diff --git a/.github/ISSUE_TEMPLATE/blank.md b/.github/ISSUE_TEMPLATE/blank.md
deleted file mode 100644
index 929d0f14..00000000
--- a/.github/ISSUE_TEMPLATE/blank.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-name: Blank issue
-about: Create an issue without a template.
-title: ''
-labels: ''
-assignees: ''
----
diff --git a/.github/ISSUE_TEMPLATE/improve-modules.md b/.github/ISSUE_TEMPLATE/improve-modules.md
deleted file mode 100644
index 55e0410b..00000000
--- a/.github/ISSUE_TEMPLATE/improve-modules.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-name: Maintenance of module
-about: Maintain the content of existing module.
-title: Maintenance of [module name] module
-labels: Enhance
-assignees: ''
----
-
-## Module to maintain
-Website:
-Code:
-
-## Work to be done
-Please ensure:
-- [ ] links, resources and exercises reflect the state-of-the-art
-- [ ] videos are of good quality and reflect the materials.
-
-If any update / re-write of the module is needed, feel free to suggest alternative material in a comment!
-If you think major changes are needed, we suggest you open an issue for new modules.
diff --git a/.github/ISSUE_TEMPLATE/new-modules.yml b/.github/ISSUE_TEMPLATE/new-modules.yml
deleted file mode 100644
index a39150f2..00000000
--- a/.github/ISSUE_TEMPLATE/new-modules.yml
+++ /dev/null
@@ -1,113 +0,0 @@
-name: Propose a new module / revise content for the existing module
-
-description: Ideas for new content for brainhack school? Submit an issue to discuss with us!
-
-# labels:
-
-body:
- - type: markdown
- attributes:
- value: |
- ## Guildeline
-
- We are very excited to see a proposal of a new module!
-
- The modules are curated resources for the student to gain neuroscience and data science skills.
-
- Once we evaluate the content, we will ask you to submit a pull request to add the new content.
-
- We would like to ensure the new module adhere to the following standards:
-
- - Use free, open source tools: to ensure all students can access the tool.
- - Python-based tool: the school focuses on Python based content.
- - Be of high quality: the material or tool should be from a quality source.
- - Have a broad appeal: a general neuroscience / data science topic suitable for entry level student.
-
- Please see the existing modules as examples!
-
- - type: markdown
- attributes:
- value: |
- ## Module info
-
- - type: textarea
- attributes:
- label: Title
- description: What's the module you would like to add? Please provide a title.
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Topic keywords
- description: "Please enter up to five keywords for the proposed topic."
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Module Description
- description: "Add a brief description of the module including the following information: a brief description of the content and module outline."
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Key tools / technology included in this module
- description: "If you are not sure what to include, feel free to raise that and\
- \ we can help!\nPlease make sure the tool included follows the listed standard:"
- value: |
- - Open-source
- - Python-based
- - Quality software (well maintained, not serving a single research project, clear feedback / bug report system)
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Prerequisites
- description: Have a look at the existing modules, pick prerequisites from there. If not, please write N/A.
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Study outcomes
- description: Please briefly describe the expected outcomes in bullet points.
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: Estimated study time
- description: We recommend to limit the time to 3 hours. Leave blank if unsure.
- validations:
- required: false
-
- - type: textarea
- attributes:
- label: Exercise examples
- description: The module must include exercises. Please give us at least one example. In the final module we will need around 5 to 10 exercises.
- validations:
- required: true
-
- - type: textarea
- attributes:
- label: References you would like to include
- description: Link to the source material you base the new module on.
- validations:
- required: false
-
- - type: markdown
- attributes:
- value: |
- ## Review checklist
-
- - type: checkboxes
- attributes:
- label: Things to check by the reviewers.
- options:
- - label: Use free, open source tools.
- - label: Python-based tool.
- - label: Be of high quality.
- - label: Have a broad appeal.
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
deleted file mode 100644
index 2ab1b38f..00000000
--- a/.github/pull_request_template.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-
-
-Closes # .
-
-
-Changes proposed in this pull request:
-
--
--
diff --git a/.github/workflows/hugo.yml b/.github/workflows/hugo.yml
deleted file mode 100644
index 0e7412fb..00000000
--- a/.github/workflows/hugo.yml
+++ /dev/null
@@ -1,76 +0,0 @@
-# Sample workflow for building and deploying a Hugo site to GitHub Pages
-name: Deploy Hugo site to Pages
-
-on:
- # Runs on pushes targeting the default branch
- push:
- branches: ["main"]
-
- # Allows you to run this workflow manually from the Actions tab
- workflow_dispatch:
-
-# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
-permissions:
- contents: read
- pages: write
- id-token: write
-
-# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
-# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
-concurrency:
- group: "pages"
- cancel-in-progress: false
-
-# Default to bash
-defaults:
- run:
- shell: bash
-
-jobs:
- # Build job
- build:
- runs-on: ubuntu-latest
- env:
- HUGO_VERSION: 0.128.0
- steps:
- - name: Install Hugo CLI
- run: |
- wget -O ${{ runner.temp }}/hugo.deb https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_linux-amd64.deb \
- && sudo dpkg -i ${{ runner.temp }}/hugo.deb
- - name: Install Dart Sass
- run: sudo snap install dart-sass
- - name: Checkout
- uses: actions/checkout@v5
- with:
- submodules: recursive
- - name: Setup Pages
- id: pages
- uses: actions/configure-pages@v6
- - name: Install Node.js dependencies
- run: "[[ -f package-lock.json || -f npm-shrinkwrap.json ]] && npm ci || true"
- - name: Build with Hugo
- env:
- HUGO_CACHEDIR: ${{ runner.temp }}/hugo_cache
- HUGO_ENVIRONMENT: production
- run: |
- hugo \
- --minify \
- --baseURL "${{ steps.pages.outputs.base_url }}/"
- - name: Disable Jekyll
- run: touch ./public/.nojekyll
- - name: Upload artifact
- uses: actions/upload-pages-artifact@v4
- with:
- path: ./public
-
- # Deployment job
- deploy:
- environment:
- name: github-pages
- url: ${{ steps.deployment.outputs.page_url }}
- runs-on: ubuntu-latest
- needs: build
- steps:
- - name: Deploy to GitHub Pages
- id: deployment
- uses: actions/deploy-pages@v5
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 4c8514d6..00000000
--- a/.gitignore
+++ /dev/null
@@ -1,9 +0,0 @@
-/public/
-/resources/
-.hugo_build.lock
-
-# vim users bewark
-*.swp
-
-#DS_Store
-.DS_Store
diff --git a/.gitmodules b/.gitmodules
deleted file mode 100644
index 2c3976ee..00000000
--- a/.gitmodules
+++ /dev/null
@@ -1,3 +0,0 @@
-[submodule "themes/hugo-universal-theme"]
- path = themes/hugo-universal-theme
- url = https://github.com/devcows/hugo-universal-theme.git
diff --git a/CLAUDE.md b/CLAUDE.md
deleted file mode 100644
index a3b8d232..00000000
--- a/CLAUDE.md
+++ /dev/null
@@ -1,259 +0,0 @@
-# CLAUDE.md — Brainhack School Website
-
-## Project Overview
-
-**Brainhack School** is a 4-week distributed neuroscience and open-science training event running simultaneously at multiple global sites. Participants complete structured training modules (Week 1) then work on collaborative data-science projects (Weeks 2–4).
-
-- **Live site:** https://school-brainhack.github.io
-- **Dev docs:** https://school-brainhack.readthedocs.io
-- **Stack:** Hugo static site + GitHub Pages deployment
-- **Theme:** `hugo-universal-theme` (Git submodule at `themes/hugo-universal-theme`)
-- **Hugo version:** 0.128.0 extended
-
----
-
-## Local Development
-
-```bash
-# 1. Initialize submodules (required after first clone)
-git submodule update --init --recursive --remote
-
-# 2. Serve locally with drafts
-hugo serve -D
-
-# 3. Production build (run by CI)
-hugo --minify
-```
-
-Site is served at `http://localhost:1313` by default.
-
----
-
-## Repository Structure
-
-```
-config.yaml # Main Hugo configuration
-content/en/ # All site content (markdown)
- modules/ # Training module pages (~26 modules)
- project/ # Student project gallery (60+ projects)
- sites/ # Distributed site hubs (toronto, polytechnique, criugm, singapore, taiwan)
- weeks/ # Schedule/week pages
- schedule/ # Calendar output page
- coc/ # Code of Conduct
- guide.md # Getting Started guide
- project_guide.md # Project submission guide
- register.md # Registration page
-data/en/ # YAML-driven dynamic content
- instructors.yaml # Global instructor/team list
- carousel/ # Homepage carousel slides (7 YAML files)
- testimonials/ # Quote testimonials (3 YAML files)
- clients/ # Sponsor/partner logos (2 YAML files)
- features/ # Features section (disabled; 6 YAML files)
-layouts/ # Custom Hugo templates (override theme)
- partials/ # Reusable components (nav, footer, carousel, instructors…)
- shortcodes/ # Custom shortcodes: gform, tabs, tab, class, content
- project/ # Project list + single page layouts
- modules/ # Module list + single page layouts
- sites/ # Site hub layouts
-static/img/ # All images
- logo_brainhack_2025.png
- carousel/ # Carousel slide images
- instructors/ # Instructor headshots
- testimonials/ # Testimonial avatars
- locations/ # Site hub images
- clients/ # Sponsor logos
-i18n/en.yaml # English UI strings
-i18n/fr.yaml # French UI strings
-.github/workflows/hugo.yml # CI/CD pipeline
-```
-
----
-
-## config.yaml Key Settings
-
-| Setting | Value | Notes |
-|---|---|---|
-| `baseURL` | `https://school-brainhack.github.io` | |
-| `theme` | `hugo-universal-theme` | Git submodule |
-| `params.style` | `green` | Theme color |
-| `params.logo` | `img/logo_brainhack_2025.png` | Update for new year |
-| `params.logo_small` | `img/logo-small_2025.png` | |
-| `params.carousel.enable` | `true` | |
-| `params.instructors.enable` | `true` | |
-| `params.testimonials.enable` | `true` | |
-| `params.features.enable` | `false` | Currently disabled |
-| `params.recent_posts.enable` | `false` | Currently disabled |
-| Markdown unsafe HTML | `true` | HTML allowed in markdown |
-
-**Main menu** (by weight): Home → Projects → Schedule → Sites → Register → Modules
-**Dropdowns:** `documents` (Getting Started, Project Guide, CoC, Dev Docs) · `past` (2024–2018 archives)
-**Topbar:** GitHub, Mastodon, email contact
-
-**Custom taxonomies:**
-- `tags` → filter projects and modules
-- `names` → author/organizer names (used for filtering)
-
----
-
-## Content Frontmatter Reference
-
-### Project page — `content/en/project//index.md`
-
-```yaml
-type: "project"
-date: "YYYY-MM-DD"
-title: "Project Title"
-names: [Author1, Author2] # must match instructor list if applicable
-github_repo: "https://github.com/..."
-website: "" # optional project website
-tags: [tag1, tag2] # LOWERCASE only
-summary: "~75 word description"
-image: "cover.png" # optional; file must exist in same directory
-```
-
-### Module page — `content/en/modules//index.md`
-
-```yaml
-type: "modules" # DO NOT change this field
-title: "Module Title"
-tags: [tag1, tag2] # lowercase
-summary: "~75 word description"
-image: "cover.png" # optional
-```
-
-### Site hub page — `content/en/sites//index.md`
-
-```yaml
-type: "sites"
-date: "YYYY-MM-DD"
-title: "Institution Name, City, Country"
-names: [Organizer1, Organizer2]
-website: "" # optional
-summary: "~75 word description"
-image: "location.png"
-```
-
----
-
-## Data Files — How to Update
-
-### Add or update an instructor (`data/en/instructors.yaml`)
-
-```yaml
-- name: Full Name
- gh: github_username # GitHub handle (no @)
- email: email@example.com
- website: https://...
- affiliation: Institution Name
- urlaff: https://institution-url.com
-```
-
-Place headshot at `static/img/instructors/.png` (or `.jpg`).
-
-### Update homepage carousel (`data/en/carousel/*.yaml`)
-
-```yaml
-weight: 0 # display order (lower = first)
-title: "Slide Title"
-description: >
-
-
Location
-
Year
-
-image: "img/carousel/filename.png"
-```
-
-### Update testimonials (`data/en/testimonials/1.yaml`)
-
-```yaml
-text: "Quote text here"
-name: "Quoted Person Name"
-link: "https://..."
-avatar: "img/testimonials/avatar.jpg"
-```
-
-### Update sponsors/clients (`data/en/clients/1.yaml`, `2.yaml`)
-
-```yaml
-title: Sponsor Name
-url: https://sponsor-site.com
-logo: img/clients/logo.png
-```
-
----
-
-## Custom Shortcodes
-
-| Shortcode | Usage | Purpose |
-|---|---|---|
-| `gform` | `{{< gform "FORM_ID" >}}` | Embed a Google Form |
-| `tabs` / `tab` | `{{< tabs >}}{{< tab name="Tab 1" >}}...{{< /tab >}}{{< /tabs >}}` | Tabbed content sections |
-| `class` | `{{< class "css-class-name" >}}...{{< /class >}}` | Wrap content in a CSS class |
-
----
-
-## Adding New Content
-
-### New student project
-
-1. Create directory: `content/en/project//` (use hyphens, no spaces)
-2. Create `index.md` with frontmatter above
-3. Add cover image to same directory (reference it as `image: "filename.png"`)
-4. Write project content in markdown — HTML is allowed
-5. Open a PR referencing the tracking issue: `closes #`
-
-### New training module
-
-1. Create `content/en/modules//index.md`
-2. Set `type: "modules"` (required — do not change)
-3. Follow existing modules (e.g., `content/en/modules/git_github/`) as examples
-4. Module list layout is at `layouts/modules/list.html`
-
-### New site hub
-
-1. Create `content/en/sites//index.md`
-2. Add site image to `static/img/locations/` and reference in frontmatter
-3. Follow `content/en/sites/toronto/` as an example
-
-### Update schedule/weeks
-
-Edit pages in `content/en/weeks/`. The schedule page at `/weeks/` also outputs an iCal calendar feed.
-
----
-
-## Deployment
-
-CI/CD is fully automated via `.github/workflows/hugo.yml`:
-
-- **Trigger:** push to `main` branch, or manual workflow dispatch
-- **Build:** Hugo 0.128.0 extended + Dart Sass, with `--minify` flag
-- **Deploy:** artifacts published to GitHub Pages automatically
-
-No manual deployment steps needed — merge to `main` and the site updates within ~2 minutes.
-
----
-
-## Key Conventions
-
-- **Tags must be lowercase** — `[machine-learning, fmri]` not `[Machine-Learning, fMRI]`
-- **Summaries ~75 words** — used in listing cards; too long breaks layout
-- **Directory names use hyphens** — `my-project/` not `my_project/` or `my project/`
-- **Do not change `type:` fields** in existing frontmatter — it controls which layout is used
-- **HTML is allowed** in markdown (Goldmark unsafe mode enabled)
-- **PR descriptions must reference the linked issue** — use `closes #`
-- **Submodule must be initialized** before `hugo serve` works locally
-- **Instructor photos** named `.png` in `static/img/instructors/`
-
----
-
-## Past Editions
-
-| Year | URL |
-|---|---|
-| 2025 | https://2025-school-brainhack.github.io/ |
-| 2024 | https://2024-school-brainhack.github.io/ |
-| 2022 | https://2022-school-brainhack.github.io/ |
-| 2021 | https://psy6983.brainhackmtl.org/ |
-| 2020 | https://school2020.brainhackmtl.org/ |
-| 2019 | https://brainhackmtl.github.io/school2019/ |
-| 2018 | https://brainhackmtl.github.io/school2018/ |
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 0848b41b..00000000
--- a/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2023 BrainhackMTL
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/README.md b/README.md
deleted file mode 100644
index 9aa3d36e..00000000
--- a/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Brainhack School
-
-This is the website of the brainhack school, built with [hugo](https://gohugo.io/).
-
-## Deploying site
-
-The deployment of the website is automated using github actions.
-
-## Run locally
-
-To test it locally, you will need to:
-
-1. Create your own fork
-
-2. Clone this GitHub repo or the fork (change the URL if it's a fork):
-
- ```bash
- git clone https://github.com/school-brainhack/school-brainhack.github.io.git
- ```
-
-3. Make sure you're inside the `school-brainhack.github.io/` directory, then **clone the submodule for themes:**
-
- ```bash
- cd school-brainhack.github.io
- git submodule update --init --recursive --remote
- ```
-
-4. If [Hugo](https://gohugo.io/) is not installed, follow the steps in their documentation to install it on your machine: https://gohugo.io/getting-started/installing/
-5. To run the website locally, make sure you are still in `school-brainhack.github.io/` directory and build the website with
- ```bash
- hugo serve -D
- ```
- The `-D` option is to serve the website including the draft .md files.
-
- Navigate the local version to make sure things are compiled correctly.
-
-
diff --git a/config.yaml b/config.yaml
deleted file mode 100644
index 41cb1a2f..00000000
--- a/config.yaml
+++ /dev/null
@@ -1,194 +0,0 @@
-baseurl: 'https://school-brainhack.github.io'
-title: 'Brainhack school'
-theme: hugo-universal-theme
-defaultContentLanguage: en
-defaultContentLanguageInSubdir: FALSE
-
-outputFormats:
- Calendar:
- mediaType: text/calendar
-
-languages:
- en:
- languageCode: en-us
- languageName: English
- contentDir: content/en
- weight: 1
- taxonomies:
- tag: tags
- category: categories
- name: names
- enableGitInfo: TRUE
- menu:
- main:
- - identifier: home
- name: Home
- url: /
- weight: 1
- - identifier: Projects
- name: Projects
- url: /project/
- weight: 2
- - identifier: Schedule
- name: Schedule
- url: /weeks/
- weight: 3
- - identifier: Sites
- name: Sites
- url: /sites/
- weight: 4
- - identifier: Register
- name: Register
- url: /register/
- weight: 5
- - identifier: Modules
- name: Modules
- url: /modules/
- weight: 6
- - parent: documents
- name: Getting started
- url: /guide/
- weight: 7
- - parent: documents
- name: Project guide
- url: /project_guide/
- weight: 7
- - parent: documents
- name: Code of Conduct
- url: /coc/
- weight: 7
- - parent: documents
- name: Dev Docs
- url: https://school-brainhack.readthedocs.io
- - parent: past
- name: 2025
- url: https://2025-school-brainhack.github.io/
- - parent: past
- name: 2024
- url: https://2024-school-brainhack.github.io/
- - parent: past
- name: 2022
- url: https://2022-school-brainhack.github.io/
- - parent: past
- name: 2021
- url: https://psy6983.brainhackmtl.org/
- - parent: past
- name: 2020
- url: https://school2020.brainhackmtl.org/
- - parent: past
- name: 2019
- url: https://brainhackmtl.github.io/school2019/
- - parent: past
- name: 2018
- url: https://brainhackmtl.github.io/school2018/
-
-
-
-
-
- topbar:
- - weight: 1
- name: GitHub
- url: 'https://github.com/school-brainhack'
- pre:
- - weight: 2
- name: Mastodon
- url: 'https://neuromatch.social/@brainhack_school'
- pre:
- - weight: 3
- name: Email
- url: 'mailto:school.brainhack@gmail.com'
- pre:
-
- params:
- outputFormats:
- Calendar:
- mediaType: text/calendar
- viewMorePostLink: /blog/
- author:
- defaultKeywords:
- - BrainHack
- - Neuroscience
- mainSections:
- - blog
- defaultDescription: Brainhack School
- facebook_site: ''
- twitter_site: ''
- default_sharing_image: img/brainhackSchool.jpg
- googleMapsApiKey:
- style: green
- email:
- contact_form_ajax: false
- about_us: >-
-
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "#plot a static map\n",
- "plotting.plot_roi(atlas.maps)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#plot an interactive map\n",
- "plotting.view_img(atlas.maps)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Fetch and view three atlases of your choice from nilearn.datasets "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You may select any 3 atlases from nilearn.datasets. Fetch and view the atlases using the above code as an example. Check out the documentation for `nilearn.plotting` at https://nilearn.github.io/stable/plotting/index.html to try different viewing methods, edit colours, change viewing options etc."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Compare and discuss"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compare the atlas parcellations and discuss your observations below. What might be some reasons that parcellation could affect your functional connectivity results?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/content/en/modules/fmri_parcellation/index.md b/content/en/modules/fmri_parcellation/index.md
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "fMRI Parcellation"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, parcellation, atlas]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The objectives of this module are to: 1) Understand the basis of the signal used in functional magnetic resonance imaging. 2) Know the main steps of preprocessing fMRI data.
-3) Know how functional connectivity is calculated, and how it is most commonly used. 4) Know the main brain parcellations and associated technical challenges
-."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "parcellation.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 1.5h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
- * the [python data analysis](/modules/python_data_analysis) module (recommended for Python familiarity).
- * the [fmri connectivity](/modules/fmri_connectivity) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-The first portion of this module was presented by Pierre Bellec during Brainhack School 2020 and the notebook was created by Desiree Lussier for this module.
-
-The tutorial slides are available [here](https://docs.google.com/presentation/d/1mTJoOSRKtGzhWeNLa9PXyKUYA0p9733UHVWrmIyi4zs/edit?usp=sharing) starting from Slide 39.
-
-The video presentation is available here:
-
-
-## Exercise
-
- * Watch the video presentation by Pierre Bellec and go over the slides.
- * Download the notebook created for this module found [here](https://github.com/school-brainhack/school-brainhack.github.io/blob/main/content/en/modules/fmri_parcellation/atlas_parcellations.ipynb).
- * Run the notebook to view the parcellation for the atlas example.
- * Create new code blocks in your notebook and the example to retrieve three other atlases using Nilearn datasets, the documentation for which you can find [here](https://nilearn.github.io/stable/modules/datasets.html), and view the atlases in your notebook.
- * Answer the discussion at the bottom of the notebook in the markdown block provided and save.
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉
-
-## More resources
-You can watch Desiree Lussier's talk on dynamic parcellation and the Dypac package (in French) [here](https://www.youtube.com/watch?v=5dA_ujGGtIY) or (in English) [here](https://www.youtube.com/watch?v=4PV_v2JAKBA&t=236s).
-
-For information on or to install the Dynamic Parcel Aggregation with Clustering (Dypac) package for Python see PyPi [here](https://pypi.org/project/dypac/) or the Github repository [here](https://github.com/courtois-neuromod/dypac), with a Jupyter notebook demo you can download and run [here](https://github.com/courtois-neuromod/dypac/blob/main/examples/dypac_demo.ipynb).
-
-If you are curious about how the embeddings of different parcellations compare you can check out Desiree Lussier's OHBM 2021 poster [here](https://drive.google.com/file/d/1O00bbKyI3Hqkah93iN83xpL10hMvygSt/view?usp=sharing) and check out Sebastian Urchs' paper on the MIST atlas [here](https://mniopenresearch.org/articles/1-3).
-
-You can also check out the Nilearn (unstable development version) tutorials [here](https://nilearn.github.io/stable/auto_examples/03_connectivity/plot_data_driven_parcellations.html).
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Using git and github"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [git, github]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Learning the basics of version control using git, as well as the social coding platform called [github](https://github.com)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "git_github.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 2h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
- * the [introduction to the terminal](/modules/introduction_to_terminal) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school.brainhack@gmail.com.
-
-## Module material
-
-This module uses material created by Software Carpentry; specifically, their [git Novice](https://swcarpentry.github.io/git-novice/) lesson.
-While the full Software Carpentry lesson material covers a half-day of content, we only cover a subset of the episodes here.
-Specifically, we cover:
-
-1. Automated Version Control
-2. Setting Up Git
-3. Creating a Repository
-4. Tracking Changes
-7. Remotes in GitHub
-8. Collaborating
-
-A video of [Elizabeth DuPre](https://elizabeth-dupre.com/#/) presenting related material is available below:
-
-
-## Additional resources
-
-For students who are interested in learning more about git and GitHub, we highly recommend reviewing the full Software Carpentry lesson.
-Software Carpentry is also actively developing an [intermediate level git and GitHub lesson](https://carpentries-incubator.github.io/byte-sized-rse-git-intermediate/), for anyone hoping to dive deeper into git and GitHub practices.
-
-
-## Exercise
-
- * send a request at the "Using Git and Github" module post at the modules channels of Discord server by typing "@ please add my " to the github organization." This will alert your local TA(s) to add you to the [school-brainhack](https://github.com/brainhack-school2026/) organization.
- * create a new repository within the [school-brainhack](https://github.com/brainhack-school2026/) organization following the naming convention `_project`. Don't worry, you will be able to change this name later, and possibly merge the content in another repository if you decide to team up with other people.
- * initialize your repo with a README and a LICENSE.
- * Create an issue for adding a short bio.
- * Using the command line, clone the repository locally to your computer.
- * Add a short bio to the README, including a picture of your github avatar. You can adapt the following snippet
- ```
-
-
- Lune Bellec
-
- ```
- This is a bit of html, which gets rendered in markdown documents. You will need to click on your profile picture to figure out what to replace `1670887` with the ID of your profile picture. Don't forget to replace `lunebellec` by your github handle.
- * Using the command line, commit this change to your local repository. Make sure you registered your github user name and email address, so the commit is accurately credited to you when you push it on github.
- * Using the command line, push your changes to the github repository.
- * Using the github interface, mention your new commit in the existing issue.
- * Using the github interface, tag your local TA (by choosing their github handle among the listed ones) in the existing issue.
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-## Even more resources
-
-* For more detailed instructions please check [GitHub's resources](https://docs.github.com/en)
-* [The Turing Way](https://the-turing-way.netlify.app/collaboration/github-novice.html) discusses using for using GitHub for open research.
-* For common Mistakes and Experiences with using Git and Github: [dangitgit.com](https://dangitgit.com/)
-* Jenny Tou wrote a git concept module and initially presented her workshops at Brigham and Women's Hospital in 2019. The video found here is a shortern version of the original presentation ; please contact Jenny for the complete presentation materials.
-
-
-* If you are curious to learn more advanced capabilities for git, you can check this tutorial "Effective use of git" by Ankur Sinha organized for the [INCF/OCNS software working group](https://ocns.github.io/SoftwareWG/2021/06/09/software-wg-tutorials-at-cns-2021-online-bash-git-and-python.html).
-
-
-* [Here](https://learngitbranching.js.org/) is an interactive page to learn Git and visually observe how the branching works under version control.
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Green computing for neuroimaging"
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/module_green_computing
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [computing resources, environmental impact, neuroimaging]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Neuroimaging is an energy-demanding field - from data collection to machine learning analyses to large-scale data storage. This module introduces strategies to evaluate and implement more sustainable neuroimaging pipelines, and encourages trainees to consider the environmental aspects related to scientific activities. The environmental impact of AI will be discussed, covering both its usage as a tool (e.g., generative assistant), and its NeuroAI specific applications (i.e., data analysis)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "green_computing.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 3h.
-
-The prerequisites to take this module are:
- * [installation](https://school-brainhack.github.io/modules/installation) module
- * [python data analysis](https://school-brainhack.github.io/modules/python_data_analysis) module
-
-Recommended but not mandatory:
- * [python scripts](https://school-brainhack.github.io/modules/python_scripts) module
- * [high performance computing](https://school-brainhack.github.io/modules/hpc) module
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-Follow up with your local TA(s) to validate you completed the exercises correctly.
-
-## Resources
-
-Coming soon!
-
-## Exercise
-
-### Exercise 1
-
-Estimate the energy consumption and the carbon footprint generated by the analyses described in the following paper using the [Green Algorithms calculator](https://calculator.green-algorithms.org/):
-- [TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction (d'Ascoli et al., 2025)](https://doi.org/10.48550/arXiv.2507.22229)
-- [Neuro-Symbolic Decoding of Neural Activity (Wang et al., 2026)](https://doi.org/10.48550/arXiv.2603.03343)
-- [An analysis of performance bottlenecks in MRI preprocessing (Dugré et al., 2025)](https://doi.org/10.1093/gigascience/giae098)
-- [Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG (Guo et al., 2026)](https://doi.org/10.48550/arXiv.2602.23410)
-- [Towards a general-purpose foundation model for fMRI analysis (Wang et al., 2025)](https://doi.org/10.48550/arXiv.2506.11167)
-
-
-Use [the template](https://github.com/school-brainhack/module_green_computing/blob/main/exercise1_template.csv) provided in the github repository to fill the relevant information, and add the corresponding output from Green Algorithms.
-
-### Exercise 2
-
-Calculate the total energy and carbon emissions for the jobs listed in [the Slurm log file](https://github.com/school-brainhack/module_green_computing/blob/main/exercise2_input.csv) in the Github repository using the formula given below. You may write [a python script](https://github.com/school-brainhack/module_green_computing/blob/main/exercise2.py) from scratch or complete [the jupyter notebook](https://github.com/school-brainhack/module_green_computing/blob/main/exercise2.ipynb) provided in the Github repository. The definition of the variables needed is provided in the glossary below. Do not forget to pay attention to the units of each variable.
-
-**Equations**
-
-(1) *CPU energy (Wh)* = *CPU time (hours)* x *TDP* / *number of CPU cores*
-
-(2) *RAM energy (Wh)* = *Max Memory* x *VMC* x *Runtime (hours)*
-
-(3) *Total energy (kWh)* = ((*CPU energy* + *RAM energy*) / 1000) x *PUE*
-
-(4) *Carbon emissions (g)* = *Total energy* x *VMC* x *Carbon intensity*
-
-**Glossary**
-
-*Carbon emissions (g)*: quantity of carbon emitted to execute a job.
-
-*Carbon intensity (g)*: Grams of carbon dioxide emitted *per KWh* used. It varies by time and location.
-- Value: assume a value of **177g** (average for the UK in 2025)
-- Where to find: In general, the online carbon intensity value can be retrieved from [Electricity Maps](https://app.electricitymaps.com/)
-
-*CPU energy*: Electrical energy used by the Central Processing Units (CPUs) while executing a job.
-- Value: computed using the formula above
-
-*CPU time*: Total time all CPU cores actually worked on a given task.
-- Value: provided in the log file under the **TotalCPU** column
-
-*Number of CPU cores*: Available number of CPU cores on a given processor.
-- Value: assume a value of **32**
-
-*Max memory*: Maximum memory (GB) used at any point within a job.
-- Value: provided in the log file under the **MaxRSS** column
-
-*Power use effectiveness (PUE)*: Energy overheads used to performed computing.
-- Value: assume a value of **1.28KWh**. This means for each **KWh** used for computing, 0.28 kWh were used for air conditioning, lighting, etc.
-
-*RAM energy*: Energy used by the volatile memory of the system (Random Access Memory; RAM), which allows to store and retrieve data during a job's execution.
-- Value: computed using the formula above
-
-*Runtime*: Total time needed for a job to finish running.
-- Value: provided in the log file under the **Elapsed** column
-
-*Thermal design power (TDP)*: Energy efficiency of a processor.
-- Value: **200W** for the processor used here (AMD EPYC 7513)
-
-*Total energy*: Sum of all electrical consumption required to execute the job.
-- Value: computed using the formula above
-
-*Volatile memory consumption (VMC)*: Energy (W) needed per GB of memory used.
-- Value: assume a value of **0.3725W**
-
-
-### Bonus
-
-Using the code provided in the [main-educational tutorial "Introduction to brain decoding in fMRI"](https://main-educational.github.io/brain_encoding_decoding/gcn_decoding.html), calculate and compare the carbon footprint of the three decoding models (SVM, MLP, GCN). For this exercise, you will have to integrate [codecarbon](https://docs.codecarbon.io/latest/) in your script.
-
-To go even further, try plotting the energy usage and the model accuracy curves for different sample size (i.e., different number of observations).
-
-
-
-
diff --git a/content/en/modules/installation/index.md b/content/en/modules/installation/index.md
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Installation"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [install, setup]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Instructions to install and setup all the tools required for the BrainHack summer school."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "installation.png"
----
-
-
-## General computing requirements
-
-There are a few computing requirements for the course that are absolutely necessary (beyond the few software packages we would like you to install, described below):
- 1. You must have administrator access to your computer (i.e., you must be able to install things yourself without requesting IT approval).
- 1. You must have at least 40 GB of free disk space on your computer (but we would recommend more, to be safe).
- 1. If you are using Windows you must be using Windows 10 or 11; Windows 7 and 8 will not be sufficient for this course.
-
-If you foresee any of these being a problem please reach out to one of the instructors for what steps you can take to ensure you are ready for the course.
-
-## Required software
-To get the most out of our course, we ask you to install the following software:
-* A command-line shell: Bash
-* A version control system: Git
-* A remote-capable text editor: VSCode
-* Python 3 via Miniconda
-* A virtualization system: Docker
-* A GitHub account
-* Discord
-* A modern browser
-
-**Note** Miniconda and VSCode are not strictly required, if you already have python and prefer to use pip, or if you already have a favorite text editor for programming, feel free to use that instead.
-
-If you already have all of the above software tools/packages installed, or are confident you’ll be able to install them by the time the course starts, you can jump straight to [checking your install](#checking-your-install). The rest of this page provides more detail on installation procedures for each of the above elements, with separate instructions for each of the three major operating systems (Windows, Mac OS, and Linux).
-
-## OS-specific installation instructions
-
-Select the tab that corresponds to your operating system and follow the instructions therein.
-
-Note: If the instructions below aren't working and you have spent more than 15-20 minutes troubleshooting on your own, reach out on the `#help-installation` channel on the BHS Discord with the exact problems you're having.
-
-{{< tabs tabTotal="3" tabID="setup" tabName1="Windows" tabName2="Mac OS" tabName3="Linux" >}}
-{{< tab tabNum="1" >}} {{% content "content/en/modules/installation/windows.md" %}} {{< /tab >}}
-{{< tab tabNum="2" >}} {{% content "content/en/modules/installation/mac.md" %}} {{< /tab >}}
-{{< tab tabNum="3" >}} {{% content "content/en/modules/installation/linux.md" %}} {{< /tab >}}
-{{< /tabs >}}
-
-### GitHub account
-
-Go to https://github.com/join/ and follow the on-screen instructions to create an account.
-It is a good idea to associate this with your university e-mail (if you have one) as this will entitle you to sign up for the [GitHub Student Developer Pack](https://education.github.com/pack) which comes with some nice free bonuses.
-
-### Discord
-
-Go to https://discord.com/download and download and install Discord.
-You will be invited to the BrainHack School Discord server via the e-mail with which you registered for the course. If you have not received the invitation, please reach out to one of the instructors.
-
-### Modern web browser
-
-Install Firefox or Chrome.
-(Safari will also work.)
-Microsoft Edge is not modern, despite what Microsoft might try and otherwise tell you.
-
-## Checking your install
-
-Now that you've installed everything it's time to check that everything works as expected!
-Type the following into your terminal:
-
-``` bash
-bash <( curl -s https://raw.githubusercontent.com/brainhackorg/school/master/content/en/modules/installation/nds_check_install.sh )
-```
-
-If you installed everything correctly you should see a message informing you as such.
-If any problems were detected you should receive some brief instructions on what is wrong with potential suggestions on how to remedy it.
-If you followed these instructions step-by-step and cannot resolve the issue please post in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com..
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-### Bash shell
-
-You already have it!
-Depending on which version of Linux you’re running you may need to type `bash` inside the terminal to access it.
-To check whether this is necessary, follow these steps:
-
-1. Open a terminal and type `echo $SHELL`.
- - If it reads `/bin/bash` then you are all set!
- - If not, whenever the instructions read "open a terminal," please assume you are to open a terminal, type `bash`, and then proceed with the instructions as specified.
-
-### Git
-
-You may already have it; try typing `sudo apt-get install git` (Ubuntu, Debian) or `sudo yum install git` (Fedora, CentOS) inside the terminal.
-If you are prompted to install it, follow the instructions on-screen to do so.
-
-### VSCode
-
-1. Go to https://code.visualstudio.com/ and click the download button for either the .deb (Ubuntu, Debian) or the .rpm (Fedora, CentOS) file.
-1. Double-click the downloaded file to install VSCode.
- (You may be prompted to type your administrator password during the install).
-
-#### VSCode extensions
-
-1. Open the Visual Studio Code application.
-1. Press `Ctrl+Shift+P` in the new window that opens and type "Extensions: Install extensions" into the search bar that appears at the top of the screen.
- - Select the appropriate entry from the dropdown menu that appears (there should be four entries; simply select the one that reads "Extensions: Install extensions").
-1. A new panel should appear on the left-hand side of the screen with a search bar.
- Search for each of the following extensions and press `Install` for the first entry that appears. (The author listed for all of these extensions should be "Microsoft".)
- - Python (n.b., you will need to reload VSCode after installing this)
- - Docker
-
-### Python
-
-1. Open a new terminal and type the following lines (separately) into the terminal, pressing `Enter` after each one:
-
- ``` bash
- wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
- bash Miniconda3-latest-Linux-x86_64.sh
- ```
-
-1. A license agreement will be displayed and the bottom of the terminal will read `--More--`.
- Press `Enter` or the space bar until you are prompted with "Do you accept the license terms? [yes|no]."
- Type `yes` and then press `Enter`
-1. The installation script will inform you that it is going to install into a default directory (e.g., `/home/$USER/miniconda3`).
- Leave this default and press `Enter`.
-1. When you are asked "Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no]," type `yes` and press `Enter`.
- Exit the terminal once the installation has finished.
-1. Re-open a new terminal.
- Type `which python` into the terminal and it should return a path (e.g., `/home/$USER/miniconda3/bin/python`).
- - If you do not see a path like this then please try typing `conda init`, closing your terminal, and repeating this step.
- If your issue is still not resolved skip the following step and contact an instructor on the `#help-installation` channel of the BHS Discord.
-1. Type the following to remove the installation script that was downloaded:
-
- ``` bash
- rm ./Miniconda3-latest-Linux-x86_64.sh
- ```
-
-#### Python packages
-
-Open a terminal and type the following commands:
-
-``` bash
-conda config --append channels conda-forge
-conda config --set channel_priority strict
-conda install -y flake8 ipython jupyter jupyterlab matplotlib nibabel nilearn numpy pandas scipy seaborn
-```
-
-### Docker
-
-1. You will be following different instructions depending on your distro ([Ubuntu](https://docs.docker.com/engine/install/ubuntu/), [Debian](https://docs.docker.com/engine/install/debian/), [Fedora](https://docs.docker.com/engine/install/fedora/), [CentOS](https://docs.docker.com/engine/install/centos/)).
- Make sure to follow the “Install using the repository” method!
-1. Once you’ve installed Docker make sure to follow the [post-install instructions](https://docs.docker.com/engine/install/linux-postinstall/) as well.
- You only need to do the “Manage Docker as a non-root user” and “Configure Docker to start on boot” steps.
-1. Open a new terminal and type `docker run hello-world`.
- A brief introductory message should be printed to the screen.
diff --git a/content/en/modules/installation/mac.md b/content/en/modules/installation/mac.md
deleted file mode 100644
index 79594f87..00000000
--- a/content/en/modules/installation/mac.md
+++ /dev/null
@@ -1,103 +0,0 @@
-### Bash shell
-
-You already have it!
-Depending on which version of Mac OS you’re running you may need to type `bash` inside the terminal to access it.
-To check whether this is necessary, follow these steps:
-
-1. Open a terminal and type `echo $SHELL`.
- - If it reads `/bin/bash` then you are all set! (The Macs now have `/bin/zsh` as default, it is compatible with bash, so you are set too!)
-
-Note: If you are using Mac Catalina (10.15.X) then it is possible your default shell is NOT CORRECT.
-To set the default to bash, type `chsh -s /bin/bash` in the terminal, enter your password when prompted, and then close + re-open the terminal.
-
-### Git
-
-You may already have it!
-Try opening a terminal and typing `git --version`.
-If you do not see something like “git version X.XX.X” printed out, then follow these steps:
-
-1. Follow [this link](https://sourceforge.net/projects/git-osx-installer/files/git-2.23.0-intel-universal-mavericks.dmg/download?use_mirror=autoselect) to automatically download an installer.
-1. Double click the downloaded file (`git-2.33.0-intel-universal-mavericks.dmg`) and then double click the `git-2.33.0-intel-universal-mavericks.pkg` icon inside the dmg that is opened.
-1. Follow the on-screen instructions to install the package.
-
-### VSCode
-
-1. Go to https://code.visualstudio.com/ and click the download button.
-1. Unzip the downloaded file (e.g., `VSCode-darwin-stable.zip`) and moving the resulting `Visual Studio Code` file to your Applications directory.
-
-#### VSCode extensions
-
-1. Open the Visual Studio Code application
-1. Type `Cmd+Shift+P` and then enter "Shell command: Install 'code' command in PATH" into the search bar that appears at the top of the screen.
- - Select the highlighted entry.
- - A notification box should appear in the bottom-right corner indicating that the command was installed successfully.
-1. Type `Cmd+Shift+P` again and then enter "Extensions: Install extensions" into the search bar.
- - Select the appropriate entry from the dropdown menu that appears (there should be four entries; simply select the one that reads "Extensions: Install extensions").
-1. A new panel should appear on the left-hand side of the screen with a search bar.
- - Search for each of the following extensions and press `Install` for the first entry that appears. (The author listed for all of these extensions should be "Microsoft".)
- - Python (n.b., you will need to reload VSCode after installing this)
- - Docker
-
-### Python
-
-1. Open a new terminal and type the following lines (separately) into the terminal, pressing `Enter` after each one:
-
- **On X86 MacOs machine :**
-
- ``` bash
- curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
- bash Miniconda3-latest-MacOSX-x86_64.sh
- ```
-
- **On ARM MacOs machine :**
-
- ``` bash
- curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh
- bash Miniconda3-latest-MacOSX-arm64.sh
- ```
-
-1. A license agreement will be displayed and the bottom of the terminal will read `--More--`.
- Press `Enter` or the space bar until you are prompted with "Do you accept the license terms? [yes|no]."
- Type `yes` and then press `Enter`
-1. The installation script will inform you that it is going to install into a default directory (e.g., `/home/$USER/miniconda3`).
- Leave this default and press `Enter`.
-1. When you are asked "Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no]," type `yes` and press `Enter`.
- Exit the terminal once the installation has finished.
-1. Re-open a terminal.
- Type `which python` into the terminal and it should return a path (e.g., `/home/$USER/miniconda3/bin/python`).
- - If you do not see a path like this then please try typing `conda init`, closing your terminal, and repeating this step.
- If your issue is still not resolved skip the following step and contact an instructor on the #help-installation channel of the BHS Slack.
-1. Type the following to remove the installation script that was downloaded:
-
- **On X86 MacOs machine :**
- ``` bash
- rm ./Miniconda3-latest-MacOSX-x86_64.sh
- ```
-
- **On ARM MacOs machine :**
- ``` bash
- rm ./Miniconda3-latest-MacOSX-arm64.sh
- ```
-
-#### Python packages
-
-Open a terminal and type the following commands:
-
-``` bash
-conda config --append channels conda-forge
-conda config --set channel_priority strict
-conda install -y flake8 ipython jupyter jupyterlab matplotlib nibabel nilearn numpy pandas scipy seaborn
-```
-
-### Docker
-
-1. Go to https://www.docker.com/products/docker-desktop/ and select the right version for your computer.
-2. Open the “Docker.dmg” file that is downloaded and drag and drop the icon to the Applications folder
-3. Open the Docker application and enter your password.
- An icon will appear in the status bar in the top-left of the screen.
- Wait until it reads “Docker Desktop is now up and running!”
-4. Open a new terminal and type `docker run hello-world`.
- A brief introductory message should be printed to the screen.
-
-(The above step-by-step Docker instructions are distilled from [here](https://docs.docker.com/desktop/install/mac-install/).
-If you have questions during the installation procedure please check that link for potential answers!)
diff --git a/content/en/modules/installation/nds_check_install.sh b/content/en/modules/installation/nds_check_install.sh
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index a201845c..00000000
--- a/content/en/modules/installation/nds_check_install.sh
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-#!/usr/bin/env bash
-
-# Script to run after following all NDS install instructions to make sure that
-# everything was installed correctly!
-#
-# Usage:
-#
-# $ bash nds_check_install.sh
-#
-# The script will try and print out any missing programs to the screen with
-# brief instructions on how to install them. If everything is installed
-# correctly you will see a message printed to screen notifying you of this.
-#
-
-missing=
-
-function check_installed() {
- func=${1}
- hash ${func} 2> /dev/null || {
- printf "Missing software program: ${func}. "
- printf "Check installation instructions\n"
- missing=true
- }
-}
-
-function get_os() {
- UNAME=$( $( command -v uname) -a | tr '[:upper:]' '[:lower:]' )
-
- if echo "${UNAME}" | grep -q "microsoft"; then
- printf "windows\n"
- elif echo "${UNAME}" | grep -q "darwin"; then
- printf "darwin\n"
- elif echo "${UNAME}" | grep -q "linux"; then
- printf "linux\n"
- else
- printf "unknown\n"
- fi
-}
-
-
-curr_os=$( get_os )
-
-# expected VSCode extensions
-extensions=(
- ms-azuretools.vscode-docker
- ms-python.python
-)
-if [ "${curr_os}" == "windows" ]; then
- extensions+=(ms-vscode-remote.remote-wsl)
-fi
-
-# expected importable python package
-packages=(
- flake8
- IPython
- jupyter
- jupyterlab
- matplotlib
- nibabel
- nilearn
- numpy
- pandas
- scipy
- seaborn
-)
-
-# check basic installations
-check_installed git
-check_installed conda
-check_installed python
-check_installed code
-check_installed docker
-
-# check vscode extensions
-
-if [ "${curr_os}" == "windows" ]; then
- cmd.exe /c "code --list-extensions" > /tmp/vscode_ext 2> /dev/null
- vscode_ext=$( cat /tmp/vscode_ext )
-else
- vscode_ext=$( code --list-extensions )
-fi
-for ext in ${extensions[@]}; do
- if [[ $vscode_ext != *${ext}* ]]; then
- ext=$( echo "${ext}" | tr '[:upper:]' '[:lower:]' )
- printf "Missing VSCode extension: "
- printf "install with $ code --install-extension ${ext}\n"
- missing=true
- fi
-done
-
-# check python packages
-for package in ${packages[@]}; do
- python -c "import ${package}" 2> /dev/null || {
- package=$( echo "${package}" | tr '[:upper:]' '[:lower:]' )
- printf "Missing Python package: "
- printf "install with $ conda install ${package}\n"
- missing=true
- }
-done
-
-# congratulations, you did it!
-if [ -z ${missing} ]; then
- if [ "${curr_os}" != "windows" ]; then
- python -c 'print("\U0001f389" * 3, end=" ")'
- fi
- printf "Everything seems to be installed correctly! "
- if [ "${curr_os}" != "windows" ]; then
- python -c 'print("\U0001f389" * 3)'
- else
- printf "\n"
- fi
- printf "Congratulations, you're all ready for the NDS course!\n"
-fi
-
-exit 0
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diff --git a/content/en/modules/installation/windows.md b/content/en/modules/installation/windows.md
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-### Prerequisites
-
-1. These installation instructions require Windows 10 version 21H1 or higher, or Windows 11 21H2 or higher. Please check with **Windows Update** to make sure your system is updated.
-2. You should enable Virtualization in your BIOS setting. Please click `ctrl+alt+del` then click `Task Manager`, navigate to "Performance" tab and select CPU. Please make sure the Virtualization is `Enabled`.
- 
- Note: If the Virtualization is “Disable”, you have to reboot your PC and get into BIOS setting to turn on the Virtualization function. Depending on your PC or motherboard manufacturer (ASUS, Acer, Dell, … etc), there are different ways to get into BIOS settings, so that we cannot provide common step-by-step instructions to help you turn on the Virtualization. If you have a problem on BIOS settings, please consult your friends or local TAs, or reach out on the `#help-installation` channel on our Discord server.
-
-### Windows Subsystem for Linux (WSL)
-Windows Subsystem for Linux (WSL) will install a Linux distribution (it is Ubuntu 22.04.1 LTS by default), then users can directly run Linux applications and Bash command-line tools on Windows. But, it should be noted that the Linux subsystem (i.e. Ubuntu) is an independent environment that has their own file structure and text command, which you will learn from another mandatory module. Some applications, such as VScode and Docker, can automatically integrate with WSL when you installed those on Windowns. But most applications, such as Python and probably your analysis softwares, should be installed under WSL. Please be cautious with these instructions in the future.
-
-1. Search for `Windows Powershell` in your applications; right click and select `Run as administrator`.
- Select `Yes` on the prompt that appears asking if you want to allow the app to make changes to your device.
- 
-
-2. Type `wsl --install` into the Powershell and then press `Enter`.
- 
- - You should see the messages about installing WSL, please wait and follow next step.
- - If WSL help text prompted and no installing takes place, you might already have WSL installed. Check if you have `Ubuntu` in your applications. Or, try `wsl --install -d Ubuntu` to install another Linux distribution.
-
-3. A meesage will prompt to ask you to reboot the computer.
- 
-4. Once your computer has rebooted, a Ubuntu window will pop up. If no window popped up, search for and open Ubuntu from your applications. If you cannot find Ubuntu, try step 2 again.
-5. It will show the message about installing Ubuntu, you will then be prompted to `Enter new UNIX username`. You can use any combination of alphanumeric characters here for your username, but a good choice is `` (e.g., `jsmith` for John Smith). You will then be prompted to enter a new password. (Choose something easy to remember as you will find yourself using it frequently.)
- 
-6. You will see the message to inform you that the installation is successful, and you can now close the window.
- 
-7. The final step is to check if we missed anything. Please search for powershell and run as administrator again. Then type `wsl -l -v` and press enter. Please make sure you have Ubuntu in the list, and the version is 2.
- 
- - If the version is not 2, please type `wsl --set-version Ubuntu 2` to update the WSL to version 2. This is necessary for installing Docker on WSL.
-8. Finally, in the same powershell window, type `wsl --update`. Hopefully, there is no updates. Then, we can move on to the next step.
-
-(The above step-by-step WSL instructions are distilled from [here](https://learn.microsoft.com/en-us/windows/wsl/install) and [here](https://learn.microsoft.com/en-us/windows/wsl/install-manual).
-If you have questions during the installation procedure those resources may have answers!)
-
-### Windows Terminal
-Microsoft have [Windows Terminal](https://aka.ms/terminal) that will provide better functionality for command-line interface. This application is now built-in on Windows 11. We will demostrate how to open terminal for WSL (it is Ubuntu by default).
-
-1. Click [here](https://aka.ms/terminal) and download. Then, you can search and open **Windows Terminal** in your applications.
-2. By default, it will open a PowerShell for Windows system. For this course, we will use bash terminal on WSL. You can click the down arrow and select Ubuntu to open the terminal.
- 
- - If you would like to open Ubuntu terminal by default, you can change the default profile in Settings.
-
-From this point on whenever the instructions specify to "open a terminal" please assume you are supposed to open the terminal for Ubuntu.
-
-### Bash shell
-
-You already have it, now that you’ve installed the WSL!
-
-### Git
-
-You already have it, now that you’ve installed the WSL!
-
-### VSCode
-
-1. Go to https://code.visualstudio.com/ and click the download button, then run the `.exe` file on Windows.
-2. Leave all the defaults during the installation with the following exception:
- - Please make sure the box labelled "Register Code as an editor for supported file types" and "add to PATH" are selected.
-3. VScode is now installed on your Windows, and also integrated with your WSL. You can now open VScode either on Windows or WSL.
-
-#### VSCode extensions
-
-1. Let's try to open VScode on Windows system and install necessary extensions. Search and open VScode on your Windows application.
-2. Press `Ctrl+Shift+P` in the new window that opens and type "Extensions: Install extensions" into the search bar that appears at the top of the screen.
- - Select the appropriate entry from the dropdown menu that appears (there should be four entries; simply select the one that reads "Extensions: Install extensions").
-3. A new panel should appear on the left-hand side of the screen with a search bar.
- - Search for each of the following extensions and press `Install` for the first entry that appears. (The author listed for all of these extensions should be "Microsoft".)
- - Python (n.b., you will need to reload VSCode after installing this)
- - Docker
- - WSL
-4. Once the WSL extension is installed, you will see a new icon appear at the bottom left corner. You can now access to WSL by this icon. Click on it and select "connect to WSL". The whole VScode will be refreshed and then connected to WSL.
- 
-5. Press `Ctrl+Shift+P` in the new window that opens and type "Extensions: Install extensions" into the search bar that appears at the top of the screen. You will find that the three extensions are not installed on WSL. Please also install the three extensions, and be cautious that Windows and WSL are isolated systems that the most of extensions, softwares or packages should be installed separately.
-6. Close the VScode. Let's try another way to open VScode on WSL. Open the terminal for WSL (it is Ubuntu by default), then type `code .` into the terminal and press `Enter`. Check the icon botom left, you will find that VScode is now on WSL.
- - You can get back to Windows by clicking the icon, and selecting "Close remote connection".
-
-### Python
-
-1. Open a new terminal for WSL and type the following lines (separately) into the terminal, pressing `Enter` after each one:
-
- ``` bash
- wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
- bash Miniconda3-latest-Linux-x86_64.sh
- ```
-
-2. A license agreement will be displayed and the bottom of the terminal will read `--More--`.
- Press `Enter` or the space bar until you are prompted with "Do you accept the license terms? [yes|no]."
- Type `yes` and then press `Enter`
-3. The installation script will inform you that it is going to install into a default directory (e.g., `/home/$USER/miniconda3`).
- Leave this default and press `Enter`.
-4. When you are asked "Do you wish the installer to initialize Miniconda3 by running conda init? [yes|no]," type `yes` and press `Enter`.
- Exit the terminal once the installation has finished.
-5. Re-open the Ubuntu application.
- Type `which python` into the terminal and it should return a path (e.g., `/home/$USER/miniconda3/bin/python`).
- - If you do not see a path like this then please try typing `conda init`, closing your terminal, and repeating this step.
- If your issue is still not resolved skip the following step and contact an instructor on the #help-installation channel on the BHS Discord.
-6. Type the following to remove the installation script that was downloaded:
-
- ``` bash
- rm ./Miniconda3-latest-Linux-x86_64.sh
- ```
-
-#### Python packages
-
-Open a terminal and type the following commands:
-
-``` bash
-conda config --append channels conda-forge
-conda config --set channel_priority strict
-conda install -y flake8 ipython jupyter jupyterlab matplotlib nibabel nilearn numpy pandas scipy seaborn
-```
-
-### Docker
-
-1. Download and run the latest [Docker Desktop installer](https://docs.docker.com/desktop/install/windows-install/). When you see the configuration, please turn on "Use WSL 2 instead of Hyper-V" (this option is not available on some Windows versions). Then the installer will ask you to log out or reboot.
-2. After login, search and open Docker Desktop, it will take a while to initialize the Docker. The initialization will be done when a tutorial guide shows up. You can skip the tutorial and move on to the next step.
-3. Docker Desktop will automatically integrate into WSL. We should test if docker is sucessfully installed on WSL. Open the terminal for WSL (i.e. Ubuntu), once you see a `$` prompt type `docker run hello-world`. A brief introductory message should be printed to the screen, and you have sucessfully installed Docker on Windows and WSL.
- 
-
-**Note**: If you restart your computer (or somehow otherwise shut down the Docker VM) you will need to re-open the "Docker Desktop" application before your `docker` commands will work again!
-If you are having problems running `docker` commands in the terminal, try re-opening the "Docker Desktop" application.
-
-(The above step-by-step instructions are distilled from [here](https://docs.docker.com/desktop/install/windows-install/) and [here](https://learn.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers).
-If you have questions during the installation procedure please check those links for potential answers!)
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-#### Short answers
-**What are some of the main advantages of using the shell?**
-Possible responses
-- High action-to-keystroke ratio
-- Support for automating repetitive tasks
-- Capacity to access remote machines
-
-**What are some of disadvantages?**
-- Response : Textual nature and how cryptic its commands and operation
-
-**Name a few command lines that enable to read/write/operate on files. What are they used for?**
-- Response : Just to name a few : ls, pwd, cd, mv, cp, mkdir
-
-**What is an option, also called flag or switch?**
-- Response : Options change the behavior of a command
-
-**What are arguments in a command line?**
-- Response : These tell the command what to operate on!
-
-
-**Can you tell the difference between relative and absolute paths?**
-Response : The top of the file system is the root directory, it holds the ENTIRE FILE SYSTEM. Inside are several other directories
-
-**What is Nano?**
-- Response : A textfile editor
-
-**You want to move a file to a folder and avoid overwriting another file with the same name. How can you make this move safely?
-True/False**
-- Response : You can ls the content of the directory to make sure names are not exactly the same. Also, you could refer to the -i flag for "interactive" moving (with warnings!).
-
-#### TRUE/FALSE
-
-**We are always located somewhere in the file system**
-
- True
-**It is possible to be located in more than one place at once**
-
- False
-**You can choose multiple options after a command**
-
- True
-**Changing one directory at a time is the same as providing the full path to the final destination**
-
- True
-**Environmental variables are preceded by $**
-
- True (in most cases)
-
-**Good naming conventions of files include special characters**
-
- False
-
-#### Exercises
-
-**Exercice 1**
-
- No: . refers to the current working directory
- No: / refers to the root directory
- No: Amanda's home directory is /Users/amanda
- No: this goes up two levels, to Users
- Yes: ~ refers to the home directory, which is Users/amanda.
- No: this would navigate to the hom directory inside Users/amanda/data (if it exists)
- Yes: unnecessarily complicated, but correct
- Yes: this is a shortcut to go back to the user's home directory!
- Yes: this goes up one directory, to /Users/amanda
-
-**Exercise 2**
-
-2.1.
-
- No: there is a directory backup in Users
- No: this is the content of /Users/thing/backup, but .. means we are one level up from that
- No: for the same reason as (2)
- Yes: ../backup refers to /Users/backup
-
-2.2.
-
- No: pwd is not the name of a directory, it is a command.
- Yes: ls without any arguments will list the contents of the current directory.
- Yes: providing the absolute path of the directory will work.
-
-**Exercice : Moving files to a new folder**
-
- mv sucrose.dat maltose.dat ../raw
-
-**Exercise : Renaming files**
-
- No: this would create a file with the correct name but would not remove the incorrectly named file
- Yes: this would rename the file!
- No, the . indicates where to move the file but does not provide a new name.
- No, the . indicates where to copy the file but does not provide a new name.
-
-**Exercise : Moving and copying**
-
- No: proteins-saved.dat is located at /Users/jamie
- Yes!
- No: proteins.dat is located at /Users/jamie/data/recombine
- No, proteins-saved.dat is located at /Users/jamie
-
-**Exercise : Copy with multiple filenames**
-
- When given multiple filenames followed by a directory all the files are copied into the directory.
- When give multiple filenames with no directory, cp throws an error:
- cp: target morse.txt is not a directory
-
-**Exercise : List filenames matching a pattern**
-
- No: This will give ethane.pdb methane.pdb octane.pdb pentane.pdb
- No: this will give octane.pdb pentane.pdb
- Yes!
- No: This only shows file starting with ethane
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "A brief introduction to the bash shell"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [bash shell, terminal]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This tutorial aims at introducing students to the use of command line terminal which offers more flexibility than built-in graphical user interfaces. We hope to provide students with an understanding of the basic command lines and advantages of working with the bash shell."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "bash_shell.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 4h.
-
-The prerequisites to take this module are:
- * The [installation](/modules/installation) module.
- - Windows: Ubuntu application (Windows Linux Subsystem)
- - Mac/Linux: Terminal
-
-Your environment should be ready to go, everything required was set up during the installation!
-In the video, you will be working with a dataset from [Software Carpentry](https://swcarpentry.github.io/shell-novice/index.html).
-Click on this link, and navigate to the section [Download files](https://swcarpentry.github.io/shell-novice/index.html#download-files).
-Download [shell-lesson-data.zip](https://swcarpentry.github.io/shell-novice/data/shell-lesson-data.zip),
-unzip it, and move the file to your Desktop.
-
-Important: Note that if you are working with Windows Subsystem for Linux (WSL), paths will be a bit different than with Mac/Linux. You will want to use : `/mnt/c/Users/${USERNAME}/Desktop/`
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server.
-If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-
-This module was presented by [Ross Markello](https://rossmarkello.com/) during the QLSC612 course in 2021.
-
-All the tutorial notes related to the video below are available [here](https://github.com/school-brainhack/course-materials-2020/blob/master/lectures/11-may/03-intro-to-shell/introduction-to-shell.ipynb).
-
-The video of the presentation is available below (duration 1h13). Follow along with the video of the presentation, typing the command line into your terminal.
-
-
-
-## Exercises
-
-### Test your knowledge
-
-Once you've completed the tutorial, try testing your understanding by answering the following questions.
-
-Short answers
-
- - *What are some of the main advantages of using the shell?*
- - *What are some of disadvantages?*
- - *Name a few command lines that enable to read/write/operate on files. What are they used for?*
- - *What is an option, also called flag or switch?*
- - *What are arguments in a command line*?
- - *Can you tell the difference between relative and absolute paths?*
- - *What is Nano?*
- - *You want to move a file to a folder and avoid overwriting another file with the same name. How can you make this move safely?*
-
-True/False
-
- - *We are always located somewhere in the file system*
- - *It is possible to be located in more than one place at once*
- - *You can choose multiple options after a command*
- - *Changing one directory at a time is the same as providing the full path to the final destination*
- - *Environmental variables are preceded by `$`*
- - *Good naming conventions of files include special characters*
-
-## Exercise
-
-Practice makes perfect.
-
-**Exercise 1**
-Starting from `/Users/amanda/data`, which of the following commands could Amanda use to navigate to her home directory, which is `/Users/amanda`?
-
- a) cd .
- b) cd /
- c) cd /home/amanda
- d) cd ../..
- e) cd ~
- f) cd home
- g) cd ~/data/..
- h) cd
- i) cd ..
-
-
-**Exercice 2**
-
-This is how your filesystem is organised.
-
-
-
-**2.1.**
-Based on the previous diagram, if `pwd` displays `/Users/things`, what will `ls -F ../backup` display?
-
- a) ../backup: No such file or directory
- b) 2012-12-01 2013-01-08 2013-01-27
- c) 2012-12-01/ 2013-01-08/ 2013-01-27/
- d) original/ pnas_final/ pnas_sub/
-
-**2.2.**
-Using the filesystem diagram, if `pwd` displays `/Users/backup`, and `-r` tells `ls` to display things in reverse (alphabetical) order, what command(s) will result in the following output:
-
- pnas_sub/ pnas_final/ original/
-
- a) ls pwd
- b) ls -rF
- c) ls -rF /Users/backup
-
-
-**Exercise 3**
-After running the following commands, Jamie realizes that she put the files `sucrose.dat` and `maltose.dat` into the wrong folder. The files should have been placed in the raw folder.*
-
- $ ls -F
- analyzed/ raw/
-
- $ ls -F analyzed
- fructose.dat glucose.dat maltose.dat sucrose.dat
-
- $ cd analyzed
-
-Fill in the blanks to move these files to the raw/ folder (i.e. the one she forgot to put them in):
-
- $ mv sucrose.dat maltose.dat ____/____
-
-Hint: the .. refers to the parent directory (i.e., one above the current directory)
-
-**Exercice 4**
-What is the output of the closing `ls` command in the sequence shown below:
-
- $ pwd
- /Users/jamie/data
- $ ls
- proteins.dat
- $ mkdir recombine
- $ mv proteins.dat recombine
- $ cp recombine/proteins.dat ../proteins-saved.dat
- $ ls
-
-
- a) proteins-saved.dat recombine
- b) recombine
- c) proteins.dat recombine
- d) proteins-saved.dat
-
-**Exercice 5 : Copy with Multiple Filenames**
-In the example below, what does `cp` do when given several filenames and a directory name?
-
- $ mkdir backup
- $ cp amino-acids.txt animals.txt backup/
-
-What does cp do when given three or more filenames?
-
- $ ls
- amino-acids.txt animals.txt backup/ elements/ morse.txt pdb/ planets.txt salmon.txt sunspot.txt
- $ cp amino-acids.txt animals.txt morse.txt
-
-
-**Exercise 6: List filenames matching a pattern**
-When run in the proteins directory, which ls command(s) will produce this output?
-
- ethane.pdb methane.pdb
-
- a) ls *t*ane.pdb
- b) ls *t?ne.*
- c) ls *t??ne.pdb
- d) ls ethane.*
-
-Hint: you can try these out using the dataset you downloaded earlier!
-
-**Exercise 7 : Renaming files**
-Suppose that you created a plain-text file in your current directory to contain a list of the statistical tests you will need to do to analyze your data, and named it: statstics.txt
-
-After creating and saving this file you realize you misspelled the filename! You want to correct the mistake and remove the incorrectly named file. Which of the following commands could you use to do so?
-
- cp statstics.txt statistics.txt
- mv statstics.txt statistics.txt
- mv statstics.txt .
- cp statstics.txt .
-
-
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-## More resources
-
-If you are curious to solidify your capabilities for using the shell, you can check this tutorial "Effective use of bash" by Ankur Sinha organized for the [INCF/OCNS software working group](https://ocns.github.io/SoftwareWG/2021/06/09/software-wg-tutorials-at-cns-2021-online-bash-git-and-python.html).
-
-You can also try out this tutorial which inspired much of the content you saw today, while exploring the shell in further detail. It covers pipes and filters, loops, shell scripts, finding things: [The Unix Shell](https://swcarpentry.github.io/shell-novice/01-intro/index.html).
diff --git a/content/en/modules/machine_learning_basics/index.md b/content/en/modules/machine_learning_basics/index.md
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index be7fac80..00000000
--- a/content/en/modules/machine_learning_basics/index.md
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Machine learning basics"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [machine learning, machine learning basics]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Learning the basics of machine learning using Jupyter Notebook."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "machine_learning.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 4h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
- * the [python scripts](/modules/python_scripts) module.
- * the [python data analysis](/modules/python_data_analysis) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access
-to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-Follow up with your local TA(s) to validate you completed the exercises correctly.
-
-## Resources
-The tutorial slides and video portion of this module were presented by Estefany Suarez and Jacob Vogel during Brainhack School 2020.
-
-The tutorial slides are available [here](https://github.com/neurodatascience/course-materials-2020/tree/master/lectures/14-may/03-intro-to-machine-learning).
-
-The notebook for the exercise is available [here](https://github.com/brainhackorg/school/blob/master/content/en/modules/machine_learning_basics/machine_learning.ipynb)
-
-The video presentations are available below. The first video (Estefany Suarez and Jacob Vogel) is here:
-
-
-## Exercise
-
- * Watch the video presentation by Estefany Suarez and go over the slides.
- * Download the [notebook](https://github.com/brainhackorg/school/blob/master/content/en/modules/machine_learning_basics/machine_learning.ipynb)
- * Follow the tutorial within the Jupyter Notebook and run the code. Feel free to play around with the code to see what happens!
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉
-
-## More resources
-Want to dive deeper into machine learning?
-
-If you are curious to learn more, you can check this tutorial "Understanding and diagnosing your machine-learning models" by Gael Varoquaux [here](http://gael-varoquaux.info/interpreting_ml_tuto/index.html).
-
-You can also take a look at the MAIN 2021 educational tutorial on model selection & validation as well as supervised learning notes [here](https://main-educational.github.io/material.html)
-
-Additional examples, tutorials, and documentation can be found at the [Scikit-Learn website](https://scikit-learn.org/stable).
diff --git a/content/en/modules/machine_learning_basics/machine_learning.ipynb b/content/en/modules/machine_learning_basics/machine_learning.ipynb
deleted file mode 100644
index 3ad2d197..00000000
--- a/content/en/modules/machine_learning_basics/machine_learning.ipynb
+++ /dev/null
@@ -1,862 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Machine Learning in Python for Neuroimaging\n",
- "\n",
- "This notebook, prepared by Désirée Lussier, is to provide an introduction to machine learning for BrainHack School 2022. It has been adapted from Brainhack Global MTL 2019 traintrack tutorial (https://github.com/BrainhackMTL/global2019-traintrack/blob/master/machine_learning.ipynb) by Désirée Lussier which was modified from the MAIN 2019 training given by Alexadre Hutton (https://github.com/main-training/main-training-nilearn-ml/blob/master/01_intro_ml.ipynb; https://github.com/main-training/main-training-nilearn-ml/blob/master/01_intro_ml_slides.odp) and sklearn tutorial (http://scipy-lectures.org/packages/scikit-learn/index.html). \n",
- "\n",
- "This training is meant to give a brief overview of the basics of machine learning in Python3. For more complete training or other specific examples please see the additional BrainHack School modules (https://school.brainhackmtl.org/modules/) above MAIN courses (https://github.com/main-training) and documentation for Scikit Learn (https://scikit-learn.org/stable/)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Training Outline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "__Machine Learning with Scikit Learn__ \n",
- "
Machine learning classification examples
\n",
- "
Model evaluation
\n",
- "
Model complexity
\n",
- "\n",
- "\n",
- "# What is machine learning?"
- ]
- },
- {
- "attachments": {
- "machine_learning.png": {
- "image/png": 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SGGkXIaUQsCuS+E/zQOwSIpDIzhHzQ5JfEXLxbAY49AAGBYgcNi5GygnxCT0GDs3eYyiB7+HTFxcX9/T09Pf3p6enl5aWwm5+/PgxETz8eFqbQiOXLG/glYEs428OmdT/wsAwxginR9fSLo1wJJI5Lw11RCSQCgBofg9Hk5OT8/Pzc3JyEM/MzHzy5Ak0C/wGWn8OjoeeunjxIlTPYfF7IKSDaOUa9JRCoQAlQ4uR7wIthhC7CEXVx/TgfuamdIogkeJM+YlxCikRoLgEykaQUigiQ8YfATA3CTMi2GV0LRL2a3eRbb9k4tAr01FIp1349ETzGEc0dQ92h0//hfihPaQgG+g8KysLwzAvL6+vr292dha7GN0YqhiwGLYYsBjONJ1GcYxrRDCuSQMApCWQTkclpYEIQHlkvDeQSf0vCRqfGJnA/oFKQxQRpOzPY7Vawd+IkDsOQAuArZuamkpKSmD+wy14+PBhTU1NZGQktAy8ipCQEPjiUC70YbXjx4/Dw6B3xugR+H6FRSCdBZBGewWk6d50VIaMvwlojEjYP3ZgFoDmMcTOnz+P0QdXnl4AIY4PDw/nOA5mNMZvXV0dnHjY3xjvZWVlGMgYwkjH+CU6RxxDHruMt8XH8whJA1AEGaAiRHUi472CTOp/SYjjNPgyDHZpBQ3iGKsUYZnEt2MxbnEUKcifkJBw69Yt6IKWlhZaoN7V1QV7H5qCpgFpFp0m1enhH/E33GuiZGgfONyS57FfQ4n6SoYMGb8ZNHwQYmRJVjJSwPEYffRFHZjU9J6nNEIxWk+cOAF3v6ioqLOzE6Y5HHew+/DwMFIw/DHGEe7XBhQXDX4GSpfxnkEm9b8kMCClEQu2xq7kgiMCpKamFhYW5og/F1ZfX2+32zs6OpAfh4qLi0HnV65cgV74QvxIKrQD3AJavwNHAUqEOBsg/UIgjQNdA4Injpfi0D5SigwZMn4raLgBv9zF0DOZTAgxMGFSw8KGKw9//cyZM7C8MXgxigEMZyRCFYDRwd/geDD96upqTU0NxntWVhbMd6gFZBDn7Zi/Dm0AjSFqFBnvD2RS/0sCoxQDkh6kYZRKc25wxOvq6jCAu7u7t7a2RkZGMjIyoBEsFktoaCgsfYz8gwcPQgXs/2YqjH3yDyIiIqBE6NEgAJ3CyHzfeh9A0jXBfRGUX4YMGf8ZpDEkjS3Yyfy+1+1MRiMMbUTA6zRbhhBDlR7Mw0aHUf71119jaGOAw1KHmY5DGNFmszk7O3t0dNTn87W2thYUFMDKLyoqhOqQfHeE0Xtvrsr4q0Mm9b8GwNwAIiBvgCJIgSOO0VhZWQkWz8zMrKqqGh4exriFYvj222/ZBzJOnMA4/1z8uYuLFy/SbB4AdUCELTE3UTXSoS/I85Yg6hqGoL7ZA3YJdOhN0ODf/r+/A3AhsTC0J13/9TfQadgWtFT2Qcz/85KjTqxewcvs/WE1pdORGZt44mtA6fsPSRcPRsRme9Ppv4JXT0G5Bah48Xqs0L/5gq+A6kX4Ly/1GrACBiHdRQIVfv9NX9kFpOKxUKyyoHtdNpa0d7PgnX76gzN+Woy579z9F/npLiJ+eYiwP/2XwNFXTpQuu/8iPwEyiOGHCKuZuOm0GJVsVIHbDQb2rB3DFBvHB2fRxLMYQO0IkUG8Evs6nkIEOB5DHux+5MgR+kgOhj/qfvPmzfHxsbKy0pycbCiNiooK5haIyiWOqRnxLRhRtxBE9fObAQcjGJPx1iGT+lsFjRP42YBkF+83kOkoIggRl8xnimCopKSkpKWlJScnDw4OLi4uwuhGNiTm5uaCqr/66iuMYbD4MfF3Qs+ePQtHXFqajsFPugBxRChkikH0ABCSUhA1yE9aA0CcVCHiezoxeBQRpGgEpi+hgni9ltMz5Qnv3ihoDIK4JNio1+jZWl9BpTbyTLEJenCSXuTa4Ba8Jk79+Ubp7G7ixvwXLSfoBA530Jl0hgit3ohSawSlXsfjsEFrNOhMYiHZVWG5KHmtktNwnFLglGJZcTuUTmvQ8SaDluM1nMagZafoeI0uXMWjmBpeq+VUvDpMrVErtYKaU+o5BYqLm3I4Wa/BuTB8TBqdnpELFR5NgAugelCvUMwI9Kw4WqNGZ+R1Bk6nVzO/SqlWhWk1MKrUaEso6uCzi2AjsO2nRhGbnJVYLxiwaXk9bqI1qAVDuNYUotWHo0rKMK0q3KLh2G+9saq9HtRTuBPrDhGiOIAMkIjWQG1Y2whoV41aI6g1Go5VLHi5veIE6xosJ1JQe7EB2EFckK6lh/xo0PDIo+F51JSHmShyklEDUdCb9TqTwJpKvDHaCFKj4XEyLs2uj0LpIK/s24E4i1GcRieg5QWjijepUTpFmE5xxcSHoU14PQQKd0LDGJWCKUStUfFKHacw82oTWlir5rVqwajncGshQiOYIIQch+GgggCiY/UazgQ5QgeIMg+hgLAKOgMzmCBtEB8tuwgrmNbAxIZVH5Vlpd2rN8vKckvAlUkG2AagCkZxM0AkxBSC1FcYCYJBUOkEntMa1XqLSh+BAuMWJo3aolVD2JiEoAp6s1ZvEIUczcvG7/4OZd0pDm2adcNApnRUOSQk9My3Z8Hr9PYKVMRXX32JAuTn50N7RMbENDQ3L6+tdff1JiQmZGakpacmxkZHgttjY4PP9Ug1AdBL0ELE2dgl1wJxpNAh2kU6pdCujLcJmdTfKthoEFei0vDAaNk/EiiOCIZEZGQkDQlkRuT27dt37txJTU0dGBh48uRJcXExLX8DkZ8+ffrQoUP0aQt6xyw0NBTpGNg0TYehLo15Gue/O6DgoJp1Wk5t0CoMel6vM2g0Fp4z88zD4AwmFTS00RxnsUQjM8erNVCWUJ3Q8qKuZCQBQDUSTeBSQb0ppuMoNCNjbcYWRh48oNYYOV2EoIvCRcCYWl2YQavU8ZxBMBq1ZpxgANfrtSqtVm2MNEXHx8ZEWU2gB56Hzw4tyhhJaYR9ADWqjVBzeg6MbY6IupZ0LSE1IS4hzmoyG5RKnUobbb12PeaaxQBy5vRmtR4WhNKs5xlFwByA2mcKnUwCtUavhsYHOUKrs2LwRg2P4lk4gzkcNzUb4+Ki4mOteh2n1ahAnCA8sVv0TLGjuOImUoXYKGALxoJGkLpWozRwStRArzUrNBFhltiwqFjTtZhrCdEJFkM0p7KAB3WwTl7fxaKVw2QAEah7dktGCRq1GkrfoFBolAo1p1apVaFq1VWOC0XxWPuLJ6FIYnGCBgzoGxtL1DFywwZWFO00WD1qGEcmlsAxE0ILwmWMB9ZVqdWQBR6Wns4Me0+nMaCsRkEwCzyoC/2JdmXlxD9UFsagCUwMxxTED/PLwPOwiqI0pusR1vi0uJi0SG2c9qpBo1QZzCqYCJyG15hVpngDOi4h5lqkPl6rjNGpjXq1SqdUgDANFp0+Xq+NvhYbHxdrgTWg16hNWs6k4YxsE2AUINBoTbzWxOlMPCQTbaTlIVSiEKFYoqyyFgFno9IwemBEIgH1Y9URG0rMyAw7CCSqg8wQD7SHWdz2qJ2y7c394HRIiUUIM2rUnM4cro8OM0SD19EgSIwQQk0aFACtYVLpLLAOMdCQE70jnv5GMLESgY5GwIoFew0WTXjolYvnz545deKrI1AbBw4ePHH6tELNx15LjE9MKioqWnwyP9zfm5WRUlZ2B7tQPtBFpI6gcKCaoKCI5kkFQUcliD8SIRE80imCFEmnyXhrkEn9rQKyDumHoCOOEKBBQingb+xihEjZWltbGxsb4Zd3dnb29fXhKH2T8uTJkzC3YXcjAiIPCwuDpibFDRYniIP59Sr+dwdUDONUnVKt1yuYn2GCvoOrBG3FCYZQ3pKaU9TR2T3U21mWn2GBSwI3KUjnxGRSOUnZYWN12QPjFcYaUGmoEzQ82BuMYIjkddFqQafiw3S6MJ0mHNrZrLPotSYNzxx6DsreZC6urH8493RxcaH7XkNcTCS4Ba4Y1By8dPi+gtak4MwWS8zNouK2jq7hicknT1Y21l3P56YrSwtu3MrrHhtZWno6Pdyfk5au0hg4Nseg1gsgUcEC7xT1Ft0v8Biv4wR48tDmGtCZxsDDDjDD3RJ0JoVGb4qNr29pWnw2+2R2srrqToQZfQOqZg9B0HHE42JTBEmd6IORI1LApzqVAT6oSh1hjs7Iu327uatuaOTB4vzyypPu+rtJBp1eoRCdN+KM1wAuHWl5mozBPSFjN4tuFxdXFRWWFeTdzM/PvVWcV1J8ozAvI/FaDEhL7AR0E/iMsQIYjJEYozHWX6IJJvKhSOooK9xukCcYGe2KooDWVLyaucXM4EN2NrkC1oQlAbMHhgwsP2b1aHAiy4Ac2HjYOlpOC9MQZh9MEA7mhS4pObOkqqmhc2Rics658vzxwL38BJNWFaLUoAcs1+ITSspr7rYPDk7PLS4/nXk4eDs9IZIP10M0jDxIPVSASZlUc7ft6cKTZ89mmhqqoyIMaE+TltfzKiOTXtQH1WTFwwYJQUlQFjA6DBIcQyOIvSP2CiNsbJKUivIZlOG9DOB1luG1pC7m2TtXJHXBIihA1TAmlPpIhT5KrTNDPs0ahUWAVLPpoT1SZx68gc2j4Ar/PjBqjHqjCZa1Qa/FlSHAGl6hUoWHK8LPnD//xZcnPj/65eFjX3514sTZM2dCr1xKSoi/39fb1nYvKyurpaWlrq6OeJqmDElTga0RRyJCxGnpLqmyqKgoipB+k/E2IZP6WwUNA5J+Gh7E30jHGIAjDv5OSkoaGhoChaekpMBBv3nzZnh4OPibWPz48eMgcvpdSChoaGfwNz1XA6CpSV+L45gBEcYZYsofB+bT6ML0unBea1TqYjhtBNOJ8Fx1fJjaKJiT23snNjy+Xb99cep+Tkosr1SBEKDlRW1IG4r6KxucPfjeUElgTqVKUAkGk8YQpdZYVTz0q8JkVnHKK3qBA6kbdKBSNpPMawSjNXro4axv97sfv99xrT7NSEsG3cMO4NjzAs5oAOtoYxMy7rV3Lz1/3tnVnXUj/25V/dPZpz6Hvaejua2vc9nj/u7F9o9eV2tdHZx5Ba/Wm+DTcXo1XEwDNDKbQhYnonm4vno2rczaHXytEkxak15n5ARdiJpLzsqee7rwYsf/v9/vzE5PxEZZtHAvGWlT90j2DTaxSUUWFTlAxwvMYIrARVXqKGt8Rd291pFHQ8+e23cC29ue+831SSa9SQ13FfWms18FhAQ3IV4PJmm1ubl5g4MTU4+X7A7/ZmDb7XI67cvzsxPDA503C2/oaJ0Wm44WSR1lYUTC/FlmcKDcjOP3GB3pAg9SR1aQMk7TsekZPTxKLdxK9vCCA0GzpwdwrMHE4uVQJgMEmE2/M4oSXWB2Iwgy7g0qg2kqwJTRafOL77TeH++ffLa2Efjx5fazB/35180mLpzjcXtTZmZ2472engcLz92BzZ3A6tPpW2mJEapQDRfK6TmVSRemMcTGZ8/OrLxg8C0+mU6+HqNRhoHUDQIYjtmYesiXnlWW6stqiGszn5v1kSiEBMSljUAiKgGVJ2cdlUI6zD4idbA7603xRByi07ELs04wa5Sgali6YG5sKAB2TRoVNpauM4DmkQ5qx4nsGRdrsWAxRFtIKgyD2EVUKrZp2FyWHlVEHEYpbFmjjjPoYBlw4Hq9ycTpDCqNLlShvHDx4jenTx3/6ujRLz4XZ+m/gp4pLCwsKSkBuw8ODkIvQVNBTUFZQXHBgyfallQZjkK/0RpeuOyIkDUg461BJvW3Csg3xgBCyDpNakHui4uLb4h48OABxgxRvtlsPnv27GHx8230dPzq1avwyDmOg4KmV1wYeYhEDlB8P4LjG+P457t/BCRSh4ZVaWPVOvjQBpCcWivATU/Pq5medwYCO7t+2wvPfGt1sZGDoodWkmhs/wYlCN33SiKbfmdPlqGHBAUHpWmKUArmEIU+Mia+raOx9d7d2GgjXC49zx49M99WC+YHY1gKy2ompmcW56e7mqrjoiLgpsJt1hotelxS4I3WmLbeIZfb83BsBJpJqVQbtObE2KQbqWnx0RHJ2Vldo6NLzxam+7tzUhLZg2emQeFpcgYBTnFQU0Ndgo1A5uIENWhN9NTZDLyobHV6lUZrjo6tbWh4Mv94bmq8qvzWnqfO1jqw/mNaWKqs2KRikkZgkwoaKHGdKtKgNfICHFfeGMPFJJa0tS95nJtb7q766us6jYnj4KnTua8FMTqJDUQI/rpeb4yOTUrJKLo/+HBra2fT73kw1p+Tef1arNFiYmyBIqFD0dagV5E8QNuMuZkVIxK5ROqop5n1jRrdY2CkaAxV8oI58s7d6t7u9py063CL2TNstBmcdRgGzB4yoGJsukZsVFwS1AVPFFdENcDsSIERZ4RTySmUeoPSGp9cXDW1uPZyd3t+vC8nRh+hCjNoDQKPGhkFY1R8dsno3HPflv/53KPi1IRoQWnQKgWjoDBowwSjwZhUUd4yOzs3Pz/ZUFMRbTYYBDU6T8uj8OgLJnLBOXZswfHCqrZv+xlxvhkouDQDD0CecR/a3kjqRN6sEahJkJU9F1AjRPugWYjsRVJnjY90bIhIm3i1IHAFaaNWZRtbMYCGVusElV4jbloO+gPyiZuylmf+POwoDc+Fq5RhV69ePn/+3IkTJ+h3mC5cuGC1WkHV8DqGRSAC3QXHA/oKLkpERAQYnZgeXgq0HEJA1Hwy3h5kUv+d8UuzVEoBnZPEAzB1Ozo6mpqaYAVPTEy0tbUhMTQ0FOQNCj948CDM5DNnztBH1Gmgwh0H00M179fOiNNRALuMHUQgQjkZYewl/nFg6l4LHaqCLuK1kZwuAv6sSm8Mh59nSWhsn7A5X3h9m17n0q5ncf5BX05inEGt0LE1zMyPEF126D5GBrQJuA4IGn4Yq5847Yu8bEaTV/MK5tnpzaEqHTz1olulNsfy3Pz49TiLoAqH8yaoQBdw+KDzdAqtQaGL0Fus0WZ9FEooKNEYgsGsZrfQqdXcjaKSxXXXzovthppyo4792IwyVK0O4bRKNa9WhKPtrFFxkdYELRfJg++hFuFfq9nDDa1JKxhRbI0G7rlBrQFz69RgcwF0yRbmQYXr4Ygz2gZJ6FXMp9RFW00xEdhnKnXPU0fHBZ+po0jswYR4CQYOMfb4ny3RE8KNgioC9KWLCOVMF7WWktZ7IPXA5kZvU12SEZ46LCjGja8FCQyACpHMoApIDgPVGuLqm3sCge2tgKevq9kohPDKizqBSZ3AZsvNnMbMlibyIAOFgdEMPYHmeF7NCUqeLQ6AQ84bwc3oMLb4z6Tm9SEq7bWMG4+fLbk3bMU5yUL4RUEdJvAczgFZa3VmkDrrehSYFUnD8VqVgI1dg81ts/V2YFy1UafWGzVhWs0ZTh9bUDa55NjZ2Vl4MJAfb45UhRp4jV7DbI6rKq01JX9gasET8C3NTpakJlhVYXqNUq1TK4w6pd6q0V4X+GhrZExEBGw6lVlQMTed0SH+MUMFIsdmHlgPalgPoKx7G3pcA6kS1GyugTUxSi1SJorP6BQl5YVgp7HcqI1Gy1ZXiG3/E6mzlkNW9vgjOAEg7rJnDFpOpeWZSDCzkJUKxlOQtsG64ty7WaU1qtm4wFnIr9Rh41WwnF9L6pAl9BoTS0GHTclCeqACkcKVIcxqcRkgisAmRcD9kAk0BmREr4OhxJY6orLwIqCC6KnfsWPHoJrIx4BqgmcCJ2RsbAwOSWtrKxQaSJ08Fug6mqJHCmm//SC+D+7I+L0hk/p/C0gn0TZCAH42mFu0UNm3HZBC0+wQbqRkZ2fPz8+Pj4+XlZUNDQ1VV1djhNPnI8Di9BGYkJAQUTswHw5EjpCN0n3aGm6WROeIIMRRRChOEQlIIWpH5I8DNBFb6MR0GZvH5KGA9BEKXVSoJjItr3T6ie3Zsr2vf+DZwsxLv9Nne9pUXmgRwnS8UgctBh0IDcQeFpoEwcjxRmhiJRhKXD7Psblc5rKCDeBQcCr40qEaI/jdyGkjUjIKR8cnvQHX48fDSdcj2dQA9J/eYtAbGCcLIAPTFR5eI6dTXDGGX4QqZ092DZZwXs+B+o2W2pYOh3fL63Y2Vd+xmqHClSadOUJv1al5pSIsjNeA3/nwsKjwK1FgNR48zIMCGAdrzBpWWoE9umcaEy6RSQP+YdTG4eZ6TgnNy9bVgfQEUL5BxdYphWhVIazivJI9nWekoGVKHrwNelTzUMdsSTyUr9iZaFe1GpdT6PXsmTpbI6izKDQRV41Rt9s6FjccPr+zt7E2yaQ3qlRs/VrQxXwVJACQB5HZGbWLjKIN5wyGyJTGtn6PN+D3OAe7m6OMMH2ugkpBHkpBr9SYw3kDWAGUphdCdXwYW6uAg2xtAvgZJVZpeCU4DwSsFxipwxBBU0QlpNV39a95/Pb150VZ1w3qKwZcj+dQTaPJqtWb0efgL1xDrVKhJIJOz4k+JVx58A7Hplng0auMWoXewMEAPK+zxBXffbDk8G1tzU4MFsRbIpWhJrUALoJBcEWli8m8NTq36N8OrC5Ml6anWFUKtL9So0SPhqPRuOhwhTk0XKnmQg1cqFWjMnFgRNAhc2E5WGaCHoXjODWakFk87EGPVg1q1Ok4VJAP17LJBuRnZpxOb4FwovLsbQEBthxImrWzHuVnQqtijQdTgGVldC7wBh7yC8pm+WACsDchYBWp1QjRFzrYDqxHOBWo2qwF6aoMzLQBxbI2QUfADAWjq5gzz1SCaIaoNbiRmg0iZu2Kq2rQxQjRyEoVrkhuugGDhYeRrdWrIK+sj9RGrdrIPHUMB05vYBMLKDGqjWszi4Fsiz3dgvsxVSKuxoDVCzo/efIkCB76iqbooaZqa2sHBgaqqqqg3KDicnNzoe5IK0IBAhQn3ZiYmEgeDnn2SEciaUjEmVaV8V9AJvX/CiS4AEQTAKPTYySILCElJeXWrVs5OTkwZqemppqbmxsaGtLT0xUKxdGjRw8fPozhAY/84sWLSMEQAmGzIStif/ydB/NdxJA9SOV1OqUuMkyIUxuTmrsGHT5Pe3dHXPy1jvauncA2eP3xeGdmUoRRCDfBfeXVUEhwDEAGah5EGx0ZnZiVU9jYdK+/H3ZPrbiGX1eYn3+/u29sZLiquswcZY6OT6hv6Zqcee7y+D1+x7r9ycTY/bGhwdHB8f7ewaLCIoPJpLVGxWfmFlbUtrZ3DXe3luemRsFJUQtgMk4fmZ17s/le1+zSusu/7XauPZ0eHh1sHxsdGB4YLs4viYuC9onPuXW7pq1zuK+3/c5NECdccpPJAGeSLRbQWFTwrDQClNHt0vKWtq7O3sHe/qH7/YMd7e0lt4pio8w62BDwigzGqPik5KyCquq6/p7Ortb6nPRkC4iVg/PHm0zsG2FQwWaLNTMrp7qmtgNXut83ODg4MjLa3X3/5s3i6Fh49yp46kZeY9BaVNrIEFP0rdZ7z93OQMDV01ibaIDiR2mIM/5dgALCeZM2Mq2xfcjr3dx0O0d6mmNNMB0uG+HgIYPBGnU9PTWnqLaxeXCgr7m2NCHGFBsVWVhQ1N7e0T/YjyZra28uLLhhAhdzapMWNMqW8lVWNww+ePzMFbD7t1yOldkHvSN9LSMDXSMjQxVV1ZaoWPAoD0tIxcH8Sk9Nramu6eruHRgeGxyd6OkbrKyoTU5KYzXhwkzaUINOyRl0F/URsTfvji+5fLsvZx6M5IHUFVfZnACcV43uKm+IzigenX3m3fQuzU3eSUuKUrOn1GxJmNlgiElIybhdWtHS0dPT3d1SmJEYJSj1KgUzz3RmXmcKB/9qDRi85WW3e7raB/p6gftDIz1D47VNrVV3y+prSpvr7jbU1d3IzoNdBZOOE4wKsLnJmJSWVFZxp6u7c3h4ZGR4YqB/tL6+Li09UaeHiQB33ABG1+ss1+KT8vMLW1tb+gf6SstuWSKMoLCystLu7p6BQYjdeEtrW05GaoRWbeJDLVoVRJUtOtSbwgVdmFpjiojKysltaGjs6+sd6O8buN93v7e7qqI8LTWZedvsXURGw4iBdw1Gc0Zmdm1tfU9P39Dw6NDIRGffYElZ1bWEJHFyQAlSN2mUJjjrbMIA7cdWM4DaRUeBpgqCbgCuKUUA0kiI4Ha4ERQXNBh9CQM0DyMlMzMTBH/v3r2HDx+KDxVvQAGiYSPFH4nHYIFWREhqExFidFKk0J/SIRn/MWRS/89B8gdJJYnELngdsgupRSKYG3YrsLq62t7ebjKxd2TPnTsH6Qdg3hKRYxhghJBHTkNFGj+AOJr+AgCjcxoTfCydDtQSAndWrY0M4+ISM8vmFldWbYv5BdlKlSozu2juiW170+9xzDfcLbDowg0ahcCFszXUWiObatZZbt2pfjy/vLTmsm/4NndeTDx8mJGZ1tHZ4nLYX2zu/u8/flx4OpOcmRKXmFxZ2zI8PuvY8Lp9dpttYeB+R19n5/2u/vbWztycGxHRURX1zcPTC0/tHg+u5FrtrSuLBscq+TDOpLFcy7tZ3tzeO/Pctu71+9xrzx4PjA22DvSDUrvzcgpv37ozODwy/Xx11bf9/dbms6He9EizOiyMLaqHsjbAf7VYImPv3i1/PP1ofd0xPbPQ3NZVXds8ODhmX1/3uO3Dgz3paQnIHx0X39J5/+H8yprD/WLL71p9VlZcYIQWVouvjbEJT0NuXkHf/YG1Nfv8/JOu7p7We63dPT2zc3Neb8BmW+/p7UxOjjUJagOYRGclT/1Wa/vvROrpje0jjNQ99pGexlhTmJG/pNModEZTVuHtoYcLz9Y8sJy2N72u5bnhntYHEw9m554sr66vra9v7W5tbvtstqX2tsZoi8kCpc8JkdaYqurG3qGJmVWXI7Dj9aw9nujs767v7W7p7eu5XV6hj7CGwb3lNdeuJ95ru2dbXVt88nSgf6Cnp7d/cGTuydK607vw5HnN3buiMXPRiGLqNed5Y2zR3YmlDd/u948fjOTGm63KKyb2EiUMLN0V3hCZWTw8t+jZ9BGpR4PUOaWCU0YmJVa39UxMrz1b9rgDfo9nraG8KEIValQrzeL7kQpOZ4qMraiunZ+bXV99/nBsqKmu5m7V3fsDo+vubZd/x+5YWX42OfNo9MH4WHlpVYQ5VqmAH2+OT0xu7mhbWFp4sjT/YGqip7e3r29gYe650+l8+my2prbSarXCFTfqLRVl1XNzT5eXVzY2XH6/58nT2b773Q8fTjx5Mr+yuurY8Ptf/MPp8S8/f9JUddsqhFg0YUYtmwFQaEwqgzW3qGRgcHhlefnZ04We7o6G+tq2ttaFhXm3272wsHDnzm2zmS2yYZMbggBHAiJkW1+Hx9zf39/b2zM4NLKwCPHzzMw9gRkRYdZr1KEWHVuFR7NlUDoCe8VCDyOHzYuxyfwgqf8SQd0kEjwcD6RAs4WFhUGhnThx4sCBA9Bv3377rVKphBrs6elZXl6uqamBJkwSfzcWbQIlSVwOhQnNiQh297O7jP8GMqn/t4AUQjQhvhBK0DnM0sePH8NWTUtLuwutFBsLIpe+z3z69OmrV6+K9rsWNA8XDRGic0T2M7q0+5cAvByVYFHrzBjgRl2YHkpDMAuG6w1tQ84Nz/3e1ugoQ7hKqbPEN3eOuv2b21uOR+PdadfNguKinlew93yhXjUGncGakX2zrqlzaHzK7vb5NrdW1pZHx4fGxwd7Ozt6OrqnpyYfPBpNyUxVaw1qrSWnoHRp1R7Ydj+aHEi4Fimu4TKaDBYdfA69ISopPf1mee/4Y9eGd8e+3FVVHGuAm6Lj9FEKbZRSa9Vb4+s7+tbcXp97ta2mMMai0PChbG2WzhIbl5CRm1/fPfDU6dv1eed723NirFq1ktMoVXo+DL0Xl9bR2+91r284Vrq7OuPiEsDRinBtZkbu0tLSD9/v+P2utrYGvUknGM2xKTkld1un51e2fV7784WS/ByjRm0CnUOhctz16wmzs/OBzW23x19T12COiNAZ4DYJUTHQif3bW9ser7OhodIKlatUaXgjSP338tTDeIsQmVnfEST10Z76eGOIibtk1Kp0Rkt86o07Ne19I9M2+8am1+FZnR/pab1bVZ2ddzMt68bNktvdfb2rtuXd3c3V5Wc383KMgmBka6zhOhvjktJhUbm2dtfWnhTmxBs1IUa2BFurMRoFg0nF6xOT00dHJzYDmzPT07lZWewjqDr2JCIuMWVg/JFv+8W6ba269GaUJsTMh2i0/FUYYoXVD557fLs/TD8YzblmsaiuGHgYRhpOo7/MmyIzbw3PLXk2/UtzU3fSkqOU4Tq1gtNwQkREdHJ20e3WicnnLn/Abn9eeys3mgs3qVV6rT5cpdEYI6sbW9fsLo/HPXi/63pMhB5GqaAxWqJrWnqXnYHtLd/so/6slHiTVmvUReh10RwXkZCYNTg24dvZnH02c6MoR2/WcuyjPfqE62nwore2vRvu9Zqau2xAa43ZWfl1tY0TEw/8fp/P5362ON/Z1VZZWX7jRk5mVtbtyrrusRmbd/u7FztPJ0ezYa8IYUYdLmdQm6ILy2qfLtu2NrdmJh8U5GQYNRyvVkZYrb39/Vvbu4HA5uPHM9cTEkGraEF4xpOTk4GAf2J8PC01FX41W2fHC8npWeNTs76tnedLz4uL8owapZEPN2tVKLVe/HQUrEvG62ytAEQImufXSB2qCYABgRC7oHaAEsHlFy5c+Oabb+hDWMCZM2fg8NTV1SUnJ9fX109NTeXn50MxklMEnYk4Qrjp0KXQoqRXZfzHkEn9PwEkD4IIREVFQVILCwsR0g+ggcjLyspgjUKsySk/efLk2bNnQ0NDaTxA+pn2eh2XS866NFqQ/peARmdQG6wgba3AW3QcrHelWns9JWdq9pl9zVacl6kOvxymVFzhDAm5JQ+eLAZ2Am7XUm1FvoG7bNTAU1ejFdibZhqjkjOGKHX5RaVLcGz9/ufLz5paaq5di2Tfg+ME2E2p6Yl6i17J5iQNGTklS2t235Zr6vFQfIxZFRbCKwSdxmiAx6jVhWrNofqouo77Gxve7fXF9rL8a+zrc3q1NjKUx2bmzNH1nf02jzfgWW65e8OqvyzwITwn6HURCpX2Qpgyp7xmxrYBJn7c1ZpuhWOj0IBGDHy4MaK0sdvp29zddI4OdEZHmMNDFCB10ERiQurw8LDLuTo/N1lQmAUlqYQfyZl0MelDY7Og+vVnc8W52eybJ1qNoGZrr+BaDQ+PLq2sPZ5fyL95S8VzCpUyTBmuVKsKC4pXl1f8Ac/gUHec1cSeHgsmhfD7kLqAJhIiuKis2o4xj3dry70+1l1/zRhi5i4aBAW6Q6G1hgiR6bl31tad32/7Zka6U2LMarVWwZmuKoVwXhMdH9c/eN/rcW04bc211VbILw5zgkotRCckj889c21tr64sFGRf03CXOS5UreGUGk04p7FEx98fHN3afrH8fDk3O5tThPGKMI1aqVIqryq5nFtlz2yO3Ze7M+P9eSBv5UWYU6h1QkHNw0Wvb+fHqQdj2dcizOorenFB3x6plxCpL89Nl6amWMNDtcownUmvNBouqw36iPSuvimnf9NmW6wtzk3QCSB1WHgqwRiXkj05t+jb3FlbXcnLShPCrwjKUF6tDlfrIhNyxqYXmb2zNHUrPx2UJaCX1RarNbmrZ8SzubNoW84vybuivByquKLRCeHhakUof7Po5tLKk+++356cegiLjYMlqOA4tQa2/obb5fVu9N3vMkCaVGEcxxZaXFJqjEn5o3PLm36ve2m2tig9Qh3CqxSc3pKSd+fxoh0mzuLThbzMZDPsSeVVtKLJbKmsrV9csa2uO9vudVojo5VKNfhxYmJie3trfm4mNTmRFUUZqlErVIrwMBVfUlW74tzY3t0Z6u9NirXqVFdM7PkXJ372BypIRy9x0EIPJkqvA/QSXJHXKitoNmRAhNIRh9KDSwN2p/n5U6dOwfmpqKiA1z4yMpKbmwvJz8vLk7hc8uBJzcr4zyCT+msAwSIhewVgcQilROeZmZmVlZWdnZ3r6+uwQCGRcMHB5fSLKXDKL1++rFarIfRE5DTHTgNAGgkAIr8EhoTE+u8+oAjURosS3gocMQ7kzBsMxvrmFufGxsPhoZyE6zEmY2RUjC72ujk9p21kwuXb3PS7Hoy0p1036vkrbBVScIWdCZo0nDcU36lasW8EdraHRwcirDo1d1XgFRo1fBSVmg/ntJy4esickX1rcdnm8Ttm58YS4q1gBT3uzN4O18NpuizoFZa4lr4xh9P9wv68p/JmvAH6UFAKkQp9bLguUmWw1nX02bze3S1ba1VOhO6SBg4h1JbGDFcyXNDnVtWLpO6b7evIio3Qcgq1VqUwcHHZeQOPlzxbO/bVhbul+YKSvVtlNERqNRadzhwTHZOSAsVk0Rt5kIDaaL4qWDXRmUMT87iU/dlcaUGuWcPrYT4wUtcYTKYouCvx16zRMZxWC0aH52SJtEK93YUiXl4FqY+O3r8ebQGpM4NDsIQYQer/9fQ7I3WrKiqntmN8P6lbuPMmLawmYxhIXRudVVixtu7a9bunBu/FmTVKlUati1LrrWEwn8ym+pYGu93mca63NdTEmAwGtlxO4AVdZHziyON5987uuu3pzdxEnXCVF8I0Rp1Sq1fqjHnFdxaer4DUn84v1FRV5Gdn3szLKs7PKczPyywoqrrX9dTp2v1ux/Z0sjInIVJxyQiK5cyJ+SB1H5F61vUIE3dFq1GASlVa3WXBZM0sGZpb8m4Gluam76SmRCnCDWoYYdpwnS5EMOsj0kDqrs0tu325pignnldE8JyWvbNgSsjIn1m0gTiXlhbTk64ZuDAQLiwuXmcxxqQPPpjf2fV7HfPlxdla9qljvcBH5uVXLDyz+3dezjybq6wvy8xLzSvKzi/Mu3GjIC+nuLGxcWX12YvvNpeXF2/evMVxOE+j1Rhqamq9XveG297Te489cefDeV7BCxrYfFz8jZbBKc/Ghn91oeVOTrQ2jOdUsH1qOgbWA9+7vIH+3q4Yk2BUXTaoQ+HEQ3eYI2Ovp2TEJ6bpTBEqHipDX1pavra6trUZmJmeqiy9nZ+dXpyXLbYqylXY0tVnc7l3X+wszE7lpifpVVdhnbIXMX4LqUsQtVSQ0bFLmooSoegoETCZTDikUCiuXr0Kl/3w4cNQj1CSUJXQb2B3m83W0tJSXV2dkZEBXwhqFqROE5+kdYFfoXkc+pWjf0/8HUmdpn1IaBBKYkEhCBsR5MEh4m/KRnGKwClvbW2FU97b24vMNMF+RPw4zIULF8LDwyGyEGU2JyXSOUKicxJ0iiCFpt8B5AEoQlyOPNKAefcBRaAymBV6C68x6dinWeB9Js/MTm9tepefzM1NjM88fDg29uD++MO+qdl5u9u1+dLn87jWn9SW5Zs0YXpNuMAp0AAavZHTGhWCoaCkdM3l3tzdHR4dsligYEPZa7VMlWl59uEtrQqEzZuycm8trdr9Ae/MzER8DHuljX3ek4dGErQGvUJvUkder+0eWXW4Xzqf91bmxeiYc6bWRav0oE+zzmhp6Ly/6vVvBda7avJjjSFaLgR+ukJrVEN/CqasO5Wzdtd3cH36OjNjzAIfrhZUSkEoqKhfAM3tvHg2NVyYHq/nwtk7TYJJp7fygoH9ao4qlFMzyuGYwWK8aog1Jhf1jM763F7H4tOy/BwzrxBf3eZ0KKTWEKLkr4YzLk9Ovl5251ZTY21H1737w0MPphccG37Phn1ioCM9PkIP701jCtNFXzFfK2ntXt1wvfA7+xsrEw2cQaWATGnZO96vgShfQfORUph8aUDqFlVURm3HCNpgx+cc722KN4aa1BcNOkiwKVQboTTF37hVtby6vuvbmB/rvh4BruNRBs4QESYYNNaYupZ7dqfL43J2NNTF6DUmdbiJLQnXRF1PGpl95tjcsS3P38qKNwuhGkHJ6QzhWovaHF1/r2fd5fX5fIsLsw+G7z8Y6ns4OjA+PDA2Ojw4NjYw8XDk0eTDyYdD97vz0hONaoWW04LUU/Mrpxdh6n0//XAi47rVyFbUhxvAcBrDFd4YkVk0tLC0sb29ODdzOy0lXqWMEZgbCoMxzBBpiM/sGp52evzrq8/rb+VFK69GqJUGtopfH52Q1jv8AG63c8NVWXFHyyyxcLVKqea1OQWlT5ft3+0Gpsd7MpPitZyGUxm1utia+u7lda/T43n6fH78Yf/waOfYRN/I6MDY2MTo6MPh4aGJicGHj8bgj+bl3VSo9GreojNEVtfUeX0bXt96b2+LxcxDnMQHDtorglV5raCxfyrg9fqWHreVZMTwlzmlIi4le2hmyb7149q6o7b8dqxRbcVg4VTs9QqNFsOAmZ5qjUp8dQ22YWd374bb4/V65uZmxoYHx4f7H44OTgzdnxgZGBsfH5l4NDbxYHp6qv9+b3JCPCxR8Y04NBFbHseepkvbr5I6BAkhCRLJFe0C0F0sB3uhg3nzErtjF+xOkbCwMJA6/e4znHh4Pqmpqf39/fDg29raCgoKoGChVKFppWeaFEEiHaIIUhL2Pr4JFS1Dwt/UUyeZAHMDEBHEARIXgPIgBcIEQKoSExPT0tLu3bvndDphhqenp8NNhzhCLunjMBcvXlQqoe3Z8ABI1gHafe8BRaDQGFXGKM4QqWKv/Orq62o3nOszUxMNd8ury+/U3q2srq6pqqm/VVlzs7Kuf2LK5fFsBzYmBrqSYi16HpytYN+V0WrV8OQ0hrzbZUt2l29rd3RsJCrCoOfD2Epdgb20o4FDrzeqtSa1zpKZW7i4YgM3zDx+lBgfrVGHi2+LqYxMNeFSBrUlvqp7zObdfOF42luRHWtQQ9somaMZrRUEi1HX0HF/yb3l21jrupt/3aDQq0OVGi6MrfeCLx1xo7Ry0rbyYtM/392VFm1Sa0LgP2k5/Z277U/c3/m2dhYf3i9MshpxBntxWw+LhH2LXq814uZcqEmn0ht1Vznded4qJBb2PHjqD+w4nj8tz8uMYl8ARVHVnMYQBoc3Lqm0qmZw8P7CzIPn85Ojg13VNeV5xbcqW3ufO/ybXtd0f2tGtKBVXFZqzZeN1y6ZU+609K+t21+4Vwcb7yQaFSgD2iYof68DhJPkk6xGpofRa1qLJja9vmPI7QkENuxjPS3XTWoTF6oTBLRwmC4y3BiTVVS2vGbb8dgXRruSrLxWHQ4W5fTmcG0Eh+Zt6lq1e9wud0djbbyRt3KhFiFM4JVR1xOH55YdW9+7Vp6WpEZZ1Fd0AvvCQKg2Um251twz7HT7N32ewZ57ualxKbHmxNiI6/FMZcfGxmBEYvxhiEbFxGmNaHaDUmcOFQyZBSVzT5f9gc2ph+PpCbGoMvu6KjxatSaMN0ZmFQw8XV7ffrEwP3cnPfWaIuSaKjyCV6t0xksGizouuW34kcuz5V5dbSzOjeNDrfBTwemcoNGbc4tujU4+hiM7/+xJRWVFYuL1mJhI2O7Tj+fcbs/C1ERRZrKRvaNhUHEmXh/b3D5kcwVglQwNdmenx12P0V6PM167Fhl3/Vrs9YTY+JjYaHN0tDUqJl5niFYJUbwuVtBFV1TV+HyuzYC9t6veYlBouBD2PFswXBFiFAnFdfcnYehuLj3qLEmO4y9xyvDY5KzxJ+vOrR/W1lbrK4pjdeE65WV0Mq+Fq2BkL9dBwnkYPGyRvM5s7RsadflgNXja2lpTUxKvx0Vfj41MiY1Ojo2+FhN8qQxhVFQkeh8iAEH4VZH53UDKkCA5LXB+zp8///XXX8MXgiK9cuUKlCpKWFlZuba2Vl9fD5ULQFcTqUMPo/Ckn6G3pXRJY8sg/B1JHaIAmYCVBymBTBCvS+yODBSShZifn3/79u3u7u5nz57V1NSkpKTQR1vB5SdOnLh06RK4HGIKXxyyC0klqSVR/vsA9KDSmJR6K2eMUvDaxOSU6akpx9rKneJCOD28KgwaSqlQwX0OUQkXwvjcW+VPny/tbvs8juWq0iIdH27UcezjHloNnDml1lBYWrFi3wBrjo+PxUWaDVyong/Xs6+1QAmB1M28PgIbSP35qi0Q8D+aGE1g75BxBva5TUbq0FbsSyaW+JrucZt3a8f+tLsiJ0YPF8egMkQrdRGCWmU16lq6Blb9L7wb6x2V+dd0YQYuXK3XKE1mTrAIakvunYop2zJIfa6nJyUG7BLK82qt2nCronV6fcvl9TufPLxbkKZVXuU5Nftgrd6sNZqhsth7xuAbrQpuT7jGeFUfrUsq6H7wxB/YXVmYKc/LiBLC9YqrUMxaQ0RSTtHw5ILd7Xc6bP3dLTmpcdEWQatTh/GatJvls8vOTd/G48F7mdEaEx/KG6wXtLEXDImlLf12u/M775pI6mHMsGBu+utlD3TOzCFRUCGiwUSNNpQzCtGpjd0jXt+W32Uf7myKN6iNqlCdhlfrjCFaS4g+Mutm6er6+gvUdaw72cqz1821GrXWCFIXIq5VNXatrouk3lQXrVOZFZfNfDinBqknjT9dd+78uL40X5YRG8GF4Jq8wRKqtYbqImvbep1uHxzT0b5ONKwm9LwQfkXLVg1yKoWCfQBIpVCrOaVaUGkNKq05XGMKEwxZeUULz55vbm1OPpxITYhDlSOYbaTW8VpksGbm9S8sre989/TJk9sZabGhl68pQy2citObLhsjVPFJzYMTLs+md3298VZ+rBAWIZI6DEU2naI3x6Wk1bXcm3/2bHl1Babk0FD/5OSj8YmJ2urqlGtRERqljlPpdSZeY1Vrouqbe+zuTWBsqC8hBk1yXqO+As+brYdg3r9KI4TDL1WqhHCVkdPFKoQowRANo9bjdW5u2vt7Gq0mlUjqEGnDFT4qPL4Qnvqmf2N7Zaq9JDWWv4iGiEpIG5h66tz8zu129bXVxeoUBlWIRhB4vYl9Rp6ZZ0b2nRmdUaXR6yyRHb39To/P6fG0d3REWiOUYVd5RaiZV+uV4WpFGMfBomUvmgOiDAQfApIwvE3g7gDpSRRAoVDQq3Gff/452D0kJATK+e7du0+fPoUfBQ8+JycnSgQ0M/wr0ttQ1+RxyaT+Cv52pA5pIIGgXURo9gbCAYDaEYf0IF5eXt7S0gI6v3//PqTn3Llz4PIj4o8TQwShfUhLAjS7TiGE9U8ZJ38uQOq8zgwnEq4YpzPWNTTZ7etjw4MJsTG8El6sGgqd/egovAqdKZw36CNi73V2b28FXu4Ehvp7rsVYNZySfUhMx757pdToCm6XrTk2tnZ2x0ZHYsx6gzqUfRwbPgkal/2MqVEpgPtNIPVlm31ra3N66kFSfLQebjqn0PIq9vkznU4BhWmNr++dWHUHAmtPuirzYoxQuPDUQU1WOI4Wva7xXu+Kd9vvsd+rzI/WhGrUYSqdRmEwsdX7nCXvTsX02vLupn+mpys5xqwWwuCQw8GOT87vn17a3P1uy/G8t6U6ymJUqtSomsZoYV8HBfFzCpHUlQJ7G11/RTDrk270Ppz3+DZXnsxVFOREaxR6xRWDho+MTegdmXQEvnN6/R33WqItGkFxUceFKpShSq0+o7hibsW55fdN9renWjUGDh6p9aI25pIhobS5d3XdvuNeu99YlmBUip4664hgl/wCpEbRftDsTHQhqFpdGGfURKXU3xvweP1bHtdod2uMTqFTXCFSD9Vawg1RObcYqW957bOj3QlWjZYPx/kgdRRFExFf29az5nA77Y7O5oYondqovIqeAndEXkscm1+xb760Ly+UZl0zKC+xpjNEwBQI00bklVQ+W1rbCQTsS09u52fqlFcNghJONWiGDSOdgPurVZyS06g0BrXerNCaQ9V6kPr8k8VAIDD5cDwtIc4kzgpYNGqDTh/G6yLSs/vnFm1bu3OzsyUZKfHKkBjFVTOnhGF1SWvkr6V0jD1yeQOO5eWam3nRfJiZ9RH76DLcXFiiYRpd9LWE7vt9z549ram5m5jIviJlMltgrmlUoWheA/tlGoNGY1FBMIoqFhZX4amvry6W385j3+DTQDYU4Sr0OBoZ1kmoSh3Ovoqoi1Jro8M5CEZEVU2d2+PwuFd7OuujIgQIGgxZntNdFSJV1/Ib+x8FvBv+pcf3bqfH8pfV4aG6iNia9gFH4IXb5XwyOZafGqtXh6hgKejY66OwT9WcID4IN4YoOV5vLK+uXVm3eze3njx9mpmRxqmY1aJThpnYYn7oK/aBGjghpLho2gZSQeLx9oFikIlJZSALA6oVOvbgwYP00i+6oL+/H9Te3Nx869Yt7EJ7QxsjAkBdA0gRdbmMIP6Onjr56BKFQziI2gGYgfTeRWtr6507dwoLC69cuQLjESYk/HLE1Wr2lSgCBgaJJoGkU4Iot38XiKRuVGqMoSohKv76w8np1bXVWzcLNWo2Ga4VVAb2lS4j9B2vMTI+4LRZN/IXFp5sBnzra8vVFaW8Cu6dAqykFjThnJB/67bN4drZ2RkbHooCV6quWMCCgorNFurh+rBvbKk0ppyC4jW7y+/3L8zPpCcnqMOugk314js/cOnDocKMEdUdA3bf9s7GantFvoV9MAN0ZdEYIrQ8h5z1rR3rvu2A19lUnm9RXxWUIWq9VmEwKjmdWqXLu1M2Y1t5sb051dOdGGVSKK/otdDrJrU28nbdPXjqP+x4HcsL1VUVepNZycOM0Iaz3xhlvyVqNWisJoEpT73hklqri0/rezDr39xeebZQnJVqUl4xc+FaThUZc31yftm98+PzNced2zdB5zr1Fb0mXMUpVDrDjdt355cdmz7P1GBHihVVuqLU6C9rIi/p48oae9bgCXvWexsr4vQKnSoUWhHO96+A9CaEk6xPMEKISstbrtW09Hq8gR2fe7i7NQLkpEA1BfZMnTeF6SJyikvX7PZNr2NquDvOzHPKELiIMF9CwBe6iLtN7XaXz7Phbq2vsWpV7Mt9ghI+d3R84tjsc/f29xvrS3eLMg3qq2qlgjWR1hKmMZtjrt8fGPb7PNt+19T4YGbyNeY0wz1XKNknz7SsByMj2edW4hKS4UaH8frLCi4rd4/UH4wlX4vRKq+Ca03i11tx1JKcMfpk0bXzcnZutjA1MTL8cpTyqlXLCwbTZUhL1LX24Qm3f9O5unr3Zl6koADf69j34xgphsHc1JurauuXVleWV5aqKitSUpKhFiKskRBuCJ5eFcosAB173VGhNkREJXT3DXm83p1t3/Sj4eyMazoNej6MZ3MNAqdW6jWqiAhTWkZWfEK6io8I58xqjbmyusbrc/u99s57tQZduMCFGaFCdMbLnEUVk93YN+HzuN3PHzeVZETxVzlYwTpzUk7Ro/nF7YA/4Fobv9+elXKdxwBRsfcLeIF93A/jwRRh1RlNGoMxMjZ2eHzCF9jy+HyDgwOJ19jCUK0yTFCEoj0xdqC+wIhwfKHrSBiAoHC8dcDCQEiiSKGUgnKeP38evH748GGo38uXL+fl5YHUOzo6aLoUihpcTmoc3QSQbpcB/I1IfX/HE3+T0YddSEZ2dnZBQQG4vKGhASmQeBIp+OXw0SFkROGSFCIDQlKRBByiESLl/PtA/PgMU/QRMfG1Tc02u2NqajIjLcXIfv9DDdcZBjmahYMDxn46wqTWmWKvJ/XeH/T7Azs72xPjI2mpiUYwv/ilWbXWcKe8yulyb29tPXownhwfFaVTWsQPj2sFDHudwH7X3KzQmFKycp8urW7t7HjdG70dbelJ1+D0JyfEJyUmGIwGAfeMjGnqGbJ7Apsba911ZXGgWUGn1pp5LYqmtlqMnfcHXIFdv2+jrbY01qxhP8Zq1CsNBhX7fK3xVlX1E8f6ztbm3MhIRmKcig9jvxanMYXDOYxOaGi953baXu74nQ77yPgE+2JXchpsxuzsjJqqssH7XTV3S81m1Bq+vSkmLWd4cn5z58X68vOKolyrRhmp43ScymiJbO7sdwR2nd7NocE+eJgROlWURRcbF51TVNw18tAZ2An4PI9HerOvW/Xw1HUGpTFabU2obb9vd7lfbnr72+uvW7VG9us5b1TRJJ+S0oT0MgGGmWWwWuLT7vWOBDZ3tnyeiYGuxGiT1SCgL9QaQ5hgVJuiCm5XOlwusO/cg/7kWLOefRoWDqBepTUaI+Ma27ocTg9Ivftea2J0hIlXoVIaQbDGxA89mvXtfr/t35ge7rmdn3U9PuZ6Ykp8SpZgjlYIhuTU9OHhIZ/b8WLTu/Rktq2pLjMjHY7Y9WvxebnZLS2NU9PTYxMPcvIKxUWR6DVTQfGdxaXVnd3d2ceTmSmJRo3KrFGxz/EK7Gf74jNyJ54seXa+X3r+vDQ3I0YIjRbCzbwKtKfQW4zXUnrGJt3eTZdtra60OFrPR2gFDFQN2lPQXUV/myN6+ge2drYDMDRta7a1NdD73PzC2Nh4S1N9cX5ObIRFFa4UeL1Wb+EEw7XrSYNDQxtuB2RraWm2taU2OzMtDh7C9et5uTfamuqnHz14+ODRzVtlgt6q4AxaU2RNQ6PX79kMbAz03YuKgOGkYl+MZSsbrMbEG51Djzb9fs/qQkt5XqyROQ8qjZEzRBbcurOytLjr39jxOJ7NTd+trsnOLUhJSUtOTim6ebO7p6eruyc5JU2t0So4PiMrZ+LBI6/Xt7m1NT8721hbjYZKiItKTbqen5cHf/fx48cTExMZGRlw2SEPf6KykmSSpJYiKA/cd0QQAvTWO/2oDFQxhBYuGRx3+F35+flQ2lDdUOOiRyYvlwvifSZ1dDYALqcpGvQ67QIkCghB6qWlpbdv366rqyspKQkLC/v666/p0Q64PDw8HNJGck/SBkCwsIsIJA9yiRApFCcxpQx/KzAH0WDOL749OPbAueEJBDZXVlbHx0Ya62uuxUQJnPiL1Bi67PVxvc4UUVRS1jsw8nRpbXP7xfb2ttfjfrIw09xUn5yclJGZ1dDc9mh6Znt7d3dnx7a6dL+ztbu5pqwwx2Jgz9zZrABz1s3hgsEcFdfS3rliswX8ni2f27a8+HRuZmJ8DOZ8VEx0Ucntlu7eJ6tOz+bujt+9PPcQxH+ntNwUEWO1xt65VdzT1fFsaRVHtzb9zxemBnrbq6oqoq7FqY2G9Kyc1pZ7k3Nzdr93K+B3r6yMDvTdramEA8erNGqeTf4bLNbKirKpyYdrNpvb63O43E63e2197enT+bmZR6PDfTcLc6GsQFNld2t7hx8srbv9Wy/8bs/C5HhHfeWtGxkRRr2C08QmZ/bg6Jrds2F/OvOwu62uqf5u2722u00t5Q1tS/aN3e1N7/rS+P2Ou3eKU9MyCu9UNXQOPF60eQPbu1uB5aczw73tNRWlsTHRcHNZO78OSJdInUU0muycnOZ73f1jj5+vudDa27Ad1pfHB3vvNdVnZWagQ9NvFNS2dY5NzfgDgW2U27441HuvtOyW2WqBbZGVV9jW2fv0+arPBx70ryw+6etou3kj22qEycsLemNpTf3c4lLA6/pxy+21r8zPTj98NNV8rysuKZV9zJxnP+je1Fi/MDu9YV9zu+wOh3193b609PzpE/DR4/v37xffKjFbowS9KSMnv6Hl3sTktNvn397ZQcbxkcGWhprM1ASjlktNTm5quTf0cHbFveXb+cHr8TweH+xrrr6dm2bWcuaIqJtl1e39Y2j/ra3dbZ/v+ex0d0tDSWG+1cp+ujec08YkJJVUVE7Nzs0tLAwM3B8ZHnz04MHM9OMl1M4b2Ap47avPB3u7s1LTYFHyHMa4Dh5zdExMTW31zOyUbX3Z5bRtuOyowOry6vOnC09mpyZGR27fvqMzWLQm643CW2ir+WeLWzswQQO2lWc9XfcKC/PY+5dmc8Htirb+ifll+/bW5ku/a3lmor2pJj0jHeavAnaVRp+ZkYYKrT6d97rsHkjahsdmsz1ffDY/P/do8lF9faM1MlrNLGb2MzPXE5La2u4tLCy4XA6P07HhsLnsqyusVZ/Ozc319fXB67VYLKS1SDb+LEiaEzJJypN26SilA4iD3aGQjxw5As18+vRphUJx586d6urq8vJy6G1oe1LmktoXJ+bZMimRCphHR+lSynuM95zU0dOw7NCRxOtIpL5HSlJSUnt7Oyw+MDrywBgEl0NoIDGgdmg9SaQkIZPxJkA5aHTG9KzchqbW5pa22trapqamttaWqsryuNgY8QdKBE4N784AvwQeYm5hcUNre33TvfqGZphTLU2N7a3NVRWl8LAzMzMbGpsbm1oaG7E1NjXUttTdba2tuF2QZTGwn4LG7Tj266eM1zmt0RwZXVh0s+Zu1b3mBrhH5aV3MjLSrZGRJov51p3bdS2t9a0dNQ1NjXU19xprm5jpdifCGmU0mkDqbc1Nd+vq79Y31jegDLXNTXUVVeUx1+KVGiEjK7u1pa2hpaWmoZ4Vpba2uaHubk0VBEmtgBdl0OjN4Sr2UyxR0VGZ2TkVlXebmltb4K/V1dwuKU5NSTAZ2XynIHDRsTHlldUtHd1365prG1oa6hs6mura6ytv5qSbDdowFa/QmIxR8Tfyi1rqa7uaaxvv3i4pyk1KSuGMFs4YdbscRahva6i513C3qvRWcnJy8Z3y6qb22pbO+iaUq6a5obarramqvDQO9tObSV0ySUmkeY5LTU2rbWiub+lsaMZAaKivuXuvqa6jub66sgzWld4IUs+vqkcGOEaNjfW1bY01rY21t28XWyKtvN6QmZ1bW9/U0NhSV1uHFmxGIRsbCm/kmI3syb0KRTEY03Oyq++WtTfdxWVrqitv5OZGRccK6DuNgf1cCgefX4iLjS7Mz62vq2ltbWlubrl7t6qkuAgFQCFhwGETcK/cgrqm1oaWtsZmtHB9XV1t+z34zzUZKQl6jTo5KQEy09DaVVHXUtt8D1LT2lDd0XT3Zm6allNarNElFbV1LV11TewQqtlaD2GoK7l5U28wclpDUnr2wOiE3e0ZmZhISU3W6wW9TjDo9TExselpWZUVdx9PPfJt2Dc9rt6OjihzBK/i2U/Xs/LDxNTExMXk5ufU1t5tv9fa1tJSX1N75+bNlGtxJr1OC+9ZDa/efKOguA5C1NCIsjc21DfW1TU11N8sLABh6Y3GvJsl1c3ttU1tDfV1TbVV7U21jbV3M7OydAYz+6gcp+HUSotek5GcUFF2G0OjuYU9Y4YBmpt7A4YRs5XR7bDWDCY4/io1m82CrrtZVIhOvdeGExogJzdv3oTeQ9eo9z5NzVr4z+b1fwkUkvQwFRWOFvyuL7/88vDhw6dOnUpJSSkqKmpoaGhtbcXQQAWZz7737jEagfQ/dilCRxk9vL94b0kdvQgQl4PFweW0/O3GjRswVAcGBsA6qampEJRjx46BzsHlISEhEHfIDak/RHCUwIRLxq+BKV+O1yqUnFLFQdkxqFXsSZ4Avxf6RocEZDOaLBodvDStgtMqOS20okqpUoSFcIowPa/Ws1kP0I2g4gSlimerz5QqrVqpV4UbOIVRw56Cwz4QNDroYpHXTfBjkE2lUvBKbGFQf+yrHUzFsd9UA7UoOEGhVKtV4WzJGg/bQjDqjSqFit2b58LCFewZOMcplOFsaRZ7Usn6m/2wKNvnlKiNWo3DnEqJC7JaCBqLJUKvN6EY7LkDLyjpw2Cs3iq1WqnG1VUKQVAbDOynurHP1jRpdKEqIUytUanUGmWoQR1i0igMWoHXGcMF01UVWkPQqBU6VYheHaJHA2i1Cq2BLVNQ8kq0qTJMowrTIoNOiyKi6XCpULS2WnyMy1YhKnRasFGwP14BmpWUI01yQqEjEZyq5DQKToeroVToBjQg+0Iqr+bRTOhQrR7dFIaqo8wCuEWhVoapcRRDQ8deD2d3ZnVVqpUqNCKaTMOxH/hiy/XEnwpRoGSKUE4RokH788zE4dHC7FfOTGAz9punuAuTFBVcMWRF6ykUrBM1Alunjdw69nl2dAdqrQNdhSmUCpUSd0IelBjShX5mb3vzmjAFH6pEOxvQB2pViE4TphPCtIJKZzBxWlOYkn02B/dShIegmuynTrU6FS+YomI77w853P51pys3P1+pFD+ZIKA5lDDZVEpBqeTAnc8XF15s+Qd6uuMiI/U8Oh50iHrCvmTLAhWQE9QBBVOyE3QqdKcSPc3ebtTrVQKk1qAS0JJoSB7iEB6KllTBtkJDsraGJ8q+P4x2VrEWVoSqUAxYDWgf9JnBwD4nrEY3oL7st2vD0QRoBsg8DzllfA7AxcWGdmdnsZ8owNUUSlU4NvbOvZq1CsAmacTZGmJ0NlL+OoDcAqgCahcaGgpP7ODBg1Dg2AW737t3r7u7O18EOW9E4aAAhGB0Yvf9Hvx7iff8mTp1LUJ0anV1NfzykZERuIaQjG+//fbQoUMw9+iROUR8PyDrbFDtI3gZvwIoBjAcbeyXOaHfeY69T6vhoY+gkRBHqyIn6I3RJnsGr1MLIHGdhmM/ewbCNoOT4GgK0KVIYx+NY6qc12h5ziCoaGM/rrqP1LHx7G1dHfuVKrUKpAitx748o2O/dQ3+UPPQouzXW6lDkUvL62BjoIg6tiAL2gFuF3tQL/5stpbpPA3zZZFfVJcib7HfBmWkiBuzCKoh4DpsWRpqQV/gEnkTVoIaZMTKIOp8jjnOeo2eeaXQ6SpBH86DBlBiTs+FGflwkDqnMyi0JrU+ApdC+dnnzASFDsVG++hMsFrECmqh5ZlBw54fgyvFHzPntTBo0A7ipCtKgGZFg7/+0Y8o1EyqRY7YM1jRbrgLhw2lxz7qoxZQWcYyOKhDyQXRasHtUDtWIdZbjK3Zq4XiVYjLxQaB1YTeYQ4jOpk5s+gJ8Xc82ZNj1jLsJQiYHawlUVkUW8d+YA1sjQ33R+HRI+K702h7VgbYISwbGpnJA1vszeSHCQCaBEdxCvpITT9yqhGMarUBfMo6WgPCu6rVhsFmYGIpmDSCmf1qOS6r42AHMJsAO4LeHB0/MP5o6+UPK+uOgqIi1kjsg8QKgePYR+xheiq5/IJ82/qKZ8NeeecW+10BldKk08GVZ/cGfbLfUjOi6Oy29HM/KIeaN6DQSGG1QjZBKUofxIfZS0xo9GhDNC37lXP2g/soExqSvVOD6yAC4WdirEWXsR/jhVAxS4uZRGyFo9iBLGSbSMwoEW2gbPHOADJDqMW+ETuYdRmdKYKkghLffVCZESGdTIMUY/PixYtQ4wcOHIBjhurAW4PPRj8hA+UPXw4+OrgcLECR95vRgfeT1EX7LPhJ18TExOHh4dHRUYTl5eUXLlyABMC4+0b85XKobMgBOB4hRITJuAjEAQgQ7YpCJeNXAC/QyJQH1LP4BT20HnSQXieYjOwXJeB+k8vBVLfIJXDX4LezD2AIGgOvNvEqI3x6trAOWXTsZXSdSas14cKMJ9hHVdUir6vhCIIW4eCC8EBp2HA1aFSjVgPLgN2LmQUiSTD1BcXLM6Wog8PHlB2YBHeEBjWwX5FBybGDHDoOyXoTW0Usamdc0ciWJjPFy8qsN8ABgm8P3ciULCsnlD57lMAOof7stT1mcxhQVPaTV6iZkRNJF5dlqwihcM1WzmBRs59xgZmiZOQNTa3Tq40WwcB+XxxcbtEoLbgUFLzOIBgsGoOZfR+PNS8agn2rz4SqCMxvZj4ZjupMKtwFlMLeUhMNjtcBjY9KkbSjd5ACvhRFG2cE2QdshVKyz4sxQmdXwh67uXg6CsD0KnqVNT/6Esd4VATlQS9iwKBcBjSgjr05rUOboDTMAmMzK+KVBFQbRoweVpXIMLC8wGbsd/F1aHPW0bgBy4NWF40A9pvmoDpmWrHysn5ipYZ/z9gUfcwKyioizimw5jBreLMAjjUYIB1qzVWNPoz9wD0anzfptVZ2cy1vMKBzmUWGhkXv6COiKxqaHd6Ad3NncnoqP/+Ghmc/hM9+aFctGPSW/NyiR5OTNvtKW0t9TITRxKlMaiX7yj+KJX4HSXyTnv1uKcoAWTQJnInjYIuhSpAsNI3OqOVgt6LOYFwN+9Uh9nUiUL1GY8BtYG9oFBAeDA40KzN0wMrMkhMHCzMiWdPhWqIYMCuHTSaxVmUb0vf6kck1QtYVLELJ1DeM0QGxucREtN1eyl8FqDtC4nWEKDxRO+1CmZ86deqLL76AeoeSr6ysHBtjX/S7f/8+vdoOdicfHXFQe5Aq3ke8b6SODqP+Q7dlZGSkp6d3dHSAzrOysr7++mu45kePHr18+TI0BQBpMJvNkAzIBEmJBByS8MqujNeCNAX7WKrYkthlPhlzH+E689DOgHgQYxIHoGaQXY9BaQLZaXkjpzSoFYzUkQwlxNS9CYwlaE3Ms2U8zxk17FUxkdTZa0jMiwVVM28G92akzl7gYSyOCxgYDbIno1CISBE/pSkwRQlfxqTTWvCHuXwwNVgSr2dfvFFqcFl2Iu6Au8B714JamDYxQvtq9WaciovDaNAyE4SpdWYksLygC7VRqzJqkV+cKmC63AQ/EGzN6xExwmVXa41KnRnutRbmi6Aya1Xi9LtBrWNTDiibUctH6NQRGp791ovWqNbAagGHGRmTiXWE0cCmNHS8GTaGln0cFFaCYICXb4Qfp4PTiGK/WVoZ24kvDpHwsy5h3cQMBjQR/Drm2u2XdnY4OE/LupW1i449LEd9wd4GUDGMLRUIi12XNZ0ZllPw+S7jZhg6sD9Y++MaqDazhGCWMSOKXQt1hOEiGC2srRj5IA+TGXAYozHRPmOJQbB2oGkePbpDXC8tlorjBQVyGrQWky5Kr4F5BJlT8ib2kUM4xqBFvWA2aqzsm0BsokTF7ADUy2QGH4cLOkvc9ebOnqW1dbfXs7L8fHTofnNDTU1lRW11XV/v0Nzs05GxsbLy2ybYa4oQq4ZHB8FZZxNE7KvGBqUO3WoEu8OUgVFj0QgRvCYCphwyMEeZE/Q8z+ak0GOwf8xawQRbF9KKcqt5Jph62HXso0nMTWc/ayR+8ADVhHUqjiA0AmsHZnuxzsF90PxoFsbojNqZxEMukVf8tTVmcxmxiaOMHYCQolnFKzBQxwKsi/9SoGIDrFriLkJIBu3CN6P1dF9++eXnn38Ot+3GjRvw2uvq6lJTU9PS0ojRrVYr/L0gYbyP+CuROj0RoQilYJdSCPDL0WHottraWjjlfX19mZmZsOBA5J999tnx48evXr0KmSa/HEIg6TjIBCSDEveDhgFJjIxfBzUUNWOw0ZiODrpoaEvSJoDY4NA9GJnIAj0EDuNADwihxkSSxAnwV0DYRuh99nOXcALRX8Fn6vBs4BgxHx0ZxEHNbiqqv+AtoBARwjUFo4vuuBacyjIzvx+OmwDHjd2ePd1lBeH10KTsdmo2ScAeS7L6iM/4QerIyKbtUTn2g/F0BV60P+D1g11xDZzCG3WsCozGmHaGyjFq2bdowM3woJntwMGjxBUMZhRJJyi1vIJZPLAYGOubmKpmnKdiP+AGwQRBapgLy2waNgfODCOwvknLsYZC0cGWaAE9mT5Mr7EZgjcoatQGYI20pxmRn/UKIGiYIcNcQESDGl88iYEyI8IyImBVhaOMxmHPzkVDij00QYexeRows86oN8A7ZqYPxhbsDNaZ4vkgJ9oY+aCPxVlr9ilfDXNMQdSsHyWBYWXhkE2c3WGFRKfCE2X+OhqTFYklI2QMzeZZcB80D5uvQalFHlVzWhWuza6sgXyh1Q1idzE7kD1twZlgXrYYDZ6zJSM7p6m15eGD8fnH09MPH0yMjt7v7W+oby4oKIqKjlZzKh7uu06wwJ+GEQMBYGVAzxpxEWa3iesDUFbYHSYejI07C2xNBWSITUaxaXSBPW8SS8iOMNJlyzkEJaQP5iDMHdZIOja1A1FkPMzajU3CMAEXfXRm04hjh3G5uAW7hUk87CiwvZF9IYdNvbCmEQ02FAI5xV7Yg9irDMH9vwhYlzMZ3JsLFJczUxxHg3pAlG2o/a+++urAgQPHjh2D2s/Pz6fPy1dVVYEpQBP7iQOQyIVC+PQUp92/Fv4ypI5GR0ODthECxOuJiYmI0FPzyMhI7I5iKN6/Pzk5WVBQcPr0aXpqjkh4eDh1P0KSDxnvEkTtLG6S8hGVGpQVCyklmEEE0mmj3TdD0mU/XYelSckicF3xKHsSTykEaUc8A3vBBDH26lWoePvTpUIG99nZ0kWQJqlaqSJ07v5ESg9i7/o/O3Hv3D8B4r1fGVH7yyzV5fWgwv8e5Q+2xr67M35kGy7PIKUDLGWvZEhkbqz4tJ7N4cPiN5vMEWYLNjP7hTG2tJDj2Ho0kAWbCBG3fdf6eRWCx/dtlCydECwJbYRgVrFnCb9Lm/xNIdI6s1IRVygUZ86coaXy33777e3bt0ENw8PDIyMjIIuoqCj2mFZcDA9OQQgKJ0JBIq2WB7Mw+vlL4a/nqVNIEeoPICsrKyMjY2BgYHp6Ojc39+TJk6Bz9OXFixcxGk0mE8Yq+hjOCvU663wZMmTI2AN5foDI4EHQ/D8zD2T81UCOO/XjlStXjh8/DlI4deoU/L3Z2dmenh6wRnZ2NugcbAIqkfhbihDXSD7kXwV/vWfqtIKRvHOEaO6qqqqlpSWE6MVjx44dOHAA/RcSEoLd/a45RWRGlyFDhoz3HvS8CR4diAAAtV+6dIkI4quvvoKbV1NTs7i4WFZWRlSCkCJgGRA5pSCCEIxD7POXwF+G1NksibiUHXF6IpKamtrV1VVcXHzr1i30GYj8s88+gyFGM+1IQb/CykacgDgzumXIkCFDxvsO6HyE4HJS+9glgg8LCwNNHDx48MsvvwS1l5aWgkHa2tqSk5OJyCMjI+Ergs6BvxadE/4ypE7eeVRUFMKbN2+urq4ODQ3dvXuX47jPRZw9exa9hU4iOqcelcBYXfbRZciQIePvARABdD4YnZQ/Ikgx7H10RKVSnT9//tNPPz18+DAOgUpGRkbguMNLhNMYERFBdI6QIn8hvKOkLj3DoAh8dHrLkL73S7+zq1QqDx06dOTIkQsXLqjVaqm30H/oPGFv1bpkpgHSrgwZMmTIeI9Bql5aTSXpf0qH144UhUJBn5SHW4jd7u7uhYWFqqqqsrIy8A6bjhefr4OGJI4HDUm77ybeXVKnViM6R8umpqZOTk6+fPkSJtXly5cPHjz42WefXbx4kXoLLA6gqyRQz8mQIUOGjL8tXvHlaJciBBAHfMJvv/0WvA4vMTw8vK6ubnd3d2JiIiUlhSbkJT5CBBA56ifP813Duzv9Lj5DZysSk5OT8/Ly2tvbm5ubQeFo9wMHDsC84sWXFAnUTzJkyJAhQ8avgLnq4pw84iAUhOB17KpUqrNnzxK1I72lpaWtrS09PR3UTowOMkpKSkIcxERvU7+b/vo7R+rUZGi+iIgI7La2tj5//jwnJwctTt75hQsXJO+c8bnM6DJkyJAh498DGB0MAvqgr9bAOaR04hSlUin9LAiy5ebmgoDgu4PCI0WAlcDokuPOSOsdw7tI6mi4jIyMrq4uMDpCsDvaF3R++vRp+ng1tT71BJlaMmTIkCFDxr8EkTrz1vfm5Mk/pHT6qAl9tQZu5NGjR8FHHR0d9fX1vb29qampFouFGJ0erhNtvVN4h0hdnG5nPvqtW7dWVlZWV1eTkpLYuvbPPz958iRamWwr9AGannhdMrJkyJAhQ4aMfwniDlA46IPYXUpHiBSwDICj4eHh33zzDfxJcBC8c5uIgoICkBQAtgpS1zuGt0fq0nyFFCHQIXpdDbZPRUXFo0ePwOvHjx+HoQTvPCwsjFoZjU59QF1C1C51iQwZMmTIkPEvAdYg4gCDAJSICDgFAL9QClx2JILa4VUeOHDg1KlTd+7cefDgAa2NhwcPzpJYDJAify7eBqlL1ZZMG7A4UhAiBRE0TXZ2dlNT0+3bt4uKiq5cuQLj6PDhw1evXiX+RssiIrU+NTr1igwZMmTIkPHvQ+IOcIpEKwBxjRShOHEQfEv46yAmRG7cuHHz5s3Gxsb09HS4o2A0sBi4jD4XT17rn4g/nNSpkjHiWwHYBX+j5snJyYijFWDsoFGKi4tXVlbGxsY4joNBBAf9woULaFDpFcP9TSxDhgwZMmS8HYB3wOugIURATPT6FdKnpqYWFxcLCgrAYhLB/emMDvzhpE5OOSJk0QDYBaxWK9oCBF9ZWTkxMZGTk3P06FE01jfffKNSqWi+He2ItqOPxIHvJfNKhgwZMmTIeAsA7/B735oFK4Ge6EH7kSNH8vPzQe2gMJC62Wym2Xhw3J9L7W+D1OGRX9/7YbvExETU32KxgMVra2vRHBUVFeHh4Z9++umxY8fQXrCG0HAIqSmJ16llKSJDhgwZMmS8NYB9wErER3AyEQ8JCQGpg7YQKS8vrxGRkpICZxUE9/6TOkIidbA7/PWIiIjS0tKtra3V1VVYNwcPHjx8+PDFixfRcGgvmmwny4ieoyMFQCTYwDJkyJAhQ8ZbAZiICAig5VwIweuInD17FvwFwEd3Op0ul+v27dv0TPlP5PU/nNSlL+UCqC1Suru7Nzc329vbv/nmG1g6p0+fRpOhjdB21F4Amo92ZciQIUOGjD8LxOXkZ9IyL0SwC87CrlqtPnXq1GeffQaCB7XBWW1paQGpg+z+LF7/3UidKgAKpwhNswNE6lTJzMzM8fHxxcXFhIQEWhB35coVemRObQfsjwBoPoDiMmTIkCFDxlsGMRRAZCTRE00t42hISAgxWmpq6tOnT4eGhtLS0qxWKzx4MCAAKgQt0hP3Pxq/G6mTOw62RgUQod1Y8R02YvRbt27NzMzAioFF8/HHH588eRLGDn2+h5ibHHS0EbWXDBkyZMiQ8c4CnAXmIvddoVB8/fXXn3zyyYULF9rb22dnZwsKCsB95NyCCsGJIEeiyz8UvxupE5cjItE57dKCwNbW1kePHqWkpIgPIA5evHgRrUBtgUZBKDrn8lNzGTJkyJDxl4FEWwZxfTcYHQR36NCh3Nxc8Hp9fT14HSQINgRAhYBImH8gfjdSp+kFUDvqgHIjgppYrda0tLSBgYHBwUH45bBijh8/Hh4ejsrTz9mC0RGiOcjkgZtOHC9DhgwZMmS84wBnqdVqREBkNBXPcRxo7tNPPwWX3b9/v7OzMzU1lVbFvwVGB37PZ+ogcgBxFJ2m3EtKSh48eHDnzp2vvvoKlTx//jxqjmqj8ogAaAvUXAIdFdtKhgwZMmTIeHcBtgJnEXnRSjo4qwDiZ86cgRP79ddf19XVgQQLCgosFgscXaLLPxS/8zN1AHGySu7du/f48WNaE/f5559fvXqVJigAiqAJ0C4geOJ4KZHSZciQIUOGjHcWRFVgLoqDv4jOQPZGozE0NBTEd/DgwbS0tLm5uebmZpqKp6Vmfxz+E1IHc9MjcwK8c4nRkQ57JCUlZVIEqgdr5cSJEyqVCvYLeeGoPMdxOIQmICCRIjiEdOzKkCFDhgwZ7zKIs4i2EGJXSqdQrVaD/j799FMQ/9TU1MTERHJyMvge7A7eJHaH30vsSfT63+M3kzruTY/PAZpspxfyUFbsms3mW7du2Wy23t5eWuV+7tw5nudB5wBqDkgNQfWXIUOGDBky3htI7E7vdp0/fx68funSpYGBgdXV1eLiYri4IE0wprQwnij1d8F/OP1OrjlCWBnE8ShZREREd3f3999/X1NTQz9og2qgeqgYMTrYHfWkyQrsirWWIUOGDBky3hOA8ojvQOfSM+WQkJCDBw8eOXKkoaFhe3v73r17FosF1JmYmPj7Mjrwn3jqAEoDOn+F0VG+8fHx0tJS0PmhQ4eUSiXsEeaSi045GS+ixx6chGcNIEOGDBkyZLxfoHVzYD0ABA/WUygU4HWQY3l5+cjISHJyMr3vTawqsuvvg/+E1BFSUcDlIsXHWa3WgoKCuro6VODTTz+FPcJxHKpBk+0A4ghRSbJfKDFYexkyZMiQIeP9AliPqB1xo9EIFxeOLv0MDHzg2tra3Nxc8Hr07/2h+P9k+p3cdERA6gCK1dzcPDo6inJ/9NFHp06dkmqCkCL7QSkyqcuQIUOGjPcP4DjyzsmbJSDRYDDA3f3yyy8//vhjcPnDhw/hCUeKP4nyO07C/zZSx40TExNRGnA5dmmBPkq2srKC0qOg9OssYHfUAXWjmiAFkLicDtGuDBkyZMiQ8T4BbIiQOI5CYndG8qL7Tm+xw3dfW1ubmJgAr4NJ4a/T2nNy3Ilk/wP8Zk8dd2UT7nFxKASKkpOT097ejsLBRydGRxylR02oMjJkyJAhQ4YM0aVlK8cRnjx5EqQJDm1pacnIyLBareLENyNyOM+IIASIdn8T/pPpdwCkDje9sbGxra1NoVCgcN988w0YHcUFUHqZ1GXIkCFDhgwJ4ETQOZx1g/hT4yBNUCcIFI5xbW0tKBUsnpiYCEaH84w4rUP/rfjN0++04h32xfT09PLyMsdxKNa3335LxQVQdJpsp12A6iNDhgwZMmT8bUGOLvgRtGixWHieP3XqFAjUZDKBTMfGxqLE783BZwbVIgzy7m/Eb/bUccuIiIiCgoJHjx6hBMToKCKsD5HBgxROcdSBqiFDhgwZMmT8nUEsKb3tRrtnzpz5+OOPweKjo6PZ2dlWq5WerINq4UIjnZj338dvI3X46EB7e/vk5KRRXOtOjC6BWHx/PFgbGTJkyJAh428MiRMRgiLBocSVoFGQKbzl6enphoaGyMhIcLm0fC3Ivv823kjq5P5TSNdFiLumpKSsrKzAoEAhjh8/rlarUTJYHDTlTpBKTyUWqyNDxh+F3ypmJJzBHfF0KSJNOAGUSDmDSXum6iugnMEcIijl34d0EQn//nUoJyGYtIdg6q+Cqhw8QYSUKCF44BegYlMYzCpCPBiElMJaSgQligd/hv1H92egdABK5hU9Qwju70v5ddD1CcEkGTL+eARlTnTWIclIwUCj99xApqDUkpKS58+fg3bhrxPnimz82/BGUqfLEanTbAB89KKiopmZmczMTPpldInRqcQyZLxTeIWhKaSIBEohBJlhD0iR8gSTRFAiIJ70M+xPpDwAjVuARkowVQRSfon96W/KRhckYAwGY+It9iOY+ot0CTgknR5M+vnFCcEDIl5JeaV4dBSQdpHhlQYB6CgBu5Qh2L4/Z33KzM4RU365uz8iXQ0XkSwACgGkU06KABRnZ4qgRBky/mhI8kaSiTjI9NixY59++ml+fv7k5OSNGzeio6NFKv7NeCOpg8hx0f0+OlK2t7cHBgaOHDly8OBB+uE1KhAKh5EjllaGjHcFNGCCO/t0NxL3K/pg6j5KAOgQgQ5RHBFiLNoVz/sJSH8tcApxJ+1SZioAbHbY6QQMbAwrQKlUKhSK8PDwsH0I3UNISMhVEVeuXLks4tIeEEfi/vSLFy9S+muxPwOdCFA6IZh05Qpuh/u+AioPlRMFBlBylB8VAVAj1E5ySqh9EFLTIY4G2d8mUuSXcYTSWbRLHSTt0lFKBNhpYuL+QxIohRBMEkUimCRDxtsFpBQDAYMIpP7VV1+Nj487HI7IyEg40vCriZH/ffzaM3VcMSEhAVwORkccdxoeHsaA//DDDzHCoaRQFIxYhBhXwdLJkPHWQUo5uLMPkExCcH8PlLL/EF2BUoD9VEFXRhwRSDvtAuAAkbAYJYPGQGwgOYwLYkSEIMvz58+fO3fuzJkzp0+f/noPp06dOnny5IkTJzB6YZvDRD569ChC4PDhw5+LgNEs4dChQwcOHPjs50DKmxA87b9D8Fq/QPD2IqCAkILMKKFUZio/KvLFF1+gXqgg4csvvzx+/DhqjbqjBQBqDbQM2ufs2bMXLlygdkMbwlAgKwEmgtTmFFK/UHcAiJBlAEi9JoHy4Kzg/l7Psv4T8cpRSpQh4+0D4gfJhPB//PHHGAgjIqKiosC8QT7+t/F6UodrTi+/44qIAEVFRQsLCxaLBYyO4UeMDr1GIcq0f6jIkPH2QaMCkLQzIlIiQKofoos45SGlT8RMniVCMDToBLwiETMoh4gZhARyAleBtIjDwGeEV+LEbQSiauI5ojrQG65GTAYnGPfCTXF3FAZlM5vN0mBOTExMSUlJT0/PzMzMycnJy8srKCi4efPmrVu3bt++fefOndLS0vLy8oqKisrKyioR1dXVNTU1tbW1dXV1DQ0NTXtofjMaGxvr6+txCk6kc3ERXLasrAzXx11KSkpwx+LiYtwaZbhx40Z2dnZGRkZqampycjIKCdMfBUax4QCghdG2ZOugJVFBVPPbb78FhcOOge1C7YAGoZYhoN2kiNi0DPsbE3FqQ5gIuA6u9s033+CyMJvQTegsNOP+eQIAZUDnQkGhYUkYSAAAEgZRLoJ2gCQwEpgMyZDxVgA5pIEDkf7ggw8wmmZnZ/Pz8zGmiJT/fbyR1HFRkDoQKX6ZdnFxEZrlo48+wljCjWk8iIVhLgvxugwZfyiCunYfRJ3MAIEkZQ1gF5mJraHZJZKGJw2CgfYHDWDkgBLgL4IeJJKG6/mJCEQAOKMgEmIR0DAy72cRXAreOWgD9zKZTBgvSUlJ4DlQb2FhIYgQpAhqBJu2t7f39vYODAzA9J6YmHj48OHk5OT09PTMzMz8/PwTEU+fPn327BlG2fPnz5eXl1dWVlZXV9fW1tbX1x0Oh9PpROhyuTZEuN1uj8fj3YPP5wsEApsiEPH7/bS7tbW1vb29IwKR1wKHdnd3EUFmOl0CrkPA9XEX3BH3BVAAlISKBNjtdhTSJgIFRrFR/qWlJdQFNUK9AFRwbm4O9Z2amkL1Hzx4gHZAg3R3d6NxYFXAjEBzwUyB0QCLIS0tDbYC1I7VakXzogfR1HBi0Ow08wEtBAMLnYK+QweB8g8cOEC9BlAPkjUAGwJ2GDJLVhTcIPHRQQhNBhD3Q1pwIxIkUaCCkGSM5O0V4BQZMv57QPzAoZAoeqINcYW/DkaHIw0u/q28/kZSB2CAw+7GLoYfTHWaHsQAg4gTi2MASKWhwsl474HuRqcDiO/vd0qUEEzdQzD15+nBJBHBpJ8zN+lTAu3uz4OzoI7hnNHzZlL6IF1yqQHyqqH0wc3Q8iBpcQqZARofh8DTyA+ah3KHBQB5pkWncI6zsrKIm+H70pcTwUCDg4MgpMePH4OowLugMfAZWA3cBpID1QHEuBLpghHBi0STEmsy+n0Dg74CnP4roFsAdDvpvhT5HYFrBm/5ZgRL/DqgglRlCftrjXNZHbze/UYDQHaDZDSgwdHssAzQBeiInp4edEpDQ8Pdu3dLS0thDcCcQseRHwLbDuKBboVUwA6AEQBhgBgEu198jgCRQAQ6jVx/yW6TuB/nQrQgYMT6JHUS9oslgYQTQCJCJtYiKBGgOELSn+JlgjnZ4T2wc8QUKQMlSqGM9wwkPOhc6m6YsJBVyGRZWRnUDrg4du878ETN18XvzVHKL/FrC+UADI+CggKMIkj2hx9+CO9EEjIZMgCmfkSI+icI6RCUFwG7ENzgYTFDMCYCbAqQZFMKIjQrTn42PbEmzobmhRam+VuIPk3MUgiqBhDBUeho6GXILa4WHR0NdZ+Xl0ckXVdX19LS0tXVBW4YHR2FeMOPhIsMLxMsAnaB2yqxzn4XlkiIIkRjIp39DIyd9hCkRBHEWL8O0Q9/r0CVotq9FsHW2TfxAASbch+kBt8P6hRA6iAA6TAIlpeXYQHA0ZmamkIXwy0hI6C+vr6yshIuCtygtLS0mJgYSB2EBKIF4ieJkqSIQgkQKlgGRPwwFCBdRPkQUQgqCTmEDReUQFItgQSbybcYoYFDKRSXQEcRvgLkpEsF92W8L9jf3RAbSNcHH3yAxEePHkFW6Ys04tQ5A+QW1B6k6l/gjaSOM3GhhISEtbW1pKSkjz766MyZM6I0MnGU8bcFCQCkjdgakTdJhZQo5QGwC5VkMpkQ4nSibWhGmiGH1Uh+FfxsGKqgamk+XHKqcAgZwO7E2fDJIO5wrG/dulVbW9ve3t7f3z8+Pj45OTk7O/vkyRNia3h70PUgDBCANPOMELsSQ0ghgJxEOeASxIl4iIcAYiwZbx/BDvi5NYA+kuLoQepTJEqdiwg9jADIYsMVYMBBuS0tLYH+YdWB/mkOACJUU1MD4s/OzoZoQQ1CSi9fvkwGJYxFSCYInnx9kk/EIZwnTpwA30NPwtGnuX3ItjS9L3Jx0K6V4jRAKC6NKaTQIaTsBx2liHiejPcE6FOEEAPqXLIFoeVAuxBCaDCwOEgZ0kjvuYGdRZZ+Pd5I6vRMHRZuc3Mz1CgMVQioJIUy/rYgBUTAbjC2BxxlTrcolNilDAjhdkN+oOmgHKHyzp49e/r0aVoBLulHmh6HpCERChSZId+gf9ikqalpubm5paWlDQ0NfX19UL4g7MXFRZvNRjO0TqcTapqcOahsKG6AFDrxNHEzQoogMzEEmAAh7YqTvuy5NaUQaF6d8YmMdxvUTdShYk+yRyGASPc/GWQkBggpQkYAQGKDCB2lU9D7MAeBlZUViNzY2BjEDyqxoqKioKAgNTU1NjbWbDaDua9evQqphuhCgGl6nwDDVHqoT6sxYIyGhITgFBpNCEmn0640dmjgvBZSfhnvDai7ofGo6yFUUJjQihCbtrY2OCogZQgbSFmain8T3kjqsE9TUlIg1rTiHboY0gZhkuXp7wxIGwQAIfQOXOT95A3gEIQSfon0LBOOC5ibVqJB2R08eBBqDioPEfJsoOBwSmRkZHJyMrRkVVVVU1NTR0fH0NDQ9PQ0TFQQNoSQuJkR8r452P1sDVAidqGOJRUvgbQ8ARcMpu7jbyn+SjqdQoky3mVQNwW7bZ+5JskDQcrDzvmFACAzZEkSKpI3SBoJGwGJBFgA2IWIwr6EuI6OjnZ2dkKAKysri4qK0tPT4VdBvK9cuQJSB7uzaf09YBdDAEMDrj8UNyxdDBml+AofTpHULMYXQRprTAXLSvj9AmlU6ndEECIFPQ4VCvKFgw7XJTExMTIyEnEgSNJvwBsXysEiKC4uhil65MgRmJ/Q1LgN3TtYEBnvO6jHCeh3AHJGgNjB1aA5cxh88D+gmOCRSBPmNFsu+SjIg1MgVzk5OVB5bW33aJJ8+vFjKMS1tTW4RNCS4krtHShQOE7kPDGIihT/RTXNNDWpYwJ2oXwRihr7p9VqdIjyELArpRMQZ+fsgc4N7ohABuIGdr6MdxX7e5NAiftTKA8g5QcQfyWPlLKXHhQDOkLWHbJBKojXCRBSePnS9D5kGNKLk+HfP3nyZGZmZmJiYnBwEJRfU1MDvod2hQaHRfv111/DtMUw+eKLLzBkMHA+/vhjmqzCoTNnzpBbj4GG4Ua6noYhQoqLo5MhOGhl/DWBzg3G9nwnRCAksPkgHjdu3CgpKQGpQ3KSkpJ+5YE6wEidKDwhIQERyh0TEwPP6fHjx5AkWApQ3HDLcKf9JqSMPxHiKGbDGN0h9QhF9h/65S5C6kQkQiMgBZBSAMqGQ6QvKEQiJAFiABUj+d/g7E9FkPMNyTt79izyIDMclOzs7PLy8paWluHhYQgSmHt97+0s6ESwNrSf+EQ7yNkE6ErylKA2mR4FVYtaVNwAqF1KFncA/GFxysC2PdYHx+/lEcF2fg5kE3MGIcWl9DeduA84KmWAO+gVQ7oOmMCxt1EepHud4raXh9KlK1Bi8JBnw4Ft39E/Drgj3VQCbso294bTw1oAoZQOUH6qKdVl//YngLqJOg6gFGB/nOUT45BAaVcCMoobMw0pA+IQRQSIi0CH/kz8yAhEhEJAPCVI9hBiJsci30O+EULWASTi+rSkn6b0h4aGWltby8rKoLihfjH6wsLCMJSgzWEiY3DRKKNpfDA9DmEYYqCB6eHWS4OUIhQXh3XwSRkiNMAJ2EUiIvtHPSUSxFwsm4y3CeoLRNAv6DgpAnfogw8+wKGHDx+Co6Oiov7lT7cFPXWarEcEUkVLhRcWFuCp0xwpiQ5uQ6GMdwE09oDg/l5KcGcPlCIdoghkBcY+9SaNfIojxFHwN70WfOHCBTjZcBpo/pyeFB48eBBeBXwI6BSLxZKamiqtUBsZGYHYQGERMwPMz/75vCWl4zgcYK/b6QVhMPLYr2RB5CCRVzcoTyhSN+M5qFfmPAHsTHYyZfhpc7vYRnn+SED3O8UyoMzeDZdvw+V1sx2kOdwbdvfGOsK90qKQPofb73D7qDoeF8gS2RhbUK2dbq8T1gxjDafXZccmHv1DQa0dJGPsiIVHO7OyeVgZgqFYIycysBZ2BSuLGlHj/7RRj+zb/hKgKgd3ROzVKGioUQZpE7vs3wKYXoI0LjAQgkNCBI0LCmH4wrkfHR3t6Oioq6uDEk5LS7NarUql8ttvv8VIxADESATg0B85cuTYsWOnTp2CqU2z9xJ/03DG6JawXw9I450OSekUIgX6AZFXgPx0ooy3A7Q2x3Ew5tDRFRUVMAHhpkdGRore95tXv0vP3gEyASBAEKOtrS2z2UyvsVGvI8Q9qNdl/LlAL2D0IiKOsp8NMzqERHrmTf1F2agHKUKJCCE0AFgcKgP8DcKG1vjkk08Q0jM/WHUXL17EKTD1wN9tbW3379+fmJgAf6+ursJNgVaiWUc4IogzpSWCed17zzWDGu5nIGcUPOEKutWvB86ljZH6nuP77+TH9kcjSOqi3g/y3F5RHRtu+4Z7nYVBtmAUCEYXSR15QNuMuUWGwMYoRDyKS3mQ+LZI/ZdAhRhv0VSBtDFSD3aTWNmfNlZgMZ1B4nJp+4tCInWEwaQ/ADQ6aJggxKghvocpjAEljSm49fPz8w8ePCC3vrS0FMrdZDKByDE8MUhp2JJbDwKALY4xi6Nw6FUqFRQC6QSodOgExDHwSQlI+hwRghSndBl/ItBH6K9L4uttMTExEBJwNFyp67/+nrrE+ZQJuXEavK7Kysrjx49DPqDxyasjBO8m408FjTrqEURo0FIKjVVp3JLFjXSKwAuHh02r0DHyYeZ//vnnUATkgp88eRICBC0AuaH5866urkePHj179gz8TQ8XyfkmQN0AUEOkjxAiA81GQv4Qwu2QJidfBzi1bBOJ8E3A6bQRKPO/k1865Y8DkTpuhPK8ltTFbR+pk2uLnZ/T9n5S/ykD8/V/rfX+KOzRMUolVo6Fv2x2ir9KeHTm/u0vCjZnsbcFk/54YLAwkheB0UTDigYaUjDQdnd3d3Z2kGi32+lrPJOTk/39/bW1tVlZWVFRURi8GMLw3WnqnqxzsD7RPD2eh0qXlIOoQphyIL2BOCKkUrD7CpjSkcn+7QI9gj5FD6JPq6qq4FMlJibSi21vQnD6HbxO5E9PQ//P//k/iMNNhxCgv3nxY1tyj747EEdi8OkXQCkYjQB6Cj46IpQTfQdr/cKFC9988w0ccVhphw4dIgrHsD979ixORNfn5+fX1NT09PSAwpeXl0mbANAm8BVIrdD04C+1D+ISeVMKZdi/+0vgsOgJ0fYmvQlS2JvzFK+35xe+yiX7QBxJ2x8N54Yb5E038uyROpUNicTrNLUAfvs1UkdrOt2vIXW0o3i1tw3Gx0E+Y73zU0+JfSvWTgr/nBK+Z6BhAruZIA0fRABEiOMpkQYjIEUwMHEUh9bW1mZmZnp7e+/evVtQUAAPDUoANE/z9jTwoQRoIR7UAi3Bg5ZANkmBkPYgxQKIykZHcSlRxlsAtTyAnoKznpaW9s9//jM1NdUqfvjyTfgACh1+PfoeiI2NpTj8M3hyh8WPwqK/0ce47mttNxl/CsTRxwxqxInFEaKDYINjlILFYZVj0KIHP/744wMHDmAYfyF+DwvdCnM+Ly+PKPzhw4dPnjyBEgFzb4kf5aCQ1ISkShCRNA7itEvqBiBFIx2V9It09A0AjXmxiWzBLsiSghROPh7ODTK6yBzIg5w/m+/dy8lO2aMZ4kgowl+59e8CXJ8enDPaZo/wGamDklEjeLYogMPltiMUM6OAQUbHJu6LpL730B11cLyW1Nld/ljstSHbKGWPxVlhHKyPgsVmmygIGygYqxdNQlAfsfb+xfZ2Vjb8Zuyv7JtAefZEi9pE2tAULGS5fidgpGDI0MAh0IDCIYR0FHE46NhFXBxbPxnWAM4lKxzjF8OZgFPgzWOY9/X1NTY2FhYWYvjDVTt+/DiUA+x7ycSnWTocCg8PhyaBeoFugdpHBLoFQAS7ovqR8ZZAq9lA4WFhYR999BF0e3d3N2gbTP1r0++gcPxBDtA5/PXk5OT6+nqcAzfu22+/DV5bBCgEnS1bar8v0GcADSFqXkqhowBSCIhTTgAsTu44eh09TYvawOIgb3GpLPv+Gozxc+fOKZVK9G9JSUlHRwe88MXFxZWVFTjWGPD0IBxagGgYISDpF0QI2CW1gjjSpQhBzMKwn8gBpLySYT8YN4AtNnxsKTh43cXuIiYzhhAftNvdLocX2dhbalBtUFoiKbr9Yv69YrD8DlAH41dxKt/psovsE3SfqQyAuMdAJ/4eQGkdbteaZ2MdBRAXR7MZeFZIF6oGkwXFpMl5ANTAKH/D7QkWzOXwOGy+jXUfswxET93jX9/w2d0+J3x6N1WN8v6hCC6zF2/HisoaUVyib9/wkpEBwwTlcyKjGy2L5mMzEC7UmlUFBIPCIhlN8RPp0eZ5d0n9jW0L8RDlhdVow7Xu3YDhhcysh5zMphHFVZQwqWt/L4j3fVVQERIokZVMHI+0Kx2iE+kQ4tJwhnVONE+P55FhdXUVSuDx48fw3G7fvg3lAE8dtv6pU6dA8FAdn3zyCa2EhWOAdPoGFLSNyWSSqH2/gvolSFlRTooD//IsGb8EtR4aH11w+vRpdBCccBhn5IEzAn8d2DN1eo6OMDIyEj7cP/7xD1wFzv6VK1fQi3R1AvVTcEfGfwTGz78AHULbUgujCwHE0RHoAjYa9h2COw5f/Pz58+hmWNw0FMkdxyDEKSkpKWVlZRi0GLrr61C+QRNetN2DX9BECtEwqYO3DCJ1h8uzDuUpqkgRbnAxGD0groqH2kUZWWYxj0OkGZdH5EXoMUYybJk7lK+XsYsdkc3NgBPs6vFseP1unx/nUh1J2Yl3DurK/x6sBs51n9u2HXAH0JZu72Zgx+fbdoDRGRe+htSJ7MW6wpZx+JzrXqftBcwqr8frC4DUbS6vw+NHs7g9sK5EXvmDgWKhJLQIXyR1FJcZW2hqbGhGl9ja7IB7A93BhAVE7UeLO3x+keNFFkT74sxXNnHKInijdwpBUt9XNiaEbq/b61m3r6M2EEJIoNdp96KPXHYMoO0X37sD22weBZ3rctvxH30UnFb5E0bQvw9xiAdpXuJ40gNMEDc2bDbb3NxcT09PeXl5ZmZmREQEnIQvv/wSKgWKBX48OB6UD46H2oEFQMpKApRSMLZHEFBQ5GJiF/oK+g1xAmVDBIcoLuPXQewAFkCngJSjoqJevnxJ6yeCHP4LsGfq4HxQOz1+T01NHRwcBJ2DKuACUtPv7wyKyPjPIPH3fkhyj9YG9keQTqdI1jQGGLoGOCgCvI7+zs7Orq6uxrCcnp6GI47RK62bBYi/ARreNNT/dDgc0IdgaJ+DUTRTjT4P2NH9cjuAbXcrsL3FPkDj39oGh2x4wXkBu9sLIwDnQUX5oKQ8Gz7oX5c94HW93NkK+L3ixz92Nrd2fVu7bt8mI32xvvTIn6r/e5E6iMDndmz7HF7XmmNtxb5mW6ZfhHHRrLUbVOn0ONmL3mBLFIMm55k5s+HzwtFb3/S4/Bv2taWnqyvPUSkX6ujddHg3bWAcNhvxFvgQpXGKL60xUmfUhnLtOehurw+8tW63I5/P694OeLGhmeH0eQIB//b25u6Ox+d3ODdgL7H3E/dxG5E6OP8dJnVUOVi4vcLClvJBVGC6bDjt6NyAB4bXmn9j3bZme7pss0HWfJuwvdy+AFks5LWz6+2BdfV+Y+GdBA0EADqBzH3QvAS4AVAjvb29dXV1N27cgCL65ptvoHDA8QAicB6giC6Jv7xAims/9usuKK79u1B0osJjQEowJuNXgXajBgQpf/755+Hh4dDzoOkY8Wvwr8VP0++gdjjrCEHtX331FTqSOozC4B1k/HfY35JoWIDiSKeeIyCOARAi/i4ZOuLw4cOfiD9qguF07tw5GFvoptLS0r6+vqmpqWfPnmGg7og/TyKNT4xVYgUMXYRvhSF+GxiXgzS8oHIoF/iLzoWZyftdHcP994cH+kaGB0ZHRgeGRsanZladHlCd3RNwef2iR8k0MJSyy2mDF+XfcKwuPhkbGhgeHhoeHh0aGrvfP9LbP9w/OL68vIYbQXMh/J1bgJXZ7bA9H+3vuF2Ym3QtjlOqzp+7lF9QvG73uDybsD/syAAPlk3TijMTKIgblfWsr695Xc4AjJGVxbtlJYrQy2FhIbdK7qysOzf82+sbfrjIiIL+qeR/JN5I6myeecPtdMIYcm76vSvPFgZ72vu77o0O9KNTegdHBobHuvv6+waHl9fsyAxaR/7gVRETeZK2dw+oMlvQIFYZG6NzaQvO7TjXPU7blscecK4OdLVZzObT5y5dS8l4ODPvgInp34KhyTx79kjol6ROLfnXAI0LVJrqDb0B7QF/gJ7NOZ3O58+fP378uL+/v6KiAmQB7wIa6ejRo7S0HjQDgr9w4YLkxEN3SbP02CUtR6CjSEd8P8HL+BWIhKDjxd/OQMt//fXXtJ7916bfyYuHmw7mT0lJQefh5I8//pjm3umKALqBQiB4Nxm/HWjD/RGEkHLAKD4gx6gAkYO26UvRGDYwjb/88surV69GRkbm5uY2NDRMTEzAH6RxiOFH69rIF5f4W4rQiKU4Rd4RoIBsqt3pdDkcfo/bD5JeX22tr9aows9/e+aLw8F5CCBcrRt+NOfc/M7u37bDUoFD6IXKtDudqy7n2pbP5XXaqkpLvvzi80PsdzIPHzx45PDhE+fOhRhNMRMPJu0OB7ubuOwIjcBiYuP8l8BFfD7P0/nJ6tLC69GWy+fOfiAiMip+bd3tggnCSN0jTsKLvhu7Z/ABM5R+wLOx7fc8fjh6+dxpOvGbM2cfTM3Y3QG7d9MT2LazB9hklv2xHYei0UdmRCoCgtwGusKOw24LeMHw6wOdzREaRei5b45/gRb+/ODBo4e/OPHNuUuGiOjRh9PgOZeXnFdmbYmVJb6k7V3Da0idJiccaAy327G+zh6sONd30IerTxOiTNRHnxw4XNPY6g7suDd34LWvw14Tq8xacQ9/OVIHaFBA2DBGACkFID8eHA8lAw2DoysrK48ePWpra4M6slgstOYOmgouBxH8+fPnkQhVBvowmUyk4hACpPpk/PvYP4mL9rx06RLaGRE46yDrN/E6e6ZObjqQl5f3P//zP7gEJBiWF07GdcXuYI9yKUI3k/EfA22IhiUWR6tC+tHUZ86cOXbsGEiJno7DEEYi7OKysrLOzk5Yyg6Qnwh6KoaQmdb7PDmMSQDZAIw9xIMH3lUwOwQq0LEOJQpiDmysry8tTI8Pg6HPfH0SEvjpp58hPHz0ZFF5/Yp7xxHYtYkLzKGEnRtwwW0+j33L65qfeqDnlJ9+8MGRQ58z1fvBh1dDhZa2wcnHi8srdrQDs3bEpXQAdklt/R5welxrOz6bZ/1ZfmaaeOsPYuOT1uCpM1L3gdSxBUkditLlYc9swfR2m8dp3/V7Vp7M3shI/vrkV199eSw5Nf3pEk7dWvcEHG7/upM9YYBW/ePpAaWj6Q+xjHukzjY03YZj2+cKuFZdS7MLE/f7mytVV9irNSC4c1e5uraesamFVZdvnT1x8DvdPpQ6SOputlLwvyZ1FOk/rL64DOBNeCOpIwJCg7D43A6PfSXgWN5yLjVXl126ePHA4WOCIWJg9IHLt8XmjVBZ0Vlnp/wnpP7TKX86iL9JnwCUSCOF1AgdBcFD7YDdAVJByDA/Pw+CKS0tTU5ODgsLoyfxAAj+iPgNHOgxeIlgJig9gJSe6BvKzuG/BshCajG0Hhrzww8/BFP/8MMPv/JYnT1Tv379OqgdHn1GRsbMzEx4eDjYRalU4nK4lkjlzP2nSPBuMn6OVxqHdtF6+40htCeAOBoTAwAeOcYAzWIdPnwYBi88cvRFTU3N2NjYkydPbDYbxs/29jZCDCEMKgADTBpmCGns0fDDLkEaigCNT0p5p+BAkcQCel0Ov5s9vNzx2P+x658ava8JvXz2m9O8IHzy2QGQdJhgGZlfXw18bwPN+eHjw3ZZ92zYd7zrfttsc/Wt48eOhV0J06oVn3926IOPjhki0lbW/Vu734uzo2DV9Q37GrwMH/s2LVPBcD98vk20pc/rgmHBnqSCg0FIbAeXJ15Cg4KZAC+afsvrDnidXuT3+52eLZc7gEM+t/NlwLnlfF5akPkJo7qP465nLNt8Lt+uywfndQOlhfXl84pLBzb82GDMoAQep23TA0fQ4bYtz04+fDw9vbzutHnhprNHDE4HyuxESaFIff5tj2fT43Z63XZGNkiBaoVBxCYtvE72LoBfXNqGaol0Ii3YBtu4N5gd4fd7fJtuDzwt9gETtgqMVU6SCkZsrMKsyqA3qj37ywwhp4P1zoZ90736z831zecPUyJ1H37w0QcffmGIzny2vhP4/v86Alt25HXTyn+PHWTu9Yjv+YklEEvlETe/143NhxA18AXcns0Nd8DJmoneuUAxYF6gmuxT8x4XRH6bLYxAA/o3Al6XH60INxrFRdNA/l2igbeBezt8uAYz3ZADA2TD63Z53Q4/W3YRcLLXCMVED9rQhbZAG7K1b8511j5oFLZOETnZug2IAVzwDcRxcefalnN5Z2PFZ19emJubePR4fsW2wuSEXdMhNhDuybqWPUTyQarYKki0gse16fUwYUOx/OLSzg30kQ9HxVWTaBib27UKg0k08vy4NSgygGriFHYxFmMl8wXYQxD2aIN1EzqOdRBGORs34hD6PSCKxM8QPLAHkhMAcdI5ACvdHsEDqAAyPHv2bHx8vL6+HgSvUChOnDhxUPw2JVQcFB08eCg96bs3BFKMjOFFSJoTQJywP/53A7UJAB4hUkeTgj6mpqbS0tLe9Fg9+J467cCjb2pqOnv2LLxGeJBodLqijDdBEsT9u4AolsGpdYrjEOwken38iy++IFlHHFKOlq+oqBgeHl5eXsbYwJjBOAGXE5HTQKIB9i8hjrjfb8T/YYBeWnd71zx+B9PODp/L7neub244XgQ2pgdb9Ze/5q9eLizKC1GEgSoPHTuTWX1/IfC/Nj94F6qfOVpQ0T/6bI7ptkjuzMlT3xbfrrgWKRz66JMPPjyriiicX3Z6NsGZLrtzzedZhcv1YsP90re16Qv4A5te/wuffxcksb1p3932+zZ3Vz07y97tdS8zApj/5WBOqnfT5d/eCgReBDy+Fx7Hrm814Lf5d17aN//PqvulEzrb497xOHYcT8pzkz8W/dfYxNwVx0vv1g+wAnxb6/6AbSfg+s6Pe3y37H255Nl1gp7YxPrKjt/2wuf4YTvwvz/8Y/fl/zi2Xi77/Os+34YLF1z93mND8QOBLf/WPz3e7wI+x4vNVRgxu17vyy22JHBj0+968cK1+Z3d/cLh3nJ44CWDONcC7udbG89Rqi2vdzPg9W/5vIEtb+A7r++7gP/F1iboBqRiB4Gi4Zk9tWFnfM6eivtgMyDZ4UHlbW63DQdh1bB1f2yZt/0H34p/5n5mhJrV9KOjuqiCmeUf7Fs/2hhjrqP72At5Dq/NubHuQ8+CHcFqATvML5djN+Dd8bp2fc4dn2vTt7EV2PRv7np833s3/8fpeeHY2HIyS8Ph99j87qUdr22HmVC7vs1/2vwv1rdggDlf+OzfoxUDgXWv38bWqbkDdvvOxvpuwL4dWAv41kGE/u0fXO5tEP53W96Xm+vbqGvgO7v3pR02nH/jxabjhc+569ve8e9seze20al+SEJgexON5Nr0e/yBLad/d827s+bdhbXkc9p2N1ZeeuwvA95/fvfyh+9+8P7w49Lm9vrGptfpZ49SfLYtr/2lZ2PTs+FHw27vunAlP27t+T7g24GtsB1w7W7b/S/sru9c3hcuZg4y48zrerrpntv2rm75ULddGBMg9S3vxpYHBWKS6d7yrcNIePGdM7Dj8PodbJrf5vasbzCxD/hh5vzrmYA/HJKeYRaROEu/JQJx+BgrKysg+MrKysTERBASXHaaoof3gvilS5fA+lCJpBuJaCTlKeOXICpBo9ErTo2NjbCc4LITcb+CD65du0Yz8NhBmJOTc/LkyVOnTpFrHrykjDcAgkiyiBCyCyAOkwpNR4fQjPC/v/nmG9iq9JgcYg2ZRpuXl5cPDg4uLi5iYIC/AeY0iCyOFKZ0fwud/7XASN3jW/X64ZdBm/ucIHV4hA4o/emBZuOl49zl820drQXFhZ98/OEHH3waYs4Yeu5dC2yug44cPo/du+UBlT3prc099cVHOnPkyPh4aqzu4Icff/DxhTAL+MbhZi/Ggb5W/e7n3/ls/tW1+fGp2qqanBu5Cck51xLSE6/HFeandne2LjxbsQW+X/b/sOyGb+T2O5zb63Ywrte7Nr2wUNPcnZmZm3Y9NjMhKjMl5vad0u7B2QXblmvrpQvOptuxbZ8vz7nGPPUPP41KyH9me/F0ZaO9uy2vMDU5KSojKb4sr2h8fGYp8OOi7/t19jwdLuaKc2V2bmKwo6GuIKcgPbugY/TRM7iJ6Hyn7Tvn8ubS7NzkcH1jc3J6cU5e+dzsA6dtcnqsr+ZOaXZKSkJCfHpezt32tic2j8P7o821BQsDtOx3r2xvLLz0PNvxuGanF+7WNaTn3oi7nhQfn5SRll1dWfN4ehJevSew6fTsiM+P7XbXmout5/OA1D1OInU43mvuDZuPPWv3rLs318ArXsd3vmX/ZHe2ScFI/cOjWmvR9NIPa5vf2+Crbtg2nTb/uivg8oOdlzecNhgZ8JJhfG04ttAP62szDyZqKirS0SQJ1xKvxWemZzbWty08wzn/sHm/d3h27ez1/OWX/ucvvE9szx513OvMzCuLyciNyUiLvxaddf1afVn19NTSuu/HZd9LePAvN5wB2/z66viDibby8ryU1JSyyqaVNe+G0zU60FV0Iwm3KilverzoAi+urS1OjXQ3lBdnp2TdLryz8nTB5Vjr7hsoLC5JS0lKS7yWm5Xe0dn13O5d3/pxyb0LkzDgsruX5pZnH/S3t5YVoi9TKlpb59DOPlhcXlhAdrimsxOjXfeKcrITU1I6B4dtHv/8wlxrfVV+Ju6ekJSSequi+tGC3e77v2uenXWvA9K44VjbdC99F1h0rz+ZmHhQVlGbnHYj8XpS6rW41GvXC28UV1TVVdbX3qquKii/W97Q+mB2wQHrK+Byoqccbr8HBgl65t+Z4X+rgDcCLof6IscdIIWGEL4KFB3UXUJCApx10BLU4AHxFdwzZ87Q/Dz0JEiLHkqS5iRFCpAWlbj/bwhqDbQA2gHUDDbJzc2FtSQy+GvApt+J1wloRFpijQvJpP7vgAng3loGhNRo8L+vXLly/PhxyC4kGHSOnkDb3rx5s6OjY3Z2FgMAEk8DAHKPwUCjgrgcEYcDTixDcNC8d3B4wOvsZW6QOtxiHxrE5drxbUwPtBkunVRcPNs/OjAyPhxyma1BO/zVpcKG7pWtHSe8Xtd2wOF64bM9mRqMNPBfHfuyprF+dXkhJ9506IOPPvjotMKSM7vs2PC77eB/Py77fHa8/0ZScuj5y6dPnuJ4rdkabzJFnf3mxGeffXL0yOG4a0mDU8+WA/9j2/qHjU3C2v4vXPWFiTs3sy5cDTt04tKFMG1UZHRanIW7dOqLA58cP3U+IevW+JNVOPLbfrd/baYyN+7QRx98+PHhMHVMQlqJUjCfu/Dt4SMHP/30k48+/PDAJ5+GhKhLGrrnHDtu34vtzcD8zNiNtDgh7NK548c+/ujjg4e+KLzbuABlzVxp1+OBznSrLuzi6S+++OKjjw5+cuBE3PWY6Fgh7Mr5r7/66vMDBz/+GCd99PmRI7FJWZMLMF++Z++UuWyb7uXvvM9scyPFBYVnzim/OHnhzJVQTtBolIpvv/zy6IHPwkJDSqurFuA87/y/a06/3bHq8a5v/Fuk7vwOnvpkd44p7FNG6oe11gJG6oEfYIjAjxfnWtj0idPjdsCWYo9XYGTYvw9sLE5OZKckf3vm3PFTZ0OUvEZvCLl09vjnB7/6/HNeLTR3DD9z/bDi+x+7D/78uvP5aM3tFFXoWfhzV5Qabex1TWTklYsXjmEQfXpYFR5R3zGBntoI7HjXlrubSznVmVOnjhw69OlHH30UrjKNjM8W5Bec+OroJ2j4Dz44+fWl9r6Jnv7BqCjzha9PHPn0EzTcsS9OpCYmWszGsxcuHT3GFnmJzfnRiZOnMgvuTC5trHlf+gK77rWl2ls3LFzYxVNfHcDlPvrIcC1hbGXdt/0/W+6dZ08WCooyFVfOnTl29CB6+aOPVHpjTHLa1bDwb099eeTzA6zrP/rowGeHBX1C5/CzFd+Ozbu64VuDp77rW3/6sD87PfH02XPHvz4n6CPSktMSoiwXTp/89JNPDxw8fOT4yW8uXDl9KVSpM7f3Ddqcbp8f48PuZg8SYLWwZ0TBUfTOgBQXQugxACnYpRSouN3d3e3tbbvdDtXX19dXUlJiNpuhEqX3eqAq4YAqxV+Zg54kdicFSyC9+jcEyIUiRC7nz58/evSoxWIhP/y1CH77nWbgkY9ehoMrSURFl5PxJlBDEyCOsEPR6BBQiCmUBVofKWjV2traycnJlZUVSDlEnFaT0jwVBgCJPkEaEsH999VTFx9bi99mEVdpsaeFG3D5tn2ex0Md+iunr5w/1zcytOZYy8tMgnaGs66KvD6x5nRs/eh3bO06HfBHWxrKPv/ihFIX8XT5ydqzhzdi9Ec/+PCDD49z0Tkzy86NgB+un9frePp4LNrIiRf5AONgambOAT5bdz2a6I+J4D6BGfDRp0m5pVNrWytb/+vw7+xsrK+OdqdHcAc/+fiTQ19Gp98anF62OdxbG0uNd9KPH2TX+ejQVzkVjfbALgq8aZspv3HtAO788eGvToWarKlZBbcqaypq6ypyctJCL51nLPjBB99e0fRPLrl9L+GNr67MD3Y3lhekKcTV7we+OJFX1bjo33Zubvo2HHMj/XeykqIMqoOf4dRDH3568ooy/Hpq1O1bBY3V1XVVFbHRkYePHGDF+PRoQUmTDbYQ7ELX6gvvyurM/WtmNfTk4ZNXcypbp5bXV21rrsWFqe5WiyoUjfDFiZM3qlqfuv+57vseje5lD8F/A6nfMIWx5YsfHtJY86cYqf+45sVZrDO9Lr8bV3A5PX4w+rrXs/69z/aot0UbehENeT5EXdHSt7DuX7a71p4+HmiqUJ87hfKcvaSs655a9P9f587/ut3rJdnxh8XlCZcuhXTfH3nudD93rC0vzlQVZh7/gi2wuMpH9M+uund+2Fhb7btXHxetDb3yrfjs44OvTlzW6GKvXA7VCyqrUTjzzenzl8Lv9Y4Mjk5kpieZNWGfM6L/6ODBrzg1fy0hoaSssq6p9VZJcYRB88Vn7K4Hj31d3tK/5nvp9ga8tqXZgY72yuJIIfwzduIHlpTM0SW7e+vHbd/ukycLZRW34iL1544fwbEPPz7wzaVQU3xCbmFRbXVFXW1pdnbC5TMnxPmbQ0Jk7qNll3sbV13d3Fx7OjmQaNHAMjt09Ovskqpny2tu+7pn7dlQd5MylK1D/PTAkaSMgvHHT6efLi+uOTe8Wy6Xz77u9KA/PGT6sxcOg2Pp3cN+xQXlJqVA40H7bW1tQVzpbfj6+vrr16+Dy0+ePAn/BwT/5Zdfnj17Fu47CIh4nTgeEYne/j5AlQGQC+IUXrx48eDBg6AV4muRvV9F8FfaAFouB+sJhHT16lWRp9hVZPwS1Dg0U8SLv5iCdjty5Aj55ceOHcPR/Pz8zs7O+fl5yLQ0JSXNrgOIYBeyTkSOCBLJWYfQI7TZbPDXKfP7B5HL7eLmAqmAFNzQXj7v9HCn9vI3F8+f7xoZ3nqxPdzVeOHkEWi6I6fPFbX1LHm/23Ju/WPDYV8Yio02fHbsYn51l2/buzo3lGPlvmKk/hUPUl9xuTc3PT54NnaQuokLO/zZgZNHT5aXV9iczp3dH1/ufvf99kZ/x91zp0/g4mH6uL659bXtf25svXQvPSmK0TNV/cEH0YnpU7Zt5+7/E9h5+f3WxmBbeei5k+jiM5fCa7uGHIHdgM+z7Vioyk9i6vujQ5b43IVlrzOwDa97e9u7vekZ7m0PO/81Dh44+m1hZZvDueljC+/Wd7wrjrnhZDMPQjp05OuC2rbFrZc2P5jUtuO0fbexOjXScf7MqQ8++Ozo12H13T1whDc3/Tv+zW0oxNWlhOuRBxitf2yKzppZsG37A5uuZc/SVG5y1KcffvDJwa9u1vStvvj/bPz4z82twP/Z8vx/N13dNXdOHj2Mc67qYrserdsC/7vh9zucNraW8N8j9cBkd64pjFk1Hx7UROaJpP4PG46KJpoTTrorsOGCI2l3e2y7m/a16cF4/upHH3zw5YkzHUPTzu//f46d/xPY/f5/drz/9C3X5CUfO4CDH5oTCh4sb9k2/+F02kpyUk4ePnT4s4PWiPiZueXAy+98u97/95++9dk+s5r9AOUXp8/fbh+0b/0Q8Ph2fB5Ybs2NFUc+R299fODz85HRmSOj4yi807b4YGK0paNn/rnNv7mzYV8e6qk/dfQQbnfuvHp47JF3a8cV2HZv7vgD/qUnszFGAUUBAV/LuTP7/2fvLACrOrYujLtDcAvubXG3kBBICCS4u7tDcE2AhBDienMtilcodX/1UmgLbV+hBVqsFIc4/zdn35zm0cJP+3gF2ruaHubMsZE9e+01Ry6KWh0TdcAYdsAQOG6AS8nCRFf5OvQbuCYsNjZ+r0U9KWk0xsdFhfn37NBSHVe4hPvQsTtjjaaEJEZ5fKLRGh85b8JgxxIqonNw7DhrdYApMSk+iRBoy/Sx/cqqm0r56z7Vc1OIMT4pMdkSvT/BkBAXPGaUZ8ECWEShJk91CcIaUvbHmhPjzElxcbuMcfFGc0yMISwqJkp7tNDmRh4f4MRwX+LBcG6yShrY9tAgW3FxAHmDDwwMDFyyZMmQIUMgb9id8QUH4U6rVq2Ka5WZeagd4FdtLvifBI1tbHcfHB0dCX2Ie7re+/PvtlfaYHTQsWPHsmXLEkLSlHfNfvxDoEdGAltuDsgR8xI6J1yqUqUKcpxWJnpycHBo167d2LFjV69eHRQUhLEmJydD52LEAIPWE5qp22w9dxowGGQpib8poHH1cyYsVV3jzAZ0XpwlyWL2XTm/Re2KtWrWnLdqrSUlOSZoyyDnjposLdCgs7NvqGmvdde+qPAl00aUKF2iUvMe3jst8UkJhh3rB7RtVIbd8pRv2s5jWyiqP9FoNphiw42RO3ZsWb9+xep1qzft2BlsVk/LJcVGR8eGbpk/dVjVCuqgGo3bL/AJi9n1giUhadmsyQ0dyuDiKzlUmDZ/aVTyi9Hx+9RXQWPCYoK2bli5ZO78+as3bQuJtcaqZ9Hi4qP8xnr2UPo1T+FOToMjzbtirPFGK8QQl5xojQza1rur8vuFSlVzHzo1Klb7LlBcxG64MnCtc6sGXKhI6cpe0xdusyRHWCw0SKIhaq85yn/DIseqFXD9JSs2XLJxa7R6sstiNZpTEhITzLHTJw2qWA6KKty8XZ/1vkG7EhP3WMJXzhvvWFXFKBWrNhg+eeniDdvnr1y9YqX3Ju/FW1csmjikf+XyqrIlylUZNtU7Iv75aGtytPY5W3lQ7gFJnatC6i0g9dB9UfEHokzWaPWxfvqUI9SPB5iNURZrlDl2x7wxHtWLqYap06jl5HlrlmwIWLR685LlK2hDX+95g3p1LllEcV61Os0XrN4WY93LIAjd4bNpjfdq71W+WwKM5sT4lF0Gc7Qpaouf95T2TeuwcxGHWp5Tl0Ym7Gcc0fiMsPlzJ5curhjZsW77jb5RiSl74uNNCRaCOqslIdFgjreYrcmW2LXLplcoSUCSr5ZjSx//UGPSLmKvWCUdiaQiRyrmzp8nX7H2rkM274wi3k4yROyPC9kX5Tehf88yhYnZ8rd3H7Qm3BSTsM+knr0wWxKtxpidPdqrzs1TsKTXmKnRybtNSSlYCgFqYorZf8X0TjXLwN5lHTtOWLIjzrInITEyzrRmhFdnbfKmWOMOgwJi9yUmx1vjgpJNYSnxYbNnjS5alGsVcKzfxsc/3Bi/K9aUQCBoiN0VZ4iPiYs0mMIM5tiY/6TJxwfisugaHBqMzippyfytl2OVrQwHIJ+/DA0NXbt27fjx47t06YJrxanC7sWKFatYsWK9evVwuTje3J75dxN6mv0l8eRCqkNY00L7ajhp9Ha+fPkIfWii+5E6dM5mJyenDh06EB/lzZsXuvpnkjptJ6SuV1/SspRZIJlj17mcdKdOnSZOnLh+/fqwsDDCT3kJDUvFZIHNinOgGzdmrZu7tsWGe+X/vQCpR5njIuAwqqr9CIrVFGdOtsRtXTnvqdoVa9auM3vt5hj1xdeIDQsm1NJmvUtWcZy8cOW+xF2x23yd2j5dqHjpXuMWbE94MT5hV1zARs929UorX1m+ebsB20Li4pKUsrHERaRYIg+kWF7Y+1xiwj7/wOBFK5aPGz+xX1/XXp2aNa9TQbnyPAVrNOm0cHOYKeVZqzF2eD+n0oUUSTRr8fQqn+3RiailBAPyKCo80RyXkJiIwzYl7Y6zJsYYDAlmQ0JMwFgvTannKda+u2dIrCXGaok1K42SYLZEBm5x66l+SaFwieoew2dGGzAMuDJyV3yYyX+ZyzN1lC4rWbHftIVbrSlR8VazKSYxLjbFGOm3bkHdKjB0vhIVG8xc7ROakGQwW4wxhgQT1B45e6Jn5TK0SeGmbd02bAtHHu4yho71cipVTJW8VNka9Zt2bNi8dYOmjZs1a/JUkybPNGncokG9OjWrV6tWqV6jZhNmeodbngu37om0JKCzH5zUvdo0UF8DyFukWSevzaH7oq0Hoo0aqZtw0AmxUXHxsYZEUzSkHhXu279zk3L58uTNU6hclYb1m3dq2KxtkxYtmzZr+lTTRi2bNGhax9GxRo1q1Wo+06qj95pN5vgUi8mUkmA8sD9l156UsOjINRvWTZ89y8vL07Vz626NalYpTmiXv2j5xp5T10UkPG+00B5h1oSIubPGlFETEHkaNu7uH5RgtCYb1NyPNnwsys4s2JUpeuXCCeXVPYsCNWq3XLc1JCY+JdYSH2NUkjEuKnj8wN5llRwv2cpl6NYwg4V4wBC+Ny74QNS2yf27lylC9+Zr6zZobaTVkLjfqH5F12S2mqNCt/Vs/5Rqj4KlBoyaHGJNiIlPVL89aDZbEuK2r5jSzbGs6g7H9uOXBhot++KtEQkJPvOneZZTVpeneoMuq7ZEJO9KSbZG702MjY3Y2t+jB/kFCpbq1XtYQIjRYEmOJYAwJBpiUgzqtYwogyk81gSpP3YyXQfNrlr+DwI/SUfA66Ldo6KifH19p06dCnVVr14dN4uzlYe95EfkcMh4Y3HIeGwgrlt8teRo/vvJBnVhKVUjgZ6sU6cOqpso5/9R6gJInWX58uUJBAiLdFb7R0FvRGlHVnU0bdpUf7Mc0KwdO3ZEl2/evBmjxCKxRbicBJT8J8z6HwZNqRsiTYZY7TVrqwG2izPtssRuWzn7aUeHqjVqT1vtE5W8J8kabQhY06/LM4qsChRt19M1Nixi1Zy5FYqXqlGv6ZIdkcEpyOs9sQGbPds5Cqm3aOsFqRsTrXHGKKshYpc1Onyn3+L5S/v1G1K/UbOylSrWb9Cwd8/uE4f3Ge7Rvkr5EsrRN+2y2DfKmvKsJSbMo2fbItrc+1OtOq3dGhybtCcmLk69q60+gWeKMJghwlj4VVUhNt4SGx8dMMbTWTnpPCXb94DUjbFWk3rBzGRONCeGB2x27daCbYWLV/cYNluRusUca4pOsYaaty10faa2IvUSkPrirfF7o4gFTTHxRkOyMcp/7bz6VRQlFK/YYPoa35Dk3bHmeM6rXtQyhM2d0K9aaSiqcKO2Hmu3RiQlJCTFBno5ty6oSp7vqTbdZy9Ys2q9j/faFStWea9evXbNyrXrVq3atN5786YVmzf7BIWZo+KfDTamhJsT4OM/TupFm3QauCl0X4zlAH0XYzTwR2RmhoUMcXuscVBbRMSWri1qwLb58pVs22vwvJVbvdf5rFq1Zu2aFWtXL1+zatmaVSt9NvusX7tx3Zr1sdEx8Shvk8FijF63bs3QkcPatG9TqXqVarVqdmrfcdKgAXOGuD1dpzpXLlquqec03/D4F4zWGIs11JoYPnfmyDJq9iBP/UY9fQOssdYkhKwhVr3LHmOMijPGxhtNyXGRK+eP10i9YNXabdZuC4uNTzGYLLFq5JpMUUGTvHqVK1RAPdXfe/iWsDjFLoaIPXEhz0Ztmdq/a1nFwfnb9h26JiI+NnGvCTEeB29bo0K29uzQXF1bI/XQ+PiY+AT1ER76KMHg7z2lS+1ybCzt2Gb8sm1G6y6LKSI5MTgqaL2HU+fCBQvkL1yyTWfn9et9YiLCg/x9hg/xKFmycIEChZ5u2XXdpiCDZXcMMp14F+szJMSp3zOE1CMJWAgpbMPobwc8p7C7ALkPu0+YMAF2Rz6JdscJyy/B46Vl3lRcN8Bpi+sm829AYVIdEtSF2rGsVasWqrtSpUr3I3X+79y5M5tlD3iLmIhwQNpITv3PgdSapgRS/ebaO2lly5aVOz3lypWjZeHydevWRUREyNvkhJkwOhAutzP6A8A2/U5LxaLu4hIgdashbp8pcvvKGS3rlq9aq9bElT6hKfst1th9pqCVM0ZVKqXEepWatSeMHt+zU89SxR0GDB0bHJ8UYk0x4f4Ctnq2q6NNv1do3nYgpG5KtCjGNUT4b1rVs0v7EsVLFyxYpNkzrcdOnuS7ZUt8XMx+6861C0c5ViYSyFutSZd5myJNifsTDZHD3buU1KZHa9RtvnD1ltiUfdGUMjrcpO4RWiPjrFEWazS9zIoxLt5isKr31HtppF5aU+rqToL2mEB8oik5PGBjn+5N2Fa4eE2PYXOjDUhJc7Q5BlKP3zbX7ZnqaMBCJSu5TV/qG78vUn2MM4Z4IckUFbB2VqPK2my5Q/1pa7btTNkbbUkwxcQloIgNoQsn9K1ZuhAc16Cd58otEUmJiSmGnUP7tC+ilaNFy86bt4Tu3veCNVl9vEW9rm5Rs0fJSXFJiQQiFqNpd5Rpd4Q5JcoUr6YU/uj0e77ijboM3hh2wGA+oL3dHs0fWsscY4zZsSNk2+bAUP+doVt7tW+snobIU7Sjy7Cg2F0Ju59DjpnNhvgEo8VqNJqMhMEJ1qSk+AQCpkRTbGTQthGDPSpXrlqgYKFqtRy9Rgxfvmp1VFjYi4lRpq0LnVprHy0o38Bz2uYwjdStlnBodN6MsWW1+/z1G/byCUAra/cUYi2qi4wRRmNkgsGUokh9nEbqRarUbr9mawSkbjSZiEasCPnInVM9e1QolD9P3tJP9R65JYziWa1xUbuNoRqpd8kh9WFrIxIMibu1D9KpmyHRIb5OHZqpKhYo7alI3RoNqUPDJmNCgsHPe3pHR/VtxDJ1npqwfKMpIcFKxGaKSTBGB233mTxpeIun6hUvURx66tCuW+tnnqlVs1rDRg2HjRjl6x8ca91tMCdHqVfU4W/VQdpzC9FxRvX7On9jUkcRyYNEuFOW2Gai9ttU0dHRPj4+sHvbtm31++6lS5d2dHTM/UEbnLYkyMGHK5/+hIMaycMEUh1InbrLS9H3JHU2EAT11J6SIyGfk+MYzgLkvP8cUOXW2q+d0oKNGzeuVq1asWLFiHKKFy9OYDhkyBD5RAz+MSUlBVOD0YXCtYl2NdLsMv3BYPvkuEF5qcTouCSYMCE29oApbIf3lFaOZWo41hm/eltAwoE4q2mvJSxq63KXjkoSFSxcuLJD9cKFHRwbtlzjGxCXkBBjspjiUyIDtw1oV18j9fLN23n6hcSaE60JltjQ7Zt7d2tfMD9OueAzrbqs27bdvCs5ITEhxRy737x9/cJRtStyUF7Hp3rN2xwbE78/2WxYOHlQTXW7Ok/+QmUGjpsVmrQ/Lnm3uu1MJxusdK8pAZaM1z5cFmfVSH20TamXbd/DK8QQY4hnL4M5LjHRuCt8+6Y+3RuxrXDRmv1tpG6JNht2WUMSt87u93RV4oeCJar0mb7cJ34/St2kvk9iSjJG71gzq7FG6iUr1pu6xj8wZb9G6gb4L8EQsmisS+1SxAMlGnQcsmJLNKa4xxK6dOqQGhVKcEipMpWnzl4Rl7wvJjE+Rn3Fzar9cE6s2RIdn2hMSEqJM++JjIlHBRotmuv8/0ldvaeuSL11I4gxT96SDboMXh++P868H3rTFGSU1WQ2RURPHjKkWd3aA4Z5bt6xebina0U1cZ2/Uq2nlq3fEZf0rDFhNypW/eqeWf1grtViTYhPtlqTEaGJcVHzxg+pUkZNBDhUe2rC7JXRCYnmpOQEs+mllFBrwMxebdQ99aIVag+YvjEs4aDJEhdvjkq0mBZMn6TdnMlbv4GLT0BidAKRijk2Np5BGWsMNRnDEg1xGqmPLa/uqRetUrvj6q2RsdZkxqvZEJ1gjrNG7pg+oGtFReplW/Qe4xuGCIfro3cZww9E66Ser33foRqpp0jLMPZjQjY5d2jKtfMUKAOph8ebY+OtsXEELMRQpi3eM1o7OrCxTJ1mE5evMyXEx8eZrdHqy3iJybEJ8TsWzR5aq7JDHceWU6YsW7Z05aZNPmERsZaE3aZEdb8/ItYUpb1XEBcXY46LVH8G1rXR87cGLlQ9bpfzOWdxp7Q2Rk6sFRkZuXbt2mHDhiFhkexQO4RVuXLlBg0a4MBx3UKB4s//BqAuxCjC6CRq1Kgh33uFvO9H6vzDEjkPr6PaaakqVarQZP8oUqe9oHOWTZo0qVOnjgSDcsucxpk7dy5cTvCIVWFb8LeQtyw1lX73c+x23B/a0+8MXEuUMSXamBJnsCTHxb5oDQtZOaV1rdLVHetP2hAUuPslY1Jikjl8V6z/sunDKpXWFHSeQgWL13QbPincFJ9oUfPg1qSUqNDtXu2bqIfE8pRv0dErIMKUkJK4K94Y6r/pqUa1yC1atJzXkAlR1kRTSrLJbEwxRT9n2e67bEwdRZz56jzjOs/XFBn/fBLSO3BVn3b1tSvlrVS7xYSlm0NTDhhTdiclpqjPlhpjkpPjomOjwyLijOr5HnNi3M6xA4XUy3XoORBFH5eoeNJiSEo2740K3Ny3Z0O2FS5ay3PEAoMx2ZwQH22J25sYtst/tuczVbhQgZJV+s5a7Zv8QlRiIpYFAcMbwevnNKtiI/Xp63fs3PNcjCXRHGtINJuSjWHLJ/SuJ6TeecSqHSa07l5rWIyfd89n1JN3nLLBU90XbQqM3r3PkJAIQyVa4pKthqQUS3Bk6IoNW3z8UbNJ5thokyFCfYL0/3v6PdZi2J8Qkbx1mVfbxorU85Wq3334xujnLfHPJZksVkuMxRKdHG8xhoW7d+5cLF/eji7dd8SF+Wxc1cJRNT76uG23/hsC44x7X7LuPRBnikkyR+2mPBZTcEjkynVbt+8ItRqiR7t2UPcb8uR/qqPH+iCDac8eS3JKvNH8nCnI5DOtZ0v19HvRCvW9Zm6NSHrJnBCbaA3flRC3dOakCorU8zVo1Gfrzl2xyXuiLPHRsfSByWAMMRtDU+KM+0wxaxeh1NmvWNU6ndf7xxoR3KYYiyFCffcgOnC2V9dKhfLlyVu+heuErZEJ1vhEc1zMbnPk84ZtMzxtpN7BbdiG6GRj8h6KajYYE+MTjOGbe3dU0zB5CpQdNGZqNOdKiI+NizfFJe9KSN66ZtYz9ZVSL1e3ydQVmy1J+xLphEgrVm/ZHRkUuNi5rWPxfAVbNO+xeJm/z5bA0LBYk/XZOOteQ0JCXFICUa7REq8CrrgIszHIEhemPnirvnX7t5XpAP8JnbOUNBC9JJnigSF4EBYWtnDhwj59+iBBRbiXKlUKIStPy4O/AX/B5TL9DuQBgooVK5YuXbpDhw73YnSgpt/hcmF0Z2dnLy+vSpUqlS9fvlmzZrSLnO7vhLvmZCQOku6HzpHm8g1XeS1tzJgxcstcgkSgm5cwN0tJaOanMgXk2HF/aNOJ6kZslFLqCSjb+JiovTGB/ksnt6hRtmLVmmOXbw6IfzbKHI8+3WuOCN3i3a1tQ42xCtao13bl1uC4BDxfbLyBwMC8Y7uva+um2mRvyaatXXwDws1Wc5IlLiYkwN2lW4H86rhn2nZdt9U/Gl8eExMVErBp+dQ+nZtpD8rlrdq4w7xNUdHJh9BfKXEBAasmt2tcO18+jspfunLdbp7D53mvDvDfEbzNz3fN8kkThnTu3mXS9HkGs/bd1eiA4f2d1JXzlGjbtV9ITFQcKlF9FTwJpR7iv6FXV0XqhYpU8hg0NTpGfQ4+ymxItoaZt8xxbV6FaxQoVqHPpEW+lj0odaNJ3YyPN8VuXz2nQUX1kECJ8rWmrdqyM3FvjDkBUk8wGhNiQxeMca1VUs3c12/vuSogNk49mBW21xi0ZfmMVo2k5CUcajfvP2rCep8toUE7QgO2+qxbMW7cqBZt2tRu3GrC9GVW6+5ESDs2DCGoTUJon4lVU9baZ2LjIq0GBKIpOs6K8I2Bho0hsZsWu7ZqoIUvxet1HrAmJDnWsMsUFWOICTPEhpuNMTu3bWvVQH2dpp1TV7/IkPh40+KpU2tXqpw3b948BUrXadF5xIwl6/wCg4IDw3b4blq5eKhX/6bNnm7eqvPSFetTEq1LJg6qo+4p5Clfvf7YuQvD1GSOOSYsOmzT2qkDetQuq0R8kTL1+k9aEx7/rMkaFW8OSrSEz506WjsoT916PTb5W6Ksyep7doYkG6nHhSWh1GMjls8bU0a9YFagUs1Wa3xDiQQt6rX68ERLjDnCf1L/TuVozjylmjsN9wk2mS1JMGeiMWpPjP94j84lC6lHFdq6eK4LNcYlJFoMUURu8WZzVPCm7m0bq2vnK+45fFy4JU5T6vEmQ3KyOWmj97Qm9VRYVrZm3UkL1xkt+6xxu6wx8XiRhH2xK73H16+oyl2gYOlyFepUreZYr16zp1t279bLfcLMqVsC/WLQCVareh3dGGkyBlMRcyyxlxVS/9vrdaXTc16Hw6+Stm3QXK6IqHgN5Gzbtm3ixIlwHnJUHl6GwtCyGrMriKt/ciH0REWaNm1arly5mjVrDho0qFevXlC2sPhdUA/KdenSRZb9+vWbM2cOx9A6cqMCtNJ+qy337Xot+wmA8LcUWE9Tkdy8LnM19evXJwIqXLgw4V7lypWdnJwWLVokj7ILnYttiUlppmWDTuSymjttx31Bu8WY1A3C6BhGrPoFjriVyxYP6Ovc/unGJQrlz1+oSK1GT3Xv6zVt1gIC9CRrgtkQNXni6AL58hUpUqxf/0ERnMACcRt3+PkNHDywbdvW5UsWzwd75M1XtGT5Vm06Dhw0yH/blsR4y4Z1a9q0aqkeoC1YsF7DRh4DBgwdNmyAh5t7H6c+vbpVKFeWYwoVL92um+uYSTN9tvmjWFPiY7ZsWNOrZ8/KlSoXKlioQIGCRYsVLV+2bPVKlWpVq1qlSqWmLZ6eu2hZeHT0smULBni4NKhbK1++vFBpxcrVPTy9li5djsCIDI9dumi5a69u1aqUVsXKX6B2nQZDhw5ftnz59h3bZ0yf1M+pY0Xogk0FCtZu/FTfQUMXLMbqQnFS48eNcerSvliRwmzMV7B46669hoyduD0wyGo2b1q/dvzIoU83rFGkgDq0TKWanZz6Tp89NyAwMN5q2pNo9F2zrI9Tz6qVqqpPdWm/pVG5kkP1qpWrV61SqUqVuo2a9Bs4YuOWYEviXqPZEhMbo348Rd2j5U9MFztXkyj0iPqte0NcrCHGx2f9oP6u3Vo3r1BCFThv3gKlKju2696nr7tXb5c+Ls4u7m5u7m7uHdp3LFK4MC3RtVevbYGBSckpljij98JFPTp3Kl+2POUpVKREidLlKlWuWr16jerVq1esVKVewyZjJ04JDY9MSrAaQgOGe7iUK1U8f4ECFSpX7tnbefiIkZ4eAzx6u3j17d2kfl1V4fyFazd8Ztjo8d4rl65ds2z0qKHPPNWcxmdL6TJVu/XqP3DoqLkLl0RHYx0Wszk2OjrUZ8PaUUMHdmj7FLEdKFykZE8n9ylT54SFhsbFRqxbu3TY4D71apXPr+qVr2zlus69By9ZvCY4KHzxogWjh/Vv4FhJzl+uSq1urh7TZ83euWN7WEjwnFkz+/buXtmhnDoub7669Rt7DR2xcv1GozkxZEfw/KlTurdtXrqE6qQCRYo3fqb9mAnTtmzdYTHF057b/H0GevWp6lCqZvUajRo1q1mjdtWKFUuVKE4T5c+fv0ChAnUb1J8wdXpwRLTBYo0xmuRLAFofKUand7Se+vtDvK6A9F3+FomFi07Ufn46NDR02bJlCHeEGW4cVKhQoWHDhsJZ+HxcvRB8bgrT0yRy5z9WkLIBIpVS2g94zps3734/vcr/CHlIvaP2i91BQUGNGjWC3jheTiQNQaPIqlzmiQD8LRQOSDRv3lxqIUQu9aqX83vARYsWpbFogXXr1mEuELk2x6NumeeOE+14SMBHxZhNUSZjlJoFRYHGhC9cONepl5pT6tqtR5eu3bt0Id1r4sTp0dEWizXFYk3y8/fr5+7m4eG+dauPxWyKt6pv627dsqWv+sGirp27dOvRs1ePHj07d+7auXMXV9e+GzZutFrjOdjf33/q1KnEau3bt3d1dR0zZgzjH1PfuXPn0KFDtWmqrl26dnV167d6w2ZLfIrZEm82maMjI3w2rZswarhT5w6d2rXs3L5Nj25dhwwessx7ZXB4tMFk3RG8c9KUcT0obLcuPZ169HJy6q5i516TJ82IiTEGBYdOmjSFsaXGWY8u3Xt0InLu3dtl9uzZW3y3DBkypKeTU2ftZVMVcXdTP5Y4edIkfNOGDRvc3NyoiKpOL9cu3Zy6dO1B2bZu8zebTd7LFvd3c+3eqVO3Lp170kI9utFWI8eM3xIQFG2i2GpW3RQVRlAyedwo5+5d27Zu2a5dW64yePDQhYuX7QgJj7MkxJoSItQvtSQazAnqIwH/QeqEWOrPEKt++sViMpjjYtav8Xbt1aNb505dOnfU5IFTt249u3bt0bVbz85dVE9RQv569HSW5fQZs0JCIkzmBLPFGm82RobuXL1i+agRw+jatu3ad+yMHu0zaMSYFWs3BoVEcHWGW1xsNNLZEBW+aOE8dzf1C1SU2dNzAM3l5+8fGRk5f/58mqh7t65UGo82Y8aMhQsXOTu7dO7crXNXp249Xbt1d+7cpSftP2XqjPDwCOXxzeaYmKhlSxf2cures0c3px5dXZxoagzLZaDXyICAQENc1MLFM3r37dK+Yys6kWO7d+vt1MNjyqR52/2DJ0+e7Ozi1LUHV+jWuUcvTt6ju9OQIcN9fHwCA7ePHjWie9fO3bp27k7nde/WqVNXJ5e+8xYvM5jit2/dMnpgP5cuHXp2pRbd8b2dOndz8/BctWZ9dHTMjOnTataoUbxY8V7Ozpt9t4VFREVHRwUFbl21fP74McPat2tduJBS8JWq1pi9YEmkKT7akhhthNotKvxS7x/+g0j9AYGjTkpKQoDhrn19fYcNGwadFy9eHMeOWpO34IQU8PzCAjoeZ6VK2YStpIQwFDEfCXxX3759O3fG9n4Hto/PAPaA0vbv38+JMKm6devKPWaagLPISSX9ZEFvER1SKSoInRcoUIAIpmnTpuPHjw8ICMC5EPTJNDu2golIeCimY8fDA6QRbVJiXf3FGaIMsRHQPCF4YlJifEKi0Qirqh8MNaqfFrVExcSR1npHBVoW1DQcAOFo5yJLPQhmslitCfxpzpyYLN5kgvXVVJ70qXQrO0vnms1mYnwyyWFpNEGGFvU+knrFjl04hTnebEg0GZJYWuISraYEq9oTyo8zyx8uNkp9+dyqasQJk5NTCDZi1M/UWKlCTAzCggDRwrnN1tjEJDNX5tSAUnEJLk3ZoGpOayB21O4gsgmTY5OF6iSmGOOTCSAQ1No0kcFsNFjiYhLMyGiCIaNyZCZLjLrZmhCDCoyNtaiHvwxJVmOixchu8RYjjRafID9Pl2iyJhmtCQZzPFI2Ks4cbaCJhNF/JXUN6uYIqpBOiY0KMxmiaIoEq/YDpmZzQmKSNV67aR1HHRJoqphYCFn97pk1PslEy6tHzRJpqJjYWKMhWpVEff3NnJCQaOLScZQpmT9zYor6EJD68XODxWQkeoDbKTD9SYMAVQ6tKbTfm1WPMNA+ZLKildFAr2EnJkuiJTHFHJ8EocYZ1Y+ZUjbok2PVsy7YlUF9vCfBYlDPn3OEIRFDM5spXkRsXIglPsKofi3IlJiQbDQkmeJ2W817jAb6MdpkibYmGiwJVqMlMc7E1bUHJOl2Y6zFbCAeVb+bFq8YxRhnUr81Z0lST79HRyWbYnZZDVYzZkaR1ftvKryxWFauWF7XUT0cUL9eXT//gPjkPUbVUMSesSnxhr0pCaEhgS4uvdihUOFiI8ZODKdnrYkwupC6MgI7qf8GjAzMQ9w1Ayo5OZl4fdq0aW3atClZsiSqvWzZso6OjnK7XRgBCgA62edOP56gLg0aqBdAOnXqtGfPnvv9nrpMzSNWACIGbmvSpAlUV6NGDWpOmEBtOSNtwVLSTwQoKl2oV0HS8qwBTaPTOR1J34eEhGAKQLlbzWsIkWMlQDMbOx4yaGazKU591MWgvVccG82olJEJC0Lf+FyjyWqGgeLM6lep1Geq1FvF2shVv3bDSVhKl5FQLKg491deJ1PjTrWVnXVW0HM0p6+cLqvsb45PhB0NRlUOKCE2JtoSF5tkUeyoXqFWX0pXJ1E/0240R7ObBf6Ks+C7leGowivHTSyifjed0IEYQgUQFit/sF8Mx2nhiKJzASZHMcT2pISUBFANDlS/0UpckbhLcXacCkcU18bGUCT+zIpXTPFQtQXKN0XBJZYEDsHjw2FGdUecHeIITQhb1JlVQMF5zLHaD8XDEJxTe2NK//tPU1dKnZALzlfUlQh3qx8pV2WDrSFO2oE/0nC5RucQtiJU0op+VFgGxXKaKHX3WjWdkXCNrZTWaEkwJ6REEVIYOUE8HUFrUyMrvE68QktqfakagZJpvSwtI20laS6gdlPBSrxJKwMRBlEFDRkdHctB6ljNzAirVHBD4Y2EJvGJ1t0GSqR+X5+IIswSH5WQRD8a1S+gmpKNcSlmY4qFEI6owhRtiTeoyEF9oneXihjURSkNdaB5FbWr78LG08Y0CPurX1unreMhe0sc2eoH8DkR0ZvFRGAxd8aUsiXVGwrNmzbZviPQnLTblLQnRvVaVFK8Yd/upOjoMFdXF3aoWKXaslVridVUuPYrqWvT73aVkQsMKJbYg7wLpzW2AomwsLD58+e3bdsW1Y7GLVGiRK1atYQUYDQheKHzx5baKBvkRRUobdWqVakIZR43btz97qmj0eWeujYJqVR7//79HRwcoL1mzZrBghK/cEaq/djW/F6gwJQfSC/Wr19fHqagaVidOXNmeHg4jh43gdcTxyEJsRV8jeY97Lz+PwCNa1BfF1XOFXduULpKsmV7DCoPlwuHa8Ik1sBCCS+WsfCNRoHqHNr+7Eo3seQQzqf1myJI/eP5DHKWkiOZ0q3qAG1vNd+M6DYI1bGC146zwk2xkXExkSaNJgkmuBq7x+DsjWbIMzYu0miKEXvhf8qMvjUpma7ek+bMWpG4HDWJiuFU6o51HBGDuqK2lYSAUklhxPzUqvYJMZhPzRxoMYoqOZc3RKvyqB9CUyCDPdHc0RphcwlqpohQ/ZgX5+MySgtzOP/HQXVGNcNBdky0OjqH0fn71c5JqWNjVZUpvFF7+iE2NpKu0MS7+lNfIKcRtKurd+IMJlV7hKz6qIukZUAZ1bx6bFRMdJR2OdpNTYrQgKoN1eS/FIvrRMFtsBaNrDpJ6yZpDYG0jyRYsknVX7UvRVBhilYqVRH20ro3jovypz5Go9qEdWIMA9EOfaROZYw2W2LijBEx6oHB2KgogpgEeF2FcwZ16y06Jly9dIlpGVV8qWImWk91HO3POakkhSSD66urR0UbFKFTY5VLabVmV6FYtNUYnWyO2eC9oEld9UZAuTKlPfoP2BYYZkjYa07alZyckBhvDA8LGjd+dNHixcpVqDBu0uRIA7FkQrRRTSAJo1M1O6nfBVoYaF2gulx1Rk6aUZ+UlBQRETFv3ryOHTuWKlUKake1161bF3aDFCA44XVAwkYbjxOkbMh0qL106dLwuqenp4hwG4f/Bmr63cnJSVZI9+nTx8/PDy2Lim3UqBHn4oycWkhdLvOkgDKzpNj0VmPth4Cg80KFCtGX06dPDw4Olm+za4ahTEHsAB8haVllKWSv7WXHQ4PWpOoPx0eT5w6k9Pan4XHDmr9X/EU+Cy1f9YjsRlrLV4pc9iEBH5DgMP205LAErALZyuFRUVGkUVOK1PnfZI6OhSLIIbijiNoNgjh1g0CxgRZMcFn2iWFnVfZo9afIXqIKAwpfsSbuGDesQBW4FpmQdDRnJIsrsjOn0nawlR8HxDWlRixZg8nVi81odGsCglYrsAkdor4za6SEakd1EiUG1bQzst6oTX0rDa3Rj5xNTqhfjjSNxx9n0K5CWCV/6oy5QXFoP5Soirpio0lrnQYFK+bW/1ilQiw1pa6t8qcqrGIJJS1joygzBacM0YQi6hF/cxSNQrPD64rpVQ+reXJChxjaU4tNtFZSZc2B2k0DmdKVXJVlDO0Pv6rZASOnh7gpvLafdhJ6iqKo7SYCjNjoOKqiJlQ4cxw9AmdHsdQ6kTokQudRkVxCGoSrUHEsijhDzEyVh9Kpo1W4o9Lkq45VfURwI5GK4nhqxrGEFMaYyPi4yF2mSGtk4IJp4+rXqoYvKlaseJOn2vQdPHrirLlTp08ZPnxwu3Yty1co93TLZxYsXkzsoX4H2KbR5bkHVVkuqK5pRy7Qlaqdc0xFMiWB7eLkExMTGekLFiyA0dDrMiEPzcERQm1A+OKxAnKUgkFYpCkt5AW1+/v7u7i4IMWFtX8LRerOzs7C/Ozn5eW1e/du4pe8efM6OjrSBFSVE4HHM5ABUjDR4norkKm6S3sNoHr16vIiI2HK5MmT5esx+uSe9H1ugyAtIM1wlUw7HjZoXvXzH3hS8c/SzNrg1Dyyps/hEqWKFAmpfmE36TXS9IvWS6qbJK0d9WtXAj1T0pIpIJMBz1LvYuUtNTZSByhwSKzGoIpvuKw4DpZCnEIokL8KANTHz0lAzMqHxMcnWSxqGpAVzdbIlVlY9dCGTA6xSkLAqjzES5qEjoTE5ISklKSklIR4lZ+URFZ8UqL6S05Sq+g7/k9SKymJybvYmUMSk3YlJKaQq9L8k5yMXlGHJ6sdk5MSkpM4PCEpifNwEnX+RG1PuQQ7CzgukfIkJKgNiRylnkVSUHtKWh2hpW2F1+olSdJqqWbr1aS9qj4NxQohCMEJoQraV62q1iMEUfPkWmvTL7QcnJrT2jnjVHqQdO6OU8ZiMqi5AzWBwlYylT3l7E8vcUoT8Rpmpr0PxgFmbVX2JNqD0aM5kGKoc6njVKzDSTgBNsC/5JOpnZbjVAynBQHqzpFWEBUysjVnN7Oa8iGh5vdVfGeJi7HEhMdHB+8yRaSYInzXeg8d5NmsRYuyDlXLVXasVa9Jw0aN8bUeHm4zZ8/cGRKMNahwR9UHg1SMrpG6Hb8DOhpIgqVuJCTUZi2BCckQCw8Pnzt3LjRRpEgRSKFatWoQhE4WcAdUQoIc9hFaebTER2GE1OvWVW9/wNEpKSnu7u73uqEObK+09erVS+bhUe0DBw4kEChVqlTFihWFIFlKDR9DSNnkZrm8W08gQlo6g7ikWLFi+fPnp/OGDRu2Y8cO5S1yHpWy4xGCgabcn0zeKmFGj7DUBiiOWFE4I5MNaoZTpLDtODVWbcNV9hYVLtvIU0dxao1r9a1ajlKi7CYJNpHGGIRstB0U32jEDFS+oiKL4mPtxUZlOeIa4DuNuUlDXBp3KQj9JUPW2h19xQFcHYkQEYEzIZgM2blTPXKPHRJub9261dfXd/PmzZs2bVq/fv26devWrFmzevXqVatWrdSwYsWKpUuXL1mybMmSJYsXL160aOHChfMXLpjH34L5c+fNnT1X/c3BSbEyZ+682XPmzpo9Z8asOTNmzuZv5iztb+asmfxvAysz5G/2LP6mz5k9e968BXPnzecUc+RM8+bOnz8fTcPFFi5auGjRIq6+ZOnSZcuX87fc25tSAQpJUdetW0u5KfyGDRt8fHyoDpXy8/MLCAigmkFBQSEhweFhoZGR4VFRtt8RhqlVuGOF+1WgItEDbSdPqKogQgUGqpEBCdXoWrOzpNdYqp0lPrB9ioSejiPKUoGWNrWgGY/y4yy1BJ2rYjVlZoRn2iSKmkFQ3l/xMays2QylA2o3jdcVlDlxBNaldtH+1J7qZEb1CXjWMS22afNAVDCWkEGFJRoN/0rGprgYqyEyPjYsPiY4JS48xRRtiYvGKvx3hm7yC9qwdbuvn9+OwICY2OiEpATCNGX0yHx1AVHm6k8rnh1/HpiEsq34+MDAwLFjx0KT8mRVrVq1hOk0SrG9NiUkojP9I4EUA3Yj8iivoU+fPoMHD9Yn138XeXr27IlGV4+/a+jcuTOMvmXLFsQ+Fa5fvz5nBBAkdXuE1bsP9LKREJCmFRwcHKDzkiVLUk/8jrgDcet2PHLgN+V2rDZta7FN9WoQf6r5T3HDsegsbU3L0GSTvluOllMUDjgHZ8L/iwIUPrARhSaOgb6/JGyn0Zw64k15Z00CQsbQEuwLV23YsHH16jXe3t4QHGQ3Z86c6dOnT5o0afz48aNHjx46dOigQYM9PQe6u/d3de3j7NzbyYko2Um9y9Spc7t27RlBeAdslVHavHnzJk2aNGrUiMFF0IlDqVmzJkFn1apVq1SpUrlyZey2QoUKDOBy5cqVViilLW0ow18ulLX9W6bsn4V2Dv00/wGuZLsqhSDJXuXUIZSNEhL0U2BKXqNGDapAXerUqYOjbNiwYePGjYmwqSwjEc/Ytm2bdu3adezYUeYMcUnOtFHv3m5ubp6enjgpGnDEiBHjxo2bPGnStGnTiD4ILJYuXUr0QOhAxIBH2r59O91BcBQdHU18AOgmzQxUv6mJdU3bS5/S73S0dL0EDRiCeifCrGyAnhd7oNPpaGU3OV8al8O1iJMMBc3aJMJUM/lQvFpB5xtjYgzqPgzMi9WpYzDlOCOhBayudreRsfC6wWyIthqiEgwRCVC7IcJqilVzGPFJcfEpxsRkS2IS44CzUEaTBY1uiCaQ5TB1PVtJ7HgoEPMAhKFYIMYMTTDWoDwsVuiDoYrdymgV4a5RzV8N/dKMKeiY8lBmFPi9XmYT2F5pYymQF9ueffZZjkTsV69enfMCqacsHzdQPHEfJFglUa9evaJFi9IKOBccBIM/OTmZEUtCG7H2UfLooViavoCB8ad0DWSaM2mmQTlTdedWeWKLulWr/kWuqT9t3hpfnaKJ5qSUlBQcNJ5aO6uSTWjjsLDQkBCUsZLFCEe4GWIgtoMnkKEzZsyYOHHiqFGjhgwZ0r9/fwiGUdCpUyfYF1tieENOsFTt2rUV6fK/Rr0MBzhMUW+lSpWrVGFRsWIl/hwcKpUv71C2bIVy5cqXLVuuQgUHGBqmq1u3ftOmzVq2bNWhQ0cIHqZ3d3f38PDw8vKCxrg6NEZkAI0hkaExZPGyZcsQwWvXroXJUPBbtyDhtm7f7hcYGBActDM0JAjNHxkZCRXRRhazEQrQmIqAFXWbsGvXrj1/BIwL9XS2altaWt1Fpgugy6godGRkWHhEcEho4M6dftsDtvj5+WzdutnHR2YUVq5cSVEpMCWfPXs2IQ7xzZgxY+BmmnTAgAG4S1dXV9wImgE6lyd9GJhEAJUqVSImwI3qII4hRCCfBc1L6wlocJpda/5adIf0CKEDIRHDnEBBJiHpvn793AcPHjR69CjakyFPFxOBqQbcSuttR5kREARTmdAw9DF/aHSIHG+AfSUm0gzqpxxAYqI2+5JIQKAmAzRjU++iqUcfsVZ1j0AFnyplQqobYF9M16C9JqDyoODoaGNMlPZkpdLaQuoar/N/rFVRe6RF/al9sNiYOJN6L8Js1R5HjI3mhNofA0BdyY6HDZmi0/o6kdAQG2bI65I9N4sL3wmtPBJQDJk/YCxAx8j0vXv3yhvq+Cuh8N/i18/EimQHjEPcDY6GEyFz5Ss0VFsu83hC+oDWRx+IQEdb4EA3b97MmKT/JKjHc+mdascjgTC2UkN4VPUDYpC6SvCHtoKb6SxNRMXjWpMTk1OSUljGm+ONBmNUVExISFjAjp1btvpt9tmydt2GFSu8ly1bunjxYnhlwoTxw4bB0B4uLs5dunRq164NcTb0DDFAG1hyKQ0IUGJz+EM4A5KAaQiEUc8YOXbEKIKBOnTowIjo16/f4MFDxo4dN23a9Hnz5kFjUBrBASQRGRlhNlmSknbt27f/4MFDr732xrvvvvfJJ58ePXr02LFjX3/99bfffvvddyd++OHU6dNnfvzxp7M/nTt79vy5c+cuXLjw888/X7p06erVq9evX79x48atW7du376dqiFNQ3oOMjRkZvJnQ1YOshVk+Svu/HHYjswBJ7ZdICuLi8lFVQk0UCApmJSTAlNyyk8tqMu1a9euXLly+fJlanfx4sXz58+fPXv2p59+ov5nzpw59cMPJ06c+Pbbf3/zzdfHjx///MjnH3704TvvvPPqq68ePHhw//79yclJmAUhC6EYjUxkQ4PT7FOmTBk7diyCHqeGLyNCatu2nUx+0GVNGjdpUL9Bvbr1oHwCKbqVyIAuLlu2rPQ4XY8B0Nf08tPPPN2+fbtu3bu5uPQiuiK0Gj9+8swZcxctWkoQQDi1bt3azT4b/Px9A3cGhoaGEt+YjZZEq3VXYvzuRAwS81RSX+4exCcmsFQ/l6OmmQxmk8ESZ1Cv2sdFx8dFWeIUZ0Pn8rCb9rwbJB9tio0yx0ZZDGoGXv2mkUFu6asvAMSa1VeC0euK1G1/dvxPgAdiSV8S0AUHBxNnE2jmy5cPU5HXvoRQ8AYy56TxzF8NeI0yEMJixgQcRKujR4/GL0HqsLbG4L8DpdRdXFxkD1HquDNigQMHDrBKdIDXk2ABcAGhz8cQlI1BywCG0WkF6k8UJsE4LA6pwyWsavN26t6eHQ8dQtg6aG0BsRSDBwhbA+1FdGhcSRv+TPhCkyEsPAw9jbpCCC5dsnTOzDmTJkweNWLUQM+B/dw8evXq3aFD55Yt2zRr1rx+3fo1q9dyqFAJjlaz0WXLqtnnMmUqV3RwrF2zSZNGrVu1QsMRnnr064fjHjN2zOQpk2fPmbNk6dK1a9b4bduGv6aEjOdnn3325Zdf/te//nX48OGvjx/74fsTFy6ch5lgXChKJ12NdhXvCuNqRKv4FRa0ceMjBsXQ/55UaCGFCiNU9KDFDXrQcPPmr3EDXUP/EBj98MMPx78+/tmnn73z1ruvvvzagQPPJSYmQcNocgT6+vXrly5dOnfu3ClTJo8ZM3rYsKEeHv2cejl17NShZatnGjduhGerUqVKzg0IGyo4lK9eo2q9+o7NmjXF73Xq1NmpR69+ffsO8RowZsTwKZMmzpw5Y+HC+atWr9ros8l/x/bQ8LBYg/oKkJpyilOknmg2JlviUiyxyVYjjG+xJpjik+OsSXGWRKMlXpsXMcabDfFxMWaDYnezetouNspgUJ8xijPGGNRTJMh0NRGAzlczT/qfHQ8BMAJjH0aAFwB+KSkpidUlS5ZA3tBHiRIloBKR6SJoJf3XQ6KKmjVrQsS9e/fet2+fs7Pz/WU6yCMPx7GE/wF7y3NzxMgTJ04kbCFGaNq0KWfnGtTwEZI6l6ZxJbCQhpbCUO3mzZvXqVOncOHCBQoUoAoQA10Fl0j/SecJFNv8g6ffaQFJSLPIBIbWMApk6i2WewdJAwlvSZCjTdjaXsEiDVWjYkCi9vvHevsTQkVERISFhYWEhOzYscPHxwdJNGfOnAkTJwwZNtStn0e3Hj1at2ndtFlzBhKiGb2lJrqrVa9SsXL5suXF45YrV75ajdrNnmrTsWM3D9e+Y4cOnT1p2vJFKzZs3Oa/MzzKZDEnJuzdl3Lo4P633nj5ow/e++LI0W++/vbUqVPnz1+AnIWTH2NKtsll28rvIIu/7DuZWXeyMu/c4Y991f7kZZGXdSebHeR44fXcf38S6hLa8ZxarWrLh42HfFba5F5hATHBzz9fPHX6h2++/frIkc8//PDDN99886WXXsJRYrmRkZEY57p165YsWTxt2rThw4YhbDp26kgE6VjbsYKm+7HD8uXKVaxYUc3x1Khes3at+g0aNGrcqHGTxm3atHF27uXh3m/ooMGTJ4xfMHfWKu9l27b4BqtHBcNCQiPCI2OiYhg2jBRtOiremmi1xKtvFJoTSMi3aywWQgNGjVL96uE8eWhA6XVtlLGwfcxABqkaxhpkbAokX9Ky1Y7fhWpNDdJomtaI37JlC/1epEiRggUL1qhRA4qBXGAZIR0dOh9J4r+HnFYPI/QEyyZNmhBrYnUzZ85cvHixPJVyf6iPz6DI72J+YgGYHmqEO+XOOmeXp8rleo8KMmcgkyE0NJAcIu58+fIRYXl6egYHBxN5MTZsvWeHBgzXlsoxaNtKDknD0KRxGbKqZ+JBgFLcGmBr9VxyrvevyIyOjoaz0dkbN25EZ69YsWLhwoWTJk0aNGiQi4tLhw4dmjVrxiAhRsRA1ex3xYrVq9eoVbtOnbr1GzZq0rRpcyR4z55O/fv3Hzdu3MIFC3GvIUHB+L6DLzz33rtvHz782RfHjn1z4uQPp85c+OnstZ8v3rp8Je0WDA0/Z2Vq09AaQ/xvqOcxgEbgGZl3MjPu3OFPiFYRr43UbfX/G4CQ5dcKqmBGVqgcW9Kz76Rnqb9MLeR4ONBDARUHpKZdv379l19+OXfuHEHhv//972PHjh0+fPi99/518OBBjD8oKGjt2rXz5s5F8+Btunfr1qpV66ZNmzVu3KR+/fq1bM9eVCMOKF26NJEAZo/xd+zY0dXV1WvgQMx7/vz52jz/uk2bN/lv9w8OCY6KisJfEQyrdxQ14MEYXzLu2MSSwSgjETA2JdomLWMWsidTtsrQZikgbceDgEam5ZEfo0ePxk2pj/pp73/Jc+KwjM61svoQIScUXiMtF5JIAouCgtu1awcdd9U+OHMXWf8W6p76bwHNExG4u7sPGzbMwcEBsd64cWOhz0dL6lRSai73PBDolIfAGUavXLnylClTYBehGbs154Z4AZa2dS2HpQx7SYv7EA+C8wJa8GrzLJg7CTR3QEAA5rVs2bIZM2aMHz8eC8FOsBm6o0GDBkRXjAfcmZrJrFBBHjyB1KF2PODIkSM5auXKlX5+/jilvXv3v/76G0eOfnHmzI+4UZnuvnnzJtIKjYWTvbd4xc3j4pVfT9P+NA4gR1vLtm3SlnDhr4Sf++/JglbmzMw7GTZVbstiLUOrolpnB1tLPMmgDvcm9Qwimyz1R501qEaQ1J/EvW3sd8DOmCXGqat/jPby5cunT52C+N986y10PwON6BYjnz59+vDhwzH73r1dOnbs0LRpU4YDg4KhAeD7qtWq1qtXD6eGs0YgDh06ZPLkyXPmzFm+fDlDLDAwEG+mD0mW+pCUnNyQUaxH5AJtoNvZ/Q8AFyfNiyyGVmBT6A+tDMvoxAcHCWT1oUBOyCXktHAcCQgX2kWpUpJRo0YRFMotRZytsPu98PukDhDrOOLk5GQkO5TJebkkYp3wQcrxF4NKcmnKIKQOWCUEpmAFChRgtMybN0+MHhOneySStUPAkFYxfA5YxXA1SaBAL6MMyKfRAK4E8t6xYweye9GiRQjuIUOGYAxt27aFoRs1auTo6Ah5I7jFN5HGYREIDhw4cNq0aatXr96+fTviY9euXS+//PIHH3zw1VdfoXsgbJkGxyeqCXA1+23zlffFr24bp67oKzv9TvaNO1nX7mTfzMhKT83KSheGV1wgFK/kLP9oXMcW7bhf/54saAXWaqKJ1NuqOqzAc2qL4jnF5ipNFi1B1Z9sUA1qZeNx+WNFkbj8k4lMV1MT0gC2f/4Y4GadyyUNSP/2jsxdq/8v2F8m/4X4iVCvXr169uzZ48ePv//++y+99FJKSgqqWlifAHfw4MEMHGijUqVK2gS/msfCp9WuXZsQmeHGoGPoMQAnTpy4YMGCzZs3y3t9jC95Nginx+BlCCNmSDOKcYCMbiBkr0NcgR33B1ERzUjr0UH4Onl0jg4SlhXqFT4SGnpYkNMKx5GQ6QEsgQL069eProTORaPLGx/30ev3JHXAYUSO+OiaNWtCnFTsUTG6QJ91l2qzhE4IpurUqQP30A2YNV2CuUPtGPE/nNdlJOuDnDaBv2X8y+0JmTPftm2bj48PvUxwOnToUEwHY4K2cS6oisqVK1evXp2YSbwMUZ2Hh8eUKVPWrFkTFhaGh3r99dc/++yzr7/++ocffrhw4QJeTG5a/6E71uwJL7G3SmhzoQKVrbnyrCwcZbamSPkP6rp5J/sqf9l3bmUqFy/+3bYz/6TeUeyHntNITv7UJtuf2vtJgSqttE62ClRuqHBFawigaXet+moVKqTSWjDzJIPq/Cep59xi0PpOW7HNVdjq+aeqi2ndayoodyZpIKYIcmdKWpnm/aaU7gZ7coiwPkL/0qVLp06dOnbs2KeffvrWW28xNuFsBiNDDFferl07mdJnDAIGI6OyYsWKDE8IxtnZecSIEbNnz/b29t6wYQORNEMSB8jQZqTLDTLSQFhKonlgcxB2/CdoOiDahuaiDfFytD8UQ5sjmqGe3KQOFC09JHBa/ZxciP6tW7duoUKFuO66desI6bp06aJPvxMICkf/Lu45/Q7k6TlsxcvLq2DBgg4ODlz4UfE6l4bUqaqUAWDiNDexDAEsfYAF6x0DSP+jzFeGq07hMmkBJEE+A56uXLt2rdzt9vT0xDhoT9gaiSAT5tWqVcOGcCWurq5oiOnTp+MsMPEXXnjh448//v7773/55RcR3DI9bnNUDwDNDf6HT8zMys7IVEty9b8cIhffR2SQwY7adLrKVFD/4tJh7VuK2lUiR5tqp2AB50NubNCOYWcOR87m/P1K8AraQba/xw8USiN123SzkHqqNuuuNlMxmaXQGogFbZJT7ycW0mGqe6gHxqAMQLrMBq1RtI22jD8MzbpsZCzp3IanQ19lTwG7aSewQbZKQj+DdjKVlkPUHlq+jt/m5AaHMLgYYgy0y5cvEyt/+OGH+/fvR6ts3Lhx6tSpeGPonEHatGlTBizDVgYv3I+fRNYPHDhw/Pjx8+fPZ7Az5CMjI8U96hCOB3aavwtqolJ7DpE0jpTAaMWKFfXq1UMuQ3/NtN+AEd6V5cMFpMZpheCaN2+OWy5SpMiwYcN8fX3x1TC6qHOW8LIw9e/inkpdzoLSx0oIHrEhGJQIUa4qk/4C0n8N2XMtrsK1AFREeYhb586dK60PkWP3wmrSPULtjxUYP1rIoe57sSqFlDRLGWOkZR8g+QI9n7RUU5YMToJKfayyNSYmJjg4GFNYsmTJ2LFj3d3dic9owIYNGxIJ6Te8WSV0GzVq1Lx58zZv3syZDxw48N5773333XdXrlyRe9vIbjyUzd/cA/fxUOB3t2puTW2460/AFTMy0rOzIfX0zEwS/08B7gIMANvnKFYuw/lyuC8TmZuuRQkaD+ZcV0ji8YN6ZzyTHqDgVETViT9K/h+g5IoFs9PuZF//e5B6Docjy9PvZFEjui8rPU09wnb7dio8qdUXO9GN6I9BHXZfo31A6CchIUSur/5u+r8BZM9gZEii7yH7b7755v333xeyZ6RD4Xh/nDYMVE77EKG6Z1+1KkKTgd+5c2d0P65g8eLFW7ZsCQ0NjY6Oxp/gLvAbeA/F8Foaf6I5GwXxNoA9c4NN4qnYpHswYNv8JENqITUiIW2ybNkygqf82rfG4do2bdpA7TJtDCUpZnoY4FRCbcKkhGgQHPzr7++PxOrYsSOkLFyOM3dychKC/13cb/pd9DoGIV+8wkSoGEwgD6kBakgJRD0DyfzfQSrM5Ro1alSwYEGimOHDh9P0tLvqEA2/NcHHCtiKCgW1YFCCD0BaHyTwsdwqE6timRvsJnYGiGOSkpLIRIIHBgYKhcPQRPG0VfXq1QktK1VS3+cCNWvWxGj69+/PqGZI79279+233z569OjJkycR36mpqTJhbvMffwnwc7/9U/nKP+LBMtLTU9PSbsNppH+59PMXhw8f/fjT40e+/ObYt18e+/bIl8c/+ezoZ4ePHP7s888/+/z4l8dOnTxz6dJV1BQn4X/8fobixOy0dLkXoM3aQo+K1EWys5fameuyM39SgMcIFD8jIzszizqlZWZfv37l+FcfHv74zWOfvvftF4e/Of71l8e/ef/IF69//NV3Z39OS7+tHjV4wkldzCA9MztD9Qddlnonkwgt/Yfvvl63ZlW3rt0nTJz29nsfqefl2IdOzYbi/1K7fdygRcAZDOFLly6dOHHis88+e/PNN5OTk9HoBOuDBg2ChBj+RPPiCvAJOAdcd+/evaH5pUuX+vj4BAQE4EbwSwgDHAs0LxC/pPkn2yP34mA1b6TAqp7D8u8HGgRHPWXKFOIkSKdOnTo4UgATwetC7f89OA/sJieEXgnFoFp6jVht0qRJsDgUDhcjs3vd+2fUddxPqcsSUucsO3bsgEGpFXWT19a5NiUQXpcC2Qr4PwNN2bZtW6KKEiVKUGfYC36C3p4ge5JhwNggIQNGRoWeYJ+78nUWZ4zJ42xBQUHr169fsGDBmDFj3NzcaBO6n34BDFoiHnII7kaPHr1ixQrOwCA/fvz4hQsXrl69SqQPYdr8wb3xFxC8uG/9TwekTglF92j+Wm1/563Xh7i7tm9cv0GVyrWqOVau2qBmnVZPt3Rp1bpHq5Ydmjdp1qhuvXat2o4eOXZHYMjLr7525co11BGuPy07Oz0LyW+7gmi8J4jUszPS72RCcFkU9+T3/547Y1z31s2eqlnJsVKF6lWqVazWsG6THq17jTTuezVNRTOiax+7ejw4VGfTK9n0FAFYalam9gzBnXRTTFiFcuotIzB5+sJrN+hbuoyAR3Wv7eB/EhgjDA/byu9BmP7atWvnz59H1r/xxhu4jrVr1+I0oPP27dvjKCpWrCh+AwfSrl07nMn48eMXLVq0YcMGdAL+R3wOxMaxMikIxEHl9mYsNff29wRVjo6OHjx4cOHChYsXL067ISyF/h4W60FtnAoy5cwsS5YsWahQIRy4n58f5AsLQ+dASFk+J6Ox9O/jnqQu4YDo/U6dOsGgUVFRWAODiqBPFPNfRucCLUJ6hjAzr/ZwHNpUrM3W9k8CKK0+JBgM+niQJfls1YNl0gwkNjHAVq1aRbSI1O7QQb0bQyPIvTRYHAofMmQIHI8FcNRbb71FwK7f+f5/6VkYlIT4CI1KFbSN/0Nwgdx/ucHVlVTPwFkrraqU+s/nP3rvZUOgd4fGVTTHXqx19+GxSYf27H959+5nrWbTdj/feXOmderYWv0KSeky40eNfW7/8zdvw3DaLfbM7HSIUVGFEDln5s9G5dpt28eS1Ak/0jOyszLSs1JTs9Nupd364vCHr+2LnTqoa5kCWitUbDRvU9TuNz7+5twvlP9ORtodgoAnH2pqna7KSs3Oui4PCrzzxotOPboXK1asVu36wWFxaRlaFyqlnp6Rmfb49dxfAX2Y6EGwbcM9wD763fozZ87gKKCrbdu2IQcR9PhznIl4lapVq+JkkHOenp64nZUrV+7cuVMcFO5IHBRLVoHQ/N+b2tFUOOEmTZpAPbQSlAcTQX/ARk7/BYTXSBAocMIKFSpwFS8vL8Qb/NuxY0e4WJ5vE5ku0Fj693E/pe7k5ES/Shpenzdvnr+/P/VBJcOp0DlFwRQoDWmteP9bcDmiJAY24dKECRMgPIzJ1upPDiizFJsxQIIhwfBgSUQMGBshISEYEDH1pEmT+vTpQ8Vr1KhBIFWpkvqiNS1Pj06bNm379u27d+9+5513jh07pk+h339gs1V20JdC5ywf3DU8LHCZ3H86KACFkfKwTE9LpXTqrvKdzAunXp8xtHM+2Cy/w4hp669SWLW/lB9v9cvX33wYGrylXs2q+fLmq1mt9poN/j9cvE4NIbqbGeqFMG1/4XVOyDL3lR8/UDoEWWZq5p2b6dm30lWUQ+bP5p2LGlQsTjPUe9r1hc++JVfdS6cdoP70vwWpq1slVD5D3VDIvkL9sjJvfvfvb156+ZUPPj587fotttEct9LUPD28bjvsHwZGCmMk97AlwWruIUw69+pdYBNDDNeBA/nqq6/efffdffv24eQhcnRh7dq1kfJyFw8pjyNCyuN51q1bFxAQEBERgRPDd8nbdHhjccjQvObn/j6gRlSTxJw5c8qWLSv3oIWGHzpw9TA67E4vzJw5k16Af4XRRa/fX6ML7ndPXWQ6FEICUofgoRy4RF6eq1u3LmEFdYPmga1Q/zNII8JtXLp58+ZEMajSJy481Ajd9owb0R8sTi2owo4dO9asWTNr1qzBgwe3adOGgVSqVCmhcBrZ1dV19uzZYWFhr7322rfffitvjjEabUPz98Bwzc3QkmDAc5SkZSn7AD0BJJ/l/xRcJvdfbnB1QXp6enZWJo5Ke9Ltyvl/75k5pE3ePHnyFqgybPqm82nqRoJW0LTs7J/v3OHvRlbG1Rf3Wp+uW1sJ2ZKVFq/f8dO1tDTtVnO69uGSX5HN+ZEu6tuxRBC2zPuC1mNntT/NaMv7HaiSZ/zqW2ldLTxRX7+TnAcFJ6AjslIz7lxNv3M1LV2TpOk/Gf1nNapYlArWfarfvg+/htGv0gRcMF1ecNPB3r9TzF9rkY4x2DLvBcqutZKCqsB9D8htYICKU216765jZDfbigYy5E1IWeUE2l/GHaXUqdxNdiGfbtICuTsS5milt52bNudwlrIKtBzb25W2rHuD3eC227cxBgqs+k6BIthg2+2xAsWiapSWpdRRyirQN0maJTtIjnb0PUFT4F4uXrz4zTff4HAg77lz56Iu8MCOjo7imlCrHTt2HD58ODy3evVqRAgOTeYXcWtIlHsR/JNL+VQtKiqqXbt2EFC1atWaNWv2EKWsTHvL09/ET7Qn3h7OFVKXO+DQuVC7pDWK/n38P6TOwXKXnrNzamdn56VLl44dO5aABbncpEkTSiMz8FJDlpKgiEBf/e/BhWjHAgUKcF2iRVoZ+8BupMUfCcRAY2JiSAAiDECRpFRQtQ62YuhyX5ygD4vnKKLdRYsWDR061MnJiQaEwkuXLs2YYfBA7UuWLAkNDX3ppZdOnDjx4PfCteF8twfSh7QO2Q2Qzr1Vz/wLwGVy/+UCIo1C4X2osXLiFFDb5fK547tnD2yllHrBagNmbv3+ljZBDzJvZmdfyci8rL3xlZGV/nPijg21ShRjxwo1mscc+ORsVvYtxfychTZMz0i/derbL/emxPv4bFy5ytt7hbf/9h2vvf7uL5ev2Qqjpn81GlGzvLeys65c+umrt57fF7xlx5oVa5d7L1+/2Sd5/wunfrogHpKII0s5wys3rv146ssPEk3mjT6Be59/+fbtWz/9eGxvSsz2rRs2rF3n5xf47KFXf/rlOifnKpCOOj9n4EpcUf1Djr60lYSCpENhd27SEOo6aaeMW6fXL1eE2tVp6bnnA0XqkB58qz4jm3GbIABS4hSs37lzOyNbfYpHISv1ysUzb738InH5yrWrV6xcsXn9xuSUPd/9cIZmVtu1F+cyVXur1Vs3Ln/z+bt7zVGhfj6b16xe6b1yxbpNYaakT746mZohn/eh6dOzMm7dunn95zPfv/byszuDAvy2B3///Xmufe7s6eT4uM2bNuyINH7yzam0zKzU29evnP/ugzcOhgYGbtkSeOTY97dup548deLA87sC/H3Wr13r7+d/4PmDZ3/+hcunZistnplxPSP95xs/f3fis/f3JCX4bPWPjE88fva86k0KTNXSr924du7bY58kWC2+m/337TpAkHbt2pWPP/4gOips04a1mzdtio4zf/zF19cztVcgVGtn3cmmlqpVsLNLFy+89NKhnUFBa9atWbVqxerVK7f7b2ew7j1wYP9zz6Xs3p+y+9nDXxy/mU7EpIJHorlf47X/gNiO/P3PcVcR1Cj4vVGcO30X9HwaAUj6t2AMEu5cv379hx9+OHToUEhIyOLFiwcNGoSbwlnhsuCCpk2b4sRGjBiBQ8O68Hji7vB1gDQ54hXxeyTwh+IVlXPUVJnIM+VGH0vWp2w4beoyf/586gsNNWrUCIKzkdMDAznKUp/eFqUqFIn6L1q0aLly5caPH4/zF/KGhUnQtiSE4CVH9Pa9cD9S/y06dOjAlaKjo6dPn16kSJESJUrIF2konA6KCEWRkDo/LFLnhPXq1cOREc5s27aN9qWhxVAeIZRJ5gBzlPKIgZLGmimncDlbw8PD/f39FyxY4Onp2bp1a8LeihUr0otVq1aln9DiBMUHDx788ssvYXGE0YOw+BMMXEquP/1fjU/lT3kd/e/OnWvnv9g3p3+r/BhBoep9ZvmdSNXkMt45K/WOev+NaAA6Iu/mhU+ed29cRbF6npI9x2x498I1mA96u3Pn+plv3/Vft6Brm5Z1azs+3aZ1286d6jSsX6F06Xo1HIeNnvb8W4dvZmWj6W+kZ6cpT3f71tUvkw1rvXq2al6jVptGrbt07NGkRbNyFcpWcKjSy3VwXPxLl25lQjBXM68fetEwZWj33s0a1CpVqlChEo1aOc1dvMC1d9eGtatUKF2qWNGiBQsWKle5ltvwyaZ9b1+6pVUwKy3jNpJTMXuGerAP3qEut++oItiInVZAgEPXGg+l3Uk9afGd3qCsUuo1nx6w64Nj2kfmpJkytQ/t3SAgupV+B8kJNacqoZ+dfv3s83F+g3p1alC3Yb0mLVp26tiydYualcpVKFe+U4++wYZd525mpKq9EbYo+GsfvX1wzsShnZ9u3KZ5/f69e3q49GrVvFnZMqWLFy/dvIPb2hDzqVtpXBfm/ffRt5dOHunatmW96pWKFClconTNXbvfeunFNwf07V2yeJECBfIXKFZ9XWjK8ZPfBvvOH9y9ZcuaVUqxX5FKoyYuneW9oUdflzp1q5UpXbJI4cLqYxiVq4+bteS9r79XnyBQvZv2yet7V4wdOKDV07UcyhcqVPipzj13f/IFvUmLpd248vKe8IlDenR4qnHl8mXY2qp51y3rtoweObxV66cdHMoUL160UKGCRYoWb93VLTDpxdOpygLuZKVnpt+kkVOvXUwyxrr3catZt/7TrdoOHzVs7NiBTzWrWaJY0ZIlylSqUadh8zbNn2r3dOuuAeGGn65cpW/k4z5Eh0RQKhamEKrZtT/1P+vyp9b/lsAp4ZouX7585MiRF154ITIyEh2P8MOJ4cpwaLVq1cJXDxgwQJ7ywafh+oTgxSWKh1TsnfN9MMkUn0niMQRFpXi48Z07dzZu3Jihh/e2MdMfgU6OJNCoJABECY3C6IDGpMUg2TZt2rC8P3nfC3+M1AFXGjly5Lp16wgfCFjKlClD+SiZzMDrFE5C8oHk/Deg2sQ4WEyhQoV69eoVFhaGidDWj9YOhMilJAQ6lATodJ6cnIwp00OEIN7e3sOGDYPI5cvP1atXJ7bt16/fypUrd+/effjw4XPnzqHF7xMv/w2he0PtTxb3YnT+hNTn2Ui9Wp9Z275L1WbToUR1V1WTl+o8MN/16yffme/eurwi9fwOzd0OfvUj2o7dTnz4/ET3jiUKFWrSrGNwmPGLf5/4+uy5YydPGkN9W1V3yJ8v/9Pte+99+/AlJL+6ZPqFUx+unDGoQtFClSo3WLLC94MjX/5w9typH0+9uC/Bo93T+fLmq1qzRUDsrsvZ2RfTbiXtsU4b4uraoGpJ7bolqjZ08Ry+YPmK8IiwmLDghTOntWigotI8efPVatglzPriTfV8vnpgAN0I2WjfcKfw6eo9LiUIH4TUPe8idVg2485N9UZY6p0s6kzGnds/X/hky8rx1coUKl2+2swlm97+7OuTZ346+9PJt19OHOTSIV/evOWqNVkWtO8nLqNa8vLHh3Z1bliH8xcv5zAvMPyDM2dOnTv348ljL8Zta1urDBWo0KBr3Muf/aJikOtHPnp+5nD33k83KZMvL4cULVXVc8j0bt36t2rxTNcOLRs2qFOkfN1VgZbPj32+df2Ukc4tGpQsrOZa8pZt2KKL24hxMxYu8vffGhzgM36ER8UyJdiSv2CZaWuDf4KHgfMAAHDtSURBVFT3zUHmN0c+2BO6dcVwN8eShdjapHvfpCP/vqJtS7t26YWUiEnDndo3raE9OJi3fNk6Lk5eUyZN8/FZG7Bj/bIlMzq2aq6KlSd/vS4ez372rXpyksbOvpF69Ye4rWvrOFTIm7dQD88xh95+/8q1S9eu/fTFZy9O8OpeRB1ToqvHlD2H3vr8i69O/nT2Sno6JbqthV10g7I1Oka1ec6f+p9s+VPr/wTgsm7duvXjjz9+/vnneDyI3NnZWb7eXapUKTw2qhQFv2zZMl9fX5ykPGEHEL7iKklo/K4EOjmaZ33sQPGIP/DqMTEx+G0EbenSpaGkPyrW2V8OEWYUioTRaSvI1M3NbcOGDUOGDNEfZROx/kfxh0m9q/ZFmt69e2/fvn3w4MEUhQCN6smUgj69QNGF11VV/mtwNgylePHiJUuWnDx5srSvmIVYwyMBV5dIk5KIdWKsRHMkQkNDly9fPmrUKDqmZs2a9BmtBKlD7WvXrt2/f//XX399/fp1Yl7b4PgHQveGmgPU/tVJXfnL3Nv5g9Qv/ErqVYXUFZmxp5oMFg8LoLIrN899sn6iW9lC0Ef+UpWb73/tC7Zd+fenMz26l8ybp0LV5kkvfaydMwfZF7cvGlW2IL68yOjFfkd+ua0+MJ7+fcCqyeXhoPxVvAN2XcFX/4rUl+J86ldQzNqx77BXvz4Jr99IS7tx+Wzy5kUVFIvkcx4+7djZa9dvp6aq2wiZaak3jrx/aLhbF0VpeQo+3dnjrSMn4Rg1a56h2Ji6wDYaQWtv0qsqadW6i9Rv/w6pq/qrNmOPzFvsTICTmaVEJfmZpw3bZ1QunT9vvqIL1++4qGheR9Z7z0U9XQ0qzduwy8znjl5SUUXW2Tcs4U9VLF+scOF67Zy2v3X4jG3nO9mXPwmcN4gL5y1SY/KGmO9vZ7B3RsaFG9d++uHjV3s0rauqnb/CM508g8KsP50+e/3a5aNHPgw3xr/+8VfXUm/cunXh4rdvDe3Wmt0Kl6i1Ncx69tq1m7duaffd029dP79x6cyyhRWddvKc/v6/f+KKWZnpGerO+I0PkkN61FUvPjR28jAfOaFInQg4/VZa2tUb107G7VhRQjVrvp69Rn751U83btxMS6PVb6el3f70rRd7tlTSqmjFBhtMz162BUs/nzhyqEdzR/JLObSwPPeh1re0IpvSDr8a37ZuDUKaqk36HvrwuLqUNm1wOytbaXTVZVpjq/bWOsnWovzDuvzlbuS/G1Tt1ei8G2SmpqZevXr12LFjaJXVq1cPGjQI742MwQEibdGduMRVq1ah73GY+hQmICGUiTtlk+ZfHzuIn581a5Y8LifvtglDPThgAYicBMfCj6g7qAEaRSoHBgYildu2bdtF+33VPyfTwR8mdSD31+fOnRseHu7q6povX77y5ctT0Da5fu5FxSEP7/lATtu4cWNqTmtClrQvjQseLalTALoZW8QiMVAs0t/fXx4qob9pE0y5Vq1aRHabNm2CyL/88ssbN26k3+P5ZGLeDO2Rb9IybIBs+luCuv3mj8XvOURbhlLq8/u3KognLlS576wt/05V72+pA7UdlIdVn167fufOpZsXPtk8Y2DpIui3AmWqNDv0+od3bp07FL6uXmk1Jd+2z/ikt7/+6t+nvvz8s6NHP/v6+OEzx97xWzqpfEnFlA079n/p8Ne47iOvRjo1xbnnqdq8t2/K2x+evvTZ0ePHP/vkhyMf/PzNe3sjVzWto+YCytVs7hd/EJrRSnFl387VSqnnLeU5Zd3ZNMUBkC4Mo23/5ZPnwlrVLs32QmXrzd4Q8+MtNdmg7vFqfAKraLcPtNfotQ2sUbv/V6nbSF27La9mLVTq1p0sNVt8/F/Jg9rVZ+fqjq19InZ9cvrcx98c++ToR18d+ej7Lw8fNO7o2rAiW4s4dF4b+TyH37lz/eqP3/3r5eee35P88jv/+upG2jVOmJWZevvnK6feDl01uQQDvkj1YStDvryVroSveuXs8rUTbw9oq7izSOnGQQnv3lQ3oAEVyMhUr+Sp+9ng2pmPhzu1ZLdipZpGp7ym7cQ+0o133t4X17GOYu5mzmOf++TfaqM06p2bn+4Kc6mnNjXq4W4+cvIX8oha5Hdos3/aG7m2tIr1inkOW3yR/leNybk5850rP325bERPwpaC5RrNCkg8nyotdPqDQ9GOZdXEQOV6vf/11UX2zL5zOTtLxUGXvnm/+1PUJV+xCk+ZnntL7t+nZdEBqjSEnOnpWXc9/MeK1lESkmrd9reGeCegxau25wpt2zSwiqNDtxw9enTXrl0IUC8vL5whLrFKlSo48759+86fP19uwKOF9Pl5VoHNwz42oEi4euEaVFnVqlWxnHr16ul894Bgf3QvgNpJo9GJeBhPtAZkOnv27I7aC2wwrDzxbmPcP4g/o9S5GBfm8gRihC3t2rUjbKFwBB1SVnluTvS6rTb/HTgPNEk7wpQQJO0LHmHfY3xwuR5p+vj4TJ48mTbBXrFaupwyT5s2jR0OHz58/vx52Npm7LmgDwM9oZ5X0WbgJYelvunvByqW2w9qf/ev7PVzX+xHqStSL1ix70xfSB1vi5PmT50IflQ/4AYHXbp59qM149zKFoTUi5ar3fHV977IuHJy1XjnUmTkK1i9Sfv2fUf0ch/o2tvZpbdz394uQ527u7R+ul7dug51Gj3TZ+irnxylN+I3L6hTggPylqvZ8BlXzx4eo9ycvPr3cBvQy3lwn+6u3Z5p0rx2pZqVm7btFWJ9+aYqYfadtDMJ/ktKUcI8lXqPWf3TLUW3lAtST1dTCBevnji4aHh7Va6CDp2GLPvy0i0YRuNsVQuNowAVUbwsxIQNwOj3v6fOBmUq6ih1LHtmZd7OVo/839wVtrJF2QL58uQr5dCsdbeBvQYM7NHPqbtbl96uTh5Oru6dOrZtULtG5ZqNnnHeHpOiXT0H2ZlpabdO/XTyjXde32U1+K6eP3ts//bN6sCe+YtXH7A08LMbMtHEVc7//M1LHk85opZLVG2f8PZpjewzstNv3Mmg1uoX+dC44MLJd4f0aEEfFC/dKiz+1RuqfunZ6j6BMvsPDxhcGyp3Wb/7qF0fKIms1Z+LXHs/JcSpjgo+mjm7JX5x4meYOT07O43ypt9JP5kcvLisUvjl3IYu/+4XdbrUOxlp2qtu1346vG1Sz3J58hSu0GJOyPOnb1IOrvXz1x8daOuoTliuWnvrgc9yz1+8sy/p6ZrV2NS4vevLH38lXZOWkZkuvywoVpvzsH1uM87U/rSeu78lP/Gg4nC58lYaaBRJ2Db/BhD8xYsXcYY4zOnTp8MO8r1qlnDKnDlz/Pz8oqOjcZjJycnC7uJpHxOCj835IDzePiAgoGbNmpiH/v6XkNQDQmavIUeovUKFCijV9u3bL1iwYMiQIWh06LWHBpqFJasa6/4x/DFSlyBCT3fo0MHJyWn9+vUeHh4UrmTJks2aNaNklBtQbiosdfgvoZO6g4PDli1b6HWAEUiLP0TQZypQzJkA0BMsgS7NMT4SlATmpukxTeru6OiIRl+xYsXBgwcvXLhw+/btuyhZeef/75US9tGP0hN/S/yW0fmTTTSRek05V1NouHnuy2fn9G+tkXr5fjM2n9RIXfPrd9JxLEg3lFb2VUj92sl3xzu3LMyeeco+5TT+6A8X037+ZnSvxsrzFy46fM7C2AMvPP/a688dPPj8CwcPHTz08v7n3nrh0OtvvPPcO++/evSLS7dv3Em/GjRnYhV1QJ4u7gMDk3cffPXNN5577eV9hw698NILBw+88NKel98+ePDN117/6PMT56+kKbENe581+y4tmYfDKrqMWnFeU42wbHpmappS7BfTz70WvnRwYeqQt0yTPrM+u3BDqwKcLbfVf4fUaY30rPQMaql2Sb2TdsLiO61B2SJQYw2l1I/nkLq6kPb9mVTtA3SC66EbZtUkhsiTt1WXoWHW5196/ZUDL+7afSjp2UPPHnzupVdeePm1V15+5dXX3n7v0xOnL+S88pf+y4Xjz+6OWbFwes8uXRo3aubSy3Xu9MnbNswfP8i5RP58eYtW6b846MjNNMg7O4t45tzF4y/1b674vkTl1qbXvyOwUsimZrfuEF5kqZ7i3L+c+mBQd5R6wSKlW4YmvsKRqoOzkHpsTP14b6h7w8qUtW6PUSkffa1OoH7Rh/pdez85pIdjJTZB6vGHv0VZo5vVV/FplszTu4MXOyilXryn1/wTlzMhdaIp1WKEeKc+8hvblSMLlm88bcfuM6kYFsi4ff37sM2za5UrWiB/oZ7OI55/6YNfrty49sul914+6OnSu0jBQo4Nm0cl772s4gatkGKg2h9QDKZZp/ZHQnG5/idH/F2Re1SSzmkJG0jnhi03B+TgGE+fPv388897e3u7ubmJfEcIQSjw/bZt2/C0Mvepu1z8cExMjNzrJJNVPS1emuX/FEIELHH+oaGhtWurN2bvQ+pqmlrLh/7YB5BA6wLyYUYEMDFNwYIFEcarVq2CRjtqGh1ihVAkgXgWdv+j+DPT76LUuV6XLl0oyuLFi2lid3d3iliuXLnGjRsLo1MTqZ5Wzf8KnEpIndAGWUzLAi76cLtTPxtmRNAgaaxKehQj02IJa3Bw8Ny5c6l+9erV4XIYffDgwTt27Hj99deJRv/Rt8n/CMQV6oxuy9UApStPofG0ek1d3Tpnh5tnvzw4e0A7jdTL9p+x4aQ2/Xs7687NrOw0JQbx70qbwh1HXolvW0vpsDwFa8xcHfLL7bSMn7+e4t5Gef4CJSat2nbymnoQS/wO5ZD/5eqQp/rZ8ps/RM4fVVtdLE93r+nvnTjHJtkzZ2nz3YqO7qTfVq/Vpd259UvyppXqc6Z5Sw2Y4H32igpOMjJuZ2beyNSUeurpVwIXehUuhNot33HY8q8u38JcNIX3+0o9Gz2aie5WpK7eU8u8fufGNxafqULq1Z/ySHn/K+qclg0xakfBZZmEEDcuX/rpl+tXaLwwn0W1NVJv12v8u1+cUmdXP493Wz3j/2ulf0V22tXPXrGOGdC5XJlipcs5eAwdG2NOOfrVievXr924fNSweVpZ9HjhSv0XhR25nq6RN3r74sXjb3o+3YRqlarYNP7Nb65InKEqkkanZKXfkmcBL554b3DPZ6h8sXKtwna9ol5ApDzqUQL+vfLZviD3hmqOvaHLhL2ffSfFUR2SefmDhEDnuqpDmzi5xn/+jVLqHEUTgbTTKYGLK6quLdhz0FyUOhZwPTs1U90XyLh2+hO/MT0h9ULl60zZYTmTRjGk327fvnZin9V/bL8uVUuUrNewy6ChcyYPH9/56aer1W44dMq8F95691o6xqVa1NbV8qdBX9P+aEXdktWflm3H/wNc5c8//4zbhMgHDBhQqVIl3CkcD7+g3XGz4ufxxrhfAa5Y6By3zKokgOaq/4fgovrldu7cSSGxtnuRuvA3+WxFjkvatk27pw6jlyhRArqk1vIZAGhUXh23Uex/hz9M6tA5ILKQOALhjl4fMWLEmjVrXF1d0etFihRp2LCh/JQN1QO22vwXEFLPp/1ePXGNNC6trLf1Q4FYDEth9KioKGF3AKOTv2HDBvi7Xr16GF/FihVpBMzxvffewzT1CXbYSBJ23B85rlDRyt0ukFxF6hqXo9XQnYoSrp/96tBsr85K5RYqO3j2xu9vKrZPVx94v5OZlZ2JblQzrtlXL53wnjKslHoYu3D9Dh6vfvIlmZlXv/NfMK5sQXx/4fpdh7/83TkJvkRgq+up+6max1dIu5N+8qWwRfh7Rm/pWl13Pv8hBKZpYgqmyqWezsuhRPJvZqNyUePn9vssdeCYPAU9xy04d0MVSHuV7na2Ir+r5786OKJXCzYXKe24yC/x9C31rXZ2YA/+sCGtBL/eU1fPxisyF6VO/s07t75N2DK9UXn1cEDNlh7JHxyHw1I5Aw2l5oE4PPvjNw+O83QdNWHy4e9+eOsFcw/t3n/pqq22mQ5SSXZJl9+x0y7xn0j77pPnx/ZoTuPlL1539LId31y8nHP/OB3aTfCdUa5AvrzFq3kuCDx6Xbvdrqbfr/705dtuLRrSuKXKN0x585jMp6tK0Y+Zmdlpt9Qj/XduXjzx1hAnSD1vkbItQ5Lf4HCN1FHqbL3y0Z6drvXh37wNnccf0O6pq4PVaa5/nBjUu65S6o169jV//t0lbZu6sUFPZJ5NCfIuo+ZUiroMWXT8fBYNfSObAUm3pF0988mWMc6EA4Uc6k7bbvxJew2S3s7Q3sjLzrp28vCeYb1bVi5Tb8mCQKsh5cWDL71/9Muz2geGuYaacacLKYKYqa3D9bVf6Vyrsf6Xs58dDwCc54ULF95///3t27fDbQ4ODjhYKHPkyJG43OjoaHmTCFef29vjkIFt5X8MeEFiC9IbN26sVk3dnbmPUhfiYxOA1MlhT1k2aNCgcOHChQoV6tevH1w2fPhwGP3PTbPfC3+Y1CFySB1Gp/UpiswbwOtubm5jx45lB2KQ4sWLU2HqIzX578F55E2+UqVKzZw5kyaG1Gnlh9upKuTTPmIsaU6OJaWkpISHhy9cuJBaly9fvmzZssQr8+bNe+mlly5dunTXzXKYQR4bsa3bcW/kuEXbnw4R6GRlZ6VnK+Wt7p3KM1/nv3t7xsDuitQLlhw2a/159S73fyI7+9LPp7dsXllZeyCucr1WgUnPXU7n2NQ7GZe+ePXZTvXUA895Clefsi7qm0vXcx1PMi0r8/b1K+e++ua7U2re/Mbl716f3k/7Km2eIj1GLTr07Y/XtF8/zYGKNlJv3/rm+x8PnzoPkSjff/vbZ33maFMEBbzGLjyfqugAH68hK+PW2cTgtQ5F1Smf7uTx1hc/KmrNhLHVl9oUBapysNBIXWsX1RpKrqdlZKcRtrD7nYzvrb5TG2kfn6nVasDeT79RgYztEoLspMhtjiULNn2656FPvrt57cSqic7qbbA8hdr0Hnvo029yft7GVi7M9ebt1G9OXfjm++9h1rf2BT+t/XJKtaaDYt48QQm1/ShVasa1I4YN08vkz5+/WLURS/2P35IPzXO21IsnPnVp2YCjyjg03/faN0JrjI00bcaBamiRz5UrP7w1rNfT7FawdMvQ3e+pD+Fx/kwon+vc/PhAlHNj5S4bOo1+4dNvtVNLUVM/SQnrVVfNzDd18Uj46hQHahegVODCrvCV5dRsRMm+Q5ecuqaiDM6Yoc6deeP8UZ+pA0pR+XL1Fgclqe/akKteI1SprBtnEkMXNq5atmHdtikpb587feHm9aup6bS2rY0AJ9I6RmNwdVVJUSWWNkbXSN22Vfuz4/+HeEvbigbcKQLp0KFDOFjcfunSpZHvUMyiRYtQtDh8hDsSSzw/FCseOzfT/4+Q+3ILFiyACFCYaFeEqzDUXYCw1FR7ixaQhfA6e4L69esjeosVK+bi4jJhwgRnZ+f27dvDLL169RJ6fSj4w6QOnQMSuYMLmL5z5860fkBAwOLFi2E+IpE6Ob9SZ6vofwHaqHnz5jQlLeLh4QHL0rW08kNX6tJtMTExCdrPEwUHBxNDUACCCaS5l5cXYePx48dTUyWQ/xUi0PGPdlJ/QOR2gaq9bP8oKCbLyszOSMtKv6noPONGVvr19LTb33/12jj3jtoUerHBU1edvqa+63nr1q2bN29ev379/OlTB1IShwwZULRooYIFC7Zr3y0mcf+ldPX4XLZilKysm9eTArc9XU0J6XyFygwYPX3Pi2/8+NOlq1euXrly6ciRD6IjNo8d5dF/yOTn3jqqnE12xgeHklxbNs2vVGC+p3q5B8Tv/ez0TxevXuWA08e/3BMfPWPGBBeP0QGG59GmyvffPnZg27RKav+Cg8YvOH355o1bGEsaJTz347c7t6+uV6sCMUmdZt1id79+RROhmenqDrh6sU3xBBko8jQuTWNgUkopQkDqSbr0LPVZmBtpt741bpxQv4yi6apP9Uv58PjttIzUtDR1mTT46GZa6rWda+aXy5PnqfaD93x6hmoceS/O3alxfjV1UfSpTgMCwuO//vp7VemrF0+d/nLv3vgZsxe6ek3eHmnh6t988sLAtupp+WIVmy3aYTp9+/bt9Ixbt1OvXT7zzvMRw5yehj3zF6owevGGkzdvZ6jb4dh8xtlvP+veUr2FX7pMwwMHj2qfxM1M094vhFrVgMi6lplx/vr3bw3qrpF6qWY7k965nJGVqtj1+p3sa1kZt9/aHdmhvvagXLfBez/4Qn1OCL7MSs/KuPmvlPCOtZVSr9/NxfzxV1e1ZiEcyMpKzUj7MT54uZpRyVO8j+eCn66mp2diNDSmirou//TFyikDaaxCZeotDbBeTlNnzUzPykxTsdDNs1+Mc2urxW2l6jTo4NK5x2BX51ETR89etSLYknz4u7OpapZfI3X1Dx1io3BiFXkmTqNz7U8RvbaX/mfHfSEPGP3uY0YY85dffgmR9+/fX4Q7TnjWrFkhISH6vCkeG44nIS/C/U8By3AhLkd66NChqFa0a7Nmze5F6rpSJw1zSSaECC3CIwj09evXw5vQucy6A2HSh4I/c09dlLpQO6WByyF4SB2MGzeOyq9bt87R0ZGAvnr16tQtt15n1Zb6I5DggHMSHzVu3Hj79u10rbS11uZ/BmIWAs4j3UZa6Dw0NBQ6b9q0adGiRanFxIkTX3jhBaLI3PaHO1OuRaNwErJJT9hxf+T2fqoFf03hLzMyM1DnGepTcUp33T713edRgRtmj+5bv6KS4LB6lYbdJs7bsGjJqjlzZo+fOKF/P7eOLZ+uW71aiZIlGj/VYsPmrYePHMO5cz7tA3HqM3GcOfP21ZeTYgb1bu1QsmChwoXLOVRp2bRFt5btOrTv1aK1U636TZ5q12GTX9jpc79woJovyEo/8sHBWeM96jkUI1QoUaasY6NGT7dr165Dz3atetav36x2o8ZTFq7+5Otz8nT3nbRTydtma1PBeSrWbtJvyIw5c9evXrF58oSJHTu0qFC5RGmH8h7DJx144+h1RdUYD3yutCCskeuLcuo+gioA7aFUIfwBt6RfuXwxOS5s+9Kxg1pWKqlCmzz5yjVzn7Z1hX/Mms0+6zes8920LmDbKp81szs0qUcRGnXyMH/4DRI4Ozv12OHX5kwaVsuhfOFCRYqVLOVYv36rdu3bdOjcpn37+k0bV2v89PAZK947clI1180fD4av7lS7TOGCBcpVqjJi9jLf2N0bg4zLvBdvWDhivFe3fHk5d/66rbqvDQzf/+qhb787/lxy8oaFCyqVVF2Dip8wflF4qCVl94unLv5yXXvGAa175dyXLyVt2bFsbNOqDnnz8F85lyEL/CwHjpwi7Ej/+fTRl5MiZ3g5ORRTirtI5YZeM1cnH3zz3MWz33/53osxAYuHuDkUVORbqnotzwUrg5IOfn78THrqzc8/fDl254phbp20Js9fvXYX7/UR+19+9wZsf/vK4fdeDN64pFUD9axynvxFmndxDwg3ffrJF+oncu5kXL/ywxsvWHu0aelQrnK9xs0d6zWoXsmhfEk8dtGChQsVKlG+aTuXNT6RJ8/8og1x1TbKmu5P6rJGQh1ix/2A5xQXKg4Tdk9PJ3JVaQHpixcvHjx4cPTo0Uh2BC48On36dESd/GoGAgynnduT/48g7EAwAS/ARHnz5q1Ro4YQkzDUb6FvgvhR6pUrV4YQ69atu3nzZk44duxYNDqMiUaHQ6FU4daHgj9G6hJZkIDRKZDMG7CkWOSQ6NChQ9++fbds2bJjxw4qU6BAgQoVKkCNeg11gv+j7M6BnIfWLF269OLFi+lOmsacc5/jD0HsQM4gCf1WOrZCJueneELn06ZNe++995ArNkPTIEQuRikJ7E+WGf/fT6XZIRC/Z/uTfyQXKKWOw0RpQXJKvH78wRvTxw8b7Obq2af3gH5u7h4D+vQb6TFgjJfXUI9+/dzc3FxdXEYNHbxuzcp9z+4/ceoHlKJ2ItU1mRmpagJf5rS5QFbqlbNfvLQ/1nvJbA83V+cunfp27T5w0Lj5awMTD/3r6x8v3E5TN5u5vhLLqkCpt6+e+vSNfYHrl4wa2KdH146dundz9Rgyc/5aY+LzR//9/eVbt9VNfYqJO7p9Oj5wfin1eF2BSrVb9vGc3H/AeI9+w/r2dRs4ZID3upXPv/HGuavX2VfbXVVNiXSbGuSClPBXpc5mxRQ5E/M//Xhq7fKFI/r1Htm31+C+fTzc+/fpN8Zt8Oz+Qyd5Dhw0ZOjgoYMHDR/kOcKz36A+ru69XWev3PD2d6duaOUCqVcvfvrWq9t9Ng4bNrBz906de/R0dh84edaC6PiEj77+5sKNW1yShqcOWTdPff5y/Kp5E9xde7l4DBs+03tzqPHlt976+adjn/3r0OiRI/v25dJDBo0Yu2Gbz2tvvLxk9pyhnl4ebm4e/dz6uff38hw+YvikxUvXHfn6BO0uXXjy+PsbF08Z6dFnsLv7IM+h/TxGuPUfPXTS3Ffe/4iyffXJG6vnTRvm5jrQrW+/fgNcPYb28Rq9YOXGz48c/tdLexaNGz7a3d3Lva97//5unl59Bw0fO23hszD3jRsp1phRg/oP7u8+wB149XUb5uk13jcg4lpa2uUrZ2NDt40b7NW/r5u7W/++7p593LxGjZ6cYE3IvH7uzBfvTB7lVbV6jTZd+sQlPXvqzI8/nT1z/OuvXnv9lYjwnaOG9q+mvcVesEx97+2J59U7CoDG0TrkP/8kX//31z87Hgy4TZngBKQBCds2DdevX3/rrbdmzJiBQy5SpAh0sGTJEpw/vC7+H+/NEk+uazPwJ6jhPuBskPrq1auR2tBQw4YNdS6Ds0W7AhIC8llt3bq1/sE40v7+/hs3bnRxcYE9YUwh00dM6rmRuxwUC6DXyezUqRNlXbhwYWBgoLOzcyFC3hIlGjRoAK9TK5ZUmCWQFnlAyIHly5endbhERESEPgnzR8FRQKI8Vukt6bDExMStW7dSZvqAC40bN+7tt9++i87t+Gthc5Dp6WnXriowvHHlgH+vXpU8G27euHGPVw9sJ8n5E2SlpaVymmvXOMu1Gzdu3k777S168OshGelpt27eYH9w7fqNW7fl0/MK6oNwqcjt1Dvp3xuC5hdRMrrC4FGrz/xy/crVa5evXL1CySleru8O6Yfa/lWQa+l/uaFycH0U+OqVK9e5/A3VDqoRrl2/ombSuYIGtXKVWrHt1u3bCB8VDthOopCRkX7z5s1rqtrXrqkf3b+t5rH/AzalmZZ6i5Ooc17jVKka34PsWzdvqstz3atXKQNtrs6Wq2ukFKyqRwVyrp6Zkc4xqmzXr9+8QV/dpAQ0jtYm2bQtZeYsWrVsnUvxCJHT01LJV5s0aJtUmVLT0nD9DE/qzqE5W1Vr3Lx5S9FDdiZFzV0wKeft21dvnH5785S+BfLlzVeufuiz78pHZ3VkZabfvnkmYdvy+mWL58lTrPf0zUfOq5v4djxa0NdvvvnmqFGjcM64aFdXV9SjTKyKMMOZSwJ/LvfdxcM/FHAhzgYfyxQ6ChNWgpJgbhLNmzeHoYTUNXJTaVCvXr1ixYoVLlyYkDMgIGDOnDmQY0ft7TUY00afDxt/ntTvAnEHpE5B0e4Uum3btqNHj/bz84Ma0dYwcY0aNaSqGkf/mVfdOKR+/foFCxYsU6bMokWLZAb+j4L+puNjYmKio6NJy20SGJ0lkSCFJBLEXPbt23f58mWbNdlhx+8B9kNWK1WRnnknLUMp7KwfooPnF1KkXnXEmI2XtWfY76JVOx41Us9/9axnS/VoRcnKLRLfPprzMGDufrr+ZnJIgwql8+YrtSho9zntt2XseBzwyy+/JCcnwzg46lq1as2ePRv+FgcO6Sp9pgl0WT4UcCp5s27NmjWVK1dGpotGFTlOQiB6nRzhdVarVasGW1WoUGHSpEm+vr7Dhg2TKXdhzP8dHg6pE3cAubnOKrxOAl7v3bv36tWrIWACFngdMm6i/Vor1E61Nab+A+AQ2kualfTOnTvlcTlb2z8YtOjNJtAlQQgWFhbm6elJSMXJKfAPP/wgBoS/RisAWbXDjtyACbAMNVmYmqZ93QyLuRAbtrCoeoit/PBRq66rV8HVTeXb2k92/kYT2/FIkHbl9EcLRrpobwQU7z9m2rvHv0vL+aX4LPVB3FuHP3zN07lz8cJFvMbO+uDEWfv4f0ygAmhtefLkyVWrVlWpUgWnPWDAgMjISJheuEDupT5EUgcEDREREZ06dYLFypUr10z7gTWIrHXr1qThbxIwFGnhqUaNGskX3eG7JUuWLF++3M3NrUOHDjLf/r/GQ1PqaHR5dE4mFgBp+Zn3WbNmbdq0idVChQrRB46Ojn+C0QFHESgQH3CeokWLjhgxAsH95/Q6YRdLxDqMTnBAW9MBbdq02b9/vz6Fq6b+0tPtT7PbcS9kZt9JgwQwj8ysrFu3z/3w3WuHzJPHOimyyFP06Zb9Yq273/34o7NXLmNSaTnz13Y8amRnZ13/9K2D/bu2LVKwYIEChdr07LfCNyhhz4svvfhCSnz0Gu8FbVo2b9a09co1W77+8YLtboTtWDseGfDDQH+YDke9Z88eZB6u29XVNTAwEOrVp9/Bw3oqHpnO2WbMmCFfjIGAoHMglKTrdRHoAB0PPRUuXJhSQXzTpk0TKhSWZPm/xsMkdZHp6HXJEXaHL8H48eN9fX0nTJjg4OBAu1SrVo0moF302fgHgRxCq9WpUwexXr58+blz59LiQFqfppfE/wtCObo8MTExKCiI5s6fPz8Ryfvvvy/8LdYDncPrkmOHHb8FriVNGYlyMdcvXYoJ9OvRvln92pUqlHMoU6ZuxYpNGzR+pt/gIftfOpSquN+u1B8T0F9Z2Vm3vz38dtCmFR5urpVr1S/v4Ni4WZvOHdr2c+48bNAA363b/vXx59dv//p8nJaw4xEDn5yq/eqhrOKccdowDrwO3cgLbwg2/Rb7gzPCvSBz+97e3jVq1MiXLx9LdDlkhDSXBHwkE++SU7lyZQiuatWqcPmWLVtGjhwJvwDIkRIKM/6v8dBIXRfoQEpvW9HIvn379u7u7hs2bCByadeuHVK7ePHijRs3pkX0kEfxtvbEIGlZ1RNAAiLZSttVqqReWq1bt+769etR2/A6PI3ypjtFhd8f7MwhUVFRbm5udBWl/fjjj8VQALYiT2D+9jlMO+zIDYxD2Uim+gzNiW++/NfbL376yRtfHP34yOGvPvnk6HsffPjhp5+dOntWPbCmvntrO8qORwv1OqF61CErM/3axYs/fnn8+PuffPruBx9+9P4HX3/51YULF+SRRvUIH//ZPmhox6MH3ljcMrBl3bnzwQcfdO7cGTfev39/KED8P0SAkwfi8B8QEgToR3EqeXq6efPmyEgUaSPt51bhb/hIKElYCdSvX79YsWJQG2SHgl29enXv3r07dOiQmw0VU/7v8dBI/V6QmlAxohWW8+fPp7EmTpxYpkwZkexyf0LYGtBSLKWxQO40kGhIduMMNDSrGzduTElJoQOkR+kYCdN+F2ylvwGhwNSpU4sWLUps8corr9gMxA47Hhi/KjgiP/XRNID3v/sOrCL+3Dvb8aihdQRdRk/9PlvTX/KFBCj9PrvZ8ZjgxRdfbNiwYeHChadMmYJjFw//J2Q6dC6z9yRYwujbt2+HmPPnz1+iRImmTZvCU1APvM5SBHrbtm3hI3kmDkqiAJxh9uzZSNmOOb/R8hfjf07qUiv5DB6VBCNHjqT5du7cSQMR19BYxDg0kHwuvkmTJoRFNJawOAlJ03BC50BWaeKyZcsSoHEgkREdIPfXRa8DrZt+H+wZGBjIeTjcz89P5Lgs7bDjQfErUfOP0HmaxgW/coDdpB5H2N4qp7NuqF/2U+/9Sbbtj46kF9msfUOA5N2Bmh2PFXDdUIA8QB0UFCRE8Edlem5Ghz4IDnbs2IEWhdFLlizZoEEDpce1+8UwkRAWq7Vr1y5evHiRIkXatWsHp3DgiBEjSHf5S56J+138RUpdEhA8VW3fvr2rq2twcDD179Onj9yEqFChgvy8mzxGSN/QXorANQoXXieHptRYXq1KBCBvrnMUej1eg+Lz+zK6WftqweTJk+kMinT06FExCzup2/GHISRgS8EN6TlfW7Hb0mMMjdQz1Y8Bparv9GZkaBG9+safonetT+lFjdQR7dKhdjzW+OyzzxDHaOXp06frT1n9OcDr8AiMzglh9GLFijVs2BCKQW1CQFASBAQZoSohL3QpS3d3dw6RT7PA6I9EoOv4K0hdF+udO3dGqZMDtbPs16/fqFGjFi1aRD6RTtGiRYl6pL3kRrveiLIqBE+OMLrsoOv1Zs2arV69mhgNyS4x2r2oHVKn193c3PLkycPV5UPuuW/S2GHHg0IjAPlX+wxdlsYX2BLa7j+/B64y7Tb2eEB1h+oh9a96gpH/MzLVd4G1LBhcfVU4K1V1qNZp2v52PM64devW/Pnzcen9+/cXpY6fF4f/4IDOOSopKWnDhg1QDGoT4VevXj24BmKCbvTpZEdHR8gezoLa5s2bN3ToUE9Pz549e6LsYbe/OakD4h2pJ0Qu36iRqQnqD53THJs3b54zZw7NRNQDQxMWwd8i2WlHlkLngAaVxiUH7gfsBq+j1+H1GjVqzJ49m76B1+/TozA6vU4ZsICtW7eKTdhluh1/BliN9ofnhyTkN761NWSe0n/6DnZSf4yguoPOgL0R50Lh6tu8WXQZVJ6RDr+nql+j0R6QkzjNjscVuuvesmULLh1mSUhIwMPHai+s25z+AwDK4EB4fenSpXXr1s2bN2+pUqXgb1gGuoF3WIL69euTX7hwYfZBE/r4+EDnsEnHjh3lwzKPltHBX3FPXd5fp8KQOtDzWULw7du3RzRDroGBgexTpkwZqF2+wwdh06AizQWi4yWT9oXjZUnTV61aNX/+/OXKlRs5cmRISAjdQyfRQ8DWaTl3Wejv+Ph4Lo0F+Pr6ilnc5310O9/bcU9gGpp1QA0aRaiERgLpdlJ/fKG6g75JVbfO4ewsfbI97U7WzTsZt++on5i7c0vrUI3Z7R336PFbP0xO7kzkNS7d2dkZ9y5i/T4QagBCCsLoEREREydOrFSpkjzrDq2ItoRx0OisojmR7yxRpLAMcrR3797QObwmSlXe6P77K/XckNqyhFNpCPietgA0Tf/+/YcNGzZ16lSovWjRolB7rVq1YHE9RKJx0ejC66xq5G67y84mcmrXrl2gQIHixYtzCTqYfkWUS7fJ8/Ck5VEIpDzhFT3H5X755RcMQl6T0E1ENxc9YYcd9wdWIn8acsg9V5Ydjxm0MEvrnZxeYlWbbdEmW3KYXOtKOx41MtTP+Srot0rFaUv6/Pnz48ePx6UPHToUzw9Ja9x9P+iMLkzh5+cHDRUrVgzarl69uhKROS9RN2vWrGbNmlCSzLePGTOmX79+gwYNIoCA0XUK1yXro8VfTeq5QVAjH5SF1GkOuJzwZ8iQIVu2bPH29kapFylSpFSpUvXq1ZPX3lDkQGN5xeKSELJnKahfvz6kjmSvW7futGnTQkNDidrosJiYGLqQzoPUATnLly+vUqWKo6PjoUOHxCxyQ8zFTud22GGHHY8DRF8Jqatvd2tf75ZMErhxfD4iG8cuP/Fyr7l30Xi6QGdnqGHWrFkIcXl1jfMIp7Rt2xbqQSvC9JARlLRs2bKNGzcOGDBAaIuljcweJzxKUgci1mXuQtqIpZubG7xOWw8fPhzeLVy4cNmyZevUqYNMzz0hT6ND6ppcV+8OSlTFkqavWrUqkr1kyZLEDZs2bYLXE7Tf2AF0Nh3JMjo6mq158uQhvvv55581s/mP0E83FzvssMMOOx4t8Mwwuvjk3KTOEgc+ZcoUZDpsAmHj8CHse30mVugcRgCou61bt/bt27d06dIwuoODAywDj6Ae4Rf0JCQCASHTUZsQBzv36dMH/QlVPdo59vvgkZE6RE6j0Dp0g7OzM2nonHzJRMEPGzYMuvXx8YHaIelChQpVqFABIS7krbe7iHWl2XOm5dkB1qc/CK/op1q1ao0dOzYsLEx/ek6one5cs2aNvGUYFBSEuYiJCK8DVgEJO7XbYYcddjxa4IcFsqp7adh9x44duHFcvXxgFPcONBH3O2IdXY7zZ7fIyMjp06c3bNgQBQhzOzo6wiBt2rSBSsiE5uUWMAS0evXqUaNGjRw5Ut5YQw0KfwmXPW54ZKROiwCaRlqHwIcEjUUadmcpv2kzevRogqOhQ4eyqVy5crnfaIe85SkGKJwEOc2aNYPpJU2C1cqVK9NhHMXqkiVL6FGr9sNu0q+kZ86cWbZs2SpVqhDWCa8D7Cb3nRvdjOywww477HgkED+sWF1LiH9OT09HsFWsWBF2wJkj23Ds2i1WdY9VWFwHbp8dRMevWLEC9Sj3ajm8Sc7Ph0Ln5cuXh845IYITRt+2bRsEBBmxP5Apd1bZCoTOHis8yul3aRT4W9J6AnYnIcJdXnsjPpozZ46vr++gQYMcHBzgacIoVLuIdaALd5mZl1l6yUGylylThp7jkF69eqHO6Vr6lVBOluj4EiVK0K8BAQHXr19X5pMrDMSAJGGHHXbYYcejgnB57sSVK1f8/Pxw3SVLlpw8eTKELRD+BhqV20C+zLf7+Pj06dMHcQiPoOjgEY1DnoHO5eF2Tti/f//NmzfPmjULMoKJIHJoCIZCWwoxASGsxxCP+J46kKYRItebiQSQTFqTdiQ9b9683bt3L1q0iFV6USbkc3+9T1S78Do59JMIepa1a9cuXLgw1F6pUiUPD49NmzbRu4mJifJIxbRp0+jIYsWKzZ49+7vvvtMjQfmZP0AOkITYk+RI2g477LDDjocC/GpmDsT9AnG2ussl8fXXX8vPoeK6ceC4cThb5ttJQ+FyT500kOfbRXNXq1YNOi9SpAikAFm0adOmcePGqHMIAnUOba9cuZJTofvhIKFz/Q66iE9JP7Z49KT+gKAdla7v2XPw4MGBgYH+/v4uLi6Ib3qC/qhTp46QN0SOTBc6b65BpuJB06ZNq1SpQihAj9aoUYPzQO1EcwQKdKG3tzeRGluJGMi5ceOGWE+G9h6F2Jmk9VXdwuywww477HgogMJRU7qgUk/E/edPYOOcd+3ahaPGXeO0ly5dCoXD3DC6JKBz0iQgckBi+/btw4cPr1u3LkIcOocIoPO2bdtCHKjzokWLlilTxt3dPTg4GOnv5eXVt29fiBz06tXr8Wfxu/DEkDqgieVD+f369Vu7du38+fNXr15NvnyvBmqvX79+o0aN6C12g9dJsITshdrpQvieHeSJenkZkVP5+PgQx0HkQUFB9GXx4sUJ/aZOnfrhhx9iWGJGmBRpndTF2uykbocddtjxcCHeFQiXS0I2paWl/etf/xo/fjzeHo2Ou96xY0ei9lNe0DlEHhsbC4ULnSclJbH09fVFvzVo0ACHj9uvWrVqkyZNIAXIQh6FYwlno84XLVq0atWqPn36dNQA3QidoySFgJ4UPEmkDjp37uzk5MSyU6dOtPXYsWOHDBkyY8YMT0/PWrVq0WcQPMEX3Qado871SXgheHJYAlS7fIsfkKAj16xZgwXEx8cvWbKEHYjmHB0dCRq++uqr3NQuRiYJu1i3ww477HiIwKMqkZ4j02252gNxR44c8fb2rlevHs4ZgQ4Hx2kPPkPnsDhpY85zcMnJyRA8wg/NLbwgfr5Zs2YIPHIICMgkwQ6TJ08eMGDAmDFjEOXt27fv0KGDi4uL3POFcSB1GEduBD8peJJInfalcaV9CaNodLnbMXDgQD8/P7S1u7s7jC7f2UeF04VEZDC0ULvMz+s5pFHtNWrUYP8CBQo4ODjQqUuXLsU4IiMjJ0yYgPWg2jECqP2TTz6R330RYG13zQjZYYcddtjx3wO/ioPVvSuO9+OPP0Z04a7x1TAxzjk4OBiBnqB9ql3UOVyONCcH740bd3Z2FtlWqlSpmjVrouJQetWqVStZsiQngSY8PDymT5/u7+8PfSARYRNdmsMvos6FZSTnCcITQ+o6ndPctLI+JUKjS7vTiwsXLiRAQ7g3aNAAPiagg6rl1XaIXLhcl++ShtobN26MKCd2g9rLlSvXtm1b4oOQkJCgoKDx48dzOHYAwXPaV1555dq1a7njRzvssMMOOx46cLNXr1596aWXcLxwMP68du3ao0aN2r59O+pc6FxuorMq2Llz55QpU9q1aycvP0PnHAKXI+urVKkCHXAS2H3OnDkxMTEIfXmUXbhcyEX4m7RQDAkhF41qnhg8YUrdltLAKpAOoGOItkjAxxhBYGDgpEmT4PKKFSsW1j5IJ70LYcsMfG693rp1a+wAWY9ql9vzdD9cPmTIkPXr1/v6+nJOtkLtRHyenp5RUVH//ve/b9++bbM+DRJdkpAYU8JMSQBtF7UqCTvssMOOfwjwe7pXJJGZ6zNfkpAcHbjW7777Dt7t168fZIywhpUnTpwIncPlu3btkmffWLKKQI+Ojl67du3gwYNx2kWLFkWbyU1Y+Ltu3bqoNVx6hQoV8PlQQ0BAwLRp0yZPngyDQNidtd9VExLRWMUGNtlSOW9mPUF4wu6p3wsi36WTIHgPDw8vLy9vb++RI0fSJVWrVsUyCNOqVasmT0mIXpdEixYtmmu/5SpkjwFJKECshylwToxg3bp18+fP5+RswmLYf/Hixc8///zp06fTc+64C8R8Ja2eqcv1VB1LybfDDjvs+IcA1wfwhPK8m6Rxhnd5TlZPnTr13HPPoaHxyYioSpUq4XJnzZolP+EBhSPH9bvmkPq2bdumT5+Ow5fPxeDh8djQeePGjVFonEF8PpoNIli+fDmSjEABpujYsWNu2v6b4W9C6hJMsaSrAN0MnLTv082cOTM4OJgYrVGjRvQxqFy5MjGdPBIv7I4NQedC81A72l2EOxIfdicgaNCgQf/+/TEvuHzEiBHsX65cOWxOvmbzyiuv/PLLL2lpaTbz1CCmLAkhdUxZNtlhhx12/EMgYkZzh7+qc22LAm7z559/xoWuXLkSHw4ro61RVkOGDNm8ebPBYBBFDp0Lr0PqISEhy5Ytg57lW+D6jXOctkgyzgCg9rlz5/r7+0+YMMHFxUXYgSiB5V26/G+GvwOpS2+xhMj1BxxEuBORkS9z8n5+flBy+/btid2KFi2KNUDbTZs2haHbaBBeh9SF7KF2iB86Z/8iRYrA7pgLm4YOHbpgwQL9uTzYvVatWlzU29v7tdde+/HHH3M/UgfElHWDtsMOO+z4R+EuPYMzxEmeOXMGLl+yZAlEiyuGhpFbrq6ueOmdO3cmJiYix+XxN9JxcXHodRTU8OHDccvyNho+mUNw0fLIM4INdV69enWk+dKlS7du3Tp79myUGM4ZIuAqkIKIPUhBMcffFH8TpQ7oJxjd2dmZJf1HDglJd+rUib5Eag8cOHDdunULFy50c3PDMojvMAtYmfgOtgbaHLztOXkSouNJQ/x169bFYrAkogEHBweCgFGjRmExhIG9e/cmZoTyAYdgprt27friiy9u3Lihv2Fphx122PFPBs7w2rVrR44cQXDPmzcPV1m2bNkyZcrgWvHe+FK4HPIWOkeayzvG0dHROO0xY8awv3z3DSeM00ZQweUcW6lSJeic8+CxPTw8Fi1atHbtWqQXDh9dh/OH0SECeSzu703ngr+JUqfDJAFEr7MUapccQI+yZE8XFxd4He0u7yZWqFABnsYmqlSpAjdD4aLaYX0YHUsSdgdsQtmjy+UjRAB76tChA8EjISHaHZNiH04IOAOs7+Pjc+jQoZ9++un27dv26Xc77LDjHwWc3q1bt3CAL774oq+vLy6xWbNmOFtcaOPGjZFDsHtgYKBR+z0O6ByQgNFjYmLgZvbHwSKi4PIiRYrA5TVr1kSaAxQ5eh3XjZRC3+PMccJz587Fw+v+H58vRA7IhA7+3hPvgr8DqdNPeldJz0kvSiYJbYttN5mEEYwfP37cuHFEiEOGDKlduzYWg4lgKDVq1CAGVJpdA3QOQ4PW2u+/yRQ94h7z0m+6y5wP5+Fsc+bMGTlyZPv27aF8duDM2OXEiRPDwsI+/PDD8+fPp6am3mcqXp+uB7lXZasObft/ZP42xw477LDjPsBj6N4G2HJzkDtT90L68l5OjHw0zLlz5/71r38FBQWNHTsWZsVblixZEjpHMo0ePXrZsmXocpPJlJSUhC6XV8yR6SEhIatWrRoxYgReF7aGyAsWLFiiRAkOxyfXr18f54yjRpqj2tFgeFrUFJcAOHkuhGNnSVo8P6uQAktoXnJ0Rvi74u8z/f6AkB5V8Zt2xx2O9/T0HDBgAFEevEumvL2GCic8rFOnDqvC6BC5TMWzlFXQokULYkbsDGOVmXmInH0GDhyIqS1evBiOd3NzQ99jlFWrVq1cuTI2DevHxsa+/PLL3377LWHsb79jwyogn+EhkB0kre+g5wB1WM54s8MOO+x4EIjHYIm3QVUrt6Iht1chQY7sJom7QCaH37x5E4f22muv4dxmzJiBBMLdVahQAW6GfV1cXJDRW7ZsQduYzeZdu3bt2bMHOkeaGwwGPz+/JUuW4DabNGki4go6R1/hMxH0wNHRkVNJZvPmzSHpadOmTZkyBdcN9IlYIH7+t/jbc7mOfyip0/eYBdDIXc3P9OrVy9nZGUOZPn06grt3795YJPEg5oUx1a1bV6dzAYEkpE6CpUh5Akn0OhZJTIDxEV2S4+rqOmHChOXLl69cuRKaZ5U9q1WrhoLntJ06dSJz69atu3fvPnr06NWrVxHxv52lzz2oZKTpq+ysxwQsJWGHHXbY8SBQ1J3LpUgCr6LjXi6FTTirK1eufPHFF3v37oWtEUXIJAQ0vIvzRA45OTmNGzdu7dq14eHhVqsV/obLU1JSZII9Ojp6w4YNkydPxisiwXG2oos4AxKoYcOGEDz5yCQkE1vRTggkiJ/gALfJyfHY+HB98lU8vB3/OFKn72F0maWHzlklLZ8JxEpk6+jRozE12H3mzJlwNpJd2B3zgt0xVthd1+sshe+JTFmi3eHy2rVrE59yCGbKsZB9+/btiRVg982bN3t7exM9DB48mEOgdggemuckmOzs2bNDQ0PffvvtM2fOXL9+PT09/beDikGojzfZKjm/3dMOO+yw4z7QnYZG7ndL89wgJy0t7caNG7gm5HhQUBAeEj7G6aGnS5UqBfXiDHFiOM/169eLIofC5WcwgcViiYmJ4UDcIN4PLSQvIhUsWBAhxEnq1KkDlzdt2pRElSpV8JxII7wueyL90eVg1KhR+GqdyJU2v7c6/2fiH0fqAFGOHWAQCHSW5MgSCM3LA5ODBg0aOXLkwoULoWFMkE1igjIzj/E1b94cI1b0rt1lBySwP1kCIk2CAAJMTBPDxUah8Hr16hE9EC6sWrWKGJaTr169euzYsW3btuX8RLgYN0EAlt2/f3/2YSS8++6733777c8///xbHa+PPRICWbXDDjvs+H/xu/wNyGQTDufixYvHjh175513cERLlizp27cvbg0fhQ/Em0G9uDvkytKlS319fUNCQoxGoyjyZA0wOtS+Y8eOdevWIa/xojKRjhctUKCAzuUoJVQNulziA+RQrVq12BnHi4ckehg+fPiAAQNwy+K3xVFLGoj3tkPwTyR1jEC3BkCOPDCJoZCWpypId+jQgXiQUBTMmTMHu5Rn5tkqM/NYJFEkJI06F72ua3eArSPEWZV8RgJminyH4AsXLoxNI9AJCzw9PTnnmjVrOD9DYtOmTYh46Bxlj4lzIeJfxgAnwb4ZOeyzb9++zz77jMF269YtYue7aN4OO+yw448CCseZ4FIuXLjw4Ycf7tmzB1ezePFiHBQCQybVIXJUCj6tT58+kyZNQo3s3LkzKioKIkeIyw1ykeZWq1VeK4fI3d3dGzduLO8PAzwn56lduzanxa1B57kfZccr4njxt4sWLQoICIDOuZazszMOGSDGWOKBZapVvLddqd+FfyKpC4TXhcJZ1fhdQVaheVHzpEnA7rDsxIkT/fz8UNVDhw7FWKFkTBBqFzOVmXkIHo0uz8lj/aRlKQQP02PfxAHyDCfUXqhQIZZodIifKJjT+vj4REdHM1T8/f0ZFVC+l5dXp06diGcheEyfSIKr9OrVa+TIkcuWLWPnt9566/vvv79y5Qpj8ndn7O9C7h0kzVJN6OfEB6xKfm7kzpEdBLYsO+yw488CThXY1nOG2G9zSJApCYGez5IhrB8imb8LNuEocBdXr1794Ycf3njjjcjISJzJ6NGjYVDcCxIcV4PDQVijj1HJsDiiYtu2bTExMTKdHq99rhUKZ0kaRR4WFrZ27VqcWO/evXF3eDl5fB0XJ7IebYNjZInzRLTgNtmEC2UrvhEH269fvzFjxnAVSqKzuPLXOa8viZcmHwi1i5e2Q8c/l9QfBGJGksCGoHbsm6WHhweWN2XKFIT1sGHDsC14mgFAHMqypvYmJSQNl2OpQKQ8vM6SNIm2bdti1hB8w4YNUfCMnwIFCmD9xKqoefJdXFzk/MTLjCJAwtfXd8GCBVyRwhBDED4TGcj8FeOEglEqBlVcXNzbb7997NixU6dOwfSM3tyuQYc+5sVH6Eud2oGeL+ncq3qOQHLssMOOPwFGkIys30K25k6wp6S1OPz/n6hjf1Q4/H369OmvvvoK52CxWNDZcn+6UaNGuCB5Hg2XAteSOWLECFwN+yAtIiIiIGz93TO0uIhy5HhsbCx6esWKFRA5Lgs3CD0jysWbcTbkCmGBCBvR5bhH0TMlSpRAr3MtirFx40bc3bhx43Ct5MhDTgBHJ+5X88e/PsEu1C5pO+6CndT/f4j1iIXJEsCgCGviysmTJxsMhnnz5hGcYtaQNMaKyWK4jBNGCKTetGlT6LxNmzZYNsDEsW8g2p0lW9kHnpYAmfCW+IAlMSyZnBZzX7RokZ+fH6OIAWk0GqH5wMBAhtP48ePd3Nzat28PrzN+iAkYnGXLlmXwUEgC3pUrV4aGhhJQI+iPHz9+4cIFIvTU1NTfvkqXG5r3ULCta64BD6LnyFZAPpBMO+yw409ABpEOhpXkyxDTE7JVNv0WbGJQyyz6+fPnjx49igTftWsXAtrb2xtXgOPCLeAccBF4J7gcRkdjuLq6opJxJkhkxENUVBTCQJ5Xl6+0shTgfOB41MXChQs5IXoGF4fL0mbW1Rc78H7Vq1eH3XFomqJRrwGzD5noctmBTThPfOmMGTO4FpeGy52cnPBXIr7ZpBO5HX8UdlJ/ICjBrkEInrGB8WGCGB8JefLO09NTbroTdQ4cOBAjlqc3AVRNTIr+Rp3rLA67S1qzfNsTduQD1DxanyCXsBdeL6R95ZjzMAg7dOjAeGAYoMjl+VLGnkyFRUdHb926denSpdOmTfPy8qKoDFeuywBm1BFkQPlyOKExYTgxOAe++eab33777S+//HLjxo3bt2/fS9b/FuJl9LQk7LDDjj8Kho8+6B5wKLE/Q5UBy7C9cuWKvCCOE8ADEP1Dt/A0KgKPwcAXCQ6d42FwVoMHD0aK4Cg2b95MuI9CwI1wrLgRoXDkOEt8C5sQLUFBQWiDCRMmuLu7t23btkaNGtAzTgnXhF+S+UJcFvwtTkx0C1dEY+DBRJfjf7j0+vXrkfWLFy8eMGAA/hMuF6WEv8Kj6j7Wjj8NO6k/EHRTY8moIIEVYo6sYpGdtG/Lk9mxY0e0O3zJoCL2ZNhguDAralueE4FcsX7iVlQ1zM0AYKs+DIAkND1vew+eqJb9OQMDicMLamBEwfcQNsdyIfmqQ3BwMLzOUDxw4AC63GQyRUZGouYZRYxzCYfbtWtXR/sRGrmPxZjn5FwCoc/WKVOmbNiwgQNfeeUVwvwTJ06cPXtW3p6/v6y3ww47/hcQ8Y3yvnTpEoPx5MmTn3322aFDh2JjYxEPcHOfPn1g2WbNmjGuERIy+w2LsyruaNy4cXikNWvW4CJQ4TC08LdQuCQAXJ6sQbT4zp07Of/s2bPxYDgNlDf0jEqBxfE/MHTlypW5BEKfS+OsKAPOCreGdIHLZboRac4+xBacZNmyZRR17NixFMbNzQ23icNkKUQuAh2QsJP6fwk7qf//EPLG8iSoFBMUmmcpdI5psmSVJWoYmu/du3f//v0xYpiVsJRQAJuGyDF3xgajDouHpxkVcsdduByOJ0EOgLAlh6UQP+OH/eU2fNmyZRk2IuJJcEKIn2IMGTKEoQiRy68jEHHv2bNH3jAh7o6JiSE2h+kZsfI5W0rFoJVzUp4qVaowejk53gG+pzrDhw9nHKIAiOj37dv3+uuvf/LJJ//+979//vlnmcZHMeSelrfDDjseHAwchg+DSKbNL168yOD69NNPUd4MN4YwZEzUPnToUFwN4xS/QVDOIEUeMGBhTaiUIcxAlh+hWLt2rZ+fH9qakc7hOmcDWBwnIFwuLM6SHMSAv7//qlWrZs6cOWjQILwcLI4TQGGLe0FFsIpzwMnghUSR45cAfgkix3tQJN25kQ9z9+3bF32PA6TwnBYfqLM4S2IOcaE4GVFHQu3iUe3407CT+v8PzA5ICEmCpZ5mq57WbVEX7pgpSxcXF6zZ3d199erVs2bNkh8EJLAtX748AS+KmZHAaHF0dISw5UY7QyK3gofgyZdMluQAcgAim1HN8GaoE6HD8ULzjMAGDRpIqC6ftAsICCBIlxHOYN67dy9Mz5BmFWkeFRW1Y8cOQgH4G01PZODs7MzAowwM40qVKlFIxiqoVq2aPJTn6ek5fvx46rJu3bqwsDCih5deeglndObMGcT9jRs3dMp/wPl8wf2Dgz8ROnCIwLb+e7hrB9KUGUimtvE/tsqqnrDjoUDaE9jWc+UAW5aWaUtpaemm3Jk69B7UIXuSr6/mTktCx1056sjfu8qDgKsguBkODAqZMD99+vThw4dffvllBiACmkE0f/58NACiFu/BuGZQM4pl0DEAGc6MffwM5A1BotEZqgxYhq28VGY2mxHZjGU4W939TkoioUMb9+pXydkHENxD/EuXLsU5MJBxUxAzHklm1FnKpDpyHL8k04p4G7wBjotikJa5AYic4rEzwAURWyAVfH19kTHEFggbzownwUlSKSA+E4jPRCaJ5xSnmtuL2vGnYSf1hw8M1JbSAgL4T6xZ5Dv8it2PGjUKlvX29ob4GU7Vq1dnADOWIGaib8JkQnIZRaLdAWnJAbqyb9Omjb5D8+bNOYqRBu8ywAgX9Ll6xp68GEqRCOfnzZu3YcMGecdUmB6C3717tywBDA3fE+bjbohC1qxZwygliofFcToMPy4tT7pyFcossw7QP5egPFSZOH3cuHELFy708fHhKsQQ77333ldffXXy5MmffvoJlX/t2rX/UuXjKAUcrids2zToOSz1hGTq0HZUIE1J9MLk3k12ALb1nMsBWbVtzrWDbd2Oe8PWUhpsWTmZtpUcSA5LvcHvguwmsGVpyL3/XZvoaLZKpurInLRAneiPgEM4IcaMSUPY586dw8hPnDjx+eefv/322/v372cIQHWLFi0aM2YMQwNXwDBhwDKI9DlzhhK0yvhlgONDBg4cSHjNUF22bBkEyeEM2NjYWBhaCFs0t74UtpZVIEKchEzOcSwjkTiA4c/4haRxCLgFnAMsjpdAYVMMWFye/sHV6P5HvA1LVAccT4RBUWV2kMJD+XDzihUr0PrUjktQTZSMaBv8Hi6OJRCXmBu/m2nHfwk7qf9FwHx1E2/fvj3DAF4fOnQoHLlt2zZCZrlVT6jLOGGkyR2pmjVrMsgZYwwq/eF5RhrjTeNx2z14QA6bGIEs2ZN95JU53WswDiUGZ/QClDc0zP4i5ZcsWQLNE7xHRETgF2B3gFMAEDyQVTwFgiA6Olrm8BnG+BpCE/h+5MiR1AVnRJFgd0rOdQGCo0qVKjgCyiCxBRdlzFN9gptVq1ZxHpzRgQMHXn311XffffeTTz75+uuvf/jhh4sXL6Jp8JJpaWmoHPHCf8LhCjRfbTtW0kDSkgkkE4iXB7b1HOTeTd9BT8hWkDttx72gGlHDXau5IZsALawbAEvsQcJB2Zp7T6AdqiBpPVMSDw4OketyOYwQU7x+/Trx6KlTp44fP/7pp5++8847r7zyCpxNZIxihnrllhbmzTBkFGDwjGKYkjibgcBwYNBJ+MswwRswZFDnDB8G0ebNm7dv3y4foYKJEd+MCwJuIWmInDSjT7S4zuICVtnKPqQjIyMZmBSGQJywwN3dvUOHDpAxsoGBj28pqn2ZlQQxBIXRH/GhSMq/aB6GQcqSTMJ0Skv52ZkDOYpa4MEY6bgsxi8ewEvDiBEjyORauDhdyRBAiAO04y+DndT/UmDoBLBi6x01kOjTpw9eABG/bt06dHzfvn0ZHoMGDWrQoAFMzDhkOJXTXpBjdDECZewx6oA+LUYmEIInIfnsBsHrqwxdOL5WrVr4F6F5OB7IPTNyCAIoHl4AxzRr1iyJvvEvuBLxHXgTCB7fITpAaB6HAtgHd4MnQkzgVkQZrF69msh9ypQp6AOiB2dnZ1qAIjVr1oyKUBL8Hb5GIM/Qogbatm1L0OPp6UngP336dJQK/i4gIAC5w+VeeOGFN99886OPPjp27Bh6SKb6b968KbP9OF9x93cR7R8Fxwps6/8J8vVLgNzXkhxJ23Ef0Eq0m56WBNDaT8G2ngv6/n8anJaTSEwgPI3ZYDyY0OXLl3/88cdvvvnm448/fuONNzAz7ByTw/A2btyIEWKKRKKYJWSGDTOaZIYcaHPkZaBwYtmGDRsy3BibDCUMvn///oymadOmLVy4cOXKlVBgWFgYQQDDBDBeGDXC00LbjDJGkwwr8vWRJZmSL5PtZHIeeRgW1zF79mzGC6NMJv9EhTO6CeUZ4KVKlRIfwqCjhET84hNwFxqP25QAmeQwPOUeOVXjwBIlSqABOAS/hA7BX8HlXFFe4gUMasB19YS4OBK5py3t+GtgJ/VHAMwd0yeBxWP9pFkS27LKMCCf8Qmnwu7z5s0jiofkCKiFiRml0DyDE8kL/zH84GzGpHA5CUamjFXciqRllbTsBtgTMIAJwyFXYgXOj1di8BPC4wUAlM+orlOnDrtRQnzTpEmTEPRbt25FSeDs0OvyMqtIeWF6lkApC+02nmTqakP8lFD+tm3bNmzYgJvjnPKzS7gMeV2VWIfiUSokPqXCreBTgNzkw90Q7lAq3JA0mqurK8UbPXo0bUVUhN8MDg7GYxJz4JpzT/ufP3/+4sWLv/zyy5UrV1Bdehzw388E2PHXQxiajsvN0NAzcR4MTUefPXuWTv/uu+8+//zzd99998UXX9y1axeMKA+KYioMtHHjxsHT8JPE2RgV7IWB6RGn2J6QIiKV8cKAYk8IG50NYTMuGK1wNlS3adOmLVu2oLYxci6E5WP/WL5QNZA0lKxiZC3BiADsDCRf31PGDmk2MdwYdAw9zJuSI8EHDhyI8TNSKBJKmhLiHNT9toIFSTN4GT4UmCGMo6BSeABhbsrPEq/CqngGtsL0RCQ4FiSEXlly2Ie4fP78+XA544uaMkIZd3gqWkwS4srEj0kCyNQjOSzt+CthJ/W/FJi4hLFE8axi9xLMypIcBolsBeTgMqA6SAt/gRuC5tmzQ4cODFQcTVHtC4sMXRwQFAhDMz6BDFRInYTG6QoajyvIVtmNVRWla6GAcDz6Q9fQDGyC/fz58+Mm4HsCdnycg4MDWzmEwhO2z5gxAxcDPaPpZdoQryS+THdJuCche8kRyKrmvtTMIQTMUpxXeHh4UFAQJ4T4iSHkN28WL16MVEKL4ILxLziUdu3aydM6hDuUVoB3E5CpJjpzgT3Zn6MoOYJm2LBhuGPCJtFPCDKui3vdvXv3oUOHXn/99bfffvtf//rXRx99dPjw4S+//BINd+LEiVOnTqHnCA70yEDCAgC1EBxIfCAhgkQJAhsR/SNhawKNg4G0j7SVzscAxXzt2rVLly7BxzTyDz/8QIPT7DQ+XYCAfv/99996661XX30Vet63bx+WRpdt374d81uxYgWhIR1Kt7q7u2MekBbc7OjoCDlhDJgECQHGwKghk6UkyGEEwW2YB8dyhsGDBxMmYnKLFi1as2YNl8BIuBZ2QkjKdWWGHOvFdDFmzaiV2bMkk7JhS8LZLIFwtiTIZAfAKmn218cIhxOPYv8YP0OeesGjw4cPJ4Zg0GHDsDUDk8EoITjDkwQDk1pQWbgZmY6dy5DXXYGE+IAczsOSfWSkQ+ToBJwJATSNgOeBkgmvGXfLli3julwdR4Q7YpPmxtS7u+xDgkyaSzIBfI/7YkkmO7AUt2bHXwk7qf+l0AcAto7RMwBIsCRHluwA2CTxL0vS7AOdQ+2MFga5n5/fkCFDxo8fP23aNI7CHzEmAeOc0Y6TQsTXq1cPemZU6yNZBjNDXWbY9Ez2wZeRL0Nd8b8WyJNmT0Y+58FNMNpxixUqVCCY4EKQPa5EZva4NI6GK3IUfIk3nDBhwpw5cygqwgX3JBP4uC0gvgwvpjg/R8RLJiBfHJwO2YFMtnIS2S0uLo5zIobQLiAiIoKrQP/r16/noggLWob2IR6iMLgnHBPREl5eXBv1pUZ4fFwkWoQWE0epplDLqI/xAVaBPAAoEwO0D06N3qEvCCwIaEaNGgWLENYQGSxZsoTgANdPMXD9+P2wsDCZzKDAOOs9e/YcOHDg+eefJ2J47bXX3nzzzXfeeYegAa767LPPjh49CnUdO3bs22+/PXnyJHx25swZVOaFCxdgOHgO6YkAJYZAiQqgwN8CqfqHYDvsP2G7gAZYlkv//PPPlIRQBu17+vRpIhsKSVEp8BdffIEOpgoffvgh1UEQv/HGGy+//PLBgwefe+45+WQC1acRDAYDnSUPbdFEcKRM1RCu0YA0I2YDj9Kw/fv3Rzpj25AHXYYR6o+GSL9IH0l/oSzpPiiNroTV6CwslkMwbFSm3OJl4Og8zVWgaqQnpoLBwJ3BwcFiS7CpCGKxWN04xQ7FMklIWidmSFpABeluncvJYQlkt9wQS+Zs7MPVfXx8KAxBydixY728vFxcXIgtqAj1yj3QSANkNGZJfRnm1JRAHHsGMtgxchnFpLFYQIJVGeYS5XAsLckoFr9Bw+JhGDLYM46FYHrp0qU4H+wckMDsGT7io3J7rdy+S1ZJswNLSYsTs+Mvhp3UH2vIaCEh7E5CH0jE71AmpEI+7IVHYB+GKwMeN8dYZfyThrEYxox8cXOMcJYMe3wlCRntkkmaYc9SHAEQyheQ1sGqzNvjVmB6In0uhK8R7wMKa6/E4JLYgT05IQXGU6OzZ8+evWrVKvGkkLHQnu7jhOlJ6FAknwPdG4qvlDT5OFnZkxyg7avAVsAmccSk8dpoLC6N3oJ9acB169ZRHhTJwoULCUSglilTphAQ4P3RfAMGDIAMZG4W1yYeE4Jv1KgRUU7ugEBmaNVErfbunyQAaehHOkI0It0BReFeEVU4ZXHHNBE9wvnbt2/fsWNHupJepq9hI+IkghIcPVEdTtZNA6UiWKF4gIYlTZwxcODAQYMGQV0PDg7hDBwOOI+k5RLi07ko1acAgJLoESetQVEpMCUXk5D4j5bBMIiWkIBUlipjHlCIcLCtUXKBRgM0ILsJMdM4ojWxQM7Ptbgol6ZIEB6qEeabOHEi3URnYfZ0HCREcEBX0qF0K51L6AA3w7LYgxiAzSZyoGwrl3XppsKSQ8S6WOpUDUhLQrM+G9gNyLFADFiT3CqOkcMxckw9JCSEUIZCEnRSBdqZ/qXTGSA0F9WnHeQWOPqbRMmSJWkWzEaCFeyNZqGdaRaWEppLkApI5J5aJ02CM9MjnJyGpalxCBL3Y40MTAyMwUgIxdgk0CHGoj3F6gCZdDQgQSYgYccTATupP2EQl0qC2Lmj9uvCpKEf+BICYJQGBgaOGzcOP8gmBjaOFWchYb4MZpwmbhcuEY8gdKL7CHHQrHIsCVZzgxzJlAMBCVbZWffpnB+HjjPCfcilC2jAVVEGnItIK/ahDHADzhpPLV+ORLpt2bIFyqcWiCccKJ4Rj6weydPez7H5YA25/S9gVSCZ+FPJlx3I5xDJIa2xvG2yVBJ4Yc0h2zwyCS7HznIqpBt+GarANQPRmpQT4A1RWhs3bkT2rV27ds2aNUQJCHe0IIEXAoiqQUJ0ELEXSghm6tevn8abblAmdIUPld5EVkJjuXtBZhToL1oV74xzp93w8sRStHDlypVZyr0GmpSGhTtp9gcHdAudcBLOwJI05+T82AkXYslF9RCEwsATlIqy0e+UE16nBzFIzJKKEHkQBxBwEB8QXlBZelbEMe0wc+ZMwiaahcZBmK5evZq2osVoN8iYZoSPaU+U4o4dO2hhYi8aHDOAFInGhE3pLL2DpO9klYRYhQ56mR4Ue5B+lKUYhiQEnBnqFeicLZtIiOUISAtIc0WZeCfNUcQQQttUBOYmzoAj6XFag/bB1GVQSAgIuULeMi4YIITgjAsan/6lrwGtLVE4IwtjEJOQQcdSErJVVmVUchUOFDvBGLhK0aJFGYM4AToO/0AH4RwIeqZrP2lK8EofYX6UULqSfsQUgeZs7HgiYSf1JxIQNuNQxc85CXGs4k9xr4sWLcL14DGRYpDHqFGjGLoMdXwHQx1yRTbhROADHDdkLN4Bv4CzIC1P0AjEd9yL5uUogeZelK/nJKyyP14G3wTE1+BcuKg4NZlLYInfQZcAEqwSecAiXIhKQX4oCSgBPkDfeHt7w/r4TbmjicPFseLcdScrrK95XQVJ6w4akMahS+IuKMefExmQuCsHSI5cSK4FJC3ITTOk5epyCAkuKoRBAvIQliJBReADeEtAWgCZAWqaG2E5gO3gvKCgIGE+ciTUkJwHB0ex5AyAtBzO2biWZMp1BYQ1UiQpG5AyUwUBldKYUVFj7kamEaS5aApAAjrUmwuQFsiq7AykAYEkWEqCc+rtyaqkc19UX2WpF0k2CdgkO+c+rVxUSngX2IGaCm0TghCULFy4ELMkXCOkJpRh9GH88DHMDY9izLC1btUAayefKAper6795InM04ieJiFjkLEDtJGkqFpfSqYMQ0mTL8ci4jkh0Zg+srgQgx0Kh7Yhb+JIIicqS4FxDgMGDCCapMCAUSZLSQDcC5JA/IwdTyLspP6kApIm9JahKNNlQIYlYFjKVsie2BxPhG5wdnaG45cvXw5T4h3UbcmcSVES8rg7vgZBBnSmx4PA0ELn+iqbBOJxAAkBW2VPIGkgabZyCAlOQppLQPa4JEQMfK/rGEoikQc+USbz8Y9kwvfsSdk4vKP2jUnilcGDB1NBaof+g/LRedASfAPBQDN4c/Hd4r7FXwt/iLMWCiGfrYDdcrt7lho7KHr4Xcimu3bQjviVYCRTzi8JoRAdGqfcE5RNlrkhZdYhFdHr8uBg/9yH6Kt3XVEryD1hq0auelFrPSGZ0ghae9jCGmklHWTKUhJA30cSLCVoIC27AVmV0+pXAaTluhSeZpFwQWqnt5KURJacB2shlsJ4tm7dSjSM1J49ezYBJWrbQ3spo0OHDhhtI+0DLDCoMLeYaEHtnRFhU4BKxpghWiJmzLuBdptfBos2aGzBMWYMZGjog4VNAslkB6JkVomMIX6A/XNaRgrDlmsxfgWscuYxY8YwEGQSaNasWX5+fpQfhyDuQvcSaHGNx5X3IIdNog3YjQQ5ukux44mDndSfSOijUcYeaXL0+FpGJiBfhqhMqbGPp6fn3LlzR48eTT4Bu4+Pz+TJk3EcVatWheZFZ0CruCqdRKF58vFluBWci+aIbDQPxEMBzSPZIDls4pwCVvFQ4rlIy25A8jlhG+3TeKzK/lwL58V10fdC+ZQQlYPzguxl3hLI3UciAPLxpDL38H/t3cuPHUWTBfAtuxEzI81qrFnMaGbBErrdZvNJQGOMDbj9Br+N8UNuP4TxCxtjwEZu4zeww0KwQPyX86s8dYPq222DxAc2os4iFRkZmZWVlRUnom7de9lzowZxyuIYHnnXrl1O04kLaK5eveqsOW70f//+fbmX1FMShjBwQJx+WL9AEzRS6JK2gH24JEIQOWwRFC1RpqSMTA8hlQJ9QVMsoW9uKGVhaPnbkS7OfciXETLJGBTSNEQ3m+XoGybICNGTM+zQrDvGAN2STZC1DVp08Ut4QXZFcnVy1dpj+P5zE30dAknnw+wHDx7cvXv31q1bN27ckLPaBvn5hPfaLybZJC+//LJ9aNvgS5sNZ9tOeayFuW0zyH6z/ejzsQXaRvBoOzdIPpuw7W1so+WRVbfpJxhueAIUwafUlF5gKAMa1vim5GY0mex2c1uzZo3W/AjM+++/LwrJieQ3ncjuere/84qLaP6gfy8HKKOPi4gDqaYxU/9LYyT1vyRyT7oPIR+fQ2mADT0kPF/XvoKiWvc2G7H81q1bcR7NwsJC3nqlnJ2d3bRpEwOuClnmyWFcSVgT0cqw+ZTmqfp3a4eOSTkke2Wc3VADlBBNlMp4tAwyROvRdZGsdA/023/b8MK8Kt9qVnmwLxbJk09lYPJQstN5/vnnee187uBAHF/ew7IavPyhQ4ekOPnol/f/5JNP8kn/nTt3Hj58mAgAFYVUQicd1TeU3Ajo19ET1CQ+mKIxiPJxCCNGJqCxVDtubGik2aGvr4ahfRDNsGN3gIbhlDLDJyOnlnOE/rSXPwmYkrOMU7GUUqv5WPxv2/vztmven3eBLl269NFHHwnajh8/LnqTrW5r33qYn5+3t9GkvWrTigsTFGYz2CTZJ7ZEqiiTYAvZIUhUgItNE9fa8HaLvTe1abNRVSPUviVQhuCD2ABlRoBsdSBnS+dTKvtT3CARb5u3+zMVR3cuGzZsYOmOFpHcu3dPKGbH5gE7RqdP7O4Gzz2uGl/h3qdpLqH7cjmNPc+eDRBoqhqojvgrYiT1vxfcq7nbhzJwf+72+AXe8Pr161KZjRs3qqK3I0eOEHA859KeL3aveWNQwPR8JWaVWIdrOZ04Kc60hKFTi2cspZJSWcEBIVCNBmIToeRClEDW0TTybN+cJTr4G/Hn8T5f6SzyuFKkwmMOn6CS4+uBc3d2esmKDOIEjWza69r7idaKh5US8afbt29HJPl62+LioqUTDXz88cfyJyspQZQmCgiwkcRR+pjPoXEwhDtLWEmihY4VBwgFKlsU0dFe+K8Qza9i5aMIqGGBDDlo0E+ooSZZTZl5TgdyRgTn6Kyde94YsCDipKX2OwTXrl2zXGLKs2fPSjSPHj2KtPbv379r1y7La0MKuYZfcnMhXN+ksC4QAnY1i6dzQYPwNNp2NV307FiUqZctgbANIjR0cYd5diE7MFC1wWpXx6B2aei52JqgNUpdCKrZn9n/yrxo4vYxq7qhcn8RTMldaSmEmO5TO03gYke5K62DYN0OBGSsFd+7Q0FT3eCPw5NbR/zVMZL63x2cAgehlLDymATsHjehlLJLgJD6li1b+Navv/5afqDKJpSJznEepuSV+NNK6PNAEvgmLizejQdUQhxfeT1ynGPk+NZoCtEAWccgTZHbwB2iZxk9RO9YQVodhR8P93PrJiwh4+KdEV8v9ef6KwhwdlxtIoB6Eps4IMi5MwtzoBmLgDnycYCogvsOfyThM5/Z2VkLbpEt5vr2dTWr/fbbb8syd+zY8e677+7Zs+fQoUNST5494cLp06dFDJgP/0lPARdeuXIlr5EDdpS8ftGALPNKOQgsHgetzBiLP9IRDGJYuHz5sqNcuHDBEc+dO+fQ5mBLYBpTQr1mePDgQQQsZdy5c6fJb9682VaRPua7cCjE1ppp3wYUbNkSWXCrYVlsnmG8ZfNYQASc7Llf3IYse1Y+q+2KuDS12pa61jmfGTmKY4WwrXm2RHZCiLbti27DpFyJbJXqWBolqKbJUMoIAWOlPeaUHbo2mJN1gl3o0f4QxSmbtgmbNtgD1tYtJgq0hlu3bs1XBixmbsmQdxF58vLcyCNGBCOpj+gj9/hfnoLL4Cx4DUpyiCc28lGQNLzavjyNDG7fvo1yVDnr2HNtaIzP5bawIIHn5bKTFXG7SYlwG7dYH6VDXKHuygjRV7V51x70sYw+lmSIDFUtIciAQZqGoKyZGLnyqkQAAZ4ocuKpuWMpo5QrcQCOAf4a36B5K4CirEYeA1RCCTSlLHTPWxv00hfDKTNISqAcomV3Hfr6BL128gX6x4FB36EhXfrK5OhDZA59pYEZTabdn8YEdZoQem6n3p1yztGx0Bs6t2JWD8FbSVSH7y0sLgwrZ9mV3eOgF16ggeFOqAtXaNezbwoYQLbBFGIMqdamCtKdEtjkQISUwMY+UdoY4kL73FkINZyXxcmyOFN6G95t4kZj78YRIj98+PDmzZtuMffUwsKCYE6ERHbfJc6GsLjwKHeZcsSIVTGS+ojeR/ApUsa4D7k7mQa7p4lDUfIpQGbDQComxeR99FKV2Mn2pG4SC75P0obvJRxcHk8d/ggZ8HSA7JEf980JIkj+Ogwa58hdcp08YDymsjnk/h094yuD5mmXueBgSq9jQOkQaYW+eblbTxl07rz57sj1NFW1s14Nrd8y1LGUjo6WnG9FBgkOEh+EEsRAgNhAxAAWCk9YSaEDtoBhABGkCmQrLKoIsuAroSnJLrlGSN+qggM5nOMGxbvyThMzw8wWTN4pJFd2Us4OnGbI2PWFRsov5BLU4kQI+kVsGC4+kLP43WoOvp1RBoHqsBfEPuhVDb1qMJOSuwNPAoU0KTOmsxBSOC8n62JZIntbaSUTBiUqslC6C4JPnDjhdnDKcnE3hWj4woULbig3TuJjyBsA7ix3HC7PjckGKCPUraocMWJVjKQ+okPoXMl9IPK8RAPcRxxKSD3KgJ6yHgDGjBBjYcGxY8fOnTu3f//+jRs3CgVOnz59586dkydPbtq0iXfL00jcEGbiDaUy0jiuEKNgDgyBG1BCPT4NDfCqHGVcLcR9Ryh0Lnk5N6QafVDKgmpz6R3qWFP66kIoOUg1eogSzK0mrDpsbRPvZx6hyqC1dOjrg6F+C6pvhFURy6kxSz9EDh1Ew2zqXKqpkFaoarpELk0wtchB69d/ZBOZMlWI3Aw7xNIguYJDNPNVRh4i82emu12XxwOikzyMEccIdJ5rv5yIucVDNiq94MZmtlf1sttt9bt37x4/fhw32+3C3IsXLx4+fNitAblxoDjbTQS5d9yDBK3MQuHuHQLkFovBiBGPw0jqf3eEziPzGtE0V9O5j6LqtKaqjGeZMuCJeLGq8lM0BOOz3LFjB++Wb/2yOX/+/E8//bS0tLS4uLhly5aXXnppZmZGHqMLl4rOuUvpTj3URfa4Pwmi3KjLapsb5XPxfTnrcspDIU0Qs+iBwNdzxGmN66dMNWY0qUYu6BVknDKI8RD0w3JV6NhLDa3fKuiblxs4eqFXTdBrV+iHSGs7p55NaYaLVpg6dKoQOUqIMihNxofSK9vU+kW2kq4jIV2GaD36ESJDqlCyoWKszLBBdgJlbFSjSauD2kIgysTcQsk1a9bYZsjb9us+P2jv2ZGl3dhdl7m5OXt4dnb2xRdf3Lp166lTp27fvv3zzz/jcrsdi+/cufPgwYPbt2/PDRLOtufdCLk7cmukFTrGbvdgDFJlwwBoYhmZwHLEiMdhJPURfxSav1oWE8SXRTM/P58fLc8vq2uS2X/33Xc3btzgJblFfk2rXP/IkSPvvPOOjvyvlIh7BRzP1f5L+zyY/xUBKCX9eSBcz/PzsWtYv3n+jjM49PBxnu1TEhjQE0ItaWITvTKgDMjMlFp1YZmf4aMkpyk2kTWxbF2XkVzQbHuqmxIK0QS9qqFX/ZNQY/Yza0hT0KtWoG8eoIaC2HSLPoGqVsIL7S/Fssg0zfAXak/3IfqGBr3SUReXjGzA2OQQ3ZEmr0SIFG2MfIrxH+0X6YWJ/zZ4NU9pO6nidbN65ZVXcPb77R+H5d92qW1pc968efPRo0f0Nu2rr766fv36N998E5fb0sOtriSHhu3eVEeM+KMxkvqIPwm8W0H15fZD05ibEOfIafJ9NFo1we7du7/88stPP/305MmTZI6VUihw7949yqNHjy4sLNAgb26aI5ZLcdOoHcc/3763pnyu/bZ23jRmBnmwH0cvReP0ZWDkRhkdPRRbQBK7QqOSHiEPNkZQhtTZZJw0ETqOWo3zghpnKBSayR+L4fSeMM9CTWxqeulL6azT1AbuaHVKP5QDVYi9qqYgaw7ksulYekLYeUKOgJV5L8HFFdjZCbaBmC8fbxP+tb0kj8Lb2wjdFxOY2QxGsOvynvnnn39ua7322mvr1q0TRx44cGBxcfHatWv5X0T71hatr4qoEpR2oBGKxaHb4g2pjhjxZ2Ik9RFPB1weJ6jkGYHMLVZCkybl3Nyckj6WmqTvcnp50t69e7laSRIDeVJ+luSLL744fvz4tm3buGZOn5fnoLlgxMD18/u4H+XjeO4+7yQH6B8NcP18PRvl/zQYQa//a9/NQwCQl7xCRbJDXF4ypiHMzMzQOGJyR/KQqwgrwawQM+jb/mD0B2voVQ29aoC+oSFnVBgaRFA6F4uQkyo0w1+aWFpAo4mx6AlhaKv9v5NXCPNgHOTZLo1UGz27XhJrVzAv0iduc/mwNTO9DOgQNpWrr69jvf7667t27RIgIu/82JxtY0e98cYbUnBR45GGKKE2ZITs0mzFIYWPGPFMYST1EU8Hkp64S74yaXrH7c1vRuA6IW40qF59vYGSZsOGDchedgV8tAHn5+d1V2J6HnxpaenMmTP79u0L3+MPPC0mOHz4MI3uxsHBKBxtyOSwRT5Jxf2EVLEILpHt5XlAEj7Z3n82/Ff7hVEID+XzfvwEFQc4bsd7E1JURii2a40dqqkQAyjj34+Qa/i1Vw0oeaiETANqYtGXZZoKThmEQdYhawJ5Bi6ftlCiq6xeHrFYz39vvwQM+aJElt0lsPKJvRjoa0AXy/V1rSXZmHjHjh3W2Ym44i7roUOHzp49e/369UePHj148MAVZ6y0SXQRCG7ZsmXz5s3ZPJLvvG1eO8o2S7Xbgu1LH8p8JaQMaAgjRjxrGEl9xNNBmLu8J48ZIZo85OQ3lZQ8L32agJOlSVM0BMoqWTKA9ZN/o+LQc4gKIJSXL19G9hcvXpS9Sf0RQ/7jbu3atfI8VKH10qVLUv89e/YgD6OJQtBVvccHIXglKoosX0T8ynwEAATIGwBhKQINs3yy2z0RnoQIeVQgRIBkqA4HIcUudW2/gJZwIQ8PVkIYsSr65gEMYrR8uS4IAee4CDgwGWwKeWoNJuxkzb9O1gnWh9PknHjKrEmEyEp9jUC2IOyN6dDiA4uMm/Hu/v37T5w44TLdunXrwIED5j87O6tJ6Pbee+998MEHp06dcoHu3LmjDO/mKudCu8qEXOsojRyNfRKZfaoxtnkgNmkyLNAUbCf67KJeNWLEM4OR1Ec8NcQ5AqfJRcaHQlrjiMvDxrcms9fUmLrLsSIwizJmUL0kYVp1VNY46UgJiQMou6M25NBYRLaH0aV9e/fuRfm4ZNOmTXkSoJfcVKL/448/fvPNNzdu3Lhy5QqOOXr0qAgAITFzCAkl1kSQCBu9ITC98ul7PtTXilCR6H+3L6njzjz8x3C64DzA+vgPCyJFMI6YAHHWU4RksQR4bvnvpE5BU4wh9hEq1ECu4eNQLyRSEcQMv5iO5lF+PptIhJFHEc5XabZGMH9mWiXQ1jP5scWxRB9++GF+1eDbb7/94YcfLK9QybXII5aNGzey3LlzJ+ODBw+6BC7Evn37XJegriPkOoaVNeUqO1w2Q8ibniZXOa0laCLX3osxMNYamxhEGTOIXjlixDOFkdRHPEOIr/xVlEv9Vfsn2/DRAQIIByj7ttbKxVcrZDSI3yegH2S/bdu2hYUFlP/WW2/RYC+Mnh9JDVsYTXdZJq76rv3vuDhgaWkJq3322Wfnz58/ffr04uLisWPHBBC7d+/evn17fmwVwxkhk5mbm5uZmZHIYk05q1AAoYoDkkBDy+27jwAEBGgYswZYOQgxM4g9bk4v1CuqMKA4w+AO4UB5F8G0zd+51O/Xmh6iNVURzJkzZ0z+6tWrTuSrr766f//+999/7wQZO9laT6dgKQySR98CIwaWy6IZE3lTZlWVekFWrLs27SrUTOghNjSxyWVK05NRI8BvkVfiya0jRjx1jKQ+YsTqKNoIhQR92wQ0YZRgyp4BDggwFm6TiVKS169fT6mK6sogSFJYqCb6QvTQZtEdJQbNcBqxhL7eoJoZ9vUBYgx9fXBoMj0BSZt8WgkJXyI3ww6qGQccKGsS9OvVYAFp2nn0iE2hVrVvHjFixOMxkvqIEb8Lv51scBtyWrt2rVLq2XNaSzGLugodm03CgipLSFOqhSJRQs+rDaoFrdWrqoXoI08dQrWfboMZpqRv8+2qc3NzSalBF4fOCE9GHWjEiBG/HyOpjxjx5wGBhSDJcnTkRwj5UUYPjUY7pLoqeosJx4dKo2/ty1CWEMscDqJciWGXIYQCynQsA1WyYZXz7TfMKVm2I4ycPWLEn4V//OP/AZChnhMgtEzAAAAAAElFTkSuQmCC"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "attachments": {
- "Picture1.png": {
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- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In __machine learning__ models with parameters which are __optimized__ according to previously-seen data.\n",
- "\n",
- "Machine learning (ML) can be divided into subcategories:\n",
- "\n",
- "## Supervised Learning\n",
- "We have observations __X__ we want to use __to predict Y__
\n",
- "__X__: data, features, inputs \n",
- "__Y__: target, labels, outputs \n",
- "The goal is to find a model which best predicts __Y based on X__: \n",
- "Y = f(X) \n",
- "\n",
- "\n",
- "\n",
- "Models are further divided: \n",
- "
Classification: Predicting ordinal numbers; determining classes for inputs
\n",
- "
Regression: Predicting continuous values
\n",
- "\n",
- "## Unsupervised Learning\n",
- "We have observations __X__ \n",
- "The goal is to __extract information about X__ \n",
- "E.g.: finding a representation, cluster the data\n",
- "\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "ML models are typically developed in some variation of: \n",
- "
Parameter training
\n",
- "
Model evaluation
\n",
- "
Model selection
\n",
- "
Model generalization
\n",
- "\n",
- "Let's start off by taking a look at some classification examples:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Scikit-learn: Classification"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Import relevant packages:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sklearn\n",
- "import numpy as np\n",
- "from matplotlib import pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Scikit-learn (\"sklearn\") uses a common interface for its estimators (models). You create an instance of a model, fit it to your data by using model.fit(data, target), and you can then use it to make predictions using model.predict(new_data)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Classification with Iris Dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sklearn contains a number of example datasets. In this section, we'll look at the iris dataset, which contains information about different flowers and we will try to predict its species based on a few of the plant's features. Load the iris dataset and print its shape:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(150, 4)\n"
- ]
- }
- ],
- "source": [
- "from sklearn.datasets import load_iris\n",
- "iris = load_iris()\n",
- "print(iris.data.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sklearn uses the data structure convention of (n_samples, n_features). The iris dataset has 150 samples, with each sample having 4 features. \n",
- "Let's look at the first few samples:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[5.1 3.5 1.4 0.2]\n",
- " [4.9 3. 1.4 0.2]\n",
- " [4.7 3.2 1.3 0.2]\n",
- " [4.6 3.1 1.5 0.2]\n",
- " [5. 3.6 1.4 0.2]]\n",
- "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
- ]
- }
- ],
- "source": [
- "print(iris.data[0:5])\n",
- "print(iris.feature_names)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's also look at the target, or label of the data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
- " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
- " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
- " 2 2]\n",
- "['setosa' 'versicolor' 'virginica']\n"
- ]
- }
- ],
- "source": [
- "print(iris.target)\n",
- "print(iris.target_names)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# From: http://scipy-lectures.org/packages/scikit-learn/auto_examples/plot_iris_scatter.html\n",
- "# The indices of the features that we are plotting\n",
- "x_index = 1\n",
- "y_index = 3\n",
- "\n",
- "# this formatter will label the colorbar with the correct target names\n",
- "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
- "\n",
- "plt.figure(figsize=(5, 4))\n",
- "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)\n",
- "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
- "plt.xlabel(iris.feature_names[x_index])\n",
- "plt.ylabel(iris.feature_names[y_index])\n",
- "\n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Classification: k nearest neighbours: kNN"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "K nearest neighbors (kNN) is one of the simplest learning strategies: given a new, unknown observation, look up in your reference database which ones have the closest features and assign the predominant class. Let’s try it out on our iris classification problem:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get the data and target\n",
- "from sklearn import neighbors\n",
- "iris = load_iris()\n",
- "X, y = iris.data, iris.target"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['virginica']\n"
- ]
- }
- ],
- "source": [
- "# Instantiate the kNN model\n",
- "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n",
- "knn.fit(X, y)\n",
- "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
- "print(iris.target_names[knn.predict([[3, 5, 4, 2]])])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And that's it! Our model, \"knn\" is now a trained classifier."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can plot our class boundaries for our kNN classifier (from http://scipy-lectures.org/packages/scikit-learn/auto_examples/plot_iris_knn.html)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\desir\\AppData\\Local\\Temp/ipykernel_7628/1884370761.py:25: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n",
- " plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(4.2, 8.0, 1.9, 4.5)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import numpy as np\n",
- "from matplotlib import pyplot as plt\n",
- "from sklearn import neighbors, datasets\n",
- "from matplotlib.colors import ListedColormap\n",
- "\n",
- "# Create color maps for 3-class classification problem, as with iris\n",
- "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
- "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
- "\n",
- "iris = datasets.load_iris()\n",
- "X = iris.data[:, :2] # we only take the first two features. We could\n",
- " # avoid this ugly slicing by using a two-dim dataset\n",
- "y = iris.target\n",
- "\n",
- "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n",
- "knn.fit(X, y)\n",
- "\n",
- "x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n",
- "y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n",
- "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
- " np.linspace(y_min, y_max, 100))\n",
- "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
- "Z = Z.reshape(xx.shape)\n",
- "plt.figure()\n",
- "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
- "\n",
- "# Plot also the training points\n",
- "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
- "plt.xlabel('sepal length (cm)')\n",
- "plt.ylabel('sepal width (cm)')\n",
- "plt.axis('tight')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Classification: Logistic Regression"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's look at a different classifier: Logistic Regression (despite its name, it does classification)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['virginica']\n"
- ]
- }
- ],
- "source": [
- "from sklearn.linear_model import LogisticRegression\n",
- "X, y = iris.data, iris.target\n",
- "logit = LogisticRegression(multi_class='auto', solver='liblinear')\n",
- "logit.fit(X, y)\n",
- "# As before: What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
- "print(iris.target_names[logit.predict([[3, 5, 4, 2]])])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We get the same result as we did before. However, we're now faced with a critical question: How good are these models? How do we know how well they perform? Are these models reliable?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Model Evaluation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Dataset splitting"
- ]
- },
- {
- "attachments": {
- "Picture4.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Datasets are typically divided into two or three subsets:
\n",
- "Training \n",
- "
Used to optimize model parameters
\n",
- "Validation \n",
- "
Used to compare the performance of models
\n",
- "
Model parameters are not directly exposed to this set
\n",
- "Test set \n",
- "
Used to evaluate generalization
\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Sklearn provides us with many useful tools for manipulating and separating datasets. We'll first look at train_test_split, which splits data into training/test sets."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X shape: (150, 4)\n",
- "y shape: (150,)\n"
- ]
- }
- ],
- "source": [
- "from sklearn.model_selection import train_test_split\n",
- "X, y = iris.data, iris.target\n",
- "print('X shape: {}'.format(X.shape))\n",
- "print('y shape: {}'.format(y.shape))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X_train shape: (90, 4)\n",
- "X_test shape: (60, 4)\n",
- "y_train shape: (90,)\n",
- "y_test shape: (60,)\n"
- ]
- }
- ],
- "source": [
- "# Split into training / testing sets. \n",
- "# We can specify the proportion we want in each set using the train_size and/or test_size parameters\n",
- "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, test_size=0.4)\n",
- "print('X_train shape: {}'.format(X_train.shape))\n",
- "print('X_test shape: {}'.format(X_test.shape))\n",
- "print('y_train shape: {}'.format(y_train.shape))\n",
- "print('y_test shape: {}'.format(y_test.shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We now have two sets: one set we use to optimize our models, and another set to evaluate how they perform. Let's recreate our previous classifiers. \n",
- "Sklearn estimators have the *score* method, which allows us to quickly evaluate the model performance on a particular dataset:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "kNN accuracy: 0.9333333333333333\n",
- "logit accuracy: 0.9333333333333333\n",
- "logit performs better!\n"
- ]
- }
- ],
- "source": [
- "# Instantiate and train our models\n",
- "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n",
- "knn.fit(X_train, y_train)\n",
- "logit = LogisticRegression(multi_class='auto',solver='liblinear')\n",
- "logit.fit(X_train, y_train)\n",
- "\n",
- "# Evaluate our models!\n",
- "knn_score = knn.score(X_test, y_test)\n",
- "logit_score = logit.score(X_test, y_test)\n",
- "print('kNN accuracy: {}'.format(knn_score))\n",
- "print('logit accuracy: {}'.format(logit_score))\n",
- "\n",
- "if(knn_score > logit_score):\n",
- " print('knn performs better!')\n",
- "else:\n",
- " print('logit performs better!')\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Try the following and rerun the previous cells: \n",
- " 1) Modify the \"n_neighbors\" parameter for the kNN classifier \n",
- " 2) Change the \"train_size\" and \"test_size\" parameters for the data split (they must sum to <= 1) \n",
- " 3) Add \"stratify=y\" to train_test_split so that the labels are balanced in the training and test sets \n",
- " 4) *Do nothing and just rerun them* "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Your model performance depends on the type of model you select and how much data is available for training/evaluation. The fourth point highlights something which is deceptively simple: your model performance also depends on which data points are present in your training and validation sets. \n",
- " \n",
- "The problem is similar when delving into model *hyperparameters*. Hyperparameters are parameters that are not directly optimized by the data, but that can have a huge impact on model performance. Hyperparameters are determined either by heuristic domain-specific information acquisition (\"guessing\") or preferably cross-validation."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Cross-Validation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Cross-validation is important for a number of reasons: \n",
- "
Evaluating model performance variability
\n",
- "
Determining hyperparameters
\n",
- "
Getting reviewers to leave you alone about doing cross-validation
\n",
- "\n",
- "K-fold cross-validation (CV) is one example. \n",
- "To do K-fold CV: \n",
- "
Take your training set
\n",
- "
Divide it into K equal subsets (“folds”)
\n",
- "
Train on (K-1) folds, and evaluate on the remaining fold
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Fit polynomials of increasing degrees:\n",
- "from sklearn.model_selection import validation_curve\n",
- "degrees = np.arange(1, 21)\n",
- "model = make_pipeline(PolynomialFeatures(), LinearRegression())\n",
- "\n",
- "# Vary the \"degrees\" on the pipeline step \"polynomialfeatures\"\n",
- "train_scores, validation_scores = validation_curve(\n",
- " model, X[:, np.newaxis], y,\n",
- " param_name='polynomialfeatures__degree',\n",
- " param_range=degrees)\n",
- "\n",
- "# Plot the mean train score and validation score across folds\n",
- "plt.plot(degrees, validation_scores.mean(axis=1), label='cross-validation') \n",
- "plt.plot(degrees, train_scores.mean(axis=1), label='training') \n",
- "plt.legend(loc='best') \n",
- "plt.xlabel('polynomial degrees')\n",
- "plt.ylabel('explained variance')\n",
- "plt.title('Validation curve');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The plot is typical for machine learning models, and demonstrates the bias/variance tradeoff. The training performance (orange) peaks at a value, then decreases, indicating that the complex models are overfitting the training data.\n",
- "\n",
- "**Bias error** refers to the error due to incorrect assumptions about the model. For example, fitting a data that follows x^3 using a model that uses x^2 will have some error that can't be improved by additional data. \n",
- "**Variance** refers to error caused by variance in the training set; a model whose parameters change significantly with small changes in the training set is susceptible to *overfitting*. \n",
- " \n",
- "**Task**: In the previous cells, try varying the amount of data, the degree of the polynomial in the generating function, and the error on the data to see the effect of these factors."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Regularization"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Regularization is a way to limit model complexity to what is helpful. This is done to avoid over-fitting and increase the interpretability of the model. It balances model performance with restrictions on model parameters.\n",
- "\n",
- "[Ridge regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) uses linear regression (as before) along with a ridge regularizer (l2 loss on the weights)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.linear_model import Ridge\n",
- "degrees = np.arange(1, 21)\n",
- "model = make_pipeline(PolynomialFeatures(), Ridge(alpha=1))\n",
- "\n",
- "# Vary the \"degrees\" on the pipeline step \"polynomialfeatures\"\n",
- "train_scores, validation_scores = validation_curve(\n",
- " model, X[:, np.newaxis], y,\n",
- " param_name='polynomialfeatures__degree',\n",
- " param_range=degrees)\n",
- "# Plot the mean train score and validation score across folds\n",
- "plt.plot(degrees, validation_scores.mean(axis=1), label='cross-validation') \n",
- "plt.plot(degrees, train_scores.mean(axis=1), label='training') \n",
- "plt.legend(loc='best') \n",
- "plt.xlabel('polynomial degrees')\n",
- "plt.ylabel('explained variance')\n",
- "plt.title('Validation curve');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Although the curves look similar to before, the variation with the polynomial degrees is much less drastic than before. Regularization is an effective way of trading variance for bias.\n",
- "Many sklearn estimators use regularization by default: our logistic regression from a previous exercise, for example, uses l2 regularization."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/content/en/modules/machine_learning_basics/machine_learning.png b/content/en/modules/machine_learning_basics/machine_learning.png
deleted file mode 100644
index b776fb73..00000000
Binary files a/content/en/modules/machine_learning_basics/machine_learning.png and /dev/null differ
diff --git a/content/en/modules/machine_learning_neuroimaging/correction.ipynb b/content/en/modules/machine_learning_neuroimaging/correction.ipynb
deleted file mode 100644
index d678835b..00000000
--- a/content/en/modules/machine_learning_neuroimaging/correction.ipynb
+++ /dev/null
@@ -1,4262 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true,
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "# Machine learning to predict age from rs-fmri\n",
- "\n",
- "Goals:\n",
- "* Extract data from several rs-fmri images\n",
- "* Use that data as features in a machine learning model to predict age. \n",
- "* Integrate what we've learned in the previous lecture to build a minimally biased model and test it on a left out sample.\n",
- "\n",
- "Link to slides: https://github.com/neurodatascience/course-materials-2020/blob/master/lectures/14-may/03-intro-to-machine-learning/IntroML_BrainHackSchool.pdf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [],
- "source": [
- "# Lets fetch the data!\n",
- "from nilearn import datasets\n",
- "data = datasets.fetch_development_fmri()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "How many individual subjects do we have?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "155"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(data.func)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "In order to do our machine learning, we will need to extract feature from our rs-fmri images. Specifically: \n",
- "* Extract signals from a brain parcellation \n",
- "* Compute a correlation matrix, representing regional coactivation between regions.\n",
- "\n",
- "We will practice on one subject first, then we'll extract data for all subjects"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "#### Retrieve the atlas for extracting features and an example subject"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Since we're using rs-fmri data, it makes sense to use an atlas defined using rs-fmri data\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "source": [
- "This paper has many excellent insights about what kind of atlas to use for an rs-fmri machine learning task. See in particular Figure 5.\n",
- "https://www.sciencedirect.com/science/article/pii/S1053811919301594?via%3Dihub"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Let's use the MIST atlas (Urchs et al. 2019) \n",
- "* Created here in Montreal using the BASC method (Bellec et al., 2015). \n",
- "* Has multiple resolutions, for larger networks or finer-grained ROIs. \n",
- "\n",
- "Let's use a 64-ROI atlas to allow some detail, but to ultimately keep our connectivity matrices manageable\n",
- "\n",
- "Here is a link to the MIST paper: https://mniopenresearch.org/articles/1-3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atlas ROIs are located in nifti image (4D) at: /Users/mepicard/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n"
- ]
- }
- ],
- "source": [
- "parcellations = datasets.fetch_atlas_basc_multiscale_2015(version='sym')\n",
- "atlas_filename = parcellations.scale064\n",
- "\n",
- "print('Atlas ROIs are located in nifti image (4D) at: %s' %\n",
- " atlas_filename)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Let's have a look at that atlas"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n",
- " anat_img = load_mni152_template()\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ],
- "text/plain": [
- " participant_id Age AgeGroup Child_Adult Gender Handedness\n",
- "0 sub-pixar123 27.06 Adult adult F R\n",
- "1 sub-pixar124 33.44 Adult adult M R\n",
- "2 sub-pixar125 31.00 Adult adult M R\n",
- "3 sub-pixar126 19.00 Adult adult F R\n",
- "4 sub-pixar127 23.00 Adult adult F R"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Let's load the phenotype data\n",
- "import pandas\n",
- "\n",
- "pheno = pandas.DataFrame(data.phenotypic)\n",
- "pheno.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Looks like there is a column labeling age. Let's capture it in a variable"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [],
- "source": [
- "y_age = pheno['Age']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Maybe we should have a look at the distribution of our target variable"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "sns.distplot(y_age)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Prepare data for machine learning\n",
- "\n",
- "Here, we will define a \"training sample\" where we can play around with our models. \n",
- "\n",
- "We will also set aside a \"validation\" sample that we will not touch until the end"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "We want to be sure that our training and test sample are matched! We can do that with a \"stratified split\". \n",
- "\n",
- "This dataset has a variable indicating AgeGroup. We can use that to make sure our training and testing sets are balanced!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5yo 34\n",
- "8-12yo 34\n",
- "Adult 33\n",
- "7yo 23\n",
- "3yo 17\n",
- "4yo 14\n",
- "Name: AgeGroup, dtype: int64"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "age_class = pheno['AgeGroup']\n",
- "age_class.value_counts()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "training: 93 testing: 62\n"
- ]
- }
- ],
- "source": [
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "# Split the sample to training/validation with a 60/40 ratio, and \n",
- "# stratify by age class, and also shuffle the data.\n",
- "\n",
- "X_train, X_val, y_train, y_val = train_test_split(\n",
- " X_features, # x\n",
- " y_age, # y\n",
- " test_size = 0.4, # 60%/40% split \n",
- " shuffle = True, # shuffle dataset\n",
- " # before splitting\n",
- " stratify = age_class, # keep\n",
- " # distribution\n",
- " # of ageclass\n",
- " # consistent\n",
- " # betw. train\n",
- " # & test sets.\n",
- " random_state = 123 # same shuffle each\n",
- " # time\n",
- " )\n",
- "\n",
- "# print the size of our training and test groups\n",
- "print('training:', len(X_train),\n",
- " 'testing:', len(X_val))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Let's visualize the distributions to be sure they are matched"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n",
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(y_train,label='train')\n",
- "sns.distplot(y_val,label='test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Run your first model!\n",
- "\n",
- "Machine learning can get pretty fancy pretty quickly. We'll start with a fairly standard regression model called a Support Vector Regressor (SVR). \n",
- "\n",
- "While this may seem unambitious, simple models can be very robust. And we probably don't have enough data to create more complex models (but we can try later)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "source": [
- "For more information, see this excellent resource: https://hal.inria.fr/hal-01824205"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Let's fit our first model!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "SVR(kernel='linear')"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from sklearn.svm import SVR\n",
- "l_svr = SVR(kernel='linear') # define the model\n",
- "\n",
- "l_svr.fit(X_train, y_train) # fit the model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Well... that was easy. Let's see how well the model learned the data!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "# predict the training data based on the model\n",
- "y_pred = l_svr.predict(X_train) \n",
- "\n",
- "# caluclate the model accuracy\n",
- "acc = l_svr.score(X_train, y_train) \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Let's view our results and plot them all at once!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "accuracy (R2) 0.9998446740477362\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Predicted Age')"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# print results\n",
- "print('accuracy (R2)', acc)\n",
- "\n",
- "sns.regplot(y_pred,y_train)\n",
- "plt.xlabel('Predicted Age')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "HOLY COW! Machine learning is amazing!!! Almost a perfect fit!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "...which means there's something wrong. What's the problem here?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "training: 69 testing: 24\n"
- ]
- }
- ],
- "source": [
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "# Split the sample to training/test with a 75/25 ratio, and \n",
- "# stratify by age class, and also shuffle the data.\n",
- "\n",
- "age_class2 = pheno.loc[y_train.index,'AgeGroup']\n",
- "\n",
- "X_train2, X_test, y_train2, y_test = train_test_split(\n",
- " X_train, # x\n",
- " y_train, # y\n",
- " test_size = 0.25, # 75%/25% split \n",
- " shuffle = True, # shuffle dataset\n",
- " # before splitting\n",
- " stratify = age_class2, # keep\n",
- " # distribution\n",
- " # of ageclass\n",
- " # consistent\n",
- " # betw. train\n",
- " # & test sets.\n",
- " random_state = 123 # same shuffle each\n",
- " # time\n",
- " )\n",
- "\n",
- "# print the size of our training and test groups\n",
- "print('training:', len(X_train2),\n",
- " 'testing:', len(X_test))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.metrics import mean_absolute_error\n",
- "\n",
- "# fit model just to training data\n",
- "l_svr.fit(X_train2,y_train2)\n",
- "\n",
- "# predict the *test* data based on the model trained on X_train2\n",
- "y_pred = l_svr.predict(X_test) \n",
- "\n",
- "# caluclate the model accuracy\n",
- "acc = l_svr.score(X_test, y_test) \n",
- "mae = mean_absolute_error(y_true=y_test,y_pred=y_pred)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "accuracy (R2) = 0.7062614753859224\n",
- "MAE = 2.9064809730437093\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Predicted Age')"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# print results\n",
- "print('accuracy (R2) = ', acc)\n",
- "print('MAE = ',mae)\n",
- "\n",
- "sns.regplot(y_pred,y_test)\n",
- "plt.xlabel('Predicted Age')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Not perfect, but as predicting with unseen data goes, not too bad! Especially with a training sample of \"only\" 69 subjects. But we can do better! "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "For example, we can increase the size our training set while simultaneously reducing bias by instead using 10-fold cross-validation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.model_selection import cross_val_predict, cross_val_score\n",
- "\n",
- "# predict\n",
- "y_pred = cross_val_predict(l_svr, X_train, y_train, cv=10)\n",
- "# scores\n",
- "acc = cross_val_score(l_svr, X_train, y_train, cv=10)\n",
- "mae = cross_val_score(l_svr, X_train, y_train, cv=10, scoring='neg_mean_absolute_error')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "We can look at the accuracy of the predictions for each fold of the cross-validation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Fold 0 -- Acc = -3.100669235914851, MAE = 3.499732445716964\n",
- "Fold 1 -- Acc = 0.6503867717466023, MAE = 2.7341708041749193\n",
- "Fold 2 -- Acc = 0.5244446059952845, MAE = 4.4667179799422\n",
- "Fold 3 -- Acc = 0.7861963587989191, MAE = 2.0792322601537143\n",
- "Fold 4 -- Acc = 0.6013797451350189, MAE = 5.073664324720509\n",
- "Fold 5 -- Acc = 0.8383831018310726, MAE = 2.661732547398206\n",
- "Fold 6 -- Acc = 0.33984112946074596, MAE = 5.492938869730001\n",
- "Fold 7 -- Acc = 0.3375078449169111, MAE = 4.5341559182588975\n",
- "Fold 8 -- Acc = -2.5192785147501793, MAE = 3.1544144963281373\n",
- "Fold 9 -- Acc = 0.7032021337313058, MAE = 2.895906039877586\n"
- ]
- }
- ],
- "source": [
- "for i in range(10):\n",
- " print('Fold {} -- Acc = {}, MAE = {}'.format(i, acc[i],-mae[i]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "We can also look at the overall accuracy of the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "R2: 0.5755837915750013\n",
- "MAE: 3.656286154909079\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Predicted Age')"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
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- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.metrics import r2_score\n",
- "\n",
- "overall_acc = r2_score(y_train, y_pred)\n",
- "overall_mae = mean_absolute_error(y_train,y_pred)\n",
- "print('R2:',overall_acc)\n",
- "print('MAE:',overall_mae)\n",
- "\n",
- "sns.regplot(y_pred, y_train)\n",
- "plt.xlabel('Predicted Age')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Not too bad at all! \n",
- "\n",
- "But more importantly, this is a more accurate estimation of our model's predictive efficacy. \n",
- "\n",
- "Our sample size is larger and this is based on several rounds of prediction of unseen data. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "For example, we can now see that the effect is being driven by the model's successful parsing of adults vs. children, but is not performing so well within the adult or children group. This was not evident during our previous iteration of the model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Tweak your model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "It's very important to learn when and where its appropriate to \"tweak\" your model."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Since we have done all of the previous analysis in our training data, it's fine to try out different models. \n",
- "\n",
- "But we **absolutely cannot** \"test\" it on our left out data. If we do, we are in great danger of overfitting."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "It is not uncommon to try other models, or tweak hyperparameters. In this case, due to our relatively small sample size, we are probably not powered sufficiently to do so, and we would once again risk overfitting. However, for the sake of demonstration, we will do some tweaking. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "We will try a few different examples:\n",
- "* Normalizing our target data\n",
- "* Tweaking our hyperparameters\n",
- "* Trying a more complicated model\n",
- "* Feature selection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "#### Normalize the target data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
- " warnings.warn(msg, FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Log-Transformed Age')"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Create a log transformer function and log transform Y (age)\n",
- "\n",
- "from sklearn.preprocessing import FunctionTransformer\n",
- "\n",
- "log_transformer = FunctionTransformer(func = np.log, validate=True)\n",
- "log_transformer.fit(y_train.values.reshape(-1,1))\n",
- "y_train_log = log_transformer.transform(y_train.values.reshape(-1,1))[:,0]\n",
- "\n",
- "sns.distplot(y_train_log)\n",
- "plt.title(\"Log-Transformed Age\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Now let's go ahead and cross-validate our model once again with this new log-transformed target"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "R2: 0.6503753590244092\n",
- "MAE: 0.28516557338254495\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Log Age')"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
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"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# re-intialize the model\n",
- "l_svr = SVR(kernel='linear') \n",
- "\n",
- "# predict\n",
- "y_pred = cross_val_predict(l_svr, X_train, y_train_log, cv=10)\n",
- "\n",
- "# scores\n",
- "acc = r2_score(y_train_log, y_pred)\n",
- "mae = mean_absolute_error(y_train_log,y_pred)\n",
- "\n",
- "print('R2:',acc)\n",
- "print('MAE:',mae)\n",
- "\n",
- "sns.regplot(y_pred, y_train_log)\n",
- "plt.xlabel('Predicted Log Age')\n",
- "plt.ylabel('Log Age')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Seems like a definite improvement, right? I think we can agree on that. \n",
- "\n",
- "But we can't forget about interpretability? The MAE is much less interpretable now."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "#### Tweak the hyperparameters"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Many machine learning algorithms have hyperparameters that can be \"tuned\" to optimize model fitting. \n",
- "\n",
- "Careful parameter tuning can really improve a model, but haphazard tuning will often lead to overfitting."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Our SVR model has multiple hyperparameters. Let's explore some approaches for tuning them"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "SVR?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "One way is to plot a \"Validation Curve\" -- this will let us view changes in training and validation accuracy of a model as we shift its hyperparameters. \n",
- "\n",
- "We can do this easily with sklearn."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.model_selection import validation_curve\n",
- "\n",
- "C_range = 10. ** np.arange(-3, 8) # A range of different values for C\n",
- "\n",
- "train_scores, valid_scores = validation_curve(l_svr, X_train, y_train_log, \n",
- " param_name= \"C\",\n",
- " param_range = C_range,\n",
- " cv=10,\n",
- " scoring='neg_mean_squared_error')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "scrolled": true,
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# And plot!\n",
- "\n",
- "g = sns.catplot(x='Degree',y='Score',hue='Type',data=ValCurves,kind='point')\n",
- "plt.xticks(range(7))\n",
- "g.set_xticklabels(degree_range, rotation=90)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "It appears that we cannot improve our model by increasing the complexity of the fit. If one looked only at the training data, one might surmise that a higher order fit could be a slightly better model. \n",
- "\n",
- "But that improvement does not generalize to the validation data."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "#### Feature selection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Right now, we have 2016 features. Are all of those really going to contribute to the model stably? "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Intuitively, models tend to perform better when there are fewer, more important features than when there are many, less imortant features. The tough part is figuring out which features are useful or important."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Here will quickly try a basic feature seclection strategy"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "The SelectPercentile() function will select the top X% of features based on univariate tests. This is a way of identifying theoretically more useful features."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "But remember, significance != prediction, as demonstrated in this figure from Bzdok et al., 2018 *bioRxiv*\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "We are also in danger of overfitting here. For starters, if we want to test this with 10-fold cross-validation, we will need to do a separate feature selection within each fold! \n",
- "\n",
- "That means we'll need to do the cross-validation manually instead of using cross_val_predict(). "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.feature_selection import SelectPercentile, f_regression\n",
- "from sklearn.model_selection import KFold\n",
- "from sklearn.pipeline import Pipeline\n",
- "\n",
- "# Build a tiny pipeline that does feature selection (top 20% of features), \n",
- "# and then prediction with our linear svr model.\n",
- "model = Pipeline([\n",
- " ('feature_selection',SelectPercentile(f_regression,percentile=20)),\n",
- " ('prediction', l_svr)\n",
- " ])\n",
- "\n",
- "y_pred = [] # a container to catch the predictions from each fold\n",
- "y_index = [] # just in case, the index for each prediction\n",
- "\n",
- "# First we create 10 splits of the data\n",
- "skf = KFold(n_splits=10, shuffle=True, random_state=123)\n",
- "\n",
- "# For each split, assemble the train and test samples \n",
- "for tr_ind, te_ind in skf.split(X_train):\n",
- " X_tr = X_train[tr_ind]\n",
- " y_tr = y_train_log[tr_ind]\n",
- " X_te = X_train[te_ind]\n",
- " y_index += list(te_ind) # store the index of samples to predict\n",
- " \n",
- " # and run our pipeline \n",
- " model.fit(X_tr, y_tr) # fit the data to the model using our mini pipeline\n",
- " predictions = model.predict(X_te).tolist() # get the predictions for this fold\n",
- " y_pred += predictions # add them to the list of predictions\n",
- "\n",
- "\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Alrighty, let's see if only using the top 20% of features improves the model at all..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "R2: 0.7275884263323822\n",
- "MAE: 0.2486199918876078\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Log Age')"
- ]
- },
- "execution_count": 54,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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4G/RnnVYHvdx2bQPvPm/+sKUmhhPwuikPePCPMjO533R2r01U3gKCMWY3cFmW5+/J+N0AX8hXG5RSk2M81UQngzGGj66q4389sY940gzbNbR5RzMuceoOtXbFSKQChyXw2Wsa+ODlC/HleELvF/S5KQt48LnH9r7p7F6bqNnXCaaUmnTTkR3Tv7j9BQvL+NI7l6W7hhZkKSN9uL2PUCxJNDEwI7msyI3PZfHRK3PPTBQRgj7njsDjGl9W/nR2r02UBgSlVE6mKjsmkbRp74sNWnimv2toqIOn+7h3W1N6TgJA0OeiuthH0pgzZgUPR0Qo8bspL/LgHmcg6Ded3WsTpQFBKTVjdIWdxe3tUUpOtPVG+e7zh3hyz/F0qQmPS6gIODOEh8s4GkpEKPW7KZuEQNBvurrXJoMGBKXUtIvEne6hWGLkInShWIKf7GjhJzubiaS2XVRRxOeua8RrCT/e2TJst1KmzPISYx1kHs1Mn3w2Eg0ISqlpk7QN7TmsU5BI2vzyteM8+MKhdPdQeZGHT129mPdeVJu+ur9yadWI+3FZQqnfCQSjzSqeiJk8+WwkGhCUUtOiJxKnvS9G0h6+e8gYw3MH2rh3WxPNHU5JCp/b4pYrFrFuZR3FOU4Om65F6wuNBgSl1JRKJG1O57C4/Rut3dzzTBOvHR0oNXHDhQv49NVLqCnJbbB4vOUl5ioNCEqpKZPLoPHRzjDf2XaQLW+dSj+3qqGS9dc10FiTW6kJn8dFeZEn5zsI5dC/LaVU3kUTSU73xojGk8Nu0xWO8x8vHubRV46lJ5adPS/IHWsauXxxbusbFHldlBflVl5CnUkDglIqb2zb0B6K0R0eftA4Gk/yyO+O8sPtR+iLOgFjXomP265t4F3nzcMapatHRCj2uSgrGvusYjWYBgSlVF6MNmhsG8NvXj/B/c8d4mRPFIBin4uPrarnjy5fhNc98ryAfMwhmOs0ICilJlUsYdPWFyUcG7576OXDHWx8pokDp5xFFd2WcPOlZ/HxqxZTNqTsw1D5nEMw12lAUEpNilwWt3/7VC+btjax41BH+rl3nFvDbdc2cFZ59oVt+mkgyD8NCEqpCRttpvGpnijffe4QT+09nl4S8eJFZWxY08h5taUj7nuqJpMpDQhKqQkYbdC4N5rgxzuaeejllnQl0vrKAJ+7roGrl1aNODfAZYkzmcyvgWCqaEBQSo1LbzRBe2+MhH3mXUEiafOL3a08+MJhulLBoiLg4VNXL+G9F9WO2OUzlkXr1eTSgKCUGpNoIkl7XyzroLExhm0HTnPftoO0pEpN+N0WH15Zx4dXLBpUEnoot2VRFvBQ6tdZxdNFA4JSKiejdQ/tOdrFxq1N7D3WDTirld14US2fWr2YquDwpSY0EMwcGhCUUqPqiyZoG6Z7qKUjxL3bDrJt/+n0c1c1VrJ+TSNLqoqH3afH5QSCEq0zNGNoQFBKDSuRtGnri9EXPbMQXUcoxoMvHOax3a3pyWfnzi9hw/WNXFpXPuw+teDczJW3gCAidcCDwALABjYZY+4ass1a4OfAwdRTjxhj7sxXm5RSuesKx3nytVZ+tL2Z1u4wtalFZy6uK+PhXS38aHszodQ4woJSP7dd28A7ltcMW2rC67YoD3gJasG5GSuf30wC+HNjzC4RKQFeFpFfG2NeH7LdNmPM+/LYDqXUGETiSdr6Ymx78xR3Pb0ft+WUiDjdG+F/P/kGxkB3xLljKPG7+fiV9dx86cJhS0143RYVAa9WHi0AefuGjDGtQGvq9x4ReQNYCAwNCEqpGWDo6mWbdzTjtgS/2yIUTXKqN0Ys6YwheFzCBy5byEdX1VM6TKkJDQSFZ0q+KRFZAlwGvJTl5dUi8ipwDPiKMWZvlvevB9YD1NfX57GlSk2/LftOsnFrE80dIeqmaD3ernCcztDgQnSt3WF8LuFoZ4RQRtlqEfjTd53DDRctyLovXYugcOW9RKCIBIGHgS8bY7qHvLwLWGyMuQT4d+Bn2fZhjNlkjFlhjFlRU1OT1/YqNZ227DvJVx/dy8meCOVFHk72RPjqo3vZsu9kXj4vEk/S0hGirTc6KBic6I4QS9gc6RgIBgK4BTyW8B8vHWZ7U/ugffk8LhaU+VlYXqTBoEDl9VsTEQ9OMPiBMeaRoa9nBghjzOMi8i0RqTbGnB66rVKz3ZZ9J/mTzb8jFEvic1tUB32UFnkIxRJs3No0qXcJSdvQ1helNzKQPbS9qZ3vv3SYptN9hGPJdM0hwZlTIAKCUFPiwxJh845mVjVW4ve4qAjoojSzQT6zjAT4DvCGMeYbw2yzADhhjDEisgrnjqUtX21SaqbqvzPoiyVwW0IiaTjW5cz0LfG7aekITdpndUecZSwz7wieP3Ca//PUm/RGE/Q/bQmsbqjkpUPtGOOki1YWeyn2ujEYTvSEqS0r0kAwi+TzDuEa4BPAayLySuq5vwHqAYwx9wC3AJ8XkQQQBtaZ4ermKjXEdPS158vGrU14XILf7SJhG6eGjw2ne6O4XcKiisCEPyPbMpbGGJ556xRff+LN9ICxCFQGvPg9Fn0xmwvPKqetL0qRxznxW5YQTdgsqQpqMJhl8pll9CzO3eZI29wN3J2vNqjZq/+K2uOSQX3td0JBBoXmjhDlRR5qSnwc64xgY0AM0YQhnjRsWNM47n0PV3Jid0sn9zzTxL7jPennXBbUBH2U+j0YDMe7w3z5Xedw19P7iSaSFPvcRBM2SZsJtUnNTDryowpS/xV1f7G0gNedl772qVJXEeBkT4QSv4ezyp31AyIJm2KvmztvumDcx5Ste+hIW4h7tzXx3NsDvbMi4MJZlrKtN4bLEiwRFpQWsXb5PCqLvXz3+UO0dIRYVOB3Y2p4GhBUQeq/os5U5HFNal/7VNqwppGvPrqXUCxB0OfGZQnxpBl3MPjVnuPc88zbHO0amGF89vwg33vhEL/c3ZoeJwh4XQR9LtyWxcmeCM5IsuFUT5TqEh//be1SFpT5WVC2gN+/cCDNdMu+k9y66cVZ0V2nBmhAUAWp/4o6s5xyOJ6clL726bB2+TzuxLnzmchVeCJp89irx/inp97EbQkugb2tnfz3/+wEgf4RurPK/dx+bSP3PHOAsiIPggB+OkIx4kmDAP/r/Rdl/fzZ1l2nBmhAUAUp84q6yOMiHE9OuK99uq1dPm/cJ1RjDJ2hOJ3hOA88fziVqWRzoieavhvAOF1D77uoli++82w8LotHXzmWHjAuLfJQUewlEk8yr8Q/bFtmW3edGqABQRWkybqiLgTDZVP1P3+4vY/5JX4+sqKOVY2VHOsK4baE491RMlP2BDirzE9zexiPy5mTum5lHf/+2wMkbJuA251TYJ1t3XVqgAYEVbAmckVdKIbrnrmlpZOfvtyCJVDsdXG6N8pdT+/ngx0LCcVsejPKVVvi/HhcFgGvi8NtvfzZj1/lRE+E+soAH1mxiBea2nMOrLOtu04N0ICg1AyWrXumLxpn07YmKgJefG5nHoDbcrKD7t7ydvq96cwhS8BAZbGXjr4YPdEEb7R2kTSGjr4YTad6+edbLsk5uM7G7jrlGLWWkYgEROTvROTe1ONlIqLlqpWaAs0dofSEMHBKTrgti1Asid9jkbSdjKBDbaF0zaHGmmI+e/USGioD2DhdRTUlXtyW0BmOOwPLIrhdFgboCMX5pyf35dymtcvncedNFzCvxE9XOM68Ev+EUmPVzJHLHcJ3gZeB1anHLcBPgcfy1SillKO/e8bvcZG0DbZtCMeT+N0Wp3qidEcGSk24BM4qL2Ljx6/AZQkfX72Y7U3t/OTlZk52RzirPMCxzgiWRXoRGxEwYmg63Temds2F7rq5KJeAsNQY8xERuRXAGBMWXfdOqSnxuWsb+LtH9xJL2Pg9FuFYkt5oEssSOsPOOIElUOJzU+R18YW1Z+OynP+ePo+Lmy9byLorB0rGn/u3T6DVYdRwcil/HRORIlJTVkRkKRDNa6uUmuOMMXSF4jTOC/LH7zibqmIfp3tjtPU5qaW90SSWQHXQS3Wxl4bqIF9+1zmsaqzE67bSZaiH1hpqqApgG6echTHOHYdtnOeVyuUO4WvAk0CdiPwAp2jdp/PZKKWm2kwqlNcTidPRFydhO8Xmakp9+L0WHaGBWkTFXhfRRJKucJy6cmcmsssS/vrh1zjWFR72GP76PefxlYdepTeaIGkbXJZQ7vPw1+85b0qPUc1Mksvto4hUAVfhjE+9OJ3rFaxYscLs3Llzuj5ezUKZqZ2ZWTNTPVD61GutbNzalC43ceNFC3ilpZMn9xxPjxMsrgzQ3hclFHPuEABsA0UeiyKvm9Iiz6jH0B/8Zvv8DTWYiLxsjFkx4jajBQQRuTzL013AYWNMIstreaUBQU22Wze9eEZefSiWYF6Jnx+tvyrvnx+JJ3l8dyv//Cun3ITXJZzqjdETSaQnli2qKOL26xr4z5eP8sbxbgzOwLDgdP8kjMHrslg2v2RajkHNfLkEhFy6jL4FXA7sxrlDuDD1e5WI3GGM+dWEW6rUNJqumbfxpE1HX4zeaIIHXziMSyCasDnWNVCd1G0JX3jHUt57US1ul8W3t7ztpJ66nGAgIlgW2AmT7mKaymNQs0suAeEQcJsxZi+AiJwP/AXwD8AjgAYEVdAmOvN2tNISQ59P2obOUIzuSAJjnMHdg229hGJONw84V17lAQ8el3DzpQud50SoqwjQ2RcDhP5kP2OcTCO3NThHRGcPq7HKJSAs7w8GAMaY10XkMmNMk2afqtkgl5m32U7uAF9/4g32n+rFY1nML/UNKi3x0K6jg0pO/N3P9/BX0eVcuLAMO9VV+0ZrN/c800RXeKD3NeB1Yds2XeE4RR4X//HCIXa3dHO8O0yJz43f66IvmiRhbGxDqvsIvC4Z9hhm0qC5mrlyGUP4MdAObE499RGgBvg48KwxZmVeWziEjiGofBhpoDXboHN3OI4BZw1i2yAiGOOUlXZZwqmeKDUlPgJet5PeaaA3Gqcy4OMbH7mEY51h7tt2kC1vnUq3wee2CHgseiKJ9FqDAa+L3miS+aU+qop9hONOZhHA6d4YAF4Lqkp8JJKGqmIvfbHkoGOYKYPmanpN1hjCp4H/BnwZ55/ps8CfA3HgHRNrolIzw0gzb7PVEzraEQZxSkm4xOm+sVMLyzRUF9MXS1LndkpLJFM5/z63xbHOEN/87QF+/soxEqlxgrNrgtxxfSOJpOEffvk6BvC6LGqCPtr6nNXLusMJqoP+dBtO9URZWlN8xkB4RbGPJ//0qlHbr+WqVTajBgRjTBj419QPIlIHfNEY889Ab36bp9T0yzbonLBtRASvyyKRNIg4ZSBiSZtwPOlc6UcT+FPF52xjONUbpSeS4OFdRwGYV+LjtmsbeNd589KlJEqK3CyuCuBKjQe0dkewUvvtV+Rx0RdLUu8ZPOlsuEHkiQyaa1fT3JLLTGVEpFpEPi8iW4EtwPwc3lMnIr8VkTdEZK+IfCnLNiIi/09EDojI7mFSXJWaVnUVAcKpwnH93JaFyxKqgz5sUjN+bYMFhGNJbrl8EfGkIRRL0BWOcfB0iK6wU3eo2Odi/XUNPPjZVfze+fOxRPC4LOaX+mmoChJNDJz8vS4L2zh/9gvHkxR7XWe0abhB5Gztz2XAub+r6WRPZFDp7S37Tub6V6cKzLABQURKROSTIvIksB04G2g0xiw1xnwlh30ngD83xpyHM6ntC6kMpUzvAZalftYD3x7PQSiVTxvWNKZP7sY4f5b43QR9btwu4awyP4ghYRsWVgT4k3cu4xNXL+HGC2tp64tzoidG0jhdSx+8fCHfv+1K1q2qx+tOBZUSH3WVAYp97jM+q8TvxjZQWuROf3Y8abj92ga6w3H2n+jhjdYu9p/ooTscz1qCeug+T/dGaOkI89aJbm7d9OKwJ/jMriYR50+PS9i4tSnff+VqmozUZXQSJxD8Lc7gsRGRD+S6Y2NMK9Ca+r1HRN4AFgKvZ2x2M/CgcUa2XxSRchGpTb1XqbzJ7AoJel2ICD3RBHUVAVY3VvJCU/ugbpI7b7pg0KDz373Xubb59jNvc6Q9xHkLyli30lmx7O1Tvfz1w7vZfqgj/XnvOLeGz17bwMLyIsBJIS0r8lBe5MGyZFB7SnzOyb8rHKehOshHV1WesYANgOEwiLMvBIZLD8lcXW7/yR56IgkqAh6qg74R10PWldHmnmGzjETkT4F1QDHwQ+DHwK+NMWNeBUNElgBbgQuNMd0Zzz8GfN0Y82zq8X8Bf2WM2Tnk/etx7iCor6+/4vDhw2NtglKAEwgyU0VL/C46Qk7K58JyP7GkzcmeGDVBL9VB37AZOfGkTUcoRm9kIF30VE+U7z53iKf2Hk+fnC9aWMYd1zdyXm1perug301lwIs71Q00niyg8c6uHsv7pnsGt5pcuWQZDdtlZIz5v8aYK4GbcLKLfgacJSJ/JSLnjKERQeBh4MuZwaD/5WwfnaUtm4wxK4wxK2pqanL9aKUG6T/xHmoP4RLBAKd7nRROlyWc7o3RHU5gCfREElm7SRJJm9O9UVo6wulg0BdN8J1nD/LJ+7fzZCoY1FcG+IebL+DfPnJJOhgU+9wsqggwr8SfDgYwvq6ZoQvnQG5X72N5X7auMl0ZbXbLJcuoCfifwP8UkYuAW4EngKWjvVdEPDjB4AfGmEeybNIC1GU8XgQcy6HdSo1Z/4k3M1XUQLoURH8mT7asnub2Ptr7YnSH4+lJZYmkzS92t/LgC4fTcwMqAh4+ffUSbryoNr0ugcdlUR30nVGKut94umbGO7t6LO/L7GrSQnhzw5jWVDbGvAa8BvzNaNumFtH5DvCGMeYbw2z2KPBFEdkMXAl06fiBypf+E29mqqglTrVQk5HJE0va6d+NMfRGE1SX+OkMxdLPbdt/mvuePUhLRxgAv9viwyvq+PDKRemTrYgzS7k84EmXmchmPCf38a5rPNb36cpoc8uYAsIYXQN8AnhNRF5JPfc3QD2AMeYe4HHgRuAAEAI+k8f2qDmu/8RbHfRxrCsM9kCfZdI2LCj1pccQgj4X8aSd7iZZt8K5kd1ztIuNW5vYe8zp/bQE3nNhLZ++ejFVQV/6s4I+N5XF3kFdQ8MZz8l9vFfvetWvRpLTeggziZauUOOVOXibSNqc6IkSTxpqS32U+D30RhMsLC/isrpyXjzYTmtXmAWlzuIzZ1X4uW/bQbbuH1gK5KrGStavaWRJVXH6udG6h0Zqm56kVT5NynoIM40GBDURw514k7aT5pk5RgDQGYrx4AuH+cXu1nRJ6nPmB7nj+qVcWlee3s4SoSLgpbTIPWL3kFLTZVJqGYnIa5yZ+dMF7AT+0RjTNv4mKjW1hvaJJ5I2bb3RdCnqfpF4kod3tfCj7c2EYs4s3/mlPm6/toF3LB8oNQFj6x5SaibLZQzhCSCJMxcBnLkJAN3AA8AfTn6zlMqvRNIpLz00ECRtw69eP8F3nzuYriYa9Ln5+FX1vP/ShXjdAyf98XYPKTVT5RIQrjHGXJPx+DURec4Yc42IfDxfDVNqPEYrxpZI2nSG487ylEO6S3ccamfjM000ne4DwOMSPnDZQj66qp7SjLRQEaEi4OF3hzv4y4d2a+E3NWvkEhCCInKlMeYlABFZBQRTr035mspKDSdz0DizGNudwDXLqukMxemNnhkIDpzsZePWJl4+PFBq4l3L53HbtQ0sKPMP2ra/e+jZ/af52i9ez/pZGhRUocolINwO3J+acSw4XUW3iUgx8L/z2TilxiJb3f++aJx/f/oAi6uLBwWC7U3tPPjCYZraeonEByahXVpXxoY1Szl3QcmgfQ/tHprIGgNaUlrNVLnMVN4BXCQiZThZSZ0ZL/8kXw1TaqwyZ/wa4yxM47KEo52hQcHgmX2n+JffvElfdKAktNsSPraqnk9evXhQltBw2UPjLfw20l1MPoKCBh81FqOmRYhImYh8A/gv4Dci8q+p4KDUjFJXESAUS5BI2sSSNknbEInbLCh1KozGkzYP72rhH594Ix0MXJYwv8THglIfr7Z0DTrpl/g91FUGKMsy03i8awxMZUlpXc9AjVUuXUb3A3uAD6cefwL4LvBH+WqUmjsm6wo2nrRZt7KOrz+5j3jS4PdYHO+K0BNN0twR5j3/thWv20VP1Bn2EoHKgJeKgAdLBIPheLdThqLI66Ky2IvP7Rq2feMtHTGVJaV16Uw1VrkEhKXGmA9mPP77jFIUSo1Z/0n2rRPd9EaTVBZ7qCoeuTb/cGIJm85wjL5okosWlfGldy5j845m9p/ooi9u0qUpoklDNJlAIHWytwj6Bv75R+I2tWVFzCv1s/Ng+4hrB9zS0skLTe2EYgliCRuvS1g2vzQdDG7d9OKwAW68RenGQ9czUGOVy0yasIhc2/9ARK4BwvlrkprNMrsxInEb2xjaep3sn7F0n8QSNid7IrR0hOjNSCFd1VjJNz5yCTaC4MyoHDqr8qaLazE4J2KDIRxPYhv443eezc6D7en2haIJp319MXoiTvtiiSTf3PI2J3siLCj1U1PiI+DzsGFNI7tbOtnw/ZfZfqiN9t4oh9p6z+iimcqS0uPt1lJzVy53CHcAD2aMG3QAn8pfk9RsltmNEUva6XUJTvVEKfF7RryC3bLvZHqFsvkl/vQKZZna+5xSE+GMzKFMBvj+9iN4LBCx8LotzplfwuevX8ra5fO4ddOL6fbFU4PSxobTvVFKizz0RBIkbPuMbph/enIfTaf7sI3B47JIGmjrjVMVZFAXzVQWlxtvt5aau3LJMnoVuERESlOPu0Xky8DuPLdNzUKZ3RjpMtTWwPoDw13B/tfrJ/jqo3uxBII+F219Ue56ej9fYhmrGisJx5P8dGczP97RcsZV8VDGGAwW80t8JA3pYJC1fbZTJru/fdGEjW9IiYoij4v9J3uddRUsQRBEwMbQFYrTYg0OcP2f1T820X9HNNlBQSubqrHKufz1kNXO/gz4t0lvjZr1MvvQB5WhNob9J3uIJw1el8WWfSdZu3wesYRTYuLff3sAS0iv9tV/xfuj7Uc41RvlgecP0dbnlJoo9bsxtk1PLPtdgkuEeNIm6PecMcia2b6aEh/HOiPYOG0KxRK4LKEsMLhfvj8A+dwD6yyAM3AdTdpnBLipTD3V9QzUWIy3GpeWc1TjktmHXuJ3U1XsxRhDItXZvyi1rvHf/XwPP9vVQktHiJ5InNauMH7PwD9XZ56Bzd7Wbv7112/R1hfD4xLWrazj+7ddScDvoSIw3PWO4HMPBJbMLqrM9gV9bqqCThZSkcdiXomfL6xdisflOmMMoKEqQInfjY3Btk16HoTbss7oopnK1FOlxmK8C+QUVs1sNWMM7cZoqA5SEfASSzr98nbqRBpL2Dzw/GG+8ZFyAGpLi2jri1LkcRGJJznVG0tfmQvwe+fP5zPXLGF+qX/Q9gGvm5PdUUQgnnT+2doYqoPOdkO7qIa2b0lVkP/9gcHdLBcvKneykE50E0savG6LYq+PhG2oKvbSE0kQTdi4LOELa5eecYWu2T9qpho2IIhID9lP/AIU5a1FatYb2o1x7T89TanfTTxpY6fWHPB7rPS8AIB1K+v419+8SVtfLF2OGuDsmiB/8QfnsGz+4FIT61bWcdfT+/G6LWrLfJzsiSFisASqir2U+N3DZviM1s3S/9pXH91LmUvS3VcClBd58LisEfvrx5J6qjON1VQaNiAYY0qGe02pyRJNJJlf4udkTyQ9PgAMmmHcE4nz8pEO2vvi6UVq/B6Lj66s52NX1WddkOaaZdVUFHt44PnDtHSEuKy+In3in4xB1myTvgAqin08+adXjfjeXLN/prrMhVL5XFNZqWGFY0k6wzHCsSQfumIRdz29n3A8id9jEYnbJGzDLZcv5Cc7m/nBS0foiTgzjKuDXj5zTQO/f/58XNaZgUBEKPW7qQh4qasM8AcX1p6xzWScTCfS7ZNr9o/ONFZTTQOCmlLhWJL2UIxoRmroqsZKvoQzw/h4d5j5JX7Oqy3l7i1vc7w7AkDA6+Kjq+qprwjwyO+O8uALh6hNrXfcPxfB73FRHfQNWsQmXyY64ziX7B8da1BTLW//c0TkfhE5KSJ7hnl9rYh0icgrqZ+v5qstavpF4klau8K0doUHBYN+/TOM//KG5UQSNpt3NnO8O4LLEt5/6Vl8/7ZVnF0T5Ntb36atL0qp352ei7DzYDs1JT7OKi+akmAAUzPjWGcaq6mWzzuEB4C7gQdH2GabMeZ9eWyDmmaReJKOkNM1NJJDbX1s2trEi03t6eeuW1bN7dc2UFfpnAA372jGbcmguQjRpM0jvzvKh1fVj9qWyRygnYpJXzrTWE21vAUEY8xWEVmSr/2rmS0US9AVjo8aCNp6ozzw/GGe2NNKaryY82tLueP6Ri5cOLjKemt3mFK/809WRHC7BK/b4mjn6KW18jFAm+9JXzrTWE216R5DWC0irwLHgK8YY/Zm20hE1gPrAerrR78SLGSFnmbYE4nTFY4TS2SfJdwvHEvy4x3N/GRnM5HUtosqirj92gauW1adNXOotrSI9lCUoM+THlAOxRI5daEU6gCtzjRWU2k6A8IuYLExpldEbgR+BizLtqExZhOwCWDFihWzdlJcoaYZGmPojSboDMWJJ222N7WzeUczrd3hMwZ+k7bhl6+18r3nD9ERigNQVuThk6sX84cX1+J2DT8G8JlrlvAvv3qTaCKZUxdKZnA91RNlQalv0Os6QKvUYNMWEDJrIxljHheRb4lItTHm9GR/VqFcdRfaVawxhp5ogq5UIABnreK7nt6P25JBA79/Ys4mYQz3bjvIkXbnJOxzW9xyxSLWrayj2Df8P0Wfx0VVsZfGmiBlRZ6culCGBtfTPVGOdkYAoTSVuaMDtEoNNm0BQUQWACeMMUZEVuFkPLVN9ucU0lV3oaQZGmPojiToDg8Egn7ZBn47w3H+8fE36IsNlJq44cIFfPrqJdSU+IbuPs1lCRXFXkr9A38nuXahDA2uC8r8tHSEOdETocTv1gFapbLIW0AQkR8Ba4FqEWkBvgZ4AIwx9wC3AJ8XkQTOgjvrTOZK6JOkkK66p3I1rfFI2obucJzuSJwXDrRl7RbKHPiNJW3aemPpZSsBVi2pYP2aRhprgiN+VtDvpqrYl3XyWS6GBtcSv4eF5Ybj3VG6wvEZMUBbKHeuau7IZ5bRraO8fjdOWmpeFcpVN8zcNMN40ilB3ZNamWy4bqEvsYza0iJntbFYks5wPL2PIo+LO2++gCsWV4z4WV63RXXQhz+jjMV4ZAuubpfF5fUV/Gj9yKUlpkIh3bmquWO6s4zybqZfdWeaaWmG0USSrpCzvGWmbN1C4XiSH24/wsLyIl5t6UxXRXSJEPS7+as/OHfEYGCJ0z1UNiR4j9dwwXV1Y+WIax5PppHuAArpzlXNHbM+IMzUq+7hzIQ0w2giSWcoTt+QQNAvs1sInDGFWNJmz7Eudh/tAsASp9xEY3WQj66qP2Opy0wjdQ+Nt1slW3Bd3VjJQ7uOTslV+Wh3AIV056rmjlkfEGbaVfdMNlog6Je5NkEoluBUb4xoai6B2xJuvvQsPn7V4lGv9kfrHppot8rQ4Jq5XjLk96p8tDuAQrpzVXPHrA8IMDOuuqfCeK+mh+saGs66lXX8y6/f5FRPND2pDODihWX8xQ3nsrB85OUyLBEqAl52HW7nL7btHra9k92tMtxV+f6TPZPejTTaHUCh3bmquWFOBIS5YDxX02MNBACneqI8s/8Ubb2x9DhBsdfFp1Yvob4ywL8+9VbWCWn9gj43lcVent1/mq/94vUR2zvZ3SrZrsrb+qL0RBKc7IlMajfSaHcAeueqZiINCLPEWK6mc+0aytQXTbB5RzMPvdyS7h6qrwzwuesauHppFTsOdgybebSqsRKPy6Iq6E23L5f2Tna3Srar8va+OBUBz6R3I21Y08hfPPQqRzvCJGwbt2VR4nfzd+89P73NXLlzVYVDA8IskcvVdCTuBIJQLPdAkEjaPLa7lQdfOJxOI60IePjU1Ut470W16YHg4TKPNu9o5vcumE9lsXdQfaJc2jvZ3SrZrso7QzGqg/kpaWEAxCnEh+hC5Grm04AwS4x0NZ1rCepMxhi2HTjNfdsO0tLhVBP1uy0+vKKOD69cNOhz4MzMI3BOrKd7I1QFz5yNnMvVfz66VbINNOdjcHfj1ibKijzUlg2Mp2haqZrpNCDMEtmupqMJmw9dsYhjOZSHzrTnaBcbtzax95hTbsoSeM+FtXzq6sVnXE33y8w8Qpz5B9FEkrrK4pzbO54F7ycqX4O7mlaqCpEGhFki82r6SHsf80v9fPiKOi6tL895Hy0dIe7bdpCt+wfqC17VWMnnrmukoTr7ib3fupV13PX0fiKJJEGfm2jCJmEz7Il1pgyq5qsdmlaqCpHkoXxQXq1YscLs3LlzupsxI42nawigMxTjwRcO84vdrSRTq9ScMz/IhjWNXFY/cqmJfiLC60e7+P5LRzRrhsFZX5l3HnfedMGc/TtR00tEXjbGrBhpG71DmAXGGwii8SQP7zrKj7YfSVciXVDq57Zrl/CO5fOwsixSk43f46KmxEdDdTHvveSsMbd/Npopd0BKjYUGhAIWjjmBIJJl0fqRJG3Dr18/wXefO8Sp3igAJX43H7+ynpsvXZjzQvUuS6gs9lLin5z6Q7ONppWqQqMBoQCNNxAA7DjUzsatTTSd6gPA4xLef+lCPnZlfXrhmFyUFnmoDHixxlmeWik182hAKCChmLNM5XgCwYGTvWzc2sTLhzvSz71r+Tw+e+2SQamRo/G4LGpKJl6eWik182hAKADOesWxUReuz+ZEd4T7nzvEb14/kZ4YdWldGRvWLOXcBSU570fEKTFRHvAMmmCmlJo9NCDMUEMXrh+r3miCH750hId3tRBPOqFgcZUzsHllQ+WYTuoBr5uqoBePK7exBaVUYdKAMMNkW7h+LOJJm0dfPcZ/vHCY7ohToqKq2Munr17CDRcuGNOSlG7LqT9U7NN/JkrNBfo/fYYwxtAdTtAVjpOwxx4IjDE889Zp7nu2iWOdEQD8Hot1K+v40Iq6dI2hXIgIZUUeKrR7SKk5RQPCNLNtQ3ckTlc4np4UNlavtXRxz9a3eaO1B3BKTbz34lo+tXoJlcXeMe3L73FRHfTlnHqqlJo98hYQROR+4H3ASWPMhVleF+Au4EYgBHzaGLMrX+2ZaZK2oTscpzsy/kBwpD3EvduaeO5AW/q5a5ZW8bnrGqmvGluJBJflrGlcqnMKlJqz8nmH8ABwN/DgMK+/B1iW+rkS+Hbqz1ktkbTpDMfpiSQYb9mQ9j6n1MRju4/RH0uWLyhhw/WNXLKofMz7G2lNY6XU3JG3gGCM2SoiS0bY5GbgQeOcFV8UkXIRqTXGtOarTdMplrDpDMfoiybHHQjC8SQP7Wxh845mwqm5CLVlfj53XQPXn1Mz5v5+j8tZ07jIq3MKlFLTO4awEGjOeNySeu6MgCAi64H1APX19VPSuMkSS9h0hmJjWqZyqKRteHLPcR54/hBtfTEASv1uPrF6MTddctaY00F1ToFSKpvpDAjZzkRZL52NMZuATeBUO81noyZL/x1Bb2T8gcAYw0sH29m0tYlDbU4dfY9L+ODli/joqnqC/rF/fTporJQaznQGhBagLuPxIuDYNLVl0kxGIAB460QPG7c28bsjnYATPd99/nw+e80S5pf6x7w/S4TKoA4aK6WGN50B4VHgiyKyGWcwuauQxw8i8STd4fiEuoYAjndFuP+5g/zmjZPp566oL2f9mkaWzc+91ESmoM9NZbEXt840VkqNIJ9ppz8C1gLVItICfA3wABhj7gEex0k5PYCTdvqZfLUln/qizmSy8RScy9QTifODl47wn787mi410VBdzIY1jaxcUjGuvn63ZVFd4j1j/WOllMomn1lGt47yugG+kK/PzydjDN2RBN3h8ZWXyBRL2Pz8laN8/6Uj9PSXmgh6+ew1Dfz++fPHnQqq5amVUmOll45jMBmTyfrZxvDbfaf4zrMHOd7tlJoIeF3cuqqOD16+aNzlpb1uJ5VUy1MrpcZKA0IO4kmbrglOJsv0anMn9zzTxJsnnFITLkt438W1fHL1YioCYys10U9EqAh4KCvSVFKl1PhoQBjBZA0U9zvU1semrU282NSefm7Nsmpuv66BRRVjKzWRqcjrpJJqeWql1ERoQMhisgaK+7X1Rnng+cM8sac1XWri/NpS7ri+kQsXlo17v7qmsVJqMmlASJnoOgTZhGNJfryzmZ/sbCYSd/a5qKKI269r4LqzqyfUtaP1h5RSk23OB4TJHCjO3Ofjr7XywPOH6AjFASgr8vDJ1Yv5w4trJzQfQOsPKaXyZc4GhETGQLE9CQPF4NxlvNDUxr1bD3K43Sk14XVbfOiKRaxbWTehlce0/pBSKt/mXECYjPLT2ew73s09zzSxu6ULcEpN/MEFC/jMNUuoKfFNaN9FXhdVxVp/SCmVX3MqIHSF47T3xSY1EBzrDPOdZw/y2zdPpZ9buaSC9WsaWVoTnNC+3ZZFZdBLUNc0VkpNgTl1pokn7UkLBl3hON9/8TA/f+UYidTYw9k1QdavaWDFksoJ799Z01hnGiulps6cCgiTIZaweeR3R/nBS4fpizppqfNKfHz2miW8+/z5WBPs3/d5XFQHvfjcOmislJpaGhByZBvDf71xku88e5CTPVEAin0uPraqng9cthDfBEtFWOKsaVxWpHMKlFLTQwNCDnYd7uCerU0cONkLgNsSbrr0LD5x5WLKAhM/getMY6XUTKABYQRNp3rZtO0g2w8OlJq4/pwabr+ugYXlRRPevy5ao5SaSTQgZHGqJ8oDzx/iqb3H06UmLlpYyh3XL+W82tJJ+YyA1011UBetUUrNHBoQMoRiCTbvaOanO1uIJpxSE3UVRaxf08jVS6smZUKYyxKqgj5NJVVKzTh6VsKZrPbL11r53vOH6Qw7pSYqAh4+dfUSbrxwwaRdxeuiNUqpmWxOBwRjDM8eaOPebU20dIQB8LstPrRiER9ZWTdpS0/qojVKqUIwZwPC3mNdbHymiT3HugGwBG64cAGfvnoJ1cGJlZroZ4lQEfBOSiaSUkrl25wLCC0dIe7bdpCt+0+nn7uqsZLPXddIQ3XxpH1OwOumKujVVFKlVMHIa0AQkRuAuwAXcJ8x5utDXl8L/Bw4mHrqEWPMnfloS1tvlH9+6k0eerklXeb6nPlBNqxp5LL6ikn7HF20RilVqPIWEETEBXwT+D2gBdghIo8aY14fsuk2Y8z78tUOcAaNb7r7OY52OuMEC0r93HZtA+9YXjPhUhOZgj43VUFdtEYpVZjyeYewCjhgjGkCEJHNwM3A0ICQd26XxcevWsy3thzgY1fW8/5LF05qKWm3ZVFd4p20QWillJoO+TyDLQSaMx63AFdm2W61iLwKHAO+YozZO3QDEVkPrAeor68fV2M+c80Sfv+C+Uz2tbumkiqlZot8jnhmO0MOrT29C1hsjLkE+HfgZ9l2ZIzZZIxZYYxZUVNTM67G+D2uSS0c53VbnFVeRHXQp8FAKTUr5DMgtAB1GY8X4dwFpBljuo0xvanfHwc8IlKdxzZNmIgzaLywvEjnFSilZpV8BoQdwDIRaRARL7AOeDRzAxFZIKl6ECKyKtWetjy2aUKKvC4WVRRRHvDqusZKqVknb2MIxpiEiHwReAon7fR+Y8xeEbkj9fo9wC3A50UkAYSBdWYy17ecJJpKqpSaC/KaFpPqBnp8yHP3ZPx+N3B3PtswUUG/m6piTSVVSs1+mic5DI/LqT9U5NVxAqXU3KABYQgRodTvprJYxwmUUnOLBoQMusC9Umou04CAViVVSinQgKBLWSqlVMqcDQhuy6Iy6NWlLJVSKmVOng1L/B6qirX+kFJKZZpTAcHjcuoPackJpZQ605wKCJNZ3E4ppWYbHUlVSikFaEBQSimVogFBKaUUoAFBKaVUigYEpZRSgAYEpZRSKRoQlFJKARoQlFJKpWhAUEopBYDMwCWMRyQip4DD43hrNXB6kpsz3WbbMc2244HZd0x6PDPfcMe02BhTM9IbCy4gjJeI7DTGrJjudkym2XZMs+14YPYdkx7PzDeRY9IuI6WUUoAGBKWUUilzKSBsmu4G5MFsO6bZdjww+45Jj2fmG/cxzZkxBKWUUiObS3cISimlRqABQSmlFDALA4KI3CAib4rIARH56yyvrxWRLhF5JfXz1eloZ65E5H4ROSkie4Z5XUTk/6WOd7eIXD7VbRyLHI6n0L6fOhH5rYi8ISJ7ReRLWbYptO8ol2MqmO9JRPwisl1EXk0dz99n2abQvqNcjmns35ExZtb8AC7gbaAR8AKvAucP2WYt8Nh0t3UMx7QGuBzYM8zrNwJPAAJcBbw03W2e4PEU2vdTC1ye+r0EeCvLv7lC+45yOaaC+Z5Sf+/B1O8e4CXgqgL/jnI5pjF/R7PtDmEVcMAY02SMiQGbgZunuU0TYozZCrSPsMnNwIPG8SJQLiK1U9O6scvheAqKMabVGLMr9XsP8AawcMhmhfYd5XJMBSP1996beuhJ/QzNpim07yiXYxqz2RYQFgLNGY9byP4PeXXqVusJEblgapqWN7kecyEpyO9HRJYAl+FcrWUq2O9ohGOCAvqeRMQlIq8AJ4FfG2MK/jvK4ZhgjN/RbAsIkuW5oVFzF05Nj0uAfwd+lu9G5Vkux1xICvL7EZEg8DDwZWNM99CXs7xlxn9HoxxTQX1PxpikMeZSYBGwSkQuHLJJwX1HORzTmL+j2RYQWoC6jMeLgGOZGxhjuvtvtYwxjwMeEameuiZOulGPuZAU4vcjIh6cE+cPjDGPZNmk4L6j0Y6pEL8nAGNMJ7AFuGHISwX3HfUb7pjG8x3NtoCwA1gmIg0i4gXWAY9mbiAiC0REUr+vwvk7aJvylk6eR4FPprIkrgK6jDGt092o8Sq07yfV1u8AbxhjvjHMZgX1HeVyTIX0PYlIjYiUp34vAt4N7BuyWaF9R6Me03i+I3deWjtNjDEJEfki8BROxtH9xpi9InJH6vV7gFuAz4tIAggD60xqSH4mEpEf4WQLVItIC/A1nAGk/uN5HCdD4gAQAj4zPS3NTQ7HU1DfD3AN8AngtVR/LsDfAPVQmN8RuR1TIX1PtcD3RMSFc1L8iTHmsSHnhUL7jnI5pjF/R1q6QimlFDD7uoyUUkqNkwYEpZRSgAYEpZRSKRoQlFJKARoQlFJKpWhAUDOWiCRTVRr3iMhPRSQwgX09ICK3pH6/T0TOH2HbtSJy9Tg+41C2iT/DPT9RIvIBETEisnyy963mJg0IaiYLG2MuNcZcCMSAOzJfTOVgj5kx5nZjzOsjbLIWGHNAmAa3As/iTMBUasI0IKhCsQ04O3X1/lsR+SHOxCmXiPyziOwQp479BkjXt79bRF4XkV8C8/p3JCJbRGRF6vcbRGRXqgDYf6WKud0B/Gnq7uS61KzQh1OfsUNErkm9t0pEfiUivxORjWSvh5OViCxOfd7u1J/1qeeXisiLqc+5U0R6h3l/EGcC2W1kBAQRsUTkW+LUyH9MRB7PuDO6QkSeEZGXReQpmcHVPNX00ICgZjwRcQPvAV5LPbUK+B/GmPNxTohdxpiVwErgcyLSAHwAOBe4CPgcWa74RaQGuBf4YKoA2IeMMYeAe4D/m7o72QbclXq8EvggcF9qF18DnjXGXIZT+qB+DId1N0655YuBHwD/L/X8XcBdqc8aqZbO+4EnjTFvAe0ysKDLHwFLUsd9O7A6dawenAJntxhjrgDuB/7nGNqr5oBZVbpCzTpFGaUTtuHU17ka2G6MOZh6/veBi/uvgoEyYBnOQjw/MsYkgWMi8nSW/V8FbO3flzFmuHUa3g2cnyoLA1AqIiWpz/ij1Ht/KSIdYzi21f3vBf4D+D8Zz78/9fsPgX8Z5v23Av+W+n1z6vEu4Frgp8YYGzguIr9NbXMucCHw69RxuIAZW6tHTQ8NCGomC6fK+6alTmZ9mU8Bf2yMeWrIdjcyevliyWEbcO6kVxtjwlnaMlm1X3Lej4hUAe8ELhQRg3NyNyLylwzfbSXAXmPM6gm3VM1a2mWkCt1TOAW8PAAico6IFANbgXWpMYZa4B1Z3vsCcH2qiwkRqUw934OzdGS/XwFf7H8gIpemft0KfCz13HuAijG0+3kG+v4/hjM4DPAiTrcUDD9YfAtOd9NiY8wSY0wdcBDn7uBZ4IOpsYT5OAPkAG8CNSKS7kKSGb6ojZp6GhBUobsPeB3YJSJ7gI04d77/CezHGXf4NvDM0DcaY04B64FHRORV4Mepl34BfKB/UBn4E2BFagD4dQaynf4eWCMiu3C6ro6M0M7dItKS+vlGap+fEZHdOJVF+xey/zLwZyKyHaeiZVeWfd2aOr5MDwMfTf3ZAvT/XbyEM8YSwwkk/5Q61lcojEwqNYW02qlSM0hqrkXYGGNEZB1wqzFmTOuCi0jQGNOb6lraDlxjjDmej/aq2UXHEJSaWa4A7hZngKIT+Ow49vGYOIuneIF/0GCgcqV3CEoppQAdQ1BKKZWiAUEppRSgAUEppVSKBgSllFKABgSllFIp/z/+Qw+vKvX6BwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "acc = r2_score(y_train_log[y_index], y_pred)\n",
- "mae = mean_absolute_error(y_train_log[y_index],y_pred)\n",
- "\n",
- "print('R2:',acc)\n",
- "print('MAE:',mae)\n",
- "\n",
- "sns.regplot(np.array(y_pred), y_train_log[y_index])\n",
- "plt.xlabel('Predicted Log Age')\n",
- "plt.ylabel('Log Age')\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "In this case, the transformation *did* result in a slight improvement. Will this generalize to left out data?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "source": [
- "See here for an explanation of different feature selection options and how to implement them in sklearn: https://scikit-learn.org/stable/modules/feature_selection.html\n",
- "\n",
- "And here is a thoughtful tutorial covering feature selection for novel machine learners: https://www.datacamp.com/community/tutorials/feature-selection-python"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "So there you have it. We've tried many different strategies, but most of our \"tweaks\" haven't really lead to improvements in the model. This is not always the case, but it is not uncommon. \n",
- "\n",
- "Can you think of some reasons why?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Moving on to our validation data, we probably should just stick to a basic model, though predicting log age might be a good idea!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "## Can our model predict age in completely un-seen data?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "Now that we've fit a model we think has possibly learned how to decode age based on rs-fmri signal, let's put it to the test. \n",
- "\n",
- "We will train our model on all of the training data, and try to predict the age of the subjects we left out at the beginning of this section."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Because we performed a log transformation on our training data, we will need to transform our testing data using the *same information!* But that's easy because we stored our transformation in an object!\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [],
- "source": [
- "# Notice how we use the Scaler that was fit to X_train and apply to X_test,\n",
- "# rather than creating a new Scaler for X_test\n",
- "y_val_log = log_transformer.transform(y_val.values.reshape(-1,1))[:,0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "And now for the moment of truth! \n",
- "\n",
- "No cross-validation needed here. We simply fit the model with the training data and use it to predict the testing data\n",
- "\n",
- "I'm so nervous. Let's just do it all in one cell"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "accuracy (r2) = 0.6963665987774859\n",
- "mae = 0.25413373211166607\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Log Age')"
- ]
- },
- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "l_svr = SVR(kernel='linear') # define the model\n",
- "l_svr.fit(X_train, y_train_log) # fit to training data\n",
- "y_pred = l_svr.predict(X_val) # classify age class using testing data\n",
- "acc = l_svr.score(X_val, y_val_log) # get accuracy (r2)\n",
- "mae = mean_absolute_error(y_val_log, y_pred) # get mae\n",
- "\n",
- "# print results\n",
- "print('accuracy (r2) =', acc)\n",
- "print('mae = ',mae)\n",
- "\n",
- "# plot results\n",
- "sns.regplot(y_pred, y_val_log)\n",
- "plt.xlabel('Predicted Log Age')\n",
- "plt.ylabel('Log Age')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "***Wow!!*** Congratulations. You just trained a machine learning model that used real rs-fmri data to predict the age of real humans."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "The proper thing to do at this point would be to repeat the train-validation split multiple times. This will ensure the results are not specific to this validation set, and will give you some confidence intervals around your results."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "As an assignment, you can give that a try below. Create 10 different splits of the entire dataset, fit the model and get your predictions. Then, plot the range of predictions.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "outputs": [],
- "source": [
- "# SPACE FOR YOUR ASSIGNMENT\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "So, it seems like something in this data does seem to be systematically related to age ... but you might be curious what what those features are? "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "#### Interpreting model feature importances"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Interpreting the feature importances of a machine learning model is a real can of worms. This is an area of active research. Unfortunately, it's hard to trust the feature importance of some models. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "source": [
- "\n",
- "You can find a whole tutorial on this subject here:\n",
- "http://gael-varoquaux.info/interpreting_ml_tuto/index.html"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "For now, we'll just eschew better judgement and take a look at our feature importances. \n",
- "\n",
- "While we can't ascribe any biological relevance to the features, it can still be helpful to know what the model is using to make its predictions. This is a good way to, for example, establish whether your model is actually learning based on a confound! Could you think of some examples?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "We can access the feature importances (weights) used by the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[-0.00873134, -0.01842607, 0.01206372, ..., 0.02364518,\n",
- " 0.00578919, 0.00532445]])"
- ]
- },
- "execution_count": 58,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "l_svr.coef_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "lets plot these weights to see their distribution better"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 59,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'weight')"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
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"
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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plotting.plot_connectome(feat_exp_matrix, coords, colorbar=True, edge_threshold=0.035)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "fragment"
- }
- },
- "source": [
- "That's definitely an improvement, but it's still a bit hard to see what's going on.\n",
- "Nilearn has a new feature that let's use view this data interactively!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 65,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plotting.view_connectome(feat_exp_matrix, coords, edge_threshold='98%',\n",
- " edge_cmap='viridis')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Exercises"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3. Tweak the pipeline in the tutorial, by applying PCA , keeping 90% of the variance, instead of SelectPercentile to reduce the dimensionality of features (feature selection). Refer to scikit-learn documentation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.decomposition import PCA\n",
- "\n",
- "model = Pipeline([\n",
- " ('dimensionality_reduction',PCA(n_components=0.90)),\n",
- " ('prediction', l_svr)\n",
- "])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "4. Implement cross-validation, but this time changing to leave-one-out. Here is to give an idea as to where changes need to be made in the code."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.model_selection import LeaveOneOut\n",
- "\n",
- "skf = LeaveOneOut()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "5. What are the features we are using in this model? What are the numbers representing the shape of the time series (168, 64), the shape of the connectivity matrix (64 x 64), and of the feature matrix (155, 2016)?\n",
- "\n",
- "Answer: 168 are the number of time points/measures and 64 is the amount of ROIs. The matrix is 64*64 because we have a connectivity measure (edges) for each pair of ROIs (nodes). We end up with a matrix 155 *2016. We have 155 subjects with 2016 connectivity measures."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "6. Using the performance of the different polynomial fit (MSE) for train and test error, try to explain why increasing complexity of models does not necessarily lead to a better model.\n",
- "\n",
- "Answer: Increasing the complexity of models can lead to overfitting on the training set. We go from high bias to low bias, and from low variance to high variance, as we add more complexity."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "7. Remember we talked about regularization in the introduction to machine learning? Variance of model estimation increases when there are more features than samples. This especially relevant when we have > 2000 features ! Apply a penalty to the SVR model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "R2: 0.5793281473508589\n",
- "MAE: 3.6158984999657733\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/mepicard/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Age')"
- ]
- },
- "execution_count": 77,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.svm import LinearSVR\n",
- "\n",
- "# re-intialize the model\n",
- "l_svr = LinearSVR(epsilon=0.5, loss=\"epsilon_insensitive\") \n",
- "\n",
- "# predict\n",
- "y_pred = cross_val_predict(l_svr, X_train, y_train, cv=10)\n",
- "\n",
- "# scores\n",
- "acc = r2_score(y_train, y_pred)\n",
- "mae = mean_absolute_error(y_train,y_pred)\n",
- "\n",
- "print('R2:',acc)\n",
- "print('MAE:',mae)\n",
- "\n",
- "sns.regplot(y_pred, y_train)\n",
- "plt.xlabel('Predicted Age')\n",
- "plt.ylabel('Age')"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "celltoolbar": "Edit Metadata",
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- },
- "rise": {
- "scroll": true
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Machine learning for neuroimaging"
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/module_machine_learning_neuroimaging
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://school-brainhack.github.io/module_machine_learning_neuroimaging
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, machine learning, neuroimaging, nilearn]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Application of machine learning to fMRI data analysis using nilearn. This module covers extracting functional connectivity features, building and evaluating predictive models with scikit-learn, and interpreting results in a neuroimaging context. Hands-on exercises apply these methods to ADHD classification."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "functional_connectome.png"
----
-
-## Information
-
-The estimated time to complete this training module is 4h.
-
-The prerequisites to take this module are:
- * [installation](https://school-brainhack.github.io/modules/installation/) module
- * [introduction to python for data analysis](https://school.brainhackmtl.org/modules/python_data_analysis/) module
- * [introduction to machine learning](https://school.brainhackmtl.org/modules/machine_learning_basics/) module
-
-Recommended but not mandatory:
- * [fmri connectivity](/modules/fmri_connectivity) module
- * [fmri parcellation](/modules/fmri_parcellation) module
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-Follow up with your local TA(s) to validate you completed the exercises correctly.
-
-## Resources
-
-This module is built around two Jupyter notebooks and one set of exercises, all available in the [module repository](https://github.com/school-brainhack/module_machine_learning_neuroimaging) and rendered as a [web book](https://school-brainhack.github.io/module_machine_learning_neuroimaging/):
-
-### Tutorial 1: Functional connectivity with nilearn
-
-An introduction to extracting functional connectivity features from resting-state fMRI data using nilearn. Topics covered:
-- A brief introduction to fMRI and the challenge of feature extraction
-- Computing time series from brain atlases with `NiftiLabelsMasker`
-- Constructing and visualizing connectomes with `ConnectivityMeasure`
-
-### Tutorial 2: Machine learning with nilearn
-
-A complete ML pipeline applied to a developmental fMRI dataset (children vs. adults classification). Topics covered:
-- Loading and exploring phenotypic data
-- Extracting connectivity features across subjects
-- Training a linear SVM classifier with scikit-learn
-- Evaluating model performance with cross-validation
-- Visualizing and interpreting results
-
-The dataset used is the **NeuroDev** developmental fMRI dataset, preprocessed and packaged by **Elizabeth Dupre** and now integrated as one of nilearn's main tutorial datasets.
-
-### Original lecture
-
-This module builds on a lecture originally delivered by **Jacob Vogel** during the QLSC 612 course in 2020. The original video (2h13) is available below:
-
-
-
-## Exercise
-
-The exercise notebook (`exercises_adhd.ipynb`) has you build a complete ML pipeline to predict **ADHD diagnosis** from functional connectivity data, using the [ADHD200 dataset](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_adhd.html) available through nilearn.
-
-The exercise is structured as follows:
-
-1. **Part 1 — Exploring phenotypic data**: examine clinical and demographic variables (number of subjects, ADHD diagnosis distribution, age, gender).
-2. **Part 2 — Extracting functional connectivity features**: use an atlas-based masker to extract time series and compute a connectivity matrix per subject.
-3. **Part 3 — Training and evaluating an SVM classifier**: split data, fit a linear SVM, and assess performance with accuracy, a classification report, and a confusion matrix.
-4. **Challenge — Cross-validation**: re-evaluate using stratified k-fold cross-validation for a more robust performance estimate.
-
-**Important**: follow the parameter specifications given in each question exactly — the exercise uses automated grading that compares results to reference values.
-
-Follow up with your local TA(s) to validate you completed the exercises correctly.
-
-## More resources
-
-- [nilearn documentation](https://nilearn.github.io/stable/)
-- [scikit-learn documentation](https://scikit-learn.org/stable/)
-- [ADHD200 dataset on nilearn](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_adhd.html)
-- [NeuroDev / development fMRI dataset on nilearn](https://nilearn.github.io/stable/modules/generated/nilearn.datasets.fetch_development_fmri.html)
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Working with MNE-Python and EEG-BIDS"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [MNE, python, EEG, BIDS, brain dynamics]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This repo includes a tutorial for Working with MNE-Python and EEG-BIDS."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_dynamics.gif"
----
-
-
-## Information
-
-The estimated time to complete this training module is 2.30h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school.brainhack [at] gmail [dot] com.
-
-## Resources
-This module was presented by Davide Momi during the [Brainhack Toronto](https://brainhackto.github.io/global-toronto-12-2022/), and the associated notebooks are available [here](https://github.com/Davi1990/mne_eeg_workshop).
-
-The video of the presentation is available below (duration 1h33):
-
-
-## Tutorial
-To run the code locally please follow the instructions.
- * Download the folder using:
-```
-git clone https://github.com/Davi1990/mne_eeg_workshop/
-```
-
-```
-cd folder_path_to_the_cloned_repo
-```
-
- * Install the required dependencies using:
-```
-pip install -r requirements.txt
-```
-
- * Download the required [data folder](https://drive.google.com/drive/folders/1DO-dXfIXzGDzmgcWRMtYvX30ZECYzRYd?usp=sharing).
-
- * Download the [raw file](https://drive.google.com/file/d/1-RSyaXp2Chx0zLuaAnlgK8o1VMo3enx8/view?usp=share_link).
-
- * Start a new jupyter notebook. For this follow the instructions below:
- - Open the terminal
- - Type `jupyter notebook`
- - If you’re not automatically directed to a webpage, copy the URL printed in the terminal and paste it in your browser
- - Once on the webpage, navigate to the `mne_eeg_workshop` you cloned
- - Open the relevant notebook.
-
-## Exercise
-
-1. Read through the notebook running all the cells
-2. Complete the exercises in the notebook
-
-**Exercise 1** Check information on the MNE-BIDS file that was saved as part of Tutorial 01 and fill the cells in the notebook with your answers to the questions below.
-
- How many channels do you have for each type of sensor?
- What is the sampling frequency?
- Have the data been filtered?
- What is the frequency of the line noise?
- Is there any bad channel?
-
-
-**Exercise 2** Import a new epoched file and generate a figure of both evoked response and principal components.
-
- Read the file `sub-s01_task-faceFO_eeg.fif` as an epoched object.
- Make a joint plot of the time-series.
- Create a variable with the array of the evoked data.
- Decompose the data using SVD (use the function `numpy.linalg.svd`)
- Make a `matplotlib` figure with 1 row and 5 columns and plot the first 5 components using the function `mne.viz.plot_topomap`.
-
-
-* After finishing your exercises, rename the notebook and save it to your local machine.
-* Follow up with your local TA(s) to validate you completed the exercises correctly.
-* :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-
- ## More resources
-
- - Other great resources to get started with NME in python:
- - [MNE-Python](https://mne.tools/stable/auto_tutorials/index.html)
- - [Neurodesk](https://www.neurodesk.org/tutorials/electrophysiology/eeg_mne-python/)
-
-
diff --git a/content/en/modules/nibabel/NiBabel.py b/content/en/modules/nibabel/NiBabel.py
deleted file mode 100644
index 9a903d5b..00000000
--- a/content/en/modules/nibabel/NiBabel.py
+++ /dev/null
@@ -1,937 +0,0 @@
-# ---
-# jupyter:
-# jupytext:
-# formats: ipynb,py:percent
-# text_representation:
-# extension: .py
-# format_name: percent
-# format_version: '1.3'
-# jupytext_version: 1.14.5
-# kernelspec:
-# display_name: Python 3 (ipykernel)
-# language: python
-# name: python3
-# ---
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# # NiBabel
-#
-#
-#
-# ## Neuroimaging data and file structures in Python
-#
-# ###### Christopher J Markiewicz
-#
-# ###### NeuroHackademy 2020
-
-# %% [markdown] slideshow={"slide_type": "notes"}
-# The goal of this presentation is to familiarize you with some broad classes of neuroimaging data that can be interacted with in Python, using the NiBabel library.
-#
-# This document is intended to be viewed as a [RISE](https://rise.readthedocs.io/) presentation. It works fine as a notebook, but blocks with images may look strange because they are formatted to work as slides.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# # NiBabel
-#
-# NiBabel is a low-level Python library that gives access to a variety of imaging formats, with a particular focus on providing a common interface to the various volumetric formats produced by scanners and used in common neuroimaging toolkits:
-#
-# | | | |
-# |:---: |:---: |:---:|
-# | NIfTI-1 | NIfTI-2 | MGH |
-# | MINC 1.0 | MINC 2.0 | AFNI BRIK/HEAD |
-# | ANALYZE | SPM99 ANALYZE | SPM2 ANALYZE |
-# | DICOM | PAR/REC | ECAT |
-#
-# It also supports surface file formats:
-#
-# | | |
-# |:--:|:--:|
-# | GIFTI | FreeSurfer (FS) geometry |
-# |FS labels | FS annotations |
-#
-# Tractography files:
-#
-# | | |
-# |:--:|:--:|
-# | TrackVis (TRK) | MRtrix (TCK) |
-#
-# As well as the CIFTI-2 format for composite volume/surface data.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Installation
-#
-# NiBabel is available on [PyPI](https://pypi.org/project/nibabel/):
-#
-# ```Shell
-# pip install nibabel
-# ```
-#
-# And [conda-forge](https://anaconda.org/conda-forge/nibabel):
-#
-# ```Shell
-# conda install -c conda-forge nibabel
-# ```
-#
-# *Note*: This notebook assumes NiBabel 3+, which requires a minimum Python version of 3.5.
-
-# %% slideshow={"slide_type": "subslide"}
-import nibabel as nb
-print(nb.__version__)
-
-# %% slideshow={"slide_type": "fragment"}
-# Some additional, useful imports
-from pathlib import Path # Combine path elements with /
-from pprint import pprint # Pretty-printing
-
-import numpy as np # Numeric Python
-from matplotlib import pyplot as plt # Matlab-ish plotting commands
-from nilearn import plotting as nlp # Nice neuroimage plotting
-import transforms3d # Work with affine algebra
-from scipy import ndimage as ndi # Operate on N-dimensional images
-import nibabel.testing # For fetching test data
-
-# %pylab inline
-
-# %%
-# Assume you have download the data to your home directory in ~/data
-data_dir = Path.home() / 'brainhackschool_nibabel' / 'data'
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Learning objectives
-#
-# 1. Be able to load and save different types of files in NiBabel
-# 1. Become familiar with the `SpatialImage` API and identify its components
-# 1. Understand the differences between array and proxy images
-# 1. Acquire a passing familiarity with the structures of surface images, CIFTI-2 files, and tractograms
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Basic I/O
-#
-# ### Loading
-
-# %%
-t1w = nb.load(data_dir / 'openneuro/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz')
-bold = nb.load(data_dir / 'openneuro/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz')
-
-# %% slideshow={"slide_type": "fragment"}
-print(t1w)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Saving
-#
-# All NiBabel images have a `.to_filename()` method:
-
-# %% slideshow={"slide_type": "-"}
-t1w.to_filename(data_dir / 'tmp/img.nii.gz')
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# `nibabel.save` will attempt to convert to a reasonable image type, if the extension doesn't match:
-
-# %%
-nb.save(t1w, data_dir / 'tmp/img.mgz')
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Some image types separate header and data into two separate images. Saving to either filename will generate both.
-
-# %%
-nb.save(t1w, data_dir / 'tmp/img.img')
-print(nb.load(data_dir / 'tmp/img.hdr'))
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Serialization
-#
-# Some NiBabel images can be serialized to and deserialized from byte strings, for performing stream-based I/O.
-
-# %%
-bstr = t1w.to_bytes()
-print(bstr[:100])
-
-# %%
-new_t1w = nb.Nifti1Image.from_bytes(bstr)
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Images that save to single files can generally be serialized. NIfTI-1/2, GIFTI and MGH are currently supported.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Spatial Images
-#
-# For MRI studies, neuroimaging data is typically acquired as one or more *volumes*, a regular grid of values. NiBabel represents these data as *spatial images*, a structure containing three things:
-#
-# 1. The image *data* array: a 3D or 4D array of image data
-# 1. An *affine* matrix: 4x4 array relating voxel coordinates and "world" coordinates
-# 1. Image *metadata*: usually a format-specific header
-#
-# Many file types encode this basic structure. NiBabel will read any of ANALYZE (plain, SPM99, SPM2 and later), NIfTI-1, NIfTI-2, MINC 1.0, MINC 2.0, AFNI BRIK/HEAD, MGH, ECAT, DICOM and Philips PAR/REC, and expose a simple interface:
-
-# %%
-data = t1w.get_fdata()
-affine = t1w.affine
-header = t1w.header
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Spatial images have some properties that should be familiar from NumPy arrays:
-
-# %%
-print(t1w.ndim)
-print(t1w.shape)
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ### The data array
-#
-# The data is a simple NumPy array. It can be accessed, sliced and generally manipulated as you would any array:
-
-# %%
-print(data.shape)
-i, j, k = np.array(data.shape) // 2
-fig, axes = plt.subplots(1, 3)
-axes[0].imshow(data[i,:,:].T, cmap='Greys_r', origin='lower')
-axes[1].imshow(data[:,j,:].T, cmap='Greys_r', origin='lower')
-_ = axes[2].imshow(data[:,:,k].T, cmap='Greys_r', origin='lower')
-
-# %% [markdown]
-# Each location in the image data array is a *voxel* (pixel with a volume), and can be referred to with *voxel coordinates* (array indices).
-#
-# This is a natural way to describe a block of data, but is practically meaningless with regard to anatomy.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# NiBabel has a basic viewer that scales voxels to reflect their size and labels orientations.
-
-# %% slideshow={"slide_type": "-"}
-_ = t1w.orthoview() # Requires matplotlib, occasionally glitchy in OSX setups
-
-# %% [markdown]
-# The crosshair is focused at the origin $(0, 0, 0)$.
-#
-# All of this information is encoded in the affine:
-
-# %%
-print(affine)
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ### Affine transforms
-#
-# The affine is a 4 x 4 numpy array. This describes the transformation from the voxel space (indices $(i, j, k)$) to a *reference* space (coordinates $(x, y, z)$). These coordinates are, by convention, distance in mm *right*, *anterior* and *superior* of an origin\*.
-#
-# $$
-# \begin{bmatrix}
-# x\\
-# y\\
-# z\\
-# 1\\
-# \end{bmatrix} =
-# \mathbf A
-# \begin{bmatrix}
-# i\\
-# j\\
-# k\\
-# 1\\
-# \end{bmatrix} =\begin{bmatrix}
-# m_{1,1} & m_{1,2} & m_{1,3} & a \\
-# m_{2,1} & m_{2,2} & m_{2,3} & b \\
-# m_{3,1} & m_{3,2} & m_{3,3} & c \\
-# 0 & 0 & 0 & 1 \\
-# \end{bmatrix}
-# \begin{bmatrix}
-# i\\
-# j\\
-# k\\
-# 1\\
-# \end{bmatrix}
-# $$
-#
-# For an unmodified image, this reference space typically refers to an origin in the isocenter of the imaging magnet, and the directions right, anterior and superior are defined assuming a subject is lying in the scanner, face up.
-#
-# 
-#
-# \* *If a file format uses an alternative convention, NiBabel converts on read/write, so affines are always RAS+.*
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### The affine as a series of transformations
-#
-#
-#
-# 
-#
-# An affine transformation can be decomposed into translation, rotation, scaling (zooms) and shear transformations, applied right-to-left.
-
-# %%
-T, R, Z, S = transforms3d.affines.decompose44(affine) # External library
-print(f"Translation: {T}\nRotation:\n{R}\nZooms: {Z}\nMax shear: {np.abs(S).max()}")
-
-# %%
-Tm, Rm, Zm, Sm = [np.eye(4) for _ in range(4)]
-Tm[:3, 3] = T
-Rm[:3, :3] = R
-Zm[:3, :3] = np.diag(Z)
-Sm[[0, 0, 1], [1, 2, 2]] = S
-np.allclose(Tm @ Rm @ Zm @ Sm, affine)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# NiBabel provides functions for extracting information from affines:
-#
-# * Orientation (or axis codes) indicates the direction an axis encodes. If increasing index along an axis moves to the right or left, the axis is coded "R" or "L", respectively.
-# * Voxel sizes (or zooms)
-# * Obliquity measures the amount of rotation from "canonical" axes.
-
-# %%
-print("Orientation:", nb.aff2axcodes(affine))
-print("Zooms:", nb.affines.voxel_sizes(affine))
-print("Obliquity:", nb.affines.obliquity(affine))
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# You can also use it to answer specific questions. For instance, the inverse affine allows you to calculate indices from RAS coordinates and look up the image intensity value at that location.
-
-# %%
-i, j, k, _ = np.linalg.pinv(affine) @ [0, 0, 0, 1]
-print(f"Center: ({int(i)}, {int(j)}, {int(k)})")
-print(f"Value: ", data[int(i), int(j), int(k)])
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### Don't Panic
-#
-#
-#
-# If you didn't follow all of the above, that's okay. Here are the important points:
-#
-# 1. Affines provide the spatial interpretation of the data.
-# 2. NiBabel has some useful methods for working with them.
-#
-# You'll go over this again with Noah Benson in [Introduction to the Geometry and Structure of the Human Brain](https://neurohackademy.org/course/introduction-to-the-geometry-and-structure-of-the-human-brain/).
-#
-# Matthew Brett's [Coordinate systems and affines](https://nipy.org/nibabel/coordinate_systems.html) tutorial is an excellent resource.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ### Image headers
-#
-# The image header is specific to each file format. Minimally, it contains information to construct the data and affine arrays, but some methods are useful beyond that.
-#
-# * `get_zooms()` method returns voxel sizes. If the data is 4D, then repetition time is included as a temporal zoom.
-# * `get_data_dtype()` returns the numpy data type in which the image data is stored (or will be stored when the image is saved).
-
-# %%
-print(bold.header.get_zooms())
-print(bold.header.get_data_dtype())
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# For images with fixed header structures, header fields are exposed through a `dict`-like interface.
-
-# %%
-print(header)
-
-# %%
-print(header['descrip'])
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# The MGH header is similarly accessible, but its structure is quite different:
-
-# %%
-mghheader = nb.load(data_dir / 'tmp/img.mgz').header
-print(mghheader)
-
-# %%
-print(mghheader['Pxyz_c'])
-print(mghheader.get_zooms())
-print(mghheader.get_data_dtype())
-
-
-# %% [markdown]
-# Other formats use text key-value pairs (PAR/REC, BRIK/HEAD) or keys in NetCDF (MINC 1.0) or HDF5 (MINC 2.0) containers, and their values are accessible by other means.
-#
-# Often, we're not particularly interested in the header, or even the affine. But it's important to know they're there and, especially, to remember to copy them when making new images, so that derivatives stay aligned with the original image.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ### Creating new images
-#
-# Reading data will only take you so far. In many cases, you will want to use the image to compute a new image in the same space. Such a function might take the form:
-#
-# ```Python
-# def my_function(image):
-# orig_data = image.get_fdata()
-# new_data = some_operation_on(orig_data)
-# return image.__class__(new_data, affine=image.affine, header=image.header)
-# ```
-#
-# Note the `image.__class__` ensures that the output image is the same type as the input. If your operation requires specific format features, you might use a specific class like `nb.Nifti1Image`.
-#
-# For example, perhaps we want to save space and rescale our T1w image to an unsigned byte:
-
-# %%
-def rescale(img):
- data = img.get_fdata()
- rescaled = ((data - data.min()) * 255. / (data.max() - data.min())).astype(np.uint8)
-
- rescaled_img = img.__class__(rescaled, affine=img.affine, header=img.header)
-
- rescaled_img.header.set_data_dtype('uint8') # Ensure data is saved as this type
- return rescaled_img
-
-rescaled_t1w = rescale(t1w)
-rescaled_t1w.to_filename(data_dir / 'tmp/rescaled_t1w.nii.gz')
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Data objects - Arrays and Array Proxies
-#
-# Recall that the spatial image contains data, affine and header objects. When creating a new image, the `data` array is typically an array.
-
-# %%
-array_img = nb.Nifti1Image(np.arange(244, 268).astype(int32).reshape(2, 3, 4), affine=np.diag([2, 2, 2, 1]))
-print(array_img.dataobj)
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# When loading a file, an `ArrayProxy` is used for most image types.
-
-# %%
-array_img.to_filename(data_dir / 'tmp/array_img.nii')
-proxy_img = nb.load(data_dir / 'tmp/array_img.nii')
-print(proxy_img.dataobj)
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# An array proxy is an object that knows how to access data, but does not read it until requested. The usual way to request data is `get_fdata()`, which returns all data as floating point:
-
-# %%
-proxy_img.get_fdata(dtype=np.float32) # The default is float64, but you can choose any floating point type.
-
-# %% [markdown]
-# `get_fdata()` provides a very predictable interface. When you need more control, you'll want to work with the `ArrayProxy` directly.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Converting array proxies to arrays
-#
-# Array proxies are designed to be one step away from a numpy array:
-
-# %%
-arr = np.asanyarray(proxy_img.dataobj) # array will create a copy; asarray passes through arrays; asanyarray passes subclasses like memmap through
-print(arr.dtype)
-arr
-
-# %% [markdown]
-# Memory maps are arrays that remain on disk, rather than in RAM. This is only possible with uncompressed images.
-#
-# We can also cast to any type we please, however unwisely.
-
-# %%
-print(np.uint8(proxy_img.dataobj)) # Values over 255 will be truncated
-print(np.complex256(proxy_img.dataobj)) # A life less ordinal
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Indexing and slicing array proxies
-#
-# One of the primary motivations for an array proxy is to avoid loading data unnecessarily. Accessing the array proxy with array indices or slices will return the requested values without loading other data:
-
-# %% slideshow={"slide_type": "-"}
-print(proxy_img.dataobj[0])
-print(proxy_img.dataobj[..., 1:3])
-
-# %% [markdown]
-# For example, this is useful for fetching a single volume from a BOLD series:
-
-# %%
-vol0 = bold.dataobj[..., 0]
-vol0.shape
-
-# %% [markdown]
-# Slicing works with compressed data, as well. Install the [indexed-gzip](https://pypi.org/project/indexed-gzip/) package for significant speedups with gzipped files.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Scaling array proxies
-#
-# Several image types, including NIfTI, have a slope and intercept (scaling factors) in the header that allows them to extend the data range and/or precision beyond those of the on-disk type.
-#
-# For example `uint16` can take the values 0-65535. If a BOLD series includes values from 500-2000, we can calculate a slope that will allow us to utilize the 16 bits of precision to encode our desired values.
-
-# %%
-pr = (500, 2000) # Plausible range
-nbits = 16 # 16-bits of precision
-scl_slope = (pr[1] - pr[0]) / (2 ** nbits) # Resolvable difference
-scl_inter = pr[0] # Minimum value
-print(scl_slope, scl_inter)
-"Saving space by collapsing plotting into one line."; x = np.arange(2 ** nbits); plt.step(x, x * scl_slope + scl_inter); vlim = np.array([120, 150]); plt.xlim(vlim); plt.ylim(vlim * scl_slope + scl_inter); plt.xlabel("On-disk value"); plt.ylabel('"True" value'); _ = plt.show();
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Let's create an image from some random values in our plausible range:
-
-# %%
-float_img = nb.Nifti1Image(np.random.default_rng().uniform(500, 2000, (2, 3, 4)), # 64-bit float
- affine=np.diag([2, 2, 2, 1]))
-print(float_img.get_fdata())
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Save as `uint16` and check its values:
-
-# %%
-float_img.header.set_data_dtype(np.uint16)
-float_img.to_filename(data_dir / "tmp/uint16_img.nii")
-
-uint16_img = nb.load(data_dir / "tmp/uint16_img.nii")
-print(uint16_img.get_fdata())
-
-# %% [markdown]
-# We clearly lost some precision...
-
-# %%
-np.max(np.abs(float_img.get_fdata() - uint16_img.get_fdata()))
-
-# %% [markdown]
-# But what's going on?
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# The `ArrayProxy` keeps track of scaling factors:
-
-# %%
-print(f"Slope: {uint16_img.dataobj.slope}; Intercept: {uint16_img.dataobj.inter}")
-print(uint16_img.dataobj.get_unscaled())
-
-# %% [markdown]
-# The scaling is done automatically when the data is accessed, by slice or whole.
-
-# %% slideshow={"slide_type": "fragment"}
-print(np.asanyarray(uint16_img.dataobj))
-print(uint16_img.dataobj[0, 0, 0])
-
-# %% [markdown]
-# The `ArrayProxy` guarantees that the data has the intended *value*, but the *type* can vary based on the on-disk type and the values of scaling factors.
-
-# %% slideshow={"slide_type": "fragment"}
-print(proxy_img.dataobj[0, 0, 0].dtype) # Our earlier integer image
-print(uint16_img.dataobj[0, 0, 0].dtype)
-
-# %% [markdown]
-# `get_fdata()` sweeps these details under the rug and always gives you the same type.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Don't Panic
-#
-#
-#
-# If you didn't follow all of the above, that's okay. Here are the important points:
-#
-# 1. When in doubt, use `img.get_fdata()` will fetch all of the data, and it will always be a float
-# 2. `img.dataobj` exists if you want to load only some data or control the data type
-# 3. Both methods transparently scale data when needed
-#
-# In the NiBabel docs, [The image data array](https://nipy.org/nibabel/nibabel_images.html#the-image-data-array) gives you an overview of both methods, and [Images and memory](https://nipy.org/nibabel/images_and_memory.html) has even more details.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Slicing images
-#
-# Slicing array proxies is nice. Wouldn't it be nicer to keep track of the affine and header?
-#
-# The `slicer` attribute provides an interface that allows you to apply slices to an image, and updates the affine to ensure that the spatial information matches.
-#
-# Consider the T1-weighted image from earlier:
-
-# %%
-_ = t1w.orthoview()
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# We can use the slicer to crop unneeded voxels in the left-right and inferior-superior directions:
-
-# %%
-cropped = t1w.slicer[40:216, :, 50:226]
-cropped.orthoview()
-
-# %% [markdown]
-# Note the origin crosshair points to the same structure. The affines now differ in translation:
-
-# %%
-print(cropped.affine - t1w.affine)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# You can even downsample an image, and the zooms will reflect the increased distance between voxels.
-
-# %%
-cheap_downsample = cropped.slicer[2::4, 2::4, 2::4]
-print(cheap_downsample.header.get_zooms())
-cheap_downsample.orthoview()
-
-
-# %% [markdown]
-# Note that this is a bad idea in *most* circumstances because it induces aliasing.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# The better approach would be to anti-alias and then slice:
-
-# %%
-def blur(img, sigma): # Isotropic in voxel space, not world space
- return img.__class__(ndi.gaussian_filter(img.dataobj, sigma), img.affine, img.header)
-
-better_downsample = blur(cropped, sigma=1.5).slicer[2::4, 2::4, 2::4]
-better_downsample.orthoview()
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# For non-spatial dimensions, slices or indices may be used to select one or more volumes.
-
-# %% slideshow={"slide_type": "-"}
-tp15 = bold.slicer[..., :5]
-tp1 = bold.slicer[..., 0]
-print(f"BOLD shape: {bold.shape}; Zooms: {bold.header.get_zooms()}")
-print(f"Time pts 1-5 shape: {tp15.shape}; Zooms: {tp15.header.get_zooms()}")
-print(f"Time pt 1 shape: {tp1.shape}; Zooms: {tp1.header.get_zooms()}")
-np.array_equal(tp15.get_fdata(), bold.dataobj[..., :5])
-
-# %% [markdown]
-# Aliasing considerations apply to time series as well, so be careful with down-sampling here, too.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-#
Break!
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Surface Images
-#
-# Although the scanner samples data in three dimensions, some brain structures are better represented as a convoluted sheet than a volume. Data may be usefully resampled onto a cortical sheet, but in the process, it loses the intrinsic geometry of a 3D array.
-#
-# To represent data on a surface, you need the following structures:
-#
-# 1. The surface *mesh*
-# 1. Vertices: a list of coordinates in world space
-# 1. Faces: a list of 3-tuples of indices into the coordinate list
-# 2. The *data* array: a 1D or 2D array of values or vectors at each vertex
-#
-# Unlike spatial images, these components are frequently kept in separate files.
-#
-# *Terminological note*: You are likely to encounter multiple names for each of these. *Vertices* may be called *coordinates* or a *point set*. *Faces* are often called *triangles*. When a data array is plotted on the surface, it might be called a *texture*.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Image-specific interfaces
-#
-# The two supported surface formats at present are GIFTI and FreeSurfer geometry files. Unlike spatial images, there is not yet a common interface for working with surface-based data.
-#
-# FreeSurfer encodes a great deal of information in its directory structure and file names, allowing the necessary data arrays to have relatively simple formats.
-#
-# GIFTI is more of an interchange format, and so has a rich metadata structure and can store different kinds of data.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### Surface meshes
-#
-# To load a surface mesh in a FreeSurfer directory, use `nb.freesurfer.read_geometry`:
-
-# %%
-fs_verts, fs_faces, fs_meta = nb.freesurfer.read_geometry(data_dir / 'ds000228-fmriprep/sourcedata/freesurfer/sub-pixar001/surf/lh.pial', read_metadata=True)
-print(fs_verts[:2])
-print(fs_faces[:2])
-pprint(fs_meta)
-
-# %% [markdown]
-# NiBabel does not have any viewer for surfaces, but Nilearn has plotting utilities:
-
-# %%
-_ = nlp.plot_surf((fs_verts, fs_faces))
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Let's do the same thing with GIFTI. Here, the file-level metadata is minimal:
-
-# %%
-gii_pial = nb.load(data_dir / 'ds000228-fmriprep/sub-pixar001/anat/sub-pixar001_hemi-L_pial.surf.gii')
-pprint(gii_pial.meta.metadata) # .meta maps onto the XML object, its .metadata property exposes a Python dict
-
-# %% [markdown]
-# The vertices and faces are stored as separate data arrays, each with their own metadata. These can be queried by NIfTI *intent code*:
-
-# %%
-pointset_darray = gii_pial.get_arrays_from_intent('pointset')[0]
-triangle_darray = gii_pial.get_arrays_from_intent('triangle')[0]
-pprint(pointset_darray.meta.metadata)
-pprint(triangle_darray.meta.metadata)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# The actual values are stored as the `.data` attribute:
-
-# %%
-gii_verts, gii_faces = pointset_darray.data, triangle_darray.data
-print(gii_verts[:2])
-print(gii_faces[:2])
-
-# %% [markdown]
-# This API can be cumbersome, if all you want is the NumPy arrays. Thanks to a project started at Neurohackademy 2019, the `agg_data()` (aggregate data) method will find the requested data array(s) and return just the data:
-
-# %%
-gii_verts, gii_faces = gii_pial.agg_data(('pointset', 'triangle'))
-print(gii_verts[:2])
-print(gii_faces[:2])
-_ = nlp.plot_surf((gii_verts, gii_faces))
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### Surface-sampled data
-#
-# For data sampled to the surface, FreeSurfer has a few data types:
-
-# %%
-# Morphometry
-curv = nb.freesurfer.read_morph_data(data_dir / 'ds000228-fmriprep/sourcedata/freesurfer/sub-pixar001/surf/lh.curv')
-# Annotations
-labels, color_table, names = nb.freesurfer.read_annot(data_dir / 'ds000228-fmriprep/sourcedata/freesurfer/sub-pixar001/label/lh.aparc.annot')
-# MGH files...
-mgh = nb.load(data_dir / 'ds000228-fmriprep/sourcedata/freesurfer/sub-pixar001/surf/lh.w-g.pct.mgh')
-print(curv.shape)
-print(labels.shape)
-print(mgh.shape)
-
-# %%
-fig, axes = plt.subplots(1, 3, subplot_kw={'projection': '3d'})
-_ = nlp.plot_surf((fs_verts, fs_faces), curv, axes=axes[0])
-_ = nlp.plot_surf((fs_verts, fs_faces), labels, axes=axes[1])
-_ = nlp.plot_surf((fs_verts, fs_faces), mgh.get_fdata(), axes=axes[2])
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# GIFTIs will be GIFTIs. This one is a BOLD series sampled to the `fsaverage5` surface:
-
-# %%
-bold_gii = nb.load(data_dir / 'ds000228-fmriprep/sub-pixar001/func/sub-pixar001_task-pixar_hemi-L_space-fsaverage5_bold.func.gii')
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Each time point is an individual data array with intent `NIFTI_INTENT_TIME_SERIES`. `agg_data()` will aggregate these into a single array.
-
-# %%
-data = bold_gii.agg_data('time series')
-data.shape
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# We can plot the mean BOLD signal. This time we will use a FreeSurfer surface, which Nilearn knows what to do with:
-
-# %%
-_ = nlp.plot_surf(str(data_dir / 'ds000228-fmriprep/sourcedata/freesurfer/fsaverage5/surf/lh.inflated'), data.mean(axis=1))
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Don't Panic
-#
-#
-#
-# If you didn't follow all of the above, that's okay. Here are the important points:
-#
-# 1. Practical considerations make dealing with surfaces a multi-file affair
-# 2. The data structures are common (vertices, faces, per-vertex data arrays), but the arrangement can vary
-# 3. For working with FreeSurfer files, learn to traverse the directory and identify file types
-# 4. For working with GIFTI, learn to query the file for its contents
-#
-# Hopefully next year, we'll be able to talk about a common interface that will simplify things.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## CIFTI-2
-#
-#
-#
-# CIFTI-2 is a file format intended to cover many use cases for connectivity analysis.
-#
-# Files have 2-3 dimensions and each dimension is described by one of 5 types of axis.
-#
-# * Brain models: each row/column is a voxel or vertex
-# * Parcels: each row/column is a group of voxels and/or vertices
-# * Scalars: each row/column has a unique name
-# * Labels: each row/column has a unique name and label table
-# * Series: each row/column is a point in a series (typically time series), which increases monotonically
-#
-# For example, a "parcellated dense connectivity" CIFTI-2 file has two dimensions, indexed by a brain models axis and a parcels axis, respectively. The interpretation is "connectivity from parcels to vertices and/or voxels".
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-#
-#
-#
-#
-# On disk, the file is a NIfTI-2 file with an alternative XML header as an extension, schematized here.
-#
-# NiBabel loads a header that closely mirrors this structure, and makes the NIfTI-2 header accessible as a `nifti_header` attribute.
-
-# %%
-cifti = nb.load(data_dir / 'ds000228-fmriprep/sub-pixar001/func/sub-pixar001_task-pixar_space-fsLR_den-91k_bold.dtseries.nii')
-cifti_data = cifti.get_fdata(dtype=np.float32)
-cifti_hdr = cifti.header
-nifti_hdr = cifti.nifti_header
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# The `Cifti2Header` is useful if you're familiar with the XML structure and need to fetch an exact value or have fine control over the header that is written.
-
-# %%
-bm0 = next(cifti_hdr.matrix[1].brain_models)
-print(bm0.voxel_indices_ijk)
-print(list(bm0.vertex_indices)[:20])
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Most of the time, the `Axis` format will be more useful:
-
-# %%
-axes = [cifti_hdr.get_axis(i) for i in range(cifti.ndim)]
-axes
-
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# The simplest way to get a handle on CIFTI-2 data is to use it. Let's take an axis and a data block and repackage the voxels as a regular NIfTI-1 image:
-
-# %%
-def volume_from_cifti(data, axis):
- assert isinstance(axis, nb.cifti2.BrainModelAxis)
- data = data.T[axis.volume_mask] # Assume brainmodels axis is last, move it to front
- volmask = axis.volume_mask # Which indices on this axis are for voxels?
- vox_indices = tuple(axis.voxel[axis.volume_mask].T) # ([x0, x1, ...], [y0, ...], [z0, ...])
- vol_data = np.zeros(axis.volume_shape + data.shape[1:], # Volume + any extra dimensions
- dtype=data.dtype)
- vol_data[vox_indices] = data # "Fancy indexing"
- return nb.Nifti1Image(vol_data, axis.affine) # Add affine for spatial interpretation
-
-
-# %%
-volume_from_cifti(cifti_data, axes[1]).orthoview()
-
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Now we can extract the values on a surface vertex. This time, as a simple numpy array:
-
-# %%
-def surf_data_from_cifti(data, axis, surf_name):
- assert isinstance(axis, nb.cifti2.BrainModelAxis)
- for name, data_indices, model in axis.iter_structures(): # Iterates over volumetric and surface structures
- if name == surf_name: # Just looking for a surface
- data = data.T[data_indices] # Assume brainmodels axis is last, move it to front
- vtx_indices = model.vertex # Generally 1-N, except medial wall vertices
- surf_data = np.zeros((vtx_indices.max() + 1,) + data.shape[1:], dtype=data.dtype)
- surf_data[vtx_indices] = data
- return surf_data
- raise ValueError(f"No structure named {surf_name}")
-
-
-# %%
-_ = nlp.plot_surf(str(data_dir / "Conte69.L.inflated.32k_fs_LR.surf.gii"),
- surf_data_from_cifti(cifti_data, axes[1], 'CIFTI_STRUCTURE_CORTEX_LEFT').mean(axis=1),
- cmap='plasma')
-
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Finally, combine into a function that will break a CIFTI-2 matrix into a volume and two surface components:
-
-# %%
-def decompose_cifti(img):
- data = img.get_fdata(dtype=np.float32)
- brain_models = img.header.get_axis(1) # Assume we know this
- return (volume_from_cifti(data, brain_models),
- surf_data_from_cifti(data, brain_models, "CIFTI_STRUCTURE_CORTEX_LEFT"),
- surf_data_from_cifti(data, brain_models, "CIFTI_STRUCTURE_CORTEX_RIGHT"))
-
-
-# %% slideshow={"slide_type": "fragment"}
-vol, left, right = decompose_cifti(cifti)
-print(vol.shape, left.shape, right.shape)
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Tractography
-#
-#
-#
-# Diffusion MRI uses estimates of the directional flow of water to detect white matter tracts. These tracts are represented as a series of points in world space.
-#
-# NiBabel represents tractogram files as a collection of these things:
-#
-# 1. A *tractogram*, which is:
-# 1. A set of *streamlines*: each streamline is a series of coordinates describing a path
-# 1. Streamline-level data: a set of data arrays associated with each streamline
-# 1. Point-level data: a set of data arrays associated with each point of each streamline
-# 1. An *affine* matrix: 4x4 array relating voxel coordinates of a reference image and world coordinates
-# 1. File-level metadata: a format-specific header
-#
-# Files of this type are considered `TractogramFile`s. Like `SpatialImage`, the goal is to provide a uniform interface where possible.
-#
-# The two currently-supported file types are TCK (MRtrix) and TRK (TrackVis). We will load some very small ones that were created for testing:
-
-# %%
-tck = nb.streamlines.load(Path(nb.testing.data_path) / 'standard.tck')
-trk = nb.streamlines.load(Path(nb.testing.data_path) / 'complex.trk')
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Like a spatial image, printing the tractogram file gives an overview:
-
-# %%
-print(trk)
-
-# %% [markdown]
-# ### The streamlines API
-#
-# The overall API is as follows:
-
-# %%
-# File-level
-tractogram = trk.tractogram
-trk_affine = trk.affine # Describes relation between voxels and coordinates in an associated image
-trk_header = trk.header
-
-# All streamlines
-streamlines = tractogram.streamlines
-data_per_point = tractogram.data_per_point
-data_per_streamline = tractogram.data_per_streamline
-
-# One streamline at a time
-tractogram_item = tractogram[2] # Most to look at
-streamline_coords = tractogram_item.streamline
-streamline_data = tractogram_item.data_for_streamline
-point_data = tractogram_item.data_for_points
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### A single streamline
-#
-# When looking at an individual streamline, the objects are familiar: NumPy arrays and Python dictionaries.
-
-# %%
-print(streamline_coords)
-pprint(streamline_data)
-pprint(point_data)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### All streamlines
-#
-# When looking at all streamlines, the objects are custom. These deal with issues arising from the fact that each streamline may have a different number of coordinates, so N-dimensional arrays are not appropriate.
-
-# %%
-print(streamlines)
-print(data_per_streamline)
-print(data_per_point)
-
-# %% [markdown]
-# The dictionaries operate like normal dictionaries, providing NumPy array and `ArraySequence` values.
-
-# %%
-print(data_per_streamline['mean_curvature'])
-print(data_per_point['fa'])
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# Each component is also indexable by integer:
-
-# %%
-print(streamlines[2])
-pprint(dict(data_per_streamline[2])); print() # For space
-pprint(dict(data_per_point[2]))
-
-# %% [markdown]
-# As we can see, there are a few ways to get at the same data, based on whether you prefer to operate by streamline or by data element (such as `fa`).
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# # Summary
-#
-#
-#
-# You made it!
-#
-# ## Learning objectives (reprise)
-#
-# 1. Be able to load and save different types of files in NiBabel
-# * `nb.load`, `nb.save`, `img.to_filename()`, `img.to_bytes()`, `img.from_bytes()`
-# 1. Become familiar with the `SpatialImage` API and identify its components
-# * `img.get_fdata()`/`img.dataobj`
-# * `img.affine`
-# * `img.header`
-# 1. Understand the differences between array and proxy images
-# * Arrays are what it says on the tin.
-# * Proxies conserve memory and automatically scale values
-# * `get_fdata()` and `dataobj` provide tradeoffs between consistency and control
-# 1. Acquire a passing familiarity with:
-# * Surfaces: Meshes and per-vertex data, idiosyncratic
-# * CIFTI-2: 2-3D NIfTI with some interesting axes; brainmodels can be decomposed into volume and surface components
-# * Tractograms: Streamlines are paths in space, and data can be attached to the point or the path
diff --git a/content/en/modules/nibabel/index.md b/content/en/modules/nibabel/index.md
deleted file mode 100644
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--- a/content/en/modules/nibabel/index.md
+++ /dev/null
@@ -1,161 +0,0 @@
----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Neuroimaging data and file structures in Python"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [python, neuroimaging, data analysis]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Learning the basics of neuroimaging file format with Nibabel."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "reggie.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 1h30m for the basic content and 1h for the optional advanced content.
-
- - **Basic** - Be able to load and save different types of neuroimaging files in Nibabel. Know how to work with volumetric data.
- - **Advanced** - Understand the difference between array and array proxy images.
- - **Optional** - Acquire a passing familiarity with the structures of surface images, CIFTI-2 files and tractogram.
-
-The prerequisites to take this module are:
- * the [introduction to the terminal](/modules/introduction_to_terminal) module.
- * the [DataLad](/modules/datalad) module.
- * the [python data analysis](/modules/python_data_analysis) module (recommended for Python familiarity).
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server.
-If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-
-We will watch the presentation **NiBabel: Neuroimaging data and file structures in Python** from Neurohackademy 2020 by Chris Markiewicz.
-
-The original presentation and the practical notebook can be found [here](https://github.com/effigies/nibabel-presentations).
-In this exercise, we will use the notebook in the brainhack school repository. Follow the instructions below to run both.
-
-### Downlad data and jupyter notebook
-
-You will need to install the example dataset with [DataLad](/modules/datalad).
-
-```bash
-# download open neuro dataset
-mkdir ~/brainhackschool_nibabel
-cd ~/brainhackschool_nibabel
-mkdir data
-cd data
-datalad install ///openneuro
-cd openneuro
-datalad get -r ds000114/sub-01
-
-cd ~/brainhackschool_nibabel/data
-datalad install git@github.com:OpenNeuroDerivatives/ds000228-fmriprep.git
-cd ds000228-fmriprep
-datalad get -r sourcedata/freesurfer/sub-pixar001/surf
-datalad get -r sourcedata/freesurfer/fsaverage5
-
-
-datalad get sub-pixar001/anat/sub-pixar001_hemi-L_pial.surf.gii
-datalad get sub-pixar001/func/sub-pixar001_task-pixar_hemi-L_space-fsaverage5_bold.func.gii
-datalad get sub-pixar001/func/sub-pixar001_task-pixar_space-fsLR_den-91k_bold.dtseries.nii
-
-# get the surface data from midnight scan club
-cd ~/brainhackschool_nibabel/data
-wget -c https://raw.githubusercontent.com/MidnightScanClub/MSCcodebase/master/Utilities/Conte69_atlas-v2.LR.32k_fs_LR.wb/Conte69.L.inflated.32k_fs_LR.surf.gii
-
-# get the tutorial
-cd ~/brainhackschool_nibabel
-wget -c https://raw.githubusercontent.com/school-brainhack/school-brainhack.github.io/main/content/en/modules/nibabel/requirements.txt
-wget -c https://raw.githubusercontent.com/school-brainhack/school-brainhack.github.io/main/content/en/modules/nibabel/NiBabel.py
-
-# make a temporary directory to dump things
-mkdir ~/brainhackschool_nibabel/data/tmp
-```
-
-### Set up environment
-
-You will need to create the environment with the `requirements.txt` file.
-
-```
-pip install virtualenv
-
-cd ~/brainhackschool_nibabel/
-virtualenv env
-source env/bin/activate
-pip install -r requirements.txt # install the environment
-```
-From here you can launch `jupyter notebook` and navigate to the presentaion `Nibabel.py`, or launch the notebook directly using:
-```
-jupyter notebook Nibabel.py
-```
-
-## Material
-
-Follow the `Nibabel.py` notebook along with the presentation.
-
-The time stamps of this video are as follows.
-You need to watch all the basic content to complete the excercise.
-You can skip the optional sections.
-
- - 0:00 Introduction
- - 0:44 Brief introduction to NiBabel / some Neurohackademy related business
- - 2:01 Start of the talk
- - 3:01 Installation of NiBabel
- - 6:01 Learning objective of the original presentation
- - 7:32 Basic file input/output (I/O) (basic)
- - 12:03 Data array of spatial image (basic)
- - 20:11 Affine (basic)
- - 32:13 Header (basic)
- - 36:55 Create an image (basic)
- - 39:57 Break
- - 47:27 Array and array proxy (advanced)
- - 1:06:52 Slicing image (basic)
- - 1:13:13 Question time
- - 1:14:17 Surface image (optional)
- - 1:25:36 CIFTI-2 image (optional)
- - 1:30:22 Tractogram (optional)
- - 1:34:38 Summary and ending
-
-The video presentation is available below:
-
-
-
-## Exercise
-
- * Watch the video presentation by Chris Markiewicz and go over the `Nibabel.py` notebook.
-
- * In a separate notebook, complete the following exercises:
-
- * Use the nilearn library function `fetch_atlas_difumo` to get the 64 parcellation image. As we learned in the video, the data in this image is just a number array and you can maniputate it with numpy. Please extract the 16th region, binarize it, and save it as a new nifti image.
-
- * Use the slicer object to view the new nifti file we created in the three different views.
-
- * Bonus: in 200 words, describe the conceptual differences between array and array proxy images.
-
- * Follow up with your local TA(s) to validate you completed the exercise correctly.
- * 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉
-
-## More resources
-
-Nilearn has some high level wrappers for nibabel I/O function, we encourage you to have a look:
-https://nilearn.github.io/stable/manipulating_images/input_output.html#understanding-neuroimaging-data
-
-Here are all the links mentioned in the presentations:
-
-- https://nipy.org/nibabel/coordinate_systems.html
-- https://nipy.org/nibabel/nibabel_images.html#the-image-data-array
-- https://nipy.org/nibabel/images_and_memory.html
-- https://neurohackademy.org/course/introduction-to-the-geometry-and-structure-of-the-human-brain/
diff --git a/content/en/modules/nibabel/reggie.png b/content/en/modules/nibabel/reggie.png
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-nibabel
-jupytext
-notebook
-rise
-nilearn
-matplotlib
-transforms3d
-pytest
diff --git a/content/en/modules/open_data/index.md b/content/en/modules/open_data/index.md
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Open data"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [open data, open science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Learning the basics of open data and open resource discovery."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "open_data.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 1.5h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-This module was presented by Sebastian Urchs during Brainhack School 2020.
-
-The presentation slides are available [here](https://docs.google.com/presentation/d/19pp-SwWI-Fi72BCsht_7SAj-uFp0-ijBqRc31sy2-Ag).
-
-Jupyter Notebook to follow is [here](https://mybinder.org/v2/gh/school-brainhack/bhs_nilearn_example/HEAD?labpath=nilearn_demo.ipynb). If you are not able to run the notebook through mybinder, you can download it from
-this [link](https://github.com/surchs/bhs_nilearn_example/blob/master/nilearn_demo.ipynb), and run it locally inside a [virtual environment](https://virtualenv.pypa.io/en/latest/).
-
-The video presentation is available below:
-
-
-
-## Exercise
-
- * Watch the video presentation by Sebastian Urchs and go over the slides.
- * Find an open neuroimaging dataset via one of the resource links described in the presentation that has at least 40 participants and including phenotypic information that could be useful to your project (age, gender, cognitive scores, diagnoses, etc.)
- * Download at least one of these participants and their accompanying phenotypic information.
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉
-
-## More resources
-### Open Access Data
-- [FCP-INDI](http://fcon_1000.projects.nitrc.org/)
-- [OpenNeuro](https://openneuro.org/)
-- [Canadian Open Neuroscience Portal](https://portal.conp.ca/)
-- [openMorph](https://github.com/cMadan/openMorph)
-- [Nilearn Data Set](https://nilearn.github.io/stable/modules/datasets.html)
-
-### Handling the Data
-- [DataLad](http://handbook.datalad.org/en/latest/#)
diff --git a/content/en/modules/packaging/index.md b/content/en/modules/packaging/index.md
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Introduction to python packaging with `pipy`"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [python, packaging]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this module, you will learn how to package python modules using `pypi`. This will let you install some of your own code with `pip`, dealing cleanly with dependencies, as well as share publicly a package."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "python_packaging.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 4h.
-
-The prerequisites to take this module are:
- * [Installation module](/modules/installation).
- * [Introduction to python for data analysis](/modules/python_data_analysis/) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-This module was presented by Valérie Hayot-Sasson during BrainHack School 2020. The slides are available [here](https://docs.google.com/presentation/d/18aMOZBbKEKTHtiZZA5bRPOBwxOVoPLRT1c5FaQZYLQI/edit?usp=sharing).
-
-The video of the presentation is available below (57:51):
-
-
-## Exercice
-
- * To run this exercise, you will need a small python module. You can create a toy python module by following this [tutorial](https://www.tutorialspoint.com/python/python_modules.htm). It would be better to start creating one (or a couple of) module(s) for your project, so you work directly with a meaningful code base.
- * Add your module to a new git repository under your personal account. This is not necessary, but putting code on git control should be second nature at this stage :) Note that the module will read in a folder inside the repository. The top level directory is for configuration files for the project, not the code itself.
- * Create and populate a `README.md` file
- * Create and populate a `license.txt` file
- * Fill a `requirements.txt` file. Include everything needed for development, including e.g. `jupyter-notebook` and `pytest`.
- * Fill a `pyproject.toml` file. Start from a [template](https://martin-thoma.com/pyproject-toml/). These two last steps are much more meaningful if you are working with a code base for your project, rather than a toy module with no dependencies.
- * Configure the meta-data of your project with `setup.cfg`. Check the [documentation of pypi](https://packaging.python.org/tutorials/packaging-projects/#configuring-metadata) for more info.
- * Add on `__init.py__` in your module folder. See this [example](https://careerkarma.com/blog/what-is-init-py/)
- * Try installing your package locally by running `pip install -e .` in your repository.
- * **Optional**: You can try to create the distribution files and publish this package on a test server of `pypi`. This step is described in this [tutorial](https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archives). The pypi test server is made to test how publishing a package works - so don't worry if this is just a test or if you make mistakes, the server exists for that purpose. If you upload the package on pipy itself, anybody will be able to install your software by typing `pip install ` but beware! `pipy` is a community resource, and you should only publish serious, well tested projects on there.
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-## More resources
-
- * The official `pipy` [packaging documentation](https://packaging.python.org/tutorials/packaging-projects).
- * The [guidelines for modern packaging](https://gist.github.com/effigies/9bbb424535d6a1d838d6325191c0a736) by [Chris Markiewicz](https://github.com/effigies).
diff --git a/content/en/modules/packaging/mri_schematic.svg b/content/en/modules/packaging/mri_schematic.svg
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-
-
-
-
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-
-
-
-
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Project management"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [open data, fair, license, standards, github]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Learning about the importance and benefits of project management adhering to community standards to achieve shareable science."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "fig_redpen_blackpen.jpg"
----
-
-
-## Information
-
-The estimated time to complete this training module is 4h.
-
-The prerequisites to take this module are:
- * the [open data](/modules/open_data) module.
- * the [terminal](/modules/introduction_to_terminal) module.
- * the [Git and GitHub](/modules/git_github) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school.brainhack [at] gmail [dot] com
-
-## Resources
-
-This module was presented by [Elizabeth Dupre](https://elizabeth-dupre.com/#/) during the QLSC 612 course in 2020, with content adapted from presentations by [Chris Gorgolewski](https://twitter.com/chrisgorgo).
-
-The slides are available [here](https://github.com/neurodatascience/course-materials-2020/blob/master/lectures/13-may/01-standards-for-project-management/NDS2020_ShareableScience.pdf).
-
-The video of her presentation is available below:
-
-
-
-## Exercise
-
-### Your understanding of project management
-
-Prepare your answers in an online document (e.g. using hackmd.io).
- * Let's pick a license...
- * you want to share code and make sure it can be used as widely as possible, but you still get credited. Which license do you pick and why?
- * you want to share data, get credited, allow for modifications but not commercial usage. Which license do you pick and why?
- * Pick a public dataset and explain if it is FAIR. You can pick from the list below if you need inspiration.
- * [ADNI](http://adni.loni.usc.edu/)
- * [UK biobank](https://www.ukbiobank.ac.uk/)
- * [CIMAQ](http://www.cima-q.ca/en/home/)
- * Find an example of a neuroimaging paper described on the open science framework (or somewhere else), with 1. Code available? 2. Documentation for data analysis available? 3. Data available? For each aspect, summarize briefly the standards followed (if any).
-
-
-### Create a project template for your Brainhack School project
-
-We have learned about the community standard of data sharing in the previous exercise and how that improves communication and collaboration with researchers.
-The same principle can be applied to your analysis code. Using a consistent project template will help when your project grow in the long run.
-For this section, please prepare your answer as a GitHub repository.
-We will follow the first lesson [Set up your project](https://goodresearch.dev/setup.html) in the [Good Research Code Handbook](https://goodresearch.dev/index.html)
-
-:memo: Please skip the [Install a project package](https://goodresearch.dev/setup.html#install-a-project-package) section. We recommend to revisit this part after completing the [python packaging](/modules/packaging) module.
-
-The end result will be a logically organized project skeleton that's synced to version control.
-The instructor should be able to verify your progress through 1. a public GitHub repository and 2. clone and test your project on their own machine.
-
- * Pick a short and descriptive name for your project....
- * Create a folder in your home directory.
- * You should have created a GitHub account when you completed the installation module. Now we want to create a GitHub repository of the same name, for convenience.
- * Now you should see some suggestions on the GitHub page to help you initialise the project repository. Which one should you use?
-
- * The next step is to create a virtual environment and an associated file describing the environment, so you can reinstall everything if you need to start fresh.
- * Create a conda environment of the same name of your project.
- * Activate this environment and install the same packages you did in the [installation](/modules/installation) module.
- * Export this environment and save it as a file named `environment.yml` at the root of your project repository.
- * Now commit and push this environment file to your GitHub repository.
-
-* Next, let's create a `.gitignore` file with the help of GitHub magic.
- * On your GitHub repository page, click on `Add file` - `create new file`.
- * Now enter `.gitignore` into the file name. You will see a drop down menu appear on the right hand side.
- * Pick the python template. Commit this file.
- * Now use the terminal and pull this change to your local directory.
-
- :bulb: With the same principle, you can also create a `LICENSE` file through GitHub.
-
- * Next we will populate the directory with a project skeleton and some basic descriptions.
- * Create the directories described in the lesson with command `mkdir`.
- * Try to add these changes to git. You should notice empty directories are not added, this is because git will only push files.
- * To work around this issue, let's create a file named `.gitkeep` in each of the empty directory using command `touch`.
- * Now try to add and commit these files again.
-
- * (Optional) Creating a project package
- Creating a project package can be difficult for the first time, but the pay off is substantial: your project structure will be clean, you won’t need to change Python's path, and your project will be pip installable. This is the standard approach in the data science world.
- This is a minimal demo for a installable package. We encourage you to revisit [Install a project package](https://goodresearch.dev/setup.html#install-a-project-package) after completing the [python packaging](/modules/packaging) module.
- * Create a file under `src/` named `helloworld.py`, with one line `print('hello world')`.
- * Create a file under `src/` named `__init__.py`.
- * Create a `setup.py` file in the root directory with the following lines:
- ```
- from setuptools import find_packages, setup
-
- setup(
- name='src',
- packages=find_packages(),
- )
-
- ```
- * Finally, activate the project virtual environment you created earlier. Install your package:
- ```
- pip install -e .
- ```
- * To verify the completion of this part, you should be able to run the following code from a python kernel as long as the virtual environment is activated:
- ```
- import src.helloworld
- >>> hello world
- ```
-
-* Follow up with your local TA(s) to validate you completed the exercise correctly.
-* :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-## More resources
-
-If you are curious to learn more about BIDS, check the [BIDS specifications](https://bids-specification.readthedocs.io/en/stable/). There will also be a training module on BIDS in week 2.
-We encourage you to revisit [Install a project package](https://goodresearch.dev/setup.html#install-a-project-package) after completing the [python packaging](/modules/packaging) module.
-
-Some documentation on standards for project organization:
- * the entire "the Turing way" documentation is relevant, but the section on [project design](https://the-turing-way.netlify.app/project-design/project-design.html) is the most important for this training module.
- * the [YODA principles](https://handbook.datalad.org/en/latest/basics/101-127-yoda.html).
- * the [Good Research Code Handbook](https://goodresearch.dev/) has lots of resources to help you learn industry standard research code management.
-
-Check out [this repository](https://github.com/SIMEXP/fmriprep-denoise-benchmark) which follows the principle of the project set up in the Good Research Code Handbook.
-
-Finally, a [blog post](http://ivory.idyll.org/blog/2015-on-licensing-in-bioinformatics.html) on choosing a license for open science projects by Titus Brown.
diff --git "a/content/en/modules/python_data_analysis/correction_\303\240jour.ipynb" "b/content/en/modules/python_data_analysis/correction_\303\240jour.ipynb"
deleted file mode 100644
index 77e50387..00000000
--- "a/content/en/modules/python_data_analysis/correction_\303\240jour.ipynb"
+++ /dev/null
@@ -1,476 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0ac80fdb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Andréanne Proulx, 2021-07-26\n",
- "# PSY6983 Python for data analysis exercice"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 164,
- "id": "27cfaae6",
- "metadata": {},
- "outputs": [],
- "source": [
- "# 1. imports\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "from sklearn.datasets import load_iris\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 165,
- "id": "3c4870e3",
- "metadata": {},
- "outputs": [],
- "source": [
- "# 2. load data\n",
- "iris = load_iris()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 166,
- "id": "f193e175",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
- ]
- },
- "execution_count": 166,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# 3. Explore the dataset using .keys()\n",
- "iris.keys()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 167,
- "id": "c8c38d2e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The shape of the data is : (150, 4)\n",
- "The type of the data is : float64\n"
- ]
- }
- ],
- "source": [
- "# 4. Print the shape and type of data\n",
- "shape = iris[\"data\"].shape\n",
- "type = iris[\"data\"].dtype\n",
- "\n",
- "print(\"The shape of the data is : {}\".format(shape))\n",
- "print(\"The type of the data is : {}\".format(type))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 168,
- "id": "1d64ca02",
- "metadata": {},
- "outputs": [],
- "source": [
- "# 5. Store 'data' and 'features_names' in variables\n",
- "data = iris[\"data\"] # same as iris.feature_names\n",
- "feature_names = iris[\"feature_names\"] # same as iris.feature_names"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 169,
- "id": "2d9550d9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
- "0 5.1 3.5 1.4 0.2\n",
- "1 4.9 3.0 1.4 0.2\n",
- "2 4.7 3.2 1.3 0.2\n",
- "3 4.6 3.1 1.5 0.2\n",
- "4 5.0 3.6 1.4 0.2\n",
- ".. ... ... ... ...\n",
- "145 6.7 3.0 5.2 2.3\n",
- "146 6.3 2.5 5.0 1.9\n",
- "147 6.5 3.0 5.2 2.0\n",
- "148 6.2 3.4 5.4 2.3\n",
- "149 5.9 3.0 5.1 1.8\n",
- "\n",
- "[150 rows x 4 columns]\n"
- ]
- }
- ],
- "source": [
- "# 6. Create a dataframe with the data and use feature_names for column names\n",
- "df = pd.DataFrame(data,columns= feature_names)\n",
- "print(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 170,
- "id": "ca246903",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 11-13. boxplot distribution\n",
- "plt.boxplot(df)\n",
- "\n",
- "# add title\n",
- "plt.title(\"TITLE\")\n",
- "plt.xlabel(\"X\")\n",
- "plt.ylabel(\"Y\")\n",
- "plt.savefig(\"iris_boxplot.png\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84fcc886",
- "metadata": {},
- "outputs": [],
- "source": [
- "# All done. Check "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/content/en/modules/python_data_analysis/index.md b/content/en/modules/python_data_analysis/index.md
deleted file mode 100644
index 0678f0e5..00000000
--- a/content/en/modules/python_data_analysis/index.md
+++ /dev/null
@@ -1,116 +0,0 @@
----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Introduction to Python for data analysis"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Python, data analysis]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This tutorial aims at introducing students to the programming language Python for data analysis. By the end of this module, students will be familiar with Python basic syntax and understand why Python serves well the purpose of data analysis."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "python.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 4h.
-
-The prerequisites to take this module are:
-
-* You should already have everything installed for this module!
-* We will be using Jupyter Notebook which is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access
-to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Before starting:
-
-1. Open the terminal
-2. Type jupyter notebook
-3. If you're not automatically directed to a webpage copy the URL printed in the terminal and paste it in your browser
-4. Once on the webpage, click "New" in the top-right corner and select "Python 3"
-5. You have Jupyter notebook
-
-The little box you see once in the notebook is called a "cell." You can enter (multi-line) code by typing in the cell, and then run the code by pressing "Shift+Enter."
-To create a new cell, press the "+" button on lefthand side of the toolbar at the top of the screen!
-
-You are ready for this tutorial and you are strongly encouraged to type along the presentation!
-
-## Resources
-This module was presented by [Ross Markello](https://rossmarkello.com/) during the QLSC 612 course in 2020.
-
-All the tutorial notes related to the video below are available [here](https://github.com/neurodatascience/course-materials-2020/blob/master/lectures/12-may/01-python-for-data-analysis/python-for-data-analysis.ipynb).
-
-
-
-
-## Exercises
-For this part, we will use the famous scikit-learn dataset iris which consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) with information about petal and sepal length and width stored in a 150x4 numpy.ndarray.
-
-1. Before starting to write some code, you want to set-up the environment so that it load the required modules. In a Jupyter Notebook import the following libraries:
-
- # imports
- import pandas as pd
- import numpy as np
- import matplotlib.pyplot as plt
- from sklearn.datasets import load_iris
- %matplotlib inline
-
-
-2. Load the iris dataset
-
- iris = load_iris()
-
-
-3. Explore the dataset using .keys()
-4. Print the shape and type of 'data'
-5. Store 'data' and 'features_names' in distinct variables
-6. Create a pandas dataframe with 'data' and use 'feature_names' for column names
-7. Get the summary statistics for this dataframe using .describe()
-8. Subset the dataframe to keep only the first 50 rows
-9. Try to answer this question using the entire dataframe : Are there any extreme sepal length values?
- * Reminder : extreme value are > 3.9 standard deviation. (value - mean) / std. For this one, you might need to use a for loop.
-10. What about other features of the flowers? Try automating the previous operation by writing a function name find_extreme_values()
-11. Read about the boxplot function in matplotlib to get familiar with python documentation. What does it tell us?
-https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html
-12. Use this function to plot the boxplot distribution for features. Try adding a title and name for axis.
-12. Save dataframe in csv format and the plot as png.
-
-Note: Internet is your best friend. Remember that whenever you are stuck, resources and blogs can help you figure it out (Stack Overflow).
-
-If you are done, you can play around with different functions (ex. other plotting functions). Try to answer interesting questions you might have using the data.
-
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-## More resources
-
-There are hundreds of excellent resources online for learning Python and/or data science. A few good ones:
-
-- [CodeAcademy](https://www.codecademy.com) offers interactive programming courses for many languages and tools, including Python and git
-- [A Whirlwind Tour of Python](https://jakevdp.github.io/WhirlwindTourOfPython/) is an excellent intro to Python by Jake VanderPlas; Jupyter notebooks are available [here](https://github.com/jakevdp/WhirlwindTourOfPython)
-- Another excellent and free online book is Allen Downey's "Think Python"
-- Object Oriented Programming in Python 3
-(https://realpython.com/python3-object-oriented-programming/)
-- Jake Vanderplas's Python Data Science Handbook is also available online as a set of notebooks
-- [Kaggle](https://www.kaggle.com) maintains a nice list of data science and Python tutorials
-
-- Neuromatch Academy also has great tutorials available for Python in a computational neuroscience context.
- - Tutorial 1: https://compneuro.neuromatch.io/tutorials/W0D1_PythonWorkshop1/student/W0D1_Tutorial1.html
- - Tutorial 2: https://compneuro.neuromatch.io/tutorials/W0D2_PythonWorkshop2/student/W0D2_Tutorial1.html
-- Introduction to Python in French (https://www.youtube.com/watch?v=cjFxd-0idHo)
-
-If you are curious, eiger to learn more, you can also try out this tutorial which inspired much of the content you saw today: [Introduction to Python](https://neurohackademy.org/course/introduction-to-python-2/)
diff --git a/content/en/modules/python_data_analysis/python.png b/content/en/modules/python_data_analysis/python.png
deleted file mode 100644
index 678b2ae9..00000000
Binary files a/content/en/modules/python_data_analysis/python.png and /dev/null differ
diff --git a/content/en/modules/python_scripts/correction/cypher_script.py b/content/en/modules/python_scripts/correction/cypher_script.py
deleted file mode 100644
index 1d51c58c..00000000
--- a/content/en/modules/python_scripts/correction/cypher_script.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import argparse
-from useful_functions import process_message
-
-
-def main(input_path, key, mode, output_path):
- with open(input_path, "r") as message_file:
- message = message_file.read()
- encrypt = mode == "encryption"
- processed_message = process_message(message, key, encrypt)
- with open(output_path, "w") as out_file:
- out_file.write(processed_message)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "-i",
- dest="input_path",
- type=str,
- required=True,
- help="Path to the input file.",
- )
- parser.add_argument(
- "-o",
- dest="output_path",
- type=str,
- required=True,
- help="Path to the output file.",
- )
- parser.add_argument("-k", dest="key", type=str, required=True, help="Key.")
- parser.add_argument(
- "-m",
- dest="mode",
- type=str,
- required=True,
- choices=["encryption", "decryption"],
- help="Wether to encrypt or decrypt the message.",
- )
- args = parser.parse_args()
- main(args.input_path, args.key, args.mode, args.output_path)
diff --git a/content/en/modules/python_scripts/correction/useful_functions.py b/content/en/modules/python_scripts/correction/useful_functions.py
deleted file mode 100644
index 7b7b6fa1..00000000
--- a/content/en/modules/python_scripts/correction/useful_functions.py
+++ /dev/null
@@ -1,47 +0,0 @@
-def encrypt_letter(letter, key):
- letter_index = ord(letter)
- key_index = ord(key)
- new_index = (letter_index + key_index) % 1114112
- return chr(new_index)
-
-
-def decrypt_letter(letter, key):
- letter_index = ord(letter)
- key_index = ord(key)
- new_index = (letter_index - key_index) % 1114112
- return chr(new_index)
-
-
-def process_message(message, key, encrypt):
- processed_message = ""
- process_letter = encrypt_letter if encrypt else decrypt_letter
- for i, letter in enumerate(message):
- key_letter = key[i % len(key)]
- processed_key = process_letter(letter, key_letter)
- processed_message += processed_key
-
- return processed_message
-
-
-if __name__ == "__main__":
- # Test 1
- test_letter = "l"
- test_key = "h"
- decrypted_letter = decrypt_letter(encrypt_letter(test_letter, test_key), test_key)
-
- if test_letter == decrypted_letter:
- print("first test passed")
- else:
- print("first test failed")
-
- # Test 2
- message = "word"
- key = "key"
-
- encrypted_msg = process_message(message, key, encrypt=True)
- decrypted_msg = process_message(encrypted_msg, key, encrypt=False)
-
- if decrypted_msg == message:
- print("second test passed")
- else:
- print("second test failed")
diff --git a/content/en/modules/python_scripts/index.md b/content/en/modules/python_scripts/index.md
deleted file mode 100644
index cf1f7699..00000000
--- a/content/en/modules/python_scripts/index.md
+++ /dev/null
@@ -1,134 +0,0 @@
----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Writing scripts in python"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [python, scripts]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Learning the basics of python scripts' structure. Turning jupyter notebooks into scripts that can be run from anywhere. Introduction to the argument parser."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "python-file-icon.jpg"
----
-
-
-## Information
-
-The estimated time to complete this training module is 2h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access
-to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-Contact your local TA if you have questions on this module, or if you want to check that you completed successfully all the exercises.
-
-## Resources
-This module is based on [Greg Kiar](https://github.com/gkiar)'s [QLSC 612 course](https://youtu.be/zpOQENxs1G4) in 2020, with [slides](https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUhkKGvjtV-dkAIsUXP-AL4ffI/edit#slide=id.g362da58057_0_1) from Joel Grus' talk at JupyterCon 2018.
-
-The video is available below:
-
-
-
-## Exercise
-
-* Watch the video and follow along the hands on part to do the exercise. If you prefer to do the execercise on your own, the instructions are also written below.
-
-
-
-
Click to show the exercise instructions ⬇
-
-In this exercise we will program a key-based encryption and decryption system. We will implement a version of the [Vigenere cipher](https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher), but instead of just using the 26 letters of the alphabet, we will use all the unicode characters.
-
-The Vigenere cipher consists in shifting the letters of the message to encrypt by the index of the corresponding letter in the key. For example the encryption of the letter B with the key D will result in the letter of new_index = index(B) + index(D) = 2 + 4 = 6, so it will be the 6th letter which is F.
-
-:warning: Note that here by index I mean the index of the letter in the alphabet and not the index of the letter in the string.
-
-
-You pair up the letters of the message with the ones of the key one by one, and repeating the key if it is shorter than the message. For example if the message is `message` and the key is `key`, the pairs will be :
-```
-(m,k),
-(e,e),
-(s,y),
-(s,k),
-(a,e),
-(g,y),
-(e,k)
-```
-
-For the indices of the letter, we will not use the the number of the letter in the alphabet, but the unicode index of the letter, which is easily obtained with the native python function `ord`. The reverse operation of getting a letter from its unicode index is obtained with native python function `chr`. There are 1114112 unicode characters handled by python, so we'll have to make sure we have indices in the range 0 to 1114111. To ensure that, we can use values modulo 1114112, i.e. `encrypted_index = (ord(message_letter) + ord(key_letter)) % 1114112`.
-
-### Step 1: Create relevant functions in `useful_functions.py`
-
- In that file, implement the following functions :
- * `encrypt_letter(letter, key)` : return the encrypted letter with the key, e.g. `encrypt_letter("l", "h")` should return `'Ô'`.
- * `decrypt_letter(letter, key)` : return the decrypted letter with the key, e.g. `decrypt_letter("Ô", "h")` should return `'l'`.
- * `process_message(message, key, encrypt)`: return the encrypted message using the letters in `key` if encrypt is `True`, and the decrypted message if encrypt is `False`. For example :
-```
-process_message('coucou', 'clef', True)
-'ÆÛÚÉÒá'
-
-process_message('ÆÛÚÉÒá', 'clef', False)
-'coucou'
-```
-
-After creating these function, try to call them in your python terminal or in a JupyterNotebook to try things out.
-Are the functions performing as you expected?
-
-To reliably make sure that the `process_message `function works correctly, let's add a test at the end of the `useful_functions.py` file.
-* Define a `message` variable with a word (e.g. `message = "word"`), then a `key` variable with an other word (e.g. `key = "key"`).
-* Use `process_message` to generate the encryption of the `message` variable with the `key` in an `encrypted_msg` variable.
-* Use the `process_message` function again to decrypt the `encrypted_msg` variable (still using the same `key`) in a `decrypted_msg` variable.
-* Verify that `message == decrypted_msg` by printing "Test passed" if it is true and "Test failed" if it is false.
-
-Now we have a proper test of our `process_message` function, and we can run it by executing the `useful_functions.py` script. However we don't want to run the test when we just import the functions from the file, so we will need to use the `if __name__ == "__main__":` statement.
-* Put the test in an `if __name__ == "__main__":` block.
-
-Now we have our functions and a test to validate them, we can conclude the first part of the exercise.
-
-### Step 2: Create a file `cypher_script.py`:
-
-* use the Argparse library introduced in the video so that a user can call the script with four arguments : `-i`, `-o`, `-k` and `-m`. `-i` will contain the path to the input text files containing the message. `-o` will contain the path for the output file where the processed message will be written. `-k` will be a string directly containing the key. `-m` will be the mode: a string that can take the value `"encryption"` or `"decryption"` to tell the script if you want to encrypt or decrypt the input message.
-* The script should import the functions from `useful_functions.py` and use them in its main function to encrypt or decrypt the text in the input file using the text in the key file as the key, and save the results in the output file. So calling `python cypher_script.py -i msg_file.txt -o msg_encrypted.txt -k my_key -m encryption` should create a `msg_encrypted.txt` file.
-* Don't forget to write the code under `if __name__ == "__main__"`. Even though in this file it won’t make a difference, it is never too early to get used to good practices.
-
-
-
-
-
-
-
-### Last step: verify your implementation
-
-Finally, decrypt the file obtained with :
-```
-wget https://raw.githubusercontent.com/school-brainhack/school-brainhack.github.io/main/content/en/modules/python_scripts/message_encrypted.txt
-```
-with the following key :
-```
-my_super_secret_key
-```
-Can you see something cool in the decrypted file ?
-
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
-
-## More resources
-
-If you are curious to learn more advanced capabilities for the Argparse library, you can check this [Argparse tutorial](https://docs.python.org/3/howto/argparse.html).
-
-To learn more about python in general, you can check the [tutorials of the official python documentation](https://docs.python.org/3/tutorial/) and choose the topic you want to learn. I also recommend the [porgramiz tutorials](https://www.programiz.com/python-programming) which have nice videos. Finally for even nicer and fancier videos there is the excellent [python programming playlist](https://www.youtube.com/playlist?list=PLi01XoE8jYohWFPpC17Z-wWhPOSuh8Er-) from the youtube channel Socratica.
diff --git a/content/en/modules/python_scripts/message.txt b/content/en/modules/python_scripts/message.txt
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--- a/content/en/modules/python_scripts/message.txt
+++ /dev/null
@@ -1,21 +0,0 @@
- | : . |
- | ' : ' |
- | . | ' | |
- .--._ _...:.._ _.--. , ' |
- ( , ` ` , ) . |
- '-/ \-' | |
- | o /\ o | :|
- \ _\/_ / : ' |
- /'._ ^^ _.;___ |
- /` `""""""` `\= |
- /` /= .|
-; '--,-----'= |
-| `\ | . |
-\ \___ : |
-/'. `\= |
-\_/`--......_ /= |
- |`-. /= : |
- | : `-.__ /` . |
- |jgs . ` | '|
- | . : ` . | |
-Art by Joan G. Stark
diff --git a/content/en/modules/python_scripts/message_encrypted.txt b/content/en/modules/python_scripts/message_encrypted.txt
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--- a/content/en/modules/python_scripts/message_encrypted.txt
+++ /dev/null
@@ -1 +0,0 @@
- á ám á ï õì î Ûu § £Ï Ñ¡ Óʦ§Û} Ù ¿ §õi Ñï á~ õä Ï Ôç ªá| Á ÌÕÒ ¢ õwÒ ÃÒ Ø´¾ÒÔ ß| ÅÓÅ ÅÕªñz ¡¿ ¯ ço´ ¡¦¦² ïoß Å¿ ð Û}Ñ ÌÄѾ ði§ ¿Ï² îiÏÄÒ¡§§¾ ¨ é ð¿ ¡îo î ÃÓ¾Ù§ìo éãÆæ Ó ß á î ³Ù îám³×èÍÞ·èÀá·²çÆÕÝo
\ No newline at end of file
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--- a/content/en/modules/python_visualization/index.md
+++ /dev/null
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Introduction to data visualization in Python"
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/module_python_visualization
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [visualization, python, matplotlib, seaborn, plotly]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this module, we will introduce the basics of plotting in python with some of most commonly used packages such as matplotlib and seaborn."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "plotting.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 3h.
-
-The prerequisites to take this module are:
- * [Installations](/modules/installation)
- * [Introduction to python for data analysis](/modules/python_data_analysis) module.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access
-to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-
-This module is built around one Jupyter notebook and one set of exercises, all available in the [module repository](https://github.com/school-brainhack/module_python_visualization).
-
-## Original lecture
-This module was first presented by Jacob Vogel during the QLSC 612 course in 2020, and the associated notebook is available [here](https://github.com/neurodatascience/course-materials-2020/blob/master/lectures/14-may/01-data-visualization/python_visualization_for_data.ipynb). (Note: if you did the BIDS module, the dataset to download is the same - ds000228! A few functions now throw warnings, you can ignore these, or fix them if you like.)
-
-The video of the presentation is available below (1h09):
-
-
-## Exercise
-
-In the exercise notebook (`exercise_visu_en.ipynb`), you will have to create static and interactive visualization to explore the phenotypic and fMRI data from the **ADHD200** dataset available through nilearn.
-
-The exercise is structured as follows:
-1. **Part 1 : Exploring phenotypic data**: you will visualize some clinical and demographic variables of the ADHD200 dataset.
-2. **Part 2 : Exploring fMRI data**: you will explore the functional fMRI data from the ADHD200 dataset through visualizations.
-
-**Important**: follow the parameter specifications given in each question exactly — the exercise uses automated grading that compares results to reference values.
-
-Follow up with your local TA(s) to validate you completed the exercises correctly.
-
-## More resources
-
-- Other great resources to get started with plotting in python:
- - [dartbrains](https://dartbrains.org/Introduction_to_Plotting/)
- - [neurohackademy](https://github.com/neurohackademy/visualization-in-python/blob/master/visualization-in-python.ipynb)
- - [PythonDataScienceHandbook](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb)
- - [matplolib tutorial - rougier](https://github.com/rougier/matplotlib-tutorial)
-
-- Interactive plotting:
- - [Interactive plots and the spectrum of data visualization](https://www.youtube.com/watch?v=FwM_6oZo_2g&t=1s)
- - [Plotly](https://plotly.com/python/)
- - [Bokeh](https://docs.bokeh.org/en/latest/index.html)
- - [Altair](https://altair-viz.github.io/)
-
-- Gallery:
- - [seaborn gallery](https://seaborn.pydata.org/examples/index.html)
- - [Python Graph Gallery](https://python-graph-gallery.com/)
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Introduction to spinal cord MRI analysis"
-
-# Your project GitHub repository URL
-github_repo:
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [spinal cord, spinal cord toolbox]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This tutorial introduces basics of spinal cord MRI data analysis using the Spinal Cord Toolbox.
-The tutorial will cover the installation of the Spinal Cord Toolbox, the processing of spinal cord MRI data, and the visualization of the results."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "spinal_cord_mri.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 3h.
-
-The prerequisites to take this module are:
- * the [installation](/modules/installation) module.
- - Windows: Ubuntu application (Windows Linux Subsystem)
- - Mac/Linux: Terminal
- * the [introduction to the terminal](/modules/introduction_to_terminal) module.
-
-Contact Jan Valosek if you have questions on this module, or if you want to check that you completed successfully all the exercises.
-
-## Resources
-
-The main resource for this module is the 2023 Spinal Cord Toolbox Course given by Prof. Julien Cohen-Adad and his team at [NeuroPoly lab](https://neuro.polymtl.ca).
-
-The workshop is available on YouTube:
-
-
-
-For the purpose of this module, we will focus on the following chapters of the workshop:
-
- * 18:39 `2. Installation`
- * 31:37 `3. Segmentation`
- * 47:35 `4. Vertebral labeling`
- * 1:07:30 `5. Shape-based analysis`
- * 1:31:03 `6. Registration to template` (only up to 1:48:00)
-
-The slides are available [here](https://docs.google.com/presentation/d/1t40Fd0fS0SwWR5FU_GWKEvHkB9d96LVddLQW6L3QAx0/edit?usp=sharing).
-
-## Exercise
-
- * Watch the selected chapters of the Spinal Cord Toolbox Course.
- * Install the Spinal Cord Toolbox on your local machine.
- * Download the sample data as described in the `2. Installation` chapter.
- * Do the segmentation of the spinal cord using two methods (e.g., `sct_deepseg_sc`, `sct_propseg`, `sct_deepseg`) and compare the results. TIP: to visualize the results, you can use viewer of your choice (e.g., `fsleyes`, `itk-snap`) or python (`nibabel` and `matplotlib`).
- * Perform the vertebral labeling.
- * Compute cross-sectional area (CSA) of the spinal cord at the C2-C3 levels.
- * Do the registration of the T2w image to the template.
- * Follow up with your local TA to validate you completed the exercises correctly.
- * 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉
-
-## More resources
-
-If you want to learn more about spinal cord MRI data analysis and Spinal Cord Toolbox, please check:
- * the [Spinal Cord Toolbox documentation](https://spinalcordtoolbox.com)
- * the [Spinal Cord Toolbox tutorials](https://spinalcordtoolbox.com/user_section/tutorials.html#)
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----
-type: "modules" # DON'T TOUCH THIS ! :)
-
-# Title of your project (we like creative title)
-title: "Software testing and continuous integration"
-
-# Your project GitHub repository URL
-github_repo: "https://github.com/gkiar/intro2testing"
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [testing, continuous integration, python]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Introduction to testing practices for software development and in particular continuous integration, with a guided hands-on example."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "testing.png"
----
-
-
-## Information
-
-The estimated time to complete this training module is 2h.
-
-The prerequisites to take this module are:
- * the [Using git and github](/modules/git_github) module.
- * the [A brief introduction to the bash shell](/modules/introduction_to_terminal) module.
-
-It is highly recommended to also have done the [python script](/modules/python_scripts) module, but that's not strictly necessary.
-
-If you have any questions regarding the module content please ask them in the relevant module channel on the school Discord server. If you do not have access to the server and would like to join, please send us an email at school [dot] brainhack [at] gmail [dot] com.
-
-## Resources
-The [slides](https://drive.google.com/file/d/1M3nr4D0-cPCjHjL23vk-BXth2TqHshtj/view?usp=sharing) are adapted from the original QLSC 612 course in 2020 by [Greg Kiar](https://twitter.com/g_kiar)
-
-The video of the presentation is available below:
-
-
-
-## Exercise
-
- * Fork [this repo](https://github.com/school-brainhack/testing_CI_module) for the hands on part.
- * Watch the video, and follow along the hands on material. You will implement the unit tests and the github action to execute the unit test.
- * Follow up with your local TA(s) to validate you completed the exercises correctly.
- * :tada: :tada: :tada: you completed this training module! :tada: :tada: :tada:
-
- **Bonus and addendum** : During the first part of the video I said we would implement unit tests, an integration test and an installation test. I forgot about the integration test during the hands on so I leave it to you as a bonus exercise, which corresponds to writing a test for the main function, since it integrates the other three functions. For the installation test, this one comes for free with the github action, as the action runs in a virtual machine and has to re-install the dependencies each time.
-
-## More resources
-
-To learn more about pytest and all the nice things it offers, refer to the [pytest documentation](https://docs.pytest.org/en/6.2.x/contents.html#toc). You can also check tutorials such as [this one](https://www.guru99.com/pytest-tutorial.html) or watch [this PyConDE keynote](https://www.youtube.com/watch?v=CMuSn9cofbI).
-
-
-
-Illustration by Nikita Golubev.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-14" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Detection of Alzheimer's through Acoustic and Semantic Markers"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Matías Caccia, Jeremías Inchauspe, Paloma Georgopulos]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/MatiasCaccia/BrainHack2024
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://udesa.edu.ar/cnc
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [alzheimer, speech-analysis, acoustic-markers, semantic-markers]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-summary: "Our project aims to identify acoustic and semantic markers from the speech of Alzheimer's patients to detect the disease and estimate MMSE scores using machine learning models. This approach offers a scalable and cost-effective method for early diagnosis."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "AcousticVsSemantic.png"
----
-
-
-## Project definition
-
-### Background
-
-Inspired by the need for scalable and cost-effective diagnostic methods for Alzheimer's disease (AD), our project at BrainHack 2024 focuses on identifying acoustic and semantic markers in the speech of patients. Alzheimer's disease is characterized by a gradual decline in cognitive functions, and early detection is crucial for managing the disease. Speech analysis offers a non-invasive and reliable data source for this purpose.
-
-### Objectives
-
-1. **Extract acoustic and semantic markers from patient speech** to detect the presence of Alzheimer's.
-2. **Estimate MMSE scores** based on voice characteristics and semantic indicators from conversations.
-3. **Corroborate cognitive decline** using the most relevant linguistic features.
-
-### Tools
-
-The project utilizes several tools and technologies:
- * **Speech pre-processing**: Noise reduction, volume normalization.
- * **Feature extraction**: Acoustic features like pitch, formants, loudness, and MFCC; Semantic analysis using Sentence-BERT and KeyBERT.
- * **Machine learning models**: XGBoost, Random Forest, SVM, and Logistic Regression for classification and regression tasks.
-
-### Data
-
-The dataset used is the **Pitt Corpus from DementiaBank**, employed in the ADReSS Challenge at Interspeech 2021. It includes:
-- 156 low-quality interview audios.
-- Transcriptions in CHAT format.
-- Metadata such as age, gender, condition, and MMSE scores.
-- Audios are from interviews of patients describig the "Cookie Theft Picture"
-
-
-
-### Deliverables
-
-At the end of this project, we will have:
- - A detailed report on the methodology and findings.
- - Visualizations of data and results, including bar graphs, scatter plots, and network graphs.
- - Code and documentation for reproducibility, available on GitHub.
-
-## Results
-
-### Progress overview
-
-Our project demonstrated that acoustic features are critical for classifying Alzheimer's, with Logistic Regression achieving 79% accuracy. The regression of MMSE scores was more challenging due to limited data, with the best model being SVM.
-
-### Tools I learned during this project
-
- * **Speech processing techniques** for noise reduction and feature extraction.
- * **Machine learning models** for both classification and regression tasks.
- * **Semantic analysis** using advanced NLP models like Sentence-BERT and KeyBERT.
-
-### Results
-
-#### Classification
-
-
-
-- **Best model**: Logistic Regression with 79% accuracy.
-- **Key features**: Duration and number of pauses, MFCC, and spectral flux.
-
-#### Regression
-
-
-
-- **Best model**: SVM with R² of 0.32, MSE of 25.76, MAE of 3.82, and RMSE of 5.07.
-- **Key features**: Proportion of adverbs and nouns, text similarity score.
-
-## Conclusion and Acknowledgement
-
-This study highlights the potential of speech analysis for early detection and cognitive assessment in Alzheimer's patients. The approach using acoustic and semantic features combined with machine learning models shows promising results. We acknowledge the contributions of our team and the support from BrainHack 2024.
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "ADHD diagnosis prediction using machine learning"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Iangola Andrianarison]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2024/andrianarison_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [machine learning ,fmri, classification, adhd]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project trains machine learning classification models to make predictions of adhd diagnosis from brain fRMI connectivity measures which are obtained from a resting state ADHD dataset . The main goals of this project are to get more practice with machine learning tools and to learn how work with brain data more precisely fMRI data."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_image.jpeg"
----
-
-
-## Project definition
-
-### Background
-
-I am a MSc student at the University of Montreal studying computer science. I completed my BSc in neuroscience at the University of Toronto and I am interested in the intersection between the two fields and hoping to learn more about the application of machine learning in neuroscience.
-
-This project's goal is to train classification machine learning models to make predictions of ADHD diagnosis from fMRI data in order to learn how to work with fMRI brain data and become more familiar with machine learning tools.
-
-### Tools
-
-The project rely on the following technologies:
- * Git and Github for version control
- * Python libraries such as ScikitLearn for machine learning, Nilearn for fMRI connectivity analysis and Pyplot, Ploty Express and Seaborn for data visualization
- * Jupyter Notebook
-
-### Data
-
-The data used for this project is the Nitric ADHD resting-state dataset which is preprocessed and available through Nilearn. The preprocessed data contains data from 30 subjects (13 ADHD, 17 control).
-
-Information about the dataset: https://nilearn.github.io/dev/modules/description/adhd.html
-
-Information on how to access the data : https://nilearn.github.io/dev/modules/description/adhd.html
-
-### Deliverables
-
- - Jupyter notebook containing the code and visualization plots for the exploration of the data as well as the fMRI connectivity analysis
- - Python script for the training, testing and evaluation of the machine models
- - Figures and results summarizing the performances of the models
- - [Github repo](https://github.com/brainhack-school2024/andrianarison_project)
-
-## Results
-
-### Tools I learned during this project
-
- * **Machine learning with scikitlearn**
- * **Github workflow**
- * **Getting fMRI connectivity using nilearn**
- * **Data visualization using pyplot, ploty express and seaborn**
-
-### Results
-
-#### Getting connectivity data using nilearn
-
-Brain connectivity measures were obtained using nilearn. The BASC multiscale atlas with 64 regions of interest (ROIs) was used for masking.
-
-Information about the atlas can found at: https://nilearn.github.io/dev/modules/description/basc_multiscale_2015.html
-
-A plot of the atlas can be seen below:
-
-
-A region x region correlation matrix was obtained for each subject. These matrices were used as features for the machine learning models (targets are adhd diagnosis). The feature matrices for adhd and controls subjects can be seen below:
-
-
-#### Classification models performances
-
-For each model, the accuracy score (# corrected prediction / total # predictions) and F score (2 * (precision * recall / precision + recall)) were calculated. The best models were the decision tree and the random forest with accuracy score of 0.67 and F1 score of 0.75.
-
-
-A confusion matrix for each model was obtained and can be seen below. The matrices shows the counts of true positive, true negative, false positive, and false negative predictions for each model.
-
-
-## Conclusion and acknowledgement
-
-Because of the very small size of the data, the results of this project are not significant in term of making predictions using machine learning. To have more meaningful results a much bigger dataset would be needed so that the models can train with low risk of overfitting and underfitting. However, completing this project was still meaningful in terms of learning how to use to tools and working with neuroscience data for the first time.
-
-Thank you to all the instructors, teaching assistants and organizers for this learning opportunity.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-15" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Analysing Variability in Frontoparietal Activity in Children with and without ADHD"
-
-# Name of the collaborator
-names: [Hu Ding Xuan]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/nauxgnid/hu_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-image: "sub-8150_run-02_rdlpfc_seed_map.png"
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, fmri, connectivity, differences]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This study examines dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) connectivity and dlPFC BOLD time series in ADHD versus typically developing (TD) children during the cued stop-signal task (CSST) using fMRI data from OpenNeuro ds005899. It is hypothesised that stronger dlPFC-PPC connectivity will be found in the ADHD group."
-
-image: "rdlpfc_timeseries_run-01.png"
----
-
-
-## Project definition
-
-### Background
-
-Childhood ADHD is one of the most prevalent neurodevelopmental disorders worldwide. Hallmarks of ADHD include hyperactivity, impulsivity and deficits in sustaining attention and behavioural control. However, its diagnostic heterogeneity makes it difficult to pinpoint its neural basis. Thus, capturing moment-to-moment brain fluctuations through fMRI has the potential to shed light on the neurobiological basis of the variability and inconsistency of ADHD symptoms. For my project, the analysis of the frontoparietal network will be focused on the dorsolateral prefrontal cortex and right posterior parietal cortex. The dlPFC is important for implementing control strategies, while the right PPC is found to be associated with more severe inattention symptoms. Understanding connectivity differences in these regions can reveal neural mechanisms driving ADHD symptoms during inhibitory tasks.
-
-### Tools
-
-The project relied on the following technologies:
- * Jupyter Notebook
- * `datalad` for downloading datasets
- * `fmriprep` for preprocessing
- * `nilearn` for ROI masking, BOLD signal extraction, Pearson correlations, brain connectivity maps
- * `matplotlib` & `seaborn` for plotting and visualiation
- * `scipy` for Welch’s t-tests
-
-### Data
-* Dataset: [OpenNeuro ds005899](https://openneuro.org/datasets/ds005899/versions/1.0.2)
-* Used only sub-7565 and sub-8150 for single-subject comparison analysis due to time constraints
-* CSST, 2 runs per subject (TR=0.49s)
-
-### Deliverables
-* Jupyter notebook
-* GitHub repository with a READme, directories
-* Processed fMRI data
-* Figures: time series plots, heatmaps, brainmaps
-
-## Results
-
-### Progress overview
-* Extracted time-series from 2 ROIs (dlPFC & PPC) using `NiftiSpheresMasker` in `nilearn`
-* Computed 2x2 Pearson correlation matrix for right dlPFC and right PPC
-* T-tests to examine group connectivity differences using `scipy`
-* Generated whole-brain connectivity maps seeded from the right dlPFC
-
-### Tools I learned during this project
-
- * `fmriprep` for preprocessing fMRI data
- * Correlation matrix construction
- * `nilearn` to visualise and analyse data
-
-### Results
-
-#### Deliverable 1: Time Series Plots
-The right dlPFC time series analysis revealed no significant group differences.
-
-#### Deliverable 2: Pearson Correlation Matrix
-Results indicated a stronger correlation in ADHD subject 7565 of 0.69 compared to typically developing subject 8150 of 0.2.
-
-#### Deliverable 3: Seed-based Connectivity Maps
-Results in subject 7565 showed a strong positive correlation of 0.73 with the right PPC and negative correlations down to -0.73 in the left hemisphere. This may suggest enhanced frontoparietal activity and suppressed default mode activity.
-
-## Challenges and Limitations
-* Long fMRI preprocessing time
-* Lack of significant results likely stems from reduced statistical power (n=2)
-* Beginner level proficiency in tools, struggled resolving coding errors
-
-### Future Directions
-As my project now is mostly exploratory, moving forward, analysing the remaining subjects could further deepen insights into the ADHD neural mechanisms.
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Classifying ADHD subtypes and sex using multimodal data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Béatrice P.De Koninck & Pénélope Pelland-Goulet]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/ADHDsubtypes_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [eeg, machine learning, adhd subtypes]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "ADHD subtypes are a controversial aspect of ADHD literature. Most subtypes classifications are based on behavioral and cognitive data but lack biomarkers.
-Using a multimodal dataset comprised of EEG data as well as self-reported symptoms and behavioral data, we tried to predict the DSM subtypes of each of our 96 participants. Since ADHD has been noted to present itself differently across sexes, we also tried to predict sex. At-rest eeg data and behavioral data proved to be poor predictors of the DSM subtypes. However, self-reported symptoms were a rich predictor of ADHD subtype. Additionally, predicting sex using EEG data yielded the highest decoding accuracies."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "eeg_generaltopo.PNG"
----
-
-
-### Background
-
-Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders among children and adolescents ([Volkow, 2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827421/)). It manifests itself through a variety of cognitive and behavioral symptoms, such as (but not limited to) hyperactivity, lack attention, impulsivity, lack of inhibition and diminished working memory ([Wilens, 2010](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724232/)). Long-term follow-up studies revealed that in 40 to 60% of children with ADHD, the disorder persists into adulthood ([American Psychiatric Association, 2012](https://psycnet.apa.org/fulltext/2011-28648-001.html); [Hechtman, L., 1999](https://onlinelibrary.wiley.com/doi/10.1002/(SICI)1098-2779(1999)5:3%3C243::AID-MRDD11%3E3.0.CO;2-D); [Klein, RG et al. 2012](https://pubmed.ncbi.nlm.nih.gov/23070149/)). Subtype classification of ADHD has not reach consensus within the literature and research on the correlates of ADHD subtypes show incoherent findings. The most common grouping of adhd subtypes (which is also the DSM categorization) are (1) inattentive, (2) impulsive/Hyperactive and (3) mixed. Those subtypes are for the majority based on criteria derived from behavioral and-self-report data and lack of neurophysiological assessment is prominent([Hegerl et al. 2016](https://pubmed.ncbi.nlm.nih.gov/27178310/); [Olbrich, Dinteren & Arns, 2015](https://pubmed.ncbi.nlm.nih.gov/26901357/)).
-
-### Project definition
-
-This project will aim to investigate the prediction potential of subtypes of ADHD between different types of measurements, those being behavioral measures, self-reporting measures and electrophysiological (EEG) data. More specifically, Principal components analysis (PCA) will be applied in order to achieve dimension reduction and k-nearest neighbor clustering will be used to predict the DSM ADHD subtypes according to each data type. An investigation of the predictive capacity of our 3 data types will be made, as well as observations about the potential prediction of gender using our dataset. For eeg data, an supplementary investigation will be conducted to compare prediction potential of ADHD subtypes according to electrode pools (paired according to brain regions) for brain oscillations of interest (measuring sepctral power).
-
-### Data
-
-The sample consisted of 96 college students with an ADHD condition. Different types of measurements are included in this data sample. EEG data recording was performed using a 19-channel electrode cap (international 10-20 system) and consisted of eyes-opened at-rest recording of 5-minute duration. Time-frequency analyses were conducted for each electrode in order to extract amplitude means for each frequency band. Neuropsychological assessment measures included were Conners questionnaire (self-report) and IVA-II behavioral test.
-For classification comparison, ADHD subtypes identified by the Conners questionnaire are used. Those subtypes are hyperactive, inattentive and mixed, as described by the DSM-IV. More information about EEG preprocessing can be found [here](https://github.com/brainhack-school2020/ADHDsubtypes_project/blob/master/eeg_preprocessing.md).
-
-#### Methods
-
-**Sample** :
- * Women (n = 57)
- * Men (n = 39)
- * Adhd subtype : hyperactive (n = 2), inattentive (n = 48), mixed (n = 46)
-
-#### Types of measures
-
-**Conners questionnaire** : standardized questionnaire. Comprizes 66 items about ADHD symptoms and behaviors. Answers are given using a Likert scale (0 = not at all/never and 3 = very often/very frequent). The items are compiled into 4 scales;
-
- * inattention/memory (IM)
- * hyperactivity/restlessness (HR)
- * impulsivity/emotional lability (IE)
- * problems with self concept (SC) (refers to self esteem).
-
- These four scores are used as the 4 self report symptoms measures. Test-retest correlation for 18-29 years old ranges from 0,8 to 0,92 depending on items.
-
-**IVA-II** : Behavioral test. Participants are presented with visual and auditive stimuli (numbers). If the stimulus is 1, whether it is visual or auditive, subjects must click as quickly as possible. If the stimulus is 2, whether it is visual or auditive, subjects must refrain from clickling. Stimuli are presented in a randomized order and at random time. 2 main scales are extracted, comprising 2 subscales each. 1st main scale is Attention Quotient (AQ) and its subscales are AQ auditive and AQ visual. 2nd main scale is Response Control Quotient (RCQ) and its subscales are RCQ auditive and RCQ visual.
-
-**Electroencephalography (EEG)** : 19 electrodes caps were used, positioned according to the 10-20 international system and referenced to both ear lobes. Recordings lasted 5 minutes, were participants were instructed to be as still as possible and to keep their eyes opened. The Mitsar System 201 and WinEEG (Mitast) software were used for recording. Test-retest and split-half correlations were higher than 0,9.
-
-
-### Deliverables predicted
-
-At the end of this project, we will have:
-
- - A Jupyter notebook markdown describing thoroughly all the steps of our project
- - Python script of main analyses
- - Complete published repository access to all commits and changes of our projects
- - An interactive platform to present the different data and analysis
-
-### Progress overview
-
-* As of may 26 2020; the data has been preprocessed and organized into pandas dataframes.
-* As of may 29 2020; the jupyter notebook for data visualization is well advanced; and we are working on our SNF.
-* As of june 1st; we decided to let go of our SNF analysis and concentrate our efforts on clustering, PCA and visualization, as it seems far more appropriate to our data.
-* As of june 8th; we have completed our data analysis and data visualization, what is left to do before final submission is some reorganisation of our repository and simplification of our code.
-
-
-## Results
-
-We were interested in sex differences and ADHD subtype differences, so we started by plotting their scalp distribution. White electrodes indicate the significant differences (computed via Mann-Whitney non parametric test and corrected with Bonferroni).
-
-Here is the scalp plot showing sex differences.
-
-
-
-And here are the scalp plots showing subtype differences.
-
-
-
-PCA analysis and KNN classification yielded interesting results. First, we compared the performance of a k-nearest neighbors classification using PCA as features versus using the data without dimension reduction. Here are the results:
-First, we tried to predict ADHD subtype (inattentive vs combined) using eeg data, separating pools of electrodes. None of the classifications were statistically higher than chance level (50%).
-
-
-
-These results might not be surprising, considering PCA resulted in very similar principal components which are hard to distinguish, for all electrode pools. Here's an example of 2D visualization of PCA, on the frontal electrode pool.
-
-
-
-Second, we tried to predict ADHD subtype again, this time using Conners scale (cognitive data) and IVA-II (behavioral data) separately, with and without PCA. Conners scale could predict ADHD subtype with a precision of 73,68%, with or without PCA. IVA-II classificaiton was not significally higher than chance level (50%).
-
-
-
-Finally, we also tried predicting sex according to Eeg distribution. This classification yielded the best results, with and without PCA.
-
-
-
-Here's an example of a confusion matrix output, in this case for the classification of sex (using PCA):
-
-
-
-
-### Tools used during this project
-
- * Git and GitHub
- * Bash shell
- * Dimension reduction via PCA
- * Machine learning ( KNN classification)
- * Jupyter Notebook/Jupyter Slides
- * Python packages : pandas, SNFpy, scikit-learn, numpy, scipy, etc.
- * Visualization packages (via python): seaborn, plotly, matplotlib, hytools, etc.
-
-### Deliverables
-
-#### Week 3 deliverable: data visualization
-
-This [deliverable](https://github.com/brainhack-school2020/SNF_ADHDsubtypes_project/blob/master/Interactive_plots_deliverable.ipynb) was done entirely as a team. Penelope developed the visualization for Conners (cognitive data) and IVA-II (behavioral data) (interactive plots of distribution for each data type), and Beatrice developed the script for eeg data visualization (interactive plot with facets of spectral power distribution as well as an example of 2D visualization of PCA). Scalp plots were created jointly.
-
-Please make sure to see the [requirements_week3deliverable.txt](https://github.com/brainhack-school2020/SNF_ADHDsubtypes_project/blob/master/requirements_week3deliverable.txt), to pull [Data](https://github.com/brainhack-school2020/SNF_ADHDsubtypes_project/tree/master/Data) file (with all the necessary files) and follow instruction in the notebook (linked). The excel_files folder has to be moved from the Data folder to the same directory as the notebook, in order for the path to stay the same.
-
-### Final deliverables
-
-* [main_analyses.ipynb](https://github.com/brainhack-school2020/ADHDsubtypes_project/blob/master/main_analyses.ipynb): jupyter notebook of all data wrangling, some interactive data visualization, and complete PCAs, KNN analyses (with comparisons of KNN without PCAs) on all 3 types of data
-* [Viz.ipynb](https://github.com/brainhack-school2020/ADHDsubtypes_project/blob/master/Viz.ipynb) : jupyter notebook of all the visualisation and descriptive stats made for all 3 datasets
-* [Scalp_Plots.ipynb](https://github.com/brainhack-school2020/ADHDsubtypes_project/blob/master/Scalp_Plots.ipynb) : jupyter notebook for scalp plots visualization and stastitical analysis (section with topography maps with significance masks for electrode comparisons).
-* [preprocessing.py](https://github.com/brainhack-school2020/ADHDsubtypes_project/blob/master/preprocessing.py) : File with all functions needed to run all the notebooks created
-* [requirement.txt](https://github.com/brainhack-school2020/ADHDsubtypes_project/blob/master/requirement.txt) : libraries required for this project
-
-
-### Conclusion and acknowledgement
-
-We would like to thank greatly the entire Brainhack school team for the initiative and amazing learning experience ! Another big thanks to the intructors for their patience and wise suggestions!
-
-### References
-
-1. [Galarnyk, M. (2017, December 4th). PCA using Python (scikit-learn). Retrieved from https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60](https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60)
-2. [Harel, Y. (2020, May 25). hytools. Retrieved from https://github.com/hyruuk/hytools/tree/master/hytools](https://github.com/hyruuk/hytools/tree/master/hytools)
-3. [Hasler, R., Perroud, N., Meziane, H. B., et al. (2016). Attention-related EEG markers in adult ADHD. Neuropsychologia.87:120‐133. doi:10.1016/j.neuropsychologia.2016.05.008](https://pubmed.ncbi.nlm.nih.gov/27178310/)
-4. [Ingram, S., Hechtman, L. & Morgenstern, G. (1999). Outcome issues in ADHD: Adolescent an dadult long-term outcome. Developmental Disabilities Research Reviews.5(3), 243-250. https://doi.org/10.1002/(SICI)1098-2779(1999)5:3<243::AID-MRDD11>3.0.CO;2-D](https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291098-2779%281999%295%3A3%3C243%3A%3AAID-MRDD11%3E3.0.CO%3B2-D)4.
-5. [La Malfa, G., Lassi, S., Bertelli, M., Pallanti, S., Albertini, G. (2008) Detecting attention-deficit/hyperactivity disorder (ADHD) in adults with intellectual disability The use of Conners' Adult ADHD Rating Scales (CAARS). Res Dev Disabil.29(2):158‐164. doi:10.1016/j.ridd.2007.02.002](https://www.sciencedirect.com/science/article/pii/S0891422207000200?via%3Dihub)
-6. [Navlani, A. (2018, August 2nd). KNN Classification using Scikit-learn. Retrieved from https://www.datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn](https://www.datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn)
-7. [Olbrich, S., van Dinteren, R., Arns, M. (2015). Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder. Neuropsychobiology.72(3-4):229‐240. doi:10.1159/000437435](https://pubmed.ncbi.nlm.nih.gov/26901357/)
-8. [Sandford, J. A., & Turner, A. (2000). Integrated visual and auditory continuous performance test manual. Richmond, VA: Brain Train.](https://www.braintrain.com/iva2/)
-9. [Sharma, A. (2020, January 1st). Principal Component Analysis in Python. Retrieved from https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python](https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python)
-10. [Sibley, M. H., Pelham, W. E., Jr., Molina, B. S. G., Gnagy, E. M., Waschbusch, D. A., Garefino, A. C., . . . Karch, K. M. (2012). Diagnosing ADHD in adolescence. Journal of Consulting and Clinical Psychology, 80(1), 139-150. http://dx.doi.org/10.1037/a0026577](https://psycnet.apa.org/fulltext/2011-28648-001.html)
-11. [Wilens, T. E., & Spencer, T. J. (2010). Understanding attention-deficit/hyperactivity disorder from childhood to adulthood. Postgraduate medicine, 122(5), 97–109. https://doi.org/10.3810/pgm.2010.09.2206](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724232/)
-12. [Volkow, N. D., & Swanson, J. M. (2013). Clinical practice: Adult attention deficit-hyperactivity disorder. The New England journal of medicine, 369(20), 1935–1944. https://doi.org/10.1056/NEJMcp1212625](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827421/)
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Age-Dependent EEG patterns for Predicting Treatment Response in ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Ingrid Campbell, Amanda/Tianyi Liu]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/Age_ADHD_Project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, eeg, age, brainhack]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this project we use EEG patterns to predict treatment responses for individuals with ADHD across different age groups. Project reports are incorporated in the BHS [website](https://school.brainhackmtl.org/project)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "aging_with_eeg.png"
----
-
-## Project definition
-
-### Background
-
-Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental and psychiatric disorders. ADHD is characterized by significant neurophysiological differences that can be observed in electroencephalography (EEG) recordings.
-EEG is a neuroimaging technique used to extract features and patterns, such as power spectral bands, where there are five bands that represent different functional characteristics (delta, theta, alpha, beta, gamma). For example, the theta band is associated with deep sleep and the beta band is associated with awake states and concentration. The theta-beta ratio is a well-known pattern in ADHD along with the alpha peak frequency associated with focus.
-A promising development in EEG research is the use of artificial intelligence (AI) and machine learning (ML) as an advanced signal processing tool. There are also age-related changes in quantitative EEG in ADHD. However, there is a lack of personalized treatment recommendations based on EEG patterns and age for individuals with ADHD
-
-
-
-### Tools
-
-The "project template" project will rely on the following technologies:
- * Python programming for signal processing and statistics: numpy, scipy, FOOOF
- * R for implementing regression and Random Forest algorithms and visualization: caret, randomForest, ggplot2
- * Jupyter notebooks + local laptops for teamwork collaboration, handling data and metadata
- * Github for project management, collaboration and pull requests.
-
-### Data
-
-The data used in this project can be downloaded from https://brainclinics.com/resources/tdbrain-dataset/introduction/downloads upon creation of a BrainClinics account. The TDBrain database is an extensive clinical EEG dataset (n=1274) with 19 electrodes collected over two decades from participants ranging age 5 to 89 years old. The database is publicly available and was originally published by Van Dijk 2022 in Scientific Data. The dataset has since been reanalyzed in several publications. At this link, metadata, raw EEG data, and preprocessing pipeline for artifact identification was provided. The raw EEG data was recording from resting state periods (2 minutes of eyes closed followed by 2 minutes of eyes open). The ADHD cohort (n=274) included a sub-cohort with treatment responses of neurofeeback treatment.
-
-### Deliverables
-
-At the end of this project, we have:
- - Workflows in Python
- - Workflow in R
- - Individual reports
-
-## Results
-
-### Progress overview
-
-The project was created by Ingrid Campbell and Amanda/TianYi Liu during Brainhack school 2025. This is the first version.
-
-### Tools I learned during this project
-
- * **Clinical EEG preprocessing and signal processing** Using signal processing techniques such as artifact detection and removal, epoching, power band feature extraction (of 2019 features from electrodes Fp1, Fp2, F3, F4, Fz, Cz, C3, C4), along with FOOOF, and data cleaning to handle missing data.
- * **Meta data extrapolation** The meta data included demographics (age, gender, education), NEO-FFI personality data, behavioural measures, and treatment responses to ADHD and neurofeedback.
- * **Feature selection** Using bootstrap validation to find the optimal number of features and top features to discriminate between age in participants with ADHD. Using random forest classifier
- * **Age-dependent analysis** age group stratification then pattern visualization. Examining age and EEG correlations as well as random forest classification model performance.
- * **Github collaboration**
-
-### Results
-
-#### Deliverable 1: Develop age-dependent models to predict treatment responses across different age groups
-
-To assess the age-specificity of EEG-based treatment response, we analyzed how age correlates with key EEG biomarkers and compared the relationships between ADHD treatment responders and non-responders. We found that older individuals (age range 36-55) with ADHD are significantly more likely to respond to neurofeedback treatment, responders (n=50) and non-responders (n=1). Youngest individuals (age range 6-17) had 14 responders and 40 non-responders. Middle aged individuals (age range 17-36) had 4 responders and 48 non-responders.
-
-#### Deliverable 2: Determine whether responders show distinct EEG signatures at different developmental stages
-
-By processing EEG data and machine learning models to identify the EEG signature of neurofeedback response across age development, we found that central delta relative power, central-paretal center frequency, and age were among the top EEG features that showed distinct EEG signatures between developmental stages. The random forest model using bootstrapping validation had a strong predictive accuracy performance with AUC of 0.865 of discriminating between responders and non-responders. Interestingly, the random forest classifier identified age was one of the top-most important features, along with delta frequency band at the frontal lobe electrodes, where patients with ADHD typically generate an abundance of low-frequency delta or theta brain waves.
-
-#### Deliverable 3: project gallery
-
-There is a project gallery, apart of the BHS 2025 github and website. You can still check out the repository [2025 BHS github age_eeg_pattern](https://github.com/brainhack-school2025/age_eeg_pattern)
-
-The repository of this project can be found [here](https://github.com/mtl-brainhack-school-2019/ecg_pupillometry_pipeline_kaufmann). The objective was to create a processing pipeline for EEG data. The motivation behind this task was to learn how to process EEG signals, develop AI tools to analyze and apply to a neurodevelopmental disorder. The repo features:
- * analysis_code: for preprocessing data loading, data cleaning and EEG feature extraction, merging the meta data demographics with the EEG features, a preprocessing notebook for statistical analysis and creating age-grouped dataframes and running analyses, including the pipeline for running the random forest model, bootstrap validation and age-stratified analyses.
- * Notebooks for all analyses.
- * Detailed requirements files, making it easy for others to replicate the environment of the notebook.
- * An overview of the results in the markdown document along with a report by Ingrid Campbell at https://github.com/school-brainhack/Age_ADHD_Project
-
-
-## Conclusion and Acknowledgement
-
-As the brain matures, brain frequencies measured by EEG are found to decrease, which is supported by literature[2](https://www.sciencedirect.com/science/article/abs/pii/S0167876008007459). Our results align with this as age was a topmost optimal feature: theta and delta bands are represented in the most important features that are age correlated.
-The dataset and preprocessing code can be found at the [Brainclinics Foundation](https://brainclinics.com/resources/tdbrain-dataset/introduction/downloads). The training modules for this project were provided by the [Brainhack School](https://school-brainhack.github.io/modules/).
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-10" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Multimodal Investigation of Neural Correlates of Athletic Performance"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Arunim Guchait]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/ArunimGuchait/BrainHack-School-2023-Project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, fslvbm, dMRI Analysis in Python, athletes]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project investigates the neural correlates of athletic performance using fMRI, dMRI, and FSLVBM to compare grey matter volume and white matter connectivity between athletes and non-athletes. The study aims to identify brain regions associated with athletic performance, explore white matter connectivity differences, and examine the relationship between brain structure and specific athletic skills. The dataset included nine Indiana University football players and nine controls."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "project_image.png"
----
-
-
-## Project definition
-
-### Background
-
-The relationship between athletic performance and brain structure and function has been a topic of interest in the field of neuroscience. Understanding the neural correlates of athletic performance can provide valuable insights into the development of training programs and interventions to enhance physical and cognitive abilities. In this project, I propose to use functional magnetic resonance imaging (fMRI), diffusion MRI (dMRI), and FSL's Voxel-Based Morphometry (FSLVBM) to investigate the differences in grey matter volume and white matter connectivity between elite athletes and non-athletes. I will perform the analysis using Python-based tools, such as Nilearn and Dipy.
-
-### Tools
-
-This project relied on numerous tools such as:
-1. Git and Github for Version Control
-2. FSLVBM to preprocess and analyse grey matter volume differences
-3. FSL FIRST tool for automatic segmentation of a number of subcortical structures.
-4. Python-based tools, such as Dipy and Nilearn, to preprocess the data, perform tractography analysis, and investigate white matter connectivity differences
-5. Google Colab and Jupyter Notebook
-
-### Data
-
-I've used a publicly available dataset on college-level athletes for this project. From the dataset, I have chosen data of nine Indiana University (IU) football players (American football) and nine controls (non-athletes) and wish to carry out an AmFB>NonAth comparison as well as NonAth>AmFB comparison.
-
-Please check the Participants' Info Used in this Project here:
-Results/Participants_INFO.csv
-
-Information about Dataset:
-https://www.nature.com/articles/s41597-021-00823-z
-
-Link to download the dataset:
-https://brainlife.io/pub/5f2c3765beafe924c962dd8d
-
-### Deliverables
-
-At the end of this project, these files will be made available:
-- Reproducible project workflow, detailed in the GitHub repository with codes
-- Jupyter notebooks of the analysis codes and visualisations
-- Figures of the results
-
-## Results
-
-### Progress overview
-
-**Data Preprocessing**
-> MRI data have been preprocessed using FSL tools.
-
-
-
-**Voxel-Based Morphometry (VBM) Analysis**
-> Grey matter volume differences between athletes and non-athletes have been investigated using FSLVBM [FMRIB's Software Library - Voxel-Based Morphometry]
-
-OUTPUT
-
-NonAth>AmFB
-
-Showing the local differences in grey matter volume between the two groups:
-
-
-
-To obtain statistical data, identify the region of significant difference and validate that:
-
-[1] E2 - Running randomise and displaying cluster-based thresholding results
-> Reference: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM/UserGuide
-
-OUTPUT
-
-
-
-[2] Reporting Cluster Information
-> Reference: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Cluster#reporting
-
-OUTPUT
-
-
-
-**Vertex Analysis**
-> Automated segmentation of subcortical structures in the brain has been completed using FSL FIRST [a model-based segmentation/registration tool] tool.
-
-OUTPUT
-
-
-
-Using this vertex analysis, marked differences in shape have been found in the two following areas:
-
-[1] Left-Putamen
-
-
-
-[2] Left-Thalamus
-
-
-
-### Future Work
-**dMRI Analysis in Python**
-> White matter connectivity will be explored using tractography methods implemented in Python-based tools such as Dipy and Nilearn.
-
-### Tools I learned during this project
-
-During the BrainHack School Project, I learned the following tools:
-
-* **Python:** I learned how to use Python to analyse and manipulate data.
-* **FSLVBM:** I learned how to operate FSLVBM to analyse grey matter volume differences between groups.
-* **Nilearn:** I learned how to utilise Nilearn to perform functional MRI analysis and visualise data.
-* **Dipy:** I learned how to use Dipy to perform diffusion MRI analysis and tractography.
-* **Git and Github:** I learned how to use Git and Github for version control and collaboration.
-* **Bash Terminal:** I learned how to employ the Bash Terminal to navigate and manipulate files and directories.
-* **FSL FIRST tool:** I learned how to utilise the FSL FIRST tool for subcortical segmentation.
-* **Google Colab and Jupyter Notebook:** I learned how to use Google Colab and Jupyter Notebook for interactive coding and data analysis.
-
-## Conclusion and acknowledgement
-Regarding the project, there are some promising results I've got. The short time at BrainHack School is overwhelming and challenging. But I am pleased with the new skills and knowledge I have gained throughout the program. I'll continue working on this project and try to investigate more athletes from different types of sports.
-
-I sincerely thank my advisor Professor Neil Muggleton and all the instructors and TAs (special shout-out to the Taiwan Hub) who have generously shared their expertise and provided guidance and support throughout the program. Thank you so much for this amazing opportunity.
-
-## References
-### Tools / Tutorials
-**FSLVBM**
-
-1. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM
-2. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM/UserGuide
-3. https://www.youtube.com/watch?v=L1B3Wm-wnyQ&ab_channel=FSLCourse
-
-**dMRI Analysis in Python**
-
-1. https://school-brainhack.github.io/modules/dmri_intro/
-2. https://davi1990.github.io/talks/2021-11-05-dMRI_analysis_in_Python
-3. https://carpentries-incubator.github.io/SDC-BIDS-dMRI/aio/index.html
-
-**Relevant Research Papers**
-
-1. https://www.frontiersin.org/articles/10.3389/fnhum.2014.00594/full
-2. https://www.sciencedirect.com/science/article/pii/S0960982214009798
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Combine EEG/MRI/Behavioral data-sets to learn more about Music/Auditory system"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Marcel Farres Franch]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/BHS-AuditoryMultimodal
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [EEG, fMRI, music, auditory-perception, preprocessing, fmriprep]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this project I aim to combine data from different modalities (fMRI, EEG, and behavioral) to understand more about sound and music processing. My main focus in this project was to try to reproduce some of the results from a published paper starting form raw data."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "data_pipeline.png"
----
-
-
-# Combine EEG/MRI/Behavioral data-sets to learn more about Music/Auditory system
-
-
-
-
-
-## Summary
-
-I'm currently a PhD student of the IPN at McGill University.
-
-In this project I aim to combine data from different modalities (fMRI, EEG, and behavioral) to understand more about sound and music processing.
-
-My main focus in this project was to try to reproduce some of the results from a published paper starting form raw data.
-
-The overall goal of the current project is to be able to organize, pre-process and do some basic analyses form a fMRI study.
-
-## Project definition
-
-### Background
-
-In my current PhD project one of the end results should be a open multimodal behavioural and neuroimaging dataset characterizing healthy human auditory processing.
-It aims to allow researchers address individual differences in auditory cognitive skills across brain functions and structures, and it will serve as a baseline for comparison with clinical populations.
-To achieve that, our core objectives are to create a standardized framework with which to administer a battery of curated tasks.
-After acquiring the data from 70 young adults and we intend to share our framework, analysis pipelines, stimuli with linked descriptors, and metadata with the community through open data repositories. The dataset contains cognitive and psychophysical tasks, as well as questionnaires designed to assess musical abilities, speech, and general auditory perception. It also includes EEG and fMRI recorded during resting state, as well as naturalistic listening to musical stimuli and speech.
-
-During this BrainHanks School project I wanted to understand what are the needs as a researcher to easily make use of public available data and learn the basics of pre-processing raw fMRI data.
-
-### Learning Goals
-
-I have good experience analyzing highly process data... but how you get there?
-
-* Raw data → BIDS formatted raw data
-
-* BIDS formatted raw data → BIDS formatted preprocess data
-
-* Basic Data quality control
-
-* Basic analysis
-
-* Implement other people analysis and reproduce results
-
-(but it is a 2 and a half week project...)
-
-
-
-### Tools
-
-The project will rely on the following technologies:
-
-* fmriprep
-
-* pandas
-
-* nipype
-
-* bids
-
-* bids-validator
-
-* nilearn
-
-* numpy
-
-* pathlib
-
-* jupiter lab
-
-* [OpenNeuro](https://openneuro.org/)
-
-### Data
-
-The first step was to search for candidates open datasets. I prioritized music/auditory related, as it is closer to my PhD project.
-
-Next there is a list of interesting datasets I found, which I choose 2 to work during this course:
-
-#### Chosen
-
-* Forrest Gump: A very interesting study that use naturalistic stimuli to understand more about how our brain interacts with the real complex world. More information in the dedicated [web-side](http://studyforrest.org/). Here you can find the [list of 29 publications](http://studyforrest.org/publications.html) that use the data made publicly available.
-
- I was particularly interested in the first 20 subjects that perform a music task (Listening to music (7T fMRI, cardiac & respiratory trace).
-
- I wanted to replicate the analyisis and findings of [this paper](https://f1000research.com/articles/4-174/v1).
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 37 | bold, T1w, T2w, angio, dwi, fieldmap | Forrest Gump, objectcategories, movielocalizer, retmapccw, retmapcon, retmapexp, movie, retmapclw, coverage, orientation, auditory perception | Maybe the most promising example |
-
-
-
-* Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression: Study looking how people with depression process differently musical stimuli.
-
- Same idea as the previous study, replicate results reported in [this paper](https://pubmed.ncbi.nlm.nih.gov/27284693/) using the publicly available data.
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 39 | T1w, bold | Music, Non-Music listening | missing stims |
-
-
-
-#### Other Options
-
-* A dataset recording joint EEG-fMRI during affective music listening.
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 21 | T1w, channels, eeg, events, bold | GeneratedMusic, classicalMusic, genMusic01, genMusic02, washout, genMusic03 | Could be very noisy EEG but also promising |
-
-
-
- * Article
-
-* Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 36 | bh bold | music listening | Non standard format |
-
-
-
-* Neuroimaging predictors of creativity in healthy adults
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 66 | T1w, T2w, dwi, bold, fieldmap | Rest | Interesting to study resting state, good number of subjects |
-
-
-
-* Functional Connectivity of Music-Induced Analgesia in Fibromyalgia
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 40 | T1w, dwi, bold | pre-control, pre-music, post-control, post-music | healthy and Fibromyalgia patients listening to music |
-
-
-
-
-
-* Music BCI (006-2015)
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 10 | eeg | oddball 3 music instruments | not much to extract maybe |
-
-
-
- Publication:
- *
-
-* OpenMIIR - Music imagery information retrieval
-
-
-
-
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 10 | eeg | imagery | very short musical exerts |
-
- Existing Publications:
-
- *
-
- * Sebastian Stober. Towards Studying Music Cognition with Information Retrieval Techniques: Lessons Learned from the OpenMIIR Initiative. In: Frontiers in Psychology Volume 8, 2017.
- doi: 10.3389/fpsyg.2017.01255
-
- * Sebastian Stober. Learning Discriminative Features from Electroencephalography Recordings by Encoding Similarity Constraints. In: Proceedings of 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’17), Pages 6175-6179, 2017.
- doi: 10.1109/ICASSP.2017.7953343
-
- * Sebastian Stober; Thomas Prätzlich & Meinard Müller. Brain Beats: Tempo Extraction from EEG Data. In: 17th International Society for Music Information Retrieval Conference (ISMIR’16), 2016.
- available from:
-
- * Sebastian Stober; Avital Sternin; Adrian M. Owen & Jessica A. Grahn. Deep Feature Learning for EEG Recordings. In: arXiv preprint arXiv:1511.04306 2015.
- available from:
-
- * Avital Sternin; Sebastian Stober; Jessica A. Grahn & Adrian M. Owen. Tempo Estimation from the EEG Signal during Perception and Imagination of Music. In: 1st International Workshop on Brain-Computer Music Interfacing / 11th International Symposium on Computer Music Multidisciplinary Research (BCMI/CMMR’15), 2015.
- available from:
-
-* The neural basis of spoken word perception in older adults
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 61 | T1w, T2w, bold, events | LISTEN02, REPEAT02, REPEAT01, LISTEN01 | Interesting, need to read more about the data. there are 375 different monosyllabic words |
-
-
-
-* A dataset recorded during development of an affective brain-computer music interface:
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 19-10-9 | channels, eeg, events | music listening and reported arousal and valance | Low subj number but interesting data |
-
- *
-
- *
-
- *
-
-* An EEG dataset recorded during affective music listening
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 31 | channels, eeg, events | music listening | Very little information about what files where use |
-
-
-
- Possible article:
-
- *
-
-* Sherlock_Merlin waiting bids validator
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 37 | mri | listen and see movie (A/B groups) | Very interesting, stimuli are provided |
-
-
-
-* Milky-Vodka - (2 different stories )
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 54 | T1w, bold, events | Milky, Synonyms, Scrambled, Vodka | stims available, |
-
-
-
-* Audiovisual Learning MEG Dataset
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 30 | coordsystem, channels, events, meg | The auditory and visual stimuli were 12 Georgian letters and 12 Finnish phonemes | mapping things? |
-
-
-
-* Auditory localization with 7T fMRI
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 10 | T1w, PD, T2star, dwi, bold, events, fieldmap | rest, auditory, TODO: full task name for rest, TODO: full task name for auditory | need to be down sampled |
-
-
-
-* FFR ?.
-
-
-
-#### Interesting but not related
-
-* Go-nogo categorization and detection task
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | | | | |
-
-
-
-* A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and MRI acquisition during a motor imagery neurofeedback task
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 10 | T1w, eeg, bold | eegNF, fmriNF, motorloc, eegfmriNF, MIpre, MIpost | Not interested |
-
-
-
-
-
-* TUH EEG Corpus Too big for the 3 weeks.
-
-
-
-* Dataset: rsfMRI before and after one session EEG NF vs Sham NF
-
-
-
-* Data relating "Alpha/beta power decreases track the fidelity of stimulus-specific information"
-
-
-
-* Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a convert attention task
-
-
-
-* EEG meditation study
-
-
-
-* EEG study of the attentional blink; before, during, and after transcranial Direct Current Stimulation (tDCS)
-
-
-
-* Simultaneous EEG-fMRI - Confidence in perceptual decisions
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 24 | T1map, bold, events, eeg | motion direction of random dot kinematograms | |
-
-
-
-* Audiocue walking study
-
-
-
-* Auditory and Visual Rhythm Omission EEG
-
-
-
-* Auditory and Visual Oddball EEG-fMRI
-
- | N | Type | Tasks | Comments |
- |--------|-----------|----------|----------|
- | 17 | T1w, inplaneT2, bold, eeg, events | auditory oddball with button response to target stimuli, visual oddball with button response to target stimuli| |
-
-
-
-#### NOT Accessible
-
-I also find some very interesting datasets that I was not able to access directly and in the period of 3 weeks I was still waiting for them.
-
-I was a little frustrated with the process, making me realize how important is real Open Data.
-
-* DEAP: A Database for Emotion Analysis Using Physiological Signals:
-
- website: [https://www.eecs.qmul.ac.uk/mmv/datasets/deap/]
-
- paper: [https://www.eecs.qmul.ac.uk/mmv/datasets/deap/doc/tac_special_issue_2011.pdf]
-
- (Not Accessible without explicit permission, which we are missing for no)
-
-### Deliverables
-
-At the end of this project, we have:
-
-* Scripts to download the dataset ([HERE](https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/scripts/download-for-test.sh) and [HERE](https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/scripts/download-for-all.sh)).
-
-* Scrips to pre-process the data using fmriprep in a cluster ([HERE](https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/scripts/run-fmriprep-test.sh) and [HERE](https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/scripts/run-fmriprep-all.sh)).
-
-* fmriprep report on the pre-processing ([HERE](http://htmlpreview.github.io/?https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/fmri-prep-results/sub-control01.html)).
-
-* Basic processing of 1 subject ([HERE](https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/BHS_AuditoryMultimodal-ds000171.ipynb)) with some need to be improve analysis.
-
-### Project plan / Objectives
-
-* [x] Download multiple interesting datasets (local and to the server)
-
- * [x] **fMRI** studyforrest
-
- * [x] **fMRI** Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression
-
- * [x] **EEG** An EEG dataset recorded during affective music listening
-
- * [x] **EEG-fMRI** A dataset recording joint EEG-fMRI during affective music listening
-
-* [x] Explore fMRI datasets
-
-* [ ] ~~Explore EEG datasets~~ (Not enough time)
-
-* [ ] ~~Replicate results for each dataset.~~ (Not enough time)
-
- * [x] ~~fMRI ~~ (data need pre-processing and the dataset was not fully BIDS compatible)
-
- * [x] fMRI
-
- * [x] Preprocess data.
-
- * [X] Do some basic visualizations.
-
- * [ ] Do connectivity and activation analysis mus/no-mus stimuli (IN Progress)
-
- * [ ] ~~Reproduce figures from the paper~~ (Not enough time)
-
- * [ ] ~~EEG ~~ (Not enough time)
-
- * [ ] ~~EEG-fMRI ~~ (Not enough time)
-
-* [ ] ~~Extract commune information from the different datasets~~ (Not enough time)
-
-* [ ] ~~Analyse data coming from the different datasets together~~ (Not enough time)
-
-* [x] Proper code documentation and add necessary comments
-
-* [x] Create and share conda env.
-
-### Installation instructions
-
-1. Clone the repo to your computer:
-
- `git clone https://github.com/brainhack-school2020/BHS-AuditoryMultimodal.git`
-
-2. Install fMRI prep using [this instructions](https://fmriprep.readthedocs.io/en/stable/installation.html)
-
-3. Download and preprocess the data using [these scripts](https://github.com/brainhack-school2020/BHS-AuditoryMultimodal/blob/master/scripts/) (you can grab a coffee or two... these may take a while).
-
-4. Create a virtual-env
- `python3 -m venv bhs-auditory`
-
-5. Install requirements
- `pip install -r requirements.txt`
-
-6. Open the notebook
- `jupyter lab BHS_AuditoryMultimodal-ds000171.ipynb`
-
-## Conclusion
-
-After the multiple problems I found trying to process the data, reproduce the analyses, and limitations imposed by the missing of information form the dataset I am more aware of what I will need to do to efficiency share my data/analyses in the near future.
-
-Don't be that researcher...
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2021-08-25" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Generating a BIDS compatible dataset and interactive graphs from the Projet Courtois NeuroMod's hearing test data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Eddy Fortier, Lili El Khalil]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/PSY6983-2021/fortier_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Auditory tests, Interactive graphs, BIDS]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In a longitudinal study, the amount of data and figures to manage and create quickly becomes too massive to be manually handled. The goal of this project is to create tools to take an auditory test database and automatically format it into a BIDS compatible dataset and generate interactive graphs."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "BIDS_with_ears.png"
----
-
-
-## Project definition
-
-### Background
-
-The Projet Courtois NeuroMod is a longitudinal fMRI data acquisition project where participants get scanned almost every week.
-One risk associated with intensive protocols like this one is the chronic exposure of the participants to high noise levels during the scan sessions.
-This is why it is important to regularly monitor their auditory health to ensure that the research protocol is not causing any damage to the participants' hearing.
-Part of the auditory perception team's job is to do this monitoring task.
-To do so, the participants go through different clinical tests every month to keep track of the evolution of their auditory health.
-For some of these tests, such as the pure-tone audiometry test, the data is more easily interpreted when rendered into graphic displays.
-But with these repeated tests comes important amount of data to process.
-A first task that could be interesting to do would be, in order to go through the data processing more efficiently, to build an automated pipeline to generate the graphs.
-
-### Example of audiogram
-
-
-
-### Example of Matrix test results graph
-
-.png)
-
-A second task that could be interesting to do would be to try and use this database to try and fingerprint the participants based on their results to the tests.
-Since every person's hearing is unique and is affected by the individual auditory experience, it could be interesting to try that kind of machine learning classification task.
-
-The third task that could be interesting to do is regarding the dataset formatting.
-Since the dataset is not currently BIDS compatible, it could be interesting to create a jupyter notebook to automatically create a BIDS format database from the data.
-
-### Example of a BIDS compatible file structure reformatting
-
-figure reference:
-
-Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. *Scientific data*, *3*, 160044. https://doi.org/10.1038/sdata.2016.44
-
-[](https://www.nature.com/articles/sdata201644)
-
-### Tools
-
-The plan is to use the following tools from the BrainHack School's tutorials in this project:
-
-- Python scripts to execute the graph generation and machine learning tasks
-- The Bash terminal environment to work on the python scripts and run them
-- Git and GitHub for the collaborative work and the version control of the project
-- Plotting libraries such as Matplotlib, Seaborn or Plotly
-- Machine learning libraries for the fingerprinting task
-- Jupyter Notebook to build and execute the BIDS formatting script
-- The BIDS validator to make sure that the formatting worked
-- Markdown to build this project report
-
-### Data
-
-The dataset used for this project was acquired through multiple sessions with each participant between November 2018 and August 2021.
-Multiple clinical tests were performed including:
-- Otoscopic inspection of the external auditory canal and tympanic membrane
-- Tympanometry
-- Stapedial reflex test
-- Pure-tone audiometry
- - Regular clinical frequencies range (from 250 Hz to 8 kHz)
- - Ultra-high frequencies extended range (from 9 kHz to 20 kHz)
-- Matrix speech-in-noise perception test
- - In the primary language of the participant: French or English (for all participants)
- - In the second language of the participant: French or English (for 5 out of the 6 participants)
-- Otoacoustic emissions
- - Transitory evoked otoacoustic emissions (TEOAE)
- - Distortion product otoacoustic emissions (DPOAE) with a L1/L2 ratio of 65/55 dB SPL
- - Growth function (DP Growth) with 2 kHz, 4 kHz and 6 kHz
-
-Baselines were acquired for each of these tests.
-Three different combinations of those tests were then designed as experimental conditions and randomly assigned to each of the participant in a way that they will all do each of those conditions four times over the course of a twelve months protocol.
-The specific dataset used for this project includes results from the tympanometry, the stapedial reflex test, the pure-tone audiometry and the Matrix speech-in-noise perception test.
-
-### Deliverables
-
-By the end of The BrainHack School, we aimed to have the following:
-
-- A README.md file to present the project
-- A [GitHub repository](https://github.com/PSY6983-2021/fortier_project) documenting the project
-- Python scripts to create graphs and execute machine learning tasks
-- Jupyter notebooks with code and explanations for the BIDS formatting task
-- A [slideshow](https://docs.google.com/presentation/d/1TveZjzR9TDlGQA-XrLYjqPEb2E-x2vvl0kyfu43ljaQ/edit?usp=sharing) presentation showing the project objectives
-
-## Results
-
-### Lili El Khalil's progress review
-
-A script that helped in building a BIDS format for organizing our data. As I divided my data into subject, which are the participants.
-Then for each subject the number of sessions they participated and in each session we have the four tests done. The four tests are : Tymp, Reflex, PTA and MTX.
-At the lower level, in each test folders, you can find the results obtained for each run of the test.
-
-#### Tools and skills that were developed during this project
-
-Many of these contribute in my part of the project:
-
-- Jupyter Notebook: an environment used to write the code
-- Windows Subsystem for Linux/Bash: Edit text files such as this README.md
-- Python Libraries: os, numpy and pandas
-- Git: Track file changes
-- GitHub: Organize team project
-
-#### Deliverables
-
-Jupyter Notebook
-
-The notebook contains the code used in order to be able to create BIDS compatible files from our data.
-
-
-
-README.md
-
-This README.md file contains all the project details done by each one of us
-
-### Mr. Fortier's progress overview
-
-A first iteration of functional python scripts to generation single test graphs and test overview graphs for each of the Projet Courtois NeuroMod participants is now available in the code folder of this repository.
-These scripts were also linted using flake8 and passed all the generic requirements of this linter.
-Unfortunately, it was not possible to create a machine learning task script on top of the graph generation scripts due to time constraint and restrictions linked to the collaborative nature of this project.
-
-#### Tools/skills that were developed during this project (and challenges encountered...)
-
-- **Python scripts**: It was a first attempt to code in python using scripts instead of the Jupyter Notebook controlled environment.
-- **Python scripts**: It was also a first attempt at using multiple scripts for a single task and importing functions across them.
-- **Git and Github**: This project is the first attempt at building a complete project from the start, using Git's functions and a Github repository.
-- **Plot.ly's `graph_objects` tools**: This project was a first on many front regarding Plot.ly.
- - It was a first attempt at doing interactive figures/graphs.
- - It was a first contact with Plot.ly's library.
- - It was a challenge to be able to build graphs that fully represent the particularities of the two different types of figure produced in this project: `plotly.express` might have been easier to use than `plotly.graph_objects`, but it was not as capable to fully build the vision planned for this project.
- - It was an important challenge to navigate in the impressive documentation of the `plotly.graph_objects` library. It is a powerful, extensively customizable tool, but it is also very complex.
-- **flake8 linter**: It was the first use of a linter to proof read the scripts' code format.
-- **Binder**: It has been a difficult journey to try to figure out a way to make available here examples of the interactive figures generated by this project. Unfortunately, Github doesn't allow for html figures to be embedded into markdown files to be displayed. Binder was a way to go around it.
-- **Jupyter Notebook**: In order to display the interactive figures through the use of Binder, two notebooks were created and adapted from the original graph_generator.py code.
-
-#### Deliverables
-
-- First, this README.md file describes multiple aspects of the project.
-- Second, the Github repository where this README.md file sits allows people to consult the evolution of the project.
- - Two python scripts generating the graphs needed for this project are available in the `code/` folder.
- - Two Binder based Jupyter Notebooks allow people to consult an interactive sample graph for each of the types of graph generated by the scripts.
- The links to access them are in this README.md file.
- - A requirements.txt file allows the Binder platform to properly run the graph generating Jupyter Notebooks.
-
-### Example of interactive HTML audiogram
-
-
-
-[](https://mybinder.org/v2/gh/PSY6983-2021/fortier_project/HEAD?filepath=code%2FPTA_sample_figure_generator.ipynb)
-
-### Example of Matrix test interactive HTML graph
-
-
-
-[](https://mybinder.org/v2/gh/PSY6983-2021/fortier_project/HEAD?filepath=code%2FMTX_sample_figure_generator.ipynb)
-
-## Conclusion
-
-In conclusion, most of the goals of this project were reached: only the machine learning task was left behind.
-This project includes:
-- a README.md file presenting the project
-- a LICENSE file
-- a requirements.txt file to be used by a Binder platform to run Jupyter Notebooks
-- two python scripts to generate interactive graph figures
-- two Jupyter Notebooks to be loaded using a Binder platform
-- one Jupyter Notebook to create and format a BIDS compatible dataset from the original dataset's spreadsheet format
-- images and html sample figures to be displayed by the README.md file
-
-We would like to thank the BrainHack School 2021 team of mentors for their availability and their help on this journey.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-08" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Unveiling the Power of B0 Field Mapping: A Comprehensive Tutorial and Analysis in MRI"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Behrouz Vejdani Afkham]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/Vejdani_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [B0 field mapping, rs-fMRI, EPI undistortion, connectivity]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to create a detailed tutorial on B0 field mapping principles in MRI and demonstrate the estimation methods interactively. Additionally, it explored the importance of B0 field maps in MRI by comparing the connectivity matrix of rs-fMRI with and without using the B0 field map in the processing pipeline."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Cover.png"
----
-
-
-## About me
-
-
-
- Behrouz Vejdani Afkham
-
-
-I am currently a first-year PhD student at Polytechnique Montreal, specializing in biomedical engineering. My previous academic background includes a major in Medical Imaging from Tehran University of Medical Sciences, which has provided me with a solid foundation in neuroimaging tools and methods. However, as part of my PhD program, I am now required to familiarize myself with cutting-edge data science packages specifically designed for neuroimaging data analysis. These packages aim to streamline the entire process and help me achieve my research goals more efficiently.
-
-## Project Overview
-The objective of my project is to develop a comprehensive tutorial on the principles of B0 field mapping in MRI. Additionally, I aim to explain the existing approaches for calculating B0 field maps in an interactive manner. Furthermore, I will investigate the significance of B0 field maps in MRI by comparing the connectivity matrix of rs-fMRI (resting-state functional MRI) from a single subject with and without utilizing the B0 field map during the processing pipeline.
-
-## Introduction
-In magnetic resonance imaging (MRI), achieving a homogeneous distribution of the main magnetic field (referred to as B0) is crucial for obtaining high-quality images without significant geometric distortions or undesirable signal dropout [1]. B0 field inhomogeneity can arise from various factors such as manufacturing imperfections or the presence of large metallic objects near the magnet. However, the most common cause of field perturbations is the difference in magnetic susceptibility (χ) among tissues within the human body. Magnetic susceptibility describes the extent to which a specific tissue becomes magnetized within the B0 field. Even with the process of shimming, which aims to compensate for magnetic inhomogeneity, it is challenging to achieve complete homogenization of the B0 field, especially at air-tissue interfaces where the susceptibility difference is particularly pronounced [2].
-
-While the achieved homogeneity after shimming is generally satisfactory for most anatomical images, even subtle remaining B0 offsets can significantly affect the acquisition of Echo Planar Imaging (EPI) sequences. EPI is a commonly used sequence in functional MRI (fMRI), making it crucial to find ways to correct for these distortions to avoid erroneous interpretation of results. The solution to this issue lies in retrospectively correcting EPI images using a B0 field map, which involves shifting pixels in the phase-encode direction [3]. A B0 field map is a quantitative image representing the intensity of the B0 offset in hertz (Hz) or radians per second (rad/s). There are three methods to calculate the B0 field map: double-echo Gradient Echo (GRE) sequence, multi-echo GRE sequence, and EPI-derived field mapping. The latter method requires acquiring two EPI images with opposite phase-encoding directions.
-
-## Background
-
-### Sources of B0 offset
-The regions of the brain that experience the most significant variations in the main magnetic field, B0, are primarily located at the skull base. This is due to the high χ difference between the paramagnetic air in sinuses and the diamagnetic brain tissues. Paramagnetic materials tend to enhance the B0 field by aligning towards it, while diamagnetic materials weaken the B0 field by aligning in the opposite direction.
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-
-***Courtesy of Allen D. Elster, MRIquestions.com***
-
-Consequently, these local susceptibility differences cause variations in the precessional frequency of spins, also known as the Larmor frequency (ω0). As a result, phase accumulation occurs over time. Therefore, the greater the difference in χ or the longer the duration, the more spread-out the phases become. The relationship between the Larmor frequency, the B0 field offset and phase accumulation can be described as follows:
-
-
ω0 = γB0
-
ω = ω0 + (ω0⋅Δχ)
-
ϕ = ∫ω(t) dt
-
-### Phase wrap concept
-The B0 offset affects magnitude and phase images differently. In magnitude images, the signal decays exponentially with increasing B0 offset, while phase accumulation increases in a linear manner.
-
-Click [here](https://brainhack-school2023.github.io/Vejdani_project/FID.html) to see the interactive figure.
-
-As a result, the reconstructed phase map from the complex MRI signal contains information about the B0 field variation. However, due to the involvement of various sources in phase variations, the phase maps can only provide an approximation of the ΔB0 field. Most of these sources do not change with different echo times, except for the phase induced by B0 inhomogeneity, which scales with the echo time. Therefore, to obtain a ΔB0 field map, at least two phase maps with different echo times need to be acquired.
-While MRI-based B0 mapping can be performed using any MRI pulse sequence, the fast gradient-echo (GRE) method is commonly used due to its speed, ease of use, and inherent sensitivity to B0 offsets. The pulse sequence diagram for GRE is as follows:
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-
-
-
-
-
-***Courtesy of Allen D. Elster, MRIquestions.com***
-
-One problem with phase images is that any frequency beyond 1/ΔTE will wrap into the range of -π to π. This is because phase is calculated using inverse tangent and cannot accommodate values outside this range.
-
-Click [here](https://brainhack-school2023.github.io/Vejdani_project/Phase_evolution.html) to see the interactive figure.
-
-To avoid phase wrapping, the echo time for the first phase data and the ΔTE (difference in echo times) for the linear fitting approach are typically kept low enough. Consequently, you would expect to observe fewer phase wraps with lower echo times, and an increasing number of wraps with longer echo times and larger B0 offsets (e.g., in the skull base). Additionally, geometric distortion has a more pronounced effect in the phase encoding direction where the sampling rate is lower.
-
-Click [here](https://brainhack-school2023.github.io/Vejdani_project/phase_wraps.html) to see the interactive figure (Note that phase enconding is along AP direction in these images).
-
-## Main Objectives
-
-- Illustration of three previously-mentioned methods of B0 mapping
-- Comparison of fMRI connectivity matrix with and without EPI undistortion
-
-## Personal Objectives
-
-- Emphasize on reproducibility of the analysis pipeline.
-- Learn more about advanced visualization of the neuroimaging data.
-- Understanding of fMRI connectivity concepts and processing steps.
-
-## Tools
-- Jupyter notebook for coding and visualization
-- Git and Github for Version Control
-- DataLad for data Reproducibility
-- Singularity container using NeuroDesk for Reproducibility
-- FSL command line tools for EPI undistortion using Neurodesk
-- Shimming toolbox for standard multi-echo field mapping
-- Python Packages: `matplotlib`, `seaborn`, `nilearn`, `plotly`, `Nibabel`, `scipy`, `Pandas`
-
-## Data
-
-The data utilized in this project were obtained from three different sources. For the double echo GRE and EPI field mapping I utilized a dataset that is part of the fmriprep data examples available on Open Neuro, accessible [here](https://github.com/OpenNeuroDatasets/ds001600).
-The second dataset used for Multi-echo GRE field mapping was obtained from our lab. Although we are not permitted to distribute this dataset, users can still follow the steps outlined in calculating the field maps, as the resulting images and analysis procedures are self-explanatory.
-The third dataset employed for the fMRI comparison, both with and without distortion correction, originates from National Taiwan University and is also available on Open Neuro, accessible [here](https://github.com/OpenNeuroDatasets/ds000172.git).
-It's worth noting that the notebook includes instructions for cloning the dataset and retrieving the necessary files using Datalad, enabling anyone to replicate the analysis steps.
-
-## Project Deliverables
-
-- A Github repository
-- A Jupyter notebook
-- An explanatory markdown document
-
-## Results
-### Double-echo GRE field mapping
-To calculate a field map using the double-echo GRE method, one needs to subtract two consecutive phase maps and divide the result by the difference in echo times between them. However, when dealing with the second phase image, there is a possibility of phase wrapping. When the real phase evolution is larger (e.g., for Δν = 250 Hz and ΔTE = 3.0 ms, resulting in Δφ = 1.5π), the calculated phase will wrap to -0.5π. In general, phase wrapping introduces an incorrect estimation of the local magnetic field. In this specific example, -0.5π would be equivalent to a -83 Hz offset not +250 Hz offset. This highlights the impact of phase unwrapping on the accurate determination of the local magnetic field. So, simply subtracting the phase maps in this case would yield erroneous phase values, as the second phase map is wrapped to a lower phase value compared to the first phase.
-
-To address this issue, there are two recommended approaches. The first is to spatially unwrap both phase images before performing the subtraction [5]. This ensures that the phase values are correctly aligned and eliminates the wrapping effect. Alternatively, a better approach is to calculate the complex difference between the two phase images and then perform spatial unwrapping on the resulting complex difference image. This method is already implemented in the shimming toolbox [6]. By taking the complex difference, we can reduce the need for additional spatial unwrapping, which is known to be error-prone. These concepts can be visualized in the following figure:
-
-
-
-
-
-### Mutli-echo GRE field mapping
-To calculate a field map using the Multi-echo GRE method, we employ the slope of the linear regression of phases versus echo times. Accurate estimates of magnetic field offsets are achieved by utilizing an increased number of evolution delays and extending the duration of these delays. While the acquired phase during longer delays may be wrapped, the initial delay is typically chosen in such a way that no phase wrapping occurs.
-Based on the linear phase-time trend established by the first delay(s), subsequent delays can be unwrapped by adding or subtracting an integer multiple of 2π to the calculated phase. This unwrapping process ensures that the phases are correctly aligned and removes the effects of wrapping. You can refer to [here](https://brainhack-school2023.github.io/Vejdani_project/Phase_evolution.html) for further details. Once all phases have been temporally unwrapped, a linear least-squares fit of the phase-time curve is performed, providing the best estimate of the magnetic field offset. This linear regression analysis allows us to determine the relationship between the phase and time, enabling accurate characterization of the magnetic field offset.
-However, the order of performing temporal and spatial unwrapping is crucial. In the following figure, we can compare the results of field maps calculated with the order of temporal+spatial unwrapping and reverse (spatial+temporal unwrapping).
-
-
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-
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-
-
-
-
-### EPI field mapping
-EPI images can be utilized to approximate a field map by acquiring two volumes with opposite phase encoding directions. The difference in distortion between these two acquisitions is then utilized in a least squares algorithm to estimate the field map. Although the resulting field map may exhibit some artifacts, as demonstrated in the following images, it is still sufficient to correct for distortion to some extent.
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-To perform this step, FSL command line tools such as TOPUP, FUGUE, and FLIRT are employed for the analysis. These tools enable the processing and correction of distortion in the EPI images, contributing to improved image quality.
-
-### rs-fMRI with and without distortion correction
-For the last part of the project, the third dataset is employed, which consists of resting-state functional MRI (rs-fMRI) data and the unwrapped phase difference image. rs-fMRI allows us to investigate brain regions that exhibit correlated temporal activity, forming functional networks. In order to analyze these networks, various brain regions from a known atlas are selected, and their signal time series are compared to construct a matrix known as the connectivity matrix.
-A figure of this atlas can be seen [here](https://brainhack-school2023.github.io/Vejdani_project/atlas.html).
-
-
-
-The connectivity matrix represents the strength of functional connections between different regions of the brain. To quantify these connections, a Pearson correlation is calculated between the time series of different regions in the matrix. The resulting correlation values are encoded using a color bar to visualize the connectivity patterns. Finally, the process of brain masking and retrieving connectivity data was accomplished for a single subject using the nilearn library[7].
-
-The results are as follows:
-
-
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-Click [here](https://brainhack-school2023.github.io/Vejdani_project/connectivity_corrected.html) for interactive plot.
-
-
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-
-
-Click [here](https://brainhack-school2023.github.io/Vejdani_project/connectivity_uncorrected.html) for interactive plot.
-
-To see the overlayed rs-fMRI images on standard MNI space click [here](https://brainhack-school2023.github.io/Vejdani_project/MNI_overlay.html).
-
-As evident from the observations, the lack of a field map to rectify the geometrical distortion in the EPI images leads to a reduced number of functional connections detected in the frontal and temporal lobes, which are particularly affected by a substantial B0 offset.
-
-## Conclusions
-
-In conclusion, these findings highlighted the significance of field mapping in mitigating distortions and improving the detection of functional connections. The integration of advanced tools like nilearn facilitated the calculation and visualization of functional connectivity matrices. Overall, this project underscores the crucial role of field mapping and distortion correction for enhancing the accuracy and reliability of functional connectivity studies.
-
-
-## Acknowledgements
-
-I extend my sincere gratitude to the Brainhack team of 2023 for their unwavering support throughout the duration of the course. I have learned a lot throughout this course. Also, I would like to express my special appreciation to Dr. Eva Alonso Ortiz for her valuable support and expertise, which proved instrumental in the successful completion of this project.
-
-
-## References
-
-1. Poot, D.H.J. et al. (2010) ‘Improved B0 field map estimation for high field EPI’, Magnetic Resonance Imaging, 28(3), pp. 441–450. Available at: https://doi.org/10.1016/j.mri.2009.12.020.
-
-2. Clare, S., Evans, J. and Jezzard, P., 2006. Requirements for room temperature shimming of the human brain. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 55(1), pp.210-214.
-
-3. Kim, T., Lee, Y., Zhao, T., Hetherington, H.P. and Pan, J.W., 2017. Gradient‐echo EPI using a high‐degree shim insert coil at 7 T: Implications for BOLD fMRI. Magnetic resonance in medicine, 78(5), pp.1734-1745.
-4. Webb, A.G. (ed.) (2016) Magnetic Resonance Technology: Hardware and System Component Design. Cambridge: Royal Society of Chemistry (New Developments in NMR). Available at: https://doi.org/10.1039/9781782623878.
-
-5. m. Jenkinson, fast, automated, n-dimensional phase-unwrapping algorithm, Magn. Reson. Med., 2003, 49, 193–197.
-
-6. D'Astous, A., Cereza, G., Papp, D., Gilbert, K.M., Stockmann, J.P., Alonso‐Ortiz, E. and Cohen‐Adad, J., 2023. Shimming toolbox: An open‐source software toolbox for B0 and B1 shimming in MRI. Magnetic Resonance in Medicine, 89(4), pp.1401-1417.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-05-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Diffusion MRI reconstruction Project"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Erjun Zhang, Brainhack School 2020]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/BHS_Project_dMRI
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Diffuision MRI reconstruction, MRI Visualization, MRI Analyzation, Machine Learning]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
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-summary: "This project is about diffusion magnetic resonance (MR) data processing and analysis. It mainly consists of three parts: brain diffusion MR data preprocessing, diffusion MRI images reconstruction, data visualization and left and right hemispherical preprocessed MR images classification. The whole procedures can be found in [this Jupyter Notebook file](https://github.com/brainhack-school2020/BHS_Project_dMRI/blob/master/dMRI%20Reconstruction%20Project.ipynb). Explanations about procedures results and other details are given in it. With reproducibility being a primary concern, this project was completed by using open-source software/tools (Python, FSL, DIPYPE...) and dataset (dHCP and PRIME)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "bhs2020.png"
----
-
-
-## Project definition
-
-### Background
-
-Low blood sugar is a common problem in both children and adults. Existing research showed that it may lead to serious both transient and permanent structural abnormalities on brains. Magnetic resonance imaging (MRI), specially diffusion MRI, has a higher in vivo sensitivity to lesions in soft tissues compared to other imaging modalities, and has potential to identify and quantify brain structure changes. Some of these changes are not appraient enough,even for experienced experts. Machine learning is a useful method to discover potential features.
-
-Currently, there are some open-source software to reconstruct and analyze diffusion MRI data. However, only using these tools, one, like me as a new student in this field, may get confused about operations and principle behind them. This seriously stops people to reach their long-term goals in their diffusion MRI project.
-
-Thus, I began this project to preprocess dMRI data, reconstruct diffusion MR images and do some analysis by using machine learning. The goals are as following:
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-* Get preprocessed diffusion MR images from raw data;
-* Reconstruct diffusion tensor images from the preprocessed data;
-* By using machine learning, try to classify two hemispherical brains from preprocessed diffusion images.
-
-
-
-### Tools
-
-The "Diffusion MRI reconstruction project" will rely on the following tools:
- * Python3: coding language used to complete this project (Python2 also needed for some special packages).
- * Github and git: to organize this project and make version control
- * Jupyter Notebook: to file code, visualizations and results
- * [Markdown](https://guides.github.com/features/mastering-markdown/), to structure the text in README and Jupyter Notebook
- * Visual Studio Code: to be code editor
- * Nipype: to use FSL command preprocessing image data
- * DIPY: to be used as diffusion tensor imaging (DTI) fitting and denoising
- * matplotlib, plotly and ipython widgets: data visualization
- * Linux (Ubuntu 20.04)
-
-### Data
-
-This project used data from online dataset offered by:
-1. [The Developing Human Connectome Project](http://www.developingconnectome.org/second-data-release/). It consists of over 800 neonatal scans and over 250 fetal scans and can be used for data analysis after image reconstruction.
-2. [PRIME](http://fcon_1000.projects.nitrc.org/indi/PRIMEdownloads.html): used this dataset to reconstruct diffusion Images. This also can be downloaded [here](https://drive.google.com/file/d/1zgxynxjUCETBC6MAl4rfh0sL0WhFtKA9/view?usp=sharing) directly.
-3. Since during preprocessing, we used epi data with two opposite phase-encoding directions to correct distortions, other data can also be used as the source data if it meets this requirement.
-4. Data used for analysis is generated from image data after preprocessing. The final data can be found in this [github project folder](https://github.com/brainhack-school2020/BHS_Project_dMRI/blob/master/brain_water.csv).
-
-### Deliverables
-
-At the end of this project, we will have: Jupyter notebook will be developed, allowing diffusion MR reconstruction and data analyzation. Weekly Deliverables are as following:
-
- - Week 1: [Assignment 2](https://github.com/zhangerjun/Zhang-EJ-QLS612)
- - Week 2: [README file](https://github.com/brainhack-school2020/BHS_Project_dMRI/blob/master/README.md)
- - Week 3: [Data visualization](https://github.com/brainhack-school2020/BHS_Project_dMRI/tree/master/Visualization)
- - Week 4: [Project report](https://github.com/brainhack-school2020/BHS_Project_dMRI) and [project prosentation](https://github.com/brainhack-school2020/BHS_Project_dMRI/blob/master/BHS_dMRI_Project.pdf)
-
-## Results
-
-### Progress overview
-
-Since May 11, this project goes well and three goals of this project have been reached. This is just to let me be familiar with steps and tools of diffusion MRI processing. Next, I will combine different processing methods into this project. Specifically, I would like to look deep into DTI model fitting and try to replace it by a new model created by myself.
-
-### Tools I learned during this project
-
-1. Python
- * FSL, Nipype.interfaces, DIPY
- * matplotlib, plotly, ipywidgets
- * Function definition and calling
-2. Markdown
- * Format this README file
- * Insert figures and animation
- * LaTex to edit mathematical equations
-3. Github
- * Version control
- * Project organization
-4. BIDS
-5. Jupter notebook
-6. Found and learnt to use some small but useful tools
- * [Peek](https://github.com/phw/peek) for GIF making
- * [Gedit](https://wiki.gnome.org/Apps/Gedit) to edit code or markdown
-7. Medical file format (NII) and data structure (BIDS)
-8. Statistical method and machine learning
-
-### Results
-
-#### Data Visualization (see [gif figure](https://github.com/brainhack-school2020/BHS_Project_dMRI/tree/master/Visualization) below):
- - 3D volume slices image
- - Interactive widgets use to show preprocess results
- - Interactive widgets use to show reconstruction results
-
-
-
-
-
-
-#### Preprocessing results
-
-
-
-
-
-#### DTI results
-
-
-
-
-
-#### Left and right hemispheres classification results
-
-
-
-
-
-## Conclusion and acknowledgement
-
-This project started with medical imaging data format, then preprocessed the diffusion weighted images, which includes MP-PCA denoising, FSL TOPUP distortion correction and head movement correction. After this, DTI images were reconstructed from these preprocessed images successfully. Additionally, a dataset for left and right hemispheres classification was generated from these preprocessed images. By using the KNN method, classification accuracy of 92% was reached. As a starter, I will continue this project in the future. Soon, self-made models, instead of DTI model, will be used to reconstruct diffusion images. After that, I would like to use machine learning methods to classify potential changes of brain microstructures.
-
-
-Thanks to [Brainhack Summer School 2020](https://school.brainhackmtl.org/) and all instructors. Specifically, I would like to thank my instructors Noor, Greg and Agah for your advice, instruction and encouragement. I feel so lucky to find and take this summer school. During this short one month, I touched so many new skills and tools and began my first neuroscience project. I would also like to thank all the summer school students, just because of you, I know how wonderful the neuroscience world is and how active the neuroscience group is!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Brain Learning Unicorn Project"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Elise Douard]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/EliseD_BLUP_Brain-Learning-Unicorn-Project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://brainhack-school2020.github.io/EliseD_BLUP_Brain-Learning-Unicorn-Project/
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Structural MRI, Brain volumes, Genetic, Open-science, Machine learning, UKBioBank, Random forest, gradient-boosted tree, plotly]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Can a model predict the genetic profile of an individual based on brain regions volumes? There is growing evidence suggesting that genetic variations formally associated to neurodevelopmental disorders have significant effects on brain structures. In this project, the performance of three classifiers will be compared when predicting the genetic status of individuals from brain region volumes in a highly imbalanced dataset (UK BioBank cohort)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "BLUPproject_website.png"
----
-
-
-# BLUP: Brain Learning Unicorn Project
-
-Click [HERE](https://brainhack-school2020.github.io/EliseD_BLUP_Brain-Learning-Unicorn-Project/) to see this readme as a website.
-
-### ***Can a model predict the genetic profile of an individual based on brain regions volumes?***
-
-*Trying to deal with new stuff learned at the amazing Brainhack school*
-
-Author: Elise Alix Douard
-
-Other project contributors:
-BHS, Hannah Kiesow [@hannahmaykiesow](https://twitter.com/hannahmaykiesow) (also working on UKBiobank), Kuldeep Kumar [@meetkd007](https://twitter.com/meetkd007) (extracting data from UKBB servers)
-
-## Personal presentation
-
-Welcome to this project dear unicorn student !
-
-
-
-
-
Source: https://media.giphy.com/
-
-I am Elise, Ph.D. student in neurosciences at the UdeM since near to 4 years, and working on **the contribution of genetic to neurodevelopmental disorders** as autism. I don't really fit in a specific domain (genetic/cognitive neurosciences/psychology). I guess it is what we call a *unicorn student*? Currently, I am working with genetic data (Copy Number Variants), clinical phenotypes and doing a lot of statistics and graphs on R. But my initial formation was in cognitive neurosciences where I started to work with multimodal data (combination of Arterial Spin Labelling MRI data and Eye-tracking data).
-
-Since I started my Ph.D., I never used MRI data nor python, and I am here to take a revenge on that.
-
-**Skills:**
-
-- Data management (feed me with multimodal data plz)
-- Statistics
-- Debugging codes
-
-# Project definition
-
-## Background
-
-
-
-
-
-
Source: Illustration inspired from freepik.com content and adapted on adobe illustrator
-
-Copy number variants (CNVs) are a family of structural variation of the chromosomes. They can be either a loss or a gain of a chromosome portion in comparison to a genome of reference. Sometimes, CNVs can be pathogenic, meaning that they are formally associated to neurodevelopmental or psychiatric disorders, such as autism spectrum disorders (ASD), Schizophrenia (SZ) or intellectual disability (ID). Such pathogenic CNVs have been associated to significant alterations of brain volume ([Modenato et al., 2020](https://www.medrxiv.org/content/10.1101/2020.04.15.20056531v1.full) ; [Martin-Brevet et al., 2018](http://www.sciencedirect.com/science/article/pii/S000632231831401X) ; [Maillard et al., 2015](https://www.nature.com/articles/mp2014145)) or connectivity ([Moreau et al., 2019](https://www.biorxiv.org/content/10.1101/862615v1.full)). Notably, there were common alterations of the insula volume when comparing structural brain alterations due to pathogenic CNVs and due to a neurodevelopmental disorder (e.g. ASD or SZ) ([Cauda et al., 2017](https://onlinelibrary.wiley.com/doi/abs/10.1002/aur.1759) ; [Goodkind et al., 2015](https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2108651)).
-
-## Problematic
-
-Can a model predict the genetic profile of an individual based on brain regions volumes?
-
-## Aims
-
-- **Main goal**:
-
-This project aims to feed a machine learning model with brain volumes to predict if an individual is carrier of a potentially pathogenic CNV.
-
-
-
-
-
-
Source: Illustration inspired from freepik.com content and adapted on adobe illustrator
-
-- **Subgoals**:
-
-- [x] Making minimal change to the distribution shape of the volumes
-- [x] Dealing with imbalanced dataset reflecting the reality of the prevalence of pathogenic CNVs in the general population
-
-- **Personal goals**:
-
-- [x] Learn how to properly share scientific content
-- [x] Learn how to python instead of R
-- [x] Learn how to interactive plot
-- [x] Learn how to machine learning
-
-# Methods
-
-## Data
-
-### Raw data
-
-For this project, 35,759 individuals from [UK Biobank](https://www.ukbiobank.ac.uk/) with genetic and derived brain volume data were available.
-- MRI data (features): All individuals have derived-brain volumes preprocessed on Freesurfer using Desikan parcellation (68 regions) [[cf. documentation for processing pipeline details](https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf)].
-- Genetic data (labels): 1,265 individuals (3.5% of the cohort) were carriers of at least one of 93 potentially pathogenic CNV identified by [Kendal et al. (2019)](https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/cognitive-performance-and-functional-outcomes-of-carriers-of-pathogenic-copy-number-variants-analysis-of-the-uk-biobank/0D144F6880A46DC94EE27ADEACB5942B). These individuals will constitute the carriers class, all the other will be in the controls class.
-- Confounders: age, sex, total intracranial volume (TIV) and site of MRI acquisition were available for all individuals.
-
-### Data transformation
-
-For all individuals, the 68 region volumes were adjusted for potential confounder effects.
-
-**Table 1:** Description of the confounders
-
-| Goup | N | Mean age (sd) | Mean TIV (sd) | N Female | N Male | N Site 1 | N Site 2 | N Site 3 |
-|:------|:-----:|:---------:|:------:|:---------:|:---------:|:---------:|:---------:|:---------:|
-| Carriers | 1265 |63.8 (7.4)| 1540824.3 (150493.6) | 671 | 594 | 781 | 320 | 164 |
-| Controls | 34494 | 64.1 (7.6) | 1549091.7 (151512.6) | 18280 | 16214 | 21411 | 8607 | 4476 |
-
-### Final dataset: training and test sets
-
-A subgoal of the project was to make minimal changes to the features used in the machine learning models. The final volumes used as features were the ones adjusted for the confounder effects without z-scoring.
-Another subgoal was to deal with imbalanced dataset which reflect the reality of carriers prevalence in the general population. As an alternative, the imbalance was reduced by pseudo-randomly resampling the controls. The final dataset included the 1,265 carriers and twice more controls.
-
-Click on the following images to open interactive pie-charts:
-
-**Figure 1**: Proportion of controls and carriers in the training and test sets used for the machine learning models.
-
-
-
-
-
-## Machine learning model used
-
-- Random Forest Classifier using `sklearn.ensemble.RandomForestClassifier` [[documentation]](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
-- Random Forest with Random Undersampling using `imblearn.ensemble.BalancedRandomForestClassifier` [[documentation]](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.ensemble.BalancedRandomForestClassifier.html)
-- Gradient-Boosted Classification Tree using `sklearn.ensemble.GradientBoostingClassifier` [[documentation]](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html)
-
-## Step followed for the three machine learning models
-
-- Step 1: Training the model on the training set
-- Step 2: Test the model on a validation set
-- Step 3: Cross validation (n fold = 10)
-- Step 4: Playing with the hyperparameters `max_depth` and `n_estimators` (and `learning_rate` for the gradient-boosted tree classifier)
-- Step 5: Testing the model on the test set
-
-## Tools (used and learned)
-
-The project rely on the following technologies:
-- Visual Studio Code
-- Git/Github
-- Python (libraries: `pandas`, `numpy`, `nilearn`, `nibabel`, `sklearn`, `scipy`, `random`, `seaborn`, `plotly`, `matplotlib`, `ipywidgets`, `itertools`)
-- Jupyter notebook (Including Markdown, Python code and Slides using the Rise extension)
-
-## Deliverables
-
-In this GitHub repository:
-- README: Readme file for this project repository
-- Welcoming_vid.mp4: A non-relevant video of welcoming
-- Prep_visualization_ML_allinone.ipynb: Jupyter notebook with all the instructions and code lines for the data preparation and visualization, the machine learning models training and testing and the results visualization
-- requirements.txt: List of all the package used for this project
-- Slides_Final_week.ipynb: Jupyter notebook using Rise extension for the final presentation of the project
-- Data_for_plots/: Data used for two interactive figures (3D Desikan parcellation map and pie-cart of the sex and site distribution in the carriers and controls)
-- Interactive_plots/: Folder with the html object of the interactive figures
-- Slide_content/: Images used for the Slides_Final_week.ipynb file
-
-### Week 3 deliverable: data visualization
-
-link to the blog created for the assignment: https://elise-douard.github.io/EliseAD_BLUP_BlogPage/
-
-# Results
-
-## Results of the models
-
-**Figure 2**: Results after testing the models (step 5)
-
-
-
-
-As you can see in the Figure 2, none of the models were better than chance to classify the carriers and the controls.
-BUT it is not a problem because... I learned a lot doing this project!
-
-## Progress overview
-
-### Week one:
-- Learning tons of new concepts
-
-### Week two:
-- 18 may 2020 Creating the README file
-- 20 May 2020 5 min presentation of the project draft using README file
-- 21 may 2020 Starting to play with data on python and creating the main jupyter notebook
-
-### Week three:
-- 29 may 2020 Finalization of data preparation (removing confounders) and visualization, starting ML part
-
-### Week four:
-- 3 may 2020 Starting slides on jupyter notebook using Rise extension and creating project illustrations on adobe illustrator
-- 5 may 2020 Final presentation of the project with UKBioBank data and the results of the machine learning models
-
-### Week five:
-- 8 may 2020 Final push of the blog for the visualization assignment
-- 12 may 2020 Final push of the project on Github and the website
-
-# Conclusion and acknowledgement
-
-This BHS project allows me to learn a lot of concepts and tools concerning the open science. It was also a nice introduction to machine learning models. Hopefully, I will spread the word and I will surely include all these new tools in my practice. I am more than grateful toward all the instructors, mentors and students, who shared their knowledge.
-
-
Thanks for this enriching experiment!
-
-
-
-
-
Source: https://media.giphy.com/
-
-# References
-
-Cauda F. et al., “Are Schizophrenia, Autistic, and Obsessive Spectrum Disorders Dissociable on the Basis of Neuroimaging Morphological Findings?: A Voxel-Based Meta-Analysis.” *Autism Research* 10, no. 6 (2017): 1079–95. https://doi.org/10.1002/aur.1759.
-
-Goodkind M. et al., “Identification of a Common Neurobiological Substrate for Mental Illness.” *JAMA Psychiatry* 72, no. 4 (April 2015): 305–15. https://doi.org/10.1001/jamapsychiatry.2014.2206.
-
-Kendall K. M. et al., “Cognitive Performance and Functional Outcomes of Carriers of Pathogenic Copy Number Variants: Analysis of the UK Biobank.” *The British Journal of Psychiatry* 214, no. 5 (May 2019): 297–304. https://doi.org/10.1192/bjp.2018.301.
-
-Maillard A. M. et al., “The 16p11.2 Locus Modulates Brain Structures Common to Autism, Schizophrenia and Obesity.” *Molecular Psychiatry* 20, no. 1 (February 2015): 140–47. https://doi.org/10.1038/mp.2014.145.
-
-Martin-Brevet S. et al., “Quantifying the Effects of 16p11.2 Copy Number Variants on Brain Structure: A Multisite Genetic-First Study.” *Biological Psychiatry*, 84, no. 4 (2018): 253–64. https://doi.org/10.1016/j.biopsych.2018.02.1176.
-
-Modenato C. et al., “Neuropsychiatric Copy Number Variants Exert Shared Effects on Human Brain Structure.” *MedRxiv*, April 17, 2020, 2020.04.15.20056531. https://doi.org/10.1101/2020.04.15.20056531.
-
-Moreau C. et al., “Neuropsychiatric Mutations Delineate Functional Brain Connectivity Dimensions Contributing to Autism and Schizophrenia.” *BioRxiv*, (2019), 862615. https://doi.org/10.1101/862615.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Biosignal processing for automatic emotion recognition"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Danielle Benesch, Achraf Kabbabi]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [classification, emotion, eeg, ecg]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Can we automatically detect changes in emotions given a user's biosignals? In this project, we used multimodal biosignal data to predict the target emotion of audiovisual stimuli."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "free_brain_emotions.jpg"
----
-
-
-## Project definition
-
-### Background
-
-#### About us
-
-##### Danielle
-
-I started my research master's this January, and my thesis project will involve the automatic classification of biosignals among other things. My background is in cognitive science, and while I've already learned about the basics of machine learning and neuroinformatics, I would like to improve my skills by working on a practical project with real data.
-
-##### Achraf
-
-I am currently studying Biomedical Engineering at Polytechnique Montréal (M.Eng) and am enrolled in the Brain Hack School 2020 adventure for growing my technical skills as well as networking. I have been modestly introduced to NeuroImaging before enrolling in the biomedical engineering path and would like to broaden my knowledge of the field and sharpen my skills in it.
-
-#### About the project
-
-Psychological stress has been found to be associated with changes in certain biosignals. Features extracted from these biosignals have increasingly been used for predicting an individual's emotional state, including the EEG alpha asymmetry index, heart rate variability, and skin conductance response [(Giannakakis et al., 2019)](https://s3.amazonaws.com/academia.edu.documents/60215566/2019_Giannakakis_Review_on_psychological_stress_detection_using_biosignals20190806-1688-7kttp7.pdf?response-content-disposition=inline%3B%20filename%3DReview_on_psychological_stress_detection.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BALIEFO2U4%2F20200521%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200521T161015Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Security-Token=IQoJb3JpZ2luX2VjENj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQCDcDSZcyT6u4Lc7Hc16sNh2InzAcekbrsiPyXV2UtM%2FQIgKLX0wumug7pgD59QsVol60DUF42liQhPUKjn7NITiNEqtAMIMBAAGgwyNTAzMTg4MTEyMDAiDDW7xJRp4wDPxgLtryqRA6P76YemmmJBvBnoRuW0%2BGaK9mS1Z61PKckoR8jxyxtvu45NdtS2qyfCAlopFhEH9zMMwHKKs7dwPdwbXuLt6HSQ06bRW8RTtGFE%2FooJvC9%2BZY2Pff1h%2Bnk8HW%2FM%2FDjlP5AzkmDpJ0KUO04PFxoqqBvFFIIy83iafxPVE0fly3fktUhE0kKsuOnBOjyABMzUPYI3nqjej%2BiJ8QpCQbqXx6r8YAuNJ5Nr4LGP2SVFKXA787KJnsrmD1uisIdjqLnjhzXaDZreoeukKMOF3yjQ1xG7srPW%2FWzoFSxLP79bNDEq54MJ145kg2i6vw8UqgdmDmNXjl3y7cLYLSJ%2BEFWkvYHCySYan1qdnjJDbTFYMm7GnRPM%2FVFfe6iwkPDI4gdwKyHnPH4JkEMvI2b7l%2FasmhHHjA932H5ziezXh%2FsJZNi5PdK26OwxxGM3MfiMjBpnf%2FtT%2FDPsq4B9i2i0azwQNiJYgDgw74VA%2BQJEtGPQnHaJjM%2FLud%2BMz35zKh%2BYRUMQqwFWPUOhA6EdYUgYcOUPWY7KMNexmvYFOusBM0Sz5ZgfGF1vsvoowJ42W%2BMbymHSuGfWGkUFug4P8pS02TC2eB9GPSMyN9CHbQ7PV2%2BbWVsPGBeqUtNSe6xOLqVR9goW4K3xWjw1cnSopEyUpXU%2F8C3qsCfUPGEUT2sw05J9F%2BlBBdSD7nZ4pH9SZxKFq5A6JRhzRcKIT11ba4UUznzwcBY6X%2FYgz3PpQYOl%2Bet5MFCw%2F3BcjRgR7nGytsLHNC9LmHGg9YAeQ7N%2Fo9%2BAGVVm6RDXpOeqExF7NkcKDofYQidkfUS6iGhOfdUA%2Fl1IkH4s%2BRbeATH2yeIOsIdEB%2FGqV8vOlEkrPg%3D%3D&X-Amz-Signature=62de6e2c0bd6f2dc619c179bf22988be24a3abdcbe8bbed4fd2c4dfdd2c6b780). This research has allowed for the development of technologies that can respond to users' emotions [(Carroll et al., 2013)](https://ieeexplore.ieee.org/abstract/document/6681439) and motivated the creation of multiple datasets containing emotion-correlated biosignal data [(Wiem & Lachiri, 2017)](https://pdfs.semanticscholar.org/3750/b635d455fee489305b24ead4b7e9233b7209.pdf). In this project, we were broadly interested in the task of emotion recognition, using a publicly available dataset to explore how we could predict emotions using biosignals.
-
-### Tools
-
-Tools and techniques we used:
- 1. Bash and Python
- 2. Git and GitHub
- 3. Preprocessing and feature extraction with Scipy and Neurokit
- 4. Data visualization with Plotly
- 5. Machine learning with Scikit-learn
-
-### Data
-
-At the start of the project, we considered using the following databases:
-
- - [DREAMER: A Database for Emotion Recognition through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices](https://ieeexplore.ieee.org/document/7887697)
- - Biosignal data: EEG and ECG
- - Emotion data: Rating on valence-arousal-dominance scale provided by participants and tags for the "target emotion" of the stimuli
- - [The MAHNOB-HCI-Tagging database](https://mahnob-db.eu/hci-tagging/)
- - Biosignal data: EEG (in the form of BDF files), ECG, respiration amplitude, and skin temperature
- - Emotion data: Rating on valence-arousal scale provided by participants, and for some of the data, emotion tags (e.g. amused) selected by participants
- - [An EEG dataset recorded during affective music listening](https://openneuro.org/datasets/ds002721/versions/1.0.1)
- - Biosignal data: EEG
- - Emotion data: Rating on valence-arousal scale provided by participants (though it's not immediately clear where this can be downloaded)
- - [AffectiveROAD system and database to assess driver's attention.](https://affect.media.mit.edu/share-data.php)
- - Biosignal data: BVP, EDA, ECG, respiration rate, skin temperature
- - Emotion data: "Stress metric" provided by observing experimenter
-
-Ultimately, we decided to use the [DREAMER](https://ieeexplore.ieee.org/document/7887697) dataset which must be requested from the authors [here](https://zenodo.org/record/546113).
-
-The dataset contains EEG and ECG data from 23 participants were shown 18 videos intended to elicit 9 different emotions - as well as "neutral videos" thought to have no valence for "baseline" data. Biosignal data was collected using the Emotiv EPOC wireless EEG headset and the Shimmer2 ECG sensor. We were especially interested in how accurately we could classify emotions using biosignal data collected by portable, inexpensive devices due to the potential of automatic emotion recognition incorporated into wearables. An image of the equipment is shown below [(from Katsigiannis & Ramzan, 2018)](https://ieeexplore.ieee.org/document/7887697).
-
-
-
-
-
-More information on how the data were collected can be found in the PDF [DREAMER_info](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/docs/DREAMER_info.pdf) (downloaded from [Zenodo](https://zenodo.org/record/546113)) or in the paper by [Katsigiannis and Ramzan (2018)](https://ieeexplore.ieee.org/document/7887697).
-
-### Deliverables
-
-We were able to complete:
- - Data preprocessing and feature extraction for at least one biosignal.
-
- - [x] [Python script](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/DREAMER_main.py)
-
- - Visualization of the relationship between the extracted features and the emotion data.
-
- - [x] [Jupyter notebook](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/DREAMER_Achraf.ipynb)
-
- - [x] Both [interactive](https://brainhack-school2020.github.io/Biosignal-Emotions-BHS-2020/HR_fear_anger_calm.html) and [static](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/RMSSD_fear_anger_calm.png) visualizations
-
- - Training a classifier to predict the emotion data.
-
- - [x] [Python script](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/DREAMER_main.py)
-
- - [x] [Evaluation of classifier performance](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Classification/DREAMER_classifier_comparison.ipynb)
-
- - [x] [Shell script](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/run.sh)
-
-More information about the contents of this repository and instructions for how to run them can be found in [the "repoInfo" Markdown file](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/docs/repoInfo.md) located in [the docs folder](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/tree/master/docs).
-
-The project presentation is available [here](https://gitpitch.com/brainhack-school2020/Biosignal-Emotions-BHS-2020).
-
-### Progress & Results
-
-We first preprocessed the biosignals and explored the relationship of extracted features with the emotion data, then we evaluated the performance of a number of classifiers. We created a minimal python script that performs the data preprocessing, feature extraction, and the classifier evaluation.
-
-#### Achraf: Preprocessing and feature extraction
-
-The preprocessing scripts I wrote were inspired by [Jiaqi1008's repository](https://github.com/Jiaqi1008/Emotion_detection). The DREAMER dataset being a .mat file, I used the library Scipy to load it: it contained EEG data, ECG data, and subjective ratings. The preprocessing for EEG data consisted of extracting the maximum of the Power Spectrum Density (PSD) for the EEG signals for three bands (theta, alpha, beta), for each of the 14 electrodes used. The library Scipy was used for filtering and PSD extraction (Welch's method). The preprocessing for ECG data was done thanks to the library [Neurokit2](https://github.com/neuropsychology/NeuroKit) by first preprocessing the data with the ecg_process() method then by extracting the features with the ecg_intervalrelated() method. The features extracted were the Mean Heart Rate and various Heart Rate Variability (HRV) metrics. I tested those preprocessing pipelines in notebooks first, then I wrote a script [DREAMER_main.py](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/DREAMER_main.py) implementing them. An output example of the script in a terminal can be found [here](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/ComputeCanadaScriptExample_Preprocessing.png)
-
-#### Danielle: Evaluation of classifiers
-
-I was interested in training a classifier to detect stress, but at the beginning of the project, I wasn't sure how exactly we should use the data to do so. I wanted to be able to compare my results to others, but there appears to be a wide range of ways to evaluate stress detection, further complicated by the fact that research on automatic emotion recognition and stress detection has relied on different models of emotion [(Wiem & Lachiri, 2017)](https://pdfs.semanticscholar.org/3750/b635d455fee489305b24ead4b7e9233b7209.pdf).
-
-We first considered predicting self-reported valence and arousal scores, but then we explored the data and looked at the relationship between some of the features we extracted and valence/arousal. It seemed like these features did not indicate changes in valence or arousal as much as we'd hoped. It would be interesting to see how well we could predict valence and arousal using different methods of preprocessing, feature extraction, scaling, thresholding etc., but for the sake of this project, we thought we'd start with an easier classification problem.
-
-In the paper describing DREAMER [(Katsigiannis & Ramzan, 2018)](https://ieeexplore.ieee.org/document/7887697), Table I contains the "target emotion" of each video clip with the video ID. The target emotion was not included in the .mat file downloaded from Zenodo, so I [added this information to each datapoint based on the video ID](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Data/load_emotion_data.py). Including this information allowed us to predict the emotions that were intended to be evoked by each stimulus, rather than self-reported emotions.
-
-There were 9 different target emotions: "calmness", "surprise", "amusement", "fear", "excitement", "disgust", "happiness", "anger", and "sadness". Before determining whether we could predict the full sprectrum of emotions, I wanted to see whether we could distinguish "calmness" from two emotions on the opposite side of the spectrum: "anger" and "fear". This constituted a binary classification task: "calmness" vs. "not calmness".
-
-To try to get an idea of how the classifiers would perform given biosignal data from completely new people, I evaluated the classifiers using [Group 10-Fold Cross-Validation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html), with the groups being the participants - meaning that the training set and validation set always consisted of data from separate participants. However, we should note that we used the entire dataset for data exploration; there was no held-out test set that we only used solely for evaluation. Evaluating the classifiers on another emotion-correlated biosignal dataset may provide a more realistic idea of the classifiers' generalizability.
-
-I selected a number of classifiers [based on a script from the sci-kit learn documentation](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html): Nearest Neighbors, Support Vector Machine with a linear kernel, Support Vector Machine with an RBF kernel, Gaussian Process, Decision Tree, Random Forest, Multi-layer Perceptron, AdaBoost, and Naive Bayes. The accuracy averaged over the splits for each classifier, with features extracted from both the EEG and ECG data, is shown below:
-
-
-| Name | Mean Accuracy |
-|-------------------|---------------|
-| Nearest Neighbors | 0.87 |
-| Linear SVM | 0.67 |
-| RBF SVM | 0.87 |
-| Gaussian Process | 0.7 |
-| Decision Tree | 0.9 |
-| Random Forest | 0.9 |
-| Neural Net | 0.73 |
-| AdaBoost | 0.98 |
-| Naive Bayes | 0.59 |
-
-
-[AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html), an ensemble method, had the highest mean accuracy. I also measured the average prediction runtime, and AdaBoost was relatively slow compared to the other classifiers (about 5-10 ms, while most of the others were around 1 ms, on my machine). While we didn't get to do this during this project, it would be interesting to see how methods to reduce runtime affect performance.
-
-#### Week 3 deliverable: data visualization
-
-##### Danielle's Week 3 deliverables
-* Interactive Figure: ["Participant Ratings of Film Clips in Valence-Arousal Space](https://brainhack-school2020.github.io/Biosignal-Emotions-BHS-2020/)
- * Bonus: [a jupyter notebook generating this figure...](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/Week3_Emot_Plot_Danielle.ipynb) written while using a [linter](https://pypi.org/project/pycodestyle/), which I know about thanks to [Greg's talk on scripting](https://www.youtube.com/watch?v=zpOQENxs1G4)! [](https://gesis.mybinder.org/binder/v2/gh/brainhack-school2020/Biosignal-Emotions-BHS-2020/85279820daf948d114d780e39d609c4f704f8cb1?filepath=Deliverables%2FWeek3_Emot_Plot_Danielle.ipynb)
-
-
-
-* Interactive Figure: ["Score vs. Prediction Runtime for all CV Iterations and Classifiers"](https://brainhack-school2020.github.io/Biosignal-Emotions-BHS-2020/classifier_comparison.html)
-
-
-
-
-
-##### Achraf's Week 3 deliverables
-* [A Jupyter Notebook generating static and interactive visuals (ipynb)](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/DREAMER_Achraf.ipynb) and the corresponding [HTML version](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/DREAMER_Achraf.html)
-* Static visuals that I found interesting during visual exploration of the data:
- * [Arousal patterns](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/Arousal.png)
- * [Valence patterns](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/Valence.png)
-* Screenshot of terminal outputs of the scripts we wrote for Compute Canada (minimal version):
- * [Preprocessing command example](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/ComputeCanadaScriptExample_Preprocessing.png)
- * [Classification command example](https://github.com/brainhack-school2020/Biosignal-Emotions-BHS-2020/blob/master/Deliverables/ComputeCanadaScriptExample_Classification.png)
-
-### Conclusion and acknowledgements
-
-During the BrainHack School, we hoped to learn as much as we could about modern (neuro)scientific practice, to improve our Python skills through a hands-on multi-disciplinary project, and to exchange information and expertise with the other participants.
-
-The first week was an intense theoretical and practical overview of many modern tools in neuroimaging, many of which we had very limited experience with or had never even heard of. The second week taught us how important it is to take enough time to clearly define a project and specify goals for it. Starting with a relatively broad project, it was difficult to narrow our goals down to one dataset and method, but ultimately we chose to pick one and see it through to the end.
-
-The third and fourth weeks were the core of the brain-hacking adventure and taught us many things such as:
-
-* Improved coding skills: Python notebooks & scripting
-* Deeper understanding of machine learning
-* Open science tools and practices
-
-While we weren't able to incorporate all of the tools that we had hoped to use in our project, we are grateful to have been exposed to them. Knowing where we can find [helpful online resources](https://school.brainhackmtl.org/schedule/) on all of the topics covered, we hope to slowly integrate what we've learned into our own work, post-BrainHack.
-
-We would like to thank the course organizers and our instructors who spent a lot of time helping us with of our project and generally enlightening us:
-
-* Greg
-* Agâh
-* Loic
-* Yann
-
-### References
-
-Carroll, E. A., Czerwinski, M., Roseway, A., Kapoor, A., Johns, P., Rowan, K., & Schraefel, M. C. (2013, September). Food and mood: Just-in-time support for emotional eating. In *2013 Humaine Association Conference on Affective Computing and Intelligent Interaction* (pp. 252-257). IEEE.
-
-Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2019). Review on psychological stress detection using biosignals. *IEEE Transactions on Affective Computing.*
-
-Katsigiannis, S., & Ramzan, N. (2017). DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. *IEEE journal of biomedical and health informatics*, 22(1), 98-107.
-
-Wiem, M. B. H., & Lachiri, Z. (2017). Emotion classification in arousal valence model using MAHNOB-HCI database. *Int. J. Adv. Comput. Sci. Appl. IJACSA*, 8(3).
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-18" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Brian Tumor Detection in MRI Using Faster R-CNN"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Yadollah (Amir) Zamanidoost]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/Zamanidoost_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI), click `manage topics`.
-# Please only lowercase letters
-tags: [MRI,Brain Tumor Detection,Faster R-CNN]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project presents a deep learning-based pipeline for detecting brain tumors in MRI scans using a customized Faster R-CNN architecture."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_Tumor_image.jpg"
----
-
----
-
-
-## Project definition
-
-
-### Background
-Brain tumors are among the most challenging medical conditions to detect and treat. Accurate identification of tumor regions in MRI scans is crucial for diagnosis, surgical planning, and treatment monitoring. Traditional methods rely heavily on expert interpretation, which can be time-consuming and prone to inter-observer variability. Deep learning approaches, especially object detection frameworks like Faster R-CNN, offer a promising alternative by automating tumor localization and classification. In this project, we enhance the classical Faster R-CNN pipeline with a false positive reduction (FPR) stage to improve robustness and reliability.
-
-
-
-### Tools
-
-- **Programming Language**: Python 3.x
-- **Deep Learning Framework**: PyTorch
-- **Computer Vision Libraries**: OpenCV, torchvision
-- **Annotation Tool**: LabelImg, Nibabel
-- **Visualization**: Matplotlib, Seaborn
-- **Notebook Environment**: Jupyter Notebook
-- **Hardware**:
- - **Training RPN**: Tesla V100 GPU (via terminal-based access)
- - **Inference and Evaluation**: CPU
-- **CUDA**: Used during RPN training for GPU acceleration
-
-### Data
-
-This project uses publicly available datasets containing 2D T1-weighted contrast-enhanced MRI images of brain tumors along with their ground-truth labels. The data is sufficient in both quantity and quality to allow for effective training and fine-tuning of object detection models such as Faster R-CNN.
-
-1. [Brain Tumor Segmentation @ Kaggle](https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation)
- - **Original Source & Credit**: [Figshare](https://figshare.com/articles/dataset/brain_tumor_dataset/1512427)
- - **Description**: This dataset contains 3064 T1-weighted contrast-enhanced brain MRI images in `.png` format, with manually labeled tumor regions provided via ground-truth masks.
- - **Use Case**: Used for training and fine-tuning the Faster R-CNN model.
-
-All images were preprocessed and annotated for object detection tasks by converting segmentation masks into bounding box labels.
-
-### Project Deliverables
-
-At the end of this project, the following materials will be available:
-
-- A completed and well-documented `README.md`.
-- Several Jupyter Notebooks.
- - For training the RPN and FPR models using GPU acceleration (Tesla V100).
- - For inference, post-processing, and evaluation (executed on CPU).
-- Performance metrics including precision, recall, F1-score, and FROC curves.
-- The slide presentation introducing the problem, dataset, model architecture, and key findings.
-
-## Results
-
-### Progress Overview
-
-The Faster R-CNN model demonstrates strong localization capability in detecting brain tumors from MRI scans, even with limited annotated data for training. Our custom RPN training and false positive reduction significantly improved precision without sacrificing recall.
-
-
-### Tools I Learned During This Project
-
-Throughout this project, I gained hands-on experience with several essential tools and libraries in deep learning and medical image analysis, including:
-
-- **PyTorch** – for building and training the Faster R-CNN and custom RPN models
-
-- **Torchvision** – for using pretrained VGG16 and manipulating datasets
-
-- **OpenCV** – for image processing and visualization tasks
-
-- **Matplotlib & Seaborn** – for plotting training metrics and visual results
-
-- **scikit-learn** – for computing classification metrics like precision, recall, and F1-score
-
-- **Git & GitHub** – for version control and project collaboration
-
-- **Markdown** – for documenting the project and creating a structured README
-
-- **Jupyter Notebooks** – for training, evaluation, and presenting visual results step-by-step
-
-
-### Deliverables
-- Developed a two-stage Faster R-CNN + FPR framework for brain tumor detection.
-
-- Trained and evaluated models using publicly available MRI data.
-
-- Provided Jupyter notebooks for data prep, model training, inference, and evaluation.
-
-- Shared trained models and test dataset via Google Drive for reproducibility.
-
-- Created a slide presentation summarizing the project and results.
-
-### Future work
-
-Optimizing the anchor shape using metaheuristic algorithms
-
-
-## Conclusion and acknowledgement
-
-This project presents a two-stage deep learning pipeline for accurate brain tumor detection in MRI images, combining the Faster R-CNN architecture with a False Positive Reduction (FPR) model. Through fine-tuning and evaluation on open-access datasets, the system achieves promising detection performance, with enhanced precision and reduced false positives.
-
-Many thanks to the BrainHack School Professor ([Dr. Eva Alonso Ortiz](https://neuro.polymtl.ca/team/faculty/eva-alonso-ortiz.html)), TA (Sebastian Rios), instructors, and fellow participants for their support, feedback, and inspiration throughout this project.
-
-## References
-[1] Brain Tumor Segmentation Dataset (Kaggle version). https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
-
-[2] Cheng, J. (2017). Brain Tumor Dataset. Figshare. https://figshare.com/articles/dataset/brain_tumor_dataset/1512427
-
-[3] Bhanothu Y, Kamalakannan A, Rajamanickam G. Detection and classification of brain tumor in MRI images using deep convolutional network. In2020 6th international conference on advanced computing and communication systems (ICACCS) 2020 Mar 6 (pp. 248-252). IEEE.
-
-[4] Ezhilarasi R, Varalakshmi P. Tumor detection in the brain using faster R-CNN. In2018 2nd International Conference on, 2018 2nd International Conference on 2018 Aug 30 (pp. 388-392). IEEE.
-
-[5] Zamanidoost Y, Alami-Chentoufi N, Ould-Bachir T, Martel S. Efficient Region Proposal Extraction of Small Lung Nodules Using Enhanced VGG16 Network Model. In2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) 2023 Jun 22 (pp. 483-488). IEEE.
-
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-15" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Brain Decoding Using Connectivity Informed Models"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Arian Morteza]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/Aarian/BrainDecoding
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://aarian.github.io/BrainDecoding/intro.html
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Brain Decoding, Graph Neural Networks, Graph Signal Processing, FMRI]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Brain Decoding is the reconstruction of the sensory and other stimuli form the information that has already been encoded and represented in the brain. For example, image genration, and task classification, from brain activity signals could be covered under this topic. In this project, a graph neural netwrok appriach is used to learn the representation of the brain regions activities, and do image classification for the end-user (patient)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "./Brain.jpg"
----
-
-
-## Brain Decoding, overall perspective
-The following provides some details about the Brain Decoding. First the raw data (including, per Voxel FMRI signal, connectome, ...) is preprocessed to a latent space (beta scores, Enc./Dec. and ...), providing a meaningful representation of the input data. Then based on the application including image generation, task classification; the latent space could be cast to the output.
-
-
-
-
-## **Brain Decoding using Beta scores and Graph Neural Networks**
-
-For this poject, we have access to the Brain activities, and its parcellation. Then the main task is decode the Brain activites into different classes. Normally as the data per voxcel in the Brain is a Fmri signal, as a preprocesing stage, this data is represented by different techniqes. Here for this project, beta scores as a linear model fitted to the fmri data has the role to represent this data. Therefor after casting the data to the a space called, beta-space, the data has more meaningful representaiton, and also the connectome as the underlying graph connection of the Brain regions is feeded to the Grpah neural netwrok for message passign between different links of the nodes. Eventually, after the trainig, the netwrok is trying to do a classification task based on the training data. Here is the over all procedure for this project:
-
-
-
-## **Tools and Deliverables**
-This project relies on the following technologies.
-
-* The main libraries used for this projects are : `torch-geometric`, `nilearn`, `matplotlib`
-* The details of coding and results analysis are fully developed in this [jupyter book](https://aarian.github.io/BrainDecoding/GraphNN_Arian.html).
-* Github repo for the code is [here](https://github.com/Aarian/BrainDecoding).
-
-
-
-## **Acknowledgements**
-I would like to extend my gratitude to the organizers of Brain Hack School for providing this incredible opportunity to delve into such an intriguing and impactful topic. Special thanks go to Dr. Pierre Bellec and Dr. Marie St-Laurent for their invaluable mentorship and guidance throughout this journey. Their expertise and support have been instrumental in my learning and growth, and I am deeply appreciative of their dedication and encouragement.
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-8" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Brain Tumor Segmentation via SAM-based fine-tuning on structural MRI images"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Li-Xuan Alex Peng]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI), click `manage topics`.
-# Please only lowercase letters
-tags: [mri,segmentation,brain_tumor,foundation_model]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project is to learn how to perform brain tumor segmentation using fine-tuning on foundation models, I followed tutorials provided by [MedSAM](https://github.com/bowang-lab/MedSAM) and perform fine-tuning on open datasets."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "qualitative_results.png"
----
-
-
-## Project definition
-
-### Background
-
-Of the most recent research in computer vision, Meta has released a foundation model i.e. [Segments Anything Model (SAM)](https://segment-anything.com/), capable of segmenting arbitrary semantics on any image.
-Researchers from the University of Toronto later fine-tune the SAM model on medical images [(MedSAM)](https://arxiv.org/pdf/2304.12306.pdf) and reach a significant improvement on such tasks compared to the original SAM.
-
-
-### Tools
-
-Computation Resource and Running Environment:
- * Packages: CUDA, Numpy, Nilearn, Pytorch, SAM, MedSAM, Seaborn
- * Environment: Custom built JupyterLab server running on NV-Docker based container
- * Hardware: Nvidia Tesla A100 x2 (For training), Nvidia GTX 1080Ti (For inference)
-
-
-### Data
-
-The two datasets below that I found in open-access data all provided 2D MRI images with corresponding ground-truth masks.
-Empirically, their quantities and qualities are adequate for fine-tuning.
-1. [Brain Tumor Segmentation@kaggle](https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation) (Original Source and Credit: [Jun Cheng](https://github.com/chengjun583/brainTumorRetrieval) (For fine-tuning)
-This brain tumor dataset containing 3064 T1-weighted contrast-enhanced images (.jpg)
-from 233 patients with three kinds of brain tumor: meningioma (708 slices),
-glioma (1426 slices), and pituitary tumor (930 slices).
-2. [BRaTS 2021 Task 1 Dataset@RSNA-ASNR-MICCAI BraTS Challenge 2021](https://www.synapse.org/#!Synapse:syn25829067/wiki/610863) (For evaluation)
-Consist of 1666 scans with T1, T2, and T2-FLAIR MRI images, delivered as NIfTI files (.nii.gz) and their corresponding ground-truth masks. For detailed explanations please refer to BraTS challenge home page.
-
-
-### Deliverables
-
-At the end of this project, we will have:
- - The current markdown document, completed and revised.
- - Two Jupyter notebooks, one for fine-tuning and the other for inference and evaluation. All are adopted from [MedSAM](https://github.com/bowang-lab/MedSAM) with my own minor modifications. Available on my [BHS github repository](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI).
- - The fine-tuned model checkpoint (.pth) also provided via this [google drive folder](https://drive.google.com/drive/folders/1MbHo0qBfkQYARUhB-DAhbD5a4lhmYNqs?usp=sharing)
- - The introduction slides of this project.
-
-## Results
-
-### Progress overview
-
-The SAM foundation model shows an amazing generalization ability to adapt downstream tasks even though the fine-tuning dataset is relatively small in scale.
-
-### Tools I learned during this project
-
- * **Docker**: Building JupyterLab running environment using Docker container
- * **MRI**: Understand the basic concept of MRI imaging
- * **Nilearn**: Pre-process MRI image using Nilearn package
- * **Foundation Model**: Understand how foundation model works and perform domain specific adaptation on custom dataset
- * **Result Visualization**: Visualize the result in quantitative and qualitative perspectives
-
-
-### Results
-
-#### Deliverable 1: Introduction Slides
-
-You can find the introduction slides of this project [here](https://docs.google.com/presentation/d/1SeMZCID98rrUaRF6O2B1kKdpq92b1gFhh22336PCRQY/edit?usp=sharing)
-
-#### Deliverable 2: Jupyter Notebooks
-
-You can find jupyter notebooks of this project at my [github repository](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI)
-
-
-#### Deliverable 3: Instructions
-
-1. For fine-tuning on your own datasets, please follow the tutorial provided by [MedSAM](https://github.com/bowang-lab/MedSAM)
-2. For reproducing my training results, please follow this [jupyter notebook](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI/blob/main/Demo_BraTS.ipynb)
-
-If you have any questions or suggestions, feel free to contact me via email
-
-## Conclusion and acknowledgement
-
-Thanks to [BoWang Team from University of Toronto](https://github.com/bowang-lab) for open sourcing their recent research, so I can learn how to fine-tune the model from their work.
-
-Thanks to SNMG of Dept. CSIE, National Central University, for providing powerful computation resources to make this project practical.
-
-Thanks to Professor Kevin Chun-Hsien Hsu, Institute of Cognitive Neuroscience, National Central University, giving me the oppotunity to attend BrainHackSchool 2023.
-
-Thanks to all the Professors, Moderators, TAs and Classmates that make this BrainHackSchool happend!
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-13" # Date you first upload your project.
-title: "CVAE-based ADHD neuroimaging analysis"
-names: [Cian-Ya Lan, Jia-Ling Sun]
-github_repo: https://github.com/Cleo-Lan-school/BHS_2025-project
-website:
-tags: [adhd, mri, cvae, iq]
-summary: "This project applies a contrastive variational autoencoder (CVAE) to Burner-preprocessed MRI data from the ADHD-200 dataset to disentangle ADHD-specific brain features from shared anatomical variation. We explore latent representations using RSA and clustering to better understand neuroanatomical heterogeneity in ADHD."
-image: "cover.png"
----
-
-## Project definition
-
-### Background
-
-- Reconstruct 3D brain MRIs using CVAEs.
-- Disentangle "salient" ADHD-related features from "background" features common to both ADHD and typically developing children.
-- Assess how well the learned latent spaces reflect behavioral and clinical variation using:
- - **Silhouette Analysis**
- - **Representational Similarity Analysis (RSA)**
-
-### Tools
-
-This project used:
-- Python (NumPy, Pandas, SciPy, Scikit-learn, Matplotlib, Seaborn)
-- Keras (TensorFlow backend) for deep learning
-- UMAP for latent space visualization
-- Representational Similarity Analysis (RSA) using Kendall’s tau
-- Silhouette analysis for latent space separability
-- GitHub for version control
-
-### Data
-
-This project used the publicly available [ADHD-200 dataset](http://fcon_1000.projects.nitrc.org/indi/adhd200/), specifically the **Burner-preprocessed version**:
-- Structural MRI data processed via voxel-based morphometry (VBM)
-- Normalized 3D gray matter volumes (64×64×64)
-- Accompanied by phenotypic variables: age, sex, diagnosis, subtype, medication, IQ, etc.
-
-### Deliverables
-
-At the end of the project, we produced:
-- A working CVAE framework for modeling neuroanatomical variation
-- Visualizations of latent features and synthetic brain reconstructions
-- RSA and clustering results relating brain features to clinical data
-- Well-documented code and reproducible analysis notebooks
-
-## Results
-
-### Progress overview
-
-We trained a CVAE model with two latent components:
-- `s` (salient features): ADHD-specific anatomical variation
-- `z` (background features): common/shared variation
-
-We evaluated the model using:
-- **Silhouette scores** for latent clustering
-- **RSA** to correlate latent dimensions with phenotypic variables
-- **GMM clustering and BIC** to test for discrete vs. continuous subtype structure
-
-### Tools I learned during this project
-
-- Contrastive representation learning in generative models
-- Implementation of CVAEs in Keras
-- Preprocessing and working with VBM MRI data
-- Representational Similarity Analysis
-- Model interpretability techniques for brain data
-
-### Results
-
-#### Deliverable 1: CVAE analysis
-
-- Salient features (`s`) were significantly correlated with:
- - **ADHD Index**
- - **Inattentive and Hyperactive/Impulsive scores**
- - **Medication status** and **age**
-
-- Shared features (`z`) were more related to:
- - **IQ**, **gender**, and **scan site**
-
-#### Deliverable 2: Clustering analysis
-
-- Gaussian Mixture Models (GMM) with Bayesian Information Criterion (BIC) showed:
- - **Lowest BIC at 1 cluster**, suggesting **continuous heterogeneity**
- - Results align with dimensional models of psychiatric disorders
-
-#### Deliverable 3: Code and notebook
-
-- Full training pipeline in `Train-CVAE-ADHD200.ipynb`
-- Visualization and RSA in helper scripts
-- Readme documentation and reproducibility checklist included
-
-## Conclusion and acknowledgement
-
-This project demonstrates the potential of contrastive deep generative models like CVAEs to disentangle disorder-specific neuroanatomical features from shared variation. Our findings suggest ADHD may be better described along a spectrum rather than discrete subtypes. We thank the Brainhack School instructors and the open neuroimaging community for providing tools and data that made this project possible.
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----
-type: "project"
-date: "2025-06-12"
-title: "Brainbeats: Classifying Music Genre with fMRI Connectivity"
-names: [Chelsea Xuan Chen]
-github_repo: https://github.com/x2583319/Chen_project_2025
-tags: [fmri, music, connectivity, classification]
-summary: "Can we predict music genres based on fMRI connectivity patterns alone? This project explores a single-subject decoding approach using ROI-to-ROI correlation matrices and machine learning classifiers on OpenNeuro dataset ds003720."
-image: "cover.png"
----
-
-## Project definition
-
-### Background
-
-Music evokes rich and complex responses in the brain—and I wanted to explore whether functional connectivity patterns alone could reflect the genre of music being heard. Inspired by Nakai et al. (2021), who used voxelwise patterns across five participants to decode music genre, I chose to narrow the focus to a single-subject analysis.
-
-Due to event file inconsistencies in sub-001 to sub-004, only sub-005 contained intact and correctly labeled annotations across all six runs. My project thus became a pilot proof-of-concept: could trial-wise ROI-to-ROI correlation matrices, extracted via the Schaefer 100-ROI atlas, support genre decoding even within just one brain?
-
-### Tools
-
-This project used:
-
-- Python & Jupyter Notebooks
-- `nilearn` for ROI time series extraction and brain visualization
-- `scikit-learn` for PCA and classification (SVM, KNN)
-- `matplotlib` & `seaborn` for plotting and visual diagnostics
-
-### Data
-
-- Dataset: [OpenNeuro ds003720](https://openneuro.org/datasets/ds003720)
- Nakai, T., Koide‑Majima, N., & Nishimoto, S. (2021). *Brain and Behavior*, 11(1), e01936.
-
-- Used only `sub-005` due to complete event labels (manual inspection revealed misalignments in other subjects)
-
-- 6 runs, each with ~40 musical trials; each trial is a 10-second clip; 10 total genres in the dataset
-
-- Brain parcellation: Schaefer 100-ROI cortical atlas (98 usable ROIs after resampling)
-
-- Final dataset: 98 usable trials, filtered to exclude clips <2 TRs or with correlation errors
-
-> Why only sub-005? The other subjects had corrupted or mismatched event files that broke alignment between music clips and fMRI data.
-
-### Deliverables
-
-- Trial-wise ROI-to-ROI correlation matrices
-- Atlas and correlation matrix visualizations
-- PCA dimensionality reduction pipeline
-- Genre classification with SVM and KNN
-- Final presentation slide deck (≤15 minutes)
-
----
-
-## Results
-
-### Progress overview
-
-- Extracted time-series from 98 ROIs using `NiftiLabelsMasker` in `nilearn`
-- Computed ROI-to-ROI Pearson correlations → 100×100 matrix → flattened to 4950D feature vector
-- Applied PCA (n=20) for dimensionality reduction
-- Trained classifiers (SVM, KNN) for multiclass and binary decoding
-
-### Tools I learned during this project
-
-- `nilearn` atlas-based ROI extraction
-- Correlation matrix construction + vectorization
-- PCA feature compression
-- Classification using `SVC`, `KNeighborsClassifier`, and cross-validation
-- Confusion matrix and performance visualization
-
-### Deliverable 1: Multiclass genre classification
-
-- SVM + PCA yielded ~13% accuracy (above 10% random baseline for 10 genres)
-- Confusion matrix showed some genre-specific signal, particularly for Reggae (not shown in final binary model)
-
-### Deliverable 2: Binary genre decoding (Pop vs. Metal)
-
-- SVM + PCA achieved ~68% accuracy (above chance), though **KNN was too unstable to evaluate**
-- Performance varied across runs, likely due to short duration and low trial count
-
-### Deliverable 3: Visualizations
-
-- Schaefer 100-ROI brain parcellation plotted to validate resampling and mask quality
-- Average correlation matrix showed strong within-region signals (clean diagonals)
-
-#### Schaefer Atlas
-
-
-#### ROI Correlation Matrix
-
-
-#### PCA Confmax
-
-
----
-
-## Reflection
-
-This pilot study suggests that even within a single brain, genre-relevant information can be gleaned from ROI connectivity patterns. But limitations remain:
-
-- Generalizability limited by sample size (n=1)
-- Low trial count vs. high feature dimension = overfitting risk
-- Classifier performance unstable across reruns
-
-## Future directions
-
-- Use more participants (once metadata is cleaned!)
-- Try higher-resolution atlases (e.g., Schaefer 200-ROI)
-- Experiment with sliding window connectivity or graph-based features
-- Combine with music theory-based audio features (e.g., tempo, tonality) for hybrid modeling
-- Explore feature importance: which ROI-pairs drive classification?
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-14" # Date you first upload your project.
-# Title of your project
-title: "Detecting ADHD through fMRI signals using ML classification models"
-
-# List the names of the collaborators within the [ ].
-names: [Agustín Rodriguez, Ezequiel Sirne, Francisco Covelli]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, fmri, machine-learning, rs-fmri, xgboost, PCA, classification, bold-signal, connectivity]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-summary: "We used the ADHD-200 Sample dataset to implement various machine learning classification models, aimed at diagnosing ADHD through resting-state fMRI signals."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "fotor-ai-20240613194347.jpg"
----
-
-
-
-
-## Project definition
-
-### Background
-
-Attention-Deficit/Hyperactivity Disorder, or ADHD, is characterized by inattention, impulsivity and hyperactivity. Currently, 5% of the child and adolescent population Argentina live with this condition. It is still diagnosed using subjective clinical criteria, which are often difficult to evaluate. However, artificial intelligence (AI) techniques hold potential for identifying neuroimaging biomarkers that could facilitate the diagnosis. Our objective is to develop machine learning classification models to detect ADHD through the analysis of resting state functional MRI (rs-fMRI) signals.
-
-### Tools we learned during this project
-
-* **Bash Terminal and Shell scripting:** for command-line operations and scripting.
-
-* **Git and GitHub:** for version control, learn to use a shared workspace, and repositories.
-
-* **Project Managemet:** as a team we collaborate to fulfill the project deadlines week by week.
-
-* **Jupyter Notebook and Google Colab:** we implemented the code within these interactive environments.
-
-* **Python Libraries:**
- * **Numpy and Pandas:** for data manipulation and analysis.
- * **Matplotlib and Seaborn:** for data visualization.
- * **Nilearn:** to facilitate the processing and visualization of neuroimaging data.
- * **IPython:** for interactive computing, providing an enhanced interactive shell and support for Jupyter notebooks.
- * **Scikit-learn:** for machine learning, providing tools for data analysis, metrics and building predictive models.
- * **XGBoost:** for implementing optimized gradient boosting algorithms, which are highly effective for supervised learning tasks such as classification and regression.
-
-* **Scikit-learn Tools:**
- * **Principal Components Analysis (PCA):** for linear dimensionality reduction of the features.
- * **StandardScaler:** standardize features by removing the mean and scaling to unit variance.
- * **RandomizedSearchCV:** for Cross-Validation and Hiperparameter Optimization.
- * **Metrics:** accuracy_score, f1_score, roc_auc_score, roc_curve, confusion_matrix, classification_report.
-
-
-* **MRIcroGL**: Allows us to view 2D slices and renderings of our brain imaging data, both for structural and for functional MRI.
-
-
-
-
-
-
-
- * **AAL atlas**: For an anatomic brain parcellation.
-
-
-
-**We also gain knowledge in the following areas:**
-
-* **Machine learning for neuroimaging**
-* **Introduction to deep learning**
-* **Functional connectivity in fMRI**
-* **Functional parcellations in fMRI**
-* **The Brain Imaging Data Structure (BIDS) ecosystem**
-
-### Data
-
-**ADHD-200 Preprocessed Sample:**
-
-*Pierre Bellec, Carlton Chu, François Chouinard-Decorte, Yassine Benhajali, Daniel S. Margulies, R. Cameron Craddock (2017). The Neuro Bureau ADHD-200 Preprocessed repository. NeuroImage, 144, Part B, pp. 275 - 286. doi:10.1016/j.neuroimage.2016.06.034*
-
-Prepared by the collaboration of 8 international neuroimaging institutes, who held a competition in 2011, to identify ADHD biomarkers.
-
-Composition:
-* 947 children and adolescents
-* 362 diagnosed with ADHD
-* 585 typically developing controls
-
-In order to make the competition accessible to a broader range of researchers, the data was preprocessed and shared by the Preprocessed Connectomes Project (PCP), what resulted in several publications, MS theses, PhD dissertations and even patents.
-
-### Project steps
-
-1. We downloaded the preprocessed ADHD-200 Sample dataset using a script that allowed us to obtain time series parcelled according to the AAL atlas, which has 116 ROIs.
-2. We started by creating our Jupyter Notebook, loading the BOLD signal data for a single subject and visualizing the time signals in the 116 ROIs using Matplotlib.
-3. With this subject's data, we constructed a Pearson correlation matrix between ROIs using the "corrcoef()" function from NumPy.
-4. To visualize the correlation matrix, which in this case would be a connectivity matrix, we used Matplotlib and the "heatmap" function from Seaborn.
-5. Then we used the "tril()" function from NumPy to retain only the lower triangle of the matrix, avoiding repeated data and the diagonal that do not provide information for our case study.
-6. Using the "tril_indices()" method, we created a list or vector that contains the indices of our matrix.
- This method orders rows-wise from the lower triangular matrix. Specifically, the order follows:
- All elements from the first row below the diagonal.
- All elements from the second row below the diagonal.
- And so on, up to the last row.
-7. Once we understood the process for a single subject, we were ready to replicate it for each of the subjects included in our dataset. For this, we used for loops, if-else conditionals, and try-except exception handling.
- For example, to iterate through the downloaded files following the phenotype subject list, avoiding errors in case the file did not exist or had any error that prevented the correct creation of our matrix.
-8. The result obtained from the previous iteration was a matrix with 6670 features (PC of ROIn x ROIm) and 820 rows, the considered subjects. We converted this vector matrix into a DataFrame using Pandas.
-9. As we had the subjects' IDs and their diagnosis in the phenotype, we could add them to our DataFrame.
-10. From this point, we could separate our data into Train and Test sets using train_test_split from the sklearn library:
- a. test_size=0.2
- b. random_state=42
- c. stratify=target
-11. Our problem: Supervised Classification Model with a Binary Target Variable.
- First approach: initially, we tried several models, including:
- a. PCA (Principal Components Analysis)
- b. LDA (Linear Discriminant Analysis)
- c. Logistic Regression
- d. XGBoost
-12. As our classification was binary, we used the following metrics to evaluate our models obtained from sklearn.metrics:
- a. Train and Test Accuracy
- b. Confusion Matrix
- c. ROC AUC
- d. Additionally, we used the Classification Report, which includes several metrics.
-13. Since we got the best results with XGBoost, we focused on optimizing the hyperparameters using RandomizedSearchCV in order to reduce the overfitting.
-14. We combined PCA and XGBoost but did not get better results.
-15. Finally we used Feature Importance from XGBoost to compare the most important features for the model with the theory about ADHD diagnosis.
-16. With all the knowledge gained during this workshop, and building this project, we prepare a conclusion, a presentation and this markdowm.
-
-
-### Deliverables
-
-At the end of this project, the following files will be made available:
-* The current markdown document.
-* A GitHub repository to share our work and pipeline, and to encourage other students to build from it: https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1
-* "ADHD_fRMI_ROI_Classification_Project.ipynb" notebook.
-* Figures of our results: Connectivity matrix, graphs for statistical analysis, visualizations, feature importance evaluation.
-* Our presentation shared at the end of the BrainHack meeting at Humai Institute.
-
-
-### Results
-
-#### Deliverable 1: Markdown document:
-
-This markdown file in order to share the project in the BrainHack School web repository.
-
-#### Deliverable 2: Github Repository:
-
-A GitHub repository to share our work and pipeline, we would like to encourage other students to build from it:
-https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1
-
-#### Deliverable 3: "ADHD_fRMI_ROI_Classification_Project.ipynb" notebook:
-
-You can access our public notebook with the step-by-step process in the Github repository:
-
-https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1/blob/main/notebooks/ADHD_fRMI_ROI_Classification_Project.ipynb
-
-#### Deliverable 4: Figures of our results:
-
-**Time Series:**
-
-
-
-Each time series represents the intensity of the BOLD signal of every ROI (Region of Interest)
-along the rs-fRMI.
-
-**Connectivity Matrix:**
-
-In order to create a connectivity matrix, we evaluated the Pearson Correlation between each of these signals.
-
-
-
-**Feature Importance Interpretation**
-
-
-
-* Temporal Pole and Frontal regions appear to be recurrently significant in feature importance, suggesting a key role in ADHD.
-* The Cingulum also appears repeatedly, indicating its relevance in connectivity differences for ADHD.
-* The Amygdala connectivity to the Cingulum (both Left and Right) highlights potential emotional regulation and cognitive control variations in ADHD.
-
-The importance values are relatively close, ranging approximately from 0.004 to 0.006. This indicates that while some connections are slightly more influential, many features are comparably important in distinguishing ADHD from controls in resting-state fMRI. These identified ROIs and their connectivity patterns could be further studied to develop diagnostic methods.
-
-#### Deliverable 5: Our presentation shared at the end of the BrainHack meeting at Humai Institute
-
-You can find the slides of our presentation in the Github repository:
-
-https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1/blob/main/ADHD%20Classification%20Project%20-%20Team%201%20Humai%20-%20Brainhack_2024.pdf
-
-
-## Conclusion and acknowledgement
-
-We can conclude that with an accuracy of 0.658, there is significant room for improvement in ADHD diagnosis based on classification models.
-
-Our best metrics are achieved using XGBoost with Hyperparameters Optimization and Cross-Validation using RandomizedSearchCV:
- ACC = 0.658
-
-Despite PCA helped us to reduce the dimensionality of our features, when combined with XGBoost the perfomance was lower:
- ACC = 0.628
-
-The identified ROIs and their connectivity patterns could be further studied to develop diagnostic methods.There are many ways to expand and improve this project, and we are excited to explore them.
-
-Finally, we would like to express our gratitude to our teaching assistants from Humai, Buenos Aires, who have supported us from day one and generously shared their diverse expertise in neuroscience and computer science.
-
-
-
-## Feel free to contact us :D
-
-If you need assistance with your artificial intelligence project, we would be happy to help!
-
- **Francisco Covelli:** fcovelli@med.unlp.edu.ar
-
- **Agustin Rodriguez:** agurodri96@hotmail.com
-
- **Ezequiel Sirne:** ezequielsirne@gmail.com
-
-
-
-## References
-Zhu CZ, Zang YF, Cao QJ, Yan CG, He Y, Jiang TZ, Sui MQ, Wang YF. Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage. 2008 Mar 1;40(1):110-20. doi: 10.1016/j.neuroimage.2007.11.029. Epub 2007 Dec 3. PMID: 18191584.
-
-Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. doi: 10.1196/annals.1440.011. PMID: 18400922.
-
-Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JR, Fischl B, Liu H, Buckner RL. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011 Sep;106(3):1125-65. doi: 10.1152/jn.00338.2011. Epub 2011 Jun 8. PMID: 21653723; PMCID: PMC3174820.
-
-Cortese S, Castellanos FX. Neuroimaging of attention-deficit/hyperactivity disorder: current neuroscience-informed perspectives for clinicians. Curr Psychiatry Rep. 2012 Oct;14(5):568-78. doi: 10.1007/s11920-012-0310-y. PMID: 22851201; PMCID: PMC3876939.
-
-Sripada C, Kessler D, Fang Y, Welsh RC, Prem Kumar K, Angstadt M. Disrupted network architecture of the resting brain in attention-deficit/hyperactivity disorder. Hum Brain Mapp. 2014 Sep;35(9):4693-705. doi: 10.1002/hbm.22504. Epub 2014 Mar 25. PMID: 24668728; PMCID: PMC6869736.
-
-Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC. The Neuro Bureau ADHD-200 Preprocessed repository. Neuroimage. 2017 Jan;144(Pt B):275-286. doi: 10.1016/j.neuroimage.2016.06.034. Epub 2016 Jul 15. PMID: 27423255.
-
-Damiani S, Tarchi L, Scalabrini A, Marini S, Provenzani U, Rocchetti M, Oliva F, Politi P. Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest. Eur Child Adolesc Psychiatry. 2021 Apr;30(4):619-631. doi: 10.1007/s00787-020-01545-0. Epub 2020 May 8. PMID: 32385695.
-
-Hroncich, Camila. “¿Qué sabemos de TDAH?” CONICET, 12 January 2023, https://www.conicet.gov.ar/que-sabemos-de-tdah/.
-
-Wang, Z., Zhou, X., Gui, Y. et al. Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study. Transl Psychiatry 13, 45 (2023). https://doi.org/10.1038/s41398-023-02309-5
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "EEG-FFR Classification in MATLAB: A Tutorial"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Isabelle Arseneau-Bruneau]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [beginner-friendly, ffr, electrophysiology, eeg-classification]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project is a tutorial. It aims for you to learn how to use the scripts of a machine-learning classifier (the Hidden Markov Model). The codes were written in MATLAB. They classify an auditory neural signal called the Frequency Following Responses (FFR), which represents how well the brain represents and process complexe sounds, such as speech or music."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Llanos_IABProjectPilot_FFR.png"
----
-
-
-## Project Definition
-
-This tutorial first aim is to provide a reproducible workflow, that is beginner-friendly, with machine-learning (ML) procedures written in MATLAB. As I am novice with coding, I aim to implement a classifier that I use repeatively in my doctoral research (an Hidden Markov Model [HMM]) into a structure that would provide some flexibility for future usages (with other variables or datasets), and would allow to build extensions (e.g. for other classifiers such as a Support Vector Machine (SVM), Cross-correlation (XCorr), LSTM). I aim to put together a set of resources that would be accessible to my research assistants, collaborators (who also have students to train on these ML procedures), other FFR researchers, and my futur self! Thus, the data of this tutorial will be in format that align with components of standards in project management (EEG-BIDS), use jupyter notebooks implemented in a virtual environment (allowing the use MATLAB scripts), and use basic matlab visualization tools to generate figures of the results.
-
-## Notes on Open Science Practices and Matlab
-
-_Is it mandatory to abandon MATLAB to have open science practices? Matlab may be a commercial software, but it would be sad to limit the open science movement to open source software users. Specialized fields, such as FFR research, have the wide majority of their resources, expertise, and tools in matlab. Thus, at the current moment, it would be extremely difficult to study FFR without using MATLAB. Nevertheless, there are many open science tools that are compatible with matlab scripts and files. Hence, I tried to implement an open approach in this ML tutorial. Feel free to contribute to it or indicate issues with the link below. We aim to build from this structure and repository!_ https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project
-
-### Hidden Markov Model - Classification Goal:
-
-The machine-learning classifier targeted in this tutorial, the Hidden Markov Model, aims to classify __if the EEG-FFRs__ of each participants __were generated by__ a stimulus that was either a __speech sound__ or a __piano tone__ of the same fundamental frequency (98 Hz) and duration (100ms). These procedures were done on 26 participants (13 musicians vs. 13 non-musicians).
-
-As the equipment used to collect FFRs and population targeted can influence the quality of the signal, this script also __tracks the number of FFR trials__ required to obtain certain levels of __decoding accuracy__. This information is useful to validate experimental procedures, adjust research designs and target research questions. As enhanced FFR reproduces features of the stimuli with more fidelity, the decoding accuracy of the classifier can be __used as a dependent measure__ to reveal experience-dependent effects in certain populations (Llanos et al., 2017; 2019).
-
-### Background
-
-Over the last decades, neuroimaging studies have provided cumulative evidence of the benefits of musical training, in particular how such training promotes neuroplasticity (Herholz et al. 2012). A clear benefit of musical training is manifested at the level of the frequency following response (FFR), an electrical potential sensitive to phase-locking captured by EEG that arises from subcortical and cortical sources (Coffey et al., 2016). The FFR is an extremely rapid (starting at 7 ms) reaction of the brain to an auditory stimulus. It is a neural representation of the periodicity of the entering auditory information and allow us to recognize speech or music sounds (Coffey et al., 20161; Kraus et al., 2010; 2014; 2017b; 2017c). With both cortical and subcortical contributors that modify its quality (Coffey, Herholz, Chepesiuk, Baillet, & Zatorre, 2016), the FFR represents an integrated response of the entire auditory system (Irvine et al., 2018).
-
-Interestingly, the FFR varies in individuals as a function of experience (enhanced in musicians, tonal language speakers, bilinguals [Coffey et al., 2016; Kraus & Chandrasekaran, 2010; Krizman, Marian, Shook, Skoe, & Kraus, 2012]) or pathologies (decreased in autism, dyslexia, traumatic brain injuries [Hornickel & Kraus, 2013; Nina Kraus et al., 2016;, for extensive literature survey, see Kraus et al., 2017]. Several authors have thus proposed that it may serve as an auditory biomarker to target treatments and educational interventions (Kraus et al., 2015; Bidelman et al., 2017). Musicians are of particular interest because of correlations between the augmentation of their FFR quality and enhancements in their perception skills (Coffey et al., 2016; Kraus & Chandraserakan, 2010). The greater the musical training, the closer the neuronal representation approaches its corresponding auditory stimuli (Kraus et al., 2010b, 2015, 2017). The signal is transmitted faster, and becomes more precise and robust, which then benefits higher-order cognitive functions (Coffey et al., 2016; Kraus, Anderson, White-Schwoch, Fay, & Popper, 2017). This is a remarkable phenomenon, but how these enhancements are achieved remains unclear because of technical limitations associated with the neural signal.
-
-A considerable challenge for the understanding of FFR enhancements mechanisms is the very small amplitude (measured in nanovolt) and the bad signal-to-noise ratio (SNR) of the neural signal. Hence, recording FFRs typically requires several thousand exposures to the same auditory stimulus (which are averaged to cancel out the noise). These constraints considerably limited the research questions that could be expored with FFRs, while this signal would remain a key component of neuroplasticity mechanisms associated with musical training.
-
-Fortunately, machine-learning classifiers recently developed with speech-evoked neurophysiological responses have considerably reduced the number of trials necessary to obtain a usable FFR signal (Xie et al., 2019). The classification of FFR is starting to emmerge as an objective mean to assess auditory perception (Holdgraf et al., 2017; Llanos et al., 2019). The current BHS project aims to run one of these classifiers, which I also use in my doctoral research, on FFR-EEG data. I hope to implement these ML procedures in a way that will facilitate access, collaborations and open science practices.
-
-#### The Frequency Following Responses: Procedures (a), Features (b-d) and Machine-Learning Analysis (e)
-
-_Figure from [Coffey et al., 2019](https://www-nature-com.proxy3.library.mcgill.ca/articles/s41467-019-13003-w), with permission)_
-
-
-
-### Tools
-
-The "EEG-FFR Classification" project will rely on the following technologies:
- * Bash Shell
- * [Markdown](https://guides.github.com/features/mastering-markdown/)
-
- * MATLAB
- * MATLAB Toolboxes (GPU, Bioinformatics, and HMM)
- * Jupyter Notebook
- * MATLAB script of an Hidden Markov Model (originally developed by F. Llanos)
- * A Virtual Environment for Jupyter Notebook with MATLAB
- * Supplementary classifiers scripts (SVM, X-Correlation and SLTM developed by F. Llanos)
-
- * GitHub
- * GitKraken
- * BIDS-EEG format
- * Data Archiving with OSF
- * Open Source Licence compatible with Matlab Scripts (MIT)
-
-### Data
-
-The dataset for this tutorial project was previously published and generously provided by Emily Coffey and colleagues from the Zatorre Lab at McGill University (see Coffey et al., 2017). It consists of a set of pre-processed EEG-FFR collected with 27 subjects (13 musicians and 13 non-musicians) and the auditory stimuli used during the data collection. Demographic and music experience information (collected with the Montreal Music History Questionnaire [Coffey et al., 2011]) will also be made available on OSF for futur analysis, but is not explored under the scope of this tutorial. Thus, the data under this project is limited to the auditory stimuli (.wav) and EEG-FFR (.eeg) files.
-
-For more details on this dataset, please see [Cortical Correlates of the Auditory Frequency-Following and Onset Responses: EEG and fMRI Evidence](https://www.jneurosci.org/content/37/4/830). Questions can also be directed to emily.coffey@concordia.ca
-
-#### 1) Stimuli
-
-The auditory stimuli used were a speech |dah| and a piano tone stimuli, with both a duration of 100ms and a fondamental frequency of 98 Hz. Stimuli were presented in blocks of da/piano, alternating polarity every second stimulus (see Krizman & Kraus, 2019 for more information about alternating the stimulus polarity).
-
-100ms 'da' syllable-stimuli vs piano tone-stimuli compared to the FFR of Subject 101
-
-
-#### 2) EEG Data
-
-The EEG data is provided from each subject separately as a MATLAB structure array with two fields: .eeg and .sti.
-The name on the structure array represents the subject number (e.g. 001). The first field (e.g., 001.eeg) contains a * .mat 2-dimentional matrix with the EEG data. Rows and columns will correspond to single-stimulus trials and EEG data-points, respectively. The second field (e.g., 001.sti) is a * .mat 1-dimensional vector including the target stimulus numbers associated to each row in the EEG matrix.
-
-If you use this classifier with a different EEG-FFR dataset, note that before creating a MATLAB matrix, you will need to make sure that your EEG data has been already re-referenced, band-pass filtered (zero phase filter, pass-band from 80 to 1000Hz) and that its sample rate is higher than 2000Hz. You will also need to adapt the scripts "setup.m" to the features of your dataset.
-
-#### 3) Important Data Information (Required to adjust the scripts if used with other dataset)
-
-* __Pre-Stimulus Duration__: 50 ms
-* __Stimulus Duration__: 100 ms
-* __Trial Duration__: 0.2228 seconds
-_(Note from 1st author: "It is a funny number because I shifted the data to account for air conduction delay such that 0 (or 50 ms into the file) is when the sound hits the ear) - in any case there are 3650 data points at 16384 Hz.")_
-
- * __EEG Sample-Rate__: 16384 Hz
- * __Artifact Rejection Criteria (if any)__: None in the current pre-processed dataset
- _(Note from 1st author: "In the data used for the Jounal of Neuroscience publication, a precedure based on similarity to the grand average was applied. It was less appropriate to use such a procedure here as it may mask some of the inter-individual differences we are interested in.)_/
-* __Electrodes used for re-referencing__: This was a simple Cz to linked mastoids montage, it's already been referenced.
-
-Here is what a grand average of the FFR looks like:
-
-100ms 'da' syllable-FFR (_N=26_)
-
-
-100ms piano tone-FFR (_N=26_)
-
-
-
-## Results
-
-### Progress Overview
-
-I faced a few challenges with the matlab jupyter notebook as it required extra installation and set-up that needed to be addressed to access the tutorial. I realized that I missed elements to have a more reproducible workflow so I decided to adapt to tutorial accordingly. I found myself with a lot more text than I would have thought for something beginner-friendly! However, by the end of this project, I became able to run classifiers on EEG-FFR and have a structure for the scripts and workflow documentation that I will be able to reuse (with more ease) in the future. I must say that there is a considerable amount of revisions needed in my HMM Notebook and on the overall documentation for this tutorial to be beginner friendly. I actually ended up making a [notebook tutorial](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/Tutorial_Introduction.ipynb) on how to use the tools of this tutorial!! There is also a lot of text epuration that I need to do, and the scripts needs a bit of modification (for the subjects selection, in order to be able to reproduce all the figures). In order to be usable with other datasets, the script setup.m still require more comments and annotations). I also have MIT licences that needs to be extended & procedures for futur scripts that needs to be archived. I think that using the "Project" section on the git repository might be the next step to keep track of what is left to do. Nevertheless, the EEG-FFR dataset is achived on osf.oi and I feel that I got my lab more invaded by open sciences practices. So at this point, the seeds are at least planted and I hope that this project, even if in a rougher shape right now, will grow from there.
-
-### Tools I learned during this project
-
- __ALL__ the tools I mentioned in the 'Tools' list above!
-
-### HMM Classification Results
-
-The figure below shows the results for overall HMM decoding accuracy of the syllable vs. tone FFRs. Recognition above chance start at about 100 trials (left panel). Differences related to music experience do not emerge until 1000 trials (center panel). As well, differences related to music experience only emerge for the HMM decoding of the tone (musicians>non musicians); they are not present for the decoding of the syllable stimuli (right panel).
-
-It is worth noting that the absence of music experience differences in overall decoding accuracy ( syllable & tone together), even with 1000 trials, may be related to the fact that both populations are familiar to the syllable (e.g. both musicians and non-musicians skpeak and use the syllable |da| in their everyday life). This possible explanation is supported by the fact that no music experience differences seem to emerge from the HMM decoding of the syllable stimulus (right panel). Hence, the music experience differences in the HMM decoding of the auditory stimuli could be related to the familiarity to the auditory stimulus, which is greater for the piano tone among musicians.
-
-As both the tone and the syllable share the same F0 contour, it is worth emphazing that the differences in the FFRs are really fine. More than 100 trials to reach accuracy over chance may be improved by abjusting the parameters of the HMM. (However, this require further exploration and would be under the scope of another tutorial.) It may also take hundreds of trials for very small context-determined perturbations to emerge in the FFR. Further experimentation will tell! :)
-
-
-
-## Course Deliverables
-
-#### ASSIGNMENT WEEK 1: [P-Hacking Exercise](https://github.com/arsisabelle/Arseneau-Bruneau-I-QLSC612)
-#### ASSIGNMENT WEEK 2: [README.md File](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/README.md) + Issues
-#### ASSIGNMENT WEEK 3: [Video Presentation of the Project](https://www.youtube.com/watch?v=6lX_-AgOXug)
-#### ASSIGNMENT WEEK 4: [Project Repository](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project)
-
-## Project Deliverables
-
- - [Project in the Gallery of the BHS website](https://school.brainhackmtl.org/project/EEG-FFR-Classification)
- - Project Report ([README.md File](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/edit/master/README.md))
-
- - [Video Introduction on this project](https://www.youtube.com/watch?v=6lX_-AgOXug)
- - Documentation for a reproducible ML workflow
- - [Tutorial Introduction Jupyter Notebook (beginner-friendly)](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/Tutorial_Introduction.ipynb)
- - [Tutorial HMM in Matlab Jupyter Notebook](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/Matlab_HMM_Scripts_Notebook.ipynb) (beginner-friendly)
- - Stand-alone versions of the [classifiers scripts](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/tree/master/FFRclassifiers) for advanced users
- - [EEG-FFR Dataset shared by the Zatorre Lab (McGill University) on OSF.oi](https://osf.io/c2b3t/?view_only=d1b30d4e0e7c4d53a8ec80d4d87b33d2)
- - Supplementary ML Scripts (SVM, x-corr, and SLTM) created by Fernando Llanos
- - MIT license (& coming soon: procedures to get open source licence with MATLAB scripts in the future)
-
-#### Supplementary Developments to Integrate to the Tutorial After the BHS 2020
-* Adding video of the Setup Procedures and the HMM Workflow
-* Fixing the data selection for the SVM and XCorr models
-* Adding a section to explain differences between the folder FFRs and Input1
-* Adding a function for subject selection
-* Testing the tutorial on different computer
-* Integrating the SLTM (script developed by Fernando Llanos)
-* Cutting excedentary text
-* Extensive proof reading for typos
-* Proof reading by a novice learner to identify terms that require explanations
-* Uploading the License procedures for future scripts & tutorials
-
-
-
-## Conclusion
-
-Through this project, the Brainhack School provided me with tools and guidance to tame the aspects of my doctoral research that I feared the most (programmation, machine-learning, neural signal analysis)! I still have a lot to learn, but I now feel that with the jupyter notebook tutorials, with organized documentation to reproduce the workflow, and an extendable structure for the scripts, I now have many elements in place to continue to learn and to share with other. Many seeds are planted, I just need to keep growing! As a matter of fact, the structure generated by this project will serve to integrate other classifiers that my research team will use. (Although, you may check the list ('Supplementary Developments') in the last section to see all the items that need to be done next). Thus, this project/tutorial does not finish with the BHS. That is a good news, right?!
-
-With an Open Science approach, I also found practices in academia that resonnate much more with values in research that I aim to encourage, accessibility being at the core of these. (Perhaps, did I become a little less cynical?) Anyways, even if we look at this only practically, it remains that the tools that I developed through this project will facilitate my work at the lab as well as the exchanges with current and future collaborators. Hopefully, these platforms will increase the impact of my research. In the very least, I will be able to direct colleagues and students to online-resources when they ask about the analysis techniques used in my research.
-
-## Acknowledgement
-
-I would like to acknowledge the assistance (and patience!) of Pierre Bellec, Tristan Glatard, and Yann Harel as my Individual Instructors at the Brainhack School Montréal 2020 and the __extremely generous__ contributions of my collaborators Fernando Llanos (for the classifiers original scripts, recommendations on the HMM report conclusions and the two aggregated figures), Emily Coffey (for providing the dataset and information about it), as well as Marcel Farres Franch (for regular rescues and technological assistance, notably with Git Hub and Git Kraken).
-
-## References
-Coffey, E. B. J., Herholz, S. C., Chepesiuk, A. M. P., Baillet, S., & Zatorre, R. J. (2016). Cortical contributions to the auditory frequency-following response revealed by MEG. Nature Communications, 7, 11070. https://doi.org/10.1038/ncomms11070
-
-Coffey, E. B. J., Musacchia, G., & Zatorre, R. J. (2016). Cortical correlates of the auditory frequency-following and onset responses: EEG and fMRI evidence. Journal of Neuroscience, 1265–16. https://doi.org/10.1523/JNEUROSCI.1265-16.2016
-
-Coffey, E. B. J., Nicol, T., White-Schwoch, T., Chandrasekaran, B., Krizman, J., Skoe, E., Zatorre, R. J., & Kraus, N. (2019). Evolving perspectives on the sources of the frequency-following response. Nature Communications, 10(1), 5036. https://doi.org/10.1038/s41467-019-13003-w
-
-Herholz, S. C., & Zatorre, R. J. (2012). Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure. Neuron, 76(3), 486–502. https://doi.org/10.1016/j.neuron.2012.10.011
-
-Holdgraf, C. R., Rieger, J. W., Micheli, C., Martin, S., Knight, R. T., & Theunissen, F. E. (2017). Encoding and Decoding Models in Cognitive Electrophysiology. Frontiers in Systems Neuroscience, 11. https://doi.org/10.3389/fnsys.2017.00061
-
-Krizman, J., & Kraus, N. (2019). Analyzing the FFR: A tutorial for decoding the richness of auditory function. Hearing Research, 382, 107779. https://doi.org/10.1016/j.heares.2019.107779
-
-Llanos, F., Xie, Z., & Chandrasekaran, B. (2019). Biometric identification of listener identity from frequency following responses to speech. Journal of Neural Engineering, 16(5), 056004. https://doi.org/10.1088/1741-2552/ab1e01
-
-Llanos, F., Xie, Z., & Chandrasekaran, B. (2017). Hidden Markov modeling of frequency-following responses to Mandarin lexical tones. Journal of Neuroscience Methods, 291, 101–112. https://doi.org/10.1016/j.jneumeth.2017.08.010
-
-Xie Z., Reetzke, R., & Chandrasekaran, B. (2019). Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses. Journal of Speech, Language & Hearing Research, 62(3), 587–601. https://doi.org/10.1044/2018_JSLHR-S-ASTM-18-0244
-
-Yi, H. G., Xie, Z., Reetzke, R., Dimakis, A. G., & Chandrasekaran, B. (2017). Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach. Brain and Behavior, 7(6), e00665. https://doi.org/10.1002/brb3.665
\ No newline at end of file
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Evaluating ANNs of the Visual System with Representational Similarity Analysis"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Cornelius Crijnen]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2024/crijnen_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [deep learning, machine learning, visual system, calcium imaging, representational similarity analysis]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to evaluate the similarity between the representations of artificial neural networks (ANNs) and the visual system in the mouse brain using Representational Similarity Analysis (RSA)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "nm_175.png"
----
-
-
-## Project definition
-
-### Background
-
-* ANN models are often used to understand the visual system in the brain, specifically the ventral pathway.
-* First, image classification models were shown to predict neural responses in the ventral pathway ([Yamins et al., 2014.](https://doi.org/10.1073/pnas.1403112111)).
-* Similarly, the representations learned by self-supervised models produce good matches to the ventral pathway ([Talia Konkle, George Alvarez, 2020.](https://doi.org/10.1167/jov.20.11.498)).
-* Recently, it was shown that it is not only possible to predict neural responses in the ventral pathway but also in the dorsal pathway with a model that has two parallel pathways ([Bakhtiari et al., 2021.](https://doi.org/10.1101/2021.06.18.448989)).
-* We trained self-supervised models on videos from treeshrew and rat's point of view with two pathways.
-* RSA is used to compare two representational spaces:
- * Response matrices are created for every brain area and every layer of the ANNs, where each element represents the response of a neuron to a video sequence.
- * Use Pearson correlation to calculate the similarity of every pair of columns in the response matrix, forming a Representation Dissimilarity Matrix (RDM).
- * The RDMs describe the representation space in a network, either a brain area or an ANN layer.
- * Kendall’s τ is used between the vectorized RSMs to quantify the similarity between the two representations.
-
-### Tools
-
-The "Evaluating ANNs of the Visual System with RSA" project will rely on the following technologies:
-* **AllenSDK** for mouse brain data
-* **PyTorch** to retrieve the representations of the ANNs
-* **rsatoolbox** for the RSA analysis
-* **Plotly**, **Matplotlib** and **Seaborn** for visualizations
-
-### Data
-
-* Our collaborators videos from treeshrew and rat's point of view, which were used to train the ANN models of the visual system using SSL.
-* Calcium imaging data from the [Allen Brain Observatory](https://observatory.brain-map.org/visualcoding/).
-
-### Deliverables
-
-At the end of this project, we have:
-* A [GitHub repository](https://github.com/brainhack-school2024/crijnen_project) containing the code and model checkpoints to reproduce my results
-* A [jupyter notebook](https://github.com/brainhack-school2024/crijnen_project/blob/main/notebooks/analysis.ipynb) of the RSA
-* A [jupyter notebook](https://github.com/brainhack-school2024/crijnen_project/blob/main/notebooks/results.ipynb) containing the visualisations of my analysis
-
-## Results
-
-### Visualizing the Representational Similarity Analysis
-
-The following figures show the results of the RSA between the visual system of the mouse brain and the ANNs of the visual system trained on treeshrew and rat videos.
-Each figure shows the similarity between the representations of the visual system and the ANNs for five different brain areas.
-The noise ceiling is shown in grey, the similarity between the representations of the visual system and the rat ANNs is shown in blue, and the similarity between the representations of the visual system and the treeshrew ANNs is shown in red.
-Since the ANN models have two parallel pathways, the similarity between the representations of the visual system and the two pathways of the ANNs are shown separately.
-Two stimuli were used: Natural Movie One and Natural Scenes. For each stimulus, I used two depths of mouse brain recordings from the allen brain observatory: 175 and 275, respectively.
-
-#### Natural Movie One Stimuli
-
-
-
-
-#### Natural Scenes Stimuli
-
-
-
-## Conclusion and acknowledgement
-
-The results show that the representations of the visual system in the mouse brain are more similar to the representations of the ANNs trained on rat videos than to the ones trained on treeshrew videos.
-
-I would like to thank the brainhack school for providing me with the opportunity to work on this project. I would also like to thank my collaborators for providing me with the data and the guidance to complete this project.
-
-## References
-
-* [Yamins et al., 2014. Performance-optimized hierarchical models predict neural responses in higher visual cortex](https://doi.org/10.1073/pnas.1403112111)
-* [Talia Konkle, George Alvarez, 2020. Deepnets do not need category supervision to predict visual system responses to objects](https://doi.org/10.1167/jov.20.11.498)
-* [Bakhtiari et al., 2021. The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning](https://doi.org/10.1101/2021.06.18.448989)
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-07-29" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Exploratory Work on the Predictive Clinical Neuroscience (PCN) Toolkit"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Andjela Dimitrijevic]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/dimitrijevic_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [pcn-toolkit, github, hpc, brainhack cloud]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "My project consists of exploring the predictive normative modelling (PCN) toolkit via their numerous tutorials. It contains a markdown file for future new users of this package. It also includes steps on how to format your own data to use this toolkit. Finally, some cloud computing user guides will be touched upon."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "FSL_recon.png"
----
-
-
-# Project definition
-
-## Background
-
-The PCN toolkit aims at providing diffferent tools of **normative modelling** for understanding *psychiatric disorders* at the individual level including:
-- Data selection,
-- Data preparation,
-- Algorithm & modelling,
-- Evaluation & interpretation
-
-I was interesting in how to use this tool because it overlaps with my PhD work on trying to find a neuroimaging atlas for pediatric population. Hence, I wanted to see if it would be useful with the data I have.
-
-## Objectives
-
-The aim of this project will be decomposed into three sub-objectives:
-* Start off with the available tutorial on the PCN toolkit documentation to get familiar to it
-* Use another set of data if possible to follow through the available steps that this toolkit offers
-* Adapt some functionalities which were found to be less intuitive to work with
-* Learn some new tools throughout this work
-
-All of these three sub-objectives will be **documented** to be able to be **reproduced** in later or other exploratory works.
-
-### Tools
-This project relied on numerous tools throughout its creation:
-- [PCN Toolkit](https://pcntoolkit.readthedocs.io/en/latest/) and their [GitHub page](https://github.com/amarquand/PCNtoolkit) to explore their tutorials
-- Google Colab to follow through some of the tutorials
-- Bash Terminal constantly when moving files or using Git
-- High performance computing (HPC) through accessing the BrainHack cloud
-- Markdown to structure the tutorial guides
-- Git and GitHub for version control and making the project reproducible
-
-
-
-
-
-
-
-
-## Data
-
-In the end, this project does not use data a lot because guides for using some neuroimaging tools are presented instead. However, FreeSurfer was used on two subjects of the publicly available longitudinal [Calgary Preschool Dataset](https://osf.io/axz5r/).
-
-
-## Results: Deliverables of this project
-
-At the end of this BrainHack school, these files will be made available:
-- A [GitHub page](https://github.com/brainhack-school2022/dimitrijevic_project) containing all the modifications that were made to the PCN-toolkit tutorials
- - A [markdown file](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/PCNTutorial_steps/where_to_start.md) on where to start with the PCN-toolkit tutorials and [another](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/PCNTutorial_steps/data_formatting.md) on how to format the data adequately all in the PCNTutorial_steps directory on GitHub
- - A [markdown file](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/BrainHackCloud_steps/get_connected.md) on getting connected to the BrainHack Cloud to access HPC clusters as well as [one](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/BrainHackCloud_steps/neurodesk_access.md) on testing out the Neurodesktop container all in the BrainHackCloud_steps directory on GitHub
-
-
-
-## Progress overview
-
-At first, I thought using the Calgary Preschool Dataset would have been enough to do the analysis with the PCN-toolkit. However, this dataset seems too small (only 64 subjects) for that type of analysis. Furthermore, it was lacking cortical thickness maps which is an essential feature to be analyzed. Hence, that's why I started connecting to the HPC during the last week of the BrainHack school to be able to run FreeSurfer recon-all function on all subjects. This too came with its specific challenges. First, getting connected to the BrainHack cloud from my own computer did not work directly. Second, it took quite long to run the wanted FreeSurfer command on the NeuroDesk test server on one subject. Also, FreeSurfer bases its cortical thickness maps on previous registration tasks using adult templates (by default the Talairach atlas). This can incorporate biases and render the results to be faulty as shown on the two figures below. Indeed, some regions are not well delineated whether it be for the white matter and gray matter segmentation on Fig. 1 or the wrong specific parcellations shown by the green rectangle on the second figure.
-
-
-
Fig. 1 Example of white matter segmentation on a subject
-
-
-
-
-
Fig. 2 Parcellations of the right and left hemisphere pial surface for cortical analyses
-
-
-
-The final presentation on this project is avaible via [this link](https://docs.google.com/presentation/d/1BBGOiibHYFYnNmN_DVhKsS1oMsixU5fQ7aqgNR5F5jc/edit?usp=sharing) for further details.
-
-
-## Detailed actions
-
-*Reported GitHub issue to alert the PCN-toolkit tutorial writers*
-
-The BLR_protocol tutorial is more easily ran locally and does not work because of the older python version available on Google Colab. The solution of running it locally has all been explained in the [where_to_start md file](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/PCNTutorial_steps/where_to_start.md). To consult the reported issue click [here](https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo/issues/6)
-
-*Accessed the neurodesktop via the BrainHack Cloud successfully*
-
-The neurodesktop test server is great and easily accessible through the browser with various neuroimaging tools. Their test server is available via this [link](https://www.neurodesk.org/docs/neurodesktop/getting-started/play/) without any installation required. Also, installing and accessing this neurodesktop container through the BrainHack cloud was succesful. However, an important note is that running the NeuroDesk container via Docker should first be done on a virtual machine and NOT directly on the cloud.
-
-
-*Obtained recon-all FreeSurfer results for one subject*
-
-This was ran directly on the test server which is an interesting tool easily usable by everyone. The results could not be loaded as a zip file in the main GitHub repository for this project because of their size. However, this took approximately 7 hours to run because it was done on the available resources on the test server. Hence, using BrainHack cloud resources to parallelize everything is suggested.
-
-## Future Work
-Some future steps which will be done to follow through this project are presented below:
-
-- Aggregating multiple pediatric datasets to have more subjects and also different sites to analyze with the PCN-Toolkit
-- Submitting a job to the slurm scheduler to obtain cortical thickness maps of the data, but with an appropriate atlas
-- Uploading this newly modified data via DataLad tools to make it more reproducible
-
-
-## Conclusion and acknowledgement
-
-To conclude, the initial objectives of this project have been slightly modified. However, I am still glab with all the new essential tools I have learned throughout the weeks during this BrainHack school. I would like to thank all the instructors, but specifically the ones from the group-analysis pod which made this experience smoother! :fireworks:
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-08-07" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Experimenting with Occlusion methods to visualize the features learned by a CNN from audio or visual inputs"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Maëlle Freteault]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/freteault_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Neural network, visualization, features, module]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project has for goal to explore, understand and learn how to create comprehensive visualizations of the features learned by a convolution neural network, whether the model is specialized in auditory or visual input."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-
-image: "cover_picture_bh_2022.png"
----
-
-
-# Weights and features visualisation of a neural network
-
-## Context
-As Deep Neural Networks become more investigated and used in many fields, the question of how and what such networks decipher in data arise along. These questions seem particularly relevant when using brain encoders and decoders, since it will greatly help in understanding how such models whether differ from actual brain processing, or work in similar way, as much as our knowledge goes about these processes. Visualisation of weights and how they specialized or not in differents features in trained networks and during the training could help in better understanding brain processing.
-
-
-## Project Definition and Overview
-
-### Current state of the project
- This project was made during Brainhack School 2022 in Montréal, and at first it was supposed to be a whole pytorch module to simplify neural networks investigations.
- As I delved further into this field, I quickly realized that this goal was far too wide for a 4-weeks project, so I began experimenting differents libraries such as Pytorch and Tensorboard.
- Even if BrainHack School has ended, this project is still a work in progress, as it give me an excuse to play and experiment with different ways to visualize model weights & features.
- Right now I'm examining occlusion as a way to investigate CNN processing, especially in visual and audio modality.
-
-
-
-### Installation of Deliverable
-Right now, the module is still quite empty as it is more a playground than anything else.
-I still intend to develop it more, so that it can be used to visualize activation maps and heatmaps resulting from occlusion in CNN, especially in audio CNN.
-
-Even so, I tried to have a public repository clean and easily shareable ; You can install this module by downloading it from GitHub and and using the following command inside the repository :
-
-`pip install -e .`
-
-## To-Do List (not ordered)
-- finish occlusion test with VGG16 and others visual networks : on it
-- implementing auditory occlusion (time, frequency): on it
-- exploring others occlusion caracteristics (visual, audio)
-- implementing real testing that makes sense for this project
-- separating user's interface (occlusion module) from source code
-- create more utils for modifying data samples
-- documenting the functions
-- ... and a lot more probably.
-
-## Tools that are used in this project
-- Python and Python libraries:
- - Deep Neural Networks : Pytorch.nn, Torchvision and Pytorch.utils.tensorboard (some trials in Keras, not present in the repo)
- - basic data manipulation : Numpy, Pandas, PIL, Librosa ...
-
-- High Performance Computing : Compute Canada (trial tests with Tensorboard)
-- Control Versioning : Git and Github
-- module packaging : pypi, setuptools
-- testing (WIP, not present in this repo) : pytest, GitHub Actions
-
- ## Bibliography
- Here's some documentation that I used to better understand the field:
- - [Visualizing Weights](https://distill.pub/2020/circuits/visualizing-weights/) by Voss and collaborators
- - [The Building Blocks of Interpretability](https://distill.pub/2018/building-blocks/) by Olah and collaborators
- - [Looking inside neural nets](https://ml4a.github.io/ml4a/looking_inside_neural_nets/)
- - [A Guide to Understanding Convolutional Neural Networks (CNNs) using Visualization](https://www.analyticsvidhya.com/blog/2019/05/understanding-visualizing-neural-networks/) by Saurabh Pal
- - [An Interactive Visualization for Feature Localization in Deep Neural Networks](https://www.frontiersin.org/articles/10.3389/frai.2020.00049/full) by Zurowitez and Nattkemper
- - [A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks](https://arxiv.org/abs/2102.01792) by Atefeh Shahroudnejad
- - [Visualizing Convolution Neural Networks using Pytorch](https://towardsdatascience.com/visualizing-convolution-neural-networks-using-pytorch-3dfa8443e74e) by Niranjan Kumar
- - [#028 PyTorch – Visualization of Convolutional Neural Networks in PyTorch](https://datahacker.rs/028-visualization-and-understanding-of-convolutional-neural-networks-in-pytorch/)
-
-
-## Conclusion and acknowledgement
-
-Thanks for everyone in the brainhackschool team !
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-18" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Images10K Compendium"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Sara Barbu, Lune Bellec]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/Barbu-Images10K
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://sarabarbu.github.io/Images10k-compendium/
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [images, visualization, markdown, jupyter]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "A web-based tool for exploring visual datasets across real-world categories using carousels, tables, and interactive views."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Images10k_logo.png"
----
-
-
-## Project definition
-
-### Background
-
-Many studies in cognitive neuroscience still use really simplified or artificial images, like objects on plain backgrounds.That can limit ecological validity and create a disconnect between what you see in the lab and real world perception. In contrast to that, Images10K provides naturalistic scenes, showing objects in their natural environments, which makes it easier to study how people understand visual scenes in real-world contexts. This approach fits with recent work that emphasizes the importance of using more realistic images in research (Hosu, Lin, Szirányi, & Saupe, 2019).
-
-This project is based on the Images10K dataset — containing over 8,000 naturalistic images, annotated by human participants on the Zooniverse platform. The overall goal is to provide a well-organized and richly annotated image set (8,382 images, 15 semantic categories) that can be reused for training visual recognition models in AI and neuroscience
-
-### Tools
-
-- **GitHub** for version control and to organize the project in a clear, collaborative, and shareable format.
-- **DataLad** to retrieve and manage the dataset in a reproducible way.
-- **Python scripts** to filter images by category, convert metadata formats, and prepare image paths for display.
-- **Jupyter Notebooks** to explore the metadata, test visualizations, and generate previews of the dataset.
-- **Dash Bootstrap Components** to build interactive UI elements like carousels and dropdown menus for browsing images.
-- **MyST Markdown** to structure the content of the interactive website and document the project cleanly within Jupyter Book.
-- **Ubuntu terminal** to navigate the file system, run scripts, and better understand how to work with files and folders at the command line.
-
-## Data
-
-The dataset includes:
-- Over 8,000 naturalistic images, stored in semantically organized folders based on object categories
-- Annotated labels for each image, including category, subcategory, and file path
-- High-level semantic classifications (e.g., living vs. non-living, natural vs. artificial)
-- Rich image-level metadata such as:
- - Number of participant annotations (via Zooniverse)
- - Inter-participant agreement scores
- - License information and usage permissions
- - Source URLs and author attributions
-
-### Deliverables
-
-- A structured GitHub repository with scripts, metadata, and documentation
-- Jupyter Notebooks for metadata preview and exploratory analysis
-- An interactive web interface built with Jupyter Book and MyST Markdown
-- Image carousels and scrollable metadata tables for category-based exploration
-- Downloadable metadata files hosted via Google Drive
-
-## Results
-
-### Tools I learned during this project
-
-Tools are listed above in the Tools section
-
-### Results
-
-#### Deliverable:
-
-##### Link to view google slide presentation
-[presentation](https://docs.google.com/presentation/d/1INdPO4mDrgXu64EogxEHda7Kbf1mZ-EG5l1t3ICp8UQ/edit?usp=sharing)
-
-##### Link to the Image10k-Compendium Website
-You can view the full project and explore the carousels here:
-[**Image10k Compendium Website**](https://sarabarbu.github.io/Images10k-compendium/)
-
- **Note** Only a limited set of images is included on the website due to GitHub storage and bandwidth constraints.
-
- **Note** The interactive carousels (Dash apps) **require Python to run**, so they won’t launch directly in the website view.
-
-##### To view the carousels interactively on the Website:
-
-1. **Clone this repository**:
- [Images10k-compendium](https://github.com/SaraBarbu/Images10k-compendium)
-
-2. **Install dependencies**:
- `pip install -r binder/requirements.txt`
-
-3. **Run both notebooks** in the `content/` folder:
- - `animated_being.ipynb`
- - `Objects.ipynb`
-
-
-
-## Conclusion and acknowledgement
-
-This project helped me learn how to organize data and build interactive visual tools using Dash and Jupyter. I also got more comfortable working with GitHub and sharing work online.
-
-Thanks to Lune, Marie, Cléo, and the whole BrainHack School team for all the help and support along the way!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-05-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "An introduction to brain decoding and comparing the results of the seven different classifier on Haxby dataset"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Shima Rastegarnia]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/BHS_project_SRastegarnia
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/BHS_project_SRastegarnia), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, machine Learning, deep learning, classification, artificial neural network, brain decoding, visual stimuli]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Brain decoding is a neuroscience field that concerned about different types of stimuli from information that has already been encoded and represented in the brain by networks of neurons. My goal for this project is learning the fundamentals of brain decoding. Moreover, I compared the performance of seven different common classification approaches including Naive Bayes, Nearest Neighbours, Neural Networks, Logistic Regression, Support vector machine, Decision tree and finally the Artificial Neural Network on Haxby dataset."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "FlowerBouquet.png"
----
-
-
-# Intro to brain decoding and classification on Haxby dataset using seven different common approaches
-
-
-
-
-
-## Project definition
-
-### Background
-I have B.S in computer software engineering and currently I am a Master’s student in computer science at Université de Montréal (Jan. 2020). Since I am still in the early steps of my master’s project, my main goal is to learn as much as possible and making use of several tools that we have learned during BHS training courses.
-
-
-
-
-
-### Brain_decoding overview
-Brain decoding or mind-reading using neuroimaging data has been an active topic for years. A part of this project focused on brain decoding using visual stimuli. In the human brain, the functional architecture of the object vision pathway can be investigated using fMRI. It can be done by considering the patterns of response in the ventral temporal cortex while subjects are looking at the different objects. Several studies indicate that the brain responses to the vision of each category of objects are widely distributed and overlapping. Therefore, a distinct pattern of response exists for each stimulus category in the ventral temporal cortex.
-
-### Project
-For BHS project, I ran and compared the results of six common classifiers ("Naive Bayes", "Nearest Neighbours", "Neural Networks", "Logistic Regression", "Support vector machine" and "Decision trees" classifiers) on Haxby dataset. During the classification stage, I was trying to find the best results for each approach by playing with parameters. As an example, for Neural Networks classifier, my accuracy result increased significantly when I changed some parameters (it is indicated in the Classifiers.ipynb).
-This change of accuracy made me curious about training ANN on Haxby dataset to examine its performance and learn better about this method (that wasn't a plan at the beginning). Moreover, to make the notebook reproducible and easy to follow for those who don't have a background of machine learning (similar to me before Brainhack school!), I have added a clear description for each cell and a quick overview of each classifier's theory.
-
-
-### Tools
-* Git/Github
-* Nilearn & sklearn
-* Python visualization, statistics & machine learning libraries (e.g. NumPy, Seaborn, scikit-learn, Matlplotlib, nibabel, graphviz, and pydotplus)
-* Compute Canada/Calcul Québec
-* Binder
-* Terminal and Shell commands
-
-### Data
-For this project I used [Haxby et al.(2001)](http://data.pymvpa.org/datasets/haxby2001/) which is a high-quality block-design fMRI dataset from a study on face & object representation in the human ventral temporal cortex (This cortex is involved in the high-level visual processing of complex stimuli). The dataset consists of 6 subjects with 12 runs per subject. In this experiment during each run, the subjects passively viewed greyscale images of 8 object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval.
-
-### Progress overview
-
-**Week 1:** During BHS training week I was trying to use the new concepts that we learned, by practicing and following different tutorials. I started using Git & Github.
-
-**Week 2:** In the second week, I created the README file, made slides and became prepare for the project draft presentation. Moreover, I started to play with brain decoding scripts, mostly by following Nilearn and sklearn tutorials. I explored how they work by experimenting and personalizing them.
-
-**Week 3:** From the third week, I started to create my main jupyter notebook called [Classifiers.ipynb](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/Classifiers.ipynb) to try six different classification methods on Haxby dataset and compare the results. For me, this approach was challenging not only because of writing scripts but also for understanding the classifiers algorithms and work follows. Since I am new in this field and have never had a machine learning course before, I had to read several references to figure out these approaches, their strengths, and their weaknesses. It gave me the opportunity to learn about several ML models and to get a much better understanding of the reasons that some classifiers return better results on certain datasets compared to the rest.
-
-**Week 4 & 5:** During these weeks, I executed some performance metrics such as "Classification Accuracy”, "Cross-Validation" and “Confusion Matrix” in order to check my different ML model results. I added a quick review for each classifier algorithm to make my notebook reproducible and easy to understand for everyone even those without ML background.
-
-Additionally, I uploaded and ran one of my scripts on Compute Canada.
-
-After finishing all the mentioned tasks, I decided to take advantage of the remaining time of the 5th week by training a multi-layer network. As was mentioned above, I chose the ANN model. This notebook can be found as [ANN_onHaxby.ipynb](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/ANN_onHaxby.ipynb). Similar to the previous notebook I added a quick review about the ANN algorithm and some explanation for all the cells in order to make it easy to follow.
-
-*I have written down all my progressions in detail, divided per week, in my BHS repository [README](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/README.md) file under "TO-DO LIST " section.*
-
-### Results
-As depicted in the following image the support vector machine classification has the best performance comparing to the other mentioned classifiers, while the decision tree returned the worst accuracy, which is understandable considering the classifiers algorithms and the nature of the data.
-
-
-
-
-
-
-
-
-The following figure demonstrates the six classifiers confusion matrices.
-
-
-
-
-
-
-Here, we see the ANN confusion matrix and the accuracy results. The accuracy is increased from each epoch to the next one that demonstrates how during this supervised phase, the network is taught what is the desired output.
-
-
-
-
-
-
-
-However, some methods seem to work better than others, It shouldn’t be forgotten that the size and type of data play an important role in the results of different models. As an example, generally multi-layer networks have better results on big datasets. In this case, the results are desirable considering the small size of the input data.
-
-
-
-## Tools I learned during this project
-Besides all the tools said earlier, I touched TensorFlow as well!
-
-## Conclusion
-Along with many open science tools that I have learned and used during this course; BHS was a great introduction to machine learning. I trained several machine learning classification methods to learn and compare their performance on the Haxby dataset that helped to strengthen my skills in Python and using ML/DL models.
-
-In the beginning, I explored brain decoding scripts by following tones of tutorials (mostly from Nilearn and sklearn). In the next step, I created a notebook to improve my coding skills and ML knowledge by evaluating the performance of seven different classifiers. I used plotly for making interactable graphs to demonstrate the results.
-In the end, I tried my first ever multi-layer algorithm which was an artificial neural network.
-Additionally, the focus on reproducible science taught me how to make a deliverable project based on an open-science concept.
-
-## Deliverables
-
-* [Jupyter Notebook with code for comparing different classifiers](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/Classifiers.ipynb)
-* [Jupyter Notebook with code for Artificial Neural Network model training](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/ANN_onHaxby.ipynb)
-* [Jupyter Notebook with code for introduction to brain decoding](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/BHS_Haxby_BrainDecoding.ipynb)
-[Jupyter Notebook with code to produce plots of my results](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/Data-visualization.ipynb) (week three deliverable)
-* [Batch script used on Compute Canada to run BHS_Haxby_BrainDecoding.py script](https://github.com/brainhack-school2020/BHS_project_SRastegarnia/blob/master/BHS_Batch.sh)
-* [Github repository including project description with all mentioned Jupyter Notebooks clearly commented](https://github.com/brainhack-school2020/BHS_project_SRastegarnia) (Please use Binder to see all the results!)
-* [First](https://drive.google.com/open?id=1ABaOXwWPks8xB28OlkiwDvqx7D0B2htQ) and [Final](https://drive.google.com/file/d/1qFHRrdjRLgfP-5dDYNs12vHyDhFCYVp6/view?usp=sharing) presentation slides
-* The final report summarizing the entire project and my progression.
-
-
-## Future directions
-My goals post brain hack school would be increasing my knowledge of machine learning and deep learning by training more complicated models including GCN on new datasets! Also, I hope to try more tools for neuroimaging analysis and preprocessing data.
-
-## Aknowledgments
-During BHS, I learned a lot of new tools and so many skills that I will definitely use in the future. I would like to thank all the BHS organizers, instructors, and TAs for sharing their knowledge and putting together this amazing course. Special thanks to Pierre Bellec and my clinic instructors Désirée, Valerie, and Jacob for helping me out with different aspects of my project.
-Thanks for giving me this opportunity!
-
-## References
-
-1. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, Haxby et al. 2001
-2. https://machinelearningmastery.com/exploding-gradients-in-neural-networks/
-3. https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
-4. https://towardsdatascience.com/the-vanishing-gradient-problem-69bf08b15484
-5. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
-6. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
-7. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
-8. https://scikit-learn.org/stable/modules/svm.html
-9. https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
-10. https://towardsdatascience.com/introduction-to-artificial-neural-networks-ann-1aea15775ef9
-11. https://towardsdatascience.com/building-your-own-artificial-neural-network-from-scratch-on-churn-modeling-dataset-using-keras-in-690782f7d051
-12. https://nilearn.github.io/auto_examples
-13. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
-14. https://towardsdatascience.com/categorical-encoding-using-label-encoding-and-one-hot-encoder-911ef77fb5bd
-15. https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier
-16. https://github.com/ncarlson01/chat_ML
-17. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html?highlight=kneighbors#sklearn.neighbors.KNeighborsClassifier
-18. https://scikit-learn.org/stable/supervised_learning.html#supervised-learning
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "fMRIPrep 101 - Pre-processing fMRI data and extracting connectivity matrices"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Frederic St-Onge]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/stong3_fMRI_processing
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, fmriprep, connectivity, nilearn]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to understand how to pre-process fMRI data using fMRIPrep. Through this learning experience, a tutorial was created."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "bhs2020.png"
----
-
-
-## Project definition
-
-### Background
-
-Having little experience with neuroimaging and my PhD thesis using fMRI, I wanted to be able to start from raw data (in BIDS format) and learn how to process the data until I obtain derivatives (i.e. connectivity matrices). Based on the current PhD project that I am working on, using various atlases leads to varying results. As such, I wanted to extract connectivity matrices from pre-processed time-series using Nilearn, which comes pre-loaded with several atlases of interest. Finally, as most of my work will involve large cohorts (which might not be feasible to do on a personal computer), I wanted to be able to realize these analyses on a HPC, where resources can be used appropriately. Please note that more information on the project is available on [Github](https://github.com/brainhack-school2020/stong3_fMRI_processing).
-
-
-
-### Tools
-
-The project used the following tools:
- * Docker (for bids-validator and fMRIPrep)
- * Bids-validator (to validate bids)
- * fMRIPrep (to pre-process data)
- * Jupyter notebook (for deliverables)
- * Nilearn (to extract connectivity matrices)
- * Github (to version control and share the project)
-
-### Data
-
-The data used is a single, anonymized, subject from the Prevent-AD. We used 2 fMRI visits. Data is not available for reproduction, but more details on the cohort can be found [here](https://portal.conp.ca/dataset?id=projects/preventad-open)
-
-### Deliverables
-
-At the end of this project, we will have:
- - A Jupyter notebook detailing the failed first project.
- - A Jupyter notebook detailing the current fMRIPrep project.
- - A complete GitHub repo detailing the process.
-
-## Results
-
-### Github repo
-
-For more details on the deliverables, please refer to the GitHub repo available [here](https://github.com/brainhack-school2020/stong3_fMRI_processing).
-
-The tutorial was designed to be: 1) Easy to use, 2) Comprehensive and 3) Hopefully fun!
-
-## Conclusion and acknowledgement
-
-In conclusion, I was able to learn a lot of new tools and gain a deeper understanding of fMRIPreprocessing. I was able to create a tutorial on using fMRIPrep that, hopefully, will also serve others learning this software!
-
-I want to thank my instructor, Desiree Lussier, and all the BHS2020 team for allowing me to learn so much in a short span of a month!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-25" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Investigating the effect of alpha band on TRFs"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Kuan-Yu Chen, Ruo-Chi Yao, Liang-Mei Lin, Ming-Feng Hsin]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/Aiame/2024_brain_hack_school_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [eeg, github, eelbrain, python]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In our study of three participants, removing the alpha band affected TRFs, with some features being suppressed and others enhanced. This simplification highlighted local signals, making brain activity clearer. However, it's unclear if these enhanced signals represent true brain activity or noise, requiring further analysis for validation."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "S38_TRFs.png"
----
-
-
-## Project definition
-
-### Background
-The alpha band (α-wave) is a type of brainwave with a frequency range of approximately 8 to 12 Hz, often associated with relaxation and closed-eye states. Temporal Response Functions (TRFs) are models used to analyze the brain's response to time-series stimuli, particularly in the context of speech processing.
-
-Given our interest in understanding whether α-waves influence TRFs during language processing, our project aims to explore the impact of including or excluding α-waves on TRFs. Using an open dataset, we seek to determine how the presence or absence of α-waves affects the modeling and interpretation of neural responses in speech processing.
-
-## Data
-### Stimuli
-For the EEG recordings in the Alice Dataset, participants listened to the first chapter of "Alice’s Adventures in Wonderland." This chapter consists of 2,129 words arranged into 84 sentences, with an average sentence length of 25.8 words (SD = 24.2). The total duration of the audio is approximately 12.4 minutes.
-
-### Participants
-
-The study included 49 adult participants who were native English speakers, aged between 18 and 29, with 14 males in the group. All participants reported having normal hearing and no neurological disorders.
-
-### Procedure
-Participants were equipped with 61 actively-amplified electrodes plus one ground electrode (actiCap, Brain Products GmbH), arranged according to the Easycap M10 layout for uniform scalp coverage. During the experiment, the audiobook was played at a volume set 45 dB above the participant’s hearing threshold. After the audio session, participants completed an eight-question multiple-choice quiz about the story. The experimental session took between 1 to 1.5 hours to complete.
-
-In our project, 3 EEG data sets were chosen randomly.
-This repository offer the analyzed TRF, you might want to download because running the analysis from scratch could be time consuming
-
-
-### Tools
-The dataset processing by using mne-python
-
-### Workflow
--- download data from the Alice repository and run estimate_trf script to get the processed data
-
--- use the raw data to do ICA, remove the reaction of alpha band and save as a new fif
-
--- run estimate_trf again to get the removed alpha data
-
-
-
-## Results
-subject 13
-
-subject 18
-
-subject 38
-
-
-## Conclusion and acknowledgement
-In our study of three participants, removing the alpha band affected TRFs, with some features being suppressed and others enhanced. This simplification highlighted local signals, making brain activity clearer. However, it's unclear if these enhanced signals represent true brain activity or noise, requiring further analysis for validation.
-
-### Reference
-Alice dataset: https://github.com/Eelbrain/Alice/tree/main
-
-Bhattasali, S., Brennan, J., Luh, W. M., Franzluebbers, B., & Hale, J. (2020). The Alice Datasets: fMRI & EEG observations of natural language comprehension. In Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 120-125).
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-14" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "MEG Meets Stories - TRF Analysis of Continuous Speech in SMN4Lang"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Wang Chi]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/Chi-WangPsy/Chi_brainhack_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [meg, trf, speech, language]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project explores how the brain responds to natural stories via TRF modeling on the SMN4Lang dataset, using acoustic envelope and word-aligned features -- and includes an exploratory attempt at word classification using machine learning."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover.png"
----
-
-
-## Project definition
-
-### Background
-
-This project was developed as part of Brainhack School 2025, where students from diverse backgrounds explore neuroscience tools through hands-on, open-science projects. Inspired by this spirit, I set out to investigate how the brain responds to naturalistic language.
-
-Specifically, I used the SMN4Lang dataset, which provides synchronized MEG and audio recordings of Mandarin stories. By extracting time-aligned word-level features and modeling their relationship to brain signals using Temporal Response Functions (TRF), I explored how different linguistic properties -such as the acoustic envelope -shape neural responses.
-
-At first, I couldn't really tell if the signals made any sense. But after much trial and error (and some brave plotting), the curves started to take shape - and although I still wasn't sure how meaningful they were, the instructor at my final presentation said they "mean something." That gave me a surprising sense of accomplishment.
-
-The project also included a bold, exploratory attempt to decode word categories using machine learning... which didn't quite work - but the learning process absolutely did.
-
-### Tools
-
-The "Chi_brainhack_project" project will rely on the following technologies:
- * MNE-Python: For preprocessing and analyzing MEG data, including filtering and visualization.
- * NumPy: For numerical operations and signal processing.
- * Matplotlib: For generating custom plots and visualizations.
- * Scikit-learn: For an exploratory (and brave) attempt at machine learning–based word classification.
- * Jupyter Notebook: To write, test, and document code in an interactive, reproducible way.
- * Git & GitHub: For version control and sharing the project with the Brainhack community.
-
-### Data
-
-This project uses the SMN4Lang dataset(https://openneuro.org/datasets/ds004078), which contains magnetoencephalography (MEG) recordings and time-aligned annotations of Mandarin speech from 60 naturalistic story-listening sessions across 12 participants. The dataset includes synchronized audio, transcripts, and preprocessed MEG signals, enabling analysis of how the brain tracks both linguistic and acoustic features during continuous speech.
-This rich, real-world dataset allows researchers to move beyond controlled laboratory stimuli and investigate language processing in more ecologically valid settings.
-
-
-### Deliverables
-
-At the end of this project, the following deliverables were completed:
- - Word-level stimulus .csv tables for each story, including POS, word frequency, and onset time.
- - TRF coefficients for acoustic envelope features, with corresponding visualizations.
- - Scripts to segment raw MEG data based on the annotated word-level features.
- - SVM classification of nouns vs. verbs using LOSO-CV accuracy and feature weight analysis.
-
-## Results
-
-### Progress overview
-
-This project was developed during Brainhack School 2025, where students are encouraged to learn open neuroscience tools through hands-on projects. Initially unfamiliar with MEG data and the TRF framework, I gradually gained confidence by replicating small parts of existing pipelines and troubleshooting errors. The process was at times frustrating, especially when interpreting signal outputs, but became deeply rewarding after discussing the results with instructors who reassured me that "this already means something.
-
-
-### Tools I learned during this project
-
- * **MNE-Python** for loading, preprocessing, and analyzing MEG data.
- * **Scikit-learn** for exploratory machine learning (SVM classification).
- * **Matplotlib** for visualization of TRF weights and decoding results.
- * **NumPy / SciPy** for signal processing and numerical computations.
- * **GitHub & Markdown** for project management and documentation
-
-
-### Results
-
-#### Deliverable 1: Integrating required annotations into a single CSV file
-
-At first, I had to repeatedly load multiple files, and I was always worried that the timestamps didn't align correctly or that data types were mismatched. Later, I realized it was more efficient to extract all necessary information and consolidate it into a single CSV file. This made subsequent data access much more convenient and allowed me to quickly visualize and verify the alignment.
-
-* a presentation [made in a Jupyter notebook](https://github.com/Chi-WangPsy/Chi_brainhack_project/blob/main/notebook/alignstimuli.ipynb)
-
-#### Deliverable 2: TRF coefficients and visualization
-
-I initially attempted to use Brodbeck's eelbrain, but the data format differences caused repeated failures. Eventually, with help from ChatGPT and a better understanding of TRF principles, I created a custom TRF pipeline. In future work, I'd still like to revisit eelbrain since it might produce cleaner TRF signals.
-
-In my current pipeline, I first save the TRF coefficients, which can later be loaded and plotted as needed. I visualized GRAD channels separately, explored hemispheric differences (left vs. right), and also generated topomaps to observe the dynamic patterns across the brain.
-
-**TRF GRAD Waveform**
-
-**TRF MAG Waveform**
-
-**TRF GRAD Topomap**
-
-**TRF MAG Topomap**
-
-
-* a presentation [made in a Jupyter notebook](https://github.com/Chi-WangPsy/Chi_brainhack_project/blob/main/notebook/TRF.ipynb) showing the analysis process and results.
-
-#### Deliverable 3: Epoching based on word-level timing
-
-Using word-level time-aligned annotations, I segmented the raw MEG data into epochs and saved them for further analysis.
-
-* a presentation [made in a Jupyter notebook](https://github.com/Chi-WangPsy/Chi_brainhack_project/blob/main/notebook/epoch.ipynb)
-
-#### Deliverable 4: SVM classification
-
-I selected nouns and verbs and fed them into an SVM classifier to test whether they could be distinguished based on brain signals. The output included LOSO-CV accuracy and feature weight analysis. However, I later realized that this approach may not be appropriate and needs further refinement.
-
-* a presentation [made in a Jupyter notebook](https://github.com/Chi-WangPsy/Chi_brainhack_project/blob/main/notebook/POVSVM.ipynb)
-
-## Conclusion and acknowledgement
-
-This project marked my first journey into analyzing MEG data in a naturalistic language context. Although I encountered early confusion and technical hurdles, I gradually gained hands-on experience in data pipelines, TRF modeling, and machine learning evaluation.
-
-At the outset, I didn't even know how to use Bash. Thanks to the clear and detailed module instructions, I felt like I had evolved from a digital novice into someone capable of confidently navigating their own computing environment.
-
-During the final presentation, the instructor suggested that I treat the Brainhack School Discord community like a real-time GPT-freely asking questions and receiving instant feedback. I believe this approach could help me clarify confusion more efficiently and explore new analysis directions. Moving forward, I hope to become more proactive in seeking support and initiating discussions.
-
-Special thanks to the Brainhack School team and instructors for fostering such a supportive learning environment-one that transformed initial uncertainty into curiosity and meaningful progress.
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-07" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "The Effects of Word Frequency, Orthographic Neighborhood and Part of Speech in Word Processing: A MEG Study"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Ruo-Chi Yao]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/Ruo-Chi/Ruo-Chi-Yao_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [meg, github, mne, python]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Word processing is influenced by different attributes of words, which can be observed vis brain imaging techniques such as MEG. Let's find evidence of the influence of word attributes by analyzing a dataset."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover image.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Word processing is influenced by different attributes of words, which can be observed vis brain imaging techniques such as MEG. This project focuses on attributes of words, including word frequency, orthographic neighborhood, and part of speech.
-- In word processing, high-frequency words are handled more easily and quickly than low-frequency words.
-- An orthographic neighborhood refers to words that are similar to a target word by differing in only one letter (addition, deletion, or substitution of a single letter).
-- Part of speech refers to the grammatical category or class that a word belongs to.
-
-We have all learned and understood that various attributes of words impact word processing, whether from textbooks or lectures. However, many of us may not have had the opportunity to obtain evidence regarding this ourselves. The main question here is whether I can find evidence of the influence of word attributes by analyzing a dataset. Therefore, the primary question here is whether I can find evidence of the influence of word attributes by analyzing a dataset.
-
-### Tools
-
-The dataset processing by useing mne-python
-
-### Methods
-
-
-
-- Read & load raw data
-
- 
-
-- Filter & down sample
-
-
-
-- Find event id
-
-
-
-- Do artifact rejection & slice the data into epochs
-
-
-
-- Working with epoch metadata & visualizing evoked data
-
-
-
-
-### Data
-
-Dataset: [Auditory single word recognition in MEG – OpenNeuro](https://openneuro.org/datasets/ds004276/versions/1.0.0)
-- There are files for 18 subjects, but 12 of them can be download successfully.
-- 1000 trials for one subject, that is each subject listens to 1000 words.
-- There are 97 extra words specifically for semantic probe tasks that involve go/no-go trials.
-- Data are recorded using MEG.
-
-
-### Deliverables
-
-At the end of this project, we will have:
- - A GitHub repository.
- - A markdown document.
- - MEG data analysis with MNE-Python.
-
-## Results
-
-### Frequency effects
-
-
-### Orthographic neighborhood
-
-
-### Part of speech
-
-
-## Conclusion
-
-### Frequency effects
-- the effect can be seen from 0ms to 200ms and 450ms to 600 ms time window
-- in consistence with the result that word frequency effects become stronger and more widespread from 200ms to 300ms and 400ms to 500ms respectively (Dufau et al., 2015)
-
-### Orthographic neighborhood
-- the effect can be seen after 300ms time window
-- in consistence with the result that the effects of orthographic neighborhood size emerge around 280 ms (Dufau et al., 2015)
-
-### Part of speech
-- the effect can be seen after 250 ms time window
-- in consistence with the result that verbs are more negative than nouns about 300 and 1,000 ms (Rösler et al., 2001)
-
-## References
-- Brysbaert, M., Mandera, P., & Keuleers, E. (2018). The word frequency effect in word processing: An updated review. Current Directions in Psychological Science, 27(1), 45-50.
-- Dufau, S., Grainger, J., Midgley, K. J., & Holcomb, P. J. (2015). A thousand words are worth a picture: Snapshots of printed-word processing in an event-related potential megastudy. Psychological science, 26(12), 1887-1897.
-- Gaston, P., Brodbeck, C., Phillips, C., & Lau, E. (2023). Auditory word comprehension is less incremental in isolated words. Neurobiology of Language, 4(1), 29-52.
-- Marian, V., Bartolotti, J., Chabal, S., & Shook, A. (2012). CLEARPOND: Cross-linguistic easy-access resource for phonological and orthographic neighborhood densities.
-- Rösler, F., Streb, J., & Haan, H. (2001). Event-related brain potentials evoked by verbs and nouns in a primed lexical decision task. Psychophysiology, 38(4), 694-703.
-
-
-
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "MethNet: Visualizing methods in citation networks"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Kendra Oudyk]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/koudyk_bhs_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [API, text-mining, fMRI, methods]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "A Python package that create a dynamic visualization the use of methods in citation networks over time."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "visualization__example.gif"
----
-
-
-## Project definition
-
-### Background
-Methods can influence results in fMRI research (e.g., Carp, 2012; Eklund et al., 2016; Glatard et al., 2015). If methods influence results, then methodological trends may stystematically influence results. In order to explore this problem space, I thought it might be helpful to visualize methodological trends in fMRI research.
-
-### Purpose
-The purpose of this project was to make a Python package that could create a citation network for a given field, colored by methods used by papers in that field. Such a figure would be created for each year in a range, so that one can see the development of the network and the use of methods over time. Using this package to look at research using fMRI, I hope to observe whether methods correspond to groupings of papers in the citation network.
-
-### Data
-This package uses the [PubMed Central Open-Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/)\*, which is a set of papers on PubMed Central that are available under an open licence. I chose this dataset because the package need access to the full text of journal articles in order to search for methods-related keywords.
-
-## Results
-### Progress overview
-I was able to create the type of figure that I had envisioned, and I practiced using almost all of tools and skills that I'd hoped to practice. However, the figure has some glitches and was not as informative as I'd hoped. Also, I did not get as far as I'd planned in terms of packaging and testing.
-
-### Deliverables
-- [Package](https://github.com/brainhack-school2020/koudyk_bhs_project) in progress,
-- [Workflow diagram](https://github.com/brainhack-school2020/koudyk_bhs_project/blob/master/images/workflow_diagrams/diagram_entire_workflow.gv.png) illustrating how the package works,
-- [Example notebook](https://github.com/brainhack-school2020/koudyk_bhs_project/blob/master/methnet/example.ipynb) demonstrating how to use the package, and
-- [Example gif](https://github.com/brainhack-school2020/koudyk_bhs_project/blob/master/images/visualization__example.gif), which is the output of the example notebook, and can be seen below.
-
-
-
-### Tools and skills practiced
-In this project, I practiced (more or less)
-- [x] Packaging
-- [x] GitHub issues & projects
-- [x] API interaction, specifically, using the [NCBI Entrez Programming E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25497/)
-- [x] XML
-- [x] Jupytext
-- [x] Python
-- [x] Data visualization
-
-## Conclusion and acknowledgement
-Although the project did not end up exactly where I'd hoped, I learned a lot along the way. I appreciate all the effort made by the BHS instructors and organizers, particularly Sam, Agah, Alexa, and Loic. I'm also especially thankful to Jérôme, who helped me even though he wasn't part of the BHS.
-
-
-***
-
-#### References
-- Carp, J. (2012). On the plurality of (methodological) worlds: Estimating the analytic flexibility of fMRI experiments. *Frontiers in Neuroscience, 6*, 149.
-- Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. *Proceedings of the National Academy of Sciences, 113*(28), 7900-7905.
-- Glatard, T., Lewis, L. B., Ferreira da Silva, R., Adalat, R., Beck, N., Lepage, C., ... & Khalili-Mahani, N. (2015). Reproducibility of neuroimaging analyses across operating systems. *Frontiers in Neuroinformatics, 9*, 12.
-
-#### \*NCBI's Disclaimer and Copyright notice
-The National Center for Biotechnology Information (NCBI), who own the data and E-utilities that this package uses, requests that we make this [Disclaimer and Copyright notice](https://www.ncbi.nlm.nih.gov/home/about/policies/) evident to users.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Comparing different methods for mice behavioral analysis"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Pilar López Maggi, Gonzalo Giordano, Ana Pavlova Contreras, Santiago D'hers]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/sdhers/Mice-Behavioral-Analysis.git
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [mice, tracking, machine learning, exploration detection]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects.
-summary: "Our project aims to develop different methods for the analysis of behavior in mice (in this case, exploration of an object) to determine which is the best approach to this kind of study. We were able to implement and compare three increasingly complex methods to determine exploration time: manual labeling; motion tracking and data analysis using a custom algorithm; training a Machine Learning classifier on our labeled data."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "DLC_example.JPG"
----
-
-
-## Project definition
-
-## Background
-
-Our project aims to develop different methods for the analysis of behavior in mice (in this case, exploration of an object) to determine which is the best approach to this kind of study. The accurate measurement of these behaviours is crucial for the study of neurodegenerative pathologies, such as Alzheimer’s disease.
-
-We aim to work with and compare three different and increasingly complex methods:
-
-* Manual labeling.
-* Motion tracking and data analysis using a custom algorithm.
-* Training a Machine Learning algorithm on our labeled data.
-
-### Tools
-
-Our project will rely on the following technologies:
-
- * Python, to write the scripts used to label and analyze our data.
- * Jupyter Notebooks, to present our results in a clear and readable format.
- * DeepLabCut, to track movements of mice in our videos. The accurate instructions on how to use DLC can be found here https://github.com/DeepLabCut/DeepLabCut
-
-### Data
-
-We worked on videos obtained with C57 mice during a Novel Object Recognition experiment developed at IFIBYNE - UBA, CONICET. The videos were first processed by DeepLabCut pose estimation software.
-
-### Deliverables
-
-At the end of this project, we will have:
- - A script to simplify the manual labeling of videos (including features to quickly label succesive frames by holding down a key and to go back if the user has made a mistake).
- - A Jupyter Notebook where the labeled data and the tracked positions are imported and processed, and where each of our exploration detection methods is applied and compared to the others.
- - A [`requirements.txt`](https://github.com/sdhers/Mice-Behavioral-Analysis/tree/main/requirements.txt) file and the data used during the project, to simplify the reproduction of our results.
-
-## Results
-
-### Progress overview
-
-During the first week, we learned the basic tools which then allowed us to work on our project and collaborate easily: basic Bash commands, Git/GitHub and Python tools for data analysis. In the course of the following weeks, we defined the scope of our project and implemented each of our ideas with the help of our TAs.
-
-### Tools we learned during this project
-
- * **Proper usage of version control systems for collaboration**: We learned how to properly use Git and Github to simplify collaboration between different team members.
- * We learned how to implement Python scripts to read and process mice positions and label frames.
- * Finally, we managed to use the positions and labels to train a Random Forest model to predict labels on new videos.
-
-### Results
-
-#### Video labeling script
-
-We developed a script to be able to process the video information and label the frames with ease. It can be found at [`Video_Processing/Label_videos.py`](https://github.com/sdhers/Mice-Behavioral-Analysis/tree/main/Video_Processing/Label_videos.py).
-
-#### Motion tracking using DLC
-
-We used Deep Lab Cut to track the positions of different parts of the mice in each of the videos. The resulting data (in `h5` format) can be found under [`Motion_Tracking/DataDLC/videos_eval/`](https://github.com/sdhers/Mice-Behavioral-Analysis/tree/main/Motion_Tracking/DataDLC/videos_eval/).
-
-#### Applying and comparing each method
-
-The most important part of our project is contained in [`exploration_detection.ipynb`](https://github.com/sdhers/Mice-Behavioral-Analysis/tree/main/Motion_Tracking/exploration_detection.ipynb). To start with, we import the labels and the tracked data for each video, and we separate a video to use later to test the model. We then develop our custom algorithm for detecting explorations based on the positions tracked by DLC. This algorithm labels a frame as an exploration if the mouse is both close to a given object and looking at it. In order to determine the proximity and orientation of the mouse, we extract the positions of its nose and its head. We then filter the points where the nose is close to the object and the angle between the head-nose vector and the head-object vector is small. Our code makes use of a series of classes defined in [`Motion_Tracking/utils.py`](https://github.com/sdhers/Mice-Behavioral-Analysis/tree/main/Motion_Tracking/utils.py) to handle the math.
-
-
-
-We then use the Random Forest model to process the positions and the given labels, and we test the model on the unseen video, by comparing its detection both to the labels obtained manually, and to those resulting from the distance-orientation algorithm.
-
-The notebook contains a detailed explanation of the process used to import and analyze our data, as well as a description of our custom algorithm and a comparison between the three detection methods.
-
-Some of the plots used to compare the three methods are shown below.
-
-The first shows a time series extracted from the test video, showing that all three methods seem to generally agree on which parts of the video constitute explorations of the different objects, even if they sometimes differ on the predicted length of each exploration event.
-
-
-
-Then, in order to compare the distance and orientation of the mouse at the explorations detected by each method, we plot the position of the mouse in each frame using polar coordinates, where the radial coordinate is the distance to one of the objects, and the angular coordinate is the orientation.
-
-To simplify the comparison between the three detection methods, the region considered for explorations by our custom algorithm is delimited by a dashed line (on the right hand side), and the points are plotted twice: on the left, we highlight the manual detections in red, while on the right, we highlight the Machine Learning algorithm's detections in blue.
-
-
-
-We can see that all three methods seem to agree in the region considered for explorations by our custom algorithm, since most points there are also detected by our manual method and by the Machine Learning algorithm.
-
-However, there are several points in the range of angles between $135$° and $180$° where both the manual method and the Machine Learning algorithm detect explorations; this range is not considered by our custom algorithm, which shows its limitations.
-
-## Conclusion
-In a video where the mouse spent 7.01% of the time exploring, the custom geometric method and the model detected 5.83% and 5.97% respectively. The custom geometric method got 16.92% of false positives and 18.44% of false negatives, while the Random Forest method got 14.83% of false positives and 16.54% of false negatives.
-
-These numbers, along with the analyses made on the plots, seem to point to the Machine Learning algorithm as the superior computional method to detect explorations.
-
-## Acknowledgements
-
-We would like to thank our TAs, and Tomás Pastore in particular, for all the help they provided while we were working on our project.
-
-We would also like to thank the BrainHack team for organizing the school and everyone at Humai for hosting the Buenos Aires hub.
-
-## Next steps
-
-In the future, we would like to improve our work by:
-
-- Having several different experimenters create manual labels for each video, in order to reduce experimenter bias.
-- Exploring different sets of hyperparameters for our Random Forest model.
-- Evaluating our Machine Learning model using a different metric, such as a ROC curve.
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-05-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Multimodal spine multiple sclerosis segmentation"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Thomas Dagonneau]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/Dagonneau_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [project, github, segmentation, brainhack]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project presents an open-source pipeline for segmenting multiple sclerosis lesions in the spinal cord using multimodal MRI data. Built for the MS-Multi-Spine Challenge, it combines nnUNet, the Spinal Cord Toolbox, Docker, and Boutiques for reproducibility and ease of use. The pipeline includes preprocessing, inference, and post-processing steps, and is packaged with full documentation and containerization to support future research and clinical applications in spinal cord imaging."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "ms_segmentation.png"
----
-
-
-
-## Project definition
-
-### Background
-
-Multiple sclerosis (MS) is a chronic disease of the central nervous system, with spinal cord involvement playing a critical role in clinical outcomes. Although MRI is essential for MS diagnosis and disease monitoring [1], spinal cord imaging remains challenging due to anatomical complexity and variability in acquisition protocols. Existing automated lesion segmentation tools have focused primarily on the brain [2–4], and very few methods were designed for spinal cord lesions, particularly across heterogeneous and multimodal MRI data. Additionally, high inter- and intra-rater variability in manual annotations [5] highlights the need for reliable, automated segmentation pipelines.
-
-In this study, we introduce a novel pipeline for the automatic segmentation of MS lesions in the spinal cord using multiple MRI acquisitions from the same patient. Our approach combines a deep learning model with a robust post-processing framework to produce accurate lesion instance segmentations and estimate prediction uncertainty, addressing the specific challenges of spinal cord imaging.
-
-### Objectives
-
-In this project, I focus on the [Multiple Sclerosis Spinal Cord Lesions Detection from MultiSequence MRIs Challenge (MS-Multi-Spine)](https://zenodo.org/records/14051168) and develop a model capable of multimodal MS lesion segmentation.
-
-- Train a multimodal nnUNet [6] on the challenge’s dataset (private)
-- Make the model and pipeline open-source on GitHub (public)
-- Containerize the pipeline and provide a Boutiques descriptor to facilitate reuse (public)
-
-### Tools
-
-This project uses the following tools:
-
-- **Terminal**: all code is designed to run in the terminal
-- **Python**: all scripts are written in Python
-- **Spinal Cord Toolbox (SCT)** [7]: used for preprocessing steps such as coregistration
-- **Git and GitHub**: for version control and open access
-- **Docker**: to ensure reproducibility
-- **Boutiques**: to automate and simplify tool usage
-
-### Data
-
-The dataset used in this project is provided by the challenge and is private. It includes:
-
-- 100 subjects in total
-- 50 subjects with T2w + STIR acquisitions
-- 25 subjects with T2w + PSIR acquisitions
-- 25 subjects with T2w + MP2RAGE acquisitions
-
-### Deliverables in Week 4
-
-- A GitHub repository containing Python scripts for data preprocessing, inference, and postprocessing
-- A Dockerfile used to create the Docker image available [here](https://hub.docker.com/repository/docker/tomdag25/ms-seg-challenge2025-multimodal/general)
-- A Boutiques descriptor to perform inference
-
-### Results
-
-Over the four weeks, I developed a pipeline for the automatic segmentation of MS lesions in a multimodal context. I gained hands-on experience with:
-
-- **Python scripting**: all processing steps are implemented in Python
-- **Git and GitHub**: all project code and resources are versioned and publicly available
-- **Spinal Cord Toolbox**: used for preprocessing (e.g., coregistration)
-- **Docker**: used to package the pipeline and publish the image to Docker Hub
-- **Boutiques**: used to create a descriptor for automated execution of the tool
-
-### Deliverable 1: GitHub Repository
-
-This repository contains everything needed to reproduce the training, inference, and submission process. Instructions to train and perform inference are available [here](multimodal-model/training-and-inference-script).
-
-### Deliverable 2: Docker
-
-The repository includes the Dockerfile used to build the image available on [Docker Hub](https://hub.docker.com/repository/docker/tomdag25/ms-seg-challenge2025-multimodal/general). Instructions for building the image locally are provided [here](multimodal-model/docker).
-
-### Deliverable 3: Boutiques Descriptor
-
-The Boutiques descriptor is available [here](multimodal-model/docker/miccai2025_challenge_descriptor_neuropoly_multimodal.json), with usage instructions [here](multimodal-model/docker).
-
-### Contribution to Open Science
-
-This project contributes to open science by:
-
-- Publishing the complete pipeline code and documentation on GitHub
-- Providing a Docker image to ensure reproducibility across environments
-- Creating a Boutiques descriptor to facilitate automated reuse by other researchers
-- Building on and extending open-source tools such as nnUNet and SCT
-- Enabling multimodal spinal cord lesion segmentation—an underexplored area in neuroimaging research
-
-### Conclusion
-
-This project presents a complete pipeline for multimodal MS lesion segmentation in the spinal cord, from preprocessing to inference and postprocessing. It leverages state-of-the-art tools and open standards to enable reproducibility and accessibility. The pipeline successfully integrates multimodal imaging data and handles the complexity of spinal cord anatomy, providing reliable segmentation outputs.
-
-### Next Steps
-
-- Evaluate the model on additional test data and quantify performance per modality
-- Extend support to include missing modalities through imputation or modality-agnostic models
-- Release a demo dataset to allow users to test the pipeline without requiring private data
-- Submit the pipeline to MICCAI MS Challenge 2025 results and benchmark against other approaches
-
-### References
-
-1. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. *Lancet Neurol*. 2018;17: 162–173.
-2. Valverde S, Cabezas M, Roura E, González-Villà S, Pareto D, Vilanova JC, et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. *Neuroimage*. 2017;155: 159–168.
-3. Aslani S, Dayan M, Storelli L, Filippi M, Murino V, Rocca MA, et al. Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. *Neuroimage*. 2019;196: 1–15.
-4. Kaur A, Kaur L, Singh A. State-of-the-art segmentation techniques and future directions for multiple sclerosis brain lesions. *Arch Comput Methods Eng*. 2021;28: 951–977.
-5. Walsh R, Meurée C, Kerbrat A, Masson A, Hussein BR, Gaubert M, et al. Expert variability and deep learning performance in spinal cord lesion segmentation for multiple sclerosis patients. *2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)*. doi:10.1109/cbms58004.2023.00263
-6. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. *Nat Methods*. 2021;18: 203–211.
-7. Leener BD, Lévy S, Dupont SM, Fonov VS, Stikov N, Collins DL, et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. *NeuroImage*. 2017;145, Part A: 24–43.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-05-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Harmonizing Multisite MRI Data Using ComBat"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [David MacDonald]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/dnmacdon_ASD_multisite_smri
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [mri, harmonization, multisite, combat]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Multisite data is becoming increasingly common in MRI-based studies with the proliferation of open datasets. This brings the benefit of increased statistical power, but there is a pitfall: increased variability due to site-specific effects. This project evaluates three methods of harmonizing multi-site data."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "harmonization_LGP.png"
----
-
-
-# Harmonizing Multi-Site Structural MRI Data Using ComBat
-
-David MacDonald
-
-## Summary
-This project involves using [ComBat](https://github.com/jfortin1/ComBatHarmonization), an open-source library for multi-site data harmonization, to remove site effects from subcortical volumetric data derived from a subset of the [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/) dataset, and to compare this method to using linear models and mixed effects models without harmonization.
-
-## Project definition
-### Background
-I have been working with a large, multi-site dataset to examine subcortical anatomy in autism. Multi-site MRI datasets can be difficult to work with, as scans are typically acquired on different equipment, using different protocols, by different operators. In addition, different sites may be sampling different populations. This can result in a dataset in which a significant portion of the variance is due to non-biological factors. Up to now, I have been using meta-analytic techniques to combine the data from the different sites.
-
-The purpose of this project is to learn and apply two other techniques for inter-site data harmonization:
- * the open source [ComBat](https://github.com/Jfortin1/ComBatHarmonization) library, which was originally intended to mitigate batch effects in genetic studies, but has since been adapted to work with neuroimaging data.
- * linear mixed-effect models.
-
-Other goals are to:
- * Use the tools we have been introduced to in the BrainHack School to make this project more transparent, easy to maintain, reproducible, and open to collaboration.
- * To compare these techniques.
-
-### Tools
-Tools learned and used during this project include:
- * git and github for version control, code sharing, project management, and collaboration
- * Bash scripting
- * Jupyter notebook
- * Conda for virtualization, to improve reproducibility
- * Python libraries for data manipulation (pandas), visualization (matplotlib, Seaborn) and analysis (numpy, statsmodels)
- * Interactive figures in Python using ipywidgets
- * [ComBat](https://github.com/Jfortin1/ComBatHarmonization)
- * R, for using ComBat and for some data visualization
- * Docker, to containerize the R- and Python-based pipeline and improve reproducibility
- * DockerHub
-
-#### ComBat
-[ComBat](https://academic.oup.com/biostatistics/article/8/1/118/252073) is a tool for mitigating batch effects in genomic data. It has since been [adapted](https://www.biorxiv.org/content/10.1101/116541v1) for use in neuroimaging, and is now [available](https://github.com/Jfortin1/ComBatHarmonization) as an open source library in R, Python, and MATLAB for use in multi-site neuroimaging studies. ComBat harmonizes data by fitting a linear model, with location and scale (L/S) terms accounting for site-specific differences, to the data, then reconstructing each data-point without the location and scale terms. These terms are fit using an Empirical Bayes approach, allowing pooling across features to improve the estimate of site effects.
-
-### Data
-This project made use of subcortical volumes, previously derived from a subset (n = 359, across three sites and two releases) of the [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/) dataset using the [MAGeT Brain](https://github.com/CobraLab/MAGeTbrain) pipeline.
-
-### Deliverables
- * github repository containing
- * Analysis code in Jupyter notebooks and the subcortical volume data to be processed
- * All visualizations for the linear mixed-models and ComBat harmonization implementations
- * Virtual environment requirements.txt file, or conda environment.yaml file
- * README.md file summarizing the background methodology, and results
- * Final presentation slides
-
-## Progress overview
-### Workflow
-The workflow encapsulated in the pipeline and jupyter notebook are described in the figure below.
-
-T1w structural MRI scans from several sites in the ABIDE dataset were preprocessed, then segmented using the [MAGeTBrain](https://github.com/CobraLab/MAGeTbrain) segmentation pipeline, which provided volumes for left and right striatum, thalamus, and globus pallidus. The pipeline developed here then masked out volumes for structures that did not pass quality control checks (QC). This data was examined using interactive visualizations in Python and Jupyter notebook. The same masked data was processed in three streams by the pipeline. In all three cases, linear models were used to quantify the effect of an autism spectrum disorder diagnosis on the volumes of the subcortical structures listed above. Age, sex, and total brain volume were used as covariates.
-
-In the first stream, linear models were fit, as shown in the figure, to the unharmonized data, with imaging site added as a covariate. In the second, linear mixed effects models were fit, with site as a random effect (random intercept). In the third, the raw volumes were harmonized using ComBat, and linear models were fit to this harmonized data. The harmonization included age and sex as covariates, to preserve variation due to those factors. Because ComBat was expected to remove site-specific effects, site was not included as a covariate in these models.
-
-Cohen's _d_ effect sizes were computed from the linear models in all three streams. These were used to generate a forest plot, to allow comparison between the three methods.
-
-Note that the Jupyter notebook also supports the examination of the harmonized data.
-
-Data harmonization and effect size visualization were done in R, where the available tools were more sophisticated.
-
-### Pipeline: Python vs. R
-Initially, the project was meant to run entirely in Python. However, two challenges arose. First, the current Python version of neuroCombat is not able to accept data with missing values. Since this data is masked based on segmentation quality (structures whose segmentation failed are not used), the data contains missing values. The R version of neuroCombat does support missing values. Also, R has much more sophisticated packages available to generate forest plots, which are used here to compare the results of the different harmonization methods. Since the language of the Brainhack School is Python, the project was reconceived as a mini-pipeline, using both R and Python.
-
-### Jupyter Notebooks
-Jupyter notebooks are used in this project both for data visualization and for presentation (using RISE). Initially, data harmonization was done in Python, and all of the code was run inside of Jupyter notebooks. When QC masking was added, it was necessary to move the ComBat harmonization code to R. For this reason, the Jupyter Notebooks depend on having access to the harmonized data from the pipeline. Several interactive visualizations are provided in the Jupyter notebooks, and conda environments are provided to allow them to be run on other machines without version conflicts.
-
-### Docker Container
-[neurodocker 0.7.0](https://github.com/ReproNim/neurodocker) was used to create the dockerfile that contained the specifications for the container. This was fairly straightforward:
- 1. The neurodocker command was written in a bash script (bdf.sh).
- 2. Ubuntu was used as the base. Packages to install in the first layer were specified with neurodocker's --pkg-manager and --install options. Only those packages that were necessary to run the pipeline were installed here.
- 3. R configuration was accomplished by instructing neurodocker to include R base packages, and through an additional script that called R to install its own packages and manage dependencies. This resulted in a slightly smaller docker image. The script:
- * Used apt-get to install Ubuntu packages that are required by R to build the R packages that will be installed: make, gcc, g++, but NOT required to run the pipeline.
- * Ran an R command to download and install the necessary packages.
- * Used apt-get to remove the temporary Ubuntu packages (make, gcc, g++ and packages that were installed to satisfy their dependencies).
- * Cleared the apt cache.
-
-The docker container was configured to run the pipeline bash script at startup in non-interactive mode. Input and output directories are set on the command line. All code that is run in the Docker container is provided on the command line (i.e. it has not been built in to the container). This is to allow for modifications, for example to use it on a different dataset, while maintaining the same environment.
-
-## Deliverables
-Note that the deliverables changed somewhat over the course of the project, and are more extensive than the original conception.
-### Deliverable 1: Github Repository
-The [Github repository](https://github.com/brainhack-school2020/dnmacdon_ASD_multisite_smri) contains the following:
-
- * Data, generated from the [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/) dataset as described above.
- * Code for the analysis "pipeline" using R and Python
- * Code used to generate the docker container in which the pipeline runs
- * Jupyter notebook for interactive data visualization
- * This README.md file describing the project
- * Examples of visualizations created using the pipeline and notebooks
- * The slides used for the final Brainhack School presentation
-
-The structure of the repository is shown below:
-
-
-
-| File/Directory | Content |
-| ----------------- | ------- |
-| code | Jupyter notebooks and pipeline code |
-| docker | Contains scripts used to create docker container |
-| images | Images used in this README.md |
-| input | Default input directory for the pipeline. Includes input data and copy of pipeline code. |
-| output | Default output directory for the pipeline |
-| Week_3_Deliverables_Data_Visualization | Deliverables for Week 3 of Brainhack School |
-| harmonization.yml | conda environment description for the pipeline and the Jupyter notebooks |
-
-
-### Deliverable 2: Mini-Pipeline to test ComBat Data Harmonization
-The pipeline takes as input a .csv file containing the data: volumes for each subcortical structure for each participant, as well as ASD diagnosis, Age, Sex, Total Brain Volume, Imaging Site, and Quality Control (QC) values describing the quality of each segmentation. Its output consists of two files: a .csv file in which the subcortical volumes and total brain volume have been harmonized using ComBat, and a forest plot showing the effect sizes of diagnosis on subcortical volumes, while controlling for Age, Sex, and Total Brain Volume. The forest plot shows the results of a linear model fit on the harmonized data, a linear model fit on the unharmonized data with Site as a covariate, and a linear mixed-effects model fit on the unharmonized data with Site as a random factor (random intercept).
-
-The pipeline consists of a bash script, which calls several R and Python scripts to harmonize the input data using ComBat, fit linear models to the harmonized and unharmonized data, fit linear mixed effect models with site as a random factor to the unharmonized data, compute effect size measures in all three cases, and display the effect sizes and confidence intervals obtained in a forest plot for comparison. It consists of:
-| File | Description |
-| -------- | ----------- |
-| harmonize_cmd.sh | Contains the bash command used to start the pipeline. This script is called on startup by the Docker container. All command-line options are specified here. |
-| harmonize_and_show_effect_sizes.sh | Bash script that manages data flow through the pipeline |
-| harmonize_data_prep.py | Python script to load the data, remove rows with missing values, and mask the dependent variables according to quality control (QC) results |
-| harmonize.R | R script that takes the masked data and performs ComBat harmonization |
-| harmonize_fit_models.py | Python script that fits linear models to both harmonized and unharmonized data, adding site as a covariate to the unharmonized models, and linear mixed effects models on the unharmonized data, with site as a random factor. |
-| forest.R | R script that operates on the effect size values computed in the previous step. Saves a forest plot comparing the three methods on each of the dependent variables. |
-
-Note that, although the pipeline is here run non-interactively inside a Docker container, each segment is built in such a way that it can be run independently, and the command line arguments can be specified at runtime.
-
-### Deliverable 3: Jupyter Notebooks
-Jupyter notebooks were written to facilitate interactive data exploration. Several interactive plots were created, using seaborn and ipywidgets. These plots allow the user to explore both the harmonized and unharmonized distributions, overlaid with different covariates (such as site and diagnosis). Note that these notebooks require harmonized data from the pipeline, as the Python version of ComBat is not able to work with masked data.
-
-### Deliverable 4: Virtualization
-The conda environment file harmonization.yml is included in the repository, allowing the Python environment used in this project to be recreated. Note that the R environment was not virtualized in the same way, necessitating the use of Docker.
-
-### Deliverable 5: Containerization with Docker
-The pipeline uses both Python and R. While virtualizing Python environments is readily done with conda, it is more difficult to do so with R. For this reason, the entire environment was constructed in a Docker container in which the pipeline runs. See above for more details. The docker folder in the repository contains:
- * bdf.sh: contains the shell command used to build the dockerfile using neurodocker
- * R_config.sh: contains a script called during dockerfile construction, to configure R
- * Dockerfile: the specification file for the Docker container, build using the scripts above.
-The Docker container is available on DockerHub at dnmacdon/harmonizer:environment_only
-
-Note that this container does NOT contain the data or code. When you run it (see below), you will need to specify the input and output directories, and the input directory must contain the pipeline code and the data. This was done to allow for greater flexibility, to make it easier to use the pipeline with other data or to modify it. For maximum reproducibility, it would make sense to produce a Docker container that incorporates the pipeline code as well.
-
-## Results
- 1. Differences between sites included different age distributions, meaning that different sites were sampling different populations. This indicates that there is biological variability between sites that should be preserved. Below are two views on the age distributions.
-
- 
-
- 2. Combat harmonization shifted the subcortical volume distributions, typically subtly. The large panel below shows one example, for the left globus pallidus. Harmonized volumes are in brown, unharmonized in blue. The smaller panels show, for the same structure, the distributions of volume with age and total brain volume, both before and after harmonization. These are biological sources of variability that we want to preserve.
-
-
-
-
- 3. The effects of ASD diagnosis on subcortical volumes were generally non-significant using all three measures.
- 4. Cohen's _d_ effect sizes and confidence intervals were similar across all three methods.
-
-
-## Instructions
-### Pipeline Instructions
-To run the code yourself, you will need to have Docker and conda installed on your system. To run the pipeline:
-1. Download this repository.
-2. Run the containerized pipeline:
- 1. Change to the input directory. This contains both the data and the pipeline code: ```cd input```
- 2. Run the pipeline. You must specify where the input and output directories are. You will most likely be running the Docker container downloaded from DockerHub, however you must specify the input directory that contains the code and data. In this repository, that is the "input" folder. Note that this will download the container, which is quite large (1.5Gb).
-```
-docker run --rm -v path_to_repository/input/:/input/:ro -v path_to_repository/output/:/output/ dnmacdon/harmonizer:environment_only
-```
- 3. The output will be in the output directory that you specified, and will consist of a .csv file of harmonized data and a forest plot showing the comparison between three modelling methods: linear models on harmonized data, linear models on unharmonized data, and linear mixed effects models on unharmonized data. The forest plot shows the Cohen's _d_ effect size of ASD diagnosis on the volume of each of six subcortical structures, controlling for Age, Sex, and Total Brain Volume.
- 4. If you want to change any of the command-line options to the pipeline, you can edit the command in input/harmonize_cmd.sh. The structure of the command line is as follows:
-
-./harmonize_and_show_effect_sizes.sh -i /input/data.csv \
- -o /output \
- -s Site \
- -x DX \
- -c Age,Sex,TBV \
- -l Control \
- -q "L_str,L_thal,L_GP,R_str,R_GP,R_thal" \
- -z "Age,Sex" \
- -t 0.5
-
-where the options are:
-| Argument | Meaning |
-| -------- | ------- |
-| -i | Input filename |
-| -o | Output directory|
-| -s | Site / Name of feature to harmonize |
-| -x | Name of independent variable / regressor, here diagnosis. Should be categorical |
-| -l | Name of control condition for independent variable |
-| -c | Comma-separated names of covariates for linear models |
-| -q | Names of QC variables / columns |
-| -t | QC threshold. All features below this QC value will be masked |
-| -z | ComBat covariates: covariates for data harmonization, not linear modeling |
-
-The dependent variables must be named the same as the QC variables, with the suffix _vol. For example, L_str and L_str_vol. They are not specified on the command line.
-
-### Instructions: Building Docker Container
-If you wish to build the Docker container that was built in this project:
-1. From the docker directory, create the Dockerfile that contains the specifications for the container:
-``` ./bdf.sh ```
-2. Build the container:
-``` docker build -t harmonizer . ```
-Please be aware that building the container will use current versions of the software and libraries, which may affect results. Building the container should not be necessary to reproduce the results reported here. Only rebuild the container if you wish to make changes to the pipeline.
-
-### Instructions: Jupyter Notebooks
-If you wish to run the Jupyter notebook for data exploration and presentation:
-1. If you are using your own data, you must first run the pipeline. This will create a file of harmonized data that the notebooks need to run. If you wish to use the data provided, you do not need to run the pipeline first. The pipeline has already been run and the results are in the output directory.
-2. Load the conda environment in which the notebook will run. From the repository's root directory, run: ``` conda env create -f harmonization.yml && conda activate harmonization```
-3. From the same directory, open the directory in jupyter and select the notebook you wish to open. They are in code/notebooks: ``` jupyter notebook ```
-4. Specify the name and location of the input files, if different from the names and locations already in the notebook.
-5. Run all cells.
-
-## Improvements
-A number of improvements are possible.
- * Using a library such as rpy2 to call R from within Python would eliminate the need for the bash scripting, the complex command-line arguments, and the use of temporary files, which would reduce the apparent complexity of the process.
- * The entire pipeline could be constructed in Python, if the Python version of neuroCombat supported missing values in the data.
- * With some minor modifications, vertex-wise analyses could be run using this code. ComBat harmonization may be better suited to vertex-wise data.
- * This design, with the Docker container used only to contain the environment, not the data or code, allows the container and code to be reused more easily in different applications. However, for reproducibility, it would also be useful to provide a Docker container that included all of the code, and perhaps the data as well.
- * As configured, the linear mixed effects models do not converge with some optimization routines and some subsets of data. It is important to troubleshoot this, as it may indicate a problem in the model specification.
- * The Docker container is very large at about 1.5 GB. This size is due mainly to the R installation, and could likely be optimized.
- * The pipeline code exists in both code/pipeline and input directories. Ideally symbolic links would be used, however Docker does not follow links (perhaps for security reasons) and GitHub stores the contents of the links. It may be worthwhile to reorganize the repository to eliminate the duplicated code.
-
-## Acknowledgements
-Thanks to all of the instructors, teaching assistants, and participants of the Brainhack School 2020! Thanks to the organizers, J.B. Poline, Pierre Belec, Tristan Glatard, and Benjamin de Leener, with particular thanks to Pierre for his patience.
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-65-08" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: Identifying Potential Biomarkers for Parkinson’s Disease Using Neurite Orientation Dispersion and Diffusion Imaging (NODDI)
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Samuelle St-Onge]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/st-onge_project/tree/main
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Parkinson's disease, spinal cord, NODDI, diffusion MRI]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this project, I have explored the use of NODDI, a diffusion MRI technique, to identify potential biomarkers for Parkinson’s disease using spinal cord images. The goal of this project was to use an existing Matlab toolbox to perform NODDI fitting, and then use Python to analyze the extracted NODDI metrics to identify potential differences in these metrics with Parkinson's disease progression."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "bhs2023.png"
----
-
-
-## Project definition
-
-### Background
-
-Parkinson’s disease is a neurogenerative disease for which we currently do not have any cure. Although there are treatment options available, early diagnosis is key to be able to start the treatment early and slow down the progression of the disease. However, since there are still a lot of things we don't quite understand about Parkinson's disease, it is oftentimes difficult to diagnose at early stages. During this project, I will be exploring the use of NODDI (a diffusion MRI technique) applied to spinal cord images in the case of Parkinson's disease. More specifically, I will be applying NODDI to spinal cord images acquired from Parkison's disease patients at various stages of the disease to identify potential biomarkers for Parkinson's disease, with the ultimate goal of helping clinicians diagnose the disease at an earlier stage, thus allowing early treatment to slow the progression of the disease. In recent work, diffusion MRI has shown a potential interest for finding biomarkers in the context of neurodegenerative diseases such as Parkinson, using Neurite Orientation Dispersion and Diffusion Imaging, commonly known as NODDI.
-
-NODDI is a diffusion MRI technique which aims to extract metrics based on the morphology of neurites, which are composed of dendrites and axons. The most common metrics that can be extracted from NODDI (Mitchell et al., 2019) are :
-
-1. Orientation Dispersion Index (ODI) : Measures the variability in neurite orientation
-2. Neurite Density Index (NDI) : Measures the density of neurites
-3. Isotropic Volume Fraction (Viso) : Measures the fractional volume of extracellular fluid
-
-
-
-My project aims to use an existing open-source NODDI toolbox to perform NODDI fitting of a spinal cord diffusion MRI dataset containing both healthy subjects and subjects diagnosed with Parkinson's disease. Then, using the data analysis tools and skills I have learned during BrainHack School, I have written a Python script to analyze the extracted metrics from the NODDI model fitting, which will then help me compare the metrics with the progression of the disease and identify potential biomarkers for Parkinson's disease.
-
-#### Why NODDI for Parkinson's disease?
-- Literature has shown an interest in diffusion MRI to identify potential Parkinson's disease biomarkers in brain images (Zhang et al., 2012)
-- Links have been found between neurite morphology and other neurodegenerative diseases, such as multiple sclerosis (Zhang et al., 2012)
-
-#### Why spinal cord images for Parkinson's disease biomarkers?
-- Spinal cord lesions have been recorded in Parkinson's disease patients (Tredici et al., 2012)
-- To our knowledge, NODDI in the spinal cord for Parkinson's disease patients has not yet been studied
-
-### Tools
-
-#### For NODDI fitting :
-Matlab :
-- [NODDI Matlab Toolbox](http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab) (UCL Microstructure Imaging Group, 2021)
-- [Matlab Parallel Computing Toolbox](https://www.mathworks.com/products/parallel-computing.html)
-- [Matlab Optimization Toolbox](https://www.mathworks.com/products/optimization.html)
-
-#### For registration and NODDI metric extraction :
-- The [Spinal Cord Toolbox](https://spinalcordtoolbox.com/) (De Leener et al., 2017)
-
-#### For data analysis :
-Python :
-- [`matplotlib`](https://matplotlib.org/)
-- [`nibabel`](https://nipy.org/nibabel/)
-- [`seaborn`](https://seaborn.pydata.org/)
-- [`nilearn`](https://nilearn.github.io/stable/index.html)
-
-#### For version control :
-- Git and Github
-
-### Data
-
-For this project, I have used a spinal cord dataset from our collaborators at McGill University. This dataset contains a total of 113 subjects, from which 87 have been diagnosed with Parkinson's disease at various stages of the disease ("low", "medium" and "advanced" stages) and the remaining 26 are healthy control subjects. The dataset contains other common MRI modalities, such as T1-weigthed and T2-weighted images. However, for the scope of BrainHack School, I have decided to focus only on the diffusion images.
-
-### Deliverables
-
-  [Matlab script to perform multi-subject NODDI fitting](https://github.com/brainhack-school2023/st-onge_project/blob/main/scripts/NODDI_multi-subject_fitting.m) using the [NODDI Matlab Toolbox](http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab) (UCL Microstructure Imaging Group, 2021)
-
-  [Python script to compute average NODDI metrics](https://github.com/brainhack-school2023/st-onge_project/blob/main/scripts/average_image_metrics.py) and display average images for each subject category (control subjects, low PD, medium PD and advanced PD)
-
-  [Python script to perform data analysis](https://github.com/brainhack-school2023/st-onge_project/blob/main/scripts/data_analysis_NODDI_metrics.py), i.e. regression plots to display and compare metrics (such as ODI, NDI and Viso) with Parkinson's disease progression.
-
-## Results
-
-### Progress overview
-
-The following diagram summarizes the key stages of the methods used for my project :
-
-
-
-#### NODDI fitting :
-Starting with the diffusion MRI images from the dataset, I first performed NODDI fitting using the [NODDI Matlab Toolbox](http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab) (UCL Microstructure Imaging Group, 2021) from UCL Microstructure Imaging Group. Using this toolbox, I wrote a [Matlab script](https://github.com/brainhack-school2023/st-onge_project/blob/main/scripts/NODDI_multi-subject_fitting.m) to be able to perform multi-subject NODDI fitting with a BIDS-compliant dataset. The NODDI Matlab Toolbox also gives the user the option of using the Matlab Parallel Computing Toolbox, which speeds up the fitting process significantly.
-
-For the inputs, the NODDI Matlab Toolbox requires diffusion MRI files (`sub-PXXX_dwi.nii`, `sub-PXXX.bvec`, `sub-PXXX.bval`). It is also possible to specify a mask, in order to restrict the fitting to a certain region of the image (for example, only the spinal cord). In this specific project, I used segmentation files that had already been performed on 42 subjects prior to BrainHack School, by a previous graduate student. Initially, my goal was to start by performing NODDI fitting and metric analysis for these 42 subjects with the segmentation files, and then go back and segment the remaining subjects and re-run the NODDI fitting and metric analysis pipeline for the whole dataset. Howerver, I did not have enough time to go back and do the segmentation during BrainHack School. For this reason, I decided to focus only on 42 of the 113 subjects in the dataset for my Brainhack School project.
-
-#### Registration to PAM50 template
-Following NODDI fitting, I decided to perform registration on the output metrics to be able to compute an average metric for each subject category : control, low disease, medium disease and advanced disease.
-To perform the registration, I used the [`Spinal Cord Toolbox (SCT)`](https://spinalcordtoolbox.com/index.html) and followed the recommended steps in the documentation.
-
-The following diagram shows a summary of the `Spinal Cord Toolbox` pipeline for registration to the [`PAM50 template`](https://spinalcordtoolbox.com/overview/concepts/pam50.html?highlight=pam50) with DWI images :
-
-
-
-*Detailed explanations for each of these steps can be found in the [Spinal Cord Toolbox DWI tutorial](https://spinalcordtoolbox.com/user_section/tutorials/diffusion-weighted-mri/template-registration-for-dmri.html).
-
-#### Metric extraction using the Spinal Cord Toolbox
-Following NODDI fitting, to extract the values of the ODI, NDI and Viso metrics (which were all in NIFTI format following the NODDI fitting process with Matlab), I used the command [`sct_extract_metric`](https://spinalcordtoolbox.com/user_section/tutorials/diffusion-weighted-mri/extracting-dti-from-specific-regions.html) from the `Spinal Cord Toolbox`. The following diagram shows a summary of the command amd the arguments that were used for my project :
-
-
-
-The command `sct_extract_metric` outputs a .csv file for the metric from the file specified in the `-i` argument. For this specific project, I chose the labels 50, 51 and 52 with the `-l` argument, which correspond to the spinal cord, white matter and gray matter. I also chose to extract metrics from C2 to C5, using the `-vert` argument.
-
-### Results
-
-#### Computing average metrics
-
-Following the registration to the PAM50 template, I have written a [Python script to compute average metrics](https://github.com/brainhack-school2023/st-onge_project/blob/main/scripts/average_image_metrics.py) and display them as images. The following image shows an example of the average orientation dispersion index (ODI) for each subject category (control, low PD, medium PD and advanced PD).
-
-
-
-
-
-By looking at the image above, we can notice an offset between the images used to compute the average. This is most likely due to a problem with the registration step. Following BrainHack School, I would like to try the registration with different parameters to see if I can fix this offset.
-
-#### Regression plots
-
-I have also written a [Python script for data analysis](https://github.com/brainhack-school2023/st-onge_project/blob/main/scripts/data_analysis_NODDI_metrics.py), which takes the metrics from the .csv files obtained with the `sct_extract_metric` command to do regression plots. The image below shows an example of the regression plots that can be achieved with this script.
-
-
-
-
-
-The plots are divided by vertebrae level (in this example, from C2 to C5) as well as by label (white matter, gray matter and spinal cord). In each plot, we can observe the metric with the progression of the disease (from control subjects to advanced Parkinson's disease subjects).
-
-In this specific project, since I have used only 42 subjects, there is not enough data to draw any conclusions, nor identify potential biomarkers for Parkinson's disease. Also, future versions of the Python script should include a regression model with p-values, to be able to perform a more rigorous statistical analysis.
-
-In future work, after having improved the python script to include p-values, I would like to perform the analysis for the entire dataset (113 subjects). By repeating the analysis with the entire dataset, I would suspect seeing an increase in ODI in the white matter and a decrease in ODI in the gray matter, since these have been linked to neurodegeneration according to literature (Zhang et al., 2012).
-
-#### Limitations and future work
-
-Following BrainHack School, I would like to pre-process the entire dataset, by also adding [motion correction using the Spinal Cord Toolbox](https://spinalcordtoolbox.com/user_section/tutorials/diffusion-weighted-mri/motion-correction-for-dmri.html). I would also like to use a larger mask to perform the NODDI fitting. As I mentionned in the "Methods" section above, in this project I used segmentation files as a mask for the NODDI fitting. However, it would be interesting to try a mask that is larger than the segmentation of the spinal cord to see if it can help reduce potential partial volume effect. Furthermore, I would like to try performing the registration to the PAM50 template prior to the NODDI fitting, to be able to extract the metric values and compute average images directly from the output metrics.
-
-Regarding the metric analysis, I would like to try the registration to the PAM50 template with different parameters to fix the offset I have with the current results. For the regression plots, it would be important to consider the age as a control variable, to avoid overestimating the effect of Parkinson's disease progression when performing the regression analysis. Finally, I would like to incorporate a regression model with p-values to perform more rirogous statistics on the different NODDI metrics.
-
-## Conclusion and acknowledgement
-
-As a new PhD student, BrainHack school was a great opportunity for me to learn about the basics of neuroscience imaging and data analysis. Although there are still some improvements to be made regarding my project, I look forward to apply the tools and skills I have learned during BrainHack School to work on improving what I have implemented so far.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2021-09-03" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Using ALE algorithm and machine learning to classify need and desire states"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Laurence Grenier]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/PSY6983-2021/lgrenier_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [AlE meta-analysis, Needs vs Desires, Machine learning, NiMARE]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The goal of this project is to make a classification of the needs and desires states from studies fMRI data"
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "./MA_map_first_study-need.png"
----
-
-
-# Using ALE algorithm and machine learning to classify need and desire states
-
-### Personal background
-
-My name is Laurence and I've completed a bachelor's degree in cognitive neuroscience at Montreal University. I am currently a master student in psychology at Montreal University. I am particularly interested in social neurosciences, thereby working at the NeSC (Neuroscience en contextes sociaux)lab. My master project is a coordinate based meta-analysis on social hierarchy.
-
-I really wish to contribute to scientific findings by adopting approaches that consider the current issues of reproductibility and social inclusion in neuroscience! I've decided to do brainhack school in order to familiarize myself with coding and neuroimaging data.
-
-
-
-* * *
-
-# Project Definition
-* * *
-
-## Using ALE algorithm and machine learning to classify need and desire states
-
-
-### Theoretical background
-* * *
-Overconsumption is associated with several environmental, social and individual problems (Lipschutz, 2001). One explanation suggested for overconsumption is that "we consume what we want belong what we need" (Stearns, 2006), (Juvénal et al., 2021). Some stimuli are sought for their survival value (food, water,...), while others are sought for their reward value (money, interesting objects,...). While motivation towards the former seems to increase, or even be triggered, by a state of deprivation, motivation towards the latter seems to increase as a function of the previously learned value of the stimulus, corresponding to the association between this stimulus and a reward. However, since a stimulus can be pursued for its *needing* value **AND** for its *wanting* value, for example some kind of food, or even some activities, we can wonder what are the similarities and the differences between those two conditions at a neural level?
-
-
-To answer this question, we previously made a coordinate-based meta-analysis with the ALE (Activation likelihood estimation) algorithm in order to find the consistent activations reported in the literature for each of those conditions. Therefore, we identified regions more commonly reported in the experiments regarding *needs* than in the expriment of *wantings*, vice versa.
-
-#### ALE
-* * *
-The ALE approach is used to show the convergence between the regions reported in the literature of a specified topic. For each of the selected studies, the coordinates of the significant voxels are taken (activation peaks). Then, the algorithm is making a gaussian matrice representing the spatial uncertainty, based on the sample of the corresponding study, around the activation peaks reported and thus making what is call a *MA map (main activation map)*.Therefore, each of the experiment has it own *MA map* where each voxels is associated with an activation probability (that increase in the closest to the peaks).Then, the union of the MA maps are taken and the algorithm is making permutations to identify which activations peaks are significantly reported across the experiments, thus representing the convergence of activations.
-
-[Acar, F et al.,2018](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208177)
-
-
-[](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208177)
-
-
-
-### Objectives
-* * *
-
-The goal of the present project is to build a machine learning model which will make the distinction between the studies focusing on the *needs* and the studies focusing on the *wantings* based on the MA maps generated by ALE algorithm during the first step.
-
-The puposes of this project are :
- - Build a machine learning model which will classify the *needs* and the *wants* state based on studies included in the meta-analysis to train the model.
- - Retrieve the most contributing features in the machine learning classification model and interpret the coefficient's value in comparison of ALE meta-analysis results.
- - Familiarize myself with open science pratices, programming and machine learning.
-
-
-### Tools
-* * *
- - Python
- - Jupyter notebooks
- - [nilearn](https://nilearn.github.io/index.html#)
- - [scikit-learn](https://scikit-learn.org/stable/index.html)
- - github
- - [nimare](https://nimare.readthedocs.io/en/latest/about.html)
-
-
-### Data
-* * *
-
-The dataset is composed of all the activation peaks of included studies in the meta-analysis.
-
-*needs* : 38 studies
-Contrasts of interest were meeting those criterions:
- - Perception of a food stimulus during a hunger state > perception of a stimulus during a satiety state.
- - Perception of a food stimulus during a hunger state > perception of a non-food stimulus during a hunger state.
-
-*wantings* : 33 studies
-Contrasts of interest were meeting those criterions:
- - Perception of a cue announcing a reward (or a larger reward) > perception of a cue not announcing a reward (or a smaller reward).
-
-Here's all the coordinates of the sample in the brain.
- - Green : Needs
- - Red : Wants
-
-
-### Deliverables
-* * *
-At the end of this project, I'll get :
- - Markdown README.md for the description of the present project
- - Jupyter notebook including the code, and the brain images
-
-
-# Results
-* * *
-#### Summary
-
-
-### Data preparation
- - Transform all of the activation peaks of each study into a MA map with [nimare](https://nimare.readthedocs.io/en/latest/about.html).This ended up with 71 MA map, because 71 studies had been included.
- - Apply a mask to select the voxels of the grey matter. I used the [MNI152 mask](https://nilearn.github.io/modules/generated/nilearn.datasets.load_mni152_brain_mask.html)
-
-##### Here is an example of a MA maps with the mask:
-
-
-### Machine learning
-I used a [linear support vector classification](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) to build the model.
-
-At first, the model classified all the data into *needs*. This problem occurred *maybe* because the value of most of the voxels is zero, or close to zero (as you can notice in the above MA map). By arranging the data in order to increase the gap between the null voxels and the non-zero ones, the classification has been improved.
-
-Finally, the model predicted the data with an accuracy of 83.3 ± 16.6 %.
-There are the confusion matrices that shows the results:
-
-
-#### Interpretation
-Even if the accuracy is quite good, because the dataset contains more *needs* than *wants*, the model have initially more than 50% of accuracy if it classifies all the data into *needs*. Therefore, we should be careful with the interpretation of that accuracy value. This also explains why the model never classifies the *needs* into *wants* and why it is only making errors by classifying the *wants* into *needs*. A solution to overcome this problem would be to balance the weight of the different conditions according to the proportions in the dataset.
-
-Moreover, I've split the data into training set and test set by myself with a proportion of 75% for the training set and 25% for the test set, and I've split again the training set into 60% training set and 40% validation set. Thus, the model is not optimal, because it is only learning with 45% of the initial dataset, which is quite small when having 71 data. A cross-validation method would have been more efficient in that case.
-
-
-
-#### Features importance
-There you can see the most contributing features on the brain. The more the region is *yellow clear*, the more changes in the value of that voxel influence the decision of the model.
-
-
-
-## Tools I learned during this project
-* * *
- - Python
- - Packages to run machine learning with scikit-learn
- - Packages ro run meta-analysis on python with nimare
- - Tools to create great brain images and data visualization (matplotlib, seaborn...)
-
-# Conclusion
-* * *
-In conclusion, the objectives of my project are mostly reach. Even if the model is not optimal and few modifications should be applied to improve it's validity, I've succeeded in building a machine learning model and in formatting the initial data so the model can process them.
-
-Finally, I've started this course without a significant background in coding and a little fear about it (I admit). One of my motivation to do brainhack school was to demystify this field in order to be ready to make great neuroscience! I'm glad I did it, learned python and many useful packages to analyse and play with neuroimaging data.
-
-I really want to thank BrainHack School team for their remarkable competence and exceptional support during all the course!
-
-
-### References
-* * *
-[L. Charbonnier, F. van Meer, A.M. Johnstone, D. Crabtree, W. Buosi, Y. Manios, O. Androutsos, A. Giannopoulou, M.A. Viergever, P.A.M. Smeets, Effects of hunger state on the brain responses to food cues across the life span,NeuroImage,Volume 171,2018,Pages 246-255,ISSN 1053-8119,https://doi.org/10.1016/j.neuroimage.2018.01.012.](https://neurovault.org/collections/3235/)
-
-[Simon B. Eickhoff, Danilo Bzdok, Angela R. Laird, Florian Kurth, Peter T. Fox, Activation likelihood estimation meta-analysis revisited, NeuroImage, Volume 59, Issue 3, 2012,Pages 2349-2361,ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2011.09.017.](https://www.sciencedirect.com/science/article/abs/pii/S1053811911010627?via%3Dihub)
-
-[Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI, Acar F, Seurinck R, Eickhoff SB, Moerkerke B (2018) Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI. PLOS ONE 13(11): e0208177. https://doi.org/10.1371/journal.pone.0208177](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208177)
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----
-type: "project"
-date: "2025-06-12"
-title: "EEG-Based Odor Preference Modeling 🌹🧀️🪷🍃"
-
-github_repo: github.com/xiao1992/BrainHack_Project
-
-tags: [ML, EEG, odor, brainhack]
-
-summary: "The human sense of smell plays a crucial role in emotional experience. Previous research has shown that EEG can distinguish between pleasant and unpleasant odors at an individual level (Kroupi et al.,2014), but the consistency of these preferences across individuals remain open questions. OPPD dataset: www.epfl.ch/labs/mmspg/downloads/page-119131-en-html"
-
-image: brain_scent.png
-
----
-
-## Project definition
-### Background
-
-Kroupi et al. (2014) demonstrated that EEG signals could classify pleasant vs. unpleasant odor perception with high accuracy in a subject-specific manner. However, subject-independent classification yielded lower performance, suggesting significant individual variation. The study did not explicitly test preference consistency across subjects or analyze EEG similarity among individuals who prefer the same odors.
-
-### Data
-
-We use the OPPD (Odor Pleasantness Perception Database) delopved by Kroupi et al. (2014) from EPFL [OPPD dataset](https://www.epfl.ch/labs/mmspg/downloads/page-119131-en-html/):
-The dataset includes EEG recordings from 5 male participants (ages 26–32), each exposed to four odors: valerian, lotus flower, cheese, and rosewater. For each subject, both eyes-open and eyes-closed conditions were tested. Each odor trial includes a baseline segment and an event segment (odor presentation). The EEG signals were recorded with a 256-electrode EGI system at 250 Hz. Participants rated each odor on intensity and pleasantness. The most and least pleasant odors were identified per subject.
-
-### Project Motivation
-This project investigates whether individuals share common EEG response patterns to odors they find pleasant or unpleasant, and whether these patterns can be generalized across subjects to predict preferences. The goal is to assess both subject-specific and cross-subject odor pleasantness classification and identify potential for real-world olfactory recommendation systems. Specifically, we seek to:
-1) replicate existing within-subject classification of odor pleasantness.
-2) Explore subjective vs objective labels correlation with brain activites.
-3) analyze whether EEG response patterns reflect those shared preferences.
-4) develop and evaluate models that generalize odor preference prediction.
-5) Visualize and distinguish the most active channels, bands and regions associated with different odors, eyes open vs eyes closed conditions.
-
-### Deliverables
-- Replication of Kroupi et al. (2014)'s within-subject EEG classification results of pleasant vs. unpleasant odors.
-- Statistical analysis of cross-subject odor pleasantness agreement.
-- Subjective vs objective labels, Eyes open vs eyes closed ML results.
-- Development of simple classifiers to predict preference class from EEG, including cross-subject generalization.
-- A set of visualizations mapping EEG response patterns (channels, bands) to odors.
-
-## Results
-1) There is some alignment between what subjects rated as pleasant/unpleasant and what is objectively pleasant/unpleasant. However, the result is not statistically significant (p~0.05). This suggests individual preferences may diverge from traditional expectation.
-2) Subjects are more likely to agree with the traditional (objective) odor lables when their eyes are open. This supports the idea that sensory and cognitive context (visual) influences how we evaluate smells (might be due to placebo)
-3) Strongest ML performance = Eyes Open + Objective (for all models).
-a) Objective Pleasantness is Much Easier to Decode. In both eyes open and eyes closed, accuracy for y_objective is consistently (90% of the case) higher across models. Best model overall: XGBoost in eyes_open (66.7%), indicating EEG contains information about odor perference.
-b) Subjective Preferences Are Less Consistent. Much lower and more variable performance for y_subjective (5/5 <50% are subjective preferences).
-c) Performance is model-sensitive: RF and KNN show scattered strengths (i.e. KNN best for eyes open, RF best for eyes closed).
-This reflects subjectivity: people disagree with objective odor rating, and neural patterns for preferences are weaker.
-4) Overall: Eyes Open - Theta, Alpha; fronto-parietal - attention, emotion encoding;
-Eyes Closed - Gamma, Beta; Temporal, occipital - internal sensory processing.
-
-### Progress overview
-
-Explored beyond the original paper on subjective vs objective odor labels ML results.
-
-### Tools I learned during this project
-
- * **Machine Learning** Explored different kinds of ML models and compare how each suits different dataset.
- * **Github workflow-** Interacted constantly with Github to updating on the project codes.
- * **Python Package** Made a python package for the first time NeuroSuite.
-
-### Results
-
-#### Deliverable 1: ML results on subjective vs objective labels and eyes open vs eyes closed conditions
-
-ML results: [Machine Learning Results](https://github.com/xiao1992/BrainHack_Project/blob/main/EEG_model_results.tex)
-
-#### Deliverable 2: Figures of which channels, locations are most active during the odor sensoring experience and different conditions
-
-You can check out the [Figures subject level and group level](https://github.com/xiao1992/BrainHack_Project/tree/main/figures)
-
-#### Deliverable 3: NeuroSuite Package
-
-You can check out the [NeuroSuite Package](https://github.com/xiao1992/NeuroSuite)
-
-## Conclusion and acknowledgement
-
-*Kroupi, E., Yazdani, A., Vesin, J.M., & Ebrahimi, T. (2014). EEG correlates of pleasant and unpleasant odor perception. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 11(1s), 1–17. [DOI: 10.1145/2637287]
-*Koelstra, S. et al. (2011). DEAP: A Database for Emotion Analysis Using Physiological Signals. IEEE Transactions on Affective Computing, 3(1), 18–31.
-
-If cross-subject generalization proves feasible, future work could work on larger and more diverse (females, different ages, more odors) pools which would improve the model’s generalization. Cross-modal integration with physiological signals (GSR, heart rate) may also enhance prediction.
diff --git a/content/en/project/Parkinson-fMRI/Brainhackproject_final.ipynb b/content/en/project/Parkinson-fMRI/Brainhackproject_final.ipynb
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-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 59,
- "id": "5c9e945f",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import glob\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import nibabel as nib\n",
- "import seaborn as sns\n",
- "from nilearn import connectome\n",
- "from nilearn import plotting, datasets, maskers, connectome\n",
- "from nilearn.interfaces.fmriprep import load_confounds\n",
- "from nilearn.connectome import ConnectivityMeasure\n",
- "from IPython.display import display\n",
- "from IPython.display import display, Markdown\n",
- "from scipy import stats\n",
- "from scipy.stats import ttest_ind\n",
- "import plotly.graph_objects as go\n",
- "import plotly.express as px\n",
- "import statsmodels.api as sm\n",
- "from sklearn.linear_model import LinearRegression\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.metrics import mean_squared_error, r2_score"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d58ecf06",
- "metadata": {},
- "source": [
- "## Functional Connectivity Analysis and Group Comparison using Harvard Atlas"
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- "[NiftiLabelsMasker.wrapped] loading data from Nifti1Image(\n",
- "shape=(91, 109, 91),\n",
- "affine=array([[ 2., 0., 0., -90.],\n",
- " [ 0., 2., 0., -126.],\n",
- " [ 0., 0., 2., -72.],\n",
- " [ 0., 0., 0., 1.]])\n",
- ")\n",
- "Resampling labels\n",
- "[Memory]0.1s, 0.0min : Loading _filter_and_extract...\n",
- "__________________________________filter_and_extract cache loaded - 0.0s, 0.0min\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/roya/miniconda3/lib/python3.9/site-packages/nilearn/connectome/connectivity_matrices.py:495: FutureWarning: The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.\n",
- " covariances_std = [\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[NiftiLabelsMasker.wrapped] loading data from Nifti1Image(\n",
- "shape=(91, 109, 91),\n",
- "affine=array([[ 2., 0., 0., -90.],\n",
- " [ 0., 2., 0., -126.],\n",
- " [ 0., 0., 2., -72.],\n",
- " [ 0., 0., 0., 1.]])\n",
- ")\n",
- "Resampling labels\n",
- "[Memory]0.1s, 0.0min : Loading _filter_and_extract...\n",
- "__________________________________filter_and_extract cache loaded - 0.0s, 0.0min\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/roya/miniconda3/lib/python3.9/site-packages/nilearn/connectome/connectivity_matrices.py:495: FutureWarning: The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.\n",
- " covariances_std = [\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[NiftiLabelsMasker.wrapped] loading data from Nifti1Image(\n",
- "shape=(91, 109, 91),\n",
- "affine=array([[ 2., 0., 0., -90.],\n",
- " [ 0., 2., 0., -126.],\n",
- " [ 0., 0., 2., -72.],\n",
- " [ 0., 0., 0., 1.]])\n",
- ")\n",
- "Resampling labels\n",
- "[Memory]0.1s, 0.0min : Loading _filter_and_extract...\n",
- "__________________________________filter_and_extract cache loaded - 0.0s, 0.0min\n"
- ]
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- "/home/roya/miniconda3/lib/python3.9/site-packages/nilearn/connectome/connectivity_matrices.py:495: FutureWarning: The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.\n",
- " covariances_std = [\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[NiftiLabelsMasker.wrapped] loading data from Nifti1Image(\n",
- "shape=(91, 109, 91),\n",
- "affine=array([[ 2., 0., 0., -90.],\n",
- " [ 0., 2., 0., -126.],\n",
- " [ 0., 0., 2., -72.],\n",
- " [ 0., 0., 0., 1.]])\n",
- ")\n",
- "Resampling labels\n",
- "[Memory]0.1s, 0.0min : Loading _filter_and_extract...\n",
- "__________________________________filter_and_extract cache loaded - 0.0s, 0.0min\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/roya/miniconda3/lib/python3.9/site-packages/nilearn/connectome/connectivity_matrices.py:495: FutureWarning: The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.\n",
- " covariances_std = [\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[NiftiLabelsMasker.wrapped] loading data from Nifti1Image(\n",
- "shape=(91, 109, 91),\n",
- "affine=array([[ 2., 0., 0., -90.],\n",
- " [ 0., 2., 0., -126.],\n",
- " [ 0., 0., 2., -72.],\n",
- " [ 0., 0., 0., 1.]])\n",
- ")\n",
- "Resampling labels\n",
- "[Memory]0.1s, 0.0min : Loading _filter_and_extract...\n",
- "__________________________________filter_and_extract cache loaded - 0.0s, 0.0min\n"
- ]
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- {
- "name": "stderr",
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- "text": [
- "/home/roya/miniconda3/lib/python3.9/site-packages/nilearn/connectome/connectivity_matrices.py:495: FutureWarning: The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.\n",
- " covariances_std = [\n"
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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Create a scatter plot\n",
- "scatter = go.Scatter(\n",
- " x=y_test, # Actual values\n",
- " y=y_pred, # Predicted values\n",
- " mode='markers',\n",
- " marker=dict(\n",
- " color='blue',\n",
- " size=6\n",
- " ),\n",
- " name='Actual vs. Predicted'\n",
- ")\n",
- "\n",
- "# Create layout\n",
- "layout = go.Layout(\n",
- " title='Actual vs. Predicted MixedLM',\n",
- " xaxis=dict(title='Actual Values'),\n",
- " yaxis=dict(title='Predicted Values')\n",
- ")\n",
- "\n",
- "# Create figure\n",
- "fig = go.Figure(data=[scatter], layout=layout)\n",
- "\n",
- "# Show the figure\n",
- "fig.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4adae091",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/content/en/project/Parkinson-fMRI/index.md b/content/en/project/Parkinson-fMRI/index.md
deleted file mode 100644
index e16fc7af..00000000
--- a/content/en/project/Parkinson-fMRI/index.md
+++ /dev/null
@@ -1,158 +0,0 @@
----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Modulation of functional connectivity in Parkinson’s disease with neuropsychiatric symptoms"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Roqaie moqadam]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/Moqadam_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [parkinson's, rsfmri, fmriprep, python, nilearn]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to investigate the functional connectivity patterns in individuals with Parkinson's disease and neuropsychiatric symptoms (PD+NPS) compared to those without the NPS. The study utilizes resting-state fMRI data to analyze the connectivity matrix and identify alterations in functional brain networks associated with PD+NPS. The project also involves familiarizing with data science packages, interpreting neuroimaging data, and advanced visualization techniques. The findings may contribute to understanding the neural mechanisms underlying PD+NPS and inform future research and interventions. The project involves BIDS validation, fMRI preprocessing, functional connectivity analysis, group comparisons, and prediction modeling for mild cognitive impairment."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "view.png"
----
-
-
-## Project definition
-
-### Background
-
-
-Inspired by the https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/trc2.12371, this project has defined which aims to investigate the functional connectivity patterns in individuals with Parkinson's disease and neuropsychiatric symptoms (PD+NPS) compared to those without the NPS. The study utilizes resting-state fMRI data to analyze the connectivity matrix and identify alterations in functional brain networks associated with PD+NPS. The project also involves familiarizing with data science packages, interpreting neuroimaging data, and advanced visualization techniques. The findings may contribute to understanding the neural mechanisms underlying PD+NPS and inform future research and interventions. The project involves BIDS validation, fMRI preprocessing, functional connectivity analysis, group comparisons, and prediction modeling for mild cognitive impairment.
-
-### Tools
-
-
- * Jupyter notebook for coding and visualization
- * Docker container
- * fMRIPrep for rs-fMRI preprocessing
- * Python Packages: matplotlib, seaborn, nilearn, plotly, Nibabel, scipy, Pandas
- * Git and Github for Version Control
-
-
-### Data
-
-The pilot version of this project currently utilizes small raw data from the Niemolab for analysis. However, the intention is to eventually incorporate data from a larger Parkinson's disease (PD) cohort for a more comprehensive investigation. The use of a larger dataset will enhance the generalizability and statistical power of the findings, allowing for a more robust understanding of the functional connectivity patterns in PD with neuropsychiatric symptoms (PD+NPS).
-
-### Deliverables
-
-Jupyter notebooks: Share interactive code, analysis, and visualizations. Organize project code, preprocessing steps, analysis, and results. Share notebook files (.ipynb)
-Jupyter notebooks for code and analysis
-GitHub for version control and collaboration
-
-## Results
-
-## BIDS Validation in Raw Neuroimaging Dataset
-
-The BIDS Validator is a tool that verifies if neuroimaging datasets adhere to the BIDS standard, promoting organized and correctly formatted data for enhanced data sharing and reproducibility in neuroimaging research. In the case of the "mp1014" subject dataset, it identified 2 errors and 1 warning in 62 files. I utilized the error log for subjectmp1014 and subsequently ran fmriprep for the dataset.
-
-
-
-## Running fMRIPrep with Docker: Preprocessing fMRI Data for Multiple Participants
-
-I executed the following command on my local machine to run fMRIPrep using Docker for 5 subjects:
-
-docker run -ti --rm \
- -v $HOME/subjectmp1014:/data:ro \
- -v $HOME/outputmp1014:/out \
- -v $HOME/freesurfer/license.txt:/opt/freesurfer/license.txt \
- nipreps/fmriprep:latest \
- /data /out \
- participant --participant_label {sub-mp0010,sub-mp0011,sub-mp0012,sub-mp0013,sub-mp0014 --skip-bids-validation
-
-
-
-
-
-## Functional Connectivity Analysis and Group Comparison using Harvard Atlas
-
-This analysis aimed to investigate functional connectivity patterns and group differences using the Harvard atlas. The preprocessed data and confound information were imported from the specified directory. The Harvard atlas was utilized for defining brain regions of interest. The functional connectivity matrix was extracted, and subjects were divided into depressed and non-depressed groups based on BDI scores.
-Connectivity matrices were computed for each subject, and mean matrices were calculated for the depressed and non-depressed groups.
-
-
-
-## Data Processing and Grouping based on BDI Scores for Connectivities Analysis
-
-Data processing and grouping based on BDI scores were performed to investigate connectivities. The data consisted of preprocessed functional connectivity data and BDI scores obtained from participants. The functional connectivity matrix was computed, and subjects were categorized into depressed and non-depressed groups based on their BDI scores.
-Connectivity matrices were created for each subject, and group-level analyses were conducted. The results demonstrated distinct patterns of connectivity between the depressed and non-depressed groups. These findings highlight the potential differences in brain network organization associated with depression.
-Updating and Filtering Correlation matrix for Analysis
-
-
-## Updating and Filtering Correlation matrix based on label of interest
-
-Updating and filtering the correlation matrix to focus our analysis on specific regions of interest and their associations with neurobehavioral data, improved the accuracy and relevance of the data, allowing us to uncover meaningful insights and generate hypotheses for further investigation.
-
-
-
-
-## Ploting of Functional Connectivity in Depressed and Non-Depressed Groups
-
-The aim of this analysis was to visualize atlas-based coordinates for depressed and non-depressed groups. Coordinates were obtained using the specified atlas, and separate visualizations were created for each group. The connectivity patterns and spatial distribution of these coordinates were examined.
-The visualizations revealed distinct patterns of connectivity and spatial organization in the depressed and non-depressed groups. These findings provide valuable insights into the differences in brain network architecture associated with depression. The visualizations serve as a powerful tool for understanding and interpreting the complex connectivity patterns in different brain states.
-
-
-
-
-## Connectivity Distribution by Group: Depressed vs. Non-Depressed
-
-This analysis aimed to explore the distribution of connectivities between depressed and non-depressed groups. The connectivities were obtained from participants belonging to these two groups. A box plot was created to visualize and compare the connectivity distributions.
-
-The results of the T-test revealed a significant difference in connectivities between the depressed and non-depressed groups (T-Statistic: -2.1286, P-Value: 0.0348). This indicates that there are distinct patterns of brain connectivity associated with depression.
-These findings suggest that neuroconnectivity analysis could potentially serve as a valuable tool for understanding and identifying markers of depression.
-
-
-
-
-## Linear Regression Model for MCI Prediction
-
-This study aimed to predict Mild Cognitive Impairment (MCI) using connectivities and BDI scores. The dataset was split into training and test sets (80% training, 20% test). A linear regression model was created, trained on the training set, and used to predict MCI values for the test set.
-The model's performance was evaluated using Mean Squared Error (MSE) and R-squared (R2). The MSE was 0.279, indicating the average squared difference between predicted and actual MCI values. The R2 value was -0.115, suggesting the model did not explain much variance in MCI.
-In summary, the linear regression model showed limited success in predicting MCI using connectivities and BDI scores.
-
-
-
-Mixed Effects Linear Regression Model for MCI Prediction:
-
-A mixed effects linear regression model was developed to predict Mild Cognitive Impairment (MCI) using connectivities and BDI scores. The dataset was split into training and test sets (80% training, 20% test).
-After fitting the model, predictions were made on the test set. The evaluation of the model yielded a Mean Squared Error (MSE) of 0.250 and an R-squared (R2) value close to zero. The MSE represents the average squared difference between predicted and actual MCI values, while R2 indicates the proportion of variance in MCI explained by the model.
-In summary, the mixed effects linear regression model showed limited success in predicting MCI using connectivities and BDI scores. The convergence warning suggests potential issues with model estimation.
-
-
-
-### Progress overview
-
-I have encountered obstacles during the project that affected the speed, particularly due to the utilization of lab data and local machine instead of server for preprocessing. However, I am actively working on removing these obstacles to improve the efficiency and speed.
-
-### Tools I learned during this project
-
-Throughout this project, I have gained proficiency in several tools and technologies, including:
-
- * Jupyter Notebooks
- * GitHub
- * BIDS Validator
- * High Performance Computing
- * Data Visualization Tools (specifically Nilearn)
-
-These tools have been instrumental in facilitating code development, version control, data validation, efficient computing, and data visualization tasks within the project.
-
-## Conclusion
-
-In conclusion, this pilot project has provided valuable insights into the functional connectivity patterns in individuals with Parkinson's disease and neuropsychiatric symptoms (PD+NPS). The analysis of resting-state fMRI data has revealed distinct alterations in functional brain networks specific to the PD+NPS population. While these findings contribute to our understanding of the condition, further research using larger datasets is needed to fully elucidate the underlying neural mechanisms and to explore the potential implications for future research and clinical interventions.
-
-## Acknowledgements:
-
-I would like to express my sincere gratitude to Prof. Bellec, and other co-founder and TAs in particular Isil and Marie-Eve who have contributed to the development of this project. I also acknowledge the support and assistance provided by the Niemolab (My Ph.D. supervisor Dr. Alexandru Hanganu) and his research team.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: Predicting general cognition from resting-state functional brain connectivity
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Ngieng Shih Yang]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/NSYYYYY/BHS2025.git
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [regularized regression, resting-state, cognition, prediction]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: The purpose of the project is to compare various predictive models to compare the effectiveness of each predictive model and to identify important features that best contribute towards predicting general cognition. Additionally, the ideal number of features were also explored for each model.
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover.png"
----
-
-
-## Project definition
-
-### Background
-
-This study was inspired by the potential to use various machine learning techniques to improve predictive performance of resting-state functional connectivity on various performances measures. Specifically for this project, 7021 features were utilised in an attempt to predict the cognition composite scores of children aged 9 - 10 years old. Participants were recruited as part of a larger multi-site study called the Adolescent Brain Cognitive Development (ABCD) study. This study aims to use the data obtained during the baseline assessment inconjunction with various regularized regression methods and predictive models in an attempt to better predict general cognition. The project consists of various steps such as 1) data consolidation and cleaning, 2) subsampling of data, 3) training the model and 4)results presentation.
-
-### Tools
-
-The "project template" project will rely on the following technologies:
- * Markdown, to structure the text.
- * Rstudio, for data cleaning, consolidation and visualization.
- * Python (various packages) for model training, testing and visualization.
- * Adding the project to the website relies on github, through pull requests.
-
-### Data
-Permission has been granted for data use of the National Institute of Mental Health (NIMH) Data Archive.
-The files utilise in this project were obtained from ABCD-HCP fMRI pipeline v0.0.2 by the DCANS lab team (Sturgeon et al., 2019) . Inclusion criteria included images that were not rejected due to incidental radiological findings, passing satisfactory protocol compliance, and passing FreeSurfer Quality Control. More details about the preprocessing process are detailed in prior work (Hagler et al., 2019).
-
-The rsfMRI data was used to construct a matrix consisting of 119 x 119 nodes, based on parcellated cortical nodes from the schaefer 100 parcel, 17 network atlas (Schaefer et al., 2018; Thomas Yeo et al., 2011), with an addition of 19 subcortical nodes, including the cerebellum from the FreeSurfer Aseg Atlas, resulting in 7,021 unique connections.
-
-### Deliverables
-
-#### Steps for running the entire analysis to visualization.
-
-##### Step 1: Data consolidation and cleaning
-Open the data_cleaning_CogComp_T1.Rmd (in RStudio). The top of the Markdown file indicates the necessary files to consolidate and clean the data.
-Ensure that all the files that is required has been retrieved.
-Once the Markdown file has been ran, files will be generated for the next step
-
-##### Step 2: Subsampling of the data
-Use the subsampling.ipynb to subsample the data into the preferred subsample size.
-In this stage, the remaining sample will be saved as a different CSV file for potential future use.
-
-##### Step 3: Training the model
-Next, use the subsample_ml_v5.ipynb to train, test and visualize the model.
-The labels for the model can be found in 7021Labels.csv.
-The output will be saved as both .joblib files and .csv files for future use.
-
-##### Step 4: Connectogram creation
-This step uses the CSV files that were obtained from step 3 to create a connectogram in RStudio.
-Use the Connectogram_Script_Rstudio_...v2.R to create th connectograms. (There are three different, one for each model)
-This step requires an additional labelsFC_schaefer119.csv file (that I am not too sure if I am allowed to share, hence not available)
-
-
-### Tools I learned during this project
-
- * **Scikit_learn-** This is such a powerful package for running machine learning modelling and various different forms of regression for predictive purposes.
- * **Matplotlib.pyplot-** Powerful visualization tool that allows generation of various different types of charts and figures to better visualize results that is otherwise very hard to understand.
- * **Tool_flexibility** Through the project I learnt to utilise various tools that are best able to do what I require. I learnt not to be too focused on just utilising only 1 specific tool and software and to explore various options that can best fit my workflow.
-
-### Results
-The results suggest that Elastic Net regression achieves the best results in balancing predictive performance and the number of features. Ridge regression achieves the best results as it utilizes all of the features to generate a prediction. Lasso regression is useful only when a small number of features were to be extracted and used for future predictions. SVR performance was poor likely due to the low sample size that was utilise for model creation due to time constraints.
-
-## Conclusion and acknowledgement
-
-Overall, the results seem to show low predictive performance of resting-state functional connectivity on predicting general cognition. This is somewhat to be expected as resting-state mri is task-free and does not involve and tasks for the participant to conduct during the mri scan. Even so, there does seem to be some merit in utilising such machine learning methods in predicting various abilities.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Regression-Modelling ERPs (and More!)"
-
-# Sole author
-name: [Szu-Chi Amanda Lin]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/amandalin047/Amanda_BrainHack_2023
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [eeg/erp, python, regression, javascript]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Emotion perception is contextualized. However, how emotional context modulates word processing is unclear. We regression-fitted raw EEG data to test for emotional valence effects. The results revealed a widespread effect of context valnece, as well as a plausibility N400 waveform, well replicating the past ERP literature. Moreover, as we plan on conducting a subsequent experiment to follow up on the findings the present study has revealed, this project also includes the code for constructing experimental stimuli."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "rerp_coef.png"
-
----
-
-
-# Regression-Modelling ERPs (and More!)
-## Background
-
-Emotion perception is contextualized. However, how emotional context modulates word processing is unclear. We tested 20 young native speakers who read 208 Taiwan Mandarin sentences with varied emotionality conveyed through the context. To deal with multiple covarying factors in natural language, we adopted a regression-based rERP method to titrate the contextual effects. Moreover, as we plan on conducting a subsequent experiment to follow up on the findings the present study has revealed, this project also includes the code for constructing experimental stimuli.
-
-## Tools
-This project has implemented the following programming languages and brain imaging techniques:
-- EEG/ERP
-- Python
-- Matlab
-- JavaScript
-
-## Data
-- **EEG Analyses**: A 20-subject language experiment EEG dataset collected by a lab senior several years ago. (_The data files on this repository are currently blank dummy files._) Besides planning on adding 10 more participants to the original and recruiting 30 young adults for the following-up experiment, my advisor (Dr. Charlene Lee) and I also aim to ultimately put our lab data in BIDS.
-- **Stimuli Construction**: Participant Gmails and Excel sheets sent as attachments.
-
-## Deliverables
-- This markdown README documenting the project
-- Multiple Jupyter Notenooks (.ipynb), which also have the results clearly illustrated with nice data visualization
-- Python scripts (.py)
-- A Google Apps Script (.gs)
-
-[Find in the `amanda_rerp_ols_package` folder under `rERP_2023` a mini pip-installable Python package I uploaded onto PyPI. Not only is it an extension of the MNE built-in `mne.stats.linear_regression` method, but I also wrapped **my calculated-by-hand solution matrix into a user-defined class** (a Python named tuple, actually) as the return object.](https://github.com/amandalin047/Amanda_BrainHack_2023/tree/master/rERP_2023/amanda_rerp_ols_package)
-
-# Results
-## Progress Overview
-- **EEG Analyses**
- + The main structure of the regression models was completed during winter break (January through February), following Brouwer et al.'s 2020 method along with advice suggested by Dr. Kevin Hsu. However, rather than performing repeated-measures ANOVAs on the regression fitted values as was done by Brouwer et al., I t-tested each regression coefficient against 0 for concerns about systematic and statistically significant conditional differences among the predictors (in regression terms, everything in the predictors shows up as linear combinations in the fitted values, thus causing confounds).
- + The problem of multiple comparisons was tackled around March after a brief discussion with Dr. Joshua Goh. The Bonferonni correction was, as I realized, stringently unrealistic in the case where each (channel x time point) cell is regressed separately, rendering the statistical resulst less susceptible to individual Type I errors. I later settled (for the mean time) with the BH-procedure, but am still looking into cluster-based correction methods to better take into account the spatial relationships between 3D-configurated scalp channels.
- + Two sessions in April with Prof. Kara Federmeier from the University of Illinois Urbana-Champaign pointed me towards a new direction, namely to group the regression coefficients into spatiotemperal ROIs, which largely reduced the number of multiple comparisons for which I needed to correct.
- + The class attributes and methods in the program were substantially modified and revised soon after to improve run-time efficiency due to issues related to automatically dying Jupyter kernels in WSL2.
- + The EEG Toolbox under Matlab differs from MNE-Python in many respects. Having been initially trained in Matlab, I aspire to create Python functions with which Matlab users will feel more familiar, such as bin descriptor text files and the moving-window peak-to-peak method for artifact detection. To this aim, other EEG-related Python notebooks/scripts included in this repository were subsequently developed in late April through May.
- + It was an incredible honor — considering that I started off more theory-oriented than applicational — to have the opportunity to give a four-hour workshop on MNE-Python to several of my lab mates with self-written scripts.
-
-- **Stimuli Construction**
- + I started developing the Python code for compiling ratings Excel sheets roughly around March.
- + A massive round of ratings was carried out in April, thus calling for a means to automatically label, upload, and reply to incoming participant emails.
-
-
-## Skillsets Learned
-- **MNE-Python**: As a math undergraduate, I was initially trained in C++ and never formally took Python (I could read and code in Python as languages that support OOP share some similarities, yet I was far from proficient). It was not easy-peasy to start a Python program from scratch with the limited syntax I knew at the time; I'd spent countless hours Googling, sifting through threads on GitHub and Stack Overflow. As such, I genuinely find amazing how much I've learned and matured this semester in terms of Python programming skills, not only through my own project, but also as a TA working with the students.
-- **Statistical Models**: Statistics was _not_ a required undergraduate course in the Department of Mathematics (basic probablity theory was, though). I only took introductory stats as an elective, so I had but a vague idea of statistical modelling when I started off with this project. Yet as hands-on experiences often are great ways to learn, I've been implementing models I never knew before, and I do look forward to trying out different, more complicated ones.
-- **JavaScript**: I really went from zero on this. The immediate need for an automated Gmail gave me a miraculous adreneline boost and had me picking up the language in several hours — not sure how I did it, but I'm glad it worked!
-
-## Results
-- **EEG Analyses**
- + A widespread effect of contextual valence from 250ms to 750ms, with words following emotional contexts showing more positive responses relative to words in neutral contexts.
- + Greater implausibility elicited more negative and then more positive responses during 250-500ms and 600-1000ms, respectively, over central-posterior regions, which well-replicated the past ERP literature on the N400 plausibility effect and thus provided justification for our regression model. Further, this plausibility effect was not modulated by context valence, suggesting no evidence for the Affective Primacy hypothesis in sentence comprehension where one would've expected a significant interaction between the two.
- + A frontally distributed effect of word emotionality during 400ms to 700ms.
- + These findings showed that linguistic emotional context modulates subsequent word processing over and beyond sentence constraint and word emotionality, corroborating robust contextualized emotion processing in reading.
-
-
-- **Stimui Construction**
- + A great learning oppurtunity as we've come to realize which response collection methods are more efficient (and less tedious).
- + A nice, albeit still under construction, pipeline for collecting and sorting ratings responses.
-
-## Conclusion and Acknowledgements
-This has been an incredible journey in myriad ways, both academically and personally. I would like to give the biggest, most heartfelt thanks to my amazing thesis advisor, Dr. Charlene Lee at the Graduate Institue of Linguistics, National Taiwan University, without whom none of this would have been possible. I am deeply grateful not only for her sound advice, incredible support, but also for the faith she had in me when I struggled with self-doubts and didn't believe I was capable of carrying out this project or taking on this TA position. Having graded 82 module exercises, I must say I really do find reading people's code interesting. I sincerely appreciate all the students who have discussed with me their projects and code on Discord — those were the moments when I got to learn the most. There are many directions in which this project can be expanded and imporved, all of which I look forward to.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-05-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Revealing similarities between deep learning models and brain EEG representations"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Simon Faghel-Soubeyrand]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/deep_representational_learning
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [ Deep neural networks, brain representations, vision, EEG, decoding]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-#Deep learning is a growing field of AI that is now reaching cognitive-neurosciences.
-summary: "Do artificial neural networks process visual images similarly to our brain? If so, how? In this project, we bridge deep learning and brain EEG signals as we aim to understand more about our ability to process common visual stimuli such as objects, faces, scenes and animals."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "logo_project_1.png"
----
-
-
-# Exploring representations of deep neural networks and their similarity with the human brain
-
-## Summary
-
-I'm a PhD student at Université de Montréal & University of Birmingham (UK). I use an aggregate of psychophysics, EEG, and computational techniques to understand individual differences in vision, specifically in object/face recognition.
-
-
-The practical objectives of this project were to develop skills in Python, data visualisation and developping better coding habits with Github.
-
-The general aim of this project is to understand and visualize how deep neural networks represent visual stimuli compared to our brain. For human representations, we will use high-density EEG signal from a simple visual task.
-
-
-The project will unfold in three steps.
-
-1) Create a custom deep convolutional neural network (DCNN; using TensorFlow & Keras).
-2) Create, from high-density EEG time series, group-averaged representational dissimialrity matrices (RDMs; we will be feeding eeg topographies to linear classifiers to do so).
-3) Compare the representations (RDMs) from each of the DCNNs' layers to human brain representations (RDMs) unfolding in time.
-
-The first two steps will be developed in parallel. The last step will integrate their output.
-
-Initially, the plan was to train from scratch a DCNN to "learn" more human/brain representations. This is a mid/long-term role, and here I chose to focus on learning programming tools (described below).
-
-## Project definition
-### Background
-
-The idea of comparing and restraining a DCNN weight representations is highly inspired by work from [Cichy et al., (2016)](https://www.nature.com/articles/srep27755)
-[Kietzmann et al., (2019)](https://www.pnas.org/content/116/43/21854).
-
-
-Deep neural networks (DNNs) encode information in a similar way to the visual brain (e.g. primary visual cortex V1 encodes similar information than convolutional filters in the first layer of a DNN).
-The idea here is to come up with a common measure of processing between DCNN and brain imaging signal, here electroencephalogrpahy (EEG), and link the computations in both modalities.
-
-
-
-
-A good summary of brain and DNN computations can be obtained with a Representational Dissimlarity Matrix (RDM, see DATA section).
-
-
-## Tools
-
-The structure of the analyses will rely on :
-
-- developping analysis in **Python** scripts: pyrsa, scikit-learn, tensorFlow & keras
-- basics in visualisation tools : jupyter notebooks, interactive widgets, seaborn, and scikit-learn for multi-dimensional scaling.
-- Considering that the data is not completely available, no need to focus on BIDS standards that much.
-- **Github**
-- **Binder**
-- I will also familiarize with **deep learning**
-
-## Data
-
-Description (general):
-
-we previously used brain EEG data to "decode" pairs of images (faces, objects, scenes, animals, etc.) that were presented to human participants.
-By doing this with all pairs of images presented (49 stimuli = 1178 pairs), we created a Dissimilarity Matrix that is a summary of how the brain of each participant encodes diverse visual stimuli.
-This Representational Dissimilarity Matrix (RDM) was used as the base data input of this project.
-
--RDMs were averaged across particpants (N=23). Preprocessed EEG recordings at 128 electrodes (BioSemi)
-
-Task: simple one-back task over a stream of images containing faces of different emotions/gender, human-made/natural objects, animals and scenes.
-
-
-
-
-## Deliverables
-
-The plan is to have at least:
-
-- Python scripts for steps 1 and 3
-- Markdown README.md explaining the whole pipeline.
-
-And perhaps :
-
-- A container that enables to reproduce these analyses
-
-For the course :
-
-**Week 2 deliverable:** [README file](https://github.com/brainhack-school2020/deep_representational_learning/blob/master/README.md)
-
-**Week 3 deliverable:** : *Binder environment for data visualization*
-
-This link redirect to an interactive visualisation of the human brain data (RDMs):
-[](https://mybinder.org/v2/gh/brainhack-school2020/deep_representational_learning/master?filepath=%2Fanalysis%2Finteractive_figures_EEGxRSA.ipynb)
-
-**Week 4 deliverable:** [presentation](https://github.com/brainhack-school2020/deep_representational_learning/blob/master/presentation/BHS_2020.pdf)
-
-
-## Results
-
-## Pipeline (analysis and visualization) with scripts.
-
-
-
-## human brain RDMs
-
-An interactive visualisation of the human brain data (RDMs) can be found here :
-[](https://mybinder.org/v2/gh/brainhack-school2020/deep_representational_learning/master?filepath=%2Fanalysis%2Finteractive_figures_EEGxRSA.ipynb)
-
-
-
-Results for group-average **Representational Dissimiarlity matrices (RDMs)** had previously been derived at every time step from image onset. These *stimulus* x *stimulus* matrices contains pairwise decoding accuracies between all images (e.g. image face vs image car), indicating the brain's representational model across various visual stimuli.
-
-Here, the 2D coordinates of the representational distances from the RDMs are computed "online" using Multi-Dimensional Scaling from sklearn MDS method (to play interactively with this, see binder link above).
-
-
-
-
-## DNN representations and link with brain data
-
-### DNN representations
-Using the same stimuli we showed to participants as input to DNNs, I computed an RDM for every layer of the VGG16 neural network (weights from imagenet).
-
-
-
-### Similarity between brain signal and DNN representations
-By correlating these DNN representations with the human RDMs, I obtained the time courses of similarity between VGG16 hierarchical processing and the EEG brain representations.
-
-
-
-
-
-## Tools & Skills I developed during this project
-
-- **Python**, and useful libraries (e.g. pingouin, pandas, scipy, sklearn, tensorflow, keras)
-- Machine learning (**deep learning**).
-- Interactive figures in **Jupyter notebooks** and visualization libraries (e.g. matplotlib, seaborn, ipywidgets)
-- **Binder** reproducible environments to share code and figures.
-- **GitHub**
-- **Markdown**
-- **Inkscape** for figures.
-
-## Conclusion and acknowledgements
-
-Big thanks to all instructors, to Peer & Valerie, and to all the BHS community. This was a full month but I learned and enjoyed it a lot. I should also thank Michelle, which reminded me all the deliverables and deadlines throughout the way!
-
-Thanks to my supervisors (F. Gosselin, I.Charest) and collaborators of this project, which will be integrated as part of my phD.
-
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diff --git a/content/en/project/Ridani_Chi_separation_project/index.md b/content/en/project/Ridani_Chi_separation_project/index.md
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Validating χ-separation using phantom simulations"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Daniel Ridani]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/Ridani_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Python, Susceptibility, Software, Visualization, Polytechnique]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "How can we validate χ-separation algorithm? In the absence of ground truth to validate χ-separation, my project aims to validate the χ-separation results using realistic in-silico head phantom simulations. Simulations offer a valuable advantage by providing a controlled environment where we can define and manipulate various parameters with known ground truth values. By choosing specific values for the simulation, we can create a ground truth against which we can compare the results obtained through the χ-separation algorithm."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Cover_image.png"
----
-
-
-## About Me
-
-
-Hello everyone, my name is Daniel Ridani. I am a first-year PhD student at Polytechnique Montreal, majoring in biomedical engineering. I did my Bachelor's in general physics and my Master's in biomedical engineering, where I fell in love with this field and decided to pursue a PhD. Currently, I am working on quantitative MRI, specifically quantitative susceptibility mapping (QSM). In my research, I focus on studying the susceptibility contributions from iron and myelin and developing methods to effectively separate them. Brain Hack School has given me the opportunity to improve my Python skills and explore new topics such as deep learning and machine learning.
-
-## Project Summary
-
-### Introduction
-
-My project aims to validate χ-separation algorithm using realistic in silico head phantom simulation. Susceptibility denoted as (χ), is a measure of how easily a material can be magnetized in response to an external magnetic field. Positive susceptibility implies that the material's spin aligns its magnetic moment parallel to the main field, while negative susceptibility indicates an opposing alignment. The main two sources of susceptibility are myelin (χ<0) and iron (χ>0) and they co-exist in almost every region in the human brain. Using the susceptibility, we can generate maps known as Quantitative susceptibility mapping (QSM) which does not have a ground truth. In the absence of ground truth for QSM, efforts have been made (QSM reconstruction challenge 2.0) [1] to create a phantom that can serve as a reference standard for validation. QSM measures the total susceptibility rather than the distinct positive and negative susceptibility sources, hence, to address this, χ-separation techniques, are employed to separate the two susceptibility sources [2]. The distinction between positive and negative sources of susceptibility plays a crucial role in understanding the underlying physiological processes and their implications for brain function. The ability to accurately discern and differentiate between these sources contributes significantly to the reliability of the assessments conducted in this study. The aforementioned phantom can be utilized to validate the accuracy of the χ-separation results.
-
-### Main Objectives
-- Get more familiar with χ-separation limitation.
-- Validate χ-separation algorithm using ground truth data (phantom simulations).
-- Implement python scripts to analyze and visualize the results.
-- Create a GitHub repo to share my results.
-
-### Personal Objectives
-- Gain a deeper understanding of susceptibility contribution in the human brain.
-- Develop the skill to manage a small project in a short period.
-- Improve my programming skill using the BrainHack school modules.
-- Share my knowledge with others to encourage open science.
-
-### Tools
-- χ-separation GUI developed by Hyeong-Geol Shin et al.
-- QSM reconstruction challenge 2.0 code for Gradient Echo (GRE) simulations of custom phantoms.
-- Git and GitHub for Version Control.
-- Python Packages: "matplotlib" [3], "seaborn" [4], "pandas" [5].
-### Data
-The dataset used in this study was simulated using the Matlab code from the QSM reconstruction challenge 2.0 article developed by José P. Marques et al. This article also provided susceptibility values for 10 different regions in the brain.
-
-A summary of the susceptibility values is as follows [6]:
-
-|Brain Regions |Susceptibility (ppm)|
-|:---------------|:----------------:|
-|Caudate Nucleus |0.044 |
-|Globus Pallidus |0.1305 |
-|Putamen |0.038 |
-|Red Nucleus |0.1 |
-|Dentate Nucleus |0.152 |
-|Substanial Nigra |0.111 |
-|Thalamus |0.02 |
-|White Matter |-0.03 |
-|Grey Matter |0.02 |
-|CSF |0.019 |
-
-Table 1: Susceptibility values of different brain region
-### Project Deliverables
-
-At the end of this project, we will have:
-- A phantom for χ-separation used as a ground truth.
-- Detailed project workflow documented in a GitHub repository.
-- Executable python scripts for data visualization and analysis.
-- Markdown file providing comprehensive project details.
-
-## Method
-
-### Phantoms
-
-To generate the phantoms used in this study, two experiments were performed:
-- The first experiment involved assigning variable values to each voxel within a specific region, with the mean value ± standard deviation being used for analysis.
-- The second experiment, a uniform value was assigned to every voxel within the same region.
-This approach will help reduce the complexity of the phantom model to see if the complexity of the phantom is a limitation of χ-separation.
-
-### χ-separation input
-
-χ-separation requires three main inputs:
-
-- Magnitude image.
-
-- Frequency shift (Δf): This map can be obtained from the phase image after we apply a couple of processing steps that will be explained in the next section.
-- R\'2 map (R\'2 = R\*2 - R2 ): In order to obtain an R\'2 map, you will need to follow these steps. First, acquire two different scan sequences: Gradient-echo (GRE) and Spin-echo (SE). From each sequence, generate magnitude images. Next, perform an exponential fitting on the magnitude images separately. This will provide you with T\*2 and T2 maps for the GRE and SE sequences, respectively.
-To calculate R\*2, take the inverse of T\*2 (denoted as 1⁄T\*2). Similarly, calculate R2 by taking the inverse of T2 (denoted as 1⁄T2). Finally, obtain R\'2 by subtracting R2 from R\*2.
-
-
-### Pipeline and preprocessing
-
-Figure 1: Reconstruction pipeline from raw data to χ-separation
-
-Figure 1 represents the entire pipeline used to get two separate images of iron and myelin. The custom phantom (χTotalSimulated) is simulated into GRE data to give magnitude and phase images. The magnitude image is used to extract a binary mask to identify the region of interest and an R\*2 using an exponential fitting. Since χ-separation requires an R\'2 map and the simulation only provides GRE data an approximation adapted from Alexey V. Dimov et al was used to estimate the R\'2 map [7]. In his work he found a strong correlation between R\*2 and R\'2 maps and establish a relation between them: R\*2 = αR\'2. Afterward a phase unwrapping algorithm is applied on the phase image to obtain the true phase followed by a background field removal to remove the induced background field from the region of interest and finally a dipole inversion technique is applied to estimate the local susceptibility. At this point we apply the χ-separation algorithm to separate the positive (χPositiveMeasured) and negative (χNegativeMeasured) susceptibilities. Finally, the positive and negative susceptibility maps are combined to obtain the total susceptibility (χTotalMeasured), which will be used for comparing the susceptibility values before and after χ-separation.
-
-## Results
-### Phantoms
-
-Figure 2: Phantom maps of variable and uniform assessments of brain region values. The left set (a, b, c) illustrates a variable assessment approach, where image a represents χPositiveSimulated, image b represents χNegativeSimulated, and image c represents χTotalSimulated values. The right set (d, e, f) presents a uniform assessment approach, with images d, e, and f mirroring the same representation as a, b, and c, respectively.
-
-Figure 2 represents the phantom maps that will be used as ground truth. This figure present two sets of maps: the first (a, b and c) has a variable assessment of brain region values and the second (d, e and f) has a uniform assessment of brain region values. The figure presented in this study exemplifies the comparative analysis of variable and uniform assessments. The findings of these experiments will shed light on the limitations of χ-separation when it comes to the complexity of the used model.
-
-### χ-separation
-#### Qualitative assessment
-
-Figure 3: Visual comparison between χPositiveMeasured and χNegativeMeasured vs χPositiveSimulated and χNegativeSimulated respectively for both variable and uniform assessment of brain regions values. Figure a represents χPositiveMeasured values, showcasing three subfigures (a.1, a.2, a.3) that focus on specific brain regions, namely the Caudate Nucleus (CN), Putamen (Put), and Globus Pallidus (GP), as well as the Thalamus, Red Nucleus (RN), and Substantia Nigra (SN). Similarly, figure b corresponds to χNegativeMeasured values. It also contains three subfigures (b.1, b.2, b.3) that highlight the same brain regions as figure a but with negative susceptibility. Figure c (c.1, c.2, c.3) and Figure d (d.1, d.2, d.3) follow the same configuration as Figure a (a.1, a.2, a.3) and Figure b (b.1, b.2, b.3) respectively.
-
-Figures 3 provide a compelling visual representation of the clear separation observed between positive and negative sources of susceptibility in the assessment of brain regions. Notably, the measured maps obtained through x-separation exhibit an resemblance to the ground truth, validating their accuracy and reliability in both the evaluation of variability and uniformity within the brain regions of interest which demonstrates the successful separation of positive and negative susceptibility sources in the assessment of brain regions. Through a meticulous comparison of the measured maps with the ground truth, it becomes evident that the applied x-separation method effectively captures the details of the brain regions under investigation. The similarity observed between the measured maps and the ground truth maps validates the effectiveness of the employed methodology and strengthens confidence in the obtained results.
-
-#### Quantitative assessment
-
-Figure 4: Quantitative comparison between simulated and measured values. Figure A illustrates the variability assessment of brain regions which comprises three subfigures: A.1: comparison between QSM, χTotalMeasured vs χTotalSimulated. A.2: comparison between χPositiveMeasured vs χPositiveSimulated. A.3: comparison between χNegativeMeasured vs χNegativeSimulated. Each plot contains a red dotted line depicting y=x. Figure B illustrates the uniformity assessment of brain region values which replicates the structure and comparisons of Figure A. In addition, each plot indicates the correlation value between the values as well as the slope value.
-
-Figure 4 presents the quantitative comparative analysis between simulated and measured susceptibility values for both variable and uniform assessment of brain region values. Each plot includes a y=x line to facilitate visual comparison and evaluate the consistency between measured and simulated data. This comparison is important to demonstrate the validity of the χ-separation algorithm. Ideally, the measured values should be equal to the simulated values, thus, the closer the value of the slope is to 1 the more accurate the measured values are to the simulated values. On the firs hand, when we took a variable assessment of brain region values approach the linear regression results of Figure A are as follows: slope=0.77, R2=0.98 when comparing QSM vs χTotalSimulated; slope=0.39, R2=0.98 when comparing χTotalMeasured vs χTotalSimulated; slope=0.36, R2=0.99 when comparing χPositiveMeasured vs χPositiveSimulated and finally slope=0.15, R2=0.53 when comparing χNegativeMeasured vs χNegativeSimulated. On the other hand, when we took a uniform assessment of brain region values approach to make the model less complex, the linear regression results of Figure B are as follows: slope=0.89, R2=0.99 when comparing QSM vs χTotalSimulated; slope=0.45, R2=0.99 when comparing χTotalMeasured vs χTotalSimulated; slope=0.41, R2=0.99 when comparing χPositiveMeasured vs χPositiveSimulated and finally slope=0.28, R2=0.70 when comparing χNegativeMeasured vs χNegativeSimulated. An immediate rise in the value of correlation and slope was observed when the model was simplified at a small rate.
-
-## Conclusion and future work
-- χ-separation is a great and easy tool to use that allows us to separate myelin from iron.
-- Simulations are important to obtain the ground truth and validate the measured values.
-- χ-separation showed great visual results compared to the simulated maps but it poorly performed numerically, especially when it comes to negative susceptibility.
-- χ-separation performed better when the χ-model was less complex, which could be one of the algorithm's limitations.
-- Using Python for data visualization and analysis is easy and produces the best plots.
-- Other χ-separation limitations should be explored
-- Other separation algorithms should be studied.
-- Add microstructure information to the algorithm to make it more accurate (such as myelin fiber orientations)
-
-
-
-### Objectives, Tools, and Deliverables
-Regarding the objectives, learning tools, and deliverables, I consider the project to be a successful endeavor. It has resulted in the creation of a comprehensive pipeline that encompasses all the necessary steps, starting from raw data processing and leading to the visualization of results. Furthermore, the project has successfully incorporated the targeted tools, enabling me to gain proficiency in their utilization. The deliverables of the project have been met, as evidenced by the provision of a detailed project walkthrough and the availability of reproducible Python scripts for future use.
-
-## Detailed guide to Reproducibility
-Requirements:
-- Download χ-separation code from GitHub, you can find the repository [here](https://github.com/SNU-LIST/chi-separation). It have a well documented manual on how to use the GUI.
-- Download QSM reconstruction challenge 2.0 simulation code. you can find the files [here](https://data.donders.ru.nl/collections/di/dccn/DSC_3015069.02_542?1). Note that you will have to request access in order to download the file, but it's really simple you just have to connect to your ORCID account. In case you don't have an ORCID-ID you can create one [here](https://orcid.org/)
-- Download and install python packages for data visualization and analysis you will need three: Seaborn, Matplotlib, Pandas. You can install them use these command line:`pip install seaborn` || `pip install matplotlib` || `pip install pandas`
-- Download qMRLab software for data fitting. you can find the software [here](http://qmrlab.org/) [8].
-- Matlab
-
-Once all the files are installed, carefully read the manuals that are provided by the developers as well as this report. I suggest also to read their corresponding articles so you can have an idea on how the algorithms work. You can find the link for the χ-separation article [here](https://www.sciencedirect.com/science/article/pii/S1053811921006479), and the link for the QSM reconstruction challenge 2.0 [here](https://onlinelibrary.wiley.com/doi/10.1002/mrm.28716).
-
-After reading everything, you have a general idea of what you should expect from these codes. First, open your simulation file and create your custom phantom. You can do that by changing, for example, the values of each region of the brain provided by a file named "parameters.mat", which will contain susceptibility values used as a reference for your work denoted as "chiref", "chiref" will present the total susceptibility, I suggest adding two more columns named "chipos" and "chineg" so you can add your positive and negative susceptibilities (always make sure that chiref=chipos+chineg). Now RUN the file named `MacroCreateSusceptibiltyPhantom.m` that will call the main function `CreateOwnRealisticPhantom.m`. Now before running you might want to consider one of these two options:
-- Run the code through chiref, chipos, chineg individually. (By default, the code will use chiref since we manually added chipos and chineg so to run the code over the values of chipos and chineg you will have to change in `CreateOwnRealisticPhantom.m` every chiref into chipos or chineg).
-- Change the code! You can automatically get the chipos and chineg then add them together and save the results as chiref. Here is my suggestion: 1) Make a copy of the original code so you don't lose it. 2) Open `CreateOwnRealisticPhantom.m` and change every chiref to chipos. 3) Copy-paste almost all the code and now change it to chineg. At this point you should have the exact code repeated twice once for chipos and once for chineg. 4) To get chiref add these few lines at the end:
-```matlab
-%Calculation of ChiTotal from ChiNeg and ChiPos
-% File paths of the two NIfTI images to be loaded
-ChiNegPath = 'data/chimodel/ChiNeg.nii';
-ChiPosPath = 'data/chimodel/ChiPos.nii';
-% Load the first NIfTI image
-chineg = load_untouch_nii(ChiNegPath);
-chinegdata = chineg.img;
-% Load the second NIfTI image
-chipos = load_untouch_nii(ChiPosPath);
-chiposdata = chipos.img;
-% Perform addition of the two images
-Chitotal = chinegdata + chiposdata;
-% Save the result as a NIfTI image
-resultNii = chineg; % Use image1 as a template for header information
-resultNii.img = Chitotal; % Assign the result to the image data
-% File path to save the result NIfTI image
-resultPath = 'data/chimodel/Chi.nii';
-% Save the result NIfTI image
-save_untouch_nii(resultNii, resultPath);
-% Display a message upon successful save
-disp('Result NIfTI image saved successfully.');
-```
-Now we have 3 images chiref saved as `Chi.nii`, chipos saved as `ChiPos.nii` and chineg saved as `ChiNeg.nii`. For the next step (data simulation) use only the `Chi.nii` file since the `ChiPos.nii` and `ChiNeg.nii` are only used for comparison reasons. Open `MacroCreateSimulationData.m` file, after you make sure you used the parameters you want, run the file and wait until the simulation ends. This simulation will result in multiple phase and magnitude images each correspond to it's TE time. You can use this file [here](https://github.com/brainhack-school2023/Ridani_project/blob/main/Scripts/Concatenate_echoes.py) to concatenate the files into one file named `Magnitude_data.nii.gz` and `Phase_data.nii.gz`. Now go ahead and open χ-separation GUI and follow the instruction to get the separated images. Note that you can use this code [here](https://github.com/brainhack-school2023/Ridani_project/blob/main/Scripts/R2Prime_calculation.py) to calculate an estimation of R\'2 map.
-
-In order to create susceptibility maps and other maps that have constant regions as I did in this project you can use this python code [here](https://github.com/brainhack-school2023/Ridani_project/blob/main/Scripts/Make_uniform_chi.py) to create uniform χ susceptibility maps and this code [here](https://github.com/brainhack-school2023/Ridani_project/blob/main/Scripts/Make_uniform_maps.py) to create other uniform maps that you will need in this work (read the code to understand which maps I am talking about).
-
-The final step is to make the plots that I showed in the results section. To do this you will have first to download this .csv file [here](https://github.com/brainhack-school2023/Ridani_project/blob/main/susceptibility.csv) that will contain all the values used to plot the figures and you can find the code [here](https://github.com/brainhack-school2023/Ridani_project/blob/main/NoteBook/Figure_plotting.ipynb) which is a Jupyter Notebook code.
-
-IMPORTANT NOTE: Always make sure that everything is on the same path (requirements and data)
-
-## CONGRATULATIONS YOU REPRODUCED MY PROJECT!!
-
-
-## Acknowledgments
-I would like to thank the Brank Hack School 2023 for organizing this course and for their support. The course has been highly beneficial, and I would like to express my appreciation to Dr. Eva Alonso Ortiz for delivering excellent instructions.
-## References
-
-1. Marques, José P., et al. "QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures." Magnetic Resonance in Medicine 86.1 (2021): 526-542.
-
-2. Shin, Hyeong-Geol, et al. "χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain." Neuroimage 240 (2021): 118371.
-
-3. Waskom, M. Seaborn: Statistical Data Visualization. Retrieved from https://seaborn.pydata.org/
-
-4. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55
-
-5. McKinney, W. (2010). Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference (pp. 56-61). https://doi.org/10.25080/Majora-92bf1922-00a
-
-6. Krebs, Nikolaus, et al. "Assessment of trace elements in human brain using inductively coupled plasma mass spectrometry." Journal of Trace Elements in Medicine and Biology 28.1 (2014): 1-7.
-
-7. Dimov, Alexey V., et al. "Susceptibility source separation from gradient echo data using magnitude decay modeling." Journal of Neuroimaging 32.5 (2022): 852-859.
-
-8. Karakuzu, A., Boudreau, M., Duval, T., Boshkovski, T., Leppert, I. R., Cabana, J. F., … & Stikov, N. (2020). qMRLab: Quantitative MRI analysis, under one umbrella. Journal of Open Source Software, 5(53), 2343. doi: 10.21105/joss.02343.
-
-
-
-
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-Seaborn version: 0.11.0
-Matplotlib version: 3.7.1
-Pandas version: 1.5.3
diff --git a/content/en/project/Ridani_Chi_separation_project/susceptibility.csv b/content/en/project/Ridani_Chi_separation_project/susceptibility.csv
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-,,Region\Map,Chi,QSM,ChiTotal,ChiPos_truth,ChiPosMap,ChiNeg_truth,ChiNegMap,,,,,Region\Map,Chi2,QSM2,ChiTotal2,ChiPos_truth2,ChiPosMap2,ChiNeg_truth2,ChiNegMap2
-,1,Caudate Nucleus,0.0379,0.0329691,0.0165532,0.0484887,0.0168559,-0.0105699,-0.000303032,,,,1,Caudate Nucleus,0.0439991,0.0343003,0.017225,0.0528013,0.0172314,-0.00880004,-6.38E-06
-,2,Globus pallidus,0.125,0.0836621,0.0420012,0.137987,0.0420068,-0.012965,-5.64E-06,,,,2,Globus pallidus,0.130501,0.0973529,0.0488464,0.1436,0.0488464,-0.0131001,0
-,3,Putamen,0.0353,0.0112188,0.00564466,0.0419966,0.00970432,-0.00671862,-0.00406152,,,,3,Putamen,0.0379977,0.0199957,0.0100854,0.0436976,0.0101783,-0.00569974,-9.31E-05
-X-separation protocol,4,Red Nucleus,0.0972,0.0808925,0.0405947,0.119403,0.0406374,-0.0221927,-4.28E-05,,,X-separation protocol no variation,4,Red Nucleus,0.100001,0.0828606,0.0415814,0.124999,0.0415814,-0.0250004,0
-,5,Dentate nucleus,0.1424,0.118571,0.0594287,0.152489,0.0594526,-0.0100528,-2.39E-05,,,,5,Dentate nucleus,0.152002,0.137211,0.0687489,0.161999,0.0687489,-0.00999979,0
-,6,substantia nigra,0.1026,0.0729951,0.0366273,0.111409,0.0367523,-0.00881363,-0.000125098,,,,6,substantia nigra,0.111001,0.0976436,0.0489544,0.118997,0.0489544,-0.00800021,0
-,7,Thalamus,0.0156,0.00598218,0.00301646,0.0245963,0.00560119,-0.00899235,-0.00258648,,,,7,Thalamus,0.0199991,0.00726592,0.00368881,0.0269979,0.00390117,-0.00700043,-0.00021292
-,8,White Matter,-0.0314,-0.0144786,-0.00728982,0.000565248,0.000710241,-0.0319229,-0.00800382,,,,8,White Matter,-0.0299957,-0.0249187,-0.0125373,0.00150182,3.34E-06,-0.0315,-0.0125451
-,9,Grey Matter,0.0138,0.00611861,0.00307884,0.0170268,0.00498429,-0.00323201,-0.00190684,,,,9,Grey Matter,0.0199991,0.0124622,0.00628209,0.0210022,0.00650682,-0.000999786,-0.000225044
-,10,CSF,0.0185,0.00302186,0.00151801,0.0183176,0.00200344,-0.000151271,-0.000485967,,,,10,CSF,0.0190017,0.00333623,0.00168477,0.0190017,0.00201875,0,-0.000334226
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-03" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Functional Connectivity in ADHD: Group Differences and Predictive Modeling During Spatial Working Memory Task"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Nilay Ozdemir Haksever]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/nilayoh/swm_fmri_project.git
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [spatial working memory, functional connectivity, machine learning, ADHD]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Firstly, this project investigates differences in frontoparietal brain connectivity between individuals diagnosed with ADHD and control participants during Spatial Working Memory Task, using fMRI-based connectivity data. In the second part of this project, To classify individuals as either having ADHD or being in control group based on functional connectivity data features machine learning models was tested by using k-fold cross validation. "
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Image.jpg"
----
-
-
-## Project definition
-
-### Background
-Working memory is a core component of executive function, and spatial working memory is often impaired in ADHD.
-Deficits in working memory contribute to:
-* Inattention, impulsivity, and poor task performance
-* Difficulty sustaining attention in spatial contexts
-Neural Mechanisms:
-SWM tasks typically activate:
-* Dorsolateral prefrontal cortex (DLPFC): for maintaining and manipulating information
-* Parietal cortex: for spatial processing
-
-In ADHD, these areas show reduced activation.
-
-### Tools
-
-* The "project template" project will rely on the following technologies:
-* Git and Github
-* Python libraries:
-* ScikitLearn for machine learning,
-* Nilearn for fMRI connectivity analysis
-* Pyplot, Matplotlib, Seaborn for data visualization
-* Jupyter Notebook
-
-### Data
-The dataset obtained from following project.
-
-Alpha oscillations and working memory deficits in ADHD: A multimodal imaging investigation (R01MH116268)
-
-https://nda.nih.gov/edit_collection.html?id=3101
-
-It was a multimodal data containing both fMRI and EEG. This project only focused on the fMRI data. A total of 68 young adults was used in these analyses (39 ADHD, 29 Control; Mage: 21.35)
-The data was based on a computerized version of the spatial working memory (SWM) task.
-
-
-
-### Deliverables
-
-At the end of this project, I have:
-- A Github repository with code scripts (https://github.com/nilayoh/swm_fmri_project.git)
-- Figures and results summarizing the performance of the model and showing the functional connectivity across regions
-- Jupyter notebook that includes analysis codes and visualizations
-
-
-## Results
-
-
-These images show mean functional connectivity across frontoparietal regions of the brain. Blue color indicate higher functional connectivity for Control than ADHD patients. Red color indicates higher functional connectivity for ADHD patients than control group.
-
-
-
-These images show significant differences between ADHD and control group across frontoparietall regions of the brain before FDR correction.
-
-
-No significant difference between ADHD and Control group across these areas after FDR correction.
-
-Even though I could not find significant difference for practice I would like to continue to investigate my second part of the project.
-I used machine learning methods to classify participants as ADHD or control. I used k-fold cross validation (k=5) to test my model.
-The cross validation accuracy across folds can be seen in following graph.
-
-
-## Conclusion and acknowledgement
-
-What I’ve Learned:
-
-Understanding fMRI Data:
-* Downloading, organizing datasets
-* Managing confounding variables
-Functional Connectivity Analysis:
-* Comparing ADHD vs. Control groups using connectivity matrices
-* Using atlases for brain parcellation
-Debugging & Code Practice:
-* Troubleshooting analysis pipelines in Python
-* Gaining confidence in working with neuroimaging libraries (e.g., Nilearn, Nibabel)
-Introduction to Machine Learning:
-* Applying classification techniques to distinguish between ADHD and Control groups
-* Exploring pipelines for diagnosis-based predictions
-
-Thank you to all TAs, Dr. Erin Dickie, for all your help in the process and this opportunity. Also, thank you to Joel Diaz for providing the data and helping.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Decoding Perceived Emotion from BOLD data using Machine Learning"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Muhammad Shahir Adha]
-
-# Your project GitHub repository URL
-github_repo: (https://github.com/MShahirAdha/Adha_project/tree/main)
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [emotion, machine learning, fmri, perception]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project applies machine learning to decode perceived emotions from fMRI data using ROI-based features. Data from the ds003548 OpenNeuro dataset are analyzed, with task labels extracted from events files. ROI time series are extracted using the MIST 64-ROI atlas, and mean signals during emotion blocks are classified using linear SVM. The goal is to distinguish between six conditions (happy, sad, angry, neutral, blank, scrambled), demonstrating key concepts and challenges in neuroimaging-based classification."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "confusion_matrix_test.png"
----
-
-
-## Project definition
-
-### Background
-
-Understanding how the brain processes emotions is a central challenge in cognitive neuroscience. Functional Magnetic Resonance Imaging (fMRI) allows researchers to non-invasively measure brain activity during emotional tasks, but traditional univariate methods often miss the distributed nature of neural representations.
-
-This project uses multivariate pattern analysis (MVPA) to decode emotional states from fMRI data in the OpenNeuro dataset ds003548. Participants viewed emotional and non-emotional face stimuli across several runs. Smoothed, denoised BOLD images were used to extract region-wise average signals based on the MIST 64-region functional atlas. A linear Support Vector Machine (SVM) was trained to classify six conditions: happy, sad, angry, neutral, scrambled, and dim. This approach demonstrates how machine learning can reveal distributed emotion-related patterns in the brain.
-
-### Tools
-
-The project will rely on the following technologies:
- * Python: nibabel, pandas, numpy, scikit-learn, nilearn, matplotlib
- * Adding the project to the website relies on github, through pull requests.
-
-### Data
-
-This project uses publicly available fMRI data from the OpenNeuro dataset ds003548, titled "Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis". The dataset includes BOLD fMRI scans from participants viewing emotional and non-emotional face stimuli across multiple runs.
-
-Preprocessing was performed using fMRIPrep, and the analysis uses smoothed, denoised BOLD images in MNI space. Task condition labels were extracted from events.tsv files, and brain activity was summarized using the MIST 64-region functional atlas.
-
-### Deliverables
-
-At the end of this project, we will have:
- - The current markdown document, completed and revised.
- - Model test accuracy results and confusion matrix
- - Jupyter notebook code
-
-## Results
-
-### Progress overview
-
-This project was developed as part of BrainHack School 2025. I adapted material from the tutorials to perform emotion classification using fMRI data from the OpenNeuro ds003548 dataset. The pipeline includes data loading, ROI-based feature extraction, and machine learning classification using a linear SVM. The process was straightforward, and community feedback may help refine future iterations.
-
-### Tools I learned during this project
-
- * **fMRI analysis** I learned how to work with BIDS-formatted fMRI data, use nibabel and nilearn to load and process brain images, and apply ROI-based feature extraction using the MIST 64 atlas.
- * **Machine Learning for Neuroimaging** I applied scikit-learn to perform classification using a linear SVM, using cross-validation strategies suited for subject-level generalization.
- * **GitHub & Collaboration** I practiced using GitHub for project version control and learned how to structure a project for reproducibility and collaboration.
-
-### Results
-
-#### Deliverable 1: Jupyter Notebook code
-
-A Jupyter notebook containing my entire project pipeline, with additional exploratory analyses, is included in the repository.
-
-#### Deliverable 2: project results
-
-The model achieved an overall accuracy of 51.5% on the test data. It model performs very well in identifying control conditions like Blank and Scrambled images, with over 90% accuracy. However, the model struggles more with the emotional categories. For example, Angry is correctly classified only 30% of the time, Happy 23%, Neutral 20%, and Sad performs somewhat better at 47%.
-These lower accuracies likely reflect the more subtle and overlapping neural signatures of emotions, making them more challenging to distinguish.
-
-
-## Conclusion and acknowledgement
-
-This project highlights the applications of MVPA on neuroimaging data to predict perceived emotion. Thanks to all Brainhack School collaborators from NTU SG and NTU TW for the wonderful learning opportunity!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Grandpa is moody, will his cognition decline? Predicting cognitive decline in Parkinson's disease from non-motor symptoms evolution"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Y Song]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/song-y/SubtypingPD
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://song-y.github.io/SubtypingPD/
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [machine learning, mri, cognition, parkinson's disease]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "While Parkinson's disease (PD) is recognized by its motor symptoms, it is also characterized by non-motor symptoms (NMS), such as anxiety, depression, pain, etc. NMS often precede cognitive decline, making them potential predictors of such decline. This project aims to investigate the longitudinal association between non-motor symptoms and cognitive and neural decline in patients with PD. Early identification of individuals at higher risk of cognitive decline through their NMS presentation can facilitate timely interventions."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover_photo.png"
----
-
-
-## Project definition
-
-### Background
-Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder globally and is rapidly growing in Canada. While PD is often associated with motor symptoms, it is in fact characterized by a combination of both motor and non-motor symptoms (NMS). NMS cover a wide range of disruptions, including mood disorders, anxiety, perceptual disturbances, and pain. They often precede cognitive decline, which makes them potential predictors of such decline. Yet to this day, NMS most associated with cognitive decline in PD patients have not been identified. As PD is a **heterogeneous** disease, NMS need to be examined together rather than individually when aiming to predict cognitive decline. Data-driven techniques like clustering algorithms are well-suited for identifying patterns in data comprising numerous attributes.
-
-#### Objectives
-
-**This project aims to investigate the association between NMS and cognitive and neural decline over time in individuals with PD.**
-
-Specifically, the objectives are to:
-1. Identify subtypes of PD with respect to NMS evolution.
-2. Verify whether a specific subtype of PD is associated with faster cognitive decline longitudinally.
-3. Verify whether a specific subtype of PD is associated with faster neural decline longitudinally.
-
-### Tools
-
-Throughout the school, I used the following tools:
-
-**1. Python scripting in jupyter notebooks:** most of my work was done in notebooks, including tasks such as data manipulation and plotting, visualization of neuroimaging data, statistical analyses, etc.
-**2. Git & Github:** allowed the version control of the project
-**3. BIDS and Freesurfer:** conversion of raw MRIs to BIDS, then going through the Freesurfer pipeline for preprocessing
-**4. Basics of ML:** learned ML basics such as the ML pipeline (training, validation and testing phases), h-parameter tuning, evaluating models, etc.
-**5. High-performance computing:** parallelization of MRI data preprocessing, clustering of high dimensional data
-**6. Bash scripting:** automation of neuroimaging preprocessing
-**7. Weights & Biases:** tracking algorithm runs and hyperparameter tuning
-**8. Sphinx:** documentation for Python deliverables
-
-### Data
-
-3 independant datasets of PD patients are used in this project, comprising cross-sectional clinical data of NMS and longitudinal cognitive and neuroimaging data:
-- Canadian Open Parkinson Network (COPN)
-- Parksinson's Progressive Markers Initiative (PPMI)
-- UK Biobank
-
-### Deliverables
-
-
-
-The deliverables of this project consist of [three pipelines in the form of Jupyter notebooks](https://song-y.github.io/SubtypingPD/):
-
-**1. Pipeline #1: Clustering**
-- Data visualization
-- Preparing and adjusting algorithm inputs
-- Running the algorithm
-- Model stability tracking
-- Model performance evaluation
-
-**2. Pipeline #2: Imaging**
-- Raw MRI conversion to BIDS
-- Preprocessing with Freesurfer
- - [Small section of the pipeline in .sh](https://github.com/song-y/SubtypingPD/tree/main/brainhack_source/resources/freesurferPreproc)
-- Manipulating neuroimaging data
-
-**3. Pipeline #3: Statistical analyses**
-- Descriptive statistics
-- Statistical comparisons of cognitive and neural decline between identified clusters
-- Visualization of results
-
-[Project source code](https://github.com/song-y/SubtypingPD)
-
-## Acknowledgement
-
-A HUGE thank you to the BHS organizers for this incredibly enriching experience. A sincere thank you to everybody who contributed to making this a great, positive experience. A special thank you to Pierre-Lune and Marie for your exceptional support and dedication!!
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----
-type: "project"
-date: "2025-06-15"
-title: "BHS_trRSA_project"
-names: [Xu Peng]
-github_repo: https://github.com/pengxu1308/BHS_trRSA_project
-tags: [tr-rsa, consciousness, decoding, rsa]
-summary: "Implementing Time-Resolved Representational Similarity Analysis to Track Cortical Representation of Consciousness Report"
-image: "RSA.png"
----
-
-## Project definition
-
-### Background
-
-Understanding the neural basis of consciousness remains a central challenge in cognitive neuroscience. A widely used framework is the study of Neuronal Correlates of Consciousness (NCC)—neural signals that reliably track the presence of conscious experience. Within EEG research, two ERP components have been proposed as NCCs: Visual Awareness Negativity (VAN) and Late Positivity (LP). These are often discussed in the context of the early-vs-late debate on the temporal onset of consciousness.
-
-However, leading theories diverge in interpretation. Recurrent Processing Theory posits that consciousness arises when early visual signals are processed recurrently, not just feedforwardly. Yet this theory lacks direct EEG evidence linking such recurrent activity to VAN or LP. Meanwhile, Global Neuronal Workspace Theory suggests that consciousness emerges through a late, all-or-none “ignition” process—possibly reflected in LP—but similarly lacks precise mapping to ERP dynamics.
-
-Recent studies challenge the binary view altogether, proposing that consciousness may unfold gradually over time. For example, Colombetti et al. identified multiple components contributing independently to awareness, while Cohen et al. modeled conscious access as a graded neural representation.
-
-Given these perspectives, traditional ERP methods may be too limited to capture the full complexity of conscious processing. This project applies time-resolved Representational Similarity Analysis (RSA) to investigate whether VAN and LP reflect recurrent processing, ignition, or graded accumulation. By comparing neural and behavioral RDMs across time using whole-scalp EEG patterns, and leveraging tools from the BrainHack School RSA pipeline, this approach aims to provide new insights into the dynamics of visual consciousness.
-
-### Tools
-
-The tr-RSA project will rely on the following technologies:
- * EEG MNE-python. Most the analysis are done within MNE-python.
- * Open Science Framework. Where the data is downloaded.
- * Adding the project to the website relies on github, through pull requests.
- * The function is written in python script in vscode.
- * The environment is controlled by ANACONDA.
-
-
-### Data
-
-The data is download from [OSF](https://osf.io/xz8jr/). All the data is collected from a published paper by [Railo et al, 2021](https://onlinelibrary.wiley.com/doi/10.1111/ejn.15354).
-
-### Deliverables
-
-After completing this project, we will obtain:
-
-- Representational Dissimilarity Matrices (RDMs) of EEG data across time bins, aligned with behavioral factors.
-- Well-documented RSA pipelines for both individual and group-level analysis, including split-half reliability tests, permutation tests, and more.
-- Insights into cortical representations through time-resolved multivariate analysis.
-
-## Results
-
-### Progress overview
-
-This project employs a multivariate pattern analysis (MVPA) approach to investigate the time series of conscious experience. Specifically, trial-by-trial neural Representational Dissimilarity Matrices (RDMs) were constructed for each 50 ms time bin. Principal Component Analysis (PCA) was applied for dimensionality reduction and result visualization, followed by split-half reliability testing to assess the consistency of neural patterns over time.
-
-In parallel, three behavioral RDMs were created to capture the structure of participants’ stimulus-response relationships. Representational Similarity Analysis (RSA) was then conducted by comparing the neural and behavioral RDMs to quantify their correspondence.
-
-### Tools I learned during this project
-
- * **EEG data analysis** I learn how to deal with EEG data from 0.
- * **Representational Similarity Analysis-** Basic concepts of doing RSA.
- * **Dimension reduction method** Through this project, I compared different methods of dimensionality reduction and evaluated various options for variance retention.
-
-### Results
-
-The RSA results revealed that the neural-behavioral correlation began to increase around 250 ms after stimulus onset, particularly with respect to the subjective clarity ratings. The correlation peaked around 400 ms, followed by a slight dip, and then entered a plateau phase between approximately 600–1000 ms. Notably, nearly the entire EEG epoch exhibited significant RSA values.
-
-These findings suggest a strong relationship between neural representations and subjective clarity reports. Future analyses—especially those incorporating reaction time or additional behavioral measures—are encouraged and may benefit from consulting the original data contributor for more detailed behavioral response information.
-
-#### Deliverable 1: Neural RDMs for each subject with vairas validation method.
-
-You can find out a interactively visualization results in the Jupyter Notebooks. Futhermore, the notebooks contain validation cells that users can play around with to find different results(such as changing to 90% variance retainsion within dimension reduction.)
-
-#### Deliverable 2: Behavioral RDSm for each sunject
-
-In the notebooks, you can also find out the behavioral RDM to see the response pattern for participants.
-
-#### Deliverable 3: Individaul RSA and group-level RSA
-
-Not only the individual RSA is done but also the group level RSA is implemented to see the trend of the time series from consciousness response.
-
-## Conclusion and acknowledgement
-
-In conclusion, although the analysis was carried out successfully, the interpretation of the results remains limited due to the absence of participants’ reaction time data. Without precise behavioral response timing, it is difficult to fully understand or contextualize the RSA values.
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-07-22" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Twin normative modelling project"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Johanna Bayer]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/likeajumprope/normative_twins_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [normative_modelling, twins, python, mri]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this project I am trying to train and test a normative model on a neuroimaging data set containing twin longitudinal data. I want to look at both changes of deviations in z-scores over time and differences in z-scores between twins "
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-
-image: "images/normative.png"
-
-
----
-
-
-## Project definition
-
-### Title
-Normative modelling on structural neuroimaging data from the Queensland Twin IMaging (QTIM) data set to compare individualized deviations of twins from the norm.
-### Background
-
-Normative modelling is known from growth charts, where it describes height in form of an individualized z-score/percentile score of deviation from the norm.
-Similarly, normative models can be used in neuroimaging to describe an individual's deviation from a the normative variation of a brain measure, for example from cortical thickness.
-
-
-
-
-## Tools
-
-The "project template" project will rely on the following technologies:
- * Normative modelling using the [PCN toolkit](https://github.com/amarquand/PCNtoolkit)
- * python packaging.
- * google collab.
- * the [brainhack cloud](https://brainhack.org/brainhack_cloud/)
-
-### Data
-
-The data for this project is the [Queensland Twin IMaging (QTIM)](https://openneuro.org/datasets/ds004169/versions/1.0.5) dataset: a multimodal neuroimaging dataset of young adult twins and siblings (18-30 years, N = 1026), including a subsample of participants, scanned a second time to assess test-retest reliability (N = 78, test-retest interval ~ 3.5 months).
-
-I am going to use the derivatives from FreeSurfer parcellation that can be downloaded from the repository:
-* cortical thickness data (mm)
-* cortical surface area data (mm^2)
-* subcortical volumes data (mm^3)
-
-
-### Deliverables
-
-At the end of this project, we will have:
- - A first run of the PNC toolkit on the twin data.
- - A package to structure the data to be able to load them into toolkit.
- - Tests that are integrated into the package to test the input data.
-
-## Results
-
-I was able to run the model on an initial region the data set, for males and females separately.
-These are preliminary results:
-
-## Predictions for the thickness of the right insula cortex (mm^2):
-
-### Predictions for women:
-
-
-### Predictions for men:
-
-
-### Progress overview
-
-* the model was trained using a **2 fold cross validation**
-* the model calculates **measures of model fit**, such as:
- * explained variance
- * standardized root mean square error
- * Pearson's correlation coefficient
-
-
-### Tools I learned during this project
-
- * **Deeper understanding of the PNC toolkit** I have now a better understanding of the Python implementation, and I customized the implementation in the Jupyter book that is included in this repository.
- * **Python packaging** I wrote my first ever Python package.
- * **Python testing** I added some tests to the package, which gave me a deeper understanding of unit tests in Python.
-
-
-##### Other projects
-Here are other good examples of repositories:
-- [The Predictive Clinical Neuroscience toolkit](https://github.com/amarquand/PCNtoolkit) by Andre Marquand's group
-
-## Next steps
-* run the model on remaining brian regions
-* look at changes of z-scores over time within individuals
-* look at twin differences
-
-
-## Conclusion and acknowledgement
-I thank the brainhack school staff for all their support and hope to be able to join Brainhack school next year again!
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----
-type: "project"
-date: "2025-06-14"
-title: "Understanding Learning Trajectories in VRIT: Dynamic Behavioral and Neural Signatures of Inference"
-names: ["Tzu-Yun Kung"]
-github_repo: "https://github.com/TYK0903/Tzu-Yun_Kung_project"
-tags: [inference, learning trajectories, python, fmri]
-image: "cover.png"
-summary: "This project aims to examine how the brain supports two types of inference—active and passive—during the process of posterior belief integration. I applied trial-by-trial analysis to track participants’ learning trajectories over time."
----
-
-## Project definition
-
-### Background
-
-Inferencing is a fundamental process through which the human brain updates its beliefs about environmental causes and future states. This belief-updating can occur via passive inference, where prior beliefs and outcomes are only loosely connected, and new beliefs primarily emerge from observing associations in the environment.
-However, inference is not always passive. It can also take an active form, involving hypothesis-driven actions that manipulate contextual elements and actively test the causal links between beliefs and outcomes.
-To explore this distinction, our study aims to examine how the brain supports these two types of inference—active and passive—during the process of posterior belief integration. To this end, our lab designed the Visual Rule Inference Task (VRIT), a paradigm specifically developed to probe the neural mechanisms involved in these distinct inferential processes.
-The goal of this project is to uncover the inference processes that underlie participants’ behavior as they perform the VRIT. To do so, I analyzed both neural and behavioral data, with a particular focus on how participants engage in inference under different task conditions—namely, active versus passive inference. More specifically, I applied trial-by-trial analysis to track participants’ learning trajectories over time.
-
-
-
-### Tools
-
-The project will rely on the following technologies:
- * Python, to organize data, analyze and visualize behavior and brain data.
- * SPM, to analyze brain data.
- * Adding the project to the website relies on github, through pull requests.
-
-### Data
-
-The dataset I used in this project is from Brain and Mind Lab, Taiwan. https://gibms.mc.ntu.edu.tw/bmlab/
-
-
-### Deliverables
- - "As the study is still unpublished, this repository contains only the Python scripts I wrote and shared as part of my learning experience during the Brainhack School."
-
-## Results
-
-### Progress overview
- * 1. Use Python to organize the data.
- * 2. Use Python to analyze and visualize the behavioral data.
- * 3. Write scripts to create batches of brain image preprocessing.
- * 4. Organize the onset time and durations by Python.
- * 5. Create contrast files.
- * 6. I used Python to troubleshoot issues by analyzing beta values.
- * 7. I created visualized brain images using Python.
-
-### Tools I learned during this project
- * Python, to organize data, analyze and visualize behavior and brain data.
- * SPM, to analyze brain data.
- * Adding the project to the website relies on github, through pull requests.
-
-
-## Conclusion and acknowledgement
-From the results, we observed notable differences between the two types of inference, as well as variations in their learning trajectories. These findings suggest the need for more in-depth analyses in the future.
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----
-type: "project"
-
-date: "2025-06-12"
-
-title: "Univariate analysis on melody evaluation test"
-
-names: [Hsin-Yu Cheng]
-
-github_repo: https://github.com/SkyStream5240/Hsin-YuCheng_project
-
-tags: [auditory, fmri]
-
-summary: "The project focused on extracting activities from functional images in a previous study about neural representation of melody-transposition. Using parcellated brain atlas as a mask, the BOLD signals underwent univariate analysis to look for effects in error detection or music-like stimulus-related brain regions/"
-
-image: "image.png"
----
-
-
-## Project definition
-
-For brainhack school TW-SG 2025
-
-__bhfunc2025.py__ includes some functions are used in other code
-
-__projectVisualizeTest.ipynb__ is the main processing steps I work on extracting the activities and images from preprocessed fMRI images
-
-The exported csv files are plotted to bar graphs in __resultplotting.ipynb__
-
-### Background
-
-Inspired by the previous paper: Y.- A. Han and P.J. Hsieh Imaging Neuroscience, Volume 2, 2024 https://doi.org/10.1162/imag_a_00352. I want to understand how simple melody stimuli can induce neuronal activities in the brain.
-
-
-### Tools
-
-The __Univariate analysis on melody evaluation test__ project will rely on the following technologies:
- * jupyter notebook
- * python packages - nilearn and nibabel
- * nilearn.dataset - Spatially constrained parcellation: msdl_rois
-
-### Data
-
-Using the functional fMRI data in the previous paper: Y.- A. Han and P.J. Hsieh Imaging Neuroscience, Volume 2, 2024 https://doi.org/10.1162/imag_a_00352.
-Will not be provided when the project is uploaded.
-
-### Deliverables
-
- - comparison of activity in a set of parcellated brain regions
-
-## Results
-
-### Progress overview
-
-The preprocessed image series went through masking and activities are extracted.
-
-### Tools I learned during this project
-
- * **Github workflow**
- * **dealing with Nifti data**
- * **basic data visualization in seaborn**
-
-### Results
-
-#### Deliverable 1: Comparison of activity difference
-
-Two subjects, one run each, are analyzed to look for the activation difference in dorsal ACC, dlPFC, and a generalized auditory region.
-Expected effect are seen in one run of one subject but the other doesn't seem obvious.
-
-
-
-
-
-
-
-
-
-
-## Conclusion and acknowledgement
-
-We can see the brain activity in different regions vary a lot in different individuals, even in identical test runs. More data needs analysis for a further, more general conclusion.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Using a machine learning model trained on functional connectivity patterns to predict ADHD
-title: "Using a machine learning model trained on functional connectivity patterns to predict ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Sebastian Morales]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/sebastianmorales-lab/BHS_project_repo
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/sebastianmorales-lab/BHS_project_repo), click `manage topics`.
-# Please only lowercase letters
-tags: [python, github, machine learning, fmri]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project uses functional magnetic resonance imaging data to study the connectivity of children with Attention Deficit Hyperactivity Disorder (ADHD).A set of children diagnosed with ADHD were given a series of memory tasks while undergoing MRI scans. In this project, data from one of these tasks was used to calculate connectivity matrices for 65 subjects from that data set and a machine learning model was trained. The data was downloaded from Openneuro [website](https://openneuro.org/datasets/ds002424/versions/1.2.0)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "portada.jpeg"
----
-
-
-## Project definition
-
-### Background
- Inspired by the work of [(Hammer et al., 2025)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576365/) that looks for neurobiological markers to differentiate a population of children with ADHD from those without the disorder. In this study, four memory tasks were applied while a group of 20 children with ADHD and 20 healthy controls underwent functional magnetic resonance imaging (fMRI) scans and a machine learning model was fitted. fMRI data from the four VSWM tasks enabled a classification accuracy of 92.5%, with high predicted ADHD probability values for most clinical cases. The project was attempted through a simple machine learning model, using the tools learned during the brainhack to predict a trait such as age from the connectivity matrices.
-
-
-
-### Tools
-
-For this project, several tools were used, such as:
- * Markdown, to structure the text.
- * Git and Github,to maintain repositories
- * Pyton for programming the analysis
- * jupyter-Notebook to analysis and visualization
- * Nibabel to fmri data processing
- * Nilearn to conectivity analysis
- * scikit-learn: machine learning in Python
- * Numpy and Pandas to structure the data
- * Adding the project to the website relies on github, through pull requests.
- * Openneuro to download the data set
-
-### Data
-
-The data was downloaded from Openneuro [website](https://openneuro.org/datasets/ds002424/versions/1.2.0)
-James R. Booth and GE Cooke and Jessica Gayda and Rubi Hammer and Marisa N. Lytle and MA Stein and Michael Tennekoon (2021). Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD). OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds002424.v1.2.0
-
-### Deliverables
-
-At the end of this project, we will have:
-
-- The current markdown document.
-
-- A repository with the dataset .
-
-- The jupyter notebook with data analysis and visualisation
-
-## Results
-
-### Progress overview
-
-### Overview of progress
-This project was developed solely for the purpose of learning new techniques applicable to magnetic resonance imaging. It was carried out exclusively for the Brainhack school and is not directly related to my line of research. It is a first approximation to an analysis of this kind and therefore it is a simple project with several aspects to improve. It is hoped that feedback from the community will lead to further rapid improvements to this first version.We split the data into a training set and a test set to train a machine learning model. We use the SVR model to try to predict the age of the children based on their functional connectivity matrices. We did not achieve a model that could effectively distinguish diagnosis.
-We tried to fit a model to predict the age of children.
-
-
-### Tools I learned during this project
-
-* **Project-Management** During brainhack school I learn how to manage a project, think about its different parts and the tools that would be useful.
-* **Github workflow** I learned how to use git and github to manage directories, organise files and be able to do collaborative work.
-* **python for data science** Through branhack school I learned to use python and its various libraries for MRI data analysis. I usually perform resonance analysis using SPM (Matlab), and incorporating these tools is very useful for my academic future.
-* *** course content *** much of the course content is new to me, from connectivity, to installing packages, to using machine learning.
-### Results
-
-#### Deliverable 1: Markdown document
-Generate a marckdown file in order to submit it to the school web repository.
-
-#### Deliverable 2: Report directory
-
-You can access all the data and analysis done during the project in [my public repository](https://github.com/sebastianmorales-lab/BHS_project_repo/).
-
-
-#### Deliverable 3: Project analysis
-#####Correlation connectivity matrix
-
-I calculate the correlation matrices per subject. The presence of two large clusters seems to be observed. One encompassing regions such as the frontal pole, the posterior occipital region, the anterior cingulate cortex and others.
-Another cluster comprises regions such as dorsolateral prefrontal cortex, posterior temporal region, auditory and motor regions. Activations in visual and motor regions may be due to the characteristics of the task presented.
-The one subject matrix is presented for ejample
-
-#####Classification
-######Using the classifier to see if I can predict the children ages looking at the connectivity matrices.
-
-First upload the atlas, and thus visualise the regions of interest
-
-extract the timeseries from the ROIs in the atlas and plotting the he feature matrix 
-
-the feture matrix is what the model was trained with.
-We want to predict age by plotting the age distribution. Only session 1 was taken because not all subjects had a second session.
-
-
-See the age distribution.
-#####Machine Learning Model
-
-Run the Machine Learning model
-
-As this is counted as a small number of participants, we decided on a standard regression model called Support Vector Regressor (SVR). This model usually gives robust results.
-
-
-
-accuracy (R2) = -0.15458416133781716
-MAE = 0.884920224737749
-
-
-The model does not fit well, there is a low accuracy and a rather high Mean Absolute Error.
-We tried to do some manipulations like cross-validation but did not get any better settings.
-
-##### Can our model predict the ages from the functional connectivity matrices?
-
-accuracy (r2) = -0.15576602592786326
-
-mae = 0.8901443045170564
-
-
-
-
-
-
-## Conclusion and acknowledgement
- The model has not been efficient in predicting the age of children using the functional connectivity matrices. It is likely that there are not many differences in the functional connectivity of children between the ages of 8 and 13. However, it is also likely that the model used is not able to distinguish efficiently and another model should be used or the hyperparameters should be adjusted. The dataset used consisted of only 65 subjects, for age prediction no distinction was made between children who received the diagnosis and those who did not.
-Further research is needed in the search for neural markers of Attention Deficit Hyperactivity Disorder (ADHD).
-
-I would like to thank Brainhack School for these weeks of intense training and learning, and the organisers for this initiative. I think this school is an important experience that strengthens the ties between the neuroscience community.
-
-### References
-Hammer R, Cooke GE, Stein MA & Booth JR (2015). Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder. Neuroimage – Clinical, 9, 244-252. (PMCID: PMC4576365)
-
-James R. Booth and GE Cooke and Jessica Gayda and Rubi Hammer and Marisa N. Lytle and MA Stein and Michael Tennekoon (2021). Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD). OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds002424.v1.2.0
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Fine-Tuning a Video Large Language Model (Vid-LLM) for Automatic Annotation of Gameplay"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Vincent Chamberland, Renaud Dagenais]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/vchamberland/VidGame-LLM
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [video LLM, automatic annotation, video games, cognitive neuroscience]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to develop a preprocessing pipeline to fine-tune a Video Large Language Model (Vid-LLM) for automatic annotation of gameplay recordings in cognitive neuroscience studies. Leveraging the Gym Retro ecosystem and the Courtois NeuroMod dataset, we convert event logs into video format and generate detailed annotations and timestamps to train the Vid-LLM. Deliverables include cleaned datasets, documentations and Jupyter notebooks."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "sub-01_ses-003_task-shinobi_run-01_level-1_rep-01_end_frame.png"
----
-
-
-## Project definition
-
-### Background
-
-Video games provide a controlled and rich environment to observe and modulate human behavior, and have been used in various research fields, such as cognitive neuroscience, psychology, and human-computer interaction. However, analyzing gameplay recordings can be time-consuming and labor-intensive, as it often requires manual annotation of player actions and events, which can be error-prone, subjective, and challenging to scale, especially when dealing with large datasets or complex game dynamics ([Harel et al., 2023](https://osf.io/preprints/psyarxiv/uakq9)).
-
-Recently, a growing number of large language models (LLMs) have been specialized for video understanding (Vid-LLMs), where they can generate detailed descriptions of video content, such as actions, objects, and events ([Tang et al., 2023](https://arxiv.org/abs/2312.17432)). These models can be fine-tuned on specific tasks to improve their performance and adapt them to new domains. For example, a model fine-tuned with sports videos can be used to analyze player performance, develop gameplay strategies, and provide detailed commentary.
-
-### Aims
-Therefore, this project aims to develop a preprocessing pipeline to fine-tune a Vid-LLM for automatic annotation of gameplay recordings in order to reduce manual annotation and increase efficiency.
-
-### Brainhack objectives
-1. Gain experience with the Gym Retro ecosystem and its Python API for video game analysis
-2. Gain experience in manipulation of video data (e.g., conversion, extraction, and processing)
-3. Gain experience with project and version management tools (e.g., Git, DataLad)
-4. Gain experience in Python scripting (e.g., dataframes, computer vision, automation)
-5. Gain experience with LLM training and testing using video data
-
-### Dataset
-The dataset is provided by the [Courtois NeuroMod project](https://www.cneuromod.ca/). It includes behavioral data recorded during gameplay of the retro video game "Shinobi III - Return of the Ninja Master", specifically capturing the movements and actions taken by three subjects. Each subject participated in four gaming sessions, with each session consisting of approximately a dozen runs. For the purposes of this project, we will focus on a subset of this dataset: analyzing runs from a single participant, specifically those in which they played level 1 of Shinobi [i.e., 89 runs].
-
-### Tools
-
-- Python scripting tools & packages
- - Jupyter notebooks
- - Gym Retro’s Python API (RetroEnv)
- - Dataframe handling (e.g., pandas, numpy)
- - Computer vision (e.g., OpenCV, moviepy)
- - Machine Learning requirements for the Vid-LLM model (pytorch, CUDA, etc.,)
-- Project management & versioning
- - Version controlling (Git, Github, Datalad)
-
-## Deliverables (Week 4 results)
-- Cleaned-up event dataset (```data/datasets```)
-- Formatted question-answers json file for supervised fine-tuning of the Vid-LLM (```data/json_files/custom.json```)
-- Jupyter notebooks for data preprocessing and exploration:
- - Dataset cleaning (```notebooks/dataset_preprocessing.ipynb```)
- - Generating training videos from all Gym Retro's bk2 files (```notebooks/training_video_generator.ipynb```)
- - Generating custom question-answers json file (```notebooks/custom_jason_generator.ipynb```)
- - Generating short testing videos (```notebooks/testing_video_generator.ipynb```)
- - Generating a single short testing video (```notebooks/single_testing_video_generator.ipynb```)
- - Generating specific frames and gifs from selected videos (```notebooks/frame_gif_generator.ipynb```)
- - Finding specific frames in a video (```notebooks/frame_finder.ipynb```)
-- Documentation on the notebooks (```doc/notebook_doc.md```)
-- Detailed documentation on how to download the raw dataset (```doc/how-to-download-raw-data.md```)
-- Examples of training and testing videos, frames and gifs (```output/```)
-- requirements.txt file for the project (```requirements.txt```)
-
-## Collaborators
-* The Pierre/Lune Bellec Lab
-* The Courtois NeuroMod Project
-
-## References
-- Harel, Y., Pinsard, B., Boyle, J. A., Cyr, A., Clei, M. L., Mignot, P.-H., St-Laurent, M., Jerbi, K. and Bellec, P. (2023). Gamer in the scanner : Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content. https://doi.org/10.31234/osf.io/uakq9
-- Tang, Y., Bi, J., Xu, S., Song, L., Liang, S., Wang, T., Zhang, D., An, J., Lin, J., Zhu, R., Vosoughi, A., Huang, C., Zhang, Z., Zheng, F., Zhang, J., Luo, P., Luo, J. and Xu, C. (2023). Video Understanding with Large Language Models: A Survey. arXiv. https://doi.org/10.48550/arxiv.2312.17432
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-05-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Working Memory in Children with and without ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Wei-Chen Chang, Shan-San Huang]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/wcnoname5/BHS2023_Project_ADHD
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/wcnoname5/BHS2023_Project_ADHD), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, fmri, connectivity, behavioral]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to using both fMRI data and the behavioral data during a n-back task to compare the difference between ADHD children and the heathly control ones."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "sub-03_conn_95.png"
----
-
-
-## Project definition
-
-### Background
-
-Attention deficit hyperactivity disorder (ADHD) is characterized by inattention, impulsivity, and hyperactivity (American Psychiatric Association, 1994). ADHD is a relatively common disorder in children, but with high diagnostic heterogeneity (Hammer et al., 2015).
-In past researches, ADHD is found to associated with an impaired function of the frontal lobe (Rubia et al., 1999). Furthermore, researchers have found neuroimaging-based biomarkers of ADHD using MRI (Johnston et al., 2014; Lim et al.). Thus we want to use fMRI data from open source data to analyze the difference between ADHD and the healthy childrens.
-
-### Tools
-
- The "ADHD" project would have two parts of analysis, behavioral data and funtional images.
- * `datalad`, which is used for download the dataset.
- * Some simpe `bash` scripts used to fetch the data in interest.
-
- For behavioral data analysis, the following tools was used:
- * `pandas` is used to cleaning and reconstructing the data.
- * `statsmodels.api` is used to perform statistical analysis (2-way ANOVA).
- * `matplotlib` and `Seaborn` are used to visualize the data.
-
- And for fMRI data analysis, the following tools was used:
- * `fmriprep`, a python-based package is used to preprocessing the functional images.
- * `nilearn`, which is used to visualize the brain images and analyze the connectivity
-
-### Data
-
-The dataset is from OpenNeuro
-[Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD)](https://openneuro.org/datasets/ds002424/versions/1.2.0), and is fetched using `Datalad`. The files structures is saved in `data/ds002424/` which is connected to Github site of the original study.
-
-### Results
-
-#### Deliverable 1: bash scripts
-There're 3 bash scripts helps to deal with the data, which are:
- * `download_anat.sh` is used to get all the interested anatomical images in current project via `datalad`.
- * `download_VLD_VLI.sh`: is used to get all the interested functional images via `datalad`.
- * `fmriprep_singSubj.sh`: is used to perform the fMRI preprocessing for single subject via `fmriprep-docker`. This script is actually downloaded and modified from the [Andy's Brain Book's tutorial](https://github.com/andrewjahn/OpenScience_Scripts).
-
-#### Deliverable 2: behavioral data analysis
-In the `Behavioral_Analysis` folder, there're files including:
- * `Behavioral_Analysis.ipynb`: a jupyter notebook go through the behavioral data analysis we've performed.
- * Lots of `.csv`s: the behavioral data used during analysis.
- * Lots of`.png`s: the results for statistical analysis and the visualization.
-
-#### Deliverable 3: fMRI analysis results
-In the `fMRI_Analysis` folder, there're files including:
- * `Analysis.ipynb`: a jupyter notebook that go through the fMRI data analysis, including visualiztion, connectivity, and connectomes.
- * `result_figs/`: A directory that stores all the result figures.
-
-## Conclusion and acknowledgement
-
-In the beginning, we want to build a model to classify the ADHD and healthly controls using the machine learning technique. But the preprocessing of the fMRI data took longer than we expected. (We've spending loads of time dealing with countless problems, and eventually find out how to preprocess the fMRI data with `fmriprep`.
-
-At that point, we’re running out of time, so we decided to look into the connectivity to see if there is any difference between the ADHD child and Healthy control. Also we have practiced our skills on drawing plots with Python, like using `matplotlib` and `Seaborn`.
-For future, we could try out and apply machine learning technique on the dataset.
-
-Thanks all the TAs and Teachers and students all contributing to Brainhack School!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Using fMRI Data to Predict Autism Diagnoses with Various Machine Learning Models and Cross-Validation Methods"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Emily Chen, Andréanne Proulx, Mikkel Schöttner]
-
-# Your project GitHub repository URL
-github_repo: "https://github.com/brainhack-school2020/abide-fmri"
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [brainhack-school, abide-data, resting-state-fmri, machine-learning, reproducible-science, project-management, open-science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Is autism associated with a distinct neurofunctional signature? If so, how accurately are we able to predict the diagnosis based on fMRI data? In this project, we set out to compare different machine learning models and cross-validation methods to see how well each one was able to predict autism from resting state fMRI data in the ABIDE dataset."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover-image.png"
----
-
-
-## Project Definition
-
-### Personal Backgrounds
-
-#### Emily [(personal repository)](https://github.com/brainhack-school2020/emilyemchen-EEGML)
-Hello! I am an (incoming) fourth year undergraduate student at McGill University studying computer science and urban health geography with a minor in cognitive science. I am a research assistant with Isabelle Arseneau-Bruneau in the Zatorre Lab and have been learning a lot about neuroscience research while (hopefully) lending some of my technical skills to Isabelle's PhD work exploring the effect of musical training on FFR.
-
-When joining the BrainHack School, I was looking forward in particular to working on a project at the intersection of neuroscience and CS because my previous classes rarely focused on the application of the abstract concepts we learned. Upon completion of the BrainHack School, I had the opportunity to learn from and collaborate with talented neuroscience researchers and participants, write a machine learning script using python and neuroscience data, and gain hands-on practice in reproducibility efforts and good project management.
-
-You can find me on GitHub at [emilyemchen](https://github.com/emilyemchen) and on Twitter at [@emilyemchen](https://twitter.com/emilyemchen).
-
-#### Andréanne [(personal repository)](https://github.com/brainhack-school2020/anproulx-fMRI-autism)
-
-Hi! I am an incoming master's student in Psychology at the University of Montreal. My background is in cognitive neuroscience and my career objective is to work on research projects aiming to discover new ways of characterizing the brain in its pathological states. At the moment, I am working with Sébastien Jacquemont and Pierre Bellec, and the focus of our research is on investigating the effect of genetic mutations on functional and structural brain phenotypes. More precisely, I have been working with resting-state functional connectivity measures in carrier populations with developmental disorders.
-
-By joining the Brainhack School, I hoped to strengthen my computational skills and my knowledge in the applications of machine learning to the field of neuroimaging. Not only did I get to work on a machine learning problem specific to my field, but I also got to learn about useful tools such as GitHub. More importantly, I also met a community of brilliant/aware researchers and got to collaborate with other students, even across the world!
-
-#### Mikkel [(personal repository)](https://github.com/brainhack-school2020/mschoettner_fMRI-ML)
-
-Hey! I am a master's student in Cognitive Neuroscience at the University of Marburg. My background is in psychology. I am especially interested in the social brain and how humans infer thoughts and emotions from the actions of others. Right now, I am doing my Masters thesis on how the capacities of Theory of Mind and empathy interact in the brain using fMRI. I am also very much interested in research methods and how we can use statistics and data science techniques on neuroimaging data.
-
-When getting into Brainhack School, I hoped to get a better understanding of machine learning, and how it is useful for analyzing fMRI data. I learned a lot about collaborative project organization, how to make pretty interactive plots and how to write better Python scripts. Brainhack School was really an awesome experience getting to know a lot of talented and motivated people and being able to collaborate with them across the Atlantic!
-
-You can find me on GitHub at [mschoettner](https://github.com/mschoettner) and on Twitter [@mikkelschoett](https://twitter.com/mikkelschoett).
-
-***
-
-### Tools
-
-We expected to use the following tools, technologies, and libraries for this project:
-
-* Git
-* GitHub
-* Visual Studio Code
-* Docker
-* Jupyter Notebook
-* HPC/Compute Canada
-* Python libraries: `numpy`, `pandas`, `matplotlib`, `scikit-learn`, `nilearn`, `seaborn`, `pyplot`, `pyplot`
-* `venv`
-
-***
-
-### Data
-
-The goal of this project was to compare different machine learning models and cross-validation methods and see how well each is able to predict autism from resting state fMRI data. We used the preprocessed open source [ABIDE database](http://fcon_1000.projects.nitrc.org/indi/abide/), which contains structural, functional, and phenotypic data of 539 individuals with autism and 573 typical controls from 20 different research sites.
-
-***
-
-### Deliverables
-
-By the end of The BrainHack School, we aimed to have the following:
-* `README.md` file
-* `requirements.txt` file that outlines the packages needed to run the script
-* Jupyter notebooks with code and explanations
-* GitHub repository documenting project workflow
-* Presentation showing project results
-
-## Project Results
-
-
-
-### Project Background
-
-Several studies have found an altered connectivity profile in the default mode network of subjects with Autism Spectrum Disorder, or ASD (Anderson, 2014). Based on these findings, resting state fMRI data have been used to predict autism by training a classifier on the multi-site ABIDE data set (Nielsen et al., 2013). This project's scientific aim is to replicate these findings and extends the literature by comparing the effects of differing cross-validation methods on various classification algorithms.
-
-### Becoming a Team
-The three of us joined forces when we realized that we shared many similar learning goals and interests. With such similar project ideas, we figured we would accomplish more working together by each taking on a different cross-validation methods to train various machine learning models.
-
-***
-
-### Team Project Management
-
-We all shared a common interest in making our project as reproducible as possible. This goal involved creating a transparent, collaborative workflow that could be tracked at any time by anyone. To achieve this objective, we utilized the many features that GitHub has to offer, all of which you can see in action at our shared repository [here](https://github.com/brainhack-school2020/abide-fmri).
-* **Branches:** used to work simultaneously on our own parts and then push changes to the master branch
-* **Pull requests:** created when making changes to the master branch
-* **Issues:** used to communicate with each other and keep track of tasks
-* **Tags:** used to keep issues organized
-* **Milestones:** used to keep our main goals in mind (Week 4 presentation and final deliverable)
-* **Projects:** used to track various aspects of our work
-
-***
-
-### Standardized Data Preparation and Jupyter Notebooks
-
-The data are processed in a standardized way using a Python script that prepares the data for the machine learning classifiers. Several Jupyter notebooks then implement different models and cross-validation techniques which are described in detail below.
-
-
-
-
-***
-
-### Tools, Technologies, and Libraries Learned
-
-Many of these contribute to open science practices!
-
-* **Jupyter Notebook:** Write code in a virtual environment
-* **Visual Studio Code:** Edit files such as `README.md`
-* **Python libraries:** `numpy`, `pandas`, `nilearn`, `matplotlib`, `plotly`, `seaborn`, `plotly`, `sklearn` (dimensionality reduction, test-train split, gridsearch, cross-validation methods, evaluate performance)
-* **Git:** Track file changes
-* **GitHub:** Organize team workflow and project
-* **Machine learning:** Apply ML concepts and tools to the neuroimaging field
-* **`venv`:** Make `requirement.txt` file for a reproducible virtual environment
-
-***
-
-### Deliverables
-
-#### Deliverable 1: Jupyter notebooks (x3)
-
-* ##### Leave-site-out cross-validation
-
-[*`leave-site-out-cv_classifier.ipynb`*](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/leave-site-out-cv_classifier.ipynb)
-
-This notebook contains code to run a linear support vector classification to predict autism from resting state data. It uses leave-group-out cross-validation using site as the group variable. The results give a good estimate of how stable the model is. While for most of the sites the prediction works above chance level, for some, autism is predicted only at or even below chance level.
-
-* ##### K-fold and leave-one-out cross-validation
-
-[*`kfold-leave-one-out-cv_classifier.ipynb`*](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/kfold-leave-one-out-cv_classifier.ipynb)
-
-This notebook contains the code to run linear support vector classification, k-nearest neighbors, decision tree, and random forest algorithms on the ABIDE dataset. The models are trained and evaluated using k-fold and leave-one out cross-validation methods. We obtain accuracy scores that represent how skilled the model is at predicting the labels of unseen data. Leave-one out cross validation gives more accurate predictions than kfold cross validation. The accuracy values range from 55.8% to 69.2%.
-
-* ##### Group k-folds cross-validation
-
-[*`group-kfolds-cv_classifier.ipynb`*](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/group-kfolds-cv_classifier.ipynb)
-
-This notebook implements a 10-fold iterator variant to split the data into non-overlapping groups. It then runs four classification algorithms on the data: linear support vector machines (`LinearSVC`), *k*-nearest neighbors (`KNeighborsClassifier`), decision tree (`DecisionTreeClassifier`), and random forest (`RandomForestClassifier`). The most accurate classifier, `LinearSVC`, has an average accuracy score for all models of 63.5%, while the least accurate classifier, `RandomForestClassifier`, has an average accuracy score for all models of 52.6%. This latter percentage is not far off from the average accuracy scores of the other two classifiers. These scores are not particularly impressive, and one reason could be the data that this model is being trained on. Each site had differing processes, methods, and amounts of data they collected, so the lack of similarity could suggest less widespread generalizability. Future work into optimizing the parameters would be the logical next step.
-
-##### Table 1: Group 10-fold cross validation average accuracy scores (percentages) per classifier algorithm
-| Algorithm | Average Score | Maximum Score | Minimum Score |
-| ----------|---------------|---------------|-------------- |
-| `LinearSVC` | 63.5% | 72.0% | 52.3% |
-| `KNeighborsClassifier` | 55.2% | 66.7% | 48.7% |
-| `DecisionTreeClassifier` | 54.3% | 61.6% | 44.9% |
-| `RandomForestClassifier` | 52.6% | 60.5% | 39.7% |
-
-##### What were the overall results?
-
-
-#### Deliverable 2: [`prepare_data.py`](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/prepare_data.py) script
-
-This script
-
-* downloads the data
-* extracts the time series of 64 regions of interest defined by the BASC brain atlas
-* computes the correlations between time series for each participant
-* uses a principal component analysis for dimensionality reduction
-
-To fetch and prepare the data set you can call the `prepare_data.py` script like this:
-
-`./prepare_data.py data_dir output_dir`
-
-or alternatively:
-
-`python prepare_data.py data_dir output_dir`
-
-where
-* `data_dir` is the directory where you want to save the data or have it already saved and
-* `output_dir` is the directory where you want to store the outputs the script generates.
-
-The notebooks also call the `prepare_data` function from the preparation script.
-
-#### Deliverable 3: [`requirements.txt`](https://github.com/brainhack-school2020/abide-fmri/blob/master/requirements.txt) file
-
-This file increases reproducibility by helping to ensure that the scripts run correctly on any machine. To make sure that all scripts work correctly, you can create a virtual environment using pythons built-in library `venv`. To do this, follow these steps:
-
-1. Clone repo and navigate to folder in a shell
-2. Create a virtual environment in a folder of your choice: `python -m venv /path/to/folder`
-3. Activate it (bash command, see [here](https://docs.python.org/3/library/venv.html) how to activate in different shells): `source /path/to/folder/bin/activate`
-4. Install all necessary requirements from requirements file: `pip install -r requirements.txt`
-5. Create kernel for jupyter notebooks: `ipython kernel install --user --name=abide-ml`
-6. Open a jupyter notebook: `jupyter-notebook`, then click the notebook you want to run
-7. Select different kernel by clicking *Kernel -> Change Kernel -> abide-ml*
-8. Run the code!
-
-#### Deliverable 4: Data visualizations (Week 3)
-
-* [Emily's GitHub Repository](https://github.com/emilyemchen/bhs2020-dataviz)
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_age.html) for the website.
-
-
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_fiq.html) for the website.
-
-
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_viq.html) for the website.
-
-
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_piq.html) for the website.
-
-
-
-* [Andréanne's GitHub Repository](https://github.com/brainhack-school2020/anproulx-fMRI-autism)
-
-Click [here](https://anproulx.github.io/publication_website/) for the website.
-
-
-
-Click [here](https://anproulx.github.io/cross_validation_plots/) for the website.
-
-
-
-* [Mikkel's GitHub Repository](https://github.com/brainhack-school2020/mschoettner_fMRI-ML)
-
-Click [here](https://mschoettner.github.io/brainhack_visualization/) for the website.
-
-
-
-#### Deliverable 5: Presentation (Week 4)
-
-The presentation slides can be viewed [here](https://www.canva.com/design/DAD-ByEQaXI/QLgHbYgnKd-xDWJXVnaGDA/view) on Canva, which is the platform we used to create the slides. A video of our presentation can be viewed on this project page. We presented our work to The BrainHack School on June 5, 2020 using the RISE integration in a Jupyter notebook, which can be found [here](https://github.com/brainhack-school2020/abide-fmri/tree/master/presentation).
-
-## Conclusion and Acknowledgement
-
-First and foremost, we would like to thank our Weeks 3 and 4 peer clinic mentor and co-organizer of the Brainhack School, Pierre Bellec. A special thank you as well to our Week 2 mentors Désirée Lussier-Lévesque, Alexa Pichet-Binette, and Sebastian Urchs.
-
-Thank you also to The BrainHack School leaders and co-organizers Jean-Baptiste Poline, Tristan Glatard, and Benjamin de Leener, as well as the instructors and mentors Karim Jerbi, Elizabeth DuPre, Ross Markello, Peer Herholz, Samuel Guay, Valerie Hayot-Sasson, Greg Kiar, Jake Vogel, and Agâh Karakuzu.
-
-## References
-
-1. Choosing appropriate estimators with `scikit-learn` https://scikit-learn.org/stable/tutorial/machine_learning_map/
-
-2. Benchmarking functional connectome-based predictive models for resting-state fMRI (for classification estimator inspiration) https://hal.inria.fr/hal-01824205
-
-3. Getting the data using `nilearn.datasets.fetch` https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_abide_pcp.html
-
-4. Python Data Science Handbook GitHub and website https://github.com/jakevdp/PythonDataScienceHandbook
-
-5. BrainHack School course materials and lectures https://github.com/neurodatascience/course-materials-2020
-
-**Scientific articles:**
-
-Anderson, J. S., Patel, V. B., Preedy, V. R., & Martin, C. R. (2014). Cortical underconnectivity hypothesis in autism: evidence from functional connectivity MRI. Comprehensive Guide to Autism, 1457, 1471.
-
-Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., ... & Anderson, J. S. (2013). Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in Human Neuroscience, 7, 599.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-14" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Functional Brain Activation During a Memory Encoding and Retrieval Task: Discovering Tools and Techniques for Analysis"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Stella Ruddy]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/Ruddy_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [task-related activation, fmri, memory, open-science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to explore tools and techniques used to analyze fMRI brain activation at the first level using data from an open-access dataset. We produced a comprehensive Jupyter Notebook that provides a step-by-step guide to running the analyses, including applying the GLM, defining contrasts, and generating various brain activation maps."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_cover.gif"
----
-
-
-I am a PhD student in clinical neuropsychology (research and intervention stream) at the Université de Montréal. I completed my undergraduate studies in Psychology with a concentration in Neuroscience. I am interested in investigating the neurobiological basis of cognitive training gains and whether it shares similar underlying neurobiological mechanisms with cognitive reserve. Brainhack School 2025 provides an exciting first opportunity for me to delve into neuroimaging analysis using task-based fMRI. Prior to Brainhack school, I had no experience in neuroimaging analyses nor coding.
-## Project definition
-
-### Background
-
-Studies have shown that there are numerous factors that might account for up to 40% of cases of Alzheimer’s disease, such as those pertaining to cognitive engagement and cognitive reserve (CR) proxies (Livingston et al., 2020). Thus, gaining a better understanding of cognitive interventions and protective mechanisms, such as CR, that may alter the cognitive aging trajectory to prevent or delay the onset of neurocognitive disorders in older age has become a priority. Shedding light on the underlying neurofunctional mechanisms put into action following cognitive interventions and the interplay with CR proxies will ultimately promote the development of better, more personalized, cognitive interventions and uncover whether they can be useful for the development of CR, even in older adults.
-
-Functional Magnetic Resonance Imaging (fMRI) represents an important technique for discerning functional changes over time and between populations. fMRI is the main technique used in previous research to investigate both the neurofunctional basis of CR and effects of cognitive training gains (Pappalettra et al., 2014), and has been shown to be sensitive to chronological age, thus qualifying it as a potential biomarker of aging and neurodegenerative disease (Belleville & Bherer, 2012).
-
-#### ENGAGE Study
-
-My doctoral project is on the ENGAGE study, which was a randomized controlled, double-blind preference trial with a comprehensive cohort design that aimed to test the efficacy and long-term impact of an intervention that combines cognitive training and cognitively stimulating leisure activities in older adults with subjective cognitive decline. More information can be found in the study protocol (Belleville et al., 2019). The objectives of my thesis will be to explore the neurofunctional basis of a multi-faceted cognitive training and cognitive stimulation intervention (ENGAGE) and the relationship between CR and task-related brain activation.
-
-## Brainhack Objectives
-The primary objective of my Brainhack School project was to analyze fMRI activation data from one open-access subject. This project consists of a first exposure and learning opportunity for neuroimaging analyses and coding, which will be useful for future analyses using data from the ENGAGE study.
-
-### Specific Objectives
-Skills development (coding, visualization, open science)
-Modeling (GLM, contrasts, activation maps)
-Conceptual notions pertaining to data handling (BIDS format, preprocessing tools)
-
-### Tools
-
-* GitHub: Sharing the project via a public repository
-* Jupyter Notebook/Python code: Code writing and documentation, narratives
-* Nilearn: GLM fitting (subsequent memory paradigm), statistical contrasts, and brain visualizations
-
-### Data
-We used raw data from an open-access dataset (OpenNeuro ds003789). We used snapshot 2.0.0, corresponding to Git commit 26d526316b558d23eea68db5cd48736950fa22ca, which follows the BIDS 1.0.1 specification and can be downloaded using Datalad. Though fMRIPrepped data is available for this dataset, it is not complete, in that it did the steps of head motion correction and coregistration, but not of spatial normalization to MNI space. Therefore, we were unable to do any second-level (group-level) analyses.
-
-This specific dataset was chosen because the task resembles that used in the ENGAGE study. The task used in the current study was composed of two phases: 1) encoding and 2) retrieval. In the first phase of the task, participants were asked to remember 9 lists of words, each presented 3 times. In the retrieval phase, which took place 24 hours later, the participants were presented with a series of words and indicated whether or not the word was presented in the lists shown on the previous day. We analyzed brain activations during memory encoding as participants were shown the lists for the first and third time (rep1 and rep3). The figure below shows a more detailed description of the task.
-
-### Deliverables
-- Markdown folder for project description
-- Creation of a GitHub repository to share my results
-- Comprehensive Jupyter Notebook with narratives
-- Memory-related brain activation maps
-- Bonus: interactive brain maps
-
-## Results
-The repository of this project, with the complete Jupyter Notebook and interactive map, can be found [here](https://github.com/brainhack-school2025/Ruddy_project).
-We created a Jupyter Notebook with narratives explaining step-by-step the analysis process, which can be found in my GitHub repository. The Notebook includes not only the code, but also descriptions of each conceptual step and provides some theoretical background to key concepts to help guide the reader.
-
-The steps include:
-
-* Importation of libraries, accessing the data, “preprocessing”
-* Familiarization with the data: display of anatomical and mean functional images, events files
-* Specify and apply the GLM, design matrix, expected BOLD signal
-* Definition and computation of the contrasts
-* Extraction of different brain activation maps (+ interactive map)
-* Extraction of predicted and observed time series
-* Exploration of statistical tests and next steps, reporting of results
-
-#### Example activation map
-The image below shows a z-map when we apply the contrast rep1 > rep3. Therefore, here, we see regions that are more active during the first presentation of the word list stimuli compared to the activity during the third presentation of the word lists. We also chose to look at specific coordinates based on the clusters table.
-
-
-
-These results remain exploratory and should be interpreted with caution as they are based on a single participant across nine runs and have not been corrected for multiple comparisons. In addition, the images have not been properly preprocessed. These preliminary results seem to align with the hypothesis that there would be more activation during rep1 than rep3 in regions involved in memory encoding, such as the Prefrontal Cortex, perhaps because the participant is generating memory strategies to memorize the words. When the list is presented for the third time, the subject may be more familiar with the word list, and therefore less effort is required to encode the information at that point.
-An interactive visualization of this map is available in my repo.
-
-#### A note on open science best practices
-Through this project, I developed a deeper appreciation for open science practices, many of which are underemphasized in formal training. I now better recognize their critical role in ensuring transparency, collaboration, and equitable access to scientific knowledge. I look forward to working to integrate these principles into my future work, whether through open-access tools, publicly shared code, or accessible community-oriented knowledge translation efforts.
-In line with best practices in open-science, we used the following tools:
-
-* GitHub: Sharing the project via a public repository, including the complete Jupyter Notebook, interactive image, and complete references to the version of the dataset used. More generally, by exploring this tool, I see potential for collaborations and sharing information/code/results with a larger audience.
-
-* Python: Learning to code using Python allows for the project to be more reproducible by automating the commands, as opposed to manually executing steps through graphical user interfaces, which are harder to track, share, and replicate.
-
-* Jupyter Notebook with narratives: The narratives provide context, which helps others follow along exactly what we did and to detect potential errors in my code or approach. This allows for full transparency and minimizes the "black box" science.
-
-* BIDS format: Though we did not directly work to organize the files in BIDS format, I became familiar with this structure, which I now see as being very important for standardization, compatibility with other tools and collaborators, transparency and reusability.
-
-## Conclusion
-Due to only using a limited amount of data from one subject that was not fully preprocessed, the results of this project are not significant in terms of scientific advancement. However, completing this project over the intensive course of four weeks was a great first learning opportunity and exposure to these tools. I look forward to continuing to build on my skills acquired during Brainhack School and apply them to my own projects.
-#### Next Steps
-Though this project focused on acquiring basic competencies for single-subject (first-level) fMRI activation analyses with open-access data, I also acquired some experience and exposure to a series of tools and theoretical concepts that I envision will be useful in the future when working with the ENGAGE study data.
-
-- Docker: Containerized environment for running neuroimaging tools (BIDS-validator, fMRIPrep)
-- BIDS-validator: Ensures raw data complies with BIDS format
-- fMRIPrep: Standardized preprocessing pipeline for fMRI data
-
-
-A heartfelt thanks to all those involved for this rich learning experience and growing opportunity.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Age-Dependent EEG patterns for Predicting Treatment Response in ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Tianyi/Amanda Liu, Ingrid Campbell]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/age_eeg_pattern
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, eeg, age, prediction]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project investigates whether there are age-dependent EEG patterns for individuals with ADHD and whether these patterns can predict neurofeedback treatment response. Using the ADHD samples from TDBrain database (n=204), we developed a random forest model to characterize age-related EEG biomarkers and assess treatment prediction across different age groups. Our model achieved AUC=0.865, identifying key EEG signatures including theta-beta ratios and frontal low-frequency patterns that vary with age and treatment response."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "eeg_adhd.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental and psychiatric disorder that exhibits significant age-related changes throughout development. Electroencephalography (EEG) has emerged as a powerful neuroimaging technique for extracting neural features and patterns associated with ADHD, such as theta-beta ratios and alpha peak frequency. While machine learning approaches have shown promise for age prediction using neural data, there remains a critical gap in developing treatments based on EEG patterns and age-specific considerations for individuals with ADHD.
-
-Current research has established that EEG biomarkers change across the lifespan, but the relationship between these developmental patterns and treatment response remains poorly understood. Therefore, this project aims to address the question: How can we use EEG patterns to predict treatment responses for individuals with ADHD across different age groups?
-
-### Tools
-
- * **Python**: `numpy`, `FOOOF`, `matplotlib` for data processing, feature extraction and visualization
- * **R**: `caret`, `randomForest`, `ggplot2` for machine learning and statistical analysis
- * **Jupyter notebooks**: interactive analysis
- * **GitHub**: version control and collaboration
-
-### Data
-
-[**TDBrain Database**](https://www.brainclinics.com/resources)
-
-We utilized the comprehensive TDBrain database, an extensive clinical EEG dataset containing:
-
-- **Sample size**: 1,274 participants collected over two decades
-- **Age range**: 5-89 years
-- **Data types**: Raw resting-state EEG recordings, NEO-FFI personality data, demographic information, and behavioral measures
-- **ADHD subset**: 204 ADHD participants, with 70 having treatment response data for neurofeedback therapy
-
-### Deliverables
-
-**[GitHub Repository](https://github.com/brainhack-school2025/age_eeg_pattern)**
-
-- Workflows in both Python and R
-- Documentation with figures and visualizations
-- Reproducible analysis pipeline
-
-### Results
-
-#### Dataset Characteristics
-
-While the ADHD sample contains 204 samples, only 70 individuals have neurofeedback treatment response data.
-
-| Age Group | Non-Responder (Number of sessions) | Responder (Number of sessions) |
-| --------- | ---------------------------------- | ------------------------------ |
-| Youngest | 14 | 40 |
-| Middle | 4 | 48 |
-| Oldest | 1 | 50 |
-
-The distribution of number of sessions for responder v.s. non-responder is very imbalanced.
-
-#### Feature selection
-
-
-
-Using bootstrap, we determined the maximum number of features to be used in the model is 60. The selected 60-feature set demonstrated high stability across bootstrap iterations, with minimal variance in feature rankings across different data samples. The above figure presents the top 15 features being selected by our model. Top tier precitors includes C3_delta_rel, CPz_fooof_alpha_cf and T8_theta_beta_ratio represents central region delta relative power, central-parietal alpha center frequency and right temporal theta-beta ratio. Notably, age emerged as a significant predictor, ranking 8th among all features, confirming the importance of developmental considerations in treatment prediction.
-
-#### Random forest classification results
-
-
-
-The AUC of our random forest model achieve 0.865, indicating strong predictive performance, although there is potential of overfitting due to imbalanced dataset and small sample size. The model is evaluated by out-of-bag bootstrap method.
-
-### Age-Related EEG feature
-
-We evaluated whether the selected features are correlated with age and has variabiltiy across age groups.
-
-
-
-The left panel shows age-EEG correlations for the top 10 features by correlation strength. Features like C4_beta_rel and Cz_low_beta_rel show significant correlations with age, indicating that these brain activity patterns change systematically across development. The right panel displays the most age-variable EEG features using coefficient of variation across age groups. F4_delta_abs shows the highest variability, followed by T8_foof_offset and C3_delta_rel. This suggests these features may be sensitive developmental markers that can play crucial role for age-specific treatment prediction.
-
-
-
-We then visualized the overlapping features identified by correlation and variability analysis. The left heatmap displays developmental patterns by response group, showing clear differences in EEG feature means across age groups between responders and non-responders. The right panel shows developmental trajectories, illustrating how EEG features change with age differently for responders versus non-responders. For example, the C3_delta_rel and C4_beta_rel features show divergent developmental patterns between groups. There is no significant visual difference between age groups, possibly due to small sample size. Additionally, due to imblanaced dataset, the pattern shown for non-responders may be not highly reliable. Further tests are needed for validate model accuracy in larger samples.
-
-### Tools Learned and Applied
-
-- **Machine Learning**: feature selection and random forest implementation using bootstrap
-- **EEG Analysis**: Frequency domain analysis and biomarker extraction
-- **Data Integration**: Merging multiple data sources and handling missing data
-
-## Conclusion
-
-This project demonstrates that age-dependent EEG patterns provide valuable information for predicting neurofeedback treatment response in ADHD. The Random Forest model achieved strong performance (AUC=0.865), with age emerging as an important predictor with several EEG features showing robust correlations with developmental stage. However, there is potential for overfit due the small sample size and imbalanced case-control sessions. There is need to validate findings in a larger dataset.
-
-## References
-
-1. van Dijk, H., van Wingen, G., Denys, D. et al. The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Sci Data 9, 333 (2022). https://doi-org.myaccess.library.utoronto.ca/10.1038/s41597-022-01409-z
-2. Arns, M., Conners, C. K., & Kraemer, H. C. (2012). A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis. Journal of Attention Disorders, 17(5), 374-383. https://doi-org.myaccess.library.utoronto.ca/10.1177/1087054712460087 (Original work published 2013)
-3. Clarke, A. R., Barry, R. J., Heaven, P. C. L., McCarthy, R., Selikowitz, M., & Byrne, M. K. (2008). EEG in adults with Attention-Deficit/Hyperactivity Disorder. International Journal of Psychophysiology, 70(3), 176-183.
-
-## Acknowledgments
-
-We thank the [Brainclinics Foundation](https://brainclinics.com/) for providing the [TDBrain dataset](https://brainclinics.com/resources/tdbrain-dataset/introduction/downloads) and the [BrainHack School](https://school.brainhackmtl.org/) community for tutorials and learning material. Special appreciation to our instructors, TA and participants who provided valuable feedback throughout the project development.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-
-date: "2025-06-15"
-
-title: "Decoding Depression via EEG Biomarkers: A Neurocomputational Approach using Machine and Deep Learning"
-
-names: [Wei-Xuan Chai]
-
-github_repo: https://github.com/ChaiWeiXuan/brainhack-EEG-depression-ml-dl
-
-website:
-
-tags: [EEG, depression, svm, eegnet]
-
-summary: "This project was conducted as part of Brainhack School 2025. It aimed to classify major depressive disorder (MDD) using temporal-domain EEG features (i.e., band power), applying both machine learning (SVM) and deep learning (EEGNet) models."
-
-image: "cover img.png"
-
----
-
-## Project definition
-
-### Background
-
-This project was inspired by a clinical case from Shanghai Ruijin Hospital, where Brain-Computer Interface (BCI) technology was applied to treat severe depression. That real case made me wonder about the potential of neural decoding in understanding and supporting mental health.
-
-As someone interested in both neuroscience and BCI, I believe it is essential to master machine learning and deep learning techniques—especially when working with high-dimensional signals like EEG or other neuroimaging data. This project is my personal attempt to explore how machine learning (SVM) and deep learning (EEGNet) models perform in classifying major depressive disorder (MDD) based on EEG band power features.
-
-
-### Tools
-
-The project will rely on the following technologies:
-
-1. Programming & Environment
-- Python
-- Jupyter Notebook: Used for data preprocessing and training the SVM machine learning model.
-- Google Colab: Used for training the EEGNet deep learning model.
-
-2. Libraries & Frameworks
-- scikit-learn, gridsearchCV: For machine learning models.
-- MNE: For EEG data processing and visualization.
-- numpy, pandas, matplotlib: For data manipulation and plotting.
-- tensorflow, keras: For deep learning models.
-
-3. External Models / Resources
-- Brainhack School Modules: Working with MNE-Python and EEG-BIDS [page](https://school-brainhack.github.io/modules/mne_python/)
-- Brainhack School Modules: Machine learning for neuroimaging [page](https://school-brainhack.github.io/modules/machine_learning_neuroimaging/)
-- EEGNet implementation from ARL (Army Research Laboratory) EEGModels Project [page](https://github.com/vlawhern/arl-eegmodels)
-
-
-### Data
-This project uses the MDD Patients and Healthy Controls EEG Data (New), downloaded from [page](https://figshare.com/articles/dataset/EEG_Data_New/4244171/2).
-- The dataset includes 30 Healthy Controls (H) and 34 Major Depressive Disorder (MDD) participants.
-- Each participant completed three conditions:
- - Eyes Closed (EC)
- - Eyes Open (EO)
- - P300 Task (TASK)
-
-For this analysis, we used:
-- Only the 5-minute EC EEG data
-- Successfully downloaded: 28 H and 30 MDD subjects
-
-
-### Deliverables
-
-At the end of this project, we will have:
-1. Preprocessing & Bandpower Feature.ipynb
-2. Machine Learning_SVM.ipynb
-3. Deep Learning_EEGNet.ipynb
-These deliverables reflect the full pipeline from raw EEG preprocessing to both traditional ML and DL models training.
-
-
-## Results
-
-### Progress overview
-
-This project started with the goal of integrating what I’ve previously learned—EEG preprocessing, machine learning, and deep learning—into a single end-to-end pipeline. While I had prior experience working with EEG data and individual modeling techniques, I had never tried building an entire classification workflow on my own, from data preparation to model comparison.
-
-I began by preprocessing the EEG data using MNE-Python and extracting temporal-domain features (band power). Then I implemented an SVM classifier, followed by a deep learning model based on EEGNet. Although the EEGNet part was more challenging due to model tuning and GPU limitations, I still managed to get a working prototype on Google Colab.
-
-Beyond modeling, I also learned how to structure a project repository, push updates to GitHub, and write clear documentation—sometimes after a few frustrating errors (and moments of panic). But overall, it was a fun and fulfilling learning experience, and a solid first attempt at decoding mental health signals using both machine learning and deep learning techniques.
-
-
-### Tools I learned during this project
-
-- Learned to preprocess EEG signals and extract bandpower features using MNE.
-- Applied classical machine learning models (SVM) with scikit-learn and gridsearchCV.
-- Implemented and trained a deep learning EEG model (EEGNet) with TensorFlow and Keras.
-- Developing, testing, and executing the entire analysis pipeline using Jupyter Notebooks and Google Colab as interactive environments
-
-
-### Results
-
-Preliminary results suggested that SVM achieved higher accuracy (> 0.95) compared to EEGNet (~0.8), possibly due to limited hyperparameter tuning and model optimization in this early-stage exploration. While deep learning provides promising end-to-end modeling capabilities, classical approaches like SVM may still offer superior performance when the dataset is small and features are carefully extracted.
-
-
-#### Deliverable 1: Preprocessing & Bandpower Feature
-
-An EEG preprocessing pipeline was implemented using MNE-Python, followed by the extraction of frequency-domain features (band power) for classification purposes.
-
-
-#### Deliverable 2: Machine Learning_SVM
-
-A Support Vector Machine (SVM) model was trained and evaluated based on the extracted band power features. Grid search with 10-fold cross-validation was used to tune key hyperparameters (e.g., C, gamma). Several trials were conducted, including the integration of feature selection and dimensionality reduction using PCA. However, these modifications did not improve the accuracy, suggesting that the original band power features already effectively captured discriminative patterns related to depression in the EEG data, or that such techniques may not be beneficial in this particular case.
-
-
-#### Deliverable 3: Deep Learning_EEGNet
-
-An EEGNet model [page](https://github.com/vlawhern/arl-eegmodels) was trained and evaluated using preprocessed EEG epoch data, without extracting band power features. The model was implemented on Google Colab with GPU support. Hyperparameter tuning was conducted using both Keras Tuner and manual for-loop implementations, each within a 10-fold cross-validation framework. Due to time constraints, only minimal tuning was performed, which may explain the lower performance (~80% accuracy). Nevertheless, this pipeline demonstrates how CNN-based architectures can be applied end-to-end to EEG data.
-
-
-
-## Conclusion and acknowledgement
-
-This project was my first attempt at an EEG decoding pipeline, from preprocessing, feature extraction, to model training with SVM and EEGNet. Though basic, it revealed skill gaps in computational neuroscience and BCI, guiding my future learning.
-
-I appreciate the supportive learning environment and resources from Brainhack School 2025, including video tutorials and coding notebooks. Special thanks to the researchers who shared the MDD EEG dataset on Figshare and the EEGNet developers for their open-source contribution. This project reflects the power of open science and community collaboration.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-08-06" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Classifying Neuropsychiatric Disorder Diagnoses Using Resting State BOLD fMRI Connectivity Data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Peter Brotherwood]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/brotherwood_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Machine-Learning, Connectivity, Diagnosis, Workflow]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Can functional connectivity data be used as a predictor for neuropsychiatric diagnosis? This project explores the usefulness of connectivity data in predicting ADHD, Bipolar Disorder, and Schizophrenia diagnoses using machine learning classification methods."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "connectivity_image.jpg"
----
-
-
-## About Me
-
-
-
- Peter Brotherwood
-
-
-I am a first year PhD student at the University of Montreal studying in computational neuroscience. I come from a fairly different background, with a BSc in Genetics and an MSci in Bioinformatics from the University of Birmingham. Much of my current work is in perception, using machine learning based approaches to model representational spaces in individual subjects. My hope is that BHS will introduce me to tools and best prectices I need to learn in order to fully integrate into the field of computational cognitive neuroscience.
-## Project Summary
-
-### Introduction
-Brain regions with correlated temporal activity are seen to form functional networks of varying scale and distribution. Regions correlated at rest form resting state networks. Aberrant functional connectivity of resting state networks has been observed in multiple populations sufering from neuropsychiatric disorders; including ADHD (Sudre *et al*., 2017), Bipolar Disorder (Syan *et al*., 2018), and Schizophrenia (Sheffield & Barch, 2016). Given these observed differences in resting state network connectivity, this project aims to apply machine learning methods to investigate if aberrations in resting state functional connectvitiy can be used to identify neuropsychiatric diagnoses.
-
-### Main Objectives
-- Provide a full neuroimaging workflow from preprocessing of raw data to visualisation of results.
-- Emphasize reproducibility, making all elements of the project as reproducible as possible.
-- Investigate ability of machine learning algorithms in predicting phenotype based on connectivity data.
-
-### Personal Objectives
-- Learn more about open data and project reproducibility.
-- Gain an understanding of fMRI and neuroimaging database structures and best practices.
-- Develop skills in proprocessing and analysis of fMRI data.
-- Apply knowledge of machine learning to neuroscientfic studies.
-
-### Tools
-- Compute Canada for Job Submission
-- Git and Github for Version Control
-- DataLad for Reproducibility
-- Singularity for Reproducibility
-- fMRIPrep for data preprocessing
-- Python Packages: `matplotlib`, `seaborn`, `scikit-learn`, `nilearn`
-
-### Data
-The dataset used in this study comes from the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study (Poldrack *et al*., 2016). The dataset is comprised of fMRI data for 122 healthy individuals, and 142 individuals with neuropsychiatric disorders. Of these 139 individuals, 50 are diagnosed with schizophrenia, 49 with bipolar disorder, and 40 with ADHD. The dataset contains fMRI data collected at rest and over a series of attentional tasks. The fMRI data is in nifti format and the dataset is provided in BIDS format. More information on this dataset can be found at https://openneuro.org/datasets/ds000030/versions/1.0.0.
-
-A summary of the dataset is as follows:
-
-| |Participants|Male|Female|Average Age|Age Std|
-|:-------------|:----------:|:--:|:----:|:---------:|:-----:|
-|Control |122 |65 |57 |32.05 |10.28 |
-|Schizophrenia |50 |38 |21 |35.29 |8.94 |
-|Bipolar |49 |28 |19 |31.59 |8.77 |
-|ADHD |40 |21 |12 |36.46 |8.79 |
-|Total |261 |152 |109 |33.29 |9.29 |
-
-
-
-
-
-### Project Deliverables
-- Reproducible project workflow, detailed in git repo and via datalad logs, reproducible via containers.
-- Executable Python scripts for data preparation and machine learning
-- Markdown file introducing the project and detailing project results
-
-## Results
-
-### Preprocessing using `fMRIprep`
-
-Preprocessing of raw fMRI data was done using `fMRIprep` (Esteban *et al*., 2018) and executed via the `preprocessing.sh` script on Alliance Canada's Beluga HPC Cluster. Due to lack of resting state data for some subjects, 260 preprocessed resting state BOLD fMRI and their associated confound files were returned. fMRIprep was run using singularity 3.8 with the following steps:
-
-- Brain masking and tissue segementation of T1w image
-- Spatial normalization of the anatomical T1w reference
-- Surface reconstruction using FreeSurfer
-- Alignment of functional and anatomical MRI data
-- Brain masking and confound extraction
-
-### Getting Connectivity Data using `nilearn`
-
-Brain masking and connectivity data retrieval was done using `nilearn` (Abraham *et al*., 2014). The BASC multiscale deterministic atlas (Bellec *et al*., 2009) with 64 regions of interest (ROIs) was used to mask the preprocessed voxel-wise BOLD activity data, in order to reduce the complexity of the features. A plot of this atlas can be seen below:
-
-
-
-
-
-
-
-Confounds detected by `fMRIprep` were loaded using `nilearn`'s `load_confounds_strategy` method and regressed out during masking. Following this, connectivity matrices showing correlations in BOLD timeseries activity between ROIs were generated for each subject. The impact of confound removal strategy on the output connectivity matrices can be seen below:
-
-
-
-
-
-
-
-The upper triangular vector (UTV) of each subject's connectivity matrix forms a set of features which will serve as input to the machine learning step. The UTVs of all matrices can be combined to form a feature matrix:
-
-
-
-
-
-
-
-### Machine Learning using `scikit-learn`
-
-Phenotype was learned and predicted using a support vector classifier with a linear kernel from `scikit-learn` (Pedregrosa *et al*., 2011). 30% of individuals were used as unseen test data, and a grid search with 5-fold cross validation was performed on the remaining 70% of subjects to find the optimum value for the the `C` regularizer. This process was done once with the full dataset, and once with a stratified dataset, as seen below:
-
-
-
-
-
-
-Using each model to predict the unseen test data, the folllowing multi-label confusion matrices were obtained:
-
-
-
-
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Control_vs_Schizophrenia_image_interactive.html) for an interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Control_vs_Schizophrenia_3D_interactive.html) for a 3D interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Control_vs_Schizophrenia_feature_connectome_interactive.html) for an interactive connectome.
-
-**Control vs ADHD**
-
-
-
-
-
-
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Control_image_interactive.html) for an interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Control_3D_interactive.html) for a 3D interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Control_feature_connectome_interactive.html) for an interactive connectome.
-
-**Control vs Bipolar Disorder**
-
-
-
-
-
-
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Bipolar_vs_Control_image_interactive.html) for an interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Bipolar_vs_Control_3D_interactive.html) for a 3D interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Bipolar_vs_Control_feature_connectome_interactive.html) for an interactive connectome.
-
-**Schizophrenia vs ADHD**
-
-
-
-
-
-
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Schizophrenia_image_interactive.html) for an interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Schizophrenia_3D_interactive.html) for a 3D interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Schizophrenia_feature_connectome_interactive.html) for an interactive connectome.
-
-**Schizophrenia vs Bipolar Disorder**
-
-
-
-
-
-
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Bipolar_vs_Schizophrenia_image_interactive.html) for an interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Bipolar_vs_Schizophrenia_3D_interactive.html) for a 3D interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/Bipolar_vs_Schizophrenia_feature_connectome_interactive.html) for an interactive connectome.
-
-**ADHD vs Bipolar Disorder**
-
-
-
-
-
-
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Bipolar_image_interactive.html) for an interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Bipolar_3D_interactive.html) for a 3D interactive map.
-Click [here](https://brainhack-school2022.github.io/brotherwood_project/ADHD_vs_Bipolar_feature_connectome_interactive.html) for an interactive connectome.
-
-## Conclusions
-
-### Can we predict diagnosis from fMRI data?
-While in this instance, the model was unable to accurately classify neuropsychiatric diagnoses based on resting state fMRI connectivity data, various factors should be considered when answering such a question. Factors such as the nature and composition of the data, the features selected for model fitting, choice of mask and confounding strategy, choice of model, and choice of performance metric should all be considered when implementing such a pipeline as this.
-
-### Objectives, Tools, and Deliverables
-In terms of objectives to achieve, tools to learn, and deliverables to produce I believe the project was a success. The output is a comprehensive pipeline from raw data to visualization of results, utilising all of the tools I had aimed to learn. The deliverables are fulfilled, with a comprehensive walkthrough of the project, and reproducible python scripts for replicable use. While complete containerization of the environment using Singularity was not possible, the following reproducibility guide serves as an adequate substitute for this.
-
-## Guide to Reproducibility
-All scripts used in the analyses are located in the `scripts` directory of the [git repo](https://github.com/brainhack-school2022/brotherwood_project) and are executable in the Linux command line. Each script is written such that it can be executed using fMRI data from alternative functional tasks from the same dataset, or with similar datasets conforming to BIDS formatting standards. The `requirements.txt` file provides all neccesary dependencies for execution of scripts following preprocessing using fMRIprep.
-
-A basic example of running the contents of the `scripts` directory on a Linux machine is as follows:
-
-### Running fMRIprep
-
-To run `mriprep.sh` it is highly recommended to use a HPC cluster, more instructions on preprocessing requirements and how to properly format `mriprep.sh` can be found at https://www.nipreps.org/apps/singularity/.
-
-Following correct installation and formatting, preprocessing can be run by running `$ sbatch mriprep.sh`
-
-### Setting up a virtual environment for running the python scripts
-```
-$ cd # go to project directory
-
-$ python3 -m venv # initialize virtual environment
-
-$ source /bin/activate # enter the virtual environment
-
-() $ pip install --upgrade pip # upgrade the package manager
-
-() $ pip install -r requirements.txt # install project dependencies
-```
-
-*What follows is a basic usage tutorial on `.py` scripts in the `scripts` directory; for more information on each script and its associated parameters use:* `scripts/.py -h`
-
-### Getting connectivity data
-```
-$ source /bin/activate # enter the virtual environment
-
-() $ scripts/format_data.py # create an index file in the derivatives folder for use with other python scripts
-
-() $ scripts/get_connectivity_data.py # get individual subject connectomes
-```
-
-### Fitting a Support Vector Classifier
-```
-$ source /bin/activate # enter the virtual environment
-
-() $ scripts/fit_svm.py # perform a parameter search and find the best fitting model
-```
-
-### Troubleshooting
-In the instance that attempting to run a script returns `permission denied`, it may be neccesary to run `() $ chmod +x scripts/.py`. Alternatively running `() $ python3 scripts/.py ` will execute the scripts.
-
-## Acknowledgements
-I would like to thank the Brainhack team of 2022 for their continued support throughout the course. With particular thanks to Hao-Ting Wang and Natasha Clarke for providing their support and expertise throughout the project.
-
-## References
-Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B. and Varoquaux, G., 2014. Machine learning for neuroimaging with scikit-learn. *Frontiers in Neuroinformatics*, 8.
-
-Bellec, P., Rosa-Neto, P., Benali, H. and Evans, A., 2009. Multi-level bootstrap analysis of stable clusters (BASC) in resting-state fMRI. *NeuroImage*, 47, p.S123.
-
-Esteban, O., Markiewicz, C., Blair, R., Moodie, C., Isik, A., Erramuzpe, A., Kent, J., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S., Wright, J., Durnez, J., Poldrack, R. and Gorgolewski, K., 2018. fMRIPrep: a robust preprocessing pipeline for functional MRI. *Nature Methods*, 16(1), pp.111-116.
-
-Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., 2011. Scikit-learn: Machine learning in Python. *The Journal of machine Learning research*, 12, pp.2825-2830.
-
-Poldrack, R., Congdon, E., Triplett, W., Gorgolewski, K., Karlsgodt, K., Mumford, J., Sabb, F., Freimer, N., London, E., Cannon, T. and Bilder, R., 2016. A phenome-wide examination of neural and cognitive function. *Scientific Data*, 3(1).
-
-Sheffield, J. and Barch, D., 2016. Cognition and resting-state functional connectivity in schizophrenia. *Neuroscience; Biobehavioral Reviews*, 61, pp.108-120.
-
-Sudre, G., Szekely, E., Sharp, W., Kasparek, S. and Shaw, P., 2017. Multimodal mapping of the brain’s functional connectivity and the adult outcome of attention deficit hyperactivity disorder. *Proceedings of the National Academy of Sciences*, 114(44), pp.11787-11792.
-
-Syan, S., Smith, M., Frey, B., Remtulla, R., Kapczinski, F., Hall, G. and Minuzzi, L., 2018. Resting-state functional connectivity in individuals with bipolar disorder during clinical remission: a systematic review. *Journal of Psychiatry and Neuroscience*, 43(5), pp.298-316.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-13" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Exploring machine learning tools for modelling calcium imaging data to behavioural events"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Zeeshan Haqqee]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/haqqeez_bhs2020_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [calcium imaging, single cell, population, mouse behaviour]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "I have calcium imaging data in mice while they performed behavioural learning tasks in a touchscreen chamber. I want to figure a way to consolidate the neural data (activity of ~100 individual cells over time (~30,000 x ~30ms time bins)) with behavioural data (time-stamped actions and decisions made by the animal during their behavioural task)"
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "neurons4.gif"
----
-
-
-## Project definition
-
-### Background: Behavioural task
-
-The goal of the task is for the mouse to learn that specific objects on the screen will give a reward only if they are touched when in specific locations. Otherwise, they are a distractor. For example, for Trial type 1, the flower will give a reward because it is displayed on the left, but the spider will not. For Trial type 6, the flower will not give a reward because it is in the middle, but the spider will, because it is on the right.
-
-6 Different trial type combinations exist from 3 object-location associations (flower-left, airplane-middle, spider-right) that the mice need to learn. These 6 trial types are presented randomly. 36 total trials (6 x 6) per session:
-
-
-
-### Background: Calcium imaging
-
-The mouse completes the task with a miniatrue microscope (miniscope) implanted on its head, which shines an LED light onto an implanted lens to illuminate genetically modified neurons that fluoresce (i.e., light-up) whenever calcium is being used by the cell. In the same way that fMRI infers brain activity from blood-oxygen levels, calcium imaging infers neuronal activity from calcium concentration in the cell (indicating action potentials, graded potentials, synaptic potentials etc.)
-
-This is what the camera sees. A MATLAB script takes each frame and determines which groups of pixels are neurons and which are not and tags and counts them.
-
-
-
-Once tagged, each neurons's brightness level is measured to infer calcium activity. The raster plots generated here looks similar to what you would see in 'spiking' data for single-unit electrophysiology.
-
-
-
-(left; zoomed in, right; same but zoomed out) The y axis is each one individual neuron. The x-axis is time (in frames; ~30ms). A more yellow colour = stronger fluoresence measured = higher likelihood of neuronal activity.
-
-Big overarching question: How does neural activity change over time as the animal gets better at the memory task? Are there clusters of cells that activate during certain events? (Incorrect vs. correct choices, left vs. right vs middle response, etc.).
-
-
-For a detailed review of general tools and methodology, see: https://www.biorxiv.org/content/10.1101/2020.02.06.937573v1.full
-
-
-### Data
-
-Behavioural data is time-stamped for every action done by the animal (touching the screen, drinking reward, incorrect choice, correct choice, etc.)
-
-Calcium data is a matrix representing NumberofCells x TotalFrames (e.g., 100 cells and 30,000 frames is a 100 x 30000 matrix). Where each value in the matrix represents fluoresence level (inferred spiking probability) for a particular cell at a particular frame in time.
-
-Hence, the design of my data uses the activity of individual neurons as its features (x-values), the behavioural events as its dependent or predictor variables (y-values). A 'sample' here is defined as each individual attempt made by animal on the task.
-
-
-
-Note: Ones and zeros here denote the cell being 'on' or 'off' for sake of simplicity. In reality, the activity is defined as a transient of activity based on the change in fluoresence measured by the image analysis software.
-
-### Learning Goals
-
-The primary issue with my analysis design is that each cell's activity is its own time-series, which makes it difficult or impossible to use with traditional machine learning tools (at least, in its current format). Thus, my first goal is to pre-process my time series data into a meaningful format for cell-by-cell and population level analyses. My second goal is to use machine learning to predict and decode correct vs incorrect events from individual cell and population level activity.
-
-
-
-1. For single-cell analysis, I have the option of using the time series data of a single cell. However, treating each frame in the time series as its own independent variable is not realible; any slight jitter in the cell activity from trial to trial will make it impossible to find recurring patterns. To fix this, I will use a sliding window that takes the average of a few frames, effectively smoothing out the time series for each trial to allow a more liberal overlap of activity between trials.
-
-2. From the single-cell analysis, I have the option of taking the cell-by-cell predicted categories and inputting them into a random forest analysis to test whether neurons show any kind of 'democratic' coding for correct vs. incorrect attempts.
-
-3. For the population level analysis, I have the option of condensing my time series data into mean firing or total firing across the time series per cell, then using this overall activity score to predict behavioural events.
-
-4. Finally, I can also apply nilearn tools to compute a correlation matrix between cells, and using those correlation values as my features to predict behaioural events.
-
-
-### Tools
-
-* Python
-* Jupyter notebooks
-* nilearn
-* scikitlearn
-* MATLAB
-
-## Results
-
-### Single Cell - Sliding window analysis
-
-Below are histograms depicting the distribution of test scores (top) and 5-fold cross validation scores (bottom) for three machine learning classifiers tested on 109 individual cells with windowed (4 frames) time-series data. Training and test data was shuffled and re-tested 50 times, and counts of cells that scored >80% accuracy on each test are tabulated next to each classifier method.
-
-
-
-Adding +1 to the Cell ID, it appears that cells 29, 25, and 4 most frequently scored above 80% prediction accuracy for correct vs. incorrect trial attempts. Let's see what they look like:
-
-
-
-Here we can see visually that these cells do indeed differ significantly between correct and incorrect responses. These results were, in fact, already known. Several cells in the hippocampus are expected to light up whenever an animal receives a reward for a correct trial. Thus, the purpose of this test was just to validate that my machine learning tools were working properly.
-
-### Population level - 'democracy'
-
-Now then, what about population level activity? Is there something about the activity of all these cells, or groups of cells, that can indicate correct vs. incorrect response choices? One idea is to test whether hippocampal cells function as some kind of 'democracy'; where the predictions of individual neurons accounted together could somehow improve the overall accuracy of the machine learning tools.
-
-To do this, I took the predctions of each individual neuron from the training set of the linear SVC analysis and put it into a Random Forest classifier. Unfortunately, the results were quite poor (41.6% +/- 11% accuracy on cross validation). This isn't too surprising, as hippocampal cells aren't really expected to work 'democratically' to predict single, specific events.
-
-### Population level - Summed Activity
-
-Equally bad were the results of the summed time-series activity all 109 cells. This is again to be expected, as temporal dynamics cannot be ignored on the large timescales over which activity was averaged (over 2 seconds). Cells with distinct and specific activity in very narrow timeframes (left image) are treated the same as cells that fire sparsely, but the same amount overall, within the same time-frame (right image). However, this type of 'summed' activity might be more useful for more specific and narrow time windows of interest (<1 second) that are tied to specific, expected events. Accuracy might also improve by 'leaving out' features (i.e., cells) with inconsistent activity from the model. Indeed, not all cells would be expected to contribute equally (or at all) to the coding of a correct or incorrect trial.
-
-
-
-### Population level - Cell-to-Cell Correlations
-
-Another way to look at population level activity is through cell-to-cell correlations, similar to what is done fMRI connectivity analyses. Except here, each region of interest is an individual neuron. Adapting the script learned from the machine learning with nilearn tutorial, I generated a 109 x 109 correlation matrix for all cells, as well as a feature matrix of 5800+ correlations between pairs of cells' windowed time series data:
-
-
-
-Feeding these featrues into a few machine learning classifiers, we get some cool test results (+ cross validation)!
-
-* SVC (rbf): 78.8% (82.5 +/- 10%)
-* Decision tree: 82.6% (65 +/- 10%)
-* Random forest: 73.5% (72.5 +/- 9%)
-
-It appears that looking at correlated activity is a reasonable approach for understanding correct vs. incorrect responses in this task, when the animal is collecting reward. This again makes intuitive sense, as we know that large populations of cells fire together whenever the animal receives a reward, so highly correlated activity would be indicative of a 'reward' event (and thus, a 'correct' event), perhaps even more so than any single cell's activity.
-
-## Conclusions
-
-Here, we see that both single cell and population level analyses can be useful for modelling correct vs. incorrect performance on a reward-driven behavioural task. However, some methods seem to work better than others, and both the methods that do and do not work help us understand the functional dynamics of the hippocampal cells that govern certain aspects of reward-driven learning.
-
-## Future directions
-
-* Use machine learning to predict and decode other events in the population activity of the cells, such as trial and object types.
-* What does the 'connectome' of relevant correlations for specific events look like? How does it change over time?
-* How does prediction accuracy for each of these tests change over time? Does this reflect anything about how the hippocampus handles the learning of new information?
-* Create a polished machine learning pipeline to systematically decode different types of events in the behaviour and pick return a rank of the best models and their respective prediction accuracies and how they change over time.
-
-## Acknowledgements
-
-I'd like to thank the BrainHack team for putting together this wonderfully fun and fascinating course on neural data science! I've learned a lot about how other neuroscientists are tackling their analyses and how best to share my data with collaborators and labmates. Special thanks to Yann and Loic for helping me out with the machine learning aspects of my project.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-14" # Date you first upload your project.
-title: "2025 Brainhack School (Mini-)Project - Cognitive Dispersion and Its Neural Correlates"
-names: [Truc (Curt) Nguyen]
-github_repo: https://github.com/curt-1711/Curt_brainhack_project
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-tags: [neuropsychology, fMRI, cognitive dispersion, default mode network]
-summary: "This mini-project for the 2025 Brainhack School is part of my PhD dissertation on Late-Life Cognitive Heterogeneity, where I examine the neural correlates of cognitive dispersion -- a measure of within-individual variability -- using neuropsychological and fMRI data from the Midnight Scan Club dataset (OpenNeuro ds000224)."
-image: "project_cover_20250617.jpg"
-
----
-
-
-
-## Project definition
-
-### Background
-
-Cognitive dispersion is a measure of intra-individual fluctuations
-across a range of neuropsychological tests, operationalized as the
-standard deviation of scores across multiple cognitive domains or tasks
-during one occasion. One of the most prevailing speculations regarding
-the mechanism of dispersion is the disruption of functional
-connectivity. However, these studies have primarily consisted of
-cross-sectional findings, i.e., scans that were acquired at a single
-time point, examined group-average brain, with RS-fMRI scan length
-lasting 5–6 minutes—an approach that might not fully capture functional
-brain organization at the individual level. While conventional fMRI
-studies have strived to increase the sample size, more recent works have
-directed attention towards *precision functional mapping*, i.e.,
-collecting large amounts of data at the individual level.
-
-Here I (a third year PhD student in Neuroscience at National Taiwan
-University / Academia Sinica as of June 2025) propose to use
-state-of-the-art precision functional mapping tools to determine brain
-activity patterns associated with cognitive dispersion. As a
-proof-of-concept (pilot) study, I will be using the neuropsychological
-and fMRI data from the Midnight Scan Club dataset (OpenNeuro ds000224)
-to examine the neural correlates of cognitive dispersion.
-
-### Tools
-
-- MATLAB;
-- CONN toolbox, an SPM-based software for the analysis of functional
- connectivity;
-- Utilities by the authors of the Midnight Scan Club dataset
- ([github](https://github.com/MidnightScanClub/MSCcodebase/tree/master));
-- Tutorials to run the *precision functional mapping* pipeline (Lynch
- *et al., Nature* 2024;
- [github](https://github.com/cjl2007/PFM-Depression/tree/main)) and
- the *multi-session hierarchical Bayesian modeling* (Kong *et al.,
- Cereb Cortex* 2019;
- [github](https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2019_MSHBM)).
-
-### Data
-
-The Midnight Scan Club dataset (Gordon *et al.*, Precision Functional
-Mapping of Individual Human Brains, *Neuron* 2017; OpenNeuro
-[ds000224](https://openneuro.org/datasets/ds000224/versions/1.0.4)).
-
-## Results
-
-### Progress overview
-
-During the three weeks that I had to work on this mini-project, I ran
-into multiple errors in MATLAB (details in the PDF file of my
-presentation). After hours (and hours) of troubleshooting and as the
-deadline to submit my project approached, I resorted to use the CONN
-toolbox for a quick analysis of the resting-state fMRI data from
-Midnight Scan participants.
-
-### Tools I learned during this project
-
-- **Human Connectome Workbench** to visualize the CIFTI files
- (derivatives).
-- **MATLAB** running code and troubleshooting.
-- **CONN toolbox** for fMRI data preprocessing and functional
- connectivity analysis.
-
-## Conclusion and acknowledgement
-
-Many, many thanks to the 2025 Brainhack School Organizers (Faculty and
-TAs)! I have learned so much from you and my peers this semester.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-18" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "CWAS4fMRI: a python package to perform Connectome Wide Association Study"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Clara El Khantour]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/elkhantour_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://brainhack-school2025.github.io/elkhantour_project/
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, cwas, connectome, package]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to develop a BIDS App for performing Connectome-Wide Association Studies (CWAS) on fMRI connectivity matrices. The result is a GitHub repository that can be installed via pip, enabling analyses on BIDS-formatted connectomes. Integration tests ensure the pipeline runs reliably, and a dedicated website provides full documentation and example outputs."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "illuminated-neural-network-stockcake.jpg"
----
-
-
-## Project definition
-
-### Background
-
-The reproducibility crisis in neuroimaging has affected many research domains, and psychiatric research is no exception [1](https://doi.org/10.1016/j.bpsc.2022.12.006). Indeed, psychiatric studies using neuroimaging tools have long struggled with poor reproducibility, largely due to small sample sizes and heterogeneity in analysis methods.
-
-To address this issue, initiatives such as the ENIGMA consortium (Enhancing NeuroImaging Genetics through Meta-Analyses) were launched [2](https://doi.org/10.1007/s11682-013-9269-5). ENIGMA is a consortium comprising international research sites and organized into several Disease Working Groups that focus on most major psychiatric conditions. It promotes a collaborative framework: while data remain at local sites, analyses follow standardized protocols, enabling large-scale meta-analyses across cohorts.
-
-This approach has proven successful for structural and diffusion MRI, thanks to the development of open and reproducible protocols (https://enigma.ini.usc.edu/protocols/). In this context, Waller et al., 2020 developed HALFpipe [3](https://doi.org/10.1002/hbm.25829), an open-source software that facilitates the preprocessing and extraction of connectivity matrices from functional MRI data. However, no standard tools has yet been proposed by ENIGMA for statistical analyses of functional connectivity matrices.
-
-#### Objectives
-This project aims to support the broader collaborative effort by developing a dedicated BIDS-App for conducting Connectome-Wide Association Studies (CWAS) based on connectivity matrices.
-
-#### Objectives for BrainHack school
-Learn how to :
-1. Write scripts in python following the BIDS-app workflow
-2. Build a GitHub repository with automated tests with GitHub Actions
-3. Develop a sustainable and easy-to-use python package
-4. Produce interactive plots and interactive website ✨
-
-### Tools
-
-This project uses the following tools and standards to ensure reproducibility, openness, and long-term usability:
-- **Python scripts** : To write functions, modules and be able to test them
-- **Git & GitHub**:
- - *Public repo*: Enables version control and promotes open-source, collaborative development of the library.
- - *GitHub Actions*: Implements automated testing and continuous integration to maintain code quality during development (Git Actions).
-- **BIDS Ecosystem**: Ensures compatibility with the BIDS standard, following the workflow of a BIDS App [4](https://doi.org/10.1371/journal.pcbi.1005209).
- - For this package, we followed the [BEP-017 proposal](https://bids.neuroimaging.io/extensions/beps/bep_017.html)
-- **Python Packaging with uv**: Distributes the tool as an installable open-source Python library for easy integration and reuse.
-- Interactive documentation : Provides documentation and examples on the website.
- - **Jupyter Notebooks**
- - **Plots with Plotly**
- - **Website with MyST**
-
-### Data
-
-To test the integration of the pipeline, toy data have been generated using `numpy` and `pandas`.
-
-Local tests have been made base on [Giga-connectome outputs](https://giga-connectome.readthedocs.io/en/latest/).
-
-### Deliverables
-
-1. Python scripts following the BIDS-App workflow
-2. A python package ready to be used available on [GitHub](https://github.com/brainhack-school2025/elkhantour_project)
-3. A public [GitHub repository](https://github.com/brainhack-school2025/elkhantour_project) with integration test
-4. A [website](https://brainhack-school2025.github.io/elkhantour_project/) with interactive plots
-
-## Results
-
-### Progress overview
-
-During Brain Hack School 2025, I developed a functional and pip-installable Python package. I also set up a website with all the documentation you need to run the CWAS pipeline. This first version of the Python package is now open to feedback and collaboration.
-
-### Tools I learned during this project
-
-- **Python scripts** : All original scripts were refactored into modular Python files. Scripts are now organized by topic and function to improve readability and maintainability.
-
-- **Git, GitHub & Testing with pytest** : This project introduced me to advanced GitHub features, including GitHub Actions for continuous integration. I learned to use `pytest` to write and integrate tests that ensure the pipeline remains functional after updates.
-
-- **Python Packaging with uv** : I discovered `uv` during one of the BHS2025 modules in Montreal, and used it to build this package.
-
-- **BIDS Ecosystem** : This project helped me to understand the importance of the BIDS ecosystem and all the related BIDS-App. I discovered BIDS extension proposals (BEPs) such as those for derived data like connectivity matrices.
-
-- **Website with MyST** : I used MyST Markdown to build and publish a clean, structured documentation website.
-
-- **Plots with Plotly** : To create interactive visualizations, particularly for complex connectivity maps involving many brain regions.
-
-### Results
-
-#### Deliverable 1: BIDS workfow implementation
-This pipeline was created to follow the BIDS-App workflow as proposed by [Gorgolewski et al., 2017](https://doi.org/10.1371/journal.pcbi.1005209) for group level anlaysis.
-
-
-
-This pipeline takes as input a directory structured according to the [BEP-017 proposal](https://bids.neuroimaging.io/extensions/beps/bep_017.html). Both the input and output directories are defined via command-line arguments.
-
-#### Deliverable 2: Python package
-This package was initialize using [`uv`](https://github.com/astral-sh/uv).
-
-Thanks to this setup, the repository can be installed using using `pip install .`. This configuration ensures that:
-- All required dependencies are installed
-- The pipeline can be tested using `pytest`
-
-###### Quick start
-To run this pipeline, you simply need to follow the next 3 steps:
-
-1. Clone the repository
-```
-git clone https://github.com/brainhack-school2025/elkhantour_project.git
-```
-2. Install the package
-
-```
-pip install -e .
-```
-
-3. Run the pipeline using the command `cwas-rsfmri` followed by the flags.
-
-```bash
-cwas-rsfmri --bids_dir=bids_directory --output_dir=results --atlas_file=atlas.txt --atlas=example_atlas --phenotype_file=participants.tsv --group=diagnosis --case_id=NDD --control_id=HC --session=timepoint1 --task=task01 --run=01 --feature=denoiseSimple
-```
-
-#### Deliverable 3: Continuous integration with GitHub Action
-
-The tests were implemented using [pytest](https://docs.pytest.org/en/stable/).
-
-Since this package serves as a *glue* between already existing pipelines, the focus is on integration testing rather than unit testing. To support this, toy datasets are automatically generated and deleted during each pytest run to avoid unnecessary disk usage.
-
-For now, two integration tests are included:
-1. One without any optional arguments (`scan`, `sequence`, `medication`).
-2. One using all optional arguments
-
-These tests are automatically run on GitHub via GitHub Actions on every push to the `main` and `dev_test` branches, as well as on pull requests.
-
-#### Deliverable 4: MyST Website
-To provide complete documentation and show example figures generated by the pipeline, a website was built using MyST and is hosted on GitHub Pages.
-
-The documentation is available directly on the site https://brainhack-school2025.github.io/elkhantour_project/
-
-To visualize the interactive figure, please see: https://brainhack-school2025.github.io/elkhantour_project/interactive-plots
-
-
-
-#### Contribution to Open Science
-This project aims to:
-1. Promote __collaborative science__ through:
-- A public GitHub repository open to contributions
-- Continuous integration with GitHub Actions, which automatically run on new pull requests
-2. Ensure __reproductible pipeline and results__ by:
-- Providing a Python package that can be run with just three command-line commands
-- Following the BIDS standard for input organization, aligning with community efforts to standardize neuroimaging workflows
-3. Offer extensive __documentation__ using MyST markdown, a tool designed for open-source projects that supports clear, structured scientific communication and web-based publishing.
-
-## Conclusion and acknowledgement
-
-This project provides an easy-to-use and fast Python package for researchers working with fMRI connectivity matrices. It's fully open-source and welcomes collaboration from the community.
-
-The package was built using recent, open-source tools and follows principles established by the neuroimaging community to align with current standards in neuroimaging research.
-
-### Next steps
-- **Expand integration tests**: Improve existing tests and add failing test cases to ensure the pipeline handles incorrect inputs and edge cases properly.
-- **Publish to PyPi**: Once the pipeline is more stable and mature, release an official version to PyPI for easier installation and distribution.
-- **Docker & Apptainer**: Containerize the pipeline to ensure full environment reproducibility using Docker and Apptainer.
-
-### Brain Hack School presentation materials
-[Project description - Week 2](https://docs.google.com/presentation/d/1BFQEd32ZGSvIpQaBQh5KjRjrZ0RL78illSvqR80Dr_E/edit?usp=sharing).
-
-[Final presentation - Week 4](https://docs.google.com/presentation/d/1AT7jvhL63toRHIYBsFZYHyxpPp-cLphSkeC5kAkJMak/edit?usp=sharing).
-
-## Aknowledgements
-This library is based on code originally published by Dr. Clara A. Moreau & Dr. Sebastian Urchs.
-
-The original version of the scripts can be found here : https://github.com/claramoreau9/NeuropsychiatricCNVs_Connectivity
-
-Thanks to Sara & Cleo for their help to create the Myst website! I would like to thank all the Professors, TAs, speakers of Brain Hack School 2025 Montreal. Special thanks to Lune Bellec who was mentoring me over the past few weeks.
-
-## References
-1. Botvinik-Nezer R, Wager TD. Reproducibility in neuroimaging analysis: Challenges and solutions. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023;8: 780–788.
-2. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8: 153–182.
-3. Waller L, Erk S, Pozzi E, Toenders YJ, Haswell CC, Büttner M, et al. ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data. Hum Brain Mapp. 2022;43: 2727–2742.
-4. Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, et al. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol. 2017;13: e1005209.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Diffusion MRI- From raw data to mapping brain connectomes."
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Michèle W. MacLean]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/mwmaclean-BHS2020
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [dMRI, data-management, preprocessing, tracking, data-visualization]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The focus of this project was to combine, use and present a set of tools to organize, preprocess, analyze and visualize diffusion MRI data. The overarching goal is to investigate the consequences of cortical blindness on structural connectivity using diffusion MRI. "
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "connectome_glassbrain.png"
----
-
-
-
-# Diffusion MRI- From raw data to mapping brain connectomes.
-## Organizing, preprocessing, analyzing and visualizing diffusion MRI data.
-### This project is part of the Brainhack Summer School 2020.
-
-### Contributors: Michèle W. MacLean & Brainhack School Members
-
-
-
-## Summary
-
-Hello! I'm currently a PhD student in Cognitive Neuroscience at l'Université de Montréal. My main focus is to study cortical visual impairment using MRI techniques. Damage to the primary visual areas can result in clinical blindness and prompt a cascade of dynamical structural and functional alterations of the neural networks at both the cortical and subcortical level. Following this damage, individuals can sometimes preserve the ability to non-consciously process visual information in their blind field, a striking phenomenon know as blindsight. My fascination resides in understanding how the brain can process visual information without conscious awareness.
-
-The overall goal of the current project was to combine, use and present the new tools learned during the BrainHack Summer School to organize, preprocess, analyze and visualize diffusion MRI data acquired at l'Unité de neuroimagerie fonctionnelle during my PhD.
-
-
-
-
-
-# Project definition
-
-## Background
-
-This project explored the steps for analyzing diffusion magnetic resonance imaging data starting from the raw data all the way to data visualization. For this, I used previously acquired data from l'Unité de Neuroimagerie Fonctionnelle in Montréal.
-
-**Project objectives during BHS 2020:**
-The first steps were to **1)** organize the MRI data in a BIDS friendly format and then **2)** preprocess the data. Then, I explored **3)** analyzing diffusion MRI (i.e. tracking) and **4)** performed data visualization.
-
-**Overarching goal:**
-The long term goal is to investigate the consequences of a primary visual cortex lesion on structural and functional connectivity using both diffusion MRI and resting state functional connectivity.
-
-## Tools
-This project will rely on the following:
-* **GitHub** for creating a repository & assembling all the resources.
-* **Bash terminal**
-* **Visual studio code** as a code editor
-* **BIDS** as a standard to organize the neuroimaging data
-* **Python**
-* **Docker container** to run preprocessing pipeline and basic tracking
-* **DIPY** for preprocessing and basic tracking of diffusion MRI
-* **Jupyter Notebook** and for data visualization
-* **Interactive data visualization tools**: Nilearn, Plotly, Numpy, Matplotlib, Seaborn, Widgets, etc.
-
-
-## Data
-MRI data was acquired with a high resolution 3 Tesla scanner (Siemens Trio system) and consists of a preliminary data set of 5 subjects, including 1 individual with cortical visual impairment with blindsight and 4 neurotypical controls. For each participant, raw structural MRI, resting state functional connectivity, fMRI and diffusion MRI data is available.
-
-Given the time course of the summer school, after the initial preprocessing of the data, I focused on working with the diffusion MRI data. This data set will allow to first become familiarized with the new neuroimaging tools learned during the BrainHack summer school. When I will acquire a larger data set, during the rest of my PhD, I plan to incorporate it to this project.
-
-
-
-The figure above is an example of an individual with cortical visual impairment to give you an idea of the type of data, where A) shows a T1-weighted anatomical scan with three different slice views showing the primary visual cortex removal in the left hemisphere and the destruction of the primary visual areas (V1) and B) the individual's visual field showing a symmetric loss across both eyes leading to a complete contralateral visual loss in the right visual field.
-
-As the dataset is not yet open access, feel free to send me a message if you have any questions and I'll be happy to answer!
-
-## Deliverables
-For the project:
-* The current markdown document, completed and revised.
-* Organization of MRI data into BIDS format
-* The diffusion MRI data preprocessed.
-* Basic tracking performed on the preprocessed diffusion MRI data with an output of streamlines & connectivity matrices.
-* Data visualization within a jupyter notebook using plotly, nilearn and matplotlib.
-* Requirements.txt for the Jupyter notebook
-
-For the course:
-* **Week 1 Deliverable:** [Assessement](https://github.com/mwmaclean/MacLean-M-QLSC612)
-* **Week 2 Deliverable:** [README file](https://github.com/brainhack-school2020/mwmaclean-BHS2020/blob/master/README.md)
-* **Week 3 Deliverable:** [Data visualization](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/data_visualization)
-* **Week 4 Deliverable:** [Presentation](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/presentation)
-
-## Methods
-1. **Data Organization/Management.**
-* Convert dicoms to a BIDS friendly dataset. [This tutorial](http://reproducibility.stanford.edu/bids-tutorial-series-part-1b/) served as a guideline.
-
-
-2. **Preprocessing diffusion MRI data.**
-* Running a preprocessing pipeline using [this docker image](https://hub.docker.com/r/gkiar/dwipreproc_fsl-5.0.11_minified)
-* [This python script](https://github.com/gkiar/stability/blob/master/code/preprocessing/preprocessing_pipeline.py) was used within the docker container.
-* Labels from Klein & Tourville (2012) were applied.
-
-3. **Creating a seed mask.**
-* [This python scricpt](https://github.com/gkiar/mask2boundary) was used to create a seed mask for each participant.
-
-4. **Tracking of diffusion weighted images (DWI)**
-* [This docker image](https://hub.docker.com/r/gkiar/dipy_tracking) was used to perform tracking of the diffusion data.
-* [This python script](https://github.com/gkiar/stability/blob/master/code/tractography/dipy/dipy_tracking.py) was used within the container. This script is based off of [this tutorial](https://dipy.org/documentation/1.0.0./examples_built/tracking_introduction_eudx/) for DIPY tracking.
-
-5. **Data visualization**
-* Data visualization was performed within a Jupyter notebook using Plotly, Nilearn, Matplotlib, Seaborn and more (see visualization examples with gif below).
-
-
-## Progress & Results
-
-### Data visualization
-1. Connectivity matrix
-2. Connectome projected on 3 views of a 2D glass brain
-3. Connectome projected on a 3D glass brain
-
-
-
-
-
-
-
-
-* The [jupyter notebook](https://github.com/brainhack-school2020/mwmaclean-BHS2020/blob/master/data_visualization/dMRI_data_visualization.ipynb) version of all three figures is interactive with a drop-down for each subject using widgets.
-* The connectivity matrices (.mat files) used for the data visualization can be found [here](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/data_visualization/connectivity_matrices)
-* The figures (saved as .png or .html) for each subject can also be found [here](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/images)
-
-### Progress overview
-This project was initiated by Michèle MacLean May 19th 2020 as part of the BrainHack Summer School 2020.
-Organization of MRI data into BIDS format, preprocessing and basic tracking of the data is complete as well as data visualization for the deliverable for week 3. See the methods section for a detailed description. This will be an ongoing project that I will continue to work on after BrainHack Summer School. Feedback is welcome!
-
-#### Tools and skills I learned during this project
-* **Bash terminal**
-* **GitHub**
-* **Markdown**
-* **Visual Studio Code**
-* How to implement **BIDS**
-* **Python** scripts
-* **Docker container**
-* **DIPY** for preprocessing and basic tracking of diffusion MRI
-* **Jupyter Notebook**
-* Interactive figures in Jupyter Notebook using **data visualization libraries**: Nilearn, plotly & matplotlib, etc.
-* **Preprocessing diffusion MRI data**
-* **Tracking diffusion MRI data**
-* **Mapping connectomes**
-
-
-
-
-
-## Conclusion
-The BrainHack Summer School 2020 provided a theoretical framework as well as an extensive set of practical neural data analysis and visualization tools that were mainly all new to me. For the current project, my goal was to maximize using these new tools, to learn and apply the open science protocols in order to analyze data within my own doctoral project. Within the time frame of the summer school, I set out to perform all the steps starting from raw diffusion MRI data, which I previously acquired at l'Unité de neuroimagerie fonctionnelle, leading to data visualization. In particular, this dataset was organized into a BIDS friendly format, preprocessed with a python script within a docker container, basic tracking was also performed using another python script within a second container and finally data visualization was done using multiple libraries within a jupyter notebook. As a neuroscientist with a limited previous background in programming, I believe I sensibly improved my skills towards performing independently the whole process to analyze neuroimaging data. As I plan to continue working on this project with the new tools learned, my next immediate step will be to increase the number of subjects. Also, my next challenge will be to specifically account for brain injuries leading to cortical visual impairment during the analyses as this requires additional algorithmic tools and modifications to the preprocessing and analysis pipelines.
-
-
-## Acknowledgements
-Thanks to the wonderful BrainHack Summer School 2020 for this experience as well as all the excellent resources provided!
-I would like to thank my instructors Noor and Benjamin for their advice and for providing specific resources for this project. Special thanks to my instructor Greg who originally developed the python scripts and docker images I used and invested a lot of time to help and contribute to this project. I would also like to thank my new friend and coding partner Daniel and my friend Simon who was a great study buddy throughout the summer school!
-
-Be sure to check out the other cool projects from the BrainHack Summer School 2020 [here](https://school.brainhackmtl.org/project/)!
-
-## References
-1. Abraham, A., Pedregosa1, F., Eickenberg1, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics 8: 14. https://doi.org/10.3389/fninf.2014.00014 (for using nilearn)
-2. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I and Dipy Contributors (2014). DIPY, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, vol.8, no.8.
-3. Gorgolewski, K.J., Auer, T., Calhoun, V.D., Craddock, R.C., Das, S., Duff, E.P., Flandin, G., Ghosh, S.S., Glatard, T., Halchenko, Y.O., Handwerker, D.A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A., 2016. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3, 160044.
-4. Kiar, G. . (2020). Dipy Tracking and Connectome Generation (Version v0.4.0). Zenodo. http://doi.org/10.5281/zenodo.3699595
-5. Kiar, G. (2020). dipy. Retrieved from https://github.com/gkiar/stability/tree/master/code/tractography/dipy
-Kiar, G. (2019). Using FSL from FMRIB at Oxford- BIDS App - FSL Diffusion Preprocessing (Version 5.0.9). Zenodo. http://doi.org/10.5281/zenodo.2566455
-6. Kiar, G. (2019). preprocessing. Retrieved from https://github.com/gkiar/stability/tree/master/code/preprocessing
-7. Kiar, G. (2019). mask2boundary. Retrieved from https://github.com/gkiar/mask2boundary
-8. Klein, A. and J. Tourville (2012). 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in Neuroscience 6: 171.https://doi.org/10.3389/fnins.2012.00171
-
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-07-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Decoding of painful stimuli using fMRI data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Dylan Sutterlin-Guindon, Marie-Eve Picard, Jeni Chen, Mathieu Landry, Pierre Rainville]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/sutterlin_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [decoding, fMRI, pain, hypnosis, python]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Painful experience involves a distributed pattern of brain activity. With hypnosis, it's possible to increase or decrease pain. This project aims to decode fMRI pain-evoked brain activity and identify pattern of activity that are associated with specific hypnotic conditions"
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain-activity.webp"
----
-### Background
-#### personal background
-
-Link to Github page :
- Dylan Suttelrin-Guindon
-
-
-Education :
- * B.Sc. Neuroscience cognitive(fundamental research option) from Université de Montréal
-
-### Project definition
-
-Pain is a multi-dimensional experience involving emotional, sensory and motivational aspects. Hypnosis is one intersting psychological
- tool among many others (like placebo or meditation) that have been shown to effectively modulate pain. Verbal suggestions are an important component of hypnosis and are defined as verbal instructions that are intended to change or induce a cognition, behavior or sensory experience. In the case of pain, suggestions can be hyperalgesic or hypoalgesic, and are intended to increase or decrease pain, respectively. Verbal suggestions to reduce pain can reduce or amplify the brain activity evoked during painful experiences (e.g. during the administration of electrical shocks).
-
-Studies show that the experience of pain is not correlated with spatially restricted brain activity, but rather is the result of activity in several regions (e.g. anterior mid-Cingulate Cortex, insula, thalamus, somatosensory cortex). Given the distributed nature of brain activity during the experience of pain, multivariate pattern analyses seem to be a good approach to study the subjective experience of pain.
-
-In order to better understand how verbal instructions (such as hyper/hypoalgesia) are integrated by the brain and then result in a modulation of brain activity during the administration of painful stimuli, this project aims to decode pain-related brain activity and identify activation patterns that predict a specific type of verbal suggestion.
-
-### Tools
-
-A machine learning approach will be used to decode pain-evoked brain activity and predict verbal suggestions that was administered prior to painful stimulation.
-
-The tools used to accomplish this task are:
-
-* Python scripts
-* Python modules (pandas, numpy, sklearn, nilearn, nibabel, matplotlib, seaborn)
-* Bash to run the scripts on elm server
-* Git and Github to keep track of the project's evolution
-
-### Data
-
-The dataset that will be used comes from Desmartaux et al., 2021 and has restricted access. It includes 24 participants (13 women and 11 males) and mean age is 26.9. Subjects participated to a fMRI scanning session where they received hypnosis to either increase or decrease pain. After hypnotic suggestions to modulate pain,a serie of either 6 or 9 painful stimuli were administered. In total, each participant received 72 electrical shocks. Across all participants, a total of 1728 trials/shocks were done.
-
-
-### Method
-
-A Support vector classifier from [sklearn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) was applied to 1726 statistical brain maps, each modeling the brain activation during a single painful shock. Those contrast maps were generated using a General Linear Model for which the code can be found [here](https://github.com/dylansutterlin/decoding_pain_experience). Those 1726 trials were used to predict the pain modulating condition administered prior to each shock.
-
-* Example of a statistical map :
-
-
-There were four different type of verbal suggestion to modulate (or not for the control sugestions) the pain experience. Hence, there were four classes to predict with the model. Those four classes are listed a the following :
-
- * Hyperalgesia
- * Neutral suggestion in Hyperalgesia run
- * Hypoalgesia
- * Neutral suggestion in hypoalgeisa run
-
-
-#### Target deliverables
-
-* Python scripts (main script that run the analyses)
-* Metrics and statistics of the performances of the model
-* Graphics (e.g. ROC curve, confusion matrix)
-* fMRI maps of the voxels that best predicted suggestions plotted as 3D interactive plots
-* A markdown README.md describing globaly the project and putting it in a scientific context
-* Github repository provinding a fairly open science/reproductible aspect to the project
-
-## Results
-
-### Overview
-
-#### Actual deliverables
-
-* - [x] Python scripts (main script that run the analyses)
-* - [x] Metrics and statistics of the performances of the model
-* - [x] Graphics (e.g. ROC curve, confusion matrix)
-* - [x] fMRI maps of the voxels that best predicted suggestions plotted as 3D interactive plots
-* - [x] A markdown README.md describing globaly the project and putting it in a scientific context
-* - [x] Github repository provinding a fairly open science/reproductible aspect to the project
-
-#### Final script's structure
-* data : Permission pending to give access to date (...)
-* docs : A readme file descripbing the pipeline
-* images : The images used in this README.me
-* results : Saved metrics' dataframe, brain maps (.nii files) showing the voxels that were the most predictive of the target
-* scripts : The main scripts with a function file that are called in main.py
-* src : All the general function that can be reused wihout context
-
-### Statistical model
-
-* The SVC model was performed with a linear kernel
-* A nifti masker was used to project the 3 dimmensional contrast maps to 1 dimensional with the argument `(mask_strategy = 'whole-brain-template', standardize = True)`
-* Then a SVC was applied using on a K-fold cross-validation with the following parameters : `GroupShuffleSplit(n_splits = 5, test_size = 0.3, random_state = 33)`
-
-* The final model was fitted all the training set and was then tested on a subset of data for that the model had never 'seen'. The final model accuracy = **0.60970874**
-
-* A Matrix confusion is presented to illustrate the model'performance
-* * Class 1 = Hyperalgesia
- * Class 2 = Neutral suggestion in Hyperalgesia run
- * Class 3 = Hypoalgesia
- * Class 4 = Neutral suggestion in hypoalgeisa run
-
-
-* With the final model, all the coefficients were reprojected to the MNI space using `masker.inverse_transform()`. Since there were four classes, six different class comparisons can be made and the highest coefficient for each comparison are presented in the plots below. The highest peaks represents the brain regions that either when activated or deactivated (red or blue) had the highest preictive value of the class(type of suggestion). For example, when looking at the first plot, the activation of the dlPFC appears to be the most predictive to predict the Neut_HYPO vs HYPO class.
-
-
-
-
-
-
-
-## Conclusion
-
-The main objective of this project was to complete a decoding model having the voxels of statistical brain maps as features and type of verbal suggestions as classes. Globally this project and it objectives have been reached. The delivarables were mostly acheived. Also, althought the code to train the decoding model has been made and is operational, the statistical maps that were used to train the model are prone to change in the sense that quality-control is still required in order to be confident about the validity of the results.
-
-## Acknowledgement
-
-Thanks to all the instructors, espicially Marie-Eve and Francois who took the time to sit down with me and precise some concepts and shed some light on many topics such as the proper workflow, debugging techniques, proper use of sklearn, nilearn modules and so on!
-
-## References
-
-
-Casiglia, E., Finatti, F., Tikhonoff, V., Stabile, M. R., Mitolo, M., Albertini, F., Gasparotti, F., Facco, E., Lapenta, A. M., & Venneri, A. (2020). MECHANISMS OF HYPNOTIC ANALGESIA EXPLAINED BY FUNCTIONAL MAGNETIC RESONANCE (fMRI). International Journal of Clinical and Experimental Hypnosis, 68(1), 1 15. https://doi.org/10.1080/00207144.2020.1685331
-
-Desmarteaux, C., & Rainville, P. (2021). Brain Responses to Hypnotic Verbal Suggestions Predict Pain Modulation. Frontiers in Pain Research, 2, 18.
-Franz, M., Schmidt, B., Hecht, H., Naumann, E., & Miltner, W. H. R. (2021). Suggested visual blockade during hypnosis : Top-down modulation of stimulus processing in a visual oddball task. PloS One, 16(9), e0257380. https://doi.org/10.1371/journal.pone.0257380
-
-Hofbauer, R. K., Rainville, P., Duncan, G. H., & Bushnell, M. C. (2001). Cortical Representation of the Sensory Dimension of Pain. Journal of Neurophysiology, 86(1), 402 411. https://doi.org/10.1152/jn.2001.86.1.402
-
-Jiang, H., White, M. P., Greicius, M. D., Waelde, L. C., & Spiegel, D. (2017). Brain Activity and Functional Connectivity Associated with Hypnosis. Cerebral Cortex (New York, N.Y.: 1991), 27(8), 4083 4093. https://doi.org/10.1093/cercor/bhw220
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-05-20" # Date you first upload your project.
-title: "Deep Electrode Mapper: Localizing EEG Electrodes from 3D Point Clouds Using Deep Learning"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Joel P. Diaz-Fong, Lucas Vidal Murakami, Aijia Ivy Zhong]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/DeepElectrodeMapper
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2023/JDaoust_project.git), click `manage topics`.
-# Please only lowercase letters
-tags: [electrode localization, deep learning, clustering]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects.
-
-summary: "Accurate EEG source localization depends on precise electrode coordinates, yet current methods are often manual, costly, and technically demanding. *Deep Electrode Mapper* attempts to address this by applying deep learning to segment electrodes from 3D head models—derived from MRI or 3D scans—and localize their coordinates using clustering. Although the project remains incomplete, it demonstrates a proof-of-concept pipeline, and progress is documented in the public repository."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "coverimage.png"
----
-
-
-## Project definition
-
-### Background
-
-In EEG analysis that aims to localize the origin of brain signals, source localization requires a realistic head model and precise electrode coordinates to ensure spatial accuracy. However, current approaches to obtaining these coordinates are either time-consuming (e.g., manual labeling) or require specialized, often costly hardware/software. Both options demand technical expertise, posing barriers to widespread or streamlined use.
-
-Further complicating matters, electrode localization must accommodate various data formats, such as MRI volumes (Pinte et al., 2021), 3D surface scans, and photogrammetry (Hirth et al., 2020). A robust solution must therefore be both format-agnostic and computationally efficient.
-
-Recent innovations have begun to address this need: pipelines using mobile phones for 3D scanning (Everitt et al., 2023), algorithms that infer missing electrodes from partial montages (Hirth et al., 2020), and deep learning models applied to MRI-based localization (Pinte et al., 2021) have all demonstrated potential.
-
-Building on these efforts, our project aims to develop a deep learning-based pipeline for electrode localization that is generalizable across file types and imaging modalities. The goal is to create a solution that reduces cost, minimizes manual labor, and increases accessibility—regardless of the acquisition method used.
-
-
-
-
-#### Objectives
-- Automate EEG electrode localization and labeling
-- Develop a flexible pipeline that can be applied to external datasets
-- Evaluate localization accuracy across data types (e.g., same-modality `.obj` vs. cross-modality T1-weighted MRI) to test generalizability
-
-
-#### Objectives for BrainHack school
-- Gain hands-on experience with open-source tools, deep learning frameworks, and collaborative coding practices
-
-### Tools
-
-This project relies on the following tools:
-
-- **GitHub** – For version control and collaborative development
-- **VS Code** and **Jupyter Notebook** – For interactive and modular code development
-
-Key libraries and components:
-
-- **Open3D** – Convert surface and volume data into 3D point cloud format (`.ply`, `.npz`)
-- **h5py** – Transform labeled and unlabeled 3D point clouds into HDF5 format
-- **PointNet++** – Deep learning model for training and testing electrode localization and segmentation
-- **K-Means Clustering** – Identify electrode centroids and extract their coordinates
-- Custom alignment tools – Align electrode labels to head scans
-
-### Data
-
-The dataset includes 3D head models acquired using a portable 3D scanner (`.obj` files). A subset of participants also has T2-weighted MRI scans (`.nii`) acquired while wearing an EEG cap.
-
-These data come from the study *Alpha Oscillations and Working Memory Deficits in ADHD: A Multimodal Imaging Investigation* (R01MH116268), available through the NIH Data Archive:
-[https://nda.nih.gov/edit_collection.html?id=3101](https://nda.nih.gov/edit_collection.html?id=3101)
-
-
-
-### Deliverables
-
-1. GitHub repository with our tools and code.
-2. Documentation on how to train and test on different datasets.
-
-## Results
-
-### Progress Overview
-
-We focused on preparing the dataset for training the PointNet++ model and developing a clustering pipeline to extract electrode coordinates once segmentation is complete. A subset of the dataset was manually labeled to create both labeled and unlabeled data for training and testing the model.
-
-We also built two tools to align electrode labels with the corresponding head model. To ensure compatibility with PointNet++, we converted the data into point cloud format and then into HDF5 files—both in labeled and unlabeled versions. Our preprocessing script accepts multiple input formats to support generalizability.
-
-Additionally, we implemented a Python script using K-Means clustering to extract centroid coordinates from the segmented point clouds. These centroids are intended to serve as the localized electrode positions.
-
-Next steps include training and evaluating the deep learning model. Given the limited dataset size, we are also exploring the possibility of augmenting the training set with synthetic data.
-
-
-### Tools I Learned During This Project
-
-- **GitHub**
- We learned how to use GitHub for version control and collaborative development, enabling us to share and track changes to code and scripts across the team.
-
-- **Jupyter Notebook**
- We used Jupyter Notebooks to develop and test code in a modular, step-by-step fashion. This structure made it easier to isolate and debug issues and helped organize different components of the pipeline.
-
-- **VS Code**
- Visual Studio Code was valuable for integrating multiple development tools in one environment. We used it to write and manage code across files, run terminals, and install packages directly, streamlining the development workflow.
-
-
-### Results
-
-#### Deliverable 1: GitHub Repository
-
-Although the project is still in progress and the GitHub repository does not yet contain the final versions of the scripts or a trained model, we have uploaded several core components. These include tools for aligning electrode labels with 3D head scans, scripts for preprocessing data for deep learning input, and post-segmentation clustering scripts to extract electrode coordinates from the model output.
-
-#### Deliverable 2: Documentation
-
-Although the deep learning model has not yet been trained or tested, we began preparing documentation that outlines the full electrode localization workflow. This includes a step-by-step description of the data preprocessing, model input/output formatting, and post-processing steps such as clustering for coordinate extraction.
-
-To aid clarity and reproducibility, we created a visual representation of the workflow:
-
-
-
-This diagram illustrates the full pipeline—from raw 3D scan input and manual labeling to PointNet++ segmentation and centroid estimation. Full documentation will be completed as the project advances and the model is finalized.
-
-
-### Conclusion and Acknowledgements
-
-Developing a generalizable and accessible tool for localizing EEG electrode coordinates would represent a meaningful advance in both clinical and research applications of EEG. While deep learning offers a promising route toward automation and scalability, it introduces limitations—particularly the need for large, labeled datasets and consistency in data acquisition setups. Despite these challenges, our approach demonstrates the potential for building a flexible pipeline that accommodates diverse file formats and EEG systems.
-
-We plan to continue developing this project to assess the feasibility and accuracy of the proposed method.
-
-We gratefully acknowledge the study *Alpha Oscillations and Working Memory Deficits in ADHD: A Multimodal Imaging Investigation* (R01MH116268) for providing access to the data used in this project. We also thank the BrainHack School for offering the time, support, and collaborative environment that enabled this work.
-
-Sample data can be accessed at: [osf.io/87av2](https://osf.io/87av2)
-
-
-## References
-
-Diaz-Fong, J. P., & Lenartowicz, A. (2025, March 8). 3D Scanning Sample Data for EEG Electrode Localization. Retrieved from osf.io/87av2
-
-Everitt, A., Richards, H., Song, Y., Smith, J., Kobylarz, E., Lukovits, T., Halter, R., & Murphy, E. (2023). EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES). Journal of Neural Engineering, 20(6), 066033.
-
-Hirth, L. N., Stanley, C. J., Damiano, D. L., & Bulea, T. C. (2020). Algorithmic localization of high-density EEG electrode positions using motion capture. Journal of neuroscience methods, 346, 108919. https://doi.org/10.1016/j.jneumeth.2020.108919.
-
-Pinte, C., Fleury, M., & Maurel, P. (2021). Deep learning-based localization of EEG electrodes within MRI acquisitions. Frontiers in Neurology, 12, 644278.
-
-Tveter, M., Tveitstøl, T., Nygaard, T., Kulashekhar, S., Bruña, R., Hammer, H. L., Hatlestad-Hall, C., & Haraldsen, I. R. H. (2024). EEG electrodes and where to find them: automated localization from 3D scans. Journal of Neural Engineering, 21(5), 056022."
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-25"
-# Title of your project (we like creative title)
-title: "Voice and Linguistic Analysis to Determine Emotions in Friends' Interactions"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Katia Djerroud]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2024/Djerroud-project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Emotion Detection, Voice Analysis, Linguistic Analysis, Social Interactions]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Analyzes emotional dynamics in social interactions using voice and linguistic analysis on the Friends TV show dataset. Utilizing tools like Praat, OpenSmile, NLTK, and Hugging Face Transformers, it detects emotions from audio and text. Deliverables include code, documentation, datasets, and analysis workflows. Achievements encompass integrated emotion detection models and a robust analysis pipeline, with future plans to enhance models and expand datasets."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "sweet-talk.jpg"
----
-
-
-## Djerroud-project
-
-Voice and Linguistic Analysis to Determine Emotions in Friends' Interactions Welcome to my project, where I explore the intersection of voice analysis, linguistic analysis, and natural language processing (NLP) to uncover the emotions generated during interactions among friends. This project analyze speech patterns, text exchanges, and vocal nuances to detect and interpret emotional states and determine the closeness betweeen the characters.
-
-## Features:
-
-Voice Analysis: Utilizes Praat for detailed spectrograms, pitch, formant analysis, and other acoustic features. Emotion Detection: Employs OpenSmile for extracting vocal features and identifying emotional cues from speech. Linguistic Analysis: Implements NLTK for tokenization, parsing, and sentiment analysis of textual interactions. NLP Models: Integrates state-of-the-art models from SpeechBrain and Hugging Face for comprehensive speech and text processing. Interactive Visualizations: Provides insights through visual representations of emotional states and linguistic patterns. Goals:
-
-To understand how friends' vocal expressions and word choices convey emotions. To develop a robust system for real-time emotion detection and linguistic analysis. To create a dataset and models that can be utilized for further research in social dynamics and emotional intelligence. Join me in this journey of decoding the emotional landscapes of social interactions through the power of voice and linguistic analysis!
-
-
-Project Definition Objective Analyzing emotional dynamics in social interactions using voice, linguistic, and text analysis techniques applied to the "Friends" TV show dataset.
-
-## Background Dataset Source:
-
-Obtained from MarieSTL, Pierre Bellec’s Lab, ensuring credibility and relevance to the research question.
-
-## Modality:
-
- Includes audio (MP3) and textual transcripts (JSON) of dialogues.
-
-## Suitability:
-
-Ideal for capturing real-life interactions among friends and exploring emotional dynamics.
-
-## Tools Voice Analysis
-
-Librosa, Praat, OpenSMILE Linguistic Analysis NLTK, SpaCy, Gensim Emotion Detection Hugging Face Transformers
-
-## Data Content
-
-Audio files (MP3) extracted from video episodes JSON transcript files containing dialogue text
-
-## Deliverables Expected Outputs
-
-GitHub repository with code scripts and Jupyter notebooks Documentation (README.md) Dataset files (audio, JSON transcripts) Analysis workflow/pipeline Training materials and models
-
-## Results Achievements
-
-Integrated voice and linguistic analysis for emotional dynamics Developed emotion detection models for audio and text inputs Established a robust analysis pipeline from data preprocessing to visualization
-
-## Progress Overview Summary
-
-Successfully implemented tools for voice and linguistic analysis Challenges included data integration and model optimization Tools I Learned During This Project Praat, OpenSMILE for voice analysis Hugging Face Transformers for emotion detection Docker, Git/GitHub for version control and project management
-
-## Conclusion and Acknowledgement Next Steps
-
-Enhance model performance and expand dataset coverage Prepare findings for publication and seek collaboration opportunities Acknowledgement Special thanks to MarieSTL, venkatesh and Pierre Bellec’s Lab for providing the "Friends" dataset Gratitude to the open-source community for tools and libraries used
-
-## More details
-
-Additional information is available in the `notebooks` and `presentation` folders.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-10" # Date your first upload your project.
-# Title of your project
-title: "Predicting feedback perception in an online language learning task using EEG and machine learning"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Dylan Scott Low Y.J., Stephanie Fu Pei-Yun]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/stephanie0404/Brainhack-school-project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [machine learning, duolingo, eeg, neurolinguistics, brainhack]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this project, we aim to use machine learning on EEG data from participants' language learning tasks on Duolingo. Specifically, we ask if EEG features can predict whether the participant has gotten a task right or wrong when they receive feedback. Using a k-nearest neighbours classifier, we achieve 98% accuracy in determining correct or incorrect answers based on EEG voltages from 8 electrodes."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "./Cz_sample_filtered.jpg"
----
-
-
-## Project definition
-
-### Background
-
-In recent years, new apps and products have been introduced based on the idea that machine learning techniques could be applied to EEG brainwaves in order to decipher a person's thoughts and generate statistics about the different kinds of thoughts a person has in a day (e.g, Neurosity; see also Houser, 2019; Fireship, 2023). Our motivation is to attempt to implement our own simple machine learning analysis on raw EEG data to discern cognitive activity, especially that related to language. We selected a dataset containing EEG data recorded while participants completed language learning tasks in the online platform, Duolingo (see "Data" below). Our main objectives in this project were to develop a pipeline for analysing EEG data (OpenBCI format) using open source tools like MNE Python and apply machine learning methods to the analysis. Our personal learning objectives were to make ourselves more familiar with EEG data analysis, the Python programming language and corresponding workflows, and other open source tools used in neurosciencce research.
-
-The main question we asked in this project was: How can we use machine learning techniques to identify, based on EEG data, if the participant was receiving positive feedback (they got the learning task correct) or negative feedback (they got the learning task wrong) from Duolingo?
-
-### Tools
-
-The project relied on the following tools:
- * R programming language (packages: tidyverse, ggplot2, readr)
- * Python programming language (libraries: pandas, scikit-learn, MNE Python)
- * Git/GitHub
- * Bash terminal
-
-### Data
-
-The data we used comes from a project titled Data in Brief: Multimodal Duolingo Bio-Signal Dataset by Notaro & Diamond (2018; click here for the corresponding article). As suggested by the title, this is a multimodal dataset. Four kinds of data were recorded: (1) EEG via a low-cost OpenBCI electrode cap, (2) Gazepoint-GP3 eye-tracking, (3) Autohotkey cursor movements and clicks, and (4) Autohotkey screen content data (screenshots taken when a click occurs). Data was recorded while participants (n = 22) were completing beginner German language lessons on the desktop version of Duolingo. Participants were either English native speakers or fluent in English.
-
-Our project focused on (1) and (4), that is, the EEG and screenshot data. In particular, we analyse only the data of participant 1.
-
-### Deliverables
-
-At the end of this project, these files will be available:
- * A .txt file (data_labels_raw.txt) containing manually selected filenames (timestamps) of the screenshots corresponding to when the participant receives feedback on their Duolingo performance.
- * An R script (data_labels.r) for cleaning the raw filenames list (data_labels_raw.txt) into machine-readable format (data_labels_clean.txt).
- * A Jupyter notebook containing Python script for preprocessing the OpenBCI EEG data (high and low pass filtering).
- * An R script (data_labelling.r) for combining the filtered EEG data with the data_labels_clean.txt file.
- * A Juyter notebook containing Python script for running machine learning classifiers (e.g., MLP, kNN, random forest) on the labelled EEG data.
-
-## Results
-
-### Progress overview
-
-At first, we thought preprocessing the data would be a simple matter. However, we quickly realised that the OpenBCI EEG format (formatted as a .txt file) is not a standard raw EEG format that could be easily imported in MNE Python. Solving this problem caused us some delays, but we eventually came up with a custom script to import the data in MNE Python. Another peculiarity we observed was that the OpenBCI set-up only consisted of 8 electrodes, as opposed to the standard 32 channels.
-
-Also, we initially intended to analyse the data of more than one participant in the data set, but realised that even data from one participant (recorded over about 58 minutes) was very large, and likely more than sufficient to train an ML model. The large size also delayed our work due to the long wait times when processing the data, sometimes leading to crashes which force us to start the process over again until it reaches fruition (an unfortunate symptom of running such analyses on weak personal machines).
-
-### Tools we learnt during this project
-
- * **MNE Python**: We learnt how to filter raw EEG data during the preprocessing stage, as well as how to import EEG data even if not in a recognised standard raw format.
- * **Github workflow**: We got well-acquainted with using Git via the Bash terminal, and also some related tools like Datalad and Git LFS (Large File System).
- * **Bash Terminal**: We gained a good understanding of how to use the Bast erminal in lieu of File Explorer (Windows)/Finder (Mac).
-
-### Results
-
-#### Figures and Plots
-
-##### Preprocessing
- Unfiltered Cz Channel during 1 second epoch.
-
- Filtered Cz Channel during 1 second epoch.
-
- Unfiltered F3 Channel during 1 second epoch.
-
- Filtered F3 Channel during 1 second epoch.
-
- Power Spectral Density (PSD) plot for the unfiltered EEG.
-
- Power Spectral Density (PSD) plot for the filtered EEG.
-
- EEG cap sensor positions.
-
-##### Machine Learning
-
- Feature Importance (Random Foreest)
-
- Confusion Matrix (Random Forest)
-
- Feature Importance (kNN)
-
- Confusion Matrix (kNN)
-
- Feature Importance (Logit)
-
- Confusion Matrix (Logit)
-
- Feature Importance (Multilayer Perceptron)
-
- Confusion Matrix (Multilayer Perceptron)
-
-#### Discussion
-
-The full results can be viewed in the Jupyter notebook ml_eeg_classifier in our GitHub repository. We briefly summarise the accuracy rates attained by each model below.
-
-Random Forest: 0.8923478119142694
-
-Logistic Regression: 0.5955504848830576
-
-k-Nearest Neighbours: 0.9759595794963736
-
-Multilayer Perceptron: 0.6984760818189226
-
-A prima facie look will show that the k-Nearest Neighbours (kNN) classifier achieved the highest performance in accurately predicting whether the participant was looking at positive or negative feedback based on the EEG data. However, it is possible that the high accuracy rate may be due to overfitting, and more sophisticated evaluation metrics will need to be consulted (unfortunately, time limits prevent us from doing so here).
-
-Future directions for this project could involve exploring more advanced machine learning techniques, such as deep learning models, to capture complex patterns in the EEG data. Additionally, incorporating other modalities available in the multimodal dataset, such as eye-tracking or cursor movements, could potentially enhance the prediction accuracy and provide a more comprehensive understanding of the cognitive processes during language learning.
-
-## Conclusion and acknowledgement
-
-In summary, this project provides a starting point for applying machine learning methods to EEG data analysis in the context of language learning. The results highlight the potential of using EEG features to predict certain cognitive tasks, but further research is required to refine this (admittedly) very coarse implementation and address the aforementioned limitations.
-
-We humbly thank the Brainhack School Team for organising this fantastic learning journey, as well as all the instructors who developed the learning modules and corresponding video lectures/lessons. We would especially like to thank the Taiwan Hub team (National Taiwan University and National Central University) for all their support and encouragement.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-07-05" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "An easy guide to not throwing your expensive computer out the window because you can't run a Python neuroimaging tool"
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Claudéric DeRoy]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/deroy_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [python, scripting, packaging, replicability]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The goal of this project was to learn how to create code that would be easy to use for unexperienced users but also to be as more open as possible while also being replicable. So I took a code already written and scripted it, packaged it, made a Docker container for it, and finally created a guide on how to use it."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "python_code.jpg"
----
-
-
-# Project definition
-
-## Personnal Background
-Hello there! My name is Claudéric DeRoy. I have completed a Bachelor in Cognitive Neuroscience at Université de Montréal and also a part of a Bachelor degree in Computer Science, also at Université de Montréal. I am actually a master student in psychology in the NeCS lab under the supervision of Professor Sébastien Hétu. I essential study some computer programmes and pipelines for preprocessing electrodermal activity (EDA) to propose guidelines on how to signal process EDA to the scientific community.
-
-
-## Project Background
-The more theoretical background is from another [project](https://github.com/brainhack-school2022/Lajoie_project/blob/main/project_description.md) of the Brainhack School 2022. The background for this project is more of a practical one. Essentially, computer programmes or computer analysis pipelines for neuroimaging are quite common and can be pretty intimidating for people who are not used to programming in general, which corresponds to a lot of undergrad and graduate students in psychology. Fortunately, there are many tools that can make the installation of libraries, modules, virtual environments and the rest easier or at least less painful. This also helps on another level of research. In 2012, Gronenschild et al. obtained a difference in percentage of volume and cortical thickness using FreeSurfer on both a Macintosh and a Hewlett-Packard machine but also between two different versions of Mac OSX (OSX 10.5 and OSX 10.6). This demonstrates the importance of packaging and containerized programmes so that scripts are always run in the same environment, but also making those containers available so that the scientific community can use the same tools in the same environment, helping rule out the difference between software as a cause for reproducible failure.
-
-
-## Objectives/Goals
-The main goals of this project are to familiarise myself with scripting, packaging Python code, making Python scripts run more easily on high performance computers (HPC) for the average user, and containeraizing the whole thing. This will help make it easy for inexperienced users to get everything from my Github repository and execute the script either on HPC or their personal machine. So the main goals are :
-- Take the existing Python code for the seed to voxel correlation and make a script that will make it easy to run from the terminal.
-- Do the same thing but for the machine learning algorithm that will use the seed to voxel correlation to classify.
-- Use Git and Github to create version control of all the scripts.
-- Getting familiar with HPC, especially Calcul Canada (Alliance), using Singularity with Docker image or file to use them on Calcul Canada.
-- Containerize everything at the end so that the entire project can be easily installed on any computer or HPC.
-- Make an easy-to-use guide so people with little to no knowledge of computer science would be able to use and deploy the Docker container or execute the script.
-
-
-## Tools used
-- Python
-- Git/Github
-- Script and packaging with Python
-- Bash
-- HPC (Calcul Canada or Brainhack ORACLE)
-
-
-## Data
-I won't work with data, per se but more with code. However, I will use the other [project](https://github.com/brainhack-school2022/Lajoie_project/blob/main/project_description.md) data to make sure everything works correctly.
-
-
-## Deliverables
-I want to produce by the end of the project :
-- The script, packaging, and containerization of at least the seed to voxel correlation code.
-- In the best case scenario, I will do the same for the machine learning code.
-- Some sort of tutorial (either a markdown file or a video) to explain to someone who has no background in computer science how to install everything and run the code.
-
-
-# Results
-I do not have results in the more general term. As previously stated, my goal is to make an existing code more accessible, easy to use, open and replicatable. So, I will lay out what I accomplished in terms of these goals.
-
-## Accessibility
-I packaged everything I produced during this project. There are four important files : the `script.py`, `batch.json`, `seed_voxel.py` and `Dockerfile`. They are part of the packaging I did, which is available on TestPyPi (https://test.pypi.org/project/seed-to-voxel-neurok8050/). So, it is more accessible because anybody can install the package with a simple `pip install` (see the [`guide.md`](https://github.com/brainhack-school2022/deroy_project/blob/main/guide.md) for a full installation explication).
-
-## Ease of use
-The idea was to make the code as easy to use as possible. Often, when you have access to code that a researcher has used for a paper and you want to use it and have access to it, it is somewhat of a daunting task; you do not really know where to start or what to modify, because everything is "baked" into the code itself (variables, values, etc.). So my original idea was to use a `.txt` with all thes variables names and their values, so that the script could just parse it and plug the variables into the right places. The user would just have to modify the `.txt` file and would not have to play around in the source code (although he is more than welcome to do so). It turns out that `.txt` are not suited for such a task, but `.json` file are perfect for this purpose. That is where the `batch.json` comes from. Finally, when the user wants to run the `script.py` he only has to pass one argument (the `.json` file) instead of 20.
-
-## Openness
-This is fairly simple, everything is accessible on my GitHub repository. So feel absolutely free to `git clone` my repository and have fun with it. I really do not mind. Have fun and make it yours, that is the joy of programming.
-
-## Replicability
-Creating a Docker container (actually writing a Dockerfile) will help (in my humble opinion) to run the code more replicable. Like I said above, Gronenschild et al. (2012) have shown a certain degree of variability between different versions of the same OS and also between different versions of the same software. Using a Docker container constrains the version of software used during the execution of the code. This version constraint will be the same for every user that runs the code inside the Docker container. But also, in each run, the same user will technically have the same software version each time, thus ruling out the software as a potential point of source failure for replicated study.
-Also, the `.json` also helps for replicability. For example, if I run the code with an edited version of the `batch.json` by me, I can publish the article with my edited `batch.json` so that any researcher who is trying to replicate my finding will only have to run the code with the `batch.json` I provided in my article.
-
-
-## The tools I learned and used for this project
-
-- Scripting in Python (shebang is the rule)
-- Packaging Python code (setup.py, pyproject.toml) and uploading it to TestPyPi
-- Docker container (This was by far the most difficult thing)
-- High performance computer (I wanted to use Singularity to make the Docker container work on Compute Canada, but didn't have time to do it)
-
-# Conclusion
-
-To conclude, I managed to complete most of my objectives. Now I want to focus on deploying it and seeing if the users are capable of using the package, and make changes depending on the user experience with it. I did not package the machine learning algorithm part, but it is part of the next steps I want to take with this project, alongside adding the Singularity part for a good integration of the Dockerfile to HPC. I am really pleased with what I learned during Brainhack school. I have sort of a template for scripting, packaging, and containerizing all of my future projects.
-
-## Reference
-Gronenschild, E. H. B. M., Habets, P., Jacobs, H. I. L., Mengelers, R., Rozendaal, N., van Os, J., & Marcelis, M. (2012). The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements. PLoS ONE, 7(6), e38234. https://doi.org/10.1371/journal.pone.0038234
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "EEG Athlete Project: Brain Activity During Golf Performance"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Samantha Bjelis, Fardin Islam]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/islam_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2023/JDaoust_project.git), click `manage topics`.
-# Please only lowercase letters
-tags: ["EEG", "sports neuroscience", "golf", "data analysis", "bootstrapping"]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Using EEG band power to investigate cognitive states during golf swings and correlate them with subjective performance ratings."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "project_image.png"
----
-
-## Project definition
----
-### Background
-
-Cognitive states such as focus, relaxation, and arousal are known to influence athletic performance, particularly in precision sports like golf. Electroencephalography (EEG) offers a non-invasive way to monitor neural activity and derive insights about cognitive engagement through frequency band analysis. Prior studies have associated increased alpha and theta power with focused attention and automaticity, while beta and gamma activity are linked to heightened alertness and motor preparation.
-
-The aim of this project is to explore how EEG-derived band power dynamics relate to subjective performance ratings of golf swings, as rated by the athletes themselves. This work contributes to a growing field of neurofeedback and cognitive training in sports.
-
-### Tools
-Python Libraries:
- MNE: EEG preprocessing and visualization
- numpy, pandas: Data manipulation
- matplotlib, seaborn: Plotting and statistical graphics
- scipy.stats: Correlation analysis
- sklearn: (optional for classification or clustering)
-
-Signal Processing: Welch’s method for power spectral density estimation.
-
-### Data
-Participants: Golf athletes performing standardized swing tasks.
-EEG Data: Multi-channel EEG signals recorded during golf swings.
-Subjective Ratings: Swing quality scores rated by the athletes on a Likert-type scale immediately post-swing.
-Electrode Layout: Standard 10-20 system with key channels such as Fz, Cz, Pz, etc.
-
-### Pipeline
-Preprocessing
-Bandpass filter: 1–50 Hz
-Artifact rejection (e.g., blink or muscle noise via thresholding or ICA)
-
-Feature Extraction
-Compute Relative Band Power for:
- Theta (4–8 Hz)
- Alpha (8–12 Hz)
- Beta (12–30 Hz)
- Gamma (30–45 Hz)
-Normalize power per epoch across channels
-
-Statistical Analysis
-Correlation analysis: Pearson’s r between each frequency band (per channel) and shot quality scores.
-Bootstrapping: Resample correlation coefficients to assess stability and confidence intervals, especially for alpha band (often hypothesized to relate to motor preparation).
-Topographical Maps: Visualize band power distributions across the scalp for high- vs. low-rated swings
-
-### Project deliverables
-At the end of this project, these files will be made available:
-1) To extract relative band power (Theta, Alpha, Beta, Gamma) from EEG recordings during golf swing preparation and execution.
-2) To assess the relationship between EEG features and subjective swing performance scores.
-3) To explore statistical and visual analysis methods (e.g., correlation, bootstrapping, topographical mapping) that may inform real-time cognitive training tools.
-
-## Results
-Correlation Between EEG Band Power and Shot Score
-Scatter plots were generated to assess linear relationships between relative EEG band power and subjective golf shot scores (rated out of 10).
-Key findings:
- Alpha band power showed a moderate positive correlation with shot scores (r = 0.43), suggesting increased alpha may relate to better perceived performance.
- Theta, Beta, and Gamma bands showed weak or no correlation with shot quality (r = -0.07, 0.00, and 0.14 respectively), indicating less consistent relationships.
-
-##### Figure 1. Correlation between EEG power bands and score
-
-
-EEG Topographical Maps
-Topographic visualizations of average relative EEG power for each frequency band (from Muse headset – 4 channels) reveal distinct spatial patterns:
- Alpha activity is more prominent over posterior regions, consistent with literature linking it to relaxed focus and motor readiness.
- Gamma activity showed asymmetric right-side prominence.
- Beta and Theta distributions were more uniformly diffuse, with Beta slightly higher at posterior sites.
-
-##### Figure 2. EEG Topographical Map for One Shot
-
-
-### Tools I learned during this project
-This project helped me build hands-on experience across several domains of neurodata analysis and scientific computing:
-MNE-Python: I learned to use the MNE library to preprocess EEG data, extract band power using Welch’s method, and visualize scalp topographies. This was my first time working with MNE, and it gave me a strong foundation in EEG signal workflows.
-Scipy.stats: I applied statistical functions such as Pearson correlation and bootstrapping to quantify relationships between brain activity and behavioral performance — gaining confidence in interpreting significance levels and confidence intervals.
-Git & GitHub Workflow: I practiced version control using Git and contributed to the BrainHack School repository via pull requests. I also learned how to structure markdown-based project documentation.
-
-## Conclusion and acknowledgement
-This preliminary analysis supports the potential of EEG-based monitoring for assessing cognitive states in sports performance. Alpha power, in particular, may serve as a target for real-time neurofeedback to enhance swing execution. Future work could involve real-time decoding models, athlete-specific baselines, and integration into wearable feedback systems.
-
-I would like to thanks all the Brainhack School organizators and all the crew. Mostly, thanks for this awesome opportunity!
-
-## References
-Crews, D. J., & Landers, D. M. (1993). Electroencephalographic measures of attentional patterns prior to the golf putt. Medicine & Science in Sports & Exercise, 25(1), 116–126. https://doi.org/10.1249/00005768-199301000-00018
-
-Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., ... & Hämäläinen, M. S. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7, 267. https://doi.org/10.3389/fnins.2013.00267
-
-Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017). Neurofeedback as supplementary training for optimizing athletes' performance: A systematic review with implications for future research. Neuroscience & Biobehavioral Reviews, 75, 419–432. https://doi.org/10.1016/j.neubiorev.2017.02.005
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-20" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Automated detection of focal epilepsy based on brain morphometrics"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Arthur Toulouse]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/epilepsy-detection
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [MRI, PCA, Epilepsy, Visualization]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "epilepsy.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer.
-FreeSurfer enables the extraction of detailed cortical morphometric features such as thickness, surface area, curvature, and volume across predefined brain regions. These quantitative features can potentially reflect subtle structural anomalies associated with focal epilepsy. By using a mixed-age dataset of patients with focal epilepsy, we aim to build a machine learning model that predicts the anatomical region of the epileptic focus based on these morphometric biomarkers. This approach could contribute to more objective and automated presurgical evaluation workflows.
-
-
-### Objectives
-
- * To develop a machine learning model capable of classifying the anatomical region of seizure onset in focal epilepsy patients using cortical morphometric features extracted from T1-weighted MRI scans.
- * To evaluate the performance of different classifiers and identify the most informative brain features associated with focal epileptogenic zones.
-
-
-### Tools
-
- * GitHub – for version control and collaborative code management
- * DataLad – to manage and track neuroimaging datasets in a reproducible way
- * Python – for data processing, feature extraction, and model development
- * Bash – for automation and scripting of preprocessing pipelines
- * FreeSurfer – to extract cortical morphometric features from T1-weighted MRI scans
- * scikit-learn – for implementing and evaluating machine learning classification models
-
-### Data
-
-
-This study uses the Imaging Database for Epilepsy and Surgery ([IDEAS](https://pubmed.ncbi.nlm.nih.gov/39636622/)), an open-source dataset available on OpenNeuro. The dataset includes 442 pre-operative T1-weighted and FLAIR MRI scans from patients with focal epilepsy, along with 100 healthy control scans acquired on the same scanner for consistency. In addition to imaging, the dataset provides rich metadata such as age of seizure onset, sex, number of medications, scan and surgery timing, histopathological findings, and surgical outcomes. The additional data can be found [here](https://openneuro.org/datasets/ds005602/versions/1.0.0)
-
-
-
-
-### Deliverables
-
-At the end of this project, we will have a [GitHub repository](https://github.com/school-brainhack/epilepsy-detection) containing the code :
- * data.ipynb to process the data from freeSurfer
- * model.ipynb to run the classification models
- * the processed csv files obtained after running the data.ipynb for testing purposes
-
-## Results
-
-The first model was a binary classification task distinguishing between epileptic patients and healthy controls based on cortical morphometric features extracted from T1-weighted MRI using FreeSurfer. The classifier achieved promising results, with a receiver operating characteristic (ROC) curve showing a clear separation between the two groups. The area under the curve (AUC) was approximately 0.85, indicating that the model was able to effectively capture structural differences associated with epilepsy.
-
-
-
-In the second model, the task was extended to multiclass classification to predict the specific anatomical region of the epileptic focus using the full set of regions. However, the performance of this model was limited. This can be attributed to both class imbalance and the increased difficulty of distinguishing between many finely segmented areas in a relatively small dataset.
-
-
-
-To address this, a third model was developed where only regions with a significant number of patients are used. This simplification led to a noticeable improvement in classification performance.
-
-
-
-
-
-## Conclusion and acknowledgement
-
-This project demonstrates the feasibility of using morphometric features extracted from structural MRI to classify focal epilepsy and localize the seizure onset zone. While binary classification between epileptic and control subjects showed strong performance, multiclass classification across specific brain regions remains a challenging task.These results highlight both the potential and the limitations of current approaches, pointing toward the need for larger, balanced datasets and more advanced modeling techniques.
-
-I gratefully acknowledge the use of the IDEAS dataset (Imaging Database for Epilepsy And Surgery), an open-access resource available on OpenNeuro. This dataset was developed to support research in epilepsy diagnosis and surgical planning, and I thank the authors and contributors for making this valuable resource publicly available.
-Taylor PN, Wang Y, Simpson C, Janiukstyte V, Horsley J, Leiberg K, Little B, Clifford H, Adler S, Vos SB, Winston GP, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS. The Imaging Database for Epilepsy And Surgery (IDEAS). Epilepsia. 2025 Feb;66(2):471-481. doi: 10.1111/epi.18192. Epub 2024 Dec 5. PMID: 39636622; PMCID: PMC11827737.
-
-I also gratefully acknowledge the developers of FreeSurfer, whose open-source neuroimaging software made it possible to extract detailed cortical morphometric features essential to this analysis.
-
-Finaly, I would also like to sincerely thank the Brainhack School for providing this unique opportunity to explore applied neuroimaging and data science in a collaborative setting. Special thanks to Eva and Sebastian for their guidance, support, and insightful feedback throughout the project.
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diff --git a/content/en/project/epilepsy-detection/data.ipynb b/content/en/project/epilepsy-detection/data.ipynb
deleted file mode 100644
index e4cc8bf3..00000000
--- a/content/en/project/epilepsy-detection/data.ipynb
+++ /dev/null
@@ -1,584 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "c213b6e7",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import re\n",
- "import os\n",
- "from pathlib import Path\n",
- "import glob\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "85f36dc2",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Transform the age range in a single value\n",
- "def convert_age_range_to_midpoint(age_string):\n",
- " \n",
- " if pd.isna(age_string):\n",
- " return None\n",
- " \n",
- " numbers = re.findall(r'\\d+', str(age_string))\n",
- " \n",
- " if len(numbers) >= 2:\n",
- " min_age = int(numbers[0])\n",
- " max_age = int(numbers[1])\n",
- " return (min_age + max_age) / 2\n",
- " elif len(numbers) == 1:\n",
- " \n",
- " return float(numbers[0])\n",
- " else:\n",
- " \n",
- " return None\n",
- "\n",
- "# Transform the files Metadata_Controls_Release.csv and Metadata_Release_Anon.csv in the Subject folder in a single dataframe\n",
- "def create_demographics_dataframe():\n",
- " \n",
- " controls_df = pd.read_csv(r'.\\Subject\\Metadata_Controls_Release.csv')\n",
- " release_df = pd.read_csv(r'.\\Subject\\Metadata_Release_Anon.csv')\n",
- " \n",
- " \n",
- " controls_subset = controls_df[['ID', 'Sex', 'Binned_Age_at_Scan']].copy()\n",
- " release_subset = release_df[['ID', 'Sex', 'Binned_Age_at_Scan']].copy()\n",
- " \n",
- " \n",
- " controls_subset['Age'] = controls_subset['Binned_Age_at_Scan'].apply(convert_age_range_to_midpoint)\n",
- " release_subset['Age'] = release_subset['Binned_Age_at_Scan'].apply(convert_age_range_to_midpoint)\n",
- " \n",
- " \n",
- " controls_subset = controls_subset[['ID', 'Sex', 'Age']] #We only keep the features in all patients\n",
- " release_subset = release_subset[['ID', 'Sex', 'Age']]\n",
- " \n",
- " \n",
- " combined_df = pd.concat([controls_subset, release_subset], ignore_index=True)\n",
- " \n",
- " combined_df = combined_df.drop_duplicates(subset=['ID'], keep='first')\n",
- " combined_df = combined_df.sort_values('ID').reset_index(drop=True)\n",
- " \n",
- " return combined_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "91e67fe4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dataframe saved as 'combined_demographics.csv'\n"
- ]
- }
- ],
- "source": [
- "demographics_df = create_demographics_dataframe()\n",
- "\n",
- "demographics_df.to_csv('combined_demographics.csv', index=False)\n",
- "print(\"\\nDataframe saved as 'combined_demographics.csv'\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "ee60a35d",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Read a FreeSurfer stats file and return a DataFrame\n",
- "def read_freesurfer_stats(filepath):\n",
- " \n",
- " with open(filepath, 'r') as file:\n",
- " lines = file.readlines()\n",
- " \n",
- " \n",
- " for i, line in enumerate(lines):\n",
- " if line.startswith('# ColHeaders'):\n",
- " headers = line.strip().split()[2:] \n",
- " data_start_idx = i + 1\n",
- " break\n",
- " else:\n",
- " raise ValueError(\"No column headers found in the file.\")\n",
- " \n",
- " \n",
- " data = []\n",
- " for line in lines[data_start_idx:]:\n",
- " if not line.startswith('#') and line.strip(): \n",
- " data.append(line.strip().split())\n",
- " \n",
- " df = pd.DataFrame(data, columns=headers)\n",
- " \n",
- " \n",
- " for col in df.columns:\n",
- " df[col] = pd.to_numeric(df[col], errors='ignore')\n",
- " \n",
- " return df\n",
- "\n",
- "def load_additional_data(csv_filepath, subject_id_column='Subject_ID'):\n",
- " \"\"\"\n",
- " Load additional patient data from CSV file\n",
- " \n",
- " Parameters:\n",
- " -----------\n",
- " csv_filepath : str\n",
- " Path to the CSV file containing additional patient data\n",
- " subject_id_column : str\n",
- " Name of the column containing subject IDs in the CSV\n",
- " \n",
- " Returns:\n",
- " --------\n",
- " pd.DataFrame\n",
- " DataFrame with additional patient data, indexed by subject ID\n",
- " \"\"\"\n",
- " try:\n",
- " # Read the CSV file\n",
- " additional_data = pd.read_csv(csv_filepath)\n",
- " \n",
- " # Convert subject ID column to string to match FreeSurfer data\n",
- " additional_data[subject_id_column] = additional_data[subject_id_column].astype(str)\n",
- " \n",
- " # Set subject ID as index\n",
- " additional_data.set_index(subject_id_column, inplace=True)\n",
- " \n",
- " print(f\"Successfully loaded additional data from: {csv_filepath}\")\n",
- " print(f\"Additional data shape: {additional_data.shape}\")\n",
- " print(f\"Additional data columns: {list(additional_data.columns)}\")\n",
- " \n",
- " return additional_data\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error loading additional data from {csv_filepath}: {str(e)}\")\n",
- " return None\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "1c014abe",
- "metadata": {},
- "outputs": [],
- "source": [
- "def process_lh_aparc_a2009s(base_dir):\n",
- " \n",
- " subject_base = os.path.join(base_dir, 'Subject')\n",
- " \n",
- " if not os.path.exists(subject_base):\n",
- " raise ValueError(f\"Subject directory not found: {subject_base}\")\n",
- " \n",
- " all_data = {}\n",
- " processed_subjects = []\n",
- " \n",
- " \n",
- " subject_dirs = [d for d in os.listdir(subject_base) \n",
- " if os.path.isdir(os.path.join(subject_base, d)) and d.isdigit()]\n",
- " \n",
- " print(f\"Found {len(subject_dirs)} subject directories: {sorted(subject_dirs)}\")\n",
- " \n",
- " for subject_id in subject_dirs:\n",
- " \n",
- " stats_path = os.path.join(subject_base, subject_id, 'stats', 'lh.aparc.a2009s.stats')\n",
- " \n",
- " if not os.path.exists(stats_path):\n",
- " print(f\"Warning: {stats_path} not found for subject {subject_id}\")\n",
- " continue\n",
- " \n",
- " try:\n",
- " \n",
- " df = read_freesurfer_stats(stats_path)\n",
- " \n",
- " # TO CHANGE TO INCLUDE LESS FEATURES\n",
- " measure_columns = ['SurfArea', 'GrayVol', 'ThickAvg', 'ThickStd', 'MeanCurv', 'GausCurv', 'FoldInd', 'CurvInd']\n",
- " \n",
- " \n",
- " available_measures = [col for col in measure_columns if col in df.columns]\n",
- " \n",
- " if not available_measures:\n",
- " print(f\"Warning: No standard measures found in {subject_id}\")\n",
- " continue\n",
- " \n",
- " \n",
- " subject_data = {}\n",
- " for _, row in df.iterrows():\n",
- " region = row['StructName'] \n",
- " for measure in available_measures:\n",
- " column_name = f\"{measure}_{region}\"\n",
- " subject_data[column_name] = row[measure]\n",
- " \n",
- " all_data[subject_id] = subject_data\n",
- " processed_subjects.append(subject_id)\n",
- " \n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error processing subject {subject_id}: {str(e)}\")\n",
- " continue\n",
- " \n",
- " if not all_data:\n",
- " raise ValueError(\"No data was successfully processed\")\n",
- " \n",
- "\n",
- " \n",
- " result_df = pd.DataFrame.from_dict(all_data, orient='index')\n",
- " result_df.index.name = 'Subject'\n",
- " \n",
- " \n",
- " result_df = result_df.reindex(sorted(result_df.index, key=lambda x: int(x)))\n",
- " \n",
- " \n",
- " def get_epileptic_status(subject_id):\n",
- " subject_num = int(subject_id)\n",
- " if 1 <= subject_num <= 463:\n",
- " return 'Epileptic'\n",
- " elif subject_num >= 4000:\n",
- " return 'Not Epileptic'\n",
- " else:\n",
- " return 'Unknown'\n",
- " \n",
- " result_df['Epileptic_Status'] = [get_epileptic_status(subj) for subj in result_df.index]\n",
- " \n",
- " cols = ['Epileptic_Status'] + [col for col in result_df.columns if col != 'Epileptic_Status']\n",
- " result_df = result_df[cols]\n",
- " \n",
- " other_cols = sorted([col for col in result_df.columns if col != 'Epileptic_Status'])\n",
- " result_df = result_df[['Epileptic_Status'] + other_cols]\n",
- " \n",
- " print(f\"\\nSuccessfully processed {len(processed_subjects)} subjects\")\n",
- " print(f\"Final dataset shape: {result_df.shape}\")\n",
- " \n",
- " return result_df\n",
- " \n",
- "def process_lh_aparc_a2009s_with_additional_data(base_dir, additional_csv_path=None, \n",
- " subject_id_column='Subject_ID'):\n",
- " \n",
- " \n",
- " \n",
- " freesurfer_data = process_lh_aparc_a2009s(base_dir)\n",
- " \n",
- " if freesurfer_data is None:\n",
- " print(\"Failed to process FreeSurfer data\")\n",
- " return None\n",
- " \n",
- " # If no additional CSV provided, return original data\n",
- " if additional_csv_path is None:\n",
- " print(\"No additional CSV provided, returning FreeSurfer data only\")\n",
- " return freesurfer_data\n",
- " \n",
- " \n",
- " additional_data = load_additional_data(additional_csv_path, subject_id_column)\n",
- " \n",
- " if additional_data is None:\n",
- " print(\"Failed to load additional data, returning FreeSurfer data only\")\n",
- " return freesurfer_data\n",
- " \n",
- " \n",
- " print(f\"FreeSurfer data subjects: {sorted(freesurfer_data.index.tolist(), key=int)}\")\n",
- " print(f\"Additional data subjects: {sorted(additional_data.index.tolist(), key=lambda x: int(x) if x.isdigit() else float('inf'))}\")\n",
- " \n",
- " \n",
- " merged_data = freesurfer_data.join(additional_data, how='left')\n",
- " \n",
- " \n",
- " freesurfer_subjects = set(freesurfer_data.index)\n",
- " additional_subjects = set(additional_data.index)\n",
- " \n",
- " matched_subjects = freesurfer_subjects.intersection(additional_subjects)\n",
- " freesurfer_only = freesurfer_subjects - additional_subjects\n",
- " additional_only = additional_subjects - freesurfer_subjects\n",
- " \n",
- " print(f\"\\nMerge Results:\")\n",
- " print(f\" Subjects with both FreeSurfer and additional data: {len(matched_subjects)}\")\n",
- " print(f\" Subjects with only FreeSurfer data: {len(freesurfer_only)}\")\n",
- " \n",
- " \n",
- " if freesurfer_only:\n",
- " print(f\" FreeSurfer-only subjects: {sorted(list(freesurfer_only), key=int)}\")\n",
- " \n",
- " if additional_only:\n",
- " print(f\" Additional-only subjects: {sorted(list(additional_only), key=lambda x: int(x) if x.isdigit() else float('inf'))}\")\n",
- " \n",
- " \n",
- " epileptic_col = ['Epileptic_Status']\n",
- " additional_cols = [col for col in additional_data.columns if col in merged_data.columns]\n",
- " freesurfer_cols = [col for col in merged_data.columns \n",
- " if col not in epileptic_col and col not in additional_cols]\n",
- " \n",
- " final_column_order = epileptic_col + additional_cols + sorted(freesurfer_cols)\n",
- " merged_data = merged_data[final_column_order]\n",
- " \n",
- " \n",
- " \n",
- " return merged_data\n",
- "\n",
- "\n",
- " \n",
- "def process_with_additional_data(): \n",
- " \n",
- " base_directory = r\"\"\n",
- " \n",
- " # Path to your additional CSV file \n",
- " additional_csv_path = r'.\\combined_demographics.csv' # UPDATE THIS PATH\n",
- " \n",
- " subject_id_column = 'ID' # UPDATE THIS IF THE COLUMN HAS A DIFFERENT NAME\n",
- " \n",
- " try:\n",
- " \n",
- " combined_data = process_lh_aparc_a2009s_with_additional_data(\n",
- " base_directory, \n",
- " additional_csv_path, \n",
- " subject_id_column\n",
- " )\n",
- " \n",
- " if combined_data is not None:\n",
- " \n",
- " \n",
- " epileptic_cols = [col for col in combined_data.columns if 'Epileptic' in col]\n",
- " additional_cols = [col for col in combined_data.columns \n",
- " if col not in epileptic_cols and not any(measure in col for measure in \n",
- " ['SurfArea', 'GrayVol', 'ThickAvg', 'ThickStd', 'MeanCurv', 'GausCurv', 'FoldInd', 'CurvInd'])]\n",
- " freesurfer_cols = [col for col in combined_data.columns \n",
- " if col not in epileptic_cols and col not in additional_cols]\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " output_file = 'lh.csv'\n",
- " combined_data.to_csv(output_file)\n",
- " print(f\"\\nCombined data saved to: {output_file}\")\n",
- " \n",
- " \n",
- " missing_summary = combined_data.isnull().sum()\n",
- " cols_with_missing = missing_summary[missing_summary > 0]\n",
- " \n",
- " if len(cols_with_missing) > 0:\n",
- " print(f\"\\nColumns with missing values:\")\n",
- " for col, count in cols_with_missing.items():\n",
- " percentage = (count / len(combined_data)) * 100\n",
- " print(f\" {col}: {count} missing ({percentage:.1f}%)\")\n",
- " else:\n",
- " print(f\"\\nNo missing values in the dataset!\")\n",
- " \n",
- " return combined_data\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error processing data: {str(e)}\")\n",
- " return None\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "82d12d1d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found 542 subject directories: ['1', '10', '100', '101', '102', '103', '104', '105', '106', '108', '109', '11', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '12', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '13', '130', '131', '132', '133', '134', '135', '136', '137', '138', '14', '141', '142', '143', '144', '145', '146', '147', '148', '149', '15', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '16', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '17', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '18', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '19', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '2', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '21', '210', '211', '213', '214', '215', '216', '217', '218', '219', '22', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '23', '230', '231', '232', '233', '234', '235', '236', '237', '238', '24', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '25', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '26', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '27', '270', '271', '273', '275', '276', '277', '278', '279', '28', '280', '281', '282', '283', '285', '286', '287', '288', '289', '29', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '3', '30', '300', '301', '302', '303', '304', '305', '306', '308', '309', '31', '310', '311', '312', '313', '314', '315', '316', '317', '319', '32', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '33', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '34', '340', '341', '343', '344', '345', '346', '347', '348', '349', '35', '350', '351', '352', '353', '354', '355', '356', '357', '358', '36', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '37', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '38', '380', '381', '382', '383', '384', '385', '387', '388', '389', '39', '390', '391', '392', '393', '394', '395', '396', '397', '399', '4', '40', '400', '4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009', '401', '4010', '4011', '4012', '4013', '4014', '4015', '4016', '4017', '4018', '4019', '402', '4020', '4021', '4022', '4023', '4024', '4025', '4026', '4027', '4028', '4029', '403', '4030', '4031', '4032', '4033', '4034', '4035', '4036', '4037', '4038', '4039', '404', '4040', '4041', '4042', '4043', '4044', '4045', '4046', '4047', '4048', '4049', '405', '4050', '4051', '4052', '4053', '4054', '4055', '4056', '4057', '4058', '4059', '406', '4060', '4061', '4062', '4063', '4064', '4065', '4066', '4067', '4068', '4069', '407', '4070', '4071', '4072', '4073', '4074', '4075', '4076', '4077', '4078', '4079', '408', '4080', '4081', '4082', '4083', '4084', '4085', '4086', '4087', '4088', '4089', '409', '4090', '4091', '4092', '4093', '4094', '4095', '4096', '4097', '4098', '4099', '41', '410', '4100', '411', '412', '413', '414', '415', '416', '417', '418', '419', '42', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '43', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '44', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '45', '450', '451', '452', '453', '454', '455', '459', '46', '460', '461', '462', '463', '47', '48', '49', '5', '50', '51', '53', '54', '55', '56', '57', '58', '59', '6', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '7', '70', '71', '72', '74', '75', '76', '77', '78', '79', '8', '81', '82', '83', '84', '85', '86', '87', '88', '89', '9', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99']\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\toulo\\AppData\\Local\\Temp\\ipykernel_18728\\3929357964.py:26: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
- " df[col] = pd.to_numeric(df[col], errors='ignore')\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Successfully processed 542 subjects\n",
- "Final dataset shape: (542, 593)\n",
- "Successfully loaded additional data from: .\\combined_demographics.csv\n",
- "Additional data shape: (542, 2)\n",
- "Additional data columns: ['Sex', 'Age']\n",
- "FreeSurfer data subjects: ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '74', '75', '76', '77', '78', '79', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '273', '275', '276', '277', '278', '279', '280', '281', '282', '283', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '300', '301', '302', '303', '304', '305', '306', '308', '309', '310', '311', '312', '313', '314', '315', '316', '317', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '343', '344', '345', '346', '347', '348', '349', '350', '351', '352', '353', '354', '355', '356', '357', '358', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '387', '388', '389', '390', '391', '392', '393', '394', '395', '396', '397', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '450', '451', '452', '453', '454', '455', '459', '460', '461', '462', '463', '4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009', '4010', '4011', '4012', '4013', '4014', '4015', '4016', '4017', '4018', '4019', '4020', '4021', '4022', '4023', '4024', '4025', '4026', '4027', '4028', '4029', '4030', '4031', '4032', '4033', '4034', '4035', '4036', '4037', '4038', '4039', '4040', '4041', '4042', '4043', '4044', '4045', '4046', '4047', '4048', '4049', '4050', '4051', '4052', '4053', '4054', '4055', '4056', '4057', '4058', '4059', '4060', '4061', '4062', '4063', '4064', '4065', '4066', '4067', '4068', '4069', '4070', '4071', '4072', '4073', '4074', '4075', '4076', '4077', '4078', '4079', '4080', '4081', '4082', '4083', '4084', '4085', '4086', '4087', '4088', '4089', '4090', '4091', '4092', '4093', '4094', '4095', '4096', '4097', '4098', '4099', '4100']\n",
- "Additional data subjects: ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '74', '75', '76', '77', '78', '79', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '273', '275', '276', '277', '278', '279', '280', '281', '282', '283', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '300', '301', '302', '303', '304', '305', '306', '308', '309', '310', '311', '312', '313', '314', '315', '316', '317', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '343', '344', '345', '346', '347', '348', '349', '350', '351', '352', '353', '354', '355', '356', '357', '358', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '387', '388', '389', '390', '391', '392', '393', '394', '395', '396', '397', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '450', '451', '452', '453', '454', '455', '459', '460', '461', '462', '463', '4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009', '4010', '4011', '4012', '4013', '4014', '4015', '4016', '4017', '4018', '4019', '4020', '4021', '4022', '4023', '4024', '4025', '4026', '4027', '4028', '4029', '4030', '4031', '4032', '4033', '4034', '4035', '4036', '4037', '4038', '4039', '4040', '4041', '4042', '4043', '4044', '4045', '4046', '4047', '4048', '4049', '4050', '4051', '4052', '4053', '4054', '4055', '4056', '4057', '4058', '4059', '4060', '4061', '4062', '4063', '4064', '4065', '4066', '4067', '4068', '4069', '4070', '4071', '4072', '4073', '4074', '4075', '4076', '4077', '4078', '4079', '4080', '4081', '4082', '4083', '4084', '4085', '4086', '4087', '4088', '4089', '4090', '4091', '4092', '4093', '4094', '4095', '4096', '4097', '4098', '4099', '4100']\n",
- "\n",
- "Merge Results:\n",
- " Subjects with both FreeSurfer and additional data: 542\n",
- " Subjects with only FreeSurfer data: 0\n",
- "\n",
- "Combined data saved to: lh.csv\n",
- "\n",
- "Columns with missing values:\n",
- " CurvInd_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " FoldInd_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " GausCurv_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " GrayVol_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " MeanCurv_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " SurfArea_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " ThickAvg_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " ThickStd_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " Epileptic_Status Sex Age CurvInd_G_Ins_lg_and_S_cent_ins \\\n",
- "Subject \n",
- "1 Epileptic M 22.0 0.8 \n",
- "2 Epileptic F 37.0 0.6 \n",
- "3 Epileptic M 27.0 1.4 \n",
- "4 Epileptic M 42.0 1.2 \n",
- "5 Epileptic F 47.0 0.7 \n",
- "... ... .. ... ... \n",
- "4096 Not Epileptic F 32.0 0.9 \n",
- "4097 Not Epileptic M 37.0 1.4 \n",
- "4098 Not Epileptic F 27.0 1.0 \n",
- "4099 Not Epileptic M 27.0 0.9 \n",
- "4100 Not Epileptic F 37.0 1.1 \n",
- "\n",
- " CurvInd_G_and_S_cingul-Ant CurvInd_G_and_S_cingul-Mid-Ant \\\n",
- "Subject \n",
- "1 2.4 0.9 \n",
- "2 2.4 1.1 \n",
- "3 2.1 0.7 \n",
- "4 1.9 0.9 \n",
- "5 2.6 1.1 \n",
- "... ... ... \n",
- "4096 3.0 1.1 \n",
- "4097 3.2 0.8 \n",
- "4098 1.7 1.1 \n",
- "4099 2.4 0.9 \n",
- "4100 1.8 0.9 \n",
- "\n",
- " CurvInd_G_and_S_cingul-Mid-Post CurvInd_G_and_S_frontomargin \\\n",
- "Subject \n",
- "1 1.7 2.0 \n",
- "2 1.3 1.2 \n",
- "3 0.9 1.1 \n",
- "4 0.9 1.7 \n",
- "5 0.9 1.9 \n",
- "... ... ... \n",
- "4096 1.1 1.2 \n",
- "4097 1.1 1.8 \n",
- "4098 1.1 1.5 \n",
- "4099 1.1 2.2 \n",
- "4100 0.9 1.8 \n",
- "\n",
- " CurvInd_G_and_S_occipital_inf CurvInd_G_and_S_paracentral ... \\\n",
- "Subject ... \n",
- "1 2.1 1.9 ... \n",
- "2 1.6 1.9 ... \n",
- "3 2.7 2.0 ... \n",
- "4 2.1 2.3 ... \n",
- "5 1.6 2.2 ... \n",
- "... ... ... ... \n",
- "4096 2.0 1.4 ... \n",
- "4097 1.7 2.7 ... \n",
- "4098 1.7 1.3 ... \n",
- "4099 1.5 1.4 ... \n",
- "4100 1.5 1.2 ... \n",
- "\n",
- " ThickStd_S_parieto_occipital ThickStd_S_pericallosal \\\n",
- "Subject \n",
- "1 0.598 0.556 \n",
- "2 0.515 0.579 \n",
- "3 0.545 0.725 \n",
- "4 0.650 0.611 \n",
- "5 0.533 0.641 \n",
- "... ... ... \n",
- "4096 0.409 0.602 \n",
- "4097 0.510 0.464 \n",
- "4098 0.476 0.613 \n",
- "4099 0.478 0.518 \n",
- "4100 0.462 0.638 \n",
- "\n",
- " ThickStd_S_postcentral ThickStd_S_precentral-inf-part \\\n",
- "Subject \n",
- "1 0.413 0.344 \n",
- "2 0.421 0.473 \n",
- "3 0.473 0.404 \n",
- "4 0.597 0.465 \n",
- "5 0.415 0.413 \n",
- "... ... ... \n",
- "4096 0.401 0.414 \n",
- "4097 0.559 0.392 \n",
- "4098 0.421 0.415 \n",
- "4099 0.497 0.354 \n",
- "4100 0.461 0.421 \n",
- "\n",
- " ThickStd_S_precentral-sup-part ThickStd_S_suborbital \\\n",
- "Subject \n",
- "1 0.341 0.810 \n",
- "2 0.396 0.721 \n",
- "3 0.407 0.604 \n",
- "4 0.541 0.680 \n",
- "5 0.374 0.809 \n",
- "... ... ... \n",
- "4096 0.413 0.514 \n",
- "4097 0.384 0.542 \n",
- "4098 0.396 0.795 \n",
- "4099 0.365 0.692 \n",
- "4100 0.458 0.637 \n",
- "\n",
- " ThickStd_S_subparietal ThickStd_S_temporal_inf \\\n",
- "Subject \n",
- "1 0.436 0.450 \n",
- "2 0.689 0.514 \n",
- "3 0.390 0.776 \n",
- "4 0.697 0.602 \n",
- "5 0.349 0.462 \n",
- "... ... ... \n",
- "4096 0.466 0.535 \n",
- "4097 0.517 0.370 \n",
- "4098 0.358 0.410 \n",
- "4099 0.485 0.461 \n",
- "4100 0.584 0.470 \n",
- "\n",
- " ThickStd_S_temporal_sup ThickStd_S_temporal_transverse \n",
- "Subject \n",
- "1 0.475 0.403 \n",
- "2 0.580 0.304 \n",
- "3 0.634 0.461 \n",
- "4 0.498 0.344 \n",
- "5 0.470 0.463 \n",
- "... ... ... \n",
- "4096 0.483 0.331 \n",
- "4097 0.514 0.761 \n",
- "4098 0.472 0.569 \n",
- "4099 0.418 0.383 \n",
- "4100 0.423 0.580 \n",
- "\n",
- "[542 rows x 595 columns]\n"
- ]
- }
- ],
- "source": [
- "data = process_with_additional_data()\n",
- "print(data)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "base",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
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- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.7"
- }
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- "nbformat": 4,
- "nbformat_minor": 5
-}
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-[ZoneTransfer]
-ZoneId=3
-ReferrerUrl=https://www.google.com/
-HostUrl=https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ8ccg49ypkUjs3omTRjb6xoGqvud-Kxroc4g&s
diff --git a/content/en/project/epilepsy-detection/index.md b/content/en/project/epilepsy-detection/index.md
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-20" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Automated detection of focal epilepsy based on brain morphometrics"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Arthur Toulouse]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/epilepsy-detection
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [MRI, PCA, Epilepsy, Visualization]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "epilepsy.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer.
-FreeSurfer enables the extraction of detailed cortical morphometric features such as thickness, surface area, curvature, and volume across predefined brain regions. These quantitative features can potentially reflect subtle structural anomalies associated with focal epilepsy. By using a mixed-age dataset of patients with focal epilepsy, we aim to build a machine learning model that predicts the anatomical region of the epileptic focus based on these morphometric biomarkers. This approach could contribute to more objective and automated presurgical evaluation workflows.
-
-
-### Objectives
-
- * To develop a machine learning model capable of classifying the anatomical region of seizure onset in focal epilepsy patients using cortical morphometric features extracted from T1-weighted MRI scans.
- * To evaluate the performance of different classifiers and identify the most informative brain features associated with focal epileptogenic zones.
-
-
-### Tools
-
- * GitHub – for version control and collaborative code management
- * DataLad – to manage and track neuroimaging datasets in a reproducible way
- * Python – for data processing, feature extraction, and model development
- * Bash – for automation and scripting of preprocessing pipelines
- * FreeSurfer – to extract cortical morphometric features from T1-weighted MRI scans
- * scikit-learn – for implementing and evaluating machine learning classification models
-
-### Data
-
-
-This study uses the Imaging Database for Epilepsy and Surgery ([IDEAS](https://pubmed.ncbi.nlm.nih.gov/39636622/)), an open-source dataset available on OpenNeuro. The dataset includes 442 pre-operative T1-weighted and FLAIR MRI scans from patients with focal epilepsy, along with 100 healthy control scans acquired on the same scanner for consistency. In addition to imaging, the dataset provides rich metadata such as age of seizure onset, sex, number of medications, scan and surgery timing, histopathological findings, and surgical outcomes. The additional data can be found [here](https://openneuro.org/datasets/ds005602/versions/1.0.0)
-
-
-
-
-### Deliverables
-
-At the end of this project, we will have a [GitHub repository](https://github.com/school-brainhack/epilepsy-detection) containing the code :
- * data.ipynb to process the data from freeSurfer
- * model.ipynb to run the classification models
- * the processed csv files obtained after running the data.ipynb for testing purposes
-
-## Results
-
-The first model was a binary classification task distinguishing between epileptic patients and healthy controls based on cortical morphometric features extracted from T1-weighted MRI using FreeSurfer. The classifier achieved promising results, with a receiver operating characteristic (ROC) curve showing a clear separation between the two groups. The area under the curve (AUC) was approximately 0.85, indicating that the model was able to effectively capture structural differences associated with epilepsy.
-
-
-
-In the second model, the task was extended to multiclass classification to predict the specific anatomical region of the epileptic focus using the full set of regions. However, the performance of this model was limited. This can be attributed to both class imbalance and the increased difficulty of distinguishing between many finely segmented areas in a relatively small dataset.
-
-
-
-To address this, a third model was developed where only regions with a significant number of patients are used. This simplification led to a noticeable improvement in classification performance.
-
-
-
-
-
-## Conclusion and acknowledgement
-
-This project demonstrates the feasibility of using morphometric features extracted from structural MRI to classify focal epilepsy and localize the seizure onset zone. While binary classification between epileptic and control subjects showed strong performance, multiclass classification across specific brain regions remains a challenging task.These results highlight both the potential and the limitations of current approaches, pointing toward the need for larger, balanced datasets and more advanced modeling techniques.
-
-I gratefully acknowledge the use of the IDEAS dataset (Imaging Database for Epilepsy And Surgery), an open-access resource available on OpenNeuro. This dataset was developed to support research in epilepsy diagnosis and surgical planning, and I thank the authors and contributors for making this valuable resource publicly available.
-Taylor PN, Wang Y, Simpson C, Janiukstyte V, Horsley J, Leiberg K, Little B, Clifford H, Adler S, Vos SB, Winston GP, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS. The Imaging Database for Epilepsy And Surgery (IDEAS). Epilepsia. 2025 Feb;66(2):471-481. doi: 10.1111/epi.18192. Epub 2024 Dec 5. PMID: 39636622; PMCID: PMC11827737.
-
-I also gratefully acknowledge the developers of FreeSurfer, whose open-source neuroimaging software made it possible to extract detailed cortical morphometric features essential to this analysis.
-
-Finaly, I would also like to sincerely thank the Brainhack School for providing this unique opportunity to explore applied neuroimaging and data science in a collaborative setting. Special thanks to Eva and Sebastian for their guidance, support, and insightful feedback throughout the project.
diff --git a/content/en/project/epilepsy-detection/lh.csv b/content/en/project/epilepsy-detection/lh.csv
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-Subject,Epileptic_Status,Sex,Age,CurvInd_G_Ins_lg_and_S_cent_ins,CurvInd_G_and_S_cingul-Ant,CurvInd_G_and_S_cingul-Mid-Ant,CurvInd_G_and_S_cingul-Mid-Post,CurvInd_G_and_S_frontomargin,CurvInd_G_and_S_occipital_inf,CurvInd_G_and_S_paracentral,CurvInd_G_and_S_subcentral,CurvInd_G_and_S_transv_frontopol,CurvInd_G_cingul-Post-dorsal,CurvInd_G_cingul-Post-ventral,CurvInd_G_cuneus,CurvInd_G_front_inf-Opercular,CurvInd_G_front_inf-Orbital,CurvInd_G_front_inf-Triangul,CurvInd_G_front_middle,CurvInd_G_front_sup,CurvInd_G_insular_short,CurvInd_G_oc-temp_lat-fusifor,CurvInd_G_oc-temp_med-Lingual,CurvInd_G_oc-temp_med-Parahip,CurvInd_G_occipital_middle,CurvInd_G_occipital_sup,CurvInd_G_orbital,CurvInd_G_pariet_inf-Angular,CurvInd_G_pariet_inf-Supramar,CurvInd_G_parietal_sup,CurvInd_G_postcentral,CurvInd_G_precentral,CurvInd_G_precuneus,CurvInd_G_rectus,CurvInd_G_subcallosal,CurvInd_G_temp_sup-G_T_transv,CurvInd_G_temp_sup-Lateral,CurvInd_G_temp_sup-Plan_polar,CurvInd_G_temp_sup-Plan_tempo,CurvInd_G_temporal_inf,CurvInd_G_temporal_middle,CurvInd_Lat_Fis-ant-Horizont,CurvInd_Lat_Fis-ant-Vertical,CurvInd_Lat_Fis-post,CurvInd_Pole_occipital,CurvInd_Pole_temporal,CurvInd_S_calcarine,CurvInd_S_central,CurvInd_S_cingul-Marginalis,CurvInd_S_circular_insula_ant,CurvInd_S_circular_insula_inf,CurvInd_S_circular_insula_sup,CurvInd_S_collat_transv_ant,CurvInd_S_collat_transv_post,CurvInd_S_front_inf,CurvInd_S_front_middle,CurvInd_S_front_sup,CurvInd_S_interm_prim-Jensen,CurvInd_S_intrapariet_and_P_trans,CurvInd_S_oc-temp_lat,CurvInd_S_oc-temp_med_and_Lingual,CurvInd_S_oc_middle_and_Lunatus,CurvInd_S_oc_sup_and_transversal,CurvInd_S_occipital_ant,CurvInd_S_orbital-H_Shaped,CurvInd_S_orbital_lateral,CurvInd_S_orbital_med-olfact,CurvInd_S_parieto_occipital,CurvInd_S_pericallosal,CurvInd_S_postcentral,CurvInd_S_precentral-inf-part,CurvInd_S_precentral-sup-part,CurvInd_S_suborbital,CurvInd_S_subparietal,CurvInd_S_temporal_inf,CurvInd_S_temporal_sup,CurvInd_S_temporal_transverse,FoldInd_G_Ins_lg_and_S_cent_ins,FoldInd_G_and_S_cingul-Ant,FoldInd_G_and_S_cingul-Mid-Ant,FoldInd_G_and_S_cingul-Mid-Post,FoldInd_G_and_S_frontomargin,FoldInd_G_and_S_occipital_inf,FoldInd_G_and_S_paracentral,FoldInd_G_and_S_subcentral,FoldInd_G_and_S_transv_frontopol,FoldInd_G_cingul-Post-dorsal,FoldInd_G_cingul-Post-ventral,FoldInd_G_cuneus,FoldInd_G_front_inf-Opercular,FoldInd_G_front_inf-Orbital,FoldInd_G_front_inf-Triangul,FoldInd_G_front_middle,FoldInd_G_front_sup,FoldInd_G_insular_short,FoldInd_G_oc-temp_lat-fusifor,FoldInd_G_oc-temp_med-Lingual,FoldInd_G_oc-temp_med-Parahip,FoldInd_G_occipital_middle,FoldInd_G_occipital_sup,FoldInd_G_orbital,FoldInd_G_pariet_inf-Angular,FoldInd_G_pariet_inf-Supramar,FoldInd_G_parietal_sup,FoldInd_G_postcentral,FoldInd_G_precentral,FoldInd_G_precuneus,FoldInd_G_rectus,FoldInd_G_subcallosal,FoldInd_G_temp_sup-G_T_transv,FoldInd_G_temp_sup-Lateral,FoldInd_G_temp_sup-Plan_polar,FoldInd_G_temp_sup-Plan_tempo,FoldInd_G_temporal_inf,FoldInd_G_temporal_middle,FoldInd_Lat_Fis-ant-Horizont,FoldInd_Lat_Fis-ant-Vertical,FoldInd_Lat_Fis-post,FoldInd_Pole_occipital,FoldInd_Pole_temporal,FoldInd_S_calcarine,FoldInd_S_central,FoldInd_S_cingul-Marginalis,FoldInd_S_circular_insula_ant,FoldInd_S_circular_insula_inf,FoldInd_S_circular_insula_sup,FoldInd_S_collat_transv_ant,FoldInd_S_collat_transv_post,FoldInd_S_front_inf,FoldInd_S_front_middle,FoldInd_S_front_sup,FoldInd_S_interm_prim-Jensen,FoldInd_S_intrapariet_and_P_trans,FoldInd_S_oc-temp_lat,FoldInd_S_oc-temp_med_and_Lingual,FoldInd_S_oc_middle_and_Lunatus,FoldInd_S_oc_sup_and_transversal,FoldInd_S_occipital_ant,FoldInd_S_orbital-H_Shaped,FoldInd_S_orbital_lateral,FoldInd_S_orbital_med-olfact,FoldInd_S_parieto_occipital,FoldInd_S_pericallosal,FoldInd_S_postcentral,FoldInd_S_precentral-inf-part,FoldInd_S_precentral-sup-part,FoldInd_S_suborbital,FoldInd_S_subparietal,FoldInd_S_temporal_inf,FoldInd_S_temporal_sup,FoldInd_S_temporal_transverse,GausCurv_G_Ins_lg_and_S_cent_ins,GausCurv_G_and_S_cingul-Ant,GausCurv_G_and_S_cingul-Mid-Ant,GausCurv_G_and_S_cingul-Mid-Post,GausCurv_G_and_S_frontomargin,GausCurv_G_and_S_occipital_inf,GausCurv_G_and_S_paracentral,GausCurv_G_and_S_subcentral,GausCurv_G_and_S_transv_frontopol,GausCurv_G_cingul-Post-dorsal,GausCurv_G_cingul-Post-ventral,GausCurv_G_cuneus,GausCurv_G_front_inf-Opercular,GausCurv_G_front_inf-Orbital,GausCurv_G_front_inf-Triangul,GausCurv_G_front_middle,GausCurv_G_front_sup,GausCurv_G_insular_short,GausCurv_G_oc-temp_lat-fusifor,GausCurv_G_oc-temp_med-Lingual,GausCurv_G_oc-temp_med-Parahip,GausCurv_G_occipital_middle,GausCurv_G_occipital_sup,GausCurv_G_orbital,GausCurv_G_pariet_inf-Angular,GausCurv_G_pariet_inf-Supramar,GausCurv_G_parietal_sup,GausCurv_G_postcentral,GausCurv_G_precentral,GausCurv_G_precuneus,GausCurv_G_rectus,GausCurv_G_subcallosal,GausCurv_G_temp_sup-G_T_transv,GausCurv_G_temp_sup-Lateral,GausCurv_G_temp_sup-Plan_polar,GausCurv_G_temp_sup-Plan_tempo,GausCurv_G_temporal_inf,GausCurv_G_temporal_middle,GausCurv_Lat_Fis-ant-Horizont,GausCurv_Lat_Fis-ant-Vertical,GausCurv_Lat_Fis-post,GausCurv_Pole_occipital,GausCurv_Pole_temporal,GausCurv_S_calcarine,GausCurv_S_central,GausCurv_S_cingul-Marginalis,GausCurv_S_circular_insula_ant,GausCurv_S_circular_insula_inf,GausCurv_S_circular_insula_sup,GausCurv_S_collat_transv_ant,GausCurv_S_collat_transv_post,GausCurv_S_front_inf,GausCurv_S_front_middle,GausCurv_S_front_sup,GausCurv_S_interm_prim-Jensen,GausCurv_S_intrapariet_and_P_trans,GausCurv_S_oc-temp_lat,GausCurv_S_oc-temp_med_and_Lingual,GausCurv_S_oc_middle_and_Lunatus,GausCurv_S_oc_sup_and_transversal,GausCurv_S_occipital_ant,GausCurv_S_orbital-H_Shaped,GausCurv_S_orbital_lateral,GausCurv_S_orbital_med-olfact,GausCurv_S_parieto_occipital,GausCurv_S_pericallosal,GausCurv_S_postcentral,GausCurv_S_precentral-inf-part,GausCurv_S_precentral-sup-part,GausCurv_S_suborbital,GausCurv_S_subparietal,GausCurv_S_temporal_inf,GausCurv_S_temporal_sup,GausCurv_S_temporal_transverse,GrayVol_G_Ins_lg_and_S_cent_ins,GrayVol_G_and_S_cingul-Ant,GrayVol_G_and_S_cingul-Mid-Ant,GrayVol_G_and_S_cingul-Mid-Post,GrayVol_G_and_S_frontomargin,GrayVol_G_and_S_occipital_inf,GrayVol_G_and_S_paracentral,GrayVol_G_and_S_subcentral,GrayVol_G_and_S_transv_frontopol,GrayVol_G_cingul-Post-dorsal,GrayVol_G_cingul-Post-ventral,GrayVol_G_cuneus,GrayVol_G_front_inf-Opercular,GrayVol_G_front_inf-Orbital,GrayVol_G_front_inf-Triangul,GrayVol_G_front_middle,GrayVol_G_front_sup,GrayVol_G_insular_short,GrayVol_G_oc-temp_lat-fusifor,GrayVol_G_oc-temp_med-Lingual,GrayVol_G_oc-temp_med-Parahip,GrayVol_G_occipital_middle,GrayVol_G_occipital_sup,GrayVol_G_orbital,GrayVol_G_pariet_inf-Angular,GrayVol_G_pariet_inf-Supramar,GrayVol_G_parietal_sup,GrayVol_G_postcentral,GrayVol_G_precentral,GrayVol_G_precuneus,GrayVol_G_rectus,GrayVol_G_subcallosal,GrayVol_G_temp_sup-G_T_transv,GrayVol_G_temp_sup-Lateral,GrayVol_G_temp_sup-Plan_polar,GrayVol_G_temp_sup-Plan_tempo,GrayVol_G_temporal_inf,GrayVol_G_temporal_middle,GrayVol_Lat_Fis-ant-Horizont,GrayVol_Lat_Fis-ant-Vertical,GrayVol_Lat_Fis-post,GrayVol_Pole_occipital,GrayVol_Pole_temporal,GrayVol_S_calcarine,GrayVol_S_central,GrayVol_S_cingul-Marginalis,GrayVol_S_circular_insula_ant,GrayVol_S_circular_insula_inf,GrayVol_S_circular_insula_sup,GrayVol_S_collat_transv_ant,GrayVol_S_collat_transv_post,GrayVol_S_front_inf,GrayVol_S_front_middle,GrayVol_S_front_sup,GrayVol_S_interm_prim-Jensen,GrayVol_S_intrapariet_and_P_trans,GrayVol_S_oc-temp_lat,GrayVol_S_oc-temp_med_and_Lingual,GrayVol_S_oc_middle_and_Lunatus,GrayVol_S_oc_sup_and_transversal,GrayVol_S_occipital_ant,GrayVol_S_orbital-H_Shaped,GrayVol_S_orbital_lateral,GrayVol_S_orbital_med-olfact,GrayVol_S_parieto_occipital,GrayVol_S_pericallosal,GrayVol_S_postcentral,GrayVol_S_precentral-inf-part,GrayVol_S_precentral-sup-part,GrayVol_S_suborbital,GrayVol_S_subparietal,GrayVol_S_temporal_inf,GrayVol_S_temporal_sup,GrayVol_S_temporal_transverse,MeanCurv_G_Ins_lg_and_S_cent_ins,MeanCurv_G_and_S_cingul-Ant,MeanCurv_G_and_S_cingul-Mid-Ant,MeanCurv_G_and_S_cingul-Mid-Post,MeanCurv_G_and_S_frontomargin,MeanCurv_G_and_S_occipital_inf,MeanCurv_G_and_S_paracentral,MeanCurv_G_and_S_subcentral,MeanCurv_G_and_S_transv_frontopol,MeanCurv_G_cingul-Post-dorsal,MeanCurv_G_cingul-Post-ventral,MeanCurv_G_cuneus,MeanCurv_G_front_inf-Opercular,MeanCurv_G_front_inf-Orbital,MeanCurv_G_front_inf-Triangul,MeanCurv_G_front_middle,MeanCurv_G_front_sup,MeanCurv_G_insular_short,MeanCurv_G_oc-temp_lat-fusifor,MeanCurv_G_oc-temp_med-Lingual,MeanCurv_G_oc-temp_med-Parahip,MeanCurv_G_occipital_middle,MeanCurv_G_occipital_sup,MeanCurv_G_orbital,MeanCurv_G_pariet_inf-Angular,MeanCurv_G_pariet_inf-Supramar,MeanCurv_G_parietal_sup,MeanCurv_G_postcentral,MeanCurv_G_precentral,MeanCurv_G_precuneus,MeanCurv_G_rectus,MeanCurv_G_subcallosal,MeanCurv_G_temp_sup-G_T_transv,MeanCurv_G_temp_sup-Lateral,MeanCurv_G_temp_sup-Plan_polar,MeanCurv_G_temp_sup-Plan_tempo,MeanCurv_G_temporal_inf,MeanCurv_G_temporal_middle,MeanCurv_Lat_Fis-ant-Horizont,MeanCurv_Lat_Fis-ant-Vertical,MeanCurv_Lat_Fis-post,MeanCurv_Pole_occipital,MeanCurv_Pole_temporal,MeanCurv_S_calcarine,MeanCurv_S_central,MeanCurv_S_cingul-Marginalis,MeanCurv_S_circular_insula_ant,MeanCurv_S_circular_insula_inf,MeanCurv_S_circular_insula_sup,MeanCurv_S_collat_transv_ant,MeanCurv_S_collat_transv_post,MeanCurv_S_front_inf,MeanCurv_S_front_middle,MeanCurv_S_front_sup,MeanCurv_S_interm_prim-Jensen,MeanCurv_S_intrapariet_and_P_trans,MeanCurv_S_oc-temp_lat,MeanCurv_S_oc-temp_med_and_Lingual,MeanCurv_S_oc_middle_and_Lunatus,MeanCurv_S_oc_sup_and_transversal,MeanCurv_S_occipital_ant,MeanCurv_S_orbital-H_Shaped,MeanCurv_S_orbital_lateral,MeanCurv_S_orbital_med-olfact,MeanCurv_S_parieto_occipital,MeanCurv_S_pericallosal,MeanCurv_S_postcentral,MeanCurv_S_precentral-inf-part,MeanCurv_S_precentral-sup-part,MeanCurv_S_suborbital,MeanCurv_S_subparietal,MeanCurv_S_temporal_inf,MeanCurv_S_temporal_sup,MeanCurv_S_temporal_transverse,SurfArea_G_Ins_lg_and_S_cent_ins,SurfArea_G_and_S_cingul-Ant,SurfArea_G_and_S_cingul-Mid-Ant,SurfArea_G_and_S_cingul-Mid-Post,SurfArea_G_and_S_frontomargin,SurfArea_G_and_S_occipital_inf,SurfArea_G_and_S_paracentral,SurfArea_G_and_S_subcentral,SurfArea_G_and_S_transv_frontopol,SurfArea_G_cingul-Post-dorsal,SurfArea_G_cingul-Post-ventral,SurfArea_G_cuneus,SurfArea_G_front_inf-Opercular,SurfArea_G_front_inf-Orbital,SurfArea_G_front_inf-Triangul,SurfArea_G_front_middle,SurfArea_G_front_sup,SurfArea_G_insular_short,SurfArea_G_oc-temp_lat-fusifor,SurfArea_G_oc-temp_med-Lingual,SurfArea_G_oc-temp_med-Parahip,SurfArea_G_occipital_middle,SurfArea_G_occipital_sup,SurfArea_G_orbital,SurfArea_G_pariet_inf-Angular,SurfArea_G_pariet_inf-Supramar,SurfArea_G_parietal_sup,SurfArea_G_postcentral,SurfArea_G_precentral,SurfArea_G_precuneus,SurfArea_G_rectus,SurfArea_G_subcallosal,SurfArea_G_temp_sup-G_T_transv,SurfArea_G_temp_sup-Lateral,SurfArea_G_temp_sup-Plan_polar,SurfArea_G_temp_sup-Plan_tempo,SurfArea_G_temporal_inf,SurfArea_G_temporal_middle,SurfArea_Lat_Fis-ant-Horizont,SurfArea_Lat_Fis-ant-Vertical,SurfArea_Lat_Fis-post,SurfArea_Pole_occipital,SurfArea_Pole_temporal,SurfArea_S_calcarine,SurfArea_S_central,SurfArea_S_cingul-Marginalis,SurfArea_S_circular_insula_ant,SurfArea_S_circular_insula_inf,SurfArea_S_circular_insula_sup,SurfArea_S_collat_transv_ant,SurfArea_S_collat_transv_post,SurfArea_S_front_inf,SurfArea_S_front_middle,SurfArea_S_front_sup,SurfArea_S_interm_prim-Jensen,SurfArea_S_intrapariet_and_P_trans,SurfArea_S_oc-temp_lat,SurfArea_S_oc-temp_med_and_Lingual,SurfArea_S_oc_middle_and_Lunatus,SurfArea_S_oc_sup_and_transversal,SurfArea_S_occipital_ant,SurfArea_S_orbital-H_Shaped,SurfArea_S_orbital_lateral,SurfArea_S_orbital_med-olfact,SurfArea_S_parieto_occipital,SurfArea_S_pericallosal,SurfArea_S_postcentral,SurfArea_S_precentral-inf-part,SurfArea_S_precentral-sup-part,SurfArea_S_suborbital,SurfArea_S_subparietal,SurfArea_S_temporal_inf,SurfArea_S_temporal_sup,SurfArea_S_temporal_transverse,ThickAvg_G_Ins_lg_and_S_cent_ins,ThickAvg_G_and_S_cingul-Ant,ThickAvg_G_and_S_cingul-Mid-Ant,ThickAvg_G_and_S_cingul-Mid-Post,ThickAvg_G_and_S_frontomargin,ThickAvg_G_and_S_occipital_inf,ThickAvg_G_and_S_paracentral,ThickAvg_G_and_S_subcentral,ThickAvg_G_and_S_transv_frontopol,ThickAvg_G_cingul-Post-dorsal,ThickAvg_G_cingul-Post-ventral,ThickAvg_G_cuneus,ThickAvg_G_front_inf-Opercular,ThickAvg_G_front_inf-Orbital,ThickAvg_G_front_inf-Triangul,ThickAvg_G_front_middle,ThickAvg_G_front_sup,ThickAvg_G_insular_short,ThickAvg_G_oc-temp_lat-fusifor,ThickAvg_G_oc-temp_med-Lingual,ThickAvg_G_oc-temp_med-Parahip,ThickAvg_G_occipital_middle,ThickAvg_G_occipital_sup,ThickAvg_G_orbital,ThickAvg_G_pariet_inf-Angular,ThickAvg_G_pariet_inf-Supramar,ThickAvg_G_parietal_sup,ThickAvg_G_postcentral,ThickAvg_G_precentral,ThickAvg_G_precuneus,ThickAvg_G_rectus,ThickAvg_G_subcallosal,ThickAvg_G_temp_sup-G_T_transv,ThickAvg_G_temp_sup-Lateral,ThickAvg_G_temp_sup-Plan_polar,ThickAvg_G_temp_sup-Plan_tempo,ThickAvg_G_temporal_inf,ThickAvg_G_temporal_middle,ThickAvg_Lat_Fis-ant-Horizont,ThickAvg_Lat_Fis-ant-Vertical,ThickAvg_Lat_Fis-post,ThickAvg_Pole_occipital,ThickAvg_Pole_temporal,ThickAvg_S_calcarine,ThickAvg_S_central,ThickAvg_S_cingul-Marginalis,ThickAvg_S_circular_insula_ant,ThickAvg_S_circular_insula_inf,ThickAvg_S_circular_insula_sup,ThickAvg_S_collat_transv_ant,ThickAvg_S_collat_transv_post,ThickAvg_S_front_inf,ThickAvg_S_front_middle,ThickAvg_S_front_sup,ThickAvg_S_interm_prim-Jensen,ThickAvg_S_intrapariet_and_P_trans,ThickAvg_S_oc-temp_lat,ThickAvg_S_oc-temp_med_and_Lingual,ThickAvg_S_oc_middle_and_Lunatus,ThickAvg_S_oc_sup_and_transversal,ThickAvg_S_occipital_ant,ThickAvg_S_orbital-H_Shaped,ThickAvg_S_orbital_lateral,ThickAvg_S_orbital_med-olfact,ThickAvg_S_parieto_occipital,ThickAvg_S_pericallosal,ThickAvg_S_postcentral,ThickAvg_S_precentral-inf-part,ThickAvg_S_precentral-sup-part,ThickAvg_S_suborbital,ThickAvg_S_subparietal,ThickAvg_S_temporal_inf,ThickAvg_S_temporal_sup,ThickAvg_S_temporal_transverse,ThickStd_G_Ins_lg_and_S_cent_ins,ThickStd_G_and_S_cingul-Ant,ThickStd_G_and_S_cingul-Mid-Ant,ThickStd_G_and_S_cingul-Mid-Post,ThickStd_G_and_S_frontomargin,ThickStd_G_and_S_occipital_inf,ThickStd_G_and_S_paracentral,ThickStd_G_and_S_subcentral,ThickStd_G_and_S_transv_frontopol,ThickStd_G_cingul-Post-dorsal,ThickStd_G_cingul-Post-ventral,ThickStd_G_cuneus,ThickStd_G_front_inf-Opercular,ThickStd_G_front_inf-Orbital,ThickStd_G_front_inf-Triangul,ThickStd_G_front_middle,ThickStd_G_front_sup,ThickStd_G_insular_short,ThickStd_G_oc-temp_lat-fusifor,ThickStd_G_oc-temp_med-Lingual,ThickStd_G_oc-temp_med-Parahip,ThickStd_G_occipital_middle,ThickStd_G_occipital_sup,ThickStd_G_orbital,ThickStd_G_pariet_inf-Angular,ThickStd_G_pariet_inf-Supramar,ThickStd_G_parietal_sup,ThickStd_G_postcentral,ThickStd_G_precentral,ThickStd_G_precuneus,ThickStd_G_rectus,ThickStd_G_subcallosal,ThickStd_G_temp_sup-G_T_transv,ThickStd_G_temp_sup-Lateral,ThickStd_G_temp_sup-Plan_polar,ThickStd_G_temp_sup-Plan_tempo,ThickStd_G_temporal_inf,ThickStd_G_temporal_middle,ThickStd_Lat_Fis-ant-Horizont,ThickStd_Lat_Fis-ant-Vertical,ThickStd_Lat_Fis-post,ThickStd_Pole_occipital,ThickStd_Pole_temporal,ThickStd_S_calcarine,ThickStd_S_central,ThickStd_S_cingul-Marginalis,ThickStd_S_circular_insula_ant,ThickStd_S_circular_insula_inf,ThickStd_S_circular_insula_sup,ThickStd_S_collat_transv_ant,ThickStd_S_collat_transv_post,ThickStd_S_front_inf,ThickStd_S_front_middle,ThickStd_S_front_sup,ThickStd_S_interm_prim-Jensen,ThickStd_S_intrapariet_and_P_trans,ThickStd_S_oc-temp_lat,ThickStd_S_oc-temp_med_and_Lingual,ThickStd_S_oc_middle_and_Lunatus,ThickStd_S_oc_sup_and_transversal,ThickStd_S_occipital_ant,ThickStd_S_orbital-H_Shaped,ThickStd_S_orbital_lateral,ThickStd_S_orbital_med-olfact,ThickStd_S_parieto_occipital,ThickStd_S_pericallosal,ThickStd_S_postcentral,ThickStd_S_precentral-inf-part,ThickStd_S_precentral-sup-part,ThickStd_S_suborbital,ThickStd_S_subparietal,ThickStd_S_temporal_inf,ThickStd_S_temporal_sup,ThickStd_S_temporal_transverse
-1,Epileptic,M,22.0,0.8,2.4,0.9,1.7,2.0,2.1,1.9,1.8,1.6,0.8,0.4,2.7,1.2,0.5,1.3,6.4,9.7,0.7,1.6,4.8,1.3,3.0,1.5,4.0,2.3,2.7,3.9,2.4,2.6,2.7,1.8,1.3,0.7,2.6,0.9,0.5,4.8,3.3,0.4,0.2,0.6,3.0,2.5,3.1,2.3,0.7,0.5,0.8,1.3,0.8,0.7,1.6,1.3,3.0,0.4,2.4,0.6,0.9,1.0,1.0,0.8,1.5,0.2,0.5,2.2,1.4,2.9,1.1,0.8,0.9,0.8,1.4,4.4,0.2,8,23,10,13,16,21,20,19,14,11,5.0,31,22,7,19,69,113,10,21,51,14,39,17,38,38,35,53,26,34,39,19,9,6,31,8,5,49,44,5,2,4,33,29,26,15,6,2,7,8,7,4,16,10,26,2,19,4,7,7,8,6,13,2,5,18,19,24,8,6,11,8,10,34,1,0.034,0.024,0.02,0.026,0.04,0.028,0.025,0.029,0.051,0.032,0.037,0.037,0.028,0.028,0.031,0.032,0.031,0.037,0.028,0.037,0.029,0.029,0.027,0.043,0.028,0.026,0.031,0.023,0.022,0.029,0.054,0.042,0.027,0.029,0.024,0.015,0.041,0.032,0.018,0.012,0.02,0.032,0.036,0.027,0.016,0.015,0.022,0.015,0.017,0.014,0.024,0.016,0.022,0.021,0.019,0.017,0.017,0.014,0.022,0.022,0.02,0.024,0.011,0.018,0.019,0.025,0.018,0.014,0.012,0.021,0.021,0.02,0.015,0.009,1560,5972,2242,2953,2392,4910,4005,3429,2050,2219,657.0,3207,3129,1360,2966,11882,22035,2275,4050,5920,4178,5916,2050,7153,6176,6670,7768,4736,7320,5980,2331,1375,1249,7631,2895,1402,8983,8375,730,492,1094,3523,6767,3617,3942,1697,839,2234,2393,2317,705,3552,1878,5716,652,3944,1296,2618,1399,1385,1622,3149,476,1017,4092,1216,5011,2709,1930,1834,1412,2253,10116,663,0.119,0.117,0.116,0.12,0.142,0.131,0.118,0.14,0.166,0.158,0.155,0.162,0.136,0.143,0.136,0.144,0.14,0.128,0.127,0.16,0.111,0.131,0.12,0.152,0.136,0.139,0.121,0.111,0.115,0.136,0.15,0.116,0.115,0.136,0.114,0.091,0.144,0.136,0.125,0.098,0.104,0.136,0.141,0.126,0.088,0.096,0.107,0.091,0.098,0.093,0.118,0.094,0.112,0.114,0.104,0.106,0.102,0.087,0.107,0.113,0.109,0.116,0.094,0.115,0.105,0.117,0.104,0.09,0.087,0.133,0.119,0.114,0.093,0.071,421,1757,788,968,853,1357,1282,1020,550,496,193.0,1448,969,324,779,3267,6090,473,986,2420,790,1678,968,1801,1648,1736,2478,1718,2031,1740,723,522,408,1647,629,493,2330,2051,290,250,507,1526,1390,1956,2259,779,311,905,1118,869,389,1615,890,2381,294,2095,562,1092,778,712,643,1071,269,472,1817,886,2480,1209,928,672,659,1063,4128,297,3.347,3.024,2.816,2.878,2.226,2.731,2.557,2.869,2.618,3.263,2.669,1.929,2.585,2.873,2.725,2.66,2.82,3.407,3.143,2.159,3.752,2.634,1.903,2.98,2.793,2.927,2.429,2.21,2.757,2.705,2.379,2.71,2.707,3.14,3.892,2.458,2.919,2.968,2.571,2.18,2.761,2.066,3.377,2.083,1.985,2.46,3.173,3.142,2.643,2.941,2.248,2.346,2.155,2.537,2.421,2.112,2.478,2.641,2.114,2.125,2.358,2.807,1.79,2.55,2.507,1.639,2.228,2.569,2.391,2.594,2.497,2.467,2.538,2.517,0.989,0.706,0.506,0.561,0.806,0.74,0.559,0.495,0.736,0.681,0.952,0.533,0.459,0.621,0.608,0.635,0.667,0.871,0.643,0.701,0.869,0.524,0.499,0.796,0.613,0.547,0.631,0.614,0.545,0.498,0.691,0.914,0.591,0.662,0.778,0.468,0.815,0.664,0.418,0.368,0.386,0.587,0.84,0.666,0.581,0.386,0.588,0.744,0.47,0.455,0.482,0.416,0.512,0.397,0.472,0.422,0.516,0.554,0.398,0.397,0.527,0.814,0.348,0.72,0.598,0.556,0.413,0.344,0.341,0.81,0.436,0.45,0.475,0.403
-2,Epileptic,F,37.0,0.6,2.4,1.1,1.3,1.2,1.6,1.9,1.4,1.4,0.4,0.4,3.7,1.2,0.5,1.1,4.8,6.8,1.0,2.2,4.9,0.9,2.7,2.0,3.5,3.3,1.8,2.9,2.0,2.3,2.4,2.3,2.1,0.4,2.6,0.4,1.2,2.7,2.2,0.1,0.3,1.1,3.3,2.9,3.4,2.4,0.6,0.3,0.9,1.5,0.8,0.5,1.4,1.4,2.1,0.2,2.3,0.6,1.5,1.7,1.7,0.6,1.5,0.3,0.6,1.7,1.0,2.8,1.0,1.1,0.5,1.2,1.0,3.6,0.3,5,18,7,10,12,16,15,13,10,5,5.0,30,15,5,14,51,59,7,20,41,7,30,23,36,38,23,28,18,20,25,22,14,4,29,2,9,26,23,2,2,7,31,20,24,14,4,1,6,8,5,3,10,12,13,1,19,5,8,10,13,3,10,2,5,13,15,23,7,8,3,7,6,30,3,0.04,0.033,0.028,0.03,0.03,0.031,0.032,0.034,0.051,0.033,0.05,0.037,0.029,0.028,0.026,0.033,0.033,0.037,0.04,0.039,0.029,0.034,0.034,0.043,0.035,0.022,0.027,0.031,0.026,0.028,0.05,0.074,0.025,0.035,0.018,0.02,0.037,0.032,0.013,0.019,0.028,0.036,0.048,0.028,0.019,0.017,0.016,0.022,0.022,0.026,0.024,0.019,0.035,0.02,0.027,0.019,0.018,0.02,0.032,0.025,0.018,0.027,0.021,0.025,0.022,0.03,0.028,0.016,0.023,0.019,0.029,0.018,0.019,0.021,1091,3498,1635,1866,1910,2090,2667,2340,1618,1014,382.0,3484,2566,982,2641,8506,14526,2038,3530,5263,2252,4289,2755,5407,5217,4377,5113,3167,5958,4922,2979,1000,841,5440,1392,2106,5300,4940,290,442,1348,3141,5313,2645,3258,1142,652,1539,2027,1051,524,2566,1511,3247,302,3706,1246,2134,1572,1863,920,2141,350,813,2490,930,3328,2099,1691,887,1224,1586,6983,444,0.128,0.127,0.113,0.128,0.136,0.121,0.111,0.138,0.162,0.128,0.148,0.14,0.13,0.135,0.123,0.139,0.127,0.141,0.141,0.149,0.097,0.132,0.123,0.147,0.124,0.117,0.112,0.111,0.106,0.13,0.168,0.153,0.105,0.15,0.084,0.108,0.129,0.135,0.094,0.104,0.124,0.135,0.167,0.127,0.099,0.102,0.086,0.099,0.111,0.121,0.123,0.099,0.141,0.1,0.15,0.105,0.101,0.105,0.129,0.123,0.105,0.125,0.117,0.125,0.108,0.135,0.124,0.095,0.106,0.105,0.138,0.103,0.099,0.113,291,1101,649,726,744,822,974,795,507,269,166.0,1560,798,273,715,2461,3745,401,1025,2211,570,1345,1092,1503,1605,1390,1823,1071,1467,1453,784,503,310,1311,418,835,1396,1292,213,256,618,1477,1020,1840,1918,565,308,698,1019,482,263,1171,727,1582,124,1959,603,1130,866,1019,438,845,231,373,1229,572,1657,900,823,419,554,806,2953,269,3.504,2.795,2.599,2.601,2.162,2.231,2.257,2.541,2.496,2.93,1.989,1.921,2.454,2.541,2.628,2.576,2.911,3.595,2.711,2.062,2.849,2.504,2.164,2.744,2.528,2.557,2.272,2.265,2.988,2.661,2.819,2.227,2.114,2.796,2.591,2.405,2.794,2.838,1.446,1.863,2.75,1.995,3.152,1.707,1.932,2.207,2.295,2.877,2.356,2.663,1.878,2.245,2.034,2.144,2.545,2.147,2.201,2.113,2.075,1.965,2.32,2.499,1.844,2.184,2.217,1.955,2.078,2.512,2.42,2.402,2.475,2.268,2.318,2.107,0.83,0.67,0.519,0.431,0.75,0.655,0.622,0.625,0.573,0.941,0.854,0.618,0.452,0.512,0.632,0.643,0.638,0.834,0.609,0.611,0.804,0.568,0.557,0.686,0.659,0.575,0.635,0.604,0.526,0.582,0.71,0.82,0.475,0.8,0.86,0.556,0.726,0.658,0.298,0.406,0.544,0.492,0.979,0.48,0.617,0.458,0.657,0.806,0.612,0.57,0.564,0.457,0.583,0.511,0.623,0.428,0.573,0.571,0.463,0.446,0.53,0.727,0.482,0.62,0.515,0.579,0.421,0.473,0.396,0.721,0.689,0.514,0.58,0.304
-3,Epileptic,M,27.0,1.4,2.1,0.7,0.9,1.1,2.7,2.0,2.2,1.0,0.9,0.6,3.5,1.8,0.6,1.3,6.9,9.9,0.9,3.0,8.0,0.7,2.7,2.3,2.6,4.2,4.6,3.1,3.2,2.8,4.0,1.1,1.1,1.3,4.0,1.4,1.3,3.1,3.2,0.1,0.5,1.7,3.9,3.1,3.9,3.2,0.7,0.5,1.4,1.4,0.8,0.6,1.9,1.4,2.6,0.5,2.4,1.0,3.0,0.8,1.6,0.8,1.1,0.2,0.6,1.8,0.5,2.9,2.3,0.8,1.0,1.5,1.2,4.0,0.8,11,20,6,9,11,22,15,18,12,9,5.0,34,18,5,10,75,81,7,34,59,5,31,22,38,50,49,37,32,25,44,18,8,15,35,9,12,33,33,2,3,8,34,29,31,18,5,2,7,7,6,4,16,10,17,4,22,8,17,6,12,6,10,2,4,16,8,20,14,5,12,9,8,30,6,0.041,0.019,0.01,0.017,0.028,0.03,0.027,0.026,0.037,0.029,0.037,0.035,0.032,0.023,0.025,0.03,0.026,0.027,0.029,0.047,0.014,0.03,0.03,0.034,0.03,0.03,0.026,0.027,0.023,0.031,0.032,0.061,0.036,0.034,0.036,0.024,0.028,0.03,0.013,0.024,0.024,0.039,0.033,0.027,0.018,0.015,0.018,0.016,0.014,0.02,0.027,0.016,0.018,0.017,0.025,0.017,0.021,0.026,0.018,0.022,0.022,0.02,0.013,0.024,0.019,0.031,0.018,0.02,0.013,0.025,0.024,0.018,0.017,0.026,2123,6014,2755,2581,2088,4435,4225,4842,2002,2058,1106.0,3494,3977,1303,3185,15168,25330,3331,7468,6174,3180,5386,3267,6355,9130,8964,6546,5340,7562,7514,2804,1017,1964,8047,2460,2901,9236,8252,459,909,2647,3921,9329,4097,4876,2112,1103,2455,3389,2086,567,4272,2777,6360,616,4747,2175,4331,1379,2310,1361,2442,523,893,3711,625,5070,4221,1780,1663,2340,2362,10159,1104,0.142,0.101,0.08,0.087,0.123,0.137,0.114,0.112,0.117,0.123,0.129,0.149,0.126,0.125,0.127,0.129,0.117,0.106,0.128,0.157,0.076,0.14,0.134,0.125,0.135,0.134,0.127,0.112,0.101,0.138,0.116,0.133,0.141,0.12,0.115,0.107,0.126,0.125,0.082,0.115,0.113,0.154,0.131,0.127,0.096,0.089,0.097,0.081,0.084,0.104,0.127,0.095,0.105,0.089,0.127,0.105,0.106,0.106,0.105,0.116,0.126,0.105,0.095,0.119,0.112,0.123,0.103,0.106,0.088,0.126,0.113,0.111,0.097,0.125,568,1840,979,929,693,1455,1226,1365,519,520,271.0,1678,1023,317,795,3846,6361,587,1823,2947,854,1555,1295,1499,2423,2602,2103,1831,1921,2269,622,348,608,1860,517,964,1863,1702,220,304,993,1738,1613,2357,2625,755,419,1246,1425,666,364,1886,1242,2486,292,2293,837,1953,711,1106,487,889,257,411,1583,264,2345,1702,824,632,1053,941,3654,474,3.743,2.981,2.692,2.926,2.613,2.614,2.771,2.965,2.833,3.186,3.247,1.864,2.943,2.811,2.977,2.94,3.18,3.988,3.06,1.888,2.799,2.715,2.182,3.038,2.81,2.919,2.441,2.404,2.98,2.656,3.106,2.926,2.551,3.112,3.599,2.775,3.558,3.391,2.436,2.849,3.175,2.024,3.769,1.918,2.125,2.691,3.311,2.27,2.801,3.59,1.897,2.359,2.409,2.699,2.495,2.298,2.781,2.523,2.277,2.383,2.854,2.612,2.503,2.314,2.621,2.498,2.393,2.931,2.5,2.7,2.664,2.966,2.979,2.318,0.719,0.573,0.612,0.49,0.64,0.639,0.587,0.641,0.542,0.493,0.769,0.465,0.541,0.544,0.587,0.578,0.6,0.803,0.773,0.695,0.909,0.473,0.608,0.656,0.55,0.627,0.442,0.552,0.448,0.597,0.744,1.155,0.743,0.764,0.972,0.447,0.736,0.667,0.457,0.583,0.608,0.624,0.667,0.671,0.656,0.687,0.497,1.122,0.618,0.744,0.33,0.575,0.628,0.521,0.48,0.348,0.475,0.511,0.382,0.384,0.51,0.825,0.418,0.92,0.545,0.725,0.473,0.404,0.407,0.604,0.39,0.776,0.634,0.461
-4,Epileptic,M,42.0,1.2,1.9,0.9,0.9,1.7,2.1,2.3,2.3,0.9,0.3,0.1,3.0,2.0,0.7,1.9,6.9,10.8,1.0,3.4,5.3,0.8,2.6,1.4,3.5,2.8,5.1,3.8,2.4,3.6,4.2,1.2,1.0,0.6,2.7,0.3,0.5,3.1,4.7,0.2,0.4,1.5,3.8,2.4,3.0,3.6,0.5,0.4,0.7,1.4,0.6,0.6,2.8,1.2,3.1,0.3,2.5,0.9,1.3,1.2,0.8,0.7,1.3,0.3,0.6,1.9,0.8,2.1,1.3,1.0,0.6,0.8,1.2,4.3,0.4,7,15,7,6,15,16,16,15,9,3,1.0,25,14,6,16,58,70,6,25,36,6,24,13,43,27,41,36,20,25,35,13,9,4,23,1,4,25,37,2,2,8,29,21,22,16,3,2,4,6,4,5,18,7,15,1,17,4,11,8,8,5,8,2,4,11,8,15,7,6,5,4,8,35,3,0.051,0.023,0.021,0.021,0.038,0.043,0.038,0.036,0.042,0.025,0.019,0.035,0.038,0.041,0.038,0.04,0.037,0.034,0.041,0.041,0.02,0.035,0.027,0.039,0.039,0.039,0.035,0.035,0.038,0.039,0.038,0.048,0.025,0.032,0.009,0.014,0.037,0.045,0.018,0.023,0.024,0.041,0.046,0.028,0.026,0.013,0.022,0.012,0.016,0.018,0.031,0.028,0.02,0.026,0.029,0.02,0.025,0.016,0.024,0.017,0.024,0.021,0.036,0.025,0.023,0.032,0.022,0.021,0.021,0.021,0.026,0.023,0.022,0.017,1786,5017,2529,1896,2328,2621,2706,3329,1904,1091,463.0,3343,3366,1103,3568,10786,19703,2691,3981,3440,2811,4249,1818,6282,4791,8313,6119,3677,5823,5982,2169,1149,1028,5007,1951,2019,4384,6492,365,895,1773,2964,4945,2694,3720,1287,747,2119,2769,1197,470,3559,1974,4420,442,3826,1246,2515,1298,1343,1113,2773,494,1013,2558,812,3587,2144,1645,928,1190,1988,8277,549,0.131,0.106,0.106,0.097,0.139,0.151,0.114,0.129,0.135,0.104,0.071,0.127,0.136,0.152,0.134,0.143,0.131,0.123,0.138,0.141,0.081,0.135,0.117,0.132,0.14,0.134,0.126,0.116,0.123,0.14,0.12,0.124,0.107,0.121,0.06,0.072,0.127,0.13,0.091,0.096,0.117,0.144,0.143,0.112,0.109,0.086,0.093,0.076,0.09,0.098,0.148,0.115,0.098,0.109,0.111,0.103,0.111,0.095,0.126,0.102,0.114,0.105,0.12,0.117,0.11,0.139,0.106,0.098,0.102,0.125,0.12,0.111,0.104,0.097,404,1294,704,678,764,942,966,1122,452,225,140.0,1373,920,270,872,3122,4914,494,1266,1991,674,1242,818,1505,1296,2303,1983,1279,1644,1765,542,380,360,1328,537,636,1308,1764,223,304,887,1447,995,1727,2101,595,308,803,1224,530,313,1632,913,1923,186,1850,580,1297,698,715,459,890,229,391,1263,373,1589,949,769,435,496,952,3526,332,3.956,3.062,3.165,2.755,2.498,2.573,2.316,2.64,2.715,3.47,2.691,2.082,2.789,2.754,3.024,2.635,3.179,3.895,2.629,1.591,3.032,2.566,2.023,3.055,2.888,2.853,2.449,2.211,2.713,2.66,2.672,2.954,2.271,2.675,3.267,2.82,2.612,2.738,2.116,3.117,2.374,1.89,3.294,1.789,2.093,2.304,3.046,3.025,2.663,2.866,1.981,2.256,2.306,2.466,2.449,2.346,2.797,2.194,2.285,2.146,2.467,2.949,2.435,2.675,2.273,2.418,2.387,2.611,2.595,2.422,2.817,2.51,2.494,2.041,0.92,0.82,0.789,0.611,0.773,0.751,0.556,0.549,0.778,0.481,0.761,0.521,0.603,0.534,0.637,0.593,0.779,0.743,0.701,0.515,0.829,0.531,0.442,0.872,0.639,0.75,0.545,0.615,0.601,0.528,0.841,0.966,0.421,0.659,0.953,0.5,0.702,0.743,0.59,0.785,0.436,0.701,0.897,0.591,0.54,0.575,0.607,0.952,0.574,0.571,0.402,0.485,0.496,0.465,0.476,0.609,0.621,0.574,0.35,0.472,0.543,0.779,0.319,1.281,0.65,0.611,0.597,0.465,0.541,0.68,0.697,0.602,0.498,0.344
-5,Epileptic,F,47.0,0.7,2.6,1.1,0.9,1.9,1.6,2.2,1.4,1.6,0.9,0.4,3.5,1.8,0.6,0.8,5.9,8.6,2.2,2.2,5.1,1.1,2.7,2.8,3.4,3.2,4.0,5.6,2.4,3.0,3.3,2.0,1.3,0.4,2.4,0.4,0.6,3.0,3.0,0.1,0.2,1.0,4.1,2.6,3.2,2.7,0.9,0.4,0.9,1.3,0.7,0.6,1.7,1.3,2.3,0.1,2.3,0.7,1.8,1.6,1.7,0.8,2.1,0.5,0.5,1.5,1.6,2.3,1.1,1.0,0.6,1.4,1.5,3.5,0.4,8,24,10,9,19,19,16,16,17,11,4.0,31,23,7,12,71,86,11,25,47,9,34,25,43,40,44,50,23,23,41,22,11,5,39,2,4,36,45,2,1,9,33,28,27,19,6,3,5,7,5,5,13,10,17,0,20,8,16,15,11,5,15,5,6,11,20,19,9,6,5,9,9,32,3,0.033,0.027,0.021,0.019,0.034,0.029,0.035,0.029,0.06,0.04,0.025,0.04,0.027,0.03,0.028,0.033,0.029,0.063,0.032,0.038,0.022,0.028,0.04,0.036,0.035,0.033,0.04,0.032,0.03,0.032,0.052,0.054,0.024,0.034,0.014,0.013,0.035,0.03,0.015,0.018,0.021,0.039,0.035,0.028,0.023,0.022,0.018,0.015,0.016,0.023,0.025,0.015,0.025,0.019,0.019,0.016,0.02,0.021,0.025,0.028,0.023,0.026,0.027,0.018,0.023,0.031,0.02,0.016,0.019,0.018,0.028,0.018,0.015,0.019,1545,4634,2165,2100,2072,2800,2792,3190,1820,1703,699.0,3340,3800,1139,2322,11210,19140,2603,3841,4783,3305,4885,2925,6611,5146,6869,6484,3530,5871,5934,2761,1262,1013,5018,1542,1986,5745,7678,324,367,1569,3154,5064,2801,2842,1401,888,1965,2223,1016,636,3566,1589,4465,102,4039,1197,3104,1712,1864,1017,2963,486,981,2051,1037,3469,2595,1674,1086,1436,2336,7751,441,0.127,0.132,0.119,0.099,0.146,0.134,0.136,0.132,0.182,0.161,0.122,0.161,0.141,0.142,0.132,0.143,0.13,0.17,0.127,0.151,0.101,0.126,0.142,0.138,0.131,0.15,0.128,0.109,0.106,0.147,0.13,0.121,0.105,0.145,0.074,0.08,0.141,0.147,0.105,0.105,0.118,0.138,0.155,0.131,0.11,0.112,0.103,0.089,0.092,0.121,0.139,0.094,0.125,0.1,0.126,0.101,0.118,0.115,0.124,0.124,0.115,0.122,0.138,0.11,0.112,0.134,0.106,0.096,0.1,0.117,0.129,0.101,0.092,0.13,429,1492,767,814,910,1028,1034,938,549,466,195.0,1502,1096,327,644,3144,4980,544,1163,2306,778,1470,1153,1745,1527,2065,2167,1287,1444,1787,799,523,281,1299,445,735,1633,1877,185,173,771,1662,1253,1821,1852,642,371,891,1110,481,342,1710,855,2007,48,2120,588,1419,1044,922,507,1154,294,527,1070,714,1847,1115,808,491,723,1207,3445,258,3.449,2.798,2.636,2.381,2.023,2.387,2.194,2.771,2.491,2.983,2.925,1.97,2.734,2.62,2.67,2.696,2.964,3.619,2.558,1.823,2.988,2.502,2.199,2.761,2.576,2.694,2.397,2.128,2.911,2.572,2.531,2.508,2.763,2.815,2.96,2.537,2.677,2.911,1.894,2.118,2.518,1.749,2.988,1.77,1.761,2.457,2.894,2.735,2.391,2.484,2.04,2.198,2.037,2.409,2.377,2.135,2.391,2.315,1.895,2.217,2.362,2.446,1.832,2.222,2.214,1.81,1.996,2.547,2.399,2.461,2.396,2.268,2.376,2.139,0.75,0.637,0.679,0.511,0.624,0.536,0.7,0.582,0.62,0.552,0.757,0.594,0.541,0.549,0.587,0.589,0.646,0.712,0.606,0.616,0.9,0.584,0.668,0.805,0.64,0.54,0.658,0.67,0.718,0.509,0.697,0.972,0.512,0.653,0.578,0.472,0.64,0.665,0.462,0.375,0.543,0.475,0.684,0.572,0.597,0.475,0.492,0.66,0.46,0.5,0.46,0.434,0.43,0.452,0.25,0.361,0.509,0.559,0.388,0.42,0.445,0.7,0.302,0.698,0.533,0.641,0.415,0.413,0.374,0.809,0.349,0.462,0.47,0.463
-6,Epileptic,F,22.0,1.3,3.3,1.4,1.2,2.6,2.2,3.6,2.8,1.8,1.0,0.4,4.0,2.4,0.6,1.6,8.1,17.1,1.1,3.4,4.7,1.5,4.9,2.6,7.0,6.6,6.1,7.5,4.6,5.7,4.1,3.4,1.5,0.6,3.1,0.8,0.9,5.9,4.2,0.2,0.2,1.5,4.1,4.7,3.3,4.3,1.1,0.6,1.0,1.9,1.0,0.8,2.0,2.3,4.2,0.4,3.2,1.2,2.3,1.4,1.4,0.8,1.9,0.3,1.2,2.1,1.6,3.6,2.7,1.4,0.7,1.6,1.8,5.2,0.5,11,25,10,9,20,21,27,26,13,13,4.0,39,22,6,16,74,121,9,45,45,11,52,24,56,71,67,67,35,45,52,23,10,5,31,5,7,62,54,1,1,9,37,32,25,20,6,3,6,9,9,7,12,15,27,3,23,8,20,9,11,5,12,1,10,18,19,25,19,7,6,11,15,40,3,0.053,0.032,0.023,0.023,0.05,0.038,0.047,0.038,0.058,0.048,0.023,0.042,0.041,0.04,0.051,0.058,0.049,0.047,0.043,0.04,0.032,0.048,0.041,0.073,0.051,0.038,0.052,0.049,0.051,0.036,0.075,0.054,0.027,0.034,0.022,0.019,0.05,0.037,0.017,0.018,0.029,0.043,0.061,0.027,0.033,0.019,0.028,0.014,0.023,0.021,0.028,0.024,0.035,0.036,0.034,0.021,0.026,0.026,0.028,0.022,0.03,0.037,0.032,0.037,0.023,0.031,0.028,0.031,0.034,0.025,0.035,0.021,0.022,0.026,1620,4979,2442,2669,3077,3450,3534,3692,2301,1749,804.0,3818,3689,1284,2505,9354,22294,2601,5609,5541,3066,5344,2424,6038,6051,8538,6414,4468,6373,6258,2394,1397,1138,6348,2339,2205,7792,8384,391,352,1504,3323,5448,3493,3272,1716,856,2713,2488,1582,746,2675,2826,4513,485,4080,1715,3734,1267,1792,879,2326,268,958,2931,1288,4125,2996,1557,1165,1153,2419,8438,509,0.147,0.124,0.108,0.109,0.148,0.139,0.137,0.153,0.171,0.17,0.117,0.17,0.15,0.148,0.163,0.164,0.151,0.141,0.137,0.148,0.116,0.146,0.135,0.18,0.167,0.141,0.148,0.139,0.151,0.149,0.172,0.127,0.112,0.134,0.099,0.101,0.154,0.144,0.09,0.103,0.117,0.149,0.168,0.12,0.12,0.096,0.109,0.079,0.106,0.113,0.14,0.106,0.131,0.129,0.147,0.111,0.109,0.123,0.13,0.119,0.124,0.135,0.128,0.125,0.116,0.135,0.121,0.118,0.119,0.12,0.151,0.12,0.105,0.13,457,1746,988,916,948,1114,1442,1255,569,440,291.0,1677,1105,299,728,2850,6457,503,1493,2271,825,1705,1126,1818,2166,2856,2644,1749,2037,2119,747,437,386,1619,615,779,2346,2143,224,176,850,1549,1437,1960,2089,849,361,1051,1140,730,434,1377,1172,2031,237,2233,770,1564,750,985,440,896,151,466,1498,763,2112,1378,741,512,640,1293,3876,286,3.414,2.485,2.407,2.801,2.499,2.459,2.113,2.49,2.757,3.016,2.543,1.99,2.595,2.842,2.563,2.388,2.62,3.7,2.805,2.038,2.797,2.401,1.952,2.491,2.244,2.433,2.059,2.097,2.403,2.297,2.31,3.058,2.373,2.722,3.208,2.519,2.571,2.857,1.926,2.118,2.271,1.865,2.727,1.955,1.801,2.194,2.886,2.973,2.634,2.455,1.972,2.15,2.044,2.271,2.397,2.019,2.457,2.476,1.945,1.989,2.406,2.478,1.96,2.221,2.051,2.079,2.108,2.296,2.292,2.404,2.125,2.253,2.314,2.272,0.829,0.665,0.592,0.608,0.831,0.63,0.539,0.579,0.831,0.687,0.533,0.487,0.475,0.499,0.507,0.679,0.69,0.86,0.701,0.627,0.736,0.645,0.46,0.759,0.654,0.606,0.558,0.592,0.651,0.609,0.754,1.028,0.527,0.739,0.825,0.489,0.759,0.593,0.389,0.334,0.478,0.692,0.841,0.661,0.471,0.56,0.54,0.901,0.585,0.574,0.414,0.48,0.694,0.501,0.439,0.432,0.467,0.546,0.399,0.377,0.433,0.601,0.419,0.811,0.523,0.608,0.454,0.491,0.554,0.724,0.444,0.482,0.492,0.382
-7,Epileptic,F,27.0,0.5,2.5,1.0,1.1,1.9,1.7,1.6,1.4,1.1,0.6,0.2,2.9,1.5,0.4,1.1,5.6,7.6,0.6,1.9,4.7,0.7,2.5,2.0,4.3,3.2,2.9,3.3,2.2,2.7,3.5,1.4,1.2,0.4,2.0,0.3,0.5,5.1,4.9,0.1,0.2,1.0,2.5,1.7,2.8,2.6,0.8,0.2,0.6,1.2,0.7,0.4,2.0,1.8,3.0,0.4,2.5,0.8,2.8,0.4,1.6,0.5,2.2,0.2,0.5,1.8,1.1,2.5,1.3,0.8,0.3,1.5,1.2,5.1,0.2,8,28,9,11,15,19,14,18,12,7,2.0,32,25,6,14,72,72,5,27,42,5,34,19,37,42,43,44,25,27,47,21,9,4,31,1,4,57,58,1,2,5,26,19,31,16,5,1,4,7,9,4,16,16,22,3,19,5,22,5,15,4,15,1,5,13,14,20,8,6,3,9,10,45,1,0.035,0.033,0.02,0.02,0.039,0.028,0.024,0.027,0.052,0.032,0.024,0.04,0.033,0.029,0.027,0.034,0.031,0.024,0.026,0.042,0.018,0.03,0.035,0.049,0.032,0.03,0.028,0.026,0.024,0.038,0.033,0.038,0.028,0.026,0.009,0.012,0.047,0.044,0.01,0.013,0.019,0.039,0.034,0.029,0.022,0.017,0.014,0.011,0.018,0.019,0.027,0.023,0.029,0.024,0.025,0.019,0.024,0.027,0.019,0.027,0.02,0.031,0.016,0.016,0.021,0.021,0.022,0.018,0.02,0.017,0.031,0.023,0.019,0.013,1305,4351,2460,2383,2434,2715,3619,2733,1584,1459,586.0,2828,3480,1126,3039,10822,16869,2012,4410,4193,2757,4390,2647,5567,6321,6067,6688,4356,6328,6909,2865,1446,887,4662,1535,1846,6094,7701,291,473,1617,2427,4728,2538,3410,1412,628,1817,2282,1712,547,3348,2089,4710,545,3950,1351,3199,613,1812,745,2708,374,905,2525,1339,3597,2441,1296,681,1699,1970,8649,539,0.122,0.14,0.11,0.11,0.15,0.138,0.116,0.13,0.2,0.129,0.094,0.168,0.14,0.144,0.128,0.15,0.132,0.104,0.127,0.158,0.075,0.135,0.151,0.157,0.142,0.143,0.135,0.118,0.115,0.162,0.138,0.117,0.104,0.128,0.059,0.086,0.163,0.166,0.076,0.112,0.102,0.142,0.142,0.119,0.105,0.1,0.078,0.078,0.099,0.114,0.125,0.113,0.128,0.119,0.128,0.108,0.118,0.122,0.121,0.132,0.114,0.133,0.099,0.107,0.117,0.106,0.115,0.099,0.107,0.122,0.132,0.127,0.106,0.086,358,1363,795,843,809,953,1021,948,432,353,149.0,1270,953,251,749,2866,4218,433,1222,1867,666,1353,961,1575,1764,1775,2040,1336,1751,1872,754,490,248,1206,428,666,1835,1792,149,240,790,1074,925,1580,1870,707,217,834,1052,640,278,1435,932,1941,256,1997,591,1602,338,985,390,1013,205,487,1198,747,1741,1055,632,300,821,881,3664,252,3.252,2.891,2.939,2.703,2.458,2.318,2.718,2.519,2.804,3.238,3.18,1.909,2.744,3.093,2.924,2.754,3.037,3.755,2.89,1.976,2.825,2.472,2.349,2.722,2.703,2.771,2.498,2.454,2.796,2.764,2.741,2.661,2.637,2.803,3.173,2.56,2.61,3.006,2.351,2.219,2.523,1.87,3.432,1.807,2.044,2.243,3.027,2.721,2.573,2.615,1.996,2.425,2.231,2.48,2.587,2.148,2.492,2.232,1.865,2.108,2.167,2.63,1.969,2.206,2.373,2.216,2.185,2.641,2.367,2.505,2.392,2.491,2.415,2.535,0.685,0.567,0.596,0.512,0.82,0.732,0.667,0.418,0.596,0.47,0.803,0.632,0.481,0.662,0.595,0.658,0.61,0.705,0.551,0.582,0.707,0.592,0.565,0.842,0.657,0.539,0.64,0.57,0.559,0.671,0.728,1.015,0.376,0.579,0.527,0.454,0.696,0.753,0.342,0.33,0.489,0.663,0.789,0.581,0.52,0.419,0.519,0.639,0.412,0.467,0.418,0.444,0.519,0.455,0.437,0.398,0.444,0.544,0.735,0.377,0.345,0.728,0.464,0.731,0.585,0.678,0.477,0.408,0.442,0.72,0.418,0.507,0.471,0.38
-8,Epileptic,F,42.0,0.5,1.9,0.7,1.5,1.3,3.1,1.3,1.6,1.1,0.9,0.5,3.1,1.8,0.7,1.9,4.0,7.9,0.6,2.1,4.8,0.9,2.8,1.3,3.0,3.1,3.5,3.6,2.4,2.9,3.0,1.4,1.1,0.3,1.9,0.5,0.3,3.6,2.9,0.2,0.3,1.1,2.8,1.8,2.7,3.2,0.5,0.4,0.8,1.3,0.7,0.7,2.3,1.0,2.4,0.1,2.5,0.8,1.8,1.0,1.2,0.5,1.7,0.3,0.3,1.8,1.6,2.2,1.2,1.2,0.4,0.7,1.1,3.9,0.4,5,21,8,11,14,24,11,15,12,7,3.0,23,18,7,16,41,73,6,24,40,6,29,13,31,37,31,35,21,23,34,16,9,3,24,2,3,41,32,1,2,6,22,17,20,18,4,2,6,7,7,6,16,9,18,1,20,6,15,5,9,4,13,2,3,15,20,18,7,7,3,5,7,31,2,0.031,0.027,0.015,0.03,0.039,0.046,0.023,0.031,0.049,0.036,0.035,0.037,0.031,0.039,0.035,0.036,0.034,0.031,0.034,0.042,0.022,0.04,0.023,0.045,0.029,0.037,0.032,0.03,0.03,0.038,0.041,0.049,0.026,0.03,0.016,0.013,0.039,0.04,0.015,0.022,0.03,0.041,0.036,0.027,0.027,0.012,0.026,0.015,0.018,0.02,0.032,0.025,0.026,0.023,0.018,0.02,0.027,0.025,0.031,0.022,0.021,0.038,0.029,0.017,0.023,0.028,0.022,0.022,0.023,0.016,0.023,0.025,0.021,0.022,1081,3491,1617,2201,1774,3142,2494,2417,1678,1261,713.0,2605,3193,1004,2993,6502,14106,1997,3481,4956,2980,3921,1751,4677,5350,4729,5140,2626,4667,4448,1879,1153,724,4597,1528,1053,5052,5046,311,498,1117,2195,4231,2781,2853,1347,698,1794,2179,1017,579,3514,1687,3880,228,3562,1013,2778,945,1438,1008,1749,545,740,2601,1438,2719,1942,1642,732,1062,1568,6539,514,0.11,0.129,0.102,0.126,0.151,0.174,0.1,0.148,0.173,0.138,0.134,0.141,0.13,0.154,0.136,0.14,0.137,0.111,0.136,0.153,0.087,0.149,0.12,0.156,0.134,0.158,0.133,0.113,0.116,0.15,0.146,0.131,0.108,0.135,0.079,0.081,0.121,0.139,0.093,0.118,0.126,0.149,0.14,0.119,0.116,0.085,0.116,0.093,0.096,0.12,0.141,0.118,0.124,0.113,0.117,0.11,0.135,0.122,0.124,0.113,0.093,0.147,0.131,0.113,0.124,0.132,0.111,0.107,0.112,0.105,0.124,0.117,0.108,0.121,311,1281,738,846,651,1123,879,810,478,369,200.0,1266,1027,313,888,1978,4076,394,1068,1948,686,1263,835,1251,1760,1486,1877,1228,1389,1491,520,393,207,1072,399,419,1643,1327,203,277,584,1122,895,1592,1844,695,271,831,1072,505,365,1504,675,1719,108,2005,494,1211,552,812,456,739,233,344,1198,838,1664,880,779,362,514,806,3005,226,3.395,2.497,2.108,2.554,2.308,2.445,2.401,2.657,2.659,2.726,2.647,1.832,2.538,2.497,2.622,2.526,2.783,3.775,2.697,2.111,3.148,2.441,1.855,2.757,2.474,2.653,2.241,1.815,2.654,2.421,2.68,2.671,2.623,2.904,3.331,2.203,2.335,2.842,1.766,1.953,2.3,1.791,3.299,1.946,1.826,2.089,3.163,2.644,2.528,2.247,1.815,2.289,2.337,2.295,2.297,1.918,2.411,2.397,2.083,2.001,2.107,2.593,2.431,2.491,2.436,2.028,1.825,2.523,2.165,2.2,2.371,2.419,2.344,2.657,0.634,0.453,0.427,0.5,0.63,0.735,0.545,0.449,0.497,0.435,0.961,0.467,0.444,0.475,0.475,0.576,0.568,0.749,0.573,0.683,0.736,0.463,0.477,0.561,0.546,0.508,0.441,0.43,0.54,0.513,0.686,0.94,0.408,0.672,0.778,0.462,0.675,0.624,0.265,0.382,0.503,0.546,0.745,0.665,0.586,0.515,0.572,0.675,0.531,0.398,0.471,0.546,0.555,0.534,0.47,0.394,0.425,0.434,0.339,0.397,0.434,0.563,0.493,0.974,0.572,0.675,0.396,0.39,0.477,0.696,0.45,0.555,0.504,0.482
-9,Epileptic,F,27.0,1.4,2.7,0.9,1.3,1.9,1.8,1.5,1.8,1.1,0.4,0.3,3.1,1.2,0.4,1.2,7.5,8.9,0.9,2.4,4.5,0.9,2.8,1.8,3.8,3.1,4.3,3.5,2.2,2.3,3.6,0.8,1.4,0.4,2.5,0.4,0.5,4.0,3.7,0.2,0.1,1.3,2.2,2.0,2.5,2.9,0.7,0.5,0.7,1.2,0.8,0.9,1.5,1.6,2.1,0.4,3.1,0.5,2.4,0.7,1.2,1.1,1.6,0.4,0.4,1.7,0.5,2.9,1.4,1.4,0.9,1.1,1.5,4.9,0.3,14,24,9,11,14,14,15,16,13,11,3.0,31,13,5,18,93,85,9,25,36,10,24,20,40,38,46,46,23,25,41,13,9,6,30,1,4,44,48,2,1,7,22,21,20,17,5,2,5,7,8,5,11,13,15,3,29,4,19,5,9,9,15,2,5,16,7,23,10,10,8,8,12,39,3,0.048,0.024,0.016,0.022,0.031,0.032,0.023,0.027,0.033,0.039,0.024,0.036,0.027,0.033,0.031,0.03,0.025,0.031,0.028,0.035,0.017,0.033,0.028,0.036,0.03,0.029,0.027,0.026,0.021,0.024,0.024,0.052,0.022,0.027,0.009,0.009,0.034,0.029,0.012,0.014,0.022,0.03,0.03,0.027,0.02,0.016,0.024,0.014,0.015,0.014,0.035,0.013,0.02,0.017,0.018,0.02,0.016,0.028,0.025,0.024,0.025,0.025,0.018,0.017,0.017,0.016,0.021,0.017,0.019,0.025,0.023,0.015,0.018,0.016,1620,5589,2662,2283,2422,2667,2821,3648,2047,795,493.0,2855,2957,1069,2858,15592,20107,2483,5161,4759,3856,4061,2660,6206,6268,9318,6320,4121,6519,6730,2038,990,901,6980,2003,2529,7788,9322,364,479,1806,2723,5091,2327,3639,1449,773,2087,2610,2350,799,3917,2672,4327,628,5058,1509,3787,820,1576,2027,2769,508,890,3274,861,4546,2768,2404,1435,1799,3109,10452,533,0.132,0.116,0.093,0.109,0.13,0.134,0.109,0.13,0.129,0.125,0.119,0.155,0.124,0.139,0.126,0.137,0.124,0.125,0.129,0.137,0.087,0.143,0.133,0.131,0.139,0.135,0.129,0.113,0.105,0.129,0.105,0.125,0.124,0.124,0.061,0.075,0.128,0.126,0.092,0.101,0.114,0.136,0.13,0.127,0.102,0.092,0.114,0.08,0.091,0.096,0.134,0.081,0.102,0.096,0.114,0.114,0.083,0.121,0.122,0.122,0.122,0.115,0.105,0.121,0.105,0.103,0.115,0.1,0.105,0.133,0.113,0.101,0.101,0.118,471,1900,937,860,873,965,1084,1108,598,229,150.0,1396,776,269,857,4374,5741,502,1381,2057,1071,1332,1051,1652,1868,2542,2167,1479,1772,2249,554,456,343,1612,546,893,1975,2226,222,149,845,1177,1134,1459,2195,712,327,882,1162,860,420,1928,1303,1930,303,2426,677,1578,499,858,815,1049,280,367,1613,442,2122,1222,1153,573,840,1468,4234,288,3.289,2.792,2.749,2.593,2.357,2.423,2.213,2.909,2.568,2.955,2.757,1.851,2.826,2.916,2.627,2.761,2.903,3.54,2.965,2.069,2.882,2.409,2.17,2.828,2.67,2.95,2.309,2.259,2.793,2.524,2.745,2.225,2.132,3.103,3.316,2.702,2.889,3.029,1.974,3.257,2.859,2.038,3.257,1.939,1.926,2.385,3.061,2.931,2.663,2.843,2.354,2.187,2.192,2.455,2.497,2.314,2.531,2.632,1.982,2.064,2.497,2.489,2.184,2.264,2.276,2.33,2.319,2.579,2.44,2.789,2.541,2.435,2.673,2.312,0.87,0.601,0.577,0.575,0.538,0.457,0.526,0.44,0.527,0.46,0.621,0.463,0.437,0.477,0.51,0.547,0.526,0.784,0.512,0.654,0.883,0.423,0.436,0.599,0.493,0.538,0.494,0.586,0.465,0.498,0.611,0.792,0.409,0.654,0.75,0.586,0.644,0.625,0.431,0.791,0.639,0.49,0.645,0.701,0.551,0.478,0.441,0.595,0.617,0.615,0.381,0.446,0.517,0.403,0.341,0.388,0.422,0.584,0.352,0.338,0.422,0.681,0.551,0.964,0.512,0.756,0.516,0.405,0.383,0.625,0.481,0.512,0.599,0.481
-10,Epileptic,M,22.0,0.6,2.4,0.9,1.0,1.8,2.2,1.6,1.6,1.0,0.6,0.4,3.0,1.4,0.4,1.2,4.5,7.2,2.1,1.8,4.6,1.4,2.7,1.6,4.3,2.1,3.5,3.2,2.3,2.1,2.8,2.2,2.3,0.6,2.1,0.5,0.6,3.3,3.1,0.3,0.1,1.0,3.1,2.5,2.6,2.5,0.5,0.7,1.1,1.3,1.5,0.7,2.1,1.6,2.5,0.2,2.6,0.5,1.8,1.9,0.8,0.5,1.7,0.4,0.6,1.9,0.9,2.5,1.2,0.8,1.1,1.0,1.9,4.3,0.4,4,24,8,12,17,20,14,17,12,9,4.0,30,18,7,15,56,71,11,24,46,18,32,20,52,33,43,38,26,21,41,23,22,7,28,3,4,35,41,3,1,7,27,22,20,15,4,3,5,7,12,5,16,13,20,1,18,4,14,14,7,3,11,5,4,14,15,20,7,4,9,6,14,35,2,0.027,0.019,0.015,0.02,0.033,0.036,0.025,0.027,0.031,0.028,0.033,0.04,0.025,0.037,0.027,0.026,0.025,0.055,0.024,0.038,0.025,0.034,0.03,0.044,0.027,0.03,0.026,0.03,0.025,0.033,0.053,0.06,0.03,0.033,0.017,0.014,0.035,0.031,0.02,0.016,0.021,0.038,0.033,0.027,0.017,0.011,0.026,0.015,0.016,0.03,0.022,0.018,0.021,0.018,0.018,0.016,0.014,0.02,0.028,0.018,0.014,0.026,0.019,0.018,0.018,0.024,0.017,0.015,0.013,0.023,0.019,0.024,0.019,0.02,1575,6030,2127,2177,2902,3151,2818,3303,2217,1407,972.0,3153,4388,1179,3222,12356,20696,2924,5006,5505,4338,4773,2765,8021,5714,8117,7228,4530,6594,5803,2568,1371,1214,5727,2222,1814,6937,8410,447,395,1644,3218,6607,2852,3907,1305,943,2641,2585,1737,661,4405,2580,5856,373,4522,1315,3243,1740,1384,850,2716,779,1140,2792,970,4277,2821,1599,1998,1485,4075,7987,552,0.099,0.112,0.096,0.108,0.13,0.15,0.1,0.128,0.144,0.137,0.127,0.164,0.12,0.142,0.121,0.127,0.119,0.151,0.121,0.151,0.103,0.137,0.145,0.148,0.125,0.135,0.118,0.117,0.108,0.143,0.155,0.138,0.118,0.132,0.082,0.09,0.138,0.135,0.116,0.093,0.103,0.129,0.132,0.12,0.094,0.085,0.113,0.083,0.087,0.127,0.122,0.095,0.11,0.097,0.116,0.096,0.09,0.097,0.135,0.101,0.093,0.114,0.135,0.106,0.108,0.12,0.101,0.087,0.087,0.125,0.105,0.112,0.106,0.101,447,2112,920,860,989,1123,1017,1037,537,376,224.0,1479,1072,294,886,3083,5207,593,1305,2325,1035,1537,1013,2113,1621,2181,2289,1542,1514,1890,834,701,331,1275,504,655,1772,1864,227,173,785,1507,1377,1546,2159,672,407,980,1178,794,443,1933,1130,2242,161,2415,623,1512,1021,749,467,1016,331,538,1469,589,2200,1200,780,717,804,1422,3470,306,3.528,2.629,2.24,2.394,2.494,2.302,2.321,2.722,2.835,2.745,3.212,1.881,3.137,2.873,2.761,2.865,3.005,3.794,2.935,2.008,2.916,2.398,2.215,2.822,2.66,2.835,2.386,2.327,3.099,2.419,2.328,1.973,2.74,3.159,3.812,2.529,2.827,3.147,2.229,2.44,2.63,1.861,3.36,2.083,2.031,2.059,2.96,3.561,2.66,2.404,1.88,2.331,2.257,2.527,2.553,2.024,2.337,2.382,1.857,1.984,2.069,2.567,2.261,2.57,2.057,2.032,2.014,2.669,2.262,2.657,2.069,2.767,2.404,2.335,0.835,0.615,0.518,0.487,0.734,0.631,0.676,0.482,0.688,0.577,0.776,0.409,0.476,0.54,0.561,0.683,0.704,0.777,0.51,0.661,0.957,0.517,0.528,0.763,0.607,0.546,0.611,0.576,0.614,0.53,0.746,0.848,0.373,0.619,0.653,0.502,0.827,0.68,0.559,0.482,0.553,0.511,0.726,0.617,0.624,0.458,0.439,0.825,0.432,0.505,0.368,0.547,0.442,0.525,0.389,0.44,0.352,0.492,0.377,0.373,0.32,0.681,0.49,0.614,0.464,0.63,0.458,0.428,0.428,0.767,0.47,0.72,0.512,0.294
-11,Epileptic,F,22.0,1.0,1.7,1.1,1.1,1.4,2.4,1.6,1.3,1.0,1.1,0.3,2.7,1.2,0.3,1.2,4.4,7.4,1.2,2.7,4.1,1.7,3.6,1.5,3.6,2.6,3.0,4.0,3.0,2.4,3.2,2.4,0.9,0.4,2.2,0.3,0.6,3.6,3.2,0.1,0.1,0.9,4.1,2.0,2.3,2.6,0.6,0.5,0.6,1.4,0.8,0.5,2.0,1.2,2.3,0.1,2.6,0.6,1.2,1.1,1.1,0.7,1.9,0.6,0.5,1.9,1.4,2.8,1.2,0.8,0.5,1.5,1.8,6.3,0.2,10,19,10,8,13,21,12,14,9,11,2.0,26,15,4,16,57,79,9,31,38,16,56,17,45,42,34,48,33,22,46,23,7,5,32,2,5,41,42,1,2,5,34,22,16,15,5,2,6,7,5,3,16,9,19,1,21,5,12,8,9,7,13,4,5,16,18,21,9,5,4,12,12,54,2,0.04,0.021,0.019,0.019,0.026,0.031,0.029,0.02,0.042,0.039,0.029,0.038,0.026,0.024,0.03,0.027,0.027,0.043,0.034,0.037,0.025,0.037,0.028,0.034,0.025,0.023,0.033,0.028,0.023,0.03,0.046,0.034,0.024,0.029,0.008,0.014,0.038,0.033,0.011,0.017,0.019,0.038,0.032,0.023,0.018,0.013,0.017,0.013,0.019,0.013,0.03,0.021,0.025,0.017,0.014,0.017,0.014,0.016,0.021,0.019,0.022,0.025,0.027,0.018,0.02,0.024,0.02,0.017,0.014,0.016,0.027,0.021,0.019,0.012,1680,4378,2594,2376,2147,3391,3252,3236,1210,1916,606.0,3176,3219,1038,2697,9959,18597,2532,4798,4916,5675,5708,2225,6773,6786,7451,6947,4966,5946,6569,2590,1102,1145,6242,2407,1862,6761,7939,238,446,1385,3365,5513,2880,4151,1253,885,1957,2221,1799,406,3476,2074,5295,508,4831,1160,3238,1530,1562,1259,3112,564,1091,2846,1662,4306,2488,1907,891,1940,2486,11945,471,0.14,0.114,0.108,0.101,0.127,0.13,0.113,0.115,0.151,0.157,0.1,0.151,0.118,0.113,0.133,0.128,0.129,0.146,0.144,0.141,0.11,0.143,0.129,0.137,0.119,0.119,0.137,0.115,0.105,0.145,0.148,0.11,0.11,0.145,0.057,0.084,0.131,0.142,0.079,0.107,0.107,0.138,0.136,0.115,0.095,0.096,0.102,0.083,0.099,0.087,0.129,0.111,0.119,0.095,0.093,0.098,0.096,0.103,0.108,0.114,0.117,0.118,0.137,0.109,0.116,0.116,0.107,0.097,0.087,0.109,0.129,0.106,0.108,0.104,428,1481,886,870,820,1249,965,1037,417,495,164.0,1317,919,249,719,2894,4931,513,1355,1981,1126,1757,919,1887,1968,2082,2300,1708,1590,2013,756,486,310,1419,571,632,1773,1882,123,205,719,1658,1125,1573,2266,663,365,883,1068,808,234,1512,783,2169,154,2377,591,1367,849,864,538,1175,342,546,1413,915,2143,1140,889,456,890,1262,5051,234,3.373,2.764,2.897,2.654,2.309,2.394,2.604,2.623,2.235,3.189,2.848,2.032,2.705,3.059,2.733,2.583,2.871,3.564,2.788,2.146,3.453,2.525,2.052,2.691,2.569,2.714,2.33,2.231,2.796,2.63,2.521,2.372,2.763,3.087,3.559,2.586,2.821,2.94,2.129,2.246,2.41,1.807,3.425,2.109,2.079,2.089,2.834,2.735,2.549,2.645,2.202,2.313,2.331,2.461,2.835,2.152,2.11,2.519,1.962,1.995,2.276,2.484,1.822,2.3,2.245,2.249,2.151,2.461,2.376,2.163,2.415,2.272,2.474,2.348,0.81,0.64,0.523,0.464,0.654,0.635,0.638,0.435,0.701,0.497,0.591,0.583,0.409,0.485,0.537,0.6,0.629,0.848,0.541,0.584,0.821,0.601,0.522,0.795,0.583,0.581,0.531,0.611,0.426,0.524,0.706,0.778,0.47,0.62,0.662,0.538,0.8,0.665,0.2,0.406,0.459,0.57,0.751,0.596,0.576,0.529,0.609,0.538,0.43,0.516,0.377,0.5,0.584,0.475,0.369,0.458,0.403,0.49,0.404,0.367,0.513,0.64,0.458,0.606,0.62,0.782,0.444,0.374,0.461,0.501,0.406,0.472,0.464,0.312
-12,Epileptic,F,27.0,0.9,1.8,1.0,1.5,1.8,1.8,1.7,1.9,1.0,0.7,0.2,2.8,1.0,0.5,0.7,4.6,9.3,0.6,2.2,4.9,0.9,2.9,1.1,3.1,2.4,2.8,3.3,2.7,2.5,4.0,1.5,0.6,0.5,2.2,0.4,0.7,3.6,2.6,0.1,0.1,1.2,2.6,2.3,2.6,2.7,0.7,0.4,0.7,1.2,0.7,0.4,1.1,0.9,2.7,0.4,2.1,0.5,1.5,1.1,0.8,0.5,1.6,0.3,0.4,1.9,0.8,3.4,1.1,0.8,0.5,1.2,0.7,4.5,0.3,8,21,10,13,19,18,14,20,10,6,2.0,22,12,7,7,53,90,6,25,38,11,31,12,33,35,37,42,29,21,42,21,4,4,24,3,6,43,31,1,1,8,24,25,21,17,5,2,6,7,6,4,8,6,21,3,19,4,12,6,6,3,12,2,3,14,11,32,10,6,4,7,8,46,3,0.037,0.021,0.016,0.026,0.033,0.033,0.026,0.026,0.037,0.032,0.022,0.035,0.027,0.026,0.024,0.031,0.029,0.024,0.029,0.04,0.019,0.035,0.022,0.033,0.028,0.025,0.024,0.026,0.026,0.033,0.032,0.028,0.025,0.027,0.015,0.018,0.038,0.025,0.01,0.02,0.026,0.036,0.034,0.032,0.021,0.016,0.02,0.013,0.016,0.012,0.025,0.02,0.02,0.023,0.016,0.019,0.014,0.019,0.03,0.019,0.024,0.027,0.019,0.017,0.023,0.03,0.02,0.018,0.016,0.02,0.025,0.016,0.02,0.016,1369,3946,1923,2537,2465,3130,3239,4266,1861,1394,552.0,2558,2535,1301,1848,9415,19687,1784,4007,4286,4200,4499,1875,5918,5315,6678,6690,4487,5467,6242,2971,871,984,5325,1943,1830,7814,6753,272,271,1350,2879,6373,2150,3467,1476,804,1878,2253,1931,604,1899,1641,4875,545,3580,1075,3129,1029,1170,752,2607,455,884,2753,614,5738,2342,1469,1075,1715,1707,8985,486,0.13,0.122,0.119,0.119,0.154,0.142,0.115,0.131,0.152,0.128,0.108,0.141,0.125,0.126,0.133,0.145,0.132,0.106,0.132,0.148,0.096,0.156,0.116,0.14,0.14,0.13,0.126,0.121,0.118,0.148,0.147,0.102,0.099,0.127,0.086,0.1,0.146,0.128,0.094,0.101,0.119,0.159,0.148,0.126,0.103,0.099,0.101,0.088,0.09,0.091,0.136,0.103,0.112,0.107,0.113,0.11,0.099,0.104,0.134,0.109,0.117,0.122,0.127,0.108,0.122,0.125,0.108,0.104,0.104,0.114,0.121,0.114,0.107,0.116,443,1390,804,962,852,1051,998,1256,464,367,186.0,1145,690,291,490,2517,5283,386,1199,1949,933,1332,753,1534,1540,2027,2268,1667,1558,2041,737,372,337,1337,494,600,1762,1690,146,121,766,1212,1219,1398,2043,673,340,847,1074,766,318,919,689,1967,307,1747,521,1357,525,649,370,971,215,408,1266,383,2823,1007,674,418,771,735,3701,271,2.996,2.661,2.39,2.743,2.522,2.544,2.755,2.786,2.81,3.099,2.534,1.98,2.76,2.995,2.878,2.849,2.99,3.615,2.738,1.958,3.372,2.581,2.137,2.996,2.661,2.679,2.349,2.244,2.692,2.526,2.926,2.391,2.253,2.886,3.401,2.773,3.235,2.889,2.097,2.785,2.302,2.145,3.4,1.749,1.971,2.437,2.996,2.738,2.548,2.682,2.056,2.373,2.549,2.541,2.182,2.117,2.374,2.672,2.325,2.051,2.647,2.816,2.425,2.557,2.59,1.931,2.141,2.552,2.566,2.999,2.621,2.54,2.553,2.249,0.728,0.516,0.623,0.516,0.617,0.482,0.645,0.485,0.673,0.423,0.631,0.53,0.394,0.397,0.454,0.501,0.554,0.701,0.584,0.553,0.765,0.462,0.523,0.545,0.499,0.46,0.476,0.495,0.651,0.49,0.598,0.87,0.509,0.599,0.697,0.502,0.698,0.569,0.316,0.465,0.529,0.627,0.78,0.658,0.62,0.442,0.506,0.775,0.453,0.605,0.458,0.505,0.473,0.556,0.291,0.369,0.414,0.629,0.475,0.382,0.392,0.611,0.356,0.744,0.49,0.631,0.392,0.366,0.434,0.723,0.385,0.5,0.482,0.359
-13,Epileptic,M,27.0,0.7,2.3,0.9,1.3,1.7,1.4,2.0,1.7,0.9,0.9,0.3,2.5,1.1,0.5,0.6,4.7,7.0,0.8,1.6,3.9,1.9,3.1,1.1,3.4,6.2,4.1,3.1,2.4,2.4,3.2,1.5,1.4,0.4,2.2,0.5,0.8,2.9,3.3,0.2,0.3,1.5,3.0,3.1,2.4,3.2,0.6,0.4,0.7,1.5,1.1,0.7,1.2,1.5,2.0,0.3,2.8,0.6,1.4,1.4,1.3,0.8,1.6,0.4,0.5,1.3,0.9,3.1,0.7,1.1,0.6,1.6,1.3,4.5,0.3,5,20,8,11,16,18,15,18,17,7,2.0,24,13,6,9,60,67,5,26,41,22,33,12,44,57,49,36,21,21,41,16,9,5,29,3,7,39,46,1,3,10,24,23,17,21,4,2,4,8,8,5,10,11,15,2,21,4,10,9,11,8,13,3,5,9,15,22,5,8,7,11,10,38,2,0.041,0.028,0.022,0.027,0.037,0.029,0.031,0.029,0.034,0.037,0.022,0.036,0.032,0.031,0.025,0.03,0.031,0.039,0.03,0.039,0.035,0.037,0.028,0.04,0.055,0.036,0.031,0.026,0.027,0.032,0.041,0.049,0.028,0.031,0.017,0.015,0.032,0.031,0.015,0.021,0.025,0.035,0.048,0.027,0.025,0.012,0.02,0.015,0.021,0.02,0.028,0.017,0.02,0.018,0.022,0.019,0.016,0.019,0.029,0.021,0.024,0.022,0.015,0.017,0.019,0.018,0.02,0.014,0.019,0.023,0.025,0.017,0.016,0.016,1574,3994,2042,2437,2054,2765,3054,3209,1829,1877,607.0,2125,2754,1214,1865,11520,15950,2234,4113,4499,4585,3753,1370,5922,5959,7782,5828,3456,5914,6384,2255,1250,960,6252,1749,2293,7490,7378,276,543,1803,2291,3738,2248,3097,1366,722,2088,2242,1754,542,2198,1927,4559,464,4041,1382,2406,1155,1731,1363,2711,532,959,1890,1146,5206,1763,2029,1252,1814,2299,9410,555,0.13,0.123,0.105,0.119,0.136,0.138,0.103,0.138,0.155,0.137,0.104,0.153,0.142,0.135,0.113,0.135,0.132,0.12,0.125,0.157,0.113,0.137,0.116,0.137,0.151,0.14,0.123,0.104,0.106,0.139,0.145,0.125,0.116,0.136,0.09,0.097,0.131,0.132,0.105,0.121,0.125,0.132,0.142,0.12,0.113,0.078,0.101,0.075,0.103,0.105,0.133,0.104,0.108,0.098,0.109,0.11,0.086,0.107,0.128,0.113,0.126,0.116,0.116,0.114,0.101,0.105,0.105,0.086,0.101,0.14,0.123,0.11,0.097,0.107,327,1264,663,842,839,897,1036,1009,557,442,175.0,1177,680,288,500,2928,4063,405,1144,1856,1103,1393,722,1719,1918,2149,1869,1396,1443,1850,656,438,298,1334,441,819,1763,1871,140,224,876,1262,964,1452,1964,723,316,751,1001,809,342,1074,1077,1833,211,2131,629,1086,721,920,515,1092,341,498,1056,797,2380,798,932,452,900,1130,4233,239,3.781,2.804,3.152,2.745,2.095,2.323,2.355,2.633,2.417,3.077,2.862,1.648,3.013,3.092,2.673,2.817,2.999,4.012,2.749,2.08,2.997,2.345,1.702,2.653,2.45,2.844,2.392,2.018,2.983,2.533,2.566,2.726,2.674,3.206,3.774,2.621,3.006,2.833,2.402,2.603,2.574,1.659,2.812,1.769,1.819,2.025,2.856,3.325,2.659,2.476,1.916,2.223,1.99,2.47,2.481,2.116,2.445,2.509,1.818,2.078,2.478,2.526,1.818,2.277,1.972,1.851,2.257,2.478,2.547,2.712,2.237,2.428,2.377,2.639,0.89,0.649,0.594,0.536,0.669,0.71,0.627,0.437,0.758,0.728,0.637,0.424,0.465,0.49,0.549,0.684,0.656,0.8,0.559,0.603,0.9,0.595,0.432,0.76,0.738,0.664,0.636,0.516,0.694,0.642,0.621,0.842,0.405,0.677,0.531,0.541,0.791,0.7,0.356,0.49,0.431,0.608,0.981,0.533,0.603,0.436,0.433,0.937,0.591,0.46,0.314,0.451,0.373,0.427,0.507,0.402,0.378,0.477,0.423,0.429,0.438,0.523,0.453,0.628,0.421,0.659,0.476,0.454,0.43,0.833,0.44,0.664,0.414,0.421
-14,Epileptic,F,22.0,0.6,2.5,1.5,0.9,1.7,1.7,1.4,1.7,1.0,0.7,0.3,3.1,1.7,0.5,1.0,6.1,8.7,0.6,2.3,3.6,1.8,2.9,1.7,2.7,3.7,3.2,4.1,3.1,2.8,3.1,0.9,1.4,0.5,2.7,0.4,1.4,3.8,2.9,0.1,0.1,1.0,2.7,2.6,1.8,2.5,0.8,0.4,0.8,1.3,1.0,0.6,1.5,1.4,2.7,0.4,1.6,0.9,1.3,0.9,1.2,0.8,1.1,0.3,0.3,2.2,1.0,2.0,1.8,0.8,0.4,0.8,1.4,4.9,0.4,7,23,18,9,17,17,14,19,11,6,4.0,26,16,4,7,67,79,6,20,28,13,29,19,34,47,37,52,32,28,40,14,12,4,33,2,15,44,33,1,1,6,22,31,15,17,7,2,5,7,9,4,12,12,21,4,14,8,12,6,11,6,9,2,4,18,12,20,12,6,5,6,13,43,2,0.026,0.02,0.019,0.018,0.033,0.027,0.019,0.026,0.036,0.04,0.033,0.034,0.028,0.03,0.025,0.03,0.027,0.025,0.031,0.031,0.028,0.031,0.027,0.031,0.033,0.023,0.029,0.027,0.024,0.027,0.022,0.049,0.021,0.026,0.009,0.019,0.029,0.027,0.013,0.013,0.018,0.036,0.035,0.021,0.019,0.019,0.015,0.012,0.015,0.016,0.03,0.017,0.018,0.02,0.013,0.014,0.025,0.013,0.022,0.019,0.035,0.021,0.017,0.014,0.021,0.027,0.016,0.019,0.014,0.012,0.024,0.02,0.02,0.02,1749,5635,3129,1890,2364,2917,3133,3507,1914,1040,559.0,2975,4417,1253,3030,12974,21512,2466,4150,3597,4293,4482,2507,5824,6855,7676,6619,5363,7139,5929,2027,1203,1084,6715,2263,3317,7867,7437,347,439,2052,2594,5920,2443,3925,1332,963,2340,2723,2174,638,2842,2748,5846,968,3267,1391,3342,1086,1781,765,2065,474,972,3627,957,3979,3594,1710,955,1112,2746,10632,493,0.109,0.109,0.116,0.105,0.137,0.132,0.101,0.125,0.144,0.161,0.131,0.14,0.125,0.112,0.108,0.135,0.125,0.108,0.12,0.119,0.105,0.141,0.133,0.132,0.143,0.121,0.136,0.123,0.11,0.137,0.104,0.139,0.103,0.127,0.07,0.097,0.123,0.122,0.087,0.096,0.094,0.138,0.135,0.105,0.103,0.113,0.098,0.076,0.089,0.099,0.139,0.101,0.097,0.097,0.093,0.094,0.129,0.084,0.111,0.108,0.144,0.109,0.11,0.104,0.113,0.122,0.102,0.106,0.097,0.1,0.123,0.109,0.106,0.11,452,1843,1160,772,828,1066,999,1120,509,290,185.0,1426,1048,285,682,3273,5354,507,1182,1772,1134,1397,987,1520,2059,2337,2356,1784,1838,1875,601,500,389,1609,601,1197,2225,1763,192,180,927,1212,1472,1372,2121,677,410,1059,1244,931,330,1393,1266,2227,500,1742,556,1647,651,962,358,788,261,442,1660,570,2030,1430,851,529,544,1231,3921,282,3.416,2.752,2.653,2.56,2.417,2.565,2.641,2.897,2.726,3.01,2.609,1.885,3.143,3.036,3.199,2.838,3.107,3.793,2.952,1.848,2.952,2.483,2.146,2.791,2.619,2.679,2.315,2.406,2.945,2.507,2.453,2.618,2.317,3.094,3.279,2.608,2.84,3.045,2.111,2.835,2.806,1.993,3.033,1.898,2.137,2.283,3.001,2.698,2.71,2.619,2.19,2.24,2.18,2.514,2.435,2.076,2.585,2.263,1.991,2.049,2.362,2.583,2.123,2.626,2.458,2.056,2.149,2.787,2.511,2.127,2.461,2.607,2.723,2.21,0.582,0.575,0.727,0.51,0.708,0.562,0.693,0.566,0.553,0.441,0.643,0.469,0.398,0.49,0.569,0.641,0.634,0.753,0.641,0.539,0.758,0.489,0.433,0.688,0.546,0.501,0.496,0.622,0.563,0.553,0.648,0.861,0.428,0.609,0.956,0.406,0.654,0.582,0.33,0.564,0.464,0.481,0.669,0.553,0.719,0.392,0.505,0.625,0.573,0.463,0.392,0.521,0.587,0.588,0.304,0.432,0.432,0.529,0.318,0.447,0.41,0.747,0.594,1.158,0.621,0.552,0.459,0.4,0.455,0.543,0.386,0.468,0.573,0.586
-15,Epileptic,F,32.0,1.3,1.9,1.1,1.3,1.3,2.2,1.7,1.3,1.1,0.8,0.3,3.5,1.3,0.6,1.8,4.8,8.1,0.7,2.2,4.3,1.1,2.9,2.5,2.7,3.5,3.3,3.7,2.8,2.5,3.3,1.5,1.4,0.7,2.4,0.5,0.5,3.1,3.2,0.2,0.1,1.3,2.4,2.2,2.8,2.5,0.6,0.4,1.1,1.4,0.8,0.7,2.0,1.7,2.4,0.1,2.7,0.6,1.9,0.9,1.8,0.7,1.5,0.4,0.7,1.5,1.8,2.7,1.4,1.0,0.6,0.9,1.2,5.7,0.5,9,16,10,11,11,16,11,11,10,6,2.0,29,15,6,18,45,69,5,16,33,11,31,19,28,36,30,32,26,20,31,13,8,4,20,3,4,30,34,2,1,9,20,16,20,15,6,2,6,8,7,6,14,15,19,1,22,5,15,7,14,5,10,3,5,11,13,20,12,7,6,6,11,44,3,0.053,0.026,0.024,0.027,0.039,0.032,0.033,0.031,0.049,0.032,0.035,0.042,0.031,0.038,0.034,0.031,0.033,0.033,0.037,0.04,0.032,0.031,0.032,0.036,0.034,0.034,0.035,0.036,0.028,0.033,0.044,0.062,0.035,0.032,0.02,0.018,0.035,0.035,0.016,0.014,0.028,0.033,0.045,0.031,0.021,0.017,0.019,0.02,0.02,0.024,0.033,0.024,0.025,0.023,0.013,0.021,0.02,0.023,0.034,0.027,0.023,0.031,0.023,0.024,0.023,0.046,0.024,0.021,0.02,0.026,0.026,0.019,0.024,0.029,1054,3186,1689,1811,1397,2789,2081,2184,1408,1135,490.0,2365,2503,799,2644,7896,14679,1600,3590,3548,3123,4234,2392,4278,5407,5332,4957,3818,5325,4432,2225,840,1000,4301,1446,1097,5014,6121,303,253,1220,1797,3800,2051,3115,1160,600,1842,1992,1297,566,2801,1894,3777,217,3259,1034,2555,774,1658,837,1885,422,871,1770,809,3057,2141,1516,954,1100,2119,8597,380,0.153,0.12,0.136,0.124,0.149,0.129,0.11,0.126,0.165,0.141,0.119,0.15,0.126,0.153,0.151,0.13,0.132,0.112,0.137,0.15,0.107,0.126,0.128,0.136,0.116,0.124,0.127,0.119,0.103,0.139,0.161,0.142,0.118,0.135,0.089,0.098,0.144,0.15,0.099,0.089,0.133,0.129,0.16,0.129,0.104,0.101,0.102,0.1,0.105,0.121,0.144,0.112,0.118,0.105,0.102,0.112,0.119,0.114,0.136,0.12,0.111,0.132,0.116,0.12,0.117,0.148,0.115,0.111,0.108,0.138,0.119,0.105,0.114,0.135,392,1164,685,814,594,1135,843,808,440,378,169.0,1405,790,242,826,2604,4274,379,1039,1730,786,1495,1134,1369,1723,1686,1914,1344,1460,1678,600,391,388,1175,424,482,1459,1579,211,143,675,1133,941,1442,1890,631,311,857,1010,521,338,1380,1070,1844,113,1960,499,1361,414,1050,450,758,265,441,1004,507,1762,1052,750,394,602,1029,3903,251,2.613,2.535,2.498,2.231,2.191,2.145,2.119,2.539,2.457,2.507,2.608,1.562,2.449,2.409,2.482,2.361,2.773,3.21,2.803,1.811,2.851,2.285,1.843,2.479,2.474,2.589,2.086,2.247,2.752,2.224,2.766,2.395,2.135,2.808,3.142,2.177,2.652,2.843,1.596,1.97,2.179,1.554,2.979,1.66,1.891,2.04,2.229,2.744,2.425,2.703,1.948,2.11,1.912,2.132,2.007,1.846,2.392,2.2,1.944,1.779,2.253,2.472,1.962,2.283,1.994,2.013,1.967,2.27,2.26,2.739,2.145,2.266,2.322,1.876,0.651,0.546,0.551,0.404,0.613,0.547,0.548,0.472,0.522,0.573,0.656,0.472,0.457,0.496,0.541,0.611,0.56,0.683,0.662,0.629,0.719,0.532,0.614,0.637,0.708,0.519,0.573,0.565,0.556,0.506,0.645,0.706,0.481,0.512,0.77,0.465,0.6,0.596,0.311,0.371,0.471,0.491,0.673,0.487,0.527,0.444,0.497,0.634,0.411,0.546,0.469,0.422,0.522,0.488,0.397,0.383,0.355,0.601,0.652,0.335,0.307,0.635,0.342,0.8,0.579,0.602,0.468,0.464,0.452,0.692,0.456,0.599,0.5,0.378
-16,Epileptic,M,57.0,0.8,2.5,1.0,1.8,2.0,2.0,1.6,1.6,0.9,1.0,0.4,3.3,1.2,0.5,0.9,5.8,7.8,1.4,2.9,4.8,0.7,2.8,3.5,3.1,4.2,4.2,3.5,2.3,2.6,2.7,1.7,1.4,0.3,2.1,0.2,1.2,3.5,2.4,0.1,0.2,1.3,3.1,1.9,3.4,2.9,0.5,0.4,0.7,1.4,0.6,0.6,1.5,1.3,2.2,0.2,2.8,0.9,1.9,1.7,1.8,0.3,1.7,0.3,0.5,2.1,0.9,2.7,1.1,1.1,0.3,1.2,1.3,4.1,0.4,6,22,8,14,16,20,15,17,11,10,5.0,34,15,6,12,64,82,11,38,43,8,33,31,38,36,49,46,23,25,37,21,11,5,26,1,11,36,39,1,2,11,24,24,27,17,4,2,5,7,6,4,13,11,21,1,25,6,16,12,16,2,13,1,5,20,19,22,9,7,4,10,12,38,3,0.037,0.024,0.016,0.03,0.039,0.026,0.025,0.032,0.038,0.036,0.032,0.029,0.029,0.025,0.021,0.034,0.028,0.035,0.033,0.042,0.02,0.032,0.033,0.038,0.042,0.029,0.027,0.027,0.025,0.029,0.043,0.044,0.023,0.03,0.014,0.018,0.037,0.029,0.012,0.014,0.023,0.037,0.032,0.027,0.02,0.009,0.018,0.014,0.018,0.022,0.027,0.018,0.02,0.021,0.017,0.019,0.021,0.021,0.03,0.024,0.014,0.027,0.024,0.018,0.021,0.032,0.02,0.018,0.019,0.016,0.026,0.022,0.018,0.024,1386,3973,2098,2587,2014,3231,3026,2472,1598,1598,836.0,3709,3094,1315,2687,9482,17377,2535,5513,4099,3204,4173,3102,5039,4914,8475,6128,4163,6240,4960,2440,1415,896,5408,1334,2338,6863,6105,345,572,1851,2505,4916,2840,3738,1371,653,1937,2094,1289,515,3069,1918,4531,256,4317,1318,2671,1522,2120,548,2526,343,809,3066,769,3916,2034,1759,759,1359,2058,7543,422,0.134,0.117,0.108,0.128,0.141,0.126,0.113,0.14,0.155,0.157,0.132,0.138,0.123,0.125,0.111,0.137,0.129,0.142,0.139,0.166,0.084,0.123,0.129,0.138,0.128,0.134,0.117,0.104,0.117,0.134,0.166,0.118,0.102,0.14,0.072,0.096,0.139,0.135,0.087,0.102,0.116,0.133,0.152,0.125,0.104,0.076,0.104,0.078,0.102,0.117,0.121,0.1,0.111,0.107,0.132,0.112,0.116,0.115,0.128,0.12,0.102,0.131,0.107,0.112,0.113,0.143,0.11,0.105,0.102,0.105,0.141,0.111,0.106,0.123,384,1530,864,985,901,1295,1094,914,465,454,257.0,1866,804,309,752,2891,4809,555,1594,2132,682,1512,1666,1503,1490,2612,2176,1422,1563,1799,730,537,281,1260,348,1033,1815,1564,203,286,902,1339,1126,2002,2175,751,324,833,1067,519,353,1356,1023,1982,142,2280,660,1401,840,1205,286,965,191,399,1624,554,2059,962,827,390,688,1013,3431,263,3.471,2.478,2.428,2.461,2.094,2.172,2.198,2.387,2.567,2.923,2.88,1.764,2.848,2.835,2.604,2.473,2.734,3.504,2.697,1.731,3.086,2.254,1.758,2.655,2.542,2.665,2.28,2.232,2.904,2.218,2.676,2.718,2.451,2.988,3.407,2.278,2.832,2.762,1.906,2.318,2.35,1.685,3.08,1.709,1.896,1.948,2.569,2.622,2.434,2.711,1.66,2.293,2.061,2.354,2.312,2.066,2.253,2.154,1.919,1.961,2.097,2.811,1.943,2.397,1.931,1.852,2.042,2.299,2.384,2.186,2.092,2.315,2.326,2.079,0.65,0.588,0.488,0.515,0.575,0.565,0.588,0.439,0.621,0.584,0.743,0.534,0.377,0.511,0.565,0.646,0.644,0.805,0.544,0.562,0.847,0.593,0.578,0.721,0.596,0.526,0.534,0.628,0.558,0.553,0.636,0.847,0.428,0.67,0.81,0.377,0.691,0.662,0.337,0.421,0.401,0.54,0.728,0.59,0.615,0.468,0.351,0.75,0.423,0.54,0.289,0.483,0.465,0.44,0.456,0.369,0.392,0.466,0.451,0.361,0.26,0.607,0.38,0.825,0.43,0.722,0.425,0.408,0.41,0.853,0.431,0.494,0.465,0.326
-17,Epileptic,F,32.0,0.6,1.8,1.1,1.3,1.5,2.3,1.4,1.0,1.4,0.7,0.3,2.0,1.9,0.6,1.6,4.3,6.6,0.5,1.9,3.5,0.6,2.5,1.2,2.4,2.7,3.3,2.8,2.0,2.2,3.3,1.3,1.2,0.5,2.4,0.3,0.3,2.6,2.9,0.1,0.1,0.8,2.4,1.3,1.7,2.3,0.6,0.2,0.7,1.0,0.5,0.8,1.8,1.1,1.5,0.1,1.9,0.4,1.8,1.1,0.8,0.5,0.6,0.3,0.3,1.3,0.8,2.1,1.6,0.8,0.3,0.9,0.9,3.5,0.3,5,15,11,12,11,18,12,12,11,6,3.0,17,21,7,13,44,59,4,24,33,5,32,14,27,33,36,35,20,22,33,21,6,6,25,2,2,23,39,1,1,4,22,17,13,13,4,1,5,5,4,5,13,9,11,1,14,4,17,7,6,3,5,2,3,12,12,15,11,6,3,6,7,30,3,0.026,0.022,0.019,0.021,0.043,0.032,0.026,0.023,0.045,0.034,0.032,0.03,0.039,0.04,0.034,0.032,0.025,0.025,0.027,0.034,0.013,0.036,0.033,0.035,0.037,0.034,0.029,0.026,0.026,0.034,0.055,0.045,0.019,0.03,0.015,0.009,0.03,0.033,0.012,0.012,0.018,0.032,0.03,0.025,0.019,0.019,0.012,0.012,0.013,0.016,0.031,0.02,0.02,0.014,0.017,0.019,0.01,0.021,0.028,0.022,0.019,0.014,0.023,0.013,0.021,0.027,0.02,0.019,0.017,0.013,0.03,0.02,0.016,0.017,1402,3777,2261,2445,1454,3488,2343,2968,1628,1160,605.0,2185,4036,990,3034,9248,16461,2113,4006,4039,2463,3985,1684,4958,5421,6716,5301,3820,5953,5308,1600,880,1325,6278,1692,1512,5908,6445,358,417,1426,2502,4363,2265,3228,1215,691,2026,2388,1711,620,3171,1618,4351,391,3181,1294,3221,1075,1162,1092,1536,400,842,2364,706,3341,3188,1795,843,1470,2408,8955,509,0.099,0.111,0.113,0.108,0.141,0.136,0.117,0.113,0.146,0.143,0.145,0.13,0.151,0.121,0.124,0.131,0.114,0.092,0.123,0.133,0.068,0.154,0.13,0.134,0.147,0.146,0.138,0.114,0.109,0.144,0.132,0.118,0.113,0.123,0.067,0.064,0.124,0.132,0.081,0.077,0.101,0.138,0.126,0.112,0.099,0.104,0.087,0.076,0.084,0.092,0.139,0.105,0.119,0.084,0.101,0.11,0.085,0.109,0.126,0.111,0.101,0.1,0.132,0.097,0.115,0.132,0.107,0.099,0.099,0.109,0.119,0.106,0.089,0.11,378,1217,852,952,594,1188,892,834,473,306,176.0,955,936,261,736,2360,4256,386,1188,1554,678,1204,679,1168,1432,1772,1823,1273,1512,1555,476,417,425,1302,381,541,1370,1432,216,158,664,1180,803,1169,1856,542,276,870,1103,521,399,1416,804,1801,116,1467,630,1475,625,612,446,547,179,337,1014,475,1531,1261,794,311,561,776,3649,282,3.225,2.811,2.656,2.547,2.238,2.475,2.267,3.107,2.533,3.129,2.851,1.941,3.194,2.624,2.863,2.787,3.054,3.83,2.814,2.153,2.718,2.518,2.095,2.93,2.801,2.989,2.413,2.392,2.925,2.638,2.434,2.165,2.562,3.207,3.538,2.578,3.125,3.042,1.963,2.965,2.676,1.933,3.287,2.088,1.981,2.448,2.907,2.842,2.677,3.283,2.001,2.278,1.979,2.446,3.613,2.407,2.299,2.398,1.996,2.13,2.626,2.618,2.246,2.653,2.712,2.046,2.305,2.748,2.496,3.319,2.672,3.109,2.53,2.202,0.782,0.826,0.696,0.451,0.532,0.55,0.477,0.596,0.598,0.598,0.815,0.482,0.644,0.643,0.608,0.551,0.62,0.852,0.591,0.688,0.666,0.453,0.389,0.743,0.548,0.67,0.484,0.561,0.473,0.596,0.617,0.888,0.379,0.664,0.892,0.558,0.772,0.827,0.375,0.401,0.645,0.483,0.844,0.762,0.593,0.578,0.579,0.604,0.525,0.732,0.416,0.548,0.637,0.513,0.973,0.567,0.507,0.521,0.348,0.369,0.4,1.096,0.54,1.298,0.739,0.75,0.478,0.42,0.571,0.84,0.676,1.098,0.533,0.56
-18,Epileptic,F,37.0,1.0,2.0,1.3,1.7,1.9,1.5,1.2,2.0,1.5,0.6,0.2,2.3,2.3,0.6,1.4,7.0,9.1,1.1,2.4,3.7,1.3,4.0,1.7,3.3,5.3,2.8,2.9,2.4,2.1,2.0,1.2,0.6,0.5,2.4,0.4,0.8,2.9,3.7,0.2,0.2,1.2,2.9,2.4,2.4,2.5,0.8,0.5,1.2,1.4,0.9,0.3,2.6,1.5,2.6,0.1,2.4,0.8,1.4,1.0,0.6,0.8,1.5,0.2,0.4,2.0,1.1,2.5,1.7,1.2,0.6,0.7,1.1,5.4,0.4,7,19,13,18,14,17,13,20,16,6,3.0,23,21,8,17,84,92,8,31,33,21,36,12,42,42,29,38,26,22,28,15,7,4,31,2,7,37,45,2,1,8,26,21,18,15,6,3,8,8,8,3,20,10,18,1,21,7,12,9,5,6,10,2,5,17,19,23,13,6,7,5,8,47,3,0.041,0.023,0.023,0.028,0.038,0.028,0.027,0.032,0.054,0.035,0.03,0.037,0.032,0.05,0.029,0.036,0.032,0.04,0.026,0.038,0.029,0.038,0.036,0.042,0.044,0.028,0.028,0.028,0.028,0.03,0.036,0.056,0.028,0.027,0.013,0.017,0.04,0.033,0.018,0.017,0.023,0.043,0.037,0.032,0.02,0.016,0.021,0.019,0.02,0.025,0.027,0.022,0.027,0.021,0.018,0.021,0.021,0.016,0.031,0.016,0.024,0.024,0.02,0.023,0.022,0.019,0.018,0.026,0.02,0.019,0.02,0.014,0.018,0.018,1424,4264,2740,2829,1734,2050,2545,2976,1798,1223,613.0,2622,3854,1023,3222,12729,19117,2113,5463,3504,3421,4037,1679,5860,5130,5626,5588,4090,5251,4302,2132,573,896,5581,1666,2315,5157,8052,381,405,1556,1898,5712,2492,3143,1486,818,2084,2355,1321,338,4184,1584,4394,354,3622,1612,3313,967,884,1008,2267,392,897,3031,1411,4543,2201,1681,971,1120,2637,9824,532,0.126,0.113,0.116,0.131,0.137,0.129,0.109,0.137,0.167,0.147,0.121,0.136,0.136,0.16,0.139,0.152,0.133,0.141,0.124,0.147,0.12,0.124,0.115,0.155,0.129,0.134,0.125,0.123,0.112,0.135,0.15,0.119,0.108,0.129,0.073,0.094,0.134,0.136,0.103,0.109,0.113,0.13,0.149,0.128,0.105,0.103,0.111,0.098,0.098,0.117,0.145,0.111,0.128,0.106,0.099,0.11,0.101,0.099,0.126,0.094,0.115,0.118,0.106,0.125,0.118,0.121,0.106,0.126,0.107,0.125,0.111,0.099,0.104,0.106,433,1519,965,1030,789,935,879,1102,517,344,162.0,1112,1197,298,840,3701,5057,487,1551,1513,796,1436,688,1626,1863,1748,1875,1401,1324,1346,584,264,298,1433,471,733,1400,1971,205,183,813,1112,1129,1354,1858,687,334,948,1098,583,199,1926,865,1983,154,1852,670,1465,582,529,552,918,207,380,1418,887,2186,973,832,478,543,1160,4511,275,3.283,2.645,2.683,2.606,1.882,2.049,2.386,2.309,2.584,2.761,2.965,2.009,2.619,2.65,2.734,2.603,2.994,3.291,2.795,2.012,3.091,2.256,2.002,2.747,2.262,2.639,2.409,2.311,2.896,2.523,2.707,2.305,2.36,2.785,3.111,2.611,2.719,2.919,2.117,2.551,2.368,1.59,3.416,2.059,1.928,2.36,2.765,2.699,2.638,2.436,1.992,2.331,1.956,2.397,2.686,2.19,2.327,2.413,1.953,1.807,2.248,2.319,1.976,2.593,2.245,1.923,2.204,2.396,2.328,2.236,2.35,2.376,2.326,2.247,0.882,0.588,0.498,0.577,0.648,0.504,0.547,0.627,0.656,0.616,0.627,0.524,0.48,0.424,0.572,0.569,0.593,0.963,0.601,0.659,0.741,0.639,0.636,0.674,0.641,0.566,0.456,0.52,0.465,0.452,0.646,0.948,0.337,0.625,0.737,0.555,0.84,0.74,0.382,0.517,0.489,0.571,0.753,0.628,0.54,0.534,0.372,0.908,0.544,0.558,0.552,0.446,0.443,0.433,0.409,0.389,0.475,0.511,0.364,0.39,0.358,0.691,0.386,0.605,0.477,0.659,0.459,0.505,0.459,0.73,0.397,0.521,0.498,0.537
-19,Epileptic,M,27.0,0.8,3.0,1.2,1.1,2.1,3.2,1.4,2.2,1.6,0.6,0.3,3.4,1.7,0.6,1.3,6.9,8.2,1.1,2.8,6.0,0.7,3.3,1.7,3.6,3.3,3.4,3.3,2.2,2.8,4.6,1.3,1.0,0.4,2.3,0.3,1.0,2.5,4.1,0.2,0.3,1.4,3.5,2.3,3.6,2.2,0.6,0.5,1.2,1.5,0.7,0.9,2.5,1.5,1.8,0.4,2.7,0.8,3.2,0.9,1.2,1.0,1.4,1.2,0.5,2.1,1.0,2.2,1.0,1.8,0.4,1.3,1.6,6.1,0.2,5,28,13,11,21,28,10,18,20,6,2.0,36,21,7,13,75,72,10,27,42,8,37,19,44,36,40,38,23,26,49,21,9,4,30,2,8,31,41,1,3,8,27,22,27,13,4,3,14,8,6,6,20,9,12,3,23,4,29,6,10,6,10,9,3,16,12,17,7,13,4,9,13,52,1,0.035,0.027,0.019,0.021,0.042,0.034,0.019,0.03,0.042,0.025,0.025,0.032,0.025,0.039,0.026,0.033,0.025,0.034,0.032,0.036,0.014,0.03,0.026,0.034,0.025,0.027,0.022,0.022,0.021,0.03,0.035,0.046,0.024,0.03,0.011,0.017,0.031,0.033,0.012,0.011,0.025,0.031,0.037,0.029,0.015,0.01,0.016,0.017,0.017,0.021,0.029,0.023,0.022,0.016,0.018,0.019,0.021,0.024,0.016,0.016,0.028,0.024,0.035,0.016,0.016,0.02,0.017,0.017,0.023,0.012,0.02,0.02,0.018,0.024,1567,6076,2747,2626,2435,5495,3316,4173,2242,1609,733.0,4314,4409,1420,3068,11744,19477,3024,5226,5413,3289,5957,2706,8216,7547,7900,7414,4207,7319,8066,2673,1360,1339,6206,1993,2475,6697,8152,608,944,1740,3832,5487,3653,4237,1746,1207,2377,2842,1455,979,4351,2460,4465,755,4986,1656,4551,1619,2122,1358,2407,1224,899,4304,1878,4178,2481,2921,866,1906,3201,13964,357,0.111,0.123,0.116,0.111,0.153,0.143,0.089,0.134,0.158,0.12,0.099,0.138,0.128,0.124,0.123,0.14,0.116,0.128,0.121,0.126,0.074,0.136,0.129,0.142,0.117,0.127,0.122,0.099,0.101,0.14,0.133,0.118,0.114,0.119,0.067,0.092,0.12,0.124,0.076,0.092,0.116,0.131,0.131,0.116,0.087,0.081,0.098,0.088,0.097,0.114,0.124,0.111,0.108,0.089,0.103,0.106,0.099,0.11,0.097,0.101,0.119,0.113,0.143,0.094,0.099,0.101,0.098,0.094,0.115,0.104,0.112,0.102,0.095,0.104,443,1708,1010,945,911,1584,1011,1236,654,395,207.0,1751,1185,348,834,3519,5222,583,1509,2626,895,1810,1079,1818,2159,2165,2464,1502,2009,2423,682,500,358,1495,518,880,1480,1936,231,416,880,1708,1107,2112,2351,867,501,966,1315,516,561,1816,1094,1768,356,2210,624,2033,844,1038,577,935,518,435,1917,880,1955,1055,1215,478,965,1317,5272,186,3.421,3.066,2.581,2.747,2.365,2.817,2.671,2.906,2.573,3.26,3.048,2.076,2.864,3.07,2.766,2.561,3.036,3.757,2.834,1.856,3.044,2.688,2.152,3.105,2.754,2.88,2.462,2.229,2.854,2.585,2.852,2.47,2.872,3.064,3.346,2.506,3.206,3.117,2.571,2.47,2.432,2.049,3.489,1.885,2.089,2.273,2.919,3.062,2.762,3.007,2.172,2.345,2.37,2.574,2.393,2.356,3.115,2.44,2.276,2.264,2.742,2.555,2.255,2.615,2.462,2.594,2.256,2.763,2.508,2.129,2.395,2.746,2.644,2.522,0.686,0.67,0.783,0.574,0.578,0.609,0.583,0.485,0.477,0.394,0.869,0.445,0.511,0.435,0.508,0.599,0.565,0.559,0.582,0.608,0.667,0.487,0.483,0.665,0.513,0.503,0.512,0.553,0.536,0.527,0.657,1.058,0.369,0.717,0.55,0.552,0.721,0.576,0.283,0.354,0.461,0.684,0.778,0.611,0.654,0.399,0.648,0.77,0.503,0.474,0.406,0.542,0.631,0.569,0.328,0.421,0.693,0.503,0.305,0.505,0.714,0.653,0.45,0.956,0.472,0.956,0.531,0.429,0.472,0.542,0.417,0.661,0.53,0.299
-21,Epileptic,M,27.0,1.1,3.5,1.1,1.7,1.3,2.7,2.1,2.8,1.9,1.1,0.3,3.8,2.8,0.4,1.4,6.8,10.0,0.9,2.7,6.3,0.9,3.7,1.9,3.1,4.3,4.3,3.8,3.3,3.3,5.1,1.3,0.8,0.7,3.3,0.5,0.6,5.6,5.3,0.2,0.1,1.3,3.3,2.3,2.9,3.7,0.7,0.5,0.8,1.4,0.7,0.9,2.9,1.0,2.6,0.2,3.1,1.1,3.0,1.1,2.0,1.1,1.1,0.4,0.4,2.5,1.4,3.8,1.5,1.4,0.5,1.2,2.1,5.5,0.4,6,31,11,15,15,26,18,27,16,12,4.0,41,26,8,14,78,90,10,28,52,8,41,21,39,54,47,47,36,26,60,27,6,7,35,3,5,60,56,2,1,8,36,24,22,25,5,4,6,7,6,6,23,8,17,1,26,9,26,9,14,9,7,3,4,19,18,33,12,8,5,7,13,41,3,0.038,0.028,0.017,0.025,0.033,0.029,0.026,0.03,0.049,0.041,0.041,0.035,0.028,0.032,0.028,0.03,0.027,0.026,0.028,0.04,0.017,0.032,0.027,0.032,0.032,0.029,0.026,0.024,0.022,0.032,0.037,0.038,0.027,0.033,0.016,0.014,0.036,0.035,0.011,0.012,0.028,0.031,0.032,0.023,0.022,0.013,0.018,0.012,0.015,0.02,0.025,0.022,0.013,0.016,0.02,0.016,0.019,0.022,0.019,0.024,0.024,0.02,0.019,0.018,0.02,0.02,0.021,0.016,0.021,0.017,0.019,0.023,0.018,0.016,1906,6589,3574,3268,2296,4550,3805,5756,2614,2197,725.0,4056,5739,1212,3163,15148,24407,3289,5147,7027,3481,6837,2706,7760,8147,9185,8112,6028,7857,9283,3096,1230,1463,7142,2238,1984,9159,10579,604,594,1795,3926,6805,3406,4304,1788,1056,2831,2843,1738,906,5841,2690,6353,268,5558,2150,4484,1631,2553,1467,2774,679,918,4179,2094,6169,4243,2315,1046,2028,2819,11778,639,0.121,0.113,0.093,0.123,0.124,0.135,0.112,0.121,0.147,0.138,0.124,0.14,0.128,0.144,0.131,0.127,0.123,0.108,0.115,0.138,0.079,0.142,0.134,0.13,0.144,0.13,0.125,0.106,0.097,0.14,0.129,0.113,0.119,0.135,0.084,0.085,0.128,0.133,0.08,0.076,0.122,0.141,0.129,0.109,0.108,0.086,0.1,0.081,0.088,0.097,0.137,0.106,0.084,0.092,0.115,0.102,0.097,0.102,0.104,0.113,0.116,0.102,0.106,0.105,0.109,0.11,0.109,0.096,0.1,0.11,0.108,0.112,0.101,0.106,513,1858,1017,1100,851,1602,1213,1705,692,532,197.0,1780,1638,295,875,3883,6058,634,1605,2727,906,1944,1208,1685,2350,2559,2612,2224,2087,2843,725,442,421,1675,534,706,2758,2601,314,213,737,1816,1329,2028,2600,828,450,1033,1302,669,482,2172,1267,2458,123,2785,972,2294,916,1281,754,855,360,402,1860,1215,2906,1507,1071,468,945,1424,4620,364,3.462,3.051,3.072,3.018,2.475,2.673,2.631,2.932,2.833,3.128,2.658,1.967,2.955,2.878,2.874,2.9,3.261,3.803,2.737,2.153,2.763,2.611,2.034,3.286,2.684,2.875,2.466,2.244,2.886,2.619,3.028,2.813,2.631,3.039,3.766,2.649,2.658,3.002,2.212,2.872,3.183,1.987,3.458,1.891,1.928,2.377,2.893,3.531,2.738,2.934,2.241,2.669,2.248,2.754,2.327,2.169,2.463,2.241,2.069,2.196,2.262,3.061,2.229,2.502,2.48,2.012,2.249,2.894,2.643,2.441,2.485,2.437,2.752,2.189,1.011,0.702,0.75,0.564,0.537,0.59,0.612,0.446,0.685,0.726,1.024,0.518,0.486,0.515,0.571,0.598,0.621,0.763,0.722,0.663,0.701,0.561,0.466,0.68,0.592,0.554,0.485,0.559,0.57,0.531,0.815,1.019,0.494,0.816,0.745,0.485,0.673,0.74,0.403,0.608,0.676,0.505,0.744,0.56,0.525,0.461,0.72,0.83,0.669,0.738,0.337,0.586,0.513,0.534,0.534,0.398,0.492,0.544,0.366,0.367,0.443,0.921,0.481,1.066,0.615,0.711,0.478,0.504,0.401,0.709,0.495,0.543,0.558,0.399
-22,Epileptic,F,32.0,0.9,2.2,0.6,0.8,1.8,2.2,1.1,1.3,1.1,0.7,0.2,1.9,1.5,0.4,1.6,5.3,7.8,0.6,2.3,3.9,0.7,2.2,2.0,3.2,2.7,2.7,3.1,2.4,1.8,2.3,1.1,0.9,0.4,1.6,0.4,0.5,2.8,2.5,0.1,0.1,0.8,2.3,1.6,2.1,2.2,0.7,0.4,0.5,0.9,0.9,0.6,1.4,1.1,2.8,0.3,1.8,0.4,1.1,0.5,0.8,0.2,1.3,0.2,0.4,1.4,1.1,1.8,1.2,0.7,1.0,0.5,0.6,2.1,0.3,7,29,7,6,17,22,8,13,12,7,2.0,20,17,4,16,51,68,6,27,36,8,20,20,34,36,29,37,23,18,29,11,6,5,24,2,3,28,33,1,1,4,21,15,18,14,7,2,5,5,7,4,10,7,21,3,13,4,10,4,6,1,9,2,6,11,13,14,9,6,12,4,6,19,2,0.043,0.031,0.016,0.022,0.044,0.046,0.023,0.026,0.043,0.043,0.029,0.03,0.035,0.043,0.044,0.034,0.03,0.031,0.03,0.037,0.019,0.038,0.033,0.039,0.036,0.031,0.031,0.03,0.021,0.028,0.032,0.044,0.028,0.025,0.016,0.013,0.04,0.036,0.012,0.014,0.019,0.038,0.028,0.024,0.018,0.018,0.02,0.013,0.016,0.03,0.029,0.018,0.021,0.025,0.021,0.02,0.018,0.017,0.018,0.022,0.02,0.024,0.02,0.017,0.02,0.029,0.021,0.02,0.02,0.031,0.019,0.024,0.013,0.021,1047,3725,1524,1761,1992,2659,1816,2839,1614,1091,629.0,2049,3088,747,2787,9346,15603,1832,3840,3833,2488,3323,2347,5282,4748,5295,4993,3708,4440,4356,1543,1171,951,4501,1487,1632,5479,5163,329,330,1267,2193,4612,1986,3152,1048,676,1685,1972,1096,481,3223,1490,4283,339,2738,1043,2380,672,1136,342,2467,504,735,2169,911,2797,2020,1327,1460,1002,1134,6209,466,0.144,0.135,0.106,0.102,0.164,0.18,0.099,0.131,0.16,0.151,0.109,0.137,0.149,0.16,0.162,0.143,0.127,0.113,0.137,0.149,0.083,0.16,0.139,0.144,0.163,0.146,0.137,0.117,0.095,0.136,0.123,0.107,0.126,0.117,0.078,0.079,0.148,0.15,0.113,0.089,0.1,0.148,0.141,0.127,0.096,0.11,0.104,0.083,0.088,0.132,0.14,0.092,0.109,0.117,0.139,0.107,0.106,0.101,0.106,0.114,0.108,0.115,0.114,0.124,0.106,0.14,0.108,0.113,0.104,0.138,0.101,0.117,0.084,0.133,306,1256,623,667,712,838,631,845,442,292,156.0,1027,864,183,686,2462,4231,336,1114,1668,693,998,937,1368,1300,1510,1828,1232,1348,1477,536,439,279,1052,376,562,1184,1253,150,142,578,1028,930,1277,1903,586,279,734,916,473,301,1353,733,1766,162,1390,490,1173,459,614,185,824,222,400,1087,507,1364,926,635,506,524,465,2594,209,3.455,2.914,2.369,2.575,2.413,2.6,2.415,2.939,2.694,2.884,3.282,1.75,2.773,2.88,2.979,2.797,2.928,3.666,2.727,1.939,2.863,2.464,2.117,2.749,2.648,2.799,2.168,2.34,2.54,2.369,2.242,2.765,2.546,2.988,3.385,2.476,3.279,2.98,2.448,2.654,2.9,1.871,3.456,1.738,1.851,1.953,3.036,2.777,2.587,2.745,1.879,2.406,2.271,2.52,2.197,2.139,2.509,2.361,1.707,1.946,2.415,2.731,2.389,2.132,2.091,2.276,2.197,2.61,2.322,3.014,2.184,2.563,2.359,3.017,0.912,0.659,0.579,0.582,0.52,0.618,0.587,0.563,0.627,0.533,0.732,0.457,0.504,0.525,0.66,0.582,0.603,0.756,0.54,0.526,0.683,0.578,0.539,0.851,0.634,0.539,0.594,0.651,0.595,0.534,0.746,0.881,0.5,0.629,0.842,0.488,0.752,0.606,0.391,0.44,0.673,0.485,0.773,0.581,0.557,0.406,0.418,0.811,0.458,0.589,0.517,0.496,0.478,0.57,0.453,0.403,0.481,0.555,0.358,0.43,0.347,0.774,0.744,0.716,0.544,0.64,0.501,0.486,0.504,0.663,0.463,0.52,0.512,0.499
-23,Epileptic,M,37.0,1.6,2.6,0.8,0.9,2.6,1.6,2.2,1.4,1.3,0.8,0.5,2.5,1.3,0.5,1.7,3.9,8.3,1.2,1.5,2.9,0.8,7.8,3.0,4.2,8.5,3.7,4.1,3.1,3.4,3.1,1.9,0.8,0.3,1.9,0.4,0.4,4.4,3.3,0.3,0.1,0.7,2.9,2.8,1.7,3.2,0.5,0.5,1.1,1.3,1.0,0.6,1.6,1.6,2.2,0.6,2.3,0.7,1.8,1.2,1.4,1.2,1.8,0.8,0.5,1.8,1.6,2.8,1.1,1.3,0.5,1.2,1.3,5.0,0.3,9,24,6,7,16,17,14,13,15,9,5.0,24,14,5,21,47,73,6,22,26,8,62,22,37,65,33,46,31,31,38,17,5,2,22,3,6,47,38,3,1,4,28,28,16,17,3,2,4,6,7,4,14,11,16,4,17,4,16,12,12,9,13,5,3,15,20,21,7,8,3,8,11,34,5,0.064,0.031,0.016,0.021,0.044,0.028,0.036,0.027,0.049,0.04,0.042,0.031,0.028,0.028,0.038,0.035,0.036,0.046,0.03,0.032,0.019,0.056,0.039,0.047,0.052,0.036,0.04,0.037,0.034,0.034,0.047,0.034,0.016,0.025,0.015,0.02,0.04,0.034,0.022,0.023,0.017,0.035,0.052,0.024,0.022,0.01,0.022,0.017,0.019,0.034,0.027,0.026,0.029,0.023,0.017,0.014,0.015,0.018,0.022,0.018,0.024,0.029,0.044,0.021,0.022,0.028,0.024,0.018,0.02,0.022,0.024,0.015,0.019,0.021,1526,4643,2075,2048,2763,3350,2556,3464,2226,1735,887.0,2676,3511,1476,2899,7376,17527,2380,3826,3733,2974,5460,2424,6631,5380,5595,5445,3570,6517,5885,2568,1151,784,5886,2096,1564,9218,6600,426,223,1374,2706,6776,1948,3785,1233,857,2218,2200,1678,747,2541,2159,4001,716,4539,1414,3656,1585,1980,1550,2726,620,895,2628,1496,3524,2054,2305,858,1432,2924,8949,591,0.148,0.135,0.102,0.11,0.151,0.134,0.108,0.125,0.18,0.154,0.168,0.134,0.118,0.13,0.15,0.144,0.137,0.13,0.133,0.126,0.09,0.155,0.14,0.152,0.144,0.124,0.129,0.116,0.118,0.141,0.148,0.112,0.084,0.122,0.08,0.075,0.146,0.131,0.135,0.133,0.096,0.13,0.182,0.117,0.107,0.08,0.1,0.088,0.095,0.137,0.129,0.117,0.121,0.112,0.109,0.087,0.1,0.101,0.126,0.105,0.107,0.125,0.17,0.117,0.112,0.121,0.115,0.102,0.104,0.121,0.123,0.091,0.091,0.151,385,1391,707,710,920,1035,889,890,501,390,204.0,1285,891,307,871,2129,4226,433,932,1604,634,2049,1176,1655,2357,1711,2101,1374,1592,1762,663,401,270,1151,464,439,1939,1598,191,84,631,1310,1182,1143,2123,616,334,831,1019,513,378,1158,949,1577,433,2305,634,1590,915,1098,744,971,263,392,1316,881,1783,877,1010,366,742,1323,3890,233,3.663,3.088,2.941,2.812,2.46,2.763,2.355,3.176,3.188,3.085,3.108,1.816,2.984,3.313,2.65,2.692,3.161,3.915,3.032,1.95,3.159,2.262,1.879,2.971,2.088,2.718,2.137,2.093,3.01,2.649,2.789,2.889,2.613,3.438,3.614,2.931,3.293,2.951,2.6,2.872,2.797,1.835,3.717,1.935,2.064,2.213,3.215,3.257,2.677,3.404,2.316,2.304,2.242,2.531,2.184,2.154,2.67,2.504,1.98,1.975,2.301,2.716,2.065,2.545,2.231,2.091,2.185,2.726,2.663,2.663,2.315,2.467,2.495,2.654,0.783,0.604,0.463,0.577,0.698,0.768,0.722,0.519,0.646,0.714,0.853,0.564,0.477,0.327,0.577,0.602,0.626,0.723,0.526,0.589,0.834,0.754,0.502,0.774,0.627,0.604,0.592,0.619,0.526,0.581,0.642,0.943,0.378,0.717,0.615,0.609,0.685,0.714,0.305,0.554,0.558,0.528,0.79,0.899,0.728,0.465,0.436,0.837,0.562,0.629,0.38,0.485,0.468,0.529,0.376,0.442,0.578,0.546,0.368,0.49,0.571,0.767,0.627,0.694,0.574,0.719,0.555,0.421,0.468,0.563,0.451,0.475,0.532,0.356
-24,Epileptic,F,47.0,0.9,3.3,1.2,1.7,2.5,2.0,2.4,1.4,1.7,1.0,0.3,3.8,1.8,0.8,0.9,6.8,10.5,1.4,2.1,4.8,1.1,2.6,2.0,5.1,3.4,3.5,4.4,2.9,4.5,4.0,2.3,2.3,0.4,3.1,0.5,0.7,5.1,3.7,0.1,0.1,1.0,4.0,3.7,2.5,3.8,1.0,0.6,1.0,1.7,1.1,0.7,2.1,1.7,3.2,0.3,3.2,0.8,2.2,1.0,1.4,0.4,1.8,0.3,0.8,1.7,1.2,3.4,1.6,1.2,0.6,1.1,1.3,3.9,0.4,7,25,7,8,16,15,18,13,12,8,2.0,29,16,5,7,56,78,11,22,39,8,22,17,41,32,37,42,25,30,38,19,23,4,28,3,4,46,37,1,1,6,32,27,18,17,8,3,5,7,8,3,15,14,20,2,21,6,17,8,12,3,11,2,6,12,11,20,10,6,3,8,8,26,4,0.046,0.037,0.025,0.031,0.05,0.04,0.047,0.03,0.062,0.044,0.029,0.044,0.036,0.052,0.036,0.04,0.045,0.056,0.043,0.047,0.033,0.037,0.035,0.066,0.039,0.041,0.04,0.039,0.045,0.04,0.065,0.071,0.027,0.043,0.023,0.018,0.057,0.044,0.021,0.018,0.024,0.046,0.066,0.029,0.028,0.022,0.036,0.016,0.024,0.032,0.028,0.028,0.039,0.029,0.017,0.023,0.02,0.027,0.026,0.025,0.021,0.033,0.031,0.03,0.021,0.028,0.026,0.024,0.022,0.026,0.029,0.024,0.021,0.024,1399,4591,1880,2209,1858,2893,2813,2514,1635,1552,619.0,2617,3038,1031,1742,10053,15466,1942,3266,3847,2118,3501,2054,5311,4765,5051,5659,3330,5661,5696,2342,925,753,5279,1435,1455,4996,6975,264,179,1091,2845,4778,2189,3233,1347,558,1796,1911,1003,549,2395,1755,4108,389,3772,1130,2850,1017,1516,607,2547,281,1099,2349,1204,3579,2136,1555,694,1248,1933,6993,394,0.14,0.136,0.112,0.117,0.146,0.146,0.137,0.135,0.173,0.152,0.112,0.162,0.136,0.157,0.135,0.148,0.147,0.15,0.148,0.157,0.106,0.138,0.126,0.168,0.148,0.154,0.128,0.126,0.131,0.147,0.16,0.164,0.105,0.162,0.09,0.096,0.159,0.159,0.115,0.115,0.11,0.153,0.169,0.121,0.117,0.118,0.116,0.087,0.107,0.116,0.125,0.119,0.138,0.118,0.109,0.11,0.108,0.115,0.13,0.126,0.1,0.13,0.144,0.134,0.108,0.115,0.113,0.112,0.101,0.132,0.127,0.117,0.103,0.132,427,1495,755,860,838,921,993,865,492,399,164.0,1390,909,262,490,3056,4479,520,1014,1688,591,1245,1003,1509,1499,1614,2166,1333,1761,1841,683,567,310,1361,426,546,1576,1732,133,101,643,1502,1182,1389,1989,697,269,911,998,513,347,1320,832,1882,212,2132,592,1388,589,865,322,898,135,443,1225,676,1970,1036,780,333,614,865,3073,245,2.89,2.835,2.46,2.517,2.13,2.679,2.288,2.53,2.7,3.026,2.99,1.751,2.614,2.782,2.651,2.523,2.695,2.867,2.62,1.999,2.63,2.308,1.812,2.687,2.527,2.449,2.106,2.027,2.549,2.35,2.486,1.841,2.017,2.787,2.842,2.359,2.279,2.999,2.171,1.709,2.116,1.751,3.039,1.806,1.838,2.143,2.45,2.367,2.306,2.153,1.816,2.059,2.209,2.149,2.198,1.871,2.029,2.127,1.967,1.883,2.021,2.719,2.207,2.512,2.158,2.126,1.98,2.192,2.19,2.352,2.341,2.302,2.333,2.01,0.981,0.63,0.543,0.622,0.585,0.619,0.675,0.614,0.598,0.6,0.98,0.451,0.511,0.407,0.487,0.553,0.641,0.657,0.744,0.619,0.807,0.493,0.485,0.879,0.603,0.578,0.515,0.513,0.587,0.679,0.802,0.768,0.446,0.641,0.768,0.558,0.829,0.678,0.444,0.362,0.457,0.497,0.823,0.577,0.532,0.548,0.348,0.678,0.572,0.687,0.461,0.37,0.521,0.499,0.366,0.425,0.584,0.629,0.427,0.404,0.399,0.677,0.418,0.919,0.508,0.732,0.463,0.442,0.426,1.019,0.428,0.6,0.557,0.359
-25,Epileptic,F,17.5,0.5,1.7,1.3,1.3,1.2,1.7,1.5,1.9,1.0,0.9,0.3,3.0,1.5,0.4,1.0,6.5,7.4,0.9,1.8,4.4,1.0,2.9,1.3,2.3,3.2,2.8,2.9,2.6,3.0,2.6,1.2,0.6,0.6,1.9,0.3,1.0,2.3,2.5,0.3,0.1,1.3,2.4,2.1,2.1,2.8,0.5,0.4,0.7,0.9,0.9,0.5,1.2,1.2,2.1,0.1,1.2,0.8,1.4,1.0,0.9,0.4,1.3,0.1,0.3,1.4,1.0,1.7,1.5,0.8,0.3,1.1,0.9,4.1,0.4,3,17,11,14,12,17,12,19,12,9,3.0,28,14,6,13,67,69,15,17,37,9,28,14,24,40,32,36,25,27,25,15,4,5,28,1,9,25,30,3,1,7,21,19,17,17,4,2,4,4,8,4,12,8,14,1,9,7,13,10,8,2,10,1,3,12,14,14,10,5,2,9,7,34,2,0.034,0.022,0.025,0.029,0.032,0.028,0.027,0.036,0.044,0.04,0.034,0.037,0.031,0.033,0.028,0.04,0.034,0.031,0.031,0.039,0.025,0.042,0.029,0.034,0.04,0.031,0.028,0.031,0.032,0.033,0.042,0.027,0.031,0.03,0.013,0.019,0.037,0.036,0.026,0.017,0.026,0.034,0.038,0.022,0.023,0.017,0.02,0.012,0.015,0.023,0.019,0.017,0.026,0.019,0.022,0.013,0.032,0.019,0.036,0.017,0.02,0.024,0.011,0.021,0.018,0.027,0.018,0.021,0.017,0.012,0.028,0.02,0.022,0.012,1299,4798,2618,2485,2466,3176,2750,4054,1554,1656,605.0,3152,3626,1005,2846,12600,18660,2381,3461,5019,3040,4795,2250,6493,6174,6598,5653,4289,6344,5262,2419,920,1361,5969,1724,2739,6366,6697,608,333,1932,2385,6512,2988,3387,1205,681,2027,2019,1766,639,2984,1653,5216,171,2943,1003,2792,1421,1768,735,2510,377,810,2768,929,3228,3080,1636,1015,1676,1680,9025,899,0.116,0.114,0.124,0.123,0.133,0.14,0.116,0.144,0.154,0.157,0.128,0.15,0.14,0.143,0.133,0.155,0.137,0.134,0.132,0.15,0.096,0.161,0.138,0.133,0.153,0.137,0.13,0.136,0.135,0.145,0.152,0.089,0.124,0.138,0.07,0.106,0.135,0.145,0.126,0.094,0.115,0.142,0.141,0.106,0.109,0.093,0.097,0.08,0.083,0.103,0.124,0.091,0.121,0.098,0.125,0.092,0.132,0.101,0.154,0.101,0.115,0.119,0.087,0.119,0.107,0.14,0.103,0.104,0.099,0.089,0.125,0.115,0.107,0.085,321,1236,781,811,699,1006,874,1065,369,372,175.0,1287,805,210,651,2924,4075,445,941,2094,715,1297,780,1287,1513,1560,1735,1352,1632,1354,557,371,364,1184,395,786,1145,1345,198,140,760,1098,1049,1586,1872,548,288,792,866,661,338,1249,711,1845,79,1369,358,1210,542,863,274,812,167,295,1186,549,1488,1108,730,337,620,668,3154,420,3.769,3.194,3.069,2.841,2.824,2.612,2.575,3.114,2.865,3.523,2.832,2.009,3.246,3.419,3.289,3.235,3.487,3.697,2.955,2.045,3.24,2.836,2.361,3.467,2.92,3.15,2.497,2.496,2.974,2.964,3.133,2.32,2.752,3.423,3.731,3.143,3.508,3.313,3.087,2.605,3.201,1.977,3.84,2.07,2.025,2.316,3.076,3.443,2.785,2.872,2.167,2.44,2.36,2.787,2.485,2.355,3.042,2.59,2.773,2.319,2.957,3.04,2.374,3.082,2.649,2.09,2.457,3.018,2.735,3.019,2.718,2.956,2.754,2.615,0.877,1.012,0.828,0.607,0.728,0.498,0.621,0.769,0.709,0.656,0.804,0.565,0.628,0.652,0.494,0.66,0.755,0.783,0.686,0.591,0.766,0.581,0.481,0.756,0.643,0.673,0.539,0.641,0.533,0.634,0.755,0.939,0.48,0.75,0.692,0.624,0.741,0.66,0.462,0.478,0.716,0.517,0.721,0.621,0.605,0.397,0.405,0.687,0.625,0.719,0.42,0.487,0.63,0.64,0.438,0.48,0.786,0.583,0.561,0.415,0.571,0.839,0.441,0.893,0.57,0.708,0.468,0.524,0.432,0.922,0.54,0.697,0.603,0.388
-26,Epileptic,F,27.0,0.5,2.4,0.5,1.0,1.6,2.6,1.5,1.9,1.0,0.9,0.5,3.5,1.5,0.6,1.7,6.7,8.8,0.7,1.7,5.2,1.4,2.9,1.6,2.6,2.6,4.0,2.6,2.3,2.8,3.3,0.8,1.0,0.4,3.0,0.4,0.9,3.4,3.9,0.2,0.2,0.9,2.6,2.5,3.0,2.8,0.6,0.4,0.8,1.1,1.1,0.8,2.7,1.4,2.7,0.7,2.1,0.8,1.6,1.3,0.8,0.4,1.4,0.5,0.3,1.3,1.8,2.7,1.5,0.9,0.4,1.4,1.5,4.6,0.4,3,30,8,10,19,23,13,20,17,11,7.0,31,21,7,18,84,95,6,26,44,10,36,15,39,36,41,36,24,25,46,14,8,4,36,2,8,37,48,2,1,5,27,21,25,20,6,2,5,7,9,7,24,10,21,9,17,7,18,10,8,4,14,3,4,10,20,24,11,6,6,10,11,42,3,0.023,0.022,0.01,0.02,0.036,0.036,0.023,0.03,0.034,0.033,0.032,0.032,0.026,0.027,0.03,0.033,0.026,0.031,0.029,0.032,0.035,0.031,0.031,0.03,0.029,0.031,0.023,0.025,0.026,0.033,0.027,0.042,0.025,0.03,0.016,0.018,0.034,0.036,0.012,0.017,0.02,0.029,0.044,0.023,0.022,0.025,0.018,0.014,0.014,0.026,0.027,0.024,0.023,0.018,0.041,0.016,0.02,0.017,0.02,0.017,0.02,0.024,0.031,0.015,0.016,0.033,0.022,0.019,0.016,0.016,0.026,0.022,0.017,0.023,1567,5246,2135,2127,2152,3496,2345,3502,2023,1703,725.0,3178,4107,1277,3262,12990,20259,2445,3861,5507,2279,4396,1912,6182,5274,7404,4988,3771,5692,5450,1722,1076,825,6166,1599,2296,6482,7760,457,464,1399,2780,5253,3553,3251,1063,871,1883,2488,1621,727,3914,2271,5800,570,3210,1409,3591,1652,1145,712,2611,569,687,2630,1082,3930,2733,1612,1182,1575,1931,9314,576,0.09,0.118,0.091,0.109,0.139,0.149,0.116,0.133,0.141,0.154,0.139,0.136,0.13,0.136,0.132,0.144,0.125,0.118,0.138,0.134,0.116,0.144,0.131,0.137,0.135,0.14,0.119,0.115,0.109,0.149,0.112,0.125,0.119,0.137,0.072,0.102,0.124,0.145,0.088,0.101,0.105,0.125,0.152,0.116,0.111,0.112,0.098,0.081,0.093,0.127,0.144,0.117,0.106,0.101,0.198,0.099,0.1,0.105,0.116,0.1,0.114,0.122,0.132,0.112,0.092,0.163,0.114,0.108,0.103,0.11,0.127,0.123,0.101,0.125,385,1771,821,816,873,1265,920,1118,566,483,234.0,1538,1077,319,898,3472,5607,404,1058,2505,609,1511,876,1691,1653,2206,1861,1413,1582,1768,519,428,277,1530,416,784,1713,1877,263,217,658,1381,1078,2175,2014,520,347,825,1115,683,448,1789,995,2438,214,1843,641,1546,957,758,366,1013,294,357,1320,657,1975,1205,785,550,796,1020,4151,248,3.813,2.73,2.466,2.57,2.203,2.504,2.194,2.738,2.572,2.918,2.656,1.899,2.907,2.853,2.662,2.793,2.866,4.201,2.889,1.918,2.868,2.277,1.888,2.725,2.504,2.744,2.161,2.155,2.689,2.549,2.458,2.6,2.549,2.898,3.048,2.666,2.916,3.073,1.958,2.564,2.852,1.833,3.399,1.876,1.838,2.207,2.882,2.794,2.65,2.745,1.954,2.222,2.308,2.423,2.648,1.968,2.636,2.394,2.009,1.699,2.188,2.546,2.279,2.053,2.13,2.262,2.125,2.592,2.448,2.347,2.214,2.317,2.488,2.791,0.922,0.552,0.458,0.482,0.582,0.54,0.6,0.431,0.626,0.388,0.788,0.5,0.439,0.498,0.484,0.541,0.599,0.69,0.452,0.674,0.958,0.49,0.504,0.572,0.477,0.476,0.485,0.501,0.51,0.489,0.573,0.76,0.404,0.601,0.782,0.55,0.779,0.595,0.491,0.429,0.595,0.536,0.885,0.635,0.528,0.355,0.592,0.74,0.584,0.614,0.331,0.503,0.543,0.524,0.55,0.368,0.435,0.56,0.385,0.385,0.462,0.718,0.367,0.821,0.454,0.607,0.412,0.429,0.409,0.639,0.403,0.526,0.504,0.52
-27,Epileptic,F,42.0,0.7,2.2,0.8,1.3,1.2,1.4,1.6,1.5,1.1,0.9,0.2,2.9,1.3,0.8,1.1,5.9,9.6,0.6,2.2,3.2,0.9,2.9,1.5,3.4,3.5,2.8,3.6,2.6,2.6,3.9,1.3,0.5,0.4,2.2,0.3,0.4,4.5,3.5,0.1,0.2,0.8,3.0,1.9,2.2,2.6,0.6,0.4,0.8,1.0,0.7,0.4,1.8,1.0,2.0,0.5,2.3,0.5,1.5,0.8,1.3,0.5,1.2,0.3,0.9,1.4,1.2,1.5,1.8,1.3,0.9,0.6,1.7,4.3,0.3,16,19,6,12,12,16,15,16,11,9,5.0,33,14,7,12,57,85,5,21,33,11,32,15,29,40,33,41,28,23,51,13,5,5,24,1,4,38,38,2,2,7,24,20,17,17,5,2,5,6,6,2,14,7,11,2,19,4,17,9,10,4,9,1,7,13,14,13,12,8,6,5,12,39,3,0.045,0.032,0.017,0.033,0.035,0.033,0.026,0.03,0.056,0.046,0.033,0.038,0.031,0.043,0.031,0.043,0.037,0.034,0.042,0.038,0.024,0.047,0.033,0.042,0.035,0.032,0.027,0.026,0.029,0.049,0.034,0.028,0.027,0.035,0.011,0.014,0.051,0.036,0.016,0.018,0.023,0.047,0.044,0.022,0.02,0.015,0.024,0.014,0.016,0.016,0.028,0.024,0.027,0.02,0.03,0.02,0.021,0.025,0.028,0.019,0.022,0.023,0.023,0.029,0.019,0.023,0.016,0.023,0.034,0.033,0.02,0.03,0.019,0.021,1161,4202,1578,1907,1910,2283,2498,2632,1403,1125,549.0,2949,2745,1144,2402,8741,15794,1905,3253,4322,3060,3918,2022,5294,6441,4837,5286,4030,4717,5569,1724,857,756,4677,1631,1256,5567,6387,222,650,1288,3050,5864,2636,2882,1171,595,1926,1898,1598,396,2410,1312,3339,539,3545,696,2575,1004,1945,968,2266,313,943,2419,1134,2718,2505,1126,1041,1062,2090,7916,550,0.113,0.131,0.105,0.138,0.134,0.15,0.113,0.14,0.177,0.177,0.131,0.152,0.133,0.173,0.138,0.161,0.145,0.12,0.149,0.154,0.105,0.161,0.133,0.141,0.152,0.149,0.137,0.123,0.106,0.171,0.123,0.089,0.133,0.134,0.067,0.09,0.153,0.133,0.102,0.106,0.119,0.162,0.167,0.113,0.104,0.097,0.123,0.085,0.092,0.104,0.138,0.117,0.117,0.103,0.146,0.109,0.107,0.125,0.147,0.106,0.112,0.12,0.127,0.129,0.112,0.121,0.099,0.109,0.133,0.149,0.117,0.119,0.107,0.141,299,1194,625,742,588,775,973,923,359,338,166.0,1288,801,287,651,2410,4658,350,846,1562,714,1138,842,1418,1808,1604,1989,1503,1449,1603,623,363,257,1139,424,506,1550,1724,146,230,599,1188,873,1571,1936,611,257,807,901,691,219,1204,627,1517,227,1765,367,1117,518,998,446,800,152,485,1174,720,1471,1198,625,389,517,925,3480,242,3.217,2.978,2.321,2.625,2.514,2.629,2.23,2.388,2.793,2.762,2.917,1.994,2.683,2.714,2.61,2.719,2.733,3.723,3.239,2.389,3.087,2.545,2.136,2.65,2.761,2.505,2.144,2.211,2.51,2.721,2.188,2.184,2.419,2.843,3.474,2.266,2.719,2.703,1.702,2.526,2.54,2.199,3.884,1.862,1.718,2.1,2.922,2.991,2.599,2.706,2.233,2.221,2.196,2.32,2.826,2.104,2.163,2.414,2.028,2.159,2.46,2.728,2.235,2.24,2.364,2.0,2.089,2.381,2.187,2.822,2.37,2.464,2.384,2.756,1.317,0.899,0.651,0.436,0.809,0.486,0.514,0.534,0.642,0.704,0.714,0.447,0.462,0.586,0.607,0.516,0.635,0.801,0.538,0.576,0.799,0.528,0.398,0.959,0.53,0.486,0.503,0.499,0.544,0.581,1.064,1.061,0.476,0.802,1.018,0.445,0.873,0.658,0.327,0.809,0.528,0.501,0.848,0.529,0.51,0.46,0.42,0.865,0.476,0.526,0.446,0.516,0.672,0.483,0.484,0.43,0.446,0.777,0.512,0.351,0.391,0.9,0.618,0.971,0.536,0.666,0.517,0.439,0.416,0.752,0.51,0.584,0.519,0.384
-28,Epileptic,M,22.0,1.0,2.5,1.0,1.6,1.6,3.0,2.2,2.1,1.5,0.7,0.3,3.7,1.5,0.9,2.1,10.0,13.4,1.0,2.4,7.0,1.2,3.3,2.1,3.5,3.3,3.4,3.1,4.3,5.8,3.2,1.4,1.4,0.5,3.4,0.5,0.7,3.5,3.3,0.4,0.2,1.0,3.9,2.6,6.4,4.0,0.5,0.5,1.2,1.3,1.2,1.0,2.2,2.4,3.8,0.6,2.3,1.1,2.4,1.3,1.1,0.9,1.6,0.2,0.3,2.5,1.0,3.4,1.5,1.5,0.5,0.7,1.7,3.8,0.4,8,26,9,17,19,27,17,16,14,7,4.0,35,17,11,21,94,113,10,19,57,10,33,23,35,37,32,31,39,36,34,24,10,5,37,2,6,41,38,3,1,5,34,30,47,22,5,2,7,7,14,6,15,15,21,4,17,10,16,11,9,7,18,3,3,17,12,24,9,11,4,6,13,27,4,0.038,0.026,0.012,0.027,0.032,0.038,0.031,0.032,0.05,0.03,0.026,0.034,0.032,0.033,0.046,0.043,0.036,0.031,0.032,0.041,0.025,0.036,0.031,0.035,0.034,0.029,0.026,0.033,0.038,0.033,0.043,0.044,0.018,0.033,0.019,0.019,0.036,0.034,0.027,0.014,0.019,0.038,0.047,0.036,0.022,0.013,0.017,0.016,0.016,0.036,0.039,0.02,0.025,0.023,0.024,0.018,0.03,0.022,0.026,0.02,0.028,0.024,0.021,0.017,0.028,0.025,0.024,0.018,0.021,0.019,0.02,0.021,0.017,0.019,1834,5417,2550,2645,2026,3347,2820,3441,1595,1416,728.0,3641,3424,1586,3114,12778,21007,2989,3726,5790,3657,4261,2579,6249,5395,6455,4656,4965,7069,5263,2158,1351,940,6691,1781,1802,7246,7201,510,519,1517,3992,7150,4286,4213,1077,954,2134,2540,1687,720,3562,2645,5896,635,3257,1539,3544,1244,1370,995,2367,476,783,2816,945,4176,3014,2416,1110,1092,2424,7882,465,0.141,0.117,0.09,0.116,0.138,0.156,0.108,0.134,0.142,0.139,0.108,0.143,0.135,0.136,0.16,0.144,0.13,0.12,0.133,0.151,0.096,0.138,0.139,0.135,0.131,0.127,0.113,0.124,0.115,0.127,0.137,0.134,0.098,0.139,0.082,0.092,0.137,0.134,0.116,0.087,0.099,0.14,0.155,0.147,0.105,0.086,0.092,0.088,0.089,0.136,0.152,0.1,0.12,0.106,0.125,0.099,0.13,0.106,0.127,0.108,0.13,0.12,0.122,0.109,0.119,0.117,0.113,0.098,0.109,0.111,0.113,0.114,0.096,0.105,475,1692,1081,1111,887,1360,1179,1108,565,368,244.0,1786,955,416,934,4021,6402,557,1197,2809,823,1478,1187,1721,1667,1935,1931,2060,2181,1686,630,587,401,1639,465,660,1767,1751,302,241,774,1690,1243,2851,2608,669,403,987,1225,566,397,1718,1347,2521,358,1914,690,1720,773,780,525,1037,219,299,1450,611,2220,1284,1149,463,597,1209,3525,358,3.724,2.87,2.334,2.412,2.193,2.268,2.041,2.829,2.235,2.828,2.539,1.814,2.833,2.535,2.619,2.439,2.639,3.829,2.608,1.885,3.272,2.328,1.971,2.696,2.552,2.793,2.017,2.029,2.54,2.444,2.564,2.504,1.916,2.89,3.31,2.418,2.978,2.961,2.004,2.178,2.511,2.029,3.425,1.774,1.819,1.847,2.828,2.619,2.579,2.916,1.864,2.211,2.106,2.4,2.137,1.898,2.583,2.382,1.79,1.877,2.184,2.219,2.095,2.545,2.004,2.047,2.046,2.64,2.39,2.727,2.105,2.438,2.423,1.609,0.694,0.692,0.421,0.663,0.446,0.55,0.587,0.53,0.541,0.464,0.903,0.514,0.51,0.563,0.537,0.654,0.681,0.766,0.712,0.6,0.823,0.539,0.475,0.655,0.538,0.564,0.498,0.523,0.625,0.52,0.609,0.965,0.383,0.703,0.805,0.581,0.726,0.637,0.438,0.408,0.567,0.61,0.865,0.519,0.571,0.375,0.418,0.542,0.443,0.672,0.431,0.499,0.504,0.536,0.341,0.398,0.518,0.546,0.434,0.357,0.663,0.562,0.493,0.971,0.482,0.572,0.537,0.431,0.525,0.571,0.496,0.565,0.555,0.367
-29,Epileptic,F,32.0,0.7,2.2,0.8,1.4,1.3,2.0,1.5,1.5,1.6,1.0,0.5,2.3,1.7,0.9,1.8,5.2,7.4,0.5,3.0,4.9,1.2,2.5,1.4,3.1,3.0,3.4,3.9,2.9,2.8,4.2,2.1,1.6,0.4,1.5,0.6,0.5,4.3,3.3,0.3,0.2,0.8,2.7,2.8,3.5,3.7,0.5,0.5,0.8,1.1,1.1,0.5,1.4,1.0,2.3,0.2,2.5,0.8,1.8,0.6,1.3,0.8,1.3,0.3,0.5,1.7,1.7,3.6,0.7,1.2,1.1,1.0,1.1,3.0,0.3,6,18,9,9,11,15,12,13,11,10,5.0,16,16,8,17,52,65,4,28,36,9,25,13,31,35,31,36,27,21,43,20,14,4,18,4,3,44,34,2,1,5,23,25,20,22,5,2,6,7,7,5,8,5,12,1,23,6,14,5,10,5,8,3,3,14,18,27,5,7,10,6,7,27,2,0.037,0.026,0.013,0.026,0.037,0.036,0.023,0.025,0.058,0.042,0.036,0.034,0.037,0.043,0.038,0.038,0.028,0.025,0.038,0.042,0.028,0.036,0.031,0.04,0.032,0.033,0.029,0.028,0.027,0.033,0.047,0.058,0.024,0.028,0.024,0.014,0.037,0.037,0.02,0.017,0.019,0.034,0.045,0.044,0.024,0.013,0.025,0.016,0.015,0.022,0.032,0.018,0.026,0.023,0.022,0.024,0.017,0.024,0.017,0.024,0.024,0.029,0.031,0.024,0.027,0.033,0.025,0.015,0.02,0.029,0.025,0.019,0.018,0.018,1214,4200,1770,2290,1855,2589,2611,3177,1958,1397,545.0,2083,3047,1405,3289,9323,15899,1963,4337,3442,2876,4326,1719,5547,5235,5751,5868,4806,5489,5742,2017,985,1024,3596,1568,1273,7382,6278,503,415,1506,2347,4842,2259,4007,1173,818,1863,2634,1412,398,2876,1445,3876,292,3249,1680,2727,971,1500,1067,1922,484,772,2263,1605,4044,1733,1990,1556,1334,2010,6879,382,0.12,0.116,0.113,0.116,0.133,0.136,0.095,0.122,0.163,0.157,0.162,0.141,0.138,0.146,0.148,0.149,0.122,0.089,0.139,0.136,0.103,0.143,0.121,0.137,0.137,0.138,0.121,0.127,0.116,0.144,0.126,0.157,0.115,0.128,0.099,0.085,0.14,0.142,0.11,0.1,0.101,0.139,0.151,0.149,0.114,0.089,0.12,0.093,0.088,0.117,0.137,0.094,0.115,0.1,0.143,0.118,0.104,0.118,0.11,0.123,0.123,0.127,0.143,0.11,0.121,0.14,0.122,0.092,0.103,0.136,0.121,0.099,0.096,0.113,346,1407,806,844,624,909,931,998,455,420,187.0,980,858,336,859,2443,4446,365,1336,1695,765,1239,796,1395,1613,1716,2029,1634,1625,2032,597,477,279,966,437,511,2066,1607,256,198,685,1275,1201,1322,2392,655,327,794,1128,658,283,1219,618,1654,117,1759,727,1307,531,839,480,720,208,406,1184,729,2100,806,939,598,673,945,2891,210,3.277,2.779,2.161,2.778,2.511,2.528,2.336,2.953,2.908,2.882,2.741,1.97,2.728,2.907,2.826,2.809,2.935,3.893,2.621,1.818,3.178,2.647,1.985,2.967,2.51,2.766,2.304,2.369,2.662,2.409,2.483,2.32,2.59,2.725,3.19,2.385,2.741,3.027,2.278,2.526,2.677,1.794,3.049,2.077,1.951,1.9,3.207,2.978,2.759,2.481,1.768,2.494,2.671,2.421,3.464,2.064,2.754,2.48,2.098,2.007,2.496,2.812,2.285,2.294,2.19,2.508,2.186,2.569,2.492,2.675,2.449,2.518,2.513,2.335,0.827,0.594,0.495,0.681,0.67,0.777,0.542,0.7,0.673,0.548,0.577,0.474,0.483,0.431,0.621,0.571,0.55,0.908,0.791,0.586,0.785,0.611,0.559,0.613,0.611,0.586,0.535,0.499,0.51,0.471,0.657,0.853,0.509,0.655,0.8,0.529,0.777,0.592,0.364,0.455,0.547,0.439,0.677,0.922,0.585,0.483,0.503,0.713,0.62,0.542,0.379,0.618,0.554,0.581,0.379,0.449,0.609,0.623,0.44,0.343,0.354,0.586,0.376,0.826,0.503,0.792,0.459,0.367,0.398,0.586,0.421,0.412,0.487,0.4
-30,Epileptic,F,57.0,1.1,2.2,1.6,1.8,1.8,2.4,2.0,1.8,1.4,0.6,0.5,2.5,1.6,0.5,1.2,5.8,9.5,1.5,2.6,3.9,1.2,2.8,1.9,2.9,3.1,3.5,3.6,2.2,3.5,2.6,1.2,0.4,0.5,2.6,0.6,0.7,4.9,3.3,0.2,0.1,1.1,3.6,2.8,3.3,2.9,0.8,0.4,1.1,1.4,1.0,0.8,2.1,1.4,4.1,0.4,2.3,1.1,1.9,1.6,1.0,0.7,1.8,0.5,0.4,2.0,1.2,2.5,2.0,1.1,0.6,0.7,1.6,4.1,0.4,12,18,14,13,19,17,15,15,15,5,4.0,21,14,5,13,56,72,14,25,30,8,26,17,34,29,30,38,17,31,28,16,4,4,27,4,6,46,33,1,1,4,24,27,23,14,5,2,7,6,7,4,14,12,30,2,17,7,14,10,7,5,11,3,3,13,16,16,10,6,4,5,10,28,3,0.048,0.027,0.03,0.034,0.05,0.042,0.032,0.037,0.055,0.035,0.033,0.031,0.032,0.036,0.029,0.039,0.037,0.05,0.039,0.039,0.024,0.049,0.038,0.038,0.039,0.042,0.038,0.028,0.031,0.037,0.041,0.02,0.029,0.033,0.019,0.022,0.049,0.037,0.015,0.014,0.022,0.047,0.04,0.043,0.021,0.02,0.02,0.017,0.022,0.023,0.033,0.023,0.026,0.028,0.017,0.027,0.026,0.025,0.036,0.021,0.028,0.033,0.038,0.018,0.026,0.025,0.025,0.023,0.017,0.022,0.023,0.03,0.02,0.025,1363,4295,2401,2144,2284,3090,2674,2638,2083,1016,691.0,2568,3115,1101,2355,9493,16591,2310,4476,3726,2821,4034,1922,5353,5114,4792,4855,3022,5926,4196,2414,910,694,5859,2050,1845,6693,5829,318,384,1291,2908,5388,1882,3419,1178,642,2156,2049,1845,540,3032,2642,6325,561,2504,1484,3089,1509,1251,780,2675,417,745,2461,1219,2786,3142,1856,925,883,1849,8183,429,0.133,0.121,0.127,0.125,0.15,0.153,0.127,0.141,0.157,0.139,0.14,0.126,0.131,0.137,0.138,0.144,0.135,0.143,0.141,0.134,0.096,0.157,0.131,0.136,0.149,0.148,0.135,0.102,0.117,0.141,0.145,0.084,0.122,0.138,0.086,0.095,0.143,0.138,0.093,0.082,0.103,0.153,0.136,0.147,0.1,0.098,0.1,0.082,0.101,0.109,0.14,0.11,0.117,0.113,0.102,0.117,0.111,0.117,0.135,0.111,0.122,0.13,0.126,0.11,0.116,0.122,0.114,0.104,0.096,0.115,0.116,0.13,0.098,0.121,446,1415,938,861,664,1049,983,908,515,282,213.0,1258,846,271,688,2755,4314,557,1266,1703,816,1124,906,1377,1376,1498,1729,1187,1763,1409,586,419,273,1320,538,599,1932,1556,189,216,653,1323,1297,1292,2040,630,294,977,944,803,340,1445,986,2442,329,1420,704,1317,735,683,410,886,205,358,1220,748,1552,1277,964,406,496,849,3324,226,3.07,2.76,2.327,2.525,2.661,2.543,2.286,2.804,2.811,2.857,2.788,1.82,2.829,2.695,2.568,2.605,2.923,2.946,2.73,1.874,2.985,2.61,1.932,2.858,2.731,2.686,2.252,2.05,2.559,2.448,2.983,2.198,2.18,3.207,3.44,2.627,2.637,2.771,2.03,1.947,2.419,1.99,3.102,1.756,1.877,2.036,2.794,2.674,2.71,2.497,2.084,2.324,2.275,2.48,2.049,2.013,2.461,2.477,2.291,2.014,2.248,2.802,2.147,2.189,2.267,1.901,2.003,2.877,2.216,2.424,2.076,2.532,2.541,2.277,0.75,0.529,0.598,0.517,0.741,0.626,0.488,0.666,0.672,0.327,0.884,0.442,0.429,0.64,0.509,0.554,0.628,0.919,0.513,0.597,0.704,0.611,0.515,0.584,0.603,0.611,0.447,0.424,0.494,0.481,0.736,1.121,0.441,0.706,0.781,0.599,0.68,0.652,0.341,0.431,0.563,0.483,0.699,0.651,0.536,0.436,0.443,0.77,0.531,0.481,0.411,0.505,0.68,0.6,0.294,0.417,0.494,0.575,0.445,0.418,0.487,0.81,0.435,0.7,0.496,0.8,0.485,0.769,0.449,0.65,0.474,0.569,0.506,0.499
-31,Epileptic,F,52.0,1.0,2.7,0.9,1.1,1.7,1.5,1.5,1.7,1.1,0.6,0.4,3.0,1.2,0.7,0.9,5.4,8.3,0.8,2.3,3.4,1.1,3.6,1.8,3.7,3.1,2.5,2.9,1.9,2.6,3.6,1.0,1.3,0.5,2.1,0.7,0.7,3.1,2.5,0.2,0.3,1.0,2.9,3.0,2.1,2.5,0.6,0.5,0.9,1.1,1.0,0.6,1.6,1.6,1.5,0.3,2.6,0.9,1.5,0.9,1.1,1.2,1.7,0.3,0.5,1.6,1.4,2.2,0.8,1.4,0.5,1.4,1.5,4.1,0.3,11,27,9,8,18,16,12,14,15,7,3.0,24,16,9,9,54,79,8,25,29,9,35,20,46,38,28,37,19,22,40,16,10,5,28,4,6,34,32,1,2,9,24,25,18,14,5,3,6,6,10,4,12,13,10,1,20,7,8,8,10,8,12,2,5,14,17,19,5,9,5,9,10,33,3,0.038,0.03,0.017,0.023,0.029,0.031,0.023,0.025,0.037,0.027,0.037,0.035,0.023,0.034,0.027,0.033,0.032,0.029,0.028,0.034,0.02,0.035,0.027,0.039,0.03,0.025,0.022,0.02,0.024,0.026,0.031,0.039,0.018,0.027,0.014,0.015,0.032,0.024,0.019,0.016,0.022,0.037,0.044,0.026,0.018,0.009,0.022,0.014,0.015,0.021,0.031,0.02,0.027,0.018,0.031,0.016,0.02,0.014,0.027,0.018,0.031,0.025,0.032,0.02,0.02,0.022,0.016,0.014,0.02,0.017,0.022,0.022,0.015,0.014,1494,4628,2074,2177,2778,2568,2382,2973,1855,1557,603.0,2741,3076,1476,1935,8696,15660,2356,4705,3777,2590,5420,2609,6211,5907,5682,6339,3630,5918,6063,1907,996,1341,4736,2239,1595,6652,6793,294,568,1598,2384,4393,2074,3702,1849,773,2292,2157,1987,598,2820,2479,3484,355,4491,1686,3194,1207,1807,1420,2565,379,844,2668,1529,4107,1668,2354,831,1753,2386,8962,544,0.134,0.124,0.101,0.106,0.13,0.134,0.098,0.118,0.145,0.114,0.124,0.137,0.119,0.15,0.119,0.133,0.126,0.118,0.126,0.135,0.076,0.146,0.123,0.151,0.139,0.118,0.11,0.098,0.099,0.128,0.117,0.12,0.091,0.116,0.071,0.084,0.117,0.109,0.114,0.1,0.122,0.146,0.131,0.123,0.094,0.075,0.118,0.082,0.091,0.11,0.141,0.105,0.113,0.092,0.134,0.098,0.109,0.085,0.128,0.109,0.144,0.126,0.127,0.117,0.116,0.115,0.096,0.086,0.106,0.107,0.114,0.112,0.091,0.092,499,1569,773,828,1028,918,898,996,577,412,189.0,1308,918,377,573,2708,4526,462,1340,1568,852,1762,1083,1835,1916,1706,2202,1386,1708,2082,591,491,420,1197,657,663,1705,1661,149,288,755,1211,1082,1334,2193,922,333,954,1040,817,331,1248,1110,1483,161,2438,773,1628,602,939,563,1007,203,485,1293,961,2125,838,1074,448,929,1155,4155,340,3.004,2.626,2.601,2.578,2.335,2.413,2.236,2.617,2.47,2.837,2.599,1.853,2.645,2.756,2.564,2.502,2.75,3.643,2.876,2.051,2.512,2.484,2.12,2.737,2.468,2.738,2.332,2.109,2.644,2.377,2.4,2.163,2.527,2.727,2.946,2.285,2.922,2.996,2.262,2.368,2.562,1.826,2.817,1.757,1.909,2.171,3.035,2.91,2.536,2.638,2.163,2.253,2.174,2.367,2.681,2.082,2.415,2.223,2.163,2.111,2.617,2.47,2.023,2.074,2.334,1.973,2.172,2.372,2.42,2.083,2.23,2.582,2.368,1.89,0.784,0.786,0.524,0.399,0.545,0.449,0.515,0.487,0.478,0.459,0.649,0.435,0.367,0.418,0.472,0.572,0.561,0.787,0.497,0.659,0.665,0.463,0.47,0.562,0.464,0.454,0.375,0.478,0.448,0.464,0.441,0.98,0.374,0.583,0.464,0.35,0.672,0.576,0.384,0.427,0.577,0.477,0.755,0.602,0.536,0.466,0.551,0.687,0.477,0.709,0.551,0.457,0.475,0.511,0.409,0.365,0.364,0.452,0.547,0.368,0.522,0.683,0.331,0.606,0.458,0.629,0.486,0.324,0.48,0.41,0.404,0.493,0.456,0.284
-32,Epileptic,F,32.0,0.9,2.7,1.0,1.6,1.7,2.0,1.5,1.3,0.9,0.7,0.4,2.5,1.3,0.5,1.5,5.3,7.9,1.7,2.5,4.4,1.8,2.9,1.5,4.1,4.3,3.9,4.3,3.1,3.8,3.2,1.6,0.5,0.4,2.0,0.5,0.9,4.7,2.9,0.4,0.2,1.1,2.8,2.0,1.9,2.4,0.7,0.5,0.7,1.4,0.8,0.7,1.7,1.1,3.0,0.7,2.6,1.3,1.6,1.2,1.0,0.5,1.5,0.2,0.6,1.7,1.1,2.2,1.3,0.8,0.5,1.2,1.1,3.8,0.4,8,29,8,14,14,24,14,15,10,6,4.0,23,16,9,19,66,78,12,29,44,18,35,15,55,50,42,50,27,32,35,22,6,4,24,3,9,52,40,2,1,6,29,18,14,11,5,2,4,8,7,6,19,9,23,5,20,9,11,10,7,4,12,1,7,15,16,19,9,5,5,9,8,32,2,0.038,0.026,0.019,0.031,0.035,0.03,0.029,0.027,0.032,0.034,0.04,0.038,0.027,0.042,0.024,0.029,0.026,0.042,0.03,0.039,0.033,0.037,0.031,0.044,0.044,0.032,0.035,0.035,0.035,0.029,0.035,0.045,0.023,0.028,0.015,0.019,0.045,0.032,0.021,0.017,0.019,0.039,0.028,0.023,0.017,0.012,0.018,0.01,0.019,0.014,0.037,0.018,0.023,0.017,0.027,0.017,0.027,0.018,0.021,0.018,0.015,0.023,0.02,0.019,0.02,0.019,0.017,0.015,0.015,0.017,0.025,0.016,0.017,0.014,1521,5718,2552,2400,2634,3644,3133,3099,2046,1558,792.0,2946,3575,1414,3938,13737,21270,2902,5467,5275,4159,4983,2455,6921,6361,8126,7762,3748,7200,6420,2612,971,1198,5973,2190,2514,7721,7615,534,442,1749,2860,6179,2444,3926,1617,997,2416,2345,1751,767,4207,1950,7038,744,4627,1400,2863,1442,1695,885,2762,369,1115,2718,1389,3914,2790,1811,832,1584,2242,8244,602,0.136,0.128,0.102,0.132,0.132,0.131,0.114,0.123,0.138,0.14,0.152,0.15,0.129,0.157,0.126,0.13,0.123,0.146,0.134,0.153,0.123,0.141,0.155,0.153,0.146,0.129,0.136,0.105,0.116,0.134,0.132,0.104,0.11,0.132,0.083,0.104,0.156,0.149,0.108,0.094,0.098,0.155,0.129,0.115,0.087,0.091,0.102,0.069,0.1,0.101,0.149,0.106,0.108,0.094,0.131,0.102,0.129,0.102,0.116,0.107,0.1,0.115,0.113,0.114,0.111,0.108,0.095,0.095,0.088,0.12,0.127,0.093,0.097,0.101,422,1789,912,898,872,1322,950,926,507,402,212.0,1231,946,324,1078,3456,5270,632,1547,2101,971,1603,883,1979,1962,2335,2358,1361,1709,1978,863,360,310,1276,584,799,2118,1753,277,219,877,1452,1300,1351,2092,810,430,955,1163,801,344,1705,859,2810,411,2284,680,1386,830,847,483,1040,180,545,1287,942,2152,1208,860,461,788,1103,3563,326,3.313,2.766,2.638,2.508,2.634,2.368,2.603,2.698,2.968,2.819,2.728,1.977,2.852,3.297,2.837,2.898,3.13,3.524,2.827,2.091,3.051,2.467,2.252,2.789,2.602,2.698,2.593,2.209,3.122,2.561,2.4,2.445,2.811,3.177,3.356,2.695,2.802,3.174,2.223,2.314,2.33,1.804,3.337,1.969,2.152,2.098,2.814,2.967,2.455,2.467,2.243,2.389,2.38,2.578,2.122,2.158,2.291,2.317,1.951,2.154,2.149,2.634,2.292,2.339,2.28,1.827,1.947,2.576,2.375,1.95,2.077,2.263,2.359,2.249,0.969,0.73,0.499,0.552,0.655,0.595,0.777,0.558,0.599,0.574,0.825,0.527,0.478,0.532,0.545,0.638,0.635,0.798,0.539,0.621,0.804,0.527,0.506,0.757,0.597,0.616,0.594,0.649,0.58,0.579,0.716,0.83,0.37,0.623,0.775,0.508,0.848,0.624,0.425,0.292,0.468,0.436,0.712,0.51,0.697,0.37,0.37,0.99,0.434,0.528,0.478,0.45,0.455,0.484,0.5,0.403,0.516,0.477,0.327,0.444,0.313,0.488,0.514,0.531,0.444,0.559,0.411,0.417,0.473,0.523,0.441,0.547,0.463,0.404
-33,Epileptic,M,37.0,0.6,2.5,0.8,1.1,1.8,2.6,1.6,1.4,1.1,0.8,0.4,2.9,1.7,0.8,2.3,6.2,9.0,1.1,3.1,4.7,1.9,2.7,2.0,3.2,3.8,4.0,4.0,2.4,3.1,3.3,1.6,1.2,0.6,2.2,0.4,1.1,4.2,3.5,0.3,0.3,1.3,3.5,2.5,3.8,2.8,0.7,0.6,1.0,1.6,1.0,0.7,2.1,1.3,2.2,0.7,2.4,1.0,2.0,1.0,1.3,0.6,1.3,0.4,0.3,2.2,1.9,2.3,1.5,0.8,0.5,1.7,2.0,4.6,0.3,4,20,7,9,20,25,16,13,12,8,4.0,29,16,10,21,62,80,7,28,41,18,28,20,30,48,37,44,24,23,40,21,8,5,24,2,7,41,41,2,1,8,27,25,29,16,5,3,5,8,10,4,17,8,14,6,21,6,18,8,10,5,10,4,4,18,15,17,10,4,6,11,14,34,4,0.029,0.026,0.012,0.021,0.034,0.038,0.023,0.03,0.043,0.041,0.041,0.032,0.034,0.025,0.039,0.036,0.028,0.035,0.039,0.038,0.033,0.033,0.03,0.034,0.029,0.033,0.028,0.028,0.028,0.029,0.04,0.05,0.028,0.029,0.012,0.026,0.035,0.034,0.027,0.023,0.025,0.039,0.034,0.033,0.021,0.01,0.022,0.014,0.02,0.023,0.032,0.021,0.021,0.02,0.022,0.015,0.02,0.023,0.025,0.021,0.032,0.024,0.025,0.015,0.02,0.036,0.019,0.022,0.014,0.022,0.028,0.021,0.021,0.027,1574,4697,2075,2207,2023,3197,2845,2752,1635,1516,607.0,2883,3429,1375,3248,10882,18218,2532,4949,4295,3174,4393,2251,5661,7325,6504,6450,3259,5977,5965,2319,956,678,4662,1410,1847,6346,6963,463,419,1447,2943,4361,2514,3439,1604,915,2458,2729,1603,520,3113,1719,3931,670,4477,1552,3075,1023,1515,533,2229,449,822,3147,1409,3383,2379,1443,939,1795,2473,8663,354,0.107,0.115,0.089,0.105,0.14,0.158,0.102,0.124,0.152,0.137,0.154,0.126,0.133,0.128,0.151,0.14,0.118,0.109,0.138,0.142,0.111,0.136,0.13,0.13,0.132,0.135,0.125,0.112,0.111,0.128,0.141,0.131,0.117,0.119,0.065,0.1,0.13,0.134,0.112,0.109,0.117,0.148,0.131,0.132,0.099,0.084,0.102,0.081,0.099,0.114,0.142,0.105,0.111,0.097,0.12,0.093,0.104,0.114,0.128,0.114,0.143,0.117,0.122,0.11,0.113,0.123,0.104,0.106,0.095,0.12,0.132,0.121,0.103,0.133,397,1559,878,832,882,1192,1102,865,494,395,164.0,1373,929,460,987,3065,5381,497,1442,2061,885,1393,1031,1552,2271,2040,2379,1334,1674,2015,683,475,357,1304,471,707,1942,1771,214,198,780,1400,1228,1685,2064,856,435,1040,1217,706,337,1651,944,1712,401,2495,760,1500,631,867,325,834,286,400,1571,757,1858,1113,709,405,896,1362,3734,223,3.577,2.827,2.335,2.568,2.164,2.49,2.199,2.787,2.448,3.129,3.123,1.863,2.737,2.278,2.637,2.669,2.756,3.785,2.741,1.858,2.922,2.515,1.953,2.704,2.495,2.543,2.199,1.992,2.727,2.425,2.651,2.186,1.674,2.645,2.81,2.345,2.549,2.925,2.256,2.294,2.278,1.925,2.735,1.689,1.882,2.06,2.627,2.844,2.7,2.376,2.084,2.022,2.018,2.443,2.168,1.956,2.378,2.248,2.008,1.966,2.027,2.629,1.756,2.212,2.146,2.231,1.969,2.452,2.356,2.678,2.382,2.301,2.486,1.994,0.899,0.61,0.481,0.555,0.549,0.468,0.533,0.534,0.563,0.48,0.747,0.493,0.497,0.578,0.496,0.531,0.605,0.666,0.557,0.609,0.933,0.508,0.491,0.682,0.526,0.484,0.498,0.437,0.449,0.523,0.708,1.002,0.288,0.519,0.77,0.501,0.593,0.597,0.399,0.411,0.499,0.488,0.696,0.537,0.585,0.473,0.692,1.052,0.561,0.436,0.491,0.433,0.564,0.533,0.341,0.362,0.426,0.519,0.322,0.412,0.435,0.761,0.432,0.901,0.563,0.775,0.482,0.405,0.477,0.849,0.579,0.447,0.641,0.424
-34,Epileptic,F,22.0,0.6,1.4,1.3,1.3,1.2,1.7,1.4,1.8,1.3,0.8,0.2,3.3,1.7,0.8,1.0,6.4,12.1,0.6,2.1,4.5,1.2,2.0,1.6,3.3,2.7,3.6,3.6,2.4,3.6,2.7,1.5,1.4,0.5,2.1,0.4,0.7,3.2,3.7,0.2,0.2,1.1,3.7,1.7,3.9,2.9,0.5,0.6,1.0,1.0,0.5,0.6,1.3,1.3,3.4,0.5,1.9,0.3,2.5,1.5,0.5,0.5,1.2,0.2,0.3,2.0,0.6,3.0,1.9,2.0,0.3,0.9,0.8,4.0,0.3,6,12,11,11,12,15,10,16,15,6,1.0,22,16,8,9,51,95,4,19,35,10,16,13,34,30,28,34,17,25,26,17,7,6,22,3,5,34,31,2,1,5,28,15,21,17,3,3,6,5,3,5,11,12,20,2,15,2,15,8,6,5,7,1,3,14,7,19,13,11,2,5,4,32,3,0.034,0.022,0.028,0.031,0.031,0.032,0.032,0.031,0.049,0.034,0.033,0.045,0.038,0.043,0.032,0.04,0.039,0.027,0.027,0.043,0.026,0.034,0.037,0.042,0.035,0.034,0.03,0.032,0.035,0.03,0.037,0.052,0.02,0.036,0.02,0.023,0.039,0.037,0.017,0.023,0.022,0.049,0.042,0.046,0.025,0.013,0.025,0.018,0.016,0.012,0.031,0.02,0.025,0.027,0.025,0.018,0.011,0.029,0.029,0.021,0.031,0.023,0.023,0.018,0.024,0.024,0.026,0.023,0.041,0.014,0.022,0.021,0.022,0.016,1375,3806,2295,2085,1693,2902,1972,2782,1348,1224,412.0,2450,3351,1526,1956,8524,15954,2188,4225,3526,3331,2630,1790,5090,4373,6370,5235,3033,5258,4583,2122,1116,1126,5413,1658,1801,5338,5910,428,525,1556,2495,3980,2385,3062,1044,932,2217,2223,1509,601,2510,2145,5345,464,3354,1394,3473,1429,835,900,2064,301,706,3103,862,3790,2955,1548,723,1293,1431,8412,457,0.131,0.109,0.125,0.127,0.124,0.132,0.119,0.13,0.143,0.134,0.096,0.147,0.135,0.143,0.13,0.128,0.131,0.104,0.113,0.148,0.099,0.132,0.139,0.132,0.144,0.134,0.124,0.117,0.122,0.129,0.142,0.123,0.105,0.13,0.096,0.096,0.139,0.141,0.111,0.11,0.107,0.158,0.128,0.147,0.116,0.091,0.117,0.098,0.085,0.082,0.143,0.1,0.105,0.107,0.128,0.1,0.069,0.109,0.133,0.121,0.117,0.103,0.128,0.112,0.117,0.117,0.12,0.115,0.153,0.09,0.115,0.107,0.103,0.117,371,1108,738,781,662,989,719,977,440,352,138.0,1100,911,339,534,2639,4959,354,1216,1824,832,963,756,1399,1303,1838,1949,1198,1682,1561,618,402,399,1093,379,562,1459,1599,189,191,740,1227,868,1474,1900,561,377,884,985,563,327,1164,914,2172,260,1606,490,1509,791,429,375,769,139,289,1393,469,2011,1380,767,320,628,635,3271,260,3.575,2.863,2.91,2.856,2.193,2.466,2.338,2.558,2.314,2.883,2.654,2.0,2.797,2.811,2.828,2.536,2.528,3.993,2.743,1.881,3.212,2.289,2.082,2.639,2.592,2.841,2.156,2.038,2.55,2.361,2.643,2.66,2.296,3.235,3.56,2.882,2.752,2.841,2.561,2.962,2.723,1.919,3.337,1.907,1.908,2.072,3.083,3.265,2.613,2.936,2.063,2.203,2.218,2.504,2.346,2.308,3.078,2.645,2.348,2.131,2.413,2.677,2.181,2.344,2.588,2.259,2.168,2.523,2.316,2.619,2.472,2.716,2.685,2.057,0.896,0.904,0.753,0.48,0.629,0.626,0.641,0.679,0.601,0.515,0.791,0.541,0.538,0.741,0.512,0.602,0.884,0.832,0.628,0.563,0.699,0.447,0.479,0.824,0.511,0.579,0.561,0.475,0.675,0.499,0.715,1.034,0.337,0.62,0.681,0.745,0.675,0.605,0.503,0.565,0.682,0.451,0.723,0.609,0.63,0.653,0.513,0.709,0.661,0.843,0.432,0.53,0.543,0.607,0.36,0.556,0.77,0.618,0.398,0.386,0.737,0.799,0.345,1.233,0.608,0.605,0.504,0.42,0.587,0.566,0.505,0.718,0.636,0.499
-35,Epileptic,F,32.0,0.7,2.5,0.9,1.3,1.4,1.3,1.7,1.4,1.2,1.0,0.5,3.0,1.7,0.7,1.6,5.7,9.9,1.0,1.6,3.1,1.3,2.4,2.4,2.6,5.0,4.5,2.9,2.8,2.1,2.8,1.9,1.2,0.9,3.2,0.3,1.3,2.7,2.5,0.4,0.4,1.2,2.8,2.3,1.8,2.7,0.7,0.4,1.2,1.4,0.7,0.6,2.0,1.5,2.3,0.5,2.1,0.6,1.8,0.6,0.9,0.5,1.4,0.4,0.4,2.0,1.2,2.9,1.2,0.9,0.4,0.9,1.3,5.3,0.4,6,23,7,12,14,15,13,14,12,12,4.0,32,20,7,17,65,96,8,20,34,15,26,21,32,54,44,35,24,21,34,22,9,9,32,1,7,27,33,3,3,7,19,27,16,16,5,2,7,7,5,6,14,14,15,3,17,4,13,6,6,4,10,2,3,14,18,22,8,5,4,5,8,50,2,0.035,0.023,0.014,0.024,0.036,0.031,0.027,0.023,0.04,0.039,0.046,0.035,0.032,0.04,0.038,0.035,0.03,0.036,0.026,0.031,0.027,0.032,0.042,0.033,0.051,0.029,0.024,0.031,0.024,0.031,0.054,0.045,0.031,0.036,0.011,0.029,0.031,0.029,0.029,0.024,0.022,0.04,0.043,0.024,0.021,0.015,0.018,0.016,0.016,0.019,0.027,0.023,0.024,0.016,0.017,0.015,0.016,0.017,0.019,0.018,0.02,0.027,0.023,0.02,0.021,0.021,0.022,0.017,0.014,0.012,0.026,0.022,0.022,0.016,1605,5191,2480,2600,1745,2586,3036,3156,1744,1736,485.0,3123,3991,860,2931,11218,20875,2236,4359,4971,4214,3534,1635,5927,4841,9175,6509,4466,5459,5271,2337,1052,1593,6424,1752,1767,5979,6996,538,828,1739,1971,5032,2137,3327,1372,789,2402,2649,1392,553,3079,2038,5031,690,4139,1137,3768,988,1525,705,2165,418,745,2997,1187,4337,2243,1902,768,1247,1857,9480,644,0.108,0.115,0.093,0.113,0.142,0.129,0.107,0.119,0.149,0.159,0.159,0.142,0.124,0.152,0.15,0.14,0.128,0.128,0.114,0.114,0.107,0.108,0.138,0.137,0.138,0.133,0.117,0.11,0.108,0.138,0.156,0.114,0.116,0.141,0.057,0.11,0.132,0.127,0.123,0.113,0.108,0.13,0.146,0.112,0.105,0.091,0.09,0.093,0.091,0.107,0.15,0.111,0.12,0.092,0.106,0.095,0.099,0.097,0.125,0.097,0.111,0.122,0.109,0.114,0.106,0.123,0.113,0.097,0.087,0.105,0.116,0.112,0.111,0.101,438,1832,945,981,680,795,998,1026,503,465,167.0,1300,1035,300,774,3185,5673,495,1153,1866,959,1011,881,1490,1583,2576,2034,1471,1423,1693,629,445,453,1550,476,662,1521,1651,252,295,817,975,1128,1214,1969,692,345,1033,1257,523,318,1444,999,2215,394,2115,575,1707,424,761,399,793,282,381,1472,851,2082,1042,864,452,596,874,4191,342,3.486,2.568,2.608,2.575,2.11,2.449,2.482,2.769,2.476,2.944,2.329,2.041,2.87,2.283,2.647,2.606,2.864,3.449,2.844,2.21,3.114,2.575,1.763,2.975,2.428,2.754,2.462,2.337,2.9,2.484,2.692,2.415,2.658,2.991,3.293,2.521,2.745,2.933,2.106,2.696,2.648,1.772,2.847,1.941,1.935,2.197,2.679,2.893,2.58,2.733,1.954,2.244,1.919,2.338,2.16,2.14,2.338,2.421,2.414,2.211,2.113,2.645,1.75,2.001,2.184,1.801,2.24,2.367,2.613,1.996,2.439,2.218,2.311,2.205,0.803,0.543,0.432,0.47,0.653,0.656,0.544,0.564,0.627,0.634,0.938,0.521,0.514,0.585,0.705,0.685,0.637,0.631,0.509,0.61,0.861,0.598,0.55,0.612,0.668,0.629,0.593,0.65,0.533,0.493,0.657,0.728,0.396,0.886,0.552,0.59,0.787,0.745,0.387,0.507,0.544,0.781,0.891,0.554,0.613,0.457,0.482,0.752,0.517,0.47,0.361,0.438,0.559,0.479,0.392,0.412,0.367,0.492,0.518,0.381,0.368,0.875,0.341,0.665,0.501,0.602,0.456,0.439,0.427,0.553,0.463,0.578,0.587,0.4
-36,Epileptic,M,32.0,0.8,2.3,0.7,1.6,1.2,1.9,1.8,1.7,0.8,1.2,0.3,2.7,1.6,0.6,1.0,6.6,9.7,1.5,3.0,5.5,0.9,3.3,1.8,2.6,3.7,2.7,2.4,2.7,2.6,2.6,1.2,1.5,0.5,2.4,0.4,0.7,3.8,4.0,0.1,0.1,1.1,3.1,3.4,2.9,2.9,1.0,0.5,0.7,1.2,0.7,0.5,1.6,1.4,2.6,0.3,2.6,0.7,1.4,1.6,1.0,0.7,1.8,0.3,0.5,2.0,1.5,2.5,1.4,1.1,0.7,0.8,0.9,5.5,0.3,7,23,8,13,12,17,14,16,8,11,4.0,22,15,7,10,66,83,11,30,42,12,37,20,28,44,34,29,24,22,32,16,9,5,30,3,6,44,42,2,1,7,25,29,21,16,8,2,5,7,8,5,14,10,16,2,25,5,10,13,10,6,16,3,4,15,19,21,12,6,5,6,9,44,3,0.036,0.019,0.012,0.027,0.027,0.031,0.025,0.024,0.032,0.036,0.022,0.034,0.025,0.029,0.026,0.03,0.028,0.042,0.032,0.041,0.02,0.03,0.033,0.027,0.031,0.026,0.026,0.026,0.025,0.029,0.037,0.041,0.019,0.029,0.013,0.016,0.034,0.032,0.01,0.017,0.02,0.037,0.043,0.022,0.021,0.019,0.019,0.012,0.015,0.019,0.034,0.017,0.019,0.018,0.015,0.02,0.014,0.014,0.026,0.02,0.016,0.025,0.017,0.017,0.024,0.025,0.015,0.017,0.017,0.019,0.02,0.017,0.02,0.011,1580,5330,2302,2548,1775,3028,2868,3827,1614,1763,991.0,2911,3857,1541,2657,14319,21514,2979,5454,5192,3182,5466,2690,6598,7262,6760,4565,4896,6461,4999,2489,1354,1222,7016,2591,2067,8458,9087,443,328,1756,3117,7449,3530,3216,1598,1034,2331,2422,1903,394,3317,2242,5231,704,3945,1920,3351,1710,1736,1445,2897,688,1091,2871,1553,4564,3298,2312,1336,1264,1732,10733,643,0.123,0.111,0.093,0.122,0.125,0.137,0.102,0.117,0.119,0.148,0.109,0.14,0.126,0.136,0.113,0.13,0.124,0.139,0.129,0.146,0.092,0.14,0.145,0.12,0.147,0.129,0.124,0.106,0.103,0.134,0.126,0.137,0.095,0.13,0.075,0.09,0.135,0.143,0.085,0.095,0.107,0.145,0.145,0.111,0.103,0.104,0.101,0.081,0.086,0.107,0.15,0.099,0.107,0.094,0.11,0.116,0.088,0.093,0.133,0.111,0.1,0.124,0.116,0.111,0.123,0.123,0.1,0.103,0.095,0.117,0.118,0.109,0.1,0.09,428,1888,956,979,758,1076,937,1204,472,531,249.0,1289,1018,327,697,3623,5697,574,1483,2206,713,1779,1000,1635,1932,1942,1534,1659,1619,1617,626,539,407,1465,584,706,2086,2136,241,165,831,1398,1473,1995,2019,837,418,934,1174,691,258,1551,1098,2169,326,2028,813,1488,969,866,669,1085,263,464,1300,926,2374,1328,1038,521,636,844,4416,352,3.452,2.525,2.375,2.595,2.274,2.436,2.569,2.769,2.763,2.893,2.894,2.028,2.884,3.115,2.878,2.92,3.055,3.615,3.027,2.071,3.288,2.474,2.445,3.077,2.862,2.824,2.328,2.294,2.958,2.519,3.001,2.565,2.365,3.23,3.681,2.707,3.056,3.148,2.198,2.343,2.72,2.019,3.369,2.115,1.794,2.096,2.977,2.984,2.571,2.787,1.822,2.325,2.29,2.611,2.676,2.247,2.647,2.633,2.09,2.062,2.317,2.626,2.734,2.709,2.309,1.984,2.111,2.743,2.676,2.839,2.338,2.466,2.584,2.379,0.669,0.687,0.563,0.472,0.488,0.48,0.636,0.514,0.606,0.588,0.887,0.526,0.485,0.505,0.537,0.634,0.57,0.915,0.631,0.7,0.762,0.44,0.468,0.608,0.566,0.505,0.449,0.609,0.591,0.458,0.643,0.928,0.363,0.798,0.706,0.586,0.672,0.559,0.223,0.348,0.535,0.646,0.833,0.681,0.522,0.372,0.401,0.702,0.437,0.714,0.41,0.548,0.517,0.432,0.411,0.405,0.497,0.668,0.428,0.44,0.394,0.594,0.603,0.879,0.575,0.529,0.387,0.38,0.449,0.62,0.413,0.491,0.477,0.323
-37,Epileptic,F,32.0,0.7,2.3,1.0,0.9,1.4,2.4,1.6,1.5,0.7,0.5,0.1,3.0,1.2,0.3,0.7,5.4,8.0,0.7,2.9,5.3,1.3,4.5,1.7,3.4,3.6,4.2,2.7,2.5,2.4,2.9,1.5,1.1,0.5,2.4,0.3,0.4,1.9,3.7,0.2,0.2,1.1,3.5,2.0,2.4,2.9,0.6,0.2,1.0,1.2,1.0,0.4,1.4,0.9,2.7,0.4,2.9,0.7,1.4,1.3,1.1,0.6,1.4,0.2,0.5,1.6,0.9,2.4,1.0,1.1,1.0,0.8,1.1,5.6,0.6,8,19,8,6,16,21,11,12,9,5,2.0,28,15,4,8,57,70,3,29,47,13,40,18,34,35,47,40,20,20,36,18,8,3,31,2,4,20,41,1,2,8,29,21,19,19,5,1,6,8,11,5,11,9,18,2,27,9,9,10,11,5,12,2,6,16,17,20,11,7,8,6,6,49,6,0.032,0.026,0.021,0.018,0.028,0.031,0.026,0.029,0.029,0.028,0.017,0.034,0.027,0.024,0.026,0.032,0.028,0.026,0.034,0.038,0.033,0.035,0.033,0.032,0.039,0.032,0.022,0.029,0.023,0.028,0.042,0.043,0.024,0.033,0.012,0.014,0.028,0.037,0.013,0.014,0.021,0.031,0.035,0.025,0.023,0.012,0.01,0.016,0.018,0.03,0.027,0.015,0.021,0.023,0.022,0.019,0.026,0.016,0.025,0.023,0.015,0.02,0.011,0.018,0.019,0.026,0.022,0.016,0.019,0.027,0.022,0.023,0.022,0.023,1196,4140,1948,1962,2003,2923,2420,2540,1323,1208,434.0,2550,2761,1002,2101,10062,16874,2007,4662,4954,3337,4391,2436,6330,4189,6769,6142,3075,5633,5763,2112,1432,793,5319,1594,1623,4869,7137,393,515,1554,2643,6464,2487,3022,1326,768,1931,2099,1762,460,2256,1630,4681,322,4434,1326,2761,1471,1255,1140,2912,294,888,2490,815,3500,2228,1473,1220,1201,1770,9213,714,0.128,0.122,0.115,0.094,0.123,0.133,0.108,0.124,0.141,0.128,0.088,0.148,0.128,0.108,0.106,0.135,0.126,0.101,0.132,0.15,0.115,0.119,0.115,0.125,0.114,0.129,0.112,0.11,0.092,0.138,0.168,0.114,0.093,0.129,0.061,0.082,0.129,0.144,0.094,0.101,0.109,0.129,0.144,0.119,0.11,0.091,0.069,0.095,0.104,0.137,0.137,0.097,0.11,0.109,0.123,0.109,0.134,0.096,0.12,0.123,0.097,0.114,0.103,0.117,0.117,0.127,0.112,0.104,0.102,0.135,0.119,0.114,0.103,0.147,394,1426,728,748,870,1192,874,841,423,308,139.0,1353,838,251,507,2845,4801,410,1403,2183,762,1656,823,1834,1346,2160,1999,1094,1424,1722,645,482,292,1305,464,595,1252,1702,185,238,819,1556,1107,1583,1976,666,347,925,982,578,308,1364,815,2000,183,2402,562,1346,830,749,597,1079,202,503,1306,535,1741,1047,820,506,596,753,4018,339,2.962,2.614,2.752,2.574,2.069,2.217,2.39,2.885,2.321,2.962,2.584,1.73,2.533,2.863,2.9,2.639,2.917,3.754,2.686,1.966,3.169,2.346,2.408,2.714,2.586,2.566,2.38,2.3,2.963,2.602,2.541,3.043,2.262,2.973,2.819,2.483,3.12,3.125,2.508,2.468,2.34,1.601,3.565,1.871,1.71,2.159,2.708,2.714,2.71,2.95,1.631,1.919,2.074,2.456,2.101,2.049,2.466,2.348,1.951,1.889,2.186,2.624,1.754,2.117,2.021,1.976,2.04,2.385,2.123,2.601,2.382,2.656,2.367,2.476,0.585,0.697,0.478,0.586,0.633,0.721,0.582,0.53,0.598,0.64,0.722,0.491,0.542,0.46,0.603,0.658,0.56,0.714,0.648,0.614,0.924,0.688,0.624,0.846,0.702,0.66,0.654,0.636,0.67,0.549,0.643,0.898,0.49,0.634,0.69,0.545,0.664,0.629,0.49,0.462,0.555,0.429,0.776,0.589,0.502,0.488,0.602,0.724,0.465,0.664,0.343,0.423,0.432,0.453,0.41,0.461,0.491,0.529,0.385,0.363,0.41,0.654,0.44,0.688,0.498,0.742,0.561,0.421,0.385,0.498,0.397,0.599,0.502,0.504
-38,Epileptic,M,32.0,1.0,2.7,1.1,0.8,3.2,2.7,2.1,1.6,1.8,0.7,0.4,3.3,1.5,0.5,1.5,8.0,9.2,1.4,2.4,5.0,1.1,3.1,2.5,6.7,7.6,4.8,4.2,2.1,4.3,3.2,2.5,2.1,0.6,2.2,0.2,1.3,8.3,4.4,0.2,0.4,1.7,4.3,4.8,3.7,2.5,1.0,0.5,0.7,1.2,1.8,0.7,1.8,1.3,3.6,0.8,1.8,1.3,2.1,0.9,1.1,0.4,2.1,0.5,0.5,2.3,1.9,1.3,1.5,2.2,0.6,1.3,1.4,4.6,0.4,7,26,14,8,25,20,21,18,13,6,4.0,29,22,6,17,86,92,10,30,46,9,34,26,62,65,36,45,24,38,37,18,12,4,30,2,11,61,46,1,3,9,28,41,30,18,9,2,4,7,10,5,13,10,24,5,17,9,15,7,7,4,18,4,4,19,20,8,11,12,6,10,10,38,2,0.044,0.025,0.019,0.014,0.047,0.034,0.04,0.026,0.054,0.034,0.038,0.038,0.029,0.031,0.032,0.037,0.031,0.052,0.036,0.045,0.02,0.039,0.037,0.058,0.051,0.037,0.043,0.035,0.035,0.029,0.061,0.062,0.032,0.03,0.011,0.025,0.055,0.037,0.013,0.02,0.027,0.04,0.061,0.038,0.021,0.024,0.023,0.012,0.017,0.033,0.027,0.019,0.017,0.022,0.033,0.022,0.021,0.022,0.02,0.019,0.02,0.03,0.029,0.017,0.024,0.032,0.019,0.017,0.028,0.018,0.024,0.017,0.018,0.019,1623,5468,2655,2542,1900,3194,2919,4576,1747,1578,768.0,2842,4516,1367,3537,14566,20104,2559,4984,5471,3322,4719,2521,5888,5451,6582,5037,3999,5477,6583,2178,1670,1045,6274,1759,2915,6411,7959,407,911,2042,2608,5112,2935,3307,2184,883,2333,2483,1753,703,3340,2346,6876,673,2816,2232,3761,1263,1794,959,3568,605,965,3302,1631,2111,4022,2353,872,1876,2733,9900,523,0.151,0.124,0.113,0.092,0.147,0.128,0.141,0.125,0.156,0.138,0.143,0.159,0.134,0.144,0.138,0.148,0.141,0.168,0.136,0.177,0.078,0.128,0.146,0.166,0.167,0.143,0.152,0.141,0.123,0.137,0.188,0.149,0.123,0.127,0.059,0.112,0.153,0.148,0.091,0.113,0.112,0.139,0.172,0.149,0.115,0.12,0.107,0.076,0.093,0.122,0.132,0.102,0.098,0.106,0.143,0.113,0.111,0.105,0.103,0.102,0.103,0.138,0.133,0.101,0.116,0.143,0.099,0.09,0.116,0.128,0.126,0.099,0.097,0.113,421,1759,983,905,992,1284,1165,1236,596,392,208.0,1555,1148,352,854,4136,5639,483,1409,2343,865,1535,1174,2055,2441,2121,1875,1193,2256,2077,802,612,303,1418,432,885,2499,2030,231,350,902,1619,1369,1748,1719,740,340,848,1125,876,389,1575,1224,2747,341,1377,936,1612,697,932,425,1255,308,512,1659,875,1092,1411,1285,460,873,1282,4063,257,3.643,2.906,2.607,2.595,1.801,2.195,2.043,3.017,2.312,3.401,3.215,1.671,2.918,2.785,2.953,2.597,2.76,3.762,2.88,2.037,2.756,2.406,1.929,2.277,1.992,2.614,2.267,2.606,2.14,2.577,2.186,2.659,2.668,3.14,3.451,2.87,2.204,2.91,2.061,2.484,2.674,1.588,2.769,1.979,2.209,2.464,3.016,3.176,2.68,2.485,2.211,2.23,2.108,2.444,2.496,2.245,2.75,2.681,2.068,2.144,2.485,2.847,1.999,2.171,2.168,2.068,2.2,2.799,1.968,1.904,2.482,2.541,2.56,2.754,0.879,0.671,0.49,0.66,0.604,0.656,0.6,0.571,0.855,0.522,0.932,0.46,0.548,0.414,0.649,0.76,0.663,0.716,0.639,0.725,0.711,0.677,0.502,0.835,0.618,0.762,0.525,0.53,0.629,0.578,0.701,0.998,0.454,0.688,0.651,0.484,0.852,0.89,0.363,0.539,0.512,0.437,0.964,0.783,0.452,0.713,0.489,0.879,0.399,0.823,0.395,0.439,0.518,0.545,0.452,0.404,0.582,0.511,0.34,0.416,0.341,0.754,0.529,0.77,0.533,0.797,0.394,0.501,0.536,0.61,0.391,0.536,0.51,0.507
-39,Epileptic,F,57.0,1.1,2.5,0.8,1.3,1.5,1.9,2.1,1.7,1.4,0.5,0.4,3.4,1.2,0.6,1.1,7.6,7.3,1.4,1.6,4.7,1.1,2.7,1.6,3.0,2.0,3.0,3.8,2.3,3.0,2.3,1.5,1.2,0.4,2.4,0.6,0.3,5.0,2.4,0.2,0.0,0.9,3.4,2.5,3.4,2.6,0.6,0.6,1.0,1.7,0.6,0.6,1.5,1.1,2.1,0.2,2.2,0.6,1.4,1.2,0.9,0.3,1.8,0.6,0.8,2.1,1.3,2.3,1.2,1.0,1.1,0.8,1.9,3.6,0.1,9,22,8,11,14,18,22,15,12,6,4.0,33,15,7,12,77,72,10,21,46,11,34,15,30,30,29,42,25,32,36,13,9,4,28,3,2,43,32,2,1,5,28,32,24,16,4,3,6,8,6,3,9,7,14,1,22,5,12,7,7,3,12,3,6,21,13,16,9,8,10,5,12,28,2,0.055,0.036,0.021,0.032,0.039,0.034,0.038,0.033,0.065,0.032,0.036,0.041,0.038,0.048,0.04,0.05,0.036,0.055,0.034,0.041,0.03,0.034,0.03,0.043,0.032,0.038,0.03,0.027,0.033,0.032,0.041,0.049,0.027,0.04,0.024,0.015,0.057,0.046,0.017,0.012,0.027,0.04,0.055,0.036,0.018,0.011,0.027,0.022,0.028,0.027,0.028,0.024,0.022,0.021,0.013,0.02,0.016,0.023,0.027,0.016,0.011,0.032,0.047,0.023,0.022,0.03,0.023,0.022,0.02,0.036,0.029,0.023,0.021,0.014,1067,3817,1585,2028,1618,2771,2582,2225,1710,1130,671.0,2930,2075,916,1732,8303,13572,1831,3794,4844,2357,4064,1784,3899,3810,4364,5129,3562,5221,3969,1531,923,845,4413,1720,1020,6245,4800,260,212,932,2729,4497,2541,3448,1394,739,1813,1699,1001,553,1641,1334,3436,369,3267,1147,2256,1093,1314,891,2138,398,845,3008,1044,2921,1815,1493,1164,1044,1964,6335,286,0.139,0.133,0.117,0.125,0.141,0.148,0.107,0.142,0.188,0.131,0.128,0.156,0.158,0.154,0.149,0.169,0.141,0.159,0.138,0.156,0.106,0.127,0.116,0.136,0.128,0.149,0.124,0.116,0.114,0.14,0.137,0.129,0.103,0.151,0.092,0.079,0.163,0.16,0.106,0.104,0.112,0.151,0.159,0.137,0.101,0.083,0.115,0.097,0.109,0.128,0.12,0.112,0.113,0.109,0.095,0.116,0.099,0.117,0.121,0.103,0.078,0.128,0.162,0.117,0.116,0.128,0.115,0.111,0.107,0.145,0.136,0.118,0.104,0.123,332,1319,599,728,705,989,963,838,435,299,201.0,1403,692,233,556,2588,3720,430,1004,2079,639,1293,860,1340,1191,1279,1992,1308,1512,1375,628,409,254,1082,473,380,1559,1202,166,106,524,1295,1105,1611,2126,755,330,774,889,386,330,887,769,1593,199,1725,631,1042,676,798,462,857,189,537,1587,565,1524,875,744,556,436,1102,2795,145,3.059,2.635,2.637,2.513,2.016,2.44,2.224,2.323,2.819,2.969,2.64,1.886,2.388,2.67,2.358,2.391,2.723,3.185,2.876,2.069,2.648,2.35,1.849,2.279,2.459,2.641,2.148,2.153,2.612,2.257,1.918,2.432,2.71,2.808,2.929,2.281,2.816,2.827,1.867,2.062,2.152,1.842,2.963,1.731,1.839,2.035,2.531,2.761,2.459,2.536,2.026,2.004,1.879,2.327,1.983,1.987,2.058,2.343,1.934,1.797,2.046,2.473,1.967,1.894,2.004,2.3,2.084,2.298,2.21,2.131,2.391,2.109,2.3,2.437,0.843,0.599,0.643,0.59,0.572,0.634,0.586,0.586,0.663,0.429,0.9,0.465,0.403,0.52,0.573,0.579,0.697,0.887,0.707,0.554,0.816,0.554,0.54,0.778,0.55,0.525,0.521,0.508,0.487,0.566,0.647,0.767,0.4,0.656,0.68,0.496,0.823,0.672,0.417,0.415,0.451,0.508,0.733,0.511,0.562,0.498,0.565,0.644,0.445,0.665,0.37,0.372,0.48,0.46,0.332,0.432,0.38,0.506,0.359,0.438,0.373,0.793,0.579,0.583,0.436,0.769,0.455,0.357,0.341,0.57,0.596,0.498,0.496,0.633
-40,Epileptic,F,42.0,1.1,2.2,1.0,1.3,1.5,2.6,1.7,1.4,1.9,0.6,0.3,2.4,1.6,0.4,1.5,4.7,8.8,1.2,2.2,4.3,1.2,2.1,1.2,4.2,2.6,3.0,2.4,2.1,2.0,2.5,1.6,1.3,0.6,1.9,0.3,0.7,1.9,3.1,0.2,0.2,1.0,3.1,2.1,2.9,2.9,0.5,0.2,1.0,1.4,0.8,0.7,1.5,0.9,2.5,0.1,1.7,0.8,1.8,0.9,0.8,0.6,2.0,0.3,0.3,1.4,1.4,2.3,1.2,0.5,0.8,0.6,0.8,4.0,0.3,9,21,13,11,12,27,15,14,19,7,4.0,24,16,7,20,60,81,10,26,39,11,26,14,42,33,36,30,18,19,31,20,10,6,23,2,8,23,32,2,2,7,30,24,26,16,4,2,6,8,7,3,13,7,19,1,17,8,12,10,6,4,16,2,5,13,19,21,7,5,16,5,6,37,2,0.039,0.025,0.018,0.025,0.036,0.033,0.021,0.023,0.059,0.029,0.027,0.031,0.026,0.023,0.026,0.028,0.027,0.034,0.024,0.039,0.02,0.027,0.024,0.04,0.028,0.027,0.023,0.023,0.02,0.025,0.035,0.064,0.019,0.022,0.01,0.015,0.026,0.03,0.012,0.02,0.02,0.034,0.032,0.028,0.025,0.012,0.012,0.015,0.016,0.017,0.032,0.018,0.015,0.018,0.016,0.015,0.02,0.019,0.018,0.015,0.023,0.032,0.018,0.014,0.017,0.021,0.022,0.017,0.011,0.021,0.018,0.014,0.017,0.017,1384,4192,2629,2353,1589,3827,3013,3054,1893,1212,700.0,2427,3760,915,3027,9897,18127,2646,4762,4255,4204,3710,1823,5555,4777,5894,4494,3343,4787,5079,2073,1104,1213,5275,1880,2138,4528,6194,372,523,1654,3161,5396,2571,3109,1352,789,2395,2610,1611,548,3263,1765,5329,278,3110,1362,3153,1213,1263,848,2602,428,754,2590,1341,3133,2304,1533,1271,1203,1849,9751,561,0.142,0.124,0.108,0.118,0.141,0.142,0.101,0.118,0.168,0.135,0.12,0.141,0.123,0.118,0.131,0.129,0.124,0.137,0.121,0.145,0.09,0.126,0.125,0.145,0.132,0.133,0.12,0.096,0.098,0.128,0.14,0.133,0.094,0.109,0.059,0.098,0.117,0.133,0.089,0.101,0.111,0.149,0.146,0.133,0.11,0.087,0.085,0.084,0.098,0.093,0.144,0.095,0.099,0.095,0.112,0.101,0.116,0.104,0.117,0.1,0.104,0.13,0.097,0.114,0.101,0.134,0.113,0.102,0.085,0.134,0.108,0.097,0.098,0.098,459,1438,933,885,713,1411,1051,971,573,328,224.0,1166,1061,266,926,2877,5262,529,1412,1905,993,1282,798,1676,1568,1854,1609,1239,1338,1614,676,396,397,1382,503,815,1244,1528,221,258,845,1485,1249,1699,1843,683,322,960,1210,665,296,1532,892,2311,139,1724,623,1485,731,726,476,1029,252,434,1301,890,1764,1028,739,615,573,868,3844,297,2.999,2.715,2.663,2.71,2.086,2.451,2.396,2.866,2.507,2.943,2.544,1.86,2.771,2.512,2.497,2.634,2.891,3.656,2.827,1.934,3.148,2.324,1.949,2.572,2.422,2.667,2.253,2.168,2.691,2.501,2.436,2.746,2.352,2.857,3.381,2.506,2.871,3.025,2.07,2.227,2.453,1.921,3.162,1.782,1.923,2.2,2.987,3.057,2.593,2.596,2.382,2.233,2.009,2.462,2.153,1.957,2.514,2.518,1.777,1.93,2.136,2.506,2.113,1.998,2.252,1.984,1.954,2.659,2.33,2.24,2.364,2.606,2.536,2.465,0.579,0.583,0.475,0.422,0.467,0.468,0.454,0.421,0.584,0.519,0.698,0.477,0.403,0.466,0.348,0.465,0.534,0.788,0.472,0.501,0.831,0.53,0.439,0.719,0.43,0.422,0.475,0.473,0.481,0.41,0.664,0.96,0.343,0.596,0.787,0.39,0.509,0.514,0.258,0.403,0.4,0.448,0.635,0.505,0.583,0.29,0.521,0.794,0.479,0.416,0.404,0.408,0.451,0.383,0.366,0.398,0.393,0.505,0.285,0.298,0.437,0.622,0.263,0.722,0.483,0.681,0.407,0.411,0.45,0.397,0.392,0.489,0.528,0.33
-41,Epileptic,M,17.5,0.5,1.8,1.1,1.3,2.0,2.3,2.0,1.4,1.2,0.5,0.4,3.6,1.4,0.7,1.3,7.2,10.4,0.6,3.0,5.4,1.9,3.5,2.0,3.6,3.3,4.5,3.3,3.1,3.7,3.4,1.9,0.6,0.6,3.2,0.3,0.8,3.1,4.4,0.2,0.2,1.0,3.4,2.1,3.4,2.6,0.7,0.4,0.8,1.2,0.5,0.7,1.5,1.2,2.9,0.4,2.1,0.5,2.8,1.8,1.2,0.7,1.3,0.2,0.9,2.0,1.1,2.4,2.2,1.4,0.4,0.5,1.1,4.1,0.4,4,14,15,11,21,19,17,15,19,6,3.0,26,13,10,14,84,90,5,26,33,18,29,19,48,37,36,25,28,29,27,18,5,3,31,2,6,34,45,1,3,5,24,24,20,14,4,2,6,6,5,4,10,10,20,2,17,3,21,13,8,4,8,1,7,14,11,17,12,8,3,3,10,29,3,0.027,0.02,0.017,0.026,0.041,0.031,0.032,0.024,0.048,0.036,0.03,0.04,0.03,0.035,0.032,0.034,0.035,0.023,0.038,0.04,0.029,0.035,0.032,0.041,0.033,0.035,0.035,0.031,0.028,0.041,0.045,0.037,0.043,0.032,0.012,0.02,0.036,0.033,0.015,0.019,0.019,0.04,0.039,0.034,0.018,0.014,0.019,0.014,0.015,0.013,0.035,0.015,0.024,0.019,0.022,0.019,0.015,0.029,0.03,0.021,0.018,0.024,0.016,0.044,0.023,0.024,0.023,0.027,0.019,0.018,0.018,0.018,0.016,0.017,1994,5320,3480,3016,2813,4193,3130,3795,2267,1313,956.0,3504,3743,1645,3167,15701,25177,2576,5385,3961,5958,5524,2760,7376,7419,8453,4992,5243,7887,6260,2779,1237,975,7516,2237,2563,6197,10769,375,774,1889,2662,5661,3033,4308,1446,813,2349,3047,1715,795,3819,2085,7427,558,3987,1679,3800,2160,2002,1391,2488,324,968,3614,1589,3640,2892,2865,1199,921,2695,11759,731,0.101,0.099,0.107,0.11,0.149,0.123,0.116,0.112,0.162,0.138,0.128,0.141,0.122,0.152,0.121,0.138,0.13,0.103,0.146,0.143,0.113,0.126,0.132,0.144,0.128,0.119,0.12,0.123,0.112,0.132,0.133,0.113,0.144,0.122,0.059,0.087,0.124,0.114,0.104,0.095,0.098,0.137,0.133,0.131,0.088,0.092,0.09,0.084,0.083,0.092,0.127,0.089,0.116,0.095,0.115,0.099,0.082,0.118,0.123,0.108,0.102,0.112,0.115,0.157,0.107,0.115,0.107,0.102,0.094,0.115,0.104,0.096,0.09,0.118,389,1407,1056,902,844,1325,1068,1001,530,304,217.0,1491,842,348,695,3480,5378,415,1300,2108,1073,1613,1157,1657,1786,2090,1640,1611,2036,1535,665,360,272,1521,498,788,1518,2029,195,242,773,1352,961,1685,2290,679,320,871,1154,629,379,1520,882,2663,254,1695,567,1715,964,923,559,799,136,340,1428,696,1665,1160,1127,333,431,1118,4158,331,4.222,3.116,2.973,3.157,2.379,2.653,2.466,3.365,3.2,3.236,3.598,2.062,3.335,3.102,3.247,3.153,3.524,4.183,3.175,1.698,3.61,2.659,2.141,2.901,3.045,3.177,2.395,2.472,2.991,2.895,2.75,3.211,2.784,3.372,3.478,3.011,2.87,3.374,2.283,3.0,3.386,1.829,3.579,2.139,2.172,2.418,3.03,3.459,3.16,2.975,2.432,2.703,2.422,2.851,2.752,2.462,3.411,2.455,2.446,2.402,3.027,2.808,2.976,2.705,2.764,2.531,2.513,2.966,2.988,3.166,2.689,2.695,3.06,3.149,0.775,0.854,0.684,0.547,0.801,0.578,0.687,0.672,0.725,0.546,0.689,0.68,0.502,0.517,0.512,0.663,0.65,0.769,0.714,0.517,0.811,0.623,0.565,0.937,0.592,0.639,0.536,0.787,0.616,0.738,1.03,0.935,0.627,0.648,0.906,0.714,0.825,0.803,0.332,0.569,0.573,0.482,0.847,0.744,0.621,0.545,0.504,0.779,0.704,0.64,0.547,0.616,0.474,0.598,0.338,0.592,0.756,0.775,0.597,0.559,0.528,0.826,0.535,0.947,0.724,0.79,0.616,0.572,0.556,0.99,0.575,0.611,0.697,0.553
-42,Epileptic,M,22.0,0.6,2.6,1.2,1.7,1.6,2.0,2.2,1.9,1.5,1.1,0.3,3.1,1.8,0.9,1.7,7.9,11.2,1.2,3.0,5.5,1.0,3.2,1.5,3.0,2.2,3.9,2.2,2.4,2.7,3.2,1.5,1.2,0.5,3.0,0.8,0.4,4.0,3.9,0.2,0.2,0.9,3.2,2.0,2.8,2.2,0.8,0.6,1.0,1.4,0.5,0.5,1.5,1.5,2.8,0.2,2.7,0.9,1.8,1.0,0.9,0.9,1.5,0.5,0.3,1.6,1.8,3.0,1.6,0.9,0.6,1.1,1.5,5.4,0.3,6,27,11,16,17,20,18,19,23,9,5.0,29,20,14,18,89,100,11,31,49,10,36,16,40,25,44,29,23,22,34,26,9,5,34,5,3,46,43,2,2,6,26,19,22,13,8,3,7,8,4,3,14,13,21,2,24,10,13,8,10,6,13,3,4,13,18,23,14,6,5,7,12,46,3,0.03,0.021,0.018,0.024,0.035,0.029,0.029,0.024,0.053,0.041,0.029,0.031,0.028,0.039,0.03,0.035,0.031,0.032,0.032,0.038,0.022,0.032,0.024,0.034,0.025,0.029,0.023,0.021,0.027,0.033,0.037,0.034,0.019,0.029,0.024,0.009,0.036,0.029,0.01,0.013,0.016,0.03,0.032,0.024,0.019,0.016,0.02,0.016,0.016,0.016,0.024,0.015,0.021,0.019,0.025,0.019,0.021,0.017,0.027,0.018,0.022,0.027,0.023,0.013,0.018,0.034,0.016,0.019,0.014,0.019,0.033,0.019,0.02,0.013,1548,5561,2608,3032,2176,3120,3392,3948,1829,1643,655.0,3358,3903,1694,3432,13246,20903,2672,5878,5165,3092,5652,2442,6140,4793,7493,5187,4600,5303,5249,2626,1524,1284,7098,1943,1974,9241,9794,435,517,1776,3412,6103,3042,2890,1570,1081,2427,2783,1399,638,3786,2355,5805,403,4154,1938,3770,1239,1578,1320,2451,687,832,2998,1313,5064,2977,1878,1060,1209,2415,11131,550,0.111,0.113,0.103,0.114,0.137,0.136,0.121,0.125,0.173,0.155,0.132,0.147,0.13,0.148,0.139,0.144,0.132,0.124,0.137,0.15,0.093,0.145,0.131,0.145,0.121,0.135,0.119,0.098,0.104,0.147,0.143,0.121,0.111,0.136,0.09,0.069,0.142,0.135,0.089,0.098,0.097,0.13,0.136,0.117,0.1,0.104,0.109,0.087,0.094,0.095,0.115,0.091,0.116,0.101,0.11,0.111,0.115,0.1,0.127,0.113,0.109,0.117,0.124,0.104,0.104,0.134,0.097,0.108,0.093,0.118,0.137,0.112,0.106,0.107,415,1990,1054,1246,805,1170,1205,1294,538,459,207.0,1524,1071,442,963,3986,6060,558,1483,2390,806,1734,1021,1580,1538,2315,1698,1671,1535,1656,720,559,406,1601,487,726,2152,2206,279,270,854,1496,1162,1832,1809,780,444,1010,1326,614,345,1710,1036,2394,139,2108,762,1657,668,830,638,874,361,437,1392,782,2593,1322,896,504,597,1122,4436,312,3.486,2.53,2.342,2.459,2.182,2.435,2.32,2.72,2.571,2.958,2.52,1.978,2.794,2.796,2.734,2.606,2.799,3.682,3.073,1.911,3.026,2.568,2.143,2.883,2.504,2.747,2.43,2.241,2.652,2.575,2.719,2.642,2.456,3.074,3.334,2.502,3.104,3.295,1.711,2.145,2.671,2.003,3.384,1.913,1.863,2.152,2.986,2.73,2.589,2.917,2.173,2.171,2.274,2.492,2.739,2.151,2.842,2.653,2.12,2.049,2.446,2.618,2.162,2.099,2.396,2.069,2.074,2.562,2.448,2.313,2.312,2.545,2.663,2.199,0.821,0.649,0.555,0.609,0.643,0.55,0.545,0.352,0.616,0.462,0.786,0.444,0.407,0.532,0.5,0.58,0.58,0.819,0.584,0.66,0.728,0.476,0.466,0.674,0.511,0.47,0.458,0.532,0.585,0.432,0.746,1.052,0.489,0.61,0.824,0.49,0.727,0.563,0.302,0.496,0.44,0.487,0.782,0.639,0.588,0.404,0.495,0.779,0.561,0.384,0.363,0.472,0.555,0.512,0.559,0.378,0.487,0.473,0.322,0.389,0.388,0.76,0.351,0.758,0.469,0.731,0.436,0.417,0.396,0.57,0.321,0.461,0.475,0.481
-43,Epileptic,F,47.0,1.2,2.2,0.7,1.9,1.7,2.8,1.9,1.7,1.9,0.6,0.2,2.5,1.8,0.5,1.1,6.9,11.1,1.2,2.7,4.8,0.5,2.1,1.5,3.8,3.6,3.7,2.6,3.0,2.7,3.1,1.0,0.8,0.6,3.6,0.3,1.1,5.0,4.0,0.1,0.1,1.1,2.4,2.2,1.8,2.4,0.9,0.4,1.1,1.5,0.7,0.9,1.7,1.3,3.6,0.7,1.8,0.7,3.4,1.0,1.0,0.7,1.5,0.3,0.3,2.0,0.6,2.8,1.5,1.3,0.6,1.1,2.4,5.2,0.4,8,19,6,23,14,22,14,17,17,6,2.0,23,19,5,13,67,97,8,23,37,4,24,14,35,44,42,29,31,22,33,11,5,7,36,2,12,53,40,1,1,7,22,23,14,15,6,2,7,7,6,6,13,9,31,5,17,6,16,10,9,5,10,2,3,14,5,21,9,8,6,9,16,42,4,0.044,0.025,0.011,0.033,0.035,0.035,0.03,0.032,0.053,0.035,0.02,0.036,0.037,0.028,0.038,0.039,0.036,0.04,0.036,0.041,0.012,0.031,0.026,0.044,0.03,0.033,0.028,0.031,0.024,0.034,0.031,0.038,0.023,0.036,0.016,0.024,0.044,0.035,0.013,0.019,0.025,0.031,0.038,0.025,0.018,0.021,0.02,0.016,0.019,0.024,0.031,0.024,0.023,0.028,0.015,0.019,0.02,0.029,0.023,0.018,0.031,0.031,0.025,0.015,0.027,0.028,0.021,0.024,0.018,0.022,0.026,0.026,0.023,0.021,1405,4116,1991,2406,2259,3271,2521,2883,1885,1090,465.0,2141,3292,863,2144,9120,17431,2213,3214,3234,2021,3127,1839,5316,6045,6156,4470,4197,5259,4954,1945,922,1237,6526,1364,2030,7204,6522,277,250,1335,2278,5231,1750,3118,1446,684,2278,2294,1217,693,2420,1940,5215,1118,2898,1204,2925,1210,1359,690,1869,309,678,2189,833,3660,1796,1895,883,1359,2526,8386,412,0.134,0.112,0.086,0.136,0.137,0.147,0.113,0.136,0.161,0.145,0.097,0.149,0.141,0.118,0.131,0.141,0.134,0.129,0.141,0.148,0.067,0.135,0.124,0.142,0.133,0.131,0.122,0.124,0.102,0.14,0.13,0.103,0.119,0.146,0.082,0.113,0.146,0.132,0.079,0.12,0.121,0.135,0.13,0.117,0.096,0.105,0.105,0.087,0.1,0.107,0.142,0.117,0.106,0.124,0.106,0.113,0.106,0.124,0.131,0.108,0.123,0.135,0.127,0.106,0.126,0.102,0.114,0.108,0.102,0.138,0.129,0.128,0.108,0.134,432,1505,853,986,865,1170,966,929,593,314,165.0,1046,967,265,599,2910,5255,472,1179,1770,617,1137,838,1466,1926,2069,1600,1565,1637,1582,527,363,403,1649,360,745,2020,1853,192,104,710,1203,1050,1212,2053,665,324,1075,1101,555,421,1096,989,2189,599,1545,583,1628,641,827,373,749,171,316,1128,398,1964,947,1001,387,668,1303,3789,240,3.049,2.534,2.338,2.442,2.292,2.38,2.176,2.795,2.354,3.013,2.628,1.898,2.758,2.303,2.573,2.359,2.616,3.66,2.296,1.691,2.573,2.257,1.903,2.77,2.416,2.474,2.248,2.135,2.467,2.455,2.788,2.613,2.578,3.02,3.219,2.35,2.745,2.656,1.664,2.81,2.404,1.71,3.267,1.627,1.717,2.552,2.602,2.519,2.629,2.592,1.955,2.155,2.049,2.234,2.357,2.093,2.483,2.154,2.002,1.857,2.357,2.546,1.93,2.448,2.151,2.301,2.068,2.202,2.115,2.574,2.313,2.314,2.36,1.883,0.75,0.636,0.535,0.524,0.604,0.492,0.582,0.524,0.59,0.508,0.776,0.505,0.507,0.704,0.608,0.59,0.619,0.751,0.647,0.53,0.648,0.528,0.457,0.74,0.462,0.623,0.476,0.502,0.561,0.504,0.654,0.989,0.437,0.624,0.72,0.499,0.701,0.627,0.405,0.522,0.569,0.457,0.789,0.526,0.508,0.666,0.514,0.537,0.515,0.657,0.526,0.554,0.634,0.575,0.513,0.416,0.454,0.599,0.572,0.337,0.478,0.742,0.396,1.139,0.517,0.661,0.47,0.452,0.577,0.922,0.45,0.539,0.515,0.311
-44,Epileptic,F,37.0,0.9,2.1,0.6,0.9,1.1,1.2,1.3,1.3,1.2,1.0,0.4,3.1,1.5,0.8,1.5,5.2,6.5,0.7,2.6,4.1,1.2,2.2,1.7,4.0,2.6,3.2,3.8,2.3,1.8,2.9,1.8,1.4,0.4,2.3,0.2,0.8,3.3,2.3,0.2,0.1,1.3,2.4,2.0,2.3,1.8,0.5,0.3,1.4,1.2,0.6,0.8,2.1,0.7,1.8,0.1,2.4,0.4,1.7,0.9,1.2,0.5,1.4,0.4,0.6,1.7,2.4,3.6,1.6,0.5,0.5,1.1,1.3,4.4,0.2,6,23,8,8,12,14,10,14,10,11,4.0,26,18,9,16,58,67,6,35,38,13,27,18,37,31,36,53,26,20,42,23,10,7,28,1,9,39,28,1,1,10,24,22,21,11,4,2,9,6,5,7,19,5,11,1,22,4,16,9,9,5,12,2,6,13,26,28,11,3,3,7,10,41,3,0.036,0.024,0.012,0.016,0.027,0.022,0.023,0.025,0.049,0.038,0.029,0.036,0.024,0.035,0.029,0.031,0.025,0.024,0.031,0.036,0.024,0.025,0.029,0.037,0.031,0.027,0.028,0.024,0.021,0.025,0.032,0.044,0.028,0.027,0.01,0.016,0.036,0.025,0.017,0.015,0.023,0.035,0.029,0.025,0.015,0.01,0.016,0.021,0.016,0.019,0.035,0.022,0.018,0.017,0.018,0.015,0.012,0.019,0.018,0.02,0.013,0.025,0.025,0.015,0.018,0.031,0.019,0.021,0.012,0.016,0.02,0.019,0.019,0.017,1339,4054,1716,1996,1844,3239,2456,2520,1483,1822,663.0,3194,3995,1485,3192,9585,15420,1908,4684,4260,3780,3636,2125,6303,4830,6605,7157,4028,4688,6246,3090,1471,997,5655,1648,2030,6634,5564,447,450,1566,2935,5956,2508,3418,1461,780,2350,2221,1150,709,3622,1068,3457,263,4114,1189,3648,1391,1473,1076,2220,392,1204,2810,1729,5301,2259,1187,989,1722,2262,8385,296,0.117,0.124,0.101,0.099,0.126,0.108,0.102,0.128,0.162,0.141,0.124,0.143,0.116,0.149,0.135,0.133,0.124,0.121,0.136,0.148,0.097,0.11,0.124,0.131,0.119,0.127,0.107,0.094,0.101,0.134,0.138,0.132,0.133,0.135,0.061,0.093,0.148,0.127,0.09,0.087,0.123,0.128,0.135,0.122,0.085,0.075,0.087,0.091,0.093,0.12,0.153,0.112,0.111,0.095,0.12,0.097,0.082,0.11,0.106,0.109,0.092,0.124,0.119,0.102,0.109,0.129,0.104,0.113,0.084,0.092,0.112,0.109,0.105,0.139,468,1478,762,812,719,977,809,898,442,507,244.0,1328,1098,357,872,2963,4331,443,1355,1840,934,1209,850,1923,1368,1924,2188,1526,1322,2007,992,545,276,1378,456,829,1688,1572,260,176,838,1107,1274,1519,1919,790,333,1005,1066,504,372,1652,559,1604,77,2282,607,1468,785,833,560,881,232,624,1365,1133,2660,1069,569,479,845,976,3666,165,2.986,2.666,2.387,2.309,2.26,2.547,2.432,2.553,2.647,2.782,2.587,1.963,2.818,3.037,2.786,2.503,2.786,3.543,2.727,2.021,2.877,2.401,2.128,2.617,2.636,2.733,2.49,2.132,2.66,2.402,2.525,2.711,2.782,2.996,3.115,2.316,2.966,2.773,2.037,2.659,2.363,2.098,3.401,1.918,1.934,1.976,2.916,3.027,2.559,2.674,2.144,2.228,2.002,2.313,3.096,1.98,2.258,2.556,1.882,1.935,2.033,2.587,1.95,2.228,2.159,1.883,2.072,2.408,2.283,2.467,2.262,2.482,2.396,2.261,0.765,0.459,0.435,0.489,0.637,0.699,0.62,0.481,0.54,0.552,0.735,0.606,0.409,0.364,0.544,0.586,0.558,0.629,0.636,0.693,0.819,0.558,0.563,0.743,0.577,0.492,0.63,0.578,0.548,0.637,0.541,0.934,0.359,0.545,0.458,0.449,0.621,0.566,0.41,0.395,0.469,0.803,0.667,0.738,0.618,0.484,0.322,0.791,0.425,0.426,0.514,0.426,0.564,0.521,0.509,0.386,0.329,0.543,0.454,0.351,0.373,0.602,0.41,0.722,0.605,0.817,0.492,0.384,0.511,0.373,0.455,0.543,0.451,0.618
-45,Epileptic,F,37.0,0.7,2.4,1.1,1.4,1.9,1.5,2.4,1.4,1.6,0.8,0.2,3.4,2.4,0.9,1.3,7.2,8.7,1.1,1.9,4.2,1.2,2.7,1.9,4.6,2.8,4.4,3.8,2.8,2.4,3.3,1.2,1.0,0.5,2.8,0.4,0.6,3.6,4.2,0.4,0.6,1.2,3.2,2.5,2.7,3.6,0.7,0.5,0.9,1.4,1.5,0.6,2.4,2.6,2.7,0.3,3.1,0.7,2.0,1.5,1.4,0.8,1.2,0.5,0.5,1.8,1.6,3.5,1.4,1.0,0.8,0.9,0.9,4.3,0.2,5,21,12,11,16,13,15,12,16,6,3.0,31,19,6,12,60,69,6,17,38,11,26,16,39,27,32,36,24,19,38,14,10,4,21,2,4,30,42,2,4,7,28,22,21,22,5,2,6,7,10,3,14,20,17,3,22,5,16,8,11,6,7,3,4,16,16,26,9,6,5,6,5,33,2,0.035,0.028,0.022,0.029,0.041,0.027,0.039,0.031,0.052,0.036,0.024,0.04,0.036,0.047,0.034,0.042,0.039,0.045,0.034,0.039,0.02,0.035,0.036,0.047,0.032,0.041,0.032,0.031,0.029,0.029,0.038,0.037,0.037,0.039,0.015,0.018,0.038,0.04,0.027,0.031,0.025,0.035,0.038,0.025,0.028,0.02,0.034,0.017,0.022,0.026,0.029,0.022,0.039,0.026,0.019,0.022,0.024,0.026,0.029,0.026,0.028,0.02,0.036,0.02,0.021,0.032,0.027,0.02,0.022,0.021,0.021,0.022,0.021,0.012,1151,4283,2044,2003,2578,2601,2725,2472,1855,1347,567.0,3231,3968,1046,2388,9279,14539,2156,3265,5067,3370,4183,2163,6002,4689,5833,5860,3625,4315,5637,2068,933,724,4968,1664,1501,5613,6852,537,684,1530,3175,5313,3193,3524,1213,581,1942,2251,2253,593,3586,3273,4092,502,4407,1052,3056,1536,1556,802,2576,492,783,2869,1318,4550,2200,1438,1613,1501,1623,7610,550,0.123,0.118,0.115,0.119,0.142,0.127,0.136,0.126,0.165,0.136,0.13,0.148,0.129,0.157,0.123,0.135,0.136,0.137,0.135,0.145,0.089,0.136,0.135,0.142,0.138,0.147,0.131,0.117,0.109,0.137,0.127,0.115,0.135,0.131,0.082,0.093,0.13,0.149,0.135,0.131,0.114,0.14,0.139,0.117,0.116,0.104,0.123,0.095,0.103,0.11,0.125,0.099,0.132,0.11,0.123,0.111,0.118,0.121,0.124,0.132,0.142,0.102,0.14,0.123,0.103,0.14,0.119,0.105,0.108,0.109,0.105,0.103,0.1,0.086,372,1424,848,799,859,887,982,767,501,346,183.0,1383,1161,308,731,2887,3773,389,977,1880,1002,1303,903,1703,1424,1834,2008,1498,1416,1862,543,418,251,1191,422,558,1520,1762,193,306,760,1459,1189,1797,1981,613,261,814,1002,918,347,1805,1199,1715,266,2217,501,1287,797,795,409,865,249,383,1381,789,2095,1073,674,597,697,711,3332,250,3.098,2.532,2.254,2.396,2.369,2.508,2.372,2.885,2.912,3.155,2.524,2.1,2.641,2.745,2.52,2.441,2.88,3.727,2.721,2.319,2.762,2.511,2.097,2.63,2.568,2.618,2.349,2.068,2.449,2.494,2.647,2.19,2.381,2.958,3.475,2.523,2.802,2.849,2.625,2.501,2.519,2.009,3.24,2.006,1.972,2.253,2.951,2.833,2.783,2.553,2.228,2.097,2.462,2.404,2.354,2.238,2.443,2.557,2.27,2.175,2.179,2.798,2.338,2.351,2.17,2.079,2.224,2.461,2.283,2.647,2.52,2.566,2.467,2.537,0.707,0.809,0.719,0.592,0.637,0.454,0.548,0.728,0.807,0.555,0.804,0.489,0.507,0.467,0.516,0.625,0.685,0.85,0.525,0.596,0.839,0.446,0.357,0.72,0.502,0.527,0.403,0.546,0.559,0.562,0.801,0.935,0.397,0.739,0.756,0.566,0.707,0.683,0.576,0.406,0.616,0.475,0.821,0.567,0.656,0.541,0.512,0.857,0.568,0.672,0.405,0.508,0.705,0.541,0.531,0.443,0.419,0.623,0.465,0.402,0.345,0.747,0.576,1.081,0.542,0.595,0.503,0.45,0.43,0.884,0.611,0.655,0.506,0.548
-46,Epileptic,F,32.0,0.8,2.1,1.1,0.8,1.2,2.8,2.4,1.2,0.9,0.8,0.4,3.4,1.1,0.5,1.0,5.3,8.1,1.6,2.6,4.2,1.3,4.5,3.2,2.8,3.8,2.3,4.7,1.8,2.7,3.3,2.0,0.6,0.5,2.7,0.4,0.8,4.5,3.3,0.1,0.2,0.9,6.8,3.2,2.9,2.4,0.6,0.4,0.7,1.1,1.1,0.7,1.2,1.1,2.2,0.3,2.3,0.8,1.9,1.4,0.8,0.5,1.1,0.2,0.6,1.5,1.0,2.1,1.2,1.0,0.5,0.9,1.5,4.1,0.3,5,22,11,9,12,29,21,13,14,9,7.0,37,12,6,11,68,98,9,28,40,11,39,23,25,48,26,42,24,23,39,19,5,4,30,2,8,40,40,1,1,5,43,28,19,14,4,1,4,6,8,4,10,7,18,2,20,5,12,10,6,3,9,1,3,12,16,16,10,7,3,6,10,31,3,0.03,0.024,0.019,0.021,0.03,0.036,0.043,0.023,0.045,0.03,0.049,0.043,0.03,0.026,0.02,0.032,0.034,0.046,0.03,0.039,0.027,0.048,0.05,0.036,0.037,0.028,0.038,0.028,0.029,0.038,0.052,0.045,0.021,0.031,0.017,0.024,0.041,0.034,0.011,0.014,0.018,0.053,0.055,0.027,0.018,0.014,0.018,0.01,0.018,0.02,0.026,0.014,0.019,0.017,0.016,0.019,0.022,0.02,0.026,0.014,0.015,0.022,0.017,0.027,0.019,0.02,0.02,0.016,0.017,0.016,0.02,0.022,0.016,0.025,1733,4700,2390,2312,1936,3790,2842,2900,2145,1595,476.0,4094,2701,1288,3029,11754,19616,2690,5795,5680,3674,3745,1984,5830,6313,5802,6045,4371,7265,5677,2456,763,1130,7368,1993,2095,6036,8401,427,424,1539,2680,5672,3399,3906,1210,804,2668,2128,1961,667,2999,1702,5257,538,3634,1572,3533,1662,1623,874,2177,344,1081,2605,1063,3159,2445,2037,796,1555,2429,9014,393,0.115,0.119,0.119,0.106,0.133,0.142,0.123,0.116,0.14,0.154,0.166,0.152,0.138,0.141,0.11,0.139,0.142,0.144,0.135,0.149,0.113,0.134,0.135,0.141,0.129,0.129,0.121,0.112,0.111,0.141,0.167,0.112,0.098,0.134,0.074,0.101,0.143,0.149,0.08,0.092,0.097,0.171,0.157,0.113,0.098,0.091,0.094,0.067,0.095,0.113,0.125,0.085,0.105,0.096,0.108,0.108,0.106,0.1,0.115,0.084,0.095,0.121,0.092,0.117,0.109,0.111,0.111,0.102,0.096,0.114,0.106,0.112,0.095,0.146,447,1488,843,751,699,1437,1028,862,522,449,191.0,1593,701,277,734,3142,4821,517,1414,2044,818,1468,1020,1404,1968,1558,2166,1391,1733,1707,707,317,402,1514,451,714,1755,1862,182,211,749,1739,1224,1747,1955,591,298,980,959,797,378,1361,885,2261,250,1836,647,1567,914,848,435,791,193,361,1230,735,1567,1117,928,364,740,1081,3801,207,3.579,2.938,2.594,2.755,2.242,2.283,2.291,2.863,2.946,2.792,2.251,2.193,2.922,3.155,3.043,2.778,3.009,3.748,3.083,2.295,3.191,2.099,1.869,3.131,2.524,2.856,2.358,2.432,3.108,2.537,2.571,2.438,2.457,3.315,3.558,2.719,2.722,3.19,2.547,2.147,2.598,1.556,3.225,2.242,2.249,2.205,3.295,3.235,2.892,2.809,2.16,2.226,2.13,2.34,2.442,2.19,2.593,2.628,2.051,2.149,2.234,2.676,1.972,3.473,2.384,1.75,2.256,2.458,2.431,2.551,2.316,2.603,2.518,2.333,0.95,0.675,0.755,0.599,0.762,0.625,0.617,0.44,0.753,0.713,0.913,0.544,0.481,0.47,0.512,0.598,0.715,0.864,0.542,0.685,0.808,0.647,0.554,0.747,0.636,0.588,0.542,0.596,0.507,0.572,0.795,0.777,0.457,0.755,0.662,0.399,0.91,0.6,0.466,0.394,0.441,0.502,1.108,0.593,0.652,0.463,0.602,0.665,0.424,0.593,0.328,0.437,0.474,0.529,0.634,0.343,0.433,0.514,0.387,0.35,0.359,0.706,0.561,0.548,0.441,0.465,0.449,0.383,0.446,0.897,0.435,0.649,0.523,0.536
-47,Epileptic,M,67.5,0.4,2.3,0.7,1.0,1.6,2.4,1.8,1.5,0.9,0.6,0.1,2.8,1.3,0.5,1.4,6.9,8.4,0.7,2.5,4.5,1.9,3.5,1.4,3.6,2.8,2.4,3.9,1.9,2.2,3.1,1.4,0.7,0.4,3.2,0.3,0.6,3.2,3.6,0.2,0.1,0.7,3.5,2.3,1.8,3.7,0.4,0.6,0.7,0.9,0.8,0.5,1.5,0.9,2.4,0.3,2.3,0.5,3.0,1.1,1.2,0.8,2.7,0.4,0.6,2.5,0.3,2.8,1.1,1.3,0.5,1.2,1.4,5.5,0.3,3,18,10,8,15,21,10,12,9,5,2.0,25,13,5,13,58,71,6,22,39,14,31,14,33,32,24,44,17,16,38,16,7,4,28,3,5,38,35,2,0,4,27,19,14,23,4,3,4,5,8,5,12,6,14,2,16,4,22,7,9,6,19,3,4,19,3,23,8,8,5,6,10,40,3,0.019,0.024,0.019,0.025,0.036,0.033,0.029,0.031,0.035,0.028,0.02,0.038,0.027,0.032,0.027,0.037,0.032,0.025,0.031,0.034,0.025,0.037,0.024,0.037,0.029,0.03,0.03,0.024,0.024,0.031,0.046,0.024,0.03,0.035,0.016,0.017,0.036,0.04,0.019,0.008,0.015,0.035,0.047,0.025,0.035,0.015,0.022,0.011,0.011,0.021,0.031,0.019,0.018,0.024,0.02,0.015,0.014,0.039,0.028,0.023,0.029,0.044,0.029,0.048,0.028,0.016,0.024,0.02,0.029,0.024,0.032,0.022,0.022,0.02,1431,5438,1614,1514,2548,3141,2359,2534,1936,902,354.0,2596,2668,1000,2403,10219,15856,2122,3099,3719,4463,4708,2236,6328,4650,4661,6196,2633,3963,5880,2642,1003,772,5248,1649,1684,7177,6171,312,276,1341,3060,5160,1783,2355,1062,997,2028,2092,1671,338,3247,1782,4506,348,4065,1212,2489,1311,1522,919,2460,643,857,2984,545,3570,2004,1297,858,1444,2123,9846,409,0.08,0.102,0.114,0.116,0.136,0.134,0.112,0.128,0.134,0.111,0.087,0.138,0.121,0.132,0.132,0.135,0.126,0.099,0.129,0.129,0.09,0.143,0.117,0.135,0.118,0.131,0.125,0.108,0.104,0.137,0.119,0.086,0.123,0.124,0.067,0.082,0.134,0.141,0.101,0.057,0.091,0.137,0.146,0.102,0.13,0.093,0.111,0.077,0.076,0.107,0.155,0.101,0.097,0.103,0.111,0.09,0.098,0.145,0.134,0.115,0.14,0.142,0.125,0.109,0.113,0.086,0.117,0.105,0.131,0.121,0.134,0.123,0.107,0.142,385,1554,674,664,771,1201,883,757,489,297,156.0,1133,732,247,795,2945,4105,450,1327,2012,1169,1508,885,1562,1556,1392,2101,1221,1237,1748,626,559,249,1179,477,596,1563,1590,182,127,663,1447,956,1251,1746,510,408,901,1053,673,276,1360,837,1695,201,2233,576,1302,605,770,448,984,267,392,1390,313,1798,888,739,411,642,954,3937,195,3.332,3.105,2.698,2.465,2.876,2.335,2.242,2.734,3.002,2.652,2.276,1.993,2.745,2.943,2.357,2.715,3.093,3.752,2.053,1.719,2.672,2.6,2.191,3.026,2.337,2.697,2.313,1.858,2.582,2.646,2.801,1.88,2.576,3.109,2.618,2.556,3.065,2.975,1.979,2.239,2.321,1.982,3.728,1.594,1.568,2.204,2.894,2.638,2.321,2.315,1.537,2.429,2.659,2.774,2.201,2.059,2.491,2.049,2.656,2.241,2.54,2.71,2.831,2.272,2.335,2.085,2.305,2.622,2.237,2.423,2.641,2.757,2.683,2.59,1.034,0.895,0.716,0.49,0.722,0.815,0.517,0.618,0.632,0.599,0.839,0.56,0.498,0.367,0.51,0.662,0.744,0.876,0.654,0.508,0.857,0.529,0.487,0.695,0.579,0.671,0.629,0.483,0.714,0.714,0.929,0.819,0.424,0.678,1.032,0.761,0.809,0.68,0.639,0.508,0.834,0.564,0.919,0.416,0.587,0.473,0.69,0.946,0.611,0.941,0.323,0.588,0.561,0.572,0.473,0.511,0.548,0.828,0.74,0.49,0.54,0.835,0.545,0.758,0.803,0.874,0.786,0.495,0.418,0.557,0.597,0.532,0.573,0.731
-48,Epileptic,M,47.0,0.4,1.6,0.8,1.4,1.4,2.0,1.4,1.7,1.0,0.9,0.1,3.3,1.2,1.1,1.3,7.8,8.9,1.0,2.2,4.1,0.8,2.3,1.0,4.1,3.0,4.2,2.8,2.4,3.3,3.5,1.7,0.9,0.7,3.0,0.2,0.8,3.9,3.5,0.3,0.2,1.2,2.4,2.5,2.2,2.8,0.6,0.6,0.9,1.2,0.5,1.1,2.2,1.4,2.6,0.4,2.6,1.0,3.1,1.0,0.8,0.7,2.2,0.4,0.6,1.5,0.7,2.9,1.3,1.0,0.7,0.9,1.9,5.9,0.5,4,18,7,12,16,18,11,14,8,7,1.0,28,12,11,13,69,82,8,26,30,9,27,9,36,32,44,32,24,24,41,19,8,5,29,1,7,38,41,4,1,7,19,29,22,17,5,2,6,6,6,4,19,9,18,2,19,9,23,8,7,5,15,3,4,15,9,23,8,6,12,7,15,43,3,0.022,0.021,0.014,0.029,0.032,0.032,0.024,0.03,0.042,0.039,0.011,0.039,0.029,0.039,0.03,0.038,0.029,0.029,0.036,0.044,0.017,0.032,0.026,0.037,0.03,0.031,0.027,0.026,0.027,0.03,0.048,0.031,0.031,0.036,0.007,0.014,0.038,0.035,0.026,0.014,0.024,0.034,0.04,0.029,0.019,0.013,0.02,0.013,0.014,0.016,0.036,0.023,0.026,0.022,0.02,0.023,0.032,0.036,0.03,0.02,0.034,0.037,0.026,0.022,0.021,0.017,0.022,0.021,0.018,0.024,0.025,0.027,0.023,0.023,1444,3562,1861,1924,2227,2879,1911,3329,1460,1117,436.0,2320,2968,1483,2538,10684,17406,2240,3994,2949,2294,3316,1553,6536,4817,7348,4890,3327,5661,5328,2525,1252,846,5527,1510,2235,7738,6911,489,506,1560,1994,5147,1946,3260,1357,974,2298,2462,1455,662,3143,1779,4266,411,3227,1564,2496,825,956,688,2703,543,975,1914,792,3795,2239,1865,1295,1431,2386,8679,581,0.088,0.108,0.093,0.123,0.135,0.132,0.099,0.129,0.141,0.142,0.058,0.164,0.12,0.154,0.123,0.141,0.121,0.114,0.134,0.144,0.088,0.14,0.12,0.147,0.135,0.138,0.12,0.109,0.097,0.14,0.145,0.101,0.141,0.139,0.046,0.081,0.149,0.139,0.127,0.083,0.109,0.14,0.145,0.131,0.101,0.093,0.106,0.079,0.089,0.112,0.199,0.112,0.115,0.111,0.109,0.113,0.135,0.123,0.134,0.123,0.138,0.135,0.116,0.119,0.119,0.094,0.109,0.101,0.098,0.137,0.13,0.129,0.116,0.114,420,1349,792,821,838,1043,871,995,419,361,188.0,1290,752,425,796,3380,5169,482,1098,1558,820,1171,687,1753,1711,2336,1879,1418,1675,1983,621,496,322,1367,476,804,1700,1828,239,249,754,1072,1235,1275,2182,746,415,1039,1167,564,639,1565,943,1887,263,1862,609,1401,543,619,364,966,320,435,1136,589,2152,1024,896,575,687,981,3874,311,3.287,2.571,2.185,2.43,2.326,2.41,1.899,2.984,2.77,2.767,2.204,1.753,2.951,2.633,2.531,2.474,2.761,3.772,2.753,1.715,2.313,2.275,1.965,2.809,2.32,2.721,2.136,1.915,2.597,2.301,2.831,2.279,2.155,2.934,2.887,2.633,3.117,2.783,2.232,2.292,2.424,1.695,2.959,1.702,1.689,1.95,2.935,2.662,2.53,2.687,1.36,2.128,2.223,2.3,1.982,1.897,2.721,1.946,1.798,1.76,2.082,2.71,1.883,2.58,1.906,1.654,1.906,2.429,2.213,2.323,2.451,2.624,2.348,2.43,0.84,0.51,0.572,0.478,0.632,0.812,0.493,0.495,0.579,0.394,0.803,0.471,0.4,0.619,0.687,0.594,0.672,0.563,0.781,0.642,0.897,0.527,0.489,0.741,0.521,0.599,0.525,0.534,0.574,0.582,0.686,1.045,0.37,0.565,0.392,0.544,0.834,0.594,0.488,0.447,0.772,0.516,0.856,0.785,0.585,0.529,0.428,0.798,0.581,0.358,0.351,0.467,0.624,0.651,0.299,0.505,0.6,0.716,0.37,0.379,0.486,0.877,0.311,0.883,0.398,0.582,0.456,0.496,0.441,0.507,0.448,0.783,0.558,0.401
-49,Epileptic,F,47.0,1.0,2.3,1.0,1.4,1.6,2.0,2.0,1.6,1.5,1.3,0.5,3.8,2.4,0.5,1.3,5.3,9.5,0.8,2.6,4.9,1.4,3.1,1.5,3.5,4.3,3.5,2.8,2.8,2.7,3.8,1.2,0.8,0.7,2.6,0.8,1.2,6.2,3.8,0.3,0.4,1.9,3.5,1.7,3.2,3.0,0.6,0.2,1.6,1.5,0.9,0.6,2.4,1.2,2.5,0.7,3.0,0.7,2.3,1.5,0.7,1.3,1.6,0.4,0.4,2.6,1.8,3.4,1.6,0.6,0.6,1.6,1.5,7.8,0.3,9,22,12,12,11,18,15,15,15,11,5.0,29,21,5,14,60,83,6,27,41,10,35,16,37,53,35,33,30,22,41,14,11,4,30,4,10,68,43,3,2,8,29,18,26,16,4,1,11,8,8,6,17,9,15,5,27,6,16,12,7,12,14,3,3,22,21,25,10,4,6,11,11,68,3,0.031,0.019,0.015,0.022,0.03,0.028,0.027,0.025,0.043,0.033,0.04,0.036,0.033,0.027,0.022,0.027,0.027,0.028,0.031,0.033,0.024,0.03,0.023,0.031,0.031,0.025,0.024,0.024,0.023,0.027,0.03,0.028,0.034,0.024,0.016,0.021,0.037,0.025,0.017,0.023,0.036,0.034,0.022,0.026,0.018,0.013,0.015,0.021,0.016,0.018,0.028,0.019,0.02,0.021,0.019,0.021,0.015,0.021,0.025,0.013,0.023,0.026,0.017,0.014,0.023,0.023,0.02,0.021,0.014,0.015,0.025,0.017,0.02,0.024,1833,4976,2511,2623,1884,3403,3260,3047,1877,1987,558.0,2995,4627,1130,3579,12092,18810,2186,4727,4877,2812,4956,2285,6550,7605,7256,5662,4983,6181,6426,1980,1671,747,6513,2570,2118,9572,9693,496,662,1261,3159,4755,2393,4084,1431,777,2753,2752,1731,638,4210,2082,4625,770,3978,1744,3143,1776,1268,2530,2284,403,968,3379,2391,4584,2348,1313,1126,1976,2928,14383,286,0.128,0.104,0.094,0.103,0.133,0.134,0.105,0.128,0.163,0.138,0.164,0.142,0.149,0.125,0.114,0.127,0.127,0.118,0.136,0.136,0.095,0.135,0.12,0.132,0.145,0.122,0.122,0.11,0.098,0.136,0.115,0.106,0.131,0.111,0.071,0.115,0.146,0.115,0.098,0.093,0.13,0.149,0.115,0.121,0.097,0.084,0.081,0.103,0.095,0.106,0.143,0.097,0.104,0.101,0.127,0.122,0.098,0.097,0.121,0.091,0.118,0.117,0.121,0.093,0.115,0.118,0.109,0.111,0.092,0.108,0.118,0.098,0.105,0.138,552,1870,1033,1079,778,1218,1082,1047,583,600,202.0,1589,1274,327,999,3455,5512,491,1355,2432,889,1761,1031,1872,2488,2369,2024,1827,1664,2295,661,648,303,1919,739,926,2907,2622,297,303,793,1617,1288,1819,2346,761,293,1147,1374,750,372,1952,1059,2034,443,2131,803,1512,945,783,1066,1033,264,534,1774,1321,2598,1140,616,606,1059,1463,6309,191,3.134,2.535,2.425,2.46,2.309,2.367,2.511,2.634,2.53,2.707,2.547,1.776,2.961,2.702,2.751,2.725,2.773,3.427,2.82,1.809,2.758,2.313,1.899,2.654,2.425,2.51,2.265,2.204,2.83,2.292,2.322,2.556,2.142,2.637,3.227,2.125,2.663,2.829,1.961,2.506,2.019,1.797,2.965,1.518,1.988,2.063,3.158,2.922,2.498,2.515,2.092,2.216,2.205,2.484,2.115,1.993,2.481,2.433,1.905,1.838,2.29,2.183,1.814,1.992,2.109,2.172,1.942,2.312,2.378,2.174,2.166,2.398,2.365,1.805,0.746,0.424,0.356,0.43,0.476,0.458,0.583,0.502,0.497,0.408,0.532,0.468,0.449,0.406,0.451,0.523,0.571,0.667,0.524,0.562,0.621,0.419,0.492,0.568,0.504,0.504,0.473,0.475,0.52,0.472,0.546,0.81,0.527,0.688,0.634,0.366,0.608,0.526,0.46,0.451,0.489,0.399,0.729,0.496,0.643,0.421,0.474,0.912,0.514,0.475,0.475,0.5,0.459,0.476,0.359,0.39,0.405,0.412,0.438,0.304,0.376,0.586,0.359,0.615,0.535,0.557,0.404,0.397,0.438,0.54,0.421,0.411,0.446,0.534
-50,Epileptic,F,52.0,0.6,1.3,0.9,0.9,1.4,1.5,1.5,1.2,0.7,0.2,0.1,2.3,1.1,0.5,1.1,4.1,6.9,0.5,1.8,3.8,0.5,1.9,0.9,2.4,2.3,2.5,2.4,2.2,1.6,2.8,1.0,0.6,0.5,1.8,0.2,0.3,1.9,1.9,0.2,0.1,0.8,2.0,1.9,2.4,1.9,0.5,0.4,0.6,0.9,0.4,0.6,1.6,1.1,1.8,0.3,1.8,0.6,1.6,0.9,0.8,0.3,0.9,0.5,0.5,1.7,0.8,1.9,0.7,0.8,0.3,0.7,0.7,2.5,0.2,5,15,8,8,15,14,10,11,8,3,1.0,17,12,6,9,47,60,3,21,34,4,19,10,22,29,25,30,20,14,30,15,6,6,23,1,2,23,25,2,1,5,17,22,18,11,4,2,4,5,4,5,13,7,16,2,14,5,11,8,6,4,6,3,4,13,7,12,4,4,3,4,6,16,2,0.038,0.022,0.019,0.024,0.037,0.036,0.026,0.029,0.036,0.031,0.016,0.042,0.03,0.035,0.041,0.034,0.029,0.025,0.03,0.038,0.012,0.033,0.026,0.039,0.036,0.028,0.027,0.031,0.023,0.03,0.04,0.049,0.025,0.025,0.012,0.011,0.037,0.031,0.029,0.02,0.019,0.037,0.044,0.038,0.019,0.015,0.023,0.014,0.014,0.017,0.035,0.025,0.025,0.02,0.018,0.017,0.026,0.021,0.029,0.023,0.023,0.022,0.039,0.035,0.021,0.027,0.025,0.019,0.015,0.024,0.026,0.018,0.017,0.015,1228,3599,2068,2002,1760,2204,1837,2262,1356,721,368.0,1247,2199,691,1679,7087,12031,1497,3647,3599,2008,2535,1298,4135,3877,4834,4252,2966,3776,4218,1591,894,1028,4596,1430,1403,4056,4535,261,226,1386,1680,3664,1716,2524,1037,708,1895,2064,1127,512,2172,1349,4277,448,3017,1091,2617,817,1008,536,1555,568,662,2153,744,2492,1272,1338,577,945,1722,5677,336,0.133,0.112,0.114,0.114,0.154,0.147,0.105,0.118,0.148,0.113,0.07,0.153,0.129,0.142,0.144,0.143,0.128,0.096,0.135,0.151,0.069,0.149,0.128,0.134,0.158,0.133,0.13,0.128,0.102,0.134,0.134,0.128,0.132,0.127,0.069,0.065,0.133,0.131,0.153,0.107,0.108,0.149,0.149,0.142,0.101,0.098,0.113,0.081,0.086,0.106,0.145,0.125,0.119,0.098,0.115,0.102,0.125,0.107,0.142,0.112,0.123,0.119,0.147,0.15,0.113,0.121,0.115,0.1,0.092,0.123,0.125,0.098,0.092,0.105,350,1083,684,697,617,745,779,746,351,182,117.0,789,636,189,471,2081,3668,293,956,1700,591,884,576,1076,1114,1452,1564,1048,1128,1543,476,290,347,1071,343,500,886,1031,115,109,640,841,783,1071,1588,557,306,816,946,343,314,966,707,1651,258,1642,369,1278,479,552,246,607,211,296,1181,368,1226,603,743,266,440,707,2373,189,3.253,2.952,2.744,2.908,2.504,2.568,2.0,2.715,2.825,3.225,2.731,1.492,2.741,2.652,2.736,2.552,2.627,3.48,3.11,1.871,2.656,2.358,1.973,2.843,2.733,2.772,2.184,2.227,2.556,2.289,2.554,2.942,2.643,2.976,3.349,2.505,3.168,2.991,2.623,2.323,2.543,1.895,3.055,1.885,1.806,2.033,2.887,2.907,2.623,2.883,1.871,2.188,2.123,2.43,2.224,2.041,3.071,2.431,1.834,1.979,2.333,2.617,2.563,2.411,2.048,2.535,2.164,2.547,2.206,2.417,2.611,2.934,2.556,2.352,0.815,0.788,0.762,0.571,0.653,0.472,0.511,0.542,0.489,0.405,0.731,0.382,0.434,0.413,0.482,0.528,0.576,0.858,0.65,0.674,0.682,0.385,0.407,0.623,0.511,0.573,0.453,0.514,0.483,0.504,0.621,1.014,0.568,0.686,0.708,0.442,0.803,0.625,0.383,0.309,0.701,0.52,0.805,0.75,0.484,0.579,0.487,0.852,0.506,0.916,0.418,0.437,0.48,0.547,0.29,0.425,0.803,0.558,0.335,0.387,0.33,0.603,0.477,0.817,0.565,0.631,0.463,0.381,0.367,0.545,0.547,0.816,0.512,0.46
-51,Epileptic,M,32.0,1.0,2.0,0.9,1.3,1.3,2.5,1.1,1.6,1.4,0.7,0.5,2.2,1.5,1.1,1.2,5.5,8.6,1.1,2.7,4.1,1.5,3.6,1.3,2.9,3.0,3.9,3.0,2.6,2.9,3.1,1.0,0.7,0.7,3.0,0.7,0.7,4.1,3.6,0.2,0.2,1.2,4.5,2.2,3.0,3.4,0.7,0.8,1.0,1.4,0.5,0.5,2.2,1.5,2.7,0.5,2.3,1.0,2.4,2.2,1.2,0.9,1.5,0.3,0.4,1.7,1.2,3.4,1.5,1.4,0.7,1.4,1.5,5.0,0.4,6,14,8,12,10,17,9,16,14,7,4.0,19,12,13,14,55,65,10,27,33,13,30,10,35,31,34,29,25,21,28,12,6,6,29,3,6,37,36,2,1,7,32,16,23,21,5,4,7,7,4,5,17,10,16,3,19,8,21,15,8,7,10,2,4,15,13,23,10,11,7,9,12,38,4,0.036,0.021,0.017,0.024,0.027,0.042,0.018,0.023,0.04,0.029,0.034,0.032,0.025,0.052,0.021,0.029,0.027,0.028,0.03,0.032,0.027,0.028,0.026,0.033,0.025,0.024,0.026,0.022,0.023,0.026,0.03,0.02,0.022,0.027,0.02,0.017,0.037,0.028,0.012,0.014,0.021,0.04,0.038,0.029,0.02,0.017,0.021,0.017,0.018,0.015,0.035,0.019,0.02,0.018,0.017,0.017,0.018,0.02,0.037,0.022,0.028,0.025,0.026,0.023,0.022,0.025,0.022,0.017,0.019,0.023,0.022,0.026,0.017,0.019,1640,4092,2259,2257,1580,2142,2297,3485,1714,1469,661.0,2268,3055,1458,2852,10888,18423,2939,4568,3272,3336,4456,2042,5042,5966,7887,4865,5419,6142,5406,1711,1139,1203,7529,2394,2200,7294,7630,418,457,1981,3445,5840,2399,4088,1172,1176,2673,2341,1392,427,3856,1786,5501,740,4033,1799,3221,1433,1561,1215,2312,471,704,2332,1321,4697,2887,2197,1339,1761,2579,10124,491,0.12,0.106,0.096,0.109,0.12,0.144,0.088,0.112,0.132,0.111,0.12,0.132,0.112,0.158,0.123,0.121,0.113,0.104,0.125,0.127,0.097,0.12,0.123,0.123,0.112,0.112,0.115,0.096,0.089,0.117,0.11,0.077,0.105,0.117,0.081,0.086,0.138,0.119,0.087,0.091,0.107,0.14,0.134,0.12,0.106,0.101,0.113,0.09,0.097,0.093,0.146,0.101,0.102,0.095,0.118,0.103,0.107,0.107,0.132,0.109,0.113,0.113,0.14,0.125,0.117,0.111,0.103,0.101,0.106,0.12,0.106,0.113,0.097,0.112,473,1378,878,948,803,969,783,1103,624,370,234.0,1101,887,365,884,3192,5044,624,1315,1976,909,1789,802,1479,1870,2484,1859,1782,1804,1819,541,577,458,1561,568,729,1835,1961,284,255,813,1675,1027,1511,2513,612,548,1082,1166,599,284,1879,1118,2351,396,2082,887,1822,987,816,599,924,199,408,1294,774,2305,1321,1159,509,954,1189,4567,320,3.281,2.649,2.528,2.435,1.871,2.272,2.422,2.773,2.258,3.198,2.611,1.86,2.616,2.811,2.604,2.534,2.943,3.552,2.784,1.606,2.977,2.134,2.199,2.584,2.466,2.698,2.171,2.381,2.709,2.436,2.422,1.97,2.193,3.32,3.588,2.735,2.935,2.818,1.585,2.118,2.957,1.901,3.745,1.75,1.825,2.145,2.806,3.041,2.401,2.66,1.739,2.086,1.8,2.474,2.405,2.174,2.456,2.028,1.632,2.094,2.233,2.605,2.485,2.037,2.048,1.984,2.177,2.435,2.167,2.691,2.024,2.501,2.445,1.77,0.886,0.872,0.619,0.543,0.47,0.596,0.614,0.513,0.425,0.527,0.66,0.481,0.447,0.539,0.439,0.582,0.627,0.838,0.764,0.431,0.836,0.554,0.497,0.735,0.551,0.719,0.513,0.493,0.593,0.525,0.63,0.825,0.439,0.687,0.724,0.466,0.674,0.588,0.338,0.471,0.773,0.545,0.787,0.572,0.534,0.518,0.628,0.871,0.568,0.518,0.422,0.52,0.575,0.604,0.469,0.621,0.582,0.57,0.326,0.418,0.588,0.668,0.774,0.896,0.613,0.734,0.536,0.4,0.413,0.728,0.441,0.526,0.52,0.451
-53,Epileptic,M,42.0,1.2,2.3,1.0,1.2,1.8,2.5,1.6,1.6,1.7,1.2,0.5,2.5,2.0,0.7,1.4,6.1,7.6,0.7,2.3,4.7,2.2,3.9,2.1,3.2,8.8,4.7,3.9,3.2,3.0,3.8,1.5,2.5,0.6,2.2,0.8,0.5,4.9,4.2,0.3,0.3,0.8,3.8,2.1,2.4,1.9,0.6,0.4,0.8,1.7,0.5,0.6,2.4,1.6,1.7,0.3,2.8,1.0,2.0,1.8,1.3,0.6,1.4,0.4,0.6,1.8,1.4,3.1,1.1,0.8,1.2,1.3,2.4,6.8,0.4,9,20,7,7,12,24,15,17,12,10,4.0,22,22,7,15,73,69,7,23,38,18,40,20,37,68,52,37,29,26,34,19,22,6,26,6,5,43,42,2,2,4,30,22,16,12,4,2,5,8,4,6,17,11,11,2,19,7,14,14,12,7,12,3,4,12,13,26,7,4,9,9,16,46,3,0.041,0.028,0.018,0.024,0.031,0.037,0.028,0.027,0.049,0.043,0.036,0.03,0.031,0.034,0.03,0.035,0.031,0.032,0.034,0.042,0.034,0.035,0.031,0.038,0.054,0.039,0.031,0.033,0.03,0.034,0.048,0.071,0.038,0.04,0.035,0.014,0.043,0.037,0.015,0.021,0.019,0.038,0.043,0.027,0.014,0.012,0.018,0.013,0.021,0.015,0.029,0.025,0.022,0.019,0.021,0.017,0.025,0.023,0.026,0.014,0.024,0.027,0.02,0.023,0.021,0.025,0.02,0.017,0.016,0.032,0.024,0.026,0.023,0.024,1699,4623,2327,1954,2761,3388,3058,3135,2398,2131,887.0,2836,4499,1482,2951,12563,17243,2227,4376,4513,5177,5049,2272,6451,6082,7506,6068,4322,5921,6071,3373,1373,997,5468,1638,1568,8129,8559,497,414,1217,3009,6065,2275,3530,1331,935,2298,2633,1559,624,3481,2288,3221,536,4129,1891,2948,1755,2097,1014,2624,599,1051,2385,1420,4524,2238,1571,2356,1895,3251,10541,557,0.136,0.124,0.102,0.112,0.135,0.118,0.107,0.133,0.169,0.155,0.147,0.131,0.134,0.139,0.133,0.137,0.131,0.122,0.129,0.158,0.129,0.112,0.124,0.146,0.152,0.132,0.105,0.103,0.101,0.138,0.16,0.179,0.118,0.149,0.121,0.081,0.156,0.148,0.099,0.114,0.096,0.146,0.154,0.126,0.086,0.086,0.103,0.081,0.103,0.088,0.143,0.115,0.115,0.097,0.107,0.096,0.124,0.118,0.125,0.096,0.118,0.124,0.11,0.119,0.108,0.108,0.109,0.093,0.091,0.131,0.123,0.124,0.112,0.125,422,1411,838,743,950,1226,923,1023,576,496,205.0,1338,1208,356,767,3368,4325,453,1321,1979,1099,1818,1088,1670,2344,2149,2078,1530,1455,1812,692,597,275,1201,413,595,1974,2027,270,207,628,1542,1035,1426,2046,709,339,889,1125,605,344,1550,1094,1425,227,2380,686,1395,1031,1335,560,939,337,423,1258,866,2300,952,727,664,906,1386,4584,267,3.642,2.999,2.919,2.681,2.501,2.393,2.566,2.673,3.069,3.196,2.928,1.847,2.873,2.941,2.831,2.772,3.094,3.837,2.646,1.964,3.117,2.165,1.855,2.89,2.205,2.838,2.326,2.265,3.011,2.539,3.293,2.615,2.722,3.075,3.178,2.358,3.023,3.054,2.104,2.318,2.345,1.765,3.451,1.828,1.966,2.161,3.496,3.373,2.788,2.795,1.937,2.356,2.211,2.517,2.284,1.941,2.789,2.422,1.927,1.765,2.176,2.733,1.89,2.616,2.035,2.024,2.093,2.579,2.591,3.357,2.304,2.552,2.465,2.706,0.861,0.694,0.607,0.577,0.853,0.707,0.704,0.532,0.595,0.633,0.917,0.535,0.528,0.465,0.621,0.653,0.627,0.824,0.657,0.607,0.862,0.648,0.622,0.7,0.719,0.61,0.566,0.614,0.493,0.668,0.759,0.816,0.429,0.774,0.789,0.528,0.76,0.71,0.511,0.476,0.536,0.589,1.052,0.688,0.635,0.519,0.659,0.851,0.705,0.448,0.584,0.482,0.553,0.533,0.764,0.466,0.517,0.559,0.528,0.478,0.572,0.793,0.466,0.738,0.713,0.634,0.535,0.463,0.507,0.907,0.634,0.523,0.616,0.369
-54,Epileptic,M,52.0,1.0,2.2,1.0,1.3,1.8,2.4,1.7,2.2,1.6,0.4,0.3,3.3,2.0,0.5,1.8,7.5,10.1,1.1,2.5,6.1,0.9,3.2,1.9,2.8,2.8,3.2,3.7,2.3,2.9,3.3,1.0,1.8,0.7,2.7,0.5,0.7,2.7,2.9,0.3,0.2,1.2,2.9,2.3,4.0,3.3,0.6,0.7,1.1,1.6,0.6,0.5,2.3,1.6,3.2,0.4,2.0,0.9,2.3,1.2,0.9,0.5,1.5,0.3,0.3,3.1,0.7,2.3,2.0,1.0,0.8,0.8,1.1,4.2,0.3,6,18,7,7,16,22,14,18,14,4,3.0,31,18,5,17,64,88,8,24,50,6,37,18,26,34,33,40,22,20,37,12,13,7,26,4,6,30,29,2,2,8,32,25,30,19,4,3,8,9,6,4,16,11,21,4,18,8,17,11,8,4,10,2,2,24,4,22,18,6,9,6,9,32,4,0.038,0.024,0.015,0.022,0.043,0.029,0.023,0.028,0.043,0.026,0.026,0.031,0.026,0.027,0.031,0.035,0.027,0.031,0.03,0.039,0.015,0.032,0.027,0.034,0.029,0.025,0.025,0.021,0.023,0.03,0.035,0.064,0.017,0.028,0.018,0.015,0.033,0.031,0.016,0.015,0.022,0.029,0.041,0.028,0.02,0.013,0.029,0.015,0.019,0.013,0.019,0.023,0.026,0.022,0.016,0.016,0.026,0.023,0.026,0.019,0.016,0.024,0.017,0.016,0.025,0.022,0.017,0.02,0.016,0.025,0.019,0.018,0.019,0.012,1345,3852,1690,2113,1995,3761,2864,3846,2104,957,933.0,3283,3932,998,3054,10129,18670,2530,4391,5179,3008,4798,2522,5636,4834,6301,6245,4190,5464,5415,1959,1449,1529,5527,1806,2076,5965,6180,439,502,1856,3648,6511,3903,4023,1302,739,2607,2626,1728,606,3175,2096,5401,629,3507,1158,3216,1443,1437,974,2244,304,942,3697,968,3883,3286,1546,1177,1524,1766,8311,699,0.132,0.103,0.094,0.102,0.129,0.136,0.09,0.125,0.142,0.113,0.11,0.139,0.119,0.121,0.133,0.127,0.115,0.13,0.126,0.144,0.073,0.148,0.127,0.121,0.133,0.115,0.119,0.103,0.095,0.141,0.126,0.142,0.091,0.123,0.08,0.089,0.129,0.126,0.093,0.098,0.107,0.136,0.142,0.123,0.101,0.084,0.119,0.09,0.102,0.092,0.108,0.111,0.123,0.102,0.122,0.101,0.121,0.113,0.122,0.103,0.103,0.119,0.106,0.098,0.124,0.082,0.095,0.108,0.095,0.119,0.102,0.112,0.101,0.091,455,1565,893,840,765,1404,1045,1353,634,295,256.0,1645,1321,300,930,3312,6080,553,1398,2533,842,1657,1106,1488,1611,2167,2365,1536,1804,1771,498,535,581,1544,502,760,1572,1591,276,274,902,1700,1104,2333,2556,722,376,1135,1278,699,380,1687,1026,2435,357,1886,611,1576,874,794,497,866,227,333,1963,413,2274,1583,866,537,728,943,3635,444,2.921,2.375,2.14,2.618,2.179,2.444,2.301,2.653,2.43,2.854,2.868,1.829,2.516,2.425,2.582,2.391,2.582,3.513,2.603,1.827,2.707,2.376,2.047,2.833,2.502,2.446,2.206,2.159,2.429,2.565,2.717,2.729,2.143,2.59,3.096,2.569,2.825,2.855,1.913,2.089,2.618,1.95,3.642,1.94,1.788,2.047,2.442,2.838,2.496,2.617,1.864,1.975,2.218,2.32,2.177,2.051,2.385,2.252,1.961,2.041,2.33,2.479,1.558,2.928,2.006,2.536,1.839,2.151,2.103,2.316,2.293,2.42,2.461,1.895,0.744,0.557,0.497,0.438,0.701,0.487,0.61,0.668,0.608,0.377,0.688,0.485,0.51,0.431,0.55,0.574,0.636,0.799,0.592,0.511,0.764,0.439,0.507,0.764,0.51,0.503,0.42,0.528,0.518,0.509,0.843,1.002,0.413,0.634,0.988,0.596,0.674,0.574,0.311,0.4,0.431,0.421,0.804,0.551,0.504,0.484,0.62,0.7,0.548,0.457,0.301,0.457,0.532,0.57,0.341,0.413,0.511,0.528,0.379,0.371,0.359,0.853,0.365,1.11,0.442,0.751,0.419,0.413,0.421,0.624,0.48,0.482,0.45,0.33
-55,Epileptic,M,37.0,1.8,2.4,0.7,0.9,1.5,2.8,1.2,1.8,1.9,0.7,0.2,2.9,1.2,0.7,1.2,6.2,7.6,0.9,2.3,6.0,1.5,3.3,2.1,3.0,2.6,3.6,3.9,1.9,4.0,3.5,0.9,0.8,0.8,2.5,0.8,0.5,4.2,3.1,0.2,0.2,1.0,3.3,2.9,3.5,2.7,0.5,0.4,1.0,1.2,0.9,0.5,2.5,1.6,2.0,0.3,2.2,0.8,1.4,1.6,1.2,0.5,1.6,0.4,0.4,1.9,0.9,2.6,2.0,0.9,0.5,1.5,1.7,3.6,0.4,10,20,13,9,13,26,10,17,15,6,3.0,25,12,8,11,59,62,7,25,42,10,31,14,28,27,35,38,19,30,36,11,88,8,23,3,3,37,30,2,2,6,28,26,26,17,4,2,6,6,8,2,19,12,12,2,17,6,14,10,11,6,9,2,3,14,8,19,16,5,3,10,11,29,4,0.063,0.025,0.013,0.018,0.029,0.034,0.02,0.028,0.049,0.034,0.023,0.034,0.025,0.034,0.036,0.034,0.029,0.031,0.038,0.043,0.025,0.034,0.033,0.037,0.029,0.027,0.032,0.021,0.029,0.033,0.039,0.047,0.026,0.031,0.039,0.014,0.04,0.033,0.013,0.015,0.021,0.035,0.048,0.029,0.019,0.009,0.023,0.014,0.017,0.019,0.025,0.028,0.026,0.021,0.025,0.016,0.029,0.02,0.032,0.02,0.02,0.03,0.021,0.03,0.025,0.041,0.016,0.021,0.019,0.02,0.029,0.02,0.014,0.019,1580,4680,1822,1988,2257,4002,2389,3294,2040,1333,604.0,2814,3023,1414,2223,10554,15608,2446,3527,4208,2656,5049,2385,5452,4634,7343,5836,3487,6572,6137,2376,910,1469,6726,1721,1795,6631,6122,355,359,1633,2932,5448,2654,3432,1413,620,2722,2356,1839,470,3342,2312,3971,468,3622,1082,2396,1454,1701,814,2109,590,727,2437,605,4579,3331,1555,864,1683,2201,8944,484,0.152,0.112,0.098,0.096,0.125,0.144,0.086,0.124,0.153,0.137,0.107,0.139,0.118,0.137,0.137,0.139,0.121,0.115,0.148,0.143,0.105,0.132,0.12,0.138,0.128,0.127,0.12,0.093,0.105,0.14,0.126,0.139,0.119,0.124,0.116,0.074,0.132,0.127,0.098,0.103,0.106,0.145,0.142,0.127,0.098,0.077,0.116,0.081,0.094,0.11,0.114,0.125,0.116,0.101,0.121,0.097,0.124,0.104,0.132,0.117,0.109,0.117,0.107,0.128,0.116,0.15,0.09,0.107,0.095,0.124,0.133,0.115,0.088,0.111,414,1574,896,787,837,1278,841,1140,645,363,202.0,1365,830,358,606,3034,4244,478,1124,2271,906,1617,938,1455,1440,2095,1983,1372,1853,1850,533,394,459,1303,365,619,1787,1538,223,222,761,1483,1244,1870,2176,800,282,1070,1065,795,275,1455,1160,1490,202,2018,521,1306,778,913,469,859,272,255,1218,340,2459,1509,714,367,805,1171,3922,308,3.48,2.735,2.155,2.557,2.307,2.585,2.308,2.659,2.475,2.892,2.627,1.842,2.772,2.805,2.688,2.662,2.915,3.862,2.606,1.736,2.545,2.5,2.186,2.822,2.549,2.885,2.288,2.063,2.74,2.557,3.11,2.324,2.558,3.349,3.807,2.592,2.831,2.958,1.834,1.884,2.635,1.882,2.941,1.629,1.766,1.902,2.861,2.909,2.649,2.548,2.083,2.186,2.011,2.564,2.724,1.99,2.322,2.077,2.145,2.031,1.948,2.359,2.082,3.04,2.105,2.099,2.011,2.405,2.497,2.641,2.378,2.395,2.375,1.93,0.768,0.704,0.441,0.621,0.651,0.634,0.71,0.53,0.584,0.401,0.66,0.549,0.485,0.348,0.573,0.64,0.633,0.784,0.71,0.547,0.753,0.571,0.528,0.65,0.524,0.662,0.549,0.523,0.629,0.55,0.702,0.993,0.472,0.705,0.869,0.471,0.753,0.681,0.321,0.38,0.767,0.514,0.966,0.477,0.52,0.495,0.668,0.809,0.569,0.571,0.464,0.554,0.533,0.722,0.688,0.399,0.565,0.589,0.447,0.49,0.484,0.693,0.993,1.096,0.537,0.57,0.417,0.505,0.531,0.929,0.551,0.642,0.518,0.406
-56,Epileptic,F,32.0,1.1,2.8,1.3,1.2,2.0,2.0,2.0,1.7,2.0,0.8,0.5,3.9,1.4,0.6,1.0,6.1,9.4,1.1,2.6,5.7,1.2,2.9,2.4,3.3,3.6,3.8,4.1,2.9,2.8,3.8,2.9,3.0,0.5,2.3,0.6,0.7,3.4,3.4,0.2,0.2,1.6,3.1,2.0,4.3,3.3,0.8,0.4,1.0,1.6,0.9,0.9,1.5,1.4,3.5,0.3,3.0,0.8,2.1,1.5,1.1,0.3,2.0,0.2,0.6,1.7,1.9,2.7,1.7,0.9,1.5,1.3,1.3,4.4,0.5,9,27,13,7,19,19,16,19,15,10,5.0,34,17,6,11,63,84,9,30,54,8,38,17,35,45,44,49,29,24,45,34,50,4,27,3,7,43,43,2,1,8,28,18,34,19,6,2,5,7,5,7,12,11,25,2,25,6,26,9,8,3,18,2,6,12,22,19,11,5,11,8,9,35,5,0.044,0.026,0.018,0.02,0.041,0.023,0.032,0.031,0.05,0.036,0.03,0.039,0.03,0.031,0.029,0.029,0.029,0.033,0.03,0.038,0.021,0.029,0.038,0.033,0.03,0.026,0.032,0.031,0.026,0.029,0.05,0.063,0.023,0.025,0.015,0.015,0.028,0.03,0.015,0.023,0.028,0.032,0.031,0.034,0.023,0.017,0.018,0.012,0.02,0.022,0.032,0.017,0.027,0.022,0.028,0.019,0.018,0.021,0.028,0.016,0.016,0.026,0.016,0.019,0.021,0.029,0.021,0.019,0.015,0.032,0.02,0.017,0.016,0.018,1819,4541,3041,2570,2433,3332,2839,3451,1915,1604,1074.0,3392,3173,1383,2127,10576,17471,2410,4900,5710,4186,5134,2422,5856,6464,8216,6250,4245,6046,6991,2543,2061,1052,6077,2157,1912,8181,7942,362,284,1563,3039,5329,3561,3829,1440,785,2286,2227,1564,906,2909,1903,5533,400,4838,1582,3639,1397,1765,823,3084,373,899,2326,2221,3831,3041,1714,1288,2088,2411,9574,732,0.136,0.124,0.108,0.096,0.151,0.12,0.115,0.127,0.159,0.14,0.134,0.148,0.139,0.137,0.131,0.131,0.127,0.131,0.137,0.145,0.091,0.124,0.13,0.133,0.131,0.133,0.125,0.105,0.099,0.127,0.161,0.147,0.097,0.124,0.079,0.094,0.127,0.138,0.108,0.107,0.115,0.125,0.125,0.132,0.107,0.098,0.097,0.078,0.102,0.108,0.134,0.1,0.124,0.109,0.135,0.103,0.101,0.124,0.119,0.1,0.084,0.124,0.108,0.115,0.11,0.125,0.108,0.105,0.088,0.135,0.1,0.102,0.094,0.123,519,1775,1155,905,893,1309,999,1057,603,421,281.0,1513,880,312,642,3341,5297,551,1368,2568,862,1642,962,1723,1960,2416,2165,1473,1632,2264,885,728,356,1472,506,731,2118,1989,196,159,888,1528,1161,2183,2240,711,323,1070,1112,652,467,1353,867,2515,195,2543,729,1641,889,958,414,1242,213,485,1181,1051,1984,1360,831,624,984,1120,4238,357,3.375,2.428,2.566,2.705,2.342,2.159,2.362,2.709,2.61,2.977,3.065,1.943,2.781,3.028,2.549,2.503,2.66,3.582,2.824,1.92,3.232,2.384,2.078,2.577,2.599,2.704,2.33,2.223,2.753,2.481,2.284,2.784,2.435,2.946,3.752,2.379,2.883,2.895,2.226,2.239,2.176,1.79,3.458,1.838,1.94,2.211,2.954,2.537,2.397,3.065,2.111,2.227,2.224,2.229,2.304,2.081,2.349,2.352,1.771,1.996,2.157,2.325,1.89,2.192,2.094,2.35,2.109,2.573,2.284,2.135,2.301,2.475,2.407,2.335,0.778,0.546,0.393,0.5,0.634,0.621,0.514,0.632,0.54,0.403,0.574,0.513,0.407,0.428,0.462,0.503,0.556,0.714,0.632,0.608,0.861,0.608,0.565,0.727,0.581,0.557,0.499,0.592,0.448,0.516,0.597,1.028,0.505,0.533,0.75,0.401,0.696,0.606,0.486,0.51,0.494,0.503,0.655,0.559,0.547,0.472,0.472,0.506,0.425,0.421,0.42,0.501,0.466,0.469,0.438,0.381,0.425,0.481,0.349,0.365,0.396,0.568,0.563,0.708,0.355,0.775,0.478,0.445,0.419,0.517,0.416,0.468,0.468,0.359
-57,Epileptic,M,42.0,1.8,2.7,0.8,1.3,1.5,1.8,1.7,2.5,1.6,0.8,0.2,3.6,1.5,0.6,1.8,7.0,10.2,0.7,3.1,5.0,1.9,3.2,1.9,3.3,3.3,3.3,3.6,3.4,3.9,2.8,1.5,1.2,0.6,4.3,0.6,1.0,3.8,4.9,0.3,0.1,1.1,2.9,2.5,3.1,3.7,0.5,0.4,1.0,1.3,0.8,0.8,2.6,1.3,3.1,0.5,2.7,1.1,1.4,0.7,0.7,0.9,1.1,0.3,0.4,2.6,1.3,3.9,1.2,1.6,1.1,1.3,1.2,5.6,0.3,12,26,9,13,11,15,13,22,9,11,3.0,30,13,8,14,67,86,6,31,46,17,29,17,35,37,32,38,32,30,33,12,9,5,36,4,8,35,50,1,1,6,29,26,24,22,5,2,8,7,6,6,15,6,21,4,23,8,12,6,6,7,7,3,3,19,18,29,8,10,11,11,10,55,3,0.065,0.028,0.016,0.02,0.041,0.029,0.032,0.036,0.054,0.033,0.024,0.036,0.035,0.039,0.036,0.044,0.039,0.025,0.04,0.04,0.029,0.036,0.033,0.047,0.034,0.033,0.034,0.035,0.037,0.027,0.034,0.052,0.036,0.039,0.021,0.024,0.04,0.037,0.014,0.011,0.022,0.036,0.045,0.029,0.028,0.011,0.023,0.019,0.015,0.018,0.032,0.025,0.023,0.026,0.017,0.018,0.022,0.016,0.025,0.016,0.024,0.026,0.024,0.017,0.024,0.022,0.022,0.019,0.026,0.038,0.026,0.017,0.019,0.018,1578,4630,1889,2419,1548,3192,2356,4068,1534,1623,550.0,3158,3526,1173,2998,12917,17392,2080,5412,5295,4155,4423,1848,5824,5889,6813,5596,4483,6469,5686,2481,1254,898,7851,2056,2051,6909,9280,474,467,1606,3020,5860,2750,3399,1471,789,2374,2733,1649,560,3886,1717,5463,565,4166,1635,3042,938,1258,1355,2223,524,773,3165,1081,4877,2064,2275,1356,1846,2200,11733,727,0.152,0.133,0.101,0.114,0.139,0.135,0.117,0.146,0.155,0.135,0.114,0.143,0.123,0.156,0.131,0.144,0.136,0.113,0.155,0.154,0.118,0.145,0.144,0.149,0.136,0.127,0.133,0.128,0.127,0.126,0.124,0.118,0.138,0.144,0.093,0.104,0.141,0.137,0.09,0.073,0.107,0.142,0.155,0.126,0.119,0.082,0.098,0.094,0.087,0.104,0.139,0.109,0.107,0.105,0.11,0.11,0.111,0.098,0.118,0.106,0.118,0.111,0.125,0.106,0.117,0.121,0.113,0.101,0.109,0.148,0.125,0.112,0.102,0.1,453,1585,822,969,679,1053,880,1282,475,462,182.0,1610,825,295,839,3115,4723,450,1450,2284,1003,1464,864,1497,1877,1888,1867,1735,1813,1773,673,481,320,1725,471,747,1701,2291,269,185,767,1342,1235,1726,2151,732,309,907,1225,713,374,1698,850,2164,343,2140,717,1356,484,681,621,694,222,395,1578,833,2605,969,1013,508,821,1084,4923,329,3.481,2.657,2.285,2.479,2.21,2.583,2.315,2.834,2.553,2.806,2.496,1.81,3.074,2.832,2.638,2.93,2.93,3.599,2.919,2.065,3.089,2.492,1.944,3.048,2.48,2.786,2.356,2.146,2.708,2.632,2.631,2.466,2.273,3.218,3.56,2.58,2.984,2.93,2.057,2.332,2.576,1.982,3.235,1.805,1.831,2.151,3.039,3.069,2.677,2.586,1.882,2.378,2.291,2.375,1.993,2.111,2.586,2.631,2.057,2.144,2.487,3.081,2.522,2.315,2.177,1.615,2.077,2.494,2.474,2.695,2.53,2.329,2.5,2.615,0.774,0.592,0.544,0.469,0.455,0.522,0.533,0.571,0.601,0.889,0.91,0.526,0.491,0.545,0.65,0.82,0.622,0.663,0.582,0.679,0.861,0.687,0.412,0.845,0.567,0.714,0.585,0.515,0.602,0.539,0.769,0.877,0.382,0.761,0.768,0.602,0.717,0.608,0.341,0.551,0.634,0.583,0.818,0.594,0.483,0.407,0.457,0.817,0.716,0.509,0.474,0.509,0.482,0.603,0.415,0.414,0.507,0.596,0.384,0.387,0.327,1.048,0.511,0.98,0.544,0.496,0.479,0.414,0.391,0.565,0.489,0.512,0.51,0.538
-58,Epileptic,M,32.0,1.2,2.6,0.8,0.8,1.1,2.6,1.7,1.9,1.0,0.9,0.3,2.9,1.5,0.5,1.3,7.0,9.5,0.8,2.8,3.7,1.0,2.8,2.1,3.0,3.6,4.4,2.5,4.4,3.2,3.3,1.1,2.1,0.5,2.9,0.5,1.0,3.9,4.0,0.1,0.2,1.5,3.4,2.2,2.7,3.8,0.6,0.6,0.7,1.1,0.8,0.8,1.9,1.0,3.2,0.4,1.5,0.9,1.7,0.7,1.1,1.3,1.2,0.6,0.2,2.4,0.8,3.0,1.6,1.2,0.6,1.0,1.4,5.8,0.4,8,25,6,6,11,24,17,19,16,6,2.0,31,17,8,14,82,89,7,30,41,12,42,20,38,49,46,33,42,28,44,13,20,6,33,4,7,43,46,1,1,11,31,24,21,20,3,3,5,6,6,6,13,6,22,3,13,5,14,6,8,13,9,4,3,19,6,21,12,6,6,6,11,60,3,0.039,0.029,0.012,0.019,0.031,0.03,0.028,0.029,0.041,0.036,0.026,0.035,0.03,0.038,0.033,0.038,0.032,0.032,0.029,0.033,0.021,0.034,0.032,0.034,0.037,0.038,0.033,0.033,0.029,0.036,0.034,0.07,0.025,0.033,0.018,0.016,0.038,0.035,0.017,0.017,0.024,0.036,0.03,0.03,0.023,0.014,0.021,0.011,0.015,0.024,0.031,0.019,0.021,0.025,0.014,0.016,0.02,0.016,0.017,0.02,0.031,0.023,0.034,0.017,0.022,0.022,0.025,0.019,0.017,0.02,0.027,0.021,0.023,0.013,1512,5525,2027,1890,1344,4168,2970,3894,1638,1342,776.0,3154,3430,1160,2787,12790,19507,2297,5862,5287,3527,4640,2895,5837,6288,7319,3604,6031,6705,5530,2210,1141,1288,6523,2208,2581,6264,7046,313,479,2177,3742,5119,3009,4539,1294,879,2392,2273,1380,788,3554,1682,6175,563,2570,1352,3621,1143,1525,1505,2264,630,834,3459,1174,3451,3058,1998,1228,1218,2439,9976,775,0.145,0.124,0.092,0.098,0.132,0.134,0.107,0.132,0.139,0.158,0.102,0.148,0.126,0.177,0.141,0.146,0.131,0.108,0.134,0.145,0.086,0.144,0.14,0.142,0.146,0.142,0.137,0.131,0.117,0.137,0.115,0.166,0.101,0.132,0.09,0.088,0.144,0.138,0.106,0.089,0.121,0.153,0.12,0.134,0.106,0.091,0.116,0.082,0.089,0.109,0.14,0.1,0.106,0.106,0.11,0.107,0.107,0.097,0.105,0.112,0.137,0.115,0.148,0.121,0.116,0.096,0.115,0.106,0.096,0.115,0.123,0.111,0.119,0.097,460,1610,835,698,560,1462,992,1152,462,327,192.0,1378,1002,273,728,3523,5204,504,1529,1935,906,1561,1129,1510,1903,2151,1389,2107,1857,1701,540,462,415,1594,541,922,1767,2030,165,212,942,1516,1323,1581,2533,598,392,991,1069,658,446,1554,749,2191,319,1358,605,1704,676,827,685,808,289,322,1644,498,1826,1215,929,457,616,1171,4255,412,3.269,2.851,2.375,2.677,2.215,2.443,2.502,2.827,2.576,3.27,3.287,1.97,2.775,2.929,2.901,2.72,2.905,3.549,3.051,2.26,2.914,2.319,2.247,2.846,2.563,2.667,2.224,2.243,2.78,2.536,2.78,2.587,2.424,3.04,3.583,2.666,2.758,2.64,2.111,2.686,2.666,2.156,2.865,2.211,2.033,2.436,2.639,3.036,2.592,2.627,2.147,2.384,2.251,2.678,2.008,2.097,2.447,2.401,1.981,2.093,2.168,2.43,2.36,2.196,2.225,2.407,2.074,2.675,2.471,2.93,2.317,2.464,2.393,2.434,0.721,0.765,0.686,0.517,0.587,0.605,0.549,0.588,0.5,0.488,0.774,0.414,0.467,0.513,0.464,0.545,0.662,0.786,0.494,0.636,0.756,0.48,0.381,0.62,0.524,0.599,0.648,0.564,0.459,0.578,0.819,1.074,0.353,0.622,0.664,0.7,0.518,0.62,0.363,0.362,0.625,0.552,0.796,0.62,0.505,0.504,0.476,0.642,0.57,0.453,0.378,0.516,0.594,0.569,0.494,0.401,0.409,0.443,0.261,0.399,0.419,0.753,0.522,0.959,0.498,0.84,0.548,0.439,0.434,0.995,0.38,0.509,0.495,0.245
-59,Epileptic,F,42.0,0.9,2.6,0.8,1.4,1.5,2.6,1.3,1.9,1.0,0.8,0.3,2.3,1.5,0.6,1.0,5.2,8.9,0.6,3.7,3.9,1.4,2.3,1.8,3.9,3.3,3.6,4.3,2.6,2.9,3.7,1.5,1.5,0.4,2.3,0.5,1.1,4.5,3.7,0.2,0.1,1.3,3.4,2.4,3.0,3.3,0.5,0.6,1.3,1.0,1.1,0.7,1.6,0.7,3.3,0.4,2.5,1.3,1.8,1.0,0.9,0.4,1.4,0.4,0.7,2.4,0.5,3.2,1.3,1.1,0.5,1.4,1.4,4.8,0.3,6,20,8,11,15,16,10,15,11,7,3.0,19,15,7,9,46,67,4,24,28,40,22,16,40,32,32,38,22,20,34,24,11,5,25,3,8,45,33,1,1,7,26,21,22,21,4,3,8,5,11,6,10,4,23,4,20,11,16,9,7,4,12,3,4,18,7,21,9,9,4,10,12,35,3,0.034,0.029,0.016,0.024,0.032,0.038,0.021,0.03,0.039,0.027,0.035,0.031,0.033,0.036,0.023,0.029,0.029,0.021,0.044,0.034,0.019,0.026,0.031,0.039,0.031,0.03,0.03,0.025,0.025,0.027,0.044,0.063,0.026,0.027,0.017,0.022,0.034,0.028,0.017,0.016,0.027,0.038,0.036,0.031,0.024,0.011,0.034,0.019,0.015,0.016,0.037,0.018,0.015,0.023,0.019,0.023,0.035,0.022,0.027,0.018,0.019,0.024,0.03,0.024,0.03,0.021,0.023,0.018,0.019,0.016,0.024,0.024,0.02,0.017,1535,4689,2274,2123,1989,2292,2207,3260,1601,1239,422.0,2256,2429,904,1912,8620,15802,1780,2594,2877,3513,4149,2396,5649,5523,6633,5779,3417,4856,5285,2082,1041,822,5049,1823,1836,6616,5709,373,367,1676,2802,4634,2080,3180,1351,717,2237,2174,2400,483,3147,1412,4982,626,3189,1224,2439,1140,1396,998,2014,431,741,2494,665,3865,2145,1629,1346,1696,2144,8411,448,0.123,0.119,0.095,0.111,0.132,0.138,0.09,0.136,0.143,0.116,0.132,0.127,0.139,0.154,0.126,0.119,0.119,0.09,0.143,0.128,0.095,0.123,0.13,0.145,0.133,0.125,0.127,0.108,0.096,0.118,0.14,0.154,0.145,0.122,0.081,0.1,0.13,0.118,0.106,0.098,0.117,0.147,0.144,0.14,0.119,0.08,0.128,0.098,0.089,0.099,0.16,0.089,0.093,0.114,0.12,0.118,0.145,0.114,0.127,0.104,0.099,0.126,0.143,0.118,0.129,0.11,0.119,0.111,0.104,0.106,0.126,0.124,0.107,0.127,481,1503,827,947,775,1104,896,940,473,413,178.0,1108,798,269,584,2738,4662,446,1285,1689,1087,1309,918,1646,1691,2035,2224,1459,1543,2042,575,422,331,1258,547,754,2126,1868,202,192,775,1384,1162,1472,2141,773,310,955,1053,1118,361,1392,743,2290,361,1756,624,1368,650,854,461,892,242,408,1358,410,2046,1049,909,490,887,1122,3821,245,3.072,2.945,2.543,2.245,2.241,2.056,2.161,3.18,2.574,2.507,2.261,1.792,2.443,2.476,2.481,2.498,2.742,3.247,1.92,1.608,2.742,2.352,2.18,2.697,2.573,2.739,2.16,1.971,2.485,2.201,2.621,2.6,2.141,2.9,3.072,2.301,2.582,2.459,2.096,1.976,2.595,1.863,3.095,1.644,1.722,1.875,2.788,2.9,2.445,2.367,1.73,2.342,2.141,2.292,2.098,1.986,2.186,2.041,1.937,1.785,2.317,2.266,2.017,2.11,2.046,2.022,2.209,2.326,1.948,2.989,2.281,2.293,2.462,2.389,1.08,0.731,0.784,0.566,0.539,0.449,0.671,0.726,0.588,0.583,0.519,0.523,0.458,0.33,0.44,0.554,0.641,0.851,0.576,0.439,0.587,0.511,0.551,0.621,0.535,0.627,0.549,0.552,0.622,0.562,0.727,1.125,0.551,0.695,0.633,0.571,0.598,0.599,0.358,0.347,0.804,0.467,0.676,0.556,0.561,0.5,0.476,0.834,0.638,0.589,0.432,0.69,0.586,0.596,0.367,0.515,0.452,0.69,0.507,0.405,0.732,0.612,0.393,0.951,0.795,0.499,0.624,0.671,0.623,0.932,0.548,0.463,0.508,0.594
-60,Epileptic,M,62.0,1.2,3.0,1.2,1.6,2.3,1.7,2.4,2.3,2.3,1.0,0.3,2.6,1.6,0.4,1.4,10.0,10.9,2.5,2.9,5.5,1.2,4.2,1.7,4.7,3.6,3.1,7.9,4.6,5.4,3.5,2.5,1.1,0.4,2.7,0.7,0.7,5.5,3.7,0.3,0.3,1.2,3.1,3.6,2.1,4.2,0.7,0.5,1.0,1.6,1.0,0.8,1.9,1.6,2.9,0.1,3.5,1.4,2.4,1.2,1.1,1.0,2.3,0.2,0.7,2.2,1.3,3.7,1.9,1.0,0.8,1.1,2.0,6.3,0.3,9,32,13,12,20,20,21,22,19,11,3.0,29,15,5,15,91,98,19,35,46,11,46,14,49,47,42,74,42,47,46,19,10,5,31,3,5,58,56,1,2,7,29,28,14,23,9,2,7,7,8,5,15,11,17,0,30,10,15,7,11,8,18,1,5,19,19,29,12,6,7,6,12,57,3,0.044,0.025,0.024,0.027,0.035,0.029,0.034,0.033,0.057,0.045,0.03,0.033,0.034,0.021,0.035,0.045,0.034,0.07,0.031,0.044,0.021,0.039,0.032,0.048,0.034,0.028,0.042,0.039,0.039,0.03,0.054,0.056,0.021,0.031,0.016,0.016,0.042,0.035,0.021,0.023,0.022,0.038,0.047,0.027,0.026,0.015,0.023,0.016,0.019,0.025,0.03,0.018,0.02,0.021,0.015,0.018,0.029,0.021,0.023,0.016,0.035,0.03,0.022,0.02,0.021,0.023,0.023,0.022,0.017,0.017,0.028,0.022,0.021,0.016,1722,5842,2337,2795,2772,3091,3406,3384,2112,1796,774.0,3197,3289,1033,2624,12431,20674,2621,6574,5591,3643,5508,2033,6506,5757,6830,6392,4883,7044,6512,2542,1249,904,6548,2295,2050,9235,10010,357,462,1533,3609,6690,2571,4362,1691,794,2428,2510,2136,846,3036,2353,5328,113,5025,1573,3577,1244,1799,885,2823,393,1169,2980,1557,5050,3076,1764,1157,1508,2903,10075,540,0.138,0.12,0.118,0.116,0.125,0.136,0.111,0.135,0.162,0.165,0.114,0.137,0.13,0.115,0.133,0.142,0.129,0.178,0.134,0.16,0.09,0.143,0.122,0.153,0.129,0.126,0.132,0.112,0.111,0.136,0.135,0.127,0.084,0.13,0.078,0.083,0.149,0.143,0.097,0.111,0.104,0.134,0.164,0.113,0.11,0.099,0.106,0.089,0.097,0.103,0.138,0.101,0.105,0.103,0.075,0.111,0.127,0.099,0.116,0.104,0.14,0.133,0.101,0.113,0.11,0.123,0.111,0.111,0.1,0.102,0.126,0.114,0.112,0.112,466,2054,851,991,1164,1104,1137,1157,712,449,189.0,1335,842,267,784,3665,5850,579,1702,2249,920,1702,911,1942,2060,2035,2721,1837,1931,2112,830,409,336,1488,583,741,2292,2252,210,208,805,1328,1269,1386,2558,814,335,947,1186,795,420,1597,1207,2212,46,2743,694,1756,791,1024,507,1143,214,551,1560,922,2593,1334,834,685,660,1357,4666,319,3.423,2.645,2.624,2.713,2.169,2.308,2.522,2.456,2.39,2.923,2.868,1.993,2.909,2.772,2.535,2.586,2.812,2.867,2.922,2.103,2.752,2.413,1.911,2.583,2.261,2.676,2.0,2.151,2.768,2.418,2.334,2.818,2.181,3.009,3.47,2.525,2.938,3.007,1.946,2.42,2.327,2.133,3.54,2.014,1.919,2.029,2.631,3.066,2.53,2.614,2.09,2.118,2.121,2.587,2.327,1.983,2.412,2.243,1.805,1.882,2.006,2.328,2.021,2.314,2.074,1.886,2.141,2.549,2.406,1.952,2.571,2.495,2.359,2.114,0.789,0.646,0.516,0.541,0.524,0.618,0.565,0.536,0.62,0.685,0.883,0.487,0.435,0.354,0.436,0.624,0.609,1.15,0.661,0.602,0.837,0.606,0.501,0.724,0.597,0.524,0.484,0.571,0.566,0.645,0.675,1.016,0.446,0.596,0.764,0.46,0.74,0.785,0.416,0.458,0.439,0.667,0.759,0.674,0.566,0.47,0.609,0.745,0.542,0.644,0.404,0.396,0.522,0.481,0.236,0.434,0.601,0.557,0.374,0.392,0.404,0.621,0.333,0.982,0.542,0.63,0.44,0.43,0.456,0.725,0.462,0.566,0.429,0.303
-61,Epileptic,M,47.0,1.0,2.6,0.9,1.4,2.0,1.9,1.6,1.6,1.6,0.7,0.3,2.4,1.8,0.5,1.3,5.1,11.0,1.3,1.8,3.8,1.2,5.2,1.9,4.3,7.8,3.8,4.2,1.9,2.5,3.0,1.7,1.1,0.5,2.2,0.6,0.9,4.8,3.0,0.2,0.1,1.2,2.5,2.4,1.7,2.3,0.6,0.7,1.0,1.7,0.7,0.5,2.6,1.4,2.6,0.5,3.1,0.5,1.7,1.0,0.7,0.6,1.7,0.4,0.5,1.8,1.3,2.8,1.5,0.8,1.0,0.9,1.5,6.5,0.4,8,20,7,10,19,19,14,18,17,7,4.0,20,16,8,13,57,96,14,19,32,9,48,15,47,58,43,45,21,20,37,22,8,5,27,4,9,55,38,1,1,7,18,23,14,11,4,3,5,8,6,3,20,10,19,3,23,4,15,5,7,6,15,4,5,14,18,22,9,5,12,5,9,52,3,0.043,0.025,0.018,0.025,0.038,0.029,0.029,0.031,0.044,0.036,0.032,0.034,0.03,0.038,0.029,0.033,0.035,0.053,0.035,0.043,0.028,0.059,0.039,0.049,0.053,0.033,0.038,0.025,0.026,0.032,0.04,0.043,0.021,0.03,0.025,0.021,0.038,0.031,0.02,0.016,0.023,0.042,0.039,0.023,0.018,0.013,0.023,0.015,0.022,0.02,0.028,0.026,0.023,0.024,0.014,0.017,0.015,0.023,0.025,0.018,0.017,0.026,0.029,0.021,0.02,0.026,0.022,0.023,0.017,0.023,0.025,0.015,0.025,0.017,1425,4721,1975,2368,2180,2911,2981,2998,2015,1379,616.0,2703,3360,1039,2317,8623,18250,2147,3777,3388,3154,2529,1493,6112,4495,6838,4899,3242,5354,4938,2524,1120,1261,5932,1691,2231,8888,7273,310,313,1498,1662,4745,2159,3266,1330,991,2319,2425,1425,407,3669,1658,3985,780,4722,1223,2674,880,1053,1517,2692,325,790,3044,1287,3829,2108,1475,1480,1138,2289,8763,530,0.139,0.119,0.103,0.115,0.149,0.129,0.106,0.134,0.146,0.146,0.133,0.126,0.132,0.151,0.133,0.137,0.135,0.157,0.132,0.147,0.104,0.151,0.12,0.143,0.156,0.135,0.126,0.103,0.107,0.139,0.151,0.134,0.091,0.135,0.104,0.102,0.143,0.137,0.099,0.096,0.111,0.129,0.148,0.117,0.095,0.085,0.113,0.087,0.104,0.113,0.141,0.117,0.118,0.111,0.104,0.1,0.098,0.122,0.115,0.104,0.098,0.124,0.147,0.119,0.105,0.13,0.112,0.107,0.095,0.128,0.111,0.103,0.111,0.107,467,1710,786,911,912,1025,1061,989,655,347,172.0,1068,1028,268,767,2797,5644,510,1025,1495,769,1201,747,1940,1987,2094,1861,1218,1455,1596,789,455,419,1397,420,709,2228,1782,173,172,791,968,1153,1222,1898,699,443,956,1181,557,234,1688,906,1811,429,2513,557,1261,526,568,654,1081,190,421,1457,801,1966,1054,749,683,609,1164,3901,325,3.107,2.673,2.581,2.57,2.19,2.304,2.341,2.553,2.353,2.895,2.783,2.028,2.594,2.745,2.373,2.454,2.592,3.118,2.808,1.946,3.101,1.855,1.827,2.521,1.989,2.662,2.129,2.11,2.725,2.478,2.567,2.517,2.378,3.053,3.643,2.725,2.846,2.958,2.183,2.017,2.301,1.486,3.153,1.943,1.948,2.146,2.727,2.997,2.476,2.732,2.087,2.242,2.055,2.254,2.321,2.042,2.439,2.501,1.955,2.124,2.373,2.546,2.206,2.251,2.281,1.906,2.073,2.332,2.24,2.372,2.244,2.277,2.451,2.053,0.699,0.507,0.389,0.428,0.507,0.655,0.569,0.476,0.486,0.536,0.69,0.499,0.501,0.446,0.41,0.489,0.578,0.758,0.522,0.619,0.841,0.729,0.761,0.566,0.578,0.535,0.513,0.477,0.488,0.619,0.553,0.833,0.401,0.565,0.567,0.485,0.728,0.714,0.239,0.293,0.334,0.747,0.699,0.548,0.535,0.466,0.342,0.698,0.443,0.531,0.365,0.37,0.38,0.362,0.309,0.401,0.441,0.46,0.371,0.357,0.47,0.578,0.419,0.616,0.468,0.506,0.439,0.392,0.44,0.66,0.36,0.467,0.49,0.391
-62,Epileptic,F,17.5,0.9,3.6,0.8,1.3,1.5,1.9,2.4,2.7,1.4,1.2,0.3,3.7,2.2,0.9,1.1,11.6,13.3,1.4,3.4,6.6,1.1,3.4,2.1,5.5,5.5,5.7,4.7,3.1,4.6,5.3,2.1,0.6,0.7,4.5,0.6,1.1,5.1,5.9,0.2,0.4,1.7,4.1,2.9,5.9,3.8,0.9,0.5,1.1,1.2,0.7,0.5,1.6,2.5,2.9,0.4,3.6,1.3,4.0,1.4,1.4,0.7,2.3,0.6,0.5,2.1,1.7,3.4,1.4,1.8,0.5,2.1,3.0,7.3,0.6,5,25,7,9,11,16,14,19,8,12,3.0,28,18,8,10,88,99,7,28,48,7,28,16,41,41,47,40,25,36,45,18,4,4,42,3,7,47,46,1,3,9,32,26,38,22,6,3,6,7,6,4,12,15,16,3,27,12,28,9,12,6,14,3,4,16,13,24,12,9,5,14,16,54,5,0.029,0.03,0.016,0.022,0.033,0.028,0.034,0.036,0.045,0.048,0.036,0.04,0.039,0.052,0.033,0.051,0.038,0.042,0.034,0.037,0.02,0.034,0.039,0.048,0.038,0.037,0.03,0.032,0.036,0.038,0.06,0.022,0.022,0.039,0.019,0.017,0.043,0.04,0.018,0.025,0.026,0.041,0.046,0.044,0.026,0.016,0.033,0.014,0.013,0.02,0.032,0.019,0.031,0.022,0.025,0.022,0.026,0.028,0.03,0.02,0.02,0.037,0.044,0.023,0.022,0.031,0.025,0.019,0.026,0.019,0.032,0.031,0.023,0.022,1933,6611,2441,2756,2620,2915,2697,4199,1888,1975,628.0,2841,4002,1592,2607,16542,24840,2614,5532,4284,2459,5204,2538,6559,7657,8371,6859,4132,8136,7177,2159,941,1239,8029,2328,2481,7292,8913,422,788,2320,3042,5826,2649,3945,1865,726,3249,3100,1616,566,3267,3545,5242,493,4816,2332,4291,1292,1960,1043,3038,738,872,3507,1716,3962,2677,2347,1111,2638,2936,11631,638,0.106,0.117,0.087,0.106,0.117,0.131,0.115,0.136,0.136,0.148,0.131,0.142,0.135,0.151,0.136,0.156,0.132,0.121,0.13,0.137,0.08,0.136,0.127,0.145,0.143,0.136,0.124,0.12,0.127,0.141,0.146,0.079,0.09,0.132,0.081,0.079,0.135,0.128,0.093,0.118,0.114,0.145,0.14,0.142,0.108,0.093,0.116,0.077,0.084,0.096,0.126,0.093,0.117,0.096,0.127,0.109,0.108,0.114,0.13,0.11,0.109,0.122,0.142,0.112,0.108,0.127,0.112,0.099,0.115,0.098,0.13,0.13,0.108,0.109,509,1999,876,935,796,1107,1035,1336,461,463,174.0,1481,1087,325,586,3869,6067,540,1569,2796,928,1651,988,1858,2403,2680,2600,1663,2203,2401,621,459,521,1909,558,970,2141,2395,217,269,1031,1530,1237,1980,2383,895,325,1197,1338,686,324,1457,1347,2189,242,2618,936,2197,770,1066,579,1058,254,431,1589,917,2126,1212,1087,536,1128,1542,5083,431,3.611,2.793,2.553,2.844,2.578,2.411,2.284,2.819,2.744,3.411,3.112,1.714,2.831,3.21,3.202,2.802,3.04,3.57,2.791,1.5,2.143,2.549,2.269,2.656,2.438,2.557,2.208,2.021,2.742,2.488,2.386,1.99,2.033,2.856,3.477,2.519,2.525,2.829,2.414,3.028,2.643,1.868,3.11,1.599,1.894,2.244,2.788,3.222,2.799,2.528,2.1,2.239,2.243,2.378,2.166,1.956,2.836,2.207,2.001,1.877,2.044,2.775,2.993,2.261,2.438,2.418,1.991,2.474,2.652,2.239,2.643,2.416,2.432,1.814,1.035,0.81,0.693,0.813,0.963,0.612,0.629,0.599,0.726,0.665,1.074,0.499,0.555,0.605,0.824,0.843,0.739,0.799,0.538,0.443,0.734,0.531,0.579,0.928,0.699,0.59,0.606,0.522,0.659,0.651,0.907,0.894,0.6,0.92,0.876,0.673,0.886,0.786,0.46,0.617,0.573,0.636,0.867,0.701,0.594,0.526,0.635,0.868,0.646,0.65,0.543,0.745,0.721,0.749,0.491,0.507,0.709,0.759,0.33,0.441,0.471,0.806,0.846,1.006,0.614,0.893,0.494,0.425,0.595,0.721,0.564,0.549,0.582,0.393
-63,Epileptic,M,27.0,0.8,2.4,0.7,1.2,1.3,2.2,1.5,2.0,0.9,0.9,0.3,2.9,1.9,0.6,1.4,6.3,9.6,0.8,2.1,4.7,1.1,3.7,2.0,2.9,3.2,3.9,3.0,3.0,3.4,4.1,1.1,0.9,0.7,1.9,0.9,1.0,5.1,3.0,0.2,0.3,1.2,3.0,2.2,2.9,2.8,0.9,0.4,0.7,1.3,0.9,0.5,1.8,1.7,3.0,0.4,2.7,0.9,2.2,1.2,1.3,0.8,1.6,0.4,0.5,1.6,1.4,2.6,1.7,1.3,0.5,1.1,1.4,3.3,0.4,8,23,10,13,16,28,13,18,13,10,3.0,32,21,6,18,84,96,6,25,44,11,42,22,38,42,39,42,30,31,48,13,7,7,26,8,7,50,35,2,3,6,27,30,22,17,7,2,4,6,9,4,15,12,21,2,24,7,24,7,11,9,16,3,5,16,19,22,12,8,5,7,9,29,2,0.033,0.021,0.012,0.023,0.029,0.03,0.025,0.032,0.035,0.034,0.04,0.032,0.038,0.027,0.029,0.033,0.029,0.027,0.031,0.034,0.022,0.031,0.03,0.036,0.035,0.034,0.025,0.029,0.029,0.031,0.033,0.04,0.031,0.029,0.029,0.021,0.04,0.038,0.013,0.02,0.021,0.031,0.039,0.026,0.023,0.016,0.024,0.012,0.014,0.024,0.028,0.017,0.021,0.021,0.026,0.017,0.023,0.019,0.02,0.018,0.026,0.028,0.017,0.019,0.018,0.025,0.019,0.022,0.019,0.015,0.023,0.022,0.015,0.015,1601,5910,2094,2569,2349,3972,2489,3952,1910,1662,697.0,3433,3799,1455,3404,12233,19900,2321,4272,5114,3206,6195,2569,5802,6012,7492,6310,4293,6788,6907,2253,925,1213,5364,2691,2390,8853,6814,357,785,1852,3215,7293,3312,3201,1935,780,2259,2679,2141,587,3869,2420,5675,581,4146,1466,3582,1573,1808,1556,2626,567,837,2773,1449,4410,3118,2182,870,1771,2041,8746,702,0.114,0.109,0.096,0.107,0.127,0.145,0.107,0.136,0.133,0.156,0.13,0.144,0.145,0.132,0.134,0.137,0.128,0.114,0.129,0.136,0.091,0.14,0.143,0.137,0.142,0.137,0.119,0.113,0.114,0.137,0.112,0.126,0.131,0.124,0.12,0.111,0.144,0.141,0.089,0.1,0.107,0.133,0.148,0.117,0.111,0.095,0.108,0.078,0.083,0.114,0.124,0.1,0.11,0.102,0.107,0.11,0.115,0.104,0.11,0.111,0.124,0.139,0.117,0.112,0.105,0.118,0.104,0.106,0.1,0.11,0.116,0.112,0.088,0.087,452,1930,811,943,859,1412,918,1114,540,426,180.0,1532,1014,350,888,3471,5542,439,1194,2273,796,1966,1129,1447,1795,2025,2239,1661,1929,2195,607,380,358,1235,525,773,2040,1537,202,286,794,1572,1281,1903,1923,885,293,857,1225,648,337,1648,1175,2378,272,2189,640,1909,884,1020,565,869,316,437,1422,938,2173,1335,1056,480,779,1010,3606,355,3.35,2.841,2.494,2.533,2.357,2.453,2.26,3.18,2.658,2.93,2.787,1.923,2.858,2.964,2.908,2.695,2.841,3.734,2.904,1.944,3.128,2.427,2.064,2.873,2.592,2.966,2.273,2.136,2.79,2.52,2.687,2.421,2.669,3.072,3.587,2.85,3.083,3.15,2.145,2.824,2.925,1.805,3.63,2.017,1.908,2.406,3.104,3.221,2.622,3.049,1.987,2.431,2.242,2.486,2.675,2.158,2.683,2.165,2.119,1.964,2.454,2.863,2.192,2.042,2.078,1.917,2.249,2.661,2.423,2.236,2.416,2.511,2.516,2.276,0.986,0.665,0.532,0.574,0.629,0.55,0.527,0.701,0.584,0.647,0.884,0.41,0.563,0.436,0.597,0.514,0.59,0.802,0.666,0.582,0.688,0.529,0.46,0.772,0.536,0.681,0.472,0.547,0.562,0.538,0.746,1.094,0.51,0.704,0.876,0.556,0.823,0.628,0.327,0.472,0.584,0.474,0.831,0.733,0.538,0.608,0.619,0.782,0.545,0.682,0.409,0.464,0.437,0.482,0.544,0.512,0.522,0.508,0.436,0.36,0.458,0.68,0.363,0.814,0.623,0.576,0.432,0.4,0.366,0.717,0.544,0.404,0.467,0.445
-64,Epileptic,M,32.0,0.6,2.8,1.1,1.0,1.1,1.6,2.0,1.7,0.6,0.9,0.1,3.4,1.1,0.6,1.2,5.4,8.1,0.9,2.6,4.7,1.9,2.9,2.5,3.4,2.7,3.1,3.2,2.0,2.2,2.7,1.3,0.9,0.6,2.2,0.5,0.8,4.3,3.1,0.1,0.1,1.0,2.9,2.2,4.2,2.6,0.6,0.5,1.0,1.1,1.0,0.7,1.9,1.4,2.6,0.3,2.7,0.5,1.8,1.1,1.3,0.4,1.1,0.2,0.4,2.2,1.2,2.5,1.3,1.1,0.8,0.8,1.7,4.1,0.3,4,28,9,8,11,13,16,16,10,7,1.0,25,13,8,11,62,79,7,23,34,19,31,21,41,35,31,36,22,21,27,19,6,7,25,3,8,44,34,1,1,5,22,23,32,17,4,2,7,6,9,5,17,10,19,2,24,5,16,9,9,2,11,2,4,17,15,21,11,9,7,6,11,29,3,0.035,0.03,0.02,0.022,0.028,0.031,0.026,0.029,0.028,0.04,0.029,0.039,0.031,0.03,0.033,0.031,0.029,0.033,0.035,0.039,0.029,0.034,0.033,0.038,0.026,0.03,0.029,0.023,0.021,0.03,0.036,0.053,0.032,0.029,0.015,0.021,0.032,0.031,0.018,0.015,0.022,0.034,0.032,0.04,0.021,0.013,0.024,0.016,0.016,0.019,0.04,0.019,0.023,0.02,0.026,0.018,0.026,0.023,0.025,0.024,0.017,0.021,0.026,0.017,0.027,0.029,0.02,0.017,0.016,0.028,0.028,0.025,0.017,0.021,1445,4369,1711,1731,1928,2047,2499,3019,1438,1159,398.0,2580,2452,1008,2188,9946,15316,2428,3551,3566,3618,3831,2191,5876,5270,5203,4837,2855,5066,4692,2147,710,752,5993,1920,1919,8103,6347,273,448,1354,2157,4552,2659,2929,1179,772,2250,2005,2196,466,3165,2036,4572,333,4270,1173,3124,1165,1451,724,2266,348,748,2512,1003,3797,2537,1902,1357,1068,2674,9446,503,0.113,0.129,0.127,0.106,0.125,0.126,0.101,0.129,0.138,0.155,0.082,0.142,0.122,0.141,0.14,0.135,0.133,0.109,0.132,0.139,0.111,0.132,0.134,0.139,0.115,0.13,0.116,0.099,0.098,0.128,0.146,0.128,0.154,0.12,0.084,0.092,0.13,0.134,0.089,0.094,0.109,0.141,0.134,0.155,0.103,0.091,0.099,0.092,0.091,0.101,0.146,0.107,0.111,0.108,0.138,0.105,0.115,0.107,0.127,0.121,0.092,0.113,0.119,0.11,0.125,0.139,0.103,0.1,0.099,0.127,0.125,0.12,0.094,0.124,359,1392,685,729,727,852,985,1047,434,338,124.0,1224,749,322,601,2832,4585,494,1097,1749,1026,1265,1102,1609,1645,1693,1805,1340,1589,1421,627,313,302,1278,456,607,2180,1604,137,186,673,1145,1166,1791,1931,684,345,950,978,823,298,1542,1025,2025,158,2292,491,1460,747,846,304,853,178,417,1331,620,2087,1136,978,465,523,1155,3806,227,3.785,2.747,2.205,2.334,2.233,2.213,2.171,2.609,2.419,2.803,2.813,1.9,2.719,2.615,2.708,2.593,2.698,3.531,2.511,1.812,2.955,2.436,1.815,2.632,2.48,2.6,2.187,1.8,2.492,2.541,2.629,2.225,2.171,3.291,3.532,2.792,2.823,3.064,2.047,2.671,2.446,1.778,2.889,1.841,1.732,1.877,2.871,2.907,2.474,2.681,1.788,2.111,2.111,2.384,2.648,1.995,2.726,2.369,1.927,1.911,2.529,2.448,2.271,2.156,2.075,2.154,1.884,2.42,2.208,2.907,2.346,2.749,2.687,2.811,0.782,0.718,0.62,0.494,0.651,0.512,0.545,0.506,0.559,0.386,0.811,0.585,0.537,0.473,0.547,0.49,0.512,1.026,0.574,0.565,0.805,0.487,0.461,0.587,0.518,0.49,0.543,0.461,0.434,0.672,0.773,0.825,0.411,0.645,0.624,0.573,0.628,0.617,0.373,0.843,0.618,0.489,0.628,0.612,0.457,0.455,0.555,0.665,0.563,0.634,0.351,0.472,0.518,0.447,0.6,0.439,0.503,0.838,0.365,0.356,0.416,0.809,0.362,0.942,0.545,0.668,0.401,0.452,0.33,0.88,0.457,0.471,0.744,0.79
-65,Epileptic,F,52.0,1.0,2.1,1.0,1.1,0.8,1.7,2.1,1.4,1.1,0.4,0.4,3.7,1.1,0.8,1.7,4.4,6.5,1.0,3.0,4.4,2.1,2.5,3.6,3.5,2.5,2.8,5.7,2.1,2.2,2.3,1.7,0.6,0.4,1.3,0.4,0.4,4.3,2.2,0.2,0.2,0.7,3.5,1.7,2.5,2.4,0.4,0.3,0.8,1.1,0.4,0.8,1.9,0.9,1.8,0.5,2.0,0.6,1.6,0.7,1.8,0.7,1.1,0.3,0.4,1.6,0.8,2.6,0.9,0.9,0.5,0.8,1.1,3.1,0.3,8,18,10,8,8,16,17,12,11,5,4.0,33,13,7,21,51,57,8,29,36,16,26,26,32,31,32,49,21,19,27,18,4,3,19,3,4,35,23,1,1,3,27,17,19,13,3,1,5,6,6,6,14,6,15,3,17,6,12,5,12,5,8,1,3,12,10,19,6,5,4,7,8,22,2,0.047,0.023,0.023,0.022,0.027,0.029,0.046,0.026,0.035,0.021,0.032,0.039,0.028,0.036,0.033,0.03,0.029,0.04,0.039,0.045,0.035,0.034,0.044,0.045,0.036,0.028,0.044,0.032,0.027,0.03,0.045,0.039,0.031,0.026,0.014,0.018,0.039,0.026,0.019,0.015,0.016,0.044,0.03,0.027,0.019,0.011,0.014,0.014,0.019,0.014,0.035,0.025,0.015,0.021,0.022,0.018,0.019,0.021,0.024,0.022,0.023,0.026,0.022,0.022,0.018,0.021,0.022,0.017,0.019,0.02,0.022,0.016,0.018,0.03,1172,4331,1811,1907,1727,2756,2381,2685,1790,1192,746.0,3307,2500,1359,3191,8504,14836,1987,4783,4165,3812,3428,2466,5693,4765,6297,6575,3342,5382,4704,2085,566,775,3625,1763,1224,5830,6267,363,470,1160,2219,5458,2563,3468,1120,693,1913,1956,1284,605,2545,1790,3947,603,3508,1247,2668,1081,2145,1089,1839,397,973,2626,944,3494,1430,1462,738,1285,2309,5818,366,0.136,0.114,0.124,0.114,0.119,0.128,0.122,0.124,0.132,0.117,0.115,0.149,0.126,0.153,0.149,0.131,0.124,0.133,0.135,0.15,0.113,0.125,0.143,0.159,0.139,0.121,0.141,0.102,0.098,0.133,0.146,0.117,0.088,0.118,0.075,0.088,0.142,0.115,0.096,0.096,0.09,0.142,0.132,0.127,0.098,0.081,0.089,0.086,0.096,0.091,0.155,0.123,0.092,0.103,0.116,0.1,0.116,0.108,0.11,0.115,0.109,0.122,0.113,0.117,0.107,0.109,0.114,0.104,0.101,0.138,0.123,0.096,0.098,0.145,396,1473,668,713,625,1010,806,831,555,312,214.0,1508,667,359,931,2539,3893,456,1407,1853,1034,1257,1238,1477,1369,1736,2274,1206,1376,1424,611,317,207,974,488,458,1648,1431,200,229,615,1173,1124,1479,1884,555,283,873,915,653,318,1205,867,1489,317,1796,516,1247,523,1153,571,665,202,337,1298,618,1758,715,734,328,562,1115,2576,145,2.958,2.831,2.777,2.518,2.478,2.365,2.39,2.737,2.498,2.891,2.705,1.971,2.773,2.733,2.665,2.607,2.939,3.364,2.705,1.963,2.962,2.204,1.881,2.883,2.745,2.804,2.38,2.188,2.906,2.568,2.557,1.908,2.916,2.777,3.226,2.451,2.652,3.168,2.236,2.387,2.437,1.794,3.345,1.954,2.105,2.255,2.779,2.773,2.612,2.174,1.941,2.181,2.368,2.58,2.371,2.18,2.352,2.356,2.302,2.062,2.151,2.576,2.161,3.172,2.265,1.89,2.202,2.213,2.366,2.734,2.531,2.497,2.491,2.927,0.595,0.557,0.465,0.526,0.484,0.464,0.673,0.417,0.503,0.607,0.65,0.488,0.392,0.467,0.429,0.522,0.589,0.933,0.607,0.533,0.837,0.485,0.592,0.578,0.486,0.497,0.605,0.55,0.482,0.497,0.699,0.818,0.348,0.481,0.636,0.423,0.646,0.519,0.323,0.481,0.431,0.501,0.678,0.509,0.561,0.384,0.505,0.563,0.387,0.428,0.404,0.44,0.377,0.445,0.369,0.38,0.494,0.456,0.493,0.372,0.352,0.626,0.293,0.623,0.392,0.58,0.444,0.461,0.411,0.5,0.483,0.438,0.372,0.403
-66,Epileptic,M,27.0,0.6,3.0,1.2,1.7,1.6,1.7,1.4,1.9,0.9,0.8,0.4,2.8,1.4,0.5,1.3,7.6,8.9,0.9,1.9,3.8,1.0,2.3,1.3,3.5,4.2,2.9,1.9,2.2,3.1,2.8,1.7,1.0,0.5,1.9,0.4,1.2,3.2,3.2,0.1,0.2,0.9,2.5,3.4,2.2,2.2,0.5,0.5,0.8,1.1,1.0,0.7,2.5,1.2,3.1,0.3,1.9,0.8,1.4,1.7,0.9,0.4,1.5,0.4,0.5,1.4,1.5,1.4,1.1,1.0,0.3,0.7,0.9,4.0,0.3,3,20,10,16,15,13,10,15,10,7,4.0,26,14,4,10,64,71,6,19,32,9,27,13,39,40,24,20,21,25,26,19,7,5,18,2,12,28,33,1,1,5,22,26,16,10,4,2,4,6,6,5,18,11,19,2,14,3,11,11,8,3,12,3,5,10,19,11,6,5,3,6,6,31,3,0.028,0.025,0.016,0.029,0.03,0.028,0.025,0.031,0.042,0.039,0.039,0.037,0.029,0.035,0.033,0.042,0.03,0.036,0.027,0.037,0.02,0.03,0.023,0.043,0.032,0.031,0.024,0.03,0.03,0.037,0.04,0.044,0.031,0.026,0.013,0.026,0.038,0.031,0.012,0.015,0.016,0.033,0.05,0.028,0.015,0.016,0.025,0.014,0.015,0.016,0.031,0.024,0.034,0.02,0.022,0.015,0.017,0.016,0.035,0.014,0.017,0.024,0.03,0.021,0.02,0.021,0.016,0.015,0.015,0.015,0.017,0.016,0.016,0.015,1560,6575,3004,2906,2394,3226,2750,3217,1771,1231,497.0,2919,3986,1311,2530,12330,19750,2053,4736,4491,3757,4780,2294,6568,7787,5516,4520,3958,6974,4745,2526,1343,914,6385,2336,2158,6939,8098,383,456,1686,3297,6596,2353,3712,1263,684,2352,2442,1692,608,4093,2038,6619,551,3671,1619,3275,1443,1666,916,2940,659,933,2288,1454,3071,2575,2060,944,1184,2438,9722,526,0.094,0.116,0.096,0.128,0.138,0.124,0.102,0.125,0.147,0.144,0.157,0.152,0.13,0.14,0.136,0.141,0.122,0.127,0.116,0.143,0.097,0.132,0.123,0.146,0.13,0.131,0.112,0.112,0.114,0.149,0.142,0.123,0.141,0.119,0.071,0.12,0.136,0.14,0.079,0.092,0.097,0.127,0.159,0.121,0.084,0.094,0.117,0.08,0.082,0.099,0.138,0.116,0.13,0.1,0.109,0.097,0.093,0.094,0.148,0.093,0.1,0.121,0.136,0.128,0.111,0.112,0.095,0.089,0.087,0.098,0.111,0.093,0.092,0.115,414,1859,1083,953,897,952,848,1038,467,335,160.0,1157,836,263,659,3200,4952,425,1140,1744,880,1368,846,1599,2274,1572,1301,1271,1760,1362,648,458,295,1231,510,742,1391,1679,194,188,738,1204,1235,1371,2079,586,260,907,1091,684,366,1629,708,2403,221,1809,623,1418,721,940,424,1013,228,396,1091,1057,1324,1046,961,377,575,880,3909,273,3.74,3.141,2.646,2.781,2.463,2.885,2.593,2.839,2.873,2.902,2.5,2.193,3.367,3.251,2.927,2.82,3.153,3.849,3.213,2.219,3.273,2.748,2.283,3.015,2.719,3.012,2.595,2.413,2.997,2.813,2.88,2.968,2.617,3.397,3.959,2.572,3.528,3.427,2.376,2.847,2.982,2.282,3.51,1.934,2.026,2.435,3.517,3.4,2.615,2.996,2.138,2.434,2.556,2.685,2.987,2.245,3.288,2.696,2.228,1.978,2.34,2.962,2.833,2.554,2.511,1.697,2.421,2.833,2.533,2.824,2.241,3.117,2.625,2.505,0.732,0.717,0.705,0.674,0.588,0.602,0.565,0.668,0.542,0.818,0.976,0.546,0.534,0.479,0.52,0.674,0.617,0.571,0.555,0.566,0.822,0.534,0.531,0.748,0.519,0.487,0.51,0.574,0.56,0.574,0.821,0.931,0.601,0.701,0.662,0.436,0.705,0.671,0.272,0.341,0.774,0.605,0.875,0.619,0.66,0.615,0.5,0.651,0.522,0.57,0.373,0.542,0.695,0.528,0.52,0.447,0.617,0.485,0.564,0.478,0.36,0.763,0.751,0.965,0.644,0.575,0.524,0.671,0.449,0.789,0.469,0.686,0.506,0.377
-67,Epileptic,F,42.0,0.8,1.8,0.9,1.1,2.0,1.6,1.3,2.4,1.4,0.6,0.2,2.2,1.5,0.7,1.2,4.7,8.0,1.6,1.1,3.9,2.5,2.9,1.8,3.4,2.8,3.0,2.9,2.3,2.0,3.0,1.6,1.0,0.5,2.0,0.4,0.6,3.0,2.4,0.2,0.2,1.1,2.9,2.3,1.2,2.3,0.7,0.5,0.8,1.4,0.7,0.5,1.6,1.4,2.7,0.2,2.1,0.5,1.4,1.1,0.9,0.6,1.6,0.3,0.4,1.6,0.8,1.5,1.5,1.0,0.7,0.7,1.5,4.4,0.4,6,15,6,8,16,17,14,22,13,8,2.0,26,17,9,17,58,88,14,19,38,22,33,17,40,36,36,44,25,21,41,22,12,7,31,3,4,37,39,1,1,8,29,27,10,13,5,2,4,7,5,3,12,12,23,1,15,4,13,10,7,8,13,2,4,11,14,14,13,6,6,6,9,40,3,0.04,0.025,0.019,0.023,0.047,0.031,0.035,0.036,0.062,0.042,0.027,0.035,0.032,0.046,0.041,0.035,0.036,0.062,0.028,0.041,0.05,0.048,0.041,0.044,0.036,0.03,0.035,0.029,0.029,0.04,0.049,0.057,0.025,0.033,0.02,0.013,0.045,0.034,0.017,0.015,0.021,0.043,0.05,0.019,0.018,0.019,0.022,0.013,0.018,0.02,0.024,0.021,0.026,0.025,0.035,0.016,0.027,0.02,0.025,0.018,0.031,0.031,0.025,0.02,0.021,0.021,0.017,0.022,0.02,0.024,0.021,0.021,0.022,0.015,1822,4153,2020,1997,2125,3418,2340,3599,1748,1112,489.0,3097,3371,1245,2376,9762,16075,1930,3545,4660,3348,4046,2060,5771,5950,6156,4978,3292,5027,5003,2304,1100,1357,5907,1356,1807,4582,6961,298,344,1561,2946,4214,1976,3219,1138,705,2463,2266,1185,641,2442,1950,4561,298,3443,876,2670,1380,1388,1129,2452,271,916,2423,877,2925,2342,1523,1197,997,2359,7149,616,0.125,0.118,0.107,0.119,0.161,0.143,0.107,0.144,0.196,0.169,0.127,0.137,0.131,0.164,0.15,0.137,0.145,0.159,0.137,0.153,0.152,0.149,0.125,0.158,0.133,0.135,0.123,0.114,0.104,0.142,0.154,0.141,0.105,0.133,0.088,0.082,0.142,0.145,0.113,0.09,0.109,0.146,0.168,0.108,0.096,0.095,0.112,0.076,0.097,0.107,0.121,0.115,0.129,0.114,0.136,0.098,0.118,0.114,0.115,0.099,0.127,0.137,0.139,0.108,0.112,0.105,0.094,0.115,0.097,0.143,0.12,0.111,0.106,0.097,400,1282,745,731,745,981,760,1103,470,299,154.0,1177,946,351,695,2818,4409,480,825,1736,846,1163,782,1575,1683,1892,1604,1296,1340,1480,695,421,383,1308,364,685,1283,1570,154,184,816,1290,1008,1072,1853,596,312,904,1054,500,312,1187,870,1948,102,1827,340,1160,721,738,435,878,158,357,1128,627,1413,1044,729,465,537,1019,3222,342,3.658,2.853,2.463,2.57,2.58,2.711,2.569,2.836,2.991,2.816,2.77,2.126,2.895,2.706,2.687,2.681,2.998,3.419,3.061,2.197,2.841,2.695,2.233,2.939,2.721,2.625,2.438,2.052,2.785,2.542,2.628,2.811,2.653,3.161,3.323,2.428,2.666,3.109,2.423,2.057,2.381,1.937,3.06,1.959,2.002,1.983,2.796,3.339,2.616,2.637,2.377,2.236,2.295,2.394,2.907,2.1,2.479,2.558,2.007,2.075,2.348,2.806,2.032,3.004,2.348,1.731,2.09,2.442,2.381,2.91,1.925,2.476,2.392,2.238,0.837,0.673,0.59,0.488,0.533,0.733,0.675,0.521,0.521,0.729,0.8,0.609,0.432,0.503,0.521,0.497,0.548,0.697,0.59,0.565,1.041,0.579,0.622,0.65,0.625,0.551,0.538,0.566,0.533,0.597,0.693,0.737,0.398,0.595,0.545,0.382,0.807,0.636,0.457,0.381,0.45,0.573,0.71,0.646,0.547,0.432,0.394,0.88,0.349,0.598,0.421,0.422,0.455,0.485,0.377,0.418,0.614,0.631,0.42,0.396,0.501,0.573,0.281,0.65,0.464,0.7,0.461,0.437,0.476,0.802,0.386,0.577,0.447,0.421
-68,Epileptic,F,42.0,0.9,1.8,0.6,1.4,1.4,1.7,1.6,1.1,0.8,0.8,0.2,2.0,1.0,0.3,1.0,5.3,7.1,0.7,2.3,4.1,1.6,2.3,1.4,2.9,2.8,3.8,2.9,2.5,2.9,2.7,1.6,1.2,0.5,2.5,0.4,0.7,2.8,3.4,0.1,0.2,1.2,2.6,2.0,2.6,2.8,0.5,0.4,0.8,1.0,0.5,0.4,1.1,1.2,2.5,0.3,1.8,0.9,1.4,0.9,1.0,0.8,1.0,0.2,0.3,1.5,1.1,2.3,1.1,1.1,0.9,0.8,1.2,4.4,0.4,4,14,6,13,10,13,11,11,8,6,3.0,17,11,5,11,45,61,7,23,32,13,26,13,25,33,38,29,24,25,28,13,17,4,22,1,4,24,33,1,2,7,23,16,15,16,5,2,5,5,5,3,8,8,12,2,17,4,12,7,8,8,7,1,4,13,11,15,7,6,6,6,9,38,4,0.041,0.027,0.015,0.026,0.04,0.027,0.023,0.028,0.036,0.037,0.029,0.029,0.027,0.026,0.034,0.039,0.026,0.029,0.032,0.036,0.026,0.031,0.028,0.045,0.03,0.029,0.029,0.028,0.03,0.027,0.05,0.059,0.018,0.036,0.015,0.017,0.035,0.035,0.015,0.016,0.025,0.032,0.037,0.032,0.021,0.01,0.022,0.018,0.015,0.021,0.02,0.022,0.03,0.022,0.02,0.015,0.024,0.018,0.022,0.018,0.032,0.024,0.026,0.02,0.022,0.03,0.02,0.019,0.02,0.033,0.021,0.021,0.019,0.016,1421,3011,1395,2286,1799,2255,2684,2336,1020,1176,453.0,2028,2414,856,2100,8060,15373,1787,4338,3890,3606,4053,1793,4281,5969,6763,4127,3582,5805,4486,1915,1004,964,4325,1368,1697,4984,6048,286,497,1503,1985,4422,1884,3641,1498,602,1650,2201,1212,569,1659,1475,3650,440,3312,1258,2853,1179,1495,755,1868,241,765,2050,1034,3487,1962,1798,1059,1202,1903,8927,511,0.124,0.114,0.093,0.115,0.138,0.124,0.099,0.121,0.123,0.134,0.106,0.126,0.117,0.115,0.129,0.129,0.119,0.117,0.126,0.142,0.092,0.134,0.12,0.135,0.135,0.135,0.119,0.109,0.111,0.13,0.141,0.133,0.099,0.124,0.07,0.098,0.12,0.134,0.085,0.116,0.115,0.137,0.147,0.122,0.103,0.086,0.11,0.089,0.086,0.092,0.114,0.11,0.117,0.1,0.108,0.096,0.107,0.097,0.113,0.104,0.136,0.114,0.121,0.107,0.11,0.136,0.098,0.1,0.101,0.14,0.122,0.111,0.099,0.109,389,1072,634,931,615,949,966,817,378,343,159.0,1045,680,236,569,2180,4410,378,1164,1784,1006,1278,814,1122,1718,2058,1602,1363,1609,1600,496,441,414,1143,406,622,1421,1691,179,210,764,1123,949,1216,2052,786,267,736,1019,528,308,857,668,1574,203,1940,553,1374,666,856,423,643,138,329,1127,547,1854,924,791,410,606,911,3943,342,3.25,2.43,2.102,2.449,2.484,2.129,2.349,2.673,2.183,2.959,2.53,1.737,2.765,2.627,2.607,2.741,2.72,3.498,2.911,1.935,2.766,2.481,1.996,2.78,2.684,2.635,2.075,2.095,2.788,2.306,2.609,2.411,1.904,2.681,2.908,2.56,2.641,2.765,1.905,2.634,2.626,1.677,3.314,1.799,2.072,2.056,2.865,2.706,2.546,2.688,2.046,2.185,2.168,2.547,2.568,1.898,2.979,2.39,2.064,1.947,2.168,2.775,1.919,2.616,2.006,2.336,2.17,2.339,2.688,2.775,2.279,2.572,2.4,1.933,0.924,0.756,0.541,0.401,0.717,0.466,0.564,0.509,0.633,0.499,0.524,0.453,0.465,0.481,0.646,0.636,0.664,0.876,0.596,0.528,0.781,0.422,0.408,0.765,0.558,0.575,0.491,0.51,0.596,0.487,0.764,0.971,0.436,0.714,1.11,0.546,0.723,0.601,0.433,0.631,0.573,0.383,0.674,0.768,0.604,0.502,0.623,0.757,0.51,0.55,0.406,0.412,0.593,0.627,0.596,0.412,0.407,0.625,0.391,0.386,0.471,0.776,0.503,0.717,0.494,0.532,0.587,0.51,0.56,0.648,0.499,0.488,0.461,0.437
-69,Epileptic,F,17.5,0.6,2.3,0.8,1.2,1.3,1.6,1.8,1.7,1.0,0.7,0.3,2.6,1.3,0.4,1.3,6.6,8.9,0.6,2.1,5.4,0.9,2.3,1.8,4.0,4.0,2.9,2.4,3.1,2.9,3.0,1.3,1.6,0.4,2.6,0.6,0.9,2.5,2.8,0.1,0.1,1.1,3.1,2.3,2.6,3.4,0.7,0.4,1.1,1.1,0.7,0.6,1.9,1.1,2.8,0.5,1.6,0.4,1.8,1.6,1.1,0.5,1.3,0.2,0.3,1.6,1.1,3.1,1.0,1.0,0.6,0.8,1.0,4.7,0.3,4,19,7,10,12,17,17,17,11,6,2.0,24,15,6,14,81,81,6,26,49,7,27,21,43,47,35,26,38,26,35,18,9,5,29,3,8,27,35,1,1,7,27,22,18,21,6,2,6,5,10,4,13,11,19,4,15,3,18,10,9,4,8,3,4,14,16,23,6,6,6,5,8,41,3,0.029,0.025,0.012,0.022,0.033,0.029,0.022,0.031,0.04,0.037,0.031,0.035,0.031,0.03,0.036,0.037,0.029,0.025,0.031,0.038,0.019,0.032,0.034,0.041,0.036,0.033,0.026,0.025,0.024,0.031,0.042,0.058,0.016,0.03,0.017,0.02,0.032,0.032,0.013,0.013,0.021,0.036,0.042,0.028,0.021,0.017,0.021,0.018,0.014,0.029,0.024,0.018,0.024,0.021,0.023,0.014,0.01,0.022,0.03,0.019,0.019,0.022,0.024,0.016,0.02,0.023,0.021,0.015,0.016,0.016,0.021,0.02,0.018,0.013,1531,5148,2740,2482,1969,2747,4056,4070,1853,1628,878.0,2412,3377,1376,2261,15154,22157,2594,4941,5611,2917,4335,2077,6636,7335,6434,4913,6117,7876,6017,2425,1366,1212,7242,1822,2929,7236,7400,312,645,1743,2870,5659,2893,4474,1602,735,2416,2420,1498,712,3570,2488,6066,622,3719,1407,3390,1483,1768,1101,2935,616,833,2474,1366,5092,2299,2320,1283,1458,1678,11186,508,0.107,0.116,0.091,0.109,0.127,0.14,0.103,0.129,0.139,0.138,0.124,0.147,0.129,0.14,0.153,0.146,0.129,0.109,0.148,0.154,0.095,0.143,0.154,0.154,0.152,0.139,0.113,0.123,0.112,0.137,0.143,0.138,0.096,0.133,0.088,0.097,0.128,0.14,0.089,0.077,0.108,0.141,0.156,0.122,0.101,0.1,0.103,0.091,0.084,0.137,0.131,0.1,0.109,0.104,0.13,0.09,0.073,0.11,0.134,0.106,0.109,0.105,0.119,0.119,0.11,0.121,0.111,0.092,0.091,0.1,0.103,0.118,0.099,0.11,351,1451,947,808,758,990,1231,1014,481,367,180.0,1290,798,275,603,3293,5372,424,1214,2502,768,1301,911,1580,1901,1772,1650,2022,1889,1678,537,530,393,1437,518,793,1453,1610,152,183,767,1400,1077,1501,2490,708,286,954,1054,494,345,1506,897,2303,316,1778,623,1499,815,891,367,910,204,389,1188,769,2262,965,984,528,649,747,4145,302,3.805,3.018,2.747,2.835,2.285,2.402,2.616,3.353,2.655,3.241,3.554,1.701,3.037,3.103,2.701,3.157,3.229,4.072,3.077,1.971,2.91,2.554,2.085,3.064,2.825,2.986,2.349,2.361,3.077,2.766,3.149,2.565,2.363,3.532,3.235,3.131,3.418,3.206,2.408,2.573,2.873,1.872,3.286,2.189,2.038,2.52,3.239,3.145,2.776,2.941,2.346,2.394,2.641,2.696,2.488,2.201,2.534,2.483,2.152,2.185,2.707,3.224,2.43,2.222,2.365,2.197,2.445,2.856,2.718,2.474,2.691,2.569,2.806,2.148,0.986,0.767,0.666,0.586,0.604,0.592,0.684,0.666,0.628,0.542,0.898,0.41,0.525,0.716,0.572,0.752,0.61,0.73,0.568,0.599,0.867,0.519,0.583,0.683,0.536,0.58,0.599,0.616,0.614,0.568,0.786,1.029,0.45,0.745,0.704,0.719,0.783,0.62,0.289,0.762,0.544,0.455,0.867,0.686,0.611,0.548,0.561,0.737,0.62,0.728,0.532,0.612,0.529,0.559,0.293,0.417,0.425,0.513,0.401,0.408,0.578,0.796,0.725,0.792,0.576,0.636,0.426,0.357,0.416,0.53,0.531,0.547,0.57,0.333
-70,Epileptic,M,47.0,1.7,2.8,1.2,0.9,2.1,2.3,2.9,2.0,1.9,0.8,0.5,3.6,1.8,0.5,2.3,5.4,9.2,1.1,2.6,4.5,1.5,3.1,2.5,3.6,4.2,3.5,5.3,2.6,2.8,3.2,2.0,0.7,0.5,2.5,0.7,0.8,4.1,3.0,0.2,0.1,1.1,4.4,2.4,3.2,2.7,1.1,0.6,1.2,1.5,0.9,0.5,2.8,1.5,2.9,0.5,3.2,0.5,1.5,1.3,1.6,1.0,1.7,0.7,0.7,2.3,1.5,2.7,2.0,1.2,1.0,1.2,1.3,3.9,0.4,11,28,15,7,17,21,22,22,15,9,5.0,35,15,6,24,54,68,11,31,43,15,45,22,32,45,38,55,25,22,36,19,24,5,27,3,9,41,38,2,1,6,38,22,27,16,9,3,9,9,6,4,19,10,21,3,26,4,11,6,13,10,11,4,5,23,18,19,12,8,9,9,12,31,4,0.053,0.027,0.019,0.021,0.039,0.032,0.041,0.029,0.049,0.033,0.035,0.032,0.028,0.024,0.036,0.032,0.038,0.034,0.031,0.033,0.025,0.029,0.028,0.043,0.03,0.028,0.034,0.025,0.027,0.03,0.061,0.104,0.024,0.029,0.019,0.018,0.042,0.029,0.011,0.015,0.02,0.048,0.036,0.026,0.02,0.018,0.026,0.018,0.018,0.014,0.028,0.023,0.022,0.024,0.027,0.018,0.014,0.016,0.019,0.022,0.026,0.031,0.033,0.025,0.025,0.033,0.018,0.027,0.025,0.026,0.02,0.021,0.015,0.018,1833,5066,2373,2017,2331,3455,2990,3635,2063,1638,660.0,3495,3351,1200,3342,10753,16794,2531,5371,4947,4591,4888,2594,5553,6378,7742,6339,4694,5651,6467,2150,1152,1206,7180,2546,2592,6823,8561,510,375,1452,3201,6514,3483,3752,1746,727,2623,2693,2027,450,4344,2123,4243,599,4892,1047,2881,1633,2093,1448,2788,763,980,3329,1122,4500,2328,1525,1580,1780,2456,9702,511,0.159,0.126,0.117,0.101,0.14,0.143,0.121,0.132,0.151,0.136,0.149,0.143,0.121,0.105,0.143,0.124,0.128,0.117,0.141,0.136,0.106,0.121,0.123,0.133,0.115,0.12,0.114,0.1,0.098,0.123,0.153,0.111,0.095,0.122,0.082,0.099,0.151,0.121,0.092,0.086,0.102,0.148,0.144,0.122,0.101,0.101,0.117,0.092,0.101,0.09,0.136,0.11,0.115,0.11,0.136,0.105,0.109,0.096,0.096,0.11,0.125,0.12,0.127,0.128,0.123,0.138,0.1,0.118,0.121,0.138,0.115,0.115,0.095,0.125,518,1652,983,715,878,1233,1042,1149,683,450,207.0,1704,1014,289,1024,2860,4393,550,1502,2231,1028,1584,1261,1532,1890,2187,2430,1546,1451,1961,704,471,372,1447,582,733,1702,1873,233,197,763,1550,1291,2158,2071,921,344,1142,1232,893,291,1928,1042,1977,256,2597,506,1414,961,1147,614,931,368,480,1624,717,2288,1175,725,542,854,1104,3900,255,3.335,2.807,2.457,2.635,2.498,2.57,2.39,2.786,2.339,2.914,2.613,1.842,2.726,2.956,2.574,2.805,2.911,3.54,2.932,1.961,3.207,2.371,1.849,2.742,2.644,2.841,2.267,2.375,2.936,2.591,2.36,2.326,2.656,3.454,3.588,2.999,3.026,3.264,2.42,2.164,2.473,1.877,3.488,1.834,2.037,2.06,2.606,2.933,2.779,2.685,1.827,2.327,2.362,2.298,2.729,2.128,2.188,2.289,1.992,2.019,2.383,3.138,2.488,2.438,2.13,1.83,2.195,2.394,2.354,2.974,2.251,2.521,2.74,2.561,0.796,0.58,0.416,0.582,0.548,0.679,0.633,0.567,0.77,0.685,0.742,0.466,0.414,0.434,0.526,0.538,0.672,0.732,0.616,0.566,0.749,0.652,0.512,0.871,0.635,0.547,0.537,0.55,0.526,0.571,0.72,1.085,0.399,0.581,0.698,0.676,0.88,0.669,0.448,0.458,0.472,0.529,0.756,0.674,0.666,0.51,0.438,0.545,0.519,0.401,0.35,0.499,0.439,0.477,0.558,0.413,0.407,0.584,0.464,0.497,0.562,0.741,0.737,0.836,0.556,0.566,0.502,0.395,0.425,0.911,0.487,0.566,0.437,0.423
-71,Epileptic,M,57.0,1.8,2.9,1.1,1.2,2.1,2.3,2.7,2.5,0.9,1.0,0.3,3.4,2.2,0.8,2.4,7.7,12.9,1.5,2.4,6.0,1.4,3.1,2.0,4.6,3.0,3.9,5.1,2.3,3.6,5.8,2.3,1.1,0.6,3.1,0.4,1.1,3.0,4.8,0.3,0.2,1.4,3.1,3.9,4.0,3.1,1.0,0.5,1.0,1.6,0.6,0.7,2.7,2.2,3.3,0.2,2.7,0.6,1.9,0.7,1.8,0.9,2.7,0.8,0.6,3.7,1.2,5.1,2.3,1.5,0.7,1.4,1.4,5.4,0.6,9,27,10,9,21,18,19,21,9,9,2.0,31,20,10,21,74,100,7,28,48,9,37,22,50,40,39,53,20,29,48,25,9,5,34,2,12,33,53,1,2,6,28,27,71,18,7,2,6,8,5,5,19,10,19,2,19,4,10,4,16,10,18,4,4,22,9,42,13,10,5,8,9,41,4,0.045,0.021,0.013,0.019,0.039,0.033,0.032,0.032,0.036,0.04,0.027,0.033,0.03,0.032,0.036,0.034,0.033,0.036,0.031,0.036,0.024,0.036,0.028,0.04,0.034,0.027,0.032,0.027,0.03,0.037,0.048,0.026,0.023,0.031,0.011,0.022,0.043,0.036,0.016,0.013,0.026,0.035,0.058,0.246,0.025,0.021,0.02,0.014,0.016,0.025,0.03,0.023,0.023,0.022,0.029,0.02,0.014,0.02,0.021,0.025,0.027,0.037,0.025,0.021,0.028,0.022,0.025,0.023,0.023,0.019,0.024,0.019,0.018,0.023,2102,6696,2826,2525,2491,2973,3032,3822,1630,1363,564.0,2960,3931,1506,3500,13843,22836,3339,5257,6865,2787,4654,2583,7717,5558,7325,8031,3950,6528,7667,2647,1755,1272,7073,2134,2702,6296,10245,453,552,1798,3274,5463,3207,3382,1501,940,2661,3257,1342,648,4496,3052,5800,337,3859,1438,3016,798,2040,1229,3023,768,935,4061,1644,7228,3228,1974,1184,1717,2230,10774,645,0.139,0.099,0.088,0.095,0.14,0.14,0.122,0.136,0.136,0.142,0.106,0.137,0.121,0.141,0.14,0.131,0.127,0.123,0.124,0.135,0.097,0.14,0.131,0.145,0.137,0.124,0.13,0.101,0.103,0.141,0.158,0.094,0.101,0.129,0.066,0.105,0.138,0.138,0.086,0.109,0.112,0.142,0.178,0.117,0.108,0.106,0.088,0.078,0.091,0.108,0.129,0.104,0.107,0.102,0.136,0.107,0.085,0.097,0.116,0.125,0.129,0.14,0.127,0.119,0.119,0.095,0.118,0.104,0.118,0.108,0.117,0.107,0.098,0.124,581,2287,1175,1000,989,1124,1241,1298,492,383,186.0,1558,1164,417,1131,3826,6415,576,1393,2697,863,1525,1121,2102,1705,2451,2889,1398,1888,2667,735,687,425,1582,567,862,1492,2367,254,280,821,1404,1159,1983,2104,767,388,1094,1511,457,397,2078,1332,2444,158,2059,689,1477,477,1201,556,1174,405,468,2041,843,3257,1463,972,614,886,1088,4568,384,3.506,2.678,2.471,2.577,2.286,2.243,2.104,2.525,2.5,2.985,2.604,1.77,2.633,2.58,2.501,2.687,2.813,4.116,2.943,2.122,2.566,2.405,1.918,2.819,2.415,2.515,2.191,2.198,2.568,2.341,2.629,2.39,2.428,3.048,3.266,2.657,3.08,2.999,2.03,2.064,2.713,2.04,3.198,1.855,1.834,2.196,2.852,2.996,2.552,3.073,2.154,2.228,2.479,2.449,2.503,2.028,2.33,2.308,1.865,1.802,2.07,2.532,2.313,2.28,2.133,2.174,2.097,2.322,2.204,2.37,2.196,2.366,2.488,2.099,0.684,0.532,0.433,0.434,0.522,0.556,0.459,0.684,0.55,0.439,0.618,0.488,0.426,0.449,0.422,0.57,0.62,0.66,0.684,0.64,0.831,0.514,0.527,0.565,0.607,0.557,0.502,0.539,0.598,0.574,0.674,0.973,0.495,0.668,0.576,0.503,0.664,0.74,0.375,0.473,0.633,0.452,0.776,0.652,0.484,0.513,0.523,0.68,0.566,0.549,0.444,0.503,0.699,0.482,0.563,0.405,0.482,0.543,0.39,0.388,0.484,0.607,0.588,0.731,0.61,0.642,0.513,0.433,0.469,0.525,0.541,0.476,0.623,0.48
-72,Epileptic,F,47.0,0.6,2.4,1.4,1.2,2.0,2.1,1.5,2.1,1.0,0.6,0.3,2.3,1.6,0.6,1.0,5.8,8.6,0.6,2.7,5.6,1.1,2.7,1.9,3.0,2.2,4.2,3.3,3.0,2.8,3.8,1.1,0.7,0.9,2.0,0.6,0.5,3.0,3.5,0.1,0.3,1.3,2.8,2.2,4.6,3.4,0.8,0.7,1.3,1.0,0.8,0.7,1.7,2.4,2.4,0.1,2.5,1.3,1.8,1.4,1.3,0.9,1.3,0.3,0.3,2.2,1.8,2.4,1.3,1.3,0.4,1.3,0.8,4.4,0.2,6,26,16,10,20,18,11,15,11,4,3.0,18,14,6,8,57,70,6,23,44,14,27,16,31,27,39,35,28,20,29,15,7,6,21,3,2,28,32,1,2,7,23,18,34,18,6,4,8,5,6,5,12,20,16,0,21,10,16,11,9,5,8,2,3,14,19,18,9,8,3,8,7,32,2,0.027,0.023,0.021,0.02,0.033,0.031,0.022,0.035,0.041,0.031,0.024,0.029,0.032,0.028,0.032,0.032,0.032,0.024,0.035,0.04,0.022,0.029,0.034,0.038,0.028,0.034,0.03,0.028,0.025,0.035,0.039,0.024,0.025,0.024,0.017,0.012,0.032,0.034,0.024,0.02,0.029,0.037,0.039,0.045,0.024,0.018,0.03,0.019,0.014,0.019,0.03,0.019,0.024,0.017,0.015,0.018,0.031,0.023,0.033,0.022,0.034,0.022,0.024,0.013,0.023,0.03,0.022,0.023,0.025,0.017,0.033,0.022,0.02,0.018,1301,4810,2831,2429,1984,2623,2325,3108,1412,1154,596.0,2207,2761,1025,1851,9968,16032,1996,3626,4358,2456,4340,2015,4909,4526,6154,5918,4622,5085,5358,1682,1226,1628,5834,1872,1748,5933,6584,154,454,1660,2277,4945,2235,3635,1344,884,2470,2265,1552,550,2830,3545,5171,152,4076,1310,2719,1315,1601,927,2069,449,696,3039,1955,3353,1837,1725,610,1522,1846,8832,467,0.117,0.115,0.117,0.109,0.134,0.134,0.098,0.132,0.137,0.119,0.106,0.125,0.13,0.126,0.119,0.13,0.127,0.106,0.129,0.142,0.088,0.127,0.133,0.134,0.129,0.136,0.124,0.105,0.102,0.142,0.135,0.095,0.109,0.113,0.091,0.075,0.121,0.139,0.119,0.12,0.117,0.142,0.132,0.156,0.107,0.1,0.11,0.103,0.081,0.111,0.134,0.104,0.121,0.096,0.076,0.112,0.13,0.109,0.14,0.115,0.129,0.116,0.115,0.102,0.112,0.145,0.114,0.113,0.116,0.107,0.132,0.117,0.103,0.11,417,1729,1042,947,887,1030,910,1021,413,313,193.0,1128,819,309,572,3055,4633,422,1329,2141,708,1476,881,1364,1379,2048,1947,1628,1512,1719,503,535,484,1336,488,565,1528,1702,111,197,787,1175,1052,1653,2253,703,412,1069,1069,673,344,1371,1542,2212,66,2096,738,1389,763,924,443,802,210,392,1486,871,1736,931,907,332,664,702,3547,223,3.194,2.636,2.614,2.557,1.982,2.172,2.217,2.835,2.649,3.127,2.412,1.769,2.688,2.38,2.552,2.558,2.797,3.45,2.357,1.863,2.802,2.378,2.011,2.721,2.668,2.6,2.37,2.296,2.605,2.651,2.435,2.283,2.815,3.14,3.255,2.878,2.937,2.959,1.644,2.871,2.665,1.805,3.379,1.636,1.814,2.18,2.739,2.972,2.535,2.769,1.735,2.277,2.247,2.398,2.197,2.219,2.181,2.197,1.985,1.934,2.422,2.539,2.453,2.126,2.308,2.605,2.117,2.285,2.198,2.086,2.584,2.962,2.688,2.557,0.686,0.534,0.585,0.472,0.535,0.461,0.627,0.754,0.619,0.466,0.596,0.441,0.445,0.485,0.46,0.478,0.483,0.661,0.666,0.525,0.681,0.507,0.453,0.639,0.483,0.459,0.485,0.555,0.516,0.524,0.579,0.901,0.488,0.563,0.527,0.461,0.703,0.589,0.266,0.524,0.699,0.534,0.673,0.601,0.559,0.487,0.487,0.761,0.573,0.635,0.348,0.391,0.494,0.455,0.231,0.583,0.46,0.486,0.435,0.344,0.43,0.723,0.731,1.017,0.645,0.996,0.437,0.391,0.388,0.387,0.801,0.684,0.484,0.756
-74,Epileptic,M,17.5,0.7,2.4,0.9,1.1,1.4,1.5,1.1,1.7,1.0,0.9,0.5,3.3,1.8,0.4,1.3,6.3,10.1,0.8,1.3,4.4,0.9,2.4,1.7,2.1,2.4,3.0,2.8,1.7,2.2,3.2,1.2,0.8,0.5,2.1,0.5,0.6,2.8,3.6,0.2,0.2,1.3,2.4,1.7,2.5,2.1,0.7,0.3,0.8,1.0,1.0,0.4,1.7,1.6,2.5,0.2,1.9,0.4,1.4,0.7,0.9,0.8,0.8,0.5,0.4,1.4,1.8,2.0,1.4,0.7,0.4,0.7,1.7,3.9,0.4,4,25,7,8,15,18,14,15,14,10,7.0,30,19,6,12,89,94,6,17,40,8,30,18,25,29,37,37,19,23,46,14,9,5,23,3,6,32,47,1,1,10,23,15,19,13,6,1,5,6,9,3,11,11,16,1,17,6,14,6,8,6,6,4,2,12,21,14,11,5,4,5,12,32,4,0.031,0.028,0.013,0.019,0.042,0.028,0.025,0.025,0.046,0.036,0.038,0.032,0.027,0.028,0.038,0.031,0.033,0.024,0.023,0.036,0.022,0.028,0.027,0.031,0.03,0.023,0.026,0.02,0.021,0.035,0.042,0.048,0.021,0.028,0.016,0.011,0.028,0.031,0.012,0.019,0.024,0.03,0.044,0.02,0.015,0.016,0.015,0.012,0.013,0.023,0.017,0.017,0.026,0.018,0.018,0.014,0.012,0.018,0.02,0.016,0.023,0.02,0.048,0.018,0.016,0.029,0.017,0.016,0.012,0.017,0.017,0.018,0.016,0.019,1562,4359,2499,2331,1748,2518,2590,3640,1358,1802,871.0,3332,4155,846,2694,13532,20771,2498,3593,4496,2974,4457,2451,4823,4827,7999,6399,3501,6699,5761,2183,963,1240,7139,2016,2563,6745,8492,453,378,1669,2796,3686,3682,3671,1436,745,2670,2175,1883,570,3301,1946,5383,342,3568,1066,3022,1126,1667,979,1323,423,767,2886,1431,3724,3088,1851,825,1591,2923,9413,556,0.108,0.126,0.094,0.102,0.146,0.136,0.113,0.124,0.16,0.146,0.145,0.142,0.127,0.119,0.141,0.137,0.139,0.102,0.113,0.153,0.078,0.136,0.135,0.134,0.13,0.12,0.122,0.098,0.102,0.142,0.14,0.134,0.104,0.12,0.087,0.08,0.115,0.139,0.086,0.086,0.122,0.132,0.126,0.107,0.089,0.095,0.092,0.076,0.084,0.111,0.107,0.096,0.119,0.096,0.117,0.097,0.098,0.101,0.109,0.104,0.124,0.116,0.163,0.092,0.098,0.116,0.101,0.1,0.086,0.126,0.103,0.106,0.096,0.117,384,1368,896,841,661,991,853,1096,413,410,255.0,1563,1062,216,682,3664,5127,480,945,2087,812,1399,978,1184,1319,2161,1905,1255,1747,1798,507,405,376,1332,459,869,1687,1916,260,173,794,1302,837,1983,2048,684,289,987,1066,730,344,1523,952,2147,156,1976,547,1401,649,863,473,550,190,316,1373,929,1783,1292,838,355,730,1421,3610,335,3.657,3.034,2.677,2.724,2.347,2.361,2.476,2.972,2.497,3.277,2.785,1.899,3.003,2.721,2.869,2.769,3.183,3.827,3.035,1.906,2.875,2.446,2.171,2.937,2.758,2.892,2.532,2.241,2.894,2.515,3.099,2.432,2.486,3.564,3.96,2.676,2.895,3.231,2.117,2.369,2.636,1.969,3.25,2.05,2.038,2.15,3.235,3.366,2.552,2.834,2.064,2.225,2.174,2.624,2.748,2.085,2.292,2.377,2.067,2.114,2.633,2.446,2.208,2.699,2.288,1.888,2.297,2.666,2.538,2.674,2.413,2.487,2.739,2.092,0.746,0.615,0.605,0.559,0.613,0.529,0.615,0.672,0.525,0.81,0.787,0.465,0.444,0.416,0.472,0.588,0.583,0.859,0.412,0.576,0.637,0.411,0.561,0.677,0.533,0.538,0.528,0.529,0.516,0.593,0.775,1.173,0.504,0.56,0.618,0.489,0.608,0.583,0.316,0.412,0.482,0.545,0.726,0.58,0.592,0.527,0.606,0.817,0.46,0.535,0.393,0.537,0.456,0.488,0.247,0.402,0.407,0.504,0.409,0.412,0.437,0.579,0.603,0.91,0.487,0.622,0.405,0.402,0.427,0.477,0.436,0.381,0.519,0.361
-75,Epileptic,F,42.0,0.5,2.5,1.1,1.5,2.6,2.4,2.8,1.8,1.1,0.9,0.3,3.5,1.3,0.4,1.0,7.0,13.5,0.8,2.5,4.8,2.6,2.8,1.3,3.9,2.9,2.6,2.9,4.2,6.1,2.7,2.0,1.3,0.8,3.1,0.7,0.5,5.1,2.8,0.1,0.1,0.9,3.7,3.1,2.1,2.8,1.0,0.3,0.9,1.3,0.7,1.2,1.7,0.9,2.7,0.3,2.0,1.0,2.5,0.9,0.8,0.7,1.7,0.3,0.5,2.1,1.5,2.7,1.7,1.6,0.5,1.0,1.5,3.8,0.3,5,21,12,11,22,19,20,16,8,5,2.0,26,14,4,8,71,92,8,24,36,18,29,12,54,29,25,29,31,39,27,16,10,7,27,3,4,43,30,1,1,6,33,24,20,15,8,2,6,7,6,7,12,7,19,1,15,8,20,7,7,6,11,2,5,19,18,20,13,10,6,7,11,26,3,0.032,0.024,0.022,0.028,0.049,0.029,0.042,0.032,0.047,0.033,0.027,0.038,0.029,0.029,0.026,0.034,0.038,0.031,0.031,0.041,0.04,0.034,0.027,0.049,0.041,0.028,0.032,0.035,0.043,0.03,0.047,0.061,0.041,0.036,0.022,0.015,0.05,0.03,0.014,0.012,0.02,0.037,0.05,0.027,0.019,0.023,0.015,0.014,0.018,0.023,0.04,0.022,0.025,0.018,0.025,0.018,0.023,0.026,0.02,0.02,0.022,0.031,0.039,0.029,0.022,0.028,0.022,0.021,0.023,0.023,0.024,0.02,0.018,0.022,1127,4568,2393,2134,2110,3718,2549,2959,1351,1355,575.0,2656,2431,951,2298,11381,17179,1956,4041,3961,3405,4573,1971,5207,4632,4963,4191,4656,5742,4436,2301,981,881,5916,1916,1333,6373,7029,258,350,1228,3258,5333,2111,3874,1339,743,2193,2006,1250,621,2486,1192,5084,303,2718,1335,3054,1007,1026,1089,2221,346,939,2650,1537,3805,3016,2139,991,1198,2138,7609,351,0.105,0.119,0.12,0.123,0.156,0.124,0.13,0.116,0.154,0.14,0.095,0.143,0.117,0.132,0.109,0.131,0.126,0.124,0.122,0.147,0.125,0.136,0.117,0.154,0.134,0.123,0.111,0.11,0.12,0.137,0.142,0.153,0.111,0.133,0.089,0.08,0.15,0.14,0.098,0.086,0.105,0.138,0.159,0.117,0.097,0.123,0.088,0.083,0.097,0.11,0.16,0.107,0.119,0.097,0.128,0.104,0.103,0.129,0.116,0.109,0.112,0.129,0.149,0.128,0.121,0.125,0.11,0.106,0.103,0.13,0.121,0.113,0.096,0.131,350,1590,880,893,914,1281,1026,1023,419,401,170.0,1465,750,234,609,3463,5568,409,1332,2028,1068,1431,815,1618,1316,1571,1577,1770,1870,1643,760,401,332,1404,470,538,1841,1655,172,192,685,1644,1264,1427,2197,684,294,930,1052,598,431,1299,642,2327,142,1608,726,1543,681,610,554,906,139,378,1446,815,1964,1355,1006,408,660,1095,3440,225,3.025,2.614,2.796,2.39,2.185,2.41,2.225,2.491,2.426,2.814,2.84,1.652,2.512,2.76,2.707,2.593,2.638,3.274,2.601,1.786,2.576,2.46,2.064,2.555,2.691,2.674,2.154,2.171,2.551,2.215,2.439,2.713,2.238,2.977,3.363,2.247,2.556,3.039,1.686,2.028,2.271,1.767,2.933,1.735,2.011,2.166,3.017,2.811,2.446,2.4,1.748,2.06,2.028,2.348,2.467,1.959,2.188,2.173,1.752,1.905,2.071,2.571,2.879,2.925,2.015,2.273,2.124,2.345,2.282,2.576,2.138,2.322,2.384,1.903,0.622,0.687,0.492,0.507,0.604,0.717,0.683,0.63,0.762,0.549,0.808,0.414,0.429,0.475,0.528,0.704,0.694,0.827,0.8,0.576,0.897,0.601,0.685,0.755,0.654,0.524,0.551,0.606,0.643,0.571,0.831,1.094,0.563,0.801,0.662,0.517,0.868,0.505,0.254,0.428,0.466,0.542,0.91,0.566,0.649,0.541,0.544,0.681,0.432,0.542,0.314,0.412,0.428,0.486,0.338,0.505,0.488,0.598,0.38,0.412,0.398,0.633,0.604,0.534,0.562,0.732,0.494,0.431,0.542,0.76,0.416,0.56,0.455,0.398
-76,Epileptic,F,27.0,1.0,2.8,1.3,1.6,1.3,1.3,1.5,1.2,0.9,0.6,0.3,2.4,1.1,0.5,1.3,5.1,6.5,0.9,2.1,3.6,1.3,3.0,1.5,2.9,3.3,2.3,3.3,2.0,2.4,3.3,1.8,1.5,0.3,1.8,0.3,0.5,3.6,2.3,0.2,0.1,0.9,2.3,3.0,1.6,2.6,0.7,0.4,0.6,1.0,0.6,0.3,1.8,1.3,2.4,0.2,2.1,0.7,1.1,0.8,0.5,0.4,1.2,0.2,0.8,0.6,1.0,2.0,1.3,0.7,0.5,0.9,1.2,4.4,0.3,6,29,13,15,17,11,14,12,10,7,3.0,19,16,6,15,56,69,7,26,30,15,33,14,31,43,29,38,22,23,36,21,13,3,26,2,5,39,30,2,1,6,15,36,12,11,5,2,5,5,5,3,12,7,17,2,18,7,8,5,4,3,8,2,5,6,17,12,9,4,5,5,8,42,2,0.048,0.031,0.025,0.031,0.034,0.025,0.026,0.027,0.038,0.028,0.034,0.039,0.029,0.039,0.033,0.035,0.029,0.043,0.027,0.044,0.031,0.041,0.037,0.039,0.037,0.024,0.03,0.03,0.032,0.029,0.056,0.051,0.02,0.025,0.014,0.015,0.04,0.028,0.018,0.01,0.017,0.039,0.055,0.025,0.019,0.014,0.021,0.012,0.016,0.018,0.023,0.019,0.026,0.02,0.013,0.017,0.019,0.016,0.028,0.015,0.024,0.025,0.013,0.028,0.012,0.02,0.021,0.02,0.016,0.019,0.02,0.019,0.018,0.026,1407,4234,2283,2375,2127,2174,2920,2912,1677,1607,532.0,1775,2991,896,2283,9619,14050,1995,4827,3203,3897,3976,1548,6119,6357,5809,6082,3755,5507,6153,2008,858,759,4701,1374,1650,6096,6638,414,356,1460,1658,4223,1785,3401,1334,714,1945,1954,1557,449,2817,1299,4099,390,3534,1198,2410,881,1093,789,1984,336,896,1650,1158,2814,2276,1455,1146,1316,1750,7786,441,0.129,0.139,0.13,0.137,0.148,0.122,0.111,0.122,0.16,0.143,0.136,0.174,0.124,0.167,0.156,0.148,0.132,0.152,0.127,0.174,0.121,0.133,0.128,0.156,0.141,0.131,0.127,0.115,0.122,0.138,0.184,0.129,0.089,0.118,0.073,0.089,0.151,0.13,0.121,0.078,0.104,0.138,0.185,0.116,0.094,0.096,0.105,0.071,0.087,0.102,0.123,0.1,0.122,0.099,0.108,0.102,0.109,0.096,0.114,0.092,0.109,0.121,0.098,0.128,0.088,0.116,0.1,0.102,0.095,0.127,0.105,0.112,0.103,0.144,383,1502,818,865,751,839,993,874,443,393,157.0,984,836,262,672,2762,4195,423,1297,1489,844,1215,733,1528,1745,1658,1994,1268,1421,1936,636,451,263,1137,414,574,1612,1487,168,171,707,977,1068,1030,1877,665,294,783,952,616,243,1385,713,1948,207,1897,592,1092,467,544,371,743,213,437,797,807,1492,1068,668,439,649,880,3673,143,3.491,2.614,2.664,2.535,2.233,2.251,2.402,2.799,2.582,2.87,3.041,1.68,2.699,2.684,2.507,2.552,2.603,3.482,2.974,2.026,3.319,2.459,1.722,2.919,2.729,2.671,2.296,2.337,2.862,2.505,2.451,1.832,2.524,2.843,2.929,2.454,2.796,3.028,2.888,2.194,2.641,1.572,2.823,2.082,2.075,2.139,2.857,2.876,2.525,2.619,2.294,2.077,2.005,2.115,2.243,2.03,2.287,2.466,2.033,2.263,2.442,2.621,1.774,2.144,2.237,1.782,2.004,2.308,2.435,2.573,2.32,2.341,2.319,3.13,0.922,0.6,0.535,0.575,0.754,0.455,0.542,0.534,0.725,0.69,0.711,0.456,0.499,0.467,0.626,0.627,0.645,0.816,0.552,0.644,0.833,0.8,0.558,0.659,0.733,0.633,0.641,0.561,0.492,0.592,0.646,0.813,0.369,0.55,0.73,0.389,0.702,0.685,0.328,0.384,0.461,0.5,0.823,0.732,0.646,0.55,0.656,0.74,0.392,0.631,0.42,0.488,0.498,0.418,0.356,0.408,0.405,0.436,0.601,0.375,0.34,0.708,0.41,0.729,0.52,0.76,0.44,0.426,0.424,0.915,0.461,0.555,0.427,0.694
-77,Epileptic,F,22.0,0.8,2.1,1.0,1.6,2.7,1.8,2.0,1.5,1.2,0.8,0.4,3.7,1.0,0.6,1.4,5.7,7.8,1.6,1.9,4.5,1.5,3.4,2.6,4.3,8.8,5.2,8.0,4.3,2.9,3.2,2.4,1.3,0.5,2.1,0.4,1.1,5.5,4.0,0.2,0.2,0.7,2.9,2.4,1.8,2.5,0.9,0.5,1.2,1.7,0.9,0.6,1.5,1.6,2.3,0.4,3.7,0.9,1.4,0.9,1.4,0.6,1.5,0.4,0.6,1.4,0.9,3.0,1.2,1.1,0.7,0.5,1.3,5.2,0.2,10,21,7,15,24,17,21,18,11,7,4.0,30,14,5,17,70,74,14,24,38,14,34,19,48,66,46,60,34,28,36,20,15,6,26,2,10,58,43,2,1,4,20,26,15,14,5,2,6,9,9,5,12,9,17,3,30,8,11,10,11,7,12,3,5,11,11,30,7,7,4,4,9,43,2,0.043,0.024,0.019,0.026,0.051,0.035,0.04,0.025,0.044,0.036,0.059,0.044,0.025,0.035,0.031,0.034,0.032,0.046,0.034,0.041,0.029,0.043,0.042,0.044,0.058,0.042,0.047,0.047,0.032,0.033,0.054,0.082,0.025,0.028,0.013,0.032,0.043,0.036,0.009,0.016,0.017,0.043,0.041,0.021,0.02,0.017,0.02,0.017,0.022,0.018,0.029,0.018,0.023,0.024,0.013,0.019,0.019,0.016,0.021,0.022,0.019,0.023,0.027,0.019,0.017,0.017,0.02,0.017,0.021,0.02,0.018,0.018,0.019,0.015,1582,4929,2551,3197,2870,2520,2728,3967,2601,1624,684.0,3210,3281,1542,3696,12970,20178,2818,4325,4209,4240,4009,2049,7888,5232,6541,6906,3654,6675,6039,2776,923,1230,6299,2409,1497,8526,9029,491,337,1251,2001,7328,2428,3411,1680,917,2452,2520,2051,695,3090,2192,4544,719,6052,1622,2932,1517,1721,1326,2897,593,1341,2397,1491,4817,2349,1843,1258,1106,2149,9845,484,0.142,0.114,0.098,0.127,0.157,0.148,0.129,0.122,0.171,0.152,0.161,0.176,0.13,0.156,0.129,0.143,0.134,0.14,0.138,0.168,0.113,0.149,0.139,0.154,0.172,0.142,0.145,0.137,0.122,0.141,0.143,0.146,0.115,0.126,0.064,0.102,0.144,0.152,0.088,0.099,0.093,0.144,0.159,0.103,0.093,0.099,0.099,0.082,0.103,0.106,0.136,0.096,0.108,0.112,0.098,0.106,0.111,0.093,0.115,0.114,0.11,0.123,0.133,0.102,0.103,0.106,0.103,0.094,0.102,0.1,0.104,0.105,0.099,0.114,436,1596,837,1065,945,943,1016,1147,590,369,154.0,1483,826,313,906,3382,4904,585,1136,1925,971,1476,1143,1976,2426,2296,2905,1591,1710,1725,865,354,398,1315,581,537,2318,2099,255,161,636,1173,1347,1440,2001,781,337,1003,1132,819,368,1447,1050,1733,437,2912,678,1391,765,983,622,1029,243,534,1156,827,2529,1036,890,549,501,1087,4314,237,3.289,2.786,2.955,2.785,2.488,2.326,2.231,2.797,3.205,3.182,3.076,1.927,3.049,3.386,2.99,2.815,3.133,3.396,2.951,1.993,3.133,2.223,1.681,2.962,1.938,2.361,2.026,1.923,2.861,2.733,2.435,2.47,2.488,3.327,3.581,2.383,2.657,3.083,2.133,2.303,2.494,1.593,3.467,1.889,1.934,2.34,3.057,2.987,2.791,2.509,2.263,2.269,2.413,2.473,1.941,2.207,2.382,2.34,2.082,1.984,2.275,2.901,2.564,2.828,2.344,2.16,2.0,2.509,2.394,2.56,2.428,2.369,2.356,2.531,0.836,0.598,0.535,0.553,0.707,0.484,0.672,0.558,0.648,0.742,0.766,0.608,0.549,0.444,0.52,0.622,0.685,1.27,0.61,0.615,0.74,0.614,0.478,0.818,0.704,0.727,0.675,0.569,0.67,0.687,0.889,1.0,0.435,0.562,0.588,0.564,0.871,0.756,0.405,0.44,0.406,0.522,0.849,0.65,0.585,0.502,0.675,0.613,0.473,0.614,0.264,0.425,0.402,0.548,0.379,0.441,0.488,0.459,0.538,0.372,0.484,0.555,0.466,0.762,0.428,0.751,0.444,0.498,0.415,0.563,0.465,0.457,0.496,0.393
-78,Epileptic,F,62.0,0.4,2.9,1.5,2.3,1.9,1.5,2.1,1.3,1.3,0.8,0.3,2.6,1.1,0.4,1.1,4.9,10.1,0.9,1.7,5.5,0.9,2.5,1.8,3.0,2.7,2.5,3.0,2.1,2.6,2.9,1.4,1.6,0.4,2.3,0.2,0.4,2.8,2.5,0.2,0.1,0.9,3.0,1.8,2.7,3.0,0.7,0.3,0.6,0.8,0.7,1.2,1.1,1.2,2.8,0.2,1.9,0.6,1.8,0.6,1.2,0.4,1.8,0.5,0.4,1.2,1.4,2.4,1.0,1.7,0.8,0.7,1.2,3.3,0.3,3,23,15,16,14,17,13,11,10,8,3.0,28,13,4,11,44,64,8,24,48,8,31,20,24,33,25,35,21,19,34,13,11,4,25,1,3,32,30,1,1,5,23,22,18,16,4,1,5,4,6,8,7,8,22,1,15,5,16,6,9,3,14,3,3,8,14,19,6,12,6,5,9,24,3,0.026,0.036,0.035,0.043,0.045,0.029,0.04,0.028,0.045,0.038,0.029,0.028,0.032,0.029,0.033,0.035,0.044,0.039,0.031,0.039,0.023,0.03,0.03,0.039,0.033,0.033,0.031,0.028,0.026,0.034,0.047,0.059,0.026,0.038,0.009,0.016,0.038,0.031,0.014,0.015,0.02,0.035,0.044,0.023,0.022,0.016,0.019,0.013,0.014,0.027,0.034,0.015,0.025,0.024,0.017,0.017,0.022,0.022,0.019,0.022,0.012,0.037,0.038,0.022,0.018,0.025,0.024,0.018,0.024,0.029,0.024,0.019,0.015,0.022,1072,4101,1966,2076,1716,2551,2354,2458,1545,1196,642.0,3162,2281,1003,2330,7733,12977,1896,3442,5734,2599,4164,2167,4890,4957,4546,4912,3392,4540,4471,1992,1005,778,4871,1502,1028,6489,5906,341,395,1213,2847,6278,3331,3154,1181,632,2044,1746,1154,1005,2171,1509,4955,201,3338,1043,2832,820,1465,926,2153,340,876,2263,1524,2898,1731,2211,821,989,1780,7260,394,0.091,0.137,0.139,0.129,0.137,0.143,0.13,0.121,0.152,0.157,0.113,0.129,0.141,0.122,0.141,0.132,0.14,0.143,0.14,0.144,0.095,0.14,0.132,0.138,0.151,0.14,0.13,0.117,0.091,0.145,0.138,0.149,0.115,0.13,0.065,0.078,0.142,0.128,0.098,0.088,0.103,0.123,0.143,0.104,0.108,0.092,0.094,0.081,0.083,0.118,0.155,0.088,0.115,0.112,0.124,0.1,0.109,0.122,0.124,0.118,0.082,0.138,0.169,0.105,0.097,0.11,0.124,0.098,0.117,0.132,0.12,0.118,0.09,0.134,285,1456,753,805,706,927,849,780,478,351,203.0,1379,660,218,604,2210,3805,379,1008,2312,632,1371,949,1297,1561,1356,1733,1120,1337,1564,548,430,259,1071,369,410,1390,1414,181,170,634,1325,915,1993,1976,634,265,792,858,365,506,1118,733,2021,114,1729,481,1352,503,830,483,811,180,368,1106,845,1461,798,1133,455,526,863,3233,205,3.376,2.638,2.587,2.454,2.314,2.379,2.298,2.721,2.82,2.839,2.799,1.936,2.68,3.179,2.875,2.669,2.856,3.586,2.813,2.073,3.009,2.305,2.059,2.856,2.511,2.725,2.218,2.34,2.62,2.308,2.633,2.515,2.541,3.01,3.433,2.23,3.146,2.924,2.198,2.288,2.314,1.898,3.653,1.827,1.809,2.035,2.889,2.851,2.396,3.134,2.071,2.166,2.288,2.387,2.18,2.071,2.45,2.327,1.859,1.995,2.22,2.85,2.293,2.666,2.137,2.438,2.142,2.248,2.132,2.147,2.135,2.347,2.437,2.197,0.609,0.799,0.63,0.592,0.64,0.525,0.568,0.623,0.635,0.479,0.589,0.471,0.447,0.558,0.533,0.584,0.599,0.757,0.53,0.563,0.862,0.44,0.409,0.681,0.483,0.491,0.503,0.548,0.44,0.479,0.841,1.004,0.329,0.619,0.782,0.596,0.821,0.689,0.361,0.606,0.424,0.464,0.932,0.517,0.478,0.328,0.477,0.972,0.502,0.841,0.565,0.427,0.572,0.496,0.231,0.416,0.666,0.462,0.415,0.385,0.492,0.657,0.418,0.586,0.484,0.893,0.569,0.496,0.389,0.697,0.405,0.566,0.54,0.421
-79,Epileptic,F,22.0,0.8,2.9,1.0,1.8,1.8,2.1,1.5,1.3,1.4,1.0,0.3,2.9,1.6,0.4,1.8,6.3,9.8,1.3,3.0,4.7,1.5,3.0,2.4,5.5,5.7,5.5,5.6,2.7,2.0,3.2,2.5,1.2,0.4,3.0,1.0,0.7,6.7,3.2,0.1,0.3,1.2,3.4,2.7,2.2,2.6,1.0,0.6,1.4,1.3,1.0,0.4,1.8,1.8,3.1,0.3,4.1,0.9,1.6,0.7,1.3,0.9,2.2,0.5,0.4,1.6,1.1,3.1,1.1,0.8,1.0,1.2,1.5,3.9,0.4,4,26,9,13,14,20,17,15,14,11,5.0,27,22,6,21,87,107,10,34,46,34,36,25,48,56,57,59,30,23,45,25,10,5,30,11,6,64,38,1,2,8,32,24,16,15,9,2,6,8,7,5,14,17,23,3,35,6,14,5,7,7,18,3,4,12,19,27,9,9,14,9,14,31,4,0.029,0.027,0.016,0.027,0.034,0.036,0.028,0.025,0.055,0.037,0.041,0.043,0.024,0.032,0.035,0.033,0.03,0.046,0.035,0.045,0.031,0.043,0.038,0.048,0.05,0.04,0.034,0.031,0.022,0.03,0.055,0.045,0.022,0.032,0.027,0.016,0.052,0.033,0.011,0.014,0.023,0.044,0.037,0.026,0.019,0.016,0.018,0.018,0.016,0.028,0.024,0.018,0.024,0.019,0.016,0.018,0.022,0.017,0.02,0.022,0.022,0.03,0.028,0.013,0.021,0.017,0.019,0.018,0.016,0.03,0.024,0.016,0.016,0.015,1795,5507,2155,2868,2402,2799,2664,2781,1604,1827,606.0,2829,4024,1168,3862,14236,22637,2565,5159,4339,3543,4243,2300,6551,5561,7637,7823,4190,6289,6972,2288,1092,1007,7757,2503,1886,7341,7564,404,591,1471,2563,6682,2261,3354,1715,919,2534,2619,1495,634,3478,2537,6244,441,6348,1445,3286,1005,1730,1152,3489,509,1216,2337,1376,5205,2256,2012,1800,1639,2685,8968,522,0.098,0.13,0.106,0.12,0.127,0.135,0.116,0.121,0.166,0.157,0.139,0.168,0.13,0.129,0.151,0.145,0.134,0.15,0.135,0.165,0.108,0.145,0.133,0.146,0.153,0.151,0.129,0.112,0.1,0.131,0.162,0.126,0.107,0.132,0.111,0.096,0.148,0.132,0.09,0.097,0.119,0.149,0.141,0.122,0.099,0.1,0.099,0.087,0.094,0.116,0.138,0.1,0.119,0.106,0.108,0.102,0.108,0.097,0.105,0.104,0.12,0.132,0.133,0.088,0.107,0.104,0.102,0.098,0.092,0.139,0.116,0.104,0.091,0.123,442,1708,937,1078,932,1130,1011,968,536,534,178.0,1274,1137,274,993,3757,5912,501,1463,1981,899,1388,1180,2019,2071,2486,2922,1595,1713,2160,773,517,318,1606,572,675,2496,1940,225,293,745,1357,1372,1352,2022,887,415,992,1204,673,306,1625,1208,2510,266,3452,683,1605,489,889,578,1156,265,566,1185,971,2653,1030,943,662,892,1333,3932,294,3.513,2.994,2.335,2.489,2.327,2.186,2.252,2.478,2.564,2.577,2.573,1.953,2.716,2.877,2.908,2.771,2.963,3.791,2.81,1.965,2.823,2.453,1.811,2.673,2.223,2.56,2.191,2.077,2.706,2.47,2.249,2.114,2.468,3.431,3.747,2.595,2.384,2.833,2.095,2.369,2.478,1.74,3.421,1.957,1.851,2.064,2.776,3.01,2.643,2.686,2.199,2.261,2.294,2.517,2.042,1.952,2.37,2.278,2.168,2.116,2.293,3.175,2.405,2.508,2.178,1.665,1.887,2.311,2.122,2.877,2.061,2.241,2.401,2.162,0.988,0.65,0.49,0.586,0.636,0.57,0.573,0.454,0.744,0.689,0.836,0.607,0.445,0.484,0.51,0.587,0.685,0.753,0.682,0.603,0.827,0.644,0.476,0.921,0.678,0.685,0.584,0.657,0.553,0.613,0.81,0.88,0.469,0.653,0.725,0.487,0.873,0.691,0.362,0.419,0.439,0.599,0.717,0.673,0.505,0.407,0.62,0.911,0.466,0.54,0.417,0.421,0.438,0.448,0.358,0.396,0.43,0.509,0.344,0.389,0.398,0.892,0.406,0.714,0.555,0.624,0.506,0.426,0.482,0.763,0.487,0.495,0.495,0.342
-81,Epileptic,M,32.0,1.2,2.8,1.1,1.2,2.7,2.7,1.8,2.4,1.6,0.7,0.3,3.8,2.3,0.7,1.8,8.9,12.0,1.5,2.8,4.9,1.5,8.8,3.7,5.3,9.0,6.6,4.6,3.0,3.3,4.2,1.3,1.0,0.8,3.4,1.2,0.7,3.3,5.4,0.4,0.2,1.5,5.3,3.6,2.2,3.9,0.9,0.7,1.2,2.0,0.6,0.6,3.4,2.1,3.0,0.5,2.6,0.8,3.0,1.6,1.9,1.1,2.5,0.4,0.8,2.2,1.1,3.1,2.4,1.2,0.5,1.0,1.8,6.4,0.6,8,26,8,10,26,32,18,20,19,7,3.0,34,27,9,20,90,108,11,34,49,12,68,28,52,62,64,62,33,29,50,18,9,7,41,6,4,40,58,3,1,9,43,29,17,19,6,4,7,9,6,5,23,15,19,3,19,6,23,10,16,7,20,4,5,18,15,26,15,7,6,6,11,45,4,0.038,0.027,0.019,0.022,0.043,0.032,0.03,0.032,0.048,0.039,0.037,0.037,0.039,0.035,0.038,0.047,0.037,0.041,0.035,0.036,0.029,0.051,0.043,0.045,0.054,0.045,0.036,0.035,0.031,0.039,0.031,0.036,0.028,0.034,0.022,0.013,0.036,0.04,0.024,0.02,0.024,0.044,0.054,0.022,0.024,0.017,0.026,0.018,0.02,0.018,0.022,0.032,0.029,0.022,0.021,0.016,0.022,0.024,0.026,0.022,0.019,0.03,0.026,0.022,0.019,0.019,0.022,0.029,0.018,0.018,0.021,0.023,0.021,0.019,1840,5169,2268,2490,2553,4225,3424,4385,2222,1578,761.0,3453,4400,1608,3340,12176,23118,3031,5441,5652,3445,5048,3409,7725,5985,7660,7113,5123,7429,7370,3182,1530,1143,7087,2375,2186,6872,10421,592,422,1702,2942,6061,2688,4121,1644,1039,2319,3091,1480,768,4322,2639,5430,663,4614,1179,4398,1711,2336,1664,3419,621,1076,3519,1562,5274,3337,2170,1015,1632,2553,10917,722,0.127,0.13,0.099,0.104,0.144,0.124,0.122,0.129,0.162,0.14,0.121,0.146,0.144,0.146,0.148,0.157,0.144,0.143,0.145,0.141,0.106,0.146,0.136,0.149,0.156,0.131,0.135,0.12,0.116,0.158,0.142,0.105,0.105,0.136,0.084,0.077,0.139,0.144,0.129,0.106,0.113,0.145,0.159,0.113,0.107,0.095,0.11,0.091,0.104,0.1,0.115,0.127,0.117,0.1,0.105,0.101,0.119,0.117,0.112,0.113,0.102,0.134,0.124,0.118,0.106,0.105,0.107,0.124,0.092,0.109,0.111,0.111,0.102,0.108,557,1722,874,884,1140,1432,1056,1410,765,345,188.0,1657,1256,416,868,3619,5874,617,1427,2415,889,2330,1331,2203,2416,2397,2383,1517,1829,2106,809,570,473,1661,649,795,1658,2279,232,195,935,1696,1187,1635,2447,806,462,965,1372,601,464,1855,1248,2229,359,2256,557,1995,1030,1296,793,1320,288,562,1672,821,2384,1291,1002,454,775,1165,4739,446,3.197,2.538,2.56,2.725,2.169,2.489,2.537,2.698,2.283,3.118,3.328,1.915,2.783,2.862,2.82,2.592,2.939,3.721,2.942,2.012,2.696,1.926,2.04,2.68,2.129,2.557,2.408,2.502,2.992,2.756,2.888,2.488,2.011,2.996,3.273,2.499,2.954,3.181,2.352,2.366,2.291,1.616,3.148,1.825,1.94,2.211,2.682,3.014,2.724,2.724,2.076,2.399,2.072,2.419,2.327,2.287,2.449,2.324,1.886,2.023,2.43,2.6,2.202,2.396,2.338,2.219,2.232,2.573,2.451,2.176,2.426,2.699,2.483,1.916,0.595,0.713,0.476,0.625,0.581,0.602,0.709,0.529,0.628,0.915,0.689,0.544,0.533,0.491,0.673,0.657,0.726,0.769,0.556,0.645,0.784,0.642,0.546,0.712,0.649,0.664,0.516,0.703,0.588,0.629,0.693,0.91,0.492,0.7,0.771,0.481,0.756,0.762,0.407,0.425,0.482,0.483,0.912,0.566,0.637,0.504,0.465,0.499,0.531,0.341,0.415,0.47,0.509,0.527,0.341,0.403,0.463,0.525,0.354,0.38,0.499,0.556,0.489,0.586,0.49,0.662,0.432,0.467,0.357,0.621,0.417,0.558,0.478,0.473
-82,Epileptic,F,22.0,1.0,3.2,0.9,1.1,1.5,2.0,1.1,1.8,1.5,0.8,0.3,2.4,1.6,0.5,1.6,5.8,8.1,0.6,1.9,4.4,0.8,3.1,1.4,3.5,4.4,3.7,3.5,2.2,2.4,3.5,0.9,0.8,0.4,2.5,0.3,0.3,3.4,4.1,0.2,0.1,0.9,3.6,2.9,2.3,2.8,0.4,0.3,0.6,1.2,0.7,0.6,1.9,1.1,2.2,0.2,2.5,0.7,1.4,0.9,0.9,1.0,1.2,0.4,0.7,1.5,1.2,2.5,1.0,1.0,0.4,1.1,1.0,5.0,0.2,8,31,8,8,15,27,9,18,13,10,4.0,27,26,6,18,63,80,7,23,46,7,35,14,40,57,44,38,24,22,38,17,6,6,33,2,2,41,46,1,1,5,37,26,18,20,3,1,4,6,7,4,14,8,16,1,19,7,12,6,7,10,9,4,6,14,17,23,9,7,6,7,8,40,2,0.039,0.032,0.02,0.024,0.032,0.03,0.02,0.025,0.048,0.028,0.037,0.032,0.035,0.03,0.04,0.037,0.035,0.03,0.026,0.041,0.019,0.036,0.031,0.044,0.035,0.033,0.031,0.024,0.026,0.03,0.029,0.041,0.026,0.036,0.015,0.008,0.039,0.033,0.011,0.016,0.021,0.036,0.04,0.024,0.02,0.008,0.019,0.011,0.016,0.016,0.027,0.025,0.022,0.023,0.024,0.019,0.018,0.018,0.023,0.017,0.026,0.026,0.024,0.023,0.017,0.024,0.02,0.018,0.018,0.02,0.023,0.017,0.017,0.012,1694,5222,2286,2133,2645,3643,2485,3977,2597,1806,630.0,3009,3361,1241,2811,10801,16217,2435,4436,5402,2920,5302,2067,5828,7998,7587,5918,4095,5693,6465,1931,653,949,5913,1517,1597,5521,7601,388,468,1382,3289,5400,2767,3779,1305,766,1972,2471,1520,620,2878,2013,3902,395,4407,1410,2779,1061,1436,1390,2277,504,924,2960,1245,4482,2323,1774,1162,1795,1998,11371,620,0.132,0.137,0.109,0.114,0.138,0.14,0.098,0.125,0.178,0.14,0.121,0.152,0.146,0.144,0.149,0.148,0.146,0.123,0.123,0.159,0.089,0.147,0.123,0.158,0.152,0.147,0.137,0.117,0.117,0.143,0.121,0.124,0.138,0.14,0.088,0.057,0.135,0.133,0.087,0.097,0.101,0.151,0.156,0.116,0.102,0.075,0.093,0.078,0.091,0.108,0.13,0.122,0.113,0.108,0.138,0.112,0.105,0.105,0.119,0.103,0.128,0.121,0.145,0.132,0.103,0.123,0.114,0.108,0.105,0.135,0.118,0.103,0.099,0.104,436,1591,738,750,863,1373,815,1177,600,453,177.0,1251,963,288,749,2815,4070,442,1182,1996,709,1572,806,1479,2272,2046,1847,1440,1511,1887,584,341,245,1304,361,603,1541,1868,217,201,672,1537,1211,1486,2205,655,279,852,1074,678,366,1206,874,1638,132,1998,673,1309,587,828,568,768,223,488,1346,777,2018,971,819,440,768,950,4459,299,3.487,3.068,2.782,2.821,2.544,2.361,2.519,2.775,3.162,3.01,2.603,2.109,2.771,3.137,2.674,2.788,3.176,4.095,2.973,2.327,3.034,2.608,2.174,2.939,2.673,3.001,2.49,2.323,2.794,2.705,2.443,2.073,2.727,3.211,3.524,2.422,2.685,2.978,2.178,2.396,2.62,1.91,3.249,2.098,1.93,2.199,3.503,2.86,2.751,2.576,2.044,2.391,2.452,2.498,3.459,2.346,2.474,2.4,2.107,2.011,2.479,2.908,2.256,2.14,2.275,1.958,2.27,2.668,2.583,2.751,2.709,2.331,2.707,2.7,0.895,0.755,0.637,0.548,0.677,0.498,0.523,0.548,0.559,0.499,0.945,0.513,0.501,0.55,0.638,0.652,0.563,0.695,0.522,0.555,0.653,0.503,0.326,0.836,0.552,0.501,0.517,0.614,0.571,0.536,0.645,0.796,0.381,0.622,0.801,0.389,0.671,0.56,0.446,0.495,0.382,0.51,0.645,0.669,0.53,0.417,0.475,0.708,0.499,0.512,0.422,0.554,0.57,0.526,0.432,0.486,0.345,0.466,0.392,0.349,0.451,0.708,0.509,0.865,0.596,0.666,0.507,0.376,0.42,0.924,0.436,0.434,0.607,0.46
-83,Epileptic,M,32.0,1.0,3.0,1.1,1.4,2.8,1.3,2.5,2.2,1.2,0.9,0.2,2.7,1.3,0.5,1.1,6.5,12.4,1.4,2.4,3.9,1.7,2.7,1.4,4.9,3.4,3.9,2.7,2.3,3.2,3.9,2.5,1.6,0.7,2.4,0.8,0.6,3.8,3.6,0.2,0.3,1.6,2.8,2.3,1.7,3.3,0.8,0.5,1.0,1.5,1.2,0.3,2.6,2.0,2.7,0.5,3.0,1.1,1.5,0.6,1.3,0.4,2.0,0.7,0.8,1.4,2.1,2.7,1.8,1.0,0.5,1.4,2.0,4.8,0.4,9,28,10,12,22,15,24,18,18,10,3.0,26,16,8,12,70,118,12,25,26,14,34,17,50,48,46,40,26,28,49,19,16,6,26,5,6,42,48,1,3,9,29,26,12,17,5,2,5,7,7,2,22,14,16,5,28,5,11,4,10,4,20,4,5,8,23,24,11,6,5,8,15,37,3,0.034,0.024,0.018,0.025,0.038,0.027,0.032,0.029,0.042,0.031,0.037,0.029,0.022,0.028,0.028,0.034,0.034,0.039,0.029,0.04,0.026,0.025,0.021,0.042,0.026,0.029,0.025,0.025,0.025,0.026,0.049,0.053,0.037,0.025,0.019,0.012,0.027,0.028,0.016,0.019,0.028,0.031,0.028,0.02,0.022,0.011,0.023,0.012,0.02,0.017,0.017,0.023,0.032,0.021,0.02,0.015,0.022,0.02,0.02,0.016,0.018,0.028,0.033,0.022,0.012,0.03,0.017,0.021,0.015,0.019,0.023,0.017,0.017,0.018,1908,5981,2984,3052,2950,3027,3684,4022,2294,2275,617.0,3296,4185,1333,2390,12350,24525,2766,5217,4255,4790,6881,3490,8122,8676,8439,6805,4815,7919,9211,2567,1499,1133,7142,3189,2311,9729,10254,287,634,1828,4018,7570,2586,4269,2097,803,2792,2640,2718,633,4449,2433,5129,833,6616,1456,3360,900,2372,1071,3503,848,1199,3446,1812,5573,3237,2001,846,1977,3286,11209,501,0.11,0.113,0.091,0.114,0.146,0.127,0.117,0.131,0.162,0.138,0.12,0.132,0.117,0.138,0.13,0.141,0.131,0.133,0.124,0.141,0.108,0.124,0.114,0.147,0.128,0.135,0.118,0.103,0.1,0.122,0.147,0.148,0.11,0.123,0.092,0.086,0.119,0.135,0.096,0.115,0.117,0.135,0.129,0.101,0.103,0.079,0.097,0.074,0.096,0.093,0.098,0.11,0.124,0.097,0.112,0.096,0.11,0.101,0.104,0.101,0.098,0.121,0.135,0.113,0.082,0.126,0.1,0.104,0.092,0.123,0.115,0.099,0.096,0.12,562,2062,1006,1026,1097,946,1259,1267,638,484,156.0,1425,998,330,661,3434,6283,608,1387,1730,1050,1723,1108,2085,2275,2352,2120,1666,1944,2626,811,609,355,1558,723,756,2319,2311,165,282,886,1416,1513,1464,2299,954,319,1125,1191,1005,311,1883,1055,2235,439,2935,657,1378,515,1192,414,1246,317,583,1578,1184,2552,1349,932,439,924,1762,4567,291,3.294,2.647,2.834,2.867,2.366,2.472,2.465,2.835,2.702,3.28,2.849,1.988,3.111,2.938,2.726,2.717,3.046,3.401,2.892,2.142,3.353,2.86,2.492,2.959,2.843,2.839,2.517,2.263,3.051,2.709,2.451,2.713,2.524,3.226,3.805,2.789,3.041,3.052,2.102,2.673,2.557,2.241,3.392,2.038,2.127,2.417,2.969,2.975,2.738,2.888,2.144,2.451,2.351,2.464,2.201,2.348,2.442,2.577,1.82,2.158,2.456,2.691,2.426,2.369,2.416,1.761,2.266,2.722,2.507,2.113,2.423,2.293,2.564,2.263,0.742,0.574,0.479,0.486,0.742,0.651,0.584,0.507,0.582,0.696,0.963,0.513,0.367,0.428,0.477,0.554,0.612,0.675,0.493,0.631,0.748,0.52,0.67,0.809,0.553,0.573,0.551,0.533,0.483,0.598,0.695,1.1,0.498,0.593,0.537,0.422,0.688,0.68,0.301,0.559,0.419,0.584,0.764,0.67,0.591,0.508,0.405,0.581,0.387,0.634,0.323,0.53,0.508,0.406,0.418,0.385,0.502,0.498,0.407,0.4,0.468,0.763,0.527,0.706,0.43,0.624,0.397,0.46,0.451,0.618,0.433,0.503,0.461,0.298
-84,Epileptic,F,22.0,0.6,2.1,0.9,1.0,2.2,2.0,1.6,1.3,1.3,0.6,0.4,2.7,1.0,0.4,1.3,4.1,5.8,0.6,1.8,4.3,1.1,3.6,2.5,3.1,2.9,2.5,6.0,1.8,3.2,2.7,1.6,0.6,0.5,2.4,0.3,0.7,4.4,2.9,0.1,0.3,1.0,3.9,2.5,1.9,2.1,0.5,0.3,0.6,1.2,0.8,0.5,2.5,1.0,2.3,0.3,3.6,0.7,1.0,1.2,1.4,1.0,1.3,0.6,0.6,2.0,1.0,3.6,0.7,1.1,0.5,0.7,1.7,3.8,0.3,4,20,6,8,18,21,12,15,18,6,4.0,30,16,7,19,52,59,4,27,45,10,38,22,33,38,28,48,22,22,35,18,7,6,30,3,8,45,40,1,2,6,32,24,17,13,4,1,4,6,5,5,21,9,19,2,31,5,11,11,10,8,9,4,4,18,17,46,3,6,4,6,12,32,3,0.032,0.023,0.021,0.021,0.041,0.034,0.034,0.026,0.044,0.029,0.034,0.039,0.025,0.024,0.025,0.031,0.028,0.024,0.029,0.041,0.019,0.041,0.042,0.039,0.036,0.026,0.043,0.029,0.033,0.028,0.04,0.055,0.027,0.032,0.017,0.013,0.046,0.032,0.01,0.013,0.018,0.042,0.036,0.024,0.018,0.014,0.016,0.011,0.016,0.016,0.021,0.024,0.028,0.022,0.013,0.021,0.019,0.013,0.022,0.021,0.029,0.022,0.026,0.022,0.021,0.022,0.03,0.013,0.016,0.018,0.015,0.021,0.016,0.011,1516,5097,2248,2089,2724,3274,2518,3429,2679,1361,843.0,3397,3623,1447,4169,9184,17192,2320,4535,5165,3709,4379,2436,6490,6106,6161,7255,3686,5871,6321,2485,773,1232,6233,1773,2241,7493,7386,506,658,1598,2431,6623,2965,2910,1298,813,2184,2172,1655,689,4441,1848,4875,550,4918,1339,3039,1405,1862,1217,2450,512,1101,3381,1074,3969,1671,1880,1101,1518,2637,8013,603,0.099,0.114,0.102,0.104,0.16,0.143,0.112,0.123,0.183,0.139,0.133,0.155,0.113,0.126,0.127,0.13,0.124,0.103,0.13,0.159,0.086,0.133,0.138,0.147,0.13,0.129,0.146,0.115,0.113,0.133,0.15,0.114,0.115,0.14,0.083,0.076,0.151,0.139,0.074,0.097,0.101,0.139,0.142,0.118,0.095,0.089,0.082,0.073,0.09,0.099,0.136,0.119,0.126,0.114,0.1,0.116,0.109,0.088,0.123,0.106,0.129,0.119,0.149,0.11,0.111,0.122,0.105,0.081,0.095,0.113,0.106,0.118,0.09,0.1,385,1538,725,752,869,1169,844,954,650,364,231.0,1381,951,298,1045,2594,4044,431,1243,1877,887,1623,1054,1628,1612,1684,2573,1264,1461,1889,761,309,349,1293,393,732,1891,1643,262,295,762,1449,1296,1447,1743,623,327,859,1039,710,348,1812,777,1788,255,2398,559,1322,856,957,587,822,274,446,1514,724,1928,779,919,428,682,1181,3434,309,3.651,2.982,2.804,2.634,2.616,2.424,2.363,2.97,3.092,2.971,2.796,2.112,2.871,3.137,2.971,2.724,3.198,3.89,2.935,2.27,2.974,2.225,2.145,2.957,2.729,2.879,2.337,2.313,2.995,2.68,2.617,2.505,2.681,3.377,3.772,2.637,2.886,3.076,2.188,2.514,2.751,1.621,3.497,2.194,1.865,2.15,2.844,3.129,2.558,2.964,2.144,2.348,2.255,2.594,2.604,2.274,2.605,2.555,1.919,2.091,2.363,3.143,1.935,3.046,2.41,1.85,2.033,2.353,2.414,2.832,2.455,2.489,2.447,2.21,0.892,0.604,0.615,0.47,0.669,0.572,0.615,0.556,0.806,0.591,0.896,0.465,0.498,0.663,0.563,0.597,0.645,0.674,0.506,0.655,0.895,0.623,0.725,0.797,0.727,0.562,0.564,0.558,0.674,0.577,0.779,0.794,0.416,0.636,0.673,0.588,0.865,0.724,0.318,0.459,0.418,0.456,0.819,0.555,0.513,0.469,0.445,0.88,0.459,0.808,0.468,0.523,0.545,0.519,0.438,0.45,0.456,0.467,0.357,0.47,0.381,0.733,0.456,0.787,0.491,0.545,0.603,0.441,0.373,0.634,0.399,0.574,0.403,0.46
-85,Epileptic,F,42.0,0.4,1.7,0.7,0.6,1.7,1.3,1.2,1.5,1.1,0.5,0.2,1.9,1.2,0.4,1.2,4.1,7.6,0.5,1.5,3.1,0.9,1.7,1.3,3.3,1.8,2.7,3.3,2.3,2.0,2.3,1.6,0.5,0.4,2.1,0.3,0.5,3.3,2.6,0.1,0.1,1.2,2.2,2.3,2.3,2.1,0.6,0.5,0.7,0.9,1.0,0.5,1.3,1.6,2.2,0.3,2.2,0.6,1.5,0.9,1.0,0.5,1.7,0.3,0.3,1.5,0.5,2.3,1.3,1.0,0.6,0.6,1.5,3.3,0.3,3,20,6,5,14,12,13,13,12,5,2.0,20,13,4,9,46,77,4,21,34,9,22,13,36,27,32,38,24,23,33,19,6,5,27,2,3,47,31,1,1,7,24,29,17,14,4,2,5,5,10,3,12,11,15,2,16,4,16,7,8,4,13,2,4,10,5,20,7,6,4,4,10,33,2,0.023,0.025,0.015,0.012,0.04,0.034,0.023,0.029,0.047,0.027,0.021,0.033,0.029,0.042,0.033,0.036,0.029,0.026,0.025,0.031,0.019,0.033,0.027,0.043,0.035,0.029,0.029,0.03,0.023,0.027,0.042,0.027,0.019,0.032,0.009,0.012,0.034,0.029,0.014,0.02,0.022,0.039,0.043,0.029,0.017,0.015,0.024,0.012,0.014,0.027,0.026,0.019,0.036,0.021,0.016,0.021,0.019,0.023,0.026,0.025,0.023,0.034,0.027,0.021,0.022,0.014,0.022,0.025,0.018,0.02,0.018,0.022,0.021,0.017,884,3343,1586,1380,2180,1765,2546,3196,1565,993,415.0,2132,2660,981,1995,7856,14763,1602,3612,4315,2300,3091,1786,5604,3889,5959,5350,3918,5005,4759,2117,649,939,5129,1614,1701,5992,6709,261,254,1613,2052,4523,1836,2943,1039,622,1910,1754,1305,487,2552,1738,3799,448,3007,1198,2612,833,1043,820,1915,405,692,1972,655,3269,1725,1538,1083,936,1819,5916,410,0.093,0.12,0.093,0.084,0.155,0.151,0.12,0.134,0.171,0.125,0.107,0.142,0.137,0.137,0.141,0.147,0.133,0.106,0.124,0.142,0.092,0.151,0.131,0.156,0.14,0.142,0.138,0.137,0.12,0.139,0.161,0.104,0.103,0.138,0.064,0.077,0.129,0.137,0.106,0.108,0.111,0.159,0.147,0.13,0.102,0.099,0.11,0.083,0.085,0.124,0.134,0.103,0.125,0.106,0.107,0.107,0.099,0.119,0.128,0.121,0.117,0.137,0.123,0.114,0.109,0.09,0.121,0.116,0.106,0.103,0.113,0.121,0.108,0.097,316,1195,758,668,716,672,823,911,432,296,148.0,886,758,224,571,2167,4335,358,913,1544,692,967,748,1418,1106,1681,1836,1246,1353,1507,589,384,316,1211,486,583,1736,1501,136,126,843,982,1025,1206,1695,548,283,843,892,675,280,1149,720,1745,266,1588,573,1198,518,579,362,813,193,322,967,484,1621,820,798,444,503,927,2671,230,2.948,2.516,2.105,2.133,2.459,2.432,2.484,2.991,2.735,2.576,2.826,2.048,2.709,3.046,2.537,2.667,2.692,3.466,3.004,2.281,2.615,2.46,2.081,2.918,2.657,2.842,2.346,2.434,2.727,2.475,2.656,1.79,2.368,2.97,2.788,2.633,2.649,3.144,2.084,2.138,2.278,1.868,3.068,1.734,1.959,2.063,2.665,2.744,2.339,2.274,2.13,2.311,2.462,2.295,2.145,2.142,2.465,2.379,1.95,2.027,2.426,2.358,2.325,2.423,2.17,1.557,2.215,2.366,2.291,2.542,2.149,2.362,2.42,2.055,0.534,0.595,0.537,0.365,0.599,0.518,0.591,0.511,0.452,0.389,0.62,0.369,0.584,0.456,0.45,0.556,0.593,0.529,0.587,0.645,0.794,0.467,0.493,0.685,0.438,0.476,0.417,0.505,0.481,0.528,0.745,0.772,0.319,0.779,0.711,0.552,0.795,0.652,0.396,0.372,0.479,0.457,0.792,0.529,0.467,0.472,0.45,0.731,0.436,0.558,0.342,0.424,0.775,0.468,0.367,0.512,0.462,0.505,0.383,0.341,0.481,0.591,0.412,0.828,0.585,0.512,0.432,0.426,0.409,0.498,0.421,0.46,0.544,0.361
-86,Epileptic,M,22.0,0.8,1.5,0.7,1.0,1.5,1.3,1.4,1.7,1.1,0.9,0.3,2.6,1.3,0.4,1.3,4.7,7.3,1.1,1.4,3.7,1.1,2.7,1.4,2.9,4.1,4.0,2.5,2.1,2.3,2.8,1.4,1.0,0.4,1.7,0.3,1.0,4.0,2.6,0.2,0.2,1.1,2.7,2.1,1.6,2.2,0.5,0.5,0.8,1.4,0.9,0.5,1.6,1.8,2.2,0.5,2.3,0.4,1.5,0.4,0.7,0.7,1.3,0.5,0.7,1.6,1.1,2.5,1.2,1.1,0.4,1.0,1.4,4.4,0.3,8,17,7,7,11,13,15,16,13,8,4.0,23,17,5,15,56,80,9,19,33,12,36,11,37,47,59,37,24,24,35,19,8,5,23,2,10,47,37,1,1,7,18,21,12,13,5,2,6,7,8,4,11,13,14,3,19,5,14,4,6,7,11,4,4,14,17,19,10,10,4,8,10,37,3,0.035,0.018,0.012,0.019,0.032,0.028,0.021,0.024,0.041,0.037,0.033,0.036,0.025,0.023,0.028,0.029,0.026,0.037,0.025,0.038,0.021,0.037,0.03,0.034,0.041,0.032,0.024,0.025,0.021,0.024,0.031,0.058,0.02,0.023,0.01,0.019,0.038,0.028,0.028,0.022,0.02,0.041,0.033,0.022,0.016,0.01,0.015,0.014,0.017,0.02,0.023,0.022,0.023,0.018,0.017,0.016,0.01,0.018,0.017,0.018,0.021,0.025,0.028,0.022,0.019,0.022,0.021,0.02,0.018,0.014,0.025,0.015,0.018,0.019,1617,4441,2308,2421,2046,2069,2714,3316,1766,1658,751.0,2360,3420,866,3033,10487,18519,2570,3809,3847,4266,3611,1573,6772,6035,7123,5439,3936,6733,6127,3025,959,982,5561,2038,2272,7835,7150,297,421,1520,2385,5798,2107,3305,1561,989,2256,2638,1770,569,2422,1958,3961,765,3834,1249,3073,705,1179,989,2046,596,989,2544,1278,3207,2246,2056,764,1568,2364,9243,335,0.121,0.105,0.088,0.094,0.136,0.139,0.095,0.123,0.153,0.146,0.124,0.149,0.128,0.117,0.138,0.135,0.125,0.129,0.124,0.155,0.097,0.141,0.118,0.145,0.135,0.133,0.113,0.11,0.105,0.121,0.138,0.129,0.099,0.126,0.066,0.11,0.158,0.132,0.118,0.091,0.108,0.131,0.143,0.11,0.09,0.08,0.097,0.089,0.095,0.106,0.123,0.106,0.12,0.094,0.098,0.096,0.089,0.107,0.106,0.109,0.122,0.12,0.134,0.1,0.109,0.119,0.11,0.107,0.107,0.1,0.115,0.101,0.102,0.131,453,1448,855,892,783,819,972,1111,521,422,211.0,1063,898,242,757,2902,4975,504,980,1695,928,1247,679,1573,1722,2199,1763,1431,1610,1839,835,387,343,1299,555,800,1981,1715,157,218,805,1049,1135,1212,2006,804,412,945,1172,733,332,1252,1105,1868,400,2135,638,1373,377,620,537,865,312,482,1250,805,1783,987,956,399,739,1223,3798,194,3.56,2.727,2.55,2.638,2.221,2.303,2.338,2.539,2.605,3.059,2.828,1.899,2.832,2.591,2.712,2.601,2.83,3.869,2.902,1.968,3.23,2.298,2.095,3.108,2.696,2.535,2.405,2.161,2.955,2.506,2.701,2.642,2.326,3.002,3.296,2.493,2.992,2.936,2.03,2.173,2.395,1.959,3.481,2.033,1.862,2.098,2.853,3.016,2.739,2.578,1.994,2.079,1.902,2.264,2.211,1.988,2.312,2.424,2.035,2.017,2.134,2.183,1.803,2.42,2.259,1.917,1.952,2.435,2.36,2.137,2.388,2.264,2.49,2.199,0.691,0.597,0.492,0.455,0.634,0.554,0.63,0.462,0.72,0.569,0.719,0.631,0.439,0.502,0.69,0.707,0.698,0.629,0.532,0.642,0.772,0.615,0.591,0.743,0.756,0.735,0.655,0.643,0.593,0.619,0.612,0.863,0.416,0.625,0.758,0.448,0.754,0.654,0.558,0.506,0.457,0.711,0.755,0.725,0.565,0.503,0.644,0.52,0.541,0.505,0.341,0.477,0.468,0.417,0.336,0.396,0.443,0.442,0.468,0.383,0.454,0.615,0.557,0.726,0.568,0.509,0.463,0.41,0.447,0.419,0.522,0.451,0.485,0.388
-87,Epileptic,F,52.0,0.8,1.8,1.2,0.8,1.1,1.4,1.4,1.1,1.4,0.3,0.2,2.3,1.0,0.7,1.1,5.2,7.9,1.1,2.2,3.6,1.0,2.4,2.0,3.1,2.3,3.5,2.9,1.9,1.6,3.1,1.6,1.5,0.4,1.6,0.2,0.5,2.9,2.9,0.1,0.1,1.0,2.9,1.8,1.9,2.2,0.5,0.7,0.7,0.9,0.9,0.5,1.3,1.2,2.0,0.2,2.3,0.4,1.2,1.0,1.0,0.6,1.1,0.3,0.3,1.5,0.6,2.0,0.8,0.8,0.3,1.1,0.8,3.9,0.2,6,16,13,8,9,17,14,12,13,4,2.0,22,13,9,13,48,78,7,25,34,7,25,22,33,29,38,36,22,20,38,17,12,4,23,2,3,40,40,1,1,6,25,17,16,15,3,4,4,6,6,4,12,9,14,1,19,3,10,7,7,5,9,2,3,10,7,16,5,5,4,10,7,32,2,0.041,0.024,0.027,0.015,0.042,0.028,0.024,0.023,0.055,0.021,0.027,0.034,0.024,0.036,0.032,0.037,0.032,0.051,0.034,0.038,0.025,0.039,0.037,0.038,0.027,0.037,0.03,0.025,0.02,0.029,0.044,0.058,0.022,0.028,0.016,0.011,0.037,0.031,0.016,0.012,0.022,0.036,0.037,0.025,0.019,0.01,0.037,0.014,0.016,0.02,0.039,0.019,0.036,0.019,0.027,0.017,0.021,0.02,0.026,0.021,0.027,0.025,0.032,0.015,0.022,0.033,0.021,0.015,0.016,0.023,0.026,0.017,0.017,0.02,1266,3376,1840,1877,1406,2095,2235,2409,1587,894,364.0,1934,2235,1158,2022,7457,13737,1809,3497,3445,2834,3492,2191,4509,4344,5865,4858,2861,4170,4720,1789,1440,658,3391,1233,1448,4516,5422,225,294,1280,2350,3527,1641,2775,1188,632,1764,1922,1333,335,2343,1788,3371,257,3800,638,2299,1023,1422,618,1715,458,711,2042,612,2693,1799,1267,711,1551,1542,7879,352,0.12,0.117,0.129,0.102,0.145,0.139,0.109,0.12,0.169,0.103,0.106,0.151,0.127,0.143,0.139,0.144,0.141,0.133,0.146,0.148,0.096,0.151,0.159,0.148,0.129,0.15,0.137,0.109,0.108,0.136,0.158,0.167,0.119,0.12,0.064,0.077,0.141,0.134,0.11,0.096,0.114,0.148,0.136,0.12,0.1,0.081,0.144,0.088,0.094,0.119,0.167,0.102,0.148,0.103,0.151,0.106,0.116,0.108,0.132,0.11,0.132,0.129,0.139,0.116,0.111,0.114,0.11,0.093,0.098,0.132,0.129,0.112,0.099,0.127,335,1189,699,736,528,848,884,807,488,242,129.0,1101,671,328,636,2240,4229,329,965,1600,651,1075,915,1405,1468,1730,1683,1152,1191,1710,519,540,244,913,367,567,1353,1470,127,160,666,1209,787,1187,1729,640,308,794,934,626,215,1121,592,1563,106,2050,306,1032,578,783,354,663,173,297,1073,287,1549,817,733,264,738,780,3440,176,3.322,2.699,2.773,2.638,2.36,2.267,2.149,2.626,2.462,3.077,2.56,1.678,2.556,2.719,2.485,2.56,2.623,3.83,2.918,1.909,3.208,2.559,2.149,2.639,2.37,2.694,2.247,1.986,2.612,2.278,2.757,2.918,2.232,2.68,2.872,2.338,2.666,2.774,1.969,2.065,2.291,1.835,3.273,1.603,1.824,2.045,2.84,2.632,2.382,2.679,1.871,2.236,2.593,2.28,2.635,2.028,2.498,2.507,2.033,2.064,2.083,2.767,2.335,2.338,2.166,2.202,1.906,2.463,2.02,3.051,2.353,2.35,2.432,2.513,1.121,0.514,0.549,0.41,0.575,0.61,0.477,0.513,0.529,0.397,0.798,0.475,0.39,0.521,0.467,0.516,0.57,0.834,0.557,0.609,0.85,0.417,0.463,0.727,0.535,0.518,0.47,0.443,0.463,0.496,0.813,0.956,0.543,0.48,0.648,0.361,0.612,0.514,0.377,0.424,0.569,0.422,0.693,0.678,0.526,0.471,0.622,0.951,0.517,0.533,0.426,0.48,0.606,0.513,0.452,0.442,0.41,0.531,0.368,0.41,0.294,0.889,0.686,1.095,0.512,0.66,0.416,0.397,0.373,0.871,0.492,0.385,0.465,0.38
-88,Epileptic,M,47.0,1.5,3.4,1.2,1.2,2.1,2.5,2.3,2.0,1.5,0.9,0.5,2.9,1.7,0.6,1.7,6.8,12.1,2.6,2.6,4.7,1.2,5.7,2.1,6.2,6.7,3.5,4.7,3.9,4.0,2.7,1.7,1.8,0.6,2.7,0.4,0.9,5.1,4.7,0.3,0.2,1.1,4.6,3.2,2.6,4.0,0.8,1.0,1.1,2.1,1.3,0.8,2.2,1.5,2.6,0.4,3.2,1.1,2.2,1.7,1.2,1.1,2.3,0.4,0.7,2.2,1.7,3.2,1.7,1.1,0.8,0.9,1.6,4.7,0.3,9,37,13,11,15,23,17,23,12,10,6.0,26,19,6,16,84,109,16,30,42,13,46,21,47,53,39,37,40,29,35,20,13,8,30,2,9,51,47,1,1,5,39,26,19,21,5,3,6,9,12,4,18,10,18,2,26,6,20,10,10,8,13,2,6,16,20,25,12,6,5,6,11,36,3,0.053,0.036,0.021,0.023,0.039,0.034,0.036,0.029,0.047,0.046,0.041,0.035,0.038,0.04,0.036,0.046,0.042,0.081,0.031,0.039,0.025,0.046,0.035,0.06,0.045,0.034,0.043,0.039,0.044,0.036,0.043,0.049,0.034,0.03,0.015,0.021,0.041,0.042,0.022,0.018,0.021,0.041,0.044,0.03,0.026,0.017,0.043,0.014,0.023,0.029,0.032,0.025,0.021,0.025,0.017,0.021,0.019,0.024,0.028,0.025,0.03,0.034,0.021,0.017,0.022,0.024,0.022,0.024,0.022,0.021,0.024,0.017,0.019,0.02,1553,4885,2122,2096,1761,3748,3097,3354,1739,1378,618.0,2941,3350,1037,2916,10350,17840,2387,5636,5026,2676,3975,1989,5920,6453,5967,4642,4587,5779,4846,1979,1348,1183,5362,1769,2081,8626,8189,358,373,1343,3007,6368,2344,3925,1252,763,2374,2523,1739,604,2893,2162,3775,427,4266,1728,3180,1532,1279,1232,2768,407,1212,2967,1545,4471,2317,1572,1238,1135,2577,9347,390,0.144,0.14,0.125,0.114,0.137,0.132,0.127,0.129,0.14,0.167,0.148,0.135,0.147,0.138,0.134,0.161,0.148,0.192,0.14,0.149,0.109,0.14,0.132,0.161,0.137,0.138,0.126,0.13,0.137,0.14,0.145,0.13,0.105,0.131,0.076,0.097,0.143,0.154,0.105,0.098,0.103,0.153,0.166,0.127,0.112,0.1,0.131,0.08,0.098,0.129,0.123,0.121,0.111,0.113,0.105,0.111,0.099,0.127,0.123,0.127,0.129,0.134,0.111,0.102,0.11,0.119,0.106,0.117,0.101,0.114,0.127,0.107,0.099,0.12,459,1694,860,819,832,1251,1113,1208,611,382,217.0,1303,943,257,848,3106,5259,539,1442,2162,766,1717,984,1877,2419,1875,1658,1865,1665,1596,697,574,378,1437,470,686,2042,2068,207,205,737,1755,1340,1386,2350,658,321,1138,1196,628,369,1376,1106,1775,249,2283,846,1461,926,759,616,1082,238,634,1601,947,2366,1141,754,493,535,1295,4045,210,3.299,2.755,2.497,2.469,1.98,2.406,2.342,2.419,2.273,2.893,2.26,1.92,2.787,2.767,2.573,2.477,2.598,3.383,3.023,1.997,2.675,2.002,1.847,2.437,2.195,2.638,2.285,1.975,2.641,2.425,2.201,2.542,2.428,2.708,3.152,2.536,2.971,2.801,2.039,2.018,2.246,1.636,3.285,1.884,1.863,2.059,2.709,2.477,2.561,2.811,2.0,2.156,1.982,2.181,2.049,2.003,2.417,2.394,1.893,1.886,2.19,2.584,1.83,2.176,2.072,2.088,1.978,2.362,2.207,2.361,2.274,2.314,2.403,2.063,0.755,0.767,0.52,0.457,0.511,0.748,0.626,0.505,0.553,0.722,0.797,0.557,0.456,0.485,0.506,0.623,0.692,0.807,0.576,0.606,0.792,0.592,0.498,0.734,0.679,0.483,0.66,0.548,0.534,0.683,0.697,0.912,0.411,0.631,0.737,0.602,0.712,0.748,0.412,0.426,0.433,0.461,0.834,0.533,0.544,0.462,0.43,0.723,0.492,0.511,0.469,0.463,0.497,0.455,0.265,0.427,0.454,0.518,0.459,0.49,0.5,0.755,0.412,0.742,0.518,0.754,0.446,0.445,0.477,0.939,0.501,0.442,0.509,0.369
-89,Epileptic,M,32.0,1.2,3.2,1.4,1.1,2.9,2.3,1.4,1.8,1.9,0.6,0.4,3.7,1.4,0.4,1.5,7.3,9.4,1.0,2.1,5.0,1.7,3.3,2.6,5.7,4.7,3.7,5.2,3.4,3.4,3.6,1.9,1.0,0.5,2.8,0.3,1.0,3.0,3.8,0.3,0.1,1.1,3.3,2.6,3.4,2.9,0.7,0.6,0.6,1.1,0.6,0.8,2.2,2.4,3.2,0.7,3.0,0.6,1.9,1.5,1.2,0.7,1.8,0.5,0.7,1.8,1.3,2.1,1.4,1.2,0.6,0.7,1.0,4.2,0.3,8,28,15,9,30,28,12,17,17,9,4.0,31,18,7,17,83,95,8,25,53,20,38,21,56,41,42,47,34,29,45,23,6,4,32,2,9,31,43,2,0,7,28,25,27,17,5,4,4,5,4,6,18,17,24,3,25,7,19,11,9,5,12,4,6,16,21,17,10,10,5,5,9,36,3,0.057,0.026,0.02,0.018,0.04,0.026,0.022,0.031,0.052,0.03,0.031,0.032,0.025,0.032,0.028,0.036,0.029,0.043,0.032,0.033,0.037,0.033,0.032,0.048,0.04,0.026,0.031,0.03,0.028,0.03,0.035,0.043,0.025,0.032,0.011,0.019,0.037,0.033,0.016,0.009,0.022,0.029,0.046,0.026,0.02,0.013,0.021,0.013,0.016,0.014,0.029,0.021,0.025,0.02,0.028,0.016,0.017,0.023,0.025,0.014,0.017,0.024,0.026,0.023,0.017,0.02,0.018,0.018,0.019,0.018,0.016,0.017,0.015,0.014,1179,6203,3266,2358,3078,4178,3011,3053,2114,1666,655.0,3727,3788,1280,3160,13137,21233,2299,4063,6512,3884,5247,2516,9058,5998,7737,7266,5070,6706,6829,3403,1094,906,7063,1575,2410,6714,7740,601,257,1617,2943,6151,3556,3575,1479,1094,2199,2267,1509,765,4297,3322,6688,741,5469,1309,3508,1715,2216,1067,3682,576,1127,3158,1626,3685,2822,2026,1209,1352,2293,11047,521,0.142,0.119,0.121,0.097,0.149,0.131,0.098,0.131,0.155,0.141,0.135,0.139,0.12,0.14,0.121,0.14,0.129,0.124,0.143,0.143,0.13,0.112,0.126,0.159,0.128,0.129,0.106,0.115,0.11,0.137,0.15,0.115,0.107,0.124,0.061,0.096,0.146,0.134,0.102,0.08,0.106,0.12,0.165,0.114,0.102,0.092,0.114,0.074,0.089,0.091,0.133,0.108,0.12,0.104,0.124,0.098,0.107,0.121,0.118,0.097,0.106,0.119,0.125,0.124,0.108,0.118,0.102,0.096,0.108,0.119,0.101,0.098,0.089,0.103,380,2031,1119,904,1153,1496,1009,960,647,421,201.0,1796,977,267,901,3469,5560,436,1168,2623,900,1758,1195,2097,1770,2225,2551,1815,1795,1957,909,484,315,1462,453,807,1550,1811,312,97,802,1711,1120,2205,2215,771,399,870,1047,640,465,1778,1517,2611,350,2826,618,1414,933,1198,593,1166,322,485,1516,991,1812,1247,1028,463,648,944,4576,274,3.063,2.9,2.679,2.544,2.392,2.295,2.479,2.787,2.514,3.131,2.789,1.823,2.854,3.239,2.665,2.76,2.905,3.652,2.75,2.125,3.233,2.316,1.908,3.118,2.713,2.733,2.345,2.251,2.786,2.68,2.696,2.287,2.318,3.264,3.076,2.556,2.973,2.987,2.332,2.603,2.542,1.536,3.557,1.801,1.822,2.105,3.292,2.911,2.767,2.545,1.874,2.408,2.44,2.544,2.497,2.104,2.18,2.444,2.063,2.028,2.24,3.046,2.137,2.587,2.251,1.904,2.156,2.553,2.273,2.641,2.33,2.632,2.568,2.343,0.967,0.627,0.553,0.49,0.789,0.82,0.673,0.481,0.795,0.805,1.002,0.487,0.5,0.651,0.574,0.665,0.695,0.719,0.579,0.684,0.76,0.568,0.62,0.896,0.633,0.56,0.565,0.629,0.574,0.588,0.701,0.991,0.403,0.714,0.561,0.594,0.83,0.65,0.513,0.393,0.381,0.493,0.948,0.509,0.542,0.511,0.409,0.66,0.441,0.558,0.476,0.519,0.493,0.513,0.403,0.405,0.457,0.674,0.418,0.439,0.291,0.916,0.432,0.894,0.518,0.701,0.46,0.421,0.453,0.94,0.448,0.545,0.464,0.457
-90,Epileptic,M,42.0,0.8,1.8,0.9,0.7,1.3,1.9,1.7,1.7,1.2,0.5,0.3,3.4,1.6,0.7,0.8,6.3,8.8,1.0,2.0,4.4,1.0,2.0,1.4,2.8,2.4,3.6,3.8,2.3,2.7,3.9,1.1,1.0,0.7,3.1,0.5,1.1,2.2,2.6,0.2,0.1,1.1,3.3,1.9,2.9,2.8,0.7,0.6,0.8,1.2,0.7,0.5,1.2,1.8,1.8,0.2,2.3,0.5,1.7,0.8,0.6,0.9,1.3,0.2,0.5,2.2,0.2,2.5,1.6,1.4,0.5,1.0,0.6,2.9,0.6,5,17,7,6,18,18,16,14,12,5,3.0,31,13,7,11,60,86,7,25,36,9,24,16,30,32,42,42,25,29,44,15,7,8,38,5,12,23,31,2,1,6,30,21,24,16,4,3,7,6,6,3,11,11,13,1,17,3,16,5,4,5,7,2,3,16,1,20,12,7,4,6,4,25,4,0.029,0.02,0.015,0.013,0.039,0.03,0.025,0.026,0.047,0.028,0.024,0.039,0.032,0.04,0.031,0.035,0.03,0.038,0.029,0.034,0.023,0.03,0.034,0.036,0.03,0.037,0.028,0.026,0.025,0.042,0.037,0.049,0.024,0.032,0.02,0.024,0.031,0.029,0.025,0.017,0.02,0.036,0.035,0.027,0.019,0.012,0.024,0.012,0.015,0.018,0.027,0.02,0.026,0.016,0.02,0.02,0.02,0.019,0.019,0.019,0.028,0.024,0.015,0.026,0.028,0.014,0.02,0.026,0.02,0.017,0.026,0.023,0.02,0.018,1344,4999,1863,1964,2184,2781,2927,3739,2087,1127,682.0,3585,3385,1136,1548,10627,21496,2387,4252,5173,2745,4191,2152,5613,4377,8158,6612,3652,6777,5781,2041,887,1424,7129,1667,2538,4790,5639,290,239,2206,3308,5599,3371,4076,1820,874,2567,2783,1343,558,2141,2879,5291,464,3657,915,3411,1155,1078,989,2432,376,816,3025,579,4200,2450,2126,864,1243,1385,6709,990,0.108,0.104,0.099,0.085,0.138,0.142,0.105,0.124,0.144,0.117,0.094,0.151,0.142,0.162,0.123,0.14,0.124,0.117,0.134,0.124,0.083,0.132,0.141,0.13,0.147,0.147,0.129,0.115,0.107,0.155,0.137,0.132,0.126,0.126,0.1,0.118,0.136,0.124,0.129,0.097,0.092,0.147,0.138,0.118,0.099,0.082,0.117,0.084,0.091,0.102,0.128,0.105,0.118,0.087,0.103,0.113,0.105,0.11,0.108,0.104,0.128,0.109,0.104,0.12,0.121,0.063,0.105,0.12,0.103,0.124,0.123,0.1,0.1,0.103,404,1409,811,779,660,1000,1045,1065,457,271,181.0,1460,864,296,462,2929,5171,413,1099,1933,779,1182,788,1345,1322,1908,2207,1356,1778,1720,490,376,495,1571,437,790,1082,1408,135,120,865,1442,1008,1915,2171,835,350,1100,1160,551,291,1031,1024,1902,184,1792,431,1521,628,520,455,780,206,321,1291,278,1882,976,1024,396,558,498,2611,469,3.229,3.207,2.464,2.566,2.432,2.45,2.317,3.124,2.85,3.311,3.149,2.07,2.929,2.841,2.514,2.728,3.135,4.15,3.085,2.233,2.818,2.634,2.274,2.911,2.695,3.209,2.34,2.183,2.843,2.623,2.9,2.499,2.353,3.099,3.214,2.83,3.154,2.892,2.47,2.086,3.203,1.992,3.595,2.003,2.133,2.446,3.108,2.923,2.898,2.634,2.345,2.331,2.414,2.656,2.796,2.287,2.517,2.54,2.286,2.252,2.754,2.984,2.121,2.539,2.338,2.302,2.313,2.57,2.519,2.408,2.667,2.774,2.57,2.619,0.843,0.773,0.784,0.493,0.722,0.563,0.574,0.845,0.732,0.366,0.674,0.506,0.483,0.322,0.522,0.649,0.665,0.802,0.479,0.672,0.811,0.493,0.517,0.743,0.531,0.636,0.519,0.522,0.497,0.588,0.78,1.054,0.346,0.719,0.547,0.607,0.803,0.594,0.447,0.467,0.822,0.531,0.816,0.773,0.648,0.748,0.581,0.569,0.622,0.623,0.364,0.404,0.656,0.632,0.386,0.501,0.49,0.568,0.341,0.366,0.38,0.797,0.35,1.211,0.534,0.744,0.573,0.562,0.409,0.649,0.54,0.626,0.501,0.633
-91,Epileptic,F,27.0,1.2,2.6,1.5,1.3,1.5,2.0,1.4,1.5,1.5,0.7,0.2,2.9,1.1,0.7,1.5,5.7,7.7,0.7,2.2,6.2,1.7,5.8,2.8,4.4,6.4,2.8,2.9,2.3,2.4,3.1,1.3,1.3,0.5,2.7,0.4,0.8,3.2,4.3,0.2,0.2,1.1,2.7,1.7,2.5,3.2,0.6,0.6,0.7,1.3,0.7,0.7,1.5,2.3,2.4,0.2,2.5,0.8,1.5,0.7,1.8,1.0,1.4,0.3,0.4,1.4,1.0,2.2,1.0,1.1,0.9,0.6,1.4,5.0,0.4,11,23,16,13,21,20,11,17,11,8,3.0,28,17,8,16,68,75,8,27,44,14,52,22,44,52,34,29,26,26,36,18,9,7,33,2,9,46,48,2,2,7,25,20,19,19,5,4,6,7,4,5,10,15,19,1,19,7,11,6,15,7,11,2,5,11,18,20,7,7,9,6,10,35,4,0.049,0.028,0.026,0.026,0.03,0.034,0.024,0.025,0.051,0.032,0.024,0.033,0.027,0.035,0.032,0.032,0.032,0.033,0.033,0.042,0.035,0.049,0.044,0.042,0.047,0.03,0.029,0.03,0.023,0.037,0.039,0.048,0.025,0.032,0.016,0.022,0.038,0.036,0.018,0.014,0.027,0.034,0.031,0.026,0.021,0.015,0.023,0.013,0.017,0.016,0.028,0.02,0.033,0.019,0.008,0.021,0.019,0.019,0.016,0.027,0.027,0.027,0.028,0.014,0.015,0.018,0.022,0.015,0.017,0.027,0.019,0.02,0.02,0.021,1629,4382,3210,2415,2542,2403,2934,3497,1905,1714,750.0,3082,2928,1411,3088,12070,17667,2418,4587,5389,4273,4027,2135,6740,6544,6363,4948,4694,7221,5299,2214,1224,1015,7666,1803,2199,7545,9310,312,404,1378,2334,5655,2617,4198,1237,868,2131,2377,1473,566,2677,2089,5047,698,3896,1581,2928,1135,1896,1353,2415,452,968,2866,1442,3561,2390,2102,1359,1273,2128,9130,944,0.157,0.13,0.123,0.119,0.141,0.136,0.107,0.122,0.16,0.15,0.117,0.144,0.131,0.147,0.153,0.142,0.139,0.126,0.131,0.158,0.116,0.144,0.151,0.146,0.146,0.141,0.125,0.118,0.112,0.145,0.144,0.126,0.143,0.146,0.078,0.102,0.147,0.153,0.134,0.111,0.122,0.119,0.13,0.121,0.103,0.097,0.117,0.086,0.096,0.096,0.125,0.102,0.135,0.105,0.068,0.111,0.107,0.108,0.104,0.128,0.126,0.119,0.129,0.101,0.093,0.116,0.111,0.091,0.097,0.135,0.114,0.111,0.103,0.122,440,1369,965,842,887,996,939,1048,477,400,172.0,1523,761,325,739,2940,4259,436,1161,2395,819,1700,994,1729,2124,1665,1643,1396,1685,1556,642,482,315,1384,418,651,1679,1913,130,213,644,1181,1026,1577,2302,595,365,877,1094,587,414,1211,977,1987,300,1805,720,1229,658,1070,561,908,192,486,1431,898,1635,1024,946,497,534,988,3787,337,3.469,2.798,3.008,2.778,2.24,2.136,2.447,2.838,2.681,3.252,2.992,1.801,2.909,2.965,2.937,2.848,3.112,3.867,3.008,1.936,3.51,2.034,1.933,2.805,2.419,2.958,2.35,2.462,3.085,2.591,2.553,2.627,2.769,3.534,3.665,2.782,3.081,3.206,2.514,2.175,2.792,1.824,3.551,1.931,2.092,2.32,2.951,3.024,2.653,2.818,1.731,2.414,2.124,2.506,2.259,2.33,2.494,2.721,2.01,1.982,2.649,2.641,2.295,2.328,2.214,1.873,2.353,2.648,2.576,2.711,2.371,2.417,2.57,3.042,0.812,0.598,0.67,0.766,0.735,0.747,0.679,0.601,0.791,0.734,0.728,0.63,0.431,0.471,0.644,0.726,0.679,0.783,0.607,0.708,0.838,0.689,0.631,0.714,0.831,0.677,0.692,0.728,0.53,0.706,0.715,0.959,0.361,0.665,0.715,0.532,0.76,0.811,0.325,0.422,0.485,0.609,0.784,0.622,0.568,0.536,0.48,0.647,0.471,0.588,0.377,0.428,0.509,0.525,0.567,0.46,0.429,0.503,0.386,0.37,0.385,0.745,0.561,0.75,0.54,0.604,0.503,0.434,0.455,0.702,0.542,0.555,0.51,0.46
-92,Epileptic,M,37.0,1.5,3.6,1.5,1.8,2.1,2.6,2.6,1.8,2.6,0.8,0.4,2.9,2.3,0.6,2.5,8.4,10.2,0.6,2.5,4.4,1.3,4.2,2.2,5.2,2.8,2.8,3.4,3.4,4.7,3.7,1.9,1.3,0.7,3.9,0.5,0.8,5.5,3.7,0.3,0.4,0.9,3.6,2.4,2.5,4.0,1.0,0.6,1.0,1.2,0.7,0.5,3.5,1.3,3.0,0.4,2.8,1.1,1.5,1.2,1.2,0.8,2.7,0.6,0.7,2.2,1.4,2.8,1.9,1.8,0.6,1.0,1.9,4.7,0.4,9,34,13,16,14,23,17,16,19,10,6.0,27,25,9,23,82,94,5,30,38,10,50,23,42,37,30,34,25,33,44,19,10,6,43,4,7,53,42,2,2,5,32,20,18,18,7,3,7,6,7,4,20,9,14,2,19,7,14,8,8,5,15,3,5,17,19,17,12,10,4,6,16,37,3,0.041,0.026,0.025,0.029,0.037,0.029,0.039,0.031,0.06,0.032,0.05,0.033,0.035,0.044,0.035,0.039,0.033,0.024,0.029,0.036,0.027,0.035,0.032,0.055,0.031,0.028,0.035,0.041,0.04,0.032,0.043,0.044,0.031,0.039,0.017,0.018,0.045,0.035,0.013,0.02,0.02,0.032,0.04,0.027,0.03,0.019,0.021,0.017,0.014,0.019,0.022,0.028,0.017,0.022,0.014,0.02,0.021,0.016,0.025,0.016,0.02,0.038,0.032,0.018,0.021,0.023,0.029,0.028,0.028,0.023,0.022,0.022,0.015,0.015,1803,6179,2403,2995,2227,4239,2986,3423,2170,1707,728.0,4189,5019,1389,5022,14236,21981,2284,5345,5228,3287,6148,3041,5788,6404,7368,7167,4197,6509,7401,2739,1325,1097,9028,2306,2237,9523,8661,582,667,1406,3506,5368,2848,3618,1756,1090,2746,2748,1831,709,4706,2098,4424,712,4333,1854,2813,1318,2043,1264,2954,564,1218,3479,1109,3158,2658,2140,1024,1544,3168,11564,881,0.12,0.126,0.127,0.126,0.123,0.126,0.124,0.128,0.173,0.141,0.146,0.132,0.141,0.139,0.14,0.136,0.128,0.102,0.127,0.141,0.106,0.13,0.118,0.147,0.133,0.125,0.122,0.121,0.125,0.136,0.131,0.113,0.113,0.148,0.08,0.093,0.15,0.132,0.084,0.107,0.101,0.132,0.138,0.119,0.114,0.103,0.105,0.088,0.087,0.115,0.103,0.113,0.098,0.1,0.092,0.106,0.106,0.092,0.118,0.095,0.108,0.132,0.139,0.1,0.11,0.121,0.118,0.115,0.121,0.112,0.113,0.116,0.09,0.098,541,2048,929,1068,972,1512,1018,993,695,427,207.0,1484,1238,306,1289,3823,5640,443,1459,2133,846,1871,1190,1837,1641,1753,1866,1340,1773,2058,759,584,307,1788,538,741,2299,1954,313,287,703,1678,1171,1535,2010,770,416,1035,1188,652,408,2127,1137,1953,387,2173,880,1477,776,1061,621,1144,288,639,1686,871,1549,1139,984,430,727,1342,4773,360,3.145,2.823,2.542,2.586,2.142,2.279,2.443,2.949,2.372,2.87,2.509,2.202,3.051,2.997,2.841,2.713,2.96,3.797,2.84,2.083,2.724,2.558,2.082,2.462,2.87,3.061,2.789,2.372,2.836,2.759,2.57,2.304,2.843,3.392,3.738,2.63,3.102,3.052,2.158,2.457,2.568,1.806,3.258,2.134,2.049,2.556,2.89,3.365,2.82,3.03,1.952,2.33,2.003,2.398,2.154,2.195,2.317,2.099,1.773,2.033,2.245,2.477,1.985,2.151,2.211,1.571,2.16,2.529,2.338,2.539,2.495,2.523,2.516,2.835,0.905,0.562,0.538,0.633,0.607,0.645,0.527,0.643,0.764,0.552,0.923,0.696,0.663,0.636,0.587,0.719,0.683,0.722,0.646,0.65,0.861,0.675,0.697,0.807,0.718,0.681,0.761,0.741,0.696,0.574,0.617,0.933,0.646,0.616,0.641,0.449,0.795,0.768,0.422,0.445,0.443,0.632,0.77,0.735,0.572,0.586,0.425,0.604,0.444,0.623,0.446,0.468,0.422,0.438,0.379,0.426,0.567,0.601,0.533,0.447,0.436,0.674,0.448,0.77,0.487,0.513,0.54,0.517,0.512,0.766,0.504,0.619,0.554,0.496
-93,Epileptic,F,47.0,0.8,3.1,1.0,1.8,2.0,2.4,2.1,1.9,1.3,1.2,0.6,3.4,2.2,0.4,1.5,8.6,10.9,0.9,2.4,4.3,1.0,6.9,2.8,4.6,4.7,4.2,4.2,3.7,2.9,4.7,1.5,1.5,1.0,3.0,0.5,0.8,3.7,3.5,0.2,0.3,1.9,3.8,3.0,2.6,3.7,1.0,0.4,0.8,1.7,1.1,0.8,2.6,1.5,2.8,0.4,3.2,0.8,1.6,1.5,1.8,1.2,1.4,0.3,0.7,3.3,1.5,3.0,1.5,1.7,0.7,1.3,1.3,6.6,0.4,8,27,8,19,15,28,26,22,17,16,7.0,28,28,7,18,98,115,5,30,35,9,72,25,61,50,53,86,46,26,49,17,12,10,36,2,7,45,47,2,2,10,29,26,17,24,6,2,5,7,7,7,19,11,20,2,27,8,13,9,17,13,12,1,5,53,20,20,10,11,7,8,10,61,4,0.032,0.029,0.016,0.028,0.036,0.033,0.034,0.028,0.04,0.041,0.047,0.039,0.037,0.03,0.03,0.038,0.034,0.036,0.026,0.035,0.023,0.043,0.034,0.044,0.038,0.028,0.043,0.033,0.027,0.039,0.033,0.052,0.029,0.031,0.016,0.012,0.039,0.033,0.014,0.02,0.029,0.036,0.058,0.022,0.026,0.019,0.015,0.014,0.021,0.029,0.029,0.022,0.025,0.02,0.022,0.021,0.015,0.017,0.02,0.019,0.023,0.024,0.021,0.015,0.028,0.023,0.021,0.02,0.02,0.022,0.023,0.016,0.019,0.016,1779,5855,2629,3251,1904,4046,3525,4339,1927,2398,901.0,3572,4450,1202,3620,14483,22367,2661,5751,4789,3478,7014,2431,7223,7366,9780,6652,6456,6527,6657,2413,1599,1718,8018,2010,2707,7469,9975,398,812,1955,2488,6552,3484,3907,1785,923,2523,2764,2030,934,3821,1909,4936,555,4487,1763,3583,1839,2618,1646,2647,323,1114,3709,1645,4448,2511,2587,1567,1790,2483,12611,773,0.123,0.119,0.096,0.128,0.147,0.127,0.139,0.131,0.156,0.161,0.149,0.143,0.135,0.129,0.123,0.146,0.135,0.118,0.121,0.137,0.1,0.121,0.132,0.15,0.126,0.129,0.132,0.132,0.116,0.154,0.141,0.135,0.109,0.128,0.076,0.075,0.145,0.138,0.091,0.108,0.119,0.132,0.164,0.107,0.113,0.105,0.096,0.085,0.093,0.117,0.137,0.106,0.121,0.104,0.135,0.114,0.094,0.097,0.109,0.108,0.122,0.113,0.117,0.107,0.115,0.111,0.109,0.109,0.105,0.121,0.112,0.104,0.105,0.099,432,1734,960,1103,834,1231,1160,1257,549,594,254.0,1441,1133,264,895,3813,5710,391,1507,1990,712,2549,1155,1810,2097,2502,2120,1946,1739,2031,674,630,517,1591,453,931,1754,1980,237,326,1017,1446,982,1821,2315,775,340,932,1145,612,464,1846,1014,2215,246,2297,799,1617,1056,1455,774,1008,197,585,1783,1067,2058,1066,1198,545,873,1155,5175,453,3.533,2.802,2.684,2.859,1.931,2.536,2.406,3.119,2.414,3.072,2.788,2.098,2.852,2.998,2.843,2.733,2.936,4.342,2.921,2.061,3.186,2.273,1.873,2.801,2.66,2.914,2.344,2.534,2.786,2.525,2.607,2.751,2.517,3.287,3.653,2.608,2.956,3.307,1.982,2.454,2.414,1.694,3.754,2.107,1.933,2.642,3.048,3.385,2.84,3.079,2.189,2.243,1.898,2.387,2.682,2.121,2.494,2.49,2.002,1.953,2.244,2.262,1.971,2.161,2.229,1.799,2.38,2.665,2.469,2.748,2.403,2.389,2.562,2.146,1.037,0.782,0.539,0.438,0.66,0.727,0.543,0.565,0.718,0.688,0.798,0.498,0.568,0.46,0.56,0.621,0.65,0.711,0.672,0.635,0.9,0.63,0.505,0.748,0.726,0.62,0.55,0.612,0.501,0.582,0.658,0.893,0.528,0.667,0.585,0.498,0.771,0.711,0.323,0.585,0.417,0.47,0.924,0.69,0.534,0.546,0.669,0.808,0.656,0.764,0.421,0.476,0.486,0.482,0.631,0.434,0.503,0.514,0.402,0.418,0.525,0.771,0.265,0.695,0.542,0.602,0.471,0.452,0.441,1.037,0.446,0.403,0.505,0.363
-94,Epileptic,F,47.0,0.9,3.0,0.9,1.2,1.2,2.1,1.3,1.7,1.3,0.8,0.5,2.8,1.3,0.7,1.4,5.5,7.1,0.9,1.8,4.4,0.8,3.6,1.7,2.3,3.3,2.9,2.5,2.5,2.3,3.4,1.3,1.5,0.7,2.7,0.4,0.3,3.4,3.0,0.2,0.2,1.0,3.9,2.0,2.9,2.6,0.5,0.5,1.3,1.1,1.0,0.6,1.4,1.0,2.2,0.5,2.7,0.6,2.3,1.4,0.7,0.6,1.0,0.2,0.2,2.0,1.1,2.2,1.4,0.7,0.4,0.8,1.3,4.1,0.3,9,28,7,10,12,17,11,17,11,9,6.0,26,15,9,14,56,63,8,19,35,6,43,16,23,38,35,28,24,21,39,16,9,6,31,3,4,37,33,1,1,7,29,23,23,15,4,2,9,5,7,6,8,8,13,3,22,6,21,10,6,4,6,2,2,15,17,18,8,4,4,5,9,35,4,0.047,0.03,0.015,0.022,0.032,0.032,0.025,0.027,0.039,0.031,0.039,0.033,0.027,0.036,0.028,0.032,0.026,0.031,0.031,0.038,0.015,0.038,0.028,0.039,0.033,0.029,0.026,0.028,0.024,0.034,0.035,0.045,0.025,0.029,0.014,0.011,0.037,0.032,0.018,0.016,0.019,0.04,0.034,0.025,0.021,0.012,0.024,0.015,0.015,0.022,0.021,0.016,0.017,0.019,0.018,0.02,0.014,0.022,0.028,0.017,0.019,0.023,0.017,0.014,0.022,0.02,0.02,0.019,0.015,0.014,0.023,0.017,0.019,0.017,1540,5062,2312,2417,1855,3015,2584,3350,2229,1583,682.0,2881,3281,1426,2992,11255,16444,2502,3145,3929,2213,5567,2102,5155,5692,6382,5140,4195,5944,5167,2101,1343,1287,7119,1455,1604,6930,7123,355,340,1549,3638,5006,2843,3434,1271,770,2846,2493,1673,691,3209,2167,4355,691,3727,1612,2968,1505,1234,810,1648,395,672,2992,1263,3717,2636,1490,750,1191,2521,8327,362,0.138,0.134,0.098,0.113,0.137,0.147,0.105,0.13,0.147,0.134,0.13,0.143,0.127,0.166,0.136,0.134,0.12,0.124,0.13,0.141,0.08,0.15,0.129,0.141,0.144,0.133,0.12,0.111,0.103,0.145,0.137,0.142,0.104,0.137,0.072,0.072,0.134,0.137,0.108,0.101,0.106,0.154,0.132,0.122,0.101,0.08,0.107,0.087,0.086,0.115,0.123,0.086,0.095,0.096,0.107,0.112,0.105,0.115,0.127,0.101,0.117,0.123,0.115,0.096,0.116,0.119,0.103,0.105,0.094,0.111,0.112,0.105,0.098,0.116,463,1642,927,914,680,1097,846,1085,556,448,242.0,1294,877,342,792,3075,4331,481,963,1908,740,1738,964,1159,1705,1856,1676,1368,1496,1685,610,570,470,1600,431,567,1748,1688,194,174,794,1545,1082,1810,1950,683,339,1262,1126,671,412,1389,967,1849,399,1990,644,1594,867,663,409,590,192,340,1404,886,1827,1118,699,413,596,1149,3530,274,3.298,2.961,2.485,2.501,2.379,2.602,2.54,2.749,2.835,2.898,2.465,2.011,2.888,3.028,2.891,2.75,2.971,3.756,2.735,1.811,2.481,2.573,1.963,3.18,2.532,2.808,2.434,2.342,2.964,2.436,2.48,2.378,2.159,3.224,3.218,2.559,3.036,3.131,1.953,2.304,2.383,2.1,3.395,1.804,1.982,2.003,2.742,2.622,2.526,2.897,1.984,2.41,2.252,2.415,2.039,2.066,2.686,2.082,2.027,2.057,2.231,2.765,2.234,2.272,2.232,1.796,2.208,2.717,2.442,2.182,2.401,2.709,2.449,1.618,0.863,0.593,0.584,0.523,0.604,0.521,0.578,0.565,0.753,0.646,0.779,0.525,0.547,0.496,0.51,0.551,0.59,0.593,0.505,0.545,0.625,0.573,0.477,0.61,0.549,0.738,0.476,0.575,0.504,0.56,0.739,0.991,0.652,0.591,0.713,0.447,0.684,0.6,0.418,0.45,0.613,0.482,0.722,0.619,0.603,0.468,0.537,0.587,0.568,0.537,0.418,0.487,0.554,0.49,0.407,0.433,0.564,0.458,0.38,0.376,0.357,0.685,0.442,1.039,0.526,0.553,0.48,0.492,0.498,0.622,0.407,0.613,0.534,0.445
-95,Epileptic,F,32.0,1.2,2.5,1.2,1.1,1.6,2.7,1.4,2.0,1.4,0.5,0.3,2.9,1.6,0.5,1.0,6.9,9.9,0.8,2.2,5.1,0.8,6.0,3.2,3.6,6.4,4.2,3.8,1.1,1.8,2.8,1.9,1.4,0.4,2.5,0.4,0.6,2.5,4.4,0.1,0.1,1.1,4.5,2.0,2.4,1.8,0.8,0.4,0.9,1.5,0.8,0.7,2.0,1.8,3.4,0.4,1.7,1.1,1.9,0.9,1.2,0.5,1.6,0.5,0.5,2.0,1.4,1.2,1.2,1.1,0.7,0.8,1.4,5.3,0.3,10,26,13,9,14,20,16,22,18,5,4.0,25,22,6,12,87,97,9,25,49,8,47,25,43,53,37,36,19,21,34,20,13,6,27,2,6,31,47,1,1,6,37,21,21,9,7,2,6,8,7,6,16,13,25,2,14,7,15,9,8,4,15,3,5,16,16,7,10,4,6,6,10,42,3,0.048,0.026,0.021,0.018,0.028,0.044,0.029,0.035,0.045,0.023,0.039,0.04,0.026,0.033,0.031,0.033,0.031,0.03,0.035,0.043,0.02,0.049,0.048,0.043,0.052,0.046,0.037,0.021,0.024,0.031,0.05,0.059,0.026,0.027,0.014,0.016,0.029,0.039,0.013,0.017,0.023,0.042,0.032,0.026,0.016,0.027,0.018,0.017,0.021,0.024,0.028,0.019,0.025,0.02,0.033,0.022,0.021,0.021,0.022,0.022,0.023,0.03,0.028,0.017,0.02,0.032,0.013,0.022,0.021,0.027,0.021,0.022,0.019,0.019,1569,4507,2571,2839,2370,2275,2877,3228,1972,1213,760.0,2618,4210,1144,2744,13724,20063,2221,4823,5387,3167,3171,1641,6232,4238,4472,4677,3222,3787,5061,2647,889,804,6706,1625,1705,7521,8197,383,389,1293,1948,5734,3293,3001,1509,712,2023,2215,1412,559,4105,2438,6918,284,2579,1539,3209,1046,1485,630,2188,510,874,3167,1259,2190,1853,1155,966,1144,2377,9155,778,0.135,0.12,0.115,0.1,0.136,0.138,0.125,0.153,0.17,0.121,0.108,0.153,0.119,0.148,0.145,0.137,0.13,0.128,0.134,0.172,0.082,0.154,0.164,0.159,0.165,0.15,0.138,0.108,0.099,0.14,0.167,0.152,0.13,0.126,0.082,0.095,0.127,0.15,0.093,0.086,0.116,0.149,0.149,0.121,0.09,0.126,0.094,0.089,0.106,0.123,0.138,0.099,0.11,0.101,0.133,0.114,0.115,0.107,0.118,0.108,0.111,0.143,0.14,0.111,0.109,0.141,0.089,0.112,0.098,0.141,0.118,0.113,0.104,0.117,448,1511,952,976,956,1011,901,1009,600,328,202.0,1202,1262,258,696,3731,5624,475,1141,2237,746,1639,1044,1571,1806,1441,1680,951,1380,1569,793,371,267,1343,411,604,1582,1968,186,161,664,1402,1131,1648,1589,496,311,841,1049,521,376,1726,1172,2836,157,1229,735,1458,715,827,333,892,253,450,1552,640,1157,910,766,427,588,1007,4081,290,3.337,2.745,2.676,2.713,2.115,2.1,2.459,2.703,2.495,2.911,2.892,1.86,2.703,2.935,2.829,2.751,2.726,3.608,3.13,2.027,3.079,1.806,1.599,2.875,2.106,2.518,2.312,2.516,2.156,2.425,2.602,2.358,2.533,3.207,3.396,2.483,3.237,2.995,2.387,2.387,2.52,1.36,3.452,2.15,2.115,2.616,2.839,2.839,2.59,2.71,1.857,2.45,2.161,2.482,2.166,2.201,2.367,2.421,1.791,2.037,2.046,2.315,2.034,2.263,2.179,2.432,2.109,2.093,1.76,2.505,2.372,2.608,2.454,2.612,0.741,0.52,0.426,0.649,0.689,0.66,0.567,0.545,0.668,0.463,0.819,0.486,0.386,0.531,0.583,0.632,0.686,0.766,0.701,0.741,0.846,0.582,0.446,0.757,0.721,0.678,0.719,0.52,0.57,0.718,0.705,0.889,0.435,0.664,0.683,0.506,0.757,0.858,0.409,0.347,0.415,0.509,0.725,0.611,0.365,0.671,0.408,0.805,0.442,0.439,0.402,0.482,0.449,0.435,0.641,0.377,0.465,0.468,0.337,0.401,0.386,0.589,0.417,0.653,0.529,0.628,0.47,0.62,0.449,0.749,0.374,0.5,0.465,0.526
-96,Epileptic,F,67.5,0.9,2.7,1.3,1.1,1.1,1.8,1.3,1.3,0.9,0.9,0.3,2.7,1.8,0.8,2.2,4.9,8.6,1.2,2.7,4.0,0.9,2.9,1.3,2.9,3.4,3.4,3.1,2.4,2.7,3.0,1.6,1.1,0.6,2.4,0.4,0.7,2.2,3.3,0.1,0.4,1.3,2.4,2.1,2.7,3.6,0.5,0.7,1.1,1.5,0.7,0.6,2.0,1.4,2.0,0.4,2.6,0.3,1.4,1.0,1.0,0.6,1.7,0.2,0.4,2.2,1.6,2.4,1.9,1.2,0.7,1.1,1.2,4.3,0.4,9,20,10,9,12,16,11,11,11,8,3.0,25,16,10,21,47,77,8,23,35,8,30,14,33,37,31,33,20,20,33,20,8,4,29,3,6,24,31,2,3,6,22,22,20,21,5,3,6,7,5,5,13,11,12,3,22,3,12,7,7,5,13,2,5,20,18,20,12,7,5,7,10,36,4,0.039,0.027,0.024,0.018,0.03,0.033,0.024,0.021,0.044,0.035,0.028,0.029,0.028,0.036,0.034,0.036,0.032,0.039,0.032,0.034,0.022,0.032,0.022,0.034,0.033,0.028,0.024,0.026,0.023,0.025,0.036,0.041,0.019,0.029,0.02,0.017,0.029,0.032,0.009,0.026,0.022,0.028,0.035,0.024,0.025,0.013,0.025,0.015,0.02,0.022,0.025,0.021,0.03,0.018,0.017,0.019,0.018,0.021,0.029,0.017,0.021,0.027,0.018,0.016,0.023,0.029,0.019,0.022,0.018,0.02,0.024,0.023,0.019,0.02,1594,4320,2213,2283,1599,2333,2187,2974,1307,1401,529.0,2529,3599,984,3056,7662,14846,2323,3606,3866,3215,4322,1952,5411,5519,5696,5182,3308,5695,5244,2381,1108,964,5445,1747,1629,5477,6120,408,549,1550,2891,6638,2580,3739,1275,872,2257,2224,1312,453,2943,1773,3858,529,3806,959,2329,942,1464,831,2478,279,938,2517,1465,3319,2884,1793,1142,1388,1899,7792,463,0.142,0.124,0.115,0.105,0.124,0.136,0.094,0.1,0.129,0.15,0.115,0.135,0.119,0.126,0.149,0.143,0.128,0.12,0.143,0.13,0.094,0.148,0.118,0.125,0.136,0.128,0.12,0.109,0.095,0.123,0.137,0.122,0.099,0.119,0.095,0.086,0.127,0.131,0.074,0.128,0.106,0.128,0.14,0.115,0.113,0.088,0.107,0.08,0.094,0.115,0.128,0.104,0.133,0.095,0.107,0.108,0.105,0.109,0.132,0.103,0.11,0.12,0.129,0.113,0.12,0.136,0.108,0.11,0.103,0.104,0.116,0.119,0.105,0.115,480,1559,855,889,647,959,783,977,398,399,187.0,1299,1156,304,1074,2419,4475,459,1245,1818,750,1480,860,1496,1921,1994,2057,1343,1619,1846,747,529,462,1367,430,708,1363,1645,274,292,834,1311,1054,1832,2253,686,405,1035,1097,477,329,1438,769,1711,310,2105,355,1173,565,838,432,939,167,418,1448,864,1872,1306,942,531,700,831,3559,329,3.197,2.606,2.578,2.631,2.33,2.204,2.363,2.601,2.505,3.154,2.568,1.731,2.552,2.375,2.322,2.493,2.768,3.739,2.53,1.901,3.237,2.385,1.978,2.751,2.337,2.512,2.095,2.122,2.782,2.367,2.448,2.127,1.788,2.786,3.412,2.195,2.931,2.774,1.719,2.475,2.312,1.938,3.703,1.615,1.886,2.038,2.688,2.672,2.439,3.08,1.581,2.206,2.159,2.435,2.09,1.957,2.835,2.289,2.031,1.99,2.244,2.708,1.846,2.106,1.854,2.027,1.894,2.545,2.242,2.506,2.254,2.583,2.408,1.774,0.796,0.531,0.405,0.417,0.646,0.471,0.568,0.502,0.686,0.515,0.813,0.472,0.36,0.449,0.47,0.535,0.547,0.735,0.659,0.523,0.749,0.475,0.504,0.573,0.52,0.481,0.464,0.474,0.5,0.535,0.667,0.99,0.363,0.79,0.738,0.418,0.754,0.647,0.295,0.394,0.432,0.459,0.969,0.519,0.545,0.416,0.562,0.731,0.574,0.509,0.299,0.487,0.625,0.561,0.325,0.404,0.693,0.763,0.472,0.357,0.56,0.787,0.337,0.967,0.604,0.623,0.434,0.402,0.342,0.355,0.558,0.611,0.503,0.364
-97,Epileptic,M,42.0,0.7,2.9,0.9,1.6,1.8,1.9,2.7,2.2,1.3,0.9,0.3,2.5,2.0,0.6,1.3,8.3,11.8,1.0,2.2,4.7,1.1,3.2,1.9,4.1,3.3,4.2,3.8,3.7,3.3,4.5,1.9,1.4,0.5,2.4,0.4,1.2,4.7,3.2,0.3,0.3,1.5,3.2,3.8,2.7,3.3,0.7,0.4,0.8,1.7,0.8,0.9,2.6,1.5,2.9,0.2,2.1,0.9,2.2,0.8,1.5,0.7,1.7,0.3,0.5,2.7,1.9,3.0,1.8,1.0,0.7,1.4,1.3,5.2,0.4,4,25,8,12,18,17,17,18,10,9,3.0,20,17,7,13,65,91,7,22,35,9,32,16,29,38,42,34,31,27,46,21,10,4,24,2,8,43,35,1,2,10,27,27,22,14,6,2,4,7,6,5,17,11,18,1,15,6,20,6,12,5,9,1,3,23,18,20,12,6,5,8,8,38,3,0.026,0.029,0.015,0.027,0.044,0.032,0.036,0.027,0.055,0.033,0.032,0.031,0.035,0.033,0.035,0.041,0.037,0.031,0.028,0.038,0.022,0.037,0.026,0.043,0.026,0.03,0.03,0.03,0.028,0.035,0.044,0.051,0.024,0.026,0.014,0.021,0.04,0.033,0.02,0.019,0.026,0.033,0.05,0.03,0.022,0.012,0.018,0.012,0.02,0.016,0.031,0.027,0.029,0.02,0.016,0.016,0.021,0.021,0.027,0.024,0.021,0.033,0.017,0.019,0.025,0.026,0.02,0.022,0.015,0.022,0.022,0.02,0.019,0.02,1833,5619,2646,2782,2287,2911,2819,4009,1926,1697,780.0,2588,3462,1491,2434,12138,19511,2703,4395,4159,3573,4854,2222,6832,6979,7772,6228,5089,6967,6775,2625,1358,847,6138,1519,2557,6923,7091,316,415,1846,2844,5998,2228,4107,1785,955,2159,2448,2031,737,3488,1790,5777,381,4053,1590,3801,848,1860,1077,2200,440,1083,3452,1739,4671,2907,2130,1247,2039,2228,10245,460,0.101,0.12,0.096,0.118,0.136,0.14,0.119,0.117,0.16,0.134,0.116,0.123,0.135,0.124,0.131,0.136,0.131,0.122,0.118,0.134,0.089,0.143,0.125,0.138,0.125,0.127,0.122,0.104,0.109,0.139,0.14,0.129,0.1,0.113,0.068,0.102,0.136,0.139,0.104,0.105,0.122,0.139,0.148,0.127,0.098,0.086,0.089,0.077,0.098,0.093,0.139,0.116,0.117,0.097,0.106,0.097,0.107,0.11,0.12,0.115,0.113,0.123,0.091,0.093,0.125,0.12,0.106,0.106,0.09,0.12,0.114,0.114,0.1,0.108,483,1678,902,997,784,1009,1130,1350,456,442,218.0,1312,1018,341,717,3457,5512,524,1302,2000,912,1470,1088,1650,2134,2284,2097,1904,1916,2180,762,569,319,1449,442,845,2002,1653,203,243,945,1403,1389,1423,2353,890,358,988,1200,810,443,1529,903,2323,177,2028,740,1790,551,1011,490,806,249,436,1819,1029,2318,1362,1031,514,992,1020,4397,304,3.679,3.112,2.655,2.817,2.43,2.546,2.215,2.568,2.87,2.955,2.884,1.775,2.65,2.878,2.555,2.587,2.769,3.666,2.686,1.837,2.849,2.552,1.861,2.946,2.614,2.813,2.338,2.122,2.716,2.464,2.541,2.434,2.145,2.912,3.042,2.791,2.708,3.163,1.904,2.052,2.478,1.843,2.97,1.839,2.029,2.303,3.087,2.66,2.529,2.624,2.123,2.335,2.052,2.548,2.313,2.197,2.579,2.372,1.76,2.031,2.418,2.714,1.968,2.769,2.077,2.05,2.26,2.488,2.475,2.493,2.422,2.744,2.536,1.878,0.699,0.852,0.67,0.402,0.657,0.687,0.593,0.513,0.525,0.567,0.718,0.486,0.443,0.604,0.55,0.637,0.652,0.707,0.514,0.547,0.793,0.485,0.399,0.762,0.582,0.599,0.639,0.615,0.685,0.486,0.734,0.976,0.457,0.665,0.775,0.549,0.717,0.553,0.408,0.38,0.555,0.649,0.82,0.82,0.585,0.476,0.631,0.573,0.437,0.543,0.565,0.507,0.58,0.541,0.397,0.473,0.479,0.529,0.41,0.353,0.436,0.667,0.499,1.004,0.554,0.629,0.493,0.527,0.395,0.877,0.462,0.442,0.523,0.399
-98,Epileptic,M,22.0,1.4,3.1,1.4,1.3,1.8,2.1,1.4,1.4,2.8,1.2,0.2,3.2,1.4,0.5,1.7,9.4,10.7,1.2,2.5,4.1,1.1,5.4,2.7,3.8,6.1,4.0,5.0,3.4,3.1,3.3,2.4,1.0,0.5,2.8,0.7,0.7,4.1,3.5,0.2,0.3,1.1,4.0,2.6,2.5,2.6,1.1,0.5,0.9,1.6,0.9,0.5,2.7,1.9,4.3,0.3,3.3,0.7,1.3,1.5,1.5,0.3,1.5,0.4,0.5,1.7,1.3,2.7,1.6,1.3,0.9,1.2,1.5,5.5,0.4,10,27,10,10,12,24,13,18,17,9,3.0,28,20,8,21,88,95,11,29,39,8,51,23,46,54,42,53,32,24,42,22,9,4,34,3,6,51,40,1,2,7,30,25,18,13,7,2,5,8,9,3,18,11,27,1,27,6,9,8,10,3,12,4,4,13,17,19,10,6,6,8,10,39,3,0.05,0.028,0.026,0.027,0.032,0.033,0.034,0.028,0.066,0.047,0.038,0.043,0.028,0.03,0.034,0.044,0.042,0.041,0.032,0.041,0.029,0.047,0.049,0.047,0.047,0.039,0.039,0.041,0.032,0.035,0.055,0.043,0.025,0.035,0.022,0.017,0.038,0.032,0.013,0.016,0.021,0.044,0.043,0.025,0.021,0.023,0.018,0.014,0.019,0.02,0.028,0.023,0.03,0.032,0.026,0.019,0.022,0.016,0.031,0.02,0.012,0.026,0.027,0.017,0.021,0.02,0.022,0.019,0.018,0.022,0.026,0.018,0.017,0.022,1676,5800,3087,2855,2449,3737,2856,3436,2022,1802,591.0,2653,4727,1520,4501,14422,18974,2945,5358,5512,3329,6144,2340,6719,7166,7119,7505,5312,6701,7402,3286,1233,1295,7398,2061,2320,9469,9903,549,792,1640,2625,5435,2989,3329,1431,994,2515,3014,2191,538,4430,2217,5843,396,5180,1590,2951,1428,2506,999,2829,590,1118,2915,1896,3948,3130,1874,1712,1632,2878,11199,487,0.155,0.116,0.111,0.118,0.13,0.149,0.127,0.127,0.175,0.158,0.134,0.166,0.123,0.143,0.131,0.146,0.146,0.143,0.133,0.16,0.106,0.151,0.143,0.159,0.153,0.129,0.135,0.13,0.118,0.128,0.153,0.108,0.099,0.127,0.087,0.091,0.146,0.135,0.081,0.085,0.105,0.143,0.151,0.118,0.099,0.105,0.096,0.076,0.096,0.113,0.138,0.107,0.125,0.121,0.105,0.104,0.104,0.091,0.127,0.103,0.084,0.121,0.131,0.099,0.108,0.107,0.108,0.098,0.095,0.112,0.116,0.096,0.096,0.121,508,1911,960,905,920,1173,902,1037,693,431,170.0,1264,1061,324,1016,3962,5021,539,1356,2045,676,1922,947,1714,2255,1990,2547,1646,1711,1978,850,497,387,1606,545,770,2116,1944,242,343,806,1404,1242,1615,1910,698,391,1042,1273,772,256,1967,1022,2279,178,2578,642,1375,762,1160,441,1004,286,505,1302,1039,1904,1304,938,694,781,1278,4626,269,3.254,2.808,2.911,2.841,2.43,2.627,2.432,2.964,2.373,3.079,2.686,1.891,3.104,3.253,3.227,2.685,2.84,4.054,3.017,2.274,3.253,2.558,2.158,2.945,2.512,2.819,2.297,2.51,2.869,2.759,2.773,2.382,2.659,3.083,3.252,2.711,3.198,3.372,2.588,2.711,2.574,1.773,2.979,2.076,1.944,2.138,3.24,3.057,2.809,2.944,2.293,2.428,2.279,2.464,2.415,2.215,2.64,2.455,2.123,2.303,2.493,2.9,2.17,2.669,2.473,2.187,2.3,2.792,2.439,2.55,2.411,2.439,2.597,2.298,0.983,0.509,0.551,0.647,0.718,0.674,0.649,0.459,0.843,0.808,0.975,0.514,0.534,0.624,0.588,0.751,0.733,0.87,0.667,0.735,0.809,0.817,0.572,0.72,0.826,0.678,0.652,0.68,0.55,0.615,0.785,0.938,0.424,0.682,0.54,0.507,0.839,0.723,0.452,0.325,0.565,0.594,0.765,0.572,0.543,0.433,0.572,0.76,0.595,0.475,0.526,0.439,0.63,0.565,0.4,0.457,0.546,0.436,0.421,0.504,0.543,0.663,0.399,0.755,0.547,0.685,0.445,0.408,0.434,0.666,0.516,0.562,0.477,0.45
-99,Epileptic,M,37.0,1.0,3.2,2.1,1.7,2.1,3.4,2.2,2.1,1.5,1.1,0.3,4.1,1.9,0.6,1.3,10.5,12.5,1.0,3.4,6.2,2.6,4.4,3.6,5.6,5.3,4.5,4.3,3.9,6.5,3.6,1.8,1.8,1.4,3.3,0.7,0.7,5.1,4.9,0.2,0.1,1.4,3.9,2.8,4.8,4.0,1.1,0.3,1.2,1.9,0.7,0.6,2.8,2.6,3.5,0.5,3.9,1.0,2.5,2.2,2.0,1.0,2.0,0.4,0.7,3.0,1.9,2.9,1.8,1.7,0.8,1.0,1.4,6.8,0.7,7,25,14,10,14,24,16,19,16,8,3.0,31,19,5,12,82,100,9,28,44,19,39,26,47,45,45,37,29,52,33,16,13,8,25,4,4,42,44,2,1,9,29,25,33,23,8,2,9,9,4,5,19,18,25,2,29,8,15,16,85,8,13,3,4,18,15,23,13,9,5,7,9,48,5,0.039,0.028,0.027,0.03,0.036,0.042,0.039,0.033,0.045,0.054,0.037,0.042,0.036,0.036,0.033,0.045,0.042,0.042,0.049,0.044,0.049,0.035,0.043,0.052,0.04,0.041,0.034,0.04,0.051,0.037,0.049,0.064,0.072,0.048,0.033,0.021,0.045,0.041,0.016,0.019,0.029,0.036,0.058,0.035,0.029,0.027,0.018,0.022,0.024,0.017,0.04,0.026,0.035,0.025,0.034,0.025,0.026,0.028,0.032,0.048,0.024,0.035,0.025,0.037,0.027,0.033,0.026,0.022,0.025,0.03,0.031,0.021,0.024,0.038,1077,5412,3359,2280,2299,2529,3044,3424,2082,1213,627.0,2868,3322,991,2485,11287,19578,1982,4040,4304,4479,3841,1799,5826,4528,5315,5218,3989,5532,5107,3166,1216,750,5330,1493,1452,7331,7181,328,343,1225,2250,4917,3184,3523,1244,782,1876,2107,1416,408,3491,2755,5419,368,4460,1493,2865,1666,1489,1309,2449,426,978,3327,1852,3595,2813,2076,1191,1011,1952,10768,538,0.129,0.124,0.111,0.117,0.125,0.143,0.129,0.128,0.148,0.169,0.117,0.145,0.129,0.123,0.131,0.137,0.137,0.144,0.158,0.15,0.134,0.123,0.15,0.158,0.121,0.14,0.112,0.125,0.131,0.133,0.16,0.15,0.169,0.149,0.112,0.094,0.146,0.137,0.104,0.105,0.125,0.142,0.169,0.132,0.12,0.119,0.096,0.108,0.113,0.091,0.162,0.118,0.124,0.114,0.129,0.111,0.117,0.113,0.137,0.126,0.109,0.131,0.119,0.128,0.116,0.116,0.112,0.11,0.11,0.13,0.133,0.114,0.111,0.149,392,1833,1314,944,999,1279,962,1234,647,405,206.0,1627,1041,270,704,3783,5451,473,1367,2322,1092,1780,1214,1828,2001,1913,2107,1569,1831,1828,704,522,343,1316,389,577,2024,2115,209,158,739,1647,1019,2219,2069,645,290,906,1099,649,268,1803,1346,2316,212,2505,676,1520,1085,1026,693,977,243,332,1739,889,1928,1302,993,447,541,965,4662,288,2.715,2.718,2.51,2.447,2.226,1.951,2.493,2.435,2.487,2.696,2.522,1.654,2.442,2.593,2.61,2.388,2.862,3.045,2.449,1.703,2.965,1.877,1.53,2.577,1.937,2.369,2.063,2.097,2.497,2.363,3.29,2.479,1.979,2.995,3.397,2.288,2.808,2.53,1.815,2.319,2.112,1.366,3.308,1.646,1.954,2.207,3.225,2.509,2.496,2.312,1.865,2.098,2.287,2.407,2.152,1.991,2.477,2.123,1.8,1.636,2.11,2.732,2.082,3.28,2.172,2.226,1.932,2.365,2.348,2.991,2.109,2.323,2.418,2.312,0.755,0.576,0.452,0.474,0.56,0.481,0.786,0.598,0.668,0.671,0.641,0.462,0.6,0.608,0.52,0.673,0.601,0.677,0.61,0.479,0.933,0.532,0.42,0.702,0.548,0.628,0.573,0.621,0.588,0.604,0.825,0.841,0.655,0.658,0.826,0.521,0.651,0.722,0.328,0.372,0.391,0.48,0.805,0.434,0.565,0.46,0.711,0.702,0.433,0.555,0.34,0.462,0.54,0.559,0.503,0.43,0.579,0.493,0.345,0.412,0.478,0.701,0.472,0.691,0.628,0.589,0.508,0.445,0.473,0.922,0.474,0.479,0.558,0.467
-100,Epileptic,M,27.0,0.6,2.2,1.0,1.3,1.6,2.0,1.8,1.9,1.1,0.6,0.4,2.9,2.0,0.7,1.1,7.1,7.7,0.6,1.8,5.2,0.8,2.1,2.0,3.0,3.6,4.6,2.6,2.5,2.6,2.4,1.2,1.0,0.8,3.0,0.5,1.2,3.3,3.3,0.4,0.3,1.5,2.7,3.0,2.8,2.7,0.9,0.5,1.2,1.1,0.7,0.9,1.9,1.4,3.0,0.6,3.3,0.6,2.0,1.2,0.9,0.9,0.9,0.4,0.4,3.0,2.1,1.9,1.9,1.0,0.3,1.1,1.2,6.5,0.5,6,24,11,13,22,18,16,16,11,6,4.0,29,21,8,14,75,70,5,22,45,8,21,20,32,35,50,30,23,26,27,20,18,7,34,3,9,43,42,3,2,9,25,29,20,14,6,3,6,7,5,6,13,12,20,2,25,4,16,7,8,5,7,2,3,18,16,14,14,7,3,8,10,56,4,0.025,0.023,0.018,0.024,0.029,0.029,0.026,0.028,0.039,0.028,0.037,0.03,0.033,0.033,0.029,0.032,0.027,0.028,0.028,0.041,0.017,0.03,0.029,0.03,0.033,0.031,0.027,0.031,0.026,0.03,0.038,0.051,0.025,0.028,0.018,0.02,0.036,0.031,0.023,0.02,0.027,0.03,0.038,0.032,0.02,0.02,0.02,0.017,0.015,0.017,0.032,0.021,0.023,0.021,0.025,0.028,0.028,0.021,0.031,0.018,0.026,0.02,0.018,0.017,0.026,0.04,0.023,0.023,0.019,0.014,0.03,0.023,0.022,0.015,1624,4693,2113,2326,2194,3052,2677,3579,1549,1218,721.0,2918,3553,1305,2196,12527,16365,2142,3725,5345,3509,3224,2158,5873,5655,8318,4737,3716,5951,3806,2105,816,1346,6950,1766,2406,5989,7014,542,546,1795,2501,6431,2466,3525,1329,836,2181,2442,1453,633,3192,2307,5140,590,3421,841,3441,1050,1258,1082,1815,591,822,3535,1289,2553,3159,1497,807,1270,1719,10815,852,0.105,0.115,0.107,0.108,0.13,0.128,0.118,0.12,0.14,0.132,0.119,0.13,0.136,0.142,0.151,0.139,0.126,0.1,0.109,0.135,0.076,0.132,0.136,0.132,0.137,0.132,0.126,0.122,0.113,0.131,0.121,0.127,0.101,0.134,0.084,0.106,0.144,0.132,0.124,0.114,0.127,0.13,0.152,0.132,0.104,0.107,0.108,0.087,0.09,0.105,0.144,0.107,0.114,0.107,0.122,0.131,0.13,0.1,0.119,0.105,0.115,0.108,0.115,0.103,0.115,0.142,0.116,0.117,0.104,0.118,0.13,0.123,0.113,0.093,436,1603,769,943,935,1144,1062,1150,496,353,209.0,1530,975,311,635,3450,4577,397,1102,2188,888,1186,1112,1557,1894,2588,1635,1305,1610,1297,641,332,486,1757,433,869,1703,1850,240,223,857,1296,1378,1476,1996,665,347,946,1123,647,428,1513,1031,2289,298,1848,380,1644,686,779,498,772,309,388,1769,773,1358,1331,788,360,612,862,4527,507,3.507,2.643,2.442,2.415,2.127,2.295,2.206,2.692,2.372,2.932,2.764,1.72,2.845,2.951,2.674,2.732,2.87,3.875,2.726,2.026,3.001,2.216,1.787,2.804,2.439,2.709,2.286,2.234,2.792,2.396,2.594,2.446,2.236,3.047,3.545,2.536,2.734,2.83,2.395,2.478,2.689,1.72,3.206,1.953,2.004,2.121,3.029,2.811,2.553,2.656,1.891,2.28,2.208,2.343,2.328,2.059,2.631,2.393,1.854,1.866,2.287,2.465,2.154,2.234,2.183,1.937,2.087,2.575,2.192,2.636,2.241,2.32,2.481,2.01,0.878,0.759,0.585,0.521,0.503,0.602,0.507,0.542,0.47,0.48,0.78,0.422,0.595,0.369,0.299,0.517,0.537,0.696,0.537,0.748,0.747,0.45,0.38,0.565,0.503,0.585,0.405,0.515,0.445,0.468,0.71,0.931,0.465,0.653,0.617,0.488,0.676,0.592,0.348,0.501,0.498,0.418,0.674,0.675,0.571,0.435,0.652,0.614,0.513,0.458,0.498,0.418,0.535,0.524,0.328,0.397,0.364,0.558,0.354,0.32,0.439,0.548,0.432,0.807,0.427,0.728,0.441,0.498,0.418,1.055,0.44,0.459,0.551,0.368
-101,Epileptic,F,22.0,0.8,2.8,1.2,1.7,2.4,2.2,1.9,1.9,1.4,0.8,0.3,3.6,1.5,0.6,1.2,6.5,10.3,1.0,2.6,5.6,1.0,3.2,1.6,3.3,3.6,3.1,3.7,2.6,3.2,4.2,1.2,0.9,0.7,2.6,0.4,0.6,4.2,2.9,0.2,0.2,1.0,3.4,2.5,3.0,2.5,1.0,0.4,1.1,1.3,0.6,0.6,1.7,1.7,2.9,0.2,2.9,1.0,1.3,1.3,1.3,0.6,1.2,0.3,0.3,1.8,1.6,3.0,1.6,1.0,0.5,1.6,1.4,4.8,0.4,7,26,10,14,23,26,17,17,26,7,5.0,30,17,10,13,74,94,9,32,49,9,36,21,39,41,34,45,24,27,48,19,6,7,26,2,4,51,36,2,1,5,33,28,18,14,6,2,6,6,6,6,13,15,23,1,25,9,10,10,12,4,10,2,4,15,13,24,12,6,5,10,14,37,3,0.034,0.025,0.017,0.024,0.04,0.03,0.026,0.028,0.044,0.035,0.034,0.035,0.032,0.034,0.028,0.032,0.033,0.035,0.033,0.039,0.018,0.038,0.031,0.042,0.029,0.035,0.029,0.026,0.026,0.031,0.038,0.042,0.027,0.03,0.014,0.014,0.041,0.027,0.014,0.016,0.019,0.034,0.048,0.03,0.018,0.014,0.021,0.015,0.018,0.018,0.021,0.016,0.023,0.02,0.027,0.021,0.023,0.016,0.023,0.023,0.021,0.025,0.03,0.015,0.023,0.022,0.021,0.018,0.016,0.013,0.027,0.02,0.017,0.02,1790,5890,3135,3291,2880,3705,3310,3504,2361,1611,736.0,3536,3374,1290,3404,12761,20649,2673,5258,5459,3428,5376,2793,6783,7116,6165,6402,4493,6987,7462,1936,1162,1285,7023,1893,1993,7241,8605,417,503,1632,3828,6139,2882,3925,2041,820,2627,2305,1693,693,3381,2892,5713,345,3840,1735,3131,1510,1499,1042,2444,365,746,2748,1865,4032,3127,2027,903,1950,2357,10783,570,0.12,0.112,0.101,0.116,0.148,0.141,0.106,0.123,0.159,0.134,0.123,0.134,0.126,0.135,0.123,0.137,0.137,0.124,0.134,0.146,0.081,0.15,0.144,0.147,0.131,0.14,0.133,0.111,0.112,0.136,0.12,0.117,0.122,0.128,0.078,0.085,0.143,0.124,0.084,0.087,0.102,0.143,0.157,0.13,0.097,0.09,0.098,0.081,0.09,0.104,0.133,0.094,0.118,0.104,0.138,0.113,0.127,0.091,0.128,0.128,0.099,0.114,0.137,0.109,0.118,0.105,0.109,0.101,0.094,0.1,0.125,0.112,0.099,0.116,464,1854,1130,1162,1085,1366,1113,1125,704,409,212.0,1540,897,333,814,3711,5463,495,1409,2353,835,1596,1049,1660,2145,1768,2220,1539,1869,2419,550,432,409,1403,463,670,1709,1854,265,251,756,1643,1140,1647,2152,999,315,1116,1097,582,421,1662,1269,2542,133,2054,713,1429,851,874,492,862,153,364,1306,1001,2193,1407,937,529,934,1066,4398,282,3.486,2.752,2.602,2.727,2.255,2.359,2.493,2.763,2.492,2.974,2.827,2.029,2.77,2.644,2.922,2.598,2.94,3.647,3.022,2.031,2.931,2.543,2.257,2.918,2.628,2.786,2.314,2.284,2.86,2.483,2.587,2.595,2.564,3.329,3.518,2.793,3.037,3.201,1.878,2.342,2.65,2.06,3.5,2.1,2.125,2.264,3.109,2.935,2.48,2.799,1.838,2.069,2.253,2.314,2.896,2.036,2.575,2.525,2.082,1.905,2.409,2.534,2.414,2.027,2.372,2.228,2.032,2.369,2.513,1.978,2.348,2.446,2.604,2.549,0.732,0.667,0.68,0.467,0.617,0.459,0.582,0.487,0.675,0.399,0.71,0.486,0.527,0.523,0.59,0.609,0.599,0.774,0.601,0.651,0.863,0.49,0.561,0.684,0.533,0.471,0.511,0.586,0.483,0.53,0.694,0.843,0.456,0.663,0.8,0.485,0.649,0.61,0.414,0.391,0.606,0.566,0.765,0.781,0.588,0.52,0.512,0.653,0.589,0.751,0.486,0.478,0.517,0.5,0.304,0.407,0.534,0.463,0.374,0.428,0.543,0.791,0.436,0.88,0.404,0.79,0.472,0.427,0.407,0.573,0.496,0.623,0.635,0.326
-102,Epileptic,F,32.0,1.1,1.9,0.9,1.5,1.7,1.6,1.8,1.5,1.6,0.6,0.3,2.7,1.4,0.4,1.3,5.1,7.8,1.4,4.0,4.2,1.8,2.8,1.6,5.0,3.0,2.6,2.6,3.0,2.3,2.2,1.9,1.6,0.6,2.1,0.4,0.9,3.3,4.5,0.2,0.2,0.8,2.5,2.5,1.9,2.3,0.7,0.4,0.8,1.6,0.5,0.7,1.9,1.1,2.7,0.3,2.1,0.7,1.7,0.9,1.0,0.7,1.7,0.4,0.6,1.5,1.1,2.2,1.2,0.7,0.5,0.7,1.3,4.4,0.2,7,20,8,9,17,18,13,17,15,6,3.0,27,19,7,14,58,74,11,28,41,20,38,15,49,45,31,34,30,20,31,24,22,8,24,2,9,29,47,1,1,4,25,24,14,13,5,2,5,8,4,4,13,10,17,2,15,9,15,6,7,4,13,2,3,10,16,18,11,5,6,4,12,38,2,0.044,0.024,0.017,0.028,0.045,0.038,0.033,0.028,0.057,0.046,0.037,0.045,0.03,0.031,0.025,0.036,0.028,0.04,0.056,0.049,0.046,0.041,0.039,0.059,0.044,0.028,0.033,0.036,0.03,0.028,0.044,0.054,0.034,0.029,0.016,0.025,0.055,0.043,0.015,0.017,0.019,0.038,0.053,0.028,0.022,0.019,0.018,0.013,0.018,0.017,0.042,0.022,0.024,0.023,0.016,0.017,0.024,0.023,0.025,0.021,0.019,0.031,0.027,0.019,0.02,0.022,0.021,0.021,0.017,0.022,0.023,0.025,0.023,0.024,1497,4054,1956,2548,1845,2468,2384,2950,2211,1222,511.0,2352,3477,1151,3060,10527,18410,2590,3948,4193,2984,4306,1972,5956,5141,5803,4463,3429,5319,4568,2651,1414,1077,6131,1713,1937,4270,7872,367,460,1208,2154,4807,1968,2919,1234,783,2481,2490,1142,534,2765,1928,4165,456,3042,1115,3050,821,1298,990,2474,358,1098,2133,1081,3187,1897,1453,950,921,2072,7545,430,0.142,0.116,0.111,0.121,0.165,0.156,0.12,0.133,0.184,0.155,0.144,0.158,0.136,0.134,0.128,0.148,0.13,0.151,0.159,0.162,0.131,0.146,0.129,0.17,0.144,0.131,0.137,0.128,0.121,0.135,0.142,0.126,0.138,0.135,0.082,0.12,0.146,0.163,0.089,0.094,0.093,0.144,0.156,0.121,0.099,0.107,0.088,0.077,0.094,0.099,0.143,0.107,0.124,0.108,0.105,0.106,0.125,0.113,0.124,0.103,0.099,0.141,0.136,0.099,0.11,0.111,0.107,0.119,0.094,0.123,0.114,0.127,0.107,0.13,446,1483,842,828,663,908,930,974,548,307,161.0,1226,996,293,828,2835,4964,573,1167,1680,905,1404,854,1700,1521,1719,1589,1376,1382,1446,788,532,380,1309,439,634,1246,1806,209,219,685,1167,1114,1223,1645,600,330,957,1227,533,269,1364,792,1796,247,1627,515,1367,505,742,479,890,191,478,1073,696,1664,919,712,420,446,877,3240,203,3.258,2.681,2.489,2.646,2.317,2.371,2.189,2.611,3.039,2.991,2.498,1.762,2.714,2.918,2.788,2.749,2.903,3.643,2.664,2.057,2.669,2.485,2.023,2.65,2.619,2.661,2.214,2.003,2.851,2.51,2.509,2.46,2.369,3.213,3.467,2.603,2.534,3.057,2.074,2.348,2.207,1.701,3.183,1.818,2.047,2.208,2.89,2.945,2.403,2.572,2.229,2.21,2.437,2.423,2.294,2.094,2.362,2.367,1.753,1.951,2.465,2.932,2.264,2.81,2.146,1.844,2.074,2.186,2.207,2.574,2.282,2.572,2.388,2.425,0.956,0.495,0.428,0.593,0.704,0.607,0.746,0.463,0.571,0.8,0.732,0.546,0.444,0.44,0.468,0.601,0.627,0.638,0.688,0.687,0.817,0.594,0.495,0.781,0.676,0.531,0.557,0.68,0.551,0.598,0.725,0.795,0.377,0.633,0.881,0.536,0.838,0.738,0.274,0.37,0.424,0.503,0.921,0.425,0.647,0.484,0.439,0.791,0.441,0.528,0.537,0.407,0.55,0.492,0.267,0.398,0.489,0.459,0.418,0.359,0.467,0.694,0.566,0.804,0.454,0.527,0.449,0.441,0.474,0.832,0.471,0.558,0.553,0.506
-103,Epileptic,M,27.0,1.4,1.6,0.9,1.2,1.6,1.7,1.5,1.2,1.5,0.6,0.4,2.6,1.0,0.4,0.6,4.7,7.0,0.8,2.8,3.9,2.9,2.0,1.8,4.0,3.7,3.2,3.6,2.2,2.6,2.1,2.9,2.7,0.4,1.8,0.2,0.7,2.4,2.6,0.1,0.1,1.2,2.1,2.0,2.9,2.2,0.6,0.5,0.8,1.1,0.7,0.4,1.3,1.4,2.0,0.1,1.3,1.0,1.5,0.9,1.0,0.7,0.9,0.1,0.8,2.2,1.0,2.4,1.0,0.8,0.3,0.7,1.2,4.0,0.3,11,14,7,10,13,19,13,14,15,8,3.0,25,11,5,8,56,64,9,34,40,32,28,16,40,43,42,33,22,24,26,27,17,5,25,1,7,26,35,1,1,9,23,26,24,15,4,2,5,6,5,3,9,11,15,1,12,5,13,10,7,5,7,1,6,16,17,20,8,7,3,6,9,33,3,0.051,0.019,0.018,0.025,0.033,0.025,0.024,0.023,0.051,0.035,0.03,0.038,0.024,0.03,0.023,0.031,0.025,0.047,0.033,0.04,0.058,0.029,0.032,0.043,0.04,0.028,0.04,0.028,0.023,0.024,0.059,0.059,0.025,0.025,0.011,0.011,0.033,0.028,0.013,0.013,0.025,0.033,0.041,0.032,0.019,0.014,0.024,0.014,0.016,0.022,0.026,0.018,0.021,0.017,0.028,0.015,0.022,0.017,0.025,0.018,0.018,0.02,0.017,0.023,0.02,0.02,0.018,0.015,0.016,0.011,0.02,0.021,0.016,0.014,1648,4507,2499,2250,1872,3402,2919,3482,2187,1483,823.0,3231,3340,1282,1929,11113,17975,2291,5597,5160,3197,4180,1998,5245,5687,7527,4441,4361,7005,5853,2012,1769,1216,6546,1583,2742,5306,7902,294,246,1574,2264,4287,2877,3203,1441,692,1987,1965,1435,715,2363,2091,4644,247,3257,1241,3208,1103,1452,1080,2188,231,1134,3243,1078,4565,2547,1969,927,1372,1978,9038,709,0.148,0.1,0.09,0.117,0.139,0.125,0.104,0.12,0.167,0.151,0.113,0.154,0.113,0.134,0.118,0.133,0.124,0.134,0.137,0.157,0.14,0.123,0.129,0.144,0.148,0.129,0.136,0.095,0.104,0.12,0.169,0.142,0.126,0.125,0.057,0.082,0.124,0.128,0.092,0.09,0.124,0.123,0.135,0.139,0.099,0.085,0.108,0.081,0.089,0.114,0.131,0.101,0.114,0.094,0.11,0.09,0.109,0.099,0.139,0.104,0.113,0.101,0.116,0.105,0.107,0.122,0.101,0.091,0.096,0.091,0.11,0.115,0.095,0.103,500,1385,822,814,759,1130,958,952,569,370,210.0,1257,747,265,485,2813,4510,459,1490,1878,1023,1238,848,1575,1483,2043,1452,1337,1624,1444,852,732,280,1307,401,961,1380,1643,170,123,779,1067,927,1538,1867,680,286,837,941,509,263,1063,997,1903,89,1544,588,1451,546,782,526,807,125,603,1643,758,2059,1115,851,391,624,915,3935,332,2.993,2.947,2.939,2.673,2.272,2.516,2.49,3.04,2.777,3.082,2.931,2.174,3.179,3.223,2.8,2.865,3.094,3.66,2.92,2.266,2.575,2.553,2.015,2.454,2.798,2.901,2.533,2.43,3.144,2.994,1.887,2.378,3.023,3.374,3.598,2.636,2.828,3.265,2.044,2.11,2.585,1.86,3.284,1.967,1.968,2.424,2.878,2.939,2.592,2.762,2.333,2.31,2.225,2.452,3.017,2.283,2.449,2.427,2.074,2.084,2.299,2.524,1.87,2.343,2.182,1.845,2.331,2.609,2.679,2.512,2.421,2.515,2.457,2.714,0.802,0.618,0.564,0.479,0.627,0.719,0.696,0.436,0.819,0.809,0.67,0.53,0.453,0.477,0.561,0.641,0.599,0.746,0.649,0.594,0.924,0.654,0.503,1.001,0.723,0.652,0.64,0.58,0.518,0.615,0.813,0.846,0.445,0.565,0.755,0.533,0.723,0.626,0.368,0.393,0.431,0.494,0.864,0.513,0.639,0.385,0.372,0.544,0.365,0.633,0.505,0.451,0.476,0.496,0.407,0.357,0.48,0.499,0.601,0.396,0.36,0.71,0.66,0.488,0.427,0.585,0.396,0.399,0.363,0.854,0.531,0.58,0.457,0.305
-104,Epileptic,M,17.5,0.8,2.3,0.8,1.2,1.8,2.2,1.4,1.5,1.1,0.8,0.3,2.5,1.7,0.5,1.6,6.1,9.4,0.8,2.0,4.3,1.1,3.2,1.8,3.1,4.2,3.3,3.1,3.4,2.4,3.9,1.6,1.2,0.6,2.2,0.4,0.6,2.4,2.7,0.1,0.2,0.9,2.7,2.0,2.7,3.0,0.6,0.3,0.8,1.1,0.6,0.4,3.0,1.5,2.9,0.5,2.3,1.0,2.1,0.7,1.0,0.9,1.4,0.4,0.3,1.5,1.1,1.9,1.3,0.6,0.6,1.8,1.4,3.9,0.4,8,24,8,11,16,16,11,13,17,8,3.0,21,17,6,16,68,104,5,22,32,9,33,18,32,47,36,35,32,23,47,23,9,6,25,2,6,32,32,1,2,5,22,21,18,17,5,1,5,5,6,3,24,12,22,3,18,7,15,5,8,7,10,2,4,10,11,15,9,4,7,11,11,28,3,0.034,0.021,0.011,0.02,0.035,0.039,0.02,0.03,0.043,0.035,0.026,0.034,0.031,0.031,0.026,0.034,0.027,0.023,0.026,0.037,0.019,0.034,0.029,0.035,0.036,0.026,0.024,0.027,0.023,0.031,0.051,0.037,0.022,0.024,0.012,0.014,0.025,0.026,0.013,0.014,0.016,0.031,0.035,0.028,0.02,0.014,0.014,0.013,0.014,0.015,0.032,0.024,0.026,0.023,0.018,0.017,0.02,0.021,0.018,0.017,0.025,0.027,0.022,0.017,0.023,0.024,0.018,0.018,0.011,0.018,0.031,0.016,0.016,0.017,1760,5445,2390,2787,3156,2678,3127,3019,2301,1849,800.0,2842,3856,1272,4484,12664,22988,2943,4697,3937,3966,5756,2565,6825,7206,7886,7291,5185,6774,7295,2777,1535,1142,7654,2135,2338,8681,9129,381,736,1940,3642,5831,2190,4543,1587,891,2764,2690,1708,375,5341,2437,5239,821,4653,1720,3923,953,1840,1133,2589,590,1002,2495,1630,3824,2764,1777,1349,2095,3086,11158,539,0.128,0.106,0.09,0.106,0.146,0.151,0.097,0.139,0.164,0.128,0.114,0.14,0.129,0.128,0.122,0.138,0.127,0.1,0.118,0.141,0.088,0.149,0.134,0.142,0.154,0.118,0.119,0.112,0.103,0.136,0.161,0.12,0.103,0.117,0.067,0.077,0.129,0.126,0.085,0.1,0.091,0.138,0.137,0.116,0.101,0.086,0.081,0.073,0.084,0.098,0.139,0.115,0.128,0.105,0.104,0.101,0.11,0.107,0.104,0.101,0.132,0.122,0.133,0.126,0.109,0.112,0.104,0.102,0.082,0.112,0.134,0.106,0.093,0.103,480,1839,1004,962,848,981,1029,878,527,420,213.0,1201,939,316,1027,3092,5969,531,1298,1781,936,1582,1006,1486,1953,1957,2173,1818,1567,2211,659,572,456,1451,521,697,1847,1801,209,309,834,1367,1041,1432,2339,781,346,1127,1163,636,196,1986,802,2142,411,2139,719,1687,573,923,540,898,252,358,1043,705,1752,1173,779,584,896,1339,3999,305,3.432,2.808,2.391,2.836,2.815,2.532,2.494,3.121,3.115,3.273,3.163,2.013,3.145,2.912,3.261,2.919,3.0,4.164,2.952,1.933,3.202,2.747,2.298,3.202,2.816,3.076,2.599,2.327,3.111,2.651,3.075,2.677,2.124,3.622,3.827,2.784,3.318,3.526,2.175,2.887,2.792,2.187,3.44,1.809,2.286,2.2,3.3,2.956,2.885,3.046,1.915,2.51,2.731,2.646,2.597,2.453,2.807,2.746,1.976,2.232,2.684,2.802,2.6,2.725,2.644,2.891,2.387,2.7,2.597,2.406,2.585,2.769,3.026,2.244,0.704,0.688,0.524,0.467,0.723,0.687,0.501,0.544,0.623,0.5,0.997,0.525,0.428,0.569,0.58,0.638,0.577,0.624,0.592,0.652,0.839,0.528,0.505,0.663,0.573,0.58,0.539,0.587,0.554,0.628,0.784,1.131,0.335,0.613,0.7,0.665,0.565,0.593,0.329,0.473,0.744,0.624,0.864,0.791,0.705,0.535,0.416,0.541,0.561,0.455,0.5,0.566,0.708,0.512,0.366,0.527,0.473,0.691,0.448,0.434,0.452,0.656,0.53,0.848,0.513,0.818,0.465,0.393,0.429,0.691,0.493,0.48,0.631,0.43
-105,Epileptic,F,37.0,0.4,2.8,1.0,1.3,1.4,1.7,1.3,1.8,1.2,0.9,0.2,2.4,1.5,0.4,1.2,4.6,6.6,0.9,1.6,3.2,0.7,3.2,1.9,3.0,2.9,3.2,2.4,2.7,2.2,2.2,1.5,0.6,0.3,2.0,0.2,0.9,2.6,2.4,0.2,0.1,1.0,3.1,1.9,2.4,2.7,0.6,0.4,0.8,1.1,0.6,0.7,1.8,0.9,2.7,0.4,2.4,0.6,1.5,1.1,1.4,0.9,1.5,0.3,0.5,1.4,1.2,2.5,1.6,1.0,0.4,0.9,1.2,3.6,0.2,5,28,14,10,18,17,13,16,13,9,2.0,25,16,4,13,57,65,6,19,31,9,36,17,32,36,34,29,26,20,28,18,7,4,25,2,8,30,29,1,1,5,25,20,16,17,3,2,5,8,4,7,12,5,18,3,21,4,16,8,12,7,12,1,5,12,16,23,11,6,4,7,10,28,2,0.027,0.026,0.017,0.026,0.028,0.031,0.018,0.029,0.04,0.041,0.025,0.031,0.028,0.018,0.03,0.028,0.023,0.032,0.031,0.029,0.017,0.034,0.035,0.032,0.03,0.032,0.021,0.028,0.02,0.024,0.032,0.037,0.019,0.023,0.01,0.023,0.032,0.027,0.012,0.015,0.022,0.036,0.03,0.026,0.022,0.011,0.017,0.012,0.015,0.024,0.026,0.019,0.02,0.02,0.017,0.017,0.024,0.018,0.031,0.025,0.028,0.024,0.02,0.021,0.019,0.028,0.021,0.02,0.016,0.016,0.03,0.02,0.017,0.017,1168,4800,2273,2089,1660,2656,2756,2874,1625,1260,345.0,2524,3486,1132,1943,9988,15784,1942,3405,4296,2790,4573,2183,5449,5414,5732,4918,3376,5378,4470,2014,1042,748,5613,1888,1916,5842,6066,367,323,1311,2597,4843,2235,2934,1364,753,2234,2370,1160,635,2748,1421,4750,537,3756,851,3172,916,1620,1135,2581,418,912,2227,907,3446,2776,1948,868,1346,2260,7846,455,0.113,0.13,0.118,0.113,0.139,0.148,0.09,0.139,0.159,0.159,0.109,0.149,0.128,0.102,0.141,0.133,0.118,0.118,0.148,0.136,0.081,0.147,0.145,0.137,0.137,0.139,0.111,0.119,0.097,0.127,0.124,0.105,0.113,0.121,0.059,0.108,0.136,0.131,0.09,0.097,0.108,0.147,0.142,0.117,0.108,0.076,0.106,0.082,0.094,0.11,0.147,0.105,0.111,0.104,0.106,0.109,0.119,0.107,0.145,0.127,0.141,0.118,0.104,0.128,0.109,0.132,0.118,0.106,0.096,0.133,0.133,0.115,0.101,0.124,317,1661,888,843,774,969,996,1012,529,383,143.0,1184,956,292,605,2728,4657,451,927,1720,701,1534,888,1581,1714,1792,1844,1406,1518,1455,647,445,261,1306,492,644,1449,1531,206,168,660,1283,1044,1346,1849,746,335,983,1120,479,414,1366,685,2143,327,2075,439,1438,553,879,535,1033,215,416,1129,628,1852,1200,940,322,593,949,3427,198,3.334,2.875,2.801,2.572,2.03,2.492,2.261,2.697,2.365,2.81,2.618,1.88,2.789,2.755,2.5,2.773,2.767,3.444,2.876,2.095,3.09,2.44,2.212,2.722,2.46,2.651,2.18,1.959,2.71,2.437,2.411,2.402,2.41,3.132,3.33,2.672,3.037,3.049,2.19,2.294,2.393,1.862,3.141,1.936,1.826,1.991,2.769,2.724,2.511,3.074,2.108,2.315,2.384,2.445,2.164,2.085,2.527,2.422,2.002,2.108,2.623,2.75,2.331,2.542,2.097,1.84,2.037,2.531,2.42,3.557,2.545,2.694,2.574,2.721,0.694,0.517,0.41,0.418,0.524,0.468,0.655,0.435,0.521,0.452,0.658,0.437,0.353,0.358,0.463,0.516,0.535,0.841,0.394,0.614,0.638,0.432,0.374,0.566,0.493,0.459,0.417,0.501,0.478,0.49,0.684,1.086,0.446,0.471,0.581,0.501,0.611,0.517,0.346,0.364,0.441,0.395,0.821,0.632,0.511,0.371,0.402,0.729,0.384,0.519,0.477,0.434,0.522,0.399,0.364,0.425,0.337,0.426,0.323,0.327,0.385,0.545,0.41,0.912,0.497,0.639,0.469,0.417,0.357,0.685,0.429,0.51,0.446,0.49
-106,Epileptic,F,22.0,0.4,1.7,0.9,1.0,2.0,1.9,1.6,1.8,1.4,0.7,0.2,2.6,1.6,0.9,1.5,7.0,7.6,1.2,2.5,4.7,2.0,2.6,1.4,2.9,2.1,3.0,2.4,2.7,2.1,3.1,1.6,1.5,0.8,2.8,0.6,0.4,3.4,4.2,0.2,0.0,0.8,2.7,3.5,2.2,2.3,0.8,0.6,0.6,1.1,1.7,0.6,1.7,1.1,2.1,0.1,1.2,0.6,2.0,0.9,1.1,0.8,1.2,0.1,0.3,1.1,0.5,2.0,0.9,0.9,0.4,0.8,1.9,4.9,0.1,4,15,8,7,16,19,17,14,16,6,3.0,26,18,8,13,75,68,6,25,42,18,31,15,30,25,30,33,25,18,38,15,7,5,29,3,3,39,39,1,0,3,22,27,19,13,6,3,4,6,14,4,11,6,15,1,9,4,13,5,7,6,9,1,3,9,7,19,6,6,3,4,13,40,1,0.026,0.021,0.017,0.022,0.039,0.042,0.024,0.037,0.054,0.032,0.024,0.038,0.033,0.045,0.042,0.036,0.029,0.042,0.036,0.044,0.035,0.04,0.03,0.035,0.033,0.031,0.028,0.031,0.024,0.029,0.048,0.049,0.031,0.034,0.02,0.009,0.035,0.039,0.013,0.029,0.015,0.042,0.053,0.028,0.019,0.017,0.027,0.014,0.017,0.04,0.027,0.017,0.019,0.022,0.027,0.015,0.012,0.021,0.026,0.023,0.028,0.027,0.016,0.02,0.016,0.025,0.019,0.014,0.016,0.017,0.024,0.024,0.02,0.01,1491,5236,1981,1926,2478,2810,3316,3115,2801,1260,527.0,2394,3610,1532,2578,13715,17198,1803,4329,4927,2761,4090,1847,6563,4065,6512,4952,3980,5118,5191,1965,997,1030,5998,2134,1915,5937,6980,431,76,1842,2434,5027,1968,3110,1284,900,1908,2541,1731,536,3200,2026,4396,198,2571,1710,3511,890,1222,1301,2173,296,900,1942,572,3595,2066,1833,1463,905,2919,9741,364,0.094,0.112,0.104,0.101,0.139,0.156,0.122,0.124,0.174,0.136,0.112,0.14,0.143,0.146,0.141,0.147,0.13,0.139,0.133,0.153,0.108,0.157,0.129,0.136,0.142,0.126,0.135,0.128,0.108,0.135,0.156,0.111,0.117,0.128,0.079,0.065,0.128,0.131,0.085,0.128,0.084,0.14,0.142,0.124,0.096,0.102,0.115,0.076,0.091,0.132,0.129,0.095,0.096,0.106,0.152,0.093,0.081,0.105,0.129,0.108,0.131,0.119,0.097,0.114,0.098,0.112,0.104,0.094,0.099,0.107,0.12,0.113,0.102,0.07,342,1258,738,706,809,869,1030,841,581,344,182.0,1103,892,326,666,3389,4407,376,1165,1874,834,1220,822,1482,1198,1737,1565,1396,1348,1749,543,399,375,1369,480,602,1746,1701,196,32,700,1018,1174,1191,1819,628,371,724,979,685,317,1525,856,1668,71,1252,704,1478,495,737,525,789,129,299,1081,339,1715,923,798,370,458,1250,3911,175,3.614,3.324,2.563,2.639,2.493,2.677,2.545,3.006,3.241,2.928,2.607,1.848,3.039,3.094,2.783,2.952,3.064,3.694,2.969,2.394,2.706,2.55,1.966,3.139,2.545,2.863,2.414,2.319,2.844,2.318,2.783,2.318,2.571,2.976,3.356,2.792,2.823,2.976,2.518,2.619,3.055,1.975,3.045,1.764,1.981,2.007,3.083,3.15,3.009,2.677,1.978,2.272,2.559,2.689,2.774,2.15,2.772,2.617,1.987,1.87,2.501,2.819,2.725,2.996,2.014,2.194,2.194,2.7,2.558,3.439,2.281,2.539,2.694,2.119,1.235,0.986,0.63,0.664,0.542,0.718,0.582,0.606,0.738,0.573,0.561,0.442,0.546,0.586,0.568,0.646,0.722,1.01,0.691,0.773,0.813,0.625,0.478,0.664,0.56,0.699,0.553,0.486,0.537,0.668,0.826,1.088,0.782,0.885,0.971,0.563,0.706,0.715,0.39,0.339,0.775,0.657,1.069,0.65,0.54,0.646,0.457,0.91,0.606,0.742,0.458,0.55,0.68,0.632,0.446,0.447,0.488,0.679,0.495,0.388,0.522,0.578,0.379,0.872,0.69,0.621,0.466,0.448,0.528,1.117,0.482,0.545,0.568,0.489
-108,Epileptic,F,32.0,1.2,2.3,0.8,1.9,1.4,1.7,1.3,1.6,1.4,0.9,0.2,2.8,1.7,0.4,1.3,6.5,8.3,1.1,2.7,3.4,1.1,2.7,2.1,3.5,3.4,3.9,3.4,3.0,2.5,3.5,1.4,1.7,0.5,2.9,0.5,0.6,3.8,3.2,0.2,0.4,1.0,3.1,1.5,2.3,2.3,0.9,0.3,0.7,1.5,0.9,0.4,1.7,1.8,3.4,0.2,2.2,1.0,1.6,1.0,0.7,0.4,1.4,0.4,0.6,2.1,1.5,2.4,0.9,0.8,0.4,0.7,1.3,5.0,0.3,9,21,6,14,16,16,12,17,17,13,5.0,28,20,6,15,72,79,9,30,33,17,36,22,37,46,45,48,31,23,38,19,16,6,33,3,6,45,39,1,2,9,28,18,20,16,6,2,5,7,8,4,14,15,28,2,19,7,14,10,6,3,14,5,5,15,23,22,8,4,4,5,12,42,4,0.046,0.025,0.016,0.027,0.033,0.028,0.021,0.026,0.041,0.036,0.03,0.033,0.032,0.027,0.028,0.031,0.029,0.039,0.035,0.037,0.024,0.03,0.036,0.036,0.035,0.025,0.022,0.027,0.024,0.032,0.036,0.055,0.016,0.032,0.014,0.012,0.031,0.03,0.013,0.023,0.018,0.037,0.032,0.023,0.017,0.019,0.016,0.011,0.017,0.02,0.026,0.019,0.027,0.021,0.017,0.019,0.021,0.015,0.019,0.015,0.013,0.021,0.024,0.018,0.019,0.023,0.017,0.014,0.014,0.016,0.02,0.015,0.018,0.011,1610,4847,2357,2955,2412,3246,3173,3504,2489,2041,417.0,3592,3839,1215,3091,12504,19646,2877,4706,4423,3632,5718,3072,6509,6177,9432,7955,5044,6732,6019,2999,1732,1436,7945,2572,2536,9379,7949,441,642,1662,3743,5034,2561,3565,1383,910,2527,2783,1970,595,2858,2064,6269,336,3591,1587,3361,1438,1506,824,2769,716,1024,3365,1392,4489,2391,1725,726,1072,2457,9859,732,0.146,0.119,0.096,0.13,0.139,0.136,0.101,0.126,0.174,0.158,0.13,0.153,0.144,0.127,0.132,0.137,0.132,0.138,0.131,0.145,0.094,0.135,0.143,0.143,0.133,0.118,0.118,0.117,0.103,0.15,0.131,0.142,0.092,0.135,0.069,0.075,0.133,0.135,0.083,0.114,0.105,0.142,0.127,0.118,0.098,0.113,0.095,0.074,0.095,0.112,0.115,0.106,0.124,0.112,0.123,0.107,0.106,0.094,0.116,0.098,0.097,0.117,0.127,0.116,0.111,0.134,0.103,0.089,0.086,0.117,0.105,0.1,0.103,0.089,465,1594,786,1059,858,1013,948,1119,687,518,164.0,1564,987,307,838,3601,5104,522,1359,1746,877,1548,1009,1785,1793,2823,2562,1752,1579,1844,742,578,527,1667,578,815,2190,1839,239,270,885,1476,969,1585,2074,630,362,968,1230,702,328,1419,1071,2604,200,1836,740,1630,810,775,389,1055,284,513,1566,954,2153,1059,807,391,604,1267,4170,445,3.319,2.73,2.794,2.762,2.269,2.641,2.717,2.692,2.796,2.972,2.146,2.014,2.953,2.76,2.615,2.56,2.979,3.952,2.746,2.168,3.019,2.813,2.487,2.803,2.66,2.697,2.42,2.243,3.131,2.526,2.798,3.018,2.231,3.246,3.578,2.771,3.021,2.992,2.042,2.719,2.322,2.197,3.447,1.864,1.978,2.375,2.81,3.077,2.815,2.997,2.117,2.242,2.066,2.505,2.06,2.18,2.615,2.394,2.085,2.2,2.189,2.478,2.094,2.467,2.43,1.858,2.215,2.435,2.471,1.958,2.094,2.254,2.46,2.028,0.876,0.658,0.524,0.485,0.887,0.723,0.75,0.439,0.71,0.527,0.488,0.554,0.504,0.584,0.639,0.654,0.677,0.69,0.612,0.635,0.941,0.551,0.689,0.758,0.599,0.631,0.634,0.627,0.576,0.641,0.705,0.915,0.514,0.611,0.784,0.455,0.804,0.675,0.461,0.334,0.433,0.593,0.871,0.579,0.636,0.393,0.637,0.859,0.51,0.54,0.372,0.418,0.539,0.474,0.468,0.442,0.414,0.533,0.37,0.367,0.534,0.684,0.652,0.707,0.57,0.601,0.467,0.416,0.406,0.538,0.436,0.488,0.463,0.274
-109,Epileptic,M,42.0,0.8,2.3,0.7,0.9,1.5,2.5,1.5,1.6,1.0,0.4,0.4,2.4,1.3,0.4,1.6,7.6,9.0,0.8,1.7,3.2,0.7,3.3,1.0,3.3,2.3,2.8,2.8,2.1,3.2,3.3,1.0,0.6,0.4,2.0,0.5,1.0,3.2,2.5,0.3,0.4,1.1,2.4,2.3,3.4,3.0,0.6,0.4,0.9,1.3,0.6,0.7,1.6,1.1,3.1,0.1,2.0,1.0,1.7,0.8,0.9,0.6,1.9,0.6,0.4,1.6,1.1,2.4,1.5,1.0,0.4,0.9,1.1,4.3,0.3,4,22,7,7,15,19,12,13,10,5,5.0,21,16,5,15,76,76,7,24,31,6,37,12,38,32,32,38,17,27,38,15,5,4,20,3,8,34,32,2,2,6,21,20,19,15,4,2,7,6,4,4,10,7,19,1,17,7,14,5,6,5,15,5,4,15,10,16,10,6,4,6,10,35,2,0.036,0.024,0.012,0.02,0.034,0.033,0.026,0.037,0.032,0.02,0.033,0.028,0.031,0.029,0.035,0.035,0.029,0.03,0.021,0.027,0.015,0.039,0.023,0.039,0.032,0.031,0.026,0.029,0.031,0.029,0.035,0.035,0.028,0.029,0.015,0.028,0.031,0.024,0.022,0.024,0.022,0.035,0.046,0.027,0.025,0.015,0.018,0.015,0.016,0.016,0.035,0.018,0.018,0.024,0.015,0.014,0.016,0.019,0.021,0.016,0.019,0.029,0.022,0.016,0.017,0.031,0.018,0.02,0.022,0.014,0.027,0.023,0.017,0.027,1347,4972,1977,1831,2001,3411,2819,3096,1921,1448,690.0,2899,3418,889,3060,12841,19388,2394,4805,4495,2696,5800,2455,5610,4881,6563,6344,3463,7014,6563,2150,752,817,5599,1751,1669,6793,8166,416,533,1663,2622,6046,3076,3533,1450,792,2184,2321,1577,862,3250,2147,5439,327,3818,2058,3044,1052,1756,1077,2537,689,759,3199,995,3793,2492,1681,1034,1302,2100,10946,423,0.104,0.111,0.091,0.104,0.124,0.14,0.109,0.131,0.133,0.113,0.143,0.119,0.128,0.124,0.131,0.134,0.123,0.115,0.104,0.116,0.082,0.153,0.112,0.13,0.139,0.131,0.124,0.106,0.115,0.139,0.113,0.1,0.111,0.118,0.074,0.12,0.131,0.116,0.114,0.113,0.106,0.132,0.149,0.109,0.106,0.088,0.107,0.092,0.09,0.091,0.131,0.092,0.099,0.104,0.098,0.092,0.095,0.098,0.108,0.098,0.109,0.131,0.12,0.108,0.104,0.126,0.096,0.103,0.112,0.095,0.115,0.121,0.091,0.136,357,1610,817,735,745,1182,843,832,550,348,199.0,1255,848,259,829,3686,4985,505,1302,1800,759,1510,824,1436,1348,1667,1938,1114,1645,1949,583,372,260,1147,500,587,1519,1745,204,234,729,1061,1044,1837,1912,686,322,931,1172,567,336,1430,979,2260,115,2038,942,1510,605,869,477,967,388,417,1466,538,1922,1158,712,454,643,861,4195,156,3.371,2.833,2.372,2.444,2.468,2.428,2.722,3.068,2.666,3.153,2.897,1.973,2.946,2.567,2.76,2.646,3.053,3.62,2.879,2.117,2.751,2.726,2.495,2.882,2.701,3.143,2.489,2.358,3.089,2.583,2.614,2.134,2.56,3.274,3.131,2.686,3.205,3.253,2.152,2.474,2.751,2.079,3.551,1.838,2.092,2.276,2.934,2.96,2.37,3.01,2.453,2.391,2.286,2.468,2.746,2.108,2.5,2.305,1.91,2.24,2.446,2.537,2.08,2.055,2.428,2.154,2.161,2.478,2.594,2.7,2.509,2.678,2.628,2.928,1.13,0.645,0.465,0.562,0.651,0.603,0.644,0.541,0.523,0.454,0.615,0.478,0.491,0.599,0.545,0.597,0.632,0.741,0.512,0.664,0.715,0.58,0.572,0.631,0.669,0.551,0.564,0.654,0.652,0.573,0.612,0.963,0.61,0.739,0.799,0.449,0.721,0.726,0.578,0.436,0.571,0.606,0.919,0.561,0.729,0.518,0.636,0.831,0.509,0.852,0.451,0.507,0.642,0.552,0.434,0.42,0.646,0.445,0.421,0.477,0.49,0.692,0.451,0.808,0.433,0.707,0.453,0.465,0.434,0.677,0.468,0.621,0.532,0.32
-110,Epileptic,M,37.0,0.8,1.7,0.8,1.5,1.3,1.5,1.6,1.7,1.2,0.6,0.4,2.4,1.5,0.5,1.2,6.7,9.0,0.7,2.0,5.8,1.7,2.3,2.0,2.3,3.0,2.5,2.0,2.9,2.8,3.2,1.7,0.8,0.6,3.1,0.6,0.9,3.8,2.8,0.2,0.1,0.9,3.7,2.5,3.6,3.8,0.7,0.6,1.0,1.1,1.0,0.5,1.7,1.6,2.3,0.2,1.8,0.6,1.3,1.0,0.9,0.7,1.3,0.7,0.4,1.9,1.0,2.9,1.3,1.4,0.7,1.5,1.5,4.8,0.3,6,14,7,14,12,14,12,15,8,5,4.0,16,13,6,11,63,70,7,15,37,15,26,16,26,34,24,23,24,20,34,20,27,5,30,4,7,42,29,1,1,5,28,23,20,20,5,3,6,6,8,3,12,11,17,2,17,5,10,9,7,7,9,4,3,14,10,21,9,10,7,8,10,40,3,0.043,0.02,0.017,0.023,0.03,0.028,0.025,0.031,0.047,0.034,0.034,0.033,0.027,0.032,0.035,0.031,0.028,0.025,0.029,0.052,0.027,0.033,0.032,0.028,0.026,0.026,0.021,0.026,0.023,0.031,0.041,0.066,0.031,0.038,0.021,0.022,0.034,0.027,0.014,0.01,0.02,0.045,0.033,0.044,0.028,0.023,0.026,0.016,0.017,0.015,0.028,0.022,0.023,0.021,0.017,0.019,0.021,0.02,0.022,0.016,0.022,0.023,0.03,0.021,0.023,0.03,0.023,0.019,0.02,0.023,0.035,0.026,0.019,0.026,1431,3868,1949,2572,1545,2737,2358,2874,1481,1078,608.0,2264,3359,1166,2105,11376,17808,2229,3223,3530,3148,4171,2372,5002,5528,5128,4392,4266,5493,5832,1792,891,866,5614,1849,1856,7410,5713,436,246,1347,2784,5497,2374,3428,1309,820,2075,2162,2188,502,2670,2173,4320,307,3169,1283,2904,1233,1572,1295,2442,735,759,2528,1104,3883,2238,1868,1111,1412,2232,10444,425,0.129,0.103,0.101,0.12,0.123,0.129,0.095,0.135,0.128,0.134,0.117,0.133,0.119,0.13,0.138,0.128,0.119,0.106,0.115,0.158,0.108,0.134,0.132,0.115,0.126,0.122,0.108,0.111,0.097,0.131,0.145,0.115,0.123,0.134,0.093,0.105,0.125,0.112,0.084,0.082,0.106,0.154,0.134,0.157,0.119,0.105,0.12,0.093,0.094,0.1,0.126,0.105,0.113,0.1,0.119,0.106,0.111,0.099,0.129,0.098,0.12,0.107,0.133,0.098,0.117,0.118,0.114,0.111,0.116,0.111,0.134,0.116,0.093,0.132,385,1426,815,1009,701,979,820,961,445,314,198.0,1063,978,261,624,3681,5200,481,964,1662,976,1252,1029,1412,1780,1619,1494,1545,1659,1780,600,404,318,1301,485,641,1946,1554,229,107,671,1283,1299,1433,2215,615,345,925,1049,954,279,1321,1146,1966,177,1636,500,1222,738,854,547,859,330,302,1248,585,1981,1049,1019,524,674,951,4268,221,3.542,2.638,2.456,2.474,2.105,2.492,2.371,2.784,2.494,3.023,2.458,1.988,2.798,2.935,2.463,2.473,2.846,3.502,2.595,2.001,2.764,2.626,2.109,2.668,2.494,2.611,2.318,2.236,2.595,2.545,2.351,2.185,2.229,3.033,3.159,2.651,2.775,2.823,2.173,2.593,2.426,2.121,3.116,1.989,1.793,2.361,3.053,2.879,2.487,2.589,2.206,2.109,2.044,2.223,2.185,2.151,2.59,2.739,1.961,2.031,2.547,2.589,2.343,2.463,2.311,2.29,2.229,2.476,2.199,2.369,2.572,2.813,2.516,2.367,0.87,0.668,0.712,0.457,0.525,0.533,0.602,0.599,0.572,0.452,0.662,0.425,0.464,0.489,0.601,0.519,0.627,0.743,0.665,0.674,0.757,0.532,0.483,0.671,0.46,0.459,0.431,0.487,0.528,0.557,0.603,0.894,0.501,0.675,0.786,0.572,0.721,0.682,0.423,0.308,0.501,0.609,0.674,0.633,0.545,0.491,0.81,0.704,0.675,0.454,0.315,0.532,0.568,0.607,0.257,0.363,0.434,0.48,0.349,0.417,0.439,0.75,0.686,1.126,0.426,0.638,0.45,0.47,0.461,0.639,0.394,0.732,0.528,0.488
-111,Epileptic,F,37.0,1.9,1.9,0.6,0.8,2.0,1.4,1.4,1.2,0.8,0.8,0.2,3.0,1.3,0.6,0.7,5.2,7.5,0.8,2.3,6.2,0.7,2.1,1.4,2.9,3.4,2.8,3.1,2.6,2.0,3.0,1.3,0.5,0.5,2.2,0.4,0.5,2.6,4.4,0.3,0.2,0.8,2.6,1.7,2.6,2.5,0.4,0.3,0.8,1.1,0.5,0.6,1.3,1.3,1.6,0.4,2.0,0.9,1.6,0.4,0.9,0.3,1.2,0.2,0.3,1.2,1.0,2.6,1.4,1.6,0.4,1.0,0.9,3.5,0.3,9,16,5,5,19,13,13,11,12,7,3.0,25,12,6,7,52,66,6,22,46,5,22,11,31,42,28,36,25,17,29,24,5,5,23,2,4,28,42,2,1,5,21,24,21,15,3,2,5,5,7,3,10,11,14,1,19,7,13,3,6,2,8,1,3,11,13,21,8,9,3,5,6,25,3,0.063,0.023,0.012,0.017,0.042,0.022,0.025,0.024,0.048,0.031,0.029,0.035,0.027,0.031,0.023,0.036,0.029,0.033,0.03,0.04,0.015,0.033,0.033,0.038,0.033,0.031,0.03,0.027,0.021,0.034,0.045,0.035,0.018,0.027,0.011,0.017,0.03,0.035,0.019,0.016,0.018,0.037,0.035,0.025,0.02,0.011,0.018,0.013,0.015,0.014,0.026,0.016,0.025,0.017,0.018,0.019,0.03,0.015,0.016,0.018,0.019,0.023,0.018,0.02,0.021,0.025,0.019,0.021,0.025,0.015,0.023,0.017,0.014,0.02,1503,4497,1896,1964,2256,2999,2702,3072,1369,1511,411.0,2585,3420,1301,2017,9595,16128,2609,4494,5058,2556,3862,1951,5772,6679,6286,5562,4148,5824,5540,2382,838,1437,5948,2188,1391,5244,8085,447,392,1441,2776,4539,2860,3568,1279,859,2534,2326,1304,577,2706,1801,4175,499,3453,1621,3665,814,1327,709,2076,393,719,2041,1251,4112,2522,1709,773,1500,2118,8887,499,0.172,0.105,0.079,0.092,0.14,0.123,0.11,0.114,0.134,0.135,0.118,0.141,0.125,0.128,0.105,0.132,0.122,0.112,0.121,0.142,0.079,0.139,0.127,0.125,0.141,0.134,0.136,0.11,0.095,0.137,0.133,0.106,0.1,0.117,0.069,0.086,0.117,0.142,0.122,0.098,0.102,0.145,0.118,0.116,0.1,0.085,0.096,0.073,0.089,0.096,0.118,0.09,0.113,0.088,0.104,0.107,0.123,0.096,0.103,0.102,0.101,0.109,0.102,0.116,0.107,0.141,0.108,0.107,0.126,0.106,0.111,0.102,0.086,0.128,457,1420,671,717,802,963,868,862,379,412,141.0,1179,800,340,528,2627,4099,432,1143,2399,665,1159,755,1341,1846,1573,1793,1449,1456,1520,533,365,435,1291,512,438,1451,1917,212,185,642,1123,940,1724,1998,615,313,918,1012,620,331,1334,861,1731,268,1695,566,1650,413,726,261,808,204,291,1015,672,2134,1047,904,341,689,873,3849,237,3.253,2.845,2.628,2.601,2.202,2.803,2.497,3.21,2.545,3.017,2.735,1.92,3.217,2.799,2.789,2.729,3.008,4.181,3.133,1.934,2.982,2.671,2.255,2.973,2.78,3.223,2.402,2.26,2.942,2.851,3.029,2.43,2.768,3.153,3.661,2.751,2.718,3.098,2.405,2.61,2.972,2.255,3.174,1.91,2.054,2.297,3.816,3.382,2.795,2.233,2.336,2.159,2.009,2.278,2.359,2.143,3.187,2.56,2.251,2.079,3.055,2.496,2.142,2.394,2.263,2.417,2.152,2.691,2.439,2.753,2.555,2.712,2.487,2.371,0.781,0.778,0.592,0.562,0.658,0.452,0.542,0.6,0.678,0.477,0.819,0.507,0.519,0.444,0.526,0.678,0.684,0.63,0.701,0.543,0.693,0.525,0.494,0.694,0.528,0.748,0.581,0.547,0.508,0.595,0.799,0.832,0.456,0.666,0.791,0.462,0.693,0.584,0.73,0.525,0.664,0.678,0.875,0.595,0.626,0.51,0.667,0.832,0.541,0.501,0.503,0.542,0.605,0.635,0.376,0.463,0.703,0.562,0.377,0.366,0.437,0.741,0.734,0.819,0.417,0.672,0.495,0.465,0.405,0.925,0.544,0.625,0.595,0.578
-112,Epileptic,M,22.0,0.9,3.0,1.9,1.2,1.9,1.9,3.0,1.9,2.2,1.2,0.9,4.5,1.8,0.5,1.9,8.5,15.0,1.8,4.3,5.2,2.5,3.6,2.0,5.3,5.7,6.2,5.2,3.9,6.7,4.1,2.5,2.7,0.9,4.6,1.1,0.8,5.7,4.7,0.3,0.2,1.3,4.0,4.4,2.4,3.0,0.7,0.4,1.0,1.9,1.9,0.6,2.0,1.6,3.6,0.2,3.7,1.4,2.5,1.2,1.2,1.2,1.9,0.4,0.8,3.4,1.0,3.5,1.4,1.3,0.7,1.1,2.2,7.2,0.5,8,22,13,8,15,17,18,19,19,8,6.0,32,23,4,20,84,123,13,34,40,16,38,18,41,50,57,45,33,45,45,21,15,9,68,8,6,46,55,2,2,6,30,39,16,15,6,2,6,11,10,4,16,13,24,1,25,9,14,8,8,9,12,2,4,22,10,24,11,7,6,5,14,59,3,0.035,0.03,0.024,0.024,0.039,0.041,0.047,0.028,0.058,0.057,0.076,0.05,0.031,0.038,0.04,0.038,0.042,0.052,0.058,0.049,0.051,0.042,0.037,0.054,0.042,0.041,0.041,0.039,0.049,0.042,0.059,0.068,0.052,0.046,0.043,0.02,0.062,0.038,0.02,0.013,0.027,0.047,0.065,0.028,0.021,0.015,0.018,0.016,0.023,0.048,0.026,0.023,0.024,0.021,0.035,0.02,0.027,0.021,0.04,0.022,0.031,0.035,0.021,0.025,0.031,0.024,0.024,0.016,0.018,0.019,0.022,0.033,0.021,0.022,1930,5387,3177,2400,2384,2729,2561,3132,2391,1545,482.0,3314,4088,850,3361,13061,22352,2729,5451,4155,3229,5144,2432,5308,7660,8861,6033,4019,6917,5864,2134,1481,1142,7185,1759,2055,5994,9818,311,561,1532,3344,6060,2386,3749,1656,744,2539,2601,1822,637,3214,2280,6830,299,5276,1894,3556,865,1500,1327,2385,579,1125,3498,1339,4801,2692,2712,1332,1348,2375,12919,721,0.129,0.129,0.113,0.106,0.146,0.147,0.137,0.121,0.176,0.174,0.184,0.169,0.139,0.112,0.147,0.137,0.14,0.162,0.154,0.161,0.143,0.149,0.125,0.158,0.135,0.138,0.129,0.117,0.135,0.156,0.15,0.151,0.15,0.151,0.131,0.09,0.161,0.155,0.108,0.088,0.102,0.148,0.185,0.113,0.09,0.091,0.089,0.081,0.105,0.144,0.118,0.109,0.112,0.1,0.148,0.101,0.12,0.102,0.132,0.107,0.134,0.137,0.111,0.108,0.13,0.104,0.102,0.094,0.091,0.104,0.108,0.132,0.106,0.103,489,1626,1159,847,903,938,1090,1116,726,369,204.0,1644,1054,244,930,3773,6377,581,1515,2043,886,1665,1048,1672,2460,2738,2304,1665,2091,1893,772,620,381,1808,524,704,1818,2408,205,285,814,1529,1405,1447,2264,774,323,1021,1252,710,359,1462,1055,2810,104,2759,837,1710,519,885,700,845,286,443,1623,671,2484,1304,1106,544,701,1090,5490,341,3.56,2.781,2.69,2.679,2.234,2.43,2.13,2.501,2.459,3.097,2.184,1.811,2.876,2.524,2.697,2.667,2.809,3.528,2.738,1.871,2.745,2.444,1.982,2.593,2.45,2.596,2.194,2.01,2.66,2.47,2.078,2.495,2.65,2.798,2.582,2.604,2.593,2.971,1.665,2.12,2.336,1.884,2.779,1.883,1.893,2.303,2.53,2.786,2.482,2.714,2.108,2.203,2.107,2.525,2.988,2.042,2.454,2.373,1.826,1.821,2.006,2.756,2.14,2.937,2.387,2.047,2.024,2.291,2.693,2.399,2.174,2.305,2.372,2.573,0.841,0.934,0.584,0.65,0.774,0.715,0.596,0.493,0.73,0.65,0.635,0.479,0.518,0.749,0.658,0.736,0.647,0.737,0.831,0.538,0.771,0.649,0.501,0.893,0.689,0.633,0.561,0.552,0.623,0.652,0.733,0.818,0.501,0.713,0.791,0.456,0.836,0.786,0.369,0.437,0.458,0.6,0.914,0.624,0.565,0.608,0.565,0.799,0.522,0.761,0.489,0.56,0.62,0.587,0.753,0.502,0.675,0.556,0.444,0.448,0.43,0.741,0.643,0.65,0.601,0.662,0.546,0.477,0.518,0.967,0.495,0.569,0.54,0.452
-113,Epileptic,M,17.5,0.6,2.4,1.5,1.8,1.6,2.6,1.2,1.7,0.9,0.7,0.3,3.6,1.9,0.7,1.0,6.4,9.8,0.6,2.6,5.6,1.6,7.5,3.0,3.2,9.0,3.1,3.7,3.2,2.6,2.7,1.8,1.6,0.3,2.1,0.6,0.5,3.4,5.6,0.2,0.2,0.9,3.5,2.0,2.8,2.4,0.5,0.4,0.6,1.3,1.3,0.6,1.8,1.5,3.2,0.6,3.0,0.9,2.1,1.5,1.8,1.3,1.9,0.4,0.6,2.3,1.5,3.5,0.9,1.0,1.0,1.0,1.9,7.0,0.2,6,20,16,16,17,30,12,20,13,8,4.0,38,26,8,13,72,101,5,28,55,16,59,25,39,70,37,45,43,24,35,18,12,4,32,4,5,42,63,2,1,4,34,22,22,15,4,2,4,6,10,5,14,10,22,4,28,6,20,10,15,7,12,4,5,20,23,31,7,7,7,8,15,61,1,0.031,0.022,0.033,0.032,0.037,0.032,0.024,0.031,0.042,0.033,0.032,0.039,0.033,0.034,0.031,0.037,0.036,0.032,0.034,0.037,0.032,0.046,0.037,0.04,0.048,0.031,0.038,0.034,0.025,0.028,0.041,0.062,0.022,0.025,0.015,0.013,0.036,0.037,0.013,0.014,0.02,0.035,0.038,0.024,0.018,0.012,0.017,0.01,0.018,0.021,0.022,0.022,0.023,0.025,0.02,0.021,0.021,0.022,0.027,0.022,0.023,0.032,0.026,0.02,0.023,0.021,0.021,0.016,0.017,0.029,0.019,0.02,0.018,0.013,1556,5368,2417,2705,2168,3576,2486,3540,1893,1684,729.0,3761,3721,1255,2334,10520,17903,2359,5673,6785,4363,4138,2128,6950,7315,6384,4891,5404,5648,5890,3101,1273,888,6841,2392,1733,7206,10774,384,460,1157,2734,6475,2935,3163,1180,868,2093,2220,1750,630,2836,1879,4843,593,4051,1328,3537,1239,2190,1406,2603,438,1071,3138,1739,5147,1998,1623,1601,1700,2689,13428,416,0.128,0.108,0.137,0.133,0.141,0.128,0.106,0.135,0.146,0.137,0.126,0.154,0.145,0.142,0.132,0.149,0.146,0.112,0.137,0.15,0.12,0.151,0.136,0.142,0.15,0.129,0.127,0.127,0.106,0.129,0.145,0.144,0.102,0.112,0.075,0.082,0.137,0.135,0.098,0.087,0.103,0.137,0.152,0.119,0.097,0.085,0.09,0.074,0.096,0.11,0.12,0.109,0.113,0.115,0.123,0.111,0.11,0.118,0.12,0.107,0.101,0.128,0.136,0.113,0.114,0.113,0.111,0.092,0.101,0.123,0.105,0.109,0.1,0.088,398,1682,855,999,872,1304,839,1144,594,429,213.0,1711,1094,332,617,3218,5167,391,1364,2869,954,2363,1216,1688,3140,1845,1970,1884,1670,1826,797,497,259,1594,626,606,1739,2578,213,216,602,1544,1218,1729,1991,618,365,840,1000,815,398,1338,1075,2146,356,2326,637,1632,879,1293,778,961,240,478,1555,1061,2616,991,854,602,796,1422,6135,192,3.694,2.782,2.744,2.501,2.119,2.338,2.375,2.678,2.407,2.973,2.826,1.918,2.592,2.767,2.7,2.477,2.643,4.071,3.028,2.023,3.257,1.72,1.727,3.074,2.043,2.741,2.104,2.287,2.58,2.449,2.886,2.681,2.827,2.998,3.322,2.466,3.042,2.921,2.098,2.142,2.431,1.645,3.332,1.946,1.812,2.128,2.832,2.98,2.732,2.451,1.899,2.262,1.936,2.366,2.005,1.88,2.303,2.351,1.634,1.874,1.999,2.651,2.063,2.58,2.26,1.865,2.108,2.268,2.208,2.761,2.257,2.209,2.302,2.529,0.931,0.651,0.63,0.711,0.637,0.631,0.56,0.484,0.646,0.779,0.792,0.604,0.55,0.406,0.586,0.562,0.647,0.724,0.698,0.648,0.886,0.518,0.534,0.655,0.548,0.599,0.614,0.527,0.516,0.701,0.756,1.017,0.501,0.682,0.591,0.475,0.823,0.827,0.443,0.454,0.481,0.516,0.876,0.568,0.508,0.492,0.514,0.715,0.479,0.487,0.518,0.475,0.432,0.465,0.363,0.432,0.489,0.559,0.311,0.386,0.459,0.681,0.577,0.783,0.558,0.707,0.457,0.433,0.4,0.891,0.435,0.448,0.472,0.308
-114,Epileptic,F,37.0,1.0,1.9,1.0,1.5,1.4,1.6,1.6,1.4,1.4,0.6,0.2,2.3,1.7,0.6,0.8,7.2,9.7,0.8,1.7,4.5,0.8,2.9,1.2,2.2,2.6,2.7,2.7,2.1,1.9,2.5,1.3,1.3,0.7,2.2,0.4,0.7,2.5,4.0,0.2,0.2,1.1,2.1,2.5,2.2,2.4,0.9,0.4,1.3,1.2,0.8,0.6,2.0,1.3,2.5,0.3,2.2,0.5,1.5,0.5,1.0,0.3,1.5,0.7,0.3,1.8,1.3,2.9,1.2,1.0,0.3,1.1,1.6,5.0,0.4,7,16,8,11,16,13,14,13,19,4,2.0,26,18,6,7,71,86,5,21,37,6,30,10,27,25,28,32,23,17,31,14,11,5,24,3,8,28,46,3,2,5,20,22,20,15,7,2,8,7,6,5,17,10,16,3,20,4,14,4,9,2,11,5,3,15,23,22,10,6,3,9,9,42,3,0.04,0.021,0.02,0.03,0.036,0.029,0.021,0.027,0.05,0.033,0.034,0.03,0.028,0.032,0.028,0.035,0.033,0.027,0.021,0.035,0.017,0.038,0.022,0.031,0.033,0.029,0.023,0.022,0.02,0.029,0.044,0.05,0.03,0.025,0.013,0.019,0.03,0.03,0.019,0.02,0.021,0.031,0.041,0.024,0.018,0.019,0.019,0.019,0.018,0.022,0.032,0.021,0.02,0.02,0.018,0.018,0.017,0.018,0.017,0.021,0.017,0.029,0.025,0.018,0.025,0.034,0.021,0.019,0.017,0.016,0.025,0.02,0.02,0.026,1485,4218,2079,2357,1926,2845,2667,2715,1938,1098,402.0,2158,3508,1256,1614,11530,17529,2286,4273,4188,2890,4471,1840,4701,4538,5310,5225,3694,4544,4278,2178,1160,938,5459,1987,1723,5638,7815,503,339,1688,2533,5476,2352,3616,1552,664,2178,2438,1303,581,3415,2294,4566,422,3660,1381,2883,757,1413,678,2029,645,819,2593,1090,4194,2309,1522,1131,1405,2574,8991,431,0.139,0.105,0.106,0.12,0.139,0.125,0.103,0.114,0.169,0.132,0.12,0.135,0.13,0.132,0.116,0.144,0.132,0.109,0.114,0.133,0.09,0.147,0.11,0.133,0.136,0.129,0.111,0.105,0.094,0.131,0.134,0.128,0.144,0.115,0.066,0.102,0.122,0.128,0.13,0.118,0.105,0.134,0.138,0.122,0.1,0.11,0.105,0.091,0.094,0.114,0.13,0.105,0.104,0.102,0.12,0.106,0.098,0.105,0.104,0.118,0.095,0.126,0.148,0.115,0.11,0.15,0.114,0.102,0.1,0.106,0.13,0.112,0.108,0.144,444,1323,777,878,719,1001,996,816,542,284,145.0,1140,1001,313,494,3221,4830,435,1183,1888,688,1320,786,1198,1415,1597,1915,1323,1332,1510,467,439,332,1242,469,649,1471,1867,211,159,707,1071,1189,1493,2110,752,305,909,1069,601,358,1634,1028,1964,241,1967,565,1372,506,779,337,795,329,340,1311,541,2111,1014,822,320,682,1179,3823,197,3.323,2.9,2.377,2.543,2.364,2.411,2.248,2.914,2.545,3.258,2.753,1.682,2.729,2.856,2.586,2.712,2.788,3.917,2.828,1.919,3.25,2.482,1.998,2.882,2.554,2.656,2.176,2.205,2.589,2.327,3.032,2.42,2.456,3.051,3.611,2.45,2.964,3.123,2.302,2.531,3.04,2.048,3.322,1.732,1.922,2.179,2.807,2.826,2.68,2.528,1.971,2.254,2.403,2.423,2.378,2.006,2.81,2.364,1.807,1.918,2.128,2.626,2.189,2.728,2.162,2.413,2.115,2.515,2.265,3.402,2.295,2.615,2.543,2.551,0.896,0.79,0.603,0.553,0.556,0.583,0.515,0.432,0.58,0.403,0.635,0.452,0.421,0.452,0.387,0.5,0.687,0.767,0.478,0.616,0.808,0.529,0.453,0.661,0.559,0.615,0.45,0.455,0.461,0.512,0.871,1.082,0.464,0.632,0.905,0.475,0.742,0.57,0.405,0.375,0.751,0.484,0.661,0.626,0.522,0.621,0.415,0.783,0.583,0.44,0.401,0.473,0.541,0.543,0.444,0.479,0.653,0.49,0.323,0.373,0.409,0.588,0.435,0.811,0.628,0.7,0.444,0.382,0.413,0.876,0.482,0.52,0.558,0.49
-115,Epileptic,F,42.0,0.8,2.6,0.9,1.4,2.1,3.1,1.3,1.6,1.4,1.4,0.2,3.4,1.3,0.7,1.5,5.6,7.8,0.6,2.5,5.3,0.9,3.3,1.6,2.1,3.8,2.3,3.9,2.4,3.1,3.7,1.5,1.3,0.5,1.9,0.4,0.5,3.3,2.8,0.1,0.2,0.8,3.2,2.0,3.1,3.1,0.5,0.3,1.0,1.1,1.4,0.9,1.9,1.1,2.0,0.1,2.8,1.2,1.8,1.3,1.3,0.6,1.0,0.4,0.6,1.9,1.2,2.9,1.2,1.1,0.5,1.3,1.6,3.8,0.3,6,22,11,11,20,24,12,14,17,13,2.0,25,13,7,12,60,68,3,20,37,8,35,14,28,35,22,40,23,24,40,17,10,5,18,2,3,26,31,2,2,4,31,17,24,18,3,1,9,6,9,6,14,8,12,1,23,9,14,10,11,5,14,3,5,14,13,24,9,8,5,10,11,28,2,0.037,0.026,0.015,0.025,0.041,0.03,0.018,0.029,0.043,0.036,0.022,0.035,0.023,0.038,0.029,0.031,0.03,0.023,0.031,0.04,0.019,0.029,0.027,0.03,0.032,0.025,0.03,0.024,0.024,0.03,0.035,0.051,0.034,0.022,0.014,0.016,0.032,0.025,0.01,0.014,0.02,0.032,0.025,0.028,0.022,0.008,0.017,0.017,0.014,0.027,0.036,0.02,0.025,0.02,0.041,0.019,0.029,0.023,0.022,0.022,0.025,0.019,0.032,0.027,0.024,0.031,0.021,0.018,0.02,0.017,0.026,0.023,0.015,0.014,1370,4386,2143,2179,2005,4450,2691,2877,1939,1887,525.0,2792,3031,995,2870,10394,14670,2214,3920,4573,3267,5546,2062,4722,6263,4892,5972,3966,5867,6084,2101,1059,836,4563,1539,1611,4966,6521,347,557,1399,3024,4217,2703,3683,1385,730,2073,2292,1630,745,3672,1523,3598,232,3869,1504,3150,1577,1573,965,2038,517,773,2520,1246,3845,2268,1951,1162,1597,2376,8686,481,0.121,0.121,0.107,0.121,0.151,0.136,0.095,0.14,0.141,0.151,0.101,0.137,0.113,0.142,0.124,0.131,0.132,0.086,0.134,0.137,0.082,0.131,0.124,0.121,0.144,0.124,0.132,0.111,0.11,0.134,0.126,0.15,0.137,0.114,0.075,0.074,0.124,0.122,0.079,0.085,0.092,0.141,0.111,0.122,0.106,0.072,0.095,0.089,0.087,0.114,0.134,0.1,0.118,0.101,0.141,0.112,0.118,0.112,0.115,0.129,0.105,0.117,0.137,0.136,0.115,0.13,0.118,0.107,0.104,0.12,0.124,0.114,0.09,0.109,369,1441,926,914,800,1573,1020,903,606,562,172.0,1449,914,282,849,3110,4250,444,1267,1995,785,1903,978,1359,1959,1533,2144,1514,1818,2088,575,451,284,1229,452,588,1574,1809,233,286,647,1596,1140,1854,2221,798,295,853,1072,787,452,1577,716,1586,94,2198,738,1353,915,852,496,817,243,372,1290,601,2103,1012,948,462,867,1146,3895,245,3.303,2.829,2.397,2.501,2.415,2.637,2.219,2.98,2.414,2.915,2.771,1.797,2.605,2.471,2.606,2.612,2.808,3.907,2.721,2.022,3.142,2.361,1.934,2.577,2.57,2.604,2.252,2.153,2.568,2.362,2.676,2.515,2.428,2.719,3.305,2.577,2.563,2.76,1.903,2.375,2.652,1.812,2.89,1.705,1.906,1.917,3.166,2.821,2.581,2.363,2.09,2.386,2.268,2.544,2.695,1.969,2.216,2.529,1.998,2.078,2.133,2.474,2.573,2.266,2.288,2.431,2.102,2.625,2.517,2.909,2.24,2.535,2.455,2.507,0.785,0.605,0.53,0.443,0.524,0.525,0.527,0.692,0.551,0.555,0.748,0.479,0.363,0.637,0.446,0.6,0.482,0.817,0.443,0.63,0.693,0.447,0.466,0.713,0.473,0.494,0.467,0.471,0.431,0.472,0.732,0.845,0.477,0.538,0.817,0.756,0.543,0.49,0.457,0.497,0.637,0.552,0.639,0.623,0.538,0.323,0.483,0.923,0.561,0.39,0.331,0.582,0.568,0.486,0.42,0.389,0.484,0.728,0.39,0.389,0.335,0.79,0.517,1.02,0.576,0.654,0.46,0.309,0.317,0.854,0.342,0.475,0.56,0.414
-116,Epileptic,M,27.0,0.9,2.4,1.2,1.4,1.7,1.6,2.6,1.8,1.5,1.2,0.4,2.8,1.3,0.5,1.7,5.3,10.2,1.1,1.9,4.7,1.2,3.7,2.1,3.7,4.3,3.2,4.2,3.1,2.7,3.7,2.4,1.4,0.5,2.6,0.6,0.6,3.6,2.7,0.3,0.2,1.1,2.6,2.2,2.8,3.4,0.8,0.5,0.7,1.3,0.9,0.6,2.2,1.7,3.2,0.3,2.7,0.9,1.8,1.0,0.9,0.6,1.7,0.3,0.5,1.7,1.0,2.5,1.2,1.2,0.6,1.2,1.4,4.6,0.3,6,19,13,9,14,15,18,15,15,13,3.0,24,17,8,16,53,76,7,23,41,14,43,21,35,44,34,41,28,22,38,15,12,5,30,4,4,43,34,2,1,6,23,20,20,16,4,2,4,6,5,5,13,12,22,1,22,5,12,8,7,3,13,2,3,11,15,22,7,7,4,8,10,33,3,0.041,0.028,0.024,0.031,0.039,0.029,0.05,0.03,0.047,0.045,0.038,0.038,0.028,0.03,0.036,0.038,0.046,0.045,0.03,0.044,0.034,0.039,0.04,0.04,0.04,0.035,0.042,0.039,0.034,0.04,0.057,0.072,0.034,0.033,0.02,0.015,0.034,0.031,0.019,0.021,0.025,0.029,0.04,0.026,0.025,0.02,0.018,0.011,0.017,0.02,0.031,0.023,0.025,0.028,0.029,0.02,0.033,0.019,0.022,0.015,0.017,0.03,0.019,0.025,0.02,0.024,0.024,0.022,0.019,0.021,0.019,0.016,0.016,0.022,1549,4674,2425,2183,2411,3394,2988,3018,2028,2238,802.0,2620,3281,1493,3379,9392,16430,2141,4817,5125,3279,4644,1855,6744,5603,5906,6201,4303,5599,6866,2634,851,867,5743,2233,1722,9465,7613,515,396,1249,2141,4754,2669,3130,1058,903,2214,2266,1805,676,2642,2296,5078,354,4114,1013,3258,1196,1638,930,2352,439,738,2581,1261,4221,1851,1965,1046,1852,2342,10148,282,0.135,0.122,0.124,0.117,0.143,0.125,0.141,0.127,0.184,0.165,0.123,0.141,0.126,0.123,0.141,0.145,0.153,0.131,0.126,0.161,0.106,0.127,0.146,0.144,0.139,0.135,0.141,0.123,0.113,0.145,0.16,0.164,0.11,0.142,0.09,0.084,0.134,0.141,0.099,0.107,0.106,0.129,0.134,0.126,0.108,0.099,0.092,0.074,0.09,0.101,0.139,0.11,0.114,0.115,0.137,0.107,0.134,0.097,0.117,0.093,0.097,0.126,0.107,0.111,0.108,0.13,0.107,0.099,0.092,0.118,0.107,0.106,0.092,0.137,412,1357,839,789,829,1010,1042,1043,561,513,187.0,1325,877,342,870,2677,4226,423,1226,1965,844,1511,878,1784,1760,1676,2129,1422,1513,1912,672,383,330,1310,535,628,2039,1681,262,209,708,1238,1073,1618,1881,561,387,864,1100,695,357,1390,1072,1990,138,2130,423,1498,711,922,450,985,270,348,1264,686,1717,869,932,484,961,1167,4308,204,3.503,2.866,2.563,2.504,2.39,2.573,2.351,2.551,2.651,3.189,3.038,1.745,2.796,3.107,2.7,2.566,2.869,3.705,3.034,2.137,2.876,2.46,1.92,2.749,2.502,2.744,2.275,2.246,2.801,2.716,2.669,2.44,2.161,3.027,3.684,2.457,3.163,3.115,2.1,1.993,2.316,1.648,3.036,1.919,1.882,1.968,2.903,2.875,2.376,2.814,1.967,1.956,2.062,2.293,2.986,2.093,2.441,2.428,1.913,1.953,2.45,2.17,1.792,2.301,2.326,2.245,2.409,2.341,2.313,2.305,2.173,2.307,2.454,1.691,0.928,0.702,0.729,0.549,0.766,0.752,0.676,0.492,0.718,0.657,1.0,0.471,0.409,0.447,0.588,0.581,0.699,0.791,0.667,0.803,0.763,0.629,0.569,0.679,0.708,0.644,0.604,0.7,0.537,0.642,0.809,0.966,0.49,0.592,0.635,0.537,0.79,0.68,0.355,0.31,0.5,0.46,0.83,0.624,0.589,0.501,0.516,0.697,0.413,0.648,0.471,0.407,0.511,0.588,0.368,0.43,0.529,0.509,0.411,0.399,0.376,0.667,0.403,0.503,0.52,0.694,0.966,0.442,0.45,0.537,0.432,0.515,0.481,0.336
-117,Epileptic,F,37.0,0.6,1.8,1.2,1.4,1.5,1.7,1.7,1.5,1.0,1.0,0.3,2.2,1.2,0.3,1.2,4.3,8.2,0.8,1.9,4.3,1.2,2.8,2.0,3.8,3.1,2.7,4.1,2.6,2.3,2.0,2.0,1.0,0.4,1.9,0.4,0.3,5.1,2.7,0.1,0.2,0.9,2.5,1.6,3.0,2.6,0.9,0.4,0.9,1.4,0.7,0.5,1.5,1.4,2.0,0.1,1.6,0.6,1.6,0.7,1.0,0.7,1.2,0.3,0.4,1.2,1.1,3.2,0.9,1.3,0.4,1.0,1.4,3.5,0.3,8,17,10,12,13,16,17,15,13,9,5.0,29,13,6,14,56,80,8,26,46,15,37,18,48,35,34,40,30,22,25,23,8,3,24,2,3,66,33,1,1,7,24,17,19,15,7,2,6,8,6,4,15,12,13,1,14,4,15,5,7,5,10,2,5,12,18,26,9,9,4,7,11,28,3,0.035,0.02,0.021,0.025,0.025,0.025,0.029,0.022,0.025,0.036,0.028,0.038,0.024,0.026,0.028,0.028,0.027,0.029,0.029,0.036,0.024,0.033,0.035,0.042,0.037,0.027,0.031,0.03,0.019,0.025,0.045,0.049,0.023,0.024,0.009,0.009,0.048,0.029,0.009,0.012,0.02,0.035,0.031,0.025,0.017,0.021,0.019,0.016,0.018,0.02,0.027,0.015,0.021,0.017,0.014,0.013,0.017,0.017,0.018,0.018,0.019,0.02,0.023,0.015,0.017,0.022,0.021,0.016,0.016,0.015,0.03,0.02,0.015,0.016,1424,4623,2347,2664,2642,3326,3025,3517,2313,1663,761.0,2972,3044,890,2907,10726,19531,2057,4850,5556,3410,4836,2473,5734,5050,6523,6047,4525,7104,4840,2258,1058,926,5604,2017,1655,7017,7462,409,464,1319,2763,4718,3427,3914,1255,669,1973,2225,1480,519,3292,2241,4599,255,3241,1172,3327,1064,1671,1158,2297,389,1138,2420,997,4805,2181,2555,706,1092,2698,7922,436,0.13,0.108,0.119,0.119,0.125,0.121,0.104,0.116,0.133,0.174,0.134,0.162,0.118,0.134,0.126,0.134,0.128,0.124,0.12,0.156,0.097,0.123,0.138,0.148,0.135,0.125,0.122,0.11,0.098,0.124,0.136,0.123,0.101,0.114,0.06,0.07,0.147,0.132,0.089,0.086,0.112,0.132,0.136,0.116,0.094,0.115,0.098,0.084,0.099,0.105,0.122,0.096,0.117,0.092,0.12,0.089,0.099,0.1,0.099,0.099,0.104,0.114,0.127,0.111,0.103,0.133,0.115,0.097,0.096,0.12,0.129,0.104,0.09,0.109,423,1603,890,1008,982,1102,1057,1157,618,479,223.0,1279,868,233,734,2926,5406,517,1270,2286,859,1688,955,1754,1547,1967,2133,1521,1837,1414,754,473,322,1339,560,635,2100,1706,210,237,724,1347,1026,1901,2301,656,307,837,1068,621,326,1568,1091,1954,129,1840,561,1632,658,839,599,927,196,475,1167,719,2364,984,1179,399,558,1224,3522,265,3.188,2.653,2.459,2.334,2.362,2.525,2.358,2.591,2.685,2.7,2.72,2.008,2.723,2.796,2.893,2.744,2.844,3.197,2.932,2.066,2.916,2.316,2.207,2.544,2.596,2.632,2.286,2.302,2.931,2.607,2.305,2.225,2.24,2.946,3.258,2.385,2.535,3.124,2.43,2.11,2.219,1.845,3.279,2.002,1.906,2.049,2.625,2.985,2.535,2.668,1.895,2.252,2.315,2.481,2.272,1.979,2.474,2.334,1.819,2.152,2.203,2.552,2.271,2.736,2.18,1.681,2.14,2.443,2.424,1.94,2.121,2.498,2.364,2.043,0.78,0.55,0.568,0.535,0.576,0.58,0.584,0.46,0.615,0.531,0.749,0.416,0.361,0.367,0.517,0.517,0.564,0.819,0.567,0.625,0.806,0.493,0.613,0.716,0.579,0.534,0.579,0.546,0.559,0.491,0.761,0.927,0.447,0.526,0.598,0.428,0.798,0.513,0.235,0.387,0.415,0.563,0.784,0.53,0.488,0.433,0.564,0.783,0.492,0.412,0.35,0.326,0.422,0.426,0.28,0.344,0.505,0.417,0.306,0.408,0.358,0.481,0.347,0.713,0.423,0.504,0.481,0.417,0.462,0.614,0.386,0.478,0.445,0.394
-118,Epileptic,M,37.0,0.5,2.4,1.4,1.4,2.2,2.1,1.7,1.8,1.5,0.7,0.4,3.4,1.6,0.5,1.4,8.7,8.8,1.0,2.6,6.3,1.0,4.0,2.3,3.6,3.6,5.0,3.7,3.0,3.4,4.8,1.6,1.3,0.6,2.4,0.5,0.9,5.7,3.9,0.1,0.3,1.2,3.7,2.5,2.9,3.3,0.8,0.6,0.8,1.2,0.9,0.9,2.0,1.4,2.5,0.2,2.9,1.5,1.9,1.1,1.8,0.9,1.9,0.5,0.4,2.5,1.0,3.3,1.3,2.0,0.8,1.3,1.7,4.2,0.3,3,21,23,14,17,19,17,15,12,11,3.0,37,18,5,12,82,78,6,28,49,8,42,21,41,44,48,46,29,29,54,25,10,6,23,2,9,50,43,1,2,6,33,22,21,17,5,2,5,6,8,6,12,9,15,1,23,11,12,7,13,7,15,3,3,19,9,26,8,13,6,8,13,29,2,0.021,0.02,0.016,0.023,0.037,0.03,0.02,0.032,0.05,0.042,0.032,0.036,0.026,0.029,0.033,0.038,0.031,0.031,0.028,0.043,0.021,0.039,0.029,0.04,0.038,0.036,0.027,0.025,0.024,0.037,0.039,0.044,0.036,0.032,0.014,0.019,0.046,0.038,0.013,0.016,0.025,0.039,0.044,0.025,0.021,0.012,0.021,0.014,0.014,0.024,0.035,0.02,0.02,0.023,0.023,0.018,0.029,0.016,0.024,0.021,0.025,0.03,0.035,0.019,0.026,0.017,0.022,0.017,0.019,0.02,0.025,0.025,0.018,0.023,1947,5354,3371,2564,2396,3289,3355,3352,1708,1981,792.0,3593,3907,1198,2695,13702,19645,2691,4868,5920,2353,6048,2938,6014,5830,8116,6732,4143,7480,7633,2651,1528,930,5640,1666,2167,6517,5263,401,495,1458,3388,4255,3385,4231,1796,878,2460,2722,1585,761,3736,2211,4390,320,4515,1639,3565,1279,2414,1194,2888,623,867,3260,1856,4732,2663,3595,1226,1753,1939,8701,530,0.084,0.104,0.098,0.109,0.14,0.132,0.093,0.129,0.153,0.146,0.13,0.145,0.123,0.136,0.138,0.148,0.13,0.113,0.128,0.146,0.087,0.155,0.134,0.147,0.159,0.148,0.128,0.111,0.1,0.15,0.154,0.129,0.158,0.121,0.076,0.094,0.149,0.139,0.094,0.11,0.118,0.138,0.146,0.11,0.101,0.087,0.109,0.084,0.082,0.107,0.132,0.097,0.111,0.102,0.132,0.106,0.13,0.095,0.116,0.113,0.121,0.127,0.137,0.106,0.119,0.093,0.109,0.095,0.103,0.124,0.12,0.133,0.097,0.103,421,1807,1383,1008,944,1139,1126,1039,531,444,209.0,1586,1050,291,730,3873,4975,503,1439,2445,713,1826,1269,1618,1722,2409,2366,1678,2043,2391,713,581,315,1189,443,800,2051,1722,186,250,735,1455,1017,1877,2447,916,393,903,1236,718,416,1673,1023,1823,137,2432,779,1697,728,1249,629,1018,237,415,1531,884,2452,1197,1580,539,817,1050,3634,222,3.741,2.652,2.476,2.535,2.372,2.395,2.463,2.766,2.605,3.397,3.228,1.969,2.95,2.951,2.809,2.705,3.135,3.854,2.748,2.041,2.664,2.54,2.044,2.856,2.625,2.735,2.293,2.025,2.822,2.505,2.795,2.455,2.625,3.107,3.19,2.443,2.527,2.381,2.487,2.371,2.404,2.019,2.835,2.068,2.011,2.16,2.861,3.242,2.588,2.379,2.145,2.408,2.344,2.562,2.596,2.084,2.418,2.437,2.081,2.168,2.129,2.705,2.495,2.48,2.179,2.431,2.099,2.528,2.503,2.408,2.401,2.184,2.459,2.687,1.165,0.606,0.551,0.491,0.52,0.55,0.558,0.51,0.527,0.64,0.897,0.551,0.515,0.45,0.517,0.584,0.574,0.997,0.529,0.618,0.783,0.541,0.438,0.704,0.563,0.582,0.485,0.485,0.594,0.609,0.603,1.105,0.627,0.654,0.471,0.541,0.774,0.577,0.359,0.343,0.589,0.517,0.847,0.715,0.684,0.461,0.533,0.889,0.545,0.626,0.468,0.516,0.606,0.586,0.502,0.473,0.563,0.499,0.407,0.378,0.303,0.729,0.543,1.044,0.548,0.809,0.471,0.333,0.415,0.647,0.505,0.594,0.636,0.379
-119,Epileptic,M,17.5,1.1,2.2,1.0,1.9,2.4,3.0,3.0,2.1,1.9,0.9,0.3,3.5,1.6,0.6,2.1,7.6,9.9,1.4,3.3,4.1,2.3,4.0,3.5,4.5,8.7,4.7,9.3,3.8,3.0,4.7,1.6,1.3,1.0,3.6,0.6,1.0,2.6,3.7,0.3,0.6,1.7,3.4,2.8,3.2,3.6,0.9,0.5,1.3,2.0,0.6,0.8,2.8,2.8,3.0,0.5,3.6,1.3,1.9,1.3,1.4,0.8,1.8,0.4,0.7,2.3,1.4,3.5,1.5,1.2,0.4,1.5,1.6,5.7,0.4,10,19,7,16,14,27,21,20,15,12,8.0,34,22,8,23,72,89,13,36,36,17,38,26,51,71,45,68,32,23,54,16,9,8,40,3,8,37,49,2,2,11,32,25,21,18,6,2,7,9,5,6,21,17,18,3,29,9,12,8,9,5,14,2,3,21,18,22,9,6,3,14,11,49,3,0.043,0.025,0.015,0.031,0.037,0.037,0.04,0.032,0.05,0.032,0.032,0.044,0.036,0.029,0.037,0.04,0.038,0.043,0.037,0.034,0.04,0.044,0.047,0.052,0.058,0.032,0.046,0.034,0.032,0.031,0.035,0.057,0.037,0.04,0.029,0.015,0.036,0.033,0.017,0.019,0.027,0.04,0.043,0.027,0.024,0.017,0.017,0.02,0.022,0.012,0.033,0.025,0.037,0.021,0.036,0.018,0.025,0.019,0.028,0.022,0.019,0.028,0.021,0.019,0.025,0.027,0.023,0.022,0.023,0.016,0.021,0.019,0.02,0.016,1927,5289,2426,3251,2465,3861,3385,4165,2031,2004,954.0,3017,4319,1488,4211,14608,22733,2901,6135,4835,4475,4576,2261,7468,6305,9134,8541,4698,7006,7646,3035,1296,1512,8451,1901,2666,6160,9999,613,921,2014,3139,5688,3604,3995,1894,965,2551,3229,1558,701,4710,2624,5705,575,6250,1575,3400,1310,1977,1202,2815,571,1035,3318,1361,5272,2562,1840,864,2910,2800,11151,749,0.135,0.113,0.099,0.129,0.135,0.143,0.131,0.134,0.143,0.146,0.111,0.149,0.13,0.127,0.141,0.141,0.134,0.139,0.142,0.134,0.128,0.135,0.144,0.151,0.158,0.121,0.139,0.117,0.118,0.129,0.129,0.146,0.123,0.153,0.097,0.09,0.13,0.142,0.089,0.1,0.117,0.14,0.153,0.121,0.103,0.094,0.088,0.092,0.102,0.086,0.134,0.113,0.139,0.098,0.129,0.101,0.116,0.102,0.133,0.115,0.106,0.118,0.105,0.098,0.122,0.124,0.103,0.104,0.103,0.098,0.11,0.102,0.101,0.101,529,1599,862,1054,1065,1404,1287,1253,709,516,251.0,1469,1102,361,1109,3639,5350,596,1693,2086,970,1490,1091,1917,2295,2623,3344,1840,1675,2737,804,454,497,1757,435,943,1513,2132,308,410,1014,1455,1227,1908,2338,830,379,1049,1340,663,423,2004,1208,2319,204,3011,750,1492,685,913,591,1047,297,517,1598,947,2459,1068,800,387,1231,1141,4574,396,3.421,3.067,2.872,2.929,2.116,2.356,2.232,2.831,2.262,2.984,2.892,1.798,2.993,2.956,2.82,2.851,3.212,3.642,2.876,1.974,3.199,2.37,1.917,2.811,2.26,2.704,2.11,2.094,2.992,2.358,2.682,2.751,2.455,3.202,3.644,2.685,2.893,3.153,2.318,2.496,2.462,1.806,2.951,2.217,2.012,2.534,2.983,2.986,2.985,2.467,2.01,2.385,2.166,2.582,3.078,2.259,2.254,2.476,2.318,2.354,2.391,2.569,2.306,2.473,2.144,1.783,2.231,2.775,2.77,2.703,2.458,2.427,2.527,2.386,0.777,0.591,0.485,0.739,0.65,0.678,0.764,0.498,0.817,0.747,0.584,0.565,0.486,0.525,0.529,0.736,0.696,0.735,0.553,0.686,0.872,0.71,0.574,0.77,0.805,0.704,0.683,0.629,0.633,0.563,0.718,1.036,0.466,0.763,0.738,0.446,0.863,0.809,0.386,0.551,0.441,0.718,1.012,0.737,0.635,0.432,0.597,0.624,0.491,0.597,0.303,0.558,0.577,0.48,0.679,0.453,0.509,0.571,0.388,0.48,0.401,0.746,0.418,0.876,0.535,0.558,0.427,0.399,0.591,0.677,0.525,0.524,0.513,0.38
-120,Epileptic,M,22.0,1.3,3.0,0.9,1.6,2.7,2.1,1.6,1.3,1.8,1.5,0.4,2.6,2.0,0.5,1.4,9.3,13.5,0.8,2.9,3.9,3.3,3.6,1.5,4.1,4.2,4.4,2.6,1.5,3.3,3.5,2.4,1.5,0.4,2.7,0.5,1.4,3.9,4.2,0.2,0.1,1.6,2.7,3.0,2.2,1.5,0.8,0.4,1.1,1.3,0.7,0.8,2.5,1.7,4.1,0.4,2.6,1.0,2.0,1.0,1.2,0.6,1.9,0.4,0.4,2.5,1.5,2.3,2.0,2.0,0.8,1.2,2.3,6.7,0.4,8,40,10,13,22,24,17,16,19,15,6.0,28,22,9,18,110,122,6,39,40,35,48,17,46,50,45,36,21,31,46,27,8,5,32,3,13,43,54,1,0,10,27,37,17,12,5,2,7,8,6,8,23,12,28,3,21,8,19,5,9,5,16,5,3,21,20,18,15,12,6,10,16,61,3,0.047,0.028,0.016,0.026,0.043,0.026,0.031,0.027,0.041,0.041,0.039,0.031,0.036,0.041,0.037,0.042,0.031,0.036,0.029,0.034,0.051,0.035,0.022,0.047,0.029,0.034,0.029,0.029,0.027,0.029,0.06,0.043,0.026,0.029,0.013,0.023,0.035,0.037,0.016,0.014,0.029,0.031,0.05,0.026,0.016,0.015,0.017,0.02,0.016,0.015,0.027,0.025,0.023,0.023,0.025,0.02,0.021,0.02,0.018,0.021,0.019,0.032,0.031,0.019,0.02,0.025,0.02,0.025,0.026,0.019,0.024,0.021,0.02,0.014,1368,5239,2542,3248,2718,3484,2520,2885,2346,2660,819.0,2757,3656,913,2834,14521,24356,1452,6897,5189,4219,5950,2453,5862,8614,6706,4501,3403,4640,7111,3005,1228,929,7928,2179,2839,8000,10415,447,213,1742,2976,5528,2551,2383,1400,719,2030,2432,1780,892,3800,2277,6348,725,4712,1891,3890,1254,1833,1101,2813,483,678,4139,1315,3200,3159,2457,1390,1815,3521,12626,1014,0.148,0.123,0.1,0.112,0.149,0.118,0.126,0.121,0.169,0.163,0.151,0.139,0.148,0.152,0.148,0.161,0.125,0.111,0.127,0.147,0.159,0.134,0.105,0.154,0.129,0.138,0.114,0.127,0.109,0.134,0.166,0.125,0.107,0.125,0.078,0.121,0.127,0.152,0.089,0.083,0.12,0.128,0.162,0.121,0.095,0.093,0.097,0.096,0.097,0.098,0.142,0.119,0.117,0.109,0.117,0.099,0.11,0.109,0.102,0.106,0.102,0.142,0.139,0.116,0.109,0.127,0.107,0.114,0.103,0.121,0.119,0.109,0.105,0.096,398,1764,858,1068,1053,1364,868,937,693,589,200.0,1386,1043,242,796,4054,7175,365,1845,1938,1113,1583,1018,1643,2382,2131,1716,1043,1939,2272,793,586,262,1713,569,930,1993,2194,236,100,910,1437,1201,1358,1434,741,313,908,1204,757,469,1777,1094,2739,303,2112,805,1703,805,933,551,964,264,368,2019,885,1538,1354,1278,564,832,1647,5369,380,2.761,2.65,2.608,2.846,2.085,2.178,2.303,2.631,2.444,3.287,3.002,1.776,2.641,2.613,2.68,2.508,2.616,3.05,2.88,2.193,2.789,2.722,2.051,2.593,2.64,2.486,2.202,2.468,1.971,2.504,2.729,2.324,2.812,3.155,3.022,2.649,2.815,3.224,2.22,2.147,2.341,1.804,3.049,2.053,1.722,2.032,2.545,2.832,2.415,2.608,2.052,2.061,2.124,2.308,2.42,2.229,2.593,2.443,1.808,2.112,2.146,2.597,1.816,2.021,2.259,1.903,2.121,2.205,2.062,2.665,2.453,2.529,2.503,2.985,0.934,0.665,0.545,0.665,0.69,0.677,0.589,0.574,0.64,0.691,0.621,0.437,0.646,0.676,0.618,0.738,0.703,0.833,0.585,0.824,0.887,0.65,0.559,0.716,0.671,0.653,0.552,0.568,0.651,0.597,0.831,0.939,0.524,0.664,0.768,0.608,0.756,0.621,0.346,0.426,0.509,0.553,0.699,0.673,0.572,0.544,0.589,0.59,0.631,0.437,0.47,0.577,0.594,0.594,0.874,0.549,0.409,0.592,0.348,0.421,0.384,0.811,0.514,0.564,0.577,0.563,0.458,0.63,0.707,0.966,0.49,0.459,0.495,0.436
-121,Epileptic,M,42.0,1.2,2.4,1.3,1.7,2.0,2.9,1.8,1.7,1.0,0.6,0.5,3.2,1.1,0.5,1.2,7.6,11.3,1.2,2.7,4.3,1.5,10.5,2.8,5.2,7.2,4.4,4.9,3.8,2.8,2.7,1.4,0.7,0.4,2.0,0.8,0.7,5.0,4.4,0.2,0.2,0.9,5.1,2.6,2.4,2.7,0.9,0.5,0.9,1.3,1.2,0.7,1.6,1.3,2.5,0.5,2.7,1.1,1.6,1.5,1.9,0.8,1.8,0.6,0.8,2.4,1.3,2.3,1.9,1.3,0.7,0.7,1.5,6.1,0.3,8,21,11,15,18,28,17,17,16,6,5.0,33,12,6,13,85,110,10,28,43,14,86,19,57,56,39,42,32,27,33,18,5,10,23,5,8,55,52,1,1,4,48,25,14,15,7,2,6,7,12,7,14,9,17,5,26,11,11,10,13,5,17,5,6,19,18,18,13,9,7,4,10,49,1,0.043,0.023,0.022,0.028,0.034,0.037,0.027,0.027,0.038,0.042,0.036,0.04,0.021,0.033,0.023,0.033,0.033,0.043,0.038,0.033,0.023,0.063,0.033,0.044,0.048,0.034,0.039,0.033,0.029,0.031,0.037,0.039,0.028,0.028,0.022,0.023,0.035,0.035,0.013,0.013,0.02,0.041,0.036,0.024,0.02,0.016,0.017,0.014,0.016,0.018,0.035,0.019,0.024,0.018,0.022,0.023,0.022,0.02,0.018,0.021,0.018,0.025,0.027,0.021,0.018,0.023,0.018,0.017,0.019,0.019,0.023,0.017,0.021,0.016,1760,5080,2893,3037,2681,3857,3447,3797,1894,1251,847.0,3525,3019,1378,2780,13976,22201,3113,4543,5532,5543,5287,2409,7713,5387,7393,5379,5033,6841,5254,2657,768,877,6195,2369,2043,10832,9578,437,530,1374,3262,7186,2872,3736,1564,1057,2447,2558,2458,864,3224,1829,4584,995,3955,2257,3149,2238,2030,1266,2975,666,1083,3963,1548,4366,3840,1836,1367,1152,3049,11756,480,0.141,0.119,0.104,0.124,0.139,0.134,0.119,0.123,0.151,0.137,0.147,0.168,0.104,0.139,0.123,0.139,0.137,0.129,0.148,0.144,0.094,0.153,0.126,0.15,0.147,0.12,0.13,0.103,0.115,0.131,0.142,0.119,0.112,0.123,0.094,0.108,0.136,0.139,0.079,0.086,0.096,0.153,0.145,0.111,0.103,0.1,0.094,0.083,0.091,0.098,0.153,0.103,0.117,0.097,0.113,0.107,0.116,0.103,0.106,0.106,0.099,0.128,0.129,0.12,0.109,0.118,0.097,0.093,0.107,0.107,0.115,0.097,0.105,0.09,504,1628,1035,1071,952,1237,1082,1122,493,301,226.0,1454,827,312,797,3911,6039,582,1258,2140,1205,2353,1148,2043,2140,2089,2055,1739,1703,1558,756,352,235,1274,555,592,2537,2277,236,285,669,1665,1434,1511,2048,785,411,1031,1121,1067,400,1449,880,2107,378,2107,800,1274,1175,1207,630,1179,369,530,1884,919,2032,1608,916,607,463,1371,4785,222,3.492,2.857,2.711,2.66,2.433,2.408,2.539,3.004,2.737,3.067,2.771,2.15,2.77,3.079,2.641,2.743,2.837,3.917,2.888,2.235,3.383,2.06,1.892,2.806,2.198,2.878,2.191,2.377,2.96,2.592,2.653,2.448,2.716,3.368,3.854,2.927,3.151,3.105,2.211,2.182,2.631,1.85,3.455,2.247,2.109,2.254,3.039,3.0,2.845,2.553,2.218,2.299,2.318,2.388,2.875,2.089,2.472,2.768,2.282,1.873,2.381,2.449,1.891,2.547,2.392,2.059,2.265,2.59,2.172,2.394,2.735,2.6,2.602,2.544,0.9,0.678,0.52,0.496,0.574,0.725,0.67,0.488,0.618,0.724,0.788,0.534,0.404,0.482,0.468,0.557,0.646,0.905,0.523,0.721,0.833,0.66,0.503,0.769,0.75,0.631,0.568,0.625,0.55,0.67,0.593,0.831,0.368,0.597,0.622,0.511,0.802,0.696,0.438,0.315,0.454,0.551,0.773,0.557,0.637,0.424,0.738,0.635,0.394,0.416,0.433,0.412,0.483,0.428,0.571,0.396,0.567,0.538,0.383,0.354,0.371,0.631,0.491,0.749,0.469,0.623,0.459,0.31,0.432,0.667,0.371,0.493,0.496,0.338
-122,Epileptic,F,57.0,1.5,1.9,0.8,1.0,2.5,3.1,1.5,1.4,3.1,0.4,0.5,3.2,1.2,0.5,2.0,7.2,9.1,1.0,2.4,5.1,1.7,3.0,3.8,5.7,5.1,2.5,5.4,3.7,3.4,3.7,2.2,1.1,0.3,2.1,0.3,1.0,3.0,3.5,0.3,0.2,0.9,5.3,2.0,3.7,3.8,0.7,0.4,0.9,1.4,0.6,0.5,2.2,1.9,2.6,0.2,2.5,0.6,1.6,1.0,1.1,0.2,1.4,0.3,0.6,1.8,1.2,2.9,1.1,0.9,0.8,0.8,0.6,5.0,0.4,14,15,6,8,15,29,13,11,22,7,5.0,24,15,6,21,61,80,7,29,143,11,26,22,40,38,27,35,26,24,36,21,9,3,25,1,9,37,33,3,2,5,44,21,19,22,4,1,5,7,4,4,19,12,17,1,18,4,13,7,9,2,10,3,5,14,17,20,6,6,6,7,5,38,3,0.067,0.03,0.021,0.024,0.046,0.044,0.029,0.033,0.071,0.032,0.045,0.046,0.032,0.036,0.047,0.047,0.037,0.044,0.035,0.119,0.037,0.04,0.064,0.06,0.051,0.035,0.048,0.045,0.037,0.04,0.059,0.046,0.02,0.031,0.016,0.024,0.049,0.046,0.023,0.021,0.025,0.064,0.041,0.037,0.029,0.014,0.021,0.017,0.02,0.023,0.027,0.025,0.034,0.026,0.017,0.018,0.02,0.023,0.03,0.023,0.016,0.028,0.022,0.019,0.029,0.028,0.032,0.022,0.019,0.027,0.023,0.025,0.024,0.022,1432,3263,1994,1765,1491,2940,2457,2311,1633,1007,476.0,2421,3143,1020,2959,8613,16436,2086,4825,3852,2287,2647,1424,4686,4661,4819,4283,3445,5486,5201,2019,1064,920,5423,1408,2147,4492,5881,590,545,1169,1724,5195,2001,3303,1327,700,1760,2304,948,641,3406,1805,3668,344,3760,915,2862,986,1426,436,2514,397,922,1896,975,2949,2000,1380,1327,1226,1171,8457,429,0.173,0.134,0.105,0.114,0.142,0.161,0.107,0.126,0.178,0.147,0.135,0.16,0.131,0.136,0.165,0.152,0.142,0.138,0.148,0.16,0.128,0.134,0.179,0.15,0.153,0.142,0.137,0.131,0.12,0.145,0.146,0.132,0.086,0.127,0.075,0.114,0.155,0.156,0.125,0.12,0.115,0.179,0.159,0.137,0.123,0.088,0.097,0.093,0.101,0.12,0.127,0.118,0.141,0.108,0.106,0.105,0.111,0.118,0.116,0.114,0.1,0.118,0.13,0.102,0.133,0.13,0.13,0.1,0.105,0.129,0.122,0.128,0.111,0.133,432,1073,676,690,809,1235,815,796,697,287,203.0,1200,797,253,814,2802,4395,403,1214,1762,687,1125,902,1573,1637,1415,1952,1345,1442,1672,645,414,292,1113,362,644,1247,1330,237,253,584,1197,959,1337,2096,668,271,764,1047,405,310,1511,882,1635,180,2026,434,1175,560,793,240,856,228,513,1019,678,1520,865,717,435,551,462,3433,235,3.079,2.877,2.866,2.611,1.976,2.103,2.365,2.639,1.952,2.723,2.064,1.723,2.907,2.932,2.707,2.402,2.924,3.864,2.974,1.864,2.667,2.024,1.625,2.36,2.333,2.738,1.889,2.114,2.758,2.424,2.246,2.475,2.507,3.192,3.277,2.803,2.699,3.068,2.694,2.345,2.544,1.425,3.538,1.74,1.801,2.2,3.164,2.849,2.808,2.854,2.058,2.384,2.397,2.453,2.323,2.156,2.453,2.704,2.122,2.107,2.072,3.182,1.917,2.279,2.109,1.85,2.274,2.671,2.227,3.098,2.381,2.614,2.667,2.137,1.02,0.652,0.481,0.475,0.53,0.699,0.721,0.678,0.585,0.727,0.86,0.535,0.49,0.505,0.551,0.673,0.681,0.83,0.742,0.778,0.952,0.64,0.488,0.914,0.791,0.626,0.562,0.612,0.654,0.705,0.798,0.82,0.468,0.684,0.847,0.511,0.752,0.768,0.399,0.305,0.416,0.456,0.805,0.488,0.549,0.488,0.472,0.654,0.45,0.457,0.561,0.409,0.512,0.499,0.38,0.452,0.466,0.562,0.451,0.536,0.396,0.658,0.49,0.593,0.572,0.617,0.507,0.356,0.383,0.744,0.576,0.731,0.52,0.435
-123,Epileptic,M,57.0,1.0,2.1,1.2,1.1,1.7,1.6,2.1,2.0,1.5,1.1,0.5,3.2,1.2,0.6,1.7,10.6,12.0,1.0,2.0,3.5,1.1,2.8,1.4,5.2,3.9,3.1,4.2,3.8,5.3,3.0,2.6,1.2,0.5,2.8,0.7,0.8,4.3,3.2,0.1,0.3,1.1,2.4,2.1,2.9,3.2,0.7,0.6,1.2,1.6,0.9,0.4,2.1,1.4,3.8,0.2,3.0,0.8,1.3,1.3,1.2,0.5,2.5,0.8,0.5,1.4,1.4,3.2,1.8,1.6,0.7,1.1,1.4,4.2,0.5,8,18,9,9,15,16,17,20,11,13,4.0,29,14,6,16,82,108,9,25,39,12,35,15,40,42,40,52,48,37,42,23,10,5,31,4,6,56,46,2,1,6,23,23,21,16,7,3,7,7,6,3,13,9,24,1,24,6,10,7,8,4,19,4,5,14,20,26,12,9,6,8,10,36,3,0.035,0.023,0.018,0.019,0.039,0.032,0.038,0.031,0.055,0.038,0.034,0.037,0.032,0.05,0.032,0.046,0.036,0.032,0.03,0.035,0.024,0.032,0.036,0.048,0.038,0.028,0.031,0.041,0.048,0.029,0.056,0.047,0.023,0.031,0.018,0.017,0.041,0.033,0.013,0.018,0.023,0.038,0.036,0.027,0.022,0.014,0.023,0.019,0.018,0.02,0.02,0.019,0.027,0.026,0.017,0.017,0.015,0.017,0.026,0.02,0.021,0.034,0.032,0.016,0.017,0.021,0.024,0.021,0.022,0.02,0.022,0.022,0.017,0.018,1866,4964,2682,2615,1848,3071,2969,4252,1840,2337,795.0,3030,2868,873,3435,11930,21748,2578,4756,4555,3912,5271,1566,5804,7483,6706,7276,3824,6588,6672,2384,1391,1408,7602,2815,2031,7851,7690,359,508,1413,2329,6537,2754,4162,1603,1011,2876,2696,1652,537,3888,1993,5392,370,5289,1652,2837,1310,1608,698,3233,568,894,2485,1522,4377,2775,2358,1461,1530,2028,8661,723,0.119,0.113,0.098,0.099,0.139,0.14,0.115,0.127,0.167,0.15,0.13,0.137,0.119,0.153,0.126,0.151,0.136,0.122,0.138,0.139,0.098,0.127,0.127,0.15,0.127,0.129,0.117,0.123,0.133,0.125,0.166,0.118,0.091,0.124,0.09,0.089,0.155,0.137,0.095,0.101,0.108,0.132,0.147,0.122,0.103,0.091,0.108,0.081,0.09,0.103,0.107,0.096,0.11,0.111,0.098,0.099,0.094,0.095,0.119,0.107,0.116,0.139,0.134,0.106,0.099,0.11,0.113,0.103,0.102,0.106,0.111,0.125,0.098,0.102,563,1635,1021,911,803,916,914,1248,529,594,249.0,1358,794,217,886,4079,5901,567,1244,1895,756,1529,763,1992,2032,2026,2566,1528,1772,1987,807,579,396,1573,633,734,2027,1936,205,241,756,1106,1264,1808,2232,800,452,1142,1260,719,298,1706,883,2432,160,2622,823,1273,756,882,360,1317,363,494,1175,1117,2286,1291,1049,650,791,937,3876,391,3.27,2.733,2.6,2.683,2.2,2.611,2.585,2.771,2.749,2.952,2.741,1.883,2.89,2.702,2.828,2.373,2.891,3.625,2.887,2.03,3.492,2.516,1.786,2.443,2.668,2.712,2.262,2.014,2.801,2.555,2.237,2.444,2.659,3.304,3.837,2.456,2.807,2.815,2.116,2.614,2.298,1.785,3.536,1.726,2.097,2.166,2.722,3.036,2.601,2.815,1.987,2.388,2.413,2.405,2.443,2.13,2.166,2.369,1.998,2.0,2.108,2.501,1.907,2.117,2.248,1.68,1.951,2.392,2.385,2.473,2.196,2.402,2.369,2.328,0.558,0.548,0.472,0.526,0.562,0.695,0.773,0.511,0.782,0.733,0.842,0.522,0.415,0.644,0.647,0.703,0.739,0.612,0.558,0.651,0.936,0.599,0.541,0.766,0.675,0.485,0.521,0.566,0.697,0.61,0.737,0.834,0.274,0.691,0.676,0.553,0.8,0.768,0.331,0.268,0.425,0.591,0.821,0.51,0.635,0.466,0.351,0.64,0.457,0.439,0.498,0.424,0.434,0.439,0.407,0.449,0.459,0.484,0.417,0.398,0.408,0.507,0.405,0.59,0.499,0.486,0.454,0.448,0.439,0.574,0.402,0.506,0.465,0.378
-124,Epileptic,M,27.0,0.6,2.5,0.7,1.5,2.1,1.5,1.7,1.8,1.3,0.8,0.2,2.8,1.6,0.4,1.2,5.9,9.0,0.7,1.3,3.1,1.1,4.2,2.2,3.6,5.6,2.7,2.6,1.8,2.1,1.8,1.1,1.6,0.3,1.8,0.3,1.1,4.2,2.7,0.1,0.2,0.7,2.8,2.1,2.0,2.4,1.0,0.4,0.7,1.0,0.8,0.5,1.7,1.3,2.3,0.4,1.9,1.0,2.0,1.2,1.2,0.5,1.4,0.3,0.3,1.3,0.8,1.4,1.3,0.9,0.4,1.0,1.2,4.5,0.3,6,25,6,12,19,14,19,19,16,7,3.0,28,16,4,13,58,84,7,19,32,11,40,17,35,48,29,23,22,21,21,13,12,4,24,2,12,45,33,1,2,4,22,24,15,17,9,2,4,6,7,3,11,9,17,3,14,6,14,8,9,3,16,3,4,9,11,11,12,5,5,8,9,41,2,0.029,0.027,0.017,0.025,0.031,0.027,0.023,0.03,0.035,0.043,0.024,0.036,0.029,0.021,0.026,0.03,0.026,0.029,0.026,0.042,0.022,0.036,0.037,0.038,0.047,0.028,0.032,0.026,0.021,0.029,0.028,0.076,0.027,0.026,0.013,0.023,0.04,0.027,0.017,0.017,0.015,0.039,0.032,0.027,0.021,0.03,0.018,0.013,0.016,0.019,0.023,0.02,0.022,0.017,0.02,0.019,0.027,0.03,0.023,0.02,0.019,0.027,0.024,0.018,0.018,0.023,0.019,0.02,0.018,0.013,0.026,0.016,0.018,0.013,1116,4262,2087,2452,2280,2757,3214,3111,2289,1089,629.0,2526,3201,1048,2540,11321,17722,1944,2952,3782,3099,4422,2198,5575,4861,4532,3145,3567,3513,3481,2135,788,766,4828,1460,2637,7445,7043,289,400,1328,2202,5136,1955,2983,1509,798,1739,1866,1496,423,2678,2373,5010,533,2644,1119,2322,1127,1385,705,2125,362,793,1951,1093,2038,2761,1098,665,1087,2415,8700,542,0.113,0.126,0.092,0.12,0.134,0.121,0.12,0.14,0.138,0.162,0.111,0.162,0.13,0.123,0.126,0.131,0.122,0.115,0.132,0.162,0.099,0.108,0.136,0.146,0.131,0.121,0.113,0.12,0.095,0.125,0.115,0.153,0.102,0.131,0.07,0.116,0.147,0.125,0.073,0.099,0.087,0.133,0.136,0.126,0.107,0.138,0.097,0.081,0.095,0.113,0.135,0.107,0.105,0.094,0.127,0.109,0.121,0.132,0.116,0.104,0.096,0.132,0.155,0.102,0.097,0.111,0.107,0.103,0.091,0.124,0.133,0.105,0.099,0.097,387,1630,765,956,1065,861,1169,1009,621,334,195.0,1275,911,250,745,3271,5568,453,825,1436,782,1436,959,1512,1721,1557,1414,1102,1474,1125,624,346,205,1159,407,834,1939,1633,127,210,678,1128,1232,1201,1751,571,331,834,926,664,258,1328,1121,2105,240,1414,563,1095,734,892,375,831,196,380,1079,577,1102,1027,770,391,548,1148,4131,269,3.063,2.581,2.63,2.497,1.938,2.495,2.202,2.699,2.672,2.828,2.522,1.736,2.651,2.958,2.606,2.59,2.581,3.373,2.652,2.125,3.024,2.429,1.955,2.815,2.301,2.417,1.957,2.453,1.972,2.473,2.591,2.63,2.663,2.992,3.026,2.67,2.902,2.968,2.613,2.238,2.406,1.782,3.116,1.814,1.891,2.342,2.792,2.585,2.498,2.483,2.039,2.177,2.149,2.367,2.616,2.045,2.347,2.451,1.763,1.733,2.019,2.504,1.921,2.379,2.07,2.143,2.004,2.544,1.654,1.977,2.288,2.42,2.289,2.535,0.823,0.503,0.478,0.568,0.577,0.773,0.583,0.441,0.551,0.451,0.746,0.51,0.415,0.488,0.503,0.639,0.643,0.564,0.551,0.716,0.82,0.595,0.615,0.63,0.717,0.677,0.516,0.492,0.627,0.65,0.56,0.753,0.428,0.503,0.674,0.517,0.681,0.57,0.276,0.337,0.414,0.511,0.586,0.567,0.466,0.628,0.538,0.544,0.424,0.466,0.44,0.438,0.5,0.536,0.473,0.407,0.454,0.435,0.327,0.327,0.472,0.665,0.389,0.616,0.515,0.676,0.434,0.726,0.52,0.427,0.37,0.439,0.404,0.46
-125,Epileptic,F,32.0,0.8,1.8,0.5,1.2,1.3,1.8,1.6,1.9,1.0,0.8,0.2,2.5,2.0,0.6,1.2,5.7,7.6,0.4,2.4,3.9,0.9,2.2,1.5,3.0,2.8,4.7,3.0,2.7,2.5,3.1,0.9,1.4,0.7,2.6,0.2,1.1,4.5,3.3,0.1,0.2,0.9,3.2,2.2,1.9,2.3,0.8,0.3,0.5,0.9,1.1,0.5,1.5,1.2,1.9,0.1,1.6,2.0,1.5,0.8,1.2,0.3,0.9,0.1,0.4,1.4,0.8,2.3,1.1,0.8,0.3,0.8,2.1,5.4,0.4,5,20,7,13,13,17,13,16,9,8,2.0,20,20,7,14,54,75,2,22,32,7,26,15,36,33,45,36,27,20,31,10,12,7,34,1,8,41,38,1,1,4,24,19,16,14,6,1,3,4,8,4,12,8,13,1,13,12,12,7,10,2,7,1,3,11,12,18,7,6,2,5,11,39,3,0.036,0.023,0.01,0.022,0.038,0.032,0.026,0.033,0.051,0.039,0.03,0.034,0.037,0.033,0.036,0.036,0.03,0.022,0.032,0.033,0.022,0.037,0.029,0.048,0.034,0.037,0.031,0.028,0.03,0.033,0.034,0.059,0.024,0.031,0.008,0.022,0.047,0.035,0.013,0.018,0.019,0.038,0.044,0.025,0.022,0.022,0.018,0.01,0.014,0.025,0.03,0.016,0.027,0.018,0.017,0.018,0.037,0.02,0.024,0.026,0.019,0.026,0.013,0.025,0.019,0.024,0.021,0.018,0.019,0.015,0.024,0.036,0.019,0.023,1685,4287,1818,2439,2092,3533,2641,3480,1683,1537,324.0,2531,4362,1162,2946,11073,17867,2002,4270,4110,2621,4038,2369,5174,5091,8588,4930,4327,4897,5195,1624,1165,1457,6668,1157,2237,5008,7084,256,379,2100,3021,3867,2240,2947,1673,623,2022,2182,2098,567,3596,1806,4773,263,2682,1666,2982,910,1346,520,1618,315,806,2847,809,3934,2269,2131,667,1455,2065,10754,548,0.124,0.111,0.093,0.116,0.14,0.137,0.109,0.131,0.157,0.146,0.098,0.13,0.14,0.143,0.126,0.145,0.131,0.092,0.13,0.132,0.096,0.148,0.121,0.153,0.141,0.148,0.139,0.125,0.122,0.141,0.136,0.144,0.125,0.127,0.059,0.108,0.135,0.13,0.083,0.09,0.09,0.135,0.143,0.115,0.108,0.115,0.097,0.068,0.084,0.107,0.141,0.093,0.112,0.094,0.132,0.105,0.138,0.104,0.131,0.125,0.102,0.12,0.105,0.12,0.102,0.13,0.107,0.099,0.098,0.092,0.115,0.138,0.095,0.12,406,1363,731,875,612,1052,888,966,369,368,114.0,1048,971,256,655,2726,4468,306,1180,1740,664,1176,867,1261,1467,2205,1611,1473,1281,1532,427,377,449,1476,337,723,1711,1567,145,159,772,1308,851,1323,1699,610,241,884,939,738,297,1518,743,1753,114,1390,812,1287,486,715,247,553,140,293,1242,530,1808,964,727,298,571,892,4232,279,3.67,2.741,2.4,2.532,2.531,2.796,2.434,3.085,3.028,3.369,2.664,2.057,3.248,2.828,2.994,2.891,3.084,4.209,2.798,2.016,3.275,2.551,2.231,2.967,2.617,3.1,2.368,2.301,2.91,2.628,2.828,2.743,2.79,3.169,2.69,2.877,2.65,3.248,2.012,2.74,3.168,2.075,3.536,1.904,1.963,2.605,3.293,2.658,2.665,2.984,2.365,2.313,2.43,2.763,2.045,2.085,2.412,2.547,2.036,2.055,2.459,3.116,2.23,2.751,2.604,1.889,2.292,2.661,3.016,2.374,2.739,2.713,2.651,2.345,1.081,0.71,0.588,0.545,0.714,0.621,0.551,0.757,0.744,0.52,0.779,0.531,0.603,0.586,0.668,0.576,0.673,0.802,0.564,0.603,0.839,0.569,0.48,0.599,0.694,0.732,0.489,0.512,0.725,0.706,0.738,0.99,0.583,0.619,0.552,0.661,0.795,0.563,0.398,0.437,0.869,0.733,0.785,0.676,0.714,0.652,0.475,0.753,0.669,0.685,0.584,0.649,0.631,0.649,0.62,0.403,0.398,0.638,0.418,0.424,0.451,0.892,0.458,1.018,0.762,0.634,0.546,0.482,0.576,0.557,0.554,0.63,0.59,0.535
-126,Epileptic,F,47.0,0.8,2.4,1.2,1.4,1.8,2.3,1.9,1.6,0.9,1.0,0.4,3.2,1.4,0.7,1.6,6.1,7.7,0.8,2.0,4.4,1.2,3.3,2.1,2.9,2.6,4.3,3.0,2.6,2.6,3.3,1.4,1.4,0.6,2.3,0.3,0.7,2.5,2.8,0.2,0.1,1.5,2.7,2.1,3.1,2.6,0.5,0.4,0.8,1.3,0.8,0.6,1.7,1.4,3.8,0.3,1.3,0.5,2.0,1.0,1.3,0.5,1.3,0.2,0.5,2.2,1.3,2.8,1.2,1.0,0.5,1.3,1.1,3.7,0.4,8,26,11,11,19,19,14,14,13,11,4.0,31,15,10,14,70,65,5,16,40,10,41,19,36,30,40,31,26,21,43,25,12,7,25,1,6,28,29,1,1,9,25,21,25,18,4,2,7,7,9,5,13,9,32,2,12,4,19,7,12,4,10,2,4,18,14,22,7,6,6,7,9,30,4,0.032,0.022,0.019,0.024,0.04,0.032,0.035,0.029,0.034,0.031,0.029,0.039,0.029,0.039,0.039,0.037,0.027,0.028,0.033,0.037,0.021,0.03,0.035,0.038,0.028,0.033,0.028,0.027,0.024,0.042,0.038,0.055,0.026,0.033,0.012,0.015,0.037,0.033,0.016,0.013,0.029,0.032,0.036,0.028,0.02,0.015,0.02,0.013,0.019,0.02,0.029,0.017,0.02,0.023,0.021,0.017,0.014,0.026,0.022,0.023,0.024,0.028,0.031,0.022,0.024,0.041,0.023,0.019,0.019,0.017,0.029,0.022,0.018,0.015,1587,4416,2211,2137,2437,3062,2178,2964,1585,1608,726.0,2846,3074,1158,2641,10810,16042,2064,2816,4290,3507,4794,2286,5200,4542,6303,4991,4182,5760,5270,2340,1402,1162,5337,1327,1819,4853,5652,297,359,1704,2540,4812,2923,3186,1043,679,1963,2278,1562,432,2774,2080,6688,315,2566,1127,2976,1321,1875,647,2214,271,871,2860,1191,3613,2378,1689,929,1339,1830,7838,527,0.125,0.119,0.11,0.113,0.139,0.141,0.133,0.127,0.147,0.147,0.125,0.16,0.14,0.12,0.145,0.143,0.12,0.12,0.129,0.139,0.102,0.147,0.142,0.138,0.139,0.145,0.131,0.118,0.106,0.141,0.146,0.149,0.134,0.135,0.074,0.09,0.132,0.131,0.09,0.108,0.123,0.141,0.142,0.127,0.104,0.091,0.117,0.088,0.098,0.107,0.146,0.102,0.108,0.111,0.133,0.097,0.091,0.129,0.117,0.12,0.118,0.122,0.142,0.125,0.121,0.129,0.118,0.103,0.097,0.104,0.131,0.116,0.093,0.106,469,1611,906,902,859,1151,883,1039,465,492,255.0,1379,827,311,729,3057,4611,426,972,1986,898,1815,1006,1397,1546,2165,1792,1568,1629,1777,652,486,383,1320,343,747,1299,1403,188,143,819,1317,1103,1762,2028,556,297,903,1097,680,283,1402,1029,2626,191,1298,550,1302,749,900,348,748,128,401,1411,547,1854,925,832,466,640,851,3357,363,3.412,2.663,2.535,2.356,2.41,2.384,2.037,2.534,2.514,2.825,2.582,1.882,2.976,2.54,2.716,2.645,2.803,3.578,2.502,1.898,3.04,2.284,2.06,2.813,2.365,2.459,2.247,2.132,2.672,2.397,2.694,2.729,2.292,2.897,3.198,2.364,2.828,2.979,1.896,2.57,2.639,1.81,3.095,1.912,1.799,1.972,3.078,2.607,2.532,2.51,1.769,2.108,2.237,2.413,2.113,2.073,2.442,2.569,2.102,2.123,2.248,2.983,2.15,2.583,2.216,2.41,2.194,2.847,2.464,2.21,2.5,2.573,2.433,1.809,0.808,0.492,0.505,0.447,0.612,0.59,0.564,0.552,0.464,0.582,0.625,0.403,0.542,0.735,0.64,0.555,0.539,0.645,0.514,0.528,0.603,0.424,0.379,0.594,0.497,0.467,0.463,0.526,0.502,0.552,0.689,0.974,0.497,0.702,0.487,0.495,0.712,0.479,0.274,0.612,0.637,0.459,0.79,0.686,0.526,0.508,0.411,0.518,0.468,0.487,0.341,0.558,0.503,0.503,0.323,0.483,0.433,0.749,0.282,0.346,0.359,0.757,0.547,0.972,0.476,0.75,0.441,0.472,0.384,0.459,0.374,0.473,0.522,0.299
-127,Epileptic,F,47.0,0.7,2.4,1.0,1.5,1.5,1.8,1.7,1.9,1.5,0.5,0.3,2.6,1.9,0.7,1.6,7.5,11.2,0.5,2.3,4.1,1.5,2.2,1.8,4.4,5.4,4.6,3.8,3.5,3.8,4.5,1.8,0.6,0.6,3.8,0.7,0.8,2.5,2.9,0.1,0.2,1.4,2.8,2.6,2.1,2.8,0.7,0.4,0.9,1.3,0.9,0.5,2.0,1.0,2.3,0.2,2.4,0.6,1.5,0.6,1.2,0.6,1.3,0.1,0.7,1.7,1.5,2.7,1.6,0.9,0.5,1.0,1.8,4.9,0.3,4,20,6,9,12,20,14,16,14,4,3.0,22,17,6,11,61,85,4,26,31,11,28,16,32,42,44,36,27,25,47,17,9,5,33,5,6,32,33,1,1,7,21,21,13,17,5,2,4,7,5,4,14,7,15,1,15,4,15,4,10,6,8,1,6,18,9,19,10,6,4,7,14,39,3,0.046,0.037,0.02,0.031,0.04,0.031,0.036,0.034,0.061,0.035,0.043,0.051,0.049,0.059,0.043,0.056,0.049,0.033,0.04,0.049,0.042,0.042,0.039,0.06,0.059,0.05,0.04,0.05,0.05,0.049,0.066,0.044,0.035,0.056,0.049,0.026,0.044,0.044,0.017,0.027,0.031,0.046,0.061,0.028,0.033,0.019,0.027,0.015,0.023,0.035,0.025,0.027,0.036,0.028,0.025,0.022,0.022,0.023,0.026,0.029,0.028,0.037,0.038,0.035,0.027,0.048,0.028,0.032,0.028,0.021,0.032,0.034,0.022,0.018,1129,3630,1540,1762,1465,2613,2236,2992,1466,1051,612.0,1719,2170,756,2143,7646,13717,1644,3864,3094,2307,3094,1805,4213,4169,5574,4928,2648,4456,4724,1744,647,794,4978,1263,1492,4757,4617,285,417,1229,2021,3784,1692,2086,1224,491,2010,1949,1226,545,2486,893,2554,224,2989,975,1912,572,1048,584,1577,175,675,1952,965,2448,1452,906,803,949,1597,7046,543,0.121,0.131,0.109,0.124,0.154,0.153,0.123,0.153,0.186,0.139,0.131,0.173,0.169,0.17,0.147,0.167,0.162,0.105,0.162,0.167,0.143,0.14,0.144,0.178,0.161,0.162,0.139,0.146,0.142,0.179,0.158,0.125,0.12,0.179,0.13,0.114,0.176,0.161,0.113,0.113,0.123,0.163,0.19,0.121,0.128,0.105,0.114,0.081,0.107,0.141,0.139,0.114,0.136,0.12,0.129,0.112,0.115,0.116,0.13,0.133,0.127,0.136,0.123,0.137,0.132,0.142,0.132,0.13,0.128,0.105,0.142,0.164,0.113,0.124,261,1234,661,739,614,903,900,936,485,246,155.0,881,759,214,671,2616,4313,310,1093,1334,663,996,742,1335,1670,1897,1743,1181,1333,1717,607,248,283,1292,319,514,1240,1350,139,173,691,936,860,1163,1431,610,231,794,884,406,307,1301,484,1352,113,1626,468,1092,370,632,357,619,101,339,1088,448,1428,836,519,362,528,742,3266,281,3.577,2.631,2.312,2.372,2.146,2.344,2.107,2.758,2.506,3.159,3.158,1.717,2.328,2.818,2.466,2.244,2.521,3.686,2.751,1.997,2.699,2.436,2.084,2.522,2.078,2.394,2.199,1.821,2.433,2.254,2.307,2.391,2.182,2.792,2.851,2.532,2.903,2.59,2.294,2.449,2.302,1.933,2.951,1.726,1.674,2.156,2.485,3.048,2.651,2.771,1.992,2.071,2.165,2.0,2.18,2.003,2.353,1.846,1.754,1.924,1.951,2.693,1.779,2.144,1.903,2.413,1.876,1.976,1.932,2.364,1.955,2.353,2.396,2.299,1.107,0.787,0.675,0.565,0.579,0.664,0.488,0.557,0.798,0.562,0.912,0.467,0.472,0.51,0.639,0.569,0.609,0.845,0.635,0.695,0.759,0.513,0.559,0.696,0.694,0.586,0.653,0.58,0.716,0.571,0.749,0.95,0.408,0.607,0.685,0.596,0.681,0.664,0.477,0.599,0.473,0.653,0.799,0.489,0.469,0.572,0.496,1.045,0.58,0.762,0.349,0.447,0.583,0.422,0.493,0.446,0.335,0.532,0.363,0.521,0.361,0.806,0.602,0.598,0.475,0.696,0.435,0.485,0.379,0.727,0.527,0.509,0.503,0.46
-128,Epileptic,F,17.5,0.7,2.4,0.7,1.2,1.0,1.9,1.6,1.4,1.1,0.6,0.3,2.9,1.8,0.6,1.7,6.8,7.5,0.6,2.6,5.8,1.0,3.5,1.7,2.8,2.7,2.4,2.6,2.8,2.4,3.1,0.9,0.7,0.4,2.0,0.5,0.5,3.8,4.0,0.1,0.1,0.9,3.8,2.4,2.4,2.9,0.6,0.4,0.8,0.9,0.6,0.4,2.2,2.6,2.2,0.3,1.9,0.6,1.8,1.0,1.5,0.6,1.2,0.3,0.3,1.6,1.2,2.2,1.3,1.0,0.4,1.1,2.1,4.3,0.1,4,23,6,10,11,18,16,13,12,6,3.0,24,18,6,18,74,66,4,28,39,6,41,16,31,35,27,31,29,23,34,12,6,3,29,3,6,43,44,1,0,4,30,19,18,15,5,2,4,5,8,5,14,20,14,2,14,4,14,7,9,4,9,2,3,10,14,18,10,6,3,7,14,35,0,0.033,0.024,0.012,0.027,0.026,0.033,0.025,0.027,0.047,0.032,0.029,0.034,0.032,0.043,0.032,0.039,0.027,0.024,0.038,0.045,0.019,0.035,0.036,0.035,0.03,0.028,0.027,0.029,0.023,0.031,0.026,0.028,0.024,0.024,0.014,0.019,0.032,0.033,0.014,0.019,0.018,0.034,0.042,0.026,0.019,0.014,0.02,0.011,0.011,0.019,0.024,0.018,0.035,0.022,0.024,0.019,0.014,0.019,0.021,0.021,0.018,0.021,0.034,0.014,0.022,0.033,0.02,0.019,0.019,0.015,0.026,0.025,0.017,0.011,1422,6509,2234,2488,1935,3277,2950,3988,2182,1304,668.0,3030,3938,1211,3192,13771,19899,2098,4458,4448,2968,5826,2057,6809,5630,6133,4718,4398,6151,6283,2347,1158,994,6100,2485,1528,8673,9729,356,326,1868,3388,5770,2586,3831,1556,801,2800,2528,1601,566,3986,2600,4554,341,3299,1633,3276,1458,1894,1168,2416,485,969,2571,1170,3415,2539,1683,949,1672,2965,11246,267,0.111,0.113,0.089,0.115,0.126,0.152,0.116,0.118,0.159,0.14,0.109,0.138,0.132,0.15,0.135,0.148,0.127,0.101,0.139,0.135,0.075,0.146,0.135,0.133,0.132,0.119,0.13,0.124,0.113,0.141,0.112,0.097,0.11,0.12,0.065,0.093,0.127,0.121,0.088,0.082,0.095,0.14,0.144,0.113,0.097,0.089,0.102,0.07,0.075,0.099,0.127,0.1,0.137,0.1,0.124,0.097,0.093,0.098,0.114,0.109,0.102,0.11,0.118,0.107,0.107,0.129,0.11,0.101,0.102,0.104,0.119,0.121,0.092,0.08,393,1701,735,809,641,1008,1107,954,445,323,168.0,1348,1063,241,864,3341,4596,380,1194,2020,825,1806,887,1441,1718,1568,1596,1627,1590,1796,577,420,282,1298,613,563,2052,1991,182,94,694,1793,991,1584,2235,736,304,944,1081,629,299,1726,1160,1723,189,1587,651,1522,734,1009,482,856,188,374,1114,650,1521,1062,835,377,668,1341,4210,100,3.263,3.227,2.821,2.984,2.453,2.817,2.23,3.567,3.214,3.342,2.762,2.03,3.005,3.232,2.679,2.959,3.354,3.626,2.898,1.922,2.843,2.567,2.106,3.254,2.556,3.061,2.376,2.239,2.831,2.729,2.799,2.612,3.173,3.11,3.497,2.617,3.139,3.576,2.374,3.567,3.168,1.799,3.748,1.85,1.975,2.37,3.273,3.35,2.766,2.8,2.065,2.422,2.282,2.58,2.498,2.213,2.854,2.579,2.258,2.158,2.778,2.588,2.957,2.875,2.542,2.303,2.358,2.549,2.566,2.82,2.741,2.691,2.759,2.58,1.047,0.782,0.679,0.619,0.786,0.658,0.608,0.765,0.728,0.592,0.839,0.642,0.578,0.443,0.721,0.731,0.69,0.868,0.66,0.597,0.691,0.617,0.455,0.707,0.517,0.641,0.488,0.544,0.486,0.644,0.708,1.09,0.658,0.854,0.813,0.571,0.772,0.745,0.36,0.662,0.812,0.404,0.758,0.694,0.581,0.5,0.602,0.923,0.665,0.534,0.6,0.584,0.609,0.691,0.557,0.481,0.466,0.572,0.571,0.388,0.576,0.825,0.496,1.04,0.658,0.708,0.536,0.455,0.446,1.038,0.654,0.584,0.631,0.493
-129,Epileptic,F,42.0,0.9,2.3,0.9,1.2,2.3,1.7,1.3,1.5,1.1,0.7,0.5,4.2,1.2,0.9,1.2,5.1,8.8,0.9,2.1,5.8,0.8,3.0,3.4,3.1,2.4,2.6,4.3,2.3,3.4,2.5,2.0,1.6,0.5,2.1,0.4,0.3,3.2,2.3,0.2,0.1,1.0,2.6,2.2,4.0,2.8,0.6,0.3,0.7,1.1,0.8,0.7,1.7,2.5,2.4,0.5,2.5,0.9,1.3,1.0,1.3,1.4,1.4,0.4,0.5,2.4,1.3,2.8,1.2,1.0,0.6,0.9,1.4,3.1,0.3,9,25,8,10,37,20,15,16,22,8,10.0,40,13,9,12,59,93,8,27,53,16,35,26,35,38,34,48,22,57,29,24,22,4,28,2,2,39,31,2,1,5,24,21,32,17,5,1,5,7,5,7,18,22,14,2,22,11,10,9,13,11,13,3,5,23,19,21,8,7,9,10,10,29,2,0.042,0.027,0.018,0.024,0.074,0.031,0.024,0.027,0.055,0.029,0.047,0.04,0.027,0.058,0.036,0.038,0.031,0.036,0.029,0.04,0.026,0.03,0.036,0.042,0.028,0.027,0.034,0.025,0.026,0.033,0.051,0.105,0.027,0.029,0.012,0.008,0.035,0.025,0.019,0.018,0.021,0.03,0.04,0.026,0.022,0.012,0.021,0.012,0.018,0.026,0.025,0.022,0.04,0.021,0.041,0.016,0.022,0.015,0.025,0.021,0.033,0.031,0.034,0.023,0.026,0.028,0.023,0.019,0.02,0.023,0.025,0.018,0.015,0.015,1365,3849,1820,2344,1864,2388,2505,2895,1634,1482,688.0,3461,2980,1216,2567,9222,17655,2011,4750,6232,2517,4316,2351,4782,5081,5884,5499,4293,6558,4861,2127,886,1081,5293,1876,1634,6616,6234,365,288,1414,1944,4713,3689,3380,1414,589,1813,1758,1238,733,2841,2603,3897,480,4434,1400,2712,996,1833,1183,2163,420,783,3118,1138,3597,2211,1745,1071,1456,2300,6642,484,0.136,0.134,0.107,0.108,0.168,0.13,0.103,0.128,0.161,0.134,0.157,0.163,0.124,0.195,0.144,0.146,0.134,0.136,0.137,0.153,0.094,0.135,0.135,0.144,0.12,0.135,0.113,0.107,0.109,0.13,0.167,0.166,0.106,0.142,0.073,0.067,0.142,0.136,0.115,0.11,0.107,0.127,0.154,0.127,0.106,0.089,0.093,0.082,0.102,0.117,0.137,0.116,0.148,0.104,0.143,0.101,0.129,0.102,0.131,0.119,0.138,0.132,0.138,0.127,0.126,0.139,0.119,0.104,0.105,0.138,0.134,0.101,0.095,0.106,445,1338,708,845,758,946,863,988,554,389,229.0,1837,831,281,654,2638,4982,437,1287,2666,667,1582,1241,1451,1440,1758,1894,1438,1720,1400,727,458,346,1200,479,581,1798,1520,167,148,707,1285,1074,2397,2047,723,213,811,886,510,381,1304,1090,1805,196,2279,651,1296,620,983,618,809,225,440,1505,717,1759,988,805,469,659,1194,3105,263,2.768,2.597,2.581,2.674,2.173,2.168,2.381,2.602,2.344,2.921,2.762,1.685,2.722,3.055,2.89,2.625,2.811,3.469,2.927,2.043,2.86,2.228,1.811,2.5,2.659,2.721,2.345,2.34,2.9,2.659,2.348,2.209,2.423,3.018,3.207,2.607,2.681,2.894,2.416,2.236,2.537,1.462,3.221,1.765,1.868,2.121,2.929,2.841,2.558,2.741,1.926,2.231,2.211,2.359,2.757,2.161,2.287,2.361,1.807,2.058,1.981,2.607,1.984,2.135,2.187,1.87,2.332,2.537,2.373,2.562,2.217,2.276,2.259,2.255,0.798,0.644,0.523,0.496,0.602,0.617,0.617,0.47,0.508,0.589,0.562,0.559,0.52,0.438,0.573,0.628,0.58,0.981,0.631,0.688,0.705,0.616,0.541,0.922,0.575,0.475,0.547,0.583,0.557,0.475,0.799,0.772,0.452,0.636,0.669,0.48,0.734,0.793,0.429,0.353,0.497,0.401,0.716,0.583,0.564,0.408,0.51,0.666,0.44,0.569,0.548,0.472,0.504,0.398,0.511,0.369,0.426,0.453,0.425,0.439,0.421,0.741,0.439,0.707,0.45,0.574,0.413,0.386,0.435,0.642,0.465,0.508,0.441,0.384
-130,Epileptic,M,37.0,0.9,2.8,1.5,1.6,1.7,2.9,1.5,2.3,1.3,1.0,0.4,3.6,1.6,0.7,1.1,6.9,8.7,1.0,2.6,5.5,1.7,3.6,1.7,3.8,3.5,5.0,4.2,3.2,3.1,3.2,1.8,1.5,0.7,3.1,0.4,0.8,4.2,3.6,0.3,0.2,1.4,4.1,2.4,3.6,3.6,1.1,0.7,1.0,1.5,0.6,1.1,1.6,2.3,3.0,0.8,2.8,0.8,2.7,0.9,1.0,1.3,1.7,0.5,0.3,2.0,1.7,3.5,1.3,1.3,0.5,1.1,1.3,6.1,0.3,5,26,16,9,17,25,13,24,14,8,3.0,30,19,8,13,73,78,5,25,41,14,41,18,39,43,55,48,31,30,37,21,8,7,37,3,8,42,39,3,2,8,36,27,25,22,8,3,7,8,4,7,13,18,25,5,24,4,18,7,9,11,13,3,4,15,17,30,8,9,5,6,10,51,3,0.033,0.026,0.026,0.033,0.026,0.035,0.022,0.027,0.04,0.036,0.033,0.035,0.027,0.035,0.03,0.031,0.031,0.036,0.035,0.04,0.03,0.035,0.027,0.04,0.028,0.026,0.03,0.025,0.027,0.03,0.045,0.044,0.022,0.037,0.012,0.02,0.037,0.03,0.018,0.019,0.023,0.043,0.037,0.03,0.023,0.024,0.023,0.016,0.017,0.015,0.031,0.019,0.026,0.021,0.024,0.019,0.022,0.027,0.019,0.018,0.035,0.024,0.02,0.015,0.022,0.03,0.019,0.016,0.018,0.014,0.026,0.018,0.024,0.017,1848,4917,2239,1987,2667,4221,2749,4803,2129,1793,655.0,3407,3834,1483,1674,12056,18170,2190,5307,4429,3731,5830,2140,6952,5592,9002,6563,5286,6854,5341,3058,1351,1373,6395,2233,2125,8268,7810,701,452,1865,3269,5882,2887,3663,1458,1078,2471,2802,1675,798,2953,3315,6002,808,4821,1693,3425,1400,1542,1671,3319,758,889,3022,1357,5888,2729,2238,1119,1239,2542,10235,382,0.114,0.121,0.129,0.122,0.121,0.146,0.103,0.124,0.146,0.131,0.134,0.142,0.129,0.144,0.143,0.138,0.127,0.126,0.135,0.141,0.113,0.148,0.125,0.143,0.13,0.123,0.135,0.112,0.112,0.135,0.149,0.125,0.11,0.146,0.069,0.098,0.144,0.132,0.106,0.122,0.106,0.157,0.143,0.129,0.109,0.117,0.111,0.087,0.094,0.1,0.135,0.1,0.124,0.107,0.124,0.108,0.101,0.12,0.113,0.114,0.139,0.117,0.107,0.105,0.107,0.123,0.107,0.09,0.098,0.101,0.122,0.106,0.112,0.119,440,1724,905,765,1066,1467,971,1545,577,465,206.0,1485,1020,350,593,3761,4808,428,1324,2271,1043,1860,1021,1794,1988,3073,2366,2041,1978,1891,709,526,512,1533,572,772,1902,2019,324,190,976,1524,1240,1882,2354,723,440,1005,1204,614,510,1414,1407,2397,451,2455,630,1617,770,816,684,1122,399,418,1436,816,2832,1225,1090,577,642,1171,4200,255,3.845,2.793,2.405,2.553,2.292,2.48,2.338,2.831,2.745,3.012,2.932,2.004,2.889,2.995,2.333,2.559,2.971,3.98,3.098,1.71,2.743,2.461,1.92,2.98,2.373,2.437,2.231,2.12,2.669,2.332,3.091,2.549,2.315,2.974,3.577,2.541,3.091,2.826,2.348,2.812,2.303,1.966,3.266,1.787,1.779,2.208,2.961,2.994,2.799,2.834,1.996,2.257,2.344,2.462,2.336,2.126,2.869,2.341,2.104,2.155,2.459,3.025,2.297,2.394,2.25,1.975,2.198,2.546,2.225,2.08,2.305,2.619,2.514,1.945,0.982,0.528,0.628,0.621,0.473,0.593,0.731,0.647,0.566,0.469,0.723,0.525,0.583,0.404,0.524,0.545,0.586,0.734,0.698,0.577,0.772,0.451,0.441,0.56,0.492,0.513,0.491,0.517,0.512,0.476,0.762,1.186,0.578,0.59,0.694,0.568,0.689,0.494,0.407,0.637,0.505,0.536,0.743,0.505,0.563,0.42,0.467,0.799,0.59,0.524,0.562,0.472,0.549,0.446,0.452,0.414,0.737,0.599,0.373,0.352,0.447,0.629,0.52,1.008,0.502,0.707,0.491,0.449,0.46,0.45,0.351,0.474,0.565,0.495
-131,Epileptic,M,22.0,1.6,2.4,1.0,1.2,1.8,2.5,2.5,2.1,1.0,0.9,0.4,2.3,2.0,0.3,1.5,7.0,10.7,1.1,2.9,5.2,1.3,3.4,1.7,4.0,3.3,3.8,3.3,2.9,3.3,3.9,1.4,1.9,0.5,2.4,0.6,0.9,2.7,3.6,0.2,0.2,1.1,2.8,2.2,2.2,3.0,0.7,0.4,1.1,1.4,0.7,0.9,2.6,1.6,2.4,0.3,2.6,0.9,1.8,1.4,0.8,0.5,1.4,0.4,0.2,2.0,1.4,3.8,2.0,0.9,0.5,0.8,1.4,4.9,0.3,11,21,7,10,15,23,19,19,10,10,3.0,23,17,4,13,68,87,10,30,43,14,42,16,35,39,38,38,24,27,42,20,12,8,29,3,10,44,41,2,1,7,30,24,17,20,5,2,6,7,6,5,18,11,16,2,25,5,14,9,7,4,13,2,2,16,16,30,15,5,4,5,12,38,3,0.046,0.018,0.012,0.02,0.03,0.031,0.033,0.028,0.032,0.032,0.039,0.03,0.031,0.022,0.03,0.033,0.028,0.033,0.03,0.036,0.025,0.032,0.033,0.036,0.025,0.026,0.025,0.025,0.027,0.036,0.04,0.056,0.027,0.025,0.014,0.019,0.029,0.031,0.017,0.012,0.02,0.038,0.038,0.021,0.022,0.015,0.018,0.016,0.014,0.014,0.028,0.019,0.018,0.017,0.018,0.018,0.02,0.017,0.021,0.014,0.02,0.023,0.016,0.012,0.017,0.031,0.021,0.021,0.017,0.021,0.02,0.016,0.016,0.015,2164,5949,2694,2519,2824,4130,3812,4432,1864,1815,852.0,3241,4698,1059,3411,15075,25795,3210,6021,5599,4847,7146,2297,7094,8381,8131,7266,5103,8301,7330,2313,1569,1363,7471,2478,2853,6772,9959,549,578,1891,3722,5868,3144,3833,1666,1062,2753,3115,1673,1144,5560,3335,6332,550,4866,1556,4169,1910,1639,1250,2951,700,912,3947,1554,6145,3937,1725,1223,1576,2903,12020,574,0.132,0.102,0.085,0.101,0.122,0.142,0.12,0.12,0.129,0.148,0.133,0.129,0.118,0.109,0.122,0.132,0.126,0.119,0.128,0.134,0.097,0.137,0.137,0.115,0.115,0.124,0.118,0.1,0.106,0.144,0.12,0.139,0.128,0.113,0.069,0.097,0.113,0.128,0.105,0.087,0.1,0.144,0.124,0.107,0.109,0.097,0.09,0.082,0.084,0.088,0.128,0.096,0.099,0.092,0.101,0.103,0.1,0.097,0.114,0.098,0.104,0.108,0.099,0.078,0.101,0.123,0.11,0.107,0.095,0.102,0.106,0.098,0.092,0.111,570,1969,1083,980,1007,1372,1191,1322,524,466,209.0,1283,1164,247,813,3870,6459,588,1598,2293,996,1976,909,1782,2146,2434,2251,1800,2039,2121,576,539,354,1647,639,863,1771,2148,191,244,893,1456,1241,1632,2227,746,385,1067,1362,756,497,2060,1344,2360,218,2300,660,1808,938,848,460,1049,343,419,1760,780,2818,1538,714,485,729,1400,4889,318,3.825,2.852,2.588,2.676,2.534,2.67,2.81,2.918,2.671,3.13,3.139,2.135,3.175,3.232,3.209,3.041,3.276,3.918,3.049,2.119,3.534,2.836,2.19,2.973,2.927,2.895,2.592,2.274,3.023,2.769,2.628,2.661,2.986,3.263,3.512,2.887,2.747,3.306,3.067,2.688,2.738,2.247,3.351,2.122,2.045,2.485,3.367,3.096,2.709,2.261,2.552,2.716,2.522,2.897,3.005,2.276,2.437,2.63,2.366,2.074,2.645,2.608,2.583,2.261,2.462,2.253,2.389,2.769,2.754,2.556,2.5,2.47,2.718,2.049,0.685,0.658,0.627,0.497,0.564,0.539,0.606,0.575,0.605,0.464,0.716,0.554,0.472,0.381,0.424,0.566,0.585,0.742,0.565,0.577,0.845,0.497,0.466,0.677,0.582,0.505,0.479,0.57,0.657,0.508,0.839,0.983,0.397,0.605,0.907,0.611,0.831,0.592,0.236,0.321,0.494,0.544,0.797,0.669,0.664,0.433,0.57,0.664,0.734,0.664,0.498,0.569,0.573,0.572,0.512,0.409,0.559,0.594,0.417,0.389,0.672,0.767,0.548,1.146,0.554,0.596,0.446,0.477,0.448,0.638,0.497,0.635,0.517,0.39
-132,Epileptic,M,47.0,1.1,3.5,2.2,2.3,1.7,3.2,2.4,3.2,1.2,1.4,0.6,2.6,3.0,1.2,4.6,8.7,13.3,1.0,4.4,6.2,1.6,4.1,1.5,5.2,4.5,6.4,5.6,6.0,6.6,5.5,1.5,1.5,0.9,4.3,1.1,2.1,6.2,5.4,0.4,0.4,2.5,4.8,3.1,5.3,6.0,1.3,0.9,1.3,2.1,1.4,1.5,3.7,2.1,4.9,0.7,3.3,1.3,3.1,1.3,1.8,0.8,2.1,1.3,0.3,3.1,1.7,6.4,2.1,2.8,0.7,2.7,2.5,8.5,0.7,8,31,18,17,14,25,16,25,10,12,4.0,20,24,11,30,63,84,8,34,40,13,36,15,40,44,51,46,40,36,42,20,9,7,34,9,16,54,46,2,2,14,39,25,28,35,8,4,22,11,11,8,26,14,30,6,27,9,19,10,15,7,13,7,3,19,16,47,12,17,8,14,13,55,4,0.031,0.027,0.026,0.03,0.028,0.036,0.039,0.031,0.036,0.037,0.043,0.034,0.032,0.04,0.045,0.035,0.036,0.03,0.037,0.039,0.024,0.031,0.03,0.041,0.028,0.032,0.033,0.039,0.037,0.034,0.033,0.036,0.029,0.032,0.02,0.026,0.037,0.034,0.021,0.018,0.035,0.04,0.034,0.043,0.036,0.025,0.027,0.019,0.019,0.023,0.055,0.025,0.025,0.025,0.016,0.021,0.02,0.021,0.025,0.029,0.019,0.037,0.045,0.018,0.034,0.024,0.029,0.022,0.03,0.018,0.031,0.023,0.023,0.037,1827,5799,3417,2912,2164,3701,2466,4116,1532,1984,675.0,2241,4981,1360,4687,12407,19801,2516,5875,4808,3866,6620,2320,6177,8118,9360,7957,5562,7108,6335,2361,1500,1251,7273,3560,3817,10404,10332,485,786,2267,4077,6326,3060,3680,1912,973,2749,3541,2543,604,5137,2710,6947,999,4838,2507,4952,1634,1864,1366,2456,670,984,2585,2132,6957,3088,2550,1506,2581,3028,15357,685,0.127,0.123,0.122,0.13,0.12,0.135,0.123,0.121,0.122,0.135,0.138,0.134,0.122,0.145,0.133,0.124,0.123,0.122,0.131,0.132,0.095,0.136,0.127,0.129,0.117,0.125,0.132,0.121,0.121,0.128,0.123,0.118,0.127,0.123,0.089,0.11,0.132,0.13,0.108,0.101,0.135,0.139,0.13,0.148,0.138,0.115,0.124,0.101,0.096,0.106,0.169,0.108,0.108,0.102,0.11,0.108,0.103,0.102,0.124,0.137,0.101,0.132,0.163,0.096,0.136,0.114,0.127,0.109,0.129,0.114,0.136,0.12,0.106,0.136,606,2081,1338,1271,1050,1482,1030,1642,589,650,226.0,1292,1574,517,1627,3924,5728,582,1908,2575,1119,2056,884,1851,2609,3253,2847,2375,2646,2634,706,691,552,2113,853,1280,2875,2704,290,375,1170,1945,1595,2013,2703,878,452,1196,1665,1004,457,2469,1344,3012,596,2555,1044,2296,921,1023,734,953,446,374,1521,1033,3589,1404,1484,641,1301,1478,5978,339,3.025,2.608,2.538,2.378,1.993,2.307,2.177,2.326,2.348,2.698,2.804,1.745,2.623,2.136,2.425,2.552,2.877,3.482,2.702,1.81,2.872,2.58,2.172,2.645,2.45,2.48,2.252,1.991,2.315,2.174,2.515,2.197,2.126,2.677,3.194,2.648,2.852,3.089,2.05,2.476,2.466,1.945,2.993,1.814,1.632,2.344,2.773,2.886,2.657,2.656,1.731,2.25,2.096,2.295,2.104,2.029,2.651,2.516,2.214,1.959,2.132,2.342,1.984,2.503,2.071,2.336,2.128,2.623,2.08,2.497,2.437,2.619,2.738,2.216,0.726,0.541,0.544,0.538,0.492,0.626,0.646,0.487,0.588,0.646,0.765,0.435,0.497,0.528,0.485,0.653,0.628,0.726,0.837,0.656,0.764,0.426,0.666,0.703,0.497,0.495,0.568,0.519,0.711,0.546,0.795,0.995,0.518,0.567,0.756,0.512,0.673,0.574,0.297,0.44,0.559,0.69,0.767,0.54,0.429,0.63,0.449,0.656,0.59,0.559,0.453,0.528,0.51,0.629,0.388,0.458,0.606,0.474,0.345,0.441,0.477,0.708,0.387,0.91,0.691,0.827,0.471,0.483,0.437,0.666,0.418,0.537,0.582,0.702
-133,Epileptic,M,37.0,0.8,2.1,0.8,1.3,1.4,1.9,1.5,2.3,1.0,1.0,0.3,2.6,2.0,0.4,1.5,5.6,9.7,1.0,2.3,3.7,2.8,2.5,1.6,2.9,2.9,4.9,2.8,2.9,2.8,3.2,1.3,2.1,0.5,2.7,0.6,0.4,3.6,3.2,0.1,0.3,0.9,2.5,2.7,3.1,2.4,1.1,0.3,1.1,1.4,0.7,0.8,2.4,1.0,2.4,0.6,3.4,0.5,1.4,0.8,1.0,0.5,1.4,0.5,0.3,1.5,1.8,2.7,1.3,1.9,0.6,0.8,1.5,4.8,0.4,7,21,10,11,14,17,13,19,12,11,4.0,26,21,7,22,64,98,10,28,31,22,31,16,30,37,51,34,31,26,40,18,14,5,28,3,3,41,38,1,2,5,22,27,20,16,9,2,9,8,6,4,18,7,16,4,26,4,10,6,7,3,11,2,3,9,21,24,11,11,5,6,13,39,3,0.026,0.017,0.01,0.02,0.026,0.027,0.022,0.028,0.035,0.031,0.032,0.034,0.029,0.024,0.03,0.029,0.024,0.027,0.031,0.034,0.04,0.031,0.027,0.028,0.03,0.03,0.021,0.026,0.021,0.029,0.03,0.055,0.023,0.027,0.019,0.012,0.034,0.028,0.014,0.018,0.019,0.028,0.038,0.031,0.018,0.019,0.013,0.016,0.014,0.014,0.028,0.02,0.016,0.015,0.027,0.018,0.013,0.017,0.022,0.019,0.016,0.023,0.024,0.015,0.02,0.025,0.019,0.016,0.021,0.019,0.019,0.02,0.017,0.017,1994,5211,2720,2803,2470,3442,2887,4394,1767,2222,542.0,2971,4432,1049,3082,12625,24153,3154,5247,5001,5318,5046,2754,7200,6524,9587,6168,4837,7444,6102,2618,1603,1075,7206,2199,1896,9002,8706,346,502,1626,3438,7442,2726,3743,2146,947,2473,3003,2014,672,4047,2039,6125,661,4984,1371,3152,1028,1591,920,2824,473,959,2593,1888,4696,3145,2865,1110,1634,2806,11111,590,0.112,0.109,0.095,0.106,0.129,0.132,0.102,0.129,0.136,0.145,0.136,0.152,0.127,0.142,0.144,0.131,0.117,0.119,0.134,0.13,0.122,0.152,0.135,0.129,0.137,0.134,0.113,0.111,0.094,0.139,0.125,0.14,0.113,0.128,0.092,0.079,0.138,0.133,0.079,0.106,0.1,0.128,0.146,0.13,0.096,0.102,0.088,0.089,0.086,0.091,0.129,0.109,0.098,0.087,0.125,0.111,0.091,0.099,0.119,0.1,0.104,0.112,0.122,0.097,0.101,0.123,0.109,0.1,0.111,0.112,0.113,0.115,0.098,0.113,534,1764,1168,1035,846,1185,1038,1336,524,527,174.0,1246,1257,273,890,3282,6615,640,1293,1807,1142,1483,986,1676,1789,2729,2245,1686,1996,1947,705,608,345,1537,521,655,1989,1893,178,248,782,1386,1379,1485,2107,917,396,1134,1408,760,385,1785,911,2366,342,2624,594,1311,534,825,423,965,257,428,1112,1029,2238,1297,1333,507,682,1201,4358,306,3.692,2.728,2.303,2.667,2.58,2.534,2.31,2.869,2.546,3.352,2.871,2.094,2.828,2.82,2.815,2.884,2.95,3.768,3.08,2.258,2.946,2.695,2.391,3.152,2.848,2.826,2.274,2.302,2.861,2.554,2.758,2.722,2.534,3.248,3.482,2.743,3.142,3.233,2.257,2.454,2.677,2.142,3.617,2.086,2.016,2.364,3.02,2.714,2.649,2.937,2.285,2.361,2.33,2.678,2.455,2.196,2.777,2.69,2.342,2.261,2.504,2.902,2.262,2.679,2.484,2.323,2.28,2.6,2.617,2.523,2.76,2.666,2.705,2.436,0.596,0.614,0.544,0.45,0.563,0.595,0.546,0.518,0.633,0.556,0.715,0.486,0.45,0.433,0.457,0.546,0.584,0.802,0.547,0.728,1.095,0.453,0.411,0.638,0.541,0.5,0.495,0.523,0.501,0.506,0.655,0.921,0.42,0.635,0.735,0.446,0.737,0.645,0.3,0.277,0.466,0.599,0.736,0.811,0.552,0.571,0.513,0.586,0.491,0.61,0.424,0.458,0.392,0.583,0.369,0.422,0.436,0.548,0.366,0.521,0.326,0.737,0.413,0.951,0.64,0.799,0.549,0.377,0.417,0.574,0.57,0.619,0.449,0.491
-134,Epileptic,M,27.0,0.9,2.2,1.4,1.6,1.3,1.9,2.2,2.0,1.0,1.6,0.4,2.7,1.7,0.5,1.2,4.5,10.9,1.1,2.6,5.1,1.5,2.8,1.1,3.5,4.3,4.7,4.0,3.0,2.4,3.7,1.4,1.0,0.4,2.3,0.4,0.4,3.0,3.9,0.2,0.2,1.3,3.2,2.3,3.0,2.7,0.7,0.4,1.0,1.8,0.9,0.6,2.0,1.2,2.6,0.3,2.5,0.7,1.8,1.6,1.1,1.1,1.6,0.2,0.9,1.5,1.6,3.5,1.2,1.3,0.7,1.2,1.2,5.1,0.3,11,19,10,11,16,20,14,19,10,12,4.0,26,23,5,14,56,103,10,29,45,10,31,12,42,44,55,44,28,24,40,16,9,5,27,2,3,37,47,2,1,6,30,24,22,16,6,2,5,10,11,6,13,8,17,3,21,7,14,12,8,8,14,1,6,12,19,31,8,10,5,6,12,37,2,0.042,0.023,0.022,0.026,0.025,0.025,0.023,0.028,0.049,0.048,0.037,0.033,0.028,0.025,0.029,0.033,0.03,0.041,0.032,0.036,0.03,0.031,0.024,0.035,0.03,0.03,0.029,0.028,0.024,0.032,0.028,0.037,0.029,0.033,0.02,0.011,0.023,0.035,0.013,0.019,0.023,0.035,0.044,0.026,0.018,0.013,0.017,0.02,0.02,0.027,0.021,0.024,0.019,0.017,0.012,0.017,0.016,0.02,0.028,0.019,0.027,0.025,0.021,0.021,0.017,0.027,0.018,0.017,0.018,0.021,0.027,0.021,0.02,0.015,1294,4251,2397,2396,1960,3056,3320,3377,1476,1916,835.0,2546,3674,1024,2522,9044,19740,2300,4600,5082,3448,3958,1725,6130,5832,8095,5260,4128,5561,6069,2626,1460,730,4858,1262,1480,7373,7145,366,372,1518,2349,5807,2838,3773,1384,769,2021,2659,1580,879,2691,1790,5486,617,3742,1450,2560,1299,1470,1163,2410,413,1103,2343,1685,5360,2222,1845,1058,1456,2265,8842,581,0.142,0.115,0.111,0.12,0.131,0.123,0.093,0.128,0.167,0.17,0.113,0.126,0.134,0.126,0.134,0.138,0.13,0.143,0.138,0.138,0.106,0.122,0.115,0.145,0.106,0.122,0.111,0.105,0.097,0.141,0.126,0.111,0.121,0.129,0.093,0.072,0.119,0.148,0.097,0.097,0.114,0.129,0.153,0.117,0.097,0.093,0.098,0.096,0.106,0.122,0.12,0.118,0.102,0.091,0.096,0.102,0.111,0.106,0.131,0.108,0.129,0.119,0.102,0.112,0.102,0.126,0.103,0.098,0.105,0.117,0.12,0.116,0.105,0.091,385,1608,970,959,814,1150,1262,1113,443,512,223.0,1226,1130,293,705,2497,5955,498,1350,2196,856,1377,763,1804,1859,2467,1998,1542,1575,1993,794,541,204,1215,351,584,1982,1915,236,187,808,1363,1101,1910,2236,768,339,794,1352,643,439,1317,925,2339,367,2095,745,1378,823,861,635,1006,199,635,1298,918,2821,985,1024,499,728,979,3997,288,3.305,2.516,2.474,2.456,2.149,2.24,2.209,2.556,2.472,2.924,2.872,1.802,2.529,2.532,2.585,2.657,2.627,3.574,2.725,1.96,2.833,2.258,1.926,2.66,2.479,2.635,2.133,2.175,2.719,2.409,2.5,2.633,2.837,2.686,3.153,2.325,2.83,2.879,1.768,2.121,2.26,1.602,3.267,1.675,1.86,1.929,2.805,2.957,2.437,2.389,1.89,2.265,2.081,2.346,2.037,1.888,2.324,2.158,1.698,1.838,2.105,2.396,2.406,2.139,1.892,2.073,1.961,2.344,1.968,2.306,2.207,2.519,2.369,2.265,0.715,0.567,0.48,0.46,0.549,0.737,0.563,0.53,0.609,0.721,0.851,0.43,0.484,0.431,0.534,0.515,0.573,0.671,0.506,0.62,0.729,0.643,0.452,0.5,0.647,0.592,0.565,0.625,0.543,0.579,0.572,0.896,0.348,0.627,0.711,0.488,0.642,0.609,0.327,0.35,0.49,0.491,0.757,0.484,0.567,0.487,0.597,0.988,0.425,0.603,0.509,0.451,0.418,0.504,0.328,0.426,0.463,0.524,0.505,0.36,0.395,0.576,0.464,0.688,0.476,0.669,0.513,0.419,0.397,0.624,0.57,0.511,0.499,0.411
-135,Epileptic,F,52.0,1.8,3.5,1.3,1.1,2.0,1.6,2.5,1.6,0.7,0.9,0.3,3.0,2.0,0.5,1.2,5.8,8.7,0.9,2.1,4.3,0.9,2.2,1.8,3.5,4.2,2.9,6.8,2.9,3.1,3.0,1.3,1.4,0.4,2.2,0.3,0.5,3.1,3.6,0.2,0.2,1.0,2.9,2.4,2.2,2.4,1.1,0.3,1.1,1.3,0.7,0.9,2.4,0.9,2.2,0.5,2.1,1.0,1.5,0.8,0.8,1.2,1.3,0.4,0.5,2.0,1.5,3.1,1.1,1.0,0.5,1.1,1.7,4.0,0.4,14,35,12,10,18,17,16,18,12,9,3.0,27,25,6,19,57,89,9,25,40,8,29,18,46,55,29,50,29,27,34,15,9,6,30,2,6,38,47,1,2,7,29,26,16,14,9,2,8,6,5,5,20,7,14,3,18,8,12,6,7,9,9,3,5,13,19,27,6,6,4,8,15,35,4,0.063,0.038,0.026,0.018,0.046,0.025,0.044,0.03,0.036,0.04,0.036,0.041,0.036,0.026,0.027,0.036,0.034,0.042,0.032,0.037,0.023,0.031,0.036,0.039,0.041,0.03,0.051,0.033,0.036,0.032,0.033,0.047,0.019,0.029,0.011,0.012,0.035,0.038,0.017,0.014,0.02,0.036,0.041,0.026,0.019,0.026,0.016,0.017,0.018,0.021,0.034,0.023,0.02,0.023,0.019,0.017,0.029,0.017,0.026,0.018,0.027,0.024,0.033,0.017,0.02,0.026,0.021,0.016,0.022,0.018,0.033,0.019,0.017,0.014,1384,4157,2219,2553,2002,3182,3555,3048,1348,1598,668.0,2710,3369,1198,2619,8832,16287,2000,4257,5044,3225,4105,2404,6467,6118,6272,5757,4790,5294,4835,2270,1188,857,5517,1533,1799,6522,7833,352,415,1365,2391,5511,2159,3379,1479,700,2062,2121,1503,621,3675,1590,3160,540,3690,1533,2734,926,1171,1203,2440,395,935,2788,1345,4165,2165,1477,971,1164,2587,8492,566,0.158,0.145,0.119,0.102,0.156,0.127,0.136,0.139,0.149,0.146,0.128,0.155,0.15,0.117,0.134,0.14,0.141,0.128,0.131,0.147,0.084,0.129,0.129,0.145,0.149,0.131,0.151,0.122,0.122,0.146,0.13,0.126,0.1,0.133,0.07,0.072,0.138,0.152,0.093,0.098,0.109,0.136,0.149,0.115,0.1,0.129,0.09,0.084,0.097,0.109,0.139,0.116,0.107,0.109,0.116,0.099,0.13,0.098,0.133,0.101,0.129,0.112,0.135,0.111,0.103,0.115,0.115,0.095,0.105,0.103,0.138,0.119,0.097,0.11,471,1398,820,908,794,1008,975,979,445,401,195.0,1235,1021,287,768,2613,4599,467,1130,2117,760,1158,909,1820,1729,1692,2089,1473,1483,1546,651,495,323,1287,427,667,1701,1879,203,258,715,1180,1146,1363,1980,673,324,971,1046,566,399,1646,789,1517,320,2013,641,1459,501,689,630,900,207,504,1494,897,2191,977,684,492,601,1328,3867,362,2.886,2.759,2.43,2.548,2.153,2.461,2.776,2.567,2.31,3.019,2.734,1.881,2.53,2.769,2.495,2.517,2.69,3.34,2.94,1.97,2.814,2.666,2.293,2.608,2.652,2.825,2.23,2.467,2.648,2.512,2.542,2.43,2.063,2.931,3.047,2.447,2.741,2.933,1.985,1.777,2.217,1.853,3.195,1.753,1.893,2.296,2.467,2.414,2.397,3.149,1.982,2.131,1.894,2.23,1.959,1.972,2.322,2.117,2.144,1.873,2.125,2.55,2.054,2.164,1.985,1.817,2.067,2.433,2.383,2.201,2.196,2.167,2.261,1.815,0.789,0.709,0.673,0.569,0.59,0.658,0.748,0.675,0.61,0.479,0.801,0.546,0.52,0.586,0.655,0.689,0.662,0.781,0.663,0.692,0.947,0.543,0.687,0.743,0.694,0.635,0.651,0.682,0.689,0.602,0.62,0.943,0.423,0.648,0.614,0.43,0.835,0.626,0.236,0.336,0.459,0.556,0.858,0.549,0.603,0.52,0.585,0.654,0.456,0.551,0.52,0.57,0.492,0.414,0.379,0.4,0.513,0.527,0.668,0.343,0.454,0.751,0.38,0.583,0.512,0.711,0.471,0.517,0.476,0.601,0.435,0.448,0.509,0.367
-136,Epileptic,M,32.0,0.7,2.3,0.6,0.9,1.5,1.8,1.2,1.7,1.2,0.7,0.2,2.5,1.4,0.6,1.7,5.2,6.7,0.8,2.1,4.7,1.6,2.9,1.7,3.5,2.9,2.6,3.0,2.4,2.9,2.7,1.4,0.7,0.4,2.2,0.5,0.7,3.4,2.9,0.1,0.2,0.9,3.3,2.9,2.4,3.2,0.4,0.3,0.9,1.4,0.9,0.7,2.9,1.0,1.9,0.2,2.6,1.1,1.8,1.0,0.9,1.0,1.8,0.3,0.4,1.2,0.9,2.8,1.3,0.9,0.4,0.7,2.4,3.4,0.2,5,19,7,11,16,16,10,15,14,6,3.0,23,15,6,17,61,62,7,22,35,13,30,17,35,34,27,34,21,25,40,17,5,4,23,3,8,38,30,1,2,6,26,22,17,18,3,2,5,7,6,5,20,7,12,3,23,6,12,7,7,7,12,2,3,12,13,23,11,7,4,6,13,24,3,0.029,0.023,0.012,0.019,0.032,0.029,0.023,0.027,0.042,0.029,0.028,0.032,0.028,0.026,0.027,0.03,0.027,0.03,0.03,0.039,0.028,0.034,0.025,0.037,0.03,0.028,0.025,0.024,0.025,0.024,0.042,0.03,0.016,0.027,0.014,0.025,0.033,0.03,0.01,0.015,0.021,0.039,0.037,0.027,0.026,0.011,0.016,0.014,0.015,0.019,0.028,0.029,0.022,0.019,0.014,0.019,0.019,0.026,0.025,0.014,0.028,0.029,0.019,0.018,0.017,0.034,0.021,0.017,0.018,0.015,0.019,0.026,0.016,0.015,1637,5262,1846,1957,2699,2826,2517,3449,2304,1472,627.0,2781,3196,1504,3339,11100,16688,2618,4462,4430,2681,4870,2289,6875,5756,5318,5519,4080,6560,6241,2329,1039,1157,5677,2278,1913,7649,7158,447,501,1418,2922,5810,2173,3194,1218,958,2241,3069,1931,612,3800,1949,3607,456,4135,2099,2380,1190,1657,1281,2635,571,855,2570,948,3947,2878,1841,1184,1338,3087,8718,425,0.113,0.111,0.091,0.111,0.135,0.141,0.102,0.125,0.159,0.14,0.113,0.134,0.128,0.125,0.134,0.141,0.122,0.111,0.131,0.143,0.099,0.146,0.125,0.14,0.135,0.138,0.123,0.114,0.114,0.127,0.124,0.094,0.092,0.127,0.068,0.108,0.133,0.129,0.075,0.087,0.108,0.144,0.132,0.124,0.114,0.08,0.095,0.084,0.093,0.102,0.135,0.131,0.102,0.101,0.119,0.111,0.099,0.116,0.116,0.095,0.128,0.124,0.112,0.111,0.102,0.13,0.113,0.101,0.1,0.11,0.107,0.114,0.09,0.114,432,1616,755,773,808,1081,824,1041,539,385,181.0,1303,866,370,961,2959,4278,465,1159,1981,802,1465,1051,1747,1685,1609,1940,1488,1885,1840,609,449,384,1301,618,527,1960,1689,234,297,645,1302,1310,1435,2068,600,361,906,1235,740,353,1525,760,1527,223,2049,844,1186,686,916,462,981,242,379,1197,519,2057,1256,825,431,630,1456,3454,253,3.502,2.905,2.433,2.594,2.776,2.439,2.517,2.942,3.068,3.268,2.939,1.901,2.832,2.991,2.681,2.716,3.131,3.747,2.896,1.908,2.738,2.527,1.986,2.992,2.719,2.783,2.335,2.198,2.71,2.682,2.685,2.22,2.496,3.17,3.413,3.047,2.875,3.147,2.295,1.99,2.828,1.984,3.425,1.842,1.819,2.07,3.32,3.141,2.864,2.866,2.127,2.528,2.774,2.444,2.376,2.195,2.925,2.312,2.069,2.032,2.552,2.922,2.538,2.522,2.309,2.208,2.159,2.527,2.471,2.979,2.534,2.619,2.691,2.004,0.921,0.75,0.552,0.504,0.599,0.495,0.529,0.607,0.595,0.498,0.719,0.51,0.37,0.604,0.453,0.61,0.661,0.889,0.551,0.633,0.834,0.566,0.382,0.597,0.519,0.554,0.459,0.475,0.579,0.472,0.782,0.969,0.493,0.628,0.768,0.525,0.69,0.58,0.469,0.37,0.538,0.535,0.767,0.635,0.55,0.442,0.53,0.599,0.591,0.484,0.461,0.524,0.633,0.655,0.308,0.43,0.625,0.439,0.452,0.387,0.454,0.715,0.725,1.11,0.567,0.61,0.404,0.359,0.423,0.871,0.351,0.561,0.522,0.358
-137,Epileptic,M,32.0,0.8,2.8,1.0,1.3,1.5,1.6,1.3,1.7,0.9,1.1,0.4,2.1,2.4,0.6,2.2,3.8,7.4,1.0,2.0,5.1,1.1,2.8,1.2,2.8,3.3,3.2,2.5,2.5,3.0,3.8,2.1,1.1,0.8,2.8,0.3,1.3,3.2,3.0,0.1,0.3,1.2,2.5,2.1,2.2,3.2,0.6,0.5,0.9,1.3,0.8,0.5,1.6,1.1,2.8,0.6,2.3,0.7,1.7,1.2,0.7,0.8,0.9,0.4,0.3,1.8,1.7,2.7,1.3,0.8,0.4,0.8,1.1,4.4,0.2,7,29,16,11,17,17,11,17,7,12,4.0,20,21,6,22,44,70,10,20,43,12,28,13,35,38,40,31,23,27,42,25,19,7,30,2,13,39,34,1,2,8,25,24,17,19,5,3,5,7,7,5,13,6,22,3,22,6,11,10,6,6,8,2,3,14,20,25,10,6,2,5,9,36,1,0.03,0.041,0.019,0.028,0.055,0.029,0.024,0.033,0.051,0.049,0.039,0.032,0.038,0.04,0.045,0.033,0.035,0.032,0.032,0.045,0.024,0.033,0.029,0.035,0.034,0.032,0.028,0.029,0.03,0.042,0.055,0.074,0.036,0.031,0.017,0.026,0.035,0.032,0.007,0.017,0.025,0.031,0.038,0.028,0.024,0.016,0.022,0.014,0.019,0.019,0.026,0.026,0.028,0.026,0.023,0.02,0.014,0.023,0.028,0.019,0.023,0.024,0.029,0.017,0.027,0.03,0.028,0.02,0.016,0.017,0.02,0.019,0.021,0.028,1636,4609,2197,2035,2726,2459,2004,3039,1372,1469,536.0,2099,3644,1114,3096,6636,13147,2512,3756,4294,3547,4163,1601,5864,5824,6198,4386,3068,5872,5142,2173,1223,737,6143,1911,2151,7362,7291,421,445,1613,2495,5288,2093,3236,1228,871,2255,2340,1493,533,2360,1230,4231,620,3571,1763,2895,1317,1001,1115,1967,407,814,2064,1353,3561,2407,1498,561,1266,2059,8874,339,0.129,0.132,0.121,0.127,0.189,0.144,0.105,0.139,0.167,0.173,0.144,0.138,0.142,0.155,0.159,0.14,0.14,0.126,0.128,0.156,0.102,0.149,0.134,0.145,0.145,0.143,0.132,0.124,0.125,0.166,0.156,0.138,0.16,0.136,0.078,0.122,0.153,0.136,0.078,0.106,0.119,0.139,0.157,0.121,0.113,0.107,0.113,0.084,0.096,0.105,0.139,0.127,0.132,0.12,0.125,0.119,0.099,0.108,0.13,0.112,0.115,0.116,0.133,0.11,0.131,0.139,0.131,0.112,0.104,0.085,0.113,0.117,0.106,0.1,457,1387,855,767,611,978,799,919,317,401,196.0,1006,1031,263,886,2088,3633,495,998,1924,796,1330,645,1379,1670,1868,1554,1317,1610,1601,613,427,315,1438,375,811,1576,1634,198,246,764,1219,1031,1334,2021,599,348,962,1003,598,327,970,546,1780,325,1814,770,1261,755,557,525,676,205,359,1062,828,1725,1051,734,345,633,818,3599,143,3.738,2.7,2.422,2.69,3.069,2.309,2.134,2.931,3.015,3.0,2.578,1.857,2.805,3.13,2.717,2.535,2.858,3.628,2.916,1.955,3.188,2.414,2.137,3.094,2.671,2.749,2.35,1.953,2.789,2.675,2.351,2.613,2.245,3.09,3.963,2.521,3.425,3.165,2.402,2.217,2.699,1.851,3.475,1.869,1.89,2.218,3.24,2.897,2.84,2.885,2.128,2.424,2.298,2.351,2.456,2.185,2.495,2.791,2.009,2.05,2.486,2.758,2.235,2.279,2.225,2.011,2.256,2.71,2.41,1.717,2.342,2.768,2.739,2.189,0.628,0.917,0.502,0.536,0.881,0.577,0.518,0.651,0.917,0.532,0.797,0.442,0.43,0.513,0.49,0.517,0.648,0.707,0.61,0.575,0.74,0.499,0.398,0.727,0.543,0.532,0.452,0.542,0.564,0.552,0.846,0.954,0.535,0.689,0.921,0.478,0.77,0.623,0.374,0.318,0.56,0.451,0.743,0.72,0.562,0.529,0.512,0.523,0.61,0.449,0.524,0.438,0.498,0.427,0.342,0.367,0.437,0.51,0.458,0.323,0.376,0.697,0.374,1.013,0.545,0.664,0.457,0.434,0.382,0.703,0.396,0.595,0.535,0.417
-138,Epileptic,M,17.5,0.6,2.0,0.8,1.1,1.3,1.7,2.0,1.8,0.8,0.6,0.3,2.2,1.8,0.7,1.5,6.2,10.0,0.6,2.9,4.3,2.5,3.8,2.4,3.7,3.6,4.6,3.1,3.5,3.1,3.6,1.1,1.5,0.5,2.7,0.4,1.1,4.9,3.6,0.4,0.2,1.1,4.7,3.0,5.6,2.9,0.6,0.5,1.2,1.2,1.0,0.5,2.7,1.9,2.4,0.3,1.8,0.8,2.5,1.1,1.3,0.5,1.2,0.6,0.6,1.5,0.6,2.8,0.8,0.9,0.4,1.4,2.1,4.5,0.3,3,20,6,8,21,15,16,18,7,6,2.0,19,18,8,14,70,101,5,23,30,20,35,20,43,36,37,31,30,25,35,13,10,4,23,3,7,49,32,4,1,6,35,26,41,19,4,3,7,6,8,4,22,20,14,2,13,7,20,7,9,4,6,3,4,10,8,22,5,6,4,8,11,37,2,0.025,0.024,0.017,0.024,0.04,0.028,0.03,0.028,0.036,0.033,0.033,0.033,0.029,0.036,0.036,0.035,0.036,0.027,0.035,0.038,0.041,0.035,0.039,0.041,0.034,0.035,0.031,0.032,0.026,0.032,0.04,0.054,0.037,0.037,0.016,0.034,0.033,0.037,0.024,0.019,0.024,0.049,0.046,0.068,0.023,0.013,0.024,0.021,0.016,0.021,0.036,0.026,0.04,0.016,0.018,0.016,0.024,0.034,0.024,0.019,0.017,0.021,0.026,0.028,0.02,0.024,0.027,0.014,0.017,0.014,0.039,0.019,0.017,0.029,1396,5003,2565,2355,2024,3115,3658,4093,1439,1566,462.0,2228,4023,1663,2781,12797,22038,2232,3888,3649,4114,6310,2650,7737,7122,6640,6512,4817,6381,6845,2344,1126,907,5637,2047,1341,12080,7106,538,434,1557,3021,7649,1582,3806,1427,904,2082,2652,2577,540,4284,3018,5521,497,4033,1539,2416,1737,2198,1379,2428,807,928,2952,782,4044,2098,2302,982,1644,3683,10995,594,0.093,0.11,0.097,0.111,0.144,0.128,0.12,0.123,0.139,0.112,0.114,0.127,0.128,0.146,0.136,0.132,0.134,0.102,0.129,0.135,0.126,0.145,0.138,0.14,0.128,0.124,0.12,0.123,0.11,0.129,0.136,0.143,0.128,0.127,0.079,0.098,0.128,0.12,0.14,0.128,0.106,0.145,0.158,0.172,0.107,0.09,0.109,0.099,0.091,0.102,0.139,0.118,0.14,0.092,0.103,0.09,0.108,0.132,0.12,0.106,0.09,0.106,0.129,0.129,0.102,0.116,0.117,0.086,0.094,0.107,0.14,0.099,0.096,0.113,405,1336,709,731,654,1060,987,1110,338,333,137.0,984,967,347,677,3050,4822,403,1325,1789,901,1823,1000,1575,1769,1987,1604,1648,1694,1753,478,472,239,1122,453,416,2442,1596,215,147,705,1438,1266,1179,2106,645,373,833,1060,819,270,1672,829,2140,247,1801,666,1289,787,1022,592,790,314,331,1206,429,1850,932,888,419,660,1629,4174,199,3.287,3.015,3.249,3.173,2.62,2.571,2.896,3.157,2.91,3.712,3.138,2.036,3.131,3.266,2.934,3.025,3.5,4.008,2.477,1.881,3.322,2.706,2.3,3.413,2.961,2.747,2.897,2.301,2.881,3.059,3.178,2.469,3.107,3.211,3.737,2.706,3.486,3.128,2.643,3.05,2.901,2.004,3.74,1.588,2.125,2.378,3.077,3.27,2.964,3.115,2.194,2.557,2.738,2.815,2.512,2.529,2.417,2.095,2.653,2.421,2.724,3.475,2.905,2.766,2.802,2.188,2.445,2.783,2.746,2.545,2.879,2.795,2.958,3.35,0.739,0.938,0.853,0.67,0.618,0.635,0.836,0.596,0.788,0.474,1.135,0.614,0.49,0.459,0.654,0.659,0.738,0.737,0.602,0.608,1.055,0.479,0.593,0.751,0.539,0.661,0.593,0.502,0.548,0.639,0.768,1.136,0.648,0.897,0.78,0.602,0.784,0.705,0.33,0.714,0.427,0.515,0.811,0.669,0.528,0.503,0.41,0.716,0.584,0.726,0.441,0.53,0.869,0.525,0.311,0.467,0.61,0.596,0.546,0.398,0.457,0.863,0.875,1.106,0.43,0.759,0.49,0.335,0.43,0.852,0.525,0.565,0.55,0.394
-141,Epileptic,M,32.0,1.3,2.8,1.4,1.9,1.7,1.8,2.1,2.0,1.6,0.8,0.5,4.1,1.6,0.7,1.9,6.2,10.0,1.0,2.3,6.0,3.3,3.2,2.4,5.5,2.6,3.2,3.8,2.9,3.0,4.0,2.0,1.6,0.5,1.6,0.5,0.5,4.3,4.1,0.4,0.4,1.2,3.7,2.6,3.6,3.6,0.7,0.5,0.7,1.6,1.4,1.1,3.1,1.4,2.5,0.3,2.6,1.2,2.2,1.2,1.0,0.6,2.2,0.4,0.7,2.0,1.5,2.7,1.6,1.0,0.6,1.2,1.3,4.6,0.3,11,34,14,18,16,25,16,18,18,8,5.0,43,19,11,23,63,98,7,29,60,29,39,25,44,40,39,42,27,26,38,29,12,5,25,2,5,43,49,2,2,7,35,23,31,21,5,2,5,9,9,9,23,13,19,2,23,11,16,11,9,6,18,3,5,15,23,20,14,7,6,10,10,44,3,0.047,0.024,0.021,0.026,0.025,0.025,0.03,0.029,0.04,0.032,0.035,0.035,0.027,0.03,0.029,0.032,0.031,0.036,0.028,0.037,0.045,0.031,0.028,0.047,0.023,0.025,0.027,0.029,0.029,0.032,0.043,0.049,0.024,0.022,0.013,0.012,0.033,0.032,0.018,0.02,0.026,0.037,0.045,0.027,0.024,0.014,0.02,0.012,0.017,0.032,0.03,0.026,0.025,0.021,0.011,0.018,0.025,0.02,0.027,0.017,0.019,0.032,0.029,0.016,0.02,0.019,0.022,0.017,0.018,0.014,0.019,0.012,0.016,0.016,1565,5955,3182,3437,2245,3588,3987,3519,2120,2059,966.0,4412,3858,1342,4029,12240,21966,2639,5125,6783,4081,5160,3254,6399,7348,8197,8193,5237,7318,8103,2749,1493,1035,5273,2201,1895,8143,9806,546,624,1633,3773,6041,4144,3868,1694,873,2375,2817,2201,996,4314,2320,4636,463,4413,1803,3821,1237,1729,1004,2888,478,1168,3288,1934,4036,3433,2169,1600,2388,2612,10584,575,0.142,0.117,0.114,0.133,0.118,0.135,0.111,0.132,0.146,0.137,0.128,0.148,0.128,0.127,0.134,0.132,0.133,0.131,0.13,0.151,0.131,0.135,0.13,0.151,0.123,0.121,0.124,0.114,0.112,0.135,0.124,0.131,0.128,0.106,0.063,0.085,0.141,0.139,0.092,0.096,0.111,0.123,0.146,0.122,0.109,0.087,0.096,0.079,0.098,0.118,0.141,0.122,0.114,0.105,0.106,0.105,0.126,0.099,0.138,0.1,0.112,0.13,0.138,0.103,0.109,0.115,0.113,0.102,0.099,0.104,0.116,0.094,0.095,0.108,523,1974,1088,1140,1019,1284,1224,1153,643,460,256.0,1909,1025,351,1082,3165,5631,514,1438,2768,1199,1653,1310,1870,1960,2157,2250,1603,1795,2171,808,600,346,1236,630,642,2040,2224,303,303,772,1723,1120,2298,2234,794,334,941,1267,744,564,1913,1036,1976,260,2200,804,1771,728,972,520,1134,266,614,1517,1271,1938,1427,899,611,923,1328,4635,249,2.936,2.729,2.779,2.841,2.098,2.334,2.583,2.784,2.477,3.391,2.734,2.043,2.835,2.72,2.805,2.79,2.977,3.75,2.76,2.085,2.723,2.44,2.113,2.568,2.744,2.955,2.709,2.485,3.092,2.875,2.541,2.561,2.639,2.962,3.181,2.583,2.865,3.138,1.933,2.459,2.594,1.995,3.515,2.002,1.992,2.381,2.993,3.065,2.709,2.713,1.999,2.41,2.251,2.575,2.13,2.219,2.391,2.495,1.952,1.916,2.15,2.495,1.962,2.359,2.355,1.746,2.277,2.593,2.761,2.637,2.673,2.287,2.422,2.582,0.801,0.541,0.542,0.577,0.533,0.619,0.75,0.416,0.562,0.95,0.925,0.574,0.447,0.497,0.565,0.654,0.679,0.701,0.597,0.712,0.862,0.597,0.592,0.824,0.678,0.569,0.687,0.649,0.537,0.622,0.703,1.014,0.473,0.546,0.444,0.534,0.765,0.673,0.494,0.423,0.531,0.6,0.995,0.712,0.625,0.489,0.485,0.761,0.402,1.021,0.336,0.448,0.468,0.398,0.272,0.364,0.461,0.533,0.406,0.358,0.345,0.714,0.394,0.641,0.457,0.642,0.431,0.359,0.47,0.898,0.58,0.397,0.447,0.48
-142,Epileptic,F,32.0,0.5,1.8,1.0,1.0,1.6,2.1,1.2,1.9,1.1,0.5,0.1,2.5,1.6,0.6,1.1,6.0,9.0,1.1,1.6,3.7,0.9,2.2,1.8,3.3,3.2,3.6,2.6,2.2,2.2,2.9,1.1,0.4,0.5,2.0,0.4,0.4,3.9,3.5,0.3,0.1,1.0,3.1,2.9,1.5,2.1,0.5,0.5,0.8,0.9,1.0,0.7,1.7,1.0,1.9,0.2,2.5,0.8,2.1,1.2,0.6,0.5,1.6,0.3,0.5,1.8,0.7,2.5,0.8,0.8,0.4,1.0,1.7,3.6,0.3,5,18,12,10,16,18,11,15,13,5,1.0,18,16,7,9,57,73,8,19,25,7,25,15,29,31,36,32,20,18,31,20,5,5,28,3,4,44,40,2,1,5,29,33,12,12,5,2,5,5,7,6,11,7,13,1,21,8,17,8,6,4,13,1,4,14,17,18,5,5,4,9,9,28,2,0.033,0.022,0.02,0.022,0.032,0.038,0.022,0.033,0.04,0.035,0.014,0.04,0.031,0.031,0.028,0.035,0.031,0.04,0.028,0.04,0.025,0.035,0.037,0.037,0.037,0.033,0.029,0.026,0.028,0.028,0.043,0.033,0.033,0.03,0.021,0.012,0.035,0.032,0.024,0.022,0.023,0.041,0.051,0.027,0.017,0.015,0.025,0.01,0.012,0.027,0.036,0.019,0.021,0.02,0.021,0.019,0.025,0.024,0.033,0.016,0.016,0.026,0.028,0.025,0.024,0.026,0.02,0.014,0.018,0.017,0.026,0.022,0.016,0.023,1356,4255,2124,1878,2275,3010,2361,3201,1657,841,300.0,2201,3608,1277,2117,10031,16968,2180,3382,3093,2076,3807,1997,5553,4517,7171,4916,3530,4492,4956,2296,582,1014,5717,1424,1634,6803,7156,570,358,1372,2853,4502,1333,2946,1259,786,2764,2344,1506,544,3017,1825,3960,335,3731,1304,2877,1213,974,1087,2439,403,806,2269,503,4147,1757,1149,864,1392,2359,8172,562,0.104,0.113,0.11,0.107,0.139,0.152,0.103,0.136,0.143,0.119,0.067,0.143,0.13,0.149,0.13,0.144,0.131,0.131,0.127,0.138,0.08,0.144,0.136,0.143,0.147,0.139,0.124,0.102,0.109,0.131,0.129,0.101,0.15,0.128,0.079,0.08,0.126,0.13,0.115,0.088,0.109,0.149,0.138,0.117,0.098,0.092,0.112,0.074,0.08,0.112,0.147,0.095,0.108,0.099,0.118,0.108,0.125,0.111,0.139,0.103,0.098,0.132,0.118,0.131,0.124,0.113,0.106,0.09,0.112,0.107,0.133,0.11,0.092,0.137,365,1346,842,757,839,974,807,966,495,276,101.0,950,996,302,595,2761,4716,403,975,1489,629,1187,787,1483,1374,1971,1603,1284,1216,1593,562,279,297,1194,343,600,1931,1747,238,127,668,1226,1161,905,1763,616,301,1016,1038,647,348,1388,853,1611,140,2024,591,1451,610,572,551,908,157,333,1132,313,2042,836,611,378,685,1131,3562,220,3.501,2.861,2.439,2.569,2.442,2.568,2.452,2.912,2.61,2.614,2.455,2.076,2.928,3.063,2.795,2.737,2.898,3.923,2.726,1.811,2.684,2.51,2.144,2.907,2.473,2.834,2.368,2.172,2.755,2.5,2.911,2.129,2.838,3.243,3.315,2.558,2.649,3.068,2.802,2.873,2.678,2.063,3.04,1.7,1.939,2.071,3.32,3.238,2.72,2.816,1.709,2.265,2.306,2.643,2.343,1.96,2.307,2.138,2.077,1.875,2.16,2.872,3.02,2.778,2.146,2.04,2.2,2.432,2.253,2.686,2.26,2.501,2.525,3.105,1.179,0.685,0.501,0.504,0.539,0.643,0.497,0.483,0.596,0.467,0.805,0.589,0.4,0.336,0.354,0.593,0.541,0.815,0.578,0.528,0.672,0.565,0.603,0.621,0.507,0.552,0.614,0.403,0.496,0.611,0.655,0.776,0.544,0.652,0.567,0.485,0.73,0.644,0.4,0.377,0.527,0.629,0.861,0.637,0.534,0.494,0.586,0.753,0.599,0.886,0.277,0.467,0.487,0.475,0.502,0.433,0.471,0.6,0.421,0.425,0.376,0.657,0.653,0.643,0.581,0.469,0.584,0.333,0.466,0.681,0.535,0.523,0.499,0.487
-143,Epileptic,F,32.0,0.8,2.2,1.8,1.1,1.2,1.9,1.9,2.0,1.0,0.4,0.3,2.8,1.8,0.4,1.3,7.1,9.6,1.1,3.3,5.1,1.0,2.0,1.1,3.8,3.6,5.0,3.6,2.6,3.1,3.2,1.2,1.1,0.5,3.0,0.3,1.0,3.2,3.4,0.2,0.1,1.7,3.1,2.0,3.3,3.3,1.0,0.6,0.7,1.3,0.5,0.6,1.8,1.4,2.6,0.6,3.2,1.1,2.3,1.0,1.3,0.6,1.4,0.2,0.5,1.9,0.4,2.8,1.4,1.3,0.4,1.6,1.0,5.8,0.4,4,22,22,9,12,16,18,16,11,7,2.0,21,15,7,12,64,72,10,29,41,7,20,13,34,40,42,37,20,21,34,17,14,5,23,2,10,34,36,1,1,9,25,17,25,24,7,3,5,7,5,4,17,9,16,4,25,11,16,6,11,4,9,1,4,16,7,22,9,9,4,11,9,38,3,0.029,0.02,0.025,0.022,0.027,0.032,0.024,0.029,0.043,0.03,0.027,0.035,0.03,0.025,0.036,0.033,0.031,0.034,0.032,0.039,0.014,0.031,0.024,0.038,0.031,0.033,0.03,0.025,0.025,0.029,0.028,0.047,0.023,0.032,0.011,0.025,0.033,0.036,0.013,0.017,0.029,0.035,0.037,0.038,0.026,0.022,0.026,0.011,0.016,0.01,0.037,0.019,0.02,0.02,0.022,0.022,0.037,0.027,0.033,0.025,0.022,0.021,0.02,0.015,0.023,0.016,0.022,0.023,0.022,0.015,0.029,0.017,0.023,0.027,1612,4863,2883,2130,1564,2401,2729,3117,1321,857,437.0,2485,3644,1112,2507,12353,17841,2267,5447,4222,3685,3025,1957,5009,5968,8085,5463,3250,5459,5457,1950,1008,1210,6072,2131,1972,5691,6978,341,392,1980,2759,5413,2195,3017,1422,908,2314,2600,1516,472,3237,2077,4835,707,4001,1734,2863,817,1317,821,2443,398,753,2654,674,3763,2028,1912,1103,1813,2062,9559,511,0.098,0.109,0.132,0.101,0.125,0.136,0.11,0.124,0.122,0.132,0.108,0.139,0.127,0.135,0.135,0.134,0.126,0.127,0.138,0.139,0.086,0.138,0.127,0.131,0.146,0.142,0.137,0.106,0.103,0.134,0.116,0.132,0.104,0.129,0.056,0.115,0.129,0.141,0.086,0.1,0.126,0.136,0.122,0.143,0.122,0.104,0.12,0.082,0.092,0.089,0.156,0.103,0.105,0.098,0.131,0.122,0.141,0.12,0.142,0.126,0.111,0.11,0.114,0.116,0.116,0.109,0.112,0.113,0.109,0.095,0.146,0.104,0.107,0.138,494,1674,1042,864,685,981,1107,1124,414,284,188.0,1118,1029,292,671,3616,4991,496,1663,1993,951,1063,802,1498,1904,2508,2042,1412,1703,1802,612,473,397,1499,523,701,1415,1701,202,150,891,1280,1035,1483,2092,744,403,979,1221,691,284,1599,1108,2180,391,2157,641,1485,496,811,415,912,202,453,1385,352,2087,1011,962,511,822,994,4030,225,3.148,2.571,2.614,2.515,2.012,2.205,2.182,2.49,2.368,2.785,2.446,1.999,2.864,2.634,2.799,2.643,3.002,3.535,2.684,1.936,3.069,2.327,2.087,2.482,2.58,2.693,2.241,1.924,2.559,2.456,2.394,2.253,2.572,2.947,3.596,2.568,2.916,2.875,1.931,2.473,2.722,1.907,3.34,1.721,1.653,2.091,2.856,2.949,2.56,2.372,2.162,2.138,1.993,2.345,2.335,2.148,2.642,2.256,2.012,1.936,2.319,2.632,2.214,1.953,2.181,2.319,1.962,2.388,2.247,2.368,2.419,2.544,2.54,2.893,1.048,0.925,0.825,0.446,0.579,0.452,0.534,0.634,0.501,0.524,0.823,0.609,0.435,0.523,0.578,0.511,0.595,0.888,0.558,0.528,0.75,0.477,0.445,0.667,0.438,0.522,0.438,0.44,0.536,0.48,0.661,0.962,0.397,0.718,0.933,0.498,0.788,0.703,0.263,0.745,0.621,0.467,0.873,0.59,0.448,0.452,0.545,0.746,0.649,0.684,0.33,0.465,0.533,0.623,0.41,0.375,0.511,0.545,0.564,0.355,0.332,0.868,0.337,0.916,0.478,0.771,0.405,0.472,0.46,1.023,0.429,0.641,0.611,0.679
-144,Epileptic,F,47.0,0.7,2.5,1.0,1.4,2.2,1.3,1.3,1.5,1.3,0.7,0.4,2.5,1.7,0.5,1.1,4.8,6.7,0.8,1.9,4.1,0.9,2.4,1.9,3.9,2.3,3.2,3.9,2.1,2.2,3.3,1.9,0.9,0.6,2.3,0.5,0.5,2.9,3.2,0.2,0.2,0.9,3.1,2.1,2.4,2.4,0.5,0.3,0.8,1.3,0.6,0.6,1.7,1.1,1.3,0.4,2.2,0.5,1.6,0.9,1.0,0.5,1.3,0.3,0.7,2.1,1.4,2.4,1.3,0.7,0.8,1.2,1.4,3.6,0.3,6,24,7,8,18,13,11,15,12,9,5.0,27,15,4,15,46,57,7,24,40,9,26,16,30,36,28,44,20,20,37,19,5,5,22,3,5,37,35,3,1,5,29,21,21,13,3,2,5,6,6,4,11,7,9,2,19,3,10,6,9,3,7,2,5,15,18,17,10,3,8,9,9,25,2,0.038,0.034,0.022,0.029,0.045,0.027,0.026,0.031,0.055,0.039,0.048,0.037,0.028,0.04,0.038,0.037,0.033,0.036,0.029,0.036,0.024,0.033,0.032,0.053,0.03,0.033,0.028,0.029,0.029,0.032,0.058,0.051,0.032,0.032,0.015,0.017,0.038,0.035,0.024,0.022,0.022,0.045,0.04,0.027,0.02,0.012,0.018,0.015,0.022,0.022,0.033,0.024,0.032,0.02,0.024,0.022,0.018,0.02,0.02,0.019,0.017,0.028,0.02,0.029,0.021,0.032,0.019,0.018,0.016,0.033,0.026,0.02,0.016,0.019,1456,4033,1905,1954,1805,2565,2543,2766,1673,1645,493.0,2680,3183,830,1959,7350,12260,2215,4123,5167,3558,4233,2107,5160,4957,5836,7000,3678,4375,5831,2130,896,867,5638,1910,1903,6124,6556,446,273,1286,2615,5331,2557,2960,1209,624,1943,1817,1409,521,2499,1254,2515,421,3270,988,2844,1140,1456,913,1960,300,761,3050,1108,3276,2538,1190,1028,1396,1853,7887,531,0.127,0.136,0.108,0.12,0.157,0.131,0.11,0.138,0.176,0.147,0.161,0.138,0.124,0.139,0.15,0.142,0.133,0.138,0.141,0.146,0.101,0.124,0.119,0.162,0.139,0.142,0.126,0.109,0.111,0.141,0.168,0.122,0.12,0.129,0.079,0.084,0.14,0.147,0.118,0.102,0.106,0.153,0.142,0.128,0.101,0.084,0.098,0.08,0.103,0.113,0.134,0.107,0.124,0.096,0.138,0.108,0.109,0.104,0.112,0.112,0.101,0.119,0.129,0.127,0.111,0.134,0.106,0.1,0.091,0.151,0.126,0.117,0.095,0.118,359,1294,699,750,777,813,867,853,448,383,157.0,1135,1019,233,579,2178,3530,415,1068,2048,733,1190,844,1333,1354,1625,2475,1133,1239,1797,592,294,259,1233,491,572,1512,1633,180,179,641,1161,1024,1512,1765,608,262,867,898,542,309,1224,596,1174,189,1632,474,1257,695,747,456,749,201,365,1473,707,1774,1098,619,436,767,938,3372,230,3.47,2.852,2.691,2.557,2.085,2.738,2.36,2.798,2.734,3.056,2.501,1.993,2.564,2.63,2.529,2.465,2.703,3.843,3.01,2.131,3.487,2.63,2.156,2.877,2.747,2.844,2.27,2.426,2.697,2.456,2.661,2.912,2.596,3.164,3.539,2.875,2.912,2.865,2.378,1.753,2.516,1.998,3.359,1.89,1.904,2.135,2.947,2.779,2.466,2.696,2.109,2.1,2.058,2.338,2.46,2.08,2.286,2.63,1.9,2.047,2.391,2.727,1.494,2.411,2.254,1.981,2.045,2.371,2.286,2.703,2.156,2.324,2.511,3.002,0.836,0.58,0.44,0.481,0.652,0.584,0.5,0.61,0.715,0.557,0.788,0.501,0.445,0.482,0.407,0.627,0.582,0.705,0.5,0.612,0.665,0.529,0.664,0.732,0.619,0.571,0.603,0.561,0.385,0.629,0.642,0.683,0.515,0.648,0.669,0.459,0.683,0.733,0.436,0.366,0.459,0.603,0.833,0.637,0.54,0.486,0.528,0.824,0.401,0.651,0.319,0.504,0.536,0.469,0.623,0.43,0.412,0.463,0.329,0.476,0.397,0.58,0.323,0.664,0.484,0.673,0.445,0.391,0.364,0.714,0.476,0.486,0.546,0.476
-145,Epileptic,F,47.0,1.6,2.9,0.9,1.1,1.3,2.7,1.5,1.5,1.7,0.7,0.4,2.2,2.3,0.9,2.0,7.0,9.4,0.9,2.4,3.9,1.1,2.8,1.8,3.6,4.1,4.3,4.3,2.8,3.1,3.5,0.9,0.8,0.7,3.0,1.0,0.4,2.5,2.8,0.2,0.5,1.1,2.9,2.3,3.8,3.4,0.7,0.4,0.9,1.2,0.5,0.5,2.1,1.8,3.0,0.1,3.4,0.4,2.0,1.4,1.2,0.6,1.9,0.4,0.2,2.1,2.0,2.9,1.2,1.4,1.1,1.1,1.1,4.6,0.4,13,32,10,12,13,22,10,13,12,7,3.0,17,17,8,17,71,80,7,25,32,8,30,15,35,46,39,46,25,27,39,18,6,6,33,8,3,28,33,2,2,6,28,19,22,18,5,2,7,6,7,6,13,11,21,1,27,3,11,10,10,5,15,3,3,18,27,23,8,11,11,6,7,31,3,0.056,0.025,0.014,0.02,0.028,0.033,0.027,0.027,0.048,0.03,0.029,0.032,0.037,0.034,0.034,0.035,0.03,0.033,0.026,0.034,0.018,0.028,0.029,0.036,0.03,0.038,0.028,0.027,0.026,0.03,0.026,0.031,0.026,0.029,0.033,0.012,0.037,0.029,0.02,0.028,0.023,0.035,0.041,0.038,0.023,0.018,0.021,0.014,0.017,0.017,0.033,0.018,0.029,0.023,0.017,0.021,0.012,0.021,0.031,0.023,0.023,0.029,0.031,0.014,0.023,0.043,0.021,0.019,0.019,0.021,0.022,0.022,0.018,0.025,1540,5248,2342,1977,1888,3386,2182,2664,2225,1364,555.0,2150,3855,1531,3530,12068,17133,2347,4267,3305,2966,4627,1826,5977,7237,6634,6997,3999,6115,5462,2167,971,1318,6868,2054,1498,5490,6680,425,558,1447,2525,4944,2473,3642,1177,805,2274,2289,1254,428,3851,2270,4994,175,4520,1187,3061,1306,1285,993,2607,471,740,2911,1141,3996,2223,2434,1351,1422,2067,8762,406,0.161,0.122,0.097,0.111,0.12,0.14,0.105,0.123,0.163,0.127,0.126,0.121,0.135,0.141,0.133,0.137,0.128,0.122,0.118,0.132,0.082,0.129,0.129,0.139,0.142,0.146,0.123,0.101,0.098,0.138,0.122,0.12,0.115,0.128,0.109,0.071,0.132,0.134,0.09,0.117,0.109,0.127,0.15,0.144,0.108,0.098,0.107,0.084,0.092,0.106,0.139,0.091,0.114,0.107,0.135,0.113,0.094,0.101,0.136,0.119,0.108,0.131,0.133,0.105,0.118,0.14,0.111,0.097,0.108,0.122,0.116,0.109,0.101,0.121,529,1993,1040,845,824,1318,839,908,565,411,194.0,1056,1109,381,966,3467,5072,451,1401,1811,953,1587,854,1589,2230,2079,2593,1529,1739,1911,631,474,472,1643,545,576,1304,1663,251,292,773,1331,1085,1558,2244,646,319,1036,1082,489,334,1943,1090,2222,79,2458,539,1489,744,811,473,1070,239,352,1498,708,2205,1012,1151,745,752,838,3865,259,2.919,2.423,2.19,2.323,2.094,2.319,2.225,2.635,2.782,2.768,2.506,1.869,2.732,2.76,2.699,2.577,2.663,3.569,2.59,1.708,2.646,2.315,1.915,2.783,2.577,2.725,2.198,2.125,2.727,2.413,2.584,2.188,2.361,2.972,2.827,2.472,3.193,2.836,2.022,2.074,2.348,1.751,3.086,1.861,1.835,1.991,2.873,2.669,2.558,2.613,1.518,2.058,2.115,2.402,2.321,2.045,2.441,2.391,2.026,1.807,2.293,2.379,2.152,2.195,2.038,1.901,1.907,2.501,2.346,1.994,2.17,2.619,2.49,1.754,0.828,0.702,0.582,0.54,0.573,0.685,0.536,0.463,0.618,0.477,0.852,0.458,0.419,0.559,0.509,0.57,0.548,0.845,0.519,0.517,0.698,0.534,0.542,0.697,0.546,0.465,0.472,0.465,0.574,0.435,0.576,0.887,0.415,0.599,0.88,0.436,0.663,0.566,0.427,0.389,0.444,0.443,0.84,0.682,0.546,0.463,0.782,0.679,0.521,0.515,0.435,0.411,0.503,0.488,0.618,0.368,0.419,0.512,0.394,0.4,0.52,0.734,0.526,0.926,0.487,0.723,0.461,0.48,0.504,0.466,0.519,0.684,0.484,0.407
-146,Epileptic,F,22.0,0.9,1.9,0.5,0.9,1.8,1.7,1.2,1.4,0.9,0.5,0.2,3.6,1.5,0.9,1.0,5.8,5.8,0.6,1.7,5.2,1.0,2.6,1.5,3.3,2.4,2.9,3.1,2.2,2.1,2.9,1.0,0.5,0.7,1.9,0.5,0.4,2.4,3.4,0.2,0.1,0.8,2.5,2.9,4.0,2.4,0.4,0.5,1.0,1.1,0.7,0.7,1.4,1.0,2.1,0.3,1.6,0.4,1.4,0.9,0.9,0.5,1.1,0.2,0.4,1.4,1.0,2.1,1.1,0.9,0.5,0.8,1.5,3.4,0.3,5,22,6,10,17,16,10,14,12,6,2.0,28,16,8,10,58,52,3,18,41,7,26,15,31,32,35,38,20,21,33,17,4,7,27,3,2,35,36,1,1,4,22,26,27,14,3,2,6,5,8,5,11,7,13,2,14,3,11,7,7,3,7,2,3,9,11,17,7,7,5,5,10,26,3,0.04,0.026,0.016,0.018,0.045,0.035,0.023,0.03,0.056,0.035,0.021,0.04,0.032,0.041,0.038,0.034,0.025,0.022,0.026,0.038,0.022,0.04,0.035,0.043,0.036,0.04,0.03,0.026,0.025,0.035,0.04,0.031,0.029,0.029,0.023,0.01,0.031,0.035,0.015,0.014,0.02,0.035,0.047,0.033,0.022,0.009,0.023,0.016,0.015,0.019,0.031,0.023,0.027,0.02,0.037,0.015,0.019,0.02,0.024,0.018,0.022,0.022,0.031,0.02,0.024,0.027,0.019,0.02,0.017,0.017,0.022,0.023,0.018,0.017,1346,4633,1578,1784,1704,2639,2300,2915,1291,1250,580.0,2896,3814,1846,2362,11363,15357,2181,3180,4296,2400,4033,1579,5262,4712,6406,4783,2820,5393,5084,1429,840,1324,5420,1360,1485,5815,7037,363,376,1421,2131,4925,3146,2813,1021,844,2155,2842,1256,512,3115,1406,4276,348,2982,959,2335,1290,1375,808,1822,329,733,2323,1004,3453,2288,2063,1002,1271,1973,8561,491,0.12,0.124,0.098,0.111,0.134,0.135,0.107,0.136,0.14,0.142,0.098,0.146,0.131,0.146,0.132,0.14,0.119,0.088,0.121,0.135,0.092,0.149,0.134,0.137,0.143,0.155,0.139,0.112,0.11,0.139,0.132,0.104,0.136,0.127,0.092,0.066,0.117,0.139,0.105,0.096,0.097,0.145,0.153,0.129,0.105,0.08,0.109,0.086,0.085,0.118,0.137,0.102,0.116,0.098,0.146,0.102,0.101,0.111,0.129,0.109,0.112,0.11,0.13,0.105,0.104,0.124,0.102,0.106,0.095,0.12,0.112,0.117,0.092,0.114,383,1208,576,710,660,881,810,848,340,296,145.0,1324,881,376,541,2956,3817,365,1038,2110,708,1092,731,1310,1350,1616,1709,1216,1359,1570,484,319,396,1220,351,548,1496,1624,180,131,662,1071,1062,2052,1740,578,360,932,1128,525,342,1174,632,1773,129,1616,423,1162,609,757,389,711,138,324,1016,572,1711,874,884,372,580,971,3256,246,3.261,3.193,2.781,2.528,2.205,2.543,2.297,3.036,2.596,3.207,2.978,1.987,3.066,3.243,3.06,2.691,3.122,3.836,2.575,1.827,2.857,2.748,1.906,2.856,2.735,3.037,2.255,1.911,2.898,2.507,2.224,2.672,2.722,2.941,3.188,2.396,2.783,3.058,2.185,2.447,2.675,1.822,3.226,1.773,1.869,1.912,3.083,2.807,2.872,2.592,1.891,2.581,2.227,2.584,3.014,2.051,2.495,2.43,2.201,2.019,2.517,2.857,2.983,2.554,2.203,2.212,2.163,2.865,2.588,2.606,2.61,2.393,2.689,2.704,1.287,1.095,0.737,0.452,0.616,0.648,0.561,0.654,0.654,0.446,1.04,0.616,0.648,0.499,0.605,0.668,0.667,1.016,0.631,0.581,0.669,0.648,0.42,0.681,0.622,0.568,0.49,0.428,0.528,0.685,0.814,0.888,0.394,0.665,0.627,0.509,0.659,0.617,0.397,0.713,0.638,0.469,0.696,0.622,0.507,0.367,0.609,0.776,0.625,0.51,0.508,0.624,0.664,0.517,0.5,0.408,0.47,0.51,0.728,0.366,0.549,0.534,0.565,1.064,0.728,0.719,0.52,0.464,0.477,0.902,0.543,0.461,0.605,0.377
-147,Epileptic,M,32.0,0.8,2.8,1.3,1.2,2.0,2.1,1.5,1.5,1.2,0.8,0.3,2.9,1.2,0.6,1.2,7.0,7.1,1.2,2.8,6.2,0.9,3.0,2.3,3.2,3.1,3.6,2.8,2.4,2.8,3.9,1.3,1.7,0.7,2.3,0.3,0.7,6.1,3.2,0.1,0.1,1.2,3.4,2.6,3.6,4.2,0.4,0.4,0.7,1.1,0.8,1.1,2.3,1.9,2.6,0.4,3.0,0.6,1.8,1.6,1.5,0.4,1.1,0.1,0.5,2.0,1.8,3.3,1.4,1.6,0.3,1.0,1.8,5.3,0.5,4,29,15,10,21,18,12,16,13,9,4.0,25,14,6,16,70,59,6,27,40,8,31,20,29,37,34,33,17,22,46,17,14,5,20,2,5,54,24,1,1,6,25,21,21,25,4,2,6,6,6,8,19,13,21,2,23,5,14,10,10,3,9,1,3,18,15,27,9,10,3,7,11,46,2,0.033,0.027,0.023,0.025,0.037,0.032,0.023,0.025,0.039,0.035,0.028,0.036,0.031,0.032,0.032,0.033,0.027,0.038,0.028,0.046,0.019,0.036,0.037,0.041,0.035,0.032,0.024,0.03,0.023,0.033,0.035,0.068,0.029,0.037,0.012,0.022,0.044,0.038,0.015,0.016,0.026,0.038,0.044,0.029,0.032,0.011,0.025,0.015,0.015,0.019,0.042,0.023,0.026,0.02,0.026,0.019,0.015,0.023,0.025,0.026,0.015,0.023,0.011,0.016,0.025,0.039,0.025,0.022,0.023,0.02,0.026,0.025,0.024,0.03,1515,5462,2272,2251,2665,3255,2043,3205,1582,1287,712.0,2723,2802,1152,2352,12122,15096,2020,4158,4303,3590,4239,2352,5954,5760,6457,5163,2525,4967,5857,2446,1194,944,4548,1678,1421,7442,4986,305,439,1490,2606,5693,2928,2780,1081,699,1877,2300,1487,745,3330,2582,5178,439,4069,1659,2659,1588,1586,615,2009,331,918,2318,1343,4249,2268,2036,900,1336,2248,9027,477,0.113,0.122,0.126,0.112,0.144,0.134,0.095,0.117,0.143,0.147,0.129,0.138,0.123,0.138,0.138,0.133,0.118,0.124,0.124,0.146,0.083,0.145,0.144,0.142,0.148,0.133,0.108,0.107,0.097,0.145,0.122,0.145,0.107,0.121,0.074,0.1,0.138,0.121,0.088,0.091,0.114,0.141,0.14,0.124,0.131,0.088,0.114,0.09,0.088,0.103,0.149,0.112,0.115,0.105,0.144,0.113,0.086,0.113,0.123,0.123,0.093,0.112,0.079,0.108,0.127,0.136,0.118,0.111,0.11,0.111,0.12,0.11,0.113,0.122,424,1675,859,787,928,1036,824,993,478,367,220.0,1312,735,296,723,3479,4099,463,1531,2003,825,1426,966,1415,1618,1886,1992,1110,1561,1880,624,436,354,1108,409,578,2157,1419,168,187,736,1211,1175,1966,2132,622,282,817,1057,700,440,1594,1171,2174,208,2293,755,1344,958,860,331,730,173,442,1265,735,2137,1011,1057,324,682,1172,3624,275,3.251,2.837,2.313,2.686,2.463,2.642,2.118,2.73,2.572,2.93,2.865,1.875,2.815,2.603,2.534,2.604,2.901,3.254,2.36,1.903,3.105,2.397,2.104,3.027,2.699,2.758,2.143,1.89,2.477,2.46,2.936,2.858,2.155,2.783,3.46,2.291,2.772,2.653,2.191,2.876,2.617,2.005,3.369,1.709,1.54,1.845,2.686,2.744,2.7,2.61,1.886,2.092,2.249,2.349,2.535,2.036,2.566,2.264,2.023,2.009,2.168,2.697,2.086,2.224,1.951,2.345,2.007,2.505,2.259,2.806,2.147,2.422,2.411,2.354,0.952,0.832,0.729,0.701,0.743,0.512,0.536,0.512,0.577,0.564,0.755,0.455,0.503,0.629,0.528,0.607,0.656,0.989,0.547,0.735,0.667,0.487,0.45,0.74,0.562,0.609,0.417,0.47,0.657,0.589,0.832,1.014,0.552,0.849,0.968,0.572,0.713,0.71,0.277,0.508,0.502,0.576,0.821,0.486,0.447,0.442,0.728,0.561,0.491,0.487,0.369,0.458,0.653,0.463,0.548,0.434,0.44,0.547,0.456,0.403,0.515,0.848,0.376,0.963,0.47,0.632,0.499,0.435,0.427,0.911,0.571,0.692,0.557,0.55
-148,Epileptic,M,42.0,0.8,2.6,1.2,1.4,2.3,1.8,2.2,2.8,1.3,1.3,0.2,2.6,2.4,1.0,2.0,6.1,11.4,1.6,3.2,4.3,1.7,2.4,1.4,4.7,4.3,5.1,4.5,3.8,3.8,4.3,1.6,0.7,0.9,4.0,1.0,1.3,5.9,4.3,0.6,0.5,1.5,3.3,3.6,2.6,3.9,1.0,0.8,1.3,2.0,1.1,0.7,2.9,1.7,3.0,0.3,2.8,0.7,1.5,1.2,1.1,0.4,1.4,0.6,0.6,1.6,1.1,2.9,1.6,1.5,0.8,1.0,1.4,5.5,0.6,7,23,8,9,19,17,16,21,13,11,2.0,24,22,7,17,57,101,13,26,33,15,26,15,35,48,48,45,33,28,53,14,6,6,34,6,11,45,40,5,2,9,25,34,18,19,7,4,7,9,10,3,19,12,21,2,22,6,10,8,7,3,8,3,7,11,12,19,10,11,4,7,10,40,4,0.041,0.025,0.02,0.028,0.045,0.033,0.037,0.044,0.047,0.046,0.034,0.031,0.05,0.059,0.051,0.043,0.042,0.052,0.043,0.038,0.032,0.032,0.022,0.05,0.046,0.036,0.039,0.039,0.036,0.042,0.051,0.04,0.038,0.047,0.031,0.024,0.055,0.046,0.045,0.032,0.029,0.038,0.056,0.023,0.026,0.022,0.03,0.02,0.025,0.027,0.031,0.026,0.026,0.024,0.024,0.023,0.026,0.017,0.023,0.023,0.017,0.032,0.028,0.048,0.02,0.022,0.027,0.022,0.024,0.027,0.02,0.024,0.027,0.025,1857,4911,2241,2248,2130,3343,2546,3370,1860,2032,561.0,3214,3541,1253,2390,8778,18808,2759,5293,4818,4383,4242,2657,6229,6998,8330,7100,5123,6524,7279,2759,765,1154,5894,1951,2732,7582,6736,557,471,1592,3237,5649,2887,3726,1424,797,2223,2380,1691,679,3302,1691,4109,636,3713,1109,2715,1515,1309,733,1944,585,899,2370,1343,3647,2474,1937,989,1624,1943,7458,577,0.115,0.113,0.108,0.117,0.149,0.13,0.119,0.142,0.151,0.167,0.108,0.132,0.136,0.153,0.146,0.14,0.14,0.146,0.151,0.137,0.115,0.132,0.115,0.15,0.165,0.141,0.145,0.121,0.118,0.158,0.163,0.097,0.112,0.149,0.124,0.103,0.163,0.157,0.141,0.117,0.118,0.133,0.182,0.114,0.11,0.114,0.121,0.096,0.109,0.118,0.135,0.108,0.118,0.113,0.1,0.115,0.119,0.096,0.112,0.116,0.103,0.121,0.109,0.153,0.106,0.108,0.114,0.104,0.107,0.125,0.111,0.111,0.116,0.128,460,1730,880,808,981,1054,1005,1223,569,537,144.0,1370,1139,341,769,2783,5143,583,1349,1908,1052,1287,1006,1626,1902,2621,2162,1591,1783,2152,619,332,375,1520,553,961,1853,1727,225,244,855,1318,1328,1729,2200,681,364,940,1193,660,324,1739,1120,1975,290,1976,499,1356,840,731,374,709,359,311,1212,777,1776,1202,960,439,786,996,3463,331,3.832,2.559,2.627,2.608,1.974,2.678,2.145,2.414,2.458,2.958,3.08,2.006,2.456,2.663,2.36,2.478,2.727,3.564,3.146,2.175,2.989,2.57,2.21,2.946,2.678,2.556,2.569,2.529,2.808,2.587,3.115,2.274,2.459,2.811,2.973,2.387,2.833,2.698,2.381,1.964,2.383,2.102,2.786,1.913,1.906,2.095,2.534,2.888,2.441,2.632,2.386,1.986,1.713,2.224,2.213,2.136,2.249,2.239,1.979,2.009,2.16,2.527,1.895,2.622,2.117,1.852,2.237,2.299,2.318,2.621,2.341,2.354,2.329,2.385,0.716,0.595,0.546,0.75,0.522,0.603,0.533,0.589,0.612,0.778,0.842,0.536,0.479,0.597,0.64,0.659,0.711,0.772,0.647,0.734,0.84,0.568,0.63,0.819,0.756,0.652,0.771,0.718,0.552,0.623,0.734,0.841,0.487,0.783,0.542,0.612,0.963,0.741,0.421,0.459,0.53,0.647,0.869,0.737,0.534,0.587,0.6,0.788,0.468,0.668,0.654,0.515,0.373,0.477,0.489,0.441,0.492,0.521,0.457,0.361,0.328,0.758,0.419,0.7,0.529,0.587,0.5,0.383,0.38,0.645,0.511,0.606,0.468,0.534
-149,Epileptic,F,27.0,0.5,2.7,0.7,1.5,1.6,1.8,1.4,1.2,1.3,0.6,0.4,2.5,1.8,0.7,1.5,6.2,9.4,0.7,2.3,3.5,1.1,2.4,1.4,3.4,3.6,3.1,2.4,2.1,2.6,3.3,1.1,1.7,0.4,2.6,0.4,0.9,3.7,2.8,0.2,0.2,0.9,2.4,2.0,2.1,2.6,0.5,0.4,0.8,1.1,1.0,0.7,1.3,1.0,2.3,0.4,2.2,0.4,1.2,0.9,0.9,0.4,1.2,0.4,0.5,1.0,0.6,2.1,1.6,0.7,0.4,0.9,1.5,3.5,0.3,3,26,8,13,26,15,12,12,17,6,3.0,17,19,9,15,70,87,5,27,25,7,24,11,43,43,28,31,19,22,35,13,9,5,28,3,8,35,30,2,1,4,19,18,13,16,4,2,5,6,7,4,11,8,15,3,18,4,9,7,8,3,9,2,4,8,13,14,11,5,4,5,10,24,2,0.025,0.025,0.012,0.03,0.037,0.038,0.019,0.024,0.052,0.026,0.049,0.037,0.037,0.045,0.037,0.035,0.028,0.034,0.029,0.032,0.022,0.037,0.03,0.045,0.038,0.032,0.026,0.026,0.023,0.038,0.038,0.046,0.031,0.034,0.023,0.023,0.04,0.034,0.018,0.014,0.018,0.041,0.036,0.024,0.02,0.01,0.015,0.015,0.014,0.04,0.035,0.018,0.023,0.017,0.018,0.019,0.013,0.015,0.03,0.018,0.015,0.024,0.034,0.022,0.018,0.025,0.02,0.019,0.013,0.012,0.027,0.028,0.016,0.018,1254,5584,2259,2361,2041,2555,2651,3208,1672,1242,352.0,2175,3200,1251,2615,11436,21169,2079,4749,3249,2964,3570,1829,5349,6093,6312,4429,3273,5767,4849,2418,1177,917,6574,1964,2380,5008,6207,406,308,1595,2581,5468,2007,3314,1403,933,2341,2515,1177,420,2581,1616,5204,661,3395,1194,2583,836,1318,665,2217,433,821,1899,741,3360,2759,1786,1159,1013,1454,8615,595,0.099,0.125,0.095,0.122,0.145,0.146,0.096,0.114,0.17,0.137,0.154,0.134,0.146,0.147,0.159,0.15,0.13,0.108,0.13,0.127,0.087,0.154,0.126,0.139,0.153,0.136,0.126,0.101,0.109,0.145,0.134,0.148,0.141,0.131,0.098,0.11,0.143,0.128,0.102,0.085,0.096,0.149,0.129,0.109,0.103,0.082,0.098,0.085,0.086,0.127,0.158,0.104,0.112,0.095,0.11,0.11,0.091,0.091,0.137,0.106,0.102,0.11,0.136,0.129,0.101,0.112,0.105,0.102,0.088,0.097,0.125,0.122,0.092,0.111,356,1749,942,919,745,862,1008,895,471,325,124.0,1040,919,298,744,3009,5544,386,1285,1501,702,1141,753,1316,1723,1700,1564,1206,1676,1560,520,515,295,1200,333,684,1553,1404,201,162,703,992,1013,1307,2043,750,397,880,1151,480,281,1302,715,2161,336,1805,550,1253,440,756,364,780,189,371,957,383,1684,1238,822,482,545,832,3322,249,3.252,2.906,2.394,2.758,2.287,2.58,2.319,3.117,2.448,2.987,2.603,1.867,2.785,2.877,2.668,2.735,2.936,3.732,3.029,1.915,3.184,2.449,2.147,2.912,2.764,3.036,2.255,2.153,2.672,2.503,3.118,2.5,2.558,3.333,3.889,3.001,2.634,3.051,2.259,1.901,2.664,2.117,3.348,1.79,1.876,2.151,2.983,3.12,2.639,2.887,1.836,2.136,2.248,2.541,2.521,2.147,2.352,2.309,2.182,1.99,2.09,2.705,2.232,2.33,2.077,2.223,2.26,2.505,2.524,2.612,2.357,2.129,2.715,2.963,0.752,0.645,0.536,0.419,0.6,0.477,0.574,0.696,0.62,0.421,0.649,0.442,0.509,0.492,0.514,0.587,0.629,0.801,0.676,0.614,0.747,0.485,0.411,0.687,0.48,0.73,0.477,0.501,0.541,0.597,0.802,0.889,0.519,0.754,0.712,0.547,0.738,0.656,0.392,0.347,0.686,0.673,0.747,0.577,0.566,0.346,0.626,0.781,0.588,0.474,0.493,0.444,0.583,0.522,0.308,0.452,0.475,0.496,0.404,0.329,0.431,0.776,0.351,0.817,0.659,0.652,0.487,0.356,0.378,0.676,0.446,0.455,0.696,0.355
-150,Epileptic,F,27.0,0.7,2.1,1.0,1.1,1.6,1.7,1.9,1.2,1.6,0.5,0.4,2.6,1.5,0.6,1.0,5.7,9.7,0.8,3.4,4.3,1.9,3.1,1.6,2.8,3.5,2.5,2.7,2.5,3.2,2.8,1.4,0.7,0.5,2.2,0.9,0.6,2.5,3.2,0.1,0.2,0.8,2.9,2.7,2.3,3.2,0.7,0.4,1.1,1.2,1.3,0.6,1.6,1.6,2.5,0.3,2.4,0.6,1.8,1.1,1.0,0.9,1.4,0.4,0.4,1.6,0.5,3.3,1.5,0.9,0.5,0.7,1.1,4.4,0.3,8,16,11,10,14,17,14,10,13,5,5.0,21,15,4,8,52,79,5,30,34,20,30,15,32,40,24,26,20,23,34,15,5,3,20,5,4,34,36,1,1,4,23,24,16,17,4,2,6,5,8,4,10,13,17,2,18,5,21,7,8,8,12,3,5,12,13,23,9,6,5,4,7,29,2,0.034,0.019,0.02,0.022,0.031,0.029,0.029,0.025,0.042,0.027,0.035,0.033,0.032,0.031,0.026,0.031,0.032,0.03,0.037,0.035,0.044,0.033,0.031,0.033,0.036,0.027,0.027,0.032,0.035,0.031,0.036,0.031,0.024,0.029,0.028,0.015,0.032,0.035,0.018,0.018,0.019,0.032,0.044,0.025,0.025,0.013,0.021,0.016,0.018,0.046,0.028,0.017,0.021,0.02,0.024,0.016,0.018,0.024,0.024,0.017,0.029,0.023,0.03,0.014,0.018,0.027,0.025,0.019,0.019,0.018,0.019,0.022,0.019,0.023,1342,5070,2428,2172,2256,2755,2894,2810,2050,1247,481.0,2786,3141,1144,2468,11188,18894,2106,5079,4120,2237,4801,1895,5159,6243,5287,4998,3425,5541,5511,2076,915,1032,6179,2154,1798,5576,6588,438,455,1424,2756,6672,2188,3211,1547,721,2492,2267,942,593,3149,2434,5032,372,4363,1255,3054,1301,1750,977,2527,601,1032,2688,598,4357,2514,1419,1028,1365,2068,8428,360,0.124,0.101,0.103,0.105,0.134,0.137,0.116,0.121,0.151,0.119,0.142,0.133,0.134,0.13,0.117,0.124,0.133,0.105,0.143,0.137,0.129,0.136,0.136,0.13,0.144,0.125,0.124,0.112,0.116,0.137,0.122,0.107,0.102,0.118,0.114,0.079,0.13,0.149,0.098,0.104,0.099,0.132,0.148,0.115,0.11,0.084,0.104,0.091,0.092,0.11,0.139,0.096,0.107,0.098,0.142,0.099,0.099,0.121,0.11,0.1,0.141,0.114,0.142,0.111,0.108,0.129,0.109,0.102,0.105,0.109,0.102,0.111,0.103,0.128,461,1685,835,880,869,1077,1047,820,597,301,191.0,1220,862,280,638,3241,4997,443,1522,1956,725,1533,864,1552,1897,1600,1607,1379,1675,1707,638,381,331,1187,550,614,1471,1722,175,189,706,1331,1384,1446,2000,720,287,1058,1011,478,322,1440,1278,2117,169,2226,583,1435,768,944,480,997,238,492,1256,364,2129,1169,709,466,637,808,3675,188,2.708,2.844,2.8,2.532,2.478,2.262,2.348,3.054,2.747,3.113,2.309,2.001,2.93,3.051,2.947,2.714,3.013,3.596,2.722,1.851,2.366,2.38,2.005,2.651,2.558,2.718,2.392,2.081,2.604,2.593,2.453,2.288,2.516,3.463,3.459,2.62,2.789,2.713,2.975,2.5,2.615,1.895,3.26,1.762,1.84,2.522,3.203,3.023,2.808,1.738,2.008,2.327,2.085,2.432,2.488,2.207,2.485,2.23,1.931,2.048,2.086,2.551,2.676,2.4,2.423,2.215,2.115,2.44,2.287,2.477,2.485,2.65,2.565,2.378,0.819,0.57,0.602,0.417,0.526,0.507,0.509,0.564,0.515,0.474,0.552,0.471,0.473,0.433,0.438,0.602,0.632,0.862,0.562,0.507,0.909,0.477,0.365,0.573,0.515,0.519,0.51,0.591,0.559,0.558,0.666,0.945,0.622,0.656,0.695,0.519,0.914,0.661,0.381,0.48,0.545,0.566,0.766,0.572,0.489,0.36,0.486,0.69,0.548,0.844,0.406,0.487,0.518,0.535,0.445,0.425,0.475,0.54,0.329,0.393,0.444,0.658,0.425,0.735,0.471,0.622,0.48,0.383,0.42,0.431,0.401,0.691,0.54,0.737
-151,Epileptic,F,32.0,1.0,1.5,0.8,1.2,1.9,1.3,3.3,1.5,1.4,0.9,0.4,2.9,1.0,0.5,0.7,4.9,11.1,0.9,7.6,6.6,3.6,5.5,2.6,4.1,13.2,4.4,8.4,2.7,3.3,3.4,1.5,1.5,0.7,2.0,0.6,0.4,4.7,3.1,0.1,0.2,1.7,4.6,2.1,4.1,2.8,0.5,0.3,0.8,1.4,0.6,0.4,1.4,1.8,2.6,0.1,3.1,0.6,3.0,1.0,1.4,0.7,1.7,0.3,0.8,1.0,1.7,2.0,0.8,0.8,0.6,0.9,1.3,5.2,0.3,8,12,6,8,19,14,26,16,15,7,3.0,21,13,5,10,67,104,8,107,61,24,50,22,42,114,47,91,27,26,46,18,11,5,22,3,3,48,37,1,1,9,27,19,27,16,4,2,4,6,5,3,10,19,19,1,24,5,39,8,9,9,15,4,5,6,24,16,6,4,6,7,9,46,2,0.04,0.023,0.016,0.023,0.038,0.027,0.045,0.024,0.049,0.038,0.038,0.036,0.022,0.033,0.028,0.029,0.037,0.031,0.079,0.051,0.051,0.048,0.042,0.04,0.067,0.033,0.047,0.028,0.032,0.032,0.046,0.046,0.051,0.028,0.022,0.01,0.048,0.029,0.011,0.014,0.032,0.056,0.034,0.042,0.023,0.013,0.016,0.015,0.02,0.011,0.02,0.018,0.026,0.02,0.029,0.017,0.024,0.033,0.023,0.022,0.024,0.026,0.019,0.019,0.014,0.032,0.016,0.013,0.014,0.021,0.026,0.017,0.02,0.014,1706,3995,2331,2381,2094,3291,2968,4637,1777,1193,618.0,2770,3826,1087,2489,12438,20691,2328,4026,5762,3596,5491,2337,6596,7240,7914,8588,5825,7645,6443,2447,1476,1104,6908,2210,2270,7925,9431,307,583,1673,1876,5858,2634,4222,1544,700,2151,2533,1685,644,3300,2373,6092,245,6165,1091,3373,1523,2183,1434,3328,473,1260,2396,1367,4676,2538,2088,1141,1784,2657,11178,751,0.127,0.112,0.098,0.114,0.127,0.128,0.126,0.117,0.169,0.156,0.139,0.141,0.108,0.125,0.121,0.135,0.132,0.12,0.182,0.161,0.13,0.144,0.136,0.137,0.166,0.136,0.138,0.12,0.116,0.142,0.157,0.124,0.113,0.128,0.095,0.067,0.155,0.139,0.09,0.085,0.119,0.169,0.141,0.157,0.106,0.084,0.093,0.083,0.1,0.087,0.112,0.097,0.122,0.101,0.101,0.105,0.125,0.13,0.128,0.108,0.124,0.121,0.125,0.109,0.093,0.137,0.095,0.088,0.09,0.111,0.113,0.107,0.101,0.097,448,1088,752,845,785,936,1084,1108,530,401,188.0,1264,836,241,545,2892,5006,493,1589,2117,987,1671,923,1677,2639,2110,2876,1627,1577,1888,625,622,287,1294,468,657,1826,1935,156,246,795,1158,1203,1616,1994,714,323,882,1049,762,346,1243,1078,2136,88,2800,459,1542,671,1028,503,1093,242,608,1030,854,1946,999,829,435,681,1101,4359,270,3.157,3.19,2.983,2.934,2.271,2.874,2.418,3.384,2.668,2.84,2.744,1.957,3.286,2.947,3.155,3.016,3.237,3.475,2.082,2.194,2.814,2.582,2.043,2.901,2.132,2.841,2.352,2.676,3.43,2.711,2.873,2.156,2.958,3.502,3.794,3.018,3.126,3.249,2.349,2.975,2.716,1.71,3.602,1.978,2.445,2.493,2.807,3.188,3.046,2.698,2.606,2.771,2.51,2.871,3.113,2.524,2.968,2.574,2.67,2.334,2.938,3.183,1.956,2.534,2.704,1.923,2.556,3.026,2.994,2.663,2.967,2.758,2.815,3.385,0.778,0.693,0.518,0.538,0.738,0.677,0.719,0.542,0.735,0.879,0.684,0.49,0.468,0.746,0.662,0.735,0.799,0.825,0.864,0.809,1.068,0.84,0.69,0.917,0.959,0.8,0.623,0.723,0.614,0.554,0.91,0.937,0.747,0.654,0.765,0.506,0.773,0.868,0.543,0.394,0.641,0.688,0.773,0.603,0.739,0.415,0.463,0.521,0.482,0.468,0.438,0.434,0.549,0.519,0.539,0.432,0.503,0.687,0.557,0.406,0.476,0.832,0.562,0.546,0.516,0.702,0.53,0.403,0.444,0.651,0.455,0.593,0.486,0.376
-152,Epileptic,F,22.0,1.0,1.9,0.8,0.9,2.0,1.7,1.0,1.5,1.2,0.6,0.3,2.7,1.3,0.5,1.6,5.2,7.9,0.8,2.2,4.9,2.3,2.5,1.5,2.7,3.5,3.1,4.9,1.9,2.8,3.3,1.3,1.0,0.4,2.5,0.2,0.7,2.6,3.4,0.1,0.2,0.9,2.3,1.8,1.9,2.6,0.6,0.2,0.7,1.2,1.1,0.6,2.0,1.3,2.3,0.4,2.4,0.9,3.8,1.3,0.9,0.7,1.0,0.2,0.4,1.7,0.8,2.1,1.3,0.9,0.4,1.1,1.4,4.1,0.3,8,17,7,6,19,17,10,16,17,5,3.0,25,13,6,19,58,83,5,29,45,13,30,15,31,42,34,48,23,23,41,15,8,4,32,2,7,35,37,1,1,5,22,14,15,16,4,1,5,6,8,7,16,9,14,3,17,4,29,8,6,6,7,1,3,16,10,17,8,6,3,7,10,31,2,0.038,0.021,0.016,0.018,0.04,0.029,0.021,0.027,0.044,0.034,0.029,0.028,0.035,0.033,0.036,0.03,0.028,0.027,0.025,0.038,0.03,0.035,0.029,0.038,0.036,0.026,0.032,0.024,0.024,0.027,0.037,0.036,0.021,0.027,0.009,0.017,0.032,0.029,0.012,0.016,0.018,0.026,0.033,0.027,0.019,0.011,0.012,0.012,0.015,0.024,0.024,0.02,0.019,0.02,0.014,0.016,0.019,0.032,0.027,0.015,0.025,0.024,0.018,0.015,0.019,0.021,0.019,0.018,0.017,0.013,0.022,0.022,0.015,0.016,1549,5141,2196,2126,2410,3021,2599,3545,2156,1432,497.0,2584,3516,1221,3399,11589,20100,2690,5512,5695,3588,4647,2337,5596,6557,7310,6262,4756,6700,7176,2015,1183,1136,7145,1983,2283,5866,7801,310,445,1594,2545,4004,2117,3695,1446,704,2214,2548,1591,795,3781,2521,4802,721,4202,1692,4000,1400,1700,958,2098,314,790,2838,1016,3589,2741,2024,796,1719,2042,10288,626,0.133,0.109,0.101,0.094,0.14,0.136,0.103,0.124,0.154,0.128,0.113,0.134,0.132,0.11,0.155,0.135,0.128,0.106,0.119,0.144,0.101,0.143,0.127,0.132,0.144,0.125,0.136,0.112,0.108,0.132,0.116,0.113,0.099,0.124,0.065,0.094,0.127,0.124,0.086,0.083,0.099,0.127,0.128,0.115,0.1,0.091,0.083,0.078,0.088,0.118,0.133,0.105,0.098,0.102,0.098,0.099,0.095,0.125,0.127,0.095,0.121,0.105,0.108,0.108,0.114,0.125,0.103,0.103,0.097,0.107,0.118,0.112,0.091,0.104,476,1476,762,768,796,1035,824,991,525,326,169.0,1409,772,244,813,2935,4909,480,1441,2229,1162,1334,899,1270,1727,1949,2204,1370,1681,2103,553,433,352,1551,498,660,1520,1804,189,184,708,1326,922,1203,2054,706,271,921,1112,655,441,1459,1036,1780,401,2045,670,1803,708,867,463,717,154,363,1337,530,1681,1082,860,369,785,978,3997,308,3.292,3.011,2.755,2.819,2.606,2.527,2.566,3.032,3.038,3.619,2.554,1.639,3.399,3.196,2.909,2.906,3.182,3.928,2.973,2.185,2.702,2.671,2.135,3.098,2.774,2.903,2.337,2.636,2.967,2.675,2.666,2.533,2.628,3.225,3.327,2.881,2.919,3.185,1.828,2.6,2.831,1.807,3.268,2.029,2.043,2.173,3.351,2.933,2.812,2.636,2.059,2.534,2.49,2.789,2.337,2.236,2.966,2.551,2.12,2.209,2.466,2.932,2.255,2.725,2.409,2.263,2.303,2.995,2.564,2.602,2.588,2.568,2.7,2.465,0.842,0.683,0.546,0.546,0.6,0.582,0.562,0.591,0.631,0.516,0.604,0.447,0.646,0.671,0.663,0.604,0.575,0.668,0.536,0.682,0.776,0.517,0.444,0.598,0.643,0.62,0.551,0.491,0.452,0.606,0.598,0.999,0.377,0.607,0.586,0.465,0.61,0.541,0.315,0.539,0.723,0.475,0.685,0.999,0.548,0.459,0.411,0.685,0.623,0.655,0.48,0.665,0.532,0.558,0.303,0.483,0.536,0.59,0.418,0.435,0.532,0.779,0.414,0.696,0.555,0.649,0.456,0.453,0.502,0.59,0.667,0.664,0.596,0.352
-153,Epileptic,F,42.0,0.7,2.5,0.7,1.2,1.4,2.0,2.4,1.8,0.9,0.8,0.2,2.8,1.4,0.6,1.0,6.3,7.3,1.2,2.7,4.9,1.5,6.2,1.8,3.2,6.8,3.1,4.1,2.8,3.4,2.9,2.2,1.3,0.4,2.7,0.6,0.6,3.6,4.1,0.3,0.2,1.0,4.7,3.6,2.5,3.5,0.9,0.4,1.0,1.5,1.4,0.2,1.8,1.2,2.9,0.3,2.9,1.2,1.6,1.3,1.9,0.4,2.1,0.6,0.9,1.9,1.0,2.8,1.1,1.2,0.6,0.9,1.6,5.5,0.2,8,29,6,9,15,20,18,16,10,7,3.0,25,16,7,11,68,62,12,28,44,20,63,20,39,62,36,48,31,27,39,21,10,5,29,4,7,35,42,3,2,6,36,35,19,22,6,2,7,12,11,2,15,9,23,2,21,7,13,8,13,4,14,3,5,18,15,22,8,8,6,7,15,39,2,0.035,0.026,0.017,0.024,0.025,0.032,0.034,0.029,0.036,0.028,0.026,0.035,0.028,0.035,0.026,0.032,0.026,0.041,0.029,0.039,0.032,0.046,0.027,0.039,0.047,0.033,0.038,0.028,0.029,0.027,0.056,0.044,0.023,0.036,0.019,0.021,0.044,0.04,0.022,0.012,0.021,0.041,0.048,0.025,0.022,0.017,0.019,0.016,0.02,0.025,0.019,0.017,0.018,0.018,0.026,0.016,0.021,0.018,0.023,0.023,0.014,0.029,0.031,0.03,0.026,0.021,0.02,0.017,0.019,0.018,0.025,0.022,0.019,0.014,1306,4621,1840,2215,2329,3055,2921,3544,1611,1609,721.0,2865,3340,1232,2320,12001,17888,2494,5629,4977,3945,4898,2852,5696,5993,5021,5761,5046,6791,6405,1820,1197,911,5567,1833,1791,5995,7771,425,625,1356,3420,5824,2742,4458,1598,721,2235,2527,1932,266,3465,2185,6027,247,5108,1770,3358,1311,2436,1113,2698,596,1144,2474,1265,4469,2193,1881,775,1336,2856,9679,413,0.132,0.123,0.1,0.115,0.121,0.127,0.107,0.134,0.145,0.139,0.118,0.148,0.129,0.134,0.112,0.133,0.117,0.157,0.124,0.152,0.107,0.135,0.121,0.144,0.136,0.135,0.116,0.107,0.106,0.121,0.154,0.131,0.124,0.121,0.082,0.09,0.128,0.139,0.134,0.099,0.109,0.137,0.161,0.123,0.105,0.1,0.093,0.089,0.092,0.104,0.125,0.097,0.105,0.1,0.133,0.096,0.103,0.097,0.115,0.109,0.083,0.132,0.134,0.131,0.142,0.117,0.107,0.096,0.104,0.128,0.122,0.114,0.101,0.106,458,1634,689,859,963,1208,1066,1073,499,440,204.0,1393,949,347,703,3602,4946,599,1584,2247,930,2063,1167,1686,2333,1695,2168,1752,1824,1993,694,506,317,1451,520,662,1744,1941,199,261,699,1906,1569,1623,2531,820,327,912,1210,874,162,1739,1048,2581,157,2650,849,1520,845,1240,522,1102,310,419,1157,774,2167,1013,909,438,639,1235,4363,185,2.871,2.501,2.529,2.548,2.18,2.288,2.267,2.662,2.494,2.772,2.931,1.849,2.72,2.668,2.519,2.558,2.904,3.068,2.79,1.996,2.982,2.043,2.08,2.638,2.243,2.499,2.212,2.286,2.799,2.498,2.143,2.243,2.529,2.756,2.808,2.422,2.692,2.906,2.202,2.329,2.336,1.619,2.611,1.969,2.017,2.177,2.58,2.624,2.494,2.378,2.039,2.097,2.158,2.391,1.926,2.119,2.358,2.451,1.87,2.188,2.259,2.423,2.18,3.266,2.378,1.924,2.191,2.5,2.297,1.896,2.268,2.416,2.373,2.666,1.081,0.536,0.485,0.53,0.545,0.547,0.599,0.538,0.629,0.608,0.573,0.439,0.489,0.384,0.482,0.525,0.559,0.814,0.511,0.576,0.834,0.588,0.55,0.666,0.643,0.598,0.487,0.51,0.549,0.546,0.635,0.778,0.461,0.71,0.687,0.515,0.842,0.701,0.397,0.626,0.451,0.604,0.88,0.613,0.5,0.38,0.483,0.909,0.507,0.71,0.22,0.398,0.442,0.43,0.383,0.388,0.482,0.421,0.443,0.369,0.385,0.54,0.468,0.7,0.497,0.694,0.485,0.465,0.416,0.505,0.552,0.498,0.46,0.457
-154,Epileptic,M,32.0,0.6,1.7,0.8,0.7,1.9,2.7,1.2,1.2,0.9,0.6,0.3,3.4,1.2,0.4,1.0,5.3,8.5,0.7,2.2,4.8,0.7,2.6,1.9,3.3,2.9,2.9,4.3,2.3,2.6,3.4,1.0,0.6,0.5,2.4,0.4,1.4,3.5,4.4,0.1,0.2,1.1,4.2,2.4,2.7,3.0,0.5,0.4,0.8,1.0,0.4,0.5,2.1,1.4,2.6,0.8,2.9,0.8,1.6,1.0,1.1,0.9,1.8,0.3,0.4,2.1,1.4,2.7,1.5,0.6,0.8,1.6,2.0,5.2,0.4,5,19,6,6,21,23,11,12,12,5,3.0,33,13,7,16,64,84,10,25,41,7,32,21,44,40,31,46,25,27,39,15,6,4,25,2,16,43,42,1,2,6,33,29,22,19,5,2,5,6,6,3,20,10,19,7,24,5,15,8,12,8,14,2,5,16,16,22,13,4,8,11,16,46,4,0.025,0.021,0.014,0.014,0.042,0.033,0.02,0.022,0.04,0.031,0.025,0.032,0.03,0.027,0.026,0.029,0.026,0.024,0.027,0.035,0.016,0.025,0.029,0.037,0.027,0.028,0.028,0.025,0.022,0.03,0.031,0.033,0.026,0.028,0.012,0.031,0.033,0.039,0.009,0.014,0.023,0.034,0.036,0.028,0.019,0.013,0.017,0.013,0.015,0.012,0.027,0.026,0.02,0.018,0.032,0.018,0.018,0.017,0.022,0.021,0.021,0.027,0.015,0.017,0.025,0.052,0.025,0.02,0.012,0.026,0.031,0.026,0.02,0.017,1549,4278,1938,1709,2569,3838,3011,3608,1449,1331,690.0,3036,2930,1522,2674,11429,20272,2812,5261,4161,2897,5037,2719,6550,6669,6369,7575,4174,7386,6345,2090,1133,898,6691,2233,2680,9459,8219,426,461,1331,3648,6048,2354,4354,1275,847,1993,2329,1464,548,3560,2226,5518,1073,5318,1772,3314,1397,1375,1610,2884,490,827,2782,926,3716,2757,1347,1879,1861,3165,11419,578,0.101,0.111,0.097,0.091,0.148,0.144,0.101,0.11,0.15,0.125,0.13,0.147,0.119,0.138,0.142,0.136,0.125,0.111,0.122,0.145,0.08,0.128,0.14,0.143,0.138,0.133,0.135,0.111,0.108,0.133,0.13,0.099,0.125,0.131,0.065,0.133,0.144,0.151,0.078,0.098,0.114,0.143,0.145,0.122,0.103,0.089,0.109,0.085,0.088,0.089,0.117,0.127,0.108,0.099,0.156,0.106,0.096,0.101,0.111,0.124,0.118,0.121,0.094,0.122,0.126,0.15,0.114,0.107,0.086,0.119,0.141,0.132,0.107,0.105,449,1465,804,684,909,1351,844,971,453,348,216.0,1629,758,361,727,3088,5311,542,1343,2157,687,1666,1113,1732,1859,1728,2397,1456,1753,2000,574,471,300,1356,508,781,1943,1952,212,252,675,1871,1326,1586,2362,666,352,883,1046,559,310,1446,1014,2276,501,2615,682,1586,751,818,643,1061,287,406,1317,490,1874,1237,650,599,799,1289,4310,313,3.376,2.706,2.396,2.453,2.339,2.503,2.736,3.075,2.417,3.285,3.044,1.743,2.885,3.024,2.833,2.739,3.036,3.8,3.093,1.751,3.015,2.423,2.113,2.906,2.732,2.918,2.457,2.241,3.031,2.501,2.641,2.47,2.339,3.384,3.614,2.933,3.316,3.053,2.134,2.345,2.679,1.804,3.121,1.772,2.122,2.066,3.087,2.819,2.833,2.665,2.017,2.414,2.234,2.559,2.603,2.262,3.046,2.37,2.193,1.785,2.456,2.67,2.043,2.37,2.425,2.306,2.096,2.461,2.5,2.906,2.629,2.791,2.794,2.253,0.566,0.709,0.578,0.481,0.6,0.535,0.631,0.578,0.584,0.489,0.866,0.54,0.502,0.528,0.465,0.633,0.553,0.743,0.679,0.479,0.742,0.411,0.555,0.65,0.54,0.553,0.56,0.58,0.563,0.539,0.645,0.862,0.533,0.629,0.828,0.563,0.651,0.687,0.461,0.357,0.531,0.524,0.727,0.86,0.605,0.427,0.48,0.433,0.572,0.469,0.461,0.479,0.518,0.444,0.41,0.396,0.511,0.502,0.262,0.416,0.404,0.58,0.438,0.867,0.518,0.51,0.473,0.359,0.386,0.835,0.515,0.408,0.564,0.583
-155,Epileptic,F,27.0,0.6,2.3,0.9,1.1,1.6,1.6,1.4,2.0,0.8,0.5,0.2,2.6,2.3,0.5,1.4,5.4,8.7,0.7,2.1,3.8,1.5,3.1,1.7,2.8,4.0,4.9,3.3,3.0,2.7,2.7,1.2,0.6,0.8,1.9,0.2,1.5,3.9,3.4,0.2,0.1,1.4,3.0,2.4,2.6,3.0,0.6,0.6,0.5,1.4,1.4,0.7,1.8,1.7,2.4,1.9,2.4,0.8,1.8,1.1,1.0,0.5,1.0,0.3,0.4,2.0,1.9,2.9,1.9,0.7,0.9,1.1,1.9,4.8,0.1,4,22,11,13,15,17,13,20,9,6,4.0,20,23,8,15,62,82,7,24,32,13,32,16,31,53,45,44,28,25,32,21,5,4,26,1,12,43,42,1,1,7,29,24,18,16,6,3,4,7,14,5,13,10,21,12,20,5,17,9,6,6,7,2,4,16,28,27,13,4,8,9,12,37,0,0.029,0.026,0.022,0.022,0.042,0.027,0.024,0.027,0.041,0.033,0.031,0.041,0.031,0.045,0.034,0.033,0.033,0.028,0.026,0.033,0.026,0.034,0.037,0.037,0.038,0.033,0.032,0.032,0.029,0.037,0.042,0.036,0.04,0.024,0.007,0.026,0.033,0.034,0.02,0.026,0.023,0.037,0.033,0.032,0.023,0.017,0.022,0.011,0.016,0.024,0.029,0.022,0.023,0.02,0.063,0.019,0.014,0.022,0.03,0.021,0.019,0.021,0.023,0.026,0.027,0.037,0.026,0.023,0.014,0.027,0.031,0.023,0.018,0.016,1931,4903,2143,2497,1750,2918,3371,4735,1462,1400,523.0,2277,5480,1106,3269,10912,17199,2808,5009,4550,4622,4859,2155,5986,7697,8849,5416,4309,5941,4835,2275,902,1038,5620,1740,2977,8078,8204,363,359,1853,2582,4650,2078,3391,1205,1223,1750,2859,2658,639,3190,2234,4927,713,3720,2105,3550,1067,1178,1366,1860,477,962,2736,1065,4391,3693,1401,995,1382,3005,9967,149,0.099,0.111,0.114,0.11,0.144,0.132,0.116,0.121,0.147,0.117,0.137,0.15,0.132,0.165,0.144,0.136,0.133,0.111,0.121,0.133,0.106,0.145,0.143,0.137,0.153,0.138,0.14,0.126,0.121,0.147,0.144,0.101,0.14,0.119,0.05,0.115,0.127,0.134,0.093,0.114,0.105,0.158,0.13,0.13,0.104,0.104,0.109,0.073,0.09,0.111,0.141,0.11,0.116,0.106,0.164,0.109,0.08,0.119,0.123,0.108,0.104,0.11,0.114,0.114,0.122,0.147,0.12,0.111,0.094,0.126,0.139,0.112,0.097,0.093,414,1569,748,912,656,1050,999,1439,413,363,173.0,1037,1250,254,755,2876,4621,455,1312,1853,1099,1503,809,1430,2066,2539,1824,1471,1567,1353,584,328,302,1244,507,908,2059,1853,186,109,886,1265,1192,1278,1961,572,379,738,1230,973,358,1350,1036,1922,386,1929,880,1399,654,693,521,690,215,359,1126,710,1931,1319,639,422,560,1248,4192,57,3.992,2.632,2.685,2.538,2.28,2.448,2.671,3.005,2.592,2.963,2.73,2.14,3.272,2.936,3.195,2.798,2.911,4.227,2.999,2.045,3.11,2.401,2.339,2.952,2.867,2.841,2.247,2.297,2.867,2.785,2.74,2.558,2.854,3.071,3.147,2.965,2.884,3.241,2.237,3.311,2.688,1.832,3.068,1.867,2.003,2.29,3.504,2.796,2.885,2.772,2.128,2.452,2.183,2.57,2.221,2.059,2.673,2.696,1.896,1.916,2.512,2.609,2.513,2.842,2.424,1.816,2.214,2.765,2.503,2.375,2.663,2.758,2.553,2.896,0.817,0.67,0.778,0.679,0.602,0.515,0.607,0.627,0.645,0.595,0.66,0.675,0.598,0.486,0.515,0.705,0.646,0.647,0.54,0.61,0.8,0.504,0.501,0.738,0.639,0.544,0.577,0.59,0.545,0.69,0.672,0.833,0.646,0.563,0.735,0.517,0.715,0.663,0.358,0.53,0.544,0.483,0.673,0.735,0.538,0.492,0.764,0.679,0.724,0.621,0.48,0.563,0.622,0.529,0.605,0.468,0.585,0.749,0.3,0.392,0.499,0.727,0.562,0.813,0.664,0.619,0.49,0.516,0.421,0.778,0.538,0.581,0.613,0.389
-156,Epileptic,M,32.0,0.9,2.6,1.7,1.4,1.7,2.1,1.5,2.0,1.0,0.7,0.3,2.4,2.1,0.5,2.4,5.4,7.9,1.0,2.3,4.1,0.8,1.8,1.5,3.3,2.4,3.1,3.2,2.3,2.0,3.9,1.0,1.3,0.3,2.0,0.4,0.4,4.2,3.4,0.2,0.3,1.5,3.1,1.9,2.2,2.3,0.7,0.7,1.1,1.4,0.7,0.4,2.6,1.9,2.9,0.2,2.8,0.8,1.3,0.5,1.0,0.6,1.7,0.5,0.3,1.1,1.2,2.3,1.0,0.9,0.5,1.0,1.4,3.3,0.3,5,26,16,11,16,20,12,16,11,6,4.0,21,18,7,23,59,66,8,29,40,9,19,16,30,31,31,39,25,17,41,13,10,3,25,2,2,42,35,1,2,10,25,21,18,12,5,4,7,8,6,2,19,12,19,2,22,5,10,4,8,4,14,3,3,11,18,21,6,6,5,9,10,27,3,0.032,0.022,0.02,0.021,0.032,0.028,0.021,0.031,0.032,0.032,0.025,0.03,0.03,0.02,0.029,0.025,0.025,0.034,0.028,0.031,0.017,0.032,0.025,0.03,0.029,0.026,0.024,0.021,0.02,0.028,0.026,0.04,0.021,0.023,0.01,0.009,0.038,0.035,0.014,0.017,0.028,0.033,0.027,0.027,0.017,0.016,0.021,0.015,0.015,0.02,0.022,0.019,0.018,0.019,0.019,0.016,0.023,0.014,0.018,0.023,0.014,0.028,0.024,0.013,0.016,0.028,0.017,0.015,0.016,0.015,0.019,0.02,0.015,0.019,1831,6014,3059,2594,1846,3561,2975,3130,1853,1491,645.0,2361,4101,1328,4783,13050,17018,2566,4617,5714,3275,3234,2349,7098,5004,6357,6565,4090,5073,6660,2278,1420,896,5361,1953,1460,7018,7291,509,653,1562,3395,4713,2869,3672,1405,1025,2201,2897,1370,478,4869,2967,6044,306,4334,1505,3143,753,1252,1091,2696,535,906,2264,1351,3965,2200,1566,1023,1632,2327,7565,380,0.108,0.108,0.116,0.109,0.147,0.125,0.1,0.126,0.136,0.122,0.126,0.136,0.128,0.122,0.133,0.121,0.118,0.129,0.128,0.14,0.084,0.125,0.132,0.13,0.139,0.124,0.118,0.102,0.095,0.135,0.109,0.117,0.092,0.127,0.065,0.063,0.129,0.135,0.084,0.094,0.126,0.148,0.126,0.116,0.092,0.095,0.113,0.085,0.091,0.117,0.114,0.098,0.099,0.1,0.141,0.098,0.107,0.087,0.104,0.127,0.093,0.127,0.123,0.099,0.101,0.131,0.102,0.088,0.098,0.104,0.116,0.117,0.092,0.117,488,1916,1219,979,815,1214,1038,1135,531,382,207.0,1153,1154,346,1338,3491,4956,527,1360,2134,889,1044,956,1812,1506,1871,2357,1597,1464,2199,661,562,316,1342,563,567,1960,1771,288,304,826,1440,1204,1559,2051,732,491,943,1349,595,277,2146,1546,2410,148,2429,581,1505,466,616,585,986,304,437,1139,677,2010,1066,805,534,803,1107,3418,226,3.62,2.914,2.438,2.559,2.287,2.438,2.426,2.586,2.648,3.193,2.66,1.81,2.804,2.699,2.681,2.814,2.859,3.56,2.732,2.254,2.875,2.425,2.166,2.865,2.59,2.81,2.295,2.126,2.659,2.517,2.572,2.53,2.362,2.956,3.241,2.473,2.625,3.077,2.164,2.378,2.418,2.164,3.053,2.056,2.059,2.092,2.65,2.74,2.579,2.733,2.159,2.323,2.278,2.539,2.68,2.029,2.749,2.333,1.91,2.14,2.259,2.86,2.163,2.41,2.201,2.333,2.081,2.52,2.185,2.388,2.276,2.522,2.397,1.865,0.819,0.608,0.522,0.517,0.439,0.533,0.592,0.408,0.478,0.422,0.819,0.45,0.389,0.46,0.479,0.459,0.545,0.784,0.451,0.657,0.762,0.517,0.362,0.569,0.48,0.438,0.456,0.679,0.488,0.497,0.61,0.934,0.316,0.655,0.71,0.438,0.743,0.55,0.313,0.459,0.385,0.515,0.643,0.534,0.632,0.494,0.364,0.704,0.671,0.435,0.429,0.591,0.421,0.445,0.616,0.412,0.591,0.485,0.305,0.326,0.33,0.603,0.327,1.061,0.517,0.681,0.543,0.374,0.433,0.506,0.448,0.514,0.468,0.442
-157,Epileptic,F,32.0,0.9,2.1,0.8,1.1,1.5,2.6,2.0,1.6,1.0,0.8,0.3,4.1,1.4,0.5,1.6,4.4,7.8,0.9,2.2,4.8,1.4,3.0,1.9,2.9,3.3,2.7,3.5,2.2,2.6,3.4,1.2,2.0,0.6,1.9,0.4,0.7,3.8,2.7,0.1,0.2,0.9,2.7,1.9,3.5,3.7,0.9,0.6,0.7,1.2,0.5,0.6,1.5,1.1,2.8,0.4,2.3,0.6,1.6,0.7,0.9,0.8,0.8,0.4,0.3,1.5,1.6,2.6,1.0,1.2,0.3,1.2,1.3,3.9,0.3,5,21,7,8,12,21,16,13,13,7,4.0,30,14,6,13,45,67,7,21,43,12,31,15,28,32,29,38,21,19,37,14,13,6,23,2,7,36,32,2,2,4,23,20,21,25,8,3,5,6,5,5,12,10,21,2,20,6,14,6,9,6,5,4,3,13,15,21,6,8,3,10,9,30,2,0.039,0.025,0.018,0.021,0.037,0.036,0.03,0.028,0.04,0.04,0.028,0.045,0.031,0.033,0.039,0.03,0.028,0.034,0.029,0.039,0.024,0.032,0.034,0.038,0.034,0.026,0.028,0.024,0.028,0.031,0.039,0.091,0.028,0.026,0.014,0.02,0.038,0.027,0.013,0.015,0.019,0.037,0.036,0.035,0.029,0.02,0.024,0.013,0.015,0.013,0.029,0.02,0.024,0.024,0.028,0.018,0.04,0.019,0.02,0.019,0.036,0.02,0.034,0.022,0.027,0.032,0.022,0.019,0.025,0.016,0.029,0.025,0.017,0.015,1370,3558,1783,2355,1445,3229,2558,2462,1538,1309,701.0,2570,2824,971,2602,8772,17063,2098,3930,4801,3171,4757,1884,4596,5530,5581,5506,3805,4735,5178,1780,1059,1075,4858,1669,1868,4812,6631,415,507,1418,2884,3800,2591,2652,1559,855,2342,2430,1210,606,2563,2002,4949,487,3728,817,3184,877,1456,691,1884,670,680,1892,1185,4229,1802,1213,772,1546,1580,8470,598,0.125,0.124,0.107,0.103,0.133,0.143,0.12,0.129,0.136,0.14,0.112,0.16,0.137,0.144,0.149,0.13,0.126,0.132,0.13,0.15,0.103,0.139,0.143,0.143,0.139,0.128,0.133,0.11,0.108,0.141,0.15,0.183,0.134,0.127,0.07,0.094,0.128,0.118,0.09,0.099,0.096,0.149,0.136,0.134,0.133,0.114,0.124,0.073,0.088,0.105,0.14,0.1,0.117,0.11,0.12,0.107,0.144,0.104,0.115,0.117,0.141,0.099,0.157,0.111,0.119,0.14,0.11,0.102,0.129,0.097,0.137,0.12,0.095,0.096,356,1279,709,816,623,1278,972,886,525,334,199.0,1436,808,271,709,2400,4601,434,1210,2093,920,1583,875,1259,1662,1742,2095,1379,1418,1735,499,477,357,1213,438,665,1620,1647,215,245,689,1252,1036,1623,1989,726,352,942,1132,546,339,1178,802,2035,258,1999,358,1463,552,739,413,628,217,286,1006,705,1984,835,761,360,706,803,3611,281,3.567,2.554,2.411,2.899,2.118,2.371,2.223,2.555,2.206,3.114,2.936,1.702,2.663,2.588,2.749,2.781,2.935,3.478,2.729,2.002,2.688,2.424,1.964,2.694,2.628,2.712,2.168,2.225,2.651,2.421,2.685,2.628,2.505,2.896,3.155,2.618,2.364,2.971,2.202,2.35,2.567,2.004,2.873,1.865,1.551,2.245,2.989,2.925,2.646,2.674,2.041,2.215,2.186,2.399,2.252,2.09,2.575,2.506,1.864,2.189,2.121,2.789,2.67,2.299,2.012,2.249,2.308,2.491,1.925,2.45,2.601,2.35,2.506,2.506,0.838,0.815,0.772,0.499,0.478,0.525,0.537,0.457,0.629,0.728,0.526,0.558,0.455,0.495,0.506,0.695,0.629,0.695,0.59,0.604,0.795,0.46,0.436,0.711,0.518,0.513,0.452,0.504,0.602,0.503,0.635,0.785,0.406,0.661,0.627,0.704,0.642,0.59,0.513,0.393,0.522,0.507,0.622,0.733,0.455,0.733,0.501,0.837,0.668,0.508,0.406,0.608,0.712,0.49,0.31,0.411,0.559,0.537,0.361,0.393,0.387,0.838,0.48,0.965,0.483,0.525,0.471,0.388,0.452,0.608,0.408,0.522,0.639,0.528
-158,Epileptic,M,22.0,1.2,2.1,1.1,1.5,2.1,2.1,3.0,2.2,1.8,0.9,0.4,3.0,1.8,0.4,1.1,5.5,9.5,0.9,2.7,4.9,1.4,4.0,2.2,3.7,4.3,4.0,4.6,4.0,3.0,3.6,1.6,0.8,0.4,1.9,0.4,0.7,3.7,3.4,0.2,0.1,1.1,3.6,2.1,3.2,3.1,0.7,0.6,0.9,1.2,0.8,0.8,2.1,1.9,2.6,0.3,2.6,0.6,1.8,1.0,1.3,1.1,1.9,0.5,0.8,1.8,1.0,2.7,1.8,1.4,0.6,0.8,1.7,5.1,0.3,7,21,11,11,17,21,20,20,17,11,4.0,33,20,7,16,70,106,8,30,46,13,51,24,40,50,44,48,35,32,44,22,7,3,25,2,5,37,56,1,1,7,35,19,21,16,4,3,4,6,7,4,19,16,17,2,17,4,19,7,10,8,16,4,6,10,12,20,11,7,5,6,14,34,3,0.046,0.023,0.024,0.026,0.038,0.03,0.04,0.033,0.049,0.047,0.053,0.04,0.031,0.036,0.029,0.035,0.033,0.035,0.04,0.044,0.029,0.043,0.039,0.041,0.039,0.03,0.04,0.039,0.036,0.036,0.043,0.041,0.033,0.027,0.01,0.016,0.04,0.035,0.016,0.01,0.022,0.037,0.035,0.03,0.021,0.014,0.018,0.014,0.018,0.021,0.028,0.022,0.023,0.018,0.017,0.016,0.017,0.024,0.019,0.019,0.031,0.025,0.019,0.023,0.019,0.018,0.018,0.021,0.02,0.017,0.022,0.018,0.02,0.024,1541,5172,2610,2684,2533,4206,3351,3739,1833,1685,667.0,3763,4092,1225,2499,11807,22423,2758,4806,5512,3940,6626,2777,6678,8002,8740,7228,4720,6689,6689,2749,875,727,6313,2312,1939,7281,8933,298,374,1381,4008,5674,3105,3901,1376,1020,2367,2417,1866,656,3741,3030,5331,591,4548,940,3105,1610,2184,1317,3514,768,1118,3027,1238,4626,2933,2098,1226,1186,2729,9321,397,0.134,0.116,0.109,0.111,0.129,0.133,0.119,0.14,0.148,0.166,0.139,0.137,0.131,0.142,0.125,0.137,0.132,0.126,0.151,0.154,0.109,0.148,0.133,0.137,0.138,0.133,0.132,0.124,0.116,0.145,0.142,0.104,0.104,0.122,0.063,0.088,0.136,0.149,0.097,0.093,0.112,0.135,0.13,0.124,0.103,0.084,0.093,0.077,0.086,0.113,0.121,0.117,0.112,0.096,0.104,0.093,0.103,0.123,0.102,0.104,0.128,0.123,0.103,0.121,0.1,0.11,0.099,0.11,0.104,0.12,0.107,0.109,0.099,0.132,413,1518,820,946,971,1230,1133,1146,578,391,177.0,1487,1029,299,702,3007,5571,508,1220,2322,929,1745,1031,1900,2128,2353,2304,1721,1622,1836,756,436,230,1325,542,629,1711,1934,149,161,709,1659,1169,1697,2219,694,464,908,1086,615,430,1568,1287,2192,274,2304,479,1368,822,1092,539,1226,416,575,1332,676,2330,1216,965,476,584,1418,3905,225,3.307,2.925,2.947,2.763,2.273,2.575,2.44,2.814,2.321,3.084,2.932,2.079,2.92,2.984,2.599,2.797,3.016,3.735,2.932,2.041,3.21,2.794,2.27,2.658,2.759,2.77,2.42,2.161,2.987,2.722,2.71,2.204,2.413,3.234,3.616,2.715,2.98,3.195,2.053,2.374,2.433,2.063,3.481,2.042,2.014,2.305,2.739,3.158,2.622,2.902,1.958,2.338,2.275,2.511,2.269,2.212,2.288,2.441,2.086,2.09,2.149,2.597,2.047,2.232,2.442,2.181,2.098,2.678,2.517,2.546,2.306,2.344,2.534,2.01,1.007,0.682,0.607,0.476,0.703,0.761,0.62,0.48,0.692,0.796,0.886,0.592,0.491,0.372,0.637,0.71,0.7,0.813,0.68,0.667,0.852,0.708,0.624,0.794,0.726,0.664,0.613,0.656,0.576,0.641,0.743,1.061,0.532,0.679,0.858,0.695,0.837,0.692,0.412,0.378,0.565,0.659,0.683,0.609,0.622,0.374,0.471,0.651,0.408,0.592,0.345,0.537,0.527,0.481,0.513,0.402,0.464,0.598,0.435,0.436,0.508,0.738,0.458,0.59,0.583,0.711,0.449,0.477,0.449,1.116,0.439,0.436,0.504,0.48
-159,Epileptic,M,27.0,2.0,2.9,1.1,1.2,1.3,1.7,1.5,1.4,1.1,0.8,0.5,3.6,1.7,0.6,1.7,7.0,10.3,0.9,2.1,4.9,2.0,3.4,3.2,4.6,6.6,3.5,5.5,3.0,3.2,4.9,1.8,1.4,0.4,2.5,0.4,0.7,3.2,3.5,0.2,0.4,1.0,3.2,2.7,3.8,2.2,0.7,0.7,0.9,1.4,0.6,0.6,2.8,1.3,2.7,0.6,2.8,0.6,1.9,1.7,1.1,0.4,1.3,0.9,0.8,1.8,1.3,2.4,1.3,1.1,0.8,1.1,1.7,3.7,0.3,10,28,12,13,14,20,17,16,11,11,5.0,34,21,7,21,76,97,11,24,47,19,41,31,41,64,34,52,29,28,55,21,12,4,27,2,10,36,39,2,3,5,29,25,32,13,6,3,5,7,5,6,21,10,18,4,26,5,17,10,9,3,10,5,5,15,17,19,9,6,12,7,12,28,2,0.057,0.026,0.02,0.024,0.03,0.025,0.023,0.027,0.034,0.036,0.031,0.044,0.03,0.038,0.03,0.038,0.034,0.035,0.033,0.043,0.034,0.043,0.047,0.044,0.05,0.03,0.033,0.03,0.03,0.041,0.045,0.058,0.023,0.032,0.012,0.013,0.036,0.034,0.016,0.018,0.022,0.042,0.037,0.03,0.017,0.012,0.02,0.013,0.018,0.013,0.026,0.026,0.024,0.02,0.015,0.018,0.021,0.023,0.026,0.02,0.012,0.027,0.032,0.026,0.02,0.028,0.017,0.017,0.016,0.032,0.019,0.02,0.015,0.018,1773,5655,2596,2707,2160,3776,2938,2577,2044,1867,1081.0,3522,3930,1029,3423,11584,19785,2689,3824,5594,4893,4132,2190,7040,6009,7324,6739,3804,6325,7031,3101,1546,945,6464,2143,2288,6965,7687,410,763,1481,2432,6175,3802,3075,1885,954,2411,2384,1482,684,3881,1885,4933,808,4662,1185,3281,1431,1493,903,2124,839,1072,2880,1557,3923,2296,1912,1135,1816,2809,8374,393,0.148,0.12,0.107,0.113,0.119,0.123,0.105,0.137,0.148,0.157,0.131,0.162,0.133,0.142,0.141,0.152,0.14,0.134,0.149,0.162,0.124,0.128,0.15,0.156,0.156,0.124,0.126,0.117,0.106,0.151,0.162,0.134,0.113,0.128,0.069,0.094,0.14,0.138,0.097,0.105,0.098,0.142,0.151,0.134,0.092,0.086,0.104,0.083,0.092,0.096,0.125,0.115,0.121,0.103,0.111,0.102,0.114,0.113,0.124,0.106,0.078,0.125,0.151,0.11,0.112,0.128,0.1,0.098,0.092,0.138,0.105,0.104,0.094,0.118,494,1963,907,954,822,1209,1033,929,581,451,298.0,1518,1171,296,1044,3549,5545,580,1048,2219,1089,1385,1132,1973,2285,2072,2649,1599,1749,2168,863,547,330,1410,520,770,1740,1823,250,406,710,1297,1261,2166,1914,913,460,984,1128,685,403,1862,863,2170,481,2384,480,1454,895,818,466,830,388,517,1407,789,2065,1102,924,451,874,1313,3630,201,3.428,2.728,2.77,2.693,2.23,2.491,2.345,2.431,2.574,3.184,3.022,1.989,2.671,2.497,2.523,2.524,2.771,3.681,2.789,2.131,3.187,2.337,1.807,2.746,2.214,2.8,2.209,2.031,2.707,2.508,2.749,2.752,2.432,3.195,3.571,2.561,2.907,3.033,1.89,2.175,2.628,1.689,3.323,2.019,1.811,2.303,2.501,2.97,2.543,2.445,1.987,2.174,2.152,2.279,2.027,2.046,2.48,2.452,1.839,1.945,2.194,2.463,2.026,2.471,2.135,1.976,2.007,2.358,2.321,2.507,2.391,2.475,2.514,2.401,0.846,0.545,0.615,0.533,0.653,0.694,0.618,0.507,0.65,0.624,0.83,0.59,0.395,0.412,0.453,0.558,0.704,0.746,0.549,0.649,0.829,0.619,0.592,0.743,0.624,0.514,0.581,0.565,0.58,0.681,0.67,1.049,0.334,0.583,0.789,0.573,0.803,0.61,0.323,0.391,0.388,0.474,0.833,0.642,0.534,0.461,0.443,0.696,0.398,0.4,0.461,0.412,0.482,0.494,0.509,0.42,0.474,0.513,0.371,0.403,0.342,0.79,0.611,0.768,0.472,0.686,0.464,0.394,0.462,0.72,0.412,0.48,0.461,0.402
-160,Epileptic,M,37.0,1.2,2.3,0.8,1.3,1.3,1.7,1.8,1.8,0.9,0.8,0.3,3.1,1.2,0.7,1.0,7.8,9.3,1.0,1.8,5.6,1.0,3.8,2.7,4.0,4.1,5.5,3.9,2.4,3.3,3.8,1.2,1.2,0.5,2.4,0.6,1.0,4.3,2.6,0.2,0.2,1.5,4.1,2.2,3.0,3.1,0.6,0.9,0.8,1.1,0.6,0.8,1.7,1.7,2.2,0.5,3.4,0.9,1.8,2.3,1.3,0.3,1.7,0.5,0.3,2.4,1.5,2.9,1.6,1.1,0.6,1.3,1.1,6.2,0.4,8,20,11,10,13,17,19,19,9,9,4.0,29,16,8,13,84,90,8,21,47,10,41,22,42,49,60,45,22,30,42,14,9,5,29,6,11,43,36,3,2,12,33,27,25,17,5,5,6,6,6,5,14,12,17,4,31,7,14,16,11,2,11,2,4,22,20,25,12,7,6,10,8,62,4,0.039,0.022,0.015,0.022,0.027,0.026,0.025,0.026,0.037,0.031,0.028,0.033,0.023,0.028,0.025,0.035,0.027,0.027,0.029,0.038,0.017,0.041,0.035,0.036,0.03,0.03,0.026,0.024,0.024,0.034,0.044,0.038,0.032,0.026,0.016,0.017,0.039,0.026,0.018,0.018,0.021,0.039,0.029,0.024,0.02,0.012,0.032,0.012,0.014,0.019,0.029,0.015,0.025,0.018,0.018,0.021,0.017,0.018,0.032,0.021,0.011,0.028,0.026,0.017,0.022,0.021,0.02,0.018,0.021,0.021,0.027,0.019,0.019,0.03,1707,4893,2269,2547,1962,3231,2541,3863,1301,1502,820.0,3147,3638,1448,2324,13292,19802,2914,4035,5048,3883,5403,2712,6443,6995,8815,7231,3206,6976,6001,2450,1704,942,5728,2739,2692,7780,7292,376,459,2101,3414,6545,3291,3771,1435,1147,2151,2637,1553,866,3334,2167,4368,824,4273,1498,3562,2001,1699,792,2245,593,718,3355,1953,4278,3524,1423,1374,1581,2020,11874,357,0.125,0.112,0.1,0.103,0.116,0.127,0.111,0.12,0.131,0.138,0.13,0.13,0.117,0.13,0.119,0.14,0.124,0.109,0.135,0.142,0.083,0.156,0.132,0.144,0.138,0.134,0.116,0.105,0.101,0.145,0.138,0.115,0.118,0.127,0.081,0.1,0.152,0.125,0.123,0.105,0.115,0.138,0.137,0.111,0.097,0.082,0.115,0.087,0.087,0.109,0.136,0.096,0.121,0.097,0.112,0.118,0.115,0.095,0.136,0.111,0.078,0.124,0.133,0.107,0.113,0.117,0.108,0.096,0.107,0.122,0.13,0.113,0.106,0.157,468,1575,928,966,803,1138,1060,1188,443,449,249.0,1496,927,409,653,3677,5422,544,1053,2329,940,1673,1181,1832,2234,2859,2546,1383,1941,1997,543,623,319,1487,637,988,1813,1766,200,227,1029,1634,1291,1935,2286,782,451,930,1158,572,417,1663,990,1942,448,2442,662,1625,1141,958,363,932,259,399,1624,1035,2295,1462,801,470,701,887,4958,202,3.48,2.867,2.381,2.55,2.286,2.498,2.103,2.897,2.396,2.832,2.85,1.881,2.98,2.831,2.747,2.785,2.915,3.949,2.956,1.894,2.903,2.503,2.069,2.707,2.385,2.587,2.261,1.933,2.79,2.358,3.007,2.632,2.475,2.84,3.557,2.506,3.227,3.044,1.967,2.433,2.505,1.892,3.392,1.903,1.909,1.964,3.129,2.987,2.849,2.847,2.283,2.165,2.3,2.403,2.086,2.032,2.606,2.416,2.032,1.972,2.354,2.357,2.533,1.989,2.163,2.195,1.982,2.667,2.102,2.913,2.36,2.806,2.457,2.149,0.567,0.542,0.532,0.528,0.488,0.435,0.576,0.529,0.601,0.571,0.655,0.546,0.455,0.391,0.399,0.514,0.674,0.686,0.671,0.574,0.872,0.556,0.559,0.614,0.556,0.486,0.548,0.514,0.564,0.608,0.844,0.915,0.408,0.573,0.694,0.568,0.689,0.573,0.406,0.505,0.616,0.536,0.823,0.634,0.604,0.627,0.543,0.636,0.505,0.711,0.429,0.455,0.502,0.507,0.382,0.474,0.53,0.52,0.412,0.404,0.517,0.727,0.453,0.868,0.554,0.664,0.473,0.352,0.477,0.77,0.552,0.48,0.488,0.571
-161,Epileptic,F,32.0,1.3,3.2,1.3,1.6,1.4,2.4,1.8,2.0,1.3,1.1,0.2,2.8,1.6,0.5,1.1,5.8,9.6,1.1,1.8,4.7,0.7,2.7,1.4,3.5,2.7,3.8,3.6,2.6,2.9,3.3,1.4,1.8,0.7,2.5,0.3,0.4,3.5,2.9,0.1,0.3,0.9,3.0,2.2,3.3,2.4,0.8,0.3,1.0,1.3,0.8,0.7,1.5,1.5,3.6,0.1,2.1,0.7,1.3,1.5,0.9,1.0,1.4,0.2,0.4,1.4,1.8,2.1,1.6,1.3,0.4,0.9,1.3,4.0,0.4,9,31,14,13,13,25,16,18,15,13,4.0,23,15,7,13,66,87,10,30,44,6,31,17,38,36,47,41,29,27,34,18,19,6,29,1,2,44,38,1,2,6,28,27,24,17,6,2,7,7,8,5,12,11,31,1,17,6,10,11,7,7,14,1,5,10,20,18,11,7,4,6,10,29,3,0.042,0.026,0.022,0.024,0.032,0.033,0.026,0.028,0.036,0.04,0.026,0.035,0.029,0.032,0.026,0.035,0.033,0.034,0.028,0.039,0.017,0.033,0.024,0.036,0.029,0.034,0.03,0.026,0.029,0.032,0.028,0.059,0.032,0.027,0.009,0.009,0.033,0.027,0.013,0.016,0.018,0.033,0.035,0.028,0.02,0.018,0.015,0.014,0.016,0.015,0.028,0.019,0.028,0.026,0.016,0.015,0.022,0.017,0.024,0.017,0.029,0.021,0.018,0.015,0.022,0.024,0.018,0.021,0.019,0.013,0.024,0.016,0.017,0.017,1751,5678,3115,2854,2165,3505,3166,4126,1926,1799,572.0,2850,3825,1152,2728,11184,19294,2719,3861,4425,2970,5341,2303,6570,6213,7115,5756,4573,6702,5790,2256,1347,1263,7004,2073,1679,8256,7141,389,546,1698,2682,5585,3393,3516,1417,837,2541,2679,1837,567,2840,2364,6467,250,4056,1396,2663,1949,1721,1049,2688,458,867,2357,2213,4395,2842,2200,927,1375,2402,8720,822,0.151,0.118,0.112,0.116,0.124,0.154,0.125,0.131,0.141,0.159,0.109,0.14,0.122,0.135,0.132,0.143,0.137,0.124,0.131,0.148,0.08,0.142,0.13,0.139,0.134,0.153,0.134,0.129,0.124,0.139,0.116,0.157,0.134,0.13,0.057,0.062,0.136,0.129,0.085,0.098,0.1,0.138,0.133,0.128,0.104,0.103,0.094,0.084,0.091,0.102,0.131,0.104,0.121,0.119,0.121,0.097,0.12,0.1,0.119,0.104,0.128,0.121,0.097,0.115,0.112,0.118,0.099,0.107,0.102,0.099,0.113,0.106,0.095,0.105,501,1862,1010,1048,779,1215,1122,1301,566,467,177.0,1240,971,297,719,3114,5059,541,1110,2062,713,1415,929,1680,1756,1937,1973,1555,1696,1696,657,529,358,1446,485,618,1927,1734,197,294,784,1389,1253,1919,1965,689,343,1057,1169,735,368,1260,996,2391,106,2062,549,1209,938,833,541,1012,186,467,1023,1113,1981,1191,984,483,665,1193,3561,310,3.202,2.737,2.872,2.674,2.423,2.538,2.353,2.829,2.446,3.13,2.715,2.031,2.948,2.758,2.76,2.713,2.948,3.574,2.877,1.903,3.218,2.748,2.12,2.94,2.702,2.95,2.33,2.315,2.914,2.733,2.561,2.599,2.814,3.266,3.628,2.565,3.074,2.902,2.104,2.297,2.662,1.824,3.083,2.073,2.058,2.28,3.023,3.075,2.765,2.574,1.966,2.353,2.287,2.598,2.387,2.133,2.863,2.646,2.244,2.254,2.494,2.659,2.353,2.053,2.556,2.374,2.292,2.694,2.724,2.283,2.322,2.314,2.641,3.107,0.707,0.676,0.682,0.532,0.604,0.518,0.495,0.49,0.703,0.534,0.839,0.534,0.381,0.616,0.534,0.63,0.652,0.805,0.644,0.579,0.785,0.542,0.511,0.685,0.541,0.482,0.458,0.602,0.472,0.499,0.588,0.903,0.577,0.623,0.51,0.442,0.767,0.632,0.592,0.391,0.489,0.557,0.797,0.761,0.607,0.509,0.488,0.558,0.554,0.536,0.56,0.482,0.522,0.561,0.54,0.445,0.526,0.458,0.434,0.419,0.463,0.658,0.592,0.896,0.623,0.802,0.529,0.403,0.377,0.55,0.463,0.467,0.51,0.601
-162,Epileptic,F,27.0,0.8,2.2,1.1,1.2,2.1,1.8,1.9,1.6,0.8,0.8,0.2,3.1,1.6,0.4,1.1,6.2,7.9,0.7,1.7,5.2,1.8,3.1,1.5,2.9,2.7,2.6,3.8,3.1,2.4,3.3,2.1,1.0,0.5,1.9,0.2,0.7,5.1,2.7,0.2,0.1,1.0,2.4,2.2,3.4,2.4,0.5,0.3,0.6,0.9,1.1,0.7,1.3,1.0,2.1,0.1,2.4,0.6,1.6,1.2,0.8,0.5,1.4,0.2,0.4,1.5,1.4,1.8,1.8,1.3,0.5,1.0,1.0,4.4,0.3,5,21,11,10,22,15,18,14,11,8,3.0,28,20,6,10,65,78,4,15,42,13,35,16,30,36,32,43,34,25,41,25,6,5,29,2,6,45,32,1,1,6,22,22,26,14,4,1,5,5,6,5,9,8,16,1,17,4,13,8,6,3,10,1,3,12,20,14,14,8,4,7,8,38,2,0.033,0.025,0.019,0.022,0.04,0.033,0.027,0.026,0.038,0.031,0.025,0.035,0.034,0.025,0.032,0.035,0.03,0.036,0.036,0.041,0.036,0.035,0.032,0.035,0.033,0.029,0.029,0.03,0.027,0.03,0.051,0.038,0.032,0.036,0.011,0.02,0.041,0.032,0.014,0.014,0.021,0.038,0.038,0.029,0.018,0.01,0.014,0.011,0.013,0.02,0.042,0.015,0.017,0.018,0.021,0.018,0.017,0.025,0.032,0.015,0.02,0.024,0.021,0.018,0.02,0.027,0.022,0.021,0.021,0.016,0.021,0.025,0.019,0.016,1544,4906,2502,2411,3082,2893,3550,3386,1978,1554,614.0,3100,3413,1167,2700,12026,17532,2001,3209,4998,3561,4814,2140,6258,6047,6288,6667,5029,5660,6842,2706,1282,907,5106,1539,1897,7204,6714,370,603,1516,2581,4503,2941,3603,1410,791,2017,2087,1956,598,3040,2139,4909,254,3927,1285,2565,1147,1465,853,2429,283,822,2741,1506,2859,2890,2151,1139,1582,1695,9127,497,0.12,0.119,0.104,0.109,0.15,0.144,0.121,0.124,0.137,0.14,0.116,0.146,0.134,0.113,0.132,0.143,0.129,0.107,0.127,0.148,0.119,0.15,0.137,0.137,0.144,0.131,0.13,0.136,0.122,0.133,0.16,0.12,0.131,0.134,0.068,0.099,0.154,0.132,0.082,0.097,0.11,0.145,0.13,0.122,0.096,0.081,0.09,0.08,0.082,0.105,0.151,0.093,0.092,0.094,0.109,0.1,0.101,0.117,0.144,0.098,0.099,0.118,0.122,0.112,0.104,0.14,0.112,0.115,0.104,0.105,0.113,0.115,0.102,0.112,414,1493,849,890,829,937,1172,1001,415,378,180.0,1374,865,271,619,3004,4505,373,898,2090,894,1476,835,1493,1631,1690,2223,1778,1567,1952,664,462,271,1068,378,612,1953,1474,215,185,688,1135,1142,1730,2051,698,329,882,1007,791,300,1338,924,1982,86,1933,557,1141,614,754,410,896,138,372,1265,819,1347,1304,942,475,717,746,3759,245,3.46,2.915,2.627,2.687,2.742,2.627,2.474,2.956,2.909,2.979,2.809,1.91,2.953,2.956,3.117,2.874,3.008,3.885,2.796,2.054,3.05,2.598,2.217,3.035,2.843,2.872,2.361,2.305,2.718,2.62,2.999,2.62,2.507,3.145,3.373,2.793,2.813,3.208,2.096,3.469,2.846,1.934,2.849,1.938,1.963,2.256,2.816,2.788,2.504,2.957,2.052,2.409,2.4,2.493,2.794,2.274,2.738,2.397,2.2,2.131,2.393,2.695,2.18,2.413,2.358,2.263,2.266,2.508,2.452,2.803,2.583,2.544,2.618,2.493,0.706,0.868,0.638,0.496,0.653,0.629,0.528,0.561,0.819,0.51,0.649,0.544,0.544,0.505,0.61,0.577,0.595,0.597,0.464,0.572,0.924,0.438,0.495,0.688,0.569,0.575,0.533,0.511,0.602,0.618,0.711,0.98,0.519,0.647,0.628,0.664,0.651,0.553,0.358,0.705,0.6,0.547,0.693,0.634,0.578,0.371,0.529,0.513,0.512,0.563,0.459,0.446,0.572,0.537,0.467,0.448,0.634,0.591,0.433,0.385,0.395,0.64,0.587,0.8,0.527,0.646,0.48,0.466,0.509,0.587,0.522,0.554,0.517,0.723
-163,Epileptic,F,27.0,1.1,2.5,1.7,1.1,1.1,1.8,1.2,2.4,0.9,0.6,0.2,1.9,1.9,0.6,1.1,6.4,8.8,0.9,1.8,4.2,1.1,2.6,1.3,4.1,2.9,1.8,1.8,1.7,2.0,2.0,1.2,0.7,0.3,2.1,0.3,0.4,3.8,3.2,0.2,0.2,1.0,2.2,3.0,3.0,2.3,0.9,0.4,1.0,1.2,1.1,0.5,1.7,1.8,3.6,0.0,1.7,0.6,1.6,0.7,0.5,0.8,1.0,0.4,0.4,1.0,1.1,1.6,1.4,0.7,0.6,0.9,1.8,4.1,0.3,6,22,16,9,11,13,13,20,9,8,3.0,18,18,7,13,57,78,8,22,36,12,28,12,29,32,20,22,18,22,21,17,4,3,25,3,2,41,36,1,1,6,22,20,24,15,9,2,7,7,9,5,13,14,31,0,15,4,13,6,6,7,8,3,4,8,13,12,8,6,8,6,14,31,2,0.051,0.028,0.029,0.017,0.027,0.036,0.027,0.033,0.05,0.034,0.036,0.034,0.031,0.031,0.03,0.034,0.035,0.032,0.027,0.04,0.023,0.031,0.034,0.051,0.037,0.024,0.021,0.026,0.022,0.027,0.042,0.03,0.022,0.027,0.015,0.014,0.034,0.032,0.014,0.022,0.027,0.03,0.041,0.033,0.017,0.029,0.022,0.021,0.017,0.028,0.028,0.018,0.026,0.023,0.029,0.018,0.015,0.022,0.019,0.013,0.024,0.025,0.032,0.022,0.018,0.025,0.02,0.021,0.02,0.024,0.024,0.022,0.018,0.015,1290,4013,2268,2103,1732,2046,1998,3343,1187,1240,452.0,1742,3566,1328,2725,10751,14112,2272,3921,4204,2443,3940,1615,4936,4192,4034,3484,3560,3231,3510,1651,708,636,4598,1556,893,6629,6369,351,380,1165,2215,3768,2211,3187,1483,653,1970,2459,1507,476,3063,2660,5914,107,2620,1288,2536,850,1024,1215,1666,496,677,1619,1190,2515,1635,967,1192,1202,2723,7095,388,0.133,0.111,0.111,0.096,0.129,0.146,0.126,0.146,0.148,0.138,0.141,0.147,0.14,0.144,0.126,0.128,0.12,0.12,0.126,0.16,0.098,0.142,0.147,0.136,0.155,0.122,0.116,0.122,0.106,0.13,0.145,0.104,0.107,0.125,0.07,0.077,0.131,0.139,0.083,0.107,0.117,0.138,0.132,0.136,0.097,0.144,0.099,0.093,0.091,0.119,0.132,0.1,0.126,0.114,0.15,0.105,0.09,0.115,0.107,0.089,0.114,0.117,0.131,0.127,0.105,0.12,0.104,0.105,0.103,0.135,0.117,0.12,0.099,0.103,364,1426,945,946,670,837,713,1137,382,325,137.0,915,1088,321,704,3204,4247,453,1060,1740,786,1339,617,1191,1332,1250,1388,1092,1357,1137,511,341,245,1167,455,430,1777,1683,242,151,580,1103,1153,1429,1848,539,286,867,1104,645,325,1565,1171,2550,46,1466,633,1220,591,620,643,704,223,325,867,656,1247,1057,604,492,597,1261,3292,208,3.315,2.573,2.432,2.239,2.186,2.283,2.28,2.577,2.421,2.851,2.85,1.711,2.699,2.81,2.804,2.612,2.624,3.653,2.888,2.119,2.693,2.333,2.193,2.881,2.515,2.664,2.081,2.463,1.953,2.461,2.473,2.091,2.271,2.779,3.086,2.023,2.888,2.935,1.654,2.935,2.528,1.804,2.672,1.752,1.917,2.65,2.629,2.769,2.657,2.514,1.79,2.112,2.21,2.305,2.49,1.942,2.433,2.412,1.726,1.8,2.106,2.357,2.384,2.261,2.037,2.138,2.142,1.839,1.775,2.709,2.335,2.367,2.326,2.16,0.892,0.645,0.535,0.593,0.724,0.436,0.663,0.455,0.448,0.468,0.666,0.365,0.522,0.346,0.662,0.648,0.652,0.814,0.533,0.716,0.808,0.555,0.409,0.632,0.574,0.657,0.58,0.546,0.521,0.516,0.66,0.81,0.556,0.612,0.651,0.449,0.734,0.646,0.337,0.38,0.674,0.5,0.845,0.601,0.45,0.612,0.661,0.878,0.609,0.561,0.363,0.481,0.546,0.57,0.276,0.463,0.477,0.494,0.361,0.351,0.443,0.685,0.648,0.699,0.558,0.718,0.528,0.477,0.543,0.801,0.547,0.498,0.577,0.44
-164,Epileptic,F,42.0,0.7,1.9,0.9,0.9,1.2,1.4,1.4,1.6,1.3,0.7,0.2,2.7,1.2,0.4,0.9,3.7,9.0,0.9,1.4,3.9,1.2,6.3,1.8,3.2,3.1,2.9,2.2,2.2,2.8,2.6,1.3,1.0,0.8,2.1,0.3,0.9,2.5,2.9,0.1,0.1,1.3,3.9,2.1,2.0,2.9,0.6,0.6,0.9,1.6,0.9,0.5,1.2,0.8,1.7,0.2,1.9,0.5,1.1,0.7,1.3,0.3,1.1,0.4,0.5,1.9,0.7,2.8,0.8,0.9,0.5,0.8,1.1,4.3,0.2,6,18,7,8,13,16,16,15,16,6,2.0,24,13,4,9,34,83,9,19,33,11,50,17,29,37,29,23,23,20,30,16,6,5,24,1,7,26,31,2,1,7,29,23,17,19,5,3,6,8,7,2,9,5,11,1,17,4,9,6,12,2,6,2,4,15,13,20,6,5,5,5,8,34,2,0.032,0.023,0.02,0.021,0.029,0.029,0.035,0.027,0.047,0.038,0.023,0.039,0.028,0.04,0.029,0.034,0.035,0.036,0.028,0.042,0.026,0.053,0.034,0.04,0.041,0.028,0.03,0.027,0.031,0.032,0.044,0.05,0.039,0.029,0.012,0.02,0.035,0.036,0.015,0.014,0.023,0.044,0.039,0.026,0.022,0.021,0.02,0.016,0.022,0.016,0.026,0.02,0.017,0.022,0.028,0.021,0.022,0.017,0.023,0.024,0.016,0.025,0.028,0.025,0.02,0.026,0.022,0.017,0.021,0.023,0.023,0.019,0.019,0.017,1629,3776,1692,2055,1626,3087,2228,3220,1978,1429,492.0,2794,2894,749,1869,6837,18426,2322,3782,3939,4174,3582,1922,5722,4303,7012,4018,4499,5480,5303,2250,831,1034,5318,1834,2380,5329,6465,460,400,1585,2320,5993,2142,3714,1067,815,2050,2153,1773,475,1906,1505,3002,280,3033,811,2383,1050,1742,685,1874,322,829,3015,723,4354,1694,1377,679,1068,1669,8419,399,0.117,0.124,0.113,0.102,0.136,0.128,0.126,0.117,0.177,0.149,0.083,0.142,0.121,0.139,0.137,0.145,0.143,0.142,0.129,0.148,0.105,0.144,0.117,0.15,0.141,0.125,0.132,0.115,0.121,0.137,0.134,0.12,0.134,0.126,0.068,0.103,0.141,0.132,0.089,0.091,0.109,0.147,0.144,0.131,0.107,0.117,0.109,0.091,0.106,0.097,0.119,0.105,0.103,0.104,0.14,0.109,0.119,0.102,0.104,0.098,0.083,0.114,0.136,0.125,0.109,0.123,0.111,0.098,0.103,0.139,0.115,0.112,0.101,0.112,390,1304,680,695,667,849,817,1049,529,334,157.0,1153,781,214,514,1893,4686,520,909,1622,890,1545,827,1374,1292,1812,1281,1455,1454,1463,531,350,282,1264,431,742,1279,1471,173,217,812,1253,1070,1240,2032,461,382,861,1063,737,262,884,722,1257,126,1492,384,1056,577,937,281,664,197,353,1482,484,2019,718,624,374,522,857,3561,213,3.642,2.535,2.579,2.714,1.936,2.876,2.186,2.629,2.7,3.154,2.579,2.028,2.781,2.809,2.667,2.69,2.971,3.406,3.107,2.11,3.338,2.065,2.17,3.087,2.517,2.94,2.425,2.409,2.913,2.79,2.743,2.466,2.742,3.025,3.501,2.871,2.914,3.016,2.497,1.975,2.446,1.683,3.466,2.025,2.098,2.285,2.55,3.028,2.456,2.509,2.266,2.141,1.933,2.43,2.557,2.135,2.378,2.513,2.026,1.94,2.702,2.548,1.864,2.414,2.252,1.909,2.339,2.487,2.784,1.982,2.505,2.329,2.374,2.493,0.815,0.834,0.426,0.543,0.759,0.546,0.58,0.556,0.633,0.597,0.831,0.577,0.484,0.553,0.472,0.705,0.675,0.7,0.583,0.59,0.938,0.726,0.491,0.687,0.794,0.611,0.648,0.575,0.632,0.634,0.748,0.692,0.383,0.575,0.749,0.575,0.81,0.759,0.33,0.448,0.477,0.613,0.93,0.621,0.581,0.489,0.565,0.779,0.444,0.789,0.456,0.54,0.61,0.52,0.435,0.417,0.413,0.668,0.356,0.513,0.439,0.748,0.49,0.825,0.45,0.549,0.494,0.541,0.494,0.479,0.422,0.612,0.535,0.527
-165,Epileptic,F,22.0,0.7,2.4,0.7,1.2,1.5,2.1,1.4,1.7,1.0,0.8,0.3,3.3,1.7,0.4,1.1,7.8,8.3,0.9,1.9,5.0,1.4,3.2,1.5,3.0,4.3,3.0,3.7,2.7,3.3,3.5,1.7,1.0,0.6,2.2,0.4,0.7,3.3,3.5,0.2,0.1,1.1,3.3,2.1,4.4,3.7,0.5,0.3,0.6,0.9,0.6,0.6,2.0,1.2,2.7,0.4,3.0,0.7,1.6,1.5,1.2,0.8,1.9,0.5,0.5,1.9,1.2,1.9,1.4,1.5,0.4,1.1,1.3,4.0,0.4,3,20,8,10,18,19,15,15,9,8,7.0,31,20,5,10,84,90,10,23,42,19,38,18,34,48,28,45,31,29,39,23,7,9,26,3,7,36,40,2,1,6,30,19,28,24,4,1,4,4,4,4,14,9,21,2,27,4,12,13,9,5,12,4,4,18,16,15,11,11,5,7,9,35,3,0.033,0.029,0.018,0.023,0.036,0.039,0.031,0.034,0.047,0.042,0.031,0.034,0.041,0.032,0.036,0.041,0.032,0.037,0.033,0.038,0.025,0.039,0.027,0.033,0.039,0.035,0.03,0.03,0.029,0.032,0.049,0.039,0.03,0.026,0.013,0.018,0.039,0.037,0.015,0.01,0.021,0.037,0.041,0.034,0.023,0.011,0.016,0.01,0.015,0.01,0.03,0.02,0.02,0.022,0.025,0.021,0.024,0.019,0.033,0.021,0.022,0.027,0.024,0.022,0.022,0.024,0.017,0.02,0.023,0.021,0.021,0.021,0.018,0.019,1601,4538,1962,1800,2463,3306,2471,3166,1881,1312,597.0,3289,3064,1386,2331,12401,18701,2507,4550,5161,5090,5178,2527,6757,6364,5725,6857,4577,6214,5952,2349,1378,1022,6137,2054,2287,7074,7154,463,373,1704,3735,5769,2905,4025,1228,917,2294,1996,1653,648,2746,2084,5290,649,4475,1074,3518,1513,1490,818,3145,533,845,2973,1040,3223,2577,2111,1393,1523,2125,8345,780,0.102,0.126,0.101,0.116,0.143,0.155,0.113,0.119,0.144,0.15,0.133,0.15,0.144,0.118,0.135,0.15,0.132,0.129,0.132,0.142,0.107,0.155,0.126,0.131,0.161,0.136,0.132,0.113,0.109,0.138,0.156,0.128,0.151,0.116,0.076,0.096,0.138,0.137,0.116,0.075,0.105,0.151,0.144,0.135,0.112,0.084,0.084,0.075,0.088,0.082,0.127,0.102,0.104,0.108,0.124,0.11,0.119,0.099,0.142,0.11,0.127,0.119,0.134,0.123,0.115,0.124,0.102,0.106,0.107,0.11,0.118,0.119,0.099,0.109,354,1375,743,769,809,966,852,929,438,372,202.0,1508,800,289,568,3559,4842,440,1062,2182,1079,1573,1021,1667,1840,1527,2182,1510,1805,1969,649,472,344,1316,471,754,1523,1656,201,187,757,1506,983,1879,2417,637,315,888,880,753,365,1397,1048,2200,265,2296,447,1502,785,867,440,1053,258,404,1461,721,1653,1150,1073,443,758,942,3508,299,3.834,2.879,2.394,2.324,2.517,2.699,2.328,2.797,2.832,2.858,2.414,1.919,2.901,3.258,2.932,2.643,2.98,3.969,3.118,2.045,3.241,2.521,2.081,2.995,2.632,2.858,2.398,2.321,2.693,2.43,2.777,2.637,2.572,3.162,3.78,2.716,3.279,2.972,2.819,2.129,2.804,2.117,3.641,1.785,1.864,2.046,3.404,2.923,2.763,2.554,2.194,2.123,2.065,2.345,2.954,2.134,2.804,2.635,2.098,1.947,2.243,2.726,1.983,2.148,2.184,1.847,2.192,2.365,2.252,3.124,2.307,2.614,2.478,3.145,0.977,0.707,0.59,0.521,0.798,0.616,0.616,0.699,0.667,0.411,0.819,0.495,0.573,0.435,0.52,0.631,0.649,0.608,0.582,0.574,0.766,0.51,0.51,0.796,0.729,0.692,0.493,0.642,0.709,0.514,0.782,1.083,0.516,0.672,0.664,0.565,0.774,0.633,0.435,0.474,0.655,0.5,0.74,0.6,0.589,0.535,0.553,0.84,0.608,0.486,0.346,0.438,0.471,0.507,0.713,0.484,0.533,0.595,0.403,0.487,0.428,0.716,0.55,0.922,0.529,0.584,0.583,0.488,0.488,0.754,0.474,0.584,0.572,0.472
-166,Epileptic,F,32.0,0.5,1.4,0.9,1.6,1.2,1.3,1.5,2.1,1.6,1.1,0.3,2.1,1.1,0.3,1.9,7.6,8.6,0.6,2.4,3.3,0.9,2.7,1.1,3.0,1.9,4.7,3.0,2.6,2.5,4.1,0.9,1.0,0.6,2.4,0.5,0.5,3.9,2.2,0.1,0.2,1.4,2.1,2.1,1.9,2.5,0.6,0.5,0.8,1.0,0.6,0.6,1.8,1.9,2.8,0.3,1.8,0.9,1.3,0.9,1.0,0.6,1.3,0.3,0.5,1.4,1.2,3.3,1.0,0.6,0.9,1.1,1.5,2.9,0.2,3,15,9,15,13,14,15,18,18,10,3.0,21,16,7,21,87,91,5,28,30,8,35,15,34,23,54,35,27,23,44,15,8,9,29,2,5,42,28,1,1,8,21,19,16,19,7,3,5,6,5,3,15,14,21,1,18,8,12,7,8,7,8,2,4,12,20,31,9,5,11,9,13,29,2,0.022,0.018,0.015,0.032,0.035,0.026,0.026,0.032,0.047,0.047,0.033,0.033,0.028,0.03,0.04,0.037,0.027,0.022,0.033,0.036,0.021,0.037,0.031,0.039,0.029,0.038,0.03,0.027,0.027,0.037,0.033,0.052,0.025,0.032,0.018,0.016,0.032,0.03,0.019,0.013,0.027,0.032,0.036,0.023,0.02,0.018,0.014,0.015,0.015,0.023,0.039,0.02,0.023,0.02,0.025,0.021,0.021,0.016,0.029,0.02,0.032,0.027,0.018,0.019,0.024,0.025,0.022,0.016,0.013,0.028,0.02,0.015,0.017,0.012,1522,3958,2177,2197,1519,1972,2738,3169,2069,1113,416.0,2018,3046,806,3681,13314,17935,2360,4125,3709,2717,4382,1461,5134,3684,7625,4798,4069,5403,6046,2136,771,977,4847,1611,1454,7031,5718,404,410,1704,2190,4133,2185,2959,1267,1030,1761,2485,1196,424,2884,2421,5660,236,2935,1498,2750,1050,1438,938,2021,501,815,1991,1136,4493,2090,1449,1306,1862,2871,6594,660,0.095,0.101,0.102,0.144,0.134,0.134,0.123,0.147,0.155,0.167,0.134,0.139,0.122,0.136,0.162,0.149,0.13,0.099,0.135,0.146,0.084,0.156,0.145,0.139,0.144,0.164,0.14,0.131,0.121,0.157,0.122,0.141,0.125,0.132,0.076,0.096,0.129,0.129,0.11,0.089,0.128,0.149,0.137,0.116,0.108,0.11,0.099,0.086,0.087,0.117,0.145,0.104,0.118,0.106,0.142,0.117,0.106,0.102,0.131,0.112,0.132,0.118,0.104,0.105,0.113,0.128,0.124,0.104,0.093,0.117,0.116,0.104,0.105,0.081,385,1278,813,842,601,766,898,1070,584,383,149.0,982,796,228,879,3658,5085,446,1262,1531,724,1420,666,1390,1146,2255,1790,1501,1475,1865,541,348,375,1284,466,548,1997,1389,154,207,828,1088,999,1409,1917,601,436,793,1109,518,265,1403,1222,2290,128,1446,760,1273,564,759,435,749,277,436,1002,837,2280,1000,675,504,794,1337,2858,363,3.627,2.888,2.621,2.611,2.096,2.246,2.512,2.59,2.506,2.521,2.848,1.83,2.829,2.582,2.931,2.722,2.802,3.617,2.637,2.062,2.949,2.449,2.0,2.637,2.46,2.817,2.194,2.193,2.78,2.66,2.769,2.366,2.131,2.881,3.118,2.588,2.802,3.1,2.646,2.191,2.68,1.854,3.167,1.73,1.733,2.227,2.908,2.53,2.664,2.854,2.116,2.076,2.149,2.538,2.148,2.122,2.284,2.398,2.178,2.185,2.405,2.783,1.945,2.259,2.135,1.638,2.157,2.449,2.476,2.658,2.625,2.364,2.4,2.2,1.018,0.698,0.616,0.542,0.631,0.471,0.57,0.464,0.611,0.718,1.149,0.524,0.466,0.665,0.52,0.637,0.593,1.084,0.602,0.614,0.765,0.443,0.357,0.702,0.5,0.481,0.468,0.49,0.493,0.546,0.771,0.803,0.415,0.575,0.741,0.543,0.583,0.667,0.543,0.521,0.505,0.493,0.626,0.49,0.494,0.426,0.622,0.788,0.547,0.477,0.407,0.591,0.483,0.559,0.235,0.402,0.404,0.497,0.343,0.362,0.437,0.816,0.594,0.75,0.563,0.529,0.423,0.422,0.352,0.778,0.564,0.531,0.471,0.572
-167,Epileptic,F,27.0,0.5,2.0,0.6,1.1,1.5,1.4,1.3,1.6,1.3,0.8,0.2,2.3,1.2,0.4,1.5,5.3,7.4,0.7,2.5,5.5,1.1,2.5,1.5,2.9,3.2,2.8,2.6,1.9,2.1,3.4,1.3,0.8,0.4,1.9,0.2,0.4,3.6,3.5,0.1,0.2,0.7,2.0,2.0,2.4,1.8,0.6,0.5,0.9,1.2,0.8,0.5,1.5,1.2,2.6,0.3,1.8,0.8,1.8,0.5,1.4,0.6,1.4,0.5,0.3,1.3,1.4,2.4,1.1,0.8,0.2,0.7,1.5,3.8,0.3,6,20,7,10,16,15,12,15,22,6,2.0,21,17,5,16,57,78,6,30,39,8,29,11,32,32,32,33,20,20,39,13,5,5,31,2,3,41,38,2,1,4,17,20,18,13,5,2,6,6,6,3,13,7,21,1,16,6,12,4,10,4,11,3,3,10,15,21,8,6,3,4,11,40,3,0.033,0.024,0.014,0.025,0.034,0.027,0.026,0.031,0.062,0.044,0.025,0.032,0.026,0.03,0.038,0.033,0.028,0.032,0.031,0.042,0.024,0.036,0.034,0.039,0.042,0.029,0.028,0.027,0.025,0.038,0.039,0.028,0.027,0.033,0.011,0.011,0.035,0.037,0.019,0.021,0.017,0.032,0.035,0.028,0.017,0.015,0.023,0.014,0.017,0.026,0.026,0.019,0.025,0.024,0.022,0.015,0.024,0.019,0.017,0.028,0.023,0.03,0.038,0.02,0.019,0.025,0.021,0.016,0.019,0.013,0.021,0.02,0.018,0.015,1417,4187,1880,1954,2123,2995,2149,2685,1656,1513,547.0,2199,3044,879,3016,10909,17041,2153,4679,4000,2730,4531,1743,6009,5331,6129,5366,3348,5031,5405,1878,972,795,5058,1314,1351,7142,7763,296,315,1274,2075,4568,2428,2698,1194,766,2421,2373,1427,442,2780,1808,4672,408,3299,1301,3382,909,1439,1000,2224,477,933,2090,1262,3755,2401,1547,682,1101,2835,9418,538,0.118,0.125,0.093,0.113,0.143,0.133,0.119,0.142,0.163,0.143,0.109,0.143,0.142,0.156,0.167,0.141,0.133,0.113,0.14,0.152,0.094,0.149,0.13,0.147,0.15,0.138,0.129,0.117,0.114,0.151,0.139,0.103,0.132,0.138,0.069,0.074,0.132,0.144,0.125,0.112,0.098,0.131,0.128,0.12,0.097,0.099,0.109,0.083,0.096,0.119,0.12,0.101,0.11,0.114,0.105,0.099,0.106,0.098,0.107,0.127,0.112,0.134,0.157,0.109,0.108,0.124,0.109,0.099,0.106,0.096,0.112,0.109,0.099,0.111,351,1285,677,759,716,932,768,891,467,342,148.0,1059,815,212,720,2699,4543,399,1285,2079,681,1264,693,1345,1411,1653,1646,1213,1294,1645,511,404,275,1108,358,500,1817,1721,141,140,626,989,1008,1415,1618,611,324,943,996,564,279,1261,772,1904,220,1767,554,1467,490,794,419,747,189,311,1037,768,1795,1024,676,319,499,1217,3571,256,3.553,3.017,2.532,2.638,2.483,2.736,2.329,2.635,2.581,3.307,2.822,1.871,2.917,2.895,2.964,2.854,2.906,3.813,2.774,1.759,3.1,2.634,2.127,3.098,2.684,2.88,2.443,2.212,2.893,2.574,2.702,2.256,2.298,3.124,3.289,2.447,2.871,3.179,2.188,2.362,2.561,1.912,3.243,1.887,1.888,2.18,2.984,3.15,2.927,2.847,1.963,2.321,2.555,2.428,2.219,2.083,2.574,2.664,1.994,2.058,2.512,3.099,2.441,2.924,2.274,2.153,2.26,2.621,2.453,2.488,2.568,2.657,2.668,2.552,0.659,0.5,0.642,0.55,0.704,0.646,0.563,0.503,0.591,0.718,0.786,0.623,0.447,0.529,0.607,0.617,0.595,0.835,0.644,0.498,0.81,0.504,0.533,0.712,0.713,0.523,0.55,0.514,0.526,0.566,0.651,0.932,0.422,0.645,0.687,0.463,0.746,0.655,0.318,0.512,0.525,0.506,0.748,0.755,0.622,0.527,0.499,0.83,0.604,0.52,0.378,0.467,0.651,0.478,0.357,0.531,0.476,0.598,0.386,0.394,0.472,0.609,0.595,1.0,0.51,0.636,0.527,0.468,0.453,0.688,0.592,0.607,0.577,0.489
-168,Epileptic,M,42.0,0.6,1.8,1.4,0.8,1.5,2.1,1.8,2.3,1.2,0.8,0.3,3.4,1.5,0.3,0.9,4.7,9.0,0.8,1.5,4.3,0.6,3.2,1.7,3.2,2.6,3.4,4.6,2.5,4.1,3.6,0.9,1.3,0.5,2.7,0.3,0.9,2.8,3.6,0.1,0.1,1.3,2.8,1.9,2.5,2.8,0.8,0.3,0.7,1.2,0.6,0.7,1.9,1.7,2.7,0.3,2.4,0.6,1.9,1.3,1.0,0.8,1.7,0.3,0.6,1.9,0.7,3.6,1.2,1.4,0.9,1.1,1.0,4.6,0.5,5,17,10,5,15,19,16,19,15,6,2.0,33,17,4,11,51,72,8,24,42,7,38,16,31,36,40,55,25,28,44,12,9,4,36,2,11,34,37,1,1,9,26,24,22,16,4,2,5,6,5,3,14,15,19,2,21,4,14,7,8,6,13,3,6,17,7,32,8,8,9,7,8,38,4,0.034,0.024,0.024,0.018,0.034,0.031,0.026,0.031,0.046,0.042,0.031,0.034,0.035,0.021,0.026,0.032,0.031,0.031,0.024,0.033,0.017,0.037,0.026,0.039,0.033,0.029,0.027,0.029,0.031,0.034,0.032,0.05,0.022,0.032,0.01,0.022,0.031,0.032,0.009,0.023,0.024,0.031,0.034,0.027,0.022,0.018,0.017,0.012,0.017,0.021,0.029,0.023,0.025,0.025,0.013,0.017,0.021,0.022,0.028,0.018,0.027,0.032,0.017,0.034,0.022,0.025,0.026,0.021,0.024,0.03,0.028,0.022,0.02,0.019,1427,3824,2120,1748,2127,3149,3220,3642,1707,1199,505.0,2836,3518,887,1938,8876,16380,2255,3552,4786,2893,4768,2184,5576,5125,7002,8376,3287,5841,5526,2092,1043,1016,6079,2042,2043,7098,6880,316,288,1693,2573,5662,2443,3019,1342,715,2309,2284,1616,680,2953,2622,3879,511,4264,1113,2708,1076,1587,821,2158,445,955,2632,766,3926,1840,1591,1279,1209,2226,7878,530,0.106,0.114,0.112,0.094,0.128,0.145,0.107,0.137,0.154,0.151,0.147,0.141,0.147,0.125,0.147,0.134,0.126,0.115,0.129,0.142,0.072,0.158,0.118,0.144,0.147,0.132,0.126,0.104,0.106,0.154,0.138,0.139,0.098,0.137,0.061,0.12,0.139,0.147,0.081,0.117,0.118,0.134,0.15,0.122,0.105,0.107,0.096,0.082,0.096,0.109,0.118,0.117,0.111,0.116,0.1,0.099,0.109,0.108,0.127,0.102,0.124,0.138,0.118,0.14,0.124,0.101,0.125,0.113,0.112,0.147,0.127,0.12,0.108,0.116,389,1247,856,689,853,1084,1021,1212,514,327,146.0,1575,882,222,583,2718,4732,393,977,2227,663,1483,969,1518,1470,2119,3020,1342,1756,1850,508,453,342,1538,535,738,1596,1671,156,137,784,1352,1048,1610,1933,611,292,873,1046,516,373,1226,1261,1718,287,2242,469,1353,657,889,483,891,271,338,1274,465,2197,829,888,525,554,819,3497,323,3.408,2.812,2.428,2.899,2.144,2.424,2.504,2.626,2.497,2.818,3.143,1.687,3.074,2.838,2.555,2.544,2.817,3.918,2.874,1.919,3.036,2.436,1.961,2.897,2.664,2.712,2.25,1.99,2.551,2.409,3.039,2.539,2.358,2.928,3.464,2.624,3.222,2.992,2.334,2.567,2.631,1.755,3.652,1.71,1.788,2.452,3.056,3.262,2.681,3.406,2.222,2.491,2.189,2.298,2.203,2.017,2.689,2.438,1.921,1.942,2.134,2.503,1.946,2.825,2.347,2.049,1.927,2.51,2.205,2.596,2.366,2.754,2.412,2.027,0.914,0.558,0.722,0.652,0.615,0.527,0.6,0.541,0.538,0.451,0.63,0.467,0.51,0.417,0.465,0.563,0.594,0.734,0.433,0.603,0.884,0.496,0.52,0.657,0.483,0.584,0.593,0.612,0.654,0.579,0.629,1.008,0.381,0.641,0.78,0.434,0.754,0.617,0.265,0.39,0.537,0.42,0.764,0.576,0.593,0.819,0.563,0.787,0.505,0.671,0.319,0.543,0.544,0.527,0.415,0.514,0.599,0.531,0.477,0.343,0.427,0.847,0.392,0.8,0.579,0.742,0.479,0.403,0.563,0.554,0.579,0.789,0.505,0.415
-169,Epileptic,M,42.0,1.1,2.6,1.4,2.1,2.5,2.0,2.3,2.1,2.1,0.6,0.3,4.2,2.8,0.5,2.3,7.0,11.9,1.2,2.6,5.4,1.1,3.3,2.5,4.3,4.7,2.1,1.9,2.9,4.2,2.3,2.1,0.9,0.6,3.1,0.6,1.3,4.9,4.6,0.4,0.6,1.2,3.4,3.0,3.7,3.4,1.0,0.5,1.2,2.1,0.9,0.7,3.9,2.7,3.8,0.5,1.4,1.3,2.1,1.5,1.4,0.8,2.2,0.3,0.6,3.0,1.2,2.2,2.1,1.7,0.5,0.6,1.9,5.9,0.4,9,25,10,18,21,21,25,19,21,6,7.0,39,26,10,29,75,104,10,27,59,7,40,24,47,52,21,24,33,42,28,24,7,6,36,4,17,44,57,2,3,8,29,30,25,27,8,3,8,10,7,5,28,19,23,3,11,10,18,9,12,9,19,3,5,26,18,17,17,9,5,5,15,50,5,0.039,0.023,0.022,0.029,0.039,0.026,0.04,0.027,0.052,0.03,0.04,0.039,0.026,0.027,0.035,0.031,0.031,0.041,0.032,0.036,0.022,0.032,0.036,0.042,0.041,0.024,0.025,0.034,0.032,0.027,0.042,0.044,0.031,0.034,0.019,0.022,0.039,0.034,0.016,0.023,0.025,0.034,0.043,0.029,0.023,0.029,0.017,0.017,0.021,0.024,0.025,0.027,0.026,0.023,0.032,0.015,0.023,0.017,0.03,0.022,0.019,0.031,0.014,0.018,0.025,0.028,0.023,0.024,0.029,0.014,0.028,0.021,0.021,0.021,1565,5045,2474,2967,2414,3298,2997,3485,2387,1098,838.0,3365,5895,1254,3973,12999,19710,2611,4852,5463,3075,5386,2455,6603,6194,4211,3318,4796,4800,3906,2734,1098,983,6018,2198,2664,7730,9309,634,670,1501,3264,6224,3347,4024,1121,1030,2593,3027,1527,655,5194,3816,6210,515,2487,2111,3686,1187,1690,1273,3039,509,1002,3315,1195,2834,2862,1206,1110,905,2789,10271,476,0.123,0.12,0.109,0.131,0.14,0.134,0.153,0.13,0.159,0.146,0.141,0.161,0.119,0.151,0.151,0.13,0.131,0.136,0.132,0.147,0.088,0.133,0.13,0.148,0.131,0.122,0.118,0.13,0.112,0.133,0.138,0.122,0.121,0.141,0.09,0.12,0.147,0.148,0.091,0.119,0.12,0.131,0.165,0.125,0.114,0.128,0.093,0.09,0.106,0.12,0.119,0.122,0.119,0.104,0.138,0.094,0.119,0.1,0.132,0.118,0.114,0.143,0.117,0.11,0.126,0.127,0.114,0.122,0.124,0.1,0.124,0.118,0.109,0.146,504,1788,973,1162,1001,1205,1105,1188,783,301,197.0,1740,1790,344,1200,3808,6307,548,1425,2638,769,1695,1133,1921,2000,1406,1314,1550,1970,1461,831,412,352,1533,584,988,2161,2419,346,364,766,1501,1379,2038,2294,608,442,1049,1435,621,413,2388,1624,2600,244,1359,905,1866,763,992,697,1172,291,517,1819,783,1459,1314,872,536,368,1399,4482,301,3.163,2.732,2.54,2.535,2.259,2.336,2.267,2.644,2.328,2.871,2.927,1.761,2.707,2.649,2.577,2.655,2.596,3.621,2.811,1.85,2.961,2.519,1.971,2.674,2.511,2.72,2.096,2.41,2.052,2.227,2.482,2.641,2.262,2.926,3.305,2.368,2.755,2.943,2.061,2.217,2.44,1.892,3.419,1.966,1.926,1.99,2.682,3.032,2.547,2.722,1.878,2.385,2.64,2.423,2.584,1.958,2.409,2.15,1.774,1.891,2.008,2.71,2.105,2.255,1.986,1.79,2.105,2.156,1.658,2.38,2.533,2.304,2.398,2.088,0.881,0.499,0.431,0.512,0.603,0.597,0.47,0.373,0.671,0.591,0.753,0.451,0.4,0.43,0.526,0.581,0.562,0.865,0.66,0.61,0.732,0.484,0.463,0.651,0.582,0.509,0.474,0.452,0.533,0.58,0.609,0.72,0.483,0.638,0.713,0.405,0.671,0.602,0.372,0.353,0.445,0.522,0.699,0.618,0.386,0.325,0.539,0.876,0.459,0.584,0.331,0.401,0.95,0.55,0.488,0.346,0.524,0.467,0.4,0.361,0.354,0.622,0.269,0.745,0.484,0.516,0.415,0.713,0.411,0.692,0.298,0.425,0.437,0.37
-170,Epileptic,M,47.0,1.2,2.4,0.7,1.6,2.2,1.6,3.0,2.0,1.6,0.7,0.3,3.5,1.5,0.5,1.1,6.4,14.2,1.2,1.8,3.5,1.6,4.6,2.0,5.0,8.2,5.9,4.9,4.2,4.4,3.5,1.7,0.8,0.5,3.4,0.4,1.2,3.1,3.4,0.2,0.3,1.3,3.0,2.3,2.3,4.4,0.9,0.9,1.1,1.7,0.9,0.8,1.5,1.2,3.4,0.9,2.5,1.0,1.8,1.7,1.0,0.9,2.3,0.5,0.7,2.5,1.2,4.1,1.3,1.8,0.8,1.0,1.5,5.5,0.5,10,18,5,13,16,16,21,18,19,6,5.0,30,14,6,11,62,102,11,20,30,11,45,17,55,65,54,42,31,33,36,18,7,4,34,3,11,32,39,2,2,8,27,21,20,23,5,5,6,9,11,6,12,9,21,5,21,8,12,12,7,6,15,5,5,19,21,28,8,11,6,7,10,51,4,0.042,0.027,0.016,0.022,0.037,0.026,0.05,0.027,0.044,0.03,0.035,0.041,0.026,0.026,0.031,0.035,0.049,0.041,0.028,0.035,0.03,0.039,0.033,0.044,0.044,0.029,0.035,0.032,0.034,0.031,0.042,0.037,0.025,0.034,0.019,0.023,0.034,0.026,0.021,0.025,0.022,0.038,0.042,0.026,0.029,0.014,0.027,0.017,0.02,0.029,0.029,0.02,0.021,0.026,0.019,0.016,0.023,0.016,0.027,0.019,0.022,0.031,0.028,0.022,0.024,0.021,0.023,0.021,0.026,0.024,0.022,0.021,0.019,0.02,1610,4198,2162,2942,2794,3474,3008,3941,2215,1620,652.0,2957,3199,875,2268,10439,19669,2541,4266,4105,3328,5222,2093,7555,6882,9799,6345,5330,7263,6580,3065,1053,1232,7312,1909,2576,6906,9574,366,386,1771,2665,5109,2344,4066,1534,1038,2612,2692,1993,721,2892,1861,4973,1143,4180,1541,3386,1564,1287,1156,2958,679,1063,3055,1240,5649,2232,2246,1288,1587,2552,12174,595,0.139,0.124,0.087,0.115,0.151,0.127,0.14,0.125,0.157,0.122,0.139,0.153,0.117,0.13,0.131,0.14,0.155,0.142,0.123,0.138,0.101,0.124,0.122,0.145,0.133,0.118,0.119,0.113,0.114,0.133,0.145,0.115,0.094,0.129,0.084,0.107,0.132,0.125,0.124,0.116,0.11,0.131,0.142,0.119,0.118,0.086,0.118,0.091,0.102,0.115,0.148,0.11,0.11,0.116,0.109,0.101,0.122,0.092,0.132,0.105,0.105,0.132,0.137,0.113,0.123,0.119,0.11,0.105,0.117,0.117,0.111,0.11,0.1,0.123,522,1438,755,1047,970,1092,1143,1250,658,414,179.0,1469,918,274,636,3281,5466,562,1147,1726,872,1711,827,2190,2359,3189,2135,1785,2006,2062,804,387,376,1732,462,935,1711,2153,212,200,913,1289,1085,1487,2332,828,507,1050,1259,639,378,1257,870,2185,650,2320,652,1658,975,770,568,1098,292,562,1564,939,2778,1038,1059,525,758,1144,4875,353,3.088,2.692,2.655,2.536,2.506,2.556,2.191,2.692,2.636,2.919,2.71,1.784,2.686,2.464,2.681,2.523,2.742,3.385,2.906,2.055,2.759,2.411,2.144,2.599,2.464,2.58,2.364,2.335,2.759,2.469,2.766,2.518,2.539,3.062,3.509,2.557,3.04,3.148,1.999,2.33,2.464,1.887,3.356,1.779,2.027,2.065,2.571,3.134,2.576,3.125,2.155,2.349,2.198,2.394,2.289,2.075,2.605,2.33,1.832,1.844,2.247,2.618,2.287,2.29,2.133,1.636,2.2,2.477,2.355,2.562,2.388,2.681,2.498,2.122,0.75,0.747,0.606,0.52,0.611,0.644,0.599,0.537,0.575,0.653,0.856,0.478,0.401,0.409,0.501,0.497,0.676,0.784,0.574,0.696,0.788,0.668,0.576,0.642,0.602,0.512,0.531,0.623,0.613,0.615,0.618,0.953,0.412,0.596,0.595,0.469,0.682,0.683,0.277,0.288,0.493,0.521,0.74,0.496,0.557,0.395,0.423,0.63,0.457,0.64,0.486,0.444,0.464,0.438,0.339,0.341,0.519,0.477,0.365,0.37,0.326,0.638,0.545,0.59,0.504,0.646,0.397,0.368,0.433,0.608,0.534,0.492,0.529,0.34
-171,Epileptic,F,37.0,0.9,2.5,0.7,1.1,1.7,2.0,2.5,1.3,1.1,0.7,0.3,3.2,1.5,0.6,1.8,4.7,7.9,0.7,2.6,3.9,1.1,2.5,1.9,3.6,4.1,4.5,6.2,2.8,3.2,2.4,1.8,1.3,0.6,2.1,0.5,0.7,3.1,1.8,0.2,0.3,1.4,3.3,2.0,2.3,2.6,0.5,0.3,0.7,1.2,0.9,0.3,2.4,0.8,1.9,0.4,2.2,1.0,1.1,0.8,0.9,0.4,1.9,0.3,0.7,2.3,1.2,2.2,0.9,1.0,0.8,0.8,1.1,3.8,0.3,7,24,5,7,17,19,19,14,13,7,4.0,32,17,8,22,55,74,6,28,33,15,26,22,37,45,50,50,25,29,32,20,10,6,28,2,6,41,26,1,3,11,25,22,19,16,3,1,5,6,6,2,21,5,14,2,19,8,9,9,7,3,12,2,5,18,17,17,5,8,8,7,9,33,3,0.048,0.032,0.019,0.024,0.033,0.035,0.04,0.028,0.043,0.035,0.025,0.033,0.028,0.032,0.036,0.039,0.032,0.028,0.035,0.041,0.026,0.037,0.032,0.04,0.051,0.029,0.041,0.032,0.034,0.027,0.052,0.06,0.025,0.029,0.015,0.015,0.036,0.027,0.018,0.016,0.026,0.039,0.038,0.028,0.021,0.01,0.016,0.012,0.017,0.015,0.016,0.029,0.024,0.02,0.024,0.015,0.026,0.016,0.021,0.016,0.028,0.031,0.025,0.034,0.024,0.024,0.018,0.016,0.016,0.025,0.02,0.021,0.018,0.012,1475,3925,1674,2083,1955,2824,2839,2748,1702,1254,589.0,3734,3298,1184,3162,7731,15739,2244,4438,3520,3409,3547,2128,6144,4501,8983,6553,3388,6206,5059,1995,942,1237,5871,1699,1866,6742,4463,411,589,1645,2419,6075,2265,3670,1233,835,2090,1944,2153,359,2548,1076,3357,514,4227,1353,2641,1159,1517,604,2687,423,866,3083,1030,3681,1874,2019,956,1232,1859,7431,685,0.138,0.131,0.101,0.108,0.144,0.139,0.135,0.121,0.154,0.151,0.132,0.138,0.125,0.138,0.146,0.156,0.139,0.122,0.142,0.15,0.102,0.129,0.119,0.146,0.147,0.132,0.128,0.106,0.113,0.127,0.15,0.147,0.102,0.125,0.077,0.094,0.143,0.122,0.095,0.118,0.12,0.139,0.151,0.128,0.099,0.084,0.088,0.082,0.095,0.1,0.111,0.134,0.118,0.101,0.136,0.097,0.13,0.097,0.123,0.092,0.117,0.133,0.137,0.134,0.119,0.126,0.1,0.092,0.098,0.147,0.114,0.123,0.102,0.088,388,1281,623,727,819,1000,954,879,481,341,196.0,1490,888,297,892,2308,4284,416,1229,1585,709,1007,965,1612,1401,2542,2283,1246,1513,1570,591,424,381,1317,448,699,1625,1146,235,288,865,1255,1017,1358,1987,642,341,894,932,856,230,1344,534,1471,216,2140,630,1179,654,844,249,992,222,405,1407,647,1893,839,934,469,633,777,3324,357,3.506,2.624,2.552,2.742,2.134,2.348,2.451,2.79,2.461,2.863,2.86,2.047,2.834,2.691,2.614,2.481,2.784,3.725,2.877,1.991,3.434,2.492,1.953,2.783,2.454,2.742,2.308,2.151,2.956,2.502,2.429,2.476,2.491,3.108,3.335,2.473,3.029,2.78,2.141,2.213,2.429,1.754,3.585,1.906,2.105,2.038,2.865,2.964,2.558,2.719,1.82,2.025,2.016,2.309,2.592,2.188,2.359,2.461,1.985,1.997,2.458,2.491,2.011,2.416,2.311,2.007,2.142,2.507,2.295,2.074,2.186,2.361,2.352,2.284,0.695,0.698,0.516,0.463,0.701,0.64,0.643,0.465,0.618,0.68,0.834,0.559,0.396,0.523,0.579,0.579,0.686,0.736,0.568,0.721,0.827,0.741,0.56,0.736,0.755,0.62,0.623,0.615,0.569,0.524,0.648,0.804,0.395,0.612,0.872,0.518,0.777,0.796,0.459,0.717,0.452,0.609,0.894,0.601,0.67,0.391,0.397,0.409,0.433,0.499,0.302,0.469,0.486,0.489,0.438,0.42,0.439,0.723,0.426,0.386,0.442,0.673,0.505,0.755,0.424,0.678,0.38,0.352,0.484,0.592,0.394,0.786,0.467,0.352
-172,Epileptic,F,22.0,0.7,2.4,1.2,1.2,1.7,3.1,2.3,1.3,1.6,0.6,0.5,2.3,1.2,0.4,1.5,4.4,8.6,1.1,2.5,3.7,1.7,3.0,1.5,3.4,6.6,3.1,3.4,2.9,2.5,3.3,1.7,0.7,0.8,1.8,0.6,0.5,2.9,2.7,0.2,0.2,0.9,3.4,2.1,1.7,2.6,0.6,0.4,1.1,1.7,0.8,0.7,1.6,1.4,1.3,0.3,2.3,0.8,1.2,0.8,0.9,0.8,1.5,0.4,0.7,1.5,2.2,2.5,1.2,1.0,0.5,1.0,1.1,3.2,0.2,6,19,7,7,17,26,15,13,16,7,5.0,28,17,5,21,51,78,10,27,40,15,33,14,35,61,42,45,33,29,36,17,7,7,25,3,5,36,34,1,2,6,29,20,13,15,5,2,5,9,6,4,11,13,9,2,18,6,9,7,8,5,11,2,4,12,32,19,10,8,4,6,9,28,4,0.035,0.031,0.021,0.023,0.033,0.05,0.031,0.022,0.05,0.026,0.045,0.04,0.026,0.025,0.029,0.032,0.035,0.041,0.042,0.037,0.028,0.039,0.029,0.04,0.047,0.028,0.024,0.032,0.025,0.03,0.045,0.041,0.035,0.028,0.022,0.016,0.026,0.026,0.014,0.014,0.02,0.045,0.038,0.025,0.019,0.017,0.018,0.016,0.02,0.014,0.029,0.018,0.023,0.022,0.02,0.02,0.018,0.02,0.022,0.019,0.018,0.024,0.023,0.031,0.023,0.039,0.019,0.017,0.017,0.02,0.023,0.016,0.016,0.02,1560,3842,2305,2200,2453,2963,3439,3196,1909,1550,589.0,2652,3603,1179,3240,8359,15330,2203,4191,4812,3910,3783,2448,6003,5778,7155,7615,4761,7312,6059,2710,933,1028,4862,1685,1326,7286,7344,326,498,1283,2661,5039,1918,3866,1327,811,2294,2453,1830,690,2803,1971,2809,596,3996,1564,2438,1095,1410,1045,2468,408,797,2189,1152,4294,2521,2157,1016,1475,2268,7809,327,0.11,0.125,0.1,0.104,0.142,0.132,0.119,0.114,0.172,0.118,0.165,0.161,0.116,0.124,0.144,0.141,0.138,0.144,0.156,0.162,0.125,0.121,0.106,0.141,0.14,0.125,0.114,0.118,0.109,0.144,0.153,0.118,0.146,0.125,0.087,0.097,0.123,0.119,0.099,0.092,0.103,0.148,0.142,0.113,0.096,0.097,0.093,0.086,0.105,0.093,0.125,0.099,0.117,0.102,0.086,0.106,0.104,0.104,0.119,0.105,0.098,0.118,0.129,0.134,0.114,0.147,0.105,0.101,0.096,0.11,0.112,0.101,0.093,0.144,425,1319,849,788,886,987,1154,965,601,388,204.0,1020,956,284,912,2404,4617,527,1132,1790,944,1312,746,1609,1978,2058,2335,1579,1896,1789,690,378,381,1191,507,443,1937,1743,200,257,675,1263,1126,1089,2073,626,319,909,1202,778,372,1349,962,1102,246,1904,645,1090,553,735,557,992,227,375,1047,756,2040,1064,955,435,683,1091,3273,207,3.421,2.679,2.533,2.769,2.268,2.52,2.493,2.736,2.398,3.017,2.54,2.264,2.901,2.882,2.745,2.603,2.675,3.344,2.947,2.237,3.226,2.363,2.524,2.837,2.436,2.826,2.541,2.396,2.906,2.61,2.974,2.453,2.285,2.957,2.955,2.534,2.814,2.969,1.884,2.343,2.373,1.765,3.229,2.052,2.173,2.231,3.142,3.055,2.601,2.569,2.357,2.337,2.058,2.605,2.535,2.289,2.562,2.444,2.357,2.12,2.248,2.588,2.148,2.439,2.261,1.992,2.315,2.707,2.49,2.511,2.5,2.465,2.521,1.907,0.742,0.802,0.565,0.426,0.695,0.805,0.557,0.48,0.641,0.556,0.694,0.551,0.418,0.44,0.444,0.575,0.597,0.668,0.556,0.598,0.689,0.622,0.668,0.594,0.634,0.594,0.603,0.561,0.522,0.559,0.653,0.846,0.438,0.608,0.586,0.589,0.726,0.632,0.391,0.319,0.549,0.668,0.874,0.568,0.613,0.488,0.739,0.757,0.494,0.564,0.347,0.458,0.499,0.838,0.537,0.465,0.519,0.611,0.483,0.365,0.31,0.546,0.396,0.6,0.447,0.596,0.43,0.358,0.643,1.003,0.501,0.475,0.454,0.346
-173,Epileptic,F,37.0,0.7,3.3,0.7,1.1,1.2,2.2,1.2,0.9,0.7,0.5,0.4,2.9,1.4,0.2,1.2,4.4,8.3,0.6,1.6,4.3,1.1,2.2,1.2,2.5,3.1,1.9,2.6,2.0,2.5,1.9,1.2,0.9,0.4,1.8,0.3,0.5,2.0,2.2,0.2,0.2,0.7,3.0,2.2,2.4,2.6,0.4,0.4,0.8,0.9,0.4,0.4,1.5,0.8,2.4,0.2,1.8,0.6,1.2,1.4,0.4,0.4,1.0,0.4,0.3,1.6,1.2,1.7,1.0,0.9,0.4,0.4,0.8,3.2,0.4,9,28,7,8,15,22,12,9,8,6,4.0,27,12,2,12,47,71,6,19,44,13,21,12,28,31,24,30,22,23,26,17,6,4,25,2,3,26,25,1,2,5,31,19,16,17,3,2,5,5,5,3,12,7,14,1,14,6,11,9,4,3,9,3,4,10,16,12,6,4,4,3,6,29,3,0.041,0.037,0.019,0.023,0.028,0.055,0.022,0.023,0.037,0.03,0.033,0.031,0.03,0.028,0.032,0.037,0.036,0.03,0.03,0.034,0.031,0.032,0.024,0.032,0.036,0.025,0.031,0.025,0.025,0.026,0.033,0.045,0.019,0.028,0.016,0.016,0.036,0.036,0.017,0.02,0.018,0.034,0.038,0.022,0.018,0.009,0.02,0.015,0.016,0.013,0.022,0.02,0.02,0.024,0.028,0.022,0.035,0.016,0.021,0.017,0.019,0.022,0.027,0.022,0.022,0.03,0.019,0.016,0.014,0.016,0.015,0.019,0.017,0.02,1285,4689,1756,2137,1844,3521,2397,2351,1624,1131,758.0,3382,2910,732,2453,8985,16419,2341,3784,5604,3417,4013,2165,5296,4803,4645,4848,3471,6104,3757,2239,1414,1208,4735,2122,1286,4876,5763,511,512,1268,3427,5234,2847,3481,1144,746,1788,1978,1119,446,2853,1463,3749,238,2645,1173,2763,2056,936,749,2219,538,882,2372,924,2991,2024,1820,701,804,1798,7177,507,0.132,0.14,0.106,0.104,0.13,0.151,0.107,0.113,0.143,0.141,0.126,0.136,0.129,0.114,0.147,0.144,0.144,0.122,0.13,0.146,0.12,0.145,0.12,0.135,0.148,0.129,0.148,0.111,0.115,0.126,0.129,0.122,0.096,0.134,0.072,0.086,0.142,0.144,0.102,0.114,0.104,0.146,0.149,0.104,0.1,0.08,0.108,0.083,0.088,0.104,0.125,0.104,0.104,0.103,0.176,0.113,0.137,0.101,0.114,0.12,0.111,0.112,0.141,0.125,0.109,0.136,0.099,0.096,0.089,0.128,0.102,0.118,0.099,0.116,362,1377,646,769,696,1111,873,761,367,285,210.0,1374,785,163,627,2320,4074,369,963,2088,703,1172,825,1312,1395,1233,1553,1213,1606,1231,572,443,398,1067,406,475,1148,1183,177,188,593,1459,971,1679,2008,601,286,784,906,458,249,1205,684,1554,96,1281,327,1261,1005,418,364,761,228,350,1063,566,1403,878,861,343,368,633,2907,294,3.184,3.184,2.691,2.931,2.303,2.751,2.365,2.755,3.071,3.146,2.755,2.123,2.785,3.167,2.963,2.938,3.191,4.079,3.084,2.256,3.388,2.684,2.264,2.959,2.741,2.973,2.497,2.28,2.917,2.4,2.857,3.063,2.415,3.023,3.565,2.434,3.144,3.343,2.798,2.926,2.788,2.113,3.719,1.898,2.004,2.15,3.229,2.694,2.708,2.637,2.153,2.522,2.378,2.608,2.82,2.348,2.972,2.53,2.429,2.323,2.721,2.813,2.497,2.723,2.346,2.07,2.38,2.639,2.664,2.419,2.426,3.069,2.695,2.229,0.876,0.703,0.518,0.432,0.611,0.551,0.51,0.493,0.719,0.646,0.783,0.442,0.45,0.487,0.41,0.553,0.523,0.761,0.591,0.579,0.884,0.457,0.544,0.658,0.438,0.51,0.532,0.509,0.475,0.499,0.646,0.925,0.367,0.652,0.985,0.372,0.748,0.605,0.435,0.485,0.509,0.443,0.718,0.557,0.594,0.336,0.473,0.601,0.54,0.398,0.333,0.518,0.475,0.422,0.628,0.41,0.657,0.593,0.337,0.411,0.382,0.777,0.432,0.899,0.364,0.549,0.478,0.409,0.367,0.539,0.541,0.878,0.433,0.337
-174,Epileptic,M,27.0,0.7,2.8,1.4,2.0,1.8,1.9,1.7,2.0,1.5,0.5,0.4,3.3,1.4,0.7,1.5,5.1,8.7,0.9,2.5,5.2,1.5,3.8,2.4,4.1,5.7,4.7,4.2,3.5,4.9,4.4,1.7,1.6,0.5,3.9,0.6,0.6,3.9,3.8,0.3,0.2,1.3,4.1,2.6,2.2,3.5,1.1,0.4,1.0,1.6,0.7,0.5,2.0,1.7,5.1,0.4,3.8,1.0,1.2,0.8,1.6,0.6,2.1,0.2,0.5,2.2,1.6,2.9,1.2,1.4,0.6,0.8,1.3,5.0,0.3,8,23,11,15,13,20,16,20,16,6,3.0,28,16,7,15,52,72,9,25,38,10,36,27,38,52,49,39,32,37,46,18,14,5,34,3,3,45,43,2,1,7,29,24,16,20,7,2,6,8,6,3,13,17,41,3,30,8,9,7,11,5,14,2,5,18,19,26,8,9,5,5,9,42,2,0.045,0.03,0.029,0.03,0.035,0.029,0.029,0.031,0.047,0.036,0.048,0.042,0.034,0.032,0.031,0.034,0.034,0.04,0.036,0.054,0.038,0.037,0.031,0.051,0.038,0.035,0.034,0.036,0.036,0.042,0.045,0.072,0.032,0.045,0.024,0.016,0.038,0.041,0.019,0.017,0.025,0.047,0.048,0.024,0.024,0.018,0.018,0.019,0.022,0.022,0.03,0.023,0.032,0.031,0.015,0.021,0.021,0.017,0.02,0.023,0.023,0.039,0.033,0.027,0.023,0.029,0.02,0.019,0.023,0.021,0.021,0.023,0.02,0.015,1329,4291,2303,2976,2263,2736,2771,3034,1889,1205,541.0,2663,2909,1150,3114,8559,16509,1954,4009,4259,2221,3901,2384,5166,5643,7608,4975,4179,6745,5865,2628,855,1062,5890,1651,1626,6816,6794,401,422,1388,2582,4786,2295,3840,1587,687,1825,2148,1325,380,2880,2193,7517,691,4634,1443,1999,944,1797,739,2197,308,832,2665,1273,4499,2296,2060,1308,1083,2078,8953,483,0.115,0.127,0.125,0.128,0.142,0.135,0.107,0.141,0.16,0.138,0.142,0.159,0.121,0.141,0.137,0.134,0.132,0.141,0.151,0.183,0.121,0.119,0.125,0.16,0.124,0.128,0.107,0.107,0.12,0.161,0.155,0.154,0.106,0.153,0.091,0.083,0.145,0.146,0.095,0.103,0.114,0.146,0.16,0.12,0.108,0.1,0.096,0.097,0.104,0.106,0.127,0.11,0.129,0.125,0.093,0.113,0.111,0.099,0.111,0.115,0.114,0.144,0.13,0.141,0.119,0.14,0.105,0.099,0.107,0.128,0.123,0.111,0.103,0.107,399,1506,763,1013,838,1031,919,1048,624,313,149.0,1288,776,338,831,2722,4357,448,1283,1721,643,1508,1195,1544,2259,2341,1970,1669,1850,1927,723,484,322,1481,450,600,1813,1772,254,215,751,1338,1160,1361,2227,869,296,813,1079,572,255,1316,947,2904,389,2697,738,1071,627,1058,403,864,167,361,1486,854,2379,1004,982,402,516,929,4101,278,3.155,2.779,2.913,2.69,2.249,2.308,2.444,2.57,2.42,3.093,2.877,1.804,2.707,2.434,2.842,2.497,2.914,3.127,2.55,2.139,2.615,2.122,1.813,2.654,2.118,2.652,2.056,2.074,2.812,2.421,2.703,2.031,2.53,2.799,3.083,2.493,2.771,2.874,1.831,2.275,2.314,1.793,2.847,1.903,1.989,2.081,2.521,2.827,2.403,2.214,1.784,2.211,2.158,2.427,2.068,1.947,2.385,2.126,1.802,1.871,2.064,2.473,2.166,2.251,1.934,1.859,1.943,2.552,2.39,3.332,2.301,2.646,2.292,2.193,0.91,0.599,0.634,0.638,0.717,0.562,0.736,0.493,0.64,0.718,0.689,0.486,0.509,0.524,0.595,0.56,0.644,0.772,0.601,0.527,0.667,0.543,0.515,0.571,0.639,0.534,0.565,0.578,0.652,0.584,0.81,0.757,0.404,0.663,0.656,0.506,0.644,0.641,0.376,0.322,0.482,0.542,0.797,0.59,0.624,0.537,0.536,0.766,0.512,0.643,0.284,0.543,0.544,0.55,0.423,0.485,0.411,0.441,0.354,0.378,0.375,0.697,0.31,0.652,0.599,0.552,0.425,0.552,0.495,0.917,0.499,0.454,0.47,0.383
-175,Epileptic,F,42.0,1.1,2.2,0.7,1.4,1.7,2.1,1.5,1.9,1.2,1.0,0.3,3.1,1.8,0.4,1.1,5.7,9.5,0.8,1.5,4.8,1.1,3.5,2.1,3.9,3.0,3.3,3.2,2.4,3.7,3.1,1.1,1.2,0.5,2.2,0.6,0.7,3.3,4.4,0.1,0.3,1.2,3.3,3.4,3.1,3.5,0.5,0.2,1.0,1.4,0.8,0.6,1.7,1.3,2.7,0.2,2.2,1.0,1.7,1.1,1.8,0.5,1.8,0.5,0.4,2.4,1.0,2.0,1.4,2.1,0.6,1.1,1.6,4.5,0.4,9,26,7,13,17,20,14,15,13,11,4.0,33,18,6,16,66,86,6,20,42,13,41,22,41,38,37,42,21,29,35,14,10,6,27,4,7,44,53,1,2,7,28,32,25,22,3,2,5,7,7,5,14,11,19,1,18,6,17,8,12,5,15,3,6,20,18,16,9,15,7,8,13,40,3,0.04,0.019,0.015,0.024,0.028,0.03,0.022,0.031,0.04,0.039,0.027,0.032,0.029,0.024,0.027,0.036,0.028,0.027,0.027,0.034,0.023,0.035,0.029,0.037,0.03,0.03,0.027,0.026,0.024,0.03,0.024,0.041,0.025,0.028,0.015,0.015,0.033,0.032,0.015,0.023,0.021,0.037,0.047,0.023,0.024,0.014,0.011,0.015,0.018,0.018,0.029,0.02,0.021,0.021,0.027,0.015,0.021,0.02,0.021,0.024,0.023,0.024,0.033,0.013,0.023,0.019,0.018,0.021,0.022,0.017,0.024,0.019,0.018,0.018,1529,4965,2219,2622,3069,3447,2806,3343,2111,1750,722.0,3647,3732,861,2299,11005,19697,2515,3704,5039,3876,5999,3010,6046,5426,6369,6066,3802,7807,5228,2190,1470,1200,5915,2312,2022,7345,10006,265,395,1733,3204,7115,4301,3900,1073,811,2070,2606,1996,686,2900,2208,4882,376,3934,1505,3609,1321,2397,759,3154,508,1035,3467,1193,3478,2608,3271,1005,1493,2730,9477,551,0.129,0.111,0.094,0.114,0.13,0.147,0.106,0.13,0.156,0.152,0.113,0.14,0.137,0.121,0.141,0.145,0.128,0.109,0.136,0.143,0.108,0.149,0.136,0.136,0.137,0.137,0.128,0.107,0.1,0.137,0.116,0.127,0.104,0.124,0.077,0.089,0.136,0.142,0.097,0.119,0.109,0.156,0.177,0.114,0.111,0.09,0.085,0.082,0.097,0.106,0.145,0.109,0.11,0.111,0.127,0.101,0.102,0.115,0.116,0.114,0.12,0.115,0.155,0.11,0.119,0.119,0.105,0.111,0.112,0.122,0.123,0.107,0.097,0.105,490,1800,818,955,1011,1205,958,1068,564,473,238.0,1570,1048,281,704,3026,5462,456,997,2273,827,1832,1214,1844,1723,1917,2071,1393,2210,1666,638,588,383,1396,601,785,1838,2323,151,218,859,1388,1283,2320,2261,582,334,934,1182,701,327,1349,957,1967,142,2061,651,1403,771,1136,408,1155,211,560,1595,850,1750,1053,1488,553,731,1264,4143,316,3.082,2.621,2.503,2.771,2.583,2.494,2.38,2.789,2.789,3.068,2.855,2.027,2.834,2.403,2.58,2.791,2.932,4.001,2.978,2.027,3.718,2.529,2.123,2.548,2.574,2.715,2.312,2.203,2.743,2.473,2.528,2.521,2.484,2.965,3.155,2.384,2.95,3.001,2.094,2.334,2.476,2.005,3.562,2.047,1.939,2.036,3.081,2.692,2.709,2.987,2.374,2.327,2.337,2.627,2.921,2.067,2.549,2.64,2.031,2.18,2.3,2.77,2.303,2.079,2.233,1.774,2.134,2.635,2.467,2.071,2.325,2.502,2.435,2.267,0.744,0.614,0.527,0.516,0.686,0.497,0.529,0.516,0.6,0.763,0.908,0.393,0.396,0.425,0.366,0.49,0.513,0.743,0.517,0.621,0.903,0.523,0.508,0.668,0.529,0.476,0.439,0.565,0.502,0.533,0.77,0.835,0.328,0.628,0.71,0.444,0.692,0.663,0.2,0.377,0.506,0.504,0.879,0.611,0.556,0.385,0.469,0.638,0.472,0.686,0.409,0.495,0.564,0.479,0.457,0.419,0.581,0.614,0.369,0.39,0.309,0.711,0.431,0.684,0.489,0.494,0.46,0.34,0.457,0.406,0.423,0.492,0.38,0.311
-176,Epileptic,F,22.0,0.9,2.6,1.1,1.3,1.3,2.2,2.0,1.4,0.8,0.7,0.2,2.4,1.8,0.5,1.1,5.4,10.9,1.0,3.0,4.0,1.2,3.3,1.4,2.6,3.5,2.7,3.8,2.2,3.3,3.3,1.5,1.0,0.5,2.9,0.8,0.8,3.1,2.9,0.1,0.2,1.0,3.7,3.3,2.8,3.3,0.6,0.6,1.0,1.3,1.6,0.7,1.8,1.0,2.6,0.2,2.7,0.8,1.6,1.1,1.0,0.5,1.5,0.2,0.3,1.8,0.8,2.1,1.3,1.1,0.4,1.0,1.5,5.2,0.4,6,23,12,10,12,18,15,12,10,6,3.0,21,16,6,8,56,93,6,25,33,8,37,12,28,36,27,41,21,21,37,18,8,4,23,4,7,28,31,1,1,5,24,22,20,20,4,3,6,7,9,5,13,8,17,1,22,6,15,9,9,6,12,2,3,15,7,21,10,7,4,9,10,39,4,0.043,0.023,0.019,0.022,0.03,0.035,0.025,0.026,0.033,0.031,0.022,0.029,0.032,0.029,0.03,0.034,0.034,0.035,0.034,0.034,0.025,0.03,0.024,0.029,0.032,0.033,0.027,0.024,0.026,0.029,0.037,0.03,0.02,0.031,0.029,0.025,0.032,0.031,0.013,0.024,0.021,0.044,0.048,0.028,0.022,0.017,0.023,0.015,0.019,0.037,0.035,0.018,0.021,0.02,0.027,0.018,0.013,0.02,0.023,0.017,0.021,0.026,0.018,0.018,0.021,0.021,0.021,0.02,0.021,0.017,0.024,0.022,0.018,0.019,1481,5101,2290,2209,2400,2506,2746,2492,1603,1138,479.0,2234,3238,1136,2224,10348,19575,2735,4098,3609,2583,5606,1893,6103,6131,4728,6007,3460,5477,5439,2489,1121,1031,5505,2055,1461,5644,6450,326,350,1408,2329,3181,2222,3734,1252,1013,2642,2361,1495,542,3535,1986,4948,301,4126,1892,2778,1485,1654,1156,2718,388,1096,3070,990,3313,2319,1677,873,1248,2385,10113,499,0.139,0.117,0.117,0.107,0.122,0.145,0.104,0.118,0.137,0.144,0.119,0.127,0.126,0.147,0.119,0.139,0.139,0.112,0.135,0.138,0.092,0.134,0.118,0.122,0.143,0.142,0.128,0.108,0.107,0.137,0.139,0.108,0.105,0.124,0.106,0.111,0.124,0.119,0.1,0.114,0.11,0.151,0.143,0.135,0.108,0.099,0.111,0.08,0.102,0.132,0.146,0.095,0.102,0.102,0.136,0.108,0.092,0.11,0.116,0.103,0.109,0.119,0.103,0.108,0.114,0.094,0.113,0.114,0.11,0.115,0.132,0.112,0.097,0.125,366,1838,896,869,728,1092,1093,852,437,329,191.0,1170,964,299,609,2765,5582,453,1387,1749,733,1746,840,1548,1915,1435,2198,1337,1800,1785,641,499,354,1419,458,540,1556,1617,155,138,674,1276,1073,1488,2324,630,403,1057,1012,713,288,1513,878,2072,117,2231,888,1356,840,930,501,929,194,349,1379,553,1664,1051,802,414,642,1111,4540,297,3.551,2.746,2.678,2.653,2.527,2.201,2.164,2.526,2.758,2.999,2.45,1.713,2.745,2.875,2.837,2.777,2.798,4.116,2.582,1.838,2.829,2.529,1.998,3.039,2.625,2.77,2.219,2.136,2.404,2.5,2.887,2.374,2.336,2.798,3.819,2.57,2.709,2.932,2.35,2.79,2.689,1.785,2.572,1.763,1.856,2.196,3.064,2.962,2.832,2.515,2.066,2.41,2.359,2.423,2.932,2.128,2.66,2.349,2.143,2.026,2.423,2.758,2.399,2.935,2.636,2.038,2.102,2.613,2.348,2.445,2.198,2.504,2.487,2.048,0.892,0.55,0.498,0.46,0.864,0.517,0.518,0.394,0.626,0.409,0.856,0.368,0.494,0.299,0.519,0.63,0.605,0.807,0.557,0.492,0.899,0.478,0.359,0.722,0.481,0.477,0.446,0.534,0.579,0.507,0.699,1.014,0.38,0.813,0.731,0.509,0.669,0.6,0.373,0.401,0.617,0.409,0.674,0.609,0.613,0.494,0.69,0.785,0.599,0.763,0.621,0.672,0.592,0.494,0.283,0.446,0.533,0.651,0.38,0.335,0.455,0.71,0.335,0.973,0.784,0.645,0.508,0.509,0.522,0.409,0.359,0.509,0.526,0.544
-177,Epileptic,F,27.0,0.6,1.7,0.7,1.1,0.9,2.3,1.4,1.4,1.0,0.5,0.4,2.4,1.5,0.5,0.8,6.2,7.9,0.7,2.4,5.2,1.4,3.0,1.8,2.8,2.7,3.9,2.9,2.1,2.5,2.5,1.3,1.2,0.6,2.3,0.3,0.3,4.0,3.6,0.3,0.1,0.9,2.2,2.2,3.1,2.5,0.5,0.5,1.1,1.0,0.5,0.6,1.4,1.2,1.7,0.3,2.2,0.7,1.3,0.6,1.0,0.5,1.2,0.2,0.3,1.2,1.2,2.5,1.2,0.8,0.3,1.1,1.0,4.0,0.3,5,17,9,13,14,16,10,13,14,5,4.0,22,15,9,10,61,71,6,24,40,8,30,16,37,33,36,34,17,18,31,14,8,5,26,2,2,39,38,2,1,5,22,17,19,16,4,2,6,6,4,5,8,8,11,1,18,5,8,5,8,4,9,1,3,10,15,16,9,6,3,8,8,27,2,0.025,0.022,0.016,0.023,0.024,0.03,0.023,0.031,0.042,0.026,0.026,0.033,0.032,0.027,0.022,0.035,0.029,0.027,0.029,0.038,0.023,0.035,0.032,0.034,0.034,0.037,0.031,0.026,0.023,0.029,0.04,0.049,0.029,0.027,0.011,0.008,0.032,0.031,0.021,0.009,0.019,0.033,0.036,0.026,0.022,0.014,0.021,0.015,0.015,0.016,0.026,0.015,0.02,0.018,0.025,0.018,0.02,0.016,0.022,0.018,0.027,0.023,0.019,0.013,0.018,0.032,0.021,0.018,0.018,0.012,0.024,0.019,0.017,0.015,1580,3743,1477,2210,1494,3136,2697,3121,1162,1174,738.0,2481,3057,1108,2249,10935,16463,2312,4609,5645,2393,4111,2130,5033,5335,6926,4886,3402,5791,4815,1462,1068,1120,5679,1935,1584,7094,8378,483,448,1556,2312,4120,3313,3099,1065,807,2375,2105,1143,686,2835,1649,4053,328,3398,1545,2710,949,1398,705,2138,276,668,2200,958,3585,2282,1716,895,1661,2123,8959,771,0.107,0.115,0.117,0.132,0.107,0.136,0.11,0.131,0.149,0.125,0.126,0.138,0.122,0.139,0.121,0.133,0.124,0.103,0.122,0.144,0.094,0.146,0.136,0.129,0.148,0.148,0.138,0.107,0.102,0.14,0.118,0.136,0.125,0.123,0.064,0.066,0.13,0.131,0.109,0.065,0.102,0.139,0.13,0.116,0.105,0.098,0.107,0.091,0.087,0.095,0.13,0.09,0.112,0.092,0.14,0.107,0.1,0.091,0.108,0.104,0.122,0.109,0.101,0.11,0.102,0.135,0.106,0.102,0.1,0.088,0.118,0.106,0.091,0.098,424,1238,702,815,685,1139,904,935,400,349,246.0,1128,867,298,618,2899,4569,441,1286,2210,741,1336,909,1312,1501,1770,1678,1248,1576,1476,445,444,349,1330,492,572,2015,1963,241,174,708,1150,1030,1822,1870,537,327,1041,1016,552,371,1262,889,1623,142,1842,653,1245,521,794,340,791,153,321,1063,617,1766,1029,767,411,742,903,3782,277,3.377,2.944,1.995,2.727,2.022,2.476,2.451,3.116,2.269,3.03,2.517,1.996,2.728,2.765,2.754,2.826,2.986,3.686,2.814,2.229,2.631,2.477,2.014,2.816,2.789,3.138,2.365,2.195,2.831,2.578,2.396,2.432,2.664,3.105,3.437,2.598,2.686,3.174,2.374,2.437,2.948,1.844,2.951,2.064,1.935,2.084,3.287,2.96,2.576,2.577,2.186,2.346,2.071,2.606,3.086,2.122,2.878,2.531,2.202,1.999,2.629,2.506,2.114,2.311,2.256,2.076,2.292,2.607,2.504,2.352,2.483,2.815,2.595,3.115,0.91,0.793,0.619,0.641,0.533,0.494,0.573,0.605,0.543,0.445,0.651,0.411,0.472,0.411,0.478,0.501,0.628,0.864,0.507,0.674,0.724,0.447,0.351,0.579,0.434,0.514,0.473,0.463,0.548,0.465,0.644,0.763,0.368,0.518,0.656,0.416,0.709,0.561,0.383,0.313,0.555,0.431,0.692,0.577,0.652,0.434,0.675,0.617,0.584,0.338,0.394,0.558,0.506,0.558,0.542,0.392,0.52,0.495,0.498,0.362,0.547,0.702,0.274,1.089,0.497,0.65,0.499,0.391,0.394,0.56,0.476,0.673,0.552,0.649
-178,Epileptic,M,27.0,1.0,1.9,1.0,1.5,1.6,1.7,1.3,1.4,0.6,0.8,0.3,2.6,1.5,0.7,2.0,5.0,8.3,0.6,2.0,5.5,1.3,2.7,1.6,3.8,3.1,3.7,2.4,1.8,2.6,2.5,1.2,0.5,0.6,3.1,0.4,0.9,2.7,3.1,0.2,0.3,1.3,3.0,2.0,3.3,2.6,0.5,0.4,1.1,1.1,0.6,0.7,3.0,1.4,2.3,0.2,2.0,0.7,1.4,1.0,1.3,0.7,1.2,0.5,0.4,1.6,0.9,2.4,1.5,1.0,0.7,1.0,1.0,5.0,0.2,6,18,7,10,17,17,10,16,8,8,4.0,25,15,8,22,51,77,4,24,43,16,27,16,42,37,39,31,20,23,29,21,7,6,31,3,9,30,38,2,2,10,27,17,24,16,4,2,7,6,5,5,29,10,15,2,20,5,12,7,11,6,12,3,4,13,15,20,9,6,9,7,7,47,1,0.039,0.023,0.02,0.028,0.044,0.026,0.019,0.024,0.041,0.035,0.025,0.031,0.029,0.04,0.035,0.029,0.028,0.021,0.027,0.041,0.022,0.036,0.027,0.035,0.037,0.028,0.027,0.018,0.024,0.028,0.036,0.025,0.032,0.029,0.011,0.02,0.031,0.028,0.012,0.012,0.024,0.035,0.03,0.027,0.019,0.011,0.019,0.017,0.016,0.012,0.033,0.022,0.026,0.017,0.022,0.02,0.015,0.017,0.023,0.031,0.02,0.023,0.026,0.014,0.019,0.019,0.019,0.019,0.016,0.021,0.026,0.018,0.022,0.016,1709,4492,2585,2287,2097,2522,2281,2686,1043,1235,739.0,2677,3332,1182,3591,9857,18758,2795,4590,3846,4167,3716,2156,7340,4732,7161,4530,3369,5960,4376,2561,1022,838,6256,2649,1949,6782,7249,501,740,1589,2885,5263,2932,3470,1250,834,2552,2362,1470,729,5941,1642,4920,386,2823,1611,2658,1271,1437,986,2708,708,1115,2638,1169,3570,2999,2115,1400,1319,2140,8874,283,0.116,0.116,0.097,0.122,0.148,0.134,0.089,0.124,0.128,0.153,0.123,0.148,0.123,0.167,0.15,0.128,0.128,0.089,0.126,0.16,0.112,0.149,0.137,0.142,0.162,0.134,0.123,0.1,0.103,0.124,0.134,0.101,0.148,0.124,0.077,0.104,0.134,0.13,0.088,0.085,0.122,0.147,0.133,0.123,0.096,0.084,0.099,0.087,0.092,0.096,0.133,0.115,0.133,0.094,0.111,0.122,0.095,0.105,0.108,0.132,0.117,0.116,0.123,0.104,0.102,0.128,0.107,0.101,0.094,0.121,0.124,0.101,0.113,0.104,383,1365,881,823,742,1054,880,942,325,382,213.0,1310,887,298,975,2781,4915,477,1264,2197,1005,1256,923,1789,1523,2152,1691,1432,1725,1500,684,387,305,1508,577,767,1573,1911,259,368,841,1268,1045,1881,2181,692,351,986,1048,631,372,2388,735,2180,165,1527,688,1315,720,717,523,913,339,503,1370,726,1928,1184,969,561,641,912,3822,154,3.913,2.904,2.954,2.721,2.332,2.244,2.2,2.571,2.52,2.726,2.887,1.838,2.863,3.018,2.826,2.685,3.02,4.044,2.83,1.703,3.153,2.462,2.051,3.054,2.524,2.701,2.231,1.946,2.75,2.329,2.862,2.344,2.592,2.917,3.76,2.348,3.182,2.783,2.196,2.359,2.367,1.977,3.461,1.778,1.837,2.065,3.049,3.146,2.849,2.588,2.047,2.292,2.361,2.558,2.367,2.022,2.622,2.339,2.082,2.163,2.127,2.641,2.373,2.488,2.12,2.007,2.082,2.933,2.622,2.69,2.391,2.616,2.382,2.281,0.828,0.615,0.535,0.584,0.657,0.536,0.579,0.629,0.469,0.661,1.017,0.508,0.49,0.512,0.505,0.57,0.557,0.782,0.601,0.46,0.627,0.401,0.444,0.769,0.495,0.548,0.476,0.516,0.422,0.563,0.548,0.931,0.598,0.653,0.905,0.522,0.704,0.646,0.38,0.372,0.507,0.545,0.675,0.46,0.563,0.343,0.603,0.847,0.48,0.397,0.456,0.555,0.541,0.438,0.566,0.326,0.606,0.47,0.356,0.385,0.308,0.783,0.719,1.128,0.404,0.769,0.469,0.464,0.37,0.53,0.532,0.65,0.406,0.519
-179,Epileptic,M,32.0,0.8,2.0,0.8,1.1,1.3,2.4,1.9,2.9,1.6,1.1,0.4,4.0,1.5,0.6,1.7,5.0,9.5,1.2,2.0,8.0,1.0,3.2,2.5,3.9,3.5,6.6,4.5,3.0,5.8,5.3,1.4,1.9,0.8,3.1,0.3,1.3,3.7,4.5,0.2,0.4,1.4,3.9,2.4,6.5,2.9,0.8,0.3,0.9,1.5,0.6,0.4,2.0,1.4,3.0,0.2,3.0,0.5,2.1,1.0,1.5,0.5,1.5,0.2,0.4,2.3,1.2,2.6,2.1,2.0,0.4,1.6,0.9,3.5,0.1,7,23,10,6,14,18,12,21,17,8,3.0,33,12,6,15,48,84,7,21,56,9,25,20,36,30,48,34,27,163,40,20,9,5,25,1,7,32,39,1,2,9,35,23,30,16,4,1,5,7,6,4,12,10,20,1,20,4,14,7,10,3,12,1,3,14,14,20,11,13,5,8,7,28,1,0.033,0.02,0.014,0.021,0.033,0.038,0.029,0.042,0.043,0.037,0.032,0.033,0.029,0.027,0.033,0.035,0.032,0.034,0.031,0.043,0.025,0.042,0.039,0.047,0.035,0.047,0.033,0.033,0.365,0.043,0.047,0.053,0.033,0.034,0.009,0.026,0.032,0.038,0.013,0.018,0.026,0.039,0.038,0.036,0.021,0.015,0.017,0.015,0.016,0.021,0.021,0.022,0.027,0.026,0.018,0.023,0.014,0.023,0.025,0.027,0.026,0.032,0.023,0.024,0.03,0.029,0.022,0.024,0.024,0.013,0.034,0.019,0.017,0.02,1765,5087,2281,2258,2122,3315,2664,3249,2263,1552,898.0,3429,3631,1387,3848,10371,19482,2958,3714,5773,3642,3920,2772,6938,4920,7842,5750,3108,6516,5867,2502,1146,1071,5423,1819,1944,8631,6682,440,559,1635,3740,7679,3643,3417,1482,870,2447,3202,1429,484,3514,2309,5000,429,3878,1404,3076,979,1500,610,2542,888,1045,2175,1060,3859,3329,2953,1078,1863,1870,8267,152,0.116,0.11,0.103,0.092,0.148,0.137,0.107,0.14,0.166,0.146,0.104,0.132,0.11,0.115,0.126,0.137,0.126,0.111,0.139,0.149,0.091,0.136,0.137,0.156,0.126,0.134,0.117,0.121,0.124,0.147,0.157,0.123,0.131,0.117,0.059,0.101,0.137,0.119,0.09,0.105,0.121,0.149,0.154,0.145,0.098,0.089,0.084,0.085,0.091,0.119,0.12,0.106,0.113,0.108,0.095,0.109,0.086,0.112,0.12,0.119,0.112,0.124,0.103,0.122,0.128,0.127,0.106,0.107,0.111,0.103,0.12,0.103,0.092,0.112,434,1512,978,825,745,1137,933,1135,590,464,224.0,1835,895,301,860,2430,5175,481,1157,2940,827,1321,1109,1469,1621,2167,2068,1463,2051,2059,582,401,339,1372,466,773,1797,1706,228,280,837,1689,1096,2571,2133,762,317,947,1293,499,319,1403,877,1868,190,2059,616,1511,608,855,296,871,189,353,1234,592,2002,1316,1299,484,783,775,3488,63,3.645,2.916,2.358,2.597,2.454,2.488,2.405,2.738,2.675,2.86,3.007,1.7,2.971,3.096,3.355,3.056,2.895,4.061,2.561,1.794,3.166,2.345,2.232,3.299,2.501,2.759,2.338,1.815,2.593,2.317,3.072,2.816,2.768,2.809,3.432,2.435,3.294,2.871,2.381,2.478,2.379,2.074,3.609,1.724,1.824,2.095,3.284,3.129,2.871,2.874,1.835,2.711,2.601,2.656,2.776,2.118,2.587,2.389,1.911,2.05,2.391,2.916,3.248,3.383,2.057,2.142,2.049,2.596,2.657,2.395,2.626,2.831,2.511,2.393,1.171,0.669,0.668,0.565,0.742,0.651,0.708,0.726,0.682,0.623,0.902,0.467,0.629,0.759,0.844,0.704,0.714,0.807,0.657,0.551,0.823,0.626,0.621,0.709,0.691,0.79,0.618,0.49,0.741,0.633,0.588,1.1,0.541,0.781,0.503,0.629,0.924,0.862,0.494,0.534,0.577,0.524,0.862,0.51,0.586,0.623,0.754,0.875,0.637,0.708,0.333,0.542,0.683,0.659,0.409,0.479,0.579,0.567,0.378,0.458,0.605,0.822,1.052,0.711,0.524,0.623,0.561,0.533,0.599,0.66,0.485,0.721,0.56,0.667
-180,Epileptic,M,32.0,0.6,2.6,1.1,0.8,1.2,2.8,1.7,1.9,1.3,0.4,0.2,3.5,1.6,0.5,1.5,5.4,8.6,0.9,2.6,6.3,1.1,3.3,1.5,3.3,2.8,2.2,3.2,3.4,2.1,3.9,0.9,0.4,0.7,2.1,0.8,0.5,3.8,3.0,0.2,0.3,1.3,3.6,2.5,3.2,2.5,0.6,0.5,0.8,1.2,0.6,1.4,2.0,2.0,2.4,0.2,3.0,0.6,2.4,1.3,1.3,0.7,1.4,0.4,0.4,1.7,0.6,2.4,1.3,1.2,0.6,1.0,1.4,4.8,0.3,6,25,11,6,13,23,15,15,19,5,2.0,24,15,6,15,56,77,6,24,42,11,37,16,31,39,27,38,27,20,40,15,5,5,23,8,4,43,35,2,1,6,29,23,23,14,4,2,4,6,4,9,14,14,16,1,28,5,14,13,10,4,13,3,4,13,7,20,10,8,6,7,11,38,3,0.029,0.025,0.014,0.018,0.027,0.039,0.025,0.035,0.041,0.021,0.021,0.038,0.034,0.029,0.033,0.029,0.032,0.031,0.031,0.04,0.017,0.03,0.029,0.035,0.029,0.03,0.026,0.03,0.024,0.036,0.029,0.022,0.03,0.026,0.028,0.016,0.036,0.029,0.019,0.016,0.025,0.037,0.035,0.034,0.019,0.011,0.02,0.011,0.015,0.018,0.059,0.019,0.022,0.022,0.025,0.02,0.021,0.022,0.027,0.021,0.025,0.023,0.027,0.018,0.022,0.025,0.021,0.019,0.021,0.021,0.022,0.018,0.019,0.022,1476,5068,2842,2018,2311,4116,2862,3574,2528,1189,572.0,2627,3725,1006,3091,11488,18514,2652,4178,4369,3038,5744,2007,6221,5628,4919,5804,3795,5211,5886,1975,1008,1301,6186,2275,1467,8013,7761,448,469,1756,3106,6676,2166,3424,1532,927,2506,2488,1459,656,3628,3372,5037,250,4190,1265,3675,1572,1854,883,2516,725,858,2582,963,3569,2632,1750,1297,1555,2971,10307,410,0.099,0.121,0.099,0.092,0.116,0.146,0.107,0.131,0.139,0.116,0.082,0.142,0.132,0.134,0.149,0.127,0.132,0.113,0.113,0.132,0.079,0.144,0.135,0.126,0.13,0.12,0.125,0.115,0.098,0.139,0.122,0.081,0.124,0.12,0.117,0.083,0.126,0.127,0.111,0.108,0.108,0.143,0.132,0.124,0.097,0.082,0.105,0.075,0.087,0.092,0.169,0.106,0.115,0.098,0.13,0.11,0.107,0.105,0.132,0.114,0.118,0.121,0.112,0.118,0.109,0.103,0.112,0.102,0.115,0.109,0.117,0.102,0.099,0.136,427,1624,1074,762,767,1288,1017,952,524,332,153.0,1295,940,286,737,3076,4681,473,1297,2391,844,1806,843,1503,1821,1336,2035,1512,1379,1888,505,399,356,1277,498,552,1904,1752,190,203,717,1408,1206,1508,2069,778,373,1032,1159,597,410,1633,1315,1888,114,2282,524,1760,824,1005,454,967,256,399,1263,407,1800,1085,844,516,753,1262,4114,201,3.225,2.804,2.53,2.62,2.55,2.76,2.353,3.051,3.091,2.858,3.021,1.845,3.101,2.536,2.945,2.787,3.077,3.93,2.72,1.7,2.842,2.543,2.126,3.041,2.46,2.886,2.298,2.016,2.824,2.538,2.711,2.342,3.11,3.162,3.643,2.473,3.048,3.133,2.361,2.571,2.806,2.046,3.48,1.679,1.925,2.164,3.145,2.864,2.579,3.048,1.826,2.462,2.493,2.691,2.425,1.997,2.718,2.537,2.019,2.101,2.537,2.687,2.565,2.324,2.297,2.536,2.126,2.507,2.433,2.724,2.488,2.616,2.619,2.199,0.857,0.674,0.755,0.42,0.526,0.681,0.604,0.658,0.651,0.437,0.699,0.493,0.583,0.606,0.573,0.555,0.603,0.713,0.575,0.59,0.853,0.526,0.423,0.657,0.545,0.653,0.474,0.534,0.568,0.535,0.741,1.125,0.496,0.793,0.706,0.555,0.79,0.778,0.453,0.445,0.913,0.649,0.883,0.621,0.579,0.472,0.528,0.521,0.52,0.492,0.6,0.452,0.577,0.563,0.368,0.392,0.339,0.649,0.438,0.353,0.343,0.834,0.576,0.859,0.643,0.67,0.451,0.366,0.404,0.622,0.412,0.507,0.591,0.33
-181,Epileptic,F,37.0,0.7,1.5,0.4,1.0,0.9,1.4,1.4,1.1,1.1,0.7,0.2,2.3,0.8,0.3,0.7,4.8,6.4,1.1,1.8,3.3,0.7,2.3,1.1,2.5,3.2,2.6,2.4,2.2,2.1,2.6,1.0,1.1,0.4,1.6,0.3,0.4,2.2,2.4,0.1,0.1,0.8,2.5,1.8,2.1,2.3,0.7,0.5,0.7,1.0,0.5,0.4,1.1,1.6,2.0,0.1,2.1,0.6,1.4,0.8,1.1,0.7,0.8,0.1,0.4,0.8,1.0,1.6,1.1,0.9,0.6,1.0,1.1,3.1,0.2,6,13,5,7,12,12,13,13,11,9,3.0,22,8,5,9,62,59,7,23,33,7,32,11,29,37,27,34,23,18,30,12,8,6,22,2,3,30,29,1,1,4,24,16,16,12,7,2,4,5,5,3,8,12,14,1,19,3,13,7,10,7,5,1,5,7,14,10,7,6,8,9,8,26,1,0.036,0.025,0.012,0.025,0.026,0.027,0.028,0.024,0.045,0.046,0.024,0.035,0.025,0.028,0.025,0.034,0.029,0.045,0.027,0.031,0.019,0.031,0.026,0.034,0.037,0.032,0.029,0.028,0.025,0.032,0.031,0.059,0.029,0.028,0.019,0.017,0.034,0.03,0.019,0.01,0.02,0.034,0.047,0.026,0.021,0.02,0.031,0.013,0.017,0.019,0.034,0.018,0.03,0.019,0.013,0.018,0.022,0.017,0.022,0.02,0.029,0.017,0.027,0.02,0.019,0.026,0.02,0.021,0.021,0.029,0.026,0.022,0.016,0.013,1191,2839,1379,1589,1337,2012,2050,2551,1489,1104,429.0,1952,2034,790,1760,9410,12700,1761,3716,3572,2565,3633,1660,4031,4995,4459,3965,3566,4025,4354,1562,872,781,4393,1449,1294,4810,5195,326,283,1200,2023,4274,1941,3067,1128,565,1633,1684,1115,335,2046,1803,3502,182,3113,1041,2614,939,1545,756,1629,301,841,1627,773,2406,1660,1211,981,1271,1494,6734,447,0.13,0.114,0.097,0.113,0.123,0.127,0.119,0.123,0.169,0.158,0.112,0.136,0.115,0.13,0.125,0.14,0.13,0.149,0.139,0.131,0.086,0.141,0.117,0.137,0.15,0.133,0.131,0.122,0.108,0.146,0.141,0.152,0.142,0.129,0.091,0.091,0.138,0.138,0.112,0.09,0.102,0.14,0.148,0.121,0.1,0.117,0.13,0.085,0.096,0.118,0.135,0.1,0.137,0.103,0.106,0.109,0.106,0.107,0.116,0.114,0.135,0.101,0.106,0.132,0.094,0.132,0.099,0.109,0.107,0.147,0.133,0.126,0.094,0.096,334,956,552,666,542,844,798,830,435,314,157.0,983,582,192,505,2715,3672,372,1024,1674,685,1289,707,1195,1561,1392,1507,1203,1184,1387,463,318,224,920,304,453,1140,1300,138,155,591,1124,758,1353,1681,551,235,738,828,423,208,994,825,1612,94,1766,475,1297,615,883,428,684,123,351,835,533,1280,834,638,341,612,760,3047,215,3.233,2.842,2.5,2.367,2.185,2.189,2.111,2.729,2.585,2.728,2.295,1.768,2.669,2.855,2.621,2.63,2.789,3.697,2.8,1.867,2.865,2.242,1.987,2.621,2.523,2.634,2.174,2.373,2.614,2.44,2.602,2.828,2.719,3.164,3.58,2.636,2.991,2.889,2.371,2.039,2.453,1.646,3.214,1.677,2.035,2.094,2.856,2.784,2.657,3.018,1.693,2.236,2.28,2.302,2.024,1.951,2.619,2.317,1.807,2.025,2.027,2.48,2.336,2.529,1.968,1.91,2.1,2.372,2.266,3.051,2.353,2.53,2.385,2.343,0.569,0.642,0.407,0.484,0.535,0.578,0.512,0.412,0.513,0.698,0.723,0.383,0.437,0.546,0.426,0.559,0.493,0.739,0.445,0.546,0.73,0.426,0.381,0.622,0.512,0.44,0.418,0.543,0.46,0.505,0.663,0.848,0.308,0.587,0.815,0.525,0.606,0.619,0.422,0.386,0.598,0.439,0.76,0.466,0.616,0.489,0.54,0.855,0.442,0.49,0.483,0.408,0.432,0.405,0.231,0.356,0.373,0.462,0.386,0.399,0.377,0.481,0.444,1.118,0.496,0.57,0.467,0.343,0.463,0.544,0.471,0.417,0.465,0.367
-182,Epileptic,F,27.0,0.7,2.6,0.6,0.7,2.0,2.6,1.4,1.6,0.9,0.5,0.2,3.0,1.3,0.2,1.0,5.1,6.4,0.6,2.4,4.7,1.5,5.4,2.9,3.8,5.8,3.5,4.4,2.2,2.3,3.1,1.2,0.9,0.4,2.1,0.3,0.8,4.2,3.1,0.2,0.2,1.1,3.6,2.3,2.6,2.5,0.5,0.2,0.6,1.1,0.6,0.9,1.6,1.1,2.0,0.3,2.4,0.9,1.7,1.7,1.3,0.8,1.5,0.3,0.5,2.0,0.7,2.5,1.9,0.6,1.0,1.1,1.6,5.6,0.3,5,25,6,8,20,25,12,22,13,6,3.0,27,17,4,17,72,64,6,26,48,22,41,21,39,38,37,51,26,19,45,14,7,4,25,2,10,43,37,1,1,6,31,22,23,14,3,1,4,6,6,5,14,8,14,2,20,6,13,13,12,7,15,3,5,16,8,18,16,4,15,7,12,42,2,0.033,0.028,0.014,0.02,0.035,0.04,0.028,0.034,0.039,0.026,0.024,0.04,0.03,0.032,0.034,0.034,0.029,0.029,0.034,0.044,0.025,0.049,0.04,0.043,0.054,0.034,0.032,0.029,0.026,0.029,0.034,0.044,0.028,0.027,0.015,0.019,0.04,0.031,0.012,0.015,0.022,0.042,0.046,0.03,0.019,0.013,0.013,0.013,0.018,0.017,0.034,0.018,0.021,0.019,0.013,0.018,0.019,0.022,0.037,0.021,0.024,0.026,0.015,0.017,0.018,0.047,0.022,0.023,0.015,0.022,0.022,0.021,0.02,0.018,1176,3964,1435,1599,2326,3027,2719,3174,1774,1268,579.0,2607,3018,685,2451,10185,14632,1837,4862,4902,4159,2921,2220,5655,4136,5779,5666,4437,5382,6075,2111,945,709,5718,1495,2036,6786,6704,371,332,1338,2062,5668,2098,3466,1159,613,1555,1832,1536,911,3156,1722,3388,417,4266,1433,3141,911,1898,1222,2488,458,839,3039,555,3295,2835,1160,1487,1463,2217,9008,558,0.114,0.128,0.098,0.099,0.153,0.135,0.114,0.148,0.151,0.12,0.102,0.16,0.13,0.118,0.146,0.148,0.13,0.115,0.133,0.161,0.116,0.152,0.151,0.149,0.148,0.134,0.126,0.116,0.107,0.143,0.127,0.118,0.097,0.127,0.069,0.101,0.144,0.127,0.095,0.101,0.111,0.151,0.158,0.13,0.102,0.083,0.08,0.083,0.097,0.1,0.135,0.099,0.114,0.1,0.099,0.1,0.104,0.109,0.151,0.108,0.111,0.131,0.114,0.113,0.105,0.15,0.112,0.121,0.093,0.133,0.113,0.116,0.097,0.108,378,1485,616,626,947,1192,890,1001,516,336,177.0,1350,931,169,677,2971,4095,438,1366,2149,974,1666,1115,1659,1768,1764,2238,1376,1328,1990,682,408,230,1340,432,707,1759,1789,211,172,705,1301,1152,1379,1929,580,275,763,915,653,429,1481,817,1642,258,2164,688,1312,677,1036,607,997,280,475,1607,272,1677,1278,578,716,772,1133,4090,227,3.146,2.629,2.335,2.523,2.181,2.169,2.483,2.728,2.562,2.957,2.721,1.751,2.654,2.868,2.715,2.566,2.769,3.308,2.764,2.002,3.146,1.669,1.803,2.629,2.047,2.634,2.062,2.387,2.917,2.466,2.317,2.39,2.482,3.07,3.148,2.505,2.762,2.663,2.048,2.174,2.309,1.58,3.264,1.799,1.986,2.138,2.506,2.485,2.468,2.453,2.173,2.201,2.081,2.165,2.042,2.087,2.336,2.5,1.618,2.058,2.131,2.437,1.89,1.981,2.037,2.335,2.095,2.341,2.191,2.213,2.154,2.17,2.272,2.778,0.695,0.526,0.48,0.488,0.65,0.755,0.548,0.496,0.61,0.422,0.521,0.552,0.495,0.427,0.527,0.634,0.597,0.664,0.548,0.561,0.793,0.482,0.563,0.757,0.659,0.588,0.631,0.589,0.574,0.635,0.664,0.729,0.455,0.685,0.814,0.578,0.781,0.749,0.277,0.489,0.516,0.508,0.84,0.639,0.641,0.479,0.454,0.575,0.425,0.626,0.432,0.469,0.445,0.452,0.251,0.449,0.397,0.458,0.283,0.463,0.466,0.721,0.308,0.584,0.513,0.671,0.475,0.39,0.337,0.607,0.459,0.44,0.477,0.457
-183,Epileptic,M,32.0,0.8,2.1,0.9,1.0,2.5,2.1,3.4,2.0,1.1,0.6,0.4,2.5,1.4,0.4,1.2,11.0,11.9,0.9,1.5,4.7,0.7,3.8,2.3,3.6,3.0,3.2,4.4,4.3,5.2,3.9,1.9,1.1,0.5,3.0,0.4,0.7,3.4,4.5,0.2,0.2,1.0,3.5,2.7,2.8,2.7,0.6,0.5,1.1,1.2,0.7,0.4,2.2,2.1,3.2,0.2,3.4,1.0,1.3,0.9,1.2,0.8,2.0,0.4,0.9,1.7,1.3,3.2,1.7,1.5,0.5,0.8,0.8,4.5,0.4,7,22,7,7,23,16,18,16,11,6,5.0,21,14,4,13,79,89,5,19,36,8,32,19,40,29,33,39,33,34,34,15,8,4,33,3,6,32,42,1,1,4,24,20,21,17,5,2,7,6,7,3,16,10,17,1,22,7,12,6,9,6,12,2,7,11,15,18,10,7,3,5,6,34,3,0.031,0.027,0.015,0.024,0.045,0.037,0.04,0.039,0.05,0.03,0.035,0.045,0.03,0.038,0.03,0.051,0.042,0.032,0.026,0.043,0.024,0.041,0.041,0.043,0.033,0.029,0.046,0.039,0.042,0.034,0.042,0.058,0.03,0.034,0.021,0.022,0.047,0.046,0.014,0.016,0.02,0.047,0.048,0.028,0.019,0.014,0.029,0.018,0.017,0.028,0.023,0.023,0.026,0.025,0.017,0.025,0.026,0.017,0.021,0.023,0.021,0.026,0.019,0.03,0.022,0.027,0.024,0.022,0.022,0.019,0.021,0.03,0.02,0.015,1657,5373,2190,2026,2528,2942,2637,3385,2013,1646,580.0,2698,3381,1049,2785,9961,16928,2449,3443,3690,2946,5002,2583,6711,5116,6660,4254,3886,6348,5660,2458,1066,941,7601,1683,2169,5891,7496,366,517,1560,2833,5965,2540,3687,1216,798,2186,2320,1282,437,3802,2387,5207,391,3742,1359,2500,991,1396,1318,2874,567,1011,2432,986,4152,3072,2350,1288,1290,1810,9003,768,0.123,0.118,0.1,0.105,0.15,0.146,0.133,0.135,0.157,0.124,0.16,0.147,0.12,0.128,0.128,0.152,0.137,0.126,0.116,0.152,0.088,0.132,0.141,0.144,0.129,0.124,0.125,0.127,0.124,0.135,0.143,0.139,0.107,0.134,0.085,0.1,0.154,0.15,0.103,0.085,0.099,0.149,0.161,0.125,0.097,0.094,0.105,0.092,0.088,0.132,0.123,0.104,0.118,0.103,0.103,0.113,0.125,0.098,0.111,0.102,0.103,0.12,0.113,0.131,0.106,0.135,0.106,0.1,0.103,0.097,0.109,0.138,0.1,0.098,458,1466,894,700,915,1023,1117,988,452,355,182.0,1077,818,246,658,3412,4739,443,1000,1754,676,1480,982,1690,1604,1902,1729,1581,1926,1795,715,390,259,1503,362,625,1356,1691,201,222,709,1228,1072,1642,2232,625,300,1017,1063,453,270,1609,1147,2131,184,1982,584,1305,638,855,583,1046,281,464,1228,681,2143,1288,1075,452,616,540,3506,384,3.396,2.972,2.493,2.704,2.25,2.541,2.193,2.83,3.139,3.225,2.915,2.126,3.0,3.049,3.176,2.424,2.912,3.923,2.649,1.883,3.151,2.485,2.236,2.909,2.566,2.753,2.043,2.067,2.594,2.502,2.622,2.537,2.669,3.437,3.527,2.981,3.084,3.087,2.003,2.844,2.704,1.977,3.456,1.821,1.934,2.105,3.164,2.837,2.636,2.968,1.883,2.389,2.38,2.65,2.623,1.972,2.448,2.201,1.861,1.798,2.388,2.508,2.195,2.322,2.043,1.898,2.186,2.658,2.548,2.911,2.412,3.365,2.62,2.375,0.624,0.88,0.61,0.487,0.683,0.787,0.695,0.587,0.763,0.581,0.701,0.565,0.521,0.555,0.53,0.654,0.774,0.773,0.678,0.56,0.756,0.698,0.742,0.785,0.73,0.64,0.555,0.666,0.778,0.686,0.829,0.883,0.645,0.732,0.873,0.801,0.753,0.855,0.338,0.366,0.691,0.623,0.806,0.697,0.664,0.571,0.649,0.624,0.533,0.746,0.375,0.524,0.598,0.623,0.51,0.566,0.508,0.539,0.461,0.428,0.514,0.811,0.5,0.934,0.623,0.63,0.633,0.585,0.557,0.717,0.515,0.805,0.714,0.446
-184,Epileptic,F,32.0,0.8,2.2,0.9,1.3,1.6,2.0,0.9,1.4,1.1,0.5,0.2,2.9,1.2,0.4,1.0,4.0,7.6,0.7,2.1,4.3,0.8,1.8,1.1,2.2,2.9,2.7,2.4,2.6,2.3,2.9,0.9,0.8,0.5,1.8,0.3,0.4,5.0,2.8,0.1,0.2,0.7,2.8,1.7,3.0,2.6,0.4,0.6,0.9,1.0,0.9,0.4,1.2,1.1,2.0,0.2,2.1,0.8,1.4,1.3,0.9,0.4,1.2,0.1,0.4,1.4,0.8,2.9,1.2,1.0,0.3,0.7,1.4,4.9,0.1,7,25,10,11,17,24,10,18,14,6,2.0,30,16,8,13,52,76,6,29,42,7,23,14,32,33,31,34,31,25,35,21,6,5,21,2,4,51,47,1,1,4,28,17,19,16,4,3,6,6,8,3,9,10,15,1,23,5,13,10,6,4,10,1,4,15,10,21,9,6,4,6,9,34,0,0.037,0.022,0.014,0.025,0.031,0.029,0.021,0.03,0.036,0.03,0.015,0.038,0.026,0.028,0.03,0.029,0.025,0.023,0.026,0.038,0.017,0.025,0.022,0.026,0.031,0.029,0.022,0.026,0.024,0.028,0.028,0.044,0.034,0.025,0.009,0.014,0.042,0.032,0.01,0.017,0.017,0.035,0.032,0.029,0.018,0.009,0.021,0.021,0.015,0.024,0.024,0.015,0.022,0.017,0.016,0.019,0.021,0.015,0.024,0.019,0.022,0.021,0.013,0.018,0.018,0.021,0.023,0.019,0.019,0.013,0.017,0.015,0.019,0.011,1401,5093,2063,2233,2204,3397,2050,2717,1639,1214,770.0,3042,3125,937,2433,9310,17477,2139,4435,4924,2760,3875,1753,5710,5761,5619,5528,4941,5638,5243,1940,879,719,5249,1948,1344,7313,7072,394,326,1093,3116,4300,2653,3903,1187,882,1582,1903,1335,470,2590,1949,4526,442,3845,1255,2994,1671,1351,736,2229,281,863,2651,968,3939,2286,1510,766,1424,2695,9300,332,0.131,0.116,0.107,0.116,0.138,0.137,0.099,0.143,0.142,0.141,0.09,0.16,0.125,0.143,0.134,0.133,0.121,0.103,0.13,0.148,0.078,0.127,0.124,0.125,0.14,0.135,0.12,0.125,0.119,0.138,0.121,0.116,0.145,0.127,0.064,0.087,0.136,0.133,0.084,0.095,0.107,0.155,0.119,0.123,0.098,0.077,0.107,0.104,0.093,0.105,0.13,0.096,0.107,0.094,0.107,0.109,0.114,0.094,0.123,0.107,0.115,0.116,0.112,0.118,0.112,0.111,0.117,0.102,0.103,0.117,0.11,0.104,0.101,0.068,368,1609,890,835,822,1204,735,917,476,300,215.0,1298,813,228,652,2423,4740,439,1280,1902,648,1147,804,1500,1565,1536,1716,1580,1454,1668,518,362,217,1128,470,449,1911,1646,199,171,553,1345,895,1607,2170,639,371,717,974,597,261,1205,863,1927,216,1933,556,1454,811,715,367,900,168,366,1273,529,1917,1067,730,355,663,1221,3779,123,3.688,2.916,2.356,2.534,2.313,2.432,2.28,2.743,2.497,3.0,3.134,2.057,2.909,2.868,2.798,2.847,2.918,3.6,2.863,2.171,2.881,2.521,1.983,2.842,2.792,2.828,2.434,2.428,2.848,2.494,2.745,2.601,2.816,3.285,3.571,2.729,2.914,3.112,2.276,2.304,2.561,2.004,3.36,1.902,2.043,2.007,2.926,2.89,2.402,2.662,2.076,2.215,2.15,2.434,2.385,2.135,2.578,2.413,2.113,2.06,2.314,2.509,1.987,2.493,2.204,2.118,2.285,2.546,2.359,2.692,2.322,2.492,2.615,2.743,0.752,0.751,0.568,0.462,0.606,0.59,0.536,0.49,0.569,0.499,0.604,0.399,0.342,0.432,0.476,0.488,0.487,0.683,0.451,0.569,0.75,0.528,0.43,0.557,0.609,0.511,0.44,0.564,0.438,0.549,0.598,1.036,0.4,0.513,0.739,0.522,0.716,0.658,0.303,0.486,0.488,0.473,0.708,0.584,0.59,0.497,0.477,0.51,0.416,0.511,0.377,0.4,0.533,0.47,0.491,0.436,0.389,0.481,0.452,0.368,0.382,0.564,0.243,0.96,0.478,0.702,0.456,0.415,0.452,0.607,0.486,0.522,0.548,0.501
-185,Epileptic,F,27.0,0.6,1.9,1.0,1.2,2.0,1.9,1.3,1.3,1.7,0.7,0.6,4.5,1.3,0.5,0.9,5.5,7.2,0.9,2.0,4.3,1.5,4.8,2.8,3.0,6.2,2.4,3.2,2.3,2.7,2.3,1.6,0.7,0.4,2.3,0.3,0.9,3.3,2.6,0.1,0.1,0.7,2.9,2.0,2.3,2.7,0.5,0.3,1.3,1.0,0.6,0.4,1.6,1.5,2.1,0.2,2.3,0.5,1.6,0.9,1.1,0.5,1.3,0.2,0.4,1.9,1.2,1.7,1.3,0.7,0.4,1.1,1.3,3.8,0.6,7,16,9,11,20,18,12,15,16,10,6.0,41,14,9,9,74,79,8,24,39,23,52,20,35,48,29,34,25,23,34,24,6,5,29,2,8,45,44,1,1,5,28,22,21,15,4,2,7,5,7,4,11,11,18,1,19,4,12,7,10,3,11,1,5,15,16,15,8,4,4,7,9,27,5,0.037,0.023,0.02,0.026,0.031,0.028,0.023,0.025,0.039,0.033,0.035,0.032,0.025,0.04,0.028,0.034,0.029,0.043,0.03,0.031,0.03,0.046,0.031,0.033,0.046,0.025,0.027,0.027,0.025,0.028,0.051,0.042,0.027,0.029,0.012,0.021,0.035,0.026,0.013,0.012,0.018,0.035,0.031,0.024,0.019,0.013,0.014,0.021,0.015,0.016,0.017,0.018,0.024,0.016,0.015,0.016,0.022,0.017,0.02,0.018,0.018,0.019,0.019,0.017,0.017,0.021,0.019,0.018,0.014,0.017,0.026,0.021,0.016,0.022,1567,4251,2195,2196,2931,3257,2699,3022,2212,1593,1014.0,4803,3483,1023,2198,11502,18317,2346,3793,5256,4712,4313,2346,6277,6564,5192,5413,4752,6988,5200,2288,863,946,6836,1880,2468,8026,7505,228,461,1539,2026,5239,3111,4093,996,915,2035,2171,1715,426,2889,2002,4632,452,3909,813,3004,1231,1846,703,2498,332,847,3442,1187,2992,2573,1779,1043,1281,2159,8419,761,0.115,0.113,0.111,0.117,0.143,0.12,0.11,0.122,0.156,0.135,0.141,0.131,0.118,0.173,0.122,0.143,0.128,0.126,0.136,0.135,0.119,0.128,0.119,0.13,0.136,0.128,0.114,0.119,0.11,0.127,0.173,0.106,0.121,0.126,0.063,0.101,0.146,0.131,0.095,0.086,0.095,0.139,0.149,0.109,0.096,0.097,0.091,0.096,0.087,0.092,0.118,0.102,0.119,0.1,0.091,0.101,0.119,0.102,0.108,0.103,0.103,0.11,0.124,0.123,0.102,0.125,0.109,0.095,0.087,0.105,0.13,0.112,0.092,0.141,409,1349,752,771,1037,1095,863,951,658,368,311.0,2072,821,251,576,2989,4662,428,1073,2200,944,1607,1287,1647,2043,1579,1902,1377,1691,1518,646,332,279,1380,466,810,1790,1781,97,221,689,1309,1080,1837,2210,534,370,942,1003,765,282,1345,1001,1950,219,2041,347,1394,736,1001,381,992,171,425,1688,789,1382,1132,784,462,601,992,3615,360,3.387,2.883,2.828,2.67,2.378,2.438,2.463,2.67,2.518,2.978,2.717,1.912,3.011,2.938,2.831,2.739,2.959,3.626,2.774,2.002,3.511,2.147,1.733,2.805,2.498,2.724,2.293,2.506,2.99,2.612,2.709,2.518,2.533,3.35,3.203,2.622,3.105,2.963,2.404,2.392,2.863,1.506,3.219,1.891,2.101,1.984,2.915,2.865,2.69,2.425,1.826,2.245,2.2,2.436,2.298,2.072,2.676,2.377,1.911,1.972,2.132,2.483,1.965,2.151,2.13,1.865,2.268,2.663,2.661,2.386,2.459,2.512,2.483,2.509,0.723,0.629,0.491,0.516,0.64,0.699,0.565,0.582,0.64,0.547,0.715,0.596,0.393,0.666,0.556,0.622,0.628,0.821,0.484,0.669,0.827,0.666,0.537,0.814,0.763,0.521,0.572,0.576,0.553,0.572,0.747,0.784,0.36,0.65,0.77,0.558,0.755,0.652,0.547,0.353,0.42,0.492,0.797,0.649,0.591,0.451,0.54,0.567,0.385,0.53,0.35,0.414,0.45,0.459,0.356,0.477,0.404,0.535,0.487,0.393,0.389,0.535,0.612,0.563,0.52,0.7,0.461,0.362,0.412,0.765,0.517,0.458,0.462,0.498
-186,Epileptic,F,27.0,0.4,2.8,1.4,1.5,1.4,2.6,1.5,1.8,1.3,0.7,0.3,2.7,1.4,0.9,1.5,5.9,7.6,0.9,2.2,3.8,0.6,2.4,1.8,2.9,2.9,3.5,4.2,2.0,2.9,4.1,1.4,0.8,0.4,2.9,0.3,0.5,3.5,3.3,0.2,0.2,0.9,2.4,1.9,1.9,1.9,0.7,0.4,0.8,1.2,0.5,0.6,1.6,1.0,2.0,0.3,2.4,0.5,1.4,0.9,1.4,0.3,1.0,0.2,0.7,1.6,1.7,2.2,0.9,1.1,0.6,1.4,1.7,3.7,0.3,3,25,20,12,12,22,12,16,12,10,4.0,23,16,10,16,61,70,6,27,35,5,28,17,30,35,34,43,21,24,36,14,6,5,31,2,3,32,39,2,1,5,25,19,15,12,7,2,4,6,6,3,12,10,15,3,18,3,11,6,9,2,5,2,3,11,17,18,6,6,5,11,12,23,3,0.023,0.029,0.023,0.026,0.034,0.039,0.028,0.034,0.045,0.036,0.032,0.035,0.03,0.04,0.043,0.045,0.032,0.034,0.034,0.035,0.014,0.037,0.031,0.038,0.035,0.036,0.035,0.027,0.03,0.037,0.045,0.031,0.026,0.037,0.017,0.014,0.039,0.041,0.017,0.022,0.018,0.035,0.039,0.027,0.016,0.022,0.021,0.011,0.015,0.026,0.023,0.022,0.028,0.021,0.02,0.018,0.019,0.017,0.03,0.022,0.021,0.026,0.028,0.027,0.022,0.026,0.02,0.015,0.019,0.023,0.036,0.035,0.018,0.014,1469,4853,2460,2503,1841,3288,2042,2810,1607,1329,532.0,2297,2740,1256,2476,8796,13847,2371,3881,3640,1842,3691,1862,5152,4428,5390,5266,2583,4361,4780,2611,991,1010,5621,1282,1583,5956,5272,368,326,1413,2427,4885,1811,2649,1194,688,2348,2368,1338,472,2294,1588,3594,321,3566,909,2558,849,1490,372,1735,352,718,2204,1616,3127,1751,1471,939,1382,1802,6940,514,0.089,0.122,0.131,0.114,0.148,0.163,0.112,0.137,0.169,0.152,0.128,0.141,0.128,0.163,0.145,0.167,0.132,0.108,0.135,0.139,0.075,0.15,0.13,0.155,0.147,0.146,0.134,0.113,0.114,0.144,0.149,0.098,0.127,0.144,0.076,0.08,0.137,0.146,0.105,0.132,0.096,0.144,0.143,0.118,0.095,0.115,0.103,0.074,0.091,0.103,0.127,0.109,0.127,0.103,0.14,0.099,0.104,0.101,0.133,0.115,0.12,0.119,0.114,0.133,0.107,0.115,0.107,0.097,0.097,0.12,0.147,0.147,0.096,0.1,335,1623,1012,902,686,1035,816,871,478,367,177.0,1131,800,355,665,2362,4130,431,1154,1744,599,1088,908,1241,1424,1634,1994,1132,1443,1732,556,400,315,1222,349,572,1472,1402,228,158,698,1143,1018,1182,1771,596,299,999,1127,477,332,1216,675,1609,206,1992,426,1336,453,886,221,597,184,314,1157,924,1793,868,834,439,665,893,3081,318,3.731,2.717,2.269,2.512,2.389,2.529,2.063,2.778,2.611,3.058,2.69,1.838,2.668,2.606,2.898,2.732,2.65,3.804,2.782,1.856,2.459,2.537,1.808,3.187,2.391,2.622,2.082,1.892,2.402,2.26,3.268,2.172,2.46,3.174,3.011,2.524,2.815,2.792,1.782,2.253,2.314,1.899,3.067,1.797,1.665,1.952,2.778,2.594,2.371,2.749,1.732,1.936,2.239,2.121,1.804,1.892,2.408,2.132,2.052,1.836,2.18,2.757,1.883,2.612,2.071,2.15,1.78,2.242,1.953,2.447,2.254,2.392,2.388,1.908,1.025,0.789,0.503,0.739,0.793,0.734,0.509,0.508,0.466,0.466,0.73,0.384,0.462,0.467,0.56,0.643,0.588,0.911,0.572,0.578,0.68,0.576,0.46,0.749,0.494,0.542,0.526,0.429,0.418,0.578,0.746,1.074,0.447,0.646,0.619,0.637,0.785,0.569,0.3,0.414,0.607,0.474,0.825,0.755,0.505,0.729,0.492,0.757,0.567,0.807,0.389,0.406,0.637,0.527,0.294,0.437,0.662,0.473,0.668,0.423,0.35,0.799,0.451,0.924,0.681,0.794,0.47,0.442,0.511,0.575,0.502,0.507,0.627,0.395
-187,Epileptic,F,27.0,0.9,1.5,0.8,1.6,1.4,1.3,2.4,1.0,1.0,0.5,0.3,3.0,1.6,0.4,1.0,6.9,9.3,0.6,1.6,3.0,1.0,2.3,1.3,2.6,2.9,3.1,2.7,3.0,3.2,3.4,1.4,1.0,0.6,1.7,0.2,0.8,3.4,2.5,0.1,0.1,0.8,3.0,1.8,3.1,4.2,0.6,0.4,0.7,0.8,1.1,0.7,1.2,1.0,2.3,0.3,2.1,0.6,1.4,0.6,1.1,0.4,1.3,0.4,0.2,2.2,0.7,2.8,0.8,1.3,0.8,0.5,1.5,3.8,0.2,6,12,8,13,13,13,18,10,10,5,1.0,23,13,6,9,69,61,4,18,24,10,25,12,25,29,31,27,23,20,27,17,6,5,17,2,6,33,23,2,1,4,22,17,19,24,5,2,7,5,11,3,8,6,14,1,18,5,13,5,9,4,7,2,3,16,8,20,7,9,7,3,11,29,1,0.046,0.021,0.018,0.031,0.04,0.03,0.036,0.022,0.054,0.024,0.035,0.036,0.031,0.037,0.031,0.038,0.035,0.03,0.034,0.03,0.017,0.034,0.023,0.037,0.041,0.037,0.027,0.033,0.031,0.041,0.042,0.054,0.03,0.027,0.012,0.026,0.036,0.031,0.013,0.021,0.019,0.042,0.03,0.038,0.031,0.012,0.022,0.016,0.014,0.019,0.028,0.017,0.021,0.023,0.016,0.02,0.018,0.019,0.026,0.025,0.022,0.028,0.029,0.023,0.022,0.017,0.024,0.018,0.024,0.027,0.017,0.024,0.019,0.015,1390,3485,2253,2458,1613,2079,2382,2501,1584,1020,436.0,2347,3376,728,2429,10313,14557,1859,3214,3326,4007,3891,2127,4725,4365,4663,4752,3522,4989,3731,1780,878,770,4673,1698,1501,6640,4950,281,318,1190,2287,4794,2102,3205,1447,716,1827,1942,2239,572,2355,1484,3793,418,3436,1273,2729,785,1236,772,1639,422,744,3126,1386,3694,1568,1702,1337,1087,2140,8013,493,0.136,0.105,0.106,0.131,0.131,0.136,0.132,0.107,0.152,0.125,0.104,0.133,0.131,0.118,0.109,0.134,0.125,0.099,0.139,0.123,0.085,0.138,0.113,0.119,0.148,0.131,0.12,0.114,0.113,0.127,0.154,0.125,0.124,0.12,0.069,0.105,0.134,0.119,0.09,0.108,0.107,0.141,0.13,0.145,0.124,0.085,0.107,0.097,0.088,0.108,0.133,0.095,0.1,0.099,0.108,0.108,0.106,0.104,0.133,0.13,0.11,0.117,0.128,0.118,0.115,0.101,0.111,0.109,0.118,0.144,0.104,0.126,0.099,0.098,380,1199,719,935,604,776,1036,752,390,315,137.0,1196,859,205,619,2972,3991,333,850,1501,934,1192,836,1273,1215,1431,1580,1331,1528,1298,517,363,250,1020,394,491,1649,1329,203,100,578,1131,954,1396,2202,756,274,773,960,844,324,1132,813,1755,206,1770,552,1226,354,693,365,672,209,267,1494,704,1990,723,911,432,452,1026,3304,180,3.456,2.711,2.838,2.659,2.22,2.472,2.142,2.826,2.785,3.128,3.033,1.764,2.894,2.472,2.812,2.567,2.946,3.722,2.998,1.979,3.265,2.544,2.171,2.741,2.716,2.704,2.427,2.138,2.569,2.31,2.836,2.552,2.62,2.992,3.609,2.696,3.082,2.757,1.558,3.501,2.562,1.946,3.216,1.746,1.737,2.188,3.141,2.735,2.512,2.741,2.274,2.168,2.043,2.303,2.367,2.179,2.688,2.453,2.484,1.962,2.597,2.533,2.071,3.081,2.396,2.449,2.068,2.331,2.291,3.483,2.626,2.428,2.62,3.031,0.875,0.745,0.732,0.477,0.63,0.616,0.664,0.653,0.687,0.605,0.966,0.486,0.705,0.531,0.824,0.674,0.679,0.722,0.544,0.697,0.795,0.535,0.462,0.748,0.579,0.825,0.58,0.662,0.705,0.633,0.844,0.766,0.445,0.588,0.605,0.481,0.78,0.573,0.294,0.424,0.597,0.539,0.808,0.627,0.452,0.504,0.504,0.906,0.458,0.563,0.366,0.572,0.54,0.603,0.473,0.487,0.584,0.639,0.686,0.324,0.61,0.56,0.599,0.99,0.663,0.758,0.468,0.498,0.375,0.538,0.524,0.603,0.571,0.435
-188,Epileptic,F,27.0,0.5,3.3,1.9,1.5,2.1,1.4,2.0,3.1,2.1,0.6,0.3,4.0,1.2,1.1,2.9,6.0,9.1,0.7,1.7,5.9,1.3,2.5,2.2,5.2,3.8,5.4,5.8,4.0,3.3,4.8,1.6,0.9,0.7,2.7,0.3,0.8,3.1,3.7,0.1,0.1,1.3,3.9,2.8,3.3,4.2,0.8,0.9,1.1,1.2,0.8,0.7,1.8,0.8,1.7,0.4,3.0,0.6,2.1,1.3,1.3,0.6,1.4,0.1,0.6,1.5,1.3,2.4,2.3,1.2,0.4,0.9,0.8,5.1,0.4,4,19,16,10,11,13,17,21,10,6,2.0,31,6,7,12,34,57,5,14,46,8,29,15,34,37,37,44,33,23,38,11,7,5,24,2,6,21,29,1,0,6,24,22,21,18,5,5,6,6,5,4,8,5,10,2,18,4,16,9,8,3,7,1,5,9,12,21,9,4,2,7,5,32,2,0.033,0.039,0.042,0.033,0.051,0.034,0.058,0.051,0.064,0.051,0.033,0.066,0.036,0.101,0.059,0.056,0.047,0.04,0.048,0.055,0.034,0.045,0.045,0.07,0.051,0.05,0.058,0.06,0.055,0.053,0.051,0.048,0.053,0.047,0.035,0.025,0.052,0.063,0.039,0.019,0.029,0.056,0.07,0.035,0.041,0.026,0.065,0.021,0.025,0.044,0.029,0.037,0.027,0.034,0.028,0.032,0.032,0.031,0.035,0.028,0.033,0.046,0.036,0.045,0.026,0.036,0.029,0.052,0.035,0.024,0.035,0.03,0.029,0.027,1484,4752,2108,1781,1658,1809,2002,3762,1425,1204,666.0,2800,2684,928,2769,7305,13235,1678,2381,4924,2270,3568,1857,3905,4318,4622,5664,3850,4761,4744,1817,1415,896,5310,1053,1353,4413,4848,136,145,1330,2042,4111,2576,2965,916,475,1596,1898,1300,423,1618,1319,2535,317,3082,787,2514,874,1191,687,1352,170,790,1912,1282,3122,1816,839,619,1358,1337,6972,565,0.112,0.136,0.132,0.133,0.16,0.14,0.156,0.154,0.16,0.145,0.118,0.185,0.122,0.219,0.176,0.159,0.141,0.133,0.157,0.172,0.123,0.148,0.151,0.176,0.159,0.157,0.157,0.164,0.148,0.162,0.151,0.102,0.133,0.143,0.098,0.109,0.163,0.175,0.099,0.107,0.121,0.162,0.188,0.141,0.131,0.112,0.149,0.097,0.111,0.158,0.144,0.122,0.105,0.114,0.111,0.129,0.139,0.128,0.148,0.125,0.121,0.146,0.11,0.148,0.116,0.128,0.121,0.165,0.107,0.108,0.138,0.12,0.12,0.125,329,1542,833,736,777,752,661,1099,527,252,170.0,1159,533,257,882,1820,3663,335,770,1990,615,1138,806,1357,1288,1755,2083,1419,1255,1552,570,400,268,1083,229,546,1056,1080,56,48,742,1239,742,1603,1638,504,179,771,788,350,350,768,588,895,164,1621,332,1265,666,770,318,544,98,271,978,642,1520,712,472,246,527,526,3042,253,3.621,2.558,2.411,2.492,1.974,2.01,2.365,2.57,2.066,3.334,3.167,1.998,3.025,2.391,2.273,2.46,2.592,3.579,2.382,2.018,2.669,2.328,1.9,2.323,2.428,2.18,2.162,2.119,2.66,2.28,2.482,2.687,2.631,2.932,3.101,2.334,2.954,3.027,2.287,3.351,2.245,1.564,3.292,1.8,2.163,1.896,2.485,2.53,2.894,2.834,1.455,2.095,2.145,2.537,1.858,2.037,2.267,2.054,1.528,1.698,2.025,2.801,1.871,2.397,2.003,2.591,2.066,2.387,2.121,2.453,2.306,2.297,2.251,2.523,0.859,0.865,0.647,0.454,0.647,0.697,0.699,0.644,0.813,0.61,0.899,0.583,0.819,0.752,0.726,0.907,0.811,0.929,0.516,0.673,0.788,0.695,0.538,0.73,0.876,0.656,0.609,0.593,0.824,0.695,0.712,1.0,0.458,0.808,0.773,0.594,0.868,0.836,0.413,0.909,0.518,0.474,0.86,0.618,0.663,0.502,0.715,0.867,0.68,0.986,0.295,0.48,0.681,0.804,0.414,0.598,0.755,0.509,0.293,0.416,0.603,0.848,0.285,0.696,0.677,0.819,0.512,0.589,0.446,0.972,0.653,0.754,0.606,0.543
-189,Epileptic,M,37.0,0.7,1.6,0.8,1.3,2.2,2.1,1.5,1.9,1.7,0.4,0.3,3.1,2.1,0.4,1.8,7.3,8.3,0.7,2.7,5.1,1.5,3.4,2.0,3.4,3.8,2.9,3.3,3.1,3.7,3.6,1.3,0.9,0.5,2.7,0.3,0.7,4.3,3.9,0.2,0.1,1.1,3.0,2.5,3.3,3.1,0.8,0.5,0.7,1.2,0.5,0.4,2.0,1.3,1.9,0.2,2.3,0.7,2.9,1.4,1.1,0.4,1.8,0.7,0.4,2.0,0.9,3.2,1.7,1.8,0.8,0.5,2.4,4.5,0.3,8,16,7,8,19,19,15,17,13,4,4.0,28,21,4,13,63,73,7,30,43,13,41,21,32,47,33,42,24,29,40,12,8,5,29,3,7,44,49,1,2,6,33,25,20,18,5,2,7,7,5,3,11,7,16,2,18,7,21,10,10,5,13,6,4,18,8,21,9,13,8,5,17,35,3,0.039,0.021,0.018,0.027,0.05,0.038,0.026,0.029,0.055,0.03,0.025,0.042,0.031,0.036,0.045,0.044,0.032,0.031,0.037,0.044,0.031,0.041,0.033,0.043,0.037,0.033,0.038,0.029,0.032,0.031,0.036,0.045,0.032,0.034,0.015,0.021,0.04,0.033,0.018,0.021,0.024,0.037,0.04,0.03,0.027,0.015,0.028,0.013,0.02,0.021,0.031,0.024,0.024,0.024,0.018,0.019,0.025,0.027,0.028,0.02,0.028,0.033,0.044,0.021,0.027,0.027,0.023,0.025,0.024,0.027,0.017,0.028,0.019,0.024,1327,3774,1751,2152,2162,2935,2393,3265,1935,947,689.0,2531,3514,946,2151,10735,15797,2212,3994,4576,2202,4663,2085,4690,6065,5166,4771,3527,5560,5414,1924,1075,883,5503,1846,1351,6100,7777,390,437,1277,3034,6287,2724,2803,1707,668,2001,1994,998,516,3255,1691,3857,314,3203,1201,3396,1344,1482,814,2546,950,791,2216,881,3696,2361,2306,1234,943,3120,7969,529,0.128,0.114,0.099,0.114,0.158,0.152,0.109,0.13,0.171,0.124,0.102,0.15,0.143,0.127,0.17,0.146,0.129,0.121,0.142,0.156,0.106,0.172,0.147,0.149,0.153,0.142,0.137,0.119,0.112,0.137,0.122,0.137,0.146,0.142,0.082,0.104,0.145,0.141,0.1,0.112,0.118,0.148,0.155,0.122,0.112,0.093,0.115,0.084,0.099,0.1,0.132,0.105,0.112,0.103,0.129,0.109,0.12,0.11,0.13,0.113,0.126,0.134,0.161,0.109,0.129,0.106,0.11,0.11,0.11,0.126,0.109,0.129,0.106,0.132,352,1226,691,791,749,1035,937,1079,535,251,198.0,1232,1097,226,685,2991,4559,412,1247,2006,753,1474,997,1424,1790,1647,1712,1563,1806,2028,605,381,286,1341,447,573,1904,2098,145,150,654,1463,1238,1658,1821,789,292,868,966,424,237,1412,801,1572,152,1784,566,1760,792,872,359,900,299,389,1175,462,2152,1033,1198,490,507,1337,3620,223,3.504,2.903,2.533,2.845,2.446,2.46,2.203,2.678,2.749,3.061,2.75,1.807,2.684,2.808,2.641,2.652,2.835,3.792,2.674,1.967,2.33,2.492,1.913,2.575,2.554,2.569,2.214,1.86,2.458,2.203,2.481,2.843,2.508,2.963,3.429,2.227,2.618,2.823,2.616,2.547,2.438,1.818,3.29,1.798,1.722,2.339,2.644,2.745,2.574,2.631,2.183,2.407,2.401,2.386,2.067,1.971,2.353,2.164,1.945,1.878,2.186,2.902,2.697,2.361,2.049,2.037,1.875,2.45,2.07,2.648,2.119,2.575,2.383,2.992,0.844,0.689,0.489,0.513,0.585,0.646,0.467,0.551,0.59,0.557,0.853,0.401,0.45,0.49,0.453,0.573,0.616,0.794,0.509,0.525,0.781,0.546,0.424,0.852,0.546,0.51,0.492,0.461,0.484,0.548,0.689,0.791,0.487,0.683,0.749,0.434,0.574,0.549,0.527,0.694,0.6,0.54,0.816,0.543,0.447,0.613,0.55,0.852,0.494,0.368,0.468,0.493,0.565,0.456,0.446,0.449,0.403,0.46,0.341,0.348,0.409,0.789,0.528,0.867,0.637,0.64,0.442,0.478,0.4,0.661,0.442,0.549,0.408,0.482
-190,Epileptic,F,42.0,0.7,2.1,1.2,1.3,1.6,2.8,1.6,1.7,1.1,0.9,0.4,2.6,1.6,0.3,1.2,7.3,8.8,1.0,1.7,5.8,1.6,4.1,1.7,2.6,4.6,2.9,2.9,2.5,2.5,3.7,1.6,0.6,0.6,3.1,0.4,1.0,2.6,3.3,0.2,0.1,1.1,2.8,2.0,3.1,2.9,0.9,0.4,0.9,1.1,0.6,0.6,1.7,1.3,2.8,0.4,2.6,0.8,2.1,1.4,1.0,0.7,0.9,0.3,0.3,2.3,1.6,1.9,1.4,1.5,0.4,0.8,1.6,4.3,0.1,8,21,10,10,14,23,14,12,14,9,4.0,21,17,5,13,69,77,10,21,46,14,42,19,30,46,29,33,26,23,43,23,6,4,33,2,10,31,43,2,1,6,27,17,23,19,6,2,7,6,5,5,11,7,21,3,19,8,19,10,8,5,7,2,3,19,19,17,10,10,3,6,9,33,1,0.034,0.023,0.019,0.023,0.037,0.04,0.024,0.029,0.038,0.03,0.028,0.033,0.029,0.037,0.033,0.037,0.03,0.041,0.027,0.042,0.026,0.041,0.026,0.035,0.034,0.031,0.024,0.027,0.025,0.035,0.041,0.031,0.03,0.031,0.011,0.029,0.03,0.03,0.024,0.023,0.024,0.037,0.034,0.031,0.022,0.021,0.017,0.015,0.017,0.012,0.032,0.02,0.022,0.02,0.019,0.022,0.021,0.023,0.026,0.019,0.021,0.02,0.027,0.016,0.025,0.029,0.02,0.019,0.022,0.013,0.021,0.024,0.018,0.014,1351,4744,2498,2370,1914,3686,2812,3326,1679,1564,796.0,2524,3769,662,2289,12008,18294,2393,3868,4642,3716,5759,2278,5262,6769,5374,5816,4204,5802,5733,2445,1006,925,5910,2015,1917,5065,7795,369,220,1653,3244,5434,2730,3679,1495,765,2174,2290,1523,626,2957,1874,5159,536,3632,1570,3310,1506,1516,1086,1753,399,744,3054,1391,3256,2280,1853,871,1407,2410,9240,430,0.118,0.114,0.112,0.107,0.136,0.159,0.115,0.12,0.144,0.143,0.125,0.128,0.128,0.162,0.139,0.152,0.132,0.135,0.118,0.146,0.103,0.147,0.129,0.135,0.145,0.135,0.122,0.13,0.111,0.151,0.141,0.105,0.117,0.129,0.063,0.114,0.121,0.126,0.126,0.12,0.109,0.148,0.121,0.127,0.103,0.111,0.1,0.082,0.09,0.09,0.149,0.1,0.105,0.102,0.122,0.116,0.109,0.119,0.123,0.111,0.103,0.106,0.123,0.106,0.117,0.139,0.106,0.109,0.119,0.103,0.104,0.112,0.097,0.075,408,1542,957,923,734,1268,1030,987,517,433,240.0,1221,1028,186,667,3412,4987,504,1113,2321,1012,1797,1009,1329,2353,1777,2030,1421,1608,1832,651,451,265,1527,512,685,1493,1987,148,104,724,1375,1138,1672,2147,683,325,969,1034,728,330,1431,869,2229,244,1841,670,1501,849,833,539,673,204,369,1494,733,1654,1038,984,406,690,1023,3983,174,3.09,2.624,2.42,2.578,2.255,2.573,2.273,2.935,2.591,2.935,2.675,1.84,2.895,2.674,2.67,2.665,2.913,3.457,2.735,1.791,2.968,2.481,1.982,2.826,2.33,2.506,2.311,2.351,2.766,2.39,2.816,2.269,2.977,2.838,3.407,2.363,2.677,2.849,2.262,2.577,2.728,2.109,3.236,1.886,1.917,2.286,2.987,2.669,2.675,2.405,2.15,2.204,2.459,2.302,2.513,2.21,2.486,2.515,2.053,2.054,2.435,2.536,2.225,2.128,2.182,2.247,2.189,2.265,2.365,2.412,2.223,2.778,2.442,2.636,0.647,0.755,0.693,0.459,0.554,0.633,0.531,0.656,0.592,0.32,0.795,0.464,0.55,0.483,0.519,0.553,0.574,0.762,0.623,0.548,0.741,0.528,0.419,0.66,0.513,0.589,0.49,0.564,0.466,0.59,0.735,0.929,0.6,0.691,0.597,0.62,0.528,0.657,0.689,0.423,0.723,0.541,0.772,0.86,0.553,0.476,0.584,0.624,0.562,0.366,0.499,0.536,0.607,0.453,0.469,0.414,0.493,0.646,0.376,0.333,0.532,0.856,0.312,0.842,0.62,0.729,0.463,0.409,0.346,0.473,0.496,0.673,0.543,0.669
-191,Epileptic,F,32.0,1.0,2.4,0.8,0.8,1.5,1.7,1.5,2.1,1.2,0.9,0.3,3.6,1.8,1.0,1.8,5.6,7.3,0.7,2.2,5.1,1.0,3.1,1.9,2.6,2.3,3.4,2.9,3.1,2.2,2.7,1.4,0.7,0.6,3.0,0.6,0.9,3.3,3.4,0.1,0.2,1.2,3.2,2.5,4.5,3.0,0.6,0.5,1.2,1.4,1.2,0.5,2.3,1.3,1.9,0.1,1.5,0.7,2.0,1.1,1.4,0.8,1.2,0.4,0.3,2.1,1.3,2.8,1.4,0.6,0.7,0.9,1.6,5.3,0.3,9,24,7,6,15,16,14,19,11,7,3.0,31,19,10,15,55,64,6,27,43,9,35,22,28,25,36,33,31,19,30,22,4,5,33,3,10,41,38,2,1,7,22,22,34,20,5,2,8,8,9,3,18,9,15,1,14,5,18,8,11,6,11,3,4,19,14,23,11,5,5,6,11,40,3,0.037,0.02,0.012,0.015,0.026,0.027,0.021,0.026,0.039,0.035,0.024,0.032,0.03,0.036,0.031,0.033,0.024,0.024,0.027,0.032,0.02,0.025,0.029,0.032,0.025,0.029,0.025,0.026,0.022,0.026,0.041,0.03,0.025,0.028,0.012,0.016,0.033,0.028,0.012,0.019,0.021,0.037,0.039,0.027,0.022,0.013,0.017,0.016,0.016,0.022,0.028,0.022,0.019,0.019,0.02,0.015,0.023,0.019,0.024,0.021,0.024,0.021,0.036,0.014,0.021,0.037,0.016,0.018,0.014,0.019,0.02,0.02,0.019,0.017,1515,5149,2020,1945,2071,3084,3055,4273,1825,1472,647.0,3701,4001,1538,3087,11378,16736,2368,4644,5103,3294,6098,2782,5958,5200,7277,5824,5445,5754,5497,1970,929,1226,7334,1925,2421,6209,8087,419,524,1740,3520,4116,4192,3740,1414,886,2376,2798,1814,594,3941,1811,3860,209,3144,1126,4116,1554,2366,1168,2017,418,813,3285,980,5610,3088,1283,889,1577,2471,10029,603,0.138,0.109,0.091,0.086,0.125,0.134,0.101,0.123,0.141,0.138,0.12,0.135,0.124,0.149,0.137,0.137,0.117,0.103,0.119,0.13,0.085,0.13,0.132,0.128,0.128,0.128,0.124,0.123,0.105,0.135,0.133,0.1,0.111,0.137,0.076,0.094,0.13,0.131,0.075,0.09,0.106,0.135,0.129,0.121,0.111,0.095,0.097,0.093,0.092,0.12,0.127,0.104,0.105,0.095,0.114,0.096,0.116,0.103,0.116,0.114,0.112,0.113,0.136,0.095,0.112,0.139,0.099,0.103,0.092,0.122,0.11,0.102,0.101,0.114,476,1845,937,789,857,1036,1056,1315,529,388,220.0,1700,1114,367,873,3009,4756,512,1389,2472,912,1921,1125,1512,1457,2112,1939,1864,1520,1627,595,397,423,1673,628,884,1799,1988,287,226,873,1384,1154,2585,2131,707,403,1073,1302,844,283,1821,1005,1738,78,1576,515,1885,809,1022,597,862,215,409,1553,528,2529,1257,651,487,750,1197,4336,321,3.199,2.626,2.11,2.412,2.061,2.677,2.36,2.803,2.551,3.066,2.739,1.985,2.804,2.807,2.68,2.812,2.881,3.693,2.753,1.836,2.922,2.583,2.189,2.83,2.742,2.926,2.458,2.381,2.8,2.69,2.453,2.256,2.273,3.269,2.877,2.564,2.734,2.982,1.657,2.548,2.608,2.195,2.838,1.822,2.012,2.2,2.691,2.82,2.505,2.593,2.513,2.157,2.029,2.319,2.853,2.112,2.633,2.481,2.179,2.269,2.404,2.285,2.382,2.294,2.221,2.225,2.316,2.67,2.236,2.238,2.497,2.325,2.485,2.401,0.88,0.521,0.64,0.534,0.57,0.385,0.585,0.516,0.726,0.4,0.719,0.534,0.5,0.71,0.533,0.609,0.589,0.757,0.577,0.531,0.784,0.492,0.474,0.7,0.608,0.528,0.59,0.613,0.577,0.471,0.675,0.898,0.418,0.526,0.491,0.411,0.647,0.525,0.356,0.424,0.442,0.527,0.708,0.667,0.621,0.522,0.494,0.542,0.549,0.579,0.388,0.523,0.45,0.504,0.652,0.416,0.443,0.626,0.428,0.491,0.446,0.692,0.432,1.028,0.506,0.656,0.496,0.471,0.414,0.779,0.332,0.464,0.452,0.283
-192,Epileptic,M,22.0,1.0,1.8,1.1,1.8,2.3,2.0,2.2,1.9,1.9,1.2,0.6,3.4,2.0,0.6,2.0,10.7,12.5,1.2,4.0,6.0,1.9,4.3,2.6,3.5,6.5,4.3,6.8,4.1,5.2,4.4,1.5,1.8,0.5,3.0,0.5,1.2,4.8,3.8,0.2,0.2,1.1,4.4,2.6,3.6,3.2,0.9,0.4,1.0,2.0,1.0,0.6,2.6,2.5,3.2,0.4,3.9,1.5,2.8,1.3,1.1,0.8,1.9,0.4,0.7,2.6,1.9,2.8,1.7,1.4,0.7,1.2,2.0,6.3,0.4,9,13,7,13,21,21,19,20,18,10,4.0,32,23,5,19,87,111,11,37,49,18,37,23,37,53,46,59,35,41,43,15,15,4,33,2,9,50,47,1,2,7,37,27,25,18,6,2,6,9,6,5,19,17,22,3,30,9,17,11,8,8,12,3,5,18,20,23,12,10,3,8,13,51,4,0.039,0.019,0.021,0.028,0.038,0.026,0.026,0.026,0.052,0.042,0.042,0.038,0.036,0.041,0.044,0.042,0.035,0.038,0.041,0.04,0.037,0.033,0.031,0.041,0.043,0.034,0.034,0.031,0.033,0.037,0.041,0.051,0.029,0.033,0.013,0.027,0.038,0.032,0.016,0.012,0.02,0.041,0.045,0.03,0.021,0.015,0.018,0.015,0.02,0.024,0.022,0.022,0.025,0.02,0.018,0.016,0.024,0.026,0.026,0.016,0.022,0.032,0.021,0.029,0.025,0.041,0.021,0.02,0.021,0.021,0.025,0.02,0.019,0.016,1856,5617,2592,3417,2610,3195,3187,3625,2347,1968,1048.0,3200,3974,1212,3508,13644,25126,2923,5512,6120,3213,4662,2838,7393,5819,6961,6861,4389,7528,7327,3249,1339,931,7201,1770,2275,11696,9071,480,539,1548,3031,6562,3078,4046,1686,831,2075,2843,2029,568,4368,2942,5736,640,6224,2166,3397,1246,1694,1138,2735,576,1119,2769,1371,3976,2923,2218,1064,1732,3086,11352,632,0.117,0.095,0.099,0.115,0.136,0.117,0.114,0.121,0.158,0.16,0.129,0.15,0.14,0.14,0.147,0.13,0.132,0.128,0.156,0.16,0.109,0.119,0.133,0.152,0.132,0.124,0.114,0.112,0.106,0.139,0.15,0.137,0.091,0.135,0.07,0.113,0.148,0.136,0.091,0.095,0.106,0.154,0.155,0.127,0.099,0.092,0.102,0.089,0.106,0.11,0.119,0.108,0.112,0.105,0.104,0.093,0.113,0.118,0.118,0.091,0.116,0.123,0.117,0.124,0.123,0.151,0.106,0.108,0.105,0.105,0.12,0.114,0.104,0.11,529,1641,877,1089,1075,1273,1279,1182,619,464,261.0,1568,1062,228,841,4151,6326,556,1650,2709,933,1816,1277,1697,2176,2250,2906,1818,2097,2199,710,603,330,1647,566,749,2299,2076,193,257,866,1690,1220,1928,2368,871,330,974,1375,656,398,1976,1485,2513,343,3623,1007,1770,834,1052,588,983,291,438,1479,743,2137,1260,1074,420,822,1478,5155,384,3.267,2.967,2.754,2.816,2.109,2.126,2.133,2.653,2.617,3.228,2.853,1.846,2.872,3.513,3.003,2.56,3.001,3.797,2.698,1.942,2.584,2.135,1.915,3.223,2.196,2.549,2.026,2.042,2.811,2.596,3.169,2.338,2.262,3.017,2.8,2.757,3.304,3.091,2.406,2.335,2.163,1.684,3.22,1.777,1.902,2.059,2.826,2.653,2.456,3.141,1.747,2.321,2.159,2.401,2.219,1.934,2.548,2.248,1.654,1.753,1.964,2.855,2.214,2.686,2.045,2.249,2.01,2.725,2.375,2.854,2.364,2.449,2.402,1.98,0.812,0.761,0.564,0.689,0.734,0.642,0.585,0.506,0.844,0.645,0.872,0.552,0.592,0.522,0.692,0.78,0.758,0.798,0.673,0.597,0.701,0.644,0.659,0.758,0.748,0.698,0.599,0.576,0.604,0.708,0.647,0.911,0.583,0.724,0.662,0.591,0.782,0.726,0.458,0.494,0.56,0.501,0.899,0.705,0.678,0.655,0.619,0.714,0.611,0.82,0.288,0.509,0.577,0.594,0.398,0.547,0.582,0.597,0.425,0.507,0.437,0.913,0.607,0.605,0.78,0.575,0.526,0.475,0.55,0.765,0.534,0.546,0.548,0.543
-193,Epileptic,F,42.0,0.7,3.0,0.8,0.8,2.1,3.1,2.1,1.5,1.2,0.5,0.1,2.9,1.5,0.6,1.6,6.0,8.6,0.7,2.6,5.0,1.6,3.0,1.4,3.3,3.2,3.5,2.5,2.8,2.6,4.2,1.4,1.3,0.5,2.8,0.5,1.1,3.8,3.4,0.1,0.0,1.1,3.1,2.5,2.8,2.9,0.7,0.3,1.0,1.1,1.0,0.7,2.4,1.4,2.4,0.2,1.6,0.8,1.8,1.3,1.0,0.7,1.1,0.4,0.3,2.0,0.5,2.6,1.4,0.9,0.4,0.7,2.0,4.4,0.3,6,23,7,7,31,21,16,14,16,6,2.0,29,15,7,19,70,82,4,28,39,16,31,17,33,38,30,27,27,23,42,23,8,5,27,4,7,46,42,1,1,8,27,20,24,18,4,1,5,5,8,5,19,7,16,1,12,4,15,11,8,5,6,2,3,14,5,20,9,6,4,5,17,29,2,0.039,0.032,0.016,0.02,0.046,0.05,0.031,0.033,0.054,0.03,0.029,0.034,0.033,0.031,0.034,0.038,0.038,0.031,0.035,0.038,0.028,0.04,0.028,0.042,0.044,0.03,0.038,0.031,0.028,0.045,0.05,0.061,0.026,0.032,0.018,0.028,0.041,0.033,0.014,0.018,0.024,0.041,0.04,0.03,0.023,0.016,0.018,0.015,0.015,0.018,0.031,0.025,0.023,0.025,0.027,0.023,0.022,0.021,0.029,0.024,0.023,0.02,0.026,0.018,0.022,0.026,0.023,0.02,0.021,0.015,0.027,0.027,0.021,0.023,1353,4706,1520,1622,2118,3628,2911,2812,1622,1066,353.0,2988,3119,1131,2664,10606,14770,1956,4493,4692,3670,4702,1908,4984,5376,5633,4054,4018,4721,5010,2104,693,1111,5822,1677,1725,6499,6692,222,219,1522,2902,4850,2688,3256,1039,670,2006,2250,1910,741,3027,2370,4074,312,2289,1347,3049,1362,1174,1040,2240,537,661,2701,523,3356,1939,1232,791,1015,2316,7849,439,0.117,0.128,0.098,0.094,0.154,0.169,0.128,0.138,0.156,0.118,0.096,0.136,0.128,0.144,0.154,0.141,0.136,0.106,0.143,0.141,0.105,0.148,0.133,0.14,0.155,0.119,0.139,0.124,0.117,0.156,0.142,0.137,0.112,0.121,0.086,0.108,0.145,0.13,0.089,0.079,0.115,0.154,0.14,0.121,0.11,0.098,0.085,0.089,0.089,0.106,0.134,0.11,0.108,0.108,0.122,0.121,0.096,0.112,0.137,0.121,0.109,0.102,0.121,0.11,0.114,0.111,0.115,0.103,0.11,0.108,0.125,0.118,0.102,0.11,388,1488,779,680,682,1079,1003,791,427,272,124.0,1369,838,325,782,2852,4002,362,1210,1933,962,1261,829,1277,1428,1798,1322,1418,1418,1569,544,336,327,1431,424,641,1823,1797,124,89,763,1243,1020,1665,1939,537,264,903,1000,801,357,1550,911,1643,127,1095,605,1365,705,665,490,789,273,298,1361,233,1692,1007,656,401,480,1128,3252,202,3.234,2.799,1.936,2.391,2.416,2.704,2.407,3.146,2.601,3.18,2.528,1.923,2.901,2.782,2.738,2.639,2.77,3.907,2.983,2.058,2.924,2.584,1.976,2.809,2.811,2.563,2.402,2.247,2.547,2.519,2.753,2.159,2.66,2.942,3.411,2.63,2.852,2.763,2.19,2.454,2.332,2.093,3.443,1.886,1.895,2.029,3.045,2.69,2.53,2.695,2.438,2.019,2.661,2.478,2.866,2.323,2.634,2.459,2.192,1.999,2.328,2.787,2.162,2.188,2.239,2.259,2.189,2.189,2.158,2.081,2.589,2.347,2.636,2.676,0.882,0.867,0.58,0.543,0.709,0.595,0.573,0.744,0.623,0.483,0.685,0.463,0.446,0.459,0.449,0.617,0.651,0.912,0.613,0.62,0.832,0.624,0.383,0.63,0.612,0.549,0.564,0.44,0.457,0.582,0.699,0.816,0.495,0.736,0.69,0.576,0.677,0.531,0.371,0.354,0.518,0.476,0.733,0.656,0.454,0.601,0.643,0.63,0.731,0.492,0.568,0.485,0.674,0.527,0.521,0.614,0.419,0.526,0.512,0.409,0.494,0.821,0.465,0.914,0.587,0.579,0.425,0.38,0.442,0.567,0.551,0.563,0.587,0.626
-194,Epileptic,F,37.0,0.6,2.0,0.6,0.9,1.5,1.8,1.2,1.5,0.9,0.7,0.3,3.2,1.8,0.4,1.4,5.6,8.9,0.8,2.8,4.6,1.0,2.9,1.3,2.8,2.3,4.1,2.5,2.7,2.4,2.6,1.1,0.7,0.5,1.7,0.3,0.3,3.3,2.8,0.1,0.1,0.8,3.2,2.6,3.3,2.5,0.8,0.3,1.1,1.4,0.6,0.4,1.6,1.8,2.7,0.4,1.9,1.1,1.6,0.9,0.7,0.7,1.4,0.2,0.3,1.8,1.8,2.7,1.6,1.2,0.5,0.8,1.6,4.5,0.3,6,22,8,6,15,21,11,14,9,10,4.0,33,16,5,17,58,82,5,31,42,12,35,14,33,29,40,28,30,23,33,14,4,6,21,4,2,38,35,1,1,5,26,29,23,15,7,1,7,7,4,3,13,15,20,2,17,7,12,5,7,6,12,3,4,16,18,18,13,10,8,5,12,37,2,0.028,0.02,0.011,0.018,0.035,0.025,0.018,0.027,0.036,0.032,0.024,0.033,0.035,0.027,0.032,0.035,0.028,0.026,0.032,0.033,0.02,0.03,0.021,0.033,0.025,0.036,0.025,0.026,0.022,0.026,0.028,0.027,0.027,0.024,0.012,0.007,0.036,0.031,0.013,0.016,0.017,0.03,0.042,0.028,0.017,0.014,0.014,0.019,0.017,0.013,0.023,0.018,0.024,0.02,0.03,0.017,0.028,0.017,0.021,0.017,0.021,0.026,0.026,0.016,0.018,0.028,0.02,0.022,0.02,0.018,0.021,0.022,0.017,0.015,1666,4729,1691,2102,1954,3153,2828,3395,1516,1496,619.0,3282,3437,829,2764,10722,19041,2645,5257,4978,3895,5132,2503,6468,5724,6556,4571,4936,6347,5354,2280,724,949,4989,1829,1365,6558,7254,360,296,1392,3241,6251,2827,3635,1714,743,2121,2681,1478,508,2814,2711,5238,396,3488,1333,3481,1226,1296,930,2176,631,886,2969,1353,4078,2789,2016,1084,1244,2328,9225,481,0.11,0.112,0.099,0.097,0.139,0.128,0.094,0.127,0.129,0.141,0.126,0.148,0.138,0.126,0.144,0.141,0.128,0.099,0.127,0.135,0.083,0.138,0.107,0.142,0.13,0.15,0.13,0.123,0.102,0.131,0.117,0.086,0.119,0.111,0.063,0.057,0.138,0.134,0.089,0.102,0.1,0.131,0.149,0.124,0.096,0.089,0.088,0.095,0.094,0.092,0.116,0.102,0.112,0.099,0.129,0.104,0.126,0.097,0.1,0.102,0.12,0.123,0.133,0.112,0.105,0.134,0.109,0.11,0.107,0.118,0.111,0.126,0.095,0.114,462,1590,713,792,797,1288,974,976,461,408,217.0,1563,918,233,770,2832,5370,480,1499,2214,947,1616,997,1559,1567,1861,1571,1641,1724,1728,582,397,302,1123,515,564,1662,1604,166,162,694,1576,1104,1809,2129,832,286,933,1226,601,279,1474,1186,2170,196,1649,664,1575,686,661,489,870,206,401,1492,849,2027,1223,955,476,606,1042,4100,219,3.422,2.747,2.303,2.588,2.173,2.235,2.366,2.833,2.544,2.989,2.656,1.882,2.819,2.718,2.729,2.798,2.864,4.018,2.852,1.982,3.099,2.438,2.13,2.951,2.701,2.977,2.326,2.404,2.802,2.459,2.788,1.828,2.535,2.965,2.989,2.365,2.926,3.126,2.393,1.924,2.492,1.891,3.445,1.756,1.882,2.127,3.095,2.902,2.651,2.897,2.056,2.108,2.278,2.442,2.524,2.228,2.364,2.571,2.002,2.139,2.369,2.431,2.979,2.397,2.184,2.101,2.26,2.417,2.28,2.421,2.336,2.619,2.379,2.788,0.744,0.588,0.491,0.518,0.528,0.436,0.531,0.591,0.484,0.659,0.712,0.502,0.522,0.441,0.504,0.582,0.545,0.845,0.554,0.579,0.687,0.538,0.473,0.614,0.503,0.506,0.527,0.509,0.473,0.595,0.643,0.785,0.387,0.609,0.781,0.425,0.733,0.637,0.277,0.39,0.515,0.433,0.791,0.633,0.543,0.492,0.53,0.715,0.549,0.462,0.428,0.388,0.58,0.468,0.32,0.407,0.469,0.487,0.375,0.487,0.392,0.572,0.547,0.839,0.662,0.582,0.456,0.442,0.552,0.679,0.557,0.653,0.534,0.44
-195,Epileptic,F,52.0,0.9,3.0,1.0,1.4,1.6,2.1,1.2,1.3,1.5,0.7,0.3,3.0,1.1,0.3,1.1,7.0,7.7,1.1,1.6,4.7,1.3,3.3,1.3,3.5,3.0,2.3,3.7,2.0,2.7,2.5,2.1,1.2,0.4,2.2,0.4,0.5,3.8,3.4,0.1,0.3,1.1,2.6,2.8,3.5,3.0,0.7,0.5,0.8,1.4,0.6,0.5,2.2,1.6,2.4,0.2,2.6,0.5,1.2,0.7,0.8,0.5,1.2,0.2,0.4,1.6,1.2,2.0,1.1,0.7,0.9,0.8,1.6,4.9,0.4,8,39,8,15,17,20,12,14,16,8,3.0,28,14,4,17,84,78,7,20,40,14,40,11,42,40,33,42,21,33,35,20,13,4,38,2,6,39,41,1,4,6,21,27,29,16,5,2,7,26,6,3,16,8,21,2,17,6,9,5,5,3,9,1,3,14,16,15,9,4,14,6,14,44,2,0.04,0.036,0.018,0.029,0.043,0.036,0.03,0.029,0.05,0.039,0.033,0.048,0.026,0.026,0.035,0.038,0.032,0.043,0.031,0.046,0.033,0.045,0.036,0.047,0.044,0.024,0.04,0.03,0.031,0.035,0.059,0.057,0.021,0.027,0.011,0.013,0.055,0.04,0.012,0.022,0.021,0.048,0.048,0.034,0.022,0.019,0.022,0.014,0.019,0.012,0.035,0.022,0.026,0.021,0.018,0.018,0.023,0.021,0.022,0.018,0.024,0.025,0.011,0.018,0.022,0.027,0.02,0.016,0.016,0.037,0.023,0.025,0.022,0.024,1510,4492,2211,2570,1999,3464,2493,2733,2033,1334,871.0,2401,3035,1005,2665,11796,16492,2312,3304,4677,3797,3802,1276,5198,4475,5350,5295,3073,5182,5062,2024,936,965,5461,1744,1776,3913,6746,277,708,1427,1645,5290,3375,3678,1188,665,1959,2187,1425,424,3051,1896,4235,454,3884,984,2711,1023,1283,729,1940,414,807,2430,1243,2763,2247,1300,956,1218,2608,7875,387,0.154,0.145,0.104,0.131,0.142,0.141,0.105,0.127,0.189,0.158,0.124,0.161,0.121,0.135,0.142,0.151,0.134,0.133,0.143,0.169,0.119,0.143,0.115,0.162,0.135,0.118,0.128,0.107,0.112,0.14,0.183,0.139,0.096,0.124,0.062,0.087,0.162,0.144,0.08,0.113,0.106,0.15,0.17,0.148,0.099,0.106,0.112,0.077,0.102,0.096,0.131,0.115,0.118,0.109,0.105,0.102,0.109,0.106,0.113,0.099,0.11,0.129,0.087,0.106,0.111,0.123,0.104,0.093,0.092,0.172,0.113,0.125,0.111,0.127,446,1548,797,875,740,1054,800,908,616,341,206.0,1101,840,247,705,3593,4626,443,909,1957,852,1354,606,1476,1445,1680,1980,1194,1604,1554,714,493,344,1353,517,713,1396,1804,164,304,761,905,1168,1758,2110,629,274,930,1086,710,244,1560,936,1873,200,2153,495,1041,542,662,357,756,221,353,1186,730,1561,1055,662,422,579,1008,3699,208,3.135,2.711,2.585,2.604,2.317,2.573,2.37,2.612,2.67,3.116,2.878,1.838,2.712,2.776,2.739,2.534,2.778,3.64,2.846,2.149,3.314,2.317,1.843,2.666,2.428,2.474,2.151,2.018,2.483,2.494,2.43,1.854,2.327,2.812,2.967,2.323,2.306,2.739,1.858,2.212,2.256,1.672,3.282,2.081,1.945,2.039,2.77,2.484,2.385,2.265,2.167,2.061,2.112,2.386,2.349,1.975,2.017,2.666,2.069,2.108,2.236,2.624,2.117,2.816,2.218,1.989,1.907,2.315,2.201,2.36,2.155,2.496,2.289,2.31,0.855,0.645,0.59,0.575,0.641,0.701,0.703,0.543,0.554,0.765,0.747,0.575,0.441,0.591,0.667,0.574,0.629,0.836,0.55,0.683,0.865,0.643,0.635,0.848,0.59,0.528,0.481,0.524,0.496,0.571,0.666,0.853,0.348,0.624,0.84,0.457,0.759,0.755,0.379,0.528,0.479,0.578,0.685,0.588,0.554,0.454,0.602,0.611,0.486,0.503,0.407,0.452,0.444,0.478,0.38,0.428,0.431,0.639,0.438,0.408,0.42,0.529,0.576,0.698,0.418,0.683,0.403,0.474,0.501,0.536,0.58,0.624,0.449,0.265
-196,Epileptic,F,37.0,0.7,2.3,1.2,0.9,1.5,1.6,1.5,1.5,0.9,0.6,0.3,3.3,1.2,0.5,1.2,4.4,8.2,0.5,2.0,4.7,1.1,3.6,1.9,3.0,4.5,4.0,2.2,2.1,1.7,2.5,2.3,0.9,0.5,3.0,0.3,0.8,2.6,3.8,0.2,0.3,0.8,3.8,2.4,3.3,2.4,0.7,0.4,0.7,1.4,0.7,0.6,1.2,1.4,2.4,0.4,2.6,0.8,1.3,1.7,0.9,0.4,1.7,0.5,0.5,1.7,0.8,2.4,1.0,1.0,0.7,1.2,1.0,5.3,0.3,5,19,9,9,15,20,15,16,13,8,4.0,29,19,8,17,51,81,4,28,41,11,39,16,36,53,47,29,25,18,28,20,7,7,36,2,9,27,51,1,2,5,32,23,24,14,6,2,4,8,9,5,9,12,17,2,22,6,10,9,7,3,17,4,5,11,13,18,5,5,7,9,6,53,2,0.035,0.027,0.021,0.02,0.038,0.026,0.03,0.029,0.044,0.038,0.038,0.039,0.031,0.034,0.031,0.033,0.034,0.027,0.037,0.036,0.031,0.038,0.035,0.039,0.047,0.037,0.029,0.031,0.023,0.028,0.062,0.062,0.029,0.038,0.018,0.023,0.048,0.049,0.019,0.015,0.019,0.043,0.048,0.027,0.018,0.015,0.018,0.012,0.021,0.018,0.026,0.017,0.029,0.022,0.023,0.026,0.03,0.016,0.029,0.021,0.022,0.029,0.037,0.021,0.018,0.028,0.021,0.017,0.017,0.029,0.028,0.019,0.026,0.021,1560,4661,2524,2404,2187,3314,3081,3193,1564,1713,634.0,3375,2885,1158,2557,8270,17416,2247,4467,5658,3630,5102,2192,5721,6104,6676,4788,4096,5057,5289,2519,1114,1138,6024,1534,1875,4916,6013,386,537,1389,2181,5840,3474,3725,1420,886,2125,2227,1522,614,2281,1637,4690,421,3268,1403,2586,1702,1241,971,2585,676,816,2861,1295,3708,1932,1858,1144,1426,1565,6921,480,0.115,0.12,0.108,0.1,0.16,0.131,0.121,0.136,0.16,0.14,0.144,0.144,0.138,0.155,0.136,0.144,0.144,0.105,0.143,0.135,0.122,0.14,0.125,0.138,0.157,0.15,0.128,0.124,0.109,0.134,0.159,0.132,0.127,0.158,0.083,0.119,0.153,0.169,0.1,0.105,0.102,0.155,0.168,0.119,0.097,0.095,0.096,0.074,0.104,0.114,0.13,0.096,0.126,0.112,0.109,0.131,0.137,0.102,0.124,0.11,0.11,0.133,0.141,0.119,0.105,0.106,0.112,0.092,0.089,0.135,0.134,0.118,0.118,0.13,370,1349,910,816,718,1062,929,994,465,342,151.0,1440,812,308,717,2376,4505,359,1080,2263,734,1540,818,1552,1778,1965,1505,1294,1309,1456,715,340,288,1331,356,583,1172,1524,195,252,664,1317,980,2009,1987,692,318,841,1029,570,357,1074,799,1836,237,1520,527,1228,905,688,374,953,278,394,1327,712,1758,877,866,416,646,708,3251,215,3.743,3.073,2.802,2.794,2.436,2.708,2.673,2.747,2.419,3.503,2.902,2.039,2.808,2.846,2.589,2.611,2.998,4.2,3.086,2.175,3.392,2.612,2.293,2.693,2.656,2.728,2.498,2.424,2.907,2.683,2.584,3.036,2.907,3.013,3.598,2.816,2.937,2.699,2.178,2.226,2.679,1.683,3.628,1.993,2.136,2.293,3.213,3.083,2.607,2.508,2.18,2.171,2.1,2.513,2.082,2.377,2.601,2.4,2.164,2.07,2.637,2.416,2.075,2.306,2.402,1.973,2.225,2.51,2.435,2.735,2.375,2.393,2.261,2.698,0.889,0.811,0.462,0.485,0.808,0.664,0.601,0.535,0.717,0.789,0.848,0.609,0.4,0.577,0.583,0.611,0.657,0.71,0.679,0.692,0.809,0.81,0.664,0.734,0.768,0.614,0.573,0.543,0.385,0.655,0.706,1.014,0.336,0.734,0.747,0.584,0.929,0.794,0.387,0.488,0.51,0.506,0.937,0.592,0.58,0.51,0.686,0.895,0.433,0.592,0.392,0.392,0.463,0.507,0.448,0.461,0.567,0.417,0.42,0.437,0.579,0.762,0.611,0.68,0.523,0.661,0.474,0.431,0.429,0.703,0.365,0.467,0.625,0.482
-197,Epileptic,F,57.0,1.0,2.3,0.9,0.9,1.4,1.7,1.8,1.9,1.3,0.7,0.4,2.7,2.2,0.6,1.9,5.2,8.7,1.0,2.4,5.7,1.7,2.8,2.9,2.6,2.5,4.4,3.8,2.8,4.0,2.9,1.7,0.7,0.7,3.6,0.5,0.8,2.5,3.0,0.2,0.2,1.4,2.9,2.6,2.2,2.6,0.4,0.3,0.9,1.4,0.5,0.7,2.4,1.7,2.9,0.2,2.7,0.6,1.7,0.9,0.8,0.4,1.1,0.4,0.4,1.8,1.0,2.8,1.5,1.2,0.6,0.9,1.0,4.8,0.3,7,24,11,7,14,14,14,13,10,6,3.0,23,14,4,12,45,67,8,31,31,13,25,23,26,23,33,36,24,33,32,19,6,6,25,2,6,34,32,1,1,8,26,21,15,17,3,1,6,7,5,4,14,11,19,1,22,4,14,9,7,3,7,2,3,12,9,20,11,7,5,7,7,37,3,0.044,0.026,0.015,0.021,0.035,0.036,0.03,0.041,0.046,0.04,0.034,0.036,0.04,0.033,0.042,0.032,0.032,0.036,0.033,0.052,0.031,0.035,0.037,0.033,0.034,0.036,0.029,0.033,0.039,0.029,0.042,0.037,0.024,0.039,0.014,0.02,0.032,0.037,0.018,0.029,0.028,0.04,0.046,0.025,0.021,0.011,0.021,0.016,0.021,0.015,0.031,0.027,0.026,0.024,0.024,0.021,0.023,0.023,0.029,0.026,0.016,0.022,0.023,0.018,0.024,0.028,0.021,0.023,0.021,0.02,0.024,0.021,0.021,0.026,1153,4203,2198,1596,2439,2143,1656,2301,1924,1123,697.0,2675,2807,949,2798,9792,13965,2495,3539,3287,2847,3699,2661,5146,3781,6572,5758,3234,4969,4374,2384,1286,1068,5499,2067,1592,5266,5739,329,405,1722,2536,5283,2350,3047,1011,680,2281,2454,1470,502,2973,2191,4847,171,3594,826,2539,1162,1077,698,2572,422,756,2753,992,3774,2446,1970,1165,1400,1679,8029,303,0.141,0.117,0.111,0.101,0.128,0.138,0.113,0.134,0.157,0.144,0.125,0.125,0.127,0.109,0.15,0.124,0.122,0.12,0.121,0.143,0.106,0.137,0.138,0.132,0.13,0.128,0.108,0.106,0.11,0.133,0.144,0.1,0.107,0.117,0.07,0.091,0.131,0.132,0.087,0.102,0.124,0.137,0.142,0.113,0.106,0.085,0.102,0.09,0.097,0.097,0.135,0.111,0.111,0.108,0.15,0.117,0.119,0.11,0.132,0.117,0.098,0.103,0.12,0.104,0.109,0.118,0.109,0.115,0.108,0.114,0.126,0.113,0.106,0.141,378,1410,914,682,724,796,789,833,503,326,190.0,1187,931,265,736,2752,4480,451,1168,1548,783,1356,1155,1325,1229,1967,2112,1371,1636,1604,673,445,462,1239,542,676,1346,1433,175,180,861,1202,1126,1383,1984,607,276,903,1103,526,350,1401,1013,1992,95,1893,394,1305,540,562,380,795,254,333,1290,571,2045,1097,840,466,642,693,3440,182,2.976,2.807,2.265,2.367,2.797,2.306,1.906,2.476,2.752,3.016,3.053,1.937,2.4,2.664,2.789,2.647,2.596,3.842,2.483,1.887,2.972,2.262,2.071,2.976,2.458,2.724,2.24,1.969,2.465,2.272,2.713,2.85,2.034,3.01,3.386,2.276,3.011,2.847,2.12,2.624,2.505,1.911,3.125,1.886,1.747,1.858,3.245,3.111,2.676,2.679,1.797,2.19,2.326,2.486,2.086,2.1,2.475,2.161,2.159,2.12,2.227,3.161,2.081,2.635,2.259,2.113,1.98,2.399,2.435,2.494,2.383,2.592,2.47,1.972,0.865,0.623,0.68,0.508,0.777,0.686,0.518,0.475,0.666,0.559,0.945,0.455,0.538,0.467,0.596,0.585,0.568,0.899,0.76,0.644,0.966,0.52,0.474,0.558,0.515,0.614,0.587,0.598,0.663,0.551,0.785,1.079,0.384,0.666,0.66,0.44,0.887,0.583,0.386,0.582,0.479,0.566,0.841,0.747,0.533,0.388,0.642,0.835,0.621,0.818,0.568,0.516,0.64,0.565,0.309,0.531,0.487,0.773,0.423,0.403,0.429,0.881,0.406,0.964,0.734,0.589,0.53,0.454,0.507,0.504,0.756,0.784,0.57,0.484
-198,Epileptic,M,32.0,2.1,2.4,1.3,2.1,2.6,2.3,2.7,2.0,1.3,1.3,0.4,3.4,2.2,0.8,1.4,5.7,9.7,1.4,2.8,6.7,1.5,4.0,2.0,5.4,5.3,3.6,5.1,3.1,5.2,5.2,4.1,1.1,1.0,3.3,1.0,0.7,4.0,6.7,0.3,0.2,1.3,4.0,1.3,3.2,3.1,1.1,0.6,0.9,1.5,0.7,0.9,1.9,1.1,2.5,0.2,3.2,1.1,2.2,1.2,1.7,0.9,1.8,0.7,0.8,2.7,1.3,2.2,1.9,1.7,0.8,1.4,1.0,5.4,0.3,48,20,13,14,23,21,26,17,13,10,5.0,34,22,8,16,57,90,14,35,67,13,46,20,50,52,42,57,28,43,53,36,6,11,37,6,7,31,64,2,1,7,36,11,26,17,7,3,5,8,4,7,14,9,16,1,23,8,15,9,12,8,13,4,6,25,16,20,14,10,6,9,7,45,4,0.085,0.027,0.02,0.031,0.049,0.03,0.046,0.032,0.047,0.052,0.035,0.041,0.031,0.04,0.034,0.039,0.035,0.047,0.032,0.054,0.038,0.048,0.032,0.05,0.041,0.035,0.039,0.039,0.035,0.042,0.068,0.039,0.035,0.043,0.043,0.018,0.056,0.057,0.019,0.017,0.024,0.047,0.062,0.028,0.021,0.019,0.018,0.021,0.02,0.028,0.029,0.019,0.022,0.021,0.016,0.021,0.029,0.021,0.023,0.022,0.026,0.028,0.041,0.031,0.025,0.021,0.021,0.024,0.02,0.031,0.028,0.027,0.02,0.017,1827,4742,2465,2800,2083,4097,2374,3486,2071,1723,701.0,2984,4474,1518,2612,10023,19087,2587,5552,6870,2859,4551,2223,6863,6614,6739,5691,3231,7400,6864,2865,911,1346,5406,1224,1695,3625,6753,416,419,1573,3422,698,3157,3763,1575,1137,1643,2510,1127,708,3078,1712,3948,412,4208,969,3307,1272,2015,980,2860,496,1108,3325,1279,3072,3140,2588,1381,1420,1231,9108,467,0.156,0.125,0.116,0.129,0.163,0.137,0.137,0.138,0.172,0.182,0.14,0.159,0.127,0.143,0.142,0.15,0.137,0.143,0.139,0.185,0.142,0.146,0.127,0.159,0.142,0.143,0.128,0.126,0.122,0.156,0.194,0.115,0.149,0.157,0.145,0.097,0.178,0.176,0.112,0.101,0.112,0.157,0.172,0.128,0.102,0.111,0.096,0.1,0.095,0.126,0.134,0.106,0.114,0.105,0.083,0.112,0.121,0.113,0.121,0.117,0.117,0.123,0.169,0.114,0.121,0.112,0.109,0.111,0.104,0.136,0.129,0.132,0.107,0.118,520,1437,967,1061,886,1304,1037,1142,562,466,231.0,1549,1341,426,733,2747,5254,586,1585,2581,676,1648,1133,1973,2248,1967,2552,1330,2227,2320,958,476,486,1397,415,686,1179,1920,204,174,847,1499,356,1852,2264,829,477,697,1153,420,481,1520,869,1813,229,2315,517,1576,784,1078,552,1039,253,492,1840,881,1741,1374,1275,474,802,597,4280,274,2.325,2.927,2.473,2.43,2.158,2.475,2.015,2.695,2.672,2.772,2.317,1.755,2.691,2.83,2.66,2.682,2.883,3.296,2.615,2.281,2.911,2.267,1.768,2.671,2.345,2.639,1.897,1.941,2.61,2.387,2.383,2.001,2.466,2.753,2.209,2.284,2.348,2.514,2.284,2.689,2.185,2.04,1.94,1.887,1.872,1.983,2.901,2.443,2.592,2.345,1.775,2.193,2.133,2.44,1.958,1.947,1.917,2.202,1.842,2.025,1.983,2.828,2.267,2.781,1.867,1.774,1.82,2.364,2.338,2.784,1.988,2.16,2.165,1.932,1.25,0.545,0.597,0.57,0.501,0.712,0.544,0.466,0.641,0.745,0.928,0.398,0.418,0.429,0.47,0.563,0.588,0.791,0.722,0.61,0.725,0.577,0.437,0.852,0.641,0.617,0.475,0.642,0.592,0.528,0.81,0.638,0.444,0.678,0.865,0.373,0.807,0.756,0.354,0.437,0.46,0.607,0.63,0.527,0.529,0.425,0.667,0.844,0.468,0.728,0.301,0.349,0.45,0.403,0.275,0.432,0.45,0.498,0.322,0.485,0.406,0.59,0.398,0.91,0.41,0.581,0.485,0.386,0.439,0.795,0.413,0.57,0.48,0.49
-199,Epileptic,F,37.0,0.8,2.0,1.1,1.2,2.0,2.2,2.0,1.3,1.3,0.6,0.4,2.7,1.2,0.5,0.9,5.2,7.4,1.3,2.1,5.0,1.6,6.0,3.6,3.9,11.8,4.0,7.1,2.8,2.8,3.6,1.5,0.8,0.5,2.7,0.4,1.0,4.4,2.7,0.2,0.1,1.0,4.4,2.6,2.4,2.9,0.7,0.4,0.9,1.2,0.7,0.6,1.2,1.2,2.5,0.7,2.7,0.6,1.4,2.0,1.6,0.6,2.4,0.4,1.0,1.3,1.3,2.6,0.6,1.1,0.6,0.9,1.4,6.6,0.2,8,17,10,9,19,24,18,15,15,8,5.0,26,12,6,10,67,69,12,27,51,14,46,24,52,73,40,58,30,25,38,20,8,4,33,2,11,47,34,1,1,5,33,39,16,17,5,2,5,6,6,3,10,10,20,3,20,6,11,16,11,4,18,2,7,11,19,25,4,8,9,7,10,47,1,0.048,0.022,0.021,0.024,0.035,0.033,0.034,0.024,0.044,0.033,0.04,0.038,0.024,0.026,0.034,0.033,0.031,0.05,0.034,0.039,0.031,0.05,0.044,0.046,0.067,0.035,0.045,0.034,0.03,0.034,0.036,0.05,0.033,0.03,0.013,0.026,0.042,0.028,0.017,0.016,0.021,0.051,0.053,0.024,0.021,0.016,0.02,0.014,0.02,0.015,0.029,0.018,0.026,0.022,0.028,0.015,0.02,0.02,0.05,0.022,0.016,0.032,0.038,0.031,0.022,0.027,0.018,0.012,0.019,0.024,0.021,0.02,0.022,0.01,1426,4470,2831,2273,2495,3713,3884,3512,2175,1391,462.0,2627,2945,1343,1811,11176,17868,2320,4235,5373,4283,4569,2242,6813,4702,6797,5671,4492,6782,6251,2280,1032,914,6421,1770,2153,7962,8123,368,293,1546,1856,6137,2334,4226,1667,771,2351,1938,1514,642,2297,1575,4680,663,4327,1339,2659,1150,2215,1033,3314,417,887,1914,884,4930,1778,1902,1041,1539,2204,9639,565,0.138,0.113,0.113,0.112,0.146,0.136,0.125,0.119,0.165,0.145,0.151,0.145,0.113,0.135,0.136,0.139,0.135,0.157,0.147,0.148,0.118,0.152,0.145,0.148,0.168,0.135,0.151,0.116,0.114,0.14,0.135,0.116,0.12,0.134,0.082,0.115,0.141,0.124,0.098,0.079,0.107,0.167,0.2,0.117,0.101,0.094,0.099,0.084,0.098,0.096,0.121,0.1,0.115,0.117,0.131,0.102,0.115,0.107,0.165,0.116,0.096,0.128,0.151,0.133,0.114,0.129,0.104,0.08,0.104,0.142,0.119,0.115,0.106,0.072,383,1446,841,800,976,1233,1071,992,550,356,164.0,1240,763,298,487,3014,4231,528,1061,2348,847,1840,1183,1766,2256,1911,2339,1419,1567,1867,686,373,261,1379,414,661,1845,1713,197,126,712,1346,1220,1507,2142,741,282,924,914,704,326,1117,781,1798,330,2386,552,1245,715,1092,550,1232,175,486,907,691,2276,809,870,396,679,1005,4332,221,3.18,2.9,2.929,2.809,2.209,2.439,2.745,2.919,2.761,3.114,2.388,1.878,2.855,2.958,2.733,2.717,3.145,3.343,3.001,1.903,3.377,2.133,1.75,2.759,1.858,2.876,2.05,2.358,3.062,2.577,2.444,2.724,2.811,3.204,3.692,2.905,2.938,3.203,2.127,2.578,2.627,1.437,3.442,1.848,2.206,2.391,3.069,3.214,2.655,2.463,2.232,2.189,2.279,2.451,2.545,1.982,2.554,2.491,1.831,2.205,2.289,2.599,2.712,2.179,2.212,1.648,2.231,2.51,2.463,2.626,2.398,2.478,2.498,2.614,0.687,0.577,0.675,0.619,0.688,0.819,0.7,0.526,0.676,0.696,0.731,0.669,0.442,0.472,0.461,0.694,0.653,0.843,0.518,0.699,0.951,0.85,0.528,0.778,0.695,0.639,0.629,0.688,0.75,0.668,0.699,0.963,0.614,0.639,0.625,0.455,0.793,0.682,0.346,0.264,0.516,0.44,0.87,0.679,0.673,0.473,0.547,0.725,0.517,0.508,0.356,0.443,0.45,0.543,0.507,0.434,0.483,0.569,0.511,0.406,0.36,0.718,0.695,0.648,0.588,0.517,0.455,0.379,0.504,0.711,0.59,0.478,0.546,0.519
-200,Epileptic,M,32.0,1.7,2.4,1.2,1.9,1.6,1.8,1.8,3.1,1.2,1.2,0.6,3.2,2.4,0.9,1.4,9.0,13.1,1.0,2.4,4.8,1.5,2.7,1.9,3.3,2.8,3.7,4.1,3.5,4.4,4.8,1.5,1.5,0.8,2.5,0.9,0.5,4.3,3.4,0.4,0.3,1.3,3.6,3.0,2.9,3.5,0.6,0.5,1.0,1.3,0.7,0.7,2.0,1.5,4.0,0.2,3.1,0.7,1.6,0.6,1.6,0.9,1.4,0.3,1.4,1.4,1.7,3.1,1.5,2.0,0.4,1.0,1.6,4.8,0.4,12,19,11,15,10,17,14,24,8,11,6.0,28,18,6,12,66,95,7,30,40,13,33,20,31,32,37,41,24,28,50,12,10,7,27,6,3,44,36,3,1,7,31,25,22,19,5,2,5,7,7,4,13,8,19,1,25,6,14,6,13,6,9,2,9,13,18,23,10,10,3,7,12,35,3,0.047,0.023,0.017,0.028,0.042,0.028,0.031,0.035,0.045,0.043,0.04,0.032,0.035,0.05,0.036,0.051,0.038,0.033,0.029,0.035,0.025,0.027,0.029,0.039,0.027,0.031,0.028,0.036,0.034,0.035,0.043,0.048,0.023,0.031,0.027,0.01,0.037,0.032,0.035,0.028,0.022,0.035,0.039,0.024,0.023,0.015,0.017,0.014,0.014,0.017,0.027,0.024,0.026,0.029,0.028,0.019,0.014,0.018,0.016,0.022,0.02,0.026,0.033,0.043,0.02,0.033,0.025,0.022,0.028,0.016,0.027,0.02,0.018,0.015,1725,6188,2585,2924,2359,3159,2938,4503,1852,1762,922.0,3835,4191,1218,2582,11989,22741,2620,5224,5756,3703,5797,3167,5107,5720,6526,7248,4347,7164,6746,2157,1181,1535,6430,2448,1960,8832,8502,435,277,1861,3392,6094,3853,4010,1316,913,2303,3062,1734,646,3160,1798,5721,228,4468,1963,3269,1381,2192,1259,1970,416,1166,2426,1560,3679,2527,2232,979,1643,2676,9799,666,0.141,0.109,0.098,0.121,0.136,0.121,0.115,0.135,0.144,0.171,0.147,0.137,0.129,0.156,0.142,0.153,0.131,0.121,0.124,0.139,0.1,0.124,0.134,0.134,0.131,0.136,0.118,0.126,0.116,0.149,0.14,0.118,0.11,0.132,0.107,0.068,0.132,0.136,0.142,0.127,0.106,0.135,0.146,0.117,0.104,0.092,0.09,0.082,0.086,0.105,0.115,0.11,0.114,0.11,0.157,0.108,0.09,0.102,0.098,0.118,0.108,0.115,0.132,0.148,0.108,0.137,0.118,0.109,0.119,0.095,0.117,0.113,0.094,0.1,523,1781,1021,1146,696,1128,983,1556,491,473,255.0,1524,1226,335,692,3250,6227,548,1391,2150,1047,1823,1186,1514,1776,2141,2624,1581,2146,2282,640,460,551,1448,598,741,2223,1950,211,141,917,1578,1329,1927,2410,690,388,997,1326,695,397,1365,863,2323,90,2437,856,1558,756,1103,595,715,170,505,1205,816,1888,1093,1085,418,706,1211,4208,383,3.297,3.211,2.635,2.47,2.86,2.448,2.457,2.541,2.872,3.187,2.853,2.18,2.89,2.83,2.934,2.791,2.968,3.679,3.083,2.241,2.7,2.495,2.22,2.642,2.494,2.611,2.208,2.194,2.665,2.415,2.585,2.715,2.286,3.217,3.465,2.45,2.937,3.244,2.286,2.268,2.649,1.97,3.182,2.182,1.868,2.077,2.796,2.771,2.7,2.564,2.077,2.496,2.428,2.538,2.728,2.051,2.557,2.401,2.125,2.168,2.358,2.993,2.857,2.585,2.196,2.168,2.065,2.654,2.441,2.73,2.569,2.605,2.486,2.209,0.736,0.682,0.554,0.543,0.696,0.476,0.686,0.625,0.779,0.473,0.595,0.492,0.61,0.609,0.498,0.689,0.742,0.65,0.626,0.588,0.89,0.504,0.637,0.74,0.49,0.43,0.544,0.543,0.551,0.502,0.734,1.0,0.506,0.665,0.815,0.399,0.667,0.584,0.434,0.415,0.464,0.592,0.791,0.621,0.485,0.534,0.597,0.614,0.605,0.662,0.547,0.514,0.494,0.521,0.24,0.428,0.356,0.485,0.388,0.341,0.424,0.796,0.451,0.905,0.472,0.644,0.477,0.459,0.456,0.635,0.581,0.437,0.528,0.261
-201,Epileptic,M,52.0,0.8,2.4,0.9,1.7,1.7,2.9,1.6,2.0,1.2,1.0,0.4,2.6,1.8,0.6,1.6,8.2,10.2,0.8,2.9,7.0,1.1,3.5,2.0,3.5,3.1,4.3,3.1,3.0,3.1,2.7,1.2,0.8,0.8,2.0,0.6,0.8,3.0,4.3,0.2,0.2,1.1,3.5,2.0,4.0,3.3,0.9,0.5,0.8,1.4,0.6,0.8,2.1,1.3,3.0,0.4,1.7,0.7,2.6,1.0,1.4,0.6,1.8,0.3,0.4,2.6,1.8,3.5,1.9,1.2,0.5,1.1,1.0,5.1,0.4,5,24,11,16,15,30,16,18,15,8,4.0,25,20,7,18,91,97,8,28,47,13,40,23,32,32,44,37,33,29,32,16,7,7,24,4,7,33,47,2,2,7,32,20,29,19,7,2,7,6,9,7,15,9,17,3,16,7,23,9,12,5,12,3,3,18,20,30,12,7,7,7,10,44,3,0.029,0.022,0.014,0.024,0.038,0.029,0.024,0.032,0.048,0.035,0.034,0.031,0.032,0.027,0.032,0.039,0.031,0.029,0.034,0.04,0.021,0.029,0.027,0.039,0.027,0.027,0.026,0.026,0.027,0.026,0.038,0.035,0.025,0.028,0.02,0.021,0.027,0.031,0.013,0.016,0.022,0.038,0.034,0.033,0.02,0.018,0.022,0.013,0.016,0.017,0.027,0.019,0.018,0.021,0.015,0.014,0.015,0.025,0.017,0.02,0.019,0.034,0.021,0.014,0.024,0.029,0.022,0.024,0.018,0.019,0.03,0.015,0.02,0.021,1582,4833,2422,2689,2211,3878,2352,3354,1847,1384,675.0,2434,3557,1072,3039,12502,17320,2310,5008,5617,3008,5764,2773,5566,5144,7114,4968,4401,5936,4818,2202,936,1165,5267,2000,1661,7376,8548,384,447,1565,3078,5729,2372,3943,1702,831,2277,2588,1992,700,3637,1928,4734,570,3275,1443,3172,1395,1929,930,2079,496,874,3206,1739,4486,2461,1907,844,1179,2236,9045,518,0.114,0.112,0.095,0.117,0.151,0.138,0.108,0.125,0.159,0.142,0.121,0.136,0.132,0.128,0.143,0.147,0.129,0.118,0.125,0.142,0.088,0.134,0.128,0.148,0.124,0.122,0.123,0.125,0.111,0.127,0.127,0.112,0.122,0.123,0.09,0.097,0.117,0.136,0.097,0.107,0.108,0.15,0.137,0.128,0.1,0.097,0.104,0.088,0.089,0.103,0.137,0.094,0.106,0.102,0.108,0.096,0.099,0.121,0.11,0.112,0.101,0.142,0.121,0.094,0.115,0.126,0.12,0.109,0.1,0.119,0.132,0.09,0.109,0.108,476,1783,1022,1154,792,1667,990,1260,517,426,207.0,1381,1084,327,965,3890,5591,515,1529,2611,897,1911,1239,1583,1741,2541,1981,1766,1799,1681,605,432,402,1318,524,676,1834,2348,237,211,814,1562,1202,1731,2440,820,371,986,1248,705,417,1871,1002,2033,339,1886,778,1738,885,1100,512,827,252,411,1688,980,2342,1231,948,517,619,1098,4065,317,3.199,2.514,2.224,2.346,2.357,2.112,2.127,2.511,2.67,2.787,2.666,1.621,2.545,2.445,2.535,2.431,2.526,3.24,2.627,1.892,2.781,2.336,1.906,2.701,2.375,2.409,2.101,2.034,2.582,2.333,2.653,2.102,2.293,2.813,3.336,2.313,2.958,2.75,1.874,2.443,2.289,1.768,3.156,1.497,1.792,2.17,2.686,2.868,2.495,2.543,1.914,2.064,2.021,2.349,2.045,1.936,2.138,2.109,1.826,1.949,2.055,2.395,2.246,2.395,2.087,2.001,2.032,2.214,2.231,1.94,2.382,2.255,2.446,2.104,0.786,0.545,0.541,0.529,0.531,0.508,0.529,0.516,0.632,0.377,0.704,0.397,0.549,0.441,0.449,0.61,0.587,0.829,0.694,0.586,0.727,0.453,0.496,0.602,0.439,0.462,0.468,0.501,0.477,0.503,0.692,1.013,0.342,0.742,0.826,0.399,0.713,0.625,0.492,0.451,0.52,0.542,0.851,0.588,0.532,0.641,0.406,0.8,0.544,0.835,0.42,0.427,0.592,0.546,0.296,0.45,0.393,0.531,0.365,0.418,0.547,0.601,0.471,0.795,0.503,0.765,0.606,0.384,0.504,0.584,0.493,0.46,0.593,0.489
-202,Epileptic,M,37.0,0.8,3.9,1.8,2.1,2.3,3.3,3.9,2.1,1.1,1.0,0.6,4.3,1.8,0.7,1.8,9.2,11.9,1.0,4.0,7.2,4.0,7.4,3.1,4.4,6.5,3.7,5.9,3.2,4.4,5.5,2.8,2.0,0.7,2.9,0.7,0.9,7.6,4.2,0.3,0.5,1.0,4.9,3.2,3.3,3.7,1.2,0.6,0.9,1.7,1.0,1.0,3.4,2.4,3.2,0.4,3.3,1.3,3.6,1.3,2.1,1.1,2.1,0.6,0.6,2.2,1.7,2.1,1.8,1.3,0.7,1.6,2.0,5.5,0.5,5,40,14,16,16,33,33,20,13,9,5.0,39,17,7,23,81,98,8,38,62,25,77,27,43,56,40,44,30,34,57,26,18,6,31,4,11,70,50,2,2,5,36,30,24,19,6,3,5,9,8,7,25,14,18,3,23,8,33,8,15,9,15,3,3,20,21,17,12,6,8,11,14,42,3,0.043,0.031,0.024,0.031,0.041,0.035,0.049,0.03,0.047,0.05,0.041,0.048,0.029,0.039,0.035,0.048,0.038,0.037,0.042,0.052,0.058,0.051,0.041,0.048,0.047,0.031,0.047,0.033,0.033,0.044,0.053,0.048,0.037,0.038,0.019,0.029,0.053,0.032,0.021,0.028,0.021,0.047,0.053,0.033,0.022,0.019,0.024,0.014,0.02,0.022,0.037,0.021,0.032,0.024,0.018,0.019,0.02,0.03,0.018,0.023,0.022,0.034,0.023,0.024,0.023,0.023,0.017,0.018,0.021,0.023,0.03,0.02,0.017,0.029,1838,7185,3686,3436,2507,4452,3323,4190,1967,1810,990.0,3551,4352,1213,3806,13724,20962,2760,6770,7179,2903,6931,2851,7686,6918,7844,5001,3847,7186,6999,3185,1891,887,6570,2433,1658,9264,10762,462,598,1373,3367,5610,3395,4412,1930,923,3026,2580,2320,863,5859,2917,4964,650,5115,2068,4609,1727,2970,1530,2616,669,1249,3025,2154,3663,3572,1654,946,1854,3170,12037,466,0.11,0.124,0.121,0.128,0.148,0.142,0.138,0.134,0.154,0.174,0.149,0.172,0.119,0.158,0.134,0.156,0.142,0.127,0.144,0.175,0.147,0.152,0.139,0.158,0.15,0.125,0.129,0.116,0.114,0.143,0.171,0.134,0.124,0.129,0.081,0.125,0.155,0.141,0.107,0.121,0.103,0.15,0.164,0.129,0.099,0.096,0.119,0.075,0.096,0.098,0.153,0.104,0.124,0.114,0.11,0.102,0.103,0.133,0.099,0.107,0.11,0.138,0.115,0.099,0.119,0.118,0.089,0.096,0.096,0.125,0.133,0.108,0.093,0.124,411,2208,1223,1140,972,1609,1355,1271,560,403,272.0,1629,1145,305,1123,3603,5527,473,1667,2741,1054,2573,1287,2036,2355,2343,1993,1642,2140,2446,996,745,348,1534,673,646,2523,2440,242,258,710,1704,1377,1809,2489,967,379,975,1168,815,444,2588,1201,1989,335,2593,958,2066,1039,1453,810,962,355,410,1541,1089,1996,1583,839,452,860,1473,5049,227,3.774,2.957,2.69,2.711,2.374,2.288,2.173,2.779,2.623,3.223,3.362,1.957,2.887,2.771,2.741,2.792,2.912,4.036,3.047,2.258,2.175,2.207,1.942,2.927,2.315,2.581,2.114,1.95,2.622,2.274,2.451,2.427,2.327,3.021,3.392,2.285,2.696,3.157,2.158,2.829,2.286,1.801,2.955,2.106,1.975,2.17,3.013,3.417,2.557,2.892,2.214,2.239,2.315,2.597,2.23,2.143,2.463,2.512,1.815,2.198,2.121,2.665,2.208,3.534,2.063,2.274,1.89,2.418,2.22,2.3,2.385,2.572,2.432,2.206,1.005,0.704,0.589,0.674,0.634,0.661,0.697,0.644,0.632,0.614,0.871,0.492,0.505,0.578,0.513,0.754,0.716,0.825,0.685,0.636,0.827,0.646,0.524,0.748,0.724,0.724,0.564,0.53,0.632,0.714,0.724,0.977,0.483,0.655,0.733,0.548,0.914,0.753,0.542,0.55,0.52,0.563,0.846,0.65,0.556,0.516,0.512,0.894,0.552,0.58,0.473,0.512,0.616,0.544,0.495,0.489,0.483,0.596,0.395,0.441,0.47,0.772,0.467,0.586,0.594,0.766,0.442,0.523,0.445,0.565,0.477,0.621,0.516,0.463
-203,Epileptic,F,57.0,0.9,2.1,0.8,0.8,2.1,1.4,1.4,1.5,1.2,0.5,0.2,2.5,1.7,0.5,1.3,5.1,6.5,0.8,2.1,4.0,0.8,2.3,1.3,3.8,2.5,3.1,2.4,2.3,2.3,3.4,1.5,1.5,0.3,1.6,0.3,0.7,2.3,4.3,0.2,0.2,1.1,2.3,2.0,1.9,2.4,0.4,0.6,0.9,1.4,0.7,0.6,1.7,1.0,1.4,0.4,2.1,0.4,1.5,0.4,0.9,0.6,1.6,0.2,0.4,1.5,0.2,2.6,1.2,1.3,0.5,1.2,1.7,3.1,0.4,6,15,7,5,17,13,11,13,12,5,3.0,22,15,6,10,49,54,8,23,31,5,23,11,37,28,33,28,23,20,39,21,8,2,20,2,6,29,42,1,1,6,22,16,34,15,3,3,5,7,7,4,11,7,9,1,19,5,15,5,8,5,13,1,5,12,2,19,9,12,3,7,12,24,2,0.045,0.028,0.019,0.02,0.053,0.033,0.024,0.032,0.053,0.03,0.028,0.035,0.035,0.031,0.037,0.043,0.029,0.036,0.03,0.036,0.019,0.027,0.026,0.043,0.034,0.032,0.027,0.027,0.025,0.032,0.036,0.066,0.022,0.027,0.013,0.023,0.029,0.036,0.017,0.018,0.024,0.029,0.037,0.026,0.021,0.01,0.023,0.017,0.019,0.017,0.041,0.024,0.02,0.018,0.034,0.02,0.02,0.02,0.017,0.019,0.016,0.026,0.02,0.024,0.018,0.025,0.023,0.022,0.019,0.02,0.03,0.02,0.018,0.03,1162,3572,1306,1438,2223,1817,2018,2663,1532,865,445.0,2145,2947,1198,2484,8263,12978,1989,3625,4114,2701,3201,1678,5342,4393,5932,3786,2916,4419,4702,2782,1068,573,3595,1212,1389,5432,7212,326,293,1269,2104,4194,1823,2791,1095,810,1556,2047,1550,510,2746,1477,3161,364,2590,884,2314,583,1290,842,2322,253,810,2367,461,3610,2044,1889,934,1151,2665,6118,287,0.129,0.113,0.102,0.097,0.153,0.147,0.098,0.127,0.165,0.133,0.109,0.141,0.138,0.128,0.14,0.145,0.116,0.12,0.126,0.141,0.075,0.128,0.113,0.145,0.141,0.147,0.123,0.108,0.107,0.146,0.152,0.149,0.1,0.117,0.076,0.1,0.112,0.136,0.099,0.123,0.12,0.133,0.142,0.117,0.104,0.081,0.111,0.089,0.101,0.113,0.147,0.108,0.113,0.087,0.144,0.117,0.123,0.11,0.11,0.102,0.102,0.125,0.123,0.124,0.109,0.084,0.111,0.107,0.105,0.118,0.132,0.107,0.097,0.136,370,1192,627,620,748,748,812,899,434,280,152.0,1029,833,266,620,2294,3611,402,1115,1752,757,1242,815,1516,1331,1685,1534,1251,1436,1636,740,394,216,979,365,535,1397,1856,188,135,645,1136,990,1276,1743,594,353,823,1006,641,267,1175,752,1389,158,1566,359,1304,412,733,491,937,133,363,1223,194,1782,873,1009,406,584,1261,2724,174,3.142,2.872,2.306,2.298,2.451,2.218,2.12,2.576,2.42,2.765,2.49,1.878,2.65,3.004,3.049,2.729,2.875,3.651,2.685,1.932,2.86,2.183,1.831,2.701,2.644,2.82,2.068,1.915,2.47,2.304,2.813,2.714,2.067,2.671,3.04,2.402,2.859,2.857,1.942,2.427,2.4,1.806,3.182,1.663,1.814,2.005,2.893,2.301,2.455,2.648,2.043,2.41,2.194,2.332,2.666,1.87,2.511,2.098,1.607,1.809,1.874,2.514,2.19,2.51,2.088,2.357,2.141,2.46,1.953,2.791,2.058,2.524,2.339,2.225,0.805,0.618,0.626,0.499,0.73,0.526,0.478,0.508,0.687,0.434,0.614,0.466,0.526,0.491,0.581,0.654,0.662,0.881,0.615,0.62,0.727,0.472,0.527,0.695,0.602,0.572,0.519,0.5,0.593,0.562,0.701,1.136,0.366,0.758,0.587,0.592,0.685,0.826,0.355,0.558,0.587,0.406,0.69,0.46,0.575,0.479,0.643,0.872,0.489,0.56,0.397,0.582,0.547,0.564,0.419,0.418,0.556,0.577,0.351,0.41,0.34,0.723,0.369,0.674,0.709,0.559,0.51,0.584,0.541,0.641,0.557,0.589,0.498,0.388
-204,Epileptic,F,32.0,1.2,2.7,0.8,0.9,1.4,2.4,1.8,1.5,0.9,0.9,0.3,2.8,1.5,0.2,1.0,6.5,9.0,0.6,3.6,4.6,1.7,3.0,1.9,3.0,3.6,4.2,5.0,2.9,2.7,4.9,1.3,1.2,0.6,2.9,0.4,1.3,3.5,3.3,0.2,0.1,1.1,2.8,2.1,2.6,3.7,1.0,0.3,1.5,1.2,0.6,0.7,1.4,1.2,2.0,0.2,2.3,0.5,1.9,1.0,1.1,0.7,0.9,0.5,0.3,3.4,1.0,2.8,1.2,1.1,0.5,1.1,1.6,4.5,0.4,11,22,9,9,12,19,15,14,11,6,3.0,24,15,6,14,65,81,7,33,34,12,35,17,41,46,43,54,25,26,45,16,8,5,29,2,12,44,36,2,1,8,26,19,23,27,9,2,13,6,6,5,8,14,15,3,19,5,14,7,9,5,8,4,2,30,11,22,9,10,4,8,10,34,3,0.047,0.027,0.012,0.016,0.041,0.031,0.026,0.031,0.041,0.034,0.031,0.036,0.029,0.018,0.028,0.038,0.029,0.028,0.038,0.035,0.033,0.031,0.03,0.044,0.032,0.03,0.03,0.027,0.027,0.035,0.042,0.05,0.028,0.032,0.014,0.025,0.046,0.038,0.018,0.013,0.022,0.033,0.039,0.026,0.028,0.02,0.016,0.022,0.016,0.015,0.029,0.016,0.025,0.019,0.016,0.016,0.021,0.02,0.023,0.021,0.03,0.025,0.037,0.016,0.028,0.026,0.022,0.02,0.022,0.019,0.028,0.028,0.018,0.02,1544,4692,2390,2294,1505,3270,2906,2781,1238,1382,465.0,2473,3273,683,1929,10230,16775,2604,5016,4310,2891,4604,2266,4189,5700,7293,7282,4097,5120,6084,1934,1171,1092,5192,1603,2737,4791,5868,394,441,1498,2680,3736,2956,3438,1583,738,2372,2542,1358,616,3265,1875,3545,545,3949,1282,3273,1271,1534,712,1393,613,615,4000,1154,4116,2237,1695,818,1488,1993,9513,444,0.147,0.124,0.089,0.097,0.143,0.143,0.11,0.13,0.126,0.135,0.129,0.146,0.132,0.117,0.124,0.138,0.128,0.102,0.134,0.134,0.104,0.148,0.137,0.145,0.143,0.135,0.135,0.126,0.123,0.15,0.14,0.142,0.113,0.13,0.072,0.109,0.139,0.143,0.097,0.091,0.114,0.138,0.137,0.121,0.123,0.115,0.098,0.1,0.093,0.094,0.131,0.09,0.113,0.096,0.115,0.1,0.098,0.104,0.12,0.117,0.137,0.107,0.151,0.106,0.135,0.116,0.111,0.105,0.116,0.105,0.123,0.139,0.1,0.111,443,1641,926,899,593,1253,1023,859,405,376,172.0,1228,855,226,624,3051,4919,474,1527,1905,871,1595,991,1170,1923,2273,2869,1586,1584,2216,515,461,380,1406,443,876,1397,1515,211,206,778,1309,918,1676,2178,814,294,1020,1113,640,394,1352,939,1788,280,2147,565,1619,715,816,366,601,240,293,1909,589,2027,997,862,407,696,881,3864,252,3.256,2.692,2.602,2.657,2.349,2.312,2.437,3.045,2.315,3.135,2.576,1.789,2.981,2.348,2.368,2.594,2.813,3.922,2.796,2.018,2.682,2.375,2.181,2.65,2.395,2.718,2.137,2.118,2.548,2.371,2.772,2.593,2.374,2.802,3.297,2.604,2.625,2.932,2.125,2.565,2.435,1.917,3.081,1.989,1.816,2.314,3.133,2.939,2.875,2.4,1.975,2.839,2.044,2.151,2.301,2.032,2.581,2.31,2.277,1.965,2.288,2.379,2.363,1.997,2.26,2.254,2.212,2.559,2.24,2.249,2.424,2.726,2.734,2.155,0.738,0.701,0.552,0.51,0.488,0.634,0.525,0.604,0.634,0.551,0.68,0.469,0.499,0.52,0.421,0.547,0.538,0.831,0.728,0.622,0.704,0.418,0.405,0.751,0.465,0.505,0.468,0.5,0.466,0.525,0.874,0.736,0.488,0.573,0.792,0.531,0.665,0.549,0.331,0.608,0.599,0.508,0.73,0.782,0.52,0.496,0.517,0.81,0.541,0.413,0.336,0.795,0.59,0.511,0.339,0.516,0.471,0.508,0.39,0.431,0.401,0.981,0.51,0.997,0.569,0.685,0.507,0.412,0.405,0.532,0.441,0.56,0.608,0.318
-205,Epileptic,F,27.0,0.6,1.6,1.4,1.7,1.8,1.7,3.0,1.4,1.4,0.9,0.7,4.3,1.5,0.7,1.6,9.5,14.2,1.7,2.8,5.2,1.9,2.6,1.6,4.4,3.1,4.7,4.6,3.4,8.1,3.6,1.7,1.1,0.7,2.8,0.7,0.9,4.8,2.6,0.2,0.3,1.7,3.0,2.4,4.2,3.5,0.9,0.3,1.0,1.5,0.9,0.7,2.8,1.6,3.0,0.4,3.0,0.9,2.9,1.0,1.3,0.4,1.6,0.2,0.7,2.8,1.1,2.9,1.8,2.3,0.8,0.9,1.3,4.0,0.3,4,8,9,9,14,17,16,12,12,9,4.0,41,13,6,17,81,116,9,23,45,15,23,17,40,28,39,42,24,52,37,13,6,9,26,5,5,42,26,1,1,10,28,18,19,16,4,1,4,6,4,3,14,10,14,1,16,6,20,7,7,2,13,1,4,16,11,21,9,9,4,5,6,23,2,0.048,0.026,0.031,0.032,0.048,0.045,0.048,0.038,0.057,0.06,0.087,0.058,0.038,0.057,0.037,0.062,0.058,0.071,0.055,0.054,0.064,0.058,0.048,0.059,0.045,0.049,0.047,0.049,0.067,0.048,0.061,0.052,0.048,0.072,0.061,0.028,0.063,0.056,0.017,0.024,0.035,0.045,0.072,0.037,0.029,0.023,0.027,0.024,0.027,0.026,0.038,0.027,0.032,0.026,0.029,0.028,0.029,0.046,0.031,0.032,0.024,0.036,0.027,0.044,0.034,0.035,0.03,0.028,0.042,0.036,0.027,0.045,0.03,0.025,1349,3831,2395,2598,2223,2400,2245,2477,2052,1416,579.0,3887,3668,1062,2896,11021,14488,2080,3933,5341,2308,3342,2293,5676,5359,6314,6675,2998,4583,6010,2113,827,1278,3751,782,1263,4630,4484,344,457,1641,3439,3705,3111,3294,1168,633,1652,1821,1063,618,3678,1849,4342,383,3256,1382,2546,1050,1397,697,2242,346,822,2747,1139,3262,2766,1831,1095,1260,1155,4975,392,0.111,0.105,0.117,0.122,0.15,0.149,0.14,0.122,0.167,0.171,0.163,0.168,0.125,0.17,0.133,0.165,0.158,0.182,0.155,0.162,0.152,0.168,0.136,0.163,0.16,0.143,0.147,0.136,0.165,0.153,0.179,0.13,0.156,0.185,0.143,0.115,0.157,0.165,0.087,0.104,0.133,0.158,0.175,0.126,0.114,0.106,0.104,0.093,0.103,0.117,0.142,0.111,0.126,0.102,0.121,0.119,0.118,0.137,0.122,0.122,0.124,0.117,0.146,0.134,0.122,0.129,0.118,0.102,0.14,0.141,0.115,0.141,0.119,0.117,284,1037,727,784,708,784,1021,755,413,337,191.0,1387,826,241,729,2997,4472,379,1094,2023,553,895,801,1425,1246,1842,2008,1246,1789,1613,515,314,337,909,200,537,1310,1029,175,202,737,1227,698,1810,1879,544,213,610,839,445,279,1628,808,1784,184,1629,601,1047,569,697,220,734,131,315,1366,646,1639,1136,838,368,563,459,2055,223,3.797,3.117,3.141,2.934,2.661,2.522,2.032,2.727,3.356,3.154,2.416,2.284,3.107,3.02,2.827,2.759,2.774,4.022,2.91,2.238,3.048,2.834,2.388,2.775,3.029,2.667,2.64,2.049,2.161,2.858,2.774,2.694,2.651,2.732,2.716,2.386,2.65,3.042,2.31,2.734,2.519,2.293,3.157,1.946,2.065,2.397,2.994,3.321,2.667,2.601,2.664,2.308,2.492,2.711,2.461,2.289,2.499,2.528,2.182,2.335,2.947,2.763,2.446,2.667,2.233,2.242,2.075,2.722,2.442,3.099,2.529,2.901,2.512,2.211,1.044,0.824,0.596,0.633,0.685,0.547,0.668,0.545,0.613,0.77,0.762,0.699,0.526,0.498,0.578,0.747,0.898,0.889,0.677,0.679,0.865,0.654,0.561,1.051,0.657,0.739,0.554,0.627,0.783,0.677,0.783,0.851,0.621,0.76,0.807,0.501,0.998,0.749,0.479,0.47,0.468,0.553,0.833,0.532,0.652,0.403,0.663,1.022,0.531,0.838,0.582,0.506,0.467,0.457,0.415,0.407,0.475,0.723,0.428,0.528,0.385,0.803,0.477,0.836,0.468,0.716,0.441,0.447,0.547,0.992,0.624,0.846,0.581,0.4
-206,Epileptic,F,42.0,0.8,2.3,1.0,1.2,1.1,2.4,1.4,1.9,0.8,0.9,0.3,2.3,1.2,0.4,1.1,6.5,6.8,0.8,2.4,5.9,0.5,3.3,1.7,2.7,2.5,4.0,2.9,2.0,2.6,3.9,1.5,1.1,0.7,2.4,0.2,0.6,2.5,2.3,0.1,0.1,1.1,3.4,1.9,3.7,3.0,0.7,0.5,1.5,1.0,0.4,0.8,1.5,1.4,2.6,0.4,2.4,0.6,1.8,0.8,1.6,0.4,0.9,0.4,0.3,1.2,1.5,2.7,1.1,1.4,0.4,1.6,1.0,3.6,0.4,10,19,9,8,15,20,19,17,10,8,4.0,17,10,4,10,56,58,7,23,37,4,27,20,26,29,33,35,19,21,37,14,8,8,25,2,4,27,27,1,1,8,32,20,22,17,5,3,9,7,5,5,11,8,20,2,22,5,14,6,12,3,7,2,3,9,16,23,8,10,3,13,8,32,3,0.05,0.024,0.016,0.025,0.033,0.032,0.019,0.03,0.031,0.032,0.02,0.032,0.033,0.033,0.036,0.036,0.027,0.032,0.029,0.041,0.012,0.037,0.034,0.033,0.03,0.036,0.025,0.022,0.025,0.032,0.042,0.041,0.019,0.029,0.012,0.013,0.031,0.033,0.014,0.018,0.026,0.039,0.033,0.043,0.022,0.014,0.026,0.02,0.014,0.016,0.039,0.024,0.019,0.022,0.036,0.021,0.023,0.02,0.023,0.027,0.016,0.02,0.024,0.017,0.018,0.026,0.021,0.017,0.018,0.018,0.031,0.021,0.02,0.014,1380,4107,2271,2158,1770,3170,3179,3387,1497,1503,560.0,2315,2573,753,2263,10493,14365,1811,4066,4889,2580,4586,2098,5187,4529,6111,4925,3426,5845,5728,1969,789,1392,4588,1146,1907,5247,4163,336,182,1652,3318,3399,2502,3568,1557,691,2227,2292,1087,479,2102,2296,5112,436,3501,1162,3064,1059,1816,669,1782,426,1015,1826,1233,4223,2157,2444,796,1892,1673,6605,707,0.135,0.109,0.1,0.111,0.129,0.145,0.1,0.136,0.131,0.131,0.098,0.124,0.137,0.15,0.122,0.133,0.12,0.128,0.123,0.143,0.063,0.148,0.152,0.125,0.135,0.146,0.125,0.096,0.111,0.134,0.136,0.125,0.113,0.13,0.078,0.082,0.134,0.133,0.098,0.116,0.124,0.146,0.124,0.149,0.107,0.095,0.123,0.094,0.09,0.103,0.16,0.106,0.104,0.107,0.141,0.118,0.11,0.1,0.113,0.133,0.105,0.11,0.113,0.111,0.103,0.139,0.108,0.097,0.107,0.107,0.139,0.113,0.106,0.089,430,1508,891,824,631,1203,1076,1092,442,442,217.0,1034,694,206,589,3079,4055,433,1296,2164,776,1441,918,1408,1430,1931,1932,1378,1593,1936,569,452,491,1238,314,706,1310,1152,169,88,762,1460,887,1519,2129,801,303,1033,1097,446,317,1057,1103,1991,153,1790,507,1561,569,883,335,714,263,349,1016,757,2114,981,1176,364,898,757,2940,383,3.281,2.556,2.46,2.591,2.389,2.47,2.444,2.801,2.583,2.775,2.342,1.99,2.815,2.743,2.693,2.565,2.798,3.303,2.559,1.993,2.605,2.51,2.043,2.785,2.549,2.66,2.184,2.04,2.835,2.403,2.594,1.849,2.289,2.685,3.142,2.622,2.997,2.705,2.108,2.345,2.799,1.942,2.761,2.025,1.918,2.161,2.826,2.701,2.603,2.755,2.025,2.231,2.334,2.386,3.105,2.134,2.55,2.386,2.189,2.404,2.494,2.52,2.095,3.165,2.016,1.976,2.091,2.342,2.464,2.413,2.47,2.569,2.415,2.298,0.672,0.718,0.638,0.45,0.633,0.59,0.614,0.578,0.472,0.44,0.546,0.5,0.552,0.275,0.603,0.588,0.591,0.829,0.678,0.652,0.688,0.528,0.409,0.671,0.479,0.58,0.493,0.511,0.528,0.508,0.584,0.745,0.474,0.618,0.507,0.45,0.7,0.588,0.334,0.477,0.514,0.588,0.783,0.719,0.614,0.503,0.645,0.673,0.538,0.708,0.357,0.494,0.701,0.496,0.296,0.443,0.521,0.51,0.49,0.389,0.385,0.664,0.404,0.925,0.376,0.625,0.579,0.493,0.488,0.718,0.408,0.604,0.496,0.384
-207,Epileptic,F,47.0,0.5,1.3,0.7,0.9,1.2,1.3,1.5,1.4,0.9,0.5,0.2,1.7,1.0,0.5,1.4,4.8,6.9,0.5,1.8,3.3,1.1,2.2,1.1,2.5,2.7,2.8,2.4,1.6,2.4,2.1,1.2,0.9,0.3,2.1,0.4,0.7,3.1,2.8,0.1,0.1,1.0,1.9,3.0,1.8,2.4,0.6,0.4,0.7,1.1,1.4,0.7,1.4,1.0,1.8,0.3,1.7,0.7,2.2,0.6,0.9,0.6,0.8,0.3,0.2,0.9,0.7,1.7,1.1,1.1,0.3,0.8,1.4,3.4,0.3,5,14,7,10,11,16,13,11,10,5,3.0,14,12,5,15,53,71,5,20,27,10,21,11,24,31,27,28,17,21,23,16,7,4,24,2,6,34,30,1,2,5,19,19,13,16,5,2,5,5,8,7,10,12,12,2,14,6,20,5,9,7,6,2,2,7,9,12,8,7,3,6,9,27,2,0.03,0.022,0.018,0.019,0.036,0.029,0.019,0.03,0.042,0.034,0.024,0.03,0.027,0.032,0.037,0.033,0.03,0.022,0.029,0.034,0.023,0.034,0.036,0.037,0.033,0.034,0.03,0.023,0.024,0.027,0.041,0.043,0.021,0.029,0.018,0.022,0.039,0.032,0.013,0.015,0.023,0.037,0.044,0.029,0.02,0.017,0.027,0.013,0.018,0.033,0.035,0.022,0.03,0.017,0.014,0.02,0.019,0.027,0.021,0.025,0.02,0.028,0.026,0.018,0.016,0.026,0.02,0.021,0.026,0.017,0.032,0.023,0.019,0.022,1198,2880,1652,1830,1800,2197,2996,2546,1263,849,466.0,1609,2499,663,2060,8000,14004,1939,3351,3105,2510,2990,1581,4102,4879,4976,3659,2966,5497,3681,1718,737,681,5225,1635,1559,5127,5134,395,389,1181,2677,4234,1447,2968,1095,555,2100,2008,1539,560,2105,1620,3180,555,2476,1108,2594,750,1006,1010,1404,402,658,1532,693,2556,1725,1319,796,984,1996,6915,483,0.122,0.113,0.104,0.103,0.132,0.146,0.095,0.131,0.162,0.134,0.11,0.138,0.124,0.143,0.155,0.145,0.138,0.11,0.135,0.135,0.085,0.16,0.146,0.141,0.145,0.142,0.139,0.113,0.113,0.14,0.16,0.107,0.112,0.132,0.076,0.106,0.142,0.129,0.098,0.1,0.111,0.154,0.136,0.127,0.106,0.1,0.112,0.079,0.094,0.123,0.15,0.107,0.128,0.096,0.099,0.11,0.1,0.126,0.112,0.127,0.112,0.122,0.124,0.103,0.099,0.123,0.108,0.111,0.118,0.121,0.136,0.119,0.099,0.105,323,994,682,818,587,826,978,786,357,248,127.0,844,743,226,635,2408,3895,363,1020,1556,810,948,564,1066,1427,1456,1350,1090,1480,1164,473,333,262,1140,369,502,1499,1401,160,189,656,933,1072,1027,1853,589,250,882,942,685,323,1076,682,1533,322,1371,592,1387,472,573,524,505,230,259,859,446,1334,832,690,329,423,941,3040,195,3.46,2.771,2.442,2.222,2.68,2.378,2.499,2.891,2.686,2.829,2.934,1.726,2.644,2.329,2.391,2.505,2.895,3.771,2.758,1.796,2.756,2.479,2.351,2.826,2.588,2.776,2.209,2.129,2.827,2.526,2.692,2.169,2.271,3.062,3.451,2.693,2.691,2.754,2.375,2.132,2.25,2.292,3.093,1.657,1.831,2.038,2.807,2.833,2.621,2.575,1.896,1.998,2.168,2.171,2.118,2.014,2.197,2.162,1.822,1.924,2.076,2.63,2.003,2.437,1.964,2.032,2.077,2.356,2.221,2.915,2.571,2.578,2.404,2.558,0.926,0.729,0.497,0.482,0.672,0.483,0.63,0.575,0.485,0.494,0.81,0.484,0.429,0.488,0.486,0.619,0.549,0.784,0.482,0.606,0.743,0.517,0.485,0.605,0.495,0.451,0.489,0.558,0.558,0.527,0.844,1.028,0.325,0.592,0.779,0.491,0.671,0.599,0.572,0.397,0.531,0.668,0.851,0.602,0.535,0.586,0.357,0.615,0.519,0.432,0.481,0.479,0.532,0.465,0.297,0.465,0.392,0.634,0.309,0.437,0.677,0.829,0.416,1.061,0.586,0.541,0.503,0.441,0.488,1.029,0.591,0.575,0.58,0.406
-208,Epileptic,F,17.5,1.1,2.8,1.0,1.0,1.9,1.1,1.5,1.7,1.6,0.8,0.3,2.5,1.4,0.3,1.1,6.5,9.1,1.0,2.1,4.6,1.3,3.2,2.4,3.0,3.1,3.3,2.8,2.6,2.2,3.3,2.0,0.9,0.4,2.1,0.4,0.6,3.5,4.4,0.1,0.1,1.0,3.3,1.9,2.2,2.4,0.6,0.4,0.7,1.2,1.0,0.6,1.6,1.2,2.5,0.2,2.4,0.9,1.1,1.8,1.2,0.9,2.0,0.3,0.5,1.5,1.0,2.9,1.2,0.9,0.6,1.2,1.1,4.4,0.3,9,26,10,11,19,13,17,16,17,10,4.0,26,19,5,15,77,101,7,27,50,13,45,24,33,40,39,37,33,24,47,19,6,3,29,2,4,41,58,1,1,6,33,25,19,16,6,2,4,6,10,4,14,8,19,1,23,9,12,14,10,7,17,2,5,14,18,24,11,7,5,11,7,38,2,0.039,0.029,0.02,0.02,0.035,0.02,0.027,0.025,0.051,0.035,0.029,0.037,0.026,0.026,0.029,0.035,0.031,0.038,0.026,0.035,0.028,0.033,0.032,0.037,0.028,0.028,0.029,0.027,0.024,0.032,0.052,0.037,0.024,0.027,0.011,0.013,0.036,0.035,0.014,0.021,0.019,0.038,0.034,0.021,0.017,0.015,0.021,0.011,0.017,0.016,0.023,0.015,0.017,0.019,0.017,0.015,0.04,0.016,0.033,0.018,0.024,0.027,0.027,0.016,0.018,0.022,0.022,0.019,0.018,0.025,0.025,0.017,0.018,0.019,1571,4759,2098,2173,2231,2437,2635,3330,2177,1685,538.0,2668,3955,972,2710,11949,19274,1993,4849,5137,3775,5703,2486,6050,7503,6851,6330,5015,6200,6530,2043,1028,799,5015,1792,1862,6562,9366,318,398,1571,2703,6189,2656,3687,1430,665,2019,2154,1729,603,3189,2023,4891,416,4557,1043,2696,1788,1790,1334,3160,397,879,2231,883,3999,2295,1340,1313,1852,2084,9384,364,0.132,0.124,0.117,0.105,0.137,0.112,0.107,0.119,0.18,0.15,0.127,0.142,0.129,0.133,0.143,0.145,0.137,0.132,0.136,0.144,0.121,0.136,0.122,0.129,0.126,0.13,0.118,0.121,0.106,0.145,0.149,0.104,0.098,0.123,0.063,0.082,0.131,0.154,0.09,0.099,0.1,0.127,0.144,0.111,0.098,0.092,0.103,0.077,0.094,0.105,0.127,0.098,0.104,0.101,0.112,0.101,0.144,0.103,0.141,0.107,0.119,0.124,0.123,0.109,0.11,0.131,0.118,0.103,0.107,0.125,0.128,0.1,0.1,0.132,419,1578,816,821,921,934,889,1182,566,447,175.0,1156,995,213,645,3060,5116,398,1332,2197,809,1634,1052,1559,1816,1876,1814,1529,1508,1850,629,382,248,1253,483,637,1720,2046,167,143,789,1280,1218,1633,2094,744,284,884,1006,810,362,1531,1033,2142,182,2420,452,1233,913,1022,547,1171,196,473,1199,640,1961,1023,718,461,810,1004,3956,183,3.41,2.661,2.504,2.543,2.024,2.198,2.4,2.523,2.669,2.88,2.587,1.941,2.966,3.026,2.864,2.744,2.845,3.745,2.816,2.05,3.5,2.548,2.079,2.795,2.936,2.843,2.554,2.404,2.956,2.662,2.359,2.615,2.426,2.901,3.252,2.631,2.805,3.113,2.151,2.886,2.585,1.829,3.247,1.843,1.946,2.073,2.725,2.745,2.629,2.436,2.015,2.11,2.004,2.279,2.873,2.057,2.35,2.367,1.983,1.934,2.457,2.485,2.113,2.083,2.044,1.859,2.234,2.426,2.178,2.579,2.316,2.483,2.531,2.537,0.674,0.647,0.5,0.452,0.714,0.557,0.603,0.531,0.719,0.724,0.69,0.54,0.54,0.421,0.588,0.655,0.656,0.62,0.55,0.626,0.737,0.646,0.548,0.846,0.63,0.54,0.612,0.59,0.53,0.582,0.716,0.839,0.356,0.6,0.67,0.441,0.679,0.718,0.323,0.623,0.484,0.551,0.82,0.565,0.6,0.53,0.552,0.725,0.405,0.38,0.498,0.47,0.489,0.499,0.477,0.469,0.417,0.507,0.494,0.406,0.473,0.765,0.322,0.763,0.618,0.539,0.525,0.449,0.405,0.666,0.65,0.519,0.464,0.42
-209,Epileptic,M,22.0,0.8,3.2,1.1,1.5,1.7,2.3,1.8,2.0,0.8,0.8,0.2,2.4,1.5,0.6,0.9,9.3,10.3,0.9,2.6,3.4,1.4,2.6,1.2,4.0,2.8,3.9,2.9,3.4,3.4,2.5,1.2,1.3,0.7,2.3,0.5,0.6,3.9,2.6,0.2,0.1,1.3,2.8,2.6,2.2,4.0,0.8,0.6,1.0,1.2,0.7,0.8,2.0,1.3,3.4,0.7,2.8,0.7,0.9,0.9,0.9,0.8,1.3,0.3,0.3,1.6,1.4,3.0,1.4,1.7,1.1,1.1,1.3,3.1,0.3,6,22,7,9,12,20,11,16,8,6,2.0,17,11,7,8,70,75,7,23,36,10,25,12,34,31,35,30,26,21,29,16,11,6,22,3,5,42,25,1,1,6,23,18,14,19,6,3,6,6,5,6,10,7,20,4,19,5,9,7,8,8,9,2,4,12,14,21,8,8,13,6,9,22,3,0.039,0.028,0.017,0.026,0.043,0.029,0.028,0.038,0.034,0.038,0.033,0.03,0.032,0.037,0.031,0.048,0.034,0.032,0.032,0.032,0.027,0.035,0.024,0.037,0.029,0.034,0.027,0.041,0.034,0.023,0.037,0.046,0.04,0.029,0.019,0.017,0.033,0.03,0.013,0.02,0.023,0.032,0.041,0.027,0.028,0.016,0.027,0.015,0.016,0.014,0.038,0.023,0.023,0.024,0.026,0.017,0.028,0.014,0.023,0.018,0.024,0.027,0.019,0.016,0.019,0.026,0.023,0.02,0.024,0.029,0.03,0.021,0.013,0.021,1347,5741,2890,2191,1844,3609,2567,3363,1404,1418,567.0,2077,3254,1283,2111,10755,17507,2585,4536,3695,3898,4466,1776,6135,6033,6970,5645,3385,5468,5566,1885,1437,1146,6314,2095,1705,8478,5460,425,369,1580,3044,5976,2431,3382,1314,907,2614,2294,1828,477,2819,2047,6154,851,4461,1090,2591,1060,1430,1077,2206,454,969,2472,1586,4028,2301,2112,1631,1215,2279,7890,448,0.121,0.119,0.093,0.114,0.129,0.13,0.099,0.133,0.126,0.142,0.111,0.129,0.118,0.144,0.121,0.137,0.124,0.114,0.131,0.13,0.103,0.132,0.108,0.136,0.119,0.12,0.11,0.116,0.106,0.121,0.138,0.129,0.118,0.119,0.081,0.086,0.127,0.126,0.085,0.091,0.111,0.134,0.135,0.118,0.117,0.095,0.119,0.087,0.086,0.088,0.155,0.102,0.106,0.105,0.144,0.099,0.115,0.088,0.123,0.106,0.123,0.117,0.106,0.109,0.106,0.116,0.106,0.099,0.107,0.141,0.119,0.107,0.082,0.115,402,1812,961,883,730,1303,902,981,444,364,153.0,1059,844,315,565,3312,4898,491,1351,1696,888,1287,783,1708,1760,2106,1899,1302,1489,1783,523,560,319,1259,495,632,1986,1464,242,153,817,1308,1128,1329,2106,738,379,1021,1103,720,336,1328,918,2290,368,2430,508,1204,609,828,574,814,268,374,1268,812,2123,1082,1063,618,636,1078,3597,247,3.44,2.843,2.791,2.573,2.279,2.426,2.425,2.918,2.438,3.025,2.938,1.754,2.919,2.872,2.704,2.618,2.938,3.621,2.781,1.932,3.389,2.686,2.002,2.727,2.692,2.765,2.336,2.13,2.727,2.491,2.713,2.658,2.681,3.346,3.723,2.384,3.02,2.789,1.976,2.636,2.449,2.009,3.483,2.165,1.861,2.055,3.006,3.113,2.528,2.583,1.644,2.184,2.461,2.704,2.916,2.05,2.459,2.419,2.084,1.96,2.202,2.478,2.005,2.625,2.173,2.33,2.065,2.476,2.41,2.543,2.331,2.482,2.335,2.31,0.634,0.693,0.616,0.507,0.522,0.614,0.622,0.575,0.543,0.435,0.65,0.427,0.486,0.465,0.575,0.642,0.663,0.773,0.635,0.522,0.775,0.548,0.501,0.662,0.581,0.524,0.483,0.519,0.544,0.465,0.697,1.068,0.616,0.787,0.629,0.62,0.706,0.644,0.313,0.642,0.563,0.63,0.83,0.817,0.58,0.44,0.68,0.69,0.641,0.688,0.353,0.492,0.625,0.594,0.526,0.513,0.635,0.581,0.387,0.397,0.392,0.812,0.275,1.038,0.607,0.587,0.518,0.387,0.422,0.542,0.478,0.491,0.625,0.545
-210,Epileptic,F,47.0,1.0,3.3,1.2,1.1,2.7,1.7,2.9,1.8,1.7,0.7,0.3,2.0,1.6,0.6,1.9,6.2,11.5,0.9,2.3,4.1,0.6,3.0,1.6,4.2,3.1,4.2,5.1,3.2,3.5,3.6,2.3,1.3,0.5,2.3,0.4,0.9,4.4,3.2,0.3,0.2,1.0,3.4,2.9,2.2,3.5,0.9,0.6,0.7,1.4,0.8,0.4,2.3,2.0,2.6,0.4,2.6,0.8,1.7,0.7,0.7,0.5,1.6,0.6,0.5,2.1,1.1,3.5,1.4,1.6,0.7,0.9,1.5,3.4,0.4,6,38,11,9,21,20,21,16,19,8,4.0,22,16,7,18,63,90,7,29,39,7,39,19,38,45,52,52,31,23,44,18,16,6,26,2,9,51,36,2,1,6,30,27,16,23,8,3,5,9,8,5,15,13,15,3,21,9,17,6,6,3,12,4,5,17,18,29,8,9,8,7,13,32,2,0.038,0.035,0.024,0.02,0.045,0.034,0.044,0.032,0.048,0.034,0.025,0.033,0.034,0.039,0.038,0.039,0.037,0.039,0.031,0.038,0.018,0.059,0.041,0.042,0.037,0.03,0.039,0.036,0.035,0.034,0.05,0.057,0.022,0.026,0.011,0.021,0.039,0.034,0.019,0.021,0.02,0.054,0.043,0.025,0.028,0.02,0.019,0.011,0.02,0.018,0.03,0.028,0.034,0.023,0.018,0.018,0.023,0.02,0.044,0.016,0.016,0.029,0.027,0.02,0.024,0.018,0.024,0.021,0.024,0.023,0.022,0.022,0.017,0.02,1737,4153,2211,2390,2409,2895,3445,3018,2051,1524,755.0,2532,3187,939,3192,9009,19752,2200,4339,4587,2601,2873,1849,5812,5530,8252,7347,3933,5697,6360,2376,917,1062,5652,1685,1864,7255,6680,363,334,1612,2385,5658,2316,3302,1594,923,2061,2439,1426,593,3258,1986,3938,502,4070,1374,2693,482,1070,757,2149,482,796,3238,1315,4881,2048,2327,750,1318,2661,7706,482,0.122,0.144,0.124,0.101,0.16,0.141,0.129,0.135,0.175,0.147,0.112,0.143,0.134,0.148,0.144,0.146,0.137,0.133,0.14,0.139,0.077,0.173,0.159,0.149,0.146,0.132,0.124,0.118,0.115,0.142,0.143,0.143,0.093,0.122,0.062,0.115,0.154,0.151,0.118,0.108,0.106,0.151,0.158,0.113,0.115,0.109,0.105,0.075,0.103,0.103,0.138,0.117,0.15,0.107,0.122,0.099,0.129,0.116,0.152,0.104,0.104,0.128,0.135,0.117,0.118,0.111,0.119,0.103,0.109,0.14,0.122,0.111,0.096,0.098,452,1593,776,901,962,948,1231,977,648,390,221.0,1041,890,279,945,2920,5475,471,1200,1913,709,932,688,1648,1564,2670,2215,1525,1459,2016,710,398,380,1373,500,643,1949,1641,186,165,779,1089,1259,1412,2004,771,392,976,1136,687,274,1409,874,1808,280,2214,643,1400,313,539,372,905,310,414,1420,869,2328,964,1009,442,661,1237,3324,286,3.369,2.407,2.619,2.508,2.019,2.433,2.31,2.707,2.264,2.958,2.677,1.984,2.802,2.499,2.549,2.398,2.837,3.576,2.81,2.012,2.664,2.294,2.157,2.692,2.648,2.565,2.508,2.114,2.89,2.473,2.388,2.54,2.225,2.928,2.982,2.559,2.797,2.907,2.206,2.208,2.591,1.842,3.122,1.855,1.884,2.145,2.772,2.554,2.542,2.314,2.134,2.237,2.083,2.304,2.232,2.064,2.387,2.223,1.672,2.133,2.606,2.202,1.769,2.258,2.327,1.865,2.139,2.452,2.462,1.854,2.231,2.441,2.401,2.018,0.824,0.554,0.67,0.499,0.736,0.761,0.621,0.439,0.731,0.735,0.951,0.572,0.41,0.42,0.574,0.613,0.697,0.871,0.535,0.606,0.693,0.651,0.734,0.664,0.621,0.536,0.683,0.602,0.569,0.601,0.74,0.669,0.373,0.634,0.668,0.503,0.714,0.7,0.432,0.427,0.571,0.573,0.775,0.606,0.595,0.469,0.493,0.678,0.503,0.354,0.404,0.475,0.551,0.509,0.234,0.387,0.471,0.475,0.494,0.539,0.645,0.628,0.337,0.75,0.526,0.584,0.503,0.394,0.436,0.501,0.437,0.475,0.441,0.323
-211,Epileptic,F,42.0,0.8,2.0,1.2,1.0,1.8,2.2,1.6,1.8,0.8,0.6,0.4,2.5,1.9,0.4,1.4,6.0,8.3,0.9,2.8,4.6,1.3,2.3,1.4,3.4,3.4,3.1,3.6,2.7,2.8,3.3,1.0,0.7,0.4,3.2,0.3,1.1,2.8,2.6,0.2,0.3,0.9,2.9,2.2,3.0,4.3,0.7,0.5,1.1,1.2,0.7,0.7,2.4,1.5,2.4,0.4,2.9,0.8,1.3,1.3,0.7,0.6,1.6,0.5,0.4,1.6,1.3,2.4,1.0,1.5,0.3,1.0,1.1,5.2,0.3,6,20,9,8,17,19,12,18,13,5,3.0,22,16,5,15,62,68,6,26,42,13,27,13,37,37,31,34,21,20,36,17,7,4,35,4,8,29,30,2,1,5,23,23,20,22,5,2,7,6,8,7,19,11,18,3,26,7,9,10,5,6,12,3,4,13,18,17,7,10,4,7,10,41,3,0.038,0.022,0.024,0.02,0.038,0.034,0.025,0.027,0.052,0.027,0.038,0.034,0.031,0.024,0.028,0.03,0.031,0.037,0.032,0.033,0.025,0.031,0.028,0.04,0.026,0.031,0.027,0.03,0.028,0.033,0.033,0.037,0.022,0.031,0.017,0.023,0.034,0.029,0.013,0.026,0.02,0.038,0.043,0.03,0.03,0.015,0.026,0.019,0.019,0.018,0.025,0.022,0.024,0.018,0.014,0.023,0.033,0.017,0.026,0.018,0.027,0.028,0.031,0.02,0.025,0.033,0.02,0.015,0.023,0.013,0.024,0.019,0.021,0.024,1194,4165,2216,1967,2002,2845,2472,2787,1156,1083,431.0,2280,3280,870,2453,10272,15068,1996,4496,4599,3138,3664,1733,5412,6000,5154,5364,3652,4683,4822,2162,922,720,5172,1488,1968,4894,5728,409,441,1284,2738,4757,2250,3071,1223,652,1628,1730,1368,827,3565,1676,4592,558,3609,952,2415,1096,993,922,2188,499,846,2181,933,3032,1819,1794,700,1335,1549,8857,355,0.129,0.115,0.112,0.096,0.153,0.138,0.106,0.133,0.168,0.123,0.147,0.137,0.14,0.128,0.131,0.137,0.132,0.136,0.137,0.131,0.106,0.138,0.13,0.145,0.125,0.126,0.119,0.114,0.11,0.14,0.135,0.109,0.126,0.131,0.085,0.109,0.133,0.13,0.084,0.104,0.108,0.144,0.139,0.124,0.127,0.1,0.122,0.101,0.097,0.115,0.129,0.112,0.116,0.1,0.11,0.118,0.15,0.091,0.126,0.097,0.121,0.128,0.127,0.12,0.114,0.135,0.112,0.098,0.113,0.111,0.122,0.118,0.106,0.144,353,1503,802,817,771,1116,978,1082,328,327,170.0,1133,994,249,787,3320,4413,390,1393,2148,818,1292,762,1518,1988,1725,2058,1376,1471,1762,576,412,298,1545,396,732,1358,1524,270,227,682,1262,1019,1520,2094,689,291,785,915,585,491,1744,953,2095,323,2085,427,1267,738,627,443,862,261,385,1136,580,1749,909,967,397,704,849,3991,182,3.192,2.589,2.676,2.475,2.165,2.262,2.208,2.381,2.543,2.707,2.343,1.739,2.668,2.62,2.399,2.479,2.767,3.456,2.723,1.865,2.969,2.313,1.907,2.751,2.372,2.468,2.133,2.13,2.476,2.268,2.822,2.196,2.1,2.539,3.048,2.466,2.804,2.768,1.758,2.288,2.281,1.885,3.1,1.722,1.728,1.954,2.628,2.691,2.217,2.387,1.906,2.093,1.858,2.208,2.121,1.945,2.467,2.206,1.712,1.755,2.021,2.467,2.172,2.197,2.111,1.949,1.933,2.256,2.12,1.915,2.222,2.112,2.372,2.452,0.565,0.558,0.517,0.432,0.694,0.506,0.454,0.554,0.54,0.293,0.653,0.403,0.393,0.455,0.43,0.483,0.509,0.889,0.652,0.516,0.715,0.443,0.503,0.52,0.434,0.508,0.527,0.478,0.51,0.471,0.649,0.793,0.426,0.567,0.932,0.5,0.628,0.485,0.378,0.35,0.551,0.605,0.83,0.53,0.433,0.417,0.305,0.676,0.496,0.768,0.423,0.464,0.47,0.451,0.374,0.418,0.59,0.481,0.263,0.337,0.338,0.698,0.466,0.882,0.492,0.605,0.387,0.351,0.398,0.422,0.485,0.484,0.476,0.333
-213,Epileptic,F,37.0,0.8,2.0,0.9,1.0,1.0,2.8,1.2,1.5,1.1,0.5,0.2,3.3,1.3,0.7,1.4,4.4,8.6,0.6,2.1,3.7,2.0,2.3,1.6,2.5,2.6,2.0,2.9,1.9,1.8,2.1,1.4,0.4,0.2,1.9,0.2,0.7,2.6,2.6,0.2,0.1,0.8,3.0,2.7,3.8,1.9,0.5,0.3,0.5,1.2,0.5,0.3,1.2,0.9,1.8,0.3,2.1,0.7,1.1,0.8,1.0,0.3,0.9,0.4,0.3,2.9,1.2,2.0,0.8,1.1,0.5,0.6,1.5,3.5,0.2,5,18,11,8,12,24,11,14,12,5,2.0,28,14,7,14,49,82,5,26,33,13,27,16,26,34,21,34,21,18,29,20,4,3,22,1,6,35,25,2,1,5,27,26,23,14,4,1,4,5,5,4,9,4,10,2,21,6,9,6,7,5,7,2,3,20,14,18,6,6,4,4,11,31,2,0.038,0.028,0.018,0.021,0.049,0.04,0.021,0.033,0.05,0.031,0.023,0.039,0.033,0.053,0.039,0.035,0.031,0.03,0.034,0.036,0.037,0.036,0.033,0.038,0.04,0.032,0.033,0.026,0.022,0.029,0.042,0.031,0.02,0.027,0.01,0.023,0.041,0.035,0.019,0.02,0.019,0.038,0.042,0.031,0.02,0.01,0.019,0.011,0.017,0.013,0.033,0.017,0.026,0.022,0.035,0.024,0.019,0.019,0.023,0.025,0.032,0.025,0.018,0.024,0.034,0.027,0.023,0.015,0.017,0.017,0.021,0.024,0.019,0.015,1351,3483,1818,1941,1451,2963,2241,2135,1646,922,690.0,2675,2736,976,2446,8662,15267,2029,3645,3929,2521,3366,1748,4196,4374,4248,4212,2919,3838,3778,1772,781,668,4222,1417,1515,4831,5067,370,279,1172,2581,2911,2786,2294,1095,589,1553,2122,1268,409,2064,1212,3323,322,2780,1115,2073,1024,1091,602,1394,519,758,2877,1116,2703,1706,1589,1006,791,1930,7081,386,0.121,0.12,0.114,0.109,0.141,0.158,0.106,0.143,0.172,0.13,0.105,0.152,0.142,0.154,0.127,0.14,0.139,0.106,0.139,0.149,0.124,0.148,0.132,0.14,0.156,0.138,0.135,0.121,0.11,0.13,0.156,0.096,0.108,0.121,0.055,0.107,0.131,0.129,0.119,0.107,0.105,0.156,0.129,0.128,0.103,0.082,0.093,0.078,0.09,0.099,0.132,0.1,0.118,0.101,0.161,0.128,0.111,0.108,0.12,0.134,0.14,0.124,0.124,0.127,0.137,0.135,0.119,0.094,0.107,0.118,0.118,0.126,0.1,0.107,362,1160,756,753,457,1137,880,792,384,258,183.0,1327,766,232,643,2301,4394,347,1048,1664,767,1167,808,1170,1246,1203,1597,1110,1220,1301,556,254,244,1020,391,488,1278,1248,162,124,588,1218,1025,1698,1602,663,242,751,1004,579,247,1073,476,1387,138,1362,520,1009,579,548,251,546,269,251,1357,649,1405,831,806,462,399,940,2998,184,3.42,2.766,2.414,2.534,2.594,2.448,2.181,2.59,2.969,3.159,2.968,1.874,2.738,2.726,2.65,2.779,2.78,3.734,2.748,1.988,2.811,2.382,1.954,2.655,2.7,2.793,2.212,2.093,2.45,2.274,2.479,2.655,2.331,2.915,3.096,2.716,2.799,2.857,2.239,2.471,2.521,1.927,2.32,1.886,1.628,1.795,2.849,2.489,2.536,2.4,1.972,2.143,2.64,2.542,2.639,2.309,2.542,2.364,2.114,2.188,2.307,2.635,2.113,2.967,2.179,2.145,2.056,2.383,2.468,2.552,2.212,2.505,2.472,2.584,0.854,0.606,0.654,0.457,0.757,0.513,0.473,0.584,0.55,0.557,0.731,0.504,0.427,0.763,0.588,0.608,0.504,0.926,0.494,0.596,0.714,0.481,0.426,0.596,0.441,0.474,0.502,0.529,0.478,0.538,0.656,1.165,0.394,0.593,0.617,0.61,0.653,0.563,0.285,0.447,0.454,0.504,0.923,0.689,0.493,0.423,0.75,0.729,0.568,0.385,0.411,0.483,0.728,0.479,0.623,0.483,0.452,0.525,0.443,0.573,0.362,0.592,0.331,0.707,0.487,0.536,0.391,0.385,0.436,0.513,0.705,0.517,0.479,0.28
-214,Epileptic,M,22.0,0.9,2.7,0.6,1.1,1.3,1.8,1.5,2.0,1.4,0.7,0.5,3.0,2.0,0.6,1.3,7.1,8.6,1.1,2.5,6.0,0.6,2.8,2.5,3.1,3.0,4.0,4.4,2.3,3.1,3.9,0.9,0.6,0.6,3.0,0.6,0.4,4.6,4.8,0.2,0.2,1.3,2.4,2.3,4.3,2.5,0.6,0.5,0.9,1.1,1.1,0.7,2.7,1.5,2.1,0.1,2.7,0.7,1.3,1.0,1.1,0.4,1.6,0.5,0.5,2.2,0.8,3.9,1.4,0.9,0.5,1.3,2.3,4.0,0.4,8,24,7,8,11,17,12,15,14,7,4.0,26,19,7,14,68,73,8,25,45,5,40,22,33,34,42,43,22,23,42,12,5,6,37,3,2,51,43,1,1,6,23,17,27,16,5,2,7,6,8,4,19,9,15,1,25,6,11,7,8,3,10,4,4,18,9,28,10,5,5,8,13,28,3,0.041,0.025,0.013,0.02,0.031,0.035,0.022,0.033,0.049,0.035,0.042,0.037,0.034,0.029,0.029,0.035,0.031,0.036,0.031,0.042,0.018,0.031,0.034,0.04,0.033,0.033,0.03,0.028,0.028,0.034,0.03,0.03,0.029,0.035,0.017,0.01,0.041,0.039,0.017,0.024,0.027,0.034,0.037,0.033,0.022,0.014,0.019,0.015,0.015,0.03,0.036,0.025,0.02,0.019,0.02,0.02,0.024,0.017,0.025,0.016,0.014,0.028,0.026,0.018,0.023,0.036,0.025,0.024,0.019,0.023,0.024,0.032,0.018,0.023,1333,4528,1498,2124,1938,2519,2693,3086,1815,1398,482.0,2583,4280,1430,2608,12279,16860,2687,4082,4858,2164,4991,2678,5337,5836,6653,6471,3256,5687,6449,1860,1033,1027,7209,2365,1394,5829,8233,303,402,1497,2863,4948,3340,2827,1247,843,2216,2118,1473,484,4454,2214,4605,147,3795,1293,2596,1129,1692,848,2375,531,745,3271,582,4781,2361,1483,1040,1771,2484,9222,545,0.134,0.125,0.094,0.101,0.122,0.139,0.102,0.137,0.16,0.118,0.171,0.138,0.136,0.144,0.142,0.141,0.129,0.124,0.13,0.146,0.081,0.145,0.133,0.14,0.136,0.144,0.133,0.109,0.113,0.146,0.117,0.096,0.123,0.136,0.071,0.066,0.135,0.142,0.096,0.111,0.119,0.142,0.141,0.136,0.108,0.094,0.099,0.088,0.09,0.135,0.143,0.116,0.109,0.095,0.153,0.113,0.115,0.098,0.12,0.099,0.092,0.125,0.129,0.119,0.117,0.135,0.123,0.111,0.105,0.121,0.122,0.134,0.094,0.121,423,1637,703,820,707,916,989,1072,539,363,177.0,1262,1091,335,790,3440,4737,520,1231,2235,569,1513,1241,1489,1750,2084,2375,1270,1619,2070,568,408,375,1473,600,533,1819,2037,186,171,695,1213,1097,1951,1861,654,355,919,1051,627,309,1795,1058,1956,59,2114,540,1243,635,978,423,849,289,354,1463,323,2417,1057,714,421,844,1158,3712,301,3.018,2.539,1.955,2.65,2.563,2.43,2.348,2.558,2.765,3.171,2.378,1.828,3.045,2.95,2.659,2.774,2.973,3.8,2.735,1.926,2.989,2.61,2.007,2.75,2.546,2.693,2.274,2.073,2.728,2.597,2.533,2.352,2.245,3.191,3.203,2.372,2.538,3.054,1.907,2.995,2.931,2.186,3.423,1.984,1.784,2.141,2.967,2.997,2.542,3.003,2.021,2.583,2.291,2.347,2.553,2.018,2.873,2.424,2.144,1.957,2.279,2.979,2.162,2.511,2.303,2.21,2.11,2.61,2.617,2.649,2.416,2.61,2.701,2.408,1.058,0.609,0.597,0.563,0.491,0.432,0.52,0.607,0.481,0.462,0.766,0.453,0.527,0.452,0.443,0.504,0.567,0.796,0.516,0.53,0.736,0.595,0.367,0.704,0.622,0.51,0.474,0.5,0.576,0.485,0.728,0.918,0.465,0.693,0.743,0.444,0.686,0.558,0.299,0.479,0.585,0.541,0.773,0.713,0.559,0.4,0.484,0.759,0.491,0.699,0.414,0.504,0.533,0.493,0.385,0.391,0.413,0.476,0.372,0.366,0.424,0.736,0.337,1.058,0.728,0.53,0.452,0.413,0.505,0.615,0.491,0.568,0.592,0.444
-215,Epileptic,F,37.0,0.8,3.1,1.0,1.4,2.0,1.7,2.3,2.3,1.4,0.7,0.4,4.7,1.6,0.9,1.6,8.0,11.6,1.1,2.4,5.0,2.0,3.4,2.3,4.8,3.9,5.0,4.9,3.9,4.0,3.8,3.0,2.1,0.4,2.8,0.6,0.8,5.1,4.1,0.1,0.2,1.2,2.8,4.7,3.2,3.6,0.7,0.5,1.0,1.4,1.2,0.7,2.6,1.1,2.9,0.3,3.5,0.6,1.7,0.8,1.5,0.5,1.7,0.3,0.6,2.7,1.3,3.2,1.6,1.2,0.5,0.8,1.7,4.7,0.3,5,29,9,15,20,17,15,19,15,6,5.0,39,22,12,63,83,89,7,26,36,12,36,23,53,41,56,48,32,34,41,23,17,5,32,3,9,53,49,1,2,9,24,36,20,20,5,2,7,8,8,5,20,6,20,2,28,4,10,6,11,4,10,3,5,23,17,24,11,6,4,6,12,37,2,0.038,0.035,0.021,0.027,0.054,0.037,0.041,0.037,0.058,0.034,0.032,0.052,0.031,0.054,0.07,0.049,0.047,0.044,0.037,0.05,0.051,0.043,0.035,0.063,0.041,0.043,0.042,0.042,0.04,0.043,0.055,0.071,0.03,0.042,0.028,0.019,0.051,0.041,0.027,0.022,0.024,0.044,0.071,0.03,0.026,0.015,0.022,0.016,0.021,0.027,0.037,0.026,0.028,0.031,0.018,0.026,0.019,0.019,0.026,0.026,0.023,0.029,0.03,0.022,0.024,0.024,0.027,0.023,0.021,0.017,0.026,0.022,0.02,0.02,1848,5337,2662,2773,2532,2515,3674,3689,2054,1400,979.0,3696,3367,1375,2613,10425,19333,2708,4929,4367,2837,4493,2727,6496,5921,7532,6709,4602,7129,5470,2958,1262,962,6218,1422,2012,7842,7076,278,453,1560,2240,5859,3169,3884,1264,782,2259,2227,1502,542,3894,1286,3946,520,3956,1348,2873,790,1753,847,2558,523,879,3750,1458,3760,2353,1702,822,1268,2147,8875,505,0.116,0.147,0.109,0.122,0.168,0.146,0.132,0.148,0.189,0.144,0.129,0.173,0.143,0.187,0.182,0.169,0.156,0.138,0.146,0.171,0.128,0.142,0.136,0.181,0.141,0.158,0.143,0.129,0.136,0.153,0.161,0.14,0.138,0.154,0.105,0.102,0.154,0.16,0.107,0.108,0.12,0.147,0.189,0.124,0.114,0.094,0.104,0.098,0.107,0.109,0.148,0.115,0.119,0.131,0.115,0.126,0.101,0.099,0.121,0.128,0.108,0.121,0.122,0.111,0.121,0.124,0.114,0.111,0.109,0.11,0.134,0.116,0.103,0.12,376,1456,790,975,717,876,1069,1114,482,328,231.0,1653,982,337,729,2951,4775,433,1259,1809,696,1378,1159,1726,1746,2174,2217,1682,1908,1724,877,460,273,1338,358,790,1992,1734,133,232,723,1111,1385,1783,2078,677,344,849,968,700,313,1700,647,1619,244,2036,526,1339,510,883,410,874,239,486,1781,825,1825,1098,806,390,542,1128,3563,208,3.892,2.867,2.834,2.675,2.607,2.392,2.7,2.92,2.851,3.051,3.097,1.996,2.578,2.811,2.7,2.643,2.901,4.119,3.001,2.082,2.87,2.422,2.049,2.6,2.579,2.745,2.361,2.153,2.803,2.335,2.435,2.448,2.995,3.106,3.3,2.403,2.806,2.784,2.481,2.177,2.785,1.79,2.902,1.992,2.102,2.194,2.72,3.327,2.862,2.418,2.181,2.2,2.131,2.404,2.271,2.144,2.596,2.421,1.82,2.154,2.17,2.599,2.251,2.069,2.219,2.167,2.19,2.445,2.426,2.349,2.593,2.377,2.503,2.874,0.988,0.871,0.827,0.526,0.809,0.595,0.659,0.618,0.751,0.644,0.812,0.561,0.482,0.605,0.622,0.666,0.772,0.864,0.583,0.75,0.818,0.651,0.642,0.809,0.627,0.518,0.675,0.621,0.606,0.699,0.871,1.054,0.462,0.742,0.602,0.45,0.865,0.73,0.302,0.52,0.607,0.521,0.972,0.651,0.651,0.451,0.496,0.743,0.565,0.476,0.559,0.496,0.495,0.469,0.432,0.404,0.705,0.506,0.315,0.441,0.418,0.856,0.707,0.603,0.588,0.623,0.448,0.396,0.432,0.546,0.592,0.47,0.55,0.448
-216,Epileptic,F,22.0,0.7,2.4,1.5,1.3,1.3,1.7,1.6,1.6,1.4,0.6,0.3,2.1,1.8,0.3,1.5,5.5,7.3,0.9,2.7,3.1,1.4,1.8,1.7,3.1,3.5,2.9,2.5,1.7,2.2,2.9,1.0,1.3,0.7,2.8,0.4,1.0,2.9,3.4,0.2,0.2,0.9,2.3,3.1,4.2,2.7,0.7,0.4,1.2,1.2,0.7,0.6,1.7,1.2,2.1,0.7,2.2,0.7,1.5,0.6,1.0,0.6,1.3,0.3,0.7,1.3,1.6,1.6,1.3,1.0,0.5,1.0,1.2,5.0,0.3,6,22,14,10,18,15,16,12,15,6,2.0,16,18,5,14,55,67,8,26,27,13,21,15,31,39,27,29,17,22,31,15,10,6,34,2,7,27,39,2,1,5,19,27,25,15,5,2,9,6,4,4,16,10,15,3,17,7,9,4,8,3,10,2,3,8,18,11,7,6,9,6,11,38,3,0.03,0.023,0.02,0.022,0.031,0.032,0.021,0.026,0.044,0.032,0.025,0.029,0.034,0.03,0.035,0.031,0.026,0.029,0.032,0.032,0.019,0.033,0.03,0.036,0.034,0.025,0.025,0.021,0.022,0.031,0.028,0.04,0.025,0.029,0.011,0.021,0.031,0.03,0.02,0.019,0.018,0.031,0.042,0.042,0.021,0.014,0.017,0.018,0.014,0.016,0.026,0.019,0.023,0.019,0.016,0.017,0.023,0.018,0.023,0.02,0.031,0.026,0.024,0.025,0.019,0.026,0.018,0.019,0.015,0.017,0.028,0.026,0.019,0.012,1653,5164,2835,2392,1599,2363,3103,3141,1792,1358,509.0,2224,3272,719,3227,9702,17154,2337,4747,3298,3639,3155,2088,5172,6944,7245,5014,3704,5665,5894,1393,1311,1561,6475,1976,2649,5674,7165,436,404,2007,2322,4443,2104,3823,1576,833,2566,2502,1651,543,3334,2075,4362,1442,4170,1550,3333,741,1316,671,2211,480,718,2325,1715,2887,2520,1932,939,1492,2159,9645,694,0.106,0.111,0.11,0.105,0.127,0.141,0.117,0.117,0.145,0.136,0.113,0.128,0.134,0.103,0.128,0.132,0.116,0.111,0.121,0.129,0.089,0.14,0.136,0.125,0.139,0.126,0.124,0.113,0.113,0.136,0.111,0.121,0.114,0.122,0.071,0.095,0.127,0.128,0.107,0.086,0.092,0.135,0.129,0.14,0.102,0.091,0.102,0.093,0.087,0.094,0.124,0.102,0.111,0.093,0.094,0.104,0.107,0.09,0.12,0.115,0.12,0.111,0.121,0.109,0.1,0.131,0.1,0.095,0.094,0.104,0.127,0.118,0.099,0.086,464,1749,1097,942,723,907,1120,1024,513,333,164.0,1039,890,229,763,2806,4413,520,1372,1534,1173,1006,837,1390,1741,1914,1624,1255,1618,1664,462,530,440,1558,586,810,1603,1915,201,204,840,1060,1176,1552,2047,775,349,1083,1208,727,345,1498,952,1900,578,1944,616,1558,416,712,350,810,202,400,1099,901,1343,1068,954,480,606,872,4201,394,3.233,2.85,2.441,2.581,1.986,2.37,2.297,2.712,2.502,3.385,2.684,1.915,2.798,2.439,2.868,2.617,2.999,3.496,2.835,1.892,2.638,2.455,2.148,2.653,2.994,3.039,2.389,2.329,2.726,2.775,2.171,2.5,2.71,2.989,3.082,2.987,2.772,2.793,2.233,2.278,2.963,1.909,2.849,1.631,2.168,2.251,2.945,2.763,2.412,2.81,2.075,2.179,2.046,2.329,3.231,2.374,2.885,2.456,2.045,2.07,2.364,2.534,2.614,2.023,2.339,2.2,2.428,2.614,2.469,2.035,2.751,2.688,2.413,2.109,0.826,0.711,0.544,0.588,0.561,0.48,0.501,0.53,0.625,0.473,0.795,0.431,0.494,0.497,0.665,0.571,0.617,0.685,0.531,0.539,0.766,0.454,0.433,0.711,0.527,0.542,0.472,0.582,0.374,0.55,0.585,0.772,0.358,0.66,0.562,0.575,0.688,0.751,0.377,0.366,0.697,0.466,0.611,0.611,0.6,0.56,0.588,0.687,0.624,0.479,0.423,0.599,0.533,0.603,0.743,0.495,0.453,0.468,0.378,0.372,0.48,0.77,0.458,0.943,0.601,0.781,0.55,0.518,0.403,0.607,0.472,0.683,0.526,0.408
-217,Epileptic,M,57.0,1.8,2.3,1.0,1.3,2.1,1.9,2.2,1.6,1.4,0.6,0.4,3.3,1.7,0.5,1.1,8.1,12.2,1.7,2.2,5.3,1.5,2.0,2.8,7.3,3.7,3.8,3.1,3.7,5.1,4.3,2.4,1.6,0.7,3.6,0.8,0.9,5.9,3.9,0.3,0.2,1.3,3.4,3.1,3.0,3.8,0.7,0.6,1.0,1.6,0.7,0.7,1.9,1.5,3.4,0.6,2.4,0.7,1.6,0.7,1.8,0.7,1.8,0.4,0.8,2.1,1.3,2.3,1.6,1.6,0.5,0.5,1.3,4.3,0.5,12,17,8,14,13,16,19,17,11,5,5.0,33,13,6,11,73,96,10,25,48,13,28,24,49,49,39,37,33,35,42,24,10,5,34,5,7,60,41,2,1,9,31,21,23,18,6,3,6,8,5,6,12,9,22,3,18,5,11,4,14,5,11,2,6,16,13,19,9,7,4,3,8,30,3,0.057,0.028,0.017,0.026,0.043,0.034,0.04,0.031,0.047,0.037,0.044,0.039,0.034,0.051,0.034,0.055,0.043,0.053,0.038,0.043,0.039,0.032,0.037,0.063,0.038,0.034,0.028,0.038,0.051,0.036,0.056,0.057,0.034,0.05,0.038,0.021,0.054,0.047,0.026,0.027,0.027,0.035,0.05,0.028,0.027,0.015,0.024,0.017,0.021,0.022,0.026,0.029,0.025,0.029,0.026,0.018,0.02,0.017,0.02,0.025,0.026,0.033,0.027,0.022,0.027,0.031,0.022,0.028,0.027,0.017,0.015,0.027,0.021,0.033,1281,4346,2357,2310,1607,2780,2812,3208,1547,1018,857.0,3088,2480,928,1984,9811,19187,2080,3986,4747,3240,3766,2482,5375,5489,6218,5133,4482,5323,6509,2348,1178,1096,5605,1379,2006,7098,5143,416,227,1439,2369,4555,2825,3673,1415,821,1850,2481,1283,637,2362,1904,4870,538,3724,1113,2582,819,1949,637,2357,409,949,2653,1738,3404,1741,1646,689,898,1669,7256,560,0.152,0.12,0.097,0.112,0.141,0.142,0.122,0.129,0.154,0.139,0.142,0.142,0.133,0.157,0.13,0.17,0.147,0.161,0.141,0.154,0.119,0.13,0.128,0.162,0.137,0.142,0.117,0.128,0.138,0.147,0.162,0.136,0.105,0.163,0.116,0.099,0.17,0.157,0.12,0.135,0.127,0.132,0.16,0.124,0.117,0.089,0.105,0.093,0.1,0.106,0.133,0.122,0.119,0.12,0.128,0.104,0.114,0.098,0.109,0.124,0.127,0.127,0.122,0.119,0.124,0.125,0.109,0.115,0.111,0.109,0.101,0.121,0.101,0.133,457,1392,892,846,786,944,1000,1032,526,277,195.0,1462,773,224,569,2990,5119,503,1167,2185,714,1184,1192,1952,1707,1970,1862,1683,1664,2036,782,522,286,1378,422,727,1971,1507,182,103,769,1384,1099,1778,2197,730,381,878,1142,575,416,1086,900,1981,298,2007,549,1375,528,1065,375,927,242,540,1287,671,1729,851,858,392,456,816,3274,269,2.971,2.852,2.545,2.605,2.041,2.404,2.255,2.671,2.23,3.181,3.058,1.836,2.552,2.584,2.627,2.459,2.837,3.094,2.74,1.89,3.116,2.443,1.884,2.226,2.542,2.578,2.257,2.164,2.434,2.5,2.3,2.354,2.703,2.866,2.759,2.522,2.733,2.626,2.341,2.42,2.291,1.605,2.917,1.814,1.919,2.162,2.476,2.486,2.575,2.629,1.786,2.283,2.395,2.394,2.288,2.041,2.326,2.123,1.775,1.983,2.044,2.593,2.117,2.05,2.301,2.544,1.973,2.334,2.236,2.121,2.029,2.27,2.366,2.709,0.645,0.648,0.562,0.565,0.536,0.673,0.579,0.553,0.576,0.564,0.756,0.462,0.491,0.646,0.522,0.607,0.709,0.732,0.601,0.615,0.791,0.612,0.501,0.756,0.591,0.53,0.529,0.566,0.603,0.561,0.77,0.714,0.389,0.624,0.699,0.538,0.755,0.642,0.264,0.315,0.407,0.389,0.768,0.619,0.564,0.504,0.481,0.586,0.538,0.551,0.374,0.487,0.485,0.483,0.479,0.374,0.402,0.407,0.323,0.44,0.436,0.596,0.361,0.614,0.568,0.789,0.463,0.406,0.476,0.704,0.428,0.556,0.422,0.528
-218,Epileptic,F,37.0,0.7,2.2,0.9,0.9,1.8,2.9,1.8,1.8,1.4,0.6,0.2,3.6,1.5,0.6,1.2,7.3,9.0,0.8,2.7,5.2,0.9,2.6,1.8,4.0,3.4,3.4,3.7,2.3,2.2,4.1,1.8,0.7,0.3,2.3,0.5,0.7,3.5,3.0,0.1,0.1,1.1,3.8,2.3,3.5,2.4,0.7,0.6,1.1,1.2,0.6,0.7,1.5,1.4,2.9,0.5,2.7,0.7,1.9,1.4,1.2,0.3,2.1,0.4,0.4,2.4,1.2,2.5,1.2,1.0,0.7,1.0,1.3,4.7,0.2,7,22,7,10,21,26,16,16,20,7,2.0,34,14,9,13,81,79,7,28,42,8,29,18,39,42,38,42,25,20,43,19,6,2,27,3,7,40,35,1,0,7,35,25,31,16,6,3,10,6,6,6,13,11,22,3,22,6,14,10,10,3,15,3,4,23,17,26,10,7,7,7,10,37,2,0.033,0.023,0.014,0.021,0.036,0.042,0.027,0.031,0.052,0.032,0.019,0.037,0.03,0.038,0.028,0.034,0.031,0.024,0.033,0.038,0.02,0.033,0.029,0.041,0.03,0.029,0.028,0.026,0.023,0.033,0.042,0.043,0.017,0.029,0.015,0.019,0.034,0.034,0.018,0.011,0.025,0.042,0.041,0.03,0.019,0.016,0.023,0.02,0.018,0.027,0.034,0.02,0.022,0.024,0.027,0.022,0.015,0.023,0.029,0.02,0.029,0.037,0.037,0.02,0.025,0.037,0.021,0.023,0.019,0.021,0.022,0.026,0.022,0.025,1156,4212,1996,1761,2323,3277,2202,2821,1646,1123,415.0,2600,2450,886,2176,10444,15180,2017,3817,3787,2925,3699,1932,5697,4886,5198,4815,2861,4212,5399,2124,825,567,4341,1948,1361,6082,5666,232,273,1347,3134,4768,2685,2729,1350,802,1682,2137,1226,551,2632,1807,4052,515,3082,1471,2651,1299,1443,473,2086,328,804,2647,809,3257,1948,1576,931,1218,1741,7311,206,0.128,0.113,0.091,0.108,0.146,0.164,0.106,0.135,0.184,0.127,0.096,0.146,0.12,0.158,0.132,0.146,0.134,0.108,0.138,0.149,0.09,0.149,0.134,0.151,0.139,0.134,0.128,0.117,0.099,0.138,0.154,0.129,0.093,0.13,0.078,0.11,0.151,0.154,0.117,0.078,0.118,0.168,0.16,0.127,0.102,0.098,0.118,0.096,0.095,0.117,0.141,0.102,0.116,0.118,0.139,0.117,0.095,0.113,0.133,0.114,0.116,0.14,0.152,0.106,0.131,0.156,0.118,0.114,0.102,0.12,0.12,0.127,0.112,0.13,411,1590,915,771,936,1288,911,932,512,330,173.0,1419,857,305,750,3728,4823,445,1404,2104,693,1269,901,1717,1885,1911,2074,1310,1363,2069,706,366,263,1173,525,654,1783,1595,152,121,708,1481,1120,1811,1886,698,397,896,1040,445,381,1378,984,1966,240,1875,730,1418,776,860,253,851,190,427,1468,511,1833,908,863,472,704,886,3450,117,2.699,2.544,2.209,2.237,2.196,2.352,2.038,2.652,2.508,2.738,2.161,1.75,2.344,2.266,2.315,2.268,2.567,3.45,2.478,1.66,2.952,2.304,1.951,2.575,2.219,2.348,1.944,1.872,2.433,2.25,2.483,2.521,1.833,2.683,3.107,2.068,2.743,2.758,1.78,2.14,2.278,1.987,3.024,1.692,1.62,2.079,2.45,2.279,2.444,2.739,1.88,2.037,1.862,2.139,2.544,1.787,2.397,2.18,2.031,1.888,2.125,2.443,1.973,2.103,2.002,2.12,1.794,2.22,2.049,2.197,2.049,2.468,2.344,2.133,0.696,0.524,0.463,0.399,0.491,0.596,0.496,0.628,0.563,0.464,0.699,0.505,0.401,0.479,0.439,0.495,0.559,0.788,0.699,0.438,0.85,0.41,0.401,0.641,0.468,0.599,0.435,0.432,0.402,0.462,0.641,1.061,0.349,0.714,0.635,0.419,0.559,0.531,0.319,0.376,0.613,0.547,0.741,0.665,0.421,0.491,0.478,0.678,0.467,0.451,0.405,0.452,0.426,0.442,0.618,0.43,0.443,0.565,0.301,0.362,0.312,0.698,0.368,0.82,0.537,0.637,0.394,0.425,0.38,0.594,0.436,0.436,0.492,0.431
-219,Epileptic,M,47.0,0.6,3.4,1.5,1.2,2.1,2.4,2.3,1.9,2.2,0.5,0.5,3.6,1.7,0.6,1.9,7.6,13.9,1.0,2.5,7.6,1.6,4.2,2.1,5.1,5.0,4.4,4.5,3.8,4.2,5.3,1.9,1.3,0.7,2.9,0.5,0.6,5.6,6.1,0.3,0.3,1.3,3.6,3.1,5.8,3.5,1.3,0.7,0.7,1.3,1.0,0.5,3.0,1.8,3.5,0.2,3.6,0.9,1.7,1.3,1.3,1.6,1.7,0.4,0.5,1.8,1.3,3.9,1.5,1.3,0.6,1.9,2.3,6.8,0.3,5,27,11,8,15,21,15,16,15,6,4.0,28,19,5,16,60,95,9,24,56,15,41,18,45,48,38,39,33,27,43,22,12,5,27,3,4,48,52,2,1,8,24,29,39,20,8,3,4,8,8,4,18,13,20,1,24,7,14,8,8,15,14,4,4,11,11,27,9,8,4,11,15,43,2,0.032,0.027,0.021,0.021,0.04,0.035,0.031,0.032,0.059,0.031,0.045,0.039,0.033,0.036,0.032,0.041,0.041,0.046,0.033,0.041,0.026,0.043,0.031,0.047,0.039,0.035,0.036,0.035,0.034,0.038,0.045,0.038,0.038,0.037,0.017,0.016,0.042,0.04,0.024,0.03,0.032,0.044,0.048,0.037,0.026,0.022,0.029,0.012,0.019,0.019,0.025,0.025,0.033,0.025,0.024,0.022,0.024,0.018,0.032,0.024,0.033,0.03,0.032,0.016,0.02,0.024,0.028,0.022,0.027,0.023,0.033,0.026,0.021,0.016,1615,6022,3066,2348,1994,3181,2689,3321,1918,1055,622.0,3096,3206,1165,2822,10946,18786,2274,4159,6097,4578,5699,2209,6827,7548,6781,5823,4227,6087,6070,2765,1915,766,7026,2108,1488,7704,8741,398,389,1330,2783,6125,4645,3203,1763,928,2117,2326,2228,587,3736,2726,5007,405,4210,1756,3573,1200,1546,2213,2489,611,865,2606,1482,4244,2619,1473,853,1790,2637,11819,569,0.111,0.12,0.101,0.1,0.144,0.143,0.12,0.136,0.16,0.127,0.134,0.136,0.139,0.134,0.136,0.143,0.139,0.112,0.129,0.145,0.101,0.147,0.129,0.148,0.138,0.142,0.122,0.126,0.115,0.139,0.129,0.116,0.131,0.134,0.084,0.084,0.132,0.132,0.097,0.107,0.118,0.146,0.149,0.14,0.117,0.111,0.113,0.077,0.097,0.098,0.121,0.111,0.124,0.108,0.123,0.107,0.114,0.092,0.131,0.117,0.139,0.125,0.142,0.103,0.101,0.101,0.116,0.103,0.117,0.134,0.13,0.121,0.102,0.094,352,2039,1219,843,921,1132,1159,1081,653,318,215.0,1516,899,291,922,3157,5696,408,1296,2993,1077,1840,1012,1968,2381,2214,2174,1705,1905,2330,791,716,265,1474,497,592,2347,2634,226,186,701,1304,1223,2707,2122,829,385,876,1097,933,360,1740,1048,2175,176,2430,708,1620,685,817,837,929,249,489,1315,729,2409,1180,781,385,899,1371,5168,278,3.581,2.699,2.448,2.941,2.149,2.5,2.164,2.965,2.49,2.722,2.519,1.827,2.797,2.849,2.542,2.727,2.859,3.733,2.696,1.911,3.249,2.479,2.029,2.641,2.461,2.623,2.217,2.073,2.517,2.224,2.611,2.594,2.346,3.263,3.572,2.339,2.648,2.649,2.034,2.454,2.29,2.019,3.138,1.986,1.75,2.386,2.85,2.965,2.523,2.66,1.913,2.199,2.444,2.45,2.87,1.947,2.728,2.39,2.128,2.113,2.422,2.565,2.718,2.021,2.167,2.146,1.98,2.497,2.209,2.602,2.232,2.412,2.399,2.455,0.851,0.657,0.637,0.66,0.416,0.591,0.512,0.659,0.484,0.455,0.6,0.444,0.495,0.596,0.492,0.623,0.639,0.845,0.579,0.541,0.884,0.626,0.4,0.653,0.568,0.551,0.606,0.509,0.611,0.494,0.759,0.897,0.411,0.87,0.776,0.53,0.673,0.568,0.463,0.677,0.515,0.566,0.961,0.64,0.456,0.733,0.614,0.722,0.652,0.547,0.438,0.569,0.704,0.597,0.444,0.451,0.438,0.624,0.485,0.416,0.559,0.747,0.505,0.745,0.677,0.58,0.428,0.504,0.472,0.677,0.493,0.469,0.612,0.451
-220,Epileptic,M,42.0,0.9,3.9,1.9,1.9,1.9,2.1,5.2,2.0,1.6,0.8,0.5,3.5,1.6,0.4,1.2,8.9,18.0,1.2,3.7,6.3,1.8,3.2,2.4,4.6,3.6,3.1,7.1,5.3,6.9,4.2,1.4,1.4,0.5,3.4,0.6,1.0,5.4,4.0,0.2,0.3,1.1,4.0,3.5,2.7,4.9,1.3,0.4,1.8,1.7,0.9,0.7,2.9,1.8,4.5,0.1,3.5,1.1,3.0,1.1,1.5,1.1,2.1,0.6,1.0,2.5,2.2,3.3,3.0,2.0,0.7,1.1,1.2,6.7,0.5,6,27,12,11,15,20,40,15,12,6,4.0,29,15,3,11,68,122,8,29,52,11,29,22,39,34,28,52,35,46,39,13,11,4,31,3,7,41,41,1,1,5,33,30,16,22,6,2,9,7,6,4,19,11,28,0,21,7,18,8,8,8,14,3,6,18,15,19,17,10,7,6,6,43,4,0.043,0.038,0.034,0.032,0.039,0.043,0.059,0.034,0.061,0.04,0.05,0.043,0.032,0.033,0.029,0.053,0.055,0.055,0.057,0.049,0.04,0.04,0.041,0.057,0.045,0.039,0.054,0.047,0.05,0.048,0.045,0.057,0.032,0.049,0.033,0.027,0.056,0.042,0.018,0.021,0.024,0.055,0.077,0.034,0.031,0.024,0.029,0.025,0.025,0.035,0.026,0.029,0.031,0.037,0.011,0.025,0.026,0.029,0.027,0.027,0.025,0.036,0.028,0.053,0.034,0.038,0.029,0.027,0.032,0.025,0.028,0.022,0.025,0.035,1562,5493,2963,2709,2668,2811,3313,3340,1185,1307,609.0,3339,3213,946,2371,8951,16853,2141,3573,5246,2793,4615,2590,5530,5813,4825,6638,4083,6715,4932,2234,1192,802,5071,1085,1416,5171,7644,290,439,1361,3253,4685,2171,4247,1437,612,2440,1925,1342,643,3920,2054,4736,326,3969,1639,3186,1177,1458,1538,2810,506,1121,2286,1600,3563,3812,2173,877,1223,1797,9430,490,0.122,0.135,0.122,0.114,0.13,0.146,0.158,0.126,0.155,0.143,0.13,0.138,0.118,0.122,0.123,0.144,0.151,0.152,0.158,0.153,0.118,0.152,0.147,0.162,0.14,0.134,0.14,0.129,0.137,0.158,0.14,0.137,0.109,0.148,0.118,0.104,0.147,0.153,0.098,0.088,0.108,0.152,0.201,0.122,0.117,0.109,0.102,0.096,0.105,0.128,0.124,0.121,0.116,0.123,0.069,0.116,0.103,0.118,0.122,0.117,0.106,0.13,0.133,0.148,0.13,0.125,0.116,0.111,0.114,0.13,0.127,0.106,0.109,0.146,381,1758,968,994,932,1033,1445,1023,474,364,216.0,1470,889,218,708,2957,5815,432,1282,2473,806,1367,1043,1572,1579,1455,2334,1771,2274,1667,562,501,252,1240,333,627,1686,1818,169,205,711,1412,950,1370,2494,759,259,995,956,460,406,1644,1009,2186,134,2067,804,1653,705,761,710,981,300,369,1214,858,1821,1737,1020,503,638,812,4282,228,3.476,2.826,2.698,2.648,2.316,2.299,2.035,2.854,2.059,2.857,2.512,1.927,2.832,3.059,2.631,2.403,2.497,3.434,2.33,1.873,2.709,2.595,2.119,2.816,2.685,2.668,2.284,1.961,2.45,2.417,2.833,2.552,2.574,2.817,2.849,2.142,2.395,2.972,2.068,2.498,2.373,1.94,3.138,1.778,1.994,2.116,2.592,2.845,2.371,2.56,1.892,2.315,2.133,2.28,2.301,2.145,2.307,2.182,1.967,2.031,2.176,2.84,1.957,3.283,1.994,2.356,2.125,2.289,2.464,2.132,2.276,2.63,2.283,2.497,0.917,0.659,0.706,0.502,0.737,0.542,0.627,0.674,0.592,0.447,0.614,0.447,0.4,0.544,0.525,0.614,0.737,0.814,0.619,0.51,0.824,0.607,0.642,0.758,0.663,0.569,0.644,0.584,0.673,0.518,0.782,1.081,0.411,0.747,0.537,0.471,0.819,0.707,0.377,0.3,0.459,0.587,0.877,0.549,0.609,0.443,0.466,0.743,0.529,0.853,0.43,0.532,0.49,0.461,0.349,0.477,0.479,0.539,0.393,0.416,0.488,0.785,0.45,0.721,0.499,0.784,0.437,0.473,0.458,0.722,0.593,0.535,0.459,0.457
-221,Epileptic,M,32.0,1.4,2.6,2.0,1.9,1.9,1.6,2.5,1.9,2.1,1.2,0.4,3.5,1.9,0.7,1.5,8.3,11.4,1.6,2.7,5.1,2.2,3.6,2.1,4.8,5.1,5.1,6.0,3.9,3.9,4.0,2.0,0.8,0.7,2.9,0.8,1.3,3.3,3.7,0.2,0.3,1.3,3.2,3.8,2.3,3.9,1.0,0.8,1.3,1.8,1.0,0.7,2.4,1.7,2.6,0.3,2.8,1.2,2.0,1.0,1.5,0.5,2.1,0.8,0.8,1.5,1.4,3.1,1.7,1.2,1.2,1.9,2.0,4.4,0.4,12,27,15,17,16,18,17,20,17,11,4.0,34,20,9,19,84,103,10,30,51,17,47,23,55,45,56,55,35,33,45,23,6,5,29,3,11,39,51,1,2,8,29,35,18,20,7,4,7,8,7,5,18,13,18,2,21,9,15,6,9,3,17,8,6,12,19,26,9,6,16,12,14,31,3,0.049,0.025,0.033,0.03,0.037,0.029,0.04,0.032,0.055,0.051,0.038,0.042,0.039,0.044,0.031,0.043,0.036,0.054,0.038,0.044,0.04,0.033,0.03,0.052,0.041,0.033,0.046,0.04,0.037,0.032,0.056,0.049,0.03,0.038,0.021,0.022,0.037,0.038,0.017,0.02,0.024,0.037,0.061,0.024,0.027,0.016,0.028,0.02,0.021,0.022,0.031,0.023,0.025,0.021,0.009,0.017,0.02,0.021,0.02,0.023,0.021,0.03,0.033,0.024,0.021,0.028,0.021,0.018,0.02,0.029,0.027,0.026,0.02,0.018,2125,5556,3176,3028,2282,3943,2842,3685,1891,1863,826.0,3546,3754,1352,3586,12916,21402,2888,4991,5834,4502,6748,3776,6810,6752,9483,7236,4612,7593,7961,2414,1021,1278,6778,1954,2724,7103,9228,228,477,1642,3465,5899,2933,3772,1921,927,2315,2515,1790,559,3973,2055,4628,869,4681,1858,3540,1486,1848,725,3039,870,933,2183,1511,4879,3143,1879,1704,2520,2485,7812,557,0.146,0.12,0.13,0.126,0.135,0.13,0.118,0.128,0.165,0.159,0.144,0.162,0.131,0.143,0.13,0.147,0.137,0.157,0.139,0.159,0.128,0.139,0.144,0.154,0.139,0.137,0.137,0.116,0.115,0.133,0.165,0.114,0.11,0.132,0.092,0.107,0.142,0.151,0.105,0.115,0.117,0.137,0.172,0.11,0.112,0.095,0.116,0.094,0.102,0.106,0.141,0.108,0.12,0.104,0.082,0.101,0.112,0.107,0.102,0.116,0.114,0.133,0.147,0.125,0.113,0.125,0.108,0.096,0.096,0.128,0.125,0.127,0.101,0.099,560,1785,1132,1110,976,1009,1097,1262,666,501,212.0,1538,1066,367,957,3565,5831,568,1264,2421,1107,1999,1240,1933,1999,2843,2340,1619,1906,2410,764,391,429,1449,567,965,1793,2000,112,227,822,1515,1377,1751,2254,911,413,981,1193,770,318,1776,1074,2079,441,2472,847,1589,834,940,369,1151,396,543,1138,884,2448,1345,910,756,1081,1177,3477,326,3.352,2.801,2.745,2.632,2.013,2.749,2.21,2.611,2.319,2.993,3.041,1.993,2.652,2.628,2.738,2.675,2.829,3.711,2.984,2.107,2.986,2.586,2.434,2.62,2.564,2.662,2.373,2.218,2.944,2.591,2.421,2.637,2.355,3.146,2.999,2.598,2.907,3.242,2.219,2.28,2.463,1.972,3.057,1.849,1.925,2.22,2.677,3.022,2.668,2.512,2.154,2.287,1.976,2.387,2.345,2.085,2.529,2.478,1.992,2.064,2.261,2.55,2.268,1.998,2.124,2.007,2.151,2.656,2.343,2.299,2.532,2.399,2.462,2.117,0.86,0.7,0.46,0.503,0.621,0.855,0.591,0.463,0.685,0.703,0.856,0.466,0.533,0.503,0.653,0.678,0.661,0.739,0.636,0.701,0.853,0.653,0.647,0.816,0.724,0.609,0.645,0.712,0.66,0.586,0.726,0.925,0.439,0.656,0.741,0.45,0.684,0.638,0.37,0.488,0.503,0.623,0.801,0.683,0.534,0.442,0.462,0.809,0.404,0.631,0.337,0.456,0.445,0.462,0.368,0.402,0.445,0.566,0.367,0.397,0.335,0.663,0.707,0.571,0.557,0.719,0.411,0.421,0.385,0.603,0.521,0.53,0.455,0.317
-222,Epileptic,F,37.0,1.0,2.2,0.5,0.9,1.5,1.5,1.0,1.4,1.1,0.6,0.2,2.5,1.4,0.5,1.1,5.5,8.2,0.6,2.7,4.9,2.7,2.3,1.6,3.1,3.0,3.2,3.0,1.7,2.0,3.8,0.9,1.7,0.5,1.9,0.2,0.7,2.4,2.1,0.2,0.1,0.9,1.9,1.8,2.3,2.2,0.6,0.4,0.8,1.0,0.6,0.7,1.9,1.3,1.9,0.4,2.6,0.4,1.9,0.7,0.9,0.2,1.1,0.1,0.5,1.7,1.0,2.2,1.5,1.1,0.4,1.3,0.8,3.5,0.2,6,22,6,6,13,13,10,12,12,5,2.0,22,15,6,14,63,77,5,21,37,14,27,15,33,38,29,39,15,23,40,13,10,5,17,1,7,22,29,1,1,4,17,16,16,14,4,2,5,5,5,4,17,11,11,3,21,3,12,4,6,3,10,1,3,13,10,16,11,8,4,8,7,30,2,0.043,0.026,0.012,0.018,0.034,0.028,0.018,0.026,0.052,0.023,0.03,0.033,0.027,0.036,0.026,0.033,0.029,0.021,0.034,0.037,0.031,0.032,0.028,0.039,0.028,0.026,0.028,0.024,0.018,0.032,0.032,0.054,0.029,0.024,0.018,0.022,0.032,0.029,0.017,0.011,0.02,0.031,0.034,0.022,0.018,0.012,0.021,0.014,0.016,0.011,0.039,0.019,0.025,0.019,0.016,0.017,0.028,0.021,0.02,0.018,0.02,0.025,0.016,0.029,0.032,0.027,0.023,0.022,0.018,0.013,0.023,0.018,0.017,0.015,1214,4056,1804,2261,1689,2424,2509,2630,1245,1484,464.0,2398,3060,1003,2775,9721,16991,1973,4491,4629,3989,4023,1553,4699,5573,6039,5611,2948,5669,6201,1593,1212,960,5253,1118,1751,5054,5391,382,372,1485,2178,4309,2857,3261,1510,721,2188,1918,1424,604,3386,1742,3582,653,4791,823,3213,929,1410,718,1852,346,546,1860,1056,2851,2344,1922,688,1971,1394,7904,488,0.146,0.121,0.089,0.096,0.14,0.133,0.096,0.124,0.179,0.114,0.099,0.129,0.121,0.144,0.126,0.137,0.13,0.109,0.129,0.134,0.108,0.136,0.129,0.133,0.132,0.127,0.132,0.101,0.095,0.133,0.126,0.147,0.135,0.116,0.08,0.101,0.128,0.12,0.095,0.089,0.103,0.142,0.133,0.109,0.098,0.082,0.115,0.08,0.096,0.087,0.142,0.108,0.122,0.099,0.098,0.105,0.099,0.11,0.109,0.103,0.119,0.118,0.092,0.116,0.135,0.131,0.118,0.111,0.105,0.11,0.117,0.116,0.098,0.097,339,1434,658,777,698,885,812,865,390,349,134.0,1101,831,250,731,2823,4880,417,1242,2001,1124,1240,804,1231,1783,1894,1973,1065,1553,2027,468,500,320,1198,256,610,1224,1332,222,196,702,986,921,1642,2015,770,302,923,928,691,288,1563,898,1562,398,2378,370,1440,516,762,287,726,188,293,902,533,1497,1130,1009,409,894,605,3401,233,3.225,2.641,2.464,2.742,2.168,2.474,2.477,2.703,2.474,3.337,2.707,1.906,2.743,2.739,2.756,2.61,2.766,3.567,2.812,2.063,2.802,2.479,1.798,2.783,2.463,2.687,2.258,2.186,2.729,2.515,2.475,2.497,2.418,3.045,3.804,2.441,3.083,2.906,1.996,2.342,2.833,1.931,3.405,2.035,1.826,2.157,2.965,2.74,2.526,2.386,2.21,2.135,1.995,2.472,2.002,2.27,2.422,2.54,2.111,2.103,2.453,2.47,1.898,1.886,2.152,2.361,2.075,2.345,2.229,1.918,2.555,2.578,2.477,2.446,0.749,0.537,0.579,0.626,0.489,0.397,0.48,0.433,0.524,0.406,0.923,0.449,0.412,0.452,0.463,0.511,0.581,0.795,0.539,0.686,0.955,0.456,0.386,0.66,0.445,0.491,0.478,0.517,0.449,0.508,0.545,0.942,0.45,0.688,0.543,0.534,0.629,0.603,0.31,0.392,0.663,0.407,0.676,0.63,0.492,0.483,0.547,0.721,0.485,0.429,0.404,0.421,0.478,0.43,0.325,0.614,0.417,0.562,0.383,0.393,0.381,0.67,0.359,0.84,0.591,0.748,0.521,0.414,0.423,0.322,0.49,0.496,0.502,0.303
-223,Epileptic,M,32.0,1.1,2.5,1.2,0.8,1.7,2.3,1.6,1.8,1.5,0.5,0.3,2.9,1.3,0.8,1.1,6.6,9.8,1.0,1.9,4.4,0.8,1.8,2.1,3.4,3.8,2.9,3.5,2.3,2.6,4.5,2.2,0.5,0.4,2.6,0.5,0.8,3.7,3.3,0.2,0.3,1.0,3.2,2.2,2.8,2.7,0.6,0.6,0.8,1.0,0.9,1.0,1.6,1.4,3.1,0.4,2.5,0.6,2.0,1.1,0.9,0.3,2.0,0.4,0.6,1.9,0.5,2.1,1.4,1.2,0.4,1.3,1.5,4.1,0.3,9,22,12,6,17,21,14,18,8,5,3.0,25,11,8,11,61,72,8,28,34,6,19,17,31,42,34,37,20,20,36,21,4,4,28,3,6,36,35,2,1,4,26,20,20,15,4,2,4,5,6,6,10,10,17,2,18,3,17,6,7,2,14,2,4,12,7,18,8,8,4,8,10,33,2,0.044,0.023,0.017,0.017,0.037,0.032,0.028,0.032,0.046,0.026,0.029,0.038,0.026,0.04,0.029,0.036,0.038,0.038,0.029,0.037,0.015,0.029,0.036,0.037,0.035,0.029,0.026,0.026,0.026,0.035,0.046,0.023,0.026,0.031,0.018,0.017,0.036,0.03,0.017,0.022,0.02,0.037,0.036,0.025,0.022,0.013,0.022,0.012,0.015,0.027,0.035,0.023,0.025,0.025,0.018,0.019,0.013,0.022,0.025,0.019,0.013,0.027,0.024,0.022,0.024,0.027,0.02,0.019,0.02,0.012,0.024,0.022,0.018,0.019,1514,5510,2657,1952,2527,3833,2292,3238,1650,1178,659.0,2707,3172,1227,2441,10998,16355,2322,4333,4428,3128,3489,2614,6640,6464,6437,5960,3262,5124,6343,2429,1013,899,6521,2070,2128,6359,7707,374,443,1458,2816,5458,2568,2821,1291,993,2394,2262,1284,685,2529,2306,4508,573,3408,1445,3147,1045,1268,688,3177,597,874,2259,846,3082,2619,1609,1169,1694,2128,9411,375,0.144,0.117,0.104,0.093,0.145,0.139,0.113,0.141,0.133,0.133,0.109,0.147,0.116,0.145,0.127,0.139,0.137,0.123,0.125,0.141,0.073,0.128,0.135,0.132,0.146,0.137,0.114,0.112,0.102,0.143,0.14,0.078,0.125,0.135,0.087,0.094,0.128,0.126,0.118,0.103,0.104,0.138,0.133,0.12,0.106,0.089,0.102,0.079,0.085,0.12,0.143,0.105,0.115,0.109,0.111,0.101,0.09,0.108,0.116,0.103,0.087,0.128,0.111,0.119,0.111,0.102,0.108,0.1,0.105,0.093,0.121,0.111,0.095,0.117,430,1749,1074,721,879,1327,977,1037,489,322,199.0,1327,860,348,664,3149,4626,467,1224,2078,797,1142,1025,1734,1869,1835,2210,1331,1671,2154,702,405,283,1464,495,723,1794,1888,188,216,686,1402,1160,1611,1847,654,403,942,1003,544,454,1157,969,1994,284,1978,662,1625,667,759,362,1127,271,435,1204,358,1661,1176,882,515,786,1052,3892,188,3.191,2.986,2.324,2.762,2.639,2.474,2.053,2.96,2.741,3.074,2.846,1.893,2.84,2.866,2.77,2.747,2.97,3.617,2.872,1.894,2.823,2.374,2.259,2.967,2.65,2.884,2.227,2.057,2.424,2.455,2.598,2.392,2.566,3.155,3.814,2.637,2.722,3.059,2.348,2.508,2.582,1.863,3.267,1.913,1.75,2.19,3.355,3.177,2.803,2.999,1.957,2.285,2.362,2.394,2.351,1.995,2.686,2.28,1.942,1.914,2.137,2.84,2.5,2.43,2.157,2.434,2.097,2.562,2.089,2.524,2.293,2.441,2.534,2.375,1.144,0.621,0.67,0.632,0.607,0.619,0.506,0.539,0.551,0.326,0.817,0.481,0.432,0.522,0.465,0.521,0.573,0.862,0.552,0.563,0.835,0.511,0.466,0.619,0.535,0.561,0.486,0.533,0.625,0.694,0.696,1.128,0.495,0.653,0.55,0.489,0.694,0.597,0.452,0.372,0.515,0.554,0.646,0.667,0.44,0.49,0.368,0.784,0.42,0.67,0.388,0.538,0.498,0.508,0.489,0.517,0.46,0.593,0.356,0.377,0.307,0.717,0.44,0.736,0.644,0.615,0.509,0.415,0.406,0.532,0.576,0.507,0.593,0.504
-224,Epileptic,F,37.0,2.7,1.6,1.0,1.2,1.7,1.9,1.6,1.6,1.2,0.9,0.3,2.3,1.4,0.4,0.9,5.3,7.6,1.3,1.3,2.6,1.1,3.1,2.1,2.7,3.1,2.5,3.0,2.8,2.8,3.1,1.2,1.9,0.4,1.5,1.6,0.4,1.9,2.7,0.1,0.1,1.0,3.1,1.2,1.5,3.0,0.6,0.5,0.8,1.1,0.6,0.4,1.4,2.2,2.6,0.2,2.4,0.5,1.1,0.7,0.7,0.6,1.0,0.2,0.6,1.6,0.9,2.9,1.3,0.9,0.4,1.2,0.7,3.8,0.3,18,14,10,10,17,27,14,15,14,10,5.0,24,14,7,9,59,82,16,20,25,12,37,21,37,38,32,37,32,26,37,18,14,4,29,21,2,25,36,1,1,5,32,15,11,15,5,2,6,6,7,3,9,17,16,1,35,4,9,5,6,3,9,1,6,12,16,20,7,6,4,7,6,26,2,0.078,0.023,0.02,0.023,0.029,0.036,0.031,0.03,0.03,0.035,0.035,0.037,0.031,0.038,0.025,0.036,0.03,0.049,0.03,0.032,0.028,0.038,0.038,0.044,0.031,0.027,0.031,0.032,0.032,0.033,0.039,0.076,0.035,0.027,0.043,0.013,0.032,0.038,0.016,0.013,0.022,0.038,0.048,0.022,0.021,0.017,0.022,0.013,0.016,0.032,0.018,0.019,0.035,0.022,0.021,0.019,0.017,0.016,0.018,0.013,0.018,0.027,0.028,0.03,0.02,0.016,0.021,0.021,0.019,0.02,0.024,0.021,0.019,0.012,1879,3652,2076,2216,2378,3025,2705,2999,2527,1946,580.0,2572,3113,968,2709,10288,17476,2525,3310,4025,3268,4893,2192,5987,7760,6555,7037,4818,7019,6453,2135,886,794,5240,1403,1298,5045,5733,345,405,1434,2028,1714,2277,3776,1333,819,2188,2096,1577,514,2357,2399,4344,250,4520,1124,2742,985,1519,1048,1894,293,863,2478,1268,4796,2096,1991,868,1917,1693,7826,579,0.188,0.116,0.114,0.116,0.132,0.146,0.124,0.127,0.133,0.144,0.142,0.151,0.134,0.156,0.114,0.14,0.128,0.161,0.128,0.124,0.098,0.132,0.121,0.159,0.13,0.123,0.131,0.116,0.119,0.142,0.143,0.146,0.118,0.118,0.14,0.075,0.145,0.14,0.104,0.084,0.111,0.14,0.146,0.115,0.102,0.09,0.106,0.082,0.087,0.13,0.108,0.097,0.139,0.103,0.126,0.106,0.095,0.101,0.105,0.091,0.101,0.123,0.114,0.122,0.109,0.109,0.104,0.102,0.091,0.123,0.114,0.113,0.102,0.09,633,1240,745,804,878,999,871,941,708,485,218.0,1066,831,248,668,2760,4751,572,937,1349,710,1352,925,1436,1789,1697,1779,1578,1646,1832,640,421,229,1195,579,463,1125,1397,176,180,695,1273,509,1176,2107,605,299,916,1046,380,313,1126,1056,1905,98,2121,527,1132,586,810,517,681,153,438,1260,897,2143,932,829,398,851,585,3124,251,2.641,2.706,2.614,2.593,2.222,2.438,2.433,2.628,2.574,2.962,2.284,2.075,2.804,2.791,2.866,2.731,2.866,3.213,2.773,2.34,3.102,2.629,2.003,2.947,2.997,2.931,2.748,2.341,3.087,2.706,2.478,2.287,2.683,2.968,2.188,2.638,3.163,2.968,1.953,2.279,2.521,1.557,2.82,2.056,2.056,2.217,3.023,2.775,2.459,2.821,1.889,2.258,2.122,2.368,2.418,2.299,2.31,2.486,1.862,2.002,2.452,2.44,2.174,2.165,2.163,1.739,2.394,2.421,2.727,2.428,2.515,2.805,2.656,2.605,0.922,0.571,0.516,0.557,0.657,0.545,0.715,0.531,0.76,0.777,0.835,0.506,0.413,0.515,0.504,0.623,0.607,1.096,0.633,0.662,1.008,0.652,0.558,0.722,0.72,0.619,0.735,0.66,0.593,0.547,0.577,0.73,0.487,0.602,0.691,0.445,0.769,0.76,0.419,0.298,0.453,0.427,0.999,0.637,0.563,0.458,0.716,0.794,0.442,0.821,0.487,0.382,0.516,0.508,0.635,0.496,0.442,0.553,0.363,0.386,0.338,0.689,0.451,0.66,0.462,0.481,0.479,0.409,0.495,0.685,0.493,0.58,0.51,0.481
-225,Epileptic,M,22.0,0.8,3.0,0.9,1.2,1.8,2.5,2.0,1.4,2.6,0.7,0.2,2.3,1.2,0.5,1.4,4.7,9.2,0.9,2.3,5.3,6.3,3.2,2.1,5.2,5.9,4.2,3.6,3.7,3.3,3.8,2.8,1.1,0.8,3.2,0.9,0.9,7.7,6.7,0.1,0.3,1.1,4.4,3.9,2.6,2.7,0.7,0.3,1.0,1.2,1.7,0.7,2.2,1.5,2.4,0.5,2.8,1.3,2.4,1.3,1.3,1.1,1.8,0.3,0.9,1.9,1.6,2.4,1.2,1.1,1.0,1.4,2.1,6.7,0.3,8,26,8,11,16,20,16,14,16,8,4.0,30,13,7,18,59,88,11,28,53,50,46,24,50,64,38,39,35,26,39,29,12,9,34,7,7,66,57,1,1,6,34,31,20,16,5,2,6,6,10,5,19,15,17,3,24,8,16,15,11,9,14,2,8,17,18,27,9,7,6,10,13,55,3,0.039,0.03,0.021,0.023,0.033,0.038,0.029,0.026,0.067,0.038,0.037,0.04,0.026,0.036,0.034,0.034,0.039,0.041,0.033,0.038,0.06,0.036,0.035,0.048,0.046,0.037,0.03,0.039,0.032,0.034,0.053,0.053,0.032,0.034,0.032,0.021,0.053,0.044,0.011,0.016,0.02,0.043,0.047,0.024,0.021,0.015,0.016,0.017,0.017,0.021,0.023,0.023,0.023,0.022,0.02,0.019,0.026,0.028,0.021,0.023,0.025,0.026,0.023,0.022,0.023,0.026,0.017,0.016,0.018,0.03,0.029,0.024,0.02,0.018,1548,6044,2391,2618,1946,3226,3585,3687,1181,1421,664.0,2294,3682,1188,3082,10738,18524,2335,4761,6296,4475,5311,2091,6124,6385,6221,6374,4505,7020,6581,2199,1420,1541,7158,2511,1916,5596,9549,309,509,1533,2208,5108,2843,3669,1496,758,2549,2380,2237,712,3510,2277,4181,676,4323,1890,3310,1555,1592,1163,3153,406,1165,2557,1508,4549,2546,2072,1285,1760,2940,12295,531,0.122,0.134,0.104,0.112,0.119,0.142,0.12,0.116,0.194,0.149,0.127,0.165,0.117,0.146,0.14,0.134,0.143,0.134,0.125,0.155,0.159,0.141,0.129,0.144,0.153,0.134,0.126,0.115,0.121,0.146,0.152,0.128,0.13,0.129,0.094,0.098,0.155,0.141,0.095,0.093,0.105,0.15,0.139,0.122,0.101,0.093,0.095,0.083,0.089,0.102,0.12,0.118,0.123,0.105,0.108,0.109,0.106,0.115,0.127,0.125,0.125,0.119,0.133,0.119,0.12,0.117,0.1,0.097,0.101,0.133,0.137,0.11,0.103,0.115,434,1723,765,869,903,1193,1196,996,549,357,182.0,1202,884,336,828,2890,4564,486,1330,2550,1686,1649,999,1930,2358,2010,2213,1817,1775,1997,918,519,495,1659,546,669,2398,2613,155,247,785,1453,1291,1723,2101,721,313,1000,1047,1132,448,1612,1042,1816,386,2222,907,1495,962,910,662,1177,194,680,1344,923,2296,1120,927,495,754,1402,5353,300,3.392,3.207,2.947,2.884,2.017,2.316,2.458,2.986,1.931,3.063,2.943,1.711,3.078,2.753,2.882,2.734,3.037,3.598,2.799,2.111,2.383,2.494,1.877,2.427,2.288,2.551,2.318,2.086,2.936,2.519,1.869,2.82,2.472,3.025,3.764,2.439,2.04,2.833,2.319,2.648,2.52,1.487,2.867,1.841,2.008,2.306,3.158,3.043,2.815,2.512,1.901,2.315,2.247,2.426,2.169,2.083,2.345,2.421,1.757,1.94,2.035,2.632,2.158,2.138,1.982,1.889,2.113,2.642,2.435,2.891,2.471,2.431,2.434,2.015,0.656,0.825,0.572,0.63,0.552,0.677,0.603,0.544,0.721,0.591,0.689,0.489,0.557,0.466,0.519,0.625,0.694,0.859,0.654,0.68,0.859,0.65,0.446,0.934,0.615,0.636,0.611,0.536,0.555,0.584,0.831,1.022,0.563,0.654,0.705,0.502,0.826,0.805,0.341,0.348,0.468,0.442,0.994,0.676,0.692,0.395,0.444,0.634,0.412,0.534,0.452,0.47,0.481,0.412,0.372,0.397,0.488,0.544,0.481,0.362,0.438,0.597,0.422,0.59,0.521,0.68,0.452,0.421,0.457,0.687,0.453,0.468,0.439,0.368
-226,Epileptic,F,17.5,0.5,1.9,0.6,1.0,1.2,1.6,1.5,1.5,1.0,0.7,0.4,2.6,1.2,0.7,1.2,5.2,7.2,0.8,2.5,4.9,1.1,2.8,1.5,3.2,3.8,3.2,5.0,2.9,2.8,3.8,1.6,2.0,0.6,2.2,0.4,0.5,2.3,2.3,0.2,0.3,0.8,3.0,2.3,2.7,3.3,0.8,0.6,0.7,1.2,1.1,0.9,1.6,0.8,3.3,0.2,2.5,0.8,1.9,0.7,1.0,0.6,1.2,0.3,0.6,1.3,0.8,2.9,1.4,1.5,0.4,1.2,1.5,3.6,0.3,3,14,5,6,12,15,13,12,12,7,4.0,23,16,8,12,49,72,6,24,38,9,28,13,29,46,35,54,32,22,45,14,16,4,21,2,3,28,29,1,2,5,27,20,24,19,5,2,5,6,8,5,12,6,27,1,19,8,14,6,10,3,10,3,4,10,10,21,11,12,4,7,11,27,1,0.025,0.024,0.014,0.018,0.03,0.029,0.022,0.028,0.039,0.042,0.034,0.037,0.031,0.033,0.035,0.033,0.028,0.03,0.035,0.041,0.022,0.037,0.031,0.039,0.036,0.032,0.034,0.029,0.024,0.034,0.055,0.071,0.032,0.028,0.013,0.014,0.029,0.032,0.018,0.018,0.019,0.041,0.044,0.029,0.025,0.012,0.025,0.013,0.016,0.023,0.049,0.015,0.019,0.023,0.022,0.016,0.023,0.02,0.023,0.022,0.021,0.022,0.022,0.034,0.019,0.025,0.023,0.023,0.02,0.018,0.032,0.026,0.018,0.019,1588,4324,1834,2243,1737,2679,2632,3114,1686,1442,509.0,2860,3705,1538,2509,10560,17521,2287,3827,3740,3087,4550,1778,6572,6688,6838,7122,4351,5713,6225,1778,1132,907,5525,1799,1819,6181,7232,338,538,1366,2789,4455,2267,3350,1764,892,2252,2674,1703,398,3472,1644,6818,322,4473,1360,3049,1128,1651,807,2610,581,954,2411,903,3784,2043,2627,965,1468,2389,8401,431,0.097,0.106,0.09,0.095,0.121,0.138,0.102,0.122,0.138,0.151,0.135,0.145,0.138,0.142,0.137,0.132,0.122,0.1,0.127,0.143,0.091,0.147,0.139,0.142,0.14,0.133,0.136,0.121,0.103,0.14,0.149,0.178,0.12,0.123,0.06,0.078,0.122,0.123,0.117,0.101,0.099,0.155,0.124,0.118,0.11,0.087,0.109,0.076,0.088,0.115,0.172,0.092,0.097,0.111,0.117,0.099,0.124,0.104,0.117,0.108,0.117,0.119,0.12,0.135,0.103,0.116,0.116,0.102,0.109,0.121,0.138,0.118,0.091,0.114,351,1213,637,821,665,926,1000,920,498,337,165.0,1085,778,380,581,2576,4342,434,1226,1821,828,1234,733,1422,1858,1705,2433,1551,1656,1933,449,441,274,1189,517,522,1412,1370,139,240,642,1178,906,1446,2137,872,369,883,1045,726,274,1573,753,2372,150,2225,536,1544,613,850,370,822,256,298,1071,511,1880,916,1214,326,552,999,3196,181,3.806,3.4,2.566,2.725,2.156,2.581,2.308,3.109,2.463,3.486,2.908,2.169,3.43,3.007,3.036,2.944,3.183,4.003,2.723,1.842,2.987,2.684,2.117,3.234,2.677,2.977,2.377,2.266,2.649,2.528,2.859,2.61,2.804,3.248,3.155,2.93,3.116,3.554,2.565,2.625,2.751,2.165,3.552,1.74,1.801,2.161,3.332,3.223,3.07,2.616,1.75,2.243,2.338,2.746,2.676,2.179,2.827,2.36,2.24,2.139,2.975,2.969,2.386,3.566,2.484,2.243,2.244,2.52,2.436,2.942,2.877,2.954,2.792,2.781,0.957,0.757,0.614,0.523,0.731,0.693,0.551,0.785,0.619,0.65,0.797,0.55,0.569,0.438,0.57,0.724,0.632,0.868,0.73,0.535,0.769,0.6,0.394,0.643,0.597,0.626,0.52,0.548,0.449,0.628,0.832,1.154,0.497,0.581,0.489,0.864,0.723,0.677,0.331,0.698,0.738,0.562,0.7,0.62,0.551,0.525,0.548,0.955,0.567,0.517,0.355,0.638,0.657,0.635,0.358,0.58,0.56,0.523,0.387,0.513,0.51,0.914,0.476,0.965,0.632,0.698,0.504,0.464,0.412,0.897,0.663,0.573,0.687,0.492
-227,Epileptic,F,42.0,0.6,2.5,1.1,1.1,1.5,1.3,1.7,1.9,1.6,0.6,0.2,3.3,1.2,0.8,1.1,6.2,9.0,0.7,2.3,5.8,0.8,2.7,1.6,2.4,2.4,3.3,3.3,2.6,3.3,2.8,1.5,0.9,0.5,3.1,0.3,0.3,3.5,3.0,0.1,0.2,0.9,2.5,1.9,3.5,2.7,0.8,0.4,0.7,1.0,1.1,0.7,2.0,1.0,2.0,0.4,1.8,1.1,2.0,0.6,1.4,0.6,0.8,0.2,0.3,1.8,2.1,3.1,1.2,1.3,0.5,1.1,1.4,2.3,0.3,3,20,13,12,17,15,17,16,16,9,2.0,29,13,9,11,69,82,5,30,48,8,27,17,25,28,29,37,27,26,32,14,5,6,35,1,2,38,27,2,1,5,26,20,24,20,6,2,5,5,6,5,16,6,16,3,16,7,22,5,13,4,6,1,3,16,19,26,8,10,5,10,11,17,2,0.032,0.026,0.015,0.021,0.043,0.023,0.024,0.034,0.051,0.031,0.022,0.037,0.025,0.057,0.026,0.04,0.031,0.029,0.031,0.042,0.018,0.038,0.029,0.033,0.037,0.031,0.029,0.029,0.029,0.029,0.047,0.032,0.031,0.047,0.016,0.01,0.032,0.033,0.011,0.018,0.02,0.031,0.039,0.03,0.024,0.019,0.021,0.014,0.015,0.031,0.031,0.027,0.022,0.02,0.033,0.018,0.028,0.021,0.02,0.023,0.025,0.02,0.018,0.021,0.024,0.038,0.021,0.02,0.023,0.027,0.029,0.025,0.014,0.018,1422,4159,2150,2294,1670,2487,2798,3131,2091,1266,562.0,3024,2735,810,2520,8496,14923,1939,3459,5844,2063,3620,2381,4393,3699,5740,4866,3721,5125,5000,1989,1004,1059,4689,1323,1508,5520,6048,385,419,1633,2556,3817,3440,3043,1398,729,2083,2304,1201,613,2939,1435,3650,356,2995,1326,3034,1011,1863,775,1821,310,744,2442,1290,4485,1843,1850,1111,1338,1734,6295,470,0.115,0.122,0.113,0.114,0.152,0.122,0.115,0.142,0.162,0.154,0.123,0.153,0.122,0.136,0.122,0.143,0.136,0.111,0.133,0.155,0.079,0.154,0.14,0.129,0.149,0.132,0.137,0.131,0.12,0.137,0.151,0.098,0.126,0.158,0.083,0.069,0.129,0.127,0.084,0.089,0.102,0.147,0.139,0.125,0.118,0.11,0.111,0.086,0.087,0.129,0.147,0.12,0.111,0.107,0.159,0.108,0.131,0.11,0.121,0.128,0.12,0.108,0.115,0.127,0.12,0.154,0.116,0.107,0.119,0.132,0.142,0.13,0.087,0.115,341,1385,1009,929,641,927,1050,944,539,398,176.0,1399,840,252,710,2496,4644,388,1138,2247,658,1178,909,1192,1184,1697,1813,1469,1677,1642,499,437,291,1094,313,568,1729,1496,232,199,710,1239,862,2042,1977,687,323,809,1004,549,370,1333,684,1745,169,1606,583,1567,526,988,382,686,170,294,1228,815,2235,923,925,323,623,895,2640,228,3.763,2.671,2.089,2.575,2.362,2.441,2.322,2.806,2.706,2.807,2.753,1.948,2.602,2.363,2.651,2.566,2.642,3.903,2.528,2.301,2.449,2.419,2.248,2.682,2.471,2.792,2.206,2.069,2.528,2.447,2.978,2.277,2.791,2.812,3.116,2.548,2.565,2.924,1.97,2.596,2.911,1.937,3.273,1.944,1.782,2.307,2.851,3.026,2.735,2.692,2.135,2.208,2.199,2.214,2.437,2.172,2.502,2.214,2.181,2.228,2.644,2.713,1.973,2.849,2.241,2.142,2.235,2.313,2.337,3.328,2.351,2.44,2.548,2.737,1.081,0.656,0.711,0.489,0.555,0.503,0.51,0.72,0.654,0.445,0.896,0.479,0.422,0.5,0.479,0.552,0.498,0.714,0.602,0.683,0.59,0.407,0.353,0.592,0.475,0.687,0.43,0.449,0.518,0.464,0.786,0.992,0.356,0.627,0.751,0.426,0.612,0.58,0.378,0.308,0.831,0.442,0.712,0.566,0.525,0.453,0.484,0.701,0.756,0.609,0.335,0.517,0.604,0.524,0.379,0.4,0.471,0.471,0.364,0.318,0.56,0.606,0.265,0.909,0.587,0.688,0.437,0.41,0.405,0.918,0.43,0.559,0.585,0.287
-228,Epileptic,M,47.0,1.0,2.9,1.3,1.8,2.0,2.0,2.3,1.6,2.4,0.5,0.5,3.1,1.8,0.7,1.6,6.8,8.6,1.1,2.6,6.0,1.0,3.3,2.2,6.5,2.8,3.3,3.4,2.3,3.2,3.7,2.1,1.2,0.8,2.9,0.5,0.7,4.2,3.5,0.2,0.3,1.2,3.2,2.2,2.7,3.6,1.1,0.5,0.7,1.6,1.1,0.8,3.1,1.7,2.6,0.2,2.2,1.1,1.7,1.6,1.1,0.7,1.9,0.6,0.5,1.6,1.5,2.1,1.7,1.4,1.0,1.1,2.0,4.3,0.6,8,27,14,14,16,21,18,15,20,7,4.0,28,17,6,13,56,66,12,34,56,8,35,19,57,36,32,36,19,23,43,18,7,5,36,4,7,45,41,2,1,8,30,22,23,19,10,3,4,8,8,6,21,13,14,1,16,8,16,11,8,6,18,3,4,14,23,17,14,8,9,8,15,33,5,0.04,0.027,0.023,0.028,0.035,0.025,0.03,0.023,0.066,0.028,0.031,0.037,0.032,0.033,0.032,0.038,0.031,0.04,0.03,0.04,0.021,0.034,0.03,0.057,0.028,0.027,0.03,0.025,0.027,0.032,0.049,0.038,0.038,0.032,0.015,0.019,0.036,0.032,0.024,0.019,0.025,0.029,0.035,0.025,0.024,0.021,0.022,0.012,0.02,0.026,0.029,0.026,0.032,0.023,0.024,0.014,0.017,0.021,0.023,0.018,0.018,0.026,0.031,0.018,0.02,0.025,0.019,0.019,0.02,0.032,0.021,0.02,0.016,0.026,1469,4967,2587,2607,1994,3028,2933,3661,1707,1505,793.0,3501,3734,1294,2454,9128,17046,2435,6078,6487,3765,4938,2377,6794,5924,5986,5180,3532,6392,6340,2123,1287,755,6589,2278,1544,7651,8519,325,404,1283,2951,5594,3371,3865,1404,875,2131,2499,1742,774,3851,2265,3874,247,3561,2024,3112,1544,1525,1099,3416,483,931,2026,1523,3379,3282,1844,1085,1476,2652,9280,763,0.125,0.129,0.117,0.127,0.129,0.126,0.11,0.109,0.166,0.136,0.117,0.144,0.127,0.139,0.126,0.135,0.122,0.145,0.135,0.162,0.092,0.13,0.118,0.163,0.126,0.12,0.123,0.101,0.099,0.138,0.136,0.119,0.139,0.141,0.083,0.11,0.134,0.135,0.141,0.104,0.125,0.123,0.134,0.121,0.105,0.112,0.111,0.076,0.101,0.118,0.138,0.117,0.132,0.103,0.123,0.093,0.1,0.115,0.12,0.098,0.102,0.125,0.124,0.114,0.117,0.116,0.106,0.104,0.101,0.149,0.115,0.115,0.096,0.141,449,1704,926,1010,943,1231,1151,1140,657,403,268.0,1406,1056,312,791,2890,4691,565,1597,2499,760,1612,1132,2119,1707,1969,1908,1299,1730,2018,720,528,294,1584,610,533,2109,2019,159,228,692,1581,1234,1843,2257,826,343,893,1162,697,428,1926,945,1782,128,2088,955,1476,985,900,635,1275,286,460,1155,1045,1708,1386,992,549,823,1443,4038,336,3.202,2.751,2.72,2.443,2.072,2.117,2.176,2.676,2.235,2.747,2.513,2.04,2.767,2.907,2.502,2.509,2.88,3.459,2.829,2.164,3.155,2.424,1.899,2.592,2.611,2.516,2.213,2.15,2.837,2.409,2.247,2.674,2.316,3.037,3.218,2.567,2.727,3.031,2.545,2.038,2.525,1.727,3.376,2.041,1.952,1.826,3.008,2.958,2.669,2.917,1.872,2.201,2.405,2.361,2.068,1.895,2.365,2.306,1.779,1.814,2.0,2.65,2.01,2.298,1.99,1.78,2.048,2.528,2.156,2.24,2.14,2.276,2.477,2.473,0.672,0.538,0.405,0.585,0.507,0.54,0.598,0.437,0.574,0.689,0.718,0.552,0.422,0.462,0.513,0.588,0.629,0.767,0.652,0.665,0.841,0.627,0.47,0.739,0.662,0.546,0.537,0.607,0.574,0.59,0.806,0.893,0.454,0.59,0.707,0.448,0.771,0.583,0.506,0.451,0.631,0.475,0.742,0.559,0.578,0.367,0.513,0.746,0.479,0.595,0.54,0.406,0.604,0.455,0.469,0.42,0.464,0.548,0.401,0.371,0.414,0.608,0.42,0.812,0.481,0.67,0.451,0.46,0.465,0.48,0.45,0.49,0.443,0.41
-229,Epileptic,F,47.0,1.5,2.5,1.5,1.6,1.8,1.4,1.6,1.3,0.8,0.6,0.2,1.9,1.6,0.5,1.1,6.2,5.8,0.7,2.5,3.4,0.8,3.4,1.3,2.8,3.0,2.3,2.5,1.9,1.8,2.0,2.0,1.1,0.5,1.9,0.2,0.9,2.4,2.0,0.2,0.1,0.8,2.4,2.4,1.4,2.0,0.8,0.4,1.0,1.2,0.5,0.7,2.2,1.3,2.9,0.3,2.5,0.6,1.4,0.7,0.9,0.2,1.3,0.4,0.2,1.7,1.6,1.9,1.3,0.5,0.4,0.9,0.8,3.0,0.2,12,23,10,10,16,13,14,13,9,6,3.0,23,17,6,13,73,57,5,27,39,12,33,15,26,35,24,30,21,18,28,24,8,5,24,1,8,31,21,1,1,6,23,19,15,12,6,2,4,6,5,5,17,8,23,2,17,7,14,4,8,3,11,2,2,15,15,14,10,3,3,6,7,26,3,0.052,0.025,0.026,0.03,0.044,0.031,0.021,0.024,0.042,0.034,0.028,0.027,0.029,0.025,0.023,0.033,0.024,0.028,0.033,0.031,0.024,0.038,0.028,0.031,0.034,0.023,0.022,0.019,0.02,0.022,0.054,0.045,0.023,0.025,0.006,0.023,0.038,0.028,0.018,0.011,0.019,0.035,0.05,0.019,0.014,0.019,0.02,0.015,0.015,0.013,0.029,0.022,0.023,0.022,0.018,0.018,0.018,0.016,0.021,0.019,0.011,0.025,0.023,0.012,0.022,0.029,0.018,0.018,0.011,0.02,0.022,0.017,0.015,0.018,1661,4912,2742,2414,1853,2525,3069,2967,1138,1342,564.0,2263,3167,1293,2442,11228,13863,2215,4299,4124,3413,4864,2066,5967,5535,5148,5543,3488,4609,4749,2568,1182,834,5148,1565,1868,5323,4789,360,472,1451,2397,4536,1881,3398,1311,728,1958,2424,1532,658,3509,1909,5385,465,3927,1125,3227,884,1423,766,2368,507,802,2513,1551,3524,2594,1324,957,1207,1598,8187,324,0.154,0.12,0.119,0.122,0.15,0.138,0.111,0.12,0.15,0.137,0.117,0.136,0.135,0.134,0.121,0.146,0.124,0.115,0.149,0.14,0.101,0.156,0.141,0.138,0.147,0.121,0.111,0.096,0.097,0.122,0.151,0.136,0.14,0.124,0.049,0.106,0.14,0.126,0.088,0.082,0.103,0.151,0.151,0.112,0.085,0.107,0.116,0.073,0.091,0.093,0.14,0.109,0.113,0.112,0.118,0.104,0.117,0.101,0.122,0.107,0.087,0.119,0.117,0.102,0.123,0.128,0.098,0.102,0.085,0.111,0.119,0.108,0.09,0.126,443,1638,855,858,739,830,1048,903,356,340,181.0,1120,825,310,773,3270,4008,407,1261,1767,737,1457,797,1439,1656,1604,1836,1302,1384,1517,623,458,330,1199,493,649,1230,1185,200,227,715,1185,843,1186,2087,656,296,870,1093,563,381,1616,875,2170,244,2027,474,1373,486,791,374,847,264,303,1162,783,1723,1209,686,415,586,744,3325,183,3.463,2.761,2.939,2.747,2.355,2.607,2.393,2.872,2.428,3.032,2.652,1.748,2.856,2.929,2.42,2.629,2.704,3.812,2.871,2.06,3.379,2.556,2.262,2.965,2.698,2.655,2.38,2.137,2.542,2.454,2.985,2.607,2.147,3.026,2.861,2.518,3.126,2.93,2.066,2.287,2.631,1.863,3.562,1.846,1.826,2.408,3.169,2.68,2.645,2.87,2.107,2.245,2.342,2.542,2.375,2.198,2.294,2.491,2.033,2.011,2.174,2.608,2.169,2.397,2.4,2.276,2.2,2.421,2.204,2.546,2.329,2.467,2.621,2.223,0.68,0.723,0.819,0.441,0.471,0.405,0.538,0.53,0.486,0.43,0.552,0.476,0.417,0.437,0.498,0.579,0.571,0.771,0.605,0.646,0.843,0.511,0.53,0.615,0.501,0.624,0.465,0.502,0.485,0.458,0.849,1.023,0.407,0.611,0.914,0.487,0.734,0.602,0.391,0.56,0.6,0.533,0.71,0.623,0.511,0.596,0.36,0.752,0.57,0.709,0.345,0.513,0.554,0.56,0.318,0.458,0.378,0.648,0.358,0.385,0.311,0.678,0.405,0.988,0.487,0.828,0.449,0.471,0.387,0.547,0.468,0.538,0.479,0.358
-230,Epileptic,M,27.0,0.7,2.5,1.1,1.3,1.8,2.8,1.7,1.5,1.2,0.8,0.3,3.4,1.3,0.7,1.1,5.1,7.8,0.6,2.6,7.1,1.6,4.0,2.0,4.1,4.2,3.9,3.6,3.6,2.7,2.5,1.0,1.2,0.4,2.0,0.4,0.5,3.6,2.9,0.2,0.2,0.9,4.9,4.3,4.5,2.3,0.8,0.4,0.9,1.3,0.7,0.6,1.6,1.4,2.0,0.5,2.3,0.8,1.8,1.6,1.0,0.5,0.9,0.3,0.6,2.9,0.9,2.6,0.9,0.9,0.5,0.8,1.8,3.5,0.3,6,21,10,10,15,25,12,12,12,6,4.0,34,14,7,11,49,60,5,26,57,14,39,21,36,38,38,45,29,22,28,12,11,4,22,2,6,38,30,1,1,5,47,33,30,15,6,2,5,7,6,4,10,9,11,4,18,6,14,13,8,4,7,3,4,25,8,21,6,5,4,5,15,25,3,0.045,0.026,0.024,0.028,0.039,0.04,0.028,0.029,0.045,0.035,0.029,0.035,0.032,0.038,0.032,0.034,0.03,0.024,0.036,0.044,0.031,0.039,0.034,0.042,0.043,0.036,0.029,0.04,0.033,0.026,0.035,0.055,0.031,0.034,0.016,0.02,0.044,0.033,0.017,0.015,0.022,0.041,0.056,0.033,0.019,0.023,0.016,0.016,0.02,0.025,0.031,0.02,0.023,0.02,0.02,0.019,0.03,0.02,0.025,0.024,0.019,0.021,0.016,0.024,0.026,0.031,0.021,0.016,0.019,0.017,0.022,0.027,0.019,0.016,1084,4665,2347,2080,2222,3396,2710,2841,2106,1249,606.0,3099,2544,1222,2487,11195,16750,2264,4439,5498,3492,4255,2070,6381,4330,5951,6284,4128,5407,4846,2186,977,822,5006,1532,1505,5827,7076,245,288,1095,3352,6966,3313,3170,1150,769,1791,1883,1296,581,2741,1948,4272,691,3517,1095,2944,1483,1148,845,1976,456,1111,3647,803,3949,2108,1558,979,1008,2485,6954,618,0.111,0.123,0.113,0.116,0.135,0.156,0.111,0.129,0.173,0.142,0.095,0.153,0.129,0.135,0.124,0.136,0.124,0.103,0.152,0.169,0.111,0.132,0.144,0.149,0.12,0.138,0.116,0.123,0.113,0.127,0.136,0.13,0.115,0.134,0.073,0.103,0.164,0.144,0.116,0.103,0.105,0.15,0.182,0.14,0.099,0.115,0.092,0.088,0.104,0.119,0.13,0.102,0.11,0.094,0.13,0.103,0.136,0.108,0.123,0.116,0.113,0.109,0.126,0.114,0.122,0.121,0.109,0.092,0.103,0.106,0.119,0.133,0.099,0.108,318,1584,802,808,765,1150,934,907,542,391,205.0,1676,761,323,643,2690,4371,461,1255,2801,892,1573,1031,1648,1490,1890,2197,1485,1283,1594,603,424,238,1140,442,496,1410,1537,144,144,619,1902,1374,2194,1869,563,328,826,964,478,353,1271,936,1598,312,1857,484,1470,984,674,418,717,263,414,1828,478,1993,890,717,431,520,992,2928,259,3.25,2.972,2.843,2.665,2.453,2.418,2.398,2.734,2.929,2.562,2.476,1.728,2.622,2.762,2.785,3.07,3.14,3.466,2.716,1.765,2.99,2.208,1.895,2.918,2.329,2.582,2.296,2.245,3.118,2.44,2.734,2.434,2.723,3.132,2.836,2.513,2.901,3.352,1.954,2.258,2.282,1.608,3.175,1.826,1.917,2.324,2.567,2.752,2.456,2.7,1.989,2.258,2.389,2.653,2.42,2.1,2.624,2.332,1.727,1.85,2.549,2.831,2.131,2.816,2.06,2.049,2.166,2.676,2.555,2.402,2.251,2.62,2.484,2.535,0.925,0.621,0.553,0.558,0.73,0.731,0.696,0.518,0.606,0.458,0.608,0.557,0.541,0.51,0.614,0.633,0.587,0.844,0.665,0.551,0.83,0.696,0.548,0.748,0.706,0.688,0.674,0.643,0.633,0.583,0.71,0.711,0.431,0.733,0.622,0.65,0.843,0.715,0.46,0.465,0.506,0.533,0.849,0.695,0.622,0.558,0.659,0.641,0.529,0.599,0.409,0.461,0.492,0.549,0.745,0.416,0.54,0.517,0.393,0.412,0.371,0.789,0.31,0.616,0.532,0.589,0.491,0.465,0.427,0.733,0.469,0.716,0.521,0.575
-231,Epileptic,M,22.0,1.7,2.9,1.3,1.7,2.3,2.0,1.5,1.8,1.7,1.0,0.5,3.6,1.5,0.8,1.7,7.7,10.0,1.4,2.7,4.5,1.1,5.0,2.1,3.9,5.6,4.8,4.0,3.9,2.8,3.3,1.8,1.5,0.9,2.7,0.7,1.0,6.0,3.7,0.2,0.3,1.2,4.6,3.3,3.8,2.7,1.1,0.5,1.0,1.8,0.8,0.6,1.6,1.7,3.2,0.4,2.6,1.2,2.0,1.3,1.5,1.0,2.1,0.5,0.6,2.5,2.0,3.5,1.2,1.3,1.3,1.7,1.7,5.1,0.5,11,25,12,12,19,21,14,19,16,13,5.0,35,21,11,20,84,94,10,30,45,8,51,22,49,58,58,40,35,27,32,16,11,7,30,4,11,56,40,2,2,9,38,35,29,17,9,3,5,10,8,5,15,14,26,3,19,9,16,12,12,7,14,5,5,21,21,25,8,9,13,10,12,42,4,0.054,0.026,0.026,0.027,0.038,0.03,0.025,0.031,0.043,0.037,0.031,0.037,0.032,0.04,0.032,0.035,0.035,0.046,0.03,0.037,0.027,0.038,0.026,0.037,0.043,0.036,0.034,0.031,0.025,0.03,0.042,0.05,0.034,0.03,0.018,0.018,0.045,0.035,0.018,0.015,0.022,0.038,0.054,0.028,0.018,0.022,0.019,0.015,0.021,0.027,0.027,0.018,0.026,0.022,0.017,0.019,0.018,0.021,0.021,0.02,0.022,0.034,0.026,0.022,0.023,0.027,0.022,0.018,0.021,0.04,0.025,0.026,0.019,0.016,1656,5392,2660,3012,2785,3204,3047,3481,2305,2021,1124.0,3812,3495,1262,3174,13071,19458,2609,5636,5373,3360,5754,2663,7160,6590,7588,5529,4594,7220,5984,2479,1378,1249,5868,2133,2485,9217,7047,354,505,1695,3829,5863,4067,3761,1780,1054,2358,2972,1690,621,3095,2736,5722,494,4046,2099,3384,1973,2111,1430,2370,666,832,3569,2227,4549,2359,2120,1561,2221,2579,9744,842,0.161,0.115,0.121,0.114,0.142,0.12,0.105,0.127,0.159,0.149,0.12,0.154,0.126,0.144,0.131,0.134,0.138,0.142,0.127,0.154,0.091,0.127,0.12,0.145,0.135,0.129,0.121,0.112,0.103,0.127,0.143,0.129,0.102,0.13,0.082,0.098,0.159,0.148,0.111,0.105,0.105,0.135,0.188,0.124,0.098,0.111,0.098,0.082,0.104,0.115,0.127,0.105,0.116,0.108,0.117,0.102,0.106,0.109,0.111,0.104,0.104,0.132,0.126,0.119,0.121,0.124,0.113,0.095,0.106,0.166,0.117,0.119,0.105,0.099,536,1805,902,1038,1055,1240,1000,1167,693,576,337.0,1746,897,368,859,3855,5036,527,1509,2271,787,2069,1312,1917,2139,2407,2007,1851,1720,1898,678,557,476,1496,611,916,2276,1791,187,256,896,1951,1266,2263,2170,836,427,997,1327,588,360,1540,1186,2374,297,2078,960,1561,970,1226,686,1027,359,448,1705,1151,2342,1089,960,602,1021,1055,4173,502,3.066,2.888,2.738,2.644,2.242,2.275,2.417,2.609,2.519,2.896,2.916,1.948,2.847,2.504,2.627,2.571,2.952,3.519,2.833,2.048,2.907,2.269,1.863,2.752,2.409,2.577,2.255,2.069,3.111,2.466,2.712,2.876,2.112,2.783,3.114,2.587,3.06,2.888,2.079,2.322,2.295,1.784,3.125,2.061,1.976,2.099,2.893,3.054,2.729,3.042,2.262,2.141,2.238,2.357,2.001,2.093,2.525,2.329,2.057,1.914,2.257,2.198,2.134,2.2,2.183,2.245,2.175,2.522,2.491,2.675,2.373,2.558,2.416,2.1,0.701,0.652,0.565,0.692,0.641,0.543,0.65,0.549,0.687,0.523,0.619,0.458,0.532,0.57,0.676,0.662,0.645,0.646,0.635,0.543,0.713,0.612,0.54,0.673,0.688,0.548,0.595,0.553,0.553,0.661,0.797,0.958,0.445,0.706,0.953,0.6,0.733,0.616,0.408,0.48,0.518,0.474,0.868,0.61,0.598,0.573,0.745,0.684,0.528,0.549,0.697,0.466,0.595,0.552,0.338,0.486,0.476,0.522,0.487,0.425,0.469,0.698,0.588,0.665,0.521,0.893,0.468,0.461,0.462,0.922,0.511,0.598,0.543,0.348
-232,Epileptic,F,37.0,0.8,1.3,0.6,1.3,1.4,2.3,1.4,1.6,1.2,0.7,0.3,3.0,1.3,0.5,1.3,5.4,8.5,1.0,2.1,4.5,0.5,2.1,2.4,4.3,2.7,3.5,3.3,2.8,2.8,3.8,1.2,0.8,0.6,2.0,0.2,1.5,2.7,2.5,0.3,0.2,1.4,2.9,2.2,2.7,3.3,0.7,0.3,0.9,1.4,0.5,0.6,1.3,1.8,2.7,0.2,2.5,0.5,1.3,1.1,1.0,0.5,1.1,0.2,0.5,1.5,0.7,2.2,0.9,1.1,0.4,0.8,1.0,3.2,0.4,7,11,5,11,18,17,12,14,16,6,3.0,25,16,7,15,53,81,9,23,35,5,22,20,42,31,35,37,25,20,39,17,6,4,21,1,17,29,28,3,1,8,25,20,24,24,5,2,6,7,4,6,9,16,21,2,20,6,12,8,10,4,9,2,4,13,13,17,7,7,5,5,8,28,2,0.034,0.017,0.011,0.026,0.029,0.037,0.022,0.029,0.038,0.03,0.025,0.034,0.032,0.032,0.033,0.033,0.029,0.033,0.029,0.033,0.011,0.029,0.029,0.036,0.033,0.03,0.025,0.028,0.024,0.032,0.033,0.031,0.021,0.026,0.007,0.027,0.03,0.026,0.019,0.018,0.026,0.032,0.034,0.029,0.027,0.015,0.017,0.016,0.018,0.011,0.03,0.018,0.026,0.021,0.02,0.019,0.018,0.018,0.03,0.026,0.024,0.021,0.017,0.019,0.021,0.03,0.021,0.017,0.02,0.015,0.024,0.02,0.019,0.017,1388,3782,1875,2108,2558,2773,2494,2723,2059,1197,530.0,2628,3275,1319,2793,9994,18180,2805,3777,3743,3008,3631,2823,6227,4987,6654,6271,4090,5099,5605,2111,988,1324,5620,2025,2159,6074,6601,553,461,1764,3053,6204,2510,2933,1269,762,2233,2541,1459,481,2641,2570,5883,420,3872,1073,2345,1233,1194,664,2117,328,848,2338,919,3271,1992,1701,834,1092,1975,6790,531,0.113,0.095,0.081,0.115,0.129,0.15,0.096,0.128,0.143,0.138,0.101,0.131,0.137,0.134,0.148,0.142,0.128,0.124,0.127,0.128,0.07,0.14,0.124,0.142,0.15,0.136,0.119,0.112,0.102,0.142,0.125,0.091,0.088,0.116,0.056,0.125,0.132,0.125,0.118,0.105,0.123,0.138,0.137,0.128,0.13,0.099,0.093,0.095,0.102,0.091,0.157,0.099,0.125,0.107,0.119,0.109,0.109,0.105,0.128,0.131,0.12,0.109,0.125,0.111,0.111,0.115,0.117,0.102,0.109,0.124,0.122,0.108,0.1,0.102,424,1254,755,855,864,1066,891,888,587,340,194.0,1398,840,318,738,2841,4871,527,1212,2032,782,1120,1285,1803,1441,2011,2254,1443,1515,1912,604,494,425,1263,528,761,1487,1477,232,193,814,1339,1131,1749,2082,653,310,975,1165,652,354,1183,1084,2292,154,1998,524,1206,671,654,339,893,175,458,1251,440,1566,892,871,433,546,858,2794,283,3.16,2.697,2.378,2.634,2.443,2.435,2.418,2.855,2.534,2.984,2.457,1.751,3.004,3.111,2.802,2.685,2.978,3.745,2.641,1.65,3.051,2.46,2.021,2.694,2.708,2.777,2.286,2.355,2.673,2.397,2.528,2.133,2.46,3.18,3.457,2.644,3.026,3.16,2.631,2.933,2.953,2.01,3.629,1.772,1.649,2.114,3.374,2.89,2.666,2.476,1.787,2.402,2.254,2.592,2.666,2.195,2.247,2.287,2.167,1.985,2.588,2.436,2.112,2.037,2.059,2.267,2.347,2.57,2.297,2.418,2.472,2.705,2.637,2.326,0.674,0.702,0.615,0.447,0.711,0.53,0.567,0.595,0.67,0.438,0.807,0.498,0.545,0.465,0.426,0.543,0.635,0.789,0.619,0.516,0.968,0.448,0.483,0.627,0.477,0.581,0.513,0.599,0.696,0.531,0.683,0.656,0.55,0.61,0.845,0.448,0.711,0.608,0.46,0.443,0.476,0.634,0.791,0.68,0.518,0.552,0.553,0.648,0.534,0.68,0.39,0.474,0.58,0.608,0.6,0.383,0.436,0.554,0.347,0.449,0.413,0.687,0.534,0.813,0.542,0.591,0.563,0.408,0.464,0.539,0.413,0.588,0.489,0.439
-233,Epileptic,M,52.0,0.7,2.0,1.4,1.5,1.6,2.1,1.2,1.8,1.2,1.0,0.5,3.9,1.4,0.5,0.8,5.3,8.1,0.9,1.6,5.8,0.9,2.4,1.7,3.3,3.0,3.3,3.3,2.1,2.3,2.6,1.3,1.3,0.3,1.7,0.3,0.5,3.5,3.6,0.1,0.2,0.7,1.8,1.8,3.8,2.1,0.6,0.4,1.0,1.1,0.4,0.6,2.2,1.4,2.5,0.3,3.0,0.4,1.9,0.6,1.6,0.9,1.8,0.3,0.3,1.8,1.8,2.4,1.7,1.0,0.6,1.0,1.4,4.0,0.3,4,27,18,14,13,20,12,17,14,8,4.0,40,13,6,8,52,87,9,19,54,8,30,17,38,40,36,39,20,19,34,16,13,3,22,2,5,41,40,1,1,3,22,20,28,13,4,2,7,6,5,5,16,10,14,2,22,3,16,7,14,9,11,3,4,14,23,19,13,5,6,8,9,36,2,0.034,0.023,0.018,0.026,0.042,0.033,0.023,0.033,0.045,0.044,0.036,0.033,0.032,0.028,0.022,0.034,0.03,0.039,0.028,0.038,0.02,0.031,0.024,0.037,0.03,0.031,0.027,0.029,0.024,0.029,0.039,0.046,0.024,0.027,0.009,0.016,0.039,0.033,0.014,0.014,0.016,0.026,0.035,0.027,0.018,0.018,0.02,0.019,0.016,0.012,0.031,0.025,0.03,0.022,0.041,0.016,0.016,0.024,0.019,0.021,0.03,0.029,0.026,0.018,0.02,0.027,0.024,0.024,0.016,0.02,0.024,0.021,0.015,0.02,1276,4344,2814,2453,1700,2831,2152,2881,1765,1415,778.0,3559,2661,1017,2059,9772,15682,2088,3730,6194,3511,4137,2237,6112,6140,5817,5692,2749,5195,4781,1850,1572,632,4917,1956,1425,6943,7620,324,399,1102,1938,5427,3972,2768,1041,762,1890,2190,1485,636,2639,1871,3830,389,4840,954,2867,725,1849,1004,2355,453,786,2783,1580,3183,2656,1771,821,1269,2436,9329,396,0.12,0.115,0.112,0.12,0.14,0.15,0.113,0.141,0.152,0.16,0.142,0.138,0.134,0.146,0.115,0.137,0.135,0.136,0.13,0.149,0.094,0.137,0.122,0.141,0.134,0.14,0.124,0.11,0.099,0.141,0.136,0.142,0.101,0.117,0.066,0.093,0.149,0.146,0.09,0.082,0.09,0.132,0.142,0.122,0.098,0.1,0.109,0.102,0.092,0.088,0.136,0.12,0.128,0.104,0.154,0.102,0.087,0.121,0.119,0.121,0.133,0.124,0.129,0.116,0.109,0.128,0.117,0.115,0.089,0.12,0.122,0.104,0.093,0.114,341,1589,1151,905,700,1097,787,948,486,374,237.0,1765,737,283,597,2741,4584,428,1010,2486,732,1400,994,1578,1854,1729,1961,1073,1479,1561,548,552,224,1144,504,528,1540,1851,207,214,594,1141,1041,2285,1833,537,319,775,993,574,365,1316,801,1718,149,2598,403,1339,482,1012,506,894,226,362,1392,990,1690,1211,901,461,680,1076,4053,187,3.595,2.643,2.333,2.488,2.13,2.287,2.314,2.613,2.736,2.908,2.837,1.795,2.754,2.795,2.681,2.7,2.69,3.873,2.92,2.149,3.426,2.324,1.948,2.895,2.572,2.751,2.279,2.063,2.634,2.456,2.469,2.691,2.33,3.033,3.503,2.527,3.09,2.961,1.842,2.178,2.44,1.57,3.466,1.94,1.719,2.051,2.972,3.011,2.594,2.473,2.124,2.15,2.301,2.372,3.142,2.047,2.622,2.541,1.65,1.995,2.308,2.706,2.391,2.082,2.124,1.895,2.005,2.403,2.313,1.991,2.114,2.511,2.445,2.595,0.713,0.437,0.496,0.557,0.62,0.451,0.502,0.517,0.59,0.663,0.664,0.441,0.454,0.415,0.425,0.551,0.562,0.746,0.53,0.607,0.771,0.508,0.482,0.607,0.523,0.442,0.45,0.458,0.518,0.522,0.574,0.849,0.319,0.642,0.857,0.503,0.78,0.605,0.267,0.434,0.628,0.443,0.815,0.551,0.442,0.397,0.473,0.737,0.496,0.583,0.319,0.427,0.44,0.385,0.37,0.398,0.49,0.554,0.344,0.46,0.345,0.647,0.432,0.892,0.484,0.823,0.443,0.388,0.319,0.484,0.398,0.468,0.409,0.401
-234,Epileptic,F,47.0,0.6,2.1,0.9,1.2,1.4,2.1,1.8,1.3,1.4,0.6,0.4,2.4,0.9,0.6,1.4,5.3,10.2,1.5,1.9,3.9,1.3,2.2,1.0,2.6,6.5,3.8,3.9,2.4,3.9,3.5,1.4,1.0,0.4,3.0,0.5,1.0,3.3,3.1,0.2,0.2,1.1,3.0,2.8,1.8,2.5,0.7,0.7,0.8,1.1,0.8,0.2,1.8,0.9,2.5,0.5,2.8,0.8,1.2,0.3,0.8,0.6,1.6,0.1,0.7,2.3,1.7,2.7,1.5,1.0,0.4,0.9,1.3,3.7,0.1,6,21,9,13,11,21,13,14,17,8,5.0,20,13,8,13,52,89,15,21,37,21,21,9,32,42,35,35,20,30,33,16,11,5,32,4,11,38,34,2,1,8,20,31,14,15,5,3,6,7,9,2,12,9,18,3,22,8,10,3,5,3,11,1,6,21,23,17,12,5,3,6,11,28,1,0.041,0.025,0.021,0.027,0.031,0.028,0.028,0.028,0.048,0.041,0.039,0.038,0.026,0.036,0.038,0.037,0.046,0.063,0.032,0.036,0.027,0.031,0.026,0.038,0.046,0.033,0.032,0.026,0.042,0.039,0.039,0.061,0.033,0.038,0.02,0.02,0.034,0.036,0.017,0.018,0.024,0.047,0.042,0.028,0.02,0.017,0.028,0.02,0.016,0.021,0.021,0.026,0.023,0.024,0.022,0.024,0.019,0.017,0.022,0.02,0.029,0.028,0.015,0.019,0.033,0.022,0.025,0.024,0.018,0.02,0.025,0.019,0.02,0.018,1525,3927,1935,2050,1974,3134,2595,2492,1713,1338,719.0,2153,2576,1099,2525,8049,16003,2124,3385,4003,3769,2265,1165,5568,3768,4754,4989,3331,5033,4674,2278,716,818,5322,2162,1802,6827,5792,355,398,1400,1428,7152,1708,3010,1196,858,1843,2147,1943,354,2078,1406,3952,542,3350,1685,2353,457,982,468,2274,229,1019,2432,1484,2866,2019,1422,743,1072,2002,6330,344,0.114,0.118,0.111,0.134,0.136,0.118,0.114,0.136,0.167,0.154,0.128,0.141,0.129,0.162,0.135,0.145,0.164,0.171,0.133,0.152,0.105,0.115,0.119,0.143,0.153,0.133,0.123,0.108,0.132,0.152,0.139,0.142,0.146,0.138,0.085,0.106,0.136,0.131,0.103,0.101,0.112,0.166,0.153,0.12,0.1,0.101,0.124,0.089,0.092,0.115,0.121,0.11,0.104,0.105,0.119,0.12,0.124,0.104,0.113,0.109,0.125,0.126,0.091,0.108,0.138,0.106,0.119,0.112,0.1,0.133,0.122,0.105,0.104,0.127,377,1407,769,797,790,1202,1005,832,592,362,222.0,1110,735,343,682,2589,4436,455,1057,1849,869,966,590,1525,1991,1792,2116,1309,1613,1644,681,331,256,1434,540,770,1846,1431,206,199,712,1040,1381,1108,1907,666,361,767,1045,649,202,1085,696,1601,351,1846,656,1192,245,556,281,885,163,543,1254,1102,1582,988,768,323,563,1100,2918,135,3.488,2.542,2.349,2.35,2.05,2.18,2.06,2.563,2.265,2.833,2.672,1.665,2.614,2.574,2.598,2.343,2.602,3.459,2.573,1.856,3.177,1.992,1.793,2.69,1.829,2.282,2.002,2.052,2.451,2.311,2.592,2.196,2.526,2.652,3.265,2.209,2.801,2.913,1.847,2.132,2.282,1.359,3.396,1.792,1.793,1.955,2.763,2.669,2.359,2.889,2.075,1.994,2.072,2.267,1.862,2.02,2.497,2.323,2.025,1.971,2.011,2.403,1.514,2.25,2.029,1.613,2.0,2.172,2.078,2.41,2.02,2.279,2.34,2.372,0.856,0.72,0.615,0.528,0.69,0.564,0.566,0.573,0.603,0.548,0.796,0.593,0.569,0.523,0.745,0.647,0.779,0.903,0.485,0.545,0.83,0.633,0.494,0.728,0.514,0.624,0.533,0.587,0.563,0.583,0.576,0.837,0.629,0.774,0.951,0.556,0.869,0.626,0.411,0.501,0.484,0.61,0.826,0.642,0.551,0.514,0.419,0.835,0.583,0.665,0.393,0.458,0.51,0.59,0.391,0.433,0.536,0.436,0.785,0.34,0.357,0.682,0.289,0.634,0.457,0.484,0.494,0.548,0.45,0.949,0.425,0.368,0.452,0.35
-235,Epileptic,M,52.0,0.5,2.1,1.0,1.0,1.2,1.9,1.1,1.7,0.9,0.9,0.4,3.0,1.6,0.3,1.3,4.3,8.5,0.9,1.8,4.3,0.6,2.1,1.8,2.6,2.3,3.4,2.8,2.0,2.1,2.7,1.7,1.2,0.6,2.4,0.4,0.6,3.6,3.3,0.1,0.2,1.3,2.9,2.5,2.5,2.3,0.6,0.5,1.0,1.2,0.7,0.3,2.1,0.9,2.9,0.2,2.6,0.9,1.2,1.1,0.9,0.5,1.3,0.3,0.4,1.5,2.1,2.4,0.9,0.9,0.5,0.8,1.2,4.8,0.3,4,19,7,7,13,21,13,13,10,10,7.0,33,17,5,12,43,76,8,22,42,8,22,17,26,31,32,39,19,17,29,17,11,6,27,2,5,39,34,1,1,8,31,22,21,12,3,3,6,6,8,3,18,8,19,2,20,7,12,10,8,4,10,3,5,12,24,20,6,4,5,6,10,34,3,0.03,0.022,0.017,0.019,0.031,0.03,0.019,0.032,0.034,0.044,0.039,0.033,0.029,0.024,0.027,0.033,0.029,0.033,0.029,0.032,0.017,0.031,0.028,0.035,0.03,0.028,0.028,0.022,0.026,0.027,0.038,0.045,0.028,0.03,0.014,0.015,0.033,0.032,0.012,0.018,0.023,0.038,0.039,0.024,0.018,0.012,0.026,0.014,0.016,0.018,0.018,0.024,0.021,0.024,0.026,0.017,0.026,0.015,0.027,0.018,0.025,0.029,0.013,0.016,0.018,0.029,0.02,0.015,0.017,0.022,0.02,0.021,0.019,0.014,1340,4865,2495,2266,1727,3314,2823,2674,1779,1503,535.0,3523,3248,1007,2456,8366,17223,2357,3643,4951,2929,3724,2354,4622,4987,6762,5157,3216,4751,5483,1962,1247,976,6115,1720,1868,7032,7144,350,409,1674,3342,5149,2923,3240,1697,760,2418,2323,1651,500,3423,1726,4792,339,4296,1358,2721,1285,1261,642,1965,546,860,2634,1554,4053,1735,1435,834,1109,2301,9056,511,0.105,0.109,0.096,0.1,0.127,0.14,0.099,0.13,0.155,0.155,0.139,0.154,0.137,0.117,0.128,0.13,0.124,0.125,0.138,0.146,0.077,0.14,0.138,0.132,0.143,0.121,0.131,0.097,0.092,0.129,0.132,0.123,0.113,0.127,0.073,0.089,0.136,0.142,0.093,0.093,0.11,0.151,0.156,0.106,0.096,0.079,0.123,0.081,0.088,0.109,0.11,0.112,0.105,0.109,0.142,0.104,0.128,0.098,0.129,0.112,0.119,0.123,0.097,0.102,0.103,0.121,0.104,0.09,0.091,0.121,0.113,0.113,0.102,0.102,341,1526,857,838,699,1185,917,914,503,410,205.0,1439,949,250,753,2348,4738,453,1049,2043,702,1161,1011,1336,1474,1987,1814,1220,1311,1763,714,532,353,1366,446,613,1937,1683,179,204,817,1387,1143,1736,1887,808,305,966,1073,668,281,1495,783,1924,126,2212,548,1298,659,675,361,744,345,488,1286,1059,1935,846,775,359,572,1067,3873,281,3.633,2.808,2.747,2.625,2.277,2.348,2.554,2.763,2.619,2.905,2.264,2.111,2.705,2.812,2.508,2.636,2.826,3.712,2.852,2.085,3.037,2.509,2.081,2.6,2.673,2.761,2.287,2.094,2.704,2.456,2.114,2.503,2.334,3.105,3.299,2.797,2.69,3.092,2.204,2.269,2.593,2.082,3.212,1.849,1.939,2.262,2.962,2.919,2.589,2.633,2.165,2.309,2.331,2.466,2.938,2.103,2.697,2.201,2.07,2.039,2.411,2.645,1.843,2.057,2.221,1.783,2.063,2.304,2.158,2.61,2.301,2.47,2.505,2.131,0.668,0.653,0.573,0.516,0.473,0.553,0.635,0.479,0.581,0.736,0.856,0.467,0.414,0.576,0.499,0.515,0.616,0.696,0.567,0.536,0.74,0.479,0.481,0.762,0.552,0.517,0.455,0.563,0.498,0.558,0.681,1.041,0.364,0.658,0.61,0.61,0.671,0.612,0.442,0.463,0.495,0.527,0.719,0.579,0.556,0.554,0.688,0.759,0.386,0.673,0.32,0.456,0.531,0.5,0.529,0.398,0.472,0.471,0.368,0.315,0.451,0.804,0.299,0.739,0.506,0.52,0.503,0.325,0.417,0.532,0.512,0.636,0.448,0.349
-236,Epileptic,F,17.5,0.6,1.7,0.9,0.9,1.1,2.3,1.2,1.9,1.0,0.8,0.2,3.2,2.2,0.6,1.4,5.4,8.6,0.9,2.2,6.6,0.9,3.1,1.5,3.2,2.7,4.2,3.1,2.6,2.4,3.4,1.0,0.9,0.7,2.8,0.4,1.0,2.7,3.1,0.2,0.4,1.0,3.0,2.0,3.0,2.5,0.5,0.6,0.9,1.1,0.6,0.5,2.0,1.2,2.3,0.5,2.3,0.9,1.9,0.9,0.7,0.5,1.3,0.4,0.5,2.2,1.0,2.4,1.1,0.6,0.4,1.0,0.7,3.7,0.5,3,14,9,10,12,20,11,15,9,7,2.0,26,22,7,17,54,77,8,22,44,8,28,16,29,29,41,33,23,18,38,11,6,8,25,2,9,29,34,1,2,5,25,15,22,17,5,2,8,7,6,4,15,9,15,4,19,6,14,6,7,4,9,3,3,16,12,22,9,4,4,9,7,26,4,0.026,0.022,0.018,0.018,0.032,0.035,0.023,0.029,0.039,0.031,0.026,0.034,0.035,0.033,0.031,0.036,0.029,0.03,0.03,0.043,0.018,0.033,0.029,0.036,0.03,0.036,0.026,0.032,0.024,0.031,0.039,0.035,0.027,0.032,0.014,0.025,0.035,0.032,0.019,0.018,0.018,0.031,0.04,0.029,0.024,0.016,0.022,0.016,0.014,0.017,0.031,0.021,0.022,0.02,0.016,0.02,0.021,0.024,0.022,0.018,0.026,0.028,0.019,0.023,0.024,0.022,0.021,0.02,0.012,0.013,0.021,0.013,0.017,0.024,1649,4037,2120,1983,1612,3247,2256,3654,1559,1422,473.0,2771,4619,1331,3224,11188,17819,2267,3622,4252,2535,4453,1891,6243,5686,7301,5489,3073,5503,5556,2037,1053,1408,6331,1502,2337,5361,6407,308,701,1688,3071,4882,2483,2756,1140,893,2082,2677,1400,493,3774,1984,4470,869,3441,1459,2808,1373,1157,599,1820,513,962,2944,1132,3555,1925,1511,776,1606,1962,8596,548,0.09,0.106,0.101,0.098,0.129,0.141,0.097,0.127,0.114,0.13,0.102,0.131,0.146,0.137,0.13,0.137,0.127,0.118,0.133,0.146,0.088,0.146,0.143,0.136,0.131,0.151,0.132,0.125,0.107,0.132,0.135,0.095,0.127,0.13,0.073,0.103,0.133,0.132,0.096,0.104,0.098,0.135,0.136,0.127,0.113,0.096,0.105,0.094,0.086,0.108,0.127,0.104,0.109,0.1,0.106,0.114,0.111,0.114,0.106,0.117,0.127,0.124,0.107,0.111,0.113,0.13,0.113,0.104,0.089,0.104,0.118,0.094,0.095,0.126,439,1296,791,758,612,1162,759,1082,420,381,139.0,1361,1090,330,835,2690,4823,486,1139,2390,717,1464,825,1513,1540,2088,1868,1252,1463,1817,495,413,463,1417,480,773,1292,1504,170,312,773,1431,897,1686,1723,552,367,968,1211,496,318,1542,910,1801,463,1718,616,1351,702,588,327,681,333,386,1365,643,1811,903,681,387,760,856,3311,293,3.446,2.847,2.575,2.583,2.297,2.46,2.424,3.162,2.597,3.07,3.272,1.822,3.097,2.842,2.794,2.926,3.043,3.653,2.653,1.693,2.946,2.428,2.06,2.94,2.735,2.893,2.409,2.025,2.879,2.517,2.852,2.359,2.457,3.164,2.738,2.665,3.021,2.969,2.179,2.654,2.835,1.915,3.643,1.731,1.876,2.172,3.216,2.709,2.713,3.257,2.037,2.502,2.267,2.546,2.355,2.244,2.58,2.549,2.287,2.172,2.408,2.632,1.9,3.045,2.366,2.207,2.154,2.444,2.652,2.3,2.329,2.554,2.791,2.251,0.764,0.777,0.591,0.557,0.594,0.604,0.558,0.614,0.578,0.478,0.836,0.494,0.507,0.726,0.501,0.645,0.565,0.612,0.584,0.487,0.609,0.524,0.524,0.726,0.549,0.523,0.472,0.443,0.551,0.585,0.888,0.896,0.383,0.539,0.606,0.52,0.692,0.595,0.301,0.461,0.605,0.474,0.835,0.653,0.575,0.537,0.456,0.699,0.464,0.664,0.386,0.505,0.593,0.577,0.287,0.387,0.436,0.635,0.41,0.476,0.269,0.713,0.359,0.944,0.648,0.834,0.465,0.389,0.473,0.696,0.509,0.665,0.578,0.624
-237,Epileptic,F,37.0,0.9,2.8,1.7,1.9,1.8,1.6,2.0,1.4,1.4,0.6,0.4,2.0,1.1,0.4,1.2,5.5,7.6,0.7,2.2,3.9,2.1,4.2,2.7,3.5,4.0,2.6,3.9,3.2,1.8,2.8,1.8,1.4,0.5,2.1,0.8,0.6,4.0,2.8,0.2,0.1,1.1,3.1,3.7,1.7,1.8,0.6,0.4,0.9,1.5,0.8,0.5,1.9,2.0,3.1,0.5,2.1,0.6,1.5,1.3,1.5,0.7,2.3,0.1,0.8,1.5,2.1,2.6,1.1,0.7,0.6,0.9,0.9,5.4,0.4,7,24,12,14,16,18,18,13,12,5,4.0,15,15,4,14,53,67,6,23,33,16,41,20,36,40,29,38,41,20,30,22,15,5,24,4,5,38,32,2,1,7,24,25,13,12,5,2,5,8,5,4,13,17,24,2,16,4,12,11,10,5,14,1,7,9,17,18,6,4,7,7,7,40,4,0.037,0.024,0.023,0.028,0.035,0.032,0.029,0.026,0.036,0.033,0.037,0.034,0.024,0.034,0.026,0.027,0.029,0.033,0.035,0.036,0.031,0.038,0.037,0.036,0.036,0.025,0.029,0.028,0.019,0.031,0.038,0.049,0.021,0.025,0.021,0.016,0.034,0.026,0.016,0.027,0.025,0.034,0.055,0.026,0.015,0.012,0.02,0.014,0.019,0.014,0.021,0.02,0.029,0.02,0.034,0.015,0.015,0.019,0.024,0.017,0.019,0.033,0.013,0.023,0.018,0.03,0.019,0.016,0.015,0.022,0.022,0.017,0.019,0.021,1521,4557,2940,2741,2240,2410,2546,2747,2250,1469,461.0,2068,2991,753,2647,10762,16401,1675,4269,4163,4571,4644,1917,5037,5918,5813,6731,4269,5496,5339,2644,1098,972,5792,2065,1584,7064,6931,422,303,1144,2125,4588,1849,3303,1564,549,2249,2188,1945,597,2732,2724,5808,460,3766,1373,2815,1280,2055,1043,2578,390,863,2248,1786,3813,2230,1525,917,1406,2014,8982,385,0.127,0.116,0.11,0.125,0.146,0.129,0.112,0.13,0.147,0.125,0.126,0.123,0.112,0.131,0.123,0.121,0.121,0.132,0.136,0.146,0.122,0.118,0.129,0.136,0.116,0.121,0.114,0.101,0.091,0.134,0.157,0.149,0.097,0.126,0.1,0.088,0.145,0.134,0.109,0.112,0.113,0.144,0.164,0.116,0.086,0.087,0.104,0.079,0.099,0.091,0.125,0.103,0.125,0.106,0.141,0.092,0.089,0.105,0.12,0.099,0.106,0.133,0.087,0.116,0.097,0.13,0.101,0.09,0.083,0.131,0.116,0.1,0.102,0.131,431,1827,1124,1150,894,879,984,920,703,358,183.0,988,780,213,733,3181,4579,469,1239,1861,1134,1535,1081,1722,1804,1799,2180,1573,1376,1668,855,476,347,1364,577,575,1917,1761,215,140,665,1276,1139,1173,1901,800,291,1032,1111,804,361,1449,1260,2513,210,2154,659,1320,826,1245,542,1099,174,508,1247,982,2073,1033,744,461,728,892,4144,243,3.116,2.369,2.403,2.381,2.149,2.324,2.17,2.52,2.524,3.122,2.194,1.789,2.827,2.51,2.637,2.577,2.804,3.035,2.691,1.95,2.975,2.334,1.704,2.332,2.555,2.601,2.366,2.171,2.961,2.491,2.523,2.305,2.242,3.008,3.127,2.43,2.835,2.904,2.284,2.436,2.187,1.573,2.855,1.799,1.904,2.008,2.289,2.621,2.331,2.736,1.889,2.022,2.027,2.214,2.566,1.89,2.237,2.417,1.748,1.795,2.213,2.38,2.289,1.925,2.028,2.167,2.016,2.383,2.393,2.211,1.998,2.474,2.349,1.963,0.698,0.501,0.582,0.566,0.71,0.524,0.62,0.499,0.676,0.559,0.532,0.543,0.444,0.562,0.574,0.666,0.613,0.83,0.652,0.502,0.71,0.553,0.481,0.61,0.692,0.597,0.621,0.579,0.634,0.568,0.691,0.7,0.402,0.574,0.575,0.661,0.713,0.537,0.543,0.456,0.508,0.523,0.877,0.466,0.592,0.504,0.502,0.733,0.438,0.515,0.375,0.457,0.559,0.578,0.603,0.474,0.432,0.542,0.385,0.436,0.429,0.516,0.72,0.496,0.498,0.581,0.442,0.492,0.46,0.574,0.553,0.617,0.527,0.444
-238,Epileptic,M,42.0,1.7,2.4,1.5,1.4,2.4,1.6,1.7,1.9,1.8,0.7,0.5,2.8,1.3,0.6,1.2,5.8,9.2,1.6,3.6,4.3,2.7,4.6,2.5,4.6,5.3,2.9,4.7,2.7,3.4,3.5,1.2,1.6,0.7,2.1,0.8,0.7,4.5,2.7,0.2,0.2,1.0,2.9,3.1,2.7,2.4,0.8,0.5,1.3,1.7,0.9,0.6,2.3,2.2,2.6,0.4,2.4,0.7,2.3,1.3,2.0,0.9,1.6,0.8,0.8,1.5,1.2,2.1,1.5,1.3,0.7,0.9,1.9,5.5,0.4,15,21,13,7,19,17,11,17,13,7,4.0,22,15,8,13,63,78,10,30,40,19,48,21,36,49,32,44,23,31,38,15,10,5,20,5,7,46,29,1,1,7,25,25,19,15,5,2,6,9,6,6,19,15,18,4,16,5,19,11,19,6,11,5,6,10,15,16,10,9,5,6,12,37,3,0.063,0.028,0.031,0.027,0.048,0.031,0.027,0.031,0.057,0.035,0.039,0.033,0.03,0.035,0.03,0.039,0.036,0.055,0.04,0.033,0.045,0.042,0.036,0.049,0.042,0.034,0.039,0.027,0.029,0.03,0.043,0.059,0.042,0.029,0.033,0.02,0.046,0.032,0.022,0.014,0.023,0.03,0.046,0.025,0.018,0.014,0.021,0.021,0.024,0.019,0.023,0.03,0.034,0.024,0.038,0.017,0.015,0.028,0.024,0.029,0.032,0.026,0.046,0.024,0.02,0.03,0.018,0.021,0.024,0.022,0.021,0.023,0.02,0.021,1373,5344,2388,2092,2880,2607,2253,3104,2065,1408,510.0,2675,2680,1194,2284,9909,16446,2224,4594,4630,4629,4656,2268,6141,5775,4926,4754,3346,6354,5503,2082,1057,935,5344,2148,1631,6181,5872,337,405,1343,2567,6126,2628,3615,1488,824,2199,2281,1698,551,2965,2270,3957,396,3530,1184,3486,1334,1893,815,2447,635,880,2259,1350,3347,2518,1707,1162,1273,2224,8898,452,0.166,0.122,0.124,0.113,0.166,0.135,0.099,0.137,0.172,0.143,0.132,0.13,0.134,0.147,0.143,0.143,0.135,0.168,0.141,0.142,0.144,0.118,0.129,0.147,0.126,0.13,0.117,0.103,0.1,0.129,0.141,0.146,0.133,0.126,0.113,0.099,0.148,0.131,0.123,0.103,0.109,0.126,0.167,0.112,0.091,0.089,0.1,0.095,0.106,0.101,0.134,0.129,0.135,0.113,0.158,0.098,0.091,0.126,0.12,0.12,0.128,0.123,0.167,0.122,0.103,0.118,0.093,0.106,0.12,0.123,0.112,0.119,0.107,0.125,475,1561,916,768,950,885,848,1023,549,353,189.0,1261,821,329,704,2943,4547,520,1416,2052,1052,1734,1023,1643,2025,1573,1945,1443,1753,1996,648,498,287,1192,474,606,1844,1473,171,209,697,1430,1278,1722,2181,783,340,915,1087,702,363,1281,1035,1720,201,1971,666,1411,863,1092,410,946,282,474,1180,737,1855,1157,855,491,688,1118,3967,228,2.779,3.032,2.596,2.576,2.567,2.568,2.267,2.711,2.714,2.981,2.42,1.871,2.604,2.778,2.376,2.594,2.933,3.374,2.628,1.927,3.237,2.299,2.025,2.795,2.369,2.614,2.081,1.987,2.748,2.289,2.489,2.349,2.667,3.14,3.489,2.433,2.692,3.009,2.27,2.097,2.554,1.627,3.185,1.825,1.901,2.102,2.971,2.919,2.637,2.66,1.918,2.312,2.363,2.389,2.2,2.028,1.999,2.346,1.741,2.001,2.22,2.638,2.061,2.243,2.086,2.074,1.977,2.46,2.455,2.718,2.144,2.444,2.433,2.318,0.76,0.573,0.579,0.552,0.823,0.711,0.655,0.512,0.578,0.607,0.7,0.503,0.5,0.589,0.564,0.621,0.553,0.769,0.711,0.604,0.896,0.56,0.638,0.731,0.618,0.552,0.506,0.561,0.434,0.607,0.715,0.927,0.465,0.673,0.717,0.499,0.825,0.67,0.522,0.377,0.452,0.533,0.945,0.667,0.562,0.453,0.671,0.597,0.44,0.563,0.377,0.511,0.598,0.499,0.557,0.437,0.368,0.638,0.429,0.581,0.502,0.661,0.594,0.523,0.426,0.668,0.411,0.371,0.47,0.761,0.488,0.675,0.525,0.389
-240,Epileptic,M,27.0,0.8,2.3,0.9,1.7,1.3,2.1,2.0,1.5,1.3,0.8,0.4,3.5,2.4,0.5,1.6,6.5,10.3,0.8,3.1,4.1,1.2,2.5,1.3,3.0,2.8,3.8,3.8,3.3,2.4,4.0,1.1,1.5,0.4,1.9,0.3,1.3,2.8,3.1,0.2,0.4,1.1,2.8,1.7,2.9,2.4,1.0,0.6,1.0,1.3,0.6,0.7,2.2,1.3,2.0,0.3,2.5,1.2,2.8,1.1,0.9,0.5,1.7,0.4,0.8,1.7,1.8,3.5,1.2,1.4,0.5,0.7,1.1,4.1,0.3,8,19,8,19,15,20,19,14,17,7,6.0,31,30,7,19,73,103,6,33,37,18,25,19,34,36,36,44,39,27,46,21,10,6,26,3,12,30,37,2,2,6,28,21,25,14,9,3,6,7,6,4,14,12,12,2,22,9,18,11,7,3,9,3,6,15,16,28,8,10,4,5,9,32,2,0.037,0.025,0.015,0.027,0.034,0.033,0.026,0.03,0.042,0.034,0.032,0.037,0.036,0.031,0.038,0.04,0.031,0.033,0.035,0.037,0.026,0.032,0.024,0.04,0.03,0.034,0.029,0.029,0.024,0.036,0.044,0.082,0.024,0.025,0.02,0.028,0.032,0.033,0.019,0.023,0.02,0.031,0.034,0.028,0.016,0.017,0.024,0.014,0.018,0.017,0.028,0.023,0.03,0.019,0.016,0.017,0.026,0.029,0.025,0.019,0.019,0.032,0.029,0.039,0.019,0.039,0.023,0.018,0.024,0.021,0.018,0.019,0.018,0.014,1537,4869,2459,3202,1977,3317,3757,3260,2197,1489,729.0,3368,4620,1002,3427,11487,20991,2535,5331,3884,3275,3454,2231,6476,6397,7000,7294,5523,5715,6889,3402,734,1082,6595,1618,2185,6729,8005,454,477,1671,2719,6492,2635,3598,1992,964,2527,2643,1695,680,3675,2095,3889,666,4431,1832,2794,1233,1342,875,2175,484,888,3211,1239,5316,2462,1785,1034,1425,2392,9305,692,0.133,0.113,0.097,0.127,0.149,0.143,0.119,0.133,0.161,0.136,0.124,0.151,0.156,0.151,0.152,0.154,0.138,0.117,0.139,0.144,0.105,0.138,0.129,0.152,0.139,0.137,0.132,0.122,0.11,0.149,0.141,0.164,0.127,0.117,0.077,0.125,0.132,0.136,0.124,0.118,0.11,0.138,0.139,0.125,0.092,0.101,0.114,0.087,0.094,0.099,0.127,0.109,0.132,0.095,0.099,0.111,0.12,0.125,0.143,0.114,0.115,0.132,0.143,0.136,0.113,0.149,0.119,0.101,0.118,0.108,0.1,0.106,0.099,0.089,432,1455,905,1114,635,1114,1259,903,547,367,205.0,1518,1255,249,860,2965,5553,440,1571,1837,849,1206,958,1451,1703,1832,2342,1951,1650,1949,590,374,326,1240,364,725,1498,1747,217,232,720,1453,1020,1713,2142,941,399,1014,1108,658,378,1544,795,1705,302,2044,728,1479,708,700,391,758,210,371,1383,670,2377,1052,888,414,668,927,3589,318,3.553,2.897,2.64,2.847,2.507,2.547,2.538,3.097,2.73,3.165,3.018,1.904,2.793,2.762,2.848,2.791,2.868,3.853,2.85,1.814,2.932,2.306,2.005,3.174,2.802,2.981,2.519,2.294,2.641,2.738,3.709,2.181,2.655,3.306,3.486,2.78,3.037,3.183,2.193,2.65,2.953,1.761,3.604,1.766,1.911,2.366,2.929,3.03,2.831,2.721,2.208,2.411,2.402,2.51,2.596,2.3,2.603,2.107,1.978,2.148,2.613,2.813,2.506,2.768,2.501,2.389,2.335,2.647,2.307,2.898,2.528,2.658,2.788,2.711,0.945,0.82,0.585,0.567,0.745,0.567,0.556,0.527,0.75,0.49,0.66,0.506,0.514,0.552,0.648,0.669,0.651,0.859,0.634,0.595,0.704,0.463,0.575,0.712,0.579,0.645,0.505,0.537,0.503,0.553,0.786,0.894,0.421,0.702,0.763,0.534,0.825,0.651,0.489,0.327,0.659,0.626,0.93,0.612,0.536,0.494,0.683,0.894,0.543,0.694,0.374,0.629,0.597,0.461,0.366,0.428,0.481,0.582,0.349,0.467,0.355,0.813,0.595,0.917,0.622,0.651,0.523,0.453,0.402,0.878,0.54,0.769,0.601,0.539
-241,Epileptic,F,27.0,1.3,1.8,1.0,1.4,1.2,3.0,1.4,1.4,1.1,1.0,0.4,3.0,1.4,0.8,1.1,6.5,8.9,0.7,3.3,6.2,1.6,2.9,1.4,2.9,3.8,2.7,3.1,2.0,2.3,2.4,1.4,1.9,0.6,2.6,0.3,0.5,4.1,2.9,0.3,0.4,0.7,3.4,2.4,3.2,3.3,0.7,0.6,0.9,1.2,1.6,0.9,1.8,1.8,1.9,0.2,2.9,1.3,2.1,1.0,1.1,1.0,1.6,0.4,0.4,1.9,1.6,2.9,1.3,0.9,0.4,0.9,1.9,4.8,0.5,8,19,9,12,17,21,14,13,13,9,3.0,22,16,9,10,64,76,5,26,46,8,30,12,35,36,25,36,19,18,30,16,10,3,28,2,6,44,30,2,2,4,26,21,21,18,4,3,8,6,9,6,12,14,12,1,25,6,13,7,8,7,10,2,3,13,16,22,9,6,4,5,13,32,4,0.047,0.018,0.017,0.027,0.027,0.043,0.022,0.029,0.034,0.035,0.033,0.033,0.032,0.049,0.03,0.031,0.033,0.026,0.039,0.039,0.027,0.031,0.027,0.032,0.029,0.028,0.024,0.025,0.023,0.028,0.038,0.051,0.026,0.027,0.011,0.021,0.032,0.026,0.016,0.027,0.015,0.031,0.035,0.033,0.025,0.014,0.021,0.015,0.016,0.051,0.049,0.019,0.023,0.017,0.013,0.018,0.021,0.021,0.031,0.021,0.023,0.025,0.023,0.017,0.019,0.028,0.022,0.018,0.02,0.017,0.024,0.018,0.018,0.03,1732,5282,2858,2653,2148,2682,2967,3160,1704,1652,702.0,2817,3197,1480,2686,12715,17865,2314,4860,4875,3219,4656,2001,6705,6227,5809,5951,3876,5297,5425,2165,1036,996,6815,2024,1281,8448,7285,484,695,1700,2897,5211,2946,3297,1381,1109,2771,2645,1101,525,3772,2690,4590,385,5568,2471,3624,1103,1517,1831,2575,586,772,3412,1891,4236,2965,1554,1035,1314,3438,11001,623,0.109,0.097,0.092,0.109,0.117,0.143,0.101,0.12,0.126,0.139,0.131,0.121,0.13,0.154,0.114,0.124,0.131,0.096,0.129,0.128,0.096,0.129,0.119,0.126,0.124,0.118,0.115,0.108,0.098,0.123,0.114,0.137,0.091,0.122,0.071,0.103,0.125,0.107,0.106,0.12,0.091,0.121,0.12,0.132,0.113,0.092,0.103,0.08,0.086,0.161,0.137,0.098,0.111,0.093,0.099,0.102,0.099,0.108,0.129,0.111,0.112,0.109,0.107,0.092,0.101,0.12,0.11,0.096,0.109,0.098,0.115,0.103,0.094,0.128,438,1603,936,932,767,1180,955,839,517,432,221.0,1268,808,305,663,3426,4373,456,1366,2337,811,1393,793,1460,1866,1552,2132,1247,1440,1547,518,522,329,1564,480,476,2052,1751,217,262,703,1396,1113,1697,2040,690,431,1021,1116,494,335,1521,1116,1802,169,2593,960,1666,603,781,759,903,283,374,1569,872,1923,1178,677,485,605,1563,4121,292,3.637,3.028,2.761,2.93,2.54,2.223,2.593,3.102,2.567,3.127,3.018,1.935,2.95,3.343,2.944,2.795,3.161,3.993,2.806,1.897,3.272,2.613,2.243,3.187,2.737,2.929,2.265,2.429,2.76,2.707,2.774,2.091,2.628,3.155,3.702,2.429,3.043,3.127,2.63,2.969,2.878,1.856,3.484,2.066,1.875,2.352,3.143,3.278,2.781,2.966,1.744,2.578,2.331,2.688,2.357,2.327,3.121,2.58,2.159,2.166,2.557,2.771,2.481,2.298,2.445,2.428,2.46,2.944,2.565,2.43,2.622,2.643,2.819,2.317,0.843,0.672,0.717,0.451,0.637,0.608,0.567,0.769,0.651,0.582,0.74,0.48,0.408,0.513,0.554,0.633,0.627,0.769,0.847,0.499,0.79,0.508,0.484,0.809,0.502,0.598,0.464,0.504,0.596,0.526,0.664,0.863,0.516,0.688,0.814,0.446,0.792,0.544,0.307,0.471,0.887,0.479,0.811,0.72,0.528,0.592,0.49,0.762,0.72,0.634,0.402,0.498,0.685,0.669,0.333,0.443,0.571,0.596,0.423,0.38,0.514,0.842,0.431,0.596,0.571,0.872,0.543,0.546,0.502,0.57,0.363,0.481,0.644,0.325
-242,Epileptic,M,52.0,0.9,3.1,1.0,0.9,2.2,1.6,2.1,1.1,1.2,0.6,0.5,2.5,1.8,0.5,0.7,5.1,7.8,1.2,2.1,3.9,1.5,2.5,2.7,4.7,3.2,2.3,4.0,3.5,2.1,3.6,2.7,0.8,0.7,2.7,0.4,0.4,5.4,3.3,0.1,0.3,0.8,2.2,2.7,2.7,2.4,0.7,0.8,1.1,1.0,1.8,0.7,1.6,1.2,2.5,0.4,2.0,1.1,1.5,0.8,1.3,0.9,1.8,0.4,0.7,1.4,1.8,2.8,0.9,1.2,1.2,1.3,1.5,4.1,0.1,7,32,7,7,17,15,21,12,11,7,4.0,25,20,6,10,54,79,9,21,38,12,27,22,40,44,25,47,33,19,30,19,11,7,30,3,5,50,36,1,2,4,22,30,24,17,5,3,6,5,13,5,11,8,20,3,16,8,14,5,9,8,10,3,6,10,18,22,7,7,11,8,10,36,1,0.049,0.032,0.02,0.019,0.04,0.035,0.034,0.027,0.044,0.035,0.049,0.037,0.037,0.03,0.022,0.032,0.033,0.036,0.034,0.038,0.026,0.035,0.038,0.048,0.032,0.025,0.04,0.038,0.028,0.039,0.065,0.043,0.034,0.035,0.021,0.019,0.051,0.032,0.014,0.02,0.018,0.036,0.045,0.03,0.018,0.016,0.026,0.014,0.015,0.027,0.033,0.018,0.021,0.023,0.03,0.016,0.027,0.023,0.024,0.024,0.031,0.03,0.03,0.025,0.018,0.032,0.02,0.015,0.019,0.026,0.024,0.023,0.016,0.01,1353,5093,1928,1960,2421,2395,2329,2345,1790,1436,670.0,2254,3130,1104,2149,8500,14875,2318,3968,3697,3659,3711,1821,5927,5797,5326,4807,3236,4225,5181,2199,976,887,5531,1619,1369,7800,6797,320,618,1207,2217,6936,2910,2884,1148,993,2508,1987,1921,425,3046,1755,4180,507,3253,1252,2429,750,1537,1055,2431,446,928,2259,1103,4041,2605,1719,1628,1561,1910,9434,523,0.131,0.134,0.107,0.099,0.143,0.151,0.112,0.109,0.164,0.14,0.138,0.142,0.146,0.143,0.111,0.144,0.132,0.131,0.136,0.155,0.113,0.135,0.147,0.173,0.129,0.13,0.137,0.121,0.099,0.138,0.172,0.112,0.144,0.139,0.101,0.09,0.163,0.133,0.094,0.101,0.095,0.144,0.165,0.128,0.098,0.096,0.128,0.072,0.084,0.113,0.136,0.095,0.11,0.112,0.126,0.097,0.121,0.111,0.121,0.114,0.139,0.125,0.132,0.116,0.102,0.131,0.106,0.08,0.097,0.126,0.118,0.123,0.091,0.066,429,1788,714,739,942,812,969,810,562,373,219.0,1196,951,289,617,2776,4547,520,1105,1773,946,1273,1026,1750,1856,1586,1902,1463,1301,1716,799,478,289,1337,401,474,2028,1866,203,276,654,1073,1430,1695,1965,605,397,1131,982,912,329,1374,817,1897,278,1852,675,1169,468,866,456,951,211,443,1179,725,2212,1050,873,699,789,958,4140,214,3.045,2.722,2.579,2.461,2.228,2.315,2.102,2.468,2.397,3.006,2.622,1.674,2.581,2.694,2.584,2.362,2.543,3.437,2.848,1.849,3.015,2.353,1.646,2.607,2.446,2.679,2.06,1.859,2.5,2.459,2.212,2.149,2.595,2.96,3.483,2.404,2.834,2.836,1.798,2.211,2.212,1.802,3.4,1.864,1.639,1.93,3.124,2.584,2.476,2.071,1.695,2.197,2.247,2.163,2.008,1.912,2.185,2.154,1.884,1.904,2.038,2.544,2.403,2.326,2.053,1.974,1.897,2.75,2.058,2.465,2.154,2.294,2.365,2.406,0.711,0.587,0.614,0.511,0.661,0.68,0.617,0.605,0.66,0.552,0.796,0.391,0.537,0.502,0.532,0.592,0.65,0.744,0.682,0.536,0.83,0.552,0.438,0.765,0.58,0.552,0.572,0.527,0.549,0.58,0.711,0.75,0.577,0.659,0.612,0.542,0.866,0.577,0.319,0.47,0.504,0.587,0.701,0.587,0.53,0.568,0.564,0.847,0.472,0.66,0.369,0.551,0.549,0.56,0.445,0.457,0.397,0.588,0.422,0.453,0.545,0.64,0.533,0.772,0.549,0.742,0.523,0.783,0.551,0.773,0.486,0.493,0.468,0.398
-243,Epileptic,F,57.0,1.0,2.5,1.0,1.1,2.0,1.9,1.2,2.2,1.5,0.9,0.3,3.6,2.0,0.8,2.0,7.1,8.8,1.2,3.2,7.2,1.4,2.5,1.7,3.5,2.6,3.9,3.2,2.6,2.3,4.4,1.2,1.0,0.5,2.4,0.3,0.7,3.8,3.7,0.3,0.2,1.5,3.4,2.5,2.7,2.9,0.6,0.4,1.2,1.2,1.6,0.9,2.2,1.8,2.2,0.5,2.5,0.7,2.7,1.5,1.2,0.9,1.5,0.4,0.4,2.0,1.7,2.4,1.1,1.4,0.4,1.1,2.0,4.9,0.4,6,24,9,11,14,16,11,16,13,8,3.0,32,18,8,17,62,69,9,27,53,281,30,15,35,30,39,41,26,16,42,14,6,5,25,1,7,38,41,2,2,7,35,15,21,16,4,2,7,7,11,7,17,12,17,2,21,6,18,9,9,6,10,3,3,16,14,18,7,8,5,8,13,35,2,0.038,0.027,0.015,0.025,0.037,0.035,0.02,0.035,0.042,0.037,0.03,0.033,0.034,0.027,0.03,0.034,0.033,0.043,0.034,0.043,0.067,0.029,0.03,0.04,0.027,0.033,0.025,0.026,0.022,0.037,0.031,0.032,0.024,0.026,0.01,0.02,0.038,0.031,0.015,0.019,0.026,0.035,0.041,0.029,0.023,0.013,0.021,0.016,0.015,0.035,0.039,0.023,0.027,0.022,0.029,0.021,0.016,0.025,0.028,0.021,0.024,0.028,0.028,0.018,0.023,0.034,0.017,0.021,0.022,0.019,0.026,0.027,0.022,0.022,1315,4517,2310,2018,2233,2449,2290,2831,1813,1351,504.0,3140,3198,1347,2969,9738,14881,2029,4460,5020,2061,4180,2338,4850,4516,6843,5545,3461,4512,5405,1899,816,981,5058,1569,1829,7101,6963,464,294,1753,2794,3203,2200,3186,1475,694,2088,2611,1374,513,3206,2301,3772,368,3469,1663,2935,1279,1474,1094,2228,516,697,2805,1258,3987,1884,1780,823,1291,2329,8713,480,0.123,0.121,0.101,0.109,0.137,0.145,0.092,0.133,0.15,0.139,0.117,0.133,0.133,0.126,0.131,0.132,0.123,0.135,0.122,0.131,0.103,0.131,0.13,0.141,0.126,0.132,0.123,0.113,0.087,0.142,0.122,0.105,0.128,0.11,0.054,0.096,0.12,0.122,0.088,0.118,0.119,0.139,0.139,0.124,0.112,0.088,0.113,0.091,0.091,0.133,0.148,0.11,0.125,0.11,0.136,0.111,0.093,0.117,0.132,0.116,0.12,0.124,0.148,0.111,0.115,0.127,0.103,0.104,0.114,0.127,0.125,0.131,0.104,0.119,411,1523,977,755,907,944,862,1031,571,398,166.0,1594,1029,400,1038,3378,4298,441,1472,2374,785,1394,972,1463,1428,2078,1997,1459,1284,1935,598,428,363,1321,517,665,1839,1839,278,177,806,1468,941,1556,2023,736,320,988,1240,768,389,1586,1138,1733,231,1944,734,1697,796,886,589,823,247,366,1454,757,2122,931,903,389,697,1241,3769,237,3.108,2.791,2.394,2.62,2.302,2.32,2.244,2.518,2.504,2.879,2.415,1.756,2.552,2.431,2.316,2.395,2.777,3.454,2.569,1.877,2.234,2.307,2.028,2.556,2.408,2.746,2.208,2.014,2.702,2.279,2.521,2.06,2.386,2.742,2.725,2.513,2.848,2.836,1.969,2.12,2.59,1.707,2.87,1.616,1.784,2.164,2.63,2.665,2.481,2.247,1.559,2.073,2.259,2.209,1.975,1.96,2.52,2.066,1.869,1.82,2.223,2.625,2.228,2.281,2.154,2.174,2.01,2.436,2.254,2.416,2.156,2.32,2.548,2.573,0.677,0.667,0.453,0.507,0.526,0.567,0.53,0.61,0.52,0.378,0.954,0.433,0.511,0.452,0.488,0.514,0.496,0.734,0.686,0.596,0.947,0.512,0.499,0.572,0.425,0.658,0.447,0.441,0.534,0.503,0.56,0.9,0.354,0.493,0.67,0.473,0.758,0.592,0.244,0.428,0.597,0.5,0.735,0.578,0.505,0.628,0.468,0.535,0.618,0.574,0.318,0.495,0.502,0.441,0.387,0.42,0.344,0.519,0.471,0.382,0.484,0.812,0.546,0.762,0.635,0.765,0.453,0.382,0.502,0.724,0.47,0.524,0.572,0.403
-244,Epileptic,M,37.0,0.5,1.8,0.9,0.8,1.1,1.7,1.2,1.3,0.5,0.8,0.4,2.3,1.4,0.4,1.3,4.2,6.3,0.9,1.8,3.4,1.0,2.4,1.4,2.5,2.8,3.3,2.2,1.9,2.3,3.5,0.9,0.8,0.6,2.6,0.6,0.5,3.5,2.5,0.1,0.2,1.1,2.7,1.9,1.5,2.4,0.9,0.4,0.8,1.0,1.0,0.4,1.1,0.8,1.6,0.3,2.1,0.5,2.1,1.1,0.8,0.7,0.9,0.3,0.3,1.5,1.5,2.3,1.2,0.5,0.3,0.9,1.3,5.1,0.4,3,21,13,8,10,19,11,14,9,8,5.0,21,13,6,13,42,61,7,24,29,10,30,12,30,30,39,27,21,22,34,13,4,5,28,2,6,38,25,1,1,7,26,14,13,16,7,2,5,5,9,2,8,8,11,1,17,4,18,8,7,8,5,2,3,11,19,20,11,3,4,6,9,43,4,0.026,0.02,0.014,0.015,0.028,0.026,0.024,0.022,0.039,0.029,0.042,0.031,0.029,0.028,0.035,0.028,0.025,0.031,0.024,0.033,0.022,0.031,0.029,0.034,0.032,0.031,0.026,0.023,0.023,0.031,0.029,0.026,0.019,0.028,0.017,0.015,0.034,0.029,0.014,0.017,0.019,0.032,0.036,0.021,0.018,0.021,0.021,0.012,0.015,0.022,0.027,0.015,0.016,0.015,0.015,0.017,0.011,0.023,0.024,0.017,0.022,0.02,0.026,0.013,0.021,0.034,0.017,0.019,0.012,0.011,0.023,0.017,0.019,0.016,1255,4145,2349,2057,1825,3222,2326,3122,1093,1604,767.0,2955,3136,1013,2740,9488,15818,2402,4332,3842,3213,4621,2129,5469,5852,6663,4944,3972,6105,5731,2292,1132,1309,7250,1757,2155,7407,6231,425,475,1900,3650,4466,2031,3820,1223,727,2799,2405,1891,525,2976,2132,4443,335,3900,1438,3197,1561,1543,1561,1910,469,764,2540,1371,4457,2543,1156,843,1352,2395,10964,677,0.098,0.108,0.106,0.095,0.121,0.135,0.11,0.114,0.136,0.144,0.152,0.138,0.142,0.142,0.138,0.132,0.118,0.12,0.117,0.13,0.097,0.139,0.126,0.14,0.13,0.137,0.116,0.112,0.109,0.137,0.127,0.101,0.102,0.12,0.085,0.072,0.128,0.117,0.092,0.087,0.099,0.135,0.129,0.111,0.096,0.11,0.108,0.073,0.087,0.115,0.121,0.09,0.098,0.086,0.1,0.103,0.085,0.12,0.119,0.099,0.11,0.105,0.107,0.103,0.105,0.122,0.1,0.104,0.086,0.102,0.115,0.106,0.099,0.099,351,1350,952,800,676,1153,827,972,337,414,204.0,1208,794,276,678,2422,4152,443,1339,1632,764,1337,796,1327,1569,1877,1576,1317,1626,1847,560,467,462,1435,476,717,1759,1517,178,207,863,1339,982,1157,2143,620,296,1063,1016,776,259,1221,850,1732,189,1821,670,1433,740,752,579,665,222,338,1166,758,2066,1074,637,414,632,1108,4228,403,3.586,2.945,2.496,2.5,2.478,2.403,2.353,2.731,2.463,3.172,3.076,2.136,3.016,2.808,3.007,2.954,3.024,3.827,2.68,2.016,3.105,2.667,2.259,3.068,2.842,2.783,2.44,2.324,2.821,2.467,2.87,2.573,2.311,3.427,3.588,2.602,3.076,3.005,2.612,2.611,2.76,2.18,3.71,1.909,2.046,2.126,3.198,3.055,2.823,2.682,2.56,2.724,2.455,2.728,2.091,2.321,2.554,2.378,2.368,2.264,2.506,3.032,2.403,2.542,2.276,2.246,2.213,2.607,2.218,2.328,2.498,2.669,2.675,2.098,0.624,0.479,0.423,0.555,0.56,0.592,0.543,0.497,0.66,0.507,0.643,0.584,0.442,0.465,0.537,0.606,0.536,0.749,0.421,0.615,0.779,0.526,0.558,0.613,0.634,0.579,0.507,0.548,0.488,0.603,0.651,1.048,0.435,0.688,0.685,0.49,0.631,0.645,0.294,0.696,0.506,0.66,0.676,0.816,0.662,0.565,0.45,0.647,0.477,0.421,0.478,0.579,0.512,0.511,0.39,0.455,0.445,0.515,0.433,0.463,0.563,0.62,0.337,1.141,0.567,0.57,0.487,0.534,0.419,0.587,0.482,0.522,0.553,0.38
-245,Epileptic,F,42.0,0.5,2.8,1.2,1.1,1.9,1.7,2.3,1.4,1.4,0.8,0.2,2.8,1.2,0.5,0.8,6.3,8.4,0.5,1.7,4.3,0.7,2.6,1.5,3.3,2.7,2.2,2.3,3.0,2.8,2.8,1.3,0.6,0.4,1.9,0.3,0.7,2.2,2.3,0.2,0.2,1.0,2.6,1.4,2.3,3.8,0.5,0.3,0.6,0.9,0.5,0.4,1.5,1.2,2.4,0.1,2.0,0.7,1.7,1.0,1.0,0.5,1.4,0.3,0.3,1.4,1.0,1.6,1.4,1.5,0.5,0.7,0.8,3.7,0.1,3,28,16,11,12,17,16,13,12,7,3.0,24,12,5,9,64,70,5,19,47,9,32,16,37,30,23,29,26,22,33,15,4,3,24,2,6,30,34,2,2,6,24,20,20,21,5,2,4,5,6,4,10,8,17,1,16,6,14,7,8,3,12,3,3,13,13,15,9,9,8,5,7,26,1,0.029,0.032,0.02,0.023,0.036,0.028,0.034,0.023,0.045,0.035,0.023,0.037,0.023,0.031,0.021,0.031,0.032,0.028,0.031,0.036,0.019,0.032,0.028,0.037,0.03,0.027,0.028,0.035,0.026,0.035,0.045,0.036,0.025,0.027,0.012,0.025,0.027,0.027,0.014,0.02,0.023,0.031,0.043,0.024,0.025,0.014,0.02,0.013,0.015,0.016,0.026,0.019,0.019,0.023,0.017,0.02,0.019,0.021,0.021,0.018,0.02,0.023,0.033,0.019,0.018,0.028,0.016,0.019,0.023,0.022,0.022,0.021,0.016,0.016,1513,4295,2446,2017,2355,2800,2737,2880,1701,1222,521.0,2770,2855,977,2396,12754,16375,2127,3399,5578,2995,4494,2085,6173,5826,4389,4349,4236,6133,4531,1796,977,782,5012,1563,1800,5287,6165,409,474,1385,2681,5281,3063,4045,1266,738,1557,1930,1265,437,2845,2013,4875,226,3350,1235,2839,1096,1592,845,2592,368,854,2486,930,3134,2398,1944,787,1329,1769,8384,391,0.102,0.132,0.116,0.114,0.128,0.14,0.116,0.12,0.159,0.143,0.101,0.147,0.123,0.136,0.124,0.132,0.127,0.103,0.137,0.152,0.088,0.141,0.139,0.147,0.135,0.129,0.135,0.121,0.107,0.147,0.136,0.111,0.107,0.125,0.067,0.121,0.132,0.132,0.095,0.107,0.116,0.134,0.144,0.116,0.109,0.096,0.115,0.082,0.086,0.113,0.132,0.099,0.107,0.105,0.103,0.108,0.109,0.111,0.116,0.107,0.106,0.118,0.133,0.11,0.108,0.138,0.101,0.103,0.114,0.143,0.113,0.112,0.094,0.124,316,1449,1045,820,829,1044,1091,950,493,331,159.0,1191,796,238,609,3513,4476,384,960,2065,652,1436,864,1594,1610,1322,1550,1469,1694,1450,534,292,248,1101,391,508,1378,1446,212,214,630,1332,773,1561,2345,612,271,680,906,497,287,1283,949,1921,96,1515,612,1271,675,858,426,927,189,336,1190,551,1526,1101,976,366,579,739,3423,133,4.085,2.729,2.308,2.439,2.337,2.34,2.223,2.777,2.609,2.928,2.588,2.049,2.786,2.998,2.912,2.713,2.882,3.951,2.763,2.263,3.146,2.45,2.08,2.834,2.748,2.691,2.278,2.314,2.78,2.534,2.431,3.046,2.762,3.098,3.08,3.064,2.825,2.96,2.151,2.735,2.79,1.821,3.762,2.155,1.971,2.259,3.054,2.853,2.618,2.606,1.942,2.376,2.345,2.604,2.402,2.366,2.583,2.43,1.857,2.066,2.44,2.641,2.082,2.533,2.289,2.202,2.136,2.597,2.299,2.254,2.667,2.657,2.632,3.28,0.705,0.614,0.54,0.484,0.596,0.524,0.582,0.553,0.459,0.492,0.753,0.406,0.396,0.427,0.477,0.618,0.557,0.766,0.548,0.64,0.839,0.413,0.38,0.647,0.568,0.534,0.5,0.632,0.49,0.466,0.827,1.057,0.417,0.679,0.995,0.578,0.593,0.574,0.296,0.393,0.505,0.475,0.8,0.564,0.618,0.386,0.433,0.822,0.464,0.498,0.345,0.442,0.629,0.484,0.44,0.515,0.429,0.408,0.312,0.315,0.37,0.752,0.41,0.987,0.496,0.621,0.393,0.405,0.375,0.708,0.47,0.56,0.573,0.545
-246,Epileptic,M,37.0,1.1,2.3,0.7,1.2,1.3,2.3,1.5,1.8,0.7,0.9,0.2,2.6,1.7,0.5,1.4,5.9,8.3,0.7,3.2,5.2,0.8,4.1,1.8,3.0,2.7,3.7,3.0,2.1,2.7,2.8,1.6,0.8,0.7,2.2,0.5,0.5,2.2,3.5,0.2,0.3,1.7,4.1,2.0,3.3,2.6,1.0,0.4,0.7,1.3,0.6,0.8,2.1,1.4,2.5,0.3,2.6,0.9,2.3,1.2,1.3,0.5,1.6,0.3,0.4,2.0,0.8,2.9,1.2,0.9,0.4,0.9,1.0,4.2,0.2,9,18,6,9,12,22,14,17,9,9,2.0,26,15,7,13,57,74,7,31,42,7,45,18,34,34,34,32,20,19,33,20,9,7,25,3,3,25,42,1,2,8,31,22,19,16,7,2,5,6,6,5,18,8,17,2,19,5,18,8,11,4,10,2,3,16,9,26,7,5,3,6,8,31,1,0.037,0.025,0.011,0.019,0.029,0.028,0.021,0.029,0.04,0.034,0.024,0.031,0.024,0.031,0.027,0.031,0.027,0.023,0.034,0.041,0.015,0.037,0.026,0.034,0.025,0.032,0.024,0.023,0.025,0.025,0.044,0.029,0.03,0.026,0.018,0.01,0.024,0.031,0.016,0.018,0.027,0.038,0.031,0.043,0.021,0.016,0.016,0.01,0.014,0.013,0.039,0.021,0.023,0.02,0.022,0.018,0.016,0.026,0.024,0.022,0.022,0.027,0.02,0.016,0.021,0.022,0.021,0.016,0.017,0.017,0.021,0.023,0.018,0.017,1724,4809,2426,2752,2119,3411,3391,3085,1540,1590,608.0,2664,4467,1150,3214,11838,19799,2372,4845,3833,3009,5980,2469,6856,6006,7357,6199,3644,5635,5683,2661,1243,1206,5307,1769,2056,6345,7767,419,471,2214,3130,5465,2162,3203,2208,1007,2921,3288,1745,514,3932,2413,5651,387,4340,2138,3224,1497,1837,780,2460,473,914,2935,1240,5010,2856,1726,830,1644,2090,9347,402,0.135,0.105,0.087,0.098,0.123,0.139,0.097,0.132,0.122,0.139,0.09,0.139,0.119,0.126,0.122,0.133,0.123,0.102,0.13,0.13,0.076,0.154,0.125,0.137,0.131,0.132,0.113,0.107,0.106,0.129,0.131,0.092,0.114,0.127,0.082,0.073,0.113,0.134,0.095,0.109,0.119,0.147,0.123,0.147,0.105,0.094,0.09,0.073,0.082,0.094,0.133,0.112,0.104,0.1,0.133,0.103,0.098,0.116,0.123,0.112,0.113,0.123,0.122,0.105,0.115,0.103,0.112,0.094,0.096,0.111,0.116,0.106,0.096,0.102,526,1557,912,1004,745,1334,1071,1109,389,445,186.0,1300,1119,298,901,3141,5115,491,1476,1966,965,1803,1048,1642,1772,2027,2137,1417,1525,1864,607,481,432,1361,474,675,1575,1953,179,213,892,1509,1131,1213,1956,978,375,1155,1396,714,359,1777,1055,2072,208,2180,800,1648,848,1001,396,879,204,447,1461,554,2297,1162,806,380,747,899,3872,202,3.314,2.93,2.627,2.831,2.43,2.344,2.534,2.599,2.724,3.065,3.137,1.904,3.046,2.82,2.787,2.779,3.01,3.713,2.674,1.773,2.662,2.585,2.053,2.906,2.613,2.893,2.34,2.102,2.867,2.464,2.968,2.283,2.322,2.846,3.465,2.761,2.957,2.933,2.697,2.675,2.652,1.946,3.22,1.984,1.895,2.525,3.26,3.148,2.853,2.569,1.783,2.472,2.5,2.705,2.335,2.215,3.049,2.309,2.057,2.039,2.295,2.629,2.508,2.497,2.23,2.403,2.285,2.824,2.585,2.463,2.593,2.721,2.637,2.323,0.778,0.643,0.493,0.472,0.588,0.574,0.563,0.528,0.643,0.488,0.766,0.46,0.52,0.4,0.4,0.571,0.591,0.664,0.652,0.575,0.74,0.569,0.439,0.636,0.465,0.541,0.488,0.541,0.565,0.491,0.682,1.071,0.48,0.597,0.606,0.454,0.802,0.6,0.485,0.386,0.626,0.561,0.831,0.809,0.656,0.572,0.4,0.626,0.484,0.713,0.37,0.539,0.632,0.517,0.326,0.467,0.756,0.658,0.487,0.397,0.441,0.609,0.477,0.698,0.604,0.601,0.516,0.424,0.424,0.709,0.447,0.723,0.558,0.43
-247,Epileptic,F,37.0,0.8,2.7,1.0,1.1,1.0,1.7,1.7,1.8,1.2,0.6,0.4,3.2,1.6,1.0,1.8,4.9,7.2,0.6,1.5,3.3,1.3,2.8,1.3,3.8,2.8,3.2,2.9,2.4,2.7,2.9,1.0,1.9,0.7,1.7,0.4,0.9,4.3,3.0,0.1,0.0,0.9,2.8,2.2,2.0,2.2,0.8,0.3,0.7,1.0,0.8,0.5,2.4,1.4,1.6,0.4,1.8,0.5,1.4,0.7,0.5,0.5,1.2,0.2,0.4,1.7,1.4,1.4,1.3,0.8,0.4,0.8,1.4,3.1,0.4,5,27,11,10,11,16,16,17,11,7,5.0,27,18,9,22,55,72,3,17,26,9,33,14,41,35,34,30,25,23,34,16,16,8,24,3,7,45,33,1,0,5,24,21,18,14,5,1,4,5,5,4,19,17,10,2,15,4,28,4,5,4,11,1,4,20,13,12,9,7,5,5,9,22,2,0.038,0.026,0.014,0.024,0.025,0.031,0.027,0.034,0.049,0.039,0.039,0.042,0.028,0.042,0.028,0.031,0.028,0.025,0.026,0.037,0.025,0.037,0.025,0.044,0.034,0.03,0.027,0.028,0.024,0.028,0.032,0.063,0.033,0.026,0.018,0.022,0.038,0.038,0.012,0.004,0.018,0.039,0.04,0.032,0.02,0.018,0.019,0.013,0.016,0.031,0.028,0.021,0.03,0.017,0.019,0.019,0.023,0.017,0.017,0.014,0.018,0.021,0.034,0.013,0.018,0.032,0.017,0.019,0.019,0.012,0.03,0.025,0.015,0.022,1231,5482,2296,2286,1681,2602,2768,3367,1770,1285,668.0,2695,4081,1569,3805,9670,16883,2068,2827,3712,2642,4833,1983,5130,5773,6599,5352,3991,5506,4991,2128,1421,1369,5509,1746,1928,7341,6912,476,266,1489,2813,4561,1851,2941,1423,692,2150,2124,1326,548,3682,2494,3527,489,3192,1191,2810,1074,1109,972,2403,374,853,3073,1139,2812,2439,1657,904,1128,2007,7891,530,0.131,0.116,0.097,0.115,0.121,0.141,0.114,0.141,0.169,0.141,0.152,0.161,0.128,0.153,0.139,0.129,0.129,0.096,0.111,0.134,0.094,0.147,0.128,0.142,0.144,0.134,0.128,0.119,0.102,0.138,0.112,0.141,0.146,0.123,0.085,0.101,0.139,0.14,0.086,0.04,0.1,0.155,0.139,0.129,0.101,0.105,0.089,0.081,0.089,0.118,0.125,0.108,0.128,0.093,0.122,0.108,0.115,0.091,0.094,0.091,0.11,0.119,0.127,0.101,0.112,0.128,0.097,0.101,0.1,0.099,0.124,0.12,0.09,0.122,359,1755,1025,740,702,946,1007,971,435,275,197.0,1240,1050,371,1137,2801,4370,396,875,1551,787,1286,873,1358,1494,1917,1714,1325,1552,1660,581,470,368,1110,366,598,1814,1504,221,88,682,1184,991,1103,1772,645,255,844,935,477,308,1746,847,1564,252,1518,419,1483,635,657,430,845,153,427,1490,580,1276,1105,725,494,474,909,3039,220,3.345,2.785,2.152,2.84,2.141,2.453,2.282,3.275,2.771,3.425,2.776,1.952,2.931,3.053,2.727,2.651,2.96,4.014,2.615,2.067,2.669,2.721,2.028,2.741,2.85,2.929,2.469,2.442,2.753,2.464,2.552,2.825,3.043,3.339,3.863,2.986,2.957,3.284,2.58,2.703,2.784,2.041,3.264,1.993,1.904,2.624,3.522,3.196,2.829,3.262,2.359,2.221,2.453,2.462,2.246,2.309,3.289,2.147,2.002,1.869,2.403,3.075,2.297,2.271,2.279,2.47,2.4,2.62,2.538,2.13,2.692,2.74,2.687,3.068,0.889,0.585,0.723,0.596,0.507,0.462,0.608,0.581,0.645,0.407,0.665,0.524,0.463,0.514,0.389,0.572,0.552,0.689,0.566,0.506,0.872,0.566,0.495,0.756,0.566,0.594,0.542,0.61,0.55,0.551,0.666,1.214,0.649,0.624,0.71,0.592,0.712,0.674,0.532,0.494,0.51,0.442,0.768,0.724,0.612,0.58,0.442,0.822,0.545,0.723,0.405,0.517,0.611,0.41,0.33,0.494,0.784,0.506,0.419,0.299,0.302,0.717,0.502,0.808,0.538,0.785,0.435,0.364,0.398,0.408,0.418,0.624,0.627,0.649
-248,Epileptic,M,27.0,0.5,2.6,0.9,1.0,1.3,2.3,1.5,1.6,0.6,0.6,0.3,3.4,1.4,0.7,2.1,6.8,7.9,0.7,1.8,6.6,1.6,2.4,1.7,2.9,2.7,3.4,4.6,3.2,3.7,3.7,1.1,2.6,0.6,2.3,0.4,0.4,5.2,3.5,0.2,0.2,0.9,3.3,2.4,4.4,4.3,1.2,0.4,0.6,1.1,0.8,1.1,1.9,1.2,2.2,0.4,2.8,0.7,1.6,1.0,1.3,0.7,0.9,0.6,0.7,1.7,0.6,3.1,1.9,1.4,0.5,0.8,1.9,3.9,0.2,3,22,9,8,11,24,12,14,7,8,3.0,29,16,7,19,70,75,5,21,46,17,27,13,29,37,29,57,28,29,39,14,15,4,26,2,4,53,32,1,1,4,23,20,32,25,10,2,4,5,6,7,18,7,14,3,22,7,13,7,12,6,6,3,4,12,7,23,12,9,4,5,10,32,2,0.022,0.027,0.015,0.018,0.035,0.029,0.022,0.025,0.037,0.035,0.031,0.034,0.029,0.03,0.035,0.035,0.026,0.026,0.027,0.038,0.025,0.032,0.026,0.042,0.034,0.031,0.027,0.031,0.03,0.032,0.035,0.074,0.029,0.031,0.014,0.014,0.045,0.035,0.014,0.016,0.017,0.032,0.038,0.038,0.029,0.026,0.022,0.012,0.014,0.014,0.032,0.021,0.019,0.018,0.019,0.017,0.02,0.019,0.021,0.023,0.024,0.022,0.029,0.027,0.022,0.028,0.021,0.021,0.022,0.021,0.021,0.023,0.018,0.023,1541,5111,2507,2469,1707,4443,2982,3735,1153,1368,686.0,3127,3899,1459,3757,12020,18585,2457,4113,6773,3938,4097,2415,6267,5649,7534,8538,5049,7132,6488,2569,1349,1097,6586,1796,1521,7987,7076,422,531,1937,3524,5523,2579,3973,1904,758,2027,2832,1988,850,3633,1948,5055,453,5223,1170,3903,1368,2027,1043,1829,599,878,3044,707,5075,3477,2226,870,1504,2645,9115,847,0.095,0.118,0.095,0.093,0.143,0.134,0.109,0.125,0.138,0.13,0.103,0.135,0.136,0.131,0.152,0.14,0.124,0.106,0.13,0.14,0.108,0.135,0.11,0.141,0.146,0.125,0.126,0.118,0.116,0.135,0.132,0.194,0.137,0.112,0.078,0.077,0.141,0.121,0.078,0.09,0.092,0.123,0.144,0.139,0.119,0.125,0.109,0.083,0.086,0.096,0.144,0.107,0.1,0.094,0.127,0.099,0.117,0.093,0.11,0.12,0.123,0.108,0.138,0.136,0.105,0.122,0.111,0.101,0.109,0.117,0.106,0.113,0.098,0.1,364,1598,961,847,614,1376,1010,1015,345,347,201.0,1457,892,327,933,3306,4883,435,1088,2768,1086,1222,1013,1336,1524,1861,2786,1673,1919,2003,487,546,286,1205,411,522,2041,1614,243,181,776,1542,1075,1774,2357,794,289,773,1147,868,527,1558,931,1936,255,2552,558,1559,681,926,453,665,273,351,1269,336,2294,1374,1031,340,725,1259,3570,201,3.903,2.933,2.651,2.842,2.716,2.736,2.472,3.276,2.593,3.236,2.888,1.954,3.298,3.018,3.076,2.704,3.048,4.024,2.979,2.081,2.965,2.626,2.096,3.308,2.825,3.169,2.454,2.405,2.833,2.604,3.27,2.472,3.474,3.498,3.834,2.675,2.835,3.203,2.119,3.18,3.099,2.069,3.364,1.765,1.991,2.544,3.462,3.238,2.929,2.631,2.183,2.355,2.469,2.679,2.299,2.335,2.252,2.698,2.313,2.364,2.664,3.014,2.728,2.606,2.683,2.347,2.377,2.68,2.632,2.801,2.537,2.656,2.709,4.472,0.756,0.792,0.619,0.643,0.557,0.56,0.449,0.597,0.613,0.532,0.72,0.571,0.547,0.446,0.584,0.572,0.626,0.747,0.571,0.775,0.757,0.485,0.478,0.694,0.526,0.696,0.499,0.551,0.561,0.54,0.937,1.14,0.738,0.612,0.723,0.555,0.731,0.645,0.279,0.66,0.893,0.565,0.846,0.776,0.592,0.518,0.435,0.766,0.66,0.5,0.402,0.508,0.569,0.51,0.44,0.426,0.552,0.511,0.368,0.416,0.427,0.691,0.417,1.071,0.464,0.484,0.542,0.367,0.497,0.43,0.618,0.489,0.498,0.532
-249,Epileptic,M,27.0,1.3,2.5,1.1,1.9,1.1,2.8,2.1,1.9,1.0,1.1,0.4,3.1,1.5,0.5,1.8,6.3,9.1,0.7,2.1,6.2,1.0,2.1,1.3,3.1,3.5,4.0,3.4,2.7,2.9,3.8,0.8,1.5,0.8,3.0,0.4,0.6,4.5,5.2,0.1,0.3,1.0,3.9,1.6,3.1,3.3,0.8,0.4,1.3,1.4,0.7,0.7,2.3,1.7,3.0,0.6,2.8,1.0,2.4,0.8,0.7,0.9,1.2,0.5,0.4,1.7,0.9,3.1,1.4,1.1,1.2,1.4,2.1,4.8,0.4,9,21,12,16,13,23,18,16,9,10,5.0,22,15,8,19,64,75,8,24,47,10,23,12,34,41,37,35,24,25,42,11,11,8,29,3,5,49,55,2,2,5,29,17,21,21,7,2,9,8,5,5,18,11,18,4,22,7,17,6,6,5,9,5,4,15,11,28,10,10,17,9,15,47,3,0.039,0.025,0.018,0.027,0.027,0.033,0.028,0.03,0.039,0.036,0.028,0.04,0.026,0.028,0.033,0.032,0.026,0.025,0.027,0.042,0.018,0.032,0.033,0.03,0.03,0.028,0.025,0.03,0.024,0.029,0.031,0.047,0.031,0.025,0.015,0.015,0.034,0.031,0.013,0.017,0.02,0.043,0.028,0.034,0.026,0.015,0.015,0.015,0.014,0.015,0.022,0.019,0.022,0.021,0.019,0.019,0.022,0.021,0.031,0.022,0.024,0.021,0.026,0.018,0.025,0.022,0.023,0.021,0.019,0.027,0.025,0.019,0.018,0.021,1732,4985,2449,2799,1838,3818,2968,3681,1488,1666,546.0,2200,4099,1229,3376,11660,18946,2859,4705,5412,3126,3469,1703,5926,6478,7822,5999,4117,5911,5989,1523,1344,1787,8220,1926,2185,8838,9905,388,608,1763,2898,4735,2364,3031,1408,924,3005,2904,1642,680,3892,2277,4895,735,4101,1811,3901,743,901,991,2369,547,801,2339,1106,4326,2393,1902,1599,1848,3346,9825,714,0.137,0.115,0.106,0.128,0.123,0.143,0.122,0.138,0.133,0.147,0.126,0.149,0.127,0.124,0.137,0.142,0.121,0.112,0.123,0.145,0.081,0.135,0.142,0.124,0.135,0.128,0.126,0.12,0.112,0.14,0.118,0.123,0.128,0.121,0.087,0.084,0.136,0.124,0.092,0.104,0.103,0.15,0.127,0.139,0.117,0.101,0.089,0.09,0.088,0.097,0.124,0.101,0.111,0.105,0.124,0.111,0.115,0.094,0.135,0.117,0.121,0.106,0.136,0.12,0.118,0.105,0.12,0.111,0.109,0.14,0.126,0.104,0.104,0.116,551,1715,1037,1141,802,1457,1181,1082,464,461,192.0,1159,1024,358,981,3272,5516,587,1337,2401,957,1180,652,1698,2038,2355,2250,1538,1828,2296,462,515,478,1876,454,739,2412,2825,232,313,789,1426,1072,1494,1994,750,378,1219,1414,691,461,1912,1133,2216,415,2114,755,1981,459,506,523,892,343,416,1218,616,2180,1084,909,711,891,1565,4343,330,3.153,2.56,2.387,2.573,2.115,2.32,2.226,3.091,2.558,3.02,2.353,1.777,3.092,2.519,2.718,2.798,2.894,3.661,2.796,2.0,2.665,2.331,2.287,2.697,2.549,2.775,2.283,2.225,2.581,2.284,2.466,2.496,2.853,3.224,3.674,2.73,2.941,2.644,1.971,2.259,2.833,1.866,3.181,1.927,1.803,2.132,3.17,3.05,2.556,2.813,1.841,2.172,2.248,2.502,2.238,2.163,3.03,2.296,1.924,1.978,2.378,2.625,2.006,2.176,2.195,2.063,2.138,2.589,2.336,2.299,2.432,2.552,2.381,2.952,0.894,0.671,0.476,0.463,0.562,0.559,0.516,0.493,0.512,0.403,0.879,0.519,0.453,0.554,0.542,0.48,0.513,0.815,0.549,0.594,0.735,0.467,0.429,0.745,0.477,0.478,0.417,0.583,0.646,0.468,0.592,1.024,0.521,0.707,0.652,0.424,0.661,0.686,0.44,0.357,0.497,0.55,0.741,0.59,0.588,0.394,0.401,0.692,0.53,0.62,0.393,0.483,0.528,0.472,0.317,0.401,0.501,0.552,0.325,0.336,0.487,0.866,0.519,0.865,0.557,0.645,0.422,0.338,0.353,0.613,0.45,0.509,0.495,0.713
-250,Epileptic,F,37.0,0.9,2.5,1.6,1.2,1.7,1.9,1.8,1.7,1.2,0.7,0.2,3.0,1.8,0.9,1.2,8.2,8.9,0.7,2.5,5.2,0.5,2.5,1.5,3.1,3.0,3.0,2.8,2.9,3.1,3.7,1.3,1.4,0.7,3.2,0.4,1.0,3.7,3.8,0.2,0.2,1.1,2.9,2.1,2.4,3.3,0.6,0.4,0.9,1.2,0.7,0.7,1.8,1.7,2.3,0.3,1.8,0.6,1.0,1.4,0.9,0.8,1.9,0.2,0.5,1.9,2.5,3.6,1.6,1.3,0.5,0.9,1.2,4.2,0.4,8,25,14,9,18,18,16,15,11,6,4.0,27,17,11,11,76,86,6,24,42,4,29,16,34,39,33,33,31,23,34,24,13,6,34,2,7,40,42,2,1,7,23,18,20,22,5,2,6,7,8,5,16,11,20,1,16,5,10,11,7,7,14,1,5,18,22,29,11,9,6,6,8,35,5,0.041,0.023,0.02,0.022,0.031,0.039,0.026,0.025,0.033,0.036,0.029,0.033,0.027,0.04,0.035,0.039,0.029,0.027,0.033,0.039,0.013,0.035,0.03,0.031,0.032,0.03,0.03,0.023,0.024,0.031,0.036,0.042,0.03,0.034,0.014,0.023,0.036,0.033,0.015,0.017,0.024,0.038,0.036,0.028,0.02,0.014,0.02,0.016,0.017,0.031,0.034,0.02,0.023,0.017,0.02,0.022,0.015,0.016,0.031,0.018,0.02,0.03,0.015,0.017,0.021,0.034,0.023,0.024,0.021,0.016,0.02,0.021,0.018,0.026,1433,4342,2803,2153,2420,2385,2323,3010,1668,1163,551.0,2462,3617,1310,2281,10746,15601,1847,3606,4125,2298,4032,2055,5344,5150,5060,4325,4673,5847,4881,1842,1570,930,5920,1513,1801,6621,7160,396,352,1400,2071,5096,2042,3765,1302,762,1970,2198,1271,531,2615,2297,4492,334,2565,1546,2228,1345,1254,1260,2157,272,717,2772,1589,4208,2100,1710,840,1416,1741,8060,601,0.143,0.117,0.114,0.106,0.134,0.153,0.106,0.125,0.139,0.134,0.123,0.14,0.13,0.163,0.125,0.151,0.127,0.114,0.138,0.152,0.074,0.146,0.132,0.138,0.148,0.137,0.132,0.111,0.098,0.132,0.143,0.137,0.142,0.14,0.078,0.104,0.136,0.136,0.097,0.099,0.114,0.148,0.135,0.122,0.102,0.095,0.112,0.086,0.095,0.139,0.139,0.105,0.116,0.098,0.119,0.113,0.095,0.099,0.133,0.105,0.11,0.135,0.105,0.125,0.113,0.128,0.122,0.111,0.106,0.102,0.109,0.116,0.101,0.143,435,1757,1218,867,991,900,1033,1073,540,327,193.0,1420,1087,365,707,3616,5120,393,1192,2043,579,1268,853,1660,1599,1728,1586,1833,1786,1888,618,657,354,1519,439,581,1816,1862,225,189,702,1166,1047,1420,2480,730,349,953,1091,477,330,1502,1169,2183,192,1375,720,1157,773,720,644,918,185,425,1534,977,2386,1073,1002,477,747,848,3645,309,3.319,2.337,2.203,2.358,2.173,2.326,1.952,2.537,2.365,2.814,2.433,1.616,2.679,2.658,2.456,2.354,2.448,3.614,2.488,1.869,2.996,2.443,2.06,2.514,2.525,2.416,2.215,2.083,2.528,2.174,2.398,2.38,2.205,2.895,3.067,2.712,2.8,2.753,2.098,2.27,2.505,1.697,3.511,1.66,1.655,1.963,2.746,2.425,2.362,2.744,1.94,1.914,2.005,2.11,2.329,1.909,2.416,2.209,2.05,1.982,2.245,2.395,1.702,2.109,1.989,1.959,1.946,2.077,2.004,1.946,2.244,2.402,2.265,2.496,0.661,0.43,0.562,0.513,0.54,0.496,0.501,0.47,0.496,0.523,0.505,0.407,0.508,0.435,0.439,0.525,0.497,0.722,0.572,0.524,0.68,0.47,0.427,0.582,0.522,0.537,0.465,0.508,0.522,0.425,0.502,0.905,0.378,0.672,0.442,0.532,0.709,0.663,0.395,0.48,0.552,0.42,0.683,0.654,0.45,0.471,0.364,0.625,0.547,0.568,0.302,0.375,0.423,0.421,0.405,0.383,0.388,0.412,0.365,0.345,0.405,0.634,0.351,0.748,0.546,0.535,0.411,0.402,0.436,0.509,0.433,0.503,0.5,0.431
-251,Epileptic,F,52.0,0.5,1.1,0.4,0.9,1.1,1.8,1.3,1.6,1.1,0.5,0.2,2.3,1.3,0.8,0.9,5.5,7.3,0.6,1.8,4.2,1.2,3.0,1.3,3.0,2.9,3.3,2.9,2.5,3.1,3.1,1.3,1.1,0.5,2.8,0.3,1.0,3.0,3.0,0.2,0.1,1.1,2.7,1.9,2.6,2.8,0.7,0.9,1.0,1.0,0.6,0.4,1.3,1.1,2.2,0.1,2.8,1.1,2.0,1.1,0.9,0.5,0.9,0.3,0.6,1.2,1.1,2.9,1.9,0.9,0.3,1.8,1.1,4.4,0.4,4,11,6,6,15,14,10,14,14,5,2.0,19,15,10,10,55,67,5,14,29,25,27,13,35,32,35,34,24,25,41,22,13,4,24,1,10,34,31,2,1,7,21,17,19,15,8,5,6,5,5,2,8,7,18,1,21,7,15,7,7,4,9,2,3,9,11,23,14,8,2,13,7,34,3,0.024,0.014,0.009,0.019,0.033,0.032,0.019,0.027,0.041,0.026,0.028,0.037,0.03,0.027,0.021,0.032,0.025,0.026,0.036,0.038,0.024,0.035,0.035,0.037,0.033,0.028,0.025,0.025,0.025,0.028,0.043,0.034,0.02,0.033,0.011,0.021,0.032,0.034,0.019,0.007,0.021,0.036,0.034,0.022,0.018,0.016,0.032,0.013,0.013,0.019,0.026,0.014,0.021,0.02,0.015,0.022,0.032,0.022,0.031,0.029,0.024,0.019,0.014,0.025,0.027,0.03,0.021,0.025,0.02,0.013,0.031,0.022,0.022,0.016,1677,3806,1648,1932,2237,3239,2650,3565,1757,1512,527.0,2362,3149,1857,2759,12695,18875,2581,2831,3770,3186,4439,1556,5479,5314,5957,6065,4159,6320,5632,2126,1835,1172,5884,1540,2471,5138,6223,337,301,1822,2674,5052,2841,4374,1630,947,2412,2419,1546,354,3354,1820,4680,351,3528,1559,3097,1116,932,724,2023,530,724,1869,1163,4538,2682,1836,696,2539,1517,8099,668,0.089,0.093,0.088,0.095,0.136,0.138,0.093,0.131,0.154,0.121,0.106,0.14,0.126,0.135,0.098,0.134,0.117,0.092,0.131,0.131,0.101,0.143,0.144,0.139,0.143,0.126,0.121,0.104,0.103,0.128,0.137,0.107,0.101,0.124,0.056,0.106,0.129,0.13,0.132,0.057,0.103,0.145,0.133,0.112,0.098,0.101,0.133,0.085,0.084,0.098,0.132,0.083,0.105,0.096,0.087,0.12,0.129,0.108,0.131,0.126,0.127,0.105,0.105,0.115,0.122,0.116,0.112,0.113,0.1,0.091,0.133,0.113,0.108,0.103,390,1314,630,725,720,957,878,1000,480,369,156.0,936,861,483,718,3013,4901,397,830,1751,841,1378,631,1433,1554,1771,1952,1431,1737,2033,620,608,362,1302,424,873,1556,1419,164,149,845,1181,876,1802,2256,679,413,973,1109,566,226,1465,846,1934,168,1833,480,1469,630,501,319,782,263,391,802,565,2187,1157,799,362,998,737,3317,375,3.511,2.816,2.476,2.682,2.428,2.799,2.537,3.084,2.685,3.245,2.804,2.181,2.744,2.818,2.818,3.057,3.051,4.211,2.821,1.866,3.076,2.46,2.187,2.845,2.748,2.688,2.438,2.266,2.756,2.21,2.525,2.668,2.488,3.153,3.254,2.648,2.628,2.989,2.296,2.066,2.755,1.993,3.704,1.84,2.186,2.186,2.945,3.001,2.603,2.753,2.227,2.357,2.217,2.453,2.434,2.16,2.913,2.426,2.082,1.891,2.553,2.57,2.299,2.161,2.368,2.381,2.25,2.538,2.515,2.266,2.54,2.448,2.66,2.365,1.103,0.556,0.559,0.569,0.688,0.623,0.683,0.599,0.71,0.635,0.937,0.484,0.619,0.673,0.544,0.704,0.64,0.727,0.773,0.614,0.721,0.544,0.464,0.586,0.538,0.564,0.46,0.556,0.556,0.634,0.675,1.11,0.44,0.676,0.796,0.47,0.625,0.586,0.299,0.523,0.603,0.506,0.856,0.529,0.674,0.723,0.541,0.763,0.553,0.763,0.459,0.647,0.659,0.53,0.429,0.452,0.652,0.789,0.363,0.398,0.401,0.652,0.385,0.822,0.666,0.797,0.504,0.515,0.431,0.453,0.771,0.67,0.506,0.417
-252,Epileptic,M,17.5,0.6,2.6,0.7,1.1,1.6,2.4,1.6,2.0,1.4,0.7,0.3,2.9,1.4,0.6,1.2,8.0,9.5,1.1,2.7,5.4,1.6,3.7,1.3,2.9,3.5,3.8,2.8,2.6,3.0,3.1,1.0,1.1,0.7,3.5,0.4,0.6,4.9,5.8,0.4,0.1,0.9,3.1,2.9,3.0,2.5,0.6,0.4,0.8,1.0,0.8,0.7,1.5,1.1,2.6,0.5,1.9,0.8,1.4,0.8,1.1,0.4,1.4,0.3,0.5,1.7,1.5,2.0,1.1,1.0,0.5,0.9,2.5,6.0,0.2,5,18,7,11,20,24,16,17,13,6,4.0,25,14,8,15,78,98,10,27,47,13,51,16,30,49,37,33,29,30,37,15,8,7,36,3,6,43,46,4,0,4,31,30,18,16,4,2,6,4,6,4,14,12,18,3,14,4,11,6,10,3,13,2,5,13,16,13,10,7,5,6,20,41,2,0.035,0.026,0.014,0.022,0.035,0.034,0.024,0.027,0.05,0.034,0.023,0.032,0.029,0.037,0.038,0.039,0.032,0.034,0.035,0.039,0.028,0.036,0.027,0.038,0.033,0.033,0.028,0.025,0.031,0.031,0.033,0.045,0.038,0.037,0.013,0.022,0.039,0.035,0.031,0.012,0.019,0.033,0.047,0.026,0.016,0.011,0.017,0.011,0.013,0.015,0.03,0.023,0.026,0.019,0.02,0.018,0.016,0.02,0.021,0.018,0.016,0.026,0.012,0.031,0.019,0.023,0.015,0.015,0.016,0.016,0.02,0.026,0.019,0.017,1724,5707,2361,2501,2570,3487,2523,4411,2094,1390,710.0,2790,3648,1317,2279,12568,20053,2521,5014,5218,3912,5234,1818,7028,6853,6611,4850,4865,5824,5874,2345,935,1028,6274,2482,1865,7471,8293,494,244,1550,2780,5875,2743,3897,1731,772,2474,2241,1903,686,3193,2754,5905,821,3104,2011,2835,1015,1663,869,2710,491,1096,2559,1954,3698,2641,2105,1265,1601,3157,10965,632,0.105,0.124,0.093,0.109,0.155,0.142,0.115,0.116,0.174,0.139,0.117,0.134,0.136,0.16,0.155,0.153,0.134,0.13,0.143,0.147,0.114,0.153,0.127,0.14,0.141,0.132,0.121,0.113,0.121,0.143,0.138,0.116,0.164,0.127,0.081,0.097,0.131,0.122,0.144,0.075,0.1,0.139,0.16,0.117,0.09,0.08,0.088,0.076,0.077,0.093,0.133,0.107,0.11,0.099,0.106,0.101,0.084,0.108,0.11,0.115,0.097,0.122,0.096,0.134,0.114,0.11,0.096,0.093,0.095,0.116,0.113,0.119,0.094,0.086,367,1566,821,840,781,1350,986,1333,531,354,224.0,1378,882,331,651,3529,5136,524,1340,2323,997,1809,846,1500,2001,1965,1793,1652,1689,1710,542,442,319,1552,552,621,2170,2496,214,106,680,1425,1245,1718,2365,772,326,961,1015,834,373,1195,929,2211,417,1575,834,1313,667,922,402,899,286,394,1238,1035,1861,1155,935,521,770,1655,4841,262,3.637,3.007,2.648,2.845,2.723,2.298,2.133,2.847,2.831,3.13,2.677,1.832,2.961,2.804,2.396,2.655,2.979,3.534,2.949,1.974,3.029,2.253,1.834,3.254,2.597,2.72,2.181,2.264,2.644,2.639,3.096,2.105,2.982,2.729,3.551,2.598,2.631,2.506,2.552,2.453,2.738,1.794,3.047,1.826,1.847,2.434,2.909,3.012,2.688,2.515,2.065,2.482,2.519,2.743,2.192,2.142,2.689,2.341,1.659,1.94,2.157,2.829,2.045,2.976,2.265,2.334,2.186,2.582,2.496,2.502,2.291,2.331,2.381,2.601,0.776,0.74,0.573,0.541,0.646,0.583,0.583,0.7,0.651,0.552,0.692,0.447,0.556,0.587,0.781,0.665,0.689,0.634,0.576,0.635,0.909,0.598,0.499,0.739,0.584,0.64,0.507,0.568,0.477,0.581,0.63,1.011,0.644,0.746,0.819,0.588,0.828,0.746,0.329,0.597,0.808,0.406,0.9,0.746,0.577,0.509,0.573,0.752,0.609,0.481,0.467,0.52,0.691,0.646,0.396,0.632,0.571,0.505,0.388,0.449,0.596,0.769,0.534,0.884,0.672,0.851,0.581,0.433,0.508,0.673,0.588,0.636,0.568,0.709
-253,Epileptic,M,27.0,1.4,3.6,1.2,1.5,2.1,2.0,1.8,1.4,1.9,0.9,0.7,3.8,1.1,0.5,1.8,7.0,9.7,1.2,2.2,6.1,1.9,3.1,2.4,5.3,3.8,3.4,5.0,3.5,4.1,2.9,1.6,1.2,0.6,3.0,0.7,0.7,3.7,3.3,0.5,0.2,1.0,3.9,2.5,3.9,4.3,0.7,0.4,1.1,1.4,0.8,0.8,2.0,2.0,3.0,0.5,3.0,0.8,2.0,1.3,1.7,1.0,2.6,0.4,0.7,2.6,2.0,2.8,1.4,1.5,0.6,0.8,1.2,4.7,0.4,12,33,12,15,25,20,15,13,23,11,9.0,43,14,7,19,76,93,10,25,65,13,40,21,55,47,42,58,32,34,44,27,8,4,33,4,6,43,44,5,2,5,31,29,30,26,5,2,5,7,8,5,15,13,21,3,22,6,17,12,15,7,25,2,6,21,23,22,10,10,6,6,9,35,4,0.046,0.027,0.021,0.025,0.034,0.029,0.023,0.023,0.051,0.031,0.039,0.031,0.026,0.028,0.032,0.03,0.03,0.039,0.027,0.035,0.026,0.035,0.034,0.04,0.033,0.025,0.028,0.028,0.028,0.025,0.035,0.044,0.023,0.03,0.019,0.016,0.032,0.03,0.025,0.016,0.022,0.032,0.035,0.026,0.026,0.012,0.016,0.014,0.017,0.013,0.023,0.018,0.022,0.02,0.027,0.017,0.021,0.021,0.025,0.022,0.02,0.032,0.021,0.018,0.024,0.024,0.02,0.018,0.019,0.017,0.021,0.016,0.018,0.018,1632,5373,2722,2685,3125,3843,3543,3464,1879,1852,953.0,4043,3434,1252,3405,13506,19669,2743,4400,6233,5537,4677,2140,7767,6158,7490,8913,5809,9395,6222,3068,1483,1215,6948,2458,1846,7958,9017,730,520,1492,2837,6416,5015,4732,1537,1006,2393,2512,2034,723,3718,2690,6228,694,5008,1380,3658,1355,2113,1286,3634,449,1118,3865,2107,4285,2983,2638,969,1475,2461,9671,534,0.142,0.128,0.11,0.12,0.127,0.128,0.101,0.111,0.145,0.134,0.142,0.144,0.107,0.123,0.131,0.122,0.13,0.133,0.123,0.142,0.108,0.112,0.12,0.137,0.125,0.119,0.115,0.108,0.108,0.125,0.138,0.117,0.092,0.13,0.082,0.092,0.133,0.131,0.127,0.097,0.101,0.134,0.153,0.116,0.112,0.086,0.089,0.078,0.092,0.098,0.122,0.097,0.109,0.098,0.132,0.099,0.115,0.109,0.12,0.113,0.11,0.136,0.115,0.105,0.113,0.13,0.11,0.093,0.098,0.122,0.11,0.099,0.094,0.116,556,2002,993,1073,1218,1227,1194,1025,734,535,291.0,1952,918,296,963,3852,5418,630,1377,3032,1242,1725,1251,2359,2022,2307,2991,1974,2478,2066,914,586,393,1699,659,697,2188,2091,330,258,737,1726,1399,2594,2674,806,410,1088,1185,943,460,1815,1430,2475,272,2658,640,1624,870,1249,686,1420,252,625,1926,1206,2147,1298,1221,514,698,1162,4188,334,2.919,2.623,2.756,2.55,2.229,2.444,2.445,2.838,2.115,2.775,2.634,1.84,2.894,2.887,2.709,2.642,2.831,3.29,2.593,1.798,3.331,2.176,1.627,2.638,2.424,2.611,2.339,2.319,2.923,2.373,2.562,2.491,2.373,2.996,3.207,2.393,2.75,3.15,2.266,2.422,2.459,1.527,3.196,2.074,2.001,2.104,2.878,2.684,2.72,2.564,1.823,2.181,2.079,2.543,2.445,2.07,2.286,2.42,1.802,1.888,2.146,2.468,1.895,2.053,2.133,2.113,2.18,2.569,2.337,2.132,2.387,2.476,2.442,1.994,0.738,0.42,0.45,0.466,0.596,0.667,0.642,0.555,0.579,0.578,0.606,0.509,0.416,0.478,0.578,0.678,0.601,0.828,0.506,0.588,0.772,0.662,0.444,0.755,0.666,0.552,0.608,0.6,0.565,0.522,0.51,0.766,0.49,0.616,0.49,0.507,0.636,0.629,0.356,0.408,0.505,0.486,0.862,0.515,0.606,0.397,0.391,0.47,0.459,0.399,0.381,0.424,0.431,0.497,0.72,0.348,0.386,0.437,0.395,0.394,0.414,0.681,0.433,0.596,0.421,0.864,0.425,0.335,0.405,0.6,0.422,0.415,0.454,0.387
-254,Epileptic,F,17.5,1.3,2.3,1.0,1.2,1.3,1.8,1.2,1.7,1.1,0.5,0.1,2.6,1.2,0.6,1.0,4.6,8.0,0.6,2.5,4.1,1.5,2.4,1.3,2.4,1.9,4.2,3.5,2.5,2.7,2.7,1.4,0.4,0.5,2.2,0.3,0.3,3.1,3.3,0.1,0.1,0.8,2.7,2.1,3.0,2.3,0.6,0.5,0.9,0.9,0.7,0.6,1.1,0.9,2.3,0.2,2.5,0.8,1.6,0.7,0.7,0.8,1.2,0.2,0.3,1.2,1.8,2.2,1.0,0.9,0.4,0.8,1.8,3.9,0.3,9,21,15,13,15,18,13,20,12,5,2.0,23,11,9,10,48,79,3,26,33,17,30,12,32,29,45,41,29,29,33,16,3,5,28,2,2,33,42,1,1,5,25,26,18,15,5,2,5,5,5,3,8,7,19,2,20,6,12,6,5,8,7,1,3,9,16,18,6,9,3,5,11,28,3,0.046,0.023,0.017,0.021,0.033,0.035,0.026,0.029,0.041,0.036,0.043,0.03,0.031,0.036,0.023,0.034,0.027,0.024,0.036,0.034,0.026,0.032,0.029,0.032,0.024,0.034,0.029,0.027,0.027,0.028,0.037,0.027,0.019,0.029,0.011,0.008,0.032,0.032,0.012,0.013,0.017,0.038,0.04,0.032,0.017,0.013,0.019,0.015,0.011,0.012,0.02,0.014,0.023,0.019,0.029,0.017,0.023,0.019,0.023,0.013,0.032,0.021,0.021,0.019,0.016,0.027,0.019,0.016,0.018,0.014,0.024,0.024,0.016,0.017,1821,5380,2843,3239,2277,3273,2386,3655,2417,1462,500.0,2559,3367,1383,2779,11871,19675,2317,4534,4233,4349,4817,1808,6916,5449,7955,7255,4045,6431,5618,2457,693,1278,6743,1935,1676,7487,7866,370,334,1595,2509,6173,2498,3635,1394,984,1963,2671,1930,770,2702,1610,4884,401,4254,1830,3238,848,1424,925,2809,377,791,2590,1877,3755,2349,2184,1317,1302,2580,9781,511,0.146,0.111,0.1,0.116,0.134,0.149,0.096,0.141,0.148,0.121,0.093,0.126,0.126,0.137,0.108,0.129,0.126,0.091,0.133,0.14,0.104,0.138,0.13,0.129,0.118,0.143,0.133,0.123,0.125,0.132,0.138,0.087,0.106,0.122,0.063,0.064,0.122,0.129,0.093,0.104,0.099,0.15,0.152,0.131,0.094,0.095,0.102,0.086,0.077,0.089,0.111,0.087,0.108,0.102,0.16,0.102,0.109,0.097,0.125,0.097,0.123,0.108,0.105,0.124,0.095,0.136,0.108,0.088,0.101,0.108,0.122,0.119,0.09,0.114,429,1519,968,937,718,941,752,1049,485,304,128.0,1217,747,295,662,2409,4878,409,1207,1858,1029,1393,759,1450,1445,2021,2064,1454,1676,1509,565,258,406,1264,469,572,1575,1786,158,115,698,1203,1097,1508,2067,660,348,850,1086,780,410,1250,653,1994,124,2154,617,1439,445,717,421,824,176,310,1179,892,1909,923,903,431,540,1186,3766,278,3.479,3.066,2.772,3.12,2.489,2.844,2.562,3.088,3.091,3.667,2.952,1.828,3.352,3.21,2.896,3.136,3.115,3.777,3.004,1.938,3.197,2.611,2.119,3.282,2.831,3.107,2.621,2.23,2.91,2.872,2.987,2.443,2.554,3.455,3.491,2.665,3.336,3.154,2.579,3.228,2.835,1.912,3.52,1.921,2.041,2.375,3.507,2.997,2.903,2.715,2.316,2.283,2.38,2.434,3.849,2.223,2.995,2.623,2.098,2.266,2.499,3.179,2.13,2.465,2.472,2.471,2.235,2.707,2.482,2.98,2.914,2.633,2.823,2.423,0.986,0.661,0.598,0.599,0.678,0.539,0.604,0.582,0.866,0.401,0.83,0.512,0.556,0.45,0.586,0.75,0.66,0.94,0.657,0.597,0.751,0.556,0.522,0.678,0.498,0.527,0.515,0.55,0.489,0.586,0.805,1.252,0.465,0.633,0.877,0.488,0.736,0.695,0.26,0.491,0.648,0.475,0.804,0.653,0.593,0.53,0.602,0.53,0.637,0.526,0.361,0.548,0.676,0.622,0.506,0.498,0.578,0.54,0.41,0.377,0.611,0.919,0.446,0.881,0.525,0.755,0.483,0.477,0.528,1.14,0.512,0.529,0.56,0.451
-255,Epileptic,F,22.0,0.4,1.4,0.6,0.9,1.5,1.3,1.5,1.3,0.9,0.7,0.3,2.2,1.3,0.5,1.1,6.4,7.3,0.7,1.9,3.4,0.9,3.0,1.6,3.0,4.0,2.8,3.2,2.1,2.4,2.9,0.9,1.6,0.4,2.6,0.2,0.7,3.5,2.9,0.1,0.3,0.9,3.6,2.0,2.9,2.2,0.7,0.5,0.7,1.0,0.5,0.5,1.5,1.4,2.1,0.2,2.4,1.0,1.5,0.9,1.1,0.7,1.4,0.1,0.3,1.4,1.7,2.5,0.8,0.9,0.4,0.9,1.2,4.3,0.1,3,19,7,8,16,16,14,16,10,7,5.0,26,18,6,11,66,79,6,25,35,12,34,15,37,49,29,40,21,22,30,17,16,4,27,2,7,50,35,1,2,5,33,26,23,14,5,2,6,5,5,3,12,12,19,1,22,6,16,6,9,4,12,1,5,12,19,21,5,8,6,6,11,35,2,0.024,0.018,0.012,0.018,0.035,0.029,0.022,0.03,0.042,0.031,0.025,0.033,0.031,0.036,0.028,0.037,0.025,0.028,0.027,0.031,0.02,0.036,0.028,0.033,0.032,0.026,0.03,0.028,0.023,0.026,0.028,0.061,0.032,0.035,0.01,0.021,0.033,0.034,0.016,0.021,0.019,0.043,0.034,0.032,0.02,0.015,0.021,0.015,0.016,0.01,0.038,0.017,0.028,0.017,0.034,0.018,0.022,0.022,0.026,0.021,0.022,0.024,0.013,0.015,0.019,0.036,0.02,0.014,0.015,0.014,0.025,0.015,0.017,0.017,1218,3809,2035,2008,1835,2361,2922,2679,1240,1399,680.0,2144,3437,1015,2459,11468,19316,2144,3933,3698,3665,4403,2201,6091,7247,5701,5601,3307,6116,5141,1553,1182,759,5528,1471,1855,7524,6774,255,602,1363,3064,5956,2208,2809,1265,794,2096,2089,1273,415,3108,2143,5285,302,3767,1612,2572,1000,1569,1089,2511,367,706,2351,847,4264,2112,2074,1204,1356,2267,9270,323,0.087,0.109,0.094,0.097,0.137,0.144,0.108,0.146,0.126,0.129,0.133,0.154,0.139,0.13,0.133,0.148,0.123,0.116,0.137,0.141,0.102,0.153,0.13,0.133,0.144,0.131,0.139,0.121,0.108,0.127,0.116,0.148,0.144,0.152,0.073,0.11,0.14,0.139,0.1,0.117,0.108,0.171,0.14,0.127,0.101,0.098,0.107,0.086,0.09,0.09,0.165,0.096,0.126,0.094,0.128,0.108,0.105,0.115,0.13,0.109,0.107,0.12,0.103,0.118,0.105,0.158,0.11,0.088,0.097,0.11,0.119,0.109,0.097,0.139,331,1334,740,792,737,902,974,838,387,356,221.0,1133,843,245,628,2922,4677,387,1107,1707,857,1440,907,1567,2107,1731,1866,1242,1507,1572,523,496,233,1177,388,606,1848,1576,141,233,650,1436,1158,1448,1677,638,328,872,973,580,221,1385,866,2130,135,2016,685,1269,537,875,467,926,176,401,1151,585,1976,901,920,508,616,1152,3798,125,3.431,2.744,2.505,2.545,2.084,2.347,2.49,2.781,2.431,3.149,2.682,1.745,3.028,2.826,2.919,2.914,3.211,3.873,2.865,1.905,3.237,2.471,2.05,2.828,2.604,2.703,2.329,2.119,2.953,2.571,2.243,2.519,2.589,3.286,3.252,2.768,2.995,3.033,2.046,2.557,2.697,1.952,3.388,1.838,1.899,2.277,3.115,2.92,2.631,2.487,2.256,2.369,2.265,2.491,2.397,2.056,2.697,2.321,2.292,2.023,2.504,2.657,2.379,1.954,2.299,1.857,2.197,2.685,2.503,2.641,2.633,2.455,2.556,2.899,0.642,0.551,0.696,0.403,0.557,0.507,0.647,0.42,0.479,0.547,0.696,0.409,0.48,0.554,0.556,0.64,0.634,0.657,0.529,0.535,0.841,0.493,0.461,0.706,0.571,0.588,0.595,0.504,0.513,0.529,0.614,0.843,0.662,0.708,0.63,0.699,0.684,0.619,0.363,0.529,0.528,0.506,0.666,0.668,0.542,0.432,0.605,0.79,0.549,0.423,0.376,0.543,0.6,0.523,0.456,0.42,0.522,0.557,0.39,0.333,0.44,0.657,0.436,0.897,0.627,0.62,0.509,0.511,0.451,0.64,0.489,0.438,0.498,0.636
-256,Epileptic,M,32.0,0.6,2.7,1.0,1.2,1.4,1.6,1.6,2.5,0.9,0.7,0.6,3.2,1.6,0.6,0.9,5.8,7.6,0.9,2.1,3.2,0.7,2.2,1.6,3.8,3.4,4.5,2.9,2.4,2.7,3.2,0.9,1.2,0.7,2.7,0.6,1.5,4.1,3.6,0.2,0.3,2.0,2.4,2.4,2.2,2.7,0.5,0.6,1.0,1.2,0.5,0.4,1.9,1.4,2.8,0.1,2.1,0.6,1.7,0.4,0.7,0.5,1.1,0.4,0.4,1.5,1.7,2.5,1.5,0.9,0.6,1.1,0.8,3.8,0.3,4,31,12,11,17,16,13,23,9,7,6.0,28,15,8,11,70,80,8,19,25,7,21,16,40,40,47,34,26,23,42,12,12,5,29,3,12,39,39,2,2,7,24,23,21,16,4,3,7,8,4,3,15,10,23,1,20,3,17,3,7,5,8,3,4,13,22,24,11,7,7,9,7,32,2,0.03,0.024,0.018,0.022,0.025,0.025,0.026,0.026,0.03,0.034,0.043,0.035,0.027,0.038,0.026,0.026,0.025,0.033,0.029,0.033,0.018,0.033,0.03,0.033,0.033,0.032,0.026,0.024,0.024,0.033,0.021,0.044,0.03,0.025,0.017,0.023,0.042,0.033,0.014,0.021,0.019,0.032,0.033,0.026,0.02,0.011,0.018,0.016,0.015,0.017,0.024,0.022,0.018,0.019,0.023,0.014,0.02,0.016,0.016,0.017,0.027,0.017,0.014,0.016,0.016,0.028,0.019,0.017,0.016,0.014,0.021,0.015,0.016,0.011,1907,5643,2463,2585,2647,2910,3029,5385,1870,1669,965.0,3307,4441,1271,2099,14097,19738,2392,4129,3499,2582,4381,2688,7871,7044,9072,5631,4800,7282,6191,2120,1687,1481,8311,2490,3522,7763,9029,448,437,2350,3069,7694,2558,3682,1453,1130,2360,2969,1500,605,4053,2499,6615,214,4637,1336,3900,800,1444,804,2999,596,1176,2916,1361,4578,3647,2067,1609,2026,2497,9297,1074,0.104,0.124,0.113,0.114,0.124,0.128,0.112,0.124,0.115,0.132,0.118,0.155,0.127,0.134,0.117,0.122,0.115,0.11,0.12,0.131,0.079,0.142,0.135,0.133,0.138,0.131,0.125,0.113,0.103,0.144,0.114,0.124,0.121,0.12,0.082,0.104,0.153,0.142,0.098,0.111,0.091,0.145,0.142,0.126,0.1,0.083,0.102,0.083,0.082,0.094,0.117,0.111,0.104,0.095,0.104,0.098,0.103,0.092,0.102,0.103,0.131,0.097,0.102,0.096,0.098,0.124,0.108,0.096,0.098,0.115,0.118,0.105,0.097,0.068,408,1787,903,885,978,1041,993,1664,565,424,272.0,1436,1058,302,652,3430,5121,460,1135,1596,737,1128,987,1929,1924,2581,1895,1526,1719,1765,630,576,369,1699,508,1099,1666,1942,232,193,1421,1303,1302,1442,2044,675,468,853,1259,518,318,1435,1101,2556,88,2252,500,1845,406,719,340,970,320,513,1437,916,2111,1424,855,638,872,950,3760,437,3.898,2.807,2.817,2.911,2.475,2.59,2.51,2.832,2.607,3.132,2.68,1.909,3.175,3.004,2.612,3.017,3.084,3.879,2.904,1.9,2.729,2.862,2.364,3.057,2.791,2.652,2.403,2.42,3.117,2.765,2.543,2.785,2.951,3.461,3.812,2.761,3.269,3.337,2.127,2.832,1.869,2.086,3.645,1.959,2.057,2.413,3.121,3.312,2.876,3.46,2.411,2.755,2.381,2.712,2.487,2.315,3.098,2.36,2.355,2.258,2.843,3.078,2.313,2.43,2.305,1.825,2.245,2.915,2.69,2.816,2.632,2.975,2.683,2.72,0.818,0.651,0.506,0.52,0.499,0.359,0.69,0.573,0.585,0.64,0.803,0.62,0.502,0.537,0.473,0.551,0.555,0.647,0.859,0.555,0.685,0.516,0.598,0.662,0.493,0.818,0.5,0.572,0.544,0.516,0.604,1.036,0.773,0.651,0.728,0.736,0.685,0.681,0.259,0.541,0.746,0.603,0.763,0.611,0.626,0.492,0.48,0.829,0.481,0.857,0.374,0.605,0.495,0.478,0.527,0.349,0.603,0.515,0.359,0.299,0.344,0.766,0.398,1.159,0.532,0.589,0.404,0.364,0.378,0.452,0.375,0.589,0.506,0.579
-257,Epileptic,F,22.0,1.0,2.2,0.8,1.3,3.4,1.4,1.5,1.6,1.4,0.9,0.3,3.5,2.1,0.3,1.1,6.8,9.4,1.1,2.2,5.6,1.9,2.1,2.3,3.8,6.2,2.9,4.0,2.5,2.8,2.8,2.3,1.8,0.5,2.8,0.5,0.9,5.1,3.7,0.1,0.1,1.0,3.6,2.8,3.5,3.1,0.6,0.4,1.0,1.2,0.6,0.7,1.4,1.8,2.5,0.4,2.3,0.8,1.2,1.5,1.0,0.4,2.0,0.4,0.8,2.0,1.0,3.5,1.0,0.9,0.7,0.9,1.6,5.0,0.2,8,20,8,13,32,16,13,18,15,12,5.0,31,22,8,19,79,91,8,28,55,16,27,20,39,58,34,48,29,25,31,18,10,4,31,2,8,61,44,1,1,6,33,26,23,15,8,2,5,7,5,5,10,12,17,3,21,5,10,9,9,4,15,2,6,15,16,33,10,5,5,6,11,41,2,0.051,0.03,0.018,0.026,0.053,0.027,0.025,0.027,0.054,0.04,0.039,0.037,0.035,0.032,0.038,0.037,0.036,0.046,0.036,0.038,0.033,0.034,0.035,0.042,0.045,0.028,0.034,0.026,0.03,0.031,0.057,0.047,0.034,0.037,0.015,0.019,0.043,0.037,0.02,0.018,0.025,0.037,0.04,0.032,0.021,0.019,0.023,0.017,0.019,0.013,0.027,0.018,0.026,0.022,0.012,0.02,0.015,0.016,0.026,0.022,0.015,0.028,0.024,0.027,0.02,0.02,0.022,0.019,0.014,0.02,0.026,0.019,0.018,0.017,1529,4515,2094,2533,1875,2728,3107,3701,1634,1636,668.0,3616,2986,840,2387,11378,18108,2342,4451,5973,4857,3934,2055,5721,7129,6785,6892,5073,6223,5070,2081,1286,743,5876,1894,2017,9129,7847,279,273,1238,3105,5793,3385,3710,1196,683,1942,2266,1730,617,2551,2332,4385,792,3997,1591,2861,1543,1425,601,3156,404,835,3410,1365,5410,2117,1758,1032,1073,2770,10064,393,0.134,0.127,0.109,0.119,0.162,0.126,0.113,0.124,0.161,0.152,0.136,0.143,0.133,0.154,0.142,0.15,0.144,0.137,0.143,0.152,0.12,0.119,0.117,0.135,0.138,0.119,0.131,0.106,0.116,0.135,0.143,0.13,0.118,0.138,0.081,0.105,0.148,0.144,0.095,0.105,0.115,0.132,0.145,0.129,0.1,0.109,0.104,0.087,0.093,0.089,0.144,0.097,0.122,0.109,0.094,0.11,0.095,0.1,0.122,0.112,0.103,0.127,0.128,0.133,0.113,0.112,0.107,0.101,0.088,0.124,0.132,0.103,0.102,0.128,426,1309,727,879,907,940,967,1050,510,417,180.0,1550,981,225,619,3115,4702,448,1176,2512,1026,1131,1012,1573,2214,1855,2203,1697,1578,1540,651,493,228,1316,497,694,2236,1853,133,134,639,1343,1297,1835,2191,556,285,847,1062,756,368,1259,1076,1882,456,1988,759,1260,844,730,332,1132,216,481,1527,815,2491,921,833,445,512,1279,4239,169,3.44,3.049,2.831,2.767,1.922,2.431,2.56,2.891,2.35,2.969,2.714,1.994,2.48,2.786,2.717,2.608,2.949,3.721,2.855,2.053,3.344,2.606,1.863,2.651,2.497,2.806,2.374,2.336,2.868,2.608,2.395,2.811,2.569,3.025,3.467,2.504,2.958,2.968,2.486,2.191,2.513,2.055,3.198,1.994,1.907,2.467,2.951,2.807,2.514,2.526,1.917,2.171,2.172,2.451,2.087,2.129,2.459,2.63,2.109,2.081,1.937,2.769,1.958,2.196,2.401,2.06,2.228,2.461,2.433,2.534,2.334,2.413,2.426,2.885,0.763,0.717,0.444,0.523,0.602,0.644,0.564,0.486,0.796,0.465,0.716,0.537,0.422,0.488,0.637,0.643,0.628,0.741,0.553,0.58,0.759,0.615,0.538,0.822,0.694,0.604,0.645,0.638,0.635,0.539,0.859,0.886,0.442,0.644,0.846,0.473,0.741,0.68,0.385,0.401,0.511,0.596,0.752,0.623,0.582,0.467,0.371,0.667,0.465,0.414,0.294,0.419,0.505,0.458,0.263,0.442,0.442,0.488,0.483,0.458,0.375,0.698,0.494,0.737,0.555,0.594,0.559,0.367,0.42,0.988,0.43,0.444,0.477,0.451
-258,Epileptic,F,52.0,0.5,2.0,1.0,1.3,1.3,2.0,2.1,1.8,1.3,0.6,0.3,2.9,1.2,0.5,1.6,5.2,8.6,0.8,1.7,4.3,0.6,2.1,1.4,2.7,2.4,3.0,2.9,2.0,2.7,2.7,1.1,0.9,0.7,2.0,0.4,0.6,2.8,3.6,0.3,0.2,1.0,2.6,2.2,2.0,2.6,0.8,0.3,0.8,1.3,0.7,0.4,1.6,1.6,2.2,0.2,2.4,0.6,1.4,0.8,0.7,0.6,1.1,0.4,0.5,1.2,1.7,2.4,1.2,1.2,0.8,0.6,1.0,5.0,0.3,3,16,14,12,13,14,18,17,12,5,6.0,21,15,7,16,60,78,6,19,38,5,27,14,35,29,36,34,18,21,30,22,7,7,27,3,5,30,41,4,1,6,24,20,17,14,5,2,6,6,5,3,12,12,14,1,17,5,9,6,5,4,8,3,3,8,21,15,7,7,8,6,9,46,3,0.026,0.027,0.021,0.025,0.039,0.036,0.035,0.032,0.043,0.032,0.032,0.035,0.031,0.028,0.038,0.038,0.034,0.03,0.031,0.037,0.013,0.033,0.026,0.042,0.031,0.031,0.029,0.026,0.026,0.032,0.042,0.043,0.032,0.031,0.013,0.018,0.031,0.038,0.026,0.014,0.023,0.034,0.041,0.029,0.022,0.02,0.021,0.016,0.017,0.03,0.024,0.018,0.029,0.021,0.016,0.02,0.022,0.019,0.024,0.016,0.023,0.026,0.05,0.025,0.018,0.034,0.018,0.019,0.025,0.031,0.022,0.021,0.021,0.015,1610,4208,2200,2381,1889,3533,2755,3575,2127,1177,565.0,2412,3013,1043,3206,10514,16383,2481,4018,4781,3210,4084,1863,5319,4845,6750,5081,3649,5684,4561,2287,681,1182,5841,1836,1702,6081,7452,578,459,1402,2543,6035,1736,2931,1477,692,2031,2390,1372,576,3193,1960,4374,323,3397,1365,2968,990,1258,972,2088,452,706,2183,1213,3904,2209,1758,1463,1334,2351,10357,524,0.092,0.119,0.115,0.118,0.143,0.14,0.132,0.137,0.167,0.138,0.149,0.142,0.126,0.134,0.16,0.144,0.139,0.115,0.128,0.147,0.079,0.145,0.117,0.151,0.137,0.136,0.126,0.101,0.103,0.145,0.148,0.125,0.141,0.125,0.072,0.085,0.136,0.156,0.136,0.093,0.115,0.14,0.153,0.125,0.105,0.103,0.103,0.089,0.095,0.117,0.116,0.099,0.13,0.104,0.104,0.108,0.122,0.099,0.127,0.1,0.111,0.123,0.159,0.109,0.102,0.143,0.1,0.1,0.113,0.141,0.121,0.112,0.109,0.111,380,1297,792,919,662,975,975,989,470,297,185.0,1156,791,332,823,2671,4429,423,903,1874,656,1104,782,1329,1369,1725,1663,1225,1474,1363,550,281,378,1188,478,543,1444,1669,220,202,671,1155,1000,1179,1805,713,274,852,1064,442,320,1403,865,1645,170,1773,439,1235,519,667,405,668,157,338,1049,777,1972,1016,784,475,533,949,3905,272,3.778,2.927,2.553,2.573,2.605,3.149,2.378,3.118,3.038,3.311,2.58,1.872,2.872,2.449,2.915,2.856,2.896,4.0,3.377,2.209,3.527,2.769,2.085,3.016,2.709,3.055,2.375,2.328,2.841,2.644,3.043,2.311,2.553,3.279,3.173,2.787,3.092,3.156,2.447,2.35,2.765,1.953,3.745,1.631,1.834,2.26,2.96,2.815,2.749,3.558,2.233,2.376,2.476,2.6,2.079,2.192,3.092,2.735,2.217,2.141,2.768,3.035,2.932,2.351,2.291,1.999,2.195,2.563,2.496,3.076,2.75,2.58,2.729,2.382,0.969,0.672,0.578,0.515,0.498,0.681,0.533,0.568,0.581,0.624,0.759,0.54,0.539,0.638,0.7,0.631,0.57,0.826,0.482,0.727,0.685,0.568,0.487,0.651,0.556,0.58,0.489,0.587,0.521,0.573,0.728,0.927,0.336,0.662,0.75,0.665,0.75,0.705,0.572,0.504,0.52,0.618,0.776,0.742,0.545,0.567,0.64,0.663,0.527,0.87,0.527,0.511,0.573,0.523,0.329,0.416,0.569,0.502,0.451,0.305,0.453,0.814,0.638,0.898,0.562,0.653,0.441,0.452,0.433,0.863,0.628,0.603,0.549,0.452
-259,Epileptic,M,47.0,0.9,2.0,1.2,1.0,1.5,1.7,1.6,1.7,1.2,0.6,0.2,1.9,1.8,0.6,1.5,4.8,6.9,0.8,1.6,4.5,0.5,3.3,1.4,2.9,2.7,3.3,2.0,2.4,2.5,3.1,1.4,1.2,0.7,2.2,0.1,0.5,3.8,3.1,0.2,0.1,0.9,2.7,1.4,2.2,2.7,0.9,0.5,0.8,1.2,0.6,0.6,1.7,0.9,2.0,0.3,1.3,0.7,1.1,0.9,0.9,0.6,1.4,0.6,0.4,1.8,1.6,2.2,1.6,0.9,0.6,0.7,1.5,4.2,0.3,9,20,10,12,18,21,16,15,14,6,2.0,20,18,6,15,60,69,9,23,39,12,32,15,34,32,34,27,22,23,36,15,9,6,28,1,4,48,30,2,1,6,23,19,19,15,8,3,6,7,7,6,13,7,12,2,13,5,8,5,8,5,15,2,6,16,13,17,10,8,5,4,13,33,2,0.039,0.022,0.021,0.015,0.031,0.029,0.02,0.03,0.03,0.039,0.029,0.027,0.03,0.028,0.032,0.028,0.024,0.035,0.025,0.035,0.015,0.036,0.028,0.03,0.03,0.032,0.02,0.023,0.022,0.031,0.029,0.05,0.035,0.029,0.007,0.013,0.034,0.032,0.02,0.012,0.02,0.033,0.03,0.025,0.021,0.023,0.02,0.017,0.016,0.021,0.029,0.016,0.017,0.016,0.035,0.014,0.018,0.012,0.02,0.017,0.017,0.02,0.024,0.016,0.025,0.036,0.017,0.019,0.015,0.024,0.018,0.025,0.019,0.018,1455,4501,2435,2350,2292,2681,2915,3078,2242,937,531.0,2423,3642,966,2682,10037,16658,2323,3518,4665,2727,4377,1688,5800,5277,5897,4441,4415,6075,5104,2167,1318,986,4815,1434,1723,7362,6524,430,439,1376,2722,5078,2380,3840,1372,898,1805,2471,1412,684,3377,1556,4160,376,2809,1368,2577,1060,1320,1127,2766,590,933,2401,1073,3615,2871,2015,1057,1189,2683,7969,371,0.14,0.114,0.105,0.106,0.138,0.147,0.101,0.126,0.142,0.152,0.113,0.135,0.132,0.119,0.14,0.132,0.119,0.13,0.125,0.139,0.087,0.149,0.118,0.128,0.141,0.145,0.111,0.103,0.099,0.146,0.124,0.125,0.139,0.129,0.051,0.084,0.153,0.136,0.119,0.087,0.112,0.141,0.136,0.118,0.101,0.121,0.111,0.094,0.093,0.115,0.145,0.096,0.104,0.091,0.141,0.096,0.104,0.085,0.112,0.106,0.098,0.117,0.125,0.117,0.124,0.133,0.104,0.103,0.099,0.136,0.105,0.126,0.102,0.111,449,1523,915,984,853,1090,974,951,614,288,175.0,1067,970,280,787,2791,4660,439,1030,2040,659,1443,808,1734,1495,1831,1629,1425,1641,1614,695,495,312,1181,409,533,1843,1545,198,223,661,1199,917,1498,2110,635,366,808,1119,524,335,1628,822,1841,170,1458,625,1311,666,764,502,1091,299,485,1176,651,1827,1327,932,430,596,1067,3446,217,3.183,2.715,2.552,2.382,2.365,2.181,2.404,2.83,2.776,2.92,2.768,1.978,2.737,2.521,2.666,2.744,2.938,3.745,2.691,1.917,2.949,2.346,1.884,2.623,2.625,2.719,2.307,2.421,2.796,2.56,2.377,2.539,2.494,2.91,2.95,2.765,3.02,3.041,2.195,2.182,2.72,2.013,3.745,1.783,2.071,2.335,3.176,2.858,2.719,2.982,2.083,2.204,2.261,2.449,2.429,2.012,2.526,2.316,1.868,1.93,2.281,2.483,2.393,2.106,2.143,1.961,2.166,2.51,2.471,2.871,2.218,2.869,2.446,2.078,0.786,0.668,0.617,0.524,0.551,0.471,0.537,0.471,0.415,0.591,0.772,0.377,0.46,0.442,0.61,0.499,0.536,0.705,0.477,0.634,0.767,0.459,0.344,0.743,0.483,0.524,0.411,0.52,0.52,0.482,0.744,0.804,0.354,0.53,0.885,0.529,0.662,0.574,0.504,0.427,0.501,0.438,0.827,0.557,0.619,0.495,0.312,0.775,0.459,0.57,0.487,0.449,0.46,0.439,0.384,0.428,0.437,0.345,0.371,0.378,0.486,0.6,0.398,0.945,0.558,0.729,0.346,0.348,0.386,0.626,0.486,0.665,0.543,0.494
-260,Epileptic,F,32.0,0.7,2.2,0.6,1.1,1.2,1.5,1.6,2.0,1.4,0.7,0.2,2.5,1.5,0.6,1.2,5.0,6.7,1.0,1.7,4.1,1.0,2.6,1.3,2.7,3.6,3.9,2.9,2.3,3.0,3.0,1.4,1.6,0.6,2.3,0.3,0.5,3.9,2.6,0.2,0.3,1.0,2.0,1.6,2.2,2.9,0.7,0.3,2.1,1.4,0.6,0.6,1.8,1.1,3.0,0.2,2.0,0.4,1.9,0.9,1.0,0.5,1.3,0.4,0.3,1.7,1.0,2.3,1.2,1.1,0.4,1.3,1.4,4.0,0.3,6,20,5,7,14,17,10,20,13,6,3.0,25,15,6,9,53,55,6,19,35,11,27,13,30,36,37,36,26,21,35,21,7,7,32,2,5,40,34,2,2,7,17,19,17,17,7,2,10,7,5,5,11,7,21,1,14,3,13,7,9,4,11,3,4,12,14,16,8,7,3,11,10,30,3,0.033,0.022,0.014,0.025,0.036,0.023,0.022,0.031,0.038,0.029,0.024,0.029,0.027,0.029,0.027,0.031,0.025,0.032,0.026,0.033,0.022,0.027,0.026,0.031,0.034,0.033,0.027,0.027,0.027,0.027,0.033,0.071,0.021,0.028,0.016,0.014,0.037,0.029,0.014,0.015,0.02,0.026,0.029,0.023,0.02,0.017,0.013,0.027,0.017,0.012,0.029,0.02,0.019,0.022,0.017,0.015,0.014,0.023,0.025,0.018,0.02,0.022,0.03,0.015,0.022,0.035,0.019,0.018,0.021,0.014,0.029,0.023,0.016,0.014,1404,4623,1907,1855,1949,3509,3025,3447,1637,1425,472.0,2527,3527,1044,2680,9877,15524,2208,3685,4143,3171,4153,1909,5597,5933,6336,5742,4003,6173,5490,2397,922,1361,6971,1679,1990,6856,7569,395,473,1646,2396,4438,2731,3624,1361,1030,2710,2408,1684,522,3002,1906,5324,225,3668,1084,3337,1187,1595,957,2749,525,805,2588,682,3660,2351,1521,844,1821,2592,9055,418,0.124,0.112,0.089,0.103,0.144,0.121,0.097,0.145,0.142,0.126,0.119,0.127,0.122,0.123,0.122,0.138,0.119,0.119,0.12,0.133,0.098,0.131,0.121,0.128,0.14,0.14,0.127,0.111,0.109,0.134,0.13,0.179,0.101,0.134,0.081,0.08,0.136,0.137,0.1,0.108,0.111,0.12,0.114,0.114,0.101,0.099,0.083,0.105,0.093,0.086,0.135,0.102,0.099,0.101,0.117,0.098,0.088,0.108,0.124,0.106,0.109,0.108,0.132,0.115,0.109,0.133,0.105,0.099,0.119,0.09,0.141,0.113,0.097,0.118,396,1617,700,701,680,1079,959,1113,493,405,172.0,1240,965,294,667,2719,4241,460,1086,1929,865,1479,779,1507,1805,1991,1920,1410,1601,1835,696,371,406,1481,399,662,1883,1675,229,236,779,1104,1074,1503,2173,657,390,1082,1182,711,346,1375,900,2130,131,1883,470,1389,555,882,407,968,281,381,1246,419,1888,1078,703,446,781,1049,3782,243,3.481,2.725,2.724,2.456,2.429,2.733,2.55,2.763,2.52,2.834,2.343,1.762,2.81,2.589,2.864,2.758,2.911,3.685,2.81,1.874,3.018,2.371,2.115,2.835,2.561,2.668,2.387,2.27,2.914,2.438,2.606,2.683,2.646,3.32,3.691,2.644,2.851,3.133,1.87,2.298,2.621,1.934,2.95,2.054,1.899,2.172,3.609,3.217,2.563,2.672,1.987,2.429,2.328,2.545,1.999,2.194,2.649,2.699,2.206,2.038,2.738,2.719,2.122,2.195,2.356,1.916,2.185,2.523,2.547,2.149,2.507,2.698,2.674,2.229,0.843,0.638,0.673,0.59,0.554,0.645,0.612,0.423,0.601,0.441,0.672,0.492,0.387,0.448,0.545,0.572,0.643,0.777,0.567,0.576,0.781,0.422,0.455,0.609,0.509,0.495,0.476,0.558,0.643,0.508,0.804,0.831,0.466,0.597,0.63,0.472,0.723,0.61,0.372,0.468,0.4,0.483,0.818,0.603,0.583,0.491,0.527,0.861,0.507,0.49,0.367,0.492,0.497,0.561,0.45,0.469,0.394,0.517,0.495,0.367,0.343,0.776,0.432,0.948,0.468,0.556,0.415,0.382,0.437,0.594,0.412,0.456,0.488,0.27
-261,Epileptic,M,42.0,0.7,1.8,0.9,1.1,1.5,3.0,1.3,1.4,0.9,0.8,0.2,2.4,1.3,0.6,1.2,4.9,6.6,0.8,2.0,3.8,1.5,2.5,1.6,3.0,2.9,3.1,2.6,2.6,3.4,3.2,1.4,1.1,0.4,2.4,0.4,0.9,3.3,3.1,0.2,0.3,1.3,3.5,2.5,2.2,3.3,0.9,0.4,0.7,1.2,0.7,0.8,2.0,2.5,2.2,0.2,2.3,0.5,2.6,1.4,0.7,0.8,1.1,0.4,0.4,1.8,1.0,2.7,1.1,1.8,0.7,0.8,0.9,4.9,0.3,5,21,9,15,13,28,13,15,10,9,3.0,22,12,9,12,51,63,10,31,41,16,25,14,37,35,38,32,23,23,39,20,8,4,27,3,6,29,37,2,2,8,31,19,18,20,6,2,4,6,8,6,16,21,17,1,23,3,20,9,6,5,12,2,5,16,15,19,7,10,4,6,6,45,3,0.03,0.022,0.016,0.025,0.036,0.041,0.023,0.025,0.04,0.04,0.029,0.035,0.028,0.032,0.027,0.033,0.029,0.03,0.027,0.034,0.024,0.034,0.035,0.038,0.032,0.027,0.026,0.03,0.032,0.033,0.034,0.031,0.022,0.03,0.016,0.019,0.041,0.033,0.012,0.019,0.024,0.036,0.043,0.032,0.025,0.017,0.022,0.011,0.016,0.022,0.043,0.024,0.029,0.017,0.021,0.019,0.016,0.028,0.032,0.017,0.027,0.023,0.023,0.018,0.028,0.018,0.025,0.021,0.023,0.024,0.024,0.019,0.019,0.028,1445,3900,2122,2191,1723,3410,2398,3267,1326,1491,491.0,2024,3230,1210,2479,8951,13712,2353,4517,4063,4142,3550,1483,4849,4800,6502,4385,3259,5185,4690,2430,1485,905,6112,1794,1902,6541,6207,345,486,1462,2262,5356,1917,3267,1294,743,2718,2219,1600,643,3132,3570,4833,257,3474,1282,3076,1078,1134,859,2092,440,971,2097,1464,2983,1608,2409,955,1174,1778,9404,328,0.112,0.123,0.102,0.112,0.144,0.167,0.1,0.115,0.153,0.152,0.113,0.156,0.118,0.133,0.124,0.139,0.13,0.131,0.135,0.154,0.095,0.138,0.14,0.151,0.144,0.134,0.127,0.114,0.111,0.144,0.14,0.104,0.101,0.132,0.084,0.099,0.148,0.145,0.092,0.116,0.119,0.149,0.154,0.139,0.113,0.11,0.114,0.074,0.088,0.112,0.172,0.118,0.133,0.091,0.112,0.113,0.087,0.119,0.143,0.101,0.125,0.12,0.119,0.119,0.136,0.121,0.123,0.109,0.109,0.121,0.123,0.108,0.106,0.147,446,1301,799,888,724,1345,865,1034,435,399,148.0,1035,840,318,742,2702,3880,439,1319,1832,1050,1268,707,1428,1513,1979,1720,1296,1502,1601,654,572,347,1333,445,699,1390,1615,210,216,797,1423,1046,1085,2073,703,312,994,1071,553,324,1381,1404,2052,123,1913,482,1606,695,672,436,830,263,445,1066,896,1624,799,1162,414,547,806,4113,171,3.405,2.896,2.54,2.415,2.344,2.398,2.283,2.697,2.534,2.933,2.506,1.812,2.878,2.716,2.563,2.622,2.843,3.681,2.788,1.98,2.906,2.271,1.899,2.782,2.497,2.637,2.094,2.047,2.664,2.463,2.84,2.546,2.255,3.246,3.683,2.557,3.244,2.915,1.95,2.676,2.278,1.535,3.708,1.88,1.832,2.01,3.081,3.212,2.486,3.067,2.348,2.315,2.535,2.422,2.386,2.101,2.922,2.197,1.879,1.884,2.313,2.689,1.978,2.647,2.324,1.95,2.07,2.464,2.453,2.838,2.458,2.49,2.403,2.138,0.675,0.608,0.472,0.474,0.463,0.489,0.576,0.538,0.525,0.713,0.802,0.5,0.392,0.347,0.419,0.498,0.494,0.694,0.643,0.592,0.798,0.472,0.403,0.622,0.522,0.508,0.472,0.525,0.503,0.549,0.546,1.091,0.541,0.533,0.735,0.519,0.787,0.582,0.391,0.415,0.454,0.352,0.577,0.642,0.518,0.369,0.438,0.85,0.549,0.676,0.522,0.469,0.576,0.438,0.472,0.446,0.842,0.55,0.481,0.382,0.395,0.653,0.377,0.834,0.478,0.651,0.506,0.393,0.453,0.829,0.487,0.593,0.552,0.782
-262,Epileptic,F,27.0,0.8,1.7,0.7,1.2,1.6,2.7,1.7,1.2,0.9,0.7,0.2,2.1,1.2,0.3,1.3,4.8,8.9,0.9,1.5,4.6,2.4,3.5,2.6,4.3,2.8,2.4,3.7,3.0,2.9,2.6,1.9,1.2,0.4,2.0,0.4,0.5,4.2,2.9,0.1,0.1,0.8,3.4,2.9,1.6,2.4,0.7,0.5,0.9,1.2,0.8,0.6,2.3,1.6,2.3,0.3,2.3,0.7,1.4,1.5,1.0,0.7,1.4,0.2,0.5,1.5,1.0,2.0,1.0,1.5,0.7,0.9,1.5,4.0,0.2,10,17,6,10,15,30,17,14,11,6,2.0,21,12,3,13,69,77,6,16,41,23,38,19,38,34,25,31,30,26,30,27,8,5,24,3,5,35,37,0,1,5,28,28,12,14,4,2,5,6,5,5,17,12,17,2,18,5,12,13,8,6,8,2,4,11,16,12,7,12,6,6,13,30,2,0.038,0.024,0.018,0.026,0.036,0.04,0.034,0.03,0.036,0.039,0.031,0.042,0.025,0.035,0.028,0.034,0.032,0.033,0.032,0.046,0.043,0.038,0.051,0.041,0.04,0.033,0.039,0.036,0.032,0.033,0.046,0.047,0.026,0.028,0.018,0.019,0.051,0.034,0.01,0.015,0.023,0.047,0.054,0.021,0.019,0.016,0.023,0.019,0.019,0.013,0.027,0.024,0.023,0.021,0.02,0.019,0.025,0.02,0.026,0.019,0.02,0.025,0.013,0.019,0.026,0.023,0.022,0.018,0.021,0.023,0.024,0.021,0.017,0.026,1510,4063,2175,2157,1741,3690,2554,2184,2158,1029,650.0,2349,3052,769,2872,10415,17470,2508,3556,4093,4107,5025,1829,6662,4456,4486,4830,3445,5658,5226,2266,937,741,5402,1703,1425,5711,6986,394,416,1040,1961,5111,2285,3127,1197,893,1952,1933,1786,577,3559,2131,4471,437,3484,1204,2870,1442,1492,1017,2501,556,918,2092,912,2713,2079,2357,1174,1319,1826,8340,291,0.145,0.115,0.094,0.116,0.124,0.151,0.119,0.139,0.146,0.151,0.11,0.157,0.121,0.13,0.138,0.146,0.137,0.128,0.135,0.174,0.131,0.139,0.159,0.148,0.132,0.14,0.123,0.121,0.118,0.143,0.145,0.125,0.137,0.122,0.07,0.102,0.144,0.145,0.07,0.095,0.11,0.151,0.168,0.108,0.097,0.091,0.097,0.08,0.093,0.087,0.126,0.11,0.122,0.106,0.102,0.106,0.12,0.108,0.135,0.11,0.114,0.116,0.1,0.107,0.12,0.132,0.104,0.097,0.114,0.12,0.125,0.12,0.098,0.153,439,1250,725,813,808,1367,951,790,526,298,186.0,945,742,173,756,2879,4776,436,950,1792,1145,1630,876,1763,1456,1367,1630,1337,1554,1522,764,387,257,1201,435,465,1609,1683,145,200,553,1201,1141,1240,1884,644,363,785,920,796,368,1563,1106,1861,240,1786,499,1187,876,769,506,887,265,422,949,575,1412,907,1112,497,529,1043,3454,151,3.364,3.006,2.792,2.527,1.886,2.371,2.274,2.485,2.806,2.595,2.883,2.152,3.065,3.085,2.932,2.697,2.894,3.808,2.982,2.005,2.831,2.391,1.946,2.842,2.514,2.593,2.399,2.078,2.748,2.732,2.326,2.344,2.593,3.118,3.433,2.475,2.631,3.018,2.718,2.477,2.357,1.574,3.173,2.103,1.903,2.065,2.881,2.987,2.609,2.488,1.983,2.413,2.124,2.327,2.122,2.169,2.524,2.47,1.901,2.109,2.241,2.883,2.252,2.539,2.277,1.979,1.959,2.685,2.143,2.632,2.486,2.063,2.54,2.203,0.921,0.599,0.494,0.599,0.616,0.579,0.642,0.477,0.751,0.799,0.825,0.517,0.357,0.554,0.479,0.57,0.704,0.892,0.522,0.623,0.923,0.64,0.701,0.722,0.644,0.539,0.592,0.582,0.562,0.596,0.704,0.861,0.371,0.571,0.706,0.539,0.803,0.792,0.349,0.522,0.561,0.445,0.805,0.518,0.564,0.501,0.457,0.671,0.407,0.485,0.278,0.408,0.498,0.489,0.452,0.43,0.651,0.568,0.399,0.424,0.364,0.58,0.381,0.687,0.431,0.622,0.52,0.431,0.53,0.601,0.526,0.552,0.45,0.479
-263,Epileptic,F,27.0,0.6,1.5,0.4,1.0,1.2,1.8,1.3,1.2,1.2,0.2,0.3,3.4,1.2,0.8,1.0,4.2,6.5,0.6,3.2,4.7,0.6,2.9,1.8,2.7,2.7,2.8,2.6,1.9,2.2,3.0,0.9,0.7,0.7,2.3,0.7,0.6,3.3,2.2,0.1,0.3,1.0,2.5,2.5,3.1,2.7,0.4,0.5,0.7,1.0,0.6,0.6,1.2,0.6,1.9,0.2,2.2,0.8,1.6,1.1,1.3,0.6,0.9,0.1,0.3,2.1,1.6,2.4,1.0,0.7,0.4,1.1,1.0,3.1,0.3,3,16,13,11,14,16,10,12,13,4,3.0,25,13,6,10,46,66,4,30,40,7,28,17,27,32,28,30,19,22,32,10,4,7,26,3,4,35,24,2,2,5,24,18,25,14,3,2,5,6,8,6,10,6,12,1,15,7,15,7,9,3,6,2,3,17,35,18,7,5,4,8,9,22,2,0.035,0.023,0.018,0.024,0.037,0.03,0.021,0.03,0.051,0.026,0.029,0.041,0.023,0.039,0.029,0.03,0.027,0.022,0.034,0.034,0.015,0.037,0.03,0.039,0.029,0.032,0.024,0.025,0.024,0.036,0.041,0.04,0.037,0.031,0.028,0.015,0.036,0.028,0.018,0.026,0.025,0.029,0.036,0.029,0.022,0.012,0.027,0.015,0.016,0.019,0.038,0.017,0.02,0.017,0.03,0.02,0.027,0.018,0.029,0.027,0.03,0.027,0.021,0.023,0.028,0.089,0.024,0.018,0.014,0.019,0.03,0.021,0.018,0.023,1108,3049,864,1441,2425,2594,2609,2429,1694,455,461.0,2909,3266,1651,2899,8834,15878,1968,4184,4300,1929,4562,2076,4789,5531,5632,5153,3546,5523,4946,2183,532,1186,5947,1717,1672,5561,4801,261,492,1380,2739,4789,2626,3259,1021,773,1902,1790,1306,510,2436,1647,4206,187,3497,1071,2852,1077,1531,771,1521,484,802,2461,262,3400,1976,1737,1013,1476,1744,6687,364,0.104,0.123,0.099,0.119,0.133,0.144,0.098,0.14,0.147,0.117,0.11,0.151,0.124,0.148,0.125,0.134,0.129,0.092,0.129,0.133,0.078,0.157,0.145,0.144,0.138,0.144,0.123,0.109,0.114,0.141,0.126,0.102,0.146,0.13,0.101,0.085,0.134,0.123,0.095,0.116,0.117,0.132,0.129,0.121,0.103,0.084,0.115,0.087,0.094,0.122,0.129,0.098,0.099,0.094,0.138,0.106,0.124,0.101,0.125,0.13,0.12,0.129,0.139,0.131,0.132,0.105,0.118,0.102,0.089,0.115,0.13,0.118,0.098,0.115,324,1023,530,673,674,966,843,715,459,182,177.0,1310,823,311,593,2284,4001,411,1502,2041,641,1403,903,1172,1563,1529,1728,1188,1415,1497,416,240,347,1222,430,582,1497,1259,140,216,619,1370,1170,1675,1864,568,308,819,861,570,337,1192,638,1630,73,1733,522,1556,589,758,368,545,123,306,1234,162,1583,819,814,366,627,824,2725,212,3.219,2.774,2.01,2.339,2.692,2.435,2.587,2.881,2.698,2.58,2.478,2.014,2.935,3.332,3.339,2.915,3.107,3.693,2.404,1.885,2.605,2.566,2.092,2.896,2.693,3.031,2.356,2.276,2.932,2.688,3.417,2.282,2.698,3.137,3.331,2.758,2.751,2.889,2.22,2.837,3.105,1.841,3.011,1.808,2.017,1.95,3.253,3.035,2.587,2.545,1.705,2.225,2.495,2.587,3.143,2.301,2.399,2.197,2.159,2.374,2.669,2.993,2.867,2.652,2.297,1.627,2.3,2.76,2.48,2.866,2.635,2.602,2.68,2.157,0.83,0.815,0.432,0.538,0.821,0.584,0.638,0.489,0.644,0.528,0.831,0.497,0.394,0.749,0.557,0.517,0.613,0.807,0.599,0.569,0.758,0.599,0.449,0.753,0.505,0.55,0.472,0.594,0.464,0.616,0.782,0.779,0.422,0.637,0.695,0.482,0.673,0.539,0.393,0.443,0.536,0.598,0.712,0.574,0.651,0.305,0.643,0.644,0.422,0.51,0.284,0.514,0.686,0.542,0.485,0.529,0.396,0.698,0.521,0.382,0.474,0.697,0.983,0.968,0.54,0.798,0.541,0.425,0.44,0.78,0.572,0.603,0.571,0.569
-264,Epileptic,F,47.0,0.6,2.0,0.7,0.8,0.8,1.8,1.2,1.1,0.7,0.3,0.2,2.8,1.0,0.6,0.6,3.5,6.3,0.5,1.5,2.9,1.0,2.4,1.0,2.1,2.1,2.2,2.0,1.6,2.2,1.8,1.1,0.9,0.4,1.8,0.2,0.4,1.7,2.0,0.1,0.1,0.8,2.7,0.9,1.7,2.3,0.4,0.2,0.7,0.9,0.4,0.5,1.1,1.1,2.0,0.3,1.7,0.5,1.4,0.7,0.8,0.5,1.0,0.1,0.3,1.3,1.1,1.9,1.1,0.9,0.7,0.9,0.9,2.3,0.3,4,20,9,7,10,14,10,10,12,4,2.0,20,10,6,5,39,59,4,15,27,10,20,10,23,30,21,22,15,20,27,17,7,5,20,2,4,23,23,1,1,5,21,13,14,15,3,2,5,5,4,4,8,11,16,2,16,4,12,7,8,3,8,1,3,9,14,17,9,7,6,7,7,14,3,0.032,0.029,0.016,0.02,0.018,0.032,0.022,0.021,0.048,0.027,0.03,0.038,0.034,0.033,0.032,0.031,0.03,0.03,0.032,0.034,0.023,0.033,0.024,0.032,0.028,0.026,0.022,0.021,0.022,0.025,0.04,0.036,0.027,0.026,0.012,0.012,0.039,0.034,0.017,0.02,0.02,0.044,0.032,0.025,0.018,0.01,0.014,0.014,0.016,0.019,0.029,0.017,0.024,0.022,0.012,0.016,0.021,0.021,0.027,0.019,0.026,0.022,0.017,0.018,0.021,0.031,0.02,0.019,0.02,0.024,0.019,0.023,0.013,0.021,1098,3741,1817,1873,1777,2652,2300,2981,1248,913,484.0,2015,1782,935,1101,7191,12725,1492,2709,3072,2761,3827,1585,4456,4808,4887,3942,3176,5195,3920,1508,1137,684,4440,1253,1539,4117,4841,335,304,1292,1935,3597,1777,3225,1229,642,1506,1776,875,456,1967,1668,3502,472,2808,951,2427,928,1218,692,1965,325,646,1991,1035,3033,2168,1360,1014,1166,1458,6233,535,0.12,0.128,0.117,0.102,0.106,0.149,0.092,0.107,0.145,0.13,0.125,0.146,0.133,0.149,0.12,0.13,0.132,0.101,0.133,0.138,0.1,0.136,0.115,0.137,0.134,0.124,0.111,0.099,0.103,0.132,0.135,0.131,0.141,0.127,0.072,0.078,0.138,0.136,0.109,0.109,0.106,0.153,0.126,0.11,0.099,0.082,0.093,0.084,0.091,0.115,0.134,0.099,0.121,0.112,0.096,0.1,0.117,0.115,0.129,0.108,0.126,0.111,0.1,0.116,0.111,0.142,0.111,0.102,0.113,0.13,0.116,0.124,0.082,0.135,289,1203,702,713,667,918,777,860,359,249,144.0,991,502,234,321,2034,3618,279,753,1397,640,1095,639,1163,1401,1392,1498,1045,1391,1254,520,421,256,908,291,518,945,1133,147,118,603,988,611,1162,1921,640,249,684,812,338,273,1031,760,1514,305,1612,402,1147,485,701,320,743,128,321,982,519,1605,985,707,430,586,626,2717,201,3.312,2.819,2.451,2.573,2.363,2.578,2.432,2.879,2.574,3.268,2.864,1.814,2.986,2.864,2.575,2.648,2.874,3.968,2.804,1.893,3.267,2.643,2.131,2.891,2.613,2.847,2.197,2.335,2.807,2.526,2.336,2.524,2.397,3.249,3.53,2.62,3.147,3.08,2.392,2.368,2.66,1.768,3.282,1.693,1.909,2.049,3.039,2.614,2.632,2.688,2.098,2.183,2.092,2.277,1.923,1.919,2.948,2.348,2.194,1.908,2.713,2.625,2.925,2.229,2.17,2.619,2.059,2.406,2.23,2.598,2.094,2.945,2.52,3.043,0.815,0.714,0.535,0.518,0.543,0.672,0.488,0.614,0.467,0.353,0.826,0.47,0.504,0.345,0.42,0.511,0.532,0.682,0.538,0.614,0.801,0.476,0.357,0.58,0.51,0.601,0.502,0.448,0.457,0.498,0.62,0.942,0.545,0.503,0.612,0.489,0.784,0.486,0.59,0.611,0.768,0.505,0.811,0.601,0.538,0.441,0.688,0.603,0.521,0.542,0.482,0.444,0.529,0.46,0.263,0.39,0.639,0.599,0.596,0.47,0.661,0.594,0.466,0.751,0.515,0.671,0.465,0.506,0.462,0.598,0.502,0.551,0.534,0.458
-265,Epileptic,F,17.5,0.5,2.2,1.1,1.1,1.6,1.3,1.5,1.5,1.6,0.6,0.3,3.2,1.2,0.4,1.3,6.1,8.3,0.5,1.9,4.7,1.5,3.1,1.6,3.6,2.8,3.2,3.8,1.8,2.6,3.8,1.5,2.4,0.5,2.2,0.4,0.3,3.5,2.9,0.2,0.3,0.8,2.5,2.2,3.1,2.2,0.9,0.4,0.9,1.1,0.7,0.5,1.7,1.6,2.8,0.3,2.5,0.5,1.5,0.9,1.0,0.6,1.6,0.1,0.5,1.5,1.4,1.9,1.4,1.0,0.4,0.6,1.8,3.0,0.2,5,22,10,8,16,14,18,16,13,8,4.0,31,18,7,19,69,85,6,24,43,14,38,17,42,35,36,49,20,26,46,19,18,5,30,2,2,41,35,3,1,5,28,24,20,17,7,2,6,6,7,3,10,9,20,1,21,3,11,8,7,5,15,1,5,13,18,14,10,7,4,4,15,25,2,0.031,0.02,0.018,0.021,0.03,0.031,0.02,0.029,0.045,0.028,0.021,0.036,0.03,0.031,0.027,0.032,0.029,0.023,0.035,0.035,0.028,0.037,0.031,0.041,0.028,0.031,0.029,0.019,0.024,0.028,0.034,0.076,0.026,0.027,0.014,0.01,0.036,0.03,0.014,0.016,0.018,0.032,0.038,0.025,0.018,0.018,0.015,0.018,0.014,0.016,0.024,0.018,0.025,0.021,0.022,0.016,0.023,0.021,0.02,0.018,0.021,0.023,0.012,0.014,0.018,0.026,0.016,0.019,0.017,0.013,0.023,0.022,0.015,0.012,1377,5825,2737,2535,2725,2379,3862,3219,1536,1393,653.0,3148,3658,1029,3088,13516,19128,2052,3212,5567,3326,4919,2216,6504,5882,7007,6786,3999,6332,6741,2271,1809,951,4850,1385,1494,5694,6930,517,634,1451,2793,4066,3321,3169,1728,1001,1792,2478,1438,537,3248,2173,5686,453,4925,979,2919,1255,1701,1068,3185,296,1004,2707,1384,4039,2748,2014,1279,1031,2597,7592,580,0.1,0.106,0.099,0.098,0.128,0.143,0.108,0.131,0.134,0.131,0.12,0.154,0.119,0.146,0.14,0.138,0.134,0.101,0.151,0.142,0.105,0.157,0.136,0.133,0.133,0.131,0.139,0.097,0.108,0.134,0.13,0.174,0.12,0.124,0.073,0.059,0.136,0.124,0.099,0.095,0.101,0.144,0.153,0.118,0.1,0.099,0.093,0.092,0.088,0.108,0.135,0.09,0.108,0.107,0.126,0.097,0.116,0.105,0.122,0.106,0.104,0.117,0.102,0.105,0.103,0.128,0.091,0.099,0.107,0.098,0.113,0.115,0.086,0.093,348,1746,879,894,894,784,1217,931,470,349,213.0,1350,928,219,851,3322,4795,416,985,2113,916,1408,868,1696,1645,1844,2185,1393,1620,2161,657,659,274,1246,452,558,1600,1643,238,268,693,1300,1093,1856,1923,762,409,828,1104,688,307,1388,924,2193,195,2475,354,1278,668,842,469,1092,153,516,1309,789,1917,1136,870,497,474,1275,3283,252,3.443,2.952,2.849,2.801,2.409,2.665,2.545,3.014,2.478,2.976,2.614,2.01,3.002,3.287,2.703,2.993,3.168,3.713,2.668,2.214,2.816,2.625,2.154,2.754,2.737,2.941,2.398,2.285,2.897,2.456,2.54,2.847,2.692,2.784,2.788,2.547,2.648,3.062,2.346,2.67,2.847,1.966,2.988,2.008,1.885,2.498,3.059,2.596,2.789,2.463,1.959,2.403,2.438,2.608,2.698,2.258,2.838,2.501,2.236,2.195,2.448,2.921,2.092,2.337,2.24,2.182,2.263,2.609,2.626,2.773,2.601,2.403,2.459,2.847,0.955,0.756,0.559,0.463,0.724,0.491,0.586,0.453,0.833,0.448,0.836,0.544,0.445,0.378,0.518,0.634,0.663,0.73,0.44,0.767,0.616,0.491,0.462,0.794,0.626,0.528,0.534,0.569,0.557,0.549,0.8,0.954,0.479,0.623,0.587,0.512,0.646,0.563,0.371,0.525,0.536,0.449,0.496,0.63,0.613,0.617,0.418,0.797,0.526,0.367,0.364,0.478,0.566,0.537,0.487,0.383,0.622,0.672,0.344,0.401,0.431,0.73,0.356,0.494,0.511,0.629,0.404,0.459,0.427,0.688,0.559,0.415,0.514,0.496
-266,Epileptic,F,17.5,0.7,2.3,0.7,1.3,1.2,1.6,1.5,1.4,0.9,0.5,0.2,2.7,1.5,0.4,1.5,6.0,8.7,0.8,3.1,4.8,0.9,3.4,1.8,3.4,2.8,3.1,4.1,2.4,2.4,3.3,1.0,0.8,0.3,2.1,0.4,0.4,2.6,2.7,0.2,0.1,1.0,2.6,1.7,2.6,2.5,0.8,0.4,1.0,1.2,0.7,1.0,1.7,1.1,2.0,0.2,5.3,0.8,2.4,1.1,1.3,0.5,1.3,0.2,0.4,1.6,1.0,3.0,1.1,0.8,0.4,1.1,0.9,3.6,0.3,6,21,9,13,14,19,14,17,13,7,3.0,30,20,3,21,70,86,7,31,40,7,39,20,38,36,39,46,25,23,41,17,9,4,27,2,5,34,34,2,1,6,25,17,18,17,5,3,6,6,7,5,13,8,14,1,33,5,16,7,11,5,9,2,4,14,13,24,10,6,4,5,7,31,3,0.036,0.024,0.013,0.022,0.029,0.032,0.023,0.025,0.046,0.03,0.025,0.031,0.033,0.034,0.039,0.035,0.029,0.03,0.037,0.041,0.024,0.04,0.029,0.041,0.033,0.03,0.032,0.026,0.027,0.036,0.034,0.054,0.012,0.03,0.015,0.017,0.035,0.031,0.016,0.015,0.02,0.035,0.036,0.03,0.02,0.016,0.02,0.019,0.017,0.02,0.036,0.02,0.022,0.018,0.023,0.028,0.021,0.025,0.022,0.021,0.021,0.025,0.02,0.02,0.017,0.023,0.021,0.018,0.019,0.02,0.025,0.019,0.019,0.022,1466,4580,2145,2470,2000,2635,2957,3460,1813,1545,661.0,3332,3513,975,3062,12068,18969,2104,5347,5156,2403,4946,2434,5971,6050,6668,7038,3568,5148,5932,2138,1024,1078,4916,1792,1312,5623,6478,445,399,1534,2909,4852,2578,3156,1624,767,1992,2360,1325,832,3306,1991,4017,481,5212,1517,3428,1347,1863,823,2343,399,793,3271,1700,4478,2196,1340,824,1328,1352,6990,388,0.128,0.113,0.1,0.111,0.126,0.152,0.11,0.124,0.149,0.128,0.124,0.141,0.143,0.133,0.156,0.148,0.134,0.118,0.144,0.139,0.096,0.158,0.134,0.154,0.15,0.141,0.13,0.112,0.122,0.142,0.141,0.135,0.079,0.134,0.078,0.099,0.144,0.14,0.102,0.101,0.113,0.144,0.145,0.12,0.102,0.096,0.11,0.092,0.094,0.121,0.151,0.107,0.109,0.102,0.089,0.129,0.103,0.114,0.113,0.116,0.129,0.119,0.105,0.129,0.105,0.126,0.112,0.112,0.112,0.12,0.116,0.112,0.102,0.122,403,1535,798,929,695,946,991,990,462,325,178.0,1336,944,231,779,3069,5102,410,1517,2064,616,1468,964,1523,1649,1854,2098,1397,1392,1770,512,322,387,1159,452,494,1374,1562,205,174,716,1253,922,1462,1971,774,336,833,1051,480,402,1425,841,1747,185,2684,644,1617,722,964,396,821,204,327,1518,699,2085,941,645,340,634,731,3069,241,3.538,2.919,2.67,2.67,2.418,2.514,2.429,2.816,2.702,3.401,3.194,2.114,2.911,3.213,2.919,2.902,2.995,3.637,2.788,2.14,2.793,2.577,2.2,2.806,2.727,2.894,2.565,2.111,2.752,2.69,2.822,3.006,2.343,2.92,3.497,2.382,2.871,2.856,2.609,2.602,2.702,1.988,3.49,2.06,1.823,2.334,2.892,2.911,2.752,2.685,2.225,2.482,2.612,2.532,2.491,2.136,2.606,2.439,2.219,2.147,2.388,2.798,2.265,2.641,2.372,2.812,2.151,2.561,2.464,2.684,2.519,2.389,2.528,1.898,0.89,0.541,0.395,0.481,0.719,0.554,0.483,0.585,0.716,0.442,0.906,0.56,0.456,0.507,0.694,0.632,0.587,0.724,0.551,0.602,0.909,0.434,0.487,0.739,0.533,0.604,0.51,0.503,0.543,0.52,0.58,0.989,0.405,0.654,0.639,0.467,0.755,0.645,0.442,0.505,0.572,0.48,0.773,0.871,0.516,0.385,0.469,0.688,0.516,0.524,0.509,0.438,0.61,0.408,0.486,0.471,0.458,0.615,0.352,0.447,0.456,0.626,0.345,0.891,0.488,0.708,0.487,0.473,0.479,0.698,0.394,0.476,0.488,0.317
-267,Epileptic,M,22.0,0.9,2.6,1.0,1.5,1.1,2.3,1.9,2.1,0.6,1.2,0.6,2.2,2.3,0.6,1.5,8.1,8.1,1.1,2.8,5.3,1.1,3.3,1.3,3.7,2.5,3.4,2.7,2.7,3.3,3.7,1.3,1.1,0.5,2.2,0.2,0.7,4.3,3.1,0.2,0.2,1.4,3.5,2.8,1.9,3.4,0.7,0.5,1.1,1.4,0.5,0.7,2.2,2.1,2.3,0.3,2.1,1.3,1.6,1.6,0.9,1.1,1.2,0.3,0.6,1.7,1.5,3.5,1.4,1.4,0.6,1.3,1.4,4.3,0.3,5,23,13,15,11,23,16,17,8,9,6.0,21,28,8,17,76,75,7,32,43,10,38,15,39,38,37,35,25,28,41,19,8,5,27,1,5,55,38,2,1,7,29,36,18,18,5,3,7,6,5,5,14,17,15,2,18,11,13,14,9,7,11,3,4,15,15,26,8,10,5,10,11,34,3,0.033,0.027,0.016,0.024,0.023,0.031,0.024,0.036,0.033,0.041,0.04,0.025,0.03,0.032,0.024,0.035,0.029,0.03,0.039,0.034,0.022,0.03,0.023,0.034,0.031,0.031,0.023,0.029,0.026,0.026,0.03,0.041,0.026,0.026,0.012,0.013,0.039,0.033,0.022,0.019,0.024,0.038,0.043,0.021,0.024,0.012,0.018,0.019,0.015,0.016,0.023,0.019,0.024,0.017,0.015,0.017,0.022,0.017,0.024,0.014,0.021,0.024,0.013,0.022,0.019,0.03,0.024,0.019,0.024,0.018,0.024,0.017,0.016,0.016,1542,5284,2673,2606,1664,3612,3428,3470,1018,2047,901.0,2971,5141,1263,3963,13578,18166,2959,5382,5934,3783,5886,2221,7723,5065,7520,5731,4404,7349,7297,2177,1281,1093,6068,1125,2191,7870,7772,417,303,1940,3101,5035,2707,4007,1660,1142,2346,3000,1331,634,4785,2762,4948,439,3590,2476,3848,2082,1922,1729,2461,676,945,3301,1556,4541,2785,2357,1316,1804,3208,10110,806,0.124,0.116,0.094,0.116,0.111,0.139,0.101,0.142,0.122,0.144,0.158,0.12,0.134,0.14,0.11,0.132,0.122,0.122,0.144,0.128,0.094,0.128,0.122,0.138,0.132,0.129,0.11,0.111,0.102,0.119,0.12,0.109,0.116,0.123,0.062,0.078,0.143,0.132,0.107,0.133,0.111,0.137,0.158,0.105,0.106,0.086,0.1,0.089,0.085,0.106,0.134,0.099,0.109,0.091,0.111,0.097,0.115,0.091,0.12,0.086,0.108,0.108,0.088,0.1,0.105,0.119,0.114,0.101,0.11,0.107,0.118,0.102,0.092,0.105,411,1637,927,1018,802,1341,1124,964,324,504,264.0,1278,1323,320,1044,3796,4585,542,1352,2497,823,1871,895,1762,1550,1985,1953,1501,1840,2273,631,500,318,1372,345,807,1950,1822,199,124,894,1383,1212,1502,2264,802,454,985,1285,476,410,1823,1360,2056,224,1846,942,1717,995,964,795,916,403,440,1526,781,2192,1138,1037,521,859,1330,4111,346,3.658,2.844,2.598,2.594,1.983,2.452,2.536,3.048,2.493,3.113,2.892,1.986,2.982,2.885,2.854,2.746,3.098,4.118,2.987,2.03,3.417,2.498,2.111,3.134,2.524,3.026,2.368,2.321,3.067,2.54,2.511,2.345,2.638,3.121,2.903,2.487,3.012,3.131,2.378,2.903,2.775,1.952,3.086,1.961,2.039,2.2,3.138,2.827,2.824,3.0,2.001,2.566,1.952,2.555,2.544,2.114,2.724,2.577,2.067,2.144,2.686,2.507,2.051,2.593,2.343,2.375,2.352,2.734,2.603,2.678,2.34,2.706,2.549,2.662,0.682,0.752,0.665,0.46,0.484,0.572,0.709,0.51,0.546,0.51,0.936,0.445,0.55,0.467,0.59,0.62,0.609,0.602,0.615,0.693,0.808,0.613,0.57,0.633,0.449,0.507,0.47,0.563,0.587,0.613,0.603,1.013,0.627,0.593,0.647,0.528,0.629,0.682,0.327,0.545,0.513,0.581,0.783,0.548,0.69,0.482,0.661,0.709,0.562,0.823,0.537,0.539,0.591,0.505,0.376,0.427,0.454,0.609,0.453,0.438,0.367,0.62,0.317,1.07,0.541,0.754,0.536,0.423,0.422,0.771,0.561,0.538,0.544,0.561
-268,Epileptic,M,62.0,0.9,2.9,1.1,1.1,1.5,2.5,1.3,2.1,1.5,0.6,0.3,2.7,1.7,0.8,1.5,7.6,9.5,1.3,2.5,4.6,2.0,2.9,1.9,4.1,3.0,3.0,3.1,3.1,2.3,2.6,1.4,1.2,0.3,3.3,0.7,0.5,5.6,2.9,0.3,0.2,1.2,3.2,4.6,3.0,3.3,0.6,0.6,0.8,1.1,1.5,0.6,2.2,1.9,2.9,0.2,1.8,1.4,1.8,1.1,1.5,0.9,1.7,0.5,0.5,2.6,1.5,2.1,1.4,1.6,1.1,0.9,2.2,5.4,0.4,7,31,13,8,15,24,10,19,15,5,5.0,24,15,9,14,80,84,7,27,35,17,34,15,52,35,31,33,30,23,29,18,6,4,39,4,3,50,38,3,1,7,27,32,22,18,4,3,5,5,10,5,16,12,20,1,16,11,13,8,11,7,15,3,5,22,18,15,10,13,11,6,18,44,2,0.035,0.027,0.021,0.019,0.027,0.033,0.022,0.029,0.039,0.029,0.036,0.029,0.029,0.039,0.032,0.031,0.029,0.039,0.027,0.031,0.031,0.029,0.025,0.041,0.026,0.027,0.023,0.025,0.018,0.026,0.034,0.032,0.013,0.035,0.022,0.011,0.04,0.031,0.021,0.018,0.022,0.033,0.059,0.023,0.019,0.012,0.022,0.015,0.013,0.028,0.034,0.018,0.02,0.019,0.039,0.014,0.033,0.021,0.021,0.022,0.024,0.025,0.021,0.017,0.023,0.025,0.016,0.018,0.022,0.031,0.023,0.026,0.017,0.02,1799,5746,2531,2258,2424,4083,2130,3763,2184,1381,731.0,2838,3293,1573,2909,13161,19126,2922,4960,5213,3765,4851,2736,7204,5845,5768,4590,4771,6276,5106,2461,1593,833,6927,1869,1750,6680,7015,464,417,1619,3282,5882,2646,4314,1494,899,2104,2448,2223,519,4301,2893,5393,291,3387,2009,3117,1215,1840,1336,2958,686,1045,3301,1408,3609,2347,2229,1650,1351,3279,11073,518,0.123,0.119,0.117,0.095,0.123,0.144,0.089,0.122,0.153,0.13,0.139,0.13,0.132,0.15,0.132,0.133,0.123,0.112,0.124,0.125,0.11,0.136,0.11,0.152,0.118,0.118,0.107,0.102,0.085,0.119,0.13,0.105,0.079,0.123,0.088,0.068,0.133,0.112,0.13,0.089,0.106,0.133,0.17,0.111,0.094,0.081,0.109,0.079,0.083,0.12,0.144,0.1,0.105,0.101,0.148,0.096,0.136,0.099,0.113,0.114,0.11,0.123,0.12,0.11,0.115,0.114,0.097,0.103,0.115,0.146,0.115,0.12,0.095,0.102,411,1697,918,880,903,1290,812,1205,648,370,223.0,1421,892,423,838,3974,5461,487,1552,2267,1008,1604,1076,1904,2033,1832,1819,1774,1829,1679,721,575,360,1456,489,694,2200,1744,216,205,860,1426,1523,1848,2580,776,425,930,1084,891,302,1980,1365,2465,124,1940,740,1541,775,1007,576,1060,331,512,1757,905,1910,1159,1118,644,666,1458,4920,309,3.833,2.928,2.62,2.452,2.379,2.534,2.227,2.866,2.665,3.013,2.747,1.778,2.939,2.946,2.72,2.579,2.851,4.104,2.641,1.93,2.808,2.411,2.148,2.943,2.338,2.537,2.069,2.182,2.638,2.412,2.7,2.553,1.907,2.934,3.312,2.365,2.517,2.851,2.289,2.488,2.19,1.987,2.816,1.623,1.838,1.988,2.669,2.709,2.673,2.682,1.986,2.268,2.377,2.255,2.633,1.905,2.604,2.29,1.804,2.077,2.348,2.544,2.158,2.367,2.008,2.046,2.028,2.332,2.223,2.587,2.325,2.342,2.283,1.981,0.879,0.536,0.636,0.553,0.477,0.776,0.633,0.693,0.496,0.912,0.85,0.55,0.462,0.479,0.397,0.526,0.619,0.795,0.702,0.667,1.084,0.494,0.581,0.542,0.495,0.598,0.492,0.616,0.579,0.59,0.551,0.899,0.438,0.852,0.824,0.584,0.759,0.656,0.445,0.337,0.545,0.68,1.102,0.609,0.612,0.625,0.517,0.896,0.594,0.771,0.462,0.52,0.566,0.444,0.565,0.458,0.606,0.602,0.46,0.359,0.472,0.71,0.47,0.788,0.527,0.886,0.531,0.547,0.531,0.455,0.464,0.621,0.542,0.464
-269,Epileptic,F,22.0,1.3,2.9,1.1,1.6,3.0,2.0,2.1,2.6,1.5,0.9,0.9,3.4,2.9,1.6,3.4,13.6,14.8,1.1,2.6,6.5,2.1,5.4,3.1,7.6,6.2,5.6,6.4,4.9,5.8,5.9,1.9,1.5,0.9,5.3,1.1,1.9,7.2,7.6,0.6,0.6,1.8,4.7,4.5,4.8,4.5,0.8,1.2,1.7,2.2,1.2,0.6,5.7,2.9,4.7,0.4,3.1,0.9,2.1,1.7,1.5,1.3,2.4,1.1,0.7,2.5,1.1,3.1,4.0,2.0,0.7,1.1,2.6,9.7,0.5,10,20,6,10,17,16,13,15,12,9,5.0,22,18,11,22,78,102,8,18,51,16,41,16,58,35,33,42,29,28,43,18,10,6,40,6,11,52,55,2,2,11,32,29,28,23,5,5,9,10,7,4,29,14,24,1,19,7,12,12,9,9,14,5,5,18,12,15,17,9,4,7,13,56,4,0.043,0.033,0.021,0.032,0.045,0.037,0.029,0.044,0.056,0.047,0.05,0.047,0.043,0.077,0.044,0.056,0.045,0.04,0.047,0.053,0.054,0.047,0.043,0.058,0.047,0.044,0.043,0.038,0.041,0.042,0.056,0.063,0.04,0.047,0.025,0.031,0.054,0.051,0.032,0.034,0.028,0.047,0.065,0.043,0.028,0.016,0.034,0.027,0.028,0.028,0.038,0.038,0.037,0.032,0.049,0.021,0.029,0.028,0.031,0.026,0.031,0.031,0.043,0.037,0.031,0.024,0.025,0.041,0.028,0.025,0.026,0.03,0.033,0.023,1691,4263,2209,2568,2010,3185,2618,2913,1169,1587,901.0,2204,3018,1068,3378,10607,21787,2541,3234,5471,3364,4863,1193,7402,4288,4621,4448,3457,4582,5583,2362,985,1035,4628,2532,2192,9202,6314,557,531,2002,2529,6364,2905,3719,1277,1292,1958,2354,1768,353,4559,2507,5325,216,4256,1124,2645,1469,1822,1540,3506,736,922,2509,1170,4155,2681,1971,750,1421,2932,9215,497,0.145,0.113,0.101,0.122,0.136,0.119,0.109,0.13,0.141,0.156,0.136,0.151,0.138,0.168,0.139,0.152,0.135,0.111,0.149,0.166,0.136,0.134,0.144,0.156,0.139,0.132,0.137,0.13,0.126,0.142,0.164,0.153,0.122,0.151,0.095,0.113,0.16,0.14,0.116,0.128,0.12,0.163,0.16,0.151,0.117,0.097,0.123,0.107,0.112,0.112,0.149,0.122,0.13,0.119,0.19,0.101,0.13,0.112,0.131,0.114,0.118,0.119,0.145,0.13,0.122,0.114,0.1,0.134,0.118,0.122,0.12,0.115,0.118,0.115,547,1523,808,853,1076,1060,1207,1048,525,423,273.0,1160,1113,420,1372,4017,6107,628,1032,2462,852,1770,962,2459,1886,2005,2294,1819,2059,2208,649,393,418,1708,730,1004,2492,2426,289,267,1056,1572,1384,1823,2446,761,580,963,1138,729,269,2554,1290,2464,115,2398,562,1312,926,944,736,1311,417,402,1290,725,2044,1450,1052,394,696,1415,4736,323,2.786,2.501,2.488,2.783,1.878,2.417,1.962,2.42,1.987,2.872,2.655,1.665,2.281,2.075,2.128,2.253,2.944,3.111,2.517,1.928,2.894,2.257,1.296,2.46,2.057,2.081,1.79,1.755,1.974,1.99,2.529,2.49,2.124,2.236,3.084,2.239,2.737,2.22,2.24,2.388,2.242,1.601,3.074,1.896,1.735,1.916,2.687,2.383,2.525,2.569,1.777,1.993,2.265,2.335,2.631,2.024,2.349,2.306,1.899,2.212,2.414,2.556,2.198,2.329,2.146,1.953,2.185,2.248,2.292,2.13,2.325,2.344,2.131,1.957,0.785,0.746,0.701,0.63,0.539,0.678,0.593,0.658,0.594,0.732,0.839,0.586,0.735,0.543,0.67,0.676,0.825,0.81,0.524,0.594,0.878,0.534,0.595,0.757,0.623,0.671,0.634,0.483,0.678,0.731,0.651,0.921,0.478,0.748,0.911,0.684,0.753,0.652,0.6,0.462,0.393,0.583,0.971,0.51,0.425,0.561,0.574,0.555,0.625,0.598,0.363,0.516,0.525,0.552,0.559,0.417,0.485,0.569,0.325,0.506,0.499,0.697,0.399,0.797,0.678,0.524,0.583,0.582,0.469,0.655,0.515,0.596,0.482,0.357
-270,Epileptic,F,17.5,0.3,2.4,0.8,1.2,1.2,2.2,1.8,1.4,0.9,0.6,0.2,2.5,1.3,0.4,1.7,4.5,8.4,0.5,3.1,4.7,1.0,3.1,1.6,2.5,2.7,2.4,3.0,2.9,2.3,4.0,2.0,0.7,0.7,2.7,0.4,0.6,2.8,3.2,0.1,0.2,1.2,2.7,2.3,2.1,2.4,0.7,0.3,0.7,0.9,0.6,0.8,1.8,0.9,2.1,0.3,1.8,1.2,1.5,1.0,1.2,0.7,1.0,0.3,0.6,1.9,1.2,2.0,1.1,1.3,0.4,1.0,1.1,3.9,0.2,2,18,6,8,9,19,15,12,11,7,2.0,23,11,6,15,47,75,3,26,34,10,38,14,24,38,26,36,28,22,38,22,5,6,32,2,6,30,32,1,1,7,26,26,12,15,5,1,4,4,6,7,13,6,13,2,13,10,12,6,9,5,9,2,5,14,12,14,9,7,3,6,7,33,2,0.016,0.028,0.017,0.022,0.036,0.034,0.033,0.025,0.04,0.03,0.052,0.039,0.039,0.035,0.044,0.035,0.031,0.027,0.038,0.038,0.027,0.034,0.03,0.032,0.03,0.031,0.028,0.03,0.024,0.04,0.058,0.036,0.026,0.037,0.025,0.016,0.035,0.037,0.013,0.016,0.022,0.035,0.059,0.026,0.02,0.018,0.02,0.014,0.012,0.015,0.035,0.024,0.024,0.021,0.017,0.017,0.035,0.019,0.021,0.024,0.024,0.022,0.027,0.039,0.021,0.026,0.019,0.018,0.021,0.015,0.023,0.028,0.017,0.017,1623,4855,2850,3028,1952,3480,2801,3709,1915,1736,369.0,2348,3463,1290,4256,10391,22553,1756,4455,3626,2302,5539,2478,5930,6651,6247,6009,4716,5812,5700,2524,782,1321,6488,1108,1974,5683,6466,348,404,1781,2713,5076,2148,2878,1387,663,2099,2326,1539,698,2422,1775,4305,482,3804,1674,2601,1309,1791,1103,1853,499,735,3135,1188,3374,2897,2085,1156,1389,1813,9130,505,0.067,0.117,0.098,0.101,0.141,0.144,0.128,0.119,0.143,0.13,0.125,0.152,0.135,0.125,0.164,0.142,0.13,0.086,0.135,0.136,0.095,0.145,0.13,0.133,0.135,0.129,0.131,0.12,0.104,0.157,0.169,0.111,0.134,0.14,0.089,0.084,0.131,0.127,0.089,0.113,0.101,0.139,0.153,0.114,0.107,0.112,0.095,0.076,0.079,0.103,0.15,0.117,0.114,0.107,0.114,0.099,0.13,0.099,0.105,0.114,0.107,0.118,0.129,0.162,0.112,0.12,0.1,0.099,0.102,0.107,0.119,0.113,0.094,0.119,310,1312,776,845,577,1107,916,911,469,329,90.0,1036,649,262,717,2234,4779,326,1340,1848,628,1580,880,1287,1645,1453,1784,1451,1518,1549,539,338,435,1257,348,586,1291,1429,168,136,752,1229,1018,1282,1770,573,277,774,990,652,359,1062,661,1550,229,1655,618,1307,721,860,502,688,190,256,1400,695,1598,947,938,365,624,698,3589,243,3.762,3.35,3.123,3.047,2.723,2.728,2.345,3.259,2.885,3.607,3.227,1.965,3.552,3.222,3.749,3.13,3.443,3.736,2.67,1.784,2.906,2.649,2.339,3.001,2.964,3.147,2.537,2.46,2.856,2.76,3.239,2.33,2.536,3.314,2.665,2.781,3.148,3.289,2.525,3.007,2.753,2.025,3.382,1.868,1.89,2.624,2.956,3.215,2.898,2.672,2.144,2.518,2.473,2.78,2.64,2.503,2.605,2.303,2.075,2.212,2.588,2.609,2.657,2.911,2.597,2.156,2.328,2.791,2.674,3.014,2.639,2.871,2.728,2.714,1.102,0.793,0.733,0.707,0.839,0.644,0.609,0.7,0.621,0.442,0.545,0.587,0.739,0.646,0.64,0.695,0.759,0.755,0.849,0.574,0.758,0.54,0.443,0.788,0.555,0.621,0.637,0.699,0.6,0.611,0.808,0.854,0.513,0.663,0.848,0.559,0.733,0.78,0.422,0.453,0.586,0.451,0.95,0.692,0.508,0.706,0.563,0.911,0.551,0.498,0.445,0.618,0.647,0.516,0.302,0.54,0.544,0.598,0.486,0.499,0.467,0.723,0.7,0.688,0.714,0.705,0.478,0.543,0.415,0.684,0.493,0.695,0.626,0.393
-271,Epileptic,F,32.0,0.7,2.6,1.3,1.5,1.6,1.8,1.5,2.1,1.0,0.7,0.4,2.5,1.4,0.7,1.3,6.0,7.7,0.7,1.8,3.3,1.2,3.2,2.0,3.4,3.2,2.4,3.1,2.8,2.5,2.8,1.6,1.0,0.3,2.7,0.6,0.5,3.6,3.2,0.1,0.3,1.0,3.2,2.7,2.3,3.6,0.6,0.7,0.9,1.3,0.6,0.7,2.0,1.6,2.5,0.3,2.0,0.7,1.3,0.9,0.9,0.7,2.4,0.3,0.5,1.7,2.1,2.4,1.1,1.3,0.6,0.9,1.2,5.7,0.3,7,20,16,10,19,16,12,19,12,7,5.0,28,18,8,19,64,80,11,21,34,12,40,19,38,47,25,36,25,21,31,15,6,3,30,4,5,41,40,2,2,5,31,31,18,23,5,5,7,8,5,6,16,15,21,2,17,4,15,7,8,7,24,2,6,18,23,20,8,11,5,6,10,50,3,0.029,0.022,0.018,0.026,0.026,0.027,0.02,0.033,0.029,0.031,0.035,0.026,0.022,0.03,0.029,0.027,0.026,0.024,0.024,0.031,0.025,0.027,0.026,0.029,0.027,0.027,0.023,0.022,0.021,0.028,0.027,0.037,0.02,0.03,0.017,0.014,0.034,0.03,0.01,0.018,0.022,0.029,0.031,0.026,0.027,0.015,0.023,0.014,0.015,0.012,0.032,0.021,0.022,0.02,0.023,0.016,0.016,0.017,0.024,0.015,0.022,0.028,0.019,0.013,0.018,0.03,0.018,0.016,0.02,0.021,0.024,0.017,0.019,0.012,1643,5215,2768,2514,2745,2767,2473,3228,1885,1357,622.0,2394,3598,1326,2859,11589,16709,2316,3881,3759,3726,5342,2476,6060,6074,5239,5587,4729,5184,4484,2333,1436,726,7005,2684,1881,6972,7593,423,518,1408,3183,6965,2239,3298,1346,1139,2162,2642,1534,591,3634,3153,5313,277,3799,1430,2894,1107,1398,1220,2990,476,1092,2666,1742,3990,2317,2111,915,1273,2232,10225,583,0.119,0.111,0.112,0.116,0.128,0.13,0.095,0.136,0.138,0.138,0.153,0.136,0.117,0.143,0.134,0.13,0.126,0.118,0.125,0.142,0.11,0.13,0.125,0.124,0.139,0.128,0.119,0.101,0.086,0.133,0.115,0.104,0.11,0.133,0.093,0.082,0.136,0.134,0.086,0.106,0.11,0.134,0.141,0.118,0.116,0.095,0.122,0.093,0.095,0.088,0.138,0.109,0.112,0.106,0.141,0.102,0.089,0.109,0.117,0.099,0.112,0.133,0.112,0.103,0.108,0.137,0.105,0.094,0.111,0.121,0.123,0.116,0.108,0.108,477,1834,1083,930,1062,1016,949,1137,577,355,201.0,1401,1028,331,816,3522,4863,504,1118,1682,885,1933,1128,1950,2121,1507,2057,1699,1532,1663,865,461,263,1530,614,620,1787,1865,250,276,692,1610,1579,1402,2136,679,426,974,1299,704,383,1577,1339,2170,164,1907,670,1318,648,871,514,1291,252,607,1474,999,1942,1110,1040,444,634,1056,4531,275,3.228,2.715,2.462,2.6,2.318,2.273,2.233,2.649,2.479,2.887,2.673,1.608,2.681,2.758,2.687,2.629,2.753,3.416,2.817,1.933,3.135,2.232,1.909,2.479,2.334,2.832,2.211,2.254,2.599,2.303,2.167,2.844,2.284,3.291,3.599,2.755,2.969,3.02,1.945,2.397,2.637,1.801,3.225,1.849,1.758,2.173,3.05,2.836,2.543,2.566,1.962,2.496,2.383,2.6,2.343,2.137,2.52,2.409,1.89,1.791,2.237,2.326,2.157,2.022,2.009,1.979,2.216,2.481,2.234,2.514,2.504,2.445,2.379,2.485,0.478,0.7,0.498,0.521,0.566,0.488,0.534,0.516,0.484,0.514,0.815,0.347,0.426,0.377,0.406,0.588,0.513,0.797,0.498,0.675,0.772,0.502,0.465,0.882,0.457,0.534,0.486,0.574,0.585,0.418,0.789,0.982,0.452,0.635,0.663,0.561,0.652,0.591,0.277,0.287,0.464,0.476,0.636,0.642,0.497,0.546,0.366,0.511,0.537,0.388,0.346,0.457,0.513,0.389,0.365,0.437,0.487,0.545,0.368,0.347,0.464,0.653,0.429,0.747,0.449,0.721,0.49,0.379,0.495,0.719,0.353,0.398,0.52,0.604
-273,Epileptic,F,22.0,0.7,2.1,0.8,1.3,1.4,1.5,1.8,0.9,0.8,0.5,0.3,3.3,0.9,0.5,0.9,4.9,6.3,0.7,1.9,5.1,2.6,2.2,2.1,3.5,3.1,2.6,3.2,1.8,1.9,3.3,2.5,1.3,0.4,1.7,0.3,0.4,2.7,2.7,0.2,0.1,0.8,3.1,2.9,2.2,1.7,0.7,0.2,0.8,1.2,0.8,0.6,1.2,1.8,2.1,0.4,2.0,0.7,1.3,1.0,1.2,0.5,1.3,0.4,0.5,1.9,0.8,2.1,0.8,0.8,0.7,0.8,1.5,4.0,0.2,5,20,8,10,17,16,14,13,9,6,3.0,35,14,6,11,64,62,5,24,46,22,31,20,41,41,34,37,19,20,45,22,12,6,23,2,3,36,45,2,1,4,32,21,18,10,6,1,5,7,8,5,9,14,21,2,17,7,13,10,10,4,12,2,5,19,13,15,6,4,6,5,13,33,2,0.034,0.027,0.021,0.026,0.029,0.023,0.029,0.023,0.038,0.031,0.041,0.038,0.025,0.028,0.032,0.033,0.031,0.032,0.028,0.041,0.045,0.028,0.031,0.04,0.033,0.028,0.031,0.024,0.024,0.037,0.062,0.065,0.028,0.026,0.011,0.014,0.03,0.032,0.014,0.012,0.019,0.036,0.046,0.02,0.016,0.016,0.013,0.015,0.018,0.023,0.025,0.019,0.024,0.018,0.016,0.015,0.02,0.016,0.026,0.019,0.017,0.022,0.022,0.015,0.021,0.018,0.019,0.013,0.014,0.023,0.018,0.021,0.017,0.013,1344,4129,2059,2475,2549,3102,3294,2812,1702,1446,751.0,3592,2937,1480,2653,10982,16546,2220,4586,4150,3830,4482,2807,5518,7331,6970,6685,4012,5568,6962,2321,806,982,5334,1823,1593,8136,8459,430,341,1328,3176,4392,2767,2731,1487,739,1821,2045,1656,557,2250,2169,5299,546,3816,1487,2631,1492,1814,944,2408,374,890,2623,1017,3564,2043,1517,1361,1402,2808,9054,437,0.11,0.132,0.111,0.115,0.133,0.123,0.124,0.125,0.154,0.129,0.135,0.151,0.121,0.138,0.132,0.137,0.133,0.109,0.119,0.152,0.131,0.131,0.118,0.147,0.132,0.124,0.128,0.104,0.116,0.152,0.168,0.137,0.115,0.127,0.065,0.069,0.13,0.141,0.095,0.088,0.101,0.127,0.138,0.114,0.089,0.097,0.074,0.091,0.094,0.117,0.134,0.103,0.117,0.106,0.104,0.097,0.109,0.092,0.127,0.108,0.1,0.113,0.141,0.11,0.118,0.112,0.1,0.088,0.085,0.117,0.111,0.112,0.097,0.122,364,1235,661,845,891,1004,1012,761,427,290,166.0,1614,746,283,611,2844,3764,403,1177,2088,989,1333,1060,1549,1645,1704,1844,1200,1296,1809,696,362,265,1084,434,466,1665,1772,219,175,648,1414,1040,1704,1606,704,278,787,913,628,362,1042,1071,1853,319,2013,636,1348,613,918,485,898,204,477,1278,684,1716,842,767,468,624,1214,3609,220,3.424,2.817,2.858,2.717,2.276,2.477,2.611,3.119,2.953,3.439,3.144,1.931,2.874,3.316,2.937,2.762,3.195,4.027,3.014,1.788,2.828,2.603,2.209,2.554,3.052,2.997,2.673,2.466,2.997,2.919,2.557,2.365,2.783,3.273,3.655,2.837,3.212,3.32,2.112,2.263,2.571,1.962,3.001,1.89,1.939,2.266,3.048,3.043,2.719,2.846,1.934,2.263,2.121,2.522,2.082,2.097,2.656,2.235,2.284,2.075,2.129,2.48,1.78,2.25,2.278,1.787,2.244,2.644,2.323,2.828,2.411,2.628,2.643,2.545,0.794,0.773,0.566,0.566,0.677,0.66,0.574,0.547,0.659,0.51,0.678,0.611,0.473,0.573,0.684,0.727,0.74,0.79,0.545,0.584,0.917,0.655,0.634,0.908,0.762,0.652,0.679,0.718,0.672,0.638,0.79,0.77,0.401,0.677,0.834,0.584,0.768,0.689,0.456,0.426,0.399,0.563,1.159,0.65,0.518,0.561,0.464,0.636,0.485,0.481,0.352,0.472,0.441,0.597,0.341,0.372,0.416,0.462,0.665,0.383,0.374,0.72,0.534,0.558,0.477,0.605,0.457,0.469,0.396,0.714,0.487,0.484,0.501,0.326
-275,Epileptic,M,17.5,1.0,2.9,0.7,1.0,1.5,1.6,1.7,2.0,1.2,0.6,0.3,2.2,1.0,0.5,0.7,6.3,9.8,0.9,2.4,4.8,1.4,2.3,1.4,3.7,4.1,4.2,3.2,2.7,3.2,3.1,1.5,0.9,0.6,2.4,0.7,0.8,3.9,4.3,0.1,0.2,0.9,2.5,2.8,2.0,3.3,0.6,0.5,0.8,1.2,1.2,0.7,1.4,1.8,3.1,0.2,2.4,0.7,1.5,0.5,0.8,0.6,1.6,0.3,0.6,2.0,1.8,2.9,1.2,1.2,0.5,0.9,1.5,6.2,0.3,8,28,9,11,16,15,13,18,13,7,4.0,22,12,6,8,79,93,12,32,41,14,29,16,42,52,43,40,29,29,39,16,9,6,30,4,6,41,53,1,1,6,25,24,17,19,4,2,6,6,10,3,12,11,21,1,22,4,13,4,7,6,15,2,5,17,22,23,11,8,6,6,12,68,3,0.045,0.019,0.012,0.021,0.03,0.027,0.023,0.026,0.037,0.024,0.029,0.029,0.023,0.034,0.02,0.029,0.027,0.027,0.029,0.034,0.029,0.029,0.024,0.032,0.034,0.026,0.026,0.022,0.025,0.029,0.035,0.042,0.024,0.025,0.022,0.017,0.034,0.031,0.009,0.019,0.021,0.033,0.046,0.018,0.02,0.012,0.016,0.015,0.013,0.026,0.025,0.016,0.02,0.017,0.04,0.02,0.015,0.016,0.017,0.018,0.018,0.02,0.02,0.02,0.019,0.025,0.018,0.013,0.018,0.015,0.016,0.017,0.018,0.012,1526,6086,2617,2414,2568,3263,3630,4760,2144,1815,594.0,2705,2788,1136,2052,15153,25209,2674,6013,6667,3634,4298,2646,7214,8146,9152,6293,6068,7787,7042,2289,1490,1161,7778,2154,1992,7864,10617,331,254,1317,3053,5323,3333,4288,1520,1044,2313,2634,1851,631,3119,2504,7404,265,4179,1582,3916,905,1249,1205,3092,457,959,3739,1888,5920,3381,2651,1623,1823,3105,13122,620,0.137,0.103,0.093,0.109,0.112,0.129,0.107,0.123,0.148,0.127,0.122,0.134,0.108,0.142,0.099,0.134,0.128,0.123,0.137,0.135,0.104,0.138,0.129,0.13,0.15,0.131,0.13,0.114,0.109,0.138,0.133,0.113,0.098,0.121,0.088,0.088,0.134,0.131,0.089,0.117,0.107,0.145,0.139,0.1,0.099,0.081,0.092,0.086,0.083,0.118,0.118,0.094,0.108,0.095,0.142,0.108,0.09,0.093,0.107,0.105,0.105,0.113,0.092,0.114,0.104,0.123,0.103,0.088,0.101,0.108,0.099,0.104,0.104,0.093,447,2114,1004,862,912,1052,1078,1310,573,387,176.0,1143,749,249,548,3810,6159,511,1554,2288,914,1258,903,1961,2134,2574,2096,1989,1930,1954,715,466,378,1522,579,711,2029,2341,189,142,656,1233,1094,1831,2503,743,460,933,1239,766,370,1500,1239,2886,89,1948,642,1661,480,682,588,1204,235,510,1735,1023,2528,1447,1091,537,841,1274,5327,328,3.267,2.671,2.488,2.696,2.379,2.688,2.686,3.072,2.709,3.263,2.798,2.033,2.839,3.042,2.794,2.981,3.235,3.704,2.977,2.361,3.119,2.561,2.314,2.722,2.808,2.946,2.389,2.443,3.035,2.794,2.506,2.785,2.449,3.413,3.546,2.599,2.944,3.108,2.068,2.388,2.615,2.176,3.249,2.095,1.97,2.28,2.847,3.061,2.607,2.957,2.039,2.251,2.294,2.638,2.948,2.32,2.837,2.606,2.111,2.023,2.304,2.525,2.189,2.207,2.353,2.127,2.492,2.512,2.807,2.997,2.613,2.614,2.581,2.297,1.08,0.577,0.673,0.454,0.625,0.614,0.714,0.451,0.691,0.48,1.175,0.499,0.497,0.497,0.532,0.62,0.642,0.792,0.492,0.726,0.847,0.471,0.554,0.855,0.605,0.554,0.551,0.54,0.591,0.682,0.897,0.956,0.366,0.741,0.824,0.474,0.78,0.689,0.225,0.367,0.5,0.54,0.9,0.738,0.508,0.371,0.517,0.617,0.479,0.469,0.365,0.474,0.436,0.461,0.734,0.4,0.378,0.525,0.386,0.318,0.34,0.642,0.302,0.882,0.527,0.875,0.433,0.385,0.454,0.863,0.393,0.507,0.474,0.372
-276,Epileptic,M,27.0,0.7,1.9,1.1,1.0,1.3,2.5,2.0,1.4,0.9,0.6,0.2,3.2,1.5,0.7,1.1,5.7,7.7,0.6,3.3,6.6,1.7,3.7,1.7,2.5,3.2,3.0,3.3,2.8,2.9,4.0,0.7,0.6,0.6,2.8,0.3,0.6,4.5,3.4,0.2,0.1,1.0,2.6,2.6,2.7,3.3,0.5,0.4,0.9,1.1,1.3,0.9,2.2,1.3,3.5,0.3,2.2,1.3,1.9,0.9,1.1,0.8,1.1,0.5,0.5,2.1,1.1,2.4,1.4,1.3,0.4,1.5,1.5,5.3,0.3,6,18,16,8,13,19,13,13,10,5,2.0,21,14,7,10,54,63,5,23,40,14,37,14,26,29,30,29,21,20,38,12,4,6,30,2,5,38,37,1,1,5,23,20,21,17,4,2,6,5,9,6,16,9,22,1,13,9,12,6,9,5,7,3,5,15,12,17,7,7,4,9,11,35,3,0.033,0.025,0.026,0.023,0.025,0.036,0.031,0.029,0.039,0.034,0.025,0.046,0.031,0.036,0.037,0.036,0.032,0.03,0.038,0.048,0.037,0.039,0.034,0.033,0.039,0.032,0.033,0.03,0.031,0.044,0.028,0.035,0.026,0.031,0.018,0.019,0.035,0.035,0.016,0.027,0.022,0.029,0.051,0.033,0.028,0.014,0.024,0.014,0.016,0.031,0.04,0.029,0.025,0.023,0.034,0.02,0.031,0.02,0.024,0.022,0.024,0.021,0.027,0.029,0.026,0.038,0.022,0.025,0.024,0.018,0.034,0.02,0.02,0.019,1255,3996,2087,2036,1748,3335,2123,2876,1350,1149,636.0,2228,3103,1384,1944,8729,15248,1909,3446,4016,2559,5271,2291,5315,4976,6716,4884,3726,5035,4922,1854,701,1198,7137,1327,1549,8214,7790,305,167,1595,3030,4255,1994,3155,1160,616,2599,2371,1651,394,2867,1430,5537,314,3138,1799,2611,1135,1358,991,2082,768,832,2881,785,3395,1933,1777,931,1650,2723,10765,396,0.123,0.122,0.134,0.099,0.116,0.138,0.115,0.123,0.148,0.119,0.102,0.155,0.131,0.137,0.146,0.139,0.121,0.103,0.127,0.15,0.112,0.151,0.132,0.131,0.139,0.131,0.133,0.119,0.114,0.152,0.118,0.098,0.116,0.124,0.087,0.086,0.137,0.135,0.105,0.14,0.103,0.135,0.148,0.13,0.116,0.091,0.108,0.087,0.086,0.121,0.169,0.111,0.118,0.106,0.127,0.099,0.125,0.104,0.116,0.117,0.122,0.115,0.123,0.133,0.116,0.124,0.11,0.114,0.114,0.102,0.138,0.116,0.099,0.119,331,1226,692,715,730,1155,870,865,417,292,198.0,1045,792,299,548,2657,4024,382,1206,2150,676,1622,811,1253,1440,1712,1680,1410,1405,1607,492,331,402,1464,297,543,2069,1826,131,80,730,1324,969,1391,1887,548,256,1061,1053,710,321,1361,787,2409,152,1688,730,1385,647,764,541,736,328,310,1384,384,1724,900,855,357,704,1132,4280,214,3.55,2.815,2.897,2.737,2.153,2.593,2.127,2.942,2.405,3.133,2.962,1.999,3.041,3.236,2.782,2.636,2.933,3.489,2.376,1.693,2.85,2.53,2.385,3.003,2.626,3.101,2.334,2.222,2.731,2.591,2.653,2.093,2.496,3.179,3.796,2.523,2.964,2.995,2.436,2.579,2.808,2.055,3.082,1.691,1.954,2.307,2.95,2.878,2.743,2.505,1.603,2.216,1.937,2.377,2.409,2.134,2.543,2.251,2.062,2.016,2.238,2.762,2.633,2.966,2.224,2.176,2.151,2.527,2.499,2.794,2.661,2.788,2.641,2.102,0.726,0.719,0.72,0.617,0.526,0.623,0.606,0.635,0.624,0.56,1.079,0.448,0.499,0.481,0.569,0.539,0.623,0.975,0.784,0.682,0.928,0.443,0.571,0.716,0.673,0.562,0.506,0.525,0.552,0.683,0.782,0.755,0.41,0.763,0.611,0.632,0.697,0.629,0.445,0.4,0.479,0.765,0.894,0.609,0.538,0.547,0.575,0.694,0.591,0.577,0.394,0.495,0.453,0.438,0.51,0.408,0.489,0.437,0.421,0.311,0.457,0.871,0.82,1.257,0.513,0.63,0.484,0.406,0.376,0.895,0.608,0.654,0.529,0.489
-277,Epileptic,M,22.0,0.5,3.2,0.9,1.4,2.2,3.1,2.4,1.6,1.1,0.8,0.3,3.7,2.0,0.8,1.4,5.8,8.7,2.2,2.9,5.8,2.5,7.6,3.7,3.4,7.9,5.7,9.8,3.5,3.9,6.4,2.0,1.3,0.5,3.0,0.6,0.5,6.7,5.4,0.4,0.4,1.3,3.8,2.8,3.5,2.5,0.8,0.4,0.8,2.2,0.9,0.9,1.9,1.4,2.4,0.4,3.6,1.2,1.7,1.4,1.6,0.4,1.4,0.3,0.6,2.3,1.0,4.4,1.4,1.2,0.4,0.8,1.7,4.6,0.3,5,28,8,9,20,33,21,18,14,7,4.0,34,27,11,15,68,84,16,33,49,22,54,27,35,52,62,75,31,31,66,18,11,6,32,4,2,68,61,2,3,7,37,26,21,14,6,2,5,12,5,5,16,10,17,2,26,10,11,9,11,3,12,3,4,18,12,33,9,7,4,5,12,44,3,0.028,0.026,0.016,0.023,0.038,0.041,0.042,0.027,0.035,0.037,0.047,0.045,0.036,0.033,0.026,0.034,0.032,0.061,0.041,0.046,0.055,0.068,0.047,0.04,0.054,0.045,0.047,0.038,0.037,0.049,0.048,0.049,0.025,0.033,0.017,0.01,0.057,0.043,0.022,0.016,0.021,0.041,0.051,0.031,0.02,0.016,0.019,0.012,0.024,0.019,0.028,0.021,0.022,0.019,0.031,0.017,0.028,0.019,0.024,0.023,0.011,0.027,0.022,0.019,0.029,0.02,0.026,0.021,0.017,0.013,0.02,0.016,0.018,0.011,1916,6227,2170,2773,3132,4545,2928,3972,2561,1245,813.0,3693,5038,2039,4122,14094,22480,3263,5173,6239,3397,4037,2318,7405,4593,9303,8601,5109,6530,7109,2976,1372,1404,7229,2744,1905,7629,11549,477,718,1727,2687,7219,3070,3183,1823,794,2533,2980,2173,786,4135,2382,5953,397,5780,1797,3109,1591,2059,960,2734,757,1095,3296,1367,5459,2353,1962,869,1584,3140,10343,755,0.095,0.123,0.104,0.118,0.148,0.152,0.129,0.124,0.145,0.157,0.164,0.177,0.149,0.139,0.12,0.141,0.137,0.181,0.169,0.168,0.138,0.173,0.15,0.147,0.17,0.146,0.146,0.122,0.127,0.164,0.152,0.129,0.105,0.138,0.088,0.065,0.154,0.147,0.112,0.104,0.104,0.14,0.154,0.13,0.103,0.093,0.099,0.076,0.107,0.093,0.123,0.105,0.106,0.099,0.138,0.1,0.13,0.104,0.114,0.119,0.078,0.126,0.125,0.107,0.121,0.102,0.12,0.111,0.096,0.112,0.111,0.101,0.096,0.09,432,1961,846,877,1042,1525,1135,1195,639,330,182.0,1614,1267,426,1000,3490,5496,653,1364,2479,865,1951,1315,1706,2443,2877,3692,1815,1849,2512,780,496,421,1623,623,714,2056,2589,262,357,911,1558,1350,1750,1962,830,306,968,1343,664,473,1667,1063,2232,216,3074,776,1329,969,1034,527,897,253,479,1389,750,2659,1041,1001,411,642,1493,4471,411,3.841,2.924,2.505,2.8,2.587,2.396,2.171,2.781,2.842,3.187,3.044,2.061,2.987,3.198,3.031,2.911,3.152,3.721,2.909,2.189,2.784,1.908,1.725,3.195,1.792,2.563,2.044,2.219,2.71,2.266,2.958,2.583,2.567,3.135,3.692,2.485,2.688,3.202,2.247,2.167,2.289,1.658,3.411,2.011,1.844,2.317,3.11,3.235,2.731,3.47,2.043,2.466,2.41,2.655,2.208,2.034,2.435,2.56,1.949,2.2,1.977,2.963,2.661,2.903,2.355,2.108,2.112,2.442,2.296,2.49,2.366,2.413,2.39,2.28,0.842,0.622,0.565,0.666,0.72,0.659,0.7,0.625,0.708,0.603,0.883,0.584,0.567,0.778,0.56,0.687,0.676,0.702,0.808,0.718,0.947,0.671,0.554,0.712,0.679,0.687,0.651,0.731,0.744,0.744,0.611,1.054,0.408,0.738,0.605,0.467,0.819,0.899,0.435,0.481,0.447,0.532,0.938,0.761,0.592,0.485,0.398,0.804,0.489,1.009,0.444,0.479,0.486,0.504,0.289,0.454,0.557,0.725,0.39,0.397,0.332,0.782,0.672,0.526,0.582,0.623,0.451,0.454,0.422,0.606,0.68,0.496,0.516,0.282
-278,Epileptic,F,27.0,0.8,2.3,1.4,1.2,1.8,2.3,1.9,1.9,1.6,0.9,0.3,2.5,1.1,0.5,1.9,6.1,9.9,0.8,1.8,4.6,1.0,3.1,1.7,2.5,2.7,3.4,2.9,2.9,2.5,2.4,1.5,0.7,0.6,2.9,0.2,0.7,2.6,3.0,0.2,0.2,1.0,2.8,1.7,2.7,2.5,0.8,0.6,1.4,1.2,0.6,0.5,1.8,1.7,2.5,0.2,1.9,0.6,1.3,1.2,1.0,0.5,1.5,0.4,0.4,1.8,1.3,2.4,1.1,1.3,0.5,1.0,1.1,5.0,0.3,8,23,16,9,15,20,16,15,14,7,3.0,26,14,8,21,65,88,9,22,39,9,30,16,35,32,37,37,35,23,29,22,5,6,33,1,9,25,42,3,2,6,25,18,18,14,5,3,7,7,7,4,14,10,18,2,12,3,10,6,7,3,15,3,4,12,14,18,7,8,5,6,7,44,2,0.032,0.023,0.019,0.022,0.03,0.03,0.032,0.029,0.04,0.04,0.03,0.035,0.025,0.024,0.033,0.034,0.031,0.024,0.028,0.042,0.025,0.039,0.032,0.03,0.03,0.028,0.028,0.028,0.024,0.032,0.034,0.042,0.034,0.03,0.009,0.021,0.031,0.031,0.014,0.013,0.02,0.034,0.03,0.028,0.019,0.018,0.019,0.021,0.014,0.022,0.023,0.023,0.023,0.019,0.011,0.016,0.035,0.013,0.028,0.026,0.015,0.026,0.015,0.017,0.02,0.025,0.017,0.017,0.019,0.019,0.026,0.023,0.02,0.017,1845,5077,3053,2303,2600,4022,2735,3488,2748,1555,711.0,2901,3383,1228,3403,11679,20507,2749,4516,4836,3180,4010,2348,6406,5817,6874,5727,4853,6325,4882,2704,967,914,7141,1699,2118,6932,7146,491,612,1499,3027,5707,2624,3663,1444,970,2147,2712,1669,536,3174,2540,4842,669,3574,1156,3390,1332,1294,934,2736,752,835,3130,1462,4301,2012,2130,1092,1308,2404,10383,423,0.127,0.113,0.114,0.106,0.136,0.137,0.125,0.136,0.157,0.149,0.128,0.147,0.122,0.13,0.147,0.145,0.133,0.116,0.123,0.144,0.097,0.151,0.15,0.129,0.134,0.132,0.127,0.116,0.107,0.14,0.121,0.106,0.152,0.134,0.058,0.108,0.131,0.136,0.099,0.094,0.108,0.146,0.126,0.123,0.099,0.099,0.103,0.09,0.085,0.121,0.129,0.11,0.112,0.102,0.086,0.101,0.116,0.089,0.127,0.124,0.1,0.116,0.098,0.105,0.105,0.114,0.102,0.096,0.102,0.113,0.121,0.119,0.108,0.113,494,1679,1148,875,879,1249,971,996,673,393,211.0,1230,838,355,957,3165,5380,538,1122,1868,712,1314,852,1588,1607,2028,1876,1642,1628,1449,702,345,318,1547,419,621,1439,1692,260,267,725,1357,1101,1523,2031,680,439,981,1269,515,316,1327,1090,1997,314,1759,407,1506,645,619,462,971,401,419,1374,781,2151,938,1020,458,568,753,3995,210,3.501,2.786,2.605,2.579,2.565,2.699,2.484,3.101,3.035,3.267,2.858,2.095,3.081,2.584,2.711,2.798,3.016,3.669,3.167,2.198,3.211,2.508,2.363,3.065,2.767,2.833,2.4,2.299,2.929,2.684,2.822,2.778,2.613,3.381,3.442,2.819,3.542,3.049,2.068,2.472,2.665,2.072,3.527,1.964,2.046,2.474,2.69,2.646,2.55,2.963,2.102,2.414,2.532,2.505,2.29,2.222,3.043,2.595,2.36,2.085,2.418,2.725,2.203,2.199,2.492,2.392,2.149,2.457,2.411,2.778,2.505,3.04,2.732,2.432,0.69,0.602,0.617,0.504,0.538,0.551,0.529,0.411,0.607,0.669,0.689,0.451,0.446,0.402,0.575,0.619,0.567,0.791,0.634,0.624,0.798,0.446,0.416,0.485,0.541,0.555,0.526,0.564,0.471,0.568,0.703,0.888,0.459,0.537,0.733,0.507,0.72,0.645,0.374,0.346,0.656,0.518,0.773,0.569,0.63,0.463,0.618,0.496,0.498,0.581,0.481,0.54,0.579,0.474,0.386,0.394,0.797,0.469,0.368,0.444,0.339,0.592,0.48,0.889,0.535,0.712,0.438,0.449,0.431,0.774,0.486,0.772,0.528,0.469
-279,Epileptic,M,37.0,1.1,3.4,1.0,1.4,2.2,1.6,1.9,2.2,0.9,0.9,0.4,3.4,2.4,0.5,1.7,6.0,11.5,1.5,1.9,5.2,1.8,3.7,1.6,4.0,4.6,3.6,4.4,3.5,2.7,3.5,1.5,1.2,0.6,2.6,0.6,0.4,4.6,3.7,0.3,0.3,1.1,3.2,2.5,3.1,3.0,0.9,0.7,0.8,1.6,0.8,0.5,2.5,2.2,2.9,0.3,3.5,0.9,2.4,1.0,1.0,0.5,2.4,0.5,0.9,2.9,1.5,3.1,1.2,0.9,1.0,1.4,1.5,5.8,0.4,11,37,8,10,20,19,15,19,12,11,5.0,41,23,6,20,80,107,28,29,49,18,42,19,42,48,48,55,37,26,52,21,10,8,32,3,3,50,49,2,2,7,29,28,24,18,7,3,5,9,5,6,21,17,20,3,27,7,20,5,8,7,17,3,6,23,21,25,10,6,12,9,12,47,3,0.037,0.026,0.016,0.022,0.036,0.031,0.026,0.031,0.033,0.034,0.039,0.042,0.028,0.047,0.038,0.032,0.032,0.048,0.027,0.036,0.036,0.038,0.029,0.04,0.041,0.029,0.03,0.029,0.025,0.033,0.037,0.042,0.022,0.033,0.012,0.008,0.047,0.035,0.018,0.017,0.02,0.038,0.04,0.023,0.018,0.017,0.02,0.01,0.016,0.018,0.029,0.02,0.024,0.018,0.015,0.016,0.019,0.022,0.021,0.017,0.016,0.032,0.021,0.021,0.022,0.02,0.021,0.015,0.014,0.022,0.027,0.021,0.019,0.015,1808,6528,2749,2850,3152,3330,2813,3906,2118,1890,865.0,3352,5146,1206,2857,12444,22368,3156,4511,5919,3778,5353,2665,7559,6405,7514,7668,4831,6214,6787,2255,1100,1613,7232,2579,1958,6736,8972,411,409,1526,3343,5820,4316,4451,1529,1165,3212,3100,1587,895,4432,2796,6075,698,5851,1316,3643,1220,1746,1312,3059,555,1202,4305,1627,4193,2659,1891,1857,1504,2458,11228,725,0.13,0.119,0.089,0.11,0.148,0.127,0.099,0.136,0.12,0.153,0.144,0.146,0.115,0.138,0.133,0.134,0.13,0.153,0.114,0.143,0.103,0.134,0.123,0.143,0.135,0.126,0.119,0.108,0.099,0.144,0.125,0.11,0.1,0.131,0.069,0.064,0.152,0.143,0.117,0.099,0.104,0.146,0.161,0.106,0.094,0.101,0.101,0.069,0.09,0.091,0.134,0.105,0.118,0.097,0.092,0.097,0.112,0.101,0.105,0.101,0.099,0.139,0.12,0.101,0.116,0.102,0.111,0.091,0.09,0.108,0.134,0.117,0.099,0.094,579,2315,1051,1033,1111,1052,1093,1301,647,507,253.0,1624,1589,308,919,3878,6801,598,1334,2614,992,1747,1041,2029,2224,2518,2914,1975,1827,2303,846,537,488,1581,714,763,1938,2190,206,229,800,1505,1376,2373,2594,776,490,1195,1465,688,388,2141,1416,2622,327,3077,662,1884,732,913,517,1212,322,566,2093,1185,2178,1267,981,856,790,1125,4792,415,3.041,2.627,2.515,2.525,2.488,2.6,2.193,2.621,2.491,2.94,2.556,1.789,2.597,2.904,2.487,2.508,2.631,3.669,2.713,1.999,2.699,2.465,2.147,2.871,2.361,2.448,2.194,1.999,2.624,2.382,2.087,2.023,2.533,3.233,3.234,2.395,2.525,2.988,2.237,2.158,2.37,1.961,3.058,1.943,1.935,2.153,2.772,3.167,2.521,2.716,2.259,2.202,2.111,2.333,2.353,2.051,2.436,2.148,1.94,2.046,2.332,2.589,2.002,2.443,2.156,1.629,2.092,2.336,2.165,2.29,2.068,2.489,2.437,2.19,0.85,0.603,0.492,0.529,0.691,0.727,0.541,0.457,0.544,0.491,0.907,0.462,0.486,0.652,0.521,0.508,0.614,0.925,0.633,0.608,0.898,0.571,0.48,0.689,0.56,0.557,0.491,0.523,0.441,0.527,0.674,0.852,0.393,0.614,0.68,0.408,0.92,0.714,0.385,0.488,0.414,0.59,0.731,0.585,0.559,0.537,0.554,0.9,0.528,0.59,0.449,0.455,0.45,0.43,0.415,0.41,0.561,0.502,0.414,0.37,0.548,0.631,0.499,0.76,0.395,0.673,0.466,0.361,0.416,0.514,0.407,0.55,0.49,0.328
-280,Epileptic,F,27.0,0.7,1.5,0.6,1.0,1.4,1.9,1.4,1.3,0.8,0.6,0.4,2.9,1.9,0.5,1.0,5.3,8.1,0.6,2.1,5.8,0.7,2.7,1.7,2.3,2.9,3.4,3.3,1.9,2.4,3.6,1.1,1.3,0.5,2.0,0.3,0.4,3.1,2.8,0.1,0.1,0.9,4.3,2.0,2.9,2.3,0.6,0.3,0.6,0.9,0.4,0.8,1.7,1.4,2.5,0.2,4.0,0.6,2.2,1.2,1.0,0.4,1.1,0.2,0.3,1.7,1.2,2.6,1.2,1.1,0.3,0.8,0.7,4.3,0.2,5,15,6,7,35,19,13,14,9,7,3.0,25,17,5,11,66,79,6,25,47,5,34,20,30,37,33,36,21,20,43,24,12,5,28,2,4,45,37,1,2,5,42,24,23,17,6,2,5,6,5,6,15,10,18,1,30,5,16,9,7,4,9,2,2,12,14,23,10,7,4,6,7,33,2,0.034,0.019,0.014,0.02,0.033,0.033,0.026,0.028,0.038,0.034,0.029,0.033,0.036,0.024,0.023,0.031,0.027,0.025,0.035,0.039,0.017,0.035,0.027,0.027,0.033,0.036,0.026,0.023,0.025,0.033,0.033,0.055,0.034,0.029,0.012,0.014,0.037,0.031,0.014,0.02,0.02,0.038,0.038,0.028,0.019,0.014,0.015,0.014,0.015,0.016,0.028,0.018,0.022,0.019,0.02,0.023,0.021,0.024,0.026,0.021,0.02,0.022,0.016,0.015,0.024,0.027,0.022,0.02,0.02,0.011,0.022,0.019,0.021,0.019,1256,3474,1789,2062,1920,3076,2744,2063,1243,1221,637.0,2794,3153,940,2688,11075,17463,2122,3948,4882,2833,4744,1940,5299,5077,5060,5890,2796,4774,6239,1967,1174,703,4798,1475,1369,5403,6127,404,595,1386,3048,4821,2599,2604,1465,820,1825,2049,1046,750,3230,1889,4774,212,4779,1206,3030,1353,1310,816,2031,438,778,2330,927,3187,1834,1916,823,1258,1798,8189,479,0.111,0.103,0.091,0.102,0.131,0.14,0.106,0.132,0.151,0.137,0.123,0.137,0.147,0.118,0.11,0.141,0.127,0.09,0.143,0.146,0.081,0.145,0.14,0.132,0.144,0.147,0.118,0.108,0.104,0.14,0.129,0.152,0.15,0.135,0.071,0.086,0.143,0.141,0.085,0.098,0.112,0.153,0.151,0.117,0.108,0.094,0.086,0.089,0.088,0.103,0.138,0.102,0.114,0.103,0.134,0.118,0.105,0.116,0.129,0.12,0.117,0.112,0.104,0.092,0.116,0.122,0.121,0.11,0.104,0.088,0.121,0.104,0.104,0.094,325,1326,702,754,775,1065,911,808,407,329,209.0,1442,856,248,777,3032,5032,450,1017,2391,695,1393,943,1381,1637,1624,2161,1202,1408,1956,559,467,245,1192,422,522,1444,1563,202,196,631,1756,955,1669,1788,715,344,794,962,448,430,1528,927,2001,126,2488,500,1477,713,714,345,830,254,333,1131,580,1802,907,873,451,596,751,3391,235,3.317,2.531,2.422,2.613,2.07,2.431,2.453,2.427,2.352,2.971,2.676,1.759,2.874,2.611,2.609,2.742,2.818,3.519,3.05,1.832,2.965,2.585,1.853,2.867,2.5,2.547,2.29,1.915,2.727,2.492,2.593,2.486,2.263,2.886,3.205,2.36,2.774,2.844,2.226,3.177,2.728,1.692,3.391,1.795,1.678,2.269,2.785,2.783,2.632,2.5,2.383,2.21,2.33,2.467,2.097,2.18,2.581,2.404,2.263,2.098,2.715,2.409,1.913,2.507,2.252,2.01,1.968,2.345,2.577,2.023,2.49,2.556,2.568,2.373,0.748,0.537,0.552,0.617,0.563,0.816,0.569,0.442,0.459,0.499,0.772,0.454,0.499,0.484,0.451,0.561,0.567,0.793,0.516,0.537,0.933,0.601,0.428,0.658,0.478,0.543,0.461,0.436,0.496,0.565,0.589,0.886,0.337,0.64,0.87,0.54,0.653,0.552,0.356,0.678,0.491,0.451,0.834,0.65,0.441,0.424,0.543,0.704,0.464,0.473,0.671,0.474,0.444,0.407,0.406,0.362,0.608,0.547,0.584,0.399,0.389,0.588,0.335,1.03,0.514,0.475,0.422,0.418,0.415,0.433,0.512,0.629,0.5,0.417
-281,Epileptic,M,37.0,1.2,2.4,1.0,1.6,1.3,2.2,1.5,2.1,1.0,0.8,0.2,3.1,1.5,0.5,1.0,6.9,9.3,1.0,2.2,4.0,1.5,2.5,1.7,3.6,2.2,3.0,3.1,2.0,3.0,4.3,1.4,1.0,0.7,3.4,0.5,0.9,2.9,2.8,0.1,0.2,1.3,2.4,3.3,2.4,2.9,0.6,0.3,1.0,1.2,0.7,0.4,2.0,1.7,2.5,0.1,2.8,0.5,1.3,1.1,1.2,0.9,1.6,0.3,1.2,2.3,1.4,2.5,1.3,1.4,0.6,1.2,1.1,4.6,0.4,7,22,7,12,12,19,13,17,10,7,3.0,31,14,5,11,67,77,8,21,37,10,32,17,35,29,35,32,18,27,43,13,5,7,38,2,9,34,36,2,1,9,22,32,21,17,4,2,5,6,6,4,14,12,17,0,24,3,11,8,10,9,13,2,11,16,18,21,9,7,7,8,6,39,3,0.043,0.026,0.02,0.024,0.027,0.033,0.023,0.03,0.038,0.042,0.031,0.033,0.03,0.027,0.025,0.034,0.03,0.034,0.038,0.039,0.024,0.036,0.032,0.036,0.032,0.03,0.03,0.022,0.024,0.038,0.029,0.049,0.02,0.031,0.012,0.019,0.038,0.032,0.01,0.017,0.024,0.032,0.041,0.022,0.021,0.013,0.016,0.014,0.016,0.018,0.022,0.022,0.024,0.02,0.014,0.022,0.022,0.018,0.027,0.021,0.027,0.027,0.03,0.031,0.023,0.026,0.023,0.028,0.018,0.018,0.028,0.015,0.02,0.016,1918,5202,1989,2620,2285,3481,2395,3814,1532,1611,642.0,3277,3236,1264,2653,11737,17705,2125,4333,4432,4382,4307,1965,5999,4124,6414,5006,3967,6445,6623,2632,1236,1431,7864,2660,2250,6401,6996,345,520,1537,2457,7335,2743,3844,1437,714,2309,2476,1333,468,3105,2563,4862,220,3771,952,2437,1077,1441,975,2758,482,1129,3053,1248,3396,2365,2182,1065,1471,2043,8886,756,0.138,0.114,0.106,0.116,0.121,0.145,0.098,0.13,0.127,0.146,0.105,0.136,0.133,0.12,0.12,0.136,0.126,0.125,0.132,0.142,0.102,0.147,0.131,0.129,0.145,0.134,0.126,0.094,0.093,0.15,0.118,0.137,0.093,0.128,0.066,0.1,0.137,0.139,0.093,0.083,0.119,0.132,0.147,0.112,0.103,0.09,0.082,0.083,0.088,0.114,0.122,0.112,0.111,0.105,0.085,0.117,0.103,0.101,0.13,0.111,0.134,0.123,0.126,0.133,0.11,0.117,0.112,0.112,0.099,0.123,0.13,0.1,0.102,0.085,492,1683,764,998,829,1131,934,1139,458,371,184.0,1507,830,307,727,3401,4997,451,1138,1786,911,1335,921,1736,1342,1889,1570,1267,1713,1949,732,378,472,1868,647,783,1507,1747,211,221,794,1214,1421,1684,2154,702,301,938,1097,535,294,1429,1137,1978,83,1961,413,1159,663,850,553,937,203,494,1547,872,1703,874,1036,490,671,921,3721,421,3.677,2.786,2.593,2.476,2.489,2.531,2.247,3.001,2.781,3.285,2.786,1.895,2.894,2.902,2.823,2.731,2.883,3.428,3.04,2.104,3.274,2.473,1.9,2.7,2.543,2.745,2.468,2.419,2.903,2.647,2.686,3.096,2.365,3.138,3.562,2.607,3.173,3.073,1.961,2.637,2.483,1.886,3.441,1.781,2.036,2.199,2.812,3.005,2.571,2.73,1.907,2.32,2.378,2.539,2.385,2.092,2.664,2.37,1.891,1.967,2.126,3.054,2.756,2.275,2.104,1.779,2.119,2.631,2.509,2.632,2.541,2.625,2.485,2.129,0.658,0.601,0.584,0.533,0.57,0.591,0.55,0.652,0.624,0.805,0.826,0.509,0.454,0.525,0.504,0.472,0.569,0.733,0.569,0.572,0.852,0.546,0.511,0.861,0.505,0.552,0.547,0.654,0.603,0.646,0.798,0.918,0.337,0.67,0.871,0.521,0.665,0.606,0.36,0.347,0.396,0.493,0.708,0.56,0.598,0.468,0.453,0.616,0.633,0.622,0.418,0.438,0.417,0.518,0.264,0.43,0.433,0.678,0.355,0.362,0.375,0.833,0.411,0.818,0.531,0.621,0.482,0.557,0.411,0.782,0.6,0.492,0.487,0.331
-282,Epileptic,M,22.0,1.6,2.8,1.7,1.5,1.6,1.8,1.6,1.7,1.2,0.9,0.3,3.3,1.3,0.3,1.4,6.3,8.6,1.1,3.4,5.5,1.2,2.6,1.4,4.0,3.2,3.8,2.6,2.7,3.4,3.0,1.2,0.7,0.5,2.4,0.5,0.8,2.5,3.0,0.1,0.2,1.2,2.6,2.7,3.7,3.4,0.7,0.5,1.0,1.4,0.5,0.6,2.5,2.0,2.7,0.5,2.9,0.6,1.5,1.3,1.6,0.8,1.6,0.4,0.4,2.1,1.7,2.8,1.4,1.1,0.8,0.9,1.3,5.2,0.4,11,34,16,11,15,16,13,16,15,9,4.0,26,16,6,17,70,77,8,41,44,9,30,16,41,40,38,27,23,23,39,17,6,5,26,2,7,27,37,1,2,8,22,23,24,19,5,2,25,7,6,3,23,16,19,3,24,5,13,9,12,6,13,2,5,15,18,23,9,6,6,6,10,41,4,0.057,0.025,0.018,0.025,0.032,0.032,0.023,0.025,0.033,0.039,0.038,0.038,0.022,0.021,0.025,0.028,0.027,0.034,0.033,0.044,0.026,0.03,0.025,0.036,0.029,0.027,0.025,0.025,0.026,0.03,0.031,0.041,0.026,0.026,0.014,0.018,0.037,0.032,0.01,0.014,0.021,0.032,0.04,0.03,0.021,0.013,0.017,0.018,0.016,0.018,0.036,0.016,0.024,0.02,0.02,0.019,0.018,0.018,0.028,0.022,0.028,0.021,0.014,0.016,0.023,0.024,0.02,0.017,0.015,0.019,0.021,0.016,0.017,0.021,1914,5832,3976,2576,2528,2611,2867,3755,2413,1479,631.0,2974,3687,1099,3334,13126,19856,2621,6949,5608,3748,4872,2056,6981,7378,8196,5540,4204,6644,5435,2512,1360,946,6395,1881,2475,6786,8411,350,688,1665,2361,5629,3225,4254,1517,843,2421,2603,1798,455,4967,3093,5163,675,4306,1309,3124,1195,1907,923,2977,554,924,2931,1978,4447,2833,2262,1101,1414,2241,11282,772,0.153,0.122,0.106,0.113,0.134,0.147,0.103,0.12,0.148,0.152,0.146,0.139,0.119,0.127,0.123,0.131,0.123,0.118,0.149,0.155,0.099,0.133,0.122,0.132,0.127,0.126,0.114,0.107,0.102,0.138,0.13,0.098,0.119,0.124,0.073,0.09,0.129,0.138,0.088,0.096,0.109,0.129,0.15,0.134,0.101,0.094,0.098,0.083,0.092,0.1,0.129,0.1,0.114,0.104,0.108,0.113,0.105,0.107,0.129,0.114,0.137,0.118,0.108,0.111,0.116,0.115,0.106,0.091,0.093,0.116,0.115,0.104,0.1,0.13,541,1891,1490,929,921,967,1082,1095,677,388,179.0,1343,951,290,943,3566,5305,493,1790,2161,840,1485,910,1893,1912,2352,1820,1646,1807,1776,645,460,316,1352,558,770,1377,1737,171,323,866,1269,1124,1997,2454,748,363,1029,1145,618,282,2333,1460,2109,369,2219,514,1365,697,1060,456,1115,316,455,1355,1016,2251,1312,988,585,654,1071,4676,322,3.346,2.785,2.605,2.641,2.491,2.579,2.266,3.062,2.797,2.959,2.555,1.971,2.978,2.83,2.73,2.8,3.032,3.944,3.032,2.171,3.377,2.516,1.923,2.893,2.853,2.821,2.396,2.106,2.846,2.474,2.693,2.651,2.444,3.363,3.244,2.774,3.311,3.424,2.136,2.502,2.458,1.742,3.259,1.877,2.011,2.244,2.834,2.898,2.709,2.806,2.086,2.239,2.32,2.578,2.303,2.191,2.782,2.585,2.034,2.042,2.726,2.713,2.056,2.519,2.351,2.259,2.179,2.526,2.59,2.22,2.507,2.438,2.623,2.741,1.109,0.509,0.535,0.423,0.505,0.415,0.594,0.481,0.517,0.699,0.923,0.516,0.392,0.35,0.428,0.493,0.496,0.754,0.491,0.641,0.923,0.517,0.533,0.566,0.571,0.569,0.549,0.559,0.552,0.554,0.626,1.053,0.543,0.502,0.542,0.626,0.751,0.55,0.341,0.442,0.455,0.473,0.782,0.524,0.593,0.475,0.6,0.645,0.472,0.677,0.393,0.491,0.443,0.474,0.344,0.478,0.686,0.644,0.425,0.366,0.363,0.569,0.46,0.842,0.472,0.841,0.432,0.341,0.443,0.511,0.424,0.441,0.581,0.611
-283,Epileptic,F,17.5,1.2,3.0,0.9,0.9,1.9,2.5,1.6,1.4,1.2,1.3,0.3,2.6,1.5,0.3,1.5,6.1,8.8,0.9,2.0,4.4,1.3,3.1,1.3,2.8,2.9,3.4,3.9,2.6,2.4,3.2,1.5,0.8,0.7,2.3,0.4,0.3,3.2,3.6,0.2,0.1,0.9,2.7,2.1,2.2,2.7,1.1,0.4,0.8,1.5,0.4,0.5,2.1,1.1,3.6,0.4,2.3,1.0,1.3,1.2,0.9,0.7,1.6,0.4,0.4,1.6,1.2,2.8,1.3,1.0,0.2,1.6,1.2,4.1,0.4,8,29,9,7,20,19,14,14,15,12,4.0,26,17,7,19,67,87,7,30,45,11,43,15,47,37,40,48,29,21,39,21,6,8,26,3,2,39,44,3,1,7,27,18,17,16,6,2,5,8,4,5,21,12,24,2,19,6,13,11,8,7,12,3,3,12,16,26,8,7,3,9,8,34,4,0.045,0.024,0.014,0.017,0.035,0.033,0.024,0.023,0.041,0.041,0.032,0.031,0.026,0.02,0.029,0.03,0.028,0.028,0.027,0.034,0.025,0.033,0.031,0.033,0.025,0.029,0.028,0.024,0.023,0.029,0.037,0.037,0.032,0.027,0.019,0.007,0.032,0.03,0.015,0.018,0.019,0.033,0.039,0.024,0.021,0.02,0.016,0.013,0.017,0.013,0.022,0.021,0.021,0.022,0.017,0.015,0.018,0.017,0.022,0.014,0.018,0.031,0.022,0.014,0.018,0.032,0.021,0.017,0.015,0.01,0.028,0.016,0.016,0.021,1585,5629,2384,2229,3077,3231,3070,3506,2144,1834,786.0,2663,3914,881,3214,12764,19321,2508,4582,5432,3315,5173,1763,6116,6433,7020,6885,4730,6068,5809,2331,1212,1244,6405,1746,1619,6992,9023,632,461,1733,3303,5048,2778,3517,1476,808,2313,2812,1177,527,4326,2106,5647,474,4785,1985,3192,1392,1631,1228,2393,556,941,2636,1235,4693,2442,2165,711,1713,2387,9336,496,0.132,0.121,0.1,0.095,0.142,0.138,0.105,0.119,0.166,0.175,0.116,0.141,0.124,0.119,0.142,0.134,0.13,0.11,0.139,0.144,0.098,0.145,0.137,0.147,0.124,0.137,0.133,0.111,0.095,0.142,0.123,0.115,0.145,0.127,0.078,0.058,0.139,0.134,0.093,0.073,0.109,0.143,0.14,0.113,0.105,0.109,0.089,0.081,0.098,0.096,0.13,0.109,0.116,0.112,0.118,0.095,0.098,0.102,0.127,0.097,0.111,0.128,0.117,0.095,0.108,0.122,0.114,0.102,0.097,0.095,0.131,0.102,0.098,0.132,467,1838,979,797,992,1152,987,1028,623,500,241.0,1218,974,280,896,3419,5305,499,1317,2114,881,1667,732,1567,1985,1943,2343,1672,1535,1826,762,481,393,1317,458,593,1615,2046,244,179,784,1356,1025,1475,2017,701,334,971,1252,514,346,1897,909,2380,270,2451,854,1391,813,947,587,829,309,441,1281,689,2286,1084,995,393,821,1060,3874,294,3.046,2.718,2.417,2.736,2.582,2.369,2.573,2.941,2.771,3.112,2.771,1.904,3.143,2.443,2.691,2.804,2.921,3.671,2.846,2.223,2.778,2.475,2.044,2.92,2.542,2.923,2.329,2.254,2.947,2.573,2.349,2.569,2.708,3.311,3.358,2.44,3.049,3.102,2.396,2.621,2.735,2.038,3.259,2.142,2.016,2.322,2.835,3.012,2.793,2.666,1.748,2.337,2.344,2.554,2.261,2.095,2.79,2.443,1.987,1.889,2.196,2.957,1.985,2.205,2.173,2.081,2.226,2.651,2.658,2.057,2.496,2.57,2.581,2.13,0.922,0.649,0.573,0.536,0.804,0.485,0.539,0.431,0.623,0.542,0.77,0.463,0.58,0.504,0.491,0.562,0.516,0.768,0.462,0.595,0.727,0.526,0.475,0.757,0.49,0.482,0.532,0.618,0.591,0.482,0.634,0.731,0.678,0.541,0.4,0.514,0.677,0.673,0.416,0.473,0.507,0.551,0.887,0.751,0.639,0.445,0.608,0.548,0.658,0.491,0.381,0.499,0.442,0.475,0.31,0.37,0.495,0.596,0.349,0.356,0.473,0.743,0.438,0.847,0.6,0.721,0.456,0.451,0.524,0.396,0.51,0.568,0.493,0.532
-285,Epileptic,F,22.0,0.7,2.6,0.6,1.9,1.6,3.4,1.3,2.2,1.0,0.9,1.3,2.9,1.3,0.7,1.0,5.9,8.7,0.7,2.7,5.4,0.5,2.8,1.2,3.6,2.7,4.6,3.3,2.4,2.9,3.4,1.1,2.1,0.5,2.2,0.4,0.3,3.9,3.3,0.2,0.2,1.1,4.5,2.0,2.2,2.3,0.9,0.4,0.7,1.1,0.7,1.2,1.3,1.5,2.7,0.4,2.0,0.8,1.6,1.1,0.7,0.6,1.3,0.4,0.5,1.5,1.1,2.7,1.7,1.0,0.4,0.8,1.4,3.5,0.3,7,29,7,12,13,26,13,19,11,9,17.0,28,14,7,8,59,79,5,27,43,4,30,16,41,34,44,40,21,22,33,18,13,6,27,2,2,38,35,1,1,7,34,15,18,18,6,2,5,6,7,8,9,12,20,2,17,7,17,9,6,5,9,2,4,11,15,20,12,6,5,6,12,30,2,0.033,0.023,0.011,0.032,0.03,0.039,0.021,0.025,0.033,0.036,0.092,0.032,0.027,0.036,0.028,0.03,0.028,0.028,0.033,0.038,0.012,0.034,0.028,0.034,0.029,0.034,0.028,0.026,0.025,0.031,0.032,0.05,0.019,0.027,0.014,0.008,0.038,0.031,0.02,0.022,0.023,0.043,0.04,0.026,0.02,0.018,0.022,0.013,0.015,0.018,0.041,0.018,0.022,0.02,0.016,0.016,0.018,0.019,0.025,0.016,0.015,0.023,0.033,0.015,0.019,0.03,0.022,0.02,0.018,0.014,0.021,0.022,0.018,0.016,1279,5078,2000,2546,2374,3807,2816,4056,1579,1383,600.0,2725,3305,1128,2083,10861,18443,2465,3982,4480,2287,4341,2033,5989,4675,7766,5755,4085,6126,5674,1988,1378,1170,4661,1582,1535,6606,7148,333,437,1681,3261,3618,2443,2798,1531,704,2270,2441,1522,657,2759,2531,4971,521,3392,1925,2851,1228,1154,1079,2271,449,870,2669,1112,4252,3209,1712,835,1454,2480,7391,488,0.122,0.112,0.09,0.131,0.129,0.154,0.106,0.123,0.144,0.149,0.154,0.14,0.127,0.149,0.137,0.134,0.13,0.098,0.127,0.135,0.064,0.144,0.131,0.133,0.139,0.144,0.134,0.12,0.115,0.139,0.114,0.153,0.102,0.121,0.067,0.06,0.13,0.13,0.106,0.103,0.11,0.152,0.137,0.119,0.11,0.102,0.107,0.082,0.092,0.098,0.154,0.099,0.11,0.108,0.104,0.103,0.099,0.107,0.125,0.103,0.1,0.111,0.147,0.1,0.108,0.116,0.118,0.105,0.104,0.102,0.115,0.11,0.096,0.1,411,1910,760,931,834,1393,948,1366,503,405,226.0,1288,887,288,562,3086,5220,455,1330,2107,696,1395,826,1738,1539,2337,2120,1416,1775,1896,654,576,429,1243,475,618,1816,1700,154,182,803,1598,842,1422,1856,725,308,928,1087,656,491,1297,1128,2053,303,1885,841,1538,742,678,568,881,211,488,1230,564,1950,1321,825,510,649,1078,3312,293,3.163,2.482,2.432,2.621,2.461,2.347,2.362,2.707,2.401,2.845,2.092,1.866,2.849,2.911,2.829,2.733,2.959,3.795,2.463,1.838,2.611,2.448,2.05,2.668,2.464,2.641,2.269,2.348,2.693,2.457,2.248,2.524,2.204,2.765,3.235,2.336,2.78,3.09,2.831,2.645,2.591,1.933,3.202,1.952,1.692,2.319,2.977,2.858,2.771,2.556,1.703,2.456,2.339,2.5,2.211,2.072,2.556,2.163,1.968,1.987,2.244,2.56,2.517,2.196,2.476,2.163,2.409,2.71,2.397,2.002,2.607,2.558,2.383,2.147,0.925,0.562,0.536,0.514,0.763,0.494,0.491,0.58,0.541,0.383,0.976,0.408,0.401,0.324,0.424,0.534,0.582,0.842,0.55,0.685,0.648,0.479,0.429,0.576,0.487,0.505,0.424,0.49,0.529,0.451,0.433,0.842,0.368,0.519,0.486,0.402,0.704,0.56,0.358,0.453,0.577,0.577,0.864,0.704,0.476,0.636,0.47,0.795,0.697,0.483,0.346,0.376,0.554,0.516,0.28,0.356,0.417,0.652,0.394,0.363,0.375,0.595,0.545,0.631,0.612,0.638,0.477,0.456,0.439,0.371,0.463,0.587,0.467,0.313
-286,Epileptic,F,27.0,0.5,2.5,1.4,1.2,1.6,1.7,1.6,1.1,1.0,0.7,0.2,3.0,1.2,0.4,1.0,5.6,7.6,0.7,1.8,4.5,1.2,3.6,2.8,3.0,2.6,1.7,2.5,2.0,2.3,2.8,1.6,0.9,0.4,2.0,0.5,0.4,3.3,2.5,0.1,0.2,0.5,3.2,2.1,2.2,2.4,0.6,0.3,0.8,1.1,0.7,0.6,1.5,1.9,2.4,0.4,1.8,1.0,1.7,0.8,1.3,0.4,1.5,0.2,0.5,1.7,1.0,1.5,1.1,0.9,0.5,0.7,1.1,3.2,0.2,5,24,17,11,18,16,13,13,13,8,2.0,29,14,6,12,62,76,6,24,46,14,44,20,37,38,22,39,24,24,30,14,5,5,25,3,5,50,33,1,2,3,22,24,20,14,4,1,5,6,6,4,10,16,22,2,14,7,14,6,10,3,10,1,5,15,13,12,6,6,5,5,7,26,2,0.036,0.03,0.03,0.029,0.037,0.028,0.031,0.025,0.047,0.038,0.023,0.039,0.033,0.04,0.038,0.039,0.036,0.04,0.027,0.042,0.025,0.042,0.046,0.042,0.041,0.026,0.033,0.029,0.028,0.032,0.046,0.047,0.019,0.032,0.024,0.016,0.036,0.031,0.014,0.021,0.014,0.04,0.035,0.027,0.019,0.016,0.021,0.014,0.019,0.014,0.022,0.02,0.032,0.022,0.02,0.016,0.026,0.023,0.016,0.019,0.015,0.025,0.022,0.026,0.017,0.021,0.017,0.017,0.016,0.02,0.023,0.017,0.014,0.012,1309,4519,2586,2487,2657,2714,2541,3043,1799,1471,632.0,3575,2798,1042,2120,9820,15595,1971,4361,5781,4296,4814,2115,5544,5339,4502,4808,3983,6086,5531,1892,635,1029,5640,1599,1226,7773,5768,260,562,995,2210,5341,2581,3225,1158,721,1850,1776,1603,759,2309,2302,4351,466,3036,1311,2936,1129,1893,996,2358,364,767,3192,1258,2694,2177,1664,696,1124,1983,8336,479,0.111,0.128,0.132,0.122,0.148,0.123,0.119,0.125,0.186,0.167,0.094,0.149,0.141,0.153,0.162,0.156,0.146,0.144,0.135,0.166,0.114,0.147,0.146,0.156,0.143,0.132,0.136,0.122,0.118,0.15,0.151,0.122,0.11,0.142,0.104,0.08,0.153,0.143,0.111,0.117,0.088,0.139,0.156,0.114,0.097,0.099,0.099,0.089,0.098,0.095,0.124,0.109,0.141,0.108,0.12,0.102,0.127,0.118,0.102,0.108,0.093,0.125,0.11,0.137,0.102,0.108,0.094,0.097,0.095,0.127,0.107,0.105,0.086,0.107,351,1458,924,801,769,974,866,803,424,323,160.0,1454,739,233,588,2796,4046,381,1181,2135,931,1602,1037,1416,1494,1237,1549,1308,1518,1599,562,288,298,1142,395,399,1721,1447,144,203,531,1307,1133,1452,1869,554,246,789,848,678,353,1135,968,1893,252,1559,548,1221,704,960,481,865,166,368,1506,798,1373,944,837,377,526,846,3571,231,3.402,2.758,2.694,2.779,2.625,2.379,2.383,2.922,2.844,3.271,2.958,2.042,2.901,3.06,2.722,2.617,2.859,3.726,2.897,2.287,3.285,2.362,1.921,2.866,2.666,2.784,2.42,2.306,2.948,2.593,2.461,2.355,2.704,3.251,3.45,2.615,3.23,2.933,1.977,2.391,2.345,1.542,3.189,1.977,1.94,2.346,3.062,2.951,2.631,2.638,2.227,2.184,2.269,2.202,2.204,2.15,2.836,2.556,1.883,2.205,2.333,2.584,2.08,2.184,2.402,1.904,2.129,2.591,2.356,2.049,2.4,2.455,2.368,2.34,0.885,0.698,0.615,0.626,0.781,0.765,0.633,0.549,0.716,0.769,0.704,0.591,0.438,0.526,0.48,0.641,0.691,0.738,0.536,0.699,0.896,0.803,0.585,0.768,0.642,0.62,0.663,0.578,0.493,0.664,0.694,0.728,0.382,0.631,0.657,0.574,0.839,0.728,0.296,0.599,0.493,0.543,0.857,0.658,0.577,0.584,0.494,0.709,0.442,0.631,0.591,0.455,0.546,0.475,0.263,0.422,0.436,0.619,0.395,0.374,0.419,0.799,0.515,0.615,0.506,0.752,0.373,0.407,0.436,0.73,0.529,0.526,0.502,0.306
-287,Epileptic,M,37.0,0.4,2.1,1.1,1.6,1.9,2.7,2.2,1.7,1.0,0.5,0.3,3.2,1.2,0.4,1.3,6.5,7.7,0.5,2.3,3.8,1.0,2.9,1.5,3.6,2.9,3.6,3.3,2.3,2.7,3.2,1.5,1.9,1.1,2.8,0.4,0.5,3.2,3.0,0.1,0.2,1.3,3.4,2.8,2.2,3.0,0.7,0.4,1.0,1.3,0.5,0.5,1.9,2.5,2.3,0.2,2.2,1.0,2.2,1.4,0.8,0.7,1.4,0.4,0.4,1.5,1.2,3.1,1.3,1.0,0.5,0.9,1.1,3.5,0.3,3,28,9,14,13,30,17,17,11,5,3.0,33,18,6,17,69,72,5,31,36,11,36,16,48,33,39,35,26,23,33,18,12,15,36,6,4,41,30,1,2,8,33,22,17,18,5,2,6,7,9,3,17,16,18,2,22,8,15,11,7,7,12,2,6,11,16,26,7,7,5,5,8,28,3,0.02,0.026,0.019,0.027,0.034,0.028,0.031,0.028,0.039,0.026,0.03,0.031,0.029,0.033,0.026,0.036,0.028,0.021,0.031,0.034,0.02,0.029,0.025,0.037,0.029,0.028,0.028,0.025,0.023,0.035,0.033,0.062,0.032,0.031,0.015,0.01,0.034,0.028,0.011,0.017,0.025,0.03,0.039,0.023,0.023,0.018,0.023,0.014,0.016,0.016,0.027,0.02,0.028,0.022,0.013,0.019,0.021,0.023,0.023,0.014,0.018,0.022,0.019,0.016,0.019,0.026,0.023,0.019,0.015,0.013,0.023,0.014,0.014,0.015,1465,4733,2362,2689,2574,4555,3058,3904,1844,1103,544.0,3662,3485,1105,3236,12211,18133,2339,5531,5226,2707,5181,2656,6518,5796,8121,5799,4254,6702,5949,2406,1194,2150,6906,2251,1991,7812,7866,355,620,1635,3749,6471,2594,3355,1474,745,2243,2525,1489,496,3804,2875,4723,356,3762,1599,3508,1743,1675,1277,2733,594,928,2995,940,4270,2321,2103,1091,1326,2613,8687,568,0.077,0.126,0.105,0.129,0.133,0.139,0.129,0.131,0.149,0.125,0.12,0.144,0.139,0.122,0.13,0.143,0.127,0.112,0.132,0.142,0.095,0.138,0.121,0.142,0.138,0.135,0.13,0.115,0.107,0.147,0.11,0.151,0.166,0.14,0.101,0.076,0.14,0.128,0.082,0.112,0.117,0.137,0.152,0.115,0.108,0.104,0.101,0.084,0.092,0.114,0.119,0.109,0.13,0.109,0.102,0.109,0.113,0.107,0.124,0.1,0.102,0.112,0.113,0.119,0.102,0.14,0.117,0.102,0.097,0.109,0.113,0.102,0.089,0.119,446,1478,876,968,886,1599,1051,1110,477,287,174.0,1588,906,263,836,3214,4609,436,1441,1940,867,1715,1017,1833,1608,2203,1905,1401,1679,1630,732,440,617,1447,513,622,1784,1664,215,246,788,1742,1173,1441,2026,631,298,993,1204,539,281,1625,1304,1805,213,1842,759,1671,929,882,660,1001,303,480,1317,673,2119,1037,955,488,610,1074,3708,283,3.092,2.919,2.581,2.671,2.517,2.474,2.383,3.073,2.808,3.214,2.462,2.103,3.04,2.956,2.839,2.881,3.18,3.782,2.966,2.202,2.48,2.392,2.282,2.685,2.686,2.898,2.421,2.397,2.944,2.8,2.571,2.69,3.025,3.399,3.822,2.795,3.071,3.212,1.857,2.802,2.5,1.927,3.636,1.975,1.951,2.379,3.395,2.868,2.557,2.625,1.937,2.389,2.4,2.597,2.144,2.117,2.498,2.366,2.135,2.163,2.141,2.852,2.124,2.177,2.523,1.857,2.182,2.623,2.556,2.391,2.543,2.646,2.468,2.299,0.645,0.689,0.682,0.542,0.682,0.518,0.636,0.497,0.583,0.402,0.725,0.592,0.392,0.483,0.523,0.52,0.583,0.768,0.58,0.662,0.817,0.535,0.473,0.741,0.549,0.544,0.517,0.569,0.653,0.593,0.789,0.9,0.546,0.569,0.636,0.479,0.78,0.787,0.318,0.583,0.597,0.521,0.864,0.633,0.564,0.474,0.493,0.753,0.496,0.652,0.38,0.468,0.492,0.426,0.277,0.4,0.541,0.448,0.387,0.415,0.393,0.7,0.362,0.816,0.52,0.604,0.526,0.444,0.379,0.687,0.469,0.522,0.493,0.293
-288,Epileptic,F,57.0,0.8,2.0,1.0,1.0,1.1,1.4,1.3,1.5,1.0,0.7,0.4,2.4,1.3,0.4,1.2,4.9,7.4,0.8,2.1,3.3,2.1,2.5,1.3,3.4,3.1,2.8,3.9,1.8,2.1,3.3,1.6,1.2,0.7,2.1,0.4,0.9,3.7,2.7,0.2,0.2,0.8,2.5,1.4,1.6,2.0,0.5,0.4,0.9,1.2,0.4,0.5,1.7,1.0,2.6,0.4,1.9,0.5,2.0,1.1,1.0,0.5,1.4,0.2,0.7,1.2,1.4,2.2,1.0,0.7,0.7,1.6,1.4,3.3,0.1,5,18,10,7,10,10,10,13,9,8,4.0,21,12,5,11,44,60,6,20,36,21,25,12,34,34,29,37,16,15,32,17,8,5,21,4,7,32,27,2,1,5,22,17,13,11,4,2,6,6,4,4,11,6,16,3,17,4,12,6,8,4,12,1,6,12,16,17,7,5,6,10,11,25,1,0.034,0.023,0.016,0.018,0.027,0.037,0.02,0.027,0.036,0.031,0.038,0.032,0.03,0.022,0.026,0.031,0.028,0.028,0.027,0.027,0.042,0.03,0.026,0.036,0.03,0.026,0.026,0.019,0.021,0.03,0.037,0.048,0.039,0.031,0.026,0.022,0.036,0.03,0.017,0.021,0.017,0.037,0.047,0.022,0.016,0.011,0.018,0.017,0.015,0.019,0.029,0.024,0.019,0.024,0.011,0.015,0.022,0.022,0.029,0.019,0.023,0.024,0.019,0.034,0.018,0.026,0.017,0.018,0.016,0.019,0.024,0.026,0.017,0.008,1116,4184,2183,2012,1823,1872,2573,2970,1466,1302,482.0,2407,2466,811,2391,8731,13912,1734,3447,3831,4498,3905,1732,5693,5793,5432,6403,3072,4802,5115,2327,1305,867,5380,1214,1804,5445,5026,367,270,1413,2201,2427,2061,3221,1422,795,2000,2071,1164,376,2676,1874,4380,660,3595,1098,2439,894,1372,716,2347,373,737,2365,1688,3600,1800,1415,1118,1824,1764,7358,359,0.118,0.114,0.101,0.096,0.125,0.138,0.096,0.123,0.13,0.134,0.128,0.144,0.128,0.13,0.126,0.135,0.125,0.121,0.127,0.121,0.142,0.142,0.125,0.147,0.143,0.129,0.129,0.092,0.093,0.133,0.139,0.123,0.155,0.129,0.12,0.108,0.13,0.129,0.094,0.098,0.099,0.143,0.13,0.108,0.091,0.077,0.1,0.086,0.088,0.089,0.138,0.107,0.09,0.109,0.096,0.102,0.104,0.108,0.126,0.105,0.12,0.117,0.116,0.138,0.102,0.12,0.096,0.096,0.095,0.114,0.121,0.126,0.094,0.064,323,1409,920,882,726,668,937,953,441,396,201.0,1021,751,234,714,2516,4270,385,1079,1697,795,1319,801,1579,1791,1817,2369,1266,1315,1777,720,468,257,1132,267,619,1635,1465,216,142,687,1061,588,1241,1986,735,359,901,1095,468,237,1205,865,1763,389,1884,479,1259,567,738,382,956,179,361,1165,769,1974,911,644,534,977,892,3228,171,3.218,2.784,2.371,2.324,2.284,2.539,2.351,2.65,2.511,2.844,2.124,2.01,2.582,2.452,2.597,2.722,2.662,3.433,2.576,1.899,3.202,2.378,1.991,2.749,2.523,2.526,2.231,1.991,2.713,2.41,2.578,2.745,2.969,3.151,3.028,2.426,2.59,2.72,2.016,2.205,2.436,1.879,2.784,1.868,1.849,2.062,2.735,2.576,2.283,2.43,1.817,2.335,2.348,2.51,2.023,2.084,2.579,2.071,2.043,2.07,2.159,2.352,2.502,2.329,2.081,2.495,1.963,2.277,2.491,2.339,2.139,2.461,2.429,2.386,0.534,0.563,0.499,0.445,0.485,0.62,0.47,0.412,0.514,0.438,0.634,0.483,0.413,0.353,0.439,0.485,0.534,0.639,0.621,0.55,1.222,0.416,0.354,0.584,0.418,0.499,0.457,0.448,0.488,0.467,0.577,0.835,0.381,0.688,0.846,0.535,0.703,0.534,0.487,0.323,0.549,0.44,0.993,0.633,0.569,0.48,0.432,0.773,0.42,1.109,0.465,0.473,0.759,0.464,0.303,0.387,0.499,0.795,0.302,0.334,0.526,0.654,0.374,0.612,0.733,0.716,0.404,0.438,0.421,0.59,0.488,0.456,0.549,0.421
-289,Epileptic,F,22.0,0.8,2.3,0.8,1.5,1.4,2.8,2.0,3.0,1.4,0.9,0.4,3.0,2.1,0.7,1.7,6.4,9.5,1.2,2.3,5.2,1.0,3.5,1.9,4.2,3.2,5.5,4.4,2.8,2.8,4.6,1.4,1.0,0.6,3.3,0.4,0.7,4.1,4.0,0.3,0.2,1.4,4.4,2.3,3.6,2.6,0.8,0.5,1.0,1.5,0.9,0.8,2.8,2.0,2.5,0.4,3.0,1.0,1.9,0.9,1.1,0.6,1.2,0.6,0.5,2.5,2.0,3.6,1.6,0.8,0.5,1.0,1.1,5.1,0.6,10,24,8,14,15,27,15,27,13,10,5.0,25,21,11,15,68,91,10,30,44,10,40,18,46,36,65,55,29,30,54,22,10,6,38,3,6,49,48,3,1,9,36,24,27,15,5,3,7,8,5,5,24,13,18,2,26,10,16,8,8,3,8,5,3,17,19,31,14,5,6,7,8,44,3,0.026,0.022,0.013,0.022,0.029,0.026,0.024,0.026,0.041,0.035,0.034,0.03,0.024,0.034,0.032,0.03,0.026,0.032,0.024,0.035,0.018,0.033,0.028,0.038,0.03,0.027,0.025,0.024,0.021,0.031,0.035,0.041,0.018,0.028,0.009,0.012,0.035,0.03,0.017,0.017,0.023,0.037,0.038,0.027,0.018,0.014,0.017,0.014,0.015,0.021,0.032,0.019,0.022,0.016,0.032,0.019,0.023,0.019,0.018,0.02,0.019,0.021,0.021,0.02,0.024,0.029,0.02,0.018,0.014,0.018,0.018,0.022,0.02,0.017,1749,4940,2260,2859,1762,4090,3166,5447,1580,1722,702.0,3005,5170,1264,2949,12973,20447,2933,4941,5412,3889,5602,2801,6424,6249,11310,8124,4941,7021,7112,1881,1086,1376,7210,2804,2414,7665,9104,627,385,2067,3448,5278,3055,3767,1750,970,2519,3075,1656,791,5138,3226,5411,508,4576,1789,3470,1412,1580,1136,1963,853,779,3619,1507,5841,3415,1816,1091,1853,2200,9752,899,0.117,0.109,0.088,0.115,0.125,0.13,0.105,0.121,0.145,0.131,0.139,0.131,0.113,0.147,0.13,0.124,0.123,0.121,0.125,0.137,0.085,0.141,0.124,0.136,0.133,0.129,0.12,0.105,0.102,0.143,0.131,0.121,0.083,0.128,0.062,0.081,0.145,0.136,0.104,0.119,0.111,0.144,0.137,0.121,0.096,0.091,0.101,0.085,0.091,0.106,0.139,0.106,0.106,0.09,0.129,0.107,0.111,0.1,0.111,0.107,0.094,0.108,0.116,0.109,0.115,0.138,0.111,0.101,0.089,0.108,0.105,0.105,0.105,0.097,523,1710,963,1088,783,1720,1167,1856,551,478,237.0,1507,1446,386,861,3674,6072,629,1453,2316,958,1762,1180,1749,1826,3335,2865,1860,1937,2512,629,444,476,1777,722,864,1965,2204,245,180,1043,1718,1117,1992,2222,861,412,1067,1455,622,437,2311,1458,2403,208,2495,771,1653,798,896,538,836,465,389,1568,1061,2870,1375,845,538,894,970,4179,495,3.296,2.546,2.303,2.494,2.042,2.254,2.373,2.644,2.345,2.975,2.437,1.782,2.807,2.52,2.693,2.663,2.788,3.677,2.731,2.034,3.0,2.529,2.11,2.657,2.729,2.757,2.28,2.151,2.859,2.363,2.25,2.427,2.305,2.922,3.31,2.702,2.825,3.03,2.513,2.411,2.566,1.86,3.091,1.778,1.939,2.254,2.845,3.071,2.494,2.911,2.394,2.241,2.229,2.371,2.729,2.054,2.552,2.363,2.085,2.004,2.396,2.283,1.829,2.247,2.586,1.796,2.244,2.453,2.28,2.009,2.382,2.495,2.469,2.313,0.523,0.626,0.578,0.47,0.531,0.458,0.658,0.601,0.476,0.727,0.802,0.494,0.461,0.498,0.519,0.548,0.594,0.724,0.545,0.704,0.783,0.463,0.514,0.643,0.531,0.55,0.486,0.529,0.568,0.467,0.612,0.99,0.448,0.696,0.906,0.467,0.69,0.648,0.458,0.389,0.46,0.476,0.725,0.643,0.659,0.509,0.655,0.586,0.648,0.527,0.426,0.509,0.55,0.494,0.478,0.383,0.532,0.501,0.418,0.371,0.545,0.605,0.482,0.852,0.518,0.547,0.469,0.527,0.449,0.717,0.579,0.515,0.438,0.333
-290,Epileptic,M,47.0,1.0,2.2,1.3,1.1,1.5,2.0,1.6,1.9,1.2,0.7,0.3,2.5,1.3,0.5,1.6,7.2,8.2,1.2,2.4,3.6,0.9,3.1,1.1,3.2,3.0,4.2,2.9,2.2,2.3,3.7,1.4,0.7,0.5,2.3,0.2,0.5,2.1,4.5,0.2,0.2,1.3,3.4,2.7,2.2,2.3,0.8,0.6,0.9,1.4,0.9,0.8,2.2,1.0,2.9,0.4,2.6,0.7,2.0,0.6,1.1,0.6,1.6,0.3,0.5,1.9,1.5,2.7,1.7,1.1,0.7,1.1,0.7,4.7,0.3,8,17,17,8,13,16,12,16,13,7,4.0,25,15,5,13,66,70,12,19,27,9,36,10,47,38,44,33,24,21,37,18,6,5,30,1,4,20,46,1,1,8,26,29,15,14,5,3,6,8,6,5,12,7,24,3,20,6,13,5,9,3,14,2,4,13,16,18,13,7,6,7,6,39,4,0.033,0.026,0.023,0.018,0.029,0.03,0.025,0.03,0.042,0.03,0.037,0.035,0.031,0.033,0.039,0.035,0.03,0.041,0.034,0.039,0.019,0.034,0.021,0.038,0.029,0.032,0.028,0.025,0.024,0.035,0.044,0.034,0.021,0.028,0.007,0.013,0.028,0.036,0.019,0.023,0.023,0.039,0.042,0.025,0.018,0.017,0.022,0.013,0.021,0.021,0.044,0.022,0.017,0.022,0.017,0.016,0.035,0.022,0.019,0.017,0.016,0.024,0.015,0.02,0.023,0.036,0.025,0.021,0.021,0.021,0.031,0.018,0.019,0.014,1659,4632,2390,2330,2242,3223,2890,3427,1758,1366,659.0,2637,3294,978,2741,12579,16762,2549,3850,3382,3372,4579,1889,6698,6301,7057,4646,3418,5618,5848,2493,1126,1139,5169,1780,1581,5019,8859,381,383,1675,2797,5617,2291,3286,1551,932,2374,2556,1644,561,3550,1748,5744,590,4743,1234,2943,899,1855,1126,2490,463,966,2498,1127,3124,2744,1610,1353,1522,2183,9541,505,0.134,0.119,0.119,0.099,0.124,0.134,0.109,0.128,0.163,0.138,0.134,0.142,0.13,0.134,0.147,0.138,0.125,0.141,0.136,0.137,0.091,0.144,0.106,0.139,0.131,0.136,0.122,0.108,0.102,0.148,0.146,0.113,0.101,0.121,0.052,0.081,0.12,0.144,0.098,0.105,0.115,0.14,0.151,0.113,0.093,0.089,0.105,0.081,0.1,0.108,0.159,0.104,0.102,0.108,0.114,0.095,0.131,0.109,0.109,0.098,0.098,0.122,0.111,0.112,0.116,0.129,0.116,0.107,0.109,0.118,0.118,0.103,0.104,0.097,490,1406,946,868,879,1161,960,1101,523,390,191.0,1174,844,236,711,3524,4670,527,1125,1452,812,1532,739,1780,1788,2230,1677,1376,1546,1818,687,458,369,1376,521,613,1238,2067,187,168,846,1330,1267,1392,1975,762,413,1051,1104,627,324,1499,908,2273,332,2431,403,1424,523,931,472,1083,242,434,1236,595,1645,1237,798,589,669,719,3883,358,3.31,2.892,2.579,2.743,2.363,2.472,2.394,2.793,2.741,2.976,2.938,1.983,3.043,2.84,2.9,2.773,2.904,3.151,2.929,2.036,3.153,2.424,2.127,2.904,2.702,2.608,2.217,2.035,2.72,2.582,2.689,2.43,2.479,2.711,3.118,2.427,2.953,2.978,2.354,2.871,2.452,1.922,3.078,1.89,1.903,2.214,2.898,2.801,2.826,2.932,2.143,2.569,2.197,2.489,2.406,2.092,3.074,2.265,2.037,2.057,2.626,2.305,2.289,2.455,2.255,2.332,2.086,2.479,2.226,2.571,2.539,2.924,2.553,1.64,0.627,0.581,0.62,0.537,0.585,0.567,0.551,0.544,0.497,0.538,0.746,0.513,0.444,0.38,0.425,0.548,0.574,0.906,0.666,0.62,0.807,0.691,0.427,0.609,0.57,0.537,0.464,0.512,0.536,0.53,0.568,0.892,0.342,0.668,0.864,0.428,0.751,0.682,0.472,0.469,0.582,0.456,0.758,0.71,0.542,0.546,0.539,0.595,0.473,0.668,0.588,0.478,0.51,0.484,0.354,0.394,0.56,0.691,0.382,0.378,0.704,0.657,0.331,0.799,0.63,0.649,0.434,0.414,0.449,0.674,0.471,0.821,0.543,0.304
-291,Epileptic,F,22.0,0.9,1.9,1.0,1.1,1.6,1.6,1.3,1.8,1.3,0.9,0.5,3.1,2.1,0.6,1.2,4.8,7.7,0.9,2.4,3.5,1.0,1.8,1.5,3.6,2.1,3.7,2.5,1.9,2.6,2.7,1.4,0.8,0.6,2.8,0.3,0.6,3.5,2.4,0.1,0.3,1.1,3.4,2.7,2.9,2.2,0.5,0.4,0.9,1.2,0.8,0.7,2.5,1.6,4.2,0.2,2.2,0.7,1.8,1.0,1.2,0.5,1.4,0.3,0.3,2.3,1.2,1.7,1.0,1.4,0.5,0.9,0.9,3.4,0.4,7,15,11,9,16,16,11,19,12,10,4.0,26,22,9,14,49,73,5,24,27,9,23,16,36,24,40,31,21,23,31,19,6,7,33,2,6,39,24,2,2,6,30,26,24,11,4,2,5,6,7,7,18,13,30,1,18,5,11,8,10,3,11,3,3,21,18,12,6,11,4,6,6,28,4,0.035,0.019,0.019,0.018,0.031,0.026,0.02,0.026,0.042,0.034,0.044,0.034,0.032,0.037,0.029,0.03,0.026,0.027,0.033,0.029,0.021,0.029,0.03,0.038,0.027,0.03,0.024,0.024,0.023,0.028,0.049,0.031,0.021,0.029,0.011,0.012,0.034,0.025,0.011,0.02,0.018,0.034,0.043,0.026,0.017,0.01,0.018,0.012,0.014,0.016,0.035,0.026,0.021,0.022,0.015,0.017,0.02,0.019,0.023,0.024,0.028,0.023,0.027,0.023,0.024,0.023,0.017,0.015,0.021,0.015,0.016,0.017,0.016,0.013,1693,4575,2383,2585,2635,3345,2953,3431,1882,1981,720.0,2682,4216,1393,3054,12271,21071,2586,4379,3815,3305,3843,2417,6721,4511,8126,5572,3727,7073,5943,2360,941,1287,7838,1581,2539,8063,7615,349,836,1766,2992,5471,2642,3893,1345,832,2678,2727,1743,564,3679,3741,7911,381,3363,1178,3196,1260,1456,674,2426,568,733,3499,1195,3449,2264,2552,1458,1917,2353,9030,681,0.11,0.103,0.106,0.098,0.126,0.135,0.092,0.128,0.139,0.136,0.152,0.141,0.136,0.153,0.13,0.126,0.118,0.109,0.133,0.118,0.096,0.14,0.143,0.144,0.129,0.137,0.118,0.103,0.105,0.126,0.145,0.114,0.103,0.128,0.065,0.077,0.145,0.122,0.093,0.1,0.101,0.142,0.145,0.121,0.084,0.08,0.097,0.074,0.083,0.104,0.151,0.11,0.099,0.106,0.091,0.11,0.105,0.096,0.119,0.119,0.115,0.115,0.121,0.119,0.121,0.119,0.092,0.095,0.106,0.114,0.103,0.1,0.092,0.093,448,1478,878,923,914,1071,984,1157,524,464,214.0,1383,1080,350,781,2951,5109,457,1245,1725,783,1080,863,1603,1317,2087,1830,1308,1764,1718,482,426,458,1582,406,791,1880,1519,218,284,851,1578,1165,1708,2185,700,350,1099,1219,674,367,1599,1354,2999,175,1802,539,1483,704,753,296,891,239,305,1586,851,1620,970,1110,439,851,899,3394,428,3.411,2.855,2.618,2.632,2.457,2.629,2.485,2.612,2.609,3.128,2.719,1.74,3.01,2.884,2.771,2.92,3.146,3.936,2.76,1.909,3.336,2.651,2.328,2.957,2.741,3.032,2.369,2.217,2.943,2.684,3.351,2.239,2.264,3.346,3.282,2.774,3.274,3.458,1.666,3.034,2.551,1.779,3.329,1.777,1.984,2.184,2.813,2.877,2.583,2.686,1.739,2.359,2.529,2.47,2.465,2.14,2.659,2.381,2.116,2.083,2.437,2.546,2.671,2.72,2.343,1.704,2.293,2.641,2.433,3.48,2.596,2.886,2.724,1.887,0.851,0.697,0.57,0.506,0.607,0.561,0.578,0.489,0.633,0.587,0.676,0.445,0.507,0.709,0.578,0.675,0.69,0.726,0.63,0.639,0.764,0.543,0.486,0.744,0.585,0.661,0.569,0.483,0.587,0.504,0.984,1.073,0.367,0.64,0.406,0.484,0.701,0.546,0.328,0.57,0.517,0.46,0.767,0.555,0.662,0.417,0.67,0.675,0.596,0.701,0.297,0.585,0.735,0.562,0.533,0.49,0.37,0.607,0.396,0.421,0.38,0.741,0.566,0.886,0.499,0.534,0.51,0.398,0.511,0.942,0.555,0.69,0.628,0.341
-292,Epileptic,M,17.5,0.9,3.4,1.2,2.0,1.6,2.4,4.0,2.7,1.6,0.6,0.5,3.2,1.6,0.4,2.3,7.9,10.4,1.8,2.9,5.2,1.9,5.0,2.3,4.4,6.1,8.4,5.5,4.7,5.4,3.5,2.7,1.6,0.8,3.6,0.9,1.4,5.1,3.7,0.2,0.2,1.4,3.3,3.2,2.9,3.7,0.9,0.6,1.0,2.0,1.3,0.8,1.8,2.7,3.2,0.6,2.6,1.5,1.9,1.2,1.1,0.9,1.9,0.5,0.5,2.4,1.4,3.5,2.2,1.4,1.2,1.1,1.0,6.8,0.4,10,35,10,16,19,21,25,26,18,8,8.0,35,17,9,31,95,111,21,27,44,20,56,23,44,62,76,65,51,46,41,25,18,6,40,5,14,54,48,2,2,8,36,35,18,21,7,15,6,9,9,4,14,20,23,3,21,10,14,10,7,7,15,3,4,26,19,34,18,10,13,9,8,57,3,0.036,0.028,0.018,0.027,0.034,0.033,0.045,0.029,0.042,0.024,0.042,0.041,0.028,0.028,0.036,0.036,0.031,0.055,0.031,0.043,0.031,0.043,0.032,0.043,0.047,0.04,0.039,0.036,0.036,0.03,0.056,0.046,0.031,0.034,0.021,0.023,0.038,0.031,0.015,0.013,0.02,0.04,0.048,0.026,0.019,0.016,0.042,0.014,0.018,0.021,0.035,0.017,0.025,0.018,0.025,0.016,0.024,0.022,0.024,0.017,0.021,0.032,0.024,0.014,0.02,0.02,0.023,0.021,0.017,0.022,0.024,0.014,0.019,0.017,2137,7211,3207,4000,2791,3613,3592,5563,2830,1962,1046.0,3188,4731,1227,4593,17118,27052,3613,5661,5427,5513,7675,2773,8514,7017,9335,7605,6613,9096,7861,2453,2099,1387,8284,3079,3277,10378,10991,417,713,2094,3689,7190,3337,5456,2022,1170,2929,3792,2460,664,4001,3612,7534,605,4905,2474,3825,1889,2168,1453,3491,718,1751,4272,2153,4944,4033,2817,1911,2150,2618,14021,731,0.116,0.121,0.101,0.122,0.133,0.13,0.129,0.126,0.155,0.122,0.145,0.162,0.122,0.121,0.156,0.144,0.13,0.153,0.137,0.157,0.116,0.141,0.14,0.145,0.14,0.137,0.127,0.122,0.125,0.131,0.142,0.117,0.103,0.131,0.082,0.11,0.135,0.13,0.092,0.087,0.103,0.145,0.164,0.117,0.099,0.092,0.074,0.083,0.093,0.096,0.128,0.096,0.116,0.097,0.121,0.097,0.111,0.108,0.108,0.095,0.107,0.128,0.123,0.08,0.112,0.101,0.111,0.108,0.099,0.119,0.116,0.088,0.099,0.103,537,2135,1038,1269,952,1303,1375,1631,752,462,347.0,1499,1132,337,1102,4352,6680,682,1460,2221,1205,2242,1225,2077,2389,3251,2661,2490,2588,2271,802,639,461,1874,676,1078,2473,2245,246,326,1043,1603,1498,1801,2823,932,448,1106,1636,973,397,1704,1616,2895,313,2392,1008,1563,980,1022,660,1036,320,582,2014,1260,2472,1673,1235,801,842,1183,5665,346,3.739,2.9,2.999,2.916,2.415,2.366,2.288,2.969,2.82,3.147,2.382,1.871,3.104,2.625,2.903,2.85,3.097,3.77,2.994,2.141,3.411,2.556,1.95,2.981,2.345,2.402,2.339,2.202,2.72,2.767,2.322,2.891,2.432,3.13,3.707,2.634,2.944,3.359,2.028,2.221,2.57,1.964,3.278,2.129,2.226,2.369,2.568,3.336,2.849,2.829,2.101,2.354,2.328,2.575,2.303,2.228,2.656,2.718,2.121,2.277,2.368,3.135,2.61,3.359,2.268,1.917,2.087,2.409,2.587,2.493,2.681,2.525,2.608,2.55,0.808,0.686,0.625,0.659,0.724,0.537,0.681,0.542,0.615,0.689,0.812,0.492,0.491,0.52,0.746,0.606,0.73,0.932,0.509,0.645,0.879,0.672,0.507,0.803,0.749,0.669,0.521,0.607,0.712,0.541,0.88,1.182,0.422,0.642,0.735,0.675,0.943,0.637,0.336,0.452,0.55,0.574,0.856,0.679,0.629,0.48,0.876,0.712,0.615,0.648,0.382,0.476,0.507,0.499,0.343,0.405,0.49,0.577,0.429,0.396,0.421,0.891,0.458,0.843,0.537,0.742,0.47,0.557,0.447,0.568,0.582,0.549,0.488,0.456
-293,Epileptic,M,52.0,1.1,2.4,0.8,0.9,0.9,2.3,1.4,1.6,0.8,0.7,0.4,3.2,1.8,0.7,1.3,6.3,7.9,1.6,2.1,4.0,1.5,2.8,2.0,2.7,2.5,4.1,2.9,1.7,2.0,3.7,1.5,0.8,0.4,2.1,0.3,0.5,2.2,3.2,0.2,0.3,1.5,2.8,2.7,2.5,2.0,0.6,0.6,1.1,1.3,0.6,0.5,1.4,1.8,3.1,0.4,2.3,0.6,1.6,1.0,0.9,0.6,1.3,0.3,0.3,1.6,1.7,2.4,1.8,1.0,0.5,1.2,0.9,3.3,0.4,11,23,8,7,12,19,12,15,10,6,3.0,32,19,9,12,70,72,8,23,34,16,35,19,38,32,47,35,18,20,41,29,9,4,25,2,4,28,36,2,2,8,26,21,21,11,5,3,7,8,6,4,12,15,25,2,21,7,13,7,8,4,12,2,4,12,18,18,13,7,6,10,8,27,4,0.04,0.029,0.015,0.017,0.022,0.031,0.019,0.025,0.035,0.031,0.033,0.032,0.034,0.032,0.027,0.027,0.026,0.041,0.029,0.029,0.027,0.033,0.026,0.03,0.03,0.029,0.023,0.018,0.019,0.03,0.032,0.031,0.02,0.026,0.012,0.011,0.027,0.031,0.014,0.017,0.026,0.031,0.045,0.022,0.015,0.011,0.022,0.019,0.017,0.011,0.027,0.016,0.023,0.02,0.026,0.013,0.018,0.021,0.017,0.017,0.021,0.022,0.016,0.012,0.021,0.035,0.015,0.024,0.016,0.017,0.025,0.016,0.015,0.023,1641,4287,2188,2150,1589,3260,2781,3108,1504,1512,691.0,3392,3656,1286,2974,11208,16269,2841,4599,4870,4319,5207,2869,6120,5108,8243,6446,3166,5022,6404,2339,1342,1155,5767,1519,2470,5749,7685,550,568,1775,3123,5318,2803,3350,1704,982,2387,2815,2019,574,3040,2608,5837,391,4359,1395,2749,1505,1630,951,2307,536,802,2652,1458,4723,2541,1679,1155,1744,2489,8392,438,0.131,0.123,0.093,0.092,0.111,0.146,0.098,0.123,0.129,0.127,0.122,0.141,0.138,0.154,0.121,0.13,0.121,0.125,0.137,0.124,0.114,0.145,0.129,0.133,0.136,0.14,0.119,0.091,0.092,0.142,0.141,0.106,0.095,0.12,0.073,0.074,0.115,0.135,0.083,0.11,0.118,0.151,0.167,0.112,0.087,0.084,0.109,0.094,0.092,0.094,0.136,0.097,0.117,0.106,0.119,0.099,0.107,0.117,0.105,0.107,0.119,0.119,0.1,0.103,0.109,0.132,0.093,0.114,0.104,0.114,0.126,0.093,0.09,0.146,504,1414,850,833,711,1201,1009,1013,456,407,210.0,1564,961,320,792,3699,4978,561,1221,2128,896,1547,1155,1610,1522,2419,2122,1337,1457,2092,731,537,323,1292,479,803,1478,1796,280,265,850,1384,1058,1819,2091,837,429,1021,1233,772,305,1462,1234,2601,206,2357,582,1233,835,867,427,896,269,432,1301,783,2446,1245,878,544,850,1015,3701,238,3.059,2.783,2.457,2.497,2.135,2.505,2.359,2.717,2.556,3.102,2.836,1.918,2.942,2.891,2.894,2.436,2.664,3.777,2.909,1.983,3.285,2.593,2.207,2.857,2.63,2.803,2.394,1.949,2.637,2.495,2.493,2.547,2.692,3.226,2.798,2.82,2.859,3.081,2.156,2.411,2.588,1.953,3.353,1.791,1.818,2.214,2.857,2.772,2.731,2.687,2.175,2.166,2.245,2.306,2.463,2.1,2.405,2.501,2.123,2.111,2.385,2.478,2.096,2.048,2.296,2.292,2.059,2.336,2.223,2.478,2.395,2.665,2.449,2.381,0.819,0.491,0.568,0.495,0.443,0.462,0.534,0.465,0.424,0.403,0.804,0.491,0.406,0.353,0.398,0.441,0.498,0.75,0.513,0.64,0.868,0.482,0.497,0.565,0.478,0.502,0.411,0.479,0.373,0.487,0.564,0.904,0.471,0.481,0.739,0.459,0.711,0.679,0.327,0.479,0.48,0.506,0.835,0.597,0.567,0.43,0.458,0.732,0.635,0.58,0.432,0.396,0.504,0.408,0.372,0.38,0.5,0.488,0.312,0.403,0.408,0.519,0.382,0.846,0.467,0.901,0.406,0.4,0.439,0.476,0.401,0.794,0.45,0.681
-294,Epileptic,M,37.0,0.9,2.1,1.4,0.9,1.2,2.4,1.3,2.3,1.0,0.8,0.3,2.5,1.9,0.6,1.1,5.8,10.5,1.2,1.9,4.7,0.7,1.3,1.0,3.1,3.1,3.6,4.2,2.1,3.1,2.3,1.2,0.5,0.5,2.4,0.6,0.5,3.9,2.6,0.2,0.1,0.9,2.8,2.4,2.8,2.3,0.5,0.4,0.8,1.3,0.6,0.7,1.8,1.1,2.0,0.3,2.4,0.8,2.0,0.6,1.1,0.2,1.7,0.2,0.4,1.4,0.9,2.3,1.3,1.6,0.5,0.9,0.8,3.4,0.4,7,19,14,11,13,21,10,22,11,8,2.0,19,19,6,11,59,83,11,23,38,5,15,10,31,31,38,44,19,28,29,15,4,5,26,3,3,37,31,2,2,6,26,25,21,15,4,1,5,6,7,5,11,7,13,2,23,5,18,4,9,2,10,2,4,10,21,20,9,11,4,6,6,30,2,0.041,0.024,0.026,0.019,0.033,0.036,0.023,0.032,0.042,0.035,0.034,0.031,0.04,0.034,0.034,0.034,0.039,0.046,0.027,0.033,0.018,0.029,0.021,0.036,0.044,0.035,0.035,0.028,0.032,0.033,0.037,0.027,0.021,0.035,0.023,0.016,0.047,0.037,0.017,0.017,0.02,0.038,0.042,0.024,0.019,0.011,0.02,0.016,0.017,0.025,0.029,0.023,0.025,0.022,0.016,0.02,0.026,0.024,0.017,0.027,0.013,0.028,0.022,0.018,0.019,0.036,0.018,0.02,0.021,0.019,0.027,0.017,0.023,0.021,1574,5090,2662,1912,2562,3560,2133,3889,1994,1344,620.0,2682,3296,1644,2608,11526,18854,2772,4629,4866,2449,2725,1917,6375,5097,7180,5313,3590,5484,4309,2419,1058,1163,5896,1942,1630,6778,6155,456,357,1424,3249,6445,2844,2680,1270,716,2152,2249,1185,777,2982,1611,3800,553,3044,1213,3113,865,1296,523,2751,575,874,2253,837,3946,2254,2304,1087,1083,1591,6137,503,0.127,0.105,0.119,0.097,0.143,0.158,0.098,0.154,0.155,0.141,0.108,0.125,0.141,0.152,0.148,0.145,0.143,0.141,0.125,0.133,0.074,0.126,0.106,0.142,0.173,0.143,0.134,0.111,0.113,0.138,0.13,0.082,0.099,0.147,0.094,0.083,0.163,0.154,0.111,0.118,0.106,0.147,0.157,0.121,0.099,0.081,0.098,0.085,0.09,0.13,0.128,0.108,0.119,0.098,0.103,0.113,0.117,0.119,0.103,0.132,0.091,0.126,0.124,0.119,0.104,0.154,0.102,0.11,0.111,0.117,0.13,0.114,0.113,0.108,391,1562,957,760,712,1087,828,1234,460,375,179.0,1240,935,364,631,3001,4679,487,1167,2220,706,859,747,1512,1391,2083,2001,1202,1602,1471,684,434,371,1211,483,520,1533,1322,186,134,740,1232,1171,1667,1756,664,274,898,1044,427,409,1273,724,1564,262,1790,495,1412,498,620,251,869,187,385,1084,393,1958,964,1092,394,556,613,2469,273,3.545,3.122,2.733,2.664,2.92,2.802,2.22,2.976,3.347,3.001,2.972,1.893,2.808,3.389,3.069,2.888,3.169,3.838,3.075,1.968,2.746,2.499,2.248,3.268,2.927,2.897,2.154,2.358,2.634,2.368,2.702,2.294,2.466,3.383,3.396,2.672,3.261,3.281,2.745,2.499,2.521,2.271,3.517,1.94,1.735,2.144,3.39,2.933,2.684,2.945,2.33,2.526,2.332,2.601,2.726,1.964,2.745,2.591,2.177,2.298,2.37,3.412,3.167,2.817,2.362,2.392,2.101,2.716,2.315,3.18,2.277,2.73,2.635,2.467,0.969,0.73,0.615,0.429,0.786,0.533,0.589,0.572,0.743,0.421,0.802,0.458,0.477,0.606,0.69,0.626,0.735,0.965,0.562,0.636,0.822,0.427,0.427,0.841,0.697,0.564,0.634,0.749,0.697,0.616,0.732,1.161,0.366,0.736,0.892,0.392,0.863,0.78,0.424,0.409,0.446,0.676,0.853,0.677,0.5,0.44,0.504,0.54,0.499,0.633,0.433,0.364,0.618,0.567,0.473,0.47,0.404,0.6,0.45,0.443,0.432,0.583,0.864,0.676,0.542,0.651,0.454,0.458,0.473,0.96,0.453,0.563,0.514,0.373
-295,Epileptic,M,47.0,0.9,1.8,1.0,0.9,1.3,2.4,1.6,1.9,1.5,0.8,0.2,4.2,1.8,0.5,1.4,5.8,10.1,0.5,2.5,4.9,0.7,3.1,1.9,3.5,4.3,4.8,5.3,2.7,3.0,3.5,2.1,1.0,0.5,3.2,1.2,1.0,4.1,2.9,0.4,0.3,1.3,3.4,2.9,3.3,2.8,0.8,0.5,1.0,1.5,0.5,0.6,1.3,1.1,2.2,0.4,2.8,0.7,2.0,1.1,1.3,0.5,0.8,0.6,0.9,2.8,1.2,3.6,1.4,1.4,0.8,1.0,1.2,3.8,0.4,7,15,7,6,14,15,12,15,10,6,3.0,34,15,5,11,48,72,4,27,37,7,24,17,39,39,43,47,22,20,33,22,7,4,33,8,8,30,28,2,2,8,29,25,25,16,5,2,5,7,3,4,11,7,12,2,20,7,17,7,10,5,4,3,5,20,14,23,7,7,12,7,8,27,3,0.035,0.027,0.019,0.024,0.043,0.045,0.035,0.037,0.06,0.043,0.029,0.044,0.036,0.035,0.042,0.049,0.04,0.022,0.041,0.039,0.019,0.042,0.039,0.052,0.043,0.039,0.042,0.037,0.037,0.037,0.067,0.042,0.03,0.042,0.034,0.023,0.05,0.039,0.028,0.023,0.028,0.058,0.061,0.032,0.026,0.017,0.028,0.019,0.019,0.023,0.043,0.031,0.033,0.029,0.024,0.021,0.018,0.021,0.033,0.028,0.021,0.024,0.048,0.041,0.032,0.038,0.033,0.025,0.024,0.036,0.026,0.025,0.021,0.025,1481,2967,1866,1593,1623,2142,2246,2744,1814,979,560.0,3436,3368,963,2220,8099,15037,1977,3446,4026,2372,3575,2554,4712,5937,7434,7023,3264,4572,4627,1386,1223,984,4854,2064,1896,4949,5350,298,369,1394,3807,4017,2783,2531,1274,619,1693,2197,1005,449,2071,1690,2958,378,3499,1345,3066,1045,1254,703,1153,534,683,2556,768,3884,1918,1760,1208,1125,1677,6793,485,0.122,0.112,0.109,0.11,0.147,0.14,0.112,0.134,0.158,0.157,0.097,0.153,0.128,0.135,0.142,0.149,0.137,0.092,0.132,0.132,0.087,0.127,0.139,0.151,0.145,0.14,0.143,0.121,0.121,0.137,0.169,0.107,0.105,0.142,0.119,0.104,0.148,0.137,0.128,0.108,0.122,0.163,0.146,0.131,0.115,0.097,0.112,0.097,0.097,0.109,0.15,0.122,0.118,0.11,0.12,0.111,0.103,0.108,0.134,0.125,0.107,0.103,0.151,0.123,0.136,0.142,0.126,0.107,0.113,0.149,0.13,0.121,0.1,0.143,433,1114,746,639,578,832,806,965,435,305,188.0,1658,960,277,656,2179,4263,444,1048,1937,619,1176,1016,1291,1745,2295,2270,1284,1354,1753,485,515,297,1152,519,760,1372,1277,192,201,776,1239,862,1732,1694,668,269,817,1136,404,285,841,641,1319,246,2044,634,1567,579,733,407,564,242,342,1451,484,1935,893,849,452,622,775,3126,249,3.222,2.328,2.55,2.406,2.227,2.171,2.319,2.599,2.694,2.729,2.549,1.814,2.656,2.574,2.571,2.683,2.747,3.459,2.593,1.821,2.781,2.336,2.103,2.644,2.474,2.596,2.328,2.059,2.571,2.139,2.14,2.124,2.392,2.963,3.035,2.22,2.654,2.761,1.769,2.152,2.256,2.381,2.914,1.792,1.746,2.118,2.548,2.467,2.287,2.643,1.906,2.277,2.395,2.233,1.938,1.924,2.271,2.138,1.989,1.859,1.919,2.026,2.242,2.039,1.909,1.905,2.056,2.271,2.333,2.437,2.309,2.443,2.249,2.497,0.6,0.743,0.575,0.54,0.691,0.656,0.529,0.549,0.772,0.6,0.711,0.461,0.494,0.588,0.68,0.699,0.64,0.671,0.568,0.508,0.752,0.586,0.529,0.68,0.618,0.667,0.617,0.558,0.599,0.551,0.736,0.904,0.667,0.657,0.868,0.541,0.677,0.749,0.382,0.402,0.506,0.724,0.855,0.638,0.483,0.636,0.505,0.701,0.544,0.65,0.392,0.539,0.62,0.573,0.312,0.366,0.502,0.561,0.485,0.361,0.348,0.56,0.523,0.562,0.492,0.512,0.441,0.454,0.499,0.815,0.532,0.66,0.568,0.591
-296,Epileptic,M,27.0,0.7,1.9,0.6,1.1,1.8,1.7,1.4,1.6,1.0,0.7,0.3,2.5,1.2,0.9,1.6,5.4,8.3,0.8,1.5,4.4,0.6,2.7,1.7,3.4,2.5,3.6,3.4,2.6,2.3,3.1,1.3,0.7,0.7,2.5,0.3,0.5,3.5,3.2,0.2,0.1,0.9,2.3,2.1,1.8,2.3,0.7,0.6,0.7,1.3,0.5,0.6,1.5,1.2,2.1,0.2,1.5,0.8,1.3,0.7,0.9,0.5,1.2,0.8,0.4,1.7,1.2,2.0,1.1,1.2,0.4,0.8,0.6,4.0,0.3,7,21,6,8,18,19,13,15,13,7,4.0,20,13,11,16,55,75,7,17,33,6,32,18,40,32,35,39,30,24,34,21,7,7,36,2,6,32,34,1,1,7,20,25,14,15,6,3,5,7,5,4,11,9,17,1,12,6,13,5,8,3,8,5,3,13,16,17,8,7,4,5,7,31,3,0.038,0.028,0.016,0.023,0.043,0.033,0.028,0.032,0.058,0.043,0.054,0.043,0.029,0.039,0.051,0.038,0.031,0.03,0.03,0.043,0.017,0.044,0.039,0.042,0.031,0.039,0.032,0.034,0.03,0.039,0.039,0.044,0.031,0.031,0.01,0.015,0.046,0.033,0.016,0.013,0.021,0.037,0.045,0.027,0.021,0.017,0.02,0.015,0.018,0.017,0.029,0.025,0.028,0.025,0.031,0.014,0.019,0.019,0.022,0.022,0.02,0.028,0.047,0.021,0.024,0.022,0.018,0.021,0.023,0.014,0.024,0.017,0.019,0.017,1307,3883,1655,2072,1973,2714,2121,2513,1813,1308,419.0,1803,2807,1465,2627,9167,15413,2206,2808,3245,2274,4018,1813,6350,4443,5976,5227,3094,4977,4904,2272,653,854,4694,1550,1512,5483,7032,353,361,1285,2116,5293,1958,2880,1227,869,1793,2338,1455,547,1966,1537,3571,255,2904,1251,2068,842,1116,798,1837,613,749,2210,1325,2900,1903,1485,855,1134,1864,7776,366,0.125,0.129,0.101,0.11,0.172,0.16,0.116,0.135,0.149,0.141,0.153,0.16,0.121,0.155,0.147,0.154,0.133,0.119,0.123,0.156,0.08,0.149,0.145,0.159,0.141,0.153,0.134,0.13,0.116,0.149,0.124,0.129,0.155,0.131,0.062,0.093,0.167,0.143,0.103,0.078,0.114,0.141,0.164,0.12,0.105,0.109,0.115,0.09,0.095,0.1,0.136,0.122,0.129,0.117,0.155,0.094,0.105,0.106,0.111,0.129,0.116,0.126,0.171,0.109,0.12,0.109,0.108,0.108,0.114,0.103,0.123,0.102,0.102,0.125,386,1216,609,738,685,960,754,883,423,329,137.0,882,766,387,695,2433,4536,434,880,1596,600,1169,734,1354,1379,1629,1861,1229,1343,1571,603,299,326,1159,470,589,1224,1554,194,138,662,941,962,1153,1764,641,422,812,1130,467,297,925,698,1557,79,1624,583,1155,506,638,372,666,257,351,1098,778,1676,828,803,410,566,716,3267,210,3.344,2.882,2.485,2.665,2.391,2.459,2.307,2.464,2.752,2.953,2.691,1.785,2.761,2.666,2.705,2.706,2.687,3.536,2.528,1.812,2.941,2.458,2.097,3.162,2.439,2.758,2.259,2.02,2.748,2.49,2.765,2.203,2.336,2.697,2.902,2.42,3.2,3.074,1.968,2.557,2.426,1.987,3.514,1.941,1.851,2.074,2.526,2.756,2.533,2.782,2.018,2.217,2.078,2.298,2.735,2.001,2.455,2.102,1.926,1.956,2.461,2.593,2.332,2.418,2.277,2.103,1.904,2.419,2.114,2.531,2.317,2.707,2.522,2.186,0.717,0.668,0.522,0.631,0.645,0.605,0.565,0.447,0.724,0.693,0.734,0.428,0.392,0.531,0.637,0.565,0.644,0.693,0.526,0.521,0.864,0.647,0.485,0.68,0.538,0.635,0.485,0.454,0.422,0.571,0.484,0.947,0.331,0.613,0.541,0.449,0.683,0.634,0.258,0.523,0.528,0.441,0.866,0.648,0.533,0.497,0.458,0.761,0.543,0.671,0.41,0.44,0.572,0.547,0.342,0.406,0.526,0.539,0.4,0.356,0.439,0.648,0.618,0.761,0.573,0.616,0.463,0.469,0.415,0.646,0.402,0.719,0.529,0.439
-297,Epileptic,F,42.0,0.8,2.6,0.7,1.2,2.3,2.8,1.6,1.6,1.3,0.7,0.2,2.2,1.4,0.7,1.1,7.7,11.1,0.8,2.4,3.0,1.2,4.2,1.1,4.2,3.7,4.2,3.5,2.5,2.6,3.5,2.0,1.3,0.4,2.3,0.4,0.5,5.0,3.1,0.2,0.2,1.3,4.1,2.4,2.6,3.1,0.6,0.4,0.7,1.4,1.3,0.9,1.8,1.1,2.2,0.3,2.8,0.7,2.3,1.2,0.5,0.9,1.6,0.5,0.5,2.2,2.3,2.0,1.1,1.1,0.6,1.1,2.1,4.5,0.4,7,24,9,8,16,25,14,14,13,8,3.0,22,14,7,15,89,102,5,29,27,8,49,10,42,42,46,43,20,24,36,17,18,5,27,3,3,50,41,2,2,8,32,19,21,18,4,2,6,7,8,8,14,10,13,2,23,6,17,7,4,8,12,3,4,18,20,16,7,8,6,11,14,37,3,0.037,0.021,0.013,0.02,0.037,0.03,0.022,0.027,0.048,0.036,0.031,0.029,0.028,0.042,0.027,0.037,0.03,0.031,0.028,0.029,0.027,0.036,0.022,0.044,0.031,0.032,0.029,0.026,0.021,0.026,0.043,0.043,0.021,0.029,0.013,0.009,0.034,0.03,0.014,0.017,0.025,0.037,0.04,0.03,0.02,0.01,0.016,0.013,0.018,0.026,0.032,0.02,0.02,0.016,0.018,0.019,0.025,0.019,0.018,0.014,0.029,0.026,0.028,0.017,0.021,0.053,0.017,0.016,0.016,0.018,0.021,0.019,0.02,0.018,1435,5103,1833,2496,1979,3798,2719,3176,1562,1447,577.0,2554,3192,1249,2430,12131,20663,2386,4838,3689,3099,6612,1840,5760,6215,7588,5954,4211,6530,6651,1982,1295,1137,6107,2130,2140,8136,6950,537,395,1790,3580,3902,2153,4144,1518,839,2223,2543,1812,875,2990,1918,4772,332,3885,1322,3888,1726,1107,1294,2432,521,898,3253,1077,3751,2190,2149,1158,1618,3084,8210,562,0.11,0.112,0.099,0.096,0.136,0.138,0.103,0.125,0.154,0.14,0.114,0.137,0.124,0.15,0.139,0.149,0.132,0.106,0.123,0.123,0.093,0.155,0.106,0.159,0.146,0.139,0.138,0.105,0.094,0.122,0.134,0.115,0.11,0.126,0.068,0.068,0.123,0.136,0.087,0.107,0.12,0.137,0.129,0.135,0.1,0.082,0.093,0.084,0.094,0.111,0.153,0.1,0.111,0.09,0.123,0.111,0.112,0.101,0.103,0.088,0.131,0.122,0.133,0.109,0.117,0.166,0.095,0.096,0.097,0.105,0.114,0.115,0.107,0.119,398,1989,867,933,875,1572,992,997,442,372,170.0,1184,909,316,709,3669,5834,418,1469,1627,760,2079,776,1666,2017,2271,2047,1468,1756,2156,745,522,354,1355,551,783,2414,1849,225,214,834,1634,1079,1396,2318,743,346,891,1142,822,442,1435,970,2081,203,2160,597,1915,944,567,525,885,262,479,1594,624,1943,1035,1029,519,824,1506,3600,325,3.307,2.523,2.308,2.62,1.942,2.258,2.321,2.869,2.762,3.089,2.839,1.925,2.767,2.929,2.614,2.538,2.792,4.165,2.679,1.954,3.089,2.477,2.111,2.503,2.435,2.811,2.305,2.321,2.869,2.45,2.176,2.502,2.499,3.008,3.127,2.623,2.67,2.893,2.415,2.22,2.75,1.976,2.996,1.73,2.011,2.197,2.91,3.073,2.594,2.635,2.096,2.163,1.973,2.401,1.994,2.023,2.537,2.283,2.034,2.089,2.445,2.712,2.109,2.321,2.265,2.116,2.091,2.431,2.433,2.411,2.322,2.562,2.479,2.311,1.018,0.508,0.559,0.529,0.528,0.456,0.537,0.462,0.593,0.467,0.75,0.4,0.413,0.515,0.457,0.592,0.658,0.692,0.556,0.608,0.817,0.485,0.463,0.737,0.571,0.452,0.534,0.484,0.517,0.551,0.715,0.92,0.402,0.693,0.815,0.374,0.641,0.505,0.359,0.504,0.49,0.477,0.774,0.66,0.599,0.438,0.521,0.869,0.493,0.528,0.487,0.46,0.52,0.53,0.298,0.428,0.429,0.504,0.402,0.322,0.349,0.696,0.536,0.905,0.574,0.661,0.431,0.391,0.5,0.77,0.394,0.451,0.48,0.438
-298,Epileptic,F,52.0,0.7,2.4,1.0,1.1,1.5,1.6,1.3,1.5,1.3,0.6,0.3,2.6,1.5,0.6,1.3,5.1,7.6,0.8,2.9,4.5,1.4,2.0,1.5,3.0,2.2,2.8,2.5,2.1,2.2,1.8,1.2,0.9,0.5,1.9,0.4,1.0,2.4,2.1,0.2,0.1,1.3,2.9,2.4,4.9,4.4,0.5,0.7,0.8,1.2,0.8,0.9,1.5,1.3,2.2,0.2,3.0,1.3,2.2,0.7,1.1,0.3,1.1,0.3,0.3,1.6,1.0,2.5,1.3,0.7,0.7,0.5,1.3,4.5,0.4,7,20,11,8,16,14,12,13,17,5,5.0,23,14,7,11,57,64,7,23,37,9,21,14,34,27,27,26,17,17,27,19,8,4,20,2,11,32,25,1,1,8,23,23,31,25,4,4,5,6,7,7,13,11,15,1,24,9,18,5,9,2,8,2,4,13,15,19,7,5,7,4,10,38,4,0.032,0.029,0.02,0.021,0.032,0.027,0.021,0.031,0.05,0.035,0.032,0.03,0.027,0.033,0.037,0.03,0.028,0.028,0.038,0.037,0.024,0.034,0.029,0.033,0.028,0.024,0.022,0.027,0.023,0.021,0.039,0.039,0.017,0.023,0.013,0.02,0.035,0.028,0.015,0.017,0.025,0.032,0.04,0.039,0.034,0.011,0.022,0.014,0.017,0.019,0.043,0.019,0.025,0.021,0.012,0.021,0.038,0.021,0.027,0.024,0.024,0.019,0.019,0.014,0.019,0.033,0.024,0.021,0.015,0.02,0.019,0.021,0.022,0.015,1496,3875,1978,1918,2126,2890,2261,2454,1840,954,382.0,2538,3179,981,2116,9477,15227,2195,3105,3781,2842,3234,1931,5404,4085,5932,4743,2853,4761,4055,1813,1220,1096,5485,1978,2317,5197,4579,343,253,1537,2796,5420,2481,2916,1130,917,2052,2100,1486,460,2624,2003,3839,491,3872,1349,2781,824,1291,624,2036,295,734,2503,742,2940,2010,1424,1158,1024,2058,7608,492,0.12,0.128,0.115,0.11,0.138,0.125,0.099,0.13,0.153,0.13,0.125,0.127,0.121,0.126,0.146,0.134,0.124,0.113,0.138,0.136,0.097,0.137,0.12,0.131,0.139,0.127,0.115,0.113,0.102,0.116,0.121,0.106,0.091,0.113,0.074,0.099,0.128,0.121,0.1,0.123,0.117,0.135,0.145,0.147,0.135,0.09,0.124,0.079,0.093,0.117,0.172,0.103,0.119,0.105,0.076,0.116,0.142,0.113,0.13,0.125,0.111,0.107,0.123,0.114,0.108,0.148,0.117,0.1,0.096,0.126,0.113,0.121,0.11,0.106,425,1270,804,798,876,946,886,868,551,276,175.0,1187,926,276,604,2769,4446,496,1235,1852,894,1037,867,1565,1353,1839,1731,1153,1447,1372,571,503,450,1255,433,873,1272,1156,203,136,816,1321,1057,1900,2053,634,440,880,1092,661,372,1314,903,1767,248,2117,609,1642,433,665,307,814,167,411,1262,460,1652,974,711,529,445,909,3230,345,3.336,2.807,2.481,2.504,2.217,2.712,2.245,2.615,2.572,3.01,2.056,1.918,2.698,2.585,2.726,2.654,2.786,3.41,2.145,1.91,2.683,2.477,2.0,2.65,2.419,2.791,2.251,2.056,2.617,2.358,2.397,2.399,1.994,3.045,3.785,2.311,3.009,2.805,1.927,2.066,2.327,1.881,3.433,1.559,1.665,2.02,2.738,2.875,2.364,2.602,1.67,2.066,2.133,2.353,2.037,2.095,2.397,1.956,2.228,2.058,2.592,2.509,2.019,2.112,2.236,2.124,1.984,2.458,2.352,2.656,2.68,2.63,2.413,1.744,0.779,0.712,0.691,0.439,0.494,0.812,0.521,0.498,0.52,0.328,0.563,0.53,0.433,0.357,0.474,0.482,0.543,0.856,0.595,0.589,0.604,0.473,0.566,0.6,0.475,0.47,0.442,0.457,0.46,0.473,0.579,0.945,0.362,0.72,0.944,0.509,0.863,0.573,0.373,0.382,0.399,0.541,0.782,0.463,0.512,0.375,0.38,0.806,0.425,0.506,0.402,0.495,0.559,0.391,0.355,0.45,0.437,0.549,0.429,0.371,0.326,0.673,0.36,0.804,0.537,0.481,0.478,0.472,0.367,0.767,0.59,0.738,0.476,0.295
-299,Epileptic,M,32.0,1.0,3.2,1.2,1.4,1.3,2.3,2.0,1.7,1.0,1.0,0.4,3.0,1.3,0.5,1.8,7.3,9.5,0.9,2.3,5.3,2.2,4.0,1.6,4.5,3.0,3.5,3.6,3.2,2.5,3.1,2.0,2.4,0.7,3.0,1.0,1.0,9.2,5.1,0.3,0.4,1.3,3.6,3.1,3.1,3.3,0.7,0.5,1.1,1.4,0.7,0.6,1.9,1.9,3.0,0.3,3.1,1.6,1.4,1.5,1.3,1.2,2.0,0.5,0.6,1.6,1.3,2.9,2.0,0.9,0.5,1.2,2.6,4.7,0.3,8,33,9,13,15,24,18,21,12,10,4.0,33,19,8,19,91,97,9,28,47,25,46,21,48,54,40,44,35,25,43,22,20,7,33,6,9,75,65,2,2,6,38,31,20,17,4,2,8,7,6,5,19,14,22,2,23,12,9,9,9,9,17,4,5,14,20,24,15,5,5,8,17,35,2,0.045,0.028,0.019,0.026,0.033,0.033,0.036,0.03,0.033,0.035,0.033,0.034,0.024,0.026,0.033,0.031,0.031,0.033,0.031,0.04,0.035,0.039,0.026,0.036,0.032,0.027,0.034,0.031,0.021,0.033,0.043,0.058,0.036,0.031,0.025,0.018,0.057,0.04,0.023,0.017,0.022,0.041,0.044,0.029,0.021,0.014,0.016,0.015,0.016,0.012,0.021,0.017,0.022,0.018,0.022,0.018,0.029,0.015,0.029,0.019,0.022,0.029,0.028,0.017,0.015,0.018,0.022,0.019,0.015,0.017,0.021,0.025,0.016,0.014,1578,6025,2688,2736,2548,3951,2880,4285,2016,2096,840.0,3109,4053,1423,3820,16069,23557,2948,4553,6103,5452,5676,2958,7399,7208,7845,5340,5262,7782,7045,2303,2014,1379,7839,2690,2430,9723,9858,393,741,1633,3917,6863,2731,4346,1438,1051,2693,2752,2283,709,4180,3231,6608,404,4694,2331,3365,1372,2030,1782,3249,579,1393,3102,1702,4851,3720,1757,935,1713,3215,10877,688,0.109,0.125,0.107,0.117,0.131,0.127,0.118,0.14,0.144,0.154,0.133,0.151,0.124,0.131,0.136,0.134,0.134,0.135,0.135,0.153,0.127,0.126,0.126,0.143,0.129,0.128,0.119,0.119,0.106,0.138,0.134,0.139,0.133,0.135,0.116,0.093,0.165,0.15,0.094,0.108,0.108,0.148,0.143,0.122,0.101,0.087,0.093,0.089,0.093,0.081,0.123,0.1,0.11,0.099,0.127,0.106,0.128,0.092,0.116,0.105,0.117,0.134,0.122,0.097,0.094,0.106,0.113,0.103,0.088,0.105,0.111,0.119,0.088,0.097,510,1920,948,954,891,1416,1059,1161,605,524,242.0,1606,1041,353,951,4548,5916,531,1318,2607,1267,1920,1169,2055,2039,2282,2111,1831,1900,2042,822,796,371,1633,643,952,2745,2462,214,320,837,1705,1508,1755,2324,726,442,1084,1282,990,438,1901,1402,2644,192,2419,924,1411,824,1049,828,1139,328,639,1582,1130,2225,1572,850,488,873,1504,4681,296,2.967,2.822,2.691,2.552,2.59,2.364,2.197,3.097,2.702,2.94,2.892,1.746,2.852,3.014,2.891,2.725,2.997,3.844,2.843,2.088,3.218,2.311,2.15,2.75,2.685,2.726,2.124,2.236,3.004,2.561,2.221,2.461,2.911,3.214,3.645,2.355,2.633,2.943,1.988,2.463,2.431,2.005,3.215,1.819,2.119,2.137,2.697,2.967,2.434,2.558,1.888,2.278,2.332,2.528,2.402,2.141,2.541,2.523,1.832,2.137,2.161,2.882,2.258,2.497,2.085,1.72,2.221,2.496,2.405,2.048,2.102,2.483,2.419,2.769,0.922,0.627,0.587,0.728,0.683,0.718,0.636,0.561,0.56,0.883,0.681,0.484,0.481,0.499,0.572,0.575,0.707,0.703,0.501,0.665,0.945,0.658,0.505,0.691,0.612,0.61,0.487,0.629,0.545,0.638,0.675,0.949,0.607,0.722,0.789,0.53,0.867,0.617,0.42,0.373,0.449,0.592,0.786,0.632,0.607,0.401,0.532,0.783,0.569,0.474,0.382,0.398,0.509,0.489,0.432,0.414,0.497,0.47,0.417,0.342,0.436,0.736,0.383,0.72,0.486,0.766,0.473,0.491,0.476,0.719,0.476,0.547,0.442,0.474
-300,Epileptic,M,37.0,0.8,2.2,1.2,1.4,1.2,3.0,2.1,2.3,1.1,0.4,0.4,3.6,2.6,0.7,2.0,7.5,9.0,0.9,2.7,4.8,0.7,3.3,1.8,3.7,3.2,4.2,3.3,3.1,3.7,3.3,1.6,0.8,0.4,2.6,0.6,1.0,4.5,3.9,0.2,0.4,1.2,4.4,3.1,4.1,3.8,1.0,0.5,1.0,1.5,0.6,0.8,2.9,1.2,2.0,0.2,2.6,1.4,2.1,0.9,0.8,1.2,1.5,0.7,0.4,3.2,0.9,3.6,1.7,1.4,0.9,0.9,2.5,5.6,0.2,8,19,10,13,10,25,13,18,9,4,3.0,33,20,8,19,63,72,9,29,37,5,33,16,36,27,37,33,27,28,28,19,7,3,26,4,9,49,38,2,2,6,35,23,31,22,8,3,6,8,7,6,21,8,14,1,20,9,17,7,6,10,10,5,4,25,13,27,11,10,9,6,15,45,1,0.026,0.021,0.016,0.022,0.034,0.03,0.026,0.025,0.046,0.021,0.029,0.03,0.036,0.046,0.035,0.038,0.028,0.027,0.025,0.031,0.014,0.031,0.028,0.037,0.034,0.031,0.023,0.026,0.026,0.029,0.032,0.031,0.02,0.029,0.015,0.023,0.036,0.032,0.02,0.021,0.023,0.034,0.041,0.029,0.022,0.018,0.017,0.016,0.014,0.014,0.03,0.022,0.027,0.017,0.016,0.021,0.023,0.018,0.018,0.016,0.026,0.031,0.029,0.015,0.029,0.021,0.02,0.019,0.023,0.028,0.022,0.027,0.02,0.017,1617,5395,2859,2956,1544,4810,2840,4334,1841,1036,595.0,3703,4673,1026,3291,12188,17834,2892,6247,5842,3270,5395,2032,6990,4442,6788,5861,5250,6668,4919,2155,1327,957,6099,2199,1962,8101,8736,315,577,1707,4056,6246,3454,4282,1537,995,2180,3231,1822,687,5310,1907,4156,270,3162,2080,3450,1305,1039,1712,2453,839,990,3427,973,5364,2839,1769,1122,1277,3012,10855,341,0.115,0.105,0.095,0.106,0.133,0.135,0.095,0.128,0.132,0.1,0.117,0.126,0.146,0.155,0.148,0.137,0.119,0.117,0.11,0.126,0.078,0.131,0.118,0.146,0.133,0.139,0.108,0.102,0.1,0.118,0.121,0.099,0.096,0.124,0.086,0.124,0.126,0.135,0.103,0.121,0.105,0.133,0.148,0.133,0.106,0.101,0.102,0.091,0.087,0.095,0.135,0.106,0.114,0.091,0.109,0.112,0.109,0.105,0.106,0.105,0.122,0.125,0.135,0.103,0.132,0.12,0.105,0.102,0.118,0.13,0.115,0.12,0.105,0.097,500,1692,1096,1093,573,1647,1133,1429,473,280,226.0,1823,1251,279,985,3250,5159,610,1770,2391,739,1818,1020,1716,1549,2166,2287,1850,2018,1869,731,482,289,1538,590,646,2155,2084,213,274,828,1954,1256,2303,2569,813,433,961,1449,711,467,2194,815,1959,166,1861,970,1818,791,655,765,825,376,473,1775,592,2769,1352,960,470,640,1464,4439,173,3.116,2.825,2.53,2.587,2.478,2.451,2.111,2.682,2.77,2.995,2.502,1.841,2.999,2.774,2.781,2.819,2.805,3.652,2.916,1.959,3.111,2.5,1.846,3.089,2.385,2.605,2.136,2.289,2.614,2.245,2.274,2.39,2.957,2.884,3.501,2.844,2.856,3.058,1.718,2.487,2.594,1.977,3.385,1.777,1.894,2.098,2.87,2.806,2.748,2.684,1.914,2.344,2.464,2.307,1.934,1.933,2.573,2.229,1.995,1.771,2.451,2.982,2.36,2.302,2.214,1.921,2.014,2.421,2.181,2.497,2.331,2.408,2.614,2.336,0.649,0.672,0.54,0.589,0.793,0.783,0.648,0.475,0.694,0.397,0.746,0.418,0.398,0.516,0.682,0.679,0.567,0.779,0.658,0.801,0.835,0.542,0.52,0.623,0.57,0.515,0.56,0.57,0.565,0.583,0.656,1.137,0.403,0.682,0.813,0.523,0.671,0.672,0.365,0.338,0.594,0.457,0.703,0.516,0.595,0.536,0.617,0.568,0.61,0.473,0.279,0.649,0.657,0.573,0.462,0.51,0.476,0.49,0.402,0.35,0.502,0.843,0.506,0.939,0.483,0.67,0.547,0.445,0.431,0.529,0.528,0.478,0.518,0.469
-301,Epileptic,F,32.0,1.1,3.1,1.9,1.9,3.2,1.9,1.9,2.1,2.5,1.1,0.3,3.0,1.9,0.8,1.2,6.6,10.5,1.2,2.5,4.8,1.5,2.7,1.4,4.0,3.9,6.1,3.7,3.0,2.6,3.8,1.3,2.0,0.8,4.8,0.5,1.2,4.0,3.7,0.2,0.3,1.7,2.6,3.5,3.5,2.8,0.8,0.6,0.8,1.5,1.1,0.7,2.8,2.2,2.9,0.4,3.0,0.9,2.1,0.9,0.9,0.5,2.1,0.2,0.6,2.1,1.9,3.9,1.9,0.7,0.6,1.3,1.8,6.1,0.5,9,28,21,15,22,18,15,18,20,10,5.0,31,20,10,18,72,96,7,30,44,20,30,15,36,46,66,47,28,26,45,14,14,8,52,3,13,37,46,1,3,12,23,21,23,19,6,4,6,7,11,5,18,16,20,3,27,6,19,6,6,4,11,1,6,16,21,31,14,6,7,7,13,51,3,0.036,0.025,0.022,0.029,0.05,0.03,0.032,0.032,0.055,0.04,0.032,0.034,0.034,0.034,0.031,0.035,0.035,0.035,0.03,0.038,0.029,0.046,0.029,0.041,0.038,0.03,0.03,0.029,0.026,0.031,0.041,0.049,0.023,0.039,0.016,0.017,0.037,0.036,0.012,0.028,0.025,0.034,0.045,0.029,0.022,0.016,0.024,0.013,0.018,0.028,0.035,0.026,0.027,0.021,0.014,0.023,0.015,0.021,0.029,0.02,0.022,0.027,0.033,0.025,0.025,0.025,0.02,0.021,0.016,0.022,0.027,0.027,0.023,0.015,2093,5582,3337,2778,2694,3044,2526,3567,2439,1931,508.0,3030,3809,1764,2708,12210,19312,2862,5016,4958,3212,4657,2014,6803,6740,11904,6017,4061,5609,6822,2136,1709,1757,9491,2364,3426,8131,7556,592,492,2152,3439,4372,3139,3312,1349,1105,1928,2804,1575,678,3001,2976,5429,554,3777,1766,3019,855,1173,1012,3328,251,960,2608,2172,5408,3054,1159,1651,1541,2408,10995,918,0.126,0.116,0.122,0.125,0.152,0.138,0.125,0.13,0.168,0.148,0.126,0.148,0.13,0.131,0.124,0.135,0.137,0.125,0.126,0.141,0.105,0.153,0.127,0.137,0.154,0.131,0.14,0.118,0.113,0.144,0.128,0.144,0.11,0.147,0.08,0.1,0.127,0.131,0.079,0.136,0.114,0.143,0.136,0.126,0.105,0.101,0.111,0.079,0.095,0.103,0.152,0.121,0.116,0.106,0.103,0.119,0.092,0.1,0.131,0.103,0.107,0.122,0.124,0.125,0.116,0.118,0.109,0.11,0.095,0.119,0.123,0.125,0.108,0.088,535,1888,1270,1013,1047,1064,918,1105,667,477,187.0,1431,1083,392,749,3343,5243,537,1417,2127,758,1178,880,1731,1902,3730,2211,1588,1653,2059,634,643,566,2230,547,1184,1982,1986,306,155,1114,1286,1123,1944,2011,675,423,907,1258,672,343,1376,1367,2308,337,2056,850,1501,473,667,393,1100,123,463,1289,1161,2811,1346,616,531,731,1050,4458,486,3.333,2.632,2.436,2.595,2.143,2.407,2.333,2.677,2.529,3.054,2.273,1.864,2.833,2.924,2.656,2.576,2.826,3.631,2.937,2.01,3.052,2.773,1.963,2.662,2.555,2.635,2.217,2.095,2.546,2.652,2.484,2.644,2.412,3.175,3.558,2.584,3.008,2.864,2.299,3.0,2.346,2.137,3.039,1.89,1.862,2.093,3.07,2.552,2.748,2.613,2.229,2.137,2.126,2.298,1.935,2.025,2.331,2.234,1.902,1.962,2.634,2.808,2.29,2.291,2.157,2.106,2.037,2.408,1.853,2.755,2.412,2.481,2.466,2.322,0.905,0.813,0.582,0.55,0.631,0.589,0.557,0.616,0.671,0.529,0.917,0.445,0.667,0.649,0.655,0.673,0.667,0.927,0.594,0.51,0.684,0.72,0.474,0.949,0.707,0.551,0.507,0.465,0.544,0.581,0.928,1.239,0.533,0.668,0.933,0.514,0.703,0.622,0.394,0.397,0.525,0.654,1.002,0.629,0.54,0.613,0.634,0.672,0.563,0.749,0.369,0.591,0.613,0.607,0.423,0.419,0.416,0.593,0.426,0.383,0.51,0.833,0.78,1.255,0.697,0.795,0.455,0.538,0.43,1.016,0.54,0.644,0.556,0.711
-302,Epileptic,M,37.0,1.6,3.2,1.4,1.8,3.5,3.2,3.3,3.6,2.7,1.0,0.5,3.7,2.3,1.4,1.9,13.1,19.4,1.5,2.8,6.2,2.4,7.0,2.5,8.0,8.5,10.9,7.9,4.1,6.3,6.1,2.8,0.7,1.5,5.9,1.2,3.3,7.1,6.5,0.3,0.3,1.7,3.9,3.5,5.0,5.7,1.0,0.9,2.2,1.9,1.2,1.1,3.7,2.1,6.8,1.2,5.1,1.6,1.7,2.3,2.1,1.0,3.1,0.5,0.7,2.5,1.7,3.5,2.1,3.2,0.9,1.5,2.7,6.8,0.8,19,30,9,14,19,25,17,22,21,13,5.0,31,20,9,15,79,124,14,31,49,26,41,18,61,52,61,54,26,31,54,25,11,9,39,7,18,49,50,1,2,8,31,32,35,25,6,4,10,9,7,7,20,12,30,9,29,9,11,17,11,6,20,4,5,15,21,21,10,19,6,9,13,39,4,0.057,0.029,0.023,0.032,0.043,0.042,0.043,0.05,0.064,0.042,0.042,0.039,0.041,0.061,0.05,0.048,0.049,0.047,0.033,0.039,0.048,0.056,0.036,0.057,0.055,0.053,0.048,0.032,0.042,0.043,0.064,0.05,0.049,0.055,0.031,0.046,0.054,0.055,0.022,0.02,0.03,0.034,0.053,0.036,0.038,0.026,0.025,0.032,0.027,0.029,0.041,0.034,0.031,0.033,0.029,0.03,0.022,0.021,0.049,0.029,0.033,0.041,0.025,0.033,0.033,0.029,0.026,0.029,0.036,0.027,0.025,0.025,0.024,0.044,1465,5182,2610,2538,2190,2365,1996,2772,1553,1631,559.0,2517,2741,907,1509,7752,14663,2291,5746,5246,3396,2835,1722,6135,4211,6559,5391,2946,3677,6113,2408,1133,1120,5014,2253,2305,6438,6929,355,555,1751,2274,4598,2792,3103,1326,1087,2249,2315,1525,866,3521,2205,5420,1067,4979,2044,2652,1032,1748,839,3121,337,768,2118,1217,4037,1868,2064,1043,1881,2844,9291,419,0.151,0.121,0.114,0.127,0.146,0.14,0.129,0.147,0.168,0.147,0.149,0.139,0.135,0.147,0.136,0.146,0.147,0.153,0.121,0.137,0.139,0.164,0.133,0.155,0.164,0.153,0.138,0.124,0.13,0.13,0.152,0.122,0.14,0.143,0.106,0.142,0.147,0.137,0.1,0.104,0.117,0.133,0.146,0.146,0.146,0.113,0.115,0.119,0.117,0.122,0.148,0.122,0.118,0.125,0.133,0.121,0.1,0.107,0.177,0.126,0.139,0.14,0.142,0.123,0.132,0.127,0.111,0.126,0.147,0.124,0.116,0.115,0.108,0.157,507,1796,958,1018,1217,1171,1061,1163,667,454,238.0,1582,922,338,630,3664,6103,576,1652,2660,939,1744,1112,2301,2149,3054,2406,1736,2005,2458,857,389,456,1543,693,1041,1982,1970,178,244,901,1707,1344,2303,2388,688,538,1066,1069,675,428,1808,1229,2863,640,2676,1013,1292,848,1084,508,1304,258,404,1199,965,2122,1041,1341,529,947,1487,4204,282,2.736,2.67,2.679,2.439,1.951,1.945,1.719,2.282,2.053,2.783,2.08,1.482,2.493,2.171,2.019,1.941,2.243,3.25,2.781,1.772,2.837,1.577,1.502,2.273,1.843,2.143,2.03,1.544,1.668,2.14,2.163,2.629,2.165,2.546,3.051,2.196,2.649,2.712,2.214,2.454,2.455,1.31,2.681,1.453,1.542,2.255,2.59,2.585,2.683,2.473,2.136,2.176,2.124,2.188,2.189,2.13,2.305,2.293,1.53,1.945,2.205,2.645,1.688,2.104,2.009,1.564,2.187,2.118,1.86,2.263,2.315,2.392,2.487,1.989,0.845,0.437,0.464,0.452,0.466,0.723,0.501,0.626,0.628,0.636,0.745,0.509,0.652,0.602,0.586,0.544,0.698,0.708,0.625,0.669,0.735,0.552,0.498,0.733,0.583,0.641,0.513,0.38,0.511,0.572,0.654,0.872,0.611,0.838,0.654,0.662,0.884,0.931,0.36,0.351,0.438,0.394,0.81,0.462,0.362,0.511,0.274,0.723,0.442,0.443,0.589,0.432,0.43,0.476,0.366,0.397,0.421,0.463,0.428,0.532,0.46,0.493,0.392,0.515,0.528,0.463,0.493,0.437,0.431,0.454,0.358,0.562,0.543,0.293
-303,Epileptic,F,37.0,0.9,2.3,0.9,1.3,1.0,1.7,1.6,1.4,1.1,0.7,0.3,3.2,1.6,0.5,1.6,5.2,8.6,0.7,2.3,5.5,1.5,2.7,1.2,3.4,2.9,3.2,3.1,2.5,2.3,3.2,1.3,1.1,0.6,2.6,0.3,1.1,2.6,3.1,0.1,0.2,1.1,2.2,2.0,2.7,2.9,0.7,0.4,1.6,1.0,0.5,0.8,2.4,1.0,2.7,0.5,2.2,1.0,1.2,0.8,1.3,0.5,1.2,0.3,0.2,1.5,1.3,3.1,1.4,1.6,0.4,0.6,1.1,4.2,0.4,7,17,10,10,9,10,12,13,8,6,4.0,26,15,6,18,52,79,8,22,39,16,29,16,29,32,34,32,20,18,32,14,8,6,28,2,9,26,35,1,1,8,20,21,21,21,5,2,9,5,5,5,17,6,17,3,21,6,9,7,11,4,11,3,3,12,11,22,11,9,5,4,8,42,3,0.038,0.024,0.017,0.024,0.027,0.034,0.021,0.025,0.042,0.042,0.032,0.033,0.033,0.031,0.031,0.03,0.029,0.03,0.029,0.039,0.027,0.029,0.024,0.036,0.028,0.029,0.027,0.027,0.024,0.033,0.036,0.047,0.023,0.029,0.011,0.026,0.032,0.035,0.016,0.017,0.023,0.029,0.035,0.026,0.023,0.013,0.019,0.019,0.014,0.011,0.042,0.023,0.031,0.021,0.02,0.019,0.024,0.019,0.02,0.023,0.027,0.025,0.022,0.015,0.02,0.033,0.025,0.024,0.025,0.017,0.02,0.022,0.018,0.016,1724,4567,2526,2361,1581,1833,2955,3238,1729,1314,579.0,2884,3497,896,3232,10061,18129,2417,3729,3857,3557,5118,1817,5858,5831,6768,5528,3963,5762,5204,1910,1169,1199,6670,1892,2150,5666,6152,312,506,1531,2067,5343,2425,3229,1549,755,2611,2259,1552,478,3409,1268,4704,696,3609,1919,2569,1268,1459,745,1988,469,798,2705,1301,3597,2341,1949,867,1244,1906,9605,562,0.115,0.113,0.099,0.113,0.125,0.13,0.095,0.12,0.151,0.132,0.117,0.139,0.131,0.14,0.134,0.133,0.127,0.111,0.133,0.14,0.109,0.131,0.125,0.138,0.132,0.135,0.121,0.109,0.099,0.141,0.125,0.14,0.119,0.13,0.072,0.122,0.132,0.151,0.088,0.091,0.123,0.12,0.137,0.134,0.109,0.089,0.094,0.09,0.084,0.095,0.156,0.106,0.117,0.103,0.109,0.109,0.105,0.1,0.112,0.121,0.122,0.119,0.132,0.107,0.107,0.128,0.117,0.117,0.111,0.122,0.113,0.128,0.094,0.11,448,1583,907,899,658,753,1094,939,461,308,183.0,1454,925,239,905,2805,4927,418,1249,2136,932,1508,872,1528,1759,1893,1908,1347,1460,1705,615,469,377,1352,478,706,1320,1526,201,204,746,1166,1065,1627,2102,787,309,1122,1088,638,329,1738,602,2154,362,1938,768,1228,708,886,335,795,238,331,1240,679,1995,1003,1013,432,554,757,4136,296,3.48,2.727,2.793,2.699,2.266,2.204,2.354,2.958,2.712,3.37,2.393,1.836,2.868,2.677,2.66,2.672,3.018,3.814,2.483,1.698,3.007,2.563,1.823,2.789,2.545,2.803,2.351,2.286,2.948,2.49,2.336,2.664,2.548,3.374,3.62,2.709,3.113,3.0,1.841,3.069,2.768,1.679,3.352,1.758,1.774,2.23,3.001,2.886,2.443,2.675,1.903,2.087,2.25,2.363,2.479,1.995,2.674,2.407,2.056,1.869,2.369,2.35,2.393,2.361,2.361,2.324,2.043,2.44,2.107,2.385,2.678,2.717,2.482,2.337,0.708,0.554,0.526,0.597,0.541,0.605,0.651,0.444,0.737,0.569,0.795,0.536,0.626,0.545,0.622,0.64,0.571,0.804,0.614,0.533,0.777,0.538,0.498,0.689,0.548,0.594,0.608,0.707,0.664,0.541,0.689,0.795,0.395,0.6,0.715,0.561,0.793,0.614,0.358,0.618,0.558,0.503,0.797,0.631,0.577,0.593,0.451,0.684,0.465,0.513,0.403,0.492,0.587,0.612,0.346,0.503,0.617,0.454,0.431,0.453,0.504,0.775,0.532,0.945,0.635,0.895,0.522,0.436,0.521,0.725,0.469,0.765,0.564,0.497
-304,Epileptic,F,27.0,1.3,1.8,0.7,1.3,1.7,2.0,1.5,1.6,1.5,0.8,0.4,3.5,1.5,0.6,1.6,7.5,9.8,0.8,2.9,4.4,1.3,2.4,2.1,4.0,3.2,2.9,4.1,2.2,2.7,2.9,1.6,1.2,0.6,2.7,0.3,0.4,3.9,2.8,0.2,0.3,1.1,3.7,2.0,2.4,2.5,0.9,0.7,0.7,1.6,0.9,0.7,2.0,1.5,2.8,0.1,2.7,0.7,1.6,1.2,0.8,0.5,1.4,0.2,0.5,2.3,0.9,2.0,1.5,0.9,0.7,0.7,1.7,4.0,0.6,11,14,7,12,18,21,13,22,10,10,4.0,31,19,10,21,82,87,5,42,40,11,35,26,43,39,32,43,23,24,36,19,10,7,31,1,4,43,34,2,2,8,32,27,19,16,8,4,5,8,9,4,16,10,17,1,24,5,13,9,8,4,11,2,5,22,19,18,12,6,6,6,11,34,7,0.041,0.022,0.011,0.027,0.033,0.03,0.029,0.027,0.052,0.037,0.03,0.041,0.029,0.032,0.031,0.036,0.031,0.031,0.036,0.04,0.026,0.032,0.04,0.042,0.038,0.025,0.032,0.028,0.024,0.03,0.042,0.041,0.032,0.032,0.01,0.012,0.045,0.032,0.016,0.019,0.019,0.041,0.031,0.026,0.02,0.017,0.025,0.012,0.018,0.017,0.038,0.019,0.023,0.021,0.017,0.02,0.013,0.018,0.025,0.019,0.016,0.023,0.015,0.014,0.023,0.018,0.018,0.019,0.018,0.021,0.019,0.02,0.018,0.024,1672,4257,1930,2373,2369,3313,2821,3867,1852,1546,934.0,3310,3787,1393,3586,14342,20977,2468,5193,4841,4643,4822,2726,7025,5558,7179,6159,3824,6929,5894,2444,1482,1169,6434,1973,1688,7293,7844,503,477,1790,2810,6419,2638,3303,1501,1095,2159,2775,2423,502,3671,1999,5120,194,4553,1430,2993,1434,1238,1049,2616,404,1015,3553,1264,4094,3148,1615,1299,1541,2895,8646,870,0.142,0.11,0.092,0.119,0.127,0.131,0.113,0.139,0.158,0.143,0.127,0.166,0.133,0.143,0.144,0.15,0.133,0.1,0.144,0.167,0.102,0.125,0.143,0.148,0.139,0.125,0.139,0.114,0.109,0.134,0.136,0.12,0.137,0.139,0.058,0.085,0.152,0.137,0.095,0.124,0.106,0.148,0.138,0.115,0.1,0.103,0.115,0.079,0.096,0.1,0.159,0.107,0.123,0.099,0.129,0.109,0.092,0.109,0.121,0.115,0.103,0.115,0.113,0.101,0.121,0.124,0.102,0.106,0.103,0.119,0.109,0.111,0.1,0.141,495,1350,784,876,933,1213,884,1102,509,416,254.0,1501,985,340,986,3881,5577,469,1516,1950,1013,1520,1098,1924,1586,1950,2156,1388,1756,1744,800,555,316,1414,473,593,1840,1734,251,235,910,1492,1255,1576,1921,780,427,918,1232,887,278,1703,931,2035,76,2222,745,1380,747,679,456,955,224,568,1669,860,1809,1213,777,520,631,1273,3557,398,3.187,2.785,2.29,2.357,2.145,2.341,2.508,2.979,2.603,2.876,2.796,1.902,2.883,2.846,2.691,2.705,2.889,4.078,2.777,2.064,3.186,2.421,2.104,2.719,2.605,2.884,2.345,2.14,2.921,2.64,2.454,2.717,2.741,3.106,3.569,2.559,2.881,3.131,2.301,2.547,2.346,1.737,3.261,1.892,1.955,2.11,2.831,2.96,2.711,2.541,2.012,2.27,2.203,2.714,2.738,2.199,2.137,2.44,1.968,2.003,2.205,2.692,1.909,2.133,2.212,1.741,2.171,2.673,2.402,2.558,2.498,2.495,2.517,2.418,0.806,0.756,0.518,0.663,0.718,0.571,0.679,0.534,0.833,0.712,0.652,0.585,0.471,0.43,0.583,0.697,0.683,0.726,0.599,0.699,0.772,0.609,0.611,0.868,0.708,0.695,0.681,0.614,0.562,0.607,0.621,0.849,0.358,0.646,0.668,0.454,0.852,0.673,0.38,0.405,0.511,0.54,0.751,0.553,0.593,0.382,0.72,0.644,0.418,0.558,0.357,0.418,0.413,0.584,0.45,0.442,0.398,0.525,0.518,0.4,0.498,0.64,0.321,0.669,0.46,0.582,0.57,0.439,0.431,0.683,0.594,0.455,0.459,0.435
-305,Epileptic,F,32.0,1.1,2.2,1.1,1.4,1.5,1.8,1.8,1.4,1.1,0.5,0.2,2.9,1.1,0.4,1.4,6.4,8.1,0.6,2.7,4.6,1.1,3.0,1.9,2.1,2.4,2.1,3.3,2.0,3.3,3.7,1.0,0.9,0.3,2.0,0.3,0.7,4.1,3.1,0.1,0.1,0.6,3.3,1.9,2.7,2.1,0.9,0.4,0.7,1.0,1.1,0.8,2.1,1.2,2.3,0.2,2.5,0.6,1.6,0.9,1.8,0.6,1.3,0.3,0.4,1.9,1.3,2.0,1.0,1.3,0.4,0.7,1.2,3.3,0.4,8,19,12,10,15,14,13,13,14,5,2.0,20,11,6,14,67,68,6,27,35,10,24,19,28,25,22,30,18,25,33,17,5,2,18,2,6,35,31,2,1,3,26,18,20,12,6,2,4,5,8,6,17,10,18,2,19,5,13,6,11,6,11,2,4,16,15,16,7,9,5,4,9,23,3,0.044,0.025,0.021,0.029,0.027,0.034,0.029,0.03,0.047,0.038,0.029,0.036,0.026,0.025,0.029,0.037,0.034,0.032,0.031,0.04,0.027,0.038,0.034,0.031,0.033,0.03,0.025,0.025,0.029,0.04,0.029,0.039,0.016,0.025,0.012,0.025,0.043,0.035,0.008,0.011,0.016,0.044,0.035,0.035,0.018,0.024,0.021,0.013,0.018,0.028,0.035,0.026,0.023,0.022,0.014,0.024,0.027,0.017,0.029,0.029,0.026,0.021,0.02,0.019,0.027,0.029,0.018,0.017,0.019,0.018,0.022,0.017,0.017,0.024,1113,4058,1989,1902,1885,2276,2598,2734,1711,1101,462.0,2219,2325,1017,2797,9830,15020,1753,4239,3455,2474,3881,2240,4843,4505,4454,5163,2809,5576,4836,1961,708,725,4322,1376,1162,5483,6172,394,441,1177,2082,4950,1832,2786,1016,767,1925,1953,936,553,3242,1898,3888,278,3134,1003,2928,947,1616,777,2374,360,766,2466,1172,2809,1857,1779,867,855,2200,6915,445,0.157,0.117,0.123,0.116,0.127,0.137,0.112,0.129,0.153,0.14,0.101,0.134,0.118,0.128,0.129,0.139,0.135,0.115,0.134,0.142,0.098,0.148,0.14,0.126,0.135,0.124,0.11,0.105,0.114,0.145,0.129,0.126,0.087,0.11,0.066,0.098,0.131,0.136,0.079,0.091,0.089,0.154,0.131,0.135,0.095,0.116,0.109,0.083,0.095,0.135,0.154,0.12,0.115,0.111,0.122,0.112,0.107,0.101,0.125,0.131,0.129,0.116,0.127,0.123,0.12,0.124,0.105,0.101,0.106,0.122,0.127,0.108,0.093,0.138,369,1305,821,756,853,943,873,797,501,285,147.0,1199,681,267,755,2902,4167,360,1421,1857,716,1195,942,1334,1308,1327,1926,1203,1609,1658,530,341,233,1111,458,462,1644,1467,256,247,585,1195,1008,1278,1725,622,276,860,836,592,351,1449,958,1711,150,1797,454,1509,487,956,415,898,211,366,1195,704,1694,870,939,403,435,1057,3212,208,3.029,2.809,2.423,2.504,1.965,2.13,2.44,3.017,2.484,2.954,2.608,1.707,2.61,2.653,2.698,2.57,2.885,3.74,2.597,1.716,2.678,2.539,2.235,2.704,2.713,2.734,2.16,1.915,2.692,2.393,2.665,2.113,2.602,2.784,2.772,2.206,2.621,3.012,1.851,1.9,2.443,1.692,3.421,1.726,1.816,1.911,3.418,2.787,2.714,1.905,1.847,2.181,1.922,2.239,2.023,1.983,2.183,2.234,2.296,1.932,2.203,2.463,1.958,2.178,2.366,2.117,1.829,2.402,2.245,2.283,2.332,2.513,2.378,2.211,0.666,0.651,0.515,0.536,0.586,0.547,0.65,0.757,0.67,0.527,0.644,0.405,0.523,0.49,0.513,0.625,0.59,0.684,0.783,0.55,0.885,0.584,0.525,0.747,0.759,0.695,0.553,0.505,0.57,0.522,0.665,1.034,0.394,0.618,0.921,0.617,0.813,0.66,0.376,0.409,0.757,0.493,0.732,0.568,0.588,0.434,0.583,0.758,0.628,0.474,0.333,0.552,0.517,0.564,0.483,0.49,0.57,0.499,0.511,0.366,0.477,0.605,0.417,0.981,0.676,0.861,0.435,0.534,0.473,0.734,0.563,0.495,0.628,0.45
-306,Epileptic,F,47.0,0.8,2.7,1.4,1.9,1.5,1.5,1.9,1.8,1.1,0.5,0.3,3.2,1.3,0.4,1.0,4.7,8.7,0.9,1.6,4.5,1.5,2.7,2.8,3.3,3.3,3.8,3.3,1.9,2.6,2.3,1.7,1.4,0.5,3.0,0.3,0.6,3.1,2.0,0.2,0.3,1.2,3.1,1.9,2.2,2.8,0.5,0.4,0.8,1.6,0.5,0.5,1.3,1.2,2.8,0.3,2.0,0.6,1.2,0.9,1.7,0.7,1.8,0.3,1.5,1.6,1.3,2.1,1.5,0.8,0.4,1.1,1.0,3.7,0.3,4,28,9,16,14,17,15,17,12,8,5.0,31,16,7,12,58,83,8,23,37,20,33,23,38,37,45,33,22,24,31,24,8,7,35,3,4,41,31,1,2,8,28,16,15,15,3,2,4,9,5,3,12,11,21,2,14,4,10,7,15,4,18,2,8,14,19,18,9,4,4,7,8,30,4,0.035,0.031,0.023,0.03,0.027,0.028,0.032,0.028,0.049,0.027,0.027,0.034,0.026,0.028,0.027,0.028,0.034,0.04,0.028,0.039,0.029,0.037,0.034,0.034,0.037,0.032,0.031,0.023,0.027,0.03,0.046,0.055,0.021,0.031,0.014,0.015,0.035,0.022,0.012,0.019,0.022,0.034,0.036,0.024,0.02,0.011,0.013,0.012,0.023,0.013,0.023,0.018,0.026,0.021,0.03,0.015,0.022,0.017,0.02,0.025,0.02,0.029,0.031,0.04,0.016,0.019,0.02,0.018,0.016,0.015,0.025,0.019,0.015,0.011,1442,4324,2330,2668,2408,2938,2859,3255,1402,1222,591.0,3184,3263,924,2492,9323,15731,1891,3475,4457,3699,3622,2839,5749,4662,6846,5503,3846,5501,5065,2197,964,1155,6167,1649,1788,5540,6417,391,521,1483,2671,4306,2307,3478,1211,810,2562,2388,1119,522,2359,1992,5115,348,3382,933,2312,1121,2002,881,2421,272,1013,2919,1395,3272,2186,1430,611,1343,1877,7747,569,0.112,0.137,0.114,0.131,0.124,0.137,0.114,0.142,0.146,0.14,0.138,0.139,0.118,0.13,0.119,0.137,0.142,0.146,0.13,0.15,0.122,0.125,0.122,0.136,0.113,0.136,0.121,0.106,0.111,0.129,0.149,0.15,0.092,0.141,0.074,0.076,0.152,0.117,0.088,0.099,0.109,0.137,0.144,0.117,0.099,0.077,0.086,0.074,0.101,0.098,0.117,0.106,0.118,0.106,0.16,0.096,0.119,0.101,0.107,0.119,0.11,0.132,0.154,0.137,0.097,0.109,0.106,0.103,0.096,0.11,0.121,0.118,0.091,0.094,375,1516,869,985,873,909,939,1046,446,327,170.0,1496,892,252,723,2889,4497,411,939,2011,859,1140,1206,1796,1287,2074,1846,1277,1498,1521,719,442,385,1520,433,695,1522,1521,224,255,809,1334,1017,1436,2026,670,391,997,1114,577,309,1196,852,2166,153,1888,467,1177,723,1004,470,1000,144,530,1540,1033,1713,1090,714,360,677,824,3646,381,3.522,2.689,2.585,2.54,2.34,2.61,2.48,2.708,2.399,2.712,2.684,1.817,2.801,2.67,2.566,2.491,2.668,3.397,2.782,1.946,3.07,2.552,2.034,2.525,2.734,2.668,2.347,2.323,2.76,2.535,2.391,2.579,2.297,3.0,3.158,2.343,2.728,2.974,2.029,2.464,2.232,1.791,2.981,1.816,1.936,1.963,2.447,2.971,2.567,2.213,2.076,2.154,2.171,2.357,2.605,2.004,2.202,2.168,1.774,2.107,2.282,2.298,2.111,2.255,2.065,1.643,2.039,2.376,2.21,1.921,2.139,2.447,2.262,1.793,0.844,0.564,0.533,0.538,0.66,0.657,0.571,0.411,0.601,0.608,0.722,0.517,0.371,0.368,0.471,0.565,0.621,0.728,0.521,0.556,0.847,0.57,0.601,0.647,0.648,0.501,0.54,0.532,0.498,0.542,0.555,0.686,0.356,0.581,0.588,0.391,0.66,0.656,0.301,0.577,0.464,0.56,0.744,0.532,0.577,0.336,0.414,1.073,0.505,0.323,0.395,0.435,0.524,0.476,0.413,0.409,0.37,0.465,0.385,0.467,0.493,0.579,0.519,0.676,0.474,0.576,0.396,0.508,0.413,0.476,0.481,0.643,0.449,0.337
-308,Epileptic,F,22.0,1.2,2.8,1.3,1.3,1.9,1.9,2.1,2.1,1.0,0.7,0.4,3.3,1.6,0.6,1.5,5.0,8.9,0.8,1.7,4.4,1.2,3.9,3.3,4.4,2.8,3.6,3.8,2.5,2.6,3.2,2.3,1.6,0.6,3.0,0.4,0.5,4.0,3.2,0.3,0.5,1.1,4.5,3.1,3.4,2.6,0.8,0.5,0.9,1.5,1.3,0.5,2.0,1.7,2.3,0.4,2.8,1.3,1.6,1.2,2.2,0.7,1.4,0.2,0.5,1.9,1.0,2.8,0.9,1.0,0.4,0.9,2.5,5.0,0.3,10,23,9,9,14,26,18,22,12,7,6.0,31,24,7,14,55,87,6,25,56,14,47,26,42,33,49,44,25,23,42,21,13,8,38,2,3,45,41,1,3,7,33,29,29,17,6,2,5,7,10,4,15,15,15,2,21,9,14,8,15,4,10,3,3,12,16,20,5,5,4,8,19,43,3,0.048,0.029,0.024,0.027,0.038,0.033,0.035,0.033,0.039,0.037,0.037,0.039,0.03,0.036,0.031,0.032,0.029,0.032,0.03,0.045,0.03,0.035,0.041,0.045,0.04,0.032,0.04,0.037,0.034,0.029,0.05,0.057,0.036,0.043,0.019,0.01,0.049,0.031,0.017,0.017,0.022,0.045,0.049,0.032,0.022,0.022,0.016,0.014,0.021,0.024,0.03,0.019,0.028,0.022,0.018,0.018,0.025,0.022,0.023,0.031,0.021,0.026,0.029,0.018,0.021,0.019,0.02,0.012,0.018,0.015,0.022,0.033,0.02,0.015,1453,5338,2532,2170,2679,3145,3454,3460,2137,1182,861.0,3834,4431,1792,3610,11423,22267,2299,4326,5923,3179,5515,2189,7820,5587,7409,7315,4400,5716,6612,2573,1153,1311,6846,2105,1999,6613,7532,508,942,1576,2384,5470,3344,3156,1197,940,2174,2434,1850,669,3921,2364,4840,443,4245,1361,3204,1461,2349,950,2620,477,1059,2625,1207,4153,1933,1807,667,1582,2464,10361,571,0.133,0.126,0.115,0.115,0.133,0.141,0.128,0.149,0.15,0.15,0.151,0.155,0.127,0.143,0.125,0.129,0.132,0.114,0.129,0.163,0.114,0.138,0.144,0.165,0.141,0.146,0.136,0.121,0.128,0.134,0.142,0.148,0.143,0.163,0.079,0.076,0.151,0.129,0.093,0.111,0.111,0.152,0.164,0.139,0.107,0.114,0.087,0.088,0.097,0.109,0.12,0.105,0.13,0.101,0.119,0.108,0.119,0.116,0.113,0.117,0.107,0.116,0.138,0.094,0.11,0.109,0.104,0.085,0.095,0.103,0.121,0.132,0.103,0.12,429,1603,856,851,947,1266,1151,1081,558,367,250.0,1608,1145,368,887,3029,5511,476,1157,2198,777,1934,1244,1833,1585,2327,2131,1523,1479,2244,814,457,327,1525,447,736,1687,2017,257,355,769,1525,1272,1837,1815,640,394,863,1070,813,345,1686,1003,1826,224,2231,681,1297,809,1214,473,874,187,436,1294,809,2051,940,782,329,725,1158,4225,288,3.08,3.009,2.737,2.333,2.566,2.26,2.431,2.864,2.93,2.468,2.712,2.089,2.982,3.331,2.924,2.802,3.095,3.702,2.924,2.263,3.115,2.29,1.675,3.17,2.723,2.613,2.5,2.223,2.889,2.36,2.354,2.344,2.925,3.216,3.649,2.504,2.764,2.678,2.354,2.654,2.596,1.553,3.01,2.048,1.969,1.868,2.838,3.135,2.793,2.239,1.933,2.375,2.308,2.694,2.079,2.058,2.267,2.603,2.02,2.12,2.293,3.007,2.497,2.729,2.134,1.909,2.144,2.369,2.559,2.215,2.126,2.198,2.44,2.47,0.961,0.707,0.568,0.618,0.594,0.627,0.632,0.562,0.565,0.561,0.624,0.444,0.493,0.637,0.672,0.592,0.656,0.736,0.606,0.59,0.628,0.651,0.501,0.805,0.69,0.6,0.632,0.65,0.613,0.537,0.846,0.971,0.491,0.65,0.662,0.428,0.918,0.74,0.536,0.414,0.409,0.407,0.821,0.5,0.515,0.475,0.623,0.84,0.509,0.617,0.492,0.507,0.514,0.634,0.533,0.422,0.495,0.507,0.434,0.427,0.439,0.617,0.429,0.735,0.44,0.583,0.44,0.443,0.459,0.748,0.518,0.626,0.564,0.513
-309,Epileptic,M,47.0,0.7,3.4,1.2,1.2,2.0,2.2,2.3,1.6,1.5,0.5,0.3,2.8,1.0,0.5,1.2,7.6,9.5,0.8,2.2,4.9,1.5,2.5,1.2,3.5,2.8,4.8,3.9,2.9,3.7,3.3,1.9,1.5,0.4,3.5,0.5,0.6,4.5,4.4,0.1,0.3,1.1,3.2,3.4,2.9,3.8,0.7,0.5,0.9,1.3,0.8,0.7,1.8,2.2,3.3,0.3,1.9,1.1,2.3,1.5,1.6,0.7,1.7,0.5,0.3,1.8,1.8,3.9,1.9,0.9,0.4,0.7,1.5,4.4,0.3,6,30,11,14,17,18,16,17,13,4,3.0,24,9,5,9,63,67,8,23,42,9,26,12,32,30,44,38,28,29,34,21,9,2,35,3,5,45,41,1,2,6,25,23,25,20,4,2,5,7,7,5,13,17,20,2,14,10,20,8,11,6,11,2,2,16,17,32,12,5,3,5,10,37,2,0.03,0.03,0.018,0.023,0.036,0.032,0.03,0.026,0.04,0.036,0.02,0.034,0.028,0.029,0.027,0.034,0.035,0.029,0.033,0.034,0.021,0.032,0.024,0.04,0.032,0.033,0.031,0.027,0.028,0.03,0.045,0.051,0.021,0.034,0.021,0.016,0.038,0.037,0.016,0.018,0.022,0.036,0.054,0.025,0.022,0.018,0.028,0.013,0.019,0.016,0.024,0.024,0.028,0.026,0.028,0.016,0.026,0.021,0.031,0.027,0.026,0.031,0.022,0.019,0.019,0.036,0.021,0.021,0.015,0.016,0.021,0.022,0.017,0.025,1511,5654,2390,2253,2335,3072,3204,3262,2113,913,608.0,2906,2369,941,2335,12311,15904,2499,3714,5115,3265,4292,1894,6133,4814,7880,5701,4925,7134,5464,2354,1648,763,6266,1767,2011,7048,8063,286,459,1578,3032,5052,2830,4321,1342,735,2533,2477,1946,785,2974,3361,5310,474,3538,2014,4088,1327,1652,909,2517,463,875,3434,1045,5763,2932,1757,872,1272,2362,9310,328,0.128,0.115,0.107,0.122,0.132,0.144,0.119,0.127,0.147,0.125,0.093,0.139,0.117,0.143,0.119,0.134,0.133,0.125,0.128,0.138,0.085,0.135,0.117,0.135,0.147,0.138,0.131,0.118,0.106,0.133,0.161,0.133,0.093,0.13,0.096,0.086,0.136,0.138,0.087,0.093,0.104,0.142,0.148,0.118,0.106,0.099,0.114,0.079,0.1,0.099,0.119,0.105,0.117,0.113,0.129,0.097,0.12,0.105,0.128,0.129,0.137,0.124,0.127,0.096,0.105,0.137,0.11,0.105,0.087,0.1,0.108,0.106,0.096,0.122,420,1833,901,849,909,1094,1141,1019,632,267,231.0,1265,697,286,632,3554,4335,480,1176,2265,1013,1383,856,1599,1487,2496,2098,1814,2055,1709,605,566,319,1618,451,705,2069,2079,174,244,804,1400,1257,1784,2517,616,287,1037,1038,819,444,1347,1499,2078,201,1842,816,1867,736,882,418,825,257,323,1619,706,2945,1382,839,389,582,1156,4117,172,3.685,2.715,2.42,2.547,2.36,2.525,2.378,2.837,2.679,2.988,2.264,2.038,2.644,2.655,2.828,2.692,2.941,3.736,2.565,1.986,2.701,2.461,2.014,2.86,2.555,2.684,2.268,2.193,2.704,2.583,2.814,2.776,2.104,2.772,3.34,2.652,2.63,2.96,2.009,2.289,2.54,2.014,3.06,1.803,1.971,2.347,3.236,2.928,2.971,2.451,2.3,2.351,2.361,2.634,2.652,2.154,2.725,2.408,2.278,2.109,2.274,2.815,2.406,2.691,2.284,1.951,2.134,2.472,2.326,2.557,2.48,2.433,2.44,2.369,0.702,0.673,0.658,0.536,0.578,0.516,0.561,0.542,0.488,0.42,0.675,0.462,0.429,0.397,0.439,0.537,0.667,0.715,0.561,0.614,0.692,0.44,0.433,0.617,0.447,0.437,0.407,0.461,0.477,0.562,0.83,1.099,0.389,0.688,0.717,0.585,0.727,0.576,0.418,0.454,0.484,0.443,0.761,0.621,0.502,0.569,0.569,0.728,0.611,0.493,0.354,0.487,0.598,0.537,0.395,0.356,0.632,0.497,0.307,0.408,0.444,0.732,0.58,0.973,0.548,0.691,0.458,0.396,0.41,0.556,0.563,0.592,0.569,0.366
-310,Epileptic,M,37.0,1.1,2.0,1.1,1.2,2.9,1.6,2.0,1.7,1.2,0.6,0.2,2.7,1.7,0.7,1.9,7.3,10.6,0.8,1.6,4.1,2.0,2.7,2.2,4.7,4.0,6.6,5.8,4.0,6.7,2.6,1.9,1.7,0.7,3.1,0.6,1.2,3.4,4.7,0.3,0.2,1.0,3.1,2.2,2.3,2.8,0.7,0.4,0.8,1.5,0.7,0.4,2.2,1.1,2.7,0.3,2.0,1.1,1.6,1.3,0.9,0.2,1.8,0.2,0.8,2.2,1.4,2.6,1.4,1.2,0.7,0.6,1.2,4.5,0.4,7,15,7,8,23,14,16,19,8,5,2.0,24,16,8,16,63,81,6,16,31,11,26,18,36,39,57,48,33,37,28,17,14,4,21,3,13,30,40,2,1,5,26,19,18,16,5,2,5,8,6,2,17,7,16,2,16,8,10,9,7,3,9,2,6,14,13,17,8,7,5,4,9,32,2,0.045,0.027,0.023,0.027,0.066,0.034,0.036,0.034,0.06,0.038,0.032,0.036,0.036,0.063,0.05,0.048,0.043,0.042,0.028,0.035,0.038,0.04,0.039,0.062,0.045,0.053,0.048,0.043,0.052,0.038,0.062,0.066,0.037,0.04,0.026,0.036,0.04,0.046,0.022,0.015,0.022,0.033,0.057,0.023,0.019,0.017,0.024,0.021,0.022,0.028,0.018,0.028,0.028,0.024,0.017,0.015,0.029,0.022,0.034,0.016,0.02,0.042,0.021,0.036,0.025,0.039,0.021,0.022,0.02,0.026,0.022,0.026,0.018,0.02,1313,4261,1637,1947,2251,1932,2223,2538,1452,1146,545.0,2528,2614,812,1685,8682,15659,1802,3597,4082,3207,3328,2125,5436,4171,5742,4713,3937,5748,4425,2008,966,793,4705,1290,1893,6067,5517,411,405,1323,2998,3677,2162,3926,1127,727,1626,2084,1250,464,2739,1326,3784,459,3230,1268,2588,997,1527,665,1969,406,973,2430,1090,4177,2281,1839,1166,880,1572,8672,450,0.139,0.11,0.121,0.115,0.17,0.124,0.114,0.127,0.156,0.152,0.129,0.14,0.121,0.154,0.148,0.149,0.14,0.135,0.124,0.14,0.123,0.126,0.136,0.163,0.127,0.155,0.127,0.116,0.126,0.137,0.152,0.158,0.101,0.124,0.098,0.127,0.146,0.137,0.141,0.089,0.104,0.133,0.166,0.12,0.099,0.099,0.104,0.105,0.111,0.134,0.11,0.127,0.12,0.106,0.113,0.099,0.132,0.102,0.132,0.094,0.102,0.127,0.11,0.131,0.119,0.137,0.104,0.104,0.099,0.121,0.118,0.116,0.101,0.1,394,1278,672,748,802,738,993,971,400,276,125.0,1240,855,238,669,2674,4355,344,921,1798,748,1034,811,1387,1412,2022,1864,1396,1669,1368,658,497,278,1101,373,640,1539,1595,166,220,686,1447,741,1493,2246,662,297,720,1011,436,314,1322,698,1790,254,1913,568,1252,602,869,253,726,194,412,1320,595,2012,947,947,449,422,745,3757,251,3.17,2.905,2.56,2.514,2.442,2.326,1.986,2.362,2.723,3.316,3.2,1.773,2.403,2.403,2.112,2.529,2.78,3.602,2.98,1.977,2.849,2.469,2.082,2.868,2.243,2.421,2.107,2.285,2.725,2.504,2.235,2.149,2.425,2.867,2.836,2.7,2.919,2.591,2.707,2.093,2.382,1.805,2.968,1.684,1.97,1.839,2.598,2.853,2.567,2.866,1.813,2.208,2.269,2.284,2.344,1.942,2.518,2.306,1.955,1.979,2.403,2.71,2.339,2.665,1.97,2.247,2.252,2.824,2.312,2.8,2.289,2.389,2.474,2.023,0.644,0.827,0.539,0.627,0.754,0.699,0.5,0.534,0.603,0.567,0.637,0.52,0.502,0.849,0.648,0.746,0.659,0.821,0.534,0.604,0.695,0.72,0.743,0.862,0.702,0.674,0.612,0.672,0.695,0.814,0.767,0.745,0.467,0.919,0.741,0.648,0.773,0.707,0.708,0.435,0.53,0.806,0.819,0.523,0.686,0.537,0.666,0.658,0.6,0.631,0.502,0.478,0.538,0.493,0.414,0.48,0.488,0.505,0.537,0.563,0.584,0.721,0.639,0.737,0.477,0.79,0.485,0.508,0.444,0.887,0.636,0.589,0.612,0.631
-311,Epileptic,F,22.0,0.8,1.8,0.8,1.3,1.7,1.3,1.1,1.5,1.4,0.4,0.2,2.7,1.2,0.7,1.5,4.5,7.6,1.1,2.1,4.7,1.0,2.7,1.4,3.5,1.8,2.8,2.3,1.8,2.0,2.1,0.8,1.1,0.5,2.1,0.5,0.6,2.6,2.7,0.2,0.2,1.0,2.7,1.8,2.1,2.2,0.4,0.5,0.9,1.2,0.6,0.6,1.6,1.7,2.5,0.3,2.6,1.1,1.5,1.2,0.7,0.7,1.2,0.4,0.5,1.8,1.1,1.7,0.7,0.7,0.8,1.0,1.2,2.6,0.2,4,18,7,9,18,17,10,13,17,5,3.0,27,17,9,20,54,81,10,27,49,9,32,16,37,25,30,27,19,23,27,16,7,5,23,3,7,34,33,1,2,5,29,17,18,14,4,3,6,6,5,4,13,13,24,2,24,10,10,9,8,5,10,2,5,15,13,16,4,5,7,6,7,19,2,0.031,0.025,0.017,0.025,0.031,0.025,0.017,0.025,0.04,0.028,0.02,0.029,0.027,0.039,0.031,0.033,0.027,0.03,0.029,0.036,0.018,0.028,0.023,0.035,0.024,0.027,0.022,0.023,0.021,0.025,0.026,0.058,0.025,0.026,0.012,0.018,0.03,0.028,0.015,0.014,0.018,0.034,0.03,0.023,0.018,0.009,0.02,0.015,0.015,0.012,0.026,0.019,0.022,0.021,0.02,0.018,0.03,0.019,0.026,0.017,0.018,0.022,0.03,0.021,0.02,0.025,0.017,0.012,0.013,0.021,0.032,0.018,0.013,0.019,1540,4332,1993,2327,2238,2704,2760,3327,2867,1031,643.0,3441,3485,1490,3846,10262,20196,2984,4296,5374,3164,4832,2463,7237,5183,6553,4883,3439,5771,5244,2225,958,1113,6462,2194,2246,6522,7568,408,632,1660,3092,5428,3213,3407,1140,1108,2494,2674,1552,608,3441,2666,5447,497,4291,1878,2900,1344,1244,1083,2427,594,960,3162,1042,3237,1955,1679,1916,1371,2019,7806,640,0.123,0.111,0.106,0.119,0.131,0.128,0.1,0.118,0.142,0.136,0.111,0.128,0.123,0.118,0.145,0.135,0.129,0.119,0.132,0.143,0.099,0.132,0.116,0.138,0.118,0.131,0.118,0.116,0.112,0.126,0.115,0.133,0.138,0.123,0.072,0.1,0.131,0.134,0.097,0.101,0.1,0.146,0.128,0.106,0.097,0.083,0.106,0.082,0.09,0.098,0.127,0.109,0.112,0.111,0.136,0.109,0.119,0.102,0.126,0.09,0.109,0.11,0.134,0.114,0.108,0.13,0.102,0.078,0.091,0.118,0.128,0.111,0.085,0.121,410,1275,679,797,898,936,904,962,673,258,200.0,1409,890,320,916,2620,4783,559,1200,2160,794,1566,905,1644,1371,1704,1573,1163,1555,1361,547,381,315,1311,629,620,1562,1559,201,276,736,1313,1092,1677,1892,585,401,961,1177,648,343,1324,1177,1990,179,2081,671,1269,763,713,506,890,207,412,1486,631,1632,851,818,607,526,901,3030,212,3.64,2.984,2.822,2.99,2.179,2.415,2.474,3.025,2.892,3.14,2.717,2.023,3.02,3.096,3.034,2.917,3.235,3.832,2.759,2.08,3.103,2.508,2.237,3.135,2.992,3.082,2.47,2.3,2.808,2.9,2.977,2.466,2.85,3.361,3.249,3.314,3.099,3.405,2.404,2.582,2.832,2.124,3.532,2.074,2.128,2.185,3.323,3.165,2.755,2.721,2.165,2.525,2.326,2.591,2.977,2.224,2.672,2.656,2.053,1.948,2.583,2.774,2.48,2.538,2.304,2.178,2.202,2.774,2.559,3.222,2.998,2.956,2.762,3.622,0.894,0.667,0.694,0.578,0.641,0.496,0.522,0.553,0.724,0.507,0.748,0.522,0.473,0.658,0.623,0.647,0.683,0.804,0.513,0.59,0.816,0.564,0.439,0.621,0.55,0.629,0.487,0.596,0.395,0.541,0.719,1.091,0.53,0.572,0.663,0.515,0.695,0.575,0.428,0.49,0.527,0.555,0.695,0.7,0.643,0.436,0.664,0.841,0.471,0.572,0.377,0.58,0.595,0.526,0.741,0.427,0.429,0.55,0.391,0.387,0.384,0.658,0.705,0.902,0.483,0.53,0.439,0.4,0.461,0.864,0.471,0.67,0.543,0.827
-312,Epileptic,M,57.0,0.7,2.7,1.4,1.6,2.0,2.9,4.4,2.2,1.2,0.9,0.4,3.2,1.7,0.7,1.4,7.9,16.8,1.2,3.0,5.0,1.8,6.0,3.1,7.0,7.4,5.5,7.4,4.2,4.6,6.1,3.0,1.1,0.9,3.5,1.2,0.8,3.3,4.0,0.3,0.3,1.2,4.3,2.7,2.6,4.2,1.4,0.7,0.9,1.5,0.8,0.7,3.0,3.1,4.3,0.4,4.0,0.9,1.6,1.5,1.8,0.7,2.7,0.3,0.6,1.9,1.2,4.4,2.2,2.3,0.9,1.8,1.4,7.3,0.2,5,18,8,11,15,26,27,19,9,6,3.0,28,15,5,17,60,116,8,25,44,19,42,23,53,49,49,68,33,35,52,25,21,7,33,7,6,37,43,2,1,6,37,19,19,18,6,3,4,7,4,4,18,22,23,3,24,4,10,10,12,5,15,2,4,15,8,27,13,13,8,9,10,49,3,0.041,0.038,0.031,0.032,0.052,0.053,0.069,0.039,0.072,0.045,0.038,0.047,0.04,0.07,0.045,0.062,0.061,0.053,0.044,0.056,0.042,0.056,0.047,0.075,0.056,0.046,0.056,0.053,0.053,0.056,0.069,0.055,0.053,0.051,0.048,0.024,0.044,0.046,0.021,0.023,0.027,0.045,0.063,0.039,0.037,0.029,0.037,0.019,0.024,0.02,0.032,0.034,0.052,0.039,0.019,0.024,0.021,0.023,0.028,0.032,0.03,0.043,0.025,0.026,0.023,0.027,0.038,0.032,0.041,0.027,0.04,0.029,0.028,0.018,1387,3847,2094,2223,1810,3102,3193,2797,1107,1292,629.0,2382,2967,890,2376,7434,17112,2509,4321,4213,4065,2932,2412,6150,5622,6588,6297,3285,4876,6681,2511,810,734,4892,1429,1483,5994,6507,360,475,1338,3171,3063,1996,2709,1325,859,1683,1925,1374,656,3111,2356,4047,523,4517,979,2453,1315,1633,584,2526,405,910,2409,1235,3531,2034,2019,937,1442,1638,7675,203,0.128,0.133,0.119,0.12,0.165,0.177,0.173,0.148,0.177,0.145,0.12,0.169,0.137,0.174,0.159,0.176,0.169,0.147,0.156,0.191,0.132,0.17,0.148,0.199,0.162,0.164,0.163,0.152,0.153,0.169,0.192,0.131,0.135,0.168,0.136,0.105,0.159,0.159,0.11,0.102,0.109,0.154,0.198,0.14,0.13,0.122,0.123,0.092,0.102,0.107,0.154,0.126,0.173,0.135,0.119,0.112,0.111,0.114,0.123,0.128,0.138,0.146,0.123,0.118,0.115,0.104,0.13,0.129,0.138,0.131,0.147,0.124,0.122,0.124,290,1321,798,841,778,1072,1234,1065,398,349,172.0,1204,864,235,702,2615,5442,446,1214,1830,871,1664,1122,1753,2253,2174,2572,1385,1702,2311,834,327,282,1292,430,606,1561,1732,201,224,728,1609,870,1158,1717,677,296,697,913,602,309,1482,964,1895,274,2437,536,1180,846,930,342,967,214,400,1267,599,1898,1008,970,517,742,849,3710,174,3.695,2.705,2.643,2.613,2.323,2.401,2.038,2.34,2.187,2.935,3.024,1.731,2.609,2.785,2.589,2.26,2.446,3.757,2.69,2.012,2.948,1.653,1.895,2.616,2.135,2.491,2.035,1.947,2.32,2.329,2.397,2.309,2.093,2.615,2.754,2.188,2.801,2.718,2.02,2.558,2.196,1.796,2.681,1.976,1.811,2.215,3.085,2.904,2.525,2.184,2.106,2.222,2.229,2.235,2.172,2.039,2.132,2.263,1.832,1.967,2.276,2.579,2.083,2.248,2.072,2.346,1.901,2.381,2.171,1.981,2.273,2.322,2.248,1.448,1.033,0.625,0.449,0.534,0.539,0.684,0.613,0.529,0.518,0.509,0.642,0.502,0.459,0.639,0.645,0.571,0.69,0.818,0.608,0.692,0.941,0.462,0.456,0.792,0.586,0.637,0.547,0.605,0.637,0.63,0.706,1.105,0.556,0.673,0.805,0.505,0.691,0.671,0.524,0.436,0.445,0.498,0.788,0.707,0.444,0.486,0.687,1.101,0.468,0.675,0.506,0.539,0.588,0.479,0.359,0.44,0.407,0.609,0.391,0.418,0.433,0.675,0.459,0.599,0.547,0.778,0.415,0.46,0.513,0.526,0.481,0.504,0.483,0.311
-313,Epileptic,F,37.0,1.7,3.2,1.2,1.6,2.4,2.0,1.9,1.7,1.7,0.7,0.4,2.7,1.4,0.6,1.8,6.1,10.8,1.0,2.9,4.3,1.8,4.7,1.3,4.1,3.2,3.6,3.4,2.9,3.1,3.0,2.6,1.2,0.7,3.0,0.7,0.6,5.4,4.5,0.1,0.3,1.2,4.2,3.2,2.1,3.2,1.0,0.6,1.4,1.5,1.3,0.4,2.3,1.6,2.3,0.4,2.1,0.8,1.3,0.9,0.7,0.6,2.0,0.7,0.5,2.5,1.4,2.9,1.6,0.9,0.7,0.9,1.6,5.5,0.5,11,31,11,11,23,19,17,17,18,10,5.0,23,19,8,23,72,109,7,30,43,20,43,14,44,36,46,45,37,34,41,23,18,6,35,4,4,61,47,1,2,8,33,29,16,21,6,3,7,7,10,3,17,12,16,1,20,7,10,6,5,5,18,6,5,19,14,23,10,4,5,5,11,51,4,0.05,0.026,0.018,0.026,0.046,0.027,0.029,0.024,0.045,0.047,0.037,0.04,0.029,0.035,0.034,0.037,0.036,0.032,0.031,0.037,0.031,0.047,0.032,0.044,0.033,0.034,0.029,0.032,0.033,0.034,0.049,0.044,0.028,0.031,0.019,0.013,0.053,0.032,0.013,0.017,0.023,0.042,0.046,0.025,0.02,0.02,0.02,0.021,0.017,0.018,0.031,0.022,0.022,0.02,0.023,0.018,0.022,0.017,0.018,0.018,0.015,0.032,0.041,0.018,0.027,0.025,0.02,0.019,0.015,0.017,0.022,0.017,0.018,0.023,1824,6489,2902,2551,2465,3734,2659,3715,2209,1693,741.0,2820,3539,1314,3461,11356,20319,2723,5457,5190,4508,4252,1828,7040,5323,6824,5970,5261,6488,5922,2875,1257,1332,7732,2801,1796,7509,9229,389,598,1507,2629,6147,2568,4219,1576,898,2570,2815,2332,552,3415,2695,4590,437,3928,1692,3066,1070,1212,1412,3071,743,1006,3062,1639,4548,2798,1452,1260,1189,2761,11526,630,0.148,0.121,0.099,0.116,0.158,0.121,0.107,0.117,0.165,0.148,0.143,0.151,0.133,0.152,0.145,0.143,0.138,0.112,0.129,0.148,0.118,0.136,0.124,0.159,0.126,0.13,0.121,0.127,0.112,0.142,0.139,0.129,0.103,0.136,0.088,0.073,0.142,0.126,0.085,0.109,0.116,0.152,0.169,0.12,0.105,0.11,0.102,0.096,0.088,0.097,0.124,0.109,0.108,0.104,0.115,0.102,0.106,0.093,0.103,0.103,0.086,0.137,0.158,0.115,0.118,0.106,0.107,0.103,0.088,0.122,0.116,0.104,0.1,0.12,543,2142,1098,925,925,1276,1027,1230,618,402,204.0,1195,967,317,967,3275,5525,483,1598,2081,1051,1552,708,1896,1616,2126,2064,1732,1825,1817,838,492,381,1651,652,666,1950,2277,214,301,799,1470,1327,1406,2385,725,371,997,1270,1009,269,1583,1102,1879,195,1911,736,1357,706,625,648,1078,313,482,1502,850,2213,1214,773,561,605,1344,4899,335,3.258,2.785,2.636,2.533,2.41,2.348,2.15,2.707,2.631,3.127,2.865,2.044,2.74,2.932,2.875,2.626,2.852,3.864,2.779,2.153,3.216,2.282,2.224,2.905,2.595,2.599,2.317,2.344,2.721,2.606,2.572,2.417,2.633,3.297,3.708,2.423,2.802,2.966,2.119,2.331,2.318,1.655,3.194,2.109,2.003,2.228,2.943,3.305,2.735,2.625,2.156,2.219,2.407,2.49,2.368,2.176,2.442,2.456,1.849,2.226,2.281,2.772,2.46,2.422,2.232,2.078,2.214,2.396,2.205,2.587,2.297,2.468,2.511,2.269,0.788,0.54,0.585,0.547,0.5,0.609,0.52,0.571,0.636,0.559,1.05,0.458,0.581,0.501,0.503,0.593,0.628,0.747,0.622,0.699,0.819,0.621,0.467,0.668,0.608,0.627,0.486,0.516,0.593,0.586,0.767,0.85,0.42,0.703,0.832,0.537,0.893,0.661,0.475,0.497,0.417,0.53,0.961,0.632,0.51,0.618,0.495,0.68,0.453,0.655,0.504,0.431,0.597,0.512,0.354,0.391,0.517,0.51,0.289,0.381,0.36,0.588,0.513,0.515,0.486,0.669,0.439,0.549,0.342,0.599,0.432,0.553,0.493,0.549
-314,Epileptic,M,27.0,1.0,2.2,1.0,1.1,1.3,2.2,1.2,1.7,1.0,0.9,0.3,2.4,1.8,0.7,1.2,6.2,10.1,1.1,2.7,5.7,1.4,3.8,1.4,2.8,3.6,3.8,3.5,2.3,2.6,2.8,1.0,1.0,0.4,3.5,0.3,0.9,3.0,2.3,0.2,0.2,1.2,4.1,2.8,2.9,2.7,0.5,0.6,1.2,1.3,0.6,0.2,1.8,0.9,1.9,0.2,2.4,0.7,2.1,1.8,0.7,0.4,1.4,0.4,0.4,1.8,1.3,3.2,1.7,1.0,0.6,1.3,0.8,4.8,0.3,8,24,11,11,16,20,11,18,15,8,3.0,20,20,8,14,66,91,8,31,45,13,37,15,32,35,39,36,23,23,36,14,9,5,40,2,12,35,35,2,1,7,36,25,22,16,4,2,7,7,7,3,17,9,12,1,20,4,17,9,5,4,13,2,3,17,19,23,15,8,6,12,7,39,3,0.036,0.023,0.02,0.021,0.031,0.026,0.021,0.03,0.044,0.03,0.026,0.028,0.033,0.037,0.03,0.036,0.032,0.033,0.03,0.038,0.025,0.039,0.025,0.032,0.028,0.029,0.029,0.024,0.026,0.034,0.027,0.044,0.025,0.034,0.013,0.018,0.029,0.026,0.012,0.012,0.02,0.037,0.05,0.027,0.02,0.009,0.017,0.02,0.016,0.013,0.015,0.021,0.019,0.016,0.012,0.022,0.02,0.021,0.025,0.019,0.028,0.024,0.023,0.014,0.022,0.025,0.025,0.018,0.018,0.02,0.034,0.014,0.017,0.016,1817,5559,2609,2457,2326,3447,2858,3334,2253,1652,695.0,2941,4165,1681,3226,10961,21942,3007,5523,5451,4082,5181,2249,6718,6198,8813,5507,4312,6166,5399,2427,1206,1026,7328,1873,2746,7237,6407,518,567,1884,3543,5676,3068,3434,1403,1038,2329,2754,1905,469,3502,2362,4341,388,3813,1525,3716,1888,1182,936,2671,500,750,3096,1529,3875,3634,1771,1329,1897,2042,10809,500,0.14,0.115,0.103,0.109,0.127,0.13,0.089,0.144,0.159,0.136,0.119,0.129,0.145,0.154,0.13,0.145,0.134,0.117,0.138,0.142,0.104,0.15,0.13,0.13,0.127,0.124,0.133,0.111,0.111,0.138,0.108,0.131,0.135,0.144,0.077,0.095,0.126,0.122,0.078,0.083,0.108,0.141,0.165,0.119,0.104,0.074,0.094,0.095,0.094,0.086,0.111,0.113,0.101,0.089,0.094,0.113,0.098,0.11,0.119,0.112,0.117,0.117,0.117,0.111,0.114,0.134,0.12,0.105,0.104,0.11,0.143,0.099,0.096,0.116,490,1643,798,895,750,1368,880,1027,502,442,188.0,1316,1029,355,793,3005,5449,527,1461,2513,1020,1577,837,1556,1923,2275,1907,1420,1601,1583,614,439,309,1682,459,882,1717,1558,290,281,820,1756,1059,1753,2016,714,442,958,1223,767,280,1491,902,1834,181,1735,634,1616,1030,571,302,900,257,390,1338,783,1964,1467,824,546,756,927,4255,252,3.509,3.149,3.033,2.707,2.565,2.29,2.556,2.994,3.022,3.015,3.325,2.036,3.046,3.194,2.97,2.818,3.29,4.048,3.015,1.91,3.195,2.565,2.227,3.063,2.636,3.115,2.399,2.348,2.91,2.665,2.823,2.564,2.559,3.151,3.635,2.8,3.029,2.929,2.087,2.348,2.946,1.902,3.704,2.049,2.003,2.148,3.015,3.063,2.72,2.586,2.118,2.427,2.473,2.596,2.459,2.39,2.785,2.57,2.121,2.306,3.029,2.897,2.33,2.361,2.423,2.326,2.264,2.626,2.453,2.784,2.597,2.478,2.756,2.604,0.839,0.649,0.746,0.583,0.626,0.422,0.649,0.596,0.732,0.45,0.836,0.504,0.504,0.399,0.619,0.603,0.614,0.647,0.538,0.625,0.923,0.47,0.533,0.689,0.485,0.681,0.527,0.608,0.598,0.591,0.633,0.944,0.466,0.725,0.596,0.459,0.656,0.53,0.431,0.342,0.536,0.549,0.616,0.679,0.608,0.352,0.434,0.688,0.617,0.503,0.471,0.468,0.607,0.473,0.276,0.453,0.391,0.567,0.396,0.451,0.709,0.754,0.391,0.74,0.546,0.869,0.481,0.442,0.396,0.614,0.426,0.508,0.525,0.328
-315,Epileptic,F,42.0,1.0,2.0,0.8,1.5,1.7,2.4,1.4,1.5,1.0,0.7,0.3,3.0,1.7,0.6,0.6,6.8,7.2,0.6,3.0,5.7,1.4,3.0,1.6,2.9,2.6,3.6,3.0,2.1,2.1,3.1,1.2,1.6,1.1,3.3,0.8,0.4,2.9,4.3,0.2,0.1,0.9,2.3,2.7,2.9,2.1,0.6,0.5,0.9,1.0,1.4,0.2,1.2,1.9,2.9,0.3,1.8,0.6,1.2,1.2,1.0,0.5,1.4,0.3,0.8,1.4,0.9,2.1,1.0,0.7,0.2,1.0,1.4,4.7,0.3,7,18,11,10,16,21,14,16,12,8,4.0,29,16,9,11,80,68,4,35,46,11,38,17,31,38,39,40,23,24,40,14,14,10,27,3,2,34,42,2,1,5,22,23,29,11,4,2,5,5,10,2,12,19,23,2,17,4,9,8,8,4,12,2,8,12,11,14,7,5,2,6,11,35,3,0.042,0.025,0.021,0.032,0.034,0.042,0.022,0.029,0.047,0.042,0.031,0.04,0.032,0.036,0.034,0.04,0.034,0.029,0.039,0.046,0.029,0.037,0.032,0.043,0.028,0.037,0.029,0.026,0.025,0.029,0.05,0.071,0.041,0.033,0.033,0.01,0.037,0.037,0.017,0.021,0.018,0.034,0.053,0.035,0.017,0.019,0.028,0.013,0.014,0.04,0.023,0.019,0.026,0.026,0.027,0.015,0.02,0.018,0.035,0.016,0.02,0.027,0.028,0.048,0.016,0.032,0.02,0.015,0.016,0.011,0.027,0.027,0.019,0.02,1787,5319,1974,2533,2366,3210,2860,3691,2535,1625,611.0,2930,2901,1553,1976,11873,17277,1925,4924,5964,2502,4406,1911,5344,6114,7175,5531,4246,5436,5814,2615,736,1504,5664,1270,1653,5836,6004,386,321,1802,2200,4345,2807,3548,1112,729,2323,2267,1065,536,2572,3291,6040,304,3998,1466,2398,1217,1666,908,2425,484,827,2620,861,3334,2642,1288,784,1210,1842,9139,596,0.14,0.114,0.124,0.123,0.136,0.151,0.107,0.126,0.152,0.156,0.141,0.147,0.139,0.169,0.127,0.151,0.131,0.107,0.147,0.156,0.105,0.156,0.133,0.146,0.124,0.14,0.127,0.114,0.116,0.146,0.148,0.157,0.164,0.116,0.101,0.061,0.135,0.129,0.098,0.099,0.095,0.143,0.166,0.134,0.088,0.098,0.117,0.074,0.084,0.143,0.107,0.098,0.114,0.116,0.141,0.093,0.099,0.104,0.147,0.101,0.115,0.126,0.145,0.179,0.1,0.124,0.11,0.093,0.097,0.097,0.125,0.132,0.097,0.129,424,1267,653,795,809,1020,994,962,459,371,186.0,1288,809,311,404,3066,4042,350,1235,1975,734,1455,850,1197,1715,1778,1848,1327,1462,1698,481,354,378,1357,356,582,1488,1755,185,110,726,1092,969,1526,1953,522,269,929,1002,575,222,1112,1224,2041,150,1955,603,1098,552,942,406,775,161,255,1290,459,1531,1042,696,240,563,747,3876,254,3.784,3.2,2.95,2.914,2.457,2.607,2.296,3.27,3.398,3.318,2.675,2.022,2.864,3.153,3.137,2.75,3.256,3.646,3.073,2.372,2.685,2.476,1.953,3.261,2.647,2.979,2.321,2.478,2.751,2.607,3.684,2.125,3.275,2.927,3.077,2.56,2.944,2.516,2.487,2.917,2.841,1.813,3.01,2.037,2.094,2.192,3.196,2.824,2.714,2.181,2.271,2.409,2.396,2.667,2.452,2.181,2.758,2.412,2.208,1.997,2.182,2.912,3.061,3.017,2.377,2.327,2.4,2.852,2.192,2.871,2.541,2.437,2.527,2.885,1.164,0.928,0.84,0.662,0.631,0.642,0.508,0.824,0.998,0.546,0.887,0.51,0.582,0.621,0.713,0.679,0.719,0.93,0.633,0.68,0.79,0.519,0.398,0.803,0.584,0.744,0.549,0.632,0.508,0.591,0.771,1.127,0.591,0.664,0.762,0.514,0.955,0.643,0.491,0.533,0.871,0.467,0.85,0.71,0.643,0.815,0.609,0.875,0.57,0.538,0.501,0.538,0.622,0.585,0.456,0.654,0.614,0.583,0.646,0.396,0.451,0.804,0.817,0.962,0.741,0.647,0.663,0.743,0.465,1.073,0.495,0.584,0.614,0.513
-316,Epileptic,M,42.0,1.3,2.5,1.0,1.4,1.1,1.7,1.6,2.2,0.9,0.8,0.4,2.7,1.3,0.8,1.9,7.8,11.2,0.9,2.5,4.6,0.8,3.3,1.5,3.6,2.5,3.8,3.5,2.6,3.3,3.4,1.4,0.8,0.8,2.5,0.6,0.9,4.0,3.6,0.3,0.2,1.3,2.8,2.0,2.5,3.5,0.6,0.7,1.2,1.6,0.8,0.7,2.2,1.9,3.5,0.4,2.0,1.0,1.5,1.3,1.0,0.9,1.4,0.6,0.4,2.4,1.6,2.0,1.7,0.8,0.5,1.3,1.6,4.4,0.4,10,22,7,11,9,17,12,19,9,9,4.0,25,12,10,20,71,78,10,28,40,8,39,18,42,31,34,36,20,24,37,22,6,5,28,2,8,45,40,5,1,8,24,23,19,20,4,3,5,10,10,5,17,13,20,2,17,9,11,10,9,6,10,4,4,19,23,13,10,6,4,8,13,34,4,0.041,0.024,0.012,0.024,0.031,0.028,0.021,0.025,0.032,0.03,0.03,0.027,0.022,0.033,0.026,0.033,0.031,0.029,0.03,0.032,0.021,0.032,0.022,0.037,0.03,0.026,0.026,0.021,0.022,0.032,0.038,0.029,0.03,0.024,0.015,0.019,0.038,0.033,0.022,0.013,0.023,0.036,0.038,0.02,0.019,0.01,0.021,0.014,0.016,0.018,0.028,0.017,0.024,0.021,0.019,0.018,0.025,0.015,0.025,0.016,0.02,0.026,0.022,0.016,0.021,0.019,0.017,0.017,0.014,0.02,0.024,0.02,0.017,0.019,1652,5061,2355,2374,1684,3347,3008,4448,1770,1685,654.0,3439,3495,1307,3481,13522,19519,2934,5240,5718,2849,5720,2648,6484,4726,7711,6310,4942,7337,5872,2573,1114,1207,6210,2237,2496,10517,8566,649,464,1745,3120,7639,2870,5057,1515,1165,3087,3076,2284,612,4106,2421,6034,456,3443,1702,3348,1882,1803,1231,2268,746,897,3540,1865,3863,3320,1967,963,1538,2582,9637,751,0.148,0.117,0.093,0.112,0.123,0.137,0.089,0.119,0.124,0.132,0.13,0.123,0.103,0.138,0.128,0.133,0.125,0.115,0.13,0.127,0.077,0.14,0.118,0.134,0.126,0.127,0.118,0.093,0.09,0.138,0.14,0.11,0.106,0.116,0.072,0.099,0.145,0.135,0.116,0.096,0.113,0.143,0.138,0.109,0.098,0.077,0.104,0.081,0.094,0.111,0.142,0.096,0.119,0.1,0.125,0.107,0.12,0.094,0.12,0.102,0.115,0.121,0.122,0.106,0.108,0.111,0.089,0.098,0.089,0.105,0.122,0.115,0.095,0.112,536,1718,995,966,625,1056,1044,1434,484,501,259.0,1520,1009,383,1147,3943,5830,533,1437,2269,746,1685,1138,1706,1444,2322,2153,1714,2182,1803,701,490,349,1537,591,800,2167,1895,336,232,849,1251,1211,1880,2847,840,473,1117,1408,850,360,2038,1188,2576,240,1692,682,1575,878,1028,607,834,429,438,1765,1248,1961,1447,905,456,795,1261,4037,401,3.256,2.764,2.237,2.391,2.528,2.646,2.394,2.698,2.816,2.652,2.29,1.963,2.752,2.523,2.547,2.704,2.771,3.935,2.996,2.179,3.067,2.669,2.049,2.85,2.584,2.823,2.4,2.367,2.639,2.589,2.816,2.237,2.64,2.946,3.491,2.95,3.437,3.195,2.066,2.405,2.634,2.21,3.867,1.733,2.012,1.908,3.095,3.427,2.654,2.875,2.173,2.155,2.241,2.481,2.437,2.256,2.528,2.445,2.165,1.911,2.234,2.511,1.999,2.214,2.175,1.731,2.144,2.63,2.38,2.378,2.156,2.419,2.476,2.325,0.788,0.635,0.518,0.52,0.814,0.563,0.53,0.492,0.837,0.61,0.689,0.455,0.318,0.535,0.422,0.536,0.515,0.87,0.55,0.57,0.717,0.599,0.437,0.611,0.561,0.455,0.569,0.497,0.494,0.472,0.617,1.007,0.512,0.543,0.738,0.475,0.803,0.696,0.448,0.423,0.453,0.487,0.756,0.484,0.563,0.392,0.611,0.814,0.621,0.574,0.338,0.492,0.426,0.475,0.84,0.478,0.546,0.428,0.44,0.382,0.366,0.659,0.363,0.918,0.369,0.736,0.368,0.493,0.502,0.531,0.46,0.465,0.529,0.46
-317,Epileptic,F,42.0,0.5,2.5,0.7,1.1,1.8,2.4,1.2,1.1,1.7,0.6,0.2,2.4,1.3,0.7,1.1,5.2,6.9,0.8,1.5,4.1,1.0,2.7,1.1,3.2,2.1,2.6,3.0,2.0,1.9,3.0,1.1,2.2,0.3,2.5,0.2,0.8,3.0,2.8,0.1,0.4,1.1,2.6,1.8,2.8,2.4,0.8,0.5,0.8,1.2,0.9,1.0,1.3,1.5,2.1,0.1,2.2,0.6,1.6,0.6,0.8,0.6,1.5,0.2,0.2,1.9,1.5,2.0,1.3,0.7,0.5,0.8,1.3,4.3,0.3,5,27,7,9,19,18,11,11,15,6,2.0,22,17,7,12,56,63,8,16,39,10,29,10,40,26,29,33,19,18,33,14,16,3,24,1,6,40,31,2,4,7,23,16,25,16,6,2,6,6,10,6,11,10,13,0,16,4,11,5,8,7,14,2,3,16,17,16,10,5,6,6,11,32,2,0.03,0.026,0.014,0.024,0.035,0.037,0.02,0.024,0.047,0.033,0.026,0.027,0.027,0.031,0.024,0.032,0.026,0.033,0.027,0.035,0.026,0.034,0.025,0.038,0.026,0.03,0.024,0.019,0.018,0.032,0.036,0.058,0.018,0.026,0.008,0.019,0.027,0.029,0.014,0.018,0.025,0.031,0.032,0.029,0.017,0.019,0.019,0.015,0.017,0.023,0.037,0.015,0.026,0.016,0.011,0.02,0.014,0.022,0.021,0.022,0.027,0.027,0.02,0.018,0.024,0.034,0.018,0.018,0.014,0.022,0.019,0.018,0.019,0.016,1016,5083,1892,1856,2302,3015,2295,2586,2120,1381,393.0,2208,2991,1149,2463,9540,15788,2158,3395,3531,3565,4143,1667,5691,4114,5044,5467,3766,5579,4832,1841,1324,743,5312,2061,1909,7174,6101,337,667,1258,2536,4275,2042,3433,1362,811,1749,2027,1497,779,2940,2020,4529,211,2734,1394,2684,1047,1146,1104,2176,287,728,2481,989,3447,2559,1480,905,1216,2589,8659,571,0.123,0.129,0.095,0.114,0.147,0.156,0.101,0.11,0.158,0.14,0.116,0.127,0.131,0.141,0.135,0.138,0.125,0.127,0.115,0.143,0.092,0.145,0.117,0.135,0.127,0.138,0.119,0.095,0.09,0.147,0.134,0.159,0.108,0.123,0.055,0.103,0.116,0.126,0.093,0.124,0.119,0.134,0.127,0.12,0.096,0.112,0.11,0.088,0.105,0.116,0.147,0.095,0.121,0.093,0.081,0.108,0.09,0.11,0.11,0.122,0.118,0.121,0.135,0.112,0.12,0.149,0.098,0.101,0.093,0.115,0.113,0.102,0.1,0.108,297,1679,714,752,876,1076,811,824,552,358,143.0,1220,890,321,732,2856,4359,453,878,1943,852,1376,700,1488,1443,1546,2038,1399,1553,1593,511,544,282,1331,534,677,1957,1525,196,321,685,1231,938,1572,2117,628,358,825,968,637,434,1388,901,1956,101,1639,639,1206,578,565,538,941,136,308,1231,679,1787,1136,777,490,647,1143,3604,254,3.255,2.801,2.464,2.528,2.278,2.525,2.342,2.697,2.608,3.067,2.52,1.659,2.68,2.587,2.53,2.559,2.951,3.542,2.949,1.697,3.348,2.377,2.06,2.804,2.377,2.678,2.245,2.19,2.745,2.486,2.611,2.546,2.162,2.947,3.456,2.601,2.767,2.979,2.008,2.274,2.318,1.895,3.339,1.503,1.839,2.377,2.926,2.591,2.586,2.458,2.215,2.262,2.245,2.464,2.096,1.92,2.743,2.614,2.145,2.173,2.128,2.346,2.289,2.263,2.185,1.926,2.1,2.452,2.288,2.207,2.239,2.501,2.499,2.519,0.511,0.469,0.464,0.431,0.704,0.62,0.614,0.466,0.79,0.547,0.65,0.438,0.465,0.531,0.491,0.57,0.546,0.716,0.483,0.494,0.744,0.439,0.374,0.629,0.502,0.522,0.475,0.486,0.464,0.478,0.613,0.9,0.428,0.543,0.774,0.567,0.646,0.624,0.303,0.553,0.475,0.492,0.789,0.521,0.534,0.342,0.466,0.63,0.48,0.609,0.339,0.414,0.573,0.441,0.293,0.388,0.408,0.576,0.369,0.381,0.39,0.559,0.387,0.718,0.68,0.527,0.453,0.465,0.381,0.427,0.466,0.606,0.494,0.569
-319,Epileptic,M,32.0,1.2,3.3,1.7,1.6,2.0,2.0,4.3,2.0,1.3,1.2,0.5,2.6,3.1,0.6,1.1,7.0,14.0,2.2,2.6,4.8,1.9,3.2,2.0,3.8,4.6,3.7,4.8,2.9,3.2,4.5,2.2,0.8,0.6,3.1,0.8,0.7,3.8,4.4,0.3,0.2,1.3,2.8,3.7,2.7,3.0,1.3,0.8,1.1,2.0,0.9,0.5,2.5,2.2,4.3,0.4,4.0,1.2,1.7,1.3,1.4,0.7,2.0,0.4,0.8,1.9,1.4,3.5,1.8,1.6,0.8,1.0,1.4,5.3,0.4,8,25,15,14,16,20,30,18,15,12,4.0,25,23,7,12,71,119,17,29,48,15,43,20,39,45,38,53,28,28,44,21,6,6,34,4,6,41,53,2,1,6,30,27,18,14,9,5,6,10,5,3,15,17,23,2,29,9,14,8,10,4,16,2,5,13,19,25,12,9,5,7,7,34,4,0.049,0.036,0.038,0.03,0.037,0.029,0.055,0.033,0.042,0.05,0.031,0.039,0.045,0.03,0.031,0.043,0.051,0.06,0.039,0.044,0.042,0.031,0.034,0.043,0.043,0.036,0.038,0.034,0.039,0.047,0.062,0.038,0.04,0.035,0.018,0.016,0.046,0.04,0.018,0.02,0.025,0.034,0.055,0.028,0.023,0.031,0.029,0.019,0.026,0.019,0.023,0.026,0.033,0.028,0.015,0.023,0.024,0.027,0.021,0.02,0.02,0.028,0.028,0.028,0.028,0.025,0.026,0.025,0.024,0.028,0.026,0.019,0.02,0.021,1497,5504,2980,2818,2358,3406,3837,3068,1846,1887,972.0,2933,4343,1139,2255,11111,21381,2831,4032,5973,3834,6752,3068,6275,5626,6606,7203,4106,6006,5944,2137,1402,902,6571,2500,1962,7157,8055,434,330,1371,3906,5514,3032,3271,1753,1074,2230,2269,1516,590,3087,3004,6003,568,4869,1497,2706,1885,2370,1036,2851,347,1001,2632,1626,4473,3001,2378,1173,1230,2188,9763,535,0.146,0.13,0.135,0.126,0.133,0.136,0.163,0.138,0.155,0.171,0.124,0.153,0.152,0.141,0.128,0.148,0.156,0.177,0.155,0.164,0.127,0.128,0.137,0.144,0.134,0.129,0.128,0.117,0.132,0.161,0.155,0.11,0.147,0.133,0.076,0.089,0.139,0.145,0.095,0.093,0.114,0.141,0.16,0.119,0.103,0.125,0.115,0.092,0.108,0.093,0.109,0.114,0.133,0.111,0.101,0.111,0.114,0.117,0.104,0.105,0.101,0.126,0.122,0.122,0.121,0.123,0.113,0.107,0.104,0.137,0.121,0.107,0.098,0.122,408,1704,897,939,888,1199,1326,1061,569,480,292.0,1243,1217,271,660,3324,5633,669,1256,2138,857,1915,1045,1765,1906,1899,2414,1583,1645,1812,727,396,312,1514,703,668,1849,2003,266,165,697,1496,1215,1617,1978,735,434,911,1130,750,356,1596,1192,2584,300,2666,757,1212,993,1090,531,1108,201,453,1145,989,2250,1251,1092,483,623,976,4244,283,3.303,2.603,2.813,2.627,2.263,2.388,2.35,2.522,2.506,2.936,2.637,2.023,2.797,2.755,2.543,2.544,2.803,3.403,2.509,2.27,3.017,2.657,2.407,2.663,2.345,2.721,2.27,2.085,2.754,2.506,2.209,3.197,2.368,2.979,3.213,2.498,2.731,2.885,1.953,2.131,2.431,2.106,2.87,2.221,1.896,2.436,2.769,2.788,2.422,2.259,2.019,2.17,2.28,2.428,2.174,2.059,2.17,2.409,2.141,2.328,2.149,2.511,2.072,2.385,2.269,1.873,2.103,2.381,2.498,2.285,2.24,2.477,2.469,2.467,0.93,0.73,0.824,0.618,0.558,0.552,0.643,0.441,0.481,0.62,0.775,0.505,0.45,0.523,0.526,0.542,0.698,0.801,0.601,0.67,1.005,0.525,0.536,0.754,0.652,0.648,0.562,0.511,0.503,0.59,0.664,1.121,0.511,0.659,0.902,0.685,0.726,0.616,0.324,0.351,0.564,0.614,0.941,0.687,0.496,0.47,0.596,0.868,0.497,0.643,0.343,0.436,0.594,0.471,0.395,0.466,0.405,0.482,0.427,0.427,0.395,0.657,0.349,0.83,0.447,0.702,0.417,0.432,0.493,0.691,0.39,0.498,0.47,0.541
-320,Epileptic,F,32.0,0.6,1.8,0.7,1.2,1.9,1.9,1.5,1.5,0.7,0.7,0.4,3.4,1.1,0.4,1.1,6.9,9.0,0.9,2.1,4.6,1.9,4.9,1.8,2.9,5.6,2.5,3.0,2.5,2.9,3.1,1.1,1.3,0.5,1.9,0.3,0.6,6.8,2.6,0.1,0.3,0.9,5.0,2.2,2.3,2.3,0.6,0.3,0.7,1.4,0.7,0.8,2.5,1.2,2.4,0.4,2.6,1.0,1.8,1.5,0.7,0.5,1.2,0.6,0.5,1.6,1.2,2.7,1.3,1.2,0.3,1.0,1.2,4.6,0.2,6,18,6,10,23,20,15,15,11,8,4.0,33,16,5,18,80,88,8,28,42,16,53,18,36,52,32,42,24,28,42,10,9,5,27,1,7,65,40,1,2,5,36,21,18,11,4,2,5,6,6,6,21,11,16,3,20,7,17,10,6,5,9,3,4,15,15,22,9,7,3,11,9,37,3,0.031,0.022,0.016,0.023,0.037,0.025,0.027,0.031,0.042,0.032,0.038,0.034,0.025,0.023,0.027,0.03,0.028,0.034,0.025,0.037,0.038,0.041,0.03,0.035,0.048,0.029,0.027,0.024,0.03,0.029,0.039,0.046,0.028,0.028,0.01,0.018,0.048,0.028,0.02,0.017,0.019,0.042,0.037,0.025,0.017,0.013,0.019,0.012,0.017,0.014,0.024,0.02,0.022,0.017,0.017,0.017,0.016,0.024,0.023,0.016,0.016,0.026,0.032,0.031,0.02,0.024,0.018,0.019,0.017,0.014,0.019,0.017,0.017,0.018,1541,4157,2316,2517,2110,4153,2904,3173,1269,1685,774.0,3456,3362,1278,3341,16033,22042,2342,5610,5621,3777,5345,2082,6570,7209,5809,6741,3963,7012,6974,2090,1296,999,6008,1791,1895,8619,7230,330,632,1466,3062,5937,2894,3557,1566,720,2323,2450,1696,813,4038,2211,5143,684,4513,2415,3258,1828,1514,948,2292,527,1159,2805,1285,5134,2563,2116,807,1811,2419,9224,544,0.111,0.112,0.093,0.118,0.139,0.115,0.108,0.123,0.134,0.138,0.137,0.141,0.123,0.121,0.127,0.132,0.125,0.127,0.123,0.137,0.122,0.131,0.118,0.15,0.143,0.125,0.116,0.094,0.1,0.131,0.138,0.125,0.122,0.114,0.061,0.089,0.139,0.132,0.095,0.096,0.102,0.133,0.146,0.122,0.091,0.083,0.103,0.076,0.092,0.103,0.121,0.108,0.108,0.096,0.114,0.102,0.098,0.11,0.111,0.094,0.092,0.12,0.131,0.134,0.109,0.118,0.094,0.101,0.099,0.1,0.11,0.097,0.097,0.11,414,1344,756,875,811,1458,993,921,363,436,243.0,1705,840,272,852,4179,5650,464,1468,2293,903,1893,977,1587,2076,1696,2277,1619,1751,1986,579,522,285,1295,506,615,2529,1811,156,278,699,1779,1129,1525,1930,761,292,903,1114,696,507,1847,998,2157,342,2249,893,1408,1075,695,515,818,274,286,1344,724,2463,1105,1001,382,925,1126,3997,235,3.596,2.692,2.752,2.649,2.237,2.431,2.379,2.92,2.547,2.898,2.885,1.835,3.088,3.199,2.799,2.839,2.964,3.834,2.905,2.079,3.052,2.264,1.983,3.269,2.62,2.815,2.406,2.002,2.941,2.67,2.87,2.437,2.752,3.222,3.308,2.562,2.516,2.875,2.147,2.574,2.55,1.616,3.632,2.09,2.143,2.153,3.1,3.244,2.782,2.483,1.924,2.285,2.286,2.516,2.367,2.173,2.644,2.483,1.934,2.309,2.02,2.863,2.31,3.591,2.072,1.911,2.109,2.706,2.332,2.237,2.089,2.518,2.433,2.634,0.872,0.794,0.562,0.593,0.655,0.603,0.618,0.491,0.578,0.614,0.529,0.48,0.385,0.582,0.513,0.579,0.706,0.677,0.66,0.661,0.935,0.614,0.479,0.594,0.754,0.606,0.509,0.522,0.467,0.541,0.773,0.906,0.421,0.665,0.588,0.615,0.878,0.779,0.447,0.372,0.538,0.558,0.765,0.639,0.593,0.466,0.541,0.715,0.398,0.494,0.35,0.472,0.511,0.472,0.383,0.402,0.523,0.534,0.41,0.4,0.356,0.583,0.57,0.55,0.483,0.759,0.54,0.47,0.488,0.591,0.488,0.55,0.553,0.414
-321,Epileptic,M,22.0,0.7,2.1,1.2,1.6,2.4,2.8,1.6,1.8,0.7,0.8,0.4,3.1,2.1,0.9,1.0,7.3,9.5,1.1,3.0,3.4,1.6,3.3,1.7,3.6,4.2,4.4,4.2,3.0,4.0,3.2,1.1,1.4,0.5,2.3,0.5,0.4,3.3,3.1,0.3,0.2,0.9,2.8,2.6,2.5,4.5,0.7,0.6,0.7,1.2,0.7,0.8,1.8,1.4,3.5,0.5,3.8,0.9,1.4,1.1,1.3,1.0,1.7,0.3,0.6,1.7,1.1,4.8,1.6,2.0,0.6,1.3,1.2,4.7,0.2,6,15,12,11,18,25,12,13,8,8,5.0,26,18,9,10,63,81,8,30,31,16,35,15,36,47,44,39,25,31,34,13,12,4,22,3,3,33,34,2,1,5,27,21,19,24,5,3,5,6,6,7,12,12,20,4,28,9,12,9,11,7,12,2,5,15,12,33,9,12,4,6,8,38,3,0.03,0.022,0.021,0.027,0.045,0.032,0.024,0.027,0.036,0.034,0.035,0.033,0.031,0.031,0.03,0.036,0.029,0.031,0.03,0.031,0.023,0.031,0.028,0.038,0.031,0.033,0.027,0.023,0.029,0.028,0.028,0.059,0.027,0.025,0.016,0.009,0.033,0.027,0.016,0.023,0.019,0.033,0.04,0.03,0.029,0.016,0.02,0.014,0.016,0.015,0.032,0.02,0.027,0.02,0.022,0.017,0.019,0.016,0.022,0.02,0.021,0.026,0.025,0.027,0.019,0.026,0.031,0.023,0.029,0.019,0.025,0.016,0.017,0.016,1694,5137,2691,2494,2999,4074,3180,3960,1262,1636,725.0,2887,4399,1724,2603,11549,20287,2424,6949,4246,4332,5616,2290,6856,7756,7670,7741,4809,7572,6287,2434,1248,1007,6387,1934,1936,7791,8617,434,295,1704,3278,5827,2379,4014,1691,1107,2299,2506,1898,679,3636,2426,6540,1055,6258,1913,3408,1335,1837,1647,2786,524,1094,2715,1313,4836,2608,2135,947,1745,2586,11922,355,0.125,0.104,0.107,0.117,0.134,0.146,0.103,0.119,0.126,0.14,0.126,0.129,0.124,0.137,0.127,0.11,0.11,0.117,0.126,0.122,0.099,0.13,0.123,0.128,0.133,0.134,0.115,0.101,0.104,0.128,0.124,0.151,0.11,0.119,0.078,0.066,0.127,0.121,0.105,0.11,0.1,0.128,0.135,0.13,0.119,0.096,0.103,0.081,0.087,0.097,0.149,0.099,0.112,0.09,0.111,0.101,0.105,0.094,0.118,0.107,0.108,0.116,0.118,0.124,0.108,0.119,0.128,0.111,0.113,0.117,0.116,0.103,0.093,0.095,443,1575,874,949,908,1501,1016,1059,335,413,227.0,1382,1074,398,626,3013,4953,510,1668,1756,1023,1776,1007,1658,2369,2150,2553,1785,1987,1864,646,443,346,1363,479,688,1700,1892,192,128,783,1389,1061,1374,2436,735,453,969,1130,698,411,1521,979,2580,350,3191,833,1443,768,984,775,1025,266,422,1343,652,2447,1089,964,451,796,1095,4558,199,3.437,3.01,2.927,2.649,2.575,2.563,2.703,3.066,2.773,3.278,2.581,1.842,3.144,3.043,3.064,2.876,3.233,3.555,3.293,2.015,2.978,2.496,1.992,2.958,2.624,2.909,2.404,2.186,2.937,2.636,2.792,2.791,2.45,3.148,3.427,2.505,3.279,3.26,2.616,2.78,2.588,2.042,3.372,1.989,1.928,2.531,3.063,3.07,2.82,2.65,1.994,2.654,2.472,2.675,2.663,2.231,2.489,2.506,2.02,2.096,2.429,2.835,2.146,2.799,2.338,2.479,2.142,2.802,2.591,2.623,2.642,2.814,2.717,2.091,0.729,0.738,0.512,0.531,0.846,0.566,0.659,0.633,0.644,0.488,0.798,0.463,0.553,0.461,0.56,0.704,0.674,0.629,0.717,0.712,0.954,0.481,0.509,0.829,0.547,0.589,0.48,0.675,0.718,0.502,0.703,1.0,0.437,0.671,0.748,0.522,0.68,0.687,0.408,0.584,0.658,0.526,0.937,0.573,0.615,0.516,0.544,0.673,0.642,0.684,0.273,0.573,0.784,0.56,0.698,0.42,0.45,0.725,0.342,0.422,0.366,0.692,0.328,1.155,0.602,0.536,0.511,0.452,0.465,0.645,0.463,0.799,0.666,0.491
-322,Epileptic,M,42.0,0.8,2.4,0.7,1.7,1.7,2.2,1.5,2.0,1.3,0.9,0.2,3.3,1.7,0.7,1.2,6.0,7.1,0.7,2.7,3.9,0.5,3.1,1.9,2.7,2.6,3.5,3.2,2.7,2.9,3.1,1.3,1.0,0.7,3.0,0.7,1.1,2.7,3.5,0.2,0.2,1.4,3.2,1.7,3.3,3.6,0.5,0.3,0.9,1.2,0.5,0.7,1.7,1.6,2.6,0.5,2.0,0.7,2.1,1.6,1.2,0.5,0.9,0.3,0.7,2.7,1.2,3.2,1.0,1.0,0.4,0.8,1.1,4.4,0.3,7,25,8,13,18,18,11,15,15,9,2.0,30,15,8,14,66,61,6,31,37,4,35,17,32,35,38,35,26,22,36,12,7,5,28,4,8,30,38,2,1,8,31,16,26,19,4,2,7,6,8,5,13,13,20,3,16,4,19,11,10,3,6,3,6,23,14,28,6,7,4,5,8,34,3,0.037,0.026,0.013,0.029,0.039,0.035,0.021,0.031,0.043,0.037,0.027,0.033,0.033,0.028,0.029,0.028,0.026,0.026,0.029,0.03,0.012,0.034,0.029,0.033,0.027,0.029,0.022,0.027,0.023,0.03,0.037,0.049,0.036,0.032,0.02,0.021,0.034,0.035,0.014,0.013,0.026,0.035,0.025,0.032,0.025,0.013,0.017,0.014,0.015,0.02,0.027,0.021,0.022,0.019,0.014,0.014,0.018,0.022,0.03,0.018,0.016,0.018,0.022,0.036,0.022,0.025,0.023,0.014,0.019,0.017,0.018,0.02,0.019,0.026,1720,5156,2106,2180,1907,3065,2950,3433,1744,1549,543.0,2977,3362,1319,2651,12909,15655,2564,5148,4992,2349,4935,2551,5101,5321,7256,6018,4028,6448,5337,2238,1274,965,6950,2610,2482,5639,7042,426,552,1627,3124,4669,2767,4107,1295,778,2416,2664,1420,602,3167,2662,5556,745,3559,1325,3188,1406,1930,873,2317,428,802,3470,1170,4241,2528,1916,662,1267,2035,8853,454,0.117,0.122,0.097,0.123,0.132,0.147,0.101,0.124,0.146,0.149,0.093,0.147,0.129,0.141,0.128,0.128,0.115,0.107,0.123,0.122,0.073,0.143,0.131,0.129,0.124,0.131,0.11,0.108,0.098,0.129,0.133,0.132,0.133,0.13,0.095,0.105,0.124,0.141,0.081,0.097,0.121,0.143,0.116,0.13,0.11,0.086,0.094,0.086,0.087,0.098,0.138,0.103,0.108,0.098,0.093,0.092,0.098,0.11,0.135,0.109,0.097,0.095,0.118,0.146,0.111,0.121,0.118,0.085,0.098,0.122,0.104,0.109,0.098,0.142,476,1565,798,880,762,1079,1015,1075,576,427,158.0,1451,907,348,680,3554,4313,458,1578,2061,658,1551,1024,1411,1643,2158,2368,1580,1753,1831,553,430,322,1605,550,824,1427,1646,242,245,802,1438,1151,1790,2253,654,324,998,1236,560,380,1368,1251,2256,438,2013,591,1611,830,1040,424,765,236,364,1868,658,2162,1091,863,345,678,958,3831,204,3.306,2.966,2.412,2.624,2.186,2.373,2.402,2.78,2.39,2.826,2.937,1.822,2.857,2.74,2.879,2.777,2.93,3.888,2.776,2.026,2.873,2.447,2.11,2.724,2.499,2.764,2.123,2.108,2.814,2.371,2.924,2.972,2.44,3.136,3.959,2.677,2.908,3.034,2.061,2.617,2.616,1.889,3.115,1.777,2.106,2.199,2.979,2.781,2.611,2.71,1.863,2.55,2.205,2.577,2.115,1.92,2.663,2.205,2.066,2.049,2.194,3.031,2.044,2.365,2.047,2.117,2.142,2.732,2.652,2.324,2.263,2.531,2.487,2.76,0.986,0.903,0.851,0.509,0.545,0.475,0.575,0.755,0.617,0.425,0.641,0.408,0.411,0.373,0.455,0.514,0.54,0.862,0.657,0.558,0.645,0.462,0.402,0.653,0.481,0.488,0.491,0.481,0.549,0.486,0.915,0.812,0.428,0.591,0.786,0.53,0.772,0.718,0.322,0.409,0.569,0.492,0.769,0.574,0.586,0.496,0.542,0.785,0.685,0.821,0.353,0.409,0.587,0.539,0.283,0.397,0.386,0.507,0.431,0.358,0.382,0.96,0.338,0.959,0.564,0.711,0.444,0.433,0.363,0.552,0.557,0.659,0.547,0.462
-323,Epileptic,M,32.0,0.6,1.4,0.7,0.7,1.7,2.4,1.1,2.0,1.3,1.0,0.7,3.2,1.7,0.4,1.6,5.5,9.0,0.8,2.4,4.9,1.4,3.1,2.1,3.1,3.2,2.3,3.0,2.6,2.4,3.6,1.2,0.8,0.5,2.3,0.6,1.0,2.8,2.7,0.1,0.3,1.1,3.0,3.4,2.7,2.7,0.7,0.6,0.6,1.3,0.9,0.8,2.2,2.0,2.4,0.1,2.4,0.8,2.4,1.4,1.3,0.5,1.4,0.4,0.5,1.5,1.0,2.5,1.3,1.5,0.4,0.8,1.1,4.9,0.3,5,13,6,6,11,24,10,18,12,10,5.0,26,20,8,13,61,89,6,33,48,15,34,17,31,38,27,36,25,21,43,13,11,4,29,4,8,33,34,1,2,6,28,23,22,16,4,3,5,7,6,7,17,15,15,1,19,6,25,9,13,4,11,3,4,19,12,21,8,10,4,6,8,38,2,0.031,0.021,0.011,0.015,0.033,0.031,0.018,0.033,0.043,0.036,0.039,0.031,0.027,0.032,0.03,0.035,0.028,0.027,0.032,0.035,0.028,0.031,0.032,0.035,0.036,0.023,0.027,0.028,0.022,0.031,0.029,0.052,0.027,0.031,0.018,0.024,0.028,0.034,0.013,0.019,0.022,0.027,0.049,0.026,0.021,0.013,0.024,0.013,0.016,0.019,0.03,0.024,0.029,0.019,0.025,0.018,0.022,0.022,0.026,0.021,0.02,0.026,0.023,0.015,0.022,0.021,0.022,0.016,0.021,0.015,0.02,0.019,0.02,0.019,1626,4387,2141,2079,2084,3305,2206,3423,1938,1638,874.0,3521,4392,1050,3227,10500,19582,2528,5551,5739,3852,5321,2759,5776,6114,5860,5547,4205,6197,5460,2361,1274,849,5364,1798,2092,5733,6776,336,518,1669,2543,5338,2933,3055,1472,960,1693,2639,1684,691,3836,2526,4236,300,3602,1452,3896,1344,1786,1002,2722,602,1023,2426,1345,4023,2734,2299,1285,1555,2024,9240,410,0.105,0.103,0.087,0.084,0.129,0.142,0.093,0.139,0.155,0.139,0.131,0.123,0.125,0.151,0.132,0.141,0.125,0.103,0.134,0.137,0.098,0.146,0.125,0.139,0.145,0.125,0.12,0.112,0.098,0.15,0.123,0.115,0.114,0.133,0.084,0.114,0.121,0.134,0.087,0.118,0.11,0.125,0.144,0.112,0.102,0.088,0.11,0.074,0.088,0.103,0.144,0.119,0.134,0.099,0.124,0.107,0.107,0.127,0.121,0.116,0.109,0.127,0.12,0.106,0.112,0.103,0.113,0.095,0.104,0.106,0.112,0.102,0.104,0.102,388,1239,838,808,809,1291,860,1100,507,452,272.0,1526,1191,271,794,2956,5195,494,1407,2392,931,1620,973,1531,1678,1672,1940,1462,1678,1867,697,400,310,1273,497,707,1504,1518,194,239,781,1569,1153,1789,1967,752,377,823,1188,692,421,1479,1025,1884,113,1882,593,1674,818,945,401,924,281,491,1193,635,1936,1162,1109,450,679,947,3713,238,3.62,3.046,2.453,2.557,2.443,2.239,2.2,2.881,2.818,2.975,2.695,2.006,2.906,2.852,2.959,2.77,2.961,3.757,2.991,1.999,2.91,2.484,2.269,2.943,2.763,2.767,2.331,2.285,2.766,2.396,2.666,2.93,2.194,2.975,2.667,2.703,2.776,3.293,1.975,2.506,2.671,1.549,3.079,1.887,1.779,2.144,3.085,2.421,2.638,2.806,1.959,2.494,2.476,2.47,2.729,2.117,2.542,2.43,1.885,2.06,2.512,2.97,2.495,2.588,2.129,2.438,2.222,2.699,2.469,3.148,2.412,2.571,2.556,2.129,0.832,0.725,0.542,0.405,0.637,0.556,0.529,0.442,0.539,0.394,0.72,0.599,0.433,0.41,0.608,0.532,0.606,0.816,0.652,0.584,0.939,0.53,0.704,0.799,0.539,0.464,0.496,0.633,0.536,0.5,0.83,1.231,0.376,0.619,0.806,0.505,0.711,0.516,0.417,0.592,0.454,0.492,0.913,0.743,0.478,0.4,0.511,0.582,0.55,0.583,0.509,0.54,0.536,0.468,0.517,0.456,0.597,0.607,0.421,0.372,0.676,0.729,0.565,0.675,0.471,0.663,0.426,0.495,0.414,0.86,0.635,0.506,0.485,0.324
-324,Epileptic,M,52.0,0.8,1.5,0.7,1.2,1.5,2.2,2.2,2.1,1.3,0.9,0.3,2.3,1.8,0.7,2.0,5.1,8.8,0.9,2.3,3.8,1.2,2.5,1.6,3.1,3.3,3.4,2.9,2.8,2.4,3.0,1.2,1.4,0.4,2.9,0.3,0.8,3.2,3.6,0.3,0.2,1.0,2.6,2.3,2.2,2.7,0.7,0.3,1.0,1.4,0.5,0.7,2.7,1.3,2.0,0.3,2.1,1.0,1.4,1.2,1.0,0.6,1.7,0.5,0.4,1.5,1.4,2.6,1.7,1.0,0.5,0.8,1.2,4.5,0.3,9,17,7,9,14,22,17,18,17,7,3.0,24,23,8,21,59,89,11,32,42,25,28,14,36,39,39,32,30,25,38,19,9,5,33,2,6,36,44,3,1,8,21,24,17,15,5,2,7,8,4,5,24,7,14,1,18,10,13,10,8,6,13,4,4,12,17,21,11,7,6,5,11,35,2,0.033,0.018,0.011,0.023,0.04,0.033,0.027,0.028,0.038,0.041,0.039,0.027,0.027,0.029,0.031,0.035,0.027,0.033,0.033,0.032,0.022,0.033,0.028,0.038,0.032,0.028,0.022,0.023,0.022,0.028,0.035,0.042,0.023,0.037,0.015,0.019,0.033,0.036,0.016,0.012,0.023,0.033,0.037,0.02,0.017,0.014,0.015,0.016,0.016,0.009,0.034,0.025,0.02,0.017,0.036,0.014,0.024,0.017,0.025,0.018,0.021,0.03,0.023,0.015,0.019,0.024,0.018,0.019,0.018,0.016,0.024,0.02,0.02,0.019,1653,4362,2197,2477,1820,3580,3185,3435,2280,1599,496.0,2672,4076,1258,3592,9272,19322,2369,4370,4558,3477,4126,2026,5528,5408,6915,5238,4955,6040,5715,2677,1346,929,6376,1697,1893,6872,7574,525,380,1500,2482,5904,2483,4315,1626,770,2363,2752,1589,589,4177,1845,4430,290,4269,1234,2765,1406,1838,1102,2459,604,811,2624,1114,4501,3040,1955,1492,1225,1930,8736,498,0.124,0.101,0.091,0.109,0.147,0.142,0.108,0.134,0.142,0.146,0.133,0.124,0.126,0.136,0.142,0.148,0.123,0.128,0.151,0.132,0.099,0.14,0.117,0.142,0.132,0.127,0.097,0.111,0.102,0.132,0.139,0.138,0.12,0.134,0.065,0.101,0.144,0.142,0.112,0.093,0.115,0.132,0.145,0.108,0.089,0.088,0.09,0.087,0.094,0.082,0.144,0.125,0.104,0.091,0.141,0.092,0.126,0.105,0.128,0.096,0.118,0.133,0.127,0.115,0.104,0.116,0.104,0.103,0.102,0.117,0.115,0.125,0.104,0.111,492,1401,885,896,717,1168,1167,1178,709,353,162.0,1228,1248,350,1136,2745,5402,526,1220,1859,803,1278,853,1525,1758,2061,2009,1778,1660,1796,650,571,316,1363,457,672,1612,1727,256,186,718,1196,1163,1559,2443,798,343,1015,1263,671,328,1761,891,1875,124,2262,646,1293,798,970,490,904,342,418,1238,811,2201,1329,857,535,573,892,3547,225,3.156,2.757,2.449,2.603,2.254,2.569,2.252,2.662,2.473,3.165,2.703,1.877,2.617,2.597,2.556,2.628,2.798,3.492,2.897,2.078,3.306,2.522,2.112,2.742,2.508,2.694,2.157,2.251,2.747,2.463,3.044,2.427,2.535,3.187,3.081,2.6,2.964,3.084,2.021,2.295,2.791,1.868,3.498,1.791,1.977,2.18,2.757,2.907,2.605,2.533,2.092,2.371,2.276,2.458,2.679,2.092,2.36,2.239,2.14,2.052,2.46,2.701,2.043,2.337,2.319,1.685,2.201,2.571,2.534,2.889,2.39,2.495,2.597,2.634,0.78,0.56,0.471,0.586,0.565,0.542,0.548,0.586,0.555,0.788,0.777,0.496,0.481,0.51,0.558,0.54,0.579,0.681,0.561,0.62,0.666,0.548,0.449,0.592,0.569,0.486,0.488,0.591,0.52,0.478,0.719,0.867,0.514,0.666,0.558,0.685,0.714,0.652,0.403,0.407,0.485,0.461,0.728,0.662,0.645,0.488,0.519,0.537,0.469,0.515,0.458,0.466,0.482,0.487,0.281,0.478,0.465,0.505,0.359,0.397,0.392,0.666,0.428,0.697,0.584,0.539,0.503,0.442,0.489,0.906,0.58,0.334,0.591,0.623
-325,Epileptic,F,32.0,0.8,3.2,1.2,1.4,1.6,1.7,2.8,1.5,2.1,0.8,0.4,2.7,2.4,0.5,1.4,5.8,9.2,1.3,2.9,5.3,0.8,3.5,2.2,5.3,4.9,2.8,5.1,3.0,4.3,3.9,2.0,1.3,0.3,2.0,0.5,0.4,4.0,2.7,0.1,0.3,1.0,3.2,2.4,4.6,3.0,0.8,0.5,0.7,1.4,0.7,0.5,2.6,1.7,2.2,0.3,3.0,1.0,2.3,1.3,1.5,1.2,1.2,0.2,0.6,2.3,1.5,2.1,2.0,1.2,0.5,0.9,1.7,4.5,0.3,9,30,9,10,14,21,16,16,21,10,6.0,28,30,8,15,58,81,10,29,47,7,45,23,47,58,30,59,29,31,47,27,11,5,29,2,3,53,33,1,2,5,28,22,33,16,5,2,4,7,9,4,17,14,14,2,26,6,18,9,12,7,9,2,5,18,18,15,15,6,5,8,14,36,3,0.043,0.033,0.026,0.029,0.032,0.023,0.042,0.027,0.072,0.042,0.034,0.034,0.034,0.041,0.037,0.041,0.039,0.047,0.034,0.039,0.024,0.032,0.034,0.049,0.037,0.03,0.037,0.033,0.04,0.038,0.052,0.043,0.016,0.024,0.012,0.01,0.041,0.029,0.015,0.025,0.022,0.034,0.042,0.033,0.024,0.019,0.02,0.013,0.021,0.026,0.017,0.024,0.035,0.024,0.022,0.019,0.022,0.021,0.024,0.023,0.033,0.02,0.028,0.019,0.026,0.026,0.022,0.022,0.02,0.017,0.022,0.024,0.019,0.014,1353,4749,2325,2391,1619,3888,3011,3454,1809,1422,862.0,3300,4174,1034,2775,8934,16826,2663,5720,6253,3036,5832,2715,6269,6837,5345,6508,3925,5759,6092,2215,1655,1003,6005,2132,1701,7840,7094,322,473,1246,3301,5565,3512,3350,1348,791,2003,2302,1510,592,2975,2023,3187,520,4106,1469,3814,1562,2122,931,2224,335,878,2903,1390,2820,3148,1700,786,1348,2085,8669,563,0.141,0.136,0.107,0.122,0.14,0.12,0.121,0.129,0.193,0.162,0.141,0.144,0.151,0.157,0.146,0.148,0.145,0.145,0.136,0.148,0.093,0.134,0.126,0.155,0.14,0.134,0.133,0.127,0.129,0.155,0.148,0.126,0.08,0.13,0.069,0.066,0.149,0.134,0.108,0.114,0.109,0.118,0.15,0.144,0.106,0.101,0.092,0.071,0.098,0.111,0.116,0.118,0.141,0.111,0.114,0.109,0.112,0.113,0.114,0.115,0.133,0.106,0.113,0.112,0.122,0.128,0.108,0.108,0.101,0.11,0.124,0.129,0.099,0.11,350,1632,770,821,753,1347,1014,977,539,357,233.0,1356,1197,292,702,2409,4244,501,1351,2464,647,1742,1091,1838,2181,1559,2426,1472,1662,1878,711,572,357,1267,589,588,1846,1530,176,215,663,1323,1067,2155,2001,653,383,845,1061,562,365,1564,824,1486,251,2280,662,1657,883,1002,477,895,185,491,1393,876,1473,1389,878,456,643,991,3733,321,3.463,2.651,2.848,2.714,1.979,2.456,2.444,2.883,2.376,3.088,2.869,2.051,2.693,2.656,2.807,2.564,2.975,3.768,3.064,2.061,3.322,2.64,2.166,2.557,2.462,2.641,2.14,2.127,2.654,2.491,2.297,2.602,2.259,3.047,3.077,2.503,3.014,3.137,1.936,2.369,2.371,2.16,3.347,1.896,1.873,2.29,2.496,2.838,2.529,2.74,1.864,2.093,2.188,2.265,2.284,2.001,2.427,2.563,1.949,2.157,2.338,2.467,1.92,2.263,2.157,1.953,2.05,2.288,2.291,2.109,2.394,2.434,2.51,2.048,0.766,0.563,0.659,0.54,0.638,0.751,0.702,0.539,0.609,0.637,0.673,0.601,0.488,0.603,0.546,0.713,0.701,0.729,0.768,0.685,0.714,0.515,0.648,0.804,0.663,0.617,0.578,0.602,0.712,0.634,0.604,1.024,0.409,0.733,0.731,0.542,0.858,0.697,0.338,0.483,0.489,0.677,0.82,0.656,0.6,0.426,0.347,0.719,0.473,0.491,0.474,0.457,0.59,0.466,0.404,0.419,0.583,0.549,0.403,0.456,0.459,0.692,0.372,0.655,0.468,0.68,0.427,0.436,0.494,0.489,0.438,0.521,0.507,0.384
-326,Epileptic,F,22.0,0.5,2.5,1.0,0.8,1.6,2.1,1.8,1.7,0.8,0.7,0.4,3.3,1.5,0.9,1.4,6.7,11.2,0.8,1.6,5.0,0.8,2.6,2.3,2.8,2.4,3.3,2.8,2.6,3.1,4.0,0.9,1.3,0.4,2.4,0.3,0.7,3.4,3.3,0.2,0.2,1.0,2.9,1.8,3.1,2.4,0.7,0.4,0.8,1.1,0.6,0.9,1.8,1.4,3.0,0.3,1.9,0.5,1.7,1.1,1.7,0.6,1.0,0.2,0.3,1.7,0.3,2.7,1.1,1.6,1.1,1.4,1.3,4.2,0.3,5,15,7,7,10,18,13,15,9,10,7.0,29,16,7,9,55,84,5,18,45,7,25,17,28,23,29,24,25,24,35,12,10,4,23,2,4,37,36,2,1,5,24,15,23,17,5,2,5,6,6,5,14,8,22,3,17,4,13,11,14,4,6,1,3,18,4,23,9,10,8,11,12,31,3,0.029,0.026,0.026,0.02,0.036,0.03,0.027,0.026,0.042,0.06,0.035,0.034,0.025,0.037,0.028,0.035,0.036,0.029,0.023,0.034,0.018,0.026,0.031,0.038,0.026,0.027,0.027,0.028,0.024,0.038,0.034,0.049,0.017,0.029,0.011,0.015,0.032,0.029,0.019,0.02,0.022,0.03,0.033,0.028,0.017,0.018,0.018,0.015,0.015,0.023,0.035,0.02,0.024,0.021,0.017,0.021,0.018,0.02,0.023,0.023,0.019,0.026,0.022,0.017,0.022,0.099,0.021,0.015,0.021,0.028,0.028,0.022,0.017,0.018,1342,4878,1734,1412,2157,2963,2361,3655,1389,706,696.0,3677,4042,1665,3547,12429,19255,2916,4039,5962,3248,4554,2510,6660,4449,7033,4659,4041,6299,5872,1676,1216,1112,6065,1818,1870,7862,8676,278,454,1529,3071,6132,2801,3158,1174,1005,2098,2657,1506,706,3390,1885,6273,580,2856,1093,3335,1265,1946,931,2168,478,954,2803,73,4111,2821,2540,1530,1706,2177,9215,471,0.104,0.109,0.111,0.101,0.127,0.141,0.111,0.116,0.132,0.19,0.125,0.132,0.109,0.15,0.112,0.12,0.123,0.106,0.118,0.137,0.092,0.124,0.126,0.119,0.116,0.119,0.108,0.105,0.099,0.132,0.117,0.14,0.091,0.118,0.057,0.082,0.125,0.124,0.116,0.089,0.097,0.129,0.124,0.123,0.098,0.096,0.092,0.087,0.084,0.099,0.144,0.101,0.104,0.095,0.103,0.112,0.1,0.106,0.127,0.118,0.097,0.102,0.09,0.107,0.118,0.24,0.116,0.092,0.107,0.129,0.131,0.114,0.094,0.122,345,1531,749,600,720,1117,952,1149,400,255,176.0,1586,1029,377,716,3063,5077,439,1071,2302,737,1551,1106,1293,1477,1908,1652,1387,1718,1715,441,439,360,1267,495,665,1764,1889,148,197,733,1465,914,1811,2047,677,343,783,1122,462,397,1511,929,2605,278,1549,492,1445,759,1136,522,677,237,356,1376,37,2023,1155,1180,590,824,1087,3710,241,3.581,3.007,2.841,2.661,2.652,2.536,2.172,2.932,2.566,2.498,2.924,2.021,3.013,3.145,3.405,2.942,3.053,4.085,2.949,2.188,3.23,2.391,2.048,3.41,2.463,2.853,2.4,2.354,2.805,2.658,2.695,2.757,2.55,3.267,3.26,2.602,3.067,3.208,2.457,2.481,2.561,1.902,4.073,1.811,1.805,2.037,3.398,3.377,2.886,3.563,2.405,2.525,2.239,2.5,2.329,2.048,2.617,2.695,1.931,1.975,2.196,3.043,2.235,2.5,2.32,2.369,2.254,2.739,2.752,2.73,2.281,2.415,2.696,2.526,0.711,0.687,0.558,0.519,0.692,0.517,0.545,0.761,0.724,0.685,0.836,0.5,0.537,0.721,0.74,0.779,0.75,0.784,0.45,0.612,0.906,0.444,0.443,0.921,0.603,0.644,0.581,0.666,0.71,0.68,0.737,0.997,0.438,0.704,0.928,0.496,0.835,0.611,0.42,0.491,0.655,0.536,0.728,0.552,0.518,0.518,0.659,0.851,0.715,0.69,0.48,0.518,0.672,0.554,0.347,0.397,0.392,0.54,0.353,0.388,0.383,0.962,0.572,1.023,0.57,0.35,0.496,0.617,0.589,0.543,0.407,0.505,0.6,0.376
-327,Epileptic,F,27.0,0.7,2.2,0.7,1.1,1.3,1.8,1.2,1.9,1.3,0.6,0.4,1.9,1.6,0.4,1.1,5.5,8.6,0.8,2.6,4.2,0.8,2.5,1.3,2.0,2.5,3.8,3.7,2.1,2.3,3.3,1.2,0.4,0.5,2.2,0.2,0.4,2.5,2.5,0.1,0.2,1.2,3.4,1.6,3.4,2.3,0.4,0.5,0.9,1.0,0.7,0.9,1.5,0.8,1.9,0.1,2.0,0.6,1.7,1.5,1.0,0.3,0.9,0.2,0.3,1.6,1.1,1.4,1.3,1.0,0.6,0.8,1.3,4.0,0.4,5,18,6,10,13,14,11,15,20,6,4.0,19,14,5,11,60,75,6,26,39,6,25,10,20,34,38,43,18,18,33,26,3,6,24,1,3,27,24,1,1,8,29,20,24,15,4,2,5,6,6,6,11,5,14,1,17,5,12,12,8,4,6,2,2,13,16,12,9,7,5,7,8,40,2,0.037,0.029,0.014,0.024,0.044,0.029,0.021,0.033,0.051,0.03,0.032,0.03,0.032,0.029,0.03,0.037,0.029,0.028,0.03,0.03,0.021,0.033,0.03,0.032,0.028,0.031,0.029,0.029,0.026,0.034,0.033,0.025,0.015,0.028,0.01,0.013,0.032,0.036,0.012,0.016,0.021,0.043,0.038,0.028,0.02,0.011,0.025,0.015,0.015,0.019,0.029,0.023,0.026,0.017,0.009,0.015,0.022,0.019,0.032,0.026,0.025,0.02,0.023,0.015,0.019,0.028,0.017,0.021,0.016,0.027,0.02,0.025,0.02,0.015,1371,4387,1922,2232,1820,2527,2083,3346,1627,1268,612.0,2417,3238,987,2288,10498,17103,2167,4025,4831,1963,3866,1625,4464,5119,6313,6238,2983,5054,5591,2250,745,1062,4894,1517,1578,4439,4876,356,355,1629,2345,4481,3134,3061,1074,722,2214,2079,1163,652,2530,1516,4393,293,3787,1007,3014,1572,1235,571,1768,457,705,2930,1058,2492,2069,1697,917,1148,1773,7483,683,0.127,0.117,0.098,0.12,0.153,0.124,0.095,0.141,0.159,0.135,0.131,0.129,0.133,0.111,0.127,0.139,0.127,0.106,0.13,0.123,0.083,0.136,0.131,0.118,0.133,0.138,0.132,0.12,0.107,0.135,0.131,0.096,0.089,0.118,0.059,0.08,0.124,0.125,0.07,0.094,0.119,0.152,0.14,0.121,0.1,0.09,0.112,0.086,0.093,0.107,0.136,0.109,0.111,0.094,0.074,0.101,0.106,0.102,0.139,0.123,0.128,0.107,0.113,0.104,0.107,0.143,0.095,0.108,0.102,0.134,0.109,0.119,0.105,0.085,354,1362,709,779,532,1014,759,1001,467,331,217.0,994,816,269,613,2618,4665,463,1335,2050,613,1265,663,1109,1582,1994,2199,1141,1413,1687,574,306,375,1242,410,545,1269,1166,193,193,783,1209,870,1841,1861,582,304,870,1001,542,404,1186,581,1776,137,1992,479,1415,794,602,266,694,185,347,1305,607,1406,920,862,350,627,828,3272,382,3.67,2.954,2.691,2.768,2.634,2.205,2.255,2.925,2.589,2.985,2.441,2.048,2.953,2.641,2.747,2.867,2.947,3.551,2.589,2.036,2.712,2.506,2.15,2.803,2.627,2.632,2.27,2.086,2.692,2.562,2.873,2.255,2.195,2.762,3.103,2.556,2.673,2.943,1.977,2.227,2.619,1.865,3.412,1.893,1.858,1.991,3.125,3.114,2.576,2.438,1.945,2.264,2.465,2.442,1.991,2.187,2.414,2.427,2.31,2.211,2.397,2.558,2.237,2.107,2.355,2.138,1.918,2.562,2.28,3.054,2.149,2.7,2.361,2.203,0.874,0.609,0.607,0.531,0.715,0.597,0.475,0.682,0.547,0.379,0.545,0.416,0.401,0.571,0.602,0.595,0.575,0.857,0.617,0.593,0.61,0.428,0.427,0.678,0.595,0.595,0.493,0.482,0.488,0.694,0.647,1.002,0.332,0.706,0.863,0.502,0.533,0.614,0.371,0.611,0.639,0.495,0.765,0.568,0.567,0.49,0.505,0.965,0.468,0.374,0.342,0.437,0.784,0.628,0.396,0.476,0.485,0.605,0.387,0.41,0.435,0.626,0.803,0.95,0.534,0.496,0.406,0.412,0.409,0.588,0.425,0.454,0.526,0.25
-328,Epileptic,M,52.0,0.9,2.7,1.0,1.4,1.7,2.8,3.1,1.9,1.3,1.0,0.2,3.3,1.5,0.4,1.1,6.3,9.9,2.0,3.0,5.2,1.3,4.7,1.4,3.3,6.6,4.9,4.9,3.5,3.7,4.8,1.9,1.0,0.5,2.9,0.7,1.6,6.7,4.2,0.3,0.2,1.1,4.3,2.2,2.4,3.5,1.0,0.4,1.4,1.6,1.0,0.9,1.9,2.5,2.9,0.8,3.7,1.2,2.2,1.2,1.6,0.6,1.8,0.4,0.6,1.7,1.3,3.4,1.6,0.9,0.6,1.4,2.0,6.8,0.1,8,29,7,12,13,31,24,21,20,8,3.0,33,16,7,17,82,121,13,31,52,11,45,17,34,79,50,53,32,33,61,21,9,5,37,7,16,67,53,2,1,6,38,20,15,20,7,2,10,6,6,5,16,21,20,8,32,8,17,11,12,5,11,2,5,13,16,27,12,5,6,11,13,61,1,0.04,0.027,0.02,0.027,0.029,0.034,0.041,0.031,0.044,0.045,0.028,0.038,0.03,0.034,0.036,0.035,0.036,0.055,0.037,0.037,0.028,0.049,0.029,0.035,0.051,0.037,0.04,0.032,0.035,0.035,0.051,0.043,0.028,0.033,0.02,0.028,0.047,0.04,0.016,0.024,0.023,0.046,0.042,0.027,0.023,0.018,0.017,0.017,0.019,0.029,0.028,0.019,0.029,0.022,0.028,0.023,0.022,0.022,0.025,0.025,0.021,0.031,0.015,0.019,0.018,0.022,0.022,0.019,0.017,0.02,0.027,0.024,0.022,0.014,1744,5036,2394,2542,2398,4491,2618,3358,1935,1595,751.0,2976,3324,1011,2086,11635,18131,2353,5341,5811,3614,4125,2191,6137,7142,7175,5699,3765,5859,6686,2759,927,896,6802,2313,2649,7561,8994,477,327,1500,3397,5914,2647,3658,1534,766,2871,2449,1365,775,3432,2674,4817,879,4648,1727,3660,1461,1810,875,2717,663,941,2701,2006,4449,2770,1558,965,1586,2885,11082,376,0.123,0.126,0.098,0.117,0.125,0.134,0.124,0.132,0.164,0.165,0.112,0.145,0.133,0.126,0.138,0.141,0.139,0.155,0.138,0.145,0.107,0.148,0.128,0.132,0.145,0.136,0.127,0.111,0.118,0.139,0.17,0.123,0.116,0.128,0.097,0.112,0.152,0.146,0.097,0.096,0.1,0.151,0.148,0.111,0.104,0.099,0.094,0.088,0.095,0.111,0.132,0.101,0.139,0.104,0.117,0.115,0.109,0.116,0.119,0.122,0.096,0.131,0.093,0.112,0.098,0.117,0.108,0.1,0.094,0.129,0.129,0.121,0.109,0.102,480,1838,845,949,873,1670,1238,1195,686,413,188.0,1505,903,285,726,3724,5626,542,1489,2712,903,1629,969,1651,2650,2525,2328,1733,1805,2550,829,436,315,1737,594,903,2490,2256,258,150,832,1666,1200,1570,2196,843,338,1198,1120,551,457,1690,1344,2161,543,2563,839,1629,822,1011,503,942,385,457,1460,855,2460,1255,734,483,850,1320,4997,163,3.115,2.534,2.66,2.543,2.404,2.237,1.872,2.458,2.265,2.902,3.002,1.697,2.813,2.716,2.336,2.46,2.511,3.425,2.683,1.904,2.89,2.067,1.987,2.757,2.198,2.357,2.01,1.82,2.558,2.144,2.703,2.311,2.468,2.794,3.359,2.449,2.316,2.9,1.979,2.468,2.207,1.817,3.296,1.936,1.883,1.898,2.755,2.839,2.569,2.565,1.986,2.05,2.107,2.305,2.038,1.947,2.392,2.421,1.934,1.923,1.989,2.833,1.996,2.443,1.997,2.591,1.881,2.342,2.339,2.093,1.958,2.424,2.35,2.387,0.689,0.649,0.71,0.55,0.553,0.621,0.513,0.52,0.556,0.66,0.876,0.464,0.428,0.419,0.43,0.494,0.623,0.76,0.639,0.559,0.81,0.579,0.526,0.742,0.676,0.587,0.537,0.508,0.521,0.582,0.652,0.809,0.421,0.691,0.518,0.604,0.699,0.617,0.396,0.476,0.449,0.511,0.809,0.665,0.587,0.437,0.382,0.664,0.522,0.536,0.373,0.447,0.424,0.461,0.383,0.401,0.426,0.476,0.424,0.418,0.366,0.796,0.306,0.708,0.604,0.918,0.401,0.455,0.446,0.613,0.43,0.536,0.493,0.384
-329,Epileptic,M,57.0,1.1,2.8,1.1,1.2,3.2,2.0,1.8,2.4,1.2,0.9,0.4,3.1,1.3,0.7,1.2,6.6,10.2,1.1,2.5,3.6,1.4,3.0,2.1,4.5,3.5,3.8,4.1,2.7,3.6,3.7,3.0,1.5,0.6,3.9,0.4,1.2,7.5,4.2,0.2,0.4,1.4,3.6,2.9,2.7,3.3,0.7,0.6,0.8,1.6,1.5,0.4,2.2,2.0,3.2,0.4,3.0,1.2,2.3,1.4,1.1,0.7,2.1,0.6,1.0,2.0,1.6,2.6,1.4,1.6,1.1,1.8,2.2,5.5,0.5,7,32,10,10,24,22,15,19,12,12,5.0,30,15,10,17,66,92,8,28,35,15,34,22,50,52,50,49,30,36,47,28,13,6,36,3,13,67,62,2,3,8,27,30,18,17,3,3,4,9,11,4,18,20,23,2,25,7,20,13,8,5,15,4,8,16,23,21,9,11,11,12,18,52,4,0.04,0.024,0.018,0.023,0.052,0.03,0.03,0.032,0.042,0.036,0.044,0.037,0.025,0.033,0.029,0.031,0.033,0.04,0.031,0.034,0.031,0.032,0.032,0.046,0.034,0.032,0.029,0.032,0.035,0.031,0.059,0.061,0.036,0.042,0.02,0.023,0.06,0.037,0.021,0.022,0.028,0.037,0.048,0.026,0.021,0.011,0.018,0.013,0.018,0.042,0.018,0.025,0.023,0.025,0.021,0.019,0.025,0.02,0.031,0.019,0.018,0.026,0.028,0.042,0.019,0.027,0.02,0.02,0.022,0.023,0.028,0.021,0.018,0.022,1658,5970,2444,2337,2835,3809,2680,3915,1861,1623,564.0,2862,3225,1572,2339,11485,19849,2316,5143,3814,3188,4840,2743,6769,6559,7592,7052,4199,6333,5945,2641,1272,945,6087,1583,2298,7491,9009,423,597,1330,2858,4105,2627,3696,1500,997,2362,2721,1502,528,3420,3755,5578,405,4629,1628,3232,1292,1698,1056,3635,588,1051,3085,1218,3723,2093,2199,1621,1942,2760,10173,688,0.132,0.128,0.104,0.111,0.17,0.135,0.101,0.133,0.156,0.157,0.147,0.136,0.121,0.126,0.133,0.13,0.131,0.142,0.134,0.125,0.108,0.12,0.131,0.145,0.125,0.141,0.118,0.105,0.108,0.126,0.157,0.139,0.133,0.136,0.084,0.115,0.158,0.149,0.108,0.12,0.128,0.13,0.142,0.119,0.106,0.076,0.09,0.083,0.095,0.151,0.12,0.114,0.111,0.112,0.117,0.103,0.111,0.113,0.132,0.101,0.1,0.122,0.125,0.125,0.101,0.132,0.105,0.103,0.108,0.123,0.131,0.116,0.104,0.114,453,2056,1010,875,1067,1254,1075,1262,573,463,212.0,1461,879,458,781,3691,5745,521,1382,1863,883,1583,1129,2072,2203,2345,2726,1677,1871,2234,937,594,296,1610,446,846,2606,2413,194,278,724,1502,1264,1618,2322,839,494,904,1283,639,367,1625,1631,2275,261,2592,806,1698,762,938,618,1328,343,471,1593,908,2151,1062,1137,765,1031,1443,4634,342,3.549,2.66,2.37,2.582,2.42,2.355,2.108,2.665,2.476,2.611,2.31,1.761,2.821,2.485,2.381,2.505,2.726,3.521,2.871,1.803,2.836,2.391,2.099,2.581,2.355,2.505,2.089,2.002,2.589,2.164,2.16,2.307,2.529,2.783,3.211,2.345,2.263,2.703,2.499,2.38,2.294,1.664,2.62,1.815,1.807,1.946,2.444,3.212,2.531,2.426,1.71,2.174,2.277,2.399,1.937,1.904,2.246,2.058,1.791,1.984,1.89,2.62,2.036,2.376,2.096,1.733,1.806,2.217,2.152,2.265,1.907,2.202,2.277,2.132,0.688,0.531,0.463,0.438,0.617,0.73,0.583,0.477,0.627,0.7,0.674,0.426,0.411,0.592,0.465,0.521,0.62,0.599,0.579,0.514,0.787,0.621,0.445,0.737,0.571,0.64,0.506,0.575,0.591,0.534,0.813,0.833,0.479,0.689,0.49,0.517,0.737,0.617,0.306,0.328,0.425,0.528,0.706,0.622,0.514,0.386,0.491,0.851,0.476,0.559,0.356,0.352,0.438,0.452,0.343,0.358,0.515,0.43,0.51,0.393,0.372,0.686,0.437,0.562,0.471,0.604,0.394,0.418,0.44,0.505,0.447,0.452,0.505,0.513
-330,Epileptic,F,42.0,0.7,2.1,0.8,1.2,2.2,1.5,3.5,1.3,1.7,0.6,0.4,2.9,1.8,0.5,1.8,7.1,11.8,0.9,2.1,4.4,1.1,3.5,2.1,3.6,3.8,2.4,4.1,3.3,3.2,4.8,1.9,1.0,0.5,3.3,0.6,0.3,3.7,3.0,0.4,0.2,0.9,4.4,3.1,2.2,4.5,0.7,0.5,0.8,1.4,0.5,0.4,2.2,1.2,3.1,0.2,2.9,1.1,1.3,1.3,1.4,0.8,1.6,0.3,0.4,1.7,1.0,3.1,1.3,1.4,1.1,0.7,1.1,4.6,0.3,5,12,6,7,10,17,19,12,14,6,3.0,23,17,6,14,66,88,5,20,37,8,32,17,36,47,25,37,27,26,43,20,6,3,29,3,2,37,29,1,1,4,25,28,15,21,4,2,4,8,4,3,11,7,17,2,18,7,9,8,10,5,10,1,3,11,13,18,6,6,7,4,7,36,2,0.041,0.028,0.021,0.023,0.049,0.038,0.053,0.028,0.073,0.04,0.038,0.047,0.048,0.041,0.046,0.056,0.053,0.043,0.038,0.047,0.03,0.043,0.035,0.06,0.042,0.033,0.04,0.046,0.042,0.051,0.057,0.049,0.036,0.048,0.028,0.01,0.046,0.04,0.024,0.026,0.024,0.055,0.066,0.027,0.032,0.017,0.026,0.014,0.022,0.015,0.027,0.028,0.034,0.031,0.013,0.021,0.026,0.021,0.028,0.023,0.03,0.033,0.029,0.03,0.023,0.023,0.029,0.024,0.025,0.054,0.022,0.023,0.021,0.025,1518,3610,2171,2194,1732,2110,2686,2522,2116,1297,802.0,2860,2917,974,2301,9355,17397,2213,3365,4587,3477,4658,2131,5288,5891,4767,5485,3466,4812,5587,2167,860,736,4494,1220,1098,5470,6566,348,408,1024,2034,4784,2245,3297,1365,592,1926,1941,1153,412,2585,1397,4005,380,3936,1540,2186,1056,1742,1001,2296,285,771,2086,987,3273,1567,1557,919,886,1449,7510,408,0.129,0.115,0.097,0.101,0.142,0.141,0.148,0.134,0.22,0.149,0.131,0.161,0.149,0.139,0.158,0.169,0.164,0.121,0.136,0.169,0.111,0.141,0.122,0.17,0.145,0.134,0.13,0.141,0.13,0.164,0.177,0.123,0.12,0.156,0.093,0.061,0.156,0.149,0.099,0.097,0.101,0.165,0.195,0.109,0.118,0.093,0.108,0.077,0.1,0.09,0.134,0.112,0.125,0.118,0.102,0.101,0.124,0.108,0.123,0.11,0.115,0.127,0.138,0.122,0.11,0.111,0.119,0.105,0.109,0.145,0.116,0.114,0.101,0.119,351,1185,717,835,713,836,1095,807,540,322,192.0,1267,822,279,737,2735,4627,405,1052,1794,748,1548,1077,1400,1862,1417,2095,1407,1561,1835,649,376,249,1207,380,431,1681,1546,225,177,572,1214,1039,1303,2102,661,274,766,990,587,239,1277,686,1742,235,2157,616,1007,683,969,470,778,143,290,1110,655,1785,830,806,346,468,721,3426,195,3.741,2.791,2.817,2.67,2.17,2.264,2.06,2.695,2.918,2.825,2.977,1.976,2.593,2.536,2.395,2.456,2.77,3.802,2.607,2.165,3.276,2.336,1.763,2.817,2.472,2.631,2.134,1.996,2.337,2.41,2.646,2.428,2.435,2.741,2.961,2.31,2.417,3.046,1.825,2.414,2.151,1.613,3.055,1.891,1.814,2.172,2.521,3.022,2.389,2.334,2.144,2.131,2.089,2.231,1.934,1.97,2.299,2.361,1.796,1.923,2.276,2.987,2.187,2.66,2.031,1.873,1.932,2.123,2.249,2.488,2.056,2.291,2.275,2.585,0.957,0.698,0.54,0.447,0.553,0.581,0.64,0.562,0.657,0.703,0.702,0.527,0.603,0.562,0.525,0.716,0.726,0.884,0.593,0.655,0.734,0.626,0.49,0.801,0.59,0.619,0.554,0.534,0.536,0.649,0.769,0.886,0.397,0.571,0.887,0.477,0.771,0.631,0.33,0.468,0.497,0.44,0.975,0.576,0.432,0.559,0.469,0.936,0.464,0.404,0.454,0.434,0.538,0.538,0.31,0.405,0.533,0.507,0.4,0.472,0.465,0.804,0.525,0.63,0.45,0.717,0.464,0.429,0.403,0.812,0.456,0.376,0.499,0.453
-331,Epileptic,F,42.0,0.8,1.9,0.8,1.4,1.3,1.5,1.3,1.2,1.3,0.5,0.3,2.0,1.4,0.7,0.8,5.5,6.4,0.6,2.5,4.0,1.2,2.6,1.3,2.5,2.6,1.8,2.0,2.1,2.3,2.6,1.0,1.2,1.0,2.4,0.2,1.1,2.2,2.4,0.2,0.2,0.9,2.6,2.2,2.4,2.8,0.4,0.5,0.7,1.2,0.5,0.6,1.5,1.8,2.9,0.2,1.7,0.7,1.4,0.7,0.7,0.5,0.8,0.4,0.8,1.5,1.6,2.0,1.0,1.1,0.3,0.9,1.2,3.6,0.1,4,18,10,12,12,13,13,11,11,5,3.0,20,14,9,11,55,63,5,24,35,12,29,13,25,25,22,22,21,19,31,12,8,8,26,1,8,27,31,1,1,5,24,15,17,15,3,2,6,6,6,3,12,15,24,1,15,5,13,5,6,5,5,2,8,11,16,20,7,7,4,6,8,30,1,0.041,0.024,0.016,0.024,0.034,0.031,0.023,0.026,0.053,0.029,0.024,0.031,0.028,0.034,0.031,0.036,0.028,0.026,0.036,0.033,0.025,0.042,0.036,0.034,0.033,0.027,0.024,0.027,0.025,0.033,0.036,0.044,0.067,0.029,0.009,0.033,0.028,0.028,0.028,0.017,0.022,0.037,0.039,0.023,0.023,0.009,0.021,0.015,0.019,0.013,0.032,0.022,0.031,0.027,0.035,0.021,0.023,0.018,0.024,0.02,0.02,0.021,0.022,0.048,0.027,0.037,0.02,0.017,0.022,0.017,0.024,0.02,0.017,0.013,1432,4050,2250,2456,1721,2332,2169,2582,1693,994,496.0,2563,2893,1151,1769,8529,13810,2133,3738,4126,3222,3848,1608,4574,3900,3960,4016,3375,5042,4015,1674,1544,888,5088,1845,1665,4772,5737,254,414,1370,2780,3776,2849,3076,1162,702,1869,2175,1286,523,2579,2509,5115,284,2598,1380,2483,815,1046,813,1750,428,622,2021,931,2999,2050,1532,823,1004,2050,8233,398,0.12,0.113,0.102,0.121,0.131,0.127,0.116,0.123,0.147,0.113,0.118,0.141,0.131,0.15,0.127,0.143,0.122,0.105,0.137,0.133,0.103,0.155,0.14,0.136,0.14,0.123,0.12,0.115,0.108,0.148,0.119,0.129,0.19,0.138,0.06,0.123,0.119,0.13,0.144,0.101,0.11,0.147,0.134,0.109,0.107,0.078,0.112,0.072,0.096,0.098,0.123,0.112,0.123,0.119,0.153,0.118,0.103,0.105,0.121,0.114,0.111,0.104,0.109,0.168,0.119,0.152,0.109,0.098,0.118,0.116,0.124,0.114,0.095,0.095,340,1328,846,922,631,839,862,752,432,283,193.0,1007,757,336,532,2601,3869,388,1111,1857,838,1175,601,1175,1261,1291,1411,1235,1365,1299,436,439,280,1273,443,573,1387,1512,116,200,641,1169,957,1650,1819,651,326,872,952,581,312,1158,965,1762,102,1266,550,1222,481,558,425,592,244,326,949,635,1642,940,770,354,550,936,3519,155,3.575,2.817,2.556,2.668,2.354,2.491,2.166,3.003,2.86,2.842,2.333,2.088,2.903,2.602,2.593,2.593,2.896,3.732,2.757,1.903,2.898,2.518,2.183,2.806,2.456,2.505,2.319,2.193,2.803,2.505,2.763,2.989,2.684,2.855,3.559,2.532,2.661,2.853,2.748,2.398,2.636,2.024,3.07,1.927,1.941,1.914,2.715,2.456,2.742,2.352,2.142,2.341,2.292,2.541,2.955,2.259,2.864,2.23,1.933,2.037,2.138,2.838,2.147,2.161,2.223,1.942,2.018,2.499,2.459,2.427,2.203,2.817,2.469,2.953,0.718,0.583,0.553,0.483,0.579,0.525,0.435,0.659,0.587,0.346,0.634,0.482,0.434,0.448,0.353,0.559,0.636,0.819,0.625,0.589,0.82,0.514,0.502,0.625,0.433,0.572,0.399,0.454,0.44,0.58,0.606,1.101,0.554,0.62,0.897,0.438,0.571,0.488,0.304,0.363,0.777,0.521,0.68,0.645,0.609,0.414,0.485,0.726,0.484,0.485,0.275,0.449,0.681,0.639,0.627,0.466,0.636,0.517,0.431,0.417,0.374,0.852,0.315,0.67,0.567,0.504,0.41,0.355,0.399,0.697,0.424,0.599,0.563,0.54
-332,Epileptic,F,27.0,0.6,1.6,0.7,1.0,1.6,2.0,1.3,1.1,1.1,0.7,0.2,2.7,1.6,0.4,1.0,4.6,5.7,0.5,1.9,5.8,0.8,2.4,1.4,2.5,3.2,3.7,3.1,1.7,1.7,3.4,1.1,0.7,0.3,2.0,0.2,0.4,3.2,2.8,0.1,0.1,0.8,2.7,1.9,3.0,1.6,0.5,0.4,0.9,1.0,1.0,0.8,1.2,0.7,1.8,0.1,2.3,0.5,2.0,0.9,1.0,0.5,0.9,0.3,0.4,1.1,1.9,1.8,0.9,0.7,0.3,1.0,1.6,4.9,0.2,7,17,8,8,13,15,12,10,11,6,3.0,24,14,6,11,48,61,3,20,44,8,21,11,27,39,40,43,20,15,37,12,8,4,21,2,4,35,30,1,1,4,25,18,22,13,3,2,7,5,9,5,7,6,11,1,20,5,17,7,8,3,6,3,4,8,16,13,6,6,3,6,9,37,2,0.032,0.022,0.015,0.024,0.044,0.042,0.03,0.028,0.049,0.035,0.025,0.037,0.04,0.034,0.031,0.036,0.027,0.022,0.029,0.041,0.021,0.042,0.034,0.034,0.04,0.038,0.03,0.025,0.023,0.037,0.044,0.053,0.025,0.031,0.011,0.014,0.041,0.035,0.013,0.016,0.021,0.035,0.032,0.028,0.018,0.013,0.022,0.017,0.017,0.021,0.041,0.017,0.026,0.02,0.021,0.02,0.015,0.03,0.033,0.021,0.023,0.022,0.035,0.026,0.018,0.048,0.017,0.017,0.017,0.017,0.022,0.031,0.024,0.019,1138,3444,1488,1737,1824,2794,2072,2156,1850,1306,450.0,2223,3004,890,2176,8852,13254,1836,3429,3757,2484,3326,1759,4536,5266,6234,5312,2579,3890,5462,2022,566,587,4536,1656,1334,4754,5915,229,311,1158,2680,4614,2727,2033,1116,653,1704,1842,1568,580,2302,1205,3171,291,3279,1231,2430,945,1317,862,1584,465,817,1900,980,2915,1693,1358,1067,1470,1490,7668,304,0.128,0.118,0.109,0.114,0.154,0.155,0.111,0.129,0.161,0.13,0.126,0.145,0.15,0.146,0.141,0.145,0.127,0.1,0.126,0.15,0.094,0.155,0.13,0.146,0.157,0.151,0.127,0.111,0.107,0.147,0.138,0.155,0.132,0.13,0.07,0.086,0.129,0.135,0.107,0.097,0.108,0.142,0.136,0.12,0.097,0.087,0.108,0.095,0.096,0.119,0.161,0.098,0.122,0.106,0.128,0.116,0.087,0.13,0.137,0.112,0.112,0.11,0.14,0.139,0.097,0.17,0.096,0.103,0.105,0.11,0.115,0.134,0.114,0.113,314,1142,655,669,563,901,730,686,403,320,124.0,1126,732,232,548,2212,3586,358,1004,2201,619,955,676,1158,1523,1756,1931,1064,1109,1596,472,242,223,987,394,463,1517,1451,134,96,612,1249,1016,1787,1378,549,281,782,862,706,326,1056,485,1408,123,1756,488,1206,525,758,343,607,169,325,973,539,1640,793,630,316,719,794,3132,145,3.302,2.817,2.205,2.522,2.548,2.692,2.288,2.778,3.213,3.404,3.263,1.771,3.137,2.825,2.85,2.979,2.958,3.858,2.723,1.627,3.089,2.568,2.125,2.908,2.624,2.757,2.158,1.953,2.687,2.614,3.029,2.403,2.226,3.148,3.635,2.549,2.584,2.884,1.887,3.38,2.349,1.972,3.299,1.786,1.675,2.204,2.817,2.737,2.623,2.501,2.337,2.42,2.33,2.558,2.559,2.068,2.433,2.298,2.068,1.923,2.578,2.67,2.556,2.719,2.076,2.387,1.955,2.513,2.399,3.86,2.307,2.341,2.603,2.385,0.565,0.726,0.597,0.565,0.753,0.659,0.55,0.607,0.658,0.505,0.979,0.472,0.499,0.385,0.606,0.586,0.531,0.684,0.575,0.476,0.837,0.549,0.482,0.571,0.746,0.617,0.564,0.457,0.512,0.697,0.639,0.882,0.461,0.724,0.905,0.432,0.668,0.743,0.337,0.631,0.506,0.691,0.675,0.616,0.492,0.483,0.493,0.775,0.456,0.461,0.534,0.475,0.701,0.469,0.382,0.45,0.481,0.736,0.403,0.354,0.552,0.79,0.532,0.816,0.488,0.707,0.422,0.346,0.419,0.78,0.514,0.549,0.576,0.419
-333,Epileptic,M,52.0,1.1,3.3,1.6,2.0,2.3,3.4,2.5,2.8,2.2,0.5,0.3,4.0,2.2,0.8,1.5,11.8,13.3,1.6,2.6,5.2,1.4,4.0,2.2,3.5,2.9,2.1,2.2,2.3,4.2,2.4,2.2,1.2,0.4,2.9,0.6,0.9,6.6,3.8,0.2,0.2,1.0,4.7,2.9,4.2,2.9,1.0,0.5,0.9,1.8,1.3,0.7,2.8,2.3,5.1,0.4,1.5,0.7,1.9,1.2,1.4,0.7,1.9,0.5,0.8,2.6,1.5,1.6,3.9,1.6,0.8,0.6,1.7,4.9,0.5,8,21,11,16,20,29,24,23,16,5,5.0,41,27,7,13,95,99,11,29,47,10,45,26,31,36,23,40,27,35,27,20,13,4,30,4,6,60,42,2,1,5,41,27,30,18,8,2,7,9,10,4,21,16,32,3,11,5,16,7,12,5,13,4,7,17,19,13,30,9,4,4,11,38,3,0.036,0.029,0.022,0.022,0.04,0.039,0.032,0.028,0.054,0.028,0.028,0.039,0.029,0.047,0.032,0.04,0.033,0.047,0.028,0.032,0.026,0.035,0.03,0.037,0.033,0.026,0.024,0.023,0.026,0.025,0.05,0.047,0.025,0.029,0.014,0.021,0.046,0.028,0.015,0.02,0.018,0.039,0.041,0.026,0.017,0.025,0.02,0.015,0.017,0.019,0.025,0.02,0.024,0.022,0.012,0.016,0.012,0.019,0.017,0.02,0.02,0.027,0.024,0.027,0.021,0.022,0.015,0.024,0.023,0.022,0.02,0.018,0.016,0.019,1934,6788,3345,3640,2557,3500,3981,4714,1964,1205,636.0,4583,5405,1252,3041,15253,23235,2823,5271,6450,3715,5761,2964,7189,5989,4803,4246,5427,5603,5105,2437,1187,896,6710,2800,2396,8755,9418,403,397,1533,4169,6702,4578,4753,1539,984,2499,3060,2278,624,4693,3093,9033,714,3107,1810,3852,1889,2207,1052,2963,640,1295,3930,1696,3098,6163,1740,1223,982,2906,10077,664,0.109,0.115,0.096,0.112,0.141,0.14,0.12,0.114,0.149,0.13,0.127,0.141,0.114,0.135,0.118,0.129,0.125,0.145,0.126,0.125,0.104,0.116,0.139,0.133,0.113,0.106,0.124,0.102,0.093,0.121,0.136,0.125,0.09,0.117,0.076,0.093,0.139,0.123,0.094,0.098,0.093,0.141,0.148,0.114,0.091,0.115,0.099,0.081,0.092,0.106,0.123,0.101,0.113,0.103,0.091,0.093,0.082,0.101,0.095,0.113,0.1,0.124,0.123,0.112,0.107,0.109,0.093,0.107,0.105,0.115,0.108,0.101,0.089,0.114,556,1988,1265,1442,1000,1465,1374,1596,659,306,208.0,1896,1521,317,853,4915,6803,602,1605,2693,905,1824,1311,1780,1798,1489,1538,1746,2534,1710,728,546,292,1655,693,779,2627,2222,221,192,786,2010,1429,2736,2588,683,402,1072,1458,1075,409,2352,1505,3558,445,1386,916,1786,1051,1080,560,1100,327,517,1836,1144,1616,2541,1048,535,479,1439,4748,336,3.406,2.774,2.469,2.362,2.366,2.155,2.335,2.559,2.407,2.917,2.472,2.046,2.785,2.763,2.734,2.53,2.742,3.463,2.668,2.08,2.936,2.461,1.942,2.995,2.616,2.69,2.317,2.442,1.988,2.409,2.384,2.276,2.493,2.969,3.109,2.632,2.597,3.077,2.112,2.432,2.332,1.906,3.303,1.938,2.012,2.154,3.026,2.81,2.599,2.429,1.84,2.089,2.129,2.484,2.038,2.322,2.203,2.331,2.032,2.235,2.102,2.828,2.234,2.757,2.203,1.732,2.096,2.366,1.904,2.352,2.043,2.401,2.281,2.493,0.621,0.801,0.505,0.523,0.65,0.557,0.647,0.597,0.719,0.605,0.7,0.445,0.438,0.498,0.555,0.624,0.642,0.691,0.56,0.609,0.83,0.594,0.547,0.685,0.593,0.604,0.504,0.497,0.657,0.636,0.866,0.777,0.397,0.63,0.817,0.541,0.9,0.69,0.425,0.466,0.408,0.505,0.889,0.542,0.399,0.473,0.595,0.744,0.466,0.591,0.308,0.435,0.464,0.57,0.309,0.431,0.396,0.55,0.355,0.361,0.492,0.626,0.365,0.815,0.535,0.584,0.375,0.564,0.582,0.705,0.441,0.478,0.434,0.356
-334,Epileptic,M,42.0,0.9,2.2,0.9,1.4,1.5,1.4,1.3,1.9,1.5,1.0,0.3,2.8,1.3,0.6,1.8,4.8,9.1,0.8,1.8,5.0,1.3,3.2,1.4,4.5,2.9,3.3,3.0,2.6,2.3,3.0,1.1,1.6,0.5,2.2,0.2,0.9,3.4,3.5,0.2,0.1,1.0,3.4,2.4,2.3,2.2,0.7,0.4,0.6,1.2,0.6,0.8,2.0,1.1,2.1,0.3,2.1,0.5,1.5,0.9,1.1,1.4,1.7,0.5,0.4,1.6,1.9,2.6,0.8,1.1,0.6,0.8,1.7,4.2,0.1,10,20,11,12,15,15,13,15,16,12,5.0,28,16,8,19,52,83,6,23,47,18,34,14,49,39,39,36,27,20,37,16,13,3,27,2,7,39,41,2,1,6,31,29,18,13,4,2,4,6,5,6,13,7,13,2,19,3,14,7,8,14,12,4,3,14,22,21,6,7,6,7,15,37,1,0.037,0.02,0.016,0.023,0.033,0.031,0.024,0.03,0.045,0.043,0.028,0.035,0.025,0.033,0.03,0.031,0.028,0.027,0.028,0.036,0.023,0.036,0.028,0.042,0.035,0.028,0.029,0.024,0.022,0.033,0.03,0.05,0.028,0.025,0.008,0.022,0.033,0.031,0.013,0.02,0.021,0.04,0.037,0.025,0.015,0.012,0.024,0.012,0.015,0.016,0.03,0.018,0.019,0.017,0.015,0.018,0.016,0.02,0.023,0.017,0.03,0.026,0.024,0.015,0.019,0.029,0.021,0.014,0.018,0.021,0.025,0.021,0.017,0.016,1752,4997,2258,2620,2680,2524,2677,3386,2377,1748,729.0,3080,3700,1241,3724,11467,21313,3082,4082,5517,4543,5136,1947,8064,5552,6497,5568,3503,5726,5622,2748,1682,890,5971,2256,2194,9146,8974,470,308,1548,3566,6663,2655,3913,1800,858,2298,2538,1773,728,4038,2465,4895,573,3331,1251,3222,1362,1653,1901,2943,837,1162,2388,1429,3490,2029,1918,1617,1170,3041,8974,263,0.126,0.108,0.115,0.12,0.135,0.147,0.091,0.123,0.162,0.177,0.13,0.149,0.119,0.149,0.138,0.125,0.122,0.113,0.136,0.147,0.094,0.151,0.132,0.154,0.154,0.133,0.123,0.095,0.098,0.142,0.129,0.143,0.096,0.112,0.054,0.108,0.137,0.129,0.089,0.095,0.104,0.151,0.15,0.118,0.09,0.083,0.099,0.073,0.089,0.089,0.146,0.099,0.102,0.093,0.107,0.105,0.093,0.102,0.125,0.102,0.131,0.122,0.124,0.101,0.108,0.12,0.111,0.091,0.101,0.119,0.134,0.11,0.097,0.093,488,1659,854,921,898,920,871,1056,584,492,229.0,1253,910,306,983,2848,5397,448,1024,2235,1038,1492,848,1878,1556,1990,1842,1482,1534,1721,675,654,253,1344,565,732,1891,2053,273,148,754,1479,1214,1525,2129,858,311,880,1126,662,364,1684,901,1960,303,1707,528,1429,687,875,803,1019,349,432,1147,991,1923,913,911,553,558,1368,3917,109,3.46,2.885,2.536,2.727,2.565,2.507,2.525,2.908,2.93,2.998,2.414,2.105,3.041,2.929,2.912,2.935,3.065,3.982,3.026,2.107,3.102,2.61,2.061,3.188,2.746,2.637,2.381,1.944,2.811,2.536,2.918,2.554,3.287,2.994,3.392,2.74,3.154,3.031,1.973,2.337,2.556,2.07,3.585,1.92,2.08,2.269,3.284,3.088,2.742,2.901,2.086,2.512,2.674,2.69,2.368,2.101,2.775,2.443,2.291,2.087,2.429,2.812,2.482,2.906,2.275,1.839,1.981,2.606,2.429,2.898,2.446,2.553,2.507,2.807,0.861,0.644,0.555,0.667,0.592,0.441,0.45,0.69,0.612,0.737,0.977,0.474,0.433,0.403,0.492,0.614,0.676,0.731,0.499,0.74,0.792,0.581,0.503,0.7,0.469,0.618,0.562,0.519,0.513,0.595,0.677,1.124,0.856,0.682,0.842,0.469,0.801,0.623,0.273,0.32,0.515,0.48,0.741,0.588,0.708,0.441,0.649,0.725,0.553,0.706,0.436,0.486,0.74,0.448,0.346,0.427,0.504,0.408,0.534,0.445,0.452,0.77,0.483,0.929,0.582,0.548,0.446,0.393,0.437,0.785,0.648,0.575,0.476,0.42
-335,Epileptic,M,27.0,0.8,3.1,1.1,1.3,1.6,1.3,4.1,1.6,1.0,1.1,0.4,2.9,1.2,0.5,0.8,5.6,10.8,1.6,1.9,5.1,1.3,3.1,2.2,4.8,4.3,2.9,4.8,4.6,2.4,5.4,2.5,1.7,0.4,2.5,0.6,0.7,3.4,3.2,0.2,0.1,0.9,2.7,2.8,3.0,2.6,0.7,0.5,0.7,1.2,0.8,0.3,1.3,2.6,3.3,0.3,2.2,0.8,1.3,1.1,1.0,0.6,1.6,0.5,0.7,2.0,1.3,2.9,1.3,1.0,0.6,1.1,1.5,5.2,0.1,7,28,8,11,19,14,34,18,11,9,5.0,24,13,6,12,69,82,11,28,43,13,37,19,59,47,34,56,41,23,51,25,11,5,32,5,9,39,44,2,1,4,22,28,24,12,4,2,4,6,6,4,10,21,24,2,18,6,13,7,8,6,13,4,6,14,18,19,9,6,6,7,11,43,1,0.04,0.034,0.02,0.028,0.037,0.027,0.066,0.029,0.039,0.044,0.037,0.039,0.03,0.031,0.025,0.033,0.045,0.057,0.029,0.046,0.028,0.035,0.034,0.052,0.041,0.027,0.042,0.042,0.026,0.056,0.071,0.062,0.024,0.031,0.021,0.021,0.033,0.03,0.017,0.015,0.018,0.037,0.05,0.028,0.019,0.014,0.02,0.012,0.017,0.015,0.017,0.017,0.033,0.026,0.013,0.015,0.017,0.016,0.022,0.017,0.017,0.023,0.018,0.028,0.029,0.022,0.02,0.019,0.021,0.021,0.026,0.018,0.02,0.009,1417,4818,2300,2306,2643,2358,3116,3495,1759,1825,705.0,2829,2643,1177,1899,11180,18098,2540,4795,4680,4443,5149,1975,6234,6160,7059,6385,4423,6283,6012,2241,1219,910,6198,1838,1908,8547,8180,386,353,1374,2222,5119,2967,3477,1362,766,2448,2007,1799,452,2386,2364,5188,565,3615,1575,2691,1172,1500,1105,2765,658,792,2146,1189,4506,2219,1332,989,1330,2397,8998,366,0.132,0.135,0.108,0.123,0.15,0.129,0.162,0.135,0.164,0.16,0.149,0.147,0.13,0.138,0.139,0.138,0.151,0.165,0.137,0.165,0.113,0.126,0.125,0.148,0.143,0.131,0.145,0.13,0.112,0.17,0.189,0.135,0.113,0.137,0.1,0.114,0.141,0.14,0.12,0.086,0.094,0.129,0.156,0.13,0.094,0.091,0.105,0.074,0.092,0.096,0.119,0.102,0.127,0.11,0.099,0.101,0.101,0.104,0.112,0.107,0.105,0.117,0.108,0.14,0.129,0.121,0.101,0.103,0.103,0.124,0.123,0.106,0.109,0.061,411,1493,827,813,864,795,1260,1010,468,443,219.0,1307,711,293,553,3318,4550,545,1199,2017,944,1582,1008,1752,1956,1909,2283,1729,1534,1990,665,444,273,1410,446,655,1860,1894,165,161,716,1148,1167,1781,1995,691,315,922,957,758,241,1180,1112,2184,295,2001,715,1266,750,860,563,1067,408,388,1153,860,2263,1041,706,423,645,1206,3973,152,3.285,2.836,2.754,2.6,2.514,2.329,2.07,2.727,2.634,3.109,2.468,1.872,2.851,2.922,2.428,2.545,2.855,3.491,2.964,1.99,3.291,2.529,1.794,2.676,2.433,2.788,2.141,2.064,2.967,2.352,2.498,2.68,2.658,2.927,3.464,2.47,3.136,3.055,2.503,1.958,2.38,1.688,3.275,1.894,1.997,2.126,2.853,3.13,2.599,2.716,1.811,2.154,2.138,2.255,2.063,1.938,2.484,2.236,1.787,1.893,2.039,2.615,1.872,2.238,2.073,1.727,2.115,2.444,2.201,2.404,2.348,2.398,2.426,2.344,0.739,0.71,0.585,0.488,0.785,0.798,0.615,0.588,0.751,0.7,0.728,0.45,0.55,0.514,0.627,0.635,0.77,0.786,0.59,0.707,0.829,0.682,0.489,0.869,0.724,0.63,0.608,0.548,0.518,0.717,0.747,1.018,0.354,0.68,0.659,0.532,0.72,0.683,0.464,0.414,0.424,0.464,0.843,0.524,0.547,0.353,0.587,0.963,0.423,0.549,0.641,0.489,0.482,0.532,0.403,0.417,0.413,0.485,0.326,0.436,0.461,0.696,0.431,0.639,0.555,0.672,0.448,0.443,0.448,0.771,0.432,0.464,0.466,0.612
-336,Epileptic,M,37.0,0.7,2.1,0.7,1.2,1.7,1.3,1.5,1.6,1.3,0.6,0.5,4.1,1.4,0.7,1.1,5.0,8.3,0.7,2.4,5.3,0.9,2.5,2.2,3.9,2.7,2.9,2.7,2.3,2.9,2.5,1.4,0.9,0.4,2.3,0.4,0.8,2.2,2.9,0.2,0.3,0.9,3.1,2.1,4.0,2.2,0.2,0.3,0.6,1.2,0.7,0.4,3.2,1.2,3.0,0.3,1.8,0.8,1.7,1.1,0.9,0.6,2.3,0.6,0.4,2.3,1.6,2.0,1.8,1.2,1.2,0.6,0.9,4.0,0.3,6,19,6,11,17,12,11,15,15,8,5.0,40,18,9,11,55,75,7,25,50,11,29,22,41,34,30,32,21,23,32,16,7,4,27,2,8,24,33,2,2,6,31,17,29,14,3,1,4,6,5,3,25,12,19,2,14,6,15,9,8,5,17,4,5,20,19,19,12,9,11,5,6,32,2,0.029,0.021,0.012,0.022,0.026,0.025,0.025,0.022,0.031,0.022,0.033,0.03,0.025,0.028,0.024,0.025,0.026,0.031,0.031,0.031,0.024,0.028,0.027,0.038,0.025,0.027,0.024,0.022,0.021,0.024,0.03,0.049,0.025,0.022,0.014,0.019,0.026,0.024,0.011,0.021,0.02,0.028,0.033,0.026,0.015,0.007,0.014,0.01,0.017,0.013,0.021,0.024,0.019,0.017,0.022,0.015,0.018,0.019,0.019,0.021,0.021,0.026,0.028,0.018,0.021,0.024,0.015,0.019,0.016,0.024,0.011,0.018,0.015,0.012,1485,5379,2591,2178,2677,2489,2075,3138,2396,1395,706.0,4777,3528,1455,2373,9454,18723,2297,4639,6390,3692,4650,2750,6705,5812,5963,5219,3598,5846,5234,2617,1221,773,6551,1975,1814,5502,7849,433,529,1427,3861,6289,4132,3814,1050,816,2020,2319,1806,515,4375,2296,5799,301,2802,1557,3611,1607,1165,1116,3283,658,850,3256,2386,3285,3375,2012,1363,1424,2017,9579,644,0.127,0.108,0.081,0.114,0.126,0.12,0.099,0.115,0.131,0.118,0.135,0.14,0.121,0.138,0.121,0.114,0.115,0.116,0.136,0.139,0.091,0.126,0.136,0.135,0.127,0.124,0.116,0.09,0.088,0.125,0.123,0.106,0.108,0.12,0.079,0.103,0.119,0.12,0.085,0.124,0.103,0.135,0.139,0.125,0.088,0.071,0.086,0.071,0.093,0.092,0.113,0.118,0.109,0.087,0.118,0.101,0.096,0.109,0.11,0.112,0.108,0.127,0.134,0.113,0.119,0.121,0.094,0.099,0.099,0.135,0.091,0.101,0.093,0.087,437,1640,936,870,1156,881,758,1094,694,459,276.0,2169,1022,365,725,2911,5011,415,1297,2728,871,1475,1253,1828,1775,1727,1876,1415,1763,1731,780,430,270,1554,422,682,1366,1814,243,209,705,1735,1151,2386,2202,572,306,868,1027,738,299,2035,1054,2590,177,1627,761,1435,909,684,427,1273,331,424,1609,1014,1992,1531,1014,612,773,778,4078,283,3.287,3.031,2.807,2.582,2.111,2.469,2.31,2.615,2.516,2.683,2.412,1.928,2.64,2.886,2.641,2.549,2.919,3.801,2.844,2.05,3.249,2.467,1.957,2.795,2.551,2.793,2.258,2.072,2.603,2.456,2.537,2.554,2.408,2.979,3.704,2.503,2.959,3.023,2.101,2.722,2.509,1.951,3.763,1.937,1.98,1.952,3.42,2.824,2.781,2.725,2.135,2.243,2.207,2.309,2.055,1.95,2.293,2.46,2.072,1.883,2.522,2.62,2.277,2.154,2.172,2.465,1.801,2.494,2.301,2.488,2.078,2.566,2.501,2.575,0.752,0.574,0.423,0.443,0.563,0.551,0.511,0.448,0.534,0.431,0.448,0.461,0.366,0.459,0.495,0.521,0.571,0.724,0.52,0.622,0.742,0.429,0.454,0.857,0.492,0.546,0.498,0.634,0.513,0.439,0.821,1.19,0.468,0.663,0.638,0.481,0.697,0.642,0.398,0.589,0.426,0.462,0.76,0.473,0.67,0.398,0.347,0.64,0.451,0.518,0.28,0.398,0.381,0.461,0.311,0.375,0.482,0.594,0.37,0.371,0.565,0.604,0.374,0.768,0.491,0.549,0.47,0.344,0.373,0.603,0.337,0.651,0.428,0.38
-337,Epileptic,F,47.0,0.8,3.0,1.1,1.4,1.8,2.0,1.4,1.6,1.5,1.0,0.3,2.7,1.7,0.4,1.2,5.7,8.3,1.0,1.7,4.0,1.1,3.1,1.4,2.4,3.0,4.3,2.3,2.0,2.4,3.0,1.4,1.2,0.6,2.4,0.3,0.4,2.6,3.0,0.1,0.2,0.9,2.3,2.2,2.5,2.5,0.6,0.3,1.0,1.2,0.5,0.6,2.1,1.3,2.5,0.2,2.3,0.4,1.6,0.7,0.9,0.7,1.0,0.1,0.3,1.0,2.1,2.5,1.3,1.1,0.5,1.1,1.5,3.6,0.3,5,28,14,15,20,19,12,16,16,10,6.0,24,15,3,13,59,80,10,25,36,12,33,16,27,34,46,33,19,20,32,18,11,8,29,1,3,32,43,1,2,6,23,23,20,15,5,1,7,6,5,5,18,11,17,2,17,2,17,6,7,6,7,1,3,8,22,22,10,6,7,7,9,28,3,0.039,0.024,0.015,0.023,0.031,0.034,0.022,0.029,0.037,0.033,0.038,0.029,0.029,0.026,0.032,0.028,0.028,0.036,0.032,0.032,0.022,0.038,0.027,0.036,0.031,0.035,0.026,0.025,0.024,0.031,0.04,0.065,0.024,0.029,0.008,0.011,0.032,0.029,0.011,0.015,0.019,0.029,0.038,0.025,0.02,0.016,0.021,0.015,0.016,0.008,0.027,0.021,0.021,0.022,0.035,0.015,0.017,0.02,0.022,0.017,0.024,0.018,0.012,0.018,0.016,0.028,0.023,0.022,0.019,0.016,0.022,0.023,0.017,0.017,1472,5139,3070,2743,2519,3141,2747,2686,2081,1772,666.0,2830,3525,1109,2691,11608,16861,2565,3745,4732,3051,4337,2115,5755,6014,7480,5030,3232,5373,5406,2192,980,1289,6482,1953,1694,5705,7252,256,430,1599,2226,5475,3127,3446,1423,778,2402,2694,1606,641,3318,2572,4752,267,4149,842,3150,915,1528,1012,2365,438,764,2119,2386,3454,2422,1933,1114,1642,2217,7614,424,0.107,0.117,0.099,0.121,0.14,0.155,0.108,0.136,0.147,0.145,0.15,0.128,0.128,0.111,0.133,0.129,0.129,0.135,0.129,0.135,0.094,0.156,0.129,0.127,0.142,0.148,0.128,0.115,0.111,0.141,0.135,0.155,0.119,0.123,0.05,0.07,0.126,0.139,0.084,0.109,0.107,0.13,0.142,0.112,0.103,0.085,0.096,0.081,0.093,0.085,0.129,0.111,0.108,0.11,0.151,0.097,0.102,0.11,0.12,0.105,0.129,0.102,0.089,0.12,0.097,0.123,0.123,0.106,0.102,0.115,0.121,0.119,0.094,0.127,404,1865,1130,997,923,1041,932,880,593,466,193.0,1279,941,256,728,3306,4866,438,1007,1843,839,1317,868,1295,1703,2155,1697,1190,1428,1611,603,366,427,1336,579,637,1465,1743,140,225,723,1168,1126,1798,1928,693,265,990,1169,760,374,1603,1127,1893,92,2196,354,1428,554,814,503,804,201,309,990,1143,1721,1005,875,446,748,921,3336,241,3.372,2.488,2.553,2.634,2.297,2.641,2.414,2.777,2.54,2.986,2.788,1.932,2.882,2.953,2.717,2.66,2.8,3.89,2.898,2.141,2.984,2.477,2.131,3.048,2.68,2.847,2.371,2.141,2.827,2.616,2.67,2.645,2.538,3.284,3.079,2.489,2.883,3.067,2.061,2.161,2.702,1.739,3.445,2.011,2.06,2.222,3.432,3.024,2.735,2.364,2.057,2.215,2.335,2.601,2.83,2.101,2.674,2.367,1.818,2.093,2.481,2.836,2.388,2.532,2.248,2.357,2.224,2.562,2.589,2.585,2.447,2.779,2.471,2.009,0.863,0.71,0.498,0.476,0.648,0.487,0.535,0.508,0.607,0.57,0.606,0.558,0.451,0.6,0.516,0.573,0.563,0.734,0.52,0.709,0.825,0.505,0.503,0.736,0.464,0.567,0.394,0.484,0.518,0.569,0.894,1.018,0.409,0.533,0.645,0.407,0.767,0.615,0.286,0.409,0.416,0.396,0.704,0.698,0.57,0.437,0.814,0.796,0.53,0.555,0.398,0.47,0.649,0.491,0.366,0.457,0.512,0.573,0.35,0.429,0.345,0.805,0.334,0.977,0.49,1.093,0.498,0.389,0.458,0.818,0.45,0.522,0.505,0.551
-338,Epileptic,M,17.5,0.5,1.6,0.7,0.9,2.7,2.4,2.8,1.6,2.1,0.6,0.5,4.1,2.8,0.4,1.8,10.2,10.3,0.7,2.8,4.1,1.9,3.3,1.5,4.0,4.8,5.4,3.9,4.1,5.5,3.1,1.1,1.1,0.4,4.0,0.2,1.1,3.5,3.5,0.1,0.1,0.8,3.7,3.1,2.6,3.7,1.0,0.6,0.6,1.0,0.6,0.3,1.2,0.7,3.3,0.6,2.6,1.2,1.5,1.5,1.0,0.4,0.9,0.2,0.4,1.8,0.9,2.4,1.3,1.3,0.6,0.4,0.8,3.6,0.3,3,11,6,6,16,17,17,13,16,5,4.0,28,22,6,14,95,82,4,20,33,14,27,10,31,37,41,31,28,40,28,10,5,4,36,2,9,35,42,1,1,5,28,28,15,19,8,4,3,5,4,2,7,4,25,3,15,11,12,8,6,2,9,1,3,11,8,17,10,6,2,3,6,22,4,0.029,0.023,0.017,0.021,0.063,0.044,0.037,0.03,0.075,0.035,0.045,0.06,0.052,0.042,0.037,0.06,0.042,0.035,0.041,0.049,0.036,0.047,0.041,0.052,0.048,0.049,0.05,0.05,0.049,0.044,0.048,0.038,0.016,0.048,0.013,0.026,0.05,0.041,0.025,0.017,0.018,0.052,0.07,0.035,0.03,0.029,0.029,0.01,0.015,0.013,0.038,0.016,0.018,0.032,0.019,0.029,0.041,0.025,0.033,0.024,0.021,0.038,0.028,0.024,0.026,0.033,0.028,0.022,0.019,0.023,0.02,0.023,0.018,0.017,1963,4515,1903,2595,2396,3177,3327,3471,2314,1108,884.0,3781,3880,1116,3559,14645,22742,2934,4327,5750,4096,4578,2224,4785,7238,7280,6004,4744,6421,5326,1414,1150,1212,6065,1940,2525,6241,8000,349,355,1596,3773,5357,2720,3238,1551,873,1973,2690,1480,539,3111,1954,5793,903,3239,2050,2852,1432,1687,670,1518,297,824,2596,1120,3426,2268,2392,803,982,1948,7737,635,0.1,0.092,0.086,0.093,0.161,0.151,0.123,0.118,0.199,0.129,0.144,0.18,0.143,0.158,0.131,0.167,0.134,0.106,0.137,0.155,0.118,0.144,0.124,0.15,0.155,0.145,0.15,0.146,0.136,0.147,0.132,0.114,0.091,0.13,0.065,0.106,0.149,0.145,0.099,0.095,0.099,0.152,0.174,0.123,0.122,0.126,0.11,0.075,0.082,0.09,0.133,0.085,0.086,0.113,0.105,0.113,0.144,0.105,0.133,0.112,0.102,0.126,0.11,0.119,0.113,0.128,0.114,0.11,0.095,0.107,0.108,0.1,0.093,0.126,328,1179,710,742,728,1020,1171,949,513,276,208.0,1113,944,201,789,3118,4547,366,1120,1607,948,1187,728,1333,1673,1970,1437,1503,1912,1386,412,425,380,1389,463,774,1376,1563,120,131,685,1266,864,1268,1936,663,299,774,1039,634,174,1236,717,1925,447,1430,574,1260,753,663,309,539,145,254,1200,451,1424,964,999,309,362,746,3210,290,4.057,3.065,2.678,2.854,2.785,2.825,2.252,3.065,3.038,3.05,3.416,2.528,2.983,3.065,2.984,2.945,3.425,4.315,2.95,2.546,3.195,2.812,2.512,2.651,2.895,2.692,2.733,2.415,2.683,2.68,2.638,2.409,2.65,2.858,3.444,2.742,3.135,3.226,2.679,2.435,2.746,2.212,3.644,2.348,1.957,2.45,3.139,3.021,2.785,2.508,2.733,2.485,2.676,2.554,2.518,2.401,2.817,2.548,2.473,2.701,2.525,2.638,2.181,3.606,2.476,2.65,2.55,2.616,2.598,3.369,3.108,2.7,2.556,2.532,0.832,1.024,0.726,0.815,0.941,0.757,0.725,0.932,0.849,0.534,0.639,0.562,0.694,0.703,0.758,0.847,0.93,0.632,0.574,0.74,0.774,0.594,0.525,0.848,0.781,0.847,0.695,0.741,0.767,0.657,0.639,0.906,0.657,0.93,0.772,0.637,0.866,0.871,0.744,0.632,0.695,0.721,0.89,0.835,0.553,0.733,0.769,0.731,0.844,0.532,0.711,0.837,0.848,0.933,0.496,0.54,0.569,0.623,0.56,0.749,0.476,0.73,0.473,0.743,0.585,0.769,0.768,0.526,0.647,0.761,1.041,0.748,0.642,0.453
-339,Epileptic,M,32.0,0.8,3.0,1.6,1.9,2.4,1.9,2.7,2.2,1.6,1.2,0.5,4.5,1.4,0.7,1.2,9.8,13.2,1.6,3.0,7.7,1.7,3.7,1.9,5.8,3.2,4.7,4.9,5.3,5.6,3.6,1.7,0.5,0.5,3.2,0.9,1.1,4.9,5.3,0.2,0.3,1.4,3.4,4.1,4.7,4.1,1.0,0.4,1.2,1.8,1.0,0.8,3.6,2.7,6.0,0.8,2.2,1.0,2.0,1.8,1.4,0.7,2.2,0.4,0.8,2.4,1.5,3.9,1.7,1.4,0.9,0.8,2.1,6.4,0.4,7,22,11,15,18,20,18,21,17,10,5.0,42,18,6,17,83,100,8,27,66,12,39,20,54,33,46,46,36,41,37,18,4,3,31,5,8,41,47,1,2,7,35,34,33,18,8,2,6,8,6,6,27,24,33,6,15,7,16,15,9,6,17,2,7,19,19,26,10,6,8,5,16,54,2,0.036,0.028,0.02,0.035,0.044,0.028,0.044,0.029,0.047,0.049,0.035,0.042,0.03,0.041,0.025,0.039,0.04,0.05,0.035,0.044,0.039,0.033,0.03,0.049,0.03,0.036,0.036,0.041,0.045,0.035,0.044,0.035,0.036,0.035,0.027,0.022,0.046,0.04,0.015,0.017,0.03,0.037,0.051,0.029,0.025,0.023,0.017,0.019,0.022,0.018,0.029,0.035,0.034,0.033,0.031,0.021,0.025,0.023,0.025,0.025,0.021,0.032,0.02,0.028,0.025,0.03,0.022,0.024,0.018,0.025,0.022,0.023,0.021,0.022,1659,5707,2878,2678,2705,3608,2508,3646,2242,1555,821.0,4671,2897,1341,2930,13064,18244,2709,5024,7017,3111,6145,3253,6443,6158,7863,6811,4706,6133,5993,2473,804,861,6479,2333,2295,6230,9675,439,587,1532,3421,7804,4393,4057,1357,740,2280,2589,1672,677,4296,3352,6931,1002,3121,1413,3079,2122,1725,941,3280,450,954,3566,1331,5329,2284,2251,1398,1217,3776,11800,480,0.123,0.117,0.112,0.144,0.149,0.12,0.129,0.13,0.169,0.173,0.125,0.163,0.12,0.133,0.126,0.136,0.135,0.127,0.14,0.159,0.118,0.138,0.136,0.153,0.129,0.134,0.128,0.129,0.128,0.149,0.137,0.106,0.098,0.132,0.088,0.108,0.142,0.148,0.091,0.092,0.115,0.139,0.17,0.121,0.108,0.117,0.086,0.084,0.103,0.096,0.135,0.127,0.122,0.123,0.144,0.106,0.111,0.109,0.123,0.115,0.113,0.137,0.119,0.125,0.119,0.123,0.105,0.103,0.09,0.136,0.102,0.12,0.105,0.109,410,1832,1102,980,1005,1274,998,1265,670,455,268.0,1889,844,326,901,4364,5554,496,1503,3026,791,2044,1176,1999,1870,2440,2427,1995,1912,1809,746,288,310,1706,631,771,1885,2394,258,318,782,1701,1664,2606,2359,676,335,920,1156,815,433,1729,1437,2728,472,1584,668,1523,1157,877,561,1203,291,480,1648,910,2694,1080,1078,566,629,1409,4984,242,3.049,2.746,2.479,2.597,2.346,2.378,2.272,2.571,2.671,2.823,2.528,2.084,2.665,2.876,2.485,2.482,2.743,3.503,2.762,2.072,2.948,2.444,2.372,2.569,2.621,2.617,2.318,2.025,2.579,2.649,2.46,2.714,2.345,2.856,3.106,2.697,2.546,2.983,1.97,2.156,2.431,1.867,3.132,1.867,1.971,2.188,2.532,2.975,2.66,2.345,1.968,2.349,2.36,2.573,2.398,2.139,2.292,2.251,1.995,2.105,1.916,2.753,1.766,2.347,2.294,1.764,2.109,2.329,2.521,2.574,2.181,2.603,2.38,2.31,1.001,0.829,0.515,0.533,0.694,0.668,0.516,0.613,0.462,0.613,0.778,0.515,0.412,0.535,0.464,0.566,0.724,1.124,0.537,0.661,0.679,0.601,0.523,0.798,0.619,0.602,0.613,0.555,0.674,0.508,0.739,0.887,0.452,0.612,0.813,0.542,0.897,0.73,0.542,0.356,0.499,0.537,0.943,0.562,0.576,0.441,0.438,0.816,0.522,0.495,0.431,0.59,0.499,0.556,0.557,0.354,0.442,0.445,0.386,0.38,0.39,0.653,0.405,0.673,0.453,0.611,0.437,0.436,0.372,0.801,0.53,0.666,0.468,0.484
-340,Epileptic,F,52.0,0.8,1.6,1.0,1.2,1.5,2.2,1.8,1.4,1.4,0.6,0.2,2.8,1.5,0.4,0.9,5.5,8.0,0.4,2.1,4.6,1.8,2.7,1.6,2.7,3.4,2.9,3.1,2.6,2.2,3.0,1.4,1.1,0.4,2.5,0.3,0.9,2.6,3.1,0.1,0.1,1.0,2.9,1.2,2.2,2.8,0.6,0.4,1.1,1.1,0.9,0.8,1.9,1.8,1.7,0.6,2.6,1.1,2.4,1.0,1.2,0.7,1.6,0.2,0.5,1.8,0.6,2.4,1.2,1.2,0.9,1.1,1.3,5.3,0.5,7,14,13,8,11,16,14,13,9,5,2.0,21,14,3,7,45,57,3,21,38,10,25,15,26,37,31,35,20,18,29,14,8,4,22,2,6,23,28,1,1,7,21,12,16,18,5,2,8,5,6,5,15,12,11,3,19,7,15,7,10,5,10,1,4,14,9,22,9,6,6,6,10,49,5,0.046,0.03,0.025,0.029,0.036,0.038,0.034,0.029,0.055,0.031,0.027,0.034,0.035,0.026,0.03,0.039,0.038,0.022,0.033,0.038,0.029,0.03,0.03,0.038,0.033,0.031,0.029,0.029,0.023,0.03,0.058,0.07,0.018,0.034,0.019,0.025,0.03,0.029,0.014,0.018,0.022,0.036,0.026,0.035,0.025,0.018,0.022,0.018,0.019,0.031,0.03,0.028,0.032,0.023,0.018,0.022,0.025,0.024,0.032,0.022,0.024,0.031,0.019,0.024,0.024,0.028,0.02,0.021,0.026,0.031,0.029,0.026,0.022,0.029,993,2626,1579,1521,1961,2607,1912,2706,1444,881,429.0,2190,2635,805,1648,8246,12491,1700,3505,4085,2525,3999,2036,4551,5248,4796,4835,3005,4673,4558,1424,763,1006,4422,987,1535,4678,6013,208,275,1346,2730,2759,1626,2554,937,635,1966,1756,1193,579,2237,2046,2716,709,3374,1554,3183,848,1381,892,1990,279,795,2090,511,3362,1912,1332,1061,1174,1727,8333,322,0.136,0.111,0.124,0.118,0.133,0.147,0.114,0.13,0.151,0.129,0.085,0.137,0.14,0.12,0.126,0.134,0.129,0.098,0.123,0.149,0.093,0.135,0.132,0.129,0.137,0.135,0.129,0.116,0.107,0.127,0.155,0.136,0.081,0.132,0.078,0.107,0.113,0.121,0.122,0.101,0.113,0.134,0.108,0.132,0.114,0.1,0.11,0.094,0.097,0.108,0.129,0.122,0.125,0.105,0.113,0.111,0.116,0.111,0.134,0.113,0.117,0.129,0.12,0.119,0.122,0.109,0.108,0.11,0.114,0.131,0.122,0.119,0.111,0.14,331,966,691,692,761,988,773,865,408,311,161.0,1228,756,204,488,2487,3552,329,1011,1927,892,1402,943,1299,1832,1614,1847,1276,1431,1631,470,300,338,1205,296,571,1417,1719,109,154,706,1184,738,1137,1810,541,268,936,839,499,425,1169,930,1240,394,1882,725,1579,540,859,454,798,145,288,1158,333,1938,945,699,460,603,897,3978,244,3.005,2.714,2.273,2.29,2.466,2.323,2.206,2.83,2.608,2.555,2.344,1.645,2.727,2.848,2.447,2.581,2.915,3.922,2.718,1.93,2.463,2.36,1.931,2.705,2.316,2.458,2.143,1.95,2.57,2.334,2.357,2.542,2.419,2.703,2.858,2.413,2.572,2.654,2.136,1.983,2.442,2.03,2.726,1.684,1.643,1.877,2.85,2.579,2.539,2.52,1.703,2.007,2.385,2.352,2.332,1.974,2.454,2.383,1.948,1.769,2.228,2.693,1.99,2.786,2.056,1.867,1.937,2.349,2.319,2.6,2.31,2.288,2.271,1.497,0.989,0.657,0.418,0.396,0.547,0.629,0.55,0.702,0.724,0.455,0.64,0.44,0.501,0.429,0.558,0.701,0.604,0.747,0.631,0.535,0.757,0.497,0.531,0.701,0.492,0.491,0.576,0.469,0.489,0.513,0.551,0.985,0.482,0.631,0.666,0.526,0.766,0.532,0.263,0.321,0.526,0.583,0.885,0.61,0.411,0.429,0.731,0.75,0.525,0.856,0.324,0.462,0.567,0.487,0.362,0.432,0.656,0.635,0.309,0.364,0.51,0.711,0.49,0.673,0.548,0.628,0.407,0.501,0.396,0.609,0.491,0.579,0.543,0.451
-341,Epileptic,M,37.0,0.7,3.4,1.1,2.0,2.7,1.7,2.1,2.9,1.2,0.8,0.4,1.6,1.9,0.7,1.6,14.5,12.3,1.1,3.7,5.0,1.4,3.4,2.3,5.3,4.7,7.2,5.8,6.3,8.6,4.7,2.0,1.6,0.7,3.4,0.6,1.2,4.3,3.4,0.3,0.4,1.5,3.1,3.5,3.9,4.0,1.3,0.7,1.3,2.3,1.0,0.8,3.0,2.3,3.2,0.9,1.9,1.2,2.2,0.6,2.1,0.7,2.8,0.4,0.7,1.8,1.6,4.4,2.1,1.6,0.7,1.1,1.9,5.5,0.5,8,28,7,13,18,17,15,30,15,9,4.0,19,26,8,15,112,98,10,33,49,10,39,24,46,50,63,55,46,53,44,19,15,6,27,3,10,42,41,2,2,8,31,40,27,20,7,3,7,11,7,4,20,16,21,7,13,7,19,8,17,6,19,2,5,19,20,31,12,8,6,7,12,39,3,0.034,0.027,0.019,0.03,0.042,0.028,0.034,0.036,0.054,0.045,0.036,0.03,0.033,0.041,0.03,0.048,0.038,0.044,0.04,0.037,0.028,0.032,0.031,0.048,0.04,0.045,0.044,0.045,0.053,0.039,0.046,0.067,0.042,0.033,0.017,0.027,0.038,0.035,0.019,0.02,0.025,0.034,0.057,0.033,0.026,0.025,0.02,0.018,0.024,0.03,0.027,0.023,0.03,0.022,0.036,0.017,0.021,0.024,0.017,0.026,0.023,0.036,0.021,0.027,0.025,0.036,0.026,0.025,0.025,0.02,0.023,0.022,0.019,0.022,1645,5718,2636,2710,2412,2387,3140,4302,1753,1497,614.0,2404,4467,1520,3157,13068,20958,2504,5567,5076,3635,4994,2891,7085,6545,7505,5521,5367,6671,6556,2762,1233,1103,6507,2023,2686,8391,8191,533,671,1810,2468,5223,3105,3916,1728,1290,2395,3090,1769,641,4331,2590,5096,868,3035,1766,3005,1220,2021,991,3440,465,916,2639,1482,5438,3335,2330,1427,1650,2553,9466,753,0.121,0.12,0.097,0.121,0.142,0.129,0.108,0.133,0.151,0.151,0.132,0.135,0.134,0.143,0.124,0.14,0.131,0.138,0.144,0.148,0.101,0.121,0.117,0.147,0.133,0.15,0.144,0.129,0.132,0.15,0.153,0.135,0.122,0.124,0.084,0.107,0.141,0.132,0.102,0.109,0.119,0.132,0.168,0.137,0.111,0.117,0.101,0.099,0.11,0.125,0.127,0.112,0.127,0.105,0.13,0.098,0.104,0.119,0.102,0.13,0.117,0.136,0.107,0.127,0.123,0.144,0.117,0.106,0.106,0.118,0.114,0.114,0.1,0.12,472,1928,910,1036,1047,1052,1051,1425,521,389,194.0,1004,1105,384,866,4518,5687,515,1635,2246,893,1567,1107,2117,1973,2665,2197,2097,2028,2099,732,511,331,1571,529,843,1950,1759,237,323,876,1450,1226,1858,2356,760,508,1043,1406,633,414,2070,1340,2364,444,1631,890,1513,679,1134,531,1293,277,415,1313,724,2663,1393,1022,543,761,1269,4373,324,3.275,2.563,2.89,2.478,2.216,2.002,2.472,2.676,2.474,3.135,2.787,2.0,2.969,2.928,2.773,2.428,2.871,3.728,2.701,1.988,2.903,2.421,2.229,2.686,2.545,2.431,2.13,2.144,2.717,2.473,2.793,2.449,2.675,2.919,3.102,2.779,2.979,3.312,2.411,2.383,2.678,1.639,2.832,1.934,1.926,2.463,2.926,2.889,2.585,2.757,1.807,2.37,2.276,2.352,2.159,2.022,2.295,2.13,2.018,2.023,2.082,2.751,1.878,2.489,2.033,2.386,2.239,2.627,2.481,2.639,2.516,2.41,2.295,2.819,0.717,0.685,0.48,0.582,0.486,0.473,0.73,0.557,0.596,0.391,0.587,0.429,0.522,0.483,0.661,0.696,0.671,0.781,0.565,0.562,0.701,0.607,0.534,0.705,0.612,0.635,0.592,0.59,0.744,0.611,0.674,0.721,0.489,0.842,0.703,0.503,0.792,0.775,0.419,0.473,0.512,0.432,0.893,0.549,0.568,0.525,0.766,0.844,0.564,0.582,0.337,0.469,0.479,0.454,0.425,0.405,0.461,0.533,0.427,0.429,0.374,0.711,0.411,0.56,0.508,0.686,0.458,0.481,0.462,0.865,0.421,0.519,0.481,0.429
-343,Epileptic,F,27.0,1.0,2.5,1.0,1.3,1.3,1.7,1.7,1.9,1.2,0.9,0.2,4.4,1.4,0.4,1.0,6.7,8.5,1.1,1.9,4.2,2.3,4.4,2.8,4.2,3.8,2.9,5.0,2.6,2.3,3.3,1.5,1.0,0.6,2.7,0.7,0.4,6.2,3.0,0.2,0.1,0.9,5.5,3.2,3.4,3.0,0.7,0.6,0.9,1.3,0.9,0.6,2.1,1.6,3.0,0.2,2.7,0.7,1.4,1.4,1.2,0.8,1.4,0.5,0.5,2.0,1.2,2.3,1.1,1.2,0.6,1.3,1.9,4.8,0.5,7,23,7,12,13,18,16,18,16,14,5.0,40,17,5,13,88,89,8,27,48,21,56,35,42,47,34,51,35,24,47,15,6,9,34,6,4,64,46,2,1,5,51,33,26,18,5,4,6,8,7,4,20,11,22,1,20,5,12,10,9,8,13,4,5,18,18,19,7,6,5,10,14,49,4,0.038,0.024,0.016,0.021,0.033,0.028,0.027,0.027,0.036,0.04,0.026,0.048,0.027,0.024,0.024,0.032,0.029,0.04,0.028,0.037,0.032,0.042,0.048,0.04,0.035,0.032,0.035,0.037,0.024,0.028,0.042,0.047,0.034,0.035,0.023,0.011,0.051,0.034,0.012,0.015,0.022,0.067,0.044,0.028,0.019,0.012,0.027,0.011,0.017,0.015,0.029,0.02,0.023,0.019,0.028,0.017,0.016,0.016,0.028,0.019,0.019,0.025,0.027,0.025,0.022,0.02,0.02,0.016,0.015,0.018,0.024,0.019,0.019,0.022,2000,5320,2979,2945,2437,2931,3610,4138,2213,2234,897.0,3573,4152,1339,2735,14630,21610,2498,5159,6036,6021,5865,2385,6855,7997,5538,8507,4556,7002,7839,2252,1136,1315,7123,2125,1455,8536,7508,438,463,1311,1873,7007,4530,4364,1733,1061,2797,2646,2375,535,4116,2373,5776,253,4648,1365,3579,1429,1872,1358,2827,856,1276,3315,1665,3849,2212,2143,1180,1915,2691,10074,879,0.126,0.119,0.087,0.111,0.131,0.126,0.105,0.127,0.156,0.144,0.115,0.168,0.13,0.118,0.119,0.14,0.129,0.123,0.131,0.153,0.111,0.147,0.155,0.146,0.134,0.136,0.127,0.118,0.103,0.132,0.142,0.112,0.153,0.137,0.11,0.08,0.151,0.128,0.087,0.082,0.105,0.161,0.153,0.123,0.098,0.085,0.13,0.077,0.092,0.089,0.121,0.102,0.11,0.103,0.15,0.099,0.109,0.095,0.126,0.103,0.113,0.119,0.145,0.113,0.114,0.101,0.108,0.097,0.091,0.113,0.119,0.108,0.103,0.124,456,1847,995,1058,825,1152,1251,1183,619,566,252.0,1623,1012,291,769,4126,5679,526,1390,2482,1372,2033,1066,1851,2385,1757,2778,1599,1802,2446,730,472,368,1546,498,528,2368,2059,292,207,647,1138,1533,2314,2344,884,376,1098,1173,996,327,1906,1184,2379,81,2379,652,1421,845,969,692,1025,309,462,1507,1080,1883,981,1014,544,907,1400,4394,362,3.714,2.739,2.845,2.653,2.419,2.263,2.395,3.035,2.605,2.974,2.9,1.919,3.011,2.961,2.629,2.7,2.927,3.574,2.894,2.096,3.363,2.331,1.92,2.813,2.62,2.627,2.435,2.254,2.872,2.564,2.448,2.374,2.824,3.257,3.784,2.276,2.68,2.729,1.799,2.453,2.661,1.427,3.254,2.154,2.121,2.002,3.324,3.074,2.759,2.673,2.019,2.205,2.224,2.479,3.167,2.109,2.35,2.611,1.963,2.085,2.124,2.835,2.634,2.994,2.294,1.77,2.123,2.562,2.438,2.243,2.31,2.166,2.388,2.664,0.996,0.704,0.522,0.577,0.664,0.541,0.669,0.549,0.647,0.622,0.679,0.486,0.641,0.551,0.635,0.601,0.654,0.701,0.515,0.6,0.868,0.716,0.666,0.876,0.667,0.582,0.613,0.642,0.601,0.518,0.821,0.95,0.435,0.721,0.631,0.563,0.975,0.682,0.456,0.305,0.472,0.655,0.947,0.591,0.644,0.49,0.623,0.788,0.485,0.495,0.424,0.501,0.492,0.462,0.351,0.451,0.454,0.629,0.37,0.388,0.37,0.774,0.656,1.197,0.453,0.676,0.564,0.392,0.483,0.737,0.463,0.509,0.463,0.49
-344,Epileptic,M,47.0,1.1,2.7,0.8,1.3,1.8,2.5,1.6,1.9,1.8,0.6,0.3,2.9,1.4,0.4,1.2,5.8,9.5,0.8,2.4,4.3,1.5,3.1,1.7,4.3,2.6,3.6,2.1,2.3,3.2,2.9,1.9,1.0,0.6,2.4,0.5,1.2,3.0,2.8,0.2,0.3,1.4,3.1,1.9,2.4,3.2,0.8,0.6,1.2,1.1,0.8,0.9,2.7,1.1,3.2,0.4,2.1,0.7,1.6,1.3,1.1,0.7,1.7,0.4,0.4,1.4,1.6,2.1,1.7,1.1,0.5,0.7,1.6,5.4,0.4,8,25,8,10,19,23,16,18,20,7,3.0,30,16,7,13,66,87,9,25,39,16,37,19,48,34,44,29,22,28,37,25,8,4,29,3,12,37,36,2,1,10,31,24,22,17,5,3,7,7,9,5,19,8,18,2,15,5,14,10,8,6,13,2,4,14,19,18,13,5,4,6,10,48,5,0.042,0.025,0.017,0.021,0.029,0.031,0.025,0.027,0.048,0.03,0.027,0.028,0.024,0.026,0.027,0.03,0.028,0.026,0.026,0.032,0.02,0.034,0.029,0.034,0.028,0.03,0.023,0.024,0.026,0.027,0.047,0.042,0.022,0.025,0.013,0.021,0.034,0.03,0.015,0.018,0.022,0.032,0.028,0.024,0.023,0.012,0.018,0.017,0.015,0.011,0.027,0.02,0.017,0.023,0.021,0.02,0.025,0.019,0.022,0.018,0.026,0.022,0.025,0.015,0.017,0.03,0.019,0.019,0.017,0.021,0.02,0.02,0.019,0.015,1683,5203,2123,2286,2578,4317,2624,3696,1976,1362,673.0,3392,3688,1483,2520,11822,20352,3008,5065,4456,5789,5345,2746,8264,5274,7263,4496,4027,6989,5596,2798,1285,1321,6612,2369,2721,6618,6715,319,547,1849,3409,4919,3024,3876,1669,1052,2902,2418,2638,758,4099,2043,5809,577,2883,1395,3574,1739,1596,980,3157,635,1142,2818,1377,3490,3074,1891,1108,1242,2297,11199,710,0.113,0.119,0.095,0.109,0.139,0.141,0.105,0.129,0.175,0.134,0.118,0.135,0.121,0.131,0.137,0.133,0.123,0.106,0.121,0.134,0.1,0.143,0.137,0.14,0.139,0.141,0.122,0.118,0.101,0.131,0.149,0.115,0.101,0.123,0.072,0.108,0.132,0.128,0.119,0.097,0.115,0.138,0.131,0.12,0.107,0.087,0.098,0.086,0.089,0.086,0.14,0.106,0.102,0.099,0.122,0.109,0.117,0.103,0.12,0.105,0.119,0.117,0.12,0.104,0.107,0.119,0.107,0.11,0.093,0.108,0.112,0.114,0.104,0.109,530,1807,868,952,1007,1358,940,1127,648,366,193.0,1620,1028,352,733,3345,5704,560,1483,2121,1292,1652,1056,2159,1669,2207,1517,1450,1814,1839,790,417,439,1562,626,938,1625,1499,173,210,926,1557,1183,1788,2205,812,472,1103,1125,1153,429,1986,918,2343,225,1548,535,1528,896,928,480,1243,266,487,1346,870,1737,1366,919,470,635,1089,4698,442,3.071,2.743,2.521,2.406,2.305,2.661,2.362,2.875,2.469,2.906,2.8,1.844,2.79,2.978,2.665,2.711,2.929,3.733,2.869,1.797,3.332,2.556,2.215,2.955,2.508,2.747,2.384,2.21,2.913,2.436,2.729,2.822,2.362,3.005,3.456,2.68,3.033,3.098,2.14,2.774,2.678,1.977,3.009,1.903,2.025,2.345,2.73,3.059,2.594,2.614,2.059,2.136,2.261,2.57,2.937,2.131,2.907,2.423,2.155,1.943,2.29,2.608,2.377,2.439,2.246,1.984,2.209,2.579,2.371,2.558,2.292,2.594,2.516,1.973,0.893,0.593,0.482,0.397,0.496,0.621,0.565,0.398,0.499,0.465,0.794,0.396,0.478,0.469,0.467,0.516,0.551,0.678,0.508,0.524,0.7,0.438,0.463,0.629,0.471,0.525,0.382,0.498,0.481,0.531,0.665,1.155,0.421,0.659,0.485,0.432,0.852,0.532,0.367,0.434,0.495,0.511,0.905,0.627,0.61,0.399,0.578,0.691,0.52,0.35,0.325,0.394,0.414,0.467,0.503,0.322,0.675,0.637,0.341,0.32,0.385,0.636,0.467,1.001,0.468,0.619,0.434,0.487,0.432,0.574,0.377,0.677,0.488,0.483
-345,Epileptic,F,27.0,1.1,2.2,0.5,1.5,1.0,3.4,1.3,1.8,1.3,0.7,0.3,2.7,1.6,0.6,0.8,8.1,7.8,0.8,2.5,3.8,1.1,3.8,1.3,3.9,2.7,2.5,3.0,2.5,2.1,3.1,1.1,0.5,0.5,2.4,0.3,0.4,3.3,3.3,0.2,0.1,0.9,4.0,1.6,2.1,2.4,0.5,0.4,0.9,1.1,0.5,1.0,1.5,1.1,2.7,0.1,1.5,0.9,1.9,0.9,0.9,0.7,1.2,0.4,0.4,1.4,1.1,2.2,1.1,0.8,0.3,0.7,1.2,3.3,0.3,7,21,7,15,10,24,11,16,13,6,2.0,20,13,5,8,67,62,6,23,29,6,33,14,34,29,25,36,19,18,33,16,13,3,23,1,3,28,34,2,1,5,32,12,14,13,4,2,5,5,5,6,10,6,15,1,12,7,16,6,7,5,8,2,3,10,12,14,7,4,4,5,8,25,3,0.042,0.023,0.011,0.028,0.037,0.04,0.026,0.031,0.045,0.033,0.035,0.034,0.03,0.035,0.024,0.045,0.033,0.029,0.033,0.035,0.022,0.038,0.03,0.042,0.032,0.028,0.029,0.03,0.027,0.031,0.032,0.026,0.025,0.028,0.011,0.013,0.036,0.035,0.012,0.014,0.018,0.038,0.034,0.029,0.023,0.014,0.025,0.015,0.017,0.016,0.041,0.02,0.021,0.026,0.031,0.015,0.026,0.019,0.022,0.019,0.024,0.025,0.025,0.024,0.019,0.023,0.022,0.019,0.019,0.012,0.023,0.025,0.016,0.015,1541,4895,1939,2163,1611,3460,2391,3073,2124,1092,536.0,2565,3139,1194,1732,12025,15195,2658,4505,3691,2667,5109,1829,6204,4952,5808,5393,3562,4920,5222,1809,922,970,6500,1803,1689,5156,6603,464,272,1423,3175,4473,2199,2659,1179,602,2219,2130,1423,497,2897,1865,4292,146,2987,1600,3256,1426,1416,886,2485,579,748,2306,1413,3124,2225,1531,718,961,2015,8415,445,0.13,0.115,0.086,0.139,0.122,0.139,0.105,0.135,0.168,0.139,0.114,0.135,0.128,0.13,0.117,0.152,0.127,0.109,0.123,0.126,0.085,0.158,0.13,0.151,0.138,0.125,0.133,0.111,0.111,0.136,0.114,0.086,0.105,0.131,0.057,0.069,0.118,0.123,0.102,0.096,0.1,0.143,0.118,0.12,0.106,0.084,0.11,0.079,0.091,0.089,0.152,0.1,0.103,0.109,0.146,0.093,0.111,0.105,0.106,0.109,0.121,0.114,0.129,0.13,0.101,0.115,0.105,0.104,0.107,0.106,0.122,0.114,0.09,0.096,433,1564,713,834,571,1393,797,1007,490,327,144.0,1262,821,299,551,3003,4001,463,1247,1729,732,1577,808,1498,1484,1565,1766,1243,1273,1707,571,388,349,1258,490,584,1450,1490,190,162,729,1580,845,1223,1674,595,268,934,959,517,355,1285,852,1753,64,1659,594,1491,732,774,481,786,229,342,1172,723,1600,946,638,419,496,877,3418,267,3.385,2.809,2.496,2.544,2.207,2.27,2.362,2.814,2.764,2.786,3.087,1.776,2.901,2.916,2.468,2.872,2.947,3.899,2.877,1.884,2.84,2.525,2.057,3.088,2.634,2.843,2.364,2.246,2.874,2.47,2.324,2.234,2.142,3.396,3.313,2.659,2.844,3.158,2.402,1.895,2.562,1.867,3.604,2.033,1.803,2.196,2.871,2.943,2.81,3.023,1.853,2.399,2.292,2.388,2.882,2.016,2.671,2.314,2.239,2.073,2.223,2.862,3.266,2.417,2.292,2.502,2.182,2.763,2.733,2.141,2.362,2.725,2.671,2.105,0.833,0.773,0.686,0.479,0.728,0.578,0.678,0.679,0.722,0.486,0.769,0.425,0.533,0.429,0.503,0.592,0.674,0.789,0.679,0.661,0.753,0.469,0.422,0.651,0.609,0.556,0.452,0.491,0.564,0.577,0.595,0.898,0.483,0.693,0.628,0.699,0.783,0.689,0.274,0.566,0.518,0.452,0.875,0.777,0.546,0.686,0.525,0.687,0.607,0.689,0.398,0.621,0.671,0.606,0.258,0.446,0.615,0.66,0.512,0.406,0.408,0.735,0.658,0.888,0.741,0.865,0.532,0.467,0.522,0.653,0.429,0.647,0.574,0.367
-346,Epileptic,M,27.0,0.6,3.0,1.1,1.4,2.2,2.1,1.8,2.1,1.5,0.8,0.4,2.6,1.5,0.7,1.1,7.2,9.7,0.9,2.5,4.6,1.1,4.6,1.5,5.5,8.0,3.0,4.0,2.5,3.3,2.7,1.9,2.2,0.5,2.5,0.7,0.5,3.9,3.3,0.2,0.2,1.0,3.3,2.2,2.8,2.5,0.7,0.5,0.9,1.5,1.1,0.6,2.7,1.9,2.4,0.4,2.2,0.6,1.6,1.6,1.3,0.5,2.0,0.5,0.6,2.1,1.4,2.7,1.6,0.9,0.4,0.8,1.8,5.0,0.4,5,30,11,13,18,23,16,21,23,12,4.0,30,20,9,17,77,93,9,35,44,11,46,17,54,61,34,46,26,27,53,21,15,5,28,3,5,48,44,1,1,6,43,24,22,16,6,2,5,8,10,5,21,15,21,3,17,5,14,10,9,3,15,5,5,20,19,20,12,7,4,5,17,48,4,0.028,0.026,0.016,0.023,0.041,0.026,0.028,0.025,0.044,0.038,0.031,0.034,0.027,0.032,0.03,0.033,0.03,0.032,0.034,0.035,0.026,0.038,0.029,0.047,0.051,0.026,0.034,0.026,0.03,0.029,0.043,0.057,0.028,0.026,0.014,0.015,0.03,0.027,0.014,0.016,0.023,0.038,0.032,0.026,0.019,0.015,0.019,0.015,0.019,0.018,0.02,0.022,0.025,0.018,0.014,0.015,0.013,0.016,0.022,0.019,0.019,0.026,0.024,0.018,0.02,0.02,0.018,0.019,0.017,0.012,0.02,0.021,0.015,0.018,1644,5740,2995,3016,2488,4359,3174,4236,2204,1890,805.0,3423,3906,1499,2571,14103,21738,2777,5300,5908,4244,5392,2015,7452,6171,7615,6239,4774,6697,6122,2754,1977,961,7783,3121,1709,9302,9867,364,402,1338,2992,7350,2948,3404,1610,932,2386,2734,2422,778,4544,2807,5466,742,4179,1432,3955,1913,1721,751,3163,667,1217,3756,1806,4220,2951,1786,913,1532,3209,13309,688,0.109,0.119,0.096,0.111,0.148,0.122,0.119,0.125,0.17,0.15,0.105,0.149,0.123,0.128,0.125,0.136,0.127,0.127,0.132,0.143,0.102,0.127,0.114,0.145,0.148,0.117,0.126,0.104,0.109,0.135,0.139,0.138,0.1,0.118,0.076,0.078,0.132,0.131,0.1,0.108,0.107,0.13,0.128,0.124,0.1,0.098,0.091,0.082,0.1,0.101,0.111,0.111,0.12,0.093,0.1,0.095,0.09,0.098,0.115,0.103,0.121,0.127,0.118,0.097,0.106,0.112,0.099,0.105,0.1,0.101,0.103,0.108,0.088,0.127,440,1984,1023,1025,906,1425,1059,1346,672,463,222.0,1390,1045,377,729,3867,5799,534,1510,2318,872,1894,841,2174,2322,2032,2147,1460,1661,1795,839,698,336,1605,687,584,2401,2157,186,146,706,1484,1363,1705,2030,749,400,984,1192,961,443,1940,1249,2265,432,2164,656,1705,1047,950,349,1177,352,632,1736,1128,2225,1241,822,461,718,1488,5368,307,3.473,2.69,2.863,2.742,2.337,2.542,2.454,2.765,2.468,3.054,2.96,2.184,2.817,2.906,2.733,2.751,2.895,3.935,2.786,2.176,3.453,2.319,2.016,2.643,2.313,2.843,2.323,2.461,2.984,2.683,2.487,2.778,2.367,3.379,3.948,2.616,2.868,3.204,2.19,2.929,2.397,1.84,3.55,1.965,1.868,2.355,2.908,2.976,2.908,2.559,2.024,2.393,2.276,2.544,2.097,2.136,2.557,2.672,2.133,2.031,2.341,2.736,2.105,2.373,2.279,1.812,2.081,2.648,2.437,2.269,2.443,2.487,2.566,2.737,0.73,0.554,0.425,0.522,0.746,0.573,0.582,0.513,0.69,0.726,0.703,0.516,0.549,0.492,0.529,0.647,0.683,0.7,0.599,0.661,0.743,0.685,0.553,0.826,0.662,0.623,0.637,0.646,0.567,0.518,0.661,0.884,0.449,0.676,0.694,0.503,0.784,0.619,0.259,0.444,0.458,0.612,0.883,0.612,0.557,0.379,0.521,0.533,0.456,0.671,0.409,0.531,0.452,0.422,0.317,0.403,0.372,0.476,0.448,0.411,0.48,0.701,0.505,0.748,0.494,0.767,0.469,0.529,0.434,0.462,0.423,0.505,0.473,0.424
-347,Epileptic,F,52.0,0.8,2.7,1.4,1.1,1.7,2.5,1.6,1.5,1.1,0.9,0.5,2.8,1.5,0.7,1.4,6.4,11.1,1.8,1.9,4.6,1.5,7.1,2.6,3.6,3.6,3.1,3.9,2.7,2.8,2.8,2.1,1.1,0.4,2.0,0.4,0.6,4.1,2.8,0.2,0.2,1.0,6.7,3.0,3.5,2.6,0.8,0.4,1.7,1.8,0.9,0.5,1.6,1.2,2.6,1.2,2.8,1.4,1.9,3.1,1.2,0.7,1.5,0.5,0.6,1.5,1.4,2.8,1.4,1.2,0.7,0.8,1.0,4.4,0.4,7,26,15,9,13,20,13,16,13,9,3.0,27,18,8,17,67,82,11,19,40,12,52,20,39,38,31,33,20,22,33,22,7,6,23,2,5,34,31,1,2,7,47,26,29,13,5,2,9,10,6,3,13,9,17,7,26,10,12,16,11,6,11,3,5,15,13,22,10,7,4,7,8,38,4,0.042,0.028,0.025,0.024,0.038,0.034,0.03,0.03,0.044,0.039,0.036,0.034,0.032,0.036,0.038,0.04,0.042,0.049,0.027,0.034,0.035,0.048,0.039,0.047,0.034,0.025,0.027,0.026,0.03,0.033,0.055,0.044,0.028,0.03,0.018,0.017,0.046,0.035,0.016,0.016,0.022,0.049,0.044,0.028,0.019,0.015,0.016,0.026,0.022,0.023,0.034,0.019,0.019,0.023,0.034,0.017,0.035,0.021,0.042,0.022,0.025,0.029,0.029,0.021,0.022,0.037,0.021,0.022,0.023,0.024,0.026,0.02,0.021,0.024,1416,4415,2205,1971,1661,2916,2678,2812,2028,1429,598.0,2724,3248,1141,2390,10618,16385,2337,3550,4551,3116,4074,1706,5075,4395,6249,4962,3682,5169,4259,2613,918,720,4660,1430,1662,6413,5966,350,508,1314,3101,5587,3016,3063,1564,749,2022,2640,1607,431,2736,1766,4114,858,4516,1968,3140,1808,1447,782,2360,511,869,2261,963,3882,1957,1460,898,1155,1616,7766,618,0.128,0.127,0.111,0.11,0.134,0.122,0.11,0.132,0.154,0.162,0.133,0.146,0.128,0.147,0.144,0.145,0.144,0.151,0.123,0.141,0.108,0.149,0.141,0.148,0.117,0.107,0.097,0.098,0.11,0.135,0.16,0.136,0.104,0.129,0.076,0.086,0.14,0.135,0.09,0.106,0.115,0.177,0.157,0.121,0.1,0.096,0.095,0.11,0.109,0.102,0.127,0.102,0.107,0.104,0.151,0.101,0.133,0.108,0.16,0.108,0.125,0.123,0.143,0.115,0.118,0.131,0.107,0.109,0.111,0.119,0.123,0.108,0.104,0.13,431,1600,920,782,745,1210,935,930,589,396,220.0,1329,922,309,729,3057,4685,528,1088,2070,739,1844,973,1541,1561,1921,1967,1411,1464,1412,744,452,234,1249,406,664,1719,1442,212,287,699,1906,1209,2025,1883,770,348,976,1235,625,250,1350,975,1937,519,2544,676,1435,1026,810,467,856,252,461,1116,580,2087,951,776,461,557,833,3618,278,3.1,2.637,2.403,2.621,1.972,2.289,2.364,2.631,2.614,2.906,2.483,1.943,2.624,2.651,2.388,2.559,2.678,3.59,2.721,1.955,3.134,2.028,1.638,2.618,2.311,2.604,2.109,2.08,2.673,2.346,2.583,2.295,2.399,2.845,3.17,2.375,2.843,2.961,1.84,1.981,2.349,1.73,3.186,1.722,1.836,2.244,2.698,2.637,2.624,2.6,2.074,2.135,1.934,2.241,2.123,1.969,2.704,2.437,2.162,1.935,2.05,2.711,2.18,2.043,2.104,1.95,2.041,2.399,2.131,2.122,2.227,2.2,2.334,2.702,0.729,0.46,0.43,0.501,0.577,0.512,0.608,0.406,0.613,0.532,0.779,0.529,0.491,0.564,0.572,0.608,0.635,0.697,0.469,0.547,0.772,0.605,0.478,0.691,0.569,0.48,0.451,0.529,0.542,0.5,0.645,0.831,0.502,0.469,0.752,0.365,0.944,0.624,0.42,0.33,0.436,0.574,0.813,0.565,0.557,0.433,0.448,0.557,0.389,0.554,0.257,0.394,0.488,0.519,0.334,0.381,0.691,0.488,0.548,0.41,0.408,0.614,0.555,0.591,0.432,0.631,0.464,0.359,0.434,0.513,0.412,0.452,0.382,0.452
-348,Epileptic,M,27.0,1.0,2.3,1.0,1.2,2.0,2.8,1.5,2.0,1.6,0.6,0.3,3.2,1.9,0.7,1.8,4.9,9.4,1.0,2.5,5.2,1.6,3.3,3.1,5.1,3.6,4.2,4.2,2.5,2.4,3.6,1.2,1.8,0.6,3.9,0.6,1.3,2.6,2.4,0.3,0.3,1.2,4.8,3.2,3.0,2.5,0.8,0.5,0.7,1.5,0.9,0.6,1.9,1.4,2.5,0.3,2.5,0.7,1.5,1.7,1.2,0.8,2.2,0.6,0.7,2.8,1.3,2.5,1.1,1.2,0.9,1.1,0.8,4.7,0.4,9,23,7,10,16,29,13,22,18,8,4.0,34,22,10,21,60,101,9,32,47,14,49,23,50,44,54,57,30,27,41,15,9,6,48,7,13,40,34,2,2,8,45,29,24,16,6,3,5,9,13,5,17,12,20,2,23,6,10,14,11,7,21,4,6,27,17,20,9,8,9,9,7,45,3,0.039,0.028,0.019,0.025,0.041,0.033,0.023,0.034,0.056,0.032,0.045,0.036,0.032,0.037,0.036,0.033,0.033,0.039,0.027,0.042,0.035,0.037,0.037,0.051,0.044,0.04,0.033,0.033,0.026,0.031,0.033,0.058,0.034,0.044,0.024,0.018,0.032,0.031,0.015,0.02,0.024,0.04,0.046,0.028,0.019,0.022,0.019,0.012,0.018,0.021,0.027,0.024,0.034,0.022,0.013,0.019,0.021,0.023,0.026,0.022,0.021,0.033,0.037,0.027,0.025,0.021,0.021,0.018,0.02,0.027,0.027,0.021,0.024,0.02,1461,4843,2258,2297,1843,4906,2778,3464,1830,1377,521.0,3028,3813,1328,2834,9466,17249,2294,4882,5226,4083,4767,2827,6444,4928,6845,6505,3514,5375,5947,2319,1240,919,5706,2253,2560,6301,5776,551,562,1602,3205,5367,2697,3248,1134,1067,1975,2728,1763,654,2965,1640,4106,448,3757,1335,2587,1599,1450,840,3082,406,1040,3794,1439,3374,2407,1676,1224,1227,1617,6955,506,0.14,0.128,0.106,0.121,0.153,0.136,0.107,0.149,0.178,0.154,0.149,0.151,0.14,0.147,0.152,0.138,0.14,0.137,0.126,0.156,0.128,0.137,0.134,0.165,0.149,0.155,0.132,0.123,0.117,0.137,0.13,0.128,0.112,0.166,0.098,0.098,0.136,0.142,0.085,0.113,0.117,0.147,0.15,0.12,0.102,0.114,0.1,0.082,0.1,0.115,0.13,0.114,0.132,0.107,0.102,0.109,0.121,0.112,0.13,0.113,0.123,0.138,0.155,0.118,0.118,0.108,0.107,0.102,0.106,0.152,0.126,0.108,0.115,0.113,437,1466,792,797,844,1372,978,1125,619,351,150.0,1502,1122,372,892,2872,5011,453,1523,2234,846,1723,1171,2037,1723,2125,2307,1447,1488,2015,657,449,307,1481,546,1104,1497,1547,319,284,809,1664,1313,1798,1944,586,413,793,1232,735,361,1387,815,1946,276,2018,514,1129,1003,768,468,1167,244,513,1872,910,1944,1020,859,528,676,690,3097,298,3.393,2.841,2.648,2.639,1.988,2.618,2.325,2.762,2.29,2.804,2.719,1.742,2.624,2.611,2.479,2.491,2.67,3.597,2.601,2.009,3.382,2.242,2.079,2.536,2.305,2.579,2.287,1.96,2.672,2.353,2.633,2.891,2.369,2.827,3.465,2.142,3.03,2.736,1.941,2.225,2.506,1.744,2.947,1.744,1.914,2.159,3.069,3.071,2.608,2.46,1.932,2.214,2.116,2.267,1.905,1.987,2.45,2.365,1.882,1.993,2.215,2.516,1.809,2.455,2.031,1.803,1.871,2.443,2.295,2.518,2.14,2.367,2.288,2.097,0.829,0.61,0.536,0.518,0.59,0.649,0.611,0.397,0.631,0.599,0.778,0.543,0.486,0.499,0.473,0.576,0.634,0.846,0.56,0.676,0.841,0.525,0.571,0.746,0.594,0.562,0.529,0.472,0.441,0.54,0.697,1.076,0.385,0.715,0.729,0.358,0.722,0.744,0.339,0.407,0.473,0.523,0.855,0.605,0.575,0.465,0.485,0.882,0.478,0.464,0.41,0.415,0.493,0.4,0.331,0.408,0.502,0.515,0.349,0.376,0.435,0.637,0.459,0.652,0.465,0.667,0.416,0.435,0.459,0.835,0.496,0.427,0.487,0.446
-349,Epileptic,M,27.0,0.6,2.5,1.0,1.0,1.2,3.2,1.6,1.8,1.0,0.9,0.2,3.3,1.7,0.7,1.2,6.1,9.9,0.8,3.3,5.4,1.5,2.8,1.4,2.6,3.5,3.6,3.8,3.0,2.2,3.1,1.5,1.2,1.0,3.1,0.5,0.5,4.3,2.7,0.2,0.3,1.1,2.9,2.6,2.9,2.5,0.7,0.6,1.1,1.3,0.9,0.7,1.8,1.2,2.0,0.3,2.8,0.9,1.8,0.5,0.9,0.8,1.4,0.2,0.4,2.1,0.8,3.6,0.7,1.1,0.4,1.4,1.9,4.6,0.2,5,21,8,8,12,27,14,16,11,10,2.0,29,20,10,12,68,107,8,34,45,10,29,18,31,48,36,43,32,23,37,24,13,10,33,3,5,45,31,2,2,5,22,27,21,17,5,3,6,7,16,5,16,8,15,2,27,6,17,5,8,7,11,2,4,17,10,30,6,9,4,9,15,43,2,0.027,0.025,0.017,0.019,0.039,0.041,0.022,0.031,0.037,0.036,0.029,0.033,0.032,0.036,0.028,0.039,0.033,0.031,0.035,0.038,0.023,0.034,0.023,0.04,0.034,0.03,0.031,0.031,0.025,0.036,0.039,0.053,0.047,0.035,0.015,0.015,0.039,0.032,0.024,0.017,0.021,0.037,0.039,0.027,0.02,0.019,0.024,0.016,0.017,0.023,0.032,0.02,0.022,0.02,0.014,0.02,0.018,0.019,0.023,0.02,0.027,0.028,0.024,0.02,0.025,0.022,0.022,0.012,0.025,0.02,0.034,0.025,0.02,0.015,1544,4898,2547,1972,1487,3774,2947,3886,1784,1459,528.0,3103,3243,1269,2662,10205,17693,2597,5472,4271,3307,3665,2076,4753,6599,6764,5300,4159,4347,5368,2207,1260,1334,7134,2161,1716,10013,6977,330,807,1762,2541,6800,3017,2801,1341,851,2442,2662,1839,534,3124,1810,4165,500,4087,2075,2908,633,1161,1176,2033,430,892,3111,1238,4919,1933,1546,954,1446,2600,10019,504,0.111,0.116,0.103,0.104,0.143,0.159,0.119,0.128,0.149,0.16,0.115,0.136,0.146,0.151,0.134,0.151,0.139,0.113,0.145,0.138,0.093,0.149,0.125,0.142,0.148,0.13,0.139,0.12,0.116,0.138,0.146,0.135,0.197,0.152,0.084,0.093,0.143,0.129,0.135,0.083,0.105,0.144,0.148,0.12,0.107,0.107,0.115,0.088,0.093,0.109,0.137,0.111,0.112,0.099,0.101,0.116,0.1,0.103,0.122,0.11,0.13,0.132,0.119,0.119,0.12,0.111,0.112,0.088,0.125,0.111,0.145,0.129,0.103,0.103,429,1614,952,802,576,1362,1037,1062,481,411,157.0,1533,924,362,703,2837,4949,493,1637,2314,961,1351,945,1279,1981,1955,2106,1631,1417,1680,677,463,365,1620,513,579,2082,1698,153,288,728,1207,1250,1752,1888,642,354,1006,1156,712,355,1378,829,1859,286,2161,748,1659,387,735,547,776,180,397,1412,542,2574,910,728,402,709,1128,3863,245,3.473,2.886,2.712,2.5,2.379,2.407,2.275,3.177,2.886,3.201,3.14,1.809,2.755,2.568,2.891,2.706,2.914,3.831,2.708,1.643,2.808,2.3,1.892,2.784,2.521,2.798,2.132,2.132,2.463,2.553,2.728,2.545,3.077,3.16,3.669,2.627,3.281,3.097,2.387,2.909,2.946,1.87,3.632,1.926,1.713,2.208,3.025,3.043,2.797,2.673,1.912,2.192,2.512,2.375,2.353,1.966,3.191,1.975,1.806,1.853,2.285,2.644,2.751,2.543,2.487,2.504,2.02,2.456,2.223,2.761,2.276,2.643,2.631,2.505,0.953,0.617,0.574,0.556,0.494,0.573,0.563,0.71,0.648,0.532,0.711,0.465,0.44,0.547,0.515,0.541,0.64,0.771,0.632,0.602,0.661,0.454,0.451,0.599,0.527,0.59,0.504,0.569,0.478,0.584,0.619,0.997,0.781,0.673,0.688,0.612,0.807,0.629,0.559,0.543,0.646,0.695,0.761,0.746,0.427,0.516,0.547,0.568,0.515,0.878,0.487,0.513,0.578,0.472,0.385,0.401,0.781,0.558,0.37,0.331,0.486,0.629,0.821,0.871,0.845,0.606,0.468,0.508,0.397,0.68,0.506,0.581,0.689,0.813
-350,Epileptic,F,42.0,0.9,2.0,0.9,0.9,2.1,2.5,1.9,1.4,1.7,0.8,0.2,3.8,2.0,1.0,2.1,8.2,10.7,0.9,2.3,6.2,0.9,3.2,2.1,6.4,3.1,3.6,3.9,3.1,3.7,3.8,1.4,0.5,0.8,2.5,0.3,0.3,4.0,3.5,0.3,0.5,0.7,3.3,2.5,4.7,4.0,0.5,0.4,0.8,1.1,0.9,0.9,2.2,1.5,3.9,0.1,3.6,1.2,2.0,1.0,1.4,0.6,1.9,0.8,0.7,2.3,1.4,2.8,1.9,1.1,0.8,1.0,1.4,3.3,0.4,6,19,7,9,14,18,12,15,11,7,3.0,27,17,13,13,68,70,6,19,46,9,27,15,129,31,31,32,23,25,35,17,4,6,24,2,2,40,32,1,1,5,24,24,34,20,4,2,6,5,7,4,13,9,21,0,25,8,14,8,10,4,11,4,4,16,18,19,14,7,5,6,13,23,4,0.036,0.027,0.016,0.021,0.051,0.034,0.031,0.028,0.07,0.032,0.023,0.043,0.039,0.062,0.05,0.051,0.039,0.03,0.032,0.041,0.014,0.035,0.033,0.063,0.035,0.038,0.033,0.035,0.033,0.033,0.05,0.025,0.03,0.031,0.012,0.011,0.036,0.037,0.027,0.041,0.019,0.041,0.051,0.039,0.026,0.012,0.023,0.013,0.016,0.021,0.036,0.022,0.028,0.033,0.028,0.026,0.031,0.021,0.025,0.024,0.026,0.028,0.035,0.025,0.025,0.033,0.024,0.022,0.022,0.034,0.024,0.02,0.016,0.021,1464,4407,2148,2212,2433,3429,2856,3087,1903,1260,499.0,2583,3424,1071,2196,10489,17097,2245,3899,5352,3639,5140,2511,6817,5253,5701,5514,4278,5699,5914,2145,748,1229,5567,1806,1155,8326,6567,437,358,1442,2694,5234,2577,3972,1224,877,2342,2226,1752,612,3166,1795,5006,92,4005,1826,3529,1144,1660,986,2438,733,1151,2779,872,3656,3056,1941,1258,1471,2264,7431,455,0.125,0.114,0.101,0.102,0.138,0.133,0.101,0.117,0.157,0.136,0.106,0.147,0.134,0.173,0.131,0.15,0.13,0.104,0.126,0.15,0.073,0.138,0.12,0.166,0.136,0.142,0.126,0.116,0.116,0.126,0.157,0.085,0.121,0.126,0.068,0.064,0.139,0.14,0.096,0.141,0.101,0.139,0.146,0.152,0.115,0.083,0.1,0.08,0.089,0.106,0.139,0.101,0.115,0.117,0.137,0.123,0.123,0.106,0.128,0.117,0.113,0.12,0.135,0.123,0.121,0.152,0.109,0.108,0.104,0.137,0.115,0.113,0.093,0.142,393,1446,790,807,800,1261,983,906,482,384,214.0,1350,956,349,685,2968,4648,472,1181,2440,943,1485,1046,1800,1615,1628,2033,1514,1815,2054,576,343,408,1274,513,475,2061,1624,201,164,649,1373,1053,1830,2356,710,318,1005,1022,703,359,1563,890,2003,41,2187,687,1683,651,919,433,996,320,423,1463,559,1896,1441,858,459,697,1047,3292,287,3.47,2.569,2.488,2.591,2.643,2.48,2.4,3.098,2.79,2.718,2.337,1.733,2.814,2.277,2.482,2.605,2.887,3.533,2.705,1.973,3.0,2.619,2.103,2.735,2.51,2.852,2.182,2.225,2.473,2.337,2.806,2.214,2.456,3.124,3.071,2.284,2.937,2.982,2.549,2.665,2.804,1.876,3.256,1.686,1.955,1.815,3.314,2.888,2.716,2.804,1.969,2.273,2.206,2.487,2.616,2.052,2.852,2.478,2.086,2.032,2.607,2.438,2.279,3.016,2.103,2.143,2.197,2.345,2.48,2.704,2.476,2.49,2.511,2.104,1.245,0.713,0.635,0.524,0.684,0.587,0.561,0.648,0.794,0.471,0.807,0.496,0.541,0.604,0.557,0.654,0.72,0.957,0.684,0.659,0.761,0.7,0.49,0.827,0.553,0.668,0.544,0.616,0.595,0.68,0.735,1.164,0.461,0.817,0.703,0.405,0.796,0.706,0.506,0.44,0.612,0.503,0.956,0.573,0.526,0.425,0.577,0.648,0.497,0.518,0.433,0.561,0.553,0.596,0.493,0.453,0.669,0.526,0.464,0.46,0.559,0.555,0.462,0.799,0.505,0.547,0.639,0.358,0.564,0.694,0.536,0.527,0.442,0.564
-351,Epileptic,M,22.0,0.6,2.0,1.0,1.2,1.9,1.5,1.6,1.7,1.7,0.6,0.3,3.6,1.1,0.5,0.7,5.8,7.0,0.8,1.6,4.7,1.3,2.9,2.1,2.8,2.5,3.4,2.7,2.3,3.5,3.1,1.6,0.4,0.4,2.7,0.7,0.3,3.1,3.7,0.1,0.1,1.0,2.6,3.3,2.4,2.2,0.5,0.3,1.1,1.1,0.8,0.8,1.5,1.4,2.6,0.1,2.3,0.4,1.1,1.1,1.4,0.7,1.3,0.2,0.5,1.5,1.2,2.6,1.3,1.1,0.3,0.6,0.7,4.7,0.2,7,17,10,10,14,13,13,14,14,6,4.0,33,10,6,7,50,61,6,22,36,14,37,20,26,28,32,31,20,26,32,14,3,5,24,4,3,27,32,2,1,6,22,22,20,14,4,2,8,6,7,6,12,12,18,1,20,3,11,7,13,6,9,1,6,12,14,21,12,9,2,4,5,38,3,0.036,0.023,0.02,0.022,0.036,0.042,0.024,0.028,0.036,0.041,0.041,0.036,0.026,0.034,0.031,0.031,0.025,0.034,0.031,0.035,0.031,0.035,0.024,0.032,0.023,0.031,0.026,0.025,0.025,0.036,0.034,0.026,0.023,0.035,0.029,0.012,0.038,0.037,0.013,0.025,0.021,0.032,0.053,0.025,0.017,0.011,0.019,0.017,0.016,0.016,0.033,0.02,0.027,0.021,0.021,0.017,0.013,0.018,0.03,0.021,0.028,0.02,0.015,0.014,0.016,0.025,0.023,0.015,0.02,0.014,0.021,0.02,0.02,0.013,1152,4611,2421,2505,2699,2055,2393,3017,1923,1232,467.0,3422,2960,1194,1759,10685,16569,2056,3378,4315,2830,5259,2563,5727,5547,6474,5193,3338,6335,5293,2052,775,694,5084,1956,1136,5657,5402,337,197,1248,2363,4140,2284,3175,1375,672,1837,2132,1465,607,2795,2200,4628,307,3662,1229,2162,919,1716,894,2973,287,936,2776,1163,3352,3032,1531,960,1283,1243,8736,485,0.127,0.118,0.111,0.107,0.137,0.154,0.102,0.13,0.153,0.136,0.15,0.145,0.12,0.147,0.133,0.137,0.118,0.116,0.142,0.14,0.112,0.138,0.113,0.122,0.119,0.128,0.119,0.11,0.104,0.15,0.118,0.083,0.114,0.133,0.109,0.073,0.133,0.135,0.1,0.146,0.111,0.135,0.153,0.118,0.092,0.081,0.095,0.103,0.092,0.094,0.146,0.108,0.119,0.105,0.111,0.097,0.083,0.106,0.126,0.114,0.124,0.102,0.123,0.101,0.099,0.12,0.115,0.094,0.107,0.101,0.112,0.112,0.097,0.105,348,1372,845,919,890,726,979,984,688,303,166.0,1609,754,249,428,2860,4367,392,1083,2053,919,1500,1265,1475,1586,1968,1746,1345,1983,1549,737,370,263,1220,440,472,1398,1550,168,76,669,1229,1125,1476,1983,690,261,858,1026,740,359,1189,1009,1967,131,2095,580,1113,586,1061,426,1002,146,551,1393,702,1809,1377,877,331,516,583,3910,236,3.116,2.968,2.827,2.508,2.569,2.415,2.106,2.671,2.263,3.108,2.449,1.874,2.911,3.168,2.96,2.852,2.998,3.748,2.592,1.868,2.615,2.484,1.815,2.863,2.653,2.701,2.298,2.033,2.593,2.659,2.152,2.009,2.074,2.825,3.515,2.216,2.94,2.522,2.255,3.037,2.322,1.782,2.676,1.748,1.81,2.117,2.857,2.659,2.515,1.97,1.866,2.316,2.166,2.416,2.512,1.881,2.315,2.076,1.794,1.747,2.246,2.741,2.191,2.01,2.296,2.164,1.976,2.345,1.997,3.194,2.643,2.237,2.259,2.43,0.72,0.778,0.634,0.578,0.756,0.714,0.551,0.528,0.605,0.493,0.859,0.476,0.576,0.441,0.691,0.632,0.688,0.83,0.655,0.501,0.921,0.675,0.556,1.049,0.713,0.612,0.551,0.53,0.734,0.712,1.071,0.925,0.428,0.733,0.859,0.455,0.868,0.751,0.426,0.523,0.567,0.481,0.953,0.527,0.587,0.562,0.814,0.655,0.503,0.694,0.438,0.526,0.55,0.577,0.674,0.492,0.721,0.527,0.476,0.489,0.623,0.841,0.477,0.641,0.647,0.605,0.526,0.477,0.501,0.64,0.709,0.66,0.618,0.456
-352,Epileptic,M,57.0,1.5,2.8,1.2,1.1,1.2,1.6,1.3,1.5,1.3,0.7,0.4,2.6,1.6,0.5,1.3,4.7,7.3,0.8,2.1,4.7,1.3,2.9,1.9,3.1,4.6,3.7,4.7,4.4,3.9,2.9,1.4,0.9,0.8,2.5,0.5,0.6,3.0,2.6,0.3,0.3,1.2,3.0,3.1,2.8,2.4,0.7,0.4,1.0,1.4,0.6,0.5,2.2,0.6,2.3,0.2,3.3,0.7,1.6,1.2,0.9,0.7,1.7,0.3,1.0,1.6,1.6,2.6,1.2,1.4,0.6,0.8,1.8,3.8,0.3,9,23,10,7,12,16,12,14,13,6,3.0,25,16,6,12,51,65,6,16,40,8,29,15,31,36,38,43,37,29,28,18,7,8,25,2,6,32,28,2,2,7,27,26,21,14,5,2,5,6,5,3,16,4,18,1,26,5,13,9,8,6,12,2,6,11,19,21,9,8,6,5,13,30,4,0.052,0.032,0.027,0.025,0.025,0.034,0.027,0.026,0.048,0.039,0.04,0.035,0.032,0.038,0.037,0.03,0.031,0.033,0.037,0.041,0.028,0.03,0.034,0.035,0.045,0.034,0.033,0.038,0.035,0.032,0.042,0.046,0.04,0.036,0.022,0.014,0.035,0.03,0.019,0.017,0.025,0.037,0.049,0.028,0.017,0.017,0.017,0.02,0.019,0.016,0.024,0.027,0.021,0.023,0.017,0.019,0.025,0.02,0.024,0.023,0.022,0.032,0.024,0.043,0.022,0.038,0.019,0.021,0.019,0.019,0.026,0.027,0.017,0.02,1228,4103,2223,1856,2419,2265,2109,3013,1813,1248,450.0,2216,3045,1027,2596,8470,15211,1806,3433,4314,2767,3271,1558,5545,3725,6210,5586,4447,5458,4829,2668,986,1184,5407,1413,1587,6471,5671,367,496,1258,2276,7003,2147,3834,1344,802,1780,2246,1287,451,2822,1141,3931,350,4242,921,2406,1114,1208,683,2259,406,785,2101,1248,4165,2419,2202,937,1048,2285,7492,332,0.167,0.135,0.11,0.109,0.117,0.135,0.097,0.12,0.166,0.144,0.14,0.142,0.14,0.138,0.138,0.12,0.121,0.119,0.139,0.15,0.097,0.102,0.124,0.143,0.116,0.119,0.103,0.12,0.111,0.136,0.155,0.134,0.128,0.143,0.093,0.086,0.143,0.141,0.122,0.119,0.113,0.14,0.172,0.121,0.089,0.093,0.102,0.096,0.094,0.098,0.128,0.122,0.103,0.111,0.089,0.106,0.127,0.111,0.113,0.103,0.12,0.125,0.124,0.161,0.109,0.136,0.101,0.102,0.096,0.138,0.12,0.123,0.096,0.132,428,1465,755,736,905,837,791,1007,525,334,149.0,1219,839,244,667,2613,3927,407,938,2061,734,1189,859,1544,1380,1979,2065,1658,1456,1479,669,448,349,1209,393,671,1495,1458,204,193,713,1319,1311,1529,2136,692,325,775,1070,577,284,1313,574,1630,196,2475,424,1244,766,710,446,900,225,386,1106,649,2136,914,1036,455,547,1074,3663,239,3.048,2.675,2.796,2.508,2.323,2.33,2.35,2.674,2.622,2.754,2.352,1.64,2.829,2.832,2.92,2.506,3.013,3.483,2.759,1.807,2.663,2.229,1.701,2.889,2.259,2.496,2.199,2.191,2.902,2.609,2.869,2.281,2.548,3.1,3.281,2.285,3.058,2.818,2.103,2.436,2.113,1.607,3.538,1.634,2.012,2.051,2.925,2.824,2.535,2.446,1.751,2.188,2.089,2.488,1.939,1.912,2.515,2.222,1.72,1.902,1.794,2.594,2.004,2.358,2.102,2.362,2.087,2.558,2.483,2.19,2.117,2.418,2.172,1.81,0.649,0.579,0.71,0.53,0.68,0.629,0.567,0.542,0.623,0.684,0.96,0.44,0.503,0.612,0.565,0.706,0.6,0.652,0.735,0.568,0.722,0.624,0.546,0.617,0.619,0.563,0.628,0.621,0.572,0.566,0.546,0.932,0.432,0.661,0.587,0.497,0.676,0.686,0.346,0.488,0.45,0.483,0.719,0.511,0.641,0.559,0.563,0.785,0.557,0.497,0.451,0.514,0.525,0.515,0.451,0.507,0.38,0.573,0.419,0.551,0.431,0.734,0.396,0.625,0.603,0.903,0.468,0.586,0.493,0.572,0.588,0.485,0.488,0.436
-353,Epileptic,F,52.0,1.3,1.6,0.6,0.8,1.6,1.0,1.4,1.2,0.7,0.6,0.2,3.1,1.4,0.5,0.8,4.3,6.5,0.5,2.0,5.2,1.4,2.4,2.2,2.8,2.7,3.5,3.7,3.3,3.1,2.9,1.2,1.0,0.4,2.3,0.3,0.8,2.7,2.4,0.1,0.1,1.2,2.9,2.5,3.1,2.5,0.6,0.3,1.1,1.3,0.9,0.3,1.7,0.9,2.1,0.7,2.1,0.5,1.9,0.7,1.5,0.2,1.5,0.2,1.0,1.4,0.5,2.2,0.9,0.9,0.3,1.1,1.1,3.8,0.2,7,14,6,6,14,10,11,11,8,4,2.0,27,14,5,9,43,59,3,18,39,10,19,21,27,23,34,38,26,24,29,11,6,3,21,2,8,26,25,2,1,8,22,23,24,16,6,1,7,7,6,2,13,10,13,3,17,5,14,6,9,2,11,2,8,13,5,18,9,7,3,9,7,30,1,0.069,0.026,0.018,0.02,0.04,0.024,0.026,0.031,0.044,0.033,0.021,0.042,0.033,0.035,0.032,0.044,0.031,0.027,0.037,0.042,0.035,0.041,0.03,0.044,0.038,0.034,0.03,0.035,0.032,0.034,0.043,0.052,0.032,0.037,0.025,0.023,0.047,0.035,0.014,0.017,0.028,0.038,0.066,0.032,0.021,0.019,0.02,0.024,0.023,0.035,0.031,0.022,0.027,0.027,0.058,0.021,0.016,0.03,0.021,0.026,0.024,0.03,0.028,0.043,0.023,0.024,0.022,0.015,0.025,0.016,0.031,0.031,0.026,0.015,896,2805,1340,1505,1920,1259,1697,2120,1058,935,504.0,2151,1995,857,1361,6269,12099,1192,2940,3797,1730,2278,2054,3461,2474,4544,3911,3118,3985,3806,1667,527,462,3577,761,1364,3405,4043,237,252,1038,1597,3856,2184,2614,887,492,1258,1746,927,248,2452,1203,2709,345,2783,650,2340,617,1474,415,1811,317,673,1893,466,2916,2004,1329,641,1120,1279,4755,278,0.16,0.126,0.112,0.107,0.169,0.121,0.098,0.118,0.152,0.132,0.097,0.153,0.138,0.149,0.137,0.154,0.132,0.109,0.159,0.17,0.131,0.125,0.133,0.146,0.123,0.132,0.118,0.126,0.104,0.148,0.147,0.152,0.112,0.134,0.101,0.112,0.152,0.14,0.116,0.114,0.133,0.139,0.184,0.139,0.105,0.115,0.097,0.107,0.109,0.133,0.108,0.118,0.142,0.119,0.171,0.109,0.115,0.134,0.117,0.119,0.098,0.132,0.137,0.144,0.12,0.118,0.109,0.097,0.114,0.106,0.137,0.135,0.122,0.099,289,989,515,613,673,632,691,685,341,289,183.0,1129,739,239,443,1855,3413,287,935,1879,599,841,1042,1146,922,1584,1716,1380,1307,1324,508,332,182,945,235,552,1079,1058,154,128,612,1066,812,1587,1764,511,214,655,863,422,170,1196,581,1312,195,1665,426,1077,466,802,187,732,165,399,1046,277,1560,924,631,279,604,619,2326,186,2.778,2.601,2.618,2.333,2.356,1.821,2.078,2.586,2.342,2.682,2.499,1.71,2.319,2.604,2.395,2.529,2.818,3.249,2.466,1.819,2.36,2.196,1.866,2.473,2.268,2.428,1.943,2.024,2.452,2.282,2.428,1.979,2.117,2.692,2.592,2.345,2.457,2.737,1.639,2.08,2.178,1.41,3.071,1.659,1.657,1.889,2.731,2.163,2.482,2.497,1.625,2.22,2.043,2.192,2.103,1.851,1.755,2.218,1.675,2.077,2.141,2.696,2.016,2.082,1.893,1.842,1.988,2.308,2.205,2.324,1.98,2.427,2.218,1.566,0.767,0.71,0.541,0.547,0.655,0.571,0.527,0.564,0.565,0.518,0.786,0.528,0.531,0.413,0.461,0.635,0.643,0.714,0.669,0.52,0.768,0.614,0.509,0.804,0.59,0.59,0.553,0.529,0.512,0.555,0.81,0.588,0.438,0.592,0.586,0.589,0.728,0.75,0.337,0.334,0.57,0.649,0.743,0.473,0.485,0.505,0.377,0.961,0.432,0.499,0.263,0.448,0.475,0.528,0.458,0.486,0.357,0.565,0.304,0.536,0.512,0.686,0.371,0.591,0.59,0.431,0.471,0.378,0.43,0.859,0.549,0.618,0.481,0.34
-354,Epileptic,F,27.0,0.8,2.0,1.2,1.4,1.6,1.5,1.2,2.2,0.9,1.1,0.6,3.2,1.5,0.5,1.0,6.0,8.2,0.8,2.4,5.1,2.1,5.3,3.8,3.5,5.9,4.3,4.2,2.5,2.5,3.5,1.1,1.2,0.5,2.3,0.3,0.5,3.0,3.5,0.2,0.2,1.1,4.7,2.7,2.6,2.8,0.5,0.7,0.9,1.3,0.6,0.6,1.3,1.3,2.0,0.4,2.8,1.0,2.3,2.7,1.5,0.3,2.2,0.4,0.5,2.1,1.0,1.8,1.3,1.1,1.1,1.3,1.2,4.6,0.3,7,15,14,16,17,17,13,22,12,14,6.0,30,18,6,12,76,91,7,29,48,13,47,32,42,47,36,54,28,27,49,18,6,8,44,2,5,47,39,1,1,7,42,28,22,20,5,3,7,8,6,6,11,10,18,2,25,8,16,21,14,3,16,3,5,19,16,18,11,8,14,8,10,34,3,0.036,0.024,0.026,0.032,0.035,0.024,0.026,0.029,0.037,0.045,0.047,0.036,0.034,0.027,0.029,0.036,0.031,0.032,0.032,0.039,0.05,0.044,0.051,0.04,0.05,0.039,0.033,0.029,0.027,0.034,0.035,0.053,0.034,0.041,0.018,0.014,0.035,0.036,0.017,0.026,0.023,0.052,0.056,0.025,0.021,0.014,0.025,0.016,0.016,0.023,0.029,0.02,0.024,0.017,0.029,0.02,0.025,0.027,0.035,0.022,0.014,0.034,0.029,0.023,0.025,0.025,0.016,0.018,0.018,0.035,0.029,0.021,0.022,0.021,1298,3983,2313,2182,2131,3187,3022,3531,1522,1556,629.0,2957,3190,1128,1807,9781,16753,2103,4243,4546,2656,3602,2741,6015,5313,5434,6096,4536,5917,6184,2160,753,830,5386,1372,1659,6274,6935,366,398,1372,2169,6483,2335,3360,1206,794,1893,2478,1161,502,2313,1834,4355,413,3811,1243,3202,1497,1850,568,2755,369,789,2596,1022,3502,2549,1855,1492,1538,1886,7515,390,0.122,0.116,0.132,0.133,0.144,0.1,0.12,0.128,0.161,0.166,0.167,0.155,0.139,0.134,0.15,0.152,0.137,0.122,0.143,0.151,0.121,0.137,0.147,0.148,0.154,0.138,0.128,0.121,0.116,0.149,0.135,0.134,0.132,0.149,0.082,0.085,0.145,0.152,0.099,0.098,0.112,0.167,0.191,0.126,0.107,0.089,0.132,0.1,0.097,0.129,0.141,0.106,0.117,0.103,0.166,0.12,0.125,0.125,0.147,0.12,0.095,0.147,0.129,0.127,0.122,0.131,0.107,0.103,0.108,0.153,0.128,0.118,0.107,0.132,355,1327,811,780,794,978,835,1270,506,406,211.0,1429,899,274,558,3042,4674,440,1206,2234,657,1694,1175,1637,1855,1697,2177,1465,1604,1807,586,384,263,1203,358,558,1675,1564,194,155,720,1419,995,1631,2015,608,363,802,1175,425,342,1118,884,1958,183,2033,595,1363,1076,1062,326,1007,237,409,1319,665,1662,1124,858,547,735,862,3250,189,3.333,2.837,2.712,2.668,2.323,2.557,2.687,2.499,2.384,2.832,2.394,1.819,2.746,2.796,2.399,2.51,2.802,3.378,2.781,1.839,2.904,1.92,2.035,2.752,2.272,2.691,2.241,2.332,2.727,2.536,2.658,2.186,2.612,2.998,3.079,2.539,2.815,3.04,2.125,2.38,2.365,1.578,3.758,1.692,1.879,2.146,2.69,2.82,2.609,2.949,1.928,2.153,2.178,2.364,2.718,2.094,2.442,2.562,1.698,1.901,2.067,2.643,1.886,2.202,2.208,1.864,2.222,2.481,2.448,2.751,2.411,2.386,2.466,2.616,0.821,0.662,0.435,0.55,0.589,0.704,0.744,0.397,0.513,0.712,0.853,0.572,0.484,0.371,0.577,0.524,0.615,0.823,0.63,0.559,0.891,0.575,0.591,0.665,0.75,0.519,0.626,0.575,0.462,0.698,0.575,0.74,0.328,0.631,0.866,0.58,0.662,0.666,0.353,0.491,0.478,0.476,0.81,0.515,0.559,0.424,0.414,0.597,0.399,0.61,0.326,0.432,0.528,0.379,0.425,0.437,0.457,0.505,0.389,0.414,0.357,0.77,0.433,0.699,0.672,0.561,0.487,0.37,0.374,0.834,0.418,0.568,0.485,0.372
-355,Epileptic,F,22.0,0.9,2.0,0.5,1.1,1.7,1.7,1.3,1.5,1.2,0.6,0.4,2.5,1.3,0.6,1.7,5.2,8.1,0.7,2.3,3.5,1.2,2.8,1.5,3.2,2.3,3.3,2.4,1.9,2.0,3.0,1.3,1.1,0.4,1.8,0.1,0.5,2.7,2.5,0.3,0.2,1.0,3.0,1.8,2.7,1.9,0.6,0.3,0.9,1.2,0.6,0.5,1.7,1.3,2.5,0.1,2.3,0.5,1.1,1.1,1.0,0.5,1.0,0.3,0.3,1.4,0.9,2.1,0.9,0.9,0.3,0.8,0.8,3.0,0.3,8,22,6,10,14,19,12,13,13,6,6.0,26,17,7,18,64,83,3,27,35,13,32,16,40,32,38,36,19,20,33,16,7,3,21,1,4,28,32,1,1,6,33,20,16,13,5,2,6,6,5,4,14,10,16,1,23,5,9,8,9,6,6,3,3,16,13,15,5,7,2,5,6,25,3,0.036,0.025,0.011,0.023,0.04,0.034,0.019,0.029,0.044,0.032,0.035,0.033,0.028,0.029,0.033,0.031,0.028,0.026,0.031,0.031,0.027,0.035,0.028,0.042,0.03,0.033,0.024,0.023,0.023,0.028,0.031,0.035,0.027,0.031,0.008,0.013,0.034,0.033,0.018,0.022,0.022,0.039,0.039,0.029,0.016,0.012,0.015,0.017,0.017,0.02,0.028,0.018,0.023,0.02,0.014,0.017,0.024,0.015,0.025,0.019,0.02,0.025,0.024,0.016,0.021,0.022,0.018,0.016,0.018,0.013,0.023,0.016,0.016,0.021,1465,4189,1687,2448,2096,2176,2689,2600,2073,1200,591.0,2678,3472,1211,3701,10426,18761,2026,4675,4056,3437,4431,1910,5361,4294,6927,5813,3475,5360,5719,2010,1203,804,4232,1477,1695,5463,6083,459,357,1446,2803,4693,2234,3005,1389,777,2019,2341,1217,587,2981,1944,4922,316,3486,1098,2607,1172,1435,1047,2219,478,798,2270,910,3446,1937,1964,775,1099,1700,7021,377,0.124,0.133,0.095,0.112,0.15,0.153,0.099,0.133,0.142,0.136,0.135,0.143,0.134,0.145,0.138,0.139,0.129,0.102,0.139,0.136,0.1,0.159,0.133,0.151,0.146,0.143,0.12,0.103,0.106,0.14,0.116,0.103,0.114,0.134,0.049,0.086,0.132,0.14,0.089,0.104,0.12,0.167,0.142,0.121,0.096,0.091,0.092,0.089,0.094,0.101,0.137,0.104,0.118,0.101,0.105,0.106,0.111,0.095,0.129,0.112,0.103,0.11,0.124,0.096,0.125,0.121,0.104,0.091,0.101,0.083,0.121,0.107,0.096,0.131,387,1248,674,872,724,958,963,861,573,308,170.0,1191,871,289,982,2858,4991,398,1264,1692,782,1366,842,1436,1267,1868,1877,1158,1340,1702,726,463,236,946,404,567,1364,1363,257,170,658,1342,924,1341,1734,696,313,839,1049,539,248,1401,848,2004,159,1929,466,1221,661,806,479,704,229,395,1095,621,1680,833,810,359,530,734,3013,213,3.387,3.13,2.384,2.671,2.413,2.118,2.334,2.731,2.667,2.999,2.977,1.924,2.964,2.883,2.76,2.741,2.957,3.805,2.95,2.043,3.063,2.501,1.959,2.775,2.599,2.881,2.398,2.332,2.949,2.555,2.138,2.559,2.597,3.076,3.123,2.556,2.822,3.045,2.058,2.651,2.912,1.883,3.079,1.885,1.977,2.144,3.118,3.135,2.774,2.227,2.171,2.263,2.28,2.756,2.08,2.055,2.41,2.322,2.035,2.035,2.171,2.94,2.391,2.327,2.285,1.853,2.305,2.747,2.691,2.325,2.382,2.52,2.526,2.268,0.487,0.609,0.394,0.593,0.647,0.495,0.448,0.394,0.603,0.489,0.934,0.444,0.409,0.398,0.556,0.592,0.579,0.729,0.606,0.623,0.795,0.489,0.439,0.762,0.46,0.512,0.508,0.516,0.517,0.557,0.691,0.795,0.398,0.522,0.831,0.624,0.733,0.551,0.443,0.402,0.631,0.453,1.031,0.706,0.597,0.526,0.452,0.708,0.438,0.605,0.48,0.486,0.506,0.53,0.279,0.411,0.414,0.578,0.478,0.487,0.405,0.761,0.472,1.012,0.569,0.651,0.48,0.365,0.48,0.569,0.457,0.505,0.546,0.493
-356,Epileptic,F,37.0,1.0,1.7,1.4,1.3,1.4,2.0,1.8,1.2,1.2,0.5,0.2,2.5,1.6,0.7,1.1,5.6,8.9,0.6,3.0,5.1,0.8,2.8,1.4,3.0,3.4,3.4,3.4,2.5,3.1,2.8,1.2,1.3,0.6,2.3,0.3,0.4,2.9,3.1,0.2,0.3,0.8,3.1,1.7,3.2,3.1,1.2,0.4,0.8,1.0,0.7,0.7,1.5,0.9,2.2,0.1,1.6,0.6,1.3,0.9,0.8,0.8,0.9,0.3,0.3,1.3,1.6,2.3,1.5,1.0,0.4,1.0,0.8,3.7,0.2,7,17,14,13,20,17,18,13,10,7,3.0,26,16,9,13,61,74,5,32,39,7,29,14,29,34,29,36,23,28,32,17,10,3,29,2,4,33,35,2,2,5,25,21,21,19,12,2,5,5,5,4,13,9,14,1,12,5,11,8,7,6,7,5,3,12,18,19,12,8,6,8,7,26,2,0.04,0.023,0.023,0.024,0.038,0.039,0.025,0.025,0.049,0.024,0.023,0.038,0.034,0.043,0.037,0.037,0.034,0.027,0.031,0.04,0.019,0.038,0.03,0.035,0.039,0.039,0.029,0.029,0.029,0.032,0.037,0.05,0.036,0.032,0.013,0.021,0.04,0.032,0.02,0.019,0.021,0.041,0.03,0.032,0.025,0.028,0.019,0.013,0.014,0.021,0.033,0.017,0.023,0.021,0.026,0.019,0.024,0.019,0.026,0.018,0.036,0.021,0.037,0.023,0.022,0.036,0.025,0.018,0.021,0.016,0.029,0.021,0.021,0.023,1393,4225,2596,2455,1756,2959,2766,2748,1532,1213,607.0,2018,2843,1309,2094,9751,17066,2460,4379,4413,2823,4105,1934,4695,4497,4747,4918,3307,5436,4568,1896,1069,884,5024,1646,1041,4698,5599,256,644,1278,2268,4309,2559,3033,1529,881,2050,2256,1141,667,2822,2085,4258,180,2739,1085,2625,1141,1149,789,1763,422,771,2299,1279,3012,2836,1683,1133,1234,1446,7943,382,0.143,0.113,0.118,0.112,0.149,0.148,0.122,0.123,0.158,0.133,0.115,0.159,0.141,0.158,0.134,0.146,0.13,0.101,0.133,0.148,0.088,0.15,0.132,0.137,0.156,0.157,0.138,0.128,0.124,0.138,0.15,0.158,0.114,0.133,0.076,0.093,0.132,0.126,0.116,0.102,0.11,0.158,0.128,0.133,0.113,0.141,0.1,0.081,0.089,0.109,0.142,0.106,0.107,0.101,0.127,0.104,0.12,0.104,0.127,0.112,0.144,0.117,0.13,0.108,0.114,0.154,0.118,0.104,0.113,0.115,0.134,0.112,0.098,0.127,410,1272,904,934,658,952,1017,869,378,333,191.0,1102,806,321,583,2653,4501,402,1386,1892,726,1262,803,1345,1454,1458,1940,1272,1670,1509,495,422,270,1164,397,400,1337,1502,155,230,600,1153,1055,1613,2011,704,324,873,1036,568,378,1333,725,1735,90,1328,489,1232,607,639,388,679,152,260,1064,691,1497,1281,772,461,581,657,3041,162,3.422,2.991,2.842,2.588,2.211,2.795,2.384,2.887,2.775,3.054,2.61,1.72,2.751,3.014,2.648,2.707,3.017,4.103,2.64,1.951,2.99,2.496,2.124,2.649,2.477,2.741,2.174,2.083,2.625,2.482,2.912,2.535,2.687,3.013,3.503,2.385,2.74,2.814,1.838,2.96,2.729,1.881,3.104,1.858,1.727,2.439,3.233,2.826,2.608,2.613,2.263,2.249,2.345,2.457,2.257,2.225,2.489,2.467,2.153,1.964,2.525,2.617,2.335,2.901,2.362,2.372,2.259,2.576,2.542,2.554,2.614,2.432,2.782,2.743,0.763,0.697,0.579,0.456,0.611,0.693,0.566,0.604,0.645,0.51,0.817,0.47,0.545,0.61,0.611,0.711,0.618,0.721,0.56,0.618,0.838,0.582,0.417,0.63,0.541,0.497,0.473,0.483,0.467,0.471,0.625,0.948,0.408,0.72,0.849,0.36,0.728,0.567,0.332,0.705,0.684,0.422,0.599,0.7,0.53,0.468,0.641,0.668,0.646,0.484,0.505,0.513,0.714,0.612,0.244,0.53,0.561,0.579,0.455,0.411,0.422,0.723,0.512,0.864,0.715,0.662,0.515,0.486,0.44,0.638,0.447,0.617,0.695,0.547
-357,Epileptic,M,27.0,1.0,1.8,0.8,0.8,2.6,2.4,1.0,1.3,0.7,0.1,,1.5,1.1,0.6,0.5,1.9,4.7,2.0,0.7,0.8,1.8,3.2,1.9,3.4,5.0,0.7,2.1,1.9,1.4,2.1,1.3,0.6,0.2,3.9,0.8,0.7,11.6,9.2,0.3,0.0,1.8,3.2,3.5,0.7,1.9,1.8,0.5,1.3,0.4,0.0,0.1,1.6,0.6,0.9,0.1,2.4,0.5,0.2,0.3,2.1,0.8,2.7,0.0,0.4,1.6,0.6,2.1,0.8,0.6,0.4,1.6,2.3,9.7,0.1,9,13,8,9,17,29,8,11,11,1,,9,10,5,6,21,49,22,9,7,12,29,12,31,50,4,16,17,13,8,25,5,2,35,7,3,107,60,2,0,6,27,33,4,11,11,5,9,5,0,1,9,6,7,1,25,2,1,4,13,3,19,1,4,11,9,22,5,5,3,6,15,68,0,0.033,0.028,0.024,0.027,0.064,0.045,0.038,0.045,0.062,0.038,,0.06,0.045,0.056,0.036,0.044,0.036,0.05,0.043,0.047,0.039,0.041,0.057,0.047,0.052,0.016,0.03,0.041,0.033,0.059,0.036,0.034,0.039,0.057,0.029,0.039,0.089,0.079,0.04,0.017,0.059,0.049,0.045,0.055,0.031,0.064,0.036,0.029,0.025,0.028,0.019,0.037,0.039,0.028,0.028,0.034,0.042,0.028,0.029,0.039,0.032,0.056,0.025,0.019,0.057,0.023,0.036,0.036,0.035,0.032,0.084,0.054,0.056,0.023,1562,3809,1613,1695,2363,1162,1833,1962,1650,256,,980,1746,706,1118,3596,10271,2264,813,674,3318,3188,900,4497,3708,1465,2585,2305,2596,1660,2509,1084,532,4071,1975,730,5424,5058,209,219,827,2236,9228,664,2366,1118,890,2303,941,15,154,1364,1408,1716,281,2213,268,159,718,1494,566,2847,184,972,1264,1355,2006,742,1354,943,788,897,5065,324,0.133,0.129,0.124,0.137,0.235,0.124,0.159,0.147,0.191,0.154,,0.217,0.121,0.181,0.152,0.164,0.153,0.165,0.124,0.152,0.13,0.144,0.213,0.164,0.18,0.09,0.125,0.15,0.135,0.196,0.157,0.136,0.1,0.175,0.108,0.126,0.212,0.207,0.232,0.087,0.155,0.172,0.166,0.184,0.134,0.169,0.166,0.126,0.101,0.123,0.091,0.161,0.159,0.134,0.105,0.139,0.138,0.091,0.111,0.177,0.115,0.206,0.222,0.122,0.194,0.135,0.133,0.145,0.153,0.131,0.304,0.151,0.151,0.089,504,1102,535,584,812,707,521,583,331,63,,422,450,193,291,884,2469,722,336,328,911,1293,567,1292,1763,656,1223,886,813,588,650,326,135,1252,649,290,2143,2049,110,47,337,1106,1614,257,1027,417,282,796,392,12,87,642,372,642,116,1173,188,102,337,961,432,900,55,398,455,484,981,358,368,335,370,758,2664,65,2.555,3.135,2.857,2.908,2.79,1.459,2.866,2.389,3.413,3.411,,2.494,2.538,2.841,2.897,3.278,3.373,2.48,1.759,1.934,2.98,2.048,1.746,2.84,1.961,2.095,1.935,2.24,2.719,2.948,2.751,2.921,3.48,2.473,3.084,2.909,1.814,1.745,2.562,3.406,3.313,1.703,3.497,2.711,2.505,2.682,2.578,3.061,2.373,1.634,1.8,2.69,3.058,2.856,3.223,1.705,1.229,1.429,2.091,1.689,1.526,2.862,3.142,2.482,2.622,3.004,1.852,2.67,3.649,2.927,2.583,1.347,1.924,3.915,0.45,0.628,0.448,0.491,0.592,0.506,0.534,0.835,0.602,0.674,,0.707,0.843,0.651,0.499,0.55,0.574,0.634,0.706,0.625,1.021,0.646,0.407,0.697,0.605,0.943,0.704,0.521,0.537,0.475,0.641,1.309,0.48,0.79,0.896,0.797,0.919,0.904,0.552,1.034,0.943,0.749,0.823,0.737,0.479,1.085,0.689,0.468,0.796,0.302,0.563,0.601,0.557,0.465,0.481,0.592,0.517,0.315,0.633,0.455,0.485,0.857,0.27,0.563,0.616,0.646,0.541,0.378,0.427,0.749,0.641,0.662,0.936,0.563
-358,Epileptic,M,57.0,0.8,2.9,1.0,1.4,2.1,2.3,3.0,1.7,1.0,1.1,0.4,4.0,1.2,0.7,1.3,8.2,12.2,1.4,2.8,6.7,2.5,4.9,2.1,4.6,4.8,3.7,6.7,2.7,5.3,4.1,2.0,1.3,0.5,2.4,0.7,0.5,4.9,3.1,0.2,0.3,1.2,5.0,2.7,5.3,3.5,0.8,0.7,1.0,1.4,0.9,1.0,2.4,1.4,3.4,0.5,3.5,0.8,2.1,1.1,1.3,0.7,1.6,0.3,0.5,2.4,1.2,3.3,1.7,1.7,0.7,1.1,1.5,4.8,0.3,6,20,7,8,19,22,22,17,10,11,3.0,38,23,6,14,73,103,10,34,51,17,43,17,46,44,42,56,27,40,48,21,9,4,30,4,3,48,39,1,2,6,41,23,27,17,4,3,6,8,8,6,15,8,20,2,26,4,19,6,8,4,12,2,4,17,16,24,10,11,6,6,11,34,2,0.034,0.03,0.017,0.024,0.044,0.032,0.044,0.027,0.038,0.049,0.034,0.047,0.032,0.036,0.033,0.046,0.043,0.044,0.038,0.051,0.039,0.053,0.032,0.046,0.045,0.031,0.041,0.03,0.042,0.037,0.055,0.042,0.035,0.03,0.023,0.012,0.052,0.036,0.017,0.018,0.024,0.044,0.051,0.041,0.02,0.013,0.025,0.015,0.018,0.019,0.04,0.02,0.024,0.025,0.022,0.017,0.015,0.025,0.02,0.018,0.023,0.027,0.016,0.029,0.023,0.024,0.022,0.02,0.024,0.023,0.021,0.021,0.019,0.01,2013,4593,2044,2373,2492,3784,2649,3611,1455,1719,665.0,3578,3036,1459,2678,10614,18888,2788,6068,6492,5723,4412,2466,7869,5424,7308,5757,2882,5827,5978,3129,1579,843,6077,1996,1603,6757,7538,350,548,1437,4383,5979,3679,4073,1532,945,2287,2118,1585,734,3649,1548,4619,553,5274,1547,3843,1525,2089,786,2706,441,1315,3095,1307,4554,2607,2154,975,1506,2559,9054,687,0.117,0.129,0.099,0.106,0.149,0.136,0.129,0.119,0.142,0.178,0.127,0.167,0.132,0.15,0.133,0.14,0.142,0.147,0.139,0.171,0.127,0.154,0.123,0.162,0.13,0.128,0.123,0.105,0.13,0.142,0.18,0.117,0.115,0.126,0.09,0.071,0.152,0.143,0.104,0.118,0.11,0.151,0.164,0.16,0.097,0.084,0.106,0.081,0.099,0.097,0.146,0.095,0.11,0.108,0.114,0.096,0.092,0.116,0.097,0.098,0.114,0.12,0.1,0.118,0.117,0.107,0.103,0.103,0.107,0.128,0.107,0.11,0.096,0.086,540,1581,865,912,970,1370,1176,1205,518,451,208.0,1676,894,355,731,3605,5966,603,1533,2582,1225,1704,1074,2065,1900,2395,2646,1427,2101,2136,823,599,245,1424,573,623,1894,1755,199,263,728,1941,1174,2225,2515,865,444,981,1135,799,443,1758,846,2152,306,2976,765,1556,863,1065,446,991,249,395,1524,837,2400,1286,1127,489,814,1234,4042,344,3.198,2.638,2.383,2.441,2.293,2.459,2.002,2.5,2.294,2.843,2.903,1.913,2.644,3.078,2.756,2.338,2.542,3.587,2.884,2.107,3.211,2.159,1.967,2.883,2.325,2.527,1.843,1.714,2.228,2.244,2.708,2.353,2.551,2.988,3.097,2.392,2.601,2.955,1.996,2.13,2.372,1.944,3.221,1.943,1.819,1.928,2.36,2.699,2.173,2.226,1.969,2.191,2.04,2.235,2.197,1.906,2.126,2.43,2.047,2.178,1.954,2.675,1.921,3.332,2.175,1.999,1.846,2.193,2.039,2.073,2.049,2.268,2.342,2.233,0.917,0.564,0.464,0.519,0.561,0.689,0.692,0.563,0.546,0.611,0.648,0.525,0.565,0.414,0.436,0.6,0.621,0.741,0.765,0.709,0.867,0.58,0.523,0.842,0.588,0.553,0.569,0.544,0.638,0.688,0.752,0.863,0.511,0.611,0.854,0.408,0.865,0.609,0.232,0.399,0.492,0.678,0.871,0.66,0.576,0.508,0.538,0.808,0.449,0.456,0.47,0.378,0.461,0.457,0.383,0.498,0.493,0.614,0.388,0.463,0.427,0.802,0.421,0.743,0.497,0.758,0.557,0.466,0.505,0.521,0.496,0.546,0.507,0.457
-360,Epileptic,M,32.0,0.7,2.0,0.9,1.1,1.5,2.6,2.6,1.4,0.5,0.5,0.1,3.5,1.6,0.5,1.5,6.2,12.0,1.2,2.7,5.7,0.9,3.6,1.9,2.8,3.3,3.1,3.8,3.3,3.7,3.6,1.3,0.9,0.5,2.3,0.7,0.7,3.2,2.6,0.1,0.2,1.1,3.2,2.7,2.7,4.5,0.6,0.5,0.9,1.1,0.7,0.5,3.3,1.0,2.6,0.2,2.7,0.5,2.1,1.2,1.2,0.4,1.1,0.3,0.4,2.9,1.5,2.8,2.1,1.4,0.6,1.4,0.8,4.3,0.5,7,19,11,7,14,21,17,14,6,5,2.0,31,14,5,14,56,92,8,26,43,9,34,15,30,40,25,39,28,24,40,19,5,3,27,4,5,33,29,1,1,7,29,34,20,25,5,2,6,6,6,5,24,10,15,2,18,4,16,10,10,4,8,2,4,17,14,22,14,8,5,12,6,37,5,0.026,0.02,0.014,0.019,0.04,0.031,0.031,0.026,0.029,0.032,0.023,0.032,0.028,0.03,0.03,0.033,0.034,0.036,0.034,0.034,0.018,0.038,0.026,0.033,0.029,0.033,0.031,0.028,0.029,0.034,0.038,0.04,0.026,0.028,0.019,0.017,0.035,0.027,0.013,0.016,0.027,0.032,0.034,0.028,0.032,0.013,0.024,0.013,0.015,0.016,0.025,0.028,0.029,0.02,0.026,0.017,0.014,0.022,0.024,0.02,0.017,0.022,0.012,0.02,0.026,0.026,0.022,0.024,0.028,0.021,0.027,0.017,0.016,0.031,1557,5055,2735,2447,1754,3743,2550,3124,1346,1337,615.0,3134,3583,1118,2756,11370,21255,2752,4458,5348,3317,5112,2276,6468,6990,5160,6147,4019,6022,5985,2502,769,822,5997,2316,1416,6565,7139,387,486,1612,3231,5978,2388,3330,1504,901,2304,2360,1528,729,4785,1911,5074,310,4185,1192,3432,1460,1698,1130,2156,544,898,3370,1444,4107,3276,1614,1329,2363,2494,9432,478,0.122,0.107,0.096,0.095,0.131,0.134,0.114,0.125,0.112,0.115,0.084,0.131,0.118,0.131,0.137,0.132,0.127,0.119,0.14,0.129,0.09,0.145,0.12,0.137,0.127,0.131,0.129,0.106,0.1,0.128,0.132,0.106,0.118,0.127,0.082,0.09,0.126,0.119,0.087,0.092,0.12,0.131,0.138,0.129,0.123,0.093,0.105,0.085,0.088,0.102,0.121,0.121,0.116,0.098,0.138,0.1,0.094,0.112,0.126,0.111,0.095,0.111,0.1,0.106,0.12,0.12,0.109,0.111,0.118,0.114,0.126,0.088,0.096,0.148,485,1612,911,923,678,1423,1116,968,377,345,162.0,1613,940,267,761,3039,5856,582,1364,2583,908,1652,1032,1478,2015,1548,2055,1683,1879,1857,605,368,324,1355,619,558,1536,1597,198,241,697,1572,1307,1600,2236,725,358,1016,1099,606,419,1888,714,1974,133,2270,579,1669,741,919,505,739,320,393,1679,796,2028,1464,859,482,913,939,4052,232,3.139,2.795,2.874,2.667,2.413,2.393,2.038,2.916,2.685,3.333,2.894,1.809,2.91,3.123,2.785,2.852,2.989,3.669,2.829,1.873,2.931,2.507,1.94,3.168,2.694,2.814,2.412,2.022,2.567,2.62,3.028,2.245,2.297,3.022,3.404,2.315,3.074,3.183,2.343,2.56,2.802,1.81,3.352,1.756,1.762,2.264,3.213,2.79,2.71,2.687,2.094,2.499,2.697,2.715,2.621,2.1,2.609,2.352,2.129,2.072,2.405,2.718,2.105,2.465,2.298,2.352,2.158,2.61,2.277,2.846,2.825,2.715,2.516,2.147,0.62,0.681,0.76,0.501,0.477,0.496,0.519,0.65,0.514,0.585,0.77,0.537,0.383,0.516,0.593,0.577,0.598,0.705,0.556,0.569,0.662,0.48,0.505,0.693,0.505,0.566,0.52,0.57,0.552,0.601,0.81,0.924,0.621,0.726,0.725,0.371,0.762,0.675,0.398,0.358,0.644,0.438,0.729,0.687,0.536,0.62,0.551,0.651,0.564,0.768,0.548,0.442,0.69,0.51,0.447,0.404,0.598,0.54,0.444,0.435,0.442,0.697,0.38,0.999,0.62,0.744,0.458,0.454,0.444,0.695,0.565,0.874,0.472,0.433
-361,Epileptic,F,42.0,1.0,2.1,1.0,1.3,1.6,1.7,1.7,1.4,1.7,0.9,0.3,2.2,1.5,0.6,1.2,5.2,8.2,0.6,1.9,3.7,1.5,2.7,1.2,2.7,2.9,4.4,2.5,1.6,2.1,3.2,1.3,1.0,0.3,1.8,0.3,0.8,3.6,2.8,0.1,0.1,1.4,2.7,1.9,2.2,1.8,0.5,0.4,0.9,1.1,1.0,0.5,1.7,1.1,2.6,0.1,2.2,0.6,1.3,0.7,0.8,0.3,1.6,0.1,0.5,1.4,1.7,2.3,1.2,0.8,0.9,1.0,1.1,3.6,0.4,9,20,9,9,15,16,15,16,15,8,4.0,22,18,7,12,60,76,6,23,38,14,26,13,35,39,45,40,17,22,41,20,7,4,24,2,9,32,38,2,1,7,24,20,21,10,4,2,6,5,7,3,13,8,19,1,18,4,13,4,7,2,10,1,4,14,17,18,12,6,9,6,9,25,3,0.042,0.023,0.015,0.024,0.033,0.037,0.025,0.025,0.051,0.033,0.03,0.028,0.031,0.035,0.036,0.033,0.03,0.031,0.033,0.035,0.029,0.037,0.024,0.036,0.03,0.033,0.024,0.019,0.024,0.029,0.046,0.035,0.021,0.028,0.014,0.019,0.039,0.038,0.012,0.013,0.026,0.042,0.036,0.023,0.014,0.011,0.017,0.016,0.017,0.02,0.019,0.021,0.028,0.021,0.02,0.016,0.029,0.019,0.022,0.017,0.02,0.027,0.02,0.02,0.023,0.037,0.018,0.016,0.015,0.026,0.02,0.019,0.017,0.015,1621,4934,2315,2258,2283,2962,3007,3195,2357,1417,475.0,2164,3653,1300,2557,10423,17310,2105,4235,4489,3868,3911,2015,5557,5927,8076,5425,3251,5503,6437,2244,1243,924,5267,1656,2139,5501,6422,403,301,1694,2600,4962,2379,3208,1478,834,1977,2107,1605,700,2499,1600,5298,266,3901,914,3064,990,1399,441,2624,281,748,2484,1360,4270,2975,1863,1632,1577,1898,7324,813,0.142,0.112,0.102,0.111,0.147,0.156,0.11,0.128,0.162,0.152,0.149,0.137,0.133,0.143,0.142,0.145,0.127,0.112,0.133,0.149,0.117,0.162,0.113,0.14,0.147,0.15,0.124,0.101,0.106,0.133,0.151,0.116,0.115,0.123,0.071,0.094,0.136,0.149,0.091,0.122,0.12,0.16,0.143,0.121,0.084,0.081,0.096,0.093,0.091,0.112,0.108,0.107,0.117,0.105,0.115,0.105,0.137,0.105,0.11,0.111,0.115,0.12,0.115,0.125,0.111,0.142,0.101,0.103,0.093,0.126,0.112,0.109,0.097,0.12,444,1568,944,805,775,887,1053,1047,552,417,176.0,1109,937,306,666,2721,4666,398,1125,1726,878,1128,830,1380,1631,2238,1881,1185,1474,1961,543,509,257,1145,398,739,1521,1446,209,127,744,1087,979,1545,1930,731,318,867,985,715,387,1232,688,2086,95,2039,386,1201,525,746,194,890,112,356,1166,722,2058,1228,855,546,701,921,3088,330,3.464,2.925,2.393,2.75,2.451,2.994,2.298,2.693,2.911,2.87,2.39,1.751,2.977,2.967,3.025,2.805,2.925,3.68,2.987,2.257,3.116,2.743,2.063,3.044,2.783,2.942,2.309,2.187,2.824,2.578,2.922,2.401,2.823,3.078,3.452,2.779,2.77,3.184,2.214,2.671,2.922,2.121,3.308,1.759,1.892,2.164,3.256,2.913,2.66,2.517,2.222,2.185,2.471,2.597,3.369,2.189,3.01,2.639,2.341,2.114,2.565,2.948,2.431,2.106,2.259,2.218,2.213,2.544,2.427,2.904,2.569,2.369,2.593,3.144,0.736,0.545,0.614,0.575,0.707,0.778,0.54,0.436,0.746,0.57,0.672,0.378,0.444,0.536,0.512,0.554,0.595,0.828,0.626,0.557,0.718,0.543,0.446,0.694,0.589,0.544,0.446,0.493,0.458,0.517,0.944,0.762,0.484,0.684,0.595,0.428,0.629,0.607,0.399,0.435,0.505,0.48,0.995,0.564,0.595,0.429,0.401,0.694,0.499,0.414,0.296,0.473,0.453,0.522,0.692,0.445,0.506,0.515,0.3,0.389,0.463,0.898,0.491,0.909,0.533,0.727,0.477,0.384,0.464,0.553,0.586,0.587,0.464,0.55
-362,Epileptic,F,47.0,1.2,2.2,1.0,1.1,1.4,1.9,1.6,1.8,1.3,0.7,0.3,2.7,2.2,0.6,1.2,5.8,8.3,1.1,2.5,3.6,0.9,2.5,1.0,2.8,2.8,4.3,2.8,1.9,2.5,3.1,0.9,0.8,0.5,3.0,0.7,0.6,4.1,3.7,0.1,0.1,1.3,2.5,2.0,1.8,2.6,1.0,0.5,0.8,1.0,0.6,0.8,1.4,1.3,3.2,0.3,2.2,0.8,1.7,0.5,0.7,0.7,1.6,0.9,0.4,1.6,1.1,2.6,1.3,0.7,1.1,1.2,1.0,4.6,0.4,5,24,9,9,16,20,15,18,13,7,4.0,25,19,9,14,63,82,9,28,38,7,30,13,42,42,47,40,20,26,40,16,6,4,34,3,4,43,42,2,1,9,21,18,15,14,8,3,5,6,7,4,10,10,21,2,17,6,17,7,7,5,12,8,4,12,17,17,9,5,11,8,8,39,3,0.04,0.023,0.02,0.018,0.033,0.03,0.022,0.031,0.051,0.03,0.059,0.034,0.037,0.044,0.03,0.031,0.03,0.045,0.032,0.031,0.016,0.031,0.023,0.034,0.032,0.028,0.025,0.024,0.029,0.029,0.024,0.034,0.019,0.029,0.021,0.014,0.035,0.035,0.01,0.015,0.023,0.032,0.032,0.024,0.02,0.016,0.026,0.013,0.014,0.024,0.032,0.014,0.027,0.022,0.012,0.015,0.017,0.019,0.016,0.015,0.018,0.026,0.044,0.013,0.016,0.019,0.022,0.019,0.012,0.026,0.025,0.019,0.016,0.021,1680,5552,2567,2445,1837,3027,3318,3000,1740,1558,565.0,2756,3684,1138,3113,11643,18528,2425,4841,4682,2775,4355,2058,6283,6161,8729,6022,3571,6043,6287,2666,1474,1416,7961,2049,2140,7958,8501,412,632,1887,2984,5228,2353,3298,1818,814,2919,2272,1473,656,3275,1843,6340,711,4488,1759,3369,1146,1611,1382,2675,756,924,3138,1449,3631,2337,1784,1631,1622,2439,11848,570,0.108,0.116,0.107,0.101,0.141,0.15,0.113,0.131,0.157,0.142,0.11,0.149,0.152,0.161,0.133,0.141,0.134,0.149,0.133,0.132,0.071,0.144,0.123,0.141,0.135,0.134,0.121,0.112,0.119,0.137,0.114,0.104,0.099,0.129,0.094,0.086,0.148,0.143,0.098,0.088,0.113,0.141,0.138,0.117,0.104,0.11,0.115,0.078,0.085,0.109,0.139,0.089,0.122,0.108,0.09,0.097,0.107,0.106,0.116,0.09,0.105,0.129,0.181,0.098,0.092,0.121,0.115,0.103,0.089,0.134,0.121,0.121,0.096,0.119,414,1711,845,916,753,1127,1177,973,505,384,176.0,1220,1005,277,787,3182,4834,489,1329,1957,823,1354,820,1638,1707,2479,2044,1251,1516,1879,696,527,432,1730,477,715,2081,1923,202,204,855,1233,1125,1335,1892,874,330,987,1062,536,357,1487,854,2458,360,2143,753,1623,491,810,604,932,320,474,1550,886,1728,1063,777,640,764,823,4564,298,3.643,2.852,2.824,2.636,2.276,2.471,2.38,2.702,2.662,3.223,2.811,1.976,2.951,2.905,2.912,2.811,3.07,3.73,2.993,2.033,2.668,2.542,2.181,2.941,2.796,2.912,2.395,2.278,2.985,2.656,2.767,2.714,2.706,3.223,3.632,2.768,3.072,3.147,2.437,2.76,2.73,2.083,3.385,1.983,2.011,2.307,3.073,3.343,2.638,2.799,2.231,2.324,2.373,2.705,2.297,2.271,2.552,2.418,2.183,2.245,2.585,2.815,2.405,2.324,2.169,1.991,2.354,2.56,2.501,2.725,2.598,3.211,2.605,2.55,0.794,0.579,0.563,0.371,0.444,0.546,0.623,0.603,0.462,0.452,0.885,0.518,0.466,0.387,0.612,0.489,0.543,0.717,0.617,0.58,0.737,0.524,0.414,0.734,0.554,0.477,0.448,0.517,0.536,0.488,0.609,1.194,0.385,0.622,0.677,0.448,0.787,0.717,0.263,0.631,0.525,0.565,0.716,0.743,0.628,0.477,0.748,0.874,0.471,0.824,0.448,0.499,0.449,0.491,0.289,0.45,0.473,0.596,0.45,0.297,0.473,0.74,0.553,0.77,0.499,0.679,0.433,0.454,0.411,0.484,0.403,0.784,0.515,0.548
-363,Epileptic,M,37.0,0.7,2.2,0.9,1.4,1.8,1.8,1.6,1.8,1.5,0.5,0.2,2.4,1.5,0.4,1.9,6.0,9.5,1.1,2.1,4.2,1.0,3.2,1.8,3.1,3.4,3.4,3.5,2.5,3.4,3.4,1.4,1.4,0.8,2.5,0.2,1.1,3.4,4.0,0.1,0.2,1.1,3.2,2.4,2.3,3.3,1.0,0.5,1.9,1.3,0.7,0.7,2.1,1.9,2.5,0.4,1.8,0.7,1.6,1.0,1.3,0.5,0.9,0.3,0.3,1.7,1.0,2.3,1.5,1.1,0.6,0.9,1.4,4.4,0.3,6,24,9,11,21,16,14,19,17,5,2.0,24,15,6,20,61,89,9,27,33,8,38,19,32,42,36,34,24,26,37,13,13,8,29,2,10,35,42,1,1,6,27,22,16,19,7,2,9,7,6,5,15,15,15,2,17,5,14,8,10,5,6,2,4,12,11,20,10,6,7,5,8,32,2,0.034,0.022,0.015,0.024,0.035,0.031,0.027,0.027,0.047,0.033,0.023,0.03,0.027,0.03,0.031,0.034,0.03,0.034,0.033,0.036,0.02,0.037,0.029,0.032,0.038,0.028,0.028,0.03,0.026,0.035,0.033,0.06,0.034,0.027,0.013,0.021,0.041,0.031,0.014,0.015,0.02,0.039,0.04,0.026,0.023,0.023,0.023,0.022,0.016,0.016,0.03,0.026,0.033,0.021,0.017,0.017,0.017,0.019,0.027,0.028,0.018,0.018,0.015,0.014,0.019,0.02,0.018,0.021,0.018,0.023,0.029,0.023,0.019,0.024,1384,4708,2186,2546,2287,2443,2686,3698,2130,1199,599.0,2642,3265,1033,3353,10532,19400,2615,4018,3874,2864,4293,2256,5400,5383,6547,5760,3927,6809,5028,1901,1315,1210,5998,1347,2401,4479,8169,353,482,1679,3034,4936,2324,3816,1346,719,2505,2623,1242,596,3330,2439,4413,654,3259,1148,2857,1144,1367,1029,2035,382,807,2638,1465,3863,2523,1757,1278,1034,1883,8739,455,0.119,0.117,0.095,0.11,0.141,0.14,0.114,0.133,0.159,0.126,0.105,0.133,0.122,0.127,0.142,0.143,0.13,0.12,0.134,0.131,0.096,0.149,0.135,0.129,0.152,0.127,0.131,0.124,0.114,0.147,0.118,0.144,0.146,0.122,0.069,0.105,0.132,0.127,0.088,0.093,0.101,0.15,0.139,0.119,0.107,0.11,0.107,0.101,0.091,0.104,0.127,0.112,0.127,0.102,0.105,0.107,0.101,0.105,0.123,0.127,0.101,0.094,0.107,0.1,0.105,0.105,0.107,0.104,0.1,0.118,0.13,0.109,0.098,0.106,396,1660,842,977,926,934,954,1175,574,311,177.0,1243,940,260,983,3135,5480,514,1099,1917,806,1481,992,1585,1662,1992,2081,1397,1970,1687,630,547,384,1526,352,852,1502,2064,209,258,843,1326,1080,1428,2193,687,320,1102,1176,584,346,1384,1021,1809,336,1686,600,1382,661,764,471,796,231,429,1317,702,1982,1137,851,468,481,929,3823,230,3.526,2.651,2.514,2.571,2.22,2.321,2.309,2.89,2.742,3.136,2.874,1.919,2.731,2.792,2.594,2.606,2.826,3.65,2.892,1.829,2.804,2.354,2.019,2.559,2.525,2.752,2.256,2.226,2.703,2.473,2.357,2.408,2.508,2.885,3.35,2.578,2.499,3.02,2.008,2.143,2.43,2.09,3.158,1.905,1.965,2.269,2.855,2.787,2.608,2.296,2.105,2.364,2.21,2.562,2.364,2.128,2.267,2.315,2.007,1.957,2.181,2.595,1.902,1.913,2.282,2.407,2.131,2.546,2.427,2.908,2.631,2.588,2.47,2.344,0.782,0.718,0.475,0.412,0.553,0.434,0.483,0.507,0.659,0.447,0.707,0.524,0.402,0.463,0.483,0.565,0.587,0.815,0.518,0.466,0.841,0.54,0.377,0.634,0.549,0.527,0.41,0.495,0.462,0.454,0.831,0.933,0.443,0.621,0.541,0.523,0.632,0.629,0.35,0.384,0.419,0.46,0.649,0.733,0.523,0.391,0.321,0.694,0.556,0.424,0.432,0.527,0.566,0.556,0.526,0.44,0.433,0.489,0.344,0.386,0.483,0.624,0.311,0.897,0.489,0.674,0.457,0.419,0.406,0.982,0.489,0.564,0.496,0.44
-364,Epileptic,F,52.0,0.9,2.8,0.9,1.1,1.7,2.0,2.7,2.4,1.8,0.7,0.3,3.2,1.5,0.5,0.9,6.9,13.4,0.9,2.8,4.0,2.0,4.7,2.3,3.5,8.0,3.0,3.9,3.7,4.0,3.9,2.0,1.0,0.6,2.7,0.4,0.7,4.5,2.7,0.1,0.1,1.1,3.1,3.3,2.8,4.0,0.8,0.4,1.2,1.4,0.6,0.5,1.6,1.7,2.5,0.6,2.6,0.6,1.9,1.5,1.0,1.0,1.3,0.3,0.5,2.5,1.3,2.8,2.0,1.6,0.8,0.7,1.6,5.6,0.4,8,23,7,7,16,18,19,18,16,7,4.0,30,16,4,11,63,112,8,31,35,15,55,22,35,81,33,38,26,27,41,19,8,6,32,2,7,50,35,1,1,6,27,35,25,23,5,2,8,6,6,3,9,13,16,4,21,4,13,13,9,8,9,2,5,19,16,19,13,9,10,4,12,51,4,0.038,0.029,0.019,0.022,0.035,0.033,0.041,0.032,0.049,0.031,0.033,0.036,0.035,0.025,0.032,0.047,0.04,0.037,0.035,0.034,0.033,0.04,0.033,0.041,0.055,0.029,0.032,0.037,0.034,0.036,0.051,0.046,0.027,0.032,0.017,0.02,0.039,0.029,0.012,0.016,0.022,0.034,0.054,0.029,0.028,0.017,0.016,0.02,0.02,0.012,0.022,0.022,0.031,0.021,0.03,0.02,0.012,0.02,0.023,0.018,0.018,0.023,0.024,0.015,0.026,0.035,0.018,0.024,0.024,0.03,0.022,0.017,0.017,0.019,1739,4737,1907,2167,2074,3001,2637,4049,2020,1359,654.0,3131,2864,1077,2299,8971,20456,2142,5257,4813,4073,5766,2921,5205,7175,6599,6241,4312,6883,5714,1931,1035,1117,5653,1825,1992,8724,7276,326,310,1654,2779,5455,2668,3633,1436,839,2312,2211,1690,607,2241,1459,3632,524,4190,1427,3385,1621,1370,2000,2458,327,811,3197,1028,4638,2687,2140,1093,1145,2720,11989,477,0.126,0.124,0.099,0.102,0.13,0.128,0.118,0.127,0.158,0.144,0.119,0.149,0.133,0.108,0.131,0.151,0.144,0.141,0.14,0.142,0.117,0.126,0.136,0.14,0.142,0.126,0.128,0.114,0.116,0.148,0.144,0.11,0.104,0.13,0.075,0.093,0.151,0.129,0.085,0.096,0.106,0.127,0.16,0.132,0.119,0.098,0.09,0.095,0.099,0.088,0.111,0.103,0.137,0.108,0.142,0.111,0.088,0.111,0.118,0.114,0.099,0.113,0.116,0.105,0.124,0.122,0.101,0.112,0.11,0.14,0.114,0.103,0.099,0.122,463,1629,798,830,846,1052,963,1202,673,330,198.0,1522,887,284,553,2689,5793,439,1336,1936,995,1880,1119,1577,2266,1889,1950,1592,1876,1852,608,445,398,1433,437,631,2181,1688,192,171,746,1278,1294,1583,2251,718,364,936,1039,765,352,1121,834,1808,275,2058,667,1397,876,739,846,913,184,457,1584,565,2274,1248,1042,471,547,1328,4962,294,3.494,2.532,2.363,2.665,1.904,2.421,2.308,2.842,2.289,3.151,2.72,1.882,2.532,2.627,2.852,2.423,2.784,3.687,2.978,2.091,3.069,2.47,2.225,2.558,2.498,2.796,2.484,2.186,2.759,2.462,2.341,2.362,2.285,2.871,3.593,2.683,3.022,3.068,2.061,1.976,2.751,1.954,2.978,1.964,1.866,2.381,2.833,2.882,2.676,2.406,2.181,2.167,1.867,2.198,2.301,2.228,2.398,2.668,1.964,2.158,2.418,2.83,1.872,2.092,2.245,2.102,2.188,2.247,2.35,2.316,2.529,2.412,2.533,2.025,0.915,0.724,0.578,0.486,0.738,0.565,0.642,0.505,0.779,0.497,0.883,0.516,0.544,0.608,0.713,0.7,0.711,0.735,0.473,0.672,0.867,0.555,0.517,0.765,0.707,0.601,0.612,0.608,0.607,0.588,0.639,0.792,0.452,0.619,0.737,0.528,0.716,0.648,0.312,0.453,0.599,0.483,0.76,0.598,0.532,0.522,0.5,0.596,0.407,0.439,0.364,0.446,0.421,0.465,0.428,0.454,0.402,0.6,0.452,0.348,0.626,0.769,0.627,0.516,0.468,0.595,0.487,0.402,0.401,0.801,0.451,0.43,0.475,0.389
-365,Epileptic,F,47.0,0.7,1.9,0.9,1.3,2.1,1.5,1.9,1.5,1.2,0.7,0.4,2.7,1.8,0.3,1.1,4.4,8.0,1.6,2.2,4.0,1.0,1.7,2.1,4.0,5.3,3.0,3.3,2.0,2.7,2.9,2.3,0.8,0.3,2.1,0.6,0.8,4.1,3.6,0.2,0.2,0.9,2.1,2.0,2.5,2.5,0.6,0.3,0.8,1.4,0.6,0.4,1.6,1.3,3.7,0.5,2.4,0.7,1.4,0.6,1.3,0.8,1.9,0.5,0.6,1.6,1.1,2.3,1.1,0.6,0.3,0.8,1.2,4.4,0.3,6,22,7,10,17,16,14,17,13,8,6.0,32,18,5,16,56,79,14,28,36,11,25,23,40,48,31,36,21,23,34,23,5,3,25,3,6,52,41,1,2,5,23,22,21,12,5,2,4,7,4,3,11,8,30,3,21,6,10,4,9,6,16,4,5,13,17,17,8,3,3,6,8,39,2,0.037,0.025,0.017,0.027,0.043,0.031,0.033,0.031,0.038,0.034,0.028,0.04,0.032,0.026,0.031,0.029,0.03,0.046,0.029,0.039,0.024,0.027,0.037,0.042,0.04,0.034,0.033,0.027,0.03,0.031,0.048,0.038,0.032,0.03,0.019,0.023,0.035,0.034,0.014,0.017,0.021,0.033,0.032,0.022,0.022,0.019,0.013,0.012,0.021,0.011,0.027,0.021,0.019,0.024,0.024,0.021,0.026,0.02,0.018,0.019,0.022,0.027,0.02,0.021,0.02,0.024,0.017,0.018,0.013,0.013,0.021,0.014,0.018,0.024,1607,4208,1929,2191,2491,2911,2585,2744,2016,1497,755.0,3016,3871,974,2670,9983,17127,2644,4944,4582,3634,4444,3051,6458,6950,5792,5674,3082,4873,5966,2703,797,781,6029,1842,1805,8505,8038,364,395,1340,2526,6775,3234,2755,1063,859,2542,2045,1639,376,2494,2134,6654,690,3684,963,2845,914,1991,1080,3028,539,1082,2473,1010,3699,2216,1121,838,1239,2567,9139,416,0.109,0.123,0.105,0.12,0.144,0.144,0.122,0.139,0.153,0.16,0.146,0.152,0.14,0.126,0.139,0.132,0.132,0.162,0.13,0.145,0.103,0.126,0.126,0.144,0.143,0.136,0.121,0.107,0.117,0.135,0.149,0.103,0.104,0.126,0.088,0.103,0.133,0.144,0.091,0.112,0.104,0.139,0.139,0.113,0.105,0.109,0.092,0.072,0.103,0.091,0.138,0.107,0.106,0.116,0.117,0.116,0.117,0.102,0.109,0.101,0.11,0.134,0.129,0.117,0.115,0.109,0.099,0.096,0.088,0.091,0.108,0.093,0.106,0.128,445,1450,845,819,905,946,915,907,564,402,261.0,1257,1019,233,738,2936,5027,600,1311,2020,769,1274,1199,1702,2343,1682,1923,1258,1433,1798,801,438,227,1348,510,645,2124,1902,194,214,652,1139,1307,1916,1665,540,360,992,998,716,225,1160,1004,2650,369,1904,450,1242,506,1031,566,1084,307,489,1182,732,2023,998,567,388,655,1157,3708,184,3.466,2.729,2.373,2.471,2.634,2.555,2.283,2.602,2.756,2.941,2.685,2.055,2.891,2.886,2.746,2.646,2.758,3.243,3.054,2.016,3.383,2.62,2.164,2.837,2.492,2.809,2.313,2.001,2.609,2.613,2.551,1.99,2.753,3.2,3.238,2.666,2.951,3.134,2.157,1.989,2.54,1.924,3.557,1.946,1.886,2.026,2.88,3.179,2.508,2.634,2.079,2.248,2.341,2.415,2.315,2.066,2.4,2.606,2.065,2.098,2.297,2.812,2.107,2.654,2.266,1.765,1.963,2.481,2.118,2.506,2.195,2.509,2.547,2.654,1.068,0.512,0.492,0.547,0.532,0.658,0.614,0.506,0.536,0.72,0.838,0.532,0.487,0.529,0.486,0.474,0.493,0.913,0.49,0.643,0.813,0.533,0.501,0.675,0.593,0.649,0.653,0.576,0.589,0.562,0.748,0.825,0.421,0.592,0.543,0.427,0.795,0.581,0.296,0.535,0.386,0.563,0.623,0.584,0.487,0.452,0.516,0.796,0.353,0.401,0.453,0.406,0.428,0.427,0.519,0.417,0.436,0.611,0.353,0.358,0.412,0.607,0.363,0.496,0.46,0.552,0.433,0.414,0.403,0.698,0.362,0.501,0.435,0.36
-366,Epileptic,M,32.0,0.9,2.3,0.7,0.9,1.4,2.2,1.8,2.4,1.4,0.7,0.4,3.0,1.9,0.6,1.6,7.0,10.5,0.5,2.7,5.5,4.3,3.2,1.5,3.4,3.4,4.3,3.4,2.4,3.0,4.5,1.6,1.6,0.8,3.9,0.4,1.9,4.0,3.5,0.2,0.3,1.1,3.6,2.2,5.5,3.8,0.7,0.4,0.9,1.0,0.7,1.8,2.3,1.3,2.8,0.7,2.8,1.1,6.6,1.5,1.0,2.1,1.5,0.5,0.8,2.0,0.4,2.4,1.7,1.6,0.3,1.0,2.4,6.2,0.3,6,24,6,6,16,16,13,19,14,6,3.0,27,20,8,17,76,89,3,14,31,33,28,13,40,35,39,38,23,27,45,25,9,7,36,2,13,35,30,2,2,7,29,21,32,21,5,2,5,5,5,6,17,6,20,3,22,5,33,10,8,9,12,4,5,13,7,22,11,10,3,7,12,46,2,0.029,0.022,0.011,0.016,0.03,0.039,0.025,0.03,0.04,0.031,0.044,0.029,0.029,0.027,0.029,0.031,0.029,0.016,0.039,0.046,0.045,0.035,0.028,0.039,0.024,0.028,0.026,0.024,0.024,0.036,0.036,0.057,0.026,0.035,0.012,0.029,0.042,0.03,0.016,0.018,0.018,0.033,0.039,0.038,0.024,0.015,0.017,0.013,0.011,0.017,0.066,0.02,0.016,0.019,0.028,0.017,0.032,0.053,0.024,0.018,0.072,0.023,0.026,0.028,0.021,0.017,0.017,0.018,0.021,0.011,0.022,0.038,0.026,0.014,2237,6275,2148,2489,2491,2484,3123,4815,2162,1577,492.0,3278,4212,1416,3811,15662,25592,2756,2411,3688,5029,5419,2038,6559,6738,7604,7131,4265,7295,6959,2917,1052,1690,7138,2093,2856,7471,6259,476,912,2456,4373,6889,3726,4193,1885,951,2830,3160,1815,688,4521,2502,6298,658,5430,1359,3470,2190,1863,956,2735,926,910,3413,851,4430,3478,2524,949,1951,2212,9725,648,0.095,0.1,0.084,0.09,0.125,0.142,0.106,0.117,0.147,0.101,0.136,0.125,0.114,0.132,0.122,0.124,0.121,0.069,0.131,0.137,0.134,0.13,0.123,0.126,0.107,0.107,0.115,0.101,0.092,0.129,0.117,0.146,0.096,0.103,0.062,0.102,0.128,0.11,0.098,0.083,0.095,0.13,0.127,0.129,0.112,0.091,0.093,0.076,0.074,0.1,0.202,0.103,0.09,0.099,0.117,0.1,0.106,0.172,0.117,0.103,0.246,0.115,0.122,0.119,0.106,0.103,0.1,0.102,0.111,0.086,0.108,0.137,0.11,0.089,492,1737,907,859,805,968,1003,1230,602,333,162.0,1571,1073,363,904,3751,6090,468,1025,1974,1650,1468,842,1485,2053,2299,2139,1454,1763,2064,654,478,459,1466,527,878,1620,1579,232,356,1014,1685,1095,2167,2385,787,354,1084,1315,691,480,1923,1156,2432,357,2728,598,1878,1015,911,610,983,326,403,1442,463,2161,1448,1134,500,834,1016,4015,321,3.868,2.925,2.464,2.804,2.517,2.426,2.55,3.439,2.661,3.553,2.668,1.821,3.018,2.8,3.101,2.99,3.244,4.051,2.166,1.796,2.816,2.807,2.14,3.046,2.556,2.789,2.468,2.296,2.987,2.654,2.981,2.3,2.698,3.281,3.4,2.951,3.264,2.944,2.598,2.695,2.975,2.271,3.636,1.917,2.018,2.64,3.557,3.15,2.827,2.797,1.983,2.477,2.479,2.836,2.264,2.256,2.628,2.041,2.533,2.256,2.102,2.538,2.518,2.414,2.627,2.217,2.201,2.785,2.555,2.036,2.826,2.696,2.68,2.435,1.099,0.785,0.605,0.683,0.674,0.69,0.706,0.635,0.674,0.628,0.974,0.496,0.566,0.615,0.602,0.555,0.683,0.802,0.628,0.688,0.923,0.497,0.456,0.814,0.515,0.518,0.501,0.549,0.498,0.563,0.74,0.926,0.666,0.882,0.72,0.578,0.876,0.791,0.378,0.719,0.694,0.634,0.873,0.63,0.594,0.781,0.618,0.662,0.916,0.62,0.447,0.59,0.528,0.545,0.395,0.494,0.743,0.743,0.467,0.44,0.613,0.849,0.716,1.0,0.7,0.778,0.468,0.471,0.494,0.642,0.419,0.676,0.621,0.402
-367,Epileptic,F,27.0,0.9,3.4,0.9,1.1,1.9,2.0,1.5,1.9,0.8,0.7,0.2,3.0,1.8,0.7,0.9,6.3,9.0,0.9,2.8,4.2,1.8,2.3,1.5,2.7,3.2,2.9,3.2,3.1,2.8,3.2,0.8,0.9,0.6,2.5,0.5,0.5,2.3,3.2,0.2,0.1,0.8,2.4,2.0,3.0,2.9,0.8,0.3,0.8,1.4,0.4,1.0,1.8,1.9,2.8,0.4,2.1,1.1,1.4,0.9,1.1,0.7,1.5,0.2,0.4,1.5,1.7,2.9,1.1,0.7,0.3,1.0,1.1,4.6,0.3,7,34,8,10,22,18,13,18,11,8,3.0,25,18,8,10,75,84,7,28,37,17,25,13,34,40,31,32,32,19,36,12,10,6,32,5,7,26,39,1,1,4,23,25,24,19,6,2,7,8,4,6,15,15,22,2,16,8,11,7,9,7,12,3,5,10,22,23,9,5,4,6,10,42,3,0.037,0.027,0.014,0.02,0.034,0.026,0.021,0.024,0.035,0.027,0.023,0.035,0.028,0.034,0.02,0.029,0.028,0.033,0.031,0.034,0.028,0.028,0.025,0.028,0.031,0.029,0.026,0.025,0.023,0.032,0.024,0.028,0.03,0.029,0.026,0.018,0.029,0.027,0.015,0.013,0.018,0.029,0.035,0.029,0.021,0.019,0.015,0.016,0.017,0.013,0.037,0.017,0.026,0.017,0.022,0.017,0.023,0.018,0.022,0.018,0.022,0.024,0.015,0.013,0.018,0.025,0.017,0.018,0.013,0.012,0.019,0.018,0.018,0.014,1446,5246,2765,2231,2469,3096,3349,3721,1590,1549,525.0,2712,4162,1265,1981,12524,18811,2286,5106,4267,4394,4127,2347,5467,5928,5671,5350,5248,5988,5131,2047,1310,1018,6117,1228,1873,4784,7158,321,221,1413,2507,4809,2631,3944,1155,911,2189,2630,1138,601,3216,2196,5779,408,3534,1653,2797,1058,1550,1353,2714,419,891,2393,1639,4630,2225,1970,687,1437,2284,9786,638,0.134,0.123,0.088,0.103,0.144,0.136,0.105,0.118,0.128,0.123,0.106,0.136,0.131,0.143,0.11,0.135,0.128,0.121,0.132,0.14,0.099,0.129,0.118,0.127,0.138,0.134,0.114,0.119,0.099,0.14,0.104,0.116,0.131,0.128,0.089,0.087,0.126,0.134,0.101,0.083,0.094,0.136,0.146,0.126,0.102,0.108,0.085,0.096,0.096,0.085,0.15,0.101,0.119,0.103,0.139,0.105,0.12,0.1,0.117,0.104,0.117,0.117,0.101,0.103,0.096,0.128,0.104,0.105,0.085,0.111,0.106,0.113,0.104,0.119,387,1879,1035,877,1016,1260,1067,1305,489,404,166.0,1356,1141,362,672,3753,5312,478,1486,1992,1061,1330,884,1653,1841,1826,1915,1874,1676,1851,585,564,341,1449,394,623,1305,1942,159,122,710,1218,1118,1727,2318,640,387,886,1213,507,397,1696,1125,2518,231,1864,741,1324,657,935,530,995,247,508,1261,986,2356,1000,907,370,741,987,4283,269,3.403,2.56,2.496,2.513,2.261,2.258,2.591,2.612,2.52,2.804,2.608,1.825,2.893,2.636,2.321,2.599,2.912,3.611,2.831,1.933,3.032,2.422,2.219,2.599,2.498,2.638,2.259,2.272,2.777,2.315,2.483,2.309,2.479,2.907,2.449,2.577,2.769,2.744,2.465,2.121,2.606,1.883,2.906,1.738,1.931,1.965,2.957,3.099,2.615,2.499,1.865,2.115,2.055,2.374,2.36,2.098,2.603,2.457,1.815,1.873,2.381,2.643,1.761,2.042,2.128,2.076,2.122,2.417,2.587,1.993,2.321,2.548,2.435,2.889,1.061,0.622,0.627,0.397,0.561,0.519,0.642,0.617,0.622,0.487,0.652,0.455,0.464,0.361,0.447,0.485,0.557,0.756,0.602,0.539,0.81,0.475,0.526,0.677,0.477,0.52,0.532,0.563,0.541,0.502,0.66,0.9,0.414,0.637,0.604,0.5,0.74,0.548,0.261,0.469,0.597,0.534,0.729,0.533,0.59,0.497,0.348,0.803,0.517,0.522,0.418,0.44,0.472,0.473,0.43,0.464,0.491,0.486,0.339,0.375,0.496,0.715,0.382,0.901,0.481,0.728,0.425,0.491,0.467,0.395,0.476,0.525,0.484,0.375
-368,Epileptic,M,42.0,0.8,2.4,0.6,1.1,1.4,1.7,1.3,1.6,1.3,0.7,0.4,2.2,1.4,0.6,1.0,4.8,7.6,0.6,2.3,4.8,1.4,2.8,1.6,2.8,2.4,3.6,3.3,2.1,2.1,4.2,1.2,2.7,0.6,2.1,0.2,0.8,3.8,2.1,0.1,0.1,0.8,4.1,2.2,2.8,2.5,0.6,0.4,0.7,1.1,1.0,0.7,1.7,1.6,3.0,0.2,2.9,0.6,2.2,1.0,1.3,0.5,1.1,0.3,0.5,1.2,1.4,3.0,1.4,0.5,0.3,1.1,2.0,3.5,0.3,4,29,8,8,11,13,11,14,8,6,5.0,15,15,6,12,44,70,5,19,31,9,30,15,31,29,32,34,17,22,38,13,14,4,23,1,6,40,27,1,1,5,29,20,18,17,5,2,4,5,9,4,12,13,24,1,22,4,16,9,13,4,8,2,4,9,13,22,10,4,4,8,12,30,2,0.048,0.03,0.014,0.026,0.033,0.034,0.023,0.037,0.045,0.045,0.037,0.03,0.03,0.029,0.027,0.033,0.03,0.03,0.036,0.049,0.027,0.033,0.034,0.032,0.031,0.033,0.026,0.023,0.024,0.033,0.039,0.072,0.031,0.026,0.01,0.021,0.041,0.026,0.014,0.014,0.019,0.043,0.037,0.039,0.021,0.014,0.022,0.015,0.017,0.02,0.033,0.021,0.032,0.027,0.035,0.02,0.016,0.032,0.024,0.022,0.02,0.021,0.025,0.028,0.022,0.038,0.026,0.017,0.014,0.013,0.029,0.031,0.019,0.016,1181,4345,1565,1938,2125,2426,1958,2947,1615,1120,616.0,2319,3010,1127,2601,8749,14671,1801,3106,3312,2038,3986,2425,5016,4335,5952,5484,3008,4683,5998,2115,1544,993,4267,1077,1983,5180,4901,337,399,1692,2993,3493,2019,3211,1206,744,1594,2093,1428,547,2644,2205,4890,147,4106,1192,2086,1139,1646,897,2345,412,705,2108,1043,3626,3102,1119,710,1333,2458,7579,554,0.13,0.134,0.109,0.121,0.136,0.129,0.097,0.138,0.151,0.165,0.161,0.13,0.127,0.143,0.12,0.13,0.132,0.11,0.126,0.151,0.095,0.143,0.134,0.13,0.132,0.127,0.12,0.111,0.106,0.137,0.132,0.175,0.112,0.115,0.056,0.096,0.129,0.118,0.097,0.091,0.091,0.162,0.137,0.144,0.102,0.099,0.111,0.085,0.092,0.118,0.141,0.106,0.134,0.113,0.152,0.108,0.101,0.124,0.129,0.127,0.117,0.114,0.134,0.139,0.107,0.154,0.118,0.099,0.09,0.12,0.13,0.133,0.1,0.097,297,1299,678,727,690,865,776,813,414,279,201.0,1027,837,304,675,2286,4190,338,1003,1636,741,1355,887,1406,1305,1733,2029,1228,1417,1928,551,556,305,1159,379,674,1620,1311,195,193,752,1397,928,1225,1950,640,308,746,952,686,343,1272,869,1871,74,2227,608,1180,669,896,391,813,187,339,1011,521,1891,1260,559,376,638,1082,3204,230,3.786,2.93,2.231,2.693,2.666,2.405,2.143,3.012,2.798,3.297,2.552,1.98,2.751,2.612,2.837,2.778,2.759,3.737,2.502,1.86,2.363,2.395,2.329,2.78,2.666,2.771,2.244,2.012,2.551,2.413,2.717,2.816,2.731,2.675,2.409,2.786,2.577,2.829,2.118,2.488,2.723,1.983,2.795,1.953,1.903,2.134,2.886,2.506,2.78,2.338,1.959,2.311,2.292,2.484,2.458,2.078,2.365,2.064,1.879,2.095,2.425,2.723,2.481,2.324,2.281,2.512,2.082,2.797,2.397,2.085,2.506,2.763,2.525,2.924,0.916,0.617,0.597,0.557,0.624,0.725,0.56,0.682,0.519,0.611,0.841,0.476,0.367,0.524,0.532,0.573,0.59,0.717,0.58,0.62,0.754,0.527,0.56,0.611,0.51,0.632,0.521,0.512,0.494,0.552,0.811,1.194,0.704,0.507,0.726,0.566,0.718,0.527,0.274,0.49,0.587,0.622,0.678,0.686,0.552,0.505,0.475,0.725,0.67,0.513,0.653,0.525,0.658,0.52,0.4,0.525,0.536,0.581,0.373,0.433,0.49,0.811,0.586,0.997,0.58,0.746,0.506,0.453,0.426,0.495,0.543,0.522,0.536,0.669
-369,Epileptic,F,37.0,1.2,1.9,1.3,0.8,2.3,2.2,1.4,1.3,1.5,0.7,0.2,4.0,1.3,0.5,1.6,5.9,8.4,0.9,2.8,5.4,2.0,3.2,2.0,3.6,2.4,2.8,2.8,2.0,3.1,2.6,1.5,2.1,0.4,1.9,0.5,0.5,3.2,3.2,0.2,0.2,1.0,3.5,2.4,3.4,2.5,0.5,0.4,1.6,1.7,0.7,0.5,1.6,1.1,2.7,0.2,2.5,0.7,1.8,1.5,1.3,1.0,1.2,0.7,0.8,2.5,1.4,2.7,1.4,0.8,0.5,0.7,1.5,5.0,0.3,8,17,13,5,17,21,11,10,13,5,2.0,34,15,6,15,51,66,7,25,45,15,37,22,37,30,25,28,20,21,29,13,14,3,19,2,4,29,37,2,2,6,31,21,30,13,4,2,10,8,5,4,12,7,20,1,19,5,12,10,10,6,8,4,6,21,17,18,9,5,4,5,12,34,2,0.041,0.023,0.024,0.02,0.042,0.027,0.027,0.032,0.041,0.038,0.028,0.037,0.027,0.04,0.034,0.036,0.032,0.033,0.033,0.037,0.043,0.029,0.033,0.045,0.028,0.029,0.033,0.027,0.03,0.034,0.039,0.07,0.024,0.026,0.019,0.017,0.036,0.03,0.015,0.021,0.026,0.032,0.043,0.03,0.021,0.015,0.018,0.026,0.021,0.014,0.024,0.024,0.023,0.025,0.044,0.02,0.016,0.022,0.026,0.021,0.026,0.028,0.047,0.026,0.026,0.033,0.026,0.023,0.018,0.019,0.025,0.019,0.021,0.026,1532,4097,2261,1442,2210,2669,2137,2389,1943,1178,624.0,2914,2668,985,3061,8755,15796,2312,4919,5562,3476,4778,2049,5290,4860,5016,4473,3481,5364,4144,2139,899,843,5487,1800,1310,6072,7661,368,532,1140,2311,5451,2867,2825,988,762,2087,2436,1644,443,2217,1453,4514,244,3299,1454,2957,1162,1675,1047,1761,401,754,3031,1087,2991,1802,1218,1067,1023,2180,8542,472,0.142,0.11,0.117,0.095,0.145,0.119,0.1,0.12,0.149,0.135,0.087,0.145,0.119,0.135,0.135,0.138,0.127,0.117,0.124,0.147,0.13,0.116,0.144,0.136,0.113,0.12,0.118,0.098,0.1,0.139,0.147,0.163,0.102,0.116,0.077,0.087,0.124,0.128,0.09,0.109,0.115,0.131,0.143,0.132,0.101,0.099,0.097,0.105,0.104,0.094,0.111,0.113,0.106,0.113,0.149,0.104,0.098,0.112,0.12,0.111,0.12,0.115,0.158,0.134,0.126,0.134,0.118,0.114,0.098,0.104,0.12,0.114,0.104,0.112,494,1397,895,662,938,1275,759,768,649,302,173.0,1655,770,290,822,2770,4577,465,1483,2363,858,1735,1043,1536,1461,1440,1581,1206,1365,1406,650,452,291,1194,527,485,1541,1884,226,257,615,1619,1138,1985,1793,521,331,895,1189,702,338,1143,859,1891,101,1950,657,1314,839,976,600,734,193,419,1613,626,1624,938,647,421,523,1079,3905,201,2.985,2.756,2.54,2.056,2.223,1.912,2.337,2.607,2.408,3.073,2.865,1.645,2.574,2.475,2.792,2.442,2.779,3.479,2.63,2.023,3.085,2.269,1.747,2.715,2.545,2.751,2.265,2.239,2.909,2.362,2.628,2.291,2.696,3.167,2.96,2.389,3.0,2.931,2.075,2.335,2.269,1.423,3.208,1.708,1.811,2.055,2.846,2.967,2.509,2.668,1.433,2.109,1.872,2.287,2.676,1.902,2.493,2.648,1.6,1.911,2.223,2.505,2.28,2.148,1.976,2.085,1.996,2.177,2.115,2.722,2.246,2.431,2.335,2.616,0.869,0.614,0.426,0.541,0.672,0.539,0.663,0.526,0.624,0.427,0.785,0.425,0.504,0.649,0.639,0.696,0.682,0.89,0.549,0.704,0.74,0.559,0.46,0.656,0.629,0.533,0.664,0.641,0.635,0.636,0.754,0.758,0.433,0.624,0.672,0.474,0.685,0.646,0.623,0.549,0.445,0.382,0.824,0.549,0.575,0.6,0.697,0.723,0.572,0.589,0.274,0.435,0.476,0.586,0.288,0.452,0.399,0.689,0.398,0.412,0.457,0.547,0.366,0.366,0.457,0.674,0.496,0.437,0.48,0.9,0.489,0.505,0.55,0.542
-370,Epileptic,M,37.0,1.1,1.7,1.4,1.4,2.1,2.0,1.8,1.8,0.9,0.9,0.4,3.4,1.1,0.6,1.7,6.8,9.6,1.2,2.8,4.5,0.8,3.7,2.0,3.9,8.4,5.9,4.8,3.3,2.8,2.8,1.4,1.2,0.7,2.7,1.0,1.6,4.4,5.3,0.5,0.1,1.2,3.3,2.8,3.5,2.2,0.9,0.5,1.1,1.7,0.7,0.7,1.8,1.1,2.5,0.6,3.0,0.9,2.2,1.0,1.6,0.9,2.2,0.2,0.6,2.2,1.2,3.0,1.1,0.9,0.7,0.9,1.5,6.6,0.4,8,14,11,11,20,18,17,20,8,10,5.0,30,15,5,15,67,85,10,26,40,8,35,18,39,60,50,48,28,31,33,14,10,6,32,8,13,40,44,3,1,9,27,30,23,14,5,2,6,10,5,6,13,6,17,4,22,7,16,8,10,7,12,1,5,19,12,29,7,6,7,8,10,50,3,0.039,0.024,0.03,0.023,0.043,0.041,0.024,0.031,0.042,0.04,0.034,0.038,0.023,0.035,0.033,0.039,0.034,0.039,0.038,0.044,0.025,0.043,0.034,0.048,0.05,0.034,0.028,0.029,0.027,0.03,0.042,0.053,0.025,0.034,0.036,0.027,0.043,0.043,0.035,0.012,0.02,0.041,0.039,0.031,0.015,0.015,0.021,0.015,0.025,0.02,0.035,0.022,0.024,0.022,0.016,0.019,0.024,0.02,0.024,0.028,0.02,0.036,0.021,0.019,0.023,0.024,0.017,0.019,0.015,0.025,0.023,0.019,0.022,0.024,1374,3552,2075,2367,2162,2065,2783,3254,1401,1515,631.0,2420,2566,1002,2563,10081,16920,2205,4382,3891,2430,3950,2301,5403,5307,7665,5466,3563,5946,4712,2428,1026,1250,5458,1555,2508,6361,6752,570,471,1579,2461,6191,2429,3594,1464,759,2395,2193,1275,561,2620,1460,3457,754,3935,1281,3097,963,1413,1089,2058,271,961,3032,1236,4733,1962,1756,1186,1150,2489,9383,406,0.126,0.116,0.128,0.115,0.159,0.148,0.097,0.131,0.156,0.156,0.132,0.149,0.116,0.129,0.129,0.145,0.135,0.148,0.146,0.16,0.093,0.139,0.138,0.15,0.141,0.121,0.102,0.099,0.101,0.13,0.147,0.125,0.103,0.134,0.109,0.114,0.153,0.14,0.122,0.08,0.106,0.154,0.153,0.131,0.088,0.089,0.099,0.088,0.114,0.103,0.15,0.11,0.11,0.114,0.102,0.109,0.12,0.107,0.123,0.124,0.114,0.138,0.109,0.107,0.11,0.113,0.103,0.098,0.09,0.128,0.117,0.11,0.112,0.125,460,1220,752,947,899,931,1049,1094,415,435,235.0,1308,804,313,833,3137,4908,519,1262,1814,694,1412,1012,1649,2257,2651,2234,1588,1716,1628,723,453,404,1395,495,984,1737,1887,241,254,885,1308,1411,1600,2126,846,363,1045,1077,578,379,1391,756,1654,418,2336,623,1673,641,854,600,889,168,490,1639,728,2668,895,904,485,643,1184,4450,270,3.001,2.731,2.756,2.476,2.124,2.079,2.206,2.529,2.459,2.784,2.457,1.676,2.526,2.524,2.38,2.503,2.759,3.422,2.79,1.843,2.611,2.297,1.992,2.562,2.108,2.432,2.045,1.877,2.677,2.264,2.564,2.245,2.465,2.855,2.742,2.278,2.746,2.64,2.129,2.133,2.174,1.653,2.945,1.747,1.883,1.868,2.715,2.79,2.575,2.488,1.799,2.015,2.015,2.28,2.168,1.86,2.283,2.021,1.764,1.758,2.124,2.337,1.785,2.279,1.971,2.057,1.881,2.573,2.189,2.637,2.11,2.453,2.222,1.849,0.656,0.522,0.523,0.507,0.545,0.466,0.531,0.499,0.562,0.484,0.637,0.369,0.374,0.581,0.507,0.561,0.549,0.604,0.573,0.558,0.658,0.636,0.46,0.608,0.556,0.56,0.532,0.475,0.458,0.592,0.666,0.766,0.593,0.592,0.774,0.496,0.695,0.751,0.481,0.381,0.484,0.471,0.811,0.502,0.56,0.405,0.476,0.773,0.437,0.437,0.376,0.347,0.472,0.438,0.37,0.438,0.43,0.485,0.354,0.362,0.437,0.568,0.373,0.689,0.449,0.659,0.434,0.406,0.437,0.686,0.477,0.429,0.484,0.39
-371,Epileptic,F,27.0,1.5,2.8,1.0,1.4,1.1,2.6,1.4,1.4,1.0,0.8,0.2,2.7,1.3,0.4,1.0,5.6,6.6,0.8,2.5,3.7,1.6,2.6,1.7,2.5,2.7,2.8,3.8,1.6,2.5,3.6,0.9,0.8,0.5,2.0,0.4,0.3,3.8,2.6,0.1,0.2,0.8,3.1,4.0,1.6,2.4,0.9,0.4,0.7,0.9,0.7,0.7,1.4,1.4,1.9,0.2,2.3,0.4,1.1,0.8,1.2,0.4,1.5,0.2,0.4,1.2,1.5,2.4,1.1,1.0,0.5,1.1,1.6,3.6,0.4,12,28,10,14,14,25,12,15,14,9,3.0,25,15,6,11,61,67,8,23,33,11,33,15,33,31,33,47,20,24,44,15,6,6,24,4,2,53,29,1,1,5,32,29,43,15,7,2,5,5,6,4,12,11,15,2,19,4,11,7,8,3,16,2,4,9,20,19,10,6,3,7,11,27,4,0.05,0.024,0.017,0.027,0.027,0.029,0.026,0.024,0.037,0.038,0.036,0.039,0.025,0.03,0.026,0.03,0.028,0.034,0.033,0.038,0.023,0.032,0.028,0.029,0.029,0.028,0.027,0.024,0.024,0.028,0.027,0.035,0.027,0.024,0.012,0.011,0.036,0.026,0.012,0.022,0.022,0.035,0.053,0.023,0.019,0.017,0.019,0.01,0.014,0.014,0.032,0.017,0.022,0.019,0.012,0.016,0.022,0.013,0.021,0.022,0.017,0.022,0.013,0.016,0.016,0.023,0.02,0.016,0.021,0.015,0.024,0.025,0.015,0.017,1600,5010,2679,2366,1777,4209,2230,3307,1762,1500,403.0,2367,3501,879,2503,11351,16027,2181,3803,3714,4200,4505,2491,6467,5465,6393,6930,2997,6234,6215,2233,1319,1077,6318,2388,1238,7051,7272,342,399,1339,2664,4320,2033,3655,1310,936,2583,2218,1857,743,2795,2025,3868,548,4390,1127,3177,990,1501,653,2836,422,1027,2293,1571,3912,2538,1633,1016,1579,2461,8942,801,0.149,0.117,0.099,0.126,0.129,0.139,0.112,0.124,0.132,0.137,0.133,0.151,0.116,0.138,0.126,0.139,0.126,0.127,0.133,0.15,0.088,0.139,0.124,0.128,0.136,0.133,0.134,0.112,0.119,0.136,0.116,0.11,0.126,0.114,0.083,0.068,0.122,0.125,0.088,0.097,0.109,0.146,0.146,0.111,0.099,0.108,0.105,0.075,0.085,0.097,0.134,0.097,0.116,0.1,0.107,0.097,0.12,0.086,0.12,0.119,0.1,0.122,0.119,0.109,0.099,0.12,0.105,0.099,0.105,0.119,0.117,0.12,0.09,0.134,458,1754,905,921,717,1424,833,982,546,357,131.0,1145,854,206,601,3137,4102,424,1174,1507,1040,1415,885,1603,1552,1752,2420,1150,1611,2125,587,444,336,1293,555,442,1870,1575,195,180,620,1356,1134,1248,1967,729,345,1023,1014,726,380,1293,959,1645,238,2182,395,1465,595,806,342,1065,204,439,1142,937,1873,1108,743,383,753,1037,3767,310,3.444,2.708,2.703,2.56,2.27,2.498,2.326,2.916,2.509,3.151,2.463,1.87,3.007,2.953,3.107,2.702,3.027,3.688,2.765,2.101,3.052,2.408,2.343,3.121,2.654,2.924,2.323,2.113,2.946,2.461,2.734,2.778,2.779,3.313,3.799,2.555,2.803,3.289,2.061,2.714,2.729,1.822,3.056,1.803,2.13,1.958,3.306,2.905,2.716,2.856,2.408,2.353,2.33,2.505,2.343,2.251,2.709,2.435,1.854,2.039,2.39,2.674,2.428,2.89,2.306,1.986,2.155,2.667,2.594,2.984,2.549,2.776,2.584,2.937,0.742,0.48,0.604,0.512,0.449,0.566,0.547,0.525,0.517,0.825,0.807,0.487,0.449,0.49,0.435,0.573,0.586,0.726,0.584,0.604,0.69,0.468,0.436,0.613,0.477,0.53,0.425,0.501,0.395,0.484,0.559,0.915,0.368,0.665,0.698,0.513,0.687,0.63,0.213,0.288,0.559,0.482,0.956,0.562,0.6,0.495,0.512,0.753,0.425,0.488,0.5,0.383,0.441,0.403,0.306,0.442,0.59,0.437,0.362,0.407,0.477,0.744,0.497,0.864,0.558,0.654,0.434,0.425,0.439,0.783,0.38,0.537,0.511,0.326
-372,Epileptic,M,52.0,0.5,2.0,0.8,1.2,1.1,1.7,1.2,1.5,1.3,0.6,0.3,2.2,1.8,0.7,1.8,5.1,7.8,0.5,2.3,3.3,0.8,2.6,1.4,2.1,2.8,2.8,1.5,2.6,2.6,1.8,0.8,0.6,0.7,3.0,0.3,0.4,2.0,3.3,0.2,0.2,1.1,2.7,1.8,1.8,2.4,0.5,0.4,0.6,0.9,0.4,0.4,2.9,1.1,1.6,0.3,1.6,0.8,1.7,1.3,0.6,0.9,1.0,0.2,0.6,1.9,1.4,2.4,1.8,0.7,0.5,1.1,1.0,3.4,0.2,5,20,6,11,10,13,10,11,10,5,3.0,20,15,6,16,42,65,5,24,26,6,26,12,20,28,25,18,21,20,18,9,3,6,26,1,3,26,34,1,1,5,23,19,16,14,3,2,4,4,5,4,19,7,11,3,14,5,14,10,6,5,5,2,3,17,10,16,12,4,5,7,8,24,2,0.029,0.028,0.015,0.027,0.031,0.035,0.023,0.027,0.05,0.048,0.033,0.031,0.03,0.044,0.045,0.037,0.033,0.028,0.033,0.033,0.021,0.035,0.029,0.039,0.034,0.034,0.021,0.032,0.028,0.027,0.04,0.04,0.028,0.037,0.015,0.016,0.032,0.043,0.018,0.017,0.021,0.036,0.043,0.023,0.02,0.014,0.027,0.012,0.014,0.022,0.023,0.025,0.023,0.016,0.025,0.017,0.023,0.022,0.034,0.02,0.035,0.028,0.034,0.035,0.023,0.034,0.023,0.024,0.015,0.025,0.038,0.026,0.018,0.018,1180,4046,1935,1789,1536,2084,1845,3091,1781,1023,721.0,2240,3504,932,2343,7717,14624,1902,3873,3809,2519,3590,1882,4068,4639,5332,2829,3243,5163,3296,2095,563,1251,5342,1533,1262,3790,6267,279,357,1551,2601,6020,1996,3005,942,632,1857,1883,992,447,4179,1664,3672,368,2579,1325,2542,1190,916,785,1448,302,815,2471,1128,3188,2850,1180,878,1049,1544,6779,356,0.107,0.122,0.095,0.119,0.128,0.137,0.091,0.107,0.167,0.142,0.136,0.136,0.121,0.165,0.151,0.13,0.122,0.101,0.128,0.125,0.093,0.145,0.123,0.137,0.137,0.137,0.108,0.125,0.112,0.118,0.129,0.11,0.119,0.131,0.067,0.073,0.131,0.153,0.093,0.1,0.108,0.136,0.148,0.11,0.1,0.09,0.113,0.073,0.082,0.118,0.124,0.111,0.106,0.087,0.13,0.099,0.101,0.105,0.14,0.106,0.141,0.113,0.101,0.124,0.121,0.121,0.109,0.112,0.089,0.115,0.154,0.121,0.092,0.112,326,1297,761,760,607,875,739,930,426,272,164.0,1046,1031,239,720,2263,4000,324,1120,1588,596,1188,773,985,1321,1484,1183,1241,1438,1052,417,259,343,1168,382,478,1026,1423,175,219,693,1161,847,1271,1906,509,246,747,920,394,314,1762,788,1594,223,1428,559,1261,642,571,402,529,126,231,1288,559,1605,1294,641,333,434,662,3091,187,3.545,2.693,2.562,2.395,2.349,2.16,2.09,2.903,2.91,3.349,3.111,1.883,2.615,2.583,2.647,2.555,2.778,3.997,2.781,2.016,2.874,2.351,2.102,2.931,2.597,2.924,1.94,2.098,2.719,2.455,3.265,2.121,3.141,3.078,3.43,2.266,2.701,3.057,1.801,1.989,2.753,1.994,3.614,1.739,1.835,1.981,2.855,2.72,2.397,2.613,1.746,2.149,2.054,2.324,2.14,1.918,2.436,2.114,1.992,1.816,2.23,2.41,2.406,2.969,2.074,2.39,2.101,2.349,2.135,2.528,2.523,2.561,2.358,2.36,0.785,0.767,0.652,0.56,0.669,0.614,0.548,0.754,0.643,0.521,0.979,0.475,0.543,0.598,0.556,0.663,0.624,0.75,0.619,0.655,0.885,0.586,0.419,0.816,0.648,0.762,0.563,0.481,0.515,0.487,0.755,0.985,1.011,0.725,1.061,0.445,0.63,0.668,0.328,0.367,0.853,0.553,0.885,0.694,0.518,0.777,0.533,0.911,0.475,0.839,0.537,0.722,0.504,0.618,0.397,0.529,0.622,0.56,0.485,0.391,0.447,0.812,0.627,1.039,0.613,0.726,0.531,0.455,0.459,0.69,0.59,0.564,0.675,0.568
-373,Epileptic,M,27.0,0.5,2.4,0.9,1.0,1.6,1.6,1.6,2.0,1.6,0.6,0.2,3.6,2.0,0.6,1.4,6.9,7.8,0.5,2.9,5.4,0.9,2.1,1.5,3.1,3.0,4.0,3.0,3.4,2.8,3.0,1.0,1.7,0.8,2.2,0.3,0.7,4.3,3.3,0.1,0.1,1.3,2.7,2.5,2.7,3.0,0.7,0.4,0.8,1.1,0.7,0.6,2.5,1.3,3.2,0.6,3.2,0.8,1.6,1.1,0.8,0.4,1.4,0.7,0.3,2.3,1.2,2.6,1.2,1.6,0.7,0.9,1.3,4.7,0.3,4,25,8,11,17,15,15,18,20,7,3.0,29,20,6,14,80,72,6,37,42,13,23,15,40,37,44,39,29,23,36,19,12,7,27,2,5,47,42,1,1,9,22,26,23,20,5,2,5,6,7,4,18,11,23,4,26,5,13,8,6,4,10,3,3,16,17,24,9,12,12,7,10,42,3,0.023,0.024,0.014,0.019,0.029,0.026,0.027,0.024,0.035,0.031,0.022,0.038,0.029,0.03,0.026,0.03,0.025,0.024,0.029,0.036,0.019,0.028,0.027,0.029,0.029,0.027,0.024,0.029,0.023,0.028,0.031,0.054,0.031,0.025,0.014,0.014,0.032,0.035,0.014,0.007,0.024,0.027,0.035,0.027,0.02,0.012,0.016,0.011,0.016,0.016,0.023,0.019,0.019,0.02,0.02,0.021,0.015,0.018,0.025,0.017,0.018,0.025,0.024,0.015,0.021,0.028,0.019,0.018,0.02,0.02,0.023,0.018,0.019,0.016,1384,5518,2096,2364,2584,3123,2606,4226,2128,1474,596.0,3542,4849,1285,3549,14534,20164,2262,6289,5961,3577,3748,2106,7297,5909,8597,5902,4754,6436,6163,2031,1263,1417,6402,1808,2276,8698,7512,349,540,1523,3784,6806,3276,3697,1709,878,2275,2310,1816,780,4944,2640,6174,735,4258,1840,3393,1154,1186,941,2793,723,976,3733,935,4806,2691,2794,1453,1426,2328,9631,388,0.105,0.118,0.101,0.103,0.119,0.122,0.119,0.127,0.138,0.141,0.12,0.145,0.128,0.132,0.12,0.129,0.115,0.11,0.134,0.143,0.087,0.129,0.127,0.13,0.133,0.121,0.123,0.111,0.1,0.134,0.118,0.145,0.133,0.12,0.07,0.084,0.133,0.141,0.09,0.064,0.12,0.118,0.149,0.121,0.102,0.085,0.103,0.084,0.091,0.105,0.118,0.103,0.1,0.103,0.109,0.119,0.091,0.099,0.118,0.106,0.109,0.108,0.118,0.1,0.108,0.133,0.107,0.096,0.11,0.13,0.113,0.102,0.105,0.126,367,1598,880,887,966,1075,973,1244,721,372,170.0,1429,1191,290,861,3736,5164,430,1654,2391,916,1178,870,1701,1728,2490,2021,1717,1809,1841,559,501,443,1417,402,786,2163,1759,183,188,796,1486,1324,1740,2276,785,326,969,994,646,393,2041,1218,2441,418,2134,792,1493,663,658,385,961,402,367,1652,676,2121,1018,1238,567,681,1169,4059,216,3.605,2.959,2.349,2.723,2.282,2.519,2.372,2.965,2.302,3.098,2.918,2.046,3.169,3.172,3.082,2.94,3.211,3.965,2.99,2.113,3.067,2.551,2.132,3.187,2.717,2.815,2.376,2.242,2.714,2.656,2.705,2.655,2.727,3.321,3.704,2.773,2.945,3.097,2.279,2.807,2.477,1.984,3.383,2.129,1.87,2.415,3.325,3.043,3.011,3.107,2.151,2.547,2.308,2.678,2.293,2.286,2.791,2.558,2.075,2.046,2.318,2.784,2.233,2.749,2.511,1.836,2.41,2.806,2.566,2.616,2.495,2.485,2.565,2.301,0.618,0.702,0.69,0.393,0.623,0.569,0.634,0.532,0.509,0.753,0.872,0.529,0.505,0.456,0.538,0.534,0.621,0.581,0.59,0.661,0.73,0.441,0.478,0.723,0.441,0.531,0.467,0.595,0.682,0.536,0.725,1.007,0.45,0.558,0.719,0.532,0.664,0.542,0.346,0.331,0.473,0.85,0.758,0.657,0.545,0.437,0.553,0.604,0.46,0.718,0.423,0.525,0.512,0.439,0.269,0.431,0.597,0.514,0.348,0.362,0.535,0.9,0.397,1.208,0.598,0.508,0.531,0.425,0.331,0.969,0.436,0.466,0.511,0.499
-374,Epileptic,M,27.0,0.7,2.7,1.2,1.6,1.8,2.8,1.5,1.7,1.0,1.4,0.3,3.3,1.4,0.6,2.0,4.7,8.1,1.3,2.1,5.5,1.4,4.7,3.0,4.1,5.7,4.8,3.8,2.2,2.6,3.5,1.2,1.7,0.5,2.6,0.4,0.6,4.6,3.7,0.2,0.2,1.1,4.1,2.7,3.3,2.6,0.8,0.3,1.3,1.5,0.8,0.7,2.5,1.2,2.3,0.3,3.0,1.0,1.7,1.4,1.5,0.9,2.4,0.6,0.7,1.8,1.4,2.7,1.6,1.0,0.8,1.5,1.6,5.5,0.3,7,24,15,13,18,21,15,19,15,14,9.0,29,17,7,21,62,81,13,27,48,14,35,26,44,45,45,47,25,27,47,16,8,6,28,2,7,56,37,1,2,6,30,32,25,17,6,2,9,8,6,5,22,8,16,1,25,11,16,11,10,7,20,5,9,13,22,22,13,8,6,12,12,37,3,0.03,0.027,0.019,0.029,0.027,0.031,0.024,0.027,0.036,0.039,0.037,0.035,0.024,0.032,0.031,0.032,0.029,0.04,0.029,0.037,0.022,0.045,0.042,0.039,0.042,0.035,0.029,0.026,0.026,0.031,0.031,0.049,0.033,0.03,0.012,0.013,0.035,0.028,0.011,0.014,0.021,0.03,0.036,0.028,0.018,0.023,0.016,0.017,0.018,0.018,0.022,0.022,0.016,0.022,0.023,0.018,0.018,0.019,0.026,0.02,0.022,0.028,0.022,0.025,0.026,0.018,0.021,0.02,0.019,0.02,0.025,0.019,0.017,0.017,1547,4424,2559,2616,2362,3879,2747,3684,1967,2483,671.0,2994,3588,1244,3957,10337,18404,2672,5209,5884,4148,3954,2293,7205,6352,7808,6796,4193,7649,6336,2493,1002,880,6864,2253,2174,8184,9238,393,597,1449,2528,6829,2872,3841,1210,816,2410,2481,1854,709,4053,2220,3623,442,4764,1961,3451,1432,1860,1242,3063,794,1025,1898,1598,3952,2945,1817,1408,1934,2356,11526,569,0.117,0.133,0.122,0.124,0.132,0.117,0.113,0.131,0.142,0.155,0.122,0.14,0.115,0.139,0.131,0.136,0.132,0.145,0.132,0.143,0.102,0.137,0.137,0.146,0.134,0.143,0.124,0.106,0.112,0.144,0.135,0.127,0.124,0.127,0.065,0.088,0.133,0.124,0.083,0.109,0.112,0.124,0.156,0.122,0.097,0.116,0.095,0.088,0.1,0.111,0.116,0.116,0.096,0.11,0.104,0.106,0.105,0.109,0.122,0.109,0.113,0.134,0.123,0.122,0.109,0.108,0.108,0.107,0.107,0.113,0.124,0.107,0.091,0.123,473,1533,959,966,1062,1315,1037,1145,547,624,201.0,1475,1032,316,1080,2860,4976,560,1283,2518,1042,1441,1108,1861,2204,2181,2361,1440,1810,1995,751,467,292,1487,564,759,2457,2041,202,260,764,1693,1470,1846,2202,596,334,1100,1148,653,492,1819,1104,1686,222,2493,868,1518,883,1015,656,1281,425,508,979,1134,2063,1268,852,635,968,1207,4871,273,3.333,2.69,2.52,2.533,1.976,2.482,2.267,2.764,2.605,2.914,2.848,1.771,2.723,2.788,2.775,2.674,2.834,3.527,3.019,2.005,2.927,2.328,1.923,2.909,2.356,2.769,2.255,2.203,2.956,2.504,2.587,2.362,2.56,3.227,3.452,2.563,2.518,3.138,2.111,2.618,2.492,1.441,3.351,1.741,1.971,2.151,2.808,2.675,2.738,2.918,1.88,2.172,2.18,2.383,2.353,2.062,2.388,2.499,1.947,2.073,2.215,2.246,1.954,2.234,2.062,1.672,2.067,2.524,2.358,2.276,2.258,2.274,2.549,2.381,0.756,0.618,0.503,0.601,0.588,0.814,0.524,0.585,0.601,0.685,0.781,0.562,0.598,0.566,0.564,0.7,0.612,0.752,0.596,0.689,0.911,0.571,0.476,0.78,0.653,0.625,0.513,0.631,0.68,0.604,0.563,0.854,0.435,0.596,0.692,0.474,0.723,0.692,0.492,0.446,0.573,0.457,0.691,0.534,0.541,0.445,0.559,0.519,0.436,0.62,0.43,0.54,0.46,0.471,0.484,0.407,0.425,0.476,0.376,0.417,0.319,0.599,0.48,0.572,0.478,0.635,0.427,0.419,0.434,0.694,0.381,0.394,0.455,0.345
-375,Epileptic,F,27.0,0.7,1.7,1.4,1.9,1.4,1.6,1.4,1.6,1.0,0.5,0.2,1.6,1.0,0.6,0.9,6.0,8.4,1.0,2.5,4.3,1.9,3.3,1.5,3.2,3.0,3.7,2.8,2.5,2.1,2.8,1.0,1.4,0.5,2.8,0.3,0.3,5.0,3.4,0.1,0.2,1.0,2.7,2.9,1.8,2.8,0.9,0.4,0.9,1.4,0.9,0.7,1.3,1.3,2.7,0.3,2.1,1.2,2.1,1.1,1.0,1.1,1.6,0.3,0.7,1.4,1.2,2.3,1.3,1.0,0.5,0.5,2.2,4.2,0.4,28,16,15,16,16,13,13,14,9,5,3.0,13,11,5,10,65,287,7,24,46,11,39,14,30,39,37,38,25,23,33,15,7,6,30,2,2,44,35,1,1,5,23,20,14,17,7,2,6,8,7,5,12,10,19,2,19,8,17,8,7,9,13,3,3,13,15,21,8,5,4,4,15,28,2,0.029,0.02,0.018,0.029,0.029,0.025,0.02,0.023,0.038,0.032,0.025,0.028,0.028,0.031,0.033,0.039,0.036,0.036,0.034,0.037,0.033,0.033,0.03,0.034,0.03,0.032,0.027,0.027,0.021,0.028,0.031,0.062,0.033,0.03,0.01,0.012,0.038,0.031,0.011,0.016,0.021,0.039,0.044,0.027,0.018,0.019,0.022,0.015,0.021,0.027,0.031,0.016,0.028,0.02,0.016,0.017,0.022,0.024,0.026,0.02,0.026,0.024,0.025,0.026,0.019,0.027,0.016,0.022,0.017,0.018,0.016,0.025,0.017,0.017,1559,4813,3145,3011,2258,2414,2519,3467,2062,1148,497.0,1894,2643,1036,1978,9706,16777,2466,4812,4975,3019,5383,1954,6021,6012,6503,5480,4326,5642,4908,2271,783,1033,6166,1702,1597,7772,7168,326,418,1576,2253,4225,2036,4023,1497,720,2174,2289,1754,537,2607,1992,5598,496,3678,1956,3480,1027,1486,1503,2619,451,872,2484,1422,4091,2184,1668,1105,884,2497,9676,738,0.109,0.102,0.106,0.126,0.124,0.123,0.106,0.116,0.148,0.124,0.123,0.131,0.118,0.132,0.128,0.152,0.134,0.133,0.129,0.145,0.103,0.143,0.128,0.13,0.135,0.136,0.127,0.119,0.106,0.135,0.121,0.149,0.144,0.133,0.062,0.065,0.127,0.125,0.082,0.124,0.104,0.15,0.132,0.116,0.097,0.107,0.11,0.085,0.09,0.109,0.142,0.095,0.112,0.097,0.113,0.104,0.113,0.102,0.122,0.107,0.123,0.114,0.131,0.127,0.105,0.115,0.097,0.113,0.097,0.113,0.106,0.127,0.095,0.103,443,1444,1196,1075,822,971,922,1046,463,292,161.0,832,690,285,558,2705,4504,469,1345,1957,842,1764,834,1577,1834,1937,1989,1509,1541,1634,589,351,315,1402,481,571,2159,1860,163,172,766,1115,1151,1076,2360,764,286,951,1062,741,356,1317,923,2249,274,1931,840,1552,653,831,692,1027,234,358,1191,730,2158,959,850,438,487,1248,3791,326,3.274,3.075,2.607,2.613,2.264,2.249,2.245,2.834,2.862,3.05,2.63,1.934,2.859,2.665,2.656,2.624,2.922,3.776,2.833,2.158,2.909,2.381,2.034,2.795,2.562,2.77,2.212,2.254,2.774,2.434,2.742,2.538,2.592,3.14,3.113,2.547,2.743,2.892,2.404,2.699,2.57,1.842,2.946,2.072,1.908,2.162,3.054,2.705,2.501,2.871,1.885,2.072,2.182,2.388,2.35,2.046,2.401,2.575,1.769,1.899,2.266,2.413,2.091,2.572,2.107,2.278,1.944,2.521,2.339,2.925,2.072,2.4,2.611,2.583,1.046,0.633,0.456,0.561,0.574,0.552,0.558,0.639,0.716,0.471,0.809,0.403,0.575,0.471,0.551,0.551,0.626,0.724,0.524,0.596,0.745,0.48,0.422,0.63,0.548,0.46,0.514,0.495,0.475,0.583,0.737,0.927,0.403,0.519,0.533,0.496,0.761,0.7,0.326,0.71,0.535,0.417,0.684,0.764,0.603,0.484,0.497,0.659,0.596,0.568,0.445,0.505,0.599,0.475,0.351,0.442,0.553,0.721,0.331,0.445,0.406,0.666,0.408,0.806,0.659,0.688,0.442,0.44,0.389,0.811,0.486,0.499,0.643,0.71
-376,Epileptic,F,32.0,1.5,2.6,0.6,0.5,1.6,2.1,1.4,1.3,1.3,0.6,0.2,2.7,1.1,0.7,1.6,7.2,7.2,0.7,1.9,4.6,0.7,2.3,1.4,3.2,2.1,3.4,3.4,2.0,2.0,3.1,1.3,0.5,0.3,2.5,0.4,0.3,3.8,3.1,0.2,0.2,0.8,2.5,1.7,2.5,1.6,0.4,0.4,0.6,1.1,0.7,0.5,1.3,1.6,1.4,0.2,2.1,0.6,1.3,1.0,0.8,0.4,1.4,0.5,0.3,1.5,1.2,2.2,1.1,0.9,0.4,1.1,1.3,3.8,0.2,10,28,7,5,16,20,13,14,14,6,4.0,25,12,9,11,71,67,4,23,40,8,30,16,31,28,32,40,22,19,37,19,4,4,27,2,3,46,35,1,2,5,23,24,16,11,4,2,4,5,6,3,8,10,13,1,14,4,10,6,6,4,10,3,3,14,9,17,9,6,3,6,8,33,2,0.05,0.031,0.014,0.013,0.045,0.033,0.027,0.037,0.047,0.031,0.022,0.039,0.031,0.038,0.049,0.045,0.03,0.033,0.032,0.039,0.021,0.037,0.033,0.041,0.031,0.042,0.029,0.027,0.024,0.036,0.034,0.043,0.023,0.037,0.014,0.011,0.036,0.032,0.021,0.023,0.02,0.032,0.031,0.027,0.016,0.013,0.017,0.011,0.017,0.035,0.028,0.021,0.028,0.016,0.019,0.021,0.017,0.022,0.028,0.022,0.016,0.03,0.044,0.018,0.022,0.043,0.026,0.021,0.017,0.02,0.029,0.023,0.02,0.017,1364,4372,1572,1553,2050,3415,2417,2903,2150,1258,493.0,2555,2499,1412,2129,10927,15314,1980,3613,4963,3021,4128,1932,5560,4437,5866,5768,3293,4604,5168,2065,832,933,5293,1798,1615,7718,6269,257,601,1389,2693,5212,3029,2572,1186,824,1836,2189,914,464,2364,1977,3995,271,2822,1325,2570,1048,1281,937,2360,405,694,2176,593,3108,2001,1497,835,1332,2319,7345,330,0.139,0.134,0.102,0.087,0.15,0.155,0.124,0.141,0.174,0.146,0.12,0.153,0.134,0.157,0.162,0.16,0.13,0.102,0.14,0.155,0.093,0.152,0.148,0.154,0.144,0.161,0.139,0.125,0.109,0.153,0.138,0.104,0.113,0.146,0.08,0.071,0.148,0.134,0.119,0.113,0.111,0.135,0.142,0.114,0.094,0.086,0.09,0.077,0.094,0.131,0.123,0.101,0.127,0.094,0.135,0.11,0.098,0.106,0.128,0.117,0.099,0.132,0.153,0.11,0.118,0.141,0.125,0.109,0.098,0.111,0.128,0.113,0.103,0.112,358,1402,723,644,670,1074,806,773,514,326,169.0,1151,704,352,558,2925,4127,390,1042,2013,625,1243,727,1353,1214,1460,2030,1148,1316,1608,557,261,312,1094,423,544,1866,1569,140,174,602,1288,1084,1614,1545,548,334,798,923,407,280,1064,870,1599,141,1401,579,1147,573,600,462,776,175,318,1053,374,1376,866,741,325,557,976,2990,190,3.527,2.809,2.299,2.334,2.565,2.548,2.666,3.372,2.93,3.029,2.422,1.966,2.7,3.073,2.961,2.769,2.937,3.737,2.827,2.178,3.343,2.564,2.251,3.061,2.773,3.219,2.335,2.255,2.72,2.589,2.795,2.794,2.327,3.293,3.637,2.618,3.114,2.968,2.332,3.005,2.901,1.893,3.565,2.049,1.854,2.26,3.186,2.679,2.808,2.726,2.053,2.291,2.378,2.431,2.356,2.379,2.769,2.616,2.322,2.302,2.324,2.826,2.29,2.31,2.181,2.046,2.443,2.547,2.419,2.679,2.605,2.808,2.532,2.249,0.898,0.78,0.596,0.412,0.637,0.589,0.644,0.83,0.702,0.564,0.655,0.555,0.517,0.453,0.606,0.629,0.603,0.871,0.482,0.596,0.828,0.589,0.366,0.653,0.546,0.753,0.472,0.562,0.445,0.615,0.719,1.039,0.497,0.708,0.786,0.552,0.631,0.663,0.628,0.486,0.775,0.477,0.673,0.689,0.509,0.618,0.602,0.681,0.6,0.592,0.362,0.567,0.615,0.593,0.346,0.456,0.434,0.525,0.455,0.32,0.331,0.739,0.656,0.917,0.568,0.494,0.6,0.539,0.382,0.817,0.595,0.508,0.542,0.643
-377,Epileptic,F,32.0,0.7,2.3,1.3,0.9,1.9,1.7,1.4,1.5,1.1,0.8,0.2,2.5,1.9,0.3,1.2,4.1,8.3,1.1,1.9,4.2,0.5,2.7,1.5,3.5,2.8,2.9,4.7,2.1,3.6,3.8,1.3,0.9,0.5,1.7,0.2,0.8,3.2,2.9,0.2,0.3,1.0,2.9,2.5,2.0,3.4,0.5,0.3,0.9,1.4,0.7,0.6,1.7,1.1,1.7,0.1,3.0,0.4,1.4,1.1,1.1,0.7,1.9,0.3,0.4,1.7,1.0,1.9,1.6,1.4,1.0,0.7,1.0,3.6,0.6,6,24,13,9,16,18,12,27,9,7,4.0,25,20,7,12,48,75,8,22,38,5,33,14,43,36,32,48,19,30,41,18,8,5,25,2,8,37,32,2,2,7,25,24,16,22,5,2,7,9,7,4,14,9,11,1,25,5,10,8,11,8,14,1,5,17,14,20,13,10,9,6,9,32,5,0.037,0.022,0.02,0.019,0.029,0.027,0.022,0.025,0.039,0.031,0.019,0.034,0.033,0.028,0.03,0.028,0.026,0.036,0.026,0.037,0.013,0.034,0.026,0.032,0.032,0.031,0.03,0.024,0.024,0.029,0.035,0.039,0.03,0.024,0.008,0.017,0.031,0.031,0.013,0.02,0.022,0.038,0.03,0.023,0.025,0.012,0.014,0.016,0.02,0.017,0.034,0.021,0.02,0.017,0.024,0.018,0.021,0.018,0.021,0.018,0.024,0.024,0.021,0.017,0.02,0.034,0.018,0.019,0.019,0.021,0.018,0.013,0.019,0.029,1620,4782,2075,1863,2710,2532,2607,3055,1829,1431,499.0,2734,3668,877,2224,8828,17203,2302,3804,5014,2328,4166,1882,5930,4905,4986,7323,3455,7213,6404,1969,905,915,4481,2123,1878,6920,6710,592,545,1432,2550,5811,2604,3474,1511,698,1997,2583,1761,512,2919,2015,3714,173,4754,1020,2799,1301,1671,1038,3233,393,786,2720,838,3360,3222,2499,1627,1521,2402,7275,566,0.123,0.117,0.123,0.1,0.13,0.144,0.103,0.127,0.152,0.136,0.129,0.144,0.133,0.147,0.14,0.131,0.126,0.13,0.131,0.144,0.074,0.144,0.122,0.133,0.144,0.139,0.136,0.112,0.108,0.141,0.132,0.12,0.123,0.126,0.05,0.096,0.126,0.141,0.1,0.107,0.115,0.15,0.14,0.115,0.114,0.085,0.094,0.087,0.106,0.109,0.134,0.112,0.113,0.09,0.15,0.106,0.113,0.1,0.108,0.114,0.13,0.115,0.117,0.12,0.115,0.14,0.11,0.104,0.106,0.119,0.109,0.09,0.107,0.156,431,1670,904,782,1040,961,950,1054,457,386,187.0,1213,1085,265,657,2441,5137,485,1127,2021,617,1448,886,1856,1611,1774,2572,1295,2106,2121,679,425,321,1170,579,698,1796,1638,220,239,710,1195,1325,1420,2244,742,318,914,1128,619,309,1332,905,1726,59,2498,407,1271,797,939,475,1282,186,435,1313,447,1696,1364,1117,667,658,1208,3085,317,3.622,2.711,2.265,2.332,2.25,2.378,2.301,2.646,2.815,3.018,2.435,1.983,2.694,2.72,2.706,2.726,2.841,3.681,2.735,2.145,2.947,2.334,1.914,2.553,2.457,2.566,2.346,2.116,2.707,2.497,2.337,2.263,2.317,2.789,3.221,2.513,2.868,3.04,2.451,2.575,2.637,1.929,3.164,2.056,1.796,2.227,2.992,2.732,2.851,3.058,2.004,2.295,2.264,2.351,2.819,2.164,2.583,2.52,1.873,1.997,2.294,2.62,2.57,1.933,2.291,2.328,2.122,2.6,2.609,2.566,2.541,2.345,2.513,2.003,0.785,0.564,0.548,0.493,0.585,0.371,0.609,0.503,0.525,0.38,0.727,0.532,0.483,0.388,0.334,0.469,0.502,0.643,0.408,0.609,0.789,0.393,0.428,0.66,0.472,0.382,0.443,0.531,0.61,0.438,0.717,0.808,0.423,0.597,0.745,0.46,0.704,0.537,0.381,0.43,0.346,0.439,0.742,0.559,0.57,0.394,0.424,0.637,0.443,0.547,0.362,0.548,0.469,0.392,0.324,0.359,0.403,0.63,0.322,0.339,0.325,0.637,0.475,0.79,0.481,0.522,0.392,0.401,0.378,0.783,0.414,0.456,0.453,0.445
-378,Epileptic,M,17.5,0.7,2.8,1.1,1.2,1.3,1.7,1.8,2.1,0.8,0.8,0.2,2.0,2.3,0.6,1.9,5.8,9.2,1.1,1.9,3.5,3.1,2.6,1.6,4.0,4.1,3.4,4.1,3.0,4.1,4.5,1.2,0.4,0.6,3.0,1.0,0.4,2.7,3.2,0.3,0.2,1.1,2.8,2.3,1.7,2.8,0.8,0.6,0.9,1.0,0.9,0.6,1.6,1.1,2.7,0.4,2.7,0.6,1.7,0.9,1.0,0.5,1.4,0.7,0.6,1.2,1.0,2.8,1.8,0.8,0.4,1.6,1.0,3.6,0.3,3,25,10,9,14,17,13,18,10,8,3.0,19,21,7,20,65,82,6,18,31,37,23,13,36,42,33,42,25,34,42,15,4,5,27,7,4,28,38,2,2,6,25,20,15,18,6,2,5,5,7,4,10,7,22,2,21,5,20,6,7,3,10,4,6,10,11,23,13,6,3,10,8,29,3,0.033,0.028,0.026,0.026,0.032,0.034,0.028,0.031,0.043,0.047,0.032,0.034,0.035,0.036,0.04,0.037,0.031,0.039,0.033,0.037,0.047,0.034,0.033,0.044,0.038,0.03,0.028,0.031,0.032,0.041,0.036,0.033,0.022,0.041,0.04,0.01,0.036,0.037,0.026,0.017,0.022,0.033,0.048,0.032,0.021,0.021,0.026,0.014,0.015,0.019,0.034,0.019,0.021,0.022,0.026,0.019,0.019,0.022,0.026,0.02,0.032,0.024,0.045,0.031,0.022,0.022,0.022,0.019,0.016,0.013,0.031,0.02,0.016,0.013,1279,5894,2416,2546,2141,2749,2801,4318,1830,1528,648.0,2011,4549,1013,3300,11413,20133,2310,3748,4068,3040,4487,2236,7099,6766,7064,7494,4392,7717,6302,2567,925,1257,6193,1757,2055,6802,7983,390,463,1674,2514,4934,1794,3652,1463,839,2494,2483,1947,611,3148,2005,5398,636,4531,1186,3222,1121,1472,470,2685,579,897,1900,1399,4442,3697,1858,891,1900,1705,8881,484,0.105,0.123,0.114,0.11,0.136,0.148,0.118,0.134,0.139,0.168,0.15,0.147,0.14,0.158,0.162,0.143,0.134,0.121,0.139,0.139,0.128,0.139,0.133,0.144,0.137,0.122,0.124,0.124,0.127,0.162,0.134,0.093,0.106,0.14,0.117,0.07,0.14,0.14,0.118,0.11,0.111,0.145,0.156,0.12,0.101,0.112,0.114,0.081,0.086,0.104,0.127,0.101,0.106,0.112,0.117,0.101,0.105,0.108,0.119,0.109,0.133,0.117,0.152,0.121,0.114,0.105,0.111,0.1,0.096,0.094,0.133,0.115,0.097,0.095,296,1601,710,744,752,907,977,1162,436,324,142.0,966,1111,263,807,2711,4975,474,932,1574,929,1218,800,1682,1827,1798,2540,1491,1968,1778,598,324,407,1141,464,649,1337,1617,186,214,715,1253,938,948,2072,613,330,840,979,690,328,1285,846,2069,209,2210,547,1430,582,760,264,887,216,368,910,743,1990,1485,763,443,761,765,3387,280,3.613,3.285,3.197,3.157,2.447,2.614,2.461,3.219,3.03,3.565,3.758,1.914,3.179,2.781,3.051,3.045,3.298,3.468,3.02,2.139,2.72,2.766,2.504,3.138,2.807,3.2,2.389,2.354,2.926,2.838,3.026,2.562,2.407,3.414,3.118,2.798,3.401,3.387,2.669,2.666,3.275,1.914,3.519,2.19,2.063,2.769,3.149,3.585,3.177,3.078,2.369,2.72,2.674,2.702,3.066,2.313,2.604,2.395,2.391,2.209,2.392,3.126,2.793,2.947,2.395,2.325,2.375,2.789,2.712,2.328,2.887,2.661,2.874,2.094,1.2,0.747,0.639,0.657,0.571,0.607,0.591,0.549,0.665,0.714,0.785,0.485,0.394,0.66,0.585,0.607,0.619,1.198,0.584,0.733,1.102,0.537,0.467,0.725,0.639,0.681,0.597,0.698,0.655,0.698,0.828,1.037,0.536,0.885,1.015,0.401,0.731,0.641,0.396,0.479,0.475,0.531,0.944,0.743,0.529,0.552,0.52,0.778,0.505,0.718,0.407,0.53,0.483,0.411,0.691,0.459,0.618,0.747,0.463,0.44,0.388,0.872,0.555,0.895,0.468,0.749,0.467,0.404,0.424,0.394,0.687,0.57,0.485,0.437
-379,Epileptic,F,52.0,0.8,2.7,1.0,1.3,2.0,3.0,1.5,1.2,1.2,0.6,0.4,2.5,1.3,0.6,1.2,5.5,9.9,0.5,1.4,3.9,2.0,2.7,1.1,3.6,2.8,3.8,3.3,2.8,2.8,4.0,1.5,1.0,0.7,2.8,0.6,0.5,2.5,2.8,0.1,0.2,0.9,3.4,4.4,1.9,2.9,0.6,0.5,1.2,1.1,0.9,0.6,1.6,1.4,2.6,0.2,1.8,0.4,1.4,1.1,0.8,0.5,1.2,0.3,0.4,1.3,1.4,2.9,1.3,1.2,0.3,0.9,1.2,3.9,0.5,5,28,9,11,21,24,15,11,13,7,4.0,25,13,6,13,62,89,7,20,36,14,29,12,42,32,39,37,24,20,42,18,8,7,31,3,5,29,30,2,1,5,33,26,17,19,5,2,6,5,7,4,14,9,17,1,18,4,14,7,7,4,8,2,5,13,14,21,10,8,5,8,9,36,3,0.038,0.03,0.014,0.026,0.038,0.033,0.022,0.026,0.045,0.025,0.036,0.027,0.027,0.031,0.032,0.037,0.029,0.026,0.022,0.031,0.035,0.033,0.024,0.041,0.032,0.04,0.031,0.027,0.024,0.033,0.043,0.041,0.03,0.029,0.025,0.014,0.039,0.036,0.012,0.012,0.019,0.037,0.069,0.026,0.022,0.013,0.027,0.022,0.015,0.027,0.029,0.02,0.031,0.023,0.026,0.016,0.022,0.016,0.027,0.016,0.019,0.02,0.023,0.017,0.014,0.037,0.024,0.023,0.019,0.015,0.021,0.019,0.017,0.019,1765,4763,2074,1970,2006,4329,2744,2695,1759,1310,630.0,2845,3025,1308,2604,9715,18853,1939,3514,4559,2908,4111,1996,4961,4888,7079,5580,4461,5737,5812,2128,1119,1222,5912,1667,1653,4640,5733,380,502,1770,3152,3517,2353,3629,1303,788,2132,2078,1073,632,3023,1816,4487,229,3098,1012,3279,1160,1352,874,2541,370,786,2819,1080,4084,2325,2052,850,1377,1584,8697,897,0.114,0.127,0.104,0.125,0.144,0.132,0.109,0.12,0.155,0.131,0.145,0.122,0.124,0.121,0.132,0.152,0.13,0.098,0.124,0.123,0.113,0.137,0.118,0.145,0.147,0.153,0.132,0.118,0.103,0.143,0.127,0.125,0.143,0.129,0.085,0.084,0.129,0.128,0.091,0.081,0.104,0.155,0.164,0.123,0.106,0.087,0.113,0.093,0.091,0.126,0.135,0.105,0.13,0.109,0.113,0.104,0.1,0.1,0.12,0.099,0.108,0.102,0.139,0.121,0.089,0.139,0.119,0.111,0.109,0.111,0.122,0.117,0.098,0.105,419,1598,887,753,849,1320,1018,861,488,376,184.0,1302,859,359,713,2717,5369,363,988,1977,882,1243,795,1556,1442,1878,1904,1546,1628,2041,607,451,379,1483,410,598,1308,1436,199,243,719,1422,1120,1388,2147,686,322,848,959,510,358,1401,723,1936,105,1766,467,1497,655,766,460,896,170,416,1507,568,1845,1024,978,413,627,877,3587,361,3.652,2.608,2.237,2.757,1.98,2.528,2.249,2.82,2.744,3.072,3.109,1.914,2.694,2.741,2.707,2.81,2.888,3.752,2.825,1.989,2.759,2.405,2.145,2.469,2.601,3.094,2.325,2.339,2.706,2.363,2.614,2.531,2.64,2.981,3.518,2.505,2.649,2.847,2.114,2.435,2.884,1.987,2.678,1.98,1.955,1.996,3.193,2.952,2.47,2.359,2.182,2.277,2.567,2.433,2.338,1.954,2.449,2.4,2.124,1.933,2.449,2.939,2.383,2.208,2.107,2.262,2.504,2.574,2.433,2.368,2.292,2.258,2.417,2.923,1.103,0.712,0.752,0.541,0.571,0.637,0.494,0.544,0.668,0.609,0.936,0.431,0.355,0.531,0.552,0.59,0.614,0.799,0.399,0.544,0.831,0.539,0.49,0.756,0.596,0.611,0.538,0.466,0.442,0.599,0.761,0.809,0.562,0.585,0.621,0.507,0.658,0.637,0.511,0.38,0.756,0.511,0.664,0.641,0.657,0.517,0.453,0.865,0.684,0.558,0.475,0.496,0.706,0.468,0.518,0.468,0.448,0.559,0.444,0.311,0.448,0.762,0.709,0.646,0.585,0.696,0.584,0.357,0.339,0.468,0.536,0.436,0.609,0.57
-380,Epileptic,M,52.0,1.6,2.4,0.8,1.2,1.3,1.9,1.5,1.6,0.9,0.6,0.2,3.2,1.7,0.7,1.4,5.0,8.5,1.2,1.9,5.0,1.2,2.7,1.4,2.8,2.7,2.8,2.0,2.4,2.0,2.5,1.0,1.2,0.6,2.2,0.4,1.1,3.3,2.7,0.3,0.3,1.0,2.8,2.9,3.8,2.5,0.8,0.2,0.9,1.6,0.5,0.6,2.0,1.2,2.6,0.3,2.0,0.8,1.4,1.3,0.6,0.8,1.5,0.4,0.4,1.8,1.3,2.1,1.4,1.3,0.4,0.9,1.3,4.6,0.2,10,25,8,9,16,17,13,14,11,5,2.0,31,17,7,16,56,76,9,21,37,9,29,12,29,26,25,28,24,18,27,16,8,4,24,2,9,37,29,3,2,6,26,26,22,16,6,1,6,8,4,4,16,8,20,2,15,7,12,10,6,7,12,2,4,13,15,18,11,8,4,7,10,33,3,0.045,0.026,0.016,0.022,0.029,0.032,0.026,0.031,0.032,0.027,0.02,0.034,0.027,0.03,0.023,0.033,0.028,0.039,0.033,0.036,0.025,0.034,0.026,0.031,0.033,0.03,0.023,0.024,0.02,0.025,0.027,0.049,0.025,0.026,0.014,0.027,0.038,0.031,0.019,0.013,0.021,0.034,0.039,0.034,0.017,0.019,0.012,0.015,0.019,0.012,0.032,0.019,0.02,0.021,0.015,0.017,0.023,0.018,0.024,0.014,0.027,0.026,0.021,0.021,0.02,0.034,0.022,0.02,0.018,0.014,0.024,0.025,0.021,0.015,1436,4175,2253,2177,2104,2654,2188,2638,1577,1072,538.0,2656,3372,1389,2914,9051,16213,2465,3321,4356,3643,4090,1897,5334,4820,4821,4482,4014,4867,4361,1749,1190,1048,5575,2204,2000,6571,6127,573,698,1276,3041,6323,2220,3551,1494,752,2350,2640,1451,447,3002,1677,4698,466,3336,1305,2371,1451,1110,1197,2349,513,1021,2758,924,3173,2282,1924,821,1192,1799,7940,357,0.133,0.129,0.104,0.109,0.135,0.143,0.111,0.138,0.144,0.132,0.093,0.14,0.13,0.122,0.128,0.147,0.131,0.139,0.133,0.129,0.096,0.143,0.119,0.132,0.141,0.134,0.117,0.11,0.092,0.127,0.12,0.134,0.105,0.123,0.072,0.115,0.142,0.136,0.121,0.085,0.111,0.144,0.157,0.133,0.096,0.097,0.075,0.084,0.104,0.085,0.136,0.106,0.108,0.106,0.117,0.102,0.119,0.101,0.123,0.094,0.136,0.125,0.126,0.122,0.11,0.148,0.117,0.111,0.101,0.121,0.115,0.131,0.109,0.104,424,1460,863,856,811,1020,854,871,482,313,179.0,1416,1018,375,924,2650,4965,492,967,2062,719,1322,895,1492,1424,1590,1577,1455,1466,1582,528,467,351,1279,547,711,1519,1445,265,394,696,1266,1209,1632,2132,739,304,969,1143,627,305,1541,902,2080,253,1743,606,1232,807,676,533,911,241,370,1372,526,1560,1088,1031,425,611,765,3455,231,3.364,2.795,2.603,2.558,2.284,2.274,2.244,2.702,2.599,2.786,2.518,1.712,2.653,2.718,2.447,2.629,2.698,3.625,2.784,1.808,3.366,2.441,1.838,2.786,2.622,2.596,2.267,2.218,2.592,2.315,2.506,2.731,2.426,3.07,3.431,2.468,3.098,2.999,2.327,2.164,2.333,2.075,3.617,1.639,1.855,2.193,3.066,2.993,2.783,2.553,2.052,2.094,2.076,2.367,2.316,2.139,2.46,2.209,1.956,1.825,2.414,2.481,2.494,3.038,2.195,2.132,2.187,2.425,2.176,2.207,2.275,2.704,2.504,1.714,0.725,0.551,0.418,0.418,0.539,0.491,0.518,0.541,0.433,0.33,0.677,0.431,0.383,0.56,0.363,0.455,0.465,0.83,0.707,0.638,0.818,0.581,0.46,0.601,0.45,0.468,0.418,0.531,0.436,0.416,0.532,0.983,0.374,0.586,0.797,0.425,0.709,0.641,0.344,0.4,0.511,0.61,0.766,0.607,0.567,0.422,0.568,0.793,0.614,0.574,0.449,0.341,0.382,0.387,0.35,0.491,0.381,0.585,0.353,0.407,0.6,0.484,0.667,0.846,0.585,0.544,0.469,0.33,0.473,0.485,0.398,0.586,0.509,0.303
-381,Epileptic,F,42.0,0.7,2.6,1.5,1.2,1.1,2.2,1.3,1.2,1.4,0.8,0.3,3.4,1.9,0.6,1.3,5.4,7.3,1.2,3.5,5.3,1.8,2.5,1.8,2.6,2.4,2.7,2.9,3.1,3.1,3.0,0.9,0.9,0.4,2.8,0.7,0.8,2.6,3.0,0.1,0.2,1.3,3.2,2.1,3.7,2.9,0.7,0.4,0.9,1.6,0.8,0.5,1.5,1.1,2.4,0.4,2.6,0.7,3.1,1.2,1.2,0.7,1.6,0.3,0.3,1.5,1.0,2.0,0.9,1.0,0.4,0.8,1.2,5.5,0.4,4,22,15,8,11,18,10,11,12,7,2.0,32,19,5,13,57,65,8,26,38,11,23,18,23,32,29,34,29,29,30,11,6,4,26,3,5,30,28,1,1,8,24,19,26,19,5,3,5,9,7,3,11,7,20,3,22,6,22,9,9,6,13,2,2,14,9,15,7,6,3,6,9,40,4,0.035,0.03,0.031,0.024,0.032,0.034,0.023,0.025,0.038,0.037,0.027,0.034,0.034,0.031,0.028,0.03,0.029,0.041,0.036,0.041,0.029,0.031,0.027,0.032,0.031,0.026,0.026,0.027,0.027,0.031,0.037,0.07,0.027,0.033,0.02,0.02,0.037,0.039,0.013,0.019,0.026,0.038,0.037,0.031,0.02,0.017,0.022,0.015,0.023,0.025,0.034,0.021,0.021,0.018,0.016,0.021,0.024,0.033,0.029,0.027,0.024,0.031,0.015,0.023,0.02,0.025,0.019,0.015,0.017,0.019,0.024,0.02,0.023,0.019,1137,4152,1945,1637,1685,2542,2278,2501,1974,1288,516.0,2780,2733,885,2254,9726,14587,2000,4311,4034,3264,3283,2074,4332,3791,5133,5036,4324,5627,4283,2476,464,839,5165,1835,1558,4982,4954,257,296,1494,2696,4908,2999,3628,1096,621,2062,2134,1163,289,2468,1655,4989,532,3456,1198,2736,1061,1207,1050,1973,485,691,1904,926,2863,1860,1657,1352,1128,1615,8094,509,0.103,0.125,0.132,0.114,0.128,0.14,0.109,0.111,0.147,0.15,0.1,0.133,0.136,0.121,0.132,0.133,0.123,0.13,0.13,0.138,0.099,0.129,0.125,0.12,0.131,0.119,0.121,0.122,0.106,0.134,0.12,0.146,0.095,0.131,0.093,0.089,0.139,0.14,0.088,0.109,0.118,0.141,0.152,0.128,0.107,0.103,0.113,0.09,0.12,0.135,0.14,0.108,0.109,0.1,0.105,0.117,0.11,0.135,0.132,0.129,0.113,0.14,0.106,0.117,0.108,0.127,0.106,0.097,0.094,0.106,0.123,0.122,0.113,0.121,371,1475,812,753,618,1030,854,863,570,352,200.0,1409,833,278,704,2949,4248,455,1518,2043,880,1281,992,1231,1315,1682,1835,1626,1662,1585,493,269,305,1350,498,650,1319,1344,164,180,810,1255,1066,1961,2127,597,291,886,1083,502,232,1237,832,2217,334,1971,546,1602,725,678,540,842,294,246,1231,514,1640,898,870,365,582,847,3781,292,2.932,2.571,2.467,2.135,2.373,2.283,2.214,2.473,2.523,2.943,2.556,1.78,2.567,2.36,2.433,2.512,2.654,3.189,2.356,1.792,2.932,2.137,1.933,2.656,2.293,2.491,2.174,2.188,2.608,2.249,3.384,2.078,2.209,2.796,3.179,2.221,2.706,2.718,1.921,1.753,2.229,1.939,3.364,1.738,1.931,2.023,2.516,2.822,2.334,2.691,1.592,2.2,2.211,2.239,2.0,2.022,2.395,1.947,1.83,1.916,2.252,2.356,1.97,2.614,1.688,2.108,1.89,2.283,2.224,3.404,2.207,2.391,2.306,2.219,1.053,0.712,0.533,0.432,0.585,0.499,0.519,0.406,0.625,0.412,0.651,0.459,0.442,0.458,0.512,0.553,0.577,0.911,0.561,0.551,0.787,0.455,0.432,0.566,0.447,0.472,0.569,0.495,0.513,0.552,0.774,0.748,0.408,0.655,0.888,0.399,0.608,0.599,0.428,0.407,0.468,0.532,0.714,0.497,0.573,0.436,0.414,0.804,0.439,0.563,0.284,0.416,0.645,0.431,0.45,0.455,0.343,0.566,0.37,0.392,0.552,0.561,0.266,0.72,0.469,0.646,0.46,0.473,0.476,1.127,0.472,0.492,0.508,0.507
-382,Epileptic,M,32.0,0.5,1.9,0.8,1.0,1.9,2.6,1.6,1.8,1.1,0.9,0.3,2.7,1.7,0.6,2.0,5.5,9.8,1.2,2.3,4.1,1.3,2.4,2.3,3.1,2.8,3.1,3.2,3.2,2.7,3.3,1.4,0.4,0.5,2.3,0.5,0.5,3.4,2.8,0.2,0.1,0.9,3.1,1.8,3.5,2.5,0.7,0.5,0.8,1.1,0.8,0.9,2.5,1.5,2.3,0.3,2.8,0.5,1.9,1.1,1.2,0.3,1.3,0.4,0.7,2.0,1.2,2.6,2.1,0.7,0.7,1.4,1.2,4.8,0.3,5,18,7,10,21,21,15,16,13,6,3.0,22,17,10,22,61,78,9,23,31,12,24,18,40,29,30,32,27,21,33,20,4,6,25,3,6,39,33,2,1,7,25,21,23,15,5,3,5,6,6,6,18,9,16,3,22,4,16,8,9,2,11,3,5,21,13,22,15,6,6,9,10,43,3,0.024,0.022,0.012,0.022,0.035,0.037,0.024,0.023,0.034,0.041,0.026,0.035,0.029,0.035,0.033,0.033,0.032,0.036,0.032,0.033,0.025,0.032,0.033,0.032,0.03,0.026,0.027,0.027,0.021,0.03,0.036,0.022,0.02,0.031,0.014,0.019,0.034,0.031,0.018,0.019,0.019,0.037,0.037,0.026,0.018,0.015,0.031,0.012,0.014,0.02,0.034,0.021,0.025,0.019,0.023,0.022,0.015,0.019,0.033,0.022,0.016,0.023,0.029,0.028,0.018,0.025,0.021,0.017,0.014,0.024,0.039,0.021,0.023,0.018,1259,5210,2157,2623,2956,3758,2504,3675,2300,1151,786.0,2520,3898,1161,3427,10608,18983,2467,3844,3286,3057,4148,2742,5943,4985,6196,5302,3732,5333,4858,2665,1023,1182,5694,1994,1441,7644,6442,358,388,1568,4063,5564,3122,3674,1490,774,2542,2540,1702,609,4040,2209,4484,438,3848,1170,2918,1042,1554,543,2433,479,909,3306,1678,3846,4176,1297,1590,1307,1850,8072,387,0.108,0.107,0.095,0.104,0.133,0.151,0.091,0.113,0.139,0.149,0.114,0.129,0.126,0.157,0.149,0.127,0.124,0.12,0.133,0.128,0.092,0.139,0.127,0.138,0.134,0.115,0.112,0.097,0.087,0.121,0.147,0.077,0.11,0.131,0.076,0.08,0.137,0.13,0.117,0.111,0.112,0.14,0.14,0.118,0.095,0.092,0.133,0.084,0.085,0.101,0.147,0.109,0.111,0.1,0.118,0.118,0.095,0.097,0.136,0.111,0.09,0.116,0.125,0.133,0.103,0.116,0.109,0.099,0.097,0.131,0.155,0.121,0.112,0.115,394,1489,856,854,984,1124,920,1175,549,333,193.0,1132,1075,324,949,2898,4958,496,1116,1769,904,1256,1049,1732,1581,1960,1826,1517,1644,1606,658,408,378,1202,504,502,1666,1519,181,145,716,1369,1057,1985,2116,768,329,995,1104,625,370,1804,977,1987,230,1962,516,1589,594,836,304,871,236,443,1648,749,1884,1757,676,497,579,879,3383,251,3.181,3.126,2.452,2.948,2.467,2.697,2.282,2.804,2.95,2.907,3.002,1.968,2.858,2.703,2.787,2.759,3.014,3.616,2.81,1.673,2.749,2.6,2.262,2.698,2.569,2.654,2.351,2.055,2.607,2.365,2.976,2.359,2.465,3.162,3.498,2.424,3.032,2.959,2.427,2.928,2.861,2.31,3.504,1.832,1.983,2.155,2.951,3.259,2.711,3.072,1.961,2.338,2.457,2.445,2.289,2.163,2.63,2.089,1.973,1.964,2.182,2.795,2.462,2.481,1.975,2.266,2.085,2.583,2.241,3.219,2.427,2.59,2.407,1.978,1.013,0.715,0.521,0.788,0.628,0.677,0.524,0.641,0.727,0.78,0.765,0.54,0.442,0.49,0.625,0.549,0.672,0.955,0.658,0.461,0.89,0.575,0.52,0.613,0.514,0.542,0.585,0.584,0.592,0.618,0.725,1.068,0.435,0.805,0.759,0.449,0.696,0.742,0.509,0.458,0.537,0.761,0.844,0.65,0.607,0.582,0.529,0.78,0.621,0.619,0.45,0.49,0.471,0.489,0.66,0.588,0.451,0.642,0.373,0.451,0.543,0.756,0.498,0.839,0.49,0.839,0.499,0.421,0.463,0.77,0.511,0.629,0.581,0.3
-383,Epileptic,M,22.0,1.1,2.7,0.8,1.3,1.4,1.7,2.0,1.9,1.8,1.0,0.3,4.3,1.3,0.5,1.4,6.1,10.9,1.1,2.5,5.4,1.6,2.8,2.4,3.3,3.2,3.5,7.1,3.4,3.1,3.3,1.9,1.4,0.8,2.6,0.4,1.3,3.1,2.8,0.1,0.1,1.4,3.4,2.1,5.7,3.6,0.8,0.5,1.0,1.6,1.0,0.7,1.4,1.6,3.2,0.1,2.5,0.8,1.6,0.8,1.4,0.5,1.9,0.3,0.5,2.8,1.4,2.6,1.6,1.1,1.0,1.1,1.5,4.2,0.6,11,28,6,10,14,18,18,26,18,10,5.0,41,16,7,21,70,123,11,33,52,13,33,27,35,46,42,75,36,29,42,24,14,5,34,2,14,38,39,1,1,9,33,24,42,21,7,2,6,8,11,4,10,16,28,1,20,6,10,6,10,5,12,3,5,22,23,24,14,7,10,7,11,38,3,0.045,0.025,0.014,0.022,0.029,0.032,0.03,0.03,0.044,0.041,0.042,0.046,0.03,0.032,0.032,0.034,0.031,0.036,0.033,0.044,0.028,0.037,0.039,0.037,0.037,0.03,0.045,0.039,0.029,0.031,0.046,0.048,0.026,0.03,0.011,0.022,0.029,0.037,0.012,0.014,0.024,0.046,0.032,0.04,0.026,0.019,0.018,0.015,0.021,0.021,0.032,0.018,0.025,0.022,0.022,0.019,0.015,0.021,0.027,0.023,0.019,0.027,0.021,0.02,0.022,0.024,0.018,0.021,0.016,0.024,0.031,0.016,0.021,0.023,1796,5615,2395,2589,2643,3425,3371,4047,2041,1945,866.0,3723,3543,1179,3166,12414,25559,2895,4816,5749,4477,3986,2550,6464,6180,8277,7338,5151,7157,6980,2648,1969,1294,6647,2824,2714,7453,6684,446,483,1811,2701,6627,4126,3686,1622,991,2668,2521,2273,602,2879,2237,6247,213,3945,1462,2685,891,1703,979,3121,375,1051,4209,1339,4986,2769,2290,1752,1340,2529,8268,749,0.135,0.116,0.095,0.105,0.127,0.137,0.117,0.143,0.166,0.152,0.123,0.183,0.129,0.134,0.153,0.143,0.132,0.132,0.14,0.165,0.099,0.142,0.145,0.137,0.141,0.131,0.149,0.138,0.119,0.137,0.151,0.124,0.105,0.136,0.065,0.123,0.127,0.141,0.084,0.084,0.107,0.151,0.134,0.154,0.11,0.103,0.098,0.082,0.095,0.114,0.14,0.099,0.118,0.109,0.133,0.108,0.097,0.103,0.14,0.118,0.097,0.12,0.128,0.114,0.116,0.121,0.104,0.111,0.094,0.125,0.133,0.103,0.106,0.107,536,1842,866,901,871,947,1161,1224,708,414,242.0,1679,842,291,805,3168,6639,593,1379,2410,1010,1383,1084,1684,1721,2235,2556,1804,1903,2088,812,633,470,1523,691,935,1837,1557,208,225,930,1431,1379,2390,2231,726,394,993,1188,804,339,1237,1078,2548,75,2018,772,1251,469,938,447,1053,212,473,1872,958,2274,1181,1028,619,602,1236,3342,363,3.328,2.771,2.696,2.741,2.554,2.63,2.451,2.782,2.321,3.526,2.69,1.975,3.055,2.903,2.983,2.866,2.975,3.642,2.785,2.053,3.243,2.288,2.08,2.855,2.762,2.843,2.297,2.289,2.797,2.602,2.471,2.961,2.263,3.15,3.481,2.693,3.012,3.067,2.449,2.571,2.499,1.708,3.467,1.931,1.911,2.593,2.663,3.379,2.551,2.86,2.159,2.434,2.181,2.467,2.724,2.187,2.227,2.505,1.883,1.921,2.274,2.857,1.892,2.514,2.46,1.602,2.333,2.607,2.566,2.599,2.578,2.437,2.529,2.552,0.79,0.675,0.5,0.442,0.686,0.779,0.68,0.508,0.638,0.554,0.656,0.534,0.443,0.509,0.456,0.658,0.683,0.829,0.571,0.727,0.826,0.585,0.495,0.804,0.612,0.602,0.645,0.618,0.692,0.604,0.756,0.883,0.467,0.7,0.78,0.479,0.69,0.639,0.285,0.428,0.457,0.532,0.599,0.695,0.619,0.461,0.685,0.824,0.486,0.565,0.28,0.462,0.388,0.473,0.529,0.427,0.471,0.47,0.557,0.39,0.412,0.693,0.36,0.815,0.498,0.577,0.47,0.386,0.426,0.76,0.467,0.533,0.508,0.404
-384,Epileptic,M,42.0,0.4,2.2,0.7,0.9,1.6,2.0,1.6,1.4,1.0,0.7,0.3,2.5,1.4,0.6,1.0,5.9,9.3,1.0,2.5,5.7,0.7,3.1,1.5,2.7,4.2,4.0,4.5,2.7,2.8,3.8,1.5,1.1,0.4,2.0,0.4,0.5,2.9,3.5,0.2,0.1,1.0,3.0,2.4,2.4,3.1,0.6,0.5,0.8,1.1,0.3,0.8,1.7,1.7,2.3,0.4,3.4,1.0,1.7,1.0,1.0,0.7,1.8,0.2,0.4,1.8,1.0,4.6,0.9,0.8,0.7,1.1,1.0,5.7,0.4,3,24,8,10,16,17,13,14,10,8,3.0,19,13,8,12,58,78,7,24,41,9,31,13,36,44,41,43,23,21,41,19,7,4,22,2,5,31,41,2,1,7,21,23,16,21,4,3,5,6,4,6,14,12,14,3,25,9,16,8,9,5,13,2,4,11,11,38,7,6,9,8,10,43,3,0.022,0.024,0.014,0.02,0.033,0.032,0.022,0.031,0.04,0.033,0.035,0.036,0.027,0.036,0.032,0.034,0.03,0.04,0.03,0.039,0.016,0.032,0.032,0.033,0.034,0.036,0.035,0.034,0.028,0.037,0.043,0.04,0.032,0.03,0.012,0.022,0.032,0.038,0.016,0.017,0.021,0.038,0.037,0.03,0.028,0.016,0.02,0.016,0.016,0.014,0.039,0.019,0.03,0.021,0.03,0.022,0.023,0.02,0.024,0.022,0.024,0.031,0.022,0.018,0.032,0.031,0.031,0.016,0.019,0.026,0.027,0.018,0.022,0.017,1545,4673,1694,2081,2314,2742,2564,3253,1420,1353,634.0,2308,3105,1268,2397,11404,18146,2201,4835,4841,2941,4586,1771,6185,6974,6964,5743,3303,5316,5666,2372,1637,742,5674,1908,1505,6760,6522,352,253,1569,2420,5807,2372,2529,1304,902,1906,2193,1277,482,3179,2623,5101,445,4774,1756,2968,1070,1502,853,2386,442,884,2023,845,4382,2028,1427,1080,1647,2381,10877,655,0.09,0.122,0.102,0.102,0.137,0.137,0.108,0.136,0.142,0.135,0.131,0.14,0.134,0.147,0.131,0.139,0.127,0.135,0.132,0.152,0.088,0.139,0.135,0.141,0.143,0.146,0.136,0.137,0.121,0.15,0.144,0.118,0.14,0.125,0.078,0.109,0.136,0.151,0.118,0.096,0.113,0.136,0.145,0.119,0.121,0.096,0.11,0.09,0.089,0.09,0.153,0.107,0.125,0.103,0.127,0.111,0.118,0.102,0.12,0.118,0.124,0.133,0.112,0.113,0.123,0.124,0.133,0.098,0.106,0.141,0.124,0.109,0.109,0.101,359,1541,692,780,820,1051,1007,890,476,357,193.0,1034,799,311,634,3070,5127,447,1271,2244,726,1475,782,1545,2088,1956,2198,1313,1467,1849,589,532,252,1187,459,469,1593,1669,188,110,704,1106,1094,1353,1805,592,357,795,990,474,342,1420,1016,1815,232,2380,689,1481,661,799,466,845,201,385,947,505,2218,900,707,454,716,985,4300,299,3.767,2.84,2.345,2.574,2.374,2.43,2.177,3.177,2.392,3.039,2.651,1.94,3.006,2.951,2.761,2.774,2.933,3.653,3.009,1.913,2.958,2.424,2.008,2.984,2.604,2.95,2.235,2.051,2.767,2.424,2.963,2.905,2.389,3.295,3.553,2.847,3.179,2.889,2.132,2.499,2.835,2.022,3.464,2.065,1.654,2.413,3.057,3.068,2.793,2.802,1.938,2.42,2.556,2.921,2.328,2.288,2.655,2.366,1.952,2.055,2.075,3.031,2.348,2.466,2.355,2.149,2.185,2.73,2.465,2.729,2.74,2.545,2.677,2.521,0.815,0.518,0.763,0.519,0.671,0.67,0.65,0.597,0.562,0.502,0.584,0.463,0.491,0.466,0.667,0.647,0.678,0.807,0.604,0.694,0.849,0.553,0.469,0.706,0.546,0.695,0.532,0.585,0.622,0.602,0.615,0.992,0.482,0.66,0.662,0.607,0.766,0.596,0.376,0.405,0.603,0.643,0.796,0.888,0.515,0.478,0.687,0.71,0.529,0.672,0.506,0.534,0.581,0.577,0.494,0.49,0.442,0.527,0.49,0.467,0.425,0.754,0.532,1.06,0.568,0.649,0.539,0.417,0.475,0.514,0.549,0.547,0.508,0.424
-385,Epileptic,F,57.0,0.3,1.8,0.7,1.7,1.5,1.7,1.6,1.6,1.0,0.8,0.3,2.7,1.2,0.7,1.1,4.0,9.1,0.4,2.1,4.6,0.8,2.5,1.2,2.4,2.8,2.3,3.0,2.5,2.8,2.5,1.1,2.3,0.5,1.7,0.3,0.7,2.1,2.8,0.2,0.2,0.7,2.7,2.3,2.8,3.0,0.6,0.6,0.6,1.5,0.8,0.5,1.1,0.9,2.0,0.3,2.3,0.6,1.8,1.4,0.9,0.7,1.0,0.4,0.8,1.1,0.4,2.8,1.4,1.1,0.6,0.9,1.2,3.3,0.3,2,19,11,15,16,13,13,13,11,7,2.0,19,11,8,11,44,75,3,25,36,8,23,11,27,30,22,29,22,21,27,17,9,6,19,2,6,26,31,1,1,4,20,22,20,21,5,3,5,7,7,5,6,6,11,3,16,3,14,8,7,5,5,2,5,10,4,23,11,7,5,5,9,22,2,0.017,0.023,0.015,0.03,0.033,0.03,0.026,0.032,0.041,0.032,0.03,0.043,0.028,0.036,0.032,0.028,0.031,0.017,0.025,0.036,0.017,0.032,0.031,0.03,0.029,0.029,0.031,0.028,0.024,0.03,0.038,0.079,0.032,0.028,0.011,0.022,0.031,0.031,0.014,0.015,0.016,0.039,0.036,0.032,0.025,0.012,0.025,0.01,0.022,0.028,0.032,0.015,0.023,0.019,0.022,0.02,0.016,0.021,0.032,0.019,0.028,0.02,0.025,0.033,0.021,0.016,0.025,0.019,0.02,0.017,0.028,0.022,0.017,0.018,1210,3964,1402,2314,2186,2191,2947,2832,1923,988,390.0,2083,2767,1326,2083,8524,16874,1809,4246,4161,2596,3717,1723,5551,5064,5131,4824,3446,5563,4599,1672,697,860,4735,1593,1440,4689,5613,353,410,1287,2455,5429,1944,3162,1519,829,1905,2214,1106,350,2424,1500,3746,547,3341,1347,2780,1152,1345,832,2095,554,682,1718,686,3769,2797,1993,962,1269,1727,7258,553,0.07,0.115,0.119,0.125,0.139,0.137,0.114,0.134,0.159,0.15,0.127,0.15,0.122,0.156,0.136,0.129,0.125,0.087,0.111,0.133,0.095,0.142,0.125,0.127,0.138,0.128,0.132,0.115,0.104,0.129,0.123,0.162,0.153,0.13,0.07,0.112,0.117,0.135,0.102,0.098,0.097,0.145,0.137,0.136,0.114,0.089,0.113,0.077,0.097,0.128,0.14,0.088,0.104,0.1,0.147,0.111,0.084,0.116,0.135,0.107,0.13,0.105,0.122,0.141,0.107,0.09,0.118,0.106,0.104,0.113,0.131,0.123,0.094,0.108,375,1383,700,948,842,893,959,848,495,323,140.0,1012,734,320,596,2391,4637,383,1312,1968,722,1208,661,1332,1638,1404,1638,1357,1635,1438,489,390,312,1053,433,473,1280,1356,184,188,642,1178,1132,1378,2036,734,385,894,1042,496,234,1120,680,1641,217,1702,597,1397,702,697,422,768,279,346,886,427,1902,1186,826,461,518,884,3093,229,3.11,2.774,2.039,2.399,2.353,2.189,2.527,2.962,2.793,2.696,2.477,1.886,2.837,2.871,2.728,2.713,2.98,3.603,2.674,1.86,2.773,2.441,2.307,2.969,2.527,2.92,2.449,2.055,2.63,2.602,2.511,2.035,2.258,3.172,3.192,2.653,2.663,2.923,2.251,2.386,2.552,1.878,3.322,1.612,1.803,2.27,2.613,2.614,2.463,2.541,1.661,2.368,2.355,2.561,3.019,2.236,2.559,2.292,1.982,2.194,2.208,2.729,2.308,2.119,2.221,2.008,2.095,2.511,2.536,2.325,2.665,2.39,2.514,2.807,0.679,0.73,0.489,0.629,0.588,0.445,0.56,0.651,0.571,0.379,0.758,0.542,0.335,0.449,0.412,0.475,0.53,0.92,0.553,0.555,0.845,0.433,0.48,0.577,0.452,0.57,0.447,0.5,0.588,0.457,0.66,0.841,0.519,0.681,0.604,0.502,0.801,0.558,0.269,0.553,0.642,0.439,0.785,0.54,0.607,0.454,0.506,0.645,0.557,0.536,0.265,0.439,0.476,0.464,0.588,0.401,0.77,0.573,0.357,0.453,0.381,0.769,0.378,0.785,0.538,0.796,0.468,0.399,0.452,0.732,0.663,0.538,0.509,0.405
-387,Epileptic,F,32.0,1.3,1.7,1.2,0.9,1.4,2.2,1.5,2.0,0.7,0.8,0.3,2.8,1.7,0.5,1.2,5.6,8.0,1.0,1.9,4.8,0.9,2.0,2.4,3.5,2.3,3.6,3.7,2.8,2.5,3.3,1.6,2.0,0.5,2.8,0.8,0.5,3.5,2.9,0.3,0.3,1.0,3.1,2.0,2.6,2.7,0.6,0.5,0.9,1.4,0.7,0.6,2.4,1.4,2.3,0.5,2.8,0.8,1.3,0.7,1.5,0.9,1.8,0.5,0.7,1.7,1.5,2.7,1.0,1.5,1.0,1.3,1.5,4.4,0.2,9,15,13,8,18,22,11,19,11,8,4.0,26,19,6,14,62,75,9,23,43,8,27,22,44,33,40,43,30,25,39,22,13,6,35,3,5,38,40,2,2,7,27,19,20,15,4,3,5,9,8,5,16,12,16,3,26,7,10,6,11,8,15,3,5,18,21,23,6,9,6,9,11,41,2,0.049,0.019,0.022,0.02,0.03,0.029,0.025,0.026,0.033,0.03,0.035,0.035,0.025,0.026,0.027,0.031,0.03,0.036,0.023,0.043,0.021,0.025,0.027,0.033,0.024,0.029,0.028,0.029,0.025,0.026,0.041,0.064,0.019,0.03,0.026,0.011,0.03,0.032,0.019,0.021,0.022,0.034,0.034,0.026,0.02,0.012,0.014,0.015,0.016,0.023,0.026,0.022,0.024,0.017,0.018,0.021,0.019,0.017,0.018,0.022,0.022,0.026,0.028,0.019,0.021,0.024,0.02,0.015,0.023,0.028,0.024,0.019,0.018,0.011,1707,4208,2401,2084,2278,3136,2557,3734,1221,1679,771.0,2507,3952,1165,2625,10164,16400,2073,4666,4231,2686,4118,3424,6571,6372,7352,6574,4819,5939,6720,2540,1404,1430,5151,1616,1867,7312,7120,382,471,1521,2275,5410,2617,3241,1373,1040,2076,2413,1544,717,3973,1737,4618,633,3911,1261,2614,1005,1795,1603,2711,480,981,2810,1713,3679,2064,1961,1139,1807,2729,9608,626,0.148,0.099,0.114,0.097,0.141,0.136,0.098,0.131,0.132,0.132,0.123,0.141,0.129,0.129,0.13,0.141,0.132,0.129,0.107,0.164,0.089,0.115,0.11,0.139,0.121,0.132,0.128,0.111,0.102,0.131,0.143,0.15,0.098,0.126,0.099,0.079,0.136,0.14,0.105,0.105,0.108,0.133,0.145,0.123,0.101,0.084,0.093,0.082,0.097,0.124,0.118,0.108,0.124,0.098,0.117,0.115,0.109,0.099,0.103,0.114,0.119,0.123,0.122,0.109,0.117,0.118,0.106,0.089,0.111,0.136,0.115,0.104,0.099,0.087,476,1438,911,777,834,1169,887,1225,480,442,199.0,1368,1155,319,841,3170,4777,495,1256,1991,666,1200,1258,1899,1735,2165,2318,1686,1554,2134,802,562,435,1394,433,700,1945,1781,222,251,775,1356,1145,1616,2010,725,452,860,1198,560,353,1747,889,2085,316,2046,657,1218,567,999,635,1137,295,544,1376,988,2129,958,949,486,874,1231,3966,340,3.562,2.688,2.528,2.606,2.165,2.351,2.419,2.746,2.061,2.975,2.853,1.708,2.68,2.688,2.478,2.466,2.755,3.396,2.757,1.903,2.944,2.582,2.281,2.572,2.753,2.689,2.221,2.22,2.814,2.537,2.44,2.547,2.497,2.709,3.281,2.423,2.791,2.861,1.948,2.095,2.514,1.631,3.175,1.862,1.872,2.023,2.649,2.762,2.429,2.693,2.081,2.197,1.879,2.285,2.194,2.068,2.203,2.406,1.929,1.919,2.55,2.281,1.854,2.168,2.141,2.156,1.928,2.384,2.466,2.304,2.299,2.484,2.448,2.303,0.814,0.505,0.377,0.421,0.715,0.568,0.498,0.49,0.606,0.563,0.822,0.51,0.462,0.369,0.445,0.608,0.553,0.912,0.55,0.573,0.699,0.485,0.578,0.661,0.659,0.504,0.635,0.566,0.558,0.471,0.536,0.819,0.365,0.63,0.649,0.426,0.686,0.665,0.436,0.314,0.441,0.389,0.809,0.609,0.557,0.42,0.498,0.825,0.445,0.595,0.529,0.49,0.445,0.468,0.572,0.326,0.373,0.462,0.393,0.339,0.637,0.613,0.419,0.591,0.479,0.826,0.41,0.404,0.349,0.643,0.399,0.448,0.435,0.319
-388,Epileptic,F,27.0,0.9,2.3,1.0,1.2,1.3,2.2,1.4,2.0,1.0,0.7,0.2,2.8,1.6,0.4,1.8,6.9,9.0,1.2,3.4,4.7,1.2,3.0,1.9,2.7,3.0,3.4,2.6,2.5,2.9,3.2,1.3,1.2,0.7,2.7,0.3,0.5,4.8,4.5,0.1,0.1,1.5,2.5,2.6,2.0,3.5,0.7,0.4,0.9,1.5,1.0,0.5,1.8,1.8,2.4,0.5,2.8,1.1,2.2,1.2,0.8,1.0,1.7,0.5,0.3,2.0,1.1,3.1,1.4,1.4,0.3,1.3,2.3,4.0,0.3,10,22,10,9,9,17,11,16,10,8,2.0,28,15,5,21,67,77,9,30,40,9,38,17,32,37,33,30,26,21,42,19,11,8,32,2,5,58,44,2,1,8,23,26,21,20,6,2,7,8,10,4,15,18,17,3,24,7,20,11,6,10,14,4,3,17,15,25,11,11,4,8,16,30,4,0.035,0.022,0.016,0.019,0.028,0.026,0.024,0.031,0.033,0.028,0.021,0.033,0.025,0.022,0.03,0.031,0.027,0.041,0.034,0.037,0.021,0.03,0.025,0.029,0.028,0.029,0.022,0.023,0.024,0.029,0.037,0.045,0.024,0.032,0.011,0.013,0.037,0.03,0.01,0.016,0.029,0.029,0.04,0.029,0.023,0.014,0.021,0.013,0.017,0.024,0.02,0.014,0.024,0.019,0.024,0.016,0.023,0.021,0.022,0.013,0.019,0.031,0.028,0.014,0.019,0.021,0.018,0.02,0.023,0.012,0.021,0.031,0.016,0.017,1715,5338,2411,2417,1928,3480,2831,3621,1517,1791,623.0,2773,3869,1102,3122,12325,18205,2365,4717,4140,2382,6057,2364,5964,6183,6541,5537,5077,6136,6405,2123,1419,1368,5552,1517,1692,8189,7216,369,274,1760,3147,5011,2307,3842,1767,766,2216,3143,1824,639,3650,3063,4842,640,5192,1994,3185,1748,1965,1808,2253,578,879,3319,1961,5164,2363,2008,867,2004,2554,8368,511,0.122,0.115,0.101,0.1,0.123,0.125,0.105,0.134,0.125,0.125,0.102,0.14,0.125,0.127,0.14,0.138,0.129,0.134,0.137,0.141,0.084,0.143,0.128,0.129,0.135,0.133,0.116,0.113,0.104,0.131,0.118,0.143,0.129,0.135,0.074,0.079,0.128,0.126,0.081,0.092,0.119,0.136,0.134,0.13,0.109,0.092,0.103,0.086,0.096,0.107,0.119,0.089,0.127,0.1,0.121,0.098,0.105,0.102,0.124,0.081,0.101,0.138,0.138,0.093,0.108,0.11,0.106,0.112,0.117,0.094,0.115,0.129,0.092,0.093,534,1768,931,990,731,1343,899,1138,477,454,196.0,1346,1078,296,975,3736,5392,501,1631,2072,878,1824,1106,1623,1817,2020,1882,1765,1792,1971,652,546,447,1355,456,631,2379,2364,212,133,828,1363,1197,1257,2261,855,338,1080,1376,817,398,1962,1251,2115,346,2562,896,1827,877,1054,823,909,279,452,1741,929,2588,1142,1016,490,945,1298,3811,323,2.949,2.84,2.501,2.423,2.463,2.3,2.586,2.756,2.573,3.144,3.05,1.803,2.791,2.728,2.558,2.655,2.746,3.496,2.417,1.819,2.32,2.525,1.997,2.78,2.63,2.726,2.31,2.344,2.696,2.602,2.399,2.538,2.438,2.994,3.092,2.434,2.717,2.543,1.988,2.321,2.525,2.115,3.224,2.028,1.931,2.186,2.783,2.529,2.733,2.437,2.003,1.999,2.401,2.411,2.406,2.112,2.691,2.035,2.257,2.088,2.312,2.546,2.597,2.176,2.119,2.382,2.18,2.343,2.279,2.001,2.56,2.301,2.325,1.817,0.839,0.53,0.508,0.459,0.595,0.541,0.49,0.554,0.791,0.505,0.788,0.455,0.501,0.541,0.456,0.499,0.525,0.771,0.612,0.604,0.73,0.492,0.438,0.545,0.456,0.585,0.485,0.488,0.5,0.573,0.636,1.121,0.436,0.636,0.815,0.474,0.76,0.605,0.317,0.285,0.578,0.535,0.826,0.893,0.617,0.604,0.53,0.616,0.674,0.574,0.31,0.488,0.664,0.603,0.513,0.471,0.561,0.614,0.361,0.515,0.63,0.629,0.481,1.067,0.651,0.825,0.49,0.38,0.477,0.527,0.524,0.636,0.557,0.611
-389,Epileptic,M,32.0,0.9,3.3,1.6,1.9,1.4,2.8,1.7,2.0,1.0,1.2,0.4,3.6,1.7,0.6,1.7,4.9,9.2,1.2,2.8,4.8,0.9,4.5,3.6,3.3,5.1,5.1,5.2,3.3,2.7,3.4,1.7,1.1,0.8,2.7,0.4,0.8,3.0,3.0,0.3,0.3,1.2,3.6,2.6,2.8,2.3,0.9,0.6,1.0,1.7,0.7,0.9,2.9,2.3,2.7,0.3,2.7,1.0,2.3,1.1,1.7,0.5,2.1,0.2,0.5,2.6,2.4,3.3,1.5,1.4,0.5,1.0,1.5,5.1,0.2,8,67,17,17,16,27,18,22,9,14,5.0,38,17,9,22,58,89,10,35,43,8,49,31,36,52,58,56,33,28,44,19,14,8,36,2,9,40,42,2,2,10,38,28,22,15,7,3,6,9,10,5,26,18,21,2,23,9,23,12,12,6,16,1,5,26,26,27,13,11,5,9,13,49,2,0.04,0.035,0.024,0.026,0.028,0.028,0.027,0.03,0.039,0.047,0.031,0.04,0.031,0.024,0.036,0.031,0.035,0.041,0.031,0.038,0.022,0.04,0.041,0.038,0.044,0.036,0.038,0.029,0.023,0.03,0.037,0.062,0.032,0.031,0.011,0.018,0.029,0.033,0.017,0.022,0.021,0.034,0.038,0.025,0.018,0.017,0.028,0.015,0.02,0.022,0.033,0.023,0.03,0.024,0.015,0.017,0.024,0.021,0.023,0.022,0.018,0.031,0.016,0.016,0.021,0.042,0.02,0.018,0.02,0.023,0.021,0.016,0.02,0.01,1554,5414,2952,3149,2326,5104,3275,3895,1647,1909,1032.0,3443,3390,1440,3564,8256,16595,2580,6135,5658,3137,4957,3056,6097,5954,8581,5956,4422,6580,6120,2660,1111,1216,6982,2343,2420,7973,8316,456,506,1946,3978,5753,3309,3414,1782,912,2765,2673,1725,688,4606,3006,4557,505,4357,1502,3358,1503,2125,1280,2758,391,1013,4505,1104,5149,2878,2338,1098,1844,2852,9351,629,0.128,0.137,0.125,0.128,0.143,0.132,0.12,0.133,0.159,0.176,0.125,0.171,0.125,0.128,0.152,0.143,0.143,0.148,0.134,0.147,0.087,0.129,0.136,0.149,0.136,0.146,0.133,0.118,0.109,0.132,0.15,0.134,0.121,0.134,0.065,0.101,0.128,0.15,0.094,0.131,0.115,0.135,0.155,0.113,0.099,0.097,0.108,0.077,0.103,0.11,0.127,0.117,0.132,0.111,0.11,0.1,0.131,0.096,0.114,0.115,0.108,0.14,0.096,0.113,0.107,0.15,0.105,0.101,0.107,0.121,0.107,0.105,0.104,0.093,459,1817,1063,1133,886,1649,1130,1258,438,502,273.0,1592,1038,379,1008,2887,4787,532,1581,2377,740,1841,1335,1743,1914,2451,2279,1747,1862,2116,805,431,362,1628,586,837,1847,1750,270,209,907,1699,1185,1837,1969,850,393,1015,1198,636,429,2121,1222,1960,275,2398,696,1692,890,1149,522,1019,206,525,2096,808,2626,1293,1075,454,915,1365,4141,336,3.429,2.807,2.52,2.663,2.505,2.561,2.314,2.684,2.793,3.086,2.796,1.89,2.615,2.74,2.774,2.317,2.728,3.676,3.048,2.035,2.976,2.217,2.069,2.677,2.487,2.702,2.157,2.048,2.714,2.361,2.631,2.456,2.638,3.001,3.625,2.732,2.998,3.265,1.966,2.657,2.787,1.996,3.341,2.023,1.95,2.289,2.786,3.179,2.738,3.092,1.998,2.193,2.452,2.374,2.069,1.998,2.409,2.213,1.963,2.055,2.328,2.7,2.057,2.391,2.249,1.718,2.063,2.485,2.346,2.506,2.251,2.411,2.433,2.336,0.837,0.543,0.566,0.503,0.529,0.65,0.652,0.6,0.558,0.681,0.716,0.541,0.551,0.494,0.512,0.62,0.611,0.785,0.605,0.645,0.846,0.56,0.573,0.737,0.615,0.642,0.523,0.569,0.503,0.537,0.582,0.988,0.409,0.639,0.756,0.461,0.748,0.687,0.405,0.444,0.542,0.643,0.766,0.544,0.622,0.589,0.484,1.022,0.583,0.557,0.38,0.475,0.585,0.496,0.418,0.44,0.462,0.555,0.364,0.508,0.498,0.597,0.401,0.561,0.57,0.587,0.574,0.384,0.442,0.692,0.507,0.436,0.449,0.274
-390,Epileptic,M,22.0,0.8,2.1,1.1,1.3,1.1,2.3,1.6,1.8,0.8,0.7,0.3,3.3,1.6,0.7,1.8,5.8,8.9,1.2,1.9,4.8,1.7,2.6,1.6,3.4,2.3,4.0,2.2,2.4,2.9,2.7,1.5,1.1,0.5,2.5,0.5,0.9,2.8,3.5,0.3,0.3,1.0,3.5,2.3,2.7,2.9,0.5,0.7,1.0,1.4,0.8,0.7,2.7,1.2,3.6,0.3,2.4,0.6,1.6,1.0,0.9,0.4,1.2,0.4,0.6,1.8,1.5,2.2,1.1,0.9,0.5,0.7,0.8,4.6,0.4,6,15,10,10,11,18,12,15,9,8,3.0,36,17,8,18,64,84,10,25,40,16,33,16,38,31,38,29,21,25,39,25,8,4,28,3,7,32,38,2,2,6,30,22,23,18,4,2,6,6,5,5,21,9,24,2,20,6,14,8,7,3,9,2,4,15,19,19,7,5,4,5,6,40,3,0.034,0.022,0.017,0.027,0.03,0.033,0.026,0.031,0.037,0.038,0.033,0.03,0.027,0.031,0.032,0.03,0.029,0.034,0.029,0.038,0.032,0.03,0.027,0.04,0.027,0.029,0.025,0.026,0.028,0.028,0.042,0.046,0.026,0.035,0.019,0.02,0.035,0.029,0.019,0.013,0.021,0.033,0.041,0.022,0.022,0.012,0.025,0.014,0.018,0.014,0.03,0.023,0.023,0.022,0.024,0.02,0.02,0.022,0.021,0.016,0.015,0.022,0.022,0.022,0.022,0.023,0.024,0.017,0.013,0.023,0.019,0.014,0.017,0.019,1814,4926,2823,2445,1791,3401,2636,3171,1497,1375,724.0,4016,3970,1794,4065,12410,19596,3152,3993,5017,4802,4967,2282,5810,5292,7555,4488,3808,6704,5230,2290,993,892,5822,1484,2140,7114,8753,421,736,1448,2869,5471,3797,3920,1243,990,2242,2276,1840,619,4056,1827,6687,411,3376,1242,3257,1204,1488,866,2354,508,765,3030,1240,3598,2504,1888,954,1138,2440,9671,617,0.105,0.112,0.095,0.117,0.125,0.135,0.102,0.137,0.135,0.154,0.127,0.138,0.122,0.137,0.131,0.133,0.129,0.126,0.138,0.139,0.111,0.137,0.125,0.139,0.132,0.135,0.113,0.109,0.112,0.132,0.135,0.13,0.102,0.128,0.09,0.111,0.135,0.129,0.089,0.082,0.109,0.13,0.154,0.112,0.107,0.087,0.103,0.087,0.096,0.085,0.138,0.11,0.116,0.106,0.128,0.116,0.117,0.111,0.117,0.095,0.098,0.11,0.126,0.122,0.118,0.119,0.111,0.098,0.083,0.122,0.117,0.09,0.099,0.116,459,1407,969,857,650,1204,922,1014,368,357,167.0,1789,1026,382,1019,3200,4994,592,1161,2051,993,1516,1022,1512,1553,2196,1568,1334,1665,1761,637,464,289,1321,375,708,1511,1973,258,365,759,1481,1053,2131,2116,647,374,996,1080,814,379,1787,776,2495,171,1757,533,1299,710,822,433,813,248,363,1364,885,1672,1035,902,348,534,953,4185,292,3.515,3.049,2.739,2.82,2.331,2.46,2.41,2.747,2.816,2.868,3.076,1.948,2.871,3.046,2.977,2.773,3.048,3.961,2.715,2.084,3.395,2.472,2.035,2.874,2.601,2.789,2.291,2.237,3.023,2.375,2.66,2.295,2.415,2.982,3.497,2.785,3.266,3.082,1.966,2.413,2.389,1.769,3.622,2.051,2.103,2.17,3.097,2.835,2.683,2.704,2.08,2.364,2.599,2.726,2.768,2.07,2.561,2.55,1.977,1.995,2.347,2.775,2.451,2.227,2.332,1.684,2.207,2.773,2.495,2.714,2.497,2.87,2.414,2.637,0.915,0.769,0.613,0.604,0.615,0.533,0.626,0.515,0.672,0.606,0.893,0.51,0.375,0.611,0.485,0.618,0.65,0.687,0.633,0.626,0.794,0.508,0.48,0.668,0.528,0.564,0.527,0.606,0.528,0.562,0.832,0.942,0.387,0.64,0.771,0.471,0.759,0.649,0.44,0.329,0.364,0.451,0.635,0.644,0.583,0.404,0.492,0.545,0.427,0.48,0.408,0.547,0.577,0.553,0.434,0.458,0.353,0.74,0.349,0.418,0.415,0.803,0.476,0.94,0.549,0.487,0.512,0.416,0.418,0.617,0.596,0.736,0.483,0.402
-391,Epileptic,M,42.0,1.2,3.7,1.4,2.1,2.3,1.8,2.6,2.1,1.1,1.4,0.4,3.4,1.5,0.8,1.8,9.8,12.7,1.3,2.4,5.6,1.7,3.5,1.6,4.0,2.7,4.3,4.3,3.7,4.1,2.7,1.6,1.1,0.6,2.7,0.7,0.6,4.0,4.0,0.3,0.3,1.2,3.8,1.9,3.3,4.0,1.0,0.7,1.4,1.5,0.8,0.3,2.7,1.7,3.6,0.1,3.4,1.1,1.8,1.2,1.1,0.8,2.2,0.5,0.4,2.7,1.3,3.9,2.0,1.5,1.1,1.2,1.8,5.4,0.4,11,36,13,15,22,14,18,17,11,11,5.0,31,16,8,15,85,98,11,25,46,16,34,15,40,32,41,44,29,28,27,25,8,5,25,4,4,44,40,3,1,7,30,21,26,22,7,4,8,8,5,2,18,10,21,1,22,6,14,8,9,7,17,4,3,19,11,28,12,7,14,11,11,42,4,0.038,0.031,0.021,0.031,0.044,0.033,0.032,0.031,0.034,0.047,0.041,0.034,0.03,0.034,0.032,0.041,0.037,0.039,0.036,0.04,0.023,0.033,0.029,0.037,0.028,0.032,0.027,0.034,0.032,0.026,0.041,0.033,0.023,0.031,0.016,0.016,0.035,0.033,0.021,0.02,0.025,0.041,0.038,0.03,0.024,0.022,0.025,0.02,0.018,0.013,0.033,0.024,0.026,0.024,0.014,0.022,0.023,0.02,0.025,0.018,0.02,0.035,0.025,0.017,0.024,0.032,0.022,0.025,0.025,0.025,0.033,0.02,0.018,0.017,2122,5816,3037,2595,2533,3029,2656,3829,1926,1838,701.0,3407,3248,1265,3313,14733,21391,3019,4654,5847,6309,6299,2150,7743,5331,7596,7822,4064,6757,5037,2409,1555,1165,6321,2720,1956,9149,9529,515,423,1487,3641,5649,3289,4292,1494,1097,2481,2671,1983,341,4432,2143,6432,417,4779,2013,3218,1488,1860,1313,3026,682,1004,3648,1405,5501,2885,1851,1700,1445,2842,11079,522,0.126,0.128,0.104,0.122,0.142,0.127,0.125,0.132,0.141,0.17,0.132,0.131,0.131,0.134,0.123,0.141,0.132,0.13,0.151,0.137,0.103,0.127,0.125,0.136,0.123,0.131,0.117,0.109,0.107,0.125,0.156,0.106,0.102,0.121,0.084,0.078,0.133,0.133,0.109,0.113,0.113,0.142,0.132,0.127,0.107,0.105,0.117,0.09,0.094,0.083,0.127,0.106,0.113,0.102,0.109,0.106,0.1,0.106,0.116,0.102,0.113,0.132,0.121,0.107,0.112,0.122,0.105,0.116,0.11,0.125,0.131,0.105,0.099,0.109,584,2041,1141,1118,962,1073,1189,1154,560,503,191.0,1565,913,374,964,4211,6142,628,1255,2365,1316,1802,935,2007,1703,2229,2634,1681,1972,1695,637,606,411,1429,683,668,2044,2219,250,210,769,1560,1165,1916,2541,748,450,1073,1245,886,169,1919,998,2497,150,2508,834,1444,793,972,648,1106,331,447,1800,724,2910,1302,902,739,711,1345,4739,332,3.418,2.653,2.689,2.366,2.411,2.415,2.033,2.947,2.567,2.872,2.802,1.916,2.817,2.624,2.654,2.69,2.815,3.601,3.002,2.04,3.412,2.715,2.034,2.826,2.593,2.76,2.362,2.023,2.652,2.458,2.721,2.386,2.335,3.136,3.543,2.782,3.176,3.141,2.241,2.512,2.403,1.983,3.241,1.866,1.953,2.243,3.045,2.883,2.677,2.4,2.268,2.431,2.305,2.633,2.564,2.143,2.767,2.542,2.233,2.148,2.214,2.697,2.271,2.357,2.222,2.203,2.057,2.562,2.463,2.218,2.295,2.603,2.536,1.919,0.659,0.622,0.51,0.472,0.565,0.586,0.434,0.56,0.66,0.446,0.624,0.494,0.45,0.53,0.489,0.618,0.642,0.636,0.551,0.684,0.849,0.466,0.462,0.67,0.521,0.537,0.461,0.49,0.601,0.498,0.747,1.132,0.368,0.639,0.864,0.481,0.708,0.633,0.528,0.402,0.45,0.582,0.81,0.511,0.572,0.436,0.58,0.616,0.616,0.468,0.382,0.589,0.58,0.628,0.384,0.396,0.56,0.711,0.434,0.427,0.347,0.77,0.378,0.953,0.492,0.644,0.396,0.446,0.385,0.58,0.377,0.544,0.448,0.431
-392,Epileptic,M,52.0,0.5,2.2,0.7,1.1,2.0,1.8,1.5,1.8,1.2,0.6,0.3,3.3,1.5,0.8,1.5,5.5,8.2,0.8,3.1,3.9,1.1,2.9,2.1,4.1,2.9,2.9,3.1,2.5,2.6,3.9,1.0,1.5,0.6,2.9,0.3,0.9,3.6,2.7,0.1,0.3,1.3,3.2,3.0,2.8,2.9,0.5,0.4,1.0,1.3,0.9,0.7,1.8,1.2,2.4,0.4,2.4,1.2,3.2,1.1,1.5,0.5,1.5,0.6,0.4,3.0,1.8,2.4,2.0,0.8,0.4,1.2,1.4,3.4,0.4,5,18,8,12,18,18,12,18,13,5,2.0,27,15,8,15,53,74,8,32,41,11,31,18,41,36,31,34,26,21,43,12,10,5,34,2,7,48,32,1,2,6,29,30,23,16,4,2,5,6,8,6,13,9,14,3,20,10,24,7,11,5,9,3,5,23,19,18,12,7,5,8,10,27,3,0.025,0.024,0.015,0.024,0.035,0.031,0.023,0.032,0.038,0.031,0.032,0.032,0.027,0.037,0.03,0.031,0.028,0.023,0.039,0.031,0.018,0.034,0.029,0.038,0.028,0.025,0.026,0.026,0.023,0.028,0.029,0.042,0.03,0.03,0.013,0.019,0.032,0.031,0.009,0.022,0.024,0.032,0.042,0.026,0.02,0.012,0.023,0.014,0.017,0.033,0.027,0.021,0.019,0.022,0.022,0.02,0.024,0.028,0.022,0.024,0.018,0.026,0.023,0.019,0.027,0.044,0.018,0.027,0.02,0.012,0.029,0.022,0.017,0.018,1491,4148,1968,2045,2352,2799,2347,3360,2174,1211,596.0,2910,3021,1298,2640,9088,16305,2676,5127,3937,3462,4335,2427,6173,5510,6428,5440,3695,4539,5919,1959,1957,1203,6001,1827,2141,7251,6460,434,497,1472,3625,6806,2557,3521,1161,824,1995,2211,1433,602,2598,1891,3652,587,3420,1567,3245,1210,1574,922,2378,636,869,3263,1070,3791,2313,1238,784,1384,1821,7705,433,0.099,0.113,0.099,0.117,0.145,0.143,0.099,0.133,0.152,0.129,0.115,0.132,0.13,0.15,0.138,0.132,0.125,0.111,0.156,0.144,0.086,0.154,0.127,0.146,0.131,0.119,0.124,0.103,0.086,0.133,0.12,0.111,0.105,0.14,0.072,0.103,0.141,0.135,0.077,0.104,0.109,0.142,0.167,0.124,0.102,0.087,0.1,0.079,0.092,0.129,0.133,0.102,0.106,0.106,0.115,0.111,0.128,0.122,0.111,0.126,0.104,0.116,0.118,0.113,0.129,0.145,0.106,0.116,0.109,0.108,0.131,0.119,0.098,0.118,398,1418,719,830,979,1036,906,1062,594,326,200.0,1506,885,353,846,2825,4677,547,1406,1971,931,1398,1033,1937,1931,1970,1932,1433,1412,2169,645,629,405,1603,426,783,1919,1601,193,279,777,1594,1353,1714,2079,653,363,945,1066,533,399,1433,948,1651,262,1840,733,1752,740,915,461,923,376,426,1668,742,1951,1151,663,449,693,948,3174,283,3.408,2.781,2.528,2.409,2.305,2.334,2.191,2.799,2.723,3.186,2.775,1.722,2.643,2.745,2.481,2.562,2.918,3.719,2.926,1.744,2.706,2.429,2.07,2.452,2.395,2.639,2.227,2.095,2.519,2.267,2.291,2.795,2.307,2.861,3.302,2.588,2.858,2.969,2.429,2.065,2.434,1.997,3.321,1.698,1.922,1.862,2.837,2.599,2.566,2.829,1.76,1.98,2.03,2.447,2.477,2.031,2.341,2.085,1.966,1.92,2.211,2.604,1.905,2.428,2.048,1.873,2.127,2.327,2.127,2.029,2.365,2.285,2.518,1.985,0.785,0.653,0.523,0.515,0.557,0.545,0.584,0.555,0.474,0.546,0.664,0.441,0.455,0.404,0.464,0.454,0.54,0.808,0.541,0.643,0.823,0.468,0.633,0.726,0.475,0.467,0.472,0.516,0.512,0.569,0.699,1.053,0.581,0.643,0.774,0.529,0.741,0.644,0.296,0.572,0.409,0.527,0.869,0.606,0.6,0.374,0.584,0.786,0.556,0.603,0.308,0.387,0.504,0.432,0.558,0.508,0.421,0.566,0.412,0.435,0.46,0.635,0.453,0.827,0.496,0.609,0.485,0.421,0.527,0.354,0.447,0.474,0.517,0.559
-393,Epileptic,F,17.5,1.0,2.3,0.6,0.7,1.4,1.8,1.7,1.5,0.7,0.4,0.1,2.5,1.6,0.5,1.3,5.2,7.0,0.6,2.1,4.1,1.0,2.3,1.2,1.8,2.5,2.4,2.3,2.0,3.1,2.6,1.0,1.1,0.6,2.3,0.9,0.7,2.3,1.9,0.1,0.2,0.6,2.5,1.9,2.3,2.8,0.5,0.6,1.0,0.9,0.6,0.6,1.4,0.8,1.6,0.4,2.4,0.6,1.4,0.9,0.8,0.8,0.8,0.2,0.3,1.6,1.0,2.3,1.2,1.0,0.3,0.7,1.0,2.9,0.3,7,19,5,6,14,13,12,12,10,4,1.0,21,16,6,12,44,58,5,19,37,8,25,12,24,29,20,27,17,22,31,15,7,4,23,7,5,25,24,1,1,3,22,14,15,16,5,2,6,5,8,3,8,4,10,2,19,4,11,5,6,8,5,1,3,12,11,18,7,5,3,4,7,19,3,0.043,0.026,0.015,0.015,0.039,0.037,0.027,0.031,0.038,0.024,0.018,0.035,0.038,0.035,0.03,0.038,0.028,0.023,0.029,0.035,0.022,0.033,0.028,0.028,0.026,0.029,0.026,0.028,0.035,0.028,0.041,0.056,0.023,0.029,0.023,0.019,0.035,0.027,0.01,0.014,0.017,0.033,0.038,0.027,0.026,0.01,0.03,0.017,0.015,0.018,0.03,0.016,0.019,0.017,0.023,0.019,0.015,0.021,0.022,0.017,0.029,0.018,0.015,0.018,0.02,0.033,0.022,0.02,0.017,0.014,0.027,0.019,0.015,0.018,1339,4733,1903,1869,1946,2352,2643,2596,1683,1063,510.0,2692,3241,1105,2666,10072,17450,2471,4311,4621,2713,3617,1867,4632,5637,5251,4925,3322,5650,5301,2011,794,1053,5896,1815,1547,4839,5766,324,515,1478,2600,4113,2508,2795,1411,695,2289,2068,1447,580,2740,1194,3707,439,3702,1147,2919,1090,1316,911,1547,288,709,2567,916,3167,2499,1889,677,1302,1944,7665,431,0.137,0.117,0.089,0.089,0.129,0.138,0.117,0.136,0.144,0.119,0.079,0.141,0.135,0.128,0.13,0.14,0.125,0.103,0.122,0.134,0.089,0.141,0.126,0.118,0.136,0.123,0.114,0.106,0.119,0.134,0.141,0.131,0.112,0.128,0.088,0.092,0.119,0.121,0.07,0.083,0.089,0.137,0.128,0.117,0.115,0.083,0.127,0.081,0.089,0.101,0.127,0.092,0.109,0.09,0.135,0.105,0.099,0.107,0.111,0.101,0.137,0.096,0.116,0.113,0.106,0.139,0.115,0.099,0.094,0.105,0.118,0.105,0.084,0.101,426,1369,606,705,631,847,856,815,377,272,162.0,1182,845,245,730,2351,4197,420,1153,1802,770,1193,740,1152,1549,1481,1563,1175,1539,1621,472,356,363,1257,510,570,1211,1227,142,258,642,1219,911,1366,1743,694,279,895,884,609,300,1260,582,1510,227,1924,554,1207,617,698,439,657,153,296,1233,454,1589,996,844,359,505,843,3112,258,2.974,3.143,2.942,2.578,2.489,2.481,2.687,2.937,2.944,3.421,2.652,1.992,2.856,3.158,2.835,3.086,3.279,3.898,2.937,2.142,3.009,2.274,2.189,2.868,2.817,2.829,2.485,2.223,2.874,2.664,2.826,2.496,2.38,3.306,3.158,2.445,2.873,3.211,2.353,2.469,2.956,1.914,3.217,2.082,1.861,2.314,3.145,3.003,2.779,2.491,2.101,2.302,2.224,2.674,2.483,2.186,2.553,2.755,2.172,2.219,2.337,2.479,2.003,2.398,2.391,2.448,2.278,2.747,2.625,2.281,2.865,2.688,2.721,1.927,1.225,0.702,0.718,0.502,0.64,0.58,0.542,0.531,0.618,0.532,0.742,0.443,0.498,0.563,0.522,0.639,0.585,0.838,0.618,0.626,0.803,0.515,0.434,0.708,0.468,0.532,0.486,0.569,0.585,0.524,0.741,0.977,0.402,0.641,0.822,0.38,0.707,0.595,0.292,0.369,0.612,0.43,0.673,0.694,0.641,0.521,0.617,0.855,0.542,0.48,0.442,0.508,0.542,0.545,0.36,0.467,0.543,0.754,0.449,0.444,0.354,0.58,0.274,1.008,0.569,0.637,0.471,0.438,0.477,0.446,0.523,0.485,0.587,0.399
-394,Epileptic,F,22.0,0.7,2.7,0.8,1.0,1.2,2.4,1.3,2.0,0.9,0.7,0.4,2.9,1.7,0.6,1.4,5.3,9.7,0.9,2.8,5.3,1.1,3.5,1.4,2.9,3.2,3.1,2.5,1.8,2.4,2.2,1.5,1.0,0.4,1.6,0.3,0.4,4.0,2.4,0.2,0.1,1.2,2.8,2.4,3.6,2.4,0.5,0.4,1.0,1.3,0.7,0.8,1.7,1.1,1.9,0.4,1.5,1.0,1.9,1.2,1.0,0.6,1.0,0.5,0.4,2.0,1.2,2.3,1.8,0.8,0.5,0.6,1.2,3.4,0.2,6,19,7,8,10,19,12,15,9,8,4.0,23,15,5,12,51,78,6,22,40,8,31,15,23,29,29,24,18,16,24,13,9,3,16,2,2,40,22,2,1,8,26,19,28,17,4,2,6,7,6,7,13,7,12,1,13,9,14,8,8,5,7,2,3,19,11,21,13,6,6,5,9,29,2,0.031,0.027,0.015,0.023,0.034,0.035,0.02,0.031,0.045,0.04,0.032,0.036,0.029,0.036,0.036,0.034,0.035,0.028,0.039,0.041,0.023,0.034,0.023,0.042,0.038,0.031,0.023,0.021,0.026,0.03,0.04,0.049,0.016,0.024,0.011,0.011,0.039,0.031,0.033,0.016,0.023,0.035,0.04,0.034,0.018,0.01,0.023,0.015,0.017,0.017,0.049,0.019,0.024,0.018,0.048,0.015,0.024,0.027,0.028,0.02,0.024,0.025,0.034,0.018,0.022,0.033,0.02,0.022,0.016,0.021,0.02,0.025,0.017,0.013,1219,3960,1607,1664,1693,2855,1917,2788,1123,913,629.0,2360,2998,907,2914,8371,13877,2033,3603,4142,3218,4682,1502,4613,4160,4587,3879,2745,3888,3506,1475,632,637,3659,1446,1157,6325,4672,304,387,1446,2329,5730,2320,2573,1166,691,2220,2203,1417,434,2617,1335,3623,302,2551,1485,2589,1179,1382,812,1951,473,724,2449,1284,2930,2500,1192,737,884,2103,6409,291,0.118,0.111,0.092,0.112,0.132,0.14,0.099,0.133,0.136,0.146,0.119,0.141,0.125,0.139,0.129,0.126,0.123,0.113,0.148,0.15,0.087,0.14,0.127,0.141,0.148,0.137,0.112,0.102,0.105,0.13,0.135,0.152,0.085,0.108,0.065,0.065,0.133,0.123,0.127,0.089,0.118,0.142,0.141,0.145,0.098,0.086,0.108,0.088,0.094,0.098,0.181,0.1,0.116,0.089,0.164,0.093,0.126,0.122,0.128,0.105,0.123,0.12,0.144,0.1,0.129,0.119,0.112,0.115,0.099,0.113,0.128,0.114,0.1,0.095,404,1547,764,739,614,1156,921,1041,336,336,205.0,1248,878,296,722,2640,4269,445,1215,1975,823,1561,871,1129,1354,1555,1623,1265,1352,1237,594,334,332,1051,465,544,1793,1323,125,174,763,1271,1026,1688,2011,660,302,1015,1114,638,323,1377,736,1729,105,1561,641,1246,713,786,419,625,224,378,1328,617,1844,1221,705,443,503,979,3145,213,2.993,2.397,2.14,2.258,2.353,2.247,1.858,2.457,2.519,2.462,2.482,1.826,2.644,2.389,2.774,2.396,2.594,3.458,2.473,1.844,3.071,2.396,1.688,2.965,2.473,2.555,2.033,1.824,2.278,2.353,2.02,2.084,1.676,2.554,2.734,2.057,2.738,2.66,2.698,2.361,2.277,1.753,3.472,1.601,1.465,1.969,2.878,2.718,2.355,2.41,1.745,1.924,1.92,2.194,3.379,1.821,2.466,2.322,1.926,2.012,2.191,2.836,2.301,2.21,2.039,2.346,1.777,2.356,1.948,2.094,2.038,2.342,2.217,1.752,0.787,0.629,0.47,0.491,0.737,0.544,0.449,0.518,0.736,0.38,0.604,0.383,0.522,0.65,0.813,0.669,0.737,1.052,0.583,0.606,0.829,0.412,0.357,0.774,0.512,0.522,0.48,0.457,0.55,0.404,0.697,1.053,0.421,0.788,1.056,0.538,0.855,0.648,0.477,0.415,0.544,0.375,0.851,0.391,0.34,0.519,0.482,0.992,0.547,0.662,0.325,0.548,0.525,0.58,0.389,0.444,0.546,0.765,0.395,0.37,0.346,0.831,0.838,0.931,0.432,0.647,0.511,0.536,0.529,0.744,0.489,0.592,0.627,0.422
-395,Epileptic,F,27.0,1.0,2.1,0.8,0.8,2.1,2.1,1.5,1.4,1.2,0.8,0.3,3.6,1.2,0.5,1.3,5.0,8.7,0.5,1.6,4.6,1.4,2.8,3.1,2.9,3.6,3.6,2.8,2.2,2.8,3.5,1.1,1.8,0.6,2.3,0.5,0.8,3.4,2.9,0.3,0.2,1.1,2.7,1.8,1.9,2.2,0.8,0.4,1.0,1.3,0.7,0.6,1.8,1.5,2.3,0.4,2.9,0.6,1.6,1.0,1.5,0.4,1.4,0.7,0.5,2.1,1.0,2.7,1.5,1.1,0.4,1.4,1.4,5.3,0.4,7,28,9,9,19,18,13,16,15,8,3.0,35,15,7,14,62,92,6,24,41,23,40,33,38,49,42,38,22,26,41,13,12,6,28,4,6,40,40,2,1,6,27,21,13,12,7,3,6,7,7,5,12,9,16,2,22,4,18,7,13,4,13,5,5,18,18,18,11,8,4,9,10,47,3,0.039,0.024,0.014,0.017,0.04,0.033,0.028,0.025,0.043,0.044,0.039,0.045,0.031,0.031,0.027,0.031,0.03,0.024,0.035,0.045,0.025,0.037,0.041,0.035,0.034,0.028,0.032,0.027,0.031,0.032,0.035,0.06,0.026,0.027,0.016,0.016,0.038,0.03,0.019,0.017,0.022,0.035,0.032,0.022,0.018,0.016,0.019,0.015,0.018,0.014,0.029,0.023,0.029,0.021,0.019,0.02,0.017,0.022,0.027,0.022,0.02,0.026,0.034,0.021,0.021,0.02,0.022,0.021,0.022,0.014,0.031,0.02,0.02,0.018,1567,3948,2158,2285,2256,2833,2767,3102,1623,1465,578.0,3455,2921,1130,2968,9770,17622,1813,3584,4343,4419,4324,2491,5482,6538,7818,5563,3985,5706,5811,1978,1546,1155,6689,1784,2449,7008,7746,413,458,1527,2877,5147,2617,3179,1429,762,2196,2282,1723,584,2361,1356,3561,584,3703,1181,3126,985,2096,697,2154,432,838,2935,1174,3647,2412,1677,602,1464,2330,9898,697,0.138,0.122,0.102,0.1,0.159,0.148,0.125,0.13,0.165,0.141,0.124,0.158,0.144,0.152,0.131,0.139,0.135,0.119,0.153,0.161,0.111,0.138,0.139,0.136,0.139,0.133,0.134,0.108,0.117,0.144,0.143,0.123,0.125,0.126,0.086,0.086,0.133,0.134,0.114,0.1,0.104,0.143,0.136,0.11,0.092,0.102,0.103,0.088,0.095,0.097,0.134,0.113,0.129,0.1,0.096,0.111,0.101,0.106,0.122,0.115,0.117,0.125,0.161,0.121,0.115,0.114,0.106,0.109,0.108,0.115,0.135,0.104,0.108,0.113,467,1579,877,810,891,998,924,969,496,371,158.0,1363,840,288,780,3000,5288,462,946,1883,1059,1382,1288,1570,2019,2401,1709,1439,1515,1884,612,546,422,1472,457,818,1796,1947,212,238,807,1226,1142,1361,1845,743,329,951,1094,790,333,1234,821,1747,332,2182,542,1435,604,1122,365,986,266,439,1507,795,1923,1153,836,393,695,1191,4287,352,3.418,2.324,2.41,2.514,2.045,2.515,2.395,2.724,2.462,2.906,2.867,2.093,2.737,2.854,2.778,2.449,2.592,3.221,2.916,2.019,2.994,2.412,1.848,2.586,2.509,2.6,2.458,2.204,2.782,2.409,2.36,2.835,2.226,3.277,3.5,2.725,2.806,2.926,2.115,2.167,2.41,1.951,3.237,2.216,1.972,2.163,2.835,2.864,2.519,2.562,2.033,1.998,1.802,2.121,1.996,1.922,2.587,2.408,1.943,2.043,2.31,2.266,1.912,2.244,2.112,1.85,2.103,2.294,2.219,1.682,2.339,2.428,2.411,2.53,0.753,0.568,0.521,0.472,0.667,0.585,0.537,0.422,0.667,0.601,0.717,0.674,0.507,0.382,0.555,0.667,0.712,0.615,0.436,0.667,0.736,0.544,0.491,0.774,0.599,0.525,0.604,0.564,0.534,0.551,0.678,0.886,0.46,0.485,0.579,0.45,0.697,0.543,0.474,0.461,0.497,0.577,0.688,0.632,0.546,0.563,0.496,0.603,0.414,0.431,0.481,0.388,0.427,0.487,0.417,0.391,0.324,0.487,0.313,0.449,0.342,0.615,0.395,0.628,0.451,0.585,0.41,0.428,0.464,0.438,0.415,0.469,0.442,0.503
-396,Epileptic,F,22.0,0.6,2.5,0.9,1.4,1.5,1.9,1.3,1.7,1.2,0.6,0.4,3.5,1.8,0.6,1.4,5.3,9.8,0.7,1.5,4.5,1.4,3.0,1.3,2.9,3.1,4.1,3.6,2.3,2.4,3.1,1.0,1.8,0.5,2.6,0.3,0.3,2.9,3.5,0.2,0.3,1.1,3.7,2.3,3.1,2.8,0.7,0.4,1.0,1.1,0.7,0.7,1.4,1.4,2.5,0.1,2.8,0.4,2.1,1.1,1.2,0.5,1.1,0.2,0.5,1.6,1.1,2.7,1.0,1.3,0.4,1.0,1.4,5.0,0.4,5,25,9,13,22,16,11,16,12,6,2.0,26,18,6,17,67,91,6,18,34,12,31,15,44,39,43,48,24,20,35,16,12,4,29,3,2,38,47,1,2,6,27,22,27,16,5,2,7,6,6,4,9,11,18,2,22,3,18,10,10,4,9,1,4,11,15,23,8,8,4,7,10,43,3,0.028,0.028,0.016,0.021,0.034,0.031,0.021,0.027,0.037,0.026,0.035,0.04,0.03,0.034,0.029,0.035,0.029,0.025,0.027,0.038,0.024,0.033,0.027,0.037,0.032,0.033,0.03,0.022,0.021,0.03,0.033,0.049,0.022,0.031,0.012,0.008,0.035,0.032,0.014,0.014,0.02,0.04,0.034,0.034,0.022,0.013,0.017,0.019,0.018,0.014,0.027,0.016,0.024,0.019,0.016,0.017,0.016,0.024,0.027,0.024,0.023,0.021,0.031,0.018,0.019,0.023,0.018,0.015,0.018,0.013,0.025,0.021,0.019,0.017,1507,5578,2447,2834,2389,3070,3027,3669,2121,1375,670.0,2933,3937,1229,3466,11019,21677,2866,4129,3542,3843,4936,2403,5585,6192,8329,7324,4255,6492,5995,2159,1525,1200,6179,1925,1583,6357,8712,363,946,1631,3159,5570,2389,3225,1694,801,2111,2479,1735,621,2986,2044,5559,572,4425,1168,3601,1428,1640,815,2198,290,906,2884,1305,4445,2536,2466,837,1519,2149,9776,507,0.107,0.116,0.097,0.114,0.133,0.139,0.109,0.124,0.139,0.118,0.104,0.15,0.136,0.14,0.132,0.138,0.131,0.107,0.123,0.14,0.103,0.145,0.135,0.136,0.139,0.145,0.135,0.112,0.096,0.137,0.119,0.128,0.099,0.13,0.072,0.058,0.134,0.13,0.083,0.085,0.107,0.144,0.134,0.135,0.102,0.086,0.098,0.09,0.094,0.093,0.127,0.091,0.122,0.106,0.104,0.106,0.097,0.115,0.132,0.122,0.121,0.102,0.152,0.118,0.103,0.118,0.103,0.095,0.103,0.109,0.122,0.115,0.098,0.114,401,1602,881,1013,875,1038,960,1069,620,353,159.0,1365,1074,310,887,2753,5634,428,1008,1947,941,1490,833,1466,1755,2271,2253,1555,1623,1863,524,578,363,1296,514,609,1603,2003,212,367,729,1439,1133,1553,1975,809,330,871,1029,735,372,1346,891,2079,175,2373,473,1531,709,823,396,848,102,434,1263,764,2246,1041,1045,414,624,1043,4202,299,3.598,3.021,2.631,2.628,2.362,2.64,2.5,3.015,2.469,3.04,3.236,1.886,2.896,2.722,2.79,2.896,3.112,4.07,3.126,1.688,3.042,2.557,2.351,2.796,2.659,2.956,2.484,2.2,3.033,2.669,2.742,2.69,2.684,3.157,3.211,2.391,2.966,3.025,2.069,2.69,2.896,2.019,3.424,1.797,1.921,2.305,3.05,2.824,2.859,2.686,2.178,2.326,2.325,2.677,2.765,2.135,2.747,2.523,2.201,2.191,2.416,2.507,2.596,2.191,2.482,2.106,2.149,2.669,2.901,2.149,2.866,2.594,2.457,2.162,0.892,0.682,0.569,0.595,0.677,0.556,0.595,0.63,0.652,0.383,0.757,0.546,0.629,0.493,0.556,0.596,0.587,0.745,0.565,0.457,0.866,0.531,0.5,0.777,0.52,0.515,0.51,0.465,0.553,0.491,0.833,0.892,0.499,0.718,0.691,0.489,0.599,0.78,0.259,0.705,0.577,0.531,0.628,0.67,0.638,0.479,0.509,0.771,0.655,0.478,0.321,0.616,0.627,0.515,0.441,0.392,0.411,0.563,0.337,0.389,0.406,0.773,0.493,1.178,0.596,0.783,0.411,0.393,0.408,0.76,0.583,0.467,0.513,0.609
-397,Epileptic,F,47.0,0.8,1.9,1.0,1.4,1.9,2.5,1.4,1.9,0.7,0.5,0.3,2.9,1.1,0.6,1.7,7.4,10.1,0.9,2.2,4.1,0.8,3.0,1.5,3.1,2.7,4.4,4.3,2.3,2.5,4.0,1.0,1.2,0.3,2.7,0.3,0.6,3.5,3.8,0.1,0.1,1.4,3.3,1.9,2.7,3.0,1.0,0.5,0.8,1.1,1.1,0.7,1.4,1.2,3.5,0.4,2.6,0.8,2.0,1.3,1.1,0.6,1.3,0.3,0.4,2.1,1.0,2.8,1.7,1.2,0.6,1.0,1.8,4.6,0.5,5,18,8,13,16,22,14,15,12,6,3.0,28,11,7,18,79,85,8,26,36,7,37,16,34,34,45,44,24,21,40,14,10,4,25,2,8,42,41,1,1,7,30,21,20,19,7,4,5,6,8,4,14,8,27,3,25,8,15,10,10,4,9,4,4,17,13,20,15,6,6,7,14,39,3,0.046,0.021,0.017,0.03,0.041,0.034,0.024,0.031,0.036,0.03,0.033,0.03,0.025,0.034,0.039,0.035,0.028,0.036,0.029,0.033,0.016,0.033,0.029,0.038,0.035,0.033,0.031,0.023,0.025,0.034,0.031,0.063,0.016,0.033,0.011,0.014,0.031,0.032,0.014,0.016,0.025,0.034,0.032,0.034,0.024,0.017,0.025,0.014,0.017,0.019,0.029,0.021,0.027,0.027,0.014,0.021,0.022,0.025,0.024,0.024,0.02,0.025,0.026,0.018,0.025,0.028,0.024,0.021,0.019,0.016,0.025,0.028,0.02,0.021,1182,3549,1970,2201,2162,3212,2190,3054,1143,1155,526.0,2722,2456,909,2439,10860,18335,1906,4651,4176,2664,4231,1970,5155,4392,6822,6096,3489,5028,5245,1803,940,844,4906,1654,2016,5856,6238,196,365,1588,2952,3915,2234,3072,1504,740,1907,2004,1554,586,2365,1532,5175,577,3384,1322,3427,1315,1140,763,2070,611,814,2640,912,3307,2806,1731,1046,1349,1805,7664,630,0.123,0.11,0.106,0.127,0.157,0.149,0.115,0.129,0.135,0.139,0.122,0.133,0.127,0.146,0.165,0.15,0.13,0.13,0.135,0.138,0.083,0.153,0.133,0.154,0.15,0.151,0.138,0.112,0.104,0.144,0.126,0.158,0.109,0.129,0.066,0.089,0.126,0.13,0.112,0.102,0.117,0.147,0.131,0.138,0.113,0.105,0.125,0.087,0.092,0.112,0.127,0.103,0.126,0.12,0.11,0.121,0.111,0.114,0.125,0.129,0.106,0.116,0.135,0.104,0.119,0.122,0.122,0.119,0.106,0.116,0.123,0.121,0.108,0.133,339,1446,865,893,769,1327,818,1000,402,350,177.0,1362,788,328,777,3385,5578,434,1300,1939,812,1515,863,1382,1514,2294,2347,1426,1646,1938,576,383,345,1325,484,739,1843,1884,121,196,754,1530,1067,1380,1981,747,334,777,992,794,351,1249,742,2287,364,1953,670,1474,777,712,436,793,250,413,1383,544,1768,1286,851,574,653,974,3598,296,3.172,2.447,2.306,2.516,2.448,2.296,2.283,2.77,2.331,2.836,2.473,1.823,2.554,2.307,2.435,2.482,2.711,3.317,2.917,1.93,2.705,2.308,2.007,2.833,2.331,2.539,2.164,2.016,2.459,2.259,2.38,2.481,2.105,2.699,3.016,2.6,2.625,2.547,1.8,2.241,2.672,1.815,2.869,1.829,1.773,2.208,2.818,2.922,2.419,2.304,2.108,2.054,2.206,2.413,2.033,1.944,2.202,2.541,2.054,1.808,2.042,2.544,2.467,2.461,2.202,1.888,2.129,2.495,2.446,2.134,2.389,2.216,2.332,2.796,1.061,0.448,0.569,0.457,0.786,0.403,0.508,0.566,0.441,0.381,0.75,0.382,0.373,0.381,0.469,0.487,0.532,0.766,0.611,0.549,0.77,0.42,0.344,0.562,0.483,0.505,0.471,0.432,0.466,0.495,0.543,0.83,0.318,0.527,0.746,0.675,0.553,0.484,0.388,0.573,0.638,0.536,0.679,0.69,0.491,0.606,0.423,0.87,0.493,0.481,0.338,0.447,0.582,0.467,0.255,0.436,0.384,0.649,0.386,0.328,0.468,0.784,0.502,0.84,0.637,0.661,0.46,0.414,0.409,0.462,0.558,0.61,0.486,0.548
-399,Epileptic,F,37.0,0.6,2.2,1.3,1.1,1.5,1.4,1.4,1.5,1.4,0.8,0.3,2.3,1.2,0.5,0.7,5.1,7.1,0.8,2.1,4.5,0.9,2.4,1.9,2.7,2.4,3.9,3.4,2.0,2.1,3.1,1.2,1.5,0.5,1.8,0.3,0.7,3.2,2.2,0.1,0.0,1.2,3.0,1.8,2.0,2.0,0.7,0.4,0.7,1.3,0.9,0.5,1.4,2.0,2.2,0.4,2.3,0.6,1.9,1.2,1.3,0.6,1.7,0.3,0.4,1.6,1.4,2.1,0.8,1.4,0.4,1.2,1.3,3.2,0.3,4,20,12,8,20,16,11,16,15,7,4.0,26,13,8,9,57,79,5,28,41,7,29,18,35,30,47,42,21,21,41,14,8,5,25,2,7,41,36,1,0,7,23,26,20,13,6,2,5,7,10,4,12,17,22,2,19,4,17,10,11,4,13,2,4,12,21,15,7,8,4,9,9,27,2,0.031,0.024,0.024,0.023,0.029,0.028,0.025,0.029,0.037,0.037,0.031,0.033,0.021,0.033,0.022,0.025,0.028,0.03,0.028,0.037,0.019,0.035,0.034,0.032,0.031,0.029,0.028,0.029,0.024,0.027,0.036,0.051,0.023,0.028,0.015,0.018,0.034,0.025,0.009,0.015,0.022,0.033,0.031,0.023,0.018,0.018,0.018,0.012,0.018,0.024,0.03,0.015,0.027,0.017,0.027,0.017,0.021,0.021,0.025,0.021,0.022,0.032,0.023,0.018,0.017,0.023,0.017,0.018,0.019,0.017,0.027,0.021,0.018,0.02,1414,4241,2439,2328,2099,2914,2589,2848,1987,1519,612.0,2385,3115,1077,1579,10809,15652,2058,5135,4512,3648,3246,1906,5732,4745,8180,7003,2829,5405,6279,2235,921,1157,5171,1406,1880,7565,6217,439,130,1558,2539,5647,2081,3129,1259,811,1972,2344,1732,534,2611,2463,5271,352,3749,967,3196,1011,1706,701,2306,346,801,2847,1283,3793,1757,2098,608,1648,1985,6731,369,0.113,0.12,0.115,0.109,0.136,0.126,0.111,0.137,0.155,0.143,0.136,0.154,0.113,0.138,0.114,0.123,0.129,0.109,0.135,0.145,0.09,0.117,0.119,0.144,0.122,0.133,0.122,0.104,0.099,0.13,0.136,0.143,0.114,0.133,0.088,0.093,0.143,0.124,0.074,0.08,0.11,0.126,0.144,0.119,0.096,0.101,0.105,0.082,0.097,0.123,0.14,0.1,0.126,0.098,0.128,0.1,0.106,0.114,0.126,0.11,0.118,0.136,0.12,0.123,0.1,0.125,0.095,0.102,0.103,0.117,0.133,0.113,0.097,0.13,335,1485,887,770,844,867,809,903,578,354,187.0,1109,845,296,536,3323,4264,399,1180,2102,769,1006,841,1478,1273,2319,2231,1118,1320,1938,577,465,343,1134,304,669,1700,1459,206,60,841,1347,1173,1376,1705,584,347,825,1120,712,296,1397,1170,2052,194,2071,460,1503,699,939,372,886,219,377,1438,869,1957,786,1062,368,750,999,3004,202,3.772,2.716,2.771,2.682,2.123,2.676,2.518,2.668,2.576,3.144,2.617,1.849,2.899,2.709,2.347,2.531,2.835,3.812,3.249,1.883,3.268,2.438,2.104,2.973,2.723,2.728,2.406,1.997,2.934,2.524,2.878,2.306,2.787,3.087,3.641,2.511,3.158,2.888,2.255,2.385,2.201,1.702,3.438,1.683,2.069,2.253,2.973,2.869,2.515,2.78,1.994,2.084,2.055,2.445,2.229,2.02,2.364,2.436,1.704,1.964,2.368,2.482,1.773,2.293,2.159,1.843,2.083,2.388,2.44,2.08,2.395,2.309,2.405,2.213,0.674,0.572,0.579,0.609,0.732,0.768,0.498,0.54,0.522,0.581,0.905,0.543,0.505,0.436,0.491,0.584,0.58,0.667,0.722,0.683,0.735,0.575,0.58,0.619,0.659,0.675,0.583,0.566,0.561,0.619,0.557,0.771,0.6,0.65,0.632,0.502,0.689,0.682,0.227,0.442,0.423,0.597,0.737,0.556,0.688,0.575,0.405,0.708,0.535,0.542,0.392,0.377,0.475,0.438,0.39,0.439,0.567,0.637,0.285,0.422,0.372,0.578,0.396,0.54,0.536,0.796,0.467,0.348,0.389,0.444,0.51,0.464,0.456,0.391
-400,Epileptic,F,42.0,0.6,1.5,0.7,1.0,0.9,1.8,1.3,1.3,0.8,0.7,0.2,3.2,1.2,0.4,0.9,3.2,7.5,0.5,1.4,3.1,0.6,2.6,2.1,2.3,2.5,2.9,2.3,2.6,2.3,2.1,0.7,0.8,0.4,1.8,0.3,0.7,2.5,2.6,0.1,0.1,0.8,2.0,1.8,2.8,2.6,0.5,0.4,0.7,0.9,0.4,0.6,1.4,1.1,1.4,0.0,2.3,0.4,1.1,0.7,1.3,0.5,0.8,0.2,0.5,1.7,1.1,2.4,1.1,0.6,0.4,0.7,1.0,4.1,0.3,4,12,6,6,12,14,11,11,9,7,3.0,25,11,4,9,33,65,5,16,27,5,23,18,25,26,29,22,24,16,19,13,6,4,23,1,6,24,34,1,1,4,19,18,16,17,4,2,5,6,7,6,12,12,12,0,19,4,9,6,10,5,6,1,4,12,13,15,9,4,5,5,8,35,3,0.034,0.026,0.016,0.024,0.034,0.032,0.026,0.025,0.036,0.034,0.029,0.033,0.029,0.035,0.034,0.027,0.033,0.027,0.024,0.031,0.015,0.032,0.036,0.036,0.03,0.033,0.028,0.027,0.024,0.028,0.031,0.054,0.022,0.026,0.015,0.018,0.033,0.034,0.018,0.016,0.019,0.029,0.035,0.03,0.02,0.016,0.022,0.012,0.016,0.011,0.044,0.025,0.03,0.016,0.011,0.02,0.015,0.016,0.023,0.027,0.031,0.023,0.023,0.028,0.02,0.048,0.021,0.019,0.014,0.021,0.027,0.019,0.019,0.02,1098,2842,1430,1688,1527,2145,1832,2593,1696,1179,472.0,2519,2224,988,1698,6451,13280,1679,2957,3171,3004,3831,2175,4472,4714,5288,3521,3992,4534,3552,1330,782,878,4354,1384,1368,4881,5502,227,243,1249,2460,4452,1805,3107,1023,737,1940,1836,1167,312,2238,1721,3239,122,3208,851,1988,787,1160,1098,1499,243,686,2251,733,3398,2087,1245,810,875,2159,7813,371,0.13,0.113,0.104,0.102,0.132,0.142,0.108,0.117,0.132,0.139,0.11,0.139,0.123,0.12,0.139,0.119,0.125,0.104,0.119,0.135,0.081,0.131,0.138,0.138,0.13,0.136,0.116,0.116,0.104,0.122,0.128,0.13,0.111,0.125,0.076,0.098,0.131,0.138,0.083,0.105,0.102,0.131,0.15,0.121,0.107,0.095,0.102,0.087,0.095,0.108,0.175,0.113,0.123,0.088,0.076,0.106,0.102,0.101,0.113,0.128,0.12,0.118,0.126,0.13,0.105,0.144,0.107,0.107,0.091,0.125,0.129,0.114,0.103,0.126,305,1001,603,651,538,907,759,832,398,325,157.0,1300,662,231,455,1926,3788,345,927,1492,647,1306,926,1111,1449,1574,1301,1476,1351,1264,435,299,311,1106,367,574,1262,1420,134,135,639,1120,913,1323,1991,535,288,858,883,533,243,961,705,1511,55,1757,459,1093,543,769,398,595,115,318,1258,410,1808,938,649,364,465,798,3482,216,3.399,2.648,2.25,2.595,2.386,2.273,2.142,2.701,2.772,2.987,2.543,1.801,2.612,2.808,2.625,2.512,2.728,3.409,2.494,1.793,3.182,2.333,1.979,2.862,2.481,2.689,2.211,2.187,2.584,2.288,2.404,2.519,2.285,2.812,3.18,2.253,3.007,2.8,1.931,1.97,2.538,1.884,3.194,1.56,1.801,2.083,2.952,2.753,2.479,2.373,1.767,2.312,2.389,2.287,2.097,1.87,2.033,2.119,1.674,1.745,2.413,2.649,2.173,2.266,2.046,2.059,2.135,2.474,2.239,2.597,2.255,2.943,2.384,2.089,0.675,0.608,0.602,0.64,0.627,0.627,0.561,0.583,0.699,0.516,0.654,0.572,0.453,0.7,0.595,0.557,0.682,0.847,0.559,0.611,0.747,0.565,0.668,0.798,0.608,0.59,0.565,0.523,0.564,0.566,0.703,0.972,0.362,0.636,1.118,0.467,0.709,0.665,0.293,0.409,0.509,0.734,0.782,0.644,0.524,0.563,0.618,0.846,0.521,0.439,0.419,0.519,0.598,0.529,0.213,0.513,0.404,0.401,0.296,0.315,0.621,0.656,0.694,0.944,0.616,0.714,0.564,0.516,0.447,0.709,0.505,0.737,0.45,0.647
-401,Epileptic,M,42.0,0.5,2.1,0.6,1.2,1.2,2.1,1.6,1.4,1.2,0.9,0.3,2.6,1.4,0.6,1.2,4.4,9.2,0.7,1.6,4.8,1.0,2.5,2.3,2.6,2.5,2.3,2.4,2.3,2.9,2.4,1.6,1.2,0.4,1.6,0.3,0.4,2.6,2.2,0.2,0.1,0.6,2.5,1.2,3.1,2.5,0.6,0.5,0.8,1.1,0.5,0.9,1.5,1.1,2.3,0.1,2.5,0.4,1.5,1.0,0.8,1.3,1.4,0.2,0.5,0.9,1.3,2.9,1.0,1.1,0.4,1.0,1.3,4.8,0.3,5,19,5,9,13,18,11,12,11,8,3.0,24,15,7,16,43,82,6,20,42,7,28,19,32,28,22,30,20,19,29,14,7,4,18,2,5,27,23,1,1,4,17,15,20,16,4,2,5,6,5,7,11,8,17,1,23,5,12,7,7,10,10,1,4,8,15,22,7,7,5,7,10,34,2,0.025,0.024,0.012,0.022,0.029,0.03,0.025,0.028,0.039,0.039,0.025,0.033,0.029,0.032,0.028,0.031,0.03,0.03,0.026,0.033,0.017,0.03,0.032,0.034,0.023,0.027,0.022,0.025,0.028,0.027,0.039,0.041,0.034,0.025,0.012,0.015,0.033,0.029,0.023,0.014,0.017,0.033,0.029,0.03,0.021,0.015,0.02,0.014,0.015,0.016,0.03,0.017,0.025,0.022,0.021,0.018,0.014,0.018,0.022,0.017,0.027,0.026,0.015,0.02,0.015,0.025,0.022,0.017,0.02,0.017,0.023,0.018,0.018,0.023,1574,4885,2283,2245,1863,3169,2707,2527,1567,1362,654.0,2579,3365,1104,2808,9198,19201,2817,4015,5360,3272,4346,2021,5748,5895,4953,5342,3556,5172,5334,2198,1330,647,5061,1537,1258,6917,6718,304,412,1145,2015,4525,2688,3300,1346,908,1961,2484,1126,731,2860,1460,4537,278,4004,1130,3220,1244,1412,1957,2488,425,903,1992,1328,4135,2188,1770,1058,1514,2394,10294,355,0.106,0.112,0.084,0.108,0.132,0.139,0.092,0.129,0.148,0.152,0.116,0.138,0.122,0.145,0.131,0.137,0.127,0.114,0.118,0.133,0.081,0.135,0.136,0.128,0.117,0.123,0.115,0.101,0.1,0.125,0.118,0.11,0.138,0.112,0.062,0.08,0.13,0.124,0.121,0.105,0.097,0.123,0.128,0.125,0.106,0.093,0.107,0.087,0.088,0.099,0.145,0.097,0.12,0.108,0.134,0.109,0.095,0.1,0.114,0.1,0.124,0.113,0.087,0.105,0.098,0.119,0.105,0.101,0.108,0.106,0.116,0.106,0.097,0.123,379,1412,751,880,715,1083,883,796,504,370,194.0,1205,855,298,764,2419,4915,439,999,2255,787,1357,974,1430,1731,1370,1731,1358,1422,1614,598,490,216,1069,411,450,1388,1292,154,187,568,1115,840,1595,2001,714,360,805,1104,460,451,1369,683,1756,101,2066,524,1419,692,785,755,851,222,414,1007,838,2132,915,789,413,719,1027,3970,164,3.832,2.983,2.741,2.642,2.365,2.452,2.533,2.777,2.491,3.077,2.845,1.909,2.938,2.735,2.85,2.939,3.109,4.363,3.119,2.043,3.137,2.512,1.933,3.016,2.61,2.949,2.356,2.123,2.812,2.622,2.748,2.668,2.544,3.332,3.409,2.334,3.297,3.455,2.197,2.542,2.564,1.748,3.836,1.863,1.909,2.141,3.182,3.198,2.742,2.808,2.074,2.368,2.256,2.644,2.924,2.123,2.525,2.601,2.177,2.064,2.605,2.835,2.038,2.589,2.134,2.022,2.196,2.727,2.408,2.661,2.531,2.758,2.718,2.761,0.735,0.687,0.648,0.467,0.566,0.545,0.671,0.565,0.398,0.629,0.8,0.453,0.528,0.427,0.447,0.562,0.616,0.542,0.5,0.666,0.617,0.495,0.469,0.579,0.536,0.617,0.558,0.463,0.577,0.561,0.719,0.991,0.529,0.693,0.724,0.476,0.743,0.666,0.295,0.386,0.619,0.489,0.808,0.514,0.596,0.564,0.641,0.842,0.545,0.574,0.518,0.439,0.416,0.493,0.307,0.445,0.433,0.508,0.465,0.385,0.473,0.84,0.314,0.756,0.473,0.674,0.561,0.462,0.562,0.872,0.354,0.619,0.6,0.553
-402,Epileptic,F,22.0,1.1,2.0,0.7,0.9,1.6,1.8,1.7,1.9,1.4,1.1,0.2,3.3,1.5,0.4,1.5,6.9,11.3,1.1,1.9,4.1,1.2,2.6,2.0,3.4,2.6,4.5,3.9,2.4,3.8,2.9,1.7,1.3,0.6,2.7,0.5,0.5,2.8,3.1,0.3,0.2,1.3,3.2,2.7,2.5,2.8,0.8,0.4,1.1,1.1,0.6,1.0,1.9,2.0,2.9,0.2,2.6,0.6,1.3,1.5,0.9,0.6,1.4,0.5,0.5,1.4,1.6,3.1,1.9,1.3,0.4,1.0,1.0,4.5,0.3,10,16,8,7,12,21,11,15,14,8,3.0,28,12,4,15,59,66,8,23,44,12,33,19,30,25,45,36,18,28,36,15,10,5,27,3,5,27,26,3,1,8,31,20,22,17,5,2,7,6,5,6,12,14,18,1,21,5,12,11,10,4,9,4,5,10,16,20,9,9,3,8,7,48,3,0.04,0.023,0.017,0.018,0.032,0.026,0.028,0.029,0.053,0.037,0.021,0.037,0.026,0.029,0.027,0.038,0.038,0.037,0.025,0.031,0.023,0.033,0.03,0.04,0.033,0.035,0.026,0.033,0.031,0.03,0.05,0.055,0.024,0.033,0.017,0.014,0.035,0.03,0.025,0.015,0.022,0.03,0.041,0.023,0.022,0.015,0.018,0.017,0.015,0.011,0.032,0.021,0.03,0.026,0.019,0.016,0.024,0.015,0.034,0.023,0.019,0.023,0.035,0.017,0.023,0.029,0.026,0.026,0.025,0.013,0.025,0.02,0.021,0.013,1694,4056,1872,1973,2098,3298,2212,3232,1983,1744,554.0,3389,3951,916,3268,11787,17036,2303,4136,5586,4331,4199,2528,4645,4447,8310,6594,3102,5513,5689,1613,1171,1345,4740,1475,1625,5380,6942,425,598,1857,3020,6149,2837,3127,1360,742,2046,2560,1481,807,3477,2670,4505,312,4607,1066,3482,1461,1337,921,2505,586,1091,2108,1334,3820,2558,1828,1006,1494,1816,8882,699,0.128,0.113,0.113,0.096,0.127,0.134,0.104,0.123,0.157,0.15,0.114,0.147,0.107,0.109,0.125,0.137,0.132,0.129,0.12,0.145,0.091,0.14,0.138,0.129,0.133,0.133,0.123,0.109,0.112,0.128,0.129,0.134,0.104,0.137,0.076,0.09,0.136,0.124,0.136,0.083,0.114,0.137,0.154,0.119,0.105,0.096,0.099,0.096,0.089,0.082,0.138,0.103,0.123,0.112,0.111,0.101,0.113,0.097,0.137,0.12,0.117,0.105,0.126,0.108,0.114,0.141,0.113,0.108,0.117,0.094,0.119,0.114,0.112,0.091,491,1398,689,772,827,1254,844,1080,586,461,182.0,1406,990,267,894,3268,4671,513,1300,2145,993,1395,1067,1469,1334,2354,2345,1171,1717,1754,607,423,423,1415,462,606,1288,1675,198,269,914,1542,1124,1762,1965,686,307,960,1168,701,468,1566,1191,1915,146,2407,493,1466,788,699,447,938,265,506,1020,819,1941,1180,898,412,735,766,3610,396,3.252,2.807,2.631,2.439,2.312,2.402,2.249,2.696,2.53,3.017,2.605,2.074,2.943,2.65,2.808,2.725,2.924,3.484,2.599,2.237,3.222,2.392,2.122,2.382,2.652,2.858,2.337,2.122,2.559,2.499,2.098,2.977,2.469,2.553,2.83,2.46,2.909,3.03,2.515,2.704,2.634,1.865,3.301,1.823,1.84,2.214,3.125,2.688,2.751,2.386,2.138,2.425,2.251,2.53,2.485,2.148,2.48,2.583,2.066,2.046,2.689,2.703,2.283,2.42,2.328,2.187,2.162,2.501,2.341,2.625,2.391,2.81,2.441,2.079,0.722,0.589,0.583,0.578,0.712,0.485,0.525,0.507,0.639,0.559,0.793,0.464,0.606,0.521,0.544,0.673,0.707,0.74,0.526,0.559,0.772,0.467,0.422,0.887,0.488,0.527,0.471,0.636,0.617,0.598,0.675,0.959,0.474,0.617,0.958,0.453,0.782,0.659,0.345,0.411,0.388,0.538,0.968,0.555,0.508,0.457,0.524,0.88,0.596,0.674,0.435,0.481,0.474,0.563,0.375,0.358,0.606,0.527,0.324,0.34,0.562,0.797,0.431,0.952,0.474,0.611,0.422,0.453,0.395,0.585,0.486,0.69,0.546,0.328
-403,Epileptic,F,22.0,1.2,2.0,0.8,1.0,1.4,2.2,1.4,1.8,1.3,0.9,0.2,3.3,1.7,0.6,0.9,5.5,8.6,1.3,2.4,5.1,0.8,3.0,1.8,2.6,3.0,3.6,3.5,2.6,2.7,3.1,1.8,1.8,0.4,2.5,0.3,0.7,3.2,3.4,0.3,0.3,1.1,2.9,1.8,3.7,3.1,0.9,0.5,1.2,1.2,0.6,0.9,1.8,1.7,1.9,0.5,2.5,0.8,2.1,1.1,1.1,0.4,1.2,0.3,0.5,2.0,1.3,2.9,1.7,0.8,0.2,1.6,1.1,5.0,0.1,8,21,8,10,14,17,15,14,16,8,3.0,26,18,7,9,60,76,8,26,38,7,33,19,34,36,36,44,25,24,31,23,9,5,30,3,5,41,42,1,2,6,24,17,24,20,7,2,9,6,6,6,15,14,14,3,20,5,18,8,11,3,9,2,5,16,16,22,14,5,3,10,9,41,1,0.042,0.02,0.012,0.018,0.03,0.034,0.022,0.026,0.034,0.038,0.025,0.034,0.026,0.027,0.027,0.032,0.024,0.037,0.029,0.036,0.016,0.034,0.029,0.029,0.027,0.031,0.026,0.027,0.024,0.029,0.036,0.055,0.022,0.029,0.014,0.016,0.028,0.028,0.017,0.016,0.02,0.033,0.03,0.032,0.024,0.019,0.019,0.019,0.015,0.015,0.033,0.019,0.024,0.015,0.024,0.019,0.019,0.022,0.023,0.02,0.02,0.02,0.027,0.02,0.024,0.026,0.024,0.017,0.015,0.009,0.034,0.019,0.022,0.008,1808,5384,1941,2262,1778,3235,2967,3569,1810,1478,637.0,2888,4400,1678,2016,10944,20834,2472,4704,4352,3093,4486,1993,5283,5724,7122,6149,4096,6356,5021,2291,1091,788,5770,1663,2309,6871,8054,492,723,1874,2439,4264,2945,3449,1637,1010,2262,2669,1478,701,3277,2715,4849,701,3928,1499,3326,1369,1559,676,2388,510,973,3065,1199,4168,3657,1837,854,2130,2146,9919,471,0.124,0.105,0.096,0.105,0.132,0.143,0.111,0.13,0.135,0.146,0.1,0.141,0.123,0.115,0.121,0.133,0.117,0.131,0.134,0.13,0.086,0.147,0.133,0.125,0.139,0.142,0.135,0.118,0.104,0.132,0.124,0.152,0.126,0.13,0.074,0.091,0.118,0.132,0.093,0.102,0.106,0.131,0.119,0.127,0.114,0.098,0.107,0.092,0.085,0.098,0.14,0.103,0.114,0.09,0.115,0.109,0.095,0.114,0.122,0.118,0.115,0.108,0.119,0.104,0.119,0.134,0.116,0.103,0.095,0.082,0.129,0.109,0.104,0.069,465,1777,871,851,758,1109,980,1090,607,410,196.0,1424,1149,377,551,2906,5512,486,1315,2066,751,1476,997,1457,1819,2011,2157,1403,1679,1676,721,515,274,1390,453,660,1953,2012,274,261,820,1245,1121,1873,2084,798,357,973,1217,620,420,1498,1138,2007,341,2007,660,1549,788,882,328,893,217,450,1382,722,1919,1493,813,412,805,992,4033,198,3.571,2.767,2.361,2.647,2.175,2.62,2.502,2.964,2.329,3.285,2.807,1.818,2.971,3.021,2.879,2.829,3.059,3.696,2.958,1.866,3.001,2.398,1.894,2.746,2.525,2.925,2.307,2.317,2.9,2.471,2.618,2.318,2.521,2.973,3.447,3.092,2.787,2.886,2.214,2.875,2.822,1.797,3.112,1.769,1.906,2.225,3.481,2.803,2.65,2.656,2.096,2.288,2.419,2.595,2.592,2.217,2.562,2.485,2.099,1.941,2.337,2.729,2.515,2.292,2.463,2.109,2.343,2.77,2.706,2.333,2.909,2.62,2.674,2.538,0.887,0.612,0.512,0.551,0.554,0.499,0.542,0.532,0.554,0.622,0.697,0.551,0.515,0.382,0.57,0.58,0.557,0.872,0.539,0.568,0.699,0.386,0.441,0.602,0.442,0.49,0.458,0.475,0.499,0.526,0.801,0.807,0.387,0.666,0.582,0.649,0.631,0.718,0.32,0.425,0.583,0.458,0.556,0.716,0.534,0.541,0.63,0.656,0.72,0.55,0.511,0.613,0.679,0.516,0.62,0.414,0.527,0.585,0.325,0.404,0.549,0.643,0.681,0.979,0.531,0.643,0.457,0.46,0.372,0.64,0.647,0.53,0.599,0.502
-404,Epileptic,M,32.0,1.1,3.0,1.0,0.9,1.2,2.0,1.8,2.8,1.0,1.1,0.5,3.3,2.1,0.5,0.9,7.0,10.8,1.0,2.4,5.5,1.2,3.8,1.8,4.0,3.0,4.3,3.0,2.4,3.7,4.4,2.6,1.4,0.6,2.4,0.8,0.6,3.9,3.8,0.2,0.2,1.2,3.4,3.1,3.6,3.0,0.9,0.7,1.0,1.4,1.0,0.8,2.1,1.5,3.3,0.2,3.2,0.9,1.7,1.2,1.2,0.5,1.6,0.4,1.1,2.5,1.7,3.7,1.7,1.6,0.4,1.1,1.3,5.4,0.4,7,30,7,6,15,19,16,19,13,14,7.0,37,22,6,10,77,95,7,26,51,13,46,19,43,38,42,39,23,26,47,28,13,6,31,6,2,46,46,1,1,8,33,31,29,19,6,4,7,9,6,5,13,11,21,1,26,6,15,9,9,4,16,4,10,21,16,28,11,8,5,8,10,43,3,0.039,0.024,0.012,0.018,0.03,0.026,0.026,0.03,0.036,0.033,0.027,0.038,0.028,0.027,0.029,0.037,0.027,0.029,0.032,0.041,0.021,0.034,0.028,0.035,0.031,0.032,0.028,0.029,0.034,0.035,0.052,0.038,0.026,0.035,0.021,0.012,0.033,0.034,0.01,0.022,0.021,0.035,0.037,0.028,0.023,0.02,0.018,0.012,0.014,0.014,0.029,0.02,0.029,0.021,0.016,0.019,0.017,0.019,0.021,0.021,0.016,0.023,0.026,0.026,0.02,0.03,0.025,0.021,0.024,0.012,0.019,0.016,0.019,0.012,1863,7047,2300,2332,1818,3854,3236,4586,2230,2249,1126.0,3705,5189,1422,2333,13271,24932,2902,4848,6822,4966,5920,2829,7853,6348,7968,5844,3956,6547,7149,2079,2049,1498,5971,2849,1830,9343,8551,443,376,1988,3161,7680,4049,3518,1453,1330,2858,3481,2434,810,3739,2275,5715,360,4879,1653,3991,1558,1909,786,2879,658,1177,3834,1720,5080,2578,2171,982,1816,2595,11068,848,0.119,0.115,0.095,0.093,0.12,0.125,0.115,0.127,0.14,0.151,0.126,0.159,0.125,0.13,0.131,0.148,0.121,0.112,0.131,0.155,0.104,0.152,0.139,0.138,0.143,0.139,0.13,0.116,0.121,0.147,0.149,0.118,0.11,0.145,0.098,0.077,0.129,0.144,0.088,0.108,0.11,0.152,0.149,0.125,0.107,0.108,0.099,0.081,0.084,0.091,0.126,0.097,0.126,0.103,0.093,0.106,0.1,0.108,0.115,0.106,0.109,0.117,0.124,0.124,0.114,0.122,0.117,0.111,0.11,0.098,0.11,0.101,0.104,0.093,502,2157,1005,860,783,1254,1088,1436,567,594,357.0,1583,1426,373,592,3488,6865,570,1362,2413,1131,1956,1079,2038,1865,2199,2032,1461,1801,2239,779,692,456,1391,656,646,2265,2083,200,170,839,1539,1586,2123,1991,696,534,1132,1450,987,458,1780,931,2482,149,2476,768,1520,904,972,427,1104,294,645,1850,850,2368,1093,994,545,875,1253,4434,467,3.54,2.913,2.454,2.525,2.252,2.591,2.462,2.818,2.823,3.099,2.841,2.052,2.985,2.883,2.962,2.885,2.989,3.949,2.899,2.337,3.499,2.398,2.258,2.814,2.606,2.964,2.347,2.167,2.88,2.58,2.092,2.891,2.518,3.064,3.74,2.648,3.16,3.045,2.436,2.486,2.974,1.845,3.452,2.116,2.037,2.412,3.022,3.12,2.712,2.823,2.231,2.391,2.542,2.503,2.355,2.094,2.576,2.799,2.004,2.147,2.243,2.575,2.38,2.302,2.204,2.085,2.153,2.482,2.594,2.193,2.402,2.53,2.553,2.309,0.642,0.68,0.633,0.586,0.495,0.483,0.631,0.531,0.67,0.483,0.803,0.506,0.541,0.442,0.49,0.566,0.529,0.765,0.553,0.673,0.758,0.486,0.477,0.871,0.517,0.499,0.433,0.459,0.505,0.53,0.719,0.925,0.393,0.763,0.704,0.443,0.642,0.695,0.253,0.354,0.588,0.501,0.738,0.6,0.564,0.487,0.516,0.718,0.517,0.628,0.317,0.47,0.62,0.478,0.379,0.364,0.451,0.554,0.366,0.345,0.276,0.676,0.536,0.811,0.49,0.714,0.502,0.442,0.445,0.353,0.422,0.543,0.495,0.385
-405,Epileptic,M,22.0,0.8,2.1,0.6,1.1,1.8,2.0,1.6,1.8,1.2,1.0,0.3,2.4,2.1,0.6,1.2,6.8,8.6,1.1,3.0,5.8,0.8,3.9,1.9,2.9,3.0,3.4,3.8,2.7,2.7,3.5,1.2,0.7,0.6,3.8,0.6,0.7,6.0,6.6,0.2,0.2,1.0,3.4,2.8,4.6,2.2,0.6,0.8,0.8,1.2,1.0,0.6,1.5,1.4,3.3,0.1,2.5,1.0,1.9,0.8,1.8,0.7,0.9,0.3,0.9,1.9,1.1,2.3,3.0,0.9,0.5,1.0,2.4,5.1,0.3,5,17,6,9,30,16,11,13,15,10,3.0,17,23,9,12,61,80,8,28,37,6,40,17,30,31,30,38,24,25,43,17,7,5,31,3,5,59,59,2,1,6,28,23,27,13,5,3,14,5,6,4,12,12,44,1,18,6,14,7,15,6,6,2,5,18,9,18,19,5,5,7,11,34,4,0.032,0.024,0.011,0.025,0.038,0.034,0.025,0.033,0.043,0.047,0.038,0.029,0.042,0.039,0.033,0.035,0.03,0.036,0.038,0.043,0.02,0.036,0.032,0.032,0.028,0.029,0.027,0.031,0.026,0.035,0.042,0.029,0.024,0.037,0.02,0.013,0.043,0.043,0.015,0.017,0.02,0.039,0.052,0.05,0.017,0.015,0.025,0.011,0.016,0.025,0.027,0.018,0.022,0.028,0.026,0.017,0.028,0.02,0.017,0.027,0.02,0.021,0.019,0.028,0.026,0.026,0.022,0.036,0.017,0.019,0.022,0.028,0.017,0.016,1707,5549,1869,2344,2184,2928,2758,3625,2296,1555,501.0,2266,4085,1450,3002,12746,20572,2697,4889,4202,2590,6400,2187,6302,6134,6917,6502,3116,6307,5700,2262,1205,1143,7721,1937,2221,9113,10299,366,405,2005,2853,4849,2434,3327,1636,1165,2770,2762,1569,644,3059,3098,5263,213,4524,1628,3150,1138,2284,1547,1912,427,827,2856,1296,3689,2860,1917,1131,1491,3066,13142,431,0.116,0.105,0.088,0.109,0.129,0.13,0.103,0.117,0.142,0.159,0.132,0.122,0.134,0.145,0.127,0.127,0.127,0.125,0.128,0.144,0.087,0.139,0.126,0.128,0.113,0.113,0.121,0.109,0.101,0.134,0.122,0.092,0.115,0.125,0.092,0.069,0.134,0.13,0.09,0.099,0.098,0.136,0.131,0.157,0.095,0.098,0.115,0.08,0.084,0.114,0.126,0.101,0.108,0.101,0.135,0.101,0.111,0.104,0.117,0.12,0.106,0.105,0.115,0.125,0.117,0.117,0.103,0.132,0.099,0.116,0.118,0.126,0.089,0.123,462,1557,770,807,820,1046,916,932,558,397,163.0,1108,997,332,710,3330,4917,495,1343,2139,668,1782,983,1560,1721,1824,2148,1216,1577,1791,533,492,415,1562,473,820,2354,2481,210,169,793,1302,1023,1624,1978,678,439,1127,1141,627,391,1430,1130,2067,72,2223,603,1566,668,1182,618,727,236,428,1320,633,1806,1263,834,463,713,1369,4794,271,3.628,2.984,2.34,2.767,2.153,2.511,2.457,3.071,2.785,3.152,2.75,1.846,2.994,2.928,2.934,2.801,3.063,3.837,2.824,1.813,2.881,2.739,1.98,2.815,2.726,2.955,2.339,2.02,2.888,2.474,2.84,2.391,2.172,3.142,3.649,2.401,2.91,3.055,2.056,2.638,2.964,1.977,3.272,1.851,1.941,2.55,3.181,2.975,2.695,2.765,2.051,2.202,2.408,2.584,2.971,2.291,3.052,2.398,1.928,2.103,2.759,2.462,2.118,2.275,2.216,2.342,2.104,2.566,2.552,2.752,2.421,2.845,2.877,1.967,0.876,0.896,0.561,0.582,0.652,0.521,0.668,0.742,0.864,0.446,0.648,0.463,0.637,0.546,0.714,0.7,0.756,0.82,0.848,0.635,0.838,0.705,0.527,0.757,0.629,0.594,0.617,0.538,0.585,0.647,0.781,1.171,0.512,0.808,0.735,0.65,0.799,0.802,0.517,0.644,0.836,0.581,0.837,0.757,0.668,0.72,0.636,0.794,0.745,0.517,0.508,0.574,0.688,0.671,0.616,0.523,0.629,0.56,0.348,0.467,0.594,0.591,0.348,0.926,0.628,0.674,0.512,0.598,0.548,0.972,0.527,0.568,0.634,0.475
-406,Epileptic,F,32.0,0.6,3.6,1.7,1.3,2.3,1.8,1.6,1.6,1.0,0.7,0.4,3.2,1.5,0.5,1.3,6.3,9.3,1.2,1.8,4.4,1.5,3.1,1.5,3.9,3.8,2.5,5.1,2.9,2.9,3.8,2.5,0.8,0.7,1.8,0.5,0.4,3.1,2.5,0.2,0.2,0.9,3.9,2.4,2.9,3.0,0.7,0.4,1.1,1.8,0.9,0.6,2.1,1.6,2.1,0.2,2.6,1.0,1.7,1.0,1.1,0.6,2.4,0.5,0.5,1.8,1.4,2.8,1.7,1.0,0.7,1.0,1.4,4.0,0.3,7,31,14,12,17,18,13,17,12,12,5.0,30,17,5,17,82,90,10,26,43,17,34,16,44,42,30,51,31,24,42,36,6,6,21,3,3,46,37,1,1,6,29,28,25,18,5,2,6,11,11,4,19,11,14,1,22,9,15,7,11,4,16,4,5,14,22,24,13,8,6,8,12,32,3,0.033,0.034,0.03,0.021,0.043,0.026,0.027,0.024,0.041,0.034,0.039,0.035,0.034,0.031,0.03,0.036,0.033,0.046,0.024,0.037,0.025,0.032,0.03,0.044,0.033,0.022,0.033,0.029,0.027,0.032,0.049,0.055,0.032,0.027,0.019,0.013,0.033,0.028,0.011,0.017,0.019,0.039,0.037,0.027,0.025,0.013,0.027,0.017,0.022,0.015,0.022,0.021,0.028,0.019,0.016,0.018,0.02,0.02,0.027,0.021,0.021,0.031,0.029,0.017,0.021,0.021,0.02,0.019,0.021,0.022,0.019,0.015,0.017,0.019,1440,5079,2864,2642,2104,3775,3059,3828,1798,1627,571.0,3186,3573,1141,3062,11970,18784,2298,4708,4658,4625,5410,2398,6202,7466,6362,7749,4843,6355,7555,2625,737,1058,5752,2124,1385,7245,7186,430,452,1337,2446,5930,2287,3110,1513,657,2195,2465,2009,619,3245,1656,4069,336,4853,1745,3181,1130,1658,1107,2763,663,921,3036,1547,4607,3322,2024,1228,1580,2131,8825,478,0.116,0.14,0.123,0.106,0.138,0.12,0.109,0.118,0.153,0.154,0.146,0.136,0.135,0.153,0.145,0.15,0.136,0.143,0.121,0.146,0.111,0.125,0.118,0.149,0.12,0.119,0.13,0.116,0.113,0.142,0.157,0.13,0.11,0.127,0.085,0.083,0.134,0.136,0.082,0.09,0.108,0.132,0.15,0.13,0.113,0.094,0.113,0.09,0.105,0.103,0.126,0.109,0.133,0.097,0.109,0.105,0.115,0.105,0.13,0.115,0.105,0.136,0.149,0.113,0.113,0.116,0.111,0.1,0.104,0.122,0.116,0.109,0.099,0.118,414,1687,986,1006,858,1224,1024,1168,507,443,190.0,1636,939,267,825,3289,5237,466,1359,2155,1077,1564,932,1816,2111,1903,2657,1647,1783,2197,765,311,342,1211,461,493,1941,1640,228,240,694,1435,1314,1614,1909,768,257,985,1191,887,371,1657,865,1830,156,2213,797,1407,608,879,473,1143,290,475,1425,1045,2243,1427,837,513,739,1175,3679,226,3.254,2.728,2.806,2.609,2.033,2.445,2.381,2.784,2.474,2.907,2.492,1.749,2.833,3.004,2.756,2.65,2.828,3.438,2.885,1.907,3.341,2.687,2.095,2.591,2.755,2.664,2.379,2.347,2.763,2.679,2.527,2.401,2.449,3.194,3.599,2.666,2.768,3.045,2.325,2.189,2.432,1.676,3.123,1.651,1.88,2.178,2.934,2.695,2.574,2.531,2.057,2.114,2.017,2.365,2.343,2.313,2.424,2.389,2.139,2.072,2.493,2.423,2.001,2.176,2.466,1.664,2.249,2.476,2.482,2.514,2.566,2.16,2.57,2.491,0.713,0.647,0.676,0.415,0.714,0.73,0.626,0.413,0.628,0.68,0.965,0.521,0.424,0.489,0.516,0.639,0.628,1.018,0.523,0.626,0.901,0.542,0.619,0.707,0.614,0.517,0.63,0.562,0.631,0.573,0.734,1.024,0.477,0.655,0.75,0.471,0.749,0.625,0.39,0.299,0.404,0.493,0.783,0.565,0.577,0.401,0.654,0.807,0.527,0.406,0.597,0.451,0.44,0.418,0.23,0.428,0.373,0.456,0.497,0.327,0.388,0.704,0.567,0.558,0.475,0.677,0.36,0.348,0.45,0.757,0.449,0.442,0.448,0.531
-407,Epileptic,F,17.5,0.8,2.4,0.8,1.0,1.1,2.3,1.1,1.8,0.7,0.9,0.3,2.8,1.7,0.7,1.5,4.9,8.0,0.6,3.8,4.4,1.6,2.3,1.6,3.2,3.2,3.8,3.3,2.3,2.3,3.5,0.9,0.9,0.5,2.7,0.4,0.6,3.2,3.5,0.1,0.1,1.0,2.9,2.4,3.4,3.0,1.1,0.5,1.0,0.8,1.1,1.7,2.3,1.1,2.2,0.2,2.3,1.0,2.3,0.8,0.9,0.4,1.0,0.2,0.2,1.8,0.4,2.4,1.6,1.1,0.4,0.8,2.0,6.3,0.5,7,20,9,8,8,15,8,14,8,8,2.0,21,12,7,14,49,65,3,33,28,11,28,15,32,34,29,34,19,17,32,12,6,5,24,2,4,35,35,1,1,5,20,21,25,18,7,3,6,4,8,13,16,13,14,1,19,8,13,5,6,2,7,2,2,13,7,18,10,7,4,6,12,40,4,0.032,0.029,0.016,0.018,0.028,0.041,0.021,0.031,0.04,0.036,0.021,0.038,0.031,0.048,0.036,0.03,0.029,0.023,0.035,0.044,0.03,0.034,0.035,0.035,0.036,0.035,0.03,0.027,0.022,0.032,0.035,0.041,0.021,0.031,0.015,0.018,0.03,0.032,0.021,0.017,0.021,0.032,0.039,0.037,0.026,0.024,0.024,0.016,0.01,0.018,0.087,0.024,0.028,0.019,0.025,0.019,0.028,0.024,0.023,0.021,0.017,0.021,0.015,0.019,0.021,0.016,0.024,0.021,0.027,0.019,0.023,0.03,0.028,0.028,1452,5053,2210,2436,1497,2923,2543,3153,1319,1237,483.0,2618,3132,1282,2857,9750,15969,1818,4266,3026,2387,3790,1973,5294,4984,6698,5429,3204,4802,5153,1370,794,1357,5735,1593,1759,6733,6754,304,364,1896,2771,3220,2207,3041,1845,796,2356,2404,1954,475,4406,2041,4567,330,3779,1548,2934,1157,1221,831,2040,321,636,2371,653,3358,2730,1518,1131,1255,2401,9347,592,0.13,0.113,0.104,0.094,0.113,0.13,0.094,0.125,0.118,0.131,0.116,0.142,0.101,0.136,0.13,0.12,0.123,0.085,0.127,0.137,0.099,0.136,0.138,0.126,0.141,0.129,0.122,0.12,0.1,0.129,0.118,0.121,0.098,0.114,0.07,0.077,0.118,0.126,0.102,0.072,0.098,0.125,0.12,0.139,0.115,0.107,0.111,0.086,0.071,0.105,0.241,0.111,0.114,0.096,0.122,0.109,0.103,0.109,0.11,0.112,0.1,0.112,0.121,0.102,0.107,0.104,0.115,0.108,0.116,0.108,0.118,0.129,0.111,0.111,407,1352,863,858,606,962,783,971,347,385,160.0,1130,914,282,661,2699,4351,403,1679,1490,839,1155,830,1540,1505,1773,1885,1257,1503,1724,388,390,424,1226,466,594,1715,1655,156,157,756,1275,991,1485,1891,716,341,1002,1163,900,402,1586,757,1913,107,1932,710,1503,582,646,381,724,161,259,1294,406,1687,1172,721,413,590,1015,3750,306,3.395,3.226,2.334,2.757,2.212,2.706,2.639,2.995,2.619,2.737,2.769,2.062,2.765,2.819,3.043,2.689,3.017,3.45,2.216,1.886,2.463,2.528,2.064,2.617,2.6,2.987,2.234,2.086,2.508,2.551,2.534,2.21,2.544,3.042,3.037,2.657,2.911,3.033,2.004,2.296,3.025,1.961,2.661,1.758,1.878,2.806,2.889,2.873,2.364,2.426,1.595,2.679,2.315,2.529,2.555,2.166,2.451,2.358,2.437,2.081,2.654,2.777,1.981,2.424,2.077,1.989,2.075,2.67,2.394,2.973,2.444,2.778,2.575,2.041,1.088,0.907,0.837,0.745,0.598,0.565,0.528,0.64,0.587,0.471,0.708,0.481,0.581,0.629,0.672,0.625,0.678,0.913,0.639,0.64,0.73,0.533,0.544,0.669,0.534,0.546,0.537,0.448,0.564,0.555,0.658,0.859,0.389,0.679,0.539,0.715,0.798,0.677,0.472,0.523,0.807,0.485,0.524,0.593,0.577,0.847,0.652,0.819,0.754,0.511,0.506,0.594,0.704,0.583,0.652,0.487,0.685,0.613,0.448,0.403,0.398,0.932,0.736,0.769,0.485,0.724,0.43,0.609,0.363,0.892,0.516,0.703,0.678,0.374
-408,Epileptic,F,37.0,0.6,2.0,1.1,0.9,1.8,1.5,1.6,1.2,0.9,0.4,0.2,2.1,1.2,0.5,0.9,4.9,6.4,0.6,1.3,3.7,1.5,2.5,2.6,3.1,2.9,2.0,5.2,1.9,2.1,2.5,2.1,1.6,0.5,2.3,0.3,0.8,2.7,2.5,0.1,0.2,0.8,3.4,1.8,1.7,1.9,0.5,0.3,0.7,0.9,0.6,0.5,1.4,1.1,1.7,0.0,2.2,0.9,1.3,0.8,1.4,0.5,1.6,0.3,0.4,1.1,1.4,1.9,0.9,1.0,1.3,0.8,1.7,3.0,0.1,8,21,10,6,18,15,16,14,12,5,3.0,19,12,6,11,57,68,5,18,36,13,31,22,31,33,26,47,20,22,34,21,12,5,27,2,8,34,29,1,1,6,27,18,14,11,4,1,4,5,5,3,10,10,11,0,20,8,10,6,10,5,11,2,5,9,21,16,6,8,13,6,15,27,0,0.033,0.022,0.02,0.018,0.031,0.028,0.025,0.028,0.03,0.025,0.023,0.035,0.024,0.029,0.024,0.03,0.027,0.027,0.023,0.036,0.025,0.029,0.042,0.037,0.032,0.028,0.034,0.024,0.025,0.028,0.049,0.055,0.029,0.029,0.014,0.02,0.033,0.032,0.012,0.016,0.019,0.039,0.033,0.022,0.014,0.011,0.015,0.011,0.015,0.012,0.022,0.017,0.022,0.016,0.018,0.015,0.024,0.017,0.024,0.023,0.02,0.023,0.023,0.017,0.015,0.039,0.017,0.014,0.018,0.035,0.019,0.018,0.012,0.007,1291,4352,2683,2151,2317,2626,3362,2901,2020,1387,517.0,2643,3393,1137,2328,9723,17663,2067,4068,4647,4165,5193,2347,5846,5611,5577,6868,3983,6597,6058,2210,1206,777,6138,1570,1783,7109,5810,348,349,1280,3080,4233,2126,3529,1248,691,1925,1944,1807,528,2731,1702,3561,162,4109,1337,2939,1029,1821,894,2707,418,780,2112,1080,3387,2356,1792,1406,1215,2854,8531,345,0.126,0.115,0.106,0.096,0.148,0.129,0.099,0.122,0.146,0.119,0.115,0.148,0.122,0.143,0.127,0.139,0.125,0.11,0.113,0.154,0.107,0.12,0.135,0.125,0.13,0.133,0.115,0.113,0.102,0.129,0.149,0.138,0.141,0.137,0.081,0.107,0.133,0.128,0.087,0.089,0.109,0.147,0.135,0.112,0.089,0.085,0.092,0.079,0.087,0.095,0.117,0.096,0.113,0.091,0.083,0.096,0.129,0.098,0.116,0.115,0.098,0.111,0.117,0.116,0.1,0.143,0.101,0.084,0.105,0.153,0.11,0.118,0.082,0.059,431,1518,857,783,923,966,1047,892,578,326,164.0,1111,857,257,641,2848,4288,406,1036,1919,1031,1371,954,1592,1578,1446,2314,1232,1414,1626,685,514,261,1340,383,664,1588,1431,194,183,631,1250,1000,1293,1940,677,306,879,907,766,333,1356,816,1624,38,2229,628,1344,569,953,442,1061,216,468,1040,537,1661,986,786,555,632,1257,3782,154,3.118,2.674,2.933,2.729,2.199,2.348,2.668,2.684,2.575,3.142,2.645,2.076,3.064,2.923,2.662,2.583,3.077,3.631,2.872,2.082,3.029,2.742,2.204,2.725,2.65,2.909,2.37,2.41,3.231,2.787,2.449,2.652,2.578,3.205,3.371,2.425,3.031,2.831,2.188,2.291,2.449,2.111,3.143,1.918,2.016,2.142,2.733,2.572,2.666,2.555,1.87,2.2,2.202,2.343,3.624,2.057,2.293,2.385,2.015,2.06,2.157,2.577,2.037,2.045,2.281,2.421,2.157,2.641,2.489,2.542,2.236,2.365,2.308,2.204,0.615,0.62,0.56,0.46,0.642,0.472,0.67,0.483,0.586,0.418,0.848,0.678,0.415,0.499,0.486,0.676,0.648,0.603,0.604,0.584,0.834,0.574,0.731,0.895,0.682,0.581,0.635,0.723,0.61,0.564,0.854,0.867,0.408,0.66,0.575,0.492,0.852,0.782,0.344,0.35,0.447,0.798,0.78,0.566,0.643,0.385,0.478,0.596,0.419,0.452,0.431,0.421,0.408,0.392,0.333,0.408,0.462,0.519,0.428,0.39,0.366,0.524,0.542,0.537,0.415,0.591,0.458,0.456,0.523,0.58,0.419,0.635,0.465,0.392
-409,Epileptic,M,17.5,1.4,2.1,0.8,1.2,2.4,1.8,1.5,1.7,1.0,1.1,0.5,3.1,1.1,0.5,1.2,6.2,8.2,1.0,1.4,5.4,1.1,2.5,2.1,3.7,4.8,5.1,7.1,2.1,3.6,4.4,1.4,1.3,0.4,2.8,0.4,1.1,5.8,4.2,0.2,0.2,1.2,4.2,2.5,2.3,1.9,0.7,0.4,0.8,1.3,0.7,0.7,2.0,2.2,3.1,0.5,2.3,1.0,1.3,1.0,1.0,0.5,1.8,0.2,0.7,2.1,0.9,2.4,1.4,1.0,0.9,0.8,1.3,5.4,0.3,9,23,7,11,16,20,14,18,12,11,5.0,32,17,8,12,77,90,8,20,50,21,29,21,46,54,68,63,24,32,59,65,8,7,34,3,11,62,51,2,1,10,30,29,19,13,4,3,5,6,5,6,18,16,28,3,17,7,12,9,7,3,14,1,6,19,17,17,12,7,6,7,9,50,3,0.05,0.026,0.018,0.024,0.038,0.034,0.03,0.025,0.037,0.057,0.034,0.036,0.027,0.031,0.029,0.033,0.029,0.036,0.027,0.043,0.038,0.037,0.039,0.035,0.044,0.03,0.042,0.027,0.033,0.04,0.049,0.068,0.022,0.034,0.014,0.02,0.058,0.033,0.014,0.013,0.022,0.041,0.036,0.022,0.015,0.014,0.021,0.014,0.018,0.013,0.032,0.021,0.034,0.02,0.041,0.012,0.022,0.019,0.022,0.02,0.02,0.027,0.015,0.02,0.023,0.017,0.02,0.016,0.014,0.023,0.024,0.017,0.021,0.013,1894,4185,2204,2363,2560,2800,2972,3524,1876,1773,784.0,3153,3454,1411,2590,13566,19609,2510,3862,5127,3678,3906,1892,6937,6721,9835,7054,3453,6754,6329,1973,873,1060,6664,2058,2560,7505,9807,426,441,1869,3062,5746,3519,3138,1358,776,2088,2387,1633,709,3567,2657,6249,470,4436,1300,2429,1185,1532,876,2921,385,917,3046,1354,3475,3038,2022,1504,1152,2476,10133,566,0.155,0.125,0.104,0.119,0.142,0.143,0.118,0.124,0.145,0.186,0.139,0.146,0.125,0.148,0.132,0.151,0.129,0.126,0.125,0.165,0.125,0.14,0.138,0.142,0.151,0.142,0.146,0.11,0.124,0.163,0.143,0.13,0.109,0.148,0.07,0.115,0.17,0.143,0.101,0.1,0.115,0.139,0.139,0.111,0.09,0.094,0.107,0.085,0.094,0.093,0.144,0.111,0.128,0.109,0.169,0.091,0.117,0.111,0.117,0.109,0.111,0.125,0.107,0.116,0.118,0.103,0.103,0.099,0.093,0.116,0.122,0.102,0.111,0.113,471,1382,737,852,982,999,950,1123,520,409,216.0,1542,873,347,688,3470,5320,511,989,2295,787,1314,912,1947,2018,2902,2800,1368,1700,2122,656,358,334,1532,509,897,2137,2250,211,198,905,1572,1312,1831,1847,700,323,861,1078,717,384,1564,1171,2553,185,2428,645,1204,712,784,355,1107,204,503,1482,884,1822,1331,983,549,557,1127,4222,307,3.755,2.775,2.838,2.594,2.232,2.277,2.448,2.799,2.5,3.096,2.675,1.801,2.931,2.913,2.643,2.806,2.782,3.63,2.879,1.942,3.296,2.388,1.995,2.616,2.641,2.658,2.078,1.992,2.874,2.309,2.255,2.591,2.465,2.927,3.46,2.739,2.596,3.01,2.2,2.204,2.597,1.775,3.046,2.068,1.948,2.119,2.83,2.932,2.687,2.768,2.195,2.402,2.206,2.445,3.154,2.071,2.551,2.32,1.943,2.219,2.444,2.509,2.029,2.274,2.235,1.833,2.135,2.624,2.422,2.566,2.434,2.472,2.515,2.422,0.786,0.656,0.613,0.552,0.748,0.741,0.686,0.529,0.785,0.787,0.66,0.531,0.494,0.431,0.603,0.697,0.667,0.712,0.512,0.56,0.83,0.727,0.535,0.827,0.695,0.691,0.642,0.664,0.619,0.69,0.785,0.936,0.233,0.732,0.667,0.585,0.808,0.773,0.53,0.483,0.55,0.499,0.796,0.552,0.628,0.524,0.466,0.753,0.471,0.383,0.302,0.521,0.575,0.532,0.975,0.492,0.583,0.482,0.389,0.513,0.549,0.726,0.336,0.554,0.506,0.522,0.5,0.463,0.419,0.922,0.463,0.513,0.463,0.239
-410,Epileptic,M,52.0,0.8,2.2,0.8,1.1,1.3,2.1,1.5,2.1,1.4,0.8,0.2,2.8,1.5,0.5,1.3,5.7,8.6,0.9,2.9,3.8,0.7,2.8,1.7,3.5,3.3,3.8,3.3,2.7,2.4,3.5,1.4,1.6,0.7,3.1,0.3,0.9,5.0,4.3,0.1,0.1,1.3,2.7,2.1,2.6,2.4,0.7,0.3,1.4,1.2,0.6,0.6,1.5,1.7,2.8,0.3,2.1,1.1,1.8,1.0,1.0,0.5,1.3,0.4,0.4,2.0,1.4,3.5,0.9,1.0,0.7,1.0,1.3,5.6,0.4,6,24,11,10,13,16,13,18,12,8,2.0,26,13,6,14,63,78,8,30,31,6,31,15,37,37,41,40,27,22,35,19,12,8,34,2,6,52,46,2,1,7,23,24,21,13,5,2,8,7,6,4,13,13,17,2,17,8,14,8,9,5,12,2,5,19,13,29,6,5,7,8,11,40,3,0.031,0.023,0.015,0.019,0.027,0.042,0.024,0.032,0.037,0.034,0.033,0.033,0.027,0.023,0.035,0.035,0.029,0.034,0.03,0.035,0.016,0.028,0.027,0.036,0.029,0.031,0.027,0.026,0.026,0.03,0.03,0.049,0.027,0.034,0.013,0.017,0.038,0.033,0.014,0.013,0.026,0.03,0.031,0.027,0.02,0.015,0.013,0.02,0.014,0.013,0.038,0.019,0.027,0.022,0.02,0.016,0.021,0.02,0.024,0.017,0.027,0.021,0.024,0.014,0.021,0.025,0.023,0.014,0.019,0.021,0.022,0.016,0.02,0.015,1867,4822,2230,2374,2521,2634,2580,3026,2359,1287,599.0,2952,3544,1066,2149,10589,17185,2499,5312,4148,3087,4933,2517,6679,6375,7278,5749,4132,4953,5606,2820,1153,1384,7302,1634,2112,9889,8793,280,377,1532,3332,5865,2322,3196,1530,893,2318,2534,2070,344,2634,2509,4657,424,3720,2108,3162,1144,1504,813,2387,371,1081,3199,1558,4436,1818,1467,1628,1486,2524,10500,615,0.113,0.107,0.099,0.101,0.123,0.143,0.102,0.132,0.137,0.143,0.108,0.135,0.12,0.121,0.143,0.139,0.127,0.121,0.135,0.137,0.083,0.131,0.13,0.134,0.129,0.136,0.123,0.108,0.102,0.133,0.133,0.126,0.119,0.135,0.075,0.087,0.138,0.131,0.1,0.096,0.114,0.126,0.125,0.119,0.099,0.094,0.088,0.088,0.087,0.093,0.156,0.102,0.12,0.105,0.124,0.094,0.105,0.108,0.122,0.105,0.126,0.11,0.115,0.106,0.116,0.12,0.117,0.094,0.1,0.131,0.118,0.104,0.103,0.101,452,1631,860,929,924,932,973,1136,634,368,168.0,1327,944,316,664,2930,4936,518,1515,1765,674,1554,925,1755,1921,2195,2096,1524,1435,1878,774,530,429,1661,394,778,2461,2226,189,183,796,1366,1348,1494,1924,748,373,1009,1212,779,265,1300,1070,2066,199,2108,834,1496,695,862,381,973,221,495,1560,854,2318,923,733,535,750,1238,4417,363,3.859,2.772,2.474,2.477,2.42,2.538,2.236,2.451,2.763,2.941,2.902,1.96,2.884,2.421,2.482,2.729,2.785,3.684,2.829,2.008,3.287,2.449,2.204,2.844,2.559,2.68,2.194,2.178,2.665,2.465,2.833,2.286,2.494,3.134,3.539,2.507,3.018,2.978,1.847,2.384,2.438,2.019,3.196,1.793,1.913,2.255,2.943,2.673,2.659,2.698,1.756,2.129,2.315,2.439,2.396,1.951,2.814,2.511,1.901,1.973,2.461,2.418,2.057,2.486,2.338,2.331,2.137,2.325,2.352,3.29,2.292,2.452,2.563,2.129,0.753,0.591,0.536,0.487,0.579,0.576,0.532,0.583,0.695,0.462,0.521,0.514,0.435,0.526,0.539,0.598,0.549,0.705,0.688,0.604,0.674,0.496,0.51,0.629,0.51,0.484,0.447,0.568,0.591,0.441,0.714,0.758,0.423,0.694,0.691,0.536,0.635,0.62,0.569,0.383,0.43,0.767,0.732,0.516,0.549,0.524,0.374,0.669,0.447,0.45,0.362,0.446,0.619,0.5,0.455,0.381,0.711,0.493,0.382,0.372,0.46,0.562,0.482,0.797,0.597,0.53,0.49,0.408,0.355,0.94,0.436,0.448,0.54,0.321
-411,Epileptic,F,47.0,1.0,2.4,1.5,0.9,1.7,1.5,1.3,1.3,1.2,0.9,0.2,3.6,1.5,0.6,0.9,6.0,8.3,1.1,1.9,4.8,1.4,3.5,1.8,3.4,3.1,2.4,2.1,2.1,2.4,2.5,1.2,0.7,0.4,1.8,0.5,1.2,3.4,4.0,0.2,0.2,1.1,3.6,2.6,3.3,2.2,0.5,0.4,0.8,1.3,1.0,0.4,1.8,1.8,2.5,0.4,2.4,0.6,1.5,1.0,1.1,0.7,1.6,0.4,0.5,1.7,1.1,2.1,1.3,1.1,0.8,1.1,1.9,5.3,0.3,9,22,12,7,19,15,13,13,11,8,3.0,34,16,7,11,62,84,9,18,46,16,35,22,40,46,25,27,21,22,36,16,16,5,21,3,19,38,44,2,2,6,32,21,24,13,4,3,5,7,8,4,12,12,18,2,17,5,12,6,10,5,11,4,4,14,15,21,10,8,10,9,13,46,3,0.038,0.028,0.028,0.017,0.035,0.026,0.024,0.024,0.04,0.039,0.021,0.036,0.03,0.026,0.029,0.036,0.028,0.04,0.027,0.038,0.029,0.038,0.031,0.037,0.03,0.023,0.026,0.029,0.024,0.03,0.037,0.041,0.022,0.024,0.013,0.024,0.03,0.03,0.015,0.02,0.024,0.039,0.041,0.026,0.018,0.011,0.018,0.015,0.02,0.02,0.02,0.022,0.036,0.021,0.018,0.016,0.017,0.02,0.019,0.022,0.022,0.027,0.033,0.021,0.019,0.022,0.018,0.019,0.019,0.027,0.024,0.021,0.016,0.017,1489,3433,2251,2160,2074,2877,2658,2726,1491,1575,899.0,3330,3060,1408,1897,9238,15528,2158,4023,5580,3567,4077,2138,6187,5921,5771,4261,3151,5894,5100,2138,1035,794,5712,2187,2327,7927,9756,389,525,1395,2117,5754,3264,3238,1229,705,1608,2034,1819,541,2819,1942,4942,562,3768,1219,2747,1206,1305,895,2351,569,805,2604,1152,3740,2200,1532,1015,1616,2847,11216,396,0.121,0.138,0.124,0.092,0.15,0.119,0.111,0.116,0.146,0.154,0.111,0.144,0.129,0.126,0.126,0.147,0.124,0.141,0.122,0.146,0.11,0.113,0.124,0.145,0.124,0.112,0.117,0.108,0.106,0.139,0.134,0.123,0.094,0.116,0.072,0.12,0.125,0.129,0.108,0.103,0.107,0.146,0.157,0.116,0.098,0.079,0.108,0.084,0.1,0.109,0.117,0.106,0.136,0.104,0.11,0.097,0.108,0.107,0.106,0.123,0.116,0.122,0.126,0.113,0.103,0.117,0.104,0.107,0.104,0.142,0.119,0.107,0.094,0.124,439,1266,842,790,822,942,896,916,503,386,237.0,1622,895,342,522,2930,4788,455,1112,2165,846,1423,1013,1734,1846,1748,1509,1229,1657,1643,606,429,283,1308,567,794,1914,2228,170,222,718,1403,1112,2043,1867,631,339,754,978,753,315,1369,810,1921,292,2063,519,1224,743,769,472,897,273,423,1375,770,1909,1045,866,475,798,1436,5069,251,3.213,2.705,2.746,2.49,2.257,2.515,2.418,2.63,2.259,3.13,3.075,1.847,2.633,2.899,2.675,2.403,2.625,3.484,2.843,2.102,3.16,2.258,1.867,2.78,2.477,2.624,2.266,2.083,2.672,2.393,2.581,2.518,2.231,3.081,3.427,2.73,2.94,3.148,2.174,2.392,2.317,1.536,3.486,1.772,1.974,2.094,2.616,2.7,2.553,2.574,2.103,2.213,2.493,2.389,2.213,1.97,2.531,2.42,1.825,1.853,2.125,2.725,2.175,2.201,2.025,1.968,2.05,2.3,2.147,2.435,2.177,2.399,2.369,2.043,0.745,0.631,0.483,0.572,0.646,0.667,0.656,0.448,0.692,0.745,0.685,0.453,0.379,0.544,0.485,0.649,0.572,0.822,0.687,0.698,0.872,0.606,0.421,0.688,0.629,0.496,0.511,0.468,0.445,0.626,0.507,0.859,0.311,0.515,0.748,0.406,0.685,0.613,0.475,0.496,0.401,0.403,0.748,0.462,0.609,0.479,0.63,0.576,0.469,0.62,0.406,0.411,0.888,0.623,0.382,0.393,0.406,0.427,0.42,0.369,0.471,0.532,0.419,0.64,0.475,0.745,0.438,0.374,0.371,0.651,0.485,0.465,0.46,0.338
-412,Epileptic,F,52.0,0.7,1.9,0.7,0.8,1.5,1.6,1.0,1.3,1.0,0.4,0.2,2.4,1.4,0.4,1.6,4.6,8.2,0.9,2.8,3.8,0.7,2.5,1.0,2.9,2.4,3.5,2.8,2.6,2.0,3.4,1.1,1.7,0.5,1.9,0.3,0.3,3.1,2.6,0.1,0.2,1.0,2.9,2.0,2.1,2.6,0.5,0.3,0.8,1.1,1.0,0.6,1.4,1.2,2.0,0.1,2.3,0.4,1.3,1.4,1.3,0.4,1.2,0.3,0.3,1.1,1.0,2.4,1.0,0.8,0.7,1.1,1.1,3.6,0.5,9,20,7,9,15,17,9,13,12,5,2.0,17,14,4,15,46,76,6,29,33,5,28,11,32,27,36,38,24,23,39,14,10,5,23,1,2,34,26,1,1,8,25,18,18,16,4,2,6,6,10,5,11,9,14,0,20,4,11,11,13,3,9,2,3,9,11,18,7,4,7,6,8,34,3,0.031,0.021,0.015,0.016,0.036,0.027,0.02,0.026,0.039,0.025,0.02,0.037,0.04,0.029,0.038,0.036,0.029,0.033,0.034,0.035,0.016,0.032,0.029,0.035,0.028,0.029,0.024,0.027,0.024,0.032,0.029,0.049,0.019,0.026,0.011,0.008,0.035,0.032,0.021,0.017,0.021,0.033,0.034,0.028,0.023,0.012,0.015,0.012,0.014,0.02,0.031,0.018,0.023,0.02,0.022,0.018,0.016,0.015,0.029,0.022,0.024,0.023,0.024,0.012,0.023,0.031,0.021,0.015,0.017,0.023,0.027,0.021,0.019,0.021,1427,4478,1760,1811,2017,2888,2515,2397,1643,1004,518.0,1940,2685,866,2919,8642,15737,2235,4131,3497,2042,3919,1527,5469,4638,6669,5533,4190,5117,5505,1843,1138,992,4977,1505,1211,5394,4828,258,425,1476,2747,4220,1764,3240,1018,730,2230,2210,1937,573,2697,1978,4054,152,3320,959,2860,1434,1553,553,2025,470,691,1841,880,3640,2161,1269,1069,1265,1666,7444,649,0.118,0.109,0.098,0.108,0.135,0.132,0.096,0.128,0.149,0.125,0.094,0.141,0.147,0.135,0.148,0.144,0.129,0.116,0.14,0.134,0.073,0.14,0.129,0.132,0.135,0.14,0.125,0.117,0.118,0.142,0.129,0.149,0.099,0.122,0.064,0.061,0.133,0.123,0.101,0.117,0.115,0.137,0.119,0.122,0.104,0.091,0.088,0.072,0.088,0.109,0.128,0.102,0.118,0.101,0.119,0.109,0.098,0.095,0.132,0.122,0.12,0.114,0.122,0.099,0.104,0.128,0.11,0.097,0.098,0.123,0.118,0.119,0.106,0.102,450,1393,673,742,683,1064,824,864,493,309,158.0,954,730,213,744,2365,4593,435,1240,1625,694,1310,657,1445,1505,2025,1939,1378,1443,1814,532,504,400,1124,420,519,1539,1326,131,180,746,1311,1004,1212,1811,580,310,1001,1096,809,341,1237,820,1619,67,1834,399,1357,760,877,307,808,193,383,865,430,1803,1007,636,442,681,790,3053,353,2.788,2.823,2.528,2.327,2.448,2.364,2.49,2.597,2.545,2.715,2.724,1.848,2.886,2.916,2.871,2.728,2.726,3.717,2.785,1.855,2.486,2.411,1.996,2.744,2.447,2.685,2.292,2.354,2.769,2.462,2.661,2.395,2.008,3.094,3.161,2.208,2.698,2.735,2.129,2.702,2.47,1.913,2.998,1.733,2.026,1.902,2.99,2.745,2.396,2.59,1.957,2.227,2.438,2.486,2.179,2.036,2.417,2.317,2.227,1.98,2.137,2.505,2.645,2.086,2.331,2.234,2.217,2.441,2.271,2.598,2.241,2.574,2.528,2.316,0.637,0.683,0.727,0.415,0.659,0.455,0.495,0.55,0.603,0.405,0.638,0.473,0.505,0.509,0.578,0.558,0.655,0.927,0.527,0.608,0.727,0.381,0.42,0.593,0.44,0.487,0.47,0.475,0.464,0.479,0.771,0.947,0.398,0.599,0.702,0.43,0.599,0.594,0.256,0.449,0.498,0.49,0.806,0.776,0.516,0.405,0.427,0.583,0.485,0.493,0.411,0.537,0.629,0.608,0.3,0.414,0.469,0.407,0.35,0.433,0.367,0.594,0.45,0.9,0.587,0.612,0.475,0.466,0.393,0.699,0.382,0.56,0.588,0.533
-413,Epileptic,F,47.0,0.6,2.3,1.0,1.1,1.9,1.5,1.5,2.1,1.4,0.7,0.2,3.5,1.7,0.6,1.6,5.6,9.2,1.0,2.2,4.7,1.4,2.4,2.3,2.7,3.4,3.6,2.8,2.9,3.1,3.1,1.1,0.9,0.6,2.6,0.4,1.1,3.0,3.5,0.2,0.3,1.2,2.7,2.2,2.7,2.9,0.8,0.4,1.0,1.0,0.6,0.8,2.5,1.0,2.6,0.5,2.5,0.8,2.7,1.2,1.0,0.4,1.6,0.6,0.3,2.1,1.4,2.6,2.2,1.7,0.6,0.7,1.8,4.2,0.5,5,18,9,7,17,14,13,17,17,6,2.0,32,16,6,19,56,71,7,22,37,10,27,21,26,40,34,34,26,24,38,16,5,5,25,2,9,31,34,2,2,6,30,22,20,18,4,2,6,6,4,6,20,7,14,3,19,6,20,7,9,2,13,4,3,18,21,20,18,12,4,4,10,34,3,0.033,0.025,0.019,0.025,0.037,0.03,0.028,0.027,0.053,0.032,0.019,0.037,0.028,0.037,0.031,0.036,0.033,0.041,0.033,0.037,0.026,0.04,0.033,0.033,0.033,0.032,0.029,0.032,0.029,0.036,0.039,0.034,0.027,0.034,0.013,0.024,0.031,0.033,0.015,0.018,0.023,0.038,0.039,0.03,0.024,0.018,0.019,0.016,0.016,0.014,0.034,0.022,0.029,0.022,0.027,0.019,0.031,0.028,0.032,0.025,0.026,0.028,0.035,0.017,0.022,0.028,0.022,0.024,0.022,0.019,0.02,0.031,0.02,0.023,1447,4438,2163,2268,2143,3066,2398,3763,1754,1160,505.0,2945,3359,1115,3148,9942,17350,2517,4569,4570,3325,3650,2478,5505,5829,6442,5160,4071,5496,4764,2544,1255,1002,5542,1784,2277,6465,7017,330,507,1532,2889,5248,2463,2858,1412,711,2411,2138,1755,812,3949,1400,4125,568,3650,1306,3832,1110,1401,593,2878,459,894,3011,1228,3342,3135,2410,1042,1130,2026,8434,544,0.112,0.119,0.105,0.1,0.14,0.135,0.115,0.123,0.162,0.129,0.087,0.15,0.123,0.141,0.136,0.143,0.133,0.128,0.137,0.139,0.097,0.16,0.147,0.13,0.155,0.141,0.134,0.131,0.121,0.15,0.126,0.099,0.128,0.123,0.073,0.11,0.121,0.125,0.091,0.113,0.108,0.159,0.142,0.117,0.113,0.095,0.103,0.086,0.091,0.086,0.145,0.11,0.124,0.105,0.118,0.104,0.118,0.121,0.127,0.127,0.125,0.127,0.144,0.095,0.118,0.125,0.119,0.117,0.119,0.113,0.105,0.128,0.101,0.113,319,1467,813,767,792,895,893,1195,524,335,163.0,1560,1095,292,898,2877,4767,421,1157,2059,941,1180,1118,1523,1747,1968,1788,1457,1761,1530,608,451,358,1324,460,745,1693,1766,233,269,759,1296,1098,1512,1881,668,286,918,975,685,356,1818,603,1893,317,2019,526,1639,602,694,241,960,241,386,1431,652,1771,1513,1162,459,566,932,3494,299,3.866,2.799,2.645,2.897,2.303,2.915,2.339,2.856,2.721,3.088,2.78,1.761,2.533,2.636,2.706,2.712,3.001,3.987,3.022,1.916,2.933,2.594,2.018,2.936,2.599,2.802,2.265,2.259,2.533,2.526,2.877,2.65,2.273,2.868,3.475,2.825,2.885,2.872,1.627,2.343,2.528,1.921,3.263,1.839,1.766,2.27,3.133,3.042,2.723,2.828,2.345,2.246,2.402,2.443,2.318,2.08,2.873,2.619,2.268,2.22,2.929,3.309,2.101,2.403,2.271,2.185,2.185,2.366,2.292,2.477,2.235,2.56,2.641,2.268,0.882,0.527,0.601,0.424,0.586,0.691,0.543,0.641,0.551,0.442,0.482,0.475,0.419,0.505,0.642,0.465,0.55,0.808,0.59,0.653,0.925,0.564,0.435,0.554,0.506,0.553,0.459,0.513,0.517,0.486,0.801,1.064,0.405,0.66,0.869,0.437,0.692,0.646,0.341,0.283,0.51,0.534,0.742,0.703,0.461,0.535,0.455,0.927,0.557,0.543,0.571,0.511,0.45,0.423,0.298,0.361,0.545,0.74,0.358,0.41,0.322,0.842,0.404,1.036,0.487,0.879,0.449,0.362,0.396,0.612,0.639,0.543,0.538,0.385
-414,Epileptic,M,22.0,0.7,2.7,0.8,1.5,2.0,1.5,1.6,1.8,1.6,0.7,0.4,3.4,1.3,0.6,1.8,6.8,8.7,1.1,1.8,4.2,2.2,4.6,1.8,3.4,6.2,5.8,3.0,1.9,2.6,2.1,1.1,1.3,0.5,2.0,0.6,1.0,4.3,3.7,0.1,0.3,1.4,4.4,2.0,2.6,2.6,0.9,0.6,1.0,1.3,0.7,0.6,2.0,1.8,3.0,0.6,2.1,1.2,3.4,1.2,0.9,0.6,1.7,0.4,0.5,2.1,1.2,1.8,1.6,1.0,0.5,1.1,1.7,3.9,0.4,7,30,7,15,22,17,15,18,19,9,7.0,28,17,6,21,80,93,10,25,36,13,42,15,50,63,69,42,22,27,29,16,9,6,26,3,13,48,50,2,2,10,31,20,19,17,7,3,7,8,8,6,14,14,25,4,17,9,26,8,8,5,15,2,5,16,20,14,12,7,6,9,13,32,3,0.035,0.022,0.012,0.029,0.032,0.027,0.026,0.025,0.035,0.035,0.039,0.037,0.024,0.035,0.03,0.029,0.027,0.04,0.026,0.036,0.039,0.042,0.033,0.039,0.05,0.03,0.028,0.02,0.022,0.024,0.028,0.05,0.028,0.024,0.02,0.019,0.034,0.031,0.015,0.015,0.027,0.044,0.034,0.023,0.02,0.02,0.022,0.014,0.017,0.025,0.032,0.018,0.021,0.019,0.016,0.015,0.016,0.028,0.02,0.021,0.018,0.027,0.017,0.014,0.018,0.021,0.015,0.014,0.02,0.018,0.026,0.019,0.016,0.024,1417,5218,2634,2461,2656,2463,3143,4052,2203,1768,716.0,3233,3865,900,3670,15724,21926,2286,4816,5278,3916,3958,1623,6024,5259,9871,5824,4452,7485,5243,2582,1384,958,6317,2313,2463,8604,9128,422,500,1635,2243,5534,3100,3289,1481,906,2561,2520,1688,680,3806,2697,6218,1087,4133,2466,4762,1543,1243,1112,2574,534,1207,3459,1265,3930,3738,1717,860,1428,3036,8736,394,0.122,0.115,0.084,0.128,0.14,0.116,0.111,0.126,0.156,0.135,0.15,0.144,0.12,0.141,0.131,0.129,0.124,0.137,0.123,0.142,0.12,0.117,0.118,0.139,0.132,0.124,0.113,0.093,0.104,0.125,0.107,0.122,0.111,0.122,0.077,0.112,0.133,0.138,0.117,0.104,0.128,0.144,0.141,0.114,0.102,0.101,0.112,0.089,0.094,0.102,0.146,0.101,0.116,0.103,0.097,0.097,0.099,0.131,0.103,0.114,0.1,0.129,0.094,0.094,0.102,0.117,0.092,0.09,0.104,0.126,0.13,0.112,0.096,0.145,424,1974,938,957,1076,995,993,1242,721,415,213.0,1470,987,274,1055,4087,5703,494,1283,1901,944,1468,887,1757,1917,3250,1870,1423,1906,1539,754,450,350,1389,559,893,2278,2123,187,260,853,1455,1123,1810,2029,700,378,991,1201,584,353,1699,1276,2646,553,2122,1027,2036,946,681,544,1026,302,587,1729,920,1871,1614,794,447,704,1399,3800,225,3.322,2.54,2.737,2.563,2.133,2.183,2.517,2.776,2.429,3.063,2.415,1.887,2.899,2.515,2.583,2.799,2.895,3.566,2.861,2.155,2.991,2.189,1.65,2.645,2.269,2.549,2.409,2.424,2.938,2.589,2.525,2.829,2.276,3.121,3.609,2.596,2.877,3.074,2.407,2.201,2.449,1.517,3.389,1.958,1.84,2.334,2.815,3.092,2.624,2.787,2.141,2.379,2.245,2.461,2.39,2.104,2.485,2.559,1.856,1.986,2.19,2.594,1.984,2.333,2.167,1.672,2.203,2.617,2.467,2.263,2.377,2.449,2.476,2.068,0.881,0.497,0.407,0.512,0.719,0.518,0.631,0.523,0.673,0.723,0.976,0.564,0.405,0.722,0.631,0.667,0.701,0.796,0.557,0.856,1.0,0.684,0.574,0.788,0.662,0.604,0.634,0.683,0.594,0.591,0.584,1.019,0.519,0.524,0.787,0.571,0.758,0.685,0.543,0.422,0.48,0.484,0.75,0.504,0.552,0.442,0.455,0.759,0.414,0.696,0.52,0.448,0.403,0.431,0.299,0.44,0.525,0.812,0.415,0.41,0.474,0.659,0.418,0.855,0.447,0.437,0.425,0.393,0.434,0.526,0.357,0.522,0.447,0.432
-415,Epileptic,M,32.0,0.9,2.1,0.9,1.5,2.2,1.6,1.8,1.9,1.1,0.7,0.3,2.9,1.2,0.7,1.3,6.2,9.0,0.8,3.0,4.4,1.1,2.3,1.2,3.2,3.3,2.7,3.3,2.5,2.4,3.2,0.9,1.7,1.1,2.6,0.2,1.2,2.3,3.3,0.1,0.2,1.0,3.7,2.4,1.7,2.4,0.8,0.4,0.7,1.0,2.9,0.8,1.8,2.4,4.4,0.3,2.0,0.8,1.0,1.2,1.5,0.5,1.9,0.5,0.4,1.3,0.9,2.4,1.6,0.7,0.6,0.9,0.6,4.6,0.3,6,22,10,15,14,14,16,19,10,8,1.0,28,14,7,14,61,80,6,46,46,7,26,11,37,42,28,37,27,23,33,11,12,11,33,2,14,22,37,1,2,8,29,26,13,16,5,2,5,5,17,6,13,20,35,3,17,7,11,7,11,5,16,4,5,12,15,19,9,5,4,6,4,34,2,0.038,0.027,0.016,0.023,0.037,0.028,0.022,0.03,0.029,0.039,0.032,0.035,0.031,0.035,0.03,0.032,0.027,0.026,0.033,0.039,0.03,0.037,0.026,0.037,0.028,0.025,0.029,0.028,0.022,0.032,0.026,0.046,0.033,0.029,0.009,0.029,0.045,0.035,0.009,0.018,0.022,0.038,0.038,0.024,0.018,0.014,0.024,0.013,0.014,0.064,0.027,0.021,0.025,0.023,0.015,0.016,0.03,0.023,0.029,0.027,0.038,0.025,0.022,0.014,0.018,0.022,0.02,0.022,0.017,0.02,0.024,0.013,0.02,0.019,1598,5404,2625,2865,1719,2850,2895,4068,1470,1515,368.0,2938,3098,1281,3057,11601,21059,2392,5133,5334,2394,3374,1787,5905,6885,6807,5961,4120,6572,5574,1837,1477,1590,7009,1833,2259,3154,6774,407,447,1565,2964,5607,2773,3688,1651,727,2438,2268,1152,825,2867,3816,8630,528,3679,991,1815,1132,1512,531,2993,723,962,2409,1048,3649,2520,1587,1454,1478,1922,8751,483,0.111,0.117,0.103,0.122,0.145,0.122,0.112,0.131,0.116,0.149,0.114,0.149,0.129,0.142,0.131,0.133,0.12,0.102,0.132,0.15,0.116,0.154,0.109,0.136,0.132,0.125,0.128,0.113,0.1,0.138,0.115,0.133,0.148,0.142,0.057,0.12,0.114,0.135,0.082,0.112,0.112,0.148,0.147,0.107,0.099,0.093,0.116,0.081,0.088,0.183,0.133,0.105,0.121,0.109,0.114,0.103,0.116,0.117,0.134,0.129,0.143,0.128,0.116,0.113,0.103,0.132,0.106,0.107,0.099,0.11,0.124,0.092,0.106,0.114,457,1463,847,1010,819,989,1155,1147,462,368,135.0,1274,773,278,768,3247,5351,483,1421,1962,557,1121,732,1713,2145,1869,1967,1446,1672,1748,564,601,516,1501,463,715,1070,1636,214,212,772,1442,1121,1256,2063,819,266,1005,1049,781,475,1347,1571,3243,290,1881,458,791,662,816,283,1119,367,456,1160,687,1911,1117,714,470,616,702,3502,269,3.397,3.185,2.75,2.599,2.176,2.41,2.171,2.968,2.527,3.226,2.366,2.02,2.997,3.022,2.916,2.718,3.067,3.72,2.697,2.239,3.017,2.44,2.064,2.644,2.607,2.913,2.443,2.223,2.914,2.52,2.533,2.572,2.569,3.345,3.459,2.696,2.244,2.853,2.158,2.557,2.615,1.904,3.275,2.236,2.01,2.231,3.407,2.871,2.666,1.8,1.985,2.333,2.351,2.598,2.315,2.167,2.273,2.385,1.976,2.015,2.155,2.673,2.182,2.487,2.296,2.072,2.052,2.713,2.484,3.366,2.606,2.309,2.6,2.232,0.874,0.734,0.739,0.644,0.569,0.525,0.467,0.609,0.645,0.585,1.017,0.486,0.463,0.591,0.461,0.554,0.598,0.839,0.712,0.629,0.892,0.521,0.463,0.677,0.581,0.577,0.524,0.59,0.569,0.582,0.608,0.998,0.338,0.52,0.772,0.521,0.85,0.782,0.32,0.534,0.59,0.502,0.858,0.812,0.593,0.458,0.397,0.699,0.451,0.855,0.4,0.479,0.518,0.513,0.305,0.4,0.687,0.633,0.466,0.4,0.34,0.623,0.513,0.771,0.641,0.605,0.536,0.38,0.396,0.813,0.549,0.708,0.641,0.303
-416,Epileptic,M,47.0,0.6,2.5,0.7,1.0,1.6,1.8,1.7,2.0,1.7,0.4,0.3,3.4,1.8,0.6,1.4,5.9,10.0,0.8,2.1,5.0,1.5,3.2,1.6,4.1,2.6,4.3,3.0,2.3,2.5,3.8,1.9,1.4,0.5,2.3,0.5,0.5,2.8,2.5,0.2,0.3,1.1,3.2,2.7,2.4,4.0,0.7,0.7,1.3,1.7,0.5,0.7,1.5,1.4,2.3,0.3,2.9,1.1,1.6,1.3,0.9,1.1,2.0,0.7,0.7,2.9,1.2,2.4,0.8,1.0,0.4,1.0,1.3,4.6,0.5,8,24,8,7,14,17,12,21,17,5,3.0,30,17,8,16,65,87,7,23,47,18,32,15,39,33,47,32,23,20,45,19,8,5,23,5,4,29,31,2,2,7,27,30,20,23,5,3,7,10,4,4,12,10,16,2,22,6,18,11,9,8,15,6,6,25,12,19,5,7,4,6,10,36,4,0.025,0.022,0.011,0.018,0.032,0.03,0.025,0.031,0.04,0.025,0.025,0.038,0.032,0.028,0.033,0.029,0.028,0.025,0.028,0.039,0.025,0.038,0.029,0.036,0.03,0.028,0.025,0.023,0.026,0.033,0.036,0.049,0.021,0.028,0.02,0.012,0.032,0.031,0.015,0.03,0.018,0.034,0.036,0.027,0.026,0.013,0.019,0.017,0.02,0.011,0.023,0.016,0.022,0.02,0.025,0.019,0.024,0.02,0.033,0.021,0.033,0.027,0.023,0.026,0.027,0.027,0.019,0.014,0.02,0.012,0.023,0.023,0.019,0.024,1481,4747,1965,2124,2195,2296,2762,2835,1768,1330,640.0,2613,3305,1330,2553,10948,18740,2933,4598,4430,4499,4001,2009,6769,4642,7679,5457,3311,4139,6024,2342,1224,1072,5243,2377,1669,6386,5838,341,353,1675,2850,6147,2418,2923,1500,1129,2622,2819,1352,641,2989,1872,4266,329,3957,1621,3219,1124,1163,859,3058,793,820,3146,1165,3702,1655,1559,954,1289,1956,8328,427,0.101,0.116,0.093,0.099,0.139,0.141,0.102,0.131,0.13,0.111,0.123,0.153,0.134,0.124,0.131,0.133,0.124,0.102,0.128,0.156,0.109,0.16,0.135,0.131,0.137,0.127,0.125,0.101,0.1,0.151,0.142,0.116,0.09,0.122,0.082,0.083,0.138,0.137,0.109,0.134,0.105,0.145,0.144,0.117,0.121,0.088,0.098,0.09,0.107,0.091,0.118,0.091,0.113,0.103,0.126,0.109,0.11,0.119,0.152,0.122,0.144,0.123,0.129,0.121,0.131,0.121,0.107,0.09,0.103,0.101,0.12,0.129,0.103,0.116,440,1748,864,848,887,977,953,1105,570,353,211.0,1355,950,390,836,3357,5677,560,1272,2045,1082,1410,929,1986,1578,2606,1941,1299,1284,2000,895,480,385,1319,518,739,1487,1461,180,182,897,1422,1373,1522,2144,752,509,1120,1303,601,409,1556,882,1939,181,2233,685,1337,650,644,525,1173,445,465,1676,665,1990,887,777,516,677,901,3646,283,3.104,2.587,2.263,2.514,2.284,2.146,2.379,2.306,2.391,3.028,2.669,1.775,2.622,2.622,2.413,2.586,2.76,3.856,2.882,1.941,3.139,2.337,1.949,2.586,2.382,2.429,2.261,2.082,2.515,2.464,2.151,2.666,2.185,2.927,3.605,2.24,3.036,2.931,2.251,2.178,2.388,1.849,3.231,1.846,1.575,2.2,2.843,2.83,2.701,2.367,1.927,2.143,2.34,2.457,2.161,1.984,2.725,2.516,1.978,1.932,2.12,2.634,2.081,2.081,2.054,2.058,2.06,2.167,2.266,2.114,2.332,2.511,2.391,1.94,0.751,0.538,0.517,0.365,0.464,0.336,0.601,0.595,0.452,0.408,0.662,0.427,0.454,0.377,0.427,0.521,0.496,0.843,0.568,0.562,0.723,0.442,0.394,0.833,0.504,0.509,0.4,0.554,0.687,0.464,0.738,0.922,0.402,0.596,0.833,0.501,0.706,0.543,0.372,0.341,0.499,0.422,0.697,0.648,0.542,0.417,0.544,0.91,0.628,0.498,0.312,0.402,0.562,0.402,0.283,0.408,0.529,0.6,0.498,0.32,0.35,0.601,0.315,0.805,0.51,0.653,0.418,0.353,0.407,0.497,0.398,0.443,0.474,0.343
-417,Epileptic,F,52.0,0.9,2.0,0.7,1.2,1.4,1.7,1.3,1.6,1.1,0.7,0.5,3.7,1.3,0.5,1.1,4.8,8.1,1.6,2.0,4.9,1.2,2.4,3.0,3.7,2.2,3.2,2.8,2.3,2.8,2.2,1.6,1.1,0.4,2.3,0.4,0.4,3.7,3.1,0.1,0.2,1.2,2.9,2.4,3.2,2.7,0.4,0.6,2.6,1.5,0.9,0.7,1.4,1.9,3.7,0.2,2.4,0.6,1.7,1.0,1.1,0.4,1.4,0.5,0.4,2.6,1.4,2.9,0.8,1.0,0.8,0.6,1.6,4.3,0.4,9,20,7,9,16,20,10,15,13,8,4.0,35,12,6,14,51,80,12,24,48,10,31,30,42,26,33,36,22,24,30,23,9,4,28,1,2,45,44,1,2,6,25,24,32,15,3,3,61,7,9,4,11,15,26,1,18,4,14,9,10,3,16,4,4,21,17,22,4,6,10,4,17,33,4,0.038,0.023,0.016,0.023,0.025,0.029,0.024,0.03,0.039,0.029,0.028,0.034,0.025,0.029,0.029,0.035,0.029,0.049,0.028,0.036,0.025,0.034,0.035,0.043,0.027,0.027,0.028,0.028,0.03,0.026,0.046,0.058,0.029,0.03,0.009,0.011,0.032,0.032,0.014,0.011,0.026,0.032,0.036,0.022,0.022,0.011,0.018,0.03,0.02,0.024,0.027,0.018,0.027,0.023,0.018,0.017,0.017,0.022,0.02,0.02,0.016,0.024,0.018,0.02,0.024,0.022,0.02,0.016,0.02,0.026,0.017,0.022,0.019,0.024,1586,3711,1829,2051,2081,2849,2321,2894,1504,1566,722.0,3680,2737,1109,2348,8409,17024,2454,4729,5893,3017,3986,2874,5566,4606,6047,6268,3518,4922,4900,2400,1003,769,4886,2065,1315,7450,7483,308,448,1259,2965,5688,4009,3546,1178,965,1924,2053,1434,532,2361,2099,5962,262,4007,982,2999,1396,1754,705,2235,587,855,3432,1154,4757,1631,1482,1032,1142,2533,7627,551,0.131,0.119,0.093,0.104,0.126,0.139,0.093,0.125,0.163,0.136,0.125,0.138,0.12,0.135,0.13,0.141,0.129,0.158,0.126,0.138,0.101,0.136,0.137,0.156,0.123,0.127,0.123,0.104,0.107,0.126,0.163,0.128,0.104,0.132,0.059,0.07,0.143,0.14,0.103,0.089,0.119,0.121,0.149,0.112,0.105,0.077,0.098,0.1,0.101,0.129,0.137,0.099,0.129,0.112,0.118,0.103,0.102,0.113,0.114,0.113,0.101,0.134,0.115,0.126,0.116,0.116,0.105,0.086,0.104,0.144,0.101,0.131,0.104,0.138,480,1351,741,823,878,1060,724,959,511,440,260.0,1680,796,340,741,2585,4871,598,1231,2280,768,1193,1273,1614,1353,1959,1954,1325,1405,1560,603,434,239,1240,602,460,2063,1821,156,238,663,1285,1247,2285,1931,597,467,892,1097,571,334,1253,1080,2416,146,2048,532,1354,748,888,374,916,402,341,1653,868,2199,712,762,501,573,1126,3373,263,3.151,2.553,2.342,2.383,2.088,2.351,2.586,2.637,2.253,2.848,2.435,1.881,2.661,2.474,2.436,2.463,2.739,3.217,2.839,2.112,3.022,2.581,1.957,2.716,2.613,2.545,2.458,2.139,2.688,2.5,2.908,2.474,2.454,2.763,3.131,2.633,2.736,2.981,1.984,2.27,2.514,1.985,3.253,1.991,2.098,2.129,2.471,2.805,2.266,2.789,2.041,1.982,1.98,2.31,1.986,2.144,2.242,2.506,2.07,2.174,2.215,2.372,1.626,2.62,2.171,1.722,2.13,2.531,2.249,2.154,2.137,2.452,2.434,2.536,0.865,0.604,0.468,0.428,0.631,0.601,0.606,0.442,0.686,0.644,0.629,0.502,0.352,0.536,0.426,0.604,0.614,0.858,0.51,0.708,0.85,0.48,0.569,0.58,0.576,0.547,0.515,0.59,0.514,0.452,0.775,0.873,0.469,0.58,0.653,0.547,0.642,0.584,0.503,0.36,0.471,0.631,0.68,0.585,0.688,0.393,0.572,0.859,0.471,0.575,0.418,0.38,0.437,0.523,0.392,0.426,0.34,0.481,0.413,0.374,0.389,0.674,0.401,0.595,0.421,0.525,0.491,0.434,0.382,0.763,0.386,0.603,0.448,0.4
-418,Epileptic,F,42.0,0.9,2.4,0.6,1.0,1.6,2.1,1.9,1.4,0.9,0.4,0.3,2.6,1.9,0.5,1.7,5.7,8.3,0.4,1.7,3.9,0.5,2.3,1.5,2.8,2.7,2.8,3.0,2.5,2.2,3.1,1.2,0.2,0.7,2.8,0.4,0.7,2.9,2.8,0.2,0.2,1.0,2.7,1.7,1.8,2.1,0.5,0.3,0.8,1.2,0.9,0.4,1.4,1.3,2.3,0.2,1.7,0.5,1.8,0.6,1.0,0.6,1.4,0.2,0.4,1.9,0.8,3.1,1.0,1.0,0.4,1.1,1.5,4.5,0.5,7,25,7,7,12,20,16,12,11,5,3.0,23,19,7,20,53,74,3,20,34,5,27,14,25,28,30,34,19,17,31,18,3,5,30,2,7,34,34,1,1,8,25,16,14,13,3,1,5,6,8,2,9,8,15,1,14,3,16,4,8,3,7,2,3,16,9,23,8,6,5,8,11,42,5,0.038,0.026,0.012,0.021,0.033,0.033,0.028,0.026,0.038,0.024,0.023,0.034,0.034,0.037,0.04,0.035,0.028,0.018,0.025,0.038,0.011,0.034,0.027,0.036,0.031,0.031,0.029,0.026,0.025,0.031,0.033,0.017,0.037,0.036,0.013,0.019,0.04,0.042,0.017,0.021,0.02,0.034,0.036,0.025,0.02,0.011,0.019,0.013,0.016,0.039,0.033,0.017,0.02,0.022,0.024,0.015,0.02,0.025,0.024,0.021,0.026,0.029,0.016,0.013,0.024,0.034,0.022,0.016,0.02,0.015,0.027,0.029,0.026,0.029,1418,4792,1685,2013,1844,3430,2677,2880,2005,1067,606.0,2420,3374,1106,3155,10596,17350,1779,3222,3048,1617,3844,2041,4830,4872,5326,5677,3642,4583,5477,2210,580,948,4970,1491,1452,4229,5252,380,371,1663,2765,3658,1863,2748,1379,816,2100,2374,1003,348,2823,2416,4530,271,3176,780,2410,692,1279,666,2151,324,714,2320,711,4344,2086,1617,933,1395,1684,7114,733,0.125,0.12,0.094,0.1,0.132,0.132,0.12,0.128,0.128,0.128,0.109,0.14,0.132,0.126,0.149,0.141,0.125,0.091,0.12,0.145,0.072,0.145,0.124,0.131,0.135,0.131,0.122,0.106,0.1,0.139,0.132,0.075,0.126,0.13,0.072,0.095,0.134,0.135,0.103,0.104,0.112,0.152,0.134,0.115,0.101,0.076,0.091,0.078,0.092,0.149,0.128,0.094,0.104,0.105,0.145,0.097,0.108,0.114,0.115,0.113,0.114,0.124,0.111,0.09,0.119,0.134,0.111,0.099,0.101,0.117,0.128,0.141,0.116,0.128,373,1531,722,796,713,1134,1022,904,482,300,193.0,1168,970,265,771,2958,4697,342,1075,1672,641,1156,860,1311,1547,1675,1858,1297,1228,1682,681,265,281,1326,411,605,1400,1304,187,157,791,1253,875,1233,1681,665,317,955,1102,454,208,1356,903,1826,97,1799,407,1268,458,761,370,750,190,394,1244,388,2203,928,788,456,637,820,3083,356,3.222,2.841,2.258,2.535,2.275,2.513,2.259,2.874,2.923,2.938,2.692,1.846,2.691,2.898,3.004,2.745,2.915,3.636,2.453,1.68,2.066,2.553,2.072,2.829,2.53,2.738,2.345,2.269,2.839,2.553,2.463,2.167,2.811,2.743,3.011,2.272,2.372,2.781,2.275,2.46,2.548,1.97,3.131,1.736,1.848,2.22,3.236,2.738,2.536,2.653,1.889,2.287,2.476,2.609,3.332,1.953,2.192,2.085,1.695,1.857,2.041,3.083,1.94,2.142,2.109,2.29,2.065,2.478,2.296,2.421,2.603,2.387,2.328,2.173,0.859,0.678,0.63,0.471,0.593,0.697,0.563,0.511,0.646,0.458,0.901,0.376,0.577,0.566,0.636,0.615,0.592,0.774,0.537,0.427,0.615,0.578,0.471,0.664,0.534,0.481,0.539,0.5,0.631,0.543,0.602,0.962,0.593,0.637,0.539,0.53,0.694,0.608,0.487,0.388,0.666,0.471,0.712,0.651,0.581,0.464,0.438,0.614,0.528,0.552,0.342,0.522,0.727,0.554,0.415,0.441,0.407,0.451,0.427,0.362,0.449,0.566,0.261,1.008,0.622,0.589,0.457,0.351,0.47,0.577,0.489,0.582,0.576,0.632
-419,Epileptic,M,32.0,0.6,2.2,0.9,1.2,1.3,1.8,1.4,1.6,1.1,0.7,0.2,2.0,1.2,0.6,1.5,6.0,8.0,0.8,1.4,3.7,0.5,2.8,1.1,3.5,2.8,3.2,3.2,2.9,2.2,3.5,1.3,1.2,0.7,2.8,0.3,0.9,3.6,3.7,0.1,0.2,1.0,3.4,2.4,1.6,2.3,0.8,0.4,0.8,1.2,0.8,0.7,1.8,1.1,2.3,0.5,1.9,0.6,1.0,1.3,1.1,0.6,1.4,0.4,0.3,1.4,0.6,3.1,1.6,0.8,0.5,1.3,1.6,4.9,0.4,4,23,8,12,12,18,15,16,16,7,3.0,21,16,7,18,68,77,7,18,38,7,32,11,33,40,38,42,30,23,44,18,9,5,37,2,9,41,46,1,1,5,26,27,13,13,6,2,5,7,7,6,14,8,15,4,19,4,10,12,8,5,13,3,3,9,8,25,11,5,5,9,13,40,3,0.028,0.025,0.016,0.022,0.028,0.034,0.024,0.025,0.04,0.028,0.022,0.033,0.026,0.033,0.035,0.035,0.027,0.028,0.023,0.037,0.015,0.031,0.024,0.037,0.032,0.03,0.027,0.028,0.023,0.031,0.041,0.051,0.035,0.032,0.013,0.02,0.035,0.035,0.015,0.016,0.02,0.039,0.039,0.021,0.016,0.022,0.019,0.013,0.014,0.019,0.032,0.021,0.019,0.02,0.023,0.016,0.021,0.015,0.027,0.019,0.022,0.025,0.023,0.017,0.02,0.028,0.021,0.021,0.014,0.015,0.025,0.022,0.019,0.017,1486,5049,2359,2162,2204,2795,3048,3849,2256,1447,574.0,2292,3691,1503,3204,11688,19582,2600,3592,4252,3645,5002,2338,6507,5580,6476,6062,4564,6441,6362,2456,1340,1102,6959,2015,2455,8436,8959,400,487,1452,3027,6975,2344,3761,1410,794,2364,2732,1690,508,3160,2141,4541,665,3409,1074,2827,1404,1592,1035,2569,529,1169,2408,455,5119,2950,1698,1027,1829,2735,9771,661,0.106,0.109,0.097,0.117,0.134,0.149,0.109,0.12,0.15,0.122,0.112,0.143,0.12,0.13,0.147,0.141,0.124,0.115,0.121,0.15,0.076,0.139,0.112,0.139,0.141,0.138,0.13,0.115,0.11,0.143,0.151,0.134,0.12,0.147,0.076,0.102,0.137,0.146,0.095,0.093,0.104,0.149,0.148,0.103,0.089,0.104,0.105,0.084,0.088,0.114,0.144,0.106,0.111,0.099,0.132,0.104,0.118,0.096,0.133,0.106,0.13,0.122,0.132,0.106,0.104,0.118,0.111,0.107,0.092,0.121,0.119,0.124,0.102,0.103,374,1563,841,862,752,875,996,1107,586,379,190.0,962,884,323,826,3032,4884,464,976,1639,739,1473,784,1601,1615,1860,2091,1627,1544,1973,609,399,321,1492,463,741,1821,1953,194,217,714,1337,1269,1335,2103,613,323,930,1186,643,327,1332,932,1872,303,1768,452,1194,796,851,415,940,256,369,1046,319,2313,1197,758,468,898,1136,3982,340,3.713,3.027,2.804,2.528,2.499,2.628,2.48,2.908,2.933,3.072,2.761,2.035,3.102,3.166,2.941,2.883,3.128,4.177,2.922,2.238,3.453,2.545,2.436,3.021,2.619,2.883,2.346,2.237,3.023,2.5,2.868,3.041,2.6,3.224,3.654,2.884,3.228,3.23,2.214,2.662,2.661,1.969,3.602,2.029,2.072,2.283,3.103,3.167,2.751,2.784,1.996,2.485,2.4,2.613,2.511,2.094,2.904,2.62,1.976,2.052,2.7,2.773,2.353,2.927,2.46,1.801,2.385,2.878,2.62,2.573,2.455,2.817,2.624,2.485,0.833,0.627,0.575,0.525,0.737,0.578,0.683,0.459,0.688,0.518,0.639,0.469,0.384,0.633,0.516,0.607,0.599,0.646,0.599,0.686,0.754,0.526,0.487,0.667,0.539,0.61,0.495,0.583,0.55,0.564,0.581,1.032,0.529,0.611,0.728,0.527,0.775,0.616,0.358,0.412,0.484,0.557,0.819,0.614,0.641,0.533,0.746,0.72,0.491,0.456,0.45,0.503,0.36,0.489,0.472,0.38,0.482,0.441,0.398,0.448,0.547,0.719,0.535,0.964,0.577,0.685,0.423,0.393,0.428,0.675,0.462,0.484,0.473,0.465
-420,Epileptic,M,42.0,1.1,2.5,1.0,1.1,1.3,1.9,1.3,1.9,1.1,0.7,0.2,3.2,1.9,0.5,1.1,5.6,9.5,1.4,1.8,4.2,1.8,2.7,1.6,3.6,2.9,4.3,1.8,2.2,3.6,2.5,1.2,1.3,0.4,3.0,0.3,0.6,3.6,3.5,0.1,0.1,1.1,2.7,2.7,3.1,3.1,0.7,0.2,0.9,1.4,0.6,0.6,1.5,2.2,3.4,0.3,2.0,0.5,1.7,0.7,1.3,0.5,1.0,0.2,1.0,1.3,1.5,2.4,1.5,1.2,0.3,1.1,1.4,3.9,0.3,8,22,7,9,10,21,12,14,10,7,3.0,24,17,7,11,64,92,15,24,38,16,30,17,38,39,50,26,25,28,28,14,12,3,36,2,6,38,41,1,1,7,23,26,18,16,5,1,5,7,6,4,11,22,22,2,14,4,17,5,12,2,8,2,7,9,19,20,10,8,4,7,12,26,3,0.041,0.027,0.017,0.021,0.036,0.035,0.024,0.036,0.046,0.034,0.031,0.039,0.039,0.034,0.029,0.037,0.035,0.044,0.029,0.039,0.027,0.032,0.034,0.042,0.037,0.04,0.025,0.032,0.032,0.029,0.041,0.067,0.025,0.036,0.013,0.017,0.04,0.037,0.013,0.015,0.026,0.036,0.048,0.027,0.023,0.015,0.014,0.012,0.021,0.015,0.024,0.02,0.037,0.026,0.015,0.018,0.017,0.022,0.022,0.02,0.015,0.026,0.017,0.032,0.018,0.027,0.025,0.021,0.019,0.012,0.026,0.023,0.016,0.021,1637,4905,2407,2533,1737,3610,2480,3292,1499,1700,761.0,2799,3503,1179,2536,11017,19584,2718,4357,5126,4893,5359,2141,6362,6436,7032,3827,3305,6574,5009,2635,983,821,7053,2057,1916,6772,7775,361,387,1658,2700,5594,2872,3537,1378,670,2701,2107,1513,564,2685,3378,5789,437,3215,1230,2851,947,1778,1087,2380,357,1419,2147,1431,3326,2841,1880,919,1569,2480,10132,506,0.128,0.122,0.102,0.103,0.141,0.144,0.102,0.139,0.159,0.128,0.113,0.142,0.149,0.151,0.129,0.14,0.139,0.149,0.128,0.144,0.108,0.137,0.143,0.151,0.143,0.15,0.115,0.127,0.126,0.135,0.128,0.16,0.112,0.138,0.071,0.102,0.145,0.138,0.087,0.092,0.111,0.149,0.15,0.118,0.105,0.094,0.09,0.073,0.1,0.1,0.129,0.097,0.145,0.11,0.109,0.106,0.101,0.109,0.117,0.116,0.091,0.113,0.116,0.125,0.101,0.13,0.122,0.104,0.103,0.092,0.12,0.126,0.09,0.134,434,1502,862,873,577,1051,904,908,378,389,226.0,1299,927,253,638,2863,4909,575,1038,1956,1110,1590,840,1448,1691,1933,1293,1236,1776,1551,585,388,293,1474,449,624,1577,1691,191,177,707,1155,1109,1637,2051,716,274,1036,1004,694,318,1227,1081,2214,240,1647,535,1415,515,993,453,712,188,596,1063,786,1483,1194,940,385,646,980,3897,219,3.442,3.103,2.696,2.788,2.648,2.789,2.301,3.024,2.909,3.158,2.808,1.963,2.82,3.231,2.954,2.785,3.038,3.394,3.147,2.139,3.051,2.607,2.16,3.204,2.82,2.774,2.319,2.135,2.817,2.542,3.148,2.587,2.351,3.378,3.774,2.872,3.152,3.336,2.196,2.419,2.689,1.964,3.338,2.031,2.021,2.175,2.949,3.174,2.643,2.519,2.209,2.355,2.669,2.638,2.12,2.163,2.645,2.295,2.119,1.973,2.53,3.075,2.11,2.625,2.202,2.175,2.328,2.596,2.415,2.522,2.559,2.867,2.727,2.611,0.777,0.625,0.514,0.535,0.66,0.65,0.467,0.538,0.534,0.603,0.628,0.45,0.545,0.472,0.534,0.66,0.612,1.028,0.7,0.71,0.799,0.561,0.458,0.66,0.65,0.579,0.464,0.562,0.507,0.503,0.744,1.039,0.488,0.586,0.697,0.822,0.758,0.605,0.494,0.378,0.541,0.488,0.881,0.607,0.532,0.488,0.521,0.816,0.608,0.488,0.64,0.474,0.714,0.566,0.301,0.445,0.468,0.496,0.403,0.377,0.522,0.694,0.424,0.966,0.432,0.587,0.557,0.414,0.409,0.532,0.495,0.605,0.499,0.34
-421,Epileptic,F,22.0,0.7,2.5,1.1,1.5,1.4,1.6,1.5,1.4,1.5,0.4,0.2,2.5,1.8,0.6,1.7,6.7,9.1,0.7,1.5,3.7,0.8,2.0,1.5,3.6,2.1,2.8,3.1,2.3,2.1,2.3,1.6,1.1,0.7,2.6,0.3,0.9,3.1,2.5,0.1,0.1,1.1,2.3,1.5,2.9,3.2,0.5,0.5,0.9,1.2,0.7,0.5,2.5,1.5,2.6,0.1,2.3,0.5,1.0,0.4,0.9,0.5,1.5,0.2,0.4,1.7,1.0,1.7,1.6,1.3,1.1,0.6,0.8,3.4,0.3,4,20,9,14,17,20,13,13,18,5,2.0,27,20,7,22,74,100,5,24,35,8,27,13,40,29,37,35,22,20,30,16,9,10,33,2,8,38,29,2,2,18,21,14,23,22,6,2,7,6,4,3,20,12,20,1,18,5,9,5,6,3,11,2,6,18,13,15,12,11,12,6,9,28,3,0.031,0.025,0.019,0.025,0.035,0.026,0.023,0.025,0.047,0.025,0.033,0.034,0.032,0.038,0.031,0.033,0.031,0.032,0.023,0.036,0.019,0.034,0.033,0.038,0.032,0.028,0.027,0.026,0.02,0.031,0.042,0.054,0.031,0.036,0.02,0.018,0.038,0.029,0.011,0.015,0.026,0.032,0.038,0.026,0.024,0.015,0.02,0.015,0.018,0.02,0.03,0.024,0.029,0.019,0.012,0.021,0.026,0.013,0.022,0.025,0.02,0.026,0.025,0.02,0.019,0.022,0.019,0.022,0.017,0.024,0.027,0.026,0.018,0.011,1238,5196,2750,2687,2607,2847,2342,2500,2112,1149,409.0,2263,3788,1052,3369,11234,18283,2244,3857,3491,2627,2778,1742,6021,4033,5455,4658,3269,5369,4076,2228,880,1165,4835,969,2019,5239,5050,442,452,1438,2634,4161,2748,2974,1283,802,1899,2146,1053,471,3349,2024,4692,389,3285,1167,2450,815,1053,806,2327,281,788,2839,1195,2648,2511,2423,1489,1031,1344,6861,494,0.1,0.119,0.101,0.125,0.14,0.14,0.111,0.122,0.158,0.115,0.118,0.149,0.13,0.154,0.153,0.145,0.139,0.118,0.127,0.148,0.09,0.153,0.142,0.145,0.146,0.137,0.133,0.116,0.1,0.146,0.137,0.148,0.132,0.147,0.089,0.092,0.153,0.125,0.082,0.099,0.113,0.144,0.157,0.115,0.11,0.099,0.108,0.09,0.093,0.111,0.142,0.121,0.135,0.105,0.083,0.116,0.105,0.087,0.124,0.12,0.105,0.119,0.143,0.123,0.121,0.126,0.108,0.119,0.109,0.131,0.116,0.128,0.1,0.109,342,1641,933,993,828,1065,930,914,538,308,111.0,1190,1020,277,979,3553,5293,393,1080,1683,657,1019,705,1638,1242,1595,1730,1294,1635,1372,644,364,413,1293,285,713,1406,1330,244,216,745,1163,746,1725,1971,650,341,876,973,492,244,1652,826,2164,185,1749,511,1188,369,562,363,929,135,404,1412,640,1404,1183,1132,674,464,624,2941,286,3.262,2.93,2.75,2.619,2.436,2.402,2.151,2.496,2.742,2.866,3.004,1.713,2.774,2.835,2.588,2.514,2.754,3.886,2.793,1.896,3.039,2.263,2.189,2.732,2.567,2.714,2.157,2.068,2.559,2.392,2.518,2.633,2.292,2.76,2.717,2.542,2.891,2.798,1.981,2.477,2.425,1.986,3.356,1.82,1.722,2.2,3.052,2.688,2.588,2.6,2.428,2.154,2.317,2.321,2.09,2.078,2.473,2.269,2.063,2.051,2.365,2.417,2.296,2.113,2.137,2.248,2.031,2.368,2.358,2.323,2.467,2.384,2.423,2.203,0.914,0.705,0.599,0.533,0.674,0.567,0.467,0.477,0.616,0.481,0.944,0.401,0.488,0.517,0.486,0.557,0.541,0.767,0.462,0.519,0.647,0.451,0.358,0.742,0.525,0.543,0.506,0.489,0.517,0.508,0.689,0.992,0.418,0.517,0.476,0.525,0.703,0.51,0.32,0.357,0.522,0.439,0.859,0.621,0.51,0.538,0.33,0.686,0.405,0.505,0.307,0.431,0.573,0.383,0.246,0.421,0.399,0.493,0.401,0.31,0.483,0.646,0.426,1.007,0.56,0.734,0.395,0.385,0.371,0.508,0.506,0.597,0.437,0.393
-422,Epileptic,M,42.0,1.7,3.0,1.2,1.3,3.4,1.7,3.8,2.2,2.3,0.9,0.5,3.7,2.6,0.4,1.7,13.2,18.8,1.8,2.4,5.5,1.4,4.9,2.3,5.1,5.7,4.4,6.2,5.5,6.4,5.5,1.9,1.3,0.5,3.4,0.4,0.5,3.8,4.4,0.3,0.4,1.1,4.4,2.7,3.5,4.4,1.1,0.5,1.0,1.6,1.0,0.9,3.2,2.6,5.1,0.2,3.1,1.1,2.7,1.6,0.9,1.2,2.5,0.6,0.7,2.7,1.8,3.7,1.8,1.8,0.7,1.1,2.3,5.1,0.4,16,27,9,10,24,17,28,21,14,8,4.0,33,21,6,19,98,132,13,29,46,11,43,17,41,57,46,52,39,41,49,24,10,3,33,2,3,39,48,2,2,6,35,26,26,24,6,3,7,7,12,5,23,18,30,1,24,7,21,13,7,12,19,3,5,23,21,25,11,10,6,7,17,41,3,0.047,0.03,0.015,0.022,0.052,0.022,0.053,0.033,0.056,0.037,0.036,0.04,0.037,0.035,0.037,0.051,0.048,0.045,0.032,0.041,0.022,0.046,0.038,0.051,0.045,0.033,0.038,0.047,0.049,0.039,0.059,0.054,0.026,0.033,0.012,0.011,0.031,0.033,0.022,0.027,0.022,0.039,0.036,0.03,0.033,0.024,0.022,0.018,0.019,0.019,0.034,0.026,0.031,0.031,0.016,0.023,0.02,0.024,0.026,0.015,0.022,0.035,0.039,0.023,0.025,0.031,0.025,0.022,0.029,0.022,0.021,0.022,0.019,0.024,1878,5209,2982,2447,2066,2833,3427,3626,1541,1760,844.0,3017,4502,794,3118,13467,22076,2608,5188,5499,3792,4658,2036,5566,5925,8325,6704,4960,6177,7592,2228,1347,943,6684,2181,1772,8830,9566,368,559,1532,3208,5896,3238,3476,1582,886,1729,2786,2724,743,4611,2463,5950,396,4198,1834,3884,1579,1576,1722,2662,482,904,3156,1577,4974,2834,2111,1012,1751,3676,10000,417,0.149,0.128,0.093,0.108,0.157,0.115,0.144,0.133,0.149,0.13,0.133,0.147,0.14,0.15,0.136,0.15,0.148,0.149,0.128,0.15,0.093,0.125,0.12,0.144,0.145,0.135,0.126,0.137,0.135,0.145,0.155,0.125,0.107,0.129,0.064,0.067,0.136,0.133,0.093,0.114,0.11,0.13,0.133,0.128,0.127,0.109,0.098,0.091,0.093,0.106,0.141,0.114,0.124,0.12,0.087,0.113,0.105,0.116,0.108,0.093,0.111,0.134,0.146,0.111,0.121,0.13,0.115,0.106,0.126,0.111,0.104,0.118,0.098,0.13,586,1776,1133,912,1040,1186,1217,1261,641,431,233.0,1573,1241,219,887,4351,6342,658,1440,2290,1055,1589,939,1795,2117,2412,2596,1893,2026,2396,733,513,335,1684,633,626,2048,2292,237,260,759,1572,1343,1881,2177,741,370,831,1238,862,376,2126,1328,2579,175,2110,838,1808,940,855,810,1096,273,494,1670,891,2274,1197,969,470,856,1578,4355,243,3.019,2.583,2.675,2.726,1.859,2.126,2.391,2.605,2.12,3.218,3.15,1.763,2.892,2.559,2.631,2.472,2.814,3.184,2.793,2.079,2.782,2.288,1.96,2.384,2.337,2.763,2.177,2.205,2.508,2.475,2.355,2.454,2.269,2.887,3.114,2.509,3.107,2.947,1.788,2.446,2.608,1.85,3.119,1.934,1.802,2.359,2.899,2.692,2.793,3.044,2.03,2.307,2.044,2.402,2.38,2.188,2.588,2.408,1.813,2.0,2.119,2.232,2.048,2.047,2.111,2.024,2.333,2.435,2.414,2.331,2.4,2.485,2.397,2.206,0.655,0.633,0.454,0.434,0.484,0.528,0.659,0.507,0.568,0.475,0.686,0.424,0.459,0.475,0.606,0.676,0.725,0.845,0.588,0.707,0.843,0.561,0.494,0.733,0.624,0.53,0.549,0.579,0.636,0.608,0.634,0.833,0.451,0.641,0.709,0.445,0.622,0.709,0.37,0.419,0.489,0.508,0.804,0.729,0.492,0.443,0.533,0.528,0.476,0.74,0.489,0.436,0.505,0.497,0.401,0.426,0.454,0.529,0.364,0.434,0.412,0.606,0.387,0.709,0.501,0.675,0.482,0.367,0.495,0.572,0.431,0.57,0.461,0.404
-423,Epileptic,M,27.0,0.8,2.7,1.0,1.8,1.6,2.5,2.1,1.7,1.1,0.4,0.4,2.1,1.5,0.4,0.9,8.6,9.3,0.5,3.1,4.0,1.5,3.2,1.3,3.7,3.6,3.8,5.5,3.1,2.8,3.8,1.6,0.9,0.4,2.5,0.4,0.6,3.8,3.1,0.1,0.1,1.2,2.4,3.1,2.4,3.0,1.1,0.3,0.8,1.3,0.7,0.4,1.7,1.5,3.5,0.1,2.1,1.0,1.9,1.1,0.7,1.1,2.1,0.4,0.5,1.7,1.4,2.4,2.1,0.8,0.7,1.4,1.9,6.2,0.3,7,26,8,16,15,25,17,15,9,6,5.0,18,14,4,12,90,88,4,28,35,13,35,13,39,46,41,52,34,29,41,19,6,3,29,2,5,40,37,2,1,7,23,29,20,19,9,2,5,6,6,3,14,11,25,1,17,9,13,8,6,10,20,3,5,14,18,22,15,5,8,8,12,51,2,0.032,0.028,0.017,0.03,0.03,0.031,0.032,0.027,0.041,0.029,0.035,0.034,0.03,0.019,0.025,0.035,0.03,0.022,0.037,0.036,0.023,0.031,0.027,0.034,0.033,0.032,0.036,0.027,0.024,0.033,0.04,0.035,0.024,0.028,0.013,0.017,0.036,0.029,0.01,0.015,0.026,0.038,0.052,0.032,0.02,0.021,0.014,0.012,0.015,0.014,0.023,0.019,0.025,0.023,0.028,0.018,0.016,0.019,0.028,0.015,0.029,0.028,0.019,0.02,0.021,0.028,0.018,0.023,0.012,0.019,0.026,0.024,0.018,0.022,1602,5544,2565,2737,2494,3811,3197,3820,1882,1355,731.0,2143,3485,1174,2607,14421,20526,2301,5812,4536,4334,6004,2051,7483,6257,7061,7095,4927,7178,6395,2273,1119,914,6408,2148,1802,9140,8536,379,780,1550,3030,6593,1903,4041,1762,861,2599,2746,2116,467,3603,2526,6375,209,3685,2244,3626,1138,1511,1353,3409,760,991,2439,1330,3915,3360,1799,1671,1830,3004,13151,297,0.103,0.123,0.1,0.136,0.134,0.142,0.13,0.128,0.141,0.131,0.152,0.151,0.134,0.125,0.129,0.145,0.13,0.092,0.147,0.151,0.101,0.142,0.125,0.139,0.149,0.14,0.135,0.116,0.106,0.141,0.148,0.104,0.097,0.125,0.068,0.095,0.146,0.137,0.081,0.084,0.114,0.154,0.181,0.137,0.101,0.115,0.088,0.082,0.089,0.094,0.116,0.1,0.117,0.109,0.141,0.101,0.096,0.099,0.125,0.092,0.132,0.127,0.114,0.122,0.114,0.121,0.102,0.111,0.085,0.122,0.127,0.12,0.103,0.143,426,1618,862,1017,918,1383,1072,1012,502,337,215.0,1031,870,268,681,4105,5422,391,1410,1852,1049,1817,809,1798,1920,1996,2324,1704,1820,1953,625,437,298,1398,546,604,1852,1875,207,188,723,1112,1088,1214,2272,810,327,966,1171,686,277,1574,1032,2479,99,1889,885,1593,633,797,567,1203,350,400,1201,830,2025,1429,901,639,829,1189,5313,166,3.469,3.063,2.811,2.698,2.373,2.463,2.412,3.265,2.616,3.174,2.658,1.829,2.986,3.017,2.807,2.732,3.021,4.039,3.284,2.032,3.069,2.645,2.153,2.993,2.598,2.889,2.438,2.334,2.887,2.633,2.766,2.496,2.549,3.231,3.462,2.698,3.301,3.26,2.209,3.2,2.797,2.389,3.658,1.84,2.036,2.414,3.226,3.301,2.814,2.948,2.056,2.421,2.416,2.641,2.145,2.12,2.535,2.628,2.122,2.125,2.448,2.749,2.586,2.584,2.299,2.099,2.119,2.657,2.3,2.67,2.615,2.907,2.622,2.283,0.922,0.642,0.587,0.502,0.618,0.562,0.586,0.541,0.689,0.624,0.918,0.443,0.508,0.519,0.567,0.628,0.581,0.581,0.607,0.62,0.943,0.459,0.649,0.661,0.5,0.682,0.593,0.604,0.596,0.555,0.659,0.838,0.427,0.64,0.862,0.529,0.769,0.55,0.362,0.76,0.564,0.684,1.029,0.716,0.523,0.538,0.529,0.528,0.578,0.564,0.313,0.454,0.58,0.538,0.464,0.385,0.435,0.54,0.423,0.345,0.4,0.823,0.591,0.746,0.525,0.669,0.461,0.439,0.43,0.671,0.435,0.568,0.537,0.592
-424,Epileptic,M,32.0,0.9,2.8,0.6,0.9,1.6,1.6,1.3,1.6,1.3,0.9,0.3,2.6,1.3,0.5,0.7,4.8,7.5,0.8,1.7,4.0,2.2,3.8,1.7,2.5,3.6,3.2,2.6,2.1,2.0,2.6,1.4,1.7,0.3,1.8,0.4,0.6,3.7,2.5,0.2,0.1,1.2,3.6,3.0,2.3,2.9,0.4,0.4,1.3,1.1,0.9,0.6,1.0,1.6,2.3,0.3,2.0,0.7,1.3,1.1,0.9,0.6,1.5,0.4,0.4,1.6,1.9,2.7,0.6,0.7,0.9,1.2,1.4,5.5,0.3,8,28,5,7,18,17,13,16,16,8,5.0,27,18,6,9,58,72,8,24,40,23,44,19,29,49,34,33,24,22,38,16,13,5,20,3,7,42,35,1,1,7,33,28,19,16,3,3,8,6,7,5,8,11,17,3,14,5,10,9,9,4,14,2,5,13,20,24,5,5,8,8,9,56,3,0.038,0.027,0.013,0.019,0.035,0.023,0.027,0.025,0.043,0.028,0.033,0.037,0.029,0.029,0.027,0.029,0.027,0.045,0.03,0.033,0.04,0.035,0.027,0.028,0.027,0.022,0.026,0.025,0.021,0.027,0.04,0.056,0.019,0.022,0.016,0.013,0.032,0.027,0.013,0.014,0.023,0.035,0.045,0.024,0.02,0.011,0.022,0.018,0.02,0.018,0.03,0.016,0.027,0.018,0.012,0.014,0.019,0.016,0.022,0.015,0.021,0.027,0.032,0.018,0.02,0.031,0.018,0.011,0.016,0.024,0.019,0.014,0.016,0.017,1374,4544,1864,2096,2319,3493,3134,3546,1754,1818,732.0,2971,3053,1206,1623,9188,16794,1954,3790,4846,5484,5910,2405,5253,7455,7923,5728,4523,6011,5729,1873,1389,857,5976,1896,2071,9099,7172,341,386,1581,3508,6252,2636,3927,1179,871,2269,1917,2039,607,2254,1843,4906,581,3593,1242,2857,1294,1620,839,2623,366,751,2588,1214,4676,1655,1421,1599,1813,2628,12089,551,0.133,0.127,0.089,0.097,0.137,0.124,0.106,0.127,0.152,0.126,0.14,0.146,0.138,0.144,0.123,0.133,0.124,0.146,0.136,0.142,0.131,0.118,0.12,0.127,0.119,0.119,0.11,0.104,0.102,0.132,0.141,0.136,0.118,0.124,0.079,0.086,0.139,0.129,0.1,0.09,0.111,0.135,0.158,0.119,0.1,0.083,0.111,0.09,0.091,0.099,0.136,0.094,0.128,0.1,0.095,0.088,0.103,0.102,0.124,0.098,0.109,0.121,0.145,0.123,0.111,0.13,0.104,0.083,0.091,0.14,0.106,0.092,0.098,0.127,399,1561,709,781,838,1157,986,1091,630,468,216.0,1326,869,279,510,2729,4656,398,1060,2128,1121,1848,979,1603,2360,2366,1951,1448,1540,1847,617,557,267,1267,460,737,2067,1757,168,184,837,1576,1319,1488,2115,608,344,988,899,772,355,1082,887,2033,357,1988,573,1295,740,968,454,1026,178,393,1290,847,2281,799,722,573,937,1344,5248,276,3.305,2.841,2.575,2.571,2.289,2.383,2.526,2.821,2.287,2.987,2.534,1.968,2.759,2.977,2.484,2.594,2.834,3.515,2.825,1.963,3.547,2.503,2.107,2.634,2.449,2.693,2.349,2.373,2.921,2.523,2.322,2.74,2.658,3.332,3.542,2.462,3.015,2.979,2.37,2.237,2.417,1.882,3.249,2.049,2.133,2.085,2.743,2.894,2.622,2.697,2.058,2.181,2.189,2.491,1.951,1.963,2.616,2.414,1.9,1.801,2.186,2.519,2.122,2.202,2.203,1.843,2.109,2.315,2.232,2.923,2.229,2.34,2.398,2.383,0.731,0.618,0.516,0.542,0.621,0.72,0.623,0.458,0.565,0.473,0.895,0.488,0.522,0.354,0.499,0.518,0.599,0.919,0.527,0.61,0.844,0.588,0.595,0.753,0.627,0.522,0.514,0.624,0.562,0.519,0.596,0.815,0.337,0.524,0.566,0.416,0.814,0.595,0.339,0.434,0.477,0.548,0.864,0.553,0.621,0.404,0.598,0.582,0.406,0.653,0.411,0.445,0.496,0.453,0.281,0.352,0.365,0.607,0.435,0.391,0.364,0.584,0.583,0.702,0.526,0.551,0.442,0.338,0.506,0.977,0.439,0.386,0.547,0.437
-425,Epileptic,M,32.0,0.5,2.0,1.6,1.5,1.5,2.2,2.6,1.2,1.5,0.9,0.3,3.4,1.5,0.2,1.9,6.8,10.0,0.9,3.0,5.8,0.9,2.6,1.9,3.6,2.5,5.0,4.5,2.2,3.7,3.6,1.1,1.8,0.7,2.7,0.3,0.9,3.0,3.9,0.2,0.3,1.5,4.1,1.8,3.2,3.6,0.8,0.6,0.7,1.3,0.6,0.7,1.9,2.2,3.2,0.3,3.0,0.8,2.3,1.3,0.9,0.6,1.5,0.3,0.5,2.3,1.4,3.2,2.0,1.0,1.0,1.3,1.1,4.1,0.3,4,17,12,15,14,18,19,11,17,10,3.0,26,17,3,22,71,79,9,28,43,8,26,14,41,30,53,45,19,27,36,16,14,5,32,1,7,28,40,1,2,10,31,22,20,16,6,2,4,6,5,4,13,20,18,2,24,7,16,8,6,4,10,2,5,15,16,23,15,7,9,8,8,27,2,0.027,0.023,0.026,0.025,0.028,0.03,0.037,0.023,0.04,0.039,0.04,0.032,0.028,0.026,0.03,0.03,0.031,0.034,0.032,0.038,0.024,0.035,0.031,0.037,0.028,0.033,0.033,0.024,0.028,0.034,0.024,0.052,0.029,0.032,0.012,0.021,0.037,0.04,0.021,0.015,0.029,0.037,0.039,0.029,0.023,0.015,0.019,0.012,0.016,0.022,0.033,0.016,0.025,0.02,0.014,0.023,0.026,0.023,0.026,0.021,0.026,0.022,0.014,0.015,0.023,0.031,0.021,0.02,0.021,0.027,0.027,0.022,0.02,0.017,1533,4774,2681,2811,2383,3364,2624,3337,2129,1525,733.0,3546,3415,675,4224,14109,21447,2613,5309,5135,3116,4294,2523,6673,5235,9342,7338,3719,6997,6459,3066,1589,1150,6143,1304,2408,5780,8248,489,643,1705,4371,6172,2824,4233,1522,992,2077,2716,1380,608,3811,3021,5998,463,4407,1521,3237,1569,1237,618,2730,521,1212,3442,1258,5033,3428,1736,1725,1644,1869,7279,602,0.105,0.113,0.116,0.116,0.128,0.132,0.12,0.11,0.154,0.171,0.125,0.128,0.133,0.113,0.139,0.132,0.125,0.131,0.132,0.141,0.097,0.14,0.12,0.138,0.127,0.136,0.127,0.093,0.101,0.137,0.116,0.14,0.113,0.132,0.071,0.098,0.136,0.149,0.106,0.097,0.124,0.131,0.144,0.123,0.101,0.096,0.097,0.076,0.089,0.103,0.139,0.092,0.108,0.101,0.105,0.114,0.125,0.106,0.121,0.108,0.116,0.112,0.101,0.105,0.106,0.126,0.104,0.107,0.099,0.12,0.12,0.109,0.099,0.102,410,1542,963,982,874,1226,1015,1009,634,436,175.0,1681,930,157,1087,3759,5336,505,1489,2437,693,1308,1000,1738,1540,2771,2365,1426,1886,1999,760,583,352,1419,378,741,1448,1777,175,317,844,1706,1002,1755,2319,767,407,878,1172,472,298,1798,1384,2400,260,2200,591,1636,838,654,344,990,284,561,1590,796,2446,1483,771,611,775,797,3193,307,3.57,2.92,2.711,2.804,2.57,2.493,2.179,2.945,2.74,2.984,3.215,1.869,2.859,3.004,2.897,2.88,3.216,3.837,2.879,1.818,3.148,2.652,2.173,2.964,2.74,2.75,2.516,2.128,2.854,2.529,2.932,2.665,2.508,3.006,3.0,2.915,2.955,3.275,2.978,2.453,2.603,2.173,3.798,1.88,2.121,2.247,3.155,2.979,2.792,2.91,2.098,2.379,2.396,2.764,2.036,2.242,2.375,2.352,2.188,2.167,2.157,2.871,2.047,2.726,2.413,1.915,2.165,2.569,2.497,3.154,2.527,2.509,2.548,2.497,0.951,0.733,0.568,0.511,0.506,0.647,0.574,0.447,0.488,0.666,0.773,0.506,0.574,0.479,0.468,0.521,0.598,0.668,0.607,0.674,0.715,0.426,0.629,0.619,0.502,0.58,0.541,0.523,0.602,0.532,0.598,0.998,0.413,0.64,0.569,0.523,0.857,0.602,0.252,0.345,0.423,0.687,0.764,0.604,0.652,0.474,0.472,0.829,0.542,0.588,0.542,0.404,0.432,0.41,0.288,0.358,0.47,0.522,0.371,0.423,0.484,0.524,0.306,0.807,0.476,0.738,0.47,0.443,0.455,0.665,0.395,0.771,0.425,0.394
-426,Epileptic,M,47.0,0.7,2.9,0.9,1.5,2.0,2.1,1.6,1.9,1.5,0.5,0.3,3.8,2.4,0.6,1.5,8.8,10.8,1.3,2.2,3.7,1.4,2.3,2.1,3.7,2.3,4.7,2.4,3.2,4.5,3.5,1.1,0.5,0.5,3.2,0.5,0.7,1.9,3.7,0.1,0.3,1.1,3.2,2.8,2.7,2.9,0.5,0.7,0.8,1.3,0.4,0.6,2.4,1.2,2.3,0.1,2.3,0.8,2.9,1.2,1.2,0.3,1.4,0.8,0.2,2.3,1.9,2.7,2.0,1.0,0.6,1.0,0.9,5.2,0.5,4,24,10,14,19,18,11,13,13,5,3.0,31,20,6,13,65,81,8,21,30,9,21,19,31,23,41,30,33,35,36,12,6,5,26,3,5,19,36,1,2,8,26,21,22,19,5,4,4,5,3,4,14,8,11,1,18,6,21,10,8,2,9,6,2,15,16,20,10,7,5,6,6,38,3,0.033,0.029,0.018,0.025,0.04,0.037,0.023,0.029,0.051,0.032,0.03,0.038,0.04,0.035,0.04,0.041,0.035,0.041,0.035,0.031,0.022,0.034,0.033,0.038,0.026,0.031,0.024,0.029,0.035,0.033,0.029,0.025,0.02,0.037,0.018,0.018,0.037,0.036,0.018,0.021,0.025,0.038,0.04,0.027,0.02,0.013,0.026,0.013,0.016,0.016,0.035,0.026,0.023,0.021,0.013,0.017,0.023,0.028,0.032,0.022,0.017,0.023,0.035,0.016,0.024,0.038,0.019,0.026,0.02,0.023,0.025,0.026,0.023,0.02,1689,5067,1966,2462,2479,3089,3088,3249,1958,1232,576.0,3162,4071,1063,2613,13553,20041,2261,4055,4262,2767,3629,2517,6401,4735,8141,5243,4753,6387,5528,2440,1267,1290,6420,1895,1751,5222,7211,269,454,1903,3447,6304,2301,3917,1443,966,2648,2701,1125,457,3845,2159,4817,457,3862,1091,3319,1185,1613,597,2303,849,832,3499,1315,4773,3105,1968,824,1206,1555,9407,563,0.092,0.129,0.11,0.124,0.142,0.151,0.095,0.12,0.148,0.122,0.122,0.143,0.14,0.135,0.143,0.137,0.135,0.12,0.136,0.122,0.096,0.144,0.145,0.138,0.113,0.124,0.104,0.109,0.101,0.141,0.122,0.088,0.094,0.136,0.08,0.088,0.136,0.143,0.094,0.112,0.112,0.144,0.141,0.117,0.1,0.091,0.123,0.078,0.089,0.097,0.143,0.11,0.104,0.095,0.084,0.102,0.123,0.121,0.139,0.11,0.105,0.109,0.151,0.107,0.117,0.136,0.101,0.111,0.111,0.124,0.128,0.127,0.108,0.113,411,1520,792,949,871,988,923,969,553,311,181.0,1514,1017,262,712,3608,5039,459,1083,1773,752,1101,1030,1533,1335,2214,1729,1761,1684,1739,580,399,404,1411,483,617,980,1638,138,239,750,1322,1169,1612,2253,702,421,991,1140,378,297,1525,926,1750,231,2008,472,1635,629,838,261,877,361,296,1472,764,2300,1207,810,364,593,519,3678,324,3.715,3.219,2.375,2.606,2.493,2.701,2.646,2.881,2.601,3.162,3.092,1.858,3.077,2.863,2.726,2.822,3.166,3.889,2.886,1.987,2.696,2.534,2.169,3.096,2.685,2.799,2.346,2.196,2.882,2.578,3.051,2.844,2.555,3.301,3.355,2.557,3.611,3.201,2.233,2.222,2.969,2.253,3.676,1.616,1.973,2.297,2.99,3.025,2.904,3.326,1.979,2.475,2.512,3.003,2.314,2.152,2.527,2.221,2.132,2.171,2.589,2.593,2.298,3.033,2.696,2.081,2.25,2.88,2.716,2.428,2.544,3.229,2.654,2.18,1.141,0.781,0.656,0.532,0.58,0.59,0.816,0.759,0.682,0.412,0.989,0.537,0.571,0.515,0.528,0.586,0.658,0.806,0.761,0.665,0.961,0.473,0.501,0.746,0.53,0.779,0.461,0.601,0.67,0.54,0.698,1.142,0.464,0.634,0.694,0.47,0.845,0.734,0.275,0.513,0.67,0.791,0.761,0.528,0.74,0.448,0.648,0.799,0.542,0.769,0.681,0.709,0.54,0.778,0.39,0.442,0.557,0.603,0.413,0.403,0.28,0.683,0.66,0.811,0.719,0.715,0.536,0.438,0.629,0.808,0.519,0.772,0.57,0.389
-427,Epileptic,F,42.0,0.7,2.0,0.7,1.2,1.1,1.4,1.3,1.4,0.7,0.6,0.4,2.7,1.4,0.5,1.0,5.9,7.5,0.7,1.9,3.9,0.6,2.5,1.5,3.3,2.3,3.0,3.1,2.0,2.2,3.2,1.0,0.9,0.5,2.0,0.4,0.5,3.3,3.2,0.1,0.1,0.8,2.1,1.5,1.9,2.3,0.4,0.3,0.8,1.1,1.0,0.7,1.2,1.4,1.9,0.4,2.3,0.7,1.5,0.9,0.9,0.5,1.0,0.3,0.2,2.1,0.2,2.9,0.9,0.7,0.6,1.2,1.4,2.9,0.2,4,19,7,9,14,13,13,11,9,6,3.0,22,11,6,12,55,70,5,27,32,5,24,14,36,31,28,37,20,18,34,15,8,7,25,2,4,32,31,1,1,5,20,18,16,14,4,2,4,6,10,5,9,10,14,3,16,5,12,8,8,3,7,2,3,17,3,24,6,6,6,8,12,22,2,0.032,0.025,0.013,0.023,0.028,0.031,0.025,0.03,0.03,0.025,0.03,0.031,0.031,0.032,0.033,0.033,0.028,0.029,0.032,0.035,0.015,0.033,0.028,0.037,0.03,0.027,0.026,0.023,0.026,0.03,0.031,0.031,0.034,0.027,0.016,0.016,0.03,0.028,0.013,0.02,0.021,0.031,0.031,0.023,0.018,0.01,0.015,0.013,0.016,0.025,0.036,0.021,0.026,0.02,0.017,0.019,0.019,0.02,0.026,0.021,0.021,0.022,0.025,0.018,0.023,0.021,0.021,0.019,0.018,0.022,0.026,0.016,0.014,0.016,1388,3809,1790,2006,1777,2148,2489,2934,1504,1034,459.0,2703,2859,812,2100,10298,14441,2233,3983,4079,2266,3344,2291,5562,4625,6618,5430,3651,4373,5303,1848,1073,926,4860,1591,1619,6621,6844,258,267,1317,2480,3861,2266,3043,1156,842,2217,2139,1744,530,2243,2174,3630,702,3373,1297,2848,1019,1262,739,1917,388,836,2919,158,4798,1712,1377,1076,1442,2482,6934,481,0.117,0.116,0.095,0.104,0.121,0.147,0.11,0.129,0.125,0.106,0.109,0.13,0.136,0.141,0.135,0.138,0.13,0.097,0.125,0.132,0.073,0.145,0.12,0.135,0.14,0.127,0.131,0.104,0.111,0.133,0.127,0.114,0.172,0.122,0.08,0.081,0.114,0.118,0.095,0.105,0.102,0.133,0.121,0.113,0.102,0.083,0.089,0.08,0.09,0.123,0.148,0.102,0.118,0.101,0.107,0.107,0.104,0.107,0.121,0.109,0.106,0.105,0.116,0.108,0.113,0.1,0.11,0.101,0.107,0.131,0.122,0.103,0.083,0.104,355,1333,806,787,731,742,895,859,410,344,199.0,1241,791,239,570,2966,4186,414,1054,1712,674,1150,879,1425,1398,1797,1916,1362,1273,1789,498,435,289,1125,415,557,1795,1728,157,112,617,1058,918,1380,1878,643,368,896,1022,706,318,1005,917,1613,381,1851,585,1316,571,693,357,714,192,328,1467,121,2198,787,625,432,689,1241,3250,206,3.517,2.557,2.202,2.485,2.203,2.553,2.244,3.104,2.687,2.651,2.394,1.941,2.848,2.624,2.72,2.652,2.73,3.734,2.856,2.02,2.76,2.292,2.234,2.895,2.522,2.921,2.282,2.182,2.705,2.454,2.72,2.412,2.756,3.055,3.388,2.623,2.775,2.847,1.864,3.024,2.571,2.056,3.094,1.836,1.903,1.869,2.861,3.136,2.494,2.836,2.041,2.24,2.342,2.36,2.279,2.052,2.604,2.487,1.93,1.928,2.602,2.775,2.386,2.646,2.214,1.639,2.317,2.505,2.415,2.831,2.403,2.305,2.274,2.791,0.815,0.658,0.557,0.6,0.523,0.563,0.439,0.743,0.685,0.437,0.834,0.455,0.428,0.377,0.638,0.556,0.563,0.853,0.513,0.607,0.583,0.469,0.471,0.565,0.423,0.608,0.45,0.488,0.454,0.548,0.63,0.99,0.463,0.589,0.678,0.582,0.754,0.532,0.31,0.681,0.708,0.517,0.665,0.609,0.477,0.458,0.522,0.955,0.521,0.58,0.419,0.557,0.552,0.491,0.283,0.382,0.417,0.485,0.325,0.398,0.422,0.797,0.261,1.324,0.498,0.385,0.535,0.469,0.392,0.844,0.483,0.481,0.484,0.434
-428,Epileptic,M,52.0,1.0,2.3,1.3,1.8,3.3,2.4,2.1,2.2,1.4,1.7,0.6,2.7,2.9,0.6,2.6,10.5,12.7,1.3,2.4,4.3,1.0,6.2,1.6,5.6,9.2,5.2,6.6,5.2,4.9,3.9,1.8,1.3,0.7,1.9,0.4,0.3,3.1,3.8,0.3,0.2,1.2,2.5,2.9,2.5,3.5,1.0,0.7,1.0,1.5,1.0,0.3,2.3,2.1,2.7,0.9,3.5,1.3,1.9,0.6,1.0,0.8,1.7,0.6,0.7,2.3,2.1,3.8,1.3,1.0,0.7,1.1,2.2,5.3,0.3,9,22,8,15,24,20,15,22,14,14,5.0,26,22,8,29,76,92,8,31,41,7,42,13,49,63,48,51,42,31,43,15,10,6,24,3,3,36,47,2,1,5,22,30,19,19,7,3,6,7,7,2,19,12,16,5,21,8,12,4,8,5,11,5,3,16,24,25,8,6,5,7,15,41,3,0.033,0.028,0.023,0.03,0.048,0.038,0.036,0.036,0.048,0.042,0.042,0.034,0.035,0.038,0.055,0.05,0.041,0.04,0.034,0.035,0.021,0.055,0.031,0.052,0.048,0.033,0.039,0.041,0.045,0.031,0.04,0.049,0.025,0.025,0.012,0.009,0.029,0.028,0.019,0.022,0.021,0.037,0.041,0.03,0.024,0.016,0.024,0.014,0.016,0.021,0.014,0.025,0.028,0.021,0.029,0.019,0.021,0.018,0.02,0.016,0.015,0.03,0.031,0.023,0.019,0.033,0.021,0.021,0.017,0.022,0.024,0.026,0.015,0.017,1642,4697,2789,2804,2306,3103,3187,3749,1707,2016,909.0,2683,4726,1130,2692,9824,18208,2629,6121,4946,3905,3276,1130,7086,6298,8098,5355,4505,5327,6558,2745,1248,1102,4718,2005,1418,8287,10559,417,251,1514,2696,6408,2155,3370,1951,1068,2290,2943,1744,517,3446,2165,4384,873,4821,1973,3321,701,1444,1276,2863,543,1027,3315,1307,5206,2119,2007,930,1522,2879,11572,490,0.129,0.117,0.106,0.122,0.147,0.143,0.117,0.126,0.143,0.16,0.141,0.128,0.128,0.141,0.152,0.149,0.141,0.126,0.133,0.146,0.084,0.15,0.125,0.169,0.14,0.127,0.127,0.126,0.132,0.125,0.124,0.123,0.116,0.11,0.064,0.06,0.121,0.128,0.097,0.116,0.099,0.133,0.156,0.131,0.104,0.088,0.11,0.083,0.086,0.111,0.101,0.106,0.116,0.101,0.13,0.097,0.102,0.097,0.109,0.097,0.092,0.119,0.142,0.101,0.1,0.139,0.1,0.099,0.092,0.121,0.115,0.115,0.089,0.105,536,1487,884,1104,1100,1014,1032,1166,576,643,267.0,1207,1364,288,848,3441,5414,547,1309,2090,894,1505,765,1976,2868,2670,2513,1914,1566,2229,797,476,410,1371,578,637,2008,2344,242,135,802,1182,1286,1353,2075,977,434,1085,1370,704,300,1787,1205,1929,466,2775,932,1601,480,913,756,997,318,501,1729,962,2873,994,930,461,791,1403,5353,303,2.987,2.819,2.769,2.561,2.086,2.565,2.511,2.798,2.285,2.588,2.718,1.875,2.838,2.707,2.49,2.33,2.727,3.759,3.287,2.035,3.124,1.94,1.442,2.579,1.983,2.488,1.856,2.008,2.718,2.338,2.476,2.412,2.224,2.624,3.243,2.177,3.01,3.21,2.034,2.032,2.36,1.854,3.399,1.82,1.89,2.186,2.755,2.532,2.569,2.79,2.065,2.064,2.081,2.488,2.279,1.89,2.333,2.259,1.705,1.744,1.843,2.608,1.836,2.337,2.097,1.724,1.933,2.263,2.453,2.38,2.161,2.457,2.319,2.027,0.718,0.683,0.68,0.531,0.534,0.827,0.763,0.629,0.655,0.603,0.882,0.658,0.547,0.555,0.575,0.723,0.72,0.721,0.657,0.665,0.959,0.639,0.505,0.782,0.598,0.61,0.528,0.55,0.635,0.632,0.577,0.996,0.453,0.618,0.678,0.413,0.741,0.675,0.406,0.351,0.451,0.693,0.786,0.652,0.636,0.542,0.651,0.679,0.583,0.673,0.347,0.447,0.42,0.511,0.625,0.496,0.535,0.514,0.36,0.354,0.412,0.68,0.441,0.87,0.546,0.59,0.51,0.468,0.572,0.768,0.422,0.513,0.512,0.502
-429,Epileptic,M,27.0,1.3,3.2,1.6,1.5,1.3,2.4,3.3,2.6,1.1,0.8,0.3,4.0,1.9,0.8,1.5,7.8,9.7,1.5,2.4,5.2,1.2,5.8,3.2,4.5,6.5,6.4,7.1,4.5,3.7,5.1,3.0,1.1,1.1,3.3,1.1,0.7,6.8,5.4,0.2,0.2,1.6,4.1,3.3,4.2,3.0,1.1,0.4,1.2,1.7,1.1,0.9,1.9,1.7,3.6,0.3,4.5,1.1,2.0,1.8,1.3,1.3,1.7,0.4,0.5,2.0,1.1,3.9,1.4,0.9,0.4,1.0,2.1,5.8,0.4,7,26,15,12,16,23,29,25,10,8,4.0,39,27,12,18,83,98,11,28,47,15,54,27,44,58,76,68,45,35,52,27,10,12,42,9,5,67,61,1,1,11,36,32,28,18,7,2,9,8,11,4,14,12,26,2,42,11,15,13,9,9,12,3,5,19,12,28,9,8,3,7,12,47,4,0.049,0.026,0.02,0.027,0.037,0.041,0.035,0.033,0.038,0.039,0.037,0.05,0.036,0.038,0.036,0.042,0.034,0.05,0.035,0.043,0.033,0.054,0.045,0.046,0.055,0.042,0.045,0.042,0.04,0.037,0.055,0.043,0.044,0.037,0.024,0.018,0.054,0.04,0.016,0.016,0.027,0.046,0.046,0.033,0.023,0.018,0.021,0.016,0.019,0.033,0.033,0.022,0.028,0.024,0.017,0.021,0.02,0.02,0.028,0.02,0.031,0.028,0.026,0.016,0.023,0.02,0.022,0.022,0.017,0.013,0.025,0.023,0.023,0.022,1834,6403,3820,3102,2413,3284,4028,3920,1916,1589,855.0,3822,4429,1729,3218,13133,20466,2992,5019,6044,3556,4803,2520,6606,5348,9308,7058,3982,5451,7654,2786,1477,1540,7923,3291,1855,9753,9757,426,455,1638,3025,6448,3816,3312,1953,840,3078,2672,1766,684,2980,2071,6181,562,5884,2246,3334,1693,1730,1073,2943,611,1090,2921,1589,5384,2128,1848,1120,1725,3066,10081,1020,0.148,0.125,0.104,0.117,0.15,0.16,0.132,0.144,0.17,0.169,0.153,0.178,0.144,0.148,0.148,0.148,0.14,0.138,0.134,0.161,0.104,0.163,0.152,0.161,0.155,0.156,0.14,0.137,0.137,0.154,0.172,0.112,0.178,0.149,0.12,0.102,0.166,0.163,0.094,0.116,0.125,0.155,0.174,0.137,0.107,0.107,0.101,0.093,0.099,0.106,0.136,0.111,0.118,0.108,0.103,0.112,0.108,0.104,0.128,0.106,0.131,0.128,0.137,0.098,0.115,0.107,0.109,0.111,0.094,0.089,0.117,0.12,0.111,0.115,464,2075,1347,1052,746,1122,1563,1384,553,392,223.0,1668,1181,414,856,3830,5643,559,1308,2340,858,1898,1317,1862,2169,2945,3120,1917,1728,2485,931,502,463,1635,689,664,2556,2376,210,186,786,1566,1364,2024,2007,900,335,1112,1204,771,416,1390,975,2625,289,3313,875,1659,1010,990,612,969,286,549,1426,778,2735,974,931,485,727,1335,4080,388,3.586,2.892,2.717,2.609,2.804,2.362,2.213,2.576,2.913,3.064,3.072,1.991,2.91,2.939,2.956,2.625,2.899,3.844,2.917,2.126,2.915,2.103,1.784,2.748,2.043,2.516,1.918,1.819,2.436,2.444,2.445,2.613,2.81,3.214,3.691,2.442,2.724,2.918,2.432,2.559,2.474,1.74,3.283,2.091,1.886,2.092,3.044,3.463,2.64,2.471,1.924,2.3,2.26,2.382,2.144,1.899,2.663,2.275,1.932,1.937,2.158,3.135,2.5,2.28,2.177,2.303,2.1,2.429,2.119,2.691,2.36,2.659,2.487,2.902,0.901,0.526,0.45,0.645,0.608,0.582,0.643,0.697,0.541,0.59,0.677,0.576,0.464,0.468,0.439,0.544,0.602,0.873,0.592,0.675,0.87,0.643,0.49,0.775,0.529,0.654,0.478,0.487,0.614,0.625,0.778,1.022,0.529,0.739,0.851,0.485,0.901,0.761,0.393,0.419,0.707,0.449,0.808,0.818,0.465,0.554,0.495,0.8,0.472,0.583,0.458,0.45,0.562,0.443,0.465,0.402,0.643,0.502,0.387,0.394,0.448,0.731,0.587,0.566,0.573,0.781,0.439,0.405,0.4,0.47,0.597,0.549,0.554,0.701
-430,Epileptic,M,37.0,1.3,3.4,1.2,1.5,2.1,3.0,2.4,2.9,2.3,1.1,0.3,3.3,3.1,0.6,2.7,8.9,12.7,1.8,4.2,6.5,2.5,3.5,2.1,5.4,3.7,5.5,5.0,3.1,4.3,4.6,2.4,1.4,0.7,4.0,0.6,0.9,4.5,3.9,0.4,0.6,1.5,4.1,3.5,5.4,3.2,0.7,0.8,1.3,1.5,0.9,0.6,3.7,2.8,3.0,0.5,4.3,0.7,3.7,1.6,1.4,0.9,1.9,0.6,0.7,3.3,1.2,3.5,1.9,1.8,1.0,1.3,1.4,5.4,0.5,10,25,7,10,20,19,15,20,20,9,4.0,30,20,7,18,70,93,8,30,44,16,34,18,40,35,48,44,22,25,42,21,11,4,29,3,5,46,37,3,2,9,30,23,38,20,5,4,8,8,6,6,24,17,18,2,29,6,24,9,10,7,12,2,4,25,13,25,11,10,6,8,11,33,4,0.047,0.03,0.02,0.025,0.044,0.039,0.032,0.036,0.057,0.042,0.03,0.031,0.046,0.049,0.049,0.039,0.04,0.046,0.042,0.04,0.038,0.035,0.031,0.056,0.035,0.04,0.032,0.03,0.034,0.036,0.062,0.042,0.019,0.046,0.019,0.019,0.044,0.042,0.019,0.036,0.027,0.041,0.066,0.04,0.024,0.012,0.029,0.018,0.017,0.018,0.037,0.029,0.034,0.025,0.028,0.024,0.018,0.036,0.028,0.024,0.021,0.03,0.027,0.023,0.029,0.022,0.024,0.026,0.03,0.033,0.034,0.026,0.023,0.019,1629,5586,2478,2206,2554,3348,2316,3160,2406,1592,709.0,3067,3079,1100,2918,11469,18030,3080,4716,4439,3262,5407,2236,6344,5959,7506,6231,3345,5250,6113,2688,1916,1384,5494,1518,1823,6828,6371,478,670,1896,3255,4714,2766,2902,1543,1041,2690,2927,1753,449,4814,3459,4672,475,4833,1832,2747,1499,1643,1573,2656,506,939,3728,1602,4136,2448,1828,1192,1357,2303,8776,664,0.149,0.12,0.101,0.11,0.143,0.144,0.116,0.136,0.166,0.145,0.099,0.131,0.142,0.14,0.152,0.136,0.135,0.12,0.15,0.139,0.116,0.136,0.13,0.155,0.131,0.143,0.125,0.112,0.118,0.133,0.17,0.113,0.088,0.142,0.083,0.092,0.15,0.136,0.106,0.107,0.12,0.145,0.154,0.145,0.11,0.083,0.123,0.093,0.095,0.101,0.171,0.12,0.126,0.109,0.135,0.119,0.101,0.144,0.121,0.121,0.115,0.121,0.126,0.123,0.132,0.111,0.115,0.118,0.122,0.131,0.138,0.122,0.106,0.1,535,2022,912,1015,918,1335,1109,1275,772,433,216.0,1581,1053,276,952,3909,5590,569,1659,2417,1012,1759,1104,1745,1983,2553,2646,1479,1869,2170,727,662,551,1392,542,766,1669,1706,284,302,931,1554,1012,2027,2091,903,430,1150,1356,760,328,2175,1426,2019,256,2679,715,1704,914,875,716,1066,298,433,1918,820,2362,1207,1004,514,727,1017,3807,412,3.183,2.641,2.715,2.298,2.213,2.334,1.937,2.333,2.369,3.041,2.607,1.753,2.38,2.533,2.342,2.38,2.646,3.896,2.364,1.702,2.695,2.426,1.888,2.736,2.453,2.514,2.01,1.908,2.288,2.29,2.674,2.637,2.101,2.854,2.643,2.397,2.838,2.76,1.952,2.381,2.507,1.886,2.974,1.626,1.59,1.848,2.756,2.814,2.669,2.414,1.757,2.299,2.372,2.34,2.437,1.98,2.617,1.889,1.983,2.085,2.246,2.503,1.986,2.465,2.212,2.245,1.947,2.362,2.11,2.48,2.286,2.585,2.516,2.08,0.881,0.571,0.662,0.568,0.67,0.568,0.483,0.552,0.549,0.609,0.8,0.451,0.518,0.679,0.601,0.585,0.605,0.835,0.721,0.554,1.009,0.494,0.427,0.817,0.521,0.598,0.498,0.472,0.67,0.525,0.712,1.118,0.563,0.617,0.777,0.446,0.71,0.602,0.361,0.492,0.553,0.536,0.831,0.609,0.44,0.466,0.731,0.704,0.617,0.554,0.355,0.473,0.628,0.541,0.427,0.453,0.564,0.66,0.391,0.375,0.414,0.799,0.361,1.054,0.612,0.813,0.5,0.589,0.461,0.51,0.478,0.649,0.625,0.581
-431,Epileptic,M,37.0,1.5,2.6,1.1,1.5,2.1,2.3,2.0,1.8,2.5,0.9,0.6,3.7,2.2,0.7,2.1,7.0,8.9,1.3,3.0,5.0,3.2,4.4,2.8,4.2,4.9,3.7,6.1,3.1,2.8,3.0,2.4,1.5,0.9,3.3,0.9,1.0,5.4,2.8,0.2,0.2,1.3,3.0,3.1,2.6,2.8,0.7,0.7,1.1,1.7,1.0,0.8,1.9,3.0,2.4,0.4,2.9,1.5,2.5,1.8,2.2,0.6,2.0,1.1,0.7,2.1,1.7,3.5,1.3,1.5,1.1,0.9,1.8,7.4,0.5,12,28,12,14,22,27,23,20,23,12,7.0,36,27,7,19,77,95,13,37,45,21,53,28,40,48,44,72,35,33,42,24,16,9,43,8,10,60,42,1,1,9,30,32,23,19,6,4,8,9,9,7,18,27,15,3,26,13,22,12,17,6,13,5,6,19,23,36,12,10,9,7,15,65,3,0.044,0.025,0.021,0.026,0.04,0.03,0.027,0.029,0.055,0.036,0.039,0.036,0.036,0.051,0.04,0.035,0.028,0.044,0.029,0.039,0.05,0.035,0.03,0.042,0.037,0.03,0.035,0.028,0.025,0.033,0.048,0.044,0.028,0.032,0.026,0.02,0.036,0.027,0.03,0.016,0.02,0.032,0.035,0.022,0.019,0.017,0.023,0.016,0.019,0.024,0.029,0.021,0.032,0.017,0.019,0.019,0.028,0.025,0.028,0.03,0.016,0.029,0.044,0.018,0.021,0.025,0.021,0.02,0.022,0.02,0.022,0.018,0.024,0.016,1589,5030,2157,2635,2073,3166,3277,3222,1890,1700,615.0,3401,4007,635,2529,11151,18097,2609,5242,4449,3864,5485,2444,4909,5198,6096,7465,4289,6007,5491,2309,1717,1287,7176,2413,2176,8235,7456,232,332,1767,2860,6861,2993,3722,1389,871,2648,2625,2112,815,3077,3716,4371,577,4195,1757,3596,1620,2038,1082,2692,820,1013,2959,1526,5169,2277,1970,1450,1289,2497,10468,791,0.126,0.116,0.113,0.117,0.146,0.142,0.115,0.137,0.171,0.175,0.143,0.143,0.142,0.146,0.152,0.145,0.129,0.144,0.136,0.151,0.135,0.139,0.121,0.142,0.128,0.125,0.127,0.112,0.103,0.146,0.145,0.132,0.115,0.142,0.114,0.109,0.146,0.13,0.113,0.099,0.111,0.129,0.141,0.111,0.102,0.096,0.117,0.093,0.105,0.109,0.14,0.116,0.141,0.096,0.114,0.108,0.131,0.122,0.126,0.124,0.101,0.128,0.162,0.114,0.109,0.119,0.117,0.113,0.105,0.117,0.116,0.112,0.116,0.102,547,1883,819,1007,1074,1384,1276,1134,756,446,261.0,1611,1234,284,901,3622,5531,555,1729,2186,1057,2024,1372,1814,1993,2132,2950,1759,1843,1813,893,686,485,1865,631,857,2516,2047,126,188,945,1508,1612,1869,2298,733,400,1123,1258,837,457,1542,1561,2169,332,2384,804,1625,978,1147,641,1064,403,593,1629,1062,2638,1111,1073,729,713,1391,4844,410,2.934,2.454,2.41,2.446,1.795,2.115,2.13,2.576,2.122,3.01,2.015,1.849,2.573,1.839,2.241,2.365,2.536,3.446,2.434,1.861,2.906,2.302,1.72,2.169,2.156,2.381,2.061,2.002,2.491,2.34,2.055,2.486,2.183,2.846,3.269,2.291,2.545,2.651,1.828,2.028,2.328,1.68,3.008,1.835,1.798,1.957,2.679,2.8,2.552,2.833,2.054,2.074,2.244,2.09,2.041,1.891,2.36,2.332,1.908,1.854,1.885,2.432,1.981,2.064,1.948,1.77,1.931,2.206,2.129,2.083,2.044,2.064,2.225,2.297,0.666,0.465,0.514,0.6,0.479,0.585,0.588,0.493,0.644,0.779,0.761,0.559,0.534,0.69,0.709,0.594,0.629,0.841,0.569,0.636,0.812,0.607,0.489,0.825,0.562,0.522,0.582,0.557,0.535,0.581,0.777,0.807,0.371,0.581,0.564,0.46,0.726,0.677,0.438,0.388,0.536,0.571,0.726,0.539,0.586,0.453,0.598,0.735,0.5,0.673,0.508,0.454,0.561,0.448,0.373,0.419,0.435,0.63,0.399,0.458,0.374,0.693,1.001,0.724,0.477,0.62,0.45,0.33,0.454,0.591,0.449,0.424,0.414,0.413
-432,Epileptic,M,27.0,0.8,1.8,0.9,0.8,1.5,2.2,1.7,1.9,0.9,0.5,0.4,2.8,1.7,0.5,1.2,5.9,7.4,0.6,2.4,4.1,1.8,2.9,2.2,2.8,3.1,5.4,3.0,4.0,2.3,2.3,1.5,1.1,0.5,2.3,0.6,0.7,4.8,3.3,0.1,0.2,0.8,3.3,2.7,3.2,2.2,0.7,0.3,0.9,1.1,1.1,0.7,1.2,1.1,1.9,0.5,2.8,0.6,1.2,0.5,1.2,0.4,1.4,0.3,0.3,1.5,1.0,1.4,1.5,0.9,0.4,0.9,2.0,4.3,0.6,5,17,6,7,13,19,14,15,10,5,5.0,26,17,6,12,62,69,4,24,38,26,32,21,29,41,41,37,33,23,36,19,8,5,25,4,7,42,38,1,1,4,27,20,25,11,5,1,6,5,10,4,12,9,14,4,21,8,10,5,8,4,12,2,3,12,16,11,9,4,4,5,13,32,4,0.032,0.02,0.017,0.017,0.027,0.029,0.025,0.027,0.038,0.023,0.036,0.031,0.027,0.028,0.031,0.032,0.025,0.025,0.034,0.029,0.027,0.031,0.028,0.027,0.031,0.044,0.025,0.043,0.021,0.023,0.035,0.039,0.038,0.028,0.012,0.022,0.037,0.03,0.01,0.017,0.014,0.031,0.036,0.023,0.015,0.017,0.014,0.017,0.013,0.016,0.031,0.013,0.014,0.018,0.02,0.017,0.019,0.016,0.016,0.02,0.029,0.024,0.014,0.014,0.017,0.036,0.014,0.017,0.011,0.015,0.018,0.023,0.016,0.031,1794,5166,2903,2300,2765,3870,3391,4273,1827,1660,744.0,3924,5121,1625,2861,13797,20865,2571,4770,5588,3985,5018,3143,8093,7203,6360,6890,4885,7944,6890,2690,1116,789,7380,2467,2296,8430,9449,372,448,1685,3604,5975,3987,4424,1452,945,2038,2761,2419,675,3536,2460,5266,840,4764,1506,2966,1023,2052,588,2970,665,811,3270,1073,3468,3623,2311,1394,1705,2700,10632,784,0.106,0.108,0.089,0.089,0.115,0.132,0.104,0.126,0.134,0.118,0.133,0.129,0.12,0.126,0.138,0.124,0.117,0.102,0.137,0.126,0.107,0.133,0.124,0.126,0.131,0.14,0.111,0.113,0.096,0.121,0.128,0.114,0.154,0.121,0.082,0.104,0.126,0.127,0.094,0.093,0.086,0.12,0.135,0.111,0.087,0.105,0.079,0.091,0.079,0.103,0.131,0.083,0.098,0.089,0.125,0.099,0.107,0.094,0.102,0.103,0.121,0.106,0.105,0.103,0.1,0.135,0.087,0.09,0.077,0.107,0.102,0.117,0.093,0.151,441,1440,844,765,851,1288,972,1078,502,344,207.0,1344,1129,308,657,3018,4764,424,1255,2158,984,1523,1145,1660,1782,1787,1986,1387,1758,1816,625,443,267,1372,641,569,2189,1847,185,206,713,1590,1267,2007,2100,621,322,830,1138,1013,363,1452,1096,1887,337,2262,572,1370,567,989,295,993,279,344,1368,536,1534,1338,1005,470,737,1253,3941,295,3.49,3.184,3.29,2.835,2.618,2.706,2.784,3.285,2.83,3.653,2.942,2.347,3.367,3.506,3.114,3.245,3.424,3.974,2.899,2.172,3.059,2.498,2.379,3.455,3.063,2.966,2.611,2.677,3.282,2.889,3.086,2.444,2.595,3.508,3.527,3.386,2.903,3.562,2.403,2.784,2.916,1.975,3.42,2.16,2.417,2.53,3.865,3.251,3.035,2.763,2.219,2.547,2.531,2.863,3.039,2.326,2.641,2.499,1.885,2.316,2.179,3.193,2.501,2.419,2.649,2.326,2.438,2.899,2.879,3.467,2.728,2.624,2.799,3.126,0.776,0.708,0.773,0.51,0.602,0.562,0.789,0.577,0.561,0.432,0.672,0.768,0.559,0.526,0.539,0.643,0.696,0.86,0.535,0.647,0.817,0.659,0.662,0.608,0.696,0.763,0.609,0.685,0.573,0.623,0.716,0.944,0.561,0.659,0.636,0.635,0.693,0.566,0.399,0.34,0.611,0.667,0.799,0.659,0.703,0.507,0.578,0.682,0.463,0.565,0.436,0.535,0.462,0.566,0.518,0.421,0.431,0.517,0.403,0.383,0.363,0.504,0.647,1.038,0.506,0.627,0.524,0.505,0.452,0.854,0.473,0.489,0.59,0.504
-433,Epileptic,F,62.0,0.3,2.8,1.0,1.4,1.6,2.3,1.5,1.7,1.0,0.7,0.2,3.1,1.7,0.5,1.1,5.3,8.4,0.8,2.1,4.4,1.5,2.7,1.8,3.9,3.6,3.4,3.1,2.8,3.1,3.3,2.0,1.0,0.6,2.2,0.3,0.3,2.7,3.2,0.1,0.2,1.0,3.2,2.5,3.0,2.9,0.8,0.7,0.7,1.2,0.6,0.6,2.2,1.9,2.3,0.0,2.3,0.7,1.8,0.6,1.7,1.1,1.6,0.3,0.5,1.3,1.3,2.8,1.7,1.1,0.4,1.4,0.7,4.5,0.3,3,29,10,9,14,25,14,16,15,6,3.0,29,17,6,12,53,75,5,29,37,14,25,20,39,35,36,43,31,28,33,19,7,5,28,2,3,32,40,2,1,5,30,23,22,17,6,4,4,7,5,6,18,13,14,0,20,5,14,4,14,8,13,2,5,11,15,24,11,8,5,10,7,45,2,0.022,0.024,0.019,0.024,0.039,0.038,0.022,0.03,0.04,0.045,0.027,0.036,0.033,0.048,0.032,0.036,0.03,0.034,0.028,0.037,0.036,0.034,0.033,0.044,0.038,0.034,0.029,0.028,0.027,0.031,0.049,0.045,0.027,0.033,0.021,0.013,0.037,0.039,0.014,0.023,0.024,0.04,0.055,0.03,0.021,0.015,0.032,0.012,0.02,0.025,0.035,0.024,0.03,0.02,0.01,0.021,0.021,0.023,0.028,0.028,0.027,0.025,0.021,0.026,0.022,0.028,0.021,0.023,0.017,0.02,0.03,0.025,0.022,0.016,1313,4687,1983,2107,2083,3133,2797,2929,1479,1242,504.0,2875,3056,834,2283,8467,15953,1802,4140,3780,3041,3837,1974,5849,4530,4881,4885,4426,5649,4948,2207,1111,1005,5012,1027,1171,5775,5430,348,350,1194,2826,4215,2504,3097,1620,826,2047,1915,1101,697,2978,2388,4308,160,3083,1196,2652,707,1620,1124,2844,409,780,1867,1140,3779,2337,1761,737,1532,1203,8176,553,0.088,0.118,0.115,0.108,0.145,0.161,0.106,0.147,0.168,0.138,0.136,0.147,0.138,0.168,0.141,0.145,0.129,0.12,0.136,0.145,0.132,0.146,0.142,0.156,0.158,0.153,0.133,0.125,0.112,0.14,0.158,0.121,0.129,0.135,0.083,0.083,0.148,0.154,0.094,0.096,0.118,0.158,0.183,0.122,0.101,0.097,0.144,0.075,0.101,0.116,0.142,0.114,0.12,0.098,0.076,0.114,0.098,0.116,0.124,0.137,0.135,0.121,0.132,0.13,0.114,0.126,0.114,0.113,0.098,0.114,0.132,0.134,0.107,0.117,292,1747,817,859,798,1083,1008,920,442,331,167.0,1343,905,216,674,2560,4655,327,1292,1829,720,1336,905,1673,1503,1596,1927,1719,1840,1795,677,389,349,1146,305,455,1407,1558,186,174,584,1340,837,1709,2097,790,371,877,921,417,367,1500,1017,1852,63,1655,525,1259,359,869,554,1043,219,380,1017,677,2081,1153,929,347,779,502,3424,251,3.635,2.557,2.322,2.333,2.201,2.38,2.298,2.924,2.533,3.019,2.682,1.908,2.575,2.747,2.626,2.581,2.742,3.857,2.549,1.831,3.077,2.367,1.94,2.656,2.48,2.601,2.117,2.091,2.426,2.231,2.478,2.789,2.501,2.92,2.811,2.387,2.918,2.745,2.036,2.314,2.667,1.918,3.296,1.708,1.643,2.246,2.72,2.809,2.413,3.107,2.027,2.067,2.209,2.432,2.173,2.018,2.547,2.333,2.176,2.12,2.446,2.735,1.921,2.126,2.0,2.085,2.03,2.301,2.197,2.275,2.071,2.473,2.471,2.467,1.143,0.568,0.731,0.489,0.669,0.551,0.472,0.541,0.654,0.453,0.763,0.504,0.403,0.417,0.54,0.498,0.539,0.837,0.505,0.518,0.765,0.435,0.408,0.759,0.481,0.559,0.395,0.492,0.536,0.492,0.826,1.105,0.457,0.649,0.626,0.464,0.703,0.596,0.368,0.353,0.603,0.481,0.878,0.562,0.478,0.581,0.537,1.019,0.465,0.599,0.373,0.405,0.555,0.372,0.326,0.389,0.549,0.486,0.337,0.388,0.405,0.669,0.412,0.694,0.407,0.669,0.403,0.383,0.41,0.461,0.483,0.446,0.534,0.48
-434,Epileptic,M,37.0,1.4,4.0,1.4,2.1,2.8,2.8,2.8,2.6,1.0,0.9,0.5,3.5,2.1,0.8,2.4,7.9,14.4,1.5,2.7,4.3,1.5,3.9,2.6,4.7,6.2,5.1,6.9,4.0,5.0,4.1,2.2,1.4,0.9,3.4,1.3,0.7,4.4,4.5,0.4,0.3,1.5,4.2,2.8,3.2,3.8,1.5,0.3,1.8,1.5,1.3,0.5,4.0,1.6,4.1,0.3,2.8,1.4,2.1,2.1,1.7,1.6,2.6,0.6,0.6,2.6,2.1,2.8,2.1,2.1,0.5,1.0,2.3,7.2,0.4,9,41,13,16,26,33,26,27,14,12,6.0,38,26,10,28,93,143,10,35,48,12,62,31,52,60,57,61,36,43,50,24,8,8,40,10,5,54,58,5,2,9,37,31,27,19,12,2,11,8,11,3,32,13,28,2,21,10,20,17,13,13,22,5,5,24,24,23,15,13,8,6,21,64,4,0.042,0.027,0.02,0.031,0.037,0.034,0.035,0.03,0.047,0.042,0.046,0.035,0.031,0.033,0.03,0.033,0.033,0.043,0.031,0.035,0.025,0.033,0.038,0.043,0.047,0.033,0.042,0.035,0.037,0.034,0.038,0.042,0.025,0.028,0.027,0.015,0.034,0.03,0.024,0.016,0.025,0.042,0.033,0.026,0.026,0.025,0.019,0.02,0.015,0.025,0.026,0.025,0.026,0.023,0.022,0.02,0.023,0.016,0.028,0.02,0.027,0.031,0.025,0.016,0.02,0.03,0.02,0.023,0.027,0.018,0.023,0.02,0.02,0.018,1954,6434,3141,3172,3028,3754,3387,5041,1776,1701,634.0,3781,5064,1306,4877,14227,25873,2791,5297,5709,4492,6989,3242,7071,6326,9014,6239,4612,7950,6027,2714,1674,1746,8119,3871,2030,9238,11386,719,667,2053,3702,7128,3650,4174,2163,751,3558,3261,2304,633,6233,2301,6497,344,3664,1998,4019,1775,2364,1743,3484,764,1204,3789,1522,3777,3076,2487,1067,1414,3733,12297,632,0.131,0.122,0.103,0.129,0.145,0.152,0.121,0.135,0.14,0.146,0.142,0.147,0.122,0.148,0.127,0.137,0.136,0.127,0.126,0.14,0.095,0.138,0.133,0.149,0.144,0.139,0.137,0.118,0.121,0.154,0.145,0.121,0.104,0.13,0.113,0.085,0.139,0.138,0.124,0.107,0.113,0.137,0.142,0.123,0.114,0.117,0.091,0.098,0.09,0.125,0.121,0.117,0.12,0.112,0.121,0.11,0.109,0.099,0.137,0.105,0.136,0.14,0.127,0.102,0.115,0.133,0.111,0.118,0.122,0.129,0.116,0.109,0.108,0.111,563,2299,1180,1148,1191,1451,1318,1461,535,429,192.0,1731,1292,329,1378,4159,7388,574,1606,2327,1040,2310,1293,2067,2239,2755,2322,1804,2198,2073,847,604,644,1870,805,767,2364,2661,339,299,1023,1569,1519,2035,2332,973,294,1292,1443,859,327,2663,1105,2741,153,2095,841,2033,1111,1421,906,1303,429,605,1882,1013,2130,1349,1202,489,707,1837,5567,369,3.403,2.609,2.628,2.727,2.21,2.191,2.275,3.076,2.416,2.996,2.561,1.895,3.008,2.822,2.731,2.634,2.799,3.599,2.705,2.044,3.126,2.296,2.168,2.674,2.287,2.661,2.182,2.109,2.781,2.386,2.487,2.615,2.354,3.031,3.757,2.441,2.91,3.01,2.216,2.504,2.483,2.034,3.397,1.99,2.049,2.244,3.019,3.387,2.769,3.238,2.065,2.358,2.138,2.468,2.406,1.968,2.453,2.211,1.652,1.788,2.222,2.602,2.096,2.279,2.134,1.829,1.993,2.483,2.406,2.229,2.265,2.315,2.36,2.069,0.751,0.556,0.452,0.52,0.672,0.535,0.534,0.5,0.691,0.616,0.746,0.514,0.509,0.649,0.598,0.613,0.583,0.782,0.633,0.693,0.779,0.597,0.531,0.772,0.645,0.607,0.575,0.58,0.634,0.518,0.701,1.113,0.481,0.682,0.848,0.418,0.761,0.654,0.523,0.365,0.517,0.56,0.887,0.589,0.588,0.47,0.483,0.726,0.51,0.589,0.331,0.496,0.455,0.496,0.65,0.38,0.564,0.536,0.437,0.387,0.51,0.658,0.423,0.681,0.51,0.612,0.435,0.384,0.423,0.711,0.497,0.414,0.456,0.35
-435,Epileptic,M,22.0,0.7,2.7,1.7,1.9,2.3,2.2,1.6,1.6,1.9,1.0,0.6,3.0,1.2,0.5,1.4,7.3,9.2,0.9,2.3,5.8,1.2,2.6,1.9,3.5,3.2,2.2,3.8,3.1,3.1,3.1,1.7,1.6,0.6,1.9,0.4,0.4,2.7,3.3,0.1,0.1,0.9,3.0,2.4,4.6,2.4,0.7,0.5,1.3,1.1,0.4,1.1,2.1,2.2,2.4,0.4,2.6,0.8,1.3,1.4,1.1,0.8,1.8,0.4,0.4,1.4,1.3,2.9,1.0,1.4,0.5,0.9,1.3,4.3,0.3,9,26,23,14,18,24,13,15,20,10,6.0,27,12,8,14,79,86,8,29,50,12,32,18,35,38,28,46,29,27,41,26,13,5,24,3,3,31,43,1,1,7,31,21,30,16,5,3,9,6,4,7,16,16,20,4,24,10,11,10,10,9,14,3,4,12,19,21,8,10,4,6,10,37,4,0.033,0.022,0.018,0.03,0.033,0.028,0.024,0.023,0.046,0.035,0.038,0.033,0.023,0.03,0.029,0.031,0.029,0.028,0.024,0.037,0.023,0.031,0.024,0.033,0.034,0.021,0.024,0.024,0.023,0.031,0.031,0.064,0.024,0.022,0.009,0.012,0.032,0.034,0.009,0.017,0.018,0.028,0.036,0.033,0.017,0.014,0.015,0.021,0.016,0.01,0.036,0.019,0.023,0.016,0.03,0.017,0.025,0.014,0.023,0.018,0.022,0.031,0.018,0.013,0.02,0.018,0.02,0.015,0.019,0.015,0.02,0.013,0.016,0.013,1363,5828,4059,3118,2716,3640,2613,3423,2484,1667,652.0,3244,2941,1211,2804,13611,19247,2565,5188,5899,4038,4983,2703,6602,5694,6078,6814,4792,6516,5729,2978,1262,1171,6423,2414,1673,6418,8421,514,381,1447,3166,6139,3892,3736,1363,988,2091,2255,1199,816,3409,3302,6001,434,4195,1489,2979,1618,1698,1197,2348,662,1153,2463,2044,3949,2365,2278,1054,1261,2788,10236,511,0.115,0.111,0.113,0.123,0.142,0.133,0.094,0.116,0.163,0.142,0.134,0.143,0.116,0.134,0.136,0.134,0.121,0.124,0.127,0.15,0.11,0.146,0.12,0.13,0.143,0.116,0.12,0.111,0.097,0.142,0.132,0.158,0.115,0.114,0.069,0.072,0.132,0.144,0.084,0.089,0.107,0.143,0.138,0.14,0.096,0.091,0.097,0.098,0.088,0.085,0.146,0.107,0.119,0.093,0.147,0.104,0.118,0.094,0.117,0.115,0.121,0.128,0.1,0.099,0.113,0.109,0.112,0.094,0.108,0.103,0.116,0.102,0.095,0.104,412,1993,1489,1086,1090,1331,988,1136,758,457,228.0,1454,837,309,825,3870,5268,508,1452,2499,908,1512,1182,1869,1683,1797,2492,1773,1804,1763,868,535,393,1410,617,607,1517,1858,242,163,761,1643,1216,2290,2130,722,433,936,1089,597,494,1634,1511,2509,218,2360,626,1448,951,881,646,973,351,554,1151,1283,2084,1132,1139,493,654,1315,4303,317,3.216,2.7,2.488,2.616,2.153,2.367,2.255,2.695,2.499,2.841,2.332,1.949,2.833,2.796,2.664,2.703,2.921,3.535,2.939,2.018,3.153,2.541,2.01,2.668,2.643,2.728,2.207,2.216,2.775,2.563,2.553,2.597,2.38,3.1,3.362,2.459,3.005,3.252,2.283,2.513,2.379,1.773,3.348,1.929,2.0,2.148,2.884,2.719,2.573,2.382,2.116,2.235,2.298,2.491,2.147,1.956,2.7,2.336,1.946,2.007,2.272,2.511,2.056,2.229,2.234,1.888,2.134,2.522,2.325,2.406,2.283,2.4,2.502,2.01,0.568,0.534,0.571,0.612,0.631,0.511,0.542,0.579,0.663,0.464,0.804,0.44,0.446,0.427,0.481,0.58,0.587,0.806,0.622,0.664,0.735,0.488,0.447,0.724,0.517,0.448,0.464,0.602,0.471,0.463,0.721,0.867,0.34,0.68,0.695,0.431,0.762,0.602,0.51,0.458,0.432,0.454,0.885,0.592,0.567,0.452,0.484,0.571,0.447,0.401,0.414,0.441,0.487,0.38,0.45,0.409,0.737,0.437,0.38,0.433,0.378,0.561,0.486,0.757,0.683,0.768,0.465,0.352,0.384,0.772,0.439,0.507,0.509,0.346
-436,Epileptic,M,27.0,0.9,2.4,0.6,1.1,1.8,1.2,1.1,2.1,1.3,0.8,0.2,1.9,1.7,0.6,0.8,4.7,7.8,1.1,1.4,4.5,0.9,2.5,1.0,4.1,3.0,2.7,2.1,2.1,1.9,2.7,1.1,1.0,0.3,2.4,0.4,0.3,3.5,2.9,0.1,0.2,0.9,3.1,1.8,2.0,2.0,0.5,0.8,1.0,1.2,0.9,0.6,1.8,1.4,3.8,0.1,2.4,0.9,1.7,1.5,0.9,0.9,1.7,0.3,0.4,1.0,1.6,2.5,1.3,0.5,0.4,0.8,1.1,4.0,0.3,8,29,9,9,16,13,12,19,16,8,4.0,15,19,7,9,54,80,10,19,37,9,31,11,42,38,34,26,23,17,34,17,8,3,29,3,3,44,36,2,2,7,26,17,15,13,4,5,6,7,8,5,15,11,30,1,27,6,12,13,7,10,17,2,5,7,17,23,8,5,4,8,8,32,3,0.032,0.022,0.013,0.022,0.029,0.024,0.016,0.033,0.032,0.028,0.025,0.026,0.029,0.029,0.026,0.028,0.024,0.032,0.02,0.035,0.015,0.028,0.025,0.037,0.027,0.028,0.021,0.019,0.02,0.022,0.025,0.037,0.015,0.026,0.012,0.011,0.03,0.025,0.01,0.014,0.018,0.036,0.027,0.028,0.015,0.01,0.022,0.016,0.015,0.023,0.032,0.019,0.022,0.022,0.018,0.018,0.018,0.018,0.028,0.015,0.02,0.024,0.019,0.014,0.015,0.029,0.018,0.02,0.012,0.012,0.021,0.017,0.016,0.014,1705,5089,1910,2378,2741,2575,3029,3612,2392,1435,733.0,2266,3841,1456,2262,10942,19780,2720,4310,5065,2780,4451,1699,6888,6430,5878,5272,4741,5025,6220,2342,1353,1216,6285,2268,1370,8891,8125,443,474,1586,3141,4958,1967,3505,1567,1180,2253,2581,1550,551,3291,2664,6791,232,4820,1865,2842,1503,1353,1527,2758,479,964,2100,1396,4440,2534,1617,1034,1644,2288,9645,521,0.126,0.118,0.103,0.108,0.133,0.132,0.091,0.135,0.136,0.147,0.126,0.119,0.128,0.158,0.121,0.126,0.115,0.127,0.108,0.145,0.081,0.131,0.125,0.141,0.136,0.133,0.119,0.102,0.095,0.128,0.112,0.106,0.076,0.127,0.074,0.068,0.138,0.119,0.094,0.096,0.11,0.15,0.128,0.12,0.088,0.08,0.114,0.086,0.09,0.12,0.147,0.108,0.109,0.114,0.122,0.111,0.101,0.092,0.132,0.1,0.113,0.121,0.113,0.106,0.094,0.134,0.107,0.098,0.096,0.1,0.11,0.106,0.095,0.108,508,1727,743,876,1055,839,1019,1178,660,433,213.0,1029,1102,304,590,2892,5319,575,1133,2100,837,1512,714,1898,1948,1779,1644,1582,1320,1932,765,523,351,1497,542,488,2065,1983,233,198,786,1435,1131,1178,2025,796,540,966,1214,648,340,1506,1123,2762,95,2276,811,1413,861,803,765,1149,253,498,1012,810,2194,1064,712,514,775,1009,3929,304,3.282,2.835,2.527,2.704,2.429,2.559,2.481,2.716,2.8,2.915,2.686,1.959,2.703,3.23,2.881,2.795,2.987,3.52,2.886,2.129,2.415,2.321,2.027,2.642,2.606,2.696,2.491,2.368,2.843,2.574,2.336,2.434,2.712,3.031,3.646,2.483,3.093,3.023,2.137,2.527,2.576,1.973,3.126,1.904,2.0,2.274,2.695,2.89,2.684,2.565,2.244,2.359,2.424,2.526,2.753,2.347,2.7,2.333,1.951,1.891,2.317,2.335,2.264,2.129,2.389,2.138,2.316,2.636,2.602,2.405,2.442,2.668,2.606,2.188,0.717,0.678,0.635,0.409,0.457,0.636,0.618,0.472,0.48,0.409,0.81,0.465,0.504,0.432,0.459,0.512,0.595,0.725,0.471,0.648,0.769,0.436,0.37,0.754,0.526,0.45,0.554,0.611,0.491,0.442,0.735,0.885,0.385,0.655,0.677,0.424,0.68,0.608,0.324,0.418,0.47,0.465,0.72,0.773,0.595,0.304,0.587,0.531,0.474,0.488,0.437,0.465,0.521,0.484,0.461,0.384,0.47,0.492,0.382,0.327,0.397,0.5,0.363,0.922,0.515,0.621,0.484,0.45,0.496,0.663,0.381,0.456,0.47,0.409
-437,Epileptic,F,27.0,0.5,2.2,0.9,1.1,1.3,1.8,1.4,1.3,1.3,0.7,0.3,3.3,1.3,0.6,1.5,5.6,8.0,0.6,3.0,4.1,0.7,2.7,1.6,2.9,2.0,2.9,3.3,1.5,2.5,2.6,1.3,0.6,0.4,2.8,0.2,0.9,1.9,3.1,0.2,0.1,1.4,3.0,1.9,2.4,2.7,0.7,0.3,1.4,1.2,0.6,0.6,1.4,1.2,2.0,0.3,2.7,0.8,1.9,1.2,1.2,0.7,2.1,0.3,0.5,1.9,1.3,2.2,0.9,1.1,0.8,0.7,0.9,4.2,0.4,3,19,10,10,13,18,11,12,11,7,4.0,26,15,7,14,56,75,4,28,31,5,26,18,41,26,34,34,15,24,34,17,7,6,32,2,8,31,38,1,1,8,27,20,20,14,5,2,7,6,4,4,18,7,14,1,22,5,13,8,12,5,14,2,4,14,16,18,7,8,8,7,9,32,4,0.032,0.027,0.017,0.02,0.034,0.032,0.021,0.026,0.051,0.042,0.031,0.032,0.029,0.033,0.031,0.035,0.031,0.025,0.033,0.036,0.016,0.035,0.027,0.033,0.034,0.033,0.03,0.022,0.02,0.025,0.037,0.04,0.016,0.035,0.012,0.022,0.028,0.033,0.019,0.01,0.023,0.036,0.036,0.026,0.022,0.015,0.015,0.021,0.018,0.021,0.045,0.018,0.024,0.018,0.035,0.022,0.034,0.019,0.029,0.025,0.024,0.032,0.023,0.024,0.021,0.026,0.022,0.015,0.017,0.028,0.023,0.024,0.023,0.016,1138,4289,2061,2078,1954,2939,2794,2540,1847,1261,577.0,2829,2744,1265,2634,9596,15795,1742,4934,3287,2507,4249,1931,5818,3685,5128,5337,2830,6420,5066,1960,665,1055,5761,1325,2152,4026,6845,392,407,1778,2750,3955,2165,3225,1235,801,2495,2098,1211,348,2588,1896,3874,187,3365,1092,2823,1176,1332,944,2183,376,854,2818,1080,3242,1981,2006,1244,1243,1879,7187,542,0.105,0.12,0.11,0.109,0.142,0.143,0.093,0.124,0.164,0.148,0.126,0.138,0.13,0.131,0.143,0.144,0.134,0.099,0.129,0.134,0.077,0.148,0.134,0.138,0.142,0.137,0.135,0.104,0.104,0.124,0.136,0.102,0.101,0.146,0.075,0.104,0.13,0.138,0.097,0.087,0.121,0.146,0.142,0.116,0.105,0.095,0.085,0.092,0.099,0.105,0.156,0.104,0.112,0.105,0.152,0.122,0.124,0.1,0.131,0.129,0.119,0.135,0.127,0.113,0.108,0.128,0.115,0.096,0.1,0.144,0.12,0.123,0.114,0.112,331,1303,798,835,698,1044,910,847,444,344,183.0,1592,786,302,729,2795,4384,372,1482,1703,660,1349,968,1578,1210,1638,1804,1120,1820,1755,621,351,401,1326,352,704,1292,1697,207,229,900,1335,931,1528,1901,667,334,956,994,446,232,1309,835,1789,95,1807,484,1479,679,777,457,943,214,439,1443,755,1621,957,918,461,572,710,2965,363,3.392,3.009,2.415,2.468,2.375,2.378,2.574,2.717,2.998,2.735,2.548,1.651,2.633,2.821,2.71,2.578,2.896,3.656,2.71,1.714,2.906,2.468,1.831,2.769,2.396,2.52,2.42,2.009,2.748,2.292,2.464,2.047,2.174,3.211,3.263,2.574,2.604,2.907,2.197,2.069,2.468,1.931,3.081,1.616,1.926,2.043,2.761,3.091,2.582,2.908,1.682,2.045,2.409,2.319,2.372,2.105,2.667,2.163,1.927,1.909,2.337,2.319,1.882,2.348,2.07,1.807,2.087,2.388,2.507,2.931,2.326,2.636,2.569,1.763,0.891,0.742,0.612,0.51,0.616,0.668,0.501,0.506,0.57,0.612,0.668,0.412,0.403,0.625,0.477,0.574,0.619,0.721,0.617,0.638,0.614,0.514,0.428,0.599,0.593,0.515,0.456,0.568,0.443,0.521,0.587,0.947,0.453,0.55,0.537,0.722,0.689,0.596,0.381,0.376,0.505,0.581,0.731,0.538,0.551,0.51,0.618,0.732,0.522,0.587,0.378,0.454,0.595,0.461,0.256,0.424,0.36,0.527,0.361,0.459,0.386,0.608,0.535,0.752,0.524,0.569,0.498,0.381,0.411,0.48,0.584,0.685,0.593,0.391
-438,Epileptic,M,42.0,1.1,2.4,1.0,1.1,3.0,1.9,2.3,1.6,2.3,0.8,0.4,3.4,1.7,0.6,1.5,5.8,14.1,1.6,2.1,4.6,2.4,5.1,2.7,5.0,4.1,3.3,5.6,3.3,3.2,2.1,3.1,2.0,0.5,2.8,1.1,0.6,6.1,3.7,0.2,0.3,1.1,4.3,4.3,2.9,2.5,1.0,0.5,1.0,1.6,1.1,0.8,1.8,1.3,3.2,0.3,2.7,1.0,1.6,2.2,1.2,1.0,2.1,0.5,0.7,3.3,1.5,3.3,1.0,1.2,0.5,0.7,1.4,5.1,0.3,8,22,8,8,25,22,13,16,18,8,4.0,33,22,6,18,66,116,11,27,42,21,59,23,39,43,40,49,31,28,29,26,17,4,28,9,5,54,45,2,2,8,44,39,22,16,8,2,5,8,9,6,12,12,21,2,22,8,17,17,10,9,13,5,5,28,19,26,6,6,4,6,12,44,3,0.047,0.022,0.015,0.021,0.048,0.027,0.034,0.03,0.058,0.036,0.03,0.034,0.031,0.035,0.032,0.033,0.036,0.044,0.029,0.035,0.035,0.033,0.038,0.044,0.035,0.026,0.041,0.031,0.029,0.025,0.051,0.068,0.023,0.033,0.026,0.015,0.053,0.033,0.018,0.014,0.022,0.034,0.051,0.026,0.02,0.019,0.025,0.015,0.019,0.017,0.027,0.02,0.026,0.023,0.019,0.021,0.017,0.019,0.029,0.025,0.021,0.034,0.029,0.019,0.028,0.026,0.021,0.015,0.016,0.017,0.022,0.017,0.019,0.014,1445,4886,2496,2206,2244,3143,2674,2825,1786,1306,948.0,3459,3432,1227,2913,9595,21933,2607,4652,4893,5649,6841,2433,5702,6296,6594,5377,4336,6200,4760,2621,1588,974,6669,3398,1645,7613,8323,432,592,1355,3325,4574,2741,3241,1491,738,2156,2469,2012,714,2868,1815,4870,519,3378,1553,3095,1769,1311,1492,2625,660,1082,3430,1488,4263,2178,1967,836,1071,2758,9429,491,0.138,0.112,0.092,0.104,0.143,0.124,0.119,0.125,0.164,0.158,0.123,0.134,0.12,0.127,0.133,0.134,0.138,0.143,0.134,0.142,0.121,0.132,0.142,0.141,0.131,0.124,0.13,0.103,0.099,0.124,0.171,0.14,0.097,0.134,0.107,0.087,0.153,0.144,0.109,0.104,0.114,0.134,0.146,0.125,0.098,0.11,0.113,0.085,0.099,0.097,0.139,0.104,0.125,0.109,0.1,0.117,0.101,0.108,0.135,0.122,0.109,0.129,0.136,0.107,0.134,0.124,0.113,0.09,0.091,0.098,0.12,0.106,0.106,0.1,430,1802,930,785,1082,1325,987,1018,658,355,230.0,1784,1029,319,869,3269,6690,589,1337,2273,1300,2559,1116,1828,2118,2205,2180,1680,1711,1538,996,587,357,1528,730,687,2228,2176,209,320,753,1877,1467,1715,1961,759,292,935,1178,944,417,1416,901,2237,234,1942,765,1499,1189,797,779,1066,368,600,1858,934,2334,1026,994,479,547,1409,4226,324,3.448,2.518,2.579,2.657,1.928,2.136,2.214,2.429,2.17,2.91,3.053,1.734,2.599,2.687,2.544,2.351,2.611,3.729,2.722,1.874,3.204,2.19,1.924,2.478,2.37,2.44,2.111,2.11,2.739,2.392,2.193,2.669,2.154,3.089,3.81,2.292,2.56,2.81,2.299,2.097,2.259,1.699,2.536,1.851,1.832,2.104,2.938,2.822,2.507,2.574,1.917,2.129,2.031,2.231,2.441,1.934,2.165,2.267,1.755,1.772,2.156,2.619,1.938,2.243,2.044,1.945,2.035,2.424,2.228,1.86,2.235,2.298,2.345,1.866,0.722,0.491,0.576,0.486,0.486,0.541,0.629,0.442,0.602,0.578,0.614,0.458,0.404,0.571,0.53,0.5,0.627,0.922,0.609,0.589,0.878,0.573,0.552,0.777,0.727,0.519,0.513,0.491,0.508,0.592,0.63,1.029,0.31,0.59,0.855,0.409,0.784,0.698,0.371,0.387,0.533,0.517,0.879,0.638,0.538,0.502,0.839,0.756,0.433,0.442,0.358,0.431,0.427,0.464,0.404,0.392,0.393,0.516,0.377,0.358,0.313,0.563,0.355,0.686,0.541,0.622,0.448,0.384,0.392,0.436,0.457,0.43,0.413,0.307
-439,Epileptic,M,32.0,0.9,1.9,0.9,1.4,2.0,2.4,1.6,1.1,0.9,0.7,0.3,2.4,1.7,0.4,1.5,7.0,7.2,0.9,2.9,3.9,1.1,3.1,1.7,3.4,3.5,1.9,2.9,2.1,2.3,3.1,1.4,1.3,0.3,2.2,1.1,0.9,3.5,3.0,0.3,0.2,0.8,3.4,2.1,2.3,2.4,0.7,0.3,0.9,1.3,1.1,0.6,1.7,1.1,2.2,0.4,2.2,0.9,2.0,1.2,1.1,0.7,1.4,0.2,0.7,1.4,1.9,2.0,1.1,1.0,0.5,1.1,1.2,5.2,0.2,9,18,7,10,17,23,13,11,10,6,2.0,21,20,5,16,73,62,6,34,34,12,34,17,32,36,19,27,22,22,31,15,10,4,22,7,9,35,31,2,2,4,27,21,18,12,4,2,6,7,8,5,12,8,14,2,15,6,12,11,8,4,14,2,7,8,22,17,6,5,5,6,10,33,1,0.035,0.019,0.017,0.025,0.033,0.025,0.026,0.023,0.034,0.033,0.03,0.031,0.03,0.032,0.025,0.032,0.027,0.035,0.024,0.034,0.024,0.026,0.026,0.036,0.029,0.025,0.028,0.025,0.02,0.027,0.029,0.059,0.027,0.029,0.031,0.025,0.03,0.031,0.017,0.022,0.016,0.036,0.047,0.027,0.015,0.012,0.017,0.017,0.017,0.033,0.022,0.018,0.019,0.014,0.024,0.017,0.018,0.02,0.022,0.014,0.02,0.02,0.025,0.02,0.014,0.037,0.017,0.017,0.016,0.015,0.021,0.018,0.017,0.014,1481,4452,1975,2306,2653,3965,2798,3106,1618,1515,673.0,2822,3622,732,3340,12936,18028,2416,6881,5244,2983,6424,2685,5513,7205,4983,5597,4435,7167,6693,2421,1153,877,6530,2555,2272,7982,7288,521,454,1284,2704,5118,2511,4416,1548,631,2082,2404,2064,688,3548,2008,6333,440,4286,1680,2887,1664,2173,1294,2898,383,1117,2811,1201,3966,2300,2054,1083,1722,2543,11086,361,0.12,0.108,0.105,0.119,0.116,0.113,0.103,0.111,0.141,0.128,0.11,0.127,0.13,0.091,0.099,0.114,0.113,0.121,0.115,0.135,0.088,0.111,0.118,0.121,0.122,0.12,0.112,0.098,0.089,0.126,0.116,0.131,0.109,0.119,0.126,0.114,0.133,0.132,0.096,0.104,0.097,0.124,0.146,0.119,0.087,0.083,0.091,0.09,0.09,0.124,0.133,0.096,0.104,0.083,0.131,0.095,0.103,0.1,0.118,0.085,0.101,0.114,0.105,0.106,0.087,0.136,0.096,0.092,0.086,0.097,0.112,0.107,0.095,0.098,446,1591,819,902,944,1543,922,922,441,379,218.0,1299,992,217,947,3413,4529,468,1994,1966,787,1841,1110,1696,1981,1328,1733,1242,1538,1818,821,486,254,1300,571,644,1873,1748,272,219,661,1392,951,1463,2273,786,282,824,1148,585,374,1648,1067,2453,211,2134,763,1463,882,1217,604,1152,171,589,1346,785,1791,1032,955,461,806,1102,4637,175,3.249,2.734,2.565,2.546,2.423,2.248,2.524,2.904,2.751,3.139,2.546,1.951,2.692,2.54,2.687,2.833,3.14,3.756,2.751,2.205,2.705,2.57,2.06,2.575,2.692,2.979,2.483,2.656,3.38,2.835,2.252,2.498,2.882,3.266,3.12,2.951,3.028,3.036,2.402,2.318,2.474,1.83,3.367,1.967,2.232,2.155,2.687,3.165,2.577,3.158,1.907,2.293,2.194,2.671,2.265,2.236,2.473,2.264,2.119,1.995,2.375,2.644,2.453,2.138,2.355,1.999,2.3,2.634,2.549,2.573,2.448,2.669,2.614,2.336,0.648,0.667,0.479,0.577,0.663,0.611,0.675,0.575,0.7,0.479,0.652,0.615,0.498,0.463,0.59,0.679,0.67,0.859,0.669,0.664,0.768,0.646,0.631,0.69,0.606,0.556,0.521,0.567,0.533,0.49,0.698,0.775,0.489,0.667,0.903,0.678,0.738,0.553,0.593,0.487,0.499,0.482,0.843,0.665,0.708,0.536,0.636,0.552,0.601,0.833,0.509,0.454,0.451,0.555,0.663,0.5,0.394,0.581,0.47,0.458,0.535,0.624,0.504,0.725,0.535,0.547,0.496,0.431,0.421,0.771,0.519,0.527,0.543,0.255
-440,Epileptic,F,52.0,0.6,2.3,0.8,1.5,1.5,1.7,1.6,1.3,1.3,0.9,0.4,2.5,1.7,0.6,1.0,4.9,7.4,0.6,2.0,3.5,1.4,5.2,1.9,3.7,4.0,2.6,2.7,2.0,2.2,2.3,0.9,1.2,0.4,1.8,0.4,0.5,3.7,2.7,0.1,0.2,1.1,3.6,2.0,2.5,2.6,0.8,0.4,0.7,1.2,0.6,0.6,1.8,1.1,2.5,0.4,2.4,0.8,1.6,1.1,1.2,0.4,1.3,0.2,0.5,1.8,1.1,2.2,1.3,1.0,0.5,0.7,1.7,4.6,0.3,5,28,6,14,18,17,20,15,13,11,7.0,26,18,8,16,71,84,6,22,40,12,62,23,40,40,33,33,21,25,32,14,10,4,24,2,4,58,42,1,1,9,35,25,20,17,6,2,5,6,5,3,14,9,19,2,21,7,12,11,9,4,12,1,5,13,18,19,9,6,5,5,14,40,2,0.029,0.023,0.014,0.024,0.032,0.025,0.024,0.027,0.047,0.043,0.037,0.032,0.03,0.034,0.027,0.029,0.028,0.026,0.031,0.033,0.027,0.047,0.03,0.047,0.042,0.026,0.027,0.021,0.022,0.028,0.031,0.051,0.02,0.025,0.011,0.014,0.036,0.029,0.018,0.013,0.021,0.038,0.03,0.023,0.019,0.016,0.018,0.013,0.018,0.014,0.029,0.018,0.024,0.019,0.019,0.017,0.019,0.018,0.024,0.018,0.014,0.026,0.013,0.02,0.019,0.024,0.021,0.017,0.016,0.016,0.025,0.019,0.02,0.017,1348,4846,2001,2752,1959,3228,2820,2913,1764,1498,616.0,3127,3384,1154,2299,10146,16439,2028,4446,5019,4169,4305,2683,5091,5177,6130,4749,4183,6326,5012,2110,1315,1117,5101,1894,1638,8031,7484,315,551,1607,2823,5780,3218,3747,1447,745,1862,2156,1266,524,3251,1631,4359,494,3902,1473,3235,1279,1556,819,2201,349,835,2885,1190,3368,2256,1960,859,1057,3068,8962,488,0.105,0.112,0.096,0.123,0.141,0.131,0.108,0.131,0.16,0.171,0.148,0.128,0.133,0.142,0.127,0.135,0.129,0.109,0.134,0.146,0.111,0.136,0.11,0.149,0.129,0.122,0.11,0.103,0.102,0.126,0.11,0.13,0.096,0.123,0.067,0.076,0.141,0.131,0.083,0.085,0.111,0.137,0.134,0.117,0.1,0.102,0.093,0.083,0.101,0.099,0.141,0.101,0.115,0.107,0.119,0.106,0.111,0.104,0.114,0.106,0.097,0.124,0.085,0.124,0.109,0.127,0.108,0.103,0.093,0.111,0.121,0.117,0.101,0.102,379,1749,779,1030,842,1056,1045,940,610,410,202.0,1357,894,316,713,3109,4881,452,1122,1920,878,1627,1094,1486,1766,1754,1657,1411,1584,1529,620,443,329,1212,544,570,2070,1719,178,287,807,1522,1266,1774,2159,755,315,820,989,598,295,1599,822,2023,281,2027,674,1431,757,894,448,888,194,416,1432,815,1786,1112,909,508,504,1338,3856,260,3.29,2.589,2.476,2.505,2.184,2.56,2.245,2.768,2.266,2.846,2.702,1.942,2.82,2.582,2.424,2.471,2.615,3.482,3.021,2.174,3.369,2.202,2.177,2.672,2.383,2.689,2.32,2.267,2.891,2.48,2.462,2.85,2.547,2.995,3.132,2.587,2.834,3.052,2.068,2.284,2.411,1.683,3.296,1.976,1.972,2.11,2.783,2.812,2.669,2.497,1.979,2.162,1.971,2.257,2.149,2.072,2.5,2.534,1.801,1.917,2.242,2.466,1.922,2.416,2.184,1.804,2.045,2.466,2.443,1.97,2.288,2.44,2.46,2.379,0.529,0.456,0.433,0.509,0.562,0.567,0.596,0.547,0.586,0.521,0.694,0.489,0.379,0.587,0.488,0.539,0.612,0.81,0.601,0.685,0.726,0.623,0.636,0.68,0.57,0.562,0.596,0.563,0.526,0.594,0.573,0.899,0.368,0.613,0.482,0.441,0.75,0.593,0.328,0.288,0.407,0.48,0.727,0.65,0.64,0.448,0.471,0.548,0.398,0.432,0.387,0.375,0.436,0.382,0.36,0.433,0.397,0.515,0.35,0.398,0.281,0.613,0.236,0.68,0.496,0.424,0.497,0.429,0.389,0.519,0.436,0.508,0.479,0.303
-441,Epileptic,F,32.0,0.6,2.5,0.7,1.0,1.8,2.1,1.7,1.3,1.9,0.6,0.3,2.3,1.5,1.3,1.0,6.7,7.5,0.8,2.3,2.8,2.3,2.2,1.5,3.9,2.0,2.4,2.6,1.8,2.2,2.3,2.0,0.9,0.7,2.5,1.0,0.8,6.2,3.2,0.2,0.2,0.9,2.6,3.7,1.4,2.0,0.6,0.7,0.7,1.2,1.5,0.3,1.6,1.7,2.7,0.4,2.1,0.8,1.5,0.8,0.8,0.4,1.6,0.4,0.3,1.7,1.3,2.2,1.4,1.2,0.4,0.6,1.6,3.2,0.4,5,26,6,10,17,16,14,11,22,8,5.0,21,13,13,12,74,80,5,31,30,13,33,12,44,28,30,40,21,22,35,25,8,9,29,5,7,57,32,2,1,5,25,28,12,11,4,6,5,6,12,3,13,12,22,2,19,4,9,5,6,6,10,3,4,11,17,15,11,9,4,5,11,25,5,0.035,0.022,0.013,0.02,0.033,0.033,0.027,0.023,0.058,0.034,0.034,0.03,0.038,0.055,0.035,0.04,0.028,0.032,0.041,0.034,0.036,0.032,0.027,0.037,0.03,0.026,0.022,0.02,0.025,0.031,0.043,0.035,0.03,0.03,0.029,0.021,0.043,0.036,0.017,0.021,0.017,0.036,0.049,0.021,0.015,0.012,0.026,0.013,0.016,0.031,0.027,0.026,0.029,0.024,0.025,0.016,0.019,0.018,0.022,0.019,0.027,0.027,0.029,0.014,0.021,0.027,0.017,0.019,0.017,0.012,0.022,0.018,0.015,0.02,1650,6534,2332,2305,3548,2935,3157,3208,2490,1377,522.0,2575,2833,2012,2564,10854,19140,2792,5135,3704,3555,4475,2044,8114,4997,5892,6491,4192,6037,4965,3110,1461,1211,8185,1908,1711,9342,7053,375,317,1425,3085,5810,1887,3308,1418,1223,2447,2697,2266,389,3103,2921,5546,422,3645,1695,3111,1081,1375,701,3041,487,1003,2854,1126,3878,2283,2122,991,1085,2492,8363,807,0.109,0.115,0.089,0.105,0.137,0.148,0.11,0.12,0.18,0.157,0.136,0.133,0.142,0.213,0.135,0.152,0.129,0.108,0.14,0.143,0.109,0.147,0.115,0.146,0.134,0.132,0.117,0.098,0.108,0.137,0.147,0.112,0.147,0.126,0.088,0.104,0.138,0.12,0.12,0.1,0.101,0.151,0.149,0.107,0.087,0.088,0.123,0.078,0.092,0.121,0.133,0.113,0.12,0.112,0.152,0.105,0.097,0.1,0.121,0.106,0.124,0.115,0.159,0.113,0.106,0.132,0.099,0.109,0.107,0.096,0.125,0.111,0.089,0.122,365,1948,835,824,993,1005,981,892,610,320,177.0,1129,713,446,618,2931,4849,410,1227,1357,928,1292,820,1835,1269,1585,2073,1385,1460,1505,727,503,377,1301,504,584,2298,1666,187,163,718,1191,1232,1119,1963,685,480,932,1066,901,212,1173,1078,1949,168,1874,672,1275,559,653,302,1016,178,423,1264,667,1901,1085,979,499,485,1214,3199,346,3.917,3.023,2.672,2.694,2.834,2.556,2.636,3.08,2.92,3.2,2.726,1.918,2.968,3.11,2.984,2.764,3.129,4.346,3.275,2.257,3.007,2.691,2.184,3.261,2.935,2.953,2.464,2.346,3.043,2.588,2.866,2.618,2.728,3.619,3.288,2.66,2.996,3.079,2.279,2.328,2.587,2.203,3.213,1.924,1.948,2.228,3.027,3.18,2.933,3.008,2.216,2.537,2.737,2.589,3.223,2.135,2.996,2.778,2.219,2.255,2.598,2.814,2.654,2.632,2.457,2.357,2.282,2.404,2.405,2.249,2.497,2.579,2.769,2.746,1.028,0.636,0.495,0.437,0.817,0.506,0.563,0.545,0.63,0.637,0.733,0.442,0.436,0.773,0.528,0.627,0.615,0.598,0.52,0.685,0.803,0.46,0.55,0.67,0.681,0.598,0.514,0.603,0.501,0.526,0.712,1.127,0.521,0.62,0.952,0.476,0.888,0.714,0.307,0.426,0.566,0.594,1.092,0.6,0.685,0.423,0.728,0.735,0.584,0.652,0.413,0.536,0.596,0.607,0.409,0.439,0.493,0.597,0.37,0.414,0.498,0.683,0.509,0.907,0.729,0.74,0.504,0.42,0.531,0.446,0.601,0.5,0.494,0.657
-442,Epileptic,F,42.0,0.6,2.9,1.0,1.6,1.7,1.8,1.7,1.9,1.2,0.6,0.5,2.1,2.1,0.7,1.4,5.9,11.9,0.9,1.7,4.1,1.1,2.4,1.3,3.5,2.5,3.0,2.9,2.0,2.3,3.4,1.5,1.0,0.5,2.8,0.3,0.9,3.3,3.7,0.2,0.2,0.8,2.1,2.1,1.7,1.9,0.8,0.3,0.8,1.2,0.9,0.5,1.9,1.5,2.3,0.2,1.9,0.6,1.5,0.6,0.7,0.6,1.7,0.5,0.4,1.4,1.5,2.2,0.8,0.7,0.6,1.2,1.4,4.2,0.4,5,22,9,13,13,16,13,15,9,6,6.0,20,20,7,11,52,111,5,24,31,10,26,12,32,33,35,38,20,23,39,16,8,4,25,2,8,39,43,1,1,5,18,22,15,11,6,1,5,6,6,3,12,9,15,1,15,3,11,5,6,3,14,3,4,10,16,15,6,4,7,6,10,38,4,0.029,0.026,0.019,0.028,0.035,0.036,0.028,0.031,0.049,0.038,0.037,0.031,0.041,0.039,0.033,0.034,0.033,0.032,0.031,0.039,0.021,0.035,0.028,0.042,0.037,0.031,0.027,0.023,0.024,0.032,0.042,0.044,0.019,0.032,0.01,0.021,0.044,0.034,0.016,0.022,0.018,0.037,0.037,0.027,0.017,0.022,0.018,0.013,0.016,0.019,0.036,0.02,0.029,0.022,0.022,0.019,0.015,0.021,0.02,0.017,0.029,0.032,0.032,0.02,0.022,0.027,0.019,0.014,0.013,0.028,0.026,0.021,0.018,0.02,1366,4601,2207,2471,2302,2667,2663,3189,2463,1292,676.0,2111,3638,1304,2717,10333,20195,2068,4400,3829,3217,4113,1711,5811,4384,5242,5037,3838,5067,5514,2484,1043,1079,6040,1552,1906,7466,8625,356,435,1583,2445,5544,1806,2886,1234,687,2106,2470,1672,426,3432,1877,4293,283,2686,1203,2583,936,1081,626,2418,497,689,2103,1470,3226,1659,1630,867,1573,2267,8964,631,0.105,0.113,0.098,0.125,0.135,0.158,0.116,0.138,0.135,0.141,0.142,0.144,0.151,0.138,0.134,0.136,0.132,0.108,0.13,0.148,0.09,0.136,0.128,0.145,0.144,0.137,0.124,0.108,0.11,0.147,0.141,0.113,0.096,0.136,0.059,0.115,0.149,0.143,0.095,0.096,0.099,0.145,0.138,0.123,0.095,0.109,0.088,0.087,0.09,0.104,0.149,0.1,0.114,0.098,0.129,0.113,0.091,0.112,0.117,0.099,0.124,0.133,0.136,0.118,0.108,0.127,0.101,0.097,0.089,0.128,0.113,0.118,0.098,0.12,443,1772,848,887,765,837,916,1015,499,322,182.0,1046,959,285,698,2910,6207,416,1044,1616,796,1231,739,1531,1283,1693,1832,1315,1494,1814,611,420,411,1407,481,695,1636,2014,192,163,729,969,1155,1164,1750,651,274,907,1108,681,245,1566,833,1870,127,1465,548,1108,491,645,353,924,267,376,1030,871,1775,816,787,413,732,1015,3701,355,3.205,2.531,2.447,2.694,2.465,2.744,2.366,2.81,2.933,3.099,2.934,1.848,2.958,3.034,2.864,2.638,2.585,3.808,3.198,2.087,3.166,2.488,2.002,2.907,2.527,2.553,2.16,2.253,2.624,2.437,2.787,2.399,2.22,3.021,2.872,2.481,3.164,3.078,2.188,2.735,2.818,2.173,3.172,1.736,1.855,2.083,3.263,2.84,2.679,2.875,2.304,2.27,2.325,2.302,2.388,2.027,2.652,2.766,2.147,1.903,2.208,2.641,2.073,2.158,2.284,2.158,2.041,2.273,2.306,2.377,2.612,2.628,2.549,2.232,0.731,0.557,0.626,0.648,0.665,0.555,0.529,0.449,0.852,0.415,0.842,0.555,0.51,0.562,0.459,0.613,0.703,0.788,0.484,0.652,0.818,0.583,0.447,0.74,0.536,0.499,0.56,0.528,0.593,0.586,0.8,1.04,0.405,0.633,0.95,0.464,0.776,0.616,0.44,0.439,0.51,0.552,0.741,0.809,0.512,0.462,0.42,0.586,0.444,0.502,0.368,0.513,0.76,0.532,0.405,0.521,0.372,0.503,0.403,0.35,0.306,0.69,0.411,0.846,0.519,0.662,0.485,0.384,0.43,0.656,0.549,0.543,0.553,0.556
-443,Epileptic,M,22.0,1.2,3.5,1.7,1.6,1.8,2.0,3.0,2.8,1.8,1.1,0.9,3.3,2.3,0.8,2.4,11.3,13.0,1.5,2.2,4.3,1.4,3.1,2.3,5.8,3.6,7.2,5.0,3.9,4.5,3.6,3.0,1.5,0.6,4.2,1.0,0.7,4.3,5.3,0.3,0.2,1.4,3.5,4.1,3.2,4.0,1.0,0.7,1.1,1.8,1.3,0.8,3.9,2.4,3.8,0.4,2.5,1.0,2.3,1.2,1.4,0.4,2.5,0.5,0.6,2.4,1.5,4.9,1.3,1.3,0.6,1.1,1.2,5.2,0.4,9,31,12,11,18,20,18,29,14,9,9.0,26,31,9,24,87,88,11,28,41,11,33,20,49,44,65,49,36,31,43,19,8,7,46,10,7,44,64,1,2,7,29,34,22,20,5,4,6,9,10,4,30,13,23,2,22,5,19,9,10,5,17,3,5,18,18,39,8,9,5,6,9,40,3,0.045,0.03,0.026,0.032,0.04,0.032,0.047,0.037,0.059,0.046,0.049,0.033,0.038,0.043,0.044,0.057,0.045,0.043,0.031,0.043,0.031,0.037,0.033,0.053,0.034,0.036,0.037,0.041,0.044,0.035,0.06,0.049,0.033,0.048,0.047,0.014,0.044,0.044,0.016,0.018,0.022,0.034,0.052,0.03,0.027,0.02,0.024,0.017,0.018,0.024,0.032,0.028,0.036,0.025,0.014,0.02,0.019,0.024,0.023,0.025,0.019,0.039,0.026,0.02,0.024,0.026,0.027,0.018,0.025,0.021,0.021,0.02,0.022,0.019,1512,6910,3012,2746,2761,3030,2748,4805,2421,2096,939.0,3278,4718,1232,3895,12952,20604,2669,4390,4794,3595,3797,2280,7113,6456,11621,6824,4953,6848,6535,2170,1538,1286,6896,2181,2601,7032,8725,504,508,1797,2718,6382,2679,3917,1476,1042,2140,2919,1790,559,5502,2175,5252,669,3570,1387,3789,1225,1473,838,3273,567,881,3196,1405,6137,2453,1725,1208,1650,2152,8413,567,0.147,0.133,0.119,0.129,0.146,0.14,0.135,0.149,0.182,0.159,0.156,0.136,0.145,0.162,0.158,0.18,0.146,0.14,0.138,0.167,0.109,0.135,0.129,0.16,0.14,0.138,0.136,0.139,0.139,0.143,0.171,0.124,0.119,0.176,0.143,0.091,0.159,0.155,0.102,0.103,0.103,0.131,0.169,0.131,0.109,0.099,0.111,0.092,0.095,0.114,0.139,0.123,0.132,0.113,0.091,0.112,0.098,0.118,0.116,0.116,0.117,0.14,0.12,0.107,0.113,0.122,0.12,0.093,0.115,0.114,0.107,0.11,0.108,0.119,448,1913,957,861,968,1032,1079,1482,654,437,264.0,1643,1362,345,1098,3662,5375,571,1234,2066,786,1316,1139,2036,1861,3441,2500,1820,1943,2052,799,508,399,1677,546,885,1830,2245,248,240,918,1513,1469,1673,2313,738,439,904,1360,737,361,2432,1064,2361,353,2007,730,1618,798,855,385,1066,305,497,1657,854,3107,1118,858,435,791,1017,3599,315,3.333,3.088,3.061,2.776,2.243,2.347,2.183,2.634,2.722,3.505,3.056,1.78,2.722,2.652,2.552,2.568,2.856,3.616,2.766,1.96,3.162,2.313,1.828,2.568,2.594,2.652,2.248,2.11,2.644,2.495,2.092,2.819,2.517,2.894,3.335,2.599,2.825,2.799,2.244,2.25,2.426,1.705,3.191,1.882,1.939,2.215,2.688,3.011,2.579,2.545,1.97,2.176,2.074,2.288,2.24,1.984,2.157,2.527,1.781,1.86,2.221,2.722,1.962,2.028,2.076,1.956,2.041,2.385,2.258,2.535,2.321,2.262,2.34,2.288,0.737,0.795,0.629,0.726,0.696,0.711,0.641,0.554,0.777,0.861,0.905,0.518,0.646,0.575,0.647,0.747,0.776,0.805,0.589,0.688,0.894,0.63,0.526,0.892,0.726,0.677,0.673,0.69,0.694,0.66,0.721,1.059,0.387,0.759,0.838,0.496,0.839,0.799,0.514,0.521,0.507,0.481,0.768,0.647,0.554,0.476,0.431,0.794,0.557,0.577,0.394,0.536,0.533,0.507,0.341,0.433,0.496,0.647,0.361,0.485,0.576,0.808,0.426,0.714,0.533,0.58,0.485,0.463,0.532,0.977,0.53,0.535,0.527,0.455
-444,Epileptic,F,27.0,0.6,3.0,1.4,1.1,1.9,1.6,1.7,1.7,1.1,1.0,0.3,2.4,1.3,0.6,1.0,5.9,8.7,1.0,1.7,4.6,0.5,2.0,1.4,4.0,3.0,4.4,2.5,2.5,2.5,4.5,1.4,1.0,0.7,2.2,0.2,0.7,3.3,3.6,0.1,0.1,1.0,2.4,2.5,2.1,2.2,0.8,0.5,0.6,1.1,0.6,0.4,1.4,1.6,3.1,0.3,2.4,0.3,1.2,0.8,0.8,0.5,1.4,0.2,0.5,1.7,1.3,3.3,1.1,1.1,0.5,1.3,0.9,3.3,0.2,6,29,13,12,19,16,17,17,14,11,6.0,28,17,7,13,67,84,11,22,49,5,27,19,45,37,52,34,27,23,53,17,9,7,29,1,7,45,40,2,1,7,26,26,19,14,5,3,4,6,7,4,13,12,21,2,27,3,14,7,7,5,13,1,5,17,18,30,9,7,6,12,8,27,3,0.027,0.027,0.022,0.021,0.036,0.026,0.022,0.031,0.035,0.037,0.023,0.03,0.028,0.031,0.034,0.029,0.029,0.035,0.026,0.033,0.012,0.027,0.028,0.036,0.035,0.033,0.025,0.024,0.022,0.031,0.034,0.04,0.03,0.034,0.01,0.02,0.032,0.038,0.012,0.017,0.022,0.031,0.043,0.018,0.016,0.015,0.019,0.011,0.016,0.017,0.02,0.016,0.022,0.022,0.019,0.018,0.011,0.016,0.027,0.016,0.019,0.019,0.017,0.018,0.02,0.022,0.022,0.016,0.02,0.016,0.021,0.017,0.017,0.014,1389,5101,2692,2081,2586,1987,2806,3151,2019,1660,652.0,2852,3045,1071,2078,10713,17448,2482,3736,5754,2571,3106,1870,6106,5229,7463,4979,4319,5328,7178,2061,1232,938,4701,1510,1585,7291,6339,314,326,1430,2250,5348,3282,3367,1590,926,1886,2133,1373,470,2886,2378,5357,377,3828,904,2803,788,1156,865,2839,309,984,2613,1346,4320,2484,1547,1120,2023,1820,6951,337,0.118,0.13,0.115,0.106,0.146,0.132,0.114,0.132,0.154,0.144,0.134,0.148,0.135,0.144,0.143,0.134,0.13,0.123,0.138,0.144,0.071,0.135,0.141,0.144,0.153,0.148,0.124,0.115,0.097,0.144,0.122,0.12,0.132,0.136,0.058,0.107,0.14,0.142,0.095,0.098,0.113,0.139,0.154,0.103,0.093,0.089,0.104,0.08,0.095,0.105,0.12,0.102,0.112,0.108,0.127,0.108,0.089,0.109,0.124,0.101,0.108,0.111,0.113,0.109,0.116,0.119,0.117,0.1,0.106,0.117,0.121,0.11,0.094,0.117,391,1727,993,884,941,966,1031,1052,575,473,211.0,1303,856,286,603,3278,4747,497,1081,2374,640,1213,826,1755,1572,2317,1816,1526,1489,2483,679,441,341,1130,394,644,1868,1565,192,160,724,1158,1037,1875,2063,804,435,826,999,607,323,1367,1166,2388,203,2145,481,1343,490,752,479,1113,185,517,1384,886,2303,1113,835,507,1038,887,3175,197,3.547,2.833,2.603,2.297,2.334,1.892,2.27,2.719,2.544,2.827,2.733,1.884,2.721,2.653,2.644,2.548,2.931,3.502,2.714,2.092,3.01,2.033,1.939,2.677,2.536,2.636,2.199,2.245,2.685,2.355,2.374,2.704,2.349,2.811,3.249,2.375,2.893,2.841,1.838,2.414,2.472,1.754,3.257,1.89,1.79,2.0,2.67,2.775,2.649,2.511,1.749,2.159,2.081,2.396,2.251,1.853,2.247,2.305,1.771,1.717,2.09,2.524,1.98,2.236,2.029,1.854,2.065,2.624,2.179,2.394,2.154,2.33,2.415,2.092,0.759,0.592,0.541,0.414,0.653,0.407,0.493,0.477,0.617,0.658,0.586,0.427,0.427,0.463,0.522,0.504,0.562,0.768,0.553,0.618,0.864,0.499,0.506,0.668,0.542,0.502,0.493,0.487,0.467,0.487,0.592,0.887,0.39,0.666,0.66,0.436,0.712,0.671,0.344,0.501,0.527,0.483,0.87,0.671,0.534,0.528,0.53,0.713,0.588,0.569,0.364,0.635,0.381,0.444,0.465,0.399,0.432,0.535,0.359,0.397,0.412,0.564,0.317,0.758,0.587,0.579,0.508,0.479,0.441,0.449,0.508,0.396,0.42,0.488
-445,Epileptic,F,37.0,0.3,1.7,1.1,0.9,1.2,1.9,1.1,1.3,0.9,0.4,0.1,2.2,1.5,0.4,1.0,4.5,6.8,0.7,1.2,3.9,0.8,2.3,1.5,2.4,2.6,3.2,2.7,2.0,1.7,2.6,1.3,0.7,0.4,1.9,0.2,0.3,3.4,2.4,0.3,0.2,1.0,2.5,2.5,2.2,2.0,0.6,0.3,0.6,0.9,0.7,0.4,1.3,0.9,2.9,0.3,1.8,0.8,1.4,0.7,0.7,0.5,0.8,0.2,0.3,1.7,0.5,2.3,0.8,0.9,0.4,1.0,1.1,4.2,0.3,2,17,11,8,10,20,11,15,9,5,2.0,19,15,4,10,49,64,7,14,34,8,21,12,30,31,33,30,18,16,29,15,8,4,25,1,3,39,30,3,1,6,22,26,18,15,5,2,4,6,7,4,10,6,20,2,16,6,18,5,6,3,8,1,4,14,7,20,5,5,4,6,10,32,2,0.021,0.022,0.022,0.018,0.028,0.03,0.02,0.028,0.036,0.023,0.018,0.029,0.027,0.038,0.03,0.031,0.025,0.03,0.023,0.035,0.022,0.033,0.029,0.033,0.033,0.033,0.027,0.023,0.02,0.023,0.034,0.046,0.03,0.026,0.01,0.009,0.037,0.033,0.021,0.017,0.024,0.03,0.05,0.03,0.018,0.016,0.018,0.013,0.016,0.026,0.019,0.019,0.023,0.023,0.032,0.016,0.02,0.02,0.024,0.018,0.023,0.021,0.019,0.015,0.02,0.028,0.021,0.015,0.014,0.019,0.021,0.019,0.019,0.018,1166,4173,2159,2071,2185,2738,2267,2747,1656,1125,472.0,2233,3367,960,2166,9450,15788,1923,3161,4040,2195,3343,1747,4616,4704,5968,5018,3405,4741,5340,1996,1201,776,4581,1256,1477,5420,5960,465,484,1284,2440,3577,2044,2983,1282,655,1578,1955,1256,435,2431,1339,5042,371,3170,1264,2692,742,1075,575,1707,316,756,2492,471,3467,1877,2056,837,1435,1634,7524,401,0.08,0.112,0.119,0.095,0.136,0.14,0.104,0.139,0.153,0.114,0.081,0.125,0.132,0.133,0.137,0.145,0.126,0.124,0.115,0.144,0.08,0.148,0.12,0.135,0.151,0.145,0.124,0.109,0.099,0.122,0.135,0.12,0.13,0.138,0.067,0.066,0.143,0.141,0.124,0.086,0.121,0.128,0.152,0.132,0.098,0.101,0.111,0.081,0.093,0.128,0.123,0.105,0.11,0.114,0.147,0.105,0.115,0.128,0.131,0.116,0.128,0.114,0.112,0.122,0.112,0.114,0.113,0.094,0.09,0.128,0.116,0.124,0.103,0.122,277,1280,796,774,698,1102,784,851,429,279,147.0,1121,909,208,548,2535,4341,415,860,1713,614,1080,782,1275,1394,1776,1670,1195,1277,1690,535,422,251,1056,329,553,1510,1407,179,205,650,1227,946,1301,1789,604,289,727,907,549,298,1113,672,1990,194,1591,618,1173,452,635,315,685,165,336,1274,266,1837,790,901,347,694,855,3245,197,3.741,3.041,2.815,2.601,2.736,2.269,2.36,2.912,2.978,3.119,2.735,1.808,2.864,3.253,2.948,2.828,2.994,3.638,2.863,1.984,2.813,2.394,1.931,2.772,2.594,2.756,2.386,2.258,2.784,2.527,2.771,2.845,2.539,2.962,3.243,2.357,2.655,3.097,2.58,2.546,2.656,1.791,2.873,1.765,1.928,2.205,2.986,2.587,2.697,2.845,1.725,2.462,2.371,2.67,2.305,2.179,2.524,2.5,1.911,2.017,2.29,2.644,2.103,2.264,2.26,1.998,2.118,2.569,2.68,2.939,2.521,2.39,2.514,2.824,0.833,0.657,0.534,0.399,0.693,0.496,0.545,0.444,0.416,0.362,0.701,0.479,0.419,0.463,0.364,0.513,0.526,0.744,0.529,0.594,0.745,0.541,0.492,0.618,0.508,0.515,0.529,0.52,0.473,0.542,0.605,1.099,0.389,0.629,0.564,0.469,0.745,0.498,0.335,0.349,0.496,0.451,0.786,0.702,0.582,0.564,0.411,0.689,0.424,0.602,0.298,0.421,0.417,0.41,0.375,0.406,0.448,0.558,0.351,0.312,0.321,0.495,0.328,0.966,0.52,0.581,0.451,0.363,0.342,0.491,0.421,0.463,0.494,0.367
-446,Epileptic,F,47.0,1.1,2.0,1.1,0.7,1.0,1.4,1.8,1.2,0.6,0.4,0.3,2.6,1.3,0.4,0.7,4.4,7.5,1.0,1.7,3.7,0.6,2.6,2.3,2.4,2.9,4.1,4.3,2.5,2.2,1.9,1.0,0.9,0.6,2.2,0.3,0.8,2.2,2.2,0.1,0.1,0.9,3.8,1.7,3.1,2.9,0.6,0.3,0.4,1.0,0.6,0.7,1.1,1.4,1.6,0.3,2.8,0.8,1.2,0.3,0.6,0.6,1.6,0.3,0.4,1.4,0.2,5.6,0.7,0.8,0.7,0.6,1.1,2.8,0.2,9,17,12,5,11,9,13,13,7,5,2.0,16,13,6,7,45,81,6,21,27,6,20,18,28,31,43,42,22,24,18,13,7,5,26,3,7,27,33,2,1,6,27,17,27,22,5,1,4,6,6,5,9,14,14,2,22,7,11,3,4,5,11,3,4,9,4,39,6,6,9,4,8,22,2,0.049,0.028,0.025,0.015,0.024,0.041,0.023,0.024,0.033,0.027,0.03,0.05,0.031,0.029,0.036,0.036,0.03,0.036,0.036,0.042,0.017,0.042,0.052,0.032,0.034,0.031,0.03,0.024,0.024,0.025,0.035,0.045,0.03,0.027,0.012,0.021,0.036,0.027,0.016,0.014,0.018,0.048,0.037,0.037,0.019,0.017,0.018,0.01,0.014,0.016,0.065,0.024,0.039,0.019,0.014,0.026,0.02,0.019,0.02,0.05,0.042,0.028,0.035,0.016,0.026,0.015,0.027,0.021,0.019,0.024,0.017,0.02,0.016,0.015,1469,3825,2091,1653,1798,1116,3307,2897,1223,803,377.0,1631,2311,821,1407,7107,15213,2267,2787,2415,2326,3194,1682,5281,4675,7506,5382,5026,5047,3382,2098,951,797,4517,1316,1741,4705,6150,292,177,1652,2920,4981,2321,5000,1323,663,1720,2194,1202,263,2030,1768,3297,534,3445,1268,2066,818,568,1032,2277,376,807,1940,537,5649,1374,1328,1216,952,2019,6230,449,0.147,0.125,0.111,0.09,0.122,0.136,0.109,0.121,0.137,0.123,0.108,0.154,0.137,0.126,0.145,0.151,0.139,0.125,0.143,0.148,0.087,0.143,0.187,0.137,0.151,0.143,0.13,0.109,0.12,0.125,0.135,0.119,0.14,0.13,0.079,0.103,0.122,0.134,0.11,0.112,0.102,0.145,0.152,0.143,0.108,0.097,0.1,0.07,0.09,0.112,0.184,0.106,0.142,0.107,0.099,0.124,0.118,0.111,0.068,0.15,0.137,0.13,0.158,0.12,0.128,0.099,0.123,0.107,0.111,0.128,0.109,0.12,0.094,0.089,407,1296,759,665,678,564,1117,893,340,251,175.0,745,713,213,413,2178,4332,413,887,1265,573,898,688,1338,1474,2349,2151,1593,1522,1153,495,386,291,1146,392,675,1099,1425,164,95,792,1093,891,1390,2386,539,253,788,1044,517,194,801,680,1386,297,1760,590,1062,275,262,256,881,175,373,778,328,3109,613,659,508,498,765,2802,236,3.23,2.635,2.672,2.509,2.259,2.021,2.517,2.694,2.7,2.729,2.398,2.044,2.69,2.696,2.615,2.577,2.907,3.779,2.54,1.794,3.034,2.473,2.213,3.002,2.55,2.758,2.106,2.49,2.602,2.519,2.932,2.469,2.312,2.841,2.97,2.431,3.045,3.06,2.033,1.945,2.688,2.141,3.558,1.902,2.277,2.422,3.187,2.6,2.454,2.798,1.596,2.236,2.439,2.34,2.174,2.122,2.355,2.237,2.897,2.155,3.154,2.565,2.692,2.335,2.507,2.078,1.888,2.325,2.398,2.461,2.253,2.756,2.388,2.249,0.984,0.587,0.607,0.458,0.719,0.475,0.52,0.48,0.587,0.512,0.772,0.669,0.476,0.522,0.359,0.584,0.536,0.925,0.583,0.67,0.726,1.005,0.596,0.633,0.507,0.533,0.586,0.428,0.481,0.634,0.706,0.975,0.449,0.666,0.591,0.523,0.795,0.796,0.431,0.42,0.558,0.864,0.762,0.763,0.425,0.414,0.57,0.803,0.686,0.541,0.393,0.705,0.567,0.531,0.475,0.454,0.47,0.724,1.07,0.769,0.635,0.701,0.522,0.926,0.587,0.656,0.48,0.439,0.395,0.644,0.468,0.821,0.516,0.406
-447,Epileptic,F,67.5,1.6,3.4,1.0,1.0,2.5,3.0,1.3,1.7,1.1,0.5,0.2,2.6,1.4,0.5,1.4,6.2,9.7,1.8,2.7,3.8,1.3,6.1,1.9,4.6,2.8,3.9,2.8,2.3,2.2,2.0,1.7,1.8,0.5,3.5,0.4,1.1,5.3,5.6,0.2,0.2,1.1,3.7,3.4,3.2,2.8,0.4,0.5,1.3,2.1,0.7,0.6,2.1,1.7,2.0,0.0,2.4,0.9,1.9,1.3,1.3,0.6,1.8,0.3,0.7,1.8,1.4,2.7,1.4,0.6,0.6,0.5,1.5,5.4,0.4,10,24,7,8,17,25,11,14,11,5,2.0,26,14,5,13,64,85,11,30,37,15,54,18,54,29,44,28,21,19,24,19,9,5,32,3,11,44,49,1,1,6,31,25,18,16,3,3,6,8,6,5,13,11,12,0,16,6,18,12,12,4,10,3,7,13,16,18,8,4,4,4,11,36,4,0.069,0.039,0.023,0.031,0.046,0.035,0.025,0.037,0.049,0.049,0.022,0.033,0.034,0.036,0.037,0.045,0.043,0.054,0.039,0.041,0.035,0.04,0.031,0.056,0.038,0.042,0.037,0.036,0.029,0.034,0.057,0.09,0.026,0.048,0.029,0.034,0.047,0.052,0.026,0.021,0.026,0.04,0.07,0.03,0.022,0.011,0.033,0.019,0.035,0.021,0.028,0.034,0.026,0.022,0.004,0.024,0.022,0.026,0.022,0.031,0.019,0.038,0.043,0.035,0.022,0.029,0.031,0.022,0.022,0.023,0.026,0.028,0.028,0.02,1276,4080,2026,1564,2339,3544,2222,2359,1559,913,539.0,2595,2774,661,1644,8507,14642,2150,3665,3211,2608,5462,1860,4243,3129,4982,4001,3116,4604,3574,2108,624,784,4980,1052,1471,5097,4661,229,297,1260,2447,4353,2452,3110,906,645,2126,1662,1385,476,2201,1834,3149,7,2878,1318,2280,1523,1388,913,1922,363,831,2308,1196,2885,2014,1122,890,883,1742,5469,451,0.161,0.121,0.105,0.122,0.155,0.126,0.099,0.135,0.147,0.157,0.094,0.129,0.126,0.129,0.132,0.148,0.145,0.164,0.14,0.142,0.114,0.134,0.122,0.14,0.123,0.139,0.113,0.105,0.103,0.138,0.148,0.153,0.096,0.129,0.104,0.108,0.143,0.14,0.112,0.101,0.113,0.141,0.199,0.126,0.102,0.082,0.112,0.089,0.123,0.111,0.143,0.129,0.117,0.103,0.047,0.115,0.105,0.119,0.114,0.118,0.099,0.13,0.166,0.137,0.106,0.13,0.119,0.107,0.104,0.11,0.118,0.115,0.118,0.12,361,1392,644,574,845,1347,841,822,440,233,162.0,1242,734,203,597,2477,3879,482,1309,1610,657,2197,931,1283,1189,1674,1375,1053,1274,1096,592,286,295,1180,303,606,1712,1845,148,161,659,1388,851,1640,1898,534,282,902,855,554,346,1062,961,1458,5,1540,680,1278,976,736,537,756,133,355,1270,773,1502,979,489,430,383,932,3058,322,3.336,2.688,2.913,2.486,2.401,2.219,2.194,2.544,2.695,3.071,2.835,1.81,2.813,2.418,2.209,2.525,2.854,3.085,2.302,1.742,2.883,2.104,1.885,2.603,2.222,2.43,2.345,2.25,2.718,2.47,2.506,2.18,2.123,2.8,2.703,2.264,2.314,2.016,1.897,2.094,2.335,1.531,3.125,1.683,1.821,1.857,2.736,2.806,2.501,2.689,1.554,2.221,1.888,2.349,1.379,2.027,2.238,1.898,1.726,1.945,2.002,2.514,2.776,2.432,1.923,2.067,2.142,2.331,2.453,2.342,2.553,2.206,1.916,1.728,0.852,0.742,0.656,0.755,0.681,0.688,0.536,0.536,0.536,0.696,0.639,0.439,0.472,0.692,0.599,0.654,0.684,0.963,0.601,0.523,0.851,0.675,0.501,0.815,0.519,0.634,0.604,0.54,0.426,0.641,0.883,0.897,0.458,0.678,0.625,0.522,0.78,0.69,0.473,0.487,0.576,0.59,0.977,0.492,0.581,0.411,0.579,0.912,0.545,0.562,0.331,0.45,0.538,0.466,0.119,0.498,0.481,0.575,0.393,0.543,0.409,0.638,0.819,0.83,0.484,0.742,0.549,0.39,0.4,0.677,0.754,0.533,0.462,0.332
-448,Epileptic,F,37.0,0.7,1.4,0.9,1.1,1.2,2.1,1.4,1.2,1.2,0.8,0.2,2.4,1.0,0.5,1.0,3.6,6.8,0.7,2.4,3.5,1.5,2.7,1.2,2.8,2.2,2.9,2.7,1.5,2.0,2.8,1.2,0.7,0.5,2.0,0.3,0.6,3.6,2.7,0.2,0.1,1.0,2.7,2.7,2.2,2.4,0.9,0.5,0.6,1.2,0.9,0.6,0.9,1.7,2.9,0.2,2.0,0.8,2.1,0.7,0.6,0.6,0.5,0.1,0.4,1.4,1.1,2.4,0.6,0.8,0.3,1.7,1.7,3.4,0.2,4,13,9,8,10,19,13,13,20,7,2.0,21,10,6,14,38,65,5,22,27,10,28,11,37,33,27,33,16,18,34,16,6,4,25,2,7,33,29,3,1,5,24,18,18,13,10,3,4,6,6,4,8,13,25,1,18,6,13,5,4,3,3,1,3,10,11,16,4,7,3,17,9,27,1,0.041,0.025,0.022,0.027,0.046,0.033,0.026,0.027,0.048,0.035,0.028,0.037,0.025,0.027,0.031,0.035,0.031,0.024,0.037,0.039,0.027,0.041,0.034,0.045,0.03,0.035,0.03,0.022,0.026,0.031,0.043,0.038,0.029,0.032,0.013,0.024,0.037,0.033,0.016,0.02,0.019,0.036,0.041,0.036,0.022,0.024,0.019,0.013,0.018,0.036,0.045,0.018,0.029,0.025,0.023,0.021,0.03,0.021,0.019,0.016,0.021,0.016,0.015,0.019,0.024,0.031,0.024,0.015,0.018,0.014,0.032,0.025,0.017,0.017,1396,3795,1784,1932,1471,3026,2759,2466,1887,1637,450.0,1957,2382,1053,2342,7104,14923,2237,3406,2376,2361,3492,1699,4687,4459,5422,5101,2799,4396,5274,1960,776,830,3892,1626,1574,6064,5046,512,508,1509,2380,3637,1457,2881,1664,879,1928,2341,1206,486,1778,2250,4534,250,3066,1184,2914,1017,1016,961,1448,212,879,2215,891,2746,1261,1731,736,1791,1786,7352,526,0.105,0.113,0.113,0.117,0.15,0.144,0.119,0.13,0.152,0.129,0.095,0.138,0.115,0.118,0.126,0.134,0.133,0.092,0.119,0.138,0.1,0.152,0.124,0.147,0.137,0.142,0.137,0.105,0.113,0.132,0.124,0.115,0.127,0.116,0.065,0.1,0.132,0.121,0.105,0.101,0.104,0.148,0.122,0.135,0.11,0.108,0.108,0.081,0.094,0.133,0.15,0.1,0.133,0.117,0.138,0.115,0.125,0.101,0.112,0.101,0.106,0.095,0.119,0.106,0.118,0.134,0.116,0.095,0.108,0.1,0.136,0.13,0.094,0.092,310,999,629,678,465,1011,864,814,485,369,119.0,926,692,306,647,1837,3794,413,1028,1399,824,1113,611,1083,1299,1339,1598,1004,1227,1613,470,387,279,969,406,514,1685,1327,206,154,694,1100,969,1021,1706,680,369,781,1003,442,272,819,841,1947,122,1489,495,1512,585,554,429,440,119,357,942,512,1501,589,706,309,887,903,3236,201,3.829,3.201,2.541,2.651,2.553,2.554,2.587,2.811,2.67,3.423,2.822,1.916,2.647,2.386,2.672,2.773,2.974,3.805,2.695,1.621,2.627,2.44,2.232,2.962,2.647,3.097,2.529,2.174,2.745,2.644,2.832,2.163,2.533,2.773,3.304,2.694,2.927,2.799,2.294,2.968,2.754,1.944,2.861,1.609,1.962,2.236,3.012,3.076,2.837,3.097,2.096,2.29,2.369,2.368,2.679,2.263,2.877,2.345,1.972,2.021,2.771,3.447,1.956,2.672,2.456,2.248,2.112,2.563,2.56,2.747,2.185,2.553,2.344,3.026,0.824,0.71,0.711,0.516,0.758,0.541,0.488,0.503,0.693,0.553,0.842,0.514,0.424,0.647,0.698,0.622,0.628,0.865,0.758,0.453,0.691,0.515,0.464,0.793,0.519,0.623,0.495,0.518,0.473,0.664,0.664,0.916,0.471,0.598,0.786,0.701,0.733,0.589,0.433,0.814,0.798,0.409,0.732,0.639,0.539,0.592,0.625,0.902,0.65,0.675,0.376,0.467,0.781,0.549,0.284,0.495,0.47,0.562,0.402,0.336,0.678,0.792,0.461,0.941,0.675,0.682,0.451,0.436,0.64,0.759,0.435,0.65,0.476,0.445
-449,Epileptic,F,67.5,0.8,2.3,0.7,1.4,1.6,1.8,1.7,1.6,1.2,0.5,0.2,3.0,1.3,0.5,1.1,6.4,9.0,0.7,2.4,3.5,1.2,2.8,1.1,3.6,2.2,3.1,2.0,2.7,2.3,3.1,1.3,0.9,0.3,2.0,0.5,0.4,3.0,2.7,0.2,0.2,1.3,2.8,2.4,2.4,2.3,0.6,0.4,1.0,1.0,0.7,0.6,1.9,1.0,2.2,0.2,2.1,0.4,1.6,0.8,0.8,0.2,1.4,0.3,0.3,1.8,0.7,3.0,1.4,1.4,0.7,1.3,1.0,3.0,0.3,7,21,7,11,13,18,13,13,16,6,2.0,27,15,6,13,68,76,6,24,31,14,33,13,34,29,30,20,28,20,34,14,6,3,20,3,4,29,27,2,1,7,25,24,21,14,4,2,6,6,5,4,14,9,16,2,19,5,15,6,5,2,10,4,4,12,10,27,9,10,6,7,9,24,2,0.028,0.024,0.013,0.023,0.035,0.029,0.023,0.027,0.039,0.028,0.028,0.036,0.029,0.033,0.027,0.036,0.031,0.028,0.035,0.034,0.025,0.035,0.027,0.043,0.028,0.032,0.023,0.027,0.022,0.03,0.036,0.035,0.014,0.027,0.015,0.015,0.037,0.033,0.015,0.016,0.025,0.036,0.036,0.025,0.017,0.011,0.017,0.015,0.013,0.024,0.032,0.021,0.022,0.021,0.032,0.021,0.014,0.027,0.021,0.014,0.02,0.028,0.034,0.015,0.02,0.02,0.023,0.02,0.023,0.022,0.033,0.02,0.016,0.018,1308,4097,2326,2221,2028,2349,2343,2616,1874,1046,444.0,2532,2717,933,2173,10329,15873,1727,3602,3318,3149,3772,1733,4858,4368,4588,3802,3775,5147,5007,2043,880,841,4162,1658,1200,5451,5298,385,427,1349,2564,5188,2508,3426,1382,786,2087,2244,1450,397,2988,1823,4186,310,3374,1100,2150,868,1310,432,1878,341,823,2782,949,3639,2251,1762,896,1479,1636,5820,431,0.115,0.122,0.085,0.109,0.149,0.152,0.095,0.112,0.154,0.132,0.107,0.147,0.135,0.147,0.135,0.148,0.132,0.111,0.145,0.136,0.106,0.153,0.123,0.144,0.133,0.135,0.111,0.115,0.096,0.141,0.142,0.102,0.083,0.122,0.074,0.087,0.138,0.134,0.115,0.087,0.121,0.143,0.145,0.117,0.092,0.079,0.098,0.081,0.086,0.102,0.143,0.11,0.106,0.105,0.132,0.111,0.093,0.13,0.123,0.092,0.099,0.128,0.164,0.109,0.106,0.102,0.121,0.104,0.117,0.131,0.134,0.119,0.094,0.102,442,1445,872,940,758,960,1006,943,543,313,155.0,1282,773,275,690,3124,4689,414,1142,1608,835,1329,764,1490,1345,1640,1400,1555,1467,1688,594,412,269,1059,486,497,1372,1362,212,213,730,1142,1194,1668,2118,780,347,977,1113,517,285,1415,883,1820,123,1622,579,1061,562,763,245,794,166,428,1341,497,2032,1082,947,493,640,811,2933,225,2.778,2.753,2.563,2.331,2.26,2.345,2.031,2.414,2.569,3.044,2.596,1.803,2.718,2.578,2.434,2.57,2.702,3.235,2.733,1.846,2.991,2.347,1.939,2.543,2.527,2.489,2.188,2.032,2.649,2.448,2.558,2.111,2.503,2.678,3.011,2.275,2.912,2.823,2.078,2.365,2.313,2.055,3.118,1.687,1.781,1.922,2.767,2.569,2.437,2.855,1.751,2.145,2.128,2.327,3.079,2.091,2.084,2.238,1.766,1.877,1.929,2.337,2.15,2.228,2.289,2.221,1.939,2.325,2.178,2.117,2.564,2.291,2.206,2.37,0.706,0.604,0.568,0.45,0.662,0.439,0.497,0.511,0.648,0.499,0.93,0.375,0.418,0.432,0.464,0.537,0.552,0.674,0.66,0.445,0.712,0.502,0.435,0.683,0.589,0.453,0.478,0.511,0.47,0.56,0.759,0.792,0.544,0.684,0.716,0.374,0.592,0.567,0.524,0.403,0.574,0.484,0.726,0.519,0.579,0.513,0.487,0.623,0.426,0.803,0.359,0.472,0.523,0.481,0.337,0.395,0.335,0.672,0.334,0.389,0.367,0.569,0.354,0.942,0.579,0.737,0.422,0.591,0.389,0.582,0.671,0.513,0.387,0.319
-450,Epileptic,M,27.0,1.6,2.9,1.4,1.7,2.6,2.4,2.8,2.3,1.7,1.7,0.6,4.3,2.0,1.1,1.8,9.6,14.3,2.7,2.5,6.1,2.5,4.4,2.7,4.7,5.2,6.1,4.4,5.4,4.3,5.1,2.1,1.2,0.8,3.1,0.8,1.3,3.6,3.2,0.2,0.3,1.4,4.1,3.6,4.5,3.8,1.4,0.4,0.7,1.3,0.9,0.5,2.6,1.7,4.4,0.3,3.2,1.0,3.0,1.3,1.5,1.0,1.7,0.3,0.8,3.4,1.3,3.3,2.0,1.8,0.6,1.9,2.3,5.3,0.4,13,31,13,16,17,21,23,24,13,16,10.0,39,25,10,23,103,125,15,29,50,24,45,26,51,64,71,55,58,38,59,30,8,10,33,6,12,46,43,1,2,9,37,37,35,24,14,3,4,7,7,3,17,11,36,2,25,10,29,8,12,6,14,2,6,31,23,28,13,12,7,15,15,43,3,0.052,0.029,0.016,0.032,0.049,0.036,0.046,0.028,0.058,0.053,0.042,0.048,0.039,0.069,0.043,0.047,0.039,0.054,0.032,0.043,0.041,0.048,0.045,0.052,0.048,0.034,0.035,0.041,0.038,0.045,0.065,0.04,0.037,0.037,0.036,0.019,0.04,0.033,0.016,0.021,0.023,0.043,0.056,0.029,0.026,0.036,0.021,0.01,0.018,0.014,0.03,0.028,0.027,0.026,0.013,0.02,0.021,0.027,0.022,0.023,0.025,0.032,0.028,0.027,0.032,0.025,0.025,0.027,0.032,0.02,0.029,0.023,0.02,0.02,1717,5737,3066,2859,2288,2908,3029,4642,2188,2582,928.0,3889,3665,1295,3349,14637,25990,3758,4863,5726,5224,5263,2844,6679,7670,11288,6081,5713,7510,6969,2362,1488,1326,7965,2079,2809,8589,8401,334,487,1980,3292,6996,4263,3786,1712,968,2232,2330,2367,358,3343,2101,6904,537,4496,2537,4913,1520,1811,1230,2758,595,1222,3127,1191,4918,2733,1868,1067,2603,3016,9436,580,0.16,0.127,0.102,0.136,0.155,0.133,0.149,0.134,0.178,0.166,0.168,0.177,0.151,0.167,0.163,0.162,0.144,0.16,0.139,0.164,0.14,0.149,0.158,0.173,0.169,0.141,0.14,0.139,0.136,0.165,0.154,0.121,0.141,0.14,0.105,0.108,0.154,0.137,0.105,0.114,0.113,0.153,0.176,0.125,0.115,0.132,0.103,0.072,0.095,0.092,0.137,0.118,0.123,0.116,0.096,0.109,0.114,0.129,0.11,0.12,0.123,0.124,0.113,0.115,0.145,0.12,0.118,0.123,0.129,0.11,0.133,0.119,0.107,0.119,549,1787,1270,1010,807,1214,1158,1437,668,611,278.0,1776,1016,309,860,4131,6678,839,1456,2665,1169,1780,1192,1899,2152,3366,2338,2334,2104,2232,790,512,406,1644,492,1092,1972,1827,197,230,985,1669,1347,2566,2243,669,356,907,1086,971,254,1597,1021,2837,308,2405,757,1916,886,988,621,1028,237,540,1514,876,2301,1184,957,519,1108,1409,3986,324,2.811,2.739,2.507,2.554,2.299,2.262,2.183,2.72,2.343,3.089,2.642,1.883,2.73,2.865,2.835,2.567,2.865,3.306,2.687,1.813,3.209,2.347,2.025,2.574,2.553,2.547,2.066,1.988,2.647,2.423,2.268,2.703,2.682,3.07,3.206,2.361,3.022,3.097,1.999,2.352,2.417,1.798,3.269,1.828,1.893,2.336,2.872,2.828,2.697,2.681,1.622,2.212,2.202,2.385,1.986,1.989,2.615,2.453,1.909,1.932,2.328,2.349,2.224,2.301,2.092,1.642,2.131,2.55,2.186,1.936,2.411,2.411,2.447,2.231,1.188,0.723,0.404,0.712,0.655,0.589,0.603,0.558,0.759,0.746,0.883,0.556,0.552,0.607,0.6,0.633,0.719,1.048,0.545,0.666,0.79,0.648,0.684,0.812,0.802,0.651,0.581,0.646,0.593,0.687,0.686,1.041,0.434,0.709,0.833,0.554,0.806,0.684,0.433,0.442,0.51,0.533,0.875,0.615,0.555,0.55,0.889,0.733,0.476,0.462,0.257,0.488,0.535,0.479,0.331,0.47,0.657,0.582,0.341,0.434,0.372,0.737,0.591,0.846,0.551,0.581,0.519,0.583,0.466,0.7,0.53,0.459,0.476,0.501
-451,Epileptic,F,42.0,0.9,1.3,0.7,0.8,1.1,1.6,1.3,1.4,1.0,0.6,0.2,2.9,1.2,0.5,0.9,6.1,8.6,0.8,1.7,4.0,0.7,2.3,1.5,2.6,2.1,2.9,4.1,2.0,2.4,2.6,0.8,0.5,0.5,2.0,0.3,0.6,2.7,2.5,0.1,0.1,1.0,3.4,1.9,2.5,2.4,0.5,0.3,0.8,1.3,0.5,0.3,1.4,1.8,2.4,0.2,2.6,0.7,1.3,1.3,0.9,0.7,1.3,0.3,0.3,2.3,1.5,2.1,1.7,0.8,0.6,1.3,1.2,4.9,0.2,7,16,6,6,11,13,12,15,13,7,1.0,23,11,6,11,56,74,6,19,37,8,26,13,30,28,29,40,18,23,30,12,6,4,23,2,6,27,27,2,1,7,30,17,22,15,4,1,5,7,5,2,12,14,22,1,19,6,10,8,8,4,10,1,3,17,16,21,13,6,6,9,8,41,3,0.041,0.021,0.015,0.018,0.028,0.031,0.02,0.026,0.043,0.035,0.024,0.036,0.03,0.031,0.026,0.036,0.03,0.03,0.028,0.035,0.018,0.028,0.027,0.038,0.026,0.034,0.03,0.026,0.025,0.027,0.038,0.048,0.019,0.025,0.01,0.018,0.032,0.029,0.018,0.013,0.019,0.036,0.039,0.028,0.021,0.012,0.016,0.013,0.018,0.016,0.042,0.023,0.027,0.026,0.025,0.021,0.026,0.017,0.033,0.018,0.037,0.029,0.028,0.018,0.025,0.032,0.021,0.023,0.02,0.025,0.033,0.026,0.023,0.016,1289,3184,1656,1794,1663,2725,2249,2559,1432,995,387.0,2592,2558,774,1994,8632,15412,2079,2953,3874,2233,3661,1843,4154,4465,4815,5831,3117,4983,4448,1732,713,997,4657,1477,1496,5321,4917,293,401,1459,2754,3526,2127,3238,1305,691,2133,2126,1273,334,2229,2313,4012,150,3799,1199,2418,1131,1244,578,1831,254,687,3052,986,3134,2394,1246,918,1463,1778,8359,413,0.141,0.111,0.102,0.097,0.13,0.132,0.098,0.136,0.156,0.157,0.085,0.141,0.131,0.15,0.124,0.142,0.13,0.114,0.123,0.141,0.082,0.135,0.121,0.139,0.13,0.145,0.131,0.11,0.107,0.129,0.127,0.125,0.095,0.125,0.068,0.096,0.129,0.125,0.095,0.085,0.113,0.145,0.133,0.13,0.101,0.083,0.094,0.089,0.099,0.101,0.149,0.111,0.128,0.122,0.15,0.113,0.134,0.099,0.129,0.11,0.147,0.131,0.134,0.122,0.119,0.136,0.118,0.118,0.112,0.151,0.145,0.131,0.116,0.101,358,1156,687,757,700,890,806,891,442,298,147.0,1215,696,246,616,2748,4574,451,968,1791,624,1250,868,1289,1489,1453,2233,1124,1430,1542,411,281,363,1148,395,550,1445,1367,191,202,735,1372,902,1537,1914,688,296,892,1055,539,159,1062,1078,1654,76,1904,443,1284,668,731,319,715,143,307,1501,654,1591,1152,616,384,632,787,3513,260,3.42,2.586,2.357,2.443,2.239,2.805,2.312,2.733,2.628,2.854,2.55,1.896,2.786,2.408,2.544,2.472,2.761,3.563,2.686,1.893,2.771,2.358,1.916,2.537,2.419,2.852,2.19,2.215,2.698,2.331,2.922,2.542,2.232,2.943,3.326,2.547,2.919,2.715,1.875,2.314,2.525,1.808,3.064,1.626,1.897,2.071,2.816,2.879,2.497,2.644,2.624,2.157,2.13,2.303,2.776,2.225,2.839,2.204,2.168,1.947,2.283,2.436,2.003,2.243,2.141,1.915,2.061,2.361,2.304,2.599,2.571,2.668,2.462,1.882,0.68,0.525,0.486,0.376,0.456,0.581,0.546,0.469,0.697,0.408,0.918,0.453,0.437,0.505,0.42,0.571,0.562,0.743,0.557,0.586,0.64,0.465,0.362,0.529,0.464,0.465,0.562,0.555,0.49,0.554,0.612,0.85,0.475,0.565,0.584,0.433,0.673,0.501,0.358,0.357,0.548,0.437,0.708,0.589,0.622,0.407,0.505,0.714,0.56,0.419,0.437,0.454,0.531,0.539,0.42,0.451,0.589,0.439,0.343,0.388,0.447,0.79,0.424,0.955,0.536,0.669,0.501,0.404,0.471,0.551,0.584,0.526,0.515,0.445
-452,Epileptic,F,37.0,1.1,2.6,1.0,1.3,1.7,2.7,1.5,1.8,1.3,0.7,0.4,3.4,2.6,0.6,1.9,6.0,11.0,0.8,2.6,5.1,0.7,4.0,2.2,3.7,4.1,3.9,4.2,3.2,3.1,4.3,1.1,1.0,0.4,2.4,0.5,0.5,3.8,4.2,0.2,0.3,1.2,2.9,2.3,3.2,2.7,1.2,0.6,0.9,1.4,1.0,1.0,2.8,1.2,2.5,0.3,2.7,1.2,2.1,1.5,1.4,0.8,2.0,0.4,0.5,2.8,1.4,3.6,1.8,1.7,0.5,1.1,1.7,6.2,0.4,8,23,7,10,17,23,15,18,13,7,5.0,31,27,10,18,67,99,6,28,49,6,43,21,42,49,47,46,33,24,47,18,7,4,27,3,6,41,48,2,3,8,26,24,22,16,8,3,7,8,9,6,20,9,15,3,20,9,15,9,14,7,17,2,4,25,16,30,9,14,6,6,13,56,3,0.039,0.023,0.014,0.021,0.029,0.037,0.019,0.028,0.039,0.042,0.031,0.033,0.036,0.027,0.037,0.033,0.029,0.026,0.03,0.033,0.015,0.037,0.029,0.036,0.036,0.026,0.028,0.028,0.024,0.032,0.029,0.032,0.018,0.031,0.014,0.014,0.033,0.034,0.015,0.026,0.021,0.039,0.039,0.027,0.02,0.018,0.019,0.013,0.016,0.029,0.033,0.027,0.021,0.022,0.017,0.018,0.036,0.024,0.03,0.02,0.021,0.027,0.026,0.017,0.024,0.033,0.024,0.023,0.023,0.015,0.025,0.019,0.021,0.014,1565,5228,2412,2410,2301,3380,2717,3407,1853,1205,624.0,2817,4031,1523,2947,10833,20550,2559,4517,5557,2287,5456,2419,6858,6410,7455,5982,4163,6310,6157,2356,1313,986,5998,1872,1815,6887,7852,463,501,1479,2653,6026,3550,3323,1802,1031,2485,2607,1519,762,3812,1855,3759,392,3698,1178,2735,1307,1808,989,2986,480,858,3076,1005,4162,2343,2300,1001,1396,2685,10116,550,0.127,0.112,0.086,0.103,0.137,0.15,0.098,0.126,0.145,0.14,0.141,0.139,0.149,0.143,0.15,0.137,0.126,0.104,0.132,0.141,0.081,0.147,0.128,0.14,0.152,0.128,0.127,0.115,0.091,0.14,0.126,0.106,0.089,0.127,0.071,0.08,0.137,0.139,0.099,0.141,0.107,0.139,0.156,0.118,0.099,0.103,0.105,0.084,0.092,0.131,0.143,0.117,0.112,0.111,0.13,0.106,0.137,0.107,0.133,0.122,0.121,0.128,0.125,0.115,0.12,0.129,0.123,0.107,0.115,0.111,0.113,0.107,0.111,0.109,502,1837,979,987,943,1204,1098,1149,538,340,226.0,1489,1221,372,853,3021,6231,491,1362,2389,693,1918,1120,1854,2096,2524,2333,1670,1715,2254,676,530,349,1390,507,658,1880,2151,234,193,850,1230,1222,2043,2037,911,448,1041,1299,562,460,1775,939,1748,255,2124,580,1475,776,1066,511,1169,240,459,1710,676,2223,1096,1159,528,677,1358,4839,326,3.051,2.66,2.406,2.384,2.133,2.284,2.106,2.648,2.539,2.872,2.118,1.732,2.652,2.999,2.571,2.683,2.682,3.815,2.725,2.002,2.58,2.251,1.88,2.771,2.425,2.5,2.132,2.041,2.721,2.234,2.589,2.48,2.241,2.957,3.336,2.439,2.668,2.789,2.184,2.63,2.097,1.888,3.315,1.909,1.848,2.159,2.866,2.783,2.395,2.816,2.073,2.225,2.137,2.257,1.848,1.856,2.417,2.105,1.834,1.83,2.095,2.505,2.357,2.168,1.854,1.875,1.966,2.37,2.063,2.353,2.23,2.293,2.222,2.135,0.795,0.54,0.596,0.476,0.544,0.532,0.529,0.485,0.62,0.515,0.647,0.381,0.482,0.374,0.616,0.629,0.604,0.76,0.508,0.548,0.707,0.461,0.489,0.628,0.471,0.469,0.481,0.532,0.629,0.439,0.656,0.97,0.453,0.626,0.615,0.51,0.627,0.545,0.314,0.642,0.448,0.481,0.736,0.578,0.564,0.562,0.42,0.788,0.429,0.512,0.54,0.501,0.568,0.531,0.289,0.444,0.543,0.468,0.384,0.392,0.388,0.605,0.45,0.758,0.441,0.581,0.513,0.462,0.467,0.666,0.524,0.467,0.482,0.362
-453,Epileptic,M,37.0,1.5,3.3,1.2,1.8,1.7,2.3,2.5,1.9,1.8,0.8,0.4,3.7,1.6,0.7,1.5,7.2,12.7,0.8,2.0,5.1,2.1,5.2,3.3,4.9,5.9,5.3,5.6,3.3,2.7,4.1,2.1,1.0,0.7,2.6,0.5,0.8,3.7,3.2,0.1,0.2,1.6,5.0,3.1,3.6,3.7,1.2,0.4,1.6,1.3,0.7,0.6,3.4,1.6,2.0,0.3,3.3,1.1,1.9,1.5,1.6,0.7,2.1,0.5,0.6,2.7,1.8,2.9,1.6,1.2,0.8,0.9,1.4,6.8,0.4,8,27,10,14,14,21,18,19,19,9,3.0,33,19,9,17,76,106,7,20,43,17,44,28,52,44,54,57,33,24,47,24,8,5,30,5,7,44,39,1,1,12,41,31,31,22,8,2,9,7,7,5,30,13,18,2,24,9,17,12,11,5,16,5,4,20,25,26,13,7,11,5,9,49,4,0.047,0.029,0.017,0.024,0.035,0.03,0.042,0.03,0.052,0.029,0.032,0.036,0.026,0.03,0.031,0.035,0.042,0.03,0.036,0.045,0.031,0.041,0.034,0.047,0.044,0.029,0.031,0.033,0.027,0.034,0.051,0.046,0.029,0.032,0.022,0.015,0.038,0.031,0.011,0.022,0.028,0.043,0.044,0.027,0.023,0.019,0.017,0.021,0.016,0.014,0.025,0.023,0.027,0.02,0.019,0.017,0.024,0.022,0.028,0.023,0.024,0.031,0.032,0.023,0.025,0.023,0.018,0.02,0.022,0.022,0.021,0.019,0.024,0.034,1641,5440,2664,2995,2299,3483,2815,3531,2008,1747,726.0,3570,3954,1558,2921,11590,17679,2151,3536,4627,5093,3621,2582,6586,5220,8471,7439,4328,6183,6272,2578,999,1006,5867,1609,1879,7011,7763,378,385,1879,2921,6377,3471,3869,1780,742,2527,2311,1884,578,5072,2123,4164,382,5293,1603,2718,1387,1658,1060,2607,537,769,3454,1653,4572,2744,1582,1133,1263,2202,8866,351,0.119,0.126,0.097,0.115,0.135,0.128,0.131,0.135,0.182,0.133,0.125,0.154,0.122,0.142,0.133,0.133,0.15,0.118,0.136,0.167,0.122,0.128,0.13,0.163,0.132,0.129,0.114,0.116,0.104,0.136,0.156,0.113,0.103,0.142,0.094,0.086,0.154,0.139,0.087,0.11,0.13,0.149,0.179,0.121,0.109,0.104,0.094,0.102,0.096,0.092,0.119,0.12,0.113,0.104,0.11,0.103,0.124,0.116,0.122,0.106,0.099,0.133,0.133,0.115,0.119,0.121,0.1,0.103,0.107,0.121,0.11,0.107,0.111,0.154,479,1916,1022,1134,899,1213,1113,1113,671,446,203.0,1700,1077,391,857,3703,5580,496,1010,2104,1089,1636,1478,1927,1822,2704,3027,1648,1673,2258,723,436,360,1416,400,818,1799,1835,210,187,972,1699,1395,2130,2314,880,324,1142,1128,784,371,2354,1112,1801,217,2747,696,1359,922,996,528,1115,308,427,1715,1139,2478,1327,837,641,648,1022,4157,218,3.252,2.654,2.495,2.546,2.295,2.391,2.163,2.645,2.436,2.952,2.996,1.89,2.784,2.773,2.66,2.406,2.539,3.415,2.747,1.906,3.312,1.944,1.635,2.676,2.38,2.53,2.047,2.118,2.769,2.254,2.597,2.367,2.31,2.997,3.312,2.283,2.936,3.071,2.08,2.328,2.367,1.616,3.303,1.886,1.929,2.228,2.76,2.923,2.516,2.564,1.85,2.098,2.011,2.278,2.142,2.141,2.497,2.262,1.764,1.83,2.236,2.414,2.105,1.915,2.146,1.78,1.915,2.278,2.225,1.969,2.314,2.49,2.293,2.02,0.545,0.539,0.51,0.585,0.576,0.578,0.531,0.477,0.608,0.706,1.142,0.497,0.432,0.506,0.549,0.608,0.569,0.831,0.584,0.686,0.882,0.584,0.508,0.695,0.626,0.551,0.522,0.596,0.5,0.63,0.634,0.776,0.48,0.627,0.613,0.489,0.628,0.658,0.352,0.415,0.447,0.525,0.722,0.466,0.597,0.508,0.456,0.614,0.416,0.361,0.352,0.446,0.46,0.459,0.307,0.41,0.463,0.576,0.452,0.43,0.354,0.664,0.383,0.519,0.531,0.744,0.526,0.363,0.422,0.47,0.384,0.418,0.418,0.439
-454,Epileptic,M,37.0,0.4,0.7,0.5,0.8,1.3,0.9,1.2,3.7,0.7,0.6,1.0,2.1,2.0,0.6,1.0,6.6,7.0,1.5,2.4,4.4,0.8,3.9,1.0,3.8,3.1,7.8,3.3,3.9,5.5,3.1,0.5,0.8,0.6,3.4,0.5,0.7,2.6,4.4,0.6,0.1,1.6,2.8,3.3,2.0,3.4,0.6,0.4,0.7,0.8,0.6,0.4,2.3,1.3,1.1,0.2,2.8,0.9,1.2,1.0,0.5,1.0,0.6,0.1,0.2,1.3,1.1,4.9,1.9,1.9,0.3,1.7,1.2,8.3,0.3,4,6,5,5,7,6,14,30,5,7,4.0,13,12,4,8,297,58,9,21,34,7,23,7,29,18,56,23,30,40,30,3,4,5,32,6,3,21,39,1,1,8,16,25,11,16,4,2,4,5,6,2,16,8,7,1,19,6,11,6,4,5,3,1,1,12,10,24,10,8,1,8,7,40,2,0.029,0.014,0.013,0.022,0.059,0.032,0.043,0.047,0.039,0.03,0.058,0.045,0.045,0.052,0.094,0.168,0.036,0.055,0.034,0.053,0.023,0.055,0.044,0.065,0.05,0.071,0.038,0.05,0.056,0.053,0.025,0.032,0.038,0.058,0.043,0.025,0.042,0.057,0.057,0.029,0.031,0.052,0.051,0.034,0.037,0.026,0.033,0.012,0.014,0.028,0.027,0.037,0.038,0.02,0.045,0.048,0.022,0.025,0.04,0.037,0.038,0.029,0.051,0.019,0.041,0.032,0.054,0.032,0.04,0.025,0.042,0.035,0.043,0.027,1269,2056,1434,1810,950,2492,2549,3256,1152,804,690.0,2813,2772,766,1966,6597,16229,1655,2977,4813,2086,4687,1520,3965,3986,4579,4618,3479,5915,4686,1507,875,1093,3099,1301,1110,3414,3622,273,386,2014,2955,2918,2097,3070,1435,788,2154,2533,909,398,2865,1776,3110,274,2367,984,1986,1081,983,968,908,192,578,1694,1191,3653,2491,1866,818,1360,1634,6449,625,0.087,0.094,0.099,0.102,0.17,0.102,0.138,0.142,0.135,0.129,0.184,0.135,0.141,0.181,0.127,0.146,0.115,0.147,0.132,0.146,0.101,0.156,0.141,0.169,0.14,0.178,0.129,0.151,0.138,0.152,0.105,0.102,0.128,0.159,0.127,0.098,0.133,0.153,0.144,0.096,0.126,0.155,0.158,0.133,0.124,0.104,0.111,0.084,0.087,0.136,0.132,0.128,0.136,0.096,0.138,0.138,0.12,0.128,0.136,0.118,0.133,0.109,0.137,0.078,0.124,0.125,0.148,0.119,0.132,0.101,0.155,0.136,0.146,0.111,307,759,505,547,342,592,595,1117,288,360,294.0,813,720,188,401,1924,3277,391,1012,1504,655,1196,438,948,1189,1765,1412,1327,1690,1200,347,365,297,968,279,463,1080,1231,153,117,846,1010,960,1131,1633,444,241,856,1047,473,241,1003,549,1096,73,1180,608,960,494,368,442,361,84,187,719,587,1477,1004,846,180,628,586,3192,244,3.704,2.404,2.43,3.018,2.426,2.939,2.859,2.485,2.708,2.227,2.561,2.482,2.867,2.536,2.894,2.497,3.244,3.494,2.367,2.52,2.732,2.628,2.556,2.821,2.579,2.344,2.361,1.964,2.579,2.718,3.265,2.439,2.806,2.214,3.365,2.306,2.567,2.27,2.401,3.066,2.949,2.353,2.43,2.228,2.238,2.69,3.944,2.996,2.97,2.299,2.277,2.475,2.885,2.882,3.341,2.096,1.977,2.311,2.612,2.535,2.093,2.518,2.22,2.862,2.221,2.303,2.271,2.622,2.558,3.687,2.492,2.917,2.28,3.028,0.988,0.962,0.913,0.771,0.531,0.956,1.012,1.112,0.827,0.63,0.677,0.695,0.819,0.765,0.915,0.773,1.059,0.853,0.665,0.802,0.726,0.817,0.793,1.131,0.696,0.973,0.826,0.846,0.829,0.79,1.068,1.097,0.708,0.937,0.712,0.669,1.016,1.053,0.569,0.793,0.99,0.601,0.912,0.628,0.593,0.859,0.683,0.909,0.756,0.53,0.458,0.707,0.751,0.924,0.727,0.539,0.424,0.633,0.849,0.697,0.734,0.725,0.522,1.016,0.618,0.566,0.692,0.749,0.712,1.135,0.718,1.17,0.701,0.831
-455,Epileptic,F,42.0,1.1,2.1,0.8,0.8,1.4,1.9,1.4,1.4,1.3,0.7,0.3,2.3,1.6,0.5,1.1,5.6,7.9,1.5,1.6,3.4,1.6,2.2,1.2,3.4,3.9,2.7,3.2,2.6,3.3,4.1,1.2,1.5,0.5,2.1,0.3,0.7,3.2,3.1,0.2,0.3,0.9,2.1,2.2,2.0,2.8,0.5,0.4,0.8,1.4,0.9,0.6,1.6,1.4,2.1,0.5,2.2,0.5,1.8,0.6,1.1,0.5,1.5,0.1,0.7,1.2,1.1,2.6,1.1,1.3,0.5,1.3,1.4,3.4,0.4,7,18,6,7,16,18,11,13,13,7,3.0,22,16,5,12,52,65,10,19,28,13,25,10,34,35,24,31,19,22,42,17,12,3,20,2,5,37,29,1,1,5,18,19,17,17,3,2,6,7,7,4,10,10,13,3,16,4,16,5,8,6,9,1,5,8,12,15,6,7,5,9,11,25,3,0.045,0.026,0.017,0.019,0.033,0.032,0.027,0.031,0.043,0.034,0.031,0.028,0.03,0.034,0.029,0.035,0.03,0.057,0.025,0.031,0.028,0.034,0.028,0.04,0.034,0.027,0.029,0.031,0.029,0.032,0.043,0.049,0.024,0.027,0.013,0.019,0.037,0.032,0.016,0.021,0.02,0.03,0.042,0.024,0.022,0.01,0.019,0.015,0.018,0.026,0.025,0.02,0.022,0.02,0.03,0.015,0.014,0.021,0.019,0.017,0.023,0.03,0.023,0.032,0.019,0.031,0.019,0.017,0.019,0.025,0.027,0.024,0.016,0.024,1674,4109,1807,1633,2242,2799,2288,2400,1595,1396,555.0,2191,3297,1068,2760,11111,16701,2192,4088,3751,3234,3684,1916,5533,6575,5341,5806,3622,5582,6453,2385,1187,1003,4607,1514,1752,5495,6368,345,451,1316,2146,4206,2093,3342,1244,731,1994,2383,1238,489,2653,2410,4368,640,3935,1002,2869,780,1789,1093,2148,262,892,1885,1038,3878,2083,1656,854,1925,2171,8661,462,0.124,0.121,0.104,0.101,0.139,0.145,0.103,0.138,0.156,0.135,0.124,0.129,0.135,0.126,0.125,0.129,0.125,0.18,0.109,0.123,0.109,0.142,0.112,0.137,0.137,0.122,0.122,0.115,0.103,0.133,0.166,0.137,0.112,0.112,0.083,0.088,0.138,0.121,0.09,0.101,0.107,0.129,0.151,0.124,0.106,0.075,0.095,0.081,0.102,0.125,0.144,0.103,0.105,0.092,0.136,0.092,0.094,0.114,0.12,0.1,0.12,0.122,0.107,0.139,0.108,0.141,0.097,0.09,0.099,0.122,0.123,0.12,0.089,0.119,437,1283,694,688,769,1027,863,813,483,372,166.0,1044,935,279,684,2826,4527,434,1134,1620,927,1182,705,1469,1947,1613,1915,1285,1582,2068,522,460,313,1130,381,602,1654,1559,202,229,658,1007,964,1351,2042,697,317,871,1083,572,310,1253,984,1755,300,2112,519,1451,467,968,425,773,86,326,964,530,1939,1002,950,398,851,974,3547,228,3.437,2.884,2.548,2.395,2.347,2.417,2.284,2.92,2.539,3.404,3.074,1.814,2.783,2.755,2.905,2.812,2.887,3.477,2.914,2.002,2.817,2.526,2.216,2.879,2.637,2.744,2.364,2.253,2.638,2.497,3.1,2.44,2.645,2.78,3.485,2.641,2.597,3.077,1.985,2.402,2.598,1.958,3.218,1.763,1.859,1.953,2.901,2.659,2.73,2.392,2.003,2.25,2.352,2.469,2.755,2.063,2.401,2.279,1.927,2.147,2.34,2.78,2.643,2.825,2.224,2.576,2.165,2.475,2.051,2.47,2.414,2.501,2.685,2.608,0.893,0.681,0.514,0.439,0.695,0.583,0.597,0.632,0.568,0.681,0.612,0.379,0.406,0.694,0.546,0.735,0.659,0.801,0.546,0.575,0.73,0.503,0.434,0.682,0.543,0.52,0.517,0.516,0.588,0.507,0.839,0.917,0.458,0.668,0.745,0.547,0.617,0.593,0.296,0.374,0.481,0.487,0.771,0.589,0.545,0.4,0.477,0.653,0.488,0.486,0.388,0.586,0.637,0.463,0.265,0.404,0.665,0.592,0.462,0.5,0.534,0.673,1.008,0.637,0.59,0.79,0.448,0.451,0.334,0.73,0.645,0.727,0.635,0.303
-459,Epileptic,M,17.5,0.8,2.1,0.9,0.7,1.7,5.8,2.3,1.5,1.1,0.4,0.4,2.7,1.0,0.5,0.6,4.3,8.1,0.8,3.4,3.7,1.4,3.4,3.0,4.2,2.2,5.1,5.5,3.3,2.2,2.6,1.1,1.0,0.5,2.6,0.4,0.8,3.7,2.7,0.1,0.1,1.2,5.2,4.0,2.2,3.5,1.1,0.6,1.0,1.1,0.6,0.8,1.3,1.1,2.2,0.3,2.3,1.3,1.4,1.2,1.3,0.7,1.7,0.2,0.5,2.1,1.5,3.7,1.1,1.0,0.4,0.7,1.7,6.0,0.6,6,25,8,9,15,45,18,18,9,4,4.0,19,12,7,6,48,67,6,33,25,16,27,21,42,25,42,47,25,24,23,16,8,6,29,2,8,44,38,1,1,8,34,28,18,24,7,3,5,6,6,5,13,6,15,2,16,8,11,12,12,5,13,2,5,17,16,21,10,6,4,6,11,45,5,0.034,0.025,0.017,0.016,0.035,0.069,0.029,0.022,0.044,0.025,0.032,0.046,0.025,0.042,0.04,0.038,0.032,0.026,0.038,0.052,0.025,0.031,0.055,0.038,0.03,0.032,0.035,0.03,0.025,0.029,0.024,0.045,0.026,0.03,0.015,0.018,0.031,0.027,0.012,0.01,0.022,0.058,0.048,0.037,0.018,0.026,0.02,0.014,0.014,0.012,0.061,0.025,0.02,0.02,0.026,0.02,0.034,0.02,0.029,0.036,0.029,0.025,0.018,0.015,0.031,0.024,0.022,0.022,0.021,0.014,0.016,0.023,0.025,0.025,1461,4717,2087,1909,2934,3385,4289,4474,1426,1074,348.0,1582,2555,1207,1304,8958,17909,2388,5729,2020,4271,3803,1769,7194,3800,9215,5211,6258,6349,3937,2715,1186,1224,7259,1932,2363,7800,7075,363,263,1952,2836,7750,1899,6730,1838,956,2824,2429,1712,379,2494,1934,4522,407,2927,1602,2230,936,676,631,3609,515,983,2291,1535,4919,1825,1940,1006,1150,2318,8255,817,0.108,0.118,0.105,0.096,0.135,0.177,0.11,0.119,0.138,0.122,0.136,0.151,0.122,0.171,0.149,0.147,0.129,0.114,0.148,0.172,0.1,0.124,0.188,0.14,0.119,0.122,0.124,0.108,0.113,0.117,0.103,0.128,0.138,0.134,0.077,0.099,0.138,0.121,0.092,0.066,0.112,0.178,0.156,0.143,0.102,0.113,0.113,0.084,0.089,0.097,0.162,0.119,0.103,0.101,0.128,0.107,0.125,0.096,0.142,0.147,0.138,0.119,0.11,0.112,0.136,0.122,0.102,0.117,0.103,0.103,0.11,0.118,0.118,0.132,444,1525,792,745,860,1886,1234,1177,407,290,175.0,920,720,267,316,2182,4369,476,1584,1120,1006,1598,890,1766,1168,2631,2399,1691,1612,1472,763,450,370,1528,463,829,2078,1848,155,133,894,1454,1474,985,2919,735,391,1042,1097,750,264,915,796,1784,184,1764,675,1151,723,564,399,1095,202,522,1156,827,2662,773,735,489,619,1143,3651,350,3.158,2.664,2.459,2.387,2.64,1.661,2.804,3.104,2.648,3.13,2.206,1.678,2.86,3.271,3.208,2.907,3.098,3.613,2.852,1.802,3.176,1.938,1.763,2.916,2.568,2.898,1.896,2.868,2.973,2.251,2.647,2.626,2.757,3.441,3.978,2.672,2.8,2.848,2.357,2.238,2.569,1.612,3.458,2.071,2.473,2.571,3.151,3.209,2.652,2.527,1.511,2.501,2.56,2.583,2.63,1.82,2.517,2.088,1.454,1.345,1.727,2.94,2.728,2.235,1.854,2.379,2.031,2.215,2.686,2.389,2.029,2.38,2.426,2.684,0.951,0.785,0.76,0.467,0.601,0.62,0.63,0.556,0.562,0.442,0.736,0.531,0.547,0.378,0.625,0.63,0.758,0.95,0.773,0.609,0.846,0.618,0.624,0.769,0.686,0.627,0.552,0.725,0.542,0.605,0.798,0.899,0.374,0.662,0.582,0.52,0.794,0.66,0.327,0.39,0.587,0.574,0.819,0.838,0.509,0.542,0.574,0.733,0.469,0.463,0.336,0.605,0.709,0.588,0.438,0.508,0.69,0.714,0.378,0.302,0.471,0.786,0.504,0.788,0.643,0.796,0.565,0.579,0.607,0.552,0.552,0.587,0.639,0.404
-460,Epileptic,M,47.0,0.8,1.9,0.8,1.5,2.7,2.6,2.4,2.2,1.2,0.8,0.4,2.8,2.3,0.8,1.9,7.2,11.0,0.9,2.7,4.4,1.1,3.0,1.4,4.5,4.5,3.5,3.7,4.0,3.6,3.4,1.5,1.9,0.5,3.5,0.5,0.9,3.7,4.1,0.2,0.5,1.0,2.5,3.1,2.4,3.4,0.7,0.5,0.7,1.6,0.4,1.0,1.9,2.0,3.4,0.5,2.0,0.7,2.5,0.8,1.3,1.0,1.5,0.3,0.5,2.0,1.1,2.9,1.6,1.4,0.7,1.4,1.4,6.0,0.3,6,15,7,10,18,22,14,16,8,7,3.0,24,20,6,12,58,84,5,24,35,8,34,12,40,40,29,32,31,24,30,14,10,4,24,4,7,38,40,2,2,5,20,24,16,16,5,2,5,8,5,7,11,12,19,3,13,4,44,6,10,8,9,2,4,16,10,17,8,6,5,8,11,44,2,0.036,0.025,0.018,0.029,0.062,0.036,0.035,0.036,0.051,0.037,0.032,0.033,0.043,0.044,0.043,0.05,0.043,0.035,0.036,0.037,0.023,0.038,0.028,0.048,0.042,0.039,0.036,0.044,0.042,0.036,0.043,0.065,0.034,0.048,0.024,0.03,0.041,0.038,0.018,0.028,0.02,0.031,0.057,0.03,0.027,0.017,0.022,0.013,0.02,0.023,0.04,0.025,0.033,0.029,0.029,0.016,0.017,0.03,0.018,0.025,0.025,0.029,0.026,0.024,0.023,0.031,0.021,0.023,0.034,0.024,0.031,0.022,0.022,0.025,1322,3825,1835,1887,2467,3723,2379,2582,1487,1293,492.0,2393,3211,1194,2428,9756,14777,2308,4266,4140,2196,4436,1745,6196,6461,4701,4777,3673,4091,5134,2554,1058,654,4824,1416,1689,6642,8049,427,368,1375,2353,4825,1988,3055,1189,801,1683,2617,1183,838,2502,2240,3929,463,3550,1490,2606,1091,1445,1624,2416,365,876,2703,708,3544,2100,1509,1296,1503,2442,10054,443,0.114,0.115,0.1,0.121,0.175,0.145,0.116,0.132,0.14,0.138,0.106,0.142,0.149,0.142,0.137,0.154,0.14,0.117,0.14,0.137,0.093,0.142,0.113,0.15,0.144,0.134,0.118,0.135,0.126,0.139,0.141,0.157,0.129,0.139,0.101,0.107,0.145,0.138,0.101,0.122,0.103,0.126,0.16,0.124,0.113,0.102,0.1,0.088,0.1,0.104,0.156,0.109,0.125,0.119,0.123,0.092,0.099,0.131,0.113,0.119,0.12,0.116,0.134,0.139,0.118,0.123,0.106,0.108,0.12,0.12,0.132,0.121,0.108,0.132,379,1257,722,837,813,1278,971,1031,438,382,185.0,1226,983,327,750,2647,4658,443,1288,1844,741,1431,789,1683,1976,1560,1748,1514,1414,1626,631,445,219,1261,428,555,1553,1975,215,224,708,1227,989,1331,2027,614,338,817,1187,414,432,1232,1031,1749,286,1879,651,1462,681,850,669,860,189,376,1401,461,2023,1023,680,465,730,1058,4419,185,3.387,2.665,2.508,2.258,2.669,2.579,2.169,2.385,2.566,2.877,2.442,1.82,2.519,2.718,2.451,2.671,2.676,3.871,2.809,1.988,2.394,2.443,1.92,2.886,2.605,2.482,2.152,2.008,2.322,2.623,2.903,2.45,2.625,2.823,3.04,2.654,3.05,2.921,2.349,2.211,2.418,1.8,3.253,1.808,1.743,2.041,2.782,2.466,2.664,3.097,2.266,2.188,2.369,2.326,2.035,2.069,2.492,2.049,1.815,1.969,2.505,2.992,2.613,2.57,2.215,1.85,1.995,2.341,2.576,2.98,2.436,2.769,2.473,2.916,0.653,0.752,0.551,0.469,0.533,0.65,0.517,0.579,0.549,0.508,0.677,0.481,0.542,0.621,0.476,0.691,0.61,0.788,0.798,0.691,0.769,0.563,0.409,0.79,0.553,0.583,0.514,0.52,0.558,0.54,0.799,0.875,0.475,0.779,0.819,0.567,0.667,0.654,0.452,0.463,0.843,0.463,0.854,0.672,0.468,0.558,0.663,0.696,0.597,0.923,0.527,0.452,0.501,0.492,0.338,0.553,0.643,0.702,0.417,0.391,0.534,0.731,0.47,0.882,0.575,0.579,0.536,0.462,0.506,0.881,0.46,0.588,0.502,0.435
-461,Epileptic,M,42.0,1.0,1.5,1.0,0.8,2.1,2.1,1.9,1.4,0.6,0.5,0.4,3.5,1.4,1.1,1.4,6.0,10.1,0.7,1.7,6.0,1.4,2.9,2.1,5.2,2.8,4.3,4.1,2.6,3.0,4.1,2.1,0.8,0.8,3.8,0.6,0.7,5.3,4.6,0.3,0.3,1.0,3.6,3.5,3.0,2.5,0.5,0.8,1.0,1.3,1.1,0.6,1.9,3.0,2.4,0.2,2.3,0.6,2.3,1.0,1.3,0.5,1.8,0.4,0.7,1.4,1.0,2.5,1.3,1.0,0.6,1.3,2.1,4.2,0.4,5,10,10,6,17,17,12,11,5,5,4.0,25,13,11,12,52,75,4,13,44,15,31,19,39,27,39,43,21,32,33,18,6,6,27,3,4,49,38,2,2,6,28,23,22,13,4,3,5,5,8,3,11,15,13,1,17,4,20,7,12,5,12,2,3,11,8,14,8,4,4,9,10,27,4,0.03,0.02,0.019,0.017,0.06,0.034,0.029,0.031,0.046,0.028,0.034,0.035,0.03,0.045,0.037,0.041,0.04,0.027,0.029,0.034,0.027,0.032,0.032,0.051,0.033,0.04,0.034,0.029,0.032,0.035,0.047,0.039,0.029,0.041,0.019,0.021,0.052,0.038,0.017,0.025,0.024,0.035,0.047,0.026,0.02,0.012,0.031,0.015,0.016,0.026,0.025,0.023,0.043,0.023,0.035,0.02,0.025,0.027,0.027,0.022,0.03,0.036,0.028,0.026,0.02,0.027,0.02,0.025,0.018,0.023,0.025,0.034,0.019,0.024,1723,4140,2337,1995,2013,3299,2633,2751,1176,1231,647.0,3257,2879,1831,2396,9792,17384,2347,2439,5632,3434,4893,2477,6028,5103,7344,6271,3365,4627,6577,2763,1324,1105,5505,1698,1421,7099,6186,476,387,1677,2929,4464,3562,3123,1094,1026,2449,2732,1507,633,2796,2545,3989,248,3363,1036,3459,1163,1827,627,2177,427,935,2570,1024,3758,1951,1659,1409,2035,1990,8200,397,0.103,0.097,0.1,0.086,0.147,0.135,0.107,0.11,0.109,0.117,0.122,0.138,0.114,0.138,0.112,0.127,0.124,0.098,0.114,0.125,0.106,0.132,0.133,0.135,0.134,0.132,0.118,0.107,0.11,0.126,0.145,0.115,0.118,0.122,0.081,0.092,0.143,0.12,0.1,0.115,0.103,0.135,0.132,0.119,0.101,0.083,0.114,0.087,0.09,0.121,0.115,0.1,0.119,0.096,0.146,0.104,0.116,0.128,0.124,0.116,0.12,0.122,0.115,0.109,0.104,0.11,0.097,0.114,0.097,0.11,0.113,0.134,0.098,0.115,520,1153,822,720,707,1044,1030,803,269,330,221.0,1480,828,459,676,2479,4367,443,867,2492,947,1435,1055,1740,1362,2059,2144,1313,1333,1914,708,424,391,1348,509,522,1955,1638,198,189,746,1475,1257,1937,1983,627,426,1023,1137,724,342,1384,995,1663,97,1828,432,1497,633,926,338,869,248,382,1226,551,1865,881,797,450,895,990,3478,255,2.978,2.957,2.626,2.692,2.336,2.611,2.166,3.041,2.97,3.079,2.709,1.955,2.654,2.932,2.639,2.76,3.014,3.73,2.428,1.979,2.888,2.506,2.074,2.555,2.672,2.786,2.308,2.077,2.617,2.588,2.702,2.963,2.344,2.732,3.029,2.51,2.751,2.789,2.528,2.497,2.715,1.959,2.792,2.098,1.839,1.923,3.045,2.945,2.98,2.558,2.407,2.158,2.262,2.609,2.644,2.056,2.765,2.65,2.313,2.09,2.38,2.53,2.073,2.839,2.246,2.183,2.204,2.537,2.514,3.126,2.388,2.633,2.654,1.986,1.2,0.909,0.679,0.672,0.632,0.601,0.594,0.687,0.79,0.541,0.458,0.494,0.564,0.766,0.606,0.778,0.749,0.985,0.538,0.562,0.882,0.503,0.407,0.85,0.62,0.668,0.578,0.543,0.571,0.68,1.104,1.202,0.495,0.816,1.12,0.662,0.864,0.815,0.442,0.701,0.757,0.581,0.848,0.662,0.516,0.488,0.791,0.946,0.731,0.697,0.541,0.584,0.64,0.692,0.525,0.538,0.384,0.718,0.524,0.378,0.482,0.78,0.521,1.083,0.572,0.681,0.554,0.527,0.528,0.822,0.582,0.643,0.657,0.409
-462,Epileptic,F,17.5,0.9,2.0,0.7,1.0,1.6,1.8,1.3,1.4,0.9,0.6,0.2,2.6,1.4,0.5,1.4,5.7,7.7,0.6,2.6,4.6,1.1,2.9,1.5,3.4,3.9,3.5,2.7,2.5,2.3,3.2,1.5,0.5,0.5,3.2,0.4,0.8,3.3,2.9,0.1,0.2,0.9,2.7,2.4,4.7,2.2,0.5,0.4,1.1,1.1,0.6,0.8,1.8,1.7,2.8,0.4,2.4,1.1,1.2,0.8,1.0,0.6,0.9,0.3,0.4,1.7,1.5,3.0,1.2,0.9,0.7,0.9,1.5,4.7,0.1,8,18,8,11,16,14,11,15,13,7,2.0,26,16,6,14,58,73,6,31,44,14,27,17,34,47,33,28,21,18,34,23,4,4,33,2,8,35,36,1,2,5,26,28,27,14,5,2,8,6,5,5,13,12,17,3,22,8,12,7,8,5,9,2,4,14,20,25,8,6,10,6,10,39,1,0.035,0.019,0.011,0.021,0.028,0.031,0.022,0.026,0.041,0.024,0.02,0.031,0.024,0.026,0.026,0.029,0.026,0.024,0.03,0.031,0.017,0.036,0.027,0.032,0.03,0.028,0.027,0.025,0.021,0.027,0.036,0.023,0.035,0.032,0.011,0.025,0.035,0.026,0.011,0.018,0.02,0.031,0.036,0.037,0.019,0.014,0.018,0.019,0.013,0.012,0.038,0.016,0.02,0.02,0.014,0.021,0.028,0.015,0.023,0.018,0.03,0.018,0.017,0.015,0.023,0.032,0.021,0.018,0.017,0.022,0.021,0.023,0.019,0.021,1434,4579,1851,2037,2620,2516,2198,3051,1714,1592,537.0,2782,4010,1272,3241,13409,20237,2178,5030,4664,3558,3857,2167,6722,6775,6564,5333,3753,5878,5617,1976,836,912,6382,1967,1864,5830,7495,390,651,1422,2673,4868,2780,2854,1213,953,2133,2744,1517,536,4321,3395,5651,943,3649,1485,2845,854,1313,680,2185,518,834,2581,1052,4971,2600,1928,1380,1461,2306,9248,520,0.144,0.112,0.095,0.111,0.133,0.141,0.109,0.125,0.141,0.116,0.105,0.129,0.121,0.123,0.126,0.131,0.123,0.095,0.137,0.135,0.095,0.142,0.137,0.136,0.144,0.125,0.128,0.117,0.098,0.13,0.133,0.079,0.131,0.126,0.06,0.101,0.135,0.124,0.092,0.103,0.109,0.137,0.142,0.143,0.096,0.098,0.094,0.089,0.085,0.098,0.152,0.095,0.105,0.096,0.097,0.114,0.126,0.093,0.128,0.111,0.146,0.101,0.102,0.1,0.112,0.141,0.115,0.097,0.095,0.128,0.112,0.118,0.104,0.073,476,1597,804,819,882,1039,874,961,507,399,191.0,1344,957,324,887,3402,4982,444,1428,2393,990,1318,950,1699,2103,2046,1716,1456,1535,1959,628,393,261,1456,553,600,1598,1798,196,243,666,1361,1170,1870,1928,671,358,909,1249,659,321,1791,1357,2279,454,1833,665,1418,521,840,329,890,286,448,1224,674,2216,1091,879,540,677,1068,3930,141,3.036,2.706,2.236,2.585,2.522,2.195,2.141,2.81,2.477,3.049,2.637,1.831,3.15,2.928,2.849,2.965,3.273,3.624,2.921,1.77,2.883,2.337,1.966,2.929,2.594,2.645,2.437,2.125,2.841,2.415,2.324,1.948,2.773,3.003,3.196,2.55,2.788,3.073,2.355,2.988,2.573,1.818,3.002,1.737,1.665,1.924,3.417,2.821,2.671,2.705,2.352,2.562,2.465,2.464,2.485,2.143,2.583,2.343,1.919,1.752,2.315,2.428,2.117,2.3,2.295,1.985,2.283,2.795,2.445,2.721,2.64,2.651,2.536,3.14,0.929,0.739,0.641,0.541,0.699,0.411,0.618,0.472,0.59,0.532,1.003,0.528,0.458,0.469,0.553,0.591,0.659,0.779,0.644,0.53,0.755,0.478,0.455,0.643,0.508,0.584,0.514,0.547,0.564,0.624,0.616,0.865,0.512,0.648,0.577,0.658,0.637,0.536,0.459,0.512,0.557,0.425,0.716,0.681,0.445,0.492,0.534,0.766,0.513,0.432,0.528,0.546,0.674,0.542,0.37,0.518,0.493,0.508,0.347,0.339,0.484,0.78,0.535,0.997,0.575,0.629,0.576,0.431,0.495,0.509,0.408,0.498,0.547,0.948
-463,Epileptic,F,27.0,0.9,2.2,1.1,1.1,1.2,2.7,1.7,1.3,1.3,0.6,0.3,3.1,1.7,1.1,1.0,5.2,8.1,0.6,2.2,2.9,1.3,3.3,1.3,2.6,4.4,2.7,3.5,2.3,2.6,3.4,1.0,0.5,0.3,2.4,0.3,0.9,3.6,2.6,0.1,0.3,0.7,3.7,1.8,3.1,2.7,0.8,0.4,1.0,1.2,0.7,0.4,1.3,1.1,2.4,0.4,2.1,0.8,1.6,1.1,1.1,0.9,1.0,0.2,0.2,1.7,0.5,2.2,1.4,0.8,0.3,0.8,1.3,5.5,0.3,7,22,9,9,14,22,15,15,14,6,3.0,21,17,11,9,56,72,6,25,21,10,32,15,35,49,29,41,25,24,38,16,4,4,25,2,8,37,35,1,3,5,30,19,39,18,7,2,7,7,7,3,9,10,16,3,18,8,14,9,9,7,8,1,3,20,8,21,12,8,4,6,9,55,2,0.046,0.025,0.02,0.023,0.031,0.038,0.028,0.024,0.056,0.03,0.039,0.044,0.028,0.057,0.031,0.037,0.031,0.03,0.03,0.035,0.022,0.034,0.026,0.037,0.035,0.032,0.032,0.026,0.026,0.034,0.033,0.031,0.023,0.024,0.014,0.026,0.037,0.029,0.012,0.022,0.017,0.045,0.029,0.035,0.022,0.022,0.019,0.015,0.019,0.013,0.027,0.015,0.024,0.021,0.019,0.022,0.023,0.02,0.027,0.025,0.033,0.019,0.019,0.017,0.024,0.02,0.018,0.016,0.019,0.018,0.023,0.026,0.021,0.018,1286,4248,2170,2040,1577,2646,2589,2598,1349,1158,494.0,2044,3415,870,1624,8913,16412,1784,3862,2612,3319,4591,1902,4151,6389,5145,4898,3489,5057,5085,1665,1089,710,5208,1373,1771,5526,5562,345,625,1124,2369,4627,2436,3289,1205,671,2228,2264,1924,481,2862,1589,4505,624,3362,1186,2607,1108,1297,1329,1656,375,716,2601,806,3889,3323,1533,777,1376,1692,10028,471,0.126,0.125,0.106,0.113,0.139,0.151,0.127,0.128,0.16,0.133,0.129,0.161,0.126,0.171,0.125,0.139,0.13,0.127,0.136,0.13,0.095,0.143,0.129,0.135,0.152,0.143,0.145,0.13,0.118,0.141,0.126,0.1,0.137,0.121,0.082,0.116,0.136,0.13,0.092,0.122,0.107,0.17,0.134,0.135,0.106,0.113,0.1,0.092,0.099,0.09,0.126,0.089,0.118,0.101,0.111,0.112,0.128,0.103,0.13,0.119,0.144,0.107,0.114,0.109,0.124,0.115,0.106,0.098,0.11,0.118,0.11,0.122,0.11,0.115,339,1424,857,788,637,1170,935,888,378,306,169.0,1012,942,281,487,2451,4459,363,1138,1236,964,1565,826,1256,2113,1556,1895,1430,1606,1686,475,365,236,1342,342,554,1672,1524,170,222,576,1226,1018,1418,1978,592,309,929,985,831,254,1303,721,1822,284,1665,586,1305,681,720,437,763,159,326,1319,398,1914,1389,740,313,627,799,4432,201,3.434,2.766,2.648,2.664,2.081,2.193,2.333,2.594,2.537,3.055,2.632,1.898,2.858,2.319,2.468,2.731,3.006,3.79,2.858,1.807,2.76,2.425,2.009,2.488,2.436,2.739,2.187,2.085,2.5,2.468,2.566,2.925,2.522,2.945,3.466,2.518,2.592,2.858,2.233,2.823,2.506,1.898,3.451,2.003,1.877,2.417,2.718,2.953,2.894,2.629,2.209,2.288,2.04,2.576,2.727,2.17,2.402,2.262,1.98,1.978,2.651,2.226,2.116,2.061,2.134,2.268,2.192,2.55,2.358,2.715,2.67,2.53,2.469,2.609,0.773,0.602,0.608,0.434,0.594,0.513,0.474,0.441,0.609,0.542,0.617,0.446,0.519,0.443,0.632,0.726,0.528,0.643,0.515,0.56,0.768,0.447,0.42,0.667,0.5,0.483,0.373,0.442,0.429,0.559,0.78,1.053,0.374,0.523,0.696,0.476,0.6,0.55,0.382,0.614,0.447,0.396,0.66,0.736,0.577,0.517,0.651,0.684,0.462,0.48,0.39,0.606,0.532,0.627,0.358,0.494,0.504,0.729,0.311,0.377,0.637,0.667,0.394,0.857,0.536,0.527,0.407,0.465,0.365,0.716,0.717,0.608,0.479,0.359
-4001,Not Epileptic,F,37.0,0.6,2.2,1.6,1.8,2.3,1.7,2.2,1.3,1.5,0.9,0.2,3.0,1.2,0.4,1.1,4.6,9.0,0.8,1.7,4.2,1.6,2.8,1.9,4.8,4.6,2.5,2.9,2.4,2.8,2.8,2.1,1.2,0.3,1.8,0.5,0.7,3.4,3.0,0.2,0.1,0.8,3.4,2.6,2.5,2.7,0.8,0.4,0.9,1.1,0.6,0.6,2.1,1.3,2.6,0.3,2.2,1.1,1.0,1.7,1.1,0.5,1.8,0.3,0.6,1.9,1.6,2.1,1.3,1.0,0.6,1.3,1.6,3.2,0.3,6,20,15,13,16,17,18,14,14,9,5.0,28,16,5,12,59,91,7,29,40,15,32,17,47,45,32,41,29,25,29,36,11,4,22,2,5,48,35,1,1,5,27,25,18,17,7,2,5,6,6,5,16,11,16,2,17,7,10,12,8,4,17,2,6,17,23,16,10,6,6,10,10,23,2,0.029,0.022,0.022,0.025,0.034,0.031,0.026,0.021,0.055,0.037,0.027,0.031,0.026,0.018,0.027,0.026,0.029,0.024,0.025,0.036,0.026,0.03,0.03,0.042,0.039,0.023,0.03,0.024,0.025,0.028,0.046,0.046,0.02,0.025,0.016,0.016,0.03,0.029,0.012,0.015,0.015,0.036,0.031,0.025,0.018,0.016,0.017,0.013,0.014,0.011,0.026,0.022,0.022,0.021,0.012,0.015,0.024,0.012,0.028,0.021,0.019,0.023,0.027,0.015,0.018,0.022,0.017,0.018,0.015,0.016,0.026,0.019,0.014,0.015,1561,4983,3381,3397,2026,2606,3533,3483,1717,1469,663.0,3685,3666,1333,2725,11513,19835,2087,4692,4670,4467,5322,2309,6212,6569,6717,5987,5855,7363,5874,2182,997,1000,5858,1889,2184,8281,7815,382,342,1584,2652,6165,2982,4270,1773,836,2121,2552,1621,752,3331,2119,4866,586,4571,1361,2912,1838,1673,1074,3325,388,1143,3678,1694,4100,2705,2131,1146,1839,2739,8026,709,0.112,0.115,0.118,0.114,0.135,0.136,0.108,0.117,0.153,0.149,0.107,0.128,0.126,0.106,0.124,0.133,0.131,0.111,0.121,0.141,0.114,0.116,0.109,0.139,0.146,0.119,0.12,0.117,0.101,0.131,0.124,0.123,0.103,0.125,0.081,0.089,0.132,0.136,0.082,0.1,0.093,0.126,0.135,0.111,0.098,0.091,0.095,0.081,0.086,0.096,0.128,0.111,0.112,0.106,0.092,0.093,0.124,0.087,0.121,0.106,0.101,0.118,0.121,0.107,0.102,0.135,0.1,0.098,0.087,0.109,0.133,0.101,0.09,0.102,441,1688,1162,1137,888,935,1173,1054,504,387,166.0,1498,938,307,671,2966,5369,476,1194,1936,999,1478,969,1908,1851,1909,1926,1731,1764,1609,628,462,270,1196,471,658,2055,1806,225,158,770,1435,1325,1658,2352,811,356,918,1141,669,351,1461,1040,1930,330,2201,632,1409,1013,848,446,1267,206,618,1677,1036,1866,1161,974,561,752,1262,3290,303,3.387,2.825,2.85,2.791,2.097,2.359,2.497,2.855,2.505,2.998,3.043,2.083,2.94,2.977,2.936,2.844,2.905,3.461,2.938,2.036,3.207,2.704,2.159,2.493,2.731,2.813,2.457,2.557,3.114,2.768,2.429,2.219,2.696,3.317,3.487,2.933,2.899,3.139,1.938,2.489,2.605,1.677,3.342,2.029,2.064,2.45,2.974,2.803,2.77,2.623,2.168,2.323,2.133,2.645,2.114,2.275,2.599,2.385,2.129,2.186,2.541,2.616,2.211,2.141,2.441,1.968,2.386,2.568,2.61,2.271,2.564,2.594,2.636,2.827,0.704,0.51,0.579,0.488,0.704,0.595,0.541,0.488,0.642,0.803,0.782,0.559,0.474,0.485,0.528,0.599,0.584,0.622,0.615,0.685,0.748,0.544,0.619,0.769,0.729,0.524,0.513,0.6,0.462,0.477,0.676,0.827,0.567,0.597,0.667,0.445,0.749,0.577,0.378,0.464,0.526,0.496,0.75,0.533,0.595,0.538,0.386,0.661,0.52,0.396,0.441,0.506,0.434,0.473,0.281,0.445,0.43,0.43,0.384,0.422,0.436,0.62,0.425,0.608,0.448,0.727,0.428,0.407,0.368,0.759,0.583,0.527,0.443,0.468
-4002,Not Epileptic,M,47.0,1.0,2.2,0.7,1.0,1.4,2.6,2.4,1.7,0.7,0.6,0.5,3.0,1.7,0.8,0.9,6.3,9.6,0.8,2.9,5.4,0.9,2.2,1.5,3.0,3.5,4.3,3.6,3.6,3.2,3.0,1.3,1.0,0.5,2.7,0.5,1.5,4.3,3.7,0.3,0.3,1.7,2.9,2.3,2.9,3.8,0.7,0.4,0.9,1.3,1.4,0.8,1.7,1.2,2.9,0.4,2.5,1.0,1.3,1.1,0.9,0.7,1.5,0.2,0.4,2.0,0.4,3.9,2.3,1.1,0.6,1.2,1.4,4.6,0.5,9,21,7,8,11,21,17,13,7,7,5.0,27,17,8,10,59,90,9,35,41,11,27,15,30,37,37,37,32,24,37,16,7,4,27,2,13,40,36,3,2,12,26,21,21,21,5,2,5,7,17,6,11,8,18,2,18,7,10,8,7,6,13,1,3,15,3,31,16,7,5,9,11,32,4,0.033,0.022,0.012,0.017,0.027,0.029,0.023,0.022,0.032,0.026,0.028,0.032,0.033,0.033,0.027,0.031,0.025,0.029,0.025,0.037,0.02,0.026,0.023,0.029,0.028,0.027,0.028,0.028,0.024,0.026,0.032,0.044,0.023,0.027,0.011,0.025,0.029,0.029,0.02,0.021,0.029,0.031,0.031,0.023,0.022,0.013,0.02,0.012,0.017,0.024,0.026,0.016,0.018,0.022,0.021,0.018,0.017,0.014,0.022,0.017,0.02,0.023,0.016,0.018,0.022,0.024,0.021,0.024,0.015,0.016,0.025,0.016,0.016,0.019,1532,5122,2019,2425,2603,4514,4461,3914,1434,1651,870.0,3569,3836,1700,2307,12814,22447,2675,6770,6109,4024,4578,2522,6989,7197,8770,6087,5266,7673,6555,2365,1082,1116,7463,2536,2981,8713,8638,505,596,1788,3087,6188,3525,4771,1562,826,2639,2535,2428,821,3342,2297,5137,701,4242,1955,3440,1421,1539,1349,2698,566,925,3147,608,6197,3893,2340,1022,1886,2636,10162,538,0.129,0.108,0.085,0.089,0.117,0.131,0.1,0.107,0.119,0.114,0.133,0.128,0.131,0.146,0.117,0.129,0.113,0.119,0.122,0.14,0.092,0.129,0.118,0.125,0.134,0.124,0.116,0.112,0.097,0.122,0.116,0.109,0.113,0.119,0.063,0.113,0.118,0.123,0.128,0.124,0.128,0.138,0.118,0.109,0.104,0.085,0.098,0.075,0.095,0.109,0.12,0.093,0.1,0.106,0.106,0.102,0.097,0.089,0.112,0.107,0.106,0.115,0.09,0.102,0.114,0.092,0.109,0.113,0.091,0.105,0.126,0.103,0.092,0.119,508,1718,821,943,824,1518,1455,1209,423,435,266.0,1497,1028,403,623,3457,6380,530,1872,2301,955,1440,1025,1756,2003,2634,2103,2006,2120,2105,645,457,369,1630,631,1040,2443,2071,227,280,944,1500,1349,2018,2647,772,327,1090,1106,942,473,1558,1005,2154,305,2056,863,1514,794,811,624,1059,240,385,1503,308,2923,1643,1049,565,821,1292,4368,343,3.097,2.768,2.417,2.637,2.684,2.538,2.645,2.943,2.599,3.14,2.724,2.048,2.902,3.163,2.846,2.86,2.962,3.55,2.929,2.263,3.208,2.452,2.108,3.012,2.807,2.816,2.403,2.105,2.768,2.551,2.524,2.465,2.391,3.258,3.415,2.693,2.825,3.072,2.524,2.491,2.34,1.899,3.341,2.043,2.069,2.21,2.885,3.049,2.837,2.613,2.177,2.369,2.44,2.573,2.711,2.343,2.538,2.568,2.048,2.107,2.435,2.514,2.728,2.364,2.297,2.201,2.28,2.566,2.574,2.109,2.7,2.459,2.48,2.003,0.683,0.516,0.518,0.423,0.714,0.516,0.602,0.475,0.522,0.416,0.736,0.419,0.476,0.326,0.352,0.513,0.533,0.831,0.497,0.676,0.799,0.421,0.484,0.57,0.441,0.532,0.5,0.537,0.479,0.582,0.67,0.927,0.52,0.562,0.811,0.45,0.668,0.617,0.325,0.37,0.445,0.419,0.696,0.569,0.586,0.442,0.513,0.844,0.499,0.639,0.391,0.351,0.479,0.434,0.453,0.36,0.351,0.494,0.383,0.357,0.45,0.599,0.411,0.893,0.447,0.756,0.468,0.39,0.469,0.405,0.462,0.463,0.499,0.314
-4003,Not Epileptic,F,32.0,0.7,1.7,0.8,1.0,1.7,1.5,1.2,1.3,0.9,0.6,0.2,3.6,1.2,0.3,0.6,4.6,7.6,1.6,2.1,6.1,0.9,2.6,1.6,2.7,3.5,3.1,3.3,2.6,2.5,2.3,1.5,1.1,0.5,2.2,0.4,0.6,3.1,2.8,0.2,0.1,1.0,4.2,2.1,3.3,2.3,0.6,0.3,1.7,1.1,0.6,0.6,2.3,0.8,2.3,0.4,2.2,0.6,1.2,1.5,1.2,0.5,1.4,0.4,0.5,1.9,1.3,1.9,1.4,1.0,0.5,0.7,1.3,4.1,0.5,5,13,7,7,13,18,12,10,10,7,2.0,27,13,3,7,54,66,12,24,58,7,34,18,28,39,29,41,21,21,32,16,8,5,23,2,5,35,32,2,1,7,39,21,26,14,5,2,12,5,6,6,18,5,16,2,15,7,11,9,9,4,11,3,5,14,16,14,9,6,5,5,9,35,5,0.046,0.019,0.016,0.021,0.036,0.023,0.023,0.027,0.034,0.028,0.025,0.038,0.029,0.029,0.021,0.029,0.028,0.055,0.029,0.04,0.023,0.031,0.033,0.035,0.046,0.032,0.033,0.029,0.027,0.026,0.047,0.062,0.028,0.027,0.013,0.014,0.034,0.031,0.016,0.017,0.022,0.038,0.04,0.026,0.016,0.011,0.023,0.026,0.019,0.024,0.022,0.021,0.019,0.019,0.033,0.018,0.012,0.014,0.023,0.019,0.015,0.026,0.02,0.023,0.019,0.025,0.019,0.02,0.016,0.017,0.024,0.019,0.018,0.029,1078,4460,2265,2288,2137,3179,2573,2669,1685,1339,534.0,3354,2989,829,1907,10144,18363,1894,4765,5666,3280,4450,2048,5501,4792,6251,6097,4326,6542,5009,2330,651,891,6494,2114,1821,6153,7841,392,315,1224,2975,5257,3238,3854,1494,615,1866,1731,1367,748,3818,1455,4986,395,3334,1411,3223,1813,1849,1104,2121,559,780,2959,1098,3386,2399,1807,972,1098,2579,9106,660,0.126,0.107,0.102,0.1,0.136,0.129,0.096,0.119,0.143,0.13,0.097,0.148,0.123,0.119,0.109,0.133,0.123,0.174,0.124,0.153,0.091,0.125,0.132,0.137,0.131,0.132,0.129,0.099,0.104,0.134,0.135,0.145,0.124,0.124,0.073,0.078,0.136,0.131,0.099,0.086,0.117,0.142,0.145,0.122,0.093,0.087,0.101,0.1,0.102,0.12,0.119,0.107,0.106,0.099,0.162,0.102,0.095,0.093,0.112,0.11,0.095,0.124,0.12,0.135,0.107,0.135,0.097,0.11,0.101,0.11,0.123,0.11,0.096,0.151,301,1344,758,825,826,1063,832,827,455,360,149.0,1531,754,187,486,2714,4535,464,1300,2566,692,1370,875,1386,1429,1621,1736,1359,1564,1496,598,325,307,1364,530,617,1525,1712,176,151,672,1543,1039,1991,2076,722,245,825,835,494,433,1606,674,1930,150,1751,614,1491,961,898,508,824,311,399,1489,700,1692,1023,888,468,458,1094,3770,280,3.287,2.965,3.006,2.673,2.19,2.496,2.502,2.835,2.609,2.936,2.847,2.012,2.823,3.08,2.796,2.791,3.134,3.267,2.92,1.964,3.269,2.51,2.048,2.983,2.598,2.903,2.651,2.384,3.037,2.613,2.687,2.166,2.651,3.274,3.418,2.626,3.048,3.271,2.36,2.442,2.429,1.775,3.387,1.868,2.104,2.382,3.11,2.806,2.647,2.829,1.968,2.328,2.213,2.658,2.788,2.176,2.284,2.375,2.258,2.312,2.385,2.475,2.001,2.207,2.19,1.843,2.279,2.749,2.586,2.37,2.687,2.593,2.537,2.688,0.767,0.793,0.398,0.49,0.751,0.53,0.556,0.405,0.744,0.53,0.713,0.624,0.433,0.437,0.524,0.571,0.584,0.754,0.558,0.596,0.745,0.558,0.491,0.772,0.667,0.579,0.667,0.616,0.488,0.527,0.713,0.958,0.472,0.536,0.721,0.476,0.751,0.515,0.44,0.244,0.414,0.472,0.761,0.626,0.648,0.425,0.495,0.552,0.381,0.591,0.336,0.519,0.528,0.458,0.51,0.355,0.413,0.414,0.368,0.428,0.356,0.662,0.531,0.734,0.488,0.547,0.432,0.425,0.398,0.518,0.503,0.573,0.445,0.51
-4004,Not Epileptic,F,37.0,0.7,2.7,1.2,1.2,1.5,1.9,1.3,1.6,0.7,1.1,0.4,2.4,1.0,0.6,1.3,6.2,8.4,0.8,2.4,3.5,1.0,2.7,1.5,2.8,2.7,3.3,2.9,2.2,2.3,2.5,1.8,0.9,0.4,2.0,0.4,0.7,4.3,3.3,0.2,0.1,0.8,2.5,2.4,2.0,1.7,0.6,0.3,0.8,1.3,1.5,0.7,1.3,1.2,2.1,0.3,2.7,0.9,1.8,1.0,0.8,0.5,1.2,0.6,0.3,1.8,1.6,2.6,1.3,0.9,0.9,1.2,1.4,4.6,0.4,6,25,15,8,11,17,12,17,10,11,5.0,21,12,9,16,66,77,5,20,31,8,25,14,31,30,32,35,24,22,32,21,5,3,24,2,5,40,34,2,1,5,25,20,17,11,4,1,6,6,8,6,13,9,16,2,20,5,21,6,7,3,10,5,3,14,17,21,11,8,9,8,12,37,3,0.033,0.028,0.025,0.023,0.034,0.028,0.022,0.027,0.032,0.04,0.038,0.027,0.025,0.031,0.029,0.033,0.031,0.027,0.037,0.031,0.023,0.034,0.023,0.03,0.036,0.031,0.023,0.024,0.023,0.026,0.041,0.039,0.022,0.022,0.011,0.023,0.039,0.028,0.012,0.011,0.019,0.032,0.036,0.02,0.014,0.013,0.017,0.014,0.017,0.035,0.028,0.018,0.02,0.017,0.017,0.017,0.018,0.022,0.021,0.014,0.016,0.02,0.032,0.015,0.019,0.027,0.019,0.019,0.022,0.024,0.033,0.019,0.019,0.019,1620,5019,2209,2068,2089,3367,2610,3002,1506,1517,692.0,3024,3018,1240,2917,11169,16689,2473,3728,4339,2380,4252,2317,5555,4863,6512,5954,3467,5337,5374,2380,1012,938,5426,1901,1643,7633,7380,323,401,1383,2903,5055,2765,3071,1255,757,1963,2333,1196,632,2412,1990,4572,469,4622,1458,3189,1182,1299,761,2495,673,797,2888,1194,3957,2273,1595,1152,1378,2537,9197,452,0.128,0.132,0.13,0.114,0.134,0.132,0.108,0.137,0.14,0.162,0.131,0.122,0.114,0.137,0.133,0.144,0.132,0.116,0.125,0.131,0.091,0.136,0.115,0.129,0.149,0.139,0.116,0.11,0.114,0.132,0.139,0.123,0.11,0.113,0.069,0.101,0.13,0.123,0.097,0.089,0.1,0.142,0.133,0.107,0.085,0.084,0.092,0.083,0.097,0.133,0.146,0.1,0.112,0.1,0.102,0.097,0.103,0.107,0.117,0.098,0.104,0.109,0.157,0.094,0.107,0.127,0.103,0.108,0.11,0.13,0.133,0.112,0.098,0.117,414,1500,800,818,699,1109,937,1018,441,461,214.0,1334,789,319,792,3228,4864,423,1160,1741,721,1308,945,1545,1447,1777,2112,1409,1604,1744,617,372,301,1330,489,561,1934,1820,212,215,627,1252,1208,1644,1845,660,325,882,1061,593,386,1222,932,1957,252,2366,688,1469,692,786,367,909,262,378,1444,868,4004,1094,751,492,650,1260,3958,256,3.568,2.968,2.706,2.573,2.549,2.645,2.342,2.721,2.673,2.71,2.94,1.951,2.871,2.776,2.766,2.648,2.871,4.149,2.795,2.112,2.596,2.479,2.11,2.795,2.61,2.986,2.297,2.025,2.659,2.457,2.791,2.743,2.491,2.901,3.455,2.55,2.884,2.974,1.786,2.3,2.78,2.029,3.052,1.909,1.921,2.04,3.012,2.604,2.699,2.263,2.134,2.078,2.197,2.427,2.251,2.017,2.609,2.386,1.974,1.843,2.22,2.645,2.619,2.558,2.134,1.76,2.12,2.361,2.343,2.682,2.305,2.439,2.476,2.432,0.738,0.735,0.618,0.457,0.773,0.443,0.478,0.606,0.552,0.554,0.652,0.39,0.427,0.564,0.509,0.551,0.602,0.656,0.707,0.552,0.719,0.485,0.49,0.62,0.609,0.576,0.441,0.502,0.521,0.473,0.6,0.942,0.371,0.609,0.786,0.509,0.782,0.547,0.409,0.341,0.651,0.497,0.803,0.628,0.568,0.418,0.538,0.679,0.459,0.474,0.393,0.391,0.427,0.463,0.401,0.408,0.567,0.623,0.408,0.413,0.47,0.69,0.779,0.933,0.447,0.562,0.525,0.402,0.468,0.635,0.445,0.423,0.418,0.396
-4005,Not Epileptic,M,37.0,1.3,2.5,1.1,1.6,2.4,2.4,2.0,1.6,2.8,1.0,0.4,4.2,1.8,0.7,1.8,8.3,12.6,1.0,3.1,6.5,1.1,5.1,2.3,5.3,2.8,3.8,5.0,4.7,3.5,4.2,2.6,2.0,0.6,2.8,0.9,0.6,5.8,3.7,0.2,0.2,1.1,4.8,2.9,4.3,4.0,0.8,0.6,1.0,1.7,0.9,0.7,2.2,2.2,4.3,0.1,3.3,1.1,2.6,1.8,1.7,0.5,2.1,0.4,0.6,2.9,2.2,3.3,2.1,1.3,0.9,1.5,2.1,4.5,0.4,10,23,10,12,20,24,18,17,20,10,3.0,42,20,9,21,82,126,11,30,59,12,55,28,51,36,46,55,47,31,53,25,15,6,36,5,4,58,47,2,1,8,47,34,33,22,7,3,7,8,8,5,16,21,28,1,29,14,25,9,13,8,19,2,4,26,25,28,16,9,8,8,20,36,4,0.039,0.02,0.012,0.023,0.036,0.029,0.025,0.024,0.042,0.037,0.034,0.039,0.027,0.029,0.029,0.037,0.029,0.033,0.036,0.04,0.024,0.035,0.031,0.043,0.029,0.032,0.037,0.03,0.025,0.034,0.051,0.047,0.021,0.028,0.018,0.013,0.042,0.029,0.012,0.013,0.022,0.035,0.042,0.029,0.022,0.014,0.022,0.017,0.017,0.021,0.027,0.019,0.03,0.025,0.018,0.025,0.024,0.022,0.025,0.023,0.019,0.034,0.026,0.019,0.024,0.031,0.021,0.021,0.021,0.018,0.027,0.019,0.019,0.017,1932,6045,3106,3077,2461,4214,3627,3595,2272,2109,777.0,4071,3903,1440,3589,13086,25987,2920,4752,6712,3422,7521,3460,7037,5210,6447,7082,6854,8535,7808,2650,1931,1240,6357,2855,1656,10209,8673,500,587,1414,4602,6493,4227,4533,1838,941,2240,2856,1845,730,3922,3068,6533,334,4120,1735,3626,1939,2072,1126,2692,429,1001,4258,2045,4688,3742,1976,1286,1807,3495,8996,553,0.13,0.109,0.095,0.109,0.145,0.131,0.106,0.12,0.125,0.139,0.121,0.16,0.123,0.122,0.136,0.132,0.12,0.116,0.134,0.153,0.087,0.145,0.138,0.151,0.14,0.137,0.12,0.113,0.102,0.137,0.162,0.133,0.094,0.129,0.087,0.079,0.148,0.126,0.081,0.094,0.111,0.147,0.151,0.13,0.109,0.091,0.109,0.093,0.096,0.104,0.117,0.1,0.126,0.11,0.089,0.124,0.125,0.119,0.113,0.119,0.092,0.139,0.127,0.106,0.118,0.136,0.114,0.106,0.103,0.123,0.124,0.115,0.102,0.111,554,2050,1256,1072,1032,1508,1245,1209,845,474,233.0,1793,1064,354,1051,3724,7281,642,1495,2855,860,2241,1342,1980,1572,2028,2544,2399,2214,2461,788,699,434,1619,788,633,2479,2250,301,252,762,2036,1402,2400,2612,896,386,967,1347,773,460,1892,1346,2806,129,2140,850,1882,1072,1137,550,1071,239,485,1975,1006,2336,1588,980,642,870,1694,3918,353,3.493,2.583,2.489,2.734,2.304,2.317,2.404,2.629,2.373,3.264,3.054,1.934,2.9,2.765,2.685,2.727,2.797,3.548,2.673,2.039,2.901,2.519,2.144,2.679,2.553,2.609,2.259,2.293,2.936,2.46,2.583,2.693,2.277,2.884,3.438,2.452,2.976,2.785,1.862,2.616,2.377,1.991,3.333,1.957,1.974,2.306,3.17,2.85,2.613,2.699,1.977,2.239,2.162,2.466,2.173,2.121,2.191,2.213,2.057,1.886,2.274,2.424,1.925,2.486,2.205,2.188,2.187,2.533,2.269,2.396,2.432,2.397,2.436,1.987,0.761,0.633,0.474,0.593,0.549,0.641,0.692,0.466,0.614,0.534,0.702,0.502,0.449,0.469,0.526,0.608,0.652,0.771,0.67,0.574,0.89,0.525,0.631,0.674,0.569,0.503,0.546,0.505,0.521,0.604,0.856,0.961,0.36,0.564,0.707,0.54,0.864,0.688,0.359,0.471,0.348,0.522,0.716,0.492,0.529,0.561,0.511,0.581,0.466,0.477,0.351,0.427,0.497,0.455,0.351,0.39,0.465,0.502,0.363,0.352,0.55,0.693,0.334,0.767,0.517,0.893,0.456,0.342,0.437,0.706,0.549,0.506,0.422,0.393
-4006,Not Epileptic,F,27.0,1.0,2.2,1.0,1.2,2.3,1.2,1.2,1.8,1.2,0.7,0.2,1.9,1.5,0.5,1.2,7.6,8.5,0.9,2.8,4.1,1.2,2.7,1.2,4.6,4.0,3.3,3.2,1.7,2.4,3.1,1.7,1.2,0.4,2.7,0.3,0.6,3.2,3.1,0.2,0.1,1.0,3.3,2.0,2.0,2.0,0.5,0.6,0.9,1.5,1.1,0.5,1.7,1.3,2.2,0.4,2.1,0.9,1.7,0.7,0.9,0.7,2.0,0.2,0.8,1.7,1.3,1.7,1.4,1.0,0.4,0.9,1.4,5.0,0.2,13,21,9,13,15,14,12,16,15,8,3.0,24,20,6,25,81,95,10,41,40,12,32,14,45,50,42,49,22,23,40,16,10,5,36,2,4,41,46,1,1,7,27,20,15,11,4,3,6,7,9,3,15,9,16,1,23,6,15,6,9,7,17,2,6,17,15,17,13,5,4,6,11,41,3,0.044,0.026,0.021,0.024,0.044,0.024,0.022,0.032,0.043,0.043,0.036,0.034,0.039,0.035,0.03,0.047,0.034,0.037,0.039,0.039,0.027,0.036,0.029,0.051,0.042,0.03,0.036,0.026,0.028,0.028,0.043,0.052,0.02,0.033,0.013,0.015,0.039,0.034,0.013,0.015,0.018,0.039,0.039,0.03,0.015,0.01,0.023,0.022,0.021,0.034,0.026,0.024,0.028,0.02,0.022,0.017,0.02,0.021,0.018,0.019,0.019,0.03,0.024,0.019,0.018,0.021,0.018,0.022,0.015,0.021,0.019,0.019,0.02,0.013,1737,4764,2676,2456,2222,2456,2581,3196,1802,1235,506.0,2349,3489,1187,2343,10035,17944,2396,6206,4466,3081,4112,1510,5559,5879,7412,4850,3133,5711,6224,2103,1274,1161,6440,1897,1959,6925,8068,448,391,1580,2847,5716,2032,3136,1498,781,2302,2274,1289,469,2297,1756,4294,434,3655,1451,3186,1033,1353,1208,2785,260,1077,2581,1332,3149,2424,1775,875,1372,2400,9076,559,0.146,0.126,0.1,0.112,0.147,0.118,0.105,0.139,0.166,0.169,0.137,0.14,0.15,0.134,0.157,0.176,0.143,0.12,0.154,0.166,0.105,0.138,0.123,0.157,0.147,0.143,0.131,0.115,0.118,0.139,0.128,0.134,0.093,0.145,0.062,0.086,0.148,0.141,0.08,0.089,0.102,0.149,0.146,0.123,0.088,0.078,0.115,0.082,0.103,0.14,0.12,0.113,0.128,0.101,0.116,0.103,0.114,0.11,0.111,0.115,0.11,0.124,0.127,0.109,0.112,0.109,0.104,0.116,0.089,0.126,0.104,0.106,0.107,0.1,457,1405,836,883,859,863,936,1040,573,300,145.0,1111,933,281,744,3058,4920,483,1461,1982,744,1311,763,1730,1830,2084,1856,1178,1484,1904,690,471,374,1410,447,650,1708,1836,246,187,800,1280,1054,1217,1864,756,351,926,1026,499,280,1126,815,1801,223,1958,660,1433,597,693,585,1081,159,615,1376,833,1546,1079,870,323,674,1111,3914,305,3.531,3.01,3.025,2.695,2.176,2.357,2.335,2.798,2.456,3.214,2.811,1.853,2.855,2.892,2.44,2.546,2.865,3.908,3.214,2.03,2.776,2.395,1.822,2.475,2.499,2.755,2.116,2.161,2.868,2.575,2.305,2.692,2.484,3.125,3.354,2.664,2.913,3.03,2.133,2.172,2.459,1.95,3.598,1.92,1.891,2.211,2.691,3.069,2.668,3.165,2.033,2.101,2.174,2.544,2.242,1.965,2.437,2.513,1.994,2.189,2.332,2.515,2.017,2.253,2.052,2.039,2.123,2.502,2.338,2.713,2.284,2.276,2.455,2.274,0.769,0.658,0.54,0.477,0.649,0.545,0.512,0.647,0.547,0.486,0.586,0.423,0.469,0.488,0.524,0.629,0.579,0.71,0.637,0.612,0.953,0.622,0.382,0.925,0.729,0.573,0.501,0.544,0.456,0.661,0.735,0.794,0.328,0.655,0.946,0.494,0.838,0.743,0.336,0.524,0.479,0.612,0.644,0.661,0.657,0.419,0.511,0.829,0.444,0.576,0.462,0.403,0.485,0.481,0.386,0.426,0.486,0.496,0.402,0.342,0.373,0.653,0.374,0.56,0.502,0.625,0.455,0.404,0.384,0.842,0.438,0.501,0.526,0.333
-4007,Not Epileptic,F,55.0,0.6,2.0,1.1,0.7,1.6,1.4,2.0,1.4,1.1,0.6,0.2,3.3,1.1,0.4,1.3,3.7,9.2,0.9,2.0,4.4,1.4,2.2,2.6,4.2,2.2,2.7,4.7,3.2,3.8,2.1,1.4,1.9,0.4,1.9,0.5,0.8,2.7,2.6,0.3,0.2,0.9,2.4,2.4,2.7,2.4,0.8,0.4,1.1,1.2,0.8,0.7,1.7,1.7,2.8,0.4,2.7,0.9,1.3,0.9,1.0,0.6,1.5,0.4,0.6,1.3,0.9,2.2,0.9,1.2,0.4,0.4,1.3,3.4,0.3,7,19,12,7,11,14,17,15,14,7,3.0,29,13,6,17,42,74,6,22,41,13,24,24,35,33,27,42,23,24,26,17,17,4,25,2,10,35,32,1,2,5,25,26,19,15,6,2,7,6,8,4,12,14,19,2,21,6,13,8,7,5,9,2,5,10,15,14,6,7,5,4,13,31,2,0.036,0.027,0.026,0.017,0.035,0.031,0.032,0.03,0.048,0.028,0.021,0.043,0.027,0.032,0.033,0.035,0.038,0.038,0.032,0.037,0.026,0.034,0.036,0.05,0.037,0.03,0.036,0.035,0.036,0.03,0.046,0.057,0.029,0.026,0.015,0.022,0.041,0.03,0.024,0.013,0.021,0.035,0.039,0.026,0.02,0.016,0.017,0.018,0.019,0.017,0.033,0.025,0.027,0.027,0.022,0.024,0.029,0.023,0.024,0.021,0.027,0.032,0.028,0.015,0.02,0.022,0.021,0.015,0.02,0.021,0.021,0.021,0.017,0.018,1183,3310,1815,1766,1840,2430,2550,2570,2205,1296,625.0,2983,2706,962,2722,7486,16611,1983,3711,4836,4422,4105,2610,4785,4745,5492,5770,3348,4901,4615,1555,1426,768,4821,1526,2102,5807,6552,328,552,1377,2594,5648,2580,3215,1308,647,1788,1819,1670,476,2527,2197,3951,414,3437,989,2372,1009,1316,741,1958,450,894,2201,1046,3261,1784,1830,624,799,2055,7890,524,0.131,0.126,0.131,0.098,0.137,0.133,0.111,0.14,0.162,0.149,0.108,0.154,0.118,0.138,0.134,0.128,0.134,0.127,0.139,0.156,0.103,0.136,0.135,0.145,0.14,0.135,0.12,0.104,0.109,0.126,0.135,0.148,0.106,0.12,0.073,0.106,0.143,0.139,0.101,0.109,0.102,0.149,0.148,0.12,0.1,0.102,0.091,0.093,0.097,0.111,0.136,0.118,0.13,0.113,0.129,0.122,0.125,0.121,0.126,0.112,0.125,0.119,0.115,0.104,0.102,0.119,0.102,0.094,0.102,0.135,0.108,0.124,0.097,0.116,340,1224,678,638,762,765,942,849,532,308,182.0,1330,762,250,796,2140,4393,438,1117,2090,952,1056,1041,1485,1213,1463,1950,1162,1345,1268,563,572,247,1238,452,691,1439,1516,196,242,726,1111,1205,1671,1887,698,307,829,921,713,304,1096,991,1725,211,1736,498,1010,579,740,374,744,225,530,1152,623,1643,805,896,349,360,999,3256,252,3.218,2.683,2.715,2.639,2.122,2.569,2.348,2.698,2.83,3.073,2.752,1.901,2.722,2.798,2.575,2.696,2.929,3.456,2.723,1.992,3.356,2.796,2.163,2.507,2.853,2.919,2.368,2.263,2.785,2.675,2.143,2.561,2.4,2.855,3.067,2.789,2.902,3.029,1.919,2.438,2.322,2.087,3.232,1.796,1.924,2.111,2.636,2.683,2.434,2.55,2.01,2.405,2.131,2.304,2.27,2.221,2.119,2.587,1.985,1.942,2.579,2.584,2.192,2.136,2.053,2.071,2.172,2.611,2.445,1.993,2.292,2.343,2.423,2.54,0.732,0.54,0.517,0.509,0.691,0.591,0.673,0.49,0.736,0.613,0.65,0.59,0.413,0.533,0.574,0.584,0.694,0.76,0.682,0.657,0.859,0.508,0.59,0.869,0.682,0.535,0.605,0.612,0.707,0.644,0.51,0.938,0.59,0.587,0.648,0.524,0.644,0.662,0.265,0.483,0.449,0.632,0.81,0.547,0.654,0.49,0.429,0.809,0.41,0.456,0.36,0.561,0.485,0.448,0.45,0.439,0.482,0.714,0.454,0.337,0.359,0.601,0.412,0.567,0.45,0.707,0.407,0.403,0.495,0.635,0.562,0.489,0.511,0.559
-4008,Not Epileptic,F,47.0,0.5,1.6,1.1,0.8,1.6,1.9,1.0,1.3,0.9,0.5,0.2,3.2,1.1,0.6,1.2,5.1,8.0,0.9,2.1,4.9,1.6,2.8,1.8,2.3,3.3,4.5,3.5,1.7,2.8,4.0,1.1,0.5,0.5,1.9,0.5,0.4,3.0,3.5,0.1,0.1,1.0,2.7,3.3,4.1,3.0,0.6,0.4,0.6,1.2,1.3,0.7,1.3,1.0,2.9,0.5,2.5,0.4,1.7,0.5,1.4,0.5,1.3,0.2,0.5,1.3,0.8,2.2,2.2,0.9,0.6,1.2,1.9,3.3,0.2,5,16,8,7,13,17,8,11,11,6,2.0,26,11,4,10,52,66,7,20,37,13,26,17,27,34,36,37,15,22,43,13,7,4,24,4,3,33,37,1,2,5,23,23,21,20,5,2,4,5,10,3,11,9,24,2,18,3,14,4,9,4,9,1,3,11,9,19,16,6,5,8,10,24,1,0.03,0.02,0.023,0.018,0.033,0.037,0.019,0.024,0.035,0.024,0.027,0.033,0.023,0.045,0.03,0.031,0.029,0.038,0.032,0.039,0.033,0.035,0.027,0.032,0.032,0.033,0.028,0.023,0.023,0.031,0.031,0.024,0.035,0.025,0.015,0.01,0.033,0.032,0.014,0.013,0.022,0.035,0.041,0.039,0.024,0.013,0.019,0.011,0.02,0.024,0.027,0.017,0.026,0.02,0.018,0.02,0.011,0.02,0.023,0.025,0.02,0.027,0.025,0.025,0.019,0.022,0.019,0.023,0.02,0.021,0.029,0.026,0.015,0.009,1190,3669,2279,1798,1916,2561,2279,2950,1544,1213,392.0,2902,2736,822,2304,8812,15893,1857,3150,4496,2594,4106,2533,4698,6326,7667,6024,3114,5581,5760,2029,772,852,4603,1830,1576,5462,6782,229,408,1674,2650,4840,2518,3164,1221,632,1993,1973,1934,583,2358,2089,5639,718,3922,818,3117,728,1715,990,1819,378,696,2162,1065,3602,3475,1565,1173,1223,2393,7438,644,0.118,0.101,0.11,0.099,0.123,0.143,0.093,0.111,0.145,0.117,0.103,0.143,0.124,0.109,0.124,0.126,0.126,0.137,0.129,0.14,0.105,0.145,0.126,0.124,0.142,0.137,0.129,0.106,0.112,0.137,0.122,0.101,0.156,0.111,0.077,0.073,0.112,0.135,0.094,0.087,0.105,0.147,0.126,0.146,0.113,0.095,0.105,0.073,0.101,0.116,0.126,0.101,0.102,0.108,0.112,0.109,0.092,0.103,0.122,0.115,0.113,0.126,0.117,0.123,0.101,0.112,0.108,0.111,0.1,0.131,0.13,0.116,0.092,0.066,310,1259,767,703,709,890,785,917,430,344,144.0,1367,750,213,567,2597,4303,420,1030,1906,814,1233,1055,1219,1863,2253,2123,1153,1708,2133,527,396,236,1173,524,551,1609,1707,139,236,754,1189,1218,1668,2058,675,264,852,911,897,321,1262,777,2228,354,2017,425,1347,360,923,443,763,138,310,1132,555,1845,1614,790,450,682,1157,3369,289,3.514,2.834,2.815,2.659,2.336,2.549,2.362,2.921,2.763,2.721,2.37,1.933,2.759,2.604,2.868,2.586,2.976,3.643,2.619,2.036,2.826,2.563,2.106,2.867,2.675,2.84,2.253,2.131,2.566,2.317,2.773,2.019,3.082,2.777,2.935,2.677,2.654,3.013,1.899,1.968,2.702,2.019,3.133,1.833,1.816,2.041,2.809,2.817,2.684,2.594,2.302,1.995,2.625,2.524,2.654,2.271,2.158,2.445,2.424,2.165,2.508,2.607,2.624,2.511,2.125,2.244,2.071,2.32,2.355,2.847,2.235,2.643,2.373,2.455,0.602,0.585,0.637,0.503,0.632,0.503,0.565,0.647,0.503,0.377,0.771,0.401,0.34,0.664,0.563,0.629,0.601,0.815,0.429,0.515,0.797,0.409,0.402,0.538,0.444,0.463,0.466,0.483,0.471,0.502,0.904,0.866,0.665,0.6,0.821,0.549,0.649,0.542,0.328,0.33,0.603,0.593,0.738,0.696,0.56,0.404,0.789,0.761,0.574,0.502,0.338,0.478,0.676,0.549,0.364,0.355,0.329,0.395,0.45,0.314,0.314,0.438,0.729,0.597,0.473,0.672,0.354,0.462,0.395,0.847,0.531,0.442,0.573,0.651
-4009,Not Epileptic,F,22.0,0.7,2.6,0.8,1.4,1.2,2.8,1.2,1.7,1.3,0.9,0.6,2.9,1.6,0.5,1.5,5.0,8.5,1.3,2.2,5.9,1.5,3.7,2.3,4.3,4.8,4.2,3.7,2.8,2.7,3.3,1.8,1.8,0.7,2.1,0.5,0.5,3.2,3.5,0.2,0.2,1.1,3.0,2.0,2.5,2.9,0.9,0.6,0.9,1.4,0.8,0.9,2.7,1.0,2.1,0.3,2.4,0.7,1.5,1.3,1.0,1.0,1.6,0.4,0.7,2.3,1.6,2.6,0.8,0.8,0.5,1.2,1.1,5.5,0.3,12,26,7,11,13,24,13,20,15,10,5.0,29,21,7,22,62,91,10,22,55,14,47,23,43,56,46,56,33,24,37,17,15,7,27,3,4,36,40,1,1,8,28,25,19,18,8,3,6,8,5,6,24,8,14,2,20,4,15,11,8,10,13,3,5,18,20,21,6,6,4,8,9,53,3,0.037,0.025,0.013,0.026,0.031,0.034,0.021,0.027,0.045,0.035,0.043,0.035,0.029,0.027,0.026,0.028,0.03,0.046,0.033,0.046,0.025,0.036,0.045,0.04,0.04,0.026,0.031,0.032,0.025,0.026,0.04,0.051,0.036,0.029,0.014,0.013,0.039,0.034,0.013,0.022,0.022,0.035,0.037,0.023,0.022,0.019,0.026,0.015,0.018,0.02,0.034,0.022,0.021,0.018,0.016,0.016,0.02,0.017,0.027,0.016,0.027,0.024,0.022,0.019,0.024,0.023,0.017,0.014,0.017,0.013,0.023,0.02,0.021,0.023,1562,6162,2347,2643,2224,4198,3075,4398,2453,1994,762.0,2805,4002,1490,3789,12224,21030,2719,4223,5884,5194,5375,2095,6812,7179,10596,7356,4862,7295,6254,2159,1622,1108,6467,1995,2076,6303,8809,412,417,1612,2322,5412,3160,3716,1881,896,2455,2472,1645,679,4847,1915,4262,570,4586,1090,3336,1423,1697,1208,2875,565,1021,3205,1775,5057,2024,1888,783,2073,2079,10618,417,0.133,0.115,0.098,0.112,0.127,0.147,0.112,0.133,0.154,0.139,0.159,0.152,0.135,0.135,0.133,0.135,0.137,0.167,0.128,0.172,0.103,0.139,0.134,0.143,0.133,0.122,0.13,0.113,0.111,0.126,0.137,0.131,0.14,0.129,0.076,0.081,0.14,0.15,0.082,0.1,0.113,0.131,0.14,0.111,0.106,0.104,0.115,0.085,0.098,0.101,0.138,0.11,0.104,0.094,0.098,0.101,0.111,0.098,0.126,0.096,0.128,0.116,0.116,0.104,0.122,0.114,0.096,0.095,0.097,0.103,0.115,0.105,0.105,0.134,471,1825,851,892,747,1378,923,1179,576,504,210.0,1378,1059,335,1089,3126,5062,540,1165,2565,1044,1751,913,1858,1984,2909,2437,1644,1728,2123,721,600,341,1345,507,712,1542,1821,248,200,823,1348,1146,1773,2107,748,379,938,1137,662,419,2089,850,1781,295,2252,474,1540,772,885,594,1081,292,553,1469,1076,2346,901,828,495,846,942,4232,208,2.971,2.934,2.782,2.847,2.348,2.509,2.654,3.107,2.84,3.075,2.777,1.826,2.852,2.899,2.632,2.809,3.085,3.505,2.9,2.026,3.417,2.483,2.021,2.638,2.704,2.798,2.359,2.348,3.11,2.42,2.266,2.793,2.602,3.173,3.448,2.668,2.961,3.324,1.943,2.64,2.658,1.632,3.306,1.911,2.027,2.423,2.817,3.173,2.779,2.8,1.943,2.231,2.329,2.523,2.415,2.31,2.728,2.378,1.993,2.188,2.475,2.609,2.194,2.205,2.482,2.012,2.306,2.514,2.602,1.879,2.462,2.458,2.549,2.522,0.814,0.652,0.479,0.65,0.9,0.652,0.706,0.496,0.829,0.594,0.886,0.557,0.576,0.487,0.616,0.684,0.673,1.095,0.539,0.6,0.837,0.725,0.549,0.857,0.774,0.615,0.688,0.609,0.619,0.596,0.803,0.833,0.435,0.663,0.819,0.47,0.788,0.802,0.422,0.375,0.482,0.463,0.75,0.706,0.696,0.447,0.655,0.741,0.443,0.417,0.42,0.526,0.468,0.43,0.346,0.426,0.451,0.47,0.539,0.406,0.42,0.767,0.452,0.594,0.525,0.732,0.398,0.374,0.498,0.633,0.432,0.477,0.484,0.561
-4010,Not Epileptic,M,37.0,0.6,2.1,1.3,1.4,1.9,2.5,1.4,2.2,1.6,0.9,0.2,2.9,1.6,0.3,2.0,5.9,7.6,1.3,3.4,3.8,0.9,2.6,2.4,3.2,4.2,4.7,3.0,2.4,2.5,3.3,1.2,1.1,0.8,2.7,0.3,0.7,4.3,4.0,0.2,0.5,1.5,3.8,2.3,3.4,2.7,0.6,0.4,0.7,1.4,1.3,0.8,2.8,0.9,1.7,0.4,2.5,1.2,2.3,1.2,1.6,0.5,1.5,0.7,0.4,2.4,1.7,2.5,1.1,1.3,0.7,1.1,2.7,4.8,0.3,4,25,14,13,16,20,12,19,14,7,3.0,24,17,4,20,67,76,10,28,31,5,30,22,38,42,55,36,25,23,38,19,9,7,27,2,5,44,40,2,3,11,24,22,25,15,5,2,5,7,11,7,19,8,11,1,21,9,15,8,13,4,9,5,4,16,16,25,6,10,9,7,18,40,4,0.026,0.025,0.02,0.027,0.037,0.033,0.019,0.028,0.044,0.042,0.021,0.031,0.024,0.023,0.036,0.032,0.026,0.042,0.035,0.033,0.014,0.028,0.033,0.04,0.034,0.031,0.026,0.023,0.022,0.032,0.039,0.041,0.029,0.028,0.01,0.013,0.037,0.034,0.021,0.024,0.028,0.041,0.041,0.038,0.018,0.013,0.023,0.011,0.016,0.027,0.043,0.025,0.021,0.015,0.046,0.015,0.032,0.02,0.036,0.022,0.027,0.026,0.036,0.019,0.024,0.029,0.019,0.014,0.022,0.024,0.024,0.03,0.018,0.014,1529,4442,2483,2664,2119,3248,2633,3599,2346,1521,461.0,2660,3730,978,3284,11189,16850,2233,4785,3329,2390,4229,2547,5423,6071,8135,5480,3842,5827,5719,2120,1227,1187,6112,1659,2251,6995,7747,367,660,1888,2524,5149,1798,3800,1441,697,2272,2801,1795,492,4163,1668,4208,484,4177,1079,3415,823,1844,841,2473,678,746,2789,1427,4501,2466,2051,1188,1539,2616,9803,423,0.101,0.12,0.115,0.115,0.141,0.135,0.089,0.127,0.149,0.146,0.116,0.129,0.118,0.11,0.152,0.137,0.12,0.132,0.127,0.128,0.066,0.135,0.149,0.15,0.135,0.132,0.112,0.099,0.089,0.132,0.135,0.125,0.123,0.117,0.069,0.078,0.119,0.127,0.118,0.118,0.116,0.154,0.132,0.143,0.097,0.089,0.112,0.078,0.093,0.119,0.158,0.118,0.107,0.083,0.165,0.101,0.138,0.098,0.14,0.115,0.111,0.113,0.139,0.119,0.12,0.132,0.105,0.089,0.113,0.134,0.119,0.13,0.103,0.111,371,1448,893,961,824,1260,944,1196,642,402,176.0,1380,1087,281,978,3167,4827,482,1518,1745,805,1438,1130,1405,1866,2651,1956,1471,1614,1912,557,454,440,1420,458,832,1958,1947,179,343,932,1250,1057,1347,2256,726,309,922,1200,835,356,1789,821,1840,148,2297,617,1764,546,1076,386,769,324,356,1443,855,2207,1128,940,466,717,1370,4133,286,3.555,2.821,2.654,2.628,2.264,2.287,2.39,2.723,2.634,3.053,2.458,1.747,2.776,2.444,2.59,2.628,2.827,3.587,2.658,1.75,2.523,2.422,2.061,2.859,2.541,2.591,2.229,2.118,2.741,2.357,2.829,2.515,2.223,3.008,3.257,2.566,2.74,2.851,2.153,2.219,2.51,1.961,3.378,1.625,1.902,2.182,2.821,2.859,2.73,2.487,1.646,2.383,2.206,2.382,3.414,2.075,2.08,2.203,1.724,1.976,2.621,2.992,2.245,2.376,2.131,2.096,2.084,2.552,2.334,2.547,2.41,2.323,2.618,1.828,0.913,0.737,0.608,0.576,0.592,0.53,0.607,0.549,0.667,0.534,0.644,0.414,0.514,0.685,0.433,0.501,0.562,0.767,0.696,0.476,0.667,0.476,0.476,0.69,0.495,0.487,0.482,0.514,0.5,0.523,0.802,1.089,0.462,0.633,0.75,0.536,0.729,0.599,0.401,0.441,0.521,0.52,0.791,0.687,0.573,0.611,0.408,0.978,0.602,0.485,0.279,0.558,0.622,0.477,0.298,0.441,0.354,0.56,0.372,0.436,0.299,0.872,0.522,0.894,0.511,0.768,0.536,0.416,0.515,0.765,0.558,0.555,0.587,0.383
-4011,Not Epileptic,M,32.0,0.6,2.4,0.9,1.2,1.6,1.7,1.2,1.6,2.1,0.7,0.3,2.9,1.7,0.5,1.4,5.6,8.7,1.0,1.4,5.4,0.8,2.7,1.9,4.1,3.1,3.0,3.4,2.7,2.5,2.1,1.5,1.2,0.4,2.9,0.4,0.6,2.9,4.6,0.1,0.1,1.0,2.7,1.5,2.9,2.2,0.5,0.5,0.9,1.5,0.7,0.7,1.7,1.3,2.0,0.2,2.4,0.6,1.2,1.1,1.4,0.7,1.5,0.4,0.5,2.3,1.4,2.3,1.2,0.7,1.1,1.2,1.5,4.2,0.4,6,25,8,13,16,17,13,18,19,8,4.0,27,22,6,17,66,89,10,18,51,9,35,20,48,43,33,48,29,25,32,15,9,4,31,3,6,31,48,1,2,7,28,16,22,14,4,2,5,9,6,4,14,10,16,2,21,4,11,8,10,8,12,3,6,16,20,22,10,4,12,8,13,33,4,0.031,0.023,0.017,0.023,0.03,0.03,0.019,0.025,0.043,0.03,0.032,0.037,0.034,0.026,0.031,0.031,0.029,0.036,0.026,0.042,0.022,0.03,0.035,0.045,0.032,0.027,0.029,0.029,0.024,0.027,0.047,0.038,0.019,0.03,0.013,0.012,0.028,0.032,0.011,0.02,0.022,0.033,0.028,0.027,0.017,0.01,0.022,0.013,0.018,0.017,0.027,0.017,0.025,0.017,0.028,0.017,0.013,0.015,0.022,0.024,0.026,0.022,0.034,0.016,0.021,0.024,0.019,0.019,0.011,0.029,0.025,0.016,0.017,0.019,1736,4901,2517,2295,2114,3525,3055,3852,2444,1645,600.0,3285,3290,1192,2716,11157,19330,2806,3932,6039,3289,5087,2152,5654,6383,6279,6024,3762,6117,5229,2144,1490,1003,6957,2188,1878,7764,9753,280,357,1339,3038,4619,3166,3693,1354,713,2234,2566,1751,686,3014,1661,4202,428,4337,1306,2613,1379,1693,1033,2688,521,954,3181,1228,4277,2415,1745,1875,1447,3266,8882,470,0.114,0.117,0.095,0.106,0.134,0.137,0.096,0.123,0.171,0.139,0.122,0.14,0.15,0.144,0.148,0.142,0.135,0.138,0.125,0.155,0.089,0.13,0.131,0.149,0.131,0.128,0.124,0.11,0.112,0.136,0.154,0.116,0.101,0.135,0.085,0.083,0.121,0.151,0.096,0.117,0.117,0.131,0.122,0.119,0.092,0.082,0.102,0.079,0.105,0.092,0.118,0.102,0.114,0.097,0.134,0.105,0.092,0.098,0.111,0.122,0.125,0.117,0.132,0.114,0.109,0.13,0.103,0.106,0.081,0.15,0.127,0.103,0.1,0.122,437,1775,844,881,891,959,931,1172,729,374,165.0,1350,948,324,804,3091,5107,553,961,2296,703,1509,918,1641,1781,1814,2074,1375,1573,1480,552,587,349,1463,521,749,1775,2262,164,166,724,1223,960,1745,1981,709,330,984,1181,645,415,1463,838,1860,125,2104,610,1231,802,893,469,1035,231,506,1567,863,1989,1017,835,635,698,1425,3865,285,3.785,2.544,2.743,2.587,2.114,2.764,2.579,2.826,2.696,3.077,3.074,2.094,2.775,2.721,2.554,2.684,2.932,3.812,3.024,2.271,3.305,2.562,2.111,2.65,2.753,2.81,2.327,2.168,2.93,2.657,2.73,2.558,2.245,3.155,3.744,2.414,3.201,3.107,2.002,2.535,2.43,2.084,3.449,2.053,2.128,2.105,2.643,2.818,2.67,2.858,2.048,2.237,2.131,2.353,2.918,2.313,2.512,2.434,2.021,2.051,2.197,2.554,2.655,2.295,2.173,1.717,2.201,2.572,2.476,3.078,2.406,2.591,2.529,2.108,0.693,0.603,0.469,0.412,0.693,0.773,0.617,0.413,0.604,0.576,0.676,0.445,0.439,0.587,0.571,0.645,0.62,0.737,0.564,0.599,0.732,0.502,0.508,0.871,0.467,0.522,0.527,0.506,0.427,0.592,0.729,0.913,0.443,0.636,0.818,0.41,0.804,0.656,0.267,0.512,0.494,0.658,0.767,0.646,0.656,0.37,0.477,0.596,0.426,0.529,0.409,0.444,0.491,0.468,1.02,0.434,0.415,0.411,0.328,0.426,0.511,0.675,0.462,0.783,0.515,0.707,0.456,0.385,0.4,0.797,0.42,0.498,0.452,0.546
-4012,Not Epileptic,M,42.0,0.8,2.0,1.1,1.4,2.6,2.5,1.7,2.1,2.1,1.1,0.5,3.3,1.8,0.9,1.0,6.9,9.0,0.9,1.8,6.2,1.4,6.0,3.6,7.5,5.7,3.8,6.7,3.0,5.2,3.6,2.5,1.7,0.4,2.7,0.8,0.8,4.0,3.5,0.2,0.3,1.2,4.1,3.1,2.3,3.5,0.7,0.7,1.2,1.3,1.4,0.6,3.7,1.7,3.5,0.3,2.6,0.9,1.7,1.6,0.9,1.1,2.7,0.3,0.9,2.5,1.5,3.2,1.8,1.6,0.8,1.0,1.6,4.5,0.2,6,16,12,10,20,25,12,21,13,10,6.0,30,17,9,9,59,72,5,24,53,7,56,34,49,49,41,61,25,35,35,17,12,3,28,4,8,43,37,2,1,8,36,21,19,21,6,2,7,7,13,5,25,11,24,1,16,8,15,9,7,8,17,2,6,17,20,24,11,9,5,6,13,33,2,0.039,0.023,0.021,0.025,0.05,0.033,0.038,0.032,0.051,0.037,0.039,0.04,0.04,0.05,0.037,0.045,0.039,0.031,0.03,0.052,0.026,0.045,0.051,0.067,0.044,0.041,0.054,0.036,0.042,0.041,0.051,0.049,0.025,0.034,0.027,0.018,0.041,0.031,0.013,0.027,0.024,0.037,0.049,0.027,0.024,0.016,0.023,0.019,0.019,0.03,0.041,0.029,0.031,0.029,0.023,0.017,0.019,0.022,0.027,0.017,0.031,0.044,0.035,0.025,0.029,0.025,0.025,0.024,0.028,0.026,0.024,0.02,0.018,0.015,1350,4638,2286,2300,2636,3067,2258,3874,1687,1938,867.0,3251,3317,1085,2049,8648,15284,1944,4044,4542,2990,6477,2229,5004,5691,6467,5287,2975,6151,5593,2424,1362,738,7178,1917,1909,7799,7942,299,347,1480,2907,5095,2279,3657,1323,766,2298,2256,1943,718,4040,1738,4642,423,3860,1363,2773,1457,1451,1035,2266,249,931,2368,1337,3581,2299,1573,1116,1362,2345,8470,423,0.131,0.109,0.111,0.12,0.149,0.144,0.116,0.144,0.161,0.154,0.147,0.156,0.127,0.161,0.131,0.151,0.139,0.113,0.132,0.169,0.1,0.16,0.166,0.173,0.142,0.147,0.14,0.123,0.119,0.158,0.148,0.147,0.105,0.135,0.108,0.105,0.157,0.136,0.099,0.131,0.117,0.143,0.15,0.128,0.108,0.1,0.106,0.097,0.094,0.138,0.156,0.126,0.123,0.123,0.125,0.096,0.113,0.118,0.119,0.093,0.134,0.144,0.144,0.117,0.129,0.128,0.116,0.113,0.119,0.126,0.113,0.121,0.099,0.102,396,1509,910,869,1015,1309,787,1166,611,477,243.0,1536,954,287,587,2828,4181,448,1085,2207,872,2216,1155,1972,2078,1877,2059,1360,1815,1659,864,582,244,1422,478,741,1827,1854,202,148,781,1658,1118,1423,2248,660,392,963,1054,746,301,1970,896,2013,151,2170,687,1305,942,902,607,1076,154,540,1263,926,2003,1151,844,430,695,1148,3885,202,3.143,2.781,2.533,2.484,2.244,2.048,2.351,2.845,2.309,3.014,2.825,1.867,2.718,2.695,2.537,2.377,2.845,3.448,2.777,1.84,2.645,2.409,1.774,2.212,2.233,2.647,2.065,1.852,2.608,2.584,2.271,2.413,2.511,3.393,3.641,2.308,3.059,2.981,1.691,2.675,2.363,1.664,3.467,1.773,1.884,2.166,2.467,2.954,2.592,2.587,2.119,2.044,2.089,2.355,2.725,1.96,2.068,2.264,1.694,1.791,1.97,2.316,1.739,2.15,2.077,1.851,1.868,2.385,2.236,2.779,2.221,2.365,2.286,2.377,0.882,0.809,0.448,0.532,0.689,0.616,0.604,0.534,0.653,0.602,0.862,0.589,0.52,0.668,0.646,0.655,0.689,0.824,0.568,0.578,0.78,0.847,0.503,0.705,0.709,0.668,0.67,0.515,0.621,0.639,0.805,0.806,0.402,0.8,0.744,0.414,0.794,0.806,0.394,0.373,0.511,0.483,0.835,0.626,0.566,0.454,0.321,0.836,0.52,0.547,0.486,0.555,0.519,0.509,0.734,0.427,0.357,0.518,0.361,0.304,0.485,0.713,0.418,0.61,0.47,0.702,0.473,0.461,0.381,0.8,0.479,0.522,0.532,0.36
-4013,Not Epileptic,M,20.0,0.7,2.9,1.0,1.1,2.6,2.2,1.4,2.0,2.3,1.0,0.3,3.0,1.3,0.4,1.2,7.1,9.6,0.9,2.8,5.4,1.2,3.8,1.8,3.3,3.3,3.4,4.0,2.7,2.8,3.3,1.5,2.2,0.4,2.5,0.5,0.9,3.9,4.7,0.1,0.2,1.0,3.3,2.2,2.9,2.3,0.5,0.3,1.0,1.3,0.8,0.8,1.7,2.6,2.4,0.4,2.4,0.9,1.7,1.1,1.2,0.7,2.4,0.3,0.7,1.7,1.3,2.8,1.9,0.8,0.5,1.4,1.5,6.1,0.3,6,28,7,9,23,27,15,18,23,12,5.0,31,18,6,17,92,110,6,37,49,13,48,19,32,45,42,48,34,31,47,17,29,5,37,3,7,48,54,1,1,6,31,24,19,14,4,1,6,7,6,6,14,25,17,2,23,7,13,8,9,8,20,2,6,16,18,23,18,6,4,11,13,54,3,0.033,0.027,0.017,0.021,0.038,0.03,0.023,0.028,0.058,0.032,0.033,0.032,0.027,0.024,0.028,0.036,0.03,0.031,0.032,0.039,0.025,0.035,0.026,0.034,0.028,0.026,0.031,0.024,0.022,0.027,0.037,0.077,0.025,0.028,0.011,0.015,0.037,0.034,0.012,0.019,0.021,0.035,0.033,0.029,0.016,0.011,0.016,0.016,0.018,0.016,0.029,0.019,0.027,0.018,0.018,0.017,0.023,0.018,0.021,0.016,0.017,0.031,0.024,0.017,0.016,0.022,0.019,0.017,0.015,0.018,0.026,0.017,0.02,0.014,1633,5731,2563,2456,3070,3810,3206,4535,2485,2291,870.0,3275,3402,1212,2918,14214,21670,2476,5810,5983,4497,6403,2482,6860,8016,8961,6630,6705,8812,7540,2590,1618,1034,7829,2613,2818,8884,12037,361,557,1663,2906,6714,2826,3908,1427,749,2276,2449,2050,706,3176,3096,5064,524,4093,1512,3213,1351,2218,1322,3349,417,1034,3328,1597,4984,4593,1728,904,2021,3186,12137,562,0.111,0.128,0.096,0.107,0.143,0.135,0.109,0.133,0.179,0.141,0.136,0.147,0.129,0.129,0.135,0.144,0.135,0.119,0.14,0.154,0.098,0.136,0.118,0.123,0.125,0.126,0.122,0.116,0.107,0.127,0.131,0.148,0.103,0.128,0.073,0.091,0.142,0.151,0.084,0.089,0.103,0.13,0.124,0.122,0.09,0.082,0.087,0.081,0.096,0.093,0.14,0.099,0.131,0.095,0.111,0.108,0.117,0.101,0.112,0.101,0.104,0.136,0.13,0.113,0.107,0.116,0.104,0.095,0.091,0.117,0.123,0.099,0.104,0.112,418,1726,883,811,1138,1347,1001,1187,789,516,219.0,1670,860,277,746,3951,5963,474,1543,2574,973,1871,1068,1864,1979,2189,2266,1908,2125,2048,715,595,280,1702,648,931,2215,2515,201,249,777,1495,1386,1611,2209,679,300,832,1057,839,422,1489,1472,2123,286,2097,670,1480,804,1091,592,1252,227,565,1542,959,2361,1856,820,434,903,1445,5021,301,3.564,3.038,2.848,2.941,2.224,2.329,2.532,3.172,2.429,3.319,3.167,1.812,2.908,2.992,2.811,2.741,2.94,3.813,3.013,2.039,3.109,2.591,2.043,2.736,2.988,3.041,2.4,2.521,3.08,2.733,2.677,2.739,2.745,3.257,3.606,2.735,2.939,3.311,1.999,2.555,2.849,1.793,3.346,2.036,1.968,2.333,3.198,3.196,2.899,2.794,2.038,2.321,2.132,2.542,2.179,2.178,2.619,2.582,1.982,2.22,2.345,2.615,1.806,2.346,2.44,1.967,2.313,2.658,2.382,2.373,2.526,2.581,2.573,2.341,0.985,0.53,0.442,0.538,0.665,0.668,0.551,0.527,0.605,0.715,0.663,0.523,0.522,0.421,0.557,0.613,0.61,0.734,0.542,0.636,0.897,0.581,0.48,0.802,0.651,0.61,0.581,0.689,0.551,0.639,0.679,1.0,0.319,0.607,0.659,0.603,0.84,0.679,0.388,0.442,0.553,0.468,0.791,0.719,0.609,0.427,0.309,0.631,0.488,0.385,0.408,0.411,0.439,0.407,0.342,0.394,0.66,0.518,0.369,0.398,0.432,0.581,0.479,0.628,0.475,0.65,0.469,0.363,0.439,0.765,0.433,0.436,0.518,0.352
-4014,Not Epileptic,M,37.0,0.7,2.1,0.8,1.3,1.4,1.8,1.9,1.3,1.1,0.7,0.3,2.9,1.2,0.4,1.5,6.1,8.8,0.9,2.2,5.0,1.2,2.6,2.3,2.8,3.0,2.9,3.3,2.1,2.7,2.8,1.2,1.1,0.3,2.5,0.3,0.5,3.7,3.4,0.2,0.1,1.0,2.9,2.0,2.1,2.8,0.9,0.4,0.8,0.8,0.6,0.7,2.1,1.3,2.7,0.4,2.8,0.9,1.7,0.5,1.0,0.8,1.2,0.3,0.4,1.7,1.6,3.0,1.3,0.8,0.4,0.8,1.6,3.6,0.3,5,21,7,12,11,17,14,11,10,6,3.0,20,12,6,15,58,72,9,23,38,6,27,19,28,31,33,31,17,20,28,18,5,4,29,2,4,38,38,2,1,4,27,17,18,18,7,3,5,5,5,5,17,9,17,3,23,5,14,4,9,7,8,3,4,13,15,25,7,5,4,7,12,27,2,0.032,0.02,0.015,0.021,0.031,0.024,0.028,0.023,0.044,0.032,0.029,0.029,0.026,0.021,0.027,0.027,0.028,0.031,0.026,0.036,0.021,0.029,0.031,0.031,0.027,0.024,0.03,0.024,0.023,0.033,0.042,0.034,0.023,0.028,0.011,0.013,0.03,0.031,0.013,0.018,0.02,0.031,0.033,0.027,0.02,0.02,0.022,0.012,0.011,0.021,0.029,0.02,0.019,0.018,0.013,0.016,0.018,0.017,0.019,0.018,0.016,0.021,0.015,0.02,0.018,0.033,0.02,0.017,0.016,0.018,0.019,0.02,0.015,0.015,1675,4903,2542,2654,1975,3232,3977,3282,1859,1439,781.0,2987,3181,1095,3373,13548,20597,2246,5392,5449,3169,5270,2752,5595,6244,7544,6265,3906,7097,5181,2038,891,1076,7128,2184,1691,8403,8338,363,438,1665,3002,4819,2148,3838,1658,849,2344,2263,1695,755,4385,2348,6526,736,5571,2315,3538,961,1719,2152,2798,601,857,2961,1116,4928,2346,1746,880,1557,3075,10048,588,0.104,0.115,0.09,0.113,0.124,0.128,0.113,0.107,0.137,0.13,0.113,0.118,0.125,0.119,0.131,0.121,0.127,0.127,0.122,0.138,0.08,0.135,0.135,0.132,0.129,0.118,0.131,0.103,0.096,0.141,0.144,0.127,0.112,0.124,0.063,0.078,0.125,0.135,0.102,0.08,0.094,0.138,0.13,0.124,0.101,0.101,0.108,0.078,0.08,0.097,0.124,0.103,0.102,0.095,0.106,0.101,0.088,0.094,0.115,0.11,0.096,0.107,0.106,0.123,0.103,0.134,0.108,0.092,0.088,0.116,0.114,0.111,0.086,0.102,415,1582,861,970,787,1150,1107,883,514,374,197.0,1446,779,300,929,3412,5204,458,1372,2270,846,1452,1164,1496,1807,2116,1826,1399,1714,1487,573,419,314,1480,543,587,2075,1821,178,185,739,1426,1025,1354,2201,717,312,963,1026,603,434,1806,1108,2387,404,2713,803,1700,448,876,791,881,366,342,1426,664,2216,1103,768,367,662,1336,3988,262,3.694,2.976,2.938,2.749,2.323,2.424,2.965,3.101,2.663,3.406,3.07,1.817,3.053,2.616,2.797,2.886,3.205,3.666,3.168,2.051,2.9,2.876,2.101,2.82,2.722,2.871,2.569,2.262,2.944,2.827,2.61,2.272,2.794,3.305,3.497,2.833,2.955,3.297,2.257,2.549,2.776,1.902,3.299,1.823,2.04,2.516,3.154,2.955,2.681,3.352,2.161,2.513,2.51,2.898,2.369,2.295,3.237,2.465,2.316,2.222,2.817,3.239,1.882,2.611,2.331,2.152,2.253,2.519,2.515,2.652,2.845,2.726,2.742,2.92,0.824,0.592,0.735,0.529,0.57,0.514,0.808,0.496,0.625,0.612,1.032,0.593,0.391,0.499,0.51,0.579,0.623,0.895,0.595,0.706,0.816,0.56,0.565,0.681,0.57,0.605,0.611,0.578,0.686,0.672,0.79,1.012,0.401,0.638,0.83,0.603,0.825,0.466,0.448,0.45,0.545,0.496,0.887,0.62,0.657,0.448,0.648,0.749,0.365,0.919,0.528,0.509,0.528,0.529,0.355,0.511,0.597,0.603,0.404,0.395,0.434,0.961,0.394,0.976,0.661,0.769,0.486,0.429,0.402,1.014,0.453,0.62,0.478,0.365
-4015,Not Epileptic,F,47.0,1.0,2.5,1.1,1.3,2.2,1.8,2.0,1.4,2.1,0.7,0.5,3.2,1.2,0.3,1.1,5.7,8.1,0.7,1.9,4.2,1.3,2.2,2.1,3.0,4.6,3.3,2.9,1.8,2.5,2.6,2.0,0.8,0.5,2.2,0.6,0.6,3.9,3.9,0.2,0.2,0.7,3.8,2.1,2.2,2.8,0.6,0.5,0.7,1.2,0.5,0.4,2.0,1.3,1.8,0.6,2.5,1.2,1.3,0.9,0.8,0.9,1.4,0.3,0.5,1.5,1.3,1.6,1.1,1.1,0.4,1.1,1.7,5.0,0.2,9,21,8,10,19,20,15,11,19,10,5.0,31,15,4,12,61,85,11,25,32,14,30,19,33,56,39,35,19,22,33,17,6,3,26,3,8,36,48,1,1,3,30,23,18,18,4,3,4,7,4,2,17,9,14,4,21,10,9,7,5,9,12,2,6,11,17,12,6,8,4,6,14,47,1,0.044,0.026,0.025,0.026,0.041,0.032,0.034,0.027,0.075,0.035,0.035,0.035,0.025,0.04,0.028,0.039,0.034,0.037,0.036,0.044,0.026,0.026,0.036,0.035,0.036,0.029,0.026,0.023,0.025,0.029,0.052,0.037,0.029,0.027,0.023,0.019,0.039,0.03,0.021,0.017,0.019,0.039,0.034,0.022,0.021,0.014,0.018,0.013,0.022,0.015,0.019,0.024,0.026,0.019,0.016,0.02,0.02,0.017,0.021,0.017,0.023,0.025,0.023,0.018,0.015,0.021,0.017,0.017,0.022,0.02,0.025,0.025,0.02,0.015,1491,5004,2805,2546,2507,3425,3085,3056,1869,1713,840.0,3898,3467,680,2317,9790,17889,2254,4388,3681,3813,4949,2577,5547,8498,7016,6792,4749,6394,6081,2544,909,888,6298,1440,2085,7007,9240,258,451,1270,2928,6054,3063,3829,1365,728,2006,1959,1365,571,3151,1544,3570,904,4296,2282,3061,1280,1274,1313,2412,318,898,2998,1457,3029,2372,1663,732,1566,2401,10236,432,0.14,0.124,0.103,0.116,0.152,0.142,0.116,0.126,0.2,0.157,0.135,0.148,0.128,0.155,0.134,0.155,0.143,0.135,0.143,0.165,0.112,0.129,0.123,0.138,0.147,0.136,0.126,0.107,0.106,0.139,0.147,0.111,0.123,0.126,0.092,0.1,0.135,0.133,0.119,0.085,0.092,0.134,0.141,0.11,0.101,0.087,0.111,0.081,0.102,0.097,0.117,0.127,0.123,0.104,0.113,0.109,0.11,0.094,0.119,0.099,0.114,0.124,0.111,0.115,0.096,0.11,0.098,0.095,0.111,0.151,0.119,0.122,0.104,0.096,414,1511,787,873,952,1111,991,877,592,421,216.0,1670,832,185,633,2813,4566,448,1152,1616,871,1324,4015,1556,2247,2048,1919,1336,1542,1636,808,431,274,1347,367,621,1758,2460,123,225,589,1486,1296,1652,2131,650,324,884,889,553,252,1371,793,1582,491,2042,915,1288,677,669,614,916,199,481,1392,963,1458,1003,803,301,681,1114,4071,188,3.286,3.02,3.121,2.826,2.304,2.516,2.553,3.054,2.476,3.166,2.8,2.004,3.005,2.735,2.725,2.685,3.057,3.758,3.121,2.031,3.158,2.715,2.14,2.74,2.855,2.8,2.697,2.634,2.981,2.819,2.543,2.278,2.652,3.158,3.177,2.778,2.988,2.889,2.248,2.556,2.814,1.868,3.377,2.132,2.037,2.438,2.628,2.783,2.776,2.826,2.638,2.415,2.057,2.502,2.235,2.36,2.508,2.619,2.171,2.131,2.253,2.795,1.598,2.12,2.328,1.856,2.288,2.757,2.478,2.461,2.631,2.444,2.61,2.656,0.695,0.589,0.603,0.5,0.68,0.602,0.574,0.436,0.69,0.742,0.809,0.565,0.433,0.649,0.528,0.544,0.564,0.786,0.573,0.625,0.813,0.567,0.659,0.884,0.616,0.584,0.586,0.674,0.585,0.607,0.95,0.866,0.649,0.693,0.62,0.442,0.848,0.568,0.57,0.293,0.489,0.561,0.808,0.69,0.557,0.377,0.892,0.502,0.436,0.421,0.562,0.425,0.504,0.396,0.408,0.394,0.47,0.495,0.37,0.368,0.393,0.531,0.364,0.642,0.528,0.51,0.374,0.423,0.374,1.003,0.436,0.589,0.399,0.443
-4016,Not Epileptic,M,22.0,0.8,2.9,0.7,1.6,1.7,2.1,1.9,1.7,1.8,0.8,0.5,2.5,1.6,0.5,1.3,8.5,10.1,0.8,2.7,5.0,1.4,2.9,1.5,3.0,2.9,3.0,2.9,2.8,2.8,3.2,1.4,2.0,0.6,4.1,0.4,1.2,3.5,4.2,0.2,0.2,1.3,3.0,2.3,1.9,3.6,0.7,0.3,0.9,1.3,0.7,0.8,2.2,1.3,2.6,0.3,2.2,0.6,1.4,0.9,1.1,0.7,1.7,0.1,0.5,2.0,0.9,4.6,1.6,0.8,0.5,1.2,1.5,6.4,0.3,7,30,8,10,22,18,15,15,18,7,6.0,24,17,7,17,84,93,8,32,39,16,33,16,31,38,34,29,26,21,34,26,13,7,40,3,11,38,39,2,1,7,23,25,17,25,5,2,8,6,6,5,15,8,17,2,17,4,13,7,9,6,14,1,5,15,12,32,12,6,4,8,14,53,2,0.031,0.028,0.011,0.025,0.038,0.036,0.025,0.03,0.052,0.033,0.035,0.034,0.028,0.026,0.029,0.037,0.028,0.028,0.029,0.044,0.026,0.032,0.035,0.037,0.03,0.026,0.032,0.027,0.025,0.034,0.037,0.064,0.025,0.038,0.016,0.023,0.03,0.036,0.012,0.012,0.023,0.043,0.038,0.024,0.027,0.015,0.016,0.013,0.015,0.015,0.039,0.021,0.022,0.019,0.02,0.02,0.016,0.016,0.027,0.022,0.023,0.032,0.013,0.017,0.019,0.021,0.029,0.021,0.016,0.017,0.028,0.02,0.02,0.012,1900,5721,2186,2710,2041,2695,2890,3459,1970,1441,596.0,2426,4119,1131,3156,13499,22391,2640,5171,3482,3738,4753,2227,5618,6304,7087,4880,3673,6365,5208,2420,1082,1261,7681,1769,2584,7072,8299,432,659,1742,2812,5542,2186,3584,1366,781,2521,2718,1378,749,3591,1938,5439,505,3213,1201,2991,880,1549,1039,2586,329,945,2895,1112,4742,2425,1749,1042,1600,2388,11702,752,0.113,0.119,0.085,0.109,0.14,0.147,0.115,0.123,0.161,0.143,0.141,0.144,0.137,0.13,0.139,0.147,0.128,0.107,0.13,0.155,0.106,0.137,0.14,0.126,0.134,0.126,0.131,0.117,0.108,0.141,0.128,0.166,0.138,0.142,0.092,0.111,0.126,0.135,0.079,0.089,0.113,0.147,0.139,0.121,0.121,0.101,0.088,0.09,0.087,0.104,0.145,0.104,0.105,0.096,0.117,0.112,0.099,0.099,0.129,0.116,0.112,0.131,0.096,0.114,0.104,0.117,0.13,0.111,0.097,0.106,0.126,0.113,0.106,0.08,480,1715,960,991,765,963,1066,972,598,375,203.0,1187,1035,312,874,3750,5834,503,1496,1813,889,1550,807,1433,1760,1869,1585,1620,1644,1710,668,481,386,1766,380,880,1836,1884,249,278,813,1140,1073,1280,2253,688,326,1109,1259,619,341,1649,985,2302,245,1654,600,1454,553,817,525,883,181,438,1446,676,2378,1105,816,485,676,1128,4935,330,3.656,2.784,2.176,2.761,2.181,2.525,2.298,3.083,2.4,3.132,2.512,1.806,3.061,2.509,2.74,2.74,3.045,3.751,2.768,1.734,3.103,2.376,2.235,2.845,2.685,3.051,2.439,1.924,2.89,2.491,2.645,2.466,2.838,3.125,3.746,2.638,2.909,3.031,2.078,2.571,2.73,2.084,3.397,1.934,1.874,2.232,2.823,2.786,2.637,2.593,2.503,2.263,1.951,2.515,2.518,2.155,2.39,2.385,1.908,2.11,2.344,2.649,2.135,2.273,2.27,2.081,2.235,2.515,2.494,2.237,2.698,2.507,2.548,2.563,0.773,0.806,0.61,0.581,0.669,0.436,0.616,0.591,0.731,0.436,0.801,0.537,0.471,0.451,0.586,0.573,0.596,0.696,0.535,0.517,0.795,0.444,0.523,0.752,0.517,0.606,0.488,0.481,0.67,0.585,0.761,0.762,0.542,0.629,0.877,0.541,0.605,0.602,0.302,0.377,0.523,0.717,0.698,0.708,0.652,0.606,0.498,0.733,0.557,0.457,0.433,0.502,0.535,0.521,0.438,0.435,0.372,0.555,0.38,0.42,0.312,0.778,0.402,0.94,0.537,0.584,0.442,0.407,0.473,0.581,0.554,0.508,0.477,0.534
-4017,Not Epileptic,F,47.0,0.8,2.8,1.4,1.5,2.0,2.1,2.3,1.6,1.8,1.5,0.4,3.4,1.4,0.5,1.1,8.0,9.8,1.2,2.9,4.1,1.5,2.8,3.3,6.5,3.7,3.9,3.9,2.6,4.7,4.0,1.3,1.2,0.7,2.6,0.4,0.8,3.6,3.6,0.2,0.1,1.0,4.1,2.5,2.6,3.2,0.8,0.5,1.4,1.2,0.9,0.7,1.8,1.5,3.1,0.5,3.1,0.9,2.0,1.1,1.5,0.6,2.3,0.3,0.5,1.9,1.8,3.5,1.3,1.5,0.6,1.0,1.9,5.1,0.3,7,26,13,14,15,22,16,15,19,10,3.0,29,16,5,9,88,94,10,35,34,14,32,26,59,39,44,45,23,29,45,17,8,7,33,3,7,43,48,2,1,6,36,31,23,17,5,2,10,6,8,6,11,12,22,4,25,10,19,10,12,5,21,2,5,16,24,33,8,9,6,9,16,41,4,0.038,0.025,0.02,0.027,0.039,0.027,0.035,0.026,0.058,0.054,0.029,0.04,0.031,0.032,0.034,0.035,0.036,0.041,0.036,0.037,0.024,0.036,0.039,0.057,0.037,0.027,0.026,0.03,0.039,0.032,0.036,0.045,0.024,0.034,0.015,0.02,0.039,0.035,0.017,0.016,0.02,0.038,0.043,0.025,0.025,0.014,0.019,0.024,0.019,0.019,0.033,0.02,0.023,0.023,0.013,0.02,0.026,0.017,0.028,0.026,0.021,0.036,0.03,0.019,0.02,0.029,0.023,0.018,0.024,0.019,0.026,0.021,0.02,0.018,1094,5117,3143,2606,1784,4000,2933,3322,2086,1837,756.0,3207,2970,1134,2123,12681,17847,2017,5566,3580,4322,4337,2579,5620,6674,7815,7734,3395,6080,7036,2171,1197,1517,5871,2143,2097,6118,8392,431,355,1528,3661,6161,2355,3262,1419,746,2263,2147,1587,670,3051,1941,5196,971,4813,1054,3721,1142,1575,905,2788,383,1056,3096,1842,4428,2127,1747,838,1427,2798,9531,463,0.125,0.122,0.104,0.126,0.132,0.136,0.108,0.118,0.156,0.165,0.119,0.15,0.129,0.111,0.133,0.14,0.139,0.125,0.145,0.145,0.107,0.127,0.132,0.164,0.133,0.131,0.117,0.102,0.115,0.144,0.13,0.121,0.105,0.139,0.077,0.095,0.137,0.15,0.102,0.096,0.106,0.15,0.145,0.123,0.112,0.09,0.101,0.104,0.096,0.105,0.143,0.098,0.115,0.107,0.09,0.109,0.127,0.106,0.125,0.129,0.123,0.133,0.117,0.108,0.11,0.131,0.111,0.1,0.117,0.13,0.12,0.119,0.104,0.115,378,1757,1143,983,866,1291,1029,1029,675,443,194.0,1509,825,277,524,4056,4934,424,1463,1871,1057,1264,1238,1982,1799,2359,2598,1371,1607,2262,640,477,503,1458,514,662,1749,2003,182,166,755,1661,1289,1616,1952,754,335,902,990,724,339,1433,1052,2275,548,2359,536,1744,680,890,387,1207,223,520,1527,958,2309,1028,854,488,697,1274,4135,293,2.968,2.697,2.637,2.611,1.916,2.523,2.454,2.74,2.38,3.358,2.898,1.769,2.723,2.793,2.86,2.456,2.791,3.573,2.972,1.743,3.075,2.513,1.877,2.318,2.77,2.742,2.395,2.046,2.859,2.48,2.334,2.596,2.5,3.017,3.552,2.663,2.694,3.034,2.258,2.362,2.452,1.882,3.357,1.655,1.875,2.112,2.72,2.986,2.586,2.396,1.975,2.263,1.971,2.335,2.243,2.216,2.235,2.3,1.909,1.937,2.459,2.34,1.833,2.345,2.15,2.261,2.089,2.35,2.296,2.12,2.406,2.544,2.466,1.843,0.626,0.624,0.492,0.573,0.558,0.686,0.712,0.392,0.637,0.658,0.613,0.674,0.482,0.543,0.55,0.583,0.606,0.846,0.655,0.661,0.812,0.553,0.606,0.708,0.667,0.499,0.536,0.558,0.693,0.562,0.751,0.914,0.394,0.469,0.72,0.509,0.724,0.536,0.451,0.403,0.511,0.657,0.754,0.561,0.574,0.409,0.614,0.896,0.45,0.38,0.522,0.446,0.398,0.45,0.332,0.416,0.464,0.57,0.416,0.433,0.493,0.549,0.435,0.649,0.517,0.655,0.437,0.438,0.516,0.523,0.341,0.429,0.438,0.469
-4018,Not Epileptic,M,55.0,1.2,3.5,1.9,1.7,2.1,2.1,3.0,1.8,3.0,1.5,0.5,2.6,1.5,0.4,1.3,6.4,15.4,1.3,3.1,4.0,2.6,4.2,2.1,5.6,5.2,3.6,4.4,3.3,5.1,5.5,1.7,1.6,0.6,2.8,0.5,1.6,5.9,4.2,0.3,0.3,1.2,2.9,3.5,2.2,4.0,0.8,0.7,0.9,1.7,1.0,0.7,1.7,2.0,3.7,1.1,2.5,1.0,1.5,1.0,1.7,1.0,1.6,0.3,0.7,2.3,2.7,2.7,1.6,0.9,0.5,1.1,1.9,4.9,0.3,10,32,16,14,14,24,23,17,16,14,5.0,25,16,6,14,71,126,10,31,40,21,45,22,56,67,44,55,28,47,55,20,10,5,40,3,17,60,55,2,4,7,34,31,18,20,5,3,5,9,8,5,14,13,33,7,18,7,13,10,12,7,14,2,6,16,29,18,14,5,4,8,16,38,3,0.043,0.03,0.031,0.025,0.038,0.032,0.046,0.029,0.062,0.049,0.041,0.036,0.031,0.031,0.032,0.041,0.043,0.044,0.037,0.033,0.036,0.04,0.033,0.048,0.044,0.028,0.033,0.032,0.037,0.036,0.042,0.048,0.032,0.034,0.017,0.02,0.042,0.036,0.013,0.025,0.024,0.04,0.045,0.025,0.025,0.017,0.018,0.015,0.021,0.015,0.024,0.021,0.029,0.024,0.026,0.017,0.021,0.018,0.021,0.025,0.022,0.026,0.015,0.021,0.02,0.031,0.018,0.021,0.019,0.014,0.016,0.02,0.017,0.015,1999,5843,3498,3388,2059,3618,3174,3230,1623,2201,658.0,2915,2947,989,2747,10457,21729,2500,5022,5802,6168,5883,2790,6723,8387,7863,7470,5071,7436,8402,2829,1733,1038,6683,2188,3507,9450,10054,541,767,1562,3414,6697,2388,4307,1611,1153,2359,2696,2451,644,2708,2156,6636,1011,4569,1529,3528,1436,1842,1165,3105,372,1081,3242,2553,4884,2971,1488,896,2153,2771,10991,618,0.134,0.126,0.113,0.11,0.13,0.129,0.119,0.137,0.167,0.161,0.147,0.146,0.133,0.135,0.127,0.142,0.147,0.149,0.142,0.144,0.127,0.139,0.126,0.143,0.148,0.13,0.123,0.106,0.122,0.14,0.142,0.135,0.116,0.129,0.09,0.108,0.143,0.142,0.093,0.113,0.11,0.131,0.15,0.12,0.109,0.097,0.105,0.082,0.1,0.091,0.117,0.113,0.124,0.113,0.12,0.095,0.112,0.092,0.11,0.123,0.115,0.114,0.111,0.109,0.11,0.124,0.094,0.106,0.102,0.1,0.104,0.114,0.093,0.107,463,2016,1156,1119,861,1189,1079,1083,605,540,206.0,1257,799,255,739,2999,6219,542,1257,2099,1268,1696,974,2020,2420,2369,2619,1634,2106,2691,844,626,301,1445,475,1233,2505,2362,265,263,759,1274,1471,1406,2464,742,500,920,1206,994,398,1327,1075,2682,577,2218,746,1551,796,967,663,1122,231,573,1599,1423,2319,1269,731,510,989,1422,4591,329,3.541,2.704,2.84,2.833,2.275,2.447,2.465,2.677,2.389,3.119,2.509,1.951,2.899,2.747,2.73,2.614,2.739,3.295,2.998,2.28,3.465,2.6,2.291,2.557,2.65,2.659,2.299,2.382,2.74,2.526,2.609,2.791,2.551,3.045,3.476,2.506,2.784,2.902,2.311,2.571,2.549,2.089,3.206,1.903,2.014,2.364,2.814,3.179,2.67,2.758,1.957,2.265,2.172,2.463,2.205,2.27,2.234,2.446,1.855,2.062,2.112,2.781,1.717,2.275,2.216,1.997,2.266,2.546,2.242,2.05,2.418,2.237,2.425,2.404,0.863,0.596,0.645,0.542,0.622,0.705,0.78,0.467,0.672,0.966,0.918,0.522,0.524,0.433,0.566,0.6,0.741,0.838,0.559,0.672,0.851,0.67,0.587,0.66,0.663,0.49,0.565,0.575,0.513,0.644,0.637,0.928,0.651,0.685,1.046,0.494,0.886,0.775,0.306,0.637,0.455,0.663,0.814,0.62,0.6,0.489,0.395,0.778,0.567,0.568,0.366,0.427,0.432,0.501,0.402,0.391,0.427,0.478,0.56,0.369,0.339,0.555,0.333,0.783,0.56,0.841,0.415,0.344,0.484,0.446,0.48,0.463,0.464,0.432
-4019,Not Epileptic,F,47.0,0.5,3.0,1.3,1.4,1.7,1.7,2.2,1.3,1.0,0.5,0.2,2.9,1.3,0.4,1.5,6.4,8.9,0.9,2.4,4.6,1.5,2.5,1.6,4.2,2.4,2.7,4.6,3.2,4.0,3.9,2.2,1.6,0.3,2.4,0.3,0.7,4.7,3.1,0.2,0.2,1.1,3.1,2.1,2.9,3.5,0.9,0.2,0.7,1.1,0.8,0.6,2.3,1.4,2.9,0.1,2.5,0.9,1.6,1.2,1.2,0.7,1.6,0.6,0.6,1.5,1.8,1.9,1.5,1.0,0.7,0.9,1.7,4.0,0.3,6,33,12,8,15,17,19,13,11,5,3.0,28,15,5,16,71,83,6,27,43,11,33,16,36,29,31,42,31,33,38,24,12,4,27,2,6,47,35,1,1,6,27,18,21,19,7,1,4,6,6,5,19,8,24,1,24,7,11,9,9,8,14,3,5,10,16,15,12,6,6,7,12,29,3,0.033,0.03,0.022,0.023,0.04,0.026,0.028,0.023,0.039,0.034,0.021,0.034,0.033,0.032,0.035,0.035,0.028,0.035,0.036,0.041,0.027,0.026,0.025,0.041,0.03,0.028,0.04,0.031,0.029,0.042,0.048,0.051,0.022,0.034,0.01,0.015,0.042,0.034,0.018,0.017,0.021,0.034,0.034,0.031,0.021,0.017,0.013,0.012,0.017,0.02,0.03,0.024,0.021,0.019,0.029,0.022,0.02,0.022,0.022,0.018,0.017,0.027,0.026,0.021,0.019,0.033,0.02,0.019,0.014,0.026,0.026,0.021,0.018,0.018,1277,4637,2667,2572,2106,2826,3362,3233,1824,1176,643.0,2587,2841,877,2623,11398,19191,1750,4626,4840,4043,5290,1834,5548,4742,5505,4397,5180,6705,5285,2399,1342,951,6013,1553,2136,7503,7123,280,416,1577,2323,4878,2239,4393,1547,643,1790,2096,1532,563,3208,1571,5695,175,3947,1521,2678,1338,1787,1112,2583,578,856,2226,1031,3292,2902,2136,983,1317,2359,7789,348,0.123,0.135,0.113,0.106,0.159,0.132,0.101,0.116,0.156,0.136,0.101,0.152,0.135,0.127,0.145,0.141,0.127,0.131,0.151,0.151,0.103,0.117,0.118,0.141,0.135,0.141,0.127,0.12,0.097,0.145,0.145,0.137,0.102,0.148,0.068,0.089,0.151,0.141,0.105,0.106,0.104,0.135,0.139,0.129,0.103,0.097,0.087,0.084,0.093,0.104,0.131,0.115,0.115,0.106,0.128,0.115,0.108,0.111,0.116,0.1,0.112,0.126,0.12,0.123,0.106,0.14,0.1,0.102,0.09,0.145,0.118,0.113,0.1,0.134,376,1615,928,938,758,1021,1139,999,480,274,182.0,1361,822,230,758,3406,5178,406,1228,2030,921,1620,937,1684,1420,1596,1903,1676,1765,1576,751,567,289,1259,418,719,2054,1759,169,206,778,1326,1043,1511,2455,782,279,790,974,690,346,1603,897,2469,72,1863,738,1208,842,984,636,1043,355,477,1139,628,1494,1261,1001,423,642,1247,3443,223,3.467,2.674,2.768,2.754,2.411,2.267,2.472,2.783,2.858,3.134,2.682,1.738,2.702,2.707,2.659,2.546,2.929,3.508,2.969,2.023,3.221,2.441,1.814,2.554,2.696,2.8,2.086,2.44,2.905,2.565,2.352,2.543,2.504,3.317,3.339,2.629,2.684,2.866,1.949,2.128,2.639,1.683,3.435,1.798,2.013,2.241,2.905,2.823,2.615,2.595,1.906,2.075,2.011,2.365,2.708,2.213,2.286,2.625,1.812,1.988,2.076,2.314,1.92,2.21,2.276,2.164,2.343,2.556,2.471,2.588,2.323,2.169,2.442,1.832,0.649,0.715,0.534,0.446,0.689,0.659,0.767,0.568,0.561,0.439,0.72,0.486,0.44,0.432,0.518,0.619,0.605,0.787,0.538,0.657,0.842,0.595,0.397,0.771,0.648,0.458,0.506,0.533,0.546,0.601,0.753,0.814,0.469,0.588,0.744,0.541,0.81,0.615,0.397,0.363,0.437,0.453,0.699,0.493,0.595,0.402,0.503,0.495,0.498,0.455,0.414,0.438,0.368,0.441,0.428,0.363,0.37,0.449,0.372,0.409,0.356,0.728,0.381,0.638,0.477,0.699,0.41,0.327,0.507,0.872,0.405,0.474,0.467,0.396
-4020,Not Epileptic,F,37.0,0.9,2.3,1.0,0.9,1.3,1.6,1.8,1.8,1.2,0.4,0.3,2.8,1.7,0.8,1.5,6.5,10.3,1.0,2.1,4.2,1.9,6.4,2.4,3.0,6.6,4.0,4.2,2.2,2.1,3.9,1.3,0.7,0.6,2.9,0.5,0.6,4.9,3.9,0.3,0.1,1.2,3.9,2.8,2.0,2.2,0.7,0.6,1.3,1.5,0.8,0.5,2.6,1.9,2.7,0.3,2.7,1.0,1.9,1.2,1.2,0.6,1.8,0.4,0.5,2.4,1.4,3.3,0.9,0.9,0.6,0.7,1.6,4.7,0.7,9,22,8,9,14,19,17,18,13,5,3.0,25,20,8,18,74,98,10,24,43,17,53,20,36,56,48,50,22,20,45,16,5,7,32,4,4,54,50,1,1,7,33,29,16,17,6,2,8,8,6,3,19,16,19,1,23,8,19,8,9,6,13,3,5,21,19,28,8,6,5,6,11,49,6,0.038,0.025,0.017,0.018,0.031,0.031,0.031,0.027,0.037,0.024,0.034,0.034,0.033,0.039,0.038,0.035,0.036,0.038,0.034,0.037,0.028,0.051,0.038,0.042,0.048,0.034,0.034,0.023,0.026,0.031,0.038,0.042,0.039,0.035,0.021,0.015,0.045,0.032,0.016,0.021,0.023,0.049,0.045,0.027,0.023,0.015,0.025,0.017,0.019,0.012,0.026,0.029,0.027,0.026,0.025,0.019,0.02,0.022,0.022,0.022,0.019,0.026,0.03,0.017,0.019,0.027,0.022,0.017,0.022,0.021,0.017,0.022,0.018,0.024,1461,4614,2390,2098,2057,3032,3382,3404,2054,1360,535.0,3371,3924,1341,2813,11208,19528,2131,4052,4319,5402,5079,1785,5700,5287,7516,6509,4238,5022,6994,2488,751,904,6838,2036,1881,8830,8865,399,179,1664,2465,6369,2408,2281,1524,672,2338,2176,2189,479,3090,2002,4234,346,4034,1573,3384,1536,1763,1093,2678,534,831,3859,1282,4529,1872,1444,1043,1512,2185,9632,871,0.122,0.119,0.096,0.093,0.133,0.13,0.13,0.128,0.157,0.121,0.116,0.147,0.137,0.161,0.15,0.151,0.143,0.142,0.139,0.161,0.118,0.146,0.14,0.145,0.145,0.135,0.133,0.113,0.11,0.14,0.127,0.111,0.149,0.141,0.093,0.086,0.17,0.137,0.096,0.119,0.108,0.158,0.158,0.122,0.11,0.098,0.111,0.096,0.1,0.083,0.121,0.125,0.126,0.111,0.126,0.112,0.112,0.117,0.107,0.108,0.105,0.124,0.153,0.115,0.113,0.131,0.11,0.1,0.104,0.124,0.107,0.119,0.097,0.135,495,1586,900,809,829,998,1097,1176,559,350,166.0,1367,1048,336,885,3358,5410,515,1155,1987,1170,1872,925,1530,2134,2252,2271,1538,1365,2288,685,352,331,1441,484,687,2099,2224,230,80,864,1210,1290,1262,1607,710,328,1020,1100,941,297,1554,1024,1892,145,2157,761,1397,892,899,515,995,261,431,1875,791,2401,921,695,474,700,1050,4350,409,2.82,2.695,2.712,2.426,2.172,2.563,2.456,2.606,2.771,3.016,2.645,2.055,2.85,2.889,2.495,2.576,2.842,3.189,2.79,1.906,3.524,2.209,1.821,2.84,2.119,2.703,2.274,2.226,2.732,2.484,2.623,2.273,2.487,3.142,3.433,2.621,3.122,2.92,2.013,2.327,2.504,1.885,3.22,2.102,1.598,2.352,2.473,2.706,2.424,2.617,2.021,2.196,2.025,2.327,2.469,2.054,2.288,2.404,1.916,2.178,2.308,2.761,2.022,2.121,2.238,1.965,2.049,2.292,2.421,2.362,2.437,2.419,2.434,2.22,0.626,0.615,0.474,0.615,0.718,0.553,0.577,0.468,0.647,0.524,0.647,0.573,0.464,0.408,0.573,0.524,0.618,0.69,0.499,0.483,0.961,0.715,0.534,0.746,0.648,0.537,0.611,0.481,0.538,0.592,0.687,0.964,0.457,0.63,0.754,0.439,0.762,0.662,0.31,0.387,0.57,0.627,0.873,0.596,0.43,0.45,0.531,0.612,0.539,0.51,0.37,0.451,0.465,0.454,0.73,0.366,0.431,0.635,0.341,0.366,0.467,0.7,0.48,0.741,0.584,0.589,0.398,0.443,0.386,0.871,0.444,0.593,0.449,0.46
-4021,Not Epileptic,F,32.0,0.7,2.2,0.5,1.0,1.2,1.9,1.3,1.7,1.4,0.9,1.0,3.7,1.7,0.4,1.7,5.4,8.2,1.2,1.9,4.5,1.7,2.9,1.0,2.7,2.5,4.0,2.4,3.0,2.1,2.9,1.1,1.1,0.5,2.2,0.4,1.2,3.8,2.5,0.2,0.3,1.4,3.0,2.5,2.3,2.6,0.8,0.6,0.8,1.1,1.1,0.6,2.2,1.5,3.3,0.4,2.3,1.4,2.3,0.8,0.9,0.8,0.9,0.3,0.6,1.2,1.3,2.7,1.9,0.9,0.4,1.0,1.5,5.1,0.4,6,21,6,9,14,13,10,11,15,7,5.0,24,13,6,12,57,67,6,19,34,16,31,11,26,31,35,24,26,18,27,13,8,5,26,2,12,34,27,1,2,9,28,19,20,16,8,2,6,6,12,5,14,10,24,2,18,10,16,6,7,6,11,2,4,9,13,21,13,7,4,7,10,40,3,0.033,0.023,0.009,0.018,0.027,0.035,0.023,0.028,0.044,0.034,0.061,0.04,0.026,0.023,0.029,0.033,0.026,0.038,0.031,0.037,0.024,0.034,0.029,0.031,0.031,0.03,0.027,0.027,0.022,0.03,0.028,0.045,0.023,0.024,0.013,0.026,0.034,0.025,0.017,0.021,0.023,0.032,0.036,0.025,0.021,0.017,0.023,0.014,0.013,0.021,0.031,0.024,0.027,0.024,0.021,0.022,0.043,0.029,0.023,0.021,0.03,0.018,0.021,0.02,0.016,0.029,0.026,0.026,0.018,0.01,0.028,0.019,0.021,0.017,1743,5011,2023,2033,1420,2474,2736,3759,2207,1433,673.0,3041,4113,1351,3707,10620,19089,2669,3259,4750,3158,4300,1743,5348,5059,8263,4232,4365,5392,4709,2096,734,1251,5282,1814,2624,5272,6136,432,692,2445,3089,4218,2697,3555,1469,1042,2577,2786,1843,717,3201,2468,5628,537,3123,1199,3033,1047,1533,1213,1791,511,965,2552,1182,3822,2635,1772,1108,1495,2624,8613,585,0.116,0.113,0.079,0.101,0.111,0.139,0.108,0.11,0.134,0.127,0.15,0.15,0.118,0.116,0.111,0.129,0.118,0.122,0.112,0.134,0.091,0.143,0.128,0.115,0.136,0.129,0.129,0.121,0.106,0.13,0.108,0.151,0.103,0.107,0.064,0.111,0.116,0.103,0.084,0.084,0.115,0.141,0.122,0.118,0.106,0.103,0.106,0.078,0.082,0.099,0.137,0.109,0.105,0.102,0.117,0.12,0.143,0.115,0.115,0.106,0.135,0.103,0.106,0.113,0.09,0.138,0.119,0.119,0.099,0.085,0.124,0.099,0.104,0.099,396,1576,812,876,628,869,833,1053,530,426,207.0,1311,1012,316,885,2774,4901,524,1181,1947,1099,1444,636,1454,1445,2194,1397,1580,1529,1510,558,403,404,1354,534,815,1642,1604,239,269,990,1488,1132,1543,1961,724,388,1019,1213,891,341,1462,979,2199,263,1663,557,1391,589,672,440,796,234,400,1252,637,1772,1122,855,560,618,1245,3843,304,3.757,2.807,2.385,2.291,2.02,2.608,2.733,3.11,2.929,3.11,2.878,2.035,3.045,3.009,2.924,2.757,3.124,3.836,2.475,2.146,2.581,2.405,2.367,2.746,2.736,3.052,2.346,2.248,2.793,2.547,2.705,1.993,2.513,2.73,3.093,2.991,2.551,2.849,2.221,2.966,3.264,1.92,3.054,1.988,2.094,2.034,3.57,3.015,2.863,2.381,2.422,2.388,2.159,2.58,2.705,2.221,2.418,2.452,2.03,2.257,2.613,2.332,2.363,2.765,2.2,2.253,2.29,2.628,2.501,2.236,2.583,2.498,2.404,2.281,1.109,0.772,0.525,0.583,0.672,0.524,0.688,0.62,0.674,0.625,0.758,0.554,0.416,0.447,0.798,0.702,0.653,0.967,0.576,0.698,0.784,0.506,0.472,0.73,0.43,0.552,0.467,0.487,0.443,0.533,0.64,0.807,0.433,0.548,0.658,0.593,0.66,0.65,0.547,0.795,0.562,0.563,0.768,0.679,0.566,0.528,0.472,0.91,0.8,0.736,0.513,0.718,0.711,0.559,0.421,0.552,0.536,0.539,0.366,0.558,0.371,0.652,0.686,1.198,0.533,0.681,0.455,0.449,0.535,0.774,0.522,0.514,0.518,0.421
-4022,Not Epileptic,M,32.0,0.7,2.1,1.0,1.1,1.7,1.4,1.6,1.6,1.0,0.8,0.5,2.6,1.4,0.5,1.2,5.4,7.1,0.7,2.8,6.0,1.2,2.7,1.4,2.8,3.0,2.9,2.6,2.4,2.0,3.2,0.8,1.0,0.2,3.1,0.3,0.5,4.1,3.2,0.2,0.1,0.7,2.6,2.0,3.0,2.4,0.7,0.4,1.3,1.0,1.1,0.5,2.0,1.3,2.5,0.4,2.0,0.7,1.9,0.8,0.8,0.9,1.1,0.3,0.3,1.2,1.0,2.4,1.2,0.9,0.8,0.8,1.5,4.1,0.2,4,20,12,10,17,15,15,14,10,7,4.0,21,15,5,12,52,78,7,31,44,9,31,15,33,33,26,30,26,17,30,15,9,4,30,4,4,42,35,1,1,4,25,18,23,13,6,1,6,6,8,5,18,12,19,2,17,5,17,5,6,6,10,3,3,9,15,16,10,7,14,5,13,27,2,0.026,0.02,0.016,0.021,0.028,0.021,0.021,0.028,0.037,0.03,0.046,0.03,0.024,0.029,0.027,0.029,0.024,0.023,0.03,0.038,0.02,0.029,0.028,0.033,0.029,0.03,0.023,0.023,0.021,0.029,0.022,0.035,0.019,0.034,0.023,0.015,0.032,0.031,0.015,0.013,0.016,0.03,0.036,0.033,0.02,0.013,0.016,0.017,0.013,0.022,0.027,0.02,0.021,0.017,0.02,0.02,0.018,0.017,0.02,0.019,0.031,0.021,0.024,0.013,0.017,0.021,0.02,0.017,0.016,0.019,0.02,0.019,0.016,0.015,1688,5032,2813,2228,1997,2804,2952,3019,1439,1486,745.0,2669,3871,980,2697,10964,18159,2439,5152,5383,3113,4911,2005,5622,6229,6143,5171,4188,5745,5644,1770,1294,940,6767,1636,1639,8046,7496,323,400,1344,3288,5339,2578,3471,1655,774,2305,2330,1889,477,3390,2233,6077,666,3264,1601,3952,1013,1228,921,2026,466,821,2238,1376,3373,2549,1802,1602,1207,2300,9397,624,0.101,0.102,0.11,0.102,0.13,0.116,0.105,0.13,0.131,0.135,0.127,0.129,0.127,0.119,0.127,0.132,0.12,0.095,0.13,0.14,0.089,0.128,0.133,0.131,0.129,0.132,0.12,0.114,0.101,0.13,0.104,0.104,0.098,0.144,0.089,0.081,0.12,0.133,0.08,0.09,0.093,0.134,0.139,0.137,0.096,0.09,0.084,0.089,0.082,0.106,0.141,0.109,0.116,0.092,0.106,0.113,0.091,0.099,0.113,0.102,0.134,0.107,0.129,0.093,0.098,0.122,0.107,0.099,0.099,0.115,0.111,0.117,0.09,0.098,458,1647,985,876,894,1091,1059,995,496,412,205.0,1237,1023,260,740,2908,4813,492,1548,2380,909,1491,826,1414,1770,1713,1815,1607,1481,1718,522,513,264,1386,395,550,2121,1704,209,164,630,1324,1052,1551,1971,808,329,964,1180,880,327,1550,1014,2391,308,1518,681,1879,595,656,468,829,217,384,1140,848,1786,1053,837,646,574,1176,3816,202,3.471,2.77,2.702,2.535,2.063,2.33,2.357,2.773,2.229,2.821,2.791,1.968,3.043,2.611,2.787,2.86,2.967,3.781,2.777,1.965,2.748,2.502,2.081,2.891,2.741,2.856,2.357,2.121,2.916,2.615,2.45,2.333,3.026,3.329,3.525,2.565,2.795,3.103,1.764,2.875,2.563,2.015,3.561,1.914,2.058,2.226,2.781,2.93,2.423,2.676,1.709,2.213,2.2,2.513,2.499,2.267,2.631,2.401,1.924,2.191,2.346,2.39,2.373,2.113,2.229,2.224,2.215,2.658,2.415,2.475,2.359,2.352,2.553,3.229,0.789,0.669,0.722,0.521,0.516,0.527,0.569,0.526,0.544,0.454,0.849,0.523,0.554,0.457,0.53,0.576,0.624,0.652,0.657,0.669,0.858,0.516,0.531,0.743,0.529,0.513,0.5,0.555,0.514,0.616,0.639,1.037,0.407,0.696,0.888,0.497,0.748,0.655,0.292,0.662,0.634,0.659,0.808,0.723,0.638,0.536,0.513,0.638,0.537,0.707,0.34,0.537,0.512,0.546,0.429,0.459,0.526,0.492,0.352,0.448,0.631,0.703,0.543,1.058,0.584,0.699,0.459,0.437,0.478,0.683,0.549,0.5,0.565,0.537
-4023,Not Epileptic,F,42.0,0.7,2.6,1.0,0.9,2.3,1.5,1.4,1.5,1.8,0.6,0.4,3.1,1.2,0.6,1.1,4.9,8.0,0.8,1.7,4.2,1.0,2.6,1.3,4.4,2.8,3.0,3.3,1.9,2.6,2.6,1.3,1.4,0.7,2.4,0.3,0.5,3.5,3.4,0.1,0.2,1.3,3.1,2.3,2.7,3.4,0.7,0.5,0.9,1.3,0.7,0.7,1.3,1.4,1.8,0.4,2.4,1.0,1.1,1.1,0.8,0.4,1.7,0.5,0.5,1.8,0.9,2.1,0.8,0.9,0.5,1.1,1.3,3.4,0.4,5,37,8,8,19,18,13,16,15,8,4.0,31,13,8,11,56,73,7,21,44,9,30,16,49,36,36,39,23,23,33,17,14,6,31,2,5,39,42,1,1,10,31,23,21,19,5,3,6,7,8,4,14,15,13,2,23,9,10,8,6,4,16,4,6,14,11,17,5,6,7,8,10,26,3,0.039,0.03,0.021,0.019,0.032,0.023,0.022,0.027,0.043,0.025,0.036,0.034,0.025,0.028,0.029,0.03,0.028,0.029,0.025,0.039,0.018,0.03,0.025,0.041,0.034,0.026,0.03,0.025,0.024,0.025,0.035,0.056,0.021,0.027,0.013,0.012,0.037,0.036,0.014,0.014,0.024,0.034,0.037,0.032,0.025,0.016,0.022,0.017,0.019,0.022,0.031,0.02,0.024,0.018,0.024,0.018,0.023,0.015,0.024,0.018,0.012,0.027,0.027,0.016,0.02,0.03,0.019,0.014,0.018,0.018,0.028,0.023,0.018,0.015,1218,4379,1825,2105,2800,3141,3453,3024,2170,1457,606.0,3205,3495,1345,1909,9846,18234,2296,4231,4843,3451,4109,2231,6198,5409,6791,6024,4045,6640,6000,2704,1219,1226,6178,1770,1920,6398,7076,233,424,1756,3561,5629,2199,3606,1467,761,1994,2158,1232,563,2615,2229,4010,561,4059,1474,2564,1145,1350,866,2685,588,888,2778,751,3145,1880,1671,1015,1623,1803,7416,601,0.117,0.14,0.104,0.095,0.13,0.127,0.102,0.132,0.152,0.128,0.138,0.151,0.118,0.138,0.132,0.131,0.123,0.119,0.121,0.162,0.091,0.134,0.104,0.141,0.144,0.129,0.121,0.101,0.111,0.133,0.124,0.131,0.093,0.136,0.068,0.076,0.15,0.15,0.102,0.093,0.117,0.136,0.139,0.126,0.108,0.097,0.108,0.09,0.103,0.109,0.132,0.106,0.129,0.096,0.113,0.105,0.119,0.099,0.123,0.102,0.09,0.125,0.139,0.112,0.116,0.132,0.109,0.086,0.101,0.124,0.125,0.129,0.099,0.106,391,1552,726,793,1140,998,1054,943,702,361,190.0,1478,769,382,563,2687,4862,461,1116,1887,752,1321,853,1827,1504,1960,1855,1264,1577,1635,722,506,431,1433,477,747,1726,1699,131,221,869,1435,1131,1403,2078,707,357,891,984,534,347,1191,1013,1663,260,2073,683,1168,690,740,491,1083,283,511,1329,464,1608,862,794,455,739,833,3126,382,3.256,2.657,2.611,2.58,2.169,2.437,2.62,2.817,2.485,3.012,2.71,1.836,3.078,2.686,2.734,2.676,2.938,3.705,2.853,2.17,3.16,2.397,2.171,2.58,2.79,2.665,2.402,2.393,3.057,2.647,2.68,2.426,2.227,3.051,3.302,2.436,2.804,3.006,1.989,2.29,2.542,2.028,3.32,1.783,1.966,2.243,2.819,2.783,2.729,2.773,2.159,2.278,2.144,2.467,2.531,2.076,2.386,2.353,1.915,1.966,2.0,2.577,2.238,2.086,2.283,1.981,2.16,2.494,2.515,2.474,2.362,2.572,2.506,1.967,0.691,0.51,0.479,0.476,0.653,0.604,0.725,0.45,0.668,0.442,0.726,0.504,0.546,0.461,0.551,0.665,0.647,0.653,0.572,0.772,0.8,0.589,0.593,0.79,0.566,0.596,0.706,0.593,0.556,0.672,0.739,0.955,0.42,0.6,0.674,0.384,0.646,0.653,0.297,0.321,0.455,0.561,0.802,0.533,0.632,0.397,0.408,0.602,0.415,0.47,0.416,0.42,0.536,0.461,0.544,0.45,0.488,0.368,0.347,0.382,0.332,0.615,0.551,0.521,0.478,0.546,0.484,0.42,0.468,0.693,0.487,0.465,0.474,0.277
-4024,Not Epileptic,M,32.0,1.1,2.8,1.1,1.4,2.2,2.2,2.9,1.6,1.0,0.9,0.4,3.3,1.5,0.8,1.2,6.0,8.7,0.8,2.4,4.6,1.3,3.1,2.4,4.9,3.8,3.3,3.9,3.4,4.9,4.3,2.8,1.5,0.7,2.4,0.5,0.6,3.4,3.4,0.2,0.4,1.1,3.4,2.8,2.6,3.8,1.1,0.6,1.0,1.5,0.7,0.9,2.1,1.8,2.5,0.4,2.5,0.8,1.7,1.3,1.2,1.0,2.5,0.6,0.9,2.1,0.9,2.4,1.7,1.3,0.7,1.3,1.3,4.7,0.4,8,25,11,12,22,21,28,17,10,9,4.0,30,17,9,14,67,81,8,32,46,10,41,24,47,51,39,42,28,36,51,27,14,6,27,2,3,38,42,2,2,6,31,27,19,20,7,3,6,6,6,5,20,15,21,2,21,8,12,9,9,6,20,4,8,17,15,20,13,9,8,10,11,36,3,0.039,0.029,0.024,0.025,0.047,0.031,0.041,0.029,0.039,0.036,0.03,0.037,0.026,0.033,0.029,0.036,0.032,0.035,0.033,0.038,0.025,0.031,0.033,0.042,0.042,0.029,0.032,0.033,0.042,0.034,0.046,0.049,0.03,0.028,0.011,0.015,0.035,0.032,0.013,0.021,0.023,0.033,0.047,0.027,0.027,0.021,0.021,0.019,0.019,0.02,0.033,0.024,0.027,0.02,0.022,0.018,0.022,0.018,0.025,0.019,0.028,0.031,0.028,0.018,0.022,0.018,0.021,0.021,0.02,0.019,0.021,0.016,0.018,0.018,1780,4071,2141,2678,1983,3372,3370,3468,1455,2018,807.0,3314,3863,1779,2828,10905,17902,2358,4844,5435,3460,5409,2608,6620,6478,7816,6150,4500,7007,7753,2338,1491,1358,7339,2451,1957,7400,8351,458,621,1448,2846,5238,2628,3634,1514,1089,2020,2192,1988,762,3594,2047,5418,425,3936,1375,3276,1426,1906,1066,3917,678,1390,3122,1329,3737,2796,1890,1311,2042,2627,9028,566,0.131,0.128,0.122,0.121,0.144,0.147,0.127,0.138,0.137,0.151,0.141,0.143,0.121,0.147,0.134,0.14,0.133,0.137,0.146,0.158,0.094,0.139,0.129,0.142,0.149,0.132,0.125,0.113,0.123,0.149,0.14,0.135,0.11,0.129,0.062,0.081,0.134,0.141,0.093,0.104,0.105,0.134,0.15,0.12,0.111,0.109,0.113,0.094,0.101,0.108,0.139,0.107,0.12,0.103,0.12,0.112,0.108,0.101,0.121,0.102,0.124,0.129,0.127,0.104,0.113,0.105,0.111,0.109,0.109,0.122,0.119,0.1,0.101,0.11,493,1362,784,899,911,1144,1237,1022,461,450,217.0,1581,985,429,763,3178,4851,464,1308,2248,799,1585,1212,2090,1886,2105,2110,1549,1843,2307,913,597,450,1477,659,669,1898,1966,252,279,772,1579,1186,1657,2229,765,424,865,1065,684,386,1656,1090,2211,237,2035,679,1545,829,1029,541,1465,361,780,1486,869,1869,1226,999,584,934,1295,3974,325,3.291,2.727,2.701,2.787,1.988,2.35,2.252,2.928,2.39,3.036,2.91,1.831,2.953,2.919,2.765,2.573,2.837,3.683,2.836,2.074,2.991,2.554,1.917,2.339,2.662,2.867,2.353,2.263,2.779,2.566,2.069,2.485,2.434,3.278,3.299,2.59,2.849,3.009,2.113,2.54,2.462,1.635,3.234,1.843,1.853,2.167,2.867,2.851,2.552,2.69,2.133,2.192,1.985,2.407,2.123,2.158,2.205,2.393,1.947,1.956,2.196,2.599,2.057,2.179,2.245,1.868,2.162,2.546,2.278,2.414,2.2,2.404,2.424,2.129,0.757,0.561,0.495,0.624,0.554,0.691,0.532,0.535,0.788,0.788,0.751,0.599,0.49,0.463,0.505,0.667,0.644,0.752,0.574,0.704,0.819,0.527,0.617,0.793,0.614,0.594,0.628,0.659,0.662,0.652,0.657,0.831,0.411,0.744,0.83,0.431,0.834,0.657,0.284,0.383,0.455,0.464,0.737,0.715,0.588,0.459,0.68,0.517,0.493,0.661,0.408,0.467,0.426,0.455,0.499,0.456,0.47,0.476,0.433,0.435,0.466,0.681,0.377,0.66,0.508,0.663,0.478,0.434,0.492,0.589,0.479,0.486,0.438,0.246
-4025,Not Epileptic,F,55.0,1.2,3.2,1.4,1.4,2.8,1.6,1.7,1.9,2.2,1.0,0.3,2.9,2.2,0.7,1.2,5.9,12.1,1.8,2.9,4.0,0.9,2.9,2.3,4.2,3.0,4.2,4.6,2.4,4.7,2.9,1.9,1.4,0.5,2.4,0.7,0.4,4.5,4.1,0.2,0.2,1.1,3.1,3.9,2.4,3.2,0.6,0.6,0.9,1.5,1.0,0.5,1.6,1.1,3.2,0.3,3.1,0.5,2.0,1.1,1.0,0.3,2.1,0.4,0.8,1.8,1.6,3.2,1.4,1.5,0.7,0.7,2.1,4.5,0.4,7,30,13,12,16,16,12,16,15,12,2.0,22,16,6,11,63,96,11,28,35,7,29,18,35,34,37,46,23,32,32,19,9,5,27,3,2,38,47,1,1,5,25,27,17,14,3,2,5,7,9,4,8,7,16,1,18,5,14,8,8,3,10,2,6,11,14,18,8,6,5,4,15,32,3,0.055,0.034,0.024,0.026,0.052,0.027,0.033,0.037,0.054,0.055,0.022,0.032,0.044,0.074,0.042,0.044,0.045,0.063,0.044,0.036,0.021,0.037,0.033,0.06,0.042,0.039,0.042,0.032,0.05,0.041,0.045,0.045,0.028,0.038,0.026,0.013,0.052,0.05,0.021,0.022,0.026,0.039,0.059,0.029,0.025,0.012,0.031,0.017,0.023,0.023,0.039,0.018,0.023,0.029,0.02,0.02,0.02,0.022,0.028,0.019,0.02,0.036,0.037,0.027,0.025,0.025,0.024,0.024,0.027,0.025,0.025,0.026,0.021,0.026,1479,4173,2296,2310,1475,2805,2287,3149,1274,1275,647.0,2729,3142,793,1952,8657,14599,2502,4214,4368,2757,3921,2065,4105,5434,6514,5612,2901,4667,4390,2318,1325,845,5948,1812,1361,5929,7673,239,405,1163,2330,5648,2132,2808,1275,602,2249,1877,1510,425,2705,1384,3701,379,3922,1525,3349,906,1586,625,2051,331,875,2134,2010,3794,1999,1391,622,1044,2827,7937,406,0.148,0.137,0.117,0.113,0.148,0.124,0.106,0.137,0.158,0.174,0.116,0.126,0.14,0.191,0.137,0.147,0.149,0.162,0.154,0.141,0.09,0.129,0.119,0.158,0.141,0.149,0.143,0.112,0.14,0.15,0.143,0.121,0.101,0.136,0.102,0.071,0.152,0.167,0.118,0.116,0.106,0.14,0.177,0.124,0.112,0.082,0.12,0.084,0.105,0.11,0.153,0.093,0.107,0.117,0.111,0.108,0.105,0.116,0.136,0.104,0.101,0.131,0.144,0.121,0.12,0.11,0.106,0.11,0.109,0.124,0.113,0.128,0.104,0.136,387,1687,968,902,727,974,848,926,622,371,187.0,1338,854,236,529,2569,4773,473,1138,1797,721,1222,944,1399,1476,1901,1897,1111,1419,1405,724,580,281,1182,440,498,1534,1646,145,177,623,1211,1120,1400,1861,693,275,898,940,620,241,1272,738,1695,208,2093,485,1468,579,848,304,843,160,489,1057,958,2023,917,736,403,504,1237,3404,253,3.254,2.397,2.408,2.405,1.984,2.409,2.212,2.788,1.865,2.687,3.037,1.798,2.856,2.497,2.66,2.518,2.487,3.428,2.82,2.095,2.848,2.522,1.952,2.323,2.606,2.66,2.247,2.075,2.593,2.383,2.379,2.353,2.29,3.102,3.366,2.463,2.644,3.109,1.867,2.209,2.332,1.733,3.253,1.671,1.733,1.985,2.588,2.782,2.316,2.629,2.027,2.265,1.977,2.167,2.055,2.046,2.563,2.386,1.852,1.968,2.395,2.662,2.054,2.079,2.22,2.488,1.928,2.375,2.084,1.792,2.15,2.401,2.415,2.053,0.662,0.63,0.476,0.468,0.545,0.62,0.624,0.679,0.5,0.591,0.879,0.495,0.542,0.522,0.589,0.602,0.634,0.886,0.616,0.621,0.761,0.603,0.477,0.746,0.607,0.702,0.574,0.523,0.593,0.71,0.673,0.96,0.381,0.667,0.773,0.459,0.797,0.729,0.29,0.56,0.5,0.498,0.825,0.654,0.476,0.504,0.348,0.886,0.517,0.521,0.419,0.537,0.477,0.504,0.329,0.471,0.603,0.572,0.389,0.481,0.488,0.732,0.354,0.696,0.552,0.965,0.498,0.585,0.489,0.511,0.613,0.524,0.504,0.472
-4026,Not Epileptic,F,22.0,0.7,2.6,0.9,1.2,1.9,1.9,1.8,1.2,1.4,0.9,0.4,3.2,1.6,0.7,1.4,6.2,8.2,0.7,1.9,4.7,1.6,3.1,1.7,3.7,3.4,3.0,6.0,2.9,2.6,3.1,3.2,0.9,0.4,3.1,1.0,0.6,3.6,3.4,0.1,0.2,0.9,3.0,2.0,3.3,2.5,0.5,0.3,0.6,1.2,0.8,0.5,2.0,1.4,2.0,0.2,3.4,0.8,1.3,0.7,1.3,0.8,1.8,0.2,0.5,1.9,1.4,1.8,1.2,1.3,0.5,0.9,1.6,6.3,0.3,7,23,7,10,17,20,16,14,15,11,5.0,31,19,8,22,83,86,5,25,47,13,49,24,37,44,37,58,33,26,44,29,7,6,32,10,4,52,45,1,2,5,30,24,26,16,5,2,4,6,8,5,16,9,16,1,27,7,9,7,14,6,17,2,6,19,20,15,11,10,4,7,12,54,3,0.041,0.027,0.016,0.019,0.036,0.028,0.032,0.022,0.044,0.04,0.03,0.042,0.033,0.037,0.031,0.033,0.03,0.028,0.03,0.037,0.031,0.031,0.028,0.038,0.032,0.025,0.045,0.029,0.025,0.03,0.05,0.044,0.022,0.038,0.027,0.013,0.034,0.031,0.01,0.016,0.019,0.038,0.033,0.027,0.018,0.012,0.014,0.011,0.018,0.022,0.019,0.018,0.024,0.019,0.016,0.022,0.021,0.015,0.019,0.023,0.023,0.026,0.015,0.016,0.021,0.022,0.017,0.019,0.02,0.012,0.024,0.018,0.019,0.014,1584,5217,2313,2867,2390,4021,4022,3556,1902,1759,968.0,3168,4236,1123,3711,13891,21478,2465,4053,5784,4361,6299,2937,7037,7421,8378,6883,4701,7853,7164,3065,1437,1222,7276,1967,2227,9211,9838,457,501,1597,2846,7151,3032,3803,1538,747,1999,2405,1762,536,3991,1790,4538,476,4909,1439,3350,1362,1998,1135,3282,328,1163,3404,1364,3302,2496,2653,1237,1474,3112,13202,567,0.125,0.126,0.104,0.106,0.142,0.128,0.112,0.111,0.154,0.162,0.133,0.164,0.144,0.146,0.137,0.15,0.137,0.109,0.139,0.151,0.117,0.141,0.131,0.144,0.133,0.131,0.142,0.116,0.106,0.14,0.158,0.118,0.116,0.152,0.104,0.082,0.145,0.137,0.072,0.1,0.099,0.141,0.135,0.129,0.096,0.093,0.089,0.08,0.093,0.114,0.132,0.105,0.123,0.099,0.106,0.114,0.107,0.091,0.111,0.119,0.126,0.125,0.109,0.092,0.111,0.13,0.099,0.104,0.103,0.095,0.123,0.109,0.102,0.111,419,1594,844,962,922,1235,1164,1018,573,412,246.0,1526,955,315,966,3544,5021,432,1145,2359,833,1847,1091,1903,1958,2054,2408,1550,1777,2091,986,405,341,1471,491,726,2129,2103,247,236,732,1409,1350,1855,2148,661,308,813,1029,663,338,1763,870,1853,195,2359,659,1410,718,1011,535,1147,176,584,1547,855,1590,1056,1078,563,670,1257,5141,291,3.5,2.869,2.719,2.831,2.283,2.611,2.748,2.955,2.393,3.213,2.957,1.933,3.219,2.595,2.799,2.851,3.14,3.894,2.875,2.11,3.498,2.563,2.182,2.765,2.773,3.013,2.315,2.333,3.098,2.712,2.266,3.126,2.694,3.284,3.338,2.764,3.139,3.262,2.133,2.422,2.751,1.866,3.627,1.921,2.035,2.598,2.923,3.2,2.853,2.822,2.034,2.38,2.106,2.601,2.75,2.29,2.464,2.593,2.096,2.24,2.563,2.632,1.869,2.207,2.448,1.954,2.39,2.717,2.6,2.182,2.585,2.655,2.592,2.327,0.769,0.699,0.432,0.514,0.727,0.588,0.652,0.516,0.684,0.629,0.931,0.481,0.463,0.646,0.585,0.658,0.657,0.814,0.61,0.665,0.812,0.616,0.591,0.861,0.701,0.543,0.65,0.668,0.579,0.563,0.785,0.796,0.492,0.646,0.646,0.438,0.774,0.785,0.363,0.409,0.418,0.554,0.633,0.639,0.585,0.545,0.378,0.665,0.49,0.445,0.366,0.494,0.454,0.437,0.572,0.398,0.318,0.488,0.406,0.335,0.412,0.748,0.463,0.565,0.456,0.587,0.464,0.393,0.369,0.703,0.413,0.527,0.482,0.303
-4027,Not Epileptic,F,32.0,0.9,1.6,0.8,1.3,2.7,1.7,1.7,1.2,1.2,0.7,0.2,3.3,1.3,0.3,1.0,5.5,9.0,1.1,2.0,5.5,1.1,2.6,1.9,3.2,2.8,2.9,3.0,2.7,2.5,3.3,1.8,2.0,0.6,2.3,0.8,0.7,3.7,3.7,0.1,0.2,1.0,3.0,2.6,2.9,2.7,0.6,0.5,0.8,1.2,0.7,0.9,1.7,1.9,2.4,0.2,2.2,0.8,1.1,1.4,1.4,0.9,1.6,0.2,0.4,2.2,1.1,3.0,1.0,0.8,0.4,0.9,1.4,3.6,0.1,9,14,6,15,24,16,14,13,14,6,2.0,29,13,4,9,71,94,8,29,48,12,38,26,41,40,35,37,33,25,36,21,15,5,31,3,5,49,42,1,2,5,27,28,22,17,5,3,4,7,5,5,14,15,20,2,19,6,10,9,10,7,16,2,4,18,18,25,10,5,4,6,11,27,1,0.043,0.019,0.013,0.022,0.039,0.028,0.026,0.021,0.039,0.034,0.025,0.036,0.028,0.023,0.027,0.032,0.031,0.037,0.028,0.038,0.023,0.031,0.028,0.038,0.029,0.033,0.027,0.031,0.025,0.034,0.045,0.057,0.029,0.026,0.024,0.019,0.036,0.03,0.019,0.014,0.023,0.03,0.039,0.028,0.019,0.016,0.016,0.012,0.02,0.017,0.028,0.019,0.022,0.019,0.017,0.018,0.019,0.015,0.026,0.022,0.022,0.03,0.02,0.013,0.025,0.022,0.02,0.014,0.015,0.013,0.023,0.015,0.013,0.012,1482,4133,2115,2675,3012,3016,3096,3267,1855,1343,681.0,3452,3039,932,2393,11322,19066,2755,4683,6055,3536,5541,2699,6767,6999,6331,6064,4257,6516,5643,2296,1228,1213,6718,1764,1285,7292,8808,258,411,1284,2129,6399,2807,3872,1293,878,2345,2099,1454,730,3036,2619,4899,321,3778,1340,2818,1515,1959,1392,2264,372,957,3065,1112,4538,2576,1607,781,1216,2376,9254,507,0.135,0.107,0.095,0.115,0.147,0.125,0.113,0.118,0.142,0.143,0.102,0.145,0.122,0.11,0.119,0.14,0.135,0.141,0.135,0.148,0.106,0.123,0.121,0.148,0.134,0.139,0.128,0.112,0.105,0.146,0.155,0.145,0.113,0.127,0.092,0.099,0.142,0.138,0.107,0.122,0.11,0.13,0.16,0.129,0.099,0.093,0.095,0.082,0.102,0.103,0.132,0.107,0.123,0.104,0.125,0.109,0.104,0.097,0.117,0.112,0.109,0.132,0.118,0.095,0.124,0.12,0.109,0.091,0.091,0.101,0.12,0.102,0.086,0.085,417,1346,797,916,1158,1029,986,895,575,333,170.0,1632,800,226,615,3265,5060,535,1266,2508,800,1564,1140,1721,1862,1760,1962,1509,1665,1654,731,550,317,1450,437,517,1891,2130,111,196,648,1405,1307,1687,2171,650,412,929,961,642,447,1442,1180,2070,168,1818,662,1309,864,974,595,939,186,521,1387,813,2325,1135,730,397,597,1221,3967,211,3.376,2.961,2.771,2.659,2.198,2.536,2.554,2.906,2.498,2.992,3.015,1.938,2.767,2.973,2.793,2.609,2.912,3.728,2.846,2.087,3.194,2.581,1.945,2.855,2.831,2.9,2.454,2.247,2.899,2.677,2.433,2.373,2.989,3.176,3.445,2.433,2.85,2.923,2.412,2.407,2.567,1.451,3.31,1.937,2.02,2.306,2.65,3.032,2.69,2.62,2.143,2.268,2.21,2.443,2.282,2.215,2.398,2.469,2.001,2.222,2.425,2.277,2.257,2.111,2.292,1.726,2.135,2.5,2.446,2.31,2.235,2.256,2.438,2.685,0.857,0.59,0.375,0.537,0.655,0.611,0.516,0.499,0.535,0.717,0.744,0.526,0.501,0.437,0.512,0.577,0.583,0.755,0.536,0.621,0.833,0.657,0.64,0.745,0.627,0.582,0.564,0.603,0.557,0.568,0.579,0.965,0.477,0.709,0.684,0.407,0.711,0.639,0.467,0.401,0.593,0.498,0.782,0.653,0.62,0.462,0.575,0.83,0.529,0.471,0.509,0.399,0.462,0.439,0.309,0.367,0.36,0.488,0.419,0.352,0.428,0.692,0.383,0.664,0.559,0.557,0.412,0.377,0.406,0.531,0.476,0.422,0.45,0.495
-4028,Not Epileptic,M,42.0,0.8,2.3,0.9,1.8,1.4,2.9,1.3,2.0,0.8,0.8,0.4,2.6,1.8,0.6,1.4,6.3,8.4,1.4,2.5,5.1,1.4,3.3,2.8,5.4,3.1,5.1,4.8,3.6,2.8,4.2,1.5,1.0,0.7,2.7,0.4,1.0,3.3,4.0,0.1,0.2,1.3,3.6,2.5,2.7,2.5,0.5,0.6,1.1,1.6,0.6,0.7,1.3,1.6,2.9,0.7,2.2,1.0,1.4,1.0,1.0,0.5,2.5,0.4,0.4,1.7,1.8,2.8,1.0,1.2,1.5,2.0,1.4,5.7,0.5,8,24,8,15,13,27,11,22,16,9,4.0,26,20,11,17,75,76,20,29,47,12,39,26,80,41,53,51,34,23,49,19,7,6,32,2,9,40,49,1,2,9,32,25,19,15,4,3,6,9,5,5,10,12,22,4,17,10,12,7,9,4,18,3,5,15,20,22,7,8,17,15,10,52,4,0.028,0.021,0.016,0.027,0.023,0.032,0.027,0.027,0.027,0.036,0.029,0.03,0.029,0.029,0.026,0.028,0.03,0.041,0.026,0.038,0.023,0.029,0.037,0.053,0.031,0.029,0.032,0.028,0.025,0.028,0.035,0.041,0.024,0.024,0.011,0.015,0.032,0.034,0.008,0.014,0.024,0.036,0.031,0.019,0.018,0.011,0.02,0.015,0.02,0.013,0.024,0.016,0.021,0.02,0.018,0.012,0.023,0.014,0.019,0.017,0.013,0.03,0.017,0.016,0.02,0.022,0.019,0.015,0.019,0.026,0.025,0.017,0.018,0.021,1779,5500,2530,3267,2302,4935,3175,4279,2023,2125,899.0,3086,3969,1462,2719,13328,19507,3115,6100,6709,4873,5820,2647,7605,6007,11529,8245,6986,6206,8390,2774,929,1447,8241,2153,3503,7221,9268,485,417,1753,3260,6806,3560,4097,1367,1011,2847,2688,1939,871,2771,2280,6008,1127,5318,1549,3740,1368,2102,1090,2956,547,930,3027,1906,5084,2301,2265,2029,2637,2346,11954,769,0.112,0.115,0.097,0.123,0.109,0.136,0.105,0.121,0.127,0.13,0.118,0.127,0.129,0.155,0.138,0.132,0.127,0.149,0.121,0.145,0.096,0.127,0.128,0.149,0.122,0.129,0.118,0.113,0.107,0.131,0.133,0.118,0.107,0.118,0.069,0.083,0.142,0.139,0.081,0.097,0.112,0.123,0.139,0.107,0.096,0.088,0.101,0.081,0.104,0.088,0.12,0.098,0.113,0.101,0.1,0.087,0.124,0.09,0.106,0.1,0.089,0.129,0.113,0.107,0.108,0.113,0.106,0.093,0.1,0.145,0.126,0.109,0.1,0.117,529,1889,876,1055,1066,1515,887,1228,559,463,219.0,1313,1060,431,806,3923,4932,721,1585,2385,1064,1684,1048,2033,1718,2872,2348,1934,1637,2502,774,425,438,1797,571,1016,1770,2057,229,242,851,1555,1432,1842,2255,631,441,1154,1225,771,455,1298,1117,2399,525,2615,704,1599,780,992,540,1264,327,493,1496,1210,2273,1009,1036,831,1129,1098,4882,411,3.438,2.731,2.813,2.843,2.075,2.612,2.812,2.926,2.657,3.52,3.301,2.035,2.817,2.511,2.572,2.643,3.035,3.315,2.984,2.317,3.427,2.649,2.263,2.792,2.636,3.077,2.632,2.654,2.886,2.62,2.64,2.267,2.523,3.215,3.37,3.088,2.954,3.183,2.246,1.891,2.714,1.913,3.296,2.282,2.106,2.242,2.924,3.095,2.773,2.8,2.192,2.305,2.158,2.562,2.427,2.333,2.488,2.551,2.03,2.291,2.345,2.313,1.894,2.147,2.204,1.825,2.457,2.616,2.536,2.492,2.46,2.496,2.606,2.336,0.716,0.5,0.539,0.648,0.461,0.653,0.76,0.524,0.441,0.965,0.804,0.586,0.422,0.609,0.459,0.56,0.64,0.735,0.513,0.655,0.777,0.546,0.598,0.764,0.607,0.654,0.639,0.716,0.518,0.657,0.607,0.852,0.606,0.595,0.646,0.507,0.792,0.647,0.385,0.39,0.383,0.573,0.808,0.67,0.599,0.518,0.463,0.578,0.418,0.45,0.326,0.419,0.425,0.438,0.346,0.42,0.34,0.394,0.341,0.409,0.349,0.663,0.463,0.759,0.519,0.719,0.431,0.401,0.376,0.864,0.472,0.603,0.466,0.447
-4029,Not Epileptic,M,55.0,1.1,2.2,1.1,1.3,2.2,1.6,1.5,1.8,1.4,1.0,0.7,3.0,1.6,0.5,1.5,4.4,9.4,2.2,2.0,5.5,1.2,3.1,1.4,4.2,2.9,2.5,4.3,3.2,3.0,3.1,2.3,1.3,0.5,2.7,0.7,0.6,2.9,2.5,0.2,0.3,0.9,3.4,2.2,3.5,2.9,0.4,0.6,1.2,1.4,0.7,0.5,2.2,1.1,2.2,0.4,2.0,0.3,1.2,1.3,1.0,0.6,1.4,0.3,0.6,1.6,1.3,2.8,1.2,1.0,0.5,0.8,1.3,4.3,0.3,11,23,9,18,15,15,12,16,14,10,9.0,33,17,4,19,48,83,16,37,52,10,43,13,43,37,26,39,26,27,37,22,10,6,26,6,5,35,33,2,2,5,29,19,26,14,3,4,7,7,6,4,16,6,17,2,17,3,11,10,8,6,11,3,6,14,18,27,9,6,4,5,10,39,4,0.057,0.022,0.025,0.025,0.033,0.023,0.025,0.027,0.049,0.033,0.038,0.035,0.025,0.032,0.031,0.03,0.034,0.056,0.028,0.037,0.022,0.031,0.024,0.035,0.029,0.026,0.033,0.031,0.026,0.027,0.046,0.047,0.026,0.027,0.026,0.016,0.029,0.029,0.016,0.025,0.021,0.03,0.033,0.027,0.021,0.01,0.021,0.016,0.021,0.015,0.018,0.024,0.021,0.02,0.037,0.015,0.015,0.016,0.021,0.015,0.023,0.024,0.013,0.018,0.021,0.024,0.022,0.016,0.018,0.018,0.019,0.019,0.017,0.019,1391,5055,2140,2551,2437,3474,2811,3449,1794,1818,834.0,3233,3582,889,3194,9163,16831,2964,3766,5933,3969,5859,1808,6492,6035,5342,5354,3883,6235,6934,2758,1052,1089,6569,1726,1675,6504,7262,441,428,1187,3077,5828,3549,3894,1312,844,2526,2092,1643,724,2991,1545,4178,399,3911,955,2708,1758,1857,780,2759,586,1062,2289,1314,4099,2413,2053,1079,1398,2220,9327,496,0.157,0.117,0.11,0.119,0.125,0.114,0.092,0.129,0.162,0.149,0.152,0.143,0.125,0.137,0.138,0.13,0.13,0.171,0.138,0.148,0.099,0.123,0.109,0.131,0.124,0.122,0.113,0.106,0.104,0.127,0.147,0.123,0.106,0.131,0.093,0.088,0.128,0.138,0.097,0.108,0.1,0.12,0.137,0.118,0.096,0.075,0.109,0.085,0.098,0.095,0.108,0.116,0.115,0.099,0.13,0.095,0.09,0.107,0.113,0.097,0.116,0.111,0.102,0.108,0.117,0.114,0.112,0.095,0.092,0.108,0.107,0.099,0.094,0.113,454,1642,748,902,939,1190,930,1088,495,465,272.0,1568,1008,245,889,2578,4621,656,1152,2630,907,1839,831,1905,1748,1565,1831,1457,1645,2094,856,532,351,1504,493,588,1717,1679,224,215,670,1636,1234,2049,2119,669,415,1068,1002,772,436,1472,730,1865,177,1997,394,1207,982,969,447,1028,350,582,1184,872,2052,1152,898,478,663,1126,4136,264,2.724,2.875,2.68,2.559,2.331,2.429,2.479,2.696,2.671,3.028,2.554,1.827,2.805,2.683,2.773,2.689,2.923,3.478,2.642,1.957,3.209,2.445,1.929,2.611,2.635,2.729,2.389,2.152,2.876,2.534,2.503,2.125,2.499,3.09,3.377,2.523,2.828,3.07,2.107,2.508,2.292,1.743,3.448,1.937,2.098,2.188,2.514,2.757,2.604,2.391,1.928,2.167,2.394,2.418,2.364,2.177,2.36,2.431,2.109,2.112,1.913,2.715,1.985,2.118,2.17,1.862,2.106,2.35,2.535,2.507,2.345,2.323,2.367,2.232,0.773,0.471,0.497,0.564,0.607,0.61,0.646,0.442,0.681,0.594,0.706,0.474,0.475,0.44,0.472,0.548,0.589,0.811,0.465,0.593,0.842,0.568,0.482,0.699,0.536,0.45,0.542,0.55,0.531,0.584,0.764,0.692,0.348,0.583,0.663,0.449,0.644,0.616,0.321,0.392,0.374,0.447,0.709,0.539,0.656,0.361,0.472,0.678,0.37,0.407,0.422,0.403,0.525,0.386,0.471,0.379,0.45,0.497,0.36,0.365,0.382,0.56,0.391,0.581,0.426,0.54,0.437,0.317,0.398,0.655,0.471,0.42,0.424,0.432
-4030,Not Epileptic,F,55.0,0.8,2.5,1.4,1.3,1.6,1.6,1.4,1.5,1.4,1.0,0.2,4.0,1.4,0.6,1.9,4.9,7.9,0.7,2.1,5.5,1.4,3.4,2.2,4.4,2.0,3.2,3.8,2.8,3.4,3.6,3.6,1.6,0.8,2.2,0.5,0.3,3.6,3.5,0.3,0.2,1.0,4.2,2.2,3.0,2.6,0.6,0.4,0.7,1.5,0.7,0.4,2.3,1.4,2.1,0.0,2.6,0.7,2.0,1.5,1.0,0.8,1.9,0.4,1.0,1.9,1.3,2.4,1.3,0.9,0.6,0.8,1.3,4.1,0.4,6,22,12,12,15,23,15,16,15,9,4.0,37,16,6,21,58,71,4,22,52,16,37,19,41,30,35,49,31,27,55,27,10,7,26,2,2,39,41,2,2,6,40,22,29,17,4,2,4,8,7,3,18,8,12,0,23,8,19,12,8,9,14,3,6,17,19,17,8,4,6,7,10,31,3,0.038,0.029,0.025,0.024,0.034,0.029,0.029,0.028,0.059,0.031,0.024,0.043,0.029,0.032,0.039,0.033,0.035,0.034,0.029,0.039,0.029,0.034,0.034,0.044,0.027,0.035,0.029,0.032,0.034,0.033,0.069,0.052,0.036,0.027,0.013,0.01,0.038,0.035,0.019,0.02,0.024,0.041,0.038,0.024,0.02,0.012,0.018,0.015,0.022,0.012,0.018,0.022,0.024,0.023,0.012,0.02,0.028,0.023,0.026,0.022,0.03,0.031,0.024,0.03,0.021,0.026,0.022,0.018,0.015,0.022,0.024,0.019,0.019,0.021,1424,3987,2243,2325,1616,2927,2574,2651,1556,1725,592.0,3586,2963,1065,3319,10123,14688,1664,4215,6084,4208,4755,2080,5529,4943,5688,6861,3587,5789,6572,1919,1317,965,5048,1709,1285,5813,6542,514,465,1178,3146,5227,3550,3443,1325,730,1345,1938,1765,786,3417,1482,3262,64,4099,942,3658,1646,1424,1249,2591,436,829,2958,1747,3524,2313,1690,1088,1312,2121,8225,496,0.126,0.127,0.118,0.113,0.132,0.14,0.101,0.128,0.172,0.154,0.104,0.163,0.131,0.13,0.155,0.138,0.137,0.12,0.14,0.155,0.12,0.129,0.134,0.144,0.117,0.144,0.119,0.108,0.115,0.142,0.177,0.117,0.139,0.121,0.069,0.066,0.139,0.142,0.114,0.11,0.111,0.146,0.14,0.117,0.101,0.082,0.096,0.084,0.109,0.091,0.107,0.112,0.118,0.106,0.085,0.11,0.135,0.121,0.123,0.116,0.124,0.127,0.137,0.126,0.114,0.126,0.107,0.098,0.087,0.138,0.119,0.109,0.099,0.123,374,1432,870,870,779,930,849,877,474,431,203.0,1612,857,290,923,2708,3983,347,1147,2404,944,1668,924,1728,1489,1620,2403,1376,1503,2143,723,498,371,1222,485,490,1626,1779,259,228,648,1618,1177,2081,2024,689,331,689,985,819,325,1688,795,1448,24,2046,466,1511,938,732,504,1060,257,459,1519,922,1739,1028,836,449,614,1066,3455,245,3.31,2.569,2.529,2.53,1.883,2.568,2.454,2.621,2.446,2.971,2.635,1.957,2.621,2.621,2.69,2.687,2.808,3.502,2.818,2.133,3.289,2.283,2.051,2.513,2.64,2.808,2.297,2.07,2.86,2.392,2.014,2.613,2.388,2.853,3.224,2.444,2.716,2.732,2.205,2.262,2.287,1.749,3.165,1.882,1.943,2.108,2.708,2.293,2.374,2.486,2.282,2.178,2.054,2.422,2.247,2.199,2.232,2.627,2.002,2.068,2.299,2.566,1.932,2.124,2.1,2.253,2.164,2.579,2.271,2.528,2.44,2.289,2.451,2.43,0.993,0.484,0.483,0.462,0.56,0.651,0.573,0.489,0.578,0.605,0.761,0.549,0.454,0.398,0.464,0.639,0.627,0.772,0.62,0.672,0.804,0.55,0.489,0.818,0.52,0.495,0.606,0.628,0.603,0.57,0.761,0.845,0.518,0.636,1.008,0.512,0.732,0.651,0.39,0.409,0.501,0.56,0.65,0.536,0.634,0.362,0.461,0.658,0.492,0.458,0.529,0.462,0.417,0.421,0.183,0.342,0.36,0.608,0.439,0.371,0.424,0.668,0.476,0.671,0.46,0.789,0.448,0.382,0.403,0.85,0.392,0.48,0.5,0.35
-4031,Not Epileptic,M,42.0,1.0,2.5,1.0,1.0,1.4,1.7,1.8,2.3,1.0,0.7,0.5,3.0,2.1,0.7,1.4,6.0,11.0,1.1,2.8,5.7,1.2,2.9,2.0,3.7,2.3,3.2,3.6,3.7,3.2,3.7,1.9,0.7,0.6,2.4,0.6,0.9,3.4,3.5,0.2,0.2,1.3,4.0,2.5,4.2,3.5,0.7,0.6,0.9,1.6,0.7,1.0,2.4,1.3,2.7,0.4,2.6,1.6,3.0,0.9,1.0,0.8,1.8,0.4,0.5,2.4,1.1,3.4,1.3,1.5,0.9,1.1,1.5,4.1,0.5,7,27,8,7,16,14,17,20,11,6,5.0,28,25,10,14,67,103,7,24,43,12,30,22,42,25,36,42,32,25,41,22,5,5,27,4,8,42,33,2,2,7,27,23,29,24,6,3,6,9,5,5,17,11,17,3,22,11,21,7,7,6,19,2,4,21,17,30,12,14,9,8,11,33,4,0.036,0.023,0.015,0.019,0.027,0.029,0.025,0.033,0.049,0.029,0.039,0.038,0.029,0.032,0.032,0.034,0.032,0.032,0.032,0.037,0.026,0.036,0.028,0.039,0.032,0.027,0.026,0.03,0.023,0.03,0.046,0.035,0.029,0.031,0.017,0.019,0.035,0.032,0.009,0.014,0.026,0.036,0.033,0.028,0.023,0.014,0.02,0.015,0.018,0.013,0.053,0.022,0.033,0.02,0.018,0.02,0.024,0.032,0.022,0.017,0.025,0.029,0.029,0.02,0.026,0.028,0.02,0.017,0.022,0.023,0.025,0.024,0.018,0.029,1415,4923,1970,2159,2251,2666,2557,3298,1717,1499,608.0,2955,4283,1450,2891,10116,18921,2787,5011,5107,3398,4160,2479,6635,4435,6666,6047,4250,5528,6123,2670,1073,1020,6261,2048,2276,7531,6630,422,594,1551,3463,5687,3346,3523,1491,1031,2507,2619,1553,535,3432,1584,4221,471,3645,2313,3859,1094,1746,1222,2577,379,814,2804,1343,4861,2756,2118,1615,1613,2253,8060,440,0.123,0.123,0.099,0.094,0.124,0.13,0.105,0.145,0.164,0.123,0.129,0.15,0.14,0.147,0.128,0.142,0.137,0.114,0.13,0.145,0.1,0.15,0.137,0.151,0.13,0.119,0.126,0.107,0.096,0.127,0.145,0.101,0.117,0.132,0.092,0.098,0.13,0.122,0.089,0.082,0.118,0.142,0.139,0.125,0.111,0.096,0.11,0.089,0.101,0.092,0.17,0.118,0.132,0.102,0.12,0.111,0.115,0.135,0.117,0.103,0.118,0.135,0.132,0.11,0.127,0.132,0.108,0.101,0.118,0.115,0.125,0.124,0.1,0.149,421,1732,875,837,909,1052,1053,1173,423,377,226.0,1330,1264,391,794,3101,5881,508,1406,2454,899,1324,1097,1808,1254,2059,2302,1800,1811,2188,697,440,359,1375,534,780,1851,1753,255,309,733,1592,1243,2201,2282,754,431,989,1195,705,306,1645,748,2007,274,1956,1003,1559,671,821,479,999,176,396,1491,674,2640,1252,1100,654,737,1049,3463,271,3.054,2.736,2.22,2.582,2.121,2.27,2.091,2.638,2.902,3.058,2.418,1.963,2.701,2.723,2.708,2.511,2.625,3.99,2.881,1.851,3.073,2.538,2.021,2.817,2.703,2.643,2.172,1.952,2.385,2.29,2.665,2.319,2.302,3.115,3.507,2.616,2.987,2.83,1.9,2.311,2.774,1.936,3.303,1.825,1.74,2.161,3.086,3.322,2.781,2.526,1.899,2.306,2.228,2.347,2.076,2.083,2.794,2.82,1.97,2.268,2.567,2.44,2.252,2.437,2.088,2.337,1.997,2.334,2.105,2.698,2.425,2.603,2.457,2.14,0.937,0.545,0.452,0.502,0.581,0.645,0.439,0.471,0.623,0.433,0.755,0.542,0.511,0.479,0.493,0.561,0.546,0.84,0.602,0.632,0.741,0.524,0.564,0.561,0.602,0.556,0.503,0.523,0.457,0.567,0.733,0.983,0.509,0.647,0.817,0.532,0.697,0.668,0.284,0.289,0.563,0.618,0.708,0.726,0.477,0.38,0.523,0.799,0.507,0.356,0.398,0.508,0.459,0.423,0.322,0.414,0.545,0.719,0.406,0.359,0.458,0.71,0.332,0.845,0.437,0.693,0.417,0.437,0.517,0.725,0.522,0.521,0.487,0.508
-4032,Not Epileptic,M,27.0,0.7,2.7,0.9,1.5,1.3,1.7,2.3,1.7,1.0,0.6,0.4,3.2,2.0,0.4,1.6,9.6,10.6,1.0,2.4,6.7,1.1,2.7,1.7,2.6,3.8,3.4,2.7,2.5,3.6,2.6,1.2,0.7,0.6,2.4,0.5,1.0,3.2,3.4,0.2,0.3,1.0,3.6,2.3,3.0,3.7,0.7,0.4,1.8,1.3,0.7,0.5,1.9,1.1,3.0,0.1,2.5,0.7,1.6,1.3,1.0,0.7,1.7,0.4,0.3,2.1,1.1,2.0,2.7,1.2,0.8,1.0,1.3,4.7,0.5,10,21,9,14,13,17,16,16,11,8,5.0,30,21,6,15,94,89,10,30,48,18,33,18,29,39,39,31,24,27,31,17,5,4,26,2,7,38,42,1,2,5,31,25,21,22,5,2,9,6,5,4,16,8,17,1,67,5,12,8,8,7,11,3,4,16,11,15,16,7,7,7,11,39,5,0.038,0.027,0.013,0.023,0.032,0.028,0.03,0.028,0.052,0.031,0.026,0.03,0.033,0.021,0.026,0.041,0.031,0.029,0.034,0.046,0.024,0.027,0.028,0.027,0.032,0.034,0.027,0.026,0.031,0.026,0.033,0.043,0.025,0.026,0.017,0.032,0.038,0.03,0.012,0.018,0.021,0.034,0.037,0.025,0.024,0.011,0.022,0.026,0.017,0.011,0.023,0.019,0.023,0.019,0.012,0.019,0.014,0.018,0.021,0.018,0.015,0.026,0.026,0.017,0.022,0.027,0.018,0.023,0.018,0.021,0.021,0.017,0.016,0.028,1496,5466,2678,3111,2098,2541,3077,3496,1782,1433,920.0,3826,4035,1188,3510,15216,22648,2718,5504,5475,3637,5705,2314,5996,7158,6301,5432,4897,7434,5856,2646,835,1207,5786,1716,1487,6330,8149,286,640,1498,3737,5188,3469,4071,1842,817,2460,2432,1826,545,4030,2006,6033,322,4339,1620,3303,1674,1684,1666,2755,630,829,3258,1332,4007,3678,2464,1765,1690,2371,11396,488,0.119,0.114,0.086,0.107,0.134,0.14,0.12,0.13,0.157,0.131,0.123,0.14,0.14,0.115,0.13,0.154,0.126,0.121,0.131,0.148,0.09,0.132,0.134,0.122,0.134,0.139,0.129,0.118,0.112,0.128,0.129,0.103,0.114,0.113,0.076,0.122,0.129,0.129,0.091,0.099,0.105,0.144,0.14,0.117,0.112,0.085,0.109,0.102,0.091,0.088,0.125,0.096,0.105,0.099,0.089,0.109,0.09,0.093,0.107,0.103,0.098,0.114,0.125,0.106,0.115,0.121,0.104,0.116,0.093,0.126,0.107,0.108,0.093,0.136,447,1662,1034,1119,758,999,1070,1067,474,377,257.0,1634,1026,312,918,3875,5762,532,1384,2390,1013,1760,1009,1567,2085,1735,1650,1526,1957,1654,598,305,388,1440,454,551,1668,1902,209,260,735,1659,1113,1919,2324,850,313,1112,1119,795,347,1731,843,2398,117,2067,735,1555,934,834,785,968,241,391,1430,641,1693,1530,1042,568,781,1167,4619,273,2.989,2.974,2.424,2.698,2.479,2.308,2.507,2.914,2.84,3.125,3.167,1.991,2.979,2.739,2.81,2.961,3.138,3.424,2.963,2.001,2.795,2.511,2.002,2.821,2.719,2.901,2.562,2.561,2.908,2.723,3.071,2.695,2.626,3.006,3.044,2.527,2.814,3.051,1.651,2.826,2.756,2.028,3.046,2.001,1.984,2.418,3.102,2.775,2.727,2.462,1.924,2.33,2.316,2.686,2.551,2.372,2.398,2.473,2.005,2.266,2.225,2.635,2.464,2.113,2.499,2.303,2.631,2.761,2.838,3.202,2.57,2.408,2.557,2.032,0.586,0.709,0.665,0.442,0.644,0.469,0.581,0.605,0.468,0.376,0.532,0.517,0.597,0.6,0.462,0.694,0.688,0.64,0.528,0.575,0.761,0.407,0.501,0.725,0.536,0.6,0.457,0.632,0.551,0.535,0.716,0.977,0.613,0.646,0.695,0.429,0.66,0.605,0.386,0.505,0.471,0.578,0.813,0.579,0.52,0.486,0.587,0.685,0.364,0.586,0.39,0.627,0.524,0.497,0.38,0.541,0.387,0.516,0.369,0.324,0.372,0.819,0.78,0.839,0.487,0.732,0.518,0.475,0.43,0.805,0.457,0.41,0.514,0.449
-4033,Not Epileptic,F,52.0,0.8,1.6,0.8,0.9,1.8,2.9,2.7,1.6,1.7,0.6,0.1,2.5,1.6,0.6,1.5,4.9,8.3,0.6,2.9,3.0,1.9,2.8,1.5,3.3,2.7,3.6,2.3,2.4,3.7,2.6,1.3,1.1,0.2,2.2,0.6,0.6,3.5,3.0,0.1,0.5,0.8,2.9,3.4,2.4,3.8,0.9,0.4,0.7,1.2,0.9,0.8,1.5,1.1,2.9,0.4,1.7,0.8,2.0,1.2,1.2,0.6,1.7,0.2,0.6,1.9,1.1,2.3,1.8,1.3,0.8,0.9,1.6,4.4,0.4,5,11,7,7,13,21,14,13,11,5,2.0,17,16,4,10,40,57,5,30,30,11,26,13,25,29,33,24,24,25,26,16,7,3,21,5,6,38,29,1,3,4,27,26,17,20,7,2,5,6,13,4,8,5,17,2,14,6,15,7,7,4,12,2,4,14,11,19,10,8,6,8,11,32,2,0.037,0.023,0.019,0.023,0.042,0.035,0.042,0.03,0.076,0.032,0.022,0.033,0.035,0.048,0.043,0.045,0.039,0.031,0.038,0.03,0.03,0.034,0.028,0.049,0.035,0.036,0.027,0.029,0.035,0.032,0.046,0.042,0.019,0.033,0.025,0.019,0.038,0.032,0.017,0.031,0.019,0.042,0.067,0.024,0.028,0.026,0.022,0.013,0.017,0.028,0.033,0.023,0.029,0.028,0.032,0.017,0.019,0.023,0.028,0.022,0.022,0.033,0.029,0.028,0.02,0.026,0.024,0.028,0.027,0.029,0.024,0.025,0.02,0.03,1253,3681,1971,1855,1600,3660,2421,3194,1446,1076,531.0,2630,3134,741,2521,8211,13303,1948,4530,3746,3447,4565,2147,4751,4658,5925,4221,3805,5095,4118,1880,756,692,4589,1733,1524,5418,5934,302,655,1338,2619,3793,2471,3459,1055,697,2028,2281,1433,778,2275,1402,4360,311,3248,1365,3181,1304,1607,963,1880,434,890,3083,1200,3500,2238,1941,1349,1169,2346,8823,390,0.113,0.106,0.112,0.102,0.149,0.135,0.138,0.122,0.177,0.139,0.086,0.128,0.133,0.134,0.137,0.142,0.126,0.103,0.129,0.121,0.102,0.133,0.116,0.13,0.134,0.138,0.123,0.124,0.128,0.126,0.13,0.117,0.103,0.121,0.088,0.09,0.122,0.121,0.094,0.124,0.1,0.144,0.16,0.111,0.119,0.117,0.103,0.079,0.092,0.108,0.126,0.099,0.111,0.116,0.158,0.1,0.099,0.111,0.122,0.106,0.105,0.133,0.129,0.125,0.108,0.127,0.115,0.124,0.114,0.121,0.128,0.115,0.097,0.14,364,1073,654,677,666,1367,888,944,404,290,168.0,1161,845,207,611,1953,3522,334,1380,1727,950,1376,847,1243,1425,1743,1405,1388,1624,1387,496,380,249,1127,450,567,1558,1405,159,299,664,1199,1014,1500,2099,507,278,825,1024,638,369,1076,593,1665,162,1594,663,1451,721,838,445,831,173,349,1502,656,1655,990,839,450,581,1078,3638,168,3.399,2.924,2.953,2.653,2.188,2.468,2.324,3.095,2.643,3.234,2.714,1.992,2.785,2.663,2.953,2.9,2.931,3.873,2.803,1.896,2.887,2.606,2.169,2.826,2.622,2.77,2.374,2.235,2.617,2.384,2.814,2.037,2.271,2.898,3.553,2.491,2.565,3.038,2.114,2.362,2.529,1.926,2.899,1.838,1.897,2.03,3.042,2.979,2.608,2.505,2.11,2.183,2.384,2.367,2.287,2.193,2.403,2.472,2.195,2.134,2.379,2.293,2.692,2.55,2.207,2.434,2.233,2.597,2.537,3.268,2.228,2.594,2.551,2.697,1.151,0.843,0.621,0.56,0.497,0.484,0.488,0.613,0.535,0.503,0.72,0.445,0.544,0.49,0.627,0.668,0.727,0.846,0.64,0.461,0.734,0.413,0.456,0.634,0.57,0.615,0.562,0.47,0.592,0.544,0.716,0.968,0.423,0.757,0.841,0.523,0.709,0.527,0.54,0.487,0.593,0.527,0.695,0.533,0.562,0.547,0.493,0.908,0.601,0.509,0.446,0.539,0.796,0.701,0.441,0.447,0.341,0.627,0.367,0.287,0.465,0.599,0.47,0.886,0.586,0.76,0.451,0.569,0.492,0.676,0.727,0.714,0.501,0.697
-4034,Not Epileptic,M,55.0,0.6,1.7,0.8,1.1,1.3,1.4,1.5,1.9,0.8,0.6,0.3,2.7,1.7,0.5,1.3,5.0,9.0,0.8,1.8,3.3,0.7,2.2,1.7,3.7,3.3,2.7,2.6,2.2,2.7,3.0,1.2,1.0,0.6,2.1,0.4,0.9,2.4,3.0,0.2,0.2,1.1,3.2,1.8,2.3,2.5,0.6,0.5,0.9,1.6,0.5,0.5,1.9,1.5,2.2,0.7,2.3,0.4,1.6,0.9,0.8,0.5,2.1,0.2,0.6,1.9,1.1,2.2,1.2,0.9,0.7,0.9,1.0,3.5,0.3,7,19,6,8,14,17,15,19,14,7,5.0,25,22,7,15,59,97,5,27,30,6,33,15,38,43,31,38,25,29,36,17,8,8,30,2,11,32,36,2,1,7,26,21,20,15,6,3,6,7,4,4,12,9,17,7,16,2,18,6,6,3,19,1,5,15,14,24,10,6,5,5,7,60,2,0.032,0.023,0.016,0.022,0.03,0.023,0.03,0.034,0.031,0.035,0.031,0.032,0.028,0.034,0.032,0.035,0.031,0.031,0.026,0.035,0.015,0.029,0.032,0.039,0.031,0.025,0.025,0.027,0.028,0.031,0.039,0.036,0.027,0.027,0.013,0.024,0.04,0.03,0.02,0.022,0.022,0.037,0.035,0.023,0.019,0.013,0.02,0.015,0.019,0.021,0.018,0.021,0.026,0.02,0.025,0.015,0.016,0.017,0.021,0.014,0.022,0.029,0.013,0.018,0.022,0.022,0.018,0.018,0.017,0.02,0.022,0.018,0.018,0.017,1596,3909,2074,2004,2068,3260,2527,3215,1627,1442,770.0,3263,3845,1009,2453,9421,18289,2164,4710,4499,3088,4737,2020,6163,6927,6441,5345,3777,5810,5424,2190,1169,1244,5998,2229,2196,5467,7577,451,265,1431,2737,5231,2500,3513,1412,876,2546,2600,993,689,2888,1539,3783,759,4360,997,3217,1030,1599,708,3334,322,989,2579,1362,3887,2397,1545,1231,1337,1982,8190,465,0.115,0.118,0.093,0.105,0.133,0.119,0.099,0.143,0.143,0.137,0.129,0.134,0.129,0.142,0.147,0.138,0.133,0.121,0.118,0.141,0.077,0.124,0.121,0.14,0.136,0.123,0.115,0.105,0.106,0.146,0.13,0.103,0.126,0.136,0.069,0.112,0.14,0.152,0.111,0.134,0.114,0.133,0.138,0.12,0.1,0.092,0.108,0.088,0.097,0.104,0.115,0.107,0.121,0.104,0.103,0.092,0.086,0.11,0.118,0.097,0.103,0.135,0.095,0.106,0.113,0.106,0.098,0.109,0.096,0.111,0.114,0.107,0.097,0.116,444,1262,741,759,820,1101,934,1016,568,337,201.0,1340,1147,249,726,2692,5255,397,1308,1643,730,1298,914,1728,1962,1947,1905,1400,1639,1590,648,466,396,1378,522,702,1316,1673,192,123,715,1255,1034,1522,2000,696,385,925,1176,446,385,1339,778,1753,419,2216,456,1396,609,828,367,1211,185,508,1307,800,2032,1031,788,510,631,874,3501,248,3.375,2.717,2.586,2.531,2.206,2.465,2.257,2.85,2.254,2.97,2.779,2.042,2.643,2.875,2.598,2.567,2.641,3.871,2.854,2.27,2.945,2.651,2.002,2.723,2.693,2.633,2.208,2.122,2.693,2.539,2.47,2.502,2.628,3.062,3.45,2.731,3.148,3.186,2.527,2.349,2.619,1.926,3.476,1.806,1.967,2.131,2.78,3.302,2.621,2.785,1.941,2.287,2.152,2.297,2.19,2.145,2.509,2.454,1.953,2.085,2.378,2.688,2.025,2.311,2.111,1.955,1.995,2.437,2.224,2.561,2.323,2.561,2.424,2.341,0.837,0.729,0.516,0.529,0.573,0.624,0.665,0.57,0.612,0.668,1.087,0.499,0.468,0.347,0.482,0.577,0.644,0.738,0.715,0.657,0.712,0.569,0.435,0.863,0.643,0.557,0.558,0.544,0.493,0.695,0.749,0.778,0.359,0.597,0.717,0.398,0.711,0.587,0.432,0.518,0.443,0.536,0.639,0.56,0.577,0.425,0.528,0.823,0.499,0.468,0.323,0.455,0.504,0.414,0.346,0.411,0.454,0.465,0.41,0.386,0.339,0.757,0.227,0.762,0.48,0.676,0.458,0.439,0.461,0.712,0.415,0.549,0.415,0.307
-4035,Not Epileptic,F,52.0,1.5,2.0,1.0,1.1,1.8,1.7,3.5,2.1,1.6,0.8,0.4,3.1,1.5,0.8,1.3,6.9,11.3,0.9,2.2,4.1,1.8,2.4,2.7,6.7,3.3,3.8,6.3,3.5,3.9,3.6,1.5,1.1,0.6,2.4,0.4,0.6,5.3,3.5,0.2,0.4,1.4,2.7,3.4,2.3,4.5,1.1,1.0,1.0,1.7,0.8,0.8,2.1,1.3,3.8,0.4,3.6,0.7,2.3,1.1,1.5,0.8,1.9,0.3,0.8,2.3,1.5,3.9,2.0,1.6,0.5,1.2,1.4,4.5,0.4,13,17,7,7,15,16,22,18,11,10,5.0,32,14,5,11,58,92,6,19,37,14,28,24,41,41,41,50,29,27,38,13,8,4,25,5,4,48,90,1,4,7,25,29,21,25,8,5,6,9,8,5,14,8,20,2,26,6,24,10,11,7,14,2,7,17,18,27,14,9,6,8,10,33,3,0.05,0.026,0.02,0.021,0.037,0.031,0.045,0.03,0.055,0.035,0.037,0.034,0.032,0.055,0.033,0.038,0.038,0.032,0.038,0.034,0.039,0.026,0.034,0.054,0.033,0.029,0.041,0.033,0.035,0.033,0.041,0.044,0.026,0.03,0.02,0.018,0.051,0.035,0.02,0.027,0.026,0.032,0.052,0.021,0.029,0.021,0.044,0.018,0.021,0.023,0.027,0.024,0.028,0.031,0.014,0.02,0.021,0.029,0.024,0.021,0.026,0.032,0.019,0.026,0.021,0.025,0.026,0.025,0.029,0.02,0.029,0.018,0.018,0.024,1537,3620,1919,1990,1971,3421,3642,3633,1335,1634,570.0,3205,3042,963,2114,10076,17805,2266,3201,4868,3562,4457,3261,5550,6208,7832,6098,4130,6474,6103,1992,1064,954,5199,1307,1617,6371,7800,344,584,1484,2448,5865,2713,3745,1455,695,1930,2300,1412,589,2761,1474,4031,543,4795,1336,3249,993,2056,982,2981,354,847,3368,1300,4523,2691,1873,942,1427,2372,8668,408,0.14,0.109,0.105,0.102,0.127,0.131,0.126,0.124,0.15,0.135,0.147,0.138,0.127,0.139,0.125,0.139,0.134,0.11,0.138,0.135,0.122,0.117,0.124,0.154,0.14,0.127,0.128,0.111,0.112,0.136,0.143,0.118,0.097,0.123,0.086,0.094,0.151,0.127,0.107,0.138,0.113,0.13,0.161,0.112,0.117,0.107,0.135,0.09,0.105,0.117,0.137,0.113,0.126,0.115,0.102,0.111,0.117,0.137,0.138,0.106,0.124,0.12,0.101,0.131,0.108,0.122,0.114,0.114,0.119,0.113,0.122,0.107,0.097,0.127,536,1424,764,785,897,973,1233,1205,500,418,203.0,1478,915,254,642,3176,5251,449,1029,2045,836,1472,1196,2009,1817,2297,2351,1670,1755,1962,594,485,293,1320,413,567,1897,1803,170,247,796,1229,1311,1743,2375,753,352,897,1139,613,386,1459,713,2008,337,2503,493,1433,650,1125,487,1050,243,523,1661,897,2346,1293,878,449,721,1171,3860,208,2.96,2.559,2.615,2.511,1.991,2.734,2.338,2.556,2.147,2.873,2.273,1.875,2.633,2.75,2.579,2.451,2.753,3.687,2.746,1.989,3.26,2.359,2.226,2.326,2.605,2.674,2.139,2.046,2.794,2.468,2.42,2.308,2.662,2.696,2.797,2.679,2.53,3.079,2.253,2.481,2.27,1.837,3.158,1.771,1.856,1.971,2.396,2.559,2.559,2.578,1.976,2.075,2.267,2.189,1.917,2.108,2.658,2.318,1.8,1.973,2.177,2.777,1.734,2.017,2.179,1.795,1.994,2.411,2.288,2.346,2.258,2.311,2.349,2.532,0.788,0.546,0.45,0.464,0.53,0.839,0.683,0.524,0.679,0.75,0.94,0.567,0.482,0.575,0.546,0.543,0.664,0.758,0.579,0.69,0.867,0.532,0.735,0.795,0.556,0.675,0.579,0.548,0.698,0.627,0.751,0.918,0.446,0.685,0.622,0.429,0.816,0.705,0.403,0.39,0.461,0.575,0.845,0.606,0.61,0.437,0.516,0.615,0.519,0.558,0.484,0.446,0.479,0.499,0.367,0.42,0.756,0.719,0.405,0.408,0.545,0.653,0.423,0.527,0.573,0.584,0.452,0.479,0.384,0.7,0.391,0.686,0.51,0.668
-4036,Not Epileptic,M,27.0,1.5,2.3,1.0,1.3,1.9,2.6,1.9,2.0,1.2,0.8,0.4,3.4,1.9,0.6,1.6,6.5,10.1,1.3,2.4,5.4,1.9,2.3,2.2,2.8,3.3,3.6,3.1,3.8,4.3,3.2,2.2,0.9,0.3,3.4,0.5,1.4,3.9,3.3,0.2,0.3,0.9,3.9,3.0,4.3,3.8,0.6,0.5,0.8,1.2,1.0,0.7,3.0,0.8,2.8,0.5,4.4,0.9,1.2,1.4,1.3,0.5,1.9,0.6,0.5,2.9,1.5,4.4,1.2,1.1,0.7,0.7,1.6,4.0,0.3,11,18,8,11,13,22,13,19,9,9,3.0,27,19,9,14,55,75,9,28,40,14,23,19,25,43,33,31,33,29,36,21,6,4,31,3,12,35,34,2,2,5,30,25,26,20,4,2,5,6,9,4,22,6,15,4,29,6,10,8,11,4,10,4,3,25,12,38,6,6,6,5,9,29,2,0.044,0.024,0.019,0.025,0.041,0.032,0.03,0.035,0.05,0.035,0.024,0.038,0.031,0.044,0.034,0.042,0.038,0.046,0.03,0.04,0.033,0.033,0.03,0.035,0.03,0.032,0.029,0.037,0.039,0.036,0.052,0.04,0.028,0.037,0.019,0.029,0.04,0.035,0.014,0.018,0.02,0.04,0.05,0.031,0.033,0.012,0.022,0.014,0.015,0.02,0.034,0.027,0.02,0.024,0.02,0.03,0.021,0.017,0.029,0.028,0.019,0.036,0.029,0.023,0.027,0.032,0.029,0.019,0.024,0.026,0.024,0.021,0.018,0.02,1755,4956,2894,2523,2481,3723,2881,3236,1446,1596,642.0,3062,4181,1333,3474,11637,19076,2584,5177,5049,3871,4018,2830,6420,6798,7512,5576,5018,6732,5252,2655,1123,789,6741,2029,2602,5549,6829,341,660,1504,2910,5774,4161,3006,1454,941,1721,2496,1760,592,5030,1627,4564,830,4689,1487,2755,1574,1494,963,2323,669,1040,3607,1454,5313,2291,1482,1644,1080,2009,9294,643,0.147,0.106,0.094,0.11,0.138,0.135,0.108,0.138,0.138,0.136,0.112,0.143,0.126,0.142,0.131,0.138,0.132,0.125,0.128,0.138,0.105,0.137,0.135,0.13,0.14,0.124,0.123,0.12,0.122,0.144,0.153,0.107,0.143,0.132,0.082,0.112,0.126,0.124,0.097,0.1,0.103,0.146,0.135,0.127,0.126,0.087,0.098,0.087,0.088,0.102,0.128,0.109,0.1,0.108,0.115,0.135,0.099,0.098,0.121,0.135,0.101,0.121,0.139,0.113,0.128,0.135,0.12,0.098,0.116,0.125,0.125,0.115,0.094,0.107,541,1562,895,907,824,1337,1066,1013,430,388,241.0,1477,1110,280,873,2766,4638,528,1430,2138,1015,1260,1131,1454,1888,1922,1840,1777,1678,1622,651,459,220,1432,490,815,1682,1631,202,317,678,1484,1206,2259,1884,719,329,806,1150,730,324,1893,749,1848,401,2250,696,1226,807,776,446,793,285,328,1759,655,2436,976,684,512,488,1045,3619,234,3.049,2.796,2.871,2.829,2.529,2.55,2.282,2.924,2.501,3.357,2.726,1.88,2.91,3.125,2.915,2.953,3.174,3.584,2.91,1.994,3.007,2.491,2.086,3.219,2.875,3.089,2.463,2.25,2.897,2.651,2.9,2.572,2.885,3.323,3.299,2.857,2.604,3.098,1.989,2.511,2.941,1.867,3.099,2.211,1.887,2.299,3.576,2.667,2.718,2.484,2.221,2.617,2.321,2.626,2.764,2.334,2.553,2.562,2.213,2.19,2.39,2.886,2.454,3.097,2.085,2.574,2.277,2.722,2.638,3.098,2.613,2.389,2.712,3.048,0.832,0.902,0.748,0.578,0.765,0.496,0.723,0.524,0.535,0.429,0.833,0.592,0.487,0.604,0.624,0.674,0.7,0.83,0.605,0.736,0.77,0.458,0.549,0.813,0.549,0.649,0.481,0.549,0.791,0.543,0.816,0.949,0.436,0.807,0.82,0.646,0.695,0.616,0.317,0.347,0.679,0.528,0.829,0.701,0.676,0.43,0.44,0.562,0.483,0.617,0.407,0.544,0.423,0.411,0.357,0.461,0.577,0.487,0.363,0.474,0.373,0.87,0.662,0.941,0.446,0.789,0.531,0.406,0.45,0.797,0.39,0.627,0.533,0.897
-4037,Not Epileptic,F,32.0,0.9,1.8,1.1,1.1,1.6,2.3,1.5,1.9,1.0,0.4,0.2,3.3,1.5,0.5,0.7,5.5,10.1,1.2,1.8,5.3,1.3,3.4,2.3,2.5,3.4,2.7,2.5,2.1,2.4,3.1,2.2,1.5,0.5,2.3,0.6,0.4,2.8,3.1,0.2,0.2,1.1,3.7,2.1,2.9,2.6,0.8,0.4,1.3,1.4,0.8,0.6,1.0,1.3,2.7,0.5,2.5,0.9,1.5,1.0,1.4,0.8,1.3,0.3,0.6,1.9,1.5,2.4,0.9,1.5,0.5,0.8,1.1,4.6,0.4,9,16,7,8,17,28,13,21,11,6,3.0,33,16,7,12,85,103,9,24,45,11,42,24,33,39,31,32,23,25,38,29,11,7,26,4,4,35,40,2,2,7,31,20,25,18,5,2,7,8,6,5,9,11,22,2,22,8,13,7,11,5,13,2,5,14,17,18,8,10,4,5,10,38,4,0.041,0.021,0.019,0.02,0.031,0.026,0.02,0.028,0.039,0.027,0.025,0.033,0.026,0.038,0.026,0.033,0.03,0.04,0.026,0.039,0.029,0.035,0.031,0.033,0.031,0.023,0.023,0.022,0.025,0.027,0.043,0.062,0.024,0.025,0.018,0.012,0.029,0.027,0.018,0.016,0.022,0.038,0.036,0.026,0.018,0.013,0.015,0.019,0.017,0.014,0.022,0.012,0.021,0.019,0.023,0.02,0.025,0.018,0.025,0.021,0.024,0.022,0.017,0.018,0.018,0.029,0.016,0.013,0.022,0.015,0.019,0.013,0.019,0.019,1441,4218,2501,2821,2304,4831,3298,3562,1639,1477,701.0,3654,3513,950,1698,10488,20751,2343,4618,5276,3802,5296,3100,5128,6550,6543,6244,4553,5506,6559,2976,1013,1239,6561,1895,1758,6668,8891,399,358,1634,2903,5080,2916,3791,1775,803,2828,2599,1777,828,2459,1950,5251,527,3901,1311,3281,1309,1865,1025,2318,384,896,3330,1414,4569,2302,2078,863,1399,2511,9476,639,0.13,0.108,0.103,0.099,0.13,0.136,0.101,0.127,0.159,0.118,0.117,0.142,0.129,0.131,0.134,0.145,0.133,0.13,0.124,0.157,0.099,0.134,0.114,0.126,0.126,0.12,0.11,0.108,0.115,0.128,0.174,0.162,0.111,0.119,0.094,0.072,0.126,0.128,0.108,0.11,0.104,0.13,0.137,0.129,0.103,0.092,0.086,0.094,0.098,0.101,0.122,0.091,0.117,0.102,0.12,0.115,0.133,0.106,0.115,0.117,0.118,0.12,0.105,0.115,0.101,0.127,0.099,0.093,0.114,0.109,0.107,0.097,0.103,0.129,450,1417,855,933,902,1398,1037,1194,481,312,191.0,1706,945,275,511,3255,5784,522,1189,2216,820,1611,1093,1434,1813,1845,1843,1499,1622,1918,859,421,331,1464,497,610,1674,1990,186,191,806,1380,1121,1724,2156,843,350,1146,1228,796,440,1182,989,2301,292,1885,558,1398,668,988,504,938,247,465,1563,746,2145,1056,1004,475,676,1212,3901,274,2.902,2.723,2.779,3.041,2.214,2.691,2.541,2.627,2.52,3.493,2.851,1.919,2.775,2.582,2.459,2.482,2.828,3.539,3.042,2.023,3.419,2.584,2.431,2.724,2.738,2.773,2.607,2.39,2.641,2.58,2.654,2.4,2.863,3.128,3.472,2.559,2.977,3.141,2.423,2.195,2.585,1.939,3.325,1.951,1.999,2.443,2.905,3.041,2.607,2.533,2.214,2.126,2.063,2.375,2.215,2.24,2.48,2.723,2.189,2.109,2.529,2.27,1.679,2.239,2.373,2.23,2.328,2.482,2.446,2.114,2.437,2.337,2.672,2.648,0.549,0.73,0.51,0.409,0.634,0.672,0.674,0.516,0.563,0.485,0.885,0.553,0.414,0.431,0.483,0.504,0.627,0.93,0.577,0.716,0.689,0.53,0.648,0.588,0.571,0.545,0.578,0.499,0.443,0.583,0.582,0.985,0.389,0.541,0.546,0.473,0.645,0.555,0.415,0.415,0.493,0.559,0.651,0.609,0.528,0.507,0.408,0.753,0.4,0.43,0.445,0.47,0.512,0.38,0.373,0.415,0.544,0.615,0.393,0.412,0.486,0.583,0.305,0.677,0.461,0.735,0.381,0.398,0.366,0.495,0.475,0.456,0.476,0.464
-4038,Not Epileptic,F,22.0,1.4,2.6,1.0,1.3,1.9,1.7,2.4,1.5,1.2,0.6,0.2,3.1,1.6,0.5,0.9,5.7,7.7,1.2,2.1,4.0,0.8,3.0,1.4,4.4,2.5,2.5,3.3,2.2,3.0,2.9,2.8,0.9,0.3,1.8,0.5,0.7,3.6,2.7,0.1,0.2,1.0,2.9,2.0,2.2,2.8,0.7,0.6,0.8,1.3,0.7,0.5,1.5,1.6,2.6,0.4,1.7,0.9,1.3,1.2,0.8,0.3,1.9,0.3,0.6,1.5,1.1,2.3,1.2,1.2,0.4,0.9,1.4,3.4,0.2,9,23,8,10,15,16,19,15,9,6,3.0,27,15,4,12,59,66,10,24,37,7,37,16,46,33,25,37,20,25,37,23,5,3,22,3,7,41,37,1,1,5,27,19,21,18,5,3,5,7,6,5,14,9,17,2,14,6,10,14,7,2,33,2,6,9,15,16,9,8,3,5,10,25,2,0.048,0.028,0.02,0.024,0.036,0.029,0.034,0.03,0.038,0.026,0.024,0.031,0.034,0.025,0.023,0.032,0.031,0.04,0.034,0.037,0.017,0.032,0.024,0.047,0.027,0.027,0.029,0.028,0.03,0.032,0.056,0.037,0.022,0.023,0.016,0.024,0.035,0.029,0.011,0.016,0.022,0.034,0.032,0.027,0.022,0.013,0.022,0.015,0.019,0.019,0.022,0.023,0.026,0.023,0.022,0.015,0.025,0.015,0.026,0.013,0.014,0.034,0.017,0.016,0.02,0.024,0.02,0.017,0.019,0.016,0.025,0.02,0.016,0.014,1565,4474,2107,2165,2218,2912,2972,2798,1820,1234,653.0,2801,3164,1144,2420,11613,16849,2346,3976,4398,3094,4465,2166,6473,5717,5381,5130,3817,6728,5685,2057,1124,832,5663,2104,1710,8667,7565,290,393,1335,2099,5783,2245,3392,1586,822,1836,2176,1641,498,2360,1980,4371,410,3308,1397,2792,1325,1694,604,2536,423,1005,2328,959,3663,2483,1789,945,1244,2252,7234,502,0.139,0.131,0.113,0.113,0.139,0.125,0.124,0.126,0.143,0.139,0.107,0.135,0.139,0.128,0.126,0.142,0.13,0.15,0.131,0.146,0.089,0.13,0.111,0.149,0.126,0.124,0.115,0.108,0.115,0.138,0.154,0.103,0.101,0.12,0.081,0.098,0.14,0.142,0.083,0.096,0.109,0.133,0.132,0.13,0.106,0.095,0.105,0.084,0.101,0.105,0.121,0.116,0.111,0.11,0.121,0.095,0.121,0.097,0.142,0.09,0.094,0.131,0.109,0.104,0.104,0.129,0.101,0.096,0.105,0.099,0.12,0.115,0.096,0.102,470,1481,744,828,894,1012,1143,953,508,332,179.0,1510,832,290,700,3088,4420,529,1073,1858,743,1474,897,1761,1488,1563,1653,1170,1702,1712,755,412,279,1242,530,601,1974,1684,183,219,673,1271,1170,1420,2013,762,368,834,1035,621,345,1131,1035,1826,203,1721,601,1331,785,903,320,1028,268,532,1106,651,1772,1097,848,413,568,1083,3204,227,3.157,2.764,2.899,2.598,2.053,2.63,2.211,2.626,2.506,2.892,2.761,1.708,2.778,2.761,2.575,2.737,2.975,3.489,2.92,2.0,2.957,2.372,2.087,2.654,2.855,2.764,2.416,2.453,2.829,2.643,2.107,2.669,2.446,3.098,3.489,2.496,3.172,3.193,1.961,2.148,2.536,1.601,3.518,1.885,1.892,2.305,2.727,2.912,2.578,2.954,1.818,2.166,2.132,2.32,2.503,2.128,2.757,2.478,1.984,2.07,2.065,2.444,1.699,2.221,2.405,1.977,2.141,2.508,2.354,2.352,2.638,2.468,2.465,2.63,0.654,0.73,0.56,0.452,0.749,0.631,0.602,0.54,0.753,0.56,0.766,0.508,0.468,0.574,0.549,0.739,0.658,0.743,0.528,0.704,0.791,0.573,0.545,0.79,0.588,0.605,0.671,0.7,0.632,0.613,0.617,0.844,0.393,0.608,0.64,0.517,0.74,0.62,0.412,0.348,0.481,0.402,0.683,0.646,0.547,0.443,0.447,0.477,0.444,0.623,0.405,0.411,0.496,0.583,0.443,0.41,0.469,0.374,0.397,0.365,0.335,0.609,0.467,0.55,0.508,0.643,0.41,0.385,0.394,0.714,0.377,0.491,0.437,0.452
-4039,Not Epileptic,F,27.0,0.8,1.7,0.6,0.9,1.1,1.9,1.3,2.0,1.1,0.5,0.1,2.5,1.5,0.5,0.9,5.5,8.0,0.7,2.2,3.9,1.0,1.9,1.0,2.1,2.5,2.7,3.0,2.3,2.7,3.4,1.0,1.0,0.6,3.2,0.3,0.9,2.9,2.7,0.1,0.1,1.0,2.7,3.0,2.2,3.0,0.8,0.3,1.5,1.3,1.6,0.7,1.7,1.4,2.3,0.2,2.0,0.7,2.1,0.6,0.6,0.3,0.9,0.5,0.6,1.4,1.2,2.6,1.0,1.8,0.5,1.0,1.2,3.5,0.5,7,13,5,7,13,21,11,18,11,5,1.0,22,14,8,11,53,73,6,24,35,8,23,10,28,33,20,32,22,22,34,14,6,6,37,2,8,25,28,1,1,5,21,23,16,17,6,1,9,7,8,3,15,9,15,2,14,6,16,3,4,2,7,2,4,10,10,16,7,9,5,7,8,27,3,0.035,0.021,0.014,0.022,0.03,0.03,0.023,0.032,0.048,0.028,0.021,0.037,0.031,0.039,0.032,0.037,0.03,0.03,0.029,0.037,0.023,0.03,0.028,0.035,0.027,0.036,0.027,0.029,0.027,0.037,0.038,0.043,0.027,0.041,0.018,0.021,0.041,0.032,0.018,0.016,0.022,0.037,0.045,0.026,0.023,0.019,0.015,0.022,0.018,0.036,0.027,0.025,0.029,0.022,0.015,0.017,0.03,0.022,0.021,0.016,0.02,0.02,0.038,0.025,0.021,0.043,0.026,0.018,0.024,0.017,0.026,0.018,0.017,0.019,1420,4187,1992,2050,1968,2969,2473,3332,1595,1232,365.0,2568,2945,1157,2111,10379,18842,2278,4472,3993,2586,4016,1752,5125,5892,5224,5145,3828,6191,5279,2372,931,1208,6697,1678,2461,3156,6062,281,340,1621,2652,5271,1905,3445,1307,728,2465,2459,1497,632,2881,1961,4338,461,3370,1366,3276,816,1321,688,1889,527,827,2201,777,3287,2271,2323,977,1463,2357,8317,865,0.123,0.105,0.087,0.103,0.127,0.15,0.102,0.135,0.155,0.127,0.078,0.151,0.134,0.155,0.131,0.141,0.13,0.113,0.122,0.137,0.094,0.131,0.126,0.134,0.13,0.132,0.121,0.12,0.112,0.147,0.121,0.11,0.126,0.142,0.095,0.101,0.126,0.132,0.097,0.104,0.109,0.142,0.153,0.117,0.107,0.104,0.091,0.097,0.098,0.144,0.133,0.112,0.114,0.105,0.116,0.1,0.121,0.114,0.113,0.094,0.092,0.109,0.143,0.146,0.116,0.146,0.119,0.101,0.115,0.117,0.124,0.096,0.093,0.115,402,1355,697,741,689,1048,850,1135,396,324,113.0,1055,770,262,556,2648,4589,410,1152,1849,719,1099,632,1179,1541,1350,1711,1352,1626,1564,460,412,357,1424,336,661,1192,1390,155,183,704,1201,1174,1282,2022,649,293,983,1032,727,379,1159,848,1758,217,1819,460,1482,440,652,299,704,205,338,1032,371,1541,916,1046,464,634,1047,3352,344,3.314,3.004,2.692,2.793,2.218,2.395,2.413,2.657,2.692,3.213,2.918,2.113,2.949,2.995,2.655,2.798,3.077,3.913,3.115,1.896,2.986,2.762,2.41,3.159,2.742,3.075,2.404,2.287,2.831,2.677,3.241,2.262,2.722,3.096,3.69,3.029,2.276,3.028,1.994,2.111,2.925,1.982,3.172,1.769,1.955,2.066,3.05,3.194,2.831,2.531,2.217,2.42,2.254,2.517,2.492,2.063,2.691,2.548,2.221,2.238,2.763,2.684,2.618,2.545,2.351,2.382,2.345,2.829,2.648,2.317,2.445,2.589,2.626,3.384,0.97,0.534,0.578,0.669,0.737,0.545,0.592,0.742,0.725,0.591,0.807,0.668,0.436,0.581,0.597,0.722,0.676,0.943,0.693,0.572,0.719,0.468,0.421,0.654,0.629,0.652,0.552,0.586,0.522,0.581,0.781,0.896,0.518,0.733,0.916,0.597,0.749,0.629,0.306,0.435,0.553,0.575,0.88,0.741,0.571,0.45,0.453,0.82,0.604,0.59,0.444,0.528,0.611,0.583,0.546,0.469,0.598,0.633,0.319,0.346,0.512,0.691,0.608,0.884,0.6,0.662,0.529,0.503,0.562,0.747,0.497,0.552,0.523,0.495
-4040,Not Epileptic,F,55.0,0.6,2.0,0.5,0.8,1.2,2.2,1.3,1.5,1.1,0.6,0.6,3.0,1.7,0.2,1.7,4.7,6.9,0.9,2.1,4.5,0.7,2.6,1.3,2.6,2.9,2.8,3.1,1.9,2.4,3.1,1.1,0.7,0.4,2.4,0.3,0.3,4.1,3.8,0.1,0.1,0.9,2.8,2.0,2.9,2.1,0.4,0.3,0.8,0.9,0.8,0.6,2.3,0.9,2.2,0.1,2.0,0.7,1.6,1.1,0.8,0.6,1.1,0.5,0.3,1.4,1.5,2.2,1.0,0.9,0.5,1.1,1.6,5.4,0.4,5,24,7,7,12,16,12,12,11,5,4.0,24,15,4,15,44,62,7,21,35,7,28,11,25,26,27,30,16,18,27,15,5,4,22,2,2,38,37,2,1,4,22,18,18,15,3,2,4,5,6,5,17,6,13,0,16,4,14,8,8,5,8,2,3,10,15,16,7,5,5,7,10,40,3,0.028,0.023,0.012,0.019,0.03,0.036,0.026,0.03,0.036,0.037,0.04,0.038,0.03,0.018,0.033,0.031,0.025,0.03,0.034,0.036,0.018,0.034,0.028,0.033,0.031,0.034,0.032,0.028,0.026,0.036,0.032,0.029,0.017,0.031,0.011,0.011,0.036,0.036,0.015,0.016,0.019,0.033,0.031,0.03,0.019,0.011,0.015,0.013,0.012,0.02,0.033,0.023,0.016,0.021,0.01,0.021,0.018,0.022,0.026,0.017,0.032,0.021,0.021,0.015,0.019,0.034,0.023,0.019,0.017,0.021,0.033,0.023,0.02,0.019,1658,3968,1556,1635,1899,3191,2214,2696,1623,1045,662.0,2979,3470,738,2605,8643,15207,2117,3265,4545,2959,4316,1526,5232,4230,5138,4383,3087,4930,4275,1974,705,1025,5294,1431,1306,5870,6489,395,344,1381,2630,4238,2384,2600,1070,753,2079,2546,1256,422,3748,1599,4239,183,3092,1151,2517,1156,1192,658,2446,506,975,2088,843,2678,1814,1289,936,1224,1964,8729,461,0.098,0.113,0.093,0.1,0.121,0.13,0.106,0.119,0.136,0.125,0.152,0.144,0.123,0.125,0.15,0.131,0.121,0.108,0.124,0.137,0.085,0.139,0.117,0.128,0.13,0.133,0.127,0.111,0.103,0.144,0.134,0.107,0.109,0.118,0.073,0.066,0.126,0.12,0.092,0.092,0.094,0.134,0.126,0.119,0.103,0.087,0.093,0.075,0.079,0.114,0.155,0.11,0.092,0.097,0.082,0.116,0.095,0.112,0.129,0.098,0.13,0.107,0.111,0.109,0.104,0.134,0.116,0.108,0.102,0.119,0.14,0.107,0.103,0.098,377,1458,661,709,745,993,786,845,450,286,219.0,1149,882,215,789,2338,4279,409,1041,1973,702,1372,745,1268,1454,1473,1636,1026,1311,1391,543,373,349,1244,440,513,1863,1751,180,153,701,1199,1035,1451,1692,550,322,905,1159,648,269,1609,824,1852,88,1525,564,1268,650,781,350,829,284,347,1079,585,1440,839,702,401,524,990,3923,300,3.641,2.464,2.303,2.221,2.276,2.692,2.308,2.693,2.478,3.052,2.673,2.174,2.932,2.635,2.743,2.698,2.789,3.982,2.532,2.004,3.007,2.526,1.886,3.033,2.41,2.803,2.236,2.287,2.747,2.405,2.749,1.965,2.318,3.007,2.948,2.34,2.504,2.752,2.304,2.339,2.256,1.945,3.082,1.801,1.755,2.23,2.847,2.655,2.507,2.432,1.838,2.376,2.218,2.338,1.969,2.246,2.639,2.271,1.964,1.692,2.141,2.86,2.052,3.294,2.079,1.947,1.989,2.43,2.174,2.657,2.535,2.396,2.381,1.882,0.671,0.653,0.567,0.524,0.504,0.843,0.549,0.678,0.737,0.493,0.712,0.577,0.509,0.444,0.498,0.523,0.622,0.799,0.597,0.533,0.809,0.562,0.475,0.581,0.534,0.748,0.586,0.629,0.607,0.668,0.67,0.796,0.28,0.728,0.829,0.53,0.753,0.845,0.381,0.599,0.781,0.658,0.606,0.596,0.447,0.643,0.517,0.873,0.669,0.339,0.403,0.526,0.569,0.531,0.234,0.637,0.573,0.691,0.352,0.375,0.381,0.821,0.441,0.829,0.591,0.65,0.459,0.502,0.415,0.482,0.611,0.754,0.55,0.453
-4041,Not Epileptic,F,52.0,0.9,2.3,1.4,0.8,1.6,1.6,2.0,1.3,1.0,0.7,0.3,2.4,1.9,0.5,1.2,5.4,9.2,0.9,2.7,3.4,1.7,3.9,2.5,3.8,3.9,2.7,3.0,3.4,4.8,3.5,1.9,1.3,0.6,2.0,0.6,0.4,4.8,2.2,0.2,0.2,1.2,3.5,2.4,2.1,4.6,0.7,0.4,1.0,1.5,0.8,0.8,2.3,1.3,1.9,0.4,2.4,1.2,1.8,2.0,0.9,0.7,1.5,0.3,0.6,1.7,1.3,2.8,1.5,0.9,0.6,0.7,1.3,3.4,0.3,7,26,10,6,16,16,18,14,9,6,4.0,23,19,5,13,60,76,6,30,26,14,42,17,39,42,29,37,25,34,36,25,9,6,22,3,6,50,35,1,2,8,29,19,16,23,4,2,5,6,7,6,16,9,14,2,20,14,12,14,8,6,14,2,6,11,17,21,9,7,7,6,10,28,2,0.037,0.024,0.026,0.015,0.028,0.026,0.033,0.027,0.039,0.042,0.032,0.035,0.028,0.027,0.029,0.032,0.033,0.035,0.034,0.04,0.029,0.035,0.046,0.041,0.038,0.028,0.028,0.029,0.039,0.029,0.053,0.049,0.025,0.026,0.023,0.014,0.038,0.027,0.015,0.024,0.027,0.039,0.038,0.024,0.03,0.013,0.016,0.016,0.021,0.021,0.029,0.024,0.024,0.02,0.024,0.017,0.025,0.021,0.025,0.019,0.018,0.022,0.015,0.02,0.02,0.023,0.021,0.02,0.02,0.02,0.017,0.016,0.015,0.018,1536,4592,2435,2092,2186,3321,3054,2915,1657,1245,701.0,2837,4009,1107,2885,10752,18306,2287,5029,3206,3877,5649,2062,6547,6684,5778,6515,4134,6747,7181,2424,1294,1208,5749,1835,1869,9207,7037,417,361,1403,2852,5660,2408,3814,1455,724,2125,1946,1435,705,3493,1634,3846,419,4597,1609,2832,2190,1447,1290,2921,413,787,2631,1197,4120,2433,1519,957,1342,2503,8538,493,0.114,0.124,0.124,0.092,0.129,0.124,0.114,0.133,0.148,0.15,0.122,0.138,0.126,0.126,0.125,0.142,0.137,0.119,0.143,0.142,0.117,0.116,0.138,0.142,0.12,0.134,0.121,0.106,0.121,0.132,0.155,0.125,0.108,0.125,0.087,0.08,0.145,0.139,0.095,0.115,0.129,0.131,0.137,0.114,0.121,0.09,0.09,0.089,0.1,0.112,0.143,0.115,0.115,0.099,0.145,0.102,0.125,0.112,0.119,0.102,0.103,0.119,0.112,0.127,0.105,0.115,0.105,0.106,0.105,0.132,0.103,0.1,0.093,0.12,423,1601,804,728,900,1027,911,858,452,320,203.0,1182,1103,274,739,2873,4814,442,1397,1466,931,1598,827,1761,1756,1680,1899,1567,1807,2076,771,530,365,1262,480,635,2266,1622,232,159,672,1377,1207,1400,2299,733,315,911,964,625,353,1565,868,1559,185,2202,817,1329,1221,769,637,1054,245,423,1287,867,2121,1109,738,495,638,1199,3535,212,3.521,2.587,2.862,2.668,2.173,2.494,2.697,2.807,2.632,3.016,2.566,2.052,2.851,2.863,2.947,2.745,2.998,3.933,2.864,1.877,3.087,2.651,2.171,2.816,2.885,2.755,2.61,2.154,2.779,2.697,2.438,2.557,2.708,3.173,3.528,2.573,2.947,3.077,2.05,2.524,2.374,1.811,3.463,1.962,1.913,2.281,2.774,2.869,2.503,2.558,2.046,2.42,2.056,2.544,2.584,2.311,2.185,2.364,2.083,2.096,2.341,2.634,1.966,2.21,2.353,1.753,2.196,2.536,2.459,2.089,2.415,2.362,2.578,3.22,0.759,0.558,0.76,0.526,0.617,0.768,0.668,0.501,0.673,0.992,0.837,0.578,0.526,0.386,0.55,0.653,0.681,0.728,0.593,0.622,0.8,0.653,0.625,0.774,0.582,0.569,0.571,0.588,0.713,0.605,0.673,0.644,0.396,0.629,0.759,0.471,0.806,0.619,0.381,0.468,0.394,0.614,0.673,0.573,0.571,0.441,0.506,0.782,0.519,0.406,0.581,0.51,0.493,0.455,0.484,0.39,0.322,0.641,0.441,0.387,0.46,0.644,0.43,0.597,0.422,0.501,0.422,0.463,0.448,0.577,0.461,0.522,0.424,0.502
-4042,Not Epileptic,M,22.0,0.9,1.9,0.9,1.0,1.7,3.1,1.9,2.5,2.7,0.8,0.3,3.5,1.7,0.5,1.3,7.9,11.4,1.1,3.1,4.1,2.7,5.2,3.6,4.8,5.8,4.0,6.1,2.9,3.4,3.3,2.6,0.6,0.5,2.9,0.6,0.5,3.5,4.0,0.2,0.3,1.1,2.9,3.6,2.5,3.0,0.8,0.6,0.9,1.3,1.3,0.7,2.2,1.5,2.7,0.5,2.7,0.5,2.2,1.4,1.3,0.6,1.1,0.4,0.4,1.9,1.0,2.9,1.7,1.0,0.5,1.0,1.5,5.6,0.3,8,15,7,8,12,34,16,23,18,9,2.0,30,19,6,13,73,89,9,34,36,22,49,28,40,48,40,59,28,31,44,18,5,4,29,4,5,45,49,2,3,6,31,27,17,15,5,3,4,6,11,6,16,8,20,3,20,5,24,10,10,4,6,2,4,15,12,21,12,6,6,6,10,42,2,0.044,0.025,0.018,0.022,0.038,0.033,0.034,0.04,0.073,0.039,0.035,0.046,0.042,0.038,0.044,0.047,0.042,0.041,0.037,0.041,0.049,0.048,0.052,0.055,0.049,0.034,0.048,0.037,0.039,0.036,0.061,0.033,0.035,0.047,0.033,0.016,0.04,0.041,0.029,0.023,0.023,0.034,0.058,0.025,0.023,0.019,0.027,0.015,0.016,0.032,0.029,0.03,0.029,0.026,0.023,0.018,0.014,0.024,0.023,0.019,0.023,0.027,0.031,0.022,0.022,0.026,0.025,0.021,0.016,0.026,0.027,0.023,0.022,0.02,1660,3666,1832,2109,2298,4276,2711,3407,2000,1660,661.0,3070,3373,930,2549,10525,18006,2146,4473,4167,3267,5081,2508,6132,5399,7708,5810,4057,5971,6121,2086,749,733,5247,1351,1760,6357,8120,288,682,1456,2294,5151,2800,3428,1319,848,2011,2286,1555,811,2895,1560,3891,579,4412,1252,3293,1609,1903,615,1840,362,837,2685,1039,3978,2853,1601,855,1472,1954,9694,354,0.131,0.115,0.102,0.101,0.142,0.136,0.123,0.151,0.193,0.161,0.127,0.16,0.147,0.164,0.157,0.161,0.145,0.132,0.148,0.155,0.134,0.133,0.15,0.174,0.146,0.146,0.145,0.127,0.127,0.149,0.183,0.097,0.113,0.151,0.126,0.09,0.15,0.148,0.141,0.115,0.114,0.133,0.187,0.117,0.104,0.104,0.111,0.081,0.084,0.118,0.139,0.129,0.12,0.115,0.117,0.105,0.098,0.127,0.111,0.11,0.122,0.11,0.124,0.108,0.12,0.111,0.119,0.105,0.094,0.135,0.132,0.117,0.102,0.13,412,1293,766,744,723,1584,946,1209,606,378,181.0,1311,942,248,625,3091,4910,481,1434,1820,961,1730,1051,1635,1944,2052,2139,1458,1707,1883,648,378,249,1226,352,583,1666,1834,147,232,709,1371,1176,1625,1914,604,356,894,1097,674,383,1236,750,1847,322,2206,561,1580,875,1008,364,646,214,370,1247,590,1848,1295,805,379,602,1018,4150,169,3.58,2.626,2.409,2.738,2.363,2.281,2.33,2.592,2.291,3.063,3.408,2.034,2.743,2.909,2.848,2.486,2.753,3.321,2.432,1.921,2.603,2.343,2.05,2.744,2.346,2.791,2.218,2.2,2.698,2.555,2.33,2.108,2.43,2.843,3.106,2.682,2.707,3.062,2.283,2.78,2.399,1.603,2.844,1.921,2.043,2.542,2.813,2.694,2.558,2.336,2.211,2.441,2.315,2.216,2.174,2.243,2.165,2.16,2.04,2.01,2.123,2.504,1.818,2.318,2.335,2.013,2.277,2.41,2.276,2.488,2.615,2.183,2.436,2.645,0.725,0.639,0.571,0.575,0.828,0.646,0.489,0.58,0.782,0.603,0.696,0.516,0.515,0.511,0.626,0.661,0.663,0.568,0.671,0.719,0.824,0.657,0.594,0.691,0.653,0.646,0.594,0.584,0.584,0.582,0.798,0.839,0.454,0.714,0.741,0.619,0.809,0.756,0.53,0.7,0.55,0.408,0.845,0.586,0.671,0.723,0.467,0.514,0.486,0.609,0.621,0.525,0.512,0.565,0.366,0.448,0.431,0.556,0.459,0.439,0.373,0.845,0.362,0.879,0.568,0.652,0.523,0.455,0.443,1.018,0.638,0.514,0.52,0.521
-4043,Not Epileptic,M,47.0,1.0,2.8,1.1,1.6,2.9,1.8,1.4,1.6,1.7,0.7,0.3,3.3,1.2,0.7,1.1,5.8,9.7,0.9,2.9,5.0,1.7,3.1,1.8,3.2,3.1,2.7,2.2,2.0,2.4,2.7,2.0,1.8,0.5,1.8,0.5,0.5,3.7,3.1,0.1,0.2,0.7,4.2,2.1,4.2,2.5,0.8,0.3,0.7,1.5,0.8,0.8,2.4,1.9,2.6,0.5,2.2,0.7,1.3,1.4,0.8,0.6,1.5,0.3,0.4,1.4,1.4,2.4,1.4,0.9,0.7,1.0,1.9,4.0,0.1,9,28,10,12,22,20,14,37,12,9,3.0,31,14,11,19,79,103,10,35,49,12,39,17,39,42,33,29,27,25,33,19,14,6,23,3,7,44,44,1,1,4,44,25,27,14,8,2,5,9,6,6,21,19,22,5,19,5,11,9,5,5,13,2,5,11,21,28,11,5,5,7,12,30,1,0.046,0.027,0.019,0.026,0.048,0.025,0.024,0.031,0.059,0.037,0.031,0.035,0.027,0.035,0.028,0.032,0.034,0.039,0.032,0.043,0.031,0.034,0.037,0.037,0.03,0.032,0.022,0.026,0.022,0.035,0.047,0.068,0.031,0.025,0.016,0.016,0.042,0.036,0.013,0.019,0.016,0.045,0.034,0.027,0.018,0.014,0.017,0.013,0.019,0.013,0.036,0.024,0.026,0.02,0.027,0.017,0.018,0.015,0.023,0.015,0.022,0.025,0.021,0.019,0.017,0.024,0.023,0.021,0.015,0.019,0.023,0.019,0.016,0.007,1710,5450,2658,3135,2998,3660,2700,3489,1834,1643,849.0,4209,3220,1315,2392,11609,20458,2199,6057,5975,4316,6191,2436,5635,6826,6075,5377,4506,7042,5281,1945,1153,892,5877,1955,1680,7839,8310,339,447,1224,3468,5959,4367,3842,1603,739,1965,2658,1970,669,3519,2491,4367,762,4063,1226,2874,1683,1475,971,2934,525,906,2813,1504,3823,2412,1982,942,1487,2845,9196,511,0.14,0.124,0.108,0.117,0.154,0.127,0.108,0.143,0.167,0.157,0.105,0.149,0.128,0.156,0.133,0.146,0.141,0.136,0.128,0.17,0.109,0.133,0.127,0.142,0.141,0.135,0.117,0.106,0.1,0.142,0.142,0.163,0.128,0.118,0.078,0.09,0.14,0.151,0.083,0.084,0.094,0.156,0.141,0.127,0.091,0.095,0.097,0.082,0.099,0.086,0.154,0.118,0.129,0.11,0.138,0.1,0.108,0.097,0.12,0.093,0.109,0.118,0.117,0.117,0.097,0.124,0.111,0.111,0.089,0.105,0.12,0.115,0.095,0.057,494,1770,902,1053,1037,1179,918,1088,553,411,217.0,1659,831,292,677,3387,5437,462,1627,2211,957,1694,973,1575,1911,1767,1686,1508,1719,1464,710,559,317,1232,480,568,1969,1769,206,250,628,1700,1186,2252,2240,794,316,869,1231,856,333,1650,1130,1996,317,2028,548,1331,912,782,446,1046,233,463,1342,939,1879,1101,916,488,613,1326,3750,220,3.28,2.75,2.953,2.739,2.423,2.498,2.358,2.829,2.458,3.157,2.937,2.094,2.815,2.982,2.634,2.604,2.9,3.706,2.866,2.22,3.335,2.714,2.144,2.718,2.721,2.747,2.448,2.278,3.025,2.728,2.066,2.289,2.453,3.246,3.392,2.537,2.955,3.248,1.806,2.193,2.49,1.831,3.342,2.081,1.896,2.249,2.7,2.884,2.472,2.601,2.192,2.19,2.205,2.364,2.514,2.149,2.572,2.447,2.053,1.993,2.326,2.638,2.197,2.241,2.319,1.998,2.102,2.489,2.492,2.245,2.555,2.494,2.539,2.506,0.96,0.605,0.572,0.592,0.72,0.629,0.592,0.51,0.807,0.663,0.875,0.648,0.48,0.606,0.614,0.589,0.635,0.817,0.658,0.816,0.902,0.682,0.638,0.86,0.649,0.61,0.623,0.659,0.5,0.621,0.751,0.774,0.429,0.63,0.765,0.477,0.741,0.672,0.348,0.427,0.481,0.571,0.714,0.532,0.601,0.45,0.613,0.647,0.526,0.457,0.551,0.425,0.464,0.468,0.485,0.452,0.337,0.606,0.429,0.354,0.495,0.683,0.426,0.771,0.386,0.73,0.474,0.42,0.392,0.464,0.398,0.414,0.469,0.315
-4044,Not Epileptic,M,32.0,0.7,3.0,0.7,1.0,1.1,3.0,1.5,2.2,1.0,1.0,0.6,3.8,1.8,0.7,2.0,7.9,9.4,0.6,2.1,5.6,1.0,4.9,2.1,3.5,2.9,3.5,3.6,3.5,3.6,2.9,1.7,1.3,1.1,3.7,0.6,0.8,4.9,4.8,0.2,0.0,0.9,4.3,2.5,3.1,2.7,1.0,0.4,1.0,1.2,0.6,0.5,2.4,1.8,2.9,0.1,2.4,1.0,2.2,1.5,1.7,1.1,1.7,0.3,0.3,2.9,1.5,3.3,1.1,1.2,0.7,0.6,1.6,5.8,0.4,5,28,7,8,10,26,14,17,9,9,7.0,30,20,7,17,72,79,4,25,46,8,57,25,47,35,35,40,27,25,35,22,11,9,35,4,7,43,46,1,0,6,36,26,24,15,9,2,6,7,6,4,18,12,22,1,18,9,14,13,14,11,12,2,3,21,16,24,7,7,5,6,12,44,4,0.03,0.025,0.012,0.022,0.023,0.032,0.025,0.034,0.035,0.037,0.042,0.035,0.028,0.032,0.029,0.04,0.029,0.021,0.027,0.037,0.018,0.037,0.026,0.037,0.027,0.027,0.026,0.03,0.031,0.029,0.048,0.045,0.036,0.035,0.024,0.018,0.037,0.032,0.013,0.012,0.022,0.038,0.035,0.024,0.021,0.021,0.016,0.014,0.014,0.011,0.025,0.018,0.026,0.022,0.025,0.016,0.022,0.021,0.026,0.023,0.025,0.031,0.015,0.015,0.026,0.034,0.022,0.016,0.02,0.018,0.017,0.021,0.02,0.026,1518,6330,1937,2369,2052,4503,2657,3590,1817,1643,840.0,3834,4105,1541,3626,13812,20208,2243,5006,5911,3100,7233,3062,6577,5939,6478,6848,4123,6150,5497,2248,1424,1446,8083,1905,1947,7646,8646,506,125,1482,3737,6086,3729,3349,1662,1045,2574,2560,1927,538,4243,2272,5536,362,3727,1974,3991,1875,2054,1657,2591,619,850,3810,949,4187,2337,1950,1425,1287,2681,10958,479,0.109,0.113,0.094,0.101,0.109,0.135,0.104,0.135,0.123,0.149,0.146,0.138,0.135,0.131,0.128,0.141,0.118,0.1,0.118,0.137,0.091,0.146,0.131,0.139,0.126,0.132,0.115,0.111,0.114,0.132,0.133,0.113,0.143,0.137,0.108,0.094,0.131,0.128,0.086,0.07,0.108,0.145,0.135,0.112,0.105,0.113,0.088,0.082,0.088,0.086,0.129,0.102,0.113,0.101,0.117,0.103,0.105,0.103,0.123,0.121,0.116,0.121,0.091,0.098,0.128,0.133,0.114,0.094,0.101,0.11,0.107,0.106,0.1,0.126,418,2106,797,853,788,1672,965,1147,530,429,265.0,1742,1156,373,1157,3689,5665,408,1469,2426,892,2299,1333,1757,1792,2101,2345,1657,1762,1759,643,514,481,1868,406,771,2224,2375,259,51,706,1791,1414,2106,1990,760,389,1053,1162,793,335,2013,1139,2188,115,2069,805,1697,972,1117,749,936,297,372,1700,648,2199,1043,914,540,566,1361,4644,278,3.485,2.592,2.339,2.735,2.376,2.445,2.265,2.748,2.602,3.085,2.568,1.946,2.88,2.992,2.547,2.818,2.845,3.89,2.767,2.093,2.794,2.43,1.987,2.881,2.524,2.522,2.305,2.041,2.71,2.469,2.609,2.621,2.426,3.055,4.074,2.477,2.801,2.816,2.373,2.293,2.731,1.914,3.115,1.964,1.959,2.316,3.261,2.909,2.601,2.601,2.026,2.278,2.204,2.639,3.128,2.076,2.423,2.577,2.067,2.012,2.373,2.521,2.219,2.532,2.412,1.825,2.078,2.618,2.553,2.786,2.352,2.377,2.509,2.23,0.76,0.718,0.506,0.524,0.587,0.5,0.514,0.582,0.562,0.803,0.673,0.508,0.488,0.452,0.544,0.683,0.672,0.607,0.545,0.724,0.781,0.526,0.547,0.721,0.489,0.548,0.485,0.49,0.632,0.624,0.708,0.933,0.53,0.732,0.596,0.463,0.752,0.546,0.48,0.334,0.64,0.533,0.838,0.597,0.61,0.577,0.445,0.852,0.529,0.443,0.372,0.522,0.533,0.494,0.624,0.468,0.415,0.501,0.462,0.385,0.392,0.776,0.639,1.226,0.549,0.552,0.48,0.453,0.406,0.694,0.555,0.473,0.48,0.395
-4045,Not Epileptic,F,22.0,1.2,2.7,1.2,1.5,2.8,3.7,2.2,2.2,2.6,0.8,0.2,3.9,2.1,0.8,2.5,10.4,8.7,0.9,2.9,4.6,3.5,5.4,2.4,7.5,10.1,10.9,9.4,4.4,4.2,3.0,2.3,2.6,0.6,4.4,0.6,1.4,7.4,5.5,0.3,0.2,0.9,4.5,2.4,2.8,3.2,1.0,0.6,0.8,1.4,0.9,0.6,2.3,1.7,2.8,0.4,5.3,0.8,1.5,1.5,2.0,1.6,2.2,0.4,0.7,2.4,1.1,3.2,1.6,0.8,0.5,1.1,2.1,5.3,0.4,9,19,10,14,22,34,20,19,24,11,3.0,35,22,12,19,100,94,7,34,49,25,53,22,75,72,74,87,36,43,43,21,19,6,45,4,12,64,59,2,1,7,42,27,22,20,7,3,5,8,8,5,18,15,21,2,38,6,11,11,21,15,20,3,6,18,18,29,14,5,3,9,16,37,3,0.042,0.027,0.019,0.025,0.036,0.038,0.029,0.033,0.061,0.037,0.029,0.042,0.025,0.04,0.044,0.042,0.027,0.037,0.032,0.04,0.042,0.047,0.035,0.053,0.053,0.052,0.046,0.038,0.031,0.032,0.041,0.056,0.025,0.04,0.013,0.019,0.049,0.039,0.014,0.017,0.018,0.041,0.032,0.026,0.023,0.019,0.018,0.012,0.018,0.013,0.029,0.019,0.023,0.018,0.016,0.025,0.017,0.02,0.023,0.026,0.037,0.027,0.018,0.018,0.02,0.019,0.019,0.017,0.013,0.017,0.026,0.019,0.017,0.015,2100,6708,2706,3009,2044,3267,4019,3754,1391,1803,762.0,3899,5568,1853,3283,14134,22796,2406,5786,5829,4866,4289,2400,6633,4657,7824,5975,5517,7663,6637,2798,2506,1566,7694,2830,2951,7976,9856,449,509,1774,2973,6787,3297,3987,1676,1200,2910,2885,2338,821,4457,2592,6149,678,6037,2088,3481,1710,2160,1693,3429,633,1309,4130,1958,6120,3817,2148,871,1928,3655,12204,664,0.126,0.119,0.113,0.125,0.145,0.143,0.119,0.115,0.174,0.168,0.106,0.171,0.124,0.15,0.141,0.142,0.126,0.13,0.131,0.162,0.133,0.151,0.116,0.152,0.161,0.157,0.139,0.122,0.105,0.136,0.129,0.138,0.116,0.125,0.078,0.097,0.142,0.14,0.123,0.101,0.108,0.133,0.128,0.126,0.113,0.107,0.105,0.081,0.101,0.092,0.145,0.105,0.112,0.1,0.098,0.119,0.096,0.099,0.112,0.128,0.118,0.127,0.117,0.103,0.109,0.116,0.105,0.093,0.087,0.108,0.126,0.111,0.094,0.113,535,1854,920,985,1210,1497,1304,1123,656,446,181.0,1645,1397,447,895,4091,5819,456,1598,2192,1265,1704,1150,2334,2739,3197,3133,1877,2005,1937,931,831,395,1657,702,1136,2319,2379,223,237,817,1532,1487,1748,2217,749,496,1021,1230,1087,350,2057,1292,2525,364,3094,740,1478,1052,1131,668,1282,324,652,1919,1022,2763,1536,922,407,701,1693,4842,352,3.555,3.102,2.953,2.874,1.692,2.11,2.518,2.983,1.969,3.204,3.102,2.117,2.986,3.054,2.845,2.593,3.034,3.834,2.856,2.3,2.831,2.158,1.845,2.278,1.661,2.24,1.748,2.371,2.961,2.686,2.276,2.726,2.921,3.24,3.575,2.584,2.6,3.0,2.179,2.427,2.804,1.781,3.425,2.117,2.096,2.545,3.111,3.451,2.921,2.672,2.403,2.44,2.175,2.614,2.286,2.226,2.804,2.69,1.796,2.15,2.513,2.635,2.236,2.312,2.36,2.254,2.396,2.566,2.704,2.358,2.649,2.589,2.734,2.444,0.935,0.655,0.504,0.497,0.489,0.607,0.621,0.555,0.668,0.661,0.721,0.54,0.52,0.652,0.75,0.779,0.744,0.648,0.663,0.621,1.061,0.714,0.592,0.852,0.686,0.778,0.702,0.687,0.681,0.599,0.735,0.988,0.458,0.714,0.594,0.549,0.972,0.821,0.484,0.429,0.493,0.608,0.711,0.676,0.561,0.533,0.583,0.766,0.468,0.519,0.452,0.41,0.498,0.437,0.349,0.507,0.584,0.464,0.384,0.44,0.588,0.647,0.644,0.714,0.545,0.712,0.453,0.395,0.396,0.705,0.471,0.523,0.487,0.468
-4046,Not Epileptic,F,37.0,0.8,2.8,0.9,1.7,1.6,1.6,1.9,1.4,1.1,0.7,0.4,3.9,1.1,0.4,1.3,5.0,8.2,0.8,1.8,3.8,1.0,3.3,2.7,3.6,3.3,3.5,3.9,3.4,3.2,4.1,2.3,1.0,0.4,1.9,0.4,0.6,2.9,3.3,0.2,0.2,1.0,2.8,2.0,3.0,4.2,0.9,0.4,0.8,1.1,0.6,0.8,1.6,1.3,2.8,0.2,3.0,0.6,1.4,0.8,2.0,0.7,1.9,0.5,0.8,2.1,1.5,3.0,1.1,1.3,0.6,1.0,1.7,5.4,0.4,6,29,7,16,17,16,15,12,13,7,4.0,40,15,4,16,59,80,10,24,41,10,31,26,46,41,36,42,30,30,44,25,9,2,22,3,5,33,45,1,2,6,26,20,26,25,7,3,5,6,6,5,12,9,20,2,26,7,13,8,17,5,14,5,7,17,21,21,8,7,6,7,14,40,2,0.041,0.025,0.017,0.028,0.037,0.026,0.028,0.025,0.04,0.033,0.036,0.039,0.025,0.029,0.034,0.031,0.028,0.032,0.029,0.034,0.024,0.031,0.038,0.042,0.03,0.028,0.03,0.032,0.029,0.033,0.049,0.062,0.024,0.027,0.015,0.017,0.033,0.032,0.015,0.017,0.022,0.036,0.035,0.031,0.028,0.016,0.016,0.017,0.017,0.013,0.036,0.02,0.025,0.024,0.016,0.019,0.027,0.02,0.029,0.027,0.025,0.029,0.03,0.026,0.022,0.034,0.022,0.02,0.02,0.025,0.022,0.024,0.019,0.019,1214,4724,2209,2960,1986,2412,3135,3214,1871,1535,685.0,2928,3190,993,2497,9778,18512,1949,3997,4320,3463,4885,2318,5466,7206,6763,7018,4810,7130,6375,2590,991,811,5669,1776,1691,6396,7820,351,302,1436,2353,4711,2620,4248,1723,762,1635,1990,1557,596,2636,1659,5043,402,4666,1106,2601,920,1991,730,2303,674,998,2798,1051,4265,2357,2139,921,1537,2633,10041,642,0.131,0.123,0.102,0.128,0.15,0.122,0.119,0.11,0.152,0.138,0.139,0.143,0.124,0.13,0.142,0.137,0.126,0.141,0.134,0.153,0.107,0.127,0.13,0.147,0.125,0.136,0.129,0.115,0.112,0.141,0.145,0.128,0.101,0.125,0.087,0.094,0.136,0.145,0.09,0.129,0.111,0.132,0.143,0.133,0.121,0.097,0.108,0.093,0.091,0.099,0.138,0.106,0.112,0.111,0.113,0.11,0.124,0.111,0.135,0.134,0.13,0.139,0.151,0.124,0.119,0.151,0.113,0.103,0.104,0.125,0.114,0.124,0.105,0.11,390,1687,821,1068,762,888,1081,945,515,375,207.0,1572,788,224,681,2656,4933,451,1075,1832,805,1505,1074,1586,1902,1941,2164,1576,1747,2087,688,405,267,1178,442,610,1504,1896,175,160,707,1199,992,1617,2358,836,335,742,980,717,363,1253,835,1967,216,2389,507,1214,562,1118,420,967,280,512,1444,707,2077,992,989,505,734,1230,4272,277,3.186,2.602,2.746,2.761,2.22,2.266,2.377,2.701,2.634,3.104,2.828,1.714,2.929,3.005,2.756,2.703,2.96,3.17,2.914,2.047,3.181,2.509,1.969,2.588,2.81,2.786,2.458,2.402,3.091,2.443,2.554,2.598,2.622,3.255,3.503,2.704,3.058,3.025,2.251,2.318,2.634,1.764,3.243,1.915,2.099,2.289,2.863,2.772,2.512,2.498,2.135,2.187,2.09,2.515,2.262,2.164,2.303,2.411,1.896,1.997,2.214,2.541,2.18,2.253,2.192,1.895,2.294,2.723,2.618,1.949,2.504,2.556,2.572,2.654,0.62,0.552,0.422,0.521,0.618,0.598,0.52,0.573,0.723,0.431,0.546,0.438,0.5,0.372,0.529,0.619,0.63,0.603,0.466,0.633,0.778,0.564,0.478,0.71,0.606,0.524,0.527,0.58,0.519,0.529,0.655,0.882,0.441,0.533,0.774,0.511,0.674,0.611,0.369,0.44,0.467,0.622,0.745,0.524,0.613,0.401,0.464,0.616,0.425,0.375,0.42,0.566,0.476,0.491,0.314,0.397,0.283,0.521,0.39,0.304,0.346,0.701,0.651,0.676,0.474,0.656,0.397,0.433,0.448,0.525,0.382,0.392,0.436,0.428
-4047,Not Epileptic,F,42.0,0.7,1.9,1.1,1.2,2.0,1.8,1.7,1.7,1.1,0.9,0.2,3.2,1.4,0.5,1.2,6.8,10.1,0.9,3.2,4.9,1.4,4.0,1.9,3.2,4.4,4.2,3.9,2.7,3.0,4.3,1.5,0.9,0.6,4.8,0.4,0.6,3.9,2.8,0.1,0.1,1.0,3.4,3.3,2.7,3.3,1.0,0.3,0.9,1.0,0.5,0.8,1.5,1.5,2.3,0.2,3.6,0.7,1.8,1.3,1.5,0.7,1.7,0.3,0.5,1.8,1.5,3.1,1.1,1.0,0.9,0.9,1.6,4.6,0.3,7,22,13,10,17,18,16,14,11,9,2.0,27,15,6,14,68,94,6,35,44,11,43,18,36,42,45,53,25,26,50,16,4,6,42,2,4,44,36,1,1,6,28,24,19,20,7,2,4,5,5,5,12,11,17,1,29,5,13,11,15,6,12,2,5,15,17,24,8,6,10,5,11,38,2,0.033,0.024,0.023,0.022,0.037,0.029,0.029,0.031,0.029,0.038,0.027,0.033,0.032,0.04,0.036,0.033,0.035,0.033,0.047,0.039,0.024,0.038,0.028,0.043,0.043,0.04,0.031,0.032,0.032,0.035,0.044,0.035,0.034,0.05,0.02,0.019,0.043,0.032,0.01,0.016,0.025,0.034,0.049,0.024,0.023,0.019,0.02,0.015,0.016,0.012,0.033,0.022,0.021,0.019,0.035,0.025,0.018,0.018,0.025,0.021,0.018,0.026,0.027,0.02,0.023,0.032,0.021,0.017,0.016,0.026,0.02,0.025,0.019,0.021,1584,4389,2376,2504,2411,2991,3077,3019,2028,1898,701.0,2957,3142,946,2084,11763,19819,2236,4746,5350,4030,5135,2163,5071,5864,6803,6974,4843,6417,7144,2213,775,1089,7568,1309,1653,7436,7232,272,381,1318,2487,5055,3057,4055,1690,631,1946,1928,1160,568,2667,2111,4930,262,4280,1196,3508,1233,2027,1309,2661,345,842,2708,974,4526,2220,1777,1324,1297,1972,9317,509,0.113,0.118,0.117,0.104,0.141,0.123,0.101,0.135,0.143,0.141,0.116,0.135,0.14,0.14,0.152,0.14,0.136,0.123,0.172,0.156,0.105,0.136,0.114,0.146,0.145,0.156,0.118,0.115,0.114,0.148,0.155,0.098,0.119,0.175,0.074,0.098,0.14,0.138,0.103,0.1,0.116,0.131,0.149,0.114,0.106,0.096,0.099,0.083,0.084,0.1,0.137,0.106,0.112,0.096,0.151,0.122,0.115,0.111,0.122,0.119,0.102,0.123,0.117,0.114,0.115,0.13,0.109,0.096,0.098,0.14,0.115,0.124,0.101,0.127,414,1338,812,912,900,1097,1031,908,567,450,159.0,1488,815,249,601,3318,5227,414,1198,2143,983,1579,1061,1344,1596,1865,2207,1499,1641,2176,614,349,284,1616,350,523,1849,1661,166,174,637,1403,1171,1694,2099,809,271,854,932,611,371,1242,1101,1991,85,2166,565,1480,785,1062,620,962,188,400,1275,663,2248,981,895,522,630,953,3729,235,3.496,2.913,2.835,2.58,2.268,2.296,2.441,2.945,2.555,3.181,3.405,1.765,2.829,2.606,2.652,2.641,2.846,3.656,3.046,2.118,3.06,2.465,1.831,2.882,2.763,2.856,2.449,2.455,2.873,2.512,2.647,2.194,2.89,3.247,3.238,2.841,2.865,3.099,1.759,2.374,2.726,1.646,3.091,2.139,2.17,2.114,2.829,2.91,2.633,2.198,1.962,2.353,2.116,2.454,2.928,2.238,2.425,2.644,1.841,2.141,2.388,2.772,1.958,2.185,2.215,1.878,2.137,2.63,2.463,2.652,2.318,2.441,2.64,2.748,0.831,0.692,0.571,0.562,0.708,0.68,0.567,0.404,0.703,0.656,0.689,0.653,0.456,0.634,0.474,0.691,0.717,0.735,0.643,0.736,0.817,0.649,0.552,0.843,0.674,0.595,0.652,0.636,0.631,0.67,0.66,0.742,0.34,0.675,0.811,0.49,0.753,0.618,0.362,0.302,0.477,0.588,0.938,0.731,0.593,0.503,0.444,0.606,0.422,0.388,0.483,0.415,0.471,0.513,0.43,0.453,0.338,0.5,0.336,0.409,0.495,0.683,0.496,0.616,0.525,0.656,0.457,0.404,0.398,0.66,0.448,0.46,0.474,0.571
-4048,Not Epileptic,M,42.0,1.1,3.4,1.9,1.7,2.2,2.3,2.7,1.3,2.4,1.3,0.4,4.6,2.4,0.7,2.3,7.9,15.1,1.5,3.5,6.6,2.1,4.2,3.1,4.3,7.9,4.0,5.7,4.6,6.3,5.0,1.7,1.5,0.7,3.5,0.9,1.0,3.2,4.2,0.2,0.5,1.3,4.2,3.2,4.5,4.1,1.1,0.5,1.1,1.8,0.9,0.9,2.8,2.1,2.7,0.6,3.2,0.7,2.2,1.5,1.9,0.8,2.3,0.5,0.4,3.2,1.4,3.9,1.8,1.7,0.8,1.0,1.5,6.2,0.4,8,25,17,13,20,21,20,11,21,10,5.0,43,23,7,24,81,133,13,40,56,17,44,21,41,68,41,54,35,39,50,21,11,7,36,4,6,43,50,2,2,7,38,31,31,22,6,2,7,8,9,7,20,15,16,3,24,6,18,9,16,6,23,3,4,22,17,26,10,9,5,6,12,58,4,0.034,0.025,0.022,0.029,0.046,0.032,0.033,0.024,0.058,0.042,0.035,0.04,0.04,0.029,0.036,0.041,0.042,0.049,0.037,0.044,0.036,0.038,0.04,0.045,0.051,0.032,0.041,0.042,0.047,0.041,0.042,0.052,0.026,0.037,0.023,0.018,0.039,0.037,0.018,0.02,0.025,0.036,0.055,0.036,0.025,0.018,0.021,0.015,0.02,0.024,0.04,0.024,0.026,0.021,0.021,0.02,0.018,0.024,0.024,0.025,0.02,0.039,0.023,0.024,0.03,0.022,0.025,0.021,0.023,0.024,0.026,0.022,0.021,0.016,1659,6176,3794,3001,2659,3253,3718,2485,2288,1811,753.0,3329,4193,1330,3693,12408,22068,2892,5398,5512,4078,5388,2266,5494,7049,7524,6367,4268,6696,6202,2368,1580,1407,7493,2466,2284,5679,8848,323,734,1638,3060,6868,3149,4195,1842,743,2581,2962,1453,602,3774,2484,4729,744,4661,1241,3429,1388,2023,1155,2677,486,802,2950,1948,4720,2576,2364,915,1472,2342,11312,796,0.126,0.111,0.107,0.115,0.15,0.136,0.116,0.113,0.175,0.156,0.127,0.153,0.145,0.127,0.139,0.14,0.143,0.153,0.152,0.157,0.125,0.136,0.137,0.15,0.149,0.128,0.138,0.121,0.123,0.16,0.137,0.12,0.096,0.134,0.097,0.094,0.146,0.149,0.098,0.096,0.11,0.141,0.168,0.139,0.113,0.098,0.105,0.084,0.099,0.113,0.151,0.111,0.119,0.1,0.104,0.107,0.104,0.119,0.12,0.126,0.108,0.143,0.12,0.125,0.128,0.107,0.114,0.102,0.107,0.132,0.12,0.11,0.107,0.102,481,2124,1399,1086,857,1170,1345,871,679,486,228.0,1922,1106,389,1096,3589,6310,591,1597,2636,943,1837,1147,1676,2460,2245,2454,1695,1986,2107,665,546,475,1687,606,848,1531,2243,202,367,817,1721,1288,2201,2425,883,343,1133,1344,625,390,1852,1302,2120,415,2479,612,1619,865,1147,622,994,295,354,1629,1085,2477,1240,1102,437,715,1126,4996,452,3.382,2.525,2.586,2.688,2.433,2.335,2.328,2.524,2.412,2.897,2.666,1.623,2.859,2.463,2.559,2.59,2.762,3.572,2.752,1.819,3.16,2.262,1.812,2.55,2.344,2.687,2.136,2.039,2.653,2.392,2.593,2.999,2.355,2.971,3.514,2.538,2.71,2.857,1.872,2.418,2.471,1.69,3.484,1.652,1.975,2.257,2.671,2.926,2.639,2.635,1.963,2.165,2.057,2.412,2.178,2.034,2.206,2.292,1.802,1.952,2.169,2.385,1.717,2.48,2.037,2.013,2.066,2.409,2.393,2.065,2.47,2.468,2.393,2.17,0.737,0.619,0.548,0.635,0.719,0.628,0.652,0.581,0.615,0.802,0.85,0.423,0.526,0.524,0.459,0.582,0.62,0.624,0.588,0.613,0.878,0.561,0.503,0.639,0.552,0.464,0.528,0.506,0.615,0.508,0.705,0.957,0.388,0.68,0.82,0.504,0.603,0.636,0.275,0.375,0.54,0.523,0.842,0.482,0.651,0.487,0.566,0.81,0.584,0.405,0.328,0.55,0.497,0.429,0.361,0.442,0.398,0.572,0.387,0.389,0.452,0.68,0.452,0.818,0.529,0.699,0.457,0.409,0.469,0.712,0.492,0.436,0.401,0.292
-4049,Not Epileptic,F,52.0,1.4,2.1,0.7,1.1,1.3,1.3,1.1,1.5,0.9,0.7,0.2,3.1,1.5,0.4,1.2,6.4,7.4,0.7,2.5,4.6,1.3,2.0,1.5,3.4,2.9,3.2,3.7,2.6,2.5,4.3,1.0,1.1,0.5,2.4,0.3,1.1,3.6,3.7,0.1,0.2,1.1,2.4,2.1,3.5,3.6,0.6,0.2,1.0,0.9,1.3,0.7,1.3,1.2,3.5,0.5,2.3,0.6,1.8,0.9,1.5,0.7,1.3,0.3,0.3,1.7,0.9,2.2,1.6,1.0,0.6,1.4,1.8,3.3,0.2,7,16,6,8,12,12,8,11,10,6,2.0,22,13,4,12,52,69,4,21,33,10,19,13,34,30,30,36,23,19,34,14,6,3,22,1,9,34,33,1,1,6,22,22,26,22,5,2,6,6,9,5,11,10,23,3,19,5,16,7,10,6,9,3,3,14,13,15,12,8,7,9,12,25,2,0.047,0.022,0.012,0.021,0.028,0.023,0.02,0.023,0.038,0.027,0.017,0.032,0.027,0.029,0.028,0.035,0.024,0.027,0.027,0.032,0.024,0.027,0.026,0.036,0.03,0.028,0.026,0.028,0.023,0.034,0.035,0.035,0.022,0.027,0.009,0.023,0.039,0.032,0.021,0.028,0.023,0.03,0.039,0.034,0.026,0.012,0.014,0.016,0.013,0.036,0.03,0.017,0.025,0.022,0.022,0.018,0.021,0.022,0.026,0.023,0.023,0.025,0.019,0.016,0.02,0.028,0.02,0.024,0.021,0.017,0.028,0.024,0.015,0.018,1568,3915,2116,2096,2047,2108,2108,2780,1408,1328,460.0,2417,2874,769,2144,9566,16392,1843,3979,4194,2479,3479,1978,6059,4666,5506,5859,3612,4553,5445,1664,923,988,5329,1759,2249,4907,6581,240,297,1749,2492,3836,2254,2943,1399,716,2110,2232,1376,665,2548,1925,5780,546,3632,1182,2837,993,1600,955,2140,419,901,2418,1003,3067,2074,1719,1110,1668,2306,7795,549,0.132,0.108,0.087,0.103,0.129,0.116,0.083,0.113,0.129,0.125,0.082,0.129,0.119,0.133,0.128,0.131,0.116,0.104,0.118,0.128,0.079,0.117,0.115,0.13,0.128,0.127,0.12,0.111,0.096,0.138,0.136,0.109,0.098,0.116,0.061,0.105,0.129,0.125,0.089,0.111,0.106,0.129,0.133,0.14,0.12,0.093,0.095,0.082,0.084,0.134,0.126,0.098,0.112,0.109,0.127,0.103,0.104,0.111,0.123,0.115,0.115,0.114,0.12,0.115,0.108,0.113,0.107,0.124,0.11,0.113,0.124,0.122,0.09,0.102,441,1477,846,853,718,899,828,933,423,388,200.0,1283,869,228,652,2912,4835,389,1342,2086,790,1120,865,1521,1535,1739,2186,1332,1502,1833,483,449,332,1324,458,761,1465,1826,123,166,780,1241,1045,1679,2127,762,317,938,1116,600,342,1394,891,2562,283,1999,519,1518,595,950,490,849,256,354,1295,560,1635,1034,825,536,868,1162,3463,207,3.298,2.482,2.486,2.514,2.258,2.194,2.138,2.772,2.394,2.81,2.211,1.769,2.592,2.558,2.534,2.498,2.748,3.442,2.482,1.819,2.61,2.39,1.984,2.754,2.448,2.654,2.197,2.227,2.478,2.435,2.456,2.014,2.697,2.935,3.504,2.768,2.57,2.814,2.248,2.09,2.797,1.895,2.848,1.607,1.597,1.938,2.616,2.816,2.374,2.7,1.928,1.982,2.168,2.244,2.471,2.032,2.522,2.236,1.976,1.895,2.105,2.534,1.995,2.571,2.145,2.127,2.08,2.316,2.266,2.271,2.256,2.416,2.443,3.117,0.974,0.569,0.492,0.575,0.684,0.519,0.539,0.524,0.582,0.439,0.779,0.518,0.417,0.517,0.397,0.561,0.569,0.721,0.578,0.537,0.835,0.357,0.477,0.781,0.444,0.589,0.442,0.571,0.595,0.489,0.676,0.936,0.549,0.69,0.732,0.496,0.667,0.677,0.461,0.376,0.562,0.481,0.737,0.509,0.418,0.606,0.405,0.674,0.544,0.476,0.629,0.518,0.573,0.541,0.42,0.523,0.374,0.49,0.336,0.381,0.352,0.66,0.432,0.944,0.557,0.614,0.499,0.441,0.504,0.464,0.408,0.432,0.581,0.732
-4050,Not Epileptic,M,52.0,0.8,2.6,0.9,1.2,1.5,2.1,3.1,2.2,1.1,0.8,0.6,2.7,1.7,0.3,1.5,4.7,7.3,2.1,2.4,3.4,1.6,4.4,2.2,4.2,4.0,2.8,3.6,3.5,3.4,2.3,1.9,1.2,0.7,2.2,0.3,0.7,3.8,4.0,0.2,0.4,0.8,3.9,2.8,2.2,2.0,0.9,0.5,0.9,1.2,1.0,0.9,2.6,1.8,1.7,0.5,2.4,0.7,1.9,1.1,0.8,0.5,1.7,1.0,0.7,1.8,1.4,2.8,1.8,1.2,0.7,0.9,1.8,3.6,0.3,6,31,10,11,16,23,18,21,16,9,5.0,21,21,5,20,63,69,17,30,32,16,44,18,44,49,36,42,32,30,32,22,10,9,26,1,8,57,51,2,2,4,33,44,16,10,8,4,6,6,8,6,23,10,11,3,20,3,13,9,7,4,15,9,6,15,17,23,15,11,7,7,13,32,3,0.036,0.028,0.017,0.025,0.032,0.027,0.04,0.032,0.032,0.038,0.047,0.034,0.029,0.027,0.027,0.032,0.026,0.052,0.029,0.037,0.027,0.042,0.039,0.041,0.038,0.028,0.035,0.032,0.028,0.027,0.041,0.044,0.025,0.027,0.009,0.018,0.032,0.036,0.013,0.022,0.019,0.034,0.04,0.025,0.016,0.023,0.026,0.012,0.016,0.02,0.035,0.022,0.025,0.014,0.022,0.019,0.021,0.02,0.021,0.015,0.024,0.028,0.033,0.016,0.02,0.026,0.019,0.019,0.021,0.019,0.021,0.021,0.015,0.014,1581,4798,2009,2314,1987,4477,3161,3317,1868,1568,713.0,2488,3998,1037,2814,10728,17616,2648,4540,3060,4435,5206,2888,5768,6775,6717,6463,4724,6641,5261,2165,1196,1229,6367,1700,1760,8224,8067,455,477,1239,3095,5750,1969,3437,1521,694,2207,2276,1885,703,3852,1982,4025,652,4014,1061,3090,1363,1342,665,2555,872,1001,3027,993,4528,3351,2039,1083,1513,2488,7567,510,0.118,0.133,0.108,0.116,0.129,0.125,0.138,0.144,0.154,0.145,0.139,0.135,0.131,0.132,0.138,0.138,0.124,0.161,0.136,0.157,0.112,0.135,0.145,0.149,0.135,0.129,0.124,0.122,0.104,0.13,0.139,0.119,0.116,0.128,0.06,0.102,0.145,0.149,0.102,0.125,0.097,0.136,0.133,0.12,0.087,0.121,0.129,0.083,0.089,0.107,0.143,0.117,0.116,0.086,0.114,0.104,0.105,0.104,0.118,0.094,0.123,0.12,0.162,0.105,0.109,0.123,0.105,0.106,0.115,0.123,0.117,0.118,0.096,0.105,472,1668,800,825,796,1364,1034,1150,553,419,207.0,1266,1022,275,945,2907,4763,670,1462,1543,1025,1753,988,1863,1922,1900,1908,1579,1827,1585,846,484,418,1369,533,615,2176,2051,216,250,673,1584,1338,1334,1969,588,319,1006,1097,758,409,1904,1035,1804,350,2002,506,1494,768,785,385,1033,460,591,1473,815,2272,1442,962,507,672,1313,3595,274,3.392,2.77,2.509,2.696,2.259,2.629,2.436,2.522,2.468,2.903,2.537,1.814,2.883,2.74,2.36,2.716,2.849,2.945,2.66,1.801,3.192,2.386,2.237,2.391,2.629,2.777,2.607,2.371,2.732,2.652,2.049,2.619,2.436,3.153,2.929,2.393,2.854,2.835,2.335,2.481,2.31,1.742,3.159,1.72,1.957,2.472,2.7,2.737,2.519,2.654,2.066,2.091,2.2,2.435,2.258,2.118,2.436,2.299,1.982,1.904,2.007,2.53,1.991,2.039,2.217,1.508,2.168,2.469,2.32,2.396,2.462,2.261,2.283,2.249,0.706,0.63,0.43,0.481,0.581,0.682,0.68,0.551,0.694,0.56,0.885,0.518,0.519,0.566,0.522,0.607,0.609,1.003,0.546,0.619,0.807,0.679,0.764,0.827,0.712,0.551,0.655,0.635,0.587,0.591,0.639,0.969,0.391,0.688,0.458,0.505,0.813,0.682,0.389,0.416,0.381,0.639,0.851,0.638,0.662,0.576,0.694,0.552,0.356,0.517,0.395,0.428,0.376,0.372,0.379,0.39,0.391,0.622,0.388,0.357,0.368,0.674,0.431,0.592,0.495,0.48,0.456,0.388,0.454,0.541,0.446,0.482,0.371,0.4
-4051,Not Epileptic,F,42.0,1.0,1.8,0.7,1.1,1.1,1.3,1.4,1.4,2.1,0.5,0.3,1.8,1.2,0.3,1.5,4.2,7.2,1.0,1.6,3.7,1.3,3.3,1.6,2.6,2.5,2.7,3.2,1.9,1.9,2.6,1.4,0.8,0.4,1.7,0.4,0.4,4.0,2.8,0.2,0.2,1.0,3.8,1.5,1.8,2.1,0.5,0.2,0.6,1.2,0.6,0.4,2.1,1.8,2.2,0.3,2.4,0.6,1.1,1.3,1.2,0.7,1.5,0.6,0.7,1.2,1.0,2.0,1.3,0.6,0.5,0.7,1.2,3.9,0.4,9,17,6,9,10,14,14,14,18,6,3.0,15,14,6,16,47,68,9,17,34,13,37,15,35,33,31,35,19,19,37,15,7,5,21,2,3,41,38,1,2,5,29,15,12,10,4,1,5,6,5,3,18,13,18,2,19,6,7,10,9,6,15,4,6,11,14,17,10,3,4,6,8,32,3,0.049,0.024,0.014,0.022,0.03,0.029,0.022,0.03,0.054,0.028,0.031,0.033,0.027,0.02,0.032,0.03,0.027,0.04,0.026,0.034,0.025,0.034,0.035,0.034,0.028,0.029,0.03,0.023,0.026,0.025,0.033,0.045,0.029,0.023,0.013,0.012,0.038,0.032,0.015,0.013,0.021,0.037,0.029,0.022,0.018,0.01,0.014,0.012,0.018,0.011,0.02,0.024,0.028,0.02,0.017,0.016,0.023,0.015,0.025,0.023,0.021,0.026,0.034,0.02,0.017,0.026,0.018,0.02,0.015,0.016,0.019,0.018,0.016,0.018,1187,3723,1872,2252,1550,2980,3230,2787,1712,1332,635.0,2219,2859,891,2591,8460,16479,2175,3942,4410,4061,4915,2179,5412,5755,5763,5080,3700,4722,5962,2273,1131,812,5034,1950,1563,7130,6578,335,563,1400,2511,4856,2092,3191,1315,649,1943,1997,1970,610,2948,1872,4173,331,4153,1122,2574,1485,1561,1243,2195,458,994,2104,993,3824,2045,1235,1014,1149,2013,9284,608,0.147,0.121,0.094,0.109,0.135,0.12,0.109,0.13,0.164,0.131,0.132,0.135,0.124,0.116,0.134,0.132,0.126,0.125,0.121,0.133,0.115,0.122,0.117,0.138,0.121,0.137,0.123,0.094,0.111,0.125,0.139,0.118,0.108,0.112,0.071,0.083,0.135,0.143,0.083,0.091,0.108,0.133,0.119,0.112,0.095,0.08,0.081,0.082,0.097,0.085,0.106,0.122,0.137,0.104,0.109,0.101,0.125,0.091,0.122,0.109,0.106,0.126,0.161,0.124,0.102,0.133,0.099,0.113,0.091,0.109,0.11,0.105,0.091,0.111,422,1276,708,823,690,841,984,858,565,341,178.0,925,820,259,756,2418,4537,486,1015,1854,912,1388,757,1422,1624,1615,1681,1201,1178,1904,668,385,272,1141,549,593,1840,1614,208,249,670,1295,973,1257,1740,676,279,804,982,806,322,1370,982,1837,191,2109,447,1165,821,797,608,961,264,503,1053,567,1857,987,589,460,588,993,3829,262,2.924,2.68,2.644,2.668,1.991,2.79,2.606,2.809,2.391,2.956,2.906,2.017,2.704,2.498,2.663,2.621,2.86,3.295,2.96,2.015,3.212,2.703,2.393,2.805,2.685,2.881,2.396,2.361,2.966,2.548,2.55,3.003,2.4,3.091,3.218,2.451,2.826,3.018,1.827,2.643,2.571,1.708,3.5,1.912,2.139,2.164,2.765,2.917,2.52,2.621,2.22,2.234,2.113,2.404,2.055,2.189,2.565,2.567,2.043,2.089,2.239,2.346,2.04,2.416,2.141,2.309,2.215,2.394,2.408,2.411,2.341,2.363,2.484,2.498,0.702,0.647,0.4,0.429,0.662,0.653,0.466,0.356,0.667,0.555,0.75,0.619,0.399,0.433,0.391,0.571,0.518,0.731,0.522,0.591,0.826,0.578,0.617,0.669,0.664,0.448,0.555,0.533,0.41,0.484,0.614,0.874,0.45,0.561,0.657,0.473,0.855,0.703,0.339,0.368,0.469,0.572,0.732,0.594,0.6,0.371,0.426,0.811,0.36,0.402,0.444,0.405,0.433,0.415,0.357,0.375,0.437,0.438,0.448,0.387,0.384,0.601,0.542,0.762,0.533,0.62,0.421,0.372,0.364,0.67,0.427,0.545,0.479,0.599
-4052,Not Epileptic,F,22.0,0.6,2.0,0.9,1.5,1.7,1.4,1.4,1.5,1.5,0.7,0.3,3.0,2.1,0.5,1.5,5.1,9.4,1.1,2.0,3.9,1.0,2.4,2.4,3.6,2.4,2.2,2.9,2.5,2.8,2.5,1.3,1.3,0.5,2.4,0.4,0.6,4.1,2.7,0.2,0.3,0.9,3.2,2.6,2.1,2.9,0.7,0.4,0.9,1.7,1.0,0.6,2.2,1.5,3.1,0.3,2.2,1.0,1.8,1.1,1.1,0.5,1.7,0.5,0.6,2.1,1.2,2.3,1.5,0.9,1.0,1.1,1.7,3.5,0.3,6,23,8,14,17,17,15,18,13,7,4.0,29,26,6,16,62,98,11,24,40,11,32,20,35,33,28,32,29,24,32,16,10,6,26,3,5,52,39,1,2,6,32,19,19,20,5,2,6,9,7,4,18,10,24,2,21,7,16,8,8,4,14,4,5,18,16,22,12,5,14,7,10,27,3,0.029,0.021,0.013,0.024,0.035,0.026,0.023,0.026,0.054,0.029,0.033,0.03,0.031,0.026,0.031,0.031,0.03,0.036,0.03,0.038,0.024,0.029,0.036,0.039,0.029,0.024,0.023,0.03,0.025,0.028,0.037,0.043,0.026,0.027,0.012,0.015,0.034,0.029,0.013,0.018,0.021,0.035,0.034,0.028,0.02,0.017,0.015,0.014,0.019,0.027,0.023,0.02,0.028,0.022,0.027,0.019,0.022,0.021,0.024,0.019,0.021,0.029,0.03,0.017,0.021,0.018,0.018,0.023,0.015,0.027,0.027,0.022,0.016,0.018,1504,4533,2224,2699,2360,3155,2987,3281,2276,1550,564.0,3109,4285,1082,3149,11161,20954,2490,4438,4245,2972,4690,2430,5335,5588,5337,5949,4860,6449,5202,2431,1130,1161,6624,1935,1897,8885,7780,313,480,1308,2419,5976,2115,3629,1235,697,2224,2801,1610,706,3389,1775,4897,379,3803,1410,3124,1236,1589,672,2566,613,966,3282,1365,4344,2310,2001,1496,1365,2329,7793,389,0.105,0.119,0.097,0.119,0.144,0.129,0.101,0.127,0.177,0.129,0.137,0.134,0.145,0.122,0.14,0.142,0.134,0.138,0.135,0.153,0.09,0.126,0.131,0.138,0.125,0.123,0.112,0.112,0.109,0.135,0.137,0.12,0.109,0.136,0.073,0.081,0.136,0.139,0.094,0.104,0.107,0.136,0.133,0.125,0.105,0.105,0.095,0.089,0.103,0.126,0.117,0.107,0.124,0.112,0.138,0.111,0.115,0.116,0.129,0.11,0.112,0.133,0.14,0.102,0.111,0.11,0.106,0.115,0.088,0.138,0.124,0.111,0.096,0.12,419,1637,870,1017,873,1060,919,1047,564,415,174.0,1523,1285,272,882,2918,5749,541,1144,1808,749,1390,1031,1541,1466,1518,2023,1454,1686,1493,659,513,377,1366,520,654,2319,1796,169,245,670,1246,1219,1305,2054,603,357,907,1246,611,406,1633,888,2140,153,1790,637,1359,678,798,359,917,263,530,1608,894,2008,1047,886,620,592,1154,3342,243,3.47,2.638,2.613,2.636,2.307,2.484,2.59,2.764,3.019,2.843,2.468,1.846,2.661,2.766,2.715,2.807,2.879,3.56,2.929,2.012,2.846,2.633,2.143,2.671,2.943,2.864,2.432,2.538,2.902,2.68,2.666,2.361,2.487,3.31,3.274,2.642,2.809,3.127,2.117,2.18,2.592,1.8,3.345,1.839,1.992,2.384,2.531,2.927,2.786,3.103,2.17,2.224,2.372,2.441,2.655,2.292,2.608,2.494,2.287,2.144,2.294,2.788,2.503,2.28,2.305,1.886,2.282,2.508,2.62,2.478,2.499,2.43,2.593,2.026,0.703,0.505,0.407,0.466,0.576,0.524,0.463,0.509,0.544,0.708,0.822,0.535,0.435,0.562,0.494,0.516,0.582,0.805,0.619,0.612,0.777,0.549,0.471,0.743,0.603,0.547,0.589,0.701,0.471,0.467,0.626,0.906,0.441,0.562,0.74,0.451,0.742,0.585,0.407,0.346,0.465,0.553,0.72,0.598,0.573,0.405,0.623,0.668,0.399,0.499,0.38,0.47,0.435,0.409,0.566,0.38,0.425,0.525,0.385,0.407,0.294,0.717,0.737,0.658,0.474,0.619,0.44,0.38,0.362,0.686,0.392,0.464,0.38,0.458
-4053,Not Epileptic,M,55.0,0.9,2.5,1.0,1.7,2.7,1.4,2.5,2.1,1.7,1.1,0.7,2.8,1.5,0.7,1.3,6.7,10.3,1.3,2.5,5.0,2.8,2.8,1.9,4.0,3.1,3.8,4.9,4.3,4.3,2.5,1.6,1.8,0.6,2.2,0.4,0.7,5.0,3.1,0.4,0.1,1.4,3.3,2.1,3.2,4.8,0.9,0.9,1.0,1.4,0.9,0.7,2.8,1.4,2.6,0.4,2.2,0.6,1.6,0.9,1.0,0.7,2.2,0.4,0.6,2.4,1.9,3.6,1.6,1.6,0.5,1.0,1.4,4.1,0.4,6,24,9,15,15,13,21,17,16,10,6.0,30,17,10,14,65,89,11,29,45,20,41,22,40,38,38,52,40,29,32,15,15,4,27,2,4,56,39,2,1,9,29,26,28,27,7,5,6,8,10,5,21,10,16,3,17,8,12,7,9,7,15,2,5,20,25,27,11,10,5,7,11,32,4,0.037,0.024,0.016,0.03,0.041,0.024,0.031,0.027,0.049,0.039,0.053,0.025,0.025,0.044,0.029,0.038,0.031,0.039,0.031,0.032,0.04,0.033,0.026,0.041,0.031,0.031,0.035,0.034,0.031,0.023,0.04,0.056,0.025,0.024,0.013,0.013,0.038,0.031,0.023,0.013,0.026,0.039,0.033,0.029,0.029,0.02,0.031,0.015,0.018,0.018,0.028,0.028,0.024,0.021,0.014,0.018,0.014,0.019,0.016,0.018,0.023,0.032,0.017,0.021,0.021,0.029,0.02,0.021,0.022,0.02,0.025,0.016,0.016,0.02,1491,4306,2158,2419,1882,2974,3082,3580,1874,1751,812.0,3477,3147,1157,2434,11102,18909,2298,4614,5479,4288,5457,2726,5699,5512,7311,6581,5221,7202,5913,2229,1443,1035,5684,1611,1997,7853,6888,464,343,1415,3031,5921,2678,4173,1412,932,1894,2161,1782,714,3732,1563,3799,697,3733,1258,3022,1333,1698,1000,2611,501,878,3442,1413,5348,2624,2315,805,1447,2450,8764,451,0.111,0.121,0.103,0.127,0.131,0.117,0.109,0.122,0.149,0.142,0.156,0.128,0.119,0.166,0.128,0.136,0.125,0.142,0.134,0.139,0.132,0.133,0.128,0.149,0.12,0.125,0.119,0.116,0.109,0.116,0.144,0.149,0.095,0.121,0.067,0.082,0.142,0.133,0.116,0.095,0.119,0.138,0.143,0.132,0.115,0.109,0.135,0.083,0.098,0.117,0.125,0.126,0.111,0.103,0.101,0.098,0.101,0.106,0.104,0.108,0.122,0.135,0.1,0.114,0.11,0.122,0.107,0.108,0.108,0.116,0.119,0.102,0.094,0.117,457,1749,926,956,939,1001,1145,1270,594,527,260.0,1649,1011,341,848,3291,5579,531,1421,2598,1101,1511,1104,1814,1649,2172,2368,1913,1961,1857,717,632,342,1486,490,742,2233,1807,248,177,829,1346,1308,1800,2569,706,423,943,1160,771,427,1643,915,1917,385,1864,677,1389,815,892,535,1071,307,479,1790,1094,2688,1198,1054,465,691,1269,3889,296,3.238,2.328,2.355,2.391,1.896,2.412,2.318,2.489,2.442,2.681,2.401,1.863,2.464,2.669,2.299,2.523,2.732,3.277,2.575,1.822,2.994,2.655,2.081,2.433,2.594,2.681,2.283,2.205,2.84,2.52,2.433,2.393,2.432,2.764,2.988,2.52,2.693,2.749,2.062,2.27,2.05,1.918,3.247,1.65,1.833,2.231,2.59,2.42,2.338,2.336,2.002,2.247,1.88,2.197,2.131,2.163,2.066,2.443,1.833,2.08,2.132,2.398,1.805,2.183,2.049,1.609,2.198,2.448,2.372,2.034,2.316,2.352,2.452,1.885,0.634,0.485,0.51,0.514,0.507,0.666,0.527,0.506,0.654,0.658,1.053,0.509,0.387,0.33,0.47,0.65,0.587,0.721,0.585,0.616,0.816,0.57,0.543,0.72,0.626,0.572,0.529,0.632,0.549,0.569,0.589,0.856,0.415,0.668,0.77,0.451,0.67,0.665,0.476,0.278,0.444,0.604,0.732,0.657,0.57,0.473,0.447,0.656,0.42,0.533,0.411,0.504,0.469,0.446,0.33,0.448,0.363,0.519,0.346,0.353,0.353,0.522,0.312,0.561,0.551,0.556,0.425,0.435,0.475,0.518,0.451,0.444,0.423,0.435
-4054,Not Epileptic,F,20.0,0.6,1.3,0.7,0.9,1.4,3.0,1.7,1.3,1.0,0.4,0.3,3.1,1.1,0.5,1.1,5.8,7.5,0.5,3.9,4.8,1.7,2.3,1.6,2.2,2.8,3.6,4.1,2.2,2.3,2.1,1.6,0.8,0.5,2.5,0.4,1.1,3.1,3.6,0.1,0.1,0.8,3.3,2.4,3.0,2.0,0.6,0.3,0.7,0.8,0.9,0.8,1.5,0.8,2.1,0.5,2.3,0.7,1.5,0.7,0.6,0.8,0.8,0.1,0.5,1.8,0.8,2.1,1.1,1.1,0.3,0.5,1.4,3.8,0.4,4,12,6,10,10,21,13,13,11,4,3.0,22,13,5,9,62,62,4,29,31,14,26,17,25,30,32,40,19,21,25,16,7,5,24,3,9,37,38,1,0,4,33,22,24,12,4,2,4,4,8,4,13,5,17,3,19,5,9,4,5,4,5,1,3,15,8,16,9,7,2,4,9,28,3,0.029,0.017,0.015,0.022,0.044,0.039,0.026,0.031,0.055,0.028,0.034,0.036,0.031,0.031,0.03,0.036,0.032,0.021,0.038,0.039,0.03,0.034,0.029,0.035,0.036,0.036,0.033,0.026,0.027,0.027,0.053,0.035,0.028,0.031,0.02,0.031,0.035,0.035,0.014,0.008,0.017,0.033,0.037,0.025,0.021,0.013,0.015,0.012,0.013,0.019,0.039,0.019,0.021,0.019,0.025,0.025,0.015,0.018,0.023,0.021,0.026,0.021,0.016,0.026,0.023,0.024,0.019,0.017,0.024,0.015,0.015,0.023,0.018,0.022,1499,4911,2370,2357,1579,3111,2805,3142,1749,959,593.0,2833,3554,1130,3529,13308,18392,2360,4630,3093,3212,4038,2334,4871,5837,6394,6625,3889,5780,4772,2454,965,1105,5859,1698,2219,5779,6622,372,325,1896,3027,5294,3204,2855,1338,885,2068,2240,1765,396,3391,1856,5126,525,3355,1625,2990,957,818,1062,1808,312,773,3296,1125,3770,2413,1617,921,1105,2029,8703,465,0.097,0.097,0.096,0.101,0.137,0.141,0.106,0.123,0.148,0.121,0.113,0.133,0.122,0.123,0.098,0.135,0.122,0.085,0.122,0.137,0.103,0.15,0.138,0.128,0.141,0.134,0.136,0.103,0.111,0.116,0.141,0.121,0.121,0.121,0.074,0.113,0.122,0.124,0.082,0.054,0.091,0.135,0.126,0.118,0.097,0.092,0.096,0.08,0.078,0.108,0.141,0.104,0.102,0.096,0.138,0.118,0.094,0.095,0.115,0.111,0.118,0.105,0.101,0.13,0.112,0.116,0.102,0.103,0.117,0.1,0.099,0.108,0.093,0.121,351,1231,687,757,504,1296,875,839,361,256,157.0,1271,735,231,603,2812,3914,402,1633,1808,845,1168,948,1113,1465,1714,2121,1288,1413,1340,491,415,363,1272,394,627,1606,1735,182,125,723,1464,1179,1923,1670,608,311,834,972,766,294,1327,734,1883,264,1490,699,1430,503,488,432,557,144,294,1329,545,1775,997,727,325,509,1015,3522,271,3.789,3.253,3.075,2.904,2.382,2.363,2.582,3.214,2.883,3.193,3.047,1.957,3.34,3.111,3.298,3.22,3.437,3.931,2.436,1.673,3.026,2.683,2.177,2.93,2.953,2.927,2.437,2.303,2.996,2.656,3.261,2.351,2.551,3.026,3.52,2.837,2.746,2.767,2.376,2.518,3.145,1.902,3.083,1.852,1.941,2.392,3.526,3.218,2.801,2.484,1.719,2.478,2.591,2.54,2.685,2.388,2.793,2.5,2.31,1.901,3.216,3.254,2.36,3.235,2.573,2.436,2.364,2.962,2.598,3.143,2.489,2.378,2.704,2.077,0.888,0.866,0.902,0.598,0.716,0.631,0.719,0.859,0.804,0.45,1.017,0.62,0.717,0.521,0.84,0.753,0.721,0.767,0.702,0.516,0.858,0.424,0.556,0.773,0.683,0.601,0.589,0.618,0.51,0.718,0.788,1.003,0.485,0.719,0.63,0.75,0.765,0.6,0.421,0.793,0.825,0.532,0.741,0.741,0.591,0.68,0.508,0.795,0.684,0.622,0.461,0.688,0.761,0.557,0.362,0.465,0.751,0.502,0.396,0.382,0.81,0.929,0.496,0.934,0.679,0.607,0.577,0.59,0.394,0.839,0.494,0.639,0.566,0.441
-4055,Not Epileptic,F,52.0,1.1,2.8,1.0,1.4,1.4,1.5,1.4,1.5,1.1,0.9,0.2,2.1,1.3,0.4,1.2,5.2,7.0,0.6,2.1,3.8,0.8,2.3,2.3,3.2,3.9,2.6,4.6,2.5,2.3,3.5,1.0,1.2,0.6,1.9,0.5,0.6,3.4,3.3,0.1,0.3,0.9,2.7,2.1,2.0,3.6,0.7,0.3,1.1,1.2,0.6,0.6,1.7,1.5,1.8,0.4,3.3,0.8,1.6,1.0,1.3,0.6,1.8,0.4,0.6,1.4,1.4,2.3,1.4,0.9,0.7,1.0,1.6,3.6,0.3,12,29,8,11,15,15,11,12,12,7,3.0,21,13,3,12,53,63,4,23,35,7,33,24,41,38,29,48,24,22,38,13,10,5,22,3,6,42,37,1,2,6,25,21,15,23,5,1,7,6,4,5,11,11,15,2,25,6,12,8,13,4,15,3,7,16,21,19,11,6,5,8,11,31,3,0.046,0.028,0.02,0.026,0.032,0.033,0.021,0.029,0.04,0.036,0.034,0.029,0.026,0.026,0.027,0.032,0.031,0.029,0.03,0.035,0.019,0.026,0.03,0.037,0.033,0.028,0.033,0.029,0.024,0.027,0.028,0.046,0.031,0.024,0.014,0.016,0.033,0.029,0.016,0.022,0.02,0.035,0.035,0.026,0.027,0.012,0.017,0.021,0.02,0.021,0.029,0.018,0.028,0.021,0.014,0.02,0.024,0.017,0.024,0.02,0.017,0.03,0.026,0.017,0.017,0.025,0.019,0.02,0.018,0.024,0.019,0.021,0.015,0.022,1348,4748,2218,2426,1807,2410,3187,2950,1705,1435,436.0,2068,3011,878,2410,8985,14910,1940,4052,3805,2877,4615,2444,5608,5910,5929,7024,4068,5575,7222,2217,1329,854,5555,2041,1666,7263,7548,268,478,1298,2083,5589,2095,3531,1588,712,1950,1912,1138,621,3069,1676,3772,751,5135,1440,3043,1247,2021,1063,2357,438,895,2576,1160,3730,2736,1635,754,1637,2269,8981,529,0.15,0.132,0.109,0.116,0.14,0.135,0.101,0.126,0.166,0.144,0.118,0.127,0.121,0.129,0.123,0.141,0.129,0.107,0.131,0.143,0.081,0.115,0.119,0.143,0.114,0.125,0.121,0.1,0.104,0.127,0.117,0.128,0.122,0.122,0.077,0.088,0.133,0.137,0.099,0.106,0.11,0.125,0.135,0.12,0.121,0.091,0.093,0.087,0.099,0.114,0.149,0.098,0.131,0.105,0.092,0.11,0.125,0.095,0.122,0.11,0.105,0.128,0.156,0.119,0.108,0.124,0.108,0.104,0.097,0.125,0.119,0.118,0.094,0.127,465,1676,813,883,817,883,958,835,502,372,144.0,1074,820,217,647,2629,3848,408,1139,1693,698,1259,1145,1613,1783,1624,2267,1310,1377,2226,638,480,297,1238,543,510,1889,1803,163,209,686,1107,1143,1305,2108,769,295,925,913,511,327,1432,811,1549,392,2508,558,1494,682,1038,439,947,209,517,1322,872,1871,1149,748,426,768,1151,3703,223,2.852,2.599,2.639,2.671,2.018,2.467,2.581,2.983,2.753,2.922,2.602,1.673,2.848,2.901,2.758,2.683,3.045,3.761,2.87,1.976,2.93,2.664,1.964,2.708,2.656,2.946,2.505,2.408,3.024,2.548,2.536,2.599,2.643,3.198,3.509,2.87,2.876,3.113,2.045,2.695,2.338,1.736,3.453,1.869,1.912,2.345,2.906,2.586,2.564,2.686,2.068,2.334,2.27,2.537,2.161,2.297,2.649,2.311,2.098,2.149,2.321,2.463,2.075,2.163,2.142,1.661,2.186,2.551,2.557,2.103,2.361,2.424,2.59,2.533,0.574,0.509,0.555,0.469,0.509,0.467,0.574,0.315,0.439,0.553,0.627,0.543,0.357,0.39,0.44,0.47,0.561,0.775,0.533,0.601,0.754,0.427,0.579,0.765,0.642,0.529,0.562,0.624,0.497,0.504,0.599,0.839,0.583,0.483,0.642,0.427,0.677,0.585,0.484,0.487,0.442,0.464,0.738,0.581,0.578,0.39,0.545,0.526,0.38,0.435,0.38,0.463,0.363,0.498,0.388,0.484,0.358,0.411,0.312,0.383,0.491,0.625,0.5,0.869,0.499,0.47,0.451,0.464,0.407,0.484,0.514,0.438,0.443,0.492
-4056,Not Epileptic,F,47.0,0.6,1.7,1.0,0.8,2.0,2.3,1.6,1.5,1.0,0.6,0.2,2.5,1.8,0.6,0.9,4.3,8.8,0.7,2.9,4.2,1.6,2.1,1.2,2.5,2.1,3.3,2.6,2.3,2.7,3.3,1.2,1.1,0.7,2.2,0.6,0.6,2.3,4.3,0.4,0.1,0.9,2.1,2.1,2.8,3.1,0.5,0.5,0.8,0.9,0.9,0.7,1.1,1.7,2.0,0.5,2.3,0.9,1.7,0.6,0.7,0.9,0.8,0.3,0.4,2.0,1.2,2.4,1.2,1.0,0.3,0.9,1.7,4.0,0.2,4,13,8,7,17,22,13,12,12,5,2.0,18,18,7,11,42,68,6,25,33,12,25,13,29,25,34,26,23,22,31,14,8,6,24,4,5,28,49,2,1,5,19,21,21,19,4,3,5,5,7,5,9,20,14,2,16,4,15,5,6,7,5,2,4,14,12,18,8,7,3,6,13,29,2,0.033,0.023,0.017,0.019,0.033,0.031,0.027,0.028,0.044,0.03,0.019,0.03,0.036,0.03,0.034,0.03,0.03,0.028,0.032,0.034,0.026,0.03,0.025,0.034,0.029,0.032,0.026,0.025,0.027,0.03,0.036,0.048,0.023,0.026,0.021,0.018,0.033,0.034,0.024,0.017,0.021,0.032,0.037,0.028,0.025,0.013,0.022,0.013,0.014,0.018,0.035,0.018,0.029,0.019,0.021,0.02,0.028,0.021,0.021,0.017,0.027,0.018,0.037,0.018,0.028,0.032,0.023,0.02,0.018,0.02,0.027,0.028,0.02,0.014,1269,3974,2557,2107,2438,3226,2506,2921,1648,1144,502.0,2265,3400,1297,1875,8509,16767,2433,4380,3491,3293,4221,1760,5304,4235,7126,4629,3907,5668,5104,1728,923,1316,6100,1912,1650,4900,8581,516,303,1498,2368,4519,2518,3255,1153,835,2183,2343,1560,438,2452,2841,4569,568,3711,1204,2544,764,1046,1283,1529,324,745,2470,883,3653,2308,1673,716,1128,2554,9069,505,0.104,0.107,0.088,0.093,0.139,0.14,0.104,0.128,0.151,0.126,0.097,0.122,0.134,0.146,0.143,0.128,0.115,0.102,0.134,0.124,0.1,0.138,0.131,0.131,0.133,0.134,0.122,0.112,0.116,0.129,0.127,0.131,0.102,0.119,0.078,0.085,0.122,0.139,0.125,0.111,0.1,0.133,0.131,0.122,0.108,0.084,0.115,0.078,0.082,0.101,0.138,0.096,0.113,0.094,0.115,0.105,0.115,0.11,0.122,0.101,0.125,0.102,0.16,0.12,0.12,0.131,0.113,0.106,0.104,0.123,0.127,0.135,0.096,0.099,324,1218,751,697,913,1177,905,887,459,324,173.0,1198,862,307,534,2301,4260,419,1404,1935,909,1220,793,1290,1265,1896,1607,1339,1593,1805,510,438,440,1331,495,562,1326,2147,206,129,669,1014,1055,1616,2020,659,329,901,1005,807,306,1069,1139,1721,307,1761,529,1307,461,655,542,591,138,353,1252,533,1705,966,882,286,539,1074,3596,227,3.687,3.024,3.041,2.724,2.363,2.457,2.267,2.914,2.737,3.008,2.689,1.678,2.977,3.159,2.852,2.759,3.111,3.898,2.678,1.647,2.805,2.609,1.928,2.936,2.522,2.949,2.289,2.288,2.725,2.352,2.553,2.401,2.498,3.155,3.353,2.556,2.791,2.883,2.838,2.618,2.889,1.991,3.231,1.743,1.876,1.827,3.259,2.829,2.755,2.34,1.894,2.45,2.408,2.722,2.342,2.304,2.764,2.216,2.005,1.88,2.545,2.668,2.404,2.23,2.236,2.281,2.381,2.734,2.314,2.995,2.484,2.872,2.689,2.699,1.007,0.59,0.78,0.86,0.641,0.603,0.614,0.74,0.538,0.536,0.877,0.45,0.5,0.604,0.548,0.579,0.717,0.804,0.714,0.535,0.782,0.477,0.513,0.75,0.532,0.551,0.524,0.61,0.664,0.621,0.648,0.892,0.544,0.599,0.956,0.598,0.79,0.657,0.703,0.479,0.716,0.654,0.794,0.63,0.669,0.48,0.639,0.888,0.681,0.404,0.442,0.415,0.616,0.538,0.353,0.622,0.516,0.557,0.428,0.434,0.504,0.692,0.447,0.877,0.557,0.713,0.539,0.449,0.434,0.792,0.54,0.569,0.628,0.504
-4057,Not Epileptic,F,55.0,0.5,2.2,0.9,1.4,2.0,1.6,1.5,1.9,1.0,0.6,0.4,2.7,1.2,0.8,0.8,6.1,9.0,1.2,1.9,3.7,1.1,2.3,1.9,3.4,2.5,3.5,4.2,3.6,2.8,3.0,2.2,1.1,0.4,2.7,0.4,0.8,4.3,2.6,0.1,0.1,1.1,3.4,2.3,2.0,2.7,0.6,0.6,1.2,1.3,1.2,0.4,1.7,1.5,2.5,0.3,1.8,0.5,1.2,1.0,1.1,0.4,2.5,0.6,0.6,1.9,1.0,2.3,1.4,1.5,0.4,0.9,1.0,4.9,0.3,7,19,8,13,19,21,15,18,11,8,3.0,28,15,7,12,76,81,7,24,44,14,34,20,40,34,42,49,34,28,33,18,8,5,29,3,7,49,36,1,1,7,34,22,16,15,5,3,8,7,10,3,14,9,21,1,15,4,11,11,11,3,21,5,5,20,17,19,13,10,4,5,9,43,3,0.032,0.025,0.016,0.029,0.041,0.025,0.026,0.028,0.038,0.029,0.033,0.034,0.028,0.036,0.024,0.034,0.031,0.042,0.027,0.034,0.021,0.027,0.035,0.036,0.03,0.033,0.034,0.04,0.029,0.026,0.051,0.038,0.021,0.031,0.014,0.019,0.039,0.032,0.016,0.012,0.021,0.038,0.03,0.024,0.018,0.015,0.025,0.018,0.019,0.018,0.018,0.018,0.022,0.018,0.024,0.014,0.018,0.017,0.023,0.02,0.026,0.033,0.033,0.018,0.022,0.02,0.019,0.019,0.02,0.016,0.019,0.013,0.021,0.02,1305,4557,2175,2201,2279,3186,3149,3411,1735,1598,517.0,3096,3230,1308,2321,11297,17231,2182,4514,4983,4535,5015,2041,6055,5411,7121,6415,4565,6380,6457,2275,1559,1218,5874,1848,1795,7951,6621,267,380,1476,2499,6623,2330,3916,1286,782,2424,2085,2466,590,3014,1748,5161,386,3533,933,2841,1774,1607,509,3053,828,942,2740,1205,3733,2466,2239,725,1562,1973,9235,444,0.11,0.12,0.095,0.133,0.145,0.136,0.105,0.13,0.15,0.131,0.118,0.154,0.131,0.152,0.118,0.148,0.138,0.141,0.121,0.153,0.099,0.129,0.136,0.134,0.124,0.133,0.14,0.125,0.108,0.133,0.158,0.105,0.096,0.138,0.075,0.1,0.155,0.144,0.108,0.087,0.112,0.141,0.125,0.117,0.096,0.098,0.116,0.095,0.101,0.103,0.096,0.098,0.119,0.102,0.128,0.096,0.118,0.101,0.132,0.111,0.121,0.133,0.133,0.103,0.119,0.124,0.104,0.111,0.107,0.097,0.103,0.099,0.106,0.125,349,1493,839,852,895,1103,997,1169,478,392,190.0,1395,918,346,682,3437,5098,454,1237,2011,1036,1484,1000,1889,1505,1982,2135,1621,1647,1939,780,565,432,1416,472,675,2083,1634,138,209,756,1421,1417,1413,2203,650,347,977,971,1033,375,1551,949,2284,171,1864,436,1252,718,863,279,1270,336,514,1475,781,1923,1071,1081,412,712,1081,3858,219,3.554,2.729,2.574,2.557,2.215,2.536,2.532,2.632,2.681,2.994,2.58,1.955,2.724,2.933,2.544,2.483,2.64,3.493,2.894,2.167,3.29,2.569,1.862,2.452,2.636,2.781,2.332,2.198,2.855,2.637,2.301,2.811,2.471,2.973,3.307,2.481,2.774,2.873,2.367,2.064,2.438,1.669,3.34,1.863,2.003,2.139,2.85,3.051,2.581,2.479,1.871,2.081,1.952,2.326,2.44,2.087,2.353,2.453,2.326,2.074,2.216,2.399,2.208,2.173,2.015,1.827,2.106,2.483,2.324,2.048,2.533,2.164,2.404,2.426,0.858,0.56,0.473,0.48,0.743,0.622,0.617,0.463,0.649,0.606,0.626,0.663,0.486,0.421,0.483,0.638,0.621,0.744,0.618,0.617,0.854,0.639,0.525,0.761,0.694,0.623,0.699,0.724,0.627,0.611,0.778,0.878,0.372,0.607,0.515,0.473,0.817,0.659,0.411,0.42,0.479,0.481,0.758,0.542,0.603,0.447,0.475,0.807,0.476,0.511,0.364,0.414,0.459,0.414,0.594,0.406,0.492,0.563,0.594,0.315,0.338,0.622,0.548,0.709,0.572,0.515,0.422,0.445,0.45,0.543,0.339,0.466,0.478,0.466
-4058,Not Epileptic,M,52.0,1.2,3.4,1.5,1.2,2.6,2.5,2.2,2.2,2.1,0.8,0.2,3.7,1.7,0.6,1.5,6.5,9.8,0.9,2.8,5.4,1.5,3.6,2.1,4.7,3.3,3.6,5.1,3.2,3.5,4.0,1.5,1.6,0.6,2.3,0.4,0.8,4.6,3.2,0.2,0.2,1.3,4.3,2.8,2.6,3.4,1.7,0.6,1.3,1.5,0.9,0.9,2.5,1.4,2.7,0.4,2.7,0.9,1.6,1.3,1.5,0.6,2.5,0.3,0.7,2.0,1.8,2.5,1.4,1.3,0.4,1.1,1.7,4.1,0.4,7,32,15,11,23,24,20,18,19,11,3.0,37,22,7,16,74,93,11,33,54,12,44,26,49,41,43,61,28,30,41,20,13,5,27,3,6,49,38,2,2,8,43,29,20,18,15,2,7,8,7,6,19,12,18,2,24,6,15,7,11,5,21,3,6,18,19,20,9,8,5,14,13,29,3,0.044,0.031,0.024,0.025,0.037,0.028,0.035,0.034,0.051,0.037,0.027,0.036,0.031,0.03,0.024,0.036,0.033,0.04,0.033,0.039,0.029,0.033,0.028,0.042,0.03,0.029,0.033,0.035,0.03,0.03,0.032,0.048,0.024,0.029,0.019,0.014,0.037,0.031,0.014,0.018,0.024,0.039,0.032,0.023,0.026,0.031,0.024,0.016,0.02,0.016,0.029,0.026,0.025,0.022,0.03,0.017,0.022,0.018,0.022,0.024,0.027,0.031,0.017,0.017,0.018,0.028,0.021,0.019,0.021,0.014,0.025,0.017,0.017,0.02,1523,5094,2329,2147,2433,4529,3116,3005,1713,1527,591.0,3682,3686,1115,2816,9925,18052,2359,5143,5889,4707,5864,2577,6622,5923,7273,6688,3774,6473,6468,2486,1450,1168,5698,1973,2342,8746,7818,472,495,1673,3430,6965,2740,3656,1842,791,2738,2239,1933,797,3416,1740,4056,456,4243,1504,3287,1365,1657,1016,3125,414,1100,3037,1457,3580,2315,1896,794,1623,2985,8436,484,0.132,0.133,0.129,0.115,0.142,0.131,0.117,0.134,0.155,0.159,0.123,0.148,0.132,0.135,0.126,0.146,0.136,0.137,0.145,0.151,0.105,0.13,0.131,0.141,0.127,0.135,0.122,0.124,0.105,0.141,0.136,0.126,0.104,0.133,0.088,0.084,0.145,0.133,0.091,0.108,0.117,0.141,0.133,0.119,0.112,0.129,0.105,0.086,0.101,0.1,0.147,0.118,0.128,0.111,0.125,0.106,0.113,0.106,0.11,0.12,0.129,0.136,0.113,0.111,0.109,0.123,0.109,0.104,0.105,0.104,0.127,0.102,0.096,0.111,444,1803,891,801,1142,1444,1109,990,624,412,181.0,1710,1096,292,932,3145,5203,513,1487,2631,985,1885,1203,1972,1911,2175,2579,1397,1766,2165,798,585,372,1349,450,820,2276,1828,259,241,832,1720,1466,1765,2048,847,358,1141,1102,801,412,1637,914,1897,233,2326,658,1545,805,958,374,1268,262,588,1556,877,1772,1095,950,474,805,1379,3614,301,3.274,2.561,2.54,2.652,1.972,2.383,2.373,2.68,2.192,2.925,2.874,1.866,2.667,2.697,2.376,2.477,2.726,3.563,2.78,1.939,3.387,2.443,1.934,2.622,2.449,2.667,2.095,2.175,2.813,2.386,2.457,2.56,2.438,3.024,3.703,2.619,2.878,3.108,2.07,2.536,2.505,1.845,3.364,1.723,2.034,2.123,2.651,2.797,2.517,2.573,2.074,2.175,2.038,2.361,2.395,1.965,2.452,2.397,1.883,1.914,2.606,2.445,1.853,2.284,2.134,2.121,2.081,2.47,2.3,1.997,2.141,2.48,2.493,1.974,0.857,0.618,0.537,0.475,0.508,0.758,0.579,0.424,0.618,0.692,0.734,0.55,0.441,0.351,0.525,0.502,0.595,0.711,0.592,0.578,0.922,0.558,0.484,0.837,0.553,0.558,0.521,0.582,0.489,0.642,0.573,0.843,0.315,0.623,0.97,0.422,0.785,0.546,0.341,0.554,0.525,0.482,0.685,0.548,0.614,0.519,0.45,0.877,0.511,0.473,0.429,0.429,0.458,0.437,0.372,0.416,0.377,0.492,0.41,0.392,0.575,0.541,0.337,0.7,0.526,0.728,0.503,0.371,0.436,0.504,0.513,0.439,0.431,0.321
-4059,Not Epileptic,M,37.0,1.1,3.6,1.1,1.7,2.1,2.0,3.3,1.6,0.9,1.3,0.5,3.0,1.9,0.5,1.6,7.1,10.1,0.9,2.1,5.0,1.6,2.8,2.0,5.5,6.7,3.5,3.6,4.6,4.7,2.8,1.7,1.4,0.8,2.5,0.5,0.7,5.3,3.5,0.2,0.4,1.1,3.2,2.3,3.0,3.0,1.0,0.6,1.0,1.8,0.8,0.6,2.2,1.6,3.0,0.2,3.4,0.7,1.9,0.8,1.1,0.8,2.8,0.6,0.8,1.6,1.9,2.8,1.3,1.8,0.6,1.2,1.9,6.7,0.4,9,30,11,16,18,20,25,21,10,16,6.0,25,24,6,18,72,91,8,24,50,13,41,17,53,60,41,38,36,38,33,16,10,7,30,3,7,57,52,2,2,6,29,23,20,16,6,3,7,10,5,4,17,13,21,2,27,7,12,7,8,9,22,3,6,14,21,23,9,10,5,11,15,54,4,0.038,0.035,0.016,0.022,0.039,0.028,0.037,0.025,0.038,0.038,0.038,0.031,0.027,0.034,0.032,0.033,0.03,0.032,0.031,0.037,0.026,0.031,0.036,0.045,0.043,0.032,0.029,0.038,0.035,0.028,0.042,0.04,0.03,0.031,0.014,0.015,0.041,0.024,0.015,0.017,0.022,0.033,0.033,0.023,0.019,0.018,0.023,0.017,0.02,0.012,0.026,0.021,0.023,0.022,0.017,0.017,0.026,0.02,0.021,0.019,0.024,0.035,0.021,0.021,0.018,0.023,0.018,0.016,0.022,0.018,0.021,0.019,0.019,0.021,1659,5584,3023,3577,2598,3340,3352,3934,1566,2104,864.0,2937,4218,869,3284,13447,21870,2653,5070,5936,5456,5316,1954,6898,7192,6777,6306,4578,7189,6378,2962,1551,1251,6994,2163,2230,9208,9111,335,595,1663,3118,6751,3407,4198,1905,918,2283,2784,1952,464,3716,2143,5003,361,4725,1215,3425,1041,1316,1405,2987,635,1113,2206,1860,4695,2497,2498,920,2025,3596,12706,494,0.131,0.137,0.094,0.115,0.139,0.131,0.119,0.129,0.154,0.165,0.141,0.131,0.124,0.128,0.139,0.136,0.129,0.124,0.131,0.161,0.108,0.128,0.133,0.148,0.142,0.134,0.115,0.12,0.108,0.129,0.143,0.118,0.114,0.132,0.08,0.086,0.147,0.125,0.101,0.103,0.105,0.132,0.132,0.111,0.097,0.098,0.112,0.095,0.106,0.087,0.134,0.107,0.112,0.106,0.126,0.107,0.109,0.102,0.116,0.112,0.123,0.144,0.109,0.115,0.111,0.116,0.099,0.096,0.108,0.117,0.117,0.107,0.105,0.129,517,1749,1062,1236,975,1223,1216,1172,452,600,235.0,1427,1219,250,957,3733,5756,559,1218,2394,1084,1658,850,2140,2475,2006,2087,1819,1902,1893,750,578,401,1479,561,729,2344,2496,185,311,807,1416,1380,2004,2341,868,371,945,1250,919,314,1763,1141,2211,164,2707,518,1498,682,819,539,1296,403,592,1177,1197,2411,1115,1177,484,889,1561,5183,292,3.215,2.788,2.644,2.687,2.271,2.297,2.297,2.847,2.529,2.82,2.823,1.78,2.707,2.423,2.548,2.608,2.875,3.738,3.161,2.129,3.438,2.43,1.996,2.462,2.309,2.727,2.35,2.117,2.889,2.523,2.764,2.666,2.502,3.207,3.566,2.692,2.896,2.724,2.175,2.298,2.641,1.898,3.435,1.921,2.033,2.256,3.124,2.879,2.718,2.581,1.808,2.246,2.011,2.343,2.463,1.952,2.51,2.648,1.69,1.852,2.445,2.197,1.83,2.207,2.037,1.806,2.065,2.396,2.436,2.107,2.383,2.449,2.559,2.189,0.77,0.733,0.562,0.525,0.633,0.632,0.636,0.479,0.553,0.76,0.81,0.491,0.586,0.612,0.552,0.711,0.659,0.638,0.604,0.736,0.799,0.616,0.585,0.783,0.745,0.548,0.684,0.632,0.677,0.604,0.709,0.813,0.503,0.674,0.664,0.529,0.803,0.691,0.456,0.412,0.602,0.637,0.779,0.536,0.649,0.563,0.454,0.698,0.429,0.416,0.439,0.396,0.453,0.501,0.722,0.469,0.507,0.499,0.335,0.384,0.577,0.666,0.389,0.834,0.457,0.542,0.45,0.389,0.444,0.629,0.49,0.561,0.466,0.572
-4060,Not Epileptic,F,47.0,0.7,2.2,0.9,1.4,1.5,1.6,1.6,1.6,0.8,1.1,0.2,3.2,1.6,0.5,1.3,6.6,8.9,1.0,2.4,5.7,1.1,2.6,1.5,2.7,3.1,3.7,3.1,2.8,2.3,3.6,1.1,0.8,0.5,1.8,0.4,0.3,3.8,4.0,0.2,0.3,0.9,2.7,2.4,2.2,2.4,0.5,0.6,0.9,1.2,1.4,0.8,1.5,1.3,2.6,0.4,1.7,1.1,1.5,0.4,0.8,0.6,0.9,0.3,0.2,1.5,1.2,3.4,0.6,1.0,0.4,1.0,1.4,4.2,0.3,8,23,9,11,16,16,15,16,7,9,4.0,25,18,7,14,65,84,8,26,41,9,25,17,37,30,35,36,29,19,39,17,5,5,22,3,2,37,40,2,2,5,24,20,18,14,4,3,5,7,11,6,12,8,16,2,16,9,13,5,9,4,7,2,4,14,29,27,5,7,4,7,11,32,3,0.027,0.024,0.016,0.022,0.032,0.035,0.024,0.031,0.033,0.038,0.024,0.039,0.027,0.027,0.036,0.032,0.029,0.027,0.036,0.041,0.022,0.036,0.033,0.029,0.032,0.035,0.024,0.027,0.021,0.032,0.029,0.032,0.028,0.025,0.013,0.009,0.034,0.037,0.014,0.019,0.023,0.035,0.032,0.024,0.017,0.013,0.018,0.015,0.018,0.023,0.031,0.016,0.024,0.018,0.019,0.013,0.022,0.019,0.016,0.018,0.027,0.019,0.015,0.011,0.02,0.026,0.02,0.012,0.018,0.014,0.023,0.021,0.017,0.016,1460,4841,2090,2445,2220,2705,2631,3342,1444,1948,627.0,2502,3338,1281,2443,10988,18342,2518,4177,4964,3481,3532,2035,5788,5625,6565,5346,4198,5404,6008,2473,827,894,5340,1907,1532,7789,7960,321,436,1469,2628,5287,2293,3602,1199,1142,2009,2326,2534,664,3420,1758,4849,462,3131,1726,2884,833,1266,725,2183,451,812,2456,1173,4953,1860,1929,1063,1845,2478,9228,397,0.122,0.121,0.104,0.109,0.136,0.144,0.107,0.137,0.129,0.146,0.121,0.139,0.13,0.136,0.141,0.143,0.135,0.115,0.142,0.141,0.088,0.155,0.14,0.133,0.141,0.147,0.116,0.116,0.1,0.145,0.123,0.097,0.135,0.128,0.076,0.067,0.132,0.142,0.114,0.101,0.105,0.149,0.122,0.113,0.095,0.089,0.102,0.083,0.097,0.102,0.146,0.091,0.115,0.101,0.123,0.09,0.116,0.107,0.107,0.108,0.13,0.107,0.106,0.109,0.112,0.136,0.111,0.085,0.103,0.104,0.117,0.113,0.095,0.127,474,1587,852,928,831,919,908,1004,385,501,180.0,1229,1094,354,710,3303,5229,577,1192,2292,833,1194,776,1614,1602,1899,2081,1568,1516,1877,603,437,314,1175,521,555,2022,1914,175,194,687,1166,1250,1488,2039,631,483,896,1045,922,400,1549,844,2126,229,1797,777,1362,379,750,360,778,253,401,1250,697,2522,877,891,496,775,1137,3998,195,3.071,2.819,2.446,2.572,2.414,2.625,2.461,2.958,2.778,3.203,2.924,1.765,2.563,2.728,2.759,2.618,2.851,3.458,2.894,1.87,3.108,2.441,2.272,2.776,2.647,2.822,2.094,2.169,2.723,2.635,2.92,2.021,2.379,3.175,3.299,2.437,2.917,3.045,2.166,2.236,2.711,2.0,3.197,1.712,2.016,2.079,3.1,2.738,2.789,2.686,1.957,2.304,2.223,2.499,2.442,1.828,2.609,2.518,2.059,1.842,2.439,2.804,1.898,2.281,2.161,2.182,2.164,2.476,2.582,2.69,2.737,2.64,2.455,2.86,0.708,0.521,0.532,0.468,0.589,0.377,0.625,0.526,0.557,0.626,0.872,0.553,0.521,0.456,0.4,0.536,0.527,0.708,0.567,0.568,0.923,0.568,0.519,0.575,0.509,0.544,0.541,0.503,0.523,0.624,0.703,0.868,0.394,0.585,0.627,0.51,0.666,0.564,0.263,0.342,0.565,0.474,0.619,0.544,0.639,0.454,0.547,0.65,0.493,0.597,0.389,0.57,0.484,0.495,0.584,0.423,0.418,0.606,0.438,0.349,0.306,0.733,0.468,0.792,0.736,0.649,0.566,0.487,0.465,0.56,0.465,0.44,0.548,0.637
-4061,Not Epileptic,M,22.0,1.2,2.8,1.2,1.7,2.5,1.8,2.5,2.1,1.4,0.7,0.5,3.4,1.9,0.5,1.3,7.0,13.8,1.4,2.8,6.7,2.3,2.4,3.2,4.0,4.2,4.1,4.3,3.3,3.2,5.2,1.4,1.9,0.7,3.0,0.7,1.3,4.4,5.4,0.2,0.2,1.4,4.5,2.4,3.4,3.4,0.7,0.5,1.1,1.9,1.2,0.8,1.9,1.9,3.3,0.6,3.4,0.7,2.0,1.0,0.6,0.9,2.0,0.4,0.7,2.7,1.5,2.6,1.5,0.9,0.8,1.0,1.8,4.5,0.4,13,29,11,13,19,21,39,22,14,8,5.0,30,24,9,17,81,130,11,30,62,19,32,34,43,51,47,48,36,30,57,14,14,7,38,4,12,47,58,2,2,8,39,25,27,21,5,3,6,10,8,6,17,16,28,5,25,5,17,5,5,9,20,2,5,23,18,23,10,7,7,8,13,37,3,0.04,0.026,0.019,0.024,0.038,0.023,0.03,0.027,0.04,0.033,0.032,0.034,0.033,0.032,0.027,0.031,0.039,0.043,0.034,0.041,0.035,0.029,0.044,0.036,0.042,0.029,0.03,0.03,0.027,0.04,0.035,0.055,0.031,0.038,0.013,0.02,0.039,0.038,0.015,0.02,0.022,0.041,0.034,0.026,0.022,0.013,0.018,0.016,0.02,0.022,0.03,0.02,0.026,0.022,0.024,0.018,0.015,0.023,0.019,0.017,0.021,0.027,0.025,0.018,0.021,0.019,0.018,0.019,0.018,0.018,0.018,0.017,0.017,0.013,1955,5458,3000,2971,2241,3787,4126,4081,2091,1616,1255.0,3349,3867,1233,3572,13632,22276,2614,6172,6710,5394,4669,2318,7576,6315,8523,7535,5497,6774,7479,2267,1530,1404,6877,2844,3348,7067,10086,427,457,2005,3420,6105,4069,4130,1723,1173,2429,3183,1979,928,3479,2448,6397,757,5933,1814,3415,1337,1163,1239,3106,432,1244,4350,2399,5745,2622,1596,1439,1694,3208,9849,691,0.137,0.12,0.1,0.112,0.143,0.114,0.123,0.131,0.138,0.136,0.123,0.131,0.14,0.15,0.126,0.132,0.15,0.137,0.131,0.156,0.114,0.111,0.14,0.141,0.137,0.128,0.121,0.12,0.113,0.145,0.128,0.148,0.115,0.141,0.078,0.1,0.137,0.148,0.09,0.107,0.107,0.14,0.143,0.115,0.107,0.088,0.092,0.09,0.1,0.111,0.136,0.104,0.125,0.112,0.125,0.101,0.089,0.113,0.098,0.104,0.117,0.121,0.118,0.1,0.115,0.107,0.095,0.101,0.101,0.127,0.111,0.1,0.096,0.104,576,1745,1004,1121,982,1310,1370,1345,639,403,281.0,1546,1034,318,905,4045,6089,578,1441,2898,1172,1388,1036,2013,1714,2489,2471,1924,1877,2258,677,591,392,1597,741,1051,2053,2460,250,209,968,1615,1325,2159,2330,816,472,1019,1393,807,390,1479,1189,2372,394,2906,778,1536,751,612,569,1195,251,636,1993,1286,2465,1145,793,653,854,1542,4085,372,3.294,2.788,2.74,2.582,2.078,2.355,2.437,2.641,2.438,3.044,3.182,1.877,2.822,2.786,2.839,2.592,2.804,3.483,3.187,1.968,3.243,2.473,2.062,2.774,2.788,2.799,2.388,2.292,2.759,2.546,2.43,2.62,2.73,3.058,3.432,2.806,2.582,3.013,1.944,2.555,2.743,1.846,3.395,2.055,2.068,2.306,2.904,3.026,2.733,2.794,2.487,2.443,2.121,2.572,2.324,2.262,2.731,2.513,2.02,2.135,2.21,2.4,2.008,2.344,2.315,2.15,2.303,2.494,2.445,2.393,2.339,2.407,2.567,2.33,0.695,0.602,0.589,0.509,0.669,0.678,0.61,0.469,0.751,0.567,0.851,0.561,0.467,0.499,0.54,0.615,0.724,0.706,0.513,0.676,0.814,0.561,0.566,0.729,0.663,0.55,0.55,0.596,0.491,0.536,0.701,1.015,0.429,0.579,0.621,0.54,0.803,0.81,0.346,0.508,0.582,0.596,0.65,0.608,0.59,0.463,0.591,0.691,0.531,0.407,0.579,0.479,0.487,0.555,0.428,0.399,0.358,0.488,0.405,0.372,0.502,0.724,0.331,0.681,0.503,0.571,0.475,0.4,0.416,0.879,0.493,0.515,0.442,0.288
-4062,Not Epileptic,F,22.0,0.8,2.6,1.4,1.4,1.1,2.1,1.8,1.5,1.6,0.5,0.3,3.4,1.7,0.4,1.1,6.1,7.2,0.9,1.4,4.7,1.3,4.3,2.3,5.1,2.3,4.3,2.4,2.1,2.6,2.1,2.2,1.5,0.6,2.2,0.5,0.9,3.5,2.8,0.2,0.2,1.3,4.1,1.8,2.4,2.6,0.5,0.7,1.0,1.4,0.9,0.5,2.5,1.6,2.8,0.2,2.3,0.7,1.8,1.6,1.1,0.4,1.9,0.5,0.6,1.6,1.2,2.1,1.1,0.9,0.6,0.7,1.5,4.3,0.3,9,27,18,13,13,23,17,16,16,9,4.0,32,23,6,14,84,70,7,21,40,13,57,23,58,35,53,38,23,24,35,19,12,7,32,3,8,37,36,1,2,11,36,17,22,17,4,4,7,8,4,3,23,15,20,1,22,6,11,12,10,3,14,5,5,12,18,18,10,6,5,5,11,41,3,0.041,0.025,0.024,0.021,0.022,0.034,0.03,0.026,0.047,0.034,0.025,0.035,0.031,0.028,0.028,0.031,0.026,0.032,0.027,0.048,0.025,0.044,0.037,0.044,0.03,0.031,0.026,0.026,0.023,0.026,0.043,0.056,0.022,0.027,0.013,0.018,0.035,0.026,0.017,0.018,0.023,0.038,0.031,0.022,0.02,0.011,0.026,0.017,0.018,0.022,0.021,0.02,0.027,0.02,0.02,0.017,0.015,0.023,0.027,0.021,0.013,0.024,0.026,0.015,0.018,0.018,0.018,0.018,0.016,0.019,0.017,0.018,0.019,0.013,1456,5227,2861,2609,1944,3301,3008,3316,1841,1397,674.0,3195,3940,1187,2664,13004,17891,2151,3667,4465,3474,4619,2234,6431,5521,8685,5598,4178,6772,5281,2648,1301,1416,6020,2013,2099,7040,8164,374,561,1967,2768,5316,2796,3565,1371,906,2189,2444,1465,531,4019,1893,5127,454,4502,1473,2678,2006,1835,757,3123,486,1146,3014,1941,3862,2783,1875,992,1254,2749,8508,599,0.144,0.12,0.117,0.113,0.119,0.151,0.117,0.128,0.159,0.143,0.122,0.149,0.133,0.142,0.137,0.141,0.124,0.131,0.133,0.168,0.11,0.15,0.139,0.148,0.133,0.136,0.118,0.112,0.108,0.124,0.146,0.135,0.109,0.128,0.073,0.102,0.132,0.13,0.095,0.113,0.125,0.144,0.125,0.114,0.1,0.08,0.12,0.09,0.096,0.1,0.118,0.107,0.124,0.107,0.11,0.109,0.1,0.109,0.116,0.115,0.087,0.12,0.134,0.099,0.102,0.111,0.098,0.097,0.09,0.115,0.117,0.104,0.105,0.1,466,1737,1058,954,926,1077,1032,1001,639,353,183.0,1616,1186,291,721,3688,4849,458,955,1920,825,1581,1006,2013,1463,2618,1738,1407,1716,1620,819,520,453,1438,503,817,1841,1948,196,221,878,1585,1084,1756,2101,686,398,965,1140,645,323,2048,945,2181,145,2085,663,1247,1022,837,431,1191,277,613,1441,1043,1932,1078,909,463,590,1246,3735,333,3.129,2.563,2.715,2.517,1.894,2.549,2.423,2.749,2.25,2.974,2.544,1.82,2.685,2.956,2.73,2.614,2.871,3.5,2.833,2.049,3.057,2.313,2.027,2.355,2.819,2.641,2.515,2.271,2.948,2.549,2.333,2.59,2.576,2.94,3.37,2.431,2.748,2.992,2.172,2.593,2.817,1.696,3.516,1.788,1.957,2.234,2.861,2.732,2.689,2.555,1.87,2.07,2.051,2.433,2.779,2.278,2.384,2.395,2.184,2.369,2.088,2.552,1.914,2.163,2.289,2.057,2.167,2.656,2.4,2.276,2.306,2.433,2.383,2.11,0.818,0.646,0.424,0.62,0.578,0.623,0.574,0.45,0.695,0.702,1.027,0.572,0.432,0.383,0.545,0.604,0.639,0.764,0.613,0.555,0.764,0.66,0.463,0.809,0.598,0.559,0.549,0.611,0.528,0.548,0.871,0.92,0.342,0.604,0.672,0.449,0.787,0.732,0.404,0.307,0.517,0.459,0.681,0.556,0.631,0.412,0.701,0.645,0.433,0.566,0.432,0.416,0.468,0.482,0.521,0.41,0.472,0.472,0.38,0.354,0.474,0.743,0.557,0.581,0.441,0.669,0.455,0.473,0.417,0.871,0.435,0.464,0.456,0.357
-4063,Not Epileptic,M,52.0,1.2,2.8,1.4,1.6,2.2,2.0,3.4,2.0,1.7,0.7,0.8,3.1,1.2,0.5,1.4,7.1,12.6,1.0,2.6,4.7,1.1,2.8,1.9,3.7,3.3,4.7,3.0,2.9,3.3,3.2,2.3,1.0,0.5,2.4,0.6,0.8,2.9,3.0,0.2,0.1,1.3,4.0,2.1,3.3,4.5,0.7,0.5,1.4,1.6,1.2,0.7,2.5,2.0,3.8,0.4,3.2,0.7,1.6,1.5,1.8,0.9,1.6,0.5,0.8,1.5,1.9,3.0,1.6,1.0,0.9,1.2,1.4,5.3,0.3,10,23,14,13,19,21,24,20,13,8,8.0,27,15,5,14,83,117,11,32,41,12,33,20,37,43,50,35,26,31,37,22,10,5,30,5,4,36,41,2,1,9,37,25,22,21,4,3,8,8,10,5,17,14,29,3,30,8,18,12,13,8,11,3,7,11,19,24,13,5,7,7,11,42,3,0.04,0.027,0.023,0.032,0.039,0.034,0.046,0.025,0.051,0.04,0.051,0.031,0.026,0.04,0.031,0.035,0.04,0.036,0.031,0.04,0.031,0.03,0.027,0.041,0.029,0.028,0.03,0.029,0.033,0.032,0.049,0.039,0.028,0.031,0.019,0.016,0.027,0.029,0.015,0.013,0.032,0.044,0.034,0.029,0.029,0.014,0.03,0.019,0.019,0.024,0.036,0.028,0.026,0.026,0.015,0.02,0.013,0.02,0.026,0.022,0.023,0.031,0.023,0.023,0.02,0.032,0.024,0.021,0.017,0.026,0.024,0.017,0.019,0.017,1501,4463,1876,2221,2049,2774,2584,3791,1680,1365,788.0,3144,3150,893,2071,11576,17188,2078,4914,4272,3052,4913,2121,5499,6345,8888,4791,3689,6257,5325,2245,1168,742,5203,2020,1673,6884,7707,422,236,1251,3565,5609,2883,4192,1458,764,2084,2219,1626,433,2672,2211,5094,653,4481,1567,2616,1176,1900,1016,2217,506,1026,2346,1336,3940,2283,1692,1286,1449,1991,8560,433,0.133,0.118,0.103,0.127,0.137,0.142,0.127,0.124,0.155,0.146,0.156,0.128,0.113,0.148,0.128,0.131,0.138,0.142,0.129,0.145,0.101,0.129,0.114,0.143,0.124,0.126,0.112,0.109,0.108,0.133,0.156,0.116,0.108,0.132,0.094,0.078,0.117,0.131,0.096,0.096,0.12,0.157,0.139,0.123,0.118,0.086,0.109,0.097,0.098,0.122,0.151,0.121,0.119,0.118,0.098,0.111,0.089,0.119,0.128,0.113,0.119,0.133,0.133,0.113,0.103,0.133,0.114,0.117,0.094,0.12,0.117,0.106,0.103,0.116,521,1779,912,890,1032,985,1138,1327,581,377,280.0,1546,894,226,676,3631,5719,553,1500,2027,770,1528,1077,1701,1912,2823,1790,1393,1724,1774,768,576,283,1365,577,693,1810,1836,222,106,781,1515,1247,1862,2337,774,352,1051,1236,780,301,1454,1176,2462,395,2412,863,1369,856,1183,609,851,276,563,1197,899,2086,1127,812,571,733,1126,4228,275,2.763,2.307,2.214,2.395,1.862,2.256,2.034,2.436,2.251,2.801,2.226,1.751,2.799,2.708,2.372,2.456,2.45,3.022,2.636,1.819,2.987,2.366,1.785,2.444,2.519,2.544,2.171,2.102,2.707,2.397,2.343,2.073,2.084,2.751,3.019,2.272,2.741,2.916,2.178,2.265,1.91,1.915,3.28,1.742,2.029,2.07,2.482,2.359,2.111,2.333,1.7,1.995,2.017,2.099,1.892,1.962,1.926,2.151,1.607,1.724,1.892,2.7,1.944,2.084,2.139,1.715,2.0,2.188,2.335,2.161,2.126,2.08,2.141,1.917,0.621,0.572,0.406,0.494,0.454,0.668,0.509,0.57,0.616,0.779,0.847,0.525,0.448,0.513,0.511,0.578,0.563,0.66,0.596,0.551,0.715,0.663,0.499,0.802,0.624,0.695,0.461,0.53,0.515,0.555,0.673,0.862,0.513,0.594,0.54,0.516,0.765,0.739,0.35,0.863,0.383,0.699,0.632,0.498,0.572,0.487,0.593,0.631,0.506,0.64,0.337,0.372,0.434,0.524,0.327,0.398,0.364,0.472,0.261,0.374,0.384,0.822,0.532,0.706,0.502,0.556,0.487,0.421,0.405,0.636,0.461,0.442,0.451,0.587
-4064,Not Epileptic,M,55.0,1.1,2.1,0.8,1.1,1.6,2.0,2.4,2.0,0.9,1.0,0.5,3.0,1.9,0.6,2.1,5.4,10.2,0.9,2.2,4.7,1.0,3.5,2.6,4.4,6.4,4.3,4.7,3.4,4.1,3.7,1.2,1.4,0.5,2.6,0.3,1.2,5.6,3.2,0.4,0.1,1.2,3.8,2.3,2.5,3.1,0.7,0.5,1.0,1.5,1.0,0.6,2.7,1.9,2.9,0.5,3.0,0.6,1.4,1.3,1.2,0.6,2.0,0.5,0.7,1.9,1.7,2.5,1.4,1.1,1.1,1.1,1.1,5.1,0.4,9,20,8,8,12,18,17,18,10,10,6.0,31,16,9,24,60,102,8,31,42,7,37,23,45,58,44,53,29,31,51,17,10,5,29,1,13,55,42,2,1,9,47,25,18,19,5,3,7,9,6,3,22,14,21,2,22,5,12,8,8,4,17,3,7,14,20,24,10,6,10,7,9,50,2,0.042,0.02,0.015,0.021,0.033,0.034,0.034,0.032,0.033,0.035,0.041,0.034,0.036,0.029,0.033,0.03,0.03,0.034,0.027,0.04,0.019,0.035,0.033,0.039,0.042,0.029,0.033,0.033,0.032,0.034,0.035,0.049,0.021,0.025,0.006,0.018,0.032,0.031,0.026,0.016,0.023,0.039,0.032,0.027,0.024,0.016,0.018,0.018,0.017,0.022,0.038,0.022,0.025,0.02,0.017,0.019,0.015,0.014,0.026,0.019,0.021,0.026,0.027,0.017,0.021,0.027,0.02,0.018,0.016,0.02,0.021,0.017,0.017,0.02,1519,5099,2212,2414,1896,2448,3644,3351,1759,1883,772.0,3196,3892,1323,4081,11573,21488,2507,5332,4005,3500,5781,2492,6983,7268,8113,7005,4371,7360,6337,2629,1277,1179,6067,2183,3134,10501,8335,475,294,1824,3686,6287,2775,3961,1526,1004,2421,2910,1703,460,4084,2058,5836,624,4725,1289,3107,1385,1965,995,3436,489,1265,2852,1375,4045,2711,2163,1644,1764,2437,11311,504,0.127,0.109,0.093,0.103,0.132,0.14,0.113,0.132,0.129,0.137,0.131,0.137,0.13,0.147,0.145,0.132,0.131,0.136,0.13,0.159,0.082,0.126,0.124,0.139,0.137,0.127,0.121,0.113,0.113,0.153,0.13,0.135,0.097,0.119,0.049,0.092,0.132,0.141,0.119,0.118,0.114,0.136,0.135,0.119,0.106,0.092,0.102,0.084,0.097,0.11,0.153,0.113,0.117,0.103,0.108,0.105,0.102,0.089,0.118,0.097,0.108,0.125,0.132,0.104,0.107,0.129,0.11,0.099,0.094,0.124,0.112,0.099,0.099,0.099,498,1745,813,848,748,1033,1134,1110,488,507,237.0,1385,966,350,1155,3251,5892,495,1440,1980,842,1556,1111,1959,2309,2488,2336,1526,1904,2019,748,499,368,1604,628,1139,2701,1969,245,128,821,1566,1354,1548,2047,725,398,939,1292,697,230,1914,1077,2353,339,2352,616,1492,792,992,466,1222,287,643,1381,894,2003,1133,1003,728,834,1094,4661,280,3.185,2.721,2.702,2.727,2.346,2.265,2.563,2.743,2.694,3.04,2.754,1.925,3.083,2.765,2.733,2.644,2.885,3.66,2.898,1.858,2.96,2.742,1.957,2.725,2.612,2.64,2.412,2.186,2.878,2.555,2.626,2.535,2.466,2.874,3.053,2.457,2.974,3.058,2.245,2.593,2.647,2.011,3.342,1.939,2.156,2.266,2.953,3.211,2.62,3.002,2.124,2.254,2.122,2.465,2.25,2.256,2.455,2.231,2.039,2.14,2.348,2.768,2.016,2.309,2.315,1.874,2.142,2.576,2.511,2.593,2.408,2.607,2.455,2.17,0.828,0.469,0.492,0.508,0.569,0.469,0.656,0.426,0.528,0.612,0.701,0.589,0.473,0.509,0.523,0.601,0.626,0.657,0.57,0.565,0.785,0.54,0.574,0.737,0.661,0.632,0.638,0.764,0.594,0.505,0.704,0.883,0.467,0.59,0.51,0.42,0.812,0.65,0.376,0.444,0.476,0.609,0.749,0.615,0.619,0.42,0.605,0.793,0.433,0.557,0.4,0.428,0.404,0.478,0.437,0.358,0.369,0.481,0.409,0.411,0.443,0.663,0.505,0.672,0.462,0.753,0.398,0.396,0.425,0.56,0.45,0.441,0.415,0.341
-4065,Not Epileptic,F,22.0,0.9,3.0,1.1,1.1,1.1,2.8,2.0,1.8,1.5,0.7,0.3,3.0,2.2,0.5,1.7,9.0,11.4,0.9,2.4,4.3,1.1,3.6,3.0,3.6,2.8,4.0,3.2,3.1,3.5,4.6,1.9,1.0,0.7,3.4,0.5,0.7,5.3,4.3,0.1,0.4,1.1,3.7,2.4,2.1,3.5,0.8,0.8,1.7,1.1,1.2,0.6,3.4,1.6,2.7,0.5,2.4,0.8,1.5,1.1,1.3,0.6,1.9,0.5,0.4,2.8,1.5,3.1,1.5,0.9,0.6,1.5,2.8,4.3,0.4,6,28,11,12,12,28,15,16,18,11,3.0,28,22,9,22,97,104,6,29,36,8,46,26,39,35,36,33,31,28,47,26,5,9,33,4,6,48,50,2,2,7,35,20,23,22,5,3,14,6,13,4,25,11,18,3,20,5,12,10,13,3,18,3,5,21,18,27,10,7,4,10,19,35,3,0.032,0.026,0.013,0.019,0.031,0.03,0.028,0.031,0.042,0.029,0.042,0.039,0.036,0.03,0.033,0.036,0.029,0.026,0.029,0.04,0.017,0.033,0.041,0.037,0.033,0.034,0.028,0.028,0.029,0.039,0.046,0.038,0.029,0.037,0.017,0.017,0.04,0.03,0.011,0.018,0.02,0.038,0.037,0.023,0.024,0.018,0.026,0.02,0.014,0.035,0.026,0.025,0.019,0.018,0.019,0.019,0.023,0.016,0.026,0.03,0.021,0.028,0.024,0.017,0.025,0.03,0.02,0.019,0.016,0.023,0.034,0.025,0.017,0.016,1708,6494,2902,2306,1941,4403,3290,3832,1972,1374,313.0,3574,4650,1354,3364,15608,24071,3029,4797,4005,2731,5525,3235,7193,4972,7213,5623,5188,7025,6097,2722,774,1629,7272,2113,2051,7753,8968,422,741,1647,4104,5306,2480,3669,1475,1038,2826,2449,1389,642,4883,3050,6344,596,3397,1527,3131,1560,1634,1152,3015,633,957,4226,1041,5111,2551,1862,1283,1771,3837,8797,559,0.123,0.114,0.091,0.104,0.122,0.145,0.114,0.132,0.146,0.141,0.126,0.154,0.141,0.123,0.143,0.148,0.125,0.106,0.123,0.145,0.084,0.144,0.152,0.133,0.144,0.145,0.126,0.125,0.118,0.161,0.142,0.11,0.123,0.153,0.09,0.087,0.147,0.126,0.079,0.111,0.109,0.155,0.13,0.123,0.114,0.101,0.112,0.1,0.087,0.123,0.128,0.119,0.108,0.096,0.117,0.11,0.109,0.093,0.127,0.13,0.092,0.129,0.118,0.111,0.122,0.14,0.109,0.101,0.097,0.111,0.135,0.122,0.098,0.1,498,2026,1278,939,677,1609,1112,1047,602,411,152.0,1364,1192,355,909,4459,6610,570,1503,1865,827,1922,1168,1878,1526,2018,1888,1816,2026,2017,683,372,468,1604,517,688,2260,2409,237,323,777,1725,1222,1437,2230,719,453,1177,1153,596,355,2068,1285,2499,320,1796,575,1581,795,759,524,1075,301,475,1889,670,2496,1212,852,425,714,1608,3919,315,3.337,2.897,2.278,2.402,2.351,2.433,2.441,3.22,2.614,2.893,2.136,2.271,2.983,2.717,2.858,2.712,3.014,3.86,2.648,1.907,2.597,2.305,2.318,2.895,2.473,2.777,2.375,2.293,2.709,2.523,2.737,2.22,2.676,3.395,3.509,2.588,2.846,2.803,2.213,2.736,2.664,2.144,3.172,1.831,1.889,2.237,2.947,2.909,2.538,2.576,2.274,2.356,2.558,2.658,2.228,2.102,2.862,2.252,2.08,2.141,2.242,2.831,2.307,2.263,2.488,2.059,2.179,2.584,2.347,3.2,2.595,2.601,2.424,2.178,0.725,0.613,0.494,0.496,0.619,0.468,0.494,0.655,0.568,0.612,0.775,0.493,0.483,0.612,0.55,0.524,0.562,0.747,0.502,0.524,0.691,0.543,0.472,0.69,0.584,0.581,0.494,0.524,0.501,0.59,0.867,0.863,0.494,0.579,0.572,0.626,0.615,0.621,0.388,0.454,0.598,0.442,0.698,0.608,0.498,0.382,0.396,0.799,0.443,0.723,0.379,0.508,0.575,0.532,0.283,0.42,0.631,0.449,0.383,0.428,0.409,0.786,0.615,0.824,0.695,0.626,0.452,0.468,0.571,0.765,0.662,0.563,0.543,0.508
-4066,Not Epileptic,F,22.0,0.8,2.0,0.9,1.4,2.2,1.5,1.7,1.4,1.1,0.6,0.3,3.0,1.4,0.5,1.0,5.0,7.4,0.7,2.2,3.8,1.4,2.5,2.8,4.0,6.0,2.7,4.3,2.4,2.7,3.2,1.9,0.7,0.6,1.9,0.4,0.7,3.2,2.3,0.1,0.3,1.0,4.4,2.8,2.4,2.9,0.6,0.5,0.7,1.3,0.7,0.4,1.9,1.0,2.0,0.3,2.9,0.5,1.0,0.9,1.0,0.4,1.7,0.5,0.6,2.3,1.1,2.5,1.4,0.8,0.7,1.1,1.2,3.9,0.3,7,18,8,10,21,15,15,13,13,8,3.0,24,16,7,11,58,75,5,25,29,16,31,22,42,63,33,52,25,22,37,17,5,6,29,2,6,33,37,1,3,6,33,26,15,14,4,2,5,7,7,3,16,11,15,1,24,5,10,6,7,3,12,3,4,18,16,17,10,5,3,7,8,35,2,0.036,0.026,0.018,0.026,0.045,0.028,0.027,0.025,0.039,0.03,0.035,0.033,0.031,0.035,0.032,0.032,0.029,0.03,0.027,0.04,0.028,0.032,0.04,0.05,0.047,0.03,0.035,0.035,0.032,0.029,0.042,0.046,0.031,0.031,0.015,0.018,0.039,0.03,0.016,0.021,0.021,0.043,0.041,0.028,0.023,0.013,0.017,0.013,0.018,0.013,0.018,0.027,0.02,0.02,0.017,0.019,0.024,0.013,0.018,0.016,0.019,0.029,0.032,0.017,0.025,0.025,0.02,0.02,0.016,0.019,0.025,0.018,0.019,0.024,1638,3973,2358,2530,2151,2908,3658,3277,1631,1560,901.0,2855,3262,1002,2548,10277,18840,1991,5185,3995,4919,5829,2593,5345,6517,6165,7962,4066,5965,7100,2918,1057,1075,4995,1751,1983,6644,7023,265,566,1498,3549,5627,2298,3499,1605,800,2082,2368,1839,575,3201,1900,4219,522,4821,1029,2683,1373,1794,644,2398,395,971,3150,1402,3941,2389,1435,944,1605,1932,8441,480,0.121,0.122,0.104,0.113,0.153,0.127,0.102,0.118,0.162,0.13,0.125,0.134,0.131,0.148,0.14,0.132,0.129,0.11,0.125,0.147,0.113,0.123,0.13,0.154,0.147,0.129,0.13,0.103,0.112,0.128,0.149,0.1,0.122,0.135,0.082,0.091,0.135,0.138,0.095,0.129,0.105,0.143,0.153,0.123,0.105,0.085,0.099,0.081,0.096,0.096,0.126,0.118,0.118,0.102,0.109,0.107,0.118,0.091,0.103,0.099,0.107,0.13,0.151,0.108,0.116,0.12,0.102,0.103,0.099,0.1,0.113,0.108,0.096,0.132,413,1283,787,874,862,931,1074,890,483,349,188.0,1384,847,257,617,2734,4504,435,1305,1640,959,1398,1079,1505,1981,1652,2298,1239,1377,1855,778,397,284,1103,424,626,1511,1466,159,241,718,1546,1136,1372,1921,751,350,871,1071,830,318,1385,888,1682,216,2340,463,1251,710,891,307,874,228,482,1562,743,1931,1054,692,451,772,949,3353,220,3.56,2.813,2.709,2.776,2.008,2.556,2.614,3.018,2.474,3.305,3.576,1.806,2.874,2.778,2.906,2.677,3.135,3.496,3.008,2.007,3.536,2.949,2.073,2.72,2.592,2.884,2.624,2.443,3.129,2.882,2.607,2.727,2.814,3.108,3.432,2.863,3.022,3.327,1.842,2.593,2.633,1.944,3.246,2.016,2.12,2.408,2.769,2.915,2.74,2.579,2.066,2.294,1.892,2.488,2.819,2.282,2.264,2.28,2.237,2.209,2.278,2.577,1.821,2.326,2.274,2.262,2.194,2.468,2.412,2.204,2.494,2.443,2.628,2.756,0.849,0.646,0.493,0.536,0.782,0.678,0.718,0.459,0.813,0.418,0.847,0.494,0.511,0.481,0.546,0.758,0.688,0.764,0.522,0.7,0.84,0.618,0.677,0.716,0.749,0.611,0.685,0.744,0.554,0.568,0.754,1.011,0.455,0.628,0.69,0.493,0.813,0.647,0.408,0.848,0.418,0.685,0.865,0.569,0.634,0.416,0.606,0.806,0.418,0.393,0.301,0.479,0.612,0.55,0.577,0.402,0.409,0.569,0.394,0.414,0.361,0.77,0.466,0.467,0.528,0.74,0.419,0.448,0.516,0.594,0.341,0.557,0.457,0.519
-4067,Not Epileptic,F,37.0,0.8,2.4,0.7,1.3,1.6,1.8,1.4,1.6,1.5,0.8,0.8,2.8,1.2,0.5,1.0,7.3,7.5,1.0,2.4,4.8,1.1,2.9,2.0,3.9,2.6,3.2,3.7,2.9,2.4,2.6,1.3,1.5,0.6,2.4,0.5,0.8,3.2,3.2,0.2,0.2,1.0,3.6,2.4,3.6,2.7,0.6,0.5,1.0,1.3,0.9,0.6,1.9,1.1,2.7,0.4,2.1,0.5,1.2,1.1,1.3,0.7,1.8,0.5,0.7,2.4,1.4,2.3,0.9,1.1,0.7,1.2,1.4,3.8,0.3,8,27,6,11,13,21,13,17,13,9,7.0,28,15,9,12,72,78,11,29,46,13,36,22,43,36,40,44,33,22,29,14,12,5,33,4,7,45,46,2,1,6,34,25,25,17,4,2,8,7,8,5,12,6,22,3,20,4,12,9,11,6,12,3,5,21,19,24,8,9,5,10,12,30,3,0.035,0.028,0.016,0.024,0.035,0.03,0.025,0.029,0.045,0.035,0.047,0.04,0.025,0.038,0.032,0.043,0.028,0.038,0.032,0.039,0.028,0.033,0.029,0.041,0.029,0.027,0.027,0.029,0.021,0.029,0.036,0.058,0.023,0.031,0.015,0.016,0.033,0.035,0.018,0.02,0.021,0.042,0.038,0.034,0.018,0.013,0.016,0.017,0.021,0.021,0.026,0.022,0.024,0.022,0.026,0.015,0.015,0.018,0.024,0.02,0.026,0.028,0.032,0.023,0.025,0.028,0.016,0.016,0.018,0.023,0.03,0.019,0.017,0.015,1370,4541,1905,2550,2027,3314,2764,3379,1960,1357,749.0,2742,2761,1029,2066,10702,18343,2223,4488,5197,3681,4928,2592,5903,5828,6454,6845,4726,7246,4955,2559,1088,1252,6523,2158,2199,6989,6920,369,321,1330,2646,5825,2791,3735,1244,931,2200,1950,1721,649,2689,1361,4395,489,4223,1251,2437,1259,1875,869,2722,522,993,2980,1191,4265,2091,1937,1281,1498,2204,7968,337,0.138,0.123,0.094,0.117,0.146,0.134,0.11,0.13,0.168,0.155,0.145,0.161,0.126,0.153,0.145,0.157,0.126,0.151,0.141,0.164,0.115,0.133,0.116,0.151,0.129,0.142,0.12,0.12,0.103,0.134,0.136,0.134,0.105,0.143,0.086,0.092,0.142,0.147,0.105,0.107,0.115,0.145,0.136,0.139,0.098,0.089,0.095,0.089,0.102,0.108,0.131,0.112,0.116,0.111,0.134,0.097,0.087,0.105,0.117,0.111,0.125,0.127,0.148,0.118,0.122,0.133,0.102,0.096,0.104,0.136,0.139,0.112,0.097,0.132,436,1623,687,855,770,1081,902,1072,552,396,221.0,1316,808,285,597,3144,4833,525,1273,2208,817,1530,1105,1708,1580,1943,2249,1670,1792,1540,696,481,427,1496,570,756,1853,1825,195,186,726,1256,1249,1675,2218,630,418,941,964,680,372,1301,725,1956,234,2244,617,1145,751,965,469,1005,255,473,1565,909,2156,971,962,465,677,1174,3575,213,3.264,2.579,2.649,2.767,2.16,2.505,2.569,2.73,2.626,2.911,2.772,1.846,2.644,2.689,2.736,2.559,2.93,3.312,2.813,2.106,3.144,2.495,2.044,2.66,2.772,2.705,2.355,2.245,2.94,2.552,2.717,2.43,2.41,3.115,3.343,2.557,2.755,2.757,2.263,1.865,2.358,1.894,3.384,1.968,1.94,2.127,2.787,2.79,2.555,2.703,2.186,2.13,1.982,2.298,2.445,2.089,2.24,2.331,1.885,2.146,2.201,2.713,2.158,2.457,2.075,1.62,2.133,2.447,2.461,2.967,2.396,2.102,2.362,2.103,0.659,0.648,0.492,0.465,0.668,0.599,0.576,0.452,0.801,0.677,0.694,0.51,0.369,0.515,0.643,0.638,0.634,0.668,0.521,0.546,0.831,0.526,0.638,0.758,0.611,0.55,0.633,0.598,0.509,0.537,0.692,0.796,0.388,0.663,0.841,0.525,0.762,0.636,0.368,0.383,0.571,0.513,0.738,0.599,0.603,0.44,0.438,0.71,0.386,0.578,0.307,0.433,0.478,0.475,0.575,0.372,0.436,0.455,0.318,0.365,0.313,0.82,0.766,0.741,0.467,0.42,0.462,0.335,0.447,0.784,0.565,0.473,0.397,0.284
-4068,Not Epileptic,M,47.0,1.6,2.6,1.0,1.6,2.1,2.3,1.8,2.1,0.9,1.3,0.3,3.0,1.4,0.5,1.3,6.6,9.7,1.2,3.3,6.0,2.2,3.6,1.8,3.2,3.7,4.5,3.4,3.9,4.2,3.6,1.6,0.9,0.5,3.5,1.1,1.2,5.5,5.0,0.1,0.2,1.4,3.2,2.7,3.8,3.5,0.7,0.4,1.3,1.2,0.7,0.9,1.9,1.3,3.1,0.3,2.5,0.9,2.3,1.3,1.0,0.6,2.0,0.3,0.6,2.2,2.3,3.1,1.7,0.9,0.8,1.3,2.6,8.0,0.4,9,21,10,11,17,20,15,18,9,11,4.0,25,14,5,15,64,87,8,25,45,18,39,16,32,38,41,31,29,28,34,16,8,5,33,15,9,42,48,2,2,8,29,23,28,21,5,2,6,6,6,5,15,9,23,2,22,7,14,11,9,4,12,3,5,18,22,23,12,6,8,8,15,69,4,0.05,0.024,0.023,0.032,0.043,0.035,0.023,0.029,0.037,0.04,0.027,0.034,0.023,0.023,0.03,0.039,0.03,0.037,0.039,0.039,0.032,0.031,0.027,0.036,0.029,0.039,0.029,0.026,0.029,0.028,0.039,0.03,0.024,0.032,0.03,0.023,0.044,0.035,0.013,0.015,0.026,0.038,0.039,0.03,0.02,0.016,0.019,0.017,0.017,0.01,0.036,0.022,0.024,0.023,0.03,0.024,0.02,0.029,0.025,0.02,0.016,0.029,0.02,0.019,0.024,0.038,0.019,0.019,0.014,0.021,0.026,0.029,0.02,0.018,1875,5105,2121,2208,2679,2706,2948,3716,2160,1951,629.0,2817,3374,970,2660,10652,19082,2727,3673,4708,3927,6261,2504,6432,5652,5774,5306,5091,6744,6241,2858,1209,1155,8382,2355,2267,6722,8752,333,516,1679,3179,5548,2405,4506,1378,866,2906,2379,2072,490,3000,2078,5794,287,3393,1341,2391,1521,1558,1418,3044,483,987,2870,1619,4511,3192,1681,1358,1862,3433,15583,448,0.15,0.113,0.109,0.128,0.144,0.145,0.098,0.121,0.143,0.152,0.123,0.132,0.111,0.12,0.139,0.148,0.129,0.124,0.138,0.139,0.123,0.142,0.124,0.127,0.112,0.136,0.121,0.101,0.101,0.128,0.125,0.097,0.117,0.128,0.097,0.095,0.131,0.126,0.106,0.086,0.12,0.145,0.143,0.129,0.098,0.099,0.092,0.083,0.09,0.083,0.156,0.108,0.113,0.107,0.132,0.115,0.113,0.118,0.134,0.108,0.086,0.12,0.109,0.106,0.12,0.137,0.106,0.103,0.094,0.111,0.111,0.121,0.105,0.118,503,1720,808,856,829,1117,1164,1167,466,520,230.0,1382,983,278,716,2955,5501,472,1454,2310,1085,1969,1042,1639,1845,1895,1863,1968,1950,2037,731,549,394,1740,551,759,1995,2323,184,271,774,1430,1216,1788,2629,692,348,1087,1108,939,363,1354,917,2270,132,1720,685,1246,816,863,620,1100,269,560,1426,963,2404,1442,915,612,871,1517,6483,267,3.512,2.627,2.503,2.511,2.623,2.185,2.154,2.874,3.094,3.063,2.473,1.832,2.665,2.588,2.805,2.697,2.781,3.918,2.218,1.886,2.889,2.543,2.096,2.869,2.409,2.659,2.371,2.164,2.705,2.386,2.768,2.175,2.454,3.319,3.676,2.71,2.659,2.827,1.859,2.415,2.818,1.948,3.195,1.59,1.944,2.168,3.045,3.19,2.606,2.468,1.799,2.277,2.293,2.494,2.847,2.135,2.211,2.085,2.243,2.03,2.682,2.619,2.1,2.079,2.215,2.035,2.088,2.502,2.231,2.247,2.406,2.65,2.526,2.118,0.974,0.66,0.698,0.556,0.732,0.575,0.663,0.496,0.749,0.538,0.808,0.508,0.415,0.511,0.489,0.611,0.674,0.673,0.587,0.633,0.843,0.496,0.474,0.598,0.531,0.624,0.521,0.653,0.661,0.558,0.726,0.931,0.575,0.611,0.814,0.561,0.701,0.597,0.458,0.408,0.671,0.502,0.773,0.554,0.638,0.496,0.476,0.932,0.448,0.36,0.37,0.488,0.532,0.566,0.361,0.46,0.53,0.489,0.543,0.376,0.461,0.679,0.358,0.552,0.644,0.732,0.51,0.456,0.452,0.544,0.529,0.488,0.505,0.591
-4069,Not Epileptic,F,52.0,0.6,2.2,0.9,0.9,1.3,1.8,1.6,1.4,1.1,0.4,0.3,3.1,1.2,0.4,0.8,5.5,7.4,0.8,1.8,5.4,1.1,2.8,2.1,4.7,3.1,3.1,2.8,2.6,3.0,3.1,1.6,1.6,0.8,2.1,0.5,0.5,3.5,2.8,0.1,0.2,1.0,3.7,2.0,2.8,3.5,0.6,0.4,0.9,1.1,0.6,0.5,1.8,1.2,2.2,0.2,2.2,1.0,1.3,1.1,1.2,0.5,1.8,0.2,0.6,2.3,1.3,2.0,1.1,1.4,0.4,1.1,1.4,4.7,0.3,7,20,7,8,12,18,14,13,11,5,4.0,27,15,4,11,69,68,8,21,54,8,33,17,44,33,27,28,22,23,38,22,11,8,21,3,3,45,38,1,2,5,27,18,19,20,4,2,5,6,5,5,23,8,18,1,18,7,12,8,10,3,12,2,5,16,14,15,8,8,4,7,11,36,2,0.034,0.029,0.018,0.022,0.029,0.029,0.03,0.028,0.043,0.027,0.027,0.044,0.027,0.034,0.03,0.036,0.031,0.033,0.033,0.048,0.026,0.038,0.046,0.048,0.041,0.037,0.033,0.033,0.034,0.035,0.039,0.047,0.04,0.03,0.021,0.015,0.034,0.032,0.014,0.017,0.025,0.052,0.037,0.03,0.028,0.012,0.021,0.017,0.017,0.016,0.029,0.03,0.023,0.022,0.028,0.021,0.027,0.022,0.027,0.026,0.023,0.033,0.023,0.018,0.024,0.038,0.022,0.019,0.023,0.025,0.024,0.018,0.021,0.02,1147,3484,1865,1902,1758,2761,2547,2735,1384,1186,633.0,2162,2940,682,1436,9213,15080,2039,3189,4225,2973,3431,1228,5034,4644,4916,4154,3387,5167,5258,2379,1391,1020,5114,1501,1478,6789,5870,241,468,1049,1451,4705,2440,3428,1199,741,1765,1832,1171,394,1797,1359,3610,264,2840,1010,2047,932,1337,695,2166,264,1000,2640,920,2308,2020,1783,746,1304,2178,7930,356,0.116,0.128,0.103,0.109,0.133,0.135,0.111,0.125,0.168,0.119,0.13,0.151,0.133,0.142,0.134,0.139,0.133,0.123,0.142,0.167,0.096,0.133,0.142,0.146,0.134,0.137,0.124,0.114,0.108,0.148,0.138,0.13,0.155,0.134,0.096,0.079,0.14,0.135,0.092,0.112,0.118,0.165,0.145,0.126,0.114,0.084,0.102,0.092,0.097,0.106,0.143,0.123,0.119,0.113,0.122,0.117,0.13,0.113,0.132,0.117,0.107,0.136,0.116,0.101,0.12,0.14,0.11,0.1,0.115,0.127,0.12,0.111,0.109,0.136,391,1255,746,720,737,969,868,865,426,292,205.0,1058,784,188,419,2584,3937,432,851,1982,710,1265,708,1567,1245,1308,1424,1164,1367,1609,757,545,340,1104,390,526,1762,1541,151,180,580,1000,904,1470,2068,675,327,791,934,554,269,1061,813,1620,131,1513,530,1076,600,767,388,841,156,545,1422,496,1388,930,847,303,698,1087,3542,180,2.833,2.626,2.453,2.605,1.998,2.48,2.371,2.653,2.498,3.021,2.718,1.817,2.816,2.682,2.471,2.55,2.87,3.384,2.83,1.892,3.149,2.195,1.648,2.53,2.764,2.785,2.278,2.248,2.807,2.476,2.357,2.488,2.443,3.144,3.555,2.448,2.91,2.851,1.725,2.34,2.21,1.483,3.632,1.855,1.92,1.981,2.902,2.783,2.462,2.518,1.893,1.812,1.805,2.216,2.073,2.042,2.297,2.19,1.852,2.001,2.186,2.631,1.997,2.161,2.024,2.182,1.886,2.265,2.456,2.907,2.1,2.29,2.431,2.548,0.751,0.729,0.545,0.521,0.658,0.645,0.682,0.494,0.701,0.406,0.628,0.51,0.482,0.386,0.559,0.68,0.633,0.596,0.582,0.571,0.735,0.608,0.408,0.81,0.678,0.61,0.595,0.641,0.592,0.565,0.613,0.723,0.424,0.603,0.64,0.401,0.697,0.629,0.386,0.465,0.42,0.409,0.685,0.482,0.585,0.378,0.343,0.644,0.42,0.351,0.443,0.519,0.385,0.504,0.532,0.348,0.314,0.394,0.344,0.321,0.296,0.658,0.386,0.668,0.502,0.536,0.413,0.308,0.457,0.624,0.367,0.482,0.421,0.348
-4070,Not Epileptic,F,37.0,0.5,2.3,0.9,0.9,1.4,2.0,1.0,1.4,1.1,0.5,0.4,2.4,1.5,0.7,1.1,5.7,6.8,0.9,2.3,4.3,1.5,2.4,1.4,2.8,2.5,2.9,3.3,2.0,2.1,2.8,1.2,0.4,0.4,2.3,0.3,1.0,3.9,2.2,0.2,0.1,0.8,2.7,1.6,1.7,2.5,0.6,0.4,0.9,1.0,0.6,0.5,2.2,1.0,2.4,0.1,2.3,0.9,1.4,0.7,0.9,0.7,1.5,0.3,0.4,0.8,1.6,2.1,1.5,1.2,0.5,0.7,2.2,3.2,0.2,5,28,9,7,12,19,12,14,12,5,3.0,22,15,7,13,61,58,8,23,36,12,29,14,29,31,31,39,22,18,35,17,4,5,25,2,10,40,28,2,1,4,22,19,13,13,4,1,5,5,5,3,16,6,20,1,17,6,10,5,7,5,15,3,4,7,15,18,10,7,4,5,13,28,2,0.026,0.025,0.015,0.019,0.032,0.031,0.02,0.028,0.049,0.029,0.03,0.03,0.026,0.031,0.024,0.034,0.028,0.037,0.035,0.036,0.029,0.034,0.026,0.039,0.031,0.033,0.03,0.026,0.024,0.032,0.036,0.026,0.029,0.027,0.013,0.03,0.038,0.027,0.014,0.015,0.018,0.039,0.034,0.021,0.021,0.015,0.02,0.016,0.013,0.014,0.025,0.022,0.02,0.023,0.023,0.018,0.024,0.016,0.019,0.019,0.023,0.028,0.028,0.016,0.016,0.04,0.02,0.024,0.022,0.016,0.019,0.032,0.014,0.016,1470,4311,2055,1931,2309,3106,2319,3015,1651,1264,641.0,2879,3721,1306,3195,10851,16129,2326,4264,5194,3389,4788,1878,5763,4899,5408,6308,3451,5319,5640,2215,711,845,5055,1505,1892,7072,6230,408,319,1432,3040,4816,2261,3071,1427,799,1899,2218,1512,586,3537,1713,4780,199,3763,994,3074,1101,1503,1023,2492,498,886,1845,1038,3391,2252,1634,1105,1151,2388,8521,438,0.107,0.125,0.103,0.098,0.137,0.143,0.109,0.126,0.165,0.124,0.132,0.134,0.132,0.156,0.122,0.139,0.125,0.122,0.149,0.133,0.1,0.143,0.127,0.145,0.14,0.15,0.137,0.123,0.108,0.137,0.126,0.089,0.142,0.126,0.076,0.116,0.133,0.117,0.085,0.095,0.102,0.152,0.139,0.103,0.103,0.091,0.099,0.086,0.085,0.098,0.126,0.107,0.103,0.111,0.128,0.099,0.126,0.099,0.11,0.103,0.115,0.132,0.145,0.108,0.095,0.146,0.105,0.116,0.11,0.114,0.115,0.135,0.087,0.106,394,1472,847,715,745,1012,778,920,444,305,199.0,1209,975,350,838,2957,4099,445,1171,1976,885,1364,852,1373,1444,1585,2037,1219,1434,1726,646,324,269,1201,387,596,1869,1445,241,141,625,1166,995,1273,1802,623,299,838,1023,679,311,1620,759,1866,91,1925,482,1312,588,752,489,827,207,392,882,555,1694,970,845,460,552,1078,3497,175,3.699,2.734,2.36,2.593,2.771,2.704,2.43,2.979,2.795,3.165,2.842,2.006,2.971,2.694,2.988,2.845,3.197,3.753,2.898,2.2,3.027,2.739,2.01,3.208,2.64,2.945,2.442,2.182,2.853,2.705,2.672,2.066,2.691,2.966,3.544,2.675,2.795,3.043,2.041,2.693,2.835,2.297,3.354,1.966,1.93,2.427,3.141,2.793,2.632,2.544,2.511,2.244,2.4,2.448,2.353,2.204,2.518,2.625,2.245,2.221,2.524,3.026,2.634,2.694,2.237,2.288,2.198,2.597,2.403,2.758,2.455,2.647,2.566,3.026,0.83,0.483,0.591,0.524,0.672,0.516,0.481,0.529,0.611,0.503,0.982,0.421,0.517,0.603,0.647,0.549,0.544,0.837,0.542,0.576,0.743,0.525,0.474,0.65,0.54,0.699,0.506,0.523,0.494,0.56,0.625,0.889,0.41,0.687,0.613,0.6,0.701,0.587,0.387,0.357,0.535,0.539,0.696,0.666,0.507,0.664,0.602,0.69,0.471,0.405,0.477,0.453,0.706,0.584,0.424,0.344,0.504,0.514,0.324,0.343,0.453,0.739,0.322,1.013,0.504,0.598,0.493,0.467,0.393,0.59,0.421,0.493,0.518,0.426
-4071,Not Epileptic,F,22.0,0.6,2.0,1.0,0.9,1.5,1.6,1.6,2.0,1.0,0.8,0.1,2.7,1.6,0.5,1.8,5.9,7.4,0.5,1.9,4.7,1.3,2.6,1.8,2.7,2.7,4.6,3.1,2.4,2.6,4.1,0.7,0.5,0.3,2.3,0.2,0.4,3.3,3.1,0.1,0.1,1.0,2.8,2.7,3.1,2.1,0.8,0.3,0.7,0.9,0.9,0.6,1.3,1.0,1.6,0.2,1.7,0.7,1.6,1.2,0.9,0.6,1.3,0.2,0.3,1.9,1.1,2.5,1.7,0.7,0.6,0.8,1.3,4.4,0.3,4,17,13,9,18,18,14,17,12,8,2.0,21,15,5,13,67,69,2,18,39,17,23,14,37,30,44,37,22,21,46,13,3,3,28,1,3,41,43,1,1,6,23,18,23,13,7,1,5,5,6,4,12,8,10,2,13,5,15,9,7,4,10,1,4,14,14,17,12,5,4,6,9,41,2,0.03,0.024,0.017,0.021,0.032,0.03,0.024,0.032,0.033,0.038,0.023,0.041,0.027,0.024,0.035,0.033,0.028,0.02,0.03,0.034,0.028,0.033,0.036,0.033,0.031,0.042,0.025,0.026,0.024,0.032,0.03,0.023,0.019,0.026,0.009,0.011,0.027,0.034,0.009,0.013,0.02,0.036,0.041,0.028,0.018,0.018,0.014,0.013,0.012,0.015,0.028,0.015,0.019,0.015,0.021,0.015,0.025,0.019,0.027,0.022,0.026,0.023,0.016,0.015,0.022,0.019,0.019,0.019,0.016,0.016,0.021,0.019,0.018,0.013,1161,5195,2457,2185,2092,2976,3276,3811,2009,1444,395.0,2331,4381,1223,3522,13326,20140,2376,3303,4939,3150,4472,2286,5905,5847,8440,6021,4461,6628,6914,1736,969,942,6492,1729,2021,7988,8347,450,663,1828,2818,4771,3068,3156,1410,923,2256,2896,2042,555,3497,1677,4089,361,3252,1349,3580,1326,1487,742,2669,304,769,2761,1161,4169,3637,1720,1131,1707,2417,9149,736,0.112,0.107,0.109,0.103,0.128,0.135,0.115,0.131,0.133,0.143,0.116,0.145,0.133,0.111,0.143,0.139,0.125,0.087,0.118,0.127,0.102,0.141,0.138,0.135,0.138,0.153,0.125,0.119,0.111,0.137,0.11,0.085,0.108,0.126,0.06,0.078,0.123,0.134,0.073,0.08,0.104,0.142,0.122,0.122,0.096,0.107,0.085,0.081,0.081,0.098,0.138,0.092,0.115,0.086,0.129,0.094,0.109,0.099,0.131,0.108,0.125,0.11,0.109,0.118,0.112,0.112,0.099,0.101,0.091,0.102,0.109,0.117,0.098,0.096,333,1414,917,794,807,976,1002,1076,519,334,129.0,1074,1021,283,811,3174,4590,392,986,2132,862,1256,814,1458,1517,2021,2037,1452,1738,2188,462,356,292,1451,416,665,2088,1834,244,260,779,1226,1115,1699,1771,678,313,921,1117,845,308,1548,752,1697,140,1675,549,1578,703,726,386,907,154,344,1320,782,1958,1390,753,471,665,1018,3964,295,3.198,3.276,2.476,2.72,2.166,2.636,2.625,3.159,2.747,3.179,2.772,1.92,3.191,2.998,3.122,3.037,3.387,3.962,2.62,2.011,2.857,2.703,2.515,2.882,2.888,3.102,2.34,2.439,2.939,2.603,2.709,2.37,2.54,3.191,3.484,2.76,2.96,3.222,2.152,2.988,2.924,2.034,3.28,2.116,2.032,2.223,3.698,3.058,3.086,2.651,2.102,2.353,2.27,2.676,2.807,2.25,2.867,2.632,2.294,2.24,2.447,2.827,2.255,2.585,2.321,1.957,2.343,2.85,2.651,2.612,2.834,2.804,2.449,3.023,0.837,0.695,0.735,0.543,0.652,0.655,0.516,0.742,0.724,0.665,0.846,0.46,0.561,0.443,0.592,0.681,0.668,0.813,0.694,0.62,0.838,0.535,0.429,0.681,0.652,0.69,0.518,0.561,0.608,0.613,0.764,1.107,0.624,0.614,0.624,0.477,0.664,0.648,0.311,0.392,0.624,0.489,0.743,0.608,0.564,0.559,0.774,0.65,0.78,0.522,0.363,0.441,0.586,0.568,0.578,0.471,0.43,0.53,0.386,0.385,0.382,0.845,0.308,0.898,0.587,0.685,0.516,0.462,0.384,0.724,0.498,0.512,0.535,0.55
-4072,Not Epileptic,F,42.0,0.6,2.6,0.9,1.3,1.5,2.4,2.3,1.7,1.5,0.9,0.6,2.7,2.2,0.4,1.5,5.1,8.7,1.0,2.4,5.4,1.9,3.3,1.8,2.7,3.4,3.7,3.5,2.9,3.6,3.7,1.7,1.0,0.6,2.1,0.4,0.7,4.3,3.1,0.3,0.3,1.1,3.4,2.9,3.2,3.4,1.1,0.3,0.8,1.3,0.9,0.7,2.3,1.1,2.2,0.6,2.6,0.8,1.7,1.3,0.8,0.5,1.3,0.2,0.2,1.6,1.5,2.8,1.6,1.1,0.9,1.3,0.9,3.9,0.3,4,27,8,11,15,22,16,14,14,8,4.0,26,17,7,17,48,75,10,26,40,14,34,17,29,42,33,36,28,26,38,23,7,6,21,3,5,48,34,3,2,7,27,24,23,21,9,2,5,8,8,5,16,8,15,4,20,7,13,10,8,4,10,2,3,13,16,24,11,7,8,8,8,32,3,0.028,0.022,0.016,0.021,0.038,0.035,0.03,0.025,0.049,0.036,0.042,0.039,0.035,0.031,0.038,0.037,0.029,0.032,0.034,0.039,0.031,0.037,0.032,0.038,0.034,0.032,0.028,0.033,0.029,0.032,0.049,0.035,0.025,0.031,0.015,0.021,0.037,0.029,0.02,0.026,0.024,0.038,0.039,0.03,0.025,0.022,0.016,0.012,0.017,0.02,0.037,0.026,0.029,0.021,0.033,0.022,0.029,0.021,0.026,0.017,0.029,0.026,0.015,0.014,0.024,0.028,0.024,0.025,0.021,0.023,0.032,0.017,0.019,0.02,1370,5085,2277,2548,1560,2775,2840,3029,1350,1445,648.0,2258,3614,740,2348,6742,14829,1919,3880,3982,3615,4189,1768,4849,5454,5795,5450,3579,5871,5175,2118,1141,964,4057,1369,1633,5508,5994,404,508,1380,2881,4672,2434,3407,1400,642,1832,2084,1718,586,3178,1531,3413,474,3642,1038,2798,1311,1194,710,1761,368,790,1854,1361,3722,2351,1672,1213,1469,1721,7481,399,0.097,0.118,0.095,0.105,0.142,0.156,0.122,0.124,0.167,0.141,0.143,0.15,0.144,0.137,0.152,0.151,0.134,0.136,0.136,0.15,0.107,0.158,0.138,0.141,0.144,0.14,0.124,0.129,0.121,0.148,0.166,0.134,0.125,0.122,0.089,0.093,0.136,0.131,0.116,0.113,0.118,0.147,0.143,0.127,0.117,0.12,0.097,0.082,0.099,0.106,0.141,0.124,0.135,0.104,0.152,0.115,0.129,0.112,0.129,0.107,0.128,0.128,0.106,0.108,0.124,0.125,0.122,0.117,0.103,0.12,0.133,0.112,0.098,0.133,398,1871,881,1047,681,1168,1091,1036,489,426,237.0,1136,1057,263,748,2277,4707,453,1170,2014,985,1478,858,1373,1818,1976,2016,1424,1779,1817,618,475,332,1066,376,553,1834,1745,188,233,729,1381,1243,1699,2069,744,286,888,1040,798,321,1395,619,1585,252,1870,526,1329,784,711,304,713,198,318,1025,805,1833,1049,843,568,714,853,3498,231,3.364,2.632,2.462,2.5,1.99,2.197,2.246,2.636,2.247,2.797,2.418,1.76,2.679,2.171,2.413,2.37,2.579,3.339,2.751,1.841,2.938,2.293,1.926,2.637,2.387,2.452,2.192,2.073,2.646,2.351,2.698,2.507,2.411,2.763,3.275,2.595,2.402,2.776,2.03,2.087,2.36,1.923,2.889,1.725,1.872,2.16,2.956,2.408,2.39,2.431,2.12,2.163,2.217,2.24,2.352,2.126,2.383,2.434,2.008,1.944,2.226,2.489,1.717,2.29,2.003,2.063,2.137,2.385,2.358,2.357,2.432,2.372,2.31,2.288,0.676,0.563,0.543,0.453,0.539,0.443,0.545,0.575,0.509,0.419,0.55,0.463,0.555,0.548,0.583,0.545,0.515,0.892,0.641,0.541,0.758,0.461,0.433,0.636,0.512,0.47,0.541,0.468,0.51,0.46,0.648,0.912,0.47,0.594,0.609,0.548,0.558,0.568,0.442,0.468,0.511,0.495,0.625,0.564,0.513,0.528,0.52,0.478,0.471,0.533,0.413,0.569,0.61,0.484,0.426,0.514,0.535,0.668,0.365,0.358,0.376,0.751,0.339,0.952,0.509,0.66,0.508,0.53,0.478,0.409,0.443,0.574,0.52,0.506
-4073,Not Epileptic,F,42.0,0.8,1.8,1.0,1.3,2.2,1.3,2.0,1.3,1.3,0.8,0.2,2.4,1.3,0.6,1.5,5.0,7.3,0.7,2.6,3.3,1.1,1.8,2.1,3.6,2.3,2.8,3.1,3.0,2.3,2.5,1.2,0.7,0.3,1.9,0.3,0.7,3.2,2.6,0.2,0.4,1.1,3.6,2.0,2.2,3.6,0.6,0.4,0.7,1.2,0.8,0.5,2.2,1.0,1.8,0.3,2.0,0.7,1.3,1.1,0.7,0.4,1.2,0.6,0.7,1.8,0.9,2.3,1.1,0.6,0.6,0.8,1.3,3.6,0.3,6,17,10,9,18,13,14,15,14,7,4.0,22,15,7,18,58,63,6,32,28,10,24,18,34,34,33,34,25,20,26,16,6,3,23,2,6,41,36,1,2,7,28,23,17,20,5,2,5,6,9,4,17,8,12,2,14,5,14,9,7,3,10,5,6,18,17,16,6,3,6,6,10,29,2,0.043,0.026,0.025,0.028,0.043,0.027,0.036,0.03,0.043,0.036,0.037,0.04,0.032,0.032,0.033,0.035,0.032,0.034,0.03,0.041,0.022,0.031,0.041,0.045,0.03,0.032,0.036,0.04,0.031,0.033,0.044,0.053,0.03,0.03,0.014,0.021,0.033,0.034,0.018,0.024,0.025,0.048,0.038,0.03,0.031,0.017,0.023,0.015,0.021,0.017,0.036,0.029,0.029,0.023,0.013,0.021,0.014,0.02,0.026,0.028,0.018,0.025,0.04,0.024,0.027,0.026,0.023,0.022,0.017,0.022,0.027,0.023,0.017,0.019,1269,3825,1848,2001,1921,2274,2381,2597,1740,1353,421.0,2249,3278,1199,3264,9271,15753,1912,4481,3461,3921,3444,2142,5104,5083,5992,4477,3820,4698,4285,2345,605,646,4864,1543,1657,6836,6120,300,563,1242,2437,6165,1633,2873,990,600,1639,1984,1667,501,3165,1339,3085,519,2905,1371,2833,1088,1044,793,2053,483,921,2135,944,3205,1649,1008,1258,1084,1725,7805,542,0.136,0.121,0.126,0.12,0.15,0.124,0.124,0.138,0.162,0.151,0.131,0.157,0.142,0.144,0.146,0.149,0.132,0.131,0.135,0.161,0.099,0.131,0.151,0.139,0.139,0.143,0.137,0.125,0.114,0.14,0.141,0.126,0.119,0.14,0.076,0.115,0.137,0.149,0.103,0.118,0.12,0.145,0.15,0.137,0.128,0.104,0.111,0.086,0.1,0.115,0.159,0.13,0.122,0.104,0.099,0.109,0.091,0.117,0.13,0.134,0.103,0.129,0.166,0.119,0.132,0.145,0.112,0.111,0.102,0.134,0.12,0.116,0.098,0.111,361,1190,673,753,855,821,807,842,533,353,147.0,1120,805,283,872,2734,4137,381,1387,1488,880,1035,851,1464,1414,1705,1454,1292,1300,1269,633,269,225,1151,389,532,1842,1510,190,256,679,1200,1144,1129,1768,502,253,750,890,681,234,1379,608,1363,284,1416,686,1153,612,490,360,763,262,493,1175,601,1627,754,456,419,539,836,3324,225,3.341,2.912,2.772,2.636,2.072,2.395,2.385,2.808,2.711,2.917,2.378,1.779,2.95,2.908,2.733,2.654,3.045,3.781,2.751,2.071,3.404,2.509,2.204,2.688,2.737,2.832,2.437,2.293,2.772,2.636,2.796,2.246,2.426,2.987,3.363,2.823,2.916,3.021,1.875,2.744,2.349,1.824,3.598,1.728,1.853,2.108,2.74,2.734,2.693,2.638,2.11,2.316,2.369,2.331,2.261,2.254,2.352,2.534,1.975,2.201,2.282,2.657,2.143,2.177,2.033,1.901,2.265,2.595,2.491,3.243,2.439,2.309,2.458,2.67,0.718,0.558,0.368,0.439,0.547,0.491,0.635,0.481,0.503,0.696,0.764,0.509,0.534,0.578,0.521,0.605,0.552,0.824,0.63,0.638,0.746,0.592,0.675,0.803,0.619,0.547,0.631,0.555,0.621,0.515,0.658,0.826,0.405,0.617,0.643,0.46,0.7,0.655,0.318,0.436,0.464,0.517,0.76,0.527,0.588,0.402,0.453,0.631,0.458,0.51,0.45,0.434,0.42,0.437,0.423,0.452,0.41,0.594,0.398,0.413,0.434,0.588,0.467,0.459,0.536,0.55,0.436,0.344,0.407,0.863,0.351,0.566,0.401,0.53
-4074,Not Epileptic,M,27.0,1.1,2.3,1.0,1.4,1.8,2.1,1.9,1.7,1.2,0.7,0.3,3.7,1.4,0.7,1.8,5.7,10.4,0.8,2.3,4.4,1.4,3.4,2.1,6.0,2.7,3.3,3.9,3.4,2.6,3.6,1.6,1.4,0.4,2.3,0.6,0.4,3.1,3.4,0.2,0.2,1.1,3.0,2.5,3.5,3.4,0.7,0.3,1.1,1.2,0.9,0.6,2.3,1.8,2.1,0.2,3.6,0.6,2.5,1.1,2.0,0.7,2.3,0.4,0.6,2.9,2.1,3.1,2.3,0.8,0.5,1.5,1.8,4.5,0.4,9,21,9,9,19,21,17,15,14,8,4.0,36,17,9,25,65,102,6,28,47,16,46,21,60,35,37,52,32,20,44,21,10,4,28,4,2,36,45,1,1,7,36,23,26,19,5,2,7,6,10,3,20,18,14,1,27,6,20,8,16,5,18,2,5,24,24,25,15,7,4,10,11,36,4,0.037,0.021,0.013,0.021,0.029,0.027,0.027,0.022,0.038,0.033,0.034,0.033,0.026,0.038,0.031,0.028,0.03,0.032,0.029,0.03,0.025,0.03,0.027,0.047,0.028,0.027,0.026,0.032,0.02,0.028,0.035,0.042,0.024,0.024,0.013,0.007,0.029,0.027,0.015,0.014,0.025,0.028,0.035,0.023,0.024,0.014,0.014,0.018,0.016,0.017,0.021,0.022,0.025,0.017,0.019,0.019,0.018,0.024,0.018,0.022,0.023,0.03,0.023,0.015,0.021,0.029,0.024,0.022,0.018,0.017,0.028,0.019,0.017,0.019,1630,5194,2471,3011,2512,3547,3289,3744,1954,1894,733.0,4190,3343,1333,3963,11626,22885,2638,4549,6512,4761,6428,3543,6938,6719,7242,7844,4846,7405,8107,3151,1664,1134,7198,2805,1915,7762,9059,404,561,1641,3695,6046,4277,3827,1570,855,2410,2458,2303,626,3820,3525,4071,248,5404,1321,3794,1475,2853,1161,3187,577,1134,4492,1631,4180,4165,1754,804,2092,3039,9584,540,0.136,0.112,0.099,0.102,0.131,0.132,0.097,0.119,0.144,0.14,0.116,0.13,0.126,0.142,0.143,0.129,0.128,0.108,0.135,0.133,0.11,0.133,0.101,0.143,0.126,0.127,0.123,0.112,0.096,0.127,0.129,0.129,0.084,0.121,0.083,0.055,0.125,0.135,0.096,0.086,0.115,0.128,0.136,0.111,0.108,0.091,0.09,0.09,0.09,0.106,0.11,0.111,0.117,0.095,0.122,0.108,0.1,0.117,0.101,0.112,0.108,0.129,0.107,0.107,0.112,0.136,0.116,0.11,0.102,0.111,0.122,0.102,0.096,0.134,494,1667,984,1006,969,1239,1048,1138,514,392,201.0,1670,871,308,1056,3193,5715,470,1286,2376,1048,1938,1277,2077,1720,1995,2495,1624,1847,2226,807,566,331,1458,686,730,1861,1973,204,258,741,1678,1262,2214,2174,752,354,1008,1125,844,402,1706,1201,1857,125,2730,630,1721,938,1344,492,1180,285,589,2083,967,2021,1582,781,439,871,1444,4073,298,3.256,2.814,2.523,2.922,2.315,2.364,2.617,2.984,2.755,3.508,3.014,2.077,3.003,2.976,2.848,2.799,3.116,3.934,2.862,2.229,3.276,2.553,2.331,2.598,2.879,2.912,2.449,2.339,3.057,2.731,2.855,2.847,2.627,3.315,3.684,2.515,3.023,3.169,2.24,2.339,2.734,1.951,3.418,2.034,2.012,2.285,2.886,3.011,2.759,2.9,1.806,2.307,2.503,2.464,2.068,2.254,2.359,2.393,1.77,2.177,2.285,2.745,2.174,2.227,2.286,2.039,2.233,2.628,2.55,2.2,2.62,2.477,2.583,2.375,0.581,0.534,0.419,0.526,0.647,0.613,0.559,0.56,0.614,0.659,0.578,0.58,0.41,0.591,0.55,0.601,0.631,0.747,0.549,0.616,0.826,0.627,0.686,0.768,0.565,0.596,0.605,0.645,0.592,0.52,0.666,1.087,0.493,0.676,0.646,0.388,0.775,0.638,0.312,0.468,0.483,0.516,0.684,0.602,0.643,0.529,0.431,0.684,0.446,0.481,0.363,0.374,0.696,0.354,0.553,0.455,0.375,0.499,0.418,0.444,0.477,0.662,0.477,0.733,0.483,0.724,0.403,0.451,0.372,0.516,0.504,0.477,0.424,0.398
-4075,Not Epileptic,F,22.0,0.6,2.2,0.6,0.9,1.9,1.9,2.1,1.3,1.4,0.7,0.2,3.0,1.5,0.8,1.7,5.9,10.1,0.8,4.3,5.1,1.9,3.7,1.9,3.6,3.3,4.7,4.9,3.3,3.7,4.6,1.4,0.8,0.6,3.2,0.3,0.9,5.7,5.9,0.2,0.2,1.0,3.8,4.2,3.3,3.0,0.9,0.4,0.7,0.9,1.2,0.8,1.4,1.8,2.1,0.2,2.7,0.9,1.5,1.1,0.9,0.6,1.0,0.2,0.6,2.2,1.4,3.2,1.4,0.9,0.4,0.7,1.7,3.6,0.3,4,20,5,8,14,16,14,11,9,6,2.0,24,13,6,16,52,72,5,28,40,23,29,14,33,25,36,39,23,25,30,17,7,5,27,2,6,49,51,1,1,4,25,33,21,17,7,2,4,4,9,5,10,13,13,1,18,7,13,8,7,5,6,1,4,14,13,22,7,6,3,6,12,26,2,0.029,0.028,0.011,0.021,0.05,0.042,0.036,0.031,0.052,0.037,0.031,0.04,0.034,0.049,0.046,0.048,0.041,0.033,0.048,0.041,0.034,0.05,0.038,0.054,0.038,0.048,0.039,0.041,0.035,0.042,0.049,0.045,0.029,0.039,0.013,0.022,0.047,0.044,0.015,0.019,0.024,0.049,0.055,0.027,0.026,0.026,0.02,0.012,0.015,0.026,0.043,0.021,0.03,0.024,0.031,0.026,0.02,0.019,0.037,0.019,0.026,0.027,0.027,0.04,0.026,0.032,0.029,0.024,0.021,0.018,0.02,0.018,0.018,0.026,1500,3855,1856,2199,2269,3102,2257,2781,1805,1180,507.0,2727,3647,1347,2662,10679,17018,2324,4011,4755,3198,4693,1693,5263,5135,6495,5667,3161,5530,5387,1804,928,979,6103,1497,1981,6541,7790,262,443,1536,2591,4611,2897,2889,1481,691,1961,2227,1658,489,2616,2190,4208,270,3503,1439,2999,1063,1239,1224,1995,442,815,2979,1041,4005,2045,1451,810,1423,2577,8266,505,0.11,0.122,0.087,0.098,0.156,0.135,0.121,0.119,0.145,0.138,0.118,0.137,0.14,0.138,0.155,0.143,0.13,0.119,0.154,0.142,0.112,0.164,0.14,0.149,0.136,0.153,0.129,0.128,0.137,0.137,0.15,0.131,0.118,0.141,0.068,0.097,0.142,0.142,0.1,0.109,0.106,0.146,0.147,0.119,0.117,0.111,0.1,0.079,0.088,0.128,0.166,0.099,0.118,0.101,0.116,0.12,0.107,0.105,0.146,0.112,0.109,0.116,0.11,0.127,0.116,0.134,0.121,0.108,0.109,0.106,0.11,0.108,0.096,0.13,356,1302,741,765,653,911,901,798,411,299,150.0,1290,787,290,721,2292,4233,434,1520,2103,938,1314,826,1284,1529,1773,2070,1285,1626,1924,536,369,340,1392,406,635,2160,2289,175,186,677,1249,1315,1894,1822,663,280,874,939,731,318,1167,914,1617,117,1724,722,1504,572,708,444,604,151,315,1363,670,1866,899,708,336,661,1475,3470,217,3.771,2.722,2.362,2.819,2.402,2.745,2.084,2.911,2.953,3.309,3.112,1.892,3.143,2.973,2.606,2.956,2.926,3.729,2.388,2.008,2.694,2.608,1.956,2.73,2.604,2.834,2.265,1.999,2.564,2.305,2.508,2.703,2.349,3.002,3.064,2.762,2.384,2.548,1.747,2.724,2.799,1.945,2.559,1.74,1.87,2.19,3.121,2.581,2.983,2.639,1.924,2.182,2.178,2.426,2.484,2.343,2.271,2.337,2.131,2.022,2.436,2.73,2.543,2.51,2.475,2.062,2.238,2.506,2.401,2.515,2.415,2.265,2.599,2.653,0.93,0.691,0.53,0.609,0.852,0.705,0.587,0.76,0.848,0.535,0.929,0.463,0.769,0.71,0.775,0.859,0.753,0.755,0.755,0.619,0.891,0.736,0.475,0.837,0.65,0.656,0.537,0.55,0.623,0.695,0.887,0.799,0.53,0.778,1.032,0.595,0.852,0.737,0.398,0.613,0.763,0.56,0.96,0.406,0.567,0.486,0.654,0.84,0.545,0.652,0.431,0.767,0.748,0.706,0.39,0.591,0.49,0.535,0.555,0.471,0.55,0.888,0.859,0.853,0.705,0.597,0.475,0.538,0.615,0.733,0.622,0.508,0.623,0.771
-4076,Not Epileptic,M,52.0,0.7,2.1,0.9,1.1,1.6,2.0,2.2,1.4,1.5,0.5,0.2,2.7,1.0,0.5,1.1,6.2,8.9,0.9,2.4,6.6,1.4,3.7,2.1,3.0,2.8,3.4,2.4,2.2,3.0,2.8,1.5,1.7,0.3,2.2,0.3,0.6,2.9,3.3,0.2,0.2,1.2,4.0,2.3,5.2,3.5,0.7,0.6,0.9,1.2,0.9,0.7,2.3,1.3,2.6,0.2,2.5,0.7,1.7,2.1,1.0,0.9,1.7,0.5,0.5,2.6,1.3,2.6,1.1,1.8,0.7,1.5,1.6,4.1,0.3,7,20,6,7,14,16,17,11,13,5,2.0,24,9,6,11,58,70,8,23,44,13,40,15,28,33,32,24,20,23,32,15,10,3,21,2,4,27,34,1,1,6,31,21,34,17,5,3,5,6,9,6,16,10,13,1,19,7,15,15,8,7,14,2,4,19,12,18,7,11,6,10,13,31,3,0.03,0.023,0.015,0.02,0.029,0.034,0.033,0.026,0.041,0.028,0.02,0.029,0.022,0.031,0.023,0.033,0.03,0.033,0.029,0.038,0.023,0.029,0.029,0.034,0.027,0.03,0.024,0.022,0.026,0.026,0.041,0.053,0.021,0.028,0.014,0.015,0.036,0.028,0.012,0.014,0.027,0.033,0.035,0.039,0.023,0.016,0.024,0.014,0.019,0.027,0.032,0.025,0.028,0.024,0.027,0.021,0.02,0.019,0.026,0.019,0.016,0.029,0.028,0.019,0.025,0.029,0.018,0.016,0.031,0.023,0.029,0.021,0.016,0.017,1526,4613,1993,2331,2853,3308,2730,2986,2324,1224,573.0,2899,3161,1363,2665,10682,17847,2369,4436,5398,4129,6600,2385,6208,5674,6574,5120,3834,5971,5456,2431,1141,911,6928,1855,1805,5347,7303,442,541,1268,3861,5950,3018,3841,1392,912,2242,2137,1339,618,3027,1826,3890,390,3780,1160,3085,2325,1437,1725,2591,415,880,3309,1170,4254,2246,1725,1357,1772,2402,9759,479,0.125,0.112,0.099,0.102,0.116,0.138,0.102,0.114,0.147,0.126,0.093,0.127,0.111,0.131,0.121,0.14,0.125,0.131,0.128,0.136,0.1,0.127,0.119,0.13,0.123,0.134,0.11,0.097,0.104,0.131,0.132,0.136,0.106,0.119,0.072,0.085,0.126,0.12,0.082,0.086,0.119,0.134,0.131,0.151,0.108,0.091,0.112,0.08,0.098,0.129,0.14,0.116,0.118,0.108,0.137,0.11,0.106,0.108,0.125,0.101,0.09,0.126,0.13,0.116,0.117,0.117,0.1,0.095,0.128,0.113,0.135,0.117,0.092,0.113,396,1511,857,857,1000,1036,1025,950,669,364,197.0,1537,770,320,754,3250,5101,487,1331,2592,1028,2186,1056,1534,1739,1958,1681,1460,1750,1825,598,562,322,1342,439,573,1482,1872,234,248,682,1867,1234,2214,2305,733,394,1046,985,585,387,1487,857,1661,132,1871,611,1543,1268,807,864,972,216,415,1647,660,2195,1025,934,532,781,1268,4066,227,3.337,2.911,2.527,2.638,2.425,2.724,2.207,2.781,2.621,3.231,2.687,1.73,3.023,2.985,2.731,2.605,2.847,3.629,2.787,1.895,3.138,2.38,1.97,2.937,2.543,2.787,2.428,2.126,2.663,2.405,2.815,2.199,2.384,3.526,3.503,2.829,2.73,2.859,2.147,2.742,2.443,1.869,3.362,1.663,1.921,2.075,2.743,2.682,2.728,2.526,1.91,2.264,2.34,2.437,3.434,2.231,2.15,2.283,2.118,1.97,2.177,2.735,2.392,2.343,2.164,2.118,2.131,2.514,2.181,2.924,2.458,2.292,2.535,2.453,0.614,0.571,0.376,0.489,0.599,0.59,0.592,0.557,0.552,0.453,0.802,0.437,0.37,0.392,0.519,0.498,0.569,0.689,0.58,0.636,0.781,0.498,0.514,0.676,0.545,0.535,0.523,0.545,0.561,0.578,0.694,0.916,0.72,0.601,0.77,0.581,0.734,0.614,0.337,0.431,0.505,0.604,0.771,0.602,0.588,0.441,0.609,0.589,0.524,0.52,0.458,0.411,0.493,0.548,0.479,0.477,0.43,0.573,0.482,0.397,0.4,0.716,0.361,0.687,0.583,0.663,0.482,0.414,0.518,0.89,0.405,0.391,0.577,0.561
-4077,Not Epileptic,F,42.0,1.1,1.9,1.0,0.9,1.2,1.6,1.5,1.8,1.5,0.8,0.3,2.6,1.4,0.4,1.5,5.9,8.6,1.1,2.3,2.9,1.1,3.1,2.0,2.8,3.2,2.8,3.0,2.9,3.5,2.9,2.1,1.1,0.7,2.0,0.4,0.9,2.8,3.6,0.2,0.3,1.1,3.3,1.7,1.7,3.2,0.7,0.4,0.8,1.3,0.7,0.4,2.4,1.2,2.6,0.2,2.1,0.9,1.4,0.9,1.7,0.5,1.5,0.6,0.6,2.1,1.2,2.7,1.8,0.8,1.0,1.0,1.2,3.9,0.3,7,18,8,8,12,18,16,16,14,8,5.0,23,19,6,19,66,77,10,29,31,9,40,19,33,37,37,39,29,28,38,21,9,11,29,3,8,34,45,1,3,5,32,18,17,18,8,2,5,6,4,3,17,8,17,2,17,7,10,7,12,4,15,3,6,16,17,18,14,6,8,7,8,31,3,0.042,0.023,0.02,0.022,0.032,0.023,0.029,0.032,0.052,0.029,0.036,0.031,0.032,0.027,0.032,0.036,0.031,0.036,0.026,0.03,0.026,0.029,0.031,0.036,0.034,0.031,0.03,0.031,0.034,0.03,0.046,0.047,0.028,0.031,0.012,0.016,0.033,0.037,0.018,0.021,0.02,0.034,0.031,0.022,0.024,0.019,0.015,0.013,0.017,0.018,0.024,0.024,0.028,0.021,0.02,0.016,0.033,0.013,0.017,0.021,0.022,0.023,0.055,0.018,0.022,0.019,0.021,0.021,0.017,0.032,0.018,0.019,0.018,0.014,1614,4176,2546,2397,2015,4212,3449,3491,2064,1859,747.0,3597,3374,954,3220,11737,19155,2337,5546,4650,3930,6894,3494,5704,6372,6539,6174,5599,6936,6617,2804,1157,1446,5743,2076,2285,6150,8228,337,603,1744,4233,5573,2171,3700,1329,775,2023,2371,1385,561,3078,1591,4701,330,3771,1202,3518,1509,2275,704,2620,419,1017,3327,1455,4021,3056,1684,1194,1850,2279,7670,710,0.127,0.112,0.1,0.099,0.131,0.122,0.109,0.136,0.177,0.131,0.131,0.146,0.144,0.134,0.139,0.149,0.131,0.136,0.12,0.142,0.1,0.125,0.137,0.133,0.129,0.14,0.128,0.122,0.118,0.14,0.165,0.12,0.116,0.129,0.066,0.099,0.13,0.149,0.095,0.117,0.101,0.128,0.127,0.12,0.108,0.113,0.093,0.082,0.091,0.103,0.123,0.119,0.121,0.101,0.132,0.104,0.137,0.089,0.102,0.11,0.116,0.122,0.21,0.111,0.112,0.111,0.115,0.108,0.093,0.163,0.104,0.102,0.1,0.089,476,1380,854,823,715,1179,983,1022,514,442,213.0,1462,967,247,947,3108,4952,523,1453,1708,841,1768,1079,1619,1721,1859,1903,1693,1833,1806,779,434,452,1280,546,843,1544,1909,194,270,819,1625,1054,1346,2122,619,341,860,1107,537,247,1527,763,2007,157,1951,449,1603,792,1144,379,1011,183,535,1510,959,1794,1334,742,466,857,1050,3394,361,3.478,2.666,2.777,2.742,2.409,2.66,2.777,2.933,3.183,3.234,2.882,2.145,2.688,2.729,2.631,2.777,3.054,3.565,2.918,2.346,3.269,2.788,2.549,2.794,2.715,2.77,2.474,2.531,2.873,2.711,2.778,2.686,2.592,3.059,3.329,2.589,2.86,3.103,1.843,2.292,2.749,2.195,3.472,1.812,1.986,2.281,2.822,2.887,2.638,3.034,2.612,2.134,2.178,2.482,2.623,2.135,2.792,2.509,2.069,2.167,2.364,2.612,2.038,2.233,2.459,1.855,2.416,2.434,2.546,2.612,2.453,2.409,2.373,2.381,0.819,0.573,0.544,0.467,0.697,0.786,0.684,0.499,0.679,0.649,0.818,0.57,0.48,0.63,0.543,0.668,0.608,0.709,0.572,0.73,0.978,0.58,0.622,0.726,0.62,0.537,0.619,0.656,0.654,0.667,0.572,0.966,0.329,0.608,0.72,0.528,0.784,0.666,0.297,0.457,0.609,0.645,0.72,0.564,0.584,0.412,0.399,0.774,0.443,0.474,0.509,0.477,0.443,0.423,0.288,0.462,0.625,0.42,0.409,0.351,0.357,0.87,0.682,0.592,0.518,0.547,0.512,0.372,0.424,0.712,0.467,0.489,0.45,0.369
-4078,Not Epileptic,M,27.0,0.7,1.8,0.8,1.1,1.8,1.8,1.1,1.2,1.3,0.7,0.2,3.1,1.3,0.8,1.3,5.2,7.8,0.9,2.8,5.2,1.5,2.4,1.7,4.0,3.0,2.9,5.7,2.4,2.6,2.9,2.1,1.3,0.4,2.0,0.3,0.4,3.4,3.2,0.2,0.3,1.0,3.4,2.4,3.5,2.2,0.4,0.4,0.9,1.4,0.8,0.7,2.0,2.2,2.6,0.4,2.7,0.6,1.9,1.4,1.0,1.3,1.6,0.4,0.7,2.2,1.6,2.8,1.0,0.8,0.5,1.1,1.6,4.3,0.4,5,17,7,8,17,21,12,15,16,10,4.0,29,17,10,16,64,84,8,32,52,19,34,18,48,43,37,58,26,31,36,20,10,5,30,1,2,38,40,2,2,6,33,27,31,12,3,2,6,8,6,4,17,15,21,3,22,5,20,14,9,12,15,2,5,16,20,21,7,5,4,7,11,31,3,0.034,0.023,0.016,0.023,0.034,0.028,0.022,0.026,0.048,0.029,0.038,0.035,0.03,0.026,0.024,0.027,0.028,0.037,0.032,0.038,0.028,0.029,0.03,0.04,0.028,0.026,0.036,0.027,0.03,0.026,0.048,0.047,0.03,0.029,0.009,0.012,0.033,0.034,0.015,0.014,0.021,0.036,0.038,0.029,0.015,0.011,0.017,0.016,0.017,0.022,0.026,0.023,0.029,0.02,0.011,0.016,0.02,0.018,0.023,0.019,0.028,0.024,0.025,0.019,0.023,0.022,0.02,0.016,0.015,0.015,0.024,0.019,0.018,0.015,1374,4714,2363,2643,2473,3418,3588,3062,2176,1948,715.0,3458,3496,1884,4172,12224,20752,2584,6085,6655,3845,5215,2199,7188,7631,8028,9113,4931,7217,7105,2836,1174,1055,6143,1864,1767,7237,9407,607,591,1548,4333,6443,4251,4003,1284,972,1827,2764,1524,838,3761,2707,5827,794,4703,1615,4446,1973,1435,2153,3307,452,1188,3310,1583,4794,2296,1594,1262,1474,2755,9063,612,0.119,0.107,0.1,0.107,0.148,0.136,0.095,0.124,0.147,0.149,0.126,0.138,0.138,0.124,0.122,0.134,0.128,0.137,0.125,0.156,0.104,0.133,0.122,0.146,0.13,0.126,0.139,0.105,0.118,0.124,0.138,0.13,0.12,0.131,0.052,0.066,0.134,0.137,0.089,0.107,0.102,0.14,0.147,0.125,0.091,0.082,0.089,0.087,0.097,0.106,0.12,0.11,0.124,0.097,0.085,0.099,0.102,0.1,0.128,0.103,0.122,0.119,0.124,0.109,0.118,0.107,0.104,0.094,0.095,0.094,0.121,0.105,0.097,0.108,382,1442,816,812,869,1163,911,903,597,431,189.0,1485,848,442,1002,3385,5047,522,1609,2537,869,1476,932,1832,1885,2083,2868,1594,1618,1995,857,481,302,1360,536,557,1797,1958,315,267,784,1711,1318,2124,2107,576,375,827,1192,581,442,1537,1114,2227,441,2422,633,1881,953,815,728,1156,232,554,1452,1087,2202,951,699,542,703,1270,3699,324,3.386,3.09,2.901,2.895,2.407,2.491,2.919,2.916,2.752,3.19,2.716,2.029,2.973,3.002,2.954,2.739,3.1,3.684,2.902,2.209,3.024,2.69,2.0,2.885,2.926,2.813,2.463,2.361,3.142,2.656,2.553,2.393,2.689,3.148,3.063,2.813,2.929,3.347,2.27,2.721,2.43,2.233,3.451,2.092,2.177,2.383,3.065,2.802,2.842,2.866,2.19,2.51,2.5,2.602,2.247,2.171,2.538,2.543,2.13,1.953,2.427,2.649,2.149,2.455,2.439,1.743,2.38,2.66,2.567,2.815,2.389,2.651,2.612,2.362,0.833,0.535,0.513,0.599,0.732,0.622,0.714,0.571,0.706,0.783,0.865,0.555,0.548,0.454,0.61,0.654,0.705,0.947,0.67,0.653,0.755,0.518,0.51,0.928,0.647,0.711,0.634,0.651,0.538,0.696,0.82,0.947,0.399,0.706,0.776,0.443,0.722,0.712,0.416,0.871,0.451,0.662,0.786,0.601,0.701,0.475,0.559,0.6,0.476,0.559,0.323,0.456,0.546,0.454,0.292,0.458,0.582,0.464,0.421,0.337,0.559,0.702,0.444,0.71,0.53,0.654,0.456,0.337,0.608,0.608,0.573,0.612,0.514,0.327
-4079,Not Epileptic,F,47.0,1.0,2.5,1.3,1.8,2.2,1.8,1.3,1.5,1.2,0.8,0.4,2.0,1.1,0.3,1.0,5.9,8.3,0.8,1.6,4.1,0.9,3.0,1.3,3.1,3.0,2.7,4.2,2.4,2.7,2.2,1.5,0.8,0.5,2.5,0.4,0.6,2.9,2.5,0.1,0.2,1.0,3.1,1.7,2.0,2.1,0.9,0.4,0.8,1.1,1.0,0.5,2.1,1.5,3.1,0.3,2.1,0.7,1.2,1.0,0.9,0.6,2.0,0.4,0.6,1.5,1.6,3.3,1.4,1.2,1.0,1.1,1.0,5.3,0.4,7,25,13,12,19,16,18,19,13,7,4.0,22,14,6,14,65,84,8,18,40,8,35,17,44,35,38,51,28,31,33,16,6,6,28,3,6,35,39,1,1,6,35,21,16,11,7,2,6,5,10,3,17,13,24,2,17,7,10,7,8,4,17,2,4,11,25,32,10,7,11,7,7,46,4,0.048,0.023,0.022,0.031,0.043,0.033,0.025,0.031,0.037,0.046,0.036,0.037,0.03,0.019,0.033,0.035,0.036,0.032,0.03,0.04,0.022,0.039,0.034,0.042,0.039,0.03,0.036,0.032,0.034,0.032,0.042,0.033,0.029,0.031,0.014,0.02,0.038,0.03,0.012,0.02,0.021,0.04,0.035,0.03,0.017,0.022,0.024,0.013,0.017,0.024,0.021,0.021,0.031,0.03,0.027,0.019,0.015,0.016,0.025,0.022,0.025,0.033,0.021,0.021,0.02,0.022,0.024,0.021,0.019,0.023,0.031,0.016,0.02,0.021,1295,5011,2519,2654,2449,3288,2781,3316,1978,1502,714.0,2394,2703,930,2538,10439,16405,1961,3949,5038,3408,4526,2143,6233,4210,5639,5107,3655,5677,4546,2200,1271,922,6939,1708,1546,6366,7153,401,387,1251,3210,4643,2109,3251,1198,700,2606,2033,1951,485,3119,1527,4065,342,2882,1436,2779,1040,1140,696,2679,361,892,2353,2291,4945,2434,1780,1574,1016,1854,9413,875,0.141,0.114,0.108,0.126,0.156,0.154,0.108,0.143,0.159,0.159,0.154,0.15,0.133,0.119,0.128,0.142,0.141,0.12,0.126,0.158,0.089,0.147,0.121,0.141,0.145,0.141,0.129,0.118,0.114,0.144,0.144,0.105,0.139,0.135,0.078,0.104,0.137,0.13,0.088,0.099,0.109,0.152,0.14,0.12,0.091,0.122,0.11,0.082,0.091,0.125,0.113,0.111,0.139,0.128,0.136,0.11,0.099,0.098,0.132,0.126,0.116,0.136,0.12,0.107,0.11,0.125,0.115,0.103,0.103,0.135,0.135,0.104,0.107,0.138,356,1746,954,928,917,894,958,1004,569,335,202.0,1046,758,216,690,3055,4481,418,926,1869,709,1377,774,1597,1286,1699,2132,1302,1495,1408,637,457,300,1452,427,547,1661,1669,207,182,667,1387,972,1273,1876,617,289,944,964,638,312,1536,766,1776,173,1614,709,1248,613,666,398,1009,202,451,1143,1072,2402,1107,888,642,563,939,4047,330,3.512,2.599,2.551,2.644,2.427,2.683,2.267,2.764,2.617,3.198,2.944,1.948,2.734,2.851,2.656,2.554,2.74,3.59,3.134,2.216,3.221,2.517,2.229,3.028,2.432,2.607,2.003,2.194,2.744,2.506,2.622,2.814,2.558,3.26,3.538,2.363,2.927,3.052,2.373,2.28,2.362,1.978,3.512,1.848,1.952,2.107,2.851,3.159,2.545,2.924,1.923,2.178,2.169,2.422,2.066,1.949,2.349,2.505,1.858,1.796,2.17,2.819,2.028,2.36,2.246,2.279,2.083,2.371,2.281,2.603,2.148,2.165,2.348,3.066,0.734,0.473,0.446,0.52,0.696,0.773,0.548,0.561,0.606,0.541,0.911,0.48,0.483,0.558,0.611,0.616,0.674,0.699,0.582,0.576,0.829,0.59,0.663,0.68,0.694,0.585,0.534,0.516,0.564,0.664,0.526,0.885,0.416,0.758,0.556,0.461,0.708,0.695,0.328,0.431,0.427,0.595,0.722,0.602,0.598,0.4,0.453,0.871,0.43,0.494,0.458,0.365,0.412,0.495,0.487,0.365,0.416,0.385,0.433,0.392,0.331,0.67,0.271,0.579,0.567,0.572,0.474,0.386,0.389,0.519,0.397,0.528,0.504,0.56
-4080,Not Epileptic,F,37.0,0.6,2.2,1.1,1.2,1.2,2.0,1.3,1.6,1.0,0.4,0.3,2.2,1.1,0.5,1.0,5.0,6.5,0.7,2.1,5.1,1.4,2.6,1.0,2.9,1.9,3.0,2.5,2.1,2.2,2.9,1.9,1.4,0.5,2.0,0.5,0.4,2.8,2.4,0.0,0.1,1.1,2.2,2.4,2.9,2.8,0.5,0.3,0.6,1.2,0.8,0.5,2.2,0.9,2.0,0.1,1.5,0.8,1.3,1.3,0.9,0.8,1.2,0.3,0.6,1.4,1.3,2.1,1.5,0.6,0.6,0.8,1.5,3.1,0.4,7,24,9,9,18,19,13,18,12,5,4.0,26,13,7,16,62,69,8,24,48,11,32,11,36,25,38,34,28,23,38,20,10,5,29,3,3,36,34,1,1,11,20,24,20,18,4,2,4,7,7,4,19,7,13,0,12,6,14,8,7,9,11,2,5,12,14,18,14,4,6,6,15,28,5,0.038,0.028,0.024,0.026,0.031,0.03,0.026,0.025,0.043,0.028,0.032,0.035,0.022,0.036,0.03,0.035,0.029,0.038,0.033,0.044,0.029,0.036,0.029,0.038,0.033,0.029,0.026,0.029,0.028,0.034,0.049,0.058,0.029,0.03,0.017,0.012,0.037,0.028,0.013,0.012,0.024,0.027,0.04,0.032,0.022,0.014,0.014,0.013,0.017,0.015,0.022,0.023,0.025,0.017,0.042,0.016,0.022,0.022,0.028,0.034,0.021,0.026,0.036,0.03,0.017,0.026,0.017,0.019,0.012,0.025,0.021,0.021,0.016,0.019,1498,4275,2281,1981,1950,3088,2424,3637,1567,1065,749.0,2390,3105,1145,2329,9239,15146,2022,3569,5357,4091,2987,1275,5840,3619,6540,4662,4140,5140,5066,1959,921,904,5715,1880,1138,7129,5605,232,321,1523,1853,5653,2518,3193,1189,726,1868,2017,2004,610,3532,1317,3770,88,2746,1239,2890,1153,896,1259,1979,390,691,2286,1158,3871,2889,1444,998,1200,2189,6864,760,0.13,0.128,0.12,0.122,0.139,0.132,0.117,0.127,0.163,0.125,0.115,0.153,0.111,0.162,0.147,0.149,0.13,0.129,0.157,0.165,0.11,0.124,0.108,0.156,0.127,0.139,0.125,0.125,0.114,0.148,0.167,0.139,0.124,0.13,0.084,0.078,0.145,0.127,0.094,0.099,0.124,0.118,0.156,0.135,0.115,0.096,0.092,0.078,0.098,0.097,0.122,0.113,0.121,0.098,0.204,0.101,0.115,0.113,0.129,0.119,0.11,0.128,0.144,0.154,0.103,0.11,0.103,0.109,0.085,0.129,0.118,0.119,0.094,0.138,392,1373,749,751,780,1075,872,1056,431,257,201.0,1129,858,280,659,2703,4066,372,987,2224,858,1154,614,1448,1094,1925,1727,1314,1358,1527,593,470,294,1192,467,468,1742,1440,127,175,749,1146,1166,1482,1815,559,290,812,980,773,338,1554,637,1684,25,1346,577,1150,715,439,612,742,178,342,1207,715,1855,1264,680,466,597,1110,3002,304,3.605,2.781,2.932,2.523,2.12,2.292,2.315,2.865,2.759,2.999,3.016,1.807,2.747,2.934,2.616,2.622,2.872,3.727,2.872,2.022,3.482,2.12,1.819,2.953,2.554,2.729,2.225,2.422,2.781,2.56,2.466,2.194,2.617,3.15,3.372,2.379,2.968,2.756,2.054,2.146,2.588,1.511,3.402,1.905,1.941,2.268,2.831,2.858,2.595,2.688,2.094,2.241,2.26,2.471,3.696,2.301,2.525,2.635,1.905,2.062,2.201,2.771,2.325,2.304,2.064,2.034,2.208,2.606,2.375,2.329,2.449,2.234,2.37,2.606,0.791,0.646,0.629,0.59,0.611,0.62,0.528,0.483,0.574,0.384,0.851,0.574,0.395,0.648,0.555,0.542,0.554,0.738,0.485,0.686,0.808,0.552,0.461,0.739,0.558,0.545,0.524,0.506,0.502,0.601,0.791,0.839,0.431,0.689,0.689,0.455,0.683,0.644,0.461,0.409,0.477,0.44,0.743,0.567,0.505,0.532,0.505,0.699,0.4,0.503,0.398,0.451,0.408,0.385,0.295,0.436,0.47,0.529,0.374,0.44,0.401,0.556,0.526,0.681,0.466,0.624,0.429,0.423,0.415,0.545,0.604,0.473,0.452,0.358
-4081,Not Epileptic,F,27.0,1.1,2.1,0.7,0.9,1.8,3.1,1.7,2.0,1.7,0.9,0.3,2.7,1.1,0.5,1.6,4.9,7.6,0.6,3.5,5.5,1.4,2.6,2.0,2.6,4.0,3.9,2.8,3.3,3.9,3.1,1.8,0.8,0.5,2.6,0.8,1.4,4.9,4.2,0.1,0.1,1.4,2.5,3.6,3.5,3.1,0.4,0.4,0.8,1.0,1.0,1.1,1.7,1.3,2.5,1.1,2.3,0.7,1.2,0.7,0.9,0.5,0.9,0.4,0.3,1.3,1.0,2.6,1.4,1.4,0.6,0.8,1.9,3.7,0.3,6,18,6,7,14,19,17,14,17,7,3.0,23,10,5,15,53,63,4,25,39,12,23,18,25,35,38,28,28,25,32,21,7,5,28,3,12,48,44,1,1,6,21,26,25,18,4,2,5,5,7,7,14,6,15,50,17,5,11,4,7,2,6,2,3,10,12,19,9,11,6,6,16,29,3,0.043,0.034,0.017,0.022,0.043,0.037,0.033,0.037,0.062,0.04,0.022,0.033,0.028,0.03,0.038,0.035,0.032,0.025,0.039,0.038,0.026,0.033,0.029,0.034,0.037,0.038,0.03,0.036,0.032,0.039,0.056,0.049,0.019,0.031,0.02,0.03,0.039,0.036,0.016,0.016,0.025,0.029,0.049,0.031,0.025,0.017,0.023,0.013,0.016,0.018,0.032,0.021,0.019,0.021,0.029,0.021,0.019,0.018,0.024,0.02,0.018,0.02,0.03,0.02,0.022,0.029,0.024,0.02,0.024,0.022,0.02,0.021,0.017,0.015,1554,4771,1793,1934,2525,3671,2654,3332,2132,1381,659.0,2678,2846,956,2860,11250,17643,2598,3949,4690,2787,3880,2421,6148,6070,6903,5090,3812,5911,5097,1968,835,1249,6309,2215,2478,6869,7630,319,422,1918,2365,4896,3206,3253,1092,818,2370,2585,2344,847,3291,2427,5031,1104,3337,1595,2661,836,1228,822,2037,499,777,2034,1078,3539,2208,1949,1427,1435,2886,8143,594,0.135,0.124,0.093,0.103,0.148,0.138,0.126,0.128,0.19,0.14,0.111,0.127,0.12,0.129,0.143,0.136,0.135,0.094,0.139,0.139,0.104,0.136,0.128,0.135,0.144,0.119,0.132,0.121,0.108,0.151,0.14,0.128,0.096,0.122,0.08,0.102,0.126,0.128,0.089,0.092,0.111,0.126,0.141,0.123,0.115,0.096,0.11,0.083,0.083,0.094,0.151,0.103,0.098,0.1,0.126,0.111,0.093,0.099,0.12,0.111,0.104,0.104,0.129,0.1,0.109,0.135,0.118,0.1,0.123,0.121,0.104,0.108,0.091,0.09,419,1202,647,700,725,1306,928,913,499,356,209.0,1314,725,262,774,2635,4158,401,1371,2261,830,1276,1102,1393,1729,1769,1644,1397,1789,1516,539,335,420,1501,660,771,2049,1904,170,198,780,1264,1328,1932,1989,478,297,912,1026,902,504,1384,1012,1912,599,1745,681,1290,429,716,388,730,212,315,1044,615,1756,1105,944,450,692,1394,3553,337,3.332,3.323,2.42,2.727,2.565,2.512,2.396,3.097,2.932,3.082,2.902,1.807,2.893,2.552,2.671,3.013,3.291,4.139,2.484,1.878,2.878,2.371,1.995,3.007,2.68,2.925,2.391,2.168,2.545,2.749,2.713,2.485,2.286,2.925,3.102,2.786,2.544,2.914,1.923,2.813,2.922,1.732,2.824,1.929,1.86,2.381,3.427,3.048,2.913,2.826,2.233,2.369,2.469,2.554,2.359,2.196,2.814,2.404,2.214,1.934,2.677,2.607,2.686,2.509,2.056,2.239,2.285,2.355,2.219,3.37,2.344,2.472,2.418,2.103,1.127,0.817,0.691,0.664,0.773,0.595,0.576,0.797,0.595,0.498,0.683,0.52,0.49,0.66,0.596,0.701,0.648,0.871,0.652,0.585,0.825,0.48,0.421,0.76,0.616,0.852,0.544,0.608,0.504,0.676,0.754,1.082,0.485,0.751,0.901,0.714,0.717,0.688,0.586,0.795,0.529,0.433,0.79,0.64,0.497,0.812,0.671,0.811,0.701,0.668,0.366,0.624,0.76,0.718,0.348,0.498,0.721,0.59,0.449,0.393,0.461,0.788,0.669,1.173,0.516,0.563,0.556,0.556,0.554,0.727,0.637,0.62,0.598,0.47
-4082,Not Epileptic,F,52.0,0.9,2.4,0.9,1.2,1.4,2.0,1.5,1.5,1.4,0.5,0.3,2.4,1.4,0.2,1.3,4.9,8.0,0.9,1.9,4.4,1.0,2.4,1.4,3.4,3.3,2.6,4.2,1.7,1.9,3.1,1.7,1.1,0.4,1.8,0.4,0.6,3.0,2.8,0.2,0.3,1.1,3.7,1.7,1.9,2.5,0.6,0.3,0.6,1.2,0.7,0.5,1.4,1.4,2.0,0.3,2.3,0.6,1.6,1.3,1.2,0.5,1.1,0.2,0.5,2.0,1.0,2.1,1.1,1.1,0.4,0.8,1.5,3.7,0.2,7,26,10,12,10,22,14,13,17,6,3.0,25,13,4,17,58,71,8,25,37,8,27,12,40,39,32,42,17,19,40,37,9,4,23,2,4,34,32,1,2,7,29,19,18,16,4,2,4,8,7,5,10,11,13,2,18,6,14,11,10,5,9,1,6,16,18,17,10,6,4,6,10,33,3,0.036,0.023,0.018,0.025,0.029,0.032,0.023,0.03,0.038,0.026,0.037,0.032,0.029,0.024,0.033,0.032,0.028,0.035,0.033,0.037,0.022,0.03,0.028,0.04,0.036,0.028,0.039,0.022,0.024,0.031,0.039,0.047,0.021,0.024,0.013,0.013,0.031,0.028,0.017,0.021,0.023,0.035,0.035,0.026,0.02,0.012,0.016,0.011,0.016,0.02,0.029,0.02,0.023,0.02,0.012,0.017,0.018,0.022,0.028,0.023,0.022,0.02,0.015,0.014,0.024,0.021,0.021,0.022,0.016,0.019,0.023,0.022,0.018,0.012,1278,4221,2033,2296,1694,2663,3256,2529,2027,1328,583.0,2409,2972,707,2273,9743,16745,2150,3636,4226,3114,3369,1995,5465,5207,5874,4919,4090,5519,5750,2637,1013,917,5283,1510,1827,5293,6267,285,503,1260,2207,5007,1706,3155,1286,694,1967,2103,1482,524,2149,1926,3787,607,3324,998,2579,1243,1582,830,2065,428,910,2371,917,2994,1979,1978,673,1282,2193,7284,322,0.128,0.122,0.114,0.12,0.125,0.147,0.106,0.13,0.149,0.13,0.121,0.152,0.142,0.111,0.154,0.142,0.122,0.131,0.142,0.15,0.099,0.121,0.118,0.143,0.138,0.13,0.131,0.101,0.098,0.145,0.122,0.107,0.095,0.122,0.071,0.083,0.127,0.138,0.107,0.108,0.113,0.134,0.135,0.128,0.107,0.084,0.093,0.075,0.098,0.112,0.132,0.106,0.12,0.098,0.095,0.103,0.113,0.117,0.126,0.118,0.122,0.107,0.093,0.105,0.123,0.126,0.115,0.108,0.096,0.112,0.118,0.116,0.101,0.123,425,1585,820,818,792,1063,993,873,738,351,176.0,1310,818,206,669,2707,4856,501,1085,2015,725,1187,814,1651,1614,1719,1908,1222,1427,1783,794,374,351,1268,442,658,1608,1630,162,250,720,1417,1036,1188,1890,682,309,870,1074,601,338,1131,958,1752,309,1924,492,1241,785,795,374,884,243,542,1267,738,1575,931,934,370,588,1071,3312,212,3.003,2.529,2.359,2.598,2.07,2.229,2.568,2.514,2.296,2.891,2.62,1.677,2.751,2.523,2.595,2.682,2.724,3.396,2.712,1.826,3.254,2.239,2.083,2.535,2.501,2.803,2.133,2.493,2.841,2.551,2.417,2.567,2.198,3.017,2.986,2.515,2.466,2.968,2.019,2.086,2.257,1.519,3.316,1.704,1.877,2.036,2.812,2.797,2.449,2.609,1.875,2.066,2.187,2.41,2.398,2.006,2.341,2.338,1.879,2.208,2.419,2.378,2.013,1.947,2.165,1.551,2.197,2.325,2.371,2.132,2.516,2.352,2.381,1.995,0.659,0.563,0.52,0.614,0.447,0.523,0.688,0.436,0.58,0.601,0.833,0.585,0.407,0.447,0.428,0.545,0.584,0.766,0.537,0.627,0.683,0.517,0.585,0.654,0.604,0.466,0.545,0.661,0.585,0.542,0.608,0.827,0.376,0.575,0.492,0.331,0.663,0.578,0.28,0.435,0.417,0.433,0.787,0.59,0.562,0.391,0.428,0.618,0.404,0.586,0.378,0.347,0.414,0.342,0.264,0.395,0.349,0.523,0.317,0.456,0.436,0.447,0.36,0.409,0.483,0.47,0.361,0.352,0.356,0.577,0.351,0.476,0.375,0.372
-4083,Not Epileptic,M,37.0,0.6,2.3,1.3,1.0,1.7,1.6,2.2,1.7,1.6,1.6,0.2,2.8,1.5,0.8,0.9,6.0,8.1,0.8,2.2,4.4,1.5,2.3,2.4,4.4,4.3,2.9,5.2,2.4,2.9,3.3,3.5,1.9,0.4,2.9,0.3,0.9,4.2,3.5,0.2,0.2,0.9,2.9,1.9,2.1,2.7,0.7,0.4,0.7,1.2,0.8,0.6,2.1,1.1,2.3,0.5,2.4,0.8,1.1,0.8,1.0,0.6,1.8,0.2,0.8,1.8,1.4,2.5,1.2,0.9,0.9,1.7,1.4,4.9,0.2,7,25,10,8,21,19,16,14,15,13,3.0,23,15,12,13,66,71,6,26,35,13,31,18,52,43,33,52,24,24,37,38,13,4,35,2,10,49,42,1,1,5,22,17,15,16,6,2,4,7,6,3,21,8,18,4,20,6,9,9,6,6,13,1,7,16,20,20,8,5,10,10,10,35,2,0.031,0.021,0.021,0.022,0.031,0.03,0.038,0.03,0.047,0.047,0.039,0.04,0.025,0.033,0.025,0.029,0.028,0.03,0.032,0.04,0.03,0.027,0.036,0.042,0.036,0.028,0.036,0.027,0.028,0.029,0.058,0.056,0.027,0.031,0.008,0.024,0.042,0.03,0.013,0.021,0.022,0.04,0.031,0.022,0.019,0.019,0.015,0.012,0.016,0.016,0.023,0.023,0.015,0.017,0.014,0.015,0.022,0.015,0.02,0.015,0.026,0.025,0.012,0.023,0.019,0.019,0.018,0.019,0.021,0.023,0.032,0.02,0.017,0.012,1558,5301,2971,2377,2406,2927,3857,3358,2008,1986,519.0,3056,3736,1745,2415,13983,18204,2285,4774,4919,4507,4839,2594,6626,6131,6521,7041,4661,6641,6685,2757,1339,988,7492,2145,2523,7421,9080,330,373,1398,2802,5957,2736,3741,1289,808,2187,2436,1802,597,3952,2259,5443,1027,4650,1183,2902,1240,1882,887,3170,335,1112,2959,1881,4295,2005,1563,1571,1982,2220,9406,633,0.118,0.111,0.105,0.103,0.138,0.138,0.115,0.131,0.149,0.168,0.125,0.147,0.118,0.157,0.118,0.123,0.124,0.103,0.127,0.154,0.116,0.13,0.122,0.148,0.134,0.128,0.133,0.111,0.107,0.134,0.167,0.142,0.122,0.136,0.059,0.111,0.145,0.139,0.106,0.118,0.107,0.134,0.125,0.116,0.098,0.1,0.089,0.078,0.091,0.098,0.125,0.109,0.101,0.096,0.096,0.097,0.115,0.093,0.124,0.092,0.124,0.121,0.095,0.123,0.107,0.113,0.106,0.102,0.099,0.135,0.133,0.115,0.096,0.088,434,1837,977,797,969,980,1064,933,610,550,158.0,1154,1005,445,627,3501,4618,450,1198,1755,937,1361,986,1960,1931,1841,2500,1490,1570,1908,895,537,278,1633,536,751,1934,2062,158,144,681,1135,1153,1492,2095,620,348,975,1141,784,347,1717,1109,2201,548,2317,551,1300,578,979,427,1117,200,510,1488,1110,2120,995,698,614,867,1066,4043,274,3.403,2.738,2.849,2.755,2.218,2.406,2.712,3.062,2.569,2.872,2.622,2.191,2.935,2.896,2.802,2.858,3.076,3.821,2.985,2.33,3.309,2.646,2.216,2.704,2.507,2.87,2.283,2.426,3.023,2.695,2.407,2.665,2.689,3.333,3.497,2.961,2.886,3.109,2.367,2.699,2.531,2.053,3.554,2.097,2.038,2.176,2.95,2.73,2.606,2.619,2.147,2.322,2.228,2.639,2.267,2.197,2.215,2.556,2.212,2.107,2.183,2.92,1.854,2.402,2.143,1.997,2.176,2.38,2.45,2.7,2.489,2.564,2.455,2.569,0.643,0.536,0.56,0.52,0.634,0.653,0.75,0.521,0.626,0.835,0.866,0.671,0.491,0.426,0.481,0.7,0.584,0.701,0.589,0.74,0.884,0.481,0.59,0.708,0.666,0.484,0.628,0.604,0.672,0.668,0.832,0.945,0.518,0.67,0.493,0.481,0.799,0.605,0.346,0.316,0.45,0.703,0.655,0.554,0.569,0.463,0.459,0.545,0.48,0.448,0.552,0.416,0.412,0.418,0.35,0.412,0.433,0.406,0.567,0.376,0.322,0.682,0.342,0.693,0.533,0.764,0.424,0.339,0.448,0.593,0.545,0.627,0.449,0.5
-4084,Not Epileptic,F,55.0,1.3,2.3,1.2,1.1,2.1,1.5,1.5,1.7,1.5,0.6,0.2,3.7,1.1,0.4,1.5,5.8,10.0,0.8,2.6,4.8,1.8,3.4,3.4,5.2,3.9,2.5,3.9,3.2,2.6,2.3,3.2,0.8,0.3,2.1,0.2,0.7,6.5,3.9,0.2,0.2,1.2,2.6,2.4,3.2,3.0,0.6,0.5,0.7,1.4,0.8,0.6,1.9,2.2,2.6,0.2,2.6,1.0,1.5,1.3,1.8,0.8,2.0,0.2,0.4,2.0,1.1,2.4,1.1,0.9,0.4,0.8,1.4,4.9,0.3,11,24,9,9,14,23,13,17,13,6,3.0,38,12,6,22,68,105,7,34,48,60,46,22,55,42,33,41,30,25,32,24,8,4,29,2,6,49,46,1,1,6,23,23,25,18,5,2,5,6,8,5,17,15,17,1,21,8,10,11,12,6,16,3,4,18,16,20,9,7,4,5,11,37,3,0.055,0.027,0.024,0.023,0.039,0.025,0.027,0.028,0.046,0.036,0.032,0.038,0.024,0.03,0.037,0.042,0.039,0.039,0.039,0.036,0.036,0.035,0.044,0.052,0.039,0.029,0.038,0.033,0.025,0.031,0.065,0.033,0.021,0.028,0.011,0.016,0.051,0.037,0.017,0.014,0.024,0.03,0.045,0.024,0.021,0.012,0.022,0.015,0.022,0.023,0.029,0.023,0.034,0.024,0.032,0.016,0.023,0.019,0.034,0.02,0.021,0.03,0.028,0.016,0.019,0.024,0.019,0.015,0.021,0.022,0.025,0.02,0.02,0.014,1572,4009,2072,2224,1783,2842,2770,2976,1285,1164,646.0,3497,2822,868,2847,8522,16505,1989,4441,5843,3490,4497,2070,5579,4691,5394,4564,4462,5549,4646,1760,1211,895,5769,1650,1697,5726,7797,360,410,1302,1901,3633,3531,3820,1295,756,1687,1835,1412,517,2700,1895,3662,201,4277,1542,2648,1428,2250,1162,2752,433,891,3293,1084,3950,2144,1452,801,1024,2325,8241,533,0.177,0.124,0.117,0.111,0.143,0.118,0.095,0.122,0.16,0.147,0.118,0.15,0.114,0.142,0.143,0.158,0.144,0.127,0.146,0.152,0.127,0.126,0.129,0.149,0.144,0.128,0.122,0.108,0.104,0.131,0.162,0.109,0.099,0.124,0.058,0.092,0.155,0.146,0.095,0.094,0.109,0.132,0.131,0.122,0.102,0.087,0.103,0.083,0.108,0.11,0.145,0.114,0.14,0.117,0.159,0.1,0.112,0.104,0.124,0.108,0.112,0.137,0.126,0.096,0.106,0.118,0.105,0.096,0.107,0.127,0.124,0.105,0.103,0.11,432,1473,826,811,865,1155,936,1065,558,315,177.0,1832,856,257,851,2819,5066,437,1233,2357,742,1681,1149,1911,1846,1724,1862,1657,1599,1647,795,469,319,1365,507,662,2022,2085,224,218,715,1238,1009,2131,2240,693,340,780,902,647,333,1454,936,1710,87,2396,761,1215,695,1257,589,1082,202,495,1665,755,1909,1050,735,379,509,1162,3821,323,3.506,2.513,2.455,2.497,1.886,2.064,2.418,2.435,1.945,2.714,2.778,1.73,2.58,2.46,2.542,2.39,2.573,3.556,2.83,2.131,3.292,2.222,1.624,2.241,2.132,2.506,2.025,2.174,2.679,2.259,1.821,2.531,2.224,2.981,3.006,2.427,2.35,2.74,1.813,2.066,2.302,1.536,2.779,1.825,1.901,2.121,2.531,2.478,2.521,2.495,1.85,2.051,2.008,2.336,2.422,2.014,2.355,2.553,2.102,1.971,2.227,2.573,2.026,2.112,2.081,1.891,2.115,2.262,2.264,2.067,2.186,2.231,2.359,2.057,0.865,0.59,0.471,0.493,0.545,0.511,0.662,0.441,0.598,0.742,0.806,0.5,0.41,0.455,0.498,0.518,0.688,0.757,0.762,0.644,0.771,0.626,0.47,0.764,0.547,0.542,0.445,0.557,0.483,0.518,0.633,0.748,0.403,0.608,0.642,0.476,0.724,0.578,0.324,0.38,0.481,0.494,0.791,0.531,0.594,0.45,0.385,0.725,0.477,0.658,0.405,0.422,0.435,0.411,0.4,0.395,0.405,0.451,0.601,0.347,0.402,0.694,0.501,0.581,0.466,0.706,0.44,0.37,0.426,0.708,0.404,0.392,0.416,0.328
-4085,Not Epileptic,F,52.0,0.8,2.4,0.9,1.4,2.0,1.6,1.8,1.9,1.2,0.6,0.3,2.3,1.7,0.5,1.7,4.8,9.7,1.0,2.6,4.8,1.2,2.6,2.1,3.9,3.3,4.2,4.4,4.0,3.3,3.4,1.2,2.3,0.6,1.9,0.6,0.6,4.2,2.6,0.3,0.3,1.2,2.2,3.3,2.2,3.5,1.0,0.7,1.1,1.3,0.8,0.6,3.0,1.7,2.9,0.2,2.6,0.9,1.3,1.0,1.4,0.6,1.5,0.4,0.6,1.6,1.5,3.7,1.6,1.4,0.8,1.3,1.3,4.2,0.3,7,22,7,9,16,17,15,16,11,7,2.0,24,16,6,18,58,94,8,32,48,12,34,22,39,41,47,42,37,28,42,15,21,8,28,3,5,50,32,2,1,8,24,29,21,22,8,3,8,7,7,4,21,14,18,2,21,4,15,6,11,5,15,3,5,16,20,28,11,8,8,8,9,32,3,0.038,0.022,0.015,0.023,0.038,0.027,0.028,0.028,0.037,0.027,0.035,0.029,0.033,0.025,0.034,0.03,0.03,0.032,0.029,0.042,0.027,0.029,0.034,0.038,0.027,0.034,0.032,0.034,0.029,0.025,0.03,0.066,0.027,0.023,0.02,0.012,0.038,0.028,0.017,0.02,0.027,0.039,0.047,0.023,0.023,0.019,0.027,0.018,0.016,0.02,0.022,0.024,0.024,0.022,0.018,0.017,0.022,0.014,0.028,0.021,0.023,0.023,0.02,0.015,0.018,0.032,0.024,0.022,0.023,0.02,0.027,0.017,0.017,0.015,1354,4893,2070,2425,1978,2682,3165,3186,1495,1518,546.0,2304,3355,1160,2838,9797,20285,2525,5292,4130,3423,4481,2554,6182,6459,7212,6521,5045,6406,6476,2353,1706,1091,5841,1875,1962,7564,6925,490,483,1314,2474,5298,2426,3807,1470,841,2188,2228,1391,577,4097,2344,4926,483,4005,1201,2965,935,1594,926,2523,475,1060,2549,1366,4859,2353,1911,1207,1549,1951,8918,576,0.133,0.116,0.095,0.105,0.142,0.133,0.107,0.129,0.141,0.135,0.119,0.134,0.134,0.124,0.144,0.131,0.127,0.119,0.131,0.161,0.103,0.13,0.136,0.129,0.117,0.144,0.122,0.113,0.099,0.124,0.117,0.159,0.129,0.112,0.09,0.077,0.147,0.136,0.087,0.086,0.128,0.143,0.15,0.12,0.113,0.109,0.121,0.093,0.094,0.107,0.125,0.111,0.117,0.104,0.104,0.103,0.107,0.096,0.135,0.113,0.109,0.119,0.115,0.098,0.109,0.135,0.119,0.109,0.107,0.11,0.124,0.111,0.094,0.101,395,1745,926,941,833,963,1028,1099,466,366,149.0,1212,962,289,884,2785,5649,506,1461,1939,823,1399,997,1772,2058,2228,2190,1839,1639,2280,744,658,373,1426,448,750,1969,1680,296,254,702,1061,1289,1524,2260,769,377,977,1098,646,372,1996,1148,2140,209,2184,575,1398,537,988,422,1085,302,621,1411,762,2402,1124,889,612,740,1021,3871,287,3.305,2.558,2.349,2.551,2.128,2.289,2.467,2.565,2.416,3.181,2.873,1.709,2.697,2.841,2.457,2.623,2.777,3.774,2.87,1.939,3.001,2.474,2.243,2.623,2.538,2.678,2.348,2.17,2.832,2.343,2.435,2.751,2.379,2.842,3.666,2.354,2.762,2.934,1.889,2.209,2.288,2.001,3.009,1.73,1.888,2.152,2.867,2.711,2.451,2.377,1.962,2.114,2.011,2.417,2.453,2.01,2.362,2.176,1.898,1.794,2.176,2.337,1.761,2.122,1.975,2.111,2.214,2.491,2.417,2.144,2.356,2.208,2.389,2.331,0.815,0.501,0.348,0.46,0.635,0.691,0.636,0.54,0.631,0.491,0.873,0.474,0.388,0.335,0.496,0.601,0.609,0.728,0.627,0.666,0.765,0.522,0.495,0.764,0.585,0.49,0.631,0.624,0.59,0.543,0.513,0.752,0.456,0.636,0.824,0.471,0.76,0.728,0.346,0.39,0.377,0.551,0.711,0.656,0.639,0.513,0.478,0.896,0.442,0.4,0.35,0.499,0.449,0.46,0.611,0.409,0.516,0.454,0.522,0.336,0.508,0.626,0.43,0.564,0.539,0.615,0.474,0.525,0.493,0.593,0.395,0.44,0.444,0.335
-4086,Not Epileptic,M,55.0,0.9,2.6,1.1,1.5,2.2,1.8,1.4,1.9,1.4,0.4,0.2,3.7,1.4,0.7,0.7,5.8,8.6,0.8,1.9,5.0,1.7,2.9,2.0,2.6,3.2,3.9,2.7,2.8,2.4,2.9,1.4,1.0,0.5,2.8,0.5,0.7,4.3,3.2,0.1,0.1,1.4,3.0,2.2,2.9,3.7,0.4,0.5,0.9,1.1,1.3,0.3,1.9,1.2,2.4,0.2,3.0,0.8,2.4,0.9,0.9,0.7,1.4,0.4,0.4,2.1,2.2,2.6,1.3,0.8,0.8,0.7,1.6,4.1,0.5,6,29,17,14,16,15,9,17,14,3,3.0,33,14,7,10,64,79,9,21,39,12,35,20,34,37,40,33,25,21,30,17,7,5,30,2,6,50,36,2,1,9,29,20,21,23,4,3,6,7,10,4,16,9,16,1,25,8,22,7,7,8,11,3,4,14,25,21,9,7,7,5,14,36,4,0.037,0.027,0.017,0.023,0.036,0.035,0.026,0.027,0.046,0.033,0.027,0.037,0.026,0.03,0.024,0.032,0.031,0.029,0.027,0.035,0.027,0.038,0.032,0.033,0.032,0.038,0.026,0.026,0.023,0.028,0.038,0.034,0.023,0.03,0.016,0.016,0.037,0.031,0.016,0.011,0.026,0.038,0.033,0.025,0.025,0.011,0.019,0.015,0.016,0.028,0.017,0.023,0.021,0.021,0.02,0.024,0.018,0.03,0.029,0.021,0.024,0.025,0.02,0.017,0.024,0.038,0.023,0.019,0.016,0.02,0.022,0.018,0.018,0.018,1153,4114,2304,2201,2428,2510,2467,3520,1881,893,619.0,3275,2691,1082,1625,10486,15435,2003,4137,4465,2972,4376,2043,5676,5106,5831,4731,4184,5832,4823,2305,1296,1023,5778,1644,1869,6434,6722,289,253,1428,3120,5126,2950,3180,1139,792,2100,2078,1386,467,2732,1876,4015,290,3234,1449,3195,981,1086,1016,2197,521,621,2788,1336,3407,2226,1544,1259,955,2372,8131,495,0.126,0.131,0.117,0.121,0.145,0.135,0.097,0.128,0.162,0.107,0.125,0.152,0.127,0.147,0.122,0.139,0.137,0.133,0.12,0.129,0.113,0.16,0.141,0.135,0.141,0.15,0.13,0.111,0.098,0.128,0.136,0.102,0.102,0.135,0.073,0.088,0.139,0.129,0.107,0.096,0.115,0.154,0.135,0.113,0.112,0.079,0.106,0.084,0.091,0.134,0.122,0.118,0.105,0.106,0.139,0.115,0.1,0.127,0.142,0.113,0.128,0.119,0.115,0.111,0.118,0.139,0.116,0.108,0.099,0.126,0.118,0.112,0.102,0.109,439,1543,1008,946,912,940,842,1105,553,250,175.0,1604,910,341,549,3182,4679,422,1181,2162,850,1449,995,1465,1740,1865,1669,1572,1648,1650,672,496,359,1504,557,657,2052,1784,177,150,728,1357,1191,1876,2115,622,366,924,1075,714,288,1302,931,1770,119,1842,748,1477,507,632,515,882,299,395,1379,855,1743,1047,770,574,533,1297,3653,333,2.841,2.582,2.264,2.311,2.378,2.353,2.356,2.72,2.543,2.96,2.714,1.853,2.444,2.463,2.325,2.601,2.741,3.247,2.65,1.789,2.73,2.48,1.85,2.862,2.383,2.647,2.267,2.167,2.691,2.39,2.65,2.357,2.208,2.905,2.747,2.593,2.584,2.744,1.782,1.841,2.482,2.093,3.223,1.742,1.7,2.063,2.605,2.734,2.378,2.356,2.001,2.193,2.174,2.32,2.694,1.976,2.279,2.3,2.189,1.951,2.213,2.576,2.125,1.765,2.235,1.929,2.027,2.457,2.161,2.36,2.171,2.184,2.439,1.749,0.77,0.571,0.577,0.445,0.54,0.588,0.588,0.649,0.527,0.535,0.747,0.483,0.374,0.391,0.522,0.475,0.541,0.771,0.543,0.549,0.82,0.521,0.476,0.619,0.5,0.484,0.452,0.57,0.507,0.432,0.703,0.825,0.334,0.615,0.812,0.557,0.609,0.51,0.38,0.509,0.467,0.485,0.63,0.564,0.45,0.339,0.491,0.872,0.488,0.415,0.499,0.401,0.457,0.504,0.425,0.397,0.411,0.642,0.434,0.375,0.305,0.689,0.465,0.668,0.535,0.649,0.394,0.345,0.394,0.539,0.512,0.42,0.48,0.337
-4087,Not Epileptic,F,37.0,0.8,2.6,1.0,0.9,2.0,1.2,1.4,1.2,1.1,1.0,0.3,2.1,1.4,0.5,1.5,4.5,7.1,0.9,1.3,3.6,1.3,2.9,2.4,3.1,2.6,3.6,3.6,2.0,2.5,4.2,1.4,1.4,0.5,1.9,0.4,0.4,3.3,3.4,0.1,0.1,1.1,3.0,2.7,1.9,2.8,0.7,0.3,1.5,1.2,0.9,0.5,2.7,1.5,1.6,0.4,3.0,0.4,1.8,1.1,1.3,1.1,2.2,0.4,0.5,1.3,1.8,2.5,1.4,0.8,1.2,1.3,1.8,3.7,0.3,6,23,8,10,18,12,12,13,13,11,4.0,22,16,7,19,53,75,9,17,30,11,29,17,34,40,39,48,20,23,50,20,11,5,26,4,3,47,38,1,1,7,23,26,14,16,6,2,11,6,8,3,20,11,12,2,22,3,14,9,9,11,19,3,5,9,23,19,10,4,9,10,14,33,3,0.031,0.026,0.019,0.016,0.032,0.024,0.023,0.021,0.033,0.037,0.028,0.035,0.022,0.022,0.025,0.027,0.026,0.037,0.028,0.034,0.023,0.027,0.034,0.03,0.029,0.028,0.028,0.025,0.026,0.033,0.037,0.056,0.023,0.023,0.017,0.011,0.031,0.029,0.01,0.016,0.022,0.039,0.038,0.022,0.022,0.016,0.012,0.019,0.017,0.022,0.02,0.022,0.02,0.018,0.015,0.016,0.01,0.021,0.022,0.019,0.022,0.028,0.022,0.019,0.02,0.028,0.021,0.018,0.015,0.029,0.024,0.021,0.014,0.014,1434,4876,2524,2418,2462,2271,3006,2928,2062,1783,619.0,2175,4263,1288,4026,10971,18897,2105,3886,3655,4634,6328,2836,6739,6672,7945,7780,3844,6749,7759,2436,1208,1233,6072,1651,1645,8238,8492,392,587,1543,2820,6503,1970,3750,1763,796,2722,2343,2063,602,4082,2267,4060,590,5275,1242,3039,1403,1989,1774,3188,552,952,2110,1364,4027,2439,1843,1589,1907,3138,9840,520,0.12,0.123,0.108,0.098,0.131,0.12,0.096,0.113,0.142,0.152,0.128,0.146,0.116,0.121,0.123,0.129,0.126,0.131,0.118,0.14,0.102,0.121,0.115,0.13,0.13,0.135,0.123,0.099,0.099,0.147,0.144,0.145,0.098,0.121,0.089,0.071,0.137,0.135,0.082,0.087,0.108,0.129,0.151,0.118,0.109,0.091,0.086,0.104,0.095,0.107,0.11,0.113,0.113,0.091,0.102,0.097,0.083,0.111,0.119,0.103,0.122,0.132,0.121,0.115,0.105,0.142,0.106,0.103,0.092,0.134,0.122,0.114,0.088,0.119,415,1618,800,891,969,812,964,903,533,438,207.0,1075,1023,325,1086,2752,4403,496,885,1684,976,1534,1100,1768,1732,2128,2372,1178,1505,2221,684,464,364,1377,407,593,2005,1980,219,251,774,1072,1229,1322,2000,794,351,1137,1038,701,352,1845,1066,1611,308,2641,573,1350,785,1010,706,1214,285,495,988,900,1856,1123,832,619,884,1379,4175,282,3.318,2.815,3.003,2.696,2.275,2.523,2.559,2.879,2.783,3.084,2.596,1.81,3.108,2.864,2.931,2.899,3.262,3.311,3.069,1.975,3.302,2.961,2.171,2.832,2.897,2.94,2.547,2.518,3.18,2.723,2.631,2.843,2.74,3.099,3.597,2.444,2.998,3.098,1.996,2.828,2.483,2.105,3.296,1.788,2.179,2.449,2.711,2.889,2.858,2.876,2.121,2.281,2.342,2.638,2.211,2.26,2.6,2.496,2.179,2.223,2.348,2.455,2.255,2.141,2.295,1.872,2.291,2.529,2.699,2.55,2.533,2.558,2.512,2.375,0.514,0.423,0.549,0.444,0.569,0.43,0.705,0.498,0.677,0.811,1.023,0.586,0.43,0.436,0.571,0.635,0.596,0.721,0.518,0.688,0.886,0.652,0.715,0.745,0.651,0.559,0.648,0.669,0.582,0.596,0.604,0.816,0.428,0.657,0.631,0.525,0.647,0.63,0.401,0.408,0.482,0.754,0.869,0.638,0.671,0.459,0.507,0.578,0.445,0.592,0.343,0.454,0.422,0.45,0.291,0.39,0.347,0.456,0.327,0.346,0.415,0.61,0.631,0.799,0.485,0.612,0.416,0.362,0.322,0.739,0.412,0.453,0.42,0.446
-4088,Not Epileptic,F,52.0,0.9,2.4,1.4,1.7,1.3,1.5,3.3,1.4,1.2,0.9,0.3,2.6,1.3,0.6,1.3,5.4,9.1,0.9,2.1,2.9,1.1,2.9,2.6,3.5,4.3,4.0,5.1,2.7,3.1,3.6,1.7,1.0,0.5,2.0,0.4,0.6,4.7,3.3,0.2,0.2,1.1,3.2,2.0,1.6,2.8,0.8,0.4,0.9,1.2,0.7,0.4,1.6,1.4,2.1,0.3,2.9,0.6,2.1,0.8,1.0,0.6,1.6,0.3,0.4,1.7,1.3,2.4,0.7,0.8,0.7,1.4,1.3,4.3,0.3,8,30,12,11,14,19,19,16,17,10,3.0,26,11,6,14,59,92,9,20,32,9,34,28,34,49,44,49,29,32,45,18,7,4,23,2,4,54,47,2,2,7,27,20,14,16,9,2,5,5,7,3,12,9,16,2,23,4,17,5,8,4,11,2,4,15,21,20,4,5,6,11,8,37,2,0.041,0.028,0.024,0.028,0.029,0.026,0.047,0.024,0.049,0.034,0.029,0.036,0.028,0.041,0.035,0.036,0.037,0.046,0.036,0.03,0.022,0.033,0.037,0.039,0.043,0.031,0.039,0.035,0.033,0.032,0.043,0.049,0.026,0.026,0.016,0.014,0.04,0.035,0.017,0.023,0.024,0.039,0.033,0.024,0.021,0.018,0.017,0.015,0.018,0.025,0.016,0.017,0.024,0.019,0.017,0.017,0.02,0.024,0.022,0.018,0.023,0.03,0.014,0.017,0.021,0.028,0.021,0.011,0.016,0.022,0.023,0.02,0.019,0.023,1574,4283,2667,2569,1871,2930,2447,3188,1736,1715,831.0,3011,3254,1096,2630,9209,15635,2065,4180,4220,3334,4760,2901,5425,6239,7140,7749,4005,6023,6738,2322,939,810,5627,1766,1925,8946,7191,403,431,1431,2800,5808,2168,3631,1454,715,2053,2273,1148,604,2757,1665,4051,335,4504,960,3030,885,1669,687,2483,504,799,2652,1399,3977,1835,1482,1097,1811,2246,8034,442,0.133,0.133,0.116,0.119,0.132,0.131,0.136,0.117,0.168,0.141,0.117,0.136,0.114,0.148,0.131,0.143,0.139,0.137,0.138,0.135,0.088,0.144,0.13,0.141,0.147,0.139,0.129,0.114,0.112,0.141,0.133,0.126,0.095,0.13,0.079,0.082,0.155,0.149,0.104,0.104,0.116,0.143,0.134,0.114,0.101,0.108,0.098,0.085,0.092,0.117,0.113,0.096,0.118,0.102,0.111,0.105,0.102,0.112,0.114,0.109,0.116,0.12,0.096,0.099,0.114,0.132,0.109,0.078,0.097,0.116,0.12,0.113,0.102,0.127,430,1474,976,903,753,983,988,949,533,442,222.0,1252,797,267,722,2594,4507,423,1048,1630,824,1352,1191,1542,1773,2149,2231,1342,1492,2020,713,362,259,1230,462,676,2053,1839,210,200,732,1184,1130,1274,2071,716,315,896,993,474,361,1414,893,1758,205,2378,469,1431,536,851,380,878,306,419,1334,700,1789,937,739,492,965,995,3609,198,3.408,2.852,2.68,2.667,2.201,2.31,2.132,2.679,2.444,2.905,2.986,1.945,2.99,2.952,2.709,2.596,2.723,3.737,2.983,2.147,2.786,2.586,2.123,2.625,2.669,2.629,2.596,2.296,2.936,2.524,2.465,2.457,2.664,3.103,3.413,2.615,3.114,2.822,2.199,2.444,2.525,2.05,3.632,1.931,1.958,2.054,2.785,2.735,2.766,2.583,1.844,2.096,2.034,2.379,2.076,2.129,2.383,2.322,1.832,2.197,2.341,2.748,1.917,2.112,2.135,2.292,2.212,2.292,2.29,2.343,2.148,2.274,2.353,2.593,0.698,0.609,0.577,0.501,0.62,0.735,0.604,0.6,0.627,0.545,0.706,0.563,0.505,0.387,0.52,0.537,0.628,0.751,0.485,0.627,0.842,0.647,0.481,0.714,0.678,0.549,0.637,0.665,0.621,0.613,0.606,0.836,0.418,0.625,0.585,0.421,0.786,0.65,0.332,0.38,0.431,0.563,0.72,0.76,0.601,0.428,0.494,0.63,0.551,0.612,0.355,0.392,0.428,0.473,0.289,0.447,0.45,0.489,0.303,0.346,0.373,0.566,0.335,0.755,0.537,0.883,0.455,0.277,0.34,0.741,0.474,0.564,0.43,0.502
-4089,Not Epileptic,M,55.0,1.1,2.8,0.8,1.3,2.6,2.5,2.1,1.8,1.8,0.7,0.3,3.6,2.6,0.3,1.5,6.5,8.5,1.1,2.5,4.4,1.4,2.6,1.9,4.4,2.6,2.9,3.3,2.2,2.7,3.5,1.3,1.1,0.5,2.4,0.3,1.0,3.8,3.4,0.3,0.4,1.1,2.9,2.7,3.4,3.0,0.6,0.4,1.2,1.4,1.0,0.7,2.3,1.6,2.2,0.4,3.1,0.7,1.5,1.4,1.1,0.6,2.1,0.3,0.7,1.8,1.6,2.6,2.0,0.7,1.2,0.8,1.1,3.3,0.5,7,23,9,11,19,27,18,15,17,8,4.0,34,27,5,15,79,80,9,28,49,9,33,18,45,35,35,40,24,28,42,16,9,6,32,2,10,42,39,4,3,7,26,26,24,16,4,2,6,7,8,4,17,11,13,3,27,5,10,8,7,4,18,3,6,16,17,17,14,4,11,5,8,23,4,0.045,0.026,0.015,0.025,0.039,0.031,0.031,0.027,0.043,0.04,0.032,0.034,0.033,0.027,0.029,0.04,0.031,0.038,0.035,0.034,0.031,0.034,0.03,0.044,0.033,0.032,0.029,0.026,0.031,0.034,0.038,0.045,0.033,0.031,0.013,0.023,0.041,0.034,0.024,0.025,0.023,0.029,0.037,0.024,0.022,0.012,0.018,0.017,0.018,0.022,0.024,0.023,0.027,0.024,0.03,0.021,0.022,0.017,0.025,0.017,0.025,0.029,0.023,0.02,0.019,0.027,0.023,0.022,0.019,0.036,0.025,0.016,0.016,0.031,1701,5364,1999,2428,2306,3956,3438,3349,2220,1461,723.0,3976,5439,953,2899,11553,17542,2557,5074,6276,3316,5055,2826,6464,5840,6255,5877,3678,6106,6332,1970,1569,967,6760,1864,2047,7537,8066,625,548,1599,2679,7143,3622,3682,1387,838,2364,2643,1470,661,3213,1829,3379,382,4755,1138,3128,1575,1797,769,3056,294,899,3236,1148,3867,3403,1314,1334,1255,2086,7886,638,0.122,0.122,0.102,0.115,0.137,0.149,0.113,0.13,0.143,0.145,0.128,0.126,0.135,0.128,0.136,0.155,0.128,0.132,0.142,0.142,0.11,0.13,0.114,0.147,0.135,0.137,0.122,0.119,0.123,0.138,0.125,0.115,0.114,0.124,0.066,0.12,0.147,0.135,0.119,0.12,0.115,0.119,0.145,0.119,0.107,0.082,0.095,0.092,0.094,0.109,0.124,0.118,0.124,0.109,0.143,0.119,0.116,0.102,0.118,0.101,0.12,0.131,0.125,0.122,0.11,0.124,0.113,0.111,0.1,0.15,0.119,0.102,0.092,0.154,439,1695,783,857,962,1344,1094,1013,710,315,220.0,1689,1343,261,806,3028,4518,530,1254,2304,729,1342,1070,1794,1473,1674,1908,1265,1496,1837,678,513,257,1316,443,682,1729,1664,282,272,740,1368,1318,2094,1973,676,336,966,1143,599,416,1541,870,1465,187,2127,503,1381,834,896,381,1147,194,516,1503,818,1695,1377,592,496,579,1040,3251,237,3.578,2.984,2.673,2.683,2.109,2.507,2.437,2.924,2.388,3.279,2.814,1.995,3.084,2.618,2.65,2.776,2.921,3.58,3.027,2.317,3.109,2.793,2.257,2.702,2.868,2.945,2.465,2.316,2.955,2.654,2.3,3.068,2.758,3.238,3.478,2.815,2.947,3.143,2.109,2.585,2.715,1.811,3.564,1.912,2.103,2.137,2.988,2.969,2.834,2.52,1.895,2.3,2.217,2.44,2.219,2.372,2.495,2.588,2.098,2.138,2.554,2.71,1.877,2.088,2.302,1.702,2.355,2.577,2.411,2.807,2.449,2.349,2.655,2.989,0.777,0.609,0.424,0.586,0.627,0.578,0.657,0.49,0.61,0.621,0.804,0.508,0.477,0.468,0.534,0.667,0.665,0.84,0.546,0.8,0.748,0.576,0.556,0.842,0.621,0.54,0.547,0.551,0.555,0.585,0.591,0.85,0.52,0.756,0.712,0.526,0.791,0.692,0.489,0.473,0.455,0.48,0.825,0.547,0.548,0.416,0.676,0.855,0.466,0.617,0.488,0.46,0.55,0.467,0.335,0.38,0.497,0.424,0.397,0.424,0.37,0.673,0.283,0.683,0.53,0.577,0.511,0.451,0.479,0.675,0.462,0.472,0.463,0.523
-4090,Not Epileptic,M,52.0,0.9,2.9,1.2,1.4,2.7,1.7,2.9,1.6,1.6,0.9,0.3,3.4,0.8,0.7,0.9,5.7,9.2,0.9,2.5,5.2,1.4,3.2,2.0,3.7,3.4,2.9,5.8,3.3,4.5,3.9,2.2,1.8,0.3,2.7,0.6,0.4,4.3,3.7,0.1,0.1,0.9,3.8,2.7,3.2,4.1,0.7,0.5,1.4,1.1,0.9,0.5,1.9,1.9,2.4,0.3,2.1,1.0,1.5,1.5,1.8,0.5,2.4,0.4,0.6,2.3,1.7,2.9,1.7,1.2,0.6,1.1,1.3,5.6,0.3,7,28,12,13,23,15,21,14,13,6,4.0,38,9,5,10,59,78,8,29,46,13,43,22,36,39,29,52,26,32,43,18,12,3,31,4,3,43,40,1,1,4,42,24,23,24,5,2,11,7,5,3,16,16,14,1,17,8,13,9,14,4,17,2,5,21,23,22,12,7,6,8,10,38,2,0.027,0.022,0.018,0.026,0.035,0.023,0.036,0.023,0.034,0.031,0.029,0.034,0.024,0.034,0.024,0.027,0.027,0.027,0.03,0.037,0.024,0.027,0.026,0.036,0.032,0.025,0.035,0.03,0.031,0.032,0.043,0.05,0.018,0.024,0.014,0.01,0.041,0.03,0.014,0.012,0.018,0.037,0.036,0.025,0.025,0.013,0.017,0.016,0.018,0.015,0.017,0.017,0.019,0.019,0.024,0.014,0.018,0.015,0.026,0.021,0.02,0.031,0.024,0.019,0.019,0.021,0.019,0.018,0.019,0.019,0.023,0.016,0.018,0.015,1773,5775,2684,2677,2770,3580,3276,3367,2180,1591,640.0,4422,2389,1176,2091,13007,22159,2502,4967,6064,4725,6698,3399,6617,6197,6437,7097,4537,6478,7480,2778,1563,814,7311,2872,1466,7415,8206,260,332,1402,3775,7153,3390,4222,1775,859,2644,1929,1772,611,3821,3180,4683,255,4226,1858,3420,1527,2435,866,3160,480,1067,3453,1496,5393,3416,2003,881,1758,2438,11264,723,0.111,0.114,0.102,0.122,0.14,0.121,0.129,0.108,0.135,0.118,0.132,0.145,0.114,0.142,0.121,0.117,0.119,0.125,0.124,0.145,0.107,0.136,0.12,0.134,0.122,0.118,0.106,0.101,0.108,0.131,0.142,0.137,0.092,0.127,0.076,0.075,0.142,0.139,0.096,0.076,0.097,0.142,0.141,0.12,0.111,0.085,0.093,0.081,0.094,0.098,0.103,0.097,0.109,0.097,0.133,0.087,0.109,0.093,0.12,0.114,0.101,0.131,0.107,0.112,0.109,0.116,0.103,0.097,0.102,0.121,0.116,0.102,0.095,0.098,511,2132,1078,967,1198,1242,1348,1124,671,448,217.0,1769,653,304,604,3567,5781,533,1501,2329,1117,1998,1178,1859,1909,1857,2379,1679,2070,2134,811,616,279,1728,754,564,1932,2059,144,155,744,1673,1468,1982,2534,816,378,1153,1016,743,418,1800,1553,1905,130,2181,839,1644,909,1289,431,1257,271,485,1820,1189,2506,1485,891,487,796,1176,4822,312,3.245,2.631,2.467,2.628,2.174,2.293,2.158,2.52,2.544,2.745,2.413,2.095,2.84,2.86,2.625,2.764,3.023,3.702,2.665,2.193,3.165,2.592,2.398,2.681,2.582,2.728,2.464,2.166,2.529,2.709,2.711,2.713,2.475,3.156,3.331,2.384,2.868,2.855,2.058,2.021,2.33,1.921,3.515,1.877,1.903,2.345,2.609,2.714,2.299,2.637,1.733,2.149,2.232,2.57,2.414,2.108,2.118,2.228,1.839,2.048,2.056,2.516,1.971,2.441,2.062,1.559,2.258,2.41,2.58,2.016,2.339,2.243,2.44,2.598,0.724,0.485,0.492,0.44,0.518,0.706,0.637,0.53,0.574,0.482,0.675,0.536,0.445,0.515,0.434,0.548,0.668,0.757,0.545,0.562,0.787,0.541,0.628,0.793,0.689,0.575,0.621,0.626,0.683,0.591,0.516,1.001,0.325,0.57,0.589,0.4,0.757,0.698,0.311,0.415,0.401,0.502,0.692,0.545,0.646,0.415,0.497,0.655,0.455,0.437,0.27,0.455,0.406,0.491,0.311,0.388,0.605,0.531,0.425,0.366,0.508,0.611,0.324,0.91,0.453,0.478,0.369,0.417,0.465,0.408,0.543,0.676,0.47,0.362
-4091,Not Epileptic,F,32.0,0.6,2.9,1.2,1.0,1.8,1.9,1.7,2.1,1.4,1.1,0.4,2.8,1.6,0.7,1.1,5.9,9.5,0.7,2.4,4.3,1.2,2.1,1.5,3.4,3.7,2.9,4.1,2.7,3.0,4.3,1.8,2.6,0.8,2.4,0.5,0.8,3.9,3.2,0.2,0.2,1.4,2.9,2.4,2.2,4.1,1.0,0.4,1.0,1.5,1.2,0.6,2.0,1.9,2.7,0.4,3.3,0.7,1.7,1.1,1.1,0.4,1.6,0.4,0.6,1.9,1.4,3.6,1.3,1.2,1.0,1.3,1.2,4.3,0.4,7,27,11,9,14,17,13,21,15,11,4.0,28,16,7,13,73,96,8,28,41,9,26,20,40,47,34,52,29,26,49,24,21,7,31,3,8,47,34,2,1,8,28,23,19,25,8,2,7,8,13,5,18,13,20,3,29,4,15,8,10,3,17,2,5,18,21,33,11,9,9,8,9,36,3,0.027,0.023,0.016,0.015,0.031,0.03,0.027,0.026,0.042,0.031,0.031,0.032,0.027,0.033,0.034,0.028,0.027,0.03,0.025,0.033,0.021,0.028,0.028,0.036,0.026,0.022,0.027,0.024,0.022,0.035,0.048,0.071,0.027,0.028,0.014,0.018,0.033,0.029,0.013,0.014,0.027,0.036,0.033,0.022,0.026,0.019,0.017,0.013,0.018,0.024,0.022,0.022,0.022,0.018,0.014,0.02,0.016,0.016,0.024,0.018,0.017,0.021,0.022,0.018,0.019,0.017,0.022,0.018,0.021,0.019,0.024,0.017,0.016,0.017,1694,5805,2776,2727,2375,3803,3351,4969,2019,2476,746.0,3098,3935,1577,2463,13770,21612,2171,5438,5427,3696,4672,2338,6076,8474,7991,7933,5632,8023,7359,2347,1566,1572,5441,2609,2311,8337,8550,472,559,1790,3233,6888,2818,4253,1719,761,2572,2852,2500,674,3044,2644,5164,740,4561,1476,3923,1111,1688,784,3206,360,1052,2980,1786,5014,2869,2031,1604,1694,2281,10285,694,0.109,0.121,0.108,0.09,0.12,0.129,0.106,0.115,0.161,0.135,0.127,0.145,0.122,0.123,0.141,0.129,0.125,0.13,0.123,0.134,0.095,0.133,0.14,0.134,0.13,0.118,0.127,0.103,0.101,0.152,0.147,0.15,0.105,0.137,0.073,0.096,0.129,0.13,0.094,0.098,0.116,0.135,0.139,0.118,0.118,0.105,0.09,0.085,0.094,0.111,0.124,0.113,0.113,0.101,0.093,0.112,0.093,0.096,0.123,0.11,0.104,0.116,0.138,0.107,0.116,0.11,0.117,0.097,0.111,0.125,0.118,0.099,0.095,0.101,485,2049,1074,996,881,1206,1048,1559,600,614,222.0,1452,1002,377,655,3677,6017,474,1651,2209,903,1257,956,1842,2360,2255,2667,1781,2026,2294,753,610,470,1376,630,789,2355,1931,257,228,857,1415,1443,1556,2469,832,340,1048,1280,888,405,1463,1330,2306,381,2559,689,1791,707,913,386,1249,195,557,1480,1231,2441,1337,943,720,847,1165,4145,375,3.452,2.74,2.57,2.635,2.347,2.575,2.581,2.756,2.602,2.956,2.643,1.823,2.953,3.019,2.858,2.82,2.869,3.653,2.82,2.087,3.021,2.656,2.041,2.636,2.757,2.787,2.318,2.446,2.976,2.523,2.447,2.654,2.565,2.979,3.581,2.571,2.756,3.163,2.059,2.512,2.659,1.957,3.399,2.024,2.017,2.329,2.745,3.181,2.776,2.925,1.968,2.247,2.195,2.478,2.24,1.981,2.449,2.421,1.759,1.891,2.362,2.589,1.975,2.183,2.262,1.704,2.192,2.429,2.508,2.359,2.339,2.383,2.5,2.298,0.82,0.525,0.433,0.39,0.619,0.594,0.6,0.518,0.657,0.688,0.896,0.442,0.504,0.481,0.462,0.572,0.557,0.725,0.452,0.612,0.787,0.477,0.492,0.8,0.602,0.568,0.59,0.596,0.536,0.575,0.74,0.848,0.423,0.624,0.581,0.455,0.715,0.59,0.437,0.525,0.464,0.56,0.799,0.686,0.572,0.413,0.418,0.7,0.458,0.545,0.317,0.456,0.441,0.383,0.343,0.404,0.365,0.443,0.362,0.372,0.356,0.733,0.619,0.879,0.543,0.679,0.454,0.352,0.415,0.591,0.444,0.412,0.527,0.275
-4092,Not Epileptic,F,20.0,1.3,2.9,1.8,0.9,2.8,2.0,1.6,2.2,1.3,0.8,0.2,3.5,2.0,0.5,1.3,5.9,10.6,0.6,2.3,4.9,0.8,4.5,2.2,4.1,6.5,2.1,2.3,1.7,2.1,3.0,1.4,1.8,0.5,2.1,0.3,0.5,4.1,3.3,0.2,0.3,0.9,3.5,1.7,3.3,1.8,0.6,0.3,0.8,1.5,0.6,0.6,2.3,1.8,2.8,0.2,1.6,0.6,1.4,0.7,1.5,0.7,1.5,0.5,0.6,1.9,1.2,1.2,1.6,0.9,0.4,0.9,1.9,5.4,0.3,8,23,12,7,23,19,16,23,13,10,3.0,29,25,7,16,69,92,6,28,43,7,52,23,43,63,25,27,24,19,37,24,13,5,27,2,4,42,46,1,1,5,25,15,20,10,4,1,4,7,5,4,22,11,17,2,15,4,13,6,12,5,14,3,5,13,17,9,11,4,3,5,15,41,2,0.047,0.028,0.023,0.018,0.049,0.032,0.041,0.029,0.038,0.032,0.031,0.036,0.028,0.026,0.025,0.031,0.03,0.025,0.032,0.039,0.019,0.041,0.037,0.043,0.049,0.026,0.029,0.03,0.023,0.032,0.044,0.058,0.028,0.032,0.025,0.012,0.044,0.034,0.014,0.013,0.02,0.038,0.031,0.032,0.016,0.014,0.012,0.013,0.02,0.026,0.026,0.021,0.025,0.018,0.019,0.019,0.015,0.014,0.019,0.021,0.019,0.025,0.017,0.019,0.017,0.027,0.015,0.015,0.021,0.01,0.018,0.021,0.017,0.02,1915,5865,3275,2341,2332,3587,2753,4904,2443,1652,702.0,3519,5053,1442,3682,13052,24458,2677,5346,5419,3500,6312,2811,5928,7088,5294,5018,4418,4738,5992,2276,1267,984,6902,1224,1884,8261,9433,509,714,1450,2480,5313,3090,3435,1588,774,2410,2720,1091,809,4402,2893,6012,342,3247,1418,3436,1288,1801,1525,3017,711,999,3849,1381,2273,4549,1193,845,1529,3003,12010,462,0.131,0.125,0.113,0.094,0.152,0.13,0.128,0.129,0.154,0.141,0.119,0.142,0.122,0.124,0.123,0.136,0.129,0.102,0.135,0.147,0.083,0.125,0.131,0.143,0.148,0.118,0.121,0.125,0.091,0.148,0.128,0.139,0.126,0.124,0.095,0.074,0.144,0.134,0.085,0.089,0.108,0.138,0.128,0.129,0.087,0.087,0.074,0.075,0.096,0.12,0.124,0.11,0.116,0.094,0.13,0.105,0.092,0.093,0.11,0.112,0.092,0.117,0.122,0.112,0.095,0.12,0.094,0.085,0.1,0.088,0.101,0.119,0.095,0.129,443,1694,1079,810,890,1179,761,1343,595,413,173.0,1627,1240,313,885,3331,5955,481,1323,2093,714,1834,995,1755,2137,1378,1439,1076,1601,1712,673,519,276,1288,291,613,1818,1901,263,331,676,1295,967,1709,1655,657,315,926,1147,435,412,1867,1068,2409,168,1428,649,1494,645,1019,693,1034,306,518,1702,704,1194,1718,692,512,714,1243,4765,221,3.691,3.007,2.98,2.889,2.287,2.499,2.66,2.973,2.934,3.3,3.06,1.921,3.072,2.966,2.912,2.882,3.083,3.815,3.067,2.209,3.201,2.593,2.327,2.644,2.542,2.984,2.592,2.878,2.334,2.691,2.45,2.566,2.752,3.634,3.62,2.711,3.031,3.277,2.33,2.541,2.736,1.817,3.567,2.022,2.399,2.632,2.856,3.154,2.931,2.953,2.36,2.431,2.493,2.714,2.347,2.289,2.661,2.577,2.182,2.082,2.644,2.649,2.249,2.323,2.527,2.508,2.123,2.638,2.093,1.936,2.484,2.655,2.67,2.733,0.935,0.61,0.598,0.449,0.68,0.663,0.595,0.533,0.756,0.568,0.577,0.61,0.474,0.629,0.696,0.679,0.674,0.915,0.558,0.687,0.762,0.685,0.577,0.807,0.828,0.634,0.741,0.512,0.608,0.559,0.749,0.857,0.559,0.584,0.534,0.505,0.88,0.736,0.502,0.401,0.465,0.555,0.733,0.67,0.393,0.41,0.468,0.678,0.47,0.468,0.368,0.484,0.615,0.395,0.247,0.553,0.366,0.404,0.42,0.455,0.556,0.69,0.581,0.633,0.558,0.753,0.46,0.614,0.545,0.515,0.388,0.536,0.498,0.45
-4093,Not Epileptic,F,42.0,0.6,2.3,0.9,1.1,1.9,1.9,1.5,2.0,0.8,0.6,0.3,3.7,1.8,0.4,1.2,5.6,6.7,0.9,2.0,5.6,0.6,3.3,2.0,2.8,3.3,3.4,3.5,3.1,2.4,2.6,1.1,0.7,0.6,2.8,0.3,1.0,3.7,3.2,0.1,0.1,1.1,2.3,2.0,2.2,3.0,0.6,0.3,1.1,1.0,0.7,0.9,1.8,1.3,2.0,0.5,2.2,0.8,1.5,1.1,0.9,0.7,1.4,0.3,0.3,1.9,1.3,3.0,0.9,0.9,1.0,1.0,1.4,4.0,0.3,6,21,13,12,17,22,16,17,11,7,4.0,29,21,4,12,58,65,6,27,47,6,27,18,30,42,40,40,25,22,35,19,6,9,28,2,7,41,41,1,1,6,22,18,18,19,4,2,7,6,6,5,13,12,13,3,20,7,12,9,6,6,12,3,3,18,12,23,7,7,10,7,10,30,3,0.034,0.024,0.015,0.02,0.036,0.035,0.026,0.03,0.034,0.032,0.03,0.042,0.034,0.029,0.029,0.03,0.023,0.034,0.027,0.038,0.016,0.037,0.034,0.031,0.033,0.03,0.032,0.029,0.024,0.029,0.038,0.03,0.027,0.03,0.016,0.023,0.038,0.03,0.01,0.015,0.022,0.029,0.034,0.026,0.025,0.013,0.018,0.016,0.014,0.024,0.028,0.023,0.023,0.018,0.02,0.021,0.021,0.018,0.03,0.018,0.027,0.022,0.026,0.015,0.02,0.038,0.022,0.017,0.016,0.023,0.03,0.021,0.017,0.019,1324,4663,2326,2460,2316,3184,2650,3195,1364,1265,737.0,2896,3677,899,2501,10302,16749,2386,4236,5328,1878,3861,2099,5805,6160,6953,5073,4087,5222,4717,1819,1023,1464,6380,1592,2197,6471,7150,350,389,1575,2669,4578,2348,3094,1216,742,2449,2489,1407,720,2866,2153,4260,535,3277,1427,2953,1155,1344,945,2665,523,748,3084,1255,4240,1973,1861,1664,1367,2316,8675,457,0.116,0.114,0.103,0.11,0.144,0.147,0.13,0.139,0.126,0.139,0.121,0.154,0.142,0.129,0.13,0.134,0.118,0.118,0.13,0.143,0.079,0.147,0.142,0.124,0.151,0.133,0.144,0.121,0.109,0.131,0.141,0.099,0.138,0.131,0.091,0.105,0.137,0.136,0.089,0.095,0.109,0.131,0.12,0.119,0.11,0.085,0.1,0.087,0.084,0.104,0.129,0.109,0.106,0.094,0.116,0.114,0.118,0.103,0.141,0.103,0.133,0.111,0.116,0.12,0.114,0.134,0.115,0.097,0.098,0.123,0.126,0.109,0.095,0.119,376,1578,970,919,896,1131,931,1057,507,352,221.0,1285,1028,216,691,3043,4646,414,1268,2379,572,1371,953,1531,1839,2036,1826,1520,1524,1581,522,428,396,1466,363,696,1731,1826,183,197,729,1231,1082,1398,1990,645,337,1034,1079,621,436,1317,949,1811,319,1638,625,1401,548,707,444,990,227,295,1547,539,2097,883,893,636,579,1104,3687,230,3.14,2.648,2.394,2.705,2.298,2.43,2.335,2.794,2.269,3.139,2.763,1.961,2.838,2.89,2.678,2.573,2.926,4.039,2.723,2.012,2.685,2.375,1.952,2.886,2.66,2.749,2.308,2.183,2.632,2.467,2.652,2.467,2.854,3.158,3.578,2.863,2.831,2.897,2.194,2.237,2.644,1.922,3.213,1.936,1.809,2.199,2.868,2.915,2.668,2.777,2.135,2.332,2.186,2.442,2.051,2.227,2.751,2.447,2.177,2.056,2.334,2.55,2.523,2.428,2.271,2.414,2.344,2.47,2.498,2.494,2.604,2.497,2.565,2.716,0.897,0.712,0.622,0.479,0.604,0.445,0.583,0.574,0.518,0.508,0.738,0.522,0.441,0.382,0.497,0.576,0.508,0.682,0.489,0.62,0.732,0.498,0.43,0.684,0.493,0.598,0.433,0.528,0.451,0.472,0.588,0.882,0.504,0.682,0.485,0.626,0.706,0.673,0.202,0.486,0.486,0.47,0.75,0.683,0.493,0.483,0.414,0.83,0.579,0.382,0.45,0.576,0.621,0.471,0.351,0.373,0.413,0.5,0.429,0.352,0.356,0.797,0.599,1.015,0.451,0.723,0.561,0.411,0.398,0.626,0.499,0.554,0.62,0.449
-4094,Not Epileptic,F,27.0,0.5,1.7,1.3,1.1,1.3,1.6,1.6,1.5,1.4,0.7,0.2,2.6,1.7,0.5,1.0,6.5,8.6,0.8,2.7,5.9,0.8,2.9,1.9,2.6,3.1,2.7,4.0,2.6,3.1,3.3,0.7,0.9,0.5,1.9,0.4,0.7,4.9,3.8,0.2,0.2,0.8,2.7,1.8,2.3,3.5,0.8,0.3,0.6,0.8,0.8,0.5,2.3,0.8,1.6,0.5,2.1,2.0,1.5,1.1,0.7,1.2,1.1,0.3,0.3,1.6,1.0,2.4,1.5,1.1,0.3,0.6,2.4,4.0,0.3,4,16,14,9,16,12,15,14,18,7,2.0,27,16,5,8,69,75,8,27,38,5,33,18,33,40,34,45,28,27,38,10,6,6,23,3,6,42,44,2,1,5,23,20,20,21,6,2,4,5,5,3,19,7,11,4,17,12,10,9,6,7,8,2,3,12,10,18,13,7,3,4,15,33,3,0.024,0.02,0.024,0.023,0.039,0.03,0.026,0.026,0.06,0.036,0.024,0.031,0.031,0.032,0.033,0.039,0.032,0.028,0.03,0.045,0.015,0.042,0.033,0.034,0.032,0.029,0.031,0.029,0.026,0.04,0.025,0.039,0.026,0.028,0.015,0.023,0.036,0.029,0.014,0.013,0.021,0.033,0.028,0.022,0.025,0.02,0.015,0.01,0.014,0.029,0.036,0.025,0.026,0.018,0.029,0.018,0.028,0.015,0.027,0.014,0.029,0.022,0.019,0.016,0.019,0.026,0.017,0.02,0.019,0.013,0.023,0.025,0.019,0.015,1346,4036,2441,2146,1650,2269,2744,2992,1643,1400,500.0,2973,3443,1023,2113,10860,18343,2142,4404,4412,2995,4519,2076,5054,6242,6001,7433,4203,7090,5026,1497,827,1030,3851,1840,1490,7761,8467,359,411,1762,2933,4357,3187,3542,1154,803,2554,2062,1145,325,3832,1413,3642,655,3529,2471,3456,1408,1356,1333,1903,499,754,2988,1216,4217,2986,2027,601,950,3252,7711,553,0.101,0.109,0.113,0.115,0.128,0.127,0.118,0.119,0.156,0.14,0.105,0.131,0.127,0.118,0.124,0.143,0.133,0.118,0.124,0.142,0.074,0.147,0.148,0.128,0.14,0.134,0.141,0.127,0.12,0.141,0.102,0.124,0.128,0.118,0.076,0.118,0.119,0.122,0.11,0.088,0.099,0.139,0.127,0.111,0.113,0.109,0.094,0.073,0.086,0.113,0.13,0.118,0.111,0.091,0.132,0.103,0.123,0.09,0.137,0.098,0.12,0.105,0.123,0.104,0.098,0.117,0.096,0.103,0.105,0.11,0.118,0.117,0.101,0.099,366,1379,805,751,641,834,1053,956,393,338,136.0,1301,904,260,548,2741,4449,445,1400,1962,776,1346,891,1283,1735,1719,2287,1478,1879,1539,454,377,363,1085,473,458,2177,2170,162,230,660,1267,1051,1666,2157,537,288,967,930,542,223,1491,586,1535,299,1767,1038,1594,647,735,644,758,189,348,1328,654,2136,1316,901,326,427,1448,3417,327,3.243,2.669,2.723,2.805,2.225,2.481,2.182,2.761,2.713,3.371,3.078,1.979,2.82,2.701,2.873,2.759,3.126,3.399,2.636,2.015,3.036,2.602,2.101,2.743,2.629,2.795,2.509,2.282,2.876,2.537,2.447,2.299,2.248,2.619,3.38,3.068,2.715,2.899,2.466,2.208,3.496,2.112,3.252,2.024,1.897,2.497,3.389,3.19,2.649,2.934,1.906,2.459,2.273,2.592,2.521,2.192,2.708,2.494,2.606,2.055,2.503,2.497,2.712,2.391,2.484,2.445,2.138,2.62,2.688,2.083,2.377,2.565,2.426,2.113,0.816,0.737,0.773,0.57,0.578,0.5,0.583,0.594,0.647,0.488,0.811,0.472,0.452,0.446,0.483,0.649,0.706,0.778,0.593,0.56,0.731,0.508,0.421,0.746,0.507,0.504,0.656,0.542,0.503,0.573,0.731,0.957,0.366,0.667,0.555,0.517,0.863,0.647,0.27,0.347,0.805,0.53,0.637,0.683,0.564,0.577,0.645,0.866,0.585,0.59,0.352,0.649,0.585,0.489,0.475,0.402,0.472,0.529,0.546,0.39,0.387,0.667,0.701,1.1,0.592,0.814,0.471,0.362,0.406,0.607,0.479,0.591,0.422,0.272
-4095,Not Epileptic,F,32.0,0.7,2.2,1.2,1.1,1.7,2.2,1.3,1.1,1.1,0.6,0.2,2.8,1.0,0.5,1.3,4.8,7.4,0.7,1.5,4.1,1.5,3.8,1.6,3.9,3.4,3.0,2.7,2.4,2.1,2.0,2.4,2.2,0.3,2.2,0.3,0.6,3.1,3.2,0.1,0.2,1.0,2.6,2.4,2.3,2.2,0.6,0.5,0.7,1.2,0.9,0.5,1.7,1.3,2.1,0.5,2.3,0.9,1.5,1.1,1.0,0.5,1.3,0.3,0.7,1.5,1.3,2.1,0.7,1.2,0.4,0.8,1.1,3.5,0.3,6,27,13,8,15,19,10,13,9,6,4.0,24,14,7,15,56,72,7,20,36,12,36,14,42,35,35,34,25,20,27,24,19,3,25,2,7,33,42,2,1,5,24,21,15,13,4,3,5,6,9,4,13,10,17,3,23,6,12,9,9,3,9,2,6,11,19,16,5,7,4,7,10,27,2,0.03,0.024,0.017,0.019,0.029,0.033,0.027,0.023,0.051,0.031,0.028,0.041,0.02,0.031,0.025,0.027,0.027,0.026,0.021,0.037,0.03,0.042,0.027,0.041,0.034,0.025,0.023,0.025,0.021,0.021,0.046,0.061,0.022,0.024,0.011,0.018,0.03,0.025,0.015,0.015,0.02,0.033,0.031,0.024,0.016,0.012,0.021,0.011,0.017,0.025,0.023,0.016,0.022,0.018,0.021,0.017,0.019,0.02,0.02,0.02,0.015,0.02,0.016,0.02,0.018,0.021,0.017,0.013,0.018,0.012,0.02,0.013,0.015,0.018,1612,4436,2919,2526,2290,3627,2910,2815,1490,1550,778.0,2846,3488,1185,3176,11831,18799,2298,4810,4840,3842,4539,2499,6590,6374,7386,6932,4973,6453,5501,2420,1666,849,6415,2036,2123,8299,9392,381,533,1454,2432,6820,2692,3706,1617,772,2118,2345,1465,699,3602,1881,4795,666,3867,1578,2610,1262,1279,911,2436,451,953,2642,1773,3863,2143,2213,736,1333,2971,9196,486,0.116,0.119,0.106,0.098,0.132,0.133,0.103,0.123,0.142,0.127,0.119,0.157,0.118,0.153,0.123,0.131,0.128,0.116,0.124,0.144,0.124,0.134,0.113,0.145,0.127,0.128,0.123,0.108,0.102,0.12,0.144,0.132,0.097,0.122,0.062,0.097,0.128,0.128,0.092,0.081,0.104,0.132,0.136,0.116,0.093,0.083,0.116,0.078,0.091,0.119,0.121,0.094,0.119,0.097,0.113,0.107,0.1,0.113,0.113,0.118,0.093,0.107,0.104,0.11,0.103,0.115,0.099,0.081,0.097,0.104,0.117,0.086,0.091,0.124,439,1488,1070,863,956,1146,855,857,407,381,213.0,1190,873,306,823,3035,4564,474,1143,1887,851,1382,804,1783,1585,2044,1885,1532,1441,1544,786,661,282,1387,545,628,1818,2183,209,259,715,1156,1340,1452,2054,740,337,894,1044,621,319,1618,905,1981,360,1977,719,1245,757,728,442,990,257,545,1310,955,1883,920,971,392,624,1417,3717,207,3.44,2.705,2.626,2.784,2.024,2.724,2.727,2.86,2.617,3.178,2.546,2.076,2.956,2.842,2.795,2.856,3.086,3.644,3.085,2.163,3.357,2.544,2.456,2.727,2.9,2.858,2.701,2.51,3.192,2.704,2.276,2.517,2.454,3.304,3.384,2.883,3.102,3.082,2.024,2.602,2.61,1.933,3.471,2.134,2.069,2.386,2.823,2.824,2.8,2.562,2.324,2.29,2.206,2.57,2.239,2.194,2.562,2.374,1.93,1.959,2.323,2.451,1.863,2.157,2.245,2.117,2.258,2.767,2.584,2.162,2.428,2.342,2.58,2.689,0.748,0.584,0.452,0.494,0.605,0.599,0.671,0.439,0.747,0.722,0.956,0.605,0.439,0.514,0.51,0.699,0.626,0.678,0.543,0.599,0.739,0.622,0.657,0.867,0.675,0.615,0.663,0.586,0.544,0.505,0.791,0.867,0.422,0.591,0.574,0.534,0.855,0.688,0.394,0.325,0.455,0.574,0.82,0.649,0.62,0.452,0.487,0.657,0.493,0.698,0.375,0.466,0.481,0.449,0.455,0.371,0.351,0.426,0.348,0.377,0.391,0.639,0.34,0.561,0.501,0.807,0.385,0.382,0.407,0.451,0.434,0.479,0.485,0.519
-4096,Not Epileptic,F,32.0,0.9,3.0,1.1,1.1,1.2,2.0,1.4,1.4,1.5,0.7,0.4,3.0,1.5,0.5,0.8,4.7,8.3,0.8,2.0,5.1,1.3,2.6,1.6,3.0,3.2,3.8,2.7,1.8,2.4,2.6,2.0,1.2,0.4,2.3,0.2,0.3,2.6,2.7,0.1,0.1,1.0,3.1,1.8,2.8,2.0,0.6,0.4,0.6,1.2,0.7,0.7,2.1,1.7,2.0,0.3,2.4,0.6,1.7,1.5,1.4,0.4,1.6,0.1,0.5,1.6,1.1,2.4,1.6,1.2,0.5,0.9,1.3,4.5,0.3,8,26,9,12,15,27,14,15,17,7,5.0,30,19,7,9,56,90,9,32,45,20,33,19,39,48,48,35,21,25,32,25,7,5,26,1,3,33,36,2,1,7,29,23,25,13,4,2,4,6,5,5,17,15,14,2,20,5,14,9,12,3,13,1,5,13,16,17,11,8,7,9,11,35,4,0.035,0.032,0.02,0.022,0.027,0.033,0.022,0.023,0.046,0.036,0.039,0.036,0.028,0.03,0.029,0.03,0.027,0.034,0.031,0.037,0.03,0.027,0.032,0.033,0.03,0.027,0.025,0.024,0.023,0.029,0.052,0.048,0.022,0.028,0.008,0.008,0.027,0.027,0.014,0.013,0.021,0.032,0.036,0.029,0.016,0.013,0.015,0.011,0.016,0.013,0.023,0.024,0.026,0.017,0.019,0.018,0.02,0.019,0.025,0.019,0.013,0.026,0.014,0.017,0.02,0.019,0.019,0.025,0.019,0.017,0.026,0.018,0.017,0.016,1546,4752,2238,2327,1940,3184,3056,3039,1891,1574,586.0,3321,3664,1081,1783,9017,18912,2182,3784,5321,3483,5431,1683,6177,6796,8172,5923,3571,6378,5751,2344,963,974,4803,1678,1678,7427,7435,325,383,1540,2577,5192,2772,3423,1507,914,1977,2368,1727,709,3490,2134,4339,510,3775,1045,3109,1519,2106,885,2737,285,933,2657,1272,3765,2386,2251,840,1458,1914,9036,468,0.132,0.138,0.11,0.113,0.134,0.154,0.108,0.12,0.165,0.14,0.148,0.152,0.13,0.153,0.128,0.134,0.125,0.126,0.134,0.145,0.108,0.126,0.14,0.135,0.135,0.127,0.115,0.103,0.104,0.132,0.139,0.126,0.103,0.123,0.054,0.066,0.131,0.132,0.1,0.092,0.109,0.128,0.138,0.14,0.092,0.086,0.089,0.072,0.089,0.094,0.126,0.121,0.128,0.096,0.116,0.1,0.108,0.108,0.113,0.108,0.091,0.127,0.102,0.12,0.112,0.11,0.102,0.118,0.102,0.123,0.129,0.105,0.099,0.116,447,1601,860,852,748,1108,995,947,562,396,184.0,1355,1020,261,509,2871,5354,447,1075,2211,769,1593,904,1711,2072,2469,1851,1259,1647,1667,740,401,332,1149,472,677,1786,1756,183,197,776,1265,1051,1536,1946,724,379,839,1120,754,437,1386,1038,1889,250,2047,509,1456,922,1156,435,1053,170,463,1235,828,1865,4096,1000,417,585,1033,3971,296,3.195,2.701,2.598,2.668,2.127,2.428,2.488,2.73,2.574,3.049,2.65,2.104,2.744,2.848,2.666,2.464,2.801,3.586,2.756,2.057,3.346,2.521,1.708,2.741,2.559,2.633,2.463,2.245,2.92,2.728,2.308,2.698,2.429,2.932,3.227,2.243,2.965,2.928,2.026,2.218,2.419,1.805,3.432,1.951,1.999,2.297,2.891,2.854,2.566,2.586,1.853,2.333,2.078,2.399,2.437,1.995,2.39,2.452,1.843,2.015,2.16,2.642,1.765,2.411,2.366,1.889,2.231,2.624,2.486,2.223,2.379,2.213,2.432,1.896,0.858,0.574,0.468,0.441,0.707,0.628,0.525,0.452,0.593,0.658,0.733,0.45,0.464,0.348,0.507,0.562,0.567,0.737,0.507,0.577,0.787,0.498,0.463,0.631,0.611,0.594,0.518,0.528,0.495,0.524,0.796,1.056,0.427,0.552,0.538,0.444,0.734,0.683,0.438,0.315,0.42,0.422,0.659,0.533,0.614,0.442,0.327,0.552,0.439,0.438,0.468,0.491,0.442,0.396,0.33,0.369,0.381,0.652,0.352,0.405,0.339,0.656,0.282,0.479,0.409,0.602,0.401,0.414,0.413,0.514,0.466,0.535,0.483,0.331
-4097,Not Epileptic,M,37.0,1.4,3.2,0.8,1.1,1.8,1.7,2.7,1.6,1.2,0.8,0.4,3.6,1.5,0.3,0.9,5.7,7.6,1.5,1.9,4.6,1.8,2.3,3.2,4.3,3.8,2.5,2.3,3.5,3.7,1.6,2.7,2.7,0.5,1.9,0.5,0.6,4.0,3.5,0.3,0.2,1.2,3.6,2.2,3.1,3.3,0.6,0.3,0.8,1.2,0.8,0.4,1.7,1.5,2.4,0.6,1.9,0.7,1.6,1.1,0.9,0.4,1.6,0.4,0.6,1.7,1.1,3.1,1.3,1.2,0.7,0.6,1.4,4.9,0.3,12,28,8,7,18,19,16,14,14,8,5.0,35,17,5,11,70,74,10,26,46,15,29,24,47,39,32,25,28,30,25,31,21,4,26,2,4,42,43,4,2,7,35,21,25,18,4,2,5,6,6,3,13,12,16,3,17,5,13,8,7,3,11,2,5,15,15,24,9,7,7,4,9,40,4,0.052,0.031,0.017,0.021,0.029,0.028,0.037,0.027,0.038,0.037,0.035,0.044,0.025,0.019,0.027,0.027,0.027,0.048,0.029,0.041,0.034,0.033,0.05,0.042,0.037,0.025,0.027,0.037,0.03,0.024,0.047,0.055,0.02,0.023,0.014,0.017,0.036,0.037,0.029,0.014,0.026,0.039,0.034,0.027,0.023,0.013,0.019,0.013,0.02,0.016,0.028,0.02,0.022,0.017,0.016,0.016,0.015,0.019,0.022,0.023,0.012,0.023,0.016,0.016,0.018,0.02,0.026,0.016,0.015,0.018,0.016,0.018,0.02,0.017,1589,5395,2236,2353,2712,2868,3119,3251,2172,1491,831.0,3347,4511,1033,2246,14520,20899,2853,4573,4930,4694,4286,1874,6629,5969,6022,4406,3544,7547,4374,2560,2089,1084,6603,2214,1537,7996,8851,565,525,1165,2778,6353,3057,3969,1202,812,2197,2391,1879,481,3447,1968,5626,956,3511,1550,2946,1368,1127,843,2652,399,1158,2833,1230,4443,2954,2513,1460,1000,2246,9245,454,0.141,0.14,0.109,0.104,0.137,0.136,0.119,0.123,0.144,0.15,0.136,0.154,0.123,0.117,0.12,0.118,0.121,0.142,0.13,0.153,0.115,0.133,0.151,0.143,0.131,0.122,0.111,0.11,0.101,0.12,0.154,0.138,0.088,0.115,0.063,0.083,0.138,0.142,0.139,0.095,0.12,0.142,0.137,0.128,0.103,0.093,0.089,0.075,0.091,0.097,0.125,0.105,0.114,0.09,0.097,0.094,0.09,0.105,0.112,0.121,0.092,0.12,0.116,0.098,0.11,0.113,0.114,0.092,0.087,0.109,0.102,0.106,0.107,0.127,473,1670,744,825,964,997,968,1012,558,336,235.0,1544,962,279,562,3718,4829,501,1124,2113,1071,1318,953,1842,1646,1768,1424,1417,1779,1234,835,792,404,1383,537,554,1880,1883,205,228,691,1431,1176,1839,2263,613,314,929,1046,790,250,1448,1020,2213,512,1810,721,1356,805,582,412,1014,239,608,1452,838,1962,1225,1129,584,485,1110,3707,272,2.974,2.97,2.764,2.681,2.441,2.453,2.59,2.803,2.794,3.124,2.842,1.883,3.286,2.783,2.833,2.85,3.224,3.779,2.941,2.033,3.16,2.479,1.818,2.668,2.759,2.622,2.429,2.028,3.135,2.675,2.315,2.543,2.221,3.247,3.66,2.532,3.064,3.299,2.54,2.511,2.115,1.808,3.557,1.868,2.011,2.232,2.965,2.813,2.774,2.533,2.11,2.449,2.033,2.544,2.352,2.13,2.505,2.404,1.991,2.141,2.267,2.596,1.85,2.319,2.148,1.799,2.188,2.733,2.593,2.627,2.243,2.453,2.557,2.086,0.743,0.748,0.615,0.533,0.663,0.578,0.688,0.534,0.694,0.715,0.828,0.495,0.525,0.771,0.702,0.639,0.721,0.836,0.556,0.615,0.871,0.672,0.521,0.88,0.75,0.537,0.723,0.577,0.691,0.623,0.773,0.858,0.436,0.704,0.756,0.479,0.814,0.638,0.498,0.415,0.406,0.586,0.849,0.57,0.751,0.383,0.846,0.661,0.476,0.485,0.495,0.488,0.419,0.467,0.395,0.382,0.378,0.534,0.385,0.428,0.353,0.606,0.36,0.522,0.51,0.464,0.559,0.392,0.384,0.542,0.517,0.37,0.514,0.761
-4098,Not Epileptic,F,27.0,1.0,1.7,1.1,1.1,1.5,1.7,1.3,1.2,1.5,0.5,0.2,2.5,1.2,0.3,1.3,4.7,6.7,0.8,1.5,3.4,1.2,2.1,2.5,4.7,2.9,3.1,3.0,2.6,2.1,2.2,1.1,0.9,0.6,1.7,0.4,0.7,3.1,2.4,0.2,0.2,0.9,5.0,2.1,1.8,2.5,0.6,0.3,0.9,1.1,0.7,0.6,1.7,1.4,3.1,0.5,1.8,0.8,1.5,1.0,1.3,0.6,1.5,0.7,0.6,1.5,1.2,2.1,1.1,1.2,0.6,0.7,1.1,3.4,0.4,6,19,14,9,20,21,11,13,17,6,2.0,26,13,4,15,59,69,5,22,36,11,28,25,43,37,39,37,24,22,27,15,7,5,21,3,7,43,30,1,1,6,39,20,16,14,6,2,4,6,5,3,14,11,25,2,13,7,13,6,10,4,11,5,6,15,16,15,7,9,6,4,10,27,5,0.048,0.025,0.021,0.023,0.032,0.028,0.024,0.026,0.052,0.025,0.028,0.036,0.024,0.029,0.028,0.031,0.028,0.034,0.025,0.035,0.025,0.029,0.04,0.046,0.03,0.028,0.027,0.032,0.023,0.025,0.035,0.052,0.036,0.023,0.016,0.018,0.036,0.034,0.016,0.014,0.021,0.051,0.038,0.024,0.023,0.018,0.014,0.016,0.02,0.016,0.027,0.023,0.021,0.022,0.025,0.014,0.027,0.019,0.022,0.02,0.022,0.026,0.032,0.021,0.018,0.031,0.019,0.016,0.024,0.02,0.023,0.018,0.017,0.018,1245,3685,2319,2073,1702,3269,2754,2463,1762,1334,630.0,2722,3097,747,2612,9168,16794,2096,3620,3744,3638,4407,1816,5424,5229,6889,5982,3700,5153,5112,2067,1130,859,5721,1920,1843,6246,5879,386,520,1258,2311,4796,2173,2769,1150,676,1921,1828,1591,534,2433,1801,5505,509,3533,995,2711,1211,2048,730,2561,653,988,2594,893,3593,2290,1551,1099,896,1858,7035,753,0.144,0.122,0.121,0.113,0.138,0.144,0.102,0.126,0.155,0.136,0.113,0.14,0.117,0.123,0.14,0.13,0.123,0.115,0.128,0.143,0.113,0.123,0.132,0.142,0.123,0.132,0.12,0.1,0.104,0.122,0.127,0.12,0.117,0.12,0.082,0.095,0.151,0.142,0.104,0.086,0.111,0.159,0.135,0.12,0.106,0.096,0.089,0.083,0.103,0.095,0.14,0.113,0.12,0.112,0.121,0.089,0.136,0.107,0.11,0.109,0.12,0.116,0.163,0.111,0.11,0.155,0.103,0.097,0.114,0.119,0.113,0.117,0.098,0.144,332,1192,806,750,842,1038,788,768,528,308,160.0,1138,831,195,718,2698,4128,393,950,1643,796,1203,947,1580,1519,1832,1765,1164,1309,1509,609,403,269,1147,424,607,1536,1340,179,272,685,1258,1068,1204,1696,569,309,857,877,660,294,1178,1040,2223,245,1863,452,1242,681,1000,393,945,304,528,1252,585,1743,994,770,422,451,955,2982,295,3.566,2.811,2.68,2.613,1.808,2.599,2.746,2.831,2.416,3.087,2.972,2.017,2.819,2.799,2.702,2.518,3.072,3.875,2.924,1.956,3.299,2.69,1.784,2.528,2.634,2.9,2.516,2.404,2.903,2.55,2.493,2.77,2.65,3.317,3.632,2.682,2.785,2.95,2.244,2.257,2.188,1.797,3.169,2.052,1.839,2.324,2.602,2.801,2.518,2.687,2.174,2.192,1.809,2.422,2.455,2.141,2.548,2.487,2.08,2.26,2.161,2.595,2.032,2.296,2.289,2.102,2.245,2.549,2.29,2.482,2.394,2.287,2.422,2.75,0.761,0.68,0.45,0.5,0.587,0.618,0.502,0.485,0.69,0.458,0.524,0.528,0.387,0.567,0.56,0.625,0.654,0.848,0.475,0.543,0.744,0.501,0.429,0.791,0.666,0.544,0.573,0.6,0.487,0.612,0.59,0.978,0.523,0.68,0.772,0.415,0.755,0.681,0.407,0.352,0.413,0.546,0.79,0.529,0.557,0.388,0.421,0.644,0.396,0.473,0.353,0.472,0.469,0.482,0.37,0.405,0.341,0.555,0.341,0.462,0.364,0.751,0.567,0.635,0.476,0.613,0.421,0.415,0.396,0.795,0.358,0.41,0.472,0.569
-4099,Not Epileptic,M,27.0,0.9,2.4,0.9,1.1,2.2,1.5,1.4,1.7,1.1,0.9,0.4,3.1,1.6,0.5,1.2,6.3,8.5,0.8,2.5,3.8,1.3,2.7,2.2,3.2,3.1,2.9,3.5,2.4,2.7,3.3,2.0,1.0,0.5,2.2,0.5,0.9,3.3,3.6,0.1,0.4,1.1,2.6,2.4,2.9,3.6,0.9,0.5,0.8,1.5,0.7,0.6,1.7,1.8,1.9,0.2,2.3,0.4,1.4,1.1,1.5,0.7,1.9,0.3,0.6,2.3,1.2,2.5,1.1,1.0,0.7,0.9,1.4,4.9,0.3,8,27,8,9,19,17,15,18,18,8,4.0,28,19,8,15,72,83,10,31,39,13,33,18,37,43,35,31,31,27,38,18,6,5,29,3,8,40,46,2,3,6,25,28,25,25,6,3,5,7,6,4,14,18,15,2,19,3,13,8,15,8,16,3,5,17,15,21,7,7,5,5,9,40,2,0.037,0.021,0.014,0.022,0.037,0.026,0.022,0.023,0.032,0.031,0.035,0.029,0.025,0.03,0.026,0.03,0.027,0.026,0.029,0.042,0.022,0.03,0.031,0.035,0.032,0.027,0.033,0.022,0.025,0.034,0.044,0.041,0.018,0.025,0.019,0.019,0.034,0.034,0.01,0.02,0.023,0.029,0.033,0.028,0.024,0.017,0.022,0.012,0.018,0.012,0.025,0.017,0.026,0.016,0.027,0.018,0.016,0.018,0.024,0.021,0.029,0.031,0.019,0.023,0.023,0.022,0.023,0.016,0.016,0.018,0.025,0.017,0.018,0.014,1702,5624,2207,2496,2538,2720,3367,3507,2076,1832,891.0,3815,4367,1102,2853,13379,19231,2641,5041,4384,4662,4683,2708,6238,6048,6260,4929,5498,7017,5849,2630,1008,1301,7184,1800,2163,7155,9374,537,745,1479,2316,5921,3128,4066,1806,947,2772,2500,1899,723,3535,2728,4490,414,3801,1079,3181,1298,2051,1117,2708,430,996,3307,1086,3978,2318,1907,997,1302,2355,9403,513,0.132,0.114,0.1,0.106,0.136,0.133,0.111,0.124,0.147,0.131,0.122,0.133,0.127,0.152,0.127,0.138,0.127,0.121,0.133,0.164,0.098,0.129,0.12,0.141,0.124,0.138,0.122,0.115,0.108,0.15,0.154,0.107,0.1,0.125,0.087,0.107,0.133,0.144,0.079,0.116,0.112,0.128,0.147,0.124,0.114,0.101,0.11,0.074,0.096,0.088,0.129,0.099,0.126,0.094,0.137,0.104,0.091,0.102,0.12,0.124,0.118,0.14,0.122,0.121,0.118,0.117,0.115,0.096,0.097,0.12,0.123,0.103,0.103,0.105,497,1906,923,861,1041,1022,1088,1204,630,472,223.0,1661,1109,284,804,3656,5273,542,1551,1811,1068,1532,1172,1665,1740,1862,1509,1776,1741,1731,740,457,419,1507,450,750,1856,2078,297,361,701,1315,1373,1759,2316,821,366,1105,1183,809,369,1622,1220,1889,146,1908,473,1334,696,1086,513,984,259,458,1516,787,1799,1037,944,518,602,1178,4083,269,3.321,2.703,2.551,2.798,2.26,2.341,2.457,2.634,2.419,3.009,3.127,1.968,2.927,2.704,2.604,2.777,2.914,3.73,2.759,2.141,3.172,2.439,2.01,2.896,2.7,2.809,2.574,2.435,2.963,2.698,2.616,2.272,2.486,3.144,3.591,2.689,2.883,3.088,2.034,2.312,2.768,1.658,3.264,2.09,1.998,2.437,3.124,3.046,2.608,2.467,2.232,2.278,2.278,2.453,2.769,2.197,2.337,2.468,2.06,2.081,2.214,2.782,1.938,2.426,2.418,1.632,2.322,2.6,2.501,2.132,2.436,2.414,2.498,2.451,0.834,0.534,0.378,0.469,0.627,0.565,0.598,0.515,0.643,0.528,0.863,0.554,0.487,0.523,0.565,0.595,0.558,0.666,0.492,0.601,0.933,0.568,0.608,0.681,0.567,0.424,0.603,0.631,0.544,0.544,0.853,0.786,0.399,0.62,0.731,0.418,0.679,0.646,0.418,0.479,0.495,0.413,0.673,0.652,0.596,0.4,0.474,0.697,0.375,0.404,0.523,0.45,0.498,0.399,0.533,0.401,0.425,0.596,0.437,0.381,0.384,0.752,0.381,0.722,0.478,0.518,0.497,0.354,0.365,0.692,0.485,0.461,0.418,0.383
-4100,Not Epileptic,F,37.0,1.1,1.8,0.9,0.9,1.8,1.5,1.2,1.4,1.1,1.0,0.4,3.1,1.0,0.6,1.1,4.7,8.0,0.8,2.2,4.6,1.1,2.8,2.2,3.8,3.1,2.4,3.2,2.6,2.4,3.5,1.8,1.2,0.5,2.2,0.3,0.3,3.7,3.4,0.2,0.2,0.9,2.4,2.5,3.0,2.2,0.6,0.5,0.6,1.4,1.0,0.8,1.5,1.3,2.2,0.2,2.6,1.2,1.4,1.1,1.4,1.2,1.3,0.5,0.6,1.4,0.9,2.4,1.0,1.0,0.7,1.0,1.2,3.6,0.3,9,18,7,9,17,22,10,17,14,14,4.0,28,17,7,14,53,78,9,25,49,10,31,19,37,33,33,35,23,17,45,19,8,4,26,2,3,46,43,2,2,6,26,27,22,13,4,3,3,8,8,6,13,12,18,1,22,10,10,8,12,12,12,2,5,13,17,20,6,6,7,9,10,29,3,0.045,0.026,0.025,0.023,0.035,0.026,0.023,0.026,0.041,0.049,0.033,0.038,0.027,0.028,0.034,0.037,0.033,0.046,0.035,0.041,0.027,0.032,0.037,0.043,0.038,0.027,0.034,0.032,0.028,0.038,0.057,0.051,0.027,0.03,0.012,0.012,0.035,0.036,0.017,0.019,0.024,0.033,0.036,0.032,0.021,0.016,0.02,0.012,0.021,0.023,0.028,0.021,0.021,0.024,0.023,0.021,0.033,0.022,0.029,0.025,0.031,0.03,0.03,0.022,0.019,0.025,0.021,0.02,0.022,0.026,0.029,0.022,0.018,0.02,1516,3647,1710,1767,2342,2870,2483,2862,1818,1328,645.0,2588,2483,1144,2393,8153,15180,2155,3841,4654,3755,4288,1964,5435,4420,5285,5016,3111,4945,4799,1910,1333,736,4230,1693,1044,6571,6992,370,350,1007,1801,5863,2707,2497,1124,842,1571,1903,1661,690,2327,1924,3626,263,3410,1354,2308,1120,1489,1107,2030,428,970,1988,807,3314,1731,1408,1031,1257,1872,6845,484,0.144,0.12,0.115,0.114,0.147,0.14,0.103,0.133,0.152,0.174,0.146,0.146,0.135,0.136,0.131,0.145,0.135,0.14,0.148,0.159,0.109,0.134,0.126,0.149,0.13,0.128,0.121,0.115,0.099,0.156,0.16,0.124,0.118,0.125,0.071,0.071,0.157,0.146,0.094,0.114,0.121,0.132,0.146,0.138,0.104,0.095,0.112,0.077,0.107,0.12,0.143,0.11,0.112,0.118,0.114,0.117,0.133,0.111,0.129,0.124,0.145,0.13,0.133,0.12,0.107,0.131,0.111,0.098,0.108,0.145,0.132,0.116,0.101,0.131,409,1226,612,677,805,1057,794,917,537,384,197.0,1320,712,313,663,2210,4163,407,1089,2031,798,1390,917,1547,1314,1472,1729,1180,1278,1662,628,466,277,1158,491,434,1821,1684,213,199,553,1147,1232,1610,1609,555,358,732,952,654,374,1095,939,1553,120,1801,603,1085,640,847,566,765,250,441,1094,589,1746,768,679,449,588,925,2977,218,3.486,2.682,2.701,2.556,2.342,2.297,2.527,2.598,2.507,2.733,2.505,1.748,2.687,2.604,2.611,2.729,2.869,3.759,2.807,1.989,3.398,2.44,1.961,2.692,2.58,2.762,2.279,2.119,2.862,2.355,2.381,2.836,2.28,2.742,3.087,2.16,2.834,3.014,1.902,2.043,2.308,1.52,3.368,1.852,1.791,2.168,2.912,2.585,2.434,2.755,1.936,2.196,1.993,2.479,2.504,2.099,2.418,2.48,1.834,1.977,2.267,2.643,1.951,2.611,1.919,1.817,2.03,2.595,2.421,2.357,2.273,2.565,2.442,2.445,0.794,0.714,0.484,0.52,0.725,0.603,0.66,0.45,0.592,0.769,0.593,0.482,0.334,0.409,0.544,0.611,0.574,0.697,0.543,0.545,0.845,0.717,0.514,0.776,0.672,0.606,0.59,0.563,0.566,0.654,0.71,0.888,0.31,0.567,0.88,0.422,0.621,0.633,0.419,0.333,0.608,0.414,0.784,0.486,0.565,0.447,0.432,0.722,0.377,0.553,0.424,0.401,0.52,0.41,0.393,0.403,0.461,0.476,0.469,0.355,0.324,0.836,0.336,0.762,0.462,0.638,0.461,0.421,0.458,0.637,0.584,0.47,0.423,0.58
diff --git a/content/en/project/epilepsy-detection/model.ipynb b/content/en/project/epilepsy-detection/model.ipynb
deleted file mode 100644
index 5d9f8482..00000000
--- a/content/en/project/epilepsy-detection/model.ipynb
+++ /dev/null
@@ -1,2527 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 84,
- "id": "df02d505",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import os\n",
- "from pathlib import Path\n",
- "import glob\n",
- "import numpy as np\n",
- "import seaborn as sns\n",
- "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
- "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
- "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.svm import SVC\n",
- "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
- "from sklearn.feature_selection import SelectKBest, f_classif\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "from sklearn.decomposition import PCA\n",
- "import matplotlib.pyplot as plt\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d3240cdc",
- "metadata": {},
- "source": [
- "# Model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4a065acf",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "import pandas as pd\n",
- "df = pd.read_csv(r'.\\lh.csv')\n",
- "\n",
- "df = df.drop('Subject', axis=1)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7324d92c",
- "metadata": {},
- "source": [
- "## Preprocessing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "id": "0a4e8b59",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "def preprocess_data(df):\n",
- " \n",
- " target_candidates = [col for col in df.columns if 'epilep' in col.lower() or 'target' in col.lower() or 'label' in col.lower()]\n",
- " \n",
- " if not target_candidates:\n",
- " print(\"No target column found. Please specify the target column name.\")\n",
- " \n",
- " return None, None, None\n",
- " \n",
- " target_col = target_candidates[0]\n",
- " print(f\"Using '{target_col}' as target variable\")\n",
- " \n",
- " \n",
- " X = df.drop(columns=[target_col])\n",
- " y = df[target_col]\n",
- " \n",
- " \n",
- " if y.dtype == 'object':\n",
- " le = LabelEncoder()\n",
- " y = le.fit_transform(y)\n",
- " print(f\"Target classes: {le.classes_}\")\n",
- " \n",
- " \n",
- " numeric_cols = X.select_dtypes(include=[np.number]).columns\n",
- " X = X[numeric_cols]\n",
- " \n",
- " print(f\"Features shape: {X.shape}\")\n",
- " print(f\"Target distribution:\")\n",
- " print(pd.Series(y).value_counts())\n",
- " \n",
- " return X, y, target_col"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "id": "9c8aaa6c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using 'Epileptic_Status' as target variable\n",
- "Target classes: ['Epileptic' 'Not Epileptic']\n",
- "Features shape: (542, 593)\n",
- "Target distribution:\n",
- "0 442\n",
- "1 100\n",
- "Name: count, dtype: int64\n"
- ]
- }
- ],
- "source": [
- "X, y, target_col = preprocess_data(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "id": "4c13b5e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "def feature_engineering(X, y):\n",
- " \"\"\"Perform feature engineering and selection\"\"\"\n",
- " \n",
- " X = X.fillna(X.median())\n",
- " \n",
- " # Remove constant features\n",
- " constant_features = X.columns[X.var() == 0]\n",
- " if len(constant_features) > 0:\n",
- " print(f\"Removing {len(constant_features)} constant features\")\n",
- " X = X.drop(columns=constant_features)\n",
- " \n",
- " \n",
- " selector = SelectKBest(score_func=f_classif, k=min(50, X.shape[1]))\n",
- " X_selected = selector.fit_transform(X, y)\n",
- " selected_features = X.columns[selector.get_support()]\n",
- " \n",
- " print(f\"Selected {len(selected_features)} features out of {X.shape[1]}\")\n",
- " \n",
- " return pd.DataFrame(X_selected, columns=selected_features), selected_features"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "67ed1a59",
- "metadata": {},
- "source": [
- "# PCA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 89,
- "id": "3105e3c7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Explained variance ratio:\n",
- "PC1: 0.1373 (13.73%)\n",
- "PC2: 0.0796 (7.96%)\n",
- "PC3: 0.0495 (4.95%)\n",
- "PC4: 0.0374 (3.74%)\n",
- "PC5: 0.0168 (1.68%)\n",
- "PC6: 0.0156 (1.56%)\n",
- "PC7: 0.0141 (1.41%)\n",
- "PC8: 0.0135 (1.35%)\n",
- "PC9: 0.0130 (1.30%)\n",
- "PC10: 0.0125 (1.25%)\n",
- "\n",
- "Cumulative explained variance:\n",
- "PC1-PC1: 0.1373 (13.73%)\n",
- "PC1-PC2: 0.2169 (21.69%)\n",
- "PC1-PC3: 0.2664 (26.64%)\n",
- "PC1-PC4: 0.3038 (30.38%)\n",
- "PC1-PC5: 0.3206 (32.06%)\n",
- "PC1-PC6: 0.3362 (33.62%)\n",
- "PC1-PC7: 0.3502 (35.02%)\n",
- "PC1-PC8: 0.3637 (36.37%)\n",
- "PC1-PC9: 0.3767 (37.67%)\n",
- "PC1-PC10: 0.3892 (38.92%)\n"
- ]
- },
- {
- "data": {
- "image/png": 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ms2HXrl3Q6XQ4evQo9Hq9R/tWSE9PxyeffILPP/8cRUVFmDhxIqZMmYIPPvigxu3LyspQVlbm+rmoqMir1yMiIiKi2rW6JPqlS5fw0EMPITs7G0ajEb1798bXX3+NwYMHAyj/w8JqteKJJ55AQUEBrrvuOnzzzTcwGAx+jpyIiIiI2iOFQoENGzZg0qRJWLNmDZKTk3HzzTdj9OjR6N27t1djLVy40PV7LwDk5uYCAMLCwtCxY0fX8vXr12Po0KEIDg4GANx+++1Yv369q9q8wpgxYzBhwgTXzw899BD+/ve/Y9y4cQDKK9wXLVqEmTNnupLoM2bMcG3ftWtXLFq0CI8//rhHSfQTJ06ge/fubstmzJiBtWvXAgCCgoKQmZlZ5xh6vd5VyV35mAFAp9Nh7dq1rjYu77zzDqxWK9577z3odDoAwOrVqzF8+HAsW7bM1fIxODgYq1evhlwux5VXXolhw4Zh+/btmDRpEkJCQiCXy2EwGKq9Xm3OnTuHUaNGuT0p4K3S0lKkpqYiOjoaALBq1SoMGzYMr732Wo1xLFmyBAsWLPD6dYiIiIiofq2uncu6deuQkZGBsrIy5OTk4Ntvv3X7Q0IQBMyfPx/Z2dkoLS3Fd999h6SkJD9GTERE1H6JooTTuWb8fr4Qp3PNEEXJ3yER+cWoUaNw4cIFbNmyBUOGDMHOnTuRnJzsUf/vyvr161fvNk6nE6mpqRg7dqxr2dixY5Gamgqn01nneL/++isWLlzoqp7X6/WYNGkSsrOzUVJSAqC8anvw4MGIioqCwWDAww8/jLy8PFgsFo+OoWq1+ezZs3HgwAHMnTvX47YwtenVq5dbH/Rjx46hT58+rgQ6ANxwww0QRRHHjx93LevZsyfkcrnr58jISOTk5DQ4junTp2Px4sW44YYbMG/ePBw8eNDrMTp37uxKoANA//79q8Vd2axZs2AymVz/zp8/3+D4iYiIiMhdq6tEJyIiotbhcJYJm/ZnIj3HjDK7CLVShvhwPUYlRyMpyujv8IianUajweDBgzF48GDMnTsXjz76KObNm4fdu3cDKJ/np0JFP+6qKieDa7N161ZkZWXh/vvvd1vudDrxzTffYOjQobWOJ4oiFixYgJEjR9YY/9mzZ3HHHXdg8uTJWLRoEUJCQrBnzx5MnDix1pgrS0hIwB9//OG2LCwsDGFhYQgPD693//pUPR5JkmptEVN5edUJVQVBgCiKDY7j0UcfxZAhQ/Dll1/im2++wZIlS/Daa69h2rRpkMlkbu81UPv7XVO8tR2PWq1uVKsgIiIiIqpdq6tEJyIiopbvcJYJK7efxKFME4K0KsR20CFIq8KhzPLlh7NM/g6RyO8SExNhsVhcfcwr9/euPMmot9atW4fRo0fjwIEDbv8efPDBGicYrSw5ORnHjx9HfHx8tX8ymQz79u2Dw+HAa6+9hpSUFFxxxRW4cOGCx7E98MADOH78OD777LMGH583EhMTceDAAbcq+b1790Imk7kmHvWESqWqVsVfn5iYGEyePBlpaWn429/+hnfeeQdA+U2D4uJit5hqer/PnTvndm5/+OEHr+MmIiIiIt9gJToRERH5lChK2LQ/E/kWG+LD9a6qSb1GgXi1Huk5ZqTtz0JiZCBksvonESRq7fLy8nDvvfdiwoQJ6N27NwwGA/bt24eXX34ZI0aMgFarRUpKCpYuXYrY2FhcvnwZL7zwQoNeKzc3F59//jm2bNlSraXhuHHjMGzYMOTm5roS91XNnTsXd955J2JiYnDvvfdCJpPh4MGDOHToEBYvXoxu3brB4XBg1apVGD58OPbu3Ys1a9Z4HN/o0aORlpaG0aNHY9asWRgyZAgiIiJw9uxZbNy40a2lii88+OCDmDdvHsaNG4f58+cjNzcX06ZNw0MPPeTqh+6J2NhY7Nq1C6NHj4ZarUaHDh3q3H7GjBkYOnQorrjiChQUFOC///0vevToAQC47rrrEBAQgOeffx7Tpk3Dzz//XGNbH41Gg3HjxuHVV19FUVERpk+fjvvuu8/jvuxERERE5DusRCciIiKfysizID3HjEijtlrbAUEQEGnU4mROMTLyPOufTNTa6fV6XHfddVixYgUGDBiApKQkzJkzB5MmTcLq1asBlE8Earfb0a9fPzz55JPVJgD1VMUEmoMGDaq2buDAgTAYDHj//fdr3X/IkCH44osvsG3bNlxzzTVISUnB8uXL0aVLFwBA3759sXz5cixbtgxJSUn44IMPsGTJEo/jEwQBGzduxOuvv46vvvoKgwYNQvfu3TFhwgTExMRgz5493h90HQICArB161bk5+fjmmuuwT333INBgwa5zrunFi5ciIyMDHTr1q3WGxCVOZ1OTJkyBT169MDtt9+O7t27uyZeDQkJwb/+9S989dVX6NWrFz766CPMnz+/2hjx8fEYOXIk7rjjDtx2221ISkryaPJWIiIiIvI9QarakI9QVFQEo9EIk8mEwMBAf4dDRETUqvx+vhAvfnkMsR10kNdQae4UJWRctmD2sB7oExPU/AGSz/B3JqKmMX/+fGze37BuvgABAABJREFUvLlRbX0qPp8xMz6BTB3gu+CIiIiI6pGxdJi/Q/CYp3/TsBKdiIiIfMqgUUCtlMFqq7l/sNXmhFopg0HDrnJERERERETU8jGJTkRERD4VG6pDfLge2SYrqj7wJkkSsk1WJIQbEBuq81OERNRUhg4dCr1eX+O/l156yeNxahtDr9dj9+7dTXgEnvHVcRIRERFR68ASMCIiIvIpmUzAqORoZBVYXb3RtSo5rDYnsk1WhOhUGJkcxUlFidqgtWvXwmq11rguJCTE43HqamMSFRXlbVg+56vjrM38+fNr7JNORERERP7BJDoRERH5XFKUEdMHJWDT/kyk55hxqUiEWilD7+ggjEyOQlKU0d8hElET8FWCOz4+3ifjNJWWkMgnIiIioubDJDoRERE1iaQoIxIjA5GRZ0FxqQMGjQKxoTpWoBMREREREVGrwiQ6ERERNRmZTEBcmN7fYRARERERERE1GCcWJSIiIiIiIiIiIiKqBZPoRERERERERERERES1YBKdiIiIiIiIiIiIiKgWTKITEREREREREREREdWCSXQiIiIiIiIiIiIiolowiU5EREREREREREREVAuFvwMgIiIiIiKipnF4wRAEBgb6OwwiIiKiVo2V6EREREREREREREREtWASnYiIiIiIiIiIiIioFkyiExERERERERERERHVgkl0IiIiIiIiIiIiIqJaMIlORERERERERERERFQLJtGJiIiIiIiIiIiIiGrBJDoRERERERERERERUS2YRCciIiIiIiIiIiIiqgWT6EREREREREREREREtVD4OwCitkgUJWTkWVBc6oBBo0BsqA4ymeDvsIiIiIionUmatxUydYC/wyAiIqIWJGPpMH+H0OowiU7kY4ezTNi0PxPpOWaU2UWolTLEh+sxKjkaSVFGf4dHREREREREREREXmASnciHDmeZsHL7SeRbbIg0aqE1ymG1OXEo04SsAiumD0pgIp2IiIiIiIiIiKgVYU90Ih8RRQmb9mci32JDfLgeeo0CcpkAvUaB+HA98i02pO3PgihK/g6ViIiIiIiIiIiIPMQkOpGPZORZkJ5jRqRRC0Fw738uCAIijVqczClGRp7FTxESERERERERERGRt5hEJ/KR4lIHyuwitCp5jeu1KjnK7CKKSx3NHBkRERERERERERE1FJPoRD5i0CigVspgtTlrXG+1OaFWymDQcCoCIiIiIiIiIiKi1oJJdCIfiQ3VIT5cj2yTFZLk3vdckiRkm6xICDcgNlTnpwiJiIiIiIiIiIjIW0yiE/mITCZgVHI0QnQqpOeYYS51wClKMJc6kJ5jRohOhZHJUZDJhPoHIyIiIiIiIiIiohaBfSWIfCgpyojpgxKwaX8m0nPMuFQkQq2UoXd0EEYmRyEpyujvEImonRJFCRl5FhSXOmDQKBAbquNNPSIiIiIiIiIPMIlO5GNJUUYkRgYyWUVELcbhLJPr5l6ZvfzmXny4HqOSo3lzj4iIiIiIiKgeTKITNQGZTEBcmN7fYRAR4XCWCSu3n0S+xYZIoxZaoxxWmxOHMk3IKrBi+qAEJtKJiIiIiIiI6sCe6ERERG2UKErYtD8T+RYb4sP10GsUkMsE6DUKxIfrkW+xIW1/FkRRqn8wIiIiIiIionaKSXQiIqI2KiPPgvQcMyKNWgiCe0spQRAQadTiZE4xMvIsfoqQqP2ZP38++vbt6+8w2qzx48fj7rvvbrLxd+7cCUEQUFhYWOs2GzZsQFBQUJ3jVL0OmjpuIiIiImocJtGJiIjaqOJSB8rsIrQqeY3rtSo5yuwiiksdzRwZUcNdvHgRTz75JOLj46HRaBAREYEbb7wRa9asQUlJSZO97qZNmyCXy3Hu3Lka11955ZWYPn26z1+3qKgIc+bMQc+ePaHVahEaGoprrrkGL7/8MgoKCjwaIzY2Fq+//rrPY2uPrr/+emRnZ8NoZBssIiIiovaEPdGJiIjaKINGAbVSBqvNCb2m+n/yrTYn1EoZDDWsI2qJTp8+jRtuuAFBQUF46aWX0KtXLzgcDpw4cQLr169Hp06dcNddd1Xbz263Q6lUNuq177rrLoSGhiI1NRVz5sxxW7d3714cP34cGzdubNRrVJWfn48bb7wRRUVFWLRoEa6++mqoVCqkp6fjww8/xIcffogpU6b49DVbKl+8h76gUqnQsWNHf4dBRERERM2MlehERERtVGyoDvHhemSbrJAk977nkiQh22RFQrgBsaE6P0VI5J0nnngCCoUC+/btw3333YcePXqgV69eGDVqFL788ksMHz4cQHm7ojVr1mDEiBHQ6XRYvHgxnE4nJk6ciK5du0Kr1aJ79+74xz/+4Rp7165dUCqVuHjxottr/u1vf8OAAQOgVCrx0EMPYcOGDdU+T+vXr8fVV1+NPn364Ny5cxgxYgT0ej0CAwNx33334dKlSw063ueffx7nzp3DTz/9hEceeQS9e/fGlVdeiTvvvBMffvghnnjiiXrHuOWWW3D27Fk89dRTEATBrbXT999/jwEDBkCr1SImJgbTp0+HxfJne6fY2FgsXrwYDz/8MPR6Pbp06YLPPvsMubm5rmPs1asX9u3b59qnopXJ5s2bccUVV0Cj0WDw4ME4f/68W1xvvvkmunXrBpVKhe7du+P99993W9+Q99Bbt9xyC6ZNm4YZM2YgODgYERERePvtt2GxWPDII4/AYDCgW7du+M9//uPap6Z2Lhs2bEDnzp0REBCA//u//0NeXl6111q6dCkiIiJgMBgwceJElJaW1hmbJEl4+eWXERcXB61Wiz59+uDf//53g4+ViIiIiBqHSXQiIqI2SiYTMCo5GiE6FdJzzDCXOuAUJZhLHUjPMSNEp8LI5CjIZEL9gxH5WV5eHr755htMmTIFOl3NN34qJ4jnzZuHESNG4NChQ5gwYQJEUUR0dDQ++eQTHD16FHPnzsXzzz+PTz75BAAwYMAAxMXFuSVzHQ4H/vWvf+GRRx4BAEycOBGnT5/Gd99959rGYrHgk08+wcSJEyFJEu6++27k5+fju+++w7Zt23Dq1Cncf//9Xh+vKIrYuHEjxo4di6ioqHqPtzZpaWmIjo7GwoULkZ2djezsbADAoUOHMGTIEIwcORIHDx7Exo0bsWfPHkydOtVt/xUrVuCGG27Ab7/9hmHDhuGhhx7Cww8/jLFjx2L//v2Ij4/Hww8/7HZjoaSkBC+++CJSU1Oxd+9eFBUVYfTo0a71n376KZ588kn87W9/w+HDh/HYY4/hkUcewY4dO9xe29v3sCFSU1PRoUMH/Pzzz5g2bRoef/xx3Hvvvbj++uuxf/9+DBkyBA899FCtrYJ++uknTJgwAU888QQOHDiAgQMHYvHixW7bfPLJJ5g3bx5efPFF7Nu3D5GRkXjjjTfqjOuFF17Au+++izfffBNHjhzBU089hbFjx7pde0RERETUfASpaikNoaioCEajESaTCYGBgf4Oh4iIqFEOZ5mwaX8m0nPMKLOLUCtlSAg3YGRyFJKi2NeXGq45f2f66aefkJKSgrS0NPzf//2fa3mHDh1cVb1TpkzBsmXLIAgCZsyYgRUrVtQ55pQpU3Dp0iVXhe/LL7+MDRs24OjRowCAzz77DGPHjsXFixddifuUlBR0794dqampAIB3330XU6ZMwYULF/DLL79g6NChOHPmDGJiYgAAR48eRc+ePfHzzz/jmmuuwfz587F582YcOHCgztguXbqEjh07Yvny5Xjqqadcy6+++mocP34cADB8+HB89NFH9Z672NhYzJgxAzNmzHAte/jhh6HVavHWW2+5lu3Zswc333wzLBYLNBoNYmNjcdNNN7luLFy8eBGRkZGYM2cOFi5cCAD48ccf0b9/f2RnZ6Njx47YsGEDHnnkEfz444+47rrrAAB//PEHevTogZ9++gnXXnstbrjhBvTs2RNvv/2267Xvu+8+WCwWfPnllwDQ4Pdw/PjxKCwsxObNm+s9L7fccgucTid2794NAHA6nTAajRg5ciTee+89t2P+4YcfkJKSgp07d2LgwIEoKChAUFAQxowZg4KCArdq9dGjR+Prr792Vatff/316NOnD958803XNikpKSgtLXVdB5Xjtlgs6NChA/773/+if//+rn0effRRlJSU4MMPP6zxeMrKylBWVub6uaioCDExMYiZ8Qlk6oB6zwcRERG1HxlLh/k7hBbD079pWIlORETUxiVFGTFnWCIW3NUTs4f1wIK7euKFYT2YQKdWqWr19c8//4wDBw6gZ8+ebgnEfv36Vdt3zZo16NevH8LCwqDX6/HOO++4TRQ6fvx4pKen48cffwRQ3qblvvvuc6t8nzhxIv7973+juLjYtc3IkSMRFBSEY8eOlSct/5dAB4DExETXOl8c76effooDBw5gyJAhsFqtDRoTAH799Vds2LABer3e9W/IkCEQRRFnzpxxbde7d2/X/4+IiAAA9OrVq9qynJwc1zKFQuF2/q+88kq3c3Ds2DHccMMNbvHccMMN1c5RQ95Db1U+PrlcjtDQ0HqPr7Jjx465JboBVPvZk20qO3r0KEpLSzF48GC39+e9997DqVOnat1vyZIlMBqNrn+Vr0MiIiIiahzOJEZERNQOyGQC4sL0/g6DqMHi4+MhCAL++OMPt+VxcXEAAK1W67a8asuXTz75BE899RRee+019O/fHwaDAa+88gp++ukn1zbh4eEYPnw43n33XcTFxeGrr77Czp073cYZPXo0nnrqKWzcuBG33HIL9uzZ46rKliSpxhYrtS2vS1hYGIKCgqodb+fOnQEABoPBrS+3t0RRxGOPPYbp06dXW1fxGgDcJvOsOIaalomi6DZGTcdbeVnV9TWdo4a8h96qOlmpIAgeHV/luH2t4rW+/PLLaq181Gp1rfvNmjULTz/9tOvnikp0IiIiImo8JtGJiIiIqMULDQ3F4MGDsXr1akybNq3Wvui12b17N66//nq3yThrqup99NFHMXr0aERHR6Nbt27VKqYNBgPuvfdevPvuuzh9+jTi4uJwyy23ACivOj937hzOnz/v1s7FZDKhR48eXsUrk8lw33334V//+hfmzJlTa190T6hUKjidTrdlycnJOHLkCOLj4xs8bm0cDgf27duHa6+9FgBw/PhxFBYW4sorrwQA9OjRA3v27MHDDz/s2uf777+v9xx5+h42p8TERNeTCxWq/tyjRw/8+OOPbsdbdZuqY6rVapw7dw4333yzx7Go1eo6k+xERERE1HBs50JERERErcIbb7wBh8OBfv36YePGjTh27BiOHz+Of/3rX/jjjz8gl8tr3Tc+Ph779u3D1q1bceLECcyZMwe//PJLte2GDBkCo9GIxYsXuyYUrWrixIn4/vvv8eabb2LChAmuauVbb70VvXv3xoMPPoj9+/fj559/xsMPP4ybb765xtYk9XnppZcQFRWF6667DuvXr8fBgwdx6tQpfPrpp/jhhx/qPN7KYmNjsWvXLmRlZeHy5csAgOeeew4//PADpkyZggMHDuDkyZPYsmULpk2b5nWcVSmVSkybNg0//fQT9u/fj0ceeQQpKSmupPqzzz6LDRs2YM2aNTh58iSWL1+OtLQ0PPPMM3WO6+l72JymT5+Or7/+Gi+//DJOnDiB1atX4+uvv3bb5sknn8T69euxfv16nDhxAvPmzcORI0dqHdNgMOCZZ57BU089hdTUVJw6dQq//fYb/vnPf7p68RMRERFR82ISnagSUZRwOteM388X4nSuGaLIeXeJiIhaim7duuG3337DrbfeilmzZqFPnz7o168fVq1ahWeeeQaLFi2qdd/Jkydj5MiRuP/++3HdddchLy/PraK5gkwmw/jx4+F0Ot0qhyu78cYb0b17dxQVFWHcuHGu5YIgYPPmzQgODsaAAQNw6623Ii4uDhs3bmzQ8YaGhroS8a+88gquvfZa9OrVC/Pnz8f999+Pd955x6NxFi5ciIyMDHTr1g1hYWEAynuBf/fddzh58iRuuukmXHXVVZgzZw4iIyMbFGtlAQEBeO655zBmzBj0798fWq0WH3/8sWv93XffjX/84x945ZVX0LNnT7z11lt49913XRX9tfH0PWxOKSkpWLt2LVatWoW+ffvim2++wQsvvOC2zf3334+5c+fiueeew9VXX42zZ8/i8ccfr3PcRYsWYe7cuViyZAl69OiBIUOG4PPPP0fXrl2b8nCIiIiIqBaC1BSN/Fo5T2dlpbblcJYJm/ZnIj3HjDK7CLVShvhwPUYlR3PyPSIiohq01d+ZJk2ahEuXLmHLli3+DqXV2bBhA2bMmNGofu3kGxWfz5gZn0CmDvB3OERERNSCZCwd5u8QWgxP/6ZhT3QilCfQV24/iXyLDZFGLbRGOaw2Jw5lmpBVYMX0QQlMpBMREbVxJpMJv/zyCz744AN89tln/g6HiIiIiIhaCLZzoXZPFCVs2p+JfIsN8eF66DUKyGUC9BoF4sP1yLfYkLY/i61diIiI2rgRI0bgrrvuwmOPPYbBgwc3+evp9fpa/+3evdujMXbv3l3nOO3VuXPn6jwv586d83eIRERERNSKsBKd2r2MPAvSc8yINGpdE4NVEAQBkUYtTuYUIyPPgriw9vvHKBERUVu3c+fOZn29AwcO1LouKirKozH69etX5zjNbfz48Rg/fry/w0CnTp3qPC+dOnVqvmCIiIiIqNVjEp3aveJSB8rsIrRGeY3rtSo5LhWJKC51NHNkVBNRlJCRZ0FxqQMGjQKxoTrIZEL9OxIREbUw8fHxjR5Dq9X6ZJy2RqFQ8LwQERERkc8wiU7tnkGjgFopg9XmhF5T/SNhtTmhVspgqGEdNS9O/kpERERERERERM2NPdGp3YsN1SE+XI9skxWS5N73XJIkZJusSAg3IDZU56cICfhz8tdDmSYEaVWI7aBDkFaFQ5nlyw9nmfwdIhERERERERERtUFMolO7J5MJGJUcjRCdCuk5ZphLHXCKEsylDqTnmBGiU2FkchRbhvgRJ38lIiIiIiIiIiJ/YRKdCEBSlBHTByWgV7QRhVYbMi5bUGi1oXd0EKYPSmCrED/zZvJXIiIiIiIiIiIiX2KTZ6L/SYoyIjEykJNWtkCc/JWIiIiIiIiIiPyFSXSiSmQyAXFhen+HQVVw8lciIiIiIiIiIvIXtnMhohaPk78SEREREREREZG/MIlORC0eJ38lIiIiIiIiIiJ/YRKdiFoFTv5KRERERERERET+wAbCRNRqcPJXIiIiIu8cXjAEgYGB/g6DiIiIqFVjEp2IWhVO/kpERERERERERM2J7VyIiIiIiIiIiIiIiGrBJDoRERERERERERERUS2YRCciIiIiIiIiIiIiqgWT6EREREREREREREREtWASnYiIiIiIiIiIiIioFkyiExERERERERERERHVgkl0IiIiIiIiIiIiIqJaKPwdABERERERETWNpHlbIVMH+DsMIiIiagEylg7zdwitVqurRF+yZAmuueYaGAwGhIeH4+6778bx48fdtpEkCfPnz0enTp2g1Wpxyy234MiRI36KmIiIiIiIiIiIiIhaq1aXRP/uu+8wZcoU/Pjjj9i2bRscDgduu+02WCwW1zYvv/wyli9fjtWrV+OXX35Bx44dMXjwYBQXF/sxciIiIiIiIiIiIiJqbVpdO5evv/7a7ed3330X4eHh+PXXXzFgwABIkoTXX38ds2fPxsiRIwEAqampiIiIwIcffojHHnvMH2ETERERERERERERUSvU6irRqzKZTACAkJAQAMCZM2dw8eJF3Hbbba5t1Go1br75Znz//fd+iZGIiIiIiIiIiIiIWqdWV4lemSRJePrpp3HjjTciKSkJAHDx4kUAQEREhNu2EREROHv2bI3jlJWVoayszPVzUVFRE0VMRERERERERERERK1Jq65Enzp1Kg4ePIiPPvqo2jpBENx+liSp2rIKS5YsgdFodP2LiYlpkniJiAgQRQmnc834/XwhTueaIYqSv0MiIiIiIiIiIqpVq61EnzZtGrZs2YJdu3YhOjratbxjx44AyivSIyMjXctzcnKqVadXmDVrFp5++mnXz0VFRUykExE1gcNZJmzan4n0HDPK7CLUShniw/UYlRyNpCijv8MjIiIiIiIiIqqm1VWiS5KEqVOnIi0tDf/973/RtWtXt/Vdu3ZFx44dsW3bNtcym82G7777Dtdff32NY6rVagQGBrr9IyIi3zqcZcLK7SdxKNOEIK0KsR10CNKqcCizfPnhLJO/QyQiIiIiIiIiqqbVVaJPmTIFH374IT777DMYDAZXD3Sj0QitVgtBEDBjxgy89NJLSEhIQEJCAl566SUEBARgzJgxfo6eiKh9EkUJm/ZnIt9iQ3y43tVeS69RIF6tR3qOGWn7s5AYGQiZrObWW0RERERERERE/tDqkuhvvvkmAOCWW25xW/7uu+9i/PjxAICZM2fCarXiiSeeQEFBAa677jp88803MBgMzRwtEREBQEaeBek5ZkQatdXmpxAEAZFGLU7mFCMjz4K4ML2foiQiIiIiIiIiqq7VJdElqf4J6ARBwPz58zF//vymD4iIWgRRlJCRZ0FxqQMGjQKxoTpWNLcgxaUOlNlFaI3yGtdrVXJcKhJRXOpo5siIiIiIiIiIiOrW6pLoRERVcbLKls+gUUCtlMFqc0Kvqf6fHqvNCbVSBkMN64iIiIiIiIiI/KnVTSxKRFQZJ6tsHWJDdYgP1yPbZK32RJEkScg2WZEQbkBsqM5PERIRERERERER1YxJdCJqtapOVqnXKCCXCeWTVYbrkW+xIW1/FkSx/jZQ1LRkMgGjkqMRolMhPccMc6kDTlGCudSB9BwzQnQqjEyOYgseImqz3n77bcTExEAmk+H111/3dzjNZv78+YiIiIAgCNi8ebO/w/GJ2NjYet/D+o43IyMDgiDgwIEDAICdO3dCEAQUFhb6LE4iIiIi8h0m0Ymo1fJmskryv6QoI6YPSkCvaCMKrTZkXLag0GpD7+ggTB+UwNY7ROR3OTk5eOyxx9C5c2eo1Wp07NgRQ4YMwQ8//NCocYuKijB16lQ899xzyMrKwl//+lfXOqvViuDgYISEhMBqtTb2EHxGkiS888476N+/PwIDA6HX69GzZ088+eSTSE9P92iMY8eOYcGCBXjrrbeQnZ2NoUOHNmnMniS3feGXX35xew+JiIiIqO1j81kiarU4WWXrkxRlRGJkICeBJaIWadSoUbDb7UhNTUVcXBwuXbqE7du3Iz8/v0HjSZIEp9OJc+fOwW63Y9iwYYiMjHTbZtOmTUhKSoIkSUhLS8ODDz5Y55h2ux1KpbJB8XhKkiSMGTMGmzdvxvPPP48VK1YgPDwcZ86cwbZt27B48WJs2LCh3nFOnToFABgxYkS1m90VbDYbVCqVL8NvcmFhYf4OgYiIiIiaGSvRiajVqjxZZU04WWXLJJMJiAvTo09MEOLC9Eyge0kUJZzONeP384U4nWtmuyIiHyksLMSePXuwbNkyDBw4EF26dMG1116LWbNmYdiwYdXab1TsIwgCdu7cCeDPlhxbt25Fv379oFar8f7776NXr14AgLi4OAiCgIyMDNcY69atw9ixYzF27FisW7euWlyCIGDNmjUYMWIEdDodFi9eDAD4/PPPcfXVV0Oj0SAuLg4LFiyAw/HnTePly5ejV69e0Ol0iImJwRNPPAGz2ezRudi4cSM+/vhjbNy4EXPmzEFKSgri4uIwaNAgLF26FO+++269Y8yfPx/Dhw8HAMhkMlcSffz48bj77ruxZMkSdOrUCVdccQUA4NChQ/jLX/4CrVaL0NBQ/PWvf3WLt2K/V199FZGRkQgNDcWUKVNgt9sBALfccgvOnj2Lp556CoIg1Jq0r2zDhg0ICgrCF198ge7duyMgIAD33HMPLBYLUlNTERsbi+DgYEybNg1O55+/a1SteD958iQGDBgAjUaDxMREbNu2rdpr/fzzz7jqqqug0WjQr18//Pbbb/XG9/3332PAgAHQarWIiYnB9OnTYbHw6ToiIiIif2BmiYh8ThSlZqk0rpis8lCmCfFqvdsfzBWTVfaODuJkldRmHM4yYdP+TKTnmFFmF6FWyhAfrseo5Gi2wyFqJL1eD71ej82bNyMlJQVqtbrBY82cOROvvvoq4uLioNFo8O233+LWW2/Fzz//jJiYGFcl86lTp/DDDz8gLS0NkiRhxowZOH36NOLi4tzGmzdvHpYsWYIVK1ZALpdj69atGDt2LFauXImbbroJp06dcrUXmTdvHoDyxPXKlSsRGxuLM2fO4IknnsDMmTPxxhtv1Bv/Rx99hO7du+Ouu+6qcb0nCepnnnkGsbGxeOSRR5Cdne22bvv27QgMDMS2bdsgSRJKSkpw++23IyUlBb/88gtycnLw6KOPYurUqW4V7zt27EBkZCR27NiB9PR03H///ejbty8mTZqEtLQ09OnTB3/9618xadKkeuOrUFJSgpUrV+Ljjz9GcXExRo4ciZEjRyIoKAhfffUVTp8+jVGjRuHGG2/E/fffX21/URQxcuRIdOjQAT/++COKioowY8YMt20sFgvuvPNO/OUvf8G//vUvnDlzBk8++WSdcR06dAhDhgzBokWLsG7dOuTm5mLq1KmYOnVqrTcxysrKUFZW5vq5qKjI4/NARERERHVjEp2IfKo5k3wVk1VmFVhdvdG1KjmsNieyTVZOVkltyuEsE1ZuP4l8i638WjeWX+uHMk3IKrCyrzxRIykUCmzYsAGTJk3CmjVrkJycjJtvvhmjR49G7969vRpr4cKFGDx4sOvn3NxcAOVtQDp27Ohavn79egwdOhTBwcEAgNtvvx3r1693VZtXGDNmDCZMmOD6+aGHHsLf//53jBs3DkB5hfuiRYswc+ZMVxK9ciK3a9euWLRoER5//HGPkugnTpxA9+7d3ZbNmDEDa9euBQAEBQUhMzOzzjH0ej2CgoIAwO2YAUCn02Ht2rWuNi7vvPMOrFYr3nvvPeh05Te+V69ejeHDh2PZsmWIiIgAAAQHB2P16tWQy+W48sorMWzYMGzfvh2TJk1CSEgI5HI5DAZDtderi91ux5tvvolu3boBAO655x68//77uHTpEvR6PRITEzFw4EDs2LGjxiT6t99+i2PHjiEjIwPR0dEAgJdeesmt//sHH3wAp9OJ9evXIyAgAD179kRmZiYef/zxWuN65ZVXMGbMGNf7mJCQgJUrV+Lmm2/Gm2++CY1GU22fJUuWYMGCBR4fOxERERF5ju1ciMhn7SEqknyHMk0I0qoQ20GHIK0KhzLLlx/OMvk4ck5WSe2jvYkoSti0PxP5Fhviw/XQaxSQywToNQrEh+uRb7EhbX9Wmzx2ouY0atQoXLhwAVu2bMGQIUOwc+dOJCcne9T/u7J+/frVu43T6URqairGjh3rWjZ27Fikpqa6tQ6pabxff/0VCxcudFXP6/V6TJo0CdnZ2SgpKQFQXrU9ePBgREVFwWAw4OGHH0ZeXp7H7UCqVpvPnj0bBw4cwNy5cz1uC1ObXr16ufVBP3bsGPr06eNKoAPADTfcAFEUcfz4cdeynj17Qi7/cx6UyMhI5OTkNCqWgIAAVwIdACIiIhAbGwu9Xu+2rLbXOXbsGDp37uxKoANA//79q23Tp08fBAQE1LpNVb/++is2bNjg9h4PGTIEoijizJkzNe4za9YsmEwm17/z58/X+RpERERE5DlWohO1c76qHK+a5Kv441uvUSBerUd6jhlp+7OQGBno88pwTlbZfrWX9iYZeRbX0xZVE1uCICDSqMXJnGJk5FkQF6avZRQi8oRGo8HgwYMxePBgzJ07F48++ijmzZuH3bt3AyhvF1ahoh93VZWTwbXZunUrsrKyqlU3O51OfPPNN26VzFXHE0URCxYswMiRI2uM/+zZs7jjjjswefJkLFq0CCEhIdizZw8mTpxYa8yVJSQk4I8//nBbFhYWhrCwMISHh9e7f32qHo8kSbW2iKm8vOqEqoIgQBTFRsVS05jevE7l66Hy9vVtUx9RFPHYY49h+vTp1dZ17ty5xn3UanWj2hARERERUe1YiU7VtIeqTirny8pxb5J8TYGTVbY//njywV+KSx0os4vQquQ1rteq5CiziyguddS4nogaLjExERaLxdXHvHJ/78qTjHpr3bp1GD16NA4cOOD278EHH6xxgtHKkpOTcfz4ccTHx1f7J5PJsG/fPjgcDrz22mtISUnBFVdcgQsXLngc2wMPPIDjx4/js88+a/DxeSMxMREHDhxwq5Lfu3cvZDKZa+JRT6hUqmpV/E0tMTER586dczu/P/zwQ7Vtfv/9d1itVteyH3/8sc5xk5OTceTIkRrf48pV/ERERETUPFiJTm7aS1Un+b5y3JXkM9ae5LtUxCQf+YY/n3zwB4NGAbVSBqvNCb2m+n+6rTYn1EoZDDWsIyLP5OXl4d5778WECRPQu3dvGAwG7Nu3Dy+//DJGjBgBrVaLlJQULF26FLGxsbh8+TJeeOGFBr1Wbm4uPv/8c2zZsgVJSUlu68aNG4dhw4YhNzfXlbivau7cubjzzjsRExODe++9FzKZDAcPHsShQ4ewePFidOvWDQ6HA6tWrcLw4cOxd+9erFmzxuP4Ro8ejbS0NIwePRqzZs3CkCFDEBERgbNnz2Ljxo1uLVV84cEHH8S8efMwbtw4zJ8/H7m5uZg2bRoeeughVz90T8TGxmLXrl0YPXo01Go1OnTo4NM4a3Lrrbeie/fuePjhh/Haa6+hqKgIs2fPdttmzJgxmD17NiZOnIgXXngBGRkZePXVV+sc97nnnkNKSgqmTJmCSZMmQafT4dixY9i2bRtWrVrVlIdERERERDVgJTq5tKeqTvJ95XjlJF9NmOQjX/L3kw/NLTZUh/hwPbJN1mptASRJQrbJioRwA2JD628hQUQ10+v1uO6667BixQoMGDAASUlJmDNnDiZNmoTVq1cDKJ8I1G63o1+/fnjyySerTQDqqYoJNAcNGlRt3cCBA2EwGPD+++/Xuv+QIUPwxRdfYNu2bbjmmmuQkpKC5cuXo0uXLgCAvn37Yvny5Vi2bBmSkpLwwQcfYMmSJR7HJwgCNm7ciNdffx1fffUVBg0ahO7du2PChAmIiYnBnj17vD/oOgQEBGDr1q3Iz8/HNddcg3vuuQeDBg1ynXdPLVy4EBkZGejWrVutNyB8TSaT4dNPP0VZWRmuvfZaPProo3jxxRfdttHr9fj8889x9OhRXHXVVZg9ezaWLVtW57i9e/fGd999h5MnT+Kmm27CVVddhTlz5iAyMrIpD4eIiIiIaiFIDWnS18YVFRXBaDTCZDIhMDDQ3+E0C1GUsOjLoziUaXKr6gTKEzTpOWb0jg7CC8N6tImqTgJ+P1+IF788htgOOshreE+dooSMyxbMHtYDfWKC6h2P1xA1J19fv61BxY3OfIsNkUYttCo5rDYnsk1WhOhUnEiX/KI9/s5E1FpUfD5jZnwCmTqg/h2IiIiozctYOszfIbQ4nv5Nw0p0AtD+qjrJ95XjMpmAUcnRCNGpkJ5jhrnUAacowVzqQHqOGSE6FUYmRzGBTj7RHp98SIoyYvqgBPSKNqLQakPGZQsKrTb0jg5iAp2IiIiIiIioCbWd7AI1CvtZtz8V7SEOZZoQr65eOZ5tsqJ3dJBX7SEqknwVffUvFZX31e8dHYSRyVFM8pHPNMX12xokRRmRGBmIjDwLiksdMGgUiA3V8eYUEXll6NCh2L17d43rnn/+eTz//PMejaPX62td95///Ac33XRTg+LzFV8dJxERERERk+gEgJPWtUcVleNZBVbXUwhV20M0pHKcST5qDk11/bYGMpmAuLDaE1dERPVZu3YtrFZrjetCQkI8HufAgQO1rouKivI2LJ/z1XESERERETEjSgDab1Vne9dUleNM8lFz4JMPREQN46sEd3x8vE/GaSotIZFPRERERG0Dk+gEoH1XdbZ3rByn1ozXLxERERERERE1NSbRyYVVne0XK8epNeP1S0RERERERERNiUl0csOqTiIiIiIiIiIiIqI/MYlO1bCqk4iIiIiIiIiIiKiczN8BEBERERERERERERG1VEyiExERERERERERERHVgkl0IiIiIiIiIiIiIqJaMIlORERERERERERERFQLTixKRERERETURh1eMASBgYH+DoOIiIioVWMlOhERERERERERERFRLViJTkRE7YIoSsjIs6C41AGDRoHYUB1kMsHfYRERERERERFRC8ckOhG5MMlIbdXhLBM27c9Eeo4ZZXYRaqUM8eF6jEqORlKU0d/hEREREREREVELxiQ6EQHwb5KRyXtqSoezTFi5/STyLTZEGrXQGuWw2pw4lGlCVoEV0wclMJFORERERERERLViEp2I/JpkZIUwNSVRlLBpfybyLTbEh+shCOU3Z/QaBeLVeqTnmJG2PwuJkYG8cUNERERERERENeLEokTtXNUko16jgFwmlCcZw/XIt9iQtj8Loij5/LUrkveHMk0I0qoQ20GHIK0KhzLLlx/OMvn8Nal9ycizID3HjEij1pVAryAIAiKNWpzMKUZGnsVPERIRERERERFRS8ckOlE7568koz+T99R+FJc6UGYXoVXJa1yvVclRZhdRXOpo5siIiIiIiIiIqLVgEp2onfNXkrEhyXtRlHA614zfzxfidK65VSfY29KxtGQGjQJqpQxWm7PG9VabE2qlDAYNu5sRERERERERUc2YNSBq5yonGfU1JBKbKsnoSt4ba0/eXyr6M3nflnqnt6VjaeliQ3WID9fjUKYJ8Wq92w0bSZKQbbKid3QQYkN1foySiIiIiIiIiFoyJtGJ2jl/JRm9Sd77c+JTX2tLx9IayGQCRiVHI6vA6nryQasqP+fZJitCdCqMTI7ipKJERNRmJc3bCpk6wN9hEBERUQuQsXSYv0NotdjOhaidq0gyhuhUSM8xw1zqgFOUYC51ID3H7FWS0ZsWJRXJ+2yTFZLkvl1F8j4h3IDOwQFtpnc6+8D7R1KUEdMHJaBXtBGFVhsyLltQaLWhd3QQb1oQERERERERUb1YiU5EriRjRYuRS0XlLUZ6RwdhZHKUR0lGb1uUeFohfK6gxOPe6XFhep+dk6bgTR/4ln4srU1SlBGJkYHIyLOguNQBg0aB2FAdK9CJiIiIiIiIqF5MohMRgMYlGRvaosST5P3v5wu96p3eknnbB558SyYTeHOCiIiIiIiIiLzGJDpRGyCKkk8qbBuSZKzaoqSiwlqvUSBerUd6jhlp+7OQGBlYY0z1Je/9NfFpU2hLx0JERERERERE1F4wU0PUynnbRsXXfNGipK7kvb8mPm0KbelYiIiIiIiIiIjaiwYn0cvKyvDzzz8jIyMDJSUlCAsLw1VXXYWuXbv6Mj4iqkND26j4UlO3KPG0d3pr6G3dlo6FiIiIiIiIiKi98DqJ/v3332PVqlXYvHkzbDYbgoKCoNVqkZ+fj7KyMsTFxeGvf/0rJk+eDIPB0BQxExEa30bFV5qjRYkvJj5tKVr7sfiqdRARERERERERUWvhVVZrxIgR+OWXXzBmzBhs3boV/fr1Q0BAgGv96dOnsXv3bnz00UdYvnw53nvvPQwePNjnQRORb9qo+EJztShpzMSnLU1rPZaK1kEnLxXDZHVALgPiwvQYf30sekcH+Ts8IiIiIiIiIqIm4VUS/bbbbsP/+3//DyqVqsb1cXFxiIuLw7hx43DkyBFcuHDBJ0ESUXVN3UbFU83ZoqQhE5+2VK3tWCpaB2UWlMBqc6LE7oTdIeF0rgX7MvLxt9u6Y0TfKH+HSURERERERETkc14l0adMmeLxtj179kTPnj29DoiIPNMcbVQ81dpblFDdKloHZRaUwGS1o8whIkClgE4lwCGKyDfb8No3JxDXQYderEgnIiIiIiIiojbGJ9m1w4cP47vvvoPT6cT111+Pfv36+WJYIqpDc7VR8VRrbVFC9cvIs+DkpWJYbU6UOUQEapSu600plyNYp0JBiQ0bvs/AK/f04XtORERERERERG2KrLED/POf/8SgQYPw3XffYceOHRg0aBBefPFFX8RGRHWoaKMSolMhPccMc6kDTlGCudSB9ByzT9uoeBNTXJgefWKCEBemZzK1jSgudcBkdaDE7kSASlGtB79SLoNSJsOpXAsy8ix+ipKIiIiIiIiIqGl4XYmemZmJ6Oho18+rV6/GkSNH0KFDBwDADz/8gLvuuguzZ8/2XZREVCO2UaHmYNAoIJcBdocEnar6jRGHKEEpFyCKUpP34CciIiIiIiIiam5eV6IPGjQI//jHPyBJEgAgNDQUW7duRVlZGYqLi/Htt98iLCzM54ESUc2SooyYMywRC+7qidnDemDBXT3xwrAeTKCTz8SG6hAXpodDFOEQxSprJZTYHAhQKWDUKpulBz8RUWO8/fbbiImJgUwmw+uvv+7vcMjHNmzYgKCgoCZ9DUEQsHnz5lrXZ2RkQBAEHDhwoNZtdu7cCUEQUFhYCKB54iYiIiKihvM6if7LL7/gjz/+wHXXXYfffvsNb7/9NpYvXw6tVougoCBs3LgRqampTRErEdWCbVSoKclkAsZfH4ugACXyzTbYHE5IkgS7U4TJaodaLoNWJUNChKHZevC3J6Io4XSuGb+fL8TpXDNEUfJ3SERNLicnB4899hg6d+4MtVqNjh07YsiQIfjhhx8aNW5RURGmTp2K5557DllZWfjrX//qWme1WhEcHIyQkBBYrdbGHoLPSJKEd955B/3790dgYCD0ej169uyJJ598Eunp6R6NMX78eNx9991NG2g7kp2djaFDh/o7DCIiIiJqRl6XDAYGBuLNN9/E3r17MX78eNx6663YvXs3nE4nnE4nKyiIiNqg3tFB+Ntt3fHaN8dRUGKHUiaDUi7AoFZCq5IhOjigzh78oihx0tkGOJxlcrVrKrOXt2uKD9djVHI0nzahNm3UqFGw2+1ITU1FXFwcLl26hO3btyM/P79B40mSBKfTiXPnzsFut2PYsGGIjIx022bTpk1ISkqCJElIS0vDgw8+WOeYdrsdSqWyQfF4SpIkjBkzBps3b8bzzz+PFStWIDw8HGfOnMG2bduwePFibNiwoUljaClsNhtUKpW/wwAAdOzY0d8hEBEREVEza/DEojfccAP27dsHo9GIq666Crt27WICnYioDRvRNwr/HJOM23pEoHNoACICNYgO1iIlrgOmD0qoNal7OMuERV8exbwtR/Dil8cwb8sRLPryKA5nmZr5CFqXw1kmrNx+EocyTQjSqhDbQYcgrQqHMsuX8/xRW1VYWIg9e/Zg2bJlGDhwILp06YJrr70Ws2bNwrBhw2pslVFYWAhBELBz504Af7bK2Lp1K/r16we1Wo33338fvXr1AgDExcVBEARkZGS4xli3bh3Gjh2LsWPHYt26ddXiEgQBa9aswYgRI6DT6bB48WIAwOeff46rr74aGo0GcXFxWLBgARyOP+eHWL58OXr16gWdToeYmBg88cQTMJvNHp2LjRs34uOPP8bGjRsxZ84cpKSkIC4uDoMGDcLSpUvx7rvv1jvG/PnzkZqais8++wyCILidp6ysLNx///0IDg5GaGgoRowY4XZOKirYX3rpJURERCAoKMh1fM8++yxCQkIQHR2N9evXu/apeH8+/vhjXH/99dBoNOjZs6frNSt89913uPbaa6FWqxEZGYm///3vbuftlltuwdSpU/H000+jQ4cOGDx4cKPPZ03npm/fvli/fj06d+4MvV6Pxx9/HE6nEy+//DI6duyI8PBwvPjii277VW3n8vPPP+Oqq66CRqNBv3798Ntvv1V7ra+++gpXXHEFtFotBg4c6Haea1PftUVEREREzcfrSnSHw4F33nkHR48eRZ8+fTB79myMHj0ajz32GDZs2IBVq1axOoOIqI3qFR2EV+7t43FVeUUiON9iQ6RRC61RDqvNiUOZJmQVWOtMvrdnoihh0/5M5FtsiA/XQxDKz69eo0C8Wo/0HDPS9mchMTKQFf314FMQrY9er4der8fmzZuRkpICtVrd4LFmzpyJV199FXFxcdBoNPj2229x66234ueff0ZMTIxrHp9Tp07hhx9+QFpaGiRJwowZM3D69GnExcW5jTdv3jwsWbIEK1asgFwux9atWzF27FisXLkSN910E06dOuVqETNv3jwAgEwmw8qVKxEbG4szZ87giSeewMyZM/HGG2/UG/9HH32E7t2746677qpxfcV3Q12eeeYZHDt2DEVFRa6ke0hICEpKSjBw4EDcdNNN2LVrFxQKBRYvXozbb78dBw8edFV9//e//0V0dDR27dqFvXv3YuLEifjhhx8wYMAA/PTTT9i4cSMmT56MwYMHIyYmxvW6zz77LF5//XUkJiZi+fLluOuuu3DmzBmEhoYiKysLd9xxB8aPH4/33nsPf/zxByZNmgSNRoP58+e7xkhNTcXjjz+OvXv3uuZjasz5rMmpU6fwn//8B19//TVOnTqFe+65B2fOnMEVV1yB7777Dt9//z0mTJiAQYMGISUlpdr+FosFd955J/7yl7/gX//6F86cOYMnn3zSbZvz589j5MiRmDx5Mh5//HHs27cPf/vb3+qMy5Nrq6qysjKUlZW5fi4qKvL2dBARERFRLbyuRJ80aRJWrVoFnU6Hd999F0899RSuuOIK7NixA0OGDEH//v3x5ptvNkWsRETUAnjag79qIlivUUAuE8oTweF65FtsSNufxR7fNcjIsyA9x4xIo7ZakkwQBEQatTiZU4yMPIufImwd+BRE66RQKLBhwwakpqYiKCgIN9xwA55//nkcPHjQ67EWLlyIwYMHo1u3boiKikJoaCgAICwsDB07doRcLgcArF+/HkOHDnX1RL/99tvdqqsrjBkzBhMmTEBcXBy6dOmCF198EX//+98xbtw4xMXFYfDgwVi0aBHeeust1z4zZszAwIED0bVrV/zlL3/BokWL8Mknn3gU/4kTJ9C9e3e3ZTNmzHDdaIiOjq53DL1eD61W6+ot37FjR6hUKnz88ceQyWRYu3YtevXqhR49euDdd9/FuXPn3KrGQ0JCsHLlSnTv3h0TJkxA9+7dUVJSgueffx4JCQmYNWsWVCoV9u7d6/a6U6dOxahRo9CjRw+8+eabMBqNrgr/N954AzExMVi9ejWuvPJK3H333ViwYAFee+01iJUmsI6Pj8fLL7+M7t2748orr2z0+ayJKIpYv349EhMTMXz4cAwcOBDHjx/H66+/ju7du+ORRx5B9+7dq1XSV/jggw/gdDqxfv169OzZE3feeSeeffZZt23efPNNxMXFYcWKFejevTsefPBBjB8/vs64PLm2qlqyZAmMRqPrX+WbGkRERETUOF4n0Tdv3oxNmzZh6dKl+Pbbb/Hll1+61j366KP46aefsHv3bp8GSURErQ8TwQ1XXOpAmV2EViWvcb1WJUeZXURxKR/rrw3b4bRuo0aNwoULF7BlyxYMGTIEO3fuRHJystf9v/v161fvNk6nE6mpqRg7dqxr2dixY5Gamgqn01nneL/++isWLlzoSmrr9XpMmjQJ2dnZKCkpAQDs2LEDgwcPRlRUFAwGAx5++GHk5eXBYvHsu6/q9+fs2bNx4MABzJ07t8FtTCpiT09Ph8FgcMUeEhKC0tJSnDp1yrVdz549IZP9+SdDRESEqy0OAMjlcoSGhiInJ8dt/P79+7v+v0KhQL9+/XDs2DEAwLFjx9C/f3+3Y7vhhhtgNpuRmZnpWlbT+9fY81lVbGwsDAaD2/ElJiZWO+aqx1fh2LFj6NOnDwICAlzLKh97xTYpKSlux1t1m6o8ubaqmjVrFkwmk+vf+fPn63wNIiIiIvKc1+1cwsPD8c0336Bbt27Yvn27q6Kn8voPP/zQZwESEVHr5EoEG2tPBF8qYiK4JgaNAmqlDFabE3pN9f9UW21OqJUyGGpYV5/20N6E7XDaBo1Gg8GDB2Pw4MGYO3cuHn30UcybN89VrFHR3gMon+SzJjqdrt7X2bp1q6s3eGVOpxPffPMNhg4dWut4oihiwYIFGDlyZI3xnz17FnfccQcmT56MRYsWISQkBHv27MHEiRNrjbmyhIQE/PHHH27LwsLCEBYWhvDw8Hr3r4soirj66qvxwQcfVFtX0eYGQLXJUwVBqHFZ5Qry2lR8FiVJqnZzoOL9rLy86vlu7PmsSWOPr/J1WBtPtqmqvmurJmq1ulHtj4iIiIiodl7/9b169WqMHTsWTz/9NCIjIxv1+CQREZVri4nNhiSC2+J5aIjYUB3iw/U4lGlCvFrvllSSJAnZJit6RwchNrT+BGFlh7NM2LQ/E+k5ZpTZRaiVMsSH6zEqObpN9ab35imIuDC9n6IkbyUmJmLz5s2uBG92djauuuoqAHCbZNRb69atw+jRozF79my35UuXLsW6devckuhVJScn4/jx44iPj69x/b59++BwOPDaa6+5Kpu9+d35gQcewJgxY/DZZ59hxIgRHu9XlUqlqlZVn5ycjI0bNyI8PByBgYENHrs2P/74IwYMGACgfE6lX3/9FVOnTgVQ/l5u2rTJLZn+/fffw2AwICoqqtYxG3s+m0JiYiLef/99WK1WaLVaAOXHXnWbyhOR1rRNVfVdW0RERETUvLxOog8ePBgXL17E5cuX3apUiIioYdpqYtPbRHBbPQ8NIZMJGJUcjawCqysZrFWVT8qabbIiRKfCyOQor24wtKdJXvkUROuWl5eHe++9FxMmTEDv3r1hMBiwb98+vPzyyxgxYgS0Wi1SUlKwdOlSxMbG4vLly3jhhRca9Fq5ubn4/PPPsWXLFiQlJbmtGzduHIYNG4bc3Nxaf+edO3cu7rzzTsTExODee++FTCbDwYMHcejQISxevBjdunWDw+HAqlWrMHz4cOzduxdr1qzxOL7Ro0cjLS0No0ePxqxZszBkyBBERETg7Nmz2Lhxo6une31iY2OxdetWHD9+HKGhoTAajXjwwQfxyiuvYMSIEVi4cCGio6Nx7tw5pKWl4dlnn/Wo33pd/vnPfyIhIQE9evTAihUrUFBQgAkTJgAAnnjiCbz++uuYNm0apk6diuPHj2PevHl4+umn3dqoVNXY89kUxowZg9mzZ2PixIl44YUXkJGRgVdffdVtm8mTJ+O1117D008/jcceewy//vprva2J6ru2iIiIiKh5ed0THSiv4goLC4PT6cSlS5eQk5NTrbqFiIjq15b7NlckgkN0KqTnmGEudcApSjCXOpCeY3ZLBLfl89BQSVFGTB+UgF7RRhRabci4bEGh1Ybe0UFeJ7zb2ySvlZ+CqElj2uFQ09Pr9bjuuuuwYsUKDBgwAElJSZgzZw4mTZqE1atXAyifCNRut6Nfv3548sknG5xUfO+996DT6TBo0KBq6wYOHAiDwYD333+/1v2HDBmCL774Atu2bcM111yDlJQULF++HF26dAEA9O3bF8uXL8eyZcuQlJSEDz74AEuWLPE4PkEQsHHjRrz++uv46quvMGjQINcEnzExMdizZ49H40yaNAndu3dHv379EBYWhr179yIgIAC7du1C586dMXLkSPTo0QMTJkyA1Wr1SWX60qVLsWzZMvTp0we7d+/GZ599hg4dOgAAoqKi8NVXX+Hnn39Gnz59MHnyZFcSui6NPZ9NQa/X4/PPP8fRo0dx1VVXYfbs2Vi2bJnbNp07d8amTZvw+eefo0+fPlizZg1eeumlOset79oiIiIiouYlSA1o0vfpp5/i1VdfdT1SCfw5YdCzzz6Lu+++29dxNquioiIYjUaYTKYmebyViAgoT2wu+vJoeaV2ePVK7fQcM3pHB+GFYT1adUuTmirME8INGJkchaQoY4s9Dy2ltYwv4jida8a8LUcQpFXV2FrHXOpAodWGBXf1bBPtTVrqNdUW8XcmqiojIwNdu3bFb7/9hr59+/o7nHat4vMZM+MTyNQB9e9AREREbV7G0mH+DqHF8fRvGq9LsN566y1Mnz4dEyZMwLPPPouIiAhIkoScnBxs3boVo0ePxqpVqzBp0qRGHQARUVvXXvo2J0UZkRgZWGsiuCWeh5bUWkYmExp93O2tvUlTtMMhIiIiIiKi9svrJPorr7yCN954AxMnTqy27u6778Y111yDF198kUl0IqJ6tKfEZl2J4JZ2Htpi7/CGTPLa2lW0w6m4GXKpqPxmSO/oINdTEET+NnToUOzevbvGdc8//zyef/55j8bR62u/0faf//wHN910U4Pia+169uyJs2fP1rjurbfewoMPPtjMERERERFRa+X1X8tZWVm48cYba11//fXX48KFC40KioioPWiPic2atKTzULV3eEVlvF6jQLxaj/QcM9L2ZyExMrBVVTF7O8lrW1HfUxBE/rZ27VpYrdYa14WEhHg8zoEDB2pdFxUV5W1YjRIbG4sGdItsEl999RXsdnuN6yIiIpo5GiIiIiJqzbzOSPTs2RNvv/02XnvttRrXv/POO+jZs2ejAyMiauvaa2KzqpZ0HlpiaxlfaM/tTXzRDoeoqfgqwR0fH++TcdoaTsJJRERERL7idRL9tddew7Bhw/D111/jtttuQ0REBARBwMWLF7Ft2zacPXsWX331VVPESkTUprTnxGZlLek8tLTWMr7E9iZEREREREREDeN1Ev3mm2/G4cOH8eabb+LHH3/ExYsXAQAdO3bEnXfeicmTJyM2NtbXcRIRtUlMbJZrKeehJbWWaQpsb0JERERERETkvQZlAWJjY7Fs2TJfx0JELZgoSky8NZH6Epvt5dy3hARvS2ot01TY3oSIiIiIiIjIO62zlI6ImtXhLJOrQrjMXl4hHB+ux6jk6HZTKd3Uaktstrdz7+8Eb0tqLUNERERERERELYPPk+jjxo3D+fPn8d///tfXQxNRHZqqWvlwlgkrt59EvsVWnlA0licUD2WakFVgxfRBCW0ymdsS8Nz7R0tpLUNERERERERELYPPk+hRUVGQyWS+HpaI6tBU1cqiKGHT/kzkW2yID/+ztYVeo0C8Wo/0HDPS9mchMTKQlbk+xnPvX4mRgdBc1xknLpkBSLgiwoC4DnqeayIiIiIiIqJ2yOdJ9JdeesnXQxJRHZqyWjkjz+JqaVG5NzQACIKASKMWJ3OKkZFnYY9lH+O595/21kKHiIiIiIiIiOrGknGiVqxqtbJeo4BcJpRXK4frkW+xIW1/FkRRatD4xaUOlNlFaFXyGtdrVXKU2UUUlzoacxhUA557/6i4KXUo04QgrQqxHXQI0qpwKLN8+eEsk79DJCIiIiIiIqJm1qBK9FWrVmHfvn0YNmwY7rvvPrz//vtYsmQJRFHEyJEjsXDhQigUnLOUqKk1dbWyQaOAWimD1eaEXlP9M221OaFWymCoYR01Ds9982MLHSIiaosOLxiCwMBAf4dBRERE1Kp5XYm+aNEizJ49GxaLBU8++SSWLVuGp556Cg8++CDGjRuHtWvXYtGiRU0RKxFV0dTVyrGhOsSH65FtskKS3KvZJUlCtsmKhHADYkN1DRrfU6Io4XSuGb+fL8TpXHODK+tbk5Zy7tsTb25KEREREREREVH74XUJ44YNG7BhwwaMHDkSv//+O66++mqkpqbiwQcfBABceeWVmDlzJhYsWODzYAFg165deOWVV/Drr78iOzsbn376Ke6++27XekmSsGDBArz99tsoKCjAddddh3/+85/o2bNnk8RD5E9NXa0skwkYlRyNrAKrK7moVZX3XM82WRGiU2FkclSTVuW21/7ULeHctzeum1LG2m9KXSpiCx0iIiIiIiKi9sbrSvTs7Gz069cPANCnTx/IZDL07dvXtT45ORkXLlzwWYBVWSwW9OnTB6tXr65x/csvv4zly5dj9erV+OWXX9CxY0cMHjwYxcXFTRYTkb80R7VyUpQR0wcloFe0EYVWGzIuW1BotaF3dFCjJi31RHvvT+3Pc98eVb4pVRO20CEiIiIiIiJqn7zOBHTs2BFHjx5F586dcfLkSTidThw9etRV6X3kyBGEh4f7PNAKQ4cOxdChQ2tcJ0kSXn/9dcyePRsjR44EAKSmpiIiIgIffvghHnvssSaLi8gfmqtaOSnKiMTIQGTkWVBc6oBBo0BsqK5Jq6DZn7qcP859e1VxU+pQpgnxar1bS5eKm1K9o4PYQoeIiIiIiIionfE6iT5mzBg8/PDDGDFiBLZv347nnnsOzzzzDPLy8iAIAl588UXcc889TRFrvc6cOYOLFy/itttucy1Tq9W4+eab8f3339eaRC8rK0NZWZnr56KioiaPlchXKqqVK1qeXCoqb3nSOzoII5OjfFatLJMJDZqctKGaetLU1qQpz70oSkzQ/w9b6BARERERERFRTbxOoi9YsABarRY//vgjHnvsMTz33HPo3bs3Zs6ciZKSEgwfPtxvE4tevHgRABAREeG2PCIiAmfPnq11vyVLljRZD3ei5tAWq5XZn7rptdd+83VprptSRERERERERNR6eJ1El8vlmD17ttuy0aNHY/To0T4LqrGqVq1KklRtWWWzZs3C008/7fq5qKgIMTExTRYfUVNo7krxptbUk6a2dxX95vMttvKKa2N5xfWhTBOyCqztuud6W7wpRUREREREREQN16ayTx07dgRQXpEeGRnpWp6Tk1OtOr0ytVoNtVrd5PERkefYn7rpWq2w33z92tpNKSIiIiIiIiJqOJk3G0+ePBnnz5/3aNuNGzfigw8+aFBQDdW1a1d07NgR27Ztcy2z2Wz47rvvcP311zdrLETUOBX9qUN0KqTnmGEudcApSjCXOpCeY27z/akPZ5mw6MujmLflCF788hjmbTmCRV8exeEsU6PH9qbfPBERERERERFRe+dVJXpYWBiSkpJw/fXX46677kK/fv3QqVMnaDQaFBQU4OjRo9izZw8+/vhjREVF4e233/Z5wGazGenp6a6fz5w5gwMHDiAkJASdO3fGjBkz8NJLLyEhIQEJCQl46aWXEBAQgDFjxvg8FiJq2okp22t/6qZutcJ+80RERO1H0rytkKkD/B0GERER+UHG0mH+DqHN8CqJvmjRIkybNg3r1q3DmjVrcPjwYbf1BoMBt956K9auXYvbbrvNp4FW2LdvHwYOHOj6uaKX+bhx47BhwwbMnDkTVqsVTzzxBAoKCnDdddfhm2++gcFgaJJ4iNqz5piYsr31p26OVivsN9/2NOXNLCIiIiIiIqL2TpAkSWrozoWFhTh79iysVis6dOiAbt261TmBZ2tRVFQEo9EIk8mEwMBAf4dD1CJVq5ZWlVdLZ5usCNGp2vXElI1xOteMeVuOIEirqjHBbS51oNBqw4K7eja4Z7coSlj05dHyfvPh1fvNp+eY0Ts6CC8M68FEbCvQHDeziGrD35mIWq6Kz2fMjE9YiU5ERNROsRK9fp7+TdOoMsOgoCAEBQU1ZggiaoU4MWXTaY5WKxX95rMKrK7e6FVvgrTlfvNtSVO3/iEiIiIiIiIiLycWJSICODFlVaIo4XSuGb+fL8TpXDNEscEP+Li1WqmJr1qtVPSb7xVtRKHVhozLFhRabegdHcTEaytR9WaWXqOAXCaU38wK1yPfYkPa/qxGXY/1vb6vrnsiIiIiIiKilowNb4nIa5yY8k++bqURG6pDfLi+vNWKunqrlWyTFb2jgxAbqmt07O2t33xb483NrIa2/qkNW8gQERERERFRe8IkOhF5jRNTlmtMK43aJoJs7lYrMpng8wQrNY+G3sxq7CSkbCFDRERERERE7U3bznARUZOoq1paFEWcuWxG1w56iJIEUZTaZGVzY/rC11fFW9FqpWKbS0Xl2/SODsLI5CgmKAlADTezJAnFZQ7YHSKUChkECdVuZjW2gpzzIRAREREREVF7xCQ6USvQ2MpRX6utWvqiqRR/XCyCU5QgAVjw+dE22+KhtlYakiShuNQBrVKO3zMLcfqyGfHhBtd6T6t4W1KrFVGUcPqyGScumQFIuCLCgLgOeiZJ/azyzaxQhwpn8iwosjrgFCXIBEAC0D8u1NX6p7Zr7+D5Qpy4WIxRV0ejb0xQndeZP1vIEBEREREREflLg5Lov//+Oz7//HOEhITgvvvuQ4cOHVzrioqKMGPGDKxfv95nQRK1Zy2193DVaunTuTZcLCqFQiagZ6dAdDRq23SLh5paaeRbbDhz2YwiqwMOUYTNIeIf357EYzd3Q1KU0esq3pbQauVwlglv7zqFfWcLYP5fWxC9RoF+XULw1wFxbeo9bW0qbmYdyy7CT2fyIQiAXq2AUi7AUuaAKAGXikpxNLsIiZGBNV57Nmd5u5eTOWak51pwRbge8RG1f79wPgQiIiIiIiJqj2Te7vDNN9/g2muvxccff4xly5ahR48e2LFjh2u91WpFamqqT4MkaiqiKOF0rhm/ny/E6VwzRFHyd0huKipHD2WaEKRVIbaDDkFaFQ5lli8/nGXya3xJUUbMGZaIecMTERemQ2SQBgO7hyEqOABymVCeHA7XI99iQ9r+rBZ3fhujcisNoDyBfjjLhHyLDSqFDFqlHCqFDGfyLK73ypsq3pbgcJYJi784il0nLqPU5oRRq4RRq4TV5sSuE7lY/OVRv1+D3mrpn3lvJUYGItyghkohg1ohQ6ldhN0pIcygxnVdQ+AQJaTtz8Lpy+Zq157rmi2xQa8ubwejkAt1fr9Uve6rai/zIRAREREREVH74vVfufPnz8czzzyDF198EZIk4dVXX8Vdd92F//f//h9uv/32poiRqEm01ArvCq2l97BMJkAmCCi02tE1VA9B5n5vrq22eKjcSqObSoczl80oczgRqFFCEACT1YFQnRpJkYFIz7UgbX8WhveJLK/iDZShuNTu6l1tUCsAQWhRVbyiKOHfv57H6csWKOTC/46r/DoLUchQWGLDmVwLNv2a6fdr0FMt/TPfEBl5FhSU2HFtbAgkAHanCKW8PIktCAJUCjlO5hTjxKVitwpySZLcrlmgvMpcJS8/J7V9v9Q1H4IkScg2WdE7OsjVQoaIiIiIiIioLfA6iX7kyBG8//77AMqTY88++yyio6Nxzz334KOPPsK1117r8yCJfM3TvtT+1Jp6DzdFi4eW1ge+JildQ3D0QhH2nytAQYkdWqUcDlFCic0BjUKOrh10EGQy13tVZO0Au1PEzxn5KLWLcIoS5DIBgVoF4jrooZTLWkwVb0aeBYeyiiBKEgJUiirXoACdWokSmwOH/ldh7+9rsD6t4TPfEBWfvQCjAvIaPh8Vnz1AcJuEtLjUgSKrw/Xe2p0i5DKhfELSOr5fapsPwWpzIttkRYhOhZHJUS3us0pERERERETUGF63c1Gr1SgsLHRb9sADD2DdunUYPXo0Pv30U1/FRtQkqlZ46zWKFtl6xGS1w1RiR6nNgeJSOyC5x6NVyVFmbxlVy75u8XA4y4RFXxzFzH8fxKxPD2Hmvw9i0Rctp3XI4SwTFn15FP/66RzMpQ4UlNhRZLWjuNQOm0Msr0CPMiJYpwLw53t12VyGPEsZLheXQSkXYNAooFLIkG+x4VBmIU7lFiMh3NAiqniLSx2u91NRQ0JU8b+ZK0vszhZxDdaltXzmG8LTz94VEXrEh+uRbbJCkiTYneU3ccrf2/IbP0atsvypCNT9/VIxH0KvaCMKrTZkXLag0GpD7+igVnszgqg1ePvttxETEwOZTIbXX3/d3+GQHwiCgM2bN/s7DCIiIqJ2yetyx759+2LHjh24+uqr3Zbff//9EEUR48aN81lwRE2hNVR4H84y4f0fz+JcQQkyC6xQKWSuauWKxGxL6j3syxYPh7NMWPzlUZzOtUCUJEACIADn8ktw7GIRXhiW6NckXdWK5kijFmFFauzLyIdcJkNCuB7RwVqg0jmw2pxQK2TYeyoPerUCTlGC1e6EIAiQywQEqOTIN9sgl8vwf1d1ahFVvAaNAlpV+ZMFDlGCUu4ek0OUAAEIUMpbxDVYl9bwmfdWxZMaJqsdYQY1zl62ICHCUOtnL66D3q2C3KBRQiYDSu1O2Jyi68mJiuu2vu+XpCgjEiMDW/zTIkTNLScnB3PmzMF//vMfXLp0CcHBwejTpw/mz5+P/v37N3jcoqIiTJ06FcuXL8eoUaNgNP7530Gr1YpOnTpBEARkZWVBq9X64lAaTZIkrF27FuvXr8eRI0cgiiK6dOmCW2+9FdOmTUN8fLy/Q2xysbGxmDFjBmbMmOHvUIiIiIiokbyuRH/88ceRlZVV47oHHngAqampGDBgQKMDI2oqrtYjqtpbj/izwrsiSXv2sgXB2vJEl1IuuCYBLLDYXMmxllK1XNHiIUSnQnqOGeZSB5yiBHOpA+k5Zo9bPIiihLd3ncYf2cVwiiICVAoEapUIUCngFEX8kV2Md3ad9lvFcG0VzR2NGnQK0qLM7sSlolK3fSreq/BANXKKStEtzICkqCCE6FSwOUSYSx2wOSSEBWoQqlNBp24ZCenYUB16RQVCJggosTkguT0JIcFSZodcENArytgirsG6tPTPvLcqnoSYt+UIlnz1B87kWpBrLsPBzMI6P3uVK8jtTicgAcVlDoQEqNyenPD0+0UmExAXpkefmCDEhemZQCcCMGrUKPz+++9ITU3FiRMnsGXLFtxyyy3Iz89v0HiSJMHhcODcuXOw2+0YNmwYIiMjERAQ4Npm06ZNSEpKQmJiItLS0uod0263NygWb0iShDFjxmD69Om444478M033+DgwYNYuXIltFotFi9e3GSvbbPZmmzspuB0OiGKor/DICIiIqJ6eJ1E/7//+z+sWLGi1vUPPPAAduzY0aigiJqSr1uP+FLlJG1ChAFXdAyEWiGH1e5EgEoOq92B4xeLvUpMNxdftHg4fdmMfWfzIQhAUIAKSnl5f2alXIagABUEAfjlbD5OXzY3wxFVV1tFsyAI6Bqmh06jRHZRKS4WlVZLZN4Y3wE2hwStSo4QnQrJnYNxdZdgXNU5CFd3CUa/LsFQymUtJpErkwm45+oYxHXQweGUUFhig83hhM3hRL7FBqcIdA3TYdTV0S3mGqxNS/7Me6viJtuhTBOCtCrEdtAhOjgAerUC5jIHMgtK6vzsJUUZMWdYIhaOSMLfh16J3tFGBGqVUMplDbrxRUR/KiwsxJ49e7Bs2TIMHDgQXbp0wbXXXotZs2Zh2LBhyMjIgCAIOHDggNs+giBg586dAICdO3dCEARs3boV/fr1g1qtxvvvv49evXoBAOLi4iAIAjIyMlxjrFu3DmPHjsXYsWOxbt26anEJgoA1a9ZgxIgR0Ol0rgT2559/jquvvhoajQZxcXFYsGABHI4//xu0fPly9OrVCzqdDjExMXjiiSdgNnv239+NGzfi448/xsaNGzFnzhykpKQgLi4OgwYNwtKlS/Huu+96NM748eNx9913Y8GCBQgPD0dgYCAee+wxt0T5LbfcgqlTp+Lpp59Ghw4dMHjwYADA0aNHcccdd0Cv1yMiIgIPPfQQLl++7NpPFEUsW7YM8fHxUKvV6Ny5M1588UXX+qysLNx///0IDg5GaGgoRowY4XbeK2J79dVXERkZidDQUEyZMsV1k+KWW27B2bNn8dRTT0EQBNfvDRs2bEBQUBC++OILJCYmQq1W4+zZs/jll18wePBgdOjQAUajETfffDP279/v0XkiIiIioqbndRK9oKAAq1atQlFRUbV1JpOp1nVELUVF65GK3sCV+bPCWxQl7D6Zi9/OFcKgUUAAEKIrrxAtr1qWIEkCCqw2dAkNaLbew6Io4XSuGb+fL8TpXHOdVeAVCboFd/XE7GE9sOCunnhhWA8kRRk9GufEpWKYSx3QqxUAqibvhPJEYakDJy4V+/YgPVRXRXOIToXe0UYEKOUoKLFXS2T2iQlyS+QKgoBArRKhejUCtUqU2kWvE7nevDcNkRRlxAt3JmLAFR2gUcnL+/Rb7dCq5Lj5ijC/t9bxVEv9zHurrt7uvaODEG7QIC5Mh1l3XOn22auqooL8/5KjMWtoD/Y2J/IRvV4PvV6PzZs3o6ysrFFjzZw5E0uWLMGxY8dw22234dtvvwUA/Pzzz8jOzkZMTAwA4NSpU/jhhx9w33334b777sP333+P06dPVxtv3rx5GDFiBA4dOoQJEyZg69atGDt2LKZPn46jR4/irbfewoYNG9ySyDKZDCtXrsThw4eRmpqK//73v5g5c6ZH8X/00Ufo3r077rrrrhrXV22tVZft27fj2LFj2LFjBz766CN8+umnWLBggds2qampUCgU2Lt3L9566y1kZ2fj5ptvRt++fbFv3z58/fXXuHTpEu677z7XPrNmzcKyZcswZ84cHD16FB9++CEiIiIAACUlJRg4cCD0ej127dqFPXv2QK/X4/bbb3dL4O/YsQOnTp3Cjh07kJqaig0bNmDDhg0AgLS0NERHR2PhwoXIzs5Gdna2a7+SkhIsWbIEa9euxZEjRxAeHo7i4mKMGzcOu3fvxo8//oiEhATccccdKC72z+88REREROTO67K71atX4+DBg5g2bVq1dUajEbt370ZRURFmz57tkwCJfK2i9UhFb+BIoxZalRxWmxPZJqtfKjAPZ5mwaX8mfjtXgPQcC3QqObIKrYjroEeIToXggODyBK7DiUtFZRib0qVZElwVcaXnmFH2vyRvfHh5b+XaXr8iQdewcTw95/6pjq1c0ayvIdmtUciREKHHpJviEBSgcusTLYqSz/rGAw17bxoiKcqI1++/Cqcvm3HikhmAhCsiDIjr0Hrad3jzma/oNd4S+3x70ts9p7gMRq3S497u7G1O5DsKhQIbNmzApEmTsGbNGiQnJ+Pmm2/G6NGj0bt3b6/GWrhwoauiGgByc3MBAGFhYejYsaNr+fr16zF06FAEBwcDAG6//XasX7++WruUMWPGYMKECa6fH3roIfz97393zWUUFxeHRYsWYebMmZg3bx4AuPXx7tq1KxYtWoTHH38cb7zxRr3xnzhxAt27d3dbNmPGDKxduxYAEBQUhMzMzHrHAQCVSoX169cjICAAPXv2xMKFC/Hss89i0aJFkMnK64Hi4+Px8ssvu/aZO3cukpOT8dJLL7mWrV+/HjExMThx4gQiIyPxj3/8A6tXr3adg27duuHGG28EAHz88ceQyWRYu3at6/v23XffRVBQEHbu3InbbrsNABAcHIzVq1dDLpfjyiuvxLBhw7B9+3ZMmjQJISEhkMvlMBgMbu8ZUN5S54033kCfPn1cy/7yl7+4bfPWW28hODgY3333He68806PzlVZWZnbDRwWNhERERH5jteV6Js2bcLkyZNrXf/YY4/h3//+d6OCImpqvmg94iuV2zMEB6ihU8shl7n3QK+oWtYqFQgKUMKoVTZrXBVtI4K0KhzKLF9+OMvk03FEUYJWKYNKIUOR1Q5Jcu8PKkkSLGUO6DUKXBGhd+3TlJXYVXlS0XxFRCBuSgir1ifaV33jAd+9N56SyQTEhxtwR69I3NGrE+LDDa0uyerJZ75yr/EXvzyGeVuOYNGXR31+PhuqqXq7t+Xe5s39HUE0atQoXLhwAVu2bMGQIUOwc+dOJCcnu6qTPdWvX796t3E6nUhNTcXYsWNdy8aOHYvU1FQ4ne7tq6qO9+uvv2LhwoWu6nm9Xo9JkyYhOzsbJSUlAMqrrAcPHoyoqCgYDAY8/PDDyMvLg8Vi8egYqt7smz17Ng4cOIC5c+d63BYGAPr06ePWA75///4wm804f/58nce3Y8cOt+O78sorAZRX7x87dgxlZWUYNGhQja/566+/Ij09HQaDwbV/SEgISktLcerUKdd2PXv2hFz+53dyZGQkcnJy6j0mlUpV7cZKTk4OJk+ejCuuuAJGoxFGoxFmsxnnzp2rd7wKS5Ysce1rNBpdTywQERERUeN5XYl+6tQpJCQk1Lo+ISHB7ZdLopaqJVRgVm3PAABZhUrkW2wI1ChQVOrAmcsWBAcoIQEeVys3tpq2alwVfwjrNQrEq/VIzzEjbX8WEiMD6xzXk3E2/ZqJzPwSfHEoG9mFVjidIiw2J8pMpQjSKqFVKeAUJZTYHBAl4JouIYjroPe6EtsXFcaNfYqhIpFbEfelovK4e0cHYWRylEc3b3z13rRHdX3mK25M5Fts5e+rUY6SMgd+OZOPoxeKMOGGrhicGOHXc1rfkxCtqbd7c2iupzWIqtJoNBg8eDAGDx6MuXPn4tFHH8W8efOwe/duAHC7CVvbJJ86Xf1PJW3dutXVt7syp9OJb775BkOHDq11PFEUsWDBAowcObLG+M+ePYs77rgDkydPxqJFixASEoI9e/Zg4sSJHk1MmpCQgD/++MNtWVhYGMLCwhAeHl7v/p6onKSv6fiGDx+OZcuWVdsvMjKyxpY3Vfe/+uqr8cEHH1RbFxYW5vr/SqV7UYMgCB5NEqrVVn+iaPz48cjNzcXrr7+OLl26QK1Wo3///l5NlDpr1iw8/fTTrp+LioqYSCciIiLyEa//0pbL5bhw4QI6d+5c4/oLFy64Hq0kaulqaj3SnGpqz9C1gx6WMhOKSh1QyWUoLLHhYlEpiksdHlUr+yJxdPqyGQczTQhQyl3Jxor4KtpGnMwpRkaepc7zV1/7iQCVAl8cvIBPf8uCQ5SgUcqg1yrhlACrzYF8iw0BdhEKuQCZIODKjnpMGhCHo9lF1RKeVpsThzJNyCqwVnuawJfJtMYmwht788aTlh6evDftVU2f+ZpuTORbbDhz2Ywiqx0ZeSVY8MUR/Hg6D6Ou9l8CtuJJCF+1BGrLaropUtd3BFFTSkxMxObNm13J1+zsbFx11VUA4DbJqLfWrVuH0aNHV2uhuHTpUqxbt84tiV5VcnIyjh8/jvj4+BrX79u3Dw6HA6+99prr9/pPPvnE49geeOABjBkzBp999hlGjBjh8X41+f3332G1WqHVagEAP/74I/R6PaKjo2vdJzk5GZs2bUJsbCwUiup/7iQkJECr1WL79u149NFHa9x/48aNrslMG0qlUlV7KqA2u3fvxhtvvIE77rgDAHD+/Hm3iVA9oVaroVarvY6TiIiIiOrndRL9qquuwubNm5GSklLj+k8//dT1hwER1c3VnsH456PAFZOJnrlshslqh8XmREGJHcmdg+tN0voicXQ4y4S3vjuFk5eKoVLIoJDJEKhVoOv/+rMD5W0jLhW5t42oqdK7puOrkG+xIT2nGIUldqgVMhi1CkgQYLU5oVHKYdQokF9ih0wmoEuIDr2jjRh1dTQSIwOx6MujHldiN0UyrbGJ8MbcvKnrnAI1vzdUt6o3JipaKZU5nAhQKRAsl8Fqd+KXs/nIKvRfArYlzufQEvFpDfKXvLw83HvvvZgwYQJ69+4Ng8GAffv24eWXX8aIESOg1WqRkpKCpUuXIjY2FpcvX8YLL7zQoNfKzc3F559/ji1btiApKclt3bhx4zBs2DDk5ua6VU1XNnfuXNx5552IiYnBvffeC5lMhoMHD+LQoUNYvHgxunXrBofDgVWrVmH48OHYu3cv1qxZ43F8o0ePRlpaGkaPHo1Zs2ZhyJAhiIiIwNmzZ7Fx40a3Fij1sdlsmDhxIl544QWcPXsW8+bNw9SpU+ss2pkyZQreeecdPPDAA3j22WfRoUMHpKen4+OPP8Y777wDjUaD5557DjNnzoRKpcINN9yA3NxcHDlyBBMnTsSDDz6IV155BSNGjMDChQsRHR2Nc+fOIS0tDc8++2ydCfzKYmNjsWvXLowePRpqtRodOnSoddv4+Hi8//776NevH4qKivDss8+6bhwQERERkf95XTI+depUvPbaa1i9erVbZYXT6cSqVauwYsUKTJkyxadBErVVldszVBaiUyG5czASI42ID9fhqVsT8MKwHnUm7qomjvQaBeQyoTxxFK5HvsWGtP1ZdfYErkg4n8mzQKWQQauUQ6WQuZKK+ZbyR4qrto2orZf0RZO1xuOTJMlV5esQJdhECXkWOwpK7LA5nLA5RGjVClwTG4LOIVpMHxSPOXcmIinK6FUlti/OSW381Ue6tmumQlts6dHUfa0r9xqvuDbLHE4EapRQymVQymUQIKBToKZR14wvtKT5HFoqb74jiHxJr9fjuuuuw4oVKzBgwAAkJSVhzpw5mDRpElavXg2gfHJLu92Ofv364cknn6w2Aain3nvvPeh0uhp7eg8cOBAGgwHvv/9+rfsPGTIEX3zxBbZt24ZrrrkGKSkpWL58Obp06QIA6Nu3L5YvX45ly5YhKSkJH3zwAZYsWeJxfIIgYOPGjXj99dfx1VdfYdCgQejevTsmTJiAmJgY7Nmzx+OxBg0ahISEBAwYMAD33Xcfhg8fjvnz59e5T6dOnbB37144nU4MGTIESUlJePLJJ2E0Gl3J9zlz5uBvf/sb5s6dix49euD+++939TMPCAjArl270LlzZ4wcORI9evTAhAkTYLVavapMX7hwITIyMtCtW7dab2hUWL9+PQoKCnDVVVfhoYcewvTp033W+oaIiIiIGk+Qqs6O54HZs2djyZIlMBgMiIuLgyAIOHXqFMxmM5599lksXbq0KWJtNkVFRTAajTCZTI16hJMIqLsXt8MhYmbaQRy7UISuYToEapSupI8kSUjPMaN3dBBeGNaj3iTt6Vwz5m05giCtqsZ+yeZSBwqtNiy4q2eNVdCiKGHRl0fLW0WE6bD/fCHyLTYYtUpIElBUakeoToW+MUE4lWtxxVWttUqlythgnQpapQwXCkvdKkKLrHb8eDoPhSU2SFJ59bRMJkCSAIdThCAAASoFru0agjyzDbOH9UCfmCAAwO/nC/Hil8cQ20EHeQ3nxClKyLhswexhPWDQKBp1Tloit/cpvHpLD2+umdagOfpaV/7siJKEX88WQKUoT54DgN0pwuYQ0S82GAKEFnHN+KLHf1vlzXdExfcKNRx/Z6KmNH78eBQWFmLz5s3+DqVVqvh8xsz4BDJ1QP07EBERUZuTsXSYv0No8Tz9m6ZBpYovvvgiRowYgQ8++ADp6emQJAkDBgzAmDFjcO211zY4aKK2pq4EIABs2p+JM7kWZBeVIqvQilCdCvERBmgU8vIkdIAS13UNwaEsU72Jssa2+XCr3pTJENdBj5IyE0xWOwJUCmiVcuRZbDhyoQidgrQYmRzlOoa62iYEBGkRHKB0az9hKrGhyGqHAEChkEEQBAgABAFQKWQosztRYnPAbLVXq6r2ZnLFttj6pD219GiuvtaVe40HByjhFCUoXOevfFLbUJ0aBrUCTgkt4prx93wOLRknYCUiIiIiIiJfa/BfkNdeey0T5kR1qCsBeCy7CJAAhyghOjgAYXo1TuQU47LFhoIz+ejaQYfOIQGQAPzrp7MeVeBWThzp1OUTgtqdIpTy8mRRfYmjqgnn4P/1Zj992YwiqwMOpwibU0TXDno8dnNc+bpcc71tE/JLbBh7XRf8eCbPNRGnxeaAIBNg1Chhd4oos4tQKmSoGEEul8HhFHGxuBQ3JYS7TZTozeSKGXmWNplMa+zkpq1Bc/a1rnxj4kKhFRIk2J0iBEFAic0BtUKGcIMa+RYbbE4RakXru2baE07AStT0hg4dit27d9e47vnnn8fzzz/v0Th6fe03A//zn/80KDYiIiIioqbgdRagpKQEzz77LDZv3gy73Y5bb70VK1eurHOiHKL2ps4EoEqH/x7PBQAM7B4GmUwGvUaB/noVikrtOJ1rQYhOCYvNgcISu8cVuBWJo59O58HhlFBU6oBTlCCXCQjUKKCQC0iJ61Br4qim6s1gnQpXBwSjuMwBU4kNVruIJ2+NR3y4AYDn1e8djRrMGZboaj9xvsCCRZ8fg1OSoFcrYHfaYXeIUMhlACQ4nCIkCQgOqF5V7U0ldltOpjV2ctOWzpu+1t5UZNfWBqXixsS/fz2Pb45eQkGJHTqVHDpV+WfhZI4ZDqeIMqeImGAtzGWt5+mF9qY9Pa1B5C9r166F1WqtcV1ISIjH4xw4cKDWdVFRUbjpppu8DY2IiIiIqEl4nUSfN28eNmzYgAcffBAajQYfffQRHn/8cfy///f/miI+olaprgSg2eaE+L+pCMxlTgRq/ze/ryAgUKtCtzAB+88XIESnQlIno8cVuDKZgL4xQfji9wuw2pwI1CqhVctR5hBxodAKrUqOPjHGWhNHtSacBQF6tQIXTaXoHR2EuA5/Jiy9aZtQuf2EQaNApyAtLphKUeYQEahRoMTmQJlDhFOUIBOAoAAlJt7Ytcaqak8rsdt6Mq0tt/RoilY89fVXr7gx0T8uFOv3ZiDPXIbiUgccogi1Qg4IcCXVV/83nRN5tmDt4WkNIn+KioryyTjx8fE+GYeIiIiIqKl5nURPS0vDunXrMHr0aADA2LFjccMNN8DpdEIurznZQdTe1JUAtDtEQAIglE9YWJVTkmAudaBbB51XFbiiKOHA+UKEGdSwO0UUlzphLnNCLhPQKUgLhVzA7+dNGN67U41J44YknBta6R0bqkPfzkEoq1Q1r1bIoVIAAUo5lAoZBiSEYXBix2pxVlQSO0UJD17XGQBgKXPWWond3pNprXUCSl/3tfa0v7pMJmBIUiQ6BWnx3KaDsNqdUCtkECUgVKdCbAe9q8e/r9rJUNNo609rEBERERERUfPxOol+/vx5t0crr732WigUCly4cAExMTE+DY6otaorAahUyFDR/Fspl1Xb1/y/ylq9Rlnj2LVV4FZUv3cLM0CvlqO4zAG7o7zXuEGtgLnMWW/7C28Tzg2t9K68X565DFHBAZDLAKcIFFltCNWrMerqaLf9RFHCtqOX8PnBC8g2lUIGQKOSuyqJ66rIbopkWmtITtdXed2S+bIVT0P6q+vUCoToVIgI1EIpF1xzC1Ts29B2MtS82vLTGkRERERERNR8vE6iO51OqFQq90EUCjgc7A9LVKGuBKBeJYesIomndq9UlyQJBSU26DUKKISaE7K1VeC6Vb8LAgxVkvCetr/wNuHc0ErvqvtZysr36xMTXG2/w1kmvL3rNPamX0aZQ4RGKUNwgAqRKkWdfeIr82UyrTUkpz2tvG6pfNmKpyH91YtLHbA5JHQKUkNew2s0pJ0MEREREREREbVOXifRJUnC+PHjoVarXctKS0sxefJk6HR/VgSmpaX5JkKiFqi+KuT6EoBxYTpAAk7lWqqtizRqEBemx4VCK+IrVb4CdVfg+rL9hbcJ57oS73WdK08S9oezTPjH9pP4/XwhRElCuEEFhwgUlNhQYnMiqVMg8iy2ZmutcTjLhH98ewLZplIEB6gQqldBLggtKjndkMrrlshXrXga0l/d1+1kfK01PAlBRERERERE1FZ4/df/uHHjqi0bO3asT4Ihag08rUKuLwEIoM51K7ef9KoC15ftLxqipsS7J+eqroR9RTI422SFAMCgUUIQZFDKgUCNEkWldmTkWZAQbmjS1hoVCUuT1Y5V/z2Jw5kmyGQCLhWVQS4TEKhVoGuorlmT+XVpSOV1S+WLVjzeJsRFUYIoSTBqlThz2YyenQIhk/3Zeqm+z1NTJ7hbw5MQRERERERERG2J10n0d999tyniIGoVvG2RUV8CsK513lbg+rL9hT/OVU0qksHBWiUumcqgqBS7IAgIUClgsjrglCSU2b1rreFporNywvJiYSnO5FkglwkIDlDCoFHAIUrIt9hgKXOiW5i+RSSnG1J53ZI1thWPNzeYKr/f+RYbLppKkVNchis7BqKjUVPv56mpE9ytvU0PERERERERUWvkn+fQiVqhhrbIqCsBWNe6hlTg+qL9hS+qaH3VTqQiGRyqV0EuE+AQJSjlf24vlwlwihLMpQ6vWmt4muisnLDsGKhBhsNSvkKSUFTqgEIug1ohd1XFZ5usCNIq/Z6cbumtSJqbpzeYjmYXuSWoI41ahOpU+ONiMY5cMCHPXIYQvarWz1NTJ7jbSpseIiIiIiIiotamfWRQqF3zVWsFf7TIaEgFbmPaX/iqitZX56oiGawQylum5FtsMGqVAMrHdIoS5LLy/ujXdg31qFWNp4nOqgnL4lIHrHYn5DIBiv8l74tL7VDrZa6q+IISGwK1Sr8np/3d2qclSooyYtpf4rFhbwZOXbZAFMvbtfSKMiIlLgR2p4h//XC2WoI6KjgAnYwaHM4uQlyoDtNvTUBcB321z1NzJLjbUpseIiIiIiIiotaESXRq03zZWqE1tchoSPK9sVW0lW9WZBWW+ORcVU4Gdw3VoaTMCZPVjgCVAnIBMJfaoZDLEGnUeNSqxptEZ0aeBemXzDBoFCiw2GCxOSAAUCsE2BwSFPLy/7U7y6vj5TKg1C6ik1Hr9+R0S2vt0xIczjIh7bcsXCoudd18USpkuGwuxb9+OofCEjvOF5QgSKtEB70awTqVa19BJkPXUD0KrDbIBKHG89YcCe7W9B1ERERERERE1JYwiU5+1ZQT8Pm6tUJbbpHR2CraqjcrnP+rdtYoZYgKDqi2vafnqnIyOM9iQ1yYDhdMVhSW2FFqF6FWyNA/LhSTBsR59F56k+g8cL4QJ3KKAQkQpfIK7hK7E1qVHE7RCbtTAgA4RREAyqvSFTLc2TvSZ9dwYz4fvmjt01ZU/S7oFBSAiyYr9p8tAAD0jg5CRKAaWQVWFJfacTjLhKQoo1sivb4EdXMkuH3xHdQU37lNPZEqERE1zuEFQxAYGOjvMIiIiIhatdaX7aM2QRQlbDt6CZ8fvIBsUylkADQquc8m4GuK1grN1SKjqRNSNY3fmCramm5WlJQ5cD6/BAczTdAq5QjRq13be3uuqiaDQwLUCNKq0MmowbDekRic2NHj8+NpovPA+UJs+jUTVpsTOpUcSoUMNocIhyiiqMSJYJ0KJTYRNqcIq02EUi5BJZchJS4UgxMjPIqlPr54iqIxrX3aihq/CyQJF4tKIZeVX9+XiqzoFq6HSiGDSiGgxObEmcsWBAcogf99HupLUDfHTbbGfgc1xaSnTT2RKhEREREREVFLwCQ6NbvDWSa8ves09qZfRplDhEYpQ3CACpEqhc8m4GuK1grN0SKjqRNStY3fK8rYoCra2m5WGLRKXBUThB9P52Hf2XwkdgyEXquEXBBwsajU63Plq2SwR4lOhQx70i+j1O5EkFaJHHMZBACSBAgon9y00GqHTqVAqF6NmOAAFFrt6GTUYtKAOJ8kqH3xFEXVmyW9ooztKnleoabvguIyB4qsDujUSgCAyeoAJLj67muVcpisdhSXOWDQKD1KUDfHTbbGfAc1xaSnTT2RKjUcnw4gIiIiIiLyLSbRqVkdzjLhH9tP4vfzhRAlCeEGFRxi+cSQJTYnkjoFIs9ia/QEfE3VWqEpW2Q0dUKqrvFPXCyG3Sl6XUVb180KCAL0GiVyzWX4LdMEmVD+JMA1XUI8ar9SUxKosZMlepLo7BIagEtFpdCplThfYIUolrdtUcplAACnTIDdIcEul6BXKyEIAq6NDfVZixRfPEXB6uA/1fRdYHeIcIoSFDIBEsonqHWIErp20MNSZoLV7oQoAqU2JwQIHt0ka64+9A35DmqKJ3OaYyJVahh+/omIiIiIiHyPSXRqNhVJl2yTFQIAg0YJQZBBKQcCNUoUldqRkWdBQrih0RPwNWVrhaZokdGQhJQ3lYb1jX/yUjHKHCKyTVavqmhru1mRb7HhcJYJZQ4n9Co5rowMhFwACqx2lNid9Z6Pw1km/PvX8ziUVQSrrbwPea+oQNxzdUyjkkCeJDpvjO+AD386h0JrGURJQphBDXOZAzaHBEmSoJCVV6T3iDTg+Tt6wKhV1njuG1oJ2tinKFgd7K6m7wKlQga5rPypAgCQywQo5TIEapVIijLixMUiFFjtyCkugzFArPcmWcV77RQljEqOwo+n85Ge23R96L39DmqKJ3OaYyJV8h4//0RERERERE2DSXRqNhVJl2CtEpdMZVBUSvgIgoAAlQImqwNOSUKZvXET8DV1awWZTGh0YqhykrWwxIb0S54npDypNKw6/slLxbWO3ykoAJkFJVArZV5V0daUoJQkCWcum1HmcEKrlMPulBCiU8GgUSJKkjyapHTxF0dx+rIFoiS5lp/PL8Ef2cV44c7ERiWB6qvkDVDJ8a+fzqGgxAaVXAYBAgK1SkAqb+kiShJsThGSBBi1yloT2Q2tBG3MUxSsDq6upu8Cg1qBQK0CeeYyCIKAUJ3KdUMtOECJEJ0KyV1CMDalc603SSrU9F53C9Nh7HVd0NGoabJWGt58BzXFkznNMZEqeYeffyIiIiIioqbDJDo1m4qkS6he5aoCVcr//ENeLhPgFCWYSx1eV4nXVPXbHK0VGqpq4s3mdCLbVIo+UUE1Vs5XTkh5UmkIoMr4IrILregdo4C+ho+9ViWHUi7DPcnROJhl8rhNRE0JyqJSO/ItNsgFwFzmQJhBDYO6/DXrq1AVRQlv7zqFPy4WQyEvv7Gi+N+1UmJz4I+LxXhn12msuL9vo967uip5RVFCgEoGU4kd5R1cBAiCAJVCgEGthM0pIjhABZkg1JggbGwlaGOeovBXdXBL7r9c29MHHQM1yCkqAyAhIlALUQKsZQ5km6wI1avxyA2x9d7wqO29PpxVhAuFpZg+KKFFVGE3xZM5zTGRKnmHTwcQERERERE1Hf51S82mIumiEATXBH5GrRJA+R/7TlGCXFbeH/3arqEeV4nXVfXbVP3LG6OmxFtucRlO51pwMLMQfTsHI1inctunIiGlU8vxwU/n6qw0fGfXaZTYnShwG78Up3LNOJhpQt+YIITUMn6fmCCM6BvlcUK0aoIyQKXAmctmFJbYy1ufyGUos4soKLG7jqmmCtWKJOzR7CL8eDofMqG8xU/F8SnlAgI1ShSW2PDL2XycvmxGfLihUe9DbZW8R7OLUGR1lPfKlgDV/270lNpFWG1lCApQItKogQRUSxD6ohK0MU9R+KM6uDX0X67t6YObrwiDBAkFJXZkXLZ49f3Qmqp+m+LJnOaYSJW8w6cDiIiIiIiImg6T6NRsKidduobqUFLmhMlqR4BKUV61XGqHQi5DpFHjcZW4J1W/c4Yltpgq2doSbxGBanQM1OBCoRWnc824OiAY+N+6ygkpAHVWGnYM1OCXs/kI0amQ1Mno2qZjoAYdA9XILizFmctmBAcEu9ZVTXh526qmIkH59q5T2HPysqvnuVopR6BGAYutvHo+KcqIYJ2qWoVq5STshUIr8ixl0CrksDlFqBV/JoMEQYBOrYDJaseJS38m0Wurgm5IdXTF+6OQCegUpEFOcRmc/+ubLRMACIBGIUOJzVljgtAXlaCNmaCyWnWwJKG4zAG7Q4RSIYMgwafVwa2p/3JtTx8A8Evv+ubUFJOeNtdEquQ5Ph1ARERERETUdPiXFDWbykmXPIsNcWE6XDBZUVhiR6ldhFohQ/+4UEwaEOdR4s3jStBhgX5PYlU4fbm8GjxAKXcl7QShvF1I1zA9TKUOZBeV4mJRKcIMmmoJKUuZs85KQ6dU3g6nWwede2JPENAtzIBiqwMXTaW4VFSGMIPaZwmvxMhASChPHmoVMpRIIuwOESU2BwI1KpQ6nDhz2YIgrcItYV81CetwSjibV4Iyp4gCiw3BOpVbIv1P5YntQ5mF2LA3A6cuWyCKEoxaJeIj9OgbE4QD5wu9ro6uSIx2CgpAB4MGTrEQVpsTKrkMSoUMTlFCgdWOmNCAGs+XrypB6+vbXtsxVL5RFepQ4UyeBUVWB5yiBJlQftb6x3n+lEddWlMldoXabhA15PuhtVX9NvSaau4xqeH4dAAR1SZp3lbI1AH+DoOIiIj8IGPpMH+H0GYwiU7NqmrSJSRAjSCtCp2MGgzrHYnBiR09Tri1xErQuqqfD2eZ8NZ3p3DyUjFUChkUMhkCtQp07aBHiE6FEJ0KvaONOHi+EAUldljKnNUSUqdzzXVWGpr/l7DTa5TlC6pUIidFG3Eoy4SCkjJYyhw+S3htO3oJ36fnQZQkGLQqBKhF5FtsKLGJsDvLEKhRIs9ShsPZRehk1GJkchQAVEvCOiURKoUMDqcIhyihuNQBtV6G8pY/EsxlDug1ClwRYcBnB7Lw2jfHUVBih1Img1Je3o89q9CKL36/gDCDGt3CDF5VR1dOjOr/P3tvHiZXWaf9f85a+9Jb0kmnszRJgBASFkEQxQ1EREVFcUNFGXyRcRxHZ3RcGGVgxGVYBv0p4wjK6DgvXIKOio4DzCsKsq8JYUs6W+9r7VVnfX5/nKqi9y2dpMHnc11eM91Vdc5znvOcanJ/7+f+qjqb29LsHiqQK7tUHB9Vhaih8e5pxPjFdILOlNs+HbVC1TO9OR7cPYKiQDykY2gKRcvFF9Cfq7CjN3fAAudSfP4OJS9F1+9C1tThOKZkYcjdARKJRCKRSCQSiURy8Fg6/7qX/NmwWKLLUnOCzpQNDXD93S/Qky1j6ioRQ0NRFEaKNkUriDppjJmEdY0Ny+Nc/JoO0lFz0tzM5jQcLdnEwzq6ojBatOmsCsBB3rxC2FBZkYrwN2dsmPL4C8H3Bb96qgfL9VmWMFEUFUNTaYop5C2Hku2RrTiYmkpHU4yPv/aIekFgogibDBs0x0P0ZcsIIbAcD9v1URSFouXg+oL1LXGe68/zz//zHKOFwK1uaCquH4jsZTsQixtiJrFQMM/xsM56M8b23hzfv2cXnzpjAx3N8UnXPVEYbYyZNEQbyFdcHM/H8QSO57G1PT3lXCy2E3S+0ToQ7ApYlgjROVhEVYIsd01VaEmEWNsUY7hoL4pDfNbnz1DZO+zw2N5RgJedsPpSdf0uZE0djmNKFobcHSCRSCQSiUQikUgkBwcpoksOC4shuiwlJ+hM2dBdo2UihspI0WbziiS269ebqibDBrmKw56hAulIui68vWZDy5SC42xOwxWpMB0tcXYO5MmWHCzPJ2rq6KqC4/kM5a26wFzLWD9QOocK7BkqoqsKFccnYgbh4SFDw9RVoqZHyXZpb4jyqTM21LPMpxJhFUXh6BVJKo5HruLgej65ioOCguUGsSpDeYvLf/k0mZJDY9TErMa9GJpC1NTIlgPBPld2yFdckhGjXlAYKdrsGy7Rna2wZVVqUrzLVMKooigkIwZCCHYOFGYURhfLCbqQPPcae4aLjJYcTl7biAAcz8fQ1Hp0kKlri+IQn+n5Gy3aPNuXI1N2uPn+vfzXkz0HrdnogczVgSBdv5KlitwdIJFIJBKJRCKRSCSLjxTRJS9ZlooTdLZs6O09WUaKNie0N6CoKh3NcUpWtt5UNWJoDBdtnu7JsTIdmVV4m81pKITgL3/6GNmyQ2PcRFMVXF9QdjxSUYN4SOMXj/eweWXqgEWV7d1ZbrhnF3uHizieoGi5REyVZCTIMlcUhbCukq9AR3OMjuYXRdvpRNjGmMnxqxt4vi/HUNEmYugULJeIqXHMihTxsE53towvIFdx0HW1npvu+6AqCp7vY7sCp5qtvr07S8X1iBjB+6KGNmW8y2IIo/Nxgk4lAO/ozU27o2EuAnStOBFN6WhTjHOxdmhM9/yNFm22dWXIlh1akmGOak1QcfyD0mx0pt0fh8JxK12/kqWK3B0gkUgkEolEIpFIJIuLFNElc+ZwOT6nY6k4QWfLhm6IGOwbLuGJoBlmQ8wM4kyqUSuu52N7Puua4/yf186tqepMTsPOwQJNsRCaolB2fDzfRVMVmmIma5vjmJq6KE7kmvu+J1MmZGhEzSCTvJaD3hgz0VSVQsUhpKucs2XFuHsxUxGkIWoQNlSWJUJkSw6+LzBUlf58BccPoSoKhqZMyk1XVQVVAc8HRQnc6S8MFKi4HqmIgeOJaha9QVtDZMrml4shjM7FCTqVANwQNRjIW7iemLSjYa4C9MHcoTHxO+Bdx7eNf/4MlWf7cmTLDqmowcblCXRNJa6pUzYbPZDvlJl2fyy2WD8T0vUrkUgkEolEIpFIJBLJyx8pokvmxOF2fE4nti0FJ+hs2dC1Jp+Fiks6agKBkH5itIG85ZIt2ZQdn78+Y3096mQuTOc0zFdcDE3lFWsbKdnepDgPzxcH7EQe674/ZmUS2wsiahpjJgXLpWR79cgaXVM5taOJMze1Thr/dEWQXYN5hgrB51GgKW7WM+QzJQcF0FQVT/hYjofjCQwtENZVRcHy/bpInCu7RE0dIaBkuzTFzPpcTNf8cjGE0ZmcoFMJwCXL5YHOEWzX55XrGusCeG1Hw1SC/1QcrB0a030HnLNlBU/sz7BzoMDeYYdM1YG+cXmCxphZ//zE+S7Z3oK/U2bb/THXuVospOtXIpFIJBKJRCKRSCSSlzdSRJfMyuF2fM4m4B9uJ+hszl+92thytGTT1jDGra4oxEM6fdkKW1alx0WdLMZ4Ko5PMmJMen0xsuLHuu9VVWVdc5yilcVyPZJhg4ihUXa8+r24+PSOKe/HVEUQUw/el4oYNMVMRooOhqYG2eRhg2zZxhegq0F8i+0JbMdDU4MYF18IQrpKxAhy0V3Px9AUchWXkKGxtvlF0XWmaJODJYxOJwALQFUCB/2e4VIgQFdfm0nwn2rci71DY7bvgE++YT3xkM5je0e5+f69HLk8TtnxGS5Y4wo4tfl+cn+G327vm7aHwLtPaKM1FZn2WZ5t98dc5+rlwFLbISSRSCQSiUQikUgkEsnLESmiS2bkcDs+5yrgH04n6KzO31yFk9Y0UrLdBYmaU4lkwLTC2aHIip/ovm+sRtTsrkXU+AIh4OjWJB9/7REzFlkmFkGe3D/KDfd0Vt3tDgXbxfF8UlGDkK4RCxkULRddVfEF+Pi4QpAtObi+T0sizHtPWsVQwWZbVxbb88GhHmcz1h19KJvP1phOAHY8H8+HeEgnW3bIWy6J8ItFkPlkmS/mDo25fAf84vEevnzO0QD8x4P7eHTvaDVKSKCpCsmIzrpqlJCpK9z7wtCUx2tyTR7fn+HpnixtqQhhU5vSnT7b7o+IobJ32OGxvaMAswrLL1Uh+nDvEJJIJBKJRCKRSCQSieTPBSmiS2bkUDk+pxOKl1Jkw3TMxfl78ekd9euZj6g5XW42KIyW7CmFs0ORFT+V+74xZtIQbSBfccmVHUqOx6fO2DCviJpdgwV+9lh30BQ1ZmJoKrbnU3Y8vKKo/05VFNY2R9k9VCIW0miOh9FUOKIlwUdetYYtq9L4vqBzqMB1d73AnqEix6xMoqpq/VyLVVCYrwA7nQBsaGq9EajnCxzXH/f6dIL/TFFHi7FDYz7fAUXLZbhoMVKwaYyb6KqG6wtGijZFK0MqYnD0ihQDucqk441Wm+s6no8vFJoTIXRVnXLHy0y7P0aLNs/25ciUHW6+fy//9WTPjMLyUhCix97DWChYF0XLm/GeHe4dQhKJRCKRSCQSiUQikfw5IUV0yYzM6vichzt2OqYTsU5Z1/iSiWyYq/N3PqLmVCJZX7bCH54fQgjBhuUJ0lEDz4en9mfGCWcHOyt+Ore7oigkwjr9uQpb5xhRU7///QWeH8hTrLgIIXA8gakrpKMGI0WB7fpkyw7piIFAULQ8tq5K8e4T22lNhSfNp6oqrF+W4JLXHsH1d7/ArsHiuIJCT6ZE2NDY3Bbck4UIzAsRYKcTgBNhnWREZygfRKAY+uyC/2znX+gOjbGibnemNKfvgGzZ4ZdP9hAP6XieoGR7RE0FXVWIGBqjRRtNVXnVEY3c8nAXEXPM8URQ8Ki4HumoQaHi4fmCdHTqgtl062+0aLOtK0O2mst+VGuCiuNPKywvBSF67D0cKdiMlm0AGqImjTFzyvV0uHcISSQSiUQikUgkEolE8ueGFNElMxILafhC0JspkYqaJEJ6PacZ5u+OnchMItaOnhyFisuKVGTKsS2GgL+YzMX5O1dRc6xIdkRLjILlMVq02DdSRAiB5Xrs6M0RMzU0VSUZ1ik73jjhbCFO5Lnet8Vyu4+9/4mwDgKSEYORks1I0UJTIaRrJMI6ubJL2XZxPZ+GmMlJaxs578TZ3cJTFRQcz8dyfXwB//eh/fz88e55u48nrt1wUmWoYPPArmGe78vz+bOPYsuq9KTPzVSAWNcUYyBnBT+LwJE+3ZweLAF4ojDvVQX8sKHS1hCd9P7ad0Cu7LBzoMARLQkcz6ezGu1Trka6tCTCpKMGzfHQpCJC3nLrDWA9HzRVwdDU+rxMLJhNuf4MlWf7cmTLDqmowcblCXRNJa6pUwrLS0GIHnsPo6bOaNmmYnsIQBE2TTFzyvspM+ElEolEIpFIJBKJRCI5tEgRXTIt27uz/OzR/XRnSmRLLmFDJRkxOHJ5gsZ4aMHu2BqziVjbe7KMlm1KlkviIDXIXGzm6/ydTrSuiWRRU+fx/RlyZRfb9clVnKABJSAQhA0NRVEYKQUu38f3jY4TzuYznvm6qg/U7T7x/o8Wg4ahMUOjKWYymLcYrLqyPSGC/HMBUUPjo69ay0WvnrpZ6VSMLSg8uT/Dzx7rQlOVceL/fMTnSWMvOTzXnyNXdvF8n+5Mmb+/bRvfOO9Yjp0gpM9UgBgu2hzVmmBZMsxoyaY/b005pwdLAJ5KmC9ZLvtHSjzVlSViaDTGQ2PmwWf3UIF1zXHylkvF9oikNOKqzonRBvKWi+P6GLpK1NDYM1wiGTEmFREct5qfrgSCelPMHPdcT1Uwm7j+9g47ZKoO9I3L4hiawkjBQteqTWQNjSe7MnQOFVi/LHHYheiJhbLH92ewXZ901ERRIFt26M9ZHN+eYudgcdz9PBQ7hCQSiUQikUgkEolEIpG8yNJRHyVLipqY1jVaQlVUXCHIVVxyFZfBvMWRyxOoqnJA7tjZRKx1jTFGijZ7hotsbksdlAaZh5OZRGvPF/VoB9v1iZo6KoKMLxAACihCQQgwdZVk2CBXcejJlMmWnSnPN5PLfKGu5gPJ3Z54/w09yAR3fUHY0EmEfUZLNr7voaoKCsG1NsRMHt4zyqlH5ObltK7FgPz4gb1Yjj8n8bk2Z9myQ67skIwYpCIGvhD1sY+WHLZ3Z7Fcj6ipo6sahuOxf7TE1//7Wb5w9tGTxjlbAWK2OZ307AgxTrBekQzPWwCeTphPRAyOb0/z4O4Rntif4eS1jURCOn3ZCs/25fCqa/JH95XpyZaJmDptDRFQlHGNUQsVl5AeONa3tKV4vj9fvwZVVRAIsmWHSEhnbfP4hrjTFczGrr/H9o5y8/17aU2E2D1cJFd2qTgetuuDAqam4gvBv9z1Av/ntUfg+eKwCtFj72HB8upO/Np1R82gwWzB9iYJ+jNlwsPSLDBKJBKJRCKRSCQSiUTyUkb+C/swMt+GhIdyXLc91kXXaIls2cFyfRqjBiXbxXKDJo87enO8adNyPv7aIxbsjp3VTRnSaYiaxEL6QWuQebiYTbR+5wlt9WiHwJmq4HiBGBi40MHzA/cuCBRFwdRUSo5HbgoRfSbBftOK5AG5mheauz3x/idCQSb4SNEmGTZwPA9VUarNRBVKtkdLIsQJ7elJzty5Mh/3ccn2uO2xLp7Yl6EnW8ZyfUKaysp0hFUNEUYKNq3JMM/157Bcj2TYqB8zbGjYrs9IwZ52nLMVIGaa07FzN1q069EpXjU6JR7WCOnavATgmeamMR5iy6o0Owfz9OYqlG2PvlwFXVU4ZmWS1lSEsuWyb6TEU10ZIoY6zrEuhGDXYB6Af/tjJ7YrqpE6Hl2jJQxNxdRVXE+weUWSxpg57rMzFczGrr//eHAf27qzuEKgq0FTWlcEWxh83yds6OweLnL93S9w3glth1WIHnsPMyUbzxfoY9aIriqUqw1mU1FznKA/XSQQzD5fS/XvjkRyqPj+97/PFVdcQXd3N9dccw2f/vSnD/eQXrL8/ve/5/Wvfz2jo6Ok0+nDOhZFUfj5z3/OO97xjkN63te97nUcd9xxXHfddYf0vBKJRCKRSCSSQ48U0Q8TC2lIeKjYM1zkhf48ZdvDcv26OBgL6TiewHI8CpaLrqtsWpEc97n5xCPMxU3ZGDO54JVreGD38EFpkHk4mEux4c4d/QgROHxrU1nTuTzx4rFGSzYlRyURMrBcj5CukZwQfTOlYG+5PLxnhB09Oc45tpUX+gskwjqjRRtDV+vZ9wcz1mLS/VcUOprjlKwsoyWbiuOjqQqaqmC5PjFTp6M5jqKqCx7TXGMwntif4b+399E1WmKkYOP6PlFDw/Z8erIVchWHTMkhYmqTHMQAri/QNZXW1MyO8IUWIGpz15ct0zlYpFJ3wQdO/uGCjaoo9GUrbG1fnLlpTYUp2y4fetUafv5YN2XX46jlcZKRoMgTH+NYf7zqWI+GdMq2x67BPIN5i5ZEiIZoaFJz1/NOXEXM1PnZo/sZLtqYujbvgtnqhigVxyVXcUhHDXIVF8/zCekqKME6UxQ4pjXJrqEiD3SOcERLjO3duXkL0YvB2PVvaC/uwjCq8TNutSBi6OokQX+hPQmW8t8diWQsAwMDXHbZZfz2t7+lv7+fhoYGtm7dyle/+lVOPfXUBR83l8vxyU9+kmuuuYbzzjuPVOrFdV8ul1m5ciWKotDd3U0kMnU/lkONEIIf/OAH3HTTTTz99NP4vs+aNWs444wz+Ku/+ivWr19/uIc4Z/71X/+V7373u+zcuRPDMFi3bh3ve9/7+PznP3/Ax+7t7aWhoWERRnnweNOb3sTdd9/NfffdxymnnDLvzx+uQoFEIpFIJBKJJECK6IeBg9UQcLHIV1yyZZeS400QBxUMTUFXFRxP0DlYHCcOzjend65uyjM3LefMTcsX1T15ON2Y44oNQL7i1GM4EiGdFakInYNFYiEdFY9s2SFqBgUMMUZAVwEUKDs+ZdsiEdZZmQqTGiOiTyXY15zL2ZLDHrvEc315LNclYuhoaiDmJSOBYN0QMw9arMVU978hZrK5LcXTPVmyZQdTCyJVmmIh1jXHaKg6lBc6plkLN5aLJ3x+9WQPg3kLp+pkTkdNQCEsBLmKg6ZoqArsGiygqQpRdeyaF5Rsl8aoSVhX2TtS5tm+/KKusbVNMda3xLljWy+e79fHB6CrQWNOVVF4oHOYMzctn9N551LUcn3Br5/s4fH9GVQUHtuXJRnRWdccpzFmVh3rKXYOFujNVdAUJRCxgZZE4GYfWzTasDzBzoEC27tzfPmco2lriHDbY1280J9nz7CLpsIRLQk+8qo1s34n3v1sP0MFG9eHgbyN7wtUFVwfhPAxNBVVUSg6QTzKzsECF7xyDT2ZymHZ6TJ2/R/REhu3C0NRoGS7NMVCxE2NnYPFSYL+fHsSLPW/OxLJWM477zwcx+Hmm2+mo6OD/v5+7r77bkZGRhZ0PCEEnuexb98+HMfhnHPOYcWKFePec9ttt7F582aEENx+++188IMfnPGYjuNgGJP7tSwmQgg+8IEP8Itf/IIvfvGLXHvttSxbtozdu3dz5513cuWVV/KjH/3ooI5hsbjxxhv5zGc+w/XXX89rX/taLMviqaeeYseOHYty/NbW1kU5zsFi37593H///Xzyk5/kxhtvXJCILpFIJBKJRCI5vKiHewB/bkwUNeNhvRq/oLN+WZyRYhD/4Pti9oMdJBJhHU0Fxx0fL1Cj5pb0fTFOxBwrwk3FdG7KxpjJzoEChUoQR1GouOwcKNAYM3nn8SvZM1xkW3cWgGPbUnS0xA9I2NreneWKO3bwlV8+zT/d8Qxf+eXTXHHHDrZXz3GwqRUbLMfj0X2jPLJnlMf2ZXhkzyiP7hul4nj4viAe0jliWSBO2q5HrmwDgSNdUwAlaLSpKaAoAoHguPbxQttEwb5rpMRj+0YZzFuEdJWQrlKwHCqOoGC5mHoQqzFStNnenWW0aB+0WIvp7r+hqaQiBsmwwdErEpy0rpETVqcDAV0I8hWH3kwJXwhioakLNtNREy57s2WEGP+MjRQsHtwzwv6RMo/sGaE7U6Y7U0FXVWoCtaIoRE2dXMVjTVMMIaBie1QcDyGCiJJs2UElcM8/vHeUrtESN927e1HXmKoqnNLRGOSRC3A8gV89f67iEDI0jmpNsnOwwJ7h4gHPTS2OZbhos3+kjIpCKqKPWysjxWB9tqYirEpHuejVa/nSOUfzF69ZR2PM5IiWxKw7VDa3pXjX8W0sT4TRVAXfh4FchZ8/3j3j3G3vznLTfXsoOz4NUYOQrlQjj8ByPXQtiAVSq01MI6aG5fi0psJ86o0bOHZVikzZZs9QkUzZZsuq9EEXlceu/12DRVqTEUxdJVOyGSnahDSV5ckQOweL0wr6m9tSXHbOJi5/+zF86Zyjufztx/DlcyZn8L8U/u5Ipsb3BZ2DBZ7cn6FzsPBncY8ymQz33nsv3/jGN3j961/PmjVrOPnkk/nCF77AOeecw549e1AUhSeeeGLcZxRF4fe//z0QxIwoisLvfvc7XvGKVxAKhfjxj3/MscceC0BHRweKorBnz576MW688UYuuOACLrjgAm688cZJ41IUhRtuuIFzzz2XWCzGlVdeCcCvfvUrTjzxRMLhMB0dHVx++eW47ov/bXTNNddw7LHHEovFaG9v59JLL6VQKMxpLm655Rb+7//9v9xyyy1cdtllnHLKKXR0dPDGN76Rr3/96/zwhz+c03EefvhhzjzzTJqbm0mlUrz2ta/lsccem3R9P/jBD3jnO99JNBplw4YN/PKXvxz3nt/85jds3LiRSCTC61//+nHzNxu/+tWvOP/887noootYv349xxxzDO9///u54oor5nyMm266iWOOOYZQKMSKFSv45Cc/OW78v/jFLwDqa+T222/n9a9/PdFolK1bt3L//fePO96//du/0d7eTjQa5Z3vfCfXXHPNuFiaCy+8cJLr+9Of/jSve93r5jzmGj/84Q9561vfyic+8QluueUWisXxf5tf97rX8alPfYrPfe5zNDY20trayle/+tX662vXrgXgne98J4qi1H+WSCQSiUQikRw6pBP9EDPfyJPDwdqmGB0tcToHi7i+j6FNdtkmQkGDxbHC6kJyemdyU25tT3H7492LGj2wFNyYibCO4/k81ZXBFWJcDMdI0SZXcmhNR+hoibFvuMQJ7Wn6chbburMkwlCwHGxXYGoqybCGpqpYro9CIKyOFdrqgr3u8Vx/jq7RMrYbxKQULQ/X8/EJhHnHE2RKFq2pCKmwwUjJZkdPlmTE4KS1jYsSazFxB8CmFckp7/9JaxvpaLHoyVSIV6Nlxjroi7ZHMqLzkwf28u4T2+d8zybGYLQmw3hC0Jcts3OgiKoEWeT7R0toSpDbn6s46JpCSA+eA01V8HxBOmqwMh2mbHsMFYMGsLqmEjN1yo5H0Q6KAsuTYVYkw/NaY3PZKdGaitCaDGN7gSO/lofeFDNZ2xwnFTHYM1Scs1t/poiQnkyJguUSD+msa44xWnLwBBjai01t9wwVaIg21IsuR7Um6WiJ8+T+DLYriJiz71DZ3p3l2/+7k5GiTXtDtH7+meauJhAXbZeYqREyNExDxctZgMBHqTrzmTIepaMlvuDmuAfKxO+/hojJKEExIh01ETBrdNVcIoEW8++OzFQ/dPy5xu/E43Hi8Ti/+MUvOOWUUwiFQrN/aBo+97nP8c///M90dHQQDoe56667OOOMM3jooYdob2+npaUFgF27dnH//fdz++23I4Tg05/+NJ2dnXR0dIw73le+8hWuuuoqrr32WjRN43e/+x0XXHAB119/Pa95zWvYtWsXH//4x+vvBVBVleuvv561a9eye/duLr30Uj73uc/x3e9+d9bx/+d//idHHnkkb3/726d8feLzPB35fJ6PfOQjXH/99QBcffXVvOUtb+GFF14gkUjU33f55ZfzzW9+k29961t8+9vf5oMf/CB79+6lsbGR/fv38653vYtLLrmET3ziEzzyyCN89rOfndP5IXCK33PPPezdu5c1a9bM+XM1vve97/GZz3yGr3/965x99tlks1nuu+++GT/zpS99iX/+539mw4YNfOlLX+L9738/O3fuRNd17rvvPi655BK+8Y1v8Pa3v5277rqLyy67bN7jmgtCCH74wx/y//1//x9HHXUUGzdu5NZbb+WjH/3ouPfdfPPNfOYzn+HBBx/k/vvv58ILL+S0007jzDPP5OGHH2bZsmX88Ic/5M1vfjOaNvXfVMuysCyr/nMulzso1ySRSCQSiUTy54gU0Q8x8408ORyoqsKFr1rLI3tGGCnYNMRMDE3F9QMBPaSpREyVDcsT44TVheb0TtVgsWC5fKcqpi2W2D3fxqcHynRi0+qGKJYb5MovS4ZQlGBDiKEpJMM6AzkLx/X58Klr+O7/28XOwSIRQ0UBVEXB84PGoq4vGC27GKpKU8wgZASNJDsHC/VzjRXsLc/Hr+4isD0fv3octXpu2xOUbJ/hgoUvBBUncFUnKi4dLRY7enMHJNzMJAhdds6mSXO1ozfH9Xe/wM6BAlFTY9dAgZLj4fsQMVRWpiJs68rSk6nMaz3UhMvv/2EXj+wdpVBxqTjB7onWVJhYSEdXVVQlmBe3uuMiFA8c6TWx2vOhKR7iA69czU8e2MtIwWZ5MkT3aJlMKThe2NToaIkRjxisD+tzWmNzFc4SYZ3GuEkqbCAAxwsiSxLhIIKpUHHnvYNguqLW2uY4voBVDVHiIa0ePZKKGHV3frYcFBwGcta4YtlcYmJChkospPEfD+578fkE8paL4/osT4boz1lTzl1NIF7XGAuauVYjUUKGiuX4GKqC7frB85YITxmPstBs+sVg4vdfbXdF0fIWTaRerL87f66i7uFgKRR8Dxe6rvOjH/2Iiy++mBtuuIETTjiB1772tbzvfe9jy5Yt8zrWP/7jP3LmmWfWfx4cHASgpaVlXPzHTTfdxNlnn13P1H7zm9/MTTfdVHeb1/jABz7Axz72sfrPH/rQh/j7v/97PvKRjwCBw/2KK67gc5/7XF1EH9u4dN26dVxxxRV84hOfmJOI/vzzz3PkkUeO+92nP/1pfvCDHwCQTqfp6uqa9ThveMMbxv38r//6rzQ0NHDPPffw1re+tf77Cy+8kPe///0AfO1rX+Pb3/42Dz30EG9+85v53ve+R0dHB9deey2KonDkkUeybds2vvGNb8x6fgiKCu9617tYu3YtGzdu5NRTT+Utb3kL7373u1HV2TfGXnnllXz2s5/lr//6r+u/O+mkk2b8zN/+7d9yzjnnAEGB4JhjjmHnzp0cddRRfPvb3+bss8/mb//2bwHYuHEjf/rTn/j1r389p+uZD3fddRelUomzzjoLoL7bYaKIvmXLlvq62bBhA9/5zne4++67OfPMM+sFn3Q6PWN0zVVXXcXll1++6NcgkUgkEolEIpFxLoec+UaeHC62rErz2TcdSWPcJFMOmihajhc40KMGqxqi0wriC4lHqIlYW6txJD9/vHvRowfm48Y8UGaKjNk3WiKka8TCQfNDx/PHRHG4xMMGph44fGtzWXZ8yo7HaMlGVRWWxUOsSIVpippomsJw0aYvb3Hzn/bwdz97ik/938f53fY+VqUidcE+YgSPu+sLEC82KhWAqamEq9nVubJL0fJQFAVTV+lojtGTqXD93S/MGkcyXfRATRDa1pUlHTFZ2xwjHTHZ1hX8fkdvbtz93zNcxPMF553QxuaVQSzJaNnGcn0cz8fxBF2jZfIVl67R0oLWQ9nxaYyZbFweNLltjoco2R67hwqYelBoMDQFBbBdr5pJHxSSkhGdXNlmw7IEZ21q5QtnH80pRzTRn7PYO1Ki5HjYno/rCXYPFRkt2nNaY7PN09j5r+386MtVSIR1muIhklVRu7bzY8OyxJx2EIy9b1FT40tnHz0uIuSCU1ZjaGrgJq82gA3rGtmyg+P5qArYrs/uKaJHZouJqY0TqD+fmZIzLuro0b0ZhgoWj+8bnTR3dYE4pNfHlas4RAwdVVXq60VXlFnjUQ4XY7//1i9LsH5Zgq3t6QOOrqqxGH935rM2X0osxbgUGb8TZKL39PTwy1/+krPOOovf//73nHDCCfPO/37FK14x63s8z+Pmm2/mggsuqP/uggsu4Oabb8bzxj8zE4/36KOP8o//+I9193w8Hufiiy+mt7eXUqkEwP/7f/+PM888k7a2NhKJBB/+8IcZHh6eFOcxHRP/e+VLX/oSTzzxBP/wD/8w51iYgYEBLrnkEjZu3EgqlSKVSlEoFNi3b9+4940tUsRiMRKJBAMDAwA888wznHLKKePGM58mrytWrOD+++9n27ZtfOpTn8JxHD7ykY/w5je/Gd/3Zx1/T08Pb3zjG+d8vonXU8vAr13Pc889x8knnzzu/RN/ni9f+9rXxq2F2vzeeOONvPe970XXg+/Y97///Tz44IM899xz0463NubaeOfKF77wBbLZbP1/+/fvP4ArkkgkEolEIpGMRTrRDzELiTw5XJx7XBsdzTF+dN8edg0V8X1BKmKwYXlixniBqZzl83FTHqzIm0O1C2A2B+HZm1sxNJUtq1LsHS6SK4+P4ljdFCNbcshXXLa2p9m0IsnOwTz/598fpT9XYXkyXHdtCcCtOFiuT0RRqDgevbkKz/XluX/XMMetSuH5gljYoOx4+ELgeQJFCT4LQdq3QEFTQVGC/zXFTDRVxfcFK9MREnNwUU/nUn3X8W3cPqYoMtUOgNse7SJsqDzVleXenUMM5CrYriBkqLTETEKaStjQESLIiq/tjBgp2WiqWhdX57IeagLVaNFm88oUo0WbvcNlwoZG2AgE2JipY2oqridQFA/b9SnZwbrQVRVdVWiKh+pC7Oa2FL4QPLpnFE1VSEcMIqZej+gpWVk2t6VIRoxp19h8d0osdOfHXO/beSesYmt7GoDOwcI4N3mtAWznUIFc2cV2fXwEm1am+Ohpa8d9N8x1nEXLq0cP7ejNUXG9cVFH+YpDpuzwxP7MuPs8ViCeOK6QruKLoGiUihlzikdZTJZK9MmB/t051Lt4DhVL1Vn/Uoh9OxSEw2HOPPNMzjzzTP7hH/6Bv/iLv+ArX/kKf/zjHwHGFeUcx5nyGLHY7P8t9bvf/Y7u7m7e+973jvu953n8z//8D2efffa0x/N9n8svv5x3vetdU45/7969vOUtb+GSSy7hiiuuoLGxkXvvvZeLLrpo2jGPZcOGDTz77LPjftfS0kJLSwvLli2b9fM1LrzwQgYHB7nuuutYs2YNoVCIU089Fdu2x71vYqNURVHqAvfEIuhC2bx5M5s3b+Yv//Ivuffee3nNa17DPffcw+tf//ppPxOJRBZ0rrHXU3uWxl7PxOdr4jWqqjrpdzPdt0suuYTzzz+//vPKlSsZGRnhF7/4BY7j8L3vfa/+mud53HTTTeOc/DPN/1wJhUIHFIEkkUgkEolEIpkeKaIfYhZL+DpUHLsqzbfes3XeQtCBxCMcLLF7rrESB7ILYC5i0707hzB1hbCuccLqBvJVN3otiqNYcRkVgu5MuT7fuqqSjpqUbY+85dbFxWzJCVy21YiWoaJNImyQDMNoyeHx/Rl8AUe2JupNQn0EGkE+tCoEng++8PH8QFDXVBVdVbE9n6aYWY8HmUm4malw8Hx/npLlsaohOqUgFDV1frejjwd3D9OTreD7gqaYyfrlCcK6xo7eHN2ZMlFTpzFm1o8RxN8Eedw9mTLZ8uyCBEDnUIFtXVkihkrBctG1IDPbrTY1jZo6luuzflmcgXyFgbyF7wts1ydiaKxIhTl+dQPvPH4lUVPj8X2jZMs2P3uki7LjkowYGLqGoigYmkIqYpAtO+weKrJxWXzaNbYQ4WymngJzEYrnGhkxlQjbEDM5MdpAruLQOVhk08ok33jXsej65A1Ocxln52CBkK7y/ECeiuuRihjUGroamkIspJMpOdy3c5h3HDfZ6V4bW21cecvFdjx6shU2tib40ClrSEWMQyZkLyWB9kD/7rwcRd2lHJfyUoh9Oxxs2rSJX/ziF/VYi97eXo4//niAcU1G58uNN97I+973Pr70pS+N+/3Xv/51brzxxnEi+kROOOEEnnvuOdavXz/l64888giu63L11VfXi9+33nrrnMf2/ve/nw984AP813/9F+eee+6cPzeRP/7xj3z3u9/lLW95CwD79+9naGhoXseozf9YHnjggQWPqXZMYFZXfiKRYO3atdx9990ziu3z4aijjuKhhx4a97tHHnlk3M8tLS1s37593O+eeOKJSWJ3jcbGRhobG8f97j/+4z9YtWrVpLm7++67ueqqq/inf/qnukN9NgzDmLQ7QiKRSCQSiURy6JAi+mHgQIWvQ82hzgs+WGL3fNyYC3WQzkVsGshZLEuG2TdcYv2yOMnIi/8YGylYPL4/g64p/PDe3YRNjfXL4hzblqq619PsGQnc6wXXx/J8wrqKACzXJ2poGJpanUeDouXguD4DeYuT1zaQCBs81Z0BETQm9YXA930qjl/PAVeAsuMRDemsbX5xnqYTbmYrHDy5P1N/bSIjRZudg3lyJQcEaAokowZ5y2VHT5DBvjwV5oXBAo7nM7GHmqIomJpKyfHIzUFE396d5YZ7dvF8fx5TU9E1lWREJ6SrFG2XVMSoNw6NmhrHt6d5uifH2qYY735FG6mISSpiULBcbn+8myf2ZejJlCk7HhXHI2JqKChY1aiYYC6CQkGmZLN7pMjJa5umdPwuVDhb6M6P+bqLpxNh+3MW7Y1RPnrauikF9LmOc21TjGWJEE90ZUiPEdCBaoyOR1M8RH+uPE6snW5sCgojJYeV6QgfO23dIf1eXYoC7YH83Xm5ibpL3Vl/KAq+S5nh4WHe85738LGPfYwtW7aQSCR45JFH+OY3v8m5555LJBLhlFNO4etf/zpr165laGiIL3/5yws61+DgIL/61a/45S9/yebNm8e99pGPfIRzzjmHwcHBunA/kX/4h3/grW99K+3t7bznPe9BVVWeeuoptm3bxpVXXskRRxyB67p8+9vf5m1vexv33XcfN9xww5zH9773vY/bb7+d973vfXzhC1/grLPOYvny5ezdu5dbbrll2uaSE1m/fj0//vGPecUrXkEul+Pv/u7v5u3uvuSSS7j66qv5zGc+w//5P/+HRx99dF7xOp/4xCdYuXIlb3jDG1i1ahW9vb1ceeWVtLS0zCkW5qtf/SqXXHIJy5Yt4+yzzyafz3PffffxV3/1V/O6jhp/9Vd/xemnn84111zD2972Nv73f/+X3/72t+P+2+0Nb3gD3/rWt/j3f/93Tj31VH7yk5+wffv2evFmLtx44428+93vnrS+1qxZw+c//3nuuOOOORdIaoWE0047jVAoVM/wl0gkEolEIpEcGmQm+mFic1uKy87ZNC53+MvnHL3kBPTDwVwzlOcbeVMT2xpjJjsHChQqQYxKoeKyc6BQd2Pu6M3V88yv/PWOcRnjs2XQ1sUmc3qxyXJ9Xr2+edI4ukdLPLh7BNv1Wd+SYF1LvJ45fNujXTieT8jQOHF1A69Y28DG5XHipk46ZtbjYDTtxUdaVxVURSUVMRguWOQtlw3L46xIRdA0FcfzsZzAfq4o4Pk+rg8CQUvcZPPKFI0xs3686YSb2QoHrakwluczVBi/bVwIwe6hApXqcW3PJxYyMDSNZNjAcj32DBUCsVtVsdwgZ3w8Asv1COnauGLEVNSEzT1DRUw9yPc2dZWRok3ZcfEFDBdsipaDqoLjCXYNFlmZjnDJ647gdUcu5/jVDZRsj+/8704e7BymJ1PG8vxqw9dgLK4vqDgeI0W7nncvhKBoe8RMfVrH74HkVo/N1J4uR3ti9nPnUGFePQIW2u9gruNUVYVXb2hGUxTKjjehV4BDyAgKSrYrpiwkHOjYFoulnGe90L87L5VeHnPlUPbHWAgH62/gS4V4PM4rX/lKrr32Wk4//XQ2b97MZZddxsUXX8x3vvMdIGgE6jgOr3jFK/jrv/7rSQ1A58q///u/E4vFpszafv3rX08ikeDHP/7xtJ8/66yz+PWvf82dd97JSSedxCmnnMI111zDmjVrADjuuOO45ppr+MY3vsHmzZv5j//4D6666qo5j09RFG655Rauu+46fvOb3/DGN76RI488ko997GO0t7dz7733zuk4N910E6Ojoxx//PF86EMf4lOf+tS84mAAVq9ezW233cavfvUrtm7dyg033MDXvva1OX/+jDPO4IEHHuA973kPGzdu5LzzziMcDnP33XfT1NQ06+c/8pGPcN111/Hd736XY445hre+9a288MIL87qGsZx22mnccMMNXHPNNWzdupX//u//5m/+5m8Ih8P195x11llcdtllfO5zn+Okk04in8/z4Q9/eM7nePTRR3nyySc577zzJr2WSCR405vexI033jjn41199dXceeedtLe3z0vIl0gkEolEIpEsDopYrJDDlxG5XI5UKkU2myWZTB7u4fxZMsnJOSF64ECEsaliFjYsC3Legfp5o6ZOb7bMaMmm4viEdJXT1jfz8dM7pj1352CBr/zyadIRc0oHYaHikinbXP72YyjZ3rhxdGVKuJ7g+PY0jfEX8yyFELzQn6fs+ESrznRFUchXHB7ZM4ovRHW8Ki2JMDUHr+P52K7PEc0xnu7LsSIZZv2yBBXX47E9I4yWHRQgHTWImBr5skvJ9jA0lVevb6IpER43hp0DBbasSvPlc44eJ34+uT/DP93xDGubY2hTiLee53PPC4M0Rk22tqfrolWu7PDo3hEs1ycRNijbXj06Zuz4j2xNsK07S76aVZ6MGONyshWgIWbyN2duZNOKZF1YGut4Xt0Q5Z9++wzburIc0RLj8ao7Phk2sF2foaJVDYkXOD6ENIWNyxOcsKZxnEvX9wVX3LGDp/ZnGCxYDOSDz4nqeAEihlrdDaAQNlS8amHC1FW+8tZNnLV5xZRrp3bsbV3Zce7YqeZ/4vVN5zyv7ah4Yn9mUtZ8OmLQOVjkmLbUuPsmRCBSW27gMv/Hc4/h+NUNk455MHK+OwcLfPbWJ8mUbMqOXy8OpSLBrghTU+vPz1S7Y5ZCBvmk7wAhyFsujutj6CqKgEzFmfYaliLzWZtLJYpsJmb9zvIFe4aKfOmco+t9AQ41B/Nv4GIj/5tJ8nLh4osv5tlnn63n7r8cqD2f7Z++FTUUPdzDkUgkEolEchjY8/VzDvcQljxz/TfNS8M2Jvmz42BG3kwXKwFwxR07GCnaNMVMtvfksKrNDWsZ4w92DlN2PP56GgFjPpExqqrUx/FsX44b793DimSYRGRyY6mV6ShdoyVCusr2niwNEYNYSCdsqAzkKmhqEBnyYgSGoGS7NMVCNMZM1jXFWNcSYzBvYTk+qqoQ0oP8b1VRcD1oTYVpjJo805fnia4sJ6/ViIb0WXOTZ40ecHxWpiJETW1c3Eau7FCwPFJhnTWNUV4YKFRzyYPj12JVTE0hFdaxHI9UVMdyfPKuh+X6eJ6PJ6Bgu/zjr56mLR2lvTECKIyW7HqRpCURYvdgkVUNUVRVZV1znKKVZaRoU3G9uis4aurENYVESCcW1nnn8SvH3eeag1VRFPpzFkIITF1DUQKh0fUFZccnYupoisKRrUlMTaE3W+GktY2cual12nU519zqHb25OWVt14pFT+zLsHu4OClrfvdwgb5chaZ4iLaGYFv/SNFm94RmoT+5fy+GptaPfTDjndY2xThudZqn9mdYngrjeqLeKwCoi7XTOXAPdfTUVIyNPhkt2vUmp7WCQDysEdK1l0z0Cbz0ennMxkshLuWlFvsmkbwU+ed//mfOPPNMYrEYv/3tb7n55pv57ne/e7iHJZFIJBKJRCJZokgRXbJkWWjW81yYSmzrHAziLVqTYZ4fyGO5HsmwURfCE2EDywlEo+nycucrNtXGka+4aIpCNDT1IxkxNWzPJ67qjBRt9g2XAAjpgdDjeLXGl4GoXLJdwrrG2qYovbkKx69u4ItnH8W+0VJdsG9NhEBRxjU1VRQFQ1fZOVigN1dBU5RZhZu5FA6OX93AO45fyc8f72Znf4G9ww62FzjrO5bFWJWO0J+vMFK06w0la6KjUY1eaYqHaI4ZWK6ga7QEQuD5AlVVSIUNPF+wb7jICwMFdFVhy6oUa5tjlG2PZ3py9OYqtMRDxMNBg9LNK5M8uHskOI+i4AlBKmJw1IokDVGDnQMFfv54D8esTNXvV77iUrE9+nMVPF8QNoIceQBTV/FdD8+HouUSC+n4vmDUclmZjnDeiatmXbuzCWfAnLK2x7pYR0v2lFnzm1ckGchZPNuXY0UqRKbssr07Wy0caTieoCFssnekxPV3v3BInK9jn5+BnFV/forW1M/PUnCeT6Qm0PZly3QOFqlUC3G13RPDBRtVUejLVtjafliHOi9eTqLufIqdh5OD+TdQsjQ4++yzp3U9f/GLX+SLX/zinI4Tj09fPPztb3/La17zmgWNbz4c6LUcjmt46KGH+OY3v0k+n6ejo4Prr7+ev/iLv1j080gkEolEIpFIXh5IEV2ypFmos3Qh4lrNQeqFBLmyS9TUx4kruqpQ8AS6ovBUV4bOoQLrlyUmHWchYtNszsi+bJm+bAUFOKG9AU8EWe6jJZuYqRE2dLb3ZBks2IQNlYaoyYpUmOGiXRcedV0dJ9jHwsaUUQatqQgVx+fDr1qNqqiAYOPyBB3N8WnndrbCwTuPX0kspLN5ZZKu0RK5ikBXFXRV4bm+AlFDp6M5TsnKki07RAyNsuORCBv0ZyusaoiytT3FHU/1snOwiFuNTlGAVMQgGTERwqcnWwEgpGv05yza0hHiIY1lyRD7Rkrs6MtyWqyZou1RtFw0FZrjJr4AzxNsbkuSjAQ58GNzkWtrMBHW8YGC5aKrSpDjUp1CTVUwNQ2bIM+74viUHI+t1fu+aUWSzsHCrGtyLjslZmqGeNTyRD2Te3kyRNdouZo1r5IMq+QqDnuGCjSsbuCo1gRP9+TY3p2lZHtYrkfE0CjZHhFD58jWBOlqQeFQNVqc6/MzVSzTVI78+bAYovzaphjrW+Lcsa0Xz/dJR01qi0RXg3WiKgoPdA5z5qblLylB9OUi6r6UnPVLYXeF5ODxgx/8gHK5POVrjY2Ncz7OE088Me1rbW1t8x3WgjjQazkc13DrrbcelONKJBKJRCKRSF6eSBFd8rJjoeJaTciuNfrUxwgoluuRKQXu6b3DJTwhuO6uF7jktUdMecz5ik0zOiN9n2f78miqwjErk6hq0Dw0HTVpa4iwc6BAR0uM972ynd881UtvtoKqKAiYUrifS5SB7Xr88vEeBgoWng+piM6G5QmOa0/zxP7MlHM7lfB5bFuKlekw3/9DJ7sGC/RkK/i+oDFqsLIhiqGp7Bwo8EDnMFvb0xy9IskLA3mGiza6opCOGGxtb6gL6KqiEDM1VOXFOIyi7RHSvWDOqvnkIV0jW3boGi3Tn6+QLTm4vqAnU+E323oxdQ3PFxRtl7CuomkqyxNhkuEXo3QipkZ/zh8Xu7G2KcaKVJhne3OYuort+hgK9fslhCBqaHgiyLb/yts30dEcrzerneuanGmnxGzNEO/bNVR/n+1649ayogSxP9myS77i0pqKMFy0aUmEeGJ/BhUFxxM0xUKsa47REJu+oHAwme35mZQXPY0jfz4sliivqgqndDTyyyd7ECJoUKupQc52yXYJGRpHNMfZOVg4ZPO5mLxcRN2Xk7Ne8tJlscTh9evXL8pxDoQDvZalcA0SiUQikUgkEslMSBFd8rLiQMS1mpD98O4RNJV6PrfleowU7SAuxdSIh3XKjseeoSL/ctfzvPvEdlpT4UlC33zEppmckbuHC3i+qMaKqOM+VxNPdw4W+NCpazjrfa2zCvezRRls78kwmLfpHCqiqyqGrlCoaHRnyvz6yR5aEiGOaElMObeXnbOpfv6+bIVfP9XNL5/swXI8XCEQQhA3dfryFr05i6ipYeoqJdvlmd4c65rjtDdEOXFNA69e38zW9nS9KehI0WZlKkx/zsLUFYqWh64puF7QuDFmarWLQFHAcjye68/jCxHk2gvBUMGmYHmYnk8qbFBSlGozVUHU1Bgp2hi6SqKaBT8xF1lVFd62ZSX37xrGcj0EUHF9NFXBr8bPKIpCRNe44JTVrF+WmHFNPt+f590nrGJre3pWR+/YrO2pqIn+tdz7SEpDEIxpqqx52/Wq165w+oZmhgsWrYkwIVMjEdJhzLqYqqBwsJnu+fF9UXfaz+TIn49rfrFF+dZUhNZkGNsL5qwWTdQUM1nbHCcVMdgzVHxJ5aK/HHm5OOslEolEIpFIJBKJRHLweVmL6N/97nf51re+RW9vL8cccwzXXXfdIcmFlBweDlRcqwnZXSMlBvIWhYpDKmIEDnTXx9RVklUBvSlmsjId5on9WZ7uzbEqHT3gOInpnJEdTTGEHzT+nIqxAudchPuZBPudAzn6qpEojYkQuqri+oFIXbaDBpwNMZNYSENRlMlze06SjpY427uz/OzR/TzZlUUIQTpqMFSw8QRkyk49RsbzfRJREyEEtid449EtnLmpdZyQNdaBXROFhai5vxV0lWoefFVcVoLXLTeIfGmsuqlHHQ9NhZChYbs+uYqDriigqXhC8Fx/nq7REpqqkghrGJrKKR3Nk3KRz9y0nJ8+uI+H9gzj+T6eD64XZLNHNJWQoXFqRxNnbmqddk06nk+2bNPXb7Gzv8CG5XE2LE/MuHbm2gyxJRGqvy8R0klG9ElZ874QvNBfYKRkEzF17niqj+GiTUPUpDk8eZ0thUaLNWrNXWdz5M/V5X0wRPlEWKcxbpIKGwiY1HegUHGXzHz+ufNycdZLJBKJRCKRSCQSieTgos7+lpcmt9xyC5/+9Kf50pe+xOOPP85rXvMazj77bPbt23e4hyaZI74v6Bws8OT+DJ2DBXxfzPj++Yhr07G5LcVfn7GRUzoaURSF/ryF5XqEjUBAt1yfkKHRGAvxdE8e1/OxXZ+muEk6YrKtK3C0bu/OLuiaN7eluOycTVz+9mP40jlHc/nbj+FTZ2ygMW5Str0pP7MQgbMm2B+7KkWmbLNnqMhoycLxBKqi0BwPYWiBUG5oKpGq8KyqCtmSM85BO3Fua6JkECsD8XAg3gohUABfgBBBI07HA1BIRYLGoE935yY5QesO7KpDOhnRsT0fU1OCbHQlcNCrigAlMFCX7GB8ieq5bTfIKA8bOsviIZrjIWIhnRXpcL1Bqev5hPSgUWhvpsJg3mJre2qScLqjN4fleYR0jaip0xI3aI4ZxAwVX8C6pigXn96BqipTrsnRos327iyjJafq+AZD02ZdO7UdBL3ZMkKMfxZqzRA3LEtw2hHNL74P6GiOE67G29iuR67sULY9BvMVoobG1rYUK1JhXE/wVFeWkYI17bEPd6NFGL8epiJialjO3F3zi/G9MZHaverLVUiEdZriIZKRoEnxUptPiUQikUgkEolEIpFIJLPzsrXBXXPNNVx00UX8xV/8BQDXXXcdv/vd7/je977HVVdddZhHJ5mNheQTzzXuYjZxbXNbiuveezx37ujjJw/s5fH9WcKGCig0xQzWNMXYM1zEcj1SEYOCFTi049GFO1fHMtEZ6ftixviV3myZLavS8xbkJkYZZEo21975PCE9cGGPJXB+gycEtufjVBt71hg7t3uGi7zQnyeka1TcwIGrqICi4Hs+qgJ+9ZhCiKA4oiqEDZWebGWSg3iiA7vWgNT1q7EtVcd5ruwQ1lQsT2C7QQ61qgQu4HzFRVEUUhEdVVVRFVFtLhpkhisE8T3ZikvM1FnZEEFXFZ7cn+VtW1bW72OtQOB6gleua2T3cJFcOYjriJjBnLWmImxakQSmWJNC0DlUoFJdO76AQsXF0BTWL5t97ZyyrpEdPTm292RZ1xgjUo2dGdsMUdfVSTsNxmbNW66Priq0paN0tMTruefHt6d5cPcIT+zPcPLaximPfTBjLuba1HOujvy5FpUW43tjIc12l0rjSolEIpFIJBKJRCKRSCSz87IU0W3b5tFHH+Xv//7vx/3+TW96E3/6058O06gkc2Wh+cSLKa6pqsJZm1dwxLI4n79tG1FDIxkxSIR18hWXXNklaup4IsiYNvRAQF1InMRcxrLYgtxE0e/YthTburP4AgxtfIZ2bQyqEuR+q1V3+ljGzu2T+zM831/A8XwKFZeS5RI2tODzArSqc9wXAkVRUNTAOd4QNVEVZZJYOTHDvSFmsrktxTO9OfosF9cTCIJIFV0VhAwNVYGyE2TZR02dhqiJpjpoqlrPuK9FsIQNDV8IHC8Ql9cvi7OqIULR8ibdx7Gu5XhYpzFmkrdcHNfH0FUUASMlu/6ZiWsyb724doJolSBP3dDUGdfO2KJSoeIyWrYZqcavNMbMSc0QJ0YDWY5Pe0OUjcsT7Bku0pYOMrvH5p43xkNsWZVm52Ce3lzQnPZQNVqcT9Fstkz/+RaVDvR7Y6axT9ds95SOJrzqThuZwS2RSCQSiUQikUgkEsnS52Upog8NDeF5HsuXLx/3++XLl9PX1zfp/ZZlYVkvRhjkcrmDPkbJ1BxIPvFii2sQRGFsWZViW1eWtoYg7sHxfDxfoKuQq7g0xUJBLEeVg9GEcbq89IUInNOJfqesayQVMchVHAqWW8/QhkBY11WFsuOTCOvjBMWxc1uwXH70pz2MlmwQAt8HD3B9t9p0E7y6qx1CukLZ9gjrGitSYQRMEiunKiJ4vqDiBMdMGFrdSe54PrbrETI0EIEA2tEc48jWBI/vzwQNYh0Ppxrd4lXd7MKHqKmjKjCYr7CqITLlfZzkWlaUamRMgOcL+vNW/TMT16Tj1tZOEOtRsl2aYmb9mqc658Si0opUhJIVOP5jIZ0LXrmGMzctn/Q8TNU0MVt2uOo3z9KSGC+g12hNhSnbLh999Tra0pFD0mhxvkWzxS4qHcj3xlzGPr7ZbpkHOkf4yYN757zDRiKRSCQSiUQikUgkEsnh52UpoteYmG8rqs7XiVx11VVcfvnlh2pYkhk4kKaBB8OxPdUxNVVBIBgtOcRMnXXNsXGC5MFqwjiVKDpfgXMm0a9rtExDzGC4qOG4PtmyQ9TU0dVq4UAITF0lYqgULW/S3L7z+JX82x93s3e4hKqAUFTCuoLl+vi+wPFE4EIniHJBgKmpNMVCrG2KMly0pxUrxxUR+gs8P5Cn7Pi0NUSwXEGu7OALP3C7E0j/jTGDoYLDjt4ciXAQwzNStCnYHoamEA/pZMoOtheI2smwEWS+l92gSWvViT32Ps7XtTxx/STCgVBfcTxsL8jXX9v8onA78fPTFZUSEYPNbSl2DhR4cPcIZ25aPmkstfOPfU46Bwuzjj9sahzVmliUXRTTRbTUfp8tO/zk/r3zLpotZlFpod8bcy74jWm2e9tj3fPeYSORSCQSiUQikUgkEonk8POyFNGbm5vRNG2S63xgYGCSOx3gC1/4Ap/5zGfqP+dyOdrb2w/6OCWTOdB84sUU16Y7ZsX2MHUVzxMcszJZz5SGA8sonwsTRdH5MBfRL5qO0JaOAFCyPcq2S9YRuL5POmrygVe2M1J0ppzbsKHyyN4RFAVa4mFGSzaeLzA1FRcf1xd4AnRVIRbS6WiO0pqKoCsKvbkKjTGTdxy/ctoiQa2I8McXBrn2rudpiIaImCqP7R3F832EAENXEQIcT5CO6rTEFUZKNs/151jTGKMxZlK0PKJmEOGiqQq+EDRGDUKGhi+CRrFD+QpFx+OkNY3j7uNCXMsTCwAoULBcWlNh1jXHaayun6k+fyBFpak4GLs1pmO6HQ/Htad5Yn+GnQMFMiWH/aMl0hGD5nho3LM02/WNLSplyw65skMyYgT31hfzKi4t5HtjPvdmbVNswTtsJBKJRCKRSCQSiUQikRx+XpYiummanHjiidx55528853vrP/+zjvv5Nxzz530/lAoRCgUOpRDlEzDYuSaL4Zje7Zj9mXL/OyxboaLNqauvSSaBs5F9Bsp2Xzw5NXcuaOfbT1ZMmWBJwQRQ6M1GWak6PCu49uIhfRxcwtw8/27yZYcEmEdU1dpiJnkKw62K1AV0NXAhf72rStJRQx2DhYYLth1sXJre4qfP949Zbb02Lkv2h6GqtKSCJEp2dhu4HLXNTUIn1FebFgaCenEPUFTLMRHX72OmKnx/Xt24Yrgul1P0JspUXF9XN+lUHGwPJ9t3TkipsZws8WO3lxdRJ3NtdwQNXjluka2dWfHrbux6+eJ/Rlue7SLiuNhakGkzHRrZ7Ga5dY4GLs1pmK6HQ8Pdg7z6yd7aEmEOKIlQUhX6R4tk684bO/OsrktNU5In+36VFWhZHv88smeGfPU59K0dL7fG/O5N4tdDJFIJBKJZD5sv/wsksnk4R6GRCKRSCQSyUual6WIDvCZz3yGD33oQ7ziFa/g1FNP5fvf/z779u3jkksuOdxDk8zAYjllD8SxPZdjbm1P09YQHedc9YVgZSrMOVtWsGnF0vmHSk1AfGzvKNmSw4pkeMr3RUyNziGbX2/rpWukRG+mgicEDVGDTa1JwqY+Lnpia3saeNFxfP+uYSqOh+uLIDs9pNMcD+F4gaDt+T5F2+OMTct58zGt48TKouXy7f/dOWXUxTO9OZYlQoyWHCwnmOeebJmIGWSzKwrVJqXBdQgRCJOqquD5AkNX0DWVtnQEVYHRskPXaJmQrqKpKiFdQ7g+w0UL1w8ia1amw6xMR+nJVLj+7hfGRW1M51puS0cQMG3edW39dLTE2bg8MSfX81yLSrGQRudgYU7i78HYrTGWaXc8hDQcz6dsB2skFgp2A5i6iqkHYvjuoSINUaMejzSXpp6zZZIDc25aOp/vjfkU/Ba7GCKRSCQSiUQikUgkEonk0PKyFdHf+973Mjw8zD/+4z/S29vL5s2b+c1vfsOaNWsO99AkM3ConLKLQc25eueOfn71VA+92Qq92Qo/eXAfD+wembZZ4FxcsYvF2EiNWnRGruJwVOv4GBqAvmyZvmwFhKBUFQAjhkbZ8XimL8/mthTrl42PntjRmxsjYobYO1ysNv0Mmnc2Rk1ChoZQBZmSSzyss3F5fJxY6fuCK+7YMWXURZNr8uDuEToHi5y8tpFoSqdsuewbKfFUV4aT1qYJGyqZksB2PPSqsztkaPXGr2FdQ1Xgyf2j/HHnMABRQ8PxfQwFipZL0XZRFWiKmRzZmmRVtYmsEGLKqI3pdiaMzjHveq6u57kUldrSEf7jgX3sHJxdJJ7v+RfCdK7rvOWSr3gkIwa5au58IqyTjOiMFG0ihka27JC3XBJhY9ai2Vziif7tD52UHG/O92U+zKfgt2e4eMA7bF6uHMrvQ4lEIpFIJBKJRCKRSBbKy/pf7JdeeimXXnrp4R6GZJ4cbKfsXJmLuLOjN1cX8laOEfynE+mmy4meSfBcKE91ZfjGb59lpGjTmopw5PI4hYrDYN5im5vh2FXpupAufJ9n+/JoqsLqpiiP78sSNXUMTcXUVbJlh91DRU5Yna5HT3QOFbjt0S56smVWJsMYehCx0pe1QAhcH3IVh5QCZcfDF3DSmkY6msc7faeNuhCC3cNFFIWgUSmgqQrxiMHx7Wnu2zXMH14YClzuAjxPYHkeqgKxsM5I0cHxfEq2h+163HBPJ64vOL49zerGGJ1DBXJlF88X2J4gZmocv7qBpviL0U71qI3+PH98YZB01By3Fjpa4vi+4BePd9NbnQdB0EB1trzrubieZysq6apCf75Cd6Y8b5H4YOzWgGliToQgU7KpuB5xM3CkO56Poiisa45TtLLBGvGhYnsoKLMWzWaLSGlNhnl47wiNMZPNK1OLlkM+9nvhlHVNdI2UZi34Hcos+vlew+EUrg/l96FEIpFIJBKJRCKRSCQHwstaRJe8dDmYTtm5MBdxZy5O2LEi3VyiJw7kmscKY72ZMv9y9/N0ZyqEdJXRkkMyotOaimB7PtmSw3N9eU5a20DZ8dk9XMDzBcesTOH54PkCvX5ehaip113CUVOnP+dz944BfrejD9v16c9aaKpCSFeJhzRKjofwBWXHQ1MVDE3lqNY4F5/eMel6pou6yFsuubJLPKRTcQLRtYaiKGgKFKt565oCXjXn3BeQLdnoapCRnoqadLTEeL4/j+cLnu4JMs5PXN1A3nIZyls805snbKoYmjppXiuOx/MDea6963lMTcPUFZYlw7x6fTPHtad5ri8/aR6SEZ2O5jgNMfOA866nKyod25ZiuGjTkykvqWaVE2NORos2nUMFRoo2RculZLnoWvC6EME6a2+I0DVapmC5DOQtUlF/1qLZbBEpnhAUKi5HNMcWLYd8qu+FhqhJxNQYLdnTFvyW0g6bpSJcz+X7UArpEolEIpFIJBKJRCJZKkgRXbJkOVhO2dmYKO6EkypDBZsHdg3zfF+ez599FFtWpefVLHBtU2xO0RONMXNesRxjx1wTxkaKNt2jZYq2SzpikAgHIvRAziJbduhoitGnVRgt2zzblycVNehoiiF8aE2FKVoumqrg+gJDC8apqwplX+C4PmWCqJY7tveSK7s0RA0MTcX1BUU7EEiXhQ2KlkPZ8VmeDHNqRxPnnTj1dUyXLe24fpBpril1IR4AIegcKlBxPTQVUhEDTVHxfJ+K41JxAxc8wqejJca6lgRCCBQUGqI6uYobZG+vTpMIGwCEh4o4rhgn1AOMFm22dWUoOx4N0RC6prCzP89TXVn+99kBlidDZEoO+YpDUyxUn4eRok3JynJMWxJVURgu2jzbl1twIWiqopIvBJf/aseSa1Y51nXd5Jo83ZOj4npETR3L8Sg7Pr7v82xvjn0jJSzXr947j2XJMB951RqOX90w61zNlkleqOaLx6v3eCLzzSGfTvTtyZRpiJlc8MrVtKYiMzYuPdw7bJaKcD3fAqREIpFIJBKJRCKRSCSHGymiSyRjmCjujJYcnuvPVWM/fLozZf7+tm1847xj8QVzbhY4m+AeNXXu3TnEinSYdU3xWcWtsa7zcXncyTC92TKeLxAicOuWHQ/PB9/3KVpguT4ntqcZKNh8+NQ1nLCmoS7Ilm1vXE51MmygKIGgrqkKuhbEbFiuh0LQLFJRFBRFwdAUUhGDbNkhbKgc0ZJitOTyN2ds4DUbWqYVw6aLujB0FbWaWd6SCNXzovOWy0jRxq9eY8HyUPBQFAVT10gYgYAaNXXWL0uQiprkyk61MMA4V30ibJAI6YEruGija+PjZHYN5ilYLisbIpiawvaeHJbrkY4aFC2XnkwFz/PxBDiej6lr9XkYLto8uHsEXVWwXZ8b793Dw3tGF+z4nVhUenJ/5qA2q1xo5EfNdd01Wubx/RkczycdNfB80FQVXRXomkKm7JC3XRqjJo4QxEydkK7yu6f7ObJ1dvF0toiU0ZJNPKyjK1MfZz455HMRfR/cPcqXz2mdcdyHc4fNUhKu51OAPByFVIlEIpFIJBKJRCKRSCYiRXSJZAxjxZ3RksP27ixW1UWrqxqG47F/tMTX//tZPnzKmjk3C5wpeqKWiWy5PiuT4fqxZoqFqblZK7ZHd7aM6wVZ30KBQiUQwi3Xw/J8HA/ChoZuaHi+oGi5PNmVob0xxglrGuq53nVBclm8nlOdqzhETY2i5ZIMm/TnLEKGiuurpCNmtVmkQzpioKoq9eiXkoOpq5y8tmlGAR2mj7pQRJCD7otAMK2JbY4buJZdHxQFdCU4hqgWNYI5DV5z/SDjZWxhIBHS8aquegjOETU1fGHSn62gKSoRU2MwX6EvZxEPG6xtirF7qEjJdokaGhAI9nnLpjGik6m4ZMrBXCmKiuX6WNXmqjHTYHkyzIpkeFEdv7M5sQ+kWeWBRn5sbkvx7hPaeLoniy8UCpUg1md5MkRD1OTZvjwVfFzPx/Z8muNh1jXHSEeNOYu5s0WkrEiF6WiJB3E3Yf2AcsgXU/Q9XDtslpJwPVsUz4EWgCQSiUQikUgkEolEIllsJgcASyR/xtTEnbChsnuogOV6JMNBXImiKIQNjZCmMlKweaBzhCNaYvRmywghxh2nJtJtWJZgbVNsnOA51TlHSzZhQ8U0xotKE8WtWhzDtq4s6YhJcyKE7QZ54U/35BguWHi+IGxooATCMlW9TCHIDtcUhaLlYbs+qxuiwIuCZGPMZOdAAVNT2bQySSKkkyk5eALSUYMtq9Kcsq6R3kyFZ/py5CsuJdujN1ehaLn4QiCEoGh7xEx9zjnPtaiLY1elyJRt9gwVyZQdjm1LsaohQne2TKHsVJuAenWx3NRUtOq9UdXADe8JgS+C661FwNQaWIZ0jWzZQSBQVYVCxWXnQIFVDVE++6aNbGlP188/UrQxtaDR6nDBpitTpmR7DBdtBvMWubKD7ws0LSgoCAGjJQfH88iVbTw/mHxTD0TTRMRg/bI4I0Wb2x/rxvfHrxnfF3QOFnhyf4bOwcKk1ydSc2LPZf1NZKZzTVxja5tjpCMm27qC32/vzs56P2vNXlNhg00rkhzXnuLENQ31xq1hQ6UlbhIPGxzVmuSE1UGT24nrfTamXjc2W1al+eszNvLx0zvqa7pQCZrI1u75fHLI66KvOb3oazlLW/RdStcw0/chHFgBSCKRSCQSiUQikUgkkoOB/BeqRDKGmrgzVLDJlR1MTcV2/bpA6/oCXVNpTYXZOVjggleuoSdTmbVZ4EzRE7brUXF8VqTCJEKTH8maKzNbdvjlkz3j4hiGC9a4rO++XAVNDZphKgRCsu8HjmxFUfBF0HAxFTYwdZV9o6W663RiZrPl+LQ1RDhhTSOnrW/iuPY0BcvlG//9LCXHIxHSaYiahPTAhT1asqk4GroWNNb82GlrZ3QtT4wL2bQiyaZzNrFnuMgT+zPcu3OIgVylKk7bjBRtGqJmUGzQVYTnMUlmVpTgupVAOI+HXhQMG2Mmm1cmeXx/Bk2FrtEymgpHtCT4yKvWsGVVmrdtWcme4SJP7s/w2+297B0u8WxvjrLj4fqCkKZg6BpCBPfN9wWW65MIG1QcjURYp2QHud+KomCqCke2JmmMmdXhTe34XYjze6HNKmc616YVyQOO/Kgdf1tXlu5MmcGCRWPMZF1zcDzH8/F8CBsqYRTSUSO4YRPW+1zF3NkiUhYjh/xguv4PFUvpGmaL4pnPLgGJRCKRSCQSiUQikUgOBUv3X/wSyQJYaI5zjZq48/tnB8iUHWoqbS3zW1UVlifCtMRD7Bku0ZoKz0mkm1nwrBDSVVakwuPExBo1cStXdibFMRiaOi7ru2z7hE2N4YKN670YV1Jzq6uKQjysc9zqNLmyO0monEmQ9H3BFXfswHJ8WpMhRksOEVMjFjKImhqjJYdESCcR1jl5XRNnbmqddp5nEnIB/nt7X7354cp0lJIV5MrHQjqvP2oZZdtjMG+RLbtUXB+jeo/dak562NBoS0fYNVgcN9fDRZu1TRFURWWkaIMC/bkKP3+8G1VR2NyWomR7/HZ7HyNFh1REpz9XCWZRgOMJNDXIh1cUUFQoWEHES9jQ2LwyRa7ssK07i6oqtMRNVjVExl372KJIzQ3+s8e6sBx/3s0eZ2tWuWlFks7BQv1eFiyX7/zvzmkbS553QtsBRX6Ma1yZDJMp2wwXggJI0cqyuS1VXbPBvC1LTC4cLUTMnSkiZTFyyF8Oou9SuoaFFoAkEolEsjA2f+V3qKHo4R6GRCKRSCSSQ8ier59zuIfwskOK6JKXDQea4wyBuHNce5pfPN6N4/oYuoquqvhCULI9dFWhKW5Srh4/FtJQFYW3b11JruyQjBikIsaUIt10gudJaxsZLtr0ZIJYjunErWTEmJQjnAhpRAyV0ZJDPBRknidCBr2ZCl41F9yoZoa7XvB/j16eIGLoVQf15K+A6QTJsZnKzfEQ27uzZMtONS9eIWJoZMoO7Y1RzjtxVV14nyhebu/J8o3fPstI0aY1FaG1KUzF8dnWlaVrpETE1Cc5oRMRg81tKXYOFHi+L0/E1GhriGDoFpmSje35CBHsEkhFDFoSIf7y9et5aM/IuLmOmTrP9ecp2i6GqmJoQdPPkaJF92iZv3rDem5/vLt+/oih0ZezQAg0FTwfLNdDV4N1kQhp5CoOQ0WL5ckIsZBO2fHwgYiusa4lMUmMLttBVvqPH9jLQK7CC/0FSo5HazJEczxEXNWJhzSWJ0N0Dhb54X27+ca7tqDrU6dvTScS7+jNccUdO158HnSVoaKF7fp0NMfwhUBVxrvM73iql4rtLSireqrGletbElTsLGXHpWy7dA4W2Lgsji+C3Pq1TdFxhaODJeYeaA75y0H0XWrXMFsB6EB7BkgkEolEIpFIJBKJRLKYSBFd8rJgnAN2nm7esfi+4In9GVqTIYSAouUifK+ah66iKgqD+Qq269OWjvCTB/aya7A4SbSfToiaSfC8/u4XZhS3oqY2Lo5htGjTOVQgV80lL1guuqagF8DUVVzPD7LBVQVVUYmFFFQFhqui89b2hnkJlWObAcZVnc1tqeD8ZZeyHwiyEVPnvBODosVURY101GB7d5bhgk1ID8T/ZERnXXOc9cvibO/OMlKyObG9YUondNTU+dOuQWxPULZ9oqZKSzxEyNAoVFxsL2g6Oly0uWNbL+dsWcEHX7maouXRkynxtd8+S6Hi0BAzMTQV1xcULLfaZLTEj/60h/5cpe7EjpgaUVPD8xUsV+CLIIokpAcxJKqiULRdkmGDdMRg73CJkK7SXnWfN0SNcdcghGDXYJ6C5aEqkIyYoEAipNcb2a5uijJUsMiVXWzXpyfbzyd++ihnb17Bce3pKQs0E0XiqZ6HzsECOwcKKMBwwSZsaPW5b4yZrEhF6MmWEbCgyI+pGlc2xMz6OhkuWPRkymiqwrFtKQqWy3DRxtS1l4Qg/VIRfWfajbPUrmExdglIJBKJRCKRSCQSiURyKJAiuuQlz1QOWJhfjnONmhC4flmS1Y0eD+8ZpWA7CCFwXIEA9gyXWNesoOcUujPleYv2U7li5yJu+b6oxzE0uSZP9+SouB6xkE5IVxkuWnieYDBvEw/rrGqIkq84eEIQNXXCuorl+vRlK7S0peYtVE7MVE5HDTYsi5Otxt7omoLnw3Ht6UmxHm5I0Jct89jeUSzXpyFqkAgbuL4YF/XREDXZN1LCFZObao4UbZ7pzZIpOYQNDc/3yZZ9chUXhSDCRRAI1bbr8/vnBrl/1zCnrW/iL17TwZ3PDJAtOTTGTQwtcFobmkIqYpAtO5Rtn10DBTwBK9PR6usqIV3D1HVAoWy75CouYUPDdgUCn4aYyWVv3cSRyxOTYlOmKooULJd4SGfD8gQjRRvfF5iGSlhRyVUcnurKEjZUoqaOosBI0eVPO4d5eM8o65piHLc6PePuiqmeh9Gizb6RUtAIVVXwhcDQlHFzn4oERYFlyRC92fK8Iz/GFlnG0hAzWSdiWK5PxbFwfR/b9VmeDAEKoyX7sIu5c2Wpi75z2Y2z1K7hQHcJSCQSiUQikUgkEolEciiQIrrkJc9UDtgac8lxHstYIdD2fDRVQZ1wTOH7DBUsdFVhy6r0AYn2Y5lN3KrFMXSNlnl8fwbH80lHDTwfbM+nKR6iJW7yfH+RVNjglI5GsmW37havuZ/HusXnw9hM5SbXZPdwkVzZxfODqBNfwKkdTaxuiPJPv32GkaJNU8zkuYE8ubJDtuxgu4E7vmR7JMIGhqaSDBvkKg57hgqsaw7E2ULFIR01X5xzIeoCOgSCflLRyZYcSo5XjQdxCekahq6RDBtoCmTLDg90jjCUtxgq2PUYlvEEDveS7WLoCtGq4B0PB/nuyUgQL5MMG0RMHVVROHplEkNV6M1WOGltI2dtap10r6cqiqxpiuL5glUNURRFoWx7FG2vXghwfB8hIB4K4fuCTCkQ2ZMxA9v1yZScWQs1k54HIegcKuB4PqauVpt7CkAZN/cbliUIGSpv27KyPu4VqQjhaqPdvqpL/B3Hr5xyXU/XuHKkaLO9J0fZdomYGhuWJdBVlZ5MmYaowQWvXENrKjxnMfdA+x7MlenOs1RF3/nsxlmq1yCRSCQSiUQikUgkEslSRYrokpc80zlga8yU4zyRmhBYslx2DxUQiMBJ7YMvBL4QWI6L7fiUHX/S5+cr2k8l1M30mc1tKd59QhtPd2dxPMiUHDRVoSlmsrY5jqEpdGcqFO1ANG+ImZwYbSBfjSyxPR/XExzXnp51LiZSE/Gf6cnx4O4RVAViIR1DUyhYLkJAf77C3c/2s3OgQNTU6m55UwuEa0NXsRyfsuNRtF3iIaMe05Itu1QcP4iqKTu0jcmHz5UdhvIWPhAzVXRNwfeDrHTH8/FEcH90LZgLqBU2DGzXoy9nkS07GLqC6wcu7LHoqkLBE0QNjY6WOPuGS3Un9rrmOEUrS67i4PmC5niIhKnTm6uwMh3hvBODZqhjG3iubYpNWRTJlh2u+s2zREwtiOMZLOCLYIeDFujdCAGjRRtdU3E8QdQMXOmGJig7HsuTIfpz1rSFmonPQ95yyZVdEmEDXwgqdhBP5AuBoqjVuXfYM1ysNoRdTltDhNse6+KJfRl6MmUszyekB81TxzZhHctUjSuFEOweKmC5HooCTbEQ6YgBilIvOD24e4Qvn3P0nITwxeh7MBe2d2f52aP72dado2x7REyNY9uSvPvE9iXpkl/M3TgSiUQikUgkEolEIpFIJiNF9CXMoXJcvtSZzgFbY6Yc54nUhMCHd4+QqzbNVFUVUwUQZMsO8bBJoeJSsl3yFZdkZHzu9VxF+4mCoKkrLEuGefX65mmzrwGKtofr+6hK0OiSqoCsEGRrp6MGfVkL2/WAQLBMhA2EEOwcKLBlVZrVDdFJou9c1tamFUmWJcN0DhVRgIoTuPWXJcKsbYoGWeTV5pSjZZuK65GqNkQVIhDRPc/H8aFQCWJNQEFTFTzfpz9X4aQ1jZQcb1wUSn/OwnL9ahSJwmDeRlTFZ1cINEXB8am6zF+8juC40BA1GS5aRAyDUjXDfOyuBcfzcXyfI5bF+cir1o6LYklFDDqaYzzdm8X3BQLBaNmuR48A4xt4ThB2xxZFOgcLwVq1XHYN5ik5HomQTt5ycX2BqDaD9YTAcTxMXQ1y01HQVSj7AtcTMxZqJj4Pjuvj+QJdDdaB7fq4flAQEtX/FSyPNU16PeJnc1sKIQTP9+dpjJm0psK0xEOUqw1gp3LCT9W40vV9Roo2ni+ImXqw06A67/MtOC1W34PZ2N6d5cpf76BzqIg/JlZo/0iJZ3vzfPmtm5ackL6Yu3EkEolEIpFIJBKJRCKRTEaK6EuUQ+W4fDkwlQO2xmw5zhOpCYE7enLsGS7RoKkIIXB9Qcl2CesaaxqjPN+fx3EFjjfZjT4X0X6iIFgxPHb253mqK8v/Pjswbfb19u4stz3aFTi2QzphQ8P1Rb0p5ea2FCtTEUaLDr3ZCmFDn9S0cWt7in/67TMLWlt7houMlmxeubYRoYDj+hi6SiKkg6Jg6ho92TIVxyNTCooQEERgBM5k0DUVT/hYrkfZ9ggbGpbjYbk+jTGTi0/vABgXhVK03fp9dT0fXQtiSTxf4Hjg+IGgPrEQ4FUzwFsSJl2jGoamIAiyx6OmXhfvR4s2jfEQF75qLceuSo+LYukctBkt24R1jVhMJxbSWZ4I887jVwLMS9itrdU/PD9IT6ZMTaOtZbkHISvUY1hSYYOQHjjK3eq1GLo6Y6Fm4vNg6CqaGjjwzWrGe0gJinT5iotAkIzofOy0dfWx+r7g9se7sRyfre1jIos0dUZn88Rs/+GCXc0/D3NES5yGmDlurHMtOB0qp7XvC77/h10825dH14IdEnp17kq2y7N9ef7tD51c+97jllRBczF340gkEolEIpFIJBKJRCKZjBTRlyCHynH5cmEqB+xE4Xg+TTQ3t6X42GnruPzXT1N2vLrbuikWYl1zjIaowf7REuWijT4hFmQuov1EQXC05LCjJ4fleqSjBkXLnTL7uva5iuPRmgwzUrIJGxqGptabY3YOFkhGDF69vpmGmMGuweK4po1b21Pc8VTvgtfWi2JdIEBPJGJqqIpCQ9Rgz3CJRCj4vaEpmLpCxfZAUYgYGq7nUbI9ilUX9prGKJ8/+6j6+cdGoQwXK/zVT5+g4niEQlrda66pgYs9yPgOIlEcz8f3BYoCZcejMWZScXxSUYN4SKfiBOJ9yfFwXIHr+zTGQ3z2TRs5dlW6vgY2rUhy544+brpvD4oK6xpjREI6Zdtj70iJ6/93JxFDnZewq6oKx7Wn+cXj3ViuT6gqcPsCXA9UIKSrJMM6I0VnjKc+EHGbYqHAuV5x8YSgO1OaNju//jwkw8TDGsMFG01ViIV1Nq9IYugqtuPRU3X/n7lpef1sB+JsHhtj82xfnpvu3R2MYcKODZj7LpEpxyNEPaYoEdZ5of/AndadQwUe2TuKqjBut4KhBfnxmZLNw3tH6BwqsH5ZYsHnWWwWczdODbkTSiKRSCQSiUQikUgkkheRIvoSQ2bbLoyJDtixwvG7Tmibd9HhzE3LeaBzmIf3jrAyGcY0tLrbWghB1NTwhUl/toKmqPMS7ccKgkA9M7om2sVCyrjs69se7SJsqDxfdaqvSEVoTgiK3dlxjmpTU+nNVWhJhrj49I5JedxjG34udG3NVaw78+hl7OjNV+NvDDQ1EM7LtgdCYGgqvlDxReDmD+kaRyyPj2viOrb54c6BPFFTw3Z9bMfD0DWqZm2oRtkIYKhg1UXpwPWuoKsKfdkKEVMnZgZjTkWM6rigoznOmZuWsTIdRNyMFQsf2D2C5ws2r0xNmq/tPVlGijYntDdMEpqDuTJ4bN8If3xhkNdsaEFVFXxf8MT+DE0xk4rjIQS4nqjedw0hlGruvoemKVQ8H8P1KDseYV1jXXOMkaLN4/sz6JrCj+7bO+VOgonPQ0gPihuqonBEc5xU1KRse4yUHFamglz3sff8QJ3NtXu3tinGw3tGAld8WF/wLpGJ4xkt2vWGuZ4vUBVAgSf2Zw5IRH++v0Ch4pKKGFMWD2KhID/++f6lJaIv5m4ckDuhJBKJRCKRSCQSiUQimYgU0ZcYMtt2PPNxQ07VyHGh7klVVTjvxFV0Z8pV17aOJ6BsufRmy6xqiHLOlhU8sT8zb9F+rCCYrwRNH6PmiwKjrir17OuoqfG7HX081Z2l7Hh0jZTIlR06WuJsbkuxe6yQqEJIUzl5TSNRMxAbJ+ZxH+jaWt0QZVk8xI7eHB0tMZJho55xPVas+8DJa3hk7ygPdI5gux6eH7jGV6QiWI7HUNFGAWIhleXJMCtSYXozFa6/+4Up3fBFy2NZIhzMn+UGMTrVAHFNUwmbQc64L0BUz6WrYLs+g3mbdNRgS1sS1xf0ZlxUQ+FDr1xNPGRwf+cQP31o/ySxMGpqM85XQ8Rg33AJb0xuNsBI0Wb3UIFs2aFoeVx71wv8/vnBccc8ujUoVAzkKph6EE1Ta75asj0EgoQeOOwzZYfmWIj1y+KUbI+nujIAHNWaojUVmXYnwcTnoS9b4f7OIXYNFtkzVJxxvS6Ws3kxdon4viBTsrE9j8G8hakp9Ya1tbiViuNRsFxue7SLjcsTByD0itnfMq/3HRoWczeO3AklkUgkEolEIpFIJBLJZKSIvsSQ2bYvshA35Fj38oEyF3f727asrIuUsVBwz4qWN8nRPJaxAqXjvdj0sUYt+7pkB8fJVVyOaNZYlggxkKswXLAoWi4dLXHWNEaxvcC5vH+0RMXxuOvZAR7ZNzpprg50bdXuR+dQkd5sha5MmaaYyYZlCcKGNk6s03WVj59+RFXEq9AQNYmHdVTg4T0jREyNTSsSRAwdU1cxdZWVqTA7B4tTuuETYZ3GuElTPERftsxIycatzltj1KBgeQghOHJZgsFiMD9FK8hR11UwdZW9IyVyZRfX87GyFf6//7eLloSJ5zOlWHj25tYZ5yseDuJJChWXdDTI+h4p2mzvzmK5HqamEjM1GqLGFMfUaY6F6B4tU7QdVEVBUYI4F0/AxuVxLn3deiqOx70vDDGQt8iUbLqzZUK6ynHtaRrjoeo4Zo6PqT0PW9uDHRZzKTItprP5QHaJ1L8D+gv0Zit0DhapDbcpHgKCnSG259OaClNxvAPaqbNxeYJ4WKdguTTq45vUgqBgucTDOhuXLx0Xeo3F2I0jd0JJJBKJRCKRSCQSiUQyNVJEX2IcjGzblyJLxQ05m7u9JlJu787yHw/um5PgP1agXJYMVTO9fUDBFz4l26M5btKfr1ByPOIhjWTEIBnWaYwFETIjJZvMvlFipoYvoOJ4+L6gNR3hqNYEFcefNFcHsrbG3o9VDVGaEyF29ucZLgYZ0euaYhy/umGcWLe5LcVfn7FxXJNJTwh0XWVDY5TRksPecrne/DMZ0WlNhqd0w4+ds+NXp8lbLtmSU70H0Lc/S0s8zMbWBBtJ0JursK0rS0gPImNGijYVxyMRNoiaGhXHo2u0xGDe4lVHNNXnIx7WWW/G2N6b41dP9uL5PmXLnTLPW1cU4mGd0ZJNW8PEaB6dXCXIMG9NhlmehJ0DBe7dOYSpB/Eye0dKGJqKqoDl+jieqDcaLVgeD+0Z4bwTVnHucW2z5ovPdSfBXItMB6PPwHx3iUz8DtjalubR/aOMFm1MTaVkewCUbJeQobG2KUZI1w5op05Hc5xXrGnkD88PkinZxEJGvbFo0XIQAk5a00hH89LcBTSXeZ5pd4/cCSWRSCQSiUQikUgkEsnUvLyV2Jcgi51t+1JkqbkhZxMe5yv4jxUo+7MVVAUGCxZCUBeUs2WHiuOjoJCMGIGwrSg0x0LsGy7h+AJNEYR0g5GSQ8UJnM+tyTC6phLX1ElztdC1NdX9iKPTFDPJVRx2DwaC2hfPPgpVVegcLNQFuk0rkmw6Z1NdtNs/WuS6u15g/3AJVwjiIZ2oqeH6gdBdrLikY+YkN/zYOdvWnR3XGNRyPVxfsCxp1htNup6PqkBYVxkoWLi+j6kbGFoQnq2pSjWQQ7BnuERjzARFqWdtjxRt9g2XCBsq+0fLHF9zflebWdYacr5iTQNl2+OF/jy6pjJUsNCV4P5FDJ11zTFQFBQCt/tAzqIlEeK+ncP4QtAYM7Fdn+GiRa2IomsqCHhqf2bc+slXXFRFIRKa+mt7sXepLHafgfnsEplyzYV1Ni6L8/i+UVxfMJCroCpKNfdeZc9wkTVNMSxn4XOgqgofP72DgXzgei/ZbpDcooCuqmxYHuPi0zuWtAt74jz7vmDnQJ7n+wvsHynyXF+BwbwVNLadUOyTO6EkEolEIpFIJBKJRCKZGimiLzEW2wH6UuSl5IZcqOBfEyi//4dd7Bwo4LgCRQkabirAcMEJXNsq5MoqXaNl2tJhhooWYUPD8H0qrk+u4uL6QXa6rqqMFC3WNEVRFGXKuVrI2prufiiKQipisn6ZykC+wt3P9vPA7pFp3fjbu7P87zOD7Bsp4/sCQwsagCZCOiFDIxUxGCnaiJJdj8YZy+a2FOdsWcHV//McoyUHFQVNhXhYI1t22dadJWIUUavNXwuWGwjero8QkC05lCyPiKnhVy3ftUaRuYpDruzyXF8ex/eJGiqer5COmPRmKzzQOUxbQ4R8xaVQFepDhkZHc4zjVqe546le9vUXKNkuuqoQNXVWN0ZpiJn18dcEyKOWJ/nD80P4QuD6glzFwfMFgkCojZsaJdvl6JUJBnJWff0cjl0qi9lnYD5Mt+aa4iFiIYN8xcFHoSFqEA/ruH4QpZMtu6xMhQ9oDja3pfjyOZu47dEutnVnKTkeUUPj2LYU55340mqsub07y/f/sItH9o6SLTlYro+iQEs8xOaVKUKGNq7YJ3dCSSSShfLVr36VX/ziFzzxxBOH7JylUokPfehD3HnnneTzeUZHR0mn04fs/BKJRCKRSCSSPy/kv4SXIIvtAH2p8VJyQx6I4L9pRZKmWIj2pihxU2PfSJnhoo3nB4I6AjwfBvMWoyWHzpiJ5fokq1EeJdulLR1h/0iZdNTA9QXZsku+4tbfM3GuFrK25nI/Ogdtvvv7XdhukE29oilKeUykzDlbVnDHU730ZMpBprUCqqJgOUEufGPUDBpszjDXvi94Yn+GWEhHUaBQ8RACHFfgej6eAFUJGpAWq+K5X/1sLZLD9gIRPMggF/h+kKe9rTvLUMHGdn00VaFQcasGZAVVCZqZPtObR1GCpqUxU2dNY5SdAwXu3zVMcyLEMSuSvDCQR68WB/aOlEhEjMDlzosCZHtTlNZUGMv1yJQcSrZb7ZGq4CtQsD0UPEYK9rj1c7h2qUzlIJ9rw9/p3jfb57Nlh2zJIaypQZPVULATI27qeELg+UHBydA1FEXF0CAZ1hnIWdgxk9UN0QO65sNVPFhMtndnufLXO3i2L4+CCDL3lcBYP1SweaIrwwmrG1i/7MVi3xfPPurPfieURHK46Ovr46qrruKOO+6gq6uLVCrFhg0buOCCC/jwhz9MNHpg32vTcdttt3H++eeze/duVq9ePen1o446ije96U1cf/31i3reXC7Ht771LW6//XY6OzuJRqN0dHTwnve8h4svvpiGhoZZj3HzzTfzxz/+kT/96U80NzeTSh28/z7es2cP69at4/HHH+e44447aOeRSCQSiUQikSxdpIi+RHk5iDgL5aXkhjwQwX/PcJGdgwXWNcWJhzRGSg5lx8PQ1Ko7OZCAQ7qK6wuGijZU3emWK2iIGbQ1ROjPWfVmpJ4vqvnqAVPN1XzX1mz3ozdTYvdwEQWIGBqjJYfuSJmO5jjrl8V5oT/Pv97TSdTUWJkK050pY7l+PbrG9QWZskNIV4iYGumISdHyppyvJ/ZlGC3YuEIQC+noqsJAPojCUYCK41N2XMqOi6pCdQqDwgSgVEVEFIGqKowUbZSqc73W4NWpCvK6CpoKJSdwsgsgHdEJGzq2G7j3FUWhYLnEQjpb2lKMlm1GinY9E33PUIGGaCAE1ATIjcvjNMZM0mGD/ZkSmZKDpoKpqShK0FjUcX12DxVJRIx6PMlCd6nMRfCeqygOc2/4O937jmtP88T+zIw7Fn78wF72jZboGi1j6irJiE5HcxxdU9CqES6eEPhCIKqO/pLtEg8FjWr3jZYOeJfKYjYpPtT4vuBnj+6nc6iIrimEDZ3hQpAlr6oKluuTrzh0DhU4cXVDvVizb7T0Z78TSiI5HHR2dnLaaaeRTqf52te+xrHHHovrujz//PPcdNNNrFy5kre//e2TPuc4DoYxuWfHfHj7299OU1MTN998M5dddtm41+677z6ee+45brnllgM6x0RGRkZ49atfTS6X44orruDEE0/ENE127tzJT3/6U37605/yl3/5l7MeZ9euXRx99NFs3rx52vfYto1pmtO+LpFIJBKJRCKRzBX1cA9AMj01EWdre5qOlvifjXBRc9z2ZsuIWqfFKjU35IZliSXhhhwrME/FTIJ/XYA3NfKWS67skggbWK4HAsJGIKq6vkDXVIQvcDzBQN6mWH3/zoECIV2lZLt4fuCiNrTgsfZ9n91DBdIRE18ErmvfF3QOFtjWnQXg2LbUrGtrpvsxUrB4Yn8W1/NJRQySEQNTVxkp2mzvzpIpOSQjJj3ZMomwjmlohHSNZNggZKj4IrintuuTDBsc0ZKgMW5OOV/ZskNPtozjB+cytKC44PmCsKGhKcGxSrZP2fHRVZWIEcxFIJxTzbYOMrRTYR1XgCsEoer7XF/gi8Cxq1azzesCPGC7goihETY0hks2fbkKni/oyVZ4aPcIzfEQYV0jV3ExNZVM2aE/Z7FzoFAXIGvFhZ5smUzJQYGgwaiqgBK4tCOmiicEOwcKmLpSn4/aToJjV6XIlG32DBXJlG22rEpP2Wx3e3eWK+7YwVd++TT/dMczfOWXT3PFHTvYXr3/c33P2Pdef/cLbOvKko6YrG2OkY6YbOsKfl/7zHTve7BzmCt/vYMHO4en/Px/PdHN9Xe/wN6hIg0RA1UFQ1Pq62moYFWLNTqxkI7vC/KVYNdBUyzEsavSGJq6JHapHE72DBfZ1p3DF4KoqQdFIBG40SFYb0IEETh5yyViavVizXzXmEQiOXAuvfRSdF3nkUce4fzzz+foo4/m2GOP5bzzzuOOO+7gbW97GxDsWLrhhhs499xzicViXHnllXiex0UXXcS6deuIRCIceeSR/Mu//Ev92H/4wx8wDIO+vr5x5/zsZz/L6aefjmEYfOhDH+JHP/rRpL/xN910EyeeeCJbt25l3759nHvuucTjcZLJJOeffz79/f0Lut4vfvGL7Nu3jwcffJCPfvSjbNmyhaOOOoq3vvWt/PSnP+XSSy+d9Rive93ruPrqq/nDH/6Aoii87nWvA2Dt2rVceeWVXHjhhaRSKS6++GIgcNwfc8wxhEIh1q5dy9VXXz3ueGvXruVrX/saH/vYx0gkEqxevZrvf//79dfXrVsHwPHHHz/ufDPx+9//npNPPplYLEY6nea0005j7969AFx44YW84x3vGPf+T3/60+OO+7rXvY5PfvKTfPKTnySdTtPU1MSXv/zlSfdJIpFIJBKJRHJoOPxWXolkAi+lXPgDidgYK8A7VWd2ICiLanPJamSFpuB4gdu22uOQhqhB2NQZKdpBNAkwUrBpSYaJmhrdo2We7cvVs7Yv/9UOGqIGoDBasmd0EE9k2vthuTy+P4MgcIWHDQ1FUTA0hVTEIFt22D1UpL0hgusJdEUhEdJJRoJxN8VCgQjuBaL3EcviZErOtPOVKweZzlFDq85C4LgVQqBWoz0c16ctFaYnWwlc80LQm62gqsEc6aqCooDjCSqOwBjzs+cHzmZdVTB1FQFUHA9DU/CU4IyuLyhWs9aDAkBwTE/AaMnB8QSrm6IMFSyyJYei7TFasjhhdeO4uJzzTljF8315Roo2RnWngaKA6wUO+WTERFVguGBxwuqGcfMx150Ec2l4C8y5Ke5c8/+PWp6Y8n2xkIbrCcq2h+sJ4iEtiGgZ8/l/vWcXEUNjw/IEoyWH7d1Zyo5H1NQoWi5do2Uszydm6mxdlcLQVRzXx9BVEiGdguVRcb0lsUvlcJKvuPXCnq4q9big4P9SF9NdX+C4PmXGF/v+nHdCSSSHmuHhYf7nf/6Hr33ta8RiU5sDxv63xVe+8hWuuuoqrr32WjRNw/d9Vq1axa233kpzczN/+tOf+PjHP86KFSs4//zzOf300+no6ODHP/4xf/d3fweA67r85Cc/4etf/zoAF110Eddccw333HNPXcQtFovceuutfPOb30QIwTve8Q5isRj33HMPruty6aWX8t73vpff//7387pe3/e55ZZbuOCCC2hra5v1eqfj9ttv5+///u/Zvn07t99++zi3+be+9S0uu+wyvvzlLwPw6KOPcv755/PVr36V9773vfzpT3/i0ksvpampiQsvvLD+uauvvporrriCL37xi/zsZz/jE5/4BKeffjpHHXUUDz30ECeffDJ33XUXxxxzzKzudtd1ecc73sHFF1/Mf/7nf2LbNg899NCcrm0sN998MxdddBEPPvggjzzyCB//+MdZs2ZNvTgwEcuysCyr/nMul5vX+SQSiUQikUgk0/PnrTRIliyHKxd+PrEWcGCC/1gBfnkyhKYGYnnNYeRWHdZNUYO+vIWmKqQieuBg93x0z8fUFAqWh64qNMRN0hGDHT05+nIVdFXhmJVJWlMR+rIV/vD8EABbVqVY2xybViydiqnuhy8EuqZwVGuSrtEybrVZaEDQXDNbdkhFdFQFRss2pqGyLBEmU3IYLdkkwgaqqqCq0JetsDIdmXa+khGDkKZiez5hIVAUJRDHq+7tWjxMSFfRqrnbvghE6caoScX1savFCgjc3ZqmoCtw5IokuwYKDBVswoaKQMH1gsx1AGriI1CwXXxfYKoK5WpxQ1GCKJqK6zFcsDmhvYH+fIXRksPfnLGB12xomdRY9rwTV7FzsIDj+hRsD8v1CRuBS19VFIqWg6oqnLa+adJ8zBY1MhfB+7ZHuxCIOTfFnWv+/327hqZ8X77ikqvm9ecqQSEiETbqn0+EdXYOFji+vQFFUWiMmWxuS7F7qECu7CJEkFW/PBHC1FUaYqbM7J6GRFgnYgYRU7Xn0tRVLMfDUEBUi1C6GkTjTDVvL+U4G4nkpcTOnTsRQnDkkUeO+31zczOVSgWAv/zLv+Qb3/gGAB/4wAf42Mc+Nu69l19+ef3/X7duHX/605+49dZbOf/884FAJP/hD39YF9HvuOMOSqVS/fVNmzbxyle+kh/+8Id1Ef3WW2/F8zze//73c9ddd/HUU0+xe/du2tvbAfjxj3/MMcccw8MPP8xJJ5005+sdHBwkk8lMut4TTzyR5557DoC3ve1t/Od//ueMx2lsbCQajWKaJq2treNee8Mb3sDf/u3f1n/+4Ac/yBvf+MZ6XM3GjRvZsWMH3/rWt8aJ6G95y1vqLvjPf/7zXHvttfz+97/nqKOOoqWlBYCmpqZJ55uKXC5HNpvlrW99K0cccQQARx999Kyfm0h7ezvXXnstiqJw5JFHsm3bNq699tppRfSrrrpq3HqQSCQSiUQikSweUkSXLFkOtRtyrlnPU41zIYL/WAG+P2cRNlQyJRuBwPYCgSsR0nFFkHMeMTViIR3XD0TSoYKNX41oMDWVS05ZwxuOWsa/3PUCigqbVyRRVBUhBH25MroWZH/35yza0pFpxdKZrrN2P7Jlhyf2Z7j14f00x02yZZvRkhOM16c+rrLt8nx/HtcXPN2T4+meHIqi1PPbK/lAIGiImZy0tpHzTpx+rlMRg5XpCD3ZCrmKQ9TU0dQgu7zsBFE2APtGS5Rsj1w5GIOmKISMYO5sL8iCboyaHL0iwUN7RoPzR002LEswUhymaHsoEDjNCSJc1DFNGV0vcLDXdgbYnkBVBJmSja6qDBUs8pZDvuJywuqGSQJ6jePa02xclkDXFMqOR1+uQsX26s1Nk2GTdNTguPb0tPdkOuYieG/rziKAlXNsijvX/P/BvDXl+xwvKGBEQhoFK9h9MRZNUXA9gTYmZKwxZtIQbQjO7Xr05ywufNUafvd0/5LfpXI4WdsU49i2JPtHSpRsl2TYIBHScTwfxxP4wkdVIB7S6c9Zct4kkiXAxO/hhx56CN/3+eAHPzjOWfyKV7xi0mdvuOEGfvCDH7B3717K5TK2bY9rfnnhhRfy5S9/mQceeIBTTjmFm266ifPPP3+c8/2iiy7i05/+NN/5zndIJBLcdNNNvOtd7yKdTvPMM8/Q3t5eF9AhEN5rr81HRJ/uen/+859j2zaf//znKZfL8z7eWCbO0TPPPMO555477nennXYa1113HZ7noWnB36stW7aMG19raysDAwMLGkNjYyMXXnghZ511FmeeeSZnnHEG559/PitWrJjXcU455ZRxc3Xqqady9dVXjxv3WL7whS/wmc98pv5zLpcbd98kEolEIpFIJAtHiuiSJc2hckPOJfpiNiF9IYL/WAH+iX0ZMiWnGrkgSEdMVFUhV3ZQCAT1XNnF9XxCukI8bFbF3iAX+u5nBljTGCNTdljXFEdRAzUyXwny06Nm8Lhny07dBTxWLO0cKqAqyozjV1WFku3xyyd7grnJlBksWERNHdcT9OYq1QDyIGvc8wRRU2N5MkR31a0exEsoxEI6JdslpGt89FVruejVHbNmsx+3Oo3VOYzrCXIVF88XmLqG6wsqjo+uBZnwyXCQy+75AkWDbNkmFTGoOB7xsMHG5QkgaDiqKgoxQ8MJBeK16wn8CedWqwp6bZeA54NVFYEVAgFYUcB2PSqOx47ePBuWxWcUJtc2xVi/vBoFtCzOuqYYecvFcYPr6M9VWNMUI1t26BwszKuANBfBu+QE2fs1x/JU7xnbFHeuDX9bEqEp32dowQ4Bq1okMPTxLTm86s4Gb8LkK4pCMmJQqCikoz7Hr27gyNbkId+l8lJCVRXefWI7z/bmebYvT6ZkEwvpJEI6mbKNLyBkaDRETTlvEslhZv369SiK8v+zd+fhUZV3+8Dvs8w+k8lOQhJMQhAEBARccLeIqBSxtSpVVF6tO1rbV3Gryqaixd2q1aJQ7av2V9FaXHDFfRcECiJbWEIgIctMZj/L8/tjkjF7BsgK9+e6uDQzZ8485zyHkHzPd+4HP/74Y5PHi4uLAQAOh6PJ480jX/75z3/iD3/4Ax544AGMGzcOHo8Hf/7zn/HVV18ltsnOzsbkyZPx3HPPobi4GG+++WaLGJapU6fiD3/4A15++WWcfPLJ+PTTTzFnzhwADWsqtPz3p63H25OVlYXU1NQWxztgwAAAgMfjQW1t7V7ts7nm56i1cbaWK958kdb4J92a/0SQvOeeew7XX3893n77bbz88sv405/+hHfffRfHHHMM5Pomh8Y0Tdvn92pgs9lgs9n2ez9ERERE1BKL6HTQSzbruaNO7X0t+DcuwK/cXoulq3Zi5bZa+CM67BYZXmf8r2lEM6AZJiyKhFSnLTFOzTDhtgHBmI6lq8pbFE8bOoDV+rGH63OQGzisCjbvieHh9zbAF9Z+7sLPcuOY4nTkeB2Jovracv/PNxtS7KgNx1AViMEf1uJFWQhIkCBMM1EMNUwT5bWRxAKdhhAwDECKGchPdUBVZJT7fu6ya+/8NnTuVwWiyEtzQpGBmmAMq8tqAcTnsioYg0WRkem2waJKqArEENFMCKEhw21DtseG9bvrUBWMAQKQJYEPf6qAKsvxfHMl3iWvKhJcVhUhTUdMF7AqMrwOFVXBGGJG/BdfGYCsxKNjYnr82E3Eu6mv+0XJXmfNOy0KKqM6fqoIIaIZ0E2Be9/8MelPRTRIpuDttCgQ9f/fXlG8ISc72fz/4wZmYvlPlS2289hVpNhV7KwNo3+qAx6b2uT1dREd/b12+MMx9EuxtRvVIssSM7s7MDzPiz/9ciie/ngTvt1aA184XpxJd1kxNNeLSSNyMaogleftALW30WTUczIyMjBhwgQ8/vjjuO6669rMRW/LJ598gmOPPbbJYpybNm1qsd3vfvc7TJ06Ffn5+Rg4cCCOO+64Js97PB6ce+65eO6557B582YUFxcnol2GDh2Kbdu2Yfv27Ymu5rVr18Ln8+11RIksyzjvvPPwwgsv4I477mgzF70zDR06FJ9++mmTxz7//HMceuihrXZzt6YhA90wWl9Ivi1HHHEEjjjiCNx6660YN24c/u///g/HHHMMsrKysGbNmibbrly5skUh/8svv2zx9aBBg5IeNxERERF1HhbR6aCXbNZzQ6xFV2gowBdnuXH2qDy8u3YXlq4qR7kvUr/wZRhRzYDdIsNlszQap0AopiPDZUNRugs7fWFIiBdAXTYFdREdwagOAQHdNAFILbqAd/nC2OWLQAJQlOmGw6tgly+MN1aX4/UfdiInxY50txUDM13YXh3CTl8ExVmu+E2GLA/CsVrsCURhmoDTqsBljcd1mJoBWUJ97Ek83x2IF/UlxCNoSrI9UGQ5cX4LM1ztFn6aR+dU1MWwrSoEU0hIdapw21RohkBUNyDLwGE5KYgZAmU1IRRmOFFRF8X63XUwTYFMtw0l2W5ENRM/7KjBnnAMUv0CpPE/EgQAl9UCi+u8jHsAAIX/SURBVGJCAMjy2BHW4t3ZiozEYqqmGY/cMQWQ4bSin9uG6mAMP2yvbXEczYtb1/2iBEtWlGHltlrs9IURihqIGgYssgy7KqOknx12VUn6UxFA04L3QKsrHp9imLAoMtw2BeW+MA7P88IUJr7bWotcrx1WVYHHrtYvQNkyXzzZ/H9VldvcTlXi2fFqfZa/w6ogFNVRWhWEy6biV0fk4est1R1GtbBAmJzheV48fP4R2LwngJ92BwAIHNrPg+JMN8/XAWxfo8mo5zzxxBM47rjjMHbsWMyaNQsjRoyALMv45ptv8OOPP2LMmDFtvrakpAR///vfsWzZMhQVFeH555/HN998g6KioibbTZw4EV6vF/PmzUt0mDd32WWX4YQTTsDatWtx4403Jn7WOPXUUzFixAhceOGFePjhhxMLi5500kmtxst05J577sHy5ctx9NFHY86cORg7dixcLhdWrVqFL774AsOHD9/rfbbnf//3f3HkkUdi7ty5OP/88/HFF1/g8ccfxxNPPJH0PrKzs+FwOPD2228jPz8fdrsdXm/bf5+2bNmCp59+GmeddRb69++P9evX46effsLFF18MIJ7b/uc//xl///vfMW7cOLzwwgtYs2YNjjjiiCb72b59O/74xz/iyiuvxPfff4/HHnsMDzzwwL6dCCIiIiLaLyyi00Ev2aznhliLribLEiYOz8WEoTmJQuEuXwQLP92MteV1cFrji4/qZryAblcVFGW64LCpkAHkeO3YVFmXiDzRDROhmIFAVIdNlZGT8nMXsDBN/LirDkr9IqSyLKMmGMPmyiAM04QQQMwwASHw+qqdqAvrcFgV1IQ0pDhUFGe6UZzpRk1IgxAmwpoBqyIjzWVBzB9/nUWRE9EniixBkRVENQNR3YBumHDbLdjtN7Fyey2e/3Jrh4Wfhs79Zf/dhfvf/hFafct7OGbAFAIeuwUumxW+sIbSqhBG5nsRjOq48uSB+MdX2xDVTRRluZBSH2dTHYzBIscL5jZZQobHCgggGDOgyjIG53iQm2LD+t0BTDtmAP7fN9vx/Q4f4jXIeIFBkuILnMoAZEnCxsogHnpvA6yKDFMI5Hrt+OWIXPRPdWDJirIWxzgi34ufdtUh3WmFKmsIxwC33YK6qI61O/0YnudFSfbefSrinNH5WLfTjw/XV8Js9JFxWZJQnBmPxnl/XQXKfRGUVoVgt8hIc1qR63UgFNNbzckempuCc0bn1d/gCUOWpFajVNpaJ+CY4kyMLPBi5fZabKwIYHNlDDXhWPxaBPDxhj1Ic1rrr7FYq1EtrRUIB2a5MK44EzleO4vqzciyhJJsD0qyPT09FOoG+xtNRj1j4MCBWLFiBe655x7ceuut2LFjB2w2G4YOHYobb7yxSZd5c1dddRVWrlyJ888/H5Ik4be//S2uueYavPXWW022k2UZ06dPxz333JMo5DZ3/PHHY/DgwdiwYQMuueSSxOOSJOG1117DddddhxNPPBGyLOP000/HY489tk/Hm5GRga+//hr33Xcf/vznP2PLli2QZRmDBg3C+eefjxtuuGGf9tuW0aNH45///CfuvPNOzJ07F7m5uZgzZ06TRUU7oqoqHn30UcyZMwd33nknTjjhhBaROI05nU78+OOPWLx4MaqqqpCbm4sZM2bgyiuvBBC/qXHHHXdg5syZiEQiuPTSS3HxxRdj9erVTfZz8cUXIxwO46ijjoKiKLjuuutwxRVX7MtpICIiIqL9JInWQgEPcn6/H16vFz6fDykpKT09HOpimysDuOv1/yLVYW011iIQ0VEbjmH2WcO6JZ+9NaYp8MKXW/HoBxtgmAKKJEFVZHgdFhRlupDmsibGeeKgTDz98WaEYwZSHBbYVBmBqI7qYAySJGFEvhcl2R6EYwa2VAVQXhvBsP5e5KU5ACHw3bYaVAfjGeKaIRCM6lBlCRFNRzBmwmGRkeayJQr4+ekObKoIwqZKCMYMDOufAkWW8MWmaqhKvLM7rBmwqTIs9atG6qaAbggcW5IBj82CHTUhOK0KorqJnBQ7DCEQiOioCcWQ67Xj96ce2qTws6bMh3vfXIfVZT7YVRl1UT3REa7IEtJcVsiShJhu4rBcD3RT4HfHF+Fvn25pMs9CCHy/rQaVdVGE6veRnWKHRYlnlfojGjJc8UVHfRENvzu+CI9+sBGbKgMwDBO6+XPWqlWNx8HURTRYFRlDclNQG46hNqQhoplQpPgNmXSXFQOzPE26rCvqInDbVBRluvDd1lpY689V4zEcMSANwaiR9LW4psyHeW+sxebKYLyILgDUL7SanWKDy6pCN+OZ9Tt94cQ4baqM40sycfmJxS3OeUPxOhIzYALI9doxeUR/TBjar9WidVsd46Yp8O7aXXj2s1IEYzqK0uM3gRrOR5rLit+MzmsSJSTLUssCoVXBLl8EP+7ywzAFcrx2pLus7Lqlg5JpCsx9Y21inYXmkUgbKwIYkZ+KP006rFNvMvFnpr7j8ssvx+7du/H666/39FAoCSeffDJGjRqFhx9+eJ/30fD3s+CGf0K2OTtvcERERNTrlc6f1NND6DOS/Z2Gneh00Es267kh1qK7JYqXuwNQZAkRzYDHacUhmS7kpzqARvEbh+d5sbM2giyPDZphoi5iIBA1oMgy8lIdCER17PJHoMoybBYZxRkuCDPevQ4AddHGi5BKUOR4t7tVlZHisCKqRxN54F6HBb6wht3+KBRZii9WqCrwOqzQdAOKjPos9nj3syHqv+EIEb8RIAMWCSj3hePxKxKQ6bbhp4o6+MN6YpuKuiie+XgzHjp/VKIA25Bhb1NluGwqooZAVDNgUSRoRjxfO8NlRcgwscsXwbiBmUhxWBDVTNhTZPjDGjTDRFQ34Q9riRiYsGYk9iNJEpxWtb6jPYijijKQ4rBABpDltqEmFEOqRYEQ8W5fVQb2BGLQTYE0p1p/XCacVhUeG7DTF0EopkMGoKWbcMsq3HYV/UQ8o12RZWh60/z6n8egoy6iw2VTk/pURMM50g2BXwzOQiBmQNNNWFQZLouCD3+qBBDFKYOzINdfG3VRHTHNwE5/pD43++d/ONrqbi33hfHK9zuQl+ZotWDd3joBX26phmEKDO/vbXUdgq+21OBPk3KaxOA0X7ugOhjDpsoATCFgCoGYbiLVbumTXbeMqKH91Ruiyah38vl8+Oabb/CPf/wD//73v3t6OEREREREfRKL6HTQSzbruScKWs2LlyPzUrFqRy2qgzFEjXjOuF1VEuM8pjgDL3y1FQOzPHDbFNRF9UTx1GNTURfRUe6PYPpxh2BITgpMITD7P2sTi0s2LuJqholQTIduCqRaVVgUGVZVRihmwDBMWBQVTquKcEyHXZWxJxBD/7R453AgCjitKoIxHVFdwKrGu6qjmgFIEmQJsKkyyuui8DosMEwFLpsFa3b6EdXjkTCKEo9YiekGPt24B++u3YWJw3MThaIcrwM1IQ2GADw2FZphQjMEZFlCVDMQiGqIGibS3fH5c1oVaIaJb0urEdbix2kKgUB9p71misTCpKH6Ln5ZAgJRA4dkqIl92K0Kcq0qQjEDYc2A06pCkSWENQOhmAFVlqAoEqK6Ca/DAkBKRM4osoywbmLLniDSnBZAkqAbAhZZRiimI1bfSa+bAhZFqn9NfKFTzTBbLPbZlibFNFmGx/5zBn5dREvEuwSiBlIcMiBJ8NgtgN0Cm0XFxspAotC2LwvvdlQQ3pdiX/PXCCGwZU8AUd1Ait0C3YzfPBES9ir6pjdghjV1ht4WTUa9x5QpU/D111/jyiuvxIQJE7r8/dzutm/SvPXWWzjhhBM63Mcnn3yCM844o83nA4HAPo2tM3XGcRIRERFR38EiOhHaznBunvXcnVorXrrtKkYNSMOmyjrs8kexanstBvVzJ8ZpmOLnIkpDYbQRp02FIknIS3UmCqSNu/AtajzDu6IuCt0wE4XlQESDIktwWhVENAP+qA4BwKrKiOkCDqscXzBSlhCMGnBa4otUhupz2O0WBaYQ8Wx0ISAgwWlTceQh6RiR78X/fbUN5b4wglEdpjARjBqJmBRVjse/vLGqHBOG5iQKRTkZdqQ4VFQHY0ixW5DutMa7qXUDuikQjBoozHDhltOHYHieF6t31KIqGEV1IIZ0txWqrMAf1hDTTcQAqIoElzV+3iK6gWidCadVQYpDxaXHFWF4nrfJ+RrePwVbqoKJrnlTxBdMzXBbYZoi0c3fMJcQAqiPffGFNdRFdXjsFlhUOd5BrwtYZSlxTA0FeKO+sK4qUtKfimivmKbpZiLaRavPu298s8VpaVpo29uCd0cFYdMU+HGXH1XBGNw2NXFeGmut2Nf8mOoiP39qouE6CZsCmm5CsvedrltmWFNn8dhV2Cxy4qZoc8nehKMDT3vZ3V1h5cqVbT6Xl5eX1D7Gjh3b7n56g844zvZ097wRERERUfv4mxRRvYYFK3tLpEJbxcs0lxVjnenY5Y+gJqTh8hOKccKgLMiyhM2VgbaLKEKgsi6CmGGiNhSDaYoWXfgOq4yIpsczvGUJFkWCKQFRI/5aAQmyJEHTTezRYpAkwKrIGD0gFScempVYMHK330Sa0wpTAC6rjByvE4oM6PX7cdstuPS4IkwY2g+lVUG88NU2VAaiiOgGIABVkeu7jeMLmwohEt3RDYWiiGaiKNONYNQHf0SD06oizWlBKCohEDMwOMeDWWcNw+H5qTBNgSUryuC2qTAMgVDMgMMCRHUDEgAT8UJ3aooVNlWGZpjwheI3Dk4bmoPxQ7KxuTKAuoiOY4oysKM6hKpgDIdmexL57bv8YQhIyEmxY0dNJBHJAsQ/7dBQKLYoMqKaGS9mI95F77AqCAdjUFUZxZluhKI++MIaHBYFYc2Ax27Bbl8EGW5bUp+KcNniNy3Ka0PwOq2JhWTrojqCMR26aUKSJFT4I/hpd139oqzxrveGBUYbCm17093aUUF40ohcrNxei1U7fNhRHUJFfXRMcaYbaS5rYp+tFfuaFwg1o2n0jV5/s8Giyi3G1VvtS5c/UVt6ezQZHTxKSkr2ex8Oh6NT9tOVevv4iIiIiKhzsYhOB7XWYid6S9dqu8VLSUKWx45g1ECq05oosLVVRKkJxhLd606Lgmc+2YzlP1UmuoNn/KIEiz4rxccbKmGIeDFSliWkOiwIxXSEY/GudEkSyPbY4bDICGsGfCENbruKi8YdglEFaZg8on+T8xmI6nh1RRk2VgQQjMa7ko8uzmzS3V+Y4UJuig2rttdClgCrRYH082ECAGQJqArG4AtrGJmf+vMxZrsxPM+LLXsC9R3h8ZzzogwXZtcX0AFg854AVu/wIcNlRYbLht11EVQHYojoJhRFgly/vLJpCpgCiOfBx988L9WOu99a16SzOs1phcOqoCakJR47viQLVcEYNu8JQJbQJJJFlRs1W4umxV4BwGlVYAordvsi6J/qxGG5KdhQUYeqYAyqFJ+HkQVpSX0qYk2ZD//6bjt2+sLwh3W4rArslvg1FNVNRGI6fPWF5bqIH5LUkGWvQpYk7KmLwhRAIBrfJtnuVpdNwT++2tZmQXjVjlo88M5PyPbYkJtihz+soSoQRVUgilDUwPA8L9Jc1jaLfc2vbYsiN4q+AUIxHRkuW+KGQV/oumWGNXWm3hxNRkRERERE1Nf13uoCURfr7TnE+/LR/NaKKBHNwOodtQhEdbjtFhye74VdVVp0B2+tDiKimbCrKpxWBYoMaEY8x9sQBgTixWCLEl8kVDcF0t1WeB0WvL6yHCPyUltdSHJ4f2+73f2yLOGoogws++9umIgvRtrArO80dljVxCKgrR3jyPxU7AnEsKu+UHTzGUMSBfQ1ZT489dEm/LS7DlZFhqrISHGoyE9zwKgWcNtUmELEc+b1eK66IkvIcNtgmALvrauAYYomndU7a8NIc1kx7egByPE6Ese1ttyPR97fgAp/FHURDalOCwwzXuBNsVsghEBtSEOWxw6nRUEgoqPcF0Z+mjMxDw3XY0GaE2MOScPxJZkYWZCa1KciGneCl2R5sLGyDoGIjupgDEC8Qz2kGYnis24K2NR4ZnvDQq1epwVum4LXVuzE8P7epLtbAbRZEG64XmtCMRyelwKPw4LiLDdCMQNR3UAwpmNTZQBDZA/K67vTmxf7ms97ToodHruKqkAUkgQ4LCqKMl1NFtrt7V23zLCmztYbo8mIiIiIiIgOBCyi00GpL+QQ7+tH85sUUXYH8FNFHcKagf5pDhRluKAqMqK6gewUG7ZUBhLdwU6LArsazzYPawZsUvymAiCwpswPzTAQM+L52XZVQYbLisJMN6yK3G63bGuF9eYOz/fCaYsXlcOGASDeua0qMlJsCkwB2BQZKQ5Ly2OsCCBaXygaN7Bpl3vDPO+sDcOqynBYFEiShOpgDD4pHk0jSRIUSUKK3YLD+qfAqsiwKDIgBFbsqEUwpmN4f2+rURtfbanBnyblJIq9w/O8+P34QXj64834bOMeVNbFEvEouV4H9gQiCER1pDotKK0KtShuNe/k35s4odaiQRwWGV9tqQak+POBqA5VkZFmV1ET0hKFdFUCdBOwWSQM6++FTVWazGky3a3BqNFmQbguoiOkGbDIMnQjfpMk3WVNfIqgOhjDbn8EXoel3WJf83m31sf+qLKE4iwXUhyWxI2JvtB1ywxr6gq9LZqMiIiIiIjoQMDfzOmg01dyiNv8aH5Ux5bqIFxWFUcXpbX62oYiyicbKvHQez8hzWmDRZGaLIQpS0BYM2CYAofnpcSLkfVFyRS7Bf6Ihsq6CA5Jd8KiyHDbFEQ0E4NzPEitz8yWpPjCl/vaLdsQp/P15moYJhILRCqyBAnxrvTakAaP3YL+aY76xTabHmNbhSLTFPFYk9owcrx2BKI66iIavI5493xtKJboEhdCINNtR26KPdHJvKbMBwAoSnftVdTG8DwvHj5/FN5duxv/WbUT5b4IZAACAscUZ+LsI/rDbVNbHXMyNxza0lo0iKrIsKkyHFYbYroBf1iH26YiGDOh13f8CxNQrBI8VhWqLMGiyC06oJPpbm0vj18zTGh6PN6mIcYGiBfS05xp8IU1bK0K4dLji3D6sJx2/941n/ddvjC+3Fwdz8zfE+xTXbfMsKausj/fS4iIiIiIiKglFtHpoNOXcoibFy8374mhJhSP5hAm8MJX2/DllupWI2hkWUKq0wqrokBVJPx3px9R3YCzvlga1gyEtfhCntXBGAozXEhxqKgOxuB1WOC0qvCFdcTqC+6hmIEsjw0F6c4m521fu2UTcTr13fIx3YAsAxZZhiniRcT44qImdMPEyHxvi2Jie4Wid9fuwjtrdyOmm9jtj8IUAhHNhGFGkeKwwmlVURfRENUFFElCvxQbDAGEo/FOZpdNhQDgsLVyXEJAN01UBWP4cZe/1YiaicNzEgundkc3aGvRIJphxhd3tcgwTAFDCPjDGiDFb1JIUnxRVd0QCEV12CwKNMNsdU47umnRXkFYVSTopgmv4+fM8gbxGycyMtxWDMnxJHV+Gs/7yIJUTBia0ye7bplhTURERERERNQ3sIhOB52+lkPcULx8d+1uPPvZFkiIFyydNrXDCBqPXYVVlbBxdx2iuoEUuyVR3GyIMjGFiV3+SHxR1Uw3QlEffGENDosC3TRh6CYEAFPE37czumUbx+l47CoggBSHBf6whphuwGWzxLuXDbO+W9zA9uoQ1pb7k+ouXlPmw7OflcIf1pHmtMCiyNBNAcMU0AwTwageLyALYHRBKrxOK2pCsSadzEcXpeOFr7a26KyuqV88tDoYQ0w3sfDTUnxTWtPmjYzuuhHTWjSIRZFhCoHKuiiierygHjMErArq515AkuLbaYYJ6CZUGW3OaePjaW1R3rYKwrt9EaQ6LXBYZQgAjUvCndFx3Ze7bplhTURERERERNT7sYhOB52+mkP85ZYqGKbA8LzW87lbi6ApzHAhO8WOVTt8SHVamhTAZSlewLSpCsIxE3VRHWn1OdWNi8Qh3cS44gzs9kdQFYzBqir71S3bPE6nJhiDKYAUuwU2VUZlIAp/RIOEeByJ0xqPmNnpi+DR9zd0mFffsP9gTIfLGs9AlyQJFkVCussKf0SDx2ZBXqodYd3EHZOHojjT3aIg3HDOG3dW1wRjWFPmQ1jTIQTQLyUeAbO3WfqtFaAbx9Dsy3OtdYJrhomIZiCqmxBCJOZdNwQgAULEu9EbnjOFwNbqEPJSne3OaXuL8rZWEB5ZkIaRBV68saqcHdetYIY1ERERERERUe/Wu6qERN2gL+YQ72sEjSxLOL4kEx/8WIFgVIfLFl+EUTcFQjE9sYimbpjQdBMAkOayYrQjFWvK/SjOcOH6UwehONONteX+TumWbX4sFlWGUj8mu0WBTZZhmibSnRbYrfFvUTHdRHGWC7v90Q7z6hv2X5TuQkw3E/E0QLyY7rSqCGs6asMajirKQHGmu81O5iad1Sl2bKysQzCmQ5El2K0KirPc8DgscNvVpLP02ytAA9in54bneVtEg+Sk2LFlTzCeLy8BEiRIkoAAEp8skCXAqsjQjHhHuiJJGJjpxhUnDWxzThs+RVAViMfi2BwyDBNYtb02cSPhjklDWy0ID8xys+O6DX25m56IiIiIiIjoQMciOh10+mIO8f5E0IwqSEVRhgs1oRgimomwKaDIEjLddmS4rNi0J4hQTEfMMGGYInEe+nsduOKkgSjJ9gBIvlu2vW7p1o7FY1MTWewOqwKtfnwWVYFFkeALa8hw2ZBit0CW5A7z6n/ev9oknqYhC14IgUDUwCEZaofz3DhqY/UOHyr8UVhVGRkuKwoz3Uh3WQEkn6XfOMYm1+uAwxtfKPab0mp8s6Uaihy/ydE/1Rl/rj6uZ91OP1DfQZ54XStRPo3Hu2qHD7v9EVhVGWlOKzTDxJ5ADAICphBQpPi4XTYVHpsFqU4LZFnC9acOSsx583ndvCeApz7ahE2VASgSUFYbgVE/Xyl2FWHNiN9ImJTS6jnoyx3XHV3XREREvdWa2RORkpLS08MgIiIi6tNYRKeDUl/LId6fCJrCDBdGDUjF6h0+9EuxQTcELKoMT/3CmbXhGDJcVuiGaJIJ3tp56Khbtr0u64Z9tXYs/Tx21IY01Ia0eFFWied1+8Ia7KqCokwXIElJ5dU33n/jeBp/WEfYFBAQSHGouPS4oqTmuaHw+/Z/d+HxDzbikAwnvA5Li08EdDS25jE2DfEwm/cE4AtpqA7GAAkoTHci02PCLatw21UMtLrw4fpKSABOGZwFSZYBtB3l8/N4y/H4h5twSLoTqQ4LhBD4cks1akKxxNwHowYG9/MgP82BTZVBjMhPRXFmy/ltmNfVO3xYV+6HZpjxhWsdFnjsKnRToDoUgyLLWLGtpt0bCX2x4zqZ67oBi+1EREREREREBx4W0emg1Ze6YvcngqZx5/1ufzTReR+IxjvO89OcmPGLErht6n6dh1a7rFvplm58LBm6FVuqgvCHdWiGiZhuQDfjed2mKZDhsqEo04W0+o7vZPLqm5+rNJcVY5xpqIvqiGkGdvojOPKQdEwY2i/pY5NlCUNyPMhwW6HKcosCejJjax5j05CvHtENWBUZsgwISNgTjCFU5sPwPC/SXVYEogbM+szyQMyAxy4n9tlWB3x8vCnIcMXHi/pc+CE5KU3e02aRYVUVbKoMtvkJjMbzalclGGZ8LMIU8Ed0qIoMm6ogxW6BP6JhZ20YvrCW9Lnt7ZK9rhu2TbbYTkRERERERER9B4vodFDrK12xextB07wbdmhuSpd23rfosgZQF9Wh6Sb6pdhaZJmfMzof68r9+GpLNSQJcNtUWBQVEgBT6FBlGYP6uVGQ5oyvfInk8+rbOlcSJFSHNPT3OnDOmPy9jqFJ9kbGgDQnNlcGWuynSYyNiEejRHQDXocFUc0E6jPKHRYFUd1A6Z4A0pxp0Ayz/k2QyK1vrK0O+NbG29CZv6myDrv8UTgtCjTDaPM6aD6vu/wRmCK+4KsiS9B0E3URDTZ3/MaCVZER0gz4D5AiemufHgBa/xTA2nJ/0sV2IiIiIiIiIupbWEQn6iOSjaBprxu2rQUf91fjLuvakJaIT2nIy7ZbmsZ8DM1NQbbHhs2VQcgSENFMKLKELI8Nh2a78d/yOvx3px+qLCPLY0NEM/cqr35v43qS6SBO5kbGyAIv7n5rXav7aRwzIyDgD+twWlUAUvx46gu0iizDqUioCsawvToEVanvPJcAiyqjubY64Nsar0WR4XVYkZ1ix29G52NkQWqb10Hz7nmrLEGRAcMwocoKVEVGTBfQDAGLAkR1I96V7rAke+n0asku6Lt5TyDpYntv/KQLEREREREREbWPRXSiXqitruiOImj2JnqiMzV0WUdVA2vL/YjoRmIhT90UqItoqA1rWLm9FsVZbpRWBVET0nDkIWkIxAxENAN2iwKbKqG0KgRFkRCM6vhua3X9Qp42HJ6fikuOPaTd8Tc/b7efcRi21YTavWmwN+esveL8yAIv3lhV3uZ+ZvyiJNEZnu60wDAF1PqxqHKihg7TNFEX1RDWTPx3px82NX4uHFYFbmvThWU76s7f3+z/5ovAWlQFTqsaX4hWj9/4EEIgppsIxUxYZBm5Xju8B0gRPdkFfX/aHUiq2N5eVjwRERERERER9V4sotMBq68u8NdRV3RbETR7Ez3R2efBY1dhU2X8VFGXiCgB4u9hUSS4bCpqQxo+21iFs0floS6iozoQw7bqEAIRHUIAkiQQ0UxYlPqualPAapERjBrY5Y/AWRnAqyvKIEtSq8Xf9s7byILUFueqtCoIX1jD819uRVUgikH9PEmds9ZuZAxIc+Lut9a1e+5fW7ETvz4iD2U1Yez0hSEgoBkmJElCKKYjxW6BZpioDMQACMhSvIM/qptQZBlCAKvKfBiY5Wk3yqe5xuP1hTX4wxpSHBY4rQpMU7R7LTRfBNZjV5HhtsLwC5jCREwXMISAbppId1qhKhKOGJDWbtxOX5Lsgr6ASKrY3t6CuERERERERETUe7GITgekvrrA3/50kicbPdEV3bCFGS5ke2xYuaMWqY0K6EC8WzoUM5DhtmG3P4zSqiC+3lKNTZUB6KaALKE+C13AFBJMIaDEJIQ0AwYAr0NFVDfhC2ltnod9XfzRF9KwrSaENIcFNSEN6fULmHZ0zprfyNhcGcCG3XXw2C2oDsYSNwKk+gU9G/bjsqnxzvDvdmDZ2l2oCWlw2xRkuKwozHDhx111CMV0mCagyIAQQLbHjsIMJ7bXhABIqA3FOuwob+0GUihm4PUfdu7V34nWctWLMt0IRg1EdQOAiVSHBQOzXaiLGEnH7XSHzriJlmwO/qH9PEkV29tbEJeIiIiIiIiIei/+Rk8HnK6INOmOrvb97SRPNnqiK7phZVnC8YMy8f6PFQhrBiRJgiJLMEyBUEyHzaKgJNsNX0jD99tq8PcvSmHUF9BtqgLDFNA0A5IkoOkCVVosXlzXDNQY8Vx1f0TD0P6eFouU7s/ij3ZFxo6aMOqiOtaU+TA8z5sopIv6DuuqQAw/7qprd85/2F6LDbsDgIT6AriEFIeKokw30l3WJud+ZEEqhuam4JjiDDz72RYEozoKM1wwhEAwpsNlVWFRZRRnuJDhscFjUwFJglVVUBOK4ncnFCHVaW03nqb5DaQ0pxUV/gh0U+zV34nWctW9DgsGZrnx4y5//GaBwwLdRKctUtsZOusmWrIL+hZnupMqth8oHfpEREREREREBxsW0emA0hWRJt3V1b6/neTJRk90VTfsyIJUFGW6UBuKIayZiUVFM1xWFGa6YVVkRFQDy/67C76whgyXFf6onog0gQSYAhD1+1NlCVaLAiEAzTARM0xU1Re/G5+H/Vn8UUDAqsqwKBLCmoHSPQGkOdNQE9KwZU8A1cEYYrqJZz/dgm9Kq1ud8zVlPvzr+x0IaQY8NhUuuwLdFKgOxhCMxgvzVkVucu5lWcLE4TnIS3Mkrq2q+vfql2JHcZa7SVc80HATRCDVaW0RT9N4LC1uIEV1fLm5CjHdxNFF6YlrI9m/E23lqk86PBfHFGcgx2vvVXFJnX0TLdlc+WSK7b3h/BARERERERHR3mMRnQ4onR1p0p1d7fvbSZ5s9ERXdcMWZrgwakAqVm2vRT+vHbohfs43B7CxIoBDMpzYWBGAKstw2lSoqoy6iIaIZgKNCugS4kV0CfEFNy2KhKhuYrc/igFpzibnobXzJoRAXSReoFdkCZGY0erijx6bihSHiupgDE6rAl9Yx46aMLbsCSKqGzBMgX4pduSm2Fud84abNlHNRE6KDTUhDQ6rAosiI8VugT8SL8Z7bCpGFrTMCm+cV/7jLj8WflqK3BQ7PK0szNnRTZC2biCJ+rgcWYr//UhzWRPPJft3oqMFbXuLrloXIJnj399FXImIiLrK8LuWQbY5e3oYRERE1MVK50/q6SEc0FhEpwNKZ0aadHdX+/50kjcU5g/P8+KnXXXYsLsO/VOd3doN2zj6osIfTXTjBqM/v//xJZn4aXcAFlWCbgrYVAU2t4yYbmJPIIqIZiYK6WgoAgPQDQG7RUE4pqMyEG1yHpqft+pgDFv2BOAP6zBMkeg2314dbHltSBKKM90IRX0IRDQYpoSfdtchqhlQZMBljUeXuB0WlNjVFnPe+KZNptuGNWU++MIanFY13kmvyNjliyArz4tfj84DEM9Pb16ILc5yozDDhW9Ka7B6hw/u+jz1BsncBGnrBpKmmzAF4LKp8IV11EU0SJIETTdhUWU4LU3/TrR1k6etBW17k65cF6Dx8bd1jvrKzQYiIiIiIiIi2jssotMBpTMjTbq7q/26X5R02El+SIYTvrCGzZWBRHGueWFeM0xEdQM7akKw1MeIdFc3bEfduE6rAq9DRSCioC6qI8VugVSf9+2xWRDRogDii2oapoCE+H9lWUKK3YKoZmCXL4JxAzMTxeTGHfgZuhVrdvoR1Y36QjZQE9JgGAIf/7QHmmG2uDbSXFYMSHdi9U4fQjENddF4prnLqmJAuhNp9bEqrc1545s2blnF8DwvNtcX8MOmgCzFC/hHFqWjrCaMf323HZsqg63GAiWbv91WQbatG0gWVYZS/5qobmB1mQ8xXSTiduz1mekeu9pnF+Rt0B3rAnR0jvrCzQYiIiIiIiIi2jssotMBpTMjTbq7q/3VFTvxqyPyWi2ibqqsQyAa76y+980fE4W7UQWpeGNVeYvCfLkvDJtFxm9G52NkQWq3dsO2141rmgKD+nniWeOGCX8k3rWtyBKU+tOsSoDLqiKsmYjpJuwWGSkOC4QQiBom0t1Ni8kNxecd1SGs2F4L3TDhdVhgCMAfiS/UOax/CqqCMUR1AztrQxjUz5OYg+pgDFurgxCmQIbLiqhuwmlRoJkCW6tD8DgsiXzy5nPe/KZNmsuKMc401EV1VAWi2F4dQjRqYOmqnXj+i61QZAlDclJQmOlqNRZofyJB2rqB1BBZU+6LIKab9dtaoMoSNMPEnrooorqJ//xQhq+31CCiGfFPMXRCdFF36+p1Aboi3omIiIiIiIiIej8W0emAsr/dvI31RFe726a2KKJqholA1IDbpiI/7eeIllXba/H+ut1w21SMyE9ttTC/usyPKaO6f0HDtrpxG88PEEI4ZiCkGdB0Ad004bIpyHRbMbS/F9WBGHb5I4hoBmK6iahuYkCaE7ecPqRFoXJ4nhe/GVOA/5b7YQoJgahRv6ipDUWZLqS5rLCqCnbUhGC3KIm5sFtk/LTLD19Ig9dhQXGWGxsqArCoMhyyBH9ESyw2KklSizlv9aaNJEE3BLZVheALa8jy2CGEgCkEJAFsqgzAYVWQ7rI2iQUa0s+DbTUhGKbAhUcPAAAEo0bSkSBt3kCSJBRmOFFaFb9R4LZZocjxOJ26iA4BwBfW8NePNkOR49dizDDhltX9zhLvbl25LkBX5a0TERERERERUe/HIjodcDprgb+e6mofWZCa6OT2hTU8/+VWyBKadE+77Sr6ee3YUBGAIsloXrLb3/znrtR4fjbsroMvrEORgYFZHhw/KANvrd6VyFQvSHdiTyCGXfU3QG4+YwgOz09tdb85XjvyU53IcFthmgIWVYbHpiay1RsW/DxnTD5Wl/mwsSKA2ioNNfWF7sE5HqQ5LdhdF0F1MAavwwKntSFHPN5R33zOW71pY5Hx4y4/fGENXqcF+ekObNgdSHR/Ny/M53odWLGtBjOXrEJlXbRFREiyc9feDaSdvggcqgKXTYVmABFNhykENMOERZFgVWXUhjS4bSqqQzEEy3wYnudFev0ipL31WmoumZtoRxelYXWZb6/zyrsyb52IiIiIiIiIejcW0emA1BkL/PVkV3tDJ/fmygAq66Lon+psUbjTDQFVlhGK6aiL6vDYLU2e74z8567S3vwMyvb8nDldfwNk3MDMpONMVFmG29n2OR5VkIqzR+WhtCqI77fW4O9fbMWQHA8URQaAxEKjvrAGh0VBVDdQVhOCLgT6ex0t5rz5TZutVRpqwxqyUuw4tJ8HEPH8cVWWIElSk8J8isOCiGZgS1UQEc1ASbZnvyJC2rqBVJzhgjCBof1TEIoZ0HQDP1UEAAikOq2IxAyYAlAVGXaL0qLQ39a11NYCmz2prXOQl+qAgMALX23bp7z37shbJyIiIiIiIqLeiUV0OmB1xgJ/Pd3V3l7hzqLIsKgSNF1Aq8+6bmx/85+7Wlvzs683QPbmHDd+73//sBNhzYS7voie5rJieJ4X68r92F0XgWaY2FARgNdpQXHmz/PTvIB8+xmHYVtNCN9vrcHi+sK8qsioi2iJ+BSLIkGRJRhmvAscQmBDRR1MU6Aoy5W4wbI/ESGtnT9TCMz+z1pENBMpDgvqIkBEM+GyWQBIEABkKf7f1gr9rV1LvXkR0ubnYJcvgn99tx01Ia3DLPO2bgx0dd46EREREREREfVe/G2fqAM92dXeXuHOY1fhtCjYE4tBVZq+bn/zn3vC/nY178s5bq/wrhkGZAD9PHaMyPdClWXsrI3g0fc3YNKIXKzcXttqAXn0IWn49w87EakvzDcs7NkQEWOYAooMRHUT26pDqKyLIstjQ0qzTxLsT0RI8xsUpimaHKemm4nueCEEYoYJp01BTDfgtCpNCv2tXUt9YYHNhnNgmgKvrSxDTUjrMMt8bbm/zRsDQ3NTuixvnYiIiIiIiIh6NxbRiZLQU13t7RV5gXiERJqwYrc/ClmS9zlupqd1Vlfz3p7j9jLN/WG9vis9FWkuK4B44XXVjlosWLYeXocFaU4rMtxWKJKUKCDP+EVJizlriIipDcWgGQKyJGFNWW08WsUQMIRATUhDev37NGgcEbI/NxmaH6fHrkKWEF+01TBhsyg4NM2JbdXxxVCtigxZBjRDYGNFoMm11NcW2Ew2y/zdtbsTx9XWjYHOinciIiIiIiIior6FRXSibrS3Xe0ddVfnpzmbdEXva9xMT+rsrua9PccdZZo3L2zXBGOoDERRF9GxszYCqyohxWFBUYYLVcEYXluxE78+Iq/JnKU4LCjOcmHNTh/CmgFVluGyKeiXYkNtSEcwomP1jloUZ7nhrF8A1WNXExEhu3xhvLaybL9uMjQ5zt0BQAICUR05XjuKMt1Id1mR4rBgU2UddvmjcFoUaIbR4lrqawtsJptl/p9VOzu8MfCnSYd1SrwTEREREREREfUtLKITdbO97WpPprt68oj+vW6Bx2S029VsdWFNuR9Pf7QJ1586CMWZ7r3qvN7bc9xQeG+ead7Yj7vqsLsuClMAwZgORZKgGRIimolg1MDALDc2VNTBZVNbzpkqIyfFjjSniYGZLlgtCtxWFd9vr8FuXwTVoRhqt9XAZVWgyDJS7CpUJb7Q6r++L0NNkjcZ2utYb3ycK7fX4pXvdiCiGbAqMgxTwKLI8DqsyE6x4zej8zGyILXFtdTXFthMJsvcFALlvgj6J3FjoDPinYiIiIiIiIiob2ERnagP6Khw1xlxMz2hra7mmmAMm/cEUB2MYVtVCGW+CEbke7t00crmi41GGi02CgBVgSjW7/LDFIAEwKrIkCQJmmFCNw0AQLkvjFSHBb6wBq/DgrNG9oc/rCHFYYE/rOGZTzYjzWlrUszNdNmwrSoEzRRQJAGbaoEpgJ21YTisCrLcdgRjelLRKcnE4jQcZ3GWG4f28+x1V3VfW2AzmQVn+3vtKPdF4LAmd2Ogr/59IyIiIiIiIqJ90zuqHETUoQOxcNdaV3NNMIY1ZT5EdAMOS/xxpyrjm9JqrN3px6XHFWLC0Jx96vxNJle8taKrEAI/7a6DZggAgCpLUOpfZ1Hl+oU6TdSE4ou8vvDFVlQEok0K2SPyvIjpokmhVgiBPcEo7BYFFtNERDcRiBmwqwr6pzpgCoENlXUYXZDWYYd0KGbsdSzOvnRVD0hzItttw9pyP4qzXPEFUevH1hsX2ExmwdlJI3Lxwlfb+syNASLaO08//TTmzp2LsrIyPPjgg7jhhht6ekhE+6SwsBA33HADr2EiIiKiHiB3vAkRUddo3NUMABACm/cEENENeB2W+gI2sL02jEp/FP/d6cfspWsxZ+l/sabMt1fvtabMh7lvrMVdr/8Xd7+xDne9/l/MfWNti/00FF3TnBasKfNhR3UIO2vDqAnFAACKBEiNiswSAFWRoRkmAhEd1UENW6tDSHVYUZjpQqrDitU7fPjX9zugGebPx4r4TQR/WEeKw4I0lw1ehwXD+3sxtjANYw5JQ67XjkBEhyFEq8fksCqIaiZ8Ya1JLI7brkKRpXjHerYb1cEYlnxfBtNsuZ+GmzMjC1JRnNV+ZM6aMh/ufmsdNu8JotwXwccb9uCLzVXYUxdFIKK3ugjp5soAfthei82VgVbfvzs0RCIdnu9FbTiG0j1B1IZjGJGfiuvHD8KEoTkoyXaj3BeGaHauG24MDMr29JobA0QHooqKClx55ZUYMGAAbDYbcnJyMHHiRHzxxRf7tV+/348ZM2bg5ptvRllZGa644orEc+FwGGlpaUhPT0c4HN7fQ+g0Qgg888wzGDduHFJSUuB2uzFs2DD8/ve/x8aNG5Pax/Tp03H22Wd37UD7uFmzZmHUqFH7/Prly5dDkiTU1tZ22piIiIiIqPdiWx0R9ZjmXd+BaLyo7LSqEALwhzVohglAwGWzwKrKCGsGvi2twc7aSNKLju7L4qUOq4rqUAzbqkMwTIGYXr8gqN2CSMxATDehKjIkKV7wiOkCigJ4bEqb0StR3cDO2hAG9fMkomAMU0CVAX9ER4bLhoI0R6Kz2223AAACER2pzqYLnAI/d0j7w1qXL/bZ+BzmpzmR6bFh4+46VAVj+GZrNYoyXDhiQFoiCiaZaJnu1FHXfUfd6g03Boioa5xzzjnQNA2LFy9GcXExdu/ejffffx/V1dX7tD8hBAzDwLZt26BpGiZNmoTc3Nwm27zyyisYPnw4hBBYsmQJLrzwwnb3qWkaLBbLPo0nWUIIXHDBBXjttddw22234aGHHkJ2dja2bNmCd999F/PmzcOiRYu6dAzUvRquVVXlr2VEREREvRk70YmoxzR0fae7rNhYEYAvFINumBBCwB+JF9BlWYK9PtZFVSRIkJDrtbfbXd1Y88VL2+vSNk2BZWt2Ye7StdhcGcDo/FQcV5KJITkeWNV4wdwwBFKdFtgs8cU4Nd2EbpiQZcBjs+CwnJQ2C9k2VYHdomBjRQCBiA5FliAgUBPSYFcVFGW6EgV0AFCl+DhrQrF2O6RTHJZ4LE47md5Rbd8X+2ztHGa6bTi6OAPHD8pEboodxVlu3HbGkEQB/dH3N2D1Dl+LjvxH39+w158i6Cztdd131K2+r4X/9rrxe0unPlFPq62txaeffor77rsPp5xyCg455BAcddRRuPXWWzFp0iSUlpZCkiSsXLmyyWskScLy5csB/NwVvGzZMowdOxY2mw3PP/88Dj/8cABAcXExJElCaWlpYh8LFy7EtGnTMG3aNCxcuLDFuCRJwlNPPYUpU6bA5XJh3rx5AID//Oc/GDNmDOx2O4qLizF79mzo+s/fXx988EEcfvjhcLlcKCgowDXXXINAIJDUuXj55Zfx0ksv4eWXX8Ydd9yBY445BsXFxRg/fjzmz5+P5557rsN9zJo1C4sXL8a///1vSJLU5DyVlZXh/PPPR1paGjIyMjBlypQm56Shg/2ee+5Bv379kJqamji+m266Cenp6cjPz8ezzz6beE3D/Lz00ks49thjYbfbMWzYsMR7dqSmpgYXXnghsrKy4HA4MGjQoMRxttbtvXLlyiZzuWjRIqSmpuK1117DoYceCrvdjgkTJmD79u1JvX9bXnjhBYwdOxYejwc5OTm44IILUFFRkTjmU045BQCQlhaPXJs+fTqA+L/P999/P4qLi+FwODBy5Ej861//Suy3tWv1k08+waZNmzBlyhT069cPbrcbRx55JN577739OgYiIiIi6jxseSCiHtVQvHzl+x1YvcOHmGECWnzhzoCpwzAFqgIxSJIEVQasqgKrqiDXqybVXd3W4qVA0y7td9fuxpebq7Bs7S74wzrcNgUxw0RRphuD+nlQF9GxvToEzTAR1SV4HRYIAWi6AX9UR6rdiky3DQ5b699WHVYFFkXGOWPysbrMh40VAURiBqyqDMMQGNY/BWmun7vNhRAo90dw5CHpCMX0djuknValSxf7bOscSpIEr8OKkmwZFXURbKsJoTDD1aTg3tFiqL3JvmTEt6e9bnwAvapTn6gnud1uuN1uvPbaazjmmGNgs9n2eV8zZ87EggULUFxcDLvdjvfeew+nnnoqvv76axQUFCArKwsAsGnTJnzxxRdYsmQJhBC44YYbsHnzZhQXFzfZ31133YV7770XDz30EBRFwbJlyzBt2jQ8+uijOOGEE7Bp06ZERMxdd90FAJBlGY8++igKCwuxZcsWXHPNNZg5cyaeeOKJDsf/4osvYvDgwTjrrLNafb75v2OtufHGG7Fu3Tr4/f5EMTo9PR2hUAinnHIKTjjhBHz88cdQVRXz5s3D6aefjlWrVsFqjf8b9MEHHyA/Px8ff/wxPvvsM1x22WX44osvcOKJJ+Krr77Cyy+/jKuuugoTJkxAQUFB4n1vuukmPPzwwxg6dCgefPBBnHXWWdiyZQsyMjLaHe8dd9yBtWvX4q233kJmZiY2bty41/E6oVAId999NxYvXgyr1YprrrkGU6dOxWeffbZX+2ksFoth7ty5GDx4MCoqKvCHP/wB06dPx5tvvomCggK88sorOOecc7B+/XqkpKTA4XAAAP70pz9hyZIlePLJJzFo0CB8/PHHmDZtGrKysnDSSScl9t/4Wk1NTcWOHTtw5plnYt68ebDb7Vi8eDEmT56M9evXY8CAAft8HERERETUOVhEJ6Ie11C83LwngIff24B15f54V7opYFPlRL52WDNhCkAzTKQ6rdjt77i7urXFSxtzWBVs3hPDs59tQTCmI6abSHPG89irgzEEoz4Mz/OiKMsNX0RHXSTeNR7VTWi6gG6ayHTbcdExA/Dxhj0dFrJHFaTi7FF5iULtLl8Y//q+DFXBGKyq0qJIfvmJ8YJOQ8F1tz9ecB2Rn5qITjFN0WIx1AadsdhnMuewYS6SvWmxP9EyXamzFvBtL0Jo3U4/IAG6IZKOFyI6kKmqikWLFuHyyy/HU089hdGjR+Okk07C1KlTMWLEiL3a15w5czBhwoTE15WVlQCArKws5OTkJB5/9tlnccYZZyAtLQ0AcPrpp+PZZ59NdJs3uOCCC3DppZcmvr7oootwyy234JJLLgEQ73CfO3cuZs6cmSiiN170saioCHPnzsXVV1+dVBH9p59+wuDBg5s8dsMNN+Bvf/sbACSKre1xu91wOByIRqNNjvmFF16ALMv429/+lvj+/NxzzyE1NRXLly/HaaedBiBecH/00UchyzIGDx6M+++/H6FQCLfddhsA4NZbb8X8+fPx2WefYerUqYn9z5gxA+eccw4A4Mknn8Tbb7+NhQsXYubMme2Od9u2bTjiiCMwduxYAPHFM/eWpml4/PHHcfTRRwMAFi9ejMMOOwxff/01jjrqqL3eH4Am815cXIxHH30URx11FAKBANxuN9LT0wEA2dnZSE1NBQAEg0E8+OCD+OCDDzBu3LjEaz/99FP89a9/bVJEb36tZmRkYOTIkYmv582bh1dffRWvv/46ZsyYkdSYo9EootFo4mu/37/3B05ERERErWKcCxH1CrIsoSTbgytPLIZmmAhpBpT671DCFDBMAasqw6JI2FoVRCiqJ9Vd3WLx0mbCUR01oRiCUR39U+yQIMGiyLAoMlLsFkR1A6V7AkhzWjAi3wuPTUWG24psjx0DMhyYMDQHj19wBC47vrjp4pRCoC6ioToQhT8ca7I4ZeNYkYnDc/H7DmJEhud5ccekoZh91jDcPukwzD5rGP406bBEobV5LE4gEu/gb22xz33R4Tls1OmeKLh3UbRMX9BehNDALBc27wliS2UQJVmuvVoEluhAds4552Dnzp14/fXXMXHiRCxfvhyjR4/e6/zvhkJsewzDwOLFizFt2rTEY9OmTcPixYthGE2/zzXf33fffYc5c+Ykuufdbjcuv/xylJeXIxQKAQA+/PBDTJgwAXl5efB4PLj44otRVVWFYDCY1DE0vwF5++23Y+XKlbjzzjuTjoVpzXfffYeNGzfC4/Ekxp6eno5IJIJNmzYlths2bBhk+edfEfr165eIxQEARVGQkZGRiDZp0FA0BuI3RsaOHYt169Z1OK6rr74aL730EkaNGoWZM2fi888/3+tja3i/BkOGDEFqairWrVuHbdu2NZmve+65J6l9rlixAlOmTMEhhxwCj8eDk08+GUC86N+WtWvXIhKJYMKECU3e8+9//3uTcwy0vLaCwSBmzpyJoUOHIjU1FW63Gz/++GO779fcvffeC6/Xm/jT+JMCRERERLR/2IlORL2Ky6Yiw2WDIknYXRdFVDdhUSTYLAo8NhWyLMEX1lBaFcRRRRkddlc3X7y0eZf2lupgYjtIgCJL0E0BixLPkXVaVfjCOuoiOuyqgkH93Lj8hGKkOq1tLk65akctQjED4ZgBzRDQzHh3+8gCb6uF7GRiRDrqkG4ci9NWx/q+6ugcNu50L60Kdmm0TF/QXjd+IGrArM+3D8QMeOw/F6r6Qqc+UVdqyLKeMGEC7rzzTvzud7/DXXfdhU8++QQAmqwNoWlaq/twuTr+xM2yZcsS2eCNGYaBd955B2eccUab+zNNE7Nnz8avf/3rVse/detWnHnmmbjqqqswd+5cpKen49NPP8Vll13W5pgbGzRoEH788ccmj2VlZSErKwvZ2dkdvr49pmlizJgx+Mc//tHiuYaYGwAtFk+VJKnVx0zT7PA9k4mfOeOMM7B161a88cYbeO+99zB+/Hhce+21WLBgQaKYn8zct/ZekiShf//+TfL0GzrI2xMMBnHaaafhtNNOwwsvvICsrCxs27YNEydORCwWa/N1DefkjTfeQF5eXpPnmscUNb+2brrpJixbtgwLFixASUkJHA4HfvOb37T7fs3deuut+OMf/5j42u/3s5BORERE1EkO3CoGEfVJdREdFkXG2MJ07PJFsH5XHTTThNumwqLI0AwTgaiBQzLUpLqrG7q0y2rCreaKu6wqhAk4bSoUCUhxqKgOxuB1WABIUGQJhikQ0w3UhDSMyE/FCYOy2iyGTxqRiwfeWY/akAZVlmFRJXitNjgsMt5YVY6BWe5WC9qdESPS2ZnejcfW3jls3OlemOHCwCwXvi2tQa7XDquqwGNXIUlSp0TL9AXtxd9oRn3RSQCa3rIA1Tgah+hgN3ToULz22muJAm95eTmOOOIIAGhSFN1bCxcuxNSpU3H77bc3eXz+/PlYuHBhkyJ6c6NHj8b69etRUlLS6vPffvstdF3HAw88kCgA//Of/0x6bL/97W9xwQUX4N///jemTJmS9Ouas1qtLbrqR48ejZdffhnZ2dlISUnZ53235csvv8SJJ54IANB1Hd99913SMSRZWVmYPn06pk+fjhNOOAE33XQTFixY0GTuG6J3Wpt7Xdfx7bffJqJb1q9fj9raWgwZMgSqqrY5X2358ccfsWfPHsyfPz9RhP7222+bbNOQId/4PA8dOhQ2mw3btm1rEt2SjE8++QTTp0/Hr371KwBAIBBosuhrMmw2236tKUBEREREbetzRfS7774bb7zxBlauXAmr1Yra2toW22zbtg3XXnstPvjgAzgcDlxwwQVYsGBB4oddIuq9GqJDIpqJ/HQnXDYVm/cE4A/riGg6BARSHCouPa4o6e7q9rq0jy5KwwtfbUt0ThdnuhGK+uALa3BaVQghYAoTpVVBpLtsOLqo7Q420xRYub0WWW4bhud5oRsCFuXnruvuWFSzszK9m0u2031tuR/VQQ3lvghKq0KwW2SkOa3I9ToQiun7HS3TFzSOv2nejW9pyCiSAIvaMlHtYOjUJ2quqqoK5557Li699FKMGDECHo8H3377Le6//35MmTIFDocDxxxzDObPn4/CwkLs2bMHf/rTn/bpvSorK/Gf//wHr7/+OoYPH97kuUsuuQSTJk1CZWVlk87sxu6880788pe/REFBAc4991zIsoxVq1Zh9erVmDdvHgYOHAhd1/HYY49h8uTJ+Oyzz/DUU08lPb6pU6diyZIlmDp1Km699VZMnDgR/fr1w9atW/Hyyy9DUVqPymqusLAQy5Ytw/r165GRkQGv14sLL7wQf/7znzFlyhTMmTMH+fn52LZtG5YsWYKbbroJ+fn5SY+zNX/5y18waNAgHHbYYXjooYdQU1PTJFe8LXfeeSfGjBmDYcOGIRqNYunSpTjssMMAACUlJSgoKMCsWbMwb948bNiwAQ888ECLfVgsFlx33XV49NFHYbFYMGPGDBxzzDEd5qGHw+EWRXm3240BAwbAarXisccew1VXXYU1a9Zg7ty5TbY75JBDIEkSli5dijPPPBMOhwMejwc33ngj/vCHP8A0TRx//PHw+/34/PPP4Xa7E1n6rSkpKcGSJUswefJkSJKEO+64I6lufyIiIiLqHn3ut/RYLIZzzz0X48aNw8KFC1s8bxgGJk2ahKysLHz66aeoqqrCJZdcAiEEHnvssR4YMRHtjebRIWkuK8Y401AX1RHTDOz0R3DkIemYMLTfXu23rS5tAPhyS3WT9xue58XmPQH4Qhr8ER2SBASjBqyqjhe+2oovt1ThnNH5LYr4DTEe/VOdrUaZ9PWojo463Rsvpjmsfwp2+sKoDcUL6tXBGI4vycTlJxYf8Itmthd/47YpkCUJEgB3s9z4g6VTn6g5t9uNo48+Gg899BA2bdoETdNQUFCAyy+/PLGY5bPPPotLL70UY8eOTSx22bAQ5t74+9//DpfLhfHjx7d47pRTToHH48Hzzz/fJBKjsYkTJ2Lp0qWYM2cO7r//flgsFgwZMgS/+93vAACjRo3Cgw8+iPvuuw+33norTjzxRNx77724+OKLkxqfJEl4+eWX8cwzz+C5557D/fffD03TkJ+fj/Hjx+PBBx9Maj+XX345li9fjrFjxyIQCODDDz/EySefjI8//hg333wzfv3rX6Ourg55eXkYP358p3Smz58/H/fddx9WrFiBgQMH4t///jcyMzM7fJ3VasWtt96K0tJSOBwOnHDCCXjppZcAxIvjL774Iq6++mqMHDkSRx55JObNm4dzzz23yT6cTiduvvlmXHDBBdixYweOP/54PPvssx2+908//ZT4dEODk046CcuXL8eiRYtw22234dFHH8Xo0aOxYMECnHXWWYnt8vLyMHv2bNxyyy34n//5H1x88cVYtGgR5s6di+zsbNx7773YvHkzUlNTMXr06MS13JaHHnoIl156KY499lhkZmbi5ptv5sKgRERERL2IJBqHDPYhixYtwg033NCiE/2tt97CL3/5S2zfvh39+/cHALz00kuYPn06Kioqkvolwe/3w+v1wufzdcnHXYmoKdMUTQqzgaiOxz/YiOpgrNXokIYFNztL4+Jv4v2iOtbu8qM6GEOGy4rDclLgsKntjuOH7bW4+411KMx0QWml09owBUr3BHH7pMMwsiC108bfG5imwNw31sYLx9n1hWMhWtz8uOOXQw/oLvQGrV5T9deOKkuABOiG6Jbrm6gr8WcmKi0tRVFREVasWIFRo0Z1+/u39TsB/fz3s+CGf0K2OXt6OERERNTFSudP6ukh9EnJ/k7T5zrRO/LFF19g+PDhiQI6EO8aikaj+O6773DKKaf04OiIqLk1Zb5EREhUi0eElGS7MWlELlZur+30RTJbMzzPixm/KMGiz0uxuTIAwwRS7ApsqoJcrx0j8lMT3cRuu4oSm7vVaJb2YjyAzo/qaH7zoTPyz/dVq4tpShI8dgtgt8BmUbGxMtBnu/D3VkfxNwC6ZBFYIiIiIiIiIup8B1wRfdeuXejXr2nMQ1paGqxWK3bt2tXqa6LRKKLRaOJrfnSSqHu06Nb1xjtyV+/woawmjOt+UQKXTe3yIvGaMh9eXVGGSn8UpgkosgSXVUUwZqIgzdkkjgOIf9y+tWiW9mI8Ojuqo62bD63FzHSH9hbTBA7OBTM7ir/pikVgiah3O+OMM/DJJ5+0+txtt93WYeRHA7e77ZuRb731Fk444YR9Gl9XuOqqq/DCCy+0+ty0adP2KjOeiIiIiKin9Ioi+qxZszB79ux2t/nmm28wduzYpPbXvOAFxAtYrT0OAPfee2+H709EyUumQ9o0BV75fgeqg7Gf4z/QtNP71RU78adJh3VpYbF5IT831YFwzMDGijqU+yPIdtta7SpvrSgsyxLOGZ2Psppwoiu7eVRHZyyquabMh0fe+wnlvgjSnFZkuK1QJClx86En4kC6uwu/r2hvodeuWgSWiHqvv/3tbwiHw60+l57e9sLVzTVfDLOxvLy8vR3WfiksLER76ZBz5szBjTfe2OpznREBNH36dEyfPn2/90NERERE1J5eUc2YMWMGpk6d2u42hYWFSe0rJycHX331VZPHampqoGlaiw71BrfeemuTBaT8fj8KCgqSej+ivqarI0CS7ZBuNf6jXlud3p2tvUJ+UZYLZbVhbKgIIMNtBZqNsa2icEcxHvtb3DZNgac/3oQfdvggS8BufxSKLCHFoaIow4WqYKxFzEx36M4ufCKivqqzCtwlJSWdsp/ukJ2djezs7J4eBhERERHRfukVRfTMzExkZmZ2yr7GjRuHu+++G+Xl5cjNzQUAvPPOO7DZbBgzZkyrr7HZbLDZbJ3y/kS9WVdHgHQUz9K4Q7o3xH+0V8hPsVuQ4bJiTzAKf0RDisOaeK6jonBHMR774921u/DZxioIIeC2W6DKEnRToDoYQzBqYGCWu8tvPrSmu7rwiYiIiIiIiIi6W68oou+Nbdu2obq6Gtu2bYNhGImPs5aUlMDtduO0007D0KFDcdFFF+HPf/4zqqurceONN+Lyyy/vlI+MEvVVe1Pg7khr3ewAOoxnadwh3RviP9or5EuShJJ+HtRsqcbmyiBKsuW9Kgp3RVSHaQosXVWOqG4iy22FLMsAAIsiIcVugT+iodwXRqrD0iPZ413dhU9ERERERERE1BP6XBH9zjvvxOLFixNfH3HEEQCADz/8ECeffDIURcEbb7yBa665BscddxwcDgcuuOACLFiwoKeGTNTjkskfTzYCpK1u9mOKMvYqnqU3xH90VMi3qwqKMl0oznShIhDt8aJwaVUQ5b4I7BYZhgDkRs9JkgSnVUVNKIYUh6XHsse7sgufiIiIiIiIiKgn9Lki+qJFi7Bo0aJ2txkwYACWLl3aPQMi6gM6K3+8vW72tTv9CER15Hodrb62eTxLb4j/SKaQf8SANNx2xhBsqwn1eFG4LqJDliSkOi2oDWnwOiwAfh6HIgMRzUR/r6NHs8e5YCYRERERERERHUj6XBGdiPZeZ+SPd9TNvqbMh5pQDKGoDkiAppuwqDI8NhWQpFbjWXo6/iPZQr6qyr2iKNzQOd/f60AkZsIX1uC0qolc9LqIBpsq45cjctn5TURERERERETUSVhEJzoIdEb+eEfd7IUZLuz2R/DF5iqosgRTAIosIcWhoijDhapgrNV4lp6O/+jpQv7eaNw5P6x/CrZUBeEP6wibArIEWBUZxxRnYMLQfj09VCIiIiIiIiKiAwaL6EQHgc7IH++omz1qmAhrZnyfigSXLf7tpbIuigp/FENyPW3Gs/R0/EdPF/KT1bhzvioYw+BsD3QhEIhoqAlr6O914PITi7tk3K0tJtvbzg8RERERERERUVdgEZ3oINAZ+ePtdbMLIbBxdx0kCRiZ70VFXRT+sA7DFLCpMkwB9PPYMTQ3pasPdZ/1dCE/Wc075xsWdz2qMKPLOufbWkz2nNH5vapTn7oHb6gQERERERHRwYZFdKKDxP7GlrTXze6PaKgKxpDpsqEgzYn8NCfqIjo0w4RFkSEBqA7FOly4lJLTnZ3z7S0mW1YTxvXjB7W4dlhkPXDxhgoREREREREdjFhEJzqI7E/xtb1u9i2VQciyhEHZbkCSIAFIcVgSrzVMgYq6aLsLl9Le6Y7O+fYWkx1odeG/O/3460eb8ftTS1Cc6YYsS91SZGWRvmfsyw0VIiLqeWtmT0RKSu/9NCARERFRX8AiOtFBZn+Kr211sw/t74XdosBmaT0vPZmFS6n3aWsx2epgDFv2BFAdjGFbdQjlvjAOz/diVEEq3lhV3qVFVnZC94z2bqiU2NzYWBHAku/LMDQ3hTc0iIiIiIiI6IDDihYR7ZXWutkHpDlx91vr9mvhUup9WltMtjoYw5oyH6K6AYdFAQTgsMhYtb0W76/bDbdNxYj81C4psrITuue0dUMFACRJQq7XgQ0VdYxsIiIiIiIiogOS3NMDIKK+p6GbfWRBKoqz3FBVGeeMzke6y4qNFQEEIvFFRQMRHRsrAkktXEq9T+PFZIH4DZEtewKI6gZS7BZIkgRVkeF1WtHPa0dtSEM4ZqL5LDcvsu6L5p3QbrsKRZbiRfpsN6qDMSz5vgymKfbzqKk1iRsq1tY/beKwKohqJiObiIiIiIiI6IDEIjoRdYqGqJfD872oDcdQuieI2nAMI/JT2SHcRzUsJlvuC0MIgbqIDn9Yh9OqQpKAUEyH12GBx6ZCNwRUWUYopqMu2rKQur9F1r3phKbO1/yGSnOMbCIiIiIiIqIDGX/bJaJOsz8Ll1Lv03wxWYdFgW6asAgJvrAOu6qgKNMFSBIsigyLKkHTBTTdbLGv/S2ythYt05jDqmC3n53QXaXhhgojm4iIiIiIiOhgxCI6EXWq/Vm4tCeZpmDxvxWNF5NdtcOHWH2BPMNlQ1GmC2kuK4B4p7LTomBPLAZVaXreOqPI2rgT2t1KIZ6d0F2r+Q2VXK8DDms8k77cF2ZkExERERERER3QWG0gooPemjIfXvl+BzZWBBDVTNgsMkqy3ThndD5jaPDzJww27wngkfc2YEtVEMNzUyDJTRPBHFYFacKK3f4oZEnu1CIrO6F7XuMbKhsrAtjtj/9dGZGfil+PzuPfFSIiIiIiIjpgsYhORAe1NWU+PPr+BlQHY/HuWm+88Lt6hw9lNWHmudeTZQkl2R5cedJAPPr+BmysDLboRs5Pc2LSiFys3F7b6UVWdkL3DoxsIiIiIiIiooMRi+hEdNAyTYFXvt+B6mAMJdk/dze77SpKbG5srAhgyfdlGJqbwiJhvWS6kSeP6N8lRVZ2QvcOfTWyiYjoYDX8rmWQbc6eHgYRERF1stL5k3p6CAcVFtGJ6KBVWhVMdDU3jgcBAEmSkOt1YENFHUqrgiwaNtJRN3JXFlnZCU1ERERERERE3Y1FdCI6aNVFdEQ1Ew6v0urzDquC3X4TdRG9m0fW+/VkNzI7oYmIiIiIiIioO7GITkQHLY9dhc0iIxwz4La3/HYYjhmwWWR4WnmuK5imYIc1EREREREREVEvwyI6ER20CjNcKMl2Y/UOH0ps7iaRLkIIlPvCGJGfisIMV5ePZU2ZL5H1HdXiWd8l2W6cMzqfWd9ERERERERERD2IRXQiOmjJsoRzRuejrCacyEZ3WBWEYwbKfWGku6z49ei8Lu8GX1Pmw6Pvb0B1MBYfgzc+htU7fCirCeP68YP2qpDOjnYiIiIiIiIios7DIjoRHdSG53lx/fhBiS7w3f54F/iI/FT8enRel3eBm6bAK9/vQHUwhpLsn7vh3XYVJTY3NlYEsOT7MgzNTUmqEM6OdiIiIiIiIiKizsUiOhEd9IbneTE0N6VHurdLq4KJLvjGcTIAIEkScr0ObKioQ2lVsMPFNDu7o52IiIiIiIiIiFhEJyICEI926ahI3RXqIjqimgmHV2n1eYdVwW6/ibqI3u5+OrujnYiIiIiIiIiI4uSeHgAR0cHMY1dhs8gIx4xWnw/HDNgsMjz29u957k1HO3U+0xTYXBnAD9trsbkyANMUPT0kIiIiIiIiIuok7EQnIupBhRkulGS7sXqHDyU2d5MCuBAC5b4wRuSnojDD1e5+OqujnfYec+iJiIiIiIiIDmzsRCci6kGyLOGc0flId1mxsSKAQESHYQoEIjo2VgSQ7rLi16PzOoxg6ayOdto7DTn0q3f4kOqwojDThVSHFat3xB9fU+br6SESERERERER0X5iEZ2IqIcNz/Pi+vGDcHi+F7XhGEr3BFEbjmFEfmrSi4E2dLSX+8IQommUSENH+6BsT4cd7ZS85jn0brsKRZbiOfTZblQHY1jyfRmjXYiIiIiIiIj6OLYkEhH1AsPzvBiam4LSqiDqIjo8dhWFGa6kFwFt6GgvqwknstEdVgXhmIFyXzjpjnZK3t7k0PfEorVERERERERE1DnYiU5E1EvIsoTiLDdGFqSiOMu91wXvzuhop+QlcuitbefQRzXm0BNR8p5++mkUFBRAlmU8/PDDPT0c6mUKCwt5XRARERH1EBbRiYgOIMPzvLhj0lDMPmsYbp90GGafNQx/mnQYC+hdgDn0RAeniooKXHnllRgwYABsNhtycnIwceJEfPHFF/u1X7/fjxkzZuDmm29GWVkZrrjiisRz4XAYaWlpSE9PRzgc3t9D6DRCCDzzzDMYN24cUlJS4Ha7MWzYMPz+97/Hxo0be3p4AIDS0lJIkoSVK1d2+3uffPLJuOGGG7r9fYmIiIio87GITkR0gNnfjnZKDnPoiQ5O55xzDn744QcsXrwYP/30E15//XWcfPLJqK6u3qf9CSGg6zq2bdsGTdMwadIk5Obmwul0JrZ55ZVXMHz4cAwdOhRLlizpcJ+apu3TWPaGEAIXXHABrr/+epx55pl45513sGrVKjz66KNwOByYN29el4/hQNAw/0RERETUu7GITkREtA8acujTXVZsrAggENFhmAKBiI6NFQHm0BMdgGpra/Hpp5/ivvvuwymnnIJDDjkERx11FG699VZMmjSp1a7n2tpaSJKE5cuXAwCWL18OSZKwbNkyjB07FjabDc8//zwOP/xwAEBxcTEkSUJpaWliHwsXLsS0adMwbdo0LFy4sMW4JEnCU089hSlTpsDlciUK2P/5z38wZswY2O12FBcXY/bs2U0Ktg8++CAOP/xwuFwuFBQU4JprrkEgEEjqXLz88st46aWX8PLLL+OOO+7AMcccg+LiYowfPx7z58/Hc889l9R+pk+fjrPPPhuzZ89GdnY2UlJScOWVVyIWiyW2iUajuP7665GdnQ273Y7jjz8e33zzTeL5mpoaXHjhhcjKyoLD4cCgQYMS719UVAQAOOKIIyBJEk4++eTE65599lkMGzYMNpsNubm5mDFjRuI5n8+HK664IjGmX/ziF/jhhx8Sz8+aNQujRo3C888/j8LCQni9XkydOhV1dXWJ4/roo4/wyCOPQJKkxJy2Nv+ffPIJNm3ahClTpqBfv35wu9048sgj8d577yV1DomIiIio67GITkREtI+YQ090cHG73XC73XjttdcQjUb3a18zZ87Evffei3Xr1uG0005LFEy//vprlJeXo6CgAACwadMmfPHFFzjvvPNw3nnn4fPPP8fmzZtb7O+uu+7ClClTsHr1alx66aVYtmwZpk2bhuuvvx5r167FX//6VyxatAh333134jWyLOPRRx/FmjVrsHjxYnzwwQeYOXNmUuN/8cUXMXjwYJx11lmtPt98weX2vP/++1i3bh0+/PBDvPjii3j11Vcxe/bsJufqlVdeweLFi/H999+jpKQEEydOTHT/33HHHVi7di3eeustrFu3Dk8++SQyMzMBxM8nALz33nsoLy9PdPI/+eSTuPbaa3HFFVdg9erVeP3111FSUgIg3h0+adIk7Nq1C2+++Sa+++47jB49GuPHj2/yiYNNmzbhtddew9KlS7F06VJ89NFHmD9/PgDgkUcewbhx43D55ZejvLy8yZw2HFPD/I8YMQKBQABnnnkm3nvvPaxYsQITJ07E5MmTsW3btqTPYzQahd/vb/KHiIiIiDoHg1qJiIj2w/A8L4bmpqC0Koi6iA6PXUVhhosd6EQHIFVVsWjRIlx++eV46qmnMHr0aJx00kmYOnUqRowYsVf7mjNnDiZMmJD4urKyEgCQlZWFnJycxOPPPvsszjjjDKSlpQEATj/9dDz77LMt4lIuuOACXHrppYmvL7roItxyyy245JJLAMQ73OfOnYuZM2firrvuAoAmed1FRUWYO3curr76ajzxxBMdjv+nn37C4MGDmzx2ww034G9/+xsAIDU1FTt27OhwPwBgtVrx7LPPwul0YtiwYZgzZw5uuukmzJ07F+FwGE8++SQWLVqEM844AwDwzDPP4N1338XChQtx0003Ydu2bTjiiCMwduxYAPEFOBtkZWUBADIyMpqc13nz5uF///d/8fvf/z7x2JFHHgkA+PDDD7F69WpUVFTAZrMBABYsWIDXXnsN//rXvxJ59aZpYtGiRfB4PADi5/z999/H3XffDa/XC6vVCqfT2eR9GzSf/4yMDIwcObLJ+F599VW8/vrrTTrk23Pvvfc2uflARERERJ2HnehERET7iTn0RAePc845Bzt37sTrr7+OiRMnYvny5Rg9ejQWLVq0V/tpKPi2xzAMLF68GNOmTUs8Nm3aNCxevBiG0XRR4+b7++677zBnzpxE97zb7U50RYdCIQDxYvGECROQl5cHj8eDiy++GFVVVQgGg0kdQ/Nu89tvvx0rV67EnXfemXQsDACMHDmySQb8uHHjEAgEsH37dmzatAmapuG4445LPG+xWHDUUUdh3bp1AICrr74aL730EkaNGoWZM2fi888/b/f9KioqsHPnTowfP77V57/77jsEAgFkZGQ0OX9btmzBpk2bEtsVFhYmCugAkJubi4qKiqSOufl8BYNBzJw5E0OHDkVqaircbjd+/PHHvepEv/XWW+Hz+RJ/tm/fnvRriYiIiKh97EQnIiIiItoLdrsdEyZMwIQJE3DnnXfid7/7He666y588sknANBkseG2Fvl0uTpedHjZsmUoKyvD+eef3+RxwzDwzjvvJDqzW9ufaZqYPXs2fv3rX7c6/q1bt+LMM8/EVVddhblz5yI9PR2ffvopLrvssqQWJh00aBB+/PHHJo9lZWUhKysL2dnZHb4+GZIkJc5l84K9ECLx2BlnnIGtW7fijTfewHvvvYfx48fj2muvxYIFC1rdr8PhaPd9TdNEbm5uIse+sdTU1MT/WyyWFuM1TbOjwwLQcr5uuukmLFu2DAsWLEBJSQkcDgd+85vfNMmG74jNZkt0zhMRERFR52InOhERERHRfhg6dCiCwWAiOqS8vDzxXONFRvfWwoULMXXqVKxcubLJnwsvvLDVBUYbGz16NNavX4+SkpIWf2RZxrfffgtd1/HAAw/gmGOOwaGHHoqdO3cmPbbf/va3WL9+Pf7973/v8/E1+OGHHxAOhxNff/nll3C73cjPz0dJSQmsVis+/fTTxPOapuHbb7/FYYcdlngsKysL06dPxwsvvICHH34YTz/9NIB4VAyAJp37Ho8HhYWFeP/991sdz+jRo7Fr1y6oqtri3DVkrSfDarW2+MRAWz755BNMnz4dv/rVr3D44YcjJyenyeKyRERERNSz2IlORERERJSEqqoqnHvuubj00ksxYsQIeDwefPvtt7j//vsxZcoUOBwOHHPMMZg/fz4KCwuxZ88e/OlPf9qn96qsrMR//vMfvP766xg+fHiT5y655BJMmjQJlZWVicJ9c3feeSd++ctfoqCgAOeeey5kWcaqVauwevVqzJs3DwMHDoSu63jssccwefJkfPbZZ3jqqaeSHt/UqVOxZMkSTJ06FbfeeismTpyIfv36YevWrXj55ZehKErS+4rFYrjsssvwpz/9CVu3bsVdd92FGTNmQJZluFwuXH311bjpppuQnp6OAQMG4P7770coFMJll12WONYxY8Zg2LBhiEajWLp0aaLAnp2dDYfDgbfffhv5+fmw2+3wer2YNWsWrrrqKmRnZ+OMM85AXV0dPvvsM1x33XU49dRTMW7cOJx99tm47777MHjwYOzcuRNvvvkmzj777KSieIB43MtXX32F0tJSuN1upKent7ltSUkJlixZgsmTJ0OSJNxxxx1Jd7UTERERUddjJzoRERERURLcbjeOPvpoPPTQQzjxxBMxfPhw3HHHHbj88svx+OOPA4gvBKppGsaOHYvf//73LRYATdbf//53uFyuVnO7TznlFHg8Hjz//PNtvn7ixIlYunQp3n33XRx55JE45phj8OCDD+KQQw4BAIwaNQoPPvgg7rvvPgwfPhz/+Mc/cO+99yY9PkmS8PLLL+Phhx/Gm2++ifHjx2Pw4MG49NJLUVBQ0KRzvCPjx4/HoEGDcOKJJ+K8887D5MmTMWvWrMTz8+fPxznnnIOLLroIo0ePxsaNG7Fs2bLEYqtWqxW33norRowYgRNPPBGKouCll14CEF8M9tFHH8Vf//pX9O/fH1OmTAEQvxHx8MMP44knnsCwYcPwy1/+Ehs2bEgc25tvvokTTzwRl156KQ499FBMnToVpaWl6NevX9LHdeONN0JRFAwdOhRZWVnt5ps/9NBDSEtLw7HHHovJkydj4sSJGD16dNLvRURERERdSxKNQxsJAOD3++H1euHz+ZCSktLTwyEion1kmgKlVUHURXR47CoKM1xc9JOoE/FnJtpf06dPR21tLV577bWeHsoBp+HvZ8EN/4Rsc3b8AiIiIupTSudP6ukhHBCS/Z2GcS5ERHRAWlPmwyvf78DGigCimgmbRUZJthvnjM7H8DxvTw+PiIiIiIiIiPoIxrkQEdEBZ02ZD4++vwGrd/iQ6rCiMNOFVIcVq3fEH19T5uvpIRIR9WpnnHEG3G53q3/uueeepPfT1j7cbjc++eSTLjwCIiIiIqLOw050IiI6oJimwCvf70B1MIaSbDckKR7f4rarKLG5sbEigCXfl2FobgqjXYiI2vC3v/0N4XC41efaWyCzuZUrV7b5XF5eHk444YS9HRoRERERUbdjEZ2IiA4opVVBbKwIINfrSBTQG0iShFyvAxsq6lBaFURxlruHRklE1Lvl5eV1yn5KSko6ZT9ERERERD2JcS5ERHRAqYvoiGomHFal1ecdVgVRzURdRO/mkRERERERERFRX8QiOhERHVA8dhU2i4xwzGj1+XDMgM0iw2Pnh7GIiIiIiIiIqGMsohMR0QGlMMOFkmw3yn1hCCGaPCeEQLkvjEHZHhRmuHpohERERERERETUl7ANj4iok5mmQGlVEHURHR67isIMFxew7EayLOGc0fkoqwknstEdVgXhmIFyXxjpLit+PTqPc0JERERERERESWERnYioE60p8+GV73dgY0UAUc2EzSKjJNuNc0bnY3iet6eHd9AYnufF9eMHJeZitz8+FyPyU/Hr0XmcCyIiIiIiIiJKGovoRESdZE2ZD4++vwHVwVi8+9kb735evcOHspowrh8/iMXbbjQ8z4uhuSn8VAARERERERER7RcW0YmIOoFpCrzy/Q5UB2MoyXZDkuKFWrddRYnNjY0VASz5vgxDc1NYxO1GsiyhOMvd08MgIiIiIiIioj6MC4sSEXWC0qpgIn+7oYDeQJIk5Hod2FBRh9KqYA+NkIiIiIiIiIiI9gU70YmIOkFdREdUM+HwKq0+77Aq2O03URfRu3lkREREdDBbM3siUlJSenoYRERERH0aO9GJiDqBx67CZpERjhmtPh+OGbBZZHjsvHdJRERERERERNSXsIhORNQJCjNcKMl2o9wXhhCiyXNCCJT7whiU7UFhhquHRkhERERERERERPuCRXQiok4gyxLOGZ2PdJcVGysCCER0GKZAIKJjY0UA6S4rfj06j4uKEhERERERERH1MSyiExF1kuF5Xlw/fhAOz/eiNhxD6Z4gasMxjMhPxfXjB2F4nrenh0hERERERERERHuJ4bxERJ1oeJ4XQ3NTUFoVRF1Eh8euojDDxQ50IiIiIiIiIqI+ikV0IqJOJssSirPcPT0MIiIiIiIiIiLqBIxzISIiIiIiIiIiIiJqA4voRERERERERERERERtYBGdiIiIiIiIiIiIiKgNLKITEREREREREREREbWBRXQiIiIiIiIiIiIiojawiE5ERERERERERERE1AYW0YmIiIiIiIiIiIiI2sAiOhERERERERERERFRG1hEJyIiIiIiIiIiIiJqA4voRERERERERERERERtYBGdiIiIiIiIiIiIiKgNLKITEREREREREREREbWBRXQiIiIiIiIiIiIiojawiE5ERERERERERERE1AYW0YmIiIiIiIiIiIiI2sAiOhERERERERERERFRG9SeHkBvJIQAAPj9/h4eCREREVHv1fCzUsPPTkTUe/B3GiIiIqKOJfs7DYvorairqwMAFBQU9PBIiIiIiHq/uro6eL3enh4GETVSVVUFgL/TEBERESWjo99pJMHWoRZM08TOnTvh8XggSVJPD6dH+P1+FBQUYPv27UhJSenp4RzUOBe9A+ehd+A89B6ci96hp+dBCIG6ujr0798fssyUQKLepLa2Fmlpadi2bRtvch2kevrfCOp5vAYObpx/4jWQnGR/p2EneitkWUZ+fn5PD6NXSElJ4V+0XoJz0TtwHnoHzkPvwbnoHXpyHlicI+qdGn4J9Hq9/D59kOO/1cRr4ODG+SdeAx1L5ncatgwREREREREREREREbWBRXQiIiIiIiIiIiIiojawiE6tstlsuOuuu2Cz2Xp6KAc9zkXvwHnoHTgPvQfnonfgPBBRW/j9gXgNEK+Bgxvnn3gNdC4uLEpERERERERERERE1AZ2ohMRERERERERERERtYFFdCIiIiIiIiIiIiKiNrCITkRERERERERERETUBhbRqYnS0lJcdtllKCoqgsPhwMCBA3HXXXchFos12W7btm2YPHkyXC4XMjMzcf3117fYhvbP3XffjWOPPRZOpxOpqamtbsN56B5PPPEEioqKYLfbMWbMGHzyySc9PaQD3scff4zJkyejf//+kCQJr732WpPnhRCYNWsW+vfvD4fDgZNPPhn//e9/e2awB7B7770XRx55JDweD7Kzs3H22Wdj/fr1TbbhXHSPJ598EiNGjEBKSgpSUlIwbtw4vPXWW4nnOQ9EB769/Xnko48+wpgxY2C321FcXIynnnqqxTavvPIKhg4dCpvNhqFDh+LVV1/tquFTJ+jsa+CZZ57BCSecgLS0NKSlpeHUU0/F119/3ZWHQPupK74PNHjppZcgSRLOPvvsTh41daauuAZqa2tx7bXXIjc3F3a7HYcddhjefPPNrjoE2k9dcQ08/PDDGDx4MBwOBwoKCvCHP/wBkUikqw6h7xJEjbz11lti+vTpYtmyZWLTpk3i3//+t8jOzhb/+7//m9hG13UxfPhwccopp4jvv/9evPvuu6J///5ixowZPTjyA8+dd94pHnzwQfHHP/5ReL3eFs9zHrrHSy+9JCwWi3jmmWfE2rVrxe9//3vhcrnE1q1be3poB7Q333xT3H777eKVV14RAMSrr77a5Pn58+cLj8cjXnnlFbF69Wpx/vnni9zcXOH3+3tmwAeoiRMniueee06sWbNGrFy5UkyaNEkMGDBABAKBxDaci+7x+uuvizfeeEOsX79erF+/Xtx2223CYrGINWvWCCE4D0QHur39eWTz5s3C6XSK3//+92Lt2rXimWeeERaLRfzrX/9KbPP5558LRVHEPffcI9atWyfuueceoaqq+PLLL7vrsGgvdMU1cMEFF4i//OUvYsWKFWLdunXif/7nf4TX6xU7duzorsOivdAV10CD0tJSkZeXJ0444QQxZcqULj4S2lddcQ1Eo1ExduxYceaZZ4pPP/1UlJaWik8++USsXLmyuw6L9kJXXAMvvPCCsNls4h//+IfYsmWLWLZsmcjNzRU33HBDdx1Wn8EiOnXo/vvvF0VFRYmv33zzTSHLsigrK0s89uKLLwqbzSZ8Pl9PDPGA9txzz7VaROc8dI+jjjpKXHXVVU0eGzJkiLjlllt6aEQHn+ZFdNM0RU5Ojpg/f37isUgkIrxer3jqqad6YIQHj4qKCgFAfPTRR0IIzkVPS0tLE3/72984D0QHgb39eWTmzJliyJAhTR678sorxTHHHJP4+rzzzhOnn356k20mTpwopk6d2kmjps7UFddAc7quC4/HIxYvXrz/A6ZO11XXgK7r4rjjjhN/+9vfxCWXXMIiei/WFdfAk08+KYqLi0UsFuv8AVOn64pr4NprrxW/+MUvmmzzxz/+URx//PGdNOoDB+NcqEM+nw/p6emJr7/44gsMHz4c/fv3Tzw2ceJERKNRfPfddz0xxIMS56HrxWIxfPfddzjttNOaPH7aaafh888/76FR0ZYtW7Br164m82Kz2XDSSSdxXrqYz+cDgMS/CZyLnmEYBl566SUEg0GMGzeO80B0gNuXn0e++OKLFttPnDgR3377LTRNa3cbft/ofbrqGmguFApB07Qmv/tR79CV18CcOXOQlZWFyy67rPMHTp2mq66B119/HePGjcO1116Lfv36Yfjw4bjnnntgGEbXHAjts666Bo4//nh89913iTivzZs3480338SkSZO64Cj6NrWnB0C926ZNm/DYY4/hgQceSDy2a9cu9OvXr8l2aWlpsFqt2LVrV3cP8aDFeeh6e/bsgWEYLc5zv379eI57UMO5b21etm7d2hNDOigIIfDHP/4Rxx9/PIYPHw6Ac9HdVq9ejXHjxiESicDtduPVV1/F0KFDEz80cx6IDkz78vNIaz8n9uvXD7quY8+ePcjNzW1zG/6M0/t01TXQ3C233IK8vDyceuqpnTd46hRddQ189tlnWLhwIVauXNlVQ6dO0lXXwObNm/HBBx/gwgsvxJtvvokNGzbg2muvha7ruPPOO7vseGjvddU1MHXqVFRWVuL444+HEAK6ruPqq6/GLbfc0mXH0lexE/0gMWvWLEiS1O6fb7/9tslrdu7cidNPPx3nnnsufve73zV5TpKkFu8hhGj1cfrZvsxDezgP3aP5+eQ57h04L91rxowZWLVqFV588cUWz3EuusfgwYOxcuVKfPnll7j66qtxySWXYO3atYnnOQ9EB7a9/Tve2vbNH+f3jb6lK66BBvfffz9efPFFLFmyBHa7vRNGS12hM6+Buro6TJs2Dc888wwyMzM7f7DUJTr7+4BpmsjOzsbTTz+NMWPGYOrUqbj99tvx5JNPdvLIqbN09jWwfPly3H333XjiiSfw/fffY8mSJVi6dCnmzp3bySPv+9iJfpCYMWMGpk6d2u42hYWFif/fuXMnTjnlFIwbNw5PP/10k+1ycnLw1VdfNXmspqYGmqa1uMNFTe3tPLSH89D1MjMzoShKi7u6FRUVPMc9KCcnB0D8rnrjLirOS9e57rrr8Prrr+Pjjz9Gfn5+4nHORfeyWq0oKSkBAIwdOxbffPMNHnnkEdx8880AOA9EB6p9+XkkJyen1e1VVUVGRka72/D7Ru/TVddAgwULFuCee+7Be++9hxEjRnTu4KlTdMU18N///helpaWYPHly4nnTNAEAqqpi/fr1GDhwYCcfCe2rrvo+kJubC4vFAkVREtscdthh2LVrF2KxGKxWaycfCe2rrroG7rjjDlx00UWJ5tnDDz8cwWAQV1xxBW6//XbIMvuvG/BMHCQyMzMxZMiQdv80dByUlZXh5JNPxujRo/Hcc8+1+Aszbtw4rFmzBuXl5YnH3nnnHdhsNowZM6Zbj6uv2Zt56AjnoetZrVaMGTMG7777bpPH3333XRx77LE9NCoqKipCTk5Ok3mJxWL46KOPOC+dTAiBGTNmYMmSJfjggw9QVFTU5HnORc8SQiAajXIeiA5w+/LzyLhx41ps/84772Ds2LGwWCztbsPvG71PV10DAPDnP/8Zc+fOxdtvv42xY8d2/uCpU3TFNTBkyBCsXr0aK1euTPw566yzcMopp2DlypUoKCjosuOhvddV3weOO+44bNy4MXEDBQB++ukn5ObmsoDey3TVNRAKhVrU/RRFgRAi0bVO9bpxEVPqA8rKykRJSYn4xS9+IXbs2CHKy8sTfxroui6GDx8uxo8fL77//nvx3nvvifz8fDFjxoweHPmBZ+vWrWLFihVi9uzZwu12ixUrVogVK1aIuro6IQTnobu89NJLwmKxiIULF4q1a9eKG264QbhcLlFaWtrTQzug1dXVJa55AOLBBx8UK1asEFu3bhVCCDF//nzh9XrFkiVLxOrVq8Vvf/tbkZubK/x+fw+P/MBy9dVXC6/XK5YvX97k34NQKJTYhnPRPW699Vbx8ccfiy1btohVq1aJ2267TciyLN555x0hBOeB6EDX0c8jt9xyi7jooosS22/evFk4nU7xhz/8Qaxdu1YsXLhQWCwW8a9//SuxzWeffSYURRHz588X69atE/Pnzxeqqoovv/yy24+POtYV18B9990nrFar+Ne//tXk3/mG3zeod+mKa6C5Sy65REyZMqWrD4X2UVdcA9u2bRNut1vMmDFDrF+/XixdulRkZ2eLefPmdfvxUce64hq46667hMfjES+++KLYvHmzeOedd8TAgQPFeeed1+3H19uxiE5NPPfccwJAq38a27p1q5g0aZJwOBwiPT1dzJgxQ0QikR4a9YHpkksuaXUePvzww8Q2nIfu8Ze//EUccsghwmq1itGjR4uPPvqop4d0wPvwww9bvf4vueQSIYQQpmmKu+66S+Tk5AibzSZOPPFEsXr16p4d9AGorX8PnnvuucQ2nIvucemllya+D2VlZYnx48cnCuhCcB6IDgbt/TxyySWXiJNOOqnJ9suXLxdHHHGEsFqtorCwUDz55JMt9vn//t//E4MHDxYWi0UMGTJEvPLKK119GLQfOvsaOOSQQ1r9d/6uu+7qhqOhfdEV3wcaYxG99+uKa+Dzzz8XRx99tLDZbKK4uFjcfffdQtf1rj4U2kedfQ1omiZmzZolBg4cKOx2uygoKBDXXHONqKmp6Yaj6VskIdibT0RERERERERERETUGmaiExERERERERERERG1gUV0IiIiIiIiIiIiIqI2sIhORERERERERERERNQGFtGJiIiIiIiIiIiIiNrAIjoRERERERERERERURtYRCciIiIiIiIiIiIiagOL6EREREREREREREREbWARnYiIiIiIiIiIiIioDSyiExH1EnfccQeuuOKKnh7GPrvxxhtx/fXX9/QwiIiIiIh6peXLl0OSJNTW1nbZe5x88sm44YYbumz/fdGsWbMwatSoxNfTp0/H2Wef3S3vRUQHDhbRiYgQ/0FKkiRIkgSLxYLi4mLceOONCAaDTbZ75ZVXcPLJJ8Pr9cLtdmPEiBGYM2cOqqurAQDl5eW44IILMHjwYMiynPQPsLt378YjjzyC2267LfHYxx9/jMmTJ6N///6QJAmvvfZai9fNmjULQ4YMgcvlQlpaGk499VR89dVX7b7XySefnDjWxn8mTZqU2OYf//gHCgoKkJ6ejptuuqnJ60tLS3HooYfC7/c3eXzmzJl47rnnsGXLlqSOmYiIiIjoQPP5559DURScfvrpPT2UpJSWlkKSJKxcubJT9rdr1y5cd911KC4uhs1mQ0FBASZPnoz333+/U/bfYG9uFtx4442d/v4AWv0dravei4h6HovoRET1Tj/9dJSXl2Pz5s2YN28ennjiCdx4442J52+//Xacf/75OPLII/HWW29hzZo1eOCBB/DDDz/g+eefBwBEo1FkZWXh9ttvx8iRI5N+74ULF2LcuHEoLCxMPBYMBjFy5Eg8/vjjbb7u0EMPxeOPP47Vq1fj008/RWFhIU477TRUVla2+ZolS5agvLw88WfNmjVQFAXnnnsuAGDPnj343e9+hwULFmDZsmVYvHgx3njjjcTrr776asyfPx8pKSlN9pudnY3TTjsNTz31VNLHTURERER0IHn22Wdx3XXX4dNPP8W2bdt6ejjdqrS0FGPGjMEHH3yA+++/H6tXr8bbb7+NU045Bddee223j0cIAV3X4Xa7kZGR0S3v2Z3vRUTdi0V0IqJ6NpsNOTk5KCgowAUXXIALL7ww0Vnw9ddf45577sEDDzyAP//5zzj22GNRWFiICRMm4JVXXsEll1wCACgsLMQjjzyCiy++GF6vN+n3fumll3DWWWc1eeyMM87AvHnz8Otf/7rN111wwQU49dRTUVxcjGHDhuHBBx+E3+/HqlWr2nxNeno6cnJyEn/effddOJ3ORBF98+bN8Hq9iRsGp5xyCtauXQsA+L//+z9YrdY2x3TWWWfhxRdfTPq4iYiIiIgOFMFgEP/85z9x9dVX45e//CUWLVrU6nafffYZRo4cCbvdjqOPPhqrV69OPLd161ZMnjwZaWlpcLlcGDZsGN58883E8x999BGOOuoo2Gw25Obm4pZbboGu622OqbVu6dTU1MTYioqKAABHHHEEJEnCySefnNjuueeew2GHHQa73Y4hQ4bgiSeeaPf4r7nmGkiShK+//hq/+c1vcOihh2LYsGH44x//iC+//DKx3bZt2zBlyhS43W6kpKTgvPPOw+7duxPPN0SiPP/88ygsLITX68XUqVNRV1cHIP4p4o8++giPPPJI4lO1paWlibicZcuWYezYsbDZbPjkk0/ajFiZPXs2srOzkZKSgiuvvBKxWCzxXGFhIR5++OEm248aNQqzZs1KPA8Av/rVryBJUuLr5u9lmibmzJmD/Px82Gw2jBo1Cm+//Xbi+YZPAixZsgSnnHIKnE4nRo4ciS+++KLdc01E3Y9FdCKiNjgcDmiaBiAeb+J2u3HNNde0um1qauo+v09NTQ3WrFmDsWPH7vM+ACAWi+Hpp5+G1+vd6y74qVOnwuVyAQAGDRqEUCiEFStWoLq6Gt988w1GjBiB6upq3Hnnne12xh911FHYvn07tm7dul/HQkRERETU17z88ssYPHgwBg8ejGnTpuG5556DEKLFdjfddBMWLFiAb775BtnZ2TjrrLMSv3dce+21iEaj+Pjjj7F69Wrcd999cLvdAICysjKceeaZOPLII/HDDz/gySefxMKFCzFv3rx9HvPXX38NAHjvvfdQXl6OJUuWAACeeeYZ3H777bj77ruxbt063HPPPbjjjjuwePHiVvdTXV2Nt99+G9dee23i94rGGn5fEkLg7LPPRnV1NT766CO8++672LRpE84///wm22/atAmvvfYali5diqVLl+Kjjz7C/PnzAQCPPPIIxo0bh8svvzzx6dqCgoLEa2fOnIl7770X69atw4gRI1od7/vvv49169bhww8/xIsvvohXX30Vs2fPTvq8ffPNNwDiNxrKy8sTXzf3yCOP4IEHHsCCBQuwatUqTJw4EWeddRY2bNjQZLvbb78dN954I1auXIlDDz0Uv/3tb9u9OUJE3U/t6QEQEfVGX3/9Nf7v//4P48ePBwBs2LABxcXFsFgsnf5eW7duhRAC/fv336fXL126FFOnTkUoFEJubi7effddZGZmJvXar7/+GmvWrMHChQsTj6WlpWHx4sW4+OKLEQ6HcfHFF2PixIm49NJLcd1112HLli2JH/RnzZqF3/zmN4nX5uXlAYh3VBxyyCH7dDxERERERH3RwoULMW3aNADxqMhAIID3338fp556apPt7rrrLkyYMAEAsHjxYuTn5+PVV1/Feeedh23btuGcc87B4YcfDgAoLi5OvO6JJ55AQUEBHn/8cUiShCFDhmDnzp24+eabceedd0KW975PMisrCwCQkZGBnJycxONz587FAw88kPgEalFREdauXYu//vWviU/hNrZx40YIITBkyJB23++9997DqlWrsGXLlkTh+/nnn8ewYcPwzTff4MgjjwQQ7+BetGgRPB4PAOCiiy7C+++/j7vvvhterxdWqxVOp7PJmBvMmTMncX7bYrVa8eyzz8LpdGLYsGGYM2cObrrpJsydOzep89hw3lJTU1sdQ4MFCxbg5ptvxtSpUwEA9913Hz788EM8/PDD+Mtf/pLY7sYbb0ysUTV79mwMGzYMGzdu7PB8ElH3YRGdiKje0qVL4Xa7oes6NE3DlClT8NhjjwGId0xIktQl7xsOhwEAdrt9n15/yimnYOXKldizZw+eeeYZnHfeefjqq6+QnZ3d4WsXLlyI4cOH46ijjmry+K9+9Sv86le/Sny9fPlyrF69Go8//jhKSkrw4osvIicnB0cddRROPPHExHs5HA4AQCgU2qdjISIiIiLqi9avX4+vv/460cmtqirOP/98PPvssy2K6OPGjUv8f3p6OgYPHox169YBAK6//npcffXVeOedd3DqqafinHPOSXRTr1u3DuPGjWvye8lxxx2HQCCAHTt2YMCAAZ1yLJWVldi+fTsuu+wyXH755YnHdV1vM7KyoeO+o9+Z1q1bh4KCgiad40OHDkVqairWrVuXKKIXFhYmCugAkJubi4qKiqTGn8wnfEeOHAmn05n4ety4cQgEAti+fXunNQP5/X7s3LkTxx13XJPHjzvuOPzwww9NHmvcMZ+bmwsAqKioYBH9/7d3byFRdW0cwP9j45iMTqiZh/EQqFMpqESWWDkgUhhlChlUmBd6EWYpinnRYcqIriIvSisZlEDKLuYiO0hRBolgJI1lB8s0KyvoMBlamjM+70Wv+2vUUev7eN/q+/9gLvZaa6+91p6bvZ9Z6xmiXwjTuRAR/W0sGN3Z2YmhoSFYLBYlOGwwGPD06VNlm+X/0tiqcZvN9lPna7VaREZGIjExEWazGWq12mlluSufP3/G2bNnkZeXN2W74eFh5Ofn4+TJk+jq6oLdbofRaMSCBQtgMBjQ2tqqtP3w4QOA/6zMICIiIiL6f2A2m2G326HX66FWq6FWq1FVVQWLxTKj5/yx4HNeXh66u7uRnZ2Ne/fuYcmSJVMu7JkueK1SqSaklJnunWZ0dBTAt5QuVqtV+XR0dDjlNv9eVFQUVCqV8mOAK64WJ40vH78DWKVSKeOazmTpZGZqbAxubm4/fN+m63PMZPfg+/mO1c10vkT0z2AQnYjob2PB6PDw8AkPbZs3b8bAwIDLP9P5+PHjT183IiICOp1O+fPO/5aIYHh4eNp2586dw/DwsLLl1JWDBw8iLS0NixcvhsPhcMrNNzIyAofDoRx3dHTA3d0dMTExPz8BIiIiIqLfiN1ux+nTp3HkyBGnoHN7ezvCw8NRV1fn1P77QLTNZsPjx4+dVhyHhoZi27ZtsFgsKCkpQXV1NYBvK7ZbWlqcgrstLS3w9vZW0iqO5+/vj9evXyvHT548cdo1qtFoAMDpmT4gIAB6vR7d3d2IjIx0+oz9Eel4vr6+WL16NY4fP47BwcEJ9WPvS9HR0Xj+/DlevHih1D148AD9/f1YtGjRpH1PRqPROI35R7W3tys7goFv34mXlxdCQkIATLxvnz59Qk9Pj1Mf7u7uU45Bp9MhODgYzc3NTuUtLS0/NFci+jUwnQsR0QwsW7YMu3btQklJCfr6+pCZmYng4GB0dXXhxIkTWLFiBQoLCwEAVqsVADAwMIC3b9/CarVCo9EgOjp60r7d3NyQmpqK5uZmZGRkKOUDAwPo6upSjnt6emC1WuHr64uwsDAMDg7i0KFDSE9PR1BQEN6/f4/Kykq8fPkSWVlZynlbt26FXq/H4cOHna5rNpuRkZEBPz8/l/O+f/8+6uvrlTktXLgQbm5uMJvNCAwMxKNHj5QtlwBw8+ZNrFy5UknrQkRERET0p7tw4QJsNhtyc3MnpDvZsGEDzGYzCgoKlLLy8nL4+fkhICAAu3fvxty5c5X3gKKiIqSlpcFgMMBms+H69etKwDU/Px8VFRXYsWMHCgoK0NnZCZPJhOLiYpd5vFNSUnDs2DEkJiZidHQUZWVlTguG5s2bB09PTzQ2NiIkJASzZ8/GnDlzsH//fuzcuRM6nQ5paWkYHh7G7du3YbPZUFxcPOm1KisrkZSUhKVLl6K8vByxsbGw2+24evUqqqqq8PDhQ6SmpiI2NhZbtmxBRUUF7HY78vPzYTQaZ5SGZcz8+fPR2tqKZ8+ewcvLC76+vjM+FwC+fv2K3Nxc7NmzB729vTCZTCgoKFDuY0pKCmpra7Fu3Tr4+Phg7969mDVr1oQxXLt2DcuXL4eHhwd8fHwmXKe0tBQmkwkRERGIj49HTU0NrFbrhB9WiOg3IEREJDk5ObJ+/fpp29XX10tycrJ4e3uLVquV2NhYKS8vF5vNprQBMOETHh4+Zb+NjY2i1+vF4XAoZU1NTZP2lZOTIyIiX758kczMTAkODhaNRiNBQUGSnp4ut27dcurbaDQq54zp7OwUAHLlyhWXYxodHZWkpCRpaGhwKm9oaJCwsDAJCAiQ6upqpzqDwSBnzpyZcq5ERERERH+StWvXypo1ayata2trEwDS1tamPN83NDRITEyMaDQaSUhIEKvVqrQvKCiQiIgI8fDwEH9/f8nOzpZ3794p9Tdu3JCEhATRaDQSGBgoZWVlMjIyotQbjUYpLCxUjvv6+mTVqlWi1WolKipKLl26JHPmzJGamhqlTXV1tYSGhoqbm5sYjUalvK6uTuLj40Wj0YiPj48kJyeLxWKZ8l68evVKtm/fLuHh4aLRaESv10t6ero0NTUpbXp7eyU9PV20Wq14e3tLVlaWvHnzRqk3mUwSFxfn1O/Ro0ed3qk6OzslMTFRPD09BYD09PQo9/f7d7PJ+ht799u3b5/4+fmJl5eX5OXlydDQkNKmv79fNm7cKDqdTkJDQ6W2tlbi4uLEZDIpbc6fPy+RkZGiVquVsY2/lsPhkAMHDoherxd3d3eJi4uTy5cvK/U9PT0CQO7cuaOU2Ww2AeB0z4jo36cSGZfkiYiI/nEigsTERBQVFWHTpk3/9nB+ysWLF1FaWoq7d+9CreZGJyIiIiIiIiL6MzAnOhHRL0ClUuHUqVNO+cZ/N4ODg6ipqWEAnYiIiIiIiIj+KFyJTkRERERERERERETkAleiExERERERERERERG5wCA6EREREREREREREZELDKITEREREREREREREbnAIDoRERERERERERERkQsMohMRERERERERERERucAgOhERERERERERERGRCwyiExERERERERERERG5wCA6EREREREREREREZELDKITEREREREREREREbnAIDoRERERERERERERkQt/AcO/gBKbp75bAAAAAElFTkSuQmCC",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Final Results:\n",
- "Best performing model: Logistic Regression\n",
- "Performance on 5 regions with ≥15 subjects\n",
- "Balanced Test Accuracy: 0.371\n",
- "Binary AUC Score: 0.828\n",
- "\n",
- "Threshold Comparison Summary:\n",
- " threshold classes_retained samples_retained retention_rate\n",
- "0 5 13 468 86.3\n",
- "1 10 7 431 79.5\n",
- "2 15 5 409 75.5\n"
- ]
- }
- ],
- "source": [
- "df = pd.read_csv(r'.\\lh_updated.csv')\n",
- "df = df.drop('Subject', axis=1)\n",
- "\n",
- "def filter_regions_by_subject_count(df, min_subjects=10):\n",
- " \n",
- " target_candidates = [col for col in df.columns if 'epilep' in col.lower() or 'target' in col.lower() or 'label' in col.lower()]\n",
- " \n",
- " if not target_candidates:\n",
- " print(\"No obvious target column found. Assuming last column is target.\")\n",
- " target_col = df.columns[-1]\n",
- " else:\n",
- " target_col = target_candidates[0]\n",
- " \n",
- " print(f\"Using '{target_col}' as target variable\")\n",
- " \n",
- " \n",
- " class_counts = df[target_col].value_counts().sort_index()\n",
- " print(f\"\\nOriginal class distribution:\")\n",
- " print(f\"Total classes: {len(class_counts)}\")\n",
- " print(f\"Class 0 (not epileptic): {class_counts.get(0, 0)} subjects\")\n",
- " print(f\"Epileptic regions (1-83): {class_counts[class_counts.index > 0].sum()} subjects\")\n",
- " \n",
- " \n",
- " sufficient_regions = class_counts[class_counts >= min_subjects].index.tolist()\n",
- " insufficient_regions = class_counts[class_counts < min_subjects].index.tolist()\n",
- " \n",
- " print(f\"\\nRegions with >= {min_subjects} subjects: {len(sufficient_regions)}\")\n",
- " print(f\"Regions with < {min_subjects} subjects: {len(insufficient_regions)}\")\n",
- " \n",
- " if len(insufficient_regions) > 0:\n",
- " print(f\"Insufficient regions: {insufficient_regions[:20]}{'...' if len(insufficient_regions) > 20 else ''}\")\n",
- " \n",
- " \n",
- " filtered_df = df[df[target_col].isin(sufficient_regions)].copy()\n",
- " \n",
- " \n",
- " label_mapping = {old_label: new_label for new_label, old_label in enumerate(sufficient_regions)}\n",
- " filtered_df[target_col] = filtered_df[target_col].map(label_mapping)\n",
- " \n",
- " \n",
- " region_stats = {\n",
- " 'original_classes': len(class_counts),\n",
- " 'filtered_classes': len(sufficient_regions),\n",
- " 'original_samples': len(df),\n",
- " 'filtered_samples': len(filtered_df),\n",
- " 'removed_samples': len(df) - len(filtered_df),\n",
- " 'min_subjects_threshold': min_subjects,\n",
- " 'sufficient_regions': sufficient_regions,\n",
- " 'insufficient_regions': insufficient_regions,\n",
- " 'label_mapping': label_mapping\n",
- " }\n",
- " \n",
- " print(f\"\\nFiltering results:\")\n",
- " print(f\"Original dataset: {region_stats['original_samples']} samples, {region_stats['original_classes']} classes\")\n",
- " print(f\"Filtered dataset: {region_stats['filtered_samples']} samples, {region_stats['filtered_classes']} classes\")\n",
- " print(f\"Removed samples: {region_stats['removed_samples']} ({region_stats['removed_samples']/region_stats['original_samples']*100:.1f}%)\")\n",
- " \n",
- " return filtered_df, region_stats\n",
- "\n",
- "def preprocess_data_with_threshold(df, min_subjects=10):\n",
- " \n",
- " \n",
- " filtered_df, region_stats = filter_regions_by_subject_count(df, min_subjects)\n",
- " \n",
- " \n",
- " target_candidates = [col for col in filtered_df.columns if 'epilep' in col.lower() or 'target' in col.lower() or 'label' in col.lower()]\n",
- " \n",
- " if not target_candidates:\n",
- " target_col = filtered_df.columns[-1]\n",
- " else:\n",
- " target_col = target_candidates[0]\n",
- " \n",
- " \n",
- " X = filtered_df.drop(columns=[target_col])\n",
- " y = filtered_df[target_col]\n",
- " \n",
- " \n",
- " if y.dtype == 'object':\n",
- " le = LabelEncoder()\n",
- " y = le.fit_transform(y)\n",
- " print(f\"Target classes: {le.classes_}\")\n",
- " \n",
- " \n",
- " numeric_cols = X.select_dtypes(include=[np.number]).columns\n",
- " X = X[numeric_cols]\n",
- " \n",
- " print(f\"\\nFinal processed data:\")\n",
- " print(f\"Features shape: {X.shape}\")\n",
- " print(f\"Target distribution:\")\n",
- " value_counts = pd.Series(y).value_counts().sort_index()\n",
- " print(value_counts)\n",
- " \n",
- " \n",
- " min_samples_per_class = value_counts.min()\n",
- " print(f\"Minimum samples per class: {min_samples_per_class}\")\n",
- " \n",
- " if min_samples_per_class < min_subjects:\n",
- " print(f\"Warning: Some classes still have fewer than {min_subjects} subjects after filtering!\")\n",
- " \n",
- " return X, y, target_col, region_stats\n",
- "\n",
- "def feature_engineering(X, y):\n",
- " \n",
- " \n",
- " X = X.fillna(X.median())\n",
- " \n",
- " \n",
- " constant_features = X.columns[X.var() == 0]\n",
- " if len(constant_features) > 0:\n",
- " print(f\"Removing {len(constant_features)} constant features\")\n",
- " X = X.drop(columns=constant_features)\n",
- " \n",
- " \n",
- " selector = SelectKBest(score_func=f_classif, k=min(50, X.shape[1]))\n",
- " X_selected = selector.fit_transform(X, y)\n",
- " selected_features = X.columns[selector.get_support()]\n",
- " \n",
- " print(f\"Selected {len(selected_features)} features out of {X.shape[1]}\")\n",
- " \n",
- " return pd.DataFrame(X_selected, columns=selected_features), selected_features\n",
- "\n",
- "def train_models(X, y):\n",
- " \n",
- " class_counts = pd.Series(y).value_counts()\n",
- " min_class_count = class_counts.min()\n",
- " \n",
- " print(f\"Minimum class count: {min_class_count}\")\n",
- " \n",
- " if min_class_count < 2:\n",
- " print(f\"Warning: Some classes have only {min_class_count} sample(s). Using random split instead of stratified split.\")\n",
- " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
- " else:\n",
- " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
- " \n",
- " \n",
- " scaler = StandardScaler()\n",
- " X_train_scaled = scaler.fit_transform(X_train)\n",
- " X_test_scaled = scaler.transform(X_test)\n",
- " \n",
- " \n",
- " models = {\n",
- " 'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced'),\n",
- " 'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
- " 'Logistic Regression': LogisticRegression(random_state=42, max_iter=1000, multi_class='ovr', class_weight='balanced'),\n",
- " 'SVM': SVC(random_state=42, probability=True, class_weight='balanced')\n",
- " }\n",
- " \n",
- " results = {}\n",
- " \n",
- " print(\"\\nTraining models on filtered data...\")\n",
- " for name, model in models.items():\n",
- " print(f\"\\nTraining {name}...\")\n",
- " \n",
- " \n",
- " if name in ['Logistic Regression', 'SVM']:\n",
- " X_train_model = X_train_scaled\n",
- " X_test_model = X_test_scaled\n",
- " else:\n",
- " X_train_model = X_train\n",
- " X_test_model = X_test\n",
- " \n",
- " \n",
- " model.fit(X_train_model, y_train)\n",
- " \n",
- " \n",
- " y_pred = model.predict(X_test_model)\n",
- " y_pred_proba = model.predict_proba(X_test_model)\n",
- " \n",
- " \n",
- " cv_scores = cross_val_score(model, X_train_model, y_train, cv=3, scoring='balanced_accuracy')\n",
- " test_accuracy = accuracy_score(y_test, y_pred)\n",
- " balanced_test_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
- " \n",
- " results[name] = {\n",
- " 'model': model,\n",
- " 'cv_balanced_accuracy': cv_scores.mean(),\n",
- " 'cv_std': cv_scores.std(),\n",
- " 'test_accuracy': test_accuracy,\n",
- " 'balanced_test_accuracy': balanced_test_accuracy,\n",
- " 'y_pred': y_pred,\n",
- " 'y_pred_proba': y_pred_proba,\n",
- " 'scaler': scaler if name in ['Logistic Regression', 'SVM'] else None\n",
- " }\n",
- " \n",
- " print(f\"CV Balanced Accuracy: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})\")\n",
- " print(f\"Test Accuracy: {test_accuracy:.3f}\")\n",
- " print(f\"Balanced Test Accuracy: {balanced_test_accuracy:.3f}\")\n",
- " \n",
- " return results, X_test, y_test\n",
- "\n",
- "def evaluate_models(results, X_test, y_test, region_stats):\n",
- " \"\"\"Evaluate and visualize model performance\"\"\"\n",
- " print(\"\\n\" + \"=\"*50)\n",
- " print(\"MODEL COMPARISON (FILTERED DATA)\")\n",
- " print(\"=\"*50)\n",
- " \n",
- " \n",
- " comparison_df = pd.DataFrame({\n",
- " 'Model': list(results.keys()),\n",
- " 'CV Balanced Accuracy': [results[model]['cv_balanced_accuracy'] for model in results],\n",
- " 'Test Accuracy': [results[model]['test_accuracy'] for model in results],\n",
- " 'Balanced Test Accuracy': [results[model]['balanced_test_accuracy'] for model in results]\n",
- " })\n",
- " \n",
- " print(comparison_df.round(3))\n",
- " \n",
- " \n",
- " best_model_name = comparison_df.loc[comparison_df['Balanced Test Accuracy'].idxmax(), 'Model']\n",
- " best_model_results = results[best_model_name]\n",
- " \n",
- " print(f\"\\nBest model: {best_model_name}\")\n",
- " print(f\"Best Balanced Test Accuracy: {best_model_results['balanced_test_accuracy']:.3f}\")\n",
- " print(f\"Regular Test Accuracy: {best_model_results['test_accuracy']:.3f}\")\n",
- " \n",
- " \n",
- " print(f\"\\nDetailed evaluation for {best_model_name}:\")\n",
- " print(\"\\nClassification Report:\")\n",
- " print(classification_report(y_test, best_model_results['y_pred']))\n",
- " \n",
- " \n",
- " \n",
- " return best_model_name, best_model_results, comparison_df\n",
- "\n",
- "def plot_roc_curves(results, X_test, y_test, region_stats):\n",
- " \n",
- " n_classes = len(np.unique(y_test))\n",
- " \n",
- " \n",
- " \n",
- " colors = cycle(['aqua', 'darkorange', 'cornflowerblue', 'red', 'green', 'purple'])\n",
- " \n",
- " \n",
- " if n_classes == 2:\n",
- " \n",
- " fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
- " ax_binary = axes[0]\n",
- " ax_comparison = axes[1]\n",
- " else:\n",
- " \n",
- " fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
- " ax_binary = axes[0, 0]\n",
- " ax_micro = axes[0, 1]\n",
- " ax_macro = axes[1, 0]\n",
- " ax_comparison = axes[1, 1]\n",
- " \n",
- " \n",
- " model_auc_scores = {}\n",
- " \n",
- " \n",
- " \n",
- " y_test_binary = (y_test == 0).astype(int)\n",
- " \n",
- " for (model_name, model_results), color in zip(results.items(), colors):\n",
- " \n",
- " y_pred_proba = model_results['y_pred_proba']\n",
- " \n",
- " \n",
- " if y_pred_proba.shape[1] > 2:\n",
- " \n",
- " y_score_binary = y_pred_proba[:, 0] \n",
- " else:\n",
- " \n",
- " y_score_binary = y_pred_proba[:, 1] \n",
- " y_test_binary = 1 - y_test_binary \n",
- " \n",
- " \n",
- " fpr, tpr, _ = roc_curve(y_test_binary, y_score_binary)\n",
- " roc_auc = auc(fpr, tpr)\n",
- " \n",
- " \n",
- " model_auc_scores[model_name] = {'binary_auc': roc_auc}\n",
- " \n",
- " \n",
- " ax_binary.plot(fpr, tpr, color=color, lw=2,\n",
- " label=f'{model_name} (AUC = {roc_auc:.3f})')\n",
- " \n",
- " \n",
- " ax_binary.plot([0, 1], [0, 1], 'k--', lw=2, label='Random (AUC = 0.500)')\n",
- " ax_binary.set_xlim([0.0, 1.0])\n",
- " ax_binary.set_ylim([0.0, 1.05])\n",
- " ax_binary.set_xlabel('False Positive Rate')\n",
- " ax_binary.set_ylabel('True Positive Rate')\n",
- " ax_binary.set_title('Binary ROC Curve\\n(Not Epileptic vs Epileptic)')\n",
- " ax_binary.legend(loc=\"lower right\")\n",
- " ax_binary.grid(True, alpha=0.3)\n",
- " \n",
- " \n",
- " if n_classes > 2:\n",
- " \n",
- " \n",
- " \n",
- " y_test_binarized = label_binarize(y_test, classes=range(n_classes))\n",
- " \n",
- " for model_name, model_results in results.items():\n",
- " y_pred_proba = model_results['y_pred_proba']\n",
- " \n",
- " \n",
- " fpr_micro, tpr_micro, _ = roc_curve(y_test_binarized.ravel(), y_pred_proba.ravel())\n",
- " roc_auc_micro = auc(fpr_micro, tpr_micro)\n",
- " \n",
- " \n",
- " fpr_macro = dict()\n",
- " tpr_macro = dict()\n",
- " roc_auc_macro = dict()\n",
- " \n",
- " for i in range(n_classes):\n",
- " if i < y_pred_proba.shape[1]: \n",
- " fpr_macro[i], tpr_macro[i], _ = roc_curve(y_test_binarized[:, i], y_pred_proba[:, i])\n",
- " roc_auc_macro[i] = auc(fpr_macro[i], tpr_macro[i])\n",
- " \n",
- " \n",
- " all_fpr = np.unique(np.concatenate([fpr_macro[i] for i in roc_auc_macro.keys()]))\n",
- " mean_tpr = np.zeros_like(all_fpr)\n",
- " for i in roc_auc_macro.keys():\n",
- " mean_tpr += np.interp(all_fpr, fpr_macro[i], tpr_macro[i])\n",
- " mean_tpr /= len(roc_auc_macro)\n",
- " roc_auc_macro_avg = auc(all_fpr, mean_tpr)\n",
- " \n",
- " model_auc_scores[model_name].update({\n",
- " 'micro_auc': roc_auc_micro,\n",
- " 'macro_auc': roc_auc_macro_avg\n",
- " })\n",
- " \n",
- " \n",
- " colors_micro = cycle(['aqua', 'darkorange', 'cornflowerblue', 'red'])\n",
- " for (model_name, model_results), color in zip(results.items(), colors_micro):\n",
- " y_pred_proba = model_results['y_pred_proba']\n",
- " fpr_micro, tpr_micro, _ = roc_curve(y_test_binarized.ravel(), y_pred_proba.ravel())\n",
- " roc_auc_micro = auc(fpr_micro, tpr_micro)\n",
- " \n",
- " ax_micro.plot(fpr_micro, tpr_micro, color=color, lw=2,\n",
- " label=f'{model_name} (AUC = {roc_auc_micro:.3f})')\n",
- " \n",
- " ax_micro.plot([0, 1], [0, 1], 'k--', lw=2, label='Random (AUC = 0.500)')\n",
- " ax_micro.set_xlim([0.0, 1.0])\n",
- " ax_micro.set_ylim([0.0, 1.05])\n",
- " ax_micro.set_xlabel('False Positive Rate')\n",
- " ax_micro.set_ylabel('True Positive Rate')\n",
- " ax_micro.set_title('Micro-average ROC Curve\\n(All Classes)')\n",
- " ax_micro.legend(loc=\"lower right\")\n",
- " ax_micro.grid(True, alpha=0.3)\n",
- "\n",
- " colors_macro = cycle(['aqua', 'darkorange', 'cornflowerblue', 'red'])\n",
- " for (model_name, model_results), color in zip(results.items(), colors_macro):\n",
- " auc_score = model_auc_scores[model_name]['macro_auc']\n",
- " \n",
- " \n",
- " y_pred_proba = model_results['y_pred_proba']\n",
- " fpr_macro = dict()\n",
- " tpr_macro = dict()\n",
- " \n",
- " for i in range(min(n_classes, y_pred_proba.shape[1])):\n",
- " fpr_macro[i], tpr_macro[i], _ = roc_curve(y_test_binarized[:, i], y_pred_proba[:, i])\n",
- " \n",
- " all_fpr = np.unique(np.concatenate([fpr_macro[i] for i in fpr_macro.keys()]))\n",
- " mean_tpr = np.zeros_like(all_fpr)\n",
- " for i in fpr_macro.keys():\n",
- " mean_tpr += np.interp(all_fpr, fpr_macro[i], tpr_macro[i])\n",
- " mean_tpr /= len(fpr_macro)\n",
- " \n",
- " ax_macro.plot(all_fpr, mean_tpr, color=color, lw=2,\n",
- " label=f'{model_name} (AUC = {auc_score:.3f})')\n",
- " \n",
- " ax_macro.plot([0, 1], [0, 1], 'k--', lw=2, label='Random (AUC = 0.500)')\n",
- " ax_macro.set_xlim([0.0, 1.0])\n",
- " ax_macro.set_ylim([0.0, 1.05])\n",
- " ax_macro.set_xlabel('False Positive Rate')\n",
- " ax_macro.set_ylabel('True Positive Rate')\n",
- " ax_macro.set_title('Macro-average ROC Curve\\n(Average Across Classes)')\n",
- " ax_macro.legend(loc=\"lower right\")\n",
- " ax_macro.grid(True, alpha=0.3)\n",
- " \n",
- "\n",
- " models = list(model_auc_scores.keys())\n",
- " binary_aucs = [model_auc_scores[model]['binary_auc'] for model in models]\n",
- " \n",
- " x_pos = np.arange(len(models))\n",
- " bars = ax_comparison.bar(x_pos, binary_aucs, alpha=0.8, color=['aqua', 'darkorange', 'cornflowerblue', 'red'][:len(models)])\n",
- "\n",
- " for i, (bar, auc_val) in enumerate(zip(bars, binary_aucs)):\n",
- " height = bar.get_height()\n",
- " ax_comparison.text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
- " f'{auc_val:.3f}', ha='center', va='bottom', fontweight='bold')\n",
- " \n",
- " ax_comparison.set_xlabel('Models')\n",
- " ax_comparison.set_ylabel('AUC Score')\n",
- " ax_comparison.set_title('Binary Classification AUC Comparison\\n(Not Epileptic vs Epileptic)')\n",
- " ax_comparison.set_xticks(x_pos)\n",
- " ax_comparison.set_xticklabels(models, rotation=45)\n",
- " ax_comparison.set_ylim([0, 1.1])\n",
- " ax_comparison.grid(True, alpha=0.3, axis='y')\n",
- " \n",
- " \n",
- " ax_comparison.axhline(y=0.5, color='red', linestyle='--', alpha=0.7, label='Random')\n",
- " ax_comparison.legend()\n",
- " \n",
- " plt.suptitle(f'ROC Analysis - {region_stats[\"filtered_classes\"]} Regions with ≥{region_stats[\"min_subjects_threshold\"]} Subjects', \n",
- " fontsize=14, fontweight='bold')\n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- " \n",
- " \n",
- " print(\"\\n\" + \"=\"*50)\n",
- " print(\"AUC SCORES SUMMARY\")\n",
- " print(\"=\"*50)\n",
- " \n",
- " auc_summary_df = pd.DataFrame({\n",
- " 'Model': models,\n",
- " 'Binary AUC': binary_aucs\n",
- " })\n",
- " \n",
- " if n_classes > 2:\n",
- " micro_aucs = [model_auc_scores[model]['micro_auc'] for model in models]\n",
- " macro_aucs = [model_auc_scores[model]['macro_auc'] for model in models]\n",
- " auc_summary_df['Micro AUC'] = micro_aucs\n",
- " auc_summary_df['Macro AUC'] = macro_aucs\n",
- " \n",
- " print(auc_summary_df.round(3))\n",
- " \n",
- "\n",
- " best_binary_model = models[np.argmax(binary_aucs)]\n",
- " print(f\"\\nBest Binary AUC: {best_binary_model} ({max(binary_aucs):.3f})\")\n",
- " \n",
- " if n_classes > 2:\n",
- " best_micro_model = models[np.argmax(micro_aucs)]\n",
- " best_macro_model = models[np.argmax(macro_aucs)]\n",
- " print(f\"Best Micro AUC: {best_micro_model} ({max(micro_aucs):.3f})\")\n",
- " print(f\"Best Macro AUC: {best_macro_model} ({max(macro_aucs):.3f})\")\n",
- " \n",
- " return model_auc_scores, auc_summary_df\n",
- "\n",
- "def plot_detailed_roc_analysis(results, X_test, y_test, region_stats, top_classes=5):\n",
- " \n",
- " \n",
- " n_classes = len(np.unique(y_test))\n",
- " \n",
- " if n_classes <= 2:\n",
- " print(\"Detailed ROC analysis is for multiclass problems (>2 classes)\")\n",
- " return\n",
- " \n",
- " print(f\"\\nGenerating detailed ROC analysis for top {top_classes} classes...\")\n",
- " \n",
- "\n",
- " class_counts = pd.Series(y_test).value_counts().sort_values(ascending=False)\n",
- " top_class_indices = class_counts.head(top_classes).index.tolist()\n",
- " \n",
- "\n",
- " fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
- " axes = axes.flatten()\n",
- "\n",
- " y_test_binarized = label_binarize(y_test, classes=range(n_classes))\n",
- "\n",
- " colors = ['aqua', 'darkorange', 'cornflowerblue', 'red']\n",
- "\n",
- " for idx, class_i in enumerate(top_class_indices):\n",
- " if idx >= 6: \n",
- " break\n",
- " \n",
- " ax = axes[idx]\n",
- " \n",
- " for (model_name, model_results), color in zip(results.items(), colors):\n",
- " y_pred_proba = model_results['y_pred_proba']\n",
- " \n",
- " if class_i < y_pred_proba.shape[1]:\n",
- " \n",
- " fpr, tpr, _ = roc_curve(y_test_binarized[:, class_i], y_pred_proba[:, class_i])\n",
- " roc_auc = auc(fpr, tpr)\n",
- " \n",
- " ax.plot(fpr, tpr, color=color, lw=2,\n",
- " label=f'{model_name} (AUC = {roc_auc:.3f})')\n",
- " \n",
- " ax.plot([0, 1], [0, 1], 'k--', lw=1, alpha=0.8)\n",
- " ax.set_xlim([0.0, 1.0])\n",
- " ax.set_ylim([0.0, 1.05])\n",
- " ax.set_xlabel('False Positive Rate')\n",
- " ax.set_ylabel('True Positive Rate')\n",
- "\n",
- " original_class = region_stats['sufficient_regions'][class_i] if class_i < len(region_stats['sufficient_regions']) else class_i\n",
- " sample_count = class_counts[class_i]\n",
- " ax.set_title(f'Class {class_i} (Orig: {original_class})\\n{sample_count} samples')\n",
- " ax.legend(loc=\"lower right\", fontsize=8)\n",
- " ax.grid(True, alpha=0.3)\n",
- " \n",
- "\n",
- " for idx in range(len(top_class_indices), 6):\n",
- " axes[idx].set_visible(False)\n",
- " \n",
- " plt.suptitle('Per-Class ROC Curves (Top Classes by Sample Count)', fontsize=14, fontweight='bold')\n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- " \n",
- "\n",
- " auc_matrix = []\n",
- " model_names = list(results.keys())\n",
- " \n",
- " for model_name in model_names:\n",
- " model_aucs = []\n",
- " y_pred_proba = results[model_name]['y_pred_proba']\n",
- " \n",
- " for class_i in range(min(n_classes, y_pred_proba.shape[1])):\n",
- " if class_i < y_test_binarized.shape[1]:\n",
- " fpr, tpr, _ = roc_curve(y_test_binarized[:, class_i], y_pred_proba[:, class_i])\n",
- " roc_auc = auc(fpr, tpr)\n",
- " model_aucs.append(roc_auc)\n",
- " else:\n",
- " model_aucs.append(0.0)\n",
- " \n",
- " auc_matrix.append(model_aucs)\n",
- " \n",
- "\n",
- " fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
- " \n",
- "\n",
- " im = ax.imshow(auc_matrix, cmap='RdYlBu_r', aspect='auto', vmin=0, vmax=1)\n",
- " \n",
- "\n",
- " ax.set_xticks(range(len(auc_matrix[0])))\n",
- " ax.set_yticks(range(len(model_names)))\n",
- " ax.set_xticklabels([f'Class {i}' for i in range(len(auc_matrix[0]))])\n",
- " ax.set_yticklabels(model_names)\n",
- " \n",
- " \n",
- " cbar = plt.colorbar(im, ax=ax)\n",
- " cbar.set_label('AUC Score', rotation=270, labelpad=20)\n",
- " \n",
- " for i in range(len(model_names)):\n",
- " for j in range(len(auc_matrix[0])):\n",
- " text = ax.text(j, i, f'{auc_matrix[i][j]:.2f}',\n",
- " ha=\"center\", va=\"center\", color=\"black\" if auc_matrix[i][j] > 0.5 else \"white\")\n",
- " \n",
- " ax.set_title('AUC Scores Heatmap by Model and Class')\n",
- " ax.set_xlabel('Classes')\n",
- " ax.set_ylabel('Models')\n",
- " \n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- " \n",
- " return auc_matrix\n",
- "\n",
- "def main(min_subjects=10):\n",
- " \n",
- " \n",
- " \n",
- "\n",
- " df_original = pd.read_csv(r'.\\lh_updated.csv')\n",
- " df_original = df_original.drop('Subject', axis=1)\n",
- "\n",
- " target_candidates = [col for col in df_original.columns if 'epilep' in col.lower() or 'target' in col.lower() or 'label' in col.lower()]\n",
- " target_col_original = target_candidates[0] if target_candidates else df_original.columns[-1]\n",
- " y_original = df_original[target_col_original]\n",
- "\n",
- " X, y, target_col, region_stats = preprocess_data_with_threshold(df_original, min_subjects)\n",
- " \n",
- " if X is not None and len(X) > 0:\n",
- " \n",
- " X, selected_features = feature_engineering(X, y)\n",
- " \n",
- " \n",
- " results, X_test, y_test = train_models(X, y)\n",
- " \n",
- " \n",
- " best_model_name, best_model_results, comparison_df = evaluate_models(results, X_test, y_test, region_stats)\n",
- " \n",
- " \n",
- " model_auc_scores, auc_summary_df = plot_roc_curves(results, X_test, y_test, region_stats)\n",
- " \n",
- " \n",
- " n_classes = len(np.unique(y))\n",
- " \n",
- " plot_detailed_roc_analysis(results, X_test, y_test, region_stats, top_classes=5)\n",
- " \n",
- " \n",
- " plot_filtering_results(region_stats, y_original, y)\n",
- " \n",
- " print(f\"\\nFinal Results:\")\n",
- " print(f\"Best performing model: {best_model_name}\")\n",
- " print(f\"Performance on {region_stats['filtered_classes']} regions with ≥{min_subjects} subjects\")\n",
- " print(f\"Balanced Test Accuracy: {best_model_results['balanced_test_accuracy']:.3f}\")\n",
- " print(f\"Binary AUC Score: {model_auc_scores[best_model_name]['binary_auc']:.3f}\")\n",
- " \n",
- " return best_model_name, best_model_results, region_stats, model_auc_scores\n",
- " else:\n",
- " print(\"No data remaining after filtering. Consider lowering the threshold.\")\n",
- " return None, None, None\n",
- "\n",
- "if __name__ == \"__main__\":\n",
- " \n",
- " MIN_SUBJECTS_THRESHOLD = 10 \n",
- " \n",
- " best_model, results, stats, auc_scores = main(min_subjects=MIN_SUBJECTS_THRESHOLD)\n",
- " \n",
- " \n",
- " \n",
- " thresholds = [5, 10, 15]\n",
- " threshold_results = []\n",
- " \n",
- " for threshold in thresholds:\n",
- " print(f\"\\nTesting threshold: {threshold} subjects\")\n",
- " try:\n",
- " _, _, stats, _ = main(min_subjects=threshold)\n",
- " if stats:\n",
- " threshold_results.append({\n",
- " 'threshold': threshold,\n",
- " 'classes_retained': stats['filtered_classes'],\n",
- " 'samples_retained': stats['filtered_samples'],\n",
- " 'retention_rate': stats['filtered_samples']/stats['original_samples']*100\n",
- " })\n",
- " except Exception as e:\n",
- " print(f\"Error with threshold {threshold}: {e}\")\n",
- " \n",
- " if threshold_results:\n",
- " threshold_df = pd.DataFrame(threshold_results)\n",
- " print(\"\\nThreshold Comparison Summary:\")\n",
- " print(threshold_df.round(1))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "base",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Visualization of functional connectivity from multiple neuroimaging modalities"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Jonathan Gallego]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/jogaru1818_BHS
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [meg, fmri, connectivity, jupyter-notebooks]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "In this project I employed some of the tools we learned at the Brainhack school to generate interactive figures to display functional connectivity from MEG and fMRI resting state data from the Human Connectome Project."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "bhs2020.png"
----
-
-
-- Hi!, I'm a first year PhD student at McGill. My BHS project aimed to use some of the visualization tools we have learned to display functional connectivity results from both MEG and fMRI data.
-- I used data from 12 subjects from the Human Connectome Project (https://www.humanconnectome.org).
-- I would love to hear from other people projects and see what they have achieved so far!
-
-Twitter: @jogaru1818
-
-### What I wanted to learn
-
-- To manage and analyze data in Python (since I have always worked on Matlab)
-- To use some specific tools for neuroimaging analysis (such as nilearn)
-- Try different visualization tools for displaying functional connectivity (that could be applicable to different neuroimaging modalities)
-- To "Live the experience" of collaborating with others through GitHub
-
-
-## Project definition
-
-### Background
-
-- Although MEG and fMRI are very different signals, they both reflect some aspects of neuronal activity
-- Functional connectivity assess the statistical dependence between the activity (time-series) of different brain regions
-- After preprocessing the data individually, we can have both modalities in the same coordinate space, parcellate the brain according to a common atlas and extract the MEG and fMRI time-series of each brain region.
-- We then could then compute connectivity measures and visualize the results from both modalities in parallel!
-
-### Tools I employed
-
-- Human Connectomme Dataset: As the Open data repository I used for my BHS project
-- Git: to keep version control of my files
-- GitHub: To create a repository compiling all the resources employed for this project and make it reproducible.
-- Python: As the programming language I used to manipulate manipulate the data.
-- Jupyter notebook: As a resource to organize and document the code (with instructions and outputs) I used my project.
-- Nilearn tools: As a tool for calculating and displaying functional connectivity from neuroimaging data
-
-- Brainstorm (for MEG processing). Works on Matlab.
-
-### Data specifications
-
-For this project I used data from 12 subjects from the Human Connectome Project, including:
-
-- Preprocessed high resolution anatomical MR scan
-- Preprocessed fMRI resting state data (sesion 1)
-- Unprocessed MEG resting state data
-- Unprocessed MEG noise recordings
-- Anatomical info for MEG registration.
-
-These data can be easily downloaded after creating an account and accessing the Human Connectome Project database (https://db.humanconnectome.org/).
-The HCP_data_dowload.md file contains a brief set of instructions of how to get the specific data used for this project
-
-### Deliverables
-
-A GitHub repository containing a small set of md files and jupyter notebooks documenting all the requirements, instructions and code needed to reproduce this project.
-
-### Progress overview and results
-
-1 The first step was to install all the required software and libraries I needed for my project, and preparing the work environment. Detailed instructions on the requirements you need to fulfil to run the code and replicate these analysis are presented in the requirements.md file
-
-2 After installing all the required dependencies and downloading the HCP data I started working on the MEG data. Since the already processed source space MEG data available in the HCP database cannot be manipulated in many ways, I decided to download the raw data and preprocess it myself. I was very interested in learning to perform all these steps using some of the new tools we explored, however I knew this would not be feasible given the limited time we had for our project, so I performed the preprocessing using a software I am already familiar with (Brainstorm). Once I preprocessed the MEG data and performed a source estimation analysis, I extracted the timeseries of the 148 regions from the Destrieux atlas (2009) for each subject, concatenated them and exported them into a text file. The order of the labels of the scouts was consisted with that used for the fMRI analysis. The detailed step by step description of the MEG processing is contained in the MEG_processing.md file.
-
-3 For the fMRI data, I opted to use the preprocessed images. I employed functions from nilearn to load the brain parcellations of the Destrieux atlas and the preprocessed fMRI data. As the HCP resting state fMRI consist on 1200 3d brain volumes, loading all the dataset at the same time may cause memory saturation on a regular machine. Therefore, I segmented the nifti files into smaller chunks of data, and created a python code to load each chunk at the time, extract the fMRI timeseries of each scout and concatenated the data for each subject and then from all subjects, and save it into a single matrix. The code used to extract the time series from the fMRI data is contained in the fMRI_timeseries.ipynb jupyter notebook.
-
-4 After having extracted the MEG and fMRI timeseries from the scouts of the atlas I ellaborated a simple code to create a couple of interactive figures to display the functional connectivity matrices and graphs I obtained from the data. These interactive figures allow to easily navigate through the data from all subjects and switch between modalities. The final output of steps 2 and 3 is stored in the all_subjects_time_series_meg.txt and all_subjects_time_series_fmri.txt files, which contain the extracted timeseries of all subjects, for each modality. This last step loads these files, and use a combination of nilearn tools and ipywidgets to create and display an interactive figure within a jupyter notebook.
-
-An example of the fMRI connectivity matrix of Subject 1
-
-
-An example of the MEG delta band connectivity matrix of Subject 1
-
-
-## Conclusion and acknowledgement
-
-During the lasts weeks I spent a lot of time getting familiar with these new tools. I knew I would face many problems and that most of the time I would spend looking at documentation files or forums trying to debug things. However, in the end I feel happy with the final result, given that I learned a lot of new cool things and that this represent a first step into adopting these practices on my daily work. I finally took the time to learn the basics from Python and now I will be able to use the rich variety of libraries they offer for analyzing and visualizing data. I also plan to adopt the use of virtual environments and containers for future projects. I also learned to document my work using git and to collaborate with other people through GitHub, which are both valuable resources to promote scietific reproducibility. Overall, I believe this was a great an useful experience for me, and I will definitively start using some of these tools to improve my practices as a student an future researcher
-
-I would like to acknowledge all the BHS organizers for investing their time and effort for developing this magnificent course! I would also like to thank to my clinic instructors for assessing me during my project (Valentina, Tristan and Pierre) and to all my peers from this BHS edition for sharing their comments and ideas throughout these weeks
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+++ /dev/null
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-10" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Predicting Neuroticism and Personality Traits from fMRI Data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Annabelle Harvey, Liz Izakson]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, machine learning, personality]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Are neuropsychiatric disorders extreme cases of connectivity patterns that are found in the overall population? Using personality traits as a measure of individual variation and knowing that neuroticism is especially linked with mental disorders we wanted to see if neuroticism in a healthy population was linked with specific patterns of connectivity that could be compared to those common to neuropsychiatric disorders."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Connectome_quanta_mag.png"
----
-
-# Predicting Neuroticism and Personality Traits from fMRI Data
-
-
-JR Bee Illustration
-# Project Definition - Week 1
-### Background
-#### Annabelle
-I am a currently doing my master's in computer science at UdeM in the bioinformatics stream. I haven't locked down my thesis project yet but it will involve the impact of rare genetic variants on functional connectivity. I did my undergrad in math and have a pretty good basis in coding in python and machine learning, but I am totally new to neuroscience and very excited about brainhack school.
-
-#### Project
-I worked on this project with Liz Izakson. We're interested in variation in human brains and am curious about neuropsychiatric disorders as extremes of characterestics that are found in the overall population. To explore this idea, we want to look at the human connectome project dataset of 1200 healthy young adults that includes resting state fMRI and personality/behaviour data. Specifically, we want to try and predict neuroticism, one of the big five personality traits that is associated with [psychosis](https://www.frontiersin.org/articles/10.3389/fpsyt.2018.00648/full) and [mental disorders](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382368/), from fMRI to see if:
-1. If there are significant/consistent connnectivity patterns for neuroticism in fMRI data.
-2. If so, how do they compare to findings about schizophrenia/anxiety/depression/ADHD in the literature.
-
-### Tools
-We plan on using the following tools:
- * compute canada servers with globus and aws s3 for file access and transfer
- * nilearn
- * similarity network fusion
- * sklearn
- * Data visualization
-
-## Learning Goals
-I especially want to use brainhack school to learn to work with fMRI data, explore different parcellation methods and ways to produce features from connectivity matrices. I also want to learn to use compute canada servers, and to improve and explore my coding skills and to learn tools for open science and reproducibility.
-
-### Data
-We will use the HCP 1200 healthy young adult dataset.
-
-### Deliverables
-I would like to produce jupyter notebooks with examples of the analysis code, an interactive data visualization, and a script for running jobs on compute canada servers.
-
-# Results - Week 4
-
-### Progress overview
-
-We started out working with preprocessed data from the HCP,and explored different ways of using parcellations and connectivity measured to produce connectivity matrices. A lot of time went into learning how to download and use data in this format, but the data is very large and was very challenging to work with. Through reading papers (particularly [Dadi et al. 2019](https://www.sciencedirect.com/science/article/pii/S1053811919301594), and [Cai et al. 2020](https://academic.oup.com/scan/article/15/3/359/5815970)) we eventually discovered that HCP data is much larger than average per subject (about an hour of scan each) and that they have published an even further processed data set of parcellations, time series, and connectivity matrices (PTN release). We decided to switch to this data set which allowed us to work with 810 subjects, which would have been impossible otherwise.
-
-With the PTN data we focused on predicting Neuroticism from the processed connectivity matrices. Using a feature selection strategy from [connectome-based predictive modeling](https://pubmed.ncbi.nlm.nih.gov/28182017/) (CPM) that is simply choosing the edges in the connectivity matrices most correlated with the score being predicted, I tried CPM, SVR, and linear regression with leave one out cross validation. I found that prediction was very weak and that we were very far from being able to point to any specific regions or networks that are meaningful for any specific trait. Liz tried to improve on the prediction by using neural nets and tweaking parameters.
-
-To try different models, I rewrote jupyter notebooks that I used to write and test code as python scripts that could be used more efficiently and that I could run on compute canada. I also produced documentation and data visualization to go along with the notebooks and scripts. Liz contributed exploratory data analysis and several models to the ptn_pipeline.ipynb notebook. In the end we found that neuroticism and personality traits are very hard to predict from fMRI data and that one month is not very long to explore and compare the many many different options for processing fMRI data that might have better prediction results. The slides for our final presentation can be found [here](https://docs.google.com/presentation/d/1qsFBFawu3MmrGuhGlogRqCZJWTqOs_ZLAXk5uCATcJQ/edit?usp=sharing).
-
-### Tools I learned during this project
-
- * Nilearn
- * Compute canada
- * Slurm
- * ssh/scp
- * venv
- * Git/github
- * Jupyter lab
- * Scipy
- * Bash
- * t-SNE
- * Monitoring memory/CPU usage and strategies for working with fMRI data on a very weak laptop
-
-
-### Results
-Here is a summary of predictions of neuroticism using the three models and their respective mean squared error (MSE) values:
-
-
-Here is an image of the networks that are correlated with neuroticism (note that the predictions are poor and the parcellation done by using group ICA and 200 nodes has no labels so these are more fun visualizations than anything meaningful):
-
-
-
-#### Deliverable 1: Jupyter notebooks
-
- * [ptn_pipeline.ipynb](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/ptn_pipeline.ipynb) - explores the models and code in ptn_script.py.
- * [data_viz.ipynb](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/data_viz.ipynb) - uses the outputs from the python scripts to create figures with seaborn and nilearn plotting, the interactive nilearn plots are saved as .html files in the figures folder of this repo.
-
-#### Deliverable 2: Python scripts
-
-* [ptn_script.py](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/ptn_script.py) - code to run the models and save the output files.
-* [clean_data.py](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/clean_data.py) - code to remove the subjects with no behavioural data from HCP PTN recon2 download (see documentation/how_to.md)
-
-#### Deliverable 3: Bash script
-
-* [run_script_locally.sh](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/run_script_locally.sh) - executable to run script for all model comparisons we did on a local machine.
-
-#### Deliverable 4: Data visualization
-
-* [figures/positive_connections.html](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/figures/positive_connections.html) - interactive version nilearn plot of connections positively correlated with neuroticism.
-* [figures/negative_connections.html](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/figures/negative_connections.html) - the same for connections negatively correlated with neuroticism.
-* [figures](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/tree/master/figures) - other contents are scatter plots of real vs. predicted values for each trait (N, E, O, A, C) and each model (LR, SVR, CPM) as well as summaries of all three models for each trait.
-
-#### Deliverable 5: Documentation
-
-* [how_to.md](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/documentation/how_to.md) that describes:
- * downloading the data from HCP
- * running the code
-* List of packages used - [requirements.txt](https://github.com/brainhack-school2020/harveyaa_fMRI_neuroticism/blob/master/requirements.txt)
-
-## Thank you!!
-Special thanks to Pierre Bellec, Desiree Lussier, Peer Herholz, Liz Izakson and everyone I got to chat with through clinics and groups! Brainhack school has been an amazing introduction to neuroimaging, I'm very grateful for everyone's time, help, resources and encouragement!
-
-
-### References
-Cai et al. 2020 Robust Prediction of Individual Personality From Brain Functional Connectome
-https://academic.oup.com/scan/article/15/3/359/5815970
-
-Dadi et al. 2019 Benchmarking functional connectome-based predictive models for resting-state fMRI https://www.sciencedirect.com/science/article/pii/S1053811919301594
-
-Moreau et al. 2020 The general impact of haploinsufficiency on brain connectivity underlies the pleiotropic effect of neuropsychiatric CNVs https://www.medrxiv.org/content/10.1101/2020.03.18.20038505v1
-
-Ormel et al. 2015 Neuroticism and Common Mental Disorders: Meaning and Utility of a Complex Relationship https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382368/
-
-Shi et al. 2018 The Relationship Between Big Five Personality Traits and Psychotic Experience in a Large Non-clinical Youth Sample: The Mediating Role of Emotion Regulation https://www.frontiersin.org/articles/10.3389/fpsyt.2018.00648/full
-
-Shen et al. 2017 Using Connectome-Based Predictive Modeling to Predict Individual Behavior From Brain Connectivity https://pubmed.ncbi.nlm.nih.gov/28182017/
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2021-08-27" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "The face of pain: predicting the facial expression of pain from fMRI data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Marie-Eve Picard]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/PSY6983-2021/picard_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [python, fmri, machine-learning, classification-algorithm, regression-algorithms]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "What can our brain tells us about our facial expression in response to painful stimulus ? This projects aims to compare different regression algorithms to see if it is possible to predict facial expression of pain from fMRI data in healthy adults."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover-image.png"
----
-
-
-## Personal Background
-
-Education:
-* B.Sc. in cognitive neuroscience
-* Currently a Master's student in Psychology at Université de Montréal
-
-I am interested in the neural correlates of pain and the communication of pain. The goal of my master project is to predict the occurrence and intensity of the facial expression of pain from brain activity (fMRI data) in response to a tonic painful stimuli.
-
-I'm also interested in the different methods used to analyze neuroimaging data.
-
-# Project definition
-
-## Background
-
-Facial expression of pain is an important non-verbal informative element of communication, signaling an immediate threat and a need for help. Facial movements related to the experience of pain are associated not only with the sensory dimension of pain (i.e., pain intensity) but also with its affective dimension (i.e., the unpleasantness of pain). The facial expression can be measured with the Facial Action Coding System (FACS, Ekman & Friesen, 1978), which is based on the possible action of muscles or groups of muscles, called action units. We can compute a composite score (FACS score) taking into account the frequency and the intensity of the contraction of these action units.
-
-
-
-
-
-**Personal goals**
-* Apply machine learning to neuroimaging data :heavy_check_mark:
-* Learn visualization tools :heavy_check_mark:
-* Improve my coding skills :heavy_check_mark:
-* Learn how to run analysis on compute canada and don't be afraid of it :heavy_check_mark:
-
-## Tools
-
-The completion of this project will require the following tool:
-* **Python scripts** to write the code for the analysis
-* Several python modules:
- * Pandas: to manipulate the behavioral dataframe
- * Numpy: to manipulate arrays
- * Nibabel: to load the fMRI contrast images (from .hdr files to Nifti objects)
- * Scikit-learn: for machine learning stuff
- * Nilearn: to extract and to visualize the fMRI data
- * Matplotlib, seaborn, plotly: to plot some figures
-* The analysis will be run on **compute canada**
-* The python scripts and figures will be added to the **github** repository
-
-## Data
-
-The dataset that will be used for this project is a secondary private dataset (not open access yet). It includes 55 participants (women = 28, men = 27) aged between 18 and 53 years old. Moderately painful stimuli were applied while they where in the MRI scanner. Their facial expression was recorded during this time by the use of a MRI-compatible camera. Each participant completed 2 runs of 8 painful trials, resulting to a total of 797 observations after removing data with movement artefacs (dataset 1: n = 533 observations; dataset 2: n = 264 observations).
-
-
-
-
Illustration of the experimental paradigme.
-
Distribution of the FACS scores across the datasets
-
-
-
-
-
-
-
-## Deliverables
-
-By the end of this course, I will provide
-* python scripts for:
- - [X] Prepping the dataset for the analysis
- - [X] Machine learning pipeline
-* jupyter notebook for:
- - [X] Visualizing the results
-* Static and interactive visualizations of the results
-* MarkDown README.md documenting all important information about the project
-* Github repository containing the scripts, graphs, report and relevant information
-
-# Results
-
-## Overview
-
-This project was initiated by Marie-Eve Picard on 2nd August 2021 as part of the PSY6983 course. The final presentation of the project was delivered on 20th August 2021. All the deliverables were completed and can be found on this repository (see the Deliverables section below).
-
-### Deliverables
-
-**Python scripts**
- Four python scripts can be found in the [scripts folder](https://github.com/PSY6983-2021/picard_project/tree/main/scripts):
-* [main.py](https://github.com/PSY6983-2021/picard_project/blob/main/scripts/main.py) : main script calling functions in prepping_data.py and building_model.py. This script also includes code to save the train and test sets (in numpy format), the coefficients of the regression model (in nii.gz format), the model (in pickle format), the output of the permutation test (in json format) and the bootstrap test (in pickle format).
-* [data_to_json.py](https://github.com/PSY6983-2021/picard_project/blob/main/scripts/data_to_json.py) : script to save the fmri data (predictive variable), the target variable (predicted variable) and the group variable (for non iid data: multiple observations per participant, optional) in json format.
-* [prepping_data.py](https://github.com/PSY6983-2021/picard_project/blob/main/scripts/prepping_data.py) : script to load the fMRI data in Nifti-like format and to extract the signal according to a nilearn pre-defined mask or to a pre-computed mask.
-* [building_model.py](https://github.com/PSY6983-2021/picard_project/blob/main/scripts/building_model.py) : script to compute the regression model, the permutation test and the bootstrap test.
-
-The requirements to run the python scripts can also found in the [scripts folder](https://github.com/PSY6983-2021/picard_project/tree/main/scripts) in the [requirements.txt](https://github.com/PSY6983-2021/picard_project/blob/main/scripts/requirements.txt) file. See also the [scripts_instruction.txt](https://github.com/PSY6983-2021/picard_project/blob/main/scripts/scripts_instruction.txt) for more details.
-
-**Jupyter notebook**
- The jupyter notebook containing the script to generate the figures can be found in the [notebook folder](https://github.com/PSY6983-2021/picard_project/tree/main/notebook).
-
-**Visualization of the results**
- The png/html format of the figures generated in this project can be found in the [images/output subfolder](https://github.com/PSY6983-2021/picard_project/tree/main/images/output). The html files for the interactive plots can found in [Marie-Eve Picard's github repository](https://github.com/me-pic/BH2021_InteractiveViz).
-
-## Regression models
-
-Regression algorithms combined with a principal component analysis (PCA) were used to determine if it is possible to predict FACS scores from fMRI data in healthy adults. Three different regression algorithms were compared ([`Lasso`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html), [`Ridge`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html), [`SVR`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html)) using three different model inputs:
-* whole-brain activity: the signal was extracted using the MNI152 brain mask (see [NiftiMasker documentation](https://nilearn.github.io/modules/generated/nilearn.input_data.NiftiMasker.html), mask_strategy='template').
-* primary motor area activity: the signal was extracted using [this mask](https://github.com/PSY6983-2021/picard_project/blob/main/masks/mask_BA4.nii).
-* whole-brain activity excluding the primary motor area: the signal was extracted using [this mask](https://github.com/PSY6983-2021/picard_project/blob/main/masks/mask_excluding_BA4.nii).
-
-The models were first trained on the first dataset (n = 533 observations) using a 5-fold cross-validation procedure (Table 1) and tested on the second dataset (n = 264 observations; Table 2).
-
-*Table 1: Performance metrics (R2) of the regression models averaged across the 5 folds for the train/validation sets*
-
-| Algorithm | Whole-brain | Primary motor area (M1) | Whole-brain excluding M1 |
-| ----------|---------------|---------------|-------------- |
-| `Lasso` | 0.19±0.06 | -0.30±0.16 | 0.18±0.03 |
-| `Ridge` | 0.20±0.06 | -0.42±0.22 | 0.17±0.05 |
-| `SVR` | 0.20±0.06 | -0.18±0.19 | 0.16±0.06 |
-
-**Visualization of the R2 violin plots:**
- [Link to the interactive violin plots](https://me-pic.github.io/BH2021_InteractiveViz/regression_violin_plots.html)
-
-
-
-**Visualization of the regression coefficients of the Lasso regression model:**
- The regression averaged coefficients across folds were plotted on the brain using the 99-th percentile of the absolute value in the image.
- [Link to the 2D interactive brain plot](https://me-pic.github.io/BH2021_InteractiveViz/coefs_reg_LASSO.html)
-
-
-
-
-
-*Table 2: Performance metrics (R2) of the final regression models tested on the second dataset*
-
-
-| Algorithm | Whole-brain | Primary motor area (M1) | Whole-brain excluding M1 |
-| ----------|---------------|---------------|-------------- |
-| `Lasso` | -0.19 | -0.46 | -0.20 |
-| `Ridge` | -0.18 | -0.56 | -0.22 |
-| `SVR` | -0.18 | -0.39 | -0.17 |
-
-## Classifier model
-
-A SVM classifier was computed to see if it is possible to predict which dataset the fMRI data came from. The linear classifier was able to predict the dataset with an accuracy of 84.2 ± 1.6 %.
-
-*Confusion matrices across the 5-fold cross-validation.*
-
-
-
-
-This result possibly highlights that the fMRI data in the two datasets come from different distributions. This might explained why the regression model computed on the first dataset performed so poorly on the test set (second dataset). The classification accuracy might be due the different parameters used to acquire the fMRI data. The only divergent acquisition parameters were the number of whole-brain volumes acquired during each functional scan (170 volumes vs 160 volumes) and the voxel sizes (3.44 x 3.44 x 3.40 mm vs 3.4 x 3.4 x 3.4 mm). The averaged age of the participants was also different between the datasets (23.4 ± 2.5 years vs 36.0 ± 10.9 years). This age difference may have contributed to the high classification accuracy, although there is no clear evidence to support a difference in brain activity related to facial expression of pain according to different age groups in the literature.
-
-## Conclusion
-
-The goals of this project were reached. I started this journey with some background in python coding, but during these past few weeks I learned some best practices in coding and how to organize scripts in a more efficient way. I applied different machine learning techniques on neuroimaging data (fMRI contrast images), such as regression and classification techniques. I also learned how to use some functions from the python plotly module. I am also proud to have learned how to perform analysis on Compute Canada, with the help of the TAs.
-
-This project will be continued as part of my master's project. Stay tuned ! :eyes::brain::computer:
-
-## Acknowledgement
-
-I would like to thank Pierre Bellec and the TAs who helped me overcome the various challenges I faced during this course:
-
-* Andréanne
-* Désirée
-* François
-
-A special mention to Catherine Landry for the shared coffees and the moral support.
-
-For any question and comments about the project, please contact me at marie-eve.picard.2@umontreal.ca.
-
-## References
-
-Ekman, P. et Friesen, W. V. (1978). Facial action coding systems. Consulting Psychologists Press.
-
-Kunz, M., Chen, J.-I., Lautenbacher, S., Vachon-Presseau, E. et Rainville, P. (2011). Cerebral regulation of facial expressions of pain. Journal of Neuroscience, 31(24), 8730-8738. https://doi.org/10.1523/JNEUROSCI.0217-11.2011
-
-Vachon-Presseau, E., Roy, M., Woo, C.-W., Kunz, M., Martel, M.-O., Sullivan, M. J., Jackson, P. L., Wager, T. D. et Rainville, P. (2016). Multiple faces of pain: effects of chronic pain on the brain regulation of facial expression. Pain, 157(8), 1819-1830. https://doi.org/10.1097/j.pain.0000000000000587
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-13" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "fMRI Signatures and Behavioural Correlates of Self and Empathic Pain"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Lyanne Zhang, Onjoli Krywiak, Leen Ghanayem, Clarize Donato, Kayla Teopiz, Quin Xie]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/Empathic_Pain
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [self pain, empathic pain, task activation, functional connectivity]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project examined neural activation and functional connectivity during self-experienced and empathic pain using an open-source fMRI dataset. Analyses focused on several regions of interest, including the anterior cingulate (ACC) and insular (IC) cortices, exploring links to loneliness and social connectedness. Results showed greater activation during self-experienced pain compared to empathic pain, with loneliness predicting ACC activation in the meditation group. No significant differences in connectivity between conditions were found, though some associations with social connectedness emerged. The workflow and results are available in a reproducible GitHub repository."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "group_selfpain_zmap.png"
----
-
-
-## Project definition
-
-### Background
-
-Empathy is conceptualized as an individual’s ability to perceive, understand and respond appropriately to someone else’s experience.1,2 In addition, empathic pain is commonly defined as an individual’s perception and emotional response to another person’s suffering or painful situation.2 The cognitive component of empathy and empathic pain includes perspective taking and maintaining distinction between the self and others.3 Taken together, it is proposed that disparate neural mechanisms subserve the cognitive processes that comprise empathy and empathic pain.3,4
-
-Meta-analytic neuroimaging data suggest that multiple brain regions are implicated in empathic pain, including but not limited to the insula cortex (IC), anterior cingulate cortex (ACC), mid-cingulate cortex (MCC), and nucleus accumbens (NAc).3,5 It is postulated that the IC and ACC are critical regions of interest (ROI) in the processing of emotional states and motivation.6 In addition, it has been observed that functional connectivity between the MCC and IC is increased during pain perception in others.7 Furthermore, the NAc has been implicated in the prediction of empathic-pain related behaviours (e.g., actions to help an individual in pain).8 However, the underlying neural circuitry and networks that subserve empathic pain remain insufficiently characterized. Moreover, it is not known how sociodemographic, clinical and/or behavioural factors (e.g., loneliness or social connectedness) may mediate or moderate neural mechanisms that subserve empathic pain.
-
-Herein, this project aimed to apply analytical methods using Python to investigate neural activation and functional connectivity in the IC, ACC and related brain regions in adults completing an fMRI and empathic pain task paradigm.
-
-### Main Objectives
-
-1. Produce a neuroimaging workflow for task activation analyses, functional connectivity analyses, and data visualization.
-2. Produce a GitHub Repository to enable reproducibility of our analyses.
-
-
-### Tools
-
-- Software
- - Python packages: Nilearn Library
- - MS Visual Studio Code
- - R and RStudio
-- Code and Project Management
- - Git and GitHub
- - Scripts
- - Jupyter Notebook
-- Computing Environment
- - Local Laptops
-
-### Data
-
-This project investigated an Open Source dataset obtained from OpenNeuro [https://openneuro.org/datasets/ds006243/versions/1.0.3]. The dataset consists of fMRI data collected from 54 participants trained in one of two meditation modalities: Loving Kindness Meditation (LKM) (n = 25) and Progressive Muscle Relaxation (PMR) (n = 29). The empathic pain task paradigm consisted of two counterbalanced runs, Experience (Self) and Observe (Other) conditions, each containing 30 trials that were 24 seconds in duration, with a total of 720 seconds for a single run. Each run (i.e., 30 trials) included 15 trials involving anticipation of fear and 15 trials involving pain experience. An audio cue was used to indicate whether the participant or study partner would receive a pressure pain stimulus. The pressure pain stimulus was transmitted through a tube to a piston secured on the participant's thumbnail.
-
-All participants completed the empathic pain task during an fMRI scan in order to examine neural responses to self-pain test condition (i.e., pain stimulus of the participant’s hand) and empathic pain (i.e., observing someone else receive the pain stimulus). MRI data was collected at the Center for Functional and Molecular Imaging at Georgetown University using a 3T Siemens TIM Trio scanner. High-resolution structural images were acquired using a T1-weighted MP-RAGE sequence (1 mm isotropic voxels), and functional images were collected using a T2*-weighted EPI sequence (3 mm isotropic voxels, TR = 2,500 ms, TE = 30 ms).
-
-### Deliverables
-
-- [GitHub repository](https://github.com/brainhack-school2025/Empathic_Pain)
-
-## Methods
-
-### Task Activation Analysis
-To examine brain regions activated during the empathic pain task, fMRI data was fitted to a first-level general linear model using Nilearn’s FirstLevelModel to compute z-maps for each task contrast. A statistical threshold (z > 3.1) was applied, and significant clusters were identified and labeled using Nilearn’s get_cluster_table and the Harvard-Oxford atlas, respectively. Further, a Multivariate Analysis of Variance (MANOVA) was used to examine whether baseline loneliness and social connectedness scores predicted activation of brain regions implicated in empathic and self-experienced pain, specifically the ACC, IC and superior temporal gyrus, following the meditation intervention. The UCLA Loneliness Scale9 and Social Connectedness Scale10 were used to measure loneliness and social connectedness at baseline.
-
-### Functional Connectivity Analysis
-We aimed to compare functional connectivity between self-experienced and empathic pain conditions. First, we applied the masker to all files using the Harvard-Oxford brain atlas, which provides good parcellation of subcortical regions. Next, we generated one connectivity matrix (based on Pearson’s correlation) per condition for each subject. We then computed a network-blocked mean connectivity matrix for each condition for visualization purposes.
-To compare connectivity between the two conditions at the edge level, we performed paired t-tests with FDR correction.
-
-We were also interested in investigating whether functional connectivity is associated with loneliness or social connectedness. To investigate this possible association, we used our participants’ previously obtained individual functional connectivity matrices, where each participant had two matrices: one for run 1 (self) and run 2 (other/empathic pain). Then, we categorized participants’ functional connectivity matrices by run type (i.e., 1 vs 2). As well, we were interested in only functional connectivity/roi-to-roi pairs that had either the insula/insular cortex or the cingulate cortex/gyrus as one of the rois in a pair.
-
-Finally, to test potential associations between functional connectivity with loneliness or social connectedness, statistical analyses were performed via linear regression models. Functional connectivity of specific roi pairs (i.e., those containing insula or cingulate cortex), and covariates including age, sex, and condition were included as predictors, while our dependent/predicted variables included loneliness or social connectedness.
-
-### Partial Least Square - Covariance (PLS-C) Analysis
-To examine changes in functional connectivity between the two conditions in an unsupervised manner, we performed PLS-C analysis on the correlation matrix between all 69 parcellated regions.
-
-## Results
-
-### Task Activation Analysis
-
-#### Finding #1: Many regions are activated during self pain and empathic pain tasks
-Various cortical and subcortical regions were activated during the self-pain and empathic-pain tasks, including brain regions in the frontal, parietal, temporal and occipital lobes. The empathic-pain condition was characterized by decreased activation of the frontal medial cortex and increased activation of the posterior division of the inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus and temporal fusiform gyrus. On the other hand, the self-pain condition was characterized by decreased activation of the cuneal cortex, middle frontal gyrus, middle temporal gyrus, and increased activation of the supracalcarine cortex.
-
-
-
- Figure 1. Heatmap displaying activation by region for self and empathic pain tasks.
-
-
-#### Finding #2: Interaction between baseline loneliness and condition predicts activation in the ACC during self pain task
-Upon examining whether baseline loneliness and social connectedness predicted brain activation during the empathic and self-pain tasks, a MANOVA revealed a significant interaction between baseline loneliness and activation of the ACC in the self-pain condition (p = .03). Specifically, participants in the LKM meditation group showed a significant decrease in ACC activation at higher baseline loneliness scores. There was no signficant interaction between
-
-
-
- Figure 2. Interaction plot visualizing the relationship between baseline loneliness scores and activation in the ACC during self pain task.
-
-
-### Functional Connectivity Analysis
-
-#### Finding #1: No difference in functional connectivity between self and empathic pain tasks
-Contrary to our hypothesis, we did not observe any statistically significant differences in connectivity between the self and empathic pain conditions at the edge level, even before correcting for multiple comparisons.
-
-
-
-
- Figure 3. Functional connectivity matrices for self pain (left) and empathic pain (right) tasks.
-
-
-#### Finding #2: Functional connections are associated with social connectedness but not loneliness
-Several functional connectivity pairs containing our rois of interest (cingulate cortex and/or insula) were negatively associated with social connectedness in both self and empathic pain tasks. Notably, these significant associations do not pass FDR correction. In contrast, our functional connectivity pairs of interest were not significantly associated with loneliness. It appears that our rois of interest, these being the cingulate cortex and/or insula, that are typically implicated in empathic pain tasks, are also functionally connected with other regions of interest and are associated with social connectedness. This highlights that functional connectivity with our rois of interest that underlie empathic pain also appears to underlie behaviour such as social connectedness.
-
-
-
-
- Figure 4. Significant functional connections between roi pairs of interest and self pain (left) and empathic pain (right) tasks.
-
-
-### PLS-C Analysis
-The first two PLS components captured the majority of the covariance between neural connectivity (Figure 5). There did not seem to be significant differences between participants in the LKM group and those in the PMR group in the first two PLS components, consistent with previous functional connectivity analyses. However, the timing of fMRI collection (before or after the task) seemed to be the major contributor to the separation on the first PLS component (Figure 6). Using a correlation test, we examined whether either PLS component was associated with loneliness or social connectedness and found no significant correlation. These findings suggest that while the two interventions might have changed functional connectivities, they did not significantly affect loneliness or trait empathy of the participants.
-
-
-
- Figure 5. Explained variance in functional connectivity by the top 5 PLS components.
-
-
-
-
- Figure 6. Projection of functional connectivity onto the first two PLS components.
-
-
-## Conclusion
-- The results of our analyses support that activation in the ACC and IC was higher in the self-experienced pain condition compared to the empathic pain condition.
-- In the LKM group, higher loneliness was associated with lower ACC activation. This effect was not observed in the PMR group.
-- Functional connectivity did not significantly differ between self-experienced pain and empathic pain conditions.
-- Functional connectivity in ROI pairs of interest (cingulate cortex and insula) was associated with social connectedness in both self and empathic runs, but these associations did not survive FDR correction.
-
-## Acknowledgements
-Thank you Brain Hack School 2025 team for all the help!
-
-## References
-1. Decety J, Smith KE, Norman GJ. Halpern A social neuroscience perspective on clinical empathy. J Halpern A social neuroscience perspective on clinical empathy World Psychiatr 2014;13:233–7.
-2. Cao Y, Zhang J, He X, et al. Empathic pain: Exploring the multidimensional impacts of biological and social aspects in pain. Neuropharmacology 2024;258(110091):110091.
-3. Jauniaux J, Khatibi A, Rainville P, Jackson PL. A meta-analysis of neuroimaging studies on pain empathy: investigating the role of visual information and observers’ perspective. Soc Cogn Affect Neurosci 2019;14(8):789–813.
-4. Tremblay MPB, Meugnot A, Jackson PL. The neural signature of empathy for physical pain … not quite there yet! In: Social and Interpersonal Dynamics in Pain. Cham: Springer; 2018.
-5. Zhang M-M, Chen T. Empathic pain: Underlying neural mechanism. Neuroscientist 2025;31(3):296–307.
-6. Zhang M, Wu YE, Jiang M, Hong W. Cortical regulation of helping behaviour towards others in pain. Nature 2024;626(7997):136–44.
-7. Zaki J, Wager TD, Singer T, Keysers C, Gazzola V. The anatomy of suffering: Understanding the relationship between nociceptive and empathic pain. Trends Cogn Sci 2016;20(4):249–59.
-8. Hein G, Silani G, Preuschoff K, Batson CD, Singer T. Neural responses to ingroup and outgroup members’ suffering predict individual differences in costly helping. Neuron 2010;68(1):149–60.
-9. Russell, D , Peplau, L. A.. & Ferguson, M. L. (1978). Developing a measure of loneliness. Journal of Personality Assessment, 42, 290-294.
-10. Lee, R. M., Draper, M., & Lee, S. (2001). Social connectedness, dysfunctional interpersonal behaviors, and psychological distress: Testing a mediator model. Journal of Counseling Psychology, 48(3), 310–318. https://doi.org/10.1037/0022-0167.48.3.310
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Can we identify sex using fMRI?"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Tajwar Sultana]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/tjays7_fmri
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, Machine Learning, Data Visualization, nilearn]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Does functional connectivity between brain regions differ in male and female? If yes then fMRI data can be used to distinguish sex on the basis of the difference in functional connectivity. I applied supervised Machine Learning algorithms on the fMRI data to classify sex."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Red-brain.jpg"
----
-
-
-## Project definition
-
-### Background
-
-This project is based on the hypothesis that there is a difference in the functional connectivity between male and female. There has been quite a lot of research on the brain connectivity differences based on gender. A [review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3250567/) paper suggests that anatomical and functional connectivity between brain regions is effected by gender. If this hypothesis holds true then sex _can_ be identified using functional connectivity.
-
-### Tools
-
- - Data Visualization (matplotlib, seaborn, plotly, pywidgets)
- - Machine learning packages (nilearn, scikit-learn)
- - Github for version control
- - VS Code
- - Bash Terminal
-
-
-### Data
-
-Many open data sources were explored but those required preprocessing. In order to save time and complete this project during 3-week period, I selected Nilearn preprocessed dataset. The Nilearn's Development fmri data was collected during a [study](https://nature.com/articles/s41467-018-03399-2) in which a short film was watched by 33 adults and 122 children (age 3-12) while undergoing fMRI. The study was meant to characterize the development of functionally specialized social brain regions. The target variable for my project was gender and features were obtained by finding correlation between different regions of interest.
-
-### Deliverables
-
- - Interactive Plots
- - Predictive models with evaluation metrics
- - Presentation slides
- - Project Report
-
-## Results
-
-#### _Functional Connectome_
-
-
-#### _Interactive Scatter Plot of Principal Components_
-
-
-
-#### _Evaluation Metrics_
-
-
-
-### Progress overview
-
-This project was initiated by Tajwar Sultana on 19th May 2020 as part of the Brainhack School and the final presentation was delivered on 5th June 2020. The deliverables including code, interactive plot, report and presentation are completed.
-
-### Tools I learned during this project
-
- * Github
- * Nilearn (Machine learning package for neuroimaging)
- * Plotly, ipywidgets (Python packages for Interactive plotting)
-
-## Conclusion
-
-This project was basically adopted to learn data visualization and machine learning on neuroimaging data. The results of machine learning models show that there is a lot of improvement required in sex classification using functional connectivity.
-
-The scatter plot of Principal Components illustrates that sex does not explain the variance in the given data. Hence,it can be concluded that the features extracted from the dataset in the form of connectivity matrices were not appropriate for this problem. There could be two reasons for this:
-* The dataset consists of subjects of multiple ages including children and adults and age could be a factor effecting functional connectivity changes
-* In this study, parcellation scheme was used with 64 brain regions. It might not be necessary that all those regions reflect functional connectivity changes between male and female. Hence, care must be taken in selecting regions of interest
-
- This is just a preliminary study which could lead to the development of more robust machine learning models for the sex classification with functional connectivity features.
-
-## Acknowledgement
-
-Gratitude to all the organizers, instructors, TAs and fellow participants who helped me learn cool open neuroscience tools. Special thanks to my instructors Desiree, Greg, Benjamin and a token of appreciation to Elizebath and Jakob for their inspiring work.
-
-## References:
-
-Susanne Weis, Kaustubh R Patil, Felix Hoffstaedter, Alessandra Nostro, B T Thomas Yeo, Simon B Eickhoff, Sex Classification by Resting State Brain Connectivity, Cerebral Cortex, Volume 30, Issue 2, February 2020, Pages 824–835, https://doi.org/10.1093/cercor/bhz129
-
-Chao Zhang Chase C. Dougherty Stefi A. Baum Tonya White Andrew M. Michael, Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity, Volume 39, Issue 4, April 2018, Pages 1765-1776, https://doi.org/10.1002/hbm.23950
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-06" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Effects of Sleepiness on Resting-State Connectivity"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Thomas Perrin]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/perrin_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [sleep, rs-fMRI, BIDS, preprocessing, connectivity, open-science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Can functional connectivity predict sleep deprivation? This project aims to explore neuroimaging data organization to build a workflow from the acquisition of an open dataset to the visualization of brain connectivity. The pipeline will be detailed and carried out for one subject, using resting state fMRI to compare the result between normal sleep and sleep deprivation (less than 3 hours of sleep the previous night)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "images/Programming-Memes-Programmer-while-sleeping.jpg"
----
-
-
-#### About Me
-
-
-
- Thomas Perrin
-
-
-Hello and welcome to my project! My name is Thomas Perrin, I am currently pursuing a Master of Engineering degree in Biomedical Engineering at Polytechnique Montreal. I have previously studied mostly medical devices and images analysis at IMT Atlantique in France. Exploring neuroimaging and learning about open-source tools is a new and inspiring part of my journey in Biomedical Engineering!
-
-#### Project Description
-
-Sleep deprivation is commonplace in modern society, but little is known about the functional mechanisms and correlates of sleepiness in the awake brain. Sleepiness is a brain state with pervasive effects on cognitive and affective functioning ([Killgore, 2010, Tamm et al., 2020](#references)). Adult fMRI studies have demonstrated associations between restricted sleep and amygdala-prefrontal functional connectivity ([Reidy et al., 2016](#references)), with inhibition of top-down-control in emotion ([Tamm et al., 2020](#references)). Therefore, it would be interesting to explore and predict whether a participant is sleep deprived or not based on a functional connectivity estimation.
-
-Main question: **Can resting-state functional connectivity predict sleep deprivation?**
-
-### Tools
-
-* [Git](https://git-scm.com/) and [GitHub](https://github.com/) for project management.
-* [DataLad](https://www.datalad.org/) for retrieval and version control of data.
-* [Neurodesk](https://www.neurodesk.org/) to use a flexible and scalable data analysis environment for reproducible neuroimaging.
-* [BIDS-validator](https://bids-standard.github.io/bids-validator/) to check updated dataset integrity.
-* [FMRIPrep](https://fmriprep.org/en/stable/) for fMRI data preprocessing
-* [Python](https://www.python.org/) for dataset management and visualization (Nilearn, Pandas, NiBabel, Matplotlib, ...), in [Visual Studio Code](https://code.visualstudio.com/) and [Jupyter Notebook](https://jupyter.org/).
-
-### Data
-
-* Data used: [Resting-State fMRI from the Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old](https://openneuro.org/datasets/ds000201/versions/1.0.3). A functional brain imaging study where 86 healthy participants underwent MRI after normal sleep and partial sleep deprivation (only 3 hours of sleep) in a crossover design. Three experiments were performed investigating emotional mimicry, empathy for pain, and cognitive reappraisal, as well as resting state fMRI (rs-fMRI).
-* Fit with the research question: This study aimed to investigate the effects of partial sleep deprivation (PSD) on resting state brain connectivity, emotional contagion, empathy, and emotional regulation.
-* Obtained from: [OpenNeuro](https://openneuro.org/). The full dataset is multimodal (T1- and T2-weighted structural images, diffusion images, raw polysomnography data, task-based and resting state fMRI images).
-* Usage: For the reduced scope of this project, only the rs-fMRI data of the two sessions for one subject will be used, but the pipeline can then be used in the same way to analyze more subjects. The full dataset structure and metadata files can be retrieved without downloading the heavy dataset thanks to DataLad. This is useful to update the dataset to be BIDS-compliant and analyze data for only one subject.
-
-Note: An old page for this dataset can also be found on the deprecated website OpenfMRI ([A multimodal brain imaging dataset on sleep deprivation in young and old humans: The Sleepy Brain Project I](https://legacy.openfmri.org/dataset/ds000201/)).
-
-### Deliverables
-
-* A GitHub repository containing all the elements of the project
-* A markdown file for the project description
-* A Python script to update the dataset to be BIDS-compliant
-* A markdown file providing the Bash commands to download the dataset, run the Python script, and run fMRI preprocessing, all using DataLad
-* A Jupyter Notebook for visualization
-* A requirements.txt file to specify the Python environment for the script and the notebook
-
-## Results
-
-### Progress overview: Retrace my Steps
-
-**1. Git & GitHub: Version Control the Project.**
- * The repository was updated throughout the following steps of the project.
- * Organization of the repository:
- * Modules: Modules completed during the first weeks of BrainHack School.
- * data: Raw data for the project, not synced to source control because the dataset is too large for Git.
- * docs: Documentation (project presentations, could also include Markdown and reStructuredText (reST) files).
- * results: Results, including figures (could also include checkpoints, hdf5 files, pickle files, and tables, had the project been more complex).
- * scripts: Scripts (Python, bash, .ipynb notebooks).
- * src: Reusable Python modules for the project (imports, empty for now).
- * tests: Tests for the code (empty for now).
- * Resources:
- * [Version control with Git](https://gcapes.github.io/git-course/)
- * [Introduction to Git and GitHub](https://emdupre.github.io/git-course/)
- * [The Good Research Code Handbook](https://goodresearch.dev/) for the project template.
-
-**2. OpenNeuro: Select the Dataset.**
- * The data was already converted from DICOM to NIfTI.
- * BIDS organization except for a few errors (see [bids_validation](https://github.com/brainhack-school2023/perrin_project/tree/e5138662c5facfead956ddd53dde26edd5aa6b3d/docs/bids_validation) folder).
-
-**3. DataLad: Version Control the Dataset**
- * The [OHBM Brainhack TrainTrack: DataLad](https://handbook.datalad.org/en/latest/code_from_chapters/OHBM.html), [The DataLad Notebook](https://handbook.datalad.org/en/latest/) and the use case [A basic automatically and computationally reproducible neuroimaging analysis](https://handbook.datalad.org/en/latest/usecases/reproducible_neuroimaging_analysis_simple.html) were the most useful tools to get me started.
- * [OpenNeuro Quickstart Guide: Accessing OpenNeuro datasets via DataLad](https://handbook.datalad.org/en/latest/usecases/openneuro.html) to properly obtain the dataset with DataLad.
- * Organization of the dataset following [the YODA principles](https://handbook.datalad.org/en/latest/basics/101-130-yodaproject.html) and *datalad run* to capture everything relevant to reproduce the analysis, i.e. to run the code to fix BIDS errors
-
-**4. BIDS-Validation: Reproducible Neuroimaging Organization.**
- * [The BIDS Starter Pack](https://bids-standard.github.io/bids-starter-kit/index.html)
- * [BIDS Validator](https://bids-standard.github.io/bids-validator/)
- * Prepare the dataset to use with tools like fMRIPrep, see [Deliverable 3](#results).
-
-**5. NeuroDesk: Connecting to a flexible and scalable data analysis environment for reproducible neuroimaging.**
- * [Getting Started with Brain Imaging Tools via the Neurodesktop Container](https://github.com/brainhack-school2022/dimitrijevic_project/blob/658c4f8bdadda289845feb707cd77c6a6363a43f/BrainHackCloud_steps/neurodesk_access.md) by [Andjela Dimitrijevic](https://github.com/Andjelaaaa).
- * Guide to use [Neurodesk](https://www.neurodesk.org/) for this project (instructions as of June 1st 2023):
- * Open 'Play' then 'Neurodesk Lab' (for keeping data across sessions)
- * Choose the link closest to your location.
- * In the left panel under 'Softwares' load the fmriprep module.
- * In the 'Notebook' section launch 'Neurodesktop'.
-
-**6. fMRIPrep: Preprocessing Pipeline for fMRI Data.**
- * [Getting started with BIDS, fMRIPrep, MRIQC](https://sarenseeley.github.io/BIDS-fmriprep-MRIQC.html).
- * [BIDS App Tutorial #2: fMRIPrep](https://andysbrainbook.readthedocs.io/en/latest/OpenScience/OS/fMRIPrep.html).
- * [Usage Notes](https://fmriprep.org/en/stable/usage.html).
- * [Outputs of *fMRIPrep*](https://fmriprep.org/en/stable/outputs.html) to have a description of the output dataset to use for visualization and analysis.
- * Estimated runtime per subject on Neurodesk: 6 to 7 hours.
-
-**7. Python: Visualization of the data.**
- * The access to the data has been done following the format of datasets using [nilearn.datasets.fetch_openneuro_dataset](https://nilearn.github.io/dev/modules/generated/nilearn.datasets.fetch_openneuro_dataset.html). [PyBIDS](https://bids-standard.github.io/pybids/) was also considered but in the end not used to work with BIDS datasets.
-
-Note:
-
-Step 5. was originally intended to be executed using Docker, but after encountering storage and outdated software issues with my laptop, Neurodesk was used to finish the project in time. Here are the resources used before the issues prevented the final installation:
- * [Using containers (Docker/Singularity) in science](https://neurohackweek.github.io/docker-for-scientists/).
- * [Install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository.)
- * Optional: [Install Docker Desktop on Ubuntu](https://docs.docker.com/desktop/install/ubuntu/).
- * [NiPreps: Executing with Docker](https://www.nipreps.org/apps/docker/)
- * And for maximal reproducibility with containers, the DataLad use case [An automatically and computationally reproducible neuroimaging analysis from scratch](https://handbook.datalad.org/en/latest/usecases/reproducible_neuroimaging_analysis.html).
-
-### Tools I learned during this project
-
-* **Neuroimaging:** Functional MRI principles, organization for neuroimaging data, and visualization of functional connectivity for resting-state networks.
-* **Open science workflow:** Tools to create a reproductible neuroimaging workflow from preprocessing to data visualization.
-
-### Results
-
-#### Deliverable 1: GitHub repository
-
-The GitHub repository contains all the elements of the project.
-
-#### Deliverable 2: project report
-
-The [README.md](https://github.com/brainhack-school2023/perrin_project/blob/e5138662c5facfead956ddd53dde26edd5aa6b3d/README.md) markdown file describes the project. It can be found on [my BrainHack School 2023 repository](https://github.com/brainhack-school2023/perrin_project/blob/e5138662c5facfead956ddd53dde26edd5aa6b3d/README.md) as well as the [BrainHack School website](https://school-brainhack.github.io/project/).
-
-#### Deliverable 3: dataset manipulation scripts
-
-* A Python script ([bids_fix.py](https://github.com/brainhack-school2023/perrin_project/blob/e5138662c5facfead956ddd53dde26edd5aa6b3d/scripts/bids_fix.py)) to update the dataset to be BIDS-compliant.
-* A Markdown file ([datalad_commands](https://github.com/brainhack-school2023/perrin_project/blob/e5138662c5facfead956ddd53dde26edd5aa6b3d/scripts/datalad_commands)) containing the DataLad bash code to obtain the dataset, run the above-mentioned Python script, and run the fMRI preprocessing, all using version-control with DataLad.
-* Just below are the results using BIDS Validator before fixing the errors by and after running the script. The two errors remaining are explained by the way the dataset was retrieved using DataLad (the dataset's structure is there but the big files are not downloaded), as shown on the screenshot from the [DataLad FAQ](https://handbook.datalad.org/en/latest/basics/101-180-FAQ.html) below.
-
-*BIDS Validator errors before and after fix.*
-
-
-
-
-
-
-
-*DataLad FAQ paragraph on BIDS Validator issues.*
-
-
-
-
-
-#### Deliverable 4: analysis notebook
-
-A Jupyter Notebook ([sleep_connectivity.ipynb](https://github.com/brainhack-school2023/perrin_project/blob/e5138662c5facfead956ddd53dde26edd5aa6b3d/scripts/sleep_connectivity.ipynb)) for visualization of connectomes and analysis. The results can also be found [here](https://github.com/brainhack-school2023/perrin_project/tree/e5138662c5facfead956ddd53dde26edd5aa6b3d/results).
-
-The results obtained for the first subject (sub-9001) are presented below.
-
-*Correlation matrices after normal sleep (left) and partial sleep deprivation, i.e. only 3 hours of sleep the night before data acquisition (right).*
-
-
-
-
-
-
-*Difference between the two correlation matrices.*
-
-
-
-
-
-*Corresponding connectomes after normal sleep (left) and partial sleep deprivation, i.e. only 3 hours of sleep the night before data acquisition (right).*
-
-
-
-
-
-
-Adult fMRI studies have demonstrated associations between restricted sleep and amygdala-prefrontal functional connectivity. Therefore, the expected result is to see differences in functional connectivity in the amygdala and the prefrontal cortex.
-
-The atlas used to define the regions of interest is the MSDL brain Probabilistic atlas available with Nilearn. A threshold has been set to keep only the 5% of edges with the highest values and make the connectomes easier to read.
-
-The biggest visible differences are:
-* Between the right anterior intraparietal sulcus (R Ant IPS), with functions related to perceptual-motor coordination and visual attention, and the default mode network (DMN).
-* Between the right insular cortex (R Ins), believed to be involved in consciousness and emotion, and the DMN.
-* Between the different parts of the DMN.
-* Between the superior temporal sulcus (STS), involved in language and social processing and the motor cortex (Motor).
-
-Overall, the connectivity seems to be a little less intense after PSD regarding the connectomes. Furthermore, a few of the connections to the frontal part of the brain which were visible after normal sleep are not present anymore.
-
-While these results seem to be in line with previous studies (effects on prefrontal functional connectivity and emotion regulation), which is encouraging, the analysis has been done on only one subject and mostly qualitatively, thus relevant conclusions cannot be drawn on so little data.
-
-#### Deliverable 5: final project presentation
-
-Slides ([Perrin_Thomas_Project_Wrap_Up.pdf](https://github.com/brainhack-school2023/perrin_project/blob/2adfa80fcc5c220127a14716bf989345251faf05/docs/Perrin_Thomas_Project_Wrap_Up.pdf)) for the final project presentation to conclude the month of BrainHack School, following the provided template. The results presented there are a snapshot at this moment but are not up-to-date, the final results are presented right above.
-
-## Conclusion and acknowledgement
-
-The objectives of the project, which were to learn about neuroimaging tools and open science, and to set up the workflow from data retrieval to visualization, were completed. Although the results are limited to only one subject, the possibility to analyze at least one subject was encouraging to envision a larger analysis in order to answer the main question properly.
-
-With more time and resources, the prospect of this project would have been to process more subjects, using a High Performance Computing infrastructure such as [Alliance Canada](https://alliancecan.ca/en) or [Brainhack cloud](https://brainhack.org/brainhack_cloud/tutorials/hpc/). This additional data would have been used to build a Machine Learning model with [NiLearn](https://nilearn.github.io/stable/index.html) and find out if resting-state functional connectivity could effectively predict sleep deprivation.
-
-A special thanks goes to the TAs at Polytechnique Montreal, Jan and Andjela, for their tips and guidance, especially for pointing me to the project-saving tool Neurodesk when all other options had failed.
-
-Also, the [NeuroStars](https://neurostars.org/) forum was helpful when I encountered issues with BIDS validation and fMRIPrep.
-
-Finally, I would like to thank the BrainHack School organizers who have built this course and modules. It really provides guidance on how to learn the most useful tools for neuroimaging and open-science, in a way that is very efficient to actively acquire new skills and knowledge.
-
-## References
-
-* Gustav Nilsonne and Sandra Tamm and Paolo d’Onofrio and Hanna Å Thuné and Johanna Schwarz and Catharina Lavebratt and Jia Jia Liu and Kristoffer NT Månsson and Tina Sundelin and John Axelsson and Peter Fransson and Göran Kecklund and Håkan Fischer and Mats Lekander and Torbjörn Åkerstedt (2020). The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old. OpenNeuro. \[Dataset] [doi:10.18112/openneuro.ds000201.v1.0.3](https://doi.org/10.18112/openneuro.ds000201.v1.0.3)
-* Killgore WD. Effects of sleep deprivation on cognition. Prog Brain Res. 2010;185:105-29. [doi:10.1016/B978-0-444-53702-7.00007-5](https://doi.org/10.1016/b978-0-444-53702-7.00007-5). PMID: 21075236.
-* Reidy BL, Hamann S, Inman C, Johnson KC, Brennan PA. Decreased sleep duration is associated with increased fMRI responses to emotional faces in children. Neuropsychologia. 2016 Apr;84:54-62. [doi:10.1016/j.neuropsychologia.2016.01.028](https://doi.org/10.1016/j.neuropsychologia.2016.01.028). Epub 2016 Jan 25. PMID: 26821063.
-* Tamm S, Schwarz J, Thuné H, Kecklund G, Petrovic P, Åkerstedt T, Fischer H, Lekander M, Nilsonne G. A combined fMRI and EMG study of emotional contagion following partial sleep deprivation in young and older humans. Sci Rep. 2020 Oct 21;10(1):17944. [doi:10.1038/s41598-020-74489-9](https://doi.org/10.1038/s41598-020-74489-9). PMID: 33087746; PMCID: PMC7578048.
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diff --git a/content/en/project/fMRI-sleep-deprivation/results/bids_validation/ds000201_errors.txt b/content/en/project/fMRI-sleep-deprivation/results/bids_validation/ds000201_errors.txt
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--- a/content/en/project/fMRI-sleep-deprivation/results/bids_validation/ds000201_errors.txt
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@@ -1,45619 +0,0 @@
-File Path: Invalid JSON file. The file is not formatted according the schema.
-
- Type: Error
- File: sub-9001_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-1_task-faces_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-1_task-rest_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-2_task-faces_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-2_task-rest_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9001_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-1_task-faces_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-1_task-rest_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-2_task-faces_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-2_task-rest_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9002_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-1_task-faces_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-1_task-rest_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-2_task-faces_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-2_task-rest_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9003_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-1_task-faces_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-1_task-rest_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-2_task-faces_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-2_task-rest_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9004_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-1_task-faces_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-1_task-rest_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-2_task-faces_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-2_task-rest_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9005_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-1_task-faces_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-1_task-rest_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-2_task-faces_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-2_task-rest_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9007_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-1_task-faces_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-1_task-rest_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-2_task-faces_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-2_task-rest_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9008_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-1_task-faces_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-1_task-rest_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-2_task-faces_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-2_task-rest_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9009_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-1_task-faces_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-1_task-rest_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-2_task-faces_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-2_task-rest_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9011_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-1_task-faces_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-1_task-rest_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-2_task-faces_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-2_task-rest_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9012_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-1_task-faces_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-1_task-rest_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-2_task-faces_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-2_task-rest_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9013_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-1_task-faces_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-1_task-rest_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-2_task-faces_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-2_task-rest_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9014_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9016_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9016_ses-1_task-faces_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9016_ses-1_task-rest_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9016_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-1_task-faces_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-1_task-rest_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-2_task-faces_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-2_task-rest_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9017_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-1_task-faces_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-1_task-rest_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-2_task-faces_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-2_task-rest_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9018_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-1_task-faces_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-1_task-rest_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-2_task-faces_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-2_task-rest_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9019_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-1_task-faces_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-1_task-rest_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-2_task-faces_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-2_task-rest_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9020_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9022_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9022_ses-1_task-faces_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9022_ses-1_task-rest_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9022_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-1_task-faces_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-1_task-rest_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-2_task-faces_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-2_task-rest_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9023_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9025_ses-1_task-faces_bold.json
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9025_ses-1_task-rest_bold.json
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9025_ses-2_task-faces_bold.json
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9025_ses-2_task-rest_bold.json
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-1_task-faces_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-1_task-rest_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-2_task-faces_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-2_task-rest_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9026_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-1_task-faces_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-1_task-rest_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-2_task-faces_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-2_task-rest_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9028_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9029_ses-1_task-faces_bold.json
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9029_ses-1_task-rest_bold.json
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9029_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9029_ses-2_task-faces_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9029_ses-2_task-rest_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9029_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-1_task-faces_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-1_task-rest_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-2_task-faces_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-2_task-rest_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9032_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-1_task-faces_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-1_task-rest_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-2_task-faces_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-2_task-rest_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9033_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-1_task-faces_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-1_task-rest_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-2_task-faces_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-2_task-rest_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9034_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-1_task-faces_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-1_task-rest_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-2_task-faces_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-2_task-rest_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9035_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-1_task-faces_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-1_task-rest_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-2_task-faces_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-2_task-rest_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9036_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-1_task-faces_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-1_task-rest_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-2_task-faces_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-2_task-rest_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9037_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-1_task-faces_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-1_task-rest_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-2_task-faces_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-2_task-rest_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9038_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-1_task-faces_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-1_task-rest_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-2_task-faces_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-2_task-rest_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9039_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-1_task-faces_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-1_task-rest_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-2_task-faces_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-2_task-rest_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9040_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-1_task-faces_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-1_task-rest_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-2_task-faces_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-2_task-rest_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9041_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-1_task-faces_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-1_task-rest_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-2_task-faces_run-1_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_run-1_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-2_task-faces_run-2_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_run-2_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-2_task-rest_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9042_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9044_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9044_ses-1_task-faces_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9044_ses-1_task-rest_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9044_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-1_task-faces_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-1_task-rest_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-2_task-faces_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-2_task-rest_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9045_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-1_task-faces_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-1_task-rest_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-2_task-faces_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-2_task-rest_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9046_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-1_task-faces_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-1_task-rest_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-2_task-faces_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-2_task-rest_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9047_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-1_task-faces_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-1_task-rest_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-2_task-faces_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-2_task-rest_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9048_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-1_task-faces_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-1_task-rest_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-2_task-faces_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-2_task-rest_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9049_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-1_task-faces_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-1_task-rest_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-2_task-faces_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-2_task-rest_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9050_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-1_task-faces_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-1_task-rest_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-2_task-faces_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-2_task-rest_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9053_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-1_task-faces_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-1_task-rest_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-2_task-faces_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-2_task-rest_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9054_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-1_task-faces_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-1_task-rest_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-2_task-faces_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-2_task-rest_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9055_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-1_task-faces_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-1_task-rest_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-2_task-faces_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-2_task-rest_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9056_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-1_task-faces_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-1_task-rest_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-2_task-faces_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-2_task-rest_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9057_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-1_task-faces_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-1_task-rest_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-2_task-faces_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-2_task-rest_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9058_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-1_task-faces_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-1_task-rest_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-2_task-faces_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-2_task-rest_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9059_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-1_task-faces_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-1_task-rest_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-2_task-faces_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-2_task-rest_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9061_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-1_task-faces_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-1_task-rest_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-2_task-faces_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-2_task-rest_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9062_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-1_task-faces_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-1_task-rest_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-2_task-faces_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-2_task-rest_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9063_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-1_task-faces_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-1_task-rest_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-2_task-faces_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-2_task-rest_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9064_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-1_task-faces_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-1_task-rest_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-2_task-faces_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-2_task-rest_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9065_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9066_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9066_ses-1_task-faces_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9066_ses-1_task-rest_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9066_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-1_task-faces_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-1_task-rest_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-2_task-faces_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-2_task-rest_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9067_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-1_task-faces_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-1_task-rest_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-2_task-faces_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-2_task-rest_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9068_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-1_task-faces_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-1_task-rest_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-2_task-faces_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-2_task-rest_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9069_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-1_task-faces_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-1_task-rest_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-2_task-faces_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-2_task-rest_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9070_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-1_task-faces_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-1_task-rest_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-2_task-faces_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-2_task-rest_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9071_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-1_task-faces_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-1_task-rest_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-2_task-faces_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-2_task-rest_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9072_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-1_task-faces_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-1_task-rest_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-2_task-faces_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-2_task-rest_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9073_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-1_task-faces_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-1_task-rest_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-2_task-faces_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-2_task-rest_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9074_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-1_task-faces_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-1_task-rest_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-2_task-faces_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-2_task-rest_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9075_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-1_task-faces_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-1_task-rest_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-2_task-faces_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-2_task-rest_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9076_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-1_task-faces_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-1_task-rest_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-2_task-faces_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-2_task-rest_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9077_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-1_task-faces_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-1_task-rest_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-2_task-faces_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-2_task-rest_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9079_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-1_task-faces_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-1_task-rest_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-2_task-faces_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-2_task-rest_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9080_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-1_task-faces_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-1_task-rest_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-2_task-faces_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-2_task-rest_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9081_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-1_task-faces_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-1_task-rest_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-2_task-faces_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-2_task-rest_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9082_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-1_task-faces_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-1_task-rest_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-2_task-faces_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-2_task-rest_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9083_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-1_task-faces_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-1_task-rest_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-2_task-faces_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-2_task-rest_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9084_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-1_task-faces_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-1_task-rest_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-2_task-faces_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-2_task-rest_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9085_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-1_task-faces_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-1_task-rest_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-2_task-faces_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-2_task-rest_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9086_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-1_task-faces_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-1_task-rest_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-2_task-faces_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-2_task-rest_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9087_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-1_task-faces_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-1_task-rest_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-2_task-faces_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-2_task-rest_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9088_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-1_task-faces_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-1_task-rest_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-2_task-faces_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-2_task-rest_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9089_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-1_task-faces_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-1_task-rest_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-2_task-faces_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-2_task-rest_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9090_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-1_task-faces_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-1_task-rest_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-2_task-faces_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-2_task-rest_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9091_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-1_task-faces_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-1_task-rest_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-2_task-faces_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-2_task-rest_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9092_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-1_task-faces_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-1_task-rest_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-2_task-faces_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-2_task-rest_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9093_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-1_task-faces_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-1_task-rest_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-2_task-faces_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-2_task-rest_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9094_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9095_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9095_ses-1_task-faces_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9095_ses-1_task-rest_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9095_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-1_task-faces_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-1_task-rest_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-2_task-faces_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-2_task-rest_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9096_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-1_task-faces_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-1_task-rest_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-2_task-faces_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-2_task-rest_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9098_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-1_task-faces_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-1_task-rest_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-2_task-faces_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-2_task-rest_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: sub-9100_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: task-arrows_bold.json
- Location: ds000201/task-arrows_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: task-faces_bold.json
- Location: ds000201/task-faces_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: task-rest_bold.json
- Location: ds000201/task-rest_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
- Type: Error
- File: task-sleepiness_bold.json
- Location: ds000201/task-sleepiness_bold.json
- Reason: Invalid JSON file. The file is not formatted according the schema.
- Evidence: .CogAtlasID should match format "uri"
-
-
-======================================================
-
-
-File Path: All rows must have the same number of columns as there are headers.
-
- Type: Error
- File: sub-9001_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9001/ses-1/beh/sub-9001_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.7
-
- Type: Error
- File: sub-9001_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9001/ses-2/beh/sub-9001_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.8
-
- Type: Error
- File: sub-9002_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9002/ses-1/beh/sub-9002_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.3
-
- Type: Error
- File: sub-9002_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9002/ses-2/beh/sub-9002_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.9
-
- Type: Error
- File: sub-9003_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9003/ses-1/beh/sub-9003_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.8
-
- Type: Error
- File: sub-9003_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9003/ses-2/beh/sub-9003_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.5
-
- Type: Error
- File: sub-9004_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9004/ses-1/beh/sub-9004_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.7
-
- Type: Error
- File: sub-9004_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9004/ses-2/beh/sub-9004_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 19.2
-
- Type: Error
- File: sub-9005_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9005/ses-1/beh/sub-9005_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.7
-
- Type: Error
- File: sub-9005_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9005/ses-2/beh/sub-9005_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.6
-
- Type: Error
- File: sub-9007_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9007/ses-1/beh/sub-9007_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.7
-
- Type: Error
- File: sub-9007_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9007/ses-2/beh/sub-9007_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.2
-
- Type: Error
- File: sub-9008_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9008/ses-1/beh/sub-9008_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.2
-
- Type: Error
- File: sub-9008_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9008/ses-2/beh/sub-9008_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.7
-
- Type: Error
- File: sub-9009_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9009/ses-1/beh/sub-9009_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.9
-
- Type: Error
- File: sub-9009_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9009/ses-2/beh/sub-9009_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.1
-
- Type: Error
- File: sub-9011_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9011/ses-1/beh/sub-9011_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.9
-
- Type: Error
- File: sub-9011_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9011/ses-2/beh/sub-9011_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.1
-
- Type: Error
- File: sub-9012_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9012/ses-1/beh/sub-9012_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.5
-
- Type: Error
- File: sub-9012_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9012/ses-2/beh/sub-9012_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.7
-
- Type: Error
- File: sub-9013_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9013/ses-1/beh/sub-9013_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.2
-
- Type: Error
- File: sub-9013_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9013/ses-2/beh/sub-9013_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.0
-
- Type: Error
- File: sub-9014_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9014/ses-1/beh/sub-9014_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.5
-
- Type: Error
- File: sub-9014_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9014/ses-2/beh/sub-9014_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.0
-
- Type: Error
- File: sub-9016_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9016/ses-1/beh/sub-9016_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.8
-
- Type: Error
- File: sub-9016_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9016/ses-2/beh/sub-9016_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.6
-
- Type: Error
- File: sub-9017_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9017/ses-1/beh/sub-9017_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.6
-
- Type: Error
- File: sub-9017_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9017/ses-2/beh/sub-9017_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.9
-
- Type: Error
- File: sub-9018_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9018/ses-1/beh/sub-9018_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.6
-
- Type: Error
- File: sub-9018_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9018/ses-2/beh/sub-9018_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.6
-
- Type: Error
- File: sub-9019_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9019/ses-1/beh/sub-9019_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.1
-
- Type: Error
- File: sub-9019_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9019/ses-2/beh/sub-9019_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.0
-
- Type: Error
- File: sub-9020_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9020/ses-1/beh/sub-9020_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.6
-
- Type: Error
- File: sub-9020_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9020/ses-2/beh/sub-9020_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.1
-
- Type: Error
- File: sub-9022_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9022/ses-1/beh/sub-9022_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.9
-
- Type: Error
- File: sub-9022_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9022/ses-2/beh/sub-9022_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 5.2
-
- Type: Error
- File: sub-9023_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9023/ses-1/beh/sub-9023_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.7
-
- Type: Error
- File: sub-9023_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9023/ses-2/beh/sub-9023_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.2
-
- Type: Error
- File: sub-9025_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9025/ses-1/beh/sub-9025_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.9
-
- Type: Error
- File: sub-9025_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9025/ses-2/beh/sub-9025_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.5
-
- Type: Error
- File: sub-9026_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9026/ses-1/beh/sub-9026_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.1
-
- Type: Error
- File: sub-9026_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9026/ses-2/beh/sub-9026_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.9
-
- Type: Error
- File: sub-9028_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9028/ses-1/beh/sub-9028_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.3
-
- Type: Error
- File: sub-9028_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9028/ses-2/beh/sub-9028_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.5
-
- Type: Error
- File: sub-9029_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9029/ses-1/beh/sub-9029_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.3
-
- Type: Error
- File: sub-9029_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9029/ses-2/beh/sub-9029_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.8
-
- Type: Error
- File: sub-9032_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9032/ses-1/beh/sub-9032_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.3
-
- Type: Error
- File: sub-9032_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9032/ses-2/beh/sub-9032_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.8
-
- Type: Error
- File: sub-9033_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9033/ses-1/beh/sub-9033_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.1
-
- Type: Error
- File: sub-9033_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9033/ses-2/beh/sub-9033_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.2
-
- Type: Error
- File: sub-9034_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9034/ses-1/beh/sub-9034_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.5
-
- Type: Error
- File: sub-9034_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9034/ses-2/beh/sub-9034_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.4
-
- Type: Error
- File: sub-9035_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9035/ses-1/beh/sub-9035_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.2
-
- Type: Error
- File: sub-9035_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9035/ses-2/beh/sub-9035_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 19.3
-
- Type: Error
- File: sub-9036_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9036/ses-1/beh/sub-9036_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 19.0
-
- Type: Error
- File: sub-9036_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9036/ses-2/beh/sub-9036_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.0
-
- Type: Error
- File: sub-9037_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9037/ses-1/beh/sub-9037_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.3
-
- Type: Error
- File: sub-9037_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9037/ses-2/beh/sub-9037_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.8
-
- Type: Error
- File: sub-9038_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9038/ses-1/beh/sub-9038_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.4
-
- Type: Error
- File: sub-9038_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9038/ses-2/beh/sub-9038_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.1
-
- Type: Error
- File: sub-9039_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9039/ses-1/beh/sub-9039_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.6
-
- Type: Error
- File: sub-9039_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9039/ses-2/beh/sub-9039_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.6
-
- Type: Error
- File: sub-9040_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9040/ses-1/beh/sub-9040_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.1
-
- Type: Error
- File: sub-9040_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9040/ses-2/beh/sub-9040_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.2
-
- Type: Error
- File: sub-9041_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9041/ses-1/beh/sub-9041_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.2
-
- Type: Error
- File: sub-9041_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9041/ses-2/beh/sub-9041_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.7
-
- Type: Error
- File: sub-9042_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9042/ses-1/beh/sub-9042_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.4
-
- Type: Error
- File: sub-9042_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9042/ses-2/beh/sub-9042_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 19.7
-
- Type: Error
- File: sub-9044_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9044/ses-1/beh/sub-9044_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.6
-
- Type: Error
- File: sub-9044_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9044/ses-2/beh/sub-9044_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.1
-
- Type: Error
- File: sub-9045_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9045/ses-1/beh/sub-9045_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.9
-
- Type: Error
- File: sub-9045_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9045/ses-2/beh/sub-9045_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.1
-
- Type: Error
- File: sub-9046_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9046/ses-1/beh/sub-9046_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.8
-
- Type: Error
- File: sub-9046_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9046/ses-2/beh/sub-9046_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.6
-
- Type: Error
- File: sub-9047_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9047/ses-1/beh/sub-9047_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.7
-
- Type: Error
- File: sub-9047_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9047/ses-2/beh/sub-9047_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 18.3
-
- Type: Error
- File: sub-9048_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9048/ses-1/beh/sub-9048_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.3
-
- Type: Error
- File: sub-9048_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9048/ses-2/beh/sub-9048_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.8
-
- Type: Error
- File: sub-9049_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9049/ses-1/beh/sub-9049_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.4
-
- Type: Error
- File: sub-9049_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9049/ses-2/beh/sub-9049_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.5
-
- Type: Error
- File: sub-9050_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9050/ses-1/beh/sub-9050_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.7
-
- Type: Error
- File: sub-9050_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9050/ses-2/beh/sub-9050_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.5
-
- Type: Error
- File: sub-9053_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9053/ses-1/beh/sub-9053_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.7
-
- Type: Error
- File: sub-9053_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9053/ses-2/beh/sub-9053_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.9
-
- Type: Error
- File: sub-9054_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9054/ses-1/beh/sub-9054_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.1
-
- Type: Error
- File: sub-9054_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9054/ses-2/beh/sub-9054_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.4
-
- Type: Error
- File: sub-9055_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9055/ses-1/beh/sub-9055_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 18.3
-
- Type: Error
- File: sub-9055_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9055/ses-2/beh/sub-9055_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.2
-
- Type: Error
- File: sub-9056_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9056/ses-1/beh/sub-9056_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.6
-
- Type: Error
- File: sub-9056_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9056/ses-2/beh/sub-9056_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.4
-
- Type: Error
- File: sub-9057_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9057/ses-1/beh/sub-9057_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.1
-
- Type: Error
- File: sub-9057_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9057/ses-2/beh/sub-9057_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.9
-
- Type: Error
- File: sub-9058_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9058/ses-1/beh/sub-9058_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.1
-
- Type: Error
- File: sub-9058_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9058/ses-2/beh/sub-9058_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.9
-
- Type: Error
- File: sub-9059_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9059/ses-1/beh/sub-9059_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.5
-
- Type: Error
- File: sub-9059_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9059/ses-2/beh/sub-9059_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 18.8
-
- Type: Error
- File: sub-9061_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9061/ses-1/beh/sub-9061_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.3
-
- Type: Error
- File: sub-9061_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9061/ses-2/beh/sub-9061_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.9
-
- Type: Error
- File: sub-9062_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9062/ses-1/beh/sub-9062_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.2
-
- Type: Error
- File: sub-9062_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9062/ses-2/beh/sub-9062_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.5
-
- Type: Error
- File: sub-9063_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9063/ses-1/beh/sub-9063_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.5
-
- Type: Error
- File: sub-9063_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9063/ses-2/beh/sub-9063_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.0
-
- Type: Error
- File: sub-9064_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9064/ses-1/beh/sub-9064_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.6
-
- Type: Error
- File: sub-9064_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9064/ses-2/beh/sub-9064_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.6
-
- Type: Error
- File: sub-9065_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9065/ses-1/beh/sub-9065_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.9
-
- Type: Error
- File: sub-9065_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9065/ses-2/beh/sub-9065_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.4
-
- Type: Error
- File: sub-9066_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9066/ses-1/beh/sub-9066_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.5
-
- Type: Error
- File: sub-9066_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9066/ses-2/beh/sub-9066_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.2
-
- Type: Error
- File: sub-9067_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9067/ses-1/beh/sub-9067_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.8
-
- Type: Error
- File: sub-9067_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9067/ses-2/beh/sub-9067_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.4
-
- Type: Error
- File: sub-9068_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9068/ses-1/beh/sub-9068_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.0
-
- Type: Error
- File: sub-9068_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9068/ses-2/beh/sub-9068_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.5
-
- Type: Error
- File: sub-9069_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9069/ses-1/beh/sub-9069_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.2
-
- Type: Error
- File: sub-9069_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9069/ses-2/beh/sub-9069_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.9
-
- Type: Error
- File: sub-9070_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9070/ses-1/beh/sub-9070_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.5
-
- Type: Error
- File: sub-9070_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9070/ses-2/beh/sub-9070_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.4
-
- Type: Error
- File: sub-9071_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9071/ses-1/beh/sub-9071_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.7
-
- Type: Error
- File: sub-9071_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9071/ses-2/beh/sub-9071_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 15.1
-
- Type: Error
- File: sub-9072_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9072/ses-1/beh/sub-9072_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.6
-
- Type: Error
- File: sub-9072_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9072/ses-2/beh/sub-9072_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.3
-
- Type: Error
- File: sub-9073_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9073/ses-1/beh/sub-9073_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.9
-
- Type: Error
- File: sub-9073_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9073/ses-2/beh/sub-9073_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.1
-
- Type: Error
- File: sub-9074_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9074/ses-1/beh/sub-9074_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.5
-
- Type: Error
- File: sub-9074_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9074/ses-2/beh/sub-9074_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.6
-
- Type: Error
- File: sub-9075_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9075/ses-1/beh/sub-9075_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.8
-
- Type: Error
- File: sub-9075_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9075/ses-2/beh/sub-9075_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.9
-
- Type: Error
- File: sub-9076_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9076/ses-1/beh/sub-9076_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.9
-
- Type: Error
- File: sub-9076_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9076/ses-2/beh/sub-9076_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.0
-
- Type: Error
- File: sub-9077_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9077/ses-1/beh/sub-9077_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 18.1
-
- Type: Error
- File: sub-9077_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9077/ses-2/beh/sub-9077_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.5
-
- Type: Error
- File: sub-9078_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9078/ses-1/beh/sub-9078_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.5
-
- Type: Error
- File: sub-9078_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9078/ses-2/beh/sub-9078_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.9
-
- Type: Error
- File: sub-9079_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9079/ses-1/beh/sub-9079_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.4
-
- Type: Error
- File: sub-9079_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9079/ses-2/beh/sub-9079_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.7
-
- Type: Error
- File: sub-9080_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9080/ses-1/beh/sub-9080_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 18.1
-
- Type: Error
- File: sub-9080_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9080/ses-2/beh/sub-9080_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.5
-
- Type: Error
- File: sub-9081_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9081/ses-1/beh/sub-9081_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.4
-
- Type: Error
- File: sub-9081_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9081/ses-2/beh/sub-9081_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.3
-
- Type: Error
- File: sub-9082_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9082/ses-1/beh/sub-9082_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.4
-
- Type: Error
- File: sub-9082_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9082/ses-2/beh/sub-9082_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.9
-
- Type: Error
- File: sub-9083_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9083/ses-1/beh/sub-9083_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.6
-
- Type: Error
- File: sub-9083_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9083/ses-2/beh/sub-9083_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.7
-
- Type: Error
- File: sub-9084_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9084/ses-1/beh/sub-9084_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.5
-
- Type: Error
- File: sub-9084_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9084/ses-2/beh/sub-9084_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.4
-
- Type: Error
- File: sub-9085_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9085/ses-1/beh/sub-9085_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.5
-
- Type: Error
- File: sub-9085_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9085/ses-2/beh/sub-9085_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 8.3
-
- Type: Error
- File: sub-9086_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9086/ses-1/beh/sub-9086_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.8
-
- Type: Error
- File: sub-9086_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9086/ses-2/beh/sub-9086_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.8
-
- Type: Error
- File: sub-9087_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9087/ses-1/beh/sub-9087_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 10.0
-
- Type: Error
- File: sub-9087_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9087/ses-2/beh/sub-9087_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.2
-
- Type: Error
- File: sub-9088_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9088/ses-1/beh/sub-9088_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 17.2
-
- Type: Error
- File: sub-9088_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9088/ses-2/beh/sub-9088_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.7
-
- Type: Error
- File: sub-9089_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9089/ses-1/beh/sub-9089_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.7
-
- Type: Error
- File: sub-9089_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9089/ses-2/beh/sub-9089_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.9
-
- Type: Error
- File: sub-9090_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9090/ses-1/beh/sub-9090_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.3
-
- Type: Error
- File: sub-9090_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9090/ses-2/beh/sub-9090_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 14.0
-
- Type: Error
- File: sub-9091_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9091/ses-1/beh/sub-9091_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 16.5
-
- Type: Error
- File: sub-9091_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9091/ses-2/beh/sub-9091_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.3
-
- Type: Error
- File: sub-9092_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9092/ses-1/beh/sub-9092_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.8
-
- Type: Error
- File: sub-9092_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9092/ses-2/beh/sub-9092_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.9
-
- Type: Error
- File: sub-9093_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9093/ses-1/beh/sub-9093_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.4
-
- Type: Error
- File: sub-9093_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9093/ses-2/beh/sub-9093_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.3
-
- Type: Error
- File: sub-9094_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9094/ses-1/beh/sub-9094_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.9
-
- Type: Error
- File: sub-9094_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9094/ses-2/beh/sub-9094_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 7.8
-
- Type: Error
- File: sub-9095_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9095/ses-1/beh/sub-9095_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.7
-
- Type: Error
- File: sub-9096_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9096/ses-1/beh/sub-9096_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 12.0
-
- Type: Error
- File: sub-9096_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9096/ses-2/beh/sub-9096_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 18.1
-
- Type: Error
- File: sub-9098_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9098/ses-1/beh/sub-9098_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 11.3
-
- Type: Error
- File: sub-9098_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9098/ses-2/beh/sub-9098_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 6.9
-
- Type: Error
- File: sub-9100_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9100/ses-1/beh/sub-9100_ses-1_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 13.9
-
- Type: Error
- File: sub-9100_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9100/ses-2/beh/sub-9100_ses-2_task-PVT_events.tsv
- Reason: All rows must have the same number of columns as there are headers.
- Line: 2
- Evidence: row 1: 9.3
-
-
-======================================================
-
-
-File Path: Empty cell in TSV file detected: The proper way of labeling missing values is "n/a".
-
- Type: Error
- File: sub-9001_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9001/ses-1/beh/sub-9001_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.395
-
- Type: Error
- File: sub-9001_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9001/ses-2/beh/sub-9001_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.208
-
- Type: Error
- File: sub-9002_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9002/ses-1/beh/sub-9002_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.234
-
- Type: Error
- File: sub-9002_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9002/ses-2/beh/sub-9002_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.189
-
- Type: Error
- File: sub-9003_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9003/ses-1/beh/sub-9003_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.231
-
- Type: Error
- File: sub-9003_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9003/ses-2/beh/sub-9003_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.302
-
- Type: Error
- File: sub-9004_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9004/ses-1/beh/sub-9004_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.334
-
- Type: Error
- File: sub-9004_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9004/ses-2/beh/sub-9004_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.230
-
- Type: Error
- File: sub-9005_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9005/ses-1/beh/sub-9005_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.238
-
- Type: Error
- File: sub-9005_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9005/ses-2/beh/sub-9005_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.277
-
- Type: Error
- File: sub-9007_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9007/ses-1/beh/sub-9007_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.234
-
- Type: Error
- File: sub-9007_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9007/ses-2/beh/sub-9007_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.319
-
- Type: Error
- File: sub-9008_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9008/ses-1/beh/sub-9008_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.208
-
- Type: Error
- File: sub-9008_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9008/ses-2/beh/sub-9008_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.268
-
- Type: Error
- File: sub-9009_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9009/ses-1/beh/sub-9009_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.209
-
- Type: Error
- File: sub-9009_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9009/ses-2/beh/sub-9009_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.272
-
- Type: Error
- File: sub-9011_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9011/ses-1/beh/sub-9011_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.290
-
- Type: Error
- File: sub-9011_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9011/ses-2/beh/sub-9011_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.305
-
- Type: Error
- File: sub-9012_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9012/ses-1/beh/sub-9012_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.214
-
- Type: Error
- File: sub-9012_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9012/ses-2/beh/sub-9012_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.337
-
- Type: Error
- File: sub-9013_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9013/ses-1/beh/sub-9013_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.330
-
- Type: Error
- File: sub-9013_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9013/ses-2/beh/sub-9013_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.220
-
- Type: Error
- File: sub-9014_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9014/ses-1/beh/sub-9014_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.173
-
- Type: Error
- File: sub-9014_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9014/ses-2/beh/sub-9014_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.198
-
- Type: Error
- File: sub-9016_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9016/ses-1/beh/sub-9016_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.230
-
- Type: Error
- File: sub-9016_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9016/ses-2/beh/sub-9016_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.220
-
- Type: Error
- File: sub-9017_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9017/ses-1/beh/sub-9017_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.257
-
- Type: Error
- File: sub-9017_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9017/ses-2/beh/sub-9017_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.239
-
- Type: Error
- File: sub-9018_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9018/ses-1/beh/sub-9018_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.344
-
- Type: Error
- File: sub-9018_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9018/ses-2/beh/sub-9018_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.285
-
- Type: Error
- File: sub-9019_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9019/ses-1/beh/sub-9019_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.239
-
- Type: Error
- File: sub-9019_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9019/ses-2/beh/sub-9019_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.292
-
- Type: Error
- File: sub-9020_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9020/ses-1/beh/sub-9020_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.365
-
- Type: Error
- File: sub-9020_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9020/ses-2/beh/sub-9020_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.261
-
- Type: Error
- File: sub-9022_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9022/ses-1/beh/sub-9022_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.218
-
- Type: Error
- File: sub-9022_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9022/ses-2/beh/sub-9022_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.260
-
- Type: Error
- File: sub-9023_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9023/ses-1/beh/sub-9023_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.220
-
- Type: Error
- File: sub-9023_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9023/ses-2/beh/sub-9023_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.266
-
- Type: Error
- File: sub-9025_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9025/ses-1/beh/sub-9025_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.281
-
- Type: Error
- File: sub-9025_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9025/ses-2/beh/sub-9025_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.240
-
- Type: Error
- File: sub-9026_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9026/ses-1/beh/sub-9026_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.210
-
- Type: Error
- File: sub-9026_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9026/ses-2/beh/sub-9026_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.230
-
- Type: Error
- File: sub-9028_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9028/ses-1/beh/sub-9028_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.201
-
- Type: Error
- File: sub-9028_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9028/ses-2/beh/sub-9028_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.239
-
- Type: Error
- File: sub-9029_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9029/ses-1/beh/sub-9029_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.283
-
- Type: Error
- File: sub-9029_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9029/ses-2/beh/sub-9029_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.272
-
- Type: Error
- File: sub-9032_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9032/ses-1/beh/sub-9032_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.263
-
- Type: Error
- File: sub-9032_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9032/ses-2/beh/sub-9032_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.228
-
- Type: Error
- File: sub-9033_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9033/ses-1/beh/sub-9033_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.230
-
- Type: Error
- File: sub-9033_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9033/ses-2/beh/sub-9033_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.206
-
- Type: Error
- File: sub-9034_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9034/ses-1/beh/sub-9034_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.199
-
- Type: Error
- File: sub-9034_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9034/ses-2/beh/sub-9034_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.178
-
- Type: Error
- File: sub-9035_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9035/ses-1/beh/sub-9035_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.301
-
- Type: Error
- File: sub-9035_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9035/ses-2/beh/sub-9035_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.224
-
- Type: Error
- File: sub-9036_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9036/ses-1/beh/sub-9036_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.247
-
- Type: Error
- File: sub-9036_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9036/ses-2/beh/sub-9036_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.213
-
- Type: Error
- File: sub-9037_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9037/ses-1/beh/sub-9037_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.240
-
- Type: Error
- File: sub-9037_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9037/ses-2/beh/sub-9037_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.249
-
- Type: Error
- File: sub-9038_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9038/ses-1/beh/sub-9038_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.219
-
- Type: Error
- File: sub-9038_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9038/ses-2/beh/sub-9038_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.238
-
- Type: Error
- File: sub-9039_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9039/ses-1/beh/sub-9039_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.382
-
- Type: Error
- File: sub-9039_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9039/ses-2/beh/sub-9039_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.168
-
- Type: Error
- File: sub-9040_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9040/ses-1/beh/sub-9040_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.282
-
- Type: Error
- File: sub-9040_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9040/ses-2/beh/sub-9040_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.261
-
- Type: Error
- File: sub-9041_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9041/ses-1/beh/sub-9041_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.230
-
- Type: Error
- File: sub-9041_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9041/ses-2/beh/sub-9041_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.226
-
- Type: Error
- File: sub-9042_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9042/ses-1/beh/sub-9042_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.214
-
- Type: Error
- File: sub-9042_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9042/ses-2/beh/sub-9042_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.193
-
- Type: Error
- File: sub-9044_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9044/ses-1/beh/sub-9044_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.156
-
- Type: Error
- File: sub-9044_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9044/ses-2/beh/sub-9044_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.175
-
- Type: Error
- File: sub-9045_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9045/ses-1/beh/sub-9045_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.314
-
- Type: Error
- File: sub-9045_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9045/ses-2/beh/sub-9045_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.248
-
- Type: Error
- File: sub-9046_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9046/ses-1/beh/sub-9046_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.310
-
- Type: Error
- File: sub-9046_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9046/ses-2/beh/sub-9046_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.265
-
- Type: Error
- File: sub-9047_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9047/ses-1/beh/sub-9047_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.271
-
- Type: Error
- File: sub-9047_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9047/ses-2/beh/sub-9047_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.210
-
- Type: Error
- File: sub-9048_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9048/ses-1/beh/sub-9048_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.184
-
- Type: Error
- File: sub-9048_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9048/ses-2/beh/sub-9048_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.197
-
- Type: Error
- File: sub-9049_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9049/ses-1/beh/sub-9049_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.222
-
- Type: Error
- File: sub-9049_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9049/ses-2/beh/sub-9049_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.221
-
- Type: Error
- File: sub-9050_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9050/ses-1/beh/sub-9050_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.203
-
- Type: Error
- File: sub-9050_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9050/ses-2/beh/sub-9050_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.388
-
- Type: Error
- File: sub-9053_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9053/ses-1/beh/sub-9053_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.248
-
- Type: Error
- File: sub-9053_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9053/ses-2/beh/sub-9053_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.307
-
- Type: Error
- File: sub-9054_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9054/ses-1/beh/sub-9054_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.210
-
- Type: Error
- File: sub-9054_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9054/ses-2/beh/sub-9054_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.227
-
- Type: Error
- File: sub-9055_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9055/ses-1/beh/sub-9055_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.379
-
- Type: Error
- File: sub-9055_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9055/ses-2/beh/sub-9055_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.237
-
- Type: Error
- File: sub-9056_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9056/ses-1/beh/sub-9056_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.197
-
- Type: Error
- File: sub-9056_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9056/ses-2/beh/sub-9056_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.225
-
- Type: Error
- File: sub-9057_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9057/ses-1/beh/sub-9057_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.389
-
- Type: Error
- File: sub-9057_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9057/ses-2/beh/sub-9057_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.225
-
- Type: Error
- File: sub-9058_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9058/ses-1/beh/sub-9058_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.250
-
- Type: Error
- File: sub-9058_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9058/ses-2/beh/sub-9058_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.251
-
- Type: Error
- File: sub-9059_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9059/ses-1/beh/sub-9059_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.252
-
- Type: Error
- File: sub-9059_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9059/ses-2/beh/sub-9059_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.300
-
- Type: Error
- File: sub-9061_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9061/ses-1/beh/sub-9061_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.183
-
- Type: Error
- File: sub-9061_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9061/ses-2/beh/sub-9061_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.252
-
- Type: Error
- File: sub-9062_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9062/ses-1/beh/sub-9062_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.236
-
- Type: Error
- File: sub-9062_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9062/ses-2/beh/sub-9062_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.194
-
- Type: Error
- File: sub-9063_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9063/ses-1/beh/sub-9063_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.201
-
- Type: Error
- File: sub-9063_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9063/ses-2/beh/sub-9063_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.233
-
- Type: Error
- File: sub-9064_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9064/ses-1/beh/sub-9064_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.278
-
- Type: Error
- File: sub-9064_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9064/ses-2/beh/sub-9064_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.281
-
- Type: Error
- File: sub-9065_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9065/ses-1/beh/sub-9065_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.200
-
- Type: Error
- File: sub-9065_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9065/ses-2/beh/sub-9065_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.296
-
- Type: Error
- File: sub-9066_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9066/ses-1/beh/sub-9066_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.181
-
- Type: Error
- File: sub-9066_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9066/ses-2/beh/sub-9066_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.260
-
- Type: Error
- File: sub-9067_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9067/ses-1/beh/sub-9067_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.222
-
- Type: Error
- File: sub-9067_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9067/ses-2/beh/sub-9067_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.318
-
- Type: Error
- File: sub-9068_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9068/ses-1/beh/sub-9068_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.231
-
- Type: Error
- File: sub-9068_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9068/ses-2/beh/sub-9068_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.215
-
- Type: Error
- File: sub-9069_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9069/ses-1/beh/sub-9069_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.203
-
- Type: Error
- File: sub-9069_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9069/ses-2/beh/sub-9069_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.204
-
- Type: Error
- File: sub-9070_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9070/ses-1/beh/sub-9070_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.219
-
- Type: Error
- File: sub-9070_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9070/ses-2/beh/sub-9070_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.179
-
- Type: Error
- File: sub-9071_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9071/ses-1/beh/sub-9071_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.243
-
- Type: Error
- File: sub-9071_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9071/ses-2/beh/sub-9071_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.204
-
- Type: Error
- File: sub-9072_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9072/ses-1/beh/sub-9072_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.236
-
- Type: Error
- File: sub-9072_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9072/ses-2/beh/sub-9072_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.212
-
- Type: Error
- File: sub-9073_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9073/ses-1/beh/sub-9073_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.207
-
- Type: Error
- File: sub-9073_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9073/ses-2/beh/sub-9073_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.248
-
- Type: Error
- File: sub-9074_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9074/ses-1/beh/sub-9074_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.246
-
- Type: Error
- File: sub-9074_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9074/ses-2/beh/sub-9074_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.220
-
- Type: Error
- File: sub-9075_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9075/ses-1/beh/sub-9075_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.188
-
- Type: Error
- File: sub-9075_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9075/ses-2/beh/sub-9075_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.001
-
- Type: Error
- File: sub-9076_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9076/ses-1/beh/sub-9076_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.232
-
- Type: Error
- File: sub-9076_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9076/ses-2/beh/sub-9076_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.256
-
- Type: Error
- File: sub-9077_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9077/ses-1/beh/sub-9077_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.261
-
- Type: Error
- File: sub-9077_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9077/ses-2/beh/sub-9077_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.266
-
- Type: Error
- File: sub-9078_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9078/ses-1/beh/sub-9078_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.252
-
- Type: Error
- File: sub-9078_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9078/ses-2/beh/sub-9078_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.237
-
- Type: Error
- File: sub-9079_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9079/ses-1/beh/sub-9079_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.218
-
- Type: Error
- File: sub-9079_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9079/ses-2/beh/sub-9079_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.315
-
- Type: Error
- File: sub-9080_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9080/ses-1/beh/sub-9080_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.246
-
- Type: Error
- File: sub-9080_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9080/ses-2/beh/sub-9080_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.242
-
- Type: Error
- File: sub-9081_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9081/ses-1/beh/sub-9081_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.203
-
- Type: Error
- File: sub-9081_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9081/ses-2/beh/sub-9081_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.186
-
- Type: Error
- File: sub-9082_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9082/ses-1/beh/sub-9082_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.230
-
- Type: Error
- File: sub-9082_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9082/ses-2/beh/sub-9082_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.297
-
- Type: Error
- File: sub-9083_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9083/ses-1/beh/sub-9083_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.241
-
- Type: Error
- File: sub-9083_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9083/ses-2/beh/sub-9083_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.240
-
- Type: Error
- File: sub-9084_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9084/ses-1/beh/sub-9084_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.204
-
- Type: Error
- File: sub-9084_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9084/ses-2/beh/sub-9084_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.285
-
- Type: Error
- File: sub-9085_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9085/ses-1/beh/sub-9085_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.323
-
- Type: Error
- File: sub-9085_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9085/ses-2/beh/sub-9085_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.233
-
- Type: Error
- File: sub-9086_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9086/ses-1/beh/sub-9086_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.198
-
- Type: Error
- File: sub-9086_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9086/ses-2/beh/sub-9086_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.255
-
- Type: Error
- File: sub-9087_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9087/ses-1/beh/sub-9087_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.380
-
- Type: Error
- File: sub-9087_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9087/ses-2/beh/sub-9087_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.196
-
- Type: Error
- File: sub-9088_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9088/ses-1/beh/sub-9088_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.228
-
- Type: Error
- File: sub-9088_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9088/ses-2/beh/sub-9088_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.186
-
- Type: Error
- File: sub-9089_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9089/ses-1/beh/sub-9089_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.201
-
- Type: Error
- File: sub-9089_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9089/ses-2/beh/sub-9089_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.221
-
- Type: Error
- File: sub-9090_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9090/ses-1/beh/sub-9090_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.238
-
- Type: Error
- File: sub-9090_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9090/ses-2/beh/sub-9090_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.273
-
- Type: Error
- File: sub-9091_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9091/ses-1/beh/sub-9091_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.214
-
- Type: Error
- File: sub-9091_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9091/ses-2/beh/sub-9091_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.243
-
- Type: Error
- File: sub-9092_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9092/ses-1/beh/sub-9092_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.289
-
- Type: Error
- File: sub-9092_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9092/ses-2/beh/sub-9092_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.247
-
- Type: Error
- File: sub-9093_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9093/ses-1/beh/sub-9093_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.209
-
- Type: Error
- File: sub-9093_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9093/ses-2/beh/sub-9093_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.270
-
- Type: Error
- File: sub-9094_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9094/ses-1/beh/sub-9094_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.235
-
- Type: Error
- File: sub-9094_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9094/ses-2/beh/sub-9094_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.281
-
- Type: Error
- File: sub-9095_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9095/ses-1/beh/sub-9095_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.249
-
- Type: Error
- File: sub-9096_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9096/ses-1/beh/sub-9096_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.263
-
- Type: Error
- File: sub-9096_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9096/ses-2/beh/sub-9096_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.459
-
- Type: Error
- File: sub-9098_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9098/ses-1/beh/sub-9098_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.225
-
- Type: Error
- File: sub-9098_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9098/ses-2/beh/sub-9098_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.240
-
- Type: Error
- File: sub-9100_ses-1_task-PVT_events.tsv
- Location: ds000201/sub-9100/ses-1/beh/sub-9100_ses-1_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.209
-
- Type: Error
- File: sub-9100_ses-2_task-PVT_events.tsv
- Location: ds000201/sub-9100/ses-2/beh/sub-9100_ses-2_task-PVT_events.tsv
- Reason: Missing value at column # 1
- Line: 3
- Evidence: row 2: n/a 0.192
-
-
-======================================================
-
-
-File Path: A json sidecar file was found without a corresponding data file
-
- Type: Error
- File: sub-9001_ses-1_T1w.json
- Location: ds000201/sub-9001/ses-1/anat/sub-9001_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_T2w.json
- Location: ds000201/sub-9001/ses-1/anat/sub-9001_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_magnitude1.json
- Location: ds000201/sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_magnitude2.json
- Location: ds000201/sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_phase1.json
- Location: ds000201/sub-9001/ses-1/fmap/sub-9001_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_phase2.json
- Location: ds000201/sub-9001/ses-1/fmap/sub-9001_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_task-faces_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_task-hands_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_task-rest_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_T1w.json
- Location: ds000201/sub-9001/ses-2/anat/sub-9001_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_magnitude1.json
- Location: ds000201/sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_magnitude2.json
- Location: ds000201/sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_phase1.json
- Location: ds000201/sub-9001/ses-2/fmap/sub-9001_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_phase2.json
- Location: ds000201/sub-9001/ses-2/fmap/sub-9001_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_task-faces_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_task-hands_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_task-rest_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9001_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_T1w.json
- Location: ds000201/sub-9002/ses-1/anat/sub-9002_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_T2w.json
- Location: ds000201/sub-9002/ses-1/anat/sub-9002_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_magnitude1.json
- Location: ds000201/sub-9002/ses-1/fmap/sub-9002_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_magnitude2.json
- Location: ds000201/sub-9002/ses-1/fmap/sub-9002_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_phase1.json
- Location: ds000201/sub-9002/ses-1/fmap/sub-9002_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_phase2.json
- Location: ds000201/sub-9002/ses-1/fmap/sub-9002_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_task-faces_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_task-faces_physio.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_task-hands_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_task-rest_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_T1w.json
- Location: ds000201/sub-9002/ses-2/anat/sub-9002_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_magnitude1.json
- Location: ds000201/sub-9002/ses-2/fmap/sub-9002_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_magnitude2.json
- Location: ds000201/sub-9002/ses-2/fmap/sub-9002_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_phase1.json
- Location: ds000201/sub-9002/ses-2/fmap/sub-9002_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_phase2.json
- Location: ds000201/sub-9002/ses-2/fmap/sub-9002_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_task-faces_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_task-faces_physio.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_task-hands_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_task-rest_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9002_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_T1w.json
- Location: ds000201/sub-9003/ses-1/anat/sub-9003_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_T2w.json
- Location: ds000201/sub-9003/ses-1/anat/sub-9003_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_magnitude1.json
- Location: ds000201/sub-9003/ses-1/fmap/sub-9003_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_magnitude2.json
- Location: ds000201/sub-9003/ses-1/fmap/sub-9003_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_phase1.json
- Location: ds000201/sub-9003/ses-1/fmap/sub-9003_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_phase2.json
- Location: ds000201/sub-9003/ses-1/fmap/sub-9003_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_task-faces_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_task-faces_physio.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_task-hands_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_task-rest_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_T1w.json
- Location: ds000201/sub-9003/ses-2/anat/sub-9003_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_magnitude1.json
- Location: ds000201/sub-9003/ses-2/fmap/sub-9003_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_magnitude2.json
- Location: ds000201/sub-9003/ses-2/fmap/sub-9003_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_phase1.json
- Location: ds000201/sub-9003/ses-2/fmap/sub-9003_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_phase2.json
- Location: ds000201/sub-9003/ses-2/fmap/sub-9003_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_task-faces_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_task-faces_physio.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_task-hands_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_task-rest_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9003_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_T1w.json
- Location: ds000201/sub-9004/ses-1/anat/sub-9004_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_T2w.json
- Location: ds000201/sub-9004/ses-1/anat/sub-9004_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_magnitude1.json
- Location: ds000201/sub-9004/ses-1/fmap/sub-9004_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_magnitude2.json
- Location: ds000201/sub-9004/ses-1/fmap/sub-9004_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_phase1.json
- Location: ds000201/sub-9004/ses-1/fmap/sub-9004_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_phase2.json
- Location: ds000201/sub-9004/ses-1/fmap/sub-9004_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_task-faces_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_task-faces_physio.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_task-hands_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_task-rest_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_T1w.json
- Location: ds000201/sub-9004/ses-2/anat/sub-9004_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_magnitude1.json
- Location: ds000201/sub-9004/ses-2/fmap/sub-9004_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_magnitude2.json
- Location: ds000201/sub-9004/ses-2/fmap/sub-9004_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_phase1.json
- Location: ds000201/sub-9004/ses-2/fmap/sub-9004_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_phase2.json
- Location: ds000201/sub-9004/ses-2/fmap/sub-9004_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_task-faces_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_task-faces_physio.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_task-hands_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_task-rest_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9004_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_T1w.json
- Location: ds000201/sub-9005/ses-1/anat/sub-9005_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_T2w.json
- Location: ds000201/sub-9005/ses-1/anat/sub-9005_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_magnitude1.json
- Location: ds000201/sub-9005/ses-1/fmap/sub-9005_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_magnitude2.json
- Location: ds000201/sub-9005/ses-1/fmap/sub-9005_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_phase1.json
- Location: ds000201/sub-9005/ses-1/fmap/sub-9005_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_phase2.json
- Location: ds000201/sub-9005/ses-1/fmap/sub-9005_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_task-faces_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_task-faces_physio.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_task-hands_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_task-rest_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_T1w.json
- Location: ds000201/sub-9005/ses-2/anat/sub-9005_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_magnitude1.json
- Location: ds000201/sub-9005/ses-2/fmap/sub-9005_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_magnitude2.json
- Location: ds000201/sub-9005/ses-2/fmap/sub-9005_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_phase1.json
- Location: ds000201/sub-9005/ses-2/fmap/sub-9005_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_phase2.json
- Location: ds000201/sub-9005/ses-2/fmap/sub-9005_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_task-faces_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_task-faces_physio.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_task-hands_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_task-rest_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9005_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_T1w.json
- Location: ds000201/sub-9007/ses-1/anat/sub-9007_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_T2w.json
- Location: ds000201/sub-9007/ses-1/anat/sub-9007_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_magnitude1.json
- Location: ds000201/sub-9007/ses-1/fmap/sub-9007_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_magnitude2.json
- Location: ds000201/sub-9007/ses-1/fmap/sub-9007_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_phase1.json
- Location: ds000201/sub-9007/ses-1/fmap/sub-9007_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_phase2.json
- Location: ds000201/sub-9007/ses-1/fmap/sub-9007_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_task-faces_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_task-hands_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_task-rest_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_T1w.json
- Location: ds000201/sub-9007/ses-2/anat/sub-9007_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_magnitude1.json
- Location: ds000201/sub-9007/ses-2/fmap/sub-9007_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_magnitude2.json
- Location: ds000201/sub-9007/ses-2/fmap/sub-9007_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_phase1.json
- Location: ds000201/sub-9007/ses-2/fmap/sub-9007_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_phase2.json
- Location: ds000201/sub-9007/ses-2/fmap/sub-9007_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_task-faces_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_task-hands_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_task-rest_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9007_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_T1w.json
- Location: ds000201/sub-9008/ses-1/anat/sub-9008_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_T2w.json
- Location: ds000201/sub-9008/ses-1/anat/sub-9008_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_magnitude1.json
- Location: ds000201/sub-9008/ses-1/fmap/sub-9008_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_magnitude2.json
- Location: ds000201/sub-9008/ses-1/fmap/sub-9008_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_phase1.json
- Location: ds000201/sub-9008/ses-1/fmap/sub-9008_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_phase2.json
- Location: ds000201/sub-9008/ses-1/fmap/sub-9008_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_task-faces_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_task-faces_physio.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_task-hands_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_task-rest_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_T1w.json
- Location: ds000201/sub-9008/ses-2/anat/sub-9008_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_magnitude1.json
- Location: ds000201/sub-9008/ses-2/fmap/sub-9008_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_magnitude2.json
- Location: ds000201/sub-9008/ses-2/fmap/sub-9008_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_phase1.json
- Location: ds000201/sub-9008/ses-2/fmap/sub-9008_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_phase2.json
- Location: ds000201/sub-9008/ses-2/fmap/sub-9008_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_task-faces_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_task-faces_physio.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_task-hands_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_task-rest_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9008_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_T1w.json
- Location: ds000201/sub-9009/ses-1/anat/sub-9009_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_T2w.json
- Location: ds000201/sub-9009/ses-1/anat/sub-9009_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_magnitude1.json
- Location: ds000201/sub-9009/ses-1/fmap/sub-9009_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_magnitude2.json
- Location: ds000201/sub-9009/ses-1/fmap/sub-9009_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_phase1.json
- Location: ds000201/sub-9009/ses-1/fmap/sub-9009_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_phase2.json
- Location: ds000201/sub-9009/ses-1/fmap/sub-9009_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_task-faces_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_task-faces_physio.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_task-hands_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_task-rest_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_T1w.json
- Location: ds000201/sub-9009/ses-2/anat/sub-9009_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_magnitude1.json
- Location: ds000201/sub-9009/ses-2/fmap/sub-9009_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_magnitude2.json
- Location: ds000201/sub-9009/ses-2/fmap/sub-9009_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_phase1.json
- Location: ds000201/sub-9009/ses-2/fmap/sub-9009_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_phase2.json
- Location: ds000201/sub-9009/ses-2/fmap/sub-9009_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_task-faces_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_task-faces_physio.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_task-hands_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_task-rest_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9009_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_T1w.json
- Location: ds000201/sub-9011/ses-1/anat/sub-9011_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_T2w.json
- Location: ds000201/sub-9011/ses-1/anat/sub-9011_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_magnitude1.json
- Location: ds000201/sub-9011/ses-1/fmap/sub-9011_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_magnitude2.json
- Location: ds000201/sub-9011/ses-1/fmap/sub-9011_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_phase1.json
- Location: ds000201/sub-9011/ses-1/fmap/sub-9011_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_phase2.json
- Location: ds000201/sub-9011/ses-1/fmap/sub-9011_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_task-faces_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_task-faces_physio.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_task-hands_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_task-rest_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_T1w.json
- Location: ds000201/sub-9011/ses-2/anat/sub-9011_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_magnitude1.json
- Location: ds000201/sub-9011/ses-2/fmap/sub-9011_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_magnitude2.json
- Location: ds000201/sub-9011/ses-2/fmap/sub-9011_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_phase1.json
- Location: ds000201/sub-9011/ses-2/fmap/sub-9011_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_phase2.json
- Location: ds000201/sub-9011/ses-2/fmap/sub-9011_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_task-faces_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_task-faces_physio.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_task-hands_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_task-rest_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9011_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_T1w.json
- Location: ds000201/sub-9012/ses-1/anat/sub-9012_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_T2w.json
- Location: ds000201/sub-9012/ses-1/anat/sub-9012_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_magnitude1.json
- Location: ds000201/sub-9012/ses-1/fmap/sub-9012_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_magnitude2.json
- Location: ds000201/sub-9012/ses-1/fmap/sub-9012_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_phase1.json
- Location: ds000201/sub-9012/ses-1/fmap/sub-9012_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_phase2.json
- Location: ds000201/sub-9012/ses-1/fmap/sub-9012_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_task-faces_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_task-hands_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_task-rest_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_T1w.json
- Location: ds000201/sub-9012/ses-2/anat/sub-9012_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_magnitude1.json
- Location: ds000201/sub-9012/ses-2/fmap/sub-9012_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_magnitude2.json
- Location: ds000201/sub-9012/ses-2/fmap/sub-9012_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_phase1.json
- Location: ds000201/sub-9012/ses-2/fmap/sub-9012_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_phase2.json
- Location: ds000201/sub-9012/ses-2/fmap/sub-9012_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_task-faces_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_task-hands_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_task-rest_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9012_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_T1w.json
- Location: ds000201/sub-9013/ses-1/anat/sub-9013_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_magnitude1.json
- Location: ds000201/sub-9013/ses-1/fmap/sub-9013_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_magnitude2.json
- Location: ds000201/sub-9013/ses-1/fmap/sub-9013_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_phase1.json
- Location: ds000201/sub-9013/ses-1/fmap/sub-9013_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_phase2.json
- Location: ds000201/sub-9013/ses-1/fmap/sub-9013_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_task-faces_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_task-hands_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_task-rest_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_T1w.json
- Location: ds000201/sub-9013/ses-2/anat/sub-9013_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_T2w.json
- Location: ds000201/sub-9013/ses-2/anat/sub-9013_ses-2_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_magnitude1.json
- Location: ds000201/sub-9013/ses-2/fmap/sub-9013_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_magnitude2.json
- Location: ds000201/sub-9013/ses-2/fmap/sub-9013_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_phase1.json
- Location: ds000201/sub-9013/ses-2/fmap/sub-9013_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_phase2.json
- Location: ds000201/sub-9013/ses-2/fmap/sub-9013_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_task-faces_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_task-hands_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_task-rest_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9013_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_T1w.json
- Location: ds000201/sub-9014/ses-1/anat/sub-9014_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_T2w.json
- Location: ds000201/sub-9014/ses-1/anat/sub-9014_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_magnitude1.json
- Location: ds000201/sub-9014/ses-1/fmap/sub-9014_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_magnitude2.json
- Location: ds000201/sub-9014/ses-1/fmap/sub-9014_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_phase1.json
- Location: ds000201/sub-9014/ses-1/fmap/sub-9014_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_phase2.json
- Location: ds000201/sub-9014/ses-1/fmap/sub-9014_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_task-faces_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_task-faces_physio.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_task-hands_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_task-rest_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_T1w.json
- Location: ds000201/sub-9014/ses-2/anat/sub-9014_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_magnitude1.json
- Location: ds000201/sub-9014/ses-2/fmap/sub-9014_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_magnitude2.json
- Location: ds000201/sub-9014/ses-2/fmap/sub-9014_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_phase1.json
- Location: ds000201/sub-9014/ses-2/fmap/sub-9014_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_phase2.json
- Location: ds000201/sub-9014/ses-2/fmap/sub-9014_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_task-faces_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_task-faces_physio.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_task-hands_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_task-rest_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9014_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_T1w.json
- Location: ds000201/sub-9016/ses-1/anat/sub-9016_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_T2w.json
- Location: ds000201/sub-9016/ses-1/anat/sub-9016_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_magnitude1.json
- Location: ds000201/sub-9016/ses-1/fmap/sub-9016_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_magnitude2.json
- Location: ds000201/sub-9016/ses-1/fmap/sub-9016_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_phase1.json
- Location: ds000201/sub-9016/ses-1/fmap/sub-9016_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_phase2.json
- Location: ds000201/sub-9016/ses-1/fmap/sub-9016_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_task-faces_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_task-hands_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_task-rest_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9016_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_T1w.json
- Location: ds000201/sub-9017/ses-1/anat/sub-9017_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_T2w.json
- Location: ds000201/sub-9017/ses-1/anat/sub-9017_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_task-faces_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_task-hands_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_task-rest_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_T1w.json
- Location: ds000201/sub-9017/ses-2/anat/sub-9017_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_magnitude1.json
- Location: ds000201/sub-9017/ses-2/fmap/sub-9017_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_magnitude2.json
- Location: ds000201/sub-9017/ses-2/fmap/sub-9017_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_phase1.json
- Location: ds000201/sub-9017/ses-2/fmap/sub-9017_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_phase2.json
- Location: ds000201/sub-9017/ses-2/fmap/sub-9017_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_task-faces_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_task-hands_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_task-rest_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9017_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_T1w.json
- Location: ds000201/sub-9018/ses-1/anat/sub-9018_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_T2w.json
- Location: ds000201/sub-9018/ses-1/anat/sub-9018_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_magnitude1.json
- Location: ds000201/sub-9018/ses-1/fmap/sub-9018_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_magnitude2.json
- Location: ds000201/sub-9018/ses-1/fmap/sub-9018_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_phase1.json
- Location: ds000201/sub-9018/ses-1/fmap/sub-9018_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_phase2.json
- Location: ds000201/sub-9018/ses-1/fmap/sub-9018_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_task-faces_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_task-faces_physio.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_task-hands_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_task-rest_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_T1w.json
- Location: ds000201/sub-9018/ses-2/anat/sub-9018_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_task-faces_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_task-faces_physio.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_task-hands_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_task-rest_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9018_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_T1w.json
- Location: ds000201/sub-9019/ses-1/anat/sub-9019_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_T2w.json
- Location: ds000201/sub-9019/ses-1/anat/sub-9019_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_magnitude1.json
- Location: ds000201/sub-9019/ses-1/fmap/sub-9019_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_magnitude2.json
- Location: ds000201/sub-9019/ses-1/fmap/sub-9019_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_phase1.json
- Location: ds000201/sub-9019/ses-1/fmap/sub-9019_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_phase2.json
- Location: ds000201/sub-9019/ses-1/fmap/sub-9019_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_task-faces_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_task-faces_physio.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_task-hands_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_task-rest_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_T1w.json
- Location: ds000201/sub-9019/ses-2/anat/sub-9019_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_magnitude1.json
- Location: ds000201/sub-9019/ses-2/fmap/sub-9019_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_magnitude2.json
- Location: ds000201/sub-9019/ses-2/fmap/sub-9019_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_phase1.json
- Location: ds000201/sub-9019/ses-2/fmap/sub-9019_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_phase2.json
- Location: ds000201/sub-9019/ses-2/fmap/sub-9019_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_task-faces_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_task-faces_physio.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_task-hands_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_task-rest_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9019_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_T1w.json
- Location: ds000201/sub-9020/ses-1/anat/sub-9020_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_T2w.json
- Location: ds000201/sub-9020/ses-1/anat/sub-9020_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_magnitude1.json
- Location: ds000201/sub-9020/ses-1/fmap/sub-9020_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_magnitude2.json
- Location: ds000201/sub-9020/ses-1/fmap/sub-9020_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_phase1.json
- Location: ds000201/sub-9020/ses-1/fmap/sub-9020_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_phase2.json
- Location: ds000201/sub-9020/ses-1/fmap/sub-9020_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_task-faces_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_task-faces_physio.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_task-hands_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_task-rest_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_T1w.json
- Location: ds000201/sub-9020/ses-2/anat/sub-9020_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_magnitude1.json
- Location: ds000201/sub-9020/ses-2/fmap/sub-9020_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_magnitude2.json
- Location: ds000201/sub-9020/ses-2/fmap/sub-9020_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_phase1.json
- Location: ds000201/sub-9020/ses-2/fmap/sub-9020_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_phase2.json
- Location: ds000201/sub-9020/ses-2/fmap/sub-9020_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_task-faces_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_task-faces_physio.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_task-hands_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_task-rest_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9020_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_T1w.json
- Location: ds000201/sub-9022/ses-1/anat/sub-9022_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_T2w.json
- Location: ds000201/sub-9022/ses-1/anat/sub-9022_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_magnitude1.json
- Location: ds000201/sub-9022/ses-1/fmap/sub-9022_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_magnitude2.json
- Location: ds000201/sub-9022/ses-1/fmap/sub-9022_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_phase1.json
- Location: ds000201/sub-9022/ses-1/fmap/sub-9022_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_phase2.json
- Location: ds000201/sub-9022/ses-1/fmap/sub-9022_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_task-faces_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_task-hands_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_task-rest_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9022_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_T1w.json
- Location: ds000201/sub-9023/ses-1/anat/sub-9023_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_T2w.json
- Location: ds000201/sub-9023/ses-1/anat/sub-9023_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_magnitude1.json
- Location: ds000201/sub-9023/ses-1/fmap/sub-9023_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_magnitude2.json
- Location: ds000201/sub-9023/ses-1/fmap/sub-9023_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_phase1.json
- Location: ds000201/sub-9023/ses-1/fmap/sub-9023_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_phase2.json
- Location: ds000201/sub-9023/ses-1/fmap/sub-9023_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_task-faces_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_task-faces_physio.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_task-hands_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_task-rest_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_T1w.json
- Location: ds000201/sub-9023/ses-2/anat/sub-9023_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_magnitude1.json
- Location: ds000201/sub-9023/ses-2/fmap/sub-9023_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_magnitude2.json
- Location: ds000201/sub-9023/ses-2/fmap/sub-9023_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_phase1.json
- Location: ds000201/sub-9023/ses-2/fmap/sub-9023_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_phase2.json
- Location: ds000201/sub-9023/ses-2/fmap/sub-9023_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_task-faces_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_task-faces_physio.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_task-hands_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_task-rest_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9023_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_T1w.json
- Location: ds000201/sub-9025/ses-1/anat/sub-9025_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_T2w.json
- Location: ds000201/sub-9025/ses-1/anat/sub-9025_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_magnitude1.json
- Location: ds000201/sub-9025/ses-1/fmap/sub-9025_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_magnitude2.json
- Location: ds000201/sub-9025/ses-1/fmap/sub-9025_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_phase1.json
- Location: ds000201/sub-9025/ses-1/fmap/sub-9025_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_phase2.json
- Location: ds000201/sub-9025/ses-1/fmap/sub-9025_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_task-faces_bold.json
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_task-faces_physio.json
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_task-hands_bold.json
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-1_task-rest_bold.json
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_T1w.json
- Location: ds000201/sub-9025/ses-2/anat/sub-9025_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_magnitude1.json
- Location: ds000201/sub-9025/ses-2/fmap/sub-9025_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_magnitude2.json
- Location: ds000201/sub-9025/ses-2/fmap/sub-9025_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_phase1.json
- Location: ds000201/sub-9025/ses-2/fmap/sub-9025_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_phase2.json
- Location: ds000201/sub-9025/ses-2/fmap/sub-9025_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_task-faces_bold.json
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_task-faces_physio.json
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_task-hands_bold.json
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9025_ses-2_task-rest_bold.json
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_T1w.json
- Location: ds000201/sub-9026/ses-1/anat/sub-9026_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_T2w.json
- Location: ds000201/sub-9026/ses-1/anat/sub-9026_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_magnitude1.json
- Location: ds000201/sub-9026/ses-1/fmap/sub-9026_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_magnitude2.json
- Location: ds000201/sub-9026/ses-1/fmap/sub-9026_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_phase1.json
- Location: ds000201/sub-9026/ses-1/fmap/sub-9026_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_phase2.json
- Location: ds000201/sub-9026/ses-1/fmap/sub-9026_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_task-faces_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_task-faces_physio.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_task-hands_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_task-rest_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_T1w.json
- Location: ds000201/sub-9026/ses-2/anat/sub-9026_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_magnitude1.json
- Location: ds000201/sub-9026/ses-2/fmap/sub-9026_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_magnitude2.json
- Location: ds000201/sub-9026/ses-2/fmap/sub-9026_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_phase1.json
- Location: ds000201/sub-9026/ses-2/fmap/sub-9026_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_phase2.json
- Location: ds000201/sub-9026/ses-2/fmap/sub-9026_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_task-faces_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_task-faces_physio.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_task-hands_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_task-rest_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9026_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_T1w.json
- Location: ds000201/sub-9028/ses-1/anat/sub-9028_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_T2w.json
- Location: ds000201/sub-9028/ses-1/anat/sub-9028_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_magnitude1.json
- Location: ds000201/sub-9028/ses-1/fmap/sub-9028_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_magnitude2.json
- Location: ds000201/sub-9028/ses-1/fmap/sub-9028_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_phase1.json
- Location: ds000201/sub-9028/ses-1/fmap/sub-9028_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_phase2.json
- Location: ds000201/sub-9028/ses-1/fmap/sub-9028_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_task-faces_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_task-faces_physio.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_task-hands_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_task-rest_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_T1w.json
- Location: ds000201/sub-9028/ses-2/anat/sub-9028_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_magnitude1.json
- Location: ds000201/sub-9028/ses-2/fmap/sub-9028_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_magnitude2.json
- Location: ds000201/sub-9028/ses-2/fmap/sub-9028_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_phase1.json
- Location: ds000201/sub-9028/ses-2/fmap/sub-9028_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_phase2.json
- Location: ds000201/sub-9028/ses-2/fmap/sub-9028_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_task-faces_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_task-faces_physio.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_task-hands_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_task-rest_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9028_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_T1w.json
- Location: ds000201/sub-9029/ses-1/anat/sub-9029_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_T2w.json
- Location: ds000201/sub-9029/ses-1/anat/sub-9029_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_magnitude1.json
- Location: ds000201/sub-9029/ses-1/fmap/sub-9029_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_magnitude2.json
- Location: ds000201/sub-9029/ses-1/fmap/sub-9029_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_phase1.json
- Location: ds000201/sub-9029/ses-1/fmap/sub-9029_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_phase2.json
- Location: ds000201/sub-9029/ses-1/fmap/sub-9029_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_task-faces_bold.json
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_task-faces_physio.json
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_task-hands_bold.json
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-1_task-rest_bold.json
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_T1w.json
- Location: ds000201/sub-9029/ses-2/anat/sub-9029_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_magnitude1.json
- Location: ds000201/sub-9029/ses-2/fmap/sub-9029_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_magnitude2.json
- Location: ds000201/sub-9029/ses-2/fmap/sub-9029_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_phase1.json
- Location: ds000201/sub-9029/ses-2/fmap/sub-9029_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_phase2.json
- Location: ds000201/sub-9029/ses-2/fmap/sub-9029_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_task-faces_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_task-faces_physio.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_task-hands_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_task-rest_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9029_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_T1w.json
- Location: ds000201/sub-9032/ses-1/anat/sub-9032_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_T2w.json
- Location: ds000201/sub-9032/ses-1/anat/sub-9032_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_magnitude1.json
- Location: ds000201/sub-9032/ses-1/fmap/sub-9032_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_magnitude2.json
- Location: ds000201/sub-9032/ses-1/fmap/sub-9032_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_phase1.json
- Location: ds000201/sub-9032/ses-1/fmap/sub-9032_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_phase2.json
- Location: ds000201/sub-9032/ses-1/fmap/sub-9032_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_task-faces_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_task-faces_physio.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_task-hands_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_task-rest_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_T1w.json
- Location: ds000201/sub-9032/ses-2/anat/sub-9032_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_magnitude1.json
- Location: ds000201/sub-9032/ses-2/fmap/sub-9032_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_magnitude2.json
- Location: ds000201/sub-9032/ses-2/fmap/sub-9032_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_phase1.json
- Location: ds000201/sub-9032/ses-2/fmap/sub-9032_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_phase2.json
- Location: ds000201/sub-9032/ses-2/fmap/sub-9032_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_task-faces_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_task-faces_physio.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_task-hands_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_task-rest_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9032_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_T1w.json
- Location: ds000201/sub-9033/ses-1/anat/sub-9033_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_T2w.json
- Location: ds000201/sub-9033/ses-1/anat/sub-9033_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_magnitude1.json
- Location: ds000201/sub-9033/ses-1/fmap/sub-9033_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_magnitude2.json
- Location: ds000201/sub-9033/ses-1/fmap/sub-9033_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_phase1.json
- Location: ds000201/sub-9033/ses-1/fmap/sub-9033_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_phase2.json
- Location: ds000201/sub-9033/ses-1/fmap/sub-9033_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_task-faces_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_task-faces_physio.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_task-hands_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_task-rest_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_T1w.json
- Location: ds000201/sub-9033/ses-2/anat/sub-9033_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_magnitude1.json
- Location: ds000201/sub-9033/ses-2/fmap/sub-9033_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_magnitude2.json
- Location: ds000201/sub-9033/ses-2/fmap/sub-9033_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_phase1.json
- Location: ds000201/sub-9033/ses-2/fmap/sub-9033_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_phase2.json
- Location: ds000201/sub-9033/ses-2/fmap/sub-9033_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_task-faces_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_task-faces_physio.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_task-hands_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_task-rest_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9033_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_T1w.json
- Location: ds000201/sub-9034/ses-1/anat/sub-9034_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_T2w.json
- Location: ds000201/sub-9034/ses-1/anat/sub-9034_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_magnitude1.json
- Location: ds000201/sub-9034/ses-1/fmap/sub-9034_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_magnitude2.json
- Location: ds000201/sub-9034/ses-1/fmap/sub-9034_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_phase1.json
- Location: ds000201/sub-9034/ses-1/fmap/sub-9034_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_phase2.json
- Location: ds000201/sub-9034/ses-1/fmap/sub-9034_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_task-faces_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_task-faces_physio.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_task-hands_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_task-rest_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_T1w.json
- Location: ds000201/sub-9034/ses-2/anat/sub-9034_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_magnitude1.json
- Location: ds000201/sub-9034/ses-2/fmap/sub-9034_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_magnitude2.json
- Location: ds000201/sub-9034/ses-2/fmap/sub-9034_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_phase1.json
- Location: ds000201/sub-9034/ses-2/fmap/sub-9034_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_phase2.json
- Location: ds000201/sub-9034/ses-2/fmap/sub-9034_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_task-faces_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_task-faces_physio.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_task-hands_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_task-rest_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9034_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_T1w.json
- Location: ds000201/sub-9035/ses-1/anat/sub-9035_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_T2w.json
- Location: ds000201/sub-9035/ses-1/anat/sub-9035_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_magnitude1.json
- Location: ds000201/sub-9035/ses-1/fmap/sub-9035_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_magnitude2.json
- Location: ds000201/sub-9035/ses-1/fmap/sub-9035_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_phase1.json
- Location: ds000201/sub-9035/ses-1/fmap/sub-9035_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_phase2.json
- Location: ds000201/sub-9035/ses-1/fmap/sub-9035_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_task-faces_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_task-hands_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_task-rest_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_T1w.json
- Location: ds000201/sub-9035/ses-2/anat/sub-9035_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_magnitude1.json
- Location: ds000201/sub-9035/ses-2/fmap/sub-9035_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_magnitude2.json
- Location: ds000201/sub-9035/ses-2/fmap/sub-9035_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_phase1.json
- Location: ds000201/sub-9035/ses-2/fmap/sub-9035_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_phase2.json
- Location: ds000201/sub-9035/ses-2/fmap/sub-9035_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_task-faces_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_task-hands_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_task-rest_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9035_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_T1w.json
- Location: ds000201/sub-9036/ses-1/anat/sub-9036_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_T2w.json
- Location: ds000201/sub-9036/ses-1/anat/sub-9036_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_magnitude1.json
- Location: ds000201/sub-9036/ses-1/fmap/sub-9036_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_magnitude2.json
- Location: ds000201/sub-9036/ses-1/fmap/sub-9036_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_phase1.json
- Location: ds000201/sub-9036/ses-1/fmap/sub-9036_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_phase2.json
- Location: ds000201/sub-9036/ses-1/fmap/sub-9036_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_task-faces_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_task-faces_physio.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_task-hands_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_task-rest_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_T1w.json
- Location: ds000201/sub-9036/ses-2/anat/sub-9036_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_magnitude1.json
- Location: ds000201/sub-9036/ses-2/fmap/sub-9036_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_magnitude2.json
- Location: ds000201/sub-9036/ses-2/fmap/sub-9036_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_phase1.json
- Location: ds000201/sub-9036/ses-2/fmap/sub-9036_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_phase2.json
- Location: ds000201/sub-9036/ses-2/fmap/sub-9036_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_task-faces_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_task-faces_physio.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_task-hands_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_task-rest_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9036_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_T1w.json
- Location: ds000201/sub-9037/ses-1/anat/sub-9037_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_T2w.json
- Location: ds000201/sub-9037/ses-1/anat/sub-9037_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_magnitude1.json
- Location: ds000201/sub-9037/ses-1/fmap/sub-9037_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_magnitude2.json
- Location: ds000201/sub-9037/ses-1/fmap/sub-9037_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_phase1.json
- Location: ds000201/sub-9037/ses-1/fmap/sub-9037_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_phase2.json
- Location: ds000201/sub-9037/ses-1/fmap/sub-9037_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_task-faces_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_task-hands_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_task-rest_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_T1w.json
- Location: ds000201/sub-9037/ses-2/anat/sub-9037_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_magnitude1.json
- Location: ds000201/sub-9037/ses-2/fmap/sub-9037_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_magnitude2.json
- Location: ds000201/sub-9037/ses-2/fmap/sub-9037_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_phase1.json
- Location: ds000201/sub-9037/ses-2/fmap/sub-9037_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_phase2.json
- Location: ds000201/sub-9037/ses-2/fmap/sub-9037_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_task-faces_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_task-hands_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_task-rest_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9037_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_T1w.json
- Location: ds000201/sub-9038/ses-1/anat/sub-9038_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_T2w.json
- Location: ds000201/sub-9038/ses-1/anat/sub-9038_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_magnitude1.json
- Location: ds000201/sub-9038/ses-1/fmap/sub-9038_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_magnitude2.json
- Location: ds000201/sub-9038/ses-1/fmap/sub-9038_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_phase1.json
- Location: ds000201/sub-9038/ses-1/fmap/sub-9038_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_phase2.json
- Location: ds000201/sub-9038/ses-1/fmap/sub-9038_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_task-faces_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_task-faces_physio.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_task-hands_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_task-rest_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_T1w.json
- Location: ds000201/sub-9038/ses-2/anat/sub-9038_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_magnitude1.json
- Location: ds000201/sub-9038/ses-2/fmap/sub-9038_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_magnitude2.json
- Location: ds000201/sub-9038/ses-2/fmap/sub-9038_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_phase1.json
- Location: ds000201/sub-9038/ses-2/fmap/sub-9038_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_phase2.json
- Location: ds000201/sub-9038/ses-2/fmap/sub-9038_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_task-faces_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_task-faces_physio.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_task-hands_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_task-rest_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9038_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_T1w.json
- Location: ds000201/sub-9039/ses-1/anat/sub-9039_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_T2w.json
- Location: ds000201/sub-9039/ses-1/anat/sub-9039_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_magnitude1.json
- Location: ds000201/sub-9039/ses-1/fmap/sub-9039_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_magnitude2.json
- Location: ds000201/sub-9039/ses-1/fmap/sub-9039_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_phase1.json
- Location: ds000201/sub-9039/ses-1/fmap/sub-9039_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_phase2.json
- Location: ds000201/sub-9039/ses-1/fmap/sub-9039_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_task-faces_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_task-faces_physio.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_task-hands_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_task-rest_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_T1w.json
- Location: ds000201/sub-9039/ses-2/anat/sub-9039_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_magnitude1.json
- Location: ds000201/sub-9039/ses-2/fmap/sub-9039_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_magnitude2.json
- Location: ds000201/sub-9039/ses-2/fmap/sub-9039_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_phase1.json
- Location: ds000201/sub-9039/ses-2/fmap/sub-9039_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_phase2.json
- Location: ds000201/sub-9039/ses-2/fmap/sub-9039_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_task-faces_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_task-faces_physio.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_task-hands_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_task-rest_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9039_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_T1w.json
- Location: ds000201/sub-9040/ses-1/anat/sub-9040_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_T2w.json
- Location: ds000201/sub-9040/ses-1/anat/sub-9040_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_magnitude1.json
- Location: ds000201/sub-9040/ses-1/fmap/sub-9040_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_magnitude2.json
- Location: ds000201/sub-9040/ses-1/fmap/sub-9040_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_phase1.json
- Location: ds000201/sub-9040/ses-1/fmap/sub-9040_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_phase2.json
- Location: ds000201/sub-9040/ses-1/fmap/sub-9040_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_task-faces_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_task-faces_physio.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_task-hands_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_task-rest_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_T1w.json
- Location: ds000201/sub-9040/ses-2/anat/sub-9040_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_magnitude1.json
- Location: ds000201/sub-9040/ses-2/fmap/sub-9040_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_magnitude2.json
- Location: ds000201/sub-9040/ses-2/fmap/sub-9040_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_phase1.json
- Location: ds000201/sub-9040/ses-2/fmap/sub-9040_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_phase2.json
- Location: ds000201/sub-9040/ses-2/fmap/sub-9040_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_task-faces_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_task-faces_physio.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_task-hands_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_task-rest_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9040_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_T1w.json
- Location: ds000201/sub-9041/ses-1/anat/sub-9041_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_T2w.json
- Location: ds000201/sub-9041/ses-1/anat/sub-9041_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_magnitude1.json
- Location: ds000201/sub-9041/ses-1/fmap/sub-9041_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_magnitude2.json
- Location: ds000201/sub-9041/ses-1/fmap/sub-9041_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_phase1.json
- Location: ds000201/sub-9041/ses-1/fmap/sub-9041_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_phase2.json
- Location: ds000201/sub-9041/ses-1/fmap/sub-9041_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_task-faces_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_task-faces_physio.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_task-hands_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_task-rest_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_T1w.json
- Location: ds000201/sub-9041/ses-2/anat/sub-9041_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_magnitude1.json
- Location: ds000201/sub-9041/ses-2/fmap/sub-9041_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_magnitude2.json
- Location: ds000201/sub-9041/ses-2/fmap/sub-9041_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_phase1.json
- Location: ds000201/sub-9041/ses-2/fmap/sub-9041_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_phase2.json
- Location: ds000201/sub-9041/ses-2/fmap/sub-9041_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_task-faces_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_task-faces_physio.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_task-hands_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_task-rest_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9041_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_T1w.json
- Location: ds000201/sub-9042/ses-1/anat/sub-9042_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_T2w.json
- Location: ds000201/sub-9042/ses-1/anat/sub-9042_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_magnitude1.json
- Location: ds000201/sub-9042/ses-1/fmap/sub-9042_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_magnitude2.json
- Location: ds000201/sub-9042/ses-1/fmap/sub-9042_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_phase1.json
- Location: ds000201/sub-9042/ses-1/fmap/sub-9042_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_phase2.json
- Location: ds000201/sub-9042/ses-1/fmap/sub-9042_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_task-faces_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_task-faces_physio.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_task-hands_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_task-rest_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_T1w.json
- Location: ds000201/sub-9042/ses-2/anat/sub-9042_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_magnitude1.json
- Location: ds000201/sub-9042/ses-2/fmap/sub-9042_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_magnitude2.json
- Location: ds000201/sub-9042/ses-2/fmap/sub-9042_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_phase1.json
- Location: ds000201/sub-9042/ses-2/fmap/sub-9042_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_phase2.json
- Location: ds000201/sub-9042/ses-2/fmap/sub-9042_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-faces_physio.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-faces_run-1_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_run-1_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-faces_run-2_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_run-2_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-hands_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-rest_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9042_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_T1w.json
- Location: ds000201/sub-9044/ses-1/anat/sub-9044_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_T2w.json
- Location: ds000201/sub-9044/ses-1/anat/sub-9044_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-1_magnitude1.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-1_magnitude2.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-1_phase1.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-1_phase2.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-2_magnitude1.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-2_magnitude2.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-2_phase1.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_run-2_phase2.json
- Location: ds000201/sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_task-faces_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_task-hands_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_task-rest_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-2_T1w.json
- Location: ds000201/sub-9044/ses-2/anat/sub-9044_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-2_magnitude1.json
- Location: ds000201/sub-9044/ses-2/fmap/sub-9044_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-2_magnitude2.json
- Location: ds000201/sub-9044/ses-2/fmap/sub-9044_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-2_phase1.json
- Location: ds000201/sub-9044/ses-2/fmap/sub-9044_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9044_ses-2_phase2.json
- Location: ds000201/sub-9044/ses-2/fmap/sub-9044_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_T1w.json
- Location: ds000201/sub-9045/ses-1/anat/sub-9045_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_T2w.json
- Location: ds000201/sub-9045/ses-1/anat/sub-9045_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_magnitude1.json
- Location: ds000201/sub-9045/ses-1/fmap/sub-9045_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_magnitude2.json
- Location: ds000201/sub-9045/ses-1/fmap/sub-9045_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_phase1.json
- Location: ds000201/sub-9045/ses-1/fmap/sub-9045_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_phase2.json
- Location: ds000201/sub-9045/ses-1/fmap/sub-9045_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_task-faces_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_task-hands_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_task-rest_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_T1w.json
- Location: ds000201/sub-9045/ses-2/anat/sub-9045_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_magnitude1.json
- Location: ds000201/sub-9045/ses-2/fmap/sub-9045_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_magnitude2.json
- Location: ds000201/sub-9045/ses-2/fmap/sub-9045_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_phase1.json
- Location: ds000201/sub-9045/ses-2/fmap/sub-9045_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_phase2.json
- Location: ds000201/sub-9045/ses-2/fmap/sub-9045_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_task-faces_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_task-faces_physio.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_task-hands_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_task-rest_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9045_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_T1w.json
- Location: ds000201/sub-9046/ses-1/anat/sub-9046_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_magnitude1.json
- Location: ds000201/sub-9046/ses-1/fmap/sub-9046_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_magnitude2.json
- Location: ds000201/sub-9046/ses-1/fmap/sub-9046_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_phase1.json
- Location: ds000201/sub-9046/ses-1/fmap/sub-9046_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_phase2.json
- Location: ds000201/sub-9046/ses-1/fmap/sub-9046_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_task-faces_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_task-faces_physio.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_task-hands_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_task-rest_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_T1w.json
- Location: ds000201/sub-9046/ses-2/anat/sub-9046_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_magnitude1.json
- Location: ds000201/sub-9046/ses-2/fmap/sub-9046_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_magnitude2.json
- Location: ds000201/sub-9046/ses-2/fmap/sub-9046_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_phase1.json
- Location: ds000201/sub-9046/ses-2/fmap/sub-9046_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_phase2.json
- Location: ds000201/sub-9046/ses-2/fmap/sub-9046_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_task-faces_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_task-faces_physio.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_task-hands_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_task-rest_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9046_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_T1w.json
- Location: ds000201/sub-9047/ses-1/anat/sub-9047_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_T2w.json
- Location: ds000201/sub-9047/ses-1/anat/sub-9047_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_magnitude1.json
- Location: ds000201/sub-9047/ses-1/fmap/sub-9047_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_magnitude2.json
- Location: ds000201/sub-9047/ses-1/fmap/sub-9047_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_phase1.json
- Location: ds000201/sub-9047/ses-1/fmap/sub-9047_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_phase2.json
- Location: ds000201/sub-9047/ses-1/fmap/sub-9047_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_task-faces_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_task-faces_physio.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_task-hands_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_task-rest_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_T1w.json
- Location: ds000201/sub-9047/ses-2/anat/sub-9047_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_magnitude1.json
- Location: ds000201/sub-9047/ses-2/fmap/sub-9047_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_magnitude2.json
- Location: ds000201/sub-9047/ses-2/fmap/sub-9047_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_phase1.json
- Location: ds000201/sub-9047/ses-2/fmap/sub-9047_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_phase2.json
- Location: ds000201/sub-9047/ses-2/fmap/sub-9047_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_task-faces_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_task-faces_physio.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_task-hands_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_task-rest_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9047_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_T1w.json
- Location: ds000201/sub-9048/ses-1/anat/sub-9048_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_T2w.json
- Location: ds000201/sub-9048/ses-1/anat/sub-9048_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_magnitude1.json
- Location: ds000201/sub-9048/ses-1/fmap/sub-9048_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_magnitude2.json
- Location: ds000201/sub-9048/ses-1/fmap/sub-9048_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_phase1.json
- Location: ds000201/sub-9048/ses-1/fmap/sub-9048_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_phase2.json
- Location: ds000201/sub-9048/ses-1/fmap/sub-9048_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_task-faces_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_task-faces_physio.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_task-hands_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_task-rest_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_T1w.json
- Location: ds000201/sub-9048/ses-2/anat/sub-9048_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_magnitude1.json
- Location: ds000201/sub-9048/ses-2/fmap/sub-9048_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_magnitude2.json
- Location: ds000201/sub-9048/ses-2/fmap/sub-9048_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_phase1.json
- Location: ds000201/sub-9048/ses-2/fmap/sub-9048_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_phase2.json
- Location: ds000201/sub-9048/ses-2/fmap/sub-9048_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_task-faces_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_task-faces_physio.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_task-hands_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_task-rest_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9048_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_T1w.json
- Location: ds000201/sub-9049/ses-1/anat/sub-9049_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_T2w.json
- Location: ds000201/sub-9049/ses-1/anat/sub-9049_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_magnitude1.json
- Location: ds000201/sub-9049/ses-1/fmap/sub-9049_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_magnitude2.json
- Location: ds000201/sub-9049/ses-1/fmap/sub-9049_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_phase1.json
- Location: ds000201/sub-9049/ses-1/fmap/sub-9049_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_phase2.json
- Location: ds000201/sub-9049/ses-1/fmap/sub-9049_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_task-faces_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_task-faces_physio.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_task-hands_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_task-rest_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_T1w.json
- Location: ds000201/sub-9049/ses-2/anat/sub-9049_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_magnitude1.json
- Location: ds000201/sub-9049/ses-2/fmap/sub-9049_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_magnitude2.json
- Location: ds000201/sub-9049/ses-2/fmap/sub-9049_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_phase1.json
- Location: ds000201/sub-9049/ses-2/fmap/sub-9049_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_phase2.json
- Location: ds000201/sub-9049/ses-2/fmap/sub-9049_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_task-faces_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_task-faces_physio.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_task-hands_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_task-rest_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9049_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_T1w.json
- Location: ds000201/sub-9050/ses-1/anat/sub-9050_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_T2w.json
- Location: ds000201/sub-9050/ses-1/anat/sub-9050_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_magnitude1.json
- Location: ds000201/sub-9050/ses-1/fmap/sub-9050_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_magnitude2.json
- Location: ds000201/sub-9050/ses-1/fmap/sub-9050_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_phase1.json
- Location: ds000201/sub-9050/ses-1/fmap/sub-9050_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_phase2.json
- Location: ds000201/sub-9050/ses-1/fmap/sub-9050_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_task-faces_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_task-hands_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_task-rest_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_T1w.json
- Location: ds000201/sub-9050/ses-2/anat/sub-9050_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_magnitude1.json
- Location: ds000201/sub-9050/ses-2/fmap/sub-9050_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_magnitude2.json
- Location: ds000201/sub-9050/ses-2/fmap/sub-9050_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_phase1.json
- Location: ds000201/sub-9050/ses-2/fmap/sub-9050_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_phase2.json
- Location: ds000201/sub-9050/ses-2/fmap/sub-9050_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_task-faces_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_task-hands_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_task-rest_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9050_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_T1w.json
- Location: ds000201/sub-9053/ses-1/anat/sub-9053_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_T2w.json
- Location: ds000201/sub-9053/ses-1/anat/sub-9053_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_magnitude1.json
- Location: ds000201/sub-9053/ses-1/fmap/sub-9053_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_magnitude2.json
- Location: ds000201/sub-9053/ses-1/fmap/sub-9053_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_phase1.json
- Location: ds000201/sub-9053/ses-1/fmap/sub-9053_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_phase2.json
- Location: ds000201/sub-9053/ses-1/fmap/sub-9053_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_task-faces_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_task-hands_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_task-rest_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_T1w.json
- Location: ds000201/sub-9053/ses-2/anat/sub-9053_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_magnitude1.json
- Location: ds000201/sub-9053/ses-2/fmap/sub-9053_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_magnitude2.json
- Location: ds000201/sub-9053/ses-2/fmap/sub-9053_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_phase1.json
- Location: ds000201/sub-9053/ses-2/fmap/sub-9053_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_phase2.json
- Location: ds000201/sub-9053/ses-2/fmap/sub-9053_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_task-faces_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_task-hands_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_task-rest_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9053_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_T1w.json
- Location: ds000201/sub-9054/ses-1/anat/sub-9054_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_T2w.json
- Location: ds000201/sub-9054/ses-1/anat/sub-9054_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_magnitude1.json
- Location: ds000201/sub-9054/ses-1/fmap/sub-9054_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_magnitude2.json
- Location: ds000201/sub-9054/ses-1/fmap/sub-9054_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_phase1.json
- Location: ds000201/sub-9054/ses-1/fmap/sub-9054_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_phase2.json
- Location: ds000201/sub-9054/ses-1/fmap/sub-9054_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_task-faces_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_task-faces_physio.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_task-hands_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_task-rest_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_T1w.json
- Location: ds000201/sub-9054/ses-2/anat/sub-9054_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_T2w.json
- Location: ds000201/sub-9054/ses-2/anat/sub-9054_ses-2_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_magnitude1.json
- Location: ds000201/sub-9054/ses-2/fmap/sub-9054_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_magnitude2.json
- Location: ds000201/sub-9054/ses-2/fmap/sub-9054_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_phase1.json
- Location: ds000201/sub-9054/ses-2/fmap/sub-9054_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_phase2.json
- Location: ds000201/sub-9054/ses-2/fmap/sub-9054_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_task-faces_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_task-faces_physio.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_task-hands_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_task-rest_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9054_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_T1w.json
- Location: ds000201/sub-9055/ses-1/anat/sub-9055_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_T2w.json
- Location: ds000201/sub-9055/ses-1/anat/sub-9055_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_magnitude1.json
- Location: ds000201/sub-9055/ses-1/fmap/sub-9055_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_magnitude2.json
- Location: ds000201/sub-9055/ses-1/fmap/sub-9055_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_phase1.json
- Location: ds000201/sub-9055/ses-1/fmap/sub-9055_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_phase2.json
- Location: ds000201/sub-9055/ses-1/fmap/sub-9055_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_task-faces_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_task-faces_physio.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_task-hands_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_task-rest_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_T1w.json
- Location: ds000201/sub-9055/ses-2/anat/sub-9055_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_magnitude1.json
- Location: ds000201/sub-9055/ses-2/fmap/sub-9055_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_magnitude2.json
- Location: ds000201/sub-9055/ses-2/fmap/sub-9055_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_phase1.json
- Location: ds000201/sub-9055/ses-2/fmap/sub-9055_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_phase2.json
- Location: ds000201/sub-9055/ses-2/fmap/sub-9055_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_task-faces_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_task-hands_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_task-rest_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9055_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_T1w.json
- Location: ds000201/sub-9056/ses-1/anat/sub-9056_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_T2w.json
- Location: ds000201/sub-9056/ses-1/anat/sub-9056_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_task-faces_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_task-faces_physio.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_task-hands_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_task-rest_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_T1w.json
- Location: ds000201/sub-9056/ses-2/anat/sub-9056_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_magnitude1.json
- Location: ds000201/sub-9056/ses-2/fmap/sub-9056_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_magnitude2.json
- Location: ds000201/sub-9056/ses-2/fmap/sub-9056_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_phase1.json
- Location: ds000201/sub-9056/ses-2/fmap/sub-9056_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_phase2.json
- Location: ds000201/sub-9056/ses-2/fmap/sub-9056_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_task-faces_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_task-hands_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_task-rest_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9056_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_T1w.json
- Location: ds000201/sub-9057/ses-1/anat/sub-9057_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_T2w.json
- Location: ds000201/sub-9057/ses-1/anat/sub-9057_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_magnitude1.json
- Location: ds000201/sub-9057/ses-1/fmap/sub-9057_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_magnitude2.json
- Location: ds000201/sub-9057/ses-1/fmap/sub-9057_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_phase1.json
- Location: ds000201/sub-9057/ses-1/fmap/sub-9057_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_phase2.json
- Location: ds000201/sub-9057/ses-1/fmap/sub-9057_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_task-faces_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_task-hands_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_task-rest_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_T1w.json
- Location: ds000201/sub-9057/ses-2/anat/sub-9057_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_magnitude1.json
- Location: ds000201/sub-9057/ses-2/fmap/sub-9057_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_magnitude2.json
- Location: ds000201/sub-9057/ses-2/fmap/sub-9057_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_phase1.json
- Location: ds000201/sub-9057/ses-2/fmap/sub-9057_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_phase2.json
- Location: ds000201/sub-9057/ses-2/fmap/sub-9057_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_task-faces_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_task-hands_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_task-rest_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9057_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_T1w.json
- Location: ds000201/sub-9058/ses-1/anat/sub-9058_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_T2w.json
- Location: ds000201/sub-9058/ses-1/anat/sub-9058_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_magnitude1.json
- Location: ds000201/sub-9058/ses-1/fmap/sub-9058_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_magnitude2.json
- Location: ds000201/sub-9058/ses-1/fmap/sub-9058_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_phase1.json
- Location: ds000201/sub-9058/ses-1/fmap/sub-9058_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_phase2.json
- Location: ds000201/sub-9058/ses-1/fmap/sub-9058_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_task-faces_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_task-faces_physio.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_task-hands_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_task-rest_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_T1w.json
- Location: ds000201/sub-9058/ses-2/anat/sub-9058_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_magnitude1.json
- Location: ds000201/sub-9058/ses-2/fmap/sub-9058_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_magnitude2.json
- Location: ds000201/sub-9058/ses-2/fmap/sub-9058_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_phase1.json
- Location: ds000201/sub-9058/ses-2/fmap/sub-9058_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_phase2.json
- Location: ds000201/sub-9058/ses-2/fmap/sub-9058_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_task-faces_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_task-faces_physio.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_task-hands_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_task-rest_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9058_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_T1w.json
- Location: ds000201/sub-9059/ses-1/anat/sub-9059_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_T2w.json
- Location: ds000201/sub-9059/ses-1/anat/sub-9059_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_task-faces_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_task-faces_physio.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_task-hands_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_task-rest_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_T1w.json
- Location: ds000201/sub-9059/ses-2/anat/sub-9059_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_magnitude1.json
- Location: ds000201/sub-9059/ses-2/fmap/sub-9059_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_magnitude2.json
- Location: ds000201/sub-9059/ses-2/fmap/sub-9059_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_phase1.json
- Location: ds000201/sub-9059/ses-2/fmap/sub-9059_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_phase2.json
- Location: ds000201/sub-9059/ses-2/fmap/sub-9059_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_task-faces_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_task-faces_physio.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_task-hands_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_task-rest_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9059_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_T1w.json
- Location: ds000201/sub-9061/ses-1/anat/sub-9061_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_T2w.json
- Location: ds000201/sub-9061/ses-1/anat/sub-9061_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_magnitude1.json
- Location: ds000201/sub-9061/ses-1/fmap/sub-9061_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_magnitude2.json
- Location: ds000201/sub-9061/ses-1/fmap/sub-9061_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_phase1.json
- Location: ds000201/sub-9061/ses-1/fmap/sub-9061_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_phase2.json
- Location: ds000201/sub-9061/ses-1/fmap/sub-9061_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_task-faces_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_task-faces_physio.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_task-hands_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_task-rest_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_T1w.json
- Location: ds000201/sub-9061/ses-2/anat/sub-9061_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_magnitude1.json
- Location: ds000201/sub-9061/ses-2/fmap/sub-9061_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_magnitude2.json
- Location: ds000201/sub-9061/ses-2/fmap/sub-9061_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_phase1.json
- Location: ds000201/sub-9061/ses-2/fmap/sub-9061_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_phase2.json
- Location: ds000201/sub-9061/ses-2/fmap/sub-9061_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_task-faces_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_task-faces_physio.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_task-hands_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_task-rest_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9061_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_T1w.json
- Location: ds000201/sub-9062/ses-1/anat/sub-9062_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_T2w.json
- Location: ds000201/sub-9062/ses-1/anat/sub-9062_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_magnitude1.json
- Location: ds000201/sub-9062/ses-1/fmap/sub-9062_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_magnitude2.json
- Location: ds000201/sub-9062/ses-1/fmap/sub-9062_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_phase1.json
- Location: ds000201/sub-9062/ses-1/fmap/sub-9062_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_phase2.json
- Location: ds000201/sub-9062/ses-1/fmap/sub-9062_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_task-faces_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_task-hands_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_task-rest_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_T1w.json
- Location: ds000201/sub-9062/ses-2/anat/sub-9062_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_magnitude1.json
- Location: ds000201/sub-9062/ses-2/fmap/sub-9062_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_magnitude2.json
- Location: ds000201/sub-9062/ses-2/fmap/sub-9062_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_phase1.json
- Location: ds000201/sub-9062/ses-2/fmap/sub-9062_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_phase2.json
- Location: ds000201/sub-9062/ses-2/fmap/sub-9062_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_task-faces_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_task-faces_physio.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_task-hands_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_task-rest_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9062_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_T1w.json
- Location: ds000201/sub-9063/ses-1/anat/sub-9063_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_T2w.json
- Location: ds000201/sub-9063/ses-1/anat/sub-9063_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_magnitude1.json
- Location: ds000201/sub-9063/ses-1/fmap/sub-9063_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_magnitude2.json
- Location: ds000201/sub-9063/ses-1/fmap/sub-9063_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_phase1.json
- Location: ds000201/sub-9063/ses-1/fmap/sub-9063_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_phase2.json
- Location: ds000201/sub-9063/ses-1/fmap/sub-9063_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_task-faces_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_task-hands_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_task-rest_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_T1w.json
- Location: ds000201/sub-9063/ses-2/anat/sub-9063_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_T2w.json
- Location: ds000201/sub-9063/ses-2/anat/sub-9063_ses-2_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_magnitude1.json
- Location: ds000201/sub-9063/ses-2/fmap/sub-9063_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_magnitude2.json
- Location: ds000201/sub-9063/ses-2/fmap/sub-9063_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_phase1.json
- Location: ds000201/sub-9063/ses-2/fmap/sub-9063_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_phase2.json
- Location: ds000201/sub-9063/ses-2/fmap/sub-9063_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_task-faces_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_task-faces_physio.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_task-hands_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_task-rest_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9063_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_T1w.json
- Location: ds000201/sub-9064/ses-1/anat/sub-9064_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_T2w.json
- Location: ds000201/sub-9064/ses-1/anat/sub-9064_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_magnitude1.json
- Location: ds000201/sub-9064/ses-1/fmap/sub-9064_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_magnitude2.json
- Location: ds000201/sub-9064/ses-1/fmap/sub-9064_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_phase1.json
- Location: ds000201/sub-9064/ses-1/fmap/sub-9064_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_phase2.json
- Location: ds000201/sub-9064/ses-1/fmap/sub-9064_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_task-faces_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_task-faces_physio.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_task-hands_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_task-rest_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_T1w.json
- Location: ds000201/sub-9064/ses-2/anat/sub-9064_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_magnitude1.json
- Location: ds000201/sub-9064/ses-2/fmap/sub-9064_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_magnitude2.json
- Location: ds000201/sub-9064/ses-2/fmap/sub-9064_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_phase1.json
- Location: ds000201/sub-9064/ses-2/fmap/sub-9064_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_phase2.json
- Location: ds000201/sub-9064/ses-2/fmap/sub-9064_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_task-faces_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_task-faces_physio.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_task-hands_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_task-rest_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9064_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_T1w.json
- Location: ds000201/sub-9065/ses-1/anat/sub-9065_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_T2w.json
- Location: ds000201/sub-9065/ses-1/anat/sub-9065_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_magnitude1.json
- Location: ds000201/sub-9065/ses-1/fmap/sub-9065_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_magnitude2.json
- Location: ds000201/sub-9065/ses-1/fmap/sub-9065_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_phase1.json
- Location: ds000201/sub-9065/ses-1/fmap/sub-9065_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_phase2.json
- Location: ds000201/sub-9065/ses-1/fmap/sub-9065_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_task-faces_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_task-faces_physio.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_task-hands_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_task-rest_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_T1w.json
- Location: ds000201/sub-9065/ses-2/anat/sub-9065_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_task-faces_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_task-faces_physio.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_task-hands_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_task-rest_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9065_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_T1w.json
- Location: ds000201/sub-9066/ses-1/anat/sub-9066_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_magnitude1.json
- Location: ds000201/sub-9066/ses-1/fmap/sub-9066_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_magnitude2.json
- Location: ds000201/sub-9066/ses-1/fmap/sub-9066_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_phase1.json
- Location: ds000201/sub-9066/ses-1/fmap/sub-9066_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_phase2.json
- Location: ds000201/sub-9066/ses-1/fmap/sub-9066_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_task-faces_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_task-hands_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_task-rest_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9066_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_T1w.json
- Location: ds000201/sub-9067/ses-1/anat/sub-9067_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_T2w.json
- Location: ds000201/sub-9067/ses-1/anat/sub-9067_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_magnitude1.json
- Location: ds000201/sub-9067/ses-1/fmap/sub-9067_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_magnitude2.json
- Location: ds000201/sub-9067/ses-1/fmap/sub-9067_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_phase1.json
- Location: ds000201/sub-9067/ses-1/fmap/sub-9067_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_phase2.json
- Location: ds000201/sub-9067/ses-1/fmap/sub-9067_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_task-faces_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_task-hands_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_task-rest_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_T1w.json
- Location: ds000201/sub-9067/ses-2/anat/sub-9067_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_magnitude1.json
- Location: ds000201/sub-9067/ses-2/fmap/sub-9067_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_magnitude2.json
- Location: ds000201/sub-9067/ses-2/fmap/sub-9067_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_phase1.json
- Location: ds000201/sub-9067/ses-2/fmap/sub-9067_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_phase2.json
- Location: ds000201/sub-9067/ses-2/fmap/sub-9067_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_task-faces_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_task-hands_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_task-rest_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9067_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_T1w.json
- Location: ds000201/sub-9068/ses-1/anat/sub-9068_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_T2w.json
- Location: ds000201/sub-9068/ses-1/anat/sub-9068_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_magnitude1.json
- Location: ds000201/sub-9068/ses-1/fmap/sub-9068_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_magnitude2.json
- Location: ds000201/sub-9068/ses-1/fmap/sub-9068_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_phase1.json
- Location: ds000201/sub-9068/ses-1/fmap/sub-9068_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_phase2.json
- Location: ds000201/sub-9068/ses-1/fmap/sub-9068_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_task-faces_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_task-faces_physio.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_task-hands_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_task-rest_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_T1w.json
- Location: ds000201/sub-9068/ses-2/anat/sub-9068_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_magnitude1.json
- Location: ds000201/sub-9068/ses-2/fmap/sub-9068_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_magnitude2.json
- Location: ds000201/sub-9068/ses-2/fmap/sub-9068_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_phase1.json
- Location: ds000201/sub-9068/ses-2/fmap/sub-9068_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_phase2.json
- Location: ds000201/sub-9068/ses-2/fmap/sub-9068_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_task-faces_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_task-faces_physio.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_task-hands_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_task-rest_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9068_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_T1w.json
- Location: ds000201/sub-9069/ses-1/anat/sub-9069_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_T2w.json
- Location: ds000201/sub-9069/ses-1/anat/sub-9069_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_magnitude1.json
- Location: ds000201/sub-9069/ses-1/fmap/sub-9069_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_magnitude2.json
- Location: ds000201/sub-9069/ses-1/fmap/sub-9069_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_phase1.json
- Location: ds000201/sub-9069/ses-1/fmap/sub-9069_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_phase2.json
- Location: ds000201/sub-9069/ses-1/fmap/sub-9069_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_task-faces_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_task-faces_physio.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_task-hands_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_task-rest_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_T1w.json
- Location: ds000201/sub-9069/ses-2/anat/sub-9069_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_magnitude1.json
- Location: ds000201/sub-9069/ses-2/fmap/sub-9069_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_magnitude2.json
- Location: ds000201/sub-9069/ses-2/fmap/sub-9069_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_phase1.json
- Location: ds000201/sub-9069/ses-2/fmap/sub-9069_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_phase2.json
- Location: ds000201/sub-9069/ses-2/fmap/sub-9069_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_task-faces_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_task-faces_physio.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_task-hands_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_task-rest_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9069_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_T1w.json
- Location: ds000201/sub-9070/ses-1/anat/sub-9070_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_T2w.json
- Location: ds000201/sub-9070/ses-1/anat/sub-9070_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_magnitude1.json
- Location: ds000201/sub-9070/ses-1/fmap/sub-9070_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_phase1.json
- Location: ds000201/sub-9070/ses-1/fmap/sub-9070_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_task-faces_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_task-faces_physio.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_task-hands_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_task-rest_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_T1w.json
- Location: ds000201/sub-9070/ses-2/anat/sub-9070_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_magnitude1.json
- Location: ds000201/sub-9070/ses-2/fmap/sub-9070_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_magnitude2.json
- Location: ds000201/sub-9070/ses-2/fmap/sub-9070_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_phase1.json
- Location: ds000201/sub-9070/ses-2/fmap/sub-9070_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_phase2.json
- Location: ds000201/sub-9070/ses-2/fmap/sub-9070_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_task-faces_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_task-faces_physio.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_task-hands_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_task-rest_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9070_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_T1w.json
- Location: ds000201/sub-9071/ses-1/anat/sub-9071_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_T2w.json
- Location: ds000201/sub-9071/ses-1/anat/sub-9071_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_magnitude1.json
- Location: ds000201/sub-9071/ses-1/fmap/sub-9071_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_magnitude2.json
- Location: ds000201/sub-9071/ses-1/fmap/sub-9071_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_phase1.json
- Location: ds000201/sub-9071/ses-1/fmap/sub-9071_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_phase2.json
- Location: ds000201/sub-9071/ses-1/fmap/sub-9071_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_task-faces_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_task-faces_physio.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_task-hands_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_task-rest_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_T1w.json
- Location: ds000201/sub-9071/ses-2/anat/sub-9071_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_magnitude1.json
- Location: ds000201/sub-9071/ses-2/fmap/sub-9071_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_magnitude2.json
- Location: ds000201/sub-9071/ses-2/fmap/sub-9071_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_phase1.json
- Location: ds000201/sub-9071/ses-2/fmap/sub-9071_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_phase2.json
- Location: ds000201/sub-9071/ses-2/fmap/sub-9071_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_task-faces_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_task-faces_physio.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_task-hands_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_task-rest_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9071_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_T1w.json
- Location: ds000201/sub-9072/ses-1/anat/sub-9072_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_T2w.json
- Location: ds000201/sub-9072/ses-1/anat/sub-9072_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_magnitude1.json
- Location: ds000201/sub-9072/ses-1/fmap/sub-9072_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_magnitude2.json
- Location: ds000201/sub-9072/ses-1/fmap/sub-9072_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_phase1.json
- Location: ds000201/sub-9072/ses-1/fmap/sub-9072_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_phase2.json
- Location: ds000201/sub-9072/ses-1/fmap/sub-9072_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_task-faces_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_task-faces_physio.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_task-hands_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_task-rest_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_T1w.json
- Location: ds000201/sub-9072/ses-2/anat/sub-9072_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_magnitude1.json
- Location: ds000201/sub-9072/ses-2/fmap/sub-9072_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_magnitude2.json
- Location: ds000201/sub-9072/ses-2/fmap/sub-9072_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_phase1.json
- Location: ds000201/sub-9072/ses-2/fmap/sub-9072_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_phase2.json
- Location: ds000201/sub-9072/ses-2/fmap/sub-9072_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_task-faces_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_task-faces_physio.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_task-hands_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_task-rest_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9072_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_T1w.json
- Location: ds000201/sub-9073/ses-1/anat/sub-9073_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_T2w.json
- Location: ds000201/sub-9073/ses-1/anat/sub-9073_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_magnitude1.json
- Location: ds000201/sub-9073/ses-1/fmap/sub-9073_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_magnitude2.json
- Location: ds000201/sub-9073/ses-1/fmap/sub-9073_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_phase1.json
- Location: ds000201/sub-9073/ses-1/fmap/sub-9073_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_phase2.json
- Location: ds000201/sub-9073/ses-1/fmap/sub-9073_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_task-faces_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_task-hands_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_task-rest_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_T1w.json
- Location: ds000201/sub-9073/ses-2/anat/sub-9073_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_magnitude1.json
- Location: ds000201/sub-9073/ses-2/fmap/sub-9073_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_magnitude2.json
- Location: ds000201/sub-9073/ses-2/fmap/sub-9073_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_phase1.json
- Location: ds000201/sub-9073/ses-2/fmap/sub-9073_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_phase2.json
- Location: ds000201/sub-9073/ses-2/fmap/sub-9073_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_task-faces_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_task-hands_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_task-rest_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9073_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_T1w.json
- Location: ds000201/sub-9074/ses-1/anat/sub-9074_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_T2w.json
- Location: ds000201/sub-9074/ses-1/anat/sub-9074_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_task-faces_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_task-faces_physio.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_task-hands_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_task-rest_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_T1w.json
- Location: ds000201/sub-9074/ses-2/anat/sub-9074_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_T2w.json
- Location: ds000201/sub-9074/ses-2/anat/sub-9074_ses-2_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_magnitude1.json
- Location: ds000201/sub-9074/ses-2/fmap/sub-9074_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_magnitude2.json
- Location: ds000201/sub-9074/ses-2/fmap/sub-9074_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_phase1.json
- Location: ds000201/sub-9074/ses-2/fmap/sub-9074_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_phase2.json
- Location: ds000201/sub-9074/ses-2/fmap/sub-9074_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_task-faces_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_task-faces_physio.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_task-hands_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_task-rest_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9074_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_T1w.json
- Location: ds000201/sub-9075/ses-1/anat/sub-9075_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_T2w.json
- Location: ds000201/sub-9075/ses-1/anat/sub-9075_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_magnitude1.json
- Location: ds000201/sub-9075/ses-1/fmap/sub-9075_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_magnitude2.json
- Location: ds000201/sub-9075/ses-1/fmap/sub-9075_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_phase1.json
- Location: ds000201/sub-9075/ses-1/fmap/sub-9075_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_phase2.json
- Location: ds000201/sub-9075/ses-1/fmap/sub-9075_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_task-faces_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_task-faces_physio.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_task-hands_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_task-rest_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_T1w.json
- Location: ds000201/sub-9075/ses-2/anat/sub-9075_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_magnitude1.json
- Location: ds000201/sub-9075/ses-2/fmap/sub-9075_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_magnitude2.json
- Location: ds000201/sub-9075/ses-2/fmap/sub-9075_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_phase1.json
- Location: ds000201/sub-9075/ses-2/fmap/sub-9075_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_phase2.json
- Location: ds000201/sub-9075/ses-2/fmap/sub-9075_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_task-faces_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_task-faces_physio.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_task-hands_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_task-rest_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9075_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_T1w.json
- Location: ds000201/sub-9076/ses-1/anat/sub-9076_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_T2w.json
- Location: ds000201/sub-9076/ses-1/anat/sub-9076_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_magnitude1.json
- Location: ds000201/sub-9076/ses-1/fmap/sub-9076_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_magnitude2.json
- Location: ds000201/sub-9076/ses-1/fmap/sub-9076_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_phase1.json
- Location: ds000201/sub-9076/ses-1/fmap/sub-9076_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_phase2.json
- Location: ds000201/sub-9076/ses-1/fmap/sub-9076_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_task-faces_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_task-hands_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_task-rest_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_T1w.json
- Location: ds000201/sub-9076/ses-2/anat/sub-9076_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_magnitude1.json
- Location: ds000201/sub-9076/ses-2/fmap/sub-9076_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_magnitude2.json
- Location: ds000201/sub-9076/ses-2/fmap/sub-9076_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_phase1.json
- Location: ds000201/sub-9076/ses-2/fmap/sub-9076_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_phase2.json
- Location: ds000201/sub-9076/ses-2/fmap/sub-9076_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_task-faces_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_task-hands_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_task-rest_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9076_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_T1w.json
- Location: ds000201/sub-9077/ses-1/anat/sub-9077_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_T2w.json
- Location: ds000201/sub-9077/ses-1/anat/sub-9077_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_magnitude1.json
- Location: ds000201/sub-9077/ses-1/fmap/sub-9077_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_magnitude2.json
- Location: ds000201/sub-9077/ses-1/fmap/sub-9077_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_phase1.json
- Location: ds000201/sub-9077/ses-1/fmap/sub-9077_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_phase2.json
- Location: ds000201/sub-9077/ses-1/fmap/sub-9077_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_task-faces_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_task-hands_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_task-rest_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_T1w.json
- Location: ds000201/sub-9077/ses-2/anat/sub-9077_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_magnitude1.json
- Location: ds000201/sub-9077/ses-2/fmap/sub-9077_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_magnitude2.json
- Location: ds000201/sub-9077/ses-2/fmap/sub-9077_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_phase1.json
- Location: ds000201/sub-9077/ses-2/fmap/sub-9077_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_phase2.json
- Location: ds000201/sub-9077/ses-2/fmap/sub-9077_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_task-faces_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_task-hands_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_task-rest_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9077_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9078_ses-1_T1w.json
- Location: ds000201/sub-9078/ses-1/anat/sub-9078_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_T1w.json
- Location: ds000201/sub-9079/ses-1/anat/sub-9079_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_T2w.json
- Location: ds000201/sub-9079/ses-1/anat/sub-9079_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_magnitude1.json
- Location: ds000201/sub-9079/ses-1/fmap/sub-9079_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_magnitude2.json
- Location: ds000201/sub-9079/ses-1/fmap/sub-9079_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_phase1.json
- Location: ds000201/sub-9079/ses-1/fmap/sub-9079_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_phase2.json
- Location: ds000201/sub-9079/ses-1/fmap/sub-9079_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_task-faces_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_task-faces_physio.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_task-hands_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_task-rest_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_T1w.json
- Location: ds000201/sub-9079/ses-2/anat/sub-9079_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_magnitude1.json
- Location: ds000201/sub-9079/ses-2/fmap/sub-9079_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_magnitude2.json
- Location: ds000201/sub-9079/ses-2/fmap/sub-9079_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_phase1.json
- Location: ds000201/sub-9079/ses-2/fmap/sub-9079_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_phase2.json
- Location: ds000201/sub-9079/ses-2/fmap/sub-9079_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_task-faces_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_task-faces_physio.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_task-hands_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_task-rest_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9079_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_T1w.json
- Location: ds000201/sub-9080/ses-1/anat/sub-9080_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_T2w.json
- Location: ds000201/sub-9080/ses-1/anat/sub-9080_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_magnitude1.json
- Location: ds000201/sub-9080/ses-1/fmap/sub-9080_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_magnitude2.json
- Location: ds000201/sub-9080/ses-1/fmap/sub-9080_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_phase1.json
- Location: ds000201/sub-9080/ses-1/fmap/sub-9080_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_phase2.json
- Location: ds000201/sub-9080/ses-1/fmap/sub-9080_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_task-faces_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_task-faces_physio.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_task-hands_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_task-rest_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_T1w.json
- Location: ds000201/sub-9080/ses-2/anat/sub-9080_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_magnitude1.json
- Location: ds000201/sub-9080/ses-2/fmap/sub-9080_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_magnitude2.json
- Location: ds000201/sub-9080/ses-2/fmap/sub-9080_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_phase1.json
- Location: ds000201/sub-9080/ses-2/fmap/sub-9080_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_phase2.json
- Location: ds000201/sub-9080/ses-2/fmap/sub-9080_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_task-faces_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_task-faces_physio.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_task-hands_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_task-rest_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9080_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_T1w.json
- Location: ds000201/sub-9081/ses-1/anat/sub-9081_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_T2w.json
- Location: ds000201/sub-9081/ses-1/anat/sub-9081_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_magnitude1.json
- Location: ds000201/sub-9081/ses-1/fmap/sub-9081_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_magnitude2.json
- Location: ds000201/sub-9081/ses-1/fmap/sub-9081_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_phase1.json
- Location: ds000201/sub-9081/ses-1/fmap/sub-9081_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_phase2.json
- Location: ds000201/sub-9081/ses-1/fmap/sub-9081_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_task-faces_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_task-faces_physio.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_task-hands_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_task-rest_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_T1w.json
- Location: ds000201/sub-9081/ses-2/anat/sub-9081_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_magnitude1.json
- Location: ds000201/sub-9081/ses-2/fmap/sub-9081_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_magnitude2.json
- Location: ds000201/sub-9081/ses-2/fmap/sub-9081_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_phase1.json
- Location: ds000201/sub-9081/ses-2/fmap/sub-9081_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_phase2.json
- Location: ds000201/sub-9081/ses-2/fmap/sub-9081_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_task-faces_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_task-faces_physio.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_task-hands_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_task-rest_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9081_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_T1w.json
- Location: ds000201/sub-9082/ses-1/anat/sub-9082_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_T2w.json
- Location: ds000201/sub-9082/ses-1/anat/sub-9082_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_magnitude1.json
- Location: ds000201/sub-9082/ses-1/fmap/sub-9082_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_magnitude2.json
- Location: ds000201/sub-9082/ses-1/fmap/sub-9082_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_phase1.json
- Location: ds000201/sub-9082/ses-1/fmap/sub-9082_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_phase2.json
- Location: ds000201/sub-9082/ses-1/fmap/sub-9082_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_task-faces_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_task-hands_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_task-rest_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_T1w.json
- Location: ds000201/sub-9082/ses-2/anat/sub-9082_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_magnitude1.json
- Location: ds000201/sub-9082/ses-2/fmap/sub-9082_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_magnitude2.json
- Location: ds000201/sub-9082/ses-2/fmap/sub-9082_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_phase1.json
- Location: ds000201/sub-9082/ses-2/fmap/sub-9082_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_phase2.json
- Location: ds000201/sub-9082/ses-2/fmap/sub-9082_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_task-faces_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_task-hands_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_task-rest_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9082_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_T1w.json
- Location: ds000201/sub-9083/ses-1/anat/sub-9083_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_T2w.json
- Location: ds000201/sub-9083/ses-1/anat/sub-9083_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_magnitude1.json
- Location: ds000201/sub-9083/ses-1/fmap/sub-9083_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_magnitude2.json
- Location: ds000201/sub-9083/ses-1/fmap/sub-9083_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_phase1.json
- Location: ds000201/sub-9083/ses-1/fmap/sub-9083_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_phase2.json
- Location: ds000201/sub-9083/ses-1/fmap/sub-9083_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_task-faces_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_task-hands_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_task-rest_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_T1w.json
- Location: ds000201/sub-9083/ses-2/anat/sub-9083_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_magnitude1.json
- Location: ds000201/sub-9083/ses-2/fmap/sub-9083_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_magnitude2.json
- Location: ds000201/sub-9083/ses-2/fmap/sub-9083_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_phase1.json
- Location: ds000201/sub-9083/ses-2/fmap/sub-9083_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_phase2.json
- Location: ds000201/sub-9083/ses-2/fmap/sub-9083_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_task-faces_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_task-hands_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_task-rest_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9083_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_T1w.json
- Location: ds000201/sub-9084/ses-1/anat/sub-9084_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_T2w.json
- Location: ds000201/sub-9084/ses-1/anat/sub-9084_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_magnitude1.json
- Location: ds000201/sub-9084/ses-1/fmap/sub-9084_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_magnitude2.json
- Location: ds000201/sub-9084/ses-1/fmap/sub-9084_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_phase1.json
- Location: ds000201/sub-9084/ses-1/fmap/sub-9084_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_phase2.json
- Location: ds000201/sub-9084/ses-1/fmap/sub-9084_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_task-faces_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_task-faces_physio.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_task-hands_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_task-rest_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_T1w.json
- Location: ds000201/sub-9084/ses-2/anat/sub-9084_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_magnitude1.json
- Location: ds000201/sub-9084/ses-2/fmap/sub-9084_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_magnitude2.json
- Location: ds000201/sub-9084/ses-2/fmap/sub-9084_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_phase1.json
- Location: ds000201/sub-9084/ses-2/fmap/sub-9084_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_phase2.json
- Location: ds000201/sub-9084/ses-2/fmap/sub-9084_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_task-faces_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_task-faces_physio.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_task-hands_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_task-rest_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9084_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_T1w.json
- Location: ds000201/sub-9085/ses-1/anat/sub-9085_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_T2w.json
- Location: ds000201/sub-9085/ses-1/anat/sub-9085_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_magnitude1.json
- Location: ds000201/sub-9085/ses-1/fmap/sub-9085_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_magnitude2.json
- Location: ds000201/sub-9085/ses-1/fmap/sub-9085_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_phase1.json
- Location: ds000201/sub-9085/ses-1/fmap/sub-9085_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_phase2.json
- Location: ds000201/sub-9085/ses-1/fmap/sub-9085_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_task-faces_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_task-faces_physio.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_task-hands_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_task-rest_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_T1w.json
- Location: ds000201/sub-9085/ses-2/anat/sub-9085_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_magnitude1.json
- Location: ds000201/sub-9085/ses-2/fmap/sub-9085_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_magnitude2.json
- Location: ds000201/sub-9085/ses-2/fmap/sub-9085_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_phase1.json
- Location: ds000201/sub-9085/ses-2/fmap/sub-9085_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_phase2.json
- Location: ds000201/sub-9085/ses-2/fmap/sub-9085_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_task-faces_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_task-faces_physio.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_task-hands_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_task-rest_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9085_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_T1w.json
- Location: ds000201/sub-9086/ses-1/anat/sub-9086_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_T2w.json
- Location: ds000201/sub-9086/ses-1/anat/sub-9086_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_magnitude1.json
- Location: ds000201/sub-9086/ses-1/fmap/sub-9086_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_magnitude2.json
- Location: ds000201/sub-9086/ses-1/fmap/sub-9086_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_phase1.json
- Location: ds000201/sub-9086/ses-1/fmap/sub-9086_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_phase2.json
- Location: ds000201/sub-9086/ses-1/fmap/sub-9086_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_task-faces_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_task-faces_physio.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_task-hands_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_task-rest_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_T1w.json
- Location: ds000201/sub-9086/ses-2/anat/sub-9086_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_magnitude1.json
- Location: ds000201/sub-9086/ses-2/fmap/sub-9086_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_magnitude2.json
- Location: ds000201/sub-9086/ses-2/fmap/sub-9086_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_phase1.json
- Location: ds000201/sub-9086/ses-2/fmap/sub-9086_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_phase2.json
- Location: ds000201/sub-9086/ses-2/fmap/sub-9086_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_task-faces_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_task-faces_physio.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_task-hands_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_task-rest_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9086_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_T1w.json
- Location: ds000201/sub-9087/ses-1/anat/sub-9087_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_T2w.json
- Location: ds000201/sub-9087/ses-1/anat/sub-9087_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_magnitude1.json
- Location: ds000201/sub-9087/ses-1/fmap/sub-9087_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_magnitude2.json
- Location: ds000201/sub-9087/ses-1/fmap/sub-9087_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_phase1.json
- Location: ds000201/sub-9087/ses-1/fmap/sub-9087_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_phase2.json
- Location: ds000201/sub-9087/ses-1/fmap/sub-9087_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_task-faces_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_task-faces_physio.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_task-hands_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_task-rest_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_T1w.json
- Location: ds000201/sub-9087/ses-2/anat/sub-9087_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_magnitude1.json
- Location: ds000201/sub-9087/ses-2/fmap/sub-9087_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_magnitude2.json
- Location: ds000201/sub-9087/ses-2/fmap/sub-9087_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_phase1.json
- Location: ds000201/sub-9087/ses-2/fmap/sub-9087_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_phase2.json
- Location: ds000201/sub-9087/ses-2/fmap/sub-9087_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_task-faces_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_task-faces_physio.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_task-hands_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_task-rest_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9087_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_T1w.json
- Location: ds000201/sub-9088/ses-1/anat/sub-9088_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_T2w.json
- Location: ds000201/sub-9088/ses-1/anat/sub-9088_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_magnitude1.json
- Location: ds000201/sub-9088/ses-1/fmap/sub-9088_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_magnitude2.json
- Location: ds000201/sub-9088/ses-1/fmap/sub-9088_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_phase1.json
- Location: ds000201/sub-9088/ses-1/fmap/sub-9088_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_phase2.json
- Location: ds000201/sub-9088/ses-1/fmap/sub-9088_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_task-faces_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_task-faces_physio.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_task-hands_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_task-rest_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_T1w.json
- Location: ds000201/sub-9088/ses-2/anat/sub-9088_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_magnitude1.json
- Location: ds000201/sub-9088/ses-2/fmap/sub-9088_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_magnitude2.json
- Location: ds000201/sub-9088/ses-2/fmap/sub-9088_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_phase1.json
- Location: ds000201/sub-9088/ses-2/fmap/sub-9088_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_phase2.json
- Location: ds000201/sub-9088/ses-2/fmap/sub-9088_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_task-faces_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_task-faces_physio.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_task-hands_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_task-rest_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9088_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_T1w.json
- Location: ds000201/sub-9089/ses-1/anat/sub-9089_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_T2w.json
- Location: ds000201/sub-9089/ses-1/anat/sub-9089_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_magnitude1.json
- Location: ds000201/sub-9089/ses-1/fmap/sub-9089_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_magnitude2.json
- Location: ds000201/sub-9089/ses-1/fmap/sub-9089_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_phase1.json
- Location: ds000201/sub-9089/ses-1/fmap/sub-9089_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_phase2.json
- Location: ds000201/sub-9089/ses-1/fmap/sub-9089_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_task-faces_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_task-faces_physio.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_task-hands_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_task-rest_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_T1w.json
- Location: ds000201/sub-9089/ses-2/anat/sub-9089_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_magnitude1.json
- Location: ds000201/sub-9089/ses-2/fmap/sub-9089_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_magnitude2.json
- Location: ds000201/sub-9089/ses-2/fmap/sub-9089_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_phase1.json
- Location: ds000201/sub-9089/ses-2/fmap/sub-9089_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_phase2.json
- Location: ds000201/sub-9089/ses-2/fmap/sub-9089_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_task-faces_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_task-faces_physio.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_task-hands_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_task-rest_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9089_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_T1w.json
- Location: ds000201/sub-9090/ses-1/anat/sub-9090_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_T2w.json
- Location: ds000201/sub-9090/ses-1/anat/sub-9090_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_magnitude1.json
- Location: ds000201/sub-9090/ses-1/fmap/sub-9090_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_magnitude2.json
- Location: ds000201/sub-9090/ses-1/fmap/sub-9090_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_phase1.json
- Location: ds000201/sub-9090/ses-1/fmap/sub-9090_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_phase2.json
- Location: ds000201/sub-9090/ses-1/fmap/sub-9090_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_task-faces_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_task-hands_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_task-rest_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_T1w.json
- Location: ds000201/sub-9090/ses-2/anat/sub-9090_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_magnitude1.json
- Location: ds000201/sub-9090/ses-2/fmap/sub-9090_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_magnitude2.json
- Location: ds000201/sub-9090/ses-2/fmap/sub-9090_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_phase1.json
- Location: ds000201/sub-9090/ses-2/fmap/sub-9090_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_phase2.json
- Location: ds000201/sub-9090/ses-2/fmap/sub-9090_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_task-faces_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_task-hands_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_task-rest_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9090_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_T1w.json
- Location: ds000201/sub-9091/ses-1/anat/sub-9091_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_T2w.json
- Location: ds000201/sub-9091/ses-1/anat/sub-9091_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_magnitude1.json
- Location: ds000201/sub-9091/ses-1/fmap/sub-9091_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_magnitude2.json
- Location: ds000201/sub-9091/ses-1/fmap/sub-9091_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_phase1.json
- Location: ds000201/sub-9091/ses-1/fmap/sub-9091_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_phase2.json
- Location: ds000201/sub-9091/ses-1/fmap/sub-9091_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_task-faces_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_task-hands_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_task-rest_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_T1w.json
- Location: ds000201/sub-9091/ses-2/anat/sub-9091_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_magnitude1.json
- Location: ds000201/sub-9091/ses-2/fmap/sub-9091_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_magnitude2.json
- Location: ds000201/sub-9091/ses-2/fmap/sub-9091_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_phase1.json
- Location: ds000201/sub-9091/ses-2/fmap/sub-9091_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_phase2.json
- Location: ds000201/sub-9091/ses-2/fmap/sub-9091_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_task-faces_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_task-hands_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_task-rest_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9091_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_T1w.json
- Location: ds000201/sub-9092/ses-1/anat/sub-9092_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_T2w.json
- Location: ds000201/sub-9092/ses-1/anat/sub-9092_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_magnitude1.json
- Location: ds000201/sub-9092/ses-1/fmap/sub-9092_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_magnitude2.json
- Location: ds000201/sub-9092/ses-1/fmap/sub-9092_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_phase1.json
- Location: ds000201/sub-9092/ses-1/fmap/sub-9092_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_phase2.json
- Location: ds000201/sub-9092/ses-1/fmap/sub-9092_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_task-faces_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_task-faces_physio.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_task-hands_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_task-rest_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_T1w.json
- Location: ds000201/sub-9092/ses-2/anat/sub-9092_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_magnitude1.json
- Location: ds000201/sub-9092/ses-2/fmap/sub-9092_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_magnitude2.json
- Location: ds000201/sub-9092/ses-2/fmap/sub-9092_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_phase1.json
- Location: ds000201/sub-9092/ses-2/fmap/sub-9092_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_phase2.json
- Location: ds000201/sub-9092/ses-2/fmap/sub-9092_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_task-faces_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_task-faces_physio.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_task-hands_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_task-rest_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9092_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_T1w.json
- Location: ds000201/sub-9093/ses-1/anat/sub-9093_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_T2w.json
- Location: ds000201/sub-9093/ses-1/anat/sub-9093_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_magnitude1.json
- Location: ds000201/sub-9093/ses-1/fmap/sub-9093_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_magnitude2.json
- Location: ds000201/sub-9093/ses-1/fmap/sub-9093_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_phase1.json
- Location: ds000201/sub-9093/ses-1/fmap/sub-9093_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_phase2.json
- Location: ds000201/sub-9093/ses-1/fmap/sub-9093_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_task-faces_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_task-faces_physio.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_task-hands_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_task-rest_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_T1w.json
- Location: ds000201/sub-9093/ses-2/anat/sub-9093_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_magnitude1.json
- Location: ds000201/sub-9093/ses-2/fmap/sub-9093_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_magnitude2.json
- Location: ds000201/sub-9093/ses-2/fmap/sub-9093_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_phase1.json
- Location: ds000201/sub-9093/ses-2/fmap/sub-9093_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_phase2.json
- Location: ds000201/sub-9093/ses-2/fmap/sub-9093_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_task-faces_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_task-faces_physio.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_task-hands_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_task-rest_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9093_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_T1w.json
- Location: ds000201/sub-9094/ses-1/anat/sub-9094_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_T2w.json
- Location: ds000201/sub-9094/ses-1/anat/sub-9094_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_magnitude1.json
- Location: ds000201/sub-9094/ses-1/fmap/sub-9094_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_magnitude2.json
- Location: ds000201/sub-9094/ses-1/fmap/sub-9094_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_phase1.json
- Location: ds000201/sub-9094/ses-1/fmap/sub-9094_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_phase2.json
- Location: ds000201/sub-9094/ses-1/fmap/sub-9094_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_task-faces_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_task-faces_physio.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_task-hands_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_task-rest_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_T1w.json
- Location: ds000201/sub-9094/ses-2/anat/sub-9094_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_magnitude1.json
- Location: ds000201/sub-9094/ses-2/fmap/sub-9094_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_magnitude2.json
- Location: ds000201/sub-9094/ses-2/fmap/sub-9094_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_phase1.json
- Location: ds000201/sub-9094/ses-2/fmap/sub-9094_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_phase2.json
- Location: ds000201/sub-9094/ses-2/fmap/sub-9094_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_task-faces_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_task-faces_physio.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_task-hands_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_task-rest_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9094_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_T1w.json
- Location: ds000201/sub-9095/ses-1/anat/sub-9095_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_T2w.json
- Location: ds000201/sub-9095/ses-1/anat/sub-9095_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_magnitude1.json
- Location: ds000201/sub-9095/ses-1/fmap/sub-9095_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_magnitude2.json
- Location: ds000201/sub-9095/ses-1/fmap/sub-9095_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_phase1.json
- Location: ds000201/sub-9095/ses-1/fmap/sub-9095_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_phase2.json
- Location: ds000201/sub-9095/ses-1/fmap/sub-9095_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_task-faces_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_task-faces_physio.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_task-hands_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_task-rest_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9095_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_T1w.json
- Location: ds000201/sub-9096/ses-1/anat/sub-9096_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_T2w.json
- Location: ds000201/sub-9096/ses-1/anat/sub-9096_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_task-faces_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_task-faces_physio.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_task-hands_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_task-rest_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_T1w.json
- Location: ds000201/sub-9096/ses-2/anat/sub-9096_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_magnitude1.json
- Location: ds000201/sub-9096/ses-2/fmap/sub-9096_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_magnitude2.json
- Location: ds000201/sub-9096/ses-2/fmap/sub-9096_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_phase1.json
- Location: ds000201/sub-9096/ses-2/fmap/sub-9096_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_phase2.json
- Location: ds000201/sub-9096/ses-2/fmap/sub-9096_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_task-faces_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_task-faces_physio.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_task-hands_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_task-rest_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9096_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_T1w.json
- Location: ds000201/sub-9098/ses-1/anat/sub-9098_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_T2w.json
- Location: ds000201/sub-9098/ses-1/anat/sub-9098_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_magnitude1.json
- Location: ds000201/sub-9098/ses-1/fmap/sub-9098_ses-1_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_magnitude2.json
- Location: ds000201/sub-9098/ses-1/fmap/sub-9098_ses-1_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_phase1.json
- Location: ds000201/sub-9098/ses-1/fmap/sub-9098_ses-1_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_phase2.json
- Location: ds000201/sub-9098/ses-1/fmap/sub-9098_ses-1_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_task-faces_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_task-faces_physio.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_task-hands_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_task-rest_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_T1w.json
- Location: ds000201/sub-9098/ses-2/anat/sub-9098_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_magnitude1.json
- Location: ds000201/sub-9098/ses-2/fmap/sub-9098_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_magnitude2.json
- Location: ds000201/sub-9098/ses-2/fmap/sub-9098_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_phase1.json
- Location: ds000201/sub-9098/ses-2/fmap/sub-9098_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_phase2.json
- Location: ds000201/sub-9098/ses-2/fmap/sub-9098_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_task-faces_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_task-faces_physio.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_task-hands_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_task-rest_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9098_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_T1w.json
- Location: ds000201/sub-9100/ses-1/anat/sub-9100_ses-1_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_T2w.json
- Location: ds000201/sub-9100/ses-1/anat/sub-9100_ses-1_T2w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_task-arrows_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_task-faces_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_task-faces_physio.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_task-hands_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_task-rest_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-1_task-sleepiness_bold.json
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_T1w.json
- Location: ds000201/sub-9100/ses-2/anat/sub-9100_ses-2_T1w.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_magnitude1.json
- Location: ds000201/sub-9100/ses-2/fmap/sub-9100_ses-2_magnitude1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_magnitude2.json
- Location: ds000201/sub-9100/ses-2/fmap/sub-9100_ses-2_magnitude2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_phase1.json
- Location: ds000201/sub-9100/ses-2/fmap/sub-9100_ses-2_phase1.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_phase2.json
- Location: ds000201/sub-9100/ses-2/fmap/sub-9100_ses-2_phase2.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_task-arrows_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-arrows_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_task-faces_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-faces_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_task-faces_physio.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-faces_physio.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_task-hands_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-hands_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_task-rest_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-rest_bold.json
- Reason: A json sidecar file was found without a corresponding data file
- Type: Error
- File: sub-9100_ses-2_task-sleepiness_bold.json
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-sleepiness_bold.json
- Reason: A json sidecar file was found without a corresponding data file
-
-======================================================
-
-
-File Path: The filename in scans.tsv file does not match what is present in the BIDS dataset.
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9001/ses-1//sub-9001/ses-1/anat/sub-9001_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9001/ses-1/anat/sub-9001_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9001/ses-1/fmap/sub-9001_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9001/ses-1/func/sub-9001_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9001/ses-1/func/sub-9001_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9001/ses-1/func/sub-9001_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9001/ses-1/func/sub-9001_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-1_scans.tsv
- Location: ds000201/sub-9001/ses-1/sub-9001_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9001/ses-1/func/sub-9001_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9001/ses-2/anat/sub-9001_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9001/ses-2/dwi/sub-9001_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9001/ses-2/fmap/sub-9001_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9001/ses-2/func/sub-9001_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9001/ses-2/func/sub-9001_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9001/ses-2/func/sub-9001_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9001/ses-2/func/sub-9001_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9001_ses-2_scans.tsv
- Location: ds000201/sub-9001/ses-2/sub-9001_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9001/ses-2/func/sub-9001_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9002/ses-1/anat/sub-9002_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9002/ses-1/anat/sub-9002_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9002/ses-1/fmap/sub-9002_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9002/ses-1/func/sub-9002_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9002/ses-1/func/sub-9002_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9002/ses-1/func/sub-9002_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9002/ses-1/func/sub-9002_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-1_scans.tsv
- Location: ds000201/sub-9002/ses-1/sub-9002_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9002/ses-1/func/sub-9002_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9002/ses-2/anat/sub-9002_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9002/ses-2/dwi/sub-9002_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9002/ses-2/fmap/sub-9002_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9002/ses-2/func/sub-9002_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9002/ses-2/func/sub-9002_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9002/ses-2/func/sub-9002_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9002/ses-2/func/sub-9002_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9002_ses-2_scans.tsv
- Location: ds000201/sub-9002/ses-2/sub-9002_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9002/ses-2/func/sub-9002_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9003/ses-1/anat/sub-9003_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9003/ses-1/anat/sub-9003_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9003/ses-1/fmap/sub-9003_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9003/ses-1/func/sub-9003_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9003/ses-1/func/sub-9003_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9003/ses-1/func/sub-9003_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9003/ses-1/func/sub-9003_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-1_scans.tsv
- Location: ds000201/sub-9003/ses-1/sub-9003_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9003/ses-1/func/sub-9003_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9003/ses-2/anat/sub-9003_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9003/ses-2/dwi/sub-9003_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9003/ses-2/fmap/sub-9003_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9003/ses-2/func/sub-9003_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9003/ses-2/func/sub-9003_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9003/ses-2/func/sub-9003_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9003/ses-2/func/sub-9003_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9003_ses-2_scans.tsv
- Location: ds000201/sub-9003/ses-2/sub-9003_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9003/ses-2/func/sub-9003_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9004/ses-1/anat/sub-9004_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9004/ses-1/anat/sub-9004_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9004/ses-1/fmap/sub-9004_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9004/ses-1/func/sub-9004_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9004/ses-1/func/sub-9004_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9004/ses-1/func/sub-9004_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9004/ses-1/func/sub-9004_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-1_scans.tsv
- Location: ds000201/sub-9004/ses-1/sub-9004_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9004/ses-1/func/sub-9004_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9004/ses-2/anat/sub-9004_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9004/ses-2/dwi/sub-9004_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9004/ses-2/fmap/sub-9004_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9004/ses-2/func/sub-9004_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9004/ses-2/func/sub-9004_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9004/ses-2/func/sub-9004_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9004/ses-2/func/sub-9004_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9004_ses-2_scans.tsv
- Location: ds000201/sub-9004/ses-2/sub-9004_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9004/ses-2/func/sub-9004_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9005/ses-1/anat/sub-9005_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9005/ses-1/anat/sub-9005_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9005/ses-1/fmap/sub-9005_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9005/ses-1/func/sub-9005_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9005/ses-1/func/sub-9005_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9005/ses-1/func/sub-9005_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9005/ses-1/func/sub-9005_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-1_scans.tsv
- Location: ds000201/sub-9005/ses-1/sub-9005_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9005/ses-1/func/sub-9005_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9005/ses-2/anat/sub-9005_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9005/ses-2/dwi/sub-9005_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9005/ses-2/fmap/sub-9005_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9005/ses-2/func/sub-9005_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9005/ses-2/func/sub-9005_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9005/ses-2/func/sub-9005_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9005/ses-2/func/sub-9005_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9005_ses-2_scans.tsv
- Location: ds000201/sub-9005/ses-2/sub-9005_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9005/ses-2/func/sub-9005_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9007/ses-1/anat/sub-9007_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9007/ses-1/anat/sub-9007_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9007/ses-1/fmap/sub-9007_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9007/ses-1/func/sub-9007_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9007/ses-1/func/sub-9007_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9007/ses-1/func/sub-9007_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9007/ses-1/func/sub-9007_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-1_scans.tsv
- Location: ds000201/sub-9007/ses-1/sub-9007_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9007/ses-1/func/sub-9007_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9007/ses-2/anat/sub-9007_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9007/ses-2/dwi/sub-9007_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9007/ses-2/fmap/sub-9007_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9007/ses-2/func/sub-9007_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9007/ses-2/func/sub-9007_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9007/ses-2/func/sub-9007_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9007/ses-2/func/sub-9007_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9007_ses-2_scans.tsv
- Location: ds000201/sub-9007/ses-2/sub-9007_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9007/ses-2/func/sub-9007_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9008/ses-1/anat/sub-9008_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9008/ses-1/anat/sub-9008_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9008/ses-1/fmap/sub-9008_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9008/ses-1/func/sub-9008_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9008/ses-1/func/sub-9008_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9008/ses-1/func/sub-9008_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9008/ses-1/func/sub-9008_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-1_scans.tsv
- Location: ds000201/sub-9008/ses-1/sub-9008_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9008/ses-1/func/sub-9008_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9008/ses-2/anat/sub-9008_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9008/ses-2/dwi/sub-9008_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9008/ses-2/fmap/sub-9008_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9008/ses-2/func/sub-9008_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9008/ses-2/func/sub-9008_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9008/ses-2/func/sub-9008_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9008/ses-2/func/sub-9008_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9008_ses-2_scans.tsv
- Location: ds000201/sub-9008/ses-2/sub-9008_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9008/ses-2/func/sub-9008_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9009/ses-1/anat/sub-9009_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9009/ses-1/anat/sub-9009_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9009/ses-1/fmap/sub-9009_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9009/ses-1/func/sub-9009_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9009/ses-1/func/sub-9009_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9009/ses-1/func/sub-9009_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9009/ses-1/func/sub-9009_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-1_scans.tsv
- Location: ds000201/sub-9009/ses-1/sub-9009_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9009/ses-1/func/sub-9009_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9009/ses-2/anat/sub-9009_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9009/ses-2/dwi/sub-9009_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9009/ses-2/fmap/sub-9009_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9009/ses-2/func/sub-9009_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9009/ses-2/func/sub-9009_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9009/ses-2/func/sub-9009_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9009/ses-2/func/sub-9009_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9009_ses-2_scans.tsv
- Location: ds000201/sub-9009/ses-2/sub-9009_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9009/ses-2/func/sub-9009_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9011/ses-1/anat/sub-9011_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9011/ses-1/anat/sub-9011_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9011/ses-1/fmap/sub-9011_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9011/ses-1/func/sub-9011_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9011/ses-1/func/sub-9011_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9011/ses-1/func/sub-9011_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9011/ses-1/func/sub-9011_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-1_scans.tsv
- Location: ds000201/sub-9011/ses-1/sub-9011_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9011/ses-1/func/sub-9011_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9011/ses-2/anat/sub-9011_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9011/ses-2/dwi/sub-9011_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9011/ses-2/fmap/sub-9011_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9011/ses-2/func/sub-9011_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9011/ses-2/func/sub-9011_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9011/ses-2/func/sub-9011_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9011/ses-2/func/sub-9011_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9011_ses-2_scans.tsv
- Location: ds000201/sub-9011/ses-2/sub-9011_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9011/ses-2/func/sub-9011_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9012/ses-1/anat/sub-9012_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9012/ses-1/anat/sub-9012_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9012/ses-1/fmap/sub-9012_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9012/ses-1/func/sub-9012_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9012/ses-1/func/sub-9012_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9012/ses-1/func/sub-9012_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9012/ses-1/func/sub-9012_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-1_scans.tsv
- Location: ds000201/sub-9012/ses-1/sub-9012_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9012/ses-1/func/sub-9012_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9012/ses-2/anat/sub-9012_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9012/ses-2/dwi/sub-9012_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9012/ses-2/fmap/sub-9012_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9012/ses-2/func/sub-9012_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9012/ses-2/func/sub-9012_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9012/ses-2/func/sub-9012_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9012/ses-2/func/sub-9012_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9012_ses-2_scans.tsv
- Location: ds000201/sub-9012/ses-2/sub-9012_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9012/ses-2/func/sub-9012_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9013/ses-1/anat/sub-9013_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9013/ses-1/dwi/sub-9013_ses-1_dwi.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9013/ses-1/fmap/sub-9013_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9013/ses-1/func/sub-9013_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9013/ses-1/func/sub-9013_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9013/ses-1/func/sub-9013_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9013/ses-1/func/sub-9013_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-1_scans.tsv
- Location: ds000201/sub-9013/ses-1/sub-9013_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9013/ses-1/func/sub-9013_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9013/ses-2/anat/sub-9013_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9013/ses-2/anat/sub-9013_ses-2_T2w.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9013/ses-2/fmap/sub-9013_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9013/ses-2/func/sub-9013_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9013/ses-2/func/sub-9013_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9013/ses-2/func/sub-9013_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9013/ses-2/func/sub-9013_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9013_ses-2_scans.tsv
- Location: ds000201/sub-9013/ses-2/sub-9013_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9013/ses-2/func/sub-9013_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9014/ses-1/anat/sub-9014_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9014/ses-1/anat/sub-9014_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9014/ses-1/fmap/sub-9014_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9014/ses-1/func/sub-9014_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9014/ses-1/func/sub-9014_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9014/ses-1/func/sub-9014_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9014/ses-1/func/sub-9014_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-1_scans.tsv
- Location: ds000201/sub-9014/ses-1/sub-9014_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9014/ses-1/func/sub-9014_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9014/ses-2/anat/sub-9014_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9014/ses-2/dwi/sub-9014_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9014/ses-2/fmap/sub-9014_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9014/ses-2/func/sub-9014_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9014/ses-2/func/sub-9014_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9014/ses-2/func/sub-9014_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9014/ses-2/func/sub-9014_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9014_ses-2_scans.tsv
- Location: ds000201/sub-9014/ses-2/sub-9014_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9014/ses-2/func/sub-9014_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9016/ses-1/anat/sub-9016_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9016/ses-1/anat/sub-9016_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9016/ses-1/fmap/sub-9016_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9016/ses-1/func/sub-9016_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9016/ses-1/func/sub-9016_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9016/ses-1/func/sub-9016_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9016/ses-1/func/sub-9016_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9016_ses-1_scans.tsv
- Location: ds000201/sub-9016/ses-1/sub-9016_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9016/ses-1/func/sub-9016_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9017/ses-1/anat/sub-9017_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9017/ses-1/anat/sub-9017_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9017/ses-1/func/sub-9017_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9017/ses-1/func/sub-9017_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9017/ses-1/func/sub-9017_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9017/ses-1/func/sub-9017_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-1_scans.tsv
- Location: ds000201/sub-9017/ses-1/sub-9017_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9017/ses-1/func/sub-9017_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9017/ses-2/anat/sub-9017_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9017/ses-2/fmap/sub-9017_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9017/ses-2/func/sub-9017_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9017/ses-2/func/sub-9017_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9017/ses-2/func/sub-9017_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9017/ses-2/func/sub-9017_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9017_ses-2_scans.tsv
- Location: ds000201/sub-9017/ses-2/sub-9017_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9017/ses-2/func/sub-9017_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9018/ses-1/anat/sub-9018_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9018/ses-1/anat/sub-9018_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9018/ses-1/fmap/sub-9018_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9018/ses-1/func/sub-9018_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9018/ses-1/func/sub-9018_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9018/ses-1/func/sub-9018_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9018/ses-1/func/sub-9018_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-1_scans.tsv
- Location: ds000201/sub-9018/ses-1/sub-9018_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9018/ses-1/func/sub-9018_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9018/ses-2/anat/sub-9018_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9018/ses-2/dwi/sub-9018_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9018/ses-2/func/sub-9018_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9018/ses-2/func/sub-9018_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9018/ses-2/func/sub-9018_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9018/ses-2/func/sub-9018_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9018_ses-2_scans.tsv
- Location: ds000201/sub-9018/ses-2/sub-9018_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9018/ses-2/func/sub-9018_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9019/ses-1/anat/sub-9019_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9019/ses-1/anat/sub-9019_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9019/ses-1/fmap/sub-9019_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9019/ses-1/func/sub-9019_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9019/ses-1/func/sub-9019_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9019/ses-1/func/sub-9019_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9019/ses-1/func/sub-9019_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-1_scans.tsv
- Location: ds000201/sub-9019/ses-1/sub-9019_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9019/ses-1/func/sub-9019_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9019/ses-2/anat/sub-9019_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9019/ses-2/dwi/sub-9019_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9019/ses-2/fmap/sub-9019_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9019/ses-2/func/sub-9019_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9019/ses-2/func/sub-9019_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9019/ses-2/func/sub-9019_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9019/ses-2/func/sub-9019_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9019_ses-2_scans.tsv
- Location: ds000201/sub-9019/ses-2/sub-9019_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9019/ses-2/func/sub-9019_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9020/ses-1/anat/sub-9020_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9020/ses-1/anat/sub-9020_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9020/ses-1/fmap/sub-9020_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9020/ses-1/func/sub-9020_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9020/ses-1/func/sub-9020_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9020/ses-1/func/sub-9020_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9020/ses-1/func/sub-9020_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-1_scans.tsv
- Location: ds000201/sub-9020/ses-1/sub-9020_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9020/ses-1/func/sub-9020_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9020/ses-2/anat/sub-9020_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9020/ses-2/dwi/sub-9020_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9020/ses-2/fmap/sub-9020_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9020/ses-2/func/sub-9020_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9020/ses-2/func/sub-9020_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9020/ses-2/func/sub-9020_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9020/ses-2/func/sub-9020_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9020_ses-2_scans.tsv
- Location: ds000201/sub-9020/ses-2/sub-9020_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9020/ses-2/func/sub-9020_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9022/ses-1/anat/sub-9022_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9022/ses-1/anat/sub-9022_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9022/ses-1/fmap/sub-9022_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9022/ses-1/func/sub-9022_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9022/ses-1/func/sub-9022_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9022/ses-1/func/sub-9022_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9022/ses-1/func/sub-9022_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9022_ses-1_scans.tsv
- Location: ds000201/sub-9022/ses-1/sub-9022_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9022/ses-1/func/sub-9022_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9023/ses-1/anat/sub-9023_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9023/ses-1/anat/sub-9023_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9023/ses-1/fmap/sub-9023_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9023/ses-1/func/sub-9023_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9023/ses-1/func/sub-9023_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9023/ses-1/func/sub-9023_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9023/ses-1/func/sub-9023_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-1_scans.tsv
- Location: ds000201/sub-9023/ses-1/sub-9023_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9023/ses-1/func/sub-9023_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9023/ses-2/anat/sub-9023_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9023/ses-2/dwi/sub-9023_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9023/ses-2/fmap/sub-9023_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9023/ses-2/func/sub-9023_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9023/ses-2/func/sub-9023_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9023/ses-2/func/sub-9023_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9023/ses-2/func/sub-9023_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9023_ses-2_scans.tsv
- Location: ds000201/sub-9023/ses-2/sub-9023_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9023/ses-2/func/sub-9023_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9025/ses-1/anat/sub-9025_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9025/ses-1/anat/sub-9025_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9025/ses-1/fmap/sub-9025_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9025/ses-1/func/sub-9025_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9025/ses-1/func/sub-9025_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9025_ses-1_scans.tsv
- Location: ds000201/sub-9025/ses-1/sub-9025_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9025/ses-1/func/sub-9025_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9025/ses-2/anat/sub-9025_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9025/ses-2/dwi/sub-9025_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9025/ses-2/fmap/sub-9025_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9025/ses-2/func/sub-9025_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9025/ses-2/func/sub-9025_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9025_ses-2_scans.tsv
- Location: ds000201/sub-9025/ses-2/sub-9025_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9025/ses-2/func/sub-9025_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9026/ses-1/anat/sub-9026_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9026/ses-1/anat/sub-9026_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9026/ses-1/fmap/sub-9026_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9026/ses-1/func/sub-9026_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9026/ses-1/func/sub-9026_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9026/ses-1/func/sub-9026_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9026/ses-1/func/sub-9026_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-1_scans.tsv
- Location: ds000201/sub-9026/ses-1/sub-9026_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9026/ses-1/func/sub-9026_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9026/ses-2/anat/sub-9026_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9026/ses-2/dwi/sub-9026_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9026/ses-2/fmap/sub-9026_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9026/ses-2/func/sub-9026_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9026/ses-2/func/sub-9026_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9026/ses-2/func/sub-9026_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9026/ses-2/func/sub-9026_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9026_ses-2_scans.tsv
- Location: ds000201/sub-9026/ses-2/sub-9026_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9026/ses-2/func/sub-9026_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9028/ses-1/anat/sub-9028_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9028/ses-1/anat/sub-9028_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9028/ses-1/fmap/sub-9028_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9028/ses-1/func/sub-9028_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9028/ses-1/func/sub-9028_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9028/ses-1/func/sub-9028_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9028/ses-1/func/sub-9028_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-1_scans.tsv
- Location: ds000201/sub-9028/ses-1/sub-9028_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9028/ses-1/func/sub-9028_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9028/ses-2/anat/sub-9028_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9028/ses-2/dwi/sub-9028_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9028/ses-2/fmap/sub-9028_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9028/ses-2/func/sub-9028_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9028/ses-2/func/sub-9028_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9028/ses-2/func/sub-9028_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9028/ses-2/func/sub-9028_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9028_ses-2_scans.tsv
- Location: ds000201/sub-9028/ses-2/sub-9028_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9028/ses-2/func/sub-9028_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9029/ses-1/anat/sub-9029_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9029/ses-1/anat/sub-9029_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9029/ses-1/fmap/sub-9029_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9029/ses-1/func/sub-9029_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9029/ses-1/func/sub-9029_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-1_scans.tsv
- Location: ds000201/sub-9029/ses-1/sub-9029_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9029/ses-1/func/sub-9029_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9029/ses-2/anat/sub-9029_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9029/ses-2/dwi/sub-9029_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9029/ses-2/fmap/sub-9029_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9029/ses-2/func/sub-9029_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9029/ses-2/func/sub-9029_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9029/ses-2/func/sub-9029_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9029/ses-2/func/sub-9029_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9029_ses-2_scans.tsv
- Location: ds000201/sub-9029/ses-2/sub-9029_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9029/ses-2/func/sub-9029_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9032/ses-1/anat/sub-9032_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9032/ses-1/anat/sub-9032_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9032/ses-1/fmap/sub-9032_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9032/ses-1/func/sub-9032_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9032/ses-1/func/sub-9032_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9032/ses-1/func/sub-9032_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9032/ses-1/func/sub-9032_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-1_scans.tsv
- Location: ds000201/sub-9032/ses-1/sub-9032_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9032/ses-1/func/sub-9032_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9032/ses-2/anat/sub-9032_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9032/ses-2/dwi/sub-9032_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9032/ses-2/fmap/sub-9032_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9032/ses-2/func/sub-9032_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9032/ses-2/func/sub-9032_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9032/ses-2/func/sub-9032_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9032/ses-2/func/sub-9032_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9032_ses-2_scans.tsv
- Location: ds000201/sub-9032/ses-2/sub-9032_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9032/ses-2/func/sub-9032_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9033/ses-1/anat/sub-9033_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9033/ses-1/anat/sub-9033_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9033/ses-1/fmap/sub-9033_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9033/ses-1/func/sub-9033_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9033/ses-1/func/sub-9033_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9033/ses-1/func/sub-9033_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9033/ses-1/func/sub-9033_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-1_scans.tsv
- Location: ds000201/sub-9033/ses-1/sub-9033_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9033/ses-1/func/sub-9033_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9033/ses-2/anat/sub-9033_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9033/ses-2/dwi/sub-9033_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9033/ses-2/fmap/sub-9033_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9033/ses-2/func/sub-9033_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9033/ses-2/func/sub-9033_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9033/ses-2/func/sub-9033_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9033/ses-2/func/sub-9033_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9033_ses-2_scans.tsv
- Location: ds000201/sub-9033/ses-2/sub-9033_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9033/ses-2/func/sub-9033_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9034/ses-1/anat/sub-9034_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9034/ses-1/anat/sub-9034_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9034/ses-1/fmap/sub-9034_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9034/ses-1/func/sub-9034_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9034/ses-1/func/sub-9034_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9034/ses-1/func/sub-9034_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9034/ses-1/func/sub-9034_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-1_scans.tsv
- Location: ds000201/sub-9034/ses-1/sub-9034_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9034/ses-1/func/sub-9034_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9034/ses-2/anat/sub-9034_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9034/ses-2/dwi/sub-9034_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9034/ses-2/fmap/sub-9034_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9034/ses-2/func/sub-9034_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9034/ses-2/func/sub-9034_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9034/ses-2/func/sub-9034_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9034/ses-2/func/sub-9034_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9034_ses-2_scans.tsv
- Location: ds000201/sub-9034/ses-2/sub-9034_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9034/ses-2/func/sub-9034_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9035/ses-1/anat/sub-9035_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9035/ses-1/anat/sub-9035_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9035/ses-1/fmap/sub-9035_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9035/ses-1/func/sub-9035_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9035/ses-1/func/sub-9035_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9035/ses-1/func/sub-9035_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9035/ses-1/func/sub-9035_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-1_scans.tsv
- Location: ds000201/sub-9035/ses-1/sub-9035_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9035/ses-1/func/sub-9035_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9035/ses-2/anat/sub-9035_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9035/ses-2/dwi/sub-9035_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9035/ses-2/fmap/sub-9035_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9035/ses-2/func/sub-9035_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9035/ses-2/func/sub-9035_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9035/ses-2/func/sub-9035_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9035/ses-2/func/sub-9035_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9035_ses-2_scans.tsv
- Location: ds000201/sub-9035/ses-2/sub-9035_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9035/ses-2/func/sub-9035_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9036/ses-1/anat/sub-9036_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9036/ses-1/anat/sub-9036_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9036/ses-1/fmap/sub-9036_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9036/ses-1/func/sub-9036_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9036/ses-1/func/sub-9036_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9036/ses-1/func/sub-9036_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9036/ses-1/func/sub-9036_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-1_scans.tsv
- Location: ds000201/sub-9036/ses-1/sub-9036_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9036/ses-1/func/sub-9036_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9036/ses-2/anat/sub-9036_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9036/ses-2/dwi/sub-9036_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9036/ses-2/fmap/sub-9036_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9036/ses-2/func/sub-9036_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9036/ses-2/func/sub-9036_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9036/ses-2/func/sub-9036_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9036/ses-2/func/sub-9036_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9036_ses-2_scans.tsv
- Location: ds000201/sub-9036/ses-2/sub-9036_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9036/ses-2/func/sub-9036_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9037/ses-1/anat/sub-9037_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9037/ses-1/anat/sub-9037_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9037/ses-1/fmap/sub-9037_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9037/ses-1/func/sub-9037_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9037/ses-1/func/sub-9037_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9037/ses-1/func/sub-9037_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9037/ses-1/func/sub-9037_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-1_scans.tsv
- Location: ds000201/sub-9037/ses-1/sub-9037_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9037/ses-1/func/sub-9037_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9037/ses-2/anat/sub-9037_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9037/ses-2/dwi/sub-9037_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9037/ses-2/fmap/sub-9037_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9037/ses-2/func/sub-9037_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9037/ses-2/func/sub-9037_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9037/ses-2/func/sub-9037_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9037/ses-2/func/sub-9037_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9037_ses-2_scans.tsv
- Location: ds000201/sub-9037/ses-2/sub-9037_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9037/ses-2/func/sub-9037_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9038/ses-1/anat/sub-9038_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9038/ses-1/anat/sub-9038_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9038/ses-1/fmap/sub-9038_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9038/ses-1/func/sub-9038_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9038/ses-1/func/sub-9038_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9038/ses-1/func/sub-9038_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9038/ses-1/func/sub-9038_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-1_scans.tsv
- Location: ds000201/sub-9038/ses-1/sub-9038_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9038/ses-1/func/sub-9038_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9038/ses-2/anat/sub-9038_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9038/ses-2/dwi/sub-9038_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9038/ses-2/fmap/sub-9038_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9038/ses-2/func/sub-9038_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9038/ses-2/func/sub-9038_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9038/ses-2/func/sub-9038_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9038/ses-2/func/sub-9038_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9038_ses-2_scans.tsv
- Location: ds000201/sub-9038/ses-2/sub-9038_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9038/ses-2/func/sub-9038_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9039/ses-1/anat/sub-9039_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9039/ses-1/anat/sub-9039_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9039/ses-1/fmap/sub-9039_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9039/ses-1/func/sub-9039_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9039/ses-1/func/sub-9039_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9039/ses-1/func/sub-9039_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9039/ses-1/func/sub-9039_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-1_scans.tsv
- Location: ds000201/sub-9039/ses-1/sub-9039_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9039/ses-1/func/sub-9039_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9039/ses-2/anat/sub-9039_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9039/ses-2/dwi/sub-9039_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9039/ses-2/fmap/sub-9039_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9039/ses-2/func/sub-9039_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9039/ses-2/func/sub-9039_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9039/ses-2/func/sub-9039_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9039/ses-2/func/sub-9039_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9039_ses-2_scans.tsv
- Location: ds000201/sub-9039/ses-2/sub-9039_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9039/ses-2/func/sub-9039_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9040/ses-1/anat/sub-9040_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9040/ses-1/anat/sub-9040_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9040/ses-1/fmap/sub-9040_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9040/ses-1/func/sub-9040_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9040/ses-1/func/sub-9040_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9040/ses-1/func/sub-9040_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9040/ses-1/func/sub-9040_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-1_scans.tsv
- Location: ds000201/sub-9040/ses-1/sub-9040_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9040/ses-1/func/sub-9040_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9040/ses-2/anat/sub-9040_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9040/ses-2/dwi/sub-9040_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9040/ses-2/fmap/sub-9040_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9040/ses-2/func/sub-9040_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9040/ses-2/func/sub-9040_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9040/ses-2/func/sub-9040_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9040/ses-2/func/sub-9040_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9040_ses-2_scans.tsv
- Location: ds000201/sub-9040/ses-2/sub-9040_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9040/ses-2/func/sub-9040_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9041/ses-1/anat/sub-9041_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9041/ses-1/anat/sub-9041_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9041/ses-1/fmap/sub-9041_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9041/ses-1/func/sub-9041_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9041/ses-1/func/sub-9041_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9041/ses-1/func/sub-9041_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9041/ses-1/func/sub-9041_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-1_scans.tsv
- Location: ds000201/sub-9041/ses-1/sub-9041_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9041/ses-1/func/sub-9041_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9041/ses-2/anat/sub-9041_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9041/ses-2/dwi/sub-9041_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9041/ses-2/fmap/sub-9041_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9041/ses-2/func/sub-9041_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9041/ses-2/func/sub-9041_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9041/ses-2/func/sub-9041_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9041/ses-2/func/sub-9041_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9041_ses-2_scans.tsv
- Location: ds000201/sub-9041/ses-2/sub-9041_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9041/ses-2/func/sub-9041_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9042/ses-1/anat/sub-9042_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9042/ses-1/anat/sub-9042_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9042/ses-1/fmap/sub-9042_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9042/ses-1/func/sub-9042_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9042/ses-1/func/sub-9042_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9042/ses-1/func/sub-9042_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9042/ses-1/func/sub-9042_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-1_scans.tsv
- Location: ds000201/sub-9042/ses-1/sub-9042_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9042/ses-1/func/sub-9042_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9042/ses-2/anat/sub-9042_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9042/ses-2/dwi/sub-9042_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9042/ses-2/fmap/sub-9042_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9042/ses-2/func/sub-9042_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9042/ses-2/func/sub-9042_ses-2_task-faces_run-1_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9042/ses-2/func/sub-9042_ses-2_task-faces_run-2_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9042/ses-2/func/sub-9042_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9042/ses-2/func/sub-9042_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9042_ses-2_scans.tsv
- Location: ds000201/sub-9042/ses-2/sub-9042_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 17
- Evidence: Filename: /sub-9042/ses-2/func/sub-9042_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9044/ses-1/anat/sub-9044_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9044/ses-1/anat/sub-9044_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_phase1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_phase2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_real1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-1_real2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_phase1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 17
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_phase2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 18
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_real1.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 19
- Evidence: Filename: /sub-9044/ses-1/fmap/sub-9044_ses-1_run-2_real2.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 20
- Evidence: Filename: /sub-9044/ses-1/func/sub-9044_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 21
- Evidence: Filename: /sub-9044/ses-1/func/sub-9044_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 22
- Evidence: Filename: /sub-9044/ses-1/func/sub-9044_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 23
- Evidence: Filename: /sub-9044/ses-1/func/sub-9044_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9044_ses-1_scans.tsv
- Location: ds000201/sub-9044/ses-1/sub-9044_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 24
- Evidence: Filename: /sub-9044/ses-1/func/sub-9044_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9044/ses-2/anat/sub-9044_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9044/ses-2/dwi/sub-9044_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9044_ses-2_scans.tsv
- Location: ds000201/sub-9044/ses-2/sub-9044_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9044/ses-2/fmap/sub-9044_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9045/ses-1/anat/sub-9045_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9045/ses-1/anat/sub-9045_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9045/ses-1/fmap/sub-9045_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9045/ses-1/func/sub-9045_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9045/ses-1/func/sub-9045_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9045/ses-1/func/sub-9045_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9045/ses-1/func/sub-9045_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-1_scans.tsv
- Location: ds000201/sub-9045/ses-1/sub-9045_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9045/ses-1/func/sub-9045_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9045/ses-2/anat/sub-9045_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9045/ses-2/dwi/sub-9045_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9045/ses-2/fmap/sub-9045_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9045/ses-2/func/sub-9045_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9045/ses-2/func/sub-9045_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9045/ses-2/func/sub-9045_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9045/ses-2/func/sub-9045_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9045_ses-2_scans.tsv
- Location: ds000201/sub-9045/ses-2/sub-9045_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9045/ses-2/func/sub-9045_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9046/ses-1/anat/sub-9046_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9046/ses-1/fmap/sub-9046_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9046/ses-1/func/sub-9046_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9046/ses-1/func/sub-9046_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9046/ses-1/func/sub-9046_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9046/ses-1/func/sub-9046_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-1_scans.tsv
- Location: ds000201/sub-9046/ses-1/sub-9046_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9046/ses-1/func/sub-9046_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9046/ses-2/anat/sub-9046_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9046/ses-2/dwi/sub-9046_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9046/ses-2/fmap/sub-9046_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9046/ses-2/func/sub-9046_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9046/ses-2/func/sub-9046_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9046/ses-2/func/sub-9046_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9046/ses-2/func/sub-9046_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9046_ses-2_scans.tsv
- Location: ds000201/sub-9046/ses-2/sub-9046_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9046/ses-2/func/sub-9046_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9047/ses-1/anat/sub-9047_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9047/ses-1/anat/sub-9047_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9047/ses-1/fmap/sub-9047_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9047/ses-1/func/sub-9047_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9047/ses-1/func/sub-9047_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9047/ses-1/func/sub-9047_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9047/ses-1/func/sub-9047_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-1_scans.tsv
- Location: ds000201/sub-9047/ses-1/sub-9047_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9047/ses-1/func/sub-9047_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9047/ses-2/anat/sub-9047_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9047/ses-2/dwi/sub-9047_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9047/ses-2/fmap/sub-9047_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9047/ses-2/func/sub-9047_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9047/ses-2/func/sub-9047_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9047/ses-2/func/sub-9047_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9047/ses-2/func/sub-9047_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9047_ses-2_scans.tsv
- Location: ds000201/sub-9047/ses-2/sub-9047_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9047/ses-2/func/sub-9047_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9048/ses-1/anat/sub-9048_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9048/ses-1/anat/sub-9048_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9048/ses-1/fmap/sub-9048_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9048/ses-1/func/sub-9048_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9048/ses-1/func/sub-9048_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9048/ses-1/func/sub-9048_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9048/ses-1/func/sub-9048_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-1_scans.tsv
- Location: ds000201/sub-9048/ses-1/sub-9048_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9048/ses-1/func/sub-9048_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9048/ses-2/anat/sub-9048_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9048/ses-2/dwi/sub-9048_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9048/ses-2/fmap/sub-9048_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9048/ses-2/func/sub-9048_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9048/ses-2/func/sub-9048_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9048/ses-2/func/sub-9048_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9048/ses-2/func/sub-9048_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9048_ses-2_scans.tsv
- Location: ds000201/sub-9048/ses-2/sub-9048_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9048/ses-2/func/sub-9048_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9049/ses-1/anat/sub-9049_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9049/ses-1/anat/sub-9049_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9049/ses-1/fmap/sub-9049_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9049/ses-1/func/sub-9049_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9049/ses-1/func/sub-9049_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9049/ses-1/func/sub-9049_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9049/ses-1/func/sub-9049_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-1_scans.tsv
- Location: ds000201/sub-9049/ses-1/sub-9049_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9049/ses-1/func/sub-9049_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9049/ses-2/anat/sub-9049_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9049/ses-2/dwi/sub-9049_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9049/ses-2/fmap/sub-9049_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9049/ses-2/func/sub-9049_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9049/ses-2/func/sub-9049_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9049/ses-2/func/sub-9049_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9049/ses-2/func/sub-9049_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9049_ses-2_scans.tsv
- Location: ds000201/sub-9049/ses-2/sub-9049_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9049/ses-2/func/sub-9049_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9050/ses-1/anat/sub-9050_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9050/ses-1/anat/sub-9050_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9050/ses-1/fmap/sub-9050_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9050/ses-1/func/sub-9050_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9050/ses-1/func/sub-9050_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9050/ses-1/func/sub-9050_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9050/ses-1/func/sub-9050_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-1_scans.tsv
- Location: ds000201/sub-9050/ses-1/sub-9050_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9050/ses-1/func/sub-9050_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9050/ses-2/anat/sub-9050_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9050/ses-2/dwi/sub-9050_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9050/ses-2/fmap/sub-9050_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9050/ses-2/func/sub-9050_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9050/ses-2/func/sub-9050_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9050/ses-2/func/sub-9050_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9050/ses-2/func/sub-9050_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9050_ses-2_scans.tsv
- Location: ds000201/sub-9050/ses-2/sub-9050_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9050/ses-2/func/sub-9050_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9053/ses-1/anat/sub-9053_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9053/ses-1/anat/sub-9053_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9053/ses-1/fmap/sub-9053_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9053/ses-1/func/sub-9053_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9053/ses-1/func/sub-9053_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9053/ses-1/func/sub-9053_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9053/ses-1/func/sub-9053_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-1_scans.tsv
- Location: ds000201/sub-9053/ses-1/sub-9053_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9053/ses-1/func/sub-9053_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9053/ses-2/anat/sub-9053_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9053/ses-2/dwi/sub-9053_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9053/ses-2/fmap/sub-9053_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9053/ses-2/func/sub-9053_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9053/ses-2/func/sub-9053_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9053/ses-2/func/sub-9053_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9053/ses-2/func/sub-9053_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9053_ses-2_scans.tsv
- Location: ds000201/sub-9053/ses-2/sub-9053_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9053/ses-2/func/sub-9053_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9054/ses-1/anat/sub-9054_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9054/ses-1/anat/sub-9054_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9054/ses-1/fmap/sub-9054_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9054/ses-1/func/sub-9054_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9054/ses-1/func/sub-9054_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9054/ses-1/func/sub-9054_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9054/ses-1/func/sub-9054_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-1_scans.tsv
- Location: ds000201/sub-9054/ses-1/sub-9054_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9054/ses-1/func/sub-9054_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9054/ses-2/anat/sub-9054_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9054/ses-2/anat/sub-9054_ses-2_T2w.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9054/ses-2/fmap/sub-9054_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9054/ses-2/func/sub-9054_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9054/ses-2/func/sub-9054_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9054/ses-2/func/sub-9054_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9054/ses-2/func/sub-9054_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9054_ses-2_scans.tsv
- Location: ds000201/sub-9054/ses-2/sub-9054_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9054/ses-2/func/sub-9054_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9055/ses-1/anat/sub-9055_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9055/ses-1/anat/sub-9055_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9055/ses-1/fmap/sub-9055_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9055/ses-1/func/sub-9055_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9055/ses-1/func/sub-9055_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9055/ses-1/func/sub-9055_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9055/ses-1/func/sub-9055_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-1_scans.tsv
- Location: ds000201/sub-9055/ses-1/sub-9055_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9055/ses-1/func/sub-9055_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9055/ses-2/anat/sub-9055_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9055/ses-2/dwi/sub-9055_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9055/ses-2/fmap/sub-9055_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9055/ses-2/func/sub-9055_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9055/ses-2/func/sub-9055_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9055/ses-2/func/sub-9055_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9055/ses-2/func/sub-9055_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9055_ses-2_scans.tsv
- Location: ds000201/sub-9055/ses-2/sub-9055_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9055/ses-2/func/sub-9055_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9056/ses-1/anat/sub-9056_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9056/ses-1/anat/sub-9056_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9056/ses-1/func/sub-9056_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9056/ses-1/func/sub-9056_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9056/ses-1/func/sub-9056_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9056/ses-1/func/sub-9056_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-1_scans.tsv
- Location: ds000201/sub-9056/ses-1/sub-9056_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9056/ses-1/func/sub-9056_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9056/ses-2/anat/sub-9056_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9056/ses-2/fmap/sub-9056_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9056/ses-2/func/sub-9056_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9056/ses-2/func/sub-9056_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9056/ses-2/func/sub-9056_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9056/ses-2/func/sub-9056_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9056_ses-2_scans.tsv
- Location: ds000201/sub-9056/ses-2/sub-9056_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9056/ses-2/func/sub-9056_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9057/ses-1/anat/sub-9057_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9057/ses-1/anat/sub-9057_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9057/ses-1/fmap/sub-9057_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9057/ses-1/func/sub-9057_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9057/ses-1/func/sub-9057_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9057/ses-1/func/sub-9057_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9057/ses-1/func/sub-9057_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-1_scans.tsv
- Location: ds000201/sub-9057/ses-1/sub-9057_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9057/ses-1/func/sub-9057_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9057/ses-2/anat/sub-9057_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9057/ses-2/dwi/sub-9057_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9057/ses-2/fmap/sub-9057_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9057/ses-2/func/sub-9057_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9057/ses-2/func/sub-9057_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9057/ses-2/func/sub-9057_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9057/ses-2/func/sub-9057_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9057_ses-2_scans.tsv
- Location: ds000201/sub-9057/ses-2/sub-9057_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9057/ses-2/func/sub-9057_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9058/ses-1/anat/sub-9058_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9058/ses-1/anat/sub-9058_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9058/ses-1/fmap/sub-9058_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9058/ses-1/func/sub-9058_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9058/ses-1/func/sub-9058_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9058/ses-1/func/sub-9058_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9058/ses-1/func/sub-9058_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-1_scans.tsv
- Location: ds000201/sub-9058/ses-1/sub-9058_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9058/ses-1/func/sub-9058_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9058/ses-2/anat/sub-9058_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9058/ses-2/dwi/sub-9058_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9058/ses-2/fmap/sub-9058_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9058/ses-2/func/sub-9058_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9058/ses-2/func/sub-9058_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9058/ses-2/func/sub-9058_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9058/ses-2/func/sub-9058_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9058_ses-2_scans.tsv
- Location: ds000201/sub-9058/ses-2/sub-9058_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9058/ses-2/func/sub-9058_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9059/ses-1/anat/sub-9059_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9059/ses-1/anat/sub-9059_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9059/ses-1/func/sub-9059_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9059/ses-1/func/sub-9059_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9059/ses-1/func/sub-9059_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9059/ses-1/func/sub-9059_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-1_scans.tsv
- Location: ds000201/sub-9059/ses-1/sub-9059_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9059/ses-1/func/sub-9059_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9059/ses-2/anat/sub-9059_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9059/ses-2/fmap/sub-9059_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9059/ses-2/func/sub-9059_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9059/ses-2/func/sub-9059_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9059/ses-2/func/sub-9059_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9059/ses-2/func/sub-9059_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9059_ses-2_scans.tsv
- Location: ds000201/sub-9059/ses-2/sub-9059_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9059/ses-2/func/sub-9059_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9061/ses-1/anat/sub-9061_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9061/ses-1/anat/sub-9061_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9061/ses-1/fmap/sub-9061_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9061/ses-1/func/sub-9061_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9061/ses-1/func/sub-9061_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9061/ses-1/func/sub-9061_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9061/ses-1/func/sub-9061_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-1_scans.tsv
- Location: ds000201/sub-9061/ses-1/sub-9061_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9061/ses-1/func/sub-9061_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9061/ses-2/anat/sub-9061_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9061/ses-2/dwi/sub-9061_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9061/ses-2/fmap/sub-9061_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9061/ses-2/func/sub-9061_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9061/ses-2/func/sub-9061_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9061/ses-2/func/sub-9061_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9061/ses-2/func/sub-9061_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9061_ses-2_scans.tsv
- Location: ds000201/sub-9061/ses-2/sub-9061_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9061/ses-2/func/sub-9061_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9062/ses-1/anat/sub-9062_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9062/ses-1/anat/sub-9062_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9062/ses-1/fmap/sub-9062_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9062/ses-1/func/sub-9062_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9062/ses-1/func/sub-9062_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9062/ses-1/func/sub-9062_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9062/ses-1/func/sub-9062_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-1_scans.tsv
- Location: ds000201/sub-9062/ses-1/sub-9062_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9062/ses-1/func/sub-9062_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9062/ses-2/anat/sub-9062_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9062/ses-2/dwi/sub-9062_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9062/ses-2/fmap/sub-9062_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9062/ses-2/func/sub-9062_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9062/ses-2/func/sub-9062_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9062/ses-2/func/sub-9062_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9062/ses-2/func/sub-9062_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9062_ses-2_scans.tsv
- Location: ds000201/sub-9062/ses-2/sub-9062_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9062/ses-2/func/sub-9062_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9063/ses-1/anat/sub-9063_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9063/ses-1/anat/sub-9063_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9063/ses-1/fmap/sub-9063_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9063/ses-1/func/sub-9063_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9063/ses-1/func/sub-9063_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9063/ses-1/func/sub-9063_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9063/ses-1/func/sub-9063_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-1_scans.tsv
- Location: ds000201/sub-9063/ses-1/sub-9063_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9063/ses-1/func/sub-9063_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9063/ses-2/anat/sub-9063_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9063/ses-2/anat/sub-9063_ses-2_T2w.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9063/ses-2/fmap/sub-9063_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9063/ses-2/func/sub-9063_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9063/ses-2/func/sub-9063_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9063/ses-2/func/sub-9063_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9063/ses-2/func/sub-9063_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9063_ses-2_scans.tsv
- Location: ds000201/sub-9063/ses-2/sub-9063_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9063/ses-2/func/sub-9063_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9064/ses-1/anat/sub-9064_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9064/ses-1/anat/sub-9064_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9064/ses-1/fmap/sub-9064_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9064/ses-1/func/sub-9064_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9064/ses-1/func/sub-9064_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9064/ses-1/func/sub-9064_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9064/ses-1/func/sub-9064_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-1_scans.tsv
- Location: ds000201/sub-9064/ses-1/sub-9064_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9064/ses-1/func/sub-9064_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9064/ses-2/anat/sub-9064_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9064/ses-2/dwi/sub-9064_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9064/ses-2/fmap/sub-9064_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9064/ses-2/func/sub-9064_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9064/ses-2/func/sub-9064_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9064/ses-2/func/sub-9064_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9064/ses-2/func/sub-9064_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9064_ses-2_scans.tsv
- Location: ds000201/sub-9064/ses-2/sub-9064_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9064/ses-2/func/sub-9064_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9065/ses-1/anat/sub-9065_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9065/ses-1/anat/sub-9065_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9065/ses-1/fmap/sub-9065_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9065/ses-1/func/sub-9065_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9065/ses-1/func/sub-9065_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9065/ses-1/func/sub-9065_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9065/ses-1/func/sub-9065_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9065/ses-1/func/sub-9065_ses-1_task-rest_run-3_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-1_scans.tsv
- Location: ds000201/sub-9065/ses-1/sub-9065_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 17
- Evidence: Filename: /sub-9065/ses-1/func/sub-9065_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9065/ses-2/anat/sub-9065_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9065/ses-2/dwi/sub-9065_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9065/ses-2/func/sub-9065_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9065/ses-2/func/sub-9065_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9065/ses-2/func/sub-9065_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9065/ses-2/func/sub-9065_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9065_ses-2_scans.tsv
- Location: ds000201/sub-9065/ses-2/sub-9065_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9065/ses-2/func/sub-9065_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9066/ses-1/anat/sub-9066_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9066/ses-1/dwi/sub-9066_ses-1_dwi.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9066/ses-1/fmap/sub-9066_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9066/ses-1/func/sub-9066_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9066/ses-1/func/sub-9066_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9066/ses-1/func/sub-9066_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9066/ses-1/func/sub-9066_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9066_ses-1_scans.tsv
- Location: ds000201/sub-9066/ses-1/sub-9066_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9066/ses-1/func/sub-9066_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9067/ses-1/anat/sub-9067_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9067/ses-1/anat/sub-9067_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9067/ses-1/fmap/sub-9067_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9067/ses-1/func/sub-9067_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9067/ses-1/func/sub-9067_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9067/ses-1/func/sub-9067_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9067/ses-1/func/sub-9067_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-1_scans.tsv
- Location: ds000201/sub-9067/ses-1/sub-9067_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9067/ses-1/func/sub-9067_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9067/ses-2/anat/sub-9067_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9067/ses-2/dwi/sub-9067_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9067/ses-2/fmap/sub-9067_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9067/ses-2/func/sub-9067_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9067/ses-2/func/sub-9067_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9067/ses-2/func/sub-9067_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9067/ses-2/func/sub-9067_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9067_ses-2_scans.tsv
- Location: ds000201/sub-9067/ses-2/sub-9067_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9067/ses-2/func/sub-9067_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9068/ses-1/anat/sub-9068_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9068/ses-1/anat/sub-9068_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9068/ses-1/fmap/sub-9068_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9068/ses-1/func/sub-9068_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9068/ses-1/func/sub-9068_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9068/ses-1/func/sub-9068_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9068/ses-1/func/sub-9068_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-1_scans.tsv
- Location: ds000201/sub-9068/ses-1/sub-9068_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9068/ses-1/func/sub-9068_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9068/ses-2/anat/sub-9068_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9068/ses-2/dwi/sub-9068_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9068/ses-2/fmap/sub-9068_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9068/ses-2/func/sub-9068_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9068/ses-2/func/sub-9068_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9068/ses-2/func/sub-9068_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9068/ses-2/func/sub-9068_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9068_ses-2_scans.tsv
- Location: ds000201/sub-9068/ses-2/sub-9068_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9068/ses-2/func/sub-9068_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9069/ses-1/anat/sub-9069_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9069/ses-1/anat/sub-9069_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9069/ses-1/fmap/sub-9069_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9069/ses-1/func/sub-9069_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9069/ses-1/func/sub-9069_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9069/ses-1/func/sub-9069_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9069/ses-1/func/sub-9069_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-1_scans.tsv
- Location: ds000201/sub-9069/ses-1/sub-9069_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9069/ses-1/func/sub-9069_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9069/ses-2/anat/sub-9069_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9069/ses-2/dwi/sub-9069_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9069/ses-2/fmap/sub-9069_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9069/ses-2/func/sub-9069_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9069/ses-2/func/sub-9069_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9069/ses-2/func/sub-9069_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9069/ses-2/func/sub-9069_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9069_ses-2_scans.tsv
- Location: ds000201/sub-9069/ses-2/sub-9069_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9069/ses-2/func/sub-9069_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9070/ses-1/anat/sub-9070_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9070/ses-1/anat/sub-9070_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9070/ses-1/fmap/sub-9070_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9070/ses-1/fmap/sub-9070_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9070/ses-1/fmap/sub-9070_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9070/ses-1/fmap/sub-9070_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9070/ses-1/fmap/sub-9070_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9070/ses-1/func/sub-9070_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9070/ses-1/func/sub-9070_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9070/ses-1/func/sub-9070_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9070/ses-1/func/sub-9070_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-1_scans.tsv
- Location: ds000201/sub-9070/ses-1/sub-9070_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9070/ses-1/func/sub-9070_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9070/ses-2/anat/sub-9070_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9070/ses-2/dwi/sub-9070_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9070/ses-2/fmap/sub-9070_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9070/ses-2/func/sub-9070_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9070/ses-2/func/sub-9070_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9070/ses-2/func/sub-9070_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9070/ses-2/func/sub-9070_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9070_ses-2_scans.tsv
- Location: ds000201/sub-9070/ses-2/sub-9070_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9070/ses-2/func/sub-9070_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9071/ses-1/anat/sub-9071_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9071/ses-1/anat/sub-9071_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9071/ses-1/fmap/sub-9071_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9071/ses-1/func/sub-9071_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9071/ses-1/func/sub-9071_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9071/ses-1/func/sub-9071_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9071/ses-1/func/sub-9071_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-1_scans.tsv
- Location: ds000201/sub-9071/ses-1/sub-9071_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9071/ses-1/func/sub-9071_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9071/ses-2/anat/sub-9071_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9071/ses-2/dwi/sub-9071_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9071/ses-2/fmap/sub-9071_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9071/ses-2/func/sub-9071_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9071/ses-2/func/sub-9071_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9071/ses-2/func/sub-9071_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9071/ses-2/func/sub-9071_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9071_ses-2_scans.tsv
- Location: ds000201/sub-9071/ses-2/sub-9071_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9071/ses-2/func/sub-9071_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9072/ses-1/anat/sub-9072_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9072/ses-1/anat/sub-9072_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9072/ses-1/fmap/sub-9072_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9072/ses-1/func/sub-9072_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9072/ses-1/func/sub-9072_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9072/ses-1/func/sub-9072_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9072/ses-1/func/sub-9072_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-1_scans.tsv
- Location: ds000201/sub-9072/ses-1/sub-9072_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9072/ses-1/func/sub-9072_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9072/ses-2/anat/sub-9072_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9072/ses-2/dwi/sub-9072_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9072/ses-2/fmap/sub-9072_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9072/ses-2/func/sub-9072_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9072/ses-2/func/sub-9072_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9072/ses-2/func/sub-9072_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9072/ses-2/func/sub-9072_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9072_ses-2_scans.tsv
- Location: ds000201/sub-9072/ses-2/sub-9072_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9072/ses-2/func/sub-9072_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9073/ses-1/anat/sub-9073_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9073/ses-1/anat/sub-9073_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9073/ses-1/fmap/sub-9073_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9073/ses-1/func/sub-9073_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9073/ses-1/func/sub-9073_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9073/ses-1/func/sub-9073_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9073/ses-1/func/sub-9073_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-1_scans.tsv
- Location: ds000201/sub-9073/ses-1/sub-9073_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9073/ses-1/func/sub-9073_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9073/ses-2/anat/sub-9073_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9073/ses-2/dwi/sub-9073_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9073/ses-2/fmap/sub-9073_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9073/ses-2/func/sub-9073_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9073/ses-2/func/sub-9073_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9073/ses-2/func/sub-9073_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9073/ses-2/func/sub-9073_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9073_ses-2_scans.tsv
- Location: ds000201/sub-9073/ses-2/sub-9073_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9073/ses-2/func/sub-9073_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9074/ses-1/anat/sub-9074_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9074/ses-1/anat/sub-9074_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9074/ses-1/func/sub-9074_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9074/ses-1/func/sub-9074_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9074/ses-1/func/sub-9074_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9074/ses-1/func/sub-9074_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-1_scans.tsv
- Location: ds000201/sub-9074/ses-1/sub-9074_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9074/ses-1/func/sub-9074_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9074/ses-2/anat/sub-9074_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9074/ses-2/anat/sub-9074_ses-2_T2w.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9074/ses-2/fmap/sub-9074_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9074/ses-2/func/sub-9074_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9074/ses-2/func/sub-9074_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9074/ses-2/func/sub-9074_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9074/ses-2/func/sub-9074_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9074_ses-2_scans.tsv
- Location: ds000201/sub-9074/ses-2/sub-9074_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9074/ses-2/func/sub-9074_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9075/ses-1/anat/sub-9075_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9075/ses-1/anat/sub-9075_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9075/ses-1/fmap/sub-9075_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9075/ses-1/func/sub-9075_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9075/ses-1/func/sub-9075_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9075/ses-1/func/sub-9075_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9075/ses-1/func/sub-9075_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-1_scans.tsv
- Location: ds000201/sub-9075/ses-1/sub-9075_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9075/ses-1/func/sub-9075_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9075/ses-2/anat/sub-9075_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9075/ses-2/dwi/sub-9075_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9075/ses-2/fmap/sub-9075_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9075/ses-2/func/sub-9075_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9075/ses-2/func/sub-9075_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9075/ses-2/func/sub-9075_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9075/ses-2/func/sub-9075_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9075_ses-2_scans.tsv
- Location: ds000201/sub-9075/ses-2/sub-9075_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9075/ses-2/func/sub-9075_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9076/ses-1/anat/sub-9076_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9076/ses-1/anat/sub-9076_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9076/ses-1/fmap/sub-9076_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9076/ses-1/func/sub-9076_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9076/ses-1/func/sub-9076_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9076/ses-1/func/sub-9076_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9076/ses-1/func/sub-9076_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-1_scans.tsv
- Location: ds000201/sub-9076/ses-1/sub-9076_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9076/ses-1/func/sub-9076_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9076/ses-2/anat/sub-9076_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9076/ses-2/dwi/sub-9076_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9076/ses-2/fmap/sub-9076_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9076/ses-2/func/sub-9076_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9076/ses-2/func/sub-9076_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9076/ses-2/func/sub-9076_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9076/ses-2/func/sub-9076_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9076_ses-2_scans.tsv
- Location: ds000201/sub-9076/ses-2/sub-9076_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9076/ses-2/func/sub-9076_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9077/ses-1/anat/sub-9077_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9077/ses-1/anat/sub-9077_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9077/ses-1/fmap/sub-9077_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9077/ses-1/func/sub-9077_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9077/ses-1/func/sub-9077_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9077/ses-1/func/sub-9077_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9077/ses-1/func/sub-9077_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-1_scans.tsv
- Location: ds000201/sub-9077/ses-1/sub-9077_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9077/ses-1/func/sub-9077_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9077/ses-2/anat/sub-9077_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9077/ses-2/dwi/sub-9077_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9077/ses-2/fmap/sub-9077_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9077/ses-2/func/sub-9077_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9077/ses-2/func/sub-9077_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9077/ses-2/func/sub-9077_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9077/ses-2/func/sub-9077_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9077_ses-2_scans.tsv
- Location: ds000201/sub-9077/ses-2/sub-9077_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9077/ses-2/func/sub-9077_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9078_ses-1_scans.tsv
- Location: ds000201/sub-9078/ses-1/sub-9078_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9078/ses-1/anat/sub-9078_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9079/ses-1/anat/sub-9079_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9079/ses-1/anat/sub-9079_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9079/ses-1/fmap/sub-9079_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9079/ses-1/func/sub-9079_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9079/ses-1/func/sub-9079_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9079/ses-1/func/sub-9079_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9079/ses-1/func/sub-9079_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-1_scans.tsv
- Location: ds000201/sub-9079/ses-1/sub-9079_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9079/ses-1/func/sub-9079_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9079/ses-2/anat/sub-9079_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9079/ses-2/dwi/sub-9079_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9079/ses-2/fmap/sub-9079_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9079/ses-2/func/sub-9079_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9079/ses-2/func/sub-9079_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9079/ses-2/func/sub-9079_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9079/ses-2/func/sub-9079_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9079_ses-2_scans.tsv
- Location: ds000201/sub-9079/ses-2/sub-9079_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9079/ses-2/func/sub-9079_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9080/ses-1/anat/sub-9080_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9080/ses-1/anat/sub-9080_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9080/ses-1/fmap/sub-9080_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9080/ses-1/func/sub-9080_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9080/ses-1/func/sub-9080_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9080/ses-1/func/sub-9080_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9080/ses-1/func/sub-9080_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-1_scans.tsv
- Location: ds000201/sub-9080/ses-1/sub-9080_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9080/ses-1/func/sub-9080_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9080/ses-2/anat/sub-9080_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9080/ses-2/dwi/sub-9080_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9080/ses-2/fmap/sub-9080_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9080/ses-2/func/sub-9080_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9080/ses-2/func/sub-9080_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9080/ses-2/func/sub-9080_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9080/ses-2/func/sub-9080_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9080_ses-2_scans.tsv
- Location: ds000201/sub-9080/ses-2/sub-9080_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9080/ses-2/func/sub-9080_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9081/ses-1/anat/sub-9081_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9081/ses-1/anat/sub-9081_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9081/ses-1/fmap/sub-9081_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9081/ses-1/func/sub-9081_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9081/ses-1/func/sub-9081_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9081/ses-1/func/sub-9081_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9081/ses-1/func/sub-9081_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-1_scans.tsv
- Location: ds000201/sub-9081/ses-1/sub-9081_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9081/ses-1/func/sub-9081_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9081/ses-2/anat/sub-9081_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9081/ses-2/dwi/sub-9081_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9081/ses-2/fmap/sub-9081_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9081/ses-2/func/sub-9081_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9081/ses-2/func/sub-9081_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9081/ses-2/func/sub-9081_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9081/ses-2/func/sub-9081_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9081_ses-2_scans.tsv
- Location: ds000201/sub-9081/ses-2/sub-9081_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9081/ses-2/func/sub-9081_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9082/ses-1/anat/sub-9082_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9082/ses-1/anat/sub-9082_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9082/ses-1/fmap/sub-9082_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9082/ses-1/func/sub-9082_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9082/ses-1/func/sub-9082_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9082/ses-1/func/sub-9082_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9082/ses-1/func/sub-9082_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-1_scans.tsv
- Location: ds000201/sub-9082/ses-1/sub-9082_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9082/ses-1/func/sub-9082_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9082/ses-2/anat/sub-9082_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9082/ses-2/dwi/sub-9082_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9082/ses-2/fmap/sub-9082_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9082/ses-2/func/sub-9082_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9082/ses-2/func/sub-9082_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9082/ses-2/func/sub-9082_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9082/ses-2/func/sub-9082_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9082_ses-2_scans.tsv
- Location: ds000201/sub-9082/ses-2/sub-9082_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9082/ses-2/func/sub-9082_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9083/ses-1/anat/sub-9083_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9083/ses-1/anat/sub-9083_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9083/ses-1/fmap/sub-9083_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9083/ses-1/func/sub-9083_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9083/ses-1/func/sub-9083_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9083/ses-1/func/sub-9083_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9083/ses-1/func/sub-9083_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-1_scans.tsv
- Location: ds000201/sub-9083/ses-1/sub-9083_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9083/ses-1/func/sub-9083_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9083/ses-2/anat/sub-9083_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9083/ses-2/dwi/sub-9083_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9083/ses-2/fmap/sub-9083_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9083/ses-2/func/sub-9083_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9083/ses-2/func/sub-9083_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9083/ses-2/func/sub-9083_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9083/ses-2/func/sub-9083_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9083_ses-2_scans.tsv
- Location: ds000201/sub-9083/ses-2/sub-9083_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9083/ses-2/func/sub-9083_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9084/ses-1/anat/sub-9084_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9084/ses-1/anat/sub-9084_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9084/ses-1/fmap/sub-9084_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9084/ses-1/func/sub-9084_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9084/ses-1/func/sub-9084_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9084/ses-1/func/sub-9084_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9084/ses-1/func/sub-9084_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-1_scans.tsv
- Location: ds000201/sub-9084/ses-1/sub-9084_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9084/ses-1/func/sub-9084_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9084/ses-2/anat/sub-9084_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9084/ses-2/dwi/sub-9084_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9084/ses-2/fmap/sub-9084_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9084/ses-2/func/sub-9084_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9084/ses-2/func/sub-9084_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9084/ses-2/func/sub-9084_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9084/ses-2/func/sub-9084_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9084_ses-2_scans.tsv
- Location: ds000201/sub-9084/ses-2/sub-9084_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9084/ses-2/func/sub-9084_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9085/ses-1/anat/sub-9085_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9085/ses-1/anat/sub-9085_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9085/ses-1/fmap/sub-9085_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9085/ses-1/func/sub-9085_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9085/ses-1/func/sub-9085_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9085/ses-1/func/sub-9085_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9085/ses-1/func/sub-9085_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-1_scans.tsv
- Location: ds000201/sub-9085/ses-1/sub-9085_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9085/ses-1/func/sub-9085_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9085/ses-2/anat/sub-9085_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9085/ses-2/dwi/sub-9085_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9085/ses-2/fmap/sub-9085_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9085/ses-2/func/sub-9085_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9085/ses-2/func/sub-9085_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9085/ses-2/func/sub-9085_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9085/ses-2/func/sub-9085_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9085_ses-2_scans.tsv
- Location: ds000201/sub-9085/ses-2/sub-9085_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9085/ses-2/func/sub-9085_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9086/ses-1/anat/sub-9086_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9086/ses-1/anat/sub-9086_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9086/ses-1/fmap/sub-9086_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9086/ses-1/func/sub-9086_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9086/ses-1/func/sub-9086_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9086/ses-1/func/sub-9086_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9086/ses-1/func/sub-9086_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-1_scans.tsv
- Location: ds000201/sub-9086/ses-1/sub-9086_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9086/ses-1/func/sub-9086_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9086/ses-2/anat/sub-9086_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9086/ses-2/dwi/sub-9086_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9086/ses-2/fmap/sub-9086_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9086/ses-2/func/sub-9086_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9086/ses-2/func/sub-9086_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9086/ses-2/func/sub-9086_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9086/ses-2/func/sub-9086_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9086_ses-2_scans.tsv
- Location: ds000201/sub-9086/ses-2/sub-9086_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9086/ses-2/func/sub-9086_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9087/ses-1/anat/sub-9087_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9087/ses-1/anat/sub-9087_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9087/ses-1/fmap/sub-9087_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9087/ses-1/func/sub-9087_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9087/ses-1/func/sub-9087_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9087/ses-1/func/sub-9087_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9087/ses-1/func/sub-9087_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-1_scans.tsv
- Location: ds000201/sub-9087/ses-1/sub-9087_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9087/ses-1/func/sub-9087_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9087/ses-2/anat/sub-9087_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9087/ses-2/dwi/sub-9087_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9087/ses-2/fmap/sub-9087_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9087/ses-2/func/sub-9087_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9087/ses-2/func/sub-9087_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9087/ses-2/func/sub-9087_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9087/ses-2/func/sub-9087_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9087_ses-2_scans.tsv
- Location: ds000201/sub-9087/ses-2/sub-9087_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9087/ses-2/func/sub-9087_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9088/ses-1/anat/sub-9088_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9088/ses-1/anat/sub-9088_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9088/ses-1/fmap/sub-9088_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9088/ses-1/func/sub-9088_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9088/ses-1/func/sub-9088_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9088/ses-1/func/sub-9088_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9088/ses-1/func/sub-9088_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-1_scans.tsv
- Location: ds000201/sub-9088/ses-1/sub-9088_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9088/ses-1/func/sub-9088_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9088/ses-2/anat/sub-9088_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9088/ses-2/dwi/sub-9088_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9088/ses-2/fmap/sub-9088_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9088/ses-2/func/sub-9088_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9088/ses-2/func/sub-9088_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9088/ses-2/func/sub-9088_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9088/ses-2/func/sub-9088_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9088_ses-2_scans.tsv
- Location: ds000201/sub-9088/ses-2/sub-9088_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9088/ses-2/func/sub-9088_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9089/ses-1/anat/sub-9089_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9089/ses-1/anat/sub-9089_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9089/ses-1/fmap/sub-9089_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9089/ses-1/func/sub-9089_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9089/ses-1/func/sub-9089_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9089/ses-1/func/sub-9089_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9089/ses-1/func/sub-9089_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-1_scans.tsv
- Location: ds000201/sub-9089/ses-1/sub-9089_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9089/ses-1/func/sub-9089_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9089/ses-2/anat/sub-9089_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9089/ses-2/dwi/sub-9089_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9089/ses-2/fmap/sub-9089_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9089/ses-2/func/sub-9089_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9089/ses-2/func/sub-9089_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9089/ses-2/func/sub-9089_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9089/ses-2/func/sub-9089_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9089_ses-2_scans.tsv
- Location: ds000201/sub-9089/ses-2/sub-9089_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9089/ses-2/func/sub-9089_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9090/ses-1/anat/sub-9090_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9090/ses-1/anat/sub-9090_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9090/ses-1/fmap/sub-9090_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9090/ses-1/func/sub-9090_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9090/ses-1/func/sub-9090_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9090/ses-1/func/sub-9090_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9090/ses-1/func/sub-9090_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-1_scans.tsv
- Location: ds000201/sub-9090/ses-1/sub-9090_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9090/ses-1/func/sub-9090_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9090/ses-2/anat/sub-9090_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9090/ses-2/dwi/sub-9090_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9090/ses-2/fmap/sub-9090_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9090/ses-2/func/sub-9090_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9090/ses-2/func/sub-9090_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9090/ses-2/func/sub-9090_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9090/ses-2/func/sub-9090_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9090_ses-2_scans.tsv
- Location: ds000201/sub-9090/ses-2/sub-9090_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9090/ses-2/func/sub-9090_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9091/ses-1/anat/sub-9091_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9091/ses-1/anat/sub-9091_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9091/ses-1/fmap/sub-9091_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9091/ses-1/func/sub-9091_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9091/ses-1/func/sub-9091_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9091/ses-1/func/sub-9091_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9091/ses-1/func/sub-9091_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-1_scans.tsv
- Location: ds000201/sub-9091/ses-1/sub-9091_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9091/ses-1/func/sub-9091_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9091/ses-2/anat/sub-9091_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9091/ses-2/dwi/sub-9091_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9091/ses-2/fmap/sub-9091_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9091/ses-2/func/sub-9091_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9091/ses-2/func/sub-9091_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9091/ses-2/func/sub-9091_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9091/ses-2/func/sub-9091_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9091_ses-2_scans.tsv
- Location: ds000201/sub-9091/ses-2/sub-9091_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9091/ses-2/func/sub-9091_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9092/ses-1/anat/sub-9092_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9092/ses-1/anat/sub-9092_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9092/ses-1/fmap/sub-9092_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9092/ses-1/func/sub-9092_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9092/ses-1/func/sub-9092_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9092/ses-1/func/sub-9092_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9092/ses-1/func/sub-9092_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-1_scans.tsv
- Location: ds000201/sub-9092/ses-1/sub-9092_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9092/ses-1/func/sub-9092_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9092/ses-2/anat/sub-9092_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9092/ses-2/dwi/sub-9092_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9092/ses-2/fmap/sub-9092_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9092/ses-2/func/sub-9092_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9092/ses-2/func/sub-9092_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9092/ses-2/func/sub-9092_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9092/ses-2/func/sub-9092_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9092_ses-2_scans.tsv
- Location: ds000201/sub-9092/ses-2/sub-9092_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9092/ses-2/func/sub-9092_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9093/ses-1/anat/sub-9093_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9093/ses-1/anat/sub-9093_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9093/ses-1/fmap/sub-9093_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9093/ses-1/func/sub-9093_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9093/ses-1/func/sub-9093_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9093/ses-1/func/sub-9093_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9093/ses-1/func/sub-9093_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-1_scans.tsv
- Location: ds000201/sub-9093/ses-1/sub-9093_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9093/ses-1/func/sub-9093_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9093/ses-2/anat/sub-9093_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9093/ses-2/dwi/sub-9093_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9093/ses-2/fmap/sub-9093_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9093/ses-2/func/sub-9093_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9093/ses-2/func/sub-9093_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9093/ses-2/func/sub-9093_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9093/ses-2/func/sub-9093_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9093_ses-2_scans.tsv
- Location: ds000201/sub-9093/ses-2/sub-9093_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9093/ses-2/func/sub-9093_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9094/ses-1/anat/sub-9094_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9094/ses-1/anat/sub-9094_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9094/ses-1/fmap/sub-9094_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9094/ses-1/func/sub-9094_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9094/ses-1/func/sub-9094_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9094/ses-1/func/sub-9094_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9094/ses-1/func/sub-9094_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-1_scans.tsv
- Location: ds000201/sub-9094/ses-1/sub-9094_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9094/ses-1/func/sub-9094_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9094/ses-2/anat/sub-9094_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9094/ses-2/dwi/sub-9094_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9094/ses-2/fmap/sub-9094_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9094/ses-2/func/sub-9094_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9094/ses-2/func/sub-9094_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9094/ses-2/func/sub-9094_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9094/ses-2/func/sub-9094_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9094_ses-2_scans.tsv
- Location: ds000201/sub-9094/ses-2/sub-9094_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9094/ses-2/func/sub-9094_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9095/ses-1/anat/sub-9095_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9095/ses-1/anat/sub-9095_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9095/ses-1/fmap/sub-9095_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9095/ses-1/func/sub-9095_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9095/ses-1/func/sub-9095_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9095/ses-1/func/sub-9095_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9095/ses-1/func/sub-9095_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9095_ses-1_scans.tsv
- Location: ds000201/sub-9095/ses-1/sub-9095_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9095/ses-1/func/sub-9095_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9096/ses-1/anat/sub-9096_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9096/ses-1/anat/sub-9096_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9096/ses-1/func/sub-9096_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9096/ses-1/func/sub-9096_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9096/ses-1/func/sub-9096_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9096/ses-1/func/sub-9096_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-1_scans.tsv
- Location: ds000201/sub-9096/ses-1/sub-9096_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9096/ses-1/func/sub-9096_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9096/ses-2/anat/sub-9096_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9096/ses-2/dwi/sub-9096_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9096/ses-2/fmap/sub-9096_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9096/ses-2/func/sub-9096_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9096/ses-2/func/sub-9096_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9096/ses-2/func/sub-9096_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9096/ses-2/func/sub-9096_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9096_ses-2_scans.tsv
- Location: ds000201/sub-9096/ses-2/sub-9096_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9096/ses-2/func/sub-9096_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9098/ses-1/anat/sub-9098_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9098/ses-1/anat/sub-9098_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_fieldmap.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_imaginary1.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_imaginary2.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_magnitude1.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_magnitude2.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_phase1.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_phase2.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_real1.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9098/ses-1/fmap/sub-9098_ses-1_real2.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9098/ses-1/func/sub-9098_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9098/ses-1/func/sub-9098_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9098/ses-1/func/sub-9098_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9098/ses-1/func/sub-9098_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-1_scans.tsv
- Location: ds000201/sub-9098/ses-1/sub-9098_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9098/ses-1/func/sub-9098_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9098/ses-2/anat/sub-9098_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9098/ses-2/dwi/sub-9098_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9098/ses-2/fmap/sub-9098_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9098/ses-2/func/sub-9098_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9098/ses-2/func/sub-9098_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9098/ses-2/func/sub-9098_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9098/ses-2/func/sub-9098_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9098_ses-2_scans.tsv
- Location: ds000201/sub-9098/ses-2/sub-9098_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9098/ses-2/func/sub-9098_ses-2_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9100/ses-1/anat/sub-9100_ses-1_T1w.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9100/ses-1/anat/sub-9100_ses-1_T2w.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9100/ses-1/func/sub-9100_ses-1_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9100/ses-1/func/sub-9100_ses-1_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9100/ses-1/func/sub-9100_ses-1_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9100/ses-1/func/sub-9100_ses-1_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-1_scans.tsv
- Location: ds000201/sub-9100/ses-1/sub-9100_ses-1_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9100/ses-1/func/sub-9100_ses-1_task-sleepiness_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 1
- Evidence: Filename: /sub-9100/ses-2/anat/sub-9100_ses-2_T1w.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 2
- Evidence: Filename: /sub-9100/ses-2/dwi/sub-9100_ses-2_dwi.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 3
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_fieldmap.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 4
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_imaginary1.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 5
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_imaginary2.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 6
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_magnitude1.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 7
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_magnitude2.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 8
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_phase1.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 9
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_phase2.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 10
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_real1.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 11
- Evidence: Filename: /sub-9100/ses-2/fmap/sub-9100_ses-2_real2.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 12
- Evidence: Filename: /sub-9100/ses-2/func/sub-9100_ses-2_task-arrows_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 13
- Evidence: Filename: /sub-9100/ses-2/func/sub-9100_ses-2_task-faces_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 14
- Evidence: Filename: /sub-9100/ses-2/func/sub-9100_ses-2_task-hands_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 15
- Evidence: Filename: /sub-9100/ses-2/func/sub-9100_ses-2_task-rest_bold.nii.gz
-
- Type: Error
- File: sub-9100_ses-2_scans.tsv
- Location: ds000201/sub-9100/ses-2/sub-9100_ses-2_scans.tsv
- Reason: The filename in scans.tsv file does not match what is present in the BIDS dataset.
- Line: 16
- Evidence: Filename: /sub-9100/ses-2/func/sub-9100_ses-2_task-sleepiness_bold.nii.gz
-
-
-======================================================
-
-
-File Path: Not all subjects contain the same files. Each subject should contain the same number of files with the same naming unless some files are known to be missing.
-
- Type: Warning
- File: sub-9001_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9001/ses-1/beh/sub-9001_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9001_ses-1_dwi.bval
- Location: /sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9001_ses-1_dwi.bvec
- Location: /sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9001_ses-1_dwi.json
- Location: /sub-9001/ses-1/dwi/sub-9001_ses-1_dwi.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_dwi.json
-
- Type: Warning
- File: sub-9001_ses-1_run-1_magnitude1.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9001_ses-1_run-1_magnitude2.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9001_ses-1_run-1_phase1.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9001_ses-1_run-1_phase2.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9001_ses-1_run-2_magnitude1.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9001_ses-1_run-2_magnitude2.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9001_ses-1_run-2_phase1.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9001_ses-1_run-2_phase2.json
- Location: /sub-9001/ses-1/fmap/sub-9001_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9001_ses-1_task-faces_physio.json
- Location: /sub-9001/ses-1/func/sub-9001_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9001_ses-2_T2w.json
- Location: /sub-9001/ses-2/anat/sub-9001_ses-2_T2w.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-2_T2w.json
-
- Type: Warning
- File: sub-9001_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9001/ses-2/beh/sub-9001_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9001_ses-2_task-faces_physio.json
- Location: /sub-9001/ses-2/func/sub-9001_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9001_ses-2_task-faces_run-1_bold.json
- Location: /sub-9001/ses-2/func/sub-9001_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9001_ses-2_task-faces_run-2_bold.json
- Location: /sub-9001/ses-2/func/sub-9001_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9001, but is present for at least one other subject.
- Evidence: Subject: sub-9001; Missing file: sub-9001_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9002_ses-1_dwi.bval
- Location: /sub-9002/ses-1/dwi/sub-9002_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9002_ses-1_dwi.bvec
- Location: /sub-9002/ses-1/dwi/sub-9002_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9002_ses-1_dwi.json
- Location: /sub-9002/ses-1/dwi/sub-9002_ses-1_dwi.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_dwi.json
-
- Type: Warning
- File: sub-9002_ses-1_run-1_magnitude1.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9002_ses-1_run-1_magnitude2.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9002_ses-1_run-1_phase1.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9002_ses-1_run-1_phase2.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9002_ses-1_run-2_magnitude1.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9002_ses-1_run-2_magnitude2.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9002_ses-1_run-2_phase1.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9002_ses-1_run-2_phase2.json
- Location: /sub-9002/ses-1/fmap/sub-9002_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9002_ses-2_T2w.json
- Location: /sub-9002/ses-2/anat/sub-9002_ses-2_T2w.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-2_T2w.json
-
- Type: Warning
- File: sub-9002_ses-2_task-faces_run-1_bold.json
- Location: /sub-9002/ses-2/func/sub-9002_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9002_ses-2_task-faces_run-2_bold.json
- Location: /sub-9002/ses-2/func/sub-9002_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9002, but is present for at least one other subject.
- Evidence: Subject: sub-9002; Missing file: sub-9002_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9003_ses-1_dwi.bval
- Location: /sub-9003/ses-1/dwi/sub-9003_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9003_ses-1_dwi.bvec
- Location: /sub-9003/ses-1/dwi/sub-9003_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9003_ses-1_dwi.json
- Location: /sub-9003/ses-1/dwi/sub-9003_ses-1_dwi.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_dwi.json
-
- Type: Warning
- File: sub-9003_ses-1_run-1_magnitude1.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9003_ses-1_run-1_magnitude2.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9003_ses-1_run-1_phase1.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9003_ses-1_run-1_phase2.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9003_ses-1_run-2_magnitude1.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9003_ses-1_run-2_magnitude2.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9003_ses-1_run-2_phase1.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9003_ses-1_run-2_phase2.json
- Location: /sub-9003/ses-1/fmap/sub-9003_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9003_ses-2_T2w.json
- Location: /sub-9003/ses-2/anat/sub-9003_ses-2_T2w.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-2_T2w.json
-
- Type: Warning
- File: sub-9003_ses-2_task-faces_run-1_bold.json
- Location: /sub-9003/ses-2/func/sub-9003_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9003_ses-2_task-faces_run-2_bold.json
- Location: /sub-9003/ses-2/func/sub-9003_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9003, but is present for at least one other subject.
- Evidence: Subject: sub-9003; Missing file: sub-9003_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9004_ses-1_dwi.bval
- Location: /sub-9004/ses-1/dwi/sub-9004_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9004_ses-1_dwi.bvec
- Location: /sub-9004/ses-1/dwi/sub-9004_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9004_ses-1_dwi.json
- Location: /sub-9004/ses-1/dwi/sub-9004_ses-1_dwi.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_dwi.json
-
- Type: Warning
- File: sub-9004_ses-1_run-1_magnitude1.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9004_ses-1_run-1_magnitude2.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9004_ses-1_run-1_phase1.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9004_ses-1_run-1_phase2.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9004_ses-1_run-2_magnitude1.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9004_ses-1_run-2_magnitude2.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9004_ses-1_run-2_phase1.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9004_ses-1_run-2_phase2.json
- Location: /sub-9004/ses-1/fmap/sub-9004_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9004_ses-2_T2w.json
- Location: /sub-9004/ses-2/anat/sub-9004_ses-2_T2w.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-2_T2w.json
-
- Type: Warning
- File: sub-9004_ses-2_task-faces_run-1_bold.json
- Location: /sub-9004/ses-2/func/sub-9004_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9004_ses-2_task-faces_run-2_bold.json
- Location: /sub-9004/ses-2/func/sub-9004_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9004, but is present for at least one other subject.
- Evidence: Subject: sub-9004; Missing file: sub-9004_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9005_ses-1_dwi.bval
- Location: /sub-9005/ses-1/dwi/sub-9005_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9005_ses-1_dwi.bvec
- Location: /sub-9005/ses-1/dwi/sub-9005_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9005_ses-1_dwi.json
- Location: /sub-9005/ses-1/dwi/sub-9005_ses-1_dwi.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_dwi.json
-
- Type: Warning
- File: sub-9005_ses-1_run-1_magnitude1.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9005_ses-1_run-1_magnitude2.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9005_ses-1_run-1_phase1.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9005_ses-1_run-1_phase2.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9005_ses-1_run-2_magnitude1.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9005_ses-1_run-2_magnitude2.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9005_ses-1_run-2_phase1.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9005_ses-1_run-2_phase2.json
- Location: /sub-9005/ses-1/fmap/sub-9005_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9005_ses-2_T2w.json
- Location: /sub-9005/ses-2/anat/sub-9005_ses-2_T2w.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-2_T2w.json
-
- Type: Warning
- File: sub-9005_ses-2_task-faces_run-1_bold.json
- Location: /sub-9005/ses-2/func/sub-9005_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9005_ses-2_task-faces_run-2_bold.json
- Location: /sub-9005/ses-2/func/sub-9005_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9005, but is present for at least one other subject.
- Evidence: Subject: sub-9005; Missing file: sub-9005_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9007_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9007/ses-1/beh/sub-9007_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9007_ses-1_dwi.bval
- Location: /sub-9007/ses-1/dwi/sub-9007_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9007_ses-1_dwi.bvec
- Location: /sub-9007/ses-1/dwi/sub-9007_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9007_ses-1_dwi.json
- Location: /sub-9007/ses-1/dwi/sub-9007_ses-1_dwi.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_dwi.json
-
- Type: Warning
- File: sub-9007_ses-1_run-1_magnitude1.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9007_ses-1_run-1_magnitude2.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9007_ses-1_run-1_phase1.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9007_ses-1_run-1_phase2.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9007_ses-1_run-2_magnitude1.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9007_ses-1_run-2_magnitude2.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9007_ses-1_run-2_phase1.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9007_ses-1_run-2_phase2.json
- Location: /sub-9007/ses-1/fmap/sub-9007_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9007_ses-1_task-faces_physio.json
- Location: /sub-9007/ses-1/func/sub-9007_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9007_ses-2_T2w.json
- Location: /sub-9007/ses-2/anat/sub-9007_ses-2_T2w.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-2_T2w.json
-
- Type: Warning
- File: sub-9007_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9007/ses-2/beh/sub-9007_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9007_ses-2_task-faces_physio.json
- Location: /sub-9007/ses-2/func/sub-9007_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9007_ses-2_task-faces_run-1_bold.json
- Location: /sub-9007/ses-2/func/sub-9007_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9007_ses-2_task-faces_run-2_bold.json
- Location: /sub-9007/ses-2/func/sub-9007_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9007, but is present for at least one other subject.
- Evidence: Subject: sub-9007; Missing file: sub-9007_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9008_ses-1_dwi.bval
- Location: /sub-9008/ses-1/dwi/sub-9008_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9008_ses-1_dwi.bvec
- Location: /sub-9008/ses-1/dwi/sub-9008_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9008_ses-1_dwi.json
- Location: /sub-9008/ses-1/dwi/sub-9008_ses-1_dwi.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_dwi.json
-
- Type: Warning
- File: sub-9008_ses-1_run-1_magnitude1.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9008_ses-1_run-1_magnitude2.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9008_ses-1_run-1_phase1.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9008_ses-1_run-1_phase2.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9008_ses-1_run-2_magnitude1.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9008_ses-1_run-2_magnitude2.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9008_ses-1_run-2_phase1.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9008_ses-1_run-2_phase2.json
- Location: /sub-9008/ses-1/fmap/sub-9008_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9008_ses-2_T2w.json
- Location: /sub-9008/ses-2/anat/sub-9008_ses-2_T2w.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-2_T2w.json
-
- Type: Warning
- File: sub-9008_ses-2_task-faces_run-1_bold.json
- Location: /sub-9008/ses-2/func/sub-9008_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9008_ses-2_task-faces_run-2_bold.json
- Location: /sub-9008/ses-2/func/sub-9008_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9008, but is present for at least one other subject.
- Evidence: Subject: sub-9008; Missing file: sub-9008_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9009_ses-1_dwi.bval
- Location: /sub-9009/ses-1/dwi/sub-9009_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9009_ses-1_dwi.bvec
- Location: /sub-9009/ses-1/dwi/sub-9009_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9009_ses-1_dwi.json
- Location: /sub-9009/ses-1/dwi/sub-9009_ses-1_dwi.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_dwi.json
-
- Type: Warning
- File: sub-9009_ses-1_run-1_magnitude1.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9009_ses-1_run-1_magnitude2.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9009_ses-1_run-1_phase1.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9009_ses-1_run-1_phase2.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9009_ses-1_run-2_magnitude1.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9009_ses-1_run-2_magnitude2.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9009_ses-1_run-2_phase1.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9009_ses-1_run-2_phase2.json
- Location: /sub-9009/ses-1/fmap/sub-9009_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9009_ses-2_T2w.json
- Location: /sub-9009/ses-2/anat/sub-9009_ses-2_T2w.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-2_T2w.json
-
- Type: Warning
- File: sub-9009_ses-2_task-faces_run-1_bold.json
- Location: /sub-9009/ses-2/func/sub-9009_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9009_ses-2_task-faces_run-2_bold.json
- Location: /sub-9009/ses-2/func/sub-9009_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9009, but is present for at least one other subject.
- Evidence: Subject: sub-9009; Missing file: sub-9009_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9011_ses-1_dwi.bval
- Location: /sub-9011/ses-1/dwi/sub-9011_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9011_ses-1_dwi.bvec
- Location: /sub-9011/ses-1/dwi/sub-9011_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9011_ses-1_dwi.json
- Location: /sub-9011/ses-1/dwi/sub-9011_ses-1_dwi.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_dwi.json
-
- Type: Warning
- File: sub-9011_ses-1_run-1_magnitude1.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9011_ses-1_run-1_magnitude2.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9011_ses-1_run-1_phase1.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9011_ses-1_run-1_phase2.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9011_ses-1_run-2_magnitude1.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9011_ses-1_run-2_magnitude2.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9011_ses-1_run-2_phase1.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9011_ses-1_run-2_phase2.json
- Location: /sub-9011/ses-1/fmap/sub-9011_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9011_ses-2_T2w.json
- Location: /sub-9011/ses-2/anat/sub-9011_ses-2_T2w.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-2_T2w.json
-
- Type: Warning
- File: sub-9011_ses-2_task-faces_run-1_bold.json
- Location: /sub-9011/ses-2/func/sub-9011_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9011_ses-2_task-faces_run-2_bold.json
- Location: /sub-9011/ses-2/func/sub-9011_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9011, but is present for at least one other subject.
- Evidence: Subject: sub-9011; Missing file: sub-9011_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9012_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9012/ses-1/beh/sub-9012_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9012_ses-1_dwi.bval
- Location: /sub-9012/ses-1/dwi/sub-9012_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9012_ses-1_dwi.bvec
- Location: /sub-9012/ses-1/dwi/sub-9012_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9012_ses-1_dwi.json
- Location: /sub-9012/ses-1/dwi/sub-9012_ses-1_dwi.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_dwi.json
-
- Type: Warning
- File: sub-9012_ses-1_run-1_magnitude1.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9012_ses-1_run-1_magnitude2.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9012_ses-1_run-1_phase1.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9012_ses-1_run-1_phase2.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9012_ses-1_run-2_magnitude1.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9012_ses-1_run-2_magnitude2.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9012_ses-1_run-2_phase1.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9012_ses-1_run-2_phase2.json
- Location: /sub-9012/ses-1/fmap/sub-9012_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9012_ses-1_task-faces_physio.json
- Location: /sub-9012/ses-1/func/sub-9012_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9012_ses-2_T2w.json
- Location: /sub-9012/ses-2/anat/sub-9012_ses-2_T2w.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-2_T2w.json
-
- Type: Warning
- File: sub-9012_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9012/ses-2/beh/sub-9012_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9012_ses-2_task-faces_physio.json
- Location: /sub-9012/ses-2/func/sub-9012_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9012_ses-2_task-faces_run-1_bold.json
- Location: /sub-9012/ses-2/func/sub-9012_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9012_ses-2_task-faces_run-2_bold.json
- Location: /sub-9012/ses-2/func/sub-9012_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9012, but is present for at least one other subject.
- Evidence: Subject: sub-9012; Missing file: sub-9012_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9013_ses-1_T2w.json
- Location: /sub-9013/ses-1/anat/sub-9013_ses-1_T2w.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_T2w.json
-
- Type: Warning
- File: sub-9013_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9013/ses-1/beh/sub-9013_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9013_ses-1_run-1_magnitude1.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9013_ses-1_run-1_magnitude2.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9013_ses-1_run-1_phase1.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9013_ses-1_run-1_phase2.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9013_ses-1_run-2_magnitude1.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9013_ses-1_run-2_magnitude2.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9013_ses-1_run-2_phase1.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9013_ses-1_run-2_phase2.json
- Location: /sub-9013/ses-1/fmap/sub-9013_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9013_ses-1_task-faces_physio.json
- Location: /sub-9013/ses-1/func/sub-9013_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9013_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9013/ses-2/beh/sub-9013_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9013_ses-2_dwi.bval
- Location: /sub-9013/ses-2/dwi/sub-9013_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9013_ses-2_dwi.bvec
- Location: /sub-9013/ses-2/dwi/sub-9013_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9013_ses-2_dwi.json
- Location: /sub-9013/ses-2/dwi/sub-9013_ses-2_dwi.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_dwi.json
-
- Type: Warning
- File: sub-9013_ses-2_task-faces_physio.json
- Location: /sub-9013/ses-2/func/sub-9013_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9013_ses-2_task-faces_run-1_bold.json
- Location: /sub-9013/ses-2/func/sub-9013_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9013_ses-2_task-faces_run-2_bold.json
- Location: /sub-9013/ses-2/func/sub-9013_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9013, but is present for at least one other subject.
- Evidence: Subject: sub-9013; Missing file: sub-9013_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9014_ses-1_dwi.bval
- Location: /sub-9014/ses-1/dwi/sub-9014_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9014_ses-1_dwi.bvec
- Location: /sub-9014/ses-1/dwi/sub-9014_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9014_ses-1_dwi.json
- Location: /sub-9014/ses-1/dwi/sub-9014_ses-1_dwi.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_dwi.json
-
- Type: Warning
- File: sub-9014_ses-1_run-1_magnitude1.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9014_ses-1_run-1_magnitude2.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9014_ses-1_run-1_phase1.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9014_ses-1_run-1_phase2.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9014_ses-1_run-2_magnitude1.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9014_ses-1_run-2_magnitude2.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9014_ses-1_run-2_phase1.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9014_ses-1_run-2_phase2.json
- Location: /sub-9014/ses-1/fmap/sub-9014_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9014_ses-2_T2w.json
- Location: /sub-9014/ses-2/anat/sub-9014_ses-2_T2w.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-2_T2w.json
-
- Type: Warning
- File: sub-9014_ses-2_task-faces_run-1_bold.json
- Location: /sub-9014/ses-2/func/sub-9014_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9014_ses-2_task-faces_run-2_bold.json
- Location: /sub-9014/ses-2/func/sub-9014_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9014, but is present for at least one other subject.
- Evidence: Subject: sub-9014; Missing file: sub-9014_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9016_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9016/ses-1/beh/sub-9016_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9016_ses-1_dwi.bval
- Location: /sub-9016/ses-1/dwi/sub-9016_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9016_ses-1_dwi.bvec
- Location: /sub-9016/ses-1/dwi/sub-9016_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9016_ses-1_dwi.json
- Location: /sub-9016/ses-1/dwi/sub-9016_ses-1_dwi.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_dwi.json
-
- Type: Warning
- File: sub-9016_ses-1_run-1_magnitude1.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9016_ses-1_run-1_magnitude2.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9016_ses-1_run-1_phase1.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9016_ses-1_run-1_phase2.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9016_ses-1_run-2_magnitude1.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9016_ses-1_run-2_magnitude2.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9016_ses-1_run-2_phase1.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9016_ses-1_run-2_phase2.json
- Location: /sub-9016/ses-1/fmap/sub-9016_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9016_ses-1_task-faces_physio.json
- Location: /sub-9016/ses-1/func/sub-9016_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9016_ses-2_T1w.json
- Location: /sub-9016/ses-2/anat/sub-9016_ses-2_T1w.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_T1w.json
-
- Type: Warning
- File: sub-9016_ses-2_T2w.json
- Location: /sub-9016/ses-2/anat/sub-9016_ses-2_T2w.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_T2w.json
-
- Type: Warning
- File: sub-9016_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9016/ses-2/beh/sub-9016_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9016_ses-2_dwi.bval
- Location: /sub-9016/ses-2/dwi/sub-9016_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9016_ses-2_dwi.bvec
- Location: /sub-9016/ses-2/dwi/sub-9016_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9016_ses-2_dwi.json
- Location: /sub-9016/ses-2/dwi/sub-9016_ses-2_dwi.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_dwi.json
-
- Type: Warning
- File: sub-9016_ses-2_magnitude1.json
- Location: /sub-9016/ses-2/fmap/sub-9016_ses-2_magnitude1.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_magnitude1.json
-
- Type: Warning
- File: sub-9016_ses-2_magnitude2.json
- Location: /sub-9016/ses-2/fmap/sub-9016_ses-2_magnitude2.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_magnitude2.json
-
- Type: Warning
- File: sub-9016_ses-2_phase1.json
- Location: /sub-9016/ses-2/fmap/sub-9016_ses-2_phase1.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_phase1.json
-
- Type: Warning
- File: sub-9016_ses-2_phase2.json
- Location: /sub-9016/ses-2/fmap/sub-9016_ses-2_phase2.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_phase2.json
-
- Type: Warning
- File: sub-9016_ses-2_task-arrows_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-arrows_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-arrows_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-arrows_events.tsv
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9016_ses-2_task-faces_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-faces_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-faces_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-faces_events.tsv
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-faces_events.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-faces_events.tsv
-
- Type: Warning
- File: sub-9016_ses-2_task-faces_physio.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9016_ses-2_task-faces_run-1_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-faces_run-2_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-hands_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-hands_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-hands_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-hands_events.tsv
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-hands_events.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-hands_events.tsv
-
- Type: Warning
- File: sub-9016_ses-2_task-rest_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-rest_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-rest_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-sleepiness_bold.json
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9016_ses-2_task-sleepiness_events.tsv
- Location: /sub-9016/ses-2/func/sub-9016_ses-2_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9016_ses-2_scans.tsv
- Location: /sub-9016/ses-2/sub-9016_ses-2_scans.tsv
- Reason: This file is missing for subject sub-9016, but is present for at least one other subject.
- Evidence: Subject: sub-9016; Missing file: sub-9016_ses-2_scans.tsv
-
- Type: Warning
- File: sub-9017_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9017/ses-1/beh/sub-9017_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9017_ses-1_dwi.bval
- Location: /sub-9017/ses-1/dwi/sub-9017_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9017_ses-1_dwi.bvec
- Location: /sub-9017/ses-1/dwi/sub-9017_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9017_ses-1_dwi.json
- Location: /sub-9017/ses-1/dwi/sub-9017_ses-1_dwi.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_dwi.json
-
- Type: Warning
- File: sub-9017_ses-1_magnitude1.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9017_ses-1_magnitude2.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9017_ses-1_phase1.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_phase1.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_phase1.json
-
- Type: Warning
- File: sub-9017_ses-1_phase2.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_phase2.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_phase2.json
-
- Type: Warning
- File: sub-9017_ses-1_run-1_magnitude1.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9017_ses-1_run-1_magnitude2.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9017_ses-1_run-1_phase1.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9017_ses-1_run-1_phase2.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9017_ses-1_run-2_magnitude1.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9017_ses-1_run-2_magnitude2.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9017_ses-1_run-2_phase1.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9017_ses-1_run-2_phase2.json
- Location: /sub-9017/ses-1/fmap/sub-9017_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9017_ses-1_task-faces_physio.json
- Location: /sub-9017/ses-1/func/sub-9017_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9017_ses-2_T2w.json
- Location: /sub-9017/ses-2/anat/sub-9017_ses-2_T2w.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_T2w.json
-
- Type: Warning
- File: sub-9017_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9017/ses-2/beh/sub-9017_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9017_ses-2_dwi.bval
- Location: /sub-9017/ses-2/dwi/sub-9017_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9017_ses-2_dwi.bvec
- Location: /sub-9017/ses-2/dwi/sub-9017_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9017_ses-2_dwi.json
- Location: /sub-9017/ses-2/dwi/sub-9017_ses-2_dwi.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_dwi.json
-
- Type: Warning
- File: sub-9017_ses-2_task-faces_physio.json
- Location: /sub-9017/ses-2/func/sub-9017_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9017_ses-2_task-faces_run-1_bold.json
- Location: /sub-9017/ses-2/func/sub-9017_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9017_ses-2_task-faces_run-2_bold.json
- Location: /sub-9017/ses-2/func/sub-9017_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9017, but is present for at least one other subject.
- Evidence: Subject: sub-9017; Missing file: sub-9017_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9018_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9018/ses-1/beh/sub-9018_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9018_ses-1_dwi.bval
- Location: /sub-9018/ses-1/dwi/sub-9018_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9018_ses-1_dwi.bvec
- Location: /sub-9018/ses-1/dwi/sub-9018_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9018_ses-1_dwi.json
- Location: /sub-9018/ses-1/dwi/sub-9018_ses-1_dwi.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_dwi.json
-
- Type: Warning
- File: sub-9018_ses-1_run-1_magnitude1.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9018_ses-1_run-1_magnitude2.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9018_ses-1_run-1_phase1.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9018_ses-1_run-1_phase2.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9018_ses-1_run-2_magnitude1.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9018_ses-1_run-2_magnitude2.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9018_ses-1_run-2_phase1.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9018_ses-1_run-2_phase2.json
- Location: /sub-9018/ses-1/fmap/sub-9018_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9018_ses-2_T2w.json
- Location: /sub-9018/ses-2/anat/sub-9018_ses-2_T2w.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_T2w.json
-
- Type: Warning
- File: sub-9018_ses-2_magnitude1.json
- Location: /sub-9018/ses-2/fmap/sub-9018_ses-2_magnitude1.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_magnitude1.json
-
- Type: Warning
- File: sub-9018_ses-2_magnitude2.json
- Location: /sub-9018/ses-2/fmap/sub-9018_ses-2_magnitude2.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_magnitude2.json
-
- Type: Warning
- File: sub-9018_ses-2_phase1.json
- Location: /sub-9018/ses-2/fmap/sub-9018_ses-2_phase1.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_phase1.json
-
- Type: Warning
- File: sub-9018_ses-2_phase2.json
- Location: /sub-9018/ses-2/fmap/sub-9018_ses-2_phase2.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_phase2.json
-
- Type: Warning
- File: sub-9018_ses-2_task-faces_run-1_bold.json
- Location: /sub-9018/ses-2/func/sub-9018_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9018_ses-2_task-faces_run-2_bold.json
- Location: /sub-9018/ses-2/func/sub-9018_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9018, but is present for at least one other subject.
- Evidence: Subject: sub-9018; Missing file: sub-9018_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9019_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9019/ses-1/beh/sub-9019_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9019_ses-1_dwi.bval
- Location: /sub-9019/ses-1/dwi/sub-9019_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9019_ses-1_dwi.bvec
- Location: /sub-9019/ses-1/dwi/sub-9019_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9019_ses-1_dwi.json
- Location: /sub-9019/ses-1/dwi/sub-9019_ses-1_dwi.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_dwi.json
-
- Type: Warning
- File: sub-9019_ses-1_run-1_magnitude1.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9019_ses-1_run-1_magnitude2.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9019_ses-1_run-1_phase1.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9019_ses-1_run-1_phase2.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9019_ses-1_run-2_magnitude1.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9019_ses-1_run-2_magnitude2.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9019_ses-1_run-2_phase1.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9019_ses-1_run-2_phase2.json
- Location: /sub-9019/ses-1/fmap/sub-9019_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9019_ses-2_T2w.json
- Location: /sub-9019/ses-2/anat/sub-9019_ses-2_T2w.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-2_T2w.json
-
- Type: Warning
- File: sub-9019_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9019/ses-2/beh/sub-9019_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9019_ses-2_task-faces_run-1_bold.json
- Location: /sub-9019/ses-2/func/sub-9019_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9019_ses-2_task-faces_run-2_bold.json
- Location: /sub-9019/ses-2/func/sub-9019_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9019, but is present for at least one other subject.
- Evidence: Subject: sub-9019; Missing file: sub-9019_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9020_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9020/ses-1/beh/sub-9020_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9020_ses-1_dwi.bval
- Location: /sub-9020/ses-1/dwi/sub-9020_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9020_ses-1_dwi.bvec
- Location: /sub-9020/ses-1/dwi/sub-9020_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9020_ses-1_dwi.json
- Location: /sub-9020/ses-1/dwi/sub-9020_ses-1_dwi.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_dwi.json
-
- Type: Warning
- File: sub-9020_ses-1_run-1_magnitude1.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9020_ses-1_run-1_magnitude2.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9020_ses-1_run-1_phase1.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9020_ses-1_run-1_phase2.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9020_ses-1_run-2_magnitude1.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9020_ses-1_run-2_magnitude2.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9020_ses-1_run-2_phase1.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9020_ses-1_run-2_phase2.json
- Location: /sub-9020/ses-1/fmap/sub-9020_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9020_ses-2_T2w.json
- Location: /sub-9020/ses-2/anat/sub-9020_ses-2_T2w.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-2_T2w.json
-
- Type: Warning
- File: sub-9020_ses-2_task-faces_run-1_bold.json
- Location: /sub-9020/ses-2/func/sub-9020_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9020_ses-2_task-faces_run-2_bold.json
- Location: /sub-9020/ses-2/func/sub-9020_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9020, but is present for at least one other subject.
- Evidence: Subject: sub-9020; Missing file: sub-9020_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9022_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9022/ses-1/beh/sub-9022_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9022_ses-1_dwi.bval
- Location: /sub-9022/ses-1/dwi/sub-9022_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9022_ses-1_dwi.bvec
- Location: /sub-9022/ses-1/dwi/sub-9022_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9022_ses-1_dwi.json
- Location: /sub-9022/ses-1/dwi/sub-9022_ses-1_dwi.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_dwi.json
-
- Type: Warning
- File: sub-9022_ses-1_run-1_magnitude1.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9022_ses-1_run-1_magnitude2.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9022_ses-1_run-1_phase1.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9022_ses-1_run-1_phase2.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9022_ses-1_run-2_magnitude1.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9022_ses-1_run-2_magnitude2.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9022_ses-1_run-2_phase1.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9022_ses-1_run-2_phase2.json
- Location: /sub-9022/ses-1/fmap/sub-9022_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9022_ses-1_task-faces_physio.json
- Location: /sub-9022/ses-1/func/sub-9022_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9022_ses-2_T1w.json
- Location: /sub-9022/ses-2/anat/sub-9022_ses-2_T1w.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_T1w.json
-
- Type: Warning
- File: sub-9022_ses-2_T2w.json
- Location: /sub-9022/ses-2/anat/sub-9022_ses-2_T2w.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_T2w.json
-
- Type: Warning
- File: sub-9022_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9022/ses-2/beh/sub-9022_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9022_ses-2_dwi.bval
- Location: /sub-9022/ses-2/dwi/sub-9022_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9022_ses-2_dwi.bvec
- Location: /sub-9022/ses-2/dwi/sub-9022_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9022_ses-2_dwi.json
- Location: /sub-9022/ses-2/dwi/sub-9022_ses-2_dwi.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_dwi.json
-
- Type: Warning
- File: sub-9022_ses-2_magnitude1.json
- Location: /sub-9022/ses-2/fmap/sub-9022_ses-2_magnitude1.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_magnitude1.json
-
- Type: Warning
- File: sub-9022_ses-2_magnitude2.json
- Location: /sub-9022/ses-2/fmap/sub-9022_ses-2_magnitude2.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_magnitude2.json
-
- Type: Warning
- File: sub-9022_ses-2_phase1.json
- Location: /sub-9022/ses-2/fmap/sub-9022_ses-2_phase1.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_phase1.json
-
- Type: Warning
- File: sub-9022_ses-2_phase2.json
- Location: /sub-9022/ses-2/fmap/sub-9022_ses-2_phase2.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_phase2.json
-
- Type: Warning
- File: sub-9022_ses-2_task-arrows_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-arrows_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-arrows_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-arrows_events.tsv
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9022_ses-2_task-faces_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-faces_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-faces_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-faces_events.tsv
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-faces_events.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-faces_events.tsv
-
- Type: Warning
- File: sub-9022_ses-2_task-faces_physio.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9022_ses-2_task-faces_run-1_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-faces_run-2_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-hands_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-hands_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-hands_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-hands_events.tsv
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-hands_events.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-hands_events.tsv
-
- Type: Warning
- File: sub-9022_ses-2_task-rest_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-rest_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-rest_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-sleepiness_bold.json
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9022_ses-2_task-sleepiness_events.tsv
- Location: /sub-9022/ses-2/func/sub-9022_ses-2_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9022_ses-2_scans.tsv
- Location: /sub-9022/ses-2/sub-9022_ses-2_scans.tsv
- Reason: This file is missing for subject sub-9022, but is present for at least one other subject.
- Evidence: Subject: sub-9022; Missing file: sub-9022_ses-2_scans.tsv
-
- Type: Warning
- File: sub-9023_ses-1_dwi.bval
- Location: /sub-9023/ses-1/dwi/sub-9023_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9023_ses-1_dwi.bvec
- Location: /sub-9023/ses-1/dwi/sub-9023_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9023_ses-1_dwi.json
- Location: /sub-9023/ses-1/dwi/sub-9023_ses-1_dwi.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_dwi.json
-
- Type: Warning
- File: sub-9023_ses-1_run-1_magnitude1.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9023_ses-1_run-1_magnitude2.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9023_ses-1_run-1_phase1.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9023_ses-1_run-1_phase2.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9023_ses-1_run-2_magnitude1.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9023_ses-1_run-2_magnitude2.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9023_ses-1_run-2_phase1.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9023_ses-1_run-2_phase2.json
- Location: /sub-9023/ses-1/fmap/sub-9023_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9023_ses-2_T2w.json
- Location: /sub-9023/ses-2/anat/sub-9023_ses-2_T2w.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-2_T2w.json
-
- Type: Warning
- File: sub-9023_ses-2_task-faces_run-1_bold.json
- Location: /sub-9023/ses-2/func/sub-9023_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9023_ses-2_task-faces_run-2_bold.json
- Location: /sub-9023/ses-2/func/sub-9023_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9023, but is present for at least one other subject.
- Evidence: Subject: sub-9023; Missing file: sub-9023_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9025_ses-1_dwi.bval
- Location: /sub-9025/ses-1/dwi/sub-9025_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9025_ses-1_dwi.bvec
- Location: /sub-9025/ses-1/dwi/sub-9025_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9025_ses-1_dwi.json
- Location: /sub-9025/ses-1/dwi/sub-9025_ses-1_dwi.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_dwi.json
-
- Type: Warning
- File: sub-9025_ses-1_run-1_magnitude1.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9025_ses-1_run-1_magnitude2.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9025_ses-1_run-1_phase1.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9025_ses-1_run-1_phase2.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9025_ses-1_run-2_magnitude1.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9025_ses-1_run-2_magnitude2.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9025_ses-1_run-2_phase1.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9025_ses-1_run-2_phase2.json
- Location: /sub-9025/ses-1/fmap/sub-9025_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9025_ses-1_task-arrows_bold.json
- Location: /sub-9025/ses-1/func/sub-9025_ses-1_task-arrows_bold.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_task-arrows_bold.json
-
- Type: Warning
- File: sub-9025_ses-1_task-arrows_events.tsv
- Location: /sub-9025/ses-1/func/sub-9025_ses-1_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9025_ses-1_task-sleepiness_bold.json
- Location: /sub-9025/ses-1/func/sub-9025_ses-1_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9025_ses-1_task-sleepiness_events.tsv
- Location: /sub-9025/ses-1/func/sub-9025_ses-1_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-1_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9025_ses-2_T2w.json
- Location: /sub-9025/ses-2/anat/sub-9025_ses-2_T2w.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_T2w.json
-
- Type: Warning
- File: sub-9025_ses-2_task-arrows_bold.json
- Location: /sub-9025/ses-2/func/sub-9025_ses-2_task-arrows_bold.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_task-arrows_bold.json
-
- Type: Warning
- File: sub-9025_ses-2_task-arrows_events.tsv
- Location: /sub-9025/ses-2/func/sub-9025_ses-2_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9025_ses-2_task-faces_run-1_bold.json
- Location: /sub-9025/ses-2/func/sub-9025_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9025_ses-2_task-faces_run-2_bold.json
- Location: /sub-9025/ses-2/func/sub-9025_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9025_ses-2_task-sleepiness_bold.json
- Location: /sub-9025/ses-2/func/sub-9025_ses-2_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9025_ses-2_task-sleepiness_events.tsv
- Location: /sub-9025/ses-2/func/sub-9025_ses-2_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9025, but is present for at least one other subject.
- Evidence: Subject: sub-9025; Missing file: sub-9025_ses-2_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9026_ses-1_dwi.bval
- Location: /sub-9026/ses-1/dwi/sub-9026_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9026_ses-1_dwi.bvec
- Location: /sub-9026/ses-1/dwi/sub-9026_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9026_ses-1_dwi.json
- Location: /sub-9026/ses-1/dwi/sub-9026_ses-1_dwi.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_dwi.json
-
- Type: Warning
- File: sub-9026_ses-1_run-1_magnitude1.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9026_ses-1_run-1_magnitude2.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9026_ses-1_run-1_phase1.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9026_ses-1_run-1_phase2.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9026_ses-1_run-2_magnitude1.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9026_ses-1_run-2_magnitude2.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9026_ses-1_run-2_phase1.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9026_ses-1_run-2_phase2.json
- Location: /sub-9026/ses-1/fmap/sub-9026_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9026_ses-2_T2w.json
- Location: /sub-9026/ses-2/anat/sub-9026_ses-2_T2w.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-2_T2w.json
-
- Type: Warning
- File: sub-9026_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9026/ses-2/beh/sub-9026_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9026_ses-2_task-faces_run-1_bold.json
- Location: /sub-9026/ses-2/func/sub-9026_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9026_ses-2_task-faces_run-2_bold.json
- Location: /sub-9026/ses-2/func/sub-9026_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9026, but is present for at least one other subject.
- Evidence: Subject: sub-9026; Missing file: sub-9026_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9028_ses-1_dwi.bval
- Location: /sub-9028/ses-1/dwi/sub-9028_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9028_ses-1_dwi.bvec
- Location: /sub-9028/ses-1/dwi/sub-9028_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9028_ses-1_dwi.json
- Location: /sub-9028/ses-1/dwi/sub-9028_ses-1_dwi.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_dwi.json
-
- Type: Warning
- File: sub-9028_ses-1_run-1_magnitude1.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9028_ses-1_run-1_magnitude2.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9028_ses-1_run-1_phase1.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9028_ses-1_run-1_phase2.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9028_ses-1_run-2_magnitude1.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9028_ses-1_run-2_magnitude2.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9028_ses-1_run-2_phase1.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9028_ses-1_run-2_phase2.json
- Location: /sub-9028/ses-1/fmap/sub-9028_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9028_ses-2_T2w.json
- Location: /sub-9028/ses-2/anat/sub-9028_ses-2_T2w.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-2_T2w.json
-
- Type: Warning
- File: sub-9028_ses-2_task-faces_run-1_bold.json
- Location: /sub-9028/ses-2/func/sub-9028_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9028_ses-2_task-faces_run-2_bold.json
- Location: /sub-9028/ses-2/func/sub-9028_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9028, but is present for at least one other subject.
- Evidence: Subject: sub-9028; Missing file: sub-9028_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9029_ses-1_dwi.bval
- Location: /sub-9029/ses-1/dwi/sub-9029_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9029_ses-1_dwi.bvec
- Location: /sub-9029/ses-1/dwi/sub-9029_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9029_ses-1_dwi.json
- Location: /sub-9029/ses-1/dwi/sub-9029_ses-1_dwi.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_dwi.json
-
- Type: Warning
- File: sub-9029_ses-1_run-1_magnitude1.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9029_ses-1_run-1_magnitude2.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9029_ses-1_run-1_phase1.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9029_ses-1_run-1_phase2.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9029_ses-1_run-2_magnitude1.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9029_ses-1_run-2_magnitude2.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9029_ses-1_run-2_phase1.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9029_ses-1_run-2_phase2.json
- Location: /sub-9029/ses-1/fmap/sub-9029_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9029_ses-1_task-arrows_bold.json
- Location: /sub-9029/ses-1/func/sub-9029_ses-1_task-arrows_bold.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_task-arrows_bold.json
-
- Type: Warning
- File: sub-9029_ses-1_task-arrows_events.tsv
- Location: /sub-9029/ses-1/func/sub-9029_ses-1_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9029_ses-1_task-sleepiness_bold.json
- Location: /sub-9029/ses-1/func/sub-9029_ses-1_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9029_ses-1_task-sleepiness_events.tsv
- Location: /sub-9029/ses-1/func/sub-9029_ses-1_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-1_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9029_ses-2_T2w.json
- Location: /sub-9029/ses-2/anat/sub-9029_ses-2_T2w.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-2_T2w.json
-
- Type: Warning
- File: sub-9029_ses-2_task-faces_run-1_bold.json
- Location: /sub-9029/ses-2/func/sub-9029_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9029_ses-2_task-faces_run-2_bold.json
- Location: /sub-9029/ses-2/func/sub-9029_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9029, but is present for at least one other subject.
- Evidence: Subject: sub-9029; Missing file: sub-9029_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9032_ses-1_dwi.bval
- Location: /sub-9032/ses-1/dwi/sub-9032_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9032_ses-1_dwi.bvec
- Location: /sub-9032/ses-1/dwi/sub-9032_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9032_ses-1_dwi.json
- Location: /sub-9032/ses-1/dwi/sub-9032_ses-1_dwi.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_dwi.json
-
- Type: Warning
- File: sub-9032_ses-1_run-1_magnitude1.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9032_ses-1_run-1_magnitude2.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9032_ses-1_run-1_phase1.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9032_ses-1_run-1_phase2.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9032_ses-1_run-2_magnitude1.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9032_ses-1_run-2_magnitude2.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9032_ses-1_run-2_phase1.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9032_ses-1_run-2_phase2.json
- Location: /sub-9032/ses-1/fmap/sub-9032_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9032_ses-2_T2w.json
- Location: /sub-9032/ses-2/anat/sub-9032_ses-2_T2w.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-2_T2w.json
-
- Type: Warning
- File: sub-9032_ses-2_task-faces_run-1_bold.json
- Location: /sub-9032/ses-2/func/sub-9032_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9032_ses-2_task-faces_run-2_bold.json
- Location: /sub-9032/ses-2/func/sub-9032_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9032, but is present for at least one other subject.
- Evidence: Subject: sub-9032; Missing file: sub-9032_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9033_ses-1_dwi.bval
- Location: /sub-9033/ses-1/dwi/sub-9033_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9033_ses-1_dwi.bvec
- Location: /sub-9033/ses-1/dwi/sub-9033_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9033_ses-1_dwi.json
- Location: /sub-9033/ses-1/dwi/sub-9033_ses-1_dwi.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_dwi.json
-
- Type: Warning
- File: sub-9033_ses-1_run-1_magnitude1.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9033_ses-1_run-1_magnitude2.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9033_ses-1_run-1_phase1.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9033_ses-1_run-1_phase2.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9033_ses-1_run-2_magnitude1.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9033_ses-1_run-2_magnitude2.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9033_ses-1_run-2_phase1.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9033_ses-1_run-2_phase2.json
- Location: /sub-9033/ses-1/fmap/sub-9033_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9033_ses-2_T2w.json
- Location: /sub-9033/ses-2/anat/sub-9033_ses-2_T2w.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-2_T2w.json
-
- Type: Warning
- File: sub-9033_ses-2_task-faces_run-1_bold.json
- Location: /sub-9033/ses-2/func/sub-9033_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9033_ses-2_task-faces_run-2_bold.json
- Location: /sub-9033/ses-2/func/sub-9033_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9033, but is present for at least one other subject.
- Evidence: Subject: sub-9033; Missing file: sub-9033_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9034_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9034/ses-1/beh/sub-9034_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9034_ses-1_dwi.bval
- Location: /sub-9034/ses-1/dwi/sub-9034_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9034_ses-1_dwi.bvec
- Location: /sub-9034/ses-1/dwi/sub-9034_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9034_ses-1_dwi.json
- Location: /sub-9034/ses-1/dwi/sub-9034_ses-1_dwi.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_dwi.json
-
- Type: Warning
- File: sub-9034_ses-1_run-1_magnitude1.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9034_ses-1_run-1_magnitude2.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9034_ses-1_run-1_phase1.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9034_ses-1_run-1_phase2.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9034_ses-1_run-2_magnitude1.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9034_ses-1_run-2_magnitude2.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9034_ses-1_run-2_phase1.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9034_ses-1_run-2_phase2.json
- Location: /sub-9034/ses-1/fmap/sub-9034_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9034_ses-2_T2w.json
- Location: /sub-9034/ses-2/anat/sub-9034_ses-2_T2w.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-2_T2w.json
-
- Type: Warning
- File: sub-9034_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9034/ses-2/beh/sub-9034_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9034_ses-2_task-faces_run-1_bold.json
- Location: /sub-9034/ses-2/func/sub-9034_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9034_ses-2_task-faces_run-2_bold.json
- Location: /sub-9034/ses-2/func/sub-9034_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9034, but is present for at least one other subject.
- Evidence: Subject: sub-9034; Missing file: sub-9034_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9035_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9035/ses-1/beh/sub-9035_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9035_ses-1_dwi.bval
- Location: /sub-9035/ses-1/dwi/sub-9035_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9035_ses-1_dwi.bvec
- Location: /sub-9035/ses-1/dwi/sub-9035_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9035_ses-1_dwi.json
- Location: /sub-9035/ses-1/dwi/sub-9035_ses-1_dwi.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_dwi.json
-
- Type: Warning
- File: sub-9035_ses-1_run-1_magnitude1.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9035_ses-1_run-1_magnitude2.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9035_ses-1_run-1_phase1.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9035_ses-1_run-1_phase2.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9035_ses-1_run-2_magnitude1.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9035_ses-1_run-2_magnitude2.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9035_ses-1_run-2_phase1.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9035_ses-1_run-2_phase2.json
- Location: /sub-9035/ses-1/fmap/sub-9035_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9035_ses-1_task-faces_physio.json
- Location: /sub-9035/ses-1/func/sub-9035_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9035_ses-2_T2w.json
- Location: /sub-9035/ses-2/anat/sub-9035_ses-2_T2w.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-2_T2w.json
-
- Type: Warning
- File: sub-9035_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9035/ses-2/beh/sub-9035_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9035_ses-2_task-faces_physio.json
- Location: /sub-9035/ses-2/func/sub-9035_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9035_ses-2_task-faces_run-1_bold.json
- Location: /sub-9035/ses-2/func/sub-9035_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9035_ses-2_task-faces_run-2_bold.json
- Location: /sub-9035/ses-2/func/sub-9035_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9035, but is present for at least one other subject.
- Evidence: Subject: sub-9035; Missing file: sub-9035_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9036_ses-1_dwi.bval
- Location: /sub-9036/ses-1/dwi/sub-9036_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9036_ses-1_dwi.bvec
- Location: /sub-9036/ses-1/dwi/sub-9036_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9036_ses-1_dwi.json
- Location: /sub-9036/ses-1/dwi/sub-9036_ses-1_dwi.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_dwi.json
-
- Type: Warning
- File: sub-9036_ses-1_run-1_magnitude1.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9036_ses-1_run-1_magnitude2.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9036_ses-1_run-1_phase1.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9036_ses-1_run-1_phase2.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9036_ses-1_run-2_magnitude1.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9036_ses-1_run-2_magnitude2.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9036_ses-1_run-2_phase1.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9036_ses-1_run-2_phase2.json
- Location: /sub-9036/ses-1/fmap/sub-9036_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9036_ses-2_T2w.json
- Location: /sub-9036/ses-2/anat/sub-9036_ses-2_T2w.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-2_T2w.json
-
- Type: Warning
- File: sub-9036_ses-2_task-faces_run-1_bold.json
- Location: /sub-9036/ses-2/func/sub-9036_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9036_ses-2_task-faces_run-2_bold.json
- Location: /sub-9036/ses-2/func/sub-9036_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9036, but is present for at least one other subject.
- Evidence: Subject: sub-9036; Missing file: sub-9036_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9037_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9037/ses-1/beh/sub-9037_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9037_ses-1_dwi.bval
- Location: /sub-9037/ses-1/dwi/sub-9037_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9037_ses-1_dwi.bvec
- Location: /sub-9037/ses-1/dwi/sub-9037_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9037_ses-1_dwi.json
- Location: /sub-9037/ses-1/dwi/sub-9037_ses-1_dwi.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_dwi.json
-
- Type: Warning
- File: sub-9037_ses-1_run-1_magnitude1.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9037_ses-1_run-1_magnitude2.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9037_ses-1_run-1_phase1.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9037_ses-1_run-1_phase2.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9037_ses-1_run-2_magnitude1.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9037_ses-1_run-2_magnitude2.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9037_ses-1_run-2_phase1.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9037_ses-1_run-2_phase2.json
- Location: /sub-9037/ses-1/fmap/sub-9037_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9037_ses-1_task-faces_physio.json
- Location: /sub-9037/ses-1/func/sub-9037_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9037_ses-2_T2w.json
- Location: /sub-9037/ses-2/anat/sub-9037_ses-2_T2w.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-2_T2w.json
-
- Type: Warning
- File: sub-9037_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9037/ses-2/beh/sub-9037_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9037_ses-2_task-faces_physio.json
- Location: /sub-9037/ses-2/func/sub-9037_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9037_ses-2_task-faces_run-1_bold.json
- Location: /sub-9037/ses-2/func/sub-9037_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9037_ses-2_task-faces_run-2_bold.json
- Location: /sub-9037/ses-2/func/sub-9037_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9037, but is present for at least one other subject.
- Evidence: Subject: sub-9037; Missing file: sub-9037_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9038_ses-1_dwi.bval
- Location: /sub-9038/ses-1/dwi/sub-9038_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9038_ses-1_dwi.bvec
- Location: /sub-9038/ses-1/dwi/sub-9038_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9038_ses-1_dwi.json
- Location: /sub-9038/ses-1/dwi/sub-9038_ses-1_dwi.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_dwi.json
-
- Type: Warning
- File: sub-9038_ses-1_run-1_magnitude1.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9038_ses-1_run-1_magnitude2.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9038_ses-1_run-1_phase1.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9038_ses-1_run-1_phase2.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9038_ses-1_run-2_magnitude1.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9038_ses-1_run-2_magnitude2.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9038_ses-1_run-2_phase1.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9038_ses-1_run-2_phase2.json
- Location: /sub-9038/ses-1/fmap/sub-9038_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9038_ses-2_T2w.json
- Location: /sub-9038/ses-2/anat/sub-9038_ses-2_T2w.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-2_T2w.json
-
- Type: Warning
- File: sub-9038_ses-2_task-faces_run-1_bold.json
- Location: /sub-9038/ses-2/func/sub-9038_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9038_ses-2_task-faces_run-2_bold.json
- Location: /sub-9038/ses-2/func/sub-9038_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9038, but is present for at least one other subject.
- Evidence: Subject: sub-9038; Missing file: sub-9038_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9039_ses-1_dwi.bval
- Location: /sub-9039/ses-1/dwi/sub-9039_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9039_ses-1_dwi.bvec
- Location: /sub-9039/ses-1/dwi/sub-9039_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9039_ses-1_dwi.json
- Location: /sub-9039/ses-1/dwi/sub-9039_ses-1_dwi.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_dwi.json
-
- Type: Warning
- File: sub-9039_ses-1_run-1_magnitude1.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9039_ses-1_run-1_magnitude2.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9039_ses-1_run-1_phase1.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9039_ses-1_run-1_phase2.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9039_ses-1_run-2_magnitude1.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9039_ses-1_run-2_magnitude2.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9039_ses-1_run-2_phase1.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9039_ses-1_run-2_phase2.json
- Location: /sub-9039/ses-1/fmap/sub-9039_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9039_ses-2_T2w.json
- Location: /sub-9039/ses-2/anat/sub-9039_ses-2_T2w.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-2_T2w.json
-
- Type: Warning
- File: sub-9039_ses-2_task-faces_run-1_bold.json
- Location: /sub-9039/ses-2/func/sub-9039_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9039_ses-2_task-faces_run-2_bold.json
- Location: /sub-9039/ses-2/func/sub-9039_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9039, but is present for at least one other subject.
- Evidence: Subject: sub-9039; Missing file: sub-9039_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9040_ses-1_dwi.bval
- Location: /sub-9040/ses-1/dwi/sub-9040_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9040_ses-1_dwi.bvec
- Location: /sub-9040/ses-1/dwi/sub-9040_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9040_ses-1_dwi.json
- Location: /sub-9040/ses-1/dwi/sub-9040_ses-1_dwi.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_dwi.json
-
- Type: Warning
- File: sub-9040_ses-1_run-1_magnitude1.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9040_ses-1_run-1_magnitude2.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9040_ses-1_run-1_phase1.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9040_ses-1_run-1_phase2.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9040_ses-1_run-2_magnitude1.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9040_ses-1_run-2_magnitude2.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9040_ses-1_run-2_phase1.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9040_ses-1_run-2_phase2.json
- Location: /sub-9040/ses-1/fmap/sub-9040_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9040_ses-2_T2w.json
- Location: /sub-9040/ses-2/anat/sub-9040_ses-2_T2w.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-2_T2w.json
-
- Type: Warning
- File: sub-9040_ses-2_task-faces_run-1_bold.json
- Location: /sub-9040/ses-2/func/sub-9040_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9040_ses-2_task-faces_run-2_bold.json
- Location: /sub-9040/ses-2/func/sub-9040_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9040, but is present for at least one other subject.
- Evidence: Subject: sub-9040; Missing file: sub-9040_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9041_ses-1_dwi.bval
- Location: /sub-9041/ses-1/dwi/sub-9041_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9041_ses-1_dwi.bvec
- Location: /sub-9041/ses-1/dwi/sub-9041_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9041_ses-1_dwi.json
- Location: /sub-9041/ses-1/dwi/sub-9041_ses-1_dwi.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_dwi.json
-
- Type: Warning
- File: sub-9041_ses-1_run-1_magnitude1.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9041_ses-1_run-1_magnitude2.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9041_ses-1_run-1_phase1.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9041_ses-1_run-1_phase2.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9041_ses-1_run-2_magnitude1.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9041_ses-1_run-2_magnitude2.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9041_ses-1_run-2_phase1.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9041_ses-1_run-2_phase2.json
- Location: /sub-9041/ses-1/fmap/sub-9041_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9041_ses-2_T2w.json
- Location: /sub-9041/ses-2/anat/sub-9041_ses-2_T2w.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-2_T2w.json
-
- Type: Warning
- File: sub-9041_ses-2_task-faces_run-1_bold.json
- Location: /sub-9041/ses-2/func/sub-9041_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9041_ses-2_task-faces_run-2_bold.json
- Location: /sub-9041/ses-2/func/sub-9041_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9041, but is present for at least one other subject.
- Evidence: Subject: sub-9041; Missing file: sub-9041_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9042_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9042/ses-1/beh/sub-9042_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9042_ses-1_dwi.bval
- Location: /sub-9042/ses-1/dwi/sub-9042_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9042_ses-1_dwi.bvec
- Location: /sub-9042/ses-1/dwi/sub-9042_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9042_ses-1_dwi.json
- Location: /sub-9042/ses-1/dwi/sub-9042_ses-1_dwi.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_dwi.json
-
- Type: Warning
- File: sub-9042_ses-1_run-1_magnitude1.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9042_ses-1_run-1_magnitude2.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9042_ses-1_run-1_phase1.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9042_ses-1_run-1_phase2.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9042_ses-1_run-2_magnitude1.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9042_ses-1_run-2_magnitude2.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9042_ses-1_run-2_phase1.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9042_ses-1_run-2_phase2.json
- Location: /sub-9042/ses-1/fmap/sub-9042_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9042_ses-2_T2w.json
- Location: /sub-9042/ses-2/anat/sub-9042_ses-2_T2w.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-2_T2w.json
-
- Type: Warning
- File: sub-9042_ses-2_task-faces_bold.json
- Location: /sub-9042/ses-2/func/sub-9042_ses-2_task-faces_bold.json
- Reason: This file is missing for subject sub-9042, but is present for at least one other subject.
- Evidence: Subject: sub-9042; Missing file: sub-9042_ses-2_task-faces_bold.json
-
- Type: Warning
- File: sub-9044_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9044/ses-1/beh/sub-9044_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9044_ses-1_dwi.bval
- Location: /sub-9044/ses-1/dwi/sub-9044_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9044_ses-1_dwi.bvec
- Location: /sub-9044/ses-1/dwi/sub-9044_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9044_ses-1_dwi.json
- Location: /sub-9044/ses-1/dwi/sub-9044_ses-1_dwi.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_dwi.json
-
- Type: Warning
- File: sub-9044_ses-1_magnitude1.json
- Location: /sub-9044/ses-1/fmap/sub-9044_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9044_ses-1_magnitude2.json
- Location: /sub-9044/ses-1/fmap/sub-9044_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9044_ses-1_phase1.json
- Location: /sub-9044/ses-1/fmap/sub-9044_ses-1_phase1.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_phase1.json
-
- Type: Warning
- File: sub-9044_ses-1_phase2.json
- Location: /sub-9044/ses-1/fmap/sub-9044_ses-1_phase2.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_phase2.json
-
- Type: Warning
- File: sub-9044_ses-1_task-faces_physio.json
- Location: /sub-9044/ses-1/func/sub-9044_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9044_ses-2_T2w.json
- Location: /sub-9044/ses-2/anat/sub-9044_ses-2_T2w.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_T2w.json
-
- Type: Warning
- File: sub-9044_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9044/ses-2/beh/sub-9044_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9044_ses-2_task-arrows_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-arrows_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-arrows_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-arrows_events.tsv
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9044_ses-2_task-faces_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-faces_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-faces_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-faces_events.tsv
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-faces_events.tsv
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-faces_events.tsv
-
- Type: Warning
- File: sub-9044_ses-2_task-faces_physio.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9044_ses-2_task-faces_run-1_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-faces_run-2_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-hands_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-hands_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-hands_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-hands_events.tsv
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-hands_events.tsv
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-hands_events.tsv
-
- Type: Warning
- File: sub-9044_ses-2_task-rest_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-rest_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-rest_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-sleepiness_bold.json
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9044_ses-2_task-sleepiness_events.tsv
- Location: /sub-9044/ses-2/func/sub-9044_ses-2_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9044, but is present for at least one other subject.
- Evidence: Subject: sub-9044; Missing file: sub-9044_ses-2_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9045_ses-1_dwi.bval
- Location: /sub-9045/ses-1/dwi/sub-9045_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9045_ses-1_dwi.bvec
- Location: /sub-9045/ses-1/dwi/sub-9045_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9045_ses-1_dwi.json
- Location: /sub-9045/ses-1/dwi/sub-9045_ses-1_dwi.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_dwi.json
-
- Type: Warning
- File: sub-9045_ses-1_run-1_magnitude1.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9045_ses-1_run-1_magnitude2.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9045_ses-1_run-1_phase1.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9045_ses-1_run-1_phase2.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9045_ses-1_run-2_magnitude1.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9045_ses-1_run-2_magnitude2.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9045_ses-1_run-2_phase1.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9045_ses-1_run-2_phase2.json
- Location: /sub-9045/ses-1/fmap/sub-9045_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9045_ses-1_task-faces_physio.json
- Location: /sub-9045/ses-1/func/sub-9045_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9045_ses-2_T2w.json
- Location: /sub-9045/ses-2/anat/sub-9045_ses-2_T2w.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-2_T2w.json
-
- Type: Warning
- File: sub-9045_ses-2_task-faces_run-1_bold.json
- Location: /sub-9045/ses-2/func/sub-9045_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9045_ses-2_task-faces_run-2_bold.json
- Location: /sub-9045/ses-2/func/sub-9045_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9045, but is present for at least one other subject.
- Evidence: Subject: sub-9045; Missing file: sub-9045_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9046_ses-1_T2w.json
- Location: /sub-9046/ses-1/anat/sub-9046_ses-1_T2w.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_T2w.json
-
- Type: Warning
- File: sub-9046_ses-1_dwi.bval
- Location: /sub-9046/ses-1/dwi/sub-9046_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9046_ses-1_dwi.bvec
- Location: /sub-9046/ses-1/dwi/sub-9046_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9046_ses-1_dwi.json
- Location: /sub-9046/ses-1/dwi/sub-9046_ses-1_dwi.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_dwi.json
-
- Type: Warning
- File: sub-9046_ses-1_run-1_magnitude1.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9046_ses-1_run-1_magnitude2.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9046_ses-1_run-1_phase1.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9046_ses-1_run-1_phase2.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9046_ses-1_run-2_magnitude1.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9046_ses-1_run-2_magnitude2.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9046_ses-1_run-2_phase1.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9046_ses-1_run-2_phase2.json
- Location: /sub-9046/ses-1/fmap/sub-9046_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9046_ses-2_T2w.json
- Location: /sub-9046/ses-2/anat/sub-9046_ses-2_T2w.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-2_T2w.json
-
- Type: Warning
- File: sub-9046_ses-2_task-faces_run-1_bold.json
- Location: /sub-9046/ses-2/func/sub-9046_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9046_ses-2_task-faces_run-2_bold.json
- Location: /sub-9046/ses-2/func/sub-9046_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9046, but is present for at least one other subject.
- Evidence: Subject: sub-9046; Missing file: sub-9046_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9047_ses-1_dwi.bval
- Location: /sub-9047/ses-1/dwi/sub-9047_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9047_ses-1_dwi.bvec
- Location: /sub-9047/ses-1/dwi/sub-9047_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9047_ses-1_dwi.json
- Location: /sub-9047/ses-1/dwi/sub-9047_ses-1_dwi.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_dwi.json
-
- Type: Warning
- File: sub-9047_ses-1_run-1_magnitude1.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9047_ses-1_run-1_magnitude2.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9047_ses-1_run-1_phase1.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9047_ses-1_run-1_phase2.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9047_ses-1_run-2_magnitude1.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9047_ses-1_run-2_magnitude2.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9047_ses-1_run-2_phase1.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9047_ses-1_run-2_phase2.json
- Location: /sub-9047/ses-1/fmap/sub-9047_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9047_ses-2_T2w.json
- Location: /sub-9047/ses-2/anat/sub-9047_ses-2_T2w.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-2_T2w.json
-
- Type: Warning
- File: sub-9047_ses-2_task-faces_run-1_bold.json
- Location: /sub-9047/ses-2/func/sub-9047_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9047_ses-2_task-faces_run-2_bold.json
- Location: /sub-9047/ses-2/func/sub-9047_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9047, but is present for at least one other subject.
- Evidence: Subject: sub-9047; Missing file: sub-9047_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9048_ses-1_dwi.bval
- Location: /sub-9048/ses-1/dwi/sub-9048_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9048_ses-1_dwi.bvec
- Location: /sub-9048/ses-1/dwi/sub-9048_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9048_ses-1_dwi.json
- Location: /sub-9048/ses-1/dwi/sub-9048_ses-1_dwi.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_dwi.json
-
- Type: Warning
- File: sub-9048_ses-1_run-1_magnitude1.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9048_ses-1_run-1_magnitude2.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9048_ses-1_run-1_phase1.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9048_ses-1_run-1_phase2.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9048_ses-1_run-2_magnitude1.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9048_ses-1_run-2_magnitude2.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9048_ses-1_run-2_phase1.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9048_ses-1_run-2_phase2.json
- Location: /sub-9048/ses-1/fmap/sub-9048_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9048_ses-2_T2w.json
- Location: /sub-9048/ses-2/anat/sub-9048_ses-2_T2w.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-2_T2w.json
-
- Type: Warning
- File: sub-9048_ses-2_task-faces_run-1_bold.json
- Location: /sub-9048/ses-2/func/sub-9048_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9048_ses-2_task-faces_run-2_bold.json
- Location: /sub-9048/ses-2/func/sub-9048_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9048, but is present for at least one other subject.
- Evidence: Subject: sub-9048; Missing file: sub-9048_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9049_ses-1_dwi.bval
- Location: /sub-9049/ses-1/dwi/sub-9049_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9049_ses-1_dwi.bvec
- Location: /sub-9049/ses-1/dwi/sub-9049_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9049_ses-1_dwi.json
- Location: /sub-9049/ses-1/dwi/sub-9049_ses-1_dwi.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_dwi.json
-
- Type: Warning
- File: sub-9049_ses-1_run-1_magnitude1.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9049_ses-1_run-1_magnitude2.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9049_ses-1_run-1_phase1.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9049_ses-1_run-1_phase2.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9049_ses-1_run-2_magnitude1.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9049_ses-1_run-2_magnitude2.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9049_ses-1_run-2_phase1.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9049_ses-1_run-2_phase2.json
- Location: /sub-9049/ses-1/fmap/sub-9049_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9049_ses-2_T2w.json
- Location: /sub-9049/ses-2/anat/sub-9049_ses-2_T2w.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-2_T2w.json
-
- Type: Warning
- File: sub-9049_ses-2_task-faces_run-1_bold.json
- Location: /sub-9049/ses-2/func/sub-9049_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9049_ses-2_task-faces_run-2_bold.json
- Location: /sub-9049/ses-2/func/sub-9049_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9049, but is present for at least one other subject.
- Evidence: Subject: sub-9049; Missing file: sub-9049_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9050_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9050/ses-1/beh/sub-9050_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9050_ses-1_dwi.bval
- Location: /sub-9050/ses-1/dwi/sub-9050_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9050_ses-1_dwi.bvec
- Location: /sub-9050/ses-1/dwi/sub-9050_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9050_ses-1_dwi.json
- Location: /sub-9050/ses-1/dwi/sub-9050_ses-1_dwi.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_dwi.json
-
- Type: Warning
- File: sub-9050_ses-1_run-1_magnitude1.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9050_ses-1_run-1_magnitude2.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9050_ses-1_run-1_phase1.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9050_ses-1_run-1_phase2.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9050_ses-1_run-2_magnitude1.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9050_ses-1_run-2_magnitude2.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9050_ses-1_run-2_phase1.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9050_ses-1_run-2_phase2.json
- Location: /sub-9050/ses-1/fmap/sub-9050_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9050_ses-1_task-faces_physio.json
- Location: /sub-9050/ses-1/func/sub-9050_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9050_ses-2_T2w.json
- Location: /sub-9050/ses-2/anat/sub-9050_ses-2_T2w.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-2_T2w.json
-
- Type: Warning
- File: sub-9050_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9050/ses-2/beh/sub-9050_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9050_ses-2_task-faces_physio.json
- Location: /sub-9050/ses-2/func/sub-9050_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9050_ses-2_task-faces_run-1_bold.json
- Location: /sub-9050/ses-2/func/sub-9050_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9050_ses-2_task-faces_run-2_bold.json
- Location: /sub-9050/ses-2/func/sub-9050_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9050, but is present for at least one other subject.
- Evidence: Subject: sub-9050; Missing file: sub-9050_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9053_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9053/ses-1/beh/sub-9053_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9053_ses-1_dwi.bval
- Location: /sub-9053/ses-1/dwi/sub-9053_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9053_ses-1_dwi.bvec
- Location: /sub-9053/ses-1/dwi/sub-9053_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9053_ses-1_dwi.json
- Location: /sub-9053/ses-1/dwi/sub-9053_ses-1_dwi.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_dwi.json
-
- Type: Warning
- File: sub-9053_ses-1_run-1_magnitude1.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9053_ses-1_run-1_magnitude2.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9053_ses-1_run-1_phase1.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9053_ses-1_run-1_phase2.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9053_ses-1_run-2_magnitude1.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9053_ses-1_run-2_magnitude2.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9053_ses-1_run-2_phase1.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9053_ses-1_run-2_phase2.json
- Location: /sub-9053/ses-1/fmap/sub-9053_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9053_ses-1_task-faces_physio.json
- Location: /sub-9053/ses-1/func/sub-9053_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9053_ses-2_T2w.json
- Location: /sub-9053/ses-2/anat/sub-9053_ses-2_T2w.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-2_T2w.json
-
- Type: Warning
- File: sub-9053_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9053/ses-2/beh/sub-9053_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9053_ses-2_task-faces_physio.json
- Location: /sub-9053/ses-2/func/sub-9053_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9053_ses-2_task-faces_run-1_bold.json
- Location: /sub-9053/ses-2/func/sub-9053_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9053_ses-2_task-faces_run-2_bold.json
- Location: /sub-9053/ses-2/func/sub-9053_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9053, but is present for at least one other subject.
- Evidence: Subject: sub-9053; Missing file: sub-9053_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9054_ses-1_dwi.bval
- Location: /sub-9054/ses-1/dwi/sub-9054_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9054_ses-1_dwi.bvec
- Location: /sub-9054/ses-1/dwi/sub-9054_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9054_ses-1_dwi.json
- Location: /sub-9054/ses-1/dwi/sub-9054_ses-1_dwi.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_dwi.json
-
- Type: Warning
- File: sub-9054_ses-1_run-1_magnitude1.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9054_ses-1_run-1_magnitude2.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9054_ses-1_run-1_phase1.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9054_ses-1_run-1_phase2.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9054_ses-1_run-2_magnitude1.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9054_ses-1_run-2_magnitude2.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9054_ses-1_run-2_phase1.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9054_ses-1_run-2_phase2.json
- Location: /sub-9054/ses-1/fmap/sub-9054_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9054_ses-2_dwi.bval
- Location: /sub-9054/ses-2/dwi/sub-9054_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9054_ses-2_dwi.bvec
- Location: /sub-9054/ses-2/dwi/sub-9054_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9054_ses-2_dwi.json
- Location: /sub-9054/ses-2/dwi/sub-9054_ses-2_dwi.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-2_dwi.json
-
- Type: Warning
- File: sub-9054_ses-2_task-faces_run-1_bold.json
- Location: /sub-9054/ses-2/func/sub-9054_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9054_ses-2_task-faces_run-2_bold.json
- Location: /sub-9054/ses-2/func/sub-9054_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9054, but is present for at least one other subject.
- Evidence: Subject: sub-9054; Missing file: sub-9054_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9055_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9055/ses-1/beh/sub-9055_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9055_ses-1_dwi.bval
- Location: /sub-9055/ses-1/dwi/sub-9055_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9055_ses-1_dwi.bvec
- Location: /sub-9055/ses-1/dwi/sub-9055_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9055_ses-1_dwi.json
- Location: /sub-9055/ses-1/dwi/sub-9055_ses-1_dwi.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_dwi.json
-
- Type: Warning
- File: sub-9055_ses-1_run-1_magnitude1.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9055_ses-1_run-1_magnitude2.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9055_ses-1_run-1_phase1.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9055_ses-1_run-1_phase2.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9055_ses-1_run-2_magnitude1.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9055_ses-1_run-2_magnitude2.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9055_ses-1_run-2_phase1.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9055_ses-1_run-2_phase2.json
- Location: /sub-9055/ses-1/fmap/sub-9055_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9055_ses-2_T2w.json
- Location: /sub-9055/ses-2/anat/sub-9055_ses-2_T2w.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-2_T2w.json
-
- Type: Warning
- File: sub-9055_ses-2_task-faces_physio.json
- Location: /sub-9055/ses-2/func/sub-9055_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9055_ses-2_task-faces_run-1_bold.json
- Location: /sub-9055/ses-2/func/sub-9055_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9055_ses-2_task-faces_run-2_bold.json
- Location: /sub-9055/ses-2/func/sub-9055_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9055, but is present for at least one other subject.
- Evidence: Subject: sub-9055; Missing file: sub-9055_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9056_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9056/ses-1/beh/sub-9056_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9056_ses-1_dwi.bval
- Location: /sub-9056/ses-1/dwi/sub-9056_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9056_ses-1_dwi.bvec
- Location: /sub-9056/ses-1/dwi/sub-9056_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9056_ses-1_dwi.json
- Location: /sub-9056/ses-1/dwi/sub-9056_ses-1_dwi.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_dwi.json
-
- Type: Warning
- File: sub-9056_ses-1_magnitude1.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9056_ses-1_magnitude2.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9056_ses-1_phase1.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_phase1.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_phase1.json
-
- Type: Warning
- File: sub-9056_ses-1_phase2.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_phase2.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_phase2.json
-
- Type: Warning
- File: sub-9056_ses-1_run-1_magnitude1.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9056_ses-1_run-1_magnitude2.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9056_ses-1_run-1_phase1.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9056_ses-1_run-1_phase2.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9056_ses-1_run-2_magnitude1.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9056_ses-1_run-2_magnitude2.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9056_ses-1_run-2_phase1.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9056_ses-1_run-2_phase2.json
- Location: /sub-9056/ses-1/fmap/sub-9056_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9056_ses-2_T2w.json
- Location: /sub-9056/ses-2/anat/sub-9056_ses-2_T2w.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_T2w.json
-
- Type: Warning
- File: sub-9056_ses-2_dwi.bval
- Location: /sub-9056/ses-2/dwi/sub-9056_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9056_ses-2_dwi.bvec
- Location: /sub-9056/ses-2/dwi/sub-9056_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9056_ses-2_dwi.json
- Location: /sub-9056/ses-2/dwi/sub-9056_ses-2_dwi.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_dwi.json
-
- Type: Warning
- File: sub-9056_ses-2_task-faces_physio.json
- Location: /sub-9056/ses-2/func/sub-9056_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9056_ses-2_task-faces_run-1_bold.json
- Location: /sub-9056/ses-2/func/sub-9056_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9056_ses-2_task-faces_run-2_bold.json
- Location: /sub-9056/ses-2/func/sub-9056_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9056, but is present for at least one other subject.
- Evidence: Subject: sub-9056; Missing file: sub-9056_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9057_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9057/ses-1/beh/sub-9057_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9057_ses-1_dwi.bval
- Location: /sub-9057/ses-1/dwi/sub-9057_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9057_ses-1_dwi.bvec
- Location: /sub-9057/ses-1/dwi/sub-9057_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9057_ses-1_dwi.json
- Location: /sub-9057/ses-1/dwi/sub-9057_ses-1_dwi.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_dwi.json
-
- Type: Warning
- File: sub-9057_ses-1_run-1_magnitude1.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9057_ses-1_run-1_magnitude2.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9057_ses-1_run-1_phase1.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9057_ses-1_run-1_phase2.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9057_ses-1_run-2_magnitude1.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9057_ses-1_run-2_magnitude2.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9057_ses-1_run-2_phase1.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9057_ses-1_run-2_phase2.json
- Location: /sub-9057/ses-1/fmap/sub-9057_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9057_ses-1_task-faces_physio.json
- Location: /sub-9057/ses-1/func/sub-9057_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9057_ses-2_T2w.json
- Location: /sub-9057/ses-2/anat/sub-9057_ses-2_T2w.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-2_T2w.json
-
- Type: Warning
- File: sub-9057_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9057/ses-2/beh/sub-9057_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9057_ses-2_task-faces_physio.json
- Location: /sub-9057/ses-2/func/sub-9057_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9057_ses-2_task-faces_run-1_bold.json
- Location: /sub-9057/ses-2/func/sub-9057_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9057_ses-2_task-faces_run-2_bold.json
- Location: /sub-9057/ses-2/func/sub-9057_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9057, but is present for at least one other subject.
- Evidence: Subject: sub-9057; Missing file: sub-9057_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9058_ses-1_dwi.bval
- Location: /sub-9058/ses-1/dwi/sub-9058_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9058_ses-1_dwi.bvec
- Location: /sub-9058/ses-1/dwi/sub-9058_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9058_ses-1_dwi.json
- Location: /sub-9058/ses-1/dwi/sub-9058_ses-1_dwi.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_dwi.json
-
- Type: Warning
- File: sub-9058_ses-1_run-1_magnitude1.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9058_ses-1_run-1_magnitude2.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9058_ses-1_run-1_phase1.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9058_ses-1_run-1_phase2.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9058_ses-1_run-2_magnitude1.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9058_ses-1_run-2_magnitude2.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9058_ses-1_run-2_phase1.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9058_ses-1_run-2_phase2.json
- Location: /sub-9058/ses-1/fmap/sub-9058_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9058_ses-2_T2w.json
- Location: /sub-9058/ses-2/anat/sub-9058_ses-2_T2w.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-2_T2w.json
-
- Type: Warning
- File: sub-9058_ses-2_task-faces_run-1_bold.json
- Location: /sub-9058/ses-2/func/sub-9058_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9058_ses-2_task-faces_run-2_bold.json
- Location: /sub-9058/ses-2/func/sub-9058_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9058, but is present for at least one other subject.
- Evidence: Subject: sub-9058; Missing file: sub-9058_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9059_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9059/ses-1/beh/sub-9059_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9059_ses-1_dwi.bval
- Location: /sub-9059/ses-1/dwi/sub-9059_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9059_ses-1_dwi.bvec
- Location: /sub-9059/ses-1/dwi/sub-9059_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9059_ses-1_dwi.json
- Location: /sub-9059/ses-1/dwi/sub-9059_ses-1_dwi.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_dwi.json
-
- Type: Warning
- File: sub-9059_ses-1_magnitude1.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9059_ses-1_magnitude2.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9059_ses-1_phase1.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_phase1.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_phase1.json
-
- Type: Warning
- File: sub-9059_ses-1_phase2.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_phase2.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_phase2.json
-
- Type: Warning
- File: sub-9059_ses-1_run-1_magnitude1.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9059_ses-1_run-1_magnitude2.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9059_ses-1_run-1_phase1.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9059_ses-1_run-1_phase2.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9059_ses-1_run-2_magnitude1.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9059_ses-1_run-2_magnitude2.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9059_ses-1_run-2_phase1.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9059_ses-1_run-2_phase2.json
- Location: /sub-9059/ses-1/fmap/sub-9059_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9059_ses-2_T2w.json
- Location: /sub-9059/ses-2/anat/sub-9059_ses-2_T2w.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-2_T2w.json
-
- Type: Warning
- File: sub-9059_ses-2_dwi.bval
- Location: /sub-9059/ses-2/dwi/sub-9059_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9059_ses-2_dwi.bvec
- Location: /sub-9059/ses-2/dwi/sub-9059_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9059_ses-2_dwi.json
- Location: /sub-9059/ses-2/dwi/sub-9059_ses-2_dwi.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-2_dwi.json
-
- Type: Warning
- File: sub-9059_ses-2_task-faces_run-1_bold.json
- Location: /sub-9059/ses-2/func/sub-9059_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9059_ses-2_task-faces_run-2_bold.json
- Location: /sub-9059/ses-2/func/sub-9059_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9059, but is present for at least one other subject.
- Evidence: Subject: sub-9059; Missing file: sub-9059_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9061_ses-1_dwi.bval
- Location: /sub-9061/ses-1/dwi/sub-9061_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9061_ses-1_dwi.bvec
- Location: /sub-9061/ses-1/dwi/sub-9061_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9061_ses-1_dwi.json
- Location: /sub-9061/ses-1/dwi/sub-9061_ses-1_dwi.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_dwi.json
-
- Type: Warning
- File: sub-9061_ses-1_run-1_magnitude1.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9061_ses-1_run-1_magnitude2.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9061_ses-1_run-1_phase1.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9061_ses-1_run-1_phase2.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9061_ses-1_run-2_magnitude1.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9061_ses-1_run-2_magnitude2.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9061_ses-1_run-2_phase1.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9061_ses-1_run-2_phase2.json
- Location: /sub-9061/ses-1/fmap/sub-9061_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9061_ses-2_T2w.json
- Location: /sub-9061/ses-2/anat/sub-9061_ses-2_T2w.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-2_T2w.json
-
- Type: Warning
- File: sub-9061_ses-2_task-faces_run-1_bold.json
- Location: /sub-9061/ses-2/func/sub-9061_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9061_ses-2_task-faces_run-2_bold.json
- Location: /sub-9061/ses-2/func/sub-9061_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9061, but is present for at least one other subject.
- Evidence: Subject: sub-9061; Missing file: sub-9061_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9062_ses-1_dwi.bval
- Location: /sub-9062/ses-1/dwi/sub-9062_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9062_ses-1_dwi.bvec
- Location: /sub-9062/ses-1/dwi/sub-9062_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9062_ses-1_dwi.json
- Location: /sub-9062/ses-1/dwi/sub-9062_ses-1_dwi.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_dwi.json
-
- Type: Warning
- File: sub-9062_ses-1_run-1_magnitude1.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9062_ses-1_run-1_magnitude2.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9062_ses-1_run-1_phase1.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9062_ses-1_run-1_phase2.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9062_ses-1_run-2_magnitude1.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9062_ses-1_run-2_magnitude2.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9062_ses-1_run-2_phase1.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9062_ses-1_run-2_phase2.json
- Location: /sub-9062/ses-1/fmap/sub-9062_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9062_ses-1_task-faces_physio.json
- Location: /sub-9062/ses-1/func/sub-9062_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9062_ses-2_T2w.json
- Location: /sub-9062/ses-2/anat/sub-9062_ses-2_T2w.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-2_T2w.json
-
- Type: Warning
- File: sub-9062_ses-2_task-faces_run-1_bold.json
- Location: /sub-9062/ses-2/func/sub-9062_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9062_ses-2_task-faces_run-2_bold.json
- Location: /sub-9062/ses-2/func/sub-9062_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9062, but is present for at least one other subject.
- Evidence: Subject: sub-9062; Missing file: sub-9062_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9063_ses-1_dwi.bval
- Location: /sub-9063/ses-1/dwi/sub-9063_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9063_ses-1_dwi.bvec
- Location: /sub-9063/ses-1/dwi/sub-9063_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9063_ses-1_dwi.json
- Location: /sub-9063/ses-1/dwi/sub-9063_ses-1_dwi.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_dwi.json
-
- Type: Warning
- File: sub-9063_ses-1_run-1_magnitude1.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9063_ses-1_run-1_magnitude2.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9063_ses-1_run-1_phase1.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9063_ses-1_run-1_phase2.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9063_ses-1_run-2_magnitude1.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9063_ses-1_run-2_magnitude2.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9063_ses-1_run-2_phase1.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9063_ses-1_run-2_phase2.json
- Location: /sub-9063/ses-1/fmap/sub-9063_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9063_ses-1_task-faces_physio.json
- Location: /sub-9063/ses-1/func/sub-9063_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9063_ses-2_dwi.bval
- Location: /sub-9063/ses-2/dwi/sub-9063_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9063_ses-2_dwi.bvec
- Location: /sub-9063/ses-2/dwi/sub-9063_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9063_ses-2_dwi.json
- Location: /sub-9063/ses-2/dwi/sub-9063_ses-2_dwi.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-2_dwi.json
-
- Type: Warning
- File: sub-9063_ses-2_task-faces_run-1_bold.json
- Location: /sub-9063/ses-2/func/sub-9063_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9063_ses-2_task-faces_run-2_bold.json
- Location: /sub-9063/ses-2/func/sub-9063_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9063, but is present for at least one other subject.
- Evidence: Subject: sub-9063; Missing file: sub-9063_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9064_ses-1_dwi.bval
- Location: /sub-9064/ses-1/dwi/sub-9064_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9064_ses-1_dwi.bvec
- Location: /sub-9064/ses-1/dwi/sub-9064_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9064_ses-1_dwi.json
- Location: /sub-9064/ses-1/dwi/sub-9064_ses-1_dwi.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_dwi.json
-
- Type: Warning
- File: sub-9064_ses-1_run-1_magnitude1.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9064_ses-1_run-1_magnitude2.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9064_ses-1_run-1_phase1.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9064_ses-1_run-1_phase2.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9064_ses-1_run-2_magnitude1.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9064_ses-1_run-2_magnitude2.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9064_ses-1_run-2_phase1.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9064_ses-1_run-2_phase2.json
- Location: /sub-9064/ses-1/fmap/sub-9064_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9064_ses-2_T2w.json
- Location: /sub-9064/ses-2/anat/sub-9064_ses-2_T2w.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-2_T2w.json
-
- Type: Warning
- File: sub-9064_ses-2_task-faces_run-1_bold.json
- Location: /sub-9064/ses-2/func/sub-9064_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9064_ses-2_task-faces_run-2_bold.json
- Location: /sub-9064/ses-2/func/sub-9064_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9064, but is present for at least one other subject.
- Evidence: Subject: sub-9064; Missing file: sub-9064_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9065_ses-1_dwi.bval
- Location: /sub-9065/ses-1/dwi/sub-9065_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9065_ses-1_dwi.bvec
- Location: /sub-9065/ses-1/dwi/sub-9065_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9065_ses-1_dwi.json
- Location: /sub-9065/ses-1/dwi/sub-9065_ses-1_dwi.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_dwi.json
-
- Type: Warning
- File: sub-9065_ses-1_run-1_magnitude1.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9065_ses-1_run-1_magnitude2.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9065_ses-1_run-1_phase1.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9065_ses-1_run-1_phase2.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9065_ses-1_run-2_magnitude1.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9065_ses-1_run-2_magnitude2.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9065_ses-1_run-2_phase1.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9065_ses-1_run-2_phase2.json
- Location: /sub-9065/ses-1/fmap/sub-9065_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9065_ses-2_T2w.json
- Location: /sub-9065/ses-2/anat/sub-9065_ses-2_T2w.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_T2w.json
-
- Type: Warning
- File: sub-9065_ses-2_magnitude1.json
- Location: /sub-9065/ses-2/fmap/sub-9065_ses-2_magnitude1.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_magnitude1.json
-
- Type: Warning
- File: sub-9065_ses-2_magnitude2.json
- Location: /sub-9065/ses-2/fmap/sub-9065_ses-2_magnitude2.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_magnitude2.json
-
- Type: Warning
- File: sub-9065_ses-2_phase1.json
- Location: /sub-9065/ses-2/fmap/sub-9065_ses-2_phase1.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_phase1.json
-
- Type: Warning
- File: sub-9065_ses-2_phase2.json
- Location: /sub-9065/ses-2/fmap/sub-9065_ses-2_phase2.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_phase2.json
-
- Type: Warning
- File: sub-9065_ses-2_task-faces_run-1_bold.json
- Location: /sub-9065/ses-2/func/sub-9065_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9065_ses-2_task-faces_run-2_bold.json
- Location: /sub-9065/ses-2/func/sub-9065_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9065, but is present for at least one other subject.
- Evidence: Subject: sub-9065; Missing file: sub-9065_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9066_ses-1_T2w.json
- Location: /sub-9066/ses-1/anat/sub-9066_ses-1_T2w.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_T2w.json
-
- Type: Warning
- File: sub-9066_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9066/ses-1/beh/sub-9066_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9066_ses-1_run-1_magnitude1.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9066_ses-1_run-1_magnitude2.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9066_ses-1_run-1_phase1.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9066_ses-1_run-1_phase2.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9066_ses-1_run-2_magnitude1.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9066_ses-1_run-2_magnitude2.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9066_ses-1_run-2_phase1.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9066_ses-1_run-2_phase2.json
- Location: /sub-9066/ses-1/fmap/sub-9066_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9066_ses-1_task-arrows_events.tsv
- Location: /sub-9066/ses-1/func/sub-9066_ses-1_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9066_ses-1_task-faces_events.tsv
- Location: /sub-9066/ses-1/func/sub-9066_ses-1_task-faces_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_task-faces_events.tsv
-
- Type: Warning
- File: sub-9066_ses-1_task-faces_physio.json
- Location: /sub-9066/ses-1/func/sub-9066_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9066_ses-2_T1w.json
- Location: /sub-9066/ses-2/anat/sub-9066_ses-2_T1w.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_T1w.json
-
- Type: Warning
- File: sub-9066_ses-2_T2w.json
- Location: /sub-9066/ses-2/anat/sub-9066_ses-2_T2w.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_T2w.json
-
- Type: Warning
- File: sub-9066_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9066/ses-2/beh/sub-9066_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9066_ses-2_dwi.bval
- Location: /sub-9066/ses-2/dwi/sub-9066_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9066_ses-2_dwi.bvec
- Location: /sub-9066/ses-2/dwi/sub-9066_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9066_ses-2_dwi.json
- Location: /sub-9066/ses-2/dwi/sub-9066_ses-2_dwi.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_dwi.json
-
- Type: Warning
- File: sub-9066_ses-2_magnitude1.json
- Location: /sub-9066/ses-2/fmap/sub-9066_ses-2_magnitude1.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_magnitude1.json
-
- Type: Warning
- File: sub-9066_ses-2_magnitude2.json
- Location: /sub-9066/ses-2/fmap/sub-9066_ses-2_magnitude2.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_magnitude2.json
-
- Type: Warning
- File: sub-9066_ses-2_phase1.json
- Location: /sub-9066/ses-2/fmap/sub-9066_ses-2_phase1.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_phase1.json
-
- Type: Warning
- File: sub-9066_ses-2_phase2.json
- Location: /sub-9066/ses-2/fmap/sub-9066_ses-2_phase2.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_phase2.json
-
- Type: Warning
- File: sub-9066_ses-2_task-arrows_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-arrows_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-arrows_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-arrows_events.tsv
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9066_ses-2_task-faces_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-faces_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-faces_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-faces_events.tsv
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-faces_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-faces_events.tsv
-
- Type: Warning
- File: sub-9066_ses-2_task-faces_physio.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9066_ses-2_task-faces_run-1_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-faces_run-2_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-hands_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-hands_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-hands_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-hands_events.tsv
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-hands_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-hands_events.tsv
-
- Type: Warning
- File: sub-9066_ses-2_task-rest_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-rest_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-rest_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-sleepiness_bold.json
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9066_ses-2_task-sleepiness_events.tsv
- Location: /sub-9066/ses-2/func/sub-9066_ses-2_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9066_ses-2_scans.tsv
- Location: /sub-9066/ses-2/sub-9066_ses-2_scans.tsv
- Reason: This file is missing for subject sub-9066, but is present for at least one other subject.
- Evidence: Subject: sub-9066; Missing file: sub-9066_ses-2_scans.tsv
-
- Type: Warning
- File: sub-9067_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9067/ses-1/beh/sub-9067_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9067_ses-1_dwi.bval
- Location: /sub-9067/ses-1/dwi/sub-9067_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9067_ses-1_dwi.bvec
- Location: /sub-9067/ses-1/dwi/sub-9067_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9067_ses-1_dwi.json
- Location: /sub-9067/ses-1/dwi/sub-9067_ses-1_dwi.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_dwi.json
-
- Type: Warning
- File: sub-9067_ses-1_run-1_magnitude1.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9067_ses-1_run-1_magnitude2.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9067_ses-1_run-1_phase1.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9067_ses-1_run-1_phase2.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9067_ses-1_run-2_magnitude1.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9067_ses-1_run-2_magnitude2.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9067_ses-1_run-2_phase1.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9067_ses-1_run-2_phase2.json
- Location: /sub-9067/ses-1/fmap/sub-9067_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9067_ses-1_task-faces_physio.json
- Location: /sub-9067/ses-1/func/sub-9067_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9067_ses-2_T2w.json
- Location: /sub-9067/ses-2/anat/sub-9067_ses-2_T2w.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-2_T2w.json
-
- Type: Warning
- File: sub-9067_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9067/ses-2/beh/sub-9067_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9067_ses-2_task-faces_physio.json
- Location: /sub-9067/ses-2/func/sub-9067_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9067_ses-2_task-faces_run-1_bold.json
- Location: /sub-9067/ses-2/func/sub-9067_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9067_ses-2_task-faces_run-2_bold.json
- Location: /sub-9067/ses-2/func/sub-9067_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9067, but is present for at least one other subject.
- Evidence: Subject: sub-9067; Missing file: sub-9067_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9068_ses-1_dwi.bval
- Location: /sub-9068/ses-1/dwi/sub-9068_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9068_ses-1_dwi.bvec
- Location: /sub-9068/ses-1/dwi/sub-9068_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9068_ses-1_dwi.json
- Location: /sub-9068/ses-1/dwi/sub-9068_ses-1_dwi.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_dwi.json
-
- Type: Warning
- File: sub-9068_ses-1_run-1_magnitude1.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9068_ses-1_run-1_magnitude2.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9068_ses-1_run-1_phase1.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9068_ses-1_run-1_phase2.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9068_ses-1_run-2_magnitude1.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9068_ses-1_run-2_magnitude2.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9068_ses-1_run-2_phase1.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9068_ses-1_run-2_phase2.json
- Location: /sub-9068/ses-1/fmap/sub-9068_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9068_ses-2_T2w.json
- Location: /sub-9068/ses-2/anat/sub-9068_ses-2_T2w.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-2_T2w.json
-
- Type: Warning
- File: sub-9068_ses-2_task-faces_run-1_bold.json
- Location: /sub-9068/ses-2/func/sub-9068_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9068_ses-2_task-faces_run-2_bold.json
- Location: /sub-9068/ses-2/func/sub-9068_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9068, but is present for at least one other subject.
- Evidence: Subject: sub-9068; Missing file: sub-9068_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9069_ses-1_dwi.bval
- Location: /sub-9069/ses-1/dwi/sub-9069_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9069_ses-1_dwi.bvec
- Location: /sub-9069/ses-1/dwi/sub-9069_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9069_ses-1_dwi.json
- Location: /sub-9069/ses-1/dwi/sub-9069_ses-1_dwi.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_dwi.json
-
- Type: Warning
- File: sub-9069_ses-1_run-1_magnitude1.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9069_ses-1_run-1_magnitude2.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9069_ses-1_run-1_phase1.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9069_ses-1_run-1_phase2.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9069_ses-1_run-2_magnitude1.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9069_ses-1_run-2_magnitude2.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9069_ses-1_run-2_phase1.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9069_ses-1_run-2_phase2.json
- Location: /sub-9069/ses-1/fmap/sub-9069_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9069_ses-2_T2w.json
- Location: /sub-9069/ses-2/anat/sub-9069_ses-2_T2w.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-2_T2w.json
-
- Type: Warning
- File: sub-9069_ses-2_task-faces_run-1_bold.json
- Location: /sub-9069/ses-2/func/sub-9069_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9069_ses-2_task-faces_run-2_bold.json
- Location: /sub-9069/ses-2/func/sub-9069_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9069, but is present for at least one other subject.
- Evidence: Subject: sub-9069; Missing file: sub-9069_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9070_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9070/ses-1/beh/sub-9070_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9070_ses-1_dwi.bval
- Location: /sub-9070/ses-1/dwi/sub-9070_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9070_ses-1_dwi.bvec
- Location: /sub-9070/ses-1/dwi/sub-9070_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9070_ses-1_dwi.json
- Location: /sub-9070/ses-1/dwi/sub-9070_ses-1_dwi.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_dwi.json
-
- Type: Warning
- File: sub-9070_ses-1_magnitude2.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9070_ses-1_phase2.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_phase2.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_phase2.json
-
- Type: Warning
- File: sub-9070_ses-1_run-1_magnitude1.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9070_ses-1_run-1_magnitude2.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9070_ses-1_run-1_phase1.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9070_ses-1_run-1_phase2.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9070_ses-1_run-2_magnitude1.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9070_ses-1_run-2_magnitude2.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9070_ses-1_run-2_phase1.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9070_ses-1_run-2_phase2.json
- Location: /sub-9070/ses-1/fmap/sub-9070_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9070_ses-2_T2w.json
- Location: /sub-9070/ses-2/anat/sub-9070_ses-2_T2w.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-2_T2w.json
-
- Type: Warning
- File: sub-9070_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9070/ses-2/beh/sub-9070_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9070_ses-2_task-faces_run-1_bold.json
- Location: /sub-9070/ses-2/func/sub-9070_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9070_ses-2_task-faces_run-2_bold.json
- Location: /sub-9070/ses-2/func/sub-9070_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9070, but is present for at least one other subject.
- Evidence: Subject: sub-9070; Missing file: sub-9070_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9071_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9071/ses-1/beh/sub-9071_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9071_ses-1_dwi.bval
- Location: /sub-9071/ses-1/dwi/sub-9071_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9071_ses-1_dwi.bvec
- Location: /sub-9071/ses-1/dwi/sub-9071_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9071_ses-1_dwi.json
- Location: /sub-9071/ses-1/dwi/sub-9071_ses-1_dwi.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_dwi.json
-
- Type: Warning
- File: sub-9071_ses-1_run-1_magnitude1.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9071_ses-1_run-1_magnitude2.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9071_ses-1_run-1_phase1.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9071_ses-1_run-1_phase2.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9071_ses-1_run-2_magnitude1.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9071_ses-1_run-2_magnitude2.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9071_ses-1_run-2_phase1.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9071_ses-1_run-2_phase2.json
- Location: /sub-9071/ses-1/fmap/sub-9071_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9071_ses-2_T2w.json
- Location: /sub-9071/ses-2/anat/sub-9071_ses-2_T2w.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-2_T2w.json
-
- Type: Warning
- File: sub-9071_ses-2_task-faces_run-1_bold.json
- Location: /sub-9071/ses-2/func/sub-9071_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9071_ses-2_task-faces_run-2_bold.json
- Location: /sub-9071/ses-2/func/sub-9071_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9071, but is present for at least one other subject.
- Evidence: Subject: sub-9071; Missing file: sub-9071_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9072_ses-1_dwi.bval
- Location: /sub-9072/ses-1/dwi/sub-9072_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9072_ses-1_dwi.bvec
- Location: /sub-9072/ses-1/dwi/sub-9072_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9072_ses-1_dwi.json
- Location: /sub-9072/ses-1/dwi/sub-9072_ses-1_dwi.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_dwi.json
-
- Type: Warning
- File: sub-9072_ses-1_run-1_magnitude1.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9072_ses-1_run-1_magnitude2.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9072_ses-1_run-1_phase1.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9072_ses-1_run-1_phase2.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9072_ses-1_run-2_magnitude1.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9072_ses-1_run-2_magnitude2.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9072_ses-1_run-2_phase1.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9072_ses-1_run-2_phase2.json
- Location: /sub-9072/ses-1/fmap/sub-9072_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9072_ses-2_T2w.json
- Location: /sub-9072/ses-2/anat/sub-9072_ses-2_T2w.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-2_T2w.json
-
- Type: Warning
- File: sub-9072_ses-2_task-faces_run-1_bold.json
- Location: /sub-9072/ses-2/func/sub-9072_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9072_ses-2_task-faces_run-2_bold.json
- Location: /sub-9072/ses-2/func/sub-9072_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9072, but is present for at least one other subject.
- Evidence: Subject: sub-9072; Missing file: sub-9072_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9073_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9073/ses-1/beh/sub-9073_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9073_ses-1_dwi.bval
- Location: /sub-9073/ses-1/dwi/sub-9073_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9073_ses-1_dwi.bvec
- Location: /sub-9073/ses-1/dwi/sub-9073_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9073_ses-1_dwi.json
- Location: /sub-9073/ses-1/dwi/sub-9073_ses-1_dwi.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_dwi.json
-
- Type: Warning
- File: sub-9073_ses-1_run-1_magnitude1.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9073_ses-1_run-1_magnitude2.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9073_ses-1_run-1_phase1.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9073_ses-1_run-1_phase2.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9073_ses-1_run-2_magnitude1.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9073_ses-1_run-2_magnitude2.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9073_ses-1_run-2_phase1.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9073_ses-1_run-2_phase2.json
- Location: /sub-9073/ses-1/fmap/sub-9073_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9073_ses-1_task-faces_physio.json
- Location: /sub-9073/ses-1/func/sub-9073_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9073_ses-2_T2w.json
- Location: /sub-9073/ses-2/anat/sub-9073_ses-2_T2w.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-2_T2w.json
-
- Type: Warning
- File: sub-9073_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9073/ses-2/beh/sub-9073_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9073_ses-2_task-faces_physio.json
- Location: /sub-9073/ses-2/func/sub-9073_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9073_ses-2_task-faces_run-1_bold.json
- Location: /sub-9073/ses-2/func/sub-9073_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9073_ses-2_task-faces_run-2_bold.json
- Location: /sub-9073/ses-2/func/sub-9073_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9073, but is present for at least one other subject.
- Evidence: Subject: sub-9073; Missing file: sub-9073_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9074_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9074/ses-1/beh/sub-9074_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9074_ses-1_dwi.bval
- Location: /sub-9074/ses-1/dwi/sub-9074_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9074_ses-1_dwi.bvec
- Location: /sub-9074/ses-1/dwi/sub-9074_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9074_ses-1_dwi.json
- Location: /sub-9074/ses-1/dwi/sub-9074_ses-1_dwi.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_dwi.json
-
- Type: Warning
- File: sub-9074_ses-1_magnitude1.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9074_ses-1_magnitude2.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9074_ses-1_phase1.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_phase1.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_phase1.json
-
- Type: Warning
- File: sub-9074_ses-1_phase2.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_phase2.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_phase2.json
-
- Type: Warning
- File: sub-9074_ses-1_run-1_magnitude1.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9074_ses-1_run-1_magnitude2.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9074_ses-1_run-1_phase1.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9074_ses-1_run-1_phase2.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9074_ses-1_run-2_magnitude1.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9074_ses-1_run-2_magnitude2.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9074_ses-1_run-2_phase1.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9074_ses-1_run-2_phase2.json
- Location: /sub-9074/ses-1/fmap/sub-9074_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9074_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9074/ses-2/beh/sub-9074_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9074_ses-2_dwi.bval
- Location: /sub-9074/ses-2/dwi/sub-9074_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9074_ses-2_dwi.bvec
- Location: /sub-9074/ses-2/dwi/sub-9074_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9074_ses-2_dwi.json
- Location: /sub-9074/ses-2/dwi/sub-9074_ses-2_dwi.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-2_dwi.json
-
- Type: Warning
- File: sub-9074_ses-2_task-faces_run-1_bold.json
- Location: /sub-9074/ses-2/func/sub-9074_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9074_ses-2_task-faces_run-2_bold.json
- Location: /sub-9074/ses-2/func/sub-9074_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9074, but is present for at least one other subject.
- Evidence: Subject: sub-9074; Missing file: sub-9074_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9075_ses-1_dwi.bval
- Location: /sub-9075/ses-1/dwi/sub-9075_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9075_ses-1_dwi.bvec
- Location: /sub-9075/ses-1/dwi/sub-9075_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9075_ses-1_dwi.json
- Location: /sub-9075/ses-1/dwi/sub-9075_ses-1_dwi.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_dwi.json
-
- Type: Warning
- File: sub-9075_ses-1_run-1_magnitude1.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9075_ses-1_run-1_magnitude2.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9075_ses-1_run-1_phase1.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9075_ses-1_run-1_phase2.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9075_ses-1_run-2_magnitude1.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9075_ses-1_run-2_magnitude2.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9075_ses-1_run-2_phase1.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9075_ses-1_run-2_phase2.json
- Location: /sub-9075/ses-1/fmap/sub-9075_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9075_ses-2_T2w.json
- Location: /sub-9075/ses-2/anat/sub-9075_ses-2_T2w.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-2_T2w.json
-
- Type: Warning
- File: sub-9075_ses-2_task-faces_run-1_bold.json
- Location: /sub-9075/ses-2/func/sub-9075_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9075_ses-2_task-faces_run-2_bold.json
- Location: /sub-9075/ses-2/func/sub-9075_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9075, but is present for at least one other subject.
- Evidence: Subject: sub-9075; Missing file: sub-9075_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9076_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9076/ses-1/beh/sub-9076_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9076_ses-1_dwi.bval
- Location: /sub-9076/ses-1/dwi/sub-9076_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9076_ses-1_dwi.bvec
- Location: /sub-9076/ses-1/dwi/sub-9076_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9076_ses-1_dwi.json
- Location: /sub-9076/ses-1/dwi/sub-9076_ses-1_dwi.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_dwi.json
-
- Type: Warning
- File: sub-9076_ses-1_run-1_magnitude1.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9076_ses-1_run-1_magnitude2.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9076_ses-1_run-1_phase1.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9076_ses-1_run-1_phase2.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9076_ses-1_run-2_magnitude1.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9076_ses-1_run-2_magnitude2.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9076_ses-1_run-2_phase1.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9076_ses-1_run-2_phase2.json
- Location: /sub-9076/ses-1/fmap/sub-9076_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9076_ses-1_task-faces_physio.json
- Location: /sub-9076/ses-1/func/sub-9076_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9076_ses-2_T2w.json
- Location: /sub-9076/ses-2/anat/sub-9076_ses-2_T2w.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-2_T2w.json
-
- Type: Warning
- File: sub-9076_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9076/ses-2/beh/sub-9076_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9076_ses-2_task-faces_physio.json
- Location: /sub-9076/ses-2/func/sub-9076_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9076_ses-2_task-faces_run-1_bold.json
- Location: /sub-9076/ses-2/func/sub-9076_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9076_ses-2_task-faces_run-2_bold.json
- Location: /sub-9076/ses-2/func/sub-9076_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9076, but is present for at least one other subject.
- Evidence: Subject: sub-9076; Missing file: sub-9076_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9077_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9077/ses-1/beh/sub-9077_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9077_ses-1_dwi.bval
- Location: /sub-9077/ses-1/dwi/sub-9077_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9077_ses-1_dwi.bvec
- Location: /sub-9077/ses-1/dwi/sub-9077_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9077_ses-1_dwi.json
- Location: /sub-9077/ses-1/dwi/sub-9077_ses-1_dwi.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_dwi.json
-
- Type: Warning
- File: sub-9077_ses-1_run-1_magnitude1.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9077_ses-1_run-1_magnitude2.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9077_ses-1_run-1_phase1.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9077_ses-1_run-1_phase2.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9077_ses-1_run-2_magnitude1.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9077_ses-1_run-2_magnitude2.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9077_ses-1_run-2_phase1.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9077_ses-1_run-2_phase2.json
- Location: /sub-9077/ses-1/fmap/sub-9077_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9077_ses-1_task-faces_physio.json
- Location: /sub-9077/ses-1/func/sub-9077_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9077_ses-2_T2w.json
- Location: /sub-9077/ses-2/anat/sub-9077_ses-2_T2w.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-2_T2w.json
-
- Type: Warning
- File: sub-9077_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9077/ses-2/beh/sub-9077_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9077_ses-2_task-faces_physio.json
- Location: /sub-9077/ses-2/func/sub-9077_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9077_ses-2_task-faces_run-1_bold.json
- Location: /sub-9077/ses-2/func/sub-9077_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9077_ses-2_task-faces_run-2_bold.json
- Location: /sub-9077/ses-2/func/sub-9077_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9077, but is present for at least one other subject.
- Evidence: Subject: sub-9077; Missing file: sub-9077_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9078_ses-1_T2w.json
- Location: /sub-9078/ses-1/anat/sub-9078_ses-1_T2w.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_T2w.json
-
- Type: Warning
- File: sub-9078_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9078/ses-1/beh/sub-9078_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9078_ses-1_dwi.bval
- Location: /sub-9078/ses-1/dwi/sub-9078_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9078_ses-1_dwi.bvec
- Location: /sub-9078/ses-1/dwi/sub-9078_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9078_ses-1_dwi.json
- Location: /sub-9078/ses-1/dwi/sub-9078_ses-1_dwi.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_dwi.json
-
- Type: Warning
- File: sub-9078_ses-1_magnitude1.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9078_ses-1_magnitude2.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9078_ses-1_phase1.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_phase1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_phase1.json
-
- Type: Warning
- File: sub-9078_ses-1_phase2.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_phase2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_phase2.json
-
- Type: Warning
- File: sub-9078_ses-1_run-1_magnitude1.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9078_ses-1_run-1_magnitude2.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9078_ses-1_run-1_phase1.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9078_ses-1_run-1_phase2.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9078_ses-1_run-2_magnitude1.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9078_ses-1_run-2_magnitude2.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9078_ses-1_run-2_phase1.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9078_ses-1_run-2_phase2.json
- Location: /sub-9078/ses-1/fmap/sub-9078_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9078_ses-1_task-arrows_bold.json
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-arrows_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-arrows_bold.json
-
- Type: Warning
- File: sub-9078_ses-1_task-arrows_events.tsv
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9078_ses-1_task-faces_bold.json
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-faces_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-faces_bold.json
-
- Type: Warning
- File: sub-9078_ses-1_task-faces_events.tsv
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-faces_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-faces_events.tsv
-
- Type: Warning
- File: sub-9078_ses-1_task-faces_physio.json
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9078_ses-1_task-hands_bold.json
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-hands_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-hands_bold.json
-
- Type: Warning
- File: sub-9078_ses-1_task-hands_events.tsv
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-hands_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-hands_events.tsv
-
- Type: Warning
- File: sub-9078_ses-1_task-rest_bold.json
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-rest_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-rest_bold.json
-
- Type: Warning
- File: sub-9078_ses-1_task-sleepiness_bold.json
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9078_ses-1_task-sleepiness_events.tsv
- Location: /sub-9078/ses-1/func/sub-9078_ses-1_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-1_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9078_ses-2_T1w.json
- Location: /sub-9078/ses-2/anat/sub-9078_ses-2_T1w.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_T1w.json
-
- Type: Warning
- File: sub-9078_ses-2_T2w.json
- Location: /sub-9078/ses-2/anat/sub-9078_ses-2_T2w.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_T2w.json
-
- Type: Warning
- File: sub-9078_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9078/ses-2/beh/sub-9078_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9078_ses-2_dwi.bval
- Location: /sub-9078/ses-2/dwi/sub-9078_ses-2_dwi.bval
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_dwi.bval
-
- Type: Warning
- File: sub-9078_ses-2_dwi.bvec
- Location: /sub-9078/ses-2/dwi/sub-9078_ses-2_dwi.bvec
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_dwi.bvec
-
- Type: Warning
- File: sub-9078_ses-2_dwi.json
- Location: /sub-9078/ses-2/dwi/sub-9078_ses-2_dwi.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_dwi.json
-
- Type: Warning
- File: sub-9078_ses-2_magnitude1.json
- Location: /sub-9078/ses-2/fmap/sub-9078_ses-2_magnitude1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_magnitude1.json
-
- Type: Warning
- File: sub-9078_ses-2_magnitude2.json
- Location: /sub-9078/ses-2/fmap/sub-9078_ses-2_magnitude2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_magnitude2.json
-
- Type: Warning
- File: sub-9078_ses-2_phase1.json
- Location: /sub-9078/ses-2/fmap/sub-9078_ses-2_phase1.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_phase1.json
-
- Type: Warning
- File: sub-9078_ses-2_phase2.json
- Location: /sub-9078/ses-2/fmap/sub-9078_ses-2_phase2.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_phase2.json
-
- Type: Warning
- File: sub-9078_ses-2_task-arrows_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-arrows_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-arrows_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-arrows_events.tsv
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-arrows_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-arrows_events.tsv
-
- Type: Warning
- File: sub-9078_ses-2_task-faces_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-faces_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-faces_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-faces_events.tsv
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-faces_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-faces_events.tsv
-
- Type: Warning
- File: sub-9078_ses-2_task-faces_physio.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9078_ses-2_task-faces_run-1_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-faces_run-2_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-hands_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-hands_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-hands_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-hands_events.tsv
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-hands_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-hands_events.tsv
-
- Type: Warning
- File: sub-9078_ses-2_task-rest_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-rest_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-rest_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-sleepiness_bold.json
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-sleepiness_bold.json
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-sleepiness_bold.json
-
- Type: Warning
- File: sub-9078_ses-2_task-sleepiness_events.tsv
- Location: /sub-9078/ses-2/func/sub-9078_ses-2_task-sleepiness_events.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_task-sleepiness_events.tsv
-
- Type: Warning
- File: sub-9078_ses-2_scans.tsv
- Location: /sub-9078/ses-2/sub-9078_ses-2_scans.tsv
- Reason: This file is missing for subject sub-9078, but is present for at least one other subject.
- Evidence: Subject: sub-9078; Missing file: sub-9078_ses-2_scans.tsv
-
- Type: Warning
- File: sub-9079_ses-1_dwi.bval
- Location: /sub-9079/ses-1/dwi/sub-9079_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9079_ses-1_dwi.bvec
- Location: /sub-9079/ses-1/dwi/sub-9079_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9079_ses-1_dwi.json
- Location: /sub-9079/ses-1/dwi/sub-9079_ses-1_dwi.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_dwi.json
-
- Type: Warning
- File: sub-9079_ses-1_run-1_magnitude1.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9079_ses-1_run-1_magnitude2.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9079_ses-1_run-1_phase1.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9079_ses-1_run-1_phase2.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9079_ses-1_run-2_magnitude1.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9079_ses-1_run-2_magnitude2.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9079_ses-1_run-2_phase1.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9079_ses-1_run-2_phase2.json
- Location: /sub-9079/ses-1/fmap/sub-9079_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9079_ses-2_T2w.json
- Location: /sub-9079/ses-2/anat/sub-9079_ses-2_T2w.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-2_T2w.json
-
- Type: Warning
- File: sub-9079_ses-2_task-faces_run-1_bold.json
- Location: /sub-9079/ses-2/func/sub-9079_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9079_ses-2_task-faces_run-2_bold.json
- Location: /sub-9079/ses-2/func/sub-9079_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9079, but is present for at least one other subject.
- Evidence: Subject: sub-9079; Missing file: sub-9079_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9080_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9080/ses-1/beh/sub-9080_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9080_ses-1_dwi.bval
- Location: /sub-9080/ses-1/dwi/sub-9080_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9080_ses-1_dwi.bvec
- Location: /sub-9080/ses-1/dwi/sub-9080_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9080_ses-1_dwi.json
- Location: /sub-9080/ses-1/dwi/sub-9080_ses-1_dwi.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_dwi.json
-
- Type: Warning
- File: sub-9080_ses-1_run-1_magnitude1.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9080_ses-1_run-1_magnitude2.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9080_ses-1_run-1_phase1.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9080_ses-1_run-1_phase2.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9080_ses-1_run-2_magnitude1.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9080_ses-1_run-2_magnitude2.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9080_ses-1_run-2_phase1.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9080_ses-1_run-2_phase2.json
- Location: /sub-9080/ses-1/fmap/sub-9080_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9080_ses-2_T2w.json
- Location: /sub-9080/ses-2/anat/sub-9080_ses-2_T2w.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-2_T2w.json
-
- Type: Warning
- File: sub-9080_ses-2_task-faces_run-1_bold.json
- Location: /sub-9080/ses-2/func/sub-9080_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9080_ses-2_task-faces_run-2_bold.json
- Location: /sub-9080/ses-2/func/sub-9080_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9080, but is present for at least one other subject.
- Evidence: Subject: sub-9080; Missing file: sub-9080_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9081_ses-1_dwi.bval
- Location: /sub-9081/ses-1/dwi/sub-9081_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9081_ses-1_dwi.bvec
- Location: /sub-9081/ses-1/dwi/sub-9081_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9081_ses-1_dwi.json
- Location: /sub-9081/ses-1/dwi/sub-9081_ses-1_dwi.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_dwi.json
-
- Type: Warning
- File: sub-9081_ses-1_run-1_magnitude1.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9081_ses-1_run-1_magnitude2.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9081_ses-1_run-1_phase1.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9081_ses-1_run-1_phase2.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9081_ses-1_run-2_magnitude1.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9081_ses-1_run-2_magnitude2.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9081_ses-1_run-2_phase1.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9081_ses-1_run-2_phase2.json
- Location: /sub-9081/ses-1/fmap/sub-9081_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9081_ses-2_T2w.json
- Location: /sub-9081/ses-2/anat/sub-9081_ses-2_T2w.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-2_T2w.json
-
- Type: Warning
- File: sub-9081_ses-2_task-faces_run-1_bold.json
- Location: /sub-9081/ses-2/func/sub-9081_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9081_ses-2_task-faces_run-2_bold.json
- Location: /sub-9081/ses-2/func/sub-9081_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9081, but is present for at least one other subject.
- Evidence: Subject: sub-9081; Missing file: sub-9081_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9082_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9082/ses-1/beh/sub-9082_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9082_ses-1_dwi.bval
- Location: /sub-9082/ses-1/dwi/sub-9082_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9082_ses-1_dwi.bvec
- Location: /sub-9082/ses-1/dwi/sub-9082_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9082_ses-1_dwi.json
- Location: /sub-9082/ses-1/dwi/sub-9082_ses-1_dwi.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_dwi.json
-
- Type: Warning
- File: sub-9082_ses-1_run-1_magnitude1.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9082_ses-1_run-1_magnitude2.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9082_ses-1_run-1_phase1.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9082_ses-1_run-1_phase2.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9082_ses-1_run-2_magnitude1.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9082_ses-1_run-2_magnitude2.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9082_ses-1_run-2_phase1.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9082_ses-1_run-2_phase2.json
- Location: /sub-9082/ses-1/fmap/sub-9082_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9082_ses-1_task-faces_physio.json
- Location: /sub-9082/ses-1/func/sub-9082_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9082_ses-2_T2w.json
- Location: /sub-9082/ses-2/anat/sub-9082_ses-2_T2w.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-2_T2w.json
-
- Type: Warning
- File: sub-9082_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9082/ses-2/beh/sub-9082_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9082_ses-2_task-faces_physio.json
- Location: /sub-9082/ses-2/func/sub-9082_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9082_ses-2_task-faces_run-1_bold.json
- Location: /sub-9082/ses-2/func/sub-9082_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9082_ses-2_task-faces_run-2_bold.json
- Location: /sub-9082/ses-2/func/sub-9082_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9082, but is present for at least one other subject.
- Evidence: Subject: sub-9082; Missing file: sub-9082_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9083_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9083/ses-1/beh/sub-9083_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9083_ses-1_dwi.bval
- Location: /sub-9083/ses-1/dwi/sub-9083_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9083_ses-1_dwi.bvec
- Location: /sub-9083/ses-1/dwi/sub-9083_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9083_ses-1_dwi.json
- Location: /sub-9083/ses-1/dwi/sub-9083_ses-1_dwi.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_dwi.json
-
- Type: Warning
- File: sub-9083_ses-1_run-1_magnitude1.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9083_ses-1_run-1_magnitude2.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9083_ses-1_run-1_phase1.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9083_ses-1_run-1_phase2.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9083_ses-1_run-2_magnitude1.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9083_ses-1_run-2_magnitude2.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9083_ses-1_run-2_phase1.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9083_ses-1_run-2_phase2.json
- Location: /sub-9083/ses-1/fmap/sub-9083_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9083_ses-1_task-faces_physio.json
- Location: /sub-9083/ses-1/func/sub-9083_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9083_ses-2_T2w.json
- Location: /sub-9083/ses-2/anat/sub-9083_ses-2_T2w.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-2_T2w.json
-
- Type: Warning
- File: sub-9083_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9083/ses-2/beh/sub-9083_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9083_ses-2_task-faces_physio.json
- Location: /sub-9083/ses-2/func/sub-9083_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9083_ses-2_task-faces_run-1_bold.json
- Location: /sub-9083/ses-2/func/sub-9083_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9083_ses-2_task-faces_run-2_bold.json
- Location: /sub-9083/ses-2/func/sub-9083_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9083, but is present for at least one other subject.
- Evidence: Subject: sub-9083; Missing file: sub-9083_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9084_ses-1_dwi.bval
- Location: /sub-9084/ses-1/dwi/sub-9084_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9084_ses-1_dwi.bvec
- Location: /sub-9084/ses-1/dwi/sub-9084_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9084_ses-1_dwi.json
- Location: /sub-9084/ses-1/dwi/sub-9084_ses-1_dwi.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_dwi.json
-
- Type: Warning
- File: sub-9084_ses-1_run-1_magnitude1.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9084_ses-1_run-1_magnitude2.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9084_ses-1_run-1_phase1.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9084_ses-1_run-1_phase2.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9084_ses-1_run-2_magnitude1.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9084_ses-1_run-2_magnitude2.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9084_ses-1_run-2_phase1.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9084_ses-1_run-2_phase2.json
- Location: /sub-9084/ses-1/fmap/sub-9084_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9084_ses-2_T2w.json
- Location: /sub-9084/ses-2/anat/sub-9084_ses-2_T2w.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-2_T2w.json
-
- Type: Warning
- File: sub-9084_ses-2_task-faces_run-1_bold.json
- Location: /sub-9084/ses-2/func/sub-9084_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9084_ses-2_task-faces_run-2_bold.json
- Location: /sub-9084/ses-2/func/sub-9084_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9084, but is present for at least one other subject.
- Evidence: Subject: sub-9084; Missing file: sub-9084_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9085_ses-1_dwi.bval
- Location: /sub-9085/ses-1/dwi/sub-9085_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9085_ses-1_dwi.bvec
- Location: /sub-9085/ses-1/dwi/sub-9085_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9085_ses-1_dwi.json
- Location: /sub-9085/ses-1/dwi/sub-9085_ses-1_dwi.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_dwi.json
-
- Type: Warning
- File: sub-9085_ses-1_run-1_magnitude1.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9085_ses-1_run-1_magnitude2.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9085_ses-1_run-1_phase1.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9085_ses-1_run-1_phase2.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9085_ses-1_run-2_magnitude1.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9085_ses-1_run-2_magnitude2.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9085_ses-1_run-2_phase1.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9085_ses-1_run-2_phase2.json
- Location: /sub-9085/ses-1/fmap/sub-9085_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9085_ses-2_T2w.json
- Location: /sub-9085/ses-2/anat/sub-9085_ses-2_T2w.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-2_T2w.json
-
- Type: Warning
- File: sub-9085_ses-2_task-faces_run-1_bold.json
- Location: /sub-9085/ses-2/func/sub-9085_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9085_ses-2_task-faces_run-2_bold.json
- Location: /sub-9085/ses-2/func/sub-9085_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9085, but is present for at least one other subject.
- Evidence: Subject: sub-9085; Missing file: sub-9085_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9086_ses-1_dwi.bval
- Location: /sub-9086/ses-1/dwi/sub-9086_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9086_ses-1_dwi.bvec
- Location: /sub-9086/ses-1/dwi/sub-9086_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9086_ses-1_dwi.json
- Location: /sub-9086/ses-1/dwi/sub-9086_ses-1_dwi.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_dwi.json
-
- Type: Warning
- File: sub-9086_ses-1_run-1_magnitude1.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9086_ses-1_run-1_magnitude2.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9086_ses-1_run-1_phase1.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9086_ses-1_run-1_phase2.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9086_ses-1_run-2_magnitude1.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9086_ses-1_run-2_magnitude2.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9086_ses-1_run-2_phase1.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9086_ses-1_run-2_phase2.json
- Location: /sub-9086/ses-1/fmap/sub-9086_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9086_ses-2_T2w.json
- Location: /sub-9086/ses-2/anat/sub-9086_ses-2_T2w.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-2_T2w.json
-
- Type: Warning
- File: sub-9086_ses-2_task-faces_run-1_bold.json
- Location: /sub-9086/ses-2/func/sub-9086_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9086_ses-2_task-faces_run-2_bold.json
- Location: /sub-9086/ses-2/func/sub-9086_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9086, but is present for at least one other subject.
- Evidence: Subject: sub-9086; Missing file: sub-9086_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9087_ses-1_dwi.bval
- Location: /sub-9087/ses-1/dwi/sub-9087_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9087_ses-1_dwi.bvec
- Location: /sub-9087/ses-1/dwi/sub-9087_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9087_ses-1_dwi.json
- Location: /sub-9087/ses-1/dwi/sub-9087_ses-1_dwi.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_dwi.json
-
- Type: Warning
- File: sub-9087_ses-1_run-1_magnitude1.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9087_ses-1_run-1_magnitude2.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9087_ses-1_run-1_phase1.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9087_ses-1_run-1_phase2.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9087_ses-1_run-2_magnitude1.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9087_ses-1_run-2_magnitude2.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9087_ses-1_run-2_phase1.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9087_ses-1_run-2_phase2.json
- Location: /sub-9087/ses-1/fmap/sub-9087_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9087_ses-2_T2w.json
- Location: /sub-9087/ses-2/anat/sub-9087_ses-2_T2w.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-2_T2w.json
-
- Type: Warning
- File: sub-9087_ses-2_task-faces_run-1_bold.json
- Location: /sub-9087/ses-2/func/sub-9087_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9087_ses-2_task-faces_run-2_bold.json
- Location: /sub-9087/ses-2/func/sub-9087_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9087, but is present for at least one other subject.
- Evidence: Subject: sub-9087; Missing file: sub-9087_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9088_ses-1_dwi.bval
- Location: /sub-9088/ses-1/dwi/sub-9088_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9088_ses-1_dwi.bvec
- Location: /sub-9088/ses-1/dwi/sub-9088_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9088_ses-1_dwi.json
- Location: /sub-9088/ses-1/dwi/sub-9088_ses-1_dwi.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_dwi.json
-
- Type: Warning
- File: sub-9088_ses-1_run-1_magnitude1.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9088_ses-1_run-1_magnitude2.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9088_ses-1_run-1_phase1.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9088_ses-1_run-1_phase2.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9088_ses-1_run-2_magnitude1.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9088_ses-1_run-2_magnitude2.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9088_ses-1_run-2_phase1.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9088_ses-1_run-2_phase2.json
- Location: /sub-9088/ses-1/fmap/sub-9088_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9088_ses-2_T2w.json
- Location: /sub-9088/ses-2/anat/sub-9088_ses-2_T2w.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-2_T2w.json
-
- Type: Warning
- File: sub-9088_ses-2_task-faces_run-1_bold.json
- Location: /sub-9088/ses-2/func/sub-9088_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9088_ses-2_task-faces_run-2_bold.json
- Location: /sub-9088/ses-2/func/sub-9088_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9088, but is present for at least one other subject.
- Evidence: Subject: sub-9088; Missing file: sub-9088_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9089_ses-1_dwi.bval
- Location: /sub-9089/ses-1/dwi/sub-9089_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9089_ses-1_dwi.bvec
- Location: /sub-9089/ses-1/dwi/sub-9089_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9089_ses-1_dwi.json
- Location: /sub-9089/ses-1/dwi/sub-9089_ses-1_dwi.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_dwi.json
-
- Type: Warning
- File: sub-9089_ses-1_run-1_magnitude1.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9089_ses-1_run-1_magnitude2.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9089_ses-1_run-1_phase1.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9089_ses-1_run-1_phase2.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9089_ses-1_run-2_magnitude1.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9089_ses-1_run-2_magnitude2.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9089_ses-1_run-2_phase1.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9089_ses-1_run-2_phase2.json
- Location: /sub-9089/ses-1/fmap/sub-9089_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9089_ses-2_T2w.json
- Location: /sub-9089/ses-2/anat/sub-9089_ses-2_T2w.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-2_T2w.json
-
- Type: Warning
- File: sub-9089_ses-2_task-faces_run-1_bold.json
- Location: /sub-9089/ses-2/func/sub-9089_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9089_ses-2_task-faces_run-2_bold.json
- Location: /sub-9089/ses-2/func/sub-9089_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9089, but is present for at least one other subject.
- Evidence: Subject: sub-9089; Missing file: sub-9089_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9090_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9090/ses-1/beh/sub-9090_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9090_ses-1_dwi.bval
- Location: /sub-9090/ses-1/dwi/sub-9090_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9090_ses-1_dwi.bvec
- Location: /sub-9090/ses-1/dwi/sub-9090_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9090_ses-1_dwi.json
- Location: /sub-9090/ses-1/dwi/sub-9090_ses-1_dwi.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_dwi.json
-
- Type: Warning
- File: sub-9090_ses-1_run-1_magnitude1.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9090_ses-1_run-1_magnitude2.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9090_ses-1_run-1_phase1.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9090_ses-1_run-1_phase2.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9090_ses-1_run-2_magnitude1.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9090_ses-1_run-2_magnitude2.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9090_ses-1_run-2_phase1.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9090_ses-1_run-2_phase2.json
- Location: /sub-9090/ses-1/fmap/sub-9090_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9090_ses-1_task-faces_physio.json
- Location: /sub-9090/ses-1/func/sub-9090_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9090_ses-2_T2w.json
- Location: /sub-9090/ses-2/anat/sub-9090_ses-2_T2w.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-2_T2w.json
-
- Type: Warning
- File: sub-9090_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9090/ses-2/beh/sub-9090_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9090_ses-2_task-faces_physio.json
- Location: /sub-9090/ses-2/func/sub-9090_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9090_ses-2_task-faces_run-1_bold.json
- Location: /sub-9090/ses-2/func/sub-9090_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9090_ses-2_task-faces_run-2_bold.json
- Location: /sub-9090/ses-2/func/sub-9090_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9090, but is present for at least one other subject.
- Evidence: Subject: sub-9090; Missing file: sub-9090_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9091_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9091/ses-1/beh/sub-9091_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9091_ses-1_dwi.bval
- Location: /sub-9091/ses-1/dwi/sub-9091_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9091_ses-1_dwi.bvec
- Location: /sub-9091/ses-1/dwi/sub-9091_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9091_ses-1_dwi.json
- Location: /sub-9091/ses-1/dwi/sub-9091_ses-1_dwi.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_dwi.json
-
- Type: Warning
- File: sub-9091_ses-1_run-1_magnitude1.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9091_ses-1_run-1_magnitude2.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9091_ses-1_run-1_phase1.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9091_ses-1_run-1_phase2.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9091_ses-1_run-2_magnitude1.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9091_ses-1_run-2_magnitude2.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9091_ses-1_run-2_phase1.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9091_ses-1_run-2_phase2.json
- Location: /sub-9091/ses-1/fmap/sub-9091_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9091_ses-1_task-faces_physio.json
- Location: /sub-9091/ses-1/func/sub-9091_ses-1_task-faces_physio.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-1_task-faces_physio.json
-
- Type: Warning
- File: sub-9091_ses-2_T2w.json
- Location: /sub-9091/ses-2/anat/sub-9091_ses-2_T2w.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-2_T2w.json
-
- Type: Warning
- File: sub-9091_ses-2_task-workingmemorytest_events.tsv
- Location: /sub-9091/ses-2/beh/sub-9091_ses-2_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-2_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9091_ses-2_task-faces_physio.json
- Location: /sub-9091/ses-2/func/sub-9091_ses-2_task-faces_physio.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-2_task-faces_physio.json
-
- Type: Warning
- File: sub-9091_ses-2_task-faces_run-1_bold.json
- Location: /sub-9091/ses-2/func/sub-9091_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9091_ses-2_task-faces_run-2_bold.json
- Location: /sub-9091/ses-2/func/sub-9091_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9091, but is present for at least one other subject.
- Evidence: Subject: sub-9091; Missing file: sub-9091_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9092_ses-1_dwi.bval
- Location: /sub-9092/ses-1/dwi/sub-9092_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9092_ses-1_dwi.bvec
- Location: /sub-9092/ses-1/dwi/sub-9092_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9092_ses-1_dwi.json
- Location: /sub-9092/ses-1/dwi/sub-9092_ses-1_dwi.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_dwi.json
-
- Type: Warning
- File: sub-9092_ses-1_run-1_magnitude1.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9092_ses-1_run-1_magnitude2.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9092_ses-1_run-1_phase1.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9092_ses-1_run-1_phase2.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9092_ses-1_run-2_magnitude1.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9092_ses-1_run-2_magnitude2.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9092_ses-1_run-2_phase1.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9092_ses-1_run-2_phase2.json
- Location: /sub-9092/ses-1/fmap/sub-9092_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9092_ses-2_T2w.json
- Location: /sub-9092/ses-2/anat/sub-9092_ses-2_T2w.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-2_T2w.json
-
- Type: Warning
- File: sub-9092_ses-2_task-faces_run-1_bold.json
- Location: /sub-9092/ses-2/func/sub-9092_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9092_ses-2_task-faces_run-2_bold.json
- Location: /sub-9092/ses-2/func/sub-9092_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9092, but is present for at least one other subject.
- Evidence: Subject: sub-9092; Missing file: sub-9092_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9093_ses-1_dwi.bval
- Location: /sub-9093/ses-1/dwi/sub-9093_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9093_ses-1_dwi.bvec
- Location: /sub-9093/ses-1/dwi/sub-9093_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9093_ses-1_dwi.json
- Location: /sub-9093/ses-1/dwi/sub-9093_ses-1_dwi.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_dwi.json
-
- Type: Warning
- File: sub-9093_ses-1_run-1_magnitude1.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9093_ses-1_run-1_magnitude2.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9093_ses-1_run-1_phase1.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9093_ses-1_run-1_phase2.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9093_ses-1_run-2_magnitude1.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9093_ses-1_run-2_magnitude2.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9093_ses-1_run-2_phase1.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9093_ses-1_run-2_phase2.json
- Location: /sub-9093/ses-1/fmap/sub-9093_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9093_ses-2_T2w.json
- Location: /sub-9093/ses-2/anat/sub-9093_ses-2_T2w.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-2_T2w.json
-
- Type: Warning
- File: sub-9093_ses-2_task-faces_run-1_bold.json
- Location: /sub-9093/ses-2/func/sub-9093_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9093_ses-2_task-faces_run-2_bold.json
- Location: /sub-9093/ses-2/func/sub-9093_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9093, but is present for at least one other subject.
- Evidence: Subject: sub-9093; Missing file: sub-9093_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9094_ses-1_dwi.bval
- Location: /sub-9094/ses-1/dwi/sub-9094_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9094_ses-1_dwi.bvec
- Location: /sub-9094/ses-1/dwi/sub-9094_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9094_ses-1_dwi.json
- Location: /sub-9094/ses-1/dwi/sub-9094_ses-1_dwi.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_dwi.json
-
- Type: Warning
- File: sub-9094_ses-1_run-1_magnitude1.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9094_ses-1_run-1_magnitude2.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9094_ses-1_run-1_phase1.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9094_ses-1_run-1_phase2.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9094_ses-1_run-2_magnitude1.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9094_ses-1_run-2_magnitude2.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9094_ses-1_run-2_phase1.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9094_ses-1_run-2_phase2.json
- Location: /sub-9094/ses-1/fmap/sub-9094_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9094_ses-2_T2w.json
- Location: /sub-9094/ses-2/anat/sub-9094_ses-2_T2w.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-2_T2w.json
-
- Type: Warning
- File: sub-9094_ses-2_task-faces_run-1_bold.json
- Location: /sub-9094/ses-2/func/sub-9094_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9094_ses-2_task-faces_run-2_bold.json
- Location: /sub-9094/ses-2/func/sub-9094_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9094, but is present for at least one other subject.
- Evidence: Subject: sub-9094; Missing file: sub-9094_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9095_ses-1_dwi.bval
- Location: /sub-9095/ses-1/dwi/sub-9095_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9095_ses-1_dwi.bvec
- Location: /sub-9095/ses-1/dwi/sub-9095_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9095_ses-1_dwi.json
- Location: /sub-9095/ses-1/dwi/sub-9095_ses-1_dwi.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_dwi.json
-
- Type: Warning
- File: sub-9095_ses-1_run-1_magnitude1.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9095_ses-1_run-1_magnitude2.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9095_ses-1_run-1_phase1.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9095_ses-1_run-1_phase2.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9095_ses-1_run-2_magnitude1.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9095_ses-1_run-2_magnitude2.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9095_ses-1_run-2_phase1.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9095_ses-1_run-2_phase2.json
- Location: /sub-9095/ses-1/fmap/sub-9095_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9095, but is present for at least one other subject.
- Evidence: Subject: sub-9095; Missing file: sub-9095_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9096_ses-1_dwi.bval
- Location: /sub-9096/ses-1/dwi/sub-9096_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9096_ses-1_dwi.bvec
- Location: /sub-9096/ses-1/dwi/sub-9096_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9096_ses-1_dwi.json
- Location: /sub-9096/ses-1/dwi/sub-9096_ses-1_dwi.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_dwi.json
-
- Type: Warning
- File: sub-9096_ses-1_magnitude1.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9096_ses-1_magnitude2.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9096_ses-1_phase1.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_phase1.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_phase1.json
-
- Type: Warning
- File: sub-9096_ses-1_phase2.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_phase2.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_phase2.json
-
- Type: Warning
- File: sub-9096_ses-1_run-1_magnitude1.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9096_ses-1_run-1_magnitude2.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9096_ses-1_run-1_phase1.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9096_ses-1_run-1_phase2.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9096_ses-1_run-2_magnitude1.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9096_ses-1_run-2_magnitude2.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9096_ses-1_run-2_phase1.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9096_ses-1_run-2_phase2.json
- Location: /sub-9096/ses-1/fmap/sub-9096_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9096_ses-2_T2w.json
- Location: /sub-9096/ses-2/anat/sub-9096_ses-2_T2w.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-2_T2w.json
-
- Type: Warning
- File: sub-9096_ses-2_task-faces_run-1_bold.json
- Location: /sub-9096/ses-2/func/sub-9096_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9096_ses-2_task-faces_run-2_bold.json
- Location: /sub-9096/ses-2/func/sub-9096_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9096, but is present for at least one other subject.
- Evidence: Subject: sub-9096; Missing file: sub-9096_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9098_ses-1_dwi.bval
- Location: /sub-9098/ses-1/dwi/sub-9098_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9098_ses-1_dwi.bvec
- Location: /sub-9098/ses-1/dwi/sub-9098_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9098_ses-1_dwi.json
- Location: /sub-9098/ses-1/dwi/sub-9098_ses-1_dwi.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_dwi.json
-
- Type: Warning
- File: sub-9098_ses-1_run-1_magnitude1.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9098_ses-1_run-1_magnitude2.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9098_ses-1_run-1_phase1.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9098_ses-1_run-1_phase2.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9098_ses-1_run-2_magnitude1.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9098_ses-1_run-2_magnitude2.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9098_ses-1_run-2_phase1.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9098_ses-1_run-2_phase2.json
- Location: /sub-9098/ses-1/fmap/sub-9098_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9098_ses-2_T2w.json
- Location: /sub-9098/ses-2/anat/sub-9098_ses-2_T2w.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-2_T2w.json
-
- Type: Warning
- File: sub-9098_ses-2_task-faces_run-1_bold.json
- Location: /sub-9098/ses-2/func/sub-9098_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9098_ses-2_task-faces_run-2_bold.json
- Location: /sub-9098/ses-2/func/sub-9098_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9098, but is present for at least one other subject.
- Evidence: Subject: sub-9098; Missing file: sub-9098_ses-2_task-faces_run-2_bold.json
-
- Type: Warning
- File: sub-9100_ses-1_task-workingmemorytest_events.tsv
- Location: /sub-9100/ses-1/beh/sub-9100_ses-1_task-workingmemorytest_events.tsv
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_task-workingmemorytest_events.tsv
-
- Type: Warning
- File: sub-9100_ses-1_dwi.bval
- Location: /sub-9100/ses-1/dwi/sub-9100_ses-1_dwi.bval
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_dwi.bval
-
- Type: Warning
- File: sub-9100_ses-1_dwi.bvec
- Location: /sub-9100/ses-1/dwi/sub-9100_ses-1_dwi.bvec
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_dwi.bvec
-
- Type: Warning
- File: sub-9100_ses-1_dwi.json
- Location: /sub-9100/ses-1/dwi/sub-9100_ses-1_dwi.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_dwi.json
-
- Type: Warning
- File: sub-9100_ses-1_magnitude1.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_magnitude1.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_magnitude1.json
-
- Type: Warning
- File: sub-9100_ses-1_magnitude2.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_magnitude2.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_magnitude2.json
-
- Type: Warning
- File: sub-9100_ses-1_phase1.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_phase1.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_phase1.json
-
- Type: Warning
- File: sub-9100_ses-1_phase2.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_phase2.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_phase2.json
-
- Type: Warning
- File: sub-9100_ses-1_run-1_magnitude1.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-1_magnitude1.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-1_magnitude1.json
-
- Type: Warning
- File: sub-9100_ses-1_run-1_magnitude2.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-1_magnitude2.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-1_magnitude2.json
-
- Type: Warning
- File: sub-9100_ses-1_run-1_phase1.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-1_phase1.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-1_phase1.json
-
- Type: Warning
- File: sub-9100_ses-1_run-1_phase2.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-1_phase2.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-1_phase2.json
-
- Type: Warning
- File: sub-9100_ses-1_run-2_magnitude1.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-2_magnitude1.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-2_magnitude1.json
-
- Type: Warning
- File: sub-9100_ses-1_run-2_magnitude2.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-2_magnitude2.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-2_magnitude2.json
-
- Type: Warning
- File: sub-9100_ses-1_run-2_phase1.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-2_phase1.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-2_phase1.json
-
- Type: Warning
- File: sub-9100_ses-1_run-2_phase2.json
- Location: /sub-9100/ses-1/fmap/sub-9100_ses-1_run-2_phase2.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-1_run-2_phase2.json
-
- Type: Warning
- File: sub-9100_ses-2_T2w.json
- Location: /sub-9100/ses-2/anat/sub-9100_ses-2_T2w.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-2_T2w.json
-
- Type: Warning
- File: sub-9100_ses-2_task-faces_run-1_bold.json
- Location: /sub-9100/ses-2/func/sub-9100_ses-2_task-faces_run-1_bold.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-2_task-faces_run-1_bold.json
-
- Type: Warning
- File: sub-9100_ses-2_task-faces_run-2_bold.json
- Location: /sub-9100/ses-2/func/sub-9100_ses-2_task-faces_run-2_bold.json
- Reason: This file is missing for subject sub-9100, but is present for at least one other subject.
- Evidence: Subject: sub-9100; Missing file: sub-9100_ses-2_task-faces_run-2_bold.json
-
-
-======================================================
-
-
-File Path: Tabular file contains custom columns not described in a data dictionary
-
- Type: Warning
- File: participants.tsv
- Location: ds000201/participants.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: PAn/aS_Positive, PAn/aS_Negative, PAn/aS_Positive_byScanner.x, PAn/aS_Negative_byScanner.x, PAn/aS_Positive_byScanner.y, PAn/aS_Negative_byScanner.y not defined, please define in: /participants.json
-
- Type: Warning
- File: sub-9001_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-arrows_events.json,/sub-9001/ses-1/sub-9001_ses-1_events.json,/sub-9001/ses-1/sub-9001_ses-1_task-arrows_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9001_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-faces_events.json,/sub-9001/ses-1/sub-9001_ses-1_events.json,/sub-9001/ses-1/sub-9001_ses-1_task-faces_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9001_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-hands_events.json,/sub-9001/ses-1/sub-9001_ses-1_events.json,/sub-9001/ses-1/sub-9001_ses-1_task-hands_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9001_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9001/ses-1/func/sub-9001_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-sleepiness_events.json,/sub-9001/ses-1/sub-9001_ses-1_events.json,/sub-9001/ses-1/sub-9001_ses-1_task-sleepiness_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_events.json,/sub-9001/ses-1/func/sub-9001_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9001_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-arrows_events.json,/sub-9001/ses-2/sub-9001_ses-2_events.json,/sub-9001/ses-2/sub-9001_ses-2_task-arrows_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9001_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-faces_events.json,/sub-9001/ses-2/sub-9001_ses-2_events.json,/sub-9001/ses-2/sub-9001_ses-2_task-faces_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9001_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-hands_events.json,/sub-9001/ses-2/sub-9001_ses-2_events.json,/sub-9001/ses-2/sub-9001_ses-2_task-hands_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9001_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9001/ses-2/func/sub-9001_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9001/sub-9001_events.json,/sub-9001/sub-9001_task-sleepiness_events.json,/sub-9001/ses-2/sub-9001_ses-2_events.json,/sub-9001/ses-2/sub-9001_ses-2_task-sleepiness_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_events.json,/sub-9001/ses-2/func/sub-9001_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9001_sessions.tsv
- Location: ds000201/sub-9001/sub-9001_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9001/sub-9001_sessions.json
-
- Type: Warning
- File: sub-9002_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9002/ses-1/beh/sub-9002_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-workingmemorytest_events.json,/sub-9002/ses-1/sub-9002_ses-1_events.json,/sub-9002/ses-1/sub-9002_ses-1_task-workingmemorytest_events.json,/sub-9002/ses-1/beh/sub-9002_ses-1_events.json,/sub-9002/ses-1/beh/sub-9002_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9002_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-arrows_events.json,/sub-9002/ses-1/sub-9002_ses-1_events.json,/sub-9002/ses-1/sub-9002_ses-1_task-arrows_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9002_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-faces_events.json,/sub-9002/ses-1/sub-9002_ses-1_events.json,/sub-9002/ses-1/sub-9002_ses-1_task-faces_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9002_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-hands_events.json,/sub-9002/ses-1/sub-9002_ses-1_events.json,/sub-9002/ses-1/sub-9002_ses-1_task-hands_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9002_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9002/ses-1/func/sub-9002_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-sleepiness_events.json,/sub-9002/ses-1/sub-9002_ses-1_events.json,/sub-9002/ses-1/sub-9002_ses-1_task-sleepiness_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_events.json,/sub-9002/ses-1/func/sub-9002_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9002_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9002/ses-2/beh/sub-9002_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-workingmemorytest_events.json,/sub-9002/ses-2/sub-9002_ses-2_events.json,/sub-9002/ses-2/sub-9002_ses-2_task-workingmemorytest_events.json,/sub-9002/ses-2/beh/sub-9002_ses-2_events.json,/sub-9002/ses-2/beh/sub-9002_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9002_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-arrows_events.json,/sub-9002/ses-2/sub-9002_ses-2_events.json,/sub-9002/ses-2/sub-9002_ses-2_task-arrows_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9002_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-faces_events.json,/sub-9002/ses-2/sub-9002_ses-2_events.json,/sub-9002/ses-2/sub-9002_ses-2_task-faces_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9002_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-hands_events.json,/sub-9002/ses-2/sub-9002_ses-2_events.json,/sub-9002/ses-2/sub-9002_ses-2_task-hands_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9002_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9002/ses-2/func/sub-9002_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9002/sub-9002_events.json,/sub-9002/sub-9002_task-sleepiness_events.json,/sub-9002/ses-2/sub-9002_ses-2_events.json,/sub-9002/ses-2/sub-9002_ses-2_task-sleepiness_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_events.json,/sub-9002/ses-2/func/sub-9002_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9002_sessions.tsv
- Location: ds000201/sub-9002/sub-9002_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9002/sub-9002_sessions.json
-
- Type: Warning
- File: sub-9003_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9003/ses-1/beh/sub-9003_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-workingmemorytest_events.json,/sub-9003/ses-1/sub-9003_ses-1_events.json,/sub-9003/ses-1/sub-9003_ses-1_task-workingmemorytest_events.json,/sub-9003/ses-1/beh/sub-9003_ses-1_events.json,/sub-9003/ses-1/beh/sub-9003_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9003_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-arrows_events.json,/sub-9003/ses-1/sub-9003_ses-1_events.json,/sub-9003/ses-1/sub-9003_ses-1_task-arrows_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9003_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-faces_events.json,/sub-9003/ses-1/sub-9003_ses-1_events.json,/sub-9003/ses-1/sub-9003_ses-1_task-faces_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9003_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-hands_events.json,/sub-9003/ses-1/sub-9003_ses-1_events.json,/sub-9003/ses-1/sub-9003_ses-1_task-hands_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9003_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9003/ses-1/func/sub-9003_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-sleepiness_events.json,/sub-9003/ses-1/sub-9003_ses-1_events.json,/sub-9003/ses-1/sub-9003_ses-1_task-sleepiness_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_events.json,/sub-9003/ses-1/func/sub-9003_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9003_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9003/ses-2/beh/sub-9003_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-workingmemorytest_events.json,/sub-9003/ses-2/sub-9003_ses-2_events.json,/sub-9003/ses-2/sub-9003_ses-2_task-workingmemorytest_events.json,/sub-9003/ses-2/beh/sub-9003_ses-2_events.json,/sub-9003/ses-2/beh/sub-9003_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9003_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-arrows_events.json,/sub-9003/ses-2/sub-9003_ses-2_events.json,/sub-9003/ses-2/sub-9003_ses-2_task-arrows_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9003_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-faces_events.json,/sub-9003/ses-2/sub-9003_ses-2_events.json,/sub-9003/ses-2/sub-9003_ses-2_task-faces_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9003_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-hands_events.json,/sub-9003/ses-2/sub-9003_ses-2_events.json,/sub-9003/ses-2/sub-9003_ses-2_task-hands_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9003_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9003/ses-2/func/sub-9003_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9003/sub-9003_events.json,/sub-9003/sub-9003_task-sleepiness_events.json,/sub-9003/ses-2/sub-9003_ses-2_events.json,/sub-9003/ses-2/sub-9003_ses-2_task-sleepiness_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_events.json,/sub-9003/ses-2/func/sub-9003_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9003_sessions.tsv
- Location: ds000201/sub-9003/sub-9003_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9003/sub-9003_sessions.json
-
- Type: Warning
- File: sub-9004_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9004/ses-1/beh/sub-9004_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-workingmemorytest_events.json,/sub-9004/ses-1/sub-9004_ses-1_events.json,/sub-9004/ses-1/sub-9004_ses-1_task-workingmemorytest_events.json,/sub-9004/ses-1/beh/sub-9004_ses-1_events.json,/sub-9004/ses-1/beh/sub-9004_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9004_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-arrows_events.json,/sub-9004/ses-1/sub-9004_ses-1_events.json,/sub-9004/ses-1/sub-9004_ses-1_task-arrows_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9004_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-faces_events.json,/sub-9004/ses-1/sub-9004_ses-1_events.json,/sub-9004/ses-1/sub-9004_ses-1_task-faces_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9004_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-hands_events.json,/sub-9004/ses-1/sub-9004_ses-1_events.json,/sub-9004/ses-1/sub-9004_ses-1_task-hands_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9004_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9004/ses-1/func/sub-9004_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-sleepiness_events.json,/sub-9004/ses-1/sub-9004_ses-1_events.json,/sub-9004/ses-1/sub-9004_ses-1_task-sleepiness_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_events.json,/sub-9004/ses-1/func/sub-9004_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9004_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9004/ses-2/beh/sub-9004_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-workingmemorytest_events.json,/sub-9004/ses-2/sub-9004_ses-2_events.json,/sub-9004/ses-2/sub-9004_ses-2_task-workingmemorytest_events.json,/sub-9004/ses-2/beh/sub-9004_ses-2_events.json,/sub-9004/ses-2/beh/sub-9004_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9004_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-arrows_events.json,/sub-9004/ses-2/sub-9004_ses-2_events.json,/sub-9004/ses-2/sub-9004_ses-2_task-arrows_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9004_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-faces_events.json,/sub-9004/ses-2/sub-9004_ses-2_events.json,/sub-9004/ses-2/sub-9004_ses-2_task-faces_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9004_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-hands_events.json,/sub-9004/ses-2/sub-9004_ses-2_events.json,/sub-9004/ses-2/sub-9004_ses-2_task-hands_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9004_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9004/ses-2/func/sub-9004_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9004/sub-9004_events.json,/sub-9004/sub-9004_task-sleepiness_events.json,/sub-9004/ses-2/sub-9004_ses-2_events.json,/sub-9004/ses-2/sub-9004_ses-2_task-sleepiness_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_events.json,/sub-9004/ses-2/func/sub-9004_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9004_sessions.tsv
- Location: ds000201/sub-9004/sub-9004_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9004/sub-9004_sessions.json
-
- Type: Warning
- File: sub-9005_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9005/ses-1/beh/sub-9005_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-workingmemorytest_events.json,/sub-9005/ses-1/sub-9005_ses-1_events.json,/sub-9005/ses-1/sub-9005_ses-1_task-workingmemorytest_events.json,/sub-9005/ses-1/beh/sub-9005_ses-1_events.json,/sub-9005/ses-1/beh/sub-9005_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9005_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-arrows_events.json,/sub-9005/ses-1/sub-9005_ses-1_events.json,/sub-9005/ses-1/sub-9005_ses-1_task-arrows_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9005_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-faces_events.json,/sub-9005/ses-1/sub-9005_ses-1_events.json,/sub-9005/ses-1/sub-9005_ses-1_task-faces_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9005_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-hands_events.json,/sub-9005/ses-1/sub-9005_ses-1_events.json,/sub-9005/ses-1/sub-9005_ses-1_task-hands_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9005_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9005/ses-1/func/sub-9005_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-sleepiness_events.json,/sub-9005/ses-1/sub-9005_ses-1_events.json,/sub-9005/ses-1/sub-9005_ses-1_task-sleepiness_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_events.json,/sub-9005/ses-1/func/sub-9005_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9005_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9005/ses-2/beh/sub-9005_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-workingmemorytest_events.json,/sub-9005/ses-2/sub-9005_ses-2_events.json,/sub-9005/ses-2/sub-9005_ses-2_task-workingmemorytest_events.json,/sub-9005/ses-2/beh/sub-9005_ses-2_events.json,/sub-9005/ses-2/beh/sub-9005_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9005_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-arrows_events.json,/sub-9005/ses-2/sub-9005_ses-2_events.json,/sub-9005/ses-2/sub-9005_ses-2_task-arrows_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9005_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-faces_events.json,/sub-9005/ses-2/sub-9005_ses-2_events.json,/sub-9005/ses-2/sub-9005_ses-2_task-faces_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9005_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-hands_events.json,/sub-9005/ses-2/sub-9005_ses-2_events.json,/sub-9005/ses-2/sub-9005_ses-2_task-hands_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9005_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9005/ses-2/func/sub-9005_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9005/sub-9005_events.json,/sub-9005/sub-9005_task-sleepiness_events.json,/sub-9005/ses-2/sub-9005_ses-2_events.json,/sub-9005/ses-2/sub-9005_ses-2_task-sleepiness_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_events.json,/sub-9005/ses-2/func/sub-9005_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9005_sessions.tsv
- Location: ds000201/sub-9005/sub-9005_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9005/sub-9005_sessions.json
-
- Type: Warning
- File: sub-9007_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-arrows_events.json,/sub-9007/ses-1/sub-9007_ses-1_events.json,/sub-9007/ses-1/sub-9007_ses-1_task-arrows_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9007_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-faces_events.json,/sub-9007/ses-1/sub-9007_ses-1_events.json,/sub-9007/ses-1/sub-9007_ses-1_task-faces_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9007_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-hands_events.json,/sub-9007/ses-1/sub-9007_ses-1_events.json,/sub-9007/ses-1/sub-9007_ses-1_task-hands_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9007_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9007/ses-1/func/sub-9007_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-sleepiness_events.json,/sub-9007/ses-1/sub-9007_ses-1_events.json,/sub-9007/ses-1/sub-9007_ses-1_task-sleepiness_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_events.json,/sub-9007/ses-1/func/sub-9007_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9007_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-arrows_events.json,/sub-9007/ses-2/sub-9007_ses-2_events.json,/sub-9007/ses-2/sub-9007_ses-2_task-arrows_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9007_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-faces_events.json,/sub-9007/ses-2/sub-9007_ses-2_events.json,/sub-9007/ses-2/sub-9007_ses-2_task-faces_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9007_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-hands_events.json,/sub-9007/ses-2/sub-9007_ses-2_events.json,/sub-9007/ses-2/sub-9007_ses-2_task-hands_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9007_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9007/ses-2/func/sub-9007_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9007/sub-9007_events.json,/sub-9007/sub-9007_task-sleepiness_events.json,/sub-9007/ses-2/sub-9007_ses-2_events.json,/sub-9007/ses-2/sub-9007_ses-2_task-sleepiness_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_events.json,/sub-9007/ses-2/func/sub-9007_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9007_sessions.tsv
- Location: ds000201/sub-9007/sub-9007_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9007/sub-9007_sessions.json
-
- Type: Warning
- File: sub-9008_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9008/ses-1/beh/sub-9008_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-workingmemorytest_events.json,/sub-9008/ses-1/sub-9008_ses-1_events.json,/sub-9008/ses-1/sub-9008_ses-1_task-workingmemorytest_events.json,/sub-9008/ses-1/beh/sub-9008_ses-1_events.json,/sub-9008/ses-1/beh/sub-9008_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9008_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-arrows_events.json,/sub-9008/ses-1/sub-9008_ses-1_events.json,/sub-9008/ses-1/sub-9008_ses-1_task-arrows_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9008_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-faces_events.json,/sub-9008/ses-1/sub-9008_ses-1_events.json,/sub-9008/ses-1/sub-9008_ses-1_task-faces_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9008_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-hands_events.json,/sub-9008/ses-1/sub-9008_ses-1_events.json,/sub-9008/ses-1/sub-9008_ses-1_task-hands_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9008_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9008/ses-1/func/sub-9008_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-sleepiness_events.json,/sub-9008/ses-1/sub-9008_ses-1_events.json,/sub-9008/ses-1/sub-9008_ses-1_task-sleepiness_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_events.json,/sub-9008/ses-1/func/sub-9008_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9008_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9008/ses-2/beh/sub-9008_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-workingmemorytest_events.json,/sub-9008/ses-2/sub-9008_ses-2_events.json,/sub-9008/ses-2/sub-9008_ses-2_task-workingmemorytest_events.json,/sub-9008/ses-2/beh/sub-9008_ses-2_events.json,/sub-9008/ses-2/beh/sub-9008_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9008_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-arrows_events.json,/sub-9008/ses-2/sub-9008_ses-2_events.json,/sub-9008/ses-2/sub-9008_ses-2_task-arrows_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9008_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-faces_events.json,/sub-9008/ses-2/sub-9008_ses-2_events.json,/sub-9008/ses-2/sub-9008_ses-2_task-faces_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9008_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-hands_events.json,/sub-9008/ses-2/sub-9008_ses-2_events.json,/sub-9008/ses-2/sub-9008_ses-2_task-hands_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9008_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9008/ses-2/func/sub-9008_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9008/sub-9008_events.json,/sub-9008/sub-9008_task-sleepiness_events.json,/sub-9008/ses-2/sub-9008_ses-2_events.json,/sub-9008/ses-2/sub-9008_ses-2_task-sleepiness_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_events.json,/sub-9008/ses-2/func/sub-9008_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9008_sessions.tsv
- Location: ds000201/sub-9008/sub-9008_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9008/sub-9008_sessions.json
-
- Type: Warning
- File: sub-9009_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9009/ses-1/beh/sub-9009_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-workingmemorytest_events.json,/sub-9009/ses-1/sub-9009_ses-1_events.json,/sub-9009/ses-1/sub-9009_ses-1_task-workingmemorytest_events.json,/sub-9009/ses-1/beh/sub-9009_ses-1_events.json,/sub-9009/ses-1/beh/sub-9009_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9009_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-arrows_events.json,/sub-9009/ses-1/sub-9009_ses-1_events.json,/sub-9009/ses-1/sub-9009_ses-1_task-arrows_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9009_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-faces_events.json,/sub-9009/ses-1/sub-9009_ses-1_events.json,/sub-9009/ses-1/sub-9009_ses-1_task-faces_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9009_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-hands_events.json,/sub-9009/ses-1/sub-9009_ses-1_events.json,/sub-9009/ses-1/sub-9009_ses-1_task-hands_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9009_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9009/ses-1/func/sub-9009_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-sleepiness_events.json,/sub-9009/ses-1/sub-9009_ses-1_events.json,/sub-9009/ses-1/sub-9009_ses-1_task-sleepiness_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_events.json,/sub-9009/ses-1/func/sub-9009_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9009_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9009/ses-2/beh/sub-9009_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-workingmemorytest_events.json,/sub-9009/ses-2/sub-9009_ses-2_events.json,/sub-9009/ses-2/sub-9009_ses-2_task-workingmemorytest_events.json,/sub-9009/ses-2/beh/sub-9009_ses-2_events.json,/sub-9009/ses-2/beh/sub-9009_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9009_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-arrows_events.json,/sub-9009/ses-2/sub-9009_ses-2_events.json,/sub-9009/ses-2/sub-9009_ses-2_task-arrows_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9009_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-faces_events.json,/sub-9009/ses-2/sub-9009_ses-2_events.json,/sub-9009/ses-2/sub-9009_ses-2_task-faces_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9009_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-hands_events.json,/sub-9009/ses-2/sub-9009_ses-2_events.json,/sub-9009/ses-2/sub-9009_ses-2_task-hands_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9009_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9009/ses-2/func/sub-9009_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9009/sub-9009_events.json,/sub-9009/sub-9009_task-sleepiness_events.json,/sub-9009/ses-2/sub-9009_ses-2_events.json,/sub-9009/ses-2/sub-9009_ses-2_task-sleepiness_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_events.json,/sub-9009/ses-2/func/sub-9009_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9009_sessions.tsv
- Location: ds000201/sub-9009/sub-9009_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9009/sub-9009_sessions.json
-
- Type: Warning
- File: sub-9011_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9011/ses-1/beh/sub-9011_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-workingmemorytest_events.json,/sub-9011/ses-1/sub-9011_ses-1_events.json,/sub-9011/ses-1/sub-9011_ses-1_task-workingmemorytest_events.json,/sub-9011/ses-1/beh/sub-9011_ses-1_events.json,/sub-9011/ses-1/beh/sub-9011_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9011_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-arrows_events.json,/sub-9011/ses-1/sub-9011_ses-1_events.json,/sub-9011/ses-1/sub-9011_ses-1_task-arrows_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9011_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-faces_events.json,/sub-9011/ses-1/sub-9011_ses-1_events.json,/sub-9011/ses-1/sub-9011_ses-1_task-faces_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9011_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-hands_events.json,/sub-9011/ses-1/sub-9011_ses-1_events.json,/sub-9011/ses-1/sub-9011_ses-1_task-hands_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9011_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9011/ses-1/func/sub-9011_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-sleepiness_events.json,/sub-9011/ses-1/sub-9011_ses-1_events.json,/sub-9011/ses-1/sub-9011_ses-1_task-sleepiness_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_events.json,/sub-9011/ses-1/func/sub-9011_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9011_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9011/ses-2/beh/sub-9011_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-workingmemorytest_events.json,/sub-9011/ses-2/sub-9011_ses-2_events.json,/sub-9011/ses-2/sub-9011_ses-2_task-workingmemorytest_events.json,/sub-9011/ses-2/beh/sub-9011_ses-2_events.json,/sub-9011/ses-2/beh/sub-9011_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9011_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-arrows_events.json,/sub-9011/ses-2/sub-9011_ses-2_events.json,/sub-9011/ses-2/sub-9011_ses-2_task-arrows_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9011_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-faces_events.json,/sub-9011/ses-2/sub-9011_ses-2_events.json,/sub-9011/ses-2/sub-9011_ses-2_task-faces_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9011_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-hands_events.json,/sub-9011/ses-2/sub-9011_ses-2_events.json,/sub-9011/ses-2/sub-9011_ses-2_task-hands_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9011_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9011/ses-2/func/sub-9011_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9011/sub-9011_events.json,/sub-9011/sub-9011_task-sleepiness_events.json,/sub-9011/ses-2/sub-9011_ses-2_events.json,/sub-9011/ses-2/sub-9011_ses-2_task-sleepiness_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_events.json,/sub-9011/ses-2/func/sub-9011_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9011_sessions.tsv
- Location: ds000201/sub-9011/sub-9011_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9011/sub-9011_sessions.json
-
- Type: Warning
- File: sub-9012_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-arrows_events.json,/sub-9012/ses-1/sub-9012_ses-1_events.json,/sub-9012/ses-1/sub-9012_ses-1_task-arrows_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9012_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-faces_events.json,/sub-9012/ses-1/sub-9012_ses-1_events.json,/sub-9012/ses-1/sub-9012_ses-1_task-faces_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9012_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-hands_events.json,/sub-9012/ses-1/sub-9012_ses-1_events.json,/sub-9012/ses-1/sub-9012_ses-1_task-hands_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9012_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9012/ses-1/func/sub-9012_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-sleepiness_events.json,/sub-9012/ses-1/sub-9012_ses-1_events.json,/sub-9012/ses-1/sub-9012_ses-1_task-sleepiness_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_events.json,/sub-9012/ses-1/func/sub-9012_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9012_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-arrows_events.json,/sub-9012/ses-2/sub-9012_ses-2_events.json,/sub-9012/ses-2/sub-9012_ses-2_task-arrows_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9012_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-faces_events.json,/sub-9012/ses-2/sub-9012_ses-2_events.json,/sub-9012/ses-2/sub-9012_ses-2_task-faces_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9012_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-hands_events.json,/sub-9012/ses-2/sub-9012_ses-2_events.json,/sub-9012/ses-2/sub-9012_ses-2_task-hands_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9012_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9012/ses-2/func/sub-9012_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9012/sub-9012_events.json,/sub-9012/sub-9012_task-sleepiness_events.json,/sub-9012/ses-2/sub-9012_ses-2_events.json,/sub-9012/ses-2/sub-9012_ses-2_task-sleepiness_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_events.json,/sub-9012/ses-2/func/sub-9012_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9012_sessions.tsv
- Location: ds000201/sub-9012/sub-9012_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9012/sub-9012_sessions.json
-
- Type: Warning
- File: sub-9013_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-arrows_events.json,/sub-9013/ses-1/sub-9013_ses-1_events.json,/sub-9013/ses-1/sub-9013_ses-1_task-arrows_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9013_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-faces_events.json,/sub-9013/ses-1/sub-9013_ses-1_events.json,/sub-9013/ses-1/sub-9013_ses-1_task-faces_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9013_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-hands_events.json,/sub-9013/ses-1/sub-9013_ses-1_events.json,/sub-9013/ses-1/sub-9013_ses-1_task-hands_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9013_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9013/ses-1/func/sub-9013_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-sleepiness_events.json,/sub-9013/ses-1/sub-9013_ses-1_events.json,/sub-9013/ses-1/sub-9013_ses-1_task-sleepiness_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_events.json,/sub-9013/ses-1/func/sub-9013_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9013_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-arrows_events.json,/sub-9013/ses-2/sub-9013_ses-2_events.json,/sub-9013/ses-2/sub-9013_ses-2_task-arrows_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9013_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-faces_events.json,/sub-9013/ses-2/sub-9013_ses-2_events.json,/sub-9013/ses-2/sub-9013_ses-2_task-faces_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9013_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-hands_events.json,/sub-9013/ses-2/sub-9013_ses-2_events.json,/sub-9013/ses-2/sub-9013_ses-2_task-hands_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9013_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9013/ses-2/func/sub-9013_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9013/sub-9013_events.json,/sub-9013/sub-9013_task-sleepiness_events.json,/sub-9013/ses-2/sub-9013_ses-2_events.json,/sub-9013/ses-2/sub-9013_ses-2_task-sleepiness_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_events.json,/sub-9013/ses-2/func/sub-9013_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9013_sessions.tsv
- Location: ds000201/sub-9013/sub-9013_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9013/sub-9013_sessions.json
-
- Type: Warning
- File: sub-9014_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9014/ses-1/beh/sub-9014_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-workingmemorytest_events.json,/sub-9014/ses-1/sub-9014_ses-1_events.json,/sub-9014/ses-1/sub-9014_ses-1_task-workingmemorytest_events.json,/sub-9014/ses-1/beh/sub-9014_ses-1_events.json,/sub-9014/ses-1/beh/sub-9014_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9014_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-arrows_events.json,/sub-9014/ses-1/sub-9014_ses-1_events.json,/sub-9014/ses-1/sub-9014_ses-1_task-arrows_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9014_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-faces_events.json,/sub-9014/ses-1/sub-9014_ses-1_events.json,/sub-9014/ses-1/sub-9014_ses-1_task-faces_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9014_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-hands_events.json,/sub-9014/ses-1/sub-9014_ses-1_events.json,/sub-9014/ses-1/sub-9014_ses-1_task-hands_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9014_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9014/ses-1/func/sub-9014_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-sleepiness_events.json,/sub-9014/ses-1/sub-9014_ses-1_events.json,/sub-9014/ses-1/sub-9014_ses-1_task-sleepiness_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_events.json,/sub-9014/ses-1/func/sub-9014_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9014_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9014/ses-2/beh/sub-9014_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-workingmemorytest_events.json,/sub-9014/ses-2/sub-9014_ses-2_events.json,/sub-9014/ses-2/sub-9014_ses-2_task-workingmemorytest_events.json,/sub-9014/ses-2/beh/sub-9014_ses-2_events.json,/sub-9014/ses-2/beh/sub-9014_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9014_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-arrows_events.json,/sub-9014/ses-2/sub-9014_ses-2_events.json,/sub-9014/ses-2/sub-9014_ses-2_task-arrows_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9014_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-faces_events.json,/sub-9014/ses-2/sub-9014_ses-2_events.json,/sub-9014/ses-2/sub-9014_ses-2_task-faces_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9014_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-hands_events.json,/sub-9014/ses-2/sub-9014_ses-2_events.json,/sub-9014/ses-2/sub-9014_ses-2_task-hands_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9014_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9014/ses-2/func/sub-9014_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9014/sub-9014_events.json,/sub-9014/sub-9014_task-sleepiness_events.json,/sub-9014/ses-2/sub-9014_ses-2_events.json,/sub-9014/ses-2/sub-9014_ses-2_task-sleepiness_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_events.json,/sub-9014/ses-2/func/sub-9014_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9014_sessions.tsv
- Location: ds000201/sub-9014/sub-9014_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9014/sub-9014_sessions.json
-
- Type: Warning
- File: sub-9016_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9016/sub-9016_events.json,/sub-9016/sub-9016_task-arrows_events.json,/sub-9016/ses-1/sub-9016_ses-1_events.json,/sub-9016/ses-1/sub-9016_ses-1_task-arrows_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9016_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9016/sub-9016_events.json,/sub-9016/sub-9016_task-faces_events.json,/sub-9016/ses-1/sub-9016_ses-1_events.json,/sub-9016/ses-1/sub-9016_ses-1_task-faces_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9016_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9016/sub-9016_events.json,/sub-9016/sub-9016_task-hands_events.json,/sub-9016/ses-1/sub-9016_ses-1_events.json,/sub-9016/ses-1/sub-9016_ses-1_task-hands_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9016_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9016/ses-1/func/sub-9016_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9016/sub-9016_events.json,/sub-9016/sub-9016_task-sleepiness_events.json,/sub-9016/ses-1/sub-9016_ses-1_events.json,/sub-9016/ses-1/sub-9016_ses-1_task-sleepiness_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_events.json,/sub-9016/ses-1/func/sub-9016_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9016_sessions.tsv
- Location: ds000201/sub-9016/sub-9016_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9016/sub-9016_sessions.json
-
- Type: Warning
- File: sub-9017_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-arrows_events.json,/sub-9017/ses-1/sub-9017_ses-1_events.json,/sub-9017/ses-1/sub-9017_ses-1_task-arrows_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9017_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-faces_events.json,/sub-9017/ses-1/sub-9017_ses-1_events.json,/sub-9017/ses-1/sub-9017_ses-1_task-faces_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9017_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-hands_events.json,/sub-9017/ses-1/sub-9017_ses-1_events.json,/sub-9017/ses-1/sub-9017_ses-1_task-hands_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9017_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9017/ses-1/func/sub-9017_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-sleepiness_events.json,/sub-9017/ses-1/sub-9017_ses-1_events.json,/sub-9017/ses-1/sub-9017_ses-1_task-sleepiness_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_events.json,/sub-9017/ses-1/func/sub-9017_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9017_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-arrows_events.json,/sub-9017/ses-2/sub-9017_ses-2_events.json,/sub-9017/ses-2/sub-9017_ses-2_task-arrows_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9017_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-faces_events.json,/sub-9017/ses-2/sub-9017_ses-2_events.json,/sub-9017/ses-2/sub-9017_ses-2_task-faces_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9017_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-hands_events.json,/sub-9017/ses-2/sub-9017_ses-2_events.json,/sub-9017/ses-2/sub-9017_ses-2_task-hands_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9017_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9017/ses-2/func/sub-9017_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9017/sub-9017_events.json,/sub-9017/sub-9017_task-sleepiness_events.json,/sub-9017/ses-2/sub-9017_ses-2_events.json,/sub-9017/ses-2/sub-9017_ses-2_task-sleepiness_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_events.json,/sub-9017/ses-2/func/sub-9017_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9017_sessions.tsv
- Location: ds000201/sub-9017/sub-9017_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9017/sub-9017_sessions.json
-
- Type: Warning
- File: sub-9018_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-arrows_events.json,/sub-9018/ses-1/sub-9018_ses-1_events.json,/sub-9018/ses-1/sub-9018_ses-1_task-arrows_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9018_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-faces_events.json,/sub-9018/ses-1/sub-9018_ses-1_events.json,/sub-9018/ses-1/sub-9018_ses-1_task-faces_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9018_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-hands_events.json,/sub-9018/ses-1/sub-9018_ses-1_events.json,/sub-9018/ses-1/sub-9018_ses-1_task-hands_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9018_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9018/ses-1/func/sub-9018_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-sleepiness_events.json,/sub-9018/ses-1/sub-9018_ses-1_events.json,/sub-9018/ses-1/sub-9018_ses-1_task-sleepiness_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_events.json,/sub-9018/ses-1/func/sub-9018_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9018_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9018/ses-2/beh/sub-9018_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-workingmemorytest_events.json,/sub-9018/ses-2/sub-9018_ses-2_events.json,/sub-9018/ses-2/sub-9018_ses-2_task-workingmemorytest_events.json,/sub-9018/ses-2/beh/sub-9018_ses-2_events.json,/sub-9018/ses-2/beh/sub-9018_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9018_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-arrows_events.json,/sub-9018/ses-2/sub-9018_ses-2_events.json,/sub-9018/ses-2/sub-9018_ses-2_task-arrows_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9018_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-faces_events.json,/sub-9018/ses-2/sub-9018_ses-2_events.json,/sub-9018/ses-2/sub-9018_ses-2_task-faces_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9018_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-hands_events.json,/sub-9018/ses-2/sub-9018_ses-2_events.json,/sub-9018/ses-2/sub-9018_ses-2_task-hands_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9018_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9018/ses-2/func/sub-9018_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9018/sub-9018_events.json,/sub-9018/sub-9018_task-sleepiness_events.json,/sub-9018/ses-2/sub-9018_ses-2_events.json,/sub-9018/ses-2/sub-9018_ses-2_task-sleepiness_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_events.json,/sub-9018/ses-2/func/sub-9018_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9018_sessions.tsv
- Location: ds000201/sub-9018/sub-9018_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9018/sub-9018_sessions.json
-
- Type: Warning
- File: sub-9019_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-arrows_events.json,/sub-9019/ses-1/sub-9019_ses-1_events.json,/sub-9019/ses-1/sub-9019_ses-1_task-arrows_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9019_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-faces_events.json,/sub-9019/ses-1/sub-9019_ses-1_events.json,/sub-9019/ses-1/sub-9019_ses-1_task-faces_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9019_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-hands_events.json,/sub-9019/ses-1/sub-9019_ses-1_events.json,/sub-9019/ses-1/sub-9019_ses-1_task-hands_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9019_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9019/ses-1/func/sub-9019_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-sleepiness_events.json,/sub-9019/ses-1/sub-9019_ses-1_events.json,/sub-9019/ses-1/sub-9019_ses-1_task-sleepiness_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_events.json,/sub-9019/ses-1/func/sub-9019_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9019_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-arrows_events.json,/sub-9019/ses-2/sub-9019_ses-2_events.json,/sub-9019/ses-2/sub-9019_ses-2_task-arrows_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9019_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-faces_events.json,/sub-9019/ses-2/sub-9019_ses-2_events.json,/sub-9019/ses-2/sub-9019_ses-2_task-faces_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9019_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-hands_events.json,/sub-9019/ses-2/sub-9019_ses-2_events.json,/sub-9019/ses-2/sub-9019_ses-2_task-hands_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9019_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9019/ses-2/func/sub-9019_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9019/sub-9019_events.json,/sub-9019/sub-9019_task-sleepiness_events.json,/sub-9019/ses-2/sub-9019_ses-2_events.json,/sub-9019/ses-2/sub-9019_ses-2_task-sleepiness_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_events.json,/sub-9019/ses-2/func/sub-9019_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9019_sessions.tsv
- Location: ds000201/sub-9019/sub-9019_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9019/sub-9019_sessions.json
-
- Type: Warning
- File: sub-9020_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-arrows_events.json,/sub-9020/ses-1/sub-9020_ses-1_events.json,/sub-9020/ses-1/sub-9020_ses-1_task-arrows_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9020_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-faces_events.json,/sub-9020/ses-1/sub-9020_ses-1_events.json,/sub-9020/ses-1/sub-9020_ses-1_task-faces_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9020_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-hands_events.json,/sub-9020/ses-1/sub-9020_ses-1_events.json,/sub-9020/ses-1/sub-9020_ses-1_task-hands_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9020_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9020/ses-1/func/sub-9020_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-sleepiness_events.json,/sub-9020/ses-1/sub-9020_ses-1_events.json,/sub-9020/ses-1/sub-9020_ses-1_task-sleepiness_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_events.json,/sub-9020/ses-1/func/sub-9020_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9020_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9020/ses-2/beh/sub-9020_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-workingmemorytest_events.json,/sub-9020/ses-2/sub-9020_ses-2_events.json,/sub-9020/ses-2/sub-9020_ses-2_task-workingmemorytest_events.json,/sub-9020/ses-2/beh/sub-9020_ses-2_events.json,/sub-9020/ses-2/beh/sub-9020_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9020_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-arrows_events.json,/sub-9020/ses-2/sub-9020_ses-2_events.json,/sub-9020/ses-2/sub-9020_ses-2_task-arrows_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9020_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-faces_events.json,/sub-9020/ses-2/sub-9020_ses-2_events.json,/sub-9020/ses-2/sub-9020_ses-2_task-faces_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9020_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-hands_events.json,/sub-9020/ses-2/sub-9020_ses-2_events.json,/sub-9020/ses-2/sub-9020_ses-2_task-hands_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9020_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9020/ses-2/func/sub-9020_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9020/sub-9020_events.json,/sub-9020/sub-9020_task-sleepiness_events.json,/sub-9020/ses-2/sub-9020_ses-2_events.json,/sub-9020/ses-2/sub-9020_ses-2_task-sleepiness_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_events.json,/sub-9020/ses-2/func/sub-9020_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9020_sessions.tsv
- Location: ds000201/sub-9020/sub-9020_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9020/sub-9020_sessions.json
-
- Type: Warning
- File: sub-9022_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9022/sub-9022_events.json,/sub-9022/sub-9022_task-arrows_events.json,/sub-9022/ses-1/sub-9022_ses-1_events.json,/sub-9022/ses-1/sub-9022_ses-1_task-arrows_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9022_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9022/sub-9022_events.json,/sub-9022/sub-9022_task-faces_events.json,/sub-9022/ses-1/sub-9022_ses-1_events.json,/sub-9022/ses-1/sub-9022_ses-1_task-faces_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9022_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9022/sub-9022_events.json,/sub-9022/sub-9022_task-hands_events.json,/sub-9022/ses-1/sub-9022_ses-1_events.json,/sub-9022/ses-1/sub-9022_ses-1_task-hands_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9022_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9022/ses-1/func/sub-9022_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9022/sub-9022_events.json,/sub-9022/sub-9022_task-sleepiness_events.json,/sub-9022/ses-1/sub-9022_ses-1_events.json,/sub-9022/ses-1/sub-9022_ses-1_task-sleepiness_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_events.json,/sub-9022/ses-1/func/sub-9022_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9022_sessions.tsv
- Location: ds000201/sub-9022/sub-9022_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9022/sub-9022_sessions.json
-
- Type: Warning
- File: sub-9023_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9023/ses-1/beh/sub-9023_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-workingmemorytest_events.json,/sub-9023/ses-1/sub-9023_ses-1_events.json,/sub-9023/ses-1/sub-9023_ses-1_task-workingmemorytest_events.json,/sub-9023/ses-1/beh/sub-9023_ses-1_events.json,/sub-9023/ses-1/beh/sub-9023_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9023_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-arrows_events.json,/sub-9023/ses-1/sub-9023_ses-1_events.json,/sub-9023/ses-1/sub-9023_ses-1_task-arrows_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9023_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-faces_events.json,/sub-9023/ses-1/sub-9023_ses-1_events.json,/sub-9023/ses-1/sub-9023_ses-1_task-faces_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9023_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-hands_events.json,/sub-9023/ses-1/sub-9023_ses-1_events.json,/sub-9023/ses-1/sub-9023_ses-1_task-hands_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9023_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9023/ses-1/func/sub-9023_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-sleepiness_events.json,/sub-9023/ses-1/sub-9023_ses-1_events.json,/sub-9023/ses-1/sub-9023_ses-1_task-sleepiness_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_events.json,/sub-9023/ses-1/func/sub-9023_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9023_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9023/ses-2/beh/sub-9023_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-workingmemorytest_events.json,/sub-9023/ses-2/sub-9023_ses-2_events.json,/sub-9023/ses-2/sub-9023_ses-2_task-workingmemorytest_events.json,/sub-9023/ses-2/beh/sub-9023_ses-2_events.json,/sub-9023/ses-2/beh/sub-9023_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9023_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-arrows_events.json,/sub-9023/ses-2/sub-9023_ses-2_events.json,/sub-9023/ses-2/sub-9023_ses-2_task-arrows_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9023_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-faces_events.json,/sub-9023/ses-2/sub-9023_ses-2_events.json,/sub-9023/ses-2/sub-9023_ses-2_task-faces_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9023_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-hands_events.json,/sub-9023/ses-2/sub-9023_ses-2_events.json,/sub-9023/ses-2/sub-9023_ses-2_task-hands_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9023_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9023/ses-2/func/sub-9023_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9023/sub-9023_events.json,/sub-9023/sub-9023_task-sleepiness_events.json,/sub-9023/ses-2/sub-9023_ses-2_events.json,/sub-9023/ses-2/sub-9023_ses-2_task-sleepiness_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_events.json,/sub-9023/ses-2/func/sub-9023_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9023_sessions.tsv
- Location: ds000201/sub-9023/sub-9023_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9023/sub-9023_sessions.json
-
- Type: Warning
- File: sub-9025_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9025/ses-1/beh/sub-9025_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9025/sub-9025_events.json,/sub-9025/sub-9025_task-workingmemorytest_events.json,/sub-9025/ses-1/sub-9025_ses-1_events.json,/sub-9025/ses-1/sub-9025_ses-1_task-workingmemorytest_events.json,/sub-9025/ses-1/beh/sub-9025_ses-1_events.json,/sub-9025/ses-1/beh/sub-9025_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9025_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9025/sub-9025_events.json,/sub-9025/sub-9025_task-faces_events.json,/sub-9025/ses-1/sub-9025_ses-1_events.json,/sub-9025/ses-1/sub-9025_ses-1_task-faces_events.json,/sub-9025/ses-1/func/sub-9025_ses-1_events.json,/sub-9025/ses-1/func/sub-9025_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9025_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9025/ses-1/func/sub-9025_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9025/sub-9025_events.json,/sub-9025/sub-9025_task-hands_events.json,/sub-9025/ses-1/sub-9025_ses-1_events.json,/sub-9025/ses-1/sub-9025_ses-1_task-hands_events.json,/sub-9025/ses-1/func/sub-9025_ses-1_events.json,/sub-9025/ses-1/func/sub-9025_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9025_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9025/ses-2/beh/sub-9025_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9025/sub-9025_events.json,/sub-9025/sub-9025_task-workingmemorytest_events.json,/sub-9025/ses-2/sub-9025_ses-2_events.json,/sub-9025/ses-2/sub-9025_ses-2_task-workingmemorytest_events.json,/sub-9025/ses-2/beh/sub-9025_ses-2_events.json,/sub-9025/ses-2/beh/sub-9025_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9025_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9025/sub-9025_events.json,/sub-9025/sub-9025_task-faces_events.json,/sub-9025/ses-2/sub-9025_ses-2_events.json,/sub-9025/ses-2/sub-9025_ses-2_task-faces_events.json,/sub-9025/ses-2/func/sub-9025_ses-2_events.json,/sub-9025/ses-2/func/sub-9025_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9025_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9025/ses-2/func/sub-9025_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9025/sub-9025_events.json,/sub-9025/sub-9025_task-hands_events.json,/sub-9025/ses-2/sub-9025_ses-2_events.json,/sub-9025/ses-2/sub-9025_ses-2_task-hands_events.json,/sub-9025/ses-2/func/sub-9025_ses-2_events.json,/sub-9025/ses-2/func/sub-9025_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9025_sessions.tsv
- Location: ds000201/sub-9025/sub-9025_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9025/sub-9025_sessions.json
-
- Type: Warning
- File: sub-9026_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9026/ses-1/beh/sub-9026_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-workingmemorytest_events.json,/sub-9026/ses-1/sub-9026_ses-1_events.json,/sub-9026/ses-1/sub-9026_ses-1_task-workingmemorytest_events.json,/sub-9026/ses-1/beh/sub-9026_ses-1_events.json,/sub-9026/ses-1/beh/sub-9026_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9026_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-arrows_events.json,/sub-9026/ses-1/sub-9026_ses-1_events.json,/sub-9026/ses-1/sub-9026_ses-1_task-arrows_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9026_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-faces_events.json,/sub-9026/ses-1/sub-9026_ses-1_events.json,/sub-9026/ses-1/sub-9026_ses-1_task-faces_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9026_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-hands_events.json,/sub-9026/ses-1/sub-9026_ses-1_events.json,/sub-9026/ses-1/sub-9026_ses-1_task-hands_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9026_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9026/ses-1/func/sub-9026_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-sleepiness_events.json,/sub-9026/ses-1/sub-9026_ses-1_events.json,/sub-9026/ses-1/sub-9026_ses-1_task-sleepiness_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_events.json,/sub-9026/ses-1/func/sub-9026_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9026_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-arrows_events.json,/sub-9026/ses-2/sub-9026_ses-2_events.json,/sub-9026/ses-2/sub-9026_ses-2_task-arrows_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9026_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-faces_events.json,/sub-9026/ses-2/sub-9026_ses-2_events.json,/sub-9026/ses-2/sub-9026_ses-2_task-faces_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9026_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-hands_events.json,/sub-9026/ses-2/sub-9026_ses-2_events.json,/sub-9026/ses-2/sub-9026_ses-2_task-hands_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9026_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9026/ses-2/func/sub-9026_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9026/sub-9026_events.json,/sub-9026/sub-9026_task-sleepiness_events.json,/sub-9026/ses-2/sub-9026_ses-2_events.json,/sub-9026/ses-2/sub-9026_ses-2_task-sleepiness_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_events.json,/sub-9026/ses-2/func/sub-9026_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9026_sessions.tsv
- Location: ds000201/sub-9026/sub-9026_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9026/sub-9026_sessions.json
-
- Type: Warning
- File: sub-9028_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9028/ses-1/beh/sub-9028_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-workingmemorytest_events.json,/sub-9028/ses-1/sub-9028_ses-1_events.json,/sub-9028/ses-1/sub-9028_ses-1_task-workingmemorytest_events.json,/sub-9028/ses-1/beh/sub-9028_ses-1_events.json,/sub-9028/ses-1/beh/sub-9028_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9028_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-arrows_events.json,/sub-9028/ses-1/sub-9028_ses-1_events.json,/sub-9028/ses-1/sub-9028_ses-1_task-arrows_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9028_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-faces_events.json,/sub-9028/ses-1/sub-9028_ses-1_events.json,/sub-9028/ses-1/sub-9028_ses-1_task-faces_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9028_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-hands_events.json,/sub-9028/ses-1/sub-9028_ses-1_events.json,/sub-9028/ses-1/sub-9028_ses-1_task-hands_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9028_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9028/ses-1/func/sub-9028_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-sleepiness_events.json,/sub-9028/ses-1/sub-9028_ses-1_events.json,/sub-9028/ses-1/sub-9028_ses-1_task-sleepiness_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_events.json,/sub-9028/ses-1/func/sub-9028_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9028_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9028/ses-2/beh/sub-9028_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-workingmemorytest_events.json,/sub-9028/ses-2/sub-9028_ses-2_events.json,/sub-9028/ses-2/sub-9028_ses-2_task-workingmemorytest_events.json,/sub-9028/ses-2/beh/sub-9028_ses-2_events.json,/sub-9028/ses-2/beh/sub-9028_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9028_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-arrows_events.json,/sub-9028/ses-2/sub-9028_ses-2_events.json,/sub-9028/ses-2/sub-9028_ses-2_task-arrows_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9028_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-faces_events.json,/sub-9028/ses-2/sub-9028_ses-2_events.json,/sub-9028/ses-2/sub-9028_ses-2_task-faces_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9028_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-hands_events.json,/sub-9028/ses-2/sub-9028_ses-2_events.json,/sub-9028/ses-2/sub-9028_ses-2_task-hands_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9028_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9028/ses-2/func/sub-9028_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9028/sub-9028_events.json,/sub-9028/sub-9028_task-sleepiness_events.json,/sub-9028/ses-2/sub-9028_ses-2_events.json,/sub-9028/ses-2/sub-9028_ses-2_task-sleepiness_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_events.json,/sub-9028/ses-2/func/sub-9028_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9028_sessions.tsv
- Location: ds000201/sub-9028/sub-9028_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9028/sub-9028_sessions.json
-
- Type: Warning
- File: sub-9029_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9029/ses-1/beh/sub-9029_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-workingmemorytest_events.json,/sub-9029/ses-1/sub-9029_ses-1_events.json,/sub-9029/ses-1/sub-9029_ses-1_task-workingmemorytest_events.json,/sub-9029/ses-1/beh/sub-9029_ses-1_events.json,/sub-9029/ses-1/beh/sub-9029_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9029_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-faces_events.json,/sub-9029/ses-1/sub-9029_ses-1_events.json,/sub-9029/ses-1/sub-9029_ses-1_task-faces_events.json,/sub-9029/ses-1/func/sub-9029_ses-1_events.json,/sub-9029/ses-1/func/sub-9029_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9029_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9029/ses-1/func/sub-9029_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-hands_events.json,/sub-9029/ses-1/sub-9029_ses-1_events.json,/sub-9029/ses-1/sub-9029_ses-1_task-hands_events.json,/sub-9029/ses-1/func/sub-9029_ses-1_events.json,/sub-9029/ses-1/func/sub-9029_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9029_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9029/ses-2/beh/sub-9029_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-workingmemorytest_events.json,/sub-9029/ses-2/sub-9029_ses-2_events.json,/sub-9029/ses-2/sub-9029_ses-2_task-workingmemorytest_events.json,/sub-9029/ses-2/beh/sub-9029_ses-2_events.json,/sub-9029/ses-2/beh/sub-9029_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9029_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-arrows_events.json,/sub-9029/ses-2/sub-9029_ses-2_events.json,/sub-9029/ses-2/sub-9029_ses-2_task-arrows_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9029_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-faces_events.json,/sub-9029/ses-2/sub-9029_ses-2_events.json,/sub-9029/ses-2/sub-9029_ses-2_task-faces_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9029_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-hands_events.json,/sub-9029/ses-2/sub-9029_ses-2_events.json,/sub-9029/ses-2/sub-9029_ses-2_task-hands_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9029_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9029/ses-2/func/sub-9029_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9029/sub-9029_events.json,/sub-9029/sub-9029_task-sleepiness_events.json,/sub-9029/ses-2/sub-9029_ses-2_events.json,/sub-9029/ses-2/sub-9029_ses-2_task-sleepiness_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_events.json,/sub-9029/ses-2/func/sub-9029_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9029_sessions.tsv
- Location: ds000201/sub-9029/sub-9029_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9029/sub-9029_sessions.json
-
- Type: Warning
- File: sub-9032_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9032/ses-1/beh/sub-9032_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-workingmemorytest_events.json,/sub-9032/ses-1/sub-9032_ses-1_events.json,/sub-9032/ses-1/sub-9032_ses-1_task-workingmemorytest_events.json,/sub-9032/ses-1/beh/sub-9032_ses-1_events.json,/sub-9032/ses-1/beh/sub-9032_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9032_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-arrows_events.json,/sub-9032/ses-1/sub-9032_ses-1_events.json,/sub-9032/ses-1/sub-9032_ses-1_task-arrows_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9032_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-faces_events.json,/sub-9032/ses-1/sub-9032_ses-1_events.json,/sub-9032/ses-1/sub-9032_ses-1_task-faces_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9032_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-hands_events.json,/sub-9032/ses-1/sub-9032_ses-1_events.json,/sub-9032/ses-1/sub-9032_ses-1_task-hands_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9032_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9032/ses-1/func/sub-9032_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-sleepiness_events.json,/sub-9032/ses-1/sub-9032_ses-1_events.json,/sub-9032/ses-1/sub-9032_ses-1_task-sleepiness_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_events.json,/sub-9032/ses-1/func/sub-9032_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9032_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9032/ses-2/beh/sub-9032_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-workingmemorytest_events.json,/sub-9032/ses-2/sub-9032_ses-2_events.json,/sub-9032/ses-2/sub-9032_ses-2_task-workingmemorytest_events.json,/sub-9032/ses-2/beh/sub-9032_ses-2_events.json,/sub-9032/ses-2/beh/sub-9032_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9032_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-arrows_events.json,/sub-9032/ses-2/sub-9032_ses-2_events.json,/sub-9032/ses-2/sub-9032_ses-2_task-arrows_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9032_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-faces_events.json,/sub-9032/ses-2/sub-9032_ses-2_events.json,/sub-9032/ses-2/sub-9032_ses-2_task-faces_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9032_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-hands_events.json,/sub-9032/ses-2/sub-9032_ses-2_events.json,/sub-9032/ses-2/sub-9032_ses-2_task-hands_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9032_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9032/ses-2/func/sub-9032_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9032/sub-9032_events.json,/sub-9032/sub-9032_task-sleepiness_events.json,/sub-9032/ses-2/sub-9032_ses-2_events.json,/sub-9032/ses-2/sub-9032_ses-2_task-sleepiness_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_events.json,/sub-9032/ses-2/func/sub-9032_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9032_sessions.tsv
- Location: ds000201/sub-9032/sub-9032_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9032/sub-9032_sessions.json
-
- Type: Warning
- File: sub-9033_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9033/ses-1/beh/sub-9033_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-workingmemorytest_events.json,/sub-9033/ses-1/sub-9033_ses-1_events.json,/sub-9033/ses-1/sub-9033_ses-1_task-workingmemorytest_events.json,/sub-9033/ses-1/beh/sub-9033_ses-1_events.json,/sub-9033/ses-1/beh/sub-9033_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9033_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-arrows_events.json,/sub-9033/ses-1/sub-9033_ses-1_events.json,/sub-9033/ses-1/sub-9033_ses-1_task-arrows_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9033_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-faces_events.json,/sub-9033/ses-1/sub-9033_ses-1_events.json,/sub-9033/ses-1/sub-9033_ses-1_task-faces_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9033_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-hands_events.json,/sub-9033/ses-1/sub-9033_ses-1_events.json,/sub-9033/ses-1/sub-9033_ses-1_task-hands_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9033_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9033/ses-1/func/sub-9033_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-sleepiness_events.json,/sub-9033/ses-1/sub-9033_ses-1_events.json,/sub-9033/ses-1/sub-9033_ses-1_task-sleepiness_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_events.json,/sub-9033/ses-1/func/sub-9033_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9033_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9033/ses-2/beh/sub-9033_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-workingmemorytest_events.json,/sub-9033/ses-2/sub-9033_ses-2_events.json,/sub-9033/ses-2/sub-9033_ses-2_task-workingmemorytest_events.json,/sub-9033/ses-2/beh/sub-9033_ses-2_events.json,/sub-9033/ses-2/beh/sub-9033_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9033_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-arrows_events.json,/sub-9033/ses-2/sub-9033_ses-2_events.json,/sub-9033/ses-2/sub-9033_ses-2_task-arrows_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9033_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-faces_events.json,/sub-9033/ses-2/sub-9033_ses-2_events.json,/sub-9033/ses-2/sub-9033_ses-2_task-faces_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9033_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-hands_events.json,/sub-9033/ses-2/sub-9033_ses-2_events.json,/sub-9033/ses-2/sub-9033_ses-2_task-hands_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9033_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9033/ses-2/func/sub-9033_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9033/sub-9033_events.json,/sub-9033/sub-9033_task-sleepiness_events.json,/sub-9033/ses-2/sub-9033_ses-2_events.json,/sub-9033/ses-2/sub-9033_ses-2_task-sleepiness_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_events.json,/sub-9033/ses-2/func/sub-9033_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9033_sessions.tsv
- Location: ds000201/sub-9033/sub-9033_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9033/sub-9033_sessions.json
-
- Type: Warning
- File: sub-9034_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-arrows_events.json,/sub-9034/ses-1/sub-9034_ses-1_events.json,/sub-9034/ses-1/sub-9034_ses-1_task-arrows_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9034_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-faces_events.json,/sub-9034/ses-1/sub-9034_ses-1_events.json,/sub-9034/ses-1/sub-9034_ses-1_task-faces_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9034_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-hands_events.json,/sub-9034/ses-1/sub-9034_ses-1_events.json,/sub-9034/ses-1/sub-9034_ses-1_task-hands_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9034_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9034/ses-1/func/sub-9034_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-sleepiness_events.json,/sub-9034/ses-1/sub-9034_ses-1_events.json,/sub-9034/ses-1/sub-9034_ses-1_task-sleepiness_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_events.json,/sub-9034/ses-1/func/sub-9034_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9034_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-arrows_events.json,/sub-9034/ses-2/sub-9034_ses-2_events.json,/sub-9034/ses-2/sub-9034_ses-2_task-arrows_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9034_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-faces_events.json,/sub-9034/ses-2/sub-9034_ses-2_events.json,/sub-9034/ses-2/sub-9034_ses-2_task-faces_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9034_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-hands_events.json,/sub-9034/ses-2/sub-9034_ses-2_events.json,/sub-9034/ses-2/sub-9034_ses-2_task-hands_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9034_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9034/ses-2/func/sub-9034_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9034/sub-9034_events.json,/sub-9034/sub-9034_task-sleepiness_events.json,/sub-9034/ses-2/sub-9034_ses-2_events.json,/sub-9034/ses-2/sub-9034_ses-2_task-sleepiness_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_events.json,/sub-9034/ses-2/func/sub-9034_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9034_sessions.tsv
- Location: ds000201/sub-9034/sub-9034_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9034/sub-9034_sessions.json
-
- Type: Warning
- File: sub-9035_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-arrows_events.json,/sub-9035/ses-1/sub-9035_ses-1_events.json,/sub-9035/ses-1/sub-9035_ses-1_task-arrows_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9035_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-faces_events.json,/sub-9035/ses-1/sub-9035_ses-1_events.json,/sub-9035/ses-1/sub-9035_ses-1_task-faces_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9035_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-hands_events.json,/sub-9035/ses-1/sub-9035_ses-1_events.json,/sub-9035/ses-1/sub-9035_ses-1_task-hands_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9035_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9035/ses-1/func/sub-9035_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-sleepiness_events.json,/sub-9035/ses-1/sub-9035_ses-1_events.json,/sub-9035/ses-1/sub-9035_ses-1_task-sleepiness_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_events.json,/sub-9035/ses-1/func/sub-9035_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9035_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-arrows_events.json,/sub-9035/ses-2/sub-9035_ses-2_events.json,/sub-9035/ses-2/sub-9035_ses-2_task-arrows_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9035_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-faces_events.json,/sub-9035/ses-2/sub-9035_ses-2_events.json,/sub-9035/ses-2/sub-9035_ses-2_task-faces_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9035_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-hands_events.json,/sub-9035/ses-2/sub-9035_ses-2_events.json,/sub-9035/ses-2/sub-9035_ses-2_task-hands_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9035_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9035/ses-2/func/sub-9035_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9035/sub-9035_events.json,/sub-9035/sub-9035_task-sleepiness_events.json,/sub-9035/ses-2/sub-9035_ses-2_events.json,/sub-9035/ses-2/sub-9035_ses-2_task-sleepiness_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_events.json,/sub-9035/ses-2/func/sub-9035_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9035_sessions.tsv
- Location: ds000201/sub-9035/sub-9035_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9035/sub-9035_sessions.json
-
- Type: Warning
- File: sub-9036_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9036/ses-1/beh/sub-9036_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-workingmemorytest_events.json,/sub-9036/ses-1/sub-9036_ses-1_events.json,/sub-9036/ses-1/sub-9036_ses-1_task-workingmemorytest_events.json,/sub-9036/ses-1/beh/sub-9036_ses-1_events.json,/sub-9036/ses-1/beh/sub-9036_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9036_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-arrows_events.json,/sub-9036/ses-1/sub-9036_ses-1_events.json,/sub-9036/ses-1/sub-9036_ses-1_task-arrows_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9036_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-faces_events.json,/sub-9036/ses-1/sub-9036_ses-1_events.json,/sub-9036/ses-1/sub-9036_ses-1_task-faces_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9036_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-hands_events.json,/sub-9036/ses-1/sub-9036_ses-1_events.json,/sub-9036/ses-1/sub-9036_ses-1_task-hands_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9036_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9036/ses-1/func/sub-9036_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-sleepiness_events.json,/sub-9036/ses-1/sub-9036_ses-1_events.json,/sub-9036/ses-1/sub-9036_ses-1_task-sleepiness_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_events.json,/sub-9036/ses-1/func/sub-9036_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9036_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9036/ses-2/beh/sub-9036_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-workingmemorytest_events.json,/sub-9036/ses-2/sub-9036_ses-2_events.json,/sub-9036/ses-2/sub-9036_ses-2_task-workingmemorytest_events.json,/sub-9036/ses-2/beh/sub-9036_ses-2_events.json,/sub-9036/ses-2/beh/sub-9036_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9036_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-arrows_events.json,/sub-9036/ses-2/sub-9036_ses-2_events.json,/sub-9036/ses-2/sub-9036_ses-2_task-arrows_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9036_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-faces_events.json,/sub-9036/ses-2/sub-9036_ses-2_events.json,/sub-9036/ses-2/sub-9036_ses-2_task-faces_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9036_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-hands_events.json,/sub-9036/ses-2/sub-9036_ses-2_events.json,/sub-9036/ses-2/sub-9036_ses-2_task-hands_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9036_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9036/ses-2/func/sub-9036_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9036/sub-9036_events.json,/sub-9036/sub-9036_task-sleepiness_events.json,/sub-9036/ses-2/sub-9036_ses-2_events.json,/sub-9036/ses-2/sub-9036_ses-2_task-sleepiness_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_events.json,/sub-9036/ses-2/func/sub-9036_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9036_sessions.tsv
- Location: ds000201/sub-9036/sub-9036_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9036/sub-9036_sessions.json
-
- Type: Warning
- File: sub-9037_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-arrows_events.json,/sub-9037/ses-1/sub-9037_ses-1_events.json,/sub-9037/ses-1/sub-9037_ses-1_task-arrows_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9037_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-faces_events.json,/sub-9037/ses-1/sub-9037_ses-1_events.json,/sub-9037/ses-1/sub-9037_ses-1_task-faces_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9037_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-hands_events.json,/sub-9037/ses-1/sub-9037_ses-1_events.json,/sub-9037/ses-1/sub-9037_ses-1_task-hands_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9037_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9037/ses-1/func/sub-9037_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-sleepiness_events.json,/sub-9037/ses-1/sub-9037_ses-1_events.json,/sub-9037/ses-1/sub-9037_ses-1_task-sleepiness_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_events.json,/sub-9037/ses-1/func/sub-9037_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9037_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-arrows_events.json,/sub-9037/ses-2/sub-9037_ses-2_events.json,/sub-9037/ses-2/sub-9037_ses-2_task-arrows_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9037_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-faces_events.json,/sub-9037/ses-2/sub-9037_ses-2_events.json,/sub-9037/ses-2/sub-9037_ses-2_task-faces_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9037_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-hands_events.json,/sub-9037/ses-2/sub-9037_ses-2_events.json,/sub-9037/ses-2/sub-9037_ses-2_task-hands_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9037_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9037/ses-2/func/sub-9037_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9037/sub-9037_events.json,/sub-9037/sub-9037_task-sleepiness_events.json,/sub-9037/ses-2/sub-9037_ses-2_events.json,/sub-9037/ses-2/sub-9037_ses-2_task-sleepiness_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_events.json,/sub-9037/ses-2/func/sub-9037_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9037_sessions.tsv
- Location: ds000201/sub-9037/sub-9037_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9037/sub-9037_sessions.json
-
- Type: Warning
- File: sub-9038_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9038/ses-1/beh/sub-9038_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-workingmemorytest_events.json,/sub-9038/ses-1/sub-9038_ses-1_events.json,/sub-9038/ses-1/sub-9038_ses-1_task-workingmemorytest_events.json,/sub-9038/ses-1/beh/sub-9038_ses-1_events.json,/sub-9038/ses-1/beh/sub-9038_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9038_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-arrows_events.json,/sub-9038/ses-1/sub-9038_ses-1_events.json,/sub-9038/ses-1/sub-9038_ses-1_task-arrows_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9038_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-faces_events.json,/sub-9038/ses-1/sub-9038_ses-1_events.json,/sub-9038/ses-1/sub-9038_ses-1_task-faces_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9038_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-hands_events.json,/sub-9038/ses-1/sub-9038_ses-1_events.json,/sub-9038/ses-1/sub-9038_ses-1_task-hands_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9038_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9038/ses-1/func/sub-9038_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-sleepiness_events.json,/sub-9038/ses-1/sub-9038_ses-1_events.json,/sub-9038/ses-1/sub-9038_ses-1_task-sleepiness_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_events.json,/sub-9038/ses-1/func/sub-9038_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9038_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9038/ses-2/beh/sub-9038_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-workingmemorytest_events.json,/sub-9038/ses-2/sub-9038_ses-2_events.json,/sub-9038/ses-2/sub-9038_ses-2_task-workingmemorytest_events.json,/sub-9038/ses-2/beh/sub-9038_ses-2_events.json,/sub-9038/ses-2/beh/sub-9038_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9038_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-arrows_events.json,/sub-9038/ses-2/sub-9038_ses-2_events.json,/sub-9038/ses-2/sub-9038_ses-2_task-arrows_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9038_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-faces_events.json,/sub-9038/ses-2/sub-9038_ses-2_events.json,/sub-9038/ses-2/sub-9038_ses-2_task-faces_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9038_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-hands_events.json,/sub-9038/ses-2/sub-9038_ses-2_events.json,/sub-9038/ses-2/sub-9038_ses-2_task-hands_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9038_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9038/ses-2/func/sub-9038_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9038/sub-9038_events.json,/sub-9038/sub-9038_task-sleepiness_events.json,/sub-9038/ses-2/sub-9038_ses-2_events.json,/sub-9038/ses-2/sub-9038_ses-2_task-sleepiness_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_events.json,/sub-9038/ses-2/func/sub-9038_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9038_sessions.tsv
- Location: ds000201/sub-9038/sub-9038_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9038/sub-9038_sessions.json
-
- Type: Warning
- File: sub-9039_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9039/ses-1/beh/sub-9039_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-workingmemorytest_events.json,/sub-9039/ses-1/sub-9039_ses-1_events.json,/sub-9039/ses-1/sub-9039_ses-1_task-workingmemorytest_events.json,/sub-9039/ses-1/beh/sub-9039_ses-1_events.json,/sub-9039/ses-1/beh/sub-9039_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9039_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-arrows_events.json,/sub-9039/ses-1/sub-9039_ses-1_events.json,/sub-9039/ses-1/sub-9039_ses-1_task-arrows_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9039_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-faces_events.json,/sub-9039/ses-1/sub-9039_ses-1_events.json,/sub-9039/ses-1/sub-9039_ses-1_task-faces_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9039_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-hands_events.json,/sub-9039/ses-1/sub-9039_ses-1_events.json,/sub-9039/ses-1/sub-9039_ses-1_task-hands_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9039_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9039/ses-1/func/sub-9039_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-sleepiness_events.json,/sub-9039/ses-1/sub-9039_ses-1_events.json,/sub-9039/ses-1/sub-9039_ses-1_task-sleepiness_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_events.json,/sub-9039/ses-1/func/sub-9039_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9039_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9039/ses-2/beh/sub-9039_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-workingmemorytest_events.json,/sub-9039/ses-2/sub-9039_ses-2_events.json,/sub-9039/ses-2/sub-9039_ses-2_task-workingmemorytest_events.json,/sub-9039/ses-2/beh/sub-9039_ses-2_events.json,/sub-9039/ses-2/beh/sub-9039_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9039_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-arrows_events.json,/sub-9039/ses-2/sub-9039_ses-2_events.json,/sub-9039/ses-2/sub-9039_ses-2_task-arrows_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9039_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-faces_events.json,/sub-9039/ses-2/sub-9039_ses-2_events.json,/sub-9039/ses-2/sub-9039_ses-2_task-faces_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9039_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-hands_events.json,/sub-9039/ses-2/sub-9039_ses-2_events.json,/sub-9039/ses-2/sub-9039_ses-2_task-hands_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9039_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9039/ses-2/func/sub-9039_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9039/sub-9039_events.json,/sub-9039/sub-9039_task-sleepiness_events.json,/sub-9039/ses-2/sub-9039_ses-2_events.json,/sub-9039/ses-2/sub-9039_ses-2_task-sleepiness_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_events.json,/sub-9039/ses-2/func/sub-9039_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9039_sessions.tsv
- Location: ds000201/sub-9039/sub-9039_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9039/sub-9039_sessions.json
-
- Type: Warning
- File: sub-9040_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9040/ses-1/beh/sub-9040_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-workingmemorytest_events.json,/sub-9040/ses-1/sub-9040_ses-1_events.json,/sub-9040/ses-1/sub-9040_ses-1_task-workingmemorytest_events.json,/sub-9040/ses-1/beh/sub-9040_ses-1_events.json,/sub-9040/ses-1/beh/sub-9040_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9040_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-arrows_events.json,/sub-9040/ses-1/sub-9040_ses-1_events.json,/sub-9040/ses-1/sub-9040_ses-1_task-arrows_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9040_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-faces_events.json,/sub-9040/ses-1/sub-9040_ses-1_events.json,/sub-9040/ses-1/sub-9040_ses-1_task-faces_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9040_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-hands_events.json,/sub-9040/ses-1/sub-9040_ses-1_events.json,/sub-9040/ses-1/sub-9040_ses-1_task-hands_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9040_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9040/ses-1/func/sub-9040_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-sleepiness_events.json,/sub-9040/ses-1/sub-9040_ses-1_events.json,/sub-9040/ses-1/sub-9040_ses-1_task-sleepiness_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_events.json,/sub-9040/ses-1/func/sub-9040_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9040_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9040/ses-2/beh/sub-9040_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-workingmemorytest_events.json,/sub-9040/ses-2/sub-9040_ses-2_events.json,/sub-9040/ses-2/sub-9040_ses-2_task-workingmemorytest_events.json,/sub-9040/ses-2/beh/sub-9040_ses-2_events.json,/sub-9040/ses-2/beh/sub-9040_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9040_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-arrows_events.json,/sub-9040/ses-2/sub-9040_ses-2_events.json,/sub-9040/ses-2/sub-9040_ses-2_task-arrows_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9040_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-faces_events.json,/sub-9040/ses-2/sub-9040_ses-2_events.json,/sub-9040/ses-2/sub-9040_ses-2_task-faces_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9040_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-hands_events.json,/sub-9040/ses-2/sub-9040_ses-2_events.json,/sub-9040/ses-2/sub-9040_ses-2_task-hands_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9040_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9040/ses-2/func/sub-9040_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9040/sub-9040_events.json,/sub-9040/sub-9040_task-sleepiness_events.json,/sub-9040/ses-2/sub-9040_ses-2_events.json,/sub-9040/ses-2/sub-9040_ses-2_task-sleepiness_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_events.json,/sub-9040/ses-2/func/sub-9040_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9040_sessions.tsv
- Location: ds000201/sub-9040/sub-9040_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9040/sub-9040_sessions.json
-
- Type: Warning
- File: sub-9041_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9041/ses-1/beh/sub-9041_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-workingmemorytest_events.json,/sub-9041/ses-1/sub-9041_ses-1_events.json,/sub-9041/ses-1/sub-9041_ses-1_task-workingmemorytest_events.json,/sub-9041/ses-1/beh/sub-9041_ses-1_events.json,/sub-9041/ses-1/beh/sub-9041_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9041_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-arrows_events.json,/sub-9041/ses-1/sub-9041_ses-1_events.json,/sub-9041/ses-1/sub-9041_ses-1_task-arrows_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9041_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-faces_events.json,/sub-9041/ses-1/sub-9041_ses-1_events.json,/sub-9041/ses-1/sub-9041_ses-1_task-faces_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9041_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-hands_events.json,/sub-9041/ses-1/sub-9041_ses-1_events.json,/sub-9041/ses-1/sub-9041_ses-1_task-hands_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9041_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9041/ses-1/func/sub-9041_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-sleepiness_events.json,/sub-9041/ses-1/sub-9041_ses-1_events.json,/sub-9041/ses-1/sub-9041_ses-1_task-sleepiness_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_events.json,/sub-9041/ses-1/func/sub-9041_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9041_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9041/ses-2/beh/sub-9041_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-workingmemorytest_events.json,/sub-9041/ses-2/sub-9041_ses-2_events.json,/sub-9041/ses-2/sub-9041_ses-2_task-workingmemorytest_events.json,/sub-9041/ses-2/beh/sub-9041_ses-2_events.json,/sub-9041/ses-2/beh/sub-9041_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9041_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-arrows_events.json,/sub-9041/ses-2/sub-9041_ses-2_events.json,/sub-9041/ses-2/sub-9041_ses-2_task-arrows_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9041_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-faces_events.json,/sub-9041/ses-2/sub-9041_ses-2_events.json,/sub-9041/ses-2/sub-9041_ses-2_task-faces_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9041_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-hands_events.json,/sub-9041/ses-2/sub-9041_ses-2_events.json,/sub-9041/ses-2/sub-9041_ses-2_task-hands_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9041_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9041/ses-2/func/sub-9041_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9041/sub-9041_events.json,/sub-9041/sub-9041_task-sleepiness_events.json,/sub-9041/ses-2/sub-9041_ses-2_events.json,/sub-9041/ses-2/sub-9041_ses-2_task-sleepiness_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_events.json,/sub-9041/ses-2/func/sub-9041_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9041_sessions.tsv
- Location: ds000201/sub-9041/sub-9041_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9041/sub-9041_sessions.json
-
- Type: Warning
- File: sub-9042_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-arrows_events.json,/sub-9042/ses-1/sub-9042_ses-1_events.json,/sub-9042/ses-1/sub-9042_ses-1_task-arrows_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9042_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-faces_events.json,/sub-9042/ses-1/sub-9042_ses-1_events.json,/sub-9042/ses-1/sub-9042_ses-1_task-faces_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9042_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-hands_events.json,/sub-9042/ses-1/sub-9042_ses-1_events.json,/sub-9042/ses-1/sub-9042_ses-1_task-hands_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9042_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9042/ses-1/func/sub-9042_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-sleepiness_events.json,/sub-9042/ses-1/sub-9042_ses-1_events.json,/sub-9042/ses-1/sub-9042_ses-1_task-sleepiness_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_events.json,/sub-9042/ses-1/func/sub-9042_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9042_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9042/ses-2/beh/sub-9042_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-workingmemorytest_events.json,/sub-9042/ses-2/sub-9042_ses-2_events.json,/sub-9042/ses-2/sub-9042_ses-2_task-workingmemorytest_events.json,/sub-9042/ses-2/beh/sub-9042_ses-2_events.json,/sub-9042/ses-2/beh/sub-9042_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9042_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-arrows_events.json,/sub-9042/ses-2/sub-9042_ses-2_events.json,/sub-9042/ses-2/sub-9042_ses-2_task-arrows_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9042_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-faces_events.json,/sub-9042/ses-2/sub-9042_ses-2_events.json,/sub-9042/ses-2/sub-9042_ses-2_task-faces_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9042_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-hands_events.json,/sub-9042/ses-2/sub-9042_ses-2_events.json,/sub-9042/ses-2/sub-9042_ses-2_task-hands_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9042_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9042/ses-2/func/sub-9042_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9042/sub-9042_events.json,/sub-9042/sub-9042_task-sleepiness_events.json,/sub-9042/ses-2/sub-9042_ses-2_events.json,/sub-9042/ses-2/sub-9042_ses-2_task-sleepiness_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_events.json,/sub-9042/ses-2/func/sub-9042_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9042_sessions.tsv
- Location: ds000201/sub-9042/sub-9042_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9042/sub-9042_sessions.json
-
- Type: Warning
- File: sub-9044_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9044/sub-9044_events.json,/sub-9044/sub-9044_task-arrows_events.json,/sub-9044/ses-1/sub-9044_ses-1_events.json,/sub-9044/ses-1/sub-9044_ses-1_task-arrows_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9044_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9044/sub-9044_events.json,/sub-9044/sub-9044_task-faces_events.json,/sub-9044/ses-1/sub-9044_ses-1_events.json,/sub-9044/ses-1/sub-9044_ses-1_task-faces_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9044_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9044/sub-9044_events.json,/sub-9044/sub-9044_task-hands_events.json,/sub-9044/ses-1/sub-9044_ses-1_events.json,/sub-9044/ses-1/sub-9044_ses-1_task-hands_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9044_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9044/ses-1/func/sub-9044_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9044/sub-9044_events.json,/sub-9044/sub-9044_task-sleepiness_events.json,/sub-9044/ses-1/sub-9044_ses-1_events.json,/sub-9044/ses-1/sub-9044_ses-1_task-sleepiness_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_events.json,/sub-9044/ses-1/func/sub-9044_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9044_sessions.tsv
- Location: ds000201/sub-9044/sub-9044_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9044/sub-9044_sessions.json
-
- Type: Warning
- File: sub-9045_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9045/ses-1/beh/sub-9045_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-workingmemorytest_events.json,/sub-9045/ses-1/sub-9045_ses-1_events.json,/sub-9045/ses-1/sub-9045_ses-1_task-workingmemorytest_events.json,/sub-9045/ses-1/beh/sub-9045_ses-1_events.json,/sub-9045/ses-1/beh/sub-9045_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9045_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-arrows_events.json,/sub-9045/ses-1/sub-9045_ses-1_events.json,/sub-9045/ses-1/sub-9045_ses-1_task-arrows_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9045_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-faces_events.json,/sub-9045/ses-1/sub-9045_ses-1_events.json,/sub-9045/ses-1/sub-9045_ses-1_task-faces_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9045_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-hands_events.json,/sub-9045/ses-1/sub-9045_ses-1_events.json,/sub-9045/ses-1/sub-9045_ses-1_task-hands_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9045_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9045/ses-1/func/sub-9045_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-sleepiness_events.json,/sub-9045/ses-1/sub-9045_ses-1_events.json,/sub-9045/ses-1/sub-9045_ses-1_task-sleepiness_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_events.json,/sub-9045/ses-1/func/sub-9045_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9045_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9045/ses-2/beh/sub-9045_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-workingmemorytest_events.json,/sub-9045/ses-2/sub-9045_ses-2_events.json,/sub-9045/ses-2/sub-9045_ses-2_task-workingmemorytest_events.json,/sub-9045/ses-2/beh/sub-9045_ses-2_events.json,/sub-9045/ses-2/beh/sub-9045_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9045_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-arrows_events.json,/sub-9045/ses-2/sub-9045_ses-2_events.json,/sub-9045/ses-2/sub-9045_ses-2_task-arrows_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9045_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-faces_events.json,/sub-9045/ses-2/sub-9045_ses-2_events.json,/sub-9045/ses-2/sub-9045_ses-2_task-faces_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9045_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-hands_events.json,/sub-9045/ses-2/sub-9045_ses-2_events.json,/sub-9045/ses-2/sub-9045_ses-2_task-hands_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9045_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9045/ses-2/func/sub-9045_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9045/sub-9045_events.json,/sub-9045/sub-9045_task-sleepiness_events.json,/sub-9045/ses-2/sub-9045_ses-2_events.json,/sub-9045/ses-2/sub-9045_ses-2_task-sleepiness_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_events.json,/sub-9045/ses-2/func/sub-9045_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9045_sessions.tsv
- Location: ds000201/sub-9045/sub-9045_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9045/sub-9045_sessions.json
-
- Type: Warning
- File: sub-9046_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9046/ses-1/beh/sub-9046_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-workingmemorytest_events.json,/sub-9046/ses-1/sub-9046_ses-1_events.json,/sub-9046/ses-1/sub-9046_ses-1_task-workingmemorytest_events.json,/sub-9046/ses-1/beh/sub-9046_ses-1_events.json,/sub-9046/ses-1/beh/sub-9046_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9046_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-arrows_events.json,/sub-9046/ses-1/sub-9046_ses-1_events.json,/sub-9046/ses-1/sub-9046_ses-1_task-arrows_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9046_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-faces_events.json,/sub-9046/ses-1/sub-9046_ses-1_events.json,/sub-9046/ses-1/sub-9046_ses-1_task-faces_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9046_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-hands_events.json,/sub-9046/ses-1/sub-9046_ses-1_events.json,/sub-9046/ses-1/sub-9046_ses-1_task-hands_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9046_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9046/ses-1/func/sub-9046_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-sleepiness_events.json,/sub-9046/ses-1/sub-9046_ses-1_events.json,/sub-9046/ses-1/sub-9046_ses-1_task-sleepiness_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_events.json,/sub-9046/ses-1/func/sub-9046_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9046_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9046/ses-2/beh/sub-9046_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-workingmemorytest_events.json,/sub-9046/ses-2/sub-9046_ses-2_events.json,/sub-9046/ses-2/sub-9046_ses-2_task-workingmemorytest_events.json,/sub-9046/ses-2/beh/sub-9046_ses-2_events.json,/sub-9046/ses-2/beh/sub-9046_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9046_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-arrows_events.json,/sub-9046/ses-2/sub-9046_ses-2_events.json,/sub-9046/ses-2/sub-9046_ses-2_task-arrows_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9046_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-faces_events.json,/sub-9046/ses-2/sub-9046_ses-2_events.json,/sub-9046/ses-2/sub-9046_ses-2_task-faces_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9046_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-hands_events.json,/sub-9046/ses-2/sub-9046_ses-2_events.json,/sub-9046/ses-2/sub-9046_ses-2_task-hands_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9046_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9046/ses-2/func/sub-9046_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9046/sub-9046_events.json,/sub-9046/sub-9046_task-sleepiness_events.json,/sub-9046/ses-2/sub-9046_ses-2_events.json,/sub-9046/ses-2/sub-9046_ses-2_task-sleepiness_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_events.json,/sub-9046/ses-2/func/sub-9046_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9046_sessions.tsv
- Location: ds000201/sub-9046/sub-9046_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9046/sub-9046_sessions.json
-
- Type: Warning
- File: sub-9047_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9047/ses-1/beh/sub-9047_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-workingmemorytest_events.json,/sub-9047/ses-1/sub-9047_ses-1_events.json,/sub-9047/ses-1/sub-9047_ses-1_task-workingmemorytest_events.json,/sub-9047/ses-1/beh/sub-9047_ses-1_events.json,/sub-9047/ses-1/beh/sub-9047_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9047_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-arrows_events.json,/sub-9047/ses-1/sub-9047_ses-1_events.json,/sub-9047/ses-1/sub-9047_ses-1_task-arrows_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9047_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-faces_events.json,/sub-9047/ses-1/sub-9047_ses-1_events.json,/sub-9047/ses-1/sub-9047_ses-1_task-faces_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9047_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-hands_events.json,/sub-9047/ses-1/sub-9047_ses-1_events.json,/sub-9047/ses-1/sub-9047_ses-1_task-hands_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9047_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9047/ses-1/func/sub-9047_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-sleepiness_events.json,/sub-9047/ses-1/sub-9047_ses-1_events.json,/sub-9047/ses-1/sub-9047_ses-1_task-sleepiness_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_events.json,/sub-9047/ses-1/func/sub-9047_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9047_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9047/ses-2/beh/sub-9047_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-workingmemorytest_events.json,/sub-9047/ses-2/sub-9047_ses-2_events.json,/sub-9047/ses-2/sub-9047_ses-2_task-workingmemorytest_events.json,/sub-9047/ses-2/beh/sub-9047_ses-2_events.json,/sub-9047/ses-2/beh/sub-9047_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9047_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-arrows_events.json,/sub-9047/ses-2/sub-9047_ses-2_events.json,/sub-9047/ses-2/sub-9047_ses-2_task-arrows_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9047_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-faces_events.json,/sub-9047/ses-2/sub-9047_ses-2_events.json,/sub-9047/ses-2/sub-9047_ses-2_task-faces_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9047_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-hands_events.json,/sub-9047/ses-2/sub-9047_ses-2_events.json,/sub-9047/ses-2/sub-9047_ses-2_task-hands_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9047_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9047/ses-2/func/sub-9047_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9047/sub-9047_events.json,/sub-9047/sub-9047_task-sleepiness_events.json,/sub-9047/ses-2/sub-9047_ses-2_events.json,/sub-9047/ses-2/sub-9047_ses-2_task-sleepiness_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_events.json,/sub-9047/ses-2/func/sub-9047_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9047_sessions.tsv
- Location: ds000201/sub-9047/sub-9047_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9047/sub-9047_sessions.json
-
- Type: Warning
- File: sub-9048_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9048/ses-1/beh/sub-9048_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-workingmemorytest_events.json,/sub-9048/ses-1/sub-9048_ses-1_events.json,/sub-9048/ses-1/sub-9048_ses-1_task-workingmemorytest_events.json,/sub-9048/ses-1/beh/sub-9048_ses-1_events.json,/sub-9048/ses-1/beh/sub-9048_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9048_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-arrows_events.json,/sub-9048/ses-1/sub-9048_ses-1_events.json,/sub-9048/ses-1/sub-9048_ses-1_task-arrows_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9048_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-faces_events.json,/sub-9048/ses-1/sub-9048_ses-1_events.json,/sub-9048/ses-1/sub-9048_ses-1_task-faces_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9048_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-hands_events.json,/sub-9048/ses-1/sub-9048_ses-1_events.json,/sub-9048/ses-1/sub-9048_ses-1_task-hands_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9048_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9048/ses-1/func/sub-9048_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-sleepiness_events.json,/sub-9048/ses-1/sub-9048_ses-1_events.json,/sub-9048/ses-1/sub-9048_ses-1_task-sleepiness_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_events.json,/sub-9048/ses-1/func/sub-9048_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9048_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9048/ses-2/beh/sub-9048_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-workingmemorytest_events.json,/sub-9048/ses-2/sub-9048_ses-2_events.json,/sub-9048/ses-2/sub-9048_ses-2_task-workingmemorytest_events.json,/sub-9048/ses-2/beh/sub-9048_ses-2_events.json,/sub-9048/ses-2/beh/sub-9048_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9048_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-arrows_events.json,/sub-9048/ses-2/sub-9048_ses-2_events.json,/sub-9048/ses-2/sub-9048_ses-2_task-arrows_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9048_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-faces_events.json,/sub-9048/ses-2/sub-9048_ses-2_events.json,/sub-9048/ses-2/sub-9048_ses-2_task-faces_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9048_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-hands_events.json,/sub-9048/ses-2/sub-9048_ses-2_events.json,/sub-9048/ses-2/sub-9048_ses-2_task-hands_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9048_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9048/ses-2/func/sub-9048_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9048/sub-9048_events.json,/sub-9048/sub-9048_task-sleepiness_events.json,/sub-9048/ses-2/sub-9048_ses-2_events.json,/sub-9048/ses-2/sub-9048_ses-2_task-sleepiness_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_events.json,/sub-9048/ses-2/func/sub-9048_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9048_sessions.tsv
- Location: ds000201/sub-9048/sub-9048_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9048/sub-9048_sessions.json
-
- Type: Warning
- File: sub-9049_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9049/ses-1/beh/sub-9049_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-workingmemorytest_events.json,/sub-9049/ses-1/sub-9049_ses-1_events.json,/sub-9049/ses-1/sub-9049_ses-1_task-workingmemorytest_events.json,/sub-9049/ses-1/beh/sub-9049_ses-1_events.json,/sub-9049/ses-1/beh/sub-9049_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9049_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-arrows_events.json,/sub-9049/ses-1/sub-9049_ses-1_events.json,/sub-9049/ses-1/sub-9049_ses-1_task-arrows_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9049_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-faces_events.json,/sub-9049/ses-1/sub-9049_ses-1_events.json,/sub-9049/ses-1/sub-9049_ses-1_task-faces_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9049_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-hands_events.json,/sub-9049/ses-1/sub-9049_ses-1_events.json,/sub-9049/ses-1/sub-9049_ses-1_task-hands_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9049_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9049/ses-1/func/sub-9049_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-sleepiness_events.json,/sub-9049/ses-1/sub-9049_ses-1_events.json,/sub-9049/ses-1/sub-9049_ses-1_task-sleepiness_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_events.json,/sub-9049/ses-1/func/sub-9049_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9049_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9049/ses-2/beh/sub-9049_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-workingmemorytest_events.json,/sub-9049/ses-2/sub-9049_ses-2_events.json,/sub-9049/ses-2/sub-9049_ses-2_task-workingmemorytest_events.json,/sub-9049/ses-2/beh/sub-9049_ses-2_events.json,/sub-9049/ses-2/beh/sub-9049_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9049_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-arrows_events.json,/sub-9049/ses-2/sub-9049_ses-2_events.json,/sub-9049/ses-2/sub-9049_ses-2_task-arrows_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9049_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-faces_events.json,/sub-9049/ses-2/sub-9049_ses-2_events.json,/sub-9049/ses-2/sub-9049_ses-2_task-faces_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9049_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-hands_events.json,/sub-9049/ses-2/sub-9049_ses-2_events.json,/sub-9049/ses-2/sub-9049_ses-2_task-hands_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9049_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9049/ses-2/func/sub-9049_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9049/sub-9049_events.json,/sub-9049/sub-9049_task-sleepiness_events.json,/sub-9049/ses-2/sub-9049_ses-2_events.json,/sub-9049/ses-2/sub-9049_ses-2_task-sleepiness_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_events.json,/sub-9049/ses-2/func/sub-9049_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9049_sessions.tsv
- Location: ds000201/sub-9049/sub-9049_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9049/sub-9049_sessions.json
-
- Type: Warning
- File: sub-9050_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-arrows_events.json,/sub-9050/ses-1/sub-9050_ses-1_events.json,/sub-9050/ses-1/sub-9050_ses-1_task-arrows_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9050_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-faces_events.json,/sub-9050/ses-1/sub-9050_ses-1_events.json,/sub-9050/ses-1/sub-9050_ses-1_task-faces_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9050_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-hands_events.json,/sub-9050/ses-1/sub-9050_ses-1_events.json,/sub-9050/ses-1/sub-9050_ses-1_task-hands_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9050_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9050/ses-1/func/sub-9050_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-sleepiness_events.json,/sub-9050/ses-1/sub-9050_ses-1_events.json,/sub-9050/ses-1/sub-9050_ses-1_task-sleepiness_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_events.json,/sub-9050/ses-1/func/sub-9050_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9050_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-arrows_events.json,/sub-9050/ses-2/sub-9050_ses-2_events.json,/sub-9050/ses-2/sub-9050_ses-2_task-arrows_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9050_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-faces_events.json,/sub-9050/ses-2/sub-9050_ses-2_events.json,/sub-9050/ses-2/sub-9050_ses-2_task-faces_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9050_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-hands_events.json,/sub-9050/ses-2/sub-9050_ses-2_events.json,/sub-9050/ses-2/sub-9050_ses-2_task-hands_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9050_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9050/ses-2/func/sub-9050_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9050/sub-9050_events.json,/sub-9050/sub-9050_task-sleepiness_events.json,/sub-9050/ses-2/sub-9050_ses-2_events.json,/sub-9050/ses-2/sub-9050_ses-2_task-sleepiness_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_events.json,/sub-9050/ses-2/func/sub-9050_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9050_sessions.tsv
- Location: ds000201/sub-9050/sub-9050_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9050/sub-9050_sessions.json
-
- Type: Warning
- File: sub-9053_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-arrows_events.json,/sub-9053/ses-1/sub-9053_ses-1_events.json,/sub-9053/ses-1/sub-9053_ses-1_task-arrows_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9053_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-faces_events.json,/sub-9053/ses-1/sub-9053_ses-1_events.json,/sub-9053/ses-1/sub-9053_ses-1_task-faces_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9053_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-hands_events.json,/sub-9053/ses-1/sub-9053_ses-1_events.json,/sub-9053/ses-1/sub-9053_ses-1_task-hands_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9053_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9053/ses-1/func/sub-9053_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-sleepiness_events.json,/sub-9053/ses-1/sub-9053_ses-1_events.json,/sub-9053/ses-1/sub-9053_ses-1_task-sleepiness_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_events.json,/sub-9053/ses-1/func/sub-9053_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9053_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-arrows_events.json,/sub-9053/ses-2/sub-9053_ses-2_events.json,/sub-9053/ses-2/sub-9053_ses-2_task-arrows_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9053_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-faces_events.json,/sub-9053/ses-2/sub-9053_ses-2_events.json,/sub-9053/ses-2/sub-9053_ses-2_task-faces_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9053_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-hands_events.json,/sub-9053/ses-2/sub-9053_ses-2_events.json,/sub-9053/ses-2/sub-9053_ses-2_task-hands_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9053_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9053/ses-2/func/sub-9053_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9053/sub-9053_events.json,/sub-9053/sub-9053_task-sleepiness_events.json,/sub-9053/ses-2/sub-9053_ses-2_events.json,/sub-9053/ses-2/sub-9053_ses-2_task-sleepiness_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_events.json,/sub-9053/ses-2/func/sub-9053_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9053_sessions.tsv
- Location: ds000201/sub-9053/sub-9053_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9053/sub-9053_sessions.json
-
- Type: Warning
- File: sub-9054_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9054/ses-1/beh/sub-9054_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-workingmemorytest_events.json,/sub-9054/ses-1/sub-9054_ses-1_events.json,/sub-9054/ses-1/sub-9054_ses-1_task-workingmemorytest_events.json,/sub-9054/ses-1/beh/sub-9054_ses-1_events.json,/sub-9054/ses-1/beh/sub-9054_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9054_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-arrows_events.json,/sub-9054/ses-1/sub-9054_ses-1_events.json,/sub-9054/ses-1/sub-9054_ses-1_task-arrows_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9054_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-faces_events.json,/sub-9054/ses-1/sub-9054_ses-1_events.json,/sub-9054/ses-1/sub-9054_ses-1_task-faces_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9054_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-hands_events.json,/sub-9054/ses-1/sub-9054_ses-1_events.json,/sub-9054/ses-1/sub-9054_ses-1_task-hands_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9054_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9054/ses-1/func/sub-9054_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-sleepiness_events.json,/sub-9054/ses-1/sub-9054_ses-1_events.json,/sub-9054/ses-1/sub-9054_ses-1_task-sleepiness_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_events.json,/sub-9054/ses-1/func/sub-9054_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9054_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9054/ses-2/beh/sub-9054_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-workingmemorytest_events.json,/sub-9054/ses-2/sub-9054_ses-2_events.json,/sub-9054/ses-2/sub-9054_ses-2_task-workingmemorytest_events.json,/sub-9054/ses-2/beh/sub-9054_ses-2_events.json,/sub-9054/ses-2/beh/sub-9054_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9054_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-arrows_events.json,/sub-9054/ses-2/sub-9054_ses-2_events.json,/sub-9054/ses-2/sub-9054_ses-2_task-arrows_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9054_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-faces_events.json,/sub-9054/ses-2/sub-9054_ses-2_events.json,/sub-9054/ses-2/sub-9054_ses-2_task-faces_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9054_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-hands_events.json,/sub-9054/ses-2/sub-9054_ses-2_events.json,/sub-9054/ses-2/sub-9054_ses-2_task-hands_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9054_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9054/ses-2/func/sub-9054_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9054/sub-9054_events.json,/sub-9054/sub-9054_task-sleepiness_events.json,/sub-9054/ses-2/sub-9054_ses-2_events.json,/sub-9054/ses-2/sub-9054_ses-2_task-sleepiness_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_events.json,/sub-9054/ses-2/func/sub-9054_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9054_sessions.tsv
- Location: ds000201/sub-9054/sub-9054_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9054/sub-9054_sessions.json
-
- Type: Warning
- File: sub-9055_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-arrows_events.json,/sub-9055/ses-1/sub-9055_ses-1_events.json,/sub-9055/ses-1/sub-9055_ses-1_task-arrows_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9055_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-faces_events.json,/sub-9055/ses-1/sub-9055_ses-1_events.json,/sub-9055/ses-1/sub-9055_ses-1_task-faces_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9055_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-hands_events.json,/sub-9055/ses-1/sub-9055_ses-1_events.json,/sub-9055/ses-1/sub-9055_ses-1_task-hands_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9055_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9055/ses-1/func/sub-9055_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-sleepiness_events.json,/sub-9055/ses-1/sub-9055_ses-1_events.json,/sub-9055/ses-1/sub-9055_ses-1_task-sleepiness_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_events.json,/sub-9055/ses-1/func/sub-9055_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9055_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9055/ses-2/beh/sub-9055_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-workingmemorytest_events.json,/sub-9055/ses-2/sub-9055_ses-2_events.json,/sub-9055/ses-2/sub-9055_ses-2_task-workingmemorytest_events.json,/sub-9055/ses-2/beh/sub-9055_ses-2_events.json,/sub-9055/ses-2/beh/sub-9055_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9055_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-arrows_events.json,/sub-9055/ses-2/sub-9055_ses-2_events.json,/sub-9055/ses-2/sub-9055_ses-2_task-arrows_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9055_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-faces_events.json,/sub-9055/ses-2/sub-9055_ses-2_events.json,/sub-9055/ses-2/sub-9055_ses-2_task-faces_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9055_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-hands_events.json,/sub-9055/ses-2/sub-9055_ses-2_events.json,/sub-9055/ses-2/sub-9055_ses-2_task-hands_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9055_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9055/ses-2/func/sub-9055_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9055/sub-9055_events.json,/sub-9055/sub-9055_task-sleepiness_events.json,/sub-9055/ses-2/sub-9055_ses-2_events.json,/sub-9055/ses-2/sub-9055_ses-2_task-sleepiness_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_events.json,/sub-9055/ses-2/func/sub-9055_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9055_sessions.tsv
- Location: ds000201/sub-9055/sub-9055_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9055/sub-9055_sessions.json
-
- Type: Warning
- File: sub-9056_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-arrows_events.json,/sub-9056/ses-1/sub-9056_ses-1_events.json,/sub-9056/ses-1/sub-9056_ses-1_task-arrows_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9056_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-faces_events.json,/sub-9056/ses-1/sub-9056_ses-1_events.json,/sub-9056/ses-1/sub-9056_ses-1_task-faces_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9056_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-hands_events.json,/sub-9056/ses-1/sub-9056_ses-1_events.json,/sub-9056/ses-1/sub-9056_ses-1_task-hands_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9056_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9056/ses-1/func/sub-9056_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-sleepiness_events.json,/sub-9056/ses-1/sub-9056_ses-1_events.json,/sub-9056/ses-1/sub-9056_ses-1_task-sleepiness_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_events.json,/sub-9056/ses-1/func/sub-9056_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9056_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9056/ses-2/beh/sub-9056_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-workingmemorytest_events.json,/sub-9056/ses-2/sub-9056_ses-2_events.json,/sub-9056/ses-2/sub-9056_ses-2_task-workingmemorytest_events.json,/sub-9056/ses-2/beh/sub-9056_ses-2_events.json,/sub-9056/ses-2/beh/sub-9056_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9056_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-arrows_events.json,/sub-9056/ses-2/sub-9056_ses-2_events.json,/sub-9056/ses-2/sub-9056_ses-2_task-arrows_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9056_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-faces_events.json,/sub-9056/ses-2/sub-9056_ses-2_events.json,/sub-9056/ses-2/sub-9056_ses-2_task-faces_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9056_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-hands_events.json,/sub-9056/ses-2/sub-9056_ses-2_events.json,/sub-9056/ses-2/sub-9056_ses-2_task-hands_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9056_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9056/ses-2/func/sub-9056_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9056/sub-9056_events.json,/sub-9056/sub-9056_task-sleepiness_events.json,/sub-9056/ses-2/sub-9056_ses-2_events.json,/sub-9056/ses-2/sub-9056_ses-2_task-sleepiness_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_events.json,/sub-9056/ses-2/func/sub-9056_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9056_sessions.tsv
- Location: ds000201/sub-9056/sub-9056_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9056/sub-9056_sessions.json
-
- Type: Warning
- File: sub-9057_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-arrows_events.json,/sub-9057/ses-1/sub-9057_ses-1_events.json,/sub-9057/ses-1/sub-9057_ses-1_task-arrows_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9057_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-faces_events.json,/sub-9057/ses-1/sub-9057_ses-1_events.json,/sub-9057/ses-1/sub-9057_ses-1_task-faces_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9057_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-hands_events.json,/sub-9057/ses-1/sub-9057_ses-1_events.json,/sub-9057/ses-1/sub-9057_ses-1_task-hands_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9057_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9057/ses-1/func/sub-9057_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-sleepiness_events.json,/sub-9057/ses-1/sub-9057_ses-1_events.json,/sub-9057/ses-1/sub-9057_ses-1_task-sleepiness_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_events.json,/sub-9057/ses-1/func/sub-9057_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9057_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-arrows_events.json,/sub-9057/ses-2/sub-9057_ses-2_events.json,/sub-9057/ses-2/sub-9057_ses-2_task-arrows_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9057_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-faces_events.json,/sub-9057/ses-2/sub-9057_ses-2_events.json,/sub-9057/ses-2/sub-9057_ses-2_task-faces_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9057_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-hands_events.json,/sub-9057/ses-2/sub-9057_ses-2_events.json,/sub-9057/ses-2/sub-9057_ses-2_task-hands_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9057_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9057/ses-2/func/sub-9057_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9057/sub-9057_events.json,/sub-9057/sub-9057_task-sleepiness_events.json,/sub-9057/ses-2/sub-9057_ses-2_events.json,/sub-9057/ses-2/sub-9057_ses-2_task-sleepiness_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_events.json,/sub-9057/ses-2/func/sub-9057_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9057_sessions.tsv
- Location: ds000201/sub-9057/sub-9057_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9057/sub-9057_sessions.json
-
- Type: Warning
- File: sub-9058_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9058/ses-1/beh/sub-9058_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-workingmemorytest_events.json,/sub-9058/ses-1/sub-9058_ses-1_events.json,/sub-9058/ses-1/sub-9058_ses-1_task-workingmemorytest_events.json,/sub-9058/ses-1/beh/sub-9058_ses-1_events.json,/sub-9058/ses-1/beh/sub-9058_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9058_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-arrows_events.json,/sub-9058/ses-1/sub-9058_ses-1_events.json,/sub-9058/ses-1/sub-9058_ses-1_task-arrows_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9058_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-faces_events.json,/sub-9058/ses-1/sub-9058_ses-1_events.json,/sub-9058/ses-1/sub-9058_ses-1_task-faces_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9058_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-hands_events.json,/sub-9058/ses-1/sub-9058_ses-1_events.json,/sub-9058/ses-1/sub-9058_ses-1_task-hands_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9058_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9058/ses-1/func/sub-9058_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-sleepiness_events.json,/sub-9058/ses-1/sub-9058_ses-1_events.json,/sub-9058/ses-1/sub-9058_ses-1_task-sleepiness_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_events.json,/sub-9058/ses-1/func/sub-9058_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9058_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9058/ses-2/beh/sub-9058_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-workingmemorytest_events.json,/sub-9058/ses-2/sub-9058_ses-2_events.json,/sub-9058/ses-2/sub-9058_ses-2_task-workingmemorytest_events.json,/sub-9058/ses-2/beh/sub-9058_ses-2_events.json,/sub-9058/ses-2/beh/sub-9058_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9058_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-arrows_events.json,/sub-9058/ses-2/sub-9058_ses-2_events.json,/sub-9058/ses-2/sub-9058_ses-2_task-arrows_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9058_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-faces_events.json,/sub-9058/ses-2/sub-9058_ses-2_events.json,/sub-9058/ses-2/sub-9058_ses-2_task-faces_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9058_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-hands_events.json,/sub-9058/ses-2/sub-9058_ses-2_events.json,/sub-9058/ses-2/sub-9058_ses-2_task-hands_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9058_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9058/ses-2/func/sub-9058_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9058/sub-9058_events.json,/sub-9058/sub-9058_task-sleepiness_events.json,/sub-9058/ses-2/sub-9058_ses-2_events.json,/sub-9058/ses-2/sub-9058_ses-2_task-sleepiness_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_events.json,/sub-9058/ses-2/func/sub-9058_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9058_sessions.tsv
- Location: ds000201/sub-9058/sub-9058_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9058/sub-9058_sessions.json
-
- Type: Warning
- File: sub-9059_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-arrows_events.json,/sub-9059/ses-1/sub-9059_ses-1_events.json,/sub-9059/ses-1/sub-9059_ses-1_task-arrows_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9059_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-faces_events.json,/sub-9059/ses-1/sub-9059_ses-1_events.json,/sub-9059/ses-1/sub-9059_ses-1_task-faces_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9059_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-hands_events.json,/sub-9059/ses-1/sub-9059_ses-1_events.json,/sub-9059/ses-1/sub-9059_ses-1_task-hands_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9059_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9059/ses-1/func/sub-9059_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-sleepiness_events.json,/sub-9059/ses-1/sub-9059_ses-1_events.json,/sub-9059/ses-1/sub-9059_ses-1_task-sleepiness_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_events.json,/sub-9059/ses-1/func/sub-9059_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9059_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9059/ses-2/beh/sub-9059_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-workingmemorytest_events.json,/sub-9059/ses-2/sub-9059_ses-2_events.json,/sub-9059/ses-2/sub-9059_ses-2_task-workingmemorytest_events.json,/sub-9059/ses-2/beh/sub-9059_ses-2_events.json,/sub-9059/ses-2/beh/sub-9059_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9059_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-arrows_events.json,/sub-9059/ses-2/sub-9059_ses-2_events.json,/sub-9059/ses-2/sub-9059_ses-2_task-arrows_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9059_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-faces_events.json,/sub-9059/ses-2/sub-9059_ses-2_events.json,/sub-9059/ses-2/sub-9059_ses-2_task-faces_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9059_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-hands_events.json,/sub-9059/ses-2/sub-9059_ses-2_events.json,/sub-9059/ses-2/sub-9059_ses-2_task-hands_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9059_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9059/ses-2/func/sub-9059_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9059/sub-9059_events.json,/sub-9059/sub-9059_task-sleepiness_events.json,/sub-9059/ses-2/sub-9059_ses-2_events.json,/sub-9059/ses-2/sub-9059_ses-2_task-sleepiness_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_events.json,/sub-9059/ses-2/func/sub-9059_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9059_sessions.tsv
- Location: ds000201/sub-9059/sub-9059_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9059/sub-9059_sessions.json
-
- Type: Warning
- File: sub-9061_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9061/ses-1/beh/sub-9061_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-workingmemorytest_events.json,/sub-9061/ses-1/sub-9061_ses-1_events.json,/sub-9061/ses-1/sub-9061_ses-1_task-workingmemorytest_events.json,/sub-9061/ses-1/beh/sub-9061_ses-1_events.json,/sub-9061/ses-1/beh/sub-9061_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9061_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-arrows_events.json,/sub-9061/ses-1/sub-9061_ses-1_events.json,/sub-9061/ses-1/sub-9061_ses-1_task-arrows_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9061_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-faces_events.json,/sub-9061/ses-1/sub-9061_ses-1_events.json,/sub-9061/ses-1/sub-9061_ses-1_task-faces_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9061_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-hands_events.json,/sub-9061/ses-1/sub-9061_ses-1_events.json,/sub-9061/ses-1/sub-9061_ses-1_task-hands_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9061_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9061/ses-1/func/sub-9061_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-sleepiness_events.json,/sub-9061/ses-1/sub-9061_ses-1_events.json,/sub-9061/ses-1/sub-9061_ses-1_task-sleepiness_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_events.json,/sub-9061/ses-1/func/sub-9061_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9061_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9061/ses-2/beh/sub-9061_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-workingmemorytest_events.json,/sub-9061/ses-2/sub-9061_ses-2_events.json,/sub-9061/ses-2/sub-9061_ses-2_task-workingmemorytest_events.json,/sub-9061/ses-2/beh/sub-9061_ses-2_events.json,/sub-9061/ses-2/beh/sub-9061_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9061_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-arrows_events.json,/sub-9061/ses-2/sub-9061_ses-2_events.json,/sub-9061/ses-2/sub-9061_ses-2_task-arrows_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9061_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-faces_events.json,/sub-9061/ses-2/sub-9061_ses-2_events.json,/sub-9061/ses-2/sub-9061_ses-2_task-faces_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9061_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-hands_events.json,/sub-9061/ses-2/sub-9061_ses-2_events.json,/sub-9061/ses-2/sub-9061_ses-2_task-hands_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9061_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9061/ses-2/func/sub-9061_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9061/sub-9061_events.json,/sub-9061/sub-9061_task-sleepiness_events.json,/sub-9061/ses-2/sub-9061_ses-2_events.json,/sub-9061/ses-2/sub-9061_ses-2_task-sleepiness_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_events.json,/sub-9061/ses-2/func/sub-9061_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9061_sessions.tsv
- Location: ds000201/sub-9061/sub-9061_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9061/sub-9061_sessions.json
-
- Type: Warning
- File: sub-9062_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9062/ses-1/beh/sub-9062_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-workingmemorytest_events.json,/sub-9062/ses-1/sub-9062_ses-1_events.json,/sub-9062/ses-1/sub-9062_ses-1_task-workingmemorytest_events.json,/sub-9062/ses-1/beh/sub-9062_ses-1_events.json,/sub-9062/ses-1/beh/sub-9062_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9062_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-arrows_events.json,/sub-9062/ses-1/sub-9062_ses-1_events.json,/sub-9062/ses-1/sub-9062_ses-1_task-arrows_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9062_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-faces_events.json,/sub-9062/ses-1/sub-9062_ses-1_events.json,/sub-9062/ses-1/sub-9062_ses-1_task-faces_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9062_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-hands_events.json,/sub-9062/ses-1/sub-9062_ses-1_events.json,/sub-9062/ses-1/sub-9062_ses-1_task-hands_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9062_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9062/ses-1/func/sub-9062_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-sleepiness_events.json,/sub-9062/ses-1/sub-9062_ses-1_events.json,/sub-9062/ses-1/sub-9062_ses-1_task-sleepiness_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_events.json,/sub-9062/ses-1/func/sub-9062_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9062_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9062/ses-2/beh/sub-9062_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-workingmemorytest_events.json,/sub-9062/ses-2/sub-9062_ses-2_events.json,/sub-9062/ses-2/sub-9062_ses-2_task-workingmemorytest_events.json,/sub-9062/ses-2/beh/sub-9062_ses-2_events.json,/sub-9062/ses-2/beh/sub-9062_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9062_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-arrows_events.json,/sub-9062/ses-2/sub-9062_ses-2_events.json,/sub-9062/ses-2/sub-9062_ses-2_task-arrows_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9062_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-faces_events.json,/sub-9062/ses-2/sub-9062_ses-2_events.json,/sub-9062/ses-2/sub-9062_ses-2_task-faces_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9062_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-hands_events.json,/sub-9062/ses-2/sub-9062_ses-2_events.json,/sub-9062/ses-2/sub-9062_ses-2_task-hands_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9062_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9062/ses-2/func/sub-9062_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9062/sub-9062_events.json,/sub-9062/sub-9062_task-sleepiness_events.json,/sub-9062/ses-2/sub-9062_ses-2_events.json,/sub-9062/ses-2/sub-9062_ses-2_task-sleepiness_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_events.json,/sub-9062/ses-2/func/sub-9062_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9062_sessions.tsv
- Location: ds000201/sub-9062/sub-9062_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9062/sub-9062_sessions.json
-
- Type: Warning
- File: sub-9063_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9063/ses-1/beh/sub-9063_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-workingmemorytest_events.json,/sub-9063/ses-1/sub-9063_ses-1_events.json,/sub-9063/ses-1/sub-9063_ses-1_task-workingmemorytest_events.json,/sub-9063/ses-1/beh/sub-9063_ses-1_events.json,/sub-9063/ses-1/beh/sub-9063_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9063_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-arrows_events.json,/sub-9063/ses-1/sub-9063_ses-1_events.json,/sub-9063/ses-1/sub-9063_ses-1_task-arrows_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9063_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-faces_events.json,/sub-9063/ses-1/sub-9063_ses-1_events.json,/sub-9063/ses-1/sub-9063_ses-1_task-faces_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9063_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-hands_events.json,/sub-9063/ses-1/sub-9063_ses-1_events.json,/sub-9063/ses-1/sub-9063_ses-1_task-hands_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9063_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9063/ses-1/func/sub-9063_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-sleepiness_events.json,/sub-9063/ses-1/sub-9063_ses-1_events.json,/sub-9063/ses-1/sub-9063_ses-1_task-sleepiness_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_events.json,/sub-9063/ses-1/func/sub-9063_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9063_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9063/ses-2/beh/sub-9063_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-workingmemorytest_events.json,/sub-9063/ses-2/sub-9063_ses-2_events.json,/sub-9063/ses-2/sub-9063_ses-2_task-workingmemorytest_events.json,/sub-9063/ses-2/beh/sub-9063_ses-2_events.json,/sub-9063/ses-2/beh/sub-9063_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9063_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-arrows_events.json,/sub-9063/ses-2/sub-9063_ses-2_events.json,/sub-9063/ses-2/sub-9063_ses-2_task-arrows_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9063_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-faces_events.json,/sub-9063/ses-2/sub-9063_ses-2_events.json,/sub-9063/ses-2/sub-9063_ses-2_task-faces_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9063_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-hands_events.json,/sub-9063/ses-2/sub-9063_ses-2_events.json,/sub-9063/ses-2/sub-9063_ses-2_task-hands_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9063_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9063/ses-2/func/sub-9063_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9063/sub-9063_events.json,/sub-9063/sub-9063_task-sleepiness_events.json,/sub-9063/ses-2/sub-9063_ses-2_events.json,/sub-9063/ses-2/sub-9063_ses-2_task-sleepiness_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_events.json,/sub-9063/ses-2/func/sub-9063_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9063_sessions.tsv
- Location: ds000201/sub-9063/sub-9063_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9063/sub-9063_sessions.json
-
- Type: Warning
- File: sub-9064_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9064/ses-1/beh/sub-9064_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-workingmemorytest_events.json,/sub-9064/ses-1/sub-9064_ses-1_events.json,/sub-9064/ses-1/sub-9064_ses-1_task-workingmemorytest_events.json,/sub-9064/ses-1/beh/sub-9064_ses-1_events.json,/sub-9064/ses-1/beh/sub-9064_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9064_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-arrows_events.json,/sub-9064/ses-1/sub-9064_ses-1_events.json,/sub-9064/ses-1/sub-9064_ses-1_task-arrows_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9064_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-faces_events.json,/sub-9064/ses-1/sub-9064_ses-1_events.json,/sub-9064/ses-1/sub-9064_ses-1_task-faces_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9064_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-hands_events.json,/sub-9064/ses-1/sub-9064_ses-1_events.json,/sub-9064/ses-1/sub-9064_ses-1_task-hands_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9064_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9064/ses-1/func/sub-9064_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-sleepiness_events.json,/sub-9064/ses-1/sub-9064_ses-1_events.json,/sub-9064/ses-1/sub-9064_ses-1_task-sleepiness_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_events.json,/sub-9064/ses-1/func/sub-9064_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9064_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9064/ses-2/beh/sub-9064_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-workingmemorytest_events.json,/sub-9064/ses-2/sub-9064_ses-2_events.json,/sub-9064/ses-2/sub-9064_ses-2_task-workingmemorytest_events.json,/sub-9064/ses-2/beh/sub-9064_ses-2_events.json,/sub-9064/ses-2/beh/sub-9064_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9064_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-arrows_events.json,/sub-9064/ses-2/sub-9064_ses-2_events.json,/sub-9064/ses-2/sub-9064_ses-2_task-arrows_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9064_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-faces_events.json,/sub-9064/ses-2/sub-9064_ses-2_events.json,/sub-9064/ses-2/sub-9064_ses-2_task-faces_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9064_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-hands_events.json,/sub-9064/ses-2/sub-9064_ses-2_events.json,/sub-9064/ses-2/sub-9064_ses-2_task-hands_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9064_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9064/ses-2/func/sub-9064_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9064/sub-9064_events.json,/sub-9064/sub-9064_task-sleepiness_events.json,/sub-9064/ses-2/sub-9064_ses-2_events.json,/sub-9064/ses-2/sub-9064_ses-2_task-sleepiness_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_events.json,/sub-9064/ses-2/func/sub-9064_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9064_sessions.tsv
- Location: ds000201/sub-9064/sub-9064_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9064/sub-9064_sessions.json
-
- Type: Warning
- File: sub-9065_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9065/ses-1/beh/sub-9065_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-workingmemorytest_events.json,/sub-9065/ses-1/sub-9065_ses-1_events.json,/sub-9065/ses-1/sub-9065_ses-1_task-workingmemorytest_events.json,/sub-9065/ses-1/beh/sub-9065_ses-1_events.json,/sub-9065/ses-1/beh/sub-9065_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9065_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-arrows_events.json,/sub-9065/ses-1/sub-9065_ses-1_events.json,/sub-9065/ses-1/sub-9065_ses-1_task-arrows_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9065_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-faces_events.json,/sub-9065/ses-1/sub-9065_ses-1_events.json,/sub-9065/ses-1/sub-9065_ses-1_task-faces_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9065_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-hands_events.json,/sub-9065/ses-1/sub-9065_ses-1_events.json,/sub-9065/ses-1/sub-9065_ses-1_task-hands_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9065_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9065/ses-1/func/sub-9065_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-sleepiness_events.json,/sub-9065/ses-1/sub-9065_ses-1_events.json,/sub-9065/ses-1/sub-9065_ses-1_task-sleepiness_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_events.json,/sub-9065/ses-1/func/sub-9065_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9065_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9065/ses-2/beh/sub-9065_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-workingmemorytest_events.json,/sub-9065/ses-2/sub-9065_ses-2_events.json,/sub-9065/ses-2/sub-9065_ses-2_task-workingmemorytest_events.json,/sub-9065/ses-2/beh/sub-9065_ses-2_events.json,/sub-9065/ses-2/beh/sub-9065_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9065_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-arrows_events.json,/sub-9065/ses-2/sub-9065_ses-2_events.json,/sub-9065/ses-2/sub-9065_ses-2_task-arrows_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9065_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-faces_events.json,/sub-9065/ses-2/sub-9065_ses-2_events.json,/sub-9065/ses-2/sub-9065_ses-2_task-faces_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9065_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-hands_events.json,/sub-9065/ses-2/sub-9065_ses-2_events.json,/sub-9065/ses-2/sub-9065_ses-2_task-hands_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9065_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9065/ses-2/func/sub-9065_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9065/sub-9065_events.json,/sub-9065/sub-9065_task-sleepiness_events.json,/sub-9065/ses-2/sub-9065_ses-2_events.json,/sub-9065/ses-2/sub-9065_ses-2_task-sleepiness_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_events.json,/sub-9065/ses-2/func/sub-9065_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9065_sessions.tsv
- Location: ds000201/sub-9065/sub-9065_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9065/sub-9065_sessions.json
-
- Type: Warning
- File: sub-9066_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: condition not defined, please define in: /events.json, /task-hands_events.json,/sub-9066/sub-9066_events.json,/sub-9066/sub-9066_task-hands_events.json,/sub-9066/ses-1/sub-9066_ses-1_events.json,/sub-9066/ses-1/sub-9066_ses-1_task-hands_events.json,/sub-9066/ses-1/func/sub-9066_ses-1_events.json,/sub-9066/ses-1/func/sub-9066_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9066_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9066/ses-1/func/sub-9066_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9066/sub-9066_events.json,/sub-9066/sub-9066_task-sleepiness_events.json,/sub-9066/ses-1/sub-9066_ses-1_events.json,/sub-9066/ses-1/sub-9066_ses-1_task-sleepiness_events.json,/sub-9066/ses-1/func/sub-9066_ses-1_events.json,/sub-9066/ses-1/func/sub-9066_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9066_sessions.tsv
- Location: ds000201/sub-9066/sub-9066_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9066/sub-9066_sessions.json
-
- Type: Warning
- File: sub-9067_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-arrows_events.json,/sub-9067/ses-1/sub-9067_ses-1_events.json,/sub-9067/ses-1/sub-9067_ses-1_task-arrows_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9067_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-faces_events.json,/sub-9067/ses-1/sub-9067_ses-1_events.json,/sub-9067/ses-1/sub-9067_ses-1_task-faces_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9067_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-hands_events.json,/sub-9067/ses-1/sub-9067_ses-1_events.json,/sub-9067/ses-1/sub-9067_ses-1_task-hands_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9067_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9067/ses-1/func/sub-9067_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-sleepiness_events.json,/sub-9067/ses-1/sub-9067_ses-1_events.json,/sub-9067/ses-1/sub-9067_ses-1_task-sleepiness_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_events.json,/sub-9067/ses-1/func/sub-9067_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9067_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-arrows_events.json,/sub-9067/ses-2/sub-9067_ses-2_events.json,/sub-9067/ses-2/sub-9067_ses-2_task-arrows_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9067_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-faces_events.json,/sub-9067/ses-2/sub-9067_ses-2_events.json,/sub-9067/ses-2/sub-9067_ses-2_task-faces_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9067_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-hands_events.json,/sub-9067/ses-2/sub-9067_ses-2_events.json,/sub-9067/ses-2/sub-9067_ses-2_task-hands_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9067_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9067/ses-2/func/sub-9067_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9067/sub-9067_events.json,/sub-9067/sub-9067_task-sleepiness_events.json,/sub-9067/ses-2/sub-9067_ses-2_events.json,/sub-9067/ses-2/sub-9067_ses-2_task-sleepiness_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_events.json,/sub-9067/ses-2/func/sub-9067_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9067_sessions.tsv
- Location: ds000201/sub-9067/sub-9067_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9067/sub-9067_sessions.json
-
- Type: Warning
- File: sub-9068_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9068/ses-1/beh/sub-9068_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-workingmemorytest_events.json,/sub-9068/ses-1/sub-9068_ses-1_events.json,/sub-9068/ses-1/sub-9068_ses-1_task-workingmemorytest_events.json,/sub-9068/ses-1/beh/sub-9068_ses-1_events.json,/sub-9068/ses-1/beh/sub-9068_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9068_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-arrows_events.json,/sub-9068/ses-1/sub-9068_ses-1_events.json,/sub-9068/ses-1/sub-9068_ses-1_task-arrows_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9068_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-faces_events.json,/sub-9068/ses-1/sub-9068_ses-1_events.json,/sub-9068/ses-1/sub-9068_ses-1_task-faces_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9068_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-hands_events.json,/sub-9068/ses-1/sub-9068_ses-1_events.json,/sub-9068/ses-1/sub-9068_ses-1_task-hands_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9068_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9068/ses-1/func/sub-9068_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-sleepiness_events.json,/sub-9068/ses-1/sub-9068_ses-1_events.json,/sub-9068/ses-1/sub-9068_ses-1_task-sleepiness_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_events.json,/sub-9068/ses-1/func/sub-9068_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9068_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9068/ses-2/beh/sub-9068_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-workingmemorytest_events.json,/sub-9068/ses-2/sub-9068_ses-2_events.json,/sub-9068/ses-2/sub-9068_ses-2_task-workingmemorytest_events.json,/sub-9068/ses-2/beh/sub-9068_ses-2_events.json,/sub-9068/ses-2/beh/sub-9068_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9068_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-arrows_events.json,/sub-9068/ses-2/sub-9068_ses-2_events.json,/sub-9068/ses-2/sub-9068_ses-2_task-arrows_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9068_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-faces_events.json,/sub-9068/ses-2/sub-9068_ses-2_events.json,/sub-9068/ses-2/sub-9068_ses-2_task-faces_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9068_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-hands_events.json,/sub-9068/ses-2/sub-9068_ses-2_events.json,/sub-9068/ses-2/sub-9068_ses-2_task-hands_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9068_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9068/ses-2/func/sub-9068_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9068/sub-9068_events.json,/sub-9068/sub-9068_task-sleepiness_events.json,/sub-9068/ses-2/sub-9068_ses-2_events.json,/sub-9068/ses-2/sub-9068_ses-2_task-sleepiness_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_events.json,/sub-9068/ses-2/func/sub-9068_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9068_sessions.tsv
- Location: ds000201/sub-9068/sub-9068_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9068/sub-9068_sessions.json
-
- Type: Warning
- File: sub-9069_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9069/ses-1/beh/sub-9069_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-workingmemorytest_events.json,/sub-9069/ses-1/sub-9069_ses-1_events.json,/sub-9069/ses-1/sub-9069_ses-1_task-workingmemorytest_events.json,/sub-9069/ses-1/beh/sub-9069_ses-1_events.json,/sub-9069/ses-1/beh/sub-9069_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9069_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-arrows_events.json,/sub-9069/ses-1/sub-9069_ses-1_events.json,/sub-9069/ses-1/sub-9069_ses-1_task-arrows_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9069_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-faces_events.json,/sub-9069/ses-1/sub-9069_ses-1_events.json,/sub-9069/ses-1/sub-9069_ses-1_task-faces_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9069_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-hands_events.json,/sub-9069/ses-1/sub-9069_ses-1_events.json,/sub-9069/ses-1/sub-9069_ses-1_task-hands_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9069_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9069/ses-1/func/sub-9069_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-sleepiness_events.json,/sub-9069/ses-1/sub-9069_ses-1_events.json,/sub-9069/ses-1/sub-9069_ses-1_task-sleepiness_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_events.json,/sub-9069/ses-1/func/sub-9069_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9069_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9069/ses-2/beh/sub-9069_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-workingmemorytest_events.json,/sub-9069/ses-2/sub-9069_ses-2_events.json,/sub-9069/ses-2/sub-9069_ses-2_task-workingmemorytest_events.json,/sub-9069/ses-2/beh/sub-9069_ses-2_events.json,/sub-9069/ses-2/beh/sub-9069_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9069_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-arrows_events.json,/sub-9069/ses-2/sub-9069_ses-2_events.json,/sub-9069/ses-2/sub-9069_ses-2_task-arrows_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9069_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-faces_events.json,/sub-9069/ses-2/sub-9069_ses-2_events.json,/sub-9069/ses-2/sub-9069_ses-2_task-faces_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9069_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-hands_events.json,/sub-9069/ses-2/sub-9069_ses-2_events.json,/sub-9069/ses-2/sub-9069_ses-2_task-hands_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9069_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9069/ses-2/func/sub-9069_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9069/sub-9069_events.json,/sub-9069/sub-9069_task-sleepiness_events.json,/sub-9069/ses-2/sub-9069_ses-2_events.json,/sub-9069/ses-2/sub-9069_ses-2_task-sleepiness_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_events.json,/sub-9069/ses-2/func/sub-9069_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9069_sessions.tsv
- Location: ds000201/sub-9069/sub-9069_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9069/sub-9069_sessions.json
-
- Type: Warning
- File: sub-9070_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-arrows_events.json,/sub-9070/ses-1/sub-9070_ses-1_events.json,/sub-9070/ses-1/sub-9070_ses-1_task-arrows_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9070_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-faces_events.json,/sub-9070/ses-1/sub-9070_ses-1_events.json,/sub-9070/ses-1/sub-9070_ses-1_task-faces_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9070_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-hands_events.json,/sub-9070/ses-1/sub-9070_ses-1_events.json,/sub-9070/ses-1/sub-9070_ses-1_task-hands_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9070_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9070/ses-1/func/sub-9070_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-sleepiness_events.json,/sub-9070/ses-1/sub-9070_ses-1_events.json,/sub-9070/ses-1/sub-9070_ses-1_task-sleepiness_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_events.json,/sub-9070/ses-1/func/sub-9070_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9070_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-arrows_events.json,/sub-9070/ses-2/sub-9070_ses-2_events.json,/sub-9070/ses-2/sub-9070_ses-2_task-arrows_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9070_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-faces_events.json,/sub-9070/ses-2/sub-9070_ses-2_events.json,/sub-9070/ses-2/sub-9070_ses-2_task-faces_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9070_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-hands_events.json,/sub-9070/ses-2/sub-9070_ses-2_events.json,/sub-9070/ses-2/sub-9070_ses-2_task-hands_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9070_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9070/ses-2/func/sub-9070_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9070/sub-9070_events.json,/sub-9070/sub-9070_task-sleepiness_events.json,/sub-9070/ses-2/sub-9070_ses-2_events.json,/sub-9070/ses-2/sub-9070_ses-2_task-sleepiness_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_events.json,/sub-9070/ses-2/func/sub-9070_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9070_sessions.tsv
- Location: ds000201/sub-9070/sub-9070_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9070/sub-9070_sessions.json
-
- Type: Warning
- File: sub-9071_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-arrows_events.json,/sub-9071/ses-1/sub-9071_ses-1_events.json,/sub-9071/ses-1/sub-9071_ses-1_task-arrows_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9071_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-faces_events.json,/sub-9071/ses-1/sub-9071_ses-1_events.json,/sub-9071/ses-1/sub-9071_ses-1_task-faces_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9071_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-hands_events.json,/sub-9071/ses-1/sub-9071_ses-1_events.json,/sub-9071/ses-1/sub-9071_ses-1_task-hands_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9071_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9071/ses-1/func/sub-9071_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-sleepiness_events.json,/sub-9071/ses-1/sub-9071_ses-1_events.json,/sub-9071/ses-1/sub-9071_ses-1_task-sleepiness_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_events.json,/sub-9071/ses-1/func/sub-9071_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9071_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9071/ses-2/beh/sub-9071_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-workingmemorytest_events.json,/sub-9071/ses-2/sub-9071_ses-2_events.json,/sub-9071/ses-2/sub-9071_ses-2_task-workingmemorytest_events.json,/sub-9071/ses-2/beh/sub-9071_ses-2_events.json,/sub-9071/ses-2/beh/sub-9071_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9071_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-arrows_events.json,/sub-9071/ses-2/sub-9071_ses-2_events.json,/sub-9071/ses-2/sub-9071_ses-2_task-arrows_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9071_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-faces_events.json,/sub-9071/ses-2/sub-9071_ses-2_events.json,/sub-9071/ses-2/sub-9071_ses-2_task-faces_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9071_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-hands_events.json,/sub-9071/ses-2/sub-9071_ses-2_events.json,/sub-9071/ses-2/sub-9071_ses-2_task-hands_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9071_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9071/ses-2/func/sub-9071_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9071/sub-9071_events.json,/sub-9071/sub-9071_task-sleepiness_events.json,/sub-9071/ses-2/sub-9071_ses-2_events.json,/sub-9071/ses-2/sub-9071_ses-2_task-sleepiness_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_events.json,/sub-9071/ses-2/func/sub-9071_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9071_sessions.tsv
- Location: ds000201/sub-9071/sub-9071_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9071/sub-9071_sessions.json
-
- Type: Warning
- File: sub-9072_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9072/ses-1/beh/sub-9072_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-workingmemorytest_events.json,/sub-9072/ses-1/sub-9072_ses-1_events.json,/sub-9072/ses-1/sub-9072_ses-1_task-workingmemorytest_events.json,/sub-9072/ses-1/beh/sub-9072_ses-1_events.json,/sub-9072/ses-1/beh/sub-9072_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9072_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-arrows_events.json,/sub-9072/ses-1/sub-9072_ses-1_events.json,/sub-9072/ses-1/sub-9072_ses-1_task-arrows_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9072_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-faces_events.json,/sub-9072/ses-1/sub-9072_ses-1_events.json,/sub-9072/ses-1/sub-9072_ses-1_task-faces_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9072_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-hands_events.json,/sub-9072/ses-1/sub-9072_ses-1_events.json,/sub-9072/ses-1/sub-9072_ses-1_task-hands_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9072_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9072/ses-1/func/sub-9072_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-sleepiness_events.json,/sub-9072/ses-1/sub-9072_ses-1_events.json,/sub-9072/ses-1/sub-9072_ses-1_task-sleepiness_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_events.json,/sub-9072/ses-1/func/sub-9072_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9072_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9072/ses-2/beh/sub-9072_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-workingmemorytest_events.json,/sub-9072/ses-2/sub-9072_ses-2_events.json,/sub-9072/ses-2/sub-9072_ses-2_task-workingmemorytest_events.json,/sub-9072/ses-2/beh/sub-9072_ses-2_events.json,/sub-9072/ses-2/beh/sub-9072_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9072_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-arrows_events.json,/sub-9072/ses-2/sub-9072_ses-2_events.json,/sub-9072/ses-2/sub-9072_ses-2_task-arrows_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9072_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-faces_events.json,/sub-9072/ses-2/sub-9072_ses-2_events.json,/sub-9072/ses-2/sub-9072_ses-2_task-faces_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9072_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-hands_events.json,/sub-9072/ses-2/sub-9072_ses-2_events.json,/sub-9072/ses-2/sub-9072_ses-2_task-hands_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9072_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9072/ses-2/func/sub-9072_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9072/sub-9072_events.json,/sub-9072/sub-9072_task-sleepiness_events.json,/sub-9072/ses-2/sub-9072_ses-2_events.json,/sub-9072/ses-2/sub-9072_ses-2_task-sleepiness_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_events.json,/sub-9072/ses-2/func/sub-9072_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9072_sessions.tsv
- Location: ds000201/sub-9072/sub-9072_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9072/sub-9072_sessions.json
-
- Type: Warning
- File: sub-9073_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-arrows_events.json,/sub-9073/ses-1/sub-9073_ses-1_events.json,/sub-9073/ses-1/sub-9073_ses-1_task-arrows_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9073_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-faces_events.json,/sub-9073/ses-1/sub-9073_ses-1_events.json,/sub-9073/ses-1/sub-9073_ses-1_task-faces_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9073_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-hands_events.json,/sub-9073/ses-1/sub-9073_ses-1_events.json,/sub-9073/ses-1/sub-9073_ses-1_task-hands_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9073_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9073/ses-1/func/sub-9073_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-sleepiness_events.json,/sub-9073/ses-1/sub-9073_ses-1_events.json,/sub-9073/ses-1/sub-9073_ses-1_task-sleepiness_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_events.json,/sub-9073/ses-1/func/sub-9073_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9073_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-arrows_events.json,/sub-9073/ses-2/sub-9073_ses-2_events.json,/sub-9073/ses-2/sub-9073_ses-2_task-arrows_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9073_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-faces_events.json,/sub-9073/ses-2/sub-9073_ses-2_events.json,/sub-9073/ses-2/sub-9073_ses-2_task-faces_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9073_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-hands_events.json,/sub-9073/ses-2/sub-9073_ses-2_events.json,/sub-9073/ses-2/sub-9073_ses-2_task-hands_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9073_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9073/ses-2/func/sub-9073_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9073/sub-9073_events.json,/sub-9073/sub-9073_task-sleepiness_events.json,/sub-9073/ses-2/sub-9073_ses-2_events.json,/sub-9073/ses-2/sub-9073_ses-2_task-sleepiness_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_events.json,/sub-9073/ses-2/func/sub-9073_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9073_sessions.tsv
- Location: ds000201/sub-9073/sub-9073_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9073/sub-9073_sessions.json
-
- Type: Warning
- File: sub-9074_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-arrows_events.json,/sub-9074/ses-1/sub-9074_ses-1_events.json,/sub-9074/ses-1/sub-9074_ses-1_task-arrows_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9074_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-faces_events.json,/sub-9074/ses-1/sub-9074_ses-1_events.json,/sub-9074/ses-1/sub-9074_ses-1_task-faces_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9074_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-hands_events.json,/sub-9074/ses-1/sub-9074_ses-1_events.json,/sub-9074/ses-1/sub-9074_ses-1_task-hands_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9074_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9074/ses-1/func/sub-9074_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-sleepiness_events.json,/sub-9074/ses-1/sub-9074_ses-1_events.json,/sub-9074/ses-1/sub-9074_ses-1_task-sleepiness_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_events.json,/sub-9074/ses-1/func/sub-9074_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9074_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-arrows_events.json,/sub-9074/ses-2/sub-9074_ses-2_events.json,/sub-9074/ses-2/sub-9074_ses-2_task-arrows_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9074_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-faces_events.json,/sub-9074/ses-2/sub-9074_ses-2_events.json,/sub-9074/ses-2/sub-9074_ses-2_task-faces_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9074_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-hands_events.json,/sub-9074/ses-2/sub-9074_ses-2_events.json,/sub-9074/ses-2/sub-9074_ses-2_task-hands_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9074_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9074/ses-2/func/sub-9074_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9074/sub-9074_events.json,/sub-9074/sub-9074_task-sleepiness_events.json,/sub-9074/ses-2/sub-9074_ses-2_events.json,/sub-9074/ses-2/sub-9074_ses-2_task-sleepiness_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_events.json,/sub-9074/ses-2/func/sub-9074_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9074_sessions.tsv
- Location: ds000201/sub-9074/sub-9074_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9074/sub-9074_sessions.json
-
- Type: Warning
- File: sub-9075_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9075/ses-1/beh/sub-9075_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-workingmemorytest_events.json,/sub-9075/ses-1/sub-9075_ses-1_events.json,/sub-9075/ses-1/sub-9075_ses-1_task-workingmemorytest_events.json,/sub-9075/ses-1/beh/sub-9075_ses-1_events.json,/sub-9075/ses-1/beh/sub-9075_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9075_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-arrows_events.json,/sub-9075/ses-1/sub-9075_ses-1_events.json,/sub-9075/ses-1/sub-9075_ses-1_task-arrows_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9075_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-faces_events.json,/sub-9075/ses-1/sub-9075_ses-1_events.json,/sub-9075/ses-1/sub-9075_ses-1_task-faces_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9075_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-hands_events.json,/sub-9075/ses-1/sub-9075_ses-1_events.json,/sub-9075/ses-1/sub-9075_ses-1_task-hands_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9075_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9075/ses-1/func/sub-9075_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-sleepiness_events.json,/sub-9075/ses-1/sub-9075_ses-1_events.json,/sub-9075/ses-1/sub-9075_ses-1_task-sleepiness_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_events.json,/sub-9075/ses-1/func/sub-9075_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9075_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9075/ses-2/beh/sub-9075_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-workingmemorytest_events.json,/sub-9075/ses-2/sub-9075_ses-2_events.json,/sub-9075/ses-2/sub-9075_ses-2_task-workingmemorytest_events.json,/sub-9075/ses-2/beh/sub-9075_ses-2_events.json,/sub-9075/ses-2/beh/sub-9075_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9075_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-arrows_events.json,/sub-9075/ses-2/sub-9075_ses-2_events.json,/sub-9075/ses-2/sub-9075_ses-2_task-arrows_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9075_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-faces_events.json,/sub-9075/ses-2/sub-9075_ses-2_events.json,/sub-9075/ses-2/sub-9075_ses-2_task-faces_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9075_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-hands_events.json,/sub-9075/ses-2/sub-9075_ses-2_events.json,/sub-9075/ses-2/sub-9075_ses-2_task-hands_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9075_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9075/ses-2/func/sub-9075_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9075/sub-9075_events.json,/sub-9075/sub-9075_task-sleepiness_events.json,/sub-9075/ses-2/sub-9075_ses-2_events.json,/sub-9075/ses-2/sub-9075_ses-2_task-sleepiness_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_events.json,/sub-9075/ses-2/func/sub-9075_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9075_sessions.tsv
- Location: ds000201/sub-9075/sub-9075_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9075/sub-9075_sessions.json
-
- Type: Warning
- File: sub-9076_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-arrows_events.json,/sub-9076/ses-1/sub-9076_ses-1_events.json,/sub-9076/ses-1/sub-9076_ses-1_task-arrows_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9076_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-faces_events.json,/sub-9076/ses-1/sub-9076_ses-1_events.json,/sub-9076/ses-1/sub-9076_ses-1_task-faces_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9076_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-hands_events.json,/sub-9076/ses-1/sub-9076_ses-1_events.json,/sub-9076/ses-1/sub-9076_ses-1_task-hands_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9076_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9076/ses-1/func/sub-9076_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-sleepiness_events.json,/sub-9076/ses-1/sub-9076_ses-1_events.json,/sub-9076/ses-1/sub-9076_ses-1_task-sleepiness_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_events.json,/sub-9076/ses-1/func/sub-9076_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9076_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-arrows_events.json,/sub-9076/ses-2/sub-9076_ses-2_events.json,/sub-9076/ses-2/sub-9076_ses-2_task-arrows_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9076_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-faces_events.json,/sub-9076/ses-2/sub-9076_ses-2_events.json,/sub-9076/ses-2/sub-9076_ses-2_task-faces_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9076_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-hands_events.json,/sub-9076/ses-2/sub-9076_ses-2_events.json,/sub-9076/ses-2/sub-9076_ses-2_task-hands_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9076_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9076/ses-2/func/sub-9076_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9076/sub-9076_events.json,/sub-9076/sub-9076_task-sleepiness_events.json,/sub-9076/ses-2/sub-9076_ses-2_events.json,/sub-9076/ses-2/sub-9076_ses-2_task-sleepiness_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_events.json,/sub-9076/ses-2/func/sub-9076_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9076_sessions.tsv
- Location: ds000201/sub-9076/sub-9076_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9076/sub-9076_sessions.json
-
- Type: Warning
- File: sub-9077_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-arrows_events.json,/sub-9077/ses-1/sub-9077_ses-1_events.json,/sub-9077/ses-1/sub-9077_ses-1_task-arrows_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9077_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-faces_events.json,/sub-9077/ses-1/sub-9077_ses-1_events.json,/sub-9077/ses-1/sub-9077_ses-1_task-faces_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9077_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-hands_events.json,/sub-9077/ses-1/sub-9077_ses-1_events.json,/sub-9077/ses-1/sub-9077_ses-1_task-hands_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9077_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9077/ses-1/func/sub-9077_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-sleepiness_events.json,/sub-9077/ses-1/sub-9077_ses-1_events.json,/sub-9077/ses-1/sub-9077_ses-1_task-sleepiness_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_events.json,/sub-9077/ses-1/func/sub-9077_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9077_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-arrows_events.json,/sub-9077/ses-2/sub-9077_ses-2_events.json,/sub-9077/ses-2/sub-9077_ses-2_task-arrows_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9077_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-faces_events.json,/sub-9077/ses-2/sub-9077_ses-2_events.json,/sub-9077/ses-2/sub-9077_ses-2_task-faces_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9077_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-hands_events.json,/sub-9077/ses-2/sub-9077_ses-2_events.json,/sub-9077/ses-2/sub-9077_ses-2_task-hands_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9077_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9077/ses-2/func/sub-9077_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9077/sub-9077_events.json,/sub-9077/sub-9077_task-sleepiness_events.json,/sub-9077/ses-2/sub-9077_ses-2_events.json,/sub-9077/ses-2/sub-9077_ses-2_task-sleepiness_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_events.json,/sub-9077/ses-2/func/sub-9077_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9077_sessions.tsv
- Location: ds000201/sub-9077/sub-9077_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9077/sub-9077_sessions.json
-
- Type: Warning
- File: sub-9078_sessions.tsv
- Location: ds000201/sub-9078/sub-9078_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9078/sub-9078_sessions.json
-
- Type: Warning
- File: sub-9079_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9079/ses-1/beh/sub-9079_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-workingmemorytest_events.json,/sub-9079/ses-1/sub-9079_ses-1_events.json,/sub-9079/ses-1/sub-9079_ses-1_task-workingmemorytest_events.json,/sub-9079/ses-1/beh/sub-9079_ses-1_events.json,/sub-9079/ses-1/beh/sub-9079_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9079_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-arrows_events.json,/sub-9079/ses-1/sub-9079_ses-1_events.json,/sub-9079/ses-1/sub-9079_ses-1_task-arrows_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9079_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-faces_events.json,/sub-9079/ses-1/sub-9079_ses-1_events.json,/sub-9079/ses-1/sub-9079_ses-1_task-faces_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9079_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-hands_events.json,/sub-9079/ses-1/sub-9079_ses-1_events.json,/sub-9079/ses-1/sub-9079_ses-1_task-hands_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9079_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9079/ses-1/func/sub-9079_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-sleepiness_events.json,/sub-9079/ses-1/sub-9079_ses-1_events.json,/sub-9079/ses-1/sub-9079_ses-1_task-sleepiness_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_events.json,/sub-9079/ses-1/func/sub-9079_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9079_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9079/ses-2/beh/sub-9079_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-workingmemorytest_events.json,/sub-9079/ses-2/sub-9079_ses-2_events.json,/sub-9079/ses-2/sub-9079_ses-2_task-workingmemorytest_events.json,/sub-9079/ses-2/beh/sub-9079_ses-2_events.json,/sub-9079/ses-2/beh/sub-9079_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9079_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-arrows_events.json,/sub-9079/ses-2/sub-9079_ses-2_events.json,/sub-9079/ses-2/sub-9079_ses-2_task-arrows_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9079_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-faces_events.json,/sub-9079/ses-2/sub-9079_ses-2_events.json,/sub-9079/ses-2/sub-9079_ses-2_task-faces_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9079_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-hands_events.json,/sub-9079/ses-2/sub-9079_ses-2_events.json,/sub-9079/ses-2/sub-9079_ses-2_task-hands_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9079_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9079/ses-2/func/sub-9079_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9079/sub-9079_events.json,/sub-9079/sub-9079_task-sleepiness_events.json,/sub-9079/ses-2/sub-9079_ses-2_events.json,/sub-9079/ses-2/sub-9079_ses-2_task-sleepiness_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_events.json,/sub-9079/ses-2/func/sub-9079_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9079_sessions.tsv
- Location: ds000201/sub-9079/sub-9079_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9079/sub-9079_sessions.json
-
- Type: Warning
- File: sub-9080_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-arrows_events.json,/sub-9080/ses-1/sub-9080_ses-1_events.json,/sub-9080/ses-1/sub-9080_ses-1_task-arrows_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9080_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-faces_events.json,/sub-9080/ses-1/sub-9080_ses-1_events.json,/sub-9080/ses-1/sub-9080_ses-1_task-faces_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9080_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-hands_events.json,/sub-9080/ses-1/sub-9080_ses-1_events.json,/sub-9080/ses-1/sub-9080_ses-1_task-hands_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9080_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9080/ses-1/func/sub-9080_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-sleepiness_events.json,/sub-9080/ses-1/sub-9080_ses-1_events.json,/sub-9080/ses-1/sub-9080_ses-1_task-sleepiness_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_events.json,/sub-9080/ses-1/func/sub-9080_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9080_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9080/ses-2/beh/sub-9080_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-workingmemorytest_events.json,/sub-9080/ses-2/sub-9080_ses-2_events.json,/sub-9080/ses-2/sub-9080_ses-2_task-workingmemorytest_events.json,/sub-9080/ses-2/beh/sub-9080_ses-2_events.json,/sub-9080/ses-2/beh/sub-9080_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9080_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-arrows_events.json,/sub-9080/ses-2/sub-9080_ses-2_events.json,/sub-9080/ses-2/sub-9080_ses-2_task-arrows_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9080_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-faces_events.json,/sub-9080/ses-2/sub-9080_ses-2_events.json,/sub-9080/ses-2/sub-9080_ses-2_task-faces_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9080_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-hands_events.json,/sub-9080/ses-2/sub-9080_ses-2_events.json,/sub-9080/ses-2/sub-9080_ses-2_task-hands_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9080_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9080/ses-2/func/sub-9080_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9080/sub-9080_events.json,/sub-9080/sub-9080_task-sleepiness_events.json,/sub-9080/ses-2/sub-9080_ses-2_events.json,/sub-9080/ses-2/sub-9080_ses-2_task-sleepiness_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_events.json,/sub-9080/ses-2/func/sub-9080_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9080_sessions.tsv
- Location: ds000201/sub-9080/sub-9080_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9080/sub-9080_sessions.json
-
- Type: Warning
- File: sub-9081_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9081/ses-1/beh/sub-9081_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-workingmemorytest_events.json,/sub-9081/ses-1/sub-9081_ses-1_events.json,/sub-9081/ses-1/sub-9081_ses-1_task-workingmemorytest_events.json,/sub-9081/ses-1/beh/sub-9081_ses-1_events.json,/sub-9081/ses-1/beh/sub-9081_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9081_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-arrows_events.json,/sub-9081/ses-1/sub-9081_ses-1_events.json,/sub-9081/ses-1/sub-9081_ses-1_task-arrows_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9081_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-faces_events.json,/sub-9081/ses-1/sub-9081_ses-1_events.json,/sub-9081/ses-1/sub-9081_ses-1_task-faces_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9081_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-hands_events.json,/sub-9081/ses-1/sub-9081_ses-1_events.json,/sub-9081/ses-1/sub-9081_ses-1_task-hands_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9081_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9081/ses-1/func/sub-9081_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-sleepiness_events.json,/sub-9081/ses-1/sub-9081_ses-1_events.json,/sub-9081/ses-1/sub-9081_ses-1_task-sleepiness_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_events.json,/sub-9081/ses-1/func/sub-9081_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9081_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9081/ses-2/beh/sub-9081_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-workingmemorytest_events.json,/sub-9081/ses-2/sub-9081_ses-2_events.json,/sub-9081/ses-2/sub-9081_ses-2_task-workingmemorytest_events.json,/sub-9081/ses-2/beh/sub-9081_ses-2_events.json,/sub-9081/ses-2/beh/sub-9081_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9081_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-arrows_events.json,/sub-9081/ses-2/sub-9081_ses-2_events.json,/sub-9081/ses-2/sub-9081_ses-2_task-arrows_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9081_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-faces_events.json,/sub-9081/ses-2/sub-9081_ses-2_events.json,/sub-9081/ses-2/sub-9081_ses-2_task-faces_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9081_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-hands_events.json,/sub-9081/ses-2/sub-9081_ses-2_events.json,/sub-9081/ses-2/sub-9081_ses-2_task-hands_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9081_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9081/ses-2/func/sub-9081_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9081/sub-9081_events.json,/sub-9081/sub-9081_task-sleepiness_events.json,/sub-9081/ses-2/sub-9081_ses-2_events.json,/sub-9081/ses-2/sub-9081_ses-2_task-sleepiness_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_events.json,/sub-9081/ses-2/func/sub-9081_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9081_sessions.tsv
- Location: ds000201/sub-9081/sub-9081_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9081/sub-9081_sessions.json
-
- Type: Warning
- File: sub-9082_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-arrows_events.json,/sub-9082/ses-1/sub-9082_ses-1_events.json,/sub-9082/ses-1/sub-9082_ses-1_task-arrows_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9082_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-faces_events.json,/sub-9082/ses-1/sub-9082_ses-1_events.json,/sub-9082/ses-1/sub-9082_ses-1_task-faces_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9082_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-hands_events.json,/sub-9082/ses-1/sub-9082_ses-1_events.json,/sub-9082/ses-1/sub-9082_ses-1_task-hands_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9082_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9082/ses-1/func/sub-9082_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-sleepiness_events.json,/sub-9082/ses-1/sub-9082_ses-1_events.json,/sub-9082/ses-1/sub-9082_ses-1_task-sleepiness_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_events.json,/sub-9082/ses-1/func/sub-9082_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9082_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-arrows_events.json,/sub-9082/ses-2/sub-9082_ses-2_events.json,/sub-9082/ses-2/sub-9082_ses-2_task-arrows_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9082_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-faces_events.json,/sub-9082/ses-2/sub-9082_ses-2_events.json,/sub-9082/ses-2/sub-9082_ses-2_task-faces_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9082_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-hands_events.json,/sub-9082/ses-2/sub-9082_ses-2_events.json,/sub-9082/ses-2/sub-9082_ses-2_task-hands_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9082_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9082/ses-2/func/sub-9082_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9082/sub-9082_events.json,/sub-9082/sub-9082_task-sleepiness_events.json,/sub-9082/ses-2/sub-9082_ses-2_events.json,/sub-9082/ses-2/sub-9082_ses-2_task-sleepiness_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_events.json,/sub-9082/ses-2/func/sub-9082_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9082_sessions.tsv
- Location: ds000201/sub-9082/sub-9082_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9082/sub-9082_sessions.json
-
- Type: Warning
- File: sub-9083_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-arrows_events.json,/sub-9083/ses-1/sub-9083_ses-1_events.json,/sub-9083/ses-1/sub-9083_ses-1_task-arrows_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9083_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-faces_events.json,/sub-9083/ses-1/sub-9083_ses-1_events.json,/sub-9083/ses-1/sub-9083_ses-1_task-faces_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9083_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-hands_events.json,/sub-9083/ses-1/sub-9083_ses-1_events.json,/sub-9083/ses-1/sub-9083_ses-1_task-hands_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9083_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9083/ses-1/func/sub-9083_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-sleepiness_events.json,/sub-9083/ses-1/sub-9083_ses-1_events.json,/sub-9083/ses-1/sub-9083_ses-1_task-sleepiness_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_events.json,/sub-9083/ses-1/func/sub-9083_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9083_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-arrows_events.json,/sub-9083/ses-2/sub-9083_ses-2_events.json,/sub-9083/ses-2/sub-9083_ses-2_task-arrows_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9083_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-faces_events.json,/sub-9083/ses-2/sub-9083_ses-2_events.json,/sub-9083/ses-2/sub-9083_ses-2_task-faces_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9083_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-hands_events.json,/sub-9083/ses-2/sub-9083_ses-2_events.json,/sub-9083/ses-2/sub-9083_ses-2_task-hands_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9083_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9083/ses-2/func/sub-9083_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9083/sub-9083_events.json,/sub-9083/sub-9083_task-sleepiness_events.json,/sub-9083/ses-2/sub-9083_ses-2_events.json,/sub-9083/ses-2/sub-9083_ses-2_task-sleepiness_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_events.json,/sub-9083/ses-2/func/sub-9083_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9083_sessions.tsv
- Location: ds000201/sub-9083/sub-9083_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9083/sub-9083_sessions.json
-
- Type: Warning
- File: sub-9084_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9084/ses-1/beh/sub-9084_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-workingmemorytest_events.json,/sub-9084/ses-1/sub-9084_ses-1_events.json,/sub-9084/ses-1/sub-9084_ses-1_task-workingmemorytest_events.json,/sub-9084/ses-1/beh/sub-9084_ses-1_events.json,/sub-9084/ses-1/beh/sub-9084_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9084_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-arrows_events.json,/sub-9084/ses-1/sub-9084_ses-1_events.json,/sub-9084/ses-1/sub-9084_ses-1_task-arrows_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9084_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-faces_events.json,/sub-9084/ses-1/sub-9084_ses-1_events.json,/sub-9084/ses-1/sub-9084_ses-1_task-faces_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9084_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-hands_events.json,/sub-9084/ses-1/sub-9084_ses-1_events.json,/sub-9084/ses-1/sub-9084_ses-1_task-hands_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9084_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9084/ses-1/func/sub-9084_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-sleepiness_events.json,/sub-9084/ses-1/sub-9084_ses-1_events.json,/sub-9084/ses-1/sub-9084_ses-1_task-sleepiness_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_events.json,/sub-9084/ses-1/func/sub-9084_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9084_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9084/ses-2/beh/sub-9084_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-workingmemorytest_events.json,/sub-9084/ses-2/sub-9084_ses-2_events.json,/sub-9084/ses-2/sub-9084_ses-2_task-workingmemorytest_events.json,/sub-9084/ses-2/beh/sub-9084_ses-2_events.json,/sub-9084/ses-2/beh/sub-9084_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9084_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-arrows_events.json,/sub-9084/ses-2/sub-9084_ses-2_events.json,/sub-9084/ses-2/sub-9084_ses-2_task-arrows_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9084_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-faces_events.json,/sub-9084/ses-2/sub-9084_ses-2_events.json,/sub-9084/ses-2/sub-9084_ses-2_task-faces_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9084_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-hands_events.json,/sub-9084/ses-2/sub-9084_ses-2_events.json,/sub-9084/ses-2/sub-9084_ses-2_task-hands_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9084_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9084/ses-2/func/sub-9084_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9084/sub-9084_events.json,/sub-9084/sub-9084_task-sleepiness_events.json,/sub-9084/ses-2/sub-9084_ses-2_events.json,/sub-9084/ses-2/sub-9084_ses-2_task-sleepiness_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_events.json,/sub-9084/ses-2/func/sub-9084_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9084_sessions.tsv
- Location: ds000201/sub-9084/sub-9084_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9084/sub-9084_sessions.json
-
- Type: Warning
- File: sub-9085_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9085/ses-1/beh/sub-9085_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-workingmemorytest_events.json,/sub-9085/ses-1/sub-9085_ses-1_events.json,/sub-9085/ses-1/sub-9085_ses-1_task-workingmemorytest_events.json,/sub-9085/ses-1/beh/sub-9085_ses-1_events.json,/sub-9085/ses-1/beh/sub-9085_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9085_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-arrows_events.json,/sub-9085/ses-1/sub-9085_ses-1_events.json,/sub-9085/ses-1/sub-9085_ses-1_task-arrows_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9085_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-faces_events.json,/sub-9085/ses-1/sub-9085_ses-1_events.json,/sub-9085/ses-1/sub-9085_ses-1_task-faces_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9085_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-hands_events.json,/sub-9085/ses-1/sub-9085_ses-1_events.json,/sub-9085/ses-1/sub-9085_ses-1_task-hands_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9085_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9085/ses-1/func/sub-9085_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-sleepiness_events.json,/sub-9085/ses-1/sub-9085_ses-1_events.json,/sub-9085/ses-1/sub-9085_ses-1_task-sleepiness_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_events.json,/sub-9085/ses-1/func/sub-9085_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9085_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9085/ses-2/beh/sub-9085_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-workingmemorytest_events.json,/sub-9085/ses-2/sub-9085_ses-2_events.json,/sub-9085/ses-2/sub-9085_ses-2_task-workingmemorytest_events.json,/sub-9085/ses-2/beh/sub-9085_ses-2_events.json,/sub-9085/ses-2/beh/sub-9085_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9085_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-arrows_events.json,/sub-9085/ses-2/sub-9085_ses-2_events.json,/sub-9085/ses-2/sub-9085_ses-2_task-arrows_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9085_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-faces_events.json,/sub-9085/ses-2/sub-9085_ses-2_events.json,/sub-9085/ses-2/sub-9085_ses-2_task-faces_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9085_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-hands_events.json,/sub-9085/ses-2/sub-9085_ses-2_events.json,/sub-9085/ses-2/sub-9085_ses-2_task-hands_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9085_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9085/ses-2/func/sub-9085_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9085/sub-9085_events.json,/sub-9085/sub-9085_task-sleepiness_events.json,/sub-9085/ses-2/sub-9085_ses-2_events.json,/sub-9085/ses-2/sub-9085_ses-2_task-sleepiness_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_events.json,/sub-9085/ses-2/func/sub-9085_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9085_sessions.tsv
- Location: ds000201/sub-9085/sub-9085_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9085/sub-9085_sessions.json
-
- Type: Warning
- File: sub-9086_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9086/ses-1/beh/sub-9086_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-workingmemorytest_events.json,/sub-9086/ses-1/sub-9086_ses-1_events.json,/sub-9086/ses-1/sub-9086_ses-1_task-workingmemorytest_events.json,/sub-9086/ses-1/beh/sub-9086_ses-1_events.json,/sub-9086/ses-1/beh/sub-9086_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9086_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-arrows_events.json,/sub-9086/ses-1/sub-9086_ses-1_events.json,/sub-9086/ses-1/sub-9086_ses-1_task-arrows_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9086_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-faces_events.json,/sub-9086/ses-1/sub-9086_ses-1_events.json,/sub-9086/ses-1/sub-9086_ses-1_task-faces_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9086_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-hands_events.json,/sub-9086/ses-1/sub-9086_ses-1_events.json,/sub-9086/ses-1/sub-9086_ses-1_task-hands_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9086_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9086/ses-1/func/sub-9086_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-sleepiness_events.json,/sub-9086/ses-1/sub-9086_ses-1_events.json,/sub-9086/ses-1/sub-9086_ses-1_task-sleepiness_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_events.json,/sub-9086/ses-1/func/sub-9086_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9086_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9086/ses-2/beh/sub-9086_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-workingmemorytest_events.json,/sub-9086/ses-2/sub-9086_ses-2_events.json,/sub-9086/ses-2/sub-9086_ses-2_task-workingmemorytest_events.json,/sub-9086/ses-2/beh/sub-9086_ses-2_events.json,/sub-9086/ses-2/beh/sub-9086_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9086_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-arrows_events.json,/sub-9086/ses-2/sub-9086_ses-2_events.json,/sub-9086/ses-2/sub-9086_ses-2_task-arrows_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9086_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-faces_events.json,/sub-9086/ses-2/sub-9086_ses-2_events.json,/sub-9086/ses-2/sub-9086_ses-2_task-faces_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9086_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-hands_events.json,/sub-9086/ses-2/sub-9086_ses-2_events.json,/sub-9086/ses-2/sub-9086_ses-2_task-hands_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9086_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9086/ses-2/func/sub-9086_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9086/sub-9086_events.json,/sub-9086/sub-9086_task-sleepiness_events.json,/sub-9086/ses-2/sub-9086_ses-2_events.json,/sub-9086/ses-2/sub-9086_ses-2_task-sleepiness_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_events.json,/sub-9086/ses-2/func/sub-9086_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9086_sessions.tsv
- Location: ds000201/sub-9086/sub-9086_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9086/sub-9086_sessions.json
-
- Type: Warning
- File: sub-9087_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9087/ses-1/beh/sub-9087_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-workingmemorytest_events.json,/sub-9087/ses-1/sub-9087_ses-1_events.json,/sub-9087/ses-1/sub-9087_ses-1_task-workingmemorytest_events.json,/sub-9087/ses-1/beh/sub-9087_ses-1_events.json,/sub-9087/ses-1/beh/sub-9087_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9087_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-arrows_events.json,/sub-9087/ses-1/sub-9087_ses-1_events.json,/sub-9087/ses-1/sub-9087_ses-1_task-arrows_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9087_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-faces_events.json,/sub-9087/ses-1/sub-9087_ses-1_events.json,/sub-9087/ses-1/sub-9087_ses-1_task-faces_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9087_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-hands_events.json,/sub-9087/ses-1/sub-9087_ses-1_events.json,/sub-9087/ses-1/sub-9087_ses-1_task-hands_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9087_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9087/ses-1/func/sub-9087_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-sleepiness_events.json,/sub-9087/ses-1/sub-9087_ses-1_events.json,/sub-9087/ses-1/sub-9087_ses-1_task-sleepiness_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_events.json,/sub-9087/ses-1/func/sub-9087_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9087_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9087/ses-2/beh/sub-9087_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-workingmemorytest_events.json,/sub-9087/ses-2/sub-9087_ses-2_events.json,/sub-9087/ses-2/sub-9087_ses-2_task-workingmemorytest_events.json,/sub-9087/ses-2/beh/sub-9087_ses-2_events.json,/sub-9087/ses-2/beh/sub-9087_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9087_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-arrows_events.json,/sub-9087/ses-2/sub-9087_ses-2_events.json,/sub-9087/ses-2/sub-9087_ses-2_task-arrows_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9087_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-faces_events.json,/sub-9087/ses-2/sub-9087_ses-2_events.json,/sub-9087/ses-2/sub-9087_ses-2_task-faces_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9087_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-hands_events.json,/sub-9087/ses-2/sub-9087_ses-2_events.json,/sub-9087/ses-2/sub-9087_ses-2_task-hands_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9087_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9087/ses-2/func/sub-9087_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9087/sub-9087_events.json,/sub-9087/sub-9087_task-sleepiness_events.json,/sub-9087/ses-2/sub-9087_ses-2_events.json,/sub-9087/ses-2/sub-9087_ses-2_task-sleepiness_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_events.json,/sub-9087/ses-2/func/sub-9087_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9087_sessions.tsv
- Location: ds000201/sub-9087/sub-9087_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9087/sub-9087_sessions.json
-
- Type: Warning
- File: sub-9088_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9088/ses-1/beh/sub-9088_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-workingmemorytest_events.json,/sub-9088/ses-1/sub-9088_ses-1_events.json,/sub-9088/ses-1/sub-9088_ses-1_task-workingmemorytest_events.json,/sub-9088/ses-1/beh/sub-9088_ses-1_events.json,/sub-9088/ses-1/beh/sub-9088_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9088_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-arrows_events.json,/sub-9088/ses-1/sub-9088_ses-1_events.json,/sub-9088/ses-1/sub-9088_ses-1_task-arrows_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9088_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-faces_events.json,/sub-9088/ses-1/sub-9088_ses-1_events.json,/sub-9088/ses-1/sub-9088_ses-1_task-faces_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9088_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-hands_events.json,/sub-9088/ses-1/sub-9088_ses-1_events.json,/sub-9088/ses-1/sub-9088_ses-1_task-hands_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9088_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9088/ses-1/func/sub-9088_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-sleepiness_events.json,/sub-9088/ses-1/sub-9088_ses-1_events.json,/sub-9088/ses-1/sub-9088_ses-1_task-sleepiness_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_events.json,/sub-9088/ses-1/func/sub-9088_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9088_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9088/ses-2/beh/sub-9088_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-workingmemorytest_events.json,/sub-9088/ses-2/sub-9088_ses-2_events.json,/sub-9088/ses-2/sub-9088_ses-2_task-workingmemorytest_events.json,/sub-9088/ses-2/beh/sub-9088_ses-2_events.json,/sub-9088/ses-2/beh/sub-9088_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9088_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-arrows_events.json,/sub-9088/ses-2/sub-9088_ses-2_events.json,/sub-9088/ses-2/sub-9088_ses-2_task-arrows_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9088_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-faces_events.json,/sub-9088/ses-2/sub-9088_ses-2_events.json,/sub-9088/ses-2/sub-9088_ses-2_task-faces_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9088_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-hands_events.json,/sub-9088/ses-2/sub-9088_ses-2_events.json,/sub-9088/ses-2/sub-9088_ses-2_task-hands_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9088_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9088/ses-2/func/sub-9088_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9088/sub-9088_events.json,/sub-9088/sub-9088_task-sleepiness_events.json,/sub-9088/ses-2/sub-9088_ses-2_events.json,/sub-9088/ses-2/sub-9088_ses-2_task-sleepiness_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_events.json,/sub-9088/ses-2/func/sub-9088_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9088_sessions.tsv
- Location: ds000201/sub-9088/sub-9088_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9088/sub-9088_sessions.json
-
- Type: Warning
- File: sub-9089_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9089/ses-1/beh/sub-9089_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-workingmemorytest_events.json,/sub-9089/ses-1/sub-9089_ses-1_events.json,/sub-9089/ses-1/sub-9089_ses-1_task-workingmemorytest_events.json,/sub-9089/ses-1/beh/sub-9089_ses-1_events.json,/sub-9089/ses-1/beh/sub-9089_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9089_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-arrows_events.json,/sub-9089/ses-1/sub-9089_ses-1_events.json,/sub-9089/ses-1/sub-9089_ses-1_task-arrows_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9089_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-faces_events.json,/sub-9089/ses-1/sub-9089_ses-1_events.json,/sub-9089/ses-1/sub-9089_ses-1_task-faces_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9089_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-hands_events.json,/sub-9089/ses-1/sub-9089_ses-1_events.json,/sub-9089/ses-1/sub-9089_ses-1_task-hands_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9089_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9089/ses-1/func/sub-9089_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-sleepiness_events.json,/sub-9089/ses-1/sub-9089_ses-1_events.json,/sub-9089/ses-1/sub-9089_ses-1_task-sleepiness_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_events.json,/sub-9089/ses-1/func/sub-9089_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9089_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9089/ses-2/beh/sub-9089_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-workingmemorytest_events.json,/sub-9089/ses-2/sub-9089_ses-2_events.json,/sub-9089/ses-2/sub-9089_ses-2_task-workingmemorytest_events.json,/sub-9089/ses-2/beh/sub-9089_ses-2_events.json,/sub-9089/ses-2/beh/sub-9089_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9089_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-arrows_events.json,/sub-9089/ses-2/sub-9089_ses-2_events.json,/sub-9089/ses-2/sub-9089_ses-2_task-arrows_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9089_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-faces_events.json,/sub-9089/ses-2/sub-9089_ses-2_events.json,/sub-9089/ses-2/sub-9089_ses-2_task-faces_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9089_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-hands_events.json,/sub-9089/ses-2/sub-9089_ses-2_events.json,/sub-9089/ses-2/sub-9089_ses-2_task-hands_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9089_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9089/ses-2/func/sub-9089_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9089/sub-9089_events.json,/sub-9089/sub-9089_task-sleepiness_events.json,/sub-9089/ses-2/sub-9089_ses-2_events.json,/sub-9089/ses-2/sub-9089_ses-2_task-sleepiness_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_events.json,/sub-9089/ses-2/func/sub-9089_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9089_sessions.tsv
- Location: ds000201/sub-9089/sub-9089_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9089/sub-9089_sessions.json
-
- Type: Warning
- File: sub-9090_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-arrows_events.json,/sub-9090/ses-1/sub-9090_ses-1_events.json,/sub-9090/ses-1/sub-9090_ses-1_task-arrows_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9090_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-faces_events.json,/sub-9090/ses-1/sub-9090_ses-1_events.json,/sub-9090/ses-1/sub-9090_ses-1_task-faces_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9090_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-hands_events.json,/sub-9090/ses-1/sub-9090_ses-1_events.json,/sub-9090/ses-1/sub-9090_ses-1_task-hands_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9090_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9090/ses-1/func/sub-9090_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-sleepiness_events.json,/sub-9090/ses-1/sub-9090_ses-1_events.json,/sub-9090/ses-1/sub-9090_ses-1_task-sleepiness_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_events.json,/sub-9090/ses-1/func/sub-9090_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9090_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-arrows_events.json,/sub-9090/ses-2/sub-9090_ses-2_events.json,/sub-9090/ses-2/sub-9090_ses-2_task-arrows_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9090_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-faces_events.json,/sub-9090/ses-2/sub-9090_ses-2_events.json,/sub-9090/ses-2/sub-9090_ses-2_task-faces_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9090_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-hands_events.json,/sub-9090/ses-2/sub-9090_ses-2_events.json,/sub-9090/ses-2/sub-9090_ses-2_task-hands_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9090_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9090/ses-2/func/sub-9090_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9090/sub-9090_events.json,/sub-9090/sub-9090_task-sleepiness_events.json,/sub-9090/ses-2/sub-9090_ses-2_events.json,/sub-9090/ses-2/sub-9090_ses-2_task-sleepiness_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_events.json,/sub-9090/ses-2/func/sub-9090_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9090_sessions.tsv
- Location: ds000201/sub-9090/sub-9090_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9090/sub-9090_sessions.json
-
- Type: Warning
- File: sub-9091_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-arrows_events.json,/sub-9091/ses-1/sub-9091_ses-1_events.json,/sub-9091/ses-1/sub-9091_ses-1_task-arrows_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9091_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-faces_events.json,/sub-9091/ses-1/sub-9091_ses-1_events.json,/sub-9091/ses-1/sub-9091_ses-1_task-faces_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9091_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-hands_events.json,/sub-9091/ses-1/sub-9091_ses-1_events.json,/sub-9091/ses-1/sub-9091_ses-1_task-hands_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9091_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9091/ses-1/func/sub-9091_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-sleepiness_events.json,/sub-9091/ses-1/sub-9091_ses-1_events.json,/sub-9091/ses-1/sub-9091_ses-1_task-sleepiness_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_events.json,/sub-9091/ses-1/func/sub-9091_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9091_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-arrows_events.json,/sub-9091/ses-2/sub-9091_ses-2_events.json,/sub-9091/ses-2/sub-9091_ses-2_task-arrows_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9091_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-faces_events.json,/sub-9091/ses-2/sub-9091_ses-2_events.json,/sub-9091/ses-2/sub-9091_ses-2_task-faces_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9091_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-hands_events.json,/sub-9091/ses-2/sub-9091_ses-2_events.json,/sub-9091/ses-2/sub-9091_ses-2_task-hands_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9091_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9091/ses-2/func/sub-9091_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9091/sub-9091_events.json,/sub-9091/sub-9091_task-sleepiness_events.json,/sub-9091/ses-2/sub-9091_ses-2_events.json,/sub-9091/ses-2/sub-9091_ses-2_task-sleepiness_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_events.json,/sub-9091/ses-2/func/sub-9091_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9091_sessions.tsv
- Location: ds000201/sub-9091/sub-9091_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9091/sub-9091_sessions.json
-
- Type: Warning
- File: sub-9092_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9092/ses-1/beh/sub-9092_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-workingmemorytest_events.json,/sub-9092/ses-1/sub-9092_ses-1_events.json,/sub-9092/ses-1/sub-9092_ses-1_task-workingmemorytest_events.json,/sub-9092/ses-1/beh/sub-9092_ses-1_events.json,/sub-9092/ses-1/beh/sub-9092_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9092_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-arrows_events.json,/sub-9092/ses-1/sub-9092_ses-1_events.json,/sub-9092/ses-1/sub-9092_ses-1_task-arrows_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9092_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-faces_events.json,/sub-9092/ses-1/sub-9092_ses-1_events.json,/sub-9092/ses-1/sub-9092_ses-1_task-faces_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9092_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-hands_events.json,/sub-9092/ses-1/sub-9092_ses-1_events.json,/sub-9092/ses-1/sub-9092_ses-1_task-hands_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9092_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9092/ses-1/func/sub-9092_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-sleepiness_events.json,/sub-9092/ses-1/sub-9092_ses-1_events.json,/sub-9092/ses-1/sub-9092_ses-1_task-sleepiness_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_events.json,/sub-9092/ses-1/func/sub-9092_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9092_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9092/ses-2/beh/sub-9092_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-workingmemorytest_events.json,/sub-9092/ses-2/sub-9092_ses-2_events.json,/sub-9092/ses-2/sub-9092_ses-2_task-workingmemorytest_events.json,/sub-9092/ses-2/beh/sub-9092_ses-2_events.json,/sub-9092/ses-2/beh/sub-9092_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9092_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-arrows_events.json,/sub-9092/ses-2/sub-9092_ses-2_events.json,/sub-9092/ses-2/sub-9092_ses-2_task-arrows_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9092_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-faces_events.json,/sub-9092/ses-2/sub-9092_ses-2_events.json,/sub-9092/ses-2/sub-9092_ses-2_task-faces_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9092_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-hands_events.json,/sub-9092/ses-2/sub-9092_ses-2_events.json,/sub-9092/ses-2/sub-9092_ses-2_task-hands_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9092_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9092/ses-2/func/sub-9092_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9092/sub-9092_events.json,/sub-9092/sub-9092_task-sleepiness_events.json,/sub-9092/ses-2/sub-9092_ses-2_events.json,/sub-9092/ses-2/sub-9092_ses-2_task-sleepiness_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_events.json,/sub-9092/ses-2/func/sub-9092_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9092_sessions.tsv
- Location: ds000201/sub-9092/sub-9092_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9092/sub-9092_sessions.json
-
- Type: Warning
- File: sub-9093_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9093/ses-1/beh/sub-9093_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-workingmemorytest_events.json,/sub-9093/ses-1/sub-9093_ses-1_events.json,/sub-9093/ses-1/sub-9093_ses-1_task-workingmemorytest_events.json,/sub-9093/ses-1/beh/sub-9093_ses-1_events.json,/sub-9093/ses-1/beh/sub-9093_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9093_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-arrows_events.json,/sub-9093/ses-1/sub-9093_ses-1_events.json,/sub-9093/ses-1/sub-9093_ses-1_task-arrows_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9093_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-faces_events.json,/sub-9093/ses-1/sub-9093_ses-1_events.json,/sub-9093/ses-1/sub-9093_ses-1_task-faces_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9093_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-hands_events.json,/sub-9093/ses-1/sub-9093_ses-1_events.json,/sub-9093/ses-1/sub-9093_ses-1_task-hands_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9093_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9093/ses-1/func/sub-9093_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-sleepiness_events.json,/sub-9093/ses-1/sub-9093_ses-1_events.json,/sub-9093/ses-1/sub-9093_ses-1_task-sleepiness_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_events.json,/sub-9093/ses-1/func/sub-9093_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9093_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9093/ses-2/beh/sub-9093_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-workingmemorytest_events.json,/sub-9093/ses-2/sub-9093_ses-2_events.json,/sub-9093/ses-2/sub-9093_ses-2_task-workingmemorytest_events.json,/sub-9093/ses-2/beh/sub-9093_ses-2_events.json,/sub-9093/ses-2/beh/sub-9093_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9093_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-arrows_events.json,/sub-9093/ses-2/sub-9093_ses-2_events.json,/sub-9093/ses-2/sub-9093_ses-2_task-arrows_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9093_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-faces_events.json,/sub-9093/ses-2/sub-9093_ses-2_events.json,/sub-9093/ses-2/sub-9093_ses-2_task-faces_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9093_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-hands_events.json,/sub-9093/ses-2/sub-9093_ses-2_events.json,/sub-9093/ses-2/sub-9093_ses-2_task-hands_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9093_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9093/ses-2/func/sub-9093_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9093/sub-9093_events.json,/sub-9093/sub-9093_task-sleepiness_events.json,/sub-9093/ses-2/sub-9093_ses-2_events.json,/sub-9093/ses-2/sub-9093_ses-2_task-sleepiness_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_events.json,/sub-9093/ses-2/func/sub-9093_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9093_sessions.tsv
- Location: ds000201/sub-9093/sub-9093_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9093/sub-9093_sessions.json
-
- Type: Warning
- File: sub-9094_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9094/ses-1/beh/sub-9094_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-workingmemorytest_events.json,/sub-9094/ses-1/sub-9094_ses-1_events.json,/sub-9094/ses-1/sub-9094_ses-1_task-workingmemorytest_events.json,/sub-9094/ses-1/beh/sub-9094_ses-1_events.json,/sub-9094/ses-1/beh/sub-9094_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9094_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-arrows_events.json,/sub-9094/ses-1/sub-9094_ses-1_events.json,/sub-9094/ses-1/sub-9094_ses-1_task-arrows_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9094_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-faces_events.json,/sub-9094/ses-1/sub-9094_ses-1_events.json,/sub-9094/ses-1/sub-9094_ses-1_task-faces_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9094_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-hands_events.json,/sub-9094/ses-1/sub-9094_ses-1_events.json,/sub-9094/ses-1/sub-9094_ses-1_task-hands_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9094_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9094/ses-1/func/sub-9094_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-sleepiness_events.json,/sub-9094/ses-1/sub-9094_ses-1_events.json,/sub-9094/ses-1/sub-9094_ses-1_task-sleepiness_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_events.json,/sub-9094/ses-1/func/sub-9094_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9094_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9094/ses-2/beh/sub-9094_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-workingmemorytest_events.json,/sub-9094/ses-2/sub-9094_ses-2_events.json,/sub-9094/ses-2/sub-9094_ses-2_task-workingmemorytest_events.json,/sub-9094/ses-2/beh/sub-9094_ses-2_events.json,/sub-9094/ses-2/beh/sub-9094_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9094_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-arrows_events.json,/sub-9094/ses-2/sub-9094_ses-2_events.json,/sub-9094/ses-2/sub-9094_ses-2_task-arrows_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9094_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-faces_events.json,/sub-9094/ses-2/sub-9094_ses-2_events.json,/sub-9094/ses-2/sub-9094_ses-2_task-faces_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9094_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-hands_events.json,/sub-9094/ses-2/sub-9094_ses-2_events.json,/sub-9094/ses-2/sub-9094_ses-2_task-hands_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9094_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9094/ses-2/func/sub-9094_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9094/sub-9094_events.json,/sub-9094/sub-9094_task-sleepiness_events.json,/sub-9094/ses-2/sub-9094_ses-2_events.json,/sub-9094/ses-2/sub-9094_ses-2_task-sleepiness_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_events.json,/sub-9094/ses-2/func/sub-9094_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9094_sessions.tsv
- Location: ds000201/sub-9094/sub-9094_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9094/sub-9094_sessions.json
-
- Type: Warning
- File: sub-9095_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9095/ses-1/beh/sub-9095_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9095/sub-9095_events.json,/sub-9095/sub-9095_task-workingmemorytest_events.json,/sub-9095/ses-1/sub-9095_ses-1_events.json,/sub-9095/ses-1/sub-9095_ses-1_task-workingmemorytest_events.json,/sub-9095/ses-1/beh/sub-9095_ses-1_events.json,/sub-9095/ses-1/beh/sub-9095_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9095_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9095/sub-9095_events.json,/sub-9095/sub-9095_task-arrows_events.json,/sub-9095/ses-1/sub-9095_ses-1_events.json,/sub-9095/ses-1/sub-9095_ses-1_task-arrows_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9095_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9095/sub-9095_events.json,/sub-9095/sub-9095_task-faces_events.json,/sub-9095/ses-1/sub-9095_ses-1_events.json,/sub-9095/ses-1/sub-9095_ses-1_task-faces_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9095_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9095/sub-9095_events.json,/sub-9095/sub-9095_task-hands_events.json,/sub-9095/ses-1/sub-9095_ses-1_events.json,/sub-9095/ses-1/sub-9095_ses-1_task-hands_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9095_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9095/ses-1/func/sub-9095_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9095/sub-9095_events.json,/sub-9095/sub-9095_task-sleepiness_events.json,/sub-9095/ses-1/sub-9095_ses-1_events.json,/sub-9095/ses-1/sub-9095_ses-1_task-sleepiness_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_events.json,/sub-9095/ses-1/func/sub-9095_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9095_sessions.tsv
- Location: ds000201/sub-9095/sub-9095_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9095/sub-9095_sessions.json
-
- Type: Warning
- File: sub-9096_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9096/ses-1/beh/sub-9096_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-workingmemorytest_events.json,/sub-9096/ses-1/sub-9096_ses-1_events.json,/sub-9096/ses-1/sub-9096_ses-1_task-workingmemorytest_events.json,/sub-9096/ses-1/beh/sub-9096_ses-1_events.json,/sub-9096/ses-1/beh/sub-9096_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9096_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-arrows_events.json,/sub-9096/ses-1/sub-9096_ses-1_events.json,/sub-9096/ses-1/sub-9096_ses-1_task-arrows_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9096_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-faces_events.json,/sub-9096/ses-1/sub-9096_ses-1_events.json,/sub-9096/ses-1/sub-9096_ses-1_task-faces_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9096_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-hands_events.json,/sub-9096/ses-1/sub-9096_ses-1_events.json,/sub-9096/ses-1/sub-9096_ses-1_task-hands_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9096_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9096/ses-1/func/sub-9096_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-sleepiness_events.json,/sub-9096/ses-1/sub-9096_ses-1_events.json,/sub-9096/ses-1/sub-9096_ses-1_task-sleepiness_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_events.json,/sub-9096/ses-1/func/sub-9096_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9096_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9096/ses-2/beh/sub-9096_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-workingmemorytest_events.json,/sub-9096/ses-2/sub-9096_ses-2_events.json,/sub-9096/ses-2/sub-9096_ses-2_task-workingmemorytest_events.json,/sub-9096/ses-2/beh/sub-9096_ses-2_events.json,/sub-9096/ses-2/beh/sub-9096_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9096_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-arrows_events.json,/sub-9096/ses-2/sub-9096_ses-2_events.json,/sub-9096/ses-2/sub-9096_ses-2_task-arrows_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9096_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-faces_events.json,/sub-9096/ses-2/sub-9096_ses-2_events.json,/sub-9096/ses-2/sub-9096_ses-2_task-faces_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9096_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-hands_events.json,/sub-9096/ses-2/sub-9096_ses-2_events.json,/sub-9096/ses-2/sub-9096_ses-2_task-hands_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9096_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9096/ses-2/func/sub-9096_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9096/sub-9096_events.json,/sub-9096/sub-9096_task-sleepiness_events.json,/sub-9096/ses-2/sub-9096_ses-2_events.json,/sub-9096/ses-2/sub-9096_ses-2_task-sleepiness_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_events.json,/sub-9096/ses-2/func/sub-9096_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9096_sessions.tsv
- Location: ds000201/sub-9096/sub-9096_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9096/sub-9096_sessions.json
-
- Type: Warning
- File: sub-9098_ses-1_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9098/ses-1/beh/sub-9098_ses-1_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-workingmemorytest_events.json,/sub-9098/ses-1/sub-9098_ses-1_events.json,/sub-9098/ses-1/sub-9098_ses-1_task-workingmemorytest_events.json,/sub-9098/ses-1/beh/sub-9098_ses-1_events.json,/sub-9098/ses-1/beh/sub-9098_ses-1_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9098_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-arrows_events.json,/sub-9098/ses-1/sub-9098_ses-1_events.json,/sub-9098/ses-1/sub-9098_ses-1_task-arrows_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9098_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-faces_events.json,/sub-9098/ses-1/sub-9098_ses-1_events.json,/sub-9098/ses-1/sub-9098_ses-1_task-faces_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9098_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-hands_events.json,/sub-9098/ses-1/sub-9098_ses-1_events.json,/sub-9098/ses-1/sub-9098_ses-1_task-hands_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9098_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9098/ses-1/func/sub-9098_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-sleepiness_events.json,/sub-9098/ses-1/sub-9098_ses-1_events.json,/sub-9098/ses-1/sub-9098_ses-1_task-sleepiness_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_events.json,/sub-9098/ses-1/func/sub-9098_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9098_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9098/ses-2/beh/sub-9098_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-workingmemorytest_events.json,/sub-9098/ses-2/sub-9098_ses-2_events.json,/sub-9098/ses-2/sub-9098_ses-2_task-workingmemorytest_events.json,/sub-9098/ses-2/beh/sub-9098_ses-2_events.json,/sub-9098/ses-2/beh/sub-9098_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9098_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-arrows_events.json,/sub-9098/ses-2/sub-9098_ses-2_events.json,/sub-9098/ses-2/sub-9098_ses-2_task-arrows_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9098_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-faces_events.json,/sub-9098/ses-2/sub-9098_ses-2_events.json,/sub-9098/ses-2/sub-9098_ses-2_task-faces_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9098_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-hands_events.json,/sub-9098/ses-2/sub-9098_ses-2_events.json,/sub-9098/ses-2/sub-9098_ses-2_task-hands_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9098_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9098/ses-2/func/sub-9098_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9098/sub-9098_events.json,/sub-9098/sub-9098_task-sleepiness_events.json,/sub-9098/ses-2/sub-9098_ses-2_events.json,/sub-9098/ses-2/sub-9098_ses-2_task-sleepiness_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_events.json,/sub-9098/ses-2/func/sub-9098_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9098_sessions.tsv
- Location: ds000201/sub-9098/sub-9098_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9098/sub-9098_sessions.json
-
- Type: Warning
- File: sub-9100_ses-1_task-arrows_events.tsv
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-arrows_events.json,/sub-9100/ses-1/sub-9100_ses-1_events.json,/sub-9100/ses-1/sub-9100_ses-1_task-arrows_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_task-arrows_events.json
-
- Type: Warning
- File: sub-9100_ses-1_task-faces_events.tsv
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-faces_events.json,/sub-9100/ses-1/sub-9100_ses-1_events.json,/sub-9100/ses-1/sub-9100_ses-1_task-faces_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_task-faces_events.json
-
- Type: Warning
- File: sub-9100_ses-1_task-hands_events.tsv
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-hands_events.json,/sub-9100/ses-1/sub-9100_ses-1_events.json,/sub-9100/ses-1/sub-9100_ses-1_task-hands_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_task-hands_events.json
-
- Type: Warning
- File: sub-9100_ses-1_task-sleepiness_events.tsv
- Location: ds000201/sub-9100/ses-1/func/sub-9100_ses-1_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-sleepiness_events.json,/sub-9100/ses-1/sub-9100_ses-1_events.json,/sub-9100/ses-1/sub-9100_ses-1_task-sleepiness_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_events.json,/sub-9100/ses-1/func/sub-9100_ses-1_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9100_ses-2_task-workingmemorytest_events.tsv
- Location: ds000201/sub-9100/ses-2/beh/sub-9100_ses-2_task-workingmemorytest_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: time, order_of_test, time_start_battery, order_in_test, order, no_grids, grid1, grid2, grid3, grid4, grid5, grid6, grid7, grid8, pres_grid, correct not defined, please define in: /events.json, /task-workingmemorytest_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-workingmemorytest_events.json,/sub-9100/ses-2/sub-9100_ses-2_events.json,/sub-9100/ses-2/sub-9100_ses-2_task-workingmemorytest_events.json,/sub-9100/ses-2/beh/sub-9100_ses-2_events.json,/sub-9100/ses-2/beh/sub-9100_ses-2_task-workingmemorytest_events.json
-
- Type: Warning
- File: sub-9100_ses-2_task-arrows_events.tsv
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-arrows_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: trial_number, event_type, cue_to_participant, stimulus_type, time_until_response, rated_success_of_regulation, onset_uncorrected not defined, please define in: /events.json, /task-arrows_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-arrows_events.json,/sub-9100/ses-2/sub-9100_ses-2_events.json,/sub-9100/ses-2/sub-9100_ses-2_task-arrows_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_task-arrows_events.json
-
- Type: Warning
- File: sub-9100_ses-2_task-faces_events.tsv
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-faces_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, time_until_response, trial_number, block_num_within_trial, image_seq_num_within_block, question_seq_num_within_block, image_seq_num, picture_number, condition, question_type, block_type, rating, rating_computed, onset_uncorrected, KDEV_imagefile not defined, please define in: /events.json, /task-faces_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-faces_events.json,/sub-9100/ses-2/sub-9100_ses-2_events.json,/sub-9100/ses-2/sub-9100_ses-2_task-faces_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_task-faces_events.json
-
- Type: Warning
- File: sub-9100_ses-2_task-hands_events.tsv
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-hands_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: event_type, trial_number, condition, picture_number, time_until_response, unpleasantness_rating, onset_uncorrected not defined, please define in: /events.json, /task-hands_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-hands_events.json,/sub-9100/ses-2/sub-9100_ses-2_events.json,/sub-9100/ses-2/sub-9100_ses-2_task-hands_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_task-hands_events.json
-
- Type: Warning
- File: sub-9100_ses-2_task-sleepiness_events.tsv
- Location: ds000201/sub-9100/ses-2/func/sub-9100_ses-2_task-sleepiness_events.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: KSS_rating, onset_uncorrected not defined, please define in: /events.json, /task-sleepiness_events.json,/sub-9100/sub-9100_events.json,/sub-9100/sub-9100_task-sleepiness_events.json,/sub-9100/ses-2/sub-9100_ses-2_events.json,/sub-9100/ses-2/sub-9100_ses-2_task-sleepiness_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_events.json,/sub-9100/ses-2/func/sub-9100_ses-2_task-sleepiness_events.json
-
- Type: Warning
- File: sub-9100_sessions.tsv
- Location: ds000201/sub-9100/sub-9100_sessions.tsv
- Reason: Tabular file contains custom columns not described in a data dictionary
- Evidence: Columns: preMRI_KSS_Q0_rating, preMRI_KSS_Q1_rating, preMRI_KSS_Q2_rating, preMRI_KSS_Q3_rating, preMRI_KSS_Q4_rating, preMRI_KSS_Q5_rating, working_mem_KSS_rating, working_mem_perform_rating, working_mem_effort_rating not defined, please define in: /sessions.json, /sub-9100/sub-9100_sessions.json
-
-
-======================================================
-
-
-File Path: Not all subjects contain the same sessions.
-
- Type: Warning
- File: ses-2
- Location: /sub-9095/ses-2
- Reason: A session is missing from one subject that is present in at least one other subject
- Evidence: Subject: sub-9095; Missing session: ses-2
-
-
-======================================================
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "fMRI Stats Exploration"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Maximilien Le Clei]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/leclei_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, visualization, statistics]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to further my intuitive understanding of fMRI data. Around 20 interactive/static figures of various statistics of raw fMRI data, confounds and atlased data were produced. Special efforts have been made to make the analysis highly and easily reproducible."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "_d_sub1_fmri_3d_isosurface.png"
----
-
-
-## Deliverables
-
-* [Project Proposal](https://docs.google.com/presentation/d/1vGJHJPePtFjPrpy33KGu-CJJTDCXfbNzGQ0N2v960NI/edit?slide=id.p#slide=id.p)
-* [Project Presentation](https://docs.google.com/presentation/d/1PdP6MeMdH7ZJHP-cQqaxqgrOz72qfQjf-hrqjtZ3Q7o/edit?usp=sharing)
-* [GitHub repository](https://github.com/brainhack-school2025/leclei_project)
-* [Reproduction steps](https://github.com/brainhack-school2025/leclei_project/blob/main/README.md#reproduction-steps)
-* [Analysis notebook](https://github.com/brainhack-school2025/leclei_project/blob/main/notebook.ipynb)
-* [Resulting figures](https://github.com/brainhack-school2025/leclei_project/tree/main/visuals)
-
-## Project description
-
-The project represents my own personal first experience handling neuroimaging data.
-
-Coming from a Deep Learning background, this project represents the type of analysis I would undertake in order to get a better feel of what exactly am I trying to train models on.
-
-One extremely important element of training DL models is to make sure that the distribution of the data is well-suited for modeling. This generally means having the data take shape of a standard or uniform distribution with values concentrated between -1 and 1. I observed that original fMRI data does not necessarily fit those criteria and that the distribution between subjects can also vary substantially (**Figure a**). This distribution looks quite unnatural to fit back into one of the mentioned distributions, however, **Figure q** demonstrates that it is possible.
-
-I then wanted to get a better feel of what does that original data looks like in 3D (**Figure b & c**) before taking a look at how does the data vary across time. In **Figure d**, I take a look at the overlapping differences between two collected timepoints in order to get a better feel of what are supposedly movements by the subject in the scanner. In **Figure e**, I generalize that to the entire series of timepoints to get a feel of where variance is most present, which we notice to be at the edges of the brain, seeming to correlate to those same brain movements (Note: I did not account for the fact that the data is in its original distribution at that point in time which could have biased my observations).
-
-Next, one important element that I wanted to get more understanding of is the various transformations applied on top of the raw fMRI data. Indeed, AI practitioners are often wary of transformations being applied to data prior to feeding it to AI models as those transformations can greatly impact the model's ability to learn something valuable. I thus took a closer at the applications of confounds. I first extracted the data that gets added to the signal as a result of applying those very confounds and visualized it in 3D (**Figure f & g**). It very much looks like noise though with a little bit of a pattern of lower values around the edges of the brain.
-
-I then moved on to taking a closer look at the 3D difference between the original data (**Figure a**), the data with confounds applied (**Figure h**) and the data with confounds and a threshold applied (as demonstrated in the course) (**Figure i**). Visibly, applying the confounds looks like it is simply getting rid of the data where no brain data is ever collected, while the thresholding observed in class is more agressive and appears to chunk out a lot of the data (some seemingly relevant? not sure, too new at this hehe).
-
-I then went back to observing variance across time as done in **Figure e** but this time with the confounds and threshold applied (**Figure j**). In that visualization we observe that the largest variance no longer only occurs at the edge of the brain but also where the white matter is supposedly located, seeming to indicate that the head movement was somewhat corrected (Also note by dragging the cursor that the variance is generally much lower in general: 2 vs 3 orders of magnitude).
-
-At that point I wanted to quickly go back to connectivity matrices to see the effects of the confounds (**Figure k** applied, **Figure l** not applied). Because while the application of confounds do not amount to much information when visualizing in 3D, they do appear very important when converting to atlases and computing connectivity matrices.
-
-From then on, I somewhat concluded that the intuition that I was building in the original 3D space was not fully transferable to the parcel space.
-
-I thus moved on to the Algonauts data and started with taking a look at their 3D -> parcel space transformation, orchestrated through the Schaefer 2018 Atlas [3] (**Figure m**).
-
-I then went on to selecting a given subject and picking a given scene (**Figure n**) and taking a look at the resulting fMRI data projected back into 3D (**Figure o & p**). This looks completely different from what I had been working on so far. The distribution of the data looks ameneable to train models with (**Figure p & q**) and the different brain regions have clear differences in activation values. A correlation matrix computed on that series of data (**Figure q**) seals the deal with clear positive and negative correlation between brain regions which is to be expected.
-
-On my end, a simple question remains unanswered: how did we go from such a noisy signal to such a clear cut signal? It seems like decades of work from the scientific community has gone into those transformations, and I look forward to learning more about those as I get more familiar with fMRI brain data :)
-
-## Data Used
-
-- Nilearn's brain development dataset [1]
-- Algonauts 2025 dataset [2]
-
-## Tools Used
-
-### Development
-- Visual Studio Code
-- Python
-- Jupyter notebooks
-- Git/GitHub
-
-### Containerization
-- Docker
-- Dev Containers
-- Docker Hub
-
-### Plotting
-- Matplotlib
-- Bokeh
-- Plotly
-
-### Neuroimaging
-- Nilearn
-- Nibabel
-
-## References
-
-[1] Hilary Richardson, Grace Lisandrelli, Alexa Riobueno-Naylor, and Rebecca Saxe. Development of the social brain from age three to twelve years. Nature communications, 9(1):1–12, 2018.
-
-[2] Gifford, Alessandro T., et al. "The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies." arXiv preprint arXiv:2501.00504 (2024).
-
-[3] Alexander Schaefer, Ru Kong, Evan M Gordon, Timothy O Laumann, Xi-Nian Zuo, Avram J Holmes, Simon B Eickhoff, and B T Thomas Yeo. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9):3095–3114, 07 2017
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: Analyzing variability of working memory and reward processing in children with and without ADHD using fMRI data
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Zara Khan, Fariah Sandhu, Clara Sun]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/csun_project/
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [project, github, markdown, brainhack, jupyter notebook, preprocessing, ADHD, fMRI]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The focus of our project was to gain experience using neuroimaging tools to preprocess, analyze, and visualize functional MRI data. We aimed to explore differential variability in brain connectivity among children with and without ADHD. Project reports are incorporated on the BHS [website](https://school.brainhackmtl.org/project)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "projectcover.png"
----
-
-
-## Project definition
-
-### Background
-
-#### About us
-Hello, we are team Pipasaurus! The three of us are undergraduate summer research students in the Translational Imaging and Genetics Research (TIGR) Laboratory at the Centre for Addiction and Mental Health (CAMH) in Toronto. We are a multidisciplinary team with diverse educational backgrounds: Zara is studying biomedical and electrical engineering at McMaster University, Fariah is studying psychology at York University, and Clara just finished her undergraduate degree studying interdisciplinary medical sciences at Western University. As newcomers to neuroimaging, Brainhack School 2023 presented the perfect opportunity for us to develop our skill sets in an immersive, beginner-friendly environment. Functional MRI is one modality of special interest to us, particularly because we will be working with fMRI data this summer.
-
-#### About the project
-
-Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders worldwide. Moreover, there is a high prevalence of ADHD among children (Morkem et al., 2020). The characteristics symptoms of ADHD, namely poor working memory, greater reliance on external feedback, and abnormal reward processing are of interest to us because these characteristics have been associated with changes in brain activity patterns in distinct networks. In addition, previous studies also suggest that there are substantial individual differences in reactions to reward and feedback manipulation among those with ADHD (Hammer et al., 2015).
-
-
-
-### Personal Goals
- * Understand the processing and application of the full neuroimaging workflow, specifically from raw fMRI data to data visualization
- * Build and develop skills in open science applications, particularly for future neuroimaging projects
- * Gaining insight into fMRI data applications, specifically in creating connectivity matrices and brain parcellations
-
-### Tools
-
-This project will rely on the following technologies:
-
-
-
Open Neuro fMRI data - doi: 10.18112/openneuro.ds002424.v1.2.0
-
Python Scripts
-
-
Nilearn - extractring and visualizing data
-
Pandas - manipulation and plotting
-
Nibabel - loading images
-
-
SciNet - Jupyter Notebook - to implement code for our data/ take first steps within our the Niagara (University of Toronto) cluster
-
Git and GitHub - practice sharing a workspace, fork repositories, version control
-
Bash - Use Terminal for quick and easy access
-
-
-### Data
-
-For this project, we were working with a Neuroimaging Dataset on Working Memory and Reward Processing in Children With and Without ADHD from OpenNeuro (Lytle et al., 2020).
-
-The dataset contains 79 children at session T1 (mean age = 10.4, 14 female). A subset of the participants 48 individuals returned two years later to complete a follow-up standardized testing session (n = 48, mean age = 12.6, SD = 0.94). 35 participants at session T1 and 18 participants at session T2 had a diagnosis of ADHD. All participants diagnosed with ADHD were male. Participants were recruited from the greater Chicago area.
-
-Participants completed eight n-back working memory tasks in the scanner which varied in three factors: reward amount, feedback delay, and judgment type. In all tasks, participants were presented with a series of letters one at a time. These letters were located in one of four positions around a fixation box.
-
-In the verbal working memory task (V), which is what we were looking at, participants were asked to judge whether the letter that appeared on the screen was the same letter as the one presented n letters back.
-
-Participants made responses by selecting one of two buttons on a right-handed button box. Prior to the beginning of each block, the program would indicate which task instructions were to be followed (1-back, 2-back or fixation) with varying amounts of trials and questions.
-
-Tasks also varied in reward amount. Participants were told that they would make $.02 or $.25 for every correct answer for the small (S) and large (L) reward tasks, although all participants were compensated the same by the end of the tasks. Participants were reminded of what reward amount was being offered by one of two images. For small reward tasks, the image was two coins, and for the large reward tasks, the was a stack of paper bills.
-
-Tasks contained one of two feedback times, immediate (I) or delayed (D). In the delayed feedback tasks, participants would continue to view a black fixation square. At the end of each experimental block, participants would be told their percentage of correct responses.
-
-In all tasks, trial timing was adhered to. Participants were presented with the letter in one of the four corners, where the letter disappeared and only the fixation square remained. Participants then continued to see a fixation period of 600 ms. In delayed feedback, the fixation square remained black.
-
-Some Limitations included:
-* Right-handed
-* Native English speakers
-* Have normal or corrected to normal vision and have no history of neurological or psychological disorder
-* Prematurity of less than 36 weeks
-* Head injury causing overnight hospitalization
-* Hearing loss, or contraindications for MRI
-* Participants could not be taking medication affecting the central nervous system other than ADHD medication, and all participants with ADHD were required to be male
-
-**Our Chosen Task:**
-Our group had chosen to focus on one task from the eight- the Verbal, Large Reward, Delayed Feedback (VLD). Of the 73 participants who completed the VLD task, 30 participants were diagnosed with ADHD as opposed to the 43 participants who were not. Due to problems our group ran into with preprocessing data (which will be discussed later), we were only able to obtain processed data for the first 10 participants, 4 of whom could not be used as a result of not completing the VLD task. Of our six participants, five individuals were diagnosed with ADHD.
-
-
-
-
Verbal Task and Large Reward with Delayed Feedback
-
-
- Figure 1. Schematic of verbal task design adapted from Lytle et al. (2020) illustrating correct (solid arrow) and incorrect (dashed arrow) trials in the (a) 1-back, (b) 2-back tasks.
-
-
-
- Figure 2. Schematic of the delayed feedback large reward adapted from Lytle et al. (2020) illustrating trial timing.
-
-
-
-
-
-### Deliverables
-✓ Preprocessed Data
-✓ Creating Brain Parcellations
-✓ Visualizations
-✓ Graphs for Statistical Analysis
-✓ Connecitivty Matrix
-
-
-## Results
-
-### Progress overview
-
-This project was initiated by Clara Sun, Fariah Sandhu and Zara Khan, based off of the OpenNeuro Dataset from James R. Booth et al on 22nd May 2023. The final presentation of this project was delivered on 2nd June 2023. All deliverables were given an attempt to be completed, where the ones that were, can be found on this repository.
-
-### Tools we learned to use during this project
-
- * **Pre-processing Data Scripts:** We learned how to pre-process data for the first time, which is running code through SciNet clusteres and being able to access the fMRI data. It felt really weird, but somehow quite fun as well.
- * **Jupyter Notebook/Jupyter Lab:** We used this platform to code our results based on the preprocessed data. That was quite the challenge because we were hit with all these new libraries that we did not know could produce _so_ many different visualizations.
- * **Git:** Through the modules, and accessing functions used for preprocessing/analysis - Git was a integral part of what we had to learn.
- * **Bash/Terminal:** To be able to locate our data once it was/after preprocessing, we had to be able to use the classic "cd" and "ls" commands to find where all our outputs from the preprocesed data was hidden.
-
-### Results
-
-#### Deliverable 1: A Github repository with code scripts and data preparation
-
-The data we obtained from Lytle et al. (2020) via OpenNeuro was already validated based on the Brain Imaging Data Structure (BIDS) standards. Thus, we were able to proceed directly to preprocessing data by forking the schizophrenia Canadian Neuroimaging Database (SCanD) project codebase developed by Erin Dickie and TIGR Lab. An overview of the general folder structure for the repository (after all scripts are run) is shown below.
-
-
-
-An example of output from the SCanD project preprocessing pipeline fMRI prep anatomical step is shown below for subject 3, a child without ADHD. Specifically, the image below shows the template T1-weighted image with contours delineating the detected brain mask (red outline) and brain tissue (blue outline) segmentations. These outputs were reviewed for quality assurance purposes.
-
-
-
-Our project GitHub repository can be accessed here: https://github.com/brainhack-school2023/csun_project/
-
-Our preprocessing scripts adapted from Erin Dickie’s SCanD_project: https://github.com/sunclara/SCanD_project-brainhack2023
-
-#### Deliverable 2: A jupyter notebook of the analysis codes and visualizations
-
-Based on resources contained within Erin Dickie’s Krembil Centre for Neuroinformatics (KCNI) summer school slides and modules, we were able to produce graphs as well as brain images via the preprocessed data. For example, using nilearn, nibabel, and matplotlib, we were able to plot slices of the brain as well as a line graph which displayed fMRI signal vs time of any brain parcellation. In the context of our data, we chose to plot the somatomotor region vs the auditory region.
-
-The code for parcellation graph comparing fMRI signal vs. time with the somatomotor and auditory regions:
-
-
-
-
-
-BOLD fMRI signal over time for somatosensory and auditory regions:
-
-
-
-
-#### Deliverable 3: A connectivity matrix based on one task
-
-Once again, following Erin Dickie’s KCNI modules, we were able to create and compare connectivity matrices based on children with and without ADHD. The analysis code can be found within this repository under “brainhacks_connectivity matrix.ipynb”. Using a list, we were able to sum up the values of each participant with ADHD, average out these values (using numpy), and then plot the models using previous code in order to see a connectivity matrix.
-
- Participant (subject 3) without ADHD:
-
-
-
-
-
-
- Participants with ADHD:
-
-
-
-
-
-## Conclusion
-
-The personal objectives within this project were met as well as some deliverable goals. We all started Brainhacks with no previous knowledge on neuroimaging and the tools used with neuro-analysis on fMRI data. Moreover, most of us had limited skills with code. During our time in Brainhacks school, we were exposed to many different skills and techniques that we did not know existed. We learned lots from the modules such as the introduction to python and fMRI modules that set the foundation for our knowledge. We then applied these skills with the homework which prepared us for our project. We would like to take these skills that we learned over the course of a month in future personal projects over the summer, and develop them further.
-
-We are also glad to have had wonderful TAs as well as our supervisor, Erin Dickie, that helped us perform analysis on SciNet within the Nigara cluster.
-
-## Acknowledgements
-We would like to thank Erin Dickie for leading and organizing Brainhacks school for the Toronto Hub and giving us the wonderful opportunity to be able to join. We would also like to thank our TAs who would join our online discord calls, and were always available to help whenever we needed it:
-* Ju-Chi Yu
-* Ryan Yeung
-
-A special mention to the 12th floor and their coffee machine, and Major League Hacking for providing us with merch and pizza on our last day!
-
-## References
- Hammer, R., Cooke, G. E., Stein, M. A., & Booth, J. R. (2015). Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder. NeuroImage Clinical, 9(C), 244–252. https://doi.org/10.1016/j.nicl.2015.08.015
-
-Lytle M. N., Hammer R., & Booth J. R. (2020). Working memory and reward in children with and without Attention Deficit Hyperactivity Disorder (ADHD). OpenNeuro.10.18112/openneuro.ds002424 Lytle MN, Hammer R & Booth JR (2020). A neuroimaging dataset on working memory and reward processing in children with and without ADHD. Data in Brief, 28,105801.
-
-Morkem, R., Handelman, K., Queenan, J. A., Birtwhistle, R., & Barber, D. (2020). Validation of an EMR algorithm to measure the prevalence of ADHD in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). BMC Medical Informatics and Decision Making, 20(1), 166–166. https://doi.org/10.1186/s12911-020-01182-2
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "The Neural mechanism of Nature-based intervention with Environmental information of Forest"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Chun-Yi, Lee]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/JamesLeeCY/ChunYi_Lee_project.git
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [nature-based intervention, environmental preference, fMRI, naturalistic paradigm]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Researchers will use fMRI to study how short-term exposure to nature affects the brain. They hypothesize that nature exposure will improve cognitive function and reduce noisy information processing in the VMPFC."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "fmri_nbi_naturalistic_cover.png"
----
-
-## Project definition
-
-### Background
-
-Increasingly, research has shown that Nature-Based Interventions (NBIs) have positive effects on cognitive function and mental health. One study showed that participants who walked through an arboretum performed significantly better on memory and directed attention tasks, and increased positive affect more than did an urban-walking group. However, the different environmental preferences of individuals might moderate or confound the cognitive benefits of NBIs. It also remains unclear what specific mechanisms might link the neural processing of environmental information with cognitive benefits.
-
-### Tools
-
-The "The Neural mechanism of Nature-based intervention with Environmental information of Forest" project will rely on the following technologies:
-
- * HeuDiConv for BIDS
- * fMRIPrep for preprocessing
- * Pliers package for automated feature extraction
-
-### Data
-
-Self-recruiting project
-Location: Taiwan Mind & Brain Imaging Center at National Chengchi University
-Participants: N = 60 (30 pro-nature, 30 pro-urban)
-Stimulus: 10 min video with 5 scenes for both natural and urban environment
-
-
-### Deliverables
-
-At the end of this project, we will have:
- - Analyze workflow for BIDS conversion and fMRIPrep
- - Code script for Naturalistic paradigm analysis
-
-## Results
-
-### Progress overview
-
-The project was swiftly initiated by Chun-Yi Lee, about the naturalistic paradigm and nature-based intervention.
-
-### Tools I learned during this project
-
- * HeuDiConv for BIDS
- * fMRIPrep for preprocessing
- * Pliers package for automated feature extraction
-
-### Results
-
-#### Deliverable: results
-
- * Convert the raw dicom files into BIDS format by HeuDiConv tools
- * Create a container in server to isolate the process and manage packages
- * Preprocessed the nifti files with fMRIPrep as a Robust Preprocessing Pipeline for fMRI Data
- * Try to automated extract the features from environment stimuli with pliers package
- * Try to analyze the whole brain Intersubject Correlation and Functional Alignment between different participants with certain environmental preference
-
-
-## Conclusion and acknowledgement
-
- I would like to thank the Brain Hack School 2023 for organizing this course and for their support. The course has been highly beneficial, and I would like to express my appreciation to organizer and TA for all your support!
-
- ## References
-
- * Kuperman, V., Stadthagen-Gonzalez, H. & Brysbaert, M. Age-of-acquisition ratings for 30,000 English words. Behav Res 44, 978–990 (2012). https://doi.org/10.3758/s13428-012-0210-4
- * Warriner, A.B., Kuperman, V. & Brysbaert, M. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav Res 45, 1191–1207 (2013). https://doi.org/10.3758/s13428-012-0314-x
- * Glerean, E., Salmi, J., Lahnakoski, J. M., Jääskeläinen, I. P., and Sams, M. (2012). Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity. Brain Connect. 2, 91–101. doi:10.1089/brain.2011.0068.
- * Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. Social Cognitive and Affective Neuroscience, Volume 14, Issue 6, June 2019, Pages 667–685, https://doi.org/10.1093/scan/nsz037
- * Nummenmaa, L., Lahnakoski, J. M., and Glerean, E. (2018). Sharing the social world via intersubject neural synchronisation. Curr. Opin. Psychol. 24. doi:10.1016/j.copsyc.2018.02.021.
- * Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., et al. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nat. Commun. 7. doi:10.1038/ncomms12141.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Replication Analysis of Brain Correlates of Speech Perception in Schizophrenia Patients with and without Auditory Hallucinations"
-
-# Your project GitHub repository URL
-github_repo: https://github.com/FuelToast/timtan_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, FSL, fMRIPrep, git]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Timothy Tan]
-
-summary: "An attempt to replicate the study by Soler-Vidal et al. (2022), using the study's dataset available on OpenNeuro. Attempted preprocessing of the first participant, sub-01, using FSL (FEAT files in 'sub-01' folder) and fMRIPrep (material found in 'code' and 'derivatives' folders). Attempted creation of timing files, found in 'Ideal_Time_Series' folder."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover.png"
----
-
-
-## Project definition
-
-### Background
-
-I took up the HP4210 Data Aanalytics for Neuroimaging module in NTU Singapore, which led me to learn materials from the Brainhack School. As I studied along the brainhack school tutorials, I decided on doing research on schizophrenia, coming across a dataset avalable on OpenNeuro. This dataset is titled "Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations", where the dataset was used for a research study of the same name. As I am a beginner in handling fMRI data, I attempted to replicate the study's findings.
-
-
-### Tools
-
-The tools I used for this project are the following:
- * git and GitHub
- * WSL Docker, Jupyter Notebook and Visual Studio Code (miniconda3 and Python)
-
-### Data
-
-This project was made possible thanks to the available dataset on OpenNeuro, as linked [here](https://openneuro.org/datasets/ds004302/versions/1.0.1). To learn more about the study by Soler-Vidal et al. (2022) that I attempted to replicate, check [here](https://doi.org/10.1371/journal.pone.0276975).
-
-### Deliverables
-
-In my attempt to replicate the study, I aimed to have:
- - Preprocessing of raw fMRI data
- - First-Level Analysis
-If I was able to conduct first-level analysis, I would attempt to do second-level analysis.
-
-## Results
-
-### Progress overview
-
-I officially started this project on 25th May, after finalising and clarifying my goals for the project based on my project pitch. Over the course of 2 weeks until presentation date (10th June), I worked on this project step-by-step through online material that guided me on conducting preprocessing of raw fMRI data and subsequent analysis procedures. I modified the preprocessing and analysis steps according to the study that I was replicating.
-
-### Tools I learned during this project
-
- * **git-annex** Used to fix broken symlinks after cloning the dataset from OpenNeuro.
- * **FSL** Used to pre-process the data and attempt to create first-level analysis.
- * **fMRIPrep** Used to automatically pre-process data and create first-level analysis with default parameters.
-
-### Results
-
-#### Deliverable 1: Preprocessing of raw fMRI Data
-
-I used FSL's modules, BET and FEAT to preprocess the dataset. The preprocessed results can be found in the folder directory 'sub-01/run1.feat'.
-
-I also used fMRIPrep to automatically preprocess the data, but the preprocessed results were incomplete due to unidentified errors. Incomplete preprocessing results are found in the 'derivatives' folder, with the code used to run fMRIPrep found in the 'code' folder.
-
-#### Deliverable 2: First-Level Analysis
-
-I was unable to produce first-level analysis results as I encountered errors while using FSL and fMRIPrep.
-In my attempt to produce first-level analysis results with FSL, I created timing files based on the time series provided by the 'task-speech_events.tsv' file. The timing files can be found in the 'Ideal_Time_Series' folder.
-
-
-## Conclusion and acknowledgement
-
-I enjoyed picking up many new practical skills as part of this project, my first foray into neuroscience. Although I was unable to get the results I wanted during this project's timeframe, I still intend to progress on my own to better understand how data analysis procedures operate when it comes to fMRI data.
-
-Thanks once again to authors of the dataset for making it available [here](https://openneuro.org/datasets/ds004302/versions/1.0.1) at OpenNeuro and for their study that I based my replication on [here](https://doi.org/10.1371/journal.pone.0276975).
-
-Soler-Vidal, J., Fuentes-Claramonte, P., Salgado-Pineda, P., Ramiro, N., García-León, M. Á., Torres, M. L., Arévalo, A., Guerrero-Pedraza, A., Munuera, J., Sarró, S., Salvador, R., Hinzen, W., McKenna, P., & Pomarol-Clotet, E. (2022). Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PloS one, 17(12), e0276975. https://doi.org/10.1371/journal.pone.0276975
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-10" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Do you feel the words? An fMRI analysis on tactile vs non-tactile words in Mandarin Chinese"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Yusuke Taira]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/NTUsuke/Yusuke_Taira_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI,parietal lobe, somatosensory cortex, tactile word]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Language consists of many thousands of words which differ in meaning and syntactic category. Some words may refer to reletevely touchable, hence concrete aspects of the world while others are abstract in meaning which do not have physical references. But which brain areas are associated with tactile word processing? This project aims to investigate brain activation of participants when listening to tactile vs non-tactile word stimuli. The preliminary result shows that large areas of parietal lobe is particularly activated to tactile words compared to non-tactile words"
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "fmri_tactile.png"
----
-
-
-## Project definition
-
-### Background
-
-This project aims to explore brain activation of participants when presented with tactile words in Mandarin Chinese. The data used is from an OpenNeuro dataset which contains 672 stimulus words and functional data of 11 Chinese participants. My personal goal of this project is to familiarize myself with python, statistics and basic fMRI analysis techniques since I had no experience in any of them. For data analysis, I used General Liniear Model analysis to investigate brain responses to the stimulus words.
-
-
-### Tools
-
-The project relies on the following technologies:
- * This README is built using [Markdown](https://guides.github.com/features/mastering-markdown/), to structure the text.
- * Extraction, analysis and visualization of fMRI data is done with `nilearn`
- * The detail of this project is stored in a [Jupyter Notebook](https://jupyter.org/index.html)
-
-### Data
-
-The dataset contains 672 words of Mandarin Chinese with semantic ratings ranged from 0-7 and functional data of 11 participants recorded while listening to the stimulus words.
-The functional data are all preprocessed and available from [OpenNeuro website](https://openneuro.org/datasets/ds004301/versions/1.0.2)
-
-### Deliverables
-
-At the end of this project, I'll have :
-
- * Markdown README.md for the description of the current project
- * Jupyter notebook including the code, and the fMRI brain images
-
-### Tools I learned during this project
- For completing this project, one will need:
- * **Nilearn** for loading and visualizing fMRI data
- * **Numpy** to manipulate arrays
- * **Pandas** to manipulate dataframe
-
-### Results
-
-Left hemisphere was more activated compared to the right in general as all the stimuli were speech sounds.
-In terms of activation areas compared to non-tactile words, tactile words activated large areas of parietal lobe where somatosensory cortex is located at but also partially activated visual cortex. It might be due to overlapping features of tactile and visual words.
-
-
-
-
-### Conclusion and Limitations
-
-This project aimed to investigate brain activation when presented with tactile vs non-tactile words. Although there was a strong tendency that the somatosensory cortex was particulary activated, no area was significantly activated when setting a threshold as 3.0 in z-score which implies the necessesity of larger amount of data. It is also unfortunate that my pc leads to kernal crash every time I try to analyze data of 5 or more runs and this inhibited me from second level analysis.
-
-## Acknowledgements
-This project taught me a lot and I can not believe that I could learn python, statistics, GLM and fMRI analysis at the same time in such a limited amount of time. About a few months ago, I had no idea where to start python and now I can use it to extract, analyze and visualize fMRI data! I'm quite sure that I couldn't have achieved this without Brainhack school.
-
-In the end, I'd like to express my huge thanks to all of the organizers and TA's, especially Yu-Shiang without whom I could never have finished my project, and Dr.Josh and Dr. Lee, who hosted such as amazing program at National Taiwan University.
-
-## References
-[Wang, S., Zhang, Y., Zhang, X., Sun, J., Lin, N., Zhang, J., & Zong, C. (2022). An fMRI Dataset for Concept Representation with Semantic Feature Annotations. Scientific data, 9(1), 721.](https://doi.org/10.1038/s41597-022-01840-2)
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-17" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "The Friends Compendium: A brief visual and statistical description of FRIENDS"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Cleo Lam]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/analyses_friends_annotations
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://cleode5a7.github.io/friends_compendium/
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [project, github, website, friends]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The main goal of this brainhack project and the website is to do basic statistical analyses to the friends annotations data and to present the results in the form of visualisations. These analyses will provide better context and information about the stimuli on different levels of analyses. Here is the github for the [website](https://github.com/cleode5a7/friends_compendium)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "friends_logo.png"
----
-
-
-## Project definition
-
-### Background
-
-This project is based on the friends annotations dataset, which contains half-episodes that are annotated. The goal of these annotations are to give context about the visual stimuli (FRIENDS half-episodes) that were shown to participants in a fmri machine. This dataset is also one of the dataset available for the Algonauts challenge, where participants use the stimuli data and the brain response data to create brain encoding models.
-
-Here are some questions that encoding models attempt to address that I took from the MAIN educational website :
-
-"Does the variation in the response depend on the stimulus?
-
-How well do the responses ‘encode’ the stimuli?
-
-How well are the responses ‘explained’ by the stimuli?
-
-Can some responses be explained by specific stimuli?
-
-How can we quantify the dependence of responses on the stimuli?" (The MAIN Educational Team, 2024)
-
-Having good robust annotations and analyses on the stimuli acts as a good base to anser these types of questions.
-
-### Tools
-
-- Jupyter notebooks
-- Python
-- Mystmd
-- git and github
-
-### Data
-
-Theses analyses are made on the friends annotations dataset, it is available as a submodule in this [repo](https://github.com/courtois-neuromod/friends_annotations.git)
-
-This dataset contains annotations made for season one to six of FRIENDS and it contains for every half-episode:
-
-Number of scene cuts (PYScene): Ai model that detects scene cuts.
-
-Number of local maxima and location of maximum in pixel space (saliency with deepgazemr): model trained to predict the likelihood of the viewer's gaze position for each movie frame.
-
-Segments annotations (manual annotations): Segments where annotated by a person. It gives detail about onset and offset of each segments depending on their modalities (location change, character entry etc.)
-
-Transcript (Speech2text): AssemblyAI speech-to-text transcription. Produces time-stamped movie transcripts.
-
-### Deliverables
-
-At the end of this project, I will have:
- - Notebooks containing my preprocessing and analyses
- - A pip installable package
- - A myst website
-### Main analyses
-
-Plot different relations between scenes, segments, duration, frames, number of local maximas and more!
-
-Types of plots: barplots for frequencies, barplots for proportions, hexbins and kde for density visualisations
-
-Describing the dataset in terms of length of scenes and segments and average number of maxima within scenes in episodes can guide further analysis on the transcripts and emotions by analysing the relation between these modalities.
-
-example of a plot:
-
-
-
-
-## Results
-Check out my [website](https://cleode5a7.github.io/friends_compendium/) for the results of my analyses
-
-Package: available on my project's github
-
-Notebooks: Go to website or my website's github for the clean notebooks. For additional plots and extras, go see my notebooks on my project's github.
-
-### Progress overview
-
-I explored 3 of the 5 annotations dataset and made a website with them. The website can still be expanded since there is still tons of possible directions to explore wuth this rich dataset.
-Notes:
-- Some of the notebooks in my "analyses friends annotations" github contain more content then presented on the website.Some notebooks were also left out for now. They were left out because of narrative reasons and fit in the article (website).
-- As of 2025/06/18 there is some missing half-episode, notably episode that were cut in 4 parts.
-
-### Tools I learned during this project
-
- * **Tools listed above** I learned a lot during this past month and it thought me debugging. Myst gave me a hard time in the beginning but I figured it out eventually,
-
-
-## Conclusion and acknowledgement
-
-Thank you Marie, Lune and the TAs of brainhack school for helping me and for guiding me. Also thank you Sara for helping me with myst, we finished brainhack school girl!!!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-20" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "g-Ratio mapping of the optic nerve"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Hugo Albert Plante]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/albert-plante_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI), click `manage topics`.
-# Please only lowercase letters
-tags: [g-ratio, qmri, dmri, multiple sclerosis]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to accurately compute the optic nerve g-ratio, a key neuroimaging marker reflecting myelin integrity in nerve fibers. Using advances quantitative MRI techniques, it analyses the human optic nerve’s microstructure non-invasively. By integrating MP2RAGE and diffusion MRI, it seeks to improve assessment of myelin and nerve health, contributing to better understanding, early detection, and diagnosis of neurological diseases like multiple sclerosis (MS)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_anatomy_eye.jpg"
----
-
----
-
-# g-Ratio mapping of the optic nerve
-## About me
-
-
- Hugo Albert Plante
-
-
-I am a second-year undergraduate student in Biomedical Engineering at Polytechnique Montreal. I am passionate about science and guided by the belief that “impossible only means that you haven’t found the solution yet.” This mindset drives me to approach challenges with curiosity and persistence, always eager to discover innovative solutions that can make a difference.
-
-Attending BHS as an undergraduate student was not a simple task, but it made the experience even more rewarding. My goal in participating was to kickstart part of my internship project, focused on developing a vendor-neutral g-ratio protocol for the optic nerve.
-
-## Project definition
-
-### Introduction
-This project focuses on quantifying myelin integrity in the optic nerve using multimodal MRI. Multiple Sclerosis (MS) causes degradation of the myelin sheath surrounding nerve fibers, impairing neural communication (Ausmed, 2025). Measuring key parameters such as the **g-ratio**, **Myelin Volume Fraction (MVF)**, and **Fiber Volume Fraction (FVF)** enables better understanding and monitoring of demyelination.
-
-Using MRI-derived maps like T1 and FA, this project aims to compute these metrics non-invasively, providing valuable insights for research and clinical applications in neurological disorders like MS.
-
-### Background
-
-#### Multiple Sclerosis and Neuronal Structure
-Multiple sclerosis (MS) is an autoimmune disease that affect 2.8 millions peoples worldwide, as of 2020 (MS Canada, 2025). This disease occur when the body’s immune system mistakenly attack the myelin sheath, causing demyelination. A common early symptom of multiple sclerosis is optic neuritis, but as of 2024, it has also become a McDonald criteria for diagnosing MS (MS Trust, 2024).
-
-#### g-Ratio: A key Metric in Myelin Studies
-the g-ratio is a crucial parameter for quantifying the relative thickness of the myelin sheath. It is defined as the ratio between the inner axonal radius (r) and the outer myelinated radius (R) of a nerve fiber:
-
-$$g=\frac{r}{R}$$
-
-This ratio provides insight into myelin integrity and neuronal health.
-
-#### Volume Fractions and their significance
-* **Myelin Volume Fraction (MVF)** represents the proportion of myelin in a given tissue volume.
-* **Fiber Volume Fraction (FVF)** represents the proportion of nerve fibers (axons plus myelin) in a given tissue volume.
-* **Macromolecular Tissue Volume Fraction (MTVF)** represents the proportion of macromolecules (including myelin) in a given tissue volume.
-* The relation between MVF and FVF relates directly to the g-ratio (Stikov et al., 2015):
-
-$$\frac{MVF}{FVF}=1-g^2$$
-
-Using two MRI modalities, MVF and FVF can be calculated non-invasively.
-#### Imaging Techniques and Quantitatives Maps
-* Macromolecular Tissue Volume Fraction is used as an indirect measure of myelin content. In white matter like the optic nerve, myelin composition approximatively 50% of MTVF (Mezer et al., 2013). It has been shown that MTVF can be derived from the actual value of T1 in an MRI voxel (Mezer et al., 2013):
-
-$$\frac{1}{1-MTVF}=\frac{0.44202}{T1}+0.94766$$
-
-* Fractionnal Anisotropy (FA) obtained from diffusion MRI, has shown have a quadratic relation FVF through simulations (Stikov et al., 2011):
-
-$$FVF=0.883{FA}^2-0.082FA+0.074$$
-
-These imaging-derived metrics allow assessment of neuronal integrity and demyelination in diseases like MS.
-
-### Main Objectives
-* Develop a pipeline for mapping the g-ratio of the optic nerve
-* Co-register MP2RAGE, DWI, and their derivatives to a common space (MNI 152)
-* Compute g-ratio, MVF, and FVF from segmentations
-* Calculate the mean g-ratio along each coronal slice of the optic nerve
-
-### Personal Objectives
-* Improve my programming skills through real-world application
-* Familiarize myself with version control tools such as Git and GitHub
-* Gain hands-on experience in data processing and medical image analysis
-* Contribute to the development of scientific research in biomedical engineering
-
-## Methods
-### Tools
-* Jupyter notebooks for scripting
-* BIDS standards for reproducibility and standardization
-* Python for data vizualisation
-* dMRI module and Spinal Cord Toolbox for inspiration and understanding
-* BASH for automation
-* **ANTs for image registration**
-* 3D Slicer and FSLeyes for image visualization and ROI definition
-* Git and GitHub for Version Control
-* Python Packages: 'matplotlib', 'nibabel', 'pandas', 'numpy'
-
-### Data
-The data used in this study comes for NYU Abu Dhabi (private dataset), where the MP2RAGE and Diffusion MRI acquisitions were performed on 10 healty subjects. Manuals segmentations of the optic nerve were also perfromed for both modalities to extract the relevant MRI metrics.
-
-For each subject, the dataset includes:
-* A raw MP2RAGE image for T1 map extraction
-* A denoised and defaced MP2RAGE UNI image
-* A DWI b0 image for registration purposes
-* A Fractional Anisotropy (FA) map obtained from a Siemens scanner
-* Manual segmentations from both MRI modalities
-
-A supplementary critical part of the project was the analysis of a maximum probability label for the optic eye comming form (Barranco Hernandez et al., 2024). The goal of this integration was to determine wether the maximum probability label could be used as a automated segmentation alternative to manual segmentations.
-
-An exampale dataset will be upload to this GitHub repository once ethics approval from NYU has been obtained.
-
-### Project Deliverables
-* A non-invasive method for mapping the g-ratio along the optic nerve
-* An executable, reproducible pipeline that takes multimodal MRI as an input and outputs the mean g-ratio along the optic nerve
-* Jupyter Notebooks for data visualization and statistical metrics extraction
-
-#### Future Deliverable
-* Interactive tutorial-style NeuroLibre publication with interactive figures and data
-
-### Methodology
-1. Data Acquisition
-The dataset was acquired using a MP2RAGE and Diffusion MRI sequences on a 3T Siemens scanner located at NYU Abu Dhabi. Ten healthy subjects were scanned, with manual segmentations of the optic nerve performed on both modalities for ground-truth extraction.
-
-2. Preprocessing
-Raw MP2RAGE UNI images were denoised and defaced to facilitate manual segmentation and registration. The denoising process reduces background noise that can interfere with the registration. Native FA maps provided by the scanner were used directly for FVF calculation. However, the FA map extraction and diffusion MRI preprocessing could be done with the diffusion MRI module's preprocessing Jupyter Notebook (Brainhack School, n.d.) or Naghizadeh_project's preprocessing pipeline (Naghizadeh, 2025).
-
-3. Image Registration
-Accurate co-registration between MP2RAGE and DWI is critical due to the small size (~4-5 voxels wide) and the natural curvature of the optic nerve. Multiple registration strategies were tested, including rigid, affine, and elastic transformations, to optimize alignment while preserving anatomical integrity. Elastic registration methods tended to deform the optic nerve, resulting in segmentations inconsistencies, such as holes within manual masks. Ultimately, a combined rigid and affine registration approach was selected as the optimal balance between alignment and standardization to a template (MNI152). Additionally, another registration challenge occured because the DWI acquisitions covered only a slab including the optic nerve. This restriction made the aligment with the MP2RAGE full-volume more complicated.
-
-4. Parameters Extraction
-Quantitative MRI parameters were extracted voxel-wise within the overlapping of both MRI modalities segmentation. This overlapping approach was necessary because DWI images are noisier, producing bulkier segmentation compared to the more precise MP2RAGE segmentation. T1 relaxation maps, obtained from MP2RAGE through qMRLab (qMRLab, n.d.), were used to compute the MTVF via the established formula. The MVF was then calculated by combining the MTVF values with the assumed myelin composition of the tissue. Native extracted FA values were converted to FVF using the quadratic relation derived form simulation (Stikov et al., 2011).
-
-5. Modeling and Analysis
-The mean myelin volume fraction and mean fiber volume fraction were then used to calculate the mean g-ratio value along each coronal slice of the optic nerve.
-
-6. Visualization and Output
-The final pipeline outputs include:
-* Coronal slice-wise plot of MVF, FVF, and g-ratio in a standardized (MNI152) space
-* Statistics metrics from each coronal slice including the dice coefficient between both modalities segmentations
-
-## Results
-Since the dataset segmentations were not all completed by the submission date, the results presented here focus on only two subjects.
-
-### Image registration using registration.ipynb
-The 'registration.ipynb' Jupyter Notebook contains the full registration workflow, starting from the raw MP2RAGE and DWI images and producing standardized results in the MNI 152 space.
-
-The first cell of the pipeline sets up the bash environment variables and creates the necessary directories. As the project follows the BIDS data structure standard, each subject's files should follow a directory tree similar to the example below:
-
-Project Directory Structure (click to expand)
-
-```plaintext
-project_root/
-└── data/
- ├── sub-XXX/
- │ ├── anat/
- │ │ └── sub-XXX_T1w.nii.gz
- │ └── dwi/
- │ └── sub-XXX_dwi.nii.gz
- │
- └── derivatives/
- ├── t1_map/
- │ └── sub-XXX/
- │ └── sub-XXX_t1_map.nii.gz
- ├── fa_map/
- │ └── sub-XXX/
- │ └── sub-XXX_scanner_FA.nii.gz
- ├── optic_nerve_segmentations/
- │ └── sub-XXX/
- │ ├── anat/
- │ │ └── sub-XXX_label-ON_seg.nii.gz
- │ └── dwi/
- │ └── sub-XXX_label-ON_seg.nii.gz
- │
- └── templates/
- ├── MNI152_T1_1mm.nii.gz
- ├── Atlas_MNI.nii.gz
- └── Optic_Nerve_Label_MNI.gz
-```
-
-
-Within this data structure, the only environment variables that require customization are 'PROJECT_ROOT' and 'SUBJECT'. In this project, manual segmentations were performed directly on pre-rigidly registered DWI images, but the raw DWI image can also be used. Temporary derivative files are created in a designated directory, which can be safely deleted once the final results files are generated.
-
-For each registration step, the type of registration can be easily modified in the notebook by changing the value the '-t' parameter to:
-* 'r' for rigid
-* 'a' for affine
-* 's' for nonlinear elastic (SyN) registration
-
-The pipeline proceeds as follows:
-1. Rigid registration of DWI (automatically the b0 volume) to MP2RAGE
-2. Affine registration of MP2RAGE to MNI 152 template
-3. Application of transformations to:
-* DWI to MNI
-* DWI segmentation to MP2RAGE then to MNI
-* FA map to MP2RAGE then to MNI
-* MP2RAGE segmentation to MNI
-* T1 map to MNI
-4. Creation of standardized Regions of Interest (ROIs) for DWI and MP2RAGE in MNI 152 space
-5. Rigid registration of DWI ROI to MP2RAGE ROI (secondary alignment)
-6. Application of ROI transformations to DWI, DWI segmentation, and FA map
-7. Generation of final results files
-8. Optional: Deletion of the temporary working directory
-
-Step 4 and 5 are necessary because the initial registration introduced an offset in the segmentations. Therefore, the ROI co-registration step corrects this misalignement, as illustrated in the example below: red represents the MP2RAGE segmentation, green represents the DWI segmentation after the initial alignment, and blue represents the DWI after the ROI alignment correction.
-
-
-
-Additionally, outlying values were identified near the edges of both manual segmentations. To ensure plausible values along the optic nerve, Regions of Interest (ROIs) were defined, as illustrated in the example figure below.
-
-
-
-### Data visualization using data_visualization.ipynb
-
-Assuming that the results directory and files were generated sucessfully, the only variable that need to be modified in the notebook are 'PROJECT_ROOT' and 'SUBJECT'.
-
-The main steps in the data visualization workflow are as follow:
-1. Load results files based on the specified 'PROJECT_ROOT' and 'SUBJECT'.
-2. Extract various slice-wise metrics, including slice index, number of voxels in each segmenation, number of overlapping voxels, Dice coefficient, as well as the mean and standard deviation of T1 and FA values.
-3. Compute slice-wise MVF, FVF, and g-ratio using detailed equations and underlying assumptions.
-4. Summarize relevant metrics that characterize the overlap between segmentations.
-5. Generate plots showing the slice-wise mean of T1, FA, FVF, MVF, and g-ratio, for visual interpretation
-
-### Segmentation Overlap Metrics Summary (whole optic nerve)
-Segmentation overlap metrics showed moderate agreement between manual segmentations with Dice coefficients of 0.4961 ± 0.1703 and 0.3958 ± 0.1234 for subjects 1 and 2, respectively.
-| Metric | Subject 1 | Subject 2 |
-|--------------------------------|---------------------|---------------------|
-| Number of slices analyzed | 32 | 22 |
-| Dice coefficient (mean ± std) | 0.4961 ± 0.1703 | 0.3958 ± 0.1234 |
-| Average overlapping voxels/slice| 20.8 | 19.0 |
-| T1 values inside overlap (mean ± std, s) | 0.99 ± 0.13 | 0.99 ± 0.17 |
-| FA values inside overlap (mean ± std) | 0.5886 ± 0.1408 | 0.4370 ± 0.1013 |
-| Mean MTVF (mean ± std) | 0.2736 ± 0.0278 | 0.2751 ± 0.0338 |
-| Mean MVF (mean ± std) | 0.1368 ± 0.0139 | 0.1375 ± 0.0169 |
-| Mean FVF (mean ± std) | 0.3486 ± 0.1594 | 0.2154 ± 0.0680 |
-| Mean g-ratio (mean ± std) | 0.7329 ± 0.1019 | 0.5781 ± 0.1614 |
-
-### Results – MVF, FVF & g-Ratio
-Plausibles results were obtained for regions of interest (ROIs) in the optic nerve, with calculated g-ratio values ranging between 0.6 and 0.8 for each subject. The calculated mean for each coronal slice includes both the right and the left optic nerves.
-#### Subject 1
-
-
-
-#### Subject 2
-
-
-
-### Tools that I learned during this project
-* Python for data processing, scripting, and analysing
-* Jupyter Notebooks to organize and run interactive data workflows
-* ANTS (Advanced Normalization Tools) for medical image registration and transformation
-* Git and GitHub for version control and project collaboration
-* BIDS for standardization of the data structure
-
-### Results
-#### Deliverable 1:
-A non-invasive method for visualize the mean g-ratio along the coronal slices of the optic nerve.
-
-#### Deliverable 2:
-An executable and fully reproducible processing pipeline that:
-* Takes multimodal MRI inputs
-* Computer and outputs the mean g-ratio along the optic nerve
-* Includes Jupyter Notebooks for data visualization and extraction of relevant statistical metrics
-
-## Conclusions
-
-### Can we visualize the g-ratio value along the optic nerve?
-Yes. Using the developed pipeline, we successfully mapped and visualized the g-ratio values along the optic nerve in a reproducible and non-invasive manner. This provides meaningful insight into myelin integrity and fiber composition in differents subjects.
-
-### Encountered issues
-#### Atlas max probability optic nerve label
-The max probability optic nerve label was initialy used to assess whether manual segmentation was truly necessary, or if this atlas-based label could serve as an automated segmentation alternative. Howerver, the Dice coefficient between the overlapping regions of both manual segmentations and the maximum probability atlas label ranged only from 15% to 20%. Given this low similarity, the maximum probability label was excluded from further analysis.
-
-#### Registration issue
-As previously stated, two registrations issues were encountered. The first was related to the small size (~4-5 voxels wide) and the natural curvature of the optic nerve. Non-affine or elastic registrations tended to deform the optic nerve and create hole in the transformed manual segmentation masks. The second issue involved the alignment of the two manual segmentations: a single rigid registration resulted in an approximate one-slice offset. However, applying a second rigid registration using ROIs was able to correct this initial misalignment.
-
-#### MTVF vs MVF
-Initialy, it was assumed that myelin constitutes nearly 100% of the macromolecular content in white mater like the optic nerve. This assumption led to an overestimation of the Myelin Volume Fraction (MVF) by Macromolecular Tissue Volume Fraction (MTVF), resulting in non-plausible results such as more myelin than fiber. An litterature search revealed that myelin accounts for only 50% of the macromolecular content in white matter. With this correction, plausible values of g-ratio were acheived.
-
-### Guide to Reproducibility
-This project follows the BIDS data structure for standardized input. To reproduce results:
-1. Clone the repository
-```
-git clone https://github.com/brainhack-school2025/albert-plante_project.git
-cd albert-plante_project
-```
-2. Set up a Python virtual environment
-```
-For Linux/Mac:
-python3 -m venv .venv
-source .venv/bin/activate
-
-For Windows:
-python -m venv .venv
-.venv\Scripts\activate
-```
-3. Install required Python packages
-```
-pip install -r requirements.txt
-```
-4. Install Jupyter Notebook
-```
-pip install notebook
-```
-5. Install [ANTs](https://github.com/ANTsX/ANTs) and [FSL](https://fsl.fmrib.ox.ac.uk/fsl/docs/#/install/index)
-6. Organize data according to BIDS format under 'PROJECT_ROOT'.
-7. Modify subject variabes in the notebooks ('PROJECT_ROOT' and 'SUBJECT')
-8. Run 'registration.ipynb' to co-register images and create results files.
-9. Run 'data_visualization.ipynb' to extract metrics and generate plots.
-10. Optional: Clean temporary directories to save space.
-
-## Troubleshooting
-* Registration issues: Because the DWI is normally a slab, multiples rigid registration between DWI and MP2RAGE can be needed because the registration algorithm can fail.
-* Missing dependencies: Ensure all Python packages and ANTs binaries are installed and accessible in your environment path.
-* File path errors: Verify your BIDS directory structure and that environment variables point to correct locations.
-* Plotting errors: Create the metrics dataframe before visualization.
-
-## Acknowledgement
-I would like to thank Nikola Stikov and Agah Karakuzu for their mentorship and expertise, as well as Bas Roker and Ameen Qadi from the Rokers Vision Laboratory in NYU Abu Dhabi. Special thanks also to Eva Alonso Ortiz and Sebastian Rios, the professor and teaching assistant of the course, for their guidance and support.
-
-## References
-Ausmed. (2025, 01). How Does Multiple Sclerosis Affect the Body? | Ausmed. https://www.ausmed.com/learn/articles/multiple-sclerosis-nursing-care
-
-Barranco Hernandez, J., Luyken, A., Kebiri, H., Stachs, P., Macias Gordaliza, P., Esteban, O., Aleman Gomez, Y., Sznitman, R., Stachs, O., Langner, S., Franceschiello, B., & Bach Cuadra, M. (2024). Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas. https://doi.org/10.1101/2024.08.15.608051
-
-Brainhack School. (n.d.). Training modules. Retrieved June 19, 2025, from https://school-brainhack.github.io/modules/
-
-Duval, T., Stikov, N., & Cohen-Adad, J. (2017). Modeling white matter microstructure. Functional Neurology, 31(4), 217–228. https://doi.org/10.11138/FNeur/2016.31.4.217
-
-Mezer, A., Yeatman, J. D., Stikov, N., Kay, K. N., Cho, N.-J., Dougherty, R. F., Perry, M. L., Parvizi, J., Hua, L. H., Butts-Pauly, K., & Wandell, B. A. (2013). Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nature Medicine, 19(12), 1667–1672. https://doi.org/10.1038/nm.3390
-
-MS Canada. (2025, February 7). Prevalence and incidence of MS in Canada and around the world | MS Canada. https://mscanada.ca/ms-research/latest-research/prevalence-and-incidence-of-ms-in-canada-and-around-the-world
-
-MS Trust. (2024, 11). McDonald criteria | MS Trust. https://mstrust.org.uk/a-z/mcdonald-criteria
-
-Naghizadeh, Y. (2025). Naghizadeh_project. https://github.com/brainhack-school2025/Naghizadeh_project
-
-NeuroLibre. (n.d.). Retrieved June 19, 2025, from https://neurolibre.org
-
-qMRLab. (n.d.). Home | qMRLab. Retrieved June 19, 2025, from https://qmrlab.org/
-
-Rokers Vision Laboratory. (n.d.). Retrieved June 19, 2025, from https://sites.nyuad.nyu.edu/rokersvisionlaboratory/
-
-State University of New York College of Optometry. (2019, December 11). Scientists Discover the Origin of Brain Mapping Diversity for Eye Dominance [Video]. SciTechDaily. https://scitechdaily.com/scientists-discover-the-origin-of-brain-mapping-diversity-for-eye-dominance-video/
-
-Stikov, N., Campbell, J. S. W., Stroh, T., Lavelée, M., Frey, S., Novek, J., Nuara, S., Ho, M.-K., Bedell, B. J., Dougherty, R. F., Leppert, I. R., Boudreau, M., Narayanan, S., Duval, T., Cohen-Adad, J., Picard, P.-A., Gasecka, A., Côté, D., & Pike, G. B. (2015). In vivo histology of the myelin g-ratio with magnetic resonance imaging. NeuroImage, 118, 397–405. https://doi.org/10.1016/j.neuroimage.2015.05.023
-
-Stikov, N., Perry, L. M., Mezer, A., Rykhlevskaia, E., Wandell, B. A., Pauly, J. M., & Dougherty, R. F. (2011). Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. NeuroImage, 54(2), 1112–1121. https://doi.org/10.1016/j.neuroimage.2010.08.068
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-20" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "g-Ratio mapping of the optic nerve"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Hugo Albert Plante]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/albert-plante_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/AlexPeng517/BHS2023_Project_SAM_MRI), click `manage topics`.
-# Please only lowercase letters
-tags: [g-ratio, qmri, dmri, multiple sclerosis]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to accurately compute the optic nerve g-ratio, a key neuroimaging marker reflecting myelin integrity in nerve fibers. Using advances quantitative MRI techniques, it analyses the human optic nerve’s microstructure non-invasively. By integrating MP2RAGE and diffusion MRI, it seeks to improve assessment of myelin and nerve health, contributing to better understanding, early detection, and diagnosis of neurological diseases like multiple sclerosis (MS)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_anatomy_eye.jpg"
----
-
----
-
-# g-Ratio mapping of the optic nerve
-## About me
-
-
- Hugo Albert Plante
-
-
-I am a second-year undergraduate student in Biomedical Engineering at Polytechnique Montreal. I am passionate about science and guided by the belief that “impossible only means that you haven’t found the solution yet.” This mindset drives me to approach challenges with curiosity and persistence, always eager to discover innovative solutions that can make a difference.
-
-Attending BHS as an undergraduate student was not a simple task, but it made the experience even more rewarding. My goal in participating was to kickstart part of my internship project, focused on developing a vendor-neutral g-ratio protocol for the optic nerve.
-
-## Project definition
-
-### Introduction
-This project focuses on quantifying myelin integrity in the optic nerve using multimodal MRI. Multiple Sclerosis (MS) causes degradation of the myelin sheath surrounding nerve fibers, impairing neural communication (Ausmed, 2025). Measuring key parameters such as the **g-ratio**, **Myelin Volume Fraction (MVF)**, and **Fiber Volume Fraction (FVF)** enables better understanding and monitoring of demyelination.
-
-Using MRI-derived maps like T1 and FA, this project aims to compute these metrics non-invasively, providing valuable insights for research and clinical applications in neurological disorders like MS.
-
-### Background
-
-#### Multiple Sclerosis and Neuronal Structure
-Multiple sclerosis (MS) is an autoimmune disease that affect 2.8 millions peoples worldwide, as of 2020 (MS Canada, 2025). This disease occur when the body’s immune system mistakenly attack the myelin sheath, causing demyelination. A common early symptom of multiple sclerosis is optic neuritis, but as of 2024, it has also become a McDonald criteria for diagnosing MS (MS Trust, 2024).
-
-#### g-Ratio: A key Metric in Myelin Studies
-the g-ratio is a crucial parameter for quantifying the relative thickness of the myelin sheath. It is defined as the ratio between the inner axonal radius (r) and the outer myelinated radius (R) of a nerve fiber:
-
-$$g=\frac{r}{R}$$
-
-This ratio provides insight into myelin integrity and neuronal health.
-
-#### Volume Fractions and their significance
-* **Myelin Volume Fraction (MVF)** represents the proportion of myelin in a given tissue volume.
-* **Fiber Volume Fraction (FVF)** represents the proportion of nerve fibers (axons plus myelin) in a given tissue volume.
-* **Macromolecular Tissue Volume Fraction (MTVF)** represents the proportion of macromolecules (including myelin) in a given tissue volume.
-* The relation between MVF and FVF relates directly to the g-ratio (Stikov et al., 2015):
-
-$$\frac{MVF}{FVF}=1-g^2$$
-
-Using two MRI modalities, MVF and FVF can be calculated non-invasively.
-#### Imaging Techniques and Quantitatives Maps
-* Macromolecular Tissue Volume Fraction is used as an indirect measure of myelin content. In white matter like the optic nerve, myelin composition approximatively 50% of MTVF (Mezer et al., 2013). It has been shown that MTVF can be derived from the actual value of T1 in an MRI voxel (Mezer et al., 2013):
-
-$$\frac{1}{1-MTVF}=\frac{0.44202}{T1}+0.94766$$
-
-* Fractionnal Anisotropy (FA) obtained from diffusion MRI, has shown have a quadratic relation FVF through simulations (Stikov et al., 2011):
-
-$$FVF=0.883{FA}^2-0.082FA+0.074$$
-
-These imaging-derived metrics allow assessment of neuronal integrity and demyelination in diseases like MS.
-
-### Main Objectives
-* Develop a pipeline for mapping the g-ratio of the optic nerve
-* Co-register MP2RAGE, DWI, and their derivatives to a common space (MNI 152)
-* Compute g-ratio, MVF, and FVF from segmentations
-* Calculate the mean g-ratio along each coronal slice of the optic nerve
-
-### Personal Objectives
-* Improve my programming skills through real-world application
-* Familiarize myself with version control tools such as Git and GitHub
-* Gain hands-on experience in data processing and medical image analysis
-* Contribute to the development of scientific research in biomedical engineering
-
-## Methods
-### Tools
-* Jupyter notebooks for scripting
-* BIDS standards for reproducibility and standardization
-* Python for data vizualisation
-* dMRI module and Spinal Cord Toolbox for inspiration and understanding
-* BASH for automation
-* **ANTs for image registration**
-* 3D Slicer and FSLeyes for image visualization and ROI definition
-* Git and GitHub for Version Control
-* Python Packages: 'matplotlib', 'nibabel', 'pandas', 'numpy'
-
-### Data
-The data used in this study comes for NYU Abu Dhabi (private dataset), where the MP2RAGE and Diffusion MRI acquisitions were performed on 10 healty subjects. Manuals segmentations of the optic nerve were also perfromed for both modalities to extract the relevant MRI metrics.
-
-For each subject, the dataset includes:
-* A raw MP2RAGE image for T1 map extraction
-* A denoised and defaced MP2RAGE UNI image
-* A DWI b0 image for registration purposes
-* A Fractional Anisotropy (FA) map obtained from a Siemens scanner
-* Manual segmentations from both MRI modalities
-
-A supplementary critical part of the project was the analysis of a maximum probability label for the optic eye comming form (Barranco Hernandez et al., 2024). The goal of this integration was to determine wether the maximum probability label could be used as a automated segmentation alternative to manual segmentations.
-
-An exampale dataset will be upload to this GitHub repository once ethics approval from NYU has been obtained.
-
-### Project Deliverables
-* A non-invasive method for mapping the g-ratio along the optic nerve
-* An executable, reproducible pipeline that takes multimodal MRI as an input and outputs the mean g-ratio along the optic nerve
-* Jupyter Notebooks for data visualization and statistical metrics extraction
-
-#### Future Deliverable
-* Interactive tutorial-style NeuroLibre publication with interactive figures and data
-
-### Methodology
-1. Data Acquisition
-The dataset was acquired using a MP2RAGE and Diffusion MRI sequences on a 3T Siemens scanner located at NYU Abu Dhabi. Ten healthy subjects were scanned, with manual segmentations of the optic nerve performed on both modalities for ground-truth extraction.
-
-2. Preprocessing
-Raw MP2RAGE UNI images were denoised and defaced to facilitate manual segmentation and registration. The denoising process reduces background noise that can interfere with the registration. Native FA maps provided by the scanner were used directly for FVF calculation. However, the FA map extraction and diffusion MRI preprocessing could be done with the diffusion MRI module's preprocessing Jupyter Notebook (Brainhack School, n.d.) or Naghizadeh_project's preprocessing pipeline (Naghizadeh, 2025).
-
-3. Image Registration
-Accurate co-registration between MP2RAGE and DWI is critical due to the small size (~4-5 voxels wide) and the natural curvature of the optic nerve. Multiple registration strategies were tested, including rigid, affine, and elastic transformations, to optimize alignment while preserving anatomical integrity. Elastic registration methods tended to deform the optic nerve, resulting in segmentations inconsistencies, such as holes within manual masks. Ultimately, a combined rigid and affine registration approach was selected as the optimal balance between alignment and standardization to a template (MNI152). Additionally, another registration challenge occured because the DWI acquisitions covered only a slab including the optic nerve. This restriction made the aligment with the MP2RAGE full-volume more complicated.
-
-4. Parameters Extraction
-Quantitative MRI parameters were extracted voxel-wise within the overlapping of both MRI modalities segmentation. This overlapping approach was necessary because DWI images are noisier, producing bulkier segmentation compared to the more precise MP2RAGE segmentation. T1 relaxation maps, obtained from MP2RAGE through qMRLab (qMRLab, n.d.), were used to compute the MTVF via the established formula. The MVF was then calculated by combining the MTVF values with the assumed myelin composition of the tissue. Native extracted FA values were converted to FVF using the quadratic relation derived form simulation (Stikov et al., 2011).
-
-5. Modeling and Analysis
-The mean myelin volume fraction and mean fiber volume fraction were then used to calculate the mean g-ratio value along each coronal slice of the optic nerve.
-
-6. Visualization and Output
-The final pipeline outputs include:
-* Coronal slice-wise plot of MVF, FVF, and g-ratio in a standardized (MNI152) space
-* Statistics metrics from each coronal slice including the dice coefficient between both modalities segmentations
-
-## Results
-Since the dataset segmentations were not all completed by the submission date, the results presented here focus on only two subjects.
-
-### Image registration using registration.ipynb
-The 'registration.ipynb' Jupyter Notebook contains the full registration workflow, starting from the raw MP2RAGE and DWI images and producing standardized results in the MNI 152 space.
-
-The first cell of the pipeline sets up the bash environment variables and creates the necessary directories. As the project follows the BIDS data structure standard, each subject's files should follow a directory tree similar to the example below:
-
-Project Directory Structure (click to expand)
-
-```plaintext
-project_root/
-└── data/
- ├── sub-XXX/
- │ ├── anat/
- │ │ └── sub-XXX_T1w.nii.gz
- │ └── dwi/
- │ └── sub-XXX_dwi.nii.gz
- │
- └── derivatives/
- ├── t1_map/
- │ └── sub-XXX/
- │ └── sub-XXX_t1_map.nii.gz
- ├── fa_map/
- │ └── sub-XXX/
- │ └── sub-XXX_scanner_FA.nii.gz
- ├── optic_nerve_segmentations/
- │ └── sub-XXX/
- │ ├── anat/
- │ │ └── sub-XXX_label-ON_seg.nii.gz
- │ └── dwi/
- │ └── sub-XXX_label-ON_seg.nii.gz
- │
- └── templates/
- ├── MNI152_T1_1mm.nii.gz
- ├── Atlas_MNI.nii.gz
- └── Optic_Nerve_Label_MNI.gz
-```
-
-
-Within this data structure, the only environment variables that require customization are 'PROJECT_ROOT' and 'SUBJECT'. In this project, manual segmentations were performed directly on pre-rigidly registered DWI images, but the raw DWI image can also be used. Temporary derivative files are created in a designated directory, which can be safely deleted once the final results files are generated.
-
-For each registration step, the type of registration can be easily modified in the notebook by changing the value the '-t' parameter to:
-* 'r' for rigid
-* 'a' for affine
-* 's' for nonlinear elastic (SyN) registration
-
-The pipeline proceeds as follows:
-1. Rigid registration of DWI (automatically the b0 volume) to MP2RAGE
-2. Affine registration of MP2RAGE to MNI 152 template
-3. Application of transformations to:
-* DWI to MNI
-* DWI segmentation to MP2RAGE then to MNI
-* FA map to MP2RAGE then to MNI
-* MP2RAGE segmentation to MNI
-* T1 map to MNI
-4. Creation of standardized Regions of Interest (ROIs) for DWI and MP2RAGE in MNI 152 space
-5. Rigid registration of DWI ROI to MP2RAGE ROI (secondary alignment)
-6. Application of ROI transformations to DWI, DWI segmentation, and FA map
-7. Generation of final results files
-8. Optional: Deletion of the temporary working directory
-
-Step 4 and 5 are necessary because the initial registration introduced an offset in the segmentations. Therefore, the ROI co-registration step corrects this misalignement, as illustrated in the example below: red represents the MP2RAGE segmentation, green represents the DWI segmentation after the initial alignment, and blue represents the DWI after the ROI alignment correction.
-
-
-
-Additionally, outlying values were identified near the edges of both manual segmentations. To ensure plausible values along the optic nerve, Regions of Interest (ROIs) were defined, as illustrated in the example figure below.
-
-
-
-### Data visualization using data_visualization.ipynb
-
-Assuming that the results directory and files were generated sucessfully, the only variable that need to be modified in the notebook are 'PROJECT_ROOT' and 'SUBJECT'.
-
-The main steps in the data visualization workflow are as follow:
-1. Load results files based on the specified 'PROJECT_ROOT' and 'SUBJECT'.
-2. Extract various slice-wise metrics, including slice index, number of voxels in each segmenation, number of overlapping voxels, Dice coefficient, as well as the mean and standard deviation of T1 and FA values.
-3. Compute slice-wise MVF, FVF, and g-ratio using detailed equations and underlying assumptions.
-4. Summarize relevant metrics that characterize the overlap between segmentations.
-5. Generate plots showing the slice-wise mean of T1, FA, FVF, MVF, and g-ratio, for visual interpretation
-
-### Segmentation Overlap Metrics Summary (whole optic nerve)
-Segmentation overlap metrics showed moderate agreement between manual segmentations with Dice coefficients of 0.4961 ± 0.1703 and 0.3958 ± 0.1234 for subjects 1 and 2, respectively.
-| Metric | Subject 1 | Subject 2 |
-|--------------------------------|---------------------|---------------------|
-| Number of slices analyzed | 32 | 22 |
-| Dice coefficient (mean ± std) | 0.4961 ± 0.1703 | 0.3958 ± 0.1234 |
-| Average overlapping voxels/slice| 20.8 | 19.0 |
-| T1 values inside overlap (mean ± std, s) | 0.99 ± 0.13 | 0.99 ± 0.17 |
-| FA values inside overlap (mean ± std) | 0.5886 ± 0.1408 | 0.4370 ± 0.1013 |
-| Mean MTVF (mean ± std) | 0.2736 ± 0.0278 | 0.2751 ± 0.0338 |
-| Mean MVF (mean ± std) | 0.1368 ± 0.0139 | 0.1375 ± 0.0169 |
-| Mean FVF (mean ± std) | 0.3486 ± 0.1594 | 0.2154 ± 0.0680 |
-| Mean g-ratio (mean ± std) | 0.7329 ± 0.1019 | 0.5781 ± 0.1614 |
-
-### Results – MVF, FVF & g-Ratio
-Plausibles results were obtained for regions of interest (ROIs) in the optic nerve, with calculated g-ratio values ranging between 0.6 and 0.8 for each subject. The calculated mean for each coronal slice includes both the right and the left optic nerves.
-#### Subject 1
-
-
-
-#### Subject 2
-
-
-
-### Tools that I learned during this project
-* Python for data processing, scripting, and analysing
-* Jupyter Notebooks to organize and run interactive data workflows
-* ANTS (Advanced Normalization Tools) for medical image registration and transformation
-* Git and GitHub for version control and project collaboration
-* BIDS for standardization of the data structure
-
-### Results
-#### Deliverable 1:
-A non-invasive method for visualize the mean g-ratio along the coronal slices of the optic nerve.
-
-#### Deliverable 2:
-An executable and fully reproducible processing pipeline that:
-* Takes multimodal MRI inputs
-* Computer and outputs the mean g-ratio along the optic nerve
-* Includes Jupyter Notebooks for data visualization and extraction of relevant statistical metrics
-
-## Conclusions
-
-### Can we visualize the g-ratio value along the optic nerve?
-Yes. Using the developed pipeline, we successfully mapped and visualized the g-ratio values along the optic nerve in a reproducible and non-invasive manner. This provides meaningful insight into myelin integrity and fiber composition in differents subjects.
-
-### Encountered issues
-#### Atlas max probability optic nerve label
-The max probability optic nerve label was initialy used to assess whether manual segmentation was truly necessary, or if this atlas-based label could serve as an automated segmentation alternative. Howerver, the Dice coefficient between the overlapping regions of both manual segmentations and the maximum probability atlas label ranged only from 15% to 20%. Given this low similarity, the maximum probability label was excluded from further analysis.
-
-#### Registration issue
-As previously stated, two registrations issues were encountered. The first was related to the small size (~4-5 voxels wide) and the natural curvature of the optic nerve. Non-affine or elastic registrations tended to deform the optic nerve and create hole in the transformed manual segmentation masks. The second issue involved the alignment of the two manual segmentations: a single rigid registration resulted in an approximate one-slice offset. However, applying a second rigid registration using ROIs was able to correct this initial misalignment.
-
-#### MTVF vs MVF
-Initialy, it was assumed that myelin constitutes nearly 100% of the macromolecular content in white mater like the optic nerve. This assumption led to an overestimation of the Myelin Volume Fraction (MVF) by Macromolecular Tissue Volume Fraction (MTVF), resulting in non-plausible results such as more myelin than fiber. An litterature search revealed that myelin accounts for only 50% of the macromolecular content in white matter. With this correction, plausible values of g-ratio were acheived.
-
-### Guide to Reproducibility
-This project follows the BIDS data structure for standardized input. To reproduce results:
-1. Clone the repository
-```
-git clone https://github.com/brainhack-school2025/albert-plante_project.git
-cd albert-plante_project
-```
-2. Set up a Python virtual environment
-```
-For Linux/Mac:
-python3 -m venv .venv
-source .venv/bin/activate
-
-For Windows:
-python -m venv .venv
-.venv\Scripts\activate
-```
-3. Install required Python packages
-```
-pip install -r requirements.txt
-```
-4. Install Jupyter Notebook
-```
-pip install notebook
-```
-5. Install [ANTs](https://github.com/ANTsX/ANTs) and [FSL](https://fsl.fmrib.ox.ac.uk/fsl/docs/#/install/index)
-6. Organize data according to BIDS format under 'PROJECT_ROOT'.
-7. Modify subject variabes in the notebooks ('PROJECT_ROOT' and 'SUBJECT')
-8. Run 'registration.ipynb' to co-register images and create results files.
-9. Run 'data_visualization.ipynb' to extract metrics and generate plots.
-10. Optional: Clean temporary directories to save space.
-
-## Troubleshooting
-* Registration issues: Because the DWI is normally a slab, multiples rigid registration between DWI and MP2RAGE can be needed because the registration algorithm can fail.
-* Missing dependencies: Ensure all Python packages and ANTs binaries are installed and accessible in your environment path.
-* File path errors: Verify your BIDS directory structure and that environment variables point to correct locations.
-* Plotting errors: Create the metrics dataframe before visualization.
-
-## Acknowledgement
-I would like to thank Nikola Stikov and Agah Karakuzu for their mentorship and expertise, as well as Bas Roker and Ameen Qadi from the Rokers Vision Laboratory in NYU Abu Dhabi. Special thanks also to Eva Alonso Ortiz and Sebastian Rios, the professor and teaching assistant of the course, for their guidance and support.
-
-## References
-Ausmed. (2025, 01). How Does Multiple Sclerosis Affect the Body? | Ausmed. https://www.ausmed.com/learn/articles/multiple-sclerosis-nursing-care
-
-Barranco Hernandez, J., Luyken, A., Kebiri, H., Stachs, P., Macias Gordaliza, P., Esteban, O., Aleman Gomez, Y., Sznitman, R., Stachs, O., Langner, S., Franceschiello, B., & Bach Cuadra, M. (2024). Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas. https://doi.org/10.1101/2024.08.15.608051
-
-Brainhack School. (n.d.). Training modules. Retrieved June 19, 2025, from https://school-brainhack.github.io/modules/
-
-Duval, T., Stikov, N., & Cohen-Adad, J. (2017). Modeling white matter microstructure. Functional Neurology, 31(4), 217–228. https://doi.org/10.11138/FNeur/2016.31.4.217
-
-Mezer, A., Yeatman, J. D., Stikov, N., Kay, K. N., Cho, N.-J., Dougherty, R. F., Perry, M. L., Parvizi, J., Hua, L. H., Butts-Pauly, K., & Wandell, B. A. (2013). Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nature Medicine, 19(12), 1667–1672. https://doi.org/10.1038/nm.3390
-
-MS Canada. (2025, February 7). Prevalence and incidence of MS in Canada and around the world | MS Canada. https://mscanada.ca/ms-research/latest-research/prevalence-and-incidence-of-ms-in-canada-and-around-the-world
-
-MS Trust. (2024, 11). McDonald criteria | MS Trust. https://mstrust.org.uk/a-z/mcdonald-criteria
-
-Naghizadeh, Y. (2025). Naghizadeh_project. https://github.com/brainhack-school2025/Naghizadeh_project
-
-NeuroLibre. (n.d.). Retrieved June 19, 2025, from https://neurolibre.org
-
-qMRLab. (n.d.). Home | qMRLab. Retrieved June 19, 2025, from https://qmrlab.org/
-
-Rokers Vision Laboratory. (n.d.). Retrieved June 19, 2025, from https://sites.nyuad.nyu.edu/rokersvisionlaboratory/
-
-State University of New York College of Optometry. (2019, December 11). Scientists Discover the Origin of Brain Mapping Diversity for Eye Dominance [Video]. SciTechDaily. https://scitechdaily.com/scientists-discover-the-origin-of-brain-mapping-diversity-for-eye-dominance-video/
-
-Stikov, N., Campbell, J. S. W., Stroh, T., Lavelée, M., Frey, S., Novek, J., Nuara, S., Ho, M.-K., Bedell, B. J., Dougherty, R. F., Leppert, I. R., Boudreau, M., Narayanan, S., Duval, T., Cohen-Adad, J., Picard, P.-A., Gasecka, A., Côté, D., & Pike, G. B. (2015). In vivo histology of the myelin g-ratio with magnetic resonance imaging. NeuroImage, 118, 397–405. https://doi.org/10.1016/j.neuroimage.2015.05.023
-
-Stikov, N., Perry, L. M., Mezer, A., Rykhlevskaia, E., Wandell, B. A., Pauly, J. M., & Dougherty, R. F. (2011). Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. NeuroImage, 54(2), 1112–1121. https://doi.org/10.1016/j.neuroimage.2010.08.068
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-05-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Spinal Cord Segmentation Generalizable Across Datasets"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: []
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/bouchard_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-# website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [mri, segmentation, spinal cord, python]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "My idea is to train a deep learning model on multiple (4) spinal cord segmentation datasets to improve generalizability to new contrasts, vendors, pathologies, etc...
-
-My project aims to train the nnU-Net model architecture, a state-of-the-art deep learning architecture for biomedical segmentation, on four aggregated datasets and compare its generalizability capabilities with the four specific models trained on each individual dataset. I will conclude by comparing the two approaches on a fifth and sixth dataset outside of the training domain."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover_image.png"
----
-
-
-## Project definition
-
-### Background
-
-
-
-Spinal cord segmentation is the process of identifying the spinal cord area in MRI images. This is clinically relevant notably to compute cross-sectional area for the diagnosis and monitoring of neurodegenerative diseases such as multiple sclerosis.
-
-Performing automatic spinal cord segmentation is complicated even with deep learning methods due to the wide range of MRIs that can be produced from various contrasts, machine vendors and pathologies of the patient. This results in very different output images that no individual model can handle yet.
-
-Typically, we use one model per data sub-set to reduce the complexity of the task, like a specific contrast (e.g. T2w), and end up with *N* models where we test each of them to select the optimal model for new images. This is not reliable and scalable.
-
-My goal is to build a more general model to automatically perform spinal cord segmentation across contrasts, vendors and pathologies.
-
-Below is a visual example of the segmentation result on one subject using a contrast outside of the training scheme comparing the ground truth or manual segmentation (left) with the aggregated model (center) and one of the specific models (right).
-
-
-
-### Tools
-
-Main tools: Python/Pytorch/Git/ssh/Jupyter Notebooks
-
-Most important: [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) -> a state-of-the-art “self-configuring method for deep learning-based biomedical image segmentation”.
-
-
-### Methodology
-Train and compare 2 frameworks (individual models vs. general model) for performing automatic spinal cord segmentation.
-
-
-
-### Data
-
-Various (6) Spinal Cord MRI datasets available at Neuropoly with manual segmentation.
-
-Our training datasets:
-* Spine Generic Public Database* (multi-subject), n=244, 3 vendors, 6 contrasts (T2w, T1w, T2*w, 3D axial GRE (MTon), GRE-T1w (MToff), DWI), pathology: Mild Compression, Healthy Controls.
-* INSPIRED, n=79, 1 contrast (acq-cspineAxial_T2w), pathology: Degenerative Cervical Myelopathy (38), Spinal Cord Injury, Healthy Controls.
-* SCI-Colorado, n=80, 1 contrast (T2w), pathology: Spinal Cord Injury.
-* Canproco, n=445, 1 contrast (T2w), pathology: MS.
-
-Test datasets (outside training distribution):
-* Basel-mp2rage, n=283, 1 contrast (MP2RAGE "UNI"), pathology: Multiple Sclerosis (180), Healthy Controls (103).
-* Extra: [Rohan](https://github.com/brainhack-school2023/banerjee_project)’s fMRI test set, n=8, 1 contrast (EPI-GRE).
-
-
-**Cohen-Adad, J., Alonso-Ortiz, E., Abramovic, M., Arneitz, C., Atcheson, N., Barlow, L., Barry, R.L., Barth, M., Battiston, M., Büchel, C., et al.: Open-access quantitative mri data of the spinal cord and reproducibility across participants, sites and manufacturers. Scientific data 8(1), 219 (2021)
-
-### Objectives
-
-* Create a script for converting N BIDS datasets into one large “nnU-Net-ready” dataset train/test split. Read more [here](https://github.com/brainhack-school2023/bouchard_project/tree/main/docs).
-* Compare a general model to specific models for spinal cord segmentation on the datasets’ test sets. Results shown [here](https://github.com/brainhack-school2023/bouchard_project/blob/main/results/results_analysis.ipynb).
-* Test the resulting 2 frameworks on a fifth and sixth dataset to demonstrate the generalizability capabilities of the larger model. Results shown [here](https://github.com/brainhack-school2023/bouchard_project/blob/main/results/results_analysis.ipynb).
-
-### Deliverables
-
-* A [Github repository](https://github.com/brainhack-school2023/bouchard_project) with codes and scripts to reproduce training and testing.
-* A [jupyter notebook](https://github.com/brainhack-school2023/bouchard_project/blob/main/results/results_analysis.ipynb) of the analysis codes and visualizations for comparing the results.
-* Documentation. This README file and the README in `docs/`.
-* Model checkpoints for the nnUnet models trained on all 4 datasets (right now available on /duke for Neuropoly members).
-
-## Results
-
-### Progress overview
-
-The project was initiated by Louis-François Bouchard, based on the existing BrainHack template. I started from existing scripts for the nnU-Net method and adapted them for my needs. There are now all necessary updated scripts in the `scripts` folder and documentation in `docs` through a clear README file to reproduce the project. A notebook is available with all results as well as the final project presentation powerpoint for more information. Ultimately, the project was a success and the hypothesis (aggregating datasets == better general model) was confirmed.
-
-### Tools I learned during this project
-
- * **Git, GitHub, Git-Annex -** I learned to better use git, github and git-annex for training models and creating a project repository with appropriate documentation and hierarchy.
- * **nnU-Net -** I learned to successfully prepare data, train a model, and analyze the results for spinal cord segmentation using the nnU-Net model framework (library).
- * **Python/Pytorch/Notebooks/BIDS/ssh/GPU training/bash -** I got more familiar with pytorch for training and testing models remotely on a server with multiple GPUs. I also used and wrote many bash scripts for pre-processing images (from BIDS to nnU-Net, but also re-orienting, resampling..) and automate tasks more efficiently. Overall, this project helped me develop important skills as well as understanding the necessary steps in working on this kind of project from scratch.
-
-### Results
-
-#### Deliverable 1: A GitHub repository with code and scripts to reproduce the project
-
-This README along with the README in the `docs` folder allows anyone to reproduce these experiments and use the `scripts/` and notebooks adapting the commands with their own paths and data.
-
-#### Deliverable 2: A notebook with results analysis
-
-The deliverable 2 is available in the [results/](https://github.com/brainhack-school2023/bouchard_project/tree/main/results) folder where I show both quantitative (mean dice results) and qualitative (visual results) results of the different (3) experiments.
-The experiments were (i) to compare the general model to specific models on their respective tasks (e.g. compare the general model to the spine generic-trained model on the spine generic test set to see if the general model kept a similar "understanding" of this sub-set of the data), (ii) compare the general model to the specific models to two unseen sets of data (with unseen image contrasts), including the Basel dataset with MP2RAGE contrast and [Rohan](https://github.com/brainhack-school2023/banerjee_project)'s fMRI test set with EPI-GRE contrast.
-
-Bellow is the most important outcome, showing how the aggregated (or general) model outperforms specific models on unseen data, here reporting the mean dice over the Basel-MP2RAGE dataset, which uses a contrast (MP2RAGE) that was not seen in any training scheme prior testing.
-
-
-
-See more interesting results in [the notebook](https://github.com/brainhack-school2023/bouchard_project/blob/main/results/results_analysis.ipynb)!
-
-
-#### Deliverable 3: Documentation
-
-I believe this documentation along with `docs/README` and the comments in the different scripts allows for anyone to re-use and replicate this project easily.
-
-#### Deliverable 4: Model checkpoints
-
-Right now the models are available on `duke/` for Neuropoly students. An improved version of the general model will be shared and pushed to [SCT](https://github.com/sct-pipeline) once the results are satisfying enough.
-
-## Conclusion and acknowledgement
-
-To conclude, I believe this project was a success, showing how aggregating datasets can build a more general model while keeping the "specialized capabilities" similar. Adding more diverse data is a promising next step to further improve the model as well as performing proper visual analysis and cross-validation to ensure the validity of the results and better understand the results' quality and problems to solve.
-
-I wanted to highlight some limitations of this project like the fact that the datasets are of various sizes, which may hurt the comparison. Likewise, most datasets use the T2w contrast only, which is not ideal for building a more general model, but was the only way to correctly split our dataset and keep contrast "not seen in training" outside any training frameworks and only in our testing datasets. Finally, the time constraints didn't allow me to perform proper cross-validation of the different models as well as in-depth analysis of the results visually, as mentioned above for an interesting next step.
-
-Still, the goal of this project was to learn how to use nnU-Net, improve my git, github and git-annex skills and improve my understanding of MRIs, contrasts and spinal cord segmentation. In those terms, the project was a success and is a promising start for my PhD work.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Increasing Accessibility of BrainAGE Open Access Calculators"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Jiya Shah, Eleanor Pak Wai Lam, Isabella Monaghan Chow, Khubaib Choudry]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/shahjiya/brainhack_brainage_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [BrainAGE, Calculator, Reproducibility, sMRI, R-tools]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to increase the accessibility and reproducibility of open-access BrainAGE calculators. We developed RMarkdown documents designed to help standardize diverse structural MRI outputs into the specific formats required by different BrainAGE calculators. By simplifying these complex input requirements and centralizing these tools in a public GitHub repository, our work helps reduce technical barriers, enabling more researchers to easily leverage BrainAGE models in their neuroimaging studies and promote broader scientific use."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Normative brain age.png"
----
-
-## Project definition
-
-### Background
-
-#### Our group
-We are a group of MSc students from the Institute of Medical Science at the University of Toronto, conducting our thesis research in collaboration with the Centre for Addiction and Mental Health (CAMH). Although our individual projects examine diverse aspects of mental health through various methodologies, we share a strong collective interest in advancing research in this field.
-
-#### Project background
-Normative brain age modelling is a technique that uses structural MRI (sMRI) data to estimate an individual’s brain age by comparing it to a normative reference group. The difference between predicted brain age and chronological age, known as the Brain Age Gap Estimate (BrainAGE),can provide insights into atypical brain aging or neurodegenerative risk. A number of open-access BrainAGE calculators have been developed, but they often differ in required input features, preprocessing steps, and anatomical atlases, making them difficult to use without specialized technical knowledge. This lack of standardization and unclear input requirements pose barriers to accessibility and reproducibility across studies.
-
-### Tools
-
-The project utilizes a range of tools and platforms to support development, reproducibility, and open dissemination:
-- R Programming Language: Core environment for data processing and script development.
-- RMarkdown: Integrates code, analysis, and narrative into reproducible documents.
-- Git & GitHub: Enables version control, collaborative development, and public access to project materials.
-- Visual Studio Code (VS Code): Integrated development environment for managing project files and workflows.
-- Targeted BrainAGE Models: Input standardization tools were developed for a variety of open-access calculators, including:
- Whole-brain models: NAPR, Developmental BrainAGE, Kauffman BrainAGE, and BrainAGE
- Region-/tissue-specific models: CentileBrain, Network-BrainAGE, and BrainChart.io
-
-### Data
-This project uses normative neuroimaging data from the Reproducible Brain Charts initiative, specifically the Healthy Brain Network (HBN) subset. The HBN provides openly available, preprocessed structural MRI features from a large developmental cohort (ages 5–21).
-
-### Deliverables
-
-Our main deliverable is a publicly accessible GitHub repository housing all RMarkdown tools and documentation for standardizing BrainAGE calculator inputs.
-
-## Results
-
-### Progress overview
-
-The final presentation of this project was delivered on May 30, 2025. All planned deliverables were completed and are available in the project’s GitHub repository. All scripts have been tested with the HBN dataset and are running successfully. Initial issues related to column matching and output formatting have been resolved.
-
-### Tools we learned during this project
-
-* Git & GitHub: Collaborating on RMarkdown scripts and resolving issues highlighted the critical role of Git. We gained hands-on experience using Git for version control and GitHub for team collaboration, particularly in troubleshooting and merging code.
-* Bash/Terminal: We developed proficiency in fundamental command-line operations (e.g., cd, ls, mkdir) for navigating directories, managing files, and organizing project outputs, which are skills essential for handling data and executing scripts efficiently.
-* R Programming: SFor some team members, this project was their first opportunity to use R extensively. It enabled us to learn and build skills in data wrangling, scripting, and applying R for more complex data manipulation and analysis within the project’s scope.
-
-### Results
-
-#### Deliverable: A GitHub Repository
-
-We developed a suite of RMarkdown scripts to standardize and streamline data preparation for open-access BrainAGE calculators. These tools are publicly available in a centralized GitHub repository (github.com/shahjiya/brainhack_brainage_project), providing researchers with easy access to reproducible workflows and comprehensive documentation.
-
-Key contributions include:
-
-HBN RBC Data Preparation Guide: An RMarkdown document (HBN_data.Rmd) that presents a reproducible pipeline for downloading structural MRI data from the Healthy Brain Network Reproducible Brain Charts (HBN RBC) dataset, aggregating files into CSV format, and reformatting regional surface statistics to ensure compatibility with multiple brain atlases and BrainAGE models.
-
-Calculator-Specific Data Wrangling Scripts: Seven RMarkdown scripts designed to transform demographic and MRI data into the precise input formats required by various BrainAGE calculators. These scripts automate column mapping, renaming, type conversions, and output generation to facilitate seamless integration. The scripts are: BrainAGE_regular.Rmd, Developmental_brain_age.Rmd, NAPR_analysis.Rmd, Network_BrainAGE.Rmd, brainchart.io.Rmd, centilebrain_template.Rmd, kauffman_brainage.Rmd
-
-
-## Conclusion
-This project lays the foundation for improving the accessibility and reproducibility of open-access BrainAGE calculators by standardizing input formats and simplifying data preparation. The tools can be adapted to other datasets or extended to additional BrainAGE models. Future work may include developing a web-based interface to further reduce technical barriers and expand the use of brain age modelling in neuroimaging research.
-
-## Acknowledgement
-We would like to thank BrainHack School 2025 for organizing this course and creating a supportive learning environment. As a team with diverse coding experience, we faced many challenges along the way. We are especially grateful to our group members, course instructor, and TA for their patience, guidance, and encouragement throughout the project.
-
-## References
- * Franke K, Ziegler G, Klöppel S, Gaser C. 2010. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. NeuroImage. 50(3):883–892. https://doi.org/10.1016/j.neuroimage.2010.01.005
- * Cole JH, Ritchie SJ, Bastin ME, Valdés Hernández MC, Muñoz Maniega S, Royle N, Corley J, Pattie A, Harris SE, Zhang Q, et al. 2018. Brain age predicts mortality. Mol Psychiatry. 23(5):1385–1392. https://doi.org/10.1038/mp.2017.62
- * Rokicki J, Wolfers T, Nordhøy W, Tesli N, Quintana DS, Alnæs D, Richard G, Lange AM, Lund MJ, Norbom LB, et al. 2021. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp. 42(6):1714–1726. https://doi.org/10.1002/hbm.25323
- * Habes M, Pomponio R, Shou H, Doshi J, Mamourian E, Erus G, Nasrallah IM, Launer LJ, Rashid T, Bilgel M, et al. 2021. The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium. Alzheimers Dement. 17(1):89–102. https://doi.org/10.1002/alz.12178
- * Boyle R, Knight SP, de Lange AMG, Abe Y, Kievit RA. 2021. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav. 15(1):327–345. https://doi.org/10.1007/s11682-020-00321-9
-
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-11" # Date you first upload your project.
-title: "Dynamic Functional Connectivity of the Default Mode Network in ADHD"
-
-names: [Malikka Begum Binte Habib Mohamed]
-
-github_repo: https://github.com/malikkabegum/malikkabegum_project
-
-website:
-
-tags: [brainhack, ADHD, neuroimaging, connectivity]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project examines dynamic functional connectivity (dFC) within the Default Mode Network (DMN) in children with ADHD using the ADHD-200 dataset. Key methods of analyses include time-varying correlation, clustering of connectivity states, and group comparisons to understand how brain network dynamics differ in ADHD"
-image: "Deliverable 2_Brain regions.png"
----
-
-## Project definition
-
-Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with disruptions in large-scale brain networks, particularly the Default Mode Network (DMN). While many studies focus on static connectivity, dynamic functional connectivity (dFC) offers a time-resolved view of how brain regions interact across time. This approach may uncover temporal patterns in connectivity that differ between typically developing children and those with ADHD.
-
-This project leverages resting-state fMRI data (.1D) from the ADHD-200 dataset and investigates time-varying functional connectivity between DMN regions using the sliding window method.
-
-### Background
-
-Resting-state fMRI data allows us to study intrinsic brain network connectivity without task constraints. The Default Mode Network (DMN, including regions such as the medial prefrontal cortex (mPFC), posterior cingulate cortext (PCC), and angular gyrus, has shown atypical patterns in children with ADHD. This project uses dynamic functional connectivity (dFC) methods to analyse how these patterns change over time in ADHD versus typically developing children.
-
-The data is from the publicly available ADHD-200 dataset (http://fcon_1000.projects.nitrc.org/indi/adhd200/) sample, using preposed time series data from the Athena pipeline and focusing on DMN-related ROIs based on the AAL atlas.
-
-### Tools
-
-The "project template" project will rely on the following technologies:
- * Markdown, to structure and format the project description text.
- * The Hugo website framework which is used by the BHS website. This makes it possible to easily add the markdown project description to the website.
- * Adding the project to the website relies on github, through pull requests.
-
-### Data
-
-The ADHD-200 dataset was used for this project. Ultimately, this project template will be used by all BrainHack School (BHS) participants. Data from various projects will be aggregated on the following page, creating an example gallery for future BrainHack School students.
-
-### Deliverables
-
-At the end of this project, we will have:
- - a completed and revised markdown document detailing the dynamic functional connectivity analysis of the Default Mode Network in ADHD using the ADHD-200 dataset.
-- a gallery entry showcasing this project alongside other student projects from BrainHack 2025.
-
-## Results
-
-Dynamic correlation matrices were computed for each participant using a sliding window approach.
-Group-level state metrics (e.g., number of transitions, mean dwell time) were compared across ADHD and control groups.
-Reduced DMN coherence in ADHD, particularly between the left medial superior frontal gyrus and PCC.
-ADHD participants spend less time in strongly connected DMN states.
-The left medial superior frontal gyrus showed a statistically significant difference (p < 0.05) in connectivity strength - possibly indicating a regional hub alteration in ADHD.
-
-### Progress overview
-
-The analysis pipeline was implemented from scratch and validated using multiple test cases. Dynamic connectivity states were successfully derived from all participants, and significant group differences were identified in key metrics, particularly involving the mPFC-PCC connection.
-
-### Tools I learned during this project
-
-How to extract and preprocess region-specific time series from fMRI.
-Dynamic functional connectivity analysis using sliding windows.
-Means clustering on correlation matrices.
-Visualization of brain network dynamics.
-Using Git and GitHub for documentation and reproducibility.
-
-### Results
-
-#### Deliverable 1: significant differences between ADHD and typically developing children.
-
-Reduced dwell time and lower connectivity strength in specific DMN states among ADHD participants (refer to images).
-
-#### Deliverable 2: specific affection regions
-
-mPFC-PCC showed the most robust group difference, suggesting a central role in DMN disruption in ADHD (refer to images).
-
-##### Dynamic Functional Connectivity in ADHD pipeline by Malikka Begum
-
-The repository of this project can be found here (https://github.com/malikkabegum/malikkabegum_project). The objective was to investigate alterations in the dynamics of the Default Mode Network (DMN) in children with ADHD using resting-state fMRI data from the ADHD-200 dataset. The motivation behind this task is to study the dynamic properties of connectivity to offer deeper insights into the neural mechanisms underlying attentional instability and cognitive variability in ADHD. The repo features:
-* a Jupyter notebook for the full dFC analysis pipeline using sliding window correlations, means clustering, statistical comparison scripts to assess group differences in state frequency, dwell time and ROI-ROI connectivity.
-* a detailed README
-* figures showing state patterns and group comparisons
-* Detailed requirements files, making it easy for others to replicate the environment of the notebook.
-
-## Conclusion and acknowledgement
-
-This project highlights altered dynamic network behaviour in ADHD, especially within the core DMN. It adds to growing evidence that temporal dynamic-not just static connectivity-may underlie attentional difficulties. Thanks to the BrainHack School Singapore and Taiwan teaching team, especially Prof Chen, Dr. Goh for their invaluable support and guidance and the supportive TAs for guidance throughout.
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-17" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "The Many Faces of Fear: Univariate, Predictive and Representational Perspectives on Fearful Neuroimaging"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Shawn Manuel]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/manuel_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://shwnmnl.github.io/neurofear
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fear, fmri, glm, ml, rsa]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project explores how different fMRI analysis methods reveal distinct aspects of the neural representation of fear, including a mass univariate approach (GLM), a machine learning (decoding) approach, and representational similarity analysis (RSA) approach. While GLM identified some expected activation patterns and machine learning failed to decode fear ratings reliably, RSA revealed modest but significant structure in frontal regions, highlighting the value of methodological triangulation in cognitive neuroscience."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "fearfaces.png"
----
-
-
-## Project definition
-
-### Background
-
-Discovering *"the neural signature of X"* is the bread and butter of neuroimaging research. There are myriad analytic approaches neuroscientists may deploy to this end, each with their own strengths and limitations, but none that can, on its own, deliver a singular, definitive neural signature of ***anything***.
-
-This limitation is both methodological and epistemological. Different methods afford different perspectives (Davis et al., 2014). Moreover, each method enables distinct types of inference about the neural underpinnings of a given construct (Popov et al., 2018).
-
-For Brainhack School 2025, I wanted to gain hands-on experience with the most popular analytic approaches to neuroimaging on a single subject; that is, mass univariate (GLM), predictive (ML) and representational (RSA).
-
-### Tools
-
-- git and github for version control and documentation
-- nibabel/nilearn for NIfTI image loading and saving, GLM fitting, decoding, and visualization
-- scipy for statistical functions, distance metrics (for RSA)
-- scikit-learn for predictive modeling, cross-validation, hyperparameter tuning
-- scipy and pandas for data manipulation and RSA
-- matplotlib/plotly for plotting
-- Jupyter Book for interactive documentation and results dissemination
-
-### Data
-
-The dataset was contributed by my colleague and labmate [Darius Valevicius](https://dariusliutas.com/), and includes fMRI recordings from participants who viewed short video clips of various animals and rated, in real time, the extent to which each clip elicited fear. The dataset is already preprocessed, including motion correction and spatial normalization.
-
-### Deliverables
-
-- [Github repo](https://github.com/brainhack-school2025/manuel_project)
-- Jupyter notebook (see repo)
-- [Jupyter Book site](https://shwnmnl.github.io/neurofear)
-- [Slide deck](https://www.canva.com/design/DAGoggkx5Qc/0rdEuKNYpgVpxGgaHn7XuA/edit?)
-
-## Sneek peek of results
-
-The repository and jupyter notebook for this project [can be found here](https://github.com/brainhack-school2025/manuel_project). Note however that since I'm using pilot data from my colleague's current reserach project, I'm unable to share the data that would render the notebook truly reproducible.
-
-In addition, [a Jupyter book site can be found here](https://shwnmnl.github.io/neurofear) that walks through each of the three analyses step by step and includes some interactive plots.
-
-### Approach 1 - GLM : Fear-related activation maps from 1 and 3 runs
-
-The data for this participant includes 3 separate runs. Analyses were conducted on a sinlge run, as well as all three, in order to cultivate an intuition about what different amounts of data look like.
-
-The question being asked here is: *Which voxels show a linear relationship with subjective fear, irrespective of animal category?*
-
-As we’re working with fear, we’d hope to recover regions such as insula, thalamus, periacqueductal gray matter, locus cerulus, parabrachial nucleus and nucleus of solitary tract, medial frontal gyrus, anterior cingulate, amygdala and hippocampus, hypothalamus and maybe some ventromedial or dorsolateral prefrontal cortex.
-
-One run
-
-
-Three runs
-
-
-See the [Jupyter book](https://shwnmnl.github.io/neurofear) for a deeper interpretation of these results, including both anatomical (Harvard-Oxford) and network-based (Schaefer) atlases, but suffice it to say that we were not able to recover fear-related subcortical regions, but some associative/integrative prefrontal regions are present.
-
-Here's an interactive version that includes all runs.
-
-
-
-
-
-### Approach 2 - ML : Decoding fear ratings
-
-Here, the question(s) being asked is/are: *Can we predict the psychological condition (animal category or fear rating) of a trial from its corresponding brain activation pattern?*
-
-Many variations were attempted, to little avail. Predicting either animal category (classification) or fear rating (regression) was unsuccessful, but since fear ratings are ordinal, multi-class classification was attempted and led to barely-higher-than-chance performance, with a mean accuracy across runs of 23% (5 classes to predict). It may be that the features used aren't informative enough, in addition to the relative paucity of data to train on.
-
-### Approach 3 - RSA : Comparing the neural and behavioral structure of fear
-
-For this approach, the question being asked is: *Do similarities and differences in brain activation patterns in different ROIs reflect similarities and differences in fear ratings across trials?*
-
-The Superior Frontal Gyrus (SFG) shows the strongest (but small in absolute terms) and statistically significant correspondence with the fear rating RDM (ρ = 0.079, p ≈ 0.0009). Since the SFG is implicated in executive function and emotion regulation, it could be counted as a plausible candidate for representing or modulating the conscious experience of fear.
-
-Here's an interactive plot to compare the fear RDM to different brain ROI RDMs.
-
-
-
-## Conclusion
-
-The aim of this project was not to pin down a singular neural signature of fear, but to explore how different analytic methods—mass univariate analysis, predictive modeling, and representational similarity analysis—characterize the neural architecture of a psychologically rich construct. The results reflect the mixed promises and limits of each approach.
-
-Taken together, these results do not point to a singular neural “signature” of fear, but rather show how fear becomes legible in different ways depending on the lens applied. The GLM captures reliable spatial contrasts, predictive modeling asks whether neural patterns are sufficient for inference, and RSA examines whether psychological similarity is mirrored in brain dynamics. Each method reveals partial truths.
-
-If there is a takeaway, it is methodological rather than anatomical: capturing the neural instantiation of psychological constructs may require triangulation across models and perspectives. In the future, I’d like to see more papers that try all three of these approaches (including both decoding and encoding) and I hope to work on this a bit more, potentially with more of Darius’ data once it’s been collected, to try to lead by example.
-
-In the meantime however, I’m quite pleased with what I able to learn and experiment with over the course of the last four weeks of Brainhack School 2025.
-
-## References
-1. Davis, T., LaRocque, K. F., Mumford, J. A., Norman, K. A., Wagner, A. D., & Poldrack, R. A. (2014). *What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis.* **Neuroimage, 97, 271-283.**
-
-2. Popov, V., Ostarek, M., & Tenison, C. (2018). *Practices and pitfalls in inferring neural representations.* **NeuroImage, 174, 340-351.**
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-07" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Impact of weight loss on fMRI food cue reactivity"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Justine Daoust]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/JDaoust_project.git
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2023/JDaoust_project.git), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, food cues, obesity, weight loss]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Functional MRI studies examining reactivity to food cues in obesity have shown BOLD differences in brain regions involved in the regulation of food intake. This project aims to characterize brain reactivity to food cues in individuals with severe obesity and to examine changes in brain reactivity to food cues by fMRI after weight loss induced by bariatric surgery."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "project_image.png"
----
-
-## Project definition
----
-### Background
-
-Neuroimaging is widely used to understand neurophysiological processes associated with obesity and responsiveness to weight loss interventions ([Carnell et al. 2012](https://onlinelibrary.wiley.com/doi/10.1111/j.1467-789X.2011.00927.x)). Functional MRI studies examining the food cue reactivity in obesity compared to lean individuals have shown BOLD differences in brain regions involved in energy regulation, cognitive control and reward valuation ([Harding et al. 2018](https://www.nature.com/articles/ijo2017237)). These alterations may be implicated in the development of obesity, as well as a predictor of lower success in weight loss intervention ([Hermann et al. 2019](https://www.sciencedirect.com/science/article/pii/S2213158219301536?via%3Dihub)). However, it is unclear if intervention targeting weight loss and cardiometabolic improvement, such as bariatric surgery, could reverse these alterations in brain functional reactivity to food cues. My project aims to identify brain regions that react to snack images in individuals with severe obesity, and examine changes in food cue reactivity after weight loss induced by bariatric surgery.
-
-### Tools
-This project relied on numerous tools such as:
-1) Git and GitHub to use and share methods;
-2) High performance computing (HPC), such as Alliance Canada, for executing scripts;
-3) Python packages, such as nilearn, for food cue reactivity analyses.
-
-### Data
-The dataset was collected from 2016 to now at Quebec Heart and Lungs Institute. Ninety-four participants with severe obesity scheduled to undergo bariatric surgery were recruited. Participants were scanned prior to, 4, 12 and 24 months after bariatric surgery. For this project, a T1 and three fMRI runs with [Becker-DeGroot-Marshack auction task](https://onlinelibrary.wiley.com/doi/10.1002/bs.3830090304) (duration: 10 minutes 27 seconds) from one participant will be used. Forty-five randomized images (15 high-sweet, 15 high-salt and 15 low caloric density) were presented to the participant for 4 seconds. ThepParticipant must bid between 0 to 5$ for each snacks in the next 4s. The auction part will not be evaluated in this project.
-
-
-
-### Project deliverables
-At the end of this project, these files will be made available:
-1) Python scripts to analyse brain reactivity to food cues;
-2) Figures of this results;
-3) A repository on GitHub to share my methods;
-4) Scripts on Alliance Canada which will be usefull to all members of my lab.
-
-## Results
----
-### Progress overview
-First, this project involved correcting the format of my dataset. I had to convert it to BIDS format and preprocess the Nifti files with fmriprep. These steps took longer than expected. So far, only one participant's files have been converted to BIDS format and preprocessed. However, I was able to run a first-level general linear model on this participant to present: 1) BOLD activity in the visual cortex and the superior frontal gyrus (SFG) when images were shown at this participant (effect of interest) and 2) no significant region of BOLD activity when contrasting high vs low calorie snacks.
-
-##### Figure 1. BOLD activity when images are shown (for run #1 of one participant)
-
-
-##### Figure 2. BOLD activity contrast between high vs low calorie snacks (for run #1 of one participant)
-
-
-### Tools I learned during this project
-1) [dcm2niix](https://github.com/rordenlab/dcm2niix): I learned how to convert data from Dicom to Nifti and what I must check to validate that the convertion went well;
-2) [BIDS-validator](https://bids-standard.github.io/bids-validator/): I learned how to standardize a dataset into BIDS format;
-3) [fmriprep](https://fmriprep.org/en/stable/): I learned how to execute fmriprep and what is the output after the execution;
-4) [Nilearn](https://nilearn.github.io/stable/index.html): I learned how to use Nilearn on Jupyter Notebook to visualize data and examine the contrast of high vs low calorie food cues in a one participant's run;
-5) [Alliance Canada](https://alliancecan.ca/fr): I learned how to manage time, cpus, and memory to run a bash file;
-6) [GitHub](https://github.com/): Before this school, I had trouble using it. But now, I'm very glad to know much and how it could help me to produce reproductible research.
-
-### Deliverables
-The results of my project was mostly:
-1) BIDS transformation of one participant's file to better use standardized pipeline;
-2) Use fmriprep to had clean preprocessed data for one participant;
-3) Create Alliance Canada scripts that could be share to all members of my lab;
-4) Use Nilearn from Jupyter Notebook to visualize contrast for high vs low calorie food cues.
-
-### Future work
-For this study, I'll have to convert all my dataset into a BIDS format and execute fmriprep on my Nifti files using Alliance Canada. Then, I would be able to go further in my analysis, which means to execute 2nd and 3rd level general linear model.
-
-## Conclusion and acknowledgement
-First, I was a bit disappointed not to have gone far in analyzing my data. But I realized that thanks to this school, I finally have the necessary notions to standardize dataset and to proceed with the analysis of my data. I have learned more than if I had simply worked on analyses in Nilearn.
-
-I would like to thanks all the Brainhack School organizators and all the crew. Mostly, thanks for this awesome opportunity!
-
-## References
-Carnell S, Gibson C, Benson L, Ochner CN, Geliebter A. Neuroimaging and obesity: current knowledge and future directions. Obes Rev. 2012 Jan;13(1):43-56. doi: 10.1111/j.1467-789X.2011.00927.x. Epub 2011 Sep 8. PMID: 21902800; PMCID: PMC3241905.
-
-Harding IH, Andrews ZB, Mata F, Orlandea S, Martínez-Zalacaín I, Soriano-Mas C, Stice E, Verdejo-Garcia A. Brain substrates of unhealthy versus healthy food choices: influence of homeostatic status and body mass index. Int J Obes (Lond). 2018 Mar;42(3):448-454. doi: 10.1038/ijo.2017.237. Epub 2017 Sep 25. PMID: 29064475.
-
-Hermann P, Gál V, Kóbor I, Kirwan CB, Kovács P, Kitka T, Lengyel Z, Bálint E, Varga B, Csekő C, Vidnyánszky Z. Efficacy of weight loss intervention can be predicted based on early alterations of fMRI food cue reactivity in the striatum. Neuroimage Clin. 2019;23:101803. doi: 10.1016/j.nicl.2019.101803. Epub 2019 Mar 27. PMID: 30991304; PMCID: PMC6463125.
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----
-type: "project"
-date: "2025-06-14"
-title: "Exploring Emotional Modulation of the P300 in EEG Data"
-
-names: [Fabiana Ojeda 歐瑩忻]
-
-github_repo: https://github.com/faojeda/p300-eeg-brainhack2025
-
-slides: https://drive.google.com/file/d/1mXoa-OyEHx_O3uHKW4EWqyjux8VKKN_g/view?usp=drive_link
-
-tags: [eeg, erp, mne, brainhack]
-
-summary: "This project explores how deviant auditory tones in a cross-modal oddball paradigm elicit a stronger P300 component using EEG data from the MNE sample dataset. The analysis focuses on ERP comparison and difference waves, setting the stage for future investigations on emotional modulation of P300."
-
-image: "thumbnail.png"
----
-
-## Project definition
-
-### Background
-
-The P300 is an event-related potential (ERP) component often associated with attention and stimulus evaluation. It is typically elicited by infrequent, task-relevant stimuli in oddball paradigms. This project used the MNE sample dataset to examine P300 responses to standard and deviant tones in a cross-modal oddball task.
-
-### Tools
-
-- Python
-- MNE-Python
-- Jupyter Notebook
-- Git & GitHub
-- EEG plotting and ERP comparison tools
-
-### Data
-
-The project uses the [MNE sample dataset](https://mne.tools/stable/overview/datasets_index.html#sample), which includes auditory and visual stimuli recorded during a multimodal oddball paradigm. For this analysis, auditory standard (left) and deviant (right) tones were selected for ERP comparison.
-
-### Deliverables
-
-- ERP plots comparing standard and deviant tones
-- A difference wave plot showing the increased P300 amplitude for deviant tones
-- A Jupyter Notebook with complete code and results
-- README and documentation hosted on GitHub
-
-## Results
-
-### Event Summary
-
-| Event | Description | Count |
-|-------|---------------------------|--------|
-| 1 | Auditory left | 72 |
-| 2 | Auditory right (standard) | 73 |
-| 3 | Visual right | 73 |
-| 4 | Visual left | 71 |
-| 5 | Auditory deviant (rare) | 15 |
-| 32 | Button press | 16 |
-
-For this project, I focused on event 2 (auditory standard) and event 5 (auditory deviant) because they align with the auditory oddball paradigm known to elicit the P300.
-
-### ERP Comparison
-
-The deviant tone produced a larger positive deflection around 300 ms, consistent with the P300 component. This supports attentional engagement triggered by unexpected stimuli.
-
-
-
-### Difference Wave
-
-Subtracting standard from deviant responses revealed a clear P300 difference peaking at ~300 ms.
-
-
-
-## Conclusion and Acknowledgements
-
-This project demonstrated the classic P300 effect using auditory tones in a cross-modal oddball paradigm. It served as a practical introduction to MNE-Python, EEG data analysis, and ERP visualization. Future directions could explore emotional modulation of the P300 in similar paradigms.
-
-Big thanks to the Brainhack School organizers, TAs, and professors!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-05" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Unveiling Children's Theory of Mind with rs-fMRI"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Wei-Hung Lin, Syuan-Yu Lin]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/WeiHungLin/Unveiling_children_ToM_with_rsfmri
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [theory of mind, rs-fmri, machine learning]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects.
-
-summary: "Can functional connectivity bed used to predict children’s theory of mind (ToM)? This project utilizes supervised machine learning algorithms on the fMRI data to predict children’s ToM ability. For better visualization, the most contributing brain region connections are displayed on the brain."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Cover_image_machine_learning.png"
----
-
-
-## Project definition
-
-### Background
-
-Theory of mind (ToM) refers to the ability to understand people's mental states and use them to predict people’s behaviors; therefore, ToM plays a crucial role in social interactions, communication, and cognitive development. Previous study showed that people with higher ToM ability exhibit stronger functional connectivity than those with lower ToM ability (Marchetti et al., 2015). With this finding, we might be able to use children's functional connectivity signatures to predict their ToM ability. Thus, our project goal is to make use of the machine learning techniques we learned in Brainhack School to predict children’s ToM ability based on their resting-state functional connectivity.
-
-### Tools
-
-This project relys on the following technologies:
- * Matplotlib and seaborn for data visualization
- * Nilearn and scikit-learn for constructing machine learning algorithms
- * VS Code and Google Colab for programming
- * GitHub for Version Control
-
-### Data
-
-We selected a preprocessed resting-state fMRI dataset from Nilearn Development fMRI to conduct our project. This rs-fMRI dataset comes from a study focusing on investigating the development of functionally specialized social brain regions, and the participants watched a short film while undergoing fMRI in the study. All children completed a custom-made explicit ToM task to measure their ToM ability. In our project, we mainly focused on the relationship between children’s ToM scores and the functional connectivity of the brain regions.
-
-### Deliverables
-
-At the end of this project, we will have:
- - Predictive models with evaluation scores and figure
- - Interactive figures
- - Presentation slides and video
- - Project Report
-
-## Project Presentation
-
-
-
-## Results
-
-### Progress overview
-
-This project was initiated by Wei-Hung Lin and Syuan-Yu Lin on 30th May 2023 as the part of the Brainhack school, and the final presentation was delivered on 1st June 2023. The deliverables include python code, interactive plot, presentation slides and video.
-
-### Tools I learned during this project
-
- * Nilearn: Python package for processing and visualizing neuroimaging data
- * Scikit-learn: Python package for conducting machine learning and evaluating the predictive model
- * Jupyter Notebook: Creating project slides
-
-### Results
-
-#### Deliverable 1: Predictive models with evaluation scores and figure
-
-
-
-#### Deliverable 2: Static and interactive figures
-
-
-
-
-
-
-## Conclusion
-
-This project aimed to employ the knowledge in data visualization and machine learning techniques we learned in Brainhack School 2023 with neuroimaging data. The results of machine learning models reveal the coherent functional connectivity findings, even though the model still requires improvement in predicting children’s ToM ability based on their functional connectivity.
-
-In this exploratory project, we observed the pivotal role of OFC connections in predicting ToM ability. This finding is intriguing as we did not set any ROIs in our machine learning analysis, yet our results are consistent with previous research suggesting the importance of the OFC/vmPFC in ToM ability (Abu-Akel & Shamay-Tsoory, 2011; Leopold et al., 2012).
-
-However, the regression figure and the evaluation scores indicate that the current model only explains limited variance in the given data, and there could be two reasons for this:
-
- * The ToM score is negatively skewed and may somehow violate the linear model assumption. In addition, age is the significant confounding variable in predicting ToM ability in children. To increase the model predictability, we can transform the ToM score and contain age into the model.
- * In the current study, a parcellation scheme comprising 64 brain regions was utilized, with a majority of regions located in the left hemisphere. Previous research indicates that the development of ToM ability is more closely associated with the left hemisphere compared to the right hemisphere (Marchetti et al., 2015). However, to capture a more comprehensive understanding of the relationship between functional connectivity and ToM ability, the use of a bilateral atlas can be considered. Alternatively, focusing on primary ROIs, such as the prefrontal cortex, may provide deeper insights into the association between functional connectivity and ToM ability.
-
-Overall, this preliminary project provides some inspiring findings, but we still need to try other machine learning algorithms and more data to develop a more robust model to predict the ToM ability with functional connectivity.
-
-## Acknowledgement
-Gratitude to all the organizers, instructors, TAs, and fellow participants from all around the world who helped us learn all these cool open neuroscience tools and give us this opportunity to connect with all the great neuroscientists in the world.
-
-Special thanks to our instructor Charlene Lee, and our TA Ingrid Chuang for their inspiring work and assistance with our project.
-
-## Reference
-* Abu-Akel, A., & Shamay-Tsoory, S. (2011). Neuroanatomical and neurochemical bases of theory of mind. Neuropsychologia, 49(11), 2971-2984.
-* Leopold, A., Krueger, F., dal Monte, O., Pardini, M., Pulaski, S. J., Solomon, J., & Grafman, J. (2012). Damage to the left ventromedial prefrontal cortex impacts affective theory of mind. Social Cognitive and Affective Neuroscience, 7(8), 871-880.
-* Marchetti, A., Baglio, F., Costantini, I., Dipasquale, O., Savazzi, F., Nemni, R., ... & Castelli, I. (2015). Theory of mind and the whole brain functional connectivity: Behavioral and neural evidences with the Amsterdam Resting State Questionnaire. Frontiers in psychology, 6, 1855.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2021-08-27" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "rs-fMRI Workflow from Preprocessing to Machine Learning Classification"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Catherine Landry]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/PSY6983-2021/clandry_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [connectivity, bids, preprocessing, machine-learning, open-science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Can functional connectivity predict sensory deprivation? This project 1. explores neuroimaging data organization and preprocessing using open science tools and 2. uses a predictive model to classify whether a participant is hearing or not. For better visualization, the most contributing coefficients in the classifier are displayed on the brain."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "repo_pic.png"
----
-
-
-
-# About me
-
-[Catherine Landry (she/her)](https://github.com/catherinelandry)
-
-Hello! I am currently doing my master's degree in psychology at the University of Montreal. My wide range of interests is reflected through my eclectic research background. I have worked with children at Sainte-Justine Hospital, trained spinal cord injured rats, and I am now doing research on individuals living with deafness. As of today, I'm particularly hyped about neuroimaging and coding, hence my learning journey with PSY6983.
-
-# Project Definition
-
-## Background
-
-Functional connectivity can be studied at different resolutions, scaling from locally functional areas to large and spatially distributed networks. At rest, brain regions with correlated temporal patterns with each other form resting state networks (RSN). Sensory deprivation leads to functional changes in the brain beyond the affected sensory modality. Various studies have found altered RSN in deaf individuals compared to controls (e.g. [Bonna et al., 2020](https://doi.org/10.1007/s11682-020-00346-y ); [Ducas et al., 2021](https://doi.org/10.21203/rs.3.rs-246296/v1)). Supervised machine learning can yield characterization of rs-fMRI for individual-level predictions ([Khosla et al., 2019](https://doi.org/10.1016/j.mri.2019.05.031)). Therefore, it would be interesting to predict whether a participant is hearing or not based on a functional connectivity estimation.
-
-### Main objectives:
-
-* Learn reproductible neuroimaging workflow from preprocessing to data visualization to equip myself with open science tools for future neuroimaging projects.
-
-* Determine the most contributing features in machine learning prediction at single-participant level and interpret the weight of the coefficients according to the RSN.
-
-### Personal objectives set for the course:
-
-* Familiarize myself with open science software and its best practices
-
-* Learn how to code with python, specifically for the neuroimaging field
-
-* Have a better understanding of machine learning and its application in neuroimaging
-
-## Tools
-
-All the tools used for the project:
-
-* Bash Terminal
-
-* Visual Studio Code
-
-* Jupyter Notebook
-
-* Git and Github for version control
-
-* BIDS for data management
-
-* fMRIPrep for data preprocessing
-
-* [`matplotlib`](https://matplotlib.org/), [`seaborn`](https://seaborn.pydata.org/), and [`plotly`](https://plotly.com/) for data visualization
-
-* [`scikit-learn`](https://scikit-learn.org/stable/) for machine learning and [`nilearn`](https://nilearn.github.io/) for neuroimaging data manipulations
-
-## Data
-
-The dataset comprises 5-minutes fMRI resting state images covering the whole brain of 34 adult participants, 16 of which have severe-to-profound prelingual deafness and 18 of which are hearing individuals that serve as controls. All participants were instructed to lie still and to avoid holding on to thoughts for the duration of the scanning.
-
-**Age Distribution Across the Participants** (Click **[here](https://catherinelandry.github.io/interactive_plot/age_description.html)** for an interactive plot!)
-
-
-
-
-
-**Education Distribution Across the Participants** (Click **[here](https://catherinelandry.github.io/interactive_plot/education_description.html)** for an interactive plot!)
-
-
-
-
-
-For further questions on the dataset and the acquisition parameters, I encourage you to reach out to me at cath.landry2@gmail.com
-
-## Deliverables
-
-At the end of this project, I will have:
-
-* Markdown file for the project repository
-* requirements.txt that lists all the packages used in the project
-* Jupiter notebooks for the presentation slides and data visualization
-* Python scripts for data prep and machine learning
-
-# Results
-
-## Progress Overview
-
-This project was initiated as part of the course PSY6983. The following sections detail the different steps taken to achieve the deliverables of the project.
-
-
-
-
-
-
Source: Illustration created with Selman Design taken in Autodraw under the license CC BY 4.0
-
-## 1. Data Management
-
-Since my data was converted from DICOM to NIfTI prior to the beginning of the course, the first phase of the project was dedicated to organize my dataset into the BIDS format. This standardized neuroimaging structure enables the [FAIR guidelines](https://www.go-fair.org/fair-principles/) criteria of interoperability (I) and reusability (R), which corresponds with my objective to familiarize myself with the best practices in data management. Different tutorials and examples available in Github repos facilitated the BIDS conversion (see [bids-starter-kit](https://github.com/bids-standard/bids-starter-kit)). [BIDS Validator](https://bids-standard.github.io/bids-validator/) web browser based version confirmed that the data structure matched the BIDS standards.
-
-
-
-
-
-## 2. Data Preprocessing
-
-- Step 1. The Open-access [fMRIPrep](https://fmriprep.org/en/stable/index.html) pipeline was used for minimal preprocessing.
-
-- Step 2. [Load_confounds](https://github.com/catherinelandry/load_confounds) was installed and added to the data prep script as part of my denosing strategy.
-
-## 3. Data Preparation
-
-
-
-The Schaefer atlas (2018) was employed with dimension selection set at 100 ROI. Pairwise correlated time series were extracted to form a correlation matrix.
-
-*Note: the matrix contains redondant information, such as the pairs (i, j) and (j, i), which can bias the classification. Therefore, only the vectorized matrix of each participant was extracted and fed to the classifier.*
-
-## 4. Machine Learning
-
-A pipeline was used to facilitate hyper-parameters tuning for model optimization. It includes:
-- Standardization (StandardScaler)
-- Dimensionality Reduction (PCA)
-- Model Selection (linear SVC)
-- Regularization Parameter (C)
-
-The following figure portrays the machine learning script:
-
-
-
-
-
-
-### Accuracy Scores
-
-| | Mean | Std |
-|-------------|:------------:|:------------:|
-|Train | 1.0 | 0.0 |
-|Validation | 0.45 | 0.1 |
-|Test (fit on validation) | 0.56 | 0.0 |
-|Test (fit on train + validation) | 1.0 | 0.0 |
-
-
-### Interpretation
-
-Using a vectorized correlation matrix was not successful to classify whether the participant was hearing or not. The prediction is close to chance level when fitting the data on the validation set only and overfits when fitting the data on the whole training set (including the validation one). Many reasons could explain these results (e.g. model/hyper-parameters/features selection, sample size, intra-individual differences, etc.). Predifining ROI known to show differences between the two groups instead of using the whole brain might also improve the prediction at the single-level.
-
-But on a good note...
-
-The most contributing coefficients to the classifier can be visualized on the brain!
-
-
-
-
-
-
-
-
-
-## Tools Learned During This Project
-
-- **Open Science Software:** I learned to use Git for local and remote control in order to share my project repository on Github. I was also able to navigate through different repositories to find the documentation needed for my project. Data organization, preprocessing and denoising strategies only used open access tools for reproductibility.
-
-- **Machine Learning Packages:** I have a better understanding of how to use `scikit-learn`, `nilearn` and their different modules for machine learning and neuroimaging data manipulations.
-
-- **Python Scripting:** I was able to use different libraries (e.g. `numpy`, `nibabel`), code in a virtual environment with Jupyter Notebook and edit code with Virtual Studio Code. The course modules helped me understand data dimensionality and how to manipulate it.
-
-- **Data Visualization:** I learned to plot static figures with `matplotlib` and `seaborn` and to generate interactive figures with `plotly`. I am now able to code this cool brain visualization:
-
-
-
-
-
-## Deliverables
-
-By the end of the project, I was able to deliver:
-
-- [README.md](https://github.com/PSY6983-2021/clandry_project): markdown file that documents the present project
-- The following python scripts:
- 1. [Data Prep](https://github.com/PSY6983-2021/clandry_project/tree/main/codes): script that loads the nifti data, applies a masker and extracts features
- 2. [Linear SVC](https://github.com/PSY6983-2021/clandry_project/tree/main/codes): script with preprocessing and machine learning classifier
-- The following Jupyter notebooks:
- 1. [Presentation slides](https://github.com/PSY6983-2021/clandry_project/tree/main/notebooks): can be launched with the Rise extension
- 2. [Data visualization](https://github.com/PSY6983-2021/clandry_project/tree/main/notebooks): code that plots the static and interactive figures in the repo. This notebook was conceptualized to run specific code cells that import the python scripts needed to retrieve the data
-- The requirements.txt: file with all the prerequisite packages used to run the codes
-
-# Conclusion and Acknowledgement
-
-The past month has been very educational and rewarding on many levels. The course format allowed for the application of new knowledge to a project that reflects my interests. While it was easy to get lost in ideas of grandeur for the project, I reached most of the goals I set for myself, notably a better understanding of neuroimaging data manipulations using open science tools and practices.
-
-The classification performance outcomes illustrate the need for further investigation on single-level prediction and features' importance. The vectorize correlation matrix was not a good discriminating feature between hearing and non hearing individuals. Although the results were not striking, I emerge of this experience equipped with improved coding skills and fueled with new ideas to try on my model.
-
-A special thanks to Pierre Bellec for his advice and his insightful takes about the future of science practices. I would have been stuck longer on data preprocessing without Desiree's help and might have shed a tear without Andreanne's and François' coding skills. You have all facilitated my learning journey and provided me with the necessary tools to overcome obstacles. A great thanks to Marie-Eve with whom I shared (lots of) coffees during our coding sessions.
-
-# References
-
-Bonna, K., Finc, K., Zimmermann, M., Bola, L., Mostowski, P., Szul, M., Rutkowski, P., Duch, W., Marchewka, A., Jednorog, K., & Szwed, M. (2020). Early deafness leads to re-shaping of functional connectivity beyond the auditory cortex. Brain Imaging and Behavior, 1-14. https://doi.org/10.1007/s11682-020-00346-y
-
-Ducas, K. D., Senra Filho, A. C. D. S., Silva, P. H. R., Secchinato, K. F., Leoni, R. F., & Santos, A. C. (2021). Functional and structural brain connectivity in congenital deafness. Brain Structure and Function, 226(4), 1323-1333. https://doi.org/10.21203/rs.3.rs-246296/v1
-
-Khosla, M., Jamison, K., Ngo, G. H., Kuceyeski, A., & Sabuncu, M. R. (2019). Machine learning in resting-state fMRI analysis. Magnetic resonance imaging, 64, 101-121. https://doi.org/10.1016/j.mri.2019.05.031
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-08-07" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Workflow of resting state connectivity from a raw dataset to longitudinal visualization"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Kevin Jamey]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/jamey_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [resting state, workflow, music, autism]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "To aim of this project was to provide a full neuroimaging workflow from preprocessing of raw data to visualisation of results,
-to explore longitudinal analysis between two treatments in this dataset and to visualise resting-state networks linked to the default mode network and attention. In my github repository you will find scripts and documentation about the the BIDS to NiFTY conversion, fMRI prep as well as resting-state visualisation of a single participant. There is also a powerpoint presentation slide to guide you through the work."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "pcc_seed_to_voxel_cor_post.jpg"
----
-
-
-## Project definition
-
-
-## _ABOUT ME_
-
-My research interests are on new technologies for music-based cognitive training in children with neurodevelopmental disorders. I completed my MSc. on music perception abilities in children on the Autism Spectrum (AS) at the University of Montreal under the supervision of Krista L. Hyde. My doctoral thesis is supervised by Simone Dalla Bella and examines the neural correlates of a tablet-based rhythmic training game (RhythmWorkers) on executive functioning and speech in children on the AS. In my free time, I am a singer-songwriter, music producer/live act and I love to create powerful musical experiences where lyrics are inflected for rhythm and melody.
-
-## _PROJECT SUMMARY_
-
-### Main objectives
-
-* Provide a full neuroimaging workflow from preprocessing of raw data to visualisation of results
-* To explore longitudinal analysis between two treatments in this dataset
-* To analyse resting-state networks linked to joint attention (dorsal attentional, visual, DMN)
-
-### Personal objectives
-
-* To learn the full workflow of neuroimaging from converting raw data to preprocessing to data visualisation
-* To set myself up with open science tools for neuroimaging work in the future
-* To get familiar with the best practices of shareable open science kits and software
-
-### Tools
-
-* Git and Github for sharing project
-* dcm2bids for converting raw data into NiFTY and a BIDS structure
-* fMRIPrep for data preprocessing
-* Alliance Canada for computing pre-processing
-* Python Packages matplotlib, nilearn, plotly
-
-
-### Data
-Dataset was collected during my masters in 2016 at the MNI. I scanned
-~50 participants with autism before and after either 12 weeks of music therapy or arts and crafts therapy. The data involves a weighted T1 scan and resting-state fMRI.
-A previous publication on this data set can be found [here](https://www.nature.com/articles/s41398-018-0287-3
-).
-
-### Project deliverables
-
-* project workflow detailed in my [GitHub repository](https://github.com/brainhack-school2022/jamey_project)
-* the bash code I used to run BIDS conversion and fMRIprep
-* a markdown file introducing the project, highlighting the results and a discussion with recommendations
-
-
-## Results
-
-### Progress overview
-
-The project was workflow was set-up for a single participant over the course of the brainhack school. It will be adapted in the future in order to run multiple participants and do group seed-based resting state fMRI.
-
-### Tools I learned during this project
-
- * **Project-Management** I learnt how to set-up the framework for making a shareable neuroimaging project using open science rules.
- * **Github workflow-** I learnt to use GitHub in order to share my project openly and invite people to collaborate on it.
- * **Project content** Here you will find all the key steps necessary to move from a raw imaging dataset to some cool visualisations.
-
-### Results
-
-#### Deliverable 1: report template
-
-You are currently reading the report template! I will let you judge whether it is useful or not. If you think there is something that could be improved, please do not hesitate to open an issue [here](https://github.com/brainhack-school2022/jamey_project/issues) and let me know.
-
-
-#### Deliverable 2: Visualisation using Nilearn
-
- After successfully convertion the raw data of this participant to BIDS and NiFTY, I preprocessed his data using fmriprep. You can see details about this in my [slides](https://github.com/brainhack-school2022/jamey_project/blob/main/docs/Project%20Final%20Presentation_PSY6983.pptx). I looked at a simple connectome before and after a music intervention for a child diagnosed on the austim spectrum. This is simply for illustration purposes since a large amount of participants are requited to make a meaningful connectome. Then visualized seed-based connectivity of the Posterior Cingulate Cortex before and after music therapy in this subject. You can see the visual results of my project [here](https://github.com/brainhack-school2022/jamey_project/blob/main/results/results.md). Behaviorally we found differences after the music therapy in [joint attention](https://www.researchgate.net/publication/349361961_Joint_engagement_and_movement_Active_ingredients_of_a_music-based_intervention_with_school-age_children_with_autism). In this single subject's seed-based conectivity there seems to be more connectivity in the medial prefrontal cortex, the visual cortex, and tempo-parietal network, which would be in line with improvement in joint attention. In order to further develop these findings for the experimental group, a seed-based connectivity for the entire experimental and control treatment will be conducted.
-
-
-## Conclusion and acknowledgement
-
-I hope you find this projet useful in outlining a standard shareable workflow from imgages fresh out of the scanner to tangible visualisations of resting-state connectivity. Many thakns to the brainhack school instructors and students who helped make this prossible in such a short amount of time. I look forward to devling deeper into the foundations that are laid here during my PhD.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Does rs-fMRI preprocessing matter for prediction performance in machine learning?"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Stephanie Alley]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/stephaniealley_bhs2020_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, preprocessing, machine-learning]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Machine learning models are often used to analyze fMRI data, whether it be a simple classification or regression problem or something more complex. While the focus of a study is often centered on the model architecture, data preprocessing also plays a vital role in a model's success. This project will explore the effect that various preprocessing options may have on the prediction performance of a machine learning model for age prediction using resting state fMRI."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "parcellation.png"
----
-
-
-## Project definition
-
-### Background
-
-I am a third year PhD student at Polytechnique with a background in MRI. My primary goal for this project is to become more proficient with tools that further open, reproducible science. Even the most valiant attempts at sharing data and code can fall short in terms of reproducibility, so I aim to incorporate multiple tools and strategies to promote the reliable reproducibility of this project.
-
-One important source of variability across studies is a lack of standardization in preprocessing steps. This project will focus on the effect that preprocessing choices may have on the prediction performance of a machine learning model. Specifically, two preprocessing steps that affect the extraction of functional signal will be examined: atlas choice and confound removal.
-
-
-
-Four different atlases will be used: functional (BASC multiscale)2,3, structural (AAL)4, clustering method (Craddock)5, and linear decomposition (MSDL)6.
-
-
-
- Various confound inclusion/exclusion options will be explored, including cerebral spinal fluid (CSF) signal, white matter (WM) signal, global signal, and motion correction.
-
-The impact of these choices will be assessed by evaluating the prediction performance of a machine learning model. This model will be based on the Support Vector Regressor model used in Week 1 for age prediction. A separate model will be trained for each preprocessing option. The prediction performance will then be evaluated by calculating the accuracy and mean absolute error of each model. The skill of the model will be assessed with repeated 10-fold cross-validation.
-
-### Tools
-
-* Python for processing, analysis, and visualization
-* Git/GitHub for version control of the project, including the markdown document and the code
-* Nilearn for implementation of the machine learning model
-* DataLad for version control of the data through processing and analysis
-* Jupyter notebooks for accessibility and sharing of the code, analysis, and visualizations
-* Visualization (plotly) for creating interactive figures
-* Binder for incorporating the GitHub repository, including all files and Jupyter notebooks, into a live environment that can be easily shared
-
-### Data
-
-rs-fMRI brain development dataset based on viewing of short animated film (obtained from OpenNeuro as ds000228)1:
-* 155 subjects
- * 122 children
- * 33 adults
-
-The dataset is available as derivative data that was pre-processed using fMRIPrep.
-
-### Deliverables
-
-* GitHub repository containing all items related to the project, including the markdown document, requirements.txt, and Jupyter notebooks
-* Complete markdown document (README.md) containing all of the relevant project information
-* requirements.txt to specify the Python environment
-* Jupyter notebook containing the code for the processing and analysis, as well as the visualizations
-* Interactive notebook using Binder for reproducible sharing of the entire project
-* Presentation slides for final project presentation
-
-## Results
-
-### Progress overview
-
-* Week 2: Implementing and practicing strategies and tools learned in Week 1
-The project was envisioned as an extension of the machine learning tutorial presented in Week 1. Initially, a directory structure was created that adheres to the TIER guidelines. After creating a virtual environment in Python, a Jupyter notebook was created to contain the code and visualizations. The pre-processed developmental fMRI data was fetched through nilearn. DataLad datasets were created for both the Original_Data and Analysis_Data directories in order to keep track of data manipulation throughout the analysis. Notable changes to the notebook code and/or the data were consistently tracked using Git/Github and DataLad in order to become more familiar with and accustomed to using these tools.
-* Week 3: Expanding upon original machine learning tutorial
-In order to explore the effects that various preprocessing options might have on the prediction performance of a machine learning model, four different atlases were chosen from which to extract features: functional (BASC multiscale), structural (AAL), clustering method (Craddock), and linear decomposition (MSDL). Furthermore, the inclusion/exclusion of various confound options was explored: all confounds, CSF signal, WM signal, global signal, and motion correction. Python code was added/modified, particularly using nilearn and scikit-learn, in order to investigate these preprocessing options. Plotting functions provided by seaborn, matplotlib and nilearn were explored to examine options for visualization of results.
-* Week 4: Visualization of results and project reproducibility
-An interactive figure depicting linear regression plots of model performance on unseen test data was created using plotly. A new Jupyter notebook, bhs2020_project_presentation.ipynb, was created to create the final project presentation using RISE. Binder was used to reproduce the interactive figure as well as the project as a whole.
-
-### Tools I learned during this project
-
-* Git/GitHub: I learned to use Git to for version control of all aspects of the project repository. Specifically, I was able to learn tactics for choosing which changes to include in a commit, how often to commit, and how to write more effective commit messages.
-* Nilearn: I was able to use nilearn for the machine learning model. Along with understanding the code used in the tutorial, I learned how to use additional modules from nilearn, particularly those that deal with probabilistic atlases.
-* DataLad: I learned to incorporate DataLad into the project for data provenance. Using this tool, I was able to track the origin and manipulation of all data involved in the project.
-* Jupyter notebook: I was able to create my final presentation in a Jupyter notebook using RISE. This made it possible to include the interactive figure directly within a slide.
-* Plotly: I learned to use plotly, in conjunction with ipywidgets, to create an interactive figure for the final results. This zoomable figure included a dropdown option for choosing an atlas, a slider for choosing a confound, as well as a hover feature that provided detailed information about each point.
-* Binder: I was able to launch the project on Binder for improved reproducibility in replicating this project.
-
-### Results
-
-#### Project results
-The connectivity between regions in a particular atlas was calculated as a correlation measure. These correlations differ for each atlas as can be seen for the correlation matrix and connectome of a given subject.
-
-
-
-These differences in connectivity, however, were not found to result in much difference in the prediction performance of the models. In fact, the values of the mean R2 and mean MAE for each atlas using all confounds were fairly similar. The results for the probabilistic atlases were slightly worse, but more work is required before asserting that any such decrease is significant.
-
-| Atlas | Mean R2 | Mean MAE |
-|:-----:|:-------:|:--------:|
-| BASC | 0.664 | 3.267 |
-| AAL | 0.617 | 3.610 |
-| CRAD | 0.526 | 3.641 |
-| MSDL | 0.520 | 3.825 |
-
-Likewise, little difference was achieved in prediction performance by using alternative confound combinations. The accuracy of models trained using features extracted from probabilistic atlases was found to be somewhat lower than that achieved for the functional and structural atlases, but it cannot be said that there is a significant difference without further analysis.
-
-#### Deliverable 1: GitHub repository
-The [GitHub repository](https://github.com/brainhack-school2020/stephaniealley_bhs2020_project) contains the complete markdown document, requirements.txt, and Jupyter notebooks.
-
-#### Deliverable 2: Complete markdown document (README.md)
-This [document](https://github.com/brainhack-school2020/stephaniealley_bhs2020_project/blob/master/README.md) provides all relevant information pertaining to the project, including a project description, goals, and results.
-
-#### Deliverable 3: requirements.txt
-This [file](https://github.com/brainhack-school2020/stephaniealley_bhs2020_project/blob/master/requirements.txt) contains all relevant dependencies required for specifying the Python virtual environment used to carry out the project.
-
-#### Deliverable 4: Jupyter notebooks
-This project contains two notebooks: [rs-fMRI_age_prediction.ipynb](https://github.com/brainhack-school2020/stephaniealley_bhs2020_project/blob/master/Command_Files/rs-fMRI_age_prediction.ipynb) and [bhs2020_project_presentation.ipynb](https://github.com/brainhack-school2020/stephaniealley_bhs2020_project/blob/master/Documents/final_presentation/bhs2020_project_presentation.ipynb). The former notebook contains the code for processing, analysis, and visualization while the latter is composed of the slides for the final project presentation.
-
-#### Deliverable 5: Interactive notebook using Binder
-The project was launched on Binder [here]().
-
-#### Deliverable 6: Presentation slides
-The slides for the final presentation are located in [bhs2020_project_presentation.ipynb](https://github.com/brainhack-school2020/stephaniealley_bhs2020_project/blob/master/Documents/final_presentation/bhs2020_project_presentation.ipynb) and can also be viewed [here](https://stephaniealley.github.io/bhs2020_project_presentation/).
-
-#### Week 3 deliverable: Data visualization
-The deliverable for week 3 can be launched on [Binder](https://mybinder.org/v2/gh/stephaniealley/stephaniealley_bhs2020_data_visualization/master). This interactive figure makes use of plotly and ipywidgets to display regression plots that illustrate the prediction performance of the SVR model on unseen (test set) data. Plots can be viewed for each atlas and confound option included in this portion of the project analysis.
-
-## Conclusion and acknowledgement
-
-I am immensely grateful to the entire BHS team for all of their assistance and support throughout the course. This was an invaluable experience that has drastically changed my view of what open, reproducible science might be able to achieve.
-
-## References
-1. Richardson, H., Lisandrelli, G., Riobueno-Naylor, A., & Saxe, R. (2018). Development of the social brain from age three to twelve years. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-03399-2
-
-2. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51(3), 1126–1139. https://doi.org/10.1016/j.neuroimage.2010.02.082
-
-3. Bellec, P. (2013). Mining the hierarchy of resting-state brain networks: Selection of representative clusters in a multiscale structure. Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013, (August), 54–57. https://doi.org/10.1109/PRNI.2013.23
-
-4. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978
-
-5. Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928. https://doi.org/10.1002/hbm.21333
-
-6. Varoquaux, G., Gramfort, A., Pedregosa, F., Michel, V., & Thirion, B. (2011). Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6801 LNCS, 562–573. https://doi.org/10.1007/978-3-642-22092-0_46
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-08-03" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Can we classify men and women based on the connectivity profile of their language network?"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Xanthy Lajoie]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/Lajoie_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [sex differences, language network, classification, resting-state]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Sex differences in the language network is a long lasting and unresolved debate in the neuroscience field. Clinical studies have shown that pathologies or developmental conditions affecting language functions can differently affect individuals based on their sex. Although the language network is bilaterally organized, the left hemisphere is dominant for language in most individuals. However, this lateralisation tends to vary between sexes.In the present project, we address the research question on whether young adults present differences in the pattern of rs-fMRI functional connectivity within the language network based on their sex. To address this issue, we propose to determine whether we can classify healthy young adults, men and women, based on their rs-fMRI functional connectivity profiles within the language network."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain.png"
----
-# rs-fmri sex classification
-
-## Project Description
-
-#### Background
-Sex differences in the language network is a long lasting and unresolved debate in the neuroscience field. Clinical studies have shown that pathologies or developmental conditions affecting language functions can differently affect individuals based on their sex (Cahill, 2006; Icer et al., 2020).
-
-Although the language network is bilaterally organized, the left hemisphere is dominant for language in most individuals (Knect et al., 2000). However, this lateralisation tends to vary between sexes (Shaywitz et al., 1995).
-
-
-
-
-Differences between men and women in the functional language network could explain sex differences in clinical conditions and offer insight for the development of interventions based on individuals’ characteristics. Yet whether and how sex impacts the functional language network organization is still largely unknown.
-
-#### Objectives
-In the present project, we address the research question on whether young adult individuals present differences in the pattern of rs-fMRI functional connectivity within the language network based on their sex. To address this issue, we propose to determine whether we can classify healthy young adult men and women, based on their rs-fMRI functional connectivity profiles within the language network.
-#### Data
-The rs-fMRI images were issued from the human connectome project (HCP) S1200 release datatbase (Van Essen et al., 2012). The rs-fMRI images of a total of 667 healthy adults (322 men and 345 women, age: 22-35 years) were included in the study. First, for each participant, we extracted the functional connectivity networks anchored in the core regions of the language network from rs-fMRI data. This step is completed.
-
-With the knowledge acquired from this class, I will test whether machine learning models can accurately classify men and women based on their functional connectivity language maps within the language network. Subsequently, I will determine which are the most discriminant functional connectivity features that allow this classification.
-
-### My objectives
-- Familiarize myself with different softwares such as Git, Github, WSL
-- Learn how to code with python, specifically for the neuroimaging field
-- Have a better understanding of machine learning and its application in neuroimaging
-- Get comfortable using the terminal to search for files and not the default file system on our laptops
-
-### Deliverables
-- Seed-to-voxel code
-- Jupyter notebook containing Linear SVC model and accuracy
-- Presentation slides
-- Project Report
-
-## Results
-Using the seed-to-voxel correlations of 8 seeds of a sub-sample of 70 participants (40 M, 38 W).
-Model pipeline:
-
-
-
-1. Results from the PCA determining the best number of features to include in our model.
- In this case, 0 features represent the best number of features (basically using the whole brain)
-
-
-
-
-2. Results from the Linear SVC classifier
-
-
-
-Accuracy (r2) = 0.875
-
-Interpretation: the classifier was able to sucessfully classify men and women with an accuracy of 87.5%
-
-### Tools Learned During This Project
-Open Science Software: I learned to use Git for local and remote control in order to share my project repository on Github. I was also able to navigate through different repositories to find the documentation needed for my project. I no longer send codes to myself via email or rely on my USB drive.
-
-Machine Learning Packages: I have a better understanding of how to use scikit-learn, nilearn and their different modules for machine learning and neuroimaging data manipulations.
-
-Python Scripting: I was able to use different libraries (e.g. numpy, nibabel), code in a virtual environment with Jupyter Notebook. The course modules helped me understand data dimensionality and how to manipulate it.
-
-Data Visualization: I learned to plot multiple static figures with matplotlib and seaborn and to generate interactive figures with plotly and also to plot connectomes.
-
-
-
-## Conclusion
-
-Apart from the main project, participating in Brainhack 2022 has had several benefits. I am more comfortable with shell coding, and the introduction to WSL makes it easier for me to work in Linux environments. I'm looking forward to trying more tools for neuroimaging analysis, such as fMRIprep for preprocessing, machine learning methods to try different classifiers and seed-to-voxel connectivity analyses. Although I've only had a brief introduction to many of these tools, I feel like they are more accessible with the knowledge Brainhack school has given me.
-
-
-### Acknowledgements
-I am immensely grateful to the entire team for all of their assistance and support throughout the course. François, Marie-Ève et Claudéric, thank you for your patience and for not sighing when I asked for help every 5 minutes. Without you, I don't know what I would've done.
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Hugo Galván,Agustina Boveda,Pablo Koss,Sebastian Galván]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/hcgalvan_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [schizophrenia, fmri, tractography, machine-learning, neuroimaging, python]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-Summary: "Schizophrenia (SZ) involves significant alterations in perception, thoughts, mood, and behavior. This project aims to develop an AI model using machine learning for complementary SZ diagnosis, utilizing prefrontal cortex connectomics and tractography techniques. It focuses on creating scripts for data separation, comparing classification models, and analyzing the connectome of healthy individuals and those with SZ. Early detection and accurate diagnosis through machine learning will enable targeted interventions, improving outcomes for individuals with SZ."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "tractografia.png"
----
-
-
-# Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models
-
-Hugo Galván, Agustina Boveda, Pablo Koss,Sebastian Galván
-
-
-
-## Summary
-
-Schizophrenia (SZ) involves significant alterations in perception, thoughts, mood, and behavior. This project aims to develop an AI model using machine learning for complementary SZ diagnosis, utilizing prefrontal cortex connectomics and tractography techniques. It focuses on creating scripts for data separation, comparing classification models, and analyzing the connectome of healthy individuals and those with SZ. Early detection and accurate diagnosis through machine learning will enable targeted interventions, improving outcomes for individuals with SZ.
-
-## Project definition
-
-### Background
-
-**Schizophrenia theories**: Theory of Disconnection Syndrome
-* The theory of disconnection syndrome is a theory that explains the symptoms of schizophrenia as the result of disruptions in the normal integration of emotion, perception, and thought.
-
-**Previous works and literature**: Use of only one method of analyses, Require additional validation afterwards
-* [Schizophrenia prediction using tractography and machine learning](https://www.sciencedirect.com/science/article/pii/S1053811919303837)
-* [Schizophrenia prediction using fMRI and machine learning](https://www.sciencedirect.com/science/article/pii/S1053811919303837)
-
-**MRI**: Now it’s only use for differential diagnosis and not for prediction
-
-### Tools
-
-The "Schizophrenia" project will rely on the following technologies:
- * Processing of Tractography data is done with `DSI Studio`
- * Processing of fMRI data is done with `SPM`
-* Machine learning anlayses rely on `scikit-learn`.
- * The main summary and results of the project are stored in a [Jupyter Notebook](https://jupyter.org/index.html)
- * Updating and version control relies on github, through commits and pull requests.
-
-### Data
-
-The preprocessed data is publicly available on [UCLA Consortium for Neuropsychiatric Phenomics LA5c Study](https://purl.stanford.edu/mg599hw5271). The data is composed of 2 groups of subjects: 1) 58 patients with schizophrenia and 2) 138 Healthy controls. The data is composed of 2 modalities: 1) fMRI and 2) Tractography. The data is already preprocessed and ready to be used.
-
-### Deliverables
-Note that the deliverables changed somewhat over the course of the project, and are more extensive than the original conception.
-
-
-The [Github repository](https://github.com/brainhack-school2023/hcgalvan_project/blob/master/analysis.ipynb) that contains all of the relevant code for the project, clearly formatted and commented.
-
-* Data, generated from the [UCLA Consortium for Neuropsychiatric Phenomics LA5c Study](https://purl.stanford.edu/mg599hw5271) dataset as described above.
- * Code for the analysis "pipeline" using Python
- * Jupyter notebook for interactive data visualization
- * This README.md file describing the project
- * Examples of visualizations created using the pipeline and notebooks
- * The slides used for the final Brainhack School presentation
-
-
-## Results
-
-### Progress overview
-
-The project was divided into 3 main parts: 1) Data preparation, 2) Machine learning, and 3) Data visualization. The first part was done using `DSI Studio` and `SPM`. The second part was done using `scikit-learn`. The third part was done using `Jupyter Notebook`.
-
-### Tools I learned during this project
-
-* `DSI Studio` for tractography data processing and visualization
-* `SPM` for fMRI data processing and visualization
-* `scikit-learn` for machine learning analyses
-* `Jupyter Notebook` for data visualization and presentation
-
-### Results
-* The results of the project are summarized in the [Github repository](
-
-#### Deliverable 1: report template
-* The report template is available in the [Github repository- Metrics](https://github.com/brainhack-school2023/hcgalvan_project/blob/main/scripts/mvp-metrics.ipynb)
-
-#### Deliverable 2: project gallery
-* The project gallery is available in the [Github repository- Pipeline](https://github.com/brainhack-school2023/hcgalvan_project/blob/main/src/pca_reduct_end.ipynb)
-
-
-## Conclusion and acknowledgement
-
-### Conclusion
-1. We have managed, among many tasks, to link the worlds of clinical medicine, laboratory neurosciences and supervised learning, looking for the models achieved to support the clinic in a format
-a) screening, or with a tolerable bias to false positives that, together with other studies, advance in depth for a final diagnosis
-b) as a tool for minimum tolerance to biases, and pathology is taken as the last study and finalizes the clinical diagnosis process.
-
-2. Under these criteria, we are putting together modeling strategies, on operational hypotheses that are on the project's work table and beyond metrics that may be low, highlighting the variances of the important characteristics in the laboratory.
-We begin to understand the noise between variances that prevent us from visualizing and obtaining objective metrics. Also where they come from and we are putting together another model that verifies these facts and penalizes when detected, which will help highlight the true attributes that are hidden.
-
-3. We are understand that they are a tool to support the clinic in the diagnosis, and that they are a tool to advance in the understanding of the disease and its treatment.
-
-## Acknowledgements
-Thanks to all of the instructors, teaching assistants, and participants of the Brainhack School 2023! Thanks to the organizers, HUMAI, Hub Buenos Aires, and the Brainhack School 2023 sponsors for making this possible. Thanks to the [UCLA Consortium for Neuropsychiatric Phenomics LA5c Study](https://purl.stanford.edu/mg599hw5271) for making the data publicly available.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Diagnosing Schizophrenia from Brain Activity"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Alexander Albury]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/Alex-A14_Brainhack2020
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [brainhack, sklearn, fmri, nilearn, machine-learning]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Computational Psychiatry is growing trend that applies machine learning methods to psychological disorders. How well can we predict schizophrenia diagnosis from brain activity? This project uses neuroimaging tools from Nilearn, and machine learning tools from scikit-learn to differentiate patients diagnosed with schizophrenia from healthy controls using resting state fmri data."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "alina-grubnyak-brain.jpg"
----
-
-
- [@albury_alex](https://twitter.com/albury_alex)
-## Project definition
-
-### Background
-
-This project uses machine learning techniques to predict schizophrenia diagnosis using fMRI data.
-
-The data used is a resting state fMRI dataset of schizophrenia patients and healthy controls. The data comes from [The Center for Biomedical Research Excellence (COBRE)](http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html).
-
-Machine learning techniques are used to classify schizophrenia patients from controls. My goal for this project was to gain basic experience with processing fMRI data. Following this, I experiment with different machine learning methods (algorithms, cross-validation methods, tuning hyperparamters) and see how they compare.
-
-
-
-### Tools
-
-The project relies on the following technologies:
- * This README is built using [Markdown](https://guides.github.com/features/mastering-markdown/), to structure the text.
- * Processing of fMRI data is done with `nilearn`
- * Machine learning anlayses rely on `scikit-learn`.
- * `plotly.express` and `ipywidgets` are used to create interactive visualizations.
- * The main summary and results of the project are stored in a [Jupyter Notebook](https://jupyter.org/index.html)
- * Some interactive visualizations rely on a [Binder](https://jupyter.org/binder) environment.
- * Updating and version control relies on github, through commits and pull requests.
-
-### Data
-
-The preprocessed data is publicly available on Nilearn and can be downloaded using `nilearn.datasets.fetch_cobre()` Documentation about the Nilearn data can be found [here](https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_cobre.html#nilearn.datasets.fetch_cobre). (**Note**: The Nilearn implementation of this dataset is deprecated and so access to the data may change or be removed.)
-
-### Deliverables
-
-Products of this project include:
- - A complete README summarizing the entire project and its results.
- - Week 3 Deliverable:
- - [Plotly Histogram](http://htmlpreview.github.io/?https://github.com/brainhack-school2020/Alex-A14_Brainhack2020/blob/master/plotly.html) of age
- - Binder With ipywidgets Correlation Matrices [](https://mybinder.org/v2/gh/brainhack-school2020/Alex-A14_Brainhack2020/master?filepath=visualization.ipynb)
- - A [Jupyter notebook](https://github.com/brainhack-school2020/Alex-A14_Brainhack2020/blob/master/analysis.ipynb) that contains all of the relevant code for the project, clearly formatted and commented.
- - A formatted [html document](http://htmlpreview.github.io/?https://github.com/brainhack-school2020/Alex-A14_Brainhack2020/blob/master/analysis.html) that contains code, descriptions, and visualizations from the project.
-
-## Results
-
-### Overview
-
-The project involved importing and cleaning the demographic data and nifti files. After this, time series and correlation matrices were generated for each subject. Visualizations of subject demogrpahics and connectivity matrices of patients and controls were generated. Finally, various machine learning models were trained to predict schizophrenia diagnosis, and were evaluated using 10-fold cross validation. The performance of the best performing model was then evaluated using a validation set.
-
-### Tools learned in this project:
-
- * **fMRI Data Processing with Nilearn**
- * **Machine Learning with Scikit Learn**
- * **Interactive plotting with Plotly Express**
- * **Pandas Data Manipulation**
- * **fMRI Data Visualization**
- * **Visualizing Machine Learning Metrics**
-
-### Method
-
-#### Data Cleaning
-
-Data preparation involved extracting subject IDs from the nifti file paths and then using this to merge the final names to the phenotypic data. This allowed for easy subsetting when creating data visualizations comparing patients and controls. The final step in data visualization was to generate time series and correlation matrices for each subject. The correlation matrices were then merged to the phenotypic data to create a column containing the correlation matrix for each subject.
-
-#### Data Visualization
-
-Feature plots were generated showing average activation for patients and controls. An [interactive Plotly Express app](http://htmlpreview.github.io/?https://github.com/brainhack-school2020/Alex-A14_Brainhack2020/blob/master/plotly.html) was generated to display a histogram of age for patients and controls. Lastly, interactive visualizations were created to display average correlation matrices for patients and controls.
-
-#### Classification
-
-The primary goal of the project was to predict schizophrenia diagnosis. Several machine learning apporaches were evaluated on the data. The data was split into training and validation sets using an 80/20 split. Each model was then evaluated on the training data using 10-fold cross validation. F1 score was used as a performance metric. In some cases, a grid search was used to optimize hyperparameters.
-
-### Results
-
-The table below displays the average performance of each model across the 10-folds.
-
-| Classifier | Average CV F1 | Min | Max |
-| --------------- | ------------- | ---- | ---- |
-| Linear SVC | 0.80 | 0.63 | 1.0 |
-| Gradient Boost | 0.58 | 0.24 | 0.83 |
-| KNN | 0.62 | 0.43 | 0.73 |
-| Random Forest | 0.73 | 0.58 | 0.92 |
-| RBF SVC (Tuned) | 0.81 | 0.63 | 1.0 |
-
-
-The best performing model was a Support Vector Machine CLassifier (SVC) using an RBF kernel, with values of 100.0 and 0.001 for the `C` and `gamma` parameters, respectively. This model was then used to predict daignosis on the left out validation set. On the validation set, the model had a final F1 score of **0.69**, and an accuracy of **0.67**.
-
-The figure below shows a confusion matrix of the model's predictions:
-
-
-
-### Discussion and Limitations
-
-An F1 of .69, and accuracy of 67% isn't bad for a binary classfication problem, especially considering the small sample size. It's possible that better performance could be achieved through several methods. This analysis uses 64 ROIs, but the atlas used supports up to 444. Increasing the number fo features could improve the model. Conversely, dimensionality reduction could be used to reduce the number of features available to the model.
-
-Hyperparameter tuning of more advanced models could be another avenue for improved performance. The Random Forest classifier showed potential, and more time spent tweaking this model could produce better results.
-
-Finally, this analysis doesn't examine feature importance. It's likely that an analysis of a similar size, or greater, could be devoted to investigating feature importance for schizophrenia prediction, and so it was far beyond the scope of this project.
-
-## Conclusion and Acknowledgements
-
-This project has been a great introduction to working with fMRI data, and using Scikit Learn. The range of functions and clear documentation for both of these packages make them accessible and easy to learn. The project has also strengthened my skills in Python, particularly data management with Pandas.
-
-Apart from the main project, participating in Brainhack 2020 has had several benefits. I am more comfortable with shell coding, and the introduction to WSL makes it easier for me to work in Linux environments. The focus on reproducible science has introduced me to numerous tools for making analyses accessible, from Git to Docker. I'm also looking forward to trying more tools for neuroimaging analysis, including fMRIPrep for preprocessing, and MNE (Python) and EEG Workshop (R) for processing EEG data. Although I've only had a brief introduction to many of these tools, I feel like they are accessible with the experience that Brainhack has given me.
-
-I'd like to thank all of the instructors, presenters, TAs, and mentors who helped make Brainhack 2020 a success (in a new online format!). It's amazing that so much was packed into 4 weeks. I look forward to seeing what Brainhack does in the future.
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+---
+type: "project" # DON'T TOUCH THIS ! :)
+date: "2026-06-26" # Date you first upload your project.
+# Title of your project (we like creative title)
+title: "Wired for Sound: Functional Connectivity of the Auditory Cortex in Schizophrenia"
+
+# Your project GitHub repository URL
+github_repo: https://github.com/brainhack-school2026/BHS_2026_Schizophrenia_AVH_Project.git
+
+# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
+website:
+
+# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
+# Please only lowercase letters
+tags: [schizophrenia, dysconnectivity, hallucinations, brainhack]
+
+# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
+
+# List the names of the collaborators within the [ ]. If alone, simple put your name within []
+names: [Garene Matossian, Anna Petroseniak, Yang Jing Zheng, Hira Zahid, Diya Shah, Denisa Lazar]
+
+summary: "This project investigates auditory cortex functional connectivity in schizophrenia using open-source fMRI data from OpenNeuro. Using a seed-to-voxel connectivity approach centered on the left auditory cortex, we examined differences in brain network communication among schizophrenia patients with and without auditory verbal hallucinations (AVH+ and AVH-) and healthy controls during a speech perception task."
+
+# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
+# below with the extension.
+image: "brain.png"
+---
+
+
+## Project Definition: Background, Research Question, Project Objectives
+
+### Background
+
+Schizophrenia is a chronic psychiatric disorder that affects approximately 24 million individuals worldwide and is increasingly regarded as a disorder of brain network dysconnectivity (Friston & Frith, 1995; Alderson-Day et al., 2015). Rather than reflecting dysfunction within a single brain region, symptoms may emerge from altered communication among distributed neural systems.
+
+Auditory verbal hallucinations (AVHs) are among the most common positive symptoms of schizophrenia and affect approximately 60-80% of individuals with schizophrenia. Neuroimaging studies suggest that AVHs involve abnormal interactions between auditory processing regions, language networks, and systems involved in self-monitoring and internally generated speech (Jardri et al., 2011; Alderson-Day et al., 2015). In addition, previous work has consistently implicated the left auditory cortex, particularly regions surrounding Heschl's gyrus, in the pathophysiology of hallucinations (Gavrilescu et al., 2010; Shinn et al., 2013).
+
+This project utilizes OpenNeuro dataset ds004302 from Soler-Vidal et al. (2022), which includes fMRI data acquired during a speech perception task from healthy controls, schizophrenia patients without auditory verbal hallucinations (AVH−), and schizophrenia patients experiencing auditory verbal hallucinations (AVH+).
+
+**Main Question:**
+Do healthy controls, AVH− participants, and AVH+ participants differ in the functional connectivity of the auditory cortex during speech perception?
+
+**Research Objectives**
+1. Learn neuroimaging preprocessing and quality control workflows using fMRIPrep.
+2. Develop skills in seed-based functional connectivity analysis using Nilearn.
+3. Apply reproducible neuroimaging workflows to open-source schizophrenia datasets.
+4. Examine auditory cortex connectivity patterns associated with hallucination status.
+5. Gain experience using high-performance computing resources and collaborative software development tools.
+
+## Methodology: Workflow, Tools Used
+
+**Workflow & Procedures**
+1. Data Organization: Raw neuroimaging data were obtained from OpenNeuro (ds004302) and organized according to BIDS standards.
+
+2. fMRI Preprocessing: Functional and anatomical MRI data were preprocessed using fMRIPrep. Processing included motion correction, anatomical-functional coregistration, tissue segmentation, normalization to MNI space, and confound extraction. Slice timing correction was omitted because the primary objective was seed-based functional connectivity analysis. Skull stripping was skipped because the raw neuroimaging data already underwent skull stripping.
+
+3. Seed Definition: A seed region corresponding to the left auditory cortex was identified using the Schaefer 2018 functional atlas. The selected parcel was the cortical region nearest to a literature-based left Heschl's gyrus coordinate (MNI: −42, −26, 10).
+
+4. Functional Connectivity: The average BOLD signal from the auditory cortex seed was extracted for each participant. Connectivity between the seed and every voxel in the brain was calculated using Pearson correlation.
+
+5. Fisher-z Transformation: Correlation coefficients were transformed using Fisher's z transformation to improve suitability for statistical analyses.
+
+6. Statistical Analysis Between Groups: Connectivity maps were compared between healthy controls, AVH− participants, and AVH+ participants using two-sample t-tests and one-way ANOVA.
+
+### Tools Used
+
+In summary, the dataset was obtained from OpenNeuro and organized according to the Brain Imaging Data Structure, or BIDS, which provides a standardized framework for managing neuroimaging data. Computationally intensive analyses were performed on SciNet, a high-performance computing cluster.
+
+For preprocessing, we used fMRIPrep and FreeSurfer, while functional connectivity analyses were conducted in Python using Nilearn. Throughout the project, we used VS Code, Jupyter Notebooks, and the command line interface for coding and analysis, while GitHub was used for version control and project organization to support reproducibility and collaboration.
+
+
+
+**Figure 1.** Pre-processed surface reconstruction for subject #1.
+
+**Neuroimaging Software**
+1. fMRIPrep (v25.2.4)
+2. Nilearn
+3. FreeSurfer
+
+**Computing Environment**
+1. SciNet Teach Cluster
+3. Python/Jupyter Notebooks
+4. VSCode/Terminal
+
+**Data Standards and Version Control**
+1. OpenNeuro
+2. Brain Imaging Data Structure (BIDS)
+3. Git and GitHub
+
+
+
+**Figure 2.** Tools used for the brainhack project.
+
+## Data
+
+### Data Characteristics
+
+The complete dataset (OpenNeuro, ds004302) contains 71 participants across three groups. Due to time constraints, analyses were initially conducted on a subset of 27 participants consisting of:
+* 9 Healthy Controls
+* 9 AVH− Participants
+* 9 AVH+ Participants
+
+### Deliverables
+
+At the end of this project, we will have:
+1. Reproducible GitHub repository
+2. README file (this project report)
+3. fMRI preprocessing script
+4. Seed-based functional connectivity script
+5. Group-level analysis script
+
+### Repository Contents
+
+The repository contains:
+1. Preprocessing scripts
+2. Functional connectivity scripts
+3. Group-level statistical analysis code
+4. Documentation and workflow descriptions (git log, commits)
+5. BrainHack presentation materials
+6. Figures and outputs
+
+## Results: Skills Learned, Preliminary Findings
+
+### Overview
+
+A complete preprocessing and functional connectivity workflow was successfully implemented using fMRIPrep and Nilearn. Preprocessing was conducted on the SciNet Teach cluster using containerized workflows. Seed-to-voxel connectivity maps were generated using the left auditory cortex as the seed region and subsequently analyzed at the group level.
+
+
+### Skills Learned
+
+**Neuroimaging Analysis**
+* BIDS-compliant dataset organization
+* fMRIPrep preprocessing workflows
+* Quality-control assessment of MRI data
+* Seed-based functional connectivity analysis
+* Group-level neuroimaging statistics
+
+**Computational Skills**
+* Teach cluster usage
+* Containerized neuroimaging workflows using Apptainer
+* Git and GitHub version control
+* Python-based neuroimaging analyses
+
+### Preliminary Findings
+
+Voxel-wise seed-based functional connectivity analyses were performed using Fisher z-transformed correlation maps from the left Heschl’s gyrus seed. Pairwise group comparisons (HC vs. AVH−, HC vs. AVH+, and AVH− vs. AVH+) were conducted using two-sample t-tests, and a one-way ANOVA was used to assess overall group differences. Statistical significance was evaluated using voxel-wise Benjamini–Hochberg false discovery rate (FDR) correction (q = 0.05).
+
+
+
+**Figure 3.** ANOVA conducted to assess group differences (HC, AVH-, AVH+).
+
+A significant difference in functional connectivity was observed between the HC and AVH+ groups, with 832,821 voxels surviving FDR correction. In contrast, no voxels survived FDR correction for the HC vs. AVH− comparison, the AVH− vs. AVH+ comparison, or the omnibus one-way ANOVA. These findings suggest robust alterations in left Heschl’s gyrus connectivity in patients with auditory verbal hallucinations relative to healthy controls, whereas differences involving the AVH− group were not statistically significant after correction for multiple comparisons.
+
+
+
+**Figure 4.** Functional connectivity analysis between HC and AVH-.
+
+
+
+**Figure 5.** Functional connectivity analysis between HC and AVH+.
+
+
+
+**Figure 6.** Functional connectivity analysis between AVH- and AVH+.
+
+However, because the AVH+ and AVH− groups did not significantly differ after correction, these results should not be interpreted as definitive evidence of hallucination-specific connectivity differences. Instead, they provide preliminary support for further examining whether auditory hallucinations are associated with broader network-level alterations in auditory, language, salience, sensorimotor, and default mode systems.
+
+### Limitations
+
+Several limitations should be noted. The current analysis used a small subset of the full dataset and focused only on the sentences condition. Although the original goal was to preprocess the full dataset and conduct both seed-to-voxel and ROI-based functional connectivity analyses, this was not feasible within the project timeline. A major challenge was that preprocessing had to be repeated after we determined that the OpenNeuro dataset had already undergone skull stripping, requiring the fMRIPrep workflow to be modified and rerun with skull stripping skipped. As a result, the present findings are based on the corrected preprocessing pipeline for the 27-participant subset and should be treated as exploratory. The large number of significant voxels in the HC vs. AVH+ contrast also requires follow-up to confirm that the effect reflects meaningful connectivity differences rather than masking, preprocessing, or variance-related issues.
+
+## Conclusion: Wrap-Up, Future Directions
+
+### Future Directions
+
+Future BrainHack groups could build on this project by extending the corrected preprocessing and seed-based functional connectivity workflow to the full ds004302 dataset. Because the present project established the core pipeline but was limited by preprocessing delays and time constraints, future work can:
+
+1. Complete corrected preprocessing of the full dataset (n = 71)
+2. Perform whole-sample seed-to-voxel and ROI-based connectivity analyses
+3. Investigate connectivity within auditory, language, salience, sensorimotor, and default mode networks
+4. Examine relationships between connectivity patterns and hallucination status
+5. Explore demographic and clinical moderators, including age, sex and medication use.
+
+We thank the BrainHack teaching team, the teaching assistants and professor, for their guidance throughout the program!
+
+## References
+
+Alderson-Day, B., McCarthy-Jones, S., & Fernyhough, C. (2015). Hearing voices in the resting brain: A review of intrinsic functional connectivity research on auditory verbal hallucinations. Neuroscience & Biobehavioral Reviews, 55, 78–87.
+
+Friston, K. J., & Frith, C. D. (1995). Schizophrenia: A disconnection syndrome? Clinical Neuroscience, 3(2), 89–97.
+
+Gavrilescu, M., Rossell, S., Stuart, G. W., Shea, T. L., Innes-Brown, H., Henshall, K., McKay, C., Sergejew, A. A., Copolov, D., & Egan, G. F. (2010). Reduced connectivity of the auditory cortex in patients with auditory hallucinations: A resting state functional magnetic resonance imaging study. Psychological Medicine, 40(7), 1149–1158.
+
+Jardri, R., Pouchet, A., Pins, D., & Thomas, P. (2011). Cortical activations during auditory verbal hallucinations in schizophrenia: A coordinate-based meta-analysis. American Journal of Psychiatry, 168(1), 73–81.
+
+Shinn, A. K., Baker, J. T., Cohen, B. M., & Öngür, D. (2013). Functional connectivity of left Heschl’s gyrus in vulnerability to auditory hallucinations in schizophrenia. Schizophrenia Research, 143(2–3), 260–268.
+
+Soler-Vidal, J., Fuentes-Claramonte, P., Salgado-Pineda, P., Ramiro, N., García-León, M. Á., Torres, M. L., Arévalo, A., Guerrero-Pedraza, A., Munuera, J., Sarró, S., Salvador, R., Hinzen, W., McKenna, P., & Pomarol-Clotet, E. (2022). Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PLOS ONE, 17(12).
+
+Cover image courtesy of the National Cancer Institute.
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Sleep detection using fMRI data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Romain DUCHADEAU]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2024/duchadeau_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, sleep, Machine_learning, health]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project utilizes fMRI data and machine learning to predict sleep states, aiming to enhance understanding of sleep patterns and disorders. By analyzing brain activity during different sleep stages, it seeks to improve diagnostics and develop personalized treatments for sleep disorders. The primary goal is to determine whether a participant is asleep or awake using resting-state fMRI data."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "images_for_README/Image_objective.png"
----
-
-## Project Summary
-
-### Introduction
-
-This project explores the use of fMRI data to predict sleep states via machine learning algorithms. By enhancing our understanding of sleep patterns and disorders, this research offers valuable insights into brain activity across various sleep stages. It holds the potential to advance diagnostic methods and develop personalized treatments for sleep disorders. The primary objective is to determine a participant's sleep or wake state using resting-state fMRI data.
-
-### Main Objectives
-
-- Develop a comprehensive neuroimaging workflow, encompassing raw data preprocessing, feature extraction, model training, and results visualization.
-- Prioritize reproducibility, ensuring every element of the project can be easily replicated, including detailed documentation and open-source code availability.
-- Assess the efficacy of machine learning algorithms in predicting sleep states using fMRI connectivity data.
-
-### Personal Objectives
-
-- Acquire a thorough understanding of fMRI and neuroimaging database structures, along with best practices.
-- Cultivate skills in preprocessing and analyzing fMRI data.
-- Apply machine learning techniques to neuroscience research.
-
-### Tools
-
-- Git and GitHub for Version Control
-- jupyter notebook for result presentation
-- BIDS for dataset standardization and exploitation
-- sklearn for Machine learning
-- nipype, FSL for Data Preprocessing
-- Python Packages: `numpy`, ``scikit-learn``, ``nibabel``, ``nilearn``, ``matplotlib``
-
-### Deliverables
-- [GitHub repository](https://github.com/brainhack-school2024/duchadeau_project) : project code
-- Markdown file (this file): project description & installation instructions
-- **Main.ipynb** : An executable Jupyter notebook with step-by-step instructions for data preprocessing and machine learning
-- **Draft_Exploration_datase.ipynb** and **Draft_preprocessinf.ipynb** : Two draft used to test ideas. Not supposed to be explored but can be helpfull.
-- **requirement.txt** : See the installation module
-- **Brainhack-school_final_presentation** : My final presentation of the project
-
-### Dataset Description
-
-The dataset utilized for this analysis includes simultaneous EEG and fMRI recordings from 33 healthy participants, collected at Penn State University with informed consent. It comprises anatomical sessions for structural imaging, two 10-minute resting-state sessions, and multiple 15-minute sleep sessions. The first resting session occurred before a visuomotor adaptation task (Albouy et al., Journal of Sleep Research, 2013), and the second followed the task. Sleep stage data for these subjects are organized in the 'sourcedata' folder, with TSV files annotating sleep stages for each 30-second interval, categorized as "w" (wake), "1" (NREM1), "2" (NREM2), "3" (NREM3), "uncertain" (uncertain periods), and "unscorable" (artifact-ridden periods). MRI data were obtained using a Siemens Prisma Fit 3 Tesla scanner with a 20-channel receive coil, featuring MPRAGE sequence for anatomical images (TR: 2300 ms, TE: 2.28 ms, 1 mm isotropic spatial resolution) and EPI sequence for BOLD fMRI data (TR: 2100 ms, TE: 25 ms, 4 mm slice thickness). Although EEG data were recorded, they are not utilized in this analysis. The dataset also includes a CSV file with sleep stages used to train the algorithm.
-
-For more information or questions about this dataset, please refer to the [open-neuro dataset page](https://openneuro.org/datasets/ds003768/versions/1.0.11)
-
-## Results
-
-To recap, the objective of this project is to process raw fMRI data, make it exploitable, and train a machine learning algorithm to identify whether a patient is asleep or awake based on their fMRI images.
-
-
-
-To achieve this, we divided our work into three steps:
-1. Select the appropriate data from the dataset.
-2. Preprocess the fMRI images to make them usable.
-3. Apply a machine learning algorithm to perform the recognition.
-
-Let's begin with the first section:
-
-### 1. Select Data
-The goal of this section is to select the correct data since the temporal resolution of the labels does not match the temporal resolution of the fMRI images.
-
-
-
-To do this, we select only the data that exactly corresponds to the labels. All these steps can be found in the `Main.ipynb` file.
-
-### 2. Preprocessing
-Once the data is selected, it must be preprocessed to make it usable, providing our algorithm with only the useful information to avoid, among other things, overfitting (we don't want our algorithm to learn to recognize patients based on skull shapes, for example).
-
-#### 2.1 Brain Preprocessing
-First, we extract the brain from the fMRI data to keep only the exploitable part:
-
-
-
-Second, we align the brain to the corresponding T1 scan of the patient to be able to apply an atlas later:
-
-
-
-#### 2.2 Atlas Definition
-Next, we choose an atlas that allows us to define regions of interest (ROI) to focus our efforts. The chosen atlas defines 64 regions of interest:
-
-
-
-To apply our atlas, we also need to align it to the T1 scan of each patient:
-
-
-
-#### 2.3 Atlas Application
-Finally, since our brain is aligned with the T1 scan and the atlas is also aligned, both are aligned, and we can apply a masker to our data to define the regions of interest on our brains:
-
-
-
-### 3. Machine Learning Algorithm
-Finally, we can apply our machine learning algorithm to our data. The chosen algorithm is an [SVC algorithm](http://un-est-tout-et-tout-est-un.blogspot.com/2018/12/ia-classification-svc-avec-scikit-learn.html), and we use the cross-validation technique to test our algorithm:
-
-
-
-Ultimately, we achieve an average of **70% accuracy**, and we can create a validation curve and a confusion matrix to see where the algorithm struggles:
-
-
-
-
-We can see that the algorithm performs well in detecting wakefulness but struggles to detect shallow sleep cases.
-
-*Note:* Due to hardware limitations, the algorithm was trained on a very limited volume of data, but we can imagine it will perform even better with a larger dataset.
-
-See `Brainhack-school Final Presentation.pdf` for more details on potential improvements.
-
-
-## Setup Instructions
-
-### Step 1: Create a Virtual Environment
-
-Create a virtual environment to install the necessary libraries in an isolated environment:
-
-```
-python -m venv fmri_env
-```
-
-### Step 2: Activate the Virtual Environment
-
-Activate the virtual environment:
-
-- **Windows:**
- ```bash
- .\fmri_env\Scripts\activate
- ```
-
-- **MacOS/Linux:**
- ```bash
- source fmri_env/bin/activate
- ```
-### Step 3: Download FSL
-Download flsinstaller.py on [this](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation) site
-
-### Step 4: Install FSL
-Install FSL by tapping:
-```
-pip install flsinstaller.py
-```
-
-### Step 4: Install Required Libraries
-Run the following command to install the libraries:
-```
-pip install -r requirements.txt
-```
-**Well done it's finish**. The last step is to dowload the dataset used on [openneuro](https://openneuro.org/datasets/ds003768/versions/1.0.11)
-
-
-## Conclusion and acknowledgement
-Thanks to the BHS team for making this project possible with their very accessible and comprehensive courses. Special thanks to the Polytechnique Montreal team for the excellent moments spent together.
-
-## References
-- https://nipype.readthedocs.io/en/latest/
-- https://nilearn.github.io/stable/index.html
-- https://mniopenresearch.org/articles/1-3
-- https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
-- http://un-est-tout-et-tout-est-un.blogspot.com/2018/12/ia-classification-svc-avec-scikit-learn.html
-- https://github.com/brainhack-school2024/duchadeau_project
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-15" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Practice extracting functional signals from specific brain region"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Yiping Lu]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/yipinnng/yiping_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, hippocampus, navigation, brainhack]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to extract and analyze fMRI signals from the hippocampus during spatial navigation, using a reproducible workflow based on open tools and data formats."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover.png"
-
----
-
-
-## Project definition
-
-### Background
-
-Spatial navigation is a complex cognitive function requiring the integration of diverse sensory inputs to compute goal locations and update self-position during movement. These inputs can be broadly categorized into allocentric and egocentric. Effective navigation depends on the dynamic interaction between these two spatial strategies.
-
-To further investigate how the brain transforms between allocentric and egocentric representations, we developed a virtual environment in which participants navigate from a first-person perspective or learn routes from a top-down map. By manipulating the learning and retrieval modalities (e.g., first-person learning followed by top-down retrieval, and vice versa), we aim to examine the neural mechanisms underlying the transformation between spatial reference frames and the role of non-primary sensory information in navigation.
-
-To address the aims, I’ll observe the connectivity between brain regions, but the virtual map still need a pilot test before recruiting participants. So for my proposal pitch in brain hack school is to practice using NiBabel to extract functional signals from specific brain regions based on EPI data. And I choose hippocampus cause it’s a region can integrate the spatial information to form a cognitive map in our brain.
-
-### Tools
-
-The project will rely on the following technologies:
- * Python, The main programming language for scripting and data analysis.
- * Nibabel, For loading and manipulating neuroimaging data in NIfTI format.
- * Nilearn, For statistical analysis and visualization of functional MRI data.
- * AAL2 Atlas, Used for anatomical parcellation and identifying brain regions of interest.
- * MNI152 Standard Brain Template, Used as the reference space for normalization and visualization.
-
-### Data
-
-The fMRI dataset used in this project comes from a previous spatial navigation experiment conducted in our lab. The data analyzed here corresponds to one subject and includes task-based functional images and corresponding anatomical scans. All data were preprocessed and organized in BIDS format prior to analysis. A manually labeled hippocampal mask was also used to extract region-specific signals.
-
-### Deliverables
-
-At the end of this project, we will have:
- - **Code**: Python scripts and Jupyter notebooks used for loading, processing, and analyzing the fMRI data.
- - **Figures**: Output images illustrating key results, such as brain activation maps and region-specific signal extractions.
- - **Slides**: A summary presentation describing the project background, methods, results, and conclusions, prepared for the final showcase.
-
-## Results
-
-### Progress overview
-
-This project began with selecting and preparing a task-based fMRI dataset from a previous spatial navigation experiment conducted in our lab. The data were converted to BIDS format and preprocessed to ensure compatibility with analysis tools.
-
-Using Python-based neuroimaging libraries such as Nibabel and Nilearn, I extracted time-series signals from the hippocampus using a predefined mask. Additionally, I aligned the subject's brain image to MNI space for standardized analysis and applied the AAL2 atlas for anatomical reference.
-
-### Tools I learned during this project
-
- - **Nibabel**: I learned how to load, manipulate, and save NIfTI images using Nibabel, which helped me extract fMRI signals from specific brain regions such as the hippocampus.
- - **Nilearn**: I used Nilearn's `resample_to_img` function to match spatial resolution between brain images and anatomical masks, ensuring accurate region-of-interest extraction.
- - **ANTs (Advanced Normalization Tools)**: I applied nonlinear image registration using `ants.registration` to align subject-specific images to the MNI standard brain. This taught me how to implement spatial normalization in a reproducible way.
- - **Python scripting**: Through scripting with loops and conditionals, I automated the processing of multiple runs and trials across different image types (e.g., t-values and betas), which helped streamline my analysis workflow.
-
-### Results
-
-#### Image registration to MNI space
-
-All beta images from the spatial navigation task (spanning 8 runs and 7 trials) were registered to the MNI152 standard brain using the ANTs library. The `SyN` nonlinear transformation ensured that each subject-specific image was accurately aligned to a common anatomical space.
-
-Each output was visually inspected to verify successful alignment, and the registered images were saved for downstream analysis. These MNI-aligned images enable consistent anatomical localization across trials and facilitate integration with standard atlases like AAL2.
-
-
-
-#### Hippocampus region extraction
-
-Following registration, a binary hippocampus mask was resampled to each aligned beta image using nearest-neighbor interpolation (`nilearn.image.resample_to_img`). The resampled mask ensured voxel-level correspondence between the mask and the functional images.
-
-We then extracted signals specific to the hippocampus by multiplying each image with the resampled mask, effectively zeroing out all non-hippocampal voxels. The resulting hippocampus-only beta images were saved for further statistical analysis or visualization.
-
-An example of the hippocampus mask from AAL2 and hippocampus-masked output are shown below:
-
-
-
-
-
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-15" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Intrinsic Connectivity of Task-Defined Language Regions"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Wei-Chen Huang]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/WeiChenPsy/WeiChen_2025BHS_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [rsfmri, taskfMRI, functionalconnectivity, language]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project examines how language-related brain regions connect with DMN, FPN, and SN during rest, using fMRI data to explore links between functional connectivity and language comprehension."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover.png"
----
-
-
-## Project definition
-
-### Background
-
-This project was conducted as part of Brainhack School 2025, a program that encourages students to engage in open neuroscience through hands-on experience with real-world neuroimaging datasets. Motivated by an interest in how language functions are represented within the brain’s intrinsic functional architecture, the present project aimed to investigate the resting-state connectivity of language-related brain regions.
-
-The analysis was based on the SMN4Lang dataset, which includes both task-based and resting-state fMRI scans. Language-related regions of interest (ROIs) were first identified through activation patterns during a language comprehension task. Functional connectivity was then assessed between these ROIs and three canonical resting-state networks: the Default Mode Network (DMN), Frontoparietal Network (FPN), and Salience Network (SN). Additionally, the influence of demographic and behavioral variables—such as age and language comprehension performance—on connectivity strength was explored.
-
-Despite numerous technical challenges, including repeated system crashes, the entire analysis pipeline was successfully implemented from scratch. This project marks a significant step toward developing practical skills in functional connectivity analysis and advancing understanding of how language-related regions interact with large-scale neural networks at rest.
-
-### Tools
-
-The "WeiChen_2025BHS_project" project will rely on the following technologies:
-* Nilearn: For extracting time series from fMRI data, applying ROI and RSN masks, and computing functional connectivity.
-* NumPy & Pandas: For numerical operations, data wrangling, and managing correlation matrices.
-* Matplotlib & Seaborn: For visualizing functional connectivity patterns and group comparisons.
-* Pingouin & SciPy: For conducting statistical analyses, including Pearson correlations and repeated-measures ANOVAs.
-* Jupyter Notebook: To develop, test, and document the analysis pipeline in a transparent and reproducible manner.
-* Git & GitHub: For version control, collaboration, and sharing the project with the Brainhack community.
-
-
-### Data
-
-This project uses the SMN4Lang dataset(https://openneuro.org/datasets/ds004078), which includes both task-based and resting-state fMRI data. During the task-based sessions, participants listened to spoken Mandarin narratives while their brain responses were recorded using fMRI. The resting-state scans were collected in the absence of external stimuli to examine the brain's intrinsic functional organization.
-
-The dataset provides preprocessed fMRI images along with corresponding behavioral and demographic data, enabling researchers to investigate the interactions between language-related brain regions and large-scale resting-state networks, and to explore individual differences in language comprehension performance.
-
-For this project, only six participants (sub-01 to sub-06) were selected for analysis, due to computational resource limitations and feasibility considerations.
-
-### Deliverables
-
-At the end of this project, the following deliverables were completed:
-
-* ROI masks and resampled RSN masks used for functional connectivity analysis.
-* Z-scored time series extracted from both task-defined ROIs and three resting-state networks (DMN, FPN, SN).
-* A correlation matrix quantifying ROI–RSN functional connectivity for each participant.
-* Repeated-measures ANOVA results examining the effects of ROI and RSN on connectivity strength.
-* Exploration of behavioral and demographic influences on ROI–RSN functional connectivity.
-
-## Results
-
-### Progress overview
-
-This project was developed during Brainhack School 2025, a program that encourages students to explore open neuroscience through hands-on experience. It marked my first step into the field of resting-state fMRI and functional connectivity analysis, and every stage—from preprocessing to final results—was a completely new learning experience.
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-Through continuous trial and error, I learned how to extract time series, apply ROI and RSN masks, and compute correlation coefficients, gradually gaining an understanding of how to analyze and validate data starting from raw inputs. The process was full of challenges—especially when my computer crashed repeatedly—but completing the full analysis pipeline and seeing meaningful results brought me great satisfaction. I hope this experience will serve as a foundation for my continued exploration into brain data analysis.
-
-### Tools I learned during this project
-
-* **DataLad**: For downloading, managing, and version-controlling open datasets, enabling reproducible data access.
-* **Jupyter Notebook**: For writing, executing, and documenting analysis code in an interactive and transparent manner.
-* **Nilearn**: For extracting time series from fMRI data and computing functional connectivity between ROIs and resting-state networks.
-* **Matplotlib / Seaborn**: For visualizing correlation matrices, group comparisons, and connectivity patterns.
-* **Pingouin**: For conducting statistical tests, including Pearson correlations and repeated-measures ANOVAs.
-* **NumPy / Pandas**: For data manipulation, matrix operations, and organizing tabular data.
-* **GitHub & Markdown**: For version control, documentation, and transparent sharing of project progress and results.
-
-### Results
-
-#### Deliverable 1: Definition of Task-Based ROIs and Resampled RSN Masks
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-To define language-related regions of interest (ROIs), I first conducted subject-level analysis of the task-based fMRI data, focusing on the contrast between story and baseline. This allowed me to identify language-related activations for each participant. I then performed a group-level analysis to extract brain regions that were consistently activated across subjects, and used these regions to construct the final language-based ROI masks.
-
-For the resting-state network (RSN) masks, I used the Yeo 7-network atlas as a reference. Since the RSN masks and the ROI/resting-state data had different affine matrices, I resampled the RSN masks to ensure proper alignment before extracting time series.
-
-**ROI 1**
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-**ROI 2**
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-#### Deliverable 2: Time Series Extraction from ROIs and RSNs
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-I concatenated the four resting-state runs into a single 4D image file for each participant. Then, I extracted time series from the combined resting-state fMRI data using both the task-defined ROI masks and the resampled RSN masks. The extracted time series were z-scored and saved separately for each participant and each mask type. These time series served as the basis for subsequent functional connectivity analysis.
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-**Time Series Extracted from ROI**
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-**Time Series Extracted from RSN**
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-During the process, I encountered a data quality issue with sub-05. By visualizing the time series from each run individually, I identified that run 1 showed abnormal signal patterns and exhibited abnormally low standard deviation. As a result, run 1 was excluded from the concatenation and omitted from all downstream analyses for sub-05.
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-**sub-05 Reconcatenated Time Series (Excluding Run 1)**
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-#### Deliverable 3: ROI–RSN Functional Connectivity Analysis
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-Based on the time series extracted in the previous step, I computed Pearson correlation coefficients between each ROI and the three resting-state networks (DMN, FPN, and SN) for every participant. The results were organized into individual functional connectivity matrices. I then visualized these matrices to examine the distribution of connectivity strength across networks, the coupling patterns between specific ROIs and RSNs, and the variability across participants.
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-**ROI–RSN functional connectivity correlation**
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-#### Deliverable 4: Effects of ROI × RSN on Connectivity Strength
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-To investigate differences in functional connectivity strength among the three RSNs within each ROI, I first performed a Friedman test, a non-parametric alternative to repeated-measures ANOVA. This was followed by pairwise Wilcoxon signed-rank tests as post-hoc comparisons.
-
-* For ROI 1, the correlation with the SN was significantly higher than with the DMN (p = .031).
-* For ROI 2, the correlation with SN was significantly higher than with both DMN and FPN (p = .031 for both comparisons).
- * No significant difference was found between DMN and FPN within ROI 2 (p = .313).
-
-In addition, I compared the connectivity strength between ROI 1 and ROI 2 within each RSN. The results showed no significant difference between the two ROIs in any of the networks (all p > .05), suggesting that the two ROIs exhibit similar connectivity profiles to each RSN.
-
-
-#### Deliverable 5: Influence of Behavioral and Demographic Factors on ROI–RSN Connectivity
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-To further explore individual variability in ROI–RSN functional connectivity, I generated participant-wise bar plots displaying the correlation values between each ROI and RSN. Upon inspecting the DMN–ROI connectivity, I noticed that sub-02 and sub-06 showed distinct patterns compared to the other participants.
-
-To investigate this further, I examined their demographic and behavioral profiles, focusing specifically on quiz performance after listening to the stories. I then used Spearman correlation to assess the relationships between age, quiz accuracy, and ROI–RSN connectivity strength.
-
-However, the results revealed no significant correlations between these variables and connectivity measures. Based on this, no clear explanation could be found for the observed individual differences. It is possible that additional data—such as cognitive or neuropsychological assessments—may be required to better understand the sources of variability in ROI–RSN connectivity.
-
-
-## Conclusion and acknowledgement
-
-### Conclusion
-This project represents my first hands-on experience with resting-state fMRI analysis, from constructing task-defined ROIs to computing functional connectivity with large-scale brain networks. By combining task-based and resting-state data from the SMN4Lang dataset, I was able to identify language-related brain regions and examine how they interact with the DMN, FPN, and SN during rest.
-
-Although the connectivity patterns did not show significant associations with behavioral or demographic factors, the results provided a valuable starting point for understanding individual differences in language network integration. The entire workflow—from time series extraction to statistical testing—helped solidify my understanding of neuroimaging pipelines and taught me to approach brain data with both caution and curiosity.
-
-### Acknowledgement
-
-In the nights leading up to my final project presentation, I found myself stuck in endless cycles of trial and error, painfully wondering why I had chosen this course at the beginning of the semester. But looking back now, I realize how much I’ve truly gained from this journey.
-
-I am deeply grateful for the Brainhack School modules, which laid the foundation for everything that followed. As someone with no programming background, I found the step-by-step guidance—starting from bash and GitHub setup, through DataLad and OpenNeuro, and eventually into more advanced analysis—indispensable. The structured progression allowed me to gradually build a coherent understanding of brain data analysis.
-
-I also want to thank the BHS instructors and TAs for creating such an open and supportive learning environment. Their encouragement and flexibility made it possible for us to freely explore topics that genuinely interested us.
-
-Lastly, I want to express my heartfelt thanks to my amazing partner, Chi Wang. We went through countless hours of debugging together, learned to code from scratch side by side, and took our first steps into the world of brain data hand in hand. This journey would have been much more difficult—and far less meaningful—without you.
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-01" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Tulpas: invisible friends in the brain."
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Jonas Mago]
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-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/jonas_project.git
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://jonasmago.github.io/brainhack2023/intro.html
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fMRI, GLM, connectome, ML]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Tulpas are invisible friends that can be cultivated on will by so called Tulpamancers. This fMRI dataset comprises scans of Tulpamancers, comparing periods where there is an experiential presence of such Tulpa and where there is not. The aim of this proejct is to study the neurophysiological signature of Tulpas using GLMs, functional connectivity measrues, machine learning, and deep neural networks. [website](https://jonasmago.github.io/brainhack2023/intro.html)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "img/brain.png"
----
-
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Tulpas: invisible friends in the brain
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-> :warning: TLDR; check out my to-do list.
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-> :warning: Practical tipps and tricks I learned during this brainhack school.
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-> :information_source: I received great feedback during my final presentation of this project! I haven't been able to incorporate all of the feedback yet, however, check out a full list of feedback here.
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-> :information_source: In the spirit of open science, I have successfully contributed to the nilearn project by correcting a typo. See my [merged pull request](https://github.com/nilearn/nilearn/pull/3738).
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Background
-Tulpamancy is an intriguing psychological and sociocultural phenomenon that presents novel opportunities for neuroimaging research. Derived from Tibetan Buddhist practices, the term "Tulpa" refers to a self-induced, autonomous mental companion created through focused thought and mental conditioning. Individuals who engage in this practice, known as Tulpamancers, report a range of experiences that suggest Tulpas operate independently of the host's conscious control, often possessing their distinct personality, preferences, and perceptual experiences.
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-Tulpas are not viewed as hallucinations but rather as fully-fledged conscious entities sharing the same neurological space as the host. The creation and interaction with Tulpas require intense concentration and visualization, often leading to the creation of a mentally constructed realm, called a "wonderland," where Tulpamancers and their Tulpas can interact.
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-Tulpamancy provides a unique framework to explore the capacities of the human mind in creating and perceiving conscious entities. This has substantial implications for our understanding of consciousness, agency, and the malleability of perception. Moreover, it brings forward questions about the neurological underpinnings of these phenomena. How does the brain of a Tulpamancer accommodate multiple conscious entities? Can we observe a distinct neurophysiological pattern associated with the presence of a Tulpa
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-Through neuroimaging, we aim to investigate these questions, seeking to shed light on the intriguing practice of Tulpamancy and its implications for our understanding of the human mind and consciousness.
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-To get a better understanding of Tulpamancy you can check the following [video](https://www.youtube.com/watch?v=f3nf6NOPyr8). The video summarises the most important tipps for creating tulpas. These tipps are sourced from the reddit thread [`r/tulpas`](https://www.reddit.com/r/Tulpas/).
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Data
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-The data used for this project was collected by Dr. Michael Lifshitz (McGill) from 2020 to 2022 at Stanford University, California, USA. The dataset comprises fMRI scans of 22 expert Tulpamancers. The data was aquired during a sentence completion task originally designed by [Walsh et al. (2015)](https://www.sciencedirect.com/science/article/pii/S0010945214003037?casa_token=rzUA8Ep2LcEAAAAA:W-24nUu1MMLfuAMWAIhnP9p_pxmfo1Me0QBdhFrxqeEJ29sWT7pi-7CtrsWeVCBt47AvVjDxowVl). The tasks consists of 10 runs. During each run, participants are given a beginning of a sentnece such as "in summer...". Participants then have 9 seconds to complete this sentence in their mind (preparation) and another 12 seconds to write down the complete sentence on a sheet of paper in front of them (write). These 10 runs are repeated multiple times for different conditions. The conditions comprise self: completing or writing the sentence as yourself; Tulpa: letting the tulpa complete or write the sentence; and a control condition friend: imagine a friend completing or writing the sentence.
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-The image below depicts an overview of the analysis. The grey boxes have already been completed prior to the brainhack school (BIDS organisation, preprocessing with fmriprep, gPPI in CONN, and GLM in SPM and FSL). The green boxes are the main focus of the brainhack school coding sprint (GLM with Nilearn, Connecttome analysis, Classification).
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- protocol of the sentence completion task
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-The data was preprocessed with [fmriprep](https://fmriprep.org/en/stable/) before any of the analyses were carried out.
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Deliverables
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-In addition to the deliverables below, you can find a more extensive report of this project on this [website](https://jonasmago.github.io/brainhack2023/intro.html).
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-
-```{warning}
-The date of publishing the final results on this [website](https://jonasmago.github.io/brainhack2023/intro.html) is depending on final confirmatino of the principal investigator of this study.
-```
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-You can also find all the code used for the results below in this [github repo](https://github.com/brainhack-school2023/jonas_project.git).
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Progress overview
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-The [brainhack school](https://school-brainhack.github.io/) provided four weeks of space during which I could work full time on the analysis of this Tulpa dataset. At the end of these four weeks I was able to complete a first draft of all major analyses intended. These analyses include:
-* GLM (first and second level).
-* Task related connectivity.
-* ML classifier to differentiate conditions using the connectome;
-* A deep learning decoding appraoch using PyTorch to distinguish the task conditions.
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Tools I learned during this project
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-This project was intended to upskill in the use of the following
- * `Nilearn` to analyse fMRI data in python.
- * `Scikit-learn`to realise ML classification tasks on fMRI related measures.
- * `Py Torch` to implement a brain decoder.
- * `Jupyter {book}`and `Github pages`to present academic work online.
- * `Markdown`, `testing`, `continuous integration` and `Github` as good open science coding practices.
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Results
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-### Deliverable 1: project report
-
-You are currently reading the project report.
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-### Deliverable 2: project website
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-In addition to this report, I made a [github website](https://jonasmago.github.io/brainhack2023/). However, please note that the website does not currently show the full report of this project as the publication is waiting for final approval from the Principal Investigator of this project.
-This project website was created using `Jupyter {books}`. This format allows for a potential submission to [neurolibre](https://neurolibre.org/). Neurolibre is a preprint server for interactive data analyses.
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-
-### Deliverable 3: project github repository
-
-
-The repository of this project can be found [here](https://github.com/mtl-brainhack-school-2019/ecg_pupillometry_pipeline_kaufmann). The objective was to create a processing pipeline for ECG and pupillometry data. The motivation behind this task is that Marcel's lab (MIST Lab @ Polytechnique Montreal) was conducting a Human-Robot-Interaction user study. The repo features:
- * a [video introduction](http://www.youtube.com/watch/8ZVCNeX42_A) to the project.
- * a presentation [made in a jupyter notebook](https://github.com/mtl-brainhack-school-2019/ecg_pupillometry_pipeline_kaufmann/blob/master/BrainHackPresentation.ipynb) on the results of the project.
- * Notebooks for all analyses.
- * Detailed requirements files, making it easy for others to replicate the environment of the notebook.
- * An overview of the results in the markdown document.
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-### Deliverable 4: GLM results
-
-I have used `Nilearn` to compute a GLM that compares different conditions of the task. Specifically, I am interested in teh respective contrasts between **self**, **tulpa**, and **friend**. I am interested in these contrasts for the preparation and writing phase respectively. I have computed the GLM on fmriprep prepreocessed data. I have then created a design matrix that integrates the timing files (first two columes) as well as well as some movement regressors (see figure below). To incorporate the movement regressors I have used the `load_confounds_strategy` from nilearn with the following parameters: `denoise_strategy="scrubbing", motion="basic", wm_csf="basic"`.
-I have then computed a second level analysis that combines the individual beta maps into a group level contrast between the different conditions (see design matrix below). As a result I receive z-scores of each voxel, indicating how much that voxel differs across the two compared condition. The plot below displays these z-scores for comparing the self-write with the tulpa-write condition. A z-score of 3.0 is equivalent to a 99% confidence interval, a z-score of 2.3 is equivalent to a 95% confidence interval.
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-I then used [Neuro Maps](https://netneurolab.github.io/neuromaps/user_guide/nulls.html) to compute the pearson-r correlation between the first level outputs computed with Nilearn to those I previously computed with SPM. To test for the significance of these comparisons, I re-computed the pearson-r for random pairs. The r values of the true (matched) pairs have a mean of 61.32 while the mean of the random (unmatched) pairs is 17.09. A two-sample t-test revleaed that this difference is significant: **T-stat: -26.24** and a **p-value=0.000**.
-
-> :information_source: matched pairs are between software, within subject, within contrast, within run.
-
-> :information_source: unmatched pairs are between software, between subject, between contrast, between run
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-
-### Deliverable 5: Connectome results
-
-I have parcellated the brain into 39 regions based on the [probabilistic msdl atlas](https://nilearn.github.io/dev/modules/generated/nilearn.datasets.fetch_atlas_msdl.html). A probabilistic atlas assigns a probability to each voxel, indicating how likely it is that that voxel belongs to a specific region. I have then computed the correlation between all of these 39 regions, resulting into a connectome of shape 39*39. Given that that the correlation measure is none directive, the upper and lower triangle of the connectome mirror each other. Below is an example of a connectome for one subject and one condition.
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-
-### Deliverable 6: Seed-to-Voxel connectivity results
-I have previously computed a gPPI for this dataset using CONN. I wanted to replicate this task dependent seed to voxel correlation using Nilearn. I have specified the seed based on the group peak activation in the SMA, using the results from the SPM and Randomise analysis. Seed coordination in MNI space are `(-4, 12, 55)`. I have then constructed a sphere around this voxel with a radius of 5 mm. Using this ROI, correlations to all voxels based by task condition were computed. Below is an illustrative example of the connectivity values for one subject and one condition.
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-
-### Deliverable 7: ML-classifier results
-
-The aim was to train a classifier that can accurately distinguish between different task conditions. I used a majority vote ensemble classifier that combines `LogisticRegression`, `RandomForestClassifier`, and a `SVC`. When classifying the conditions `prep` vs. `write`, the classifier achievs an acciracy of ~80%, so well above chance. However, this is not surprising as the writing conditions will have muhc stronger motor cortex activation and the two conditions are quite different. I then classified the `self`, `tulpa`, and `friend` conditions for preparation and writing respectively. In both conditions, we have an accuracy of ~51% for this 3-group classification problem. Given the three groups, chance levels are at 33.3%, thus an accuracy of 51% is well above chance, YAY!
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-> :information_source: all classifiers were trained and tested on the **connectome**. Next, the plan is to train classifiers on the beta maps of the GLM. See my [to-do list](#My-to-do-list) for further details.
-
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-**Prep-write condition**
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-> Average accuracy = 0.76
-> P-value (on 100 permutations): p=0.00
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-**Write condition**
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-> Average accuracy = 0.52
-> P-value (on 100 permutations): p=0.02
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-**Prep condition**
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-> Average accuracy = 0.37
-> P-value (on 100 permutations): p=0.32
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-
-### Deliverable 8: Deep neural network encoding results
-
-Building on what I have done with the ML classifiers, I wanted to explore if I could achieve the same resutls with a Neural Netowrk. I used `PyTorch` to build a `Multilayer Perceptron` with 4 linear layers, 3 rectified linear unit, and one dropout layer with a threshold of 0.3. I then trained this model with a `learning rate of 0.01` and a `weight decay of 0.01`. The results show an `accuracy of 0.73` which is simialr, though slightly lower, than the accuracy of the [scikit learn ensemble classifier](#Deliverable-7-ML-classifier-results) of 0.76.
-Below you can see the definitino of the model, the confusion matrix, the learning rate of the model, as well as the weights of the trained model.
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- results of the neural network
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My to-do list
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-* [X] preprocess the fMRI data with fmriprep
-* [X] run GLMs using SPM for the first level and FSL Randomise for the second level
-* [X] replicate GLM in Nilearn
- * [X] replicate a GLM in Nilearn
- * [X] compute the correlation between first-level beta maps in the SPM and Nilearn analysis
- * [ ] replicate the Nilearn GLM with the exact same parameters as in SPM
- * fd_threshold = 1 (default is 0.5mm)
- * change from `load_confound_strategy` to `load_confound`as it's more custamizable and allows to match parameters of SPM.
- * check if I used a high pass filter in SPM, if not deactivate it in Nilearn.
- * check if I standardized in SPM, if not deactivte it in Nilearn.
- * [ ] Cluster correct the Nilearn second-level contrasts using [`cluster_level-inference`](https://nilearn.github.io/dev/modules/generated/nilearn.glm.cluster_level_inference.html#nilearn.glm.cluster_level_inference).
- * [ ] re-run the GLMs including a brain mask that excludes the cerebellum and brain stem. As we are not interested in the analysis of these regions their exclusion is legit. I can use a mask from brainvolt that I can [download here](https://neurovault.org/collections/6196/).
-* [X] connectivity analysis
- * [X] compute connectome for all conditions and save as `.npy`
- * [ ] compute `graph theory` metrics to characterise and compare the connectomes. François Paugam recommended to look into the [Networks of the Brain](http://www.cs.cmu.edu/~saketn/files/Sporns_Book.pdf) as an introduction.
-* [X] compute seed-to-voxel correlations
-* [X] ML classifier
- * [X] ML classifier on connectomes
- * [ ] ML classifier on seed-to-voxel maps
- * [ ] ML classifier on the average activity for each of the 10 trials (not entire runs) cropped to an ROI in the SMA
-* [X] Deep Neural Network Decoder using PyTorch
-
-
Feedback from the final presentation
-- visualise things in MRIcroGL, soon implemented in nilearn as `niview`
-- change my pearson-r values to [-1, 1] (currently [-100,100])
-- for comparing nilearn and spm beta maps: specify not-matched. E.g.:
- - between software, within subject, within contrast, within run
- - between software, between subject, between contrast, between run
-- feature selection must be either on (A) train set or (B) integrated in cross-validation. Make sure it's not selecting featuers on the entier dataset --> danger of overfitting!
-- r2 is a measure for the percentage of variance explained. Not relevant here, thus, remove it.
-- explain permutation test for significance testing.
-- apply the classifer on beta maps per trials (there are 10 trials per run). I can use ROIs or entire beta masks. The model will take care of the many variables.
-- try a searchlight approach: train a classifer on different ROIs or voxels and then compare the accuracy. This could lead to an accuracy map with voxel resolution, however, this is computationally expensive.
-- it's important to **de-mean** my data before doing this (e.g. use z-score).
-- look into **stratified cross validation** to deal with multiple runs per subject in the classifier. If I use stratification this shouldn't be a problem.
-
-
Tipps and Tricks
-
-**useful hacks, and notes for next iterations of this project**
-- Plotting results
- - [plotly](https://plotly.com/)
- - histograms are powerful because they don't collapse either time or space
- - raindrop plots are powerful as they show all individual points
- - examples in the [python graph gallery](https://www.python-graph-gallery.com/)
-- Python virtual environment
- - To make and actiavte a python virtual environment, use the following two lines:
- - `python3 -m venv venv`
- - `source venv/bin/activate`
- - To generate a `requirements.txt` file
- - Option 1: `pip freeze`.
- - Option 2: [Pipreqs](https://pypi.org/project/pipreqs/) automatically detects what libraries are actually used in a codebase.
-- Python local pip install
- - make sure to have an __init__.py file in the src directory
- - also make sure to have a setup.py file
- - Navigate to root directory and run: `pip install -e .`
-- Python ipykernal allows to install kernals from python and conda environments
- - `python -m ipykernel install --user --name=`
-- VIM: create and edit python files in the Terminal
- - `vim .py`
- - `%s` search and replace
- - `i` insertion mode
- - doesn't leave a mess in the terminal after closing
- - alternative to `vim` is `nano`
-- [Cookiecutter](https://www.cookiecutter.io/templates) as template for repos.
- - [Teamplate for neuroimaging](https://github.com/patrickmineault/true-neutral-cookiecutter).
-- Debugging scripst
- - `breakpoint()`can replace `import pdb; pdb.set_trace()``
- - `python -m pdb
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----
-
-# Brainhack School Code of Conduct
-
-Hello :wave:, and
-:computer::brain: Welcome to Brainhack School! :brain::computer:
-
-Brainhack is dedicated to providing an environment where people are kind and respectful to each other, a harassment-free Brainhack experience for everyone, regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age or religion. We do not tolerate harassment of event participants in any form. Sexual language and imagery is not appropriate for any event venue, including talks. Brainhack participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the event organizers.
-
-This could really be the end of that code of conduct, but some forms of harassment and negative behavior are fairly hard to identify at first. Please read carefully through the rest of the document to make sure you avoid them. There is also a section to know what to do and expect if you experience behavior that deviates from this code of conduct.
-
-Harassment includes, but _is not limited to_:
-:no_entry: Verbal comments that reinforce social structures of domination related to gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age or religion.
-:no_entry: Sexual images in public spaces
-:no_entry: Deliberate intimidation, stalking, or following
-:no_entry: Harassing photography or recording
-:no_entry: Sustained disruption of talks or other events
-:no_entry: Inappropriate physical contact
-:no_entry: Advocating for, or encouraging, any of the above behaviour
-:no_entry: Unwelcome sexual attention
-
-### *Microaggressions*
-Incidents can take the form of “microaggressions,” which is a damaging form of harassment. Microaggressions are the everyday slights or insults which communicate negative messages to target individuals, often based upon their marginalized group membership. Over time, microaggressions can take a great toll on mental and emotional health, and the target’s feeling of belonging in science and academia. The following examples can all be labeled micro-aggressions:
-:no_entry: Commenting on a woman’s appearance rather than her work;
-:no_entry: Only directing questions at male colleagues when there are female experts in the room;
-:no_entry: Telling someone of colour that they “speak such good English”;
-:no_entry: Forcefully praising meat to an individual with a vegetarian diet;
-:no_entry: Praising alcoholic drinks to an individual who do not consume them.
-:no_entry: Exclusion from a group can be a common nonverbal form of microaggression.
-:no_entry: Microaggressions can be couched in the form of a “compliment,” (e.g. “you’re too attractive to be a scientist”).
-
-### *Respecting differences*
-Brainhack participants come from many cultures and backgrounds. We therefore expect everyone to be very respectful of different cultural practices, attitudes, and beliefs. This includes being aware of preferred titles and pronouns, as well as using a respectful tone of voice.
-While we do not assume Brainhack participants know the cultural practices of every ethnic and cultural group, we expect members to recognize and respect differences within our community. This means being open to learning from and educating others, as well as educating yourself.
-
-### *How to treat each other*
-:+1: Be respectful and value each other’s ideas, styles and viewpoints.
-:+1: Be direct but professional; we cannot withhold hard truths.
-:+1: Be inclusive and help new perspectives be heard.
-:+1: Appreciate and accommodate our many cultural practices, attitudes and beliefs.
-:+1: Be open to learning from others.
-:+1: Lead by example and match your actions with your words.
-
-
-## **Enforcement**
-Participants asked to stop any harassing behavior are expected to comply immediately. Organizers and presenters are also subject to the anti-harassment policy. In particular, they should not use sexualized images, activities, or other material. Event organisers may take action to redress anything designed to, or with the clear impact of, disrupting the event or making the environment hostile for any participants.
-
-## **Reporting**
-If someone makes you or anyone else feel unsafe or unwelcome, please report it as soon as possible to your local hub organizer. You can also contact one of the main school organizers on Discord or by email at school [dot] brainhack [at] gmail [dot] com. Depending on the issue, you may need to follow your local hub policies and guidelines for reporting.
-
-### *Reporting Issues at the UdeM hub*
-For students attending the Universite de Montreal hub, Brainhack school is supported by their disciplinary board in enforcing appropriate behavior throughout the course. Any incident of unacceptable behavior hurts the learning environment for everyone. We are therefore committed to supporting students to access these resources and ensure a supportive environment.
-
-The [University of Montreal Bureau d’intervention en matière de harcèlement (BIMH)](https://neurosciences.umontreal.ca/bureau-dintervention-en-matiere-de-harcelement-ou-de-lintimidation-bimh-514-343-7020/) exists to promote a healthy student environment across the university, and we are ready to assist students in submitting a report to the disciplinary board.
-
-When necessary, a support team member will assist students to file a report. Complaints can be filed, without limitation periods, for any incident of harassment that has occurred in the course of university activities, whether on or off-campus. If one feels more comfortable to report an issue directly to the BIMH, they can visit their website (https://harcelement.umontreal.ca) or contact them directly (phone 514 343-7020; e-mail harcelement@umontreal.ca).
-
-Also, if you need moral support, feel welcome to ping the school organizers on Discord or by email at school [dot] brainhack [at] gmail [dot] com.
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-# Quick Start Guide
-
-During BrainHack School you will be exposed to a wide variety of new tools, from collaboration tools to analysis tools. At times this might make you feel a little anxious, even a little bit imposter that you didn't know them already. Don't worry if you feel overwhelmed, we are all here to help.
-
-If you complete the school more equipped than when you started, we will have achieved our main goal. Indeed, one of the difficulties in neuro-data science is not necessarily learning _how_ to use a tool, but rather learning that the tool even exists, and who to get in touch with that might know how to use it. (Keep in mind that everyone knows some tools, but not all of them!)
-
-That said, here's a little *Getting Started Guide* on the common tools we will use throughout the BrainHack School:
-
-***
-We made a bunch of GIFs because GIFs are worth 5000 words. You can see them when you click on
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-#
-# ## Neuroimaging data and file structures in Python
-#
-# ###### Christopher J Markiewicz
-#
-# ###### NeuroHackademy 2020
-
-# %% [markdown] slideshow={"slide_type": "notes"}
-# The goal of this presentation is to familiarize you with some broad classes of neuroimaging data that can be interacted with in Python, using the NiBabel library.
-#
-# This document is intended to be viewed as a [RISE](https://rise.readthedocs.io/) presentation. It works fine as a notebook, but blocks with images may look strange because they are formatted to work as slides.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# # NiBabel
-#
-# NiBabel is a low-level Python library that gives access to a variety of imaging formats, with a particular focus on providing a common interface to the various volumetric formats produced by scanners and used in common neuroimaging toolkits:
-#
-# | | | |
-# |:---: |:---: |:---:|
-# | NIfTI-1 | NIfTI-2 | MGH |
-# | MINC 1.0 | MINC 2.0 | AFNI BRIK/HEAD |
-# | ANALYZE | SPM99 ANALYZE | SPM2 ANALYZE |
-# | DICOM | PAR/REC | ECAT |
-#
-# It also supports surface file formats:
-#
-# | | |
-# |:--:|:--:|
-# | GIFTI | FreeSurfer (FS) geometry |
-# |FS labels | FS annotations |
-#
-# Tractography files:
-#
-# | | |
-# |:--:|:--:|
-# | TrackVis (TRK) | MRtrix (TCK) |
-#
-# As well as the CIFTI-2 format for composite volume/surface data.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Installation
-#
-# NiBabel is available on [PyPI](https://pypi.org/project/nibabel/):
-#
-# ```Shell
-# pip install nibabel
-# ```
-#
-# And [conda-forge](https://anaconda.org/conda-forge/nibabel):
-#
-# ```Shell
-# conda install -c conda-forge nibabel
-# ```
-#
-# *Note*: This notebook assumes NiBabel 3+, which requires a minimum Python version of 3.5.
-
-# %% slideshow={"slide_type": "subslide"}
-import nibabel as nb
-print(nb.__version__)
-
-# %% slideshow={"slide_type": "fragment"}
-# Some additional, useful imports
-from pathlib import Path # Combine path elements with /
-from pprint import pprint # Pretty-printing
-
-import numpy as np # Numeric Python
-from matplotlib import pyplot as plt # Matlab-ish plotting commands
-from nilearn import plotting as nlp # Nice neuroimage plotting
-import transforms3d # Work with affine algebra
-from scipy import ndimage as ndi # Operate on N-dimensional images
-import nibabel.testing # For fetching test data
-
-# %pylab inline
-
-# %%
-# Assume you have download the data to your home directory in ~/data
-data_dir = Path.home() / 'brainhackschool_nibabel' / 'data'
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Learning objectives
-#
-# 1. Be able to load and save different types of files in NiBabel
-# 1. Become familiar with the `SpatialImage` API and identify its components
-# 1. Understand the differences between array and proxy images
-# 1. Acquire a passing familiarity with the structures of surface images, CIFTI-2 files, and tractograms
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Basic I/O
-#
-# ### Loading
-
-# %%
-t1w = nb.load(data_dir / 'openneuro/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz')
-bold = nb.load(data_dir / 'openneuro/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz')
-
-# %% slideshow={"slide_type": "fragment"}
-print(t1w)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Saving
-#
-# All NiBabel images have a `.to_filename()` method:
-
-# %% slideshow={"slide_type": "-"}
-t1w.to_filename(data_dir / 'tmp/img.nii.gz')
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# `nibabel.save` will attempt to convert to a reasonable image type, if the extension doesn't match:
-
-# %%
-nb.save(t1w, data_dir / 'tmp/img.mgz')
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Some image types separate header and data into two separate images. Saving to either filename will generate both.
-
-# %%
-nb.save(t1w, data_dir / 'tmp/img.img')
-print(nb.load(data_dir / 'tmp/img.hdr'))
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# ### Serialization
-#
-# Some NiBabel images can be serialized to and deserialized from byte strings, for performing stream-based I/O.
-
-# %%
-bstr = t1w.to_bytes()
-print(bstr[:100])
-
-# %%
-new_t1w = nb.Nifti1Image.from_bytes(bstr)
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Images that save to single files can generally be serialized. NIfTI-1/2, GIFTI and MGH are currently supported.
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ## Spatial Images
-#
-# For MRI studies, neuroimaging data is typically acquired as one or more *volumes*, a regular grid of values. NiBabel represents these data as *spatial images*, a structure containing three things:
-#
-# 1. The image *data* array: a 3D or 4D array of image data
-# 1. An *affine* matrix: 4x4 array relating voxel coordinates and "world" coordinates
-# 1. Image *metadata*: usually a format-specific header
-#
-# Many file types encode this basic structure. NiBabel will read any of ANALYZE (plain, SPM99, SPM2 and later), NIfTI-1, NIfTI-2, MINC 1.0, MINC 2.0, AFNI BRIK/HEAD, MGH, ECAT, DICOM and Philips PAR/REC, and expose a simple interface:
-
-# %%
-data = t1w.get_fdata()
-affine = t1w.affine
-header = t1w.header
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# Spatial images have some properties that should be familiar from NumPy arrays:
-
-# %%
-print(t1w.ndim)
-print(t1w.shape)
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ### The data array
-#
-# The data is a simple NumPy array. It can be accessed, sliced and generally manipulated as you would any array:
-
-# %%
-print(data.shape)
-i, j, k = np.array(data.shape) // 2
-fig, axes = plt.subplots(1, 3)
-axes[0].imshow(data[i,:,:].T, cmap='Greys_r', origin='lower')
-axes[1].imshow(data[:,j,:].T, cmap='Greys_r', origin='lower')
-_ = axes[2].imshow(data[:,:,k].T, cmap='Greys_r', origin='lower')
-
-# %% [markdown]
-# Each location in the image data array is a *voxel* (pixel with a volume), and can be referred to with *voxel coordinates* (array indices).
-#
-# This is a natural way to describe a block of data, but is practically meaningless with regard to anatomy.
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# NiBabel has a basic viewer that scales voxels to reflect their size and labels orientations.
-
-# %% slideshow={"slide_type": "-"}
-_ = t1w.orthoview() # Requires matplotlib, occasionally glitchy in OSX setups
-
-# %% [markdown]
-# The crosshair is focused at the origin $(0, 0, 0)$.
-#
-# All of this information is encoded in the affine:
-
-# %%
-print(affine)
-
-# %% [markdown] slideshow={"slide_type": "slide"}
-# ### Affine transforms
-#
-# The affine is a 4 x 4 numpy array. This describes the transformation from the voxel space (indices $(i, j, k)$) to a *reference* space (coordinates $(x, y, z)$). These coordinates are, by convention, distance in mm *right*, *anterior* and *superior* of an origin\*.
-#
-# $$
-# \begin{bmatrix}
-# x\\
-# y\\
-# z\\
-# 1\\
-# \end{bmatrix} =
-# \mathbf A
-# \begin{bmatrix}
-# i\\
-# j\\
-# k\\
-# 1\\
-# \end{bmatrix} =\begin{bmatrix}
-# m_{1,1} & m_{1,2} & m_{1,3} & a \\
-# m_{2,1} & m_{2,2} & m_{2,3} & b \\
-# m_{3,1} & m_{3,2} & m_{3,3} & c \\
-# 0 & 0 & 0 & 1 \\
-# \end{bmatrix}
-# \begin{bmatrix}
-# i\\
-# j\\
-# k\\
-# 1\\
-# \end{bmatrix}
-# $$
-#
-# For an unmodified image, this reference space typically refers to an origin in the isocenter of the imaging magnet, and the directions right, anterior and superior are defined assuming a subject is lying in the scanner, face up.
-#
-# 
-#
-# \* *If a file format uses an alternative convention, NiBabel converts on read/write, so affines are always RAS+.*
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### The affine as a series of transformations
-#
-#
-#
-# 
-#
-# An affine transformation can be decomposed into translation, rotation, scaling (zooms) and shear transformations, applied right-to-left.
-
-# %%
-T, R, Z, S = transforms3d.affines.decompose44(affine) # External library
-print(f"Translation: {T}\nRotation:\n{R}\nZooms: {Z}\nMax shear: {np.abs(S).max()}")
-
-# %%
-Tm, Rm, Zm, Sm = [np.eye(4) for _ in range(4)]
-Tm[:3, 3] = T
-Rm[:3, :3] = R
-Zm[:3, :3] = np.diag(Z)
-Sm[[0, 0, 1], [1, 2, 2]] = S
-np.allclose(Tm @ Rm @ Zm @ Sm, affine)
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# NiBabel provides functions for extracting information from affines:
-#
-# * Orientation (or axis codes) indicates the direction an axis encodes. If increasing index along an axis moves to the right or left, the axis is coded "R" or "L", respectively.
-# * Voxel sizes (or zooms)
-# * Obliquity measures the amount of rotation from "canonical" axes.
-
-# %%
-print("Orientation:", nb.aff2axcodes(affine))
-print("Zooms:", nb.affines.voxel_sizes(affine))
-print("Obliquity:", nb.affines.obliquity(affine))
-
-# %% [markdown] slideshow={"slide_type": "fragment"}
-# You can also use it to answer specific questions. For instance, the inverse affine allows you to calculate indices from RAS coordinates and look up the image intensity value at that location.
-
-# %%
-i, j, k, _ = np.linalg.pinv(affine) @ [0, 0, 0, 1]
-print(f"Center: ({int(i)}, {int(j)}, {int(k)})")
-print(f"Value: ", data[int(i), int(j), int(k)])
-
-# %% [markdown] slideshow={"slide_type": "subslide"}
-# #### Don't Panic
-#
-# 
-# 
- 
- 2. Git and GitHub 
- 3. Preprocessing and feature extraction with Scipy and Neurokit 
- 4. Data visualization with Plotly 
- 5. Machine learning with Scikit-learn 
-
-### Data
-
-At the start of the project, we considered using the following databases:
-
- - [DREAMER: A Database for Emotion Recognition through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices](https://ieeexplore.ieee.org/document/7887697)
- - Biosignal data: EEG and ECG
- - Emotion data: Rating on valence-arousal-dominance scale provided by participants and tags for the "target emotion" of the stimuli
- - [The MAHNOB-HCI-Tagging database](https://mahnob-db.eu/hci-tagging/)
- - Biosignal data: EEG (in the form of BDF files), ECG, respiration amplitude, and skin temperature
- - Emotion data: Rating on valence-arousal scale provided by participants, and for some of the data, emotion tags (e.g. amused) selected by participants
- - [An EEG dataset recorded during affective music listening](https://openneuro.org/datasets/ds002721/versions/1.0.1)
- - Biosignal data: EEG
- - Emotion data: Rating on valence-arousal scale provided by participants (though it's not immediately clear where this can be downloaded)
- - [AffectiveROAD system and database to assess driver's attention.](https://affect.media.mit.edu/share-data.php)
- - Biosignal data: BVP, EDA, ECG, respiration rate, skin temperature
- - Emotion data: "Stress metric" provided by observing experimenter
-
-Ultimately, we decided to use the [DREAMER](https://ieeexplore.ieee.org/document/7887697) dataset which must be requested from the authors [here](https://zenodo.org/record/546113).
-
-The dataset contains EEG and ECG data from 23 participants were shown 18 videos intended to elicit 9 different emotions - as well as "neutral videos" thought to have no valence for "baseline" data. Biosignal data was collected using the Emotiv EPOC wireless EEG headset and the Shimmer2 ECG sensor. We were especially interested in how accurately we could classify emotions using biosignal data collected by portable, inexpensive devices due to the potential of automatic emotion recognition incorporated into wearables. An image of the equipment is shown below [(from Katsigiannis & Ramzan, 2018)](https://ieeexplore.ieee.org/document/7887697).
-
-
-
-
-
-**We also gain knowledge in the following areas:**
-
-* **Machine learning for neuroimaging**
-* **Introduction to deep learning**
-* **Functional connectivity in fMRI**
-* **Functional parcellations in fMRI**
-* **The Brain Imaging Data Structure (BIDS) ecosystem**
-
-### Data
-
-**ADHD-200 Preprocessed Sample:**
-
-*Pierre Bellec, Carlton Chu, François Chouinard-Decorte, Yassine Benhajali, Daniel S. Margulies, R. Cameron Craddock (2017). The Neuro Bureau ADHD-200 Preprocessed repository. NeuroImage, 144, Part B, pp. 275 - 286. doi:10.1016/j.neuroimage.2016.06.034*
-
-Prepared by the collaboration of 8 international neuroimaging institutes, who held a competition in 2011, to identify ADHD biomarkers.
-
-Composition:
-* 947 children and adolescents
-* 362 diagnosed with ADHD
-* 585 typically developing controls
-
-In order to make the competition accessible to a broader range of researchers, the data was preprocessed and shared by the Preprocessed Connectomes Project (PCP), what resulted in several publications, MS theses, PhD dissertations and even patents.
-
-### Project steps
-
-1. We downloaded the preprocessed ADHD-200 Sample dataset using a script that allowed us to obtain time series parcelled according to the AAL atlas, which has 116 ROIs.
-2. We started by creating our Jupyter Notebook, loading the BOLD signal data for a single subject and visualizing the time signals in the 116 ROIs using Matplotlib.
-3. With this subject's data, we constructed a Pearson correlation matrix between ROIs using the "corrcoef()" function from NumPy.
-4. To visualize the correlation matrix, which in this case would be a connectivity matrix, we used Matplotlib and the "heatmap" function from Seaborn.
-5. Then we used the "tril()" function from NumPy to retain only the lower triangle of the matrix, avoiding repeated data and the diagonal that do not provide information for our case study.
-6. Using the "tril_indices()" method, we created a list or vector that contains the indices of our matrix.
- This method orders rows-wise from the lower triangular matrix. Specifically, the order follows:
- All elements from the first row below the diagonal.
- All elements from the second row below the diagonal.
- And so on, up to the last row.
-7. Once we understood the process for a single subject, we were ready to replicate it for each of the subjects included in our dataset. For this, we used for loops, if-else conditionals, and try-except exception handling.
- For example, to iterate through the downloaded files following the phenotype subject list, avoiding errors in case the file did not exist or had any error that prevented the correct creation of our matrix.
-8. The result obtained from the previous iteration was a matrix with 6670 features (PC of ROIn x ROIm) and 820 rows, the considered subjects. We converted this vector matrix into a DataFrame using Pandas.
-9. As we had the subjects' IDs and their diagnosis in the phenotype, we could add them to our DataFrame.
-10. From this point, we could separate our data into Train and Test sets using train_test_split from the sklearn library:
- a. test_size=0.2
- b. random_state=42
- c. stratify=target
-11. Our problem: Supervised Classification Model with a Binary Target Variable.
- First approach: initially, we tried several models, including:
- a. PCA (Principal Components Analysis)
- b. LDA (Linear Discriminant Analysis)
- c. Logistic Regression
- d. XGBoost
-12. As our classification was binary, we used the following metrics to evaluate our models obtained from sklearn.metrics:
- a. Train and Test Accuracy
- b. Confusion Matrix
- c. ROC AUC
- d. Additionally, we used the Classification Report, which includes several metrics.
-13. Since we got the best results with XGBoost, we focused on optimizing the hyperparameters using RandomizedSearchCV in order to reduce the overfitting.
-14. We combined PCA and XGBoost but did not get better results.
-15. Finally we used Feature Importance from XGBoost to compare the most important features for the model with the theory about ADHD diagnosis.
-16. With all the knowledge gained during this workshop, and building this project, we prepare a conclusion, a presentation and this markdowm.
-
-
-### Deliverables
-
-At the end of this project, the following files will be made available:
-* The current markdown document.
-* A GitHub repository to share our work and pipeline, and to encourage other students to build from it: https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1
-* "ADHD_fRMI_ROI_Classification_Project.ipynb" notebook.
-* Figures of our results: Connectivity matrix, graphs for statistical analysis, visualizations, feature importance evaluation.
-* Our presentation shared at the end of the BrainHack meeting at Humai Institute.
-
-
-### Results
-
-#### Deliverable 1: Markdown document:
-
-This markdown file in order to share the project in the BrainHack School web repository.
-
-#### Deliverable 2: Github Repository:
-
-A GitHub repository to share our work and pipeline, we would like to encourage other students to build from it:
-https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1
-
-#### Deliverable 3: "ADHD_fRMI_ROI_Classification_Project.ipynb" notebook:
-
-You can access our public notebook with the step-by-step process in the Github repository:
-
-https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1/blob/main/notebooks/ADHD_fRMI_ROI_Classification_Project.ipynb
-
-#### Deliverable 4: Figures of our results:
-
-**Time Series:**
-
-
-
-Each time series represents the intensity of the BOLD signal of every ROI (Region of Interest)
-along the rs-fRMI.
-
-**Connectivity Matrix:**
-
-In order to create a connectivity matrix, we evaluated the Pearson Correlation between each of these signals.
-
-
-
-**Feature Importance Interpretation**
-
-
-
-* Temporal Pole and Frontal regions appear to be recurrently significant in feature importance, suggesting a key role in ADHD.
-* The Cingulum also appears repeatedly, indicating its relevance in connectivity differences for ADHD.
-* The Amygdala connectivity to the Cingulum (both Left and Right) highlights potential emotional regulation and cognitive control variations in ADHD.
-
-The importance values are relatively close, ranging approximately from 0.004 to 0.006. This indicates that while some connections are slightly more influential, many features are comparably important in distinguishing ADHD from controls in resting-state fMRI. These identified ROIs and their connectivity patterns could be further studied to develop diagnostic methods.
-
-#### Deliverable 5: Our presentation shared at the end of the BrainHack meeting at Humai Institute
-
-You can find the slides of our presentation in the Github repository:
-
-https://github.com/AgustinNR/BrainHack-BsAs-Humai-Team1/blob/main/ADHD%20Classification%20Project%20-%20Team%201%20Humai%20-%20Brainhack_2024.pdf
-
-
-## Conclusion and acknowledgement
-
-We can conclude that with an accuracy of 0.658, there is significant room for improvement in ADHD diagnosis based on classification models.
-
-Our best metrics are achieved using XGBoost with Hyperparameters Optimization and Cross-Validation using RandomizedSearchCV:
- ACC = 0.658
-
-Despite PCA helped us to reduce the dimensionality of our features, when combined with XGBoost the perfomance was lower:
- ACC = 0.628
-
-The identified ROIs and their connectivity patterns could be further studied to develop diagnostic methods.There are many ways to expand and improve this project, and we are excited to explore them.
-
-Finally, we would like to express our gratitude to our teaching assistants from Humai, Buenos Aires, who have supported us from day one and generously shared their diverse expertise in neuroscience and computer science.
-
-
-
-## Feel free to contact us :D
-
-If you need assistance with your artificial intelligence project, we would be happy to help!
-
- **Francisco Covelli:** fcovelli@med.unlp.edu.ar
-
- **Agustin Rodriguez:** agurodri96@hotmail.com
-
- **Ezequiel Sirne:** ezequielsirne@gmail.com
-
-
-
-## References
-Zhu CZ, Zang YF, Cao QJ, Yan CG, He Y, Jiang TZ, Sui MQ, Wang YF. Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage. 2008 Mar 1;40(1):110-20. doi: 10.1016/j.neuroimage.2007.11.029. Epub 2007 Dec 3. PMID: 18191584.
-
-Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. doi: 10.1196/annals.1440.011. PMID: 18400922.
-
-Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JR, Fischl B, Liu H, Buckner RL. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011 Sep;106(3):1125-65. doi: 10.1152/jn.00338.2011. Epub 2011 Jun 8. PMID: 21653723; PMCID: PMC3174820.
-
-Cortese S, Castellanos FX. Neuroimaging of attention-deficit/hyperactivity disorder: current neuroscience-informed perspectives for clinicians. Curr Psychiatry Rep. 2012 Oct;14(5):568-78. doi: 10.1007/s11920-012-0310-y. PMID: 22851201; PMCID: PMC3876939.
-
-Sripada C, Kessler D, Fang Y, Welsh RC, Prem Kumar K, Angstadt M. Disrupted network architecture of the resting brain in attention-deficit/hyperactivity disorder. Hum Brain Mapp. 2014 Sep;35(9):4693-705. doi: 10.1002/hbm.22504. Epub 2014 Mar 25. PMID: 24668728; PMCID: PMC6869736.
-
-Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC. The Neuro Bureau ADHD-200 Preprocessed repository. Neuroimage. 2017 Jan;144(Pt B):275-286. doi: 10.1016/j.neuroimage.2016.06.034. Epub 2016 Jul 15. PMID: 27423255.
-
-Damiani S, Tarchi L, Scalabrini A, Marini S, Provenzani U, Rocchetti M, Oliva F, Politi P. Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest. Eur Child Adolesc Psychiatry. 2021 Apr;30(4):619-631. doi: 10.1007/s00787-020-01545-0. Epub 2020 May 8. PMID: 32385695.
-
-Hroncich, Camila. “¿Qué sabemos de TDAH?” CONICET, 12 January 2023, https://www.conicet.gov.ar/que-sabemos-de-tdah/.
-
-Wang, Z., Zhou, X., Gui, Y. et al. Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study. Transl Psychiatry 13, 45 (2023). https://doi.org/10.1038/s41398-023-02309-5
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "EEG-FFR Classification in MATLAB: A Tutorial"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Isabelle Arseneau-Bruneau]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [beginner-friendly, ffr, electrophysiology, eeg-classification]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project is a tutorial. It aims for you to learn how to use the scripts of a machine-learning classifier (the Hidden Markov Model). The codes were written in MATLAB. They classify an auditory neural signal called the Frequency Following Responses (FFR), which represents how well the brain represents and process complexe sounds, such as speech or music."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Llanos_IABProjectPilot_FFR.png"
----
-
-
-## Project Definition
-
-This tutorial first aim is to provide a reproducible workflow, that is beginner-friendly, with machine-learning (ML) procedures written in MATLAB. As I am novice with coding, I aim to implement a classifier that I use repeatively in my doctoral research (an Hidden Markov Model [HMM]) into a structure that would provide some flexibility for future usages (with other variables or datasets), and would allow to build extensions (e.g. for other classifiers such as a Support Vector Machine (SVM), Cross-correlation (XCorr), LSTM). I aim to put together a set of resources that would be accessible to my research assistants, collaborators (who also have students to train on these ML procedures), other FFR researchers, and my futur self! Thus, the data of this tutorial will be in format that align with components of standards in project management (EEG-BIDS), use jupyter notebooks implemented in a virtual environment (allowing the use MATLAB scripts), and use basic matlab visualization tools to generate figures of the results.
-
-## Notes on Open Science Practices and Matlab
-
-_Is it mandatory to abandon MATLAB to have open science practices? Matlab may be a commercial software, but it would be sad to limit the open science movement to open source software users. Specialized fields, such as FFR research, have the wide majority of their resources, expertise, and tools in matlab. Thus, at the current moment, it would be extremely difficult to study FFR without using MATLAB. Nevertheless, there are many open science tools that are compatible with matlab scripts and files. Hence, I tried to implement an open approach in this ML tutorial. Feel free to contribute to it or indicate issues with the link below. We aim to build from this structure and repository!_ https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project
-
-### Hidden Markov Model - Classification Goal:
-
-The machine-learning classifier targeted in this tutorial, the Hidden Markov Model, aims to classify __if the EEG-FFRs__ of each participants __were generated by__ a stimulus that was either a __speech sound__ or a __piano tone__ of the same fundamental frequency (98 Hz) and duration (100ms). These procedures were done on 26 participants (13 musicians vs. 13 non-musicians).
-
-As the equipment used to collect FFRs and population targeted can influence the quality of the signal, this script also __tracks the number of FFR trials__ required to obtain certain levels of __decoding accuracy__. This information is useful to validate experimental procedures, adjust research designs and target research questions. As enhanced FFR reproduces features of the stimuli with more fidelity, the decoding accuracy of the classifier can be __used as a dependent measure__ to reveal experience-dependent effects in certain populations (Llanos et al., 2017; 2019).
-
-### Background
-
-Over the last decades, neuroimaging studies have provided cumulative evidence of the benefits of musical training, in particular how such training promotes neuroplasticity (Herholz et al. 2012). A clear benefit of musical training is manifested at the level of the frequency following response (FFR), an electrical potential sensitive to phase-locking captured by EEG that arises from subcortical and cortical sources (Coffey et al., 2016). The FFR is an extremely rapid (starting at 7 ms) reaction of the brain to an auditory stimulus. It is a neural representation of the periodicity of the entering auditory information and allow us to recognize speech or music sounds (Coffey et al., 20161; Kraus et al., 2010; 2014; 2017b; 2017c). With both cortical and subcortical contributors that modify its quality (Coffey, Herholz, Chepesiuk, Baillet, & Zatorre, 2016), the FFR represents an integrated response of the entire auditory system (Irvine et al., 2018).
-
-Interestingly, the FFR varies in individuals as a function of experience (enhanced in musicians, tonal language speakers, bilinguals [Coffey et al., 2016; Kraus & Chandrasekaran, 2010; Krizman, Marian, Shook, Skoe, & Kraus, 2012]) or pathologies (decreased in autism, dyslexia, traumatic brain injuries [Hornickel & Kraus, 2013; Nina Kraus et al., 2016;, for extensive literature survey, see Kraus et al., 2017]. Several authors have thus proposed that it may serve as an auditory biomarker to target treatments and educational interventions (Kraus et al., 2015; Bidelman et al., 2017). Musicians are of particular interest because of correlations between the augmentation of their FFR quality and enhancements in their perception skills (Coffey et al., 2016; Kraus & Chandraserakan, 2010). The greater the musical training, the closer the neuronal representation approaches its corresponding auditory stimuli (Kraus et al., 2010b, 2015, 2017). The signal is transmitted faster, and becomes more precise and robust, which then benefits higher-order cognitive functions (Coffey et al., 2016; Kraus, Anderson, White-Schwoch, Fay, & Popper, 2017). This is a remarkable phenomenon, but how these enhancements are achieved remains unclear because of technical limitations associated with the neural signal.
-
-A considerable challenge for the understanding of FFR enhancements mechanisms is the very small amplitude (measured in nanovolt) and the bad signal-to-noise ratio (SNR) of the neural signal. Hence, recording FFRs typically requires several thousand exposures to the same auditory stimulus (which are averaged to cancel out the noise). These constraints considerably limited the research questions that could be expored with FFRs, while this signal would remain a key component of neuroplasticity mechanisms associated with musical training.
-
-Fortunately, machine-learning classifiers recently developed with speech-evoked neurophysiological responses have considerably reduced the number of trials necessary to obtain a usable FFR signal (Xie et al., 2019). The classification of FFR is starting to emmerge as an objective mean to assess auditory perception (Holdgraf et al., 2017; Llanos et al., 2019). The current BHS project aims to run one of these classifiers, which I also use in my doctoral research, on FFR-EEG data. I hope to implement these ML procedures in a way that will facilitate access, collaborations and open science practices.
-
-#### The Frequency Following Responses: Procedures (a), Features (b-d) and Machine-Learning Analysis (e)
-
-_Figure from [Coffey et al., 2019](https://www-nature-com.proxy3.library.mcgill.ca/articles/s41467-019-13003-w), with permission)_
-
-
-
-### Tools
-
-The "EEG-FFR Classification" project will rely on the following technologies:
- * Bash Shell
- * [Markdown](https://guides.github.com/features/mastering-markdown/)
-
- * MATLAB
- * MATLAB Toolboxes (GPU, Bioinformatics, and HMM)
- * Jupyter Notebook
- * MATLAB script of an Hidden Markov Model (originally developed by F. Llanos)
- * A Virtual Environment for Jupyter Notebook with MATLAB
- * Supplementary classifiers scripts (SVM, X-Correlation and SLTM developed by F. Llanos)
-
- * GitHub
- * GitKraken
- * BIDS-EEG format
- * Data Archiving with OSF
- * Open Source Licence compatible with Matlab Scripts (MIT)
-
-### Data
-
-The dataset for this tutorial project was previously published and generously provided by Emily Coffey and colleagues from the Zatorre Lab at McGill University (see Coffey et al., 2017). It consists of a set of pre-processed EEG-FFR collected with 27 subjects (13 musicians and 13 non-musicians) and the auditory stimuli used during the data collection. Demographic and music experience information (collected with the Montreal Music History Questionnaire [Coffey et al., 2011]) will also be made available on OSF for futur analysis, but is not explored under the scope of this tutorial. Thus, the data under this project is limited to the auditory stimuli (.wav) and EEG-FFR (.eeg) files.
-
-For more details on this dataset, please see [Cortical Correlates of the Auditory Frequency-Following and Onset Responses: EEG and fMRI Evidence](https://www.jneurosci.org/content/37/4/830). Questions can also be directed to emily.coffey@concordia.ca
-
-#### 1) Stimuli
-
-The auditory stimuli used were a speech |dah| and a piano tone stimuli, with both a duration of 100ms and a fondamental frequency of 98 Hz. Stimuli were presented in blocks of da/piano, alternating polarity every second stimulus (see Krizman & Kraus, 2019 for more information about alternating the stimulus polarity).
-
-100ms 'da' syllable-stimuli vs piano tone-stimuli compared to the FFR of Subject 101
-
-
-#### 2) EEG Data
-
-The EEG data is provided from each subject separately as a MATLAB structure array with two fields: .eeg and .sti.
-The name on the structure array represents the subject number (e.g. 001). The first field (e.g., 001.eeg) contains a * .mat 2-dimentional matrix with the EEG data. Rows and columns will correspond to single-stimulus trials and EEG data-points, respectively. The second field (e.g., 001.sti) is a * .mat 1-dimensional vector including the target stimulus numbers associated to each row in the EEG matrix.
-
-If you use this classifier with a different EEG-FFR dataset, note that before creating a MATLAB matrix, you will need to make sure that your EEG data has been already re-referenced, band-pass filtered (zero phase filter, pass-band from 80 to 1000Hz) and that its sample rate is higher than 2000Hz. You will also need to adapt the scripts "setup.m" to the features of your dataset.
-
-#### 3) Important Data Information (Required to adjust the scripts if used with other dataset)
-
-* __Pre-Stimulus Duration__: 50 ms
-* __Stimulus Duration__: 100 ms
-* __Trial Duration__: 0.2228 seconds
-_(Note from 1st author: "It is a funny number because I shifted the data to account for air conduction delay such that 0 (or 50 ms into the file) is when the sound hits the ear) - in any case there are 3650 data points at 16384 Hz.")_
-
- * __EEG Sample-Rate__: 16384 Hz
- * __Artifact Rejection Criteria (if any)__: None in the current pre-processed dataset
- _(Note from 1st author: "In the data used for the Jounal of Neuroscience publication, a precedure based on similarity to the grand average was applied. It was less appropriate to use such a procedure here as it may mask some of the inter-individual differences we are interested in.)_/
-* __Electrodes used for re-referencing__: This was a simple Cz to linked mastoids montage, it's already been referenced.
-
-Here is what a grand average of the FFR looks like:
-
-100ms 'da' syllable-FFR (_N=26_)
-
-
-100ms piano tone-FFR (_N=26_)
-
-
-
-## Results
-
-### Progress Overview
-
-I faced a few challenges with the matlab jupyter notebook as it required extra installation and set-up that needed to be addressed to access the tutorial. I realized that I missed elements to have a more reproducible workflow so I decided to adapt to tutorial accordingly. I found myself with a lot more text than I would have thought for something beginner-friendly! However, by the end of this project, I became able to run classifiers on EEG-FFR and have a structure for the scripts and workflow documentation that I will be able to reuse (with more ease) in the future. I must say that there is a considerable amount of revisions needed in my HMM Notebook and on the overall documentation for this tutorial to be beginner friendly. I actually ended up making a [notebook tutorial](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/Tutorial_Introduction.ipynb) on how to use the tools of this tutorial!! There is also a lot of text epuration that I need to do, and the scripts needs a bit of modification (for the subjects selection, in order to be able to reproduce all the figures). In order to be usable with other datasets, the script setup.m still require more comments and annotations). I also have MIT licences that needs to be extended & procedures for futur scripts that needs to be archived. I think that using the "Project" section on the git repository might be the next step to keep track of what is left to do. Nevertheless, the EEG-FFR dataset is achived on osf.oi and I feel that I got my lab more invaded by open sciences practices. So at this point, the seeds are at least planted and I hope that this project, even if in a rougher shape right now, will grow from there.
-
-### Tools I learned during this project
-
- __ALL__ the tools I mentioned in the 'Tools' list above!
-
-### HMM Classification Results
-
-The figure below shows the results for overall HMM decoding accuracy of the syllable vs. tone FFRs. Recognition above chance start at about 100 trials (left panel). Differences related to music experience do not emerge until 1000 trials (center panel). As well, differences related to music experience only emerge for the HMM decoding of the tone (musicians>non musicians); they are not present for the decoding of the syllable stimuli (right panel).
-
-It is worth noting that the absence of music experience differences in overall decoding accuracy ( syllable & tone together), even with 1000 trials, may be related to the fact that both populations are familiar to the syllable (e.g. both musicians and non-musicians skpeak and use the syllable |da| in their everyday life). This possible explanation is supported by the fact that no music experience differences seem to emerge from the HMM decoding of the syllable stimulus (right panel). Hence, the music experience differences in the HMM decoding of the auditory stimuli could be related to the familiarity to the auditory stimulus, which is greater for the piano tone among musicians.
-
-As both the tone and the syllable share the same F0 contour, it is worth emphazing that the differences in the FFRs are really fine. More than 100 trials to reach accuracy over chance may be improved by abjusting the parameters of the HMM. (However, this require further exploration and would be under the scope of another tutorial.) It may also take hundreds of trials for very small context-determined perturbations to emerge in the FFR. Further experimentation will tell! :)
-
-
-
-## Course Deliverables
-
-#### ASSIGNMENT WEEK 1: [P-Hacking Exercise](https://github.com/arsisabelle/Arseneau-Bruneau-I-QLSC612)
-#### ASSIGNMENT WEEK 2: [README.md File](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/README.md) + Issues
-#### ASSIGNMENT WEEK 3: [Video Presentation of the Project](https://www.youtube.com/watch?v=6lX_-AgOXug)
-#### ASSIGNMENT WEEK 4: [Project Repository](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project)
-
-## Project Deliverables
-
- - [Project in the Gallery of the BHS website](https://school.brainhackmtl.org/project/EEG-FFR-Classification)
- - Project Report ([README.md File](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/edit/master/README.md))
-
- - [Video Introduction on this project](https://www.youtube.com/watch?v=6lX_-AgOXug)
- - Documentation for a reproducible ML workflow
- - [Tutorial Introduction Jupyter Notebook (beginner-friendly)](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/Tutorial_Introduction.ipynb)
- - [Tutorial HMM in Matlab Jupyter Notebook](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/blob/master/Matlab_HMM_Scripts_Notebook.ipynb) (beginner-friendly)
- - Stand-alone versions of the [classifiers scripts](https://github.com/brainhack-school2020/ArsIsabelle_BHS_Project/tree/master/FFRclassifiers) for advanced users
- - [EEG-FFR Dataset shared by the Zatorre Lab (McGill University) on OSF.oi](https://osf.io/c2b3t/?view_only=d1b30d4e0e7c4d53a8ec80d4d87b33d2)
- - Supplementary ML Scripts (SVM, x-corr, and SLTM) created by Fernando Llanos
- - MIT license (& coming soon: procedures to get open source licence with MATLAB scripts in the future)
-
-#### Supplementary Developments to Integrate to the Tutorial After the BHS 2020
-* Adding video of the Setup Procedures and the HMM Workflow
-* Fixing the data selection for the SVM and XCorr models
-* Adding a section to explain differences between the folder FFRs and Input1
-* Adding a function for subject selection
-* Testing the tutorial on different computer
-* Integrating the SLTM (script developed by Fernando Llanos)
-* Cutting excedentary text
-* Extensive proof reading for typos
-* Proof reading by a novice learner to identify terms that require explanations
-* Uploading the License procedures for future scripts & tutorials
-
-
-
-## Conclusion
-
-Through this project, the Brainhack School provided me with tools and guidance to tame the aspects of my doctoral research that I feared the most (programmation, machine-learning, neural signal analysis)! I still have a lot to learn, but I now feel that with the jupyter notebook tutorials, with organized documentation to reproduce the workflow, and an extendable structure for the scripts, I now have many elements in place to continue to learn and to share with other. Many seeds are planted, I just need to keep growing! As a matter of fact, the structure generated by this project will serve to integrate other classifiers that my research team will use. (Although, you may check the list ('Supplementary Developments') in the last section to see all the items that need to be done next). Thus, this project/tutorial does not finish with the BHS. That is a good news, right?!
-
-With an Open Science approach, I also found practices in academia that resonnate much more with values in research that I aim to encourage, accessibility being at the core of these. (Perhaps, did I become a little less cynical?) Anyways, even if we look at this only practically, it remains that the tools that I developed through this project will facilitate my work at the lab as well as the exchanges with current and future collaborators. Hopefully, these platforms will increase the impact of my research. In the very least, I will be able to direct colleagues and students to online-resources when they ask about the analysis techniques used in my research.
-
-## Acknowledgement
-
-I would like to acknowledge the assistance (and patience!) of Pierre Bellec, Tristan Glatard, and Yann Harel as my Individual Instructors at the Brainhack School Montréal 2020 and the __extremely generous__ contributions of my collaborators Fernando Llanos (for the classifiers original scripts, recommendations on the HMM report conclusions and the two aggregated figures), Emily Coffey (for providing the dataset and information about it), as well as Marcel Farres Franch (for regular rescues and technological assistance, notably with Git Hub and Git Kraken).
-
-## References
-Coffey, E. B. J., Herholz, S. C., Chepesiuk, A. M. P., Baillet, S., & Zatorre, R. J. (2016). Cortical contributions to the auditory frequency-following response revealed by MEG. Nature Communications, 7, 11070. https://doi.org/10.1038/ncomms11070
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-Coffey, E. B. J., Musacchia, G., & Zatorre, R. J. (2016). Cortical correlates of the auditory frequency-following and onset responses: EEG and fMRI evidence. Journal of Neuroscience, 1265–16. https://doi.org/10.1523/JNEUROSCI.1265-16.2016
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-Coffey, E. B. J., Nicol, T., White-Schwoch, T., Chandrasekaran, B., Krizman, J., Skoe, E., Zatorre, R. J., & Kraus, N. (2019). Evolving perspectives on the sources of the frequency-following response. Nature Communications, 10(1), 5036. https://doi.org/10.1038/s41467-019-13003-w
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-Herholz, S. C., & Zatorre, R. J. (2012). Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure. Neuron, 76(3), 486–502. https://doi.org/10.1016/j.neuron.2012.10.011
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-Holdgraf, C. R., Rieger, J. W., Micheli, C., Martin, S., Knight, R. T., & Theunissen, F. E. (2017). Encoding and Decoding Models in Cognitive Electrophysiology. Frontiers in Systems Neuroscience, 11. https://doi.org/10.3389/fnsys.2017.00061
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-Krizman, J., & Kraus, N. (2019). Analyzing the FFR: A tutorial for decoding the richness of auditory function. Hearing Research, 382, 107779. https://doi.org/10.1016/j.heares.2019.107779
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-Llanos, F., Xie, Z., & Chandrasekaran, B. (2019). Biometric identification of listener identity from frequency following responses to speech. Journal of Neural Engineering, 16(5), 056004. https://doi.org/10.1088/1741-2552/ab1e01
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-Llanos, F., Xie, Z., & Chandrasekaran, B. (2017). Hidden Markov modeling of frequency-following responses to Mandarin lexical tones. Journal of Neuroscience Methods, 291, 101–112. https://doi.org/10.1016/j.jneumeth.2017.08.010
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-Xie Z., Reetzke, R., & Chandrasekaran, B. (2019). Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses. Journal of Speech, Language & Hearing Research, 62(3), 587–601. https://doi.org/10.1044/2018_JSLHR-S-ASTM-18-0244
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-Yi, H. G., Xie, Z., Reetzke, R., Dimakis, A. G., & Chandrasekaran, B. (2017). Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach. Brain and Behavior, 7(6), e00665. https://doi.org/10.1002/brb3.665
\ No newline at end of file
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Evaluating ANNs of the Visual System with Representational Similarity Analysis"
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-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Cornelius Crijnen]
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-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2024/crijnen_project
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-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
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-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [deep learning, machine learning, visual system, calcium imaging, representational similarity analysis]
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-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
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-summary: "This project aims to evaluate the similarity between the representations of artificial neural networks (ANNs) and the visual system in the mouse brain using Representational Similarity Analysis (RSA)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "nm_175.png"
----
-
-
-## Project definition
-
-### Background
-
-* ANN models are often used to understand the visual system in the brain, specifically the ventral pathway.
-* First, image classification models were shown to predict neural responses in the ventral pathway ([Yamins et al., 2014.](https://doi.org/10.1073/pnas.1403112111)).
-* Similarly, the representations learned by self-supervised models produce good matches to the ventral pathway ([Talia Konkle, George Alvarez, 2020.](https://doi.org/10.1167/jov.20.11.498)).
-* Recently, it was shown that it is not only possible to predict neural responses in the ventral pathway but also in the dorsal pathway with a model that has two parallel pathways ([Bakhtiari et al., 2021.](https://doi.org/10.1101/2021.06.18.448989)).
-* We trained self-supervised models on videos from treeshrew and rat's point of view with two pathways.
-* RSA is used to compare two representational spaces:
- * Response matrices are created for every brain area and every layer of the ANNs, where each element represents the response of a neuron to a video sequence.
- * Use Pearson correlation to calculate the similarity of every pair of columns in the response matrix, forming a Representation Dissimilarity Matrix (RDM).
- * The RDMs describe the representation space in a network, either a brain area or an ANN layer.
- * Kendall’s τ is used between the vectorized RSMs to quantify the similarity between the two representations.
-
-### Tools
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-The "Evaluating ANNs of the Visual System with RSA" project will rely on the following technologies:
-* **AllenSDK** for mouse brain data
-* **PyTorch** to retrieve the representations of the ANNs
-* **rsatoolbox** for the RSA analysis
-* **Plotly**, **Matplotlib** and **Seaborn** for visualizations
-
-### Data
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-* Our collaborators videos from treeshrew and rat's point of view, which were used to train the ANN models of the visual system using SSL.
-* Calcium imaging data from the [Allen Brain Observatory](https://observatory.brain-map.org/visualcoding/).
-
-### Deliverables
-
-At the end of this project, we have:
-* A [GitHub repository](https://github.com/brainhack-school2024/crijnen_project) containing the code and model checkpoints to reproduce my results
-* A [jupyter notebook](https://github.com/brainhack-school2024/crijnen_project/blob/main/notebooks/analysis.ipynb) of the RSA
-* A [jupyter notebook](https://github.com/brainhack-school2024/crijnen_project/blob/main/notebooks/results.ipynb) containing the visualisations of my analysis
-
-## Results
-
-### Visualizing the Representational Similarity Analysis
-
-The following figures show the results of the RSA between the visual system of the mouse brain and the ANNs of the visual system trained on treeshrew and rat videos.
-Each figure shows the similarity between the representations of the visual system and the ANNs for five different brain areas.
-The noise ceiling is shown in grey, the similarity between the representations of the visual system and the rat ANNs is shown in blue, and the similarity between the representations of the visual system and the treeshrew ANNs is shown in red.
-Since the ANN models have two parallel pathways, the similarity between the representations of the visual system and the two pathways of the ANNs are shown separately.
-Two stimuli were used: Natural Movie One and Natural Scenes. For each stimulus, I used two depths of mouse brain recordings from the allen brain observatory: 175 and 275, respectively.
-
-#### Natural Movie One Stimuli
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-
-#### Natural Scenes Stimuli
-
-
-
-## Conclusion and acknowledgement
-
-The results show that the representations of the visual system in the mouse brain are more similar to the representations of the ANNs trained on rat videos than to the ones trained on treeshrew videos.
-
-I would like to thank the brainhack school for providing me with the opportunity to work on this project. I would also like to thank my collaborators for providing me with the data and the guidance to complete this project.
-
-## References
-
-* [Yamins et al., 2014. Performance-optimized hierarchical models predict neural responses in higher visual cortex](https://doi.org/10.1073/pnas.1403112111)
-* [Talia Konkle, George Alvarez, 2020. Deepnets do not need category supervision to predict visual system responses to objects](https://doi.org/10.1167/jov.20.11.498)
-* [Bakhtiari et al., 2021. The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning](https://doi.org/10.1101/2021.06.18.448989)
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-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-07-29" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Exploratory Work on the Predictive Clinical Neuroscience (PCN) Toolkit"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Andjela Dimitrijevic]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/dimitrijevic_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [pcn-toolkit, github, hpc, brainhack cloud]
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-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "My project consists of exploring the predictive normative modelling (PCN) toolkit via their numerous tutorials. It contains a markdown file for future new users of this package. It also includes steps on how to format your own data to use this toolkit. Finally, some cloud computing user guides will be touched upon."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "FSL_recon.png"
----
-
-
-# Project definition
-
-## Background
-
-The PCN toolkit aims at providing diffferent tools of **normative modelling** for understanding *psychiatric disorders* at the individual level including: 
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-
-## Data
-
-In the end, this project does not use data a lot because guides for using some neuroimaging tools are presented instead. However, FreeSurfer was used on two subjects of the publicly available longitudinal [Calgary Preschool Dataset](https://osf.io/axz5r/).
-
-
-## Results: Deliverables of this project
-
-At the end of this BrainHack school, these files will be made available:
-- A [GitHub page](https://github.com/brainhack-school2022/dimitrijevic_project) containing all the modifications that were made to the PCN-toolkit tutorials
- - A [markdown file](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/PCNTutorial_steps/where_to_start.md) on where to start with the PCN-toolkit tutorials and [another](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/PCNTutorial_steps/data_formatting.md) on how to format the data adequately all in the PCNTutorial_steps directory on GitHub
- - A [markdown file](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/BrainHackCloud_steps/get_connected.md) on getting connected to the BrainHack Cloud to access HPC clusters as well as [one](https://github.com/brainhack-school2022/dimitrijevic_project/blob/main/BrainHackCloud_steps/neurodesk_access.md) on testing out the Neurodesktop container all in the BrainHackCloud_steps directory on GitHub
-
-
-
-## Progress overview
-
-At first, I thought using the Calgary Preschool Dataset would have been enough to do the analysis with the PCN-toolkit. However, this dataset seems too small (only 64 subjects) for that type of analysis. Furthermore, it was lacking cortical thickness maps which is an essential feature to be analyzed. Hence, that's why I started connecting to the HPC during the last week of the BrainHack school to be able to run FreeSurfer recon-all function on all subjects. This too came with its specific challenges. First, getting connected to the BrainHack cloud from my own computer did not work directly. Second, it took quite long to run the wanted FreeSurfer command on the NeuroDesk test server on one subject. Also, FreeSurfer bases its cortical thickness maps on previous registration tasks using adult templates (by default the Talairach atlas). This can incorporate biases and render the results to be faulty as shown on the two figures below. Indeed, some regions are not well delineated whether it be for the white matter and gray matter segmentation on Fig. 1 or the wrong specific parcellations shown by the green rectangle on the second figure.
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Fig. 1 Example of white matter segmentation on a subject
Fig. 2 Parcellations of the right and left hemisphere pial surface for cortical analyses 
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-Emotion perception is contextualized. However, how emotional context modulates word processing is unclear. We tested 20 young native speakers who read 208 Taiwan Mandarin sentences with varied emotionality conveyed through the context. To deal with multiple covarying factors in natural language, we adopted a regression-based rERP method to titrate the contextual effects. Moreover, as we plan on conducting a subsequent experiment to follow up on the findings the present study has revealed, this project also includes the code for constructing experimental stimuli.
-
-## Tools
-This project has implemented the following programming languages and brain imaging techniques:
-- EEG/ERP
-- Python
-- Matlab
-- JavaScript
-
-## Data
-- **EEG Analyses**: A 20-subject language experiment EEG dataset collected by a lab senior several years ago. (_The data files on this repository are currently blank dummy files._) Besides planning on adding 10 more participants to the original and recruiting 30 young adults for the following-up experiment, my advisor (Dr. Charlene Lee) and I also aim to ultimately put our lab data in BIDS.
-- **Stimuli Construction**: Participant Gmails and Excel sheets sent as attachments.
-
-## Deliverables
-- This markdown README documenting the project
-- Multiple Jupyter Notenooks (.ipynb), which also have the results clearly illustrated with nice data visualization
-- Python scripts (.py)
-- A Google Apps Script (.gs)
-
-[Find in the `amanda_rerp_ols_package` folder under `rERP_2023` a mini pip-installable Python package I uploaded onto PyPI. Not only is it an extension of the MNE built-in `mne.stats.linear_regression` method, but I also wrapped **my calculated-by-hand solution matrix into a user-defined class** (a Python named tuple, actually) as the return object.](https://github.com/amandalin047/Amanda_BrainHack_2023/tree/master/rERP_2023/amanda_rerp_ols_package)
-
-# Results
-## Progress Overview
-- **EEG Analyses**
- + The main structure of the regression models was completed during winter break (January through February), following Brouwer et al.'s 2020 method along with advice suggested by Dr. Kevin Hsu. However, rather than performing repeated-measures ANOVAs on the regression fitted values as was done by Brouwer et al., I t-tested each regression coefficient against 0 for concerns about systematic and statistically significant conditional differences among the predictors (in regression terms, everything in the predictors shows up as linear combinations in the fitted values, thus causing confounds).
- + The problem of multiple comparisons was tackled around March after a brief discussion with Dr. Joshua Goh. The Bonferonni correction was, as I realized, stringently unrealistic in the case where each (channel x time point) cell is regressed separately, rendering the statistical resulst less susceptible to individual Type I errors. I later settled (for the mean time) with the BH-procedure, but am still looking into cluster-based correction methods to better take into account the spatial relationships between 3D-configurated scalp channels.
- + Two sessions in April with Prof. Kara Federmeier from the University of Illinois Urbana-Champaign pointed me towards a new direction, namely to group the regression coefficients into spatiotemperal ROIs, which largely reduced the number of multiple comparisons for which I needed to correct.
- + The class attributes and methods in the program were substantially modified and revised soon after to improve run-time efficiency due to issues related to automatically dying Jupyter kernels in WSL2.
- + The EEG Toolbox under Matlab differs from MNE-Python in many respects. Having been initially trained in Matlab, I aspire to create Python functions with which Matlab users will feel more familiar, such as bin descriptor text files and the moving-window peak-to-peak method for artifact detection. To this aim, other EEG-related Python notebooks/scripts included in this repository were subsequently developed in late April through May.
- + It was an incredible honor — considering that I started off more theory-oriented than applicational — to have the opportunity to give a four-hour workshop on MNE-Python to several of my lab mates with self-written scripts.
-
-- **Stimuli Construction**
- + I started developing the Python code for compiling ratings Excel sheets roughly around March.
- + A massive round of ratings was carried out in April, thus calling for a means to automatically label, upload, and reply to incoming participant emails.
-
-
-## Skillsets Learned
-- **MNE-Python**: As a math undergraduate, I was initially trained in C++ and never formally took Python (I could read and code in Python as languages that support OOP share some similarities, yet I was far from proficient). It was not easy-peasy to start a Python program from scratch with the limited syntax I knew at the time; I'd spent countless hours Googling, sifting through threads on GitHub and Stack Overflow. As such, I genuinely find amazing how much I've learned and matured this semester in terms of Python programming skills, not only through my own project, but also as a TA working with the students.
-- **Statistical Models**: Statistics was _not_ a required undergraduate course in the Department of Mathematics (basic probablity theory was, though). I only took introductory stats as an elective, so I had but a vague idea of statistical modelling when I started off with this project. Yet as hands-on experiences often are great ways to learn, I've been implementing models I never knew before, and I do look forward to trying out different, more complicated ones.
-- **JavaScript**: I really went from zero on this. The immediate need for an automated Gmail gave me a miraculous adreneline boost and had me picking up the language in several hours — not sure how I did it, but I'm glad it worked!
-
-## Results
-- **EEG Analyses**
- + A widespread effect of contextual valence from 250ms to 750ms, with words following emotional contexts showing more positive responses relative to words in neutral contexts.
- + Greater implausibility elicited more negative and then more positive responses during 250-500ms and 600-1000ms, respectively, over central-posterior regions, which well-replicated the past ERP literature on the N400 plausibility effect and thus provided justification for our regression model. Further, this plausibility effect was not modulated by context valence, suggesting no evidence for the Affective Primacy hypothesis in sentence comprehension where one would've expected a significant interaction between the two.
- + A frontally distributed effect of word emotionality during 400ms to 700ms.
- + These findings showed that linguistic emotional context modulates subsequent word processing over and beyond sentence constraint and word emotionality, corroborating robust contextualized emotion processing in reading.
-
-
-- **Stimui Construction**
- + A great learning oppurtunity as we've come to realize which response collection methods are more efficient (and less tedious).
- + A nice, albeit still under construction, pipeline for collecting and sorting ratings responses.
-
-## Conclusion and Acknowledgements
-This has been an incredible journey in myriad ways, both academically and personally. I would like to give the biggest, most heartfelt thanks to my amazing thesis advisor, Dr. Charlene Lee at the Graduate Institue of Linguistics, National Taiwan University, without whom none of this would have been possible. I am deeply grateful not only for her sound advice, incredible support, but also for the faith she had in me when I struggled with self-doubts and didn't believe I was capable of carrying out this project or taking on this TA position. Having graded 82 module exercises, I must say I really do find reading people's code interesting. I sincerely appreciate all the students who have discussed with me their projects and code on Discord — those were the moments when I got to learn the most. There are many directions in which this project can be expanded and imporved, all of which I look forward to.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-05-16" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Revealing similarities between deep learning models and brain EEG representations"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Simon Faghel-Soubeyrand]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/deep_representational_learning
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [ Deep neural networks, brain representations, vision, EEG, decoding]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-#Deep learning is a growing field of AI that is now reaching cognitive-neurosciences.
-summary: "Do artificial neural networks process visual images similarly to our brain? If so, how? In this project, we bridge deep learning and brain EEG signals as we aim to understand more about our ability to process common visual stimuli such as objects, faces, scenes and animals."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "logo_project_1.png"
----
-
-
-# Exploring representations of deep neural networks and their similarity with the human brain
-
-## Summary
-
-I'm a PhD student at Université de Montréal & University of Birmingham (UK). I use an aggregate of psychophysics, EEG, and computational techniques to understand individual differences in vision, specifically in object/face recognition.
-
-
-The practical objectives of this project were to develop skills in Python, data visualisation and developping better coding habits with Github.
-
-The general aim of this project is to understand and visualize how deep neural networks represent visual stimuli compared to our brain. For human representations, we will use high-density EEG signal from a simple visual task.
-
-
-The project will unfold in three steps.
-
-1) Create a custom deep convolutional neural network (DCNN; using TensorFlow & Keras).
-2) Create, from high-density EEG time series, group-averaged representational dissimialrity matrices (RDMs; we will be feeding eeg topographies to linear classifiers to do so).
-3) Compare the representations (RDMs) from each of the DCNNs' layers to human brain representations (RDMs) unfolding in time.
-
-The first two steps will be developed in parallel. The last step will integrate their output.
-
-Initially, the plan was to train from scratch a DCNN to "learn" more human/brain representations. This is a mid/long-term role, and here I chose to focus on learning programming tools (described below).
-
-## Project definition
-### Background
-
-The idea of comparing and restraining a DCNN weight representations is highly inspired by work from [Cichy et al., (2016)](https://www.nature.com/articles/srep27755)
-[Kietzmann et al., (2019)](https://www.pnas.org/content/116/43/21854).
-
-
-Deep neural networks (DNNs) encode information in a similar way to the visual brain (e.g. primary visual cortex V1 encodes similar information than convolutional filters in the first layer of a DNN).
-The idea here is to come up with a common measure of processing between DCNN and brain imaging signal, here electroencephalogrpahy (EEG), and link the computations in both modalities.
-
-
-
-
-A good summary of brain and DNN computations can be obtained with a Representational Dissimlarity Matrix (RDM, see DATA section).
-
-
-## Tools
-
-The structure of the analyses will rely on :
-
-- developping analysis in **Python** scripts: pyrsa, scikit-learn, tensorFlow & keras
-- basics in visualisation tools : jupyter notebooks, interactive widgets, seaborn, and scikit-learn for multi-dimensional scaling.
-- Considering that the data is not completely available, no need to focus on BIDS standards that much.
-- **Github**
-- **Binder**
-- I will also familiarize with **deep learning**
-
-## Data
-
-Description (general):
-
-we previously used brain EEG data to "decode" pairs of images (faces, objects, scenes, animals, etc.) that were presented to human participants.
-By doing this with all pairs of images presented (49 stimuli = 1178 pairs), we created a Dissimilarity Matrix that is a summary of how the brain of each participant encodes diverse visual stimuli.
-This Representational Dissimilarity Matrix (RDM) was used as the base data input of this project.
-
--RDMs were averaged across particpants (N=23). Preprocessed EEG recordings at 128 electrodes (BioSemi)
-
-Task: simple one-back task over a stream of images containing faces of different emotions/gender, human-made/natural objects, animals and scenes.
-
-
-
-
-## Deliverables
-
-The plan is to have at least:
-
-- Python scripts for steps 1 and 3
-- Markdown README.md explaining the whole pipeline.
-
-And perhaps :
-
-- A container that enables to reproduce these analyses
-
-For the course :
-
-**Week 2 deliverable:** [README file](https://github.com/brainhack-school2020/deep_representational_learning/blob/master/README.md)
-
-**Week 3 deliverable:** : *Binder environment for data visualization*
-
-This link redirect to an interactive visualisation of the human brain data (RDMs):
-[](https://mybinder.org/v2/gh/brainhack-school2020/deep_representational_learning/master?filepath=%2Fanalysis%2Finteractive_figures_EEGxRSA.ipynb)
-
-**Week 4 deliverable:** [presentation](https://github.com/brainhack-school2020/deep_representational_learning/blob/master/presentation/BHS_2020.pdf)
-
-
-## Results
-
-## Pipeline (analysis and visualization) with scripts.
-
-
-
-## human brain RDMs
-
-An interactive visualisation of the human brain data (RDMs) can be found here :
-[](https://mybinder.org/v2/gh/brainhack-school2020/deep_representational_learning/master?filepath=%2Fanalysis%2Finteractive_figures_EEGxRSA.ipynb)
-
-
-
-Results for group-average **Representational Dissimiarlity matrices (RDMs)** had previously been derived at every time step from image onset. These *stimulus* x *stimulus* matrices contains pairwise decoding accuracies between all images (e.g. image face vs image car), indicating the brain's representational model across various visual stimuli.
-
-Here, the 2D coordinates of the representational distances from the RDMs are computed "online" using Multi-Dimensional Scaling from sklearn MDS method (to play interactively with this, see binder link above).
-
-
-
-
-## DNN representations and link with brain data
-
-### DNN representations
-Using the same stimuli we showed to participants as input to DNNs, I computed an RDM for every layer of the VGG16 neural network (weights from imagenet).
-
-
-
-### Similarity between brain signal and DNN representations
-By correlating these DNN representations with the human RDMs, I obtained the time courses of similarity between VGG16 hierarchical processing and the EEG brain representations.
-
-
-
-
-
-## Tools & Skills I developed during this project
-
-- **Python**, and useful libraries (e.g. pingouin, pandas, scipy, sklearn, tensorflow, keras)
-- Machine learning (**deep learning**).
-- Interactive figures in **Jupyter notebooks** and visualization libraries (e.g. matplotlib, seaborn, ipywidgets)
-- **Binder** reproducible environments to share code and figures.
-- **GitHub**
-- **Markdown**
-- **Inkscape** for figures.
-
-## Conclusion and acknowledgements
-
-Big thanks to all instructors, to Peer & Valerie, and to all the BHS community. This was a full month but I learned and enjoyed it a lot. I should also thank Michelle, which reminded me all the deliverables and deadlines throughout the way!
-
-Thanks to my supervisors (F. Gosselin, I.Charest) and collaborators of this project, which will be integrated as part of my phD.
-
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diff --git a/content/en/project/Ridani_Chi_separation_project/index.md b/content/en/project/Ridani_Chi_separation_project/index.md
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Validating χ-separation using phantom simulations"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Daniel Ridani]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2023/Ridani_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Python, Susceptibility, Software, Visualization, Polytechnique]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "How can we validate χ-separation algorithm? In the absence of ground truth to validate χ-separation, my project aims to validate the χ-separation results using realistic in-silico head phantom simulations. Simulations offer a valuable advantage by providing a controlled environment where we can define and manipulate various parameters with known ground truth values. By choosing specific values for the simulation, we can create a ground truth against which we can compare the results obtained through the χ-separation algorithm."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "Cover_image.png"
----
-
-
-## About Me
-
-
-Hello everyone, my name is Daniel Ridani. I am a first-year PhD student at Polytechnique Montreal, majoring in biomedical engineering. I did my Bachelor's in general physics and my Master's in biomedical engineering, where I fell in love with this field and decided to pursue a PhD. Currently, I am working on quantitative MRI, specifically quantitative susceptibility mapping (QSM). In my research, I focus on studying the susceptibility contributions from iron and myelin and developing methods to effectively separate them. Brain Hack School has given me the opportunity to improve my Python skills and explore new topics such as deep learning and machine learning.
-
-## Project Summary
-
-### Introduction
-
-My project aims to validate χ-separation algorithm using realistic in silico head phantom simulation. Susceptibility denoted as (χ), is a measure of how easily a material can be magnetized in response to an external magnetic field. Positive susceptibility implies that the material's spin aligns its magnetic moment parallel to the main field, while negative susceptibility indicates an opposing alignment. The main two sources of susceptibility are myelin (χ<0) and iron (χ>0) and they co-exist in almost every region in the human brain. Using the susceptibility, we can generate maps known as Quantitative susceptibility mapping (QSM) which does not have a ground truth. In the absence of ground truth for QSM, efforts have been made (QSM reconstruction challenge 2.0) [1] to create a phantom that can serve as a reference standard for validation. QSM measures the total susceptibility rather than the distinct positive and negative susceptibility sources, hence, to address this, χ-separation techniques, are employed to separate the two susceptibility sources [2]. The distinction between positive and negative sources of susceptibility plays a crucial role in understanding the underlying physiological processes and their implications for brain function. The ability to accurately discern and differentiate between these sources contributes significantly to the reliability of the assessments conducted in this study. The aforementioned phantom can be utilized to validate the accuracy of the χ-separation results.
-
-### Main Objectives
-- Get more familiar with χ-separation limitation.
-- Validate χ-separation algorithm using ground truth data (phantom simulations).
-- Implement python scripts to analyze and visualize the results.
-- Create a GitHub repo to share my results.
-
-### Personal Objectives
-- Gain a deeper understanding of susceptibility contribution in the human brain.
-- Develop the skill to manage a small project in a short period.
-- Improve my programming skill using the BrainHack school modules.
-- Share my knowledge with others to encourage open science.
-
-### Tools
-- χ-separation GUI developed by Hyeong-Geol Shin et al.
-- QSM reconstruction challenge 2.0 code for Gradient Echo (GRE) simulations of custom phantoms.
-- Git and GitHub for Version Control.
-- Python Packages: "matplotlib" [3], "seaborn" [4], "pandas" [5].
-### Data
-The dataset used in this study was simulated using the Matlab code from the QSM reconstruction challenge 2.0 article developed by José P. Marques et al. This article also provided susceptibility values for 10 different regions in the brain.
-
-A summary of the susceptibility values is as follows [6]:
-
-|Brain Regions |Susceptibility (ppm)|
-|:---------------|:----------------:|
-|Caudate Nucleus |0.044 |
-|Globus Pallidus |0.1305 |
-|Putamen |0.038 |
-|Red Nucleus |0.1 |
-|Dentate Nucleus |0.152 |
-|Substanial Nigra |0.111 |
-|Thalamus |0.02 |
-|White Matter |-0.03 |
-|Grey Matter |0.02 |
-|CSF |0.019 |
-
-Table 1: Susceptibility values of different brain region
-### Project Deliverables
-
-At the end of this project, we will have:
-- A phantom for χ-separation used as a ground truth.
-- Detailed project workflow documented in a GitHub repository.
-- Executable python scripts for data visualization and analysis.
-- Markdown file providing comprehensive project details.
-
-## Method
-
-### Phantoms
-
-To generate the phantoms used in this study, two experiments were performed:
-- The first experiment involved assigning variable values to each voxel within a specific region, with the mean value ± standard deviation being used for analysis.
-- The second experiment, a uniform value was assigned to every voxel within the same region.
-This approach will help reduce the complexity of the phantom model to see if the complexity of the phantom is a limitation of χ-separation.
-
-### χ-separation input
-
-χ-separation requires three main inputs:
-
-- Magnitude image.
-
-- Frequency shift (Δf): This map can be obtained from the phase image after we apply a couple of processing steps that will be explained in the next section.
-- R\'2 map (R\'2 = R\*2 - R2 ): In order to obtain an R\'2 map, you will need to follow these steps. First, acquire two different scan sequences: Gradient-echo (GRE) and Spin-echo (SE). From each sequence, generate magnitude images. Next, perform an exponential fitting on the magnitude images separately. This will provide you with T\*2 and T2 maps for the GRE and SE sequences, respectively.
-To calculate R\*2, take the inverse of T\*2 (denoted as 1⁄T\*2). Similarly, calculate R2 by taking the inverse of T2 (denoted as 1⁄T2). Finally, obtain R\'2 by subtracting R2 from R\*2.
-
-
-### Pipeline and preprocessing
-
-Figure 1: Reconstruction pipeline from raw data to χ-separation
-Figure 2: Phantom maps of variable and uniform assessments of brain region values. The left set (a, b, c) illustrates a variable assessment approach, where image a represents χPositiveSimulated, image b represents χNegativeSimulated, and image c represents χTotalSimulated values. The right set (d, e, f) presents a uniform assessment approach, with images d, e, and f mirroring the same representation as a, b, and c, respectively. 
-Figure 3: Visual comparison between χPositiveMeasured and χNegativeMeasured vs χPositiveSimulated and χNegativeSimulated respectively for both variable and uniform assessment of brain regions values. Figure a represents χPositiveMeasured values, showcasing three subfigures (a.1, a.2, a.3) that focus on specific brain regions, namely the Caudate Nucleus (CN), Putamen (Put), and Globus Pallidus (GP), as well as the Thalamus, Red Nucleus (RN), and Substantia Nigra (SN). Similarly, figure b corresponds to χNegativeMeasured values. It also contains three subfigures (b.1, b.2, b.3) that highlight the same brain regions as figure a but with negative susceptibility. Figure c (c.1, c.2, c.3) and Figure d (d.1, d.2, d.3) follow the same configuration as Figure a (a.1, a.2, a.3) and Figure b (b.1, b.2, b.3) respectively.
-Figure 4: Quantitative comparison between simulated and measured values. Figure A illustrates the variability assessment of brain regions which comprises three subfigures: A.1: comparison between QSM, χTotalMeasured vs χTotalSimulated. A.2: comparison between χPositiveMeasured vs χPositiveSimulated. A.3: comparison between χNegativeMeasured vs χNegativeSimulated. Each plot contains a red dotted line depicting y=x. Figure B illustrates the uniformity assessment of brain region values which replicates the structure and comparisons of Figure A. In addition, each plot indicates the correlation value between the values as well as the slope value. 
-
-## Tools
-
-The "project template" project will rely on the following technologies:
- * Normative modelling using the [PCN toolkit](https://github.com/amarquand/PCNtoolkit)
- * python packaging.
- * google collab.
- * the [brainhack cloud](https://brainhack.org/brainhack_cloud/)
-
-### Data
-
-The data for this project is the [Queensland Twin IMaging (QTIM)](https://openneuro.org/datasets/ds004169/versions/1.0.5) dataset: a multimodal neuroimaging dataset of young adult twins and siblings (18-30 years, N = 1026), including a subsample of participants, scanned a second time to assess test-retest reliability (N = 78, test-retest interval ~ 3.5 months).
-
-I am going to use the derivatives from FreeSurfer parcellation that can be downloaded from the repository:
-* cortical thickness data (mm)
-* cortical surface area data (mm^2)
-* subcortical volumes data (mm^3)
-
-
-### Deliverables
-
-At the end of this project, we will have:
- - A first run of the PNC toolkit on the twin data.
- - A package to structure the data to be able to load them into toolkit.
- - Tests that are integrated into the package to test the input data.
-
-## Results
-
-I was able to run the model on an initial region the data set, for males and females separately.
-These are preliminary results:
-
-## Predictions for the thickness of the right insula cortex (mm^2):
-
-### Predictions for women:
-
-
-### Predictions for men:
-
-
-### Progress overview
-
-* the model was trained using a **2 fold cross validation**
-* the model calculates **measures of model fit**, such as:
- * explained variance
- * standardized root mean square error
- * Pearson's correlation coefficient
-
-
-### Tools I learned during this project
-
- * **Deeper understanding of the PNC toolkit** I have now a better understanding of the Python implementation, and I customized the implementation in the Jupyter book that is included in this repository.
- * **Python packaging** I wrote my first ever Python package.
- * **Python testing** I added some tests to the package, which gave me a deeper understanding of unit tests in Python.
-
-
-##### Other projects
-Here are other good examples of repositories:
-- [The Predictive Clinical Neuroscience toolkit](https://github.com/amarquand/PCNtoolkit) by Andre Marquand's group
-
-## Next steps
-* run the model on remaining brian regions
-* look at changes of z-scores over time within individuals
-* look at twin differences
-
-
-## Conclusion and acknowledgement
-I thank the brainhack school staff for all their support and hope to be able to join Brainhack school next year again!
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----
-type: "project"
-date: "2025-06-14"
-title: "Understanding Learning Trajectories in VRIT: Dynamic Behavioral and Neural Signatures of Inference"
-names: ["Tzu-Yun Kung"]
-github_repo: "https://github.com/TYK0903/Tzu-Yun_Kung_project"
-tags: [inference, learning trajectories, python, fmri]
-image: "cover.png"
-summary: "This project aims to examine how the brain supports two types of inference—active and passive—during the process of posterior belief integration. I applied trial-by-trial analysis to track participants’ learning trajectories over time."
----
-
-## Project definition
-
-### Background
-
-Inferencing is a fundamental process through which the human brain updates its beliefs about environmental causes and future states. This belief-updating can occur via passive inference, where prior beliefs and outcomes are only loosely connected, and new beliefs primarily emerge from observing associations in the environment.
-However, inference is not always passive. It can also take an active form, involving hypothesis-driven actions that manipulate contextual elements and actively test the causal links between beliefs and outcomes.
-To explore this distinction, our study aims to examine how the brain supports these two types of inference—active and passive—during the process of posterior belief integration. To this end, our lab designed the Visual Rule Inference Task (VRIT), a paradigm specifically developed to probe the neural mechanisms involved in these distinct inferential processes.
-The goal of this project is to uncover the inference processes that underlie participants’ behavior as they perform the VRIT. To do so, I analyzed both neural and behavioral data, with a particular focus on how participants engage in inference under different task conditions—namely, active versus passive inference. More specifically, I applied trial-by-trial analysis to track participants’ learning trajectories over time.
-
-
-
-### Tools
-
-The project will rely on the following technologies:
- * Python, to organize data, analyze and visualize behavior and brain data.
- * SPM, to analyze brain data.
- * Adding the project to the website relies on github, through pull requests.
-
-### Data
-
-The dataset I used in this project is from Brain and Mind Lab, Taiwan. https://gibms.mc.ntu.edu.tw/bmlab/
-
-
-### Deliverables
- - "As the study is still unpublished, this repository contains only the Python scripts I wrote and shared as part of my learning experience during the Brainhack School."
-
-## Results
-
-### Progress overview
- * 1. Use Python to organize the data.
- * 2. Use Python to analyze and visualize the behavioral data.
- * 3. Write scripts to create batches of brain image preprocessing.
- * 4. Organize the onset time and durations by Python.
- * 5. Create contrast files.
- * 6. I used Python to troubleshoot issues by analyzing beta values.
- * 7. I created visualized brain images using Python.
-
-### Tools I learned during this project
- * Python, to organize data, analyze and visualize behavior and brain data.
- * SPM, to analyze brain data.
- * Adding the project to the website relies on github, through pull requests.
-
-
-## Conclusion and acknowledgement
-From the results, we observed notable differences between the two types of inference, as well as variations in their learning trajectories. These findings suggest the need for more in-depth analyses in the future.
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----
-type: "project"
-
-date: "2025-06-12"
-
-title: "Univariate analysis on melody evaluation test"
-
-names: [Hsin-Yu Cheng]
-
-github_repo: https://github.com/SkyStream5240/Hsin-YuCheng_project
-
-tags: [auditory, fmri]
-
-summary: "The project focused on extracting activities from functional images in a previous study about neural representation of melody-transposition. Using parcellated brain atlas as a mask, the BOLD signals underwent univariate analysis to look for effects in error detection or music-like stimulus-related brain regions/"
-
-image: "image.png"
----
-
-
-## Project definition
-
-For brainhack school TW-SG 2025
-
-__bhfunc2025.py__ includes some functions are used in other code
-
-__projectVisualizeTest.ipynb__ is the main processing steps I work on extracting the activities and images from preprocessed fMRI images
-
-The exported csv files are plotted to bar graphs in __resultplotting.ipynb__
-
-### Background
-
-Inspired by the previous paper: Y.- A. Han and P.J. Hsieh Imaging Neuroscience, Volume 2, 2024 https://doi.org/10.1162/imag_a_00352. I want to understand how simple melody stimuli can induce neuronal activities in the brain.
-
-
-### Tools
-
-The __Univariate analysis on melody evaluation test__ project will rely on the following technologies:
- * jupyter notebook
- * python packages - nilearn and nibabel
- * nilearn.dataset - Spatially constrained parcellation: msdl_rois
-
-### Data
-
-Using the functional fMRI data in the previous paper: Y.- A. Han and P.J. Hsieh Imaging Neuroscience, Volume 2, 2024 https://doi.org/10.1162/imag_a_00352.
-Will not be provided when the project is uploaded.
-
-### Deliverables
-
- - comparison of activity in a set of parcellated brain regions
-
-## Results
-
-### Progress overview
-
-The preprocessed image series went through masking and activities are extracted.
-
-### Tools I learned during this project
-
- * **Github workflow**
- * **dealing with Nifti data**
- * **basic data visualization in seaborn**
-
-### Results
-
-#### Deliverable 1: Comparison of activity difference
-
-Two subjects, one run each, are analyzed to look for the activation difference in dorsal ACC, dlPFC, and a generalized auditory region. 
-Expected effect are seen in one run of one subject but the other doesn't seem obvious.
-
-
-
-
-
-
-
-
-
-
-## Conclusion and acknowledgement
-
-We can see the brain activity in different regions vary a lot in different individuals, even in identical test runs. More data needs analysis for a further, more general conclusion.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-06-09" # Date you first upload your project.
-# Using a machine learning model trained on functional connectivity patterns to predict ADHD
-title: "Using a machine learning model trained on functional connectivity patterns to predict ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Sebastian Morales]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/sebastianmorales-lab/BHS_project_repo
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/sebastianmorales-lab/BHS_project_repo), click `manage topics`.
-# Please only lowercase letters
-tags: [python, github, machine learning, fmri]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project uses functional magnetic resonance imaging data to study the connectivity of children with Attention Deficit Hyperactivity Disorder (ADHD).A set of children diagnosed with ADHD were given a series of memory tasks while undergoing MRI scans. In this project, data from one of these tasks was used to calculate connectivity matrices for 65 subjects from that data set and a machine learning model was trained. The data was downloaded from Openneuro [website](https://openneuro.org/datasets/ds002424/versions/1.2.0)."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "portada.jpeg"
----
-
-
-## Project definition
-
-### Background
- Inspired by the work of [(Hammer et al., 2025)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576365/) that looks for neurobiological markers to differentiate a population of children with ADHD from those without the disorder. In this study, four memory tasks were applied while a group of 20 children with ADHD and 20 healthy controls underwent functional magnetic resonance imaging (fMRI) scans and a machine learning model was fitted. fMRI data from the four VSWM tasks enabled a classification accuracy of 92.5%, with high predicted ADHD probability values for most clinical cases. The project was attempted through a simple machine learning model, using the tools learned during the brainhack to predict a trait such as age from the connectivity matrices.
-
-
-
-### Tools
-
-For this project, several tools were used, such as:
- * Markdown, to structure the text.
- * Git and Github,to maintain repositories
- * Pyton for programming the analysis
- * jupyter-Notebook to analysis and visualization
- * Nibabel to fmri data processing
- * Nilearn to conectivity analysis
- * scikit-learn: machine learning in Python
- * Numpy and Pandas to structure the data
- * Adding the project to the website relies on github, through pull requests.
- * Openneuro to download the data set
-
-### Data
-
-The data was downloaded from Openneuro [website](https://openneuro.org/datasets/ds002424/versions/1.2.0)
-James R. Booth and GE Cooke and Jessica Gayda and Rubi Hammer and Marisa N. Lytle and MA Stein and Michael Tennekoon (2021). Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD). OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds002424.v1.2.0
-
-### Deliverables
-
-At the end of this project, we will have:
-
-- The current markdown document.
-
-- A repository with the dataset .
-
-- The jupyter notebook with data analysis and visualisation
-
-## Results
-
-### Progress overview
-
-### Overview of progress
-This project was developed solely for the purpose of learning new techniques applicable to magnetic resonance imaging. It was carried out exclusively for the Brainhack school and is not directly related to my line of research. It is a first approximation to an analysis of this kind and therefore it is a simple project with several aspects to improve. It is hoped that feedback from the community will lead to further rapid improvements to this first version.We split the data into a training set and a test set to train a machine learning model. We use the SVR model to try to predict the age of the children based on their functional connectivity matrices. We did not achieve a model that could effectively distinguish diagnosis.
-We tried to fit a model to predict the age of children.
-
-
-### Tools I learned during this project
-
-* **Project-Management** During brainhack school I learn how to manage a project, think about its different parts and the tools that would be useful.
-* **Github workflow** I learned how to use git and github to manage directories, organise files and be able to do collaborative work.
-* **python for data science** Through branhack school I learned to use python and its various libraries for MRI data analysis. I usually perform resonance analysis using SPM (Matlab), and incorporating these tools is very useful for my academic future.
-* *** course content *** much of the course content is new to me, from connectivity, to installing packages, to using machine learning.
-### Results
-
-#### Deliverable 1: Markdown document
-Generate a marckdown file in order to submit it to the school web repository.
-
-#### Deliverable 2: Report directory
-
-You can access all the data and analysis done during the project in [my public repository](https://github.com/sebastianmorales-lab/BHS_project_repo/).
-
-
-#### Deliverable 3: Project analysis
-#####Correlation connectivity matrix
-
-I calculate the correlation matrices per subject. The presence of two large clusters seems to be observed. One encompassing regions such as the frontal pole, the posterior occipital region, the anterior cingulate cortex and others.
-Another cluster comprises regions such as dorsolateral prefrontal cortex, posterior temporal region, auditory and motor regions. Activations in visual and motor regions may be due to the characteristics of the task presented.
-The one subject matrix is presented for ejample
-
-#####Classification
-######Using the classifier to see if I can predict the children ages looking at the connectivity matrices.
-
-First upload the atlas, and thus visualise the regions of interest
-
-extract the timeseries from the ROIs in the atlas and plotting the he feature matrix 
-
-the feture matrix is what the model was trained with.
-We want to predict age by plotting the age distribution. Only session 1 was taken because not all subjects had a second session.
-
-
-See the age distribution.
-#####Machine Learning Model
-
-Run the Machine Learning model
-
-As this is counted as a small number of participants, we decided on a standard regression model called Support Vector Regressor (SVR). This model usually gives robust results.
-
-
-
-accuracy (R2) = -0.15458416133781716
-MAE = 0.884920224737749
-
-
-The model does not fit well, there is a low accuracy and a rather high Mean Absolute Error.
-We tried to do some manipulations like cross-validation but did not get any better settings.
-
-##### Can our model predict the ages from the functional connectivity matrices?
-
-accuracy (r2) = -0.15576602592786326
-
-mae = 0.8901443045170564
-
-
-
-
-
-
-## Conclusion and acknowledgement
- The model has not been efficient in predicting the age of children using the functional connectivity matrices. It is likely that there are not many differences in the functional connectivity of children between the ages of 8 and 13. However, it is also likely that the model used is not able to distinguish efficiently and another model should be used or the hyperparameters should be adjusted. The dataset used consisted of only 65 subjects, for age prediction no distinction was made between children who received the diagnosis and those who did not.
-Further research is needed in the search for neural markers of Attention Deficit Hyperactivity Disorder (ADHD).
-
-I would like to thank Brainhack School for these weeks of intense training and learning, and the organisers for this initiative. I think this school is an important experience that strengthens the ties between the neuroscience community.
-
-### References
-Hammer R, Cooke GE, Stein MA & Booth JR (2015). Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder. Neuroimage – Clinical, 9, 244-252. (PMCID: PMC4576365)
-
-James R. Booth and GE Cooke and Jessica Gayda and Rubi Hammer and Marisa N. Lytle and MA Stein and Michael Tennekoon (2021). Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD). OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds002424.v1.2.0
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2024-06-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Fine-Tuning a Video Large Language Model (Vid-LLM) for Automatic Annotation of Gameplay"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Vincent Chamberland, Renaud Dagenais]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/vchamberland/VidGame-LLM
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [video LLM, automatic annotation, video games, cognitive neuroscience]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to develop a preprocessing pipeline to fine-tune a Video Large Language Model (Vid-LLM) for automatic annotation of gameplay recordings in cognitive neuroscience studies. Leveraging the Gym Retro ecosystem and the Courtois NeuroMod dataset, we convert event logs into video format and generate detailed annotations and timestamps to train the Vid-LLM. Deliverables include cleaned datasets, documentations and Jupyter notebooks."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "sub-01_ses-003_task-shinobi_run-01_level-1_rep-01_end_frame.png"
----
-
-
-## Project definition
-
-### Background
-
-Video games provide a controlled and rich environment to observe and modulate human behavior, and have been used in various research fields, such as cognitive neuroscience, psychology, and human-computer interaction. However, analyzing gameplay recordings can be time-consuming and labor-intensive, as it often requires manual annotation of player actions and events, which can be error-prone, subjective, and challenging to scale, especially when dealing with large datasets or complex game dynamics ([Harel et al., 2023](https://osf.io/preprints/psyarxiv/uakq9)).
-
-Recently, a growing number of large language models (LLMs) have been specialized for video understanding (Vid-LLMs), where they can generate detailed descriptions of video content, such as actions, objects, and events ([Tang et al., 2023](https://arxiv.org/abs/2312.17432)). These models can be fine-tuned on specific tasks to improve their performance and adapt them to new domains. For example, a model fine-tuned with sports videos can be used to analyze player performance, develop gameplay strategies, and provide detailed commentary.
-
-### Aims
-Therefore, this project aims to develop a preprocessing pipeline to fine-tune a Vid-LLM for automatic annotation of gameplay recordings in order to reduce manual annotation and increase efficiency.
-
-### Brainhack objectives
-1. Gain experience with the Gym Retro ecosystem and its Python API for video game analysis
-2. Gain experience in manipulation of video data (e.g., conversion, extraction, and processing)
-3. Gain experience with project and version management tools (e.g., Git, DataLad)
-4. Gain experience in Python scripting (e.g., dataframes, computer vision, automation)
-5. Gain experience with LLM training and testing using video data
-
-### Dataset
-The dataset is provided by the [Courtois NeuroMod project](https://www.cneuromod.ca/). It includes behavioral data recorded during gameplay of the retro video game "Shinobi III - Return of the Ninja Master", specifically capturing the movements and actions taken by three subjects. Each subject participated in four gaming sessions, with each session consisting of approximately a dozen runs. For the purposes of this project, we will focus on a subset of this dataset: analyzing runs from a single participant, specifically those in which they played level 1 of Shinobi [i.e., 89 runs].
-
-### Tools
-
-- Python scripting tools & packages
- - Jupyter notebooks
- - Gym Retro’s Python API (RetroEnv)
- - Dataframe handling (e.g., pandas, numpy)
- - Computer vision (e.g., OpenCV, moviepy)
- - Machine Learning requirements for the Vid-LLM model (pytorch, CUDA, etc.,)
-- Project management & versioning
- - Version controlling (Git, Github, Datalad)
-
-## Deliverables (Week 4 results)
-- Cleaned-up event dataset (```data/datasets```)
-- Formatted question-answers json file for supervised fine-tuning of the Vid-LLM (```data/json_files/custom.json```)
-- Jupyter notebooks for data preprocessing and exploration:
- - Dataset cleaning (```notebooks/dataset_preprocessing.ipynb```)
- - Generating training videos from all Gym Retro's bk2 files (```notebooks/training_video_generator.ipynb```)
- - Generating custom question-answers json file (```notebooks/custom_jason_generator.ipynb```)
- - Generating short testing videos (```notebooks/testing_video_generator.ipynb```)
- - Generating a single short testing video (```notebooks/single_testing_video_generator.ipynb```)
- - Generating specific frames and gifs from selected videos (```notebooks/frame_gif_generator.ipynb```)
- - Finding specific frames in a video (```notebooks/frame_finder.ipynb```)
-- Documentation on the notebooks (```doc/notebook_doc.md```)
-- Detailed documentation on how to download the raw dataset (```doc/how-to-download-raw-data.md```)
-- Examples of training and testing videos, frames and gifs (```output/```)
-- requirements.txt file for the project (```requirements.txt```)
-
-## Collaborators
-* The Pierre/Lune Bellec Lab
-* The Courtois NeuroMod Project
-
-## References
-- Harel, Y., Pinsard, B., Boyle, J. A., Cyr, A., Clei, M. L., Mignot, P.-H., St-Laurent, M., Jerbi, K. and Bellec, P. (2023). Gamer in the scanner : Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content. https://doi.org/10.31234/osf.io/uakq9
-- Tang, Y., Bi, J., Xu, S., Song, L., Liang, S., Wang, T., Zhang, D., An, J., Lin, J., Zhu, R., Vosoughi, A., Huang, C., Zhang, Z., Zheng, F., Zhang, J., Luo, P., Luo, J. and Xu, C. (2023). Video Understanding with Large Language Models: A Survey. arXiv. https://doi.org/10.48550/arxiv.2312.17432
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2023-05-21" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Working Memory in Children with and without ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Wei-Chen Chang, Shan-San Huang]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/wcnoname5/BHS2023_Project_ADHD
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/wcnoname5/BHS2023_Project_ADHD), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, fmri, connectivity, behavioral]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aims to using both fMRI data and the behavioral data during a n-back task to compare the difference between ADHD children and the heathly control ones."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "sub-03_conn_95.png"
----
-
-
-## Project definition
-
-### Background
-
-Attention deficit hyperactivity disorder (ADHD) is characterized by inattention, impulsivity, and hyperactivity (American Psychiatric Association, 1994). ADHD is a relatively common disorder in children, but with high diagnostic heterogeneity (Hammer et al., 2015).
-In past researches, ADHD is found to associated with an impaired function of the frontal lobe (Rubia et al., 1999). Furthermore, researchers have found neuroimaging-based biomarkers of ADHD using MRI (Johnston et al., 2014; Lim et al.). Thus we want to use fMRI data from open source data to analyze the difference between ADHD and the healthy childrens.
-
-### Tools
-
- The "ADHD" project would have two parts of analysis, behavioral data and funtional images.
- * `datalad`, which is used for download the dataset.
- * Some simpe `bash` scripts used to fetch the data in interest.
-
- For behavioral data analysis, the following tools was used:
- * `pandas` is used to cleaning and reconstructing the data.
- * `statsmodels.api` is used to perform statistical analysis (2-way ANOVA).
- * `matplotlib` and `Seaborn` are used to visualize the data.
-
- And for fMRI data analysis, the following tools was used:
- * `fmriprep`, a python-based package is used to preprocessing the functional images.
- * `nilearn`, which is used to visualize the brain images and analyze the connectivity
-
-### Data
-
-The dataset is from OpenNeuro
-[Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD)](https://openneuro.org/datasets/ds002424/versions/1.2.0), and is fetched using `Datalad`. The files structures is saved in `data/ds002424/` which is connected to Github site of the original study.
-
-### Results
-
-#### Deliverable 1: bash scripts
-There're 3 bash scripts helps to deal with the data, which are:
- * `download_anat.sh` is used to get all the interested anatomical images in current project via `datalad`.
- * `download_VLD_VLI.sh`: is used to get all the interested functional images via `datalad`.
- * `fmriprep_singSubj.sh`: is used to perform the fMRI preprocessing for single subject via `fmriprep-docker`. This script is actually downloaded and modified from the [Andy's Brain Book's tutorial](https://github.com/andrewjahn/OpenScience_Scripts).
-
-#### Deliverable 2: behavioral data analysis
-In the `Behavioral_Analysis` folder, there're files including:
- * `Behavioral_Analysis.ipynb`: a jupyter notebook go through the behavioral data analysis we've performed.
- * Lots of `.csv`s: the behavioral data used during analysis.
- * Lots of`.png`s: the results for statistical analysis and the visualization.
-
-#### Deliverable 3: fMRI analysis results
-In the `fMRI_Analysis` folder, there're files including:
- * `Analysis.ipynb`: a jupyter notebook that go through the fMRI data analysis, including visualiztion, connectivity, and connectomes.
- * `result_figs/`: A directory that stores all the result figures.
-
-## Conclusion and acknowledgement
-
-In the beginning, we want to build a model to classify the ADHD and healthly controls using the machine learning technique. But the preprocessing of the fMRI data took longer than we expected. (We've spending loads of time dealing with countless problems, and eventually find out how to preprocess the fMRI data with `fmriprep`.
-
-At that point, we’re running out of time, so we decided to look into the connectivity to see if there is any difference between the ADHD child and Healthy control. Also we have practiced our skills on drawing plots with Python, like using `matplotlib` and `Seaborn`.
-For future, we could try out and apply machine learning technique on the dataset.
-
-Thanks all the TAs and Teachers and students all contributing to Brainhack School!
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-11" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Using fMRI Data to Predict Autism Diagnoses with Various Machine Learning Models and Cross-Validation Methods"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Emily Chen, Andréanne Proulx, Mikkel Schöttner]
-
-# Your project GitHub repository URL
-github_repo: "https://github.com/brainhack-school2020/abide-fmri"
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [brainhack-school, abide-data, resting-state-fmri, machine-learning, reproducible-science, project-management, open-science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Is autism associated with a distinct neurofunctional signature? If so, how accurately are we able to predict the diagnosis based on fMRI data? In this project, we set out to compare different machine learning models and cross-validation methods to see how well each one was able to predict autism from resting state fMRI data in the ABIDE dataset."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "cover-image.png"
----
-
-
-## Project Definition
-
-### Personal Backgrounds
-
-#### Emily [(personal repository)](https://github.com/brainhack-school2020/emilyemchen-EEGML)
-Hello! I am an (incoming) fourth year undergraduate student at McGill University studying computer science and urban health geography with a minor in cognitive science. I am a research assistant with Isabelle Arseneau-Bruneau in the Zatorre Lab and have been learning a lot about neuroscience research while (hopefully) lending some of my technical skills to Isabelle's PhD work exploring the effect of musical training on FFR.
-
-When joining the BrainHack School, I was looking forward in particular to working on a project at the intersection of neuroscience and CS because my previous classes rarely focused on the application of the abstract concepts we learned. Upon completion of the BrainHack School, I had the opportunity to learn from and collaborate with talented neuroscience researchers and participants, write a machine learning script using python and neuroscience data, and gain hands-on practice in reproducibility efforts and good project management.
-
-You can find me on GitHub at [emilyemchen](https://github.com/emilyemchen) and on Twitter at [@emilyemchen](https://twitter.com/emilyemchen).
-
-#### Andréanne [(personal repository)](https://github.com/brainhack-school2020/anproulx-fMRI-autism)
-
-Hi! I am an incoming master's student in Psychology at the University of Montreal. My background is in cognitive neuroscience and my career objective is to work on research projects aiming to discover new ways of characterizing the brain in its pathological states. At the moment, I am working with Sébastien Jacquemont and Pierre Bellec, and the focus of our research is on investigating the effect of genetic mutations on functional and structural brain phenotypes. More precisely, I have been working with resting-state functional connectivity measures in carrier populations with developmental disorders.
-
-By joining the Brainhack School, I hoped to strengthen my computational skills and my knowledge in the applications of machine learning to the field of neuroimaging. Not only did I get to work on a machine learning problem specific to my field, but I also got to learn about useful tools such as GitHub. More importantly, I also met a community of brilliant/aware researchers and got to collaborate with other students, even across the world!
-
-#### Mikkel [(personal repository)](https://github.com/brainhack-school2020/mschoettner_fMRI-ML)
-
-Hey! I am a master's student in Cognitive Neuroscience at the University of Marburg. My background is in psychology. I am especially interested in the social brain and how humans infer thoughts and emotions from the actions of others. Right now, I am doing my Masters thesis on how the capacities of Theory of Mind and empathy interact in the brain using fMRI. I am also very much interested in research methods and how we can use statistics and data science techniques on neuroimaging data.
-
-When getting into Brainhack School, I hoped to get a better understanding of machine learning, and how it is useful for analyzing fMRI data. I learned a lot about collaborative project organization, how to make pretty interactive plots and how to write better Python scripts. Brainhack School was really an awesome experience getting to know a lot of talented and motivated people and being able to collaborate with them across the Atlantic!
-
-You can find me on GitHub at [mschoettner](https://github.com/mschoettner) and on Twitter [@mikkelschoett](https://twitter.com/mikkelschoett).
-
-***
-
-### Tools
-
-We expected to use the following tools, technologies, and libraries for this project:
-
-* Git
-* GitHub
-* Visual Studio Code
-* Docker
-* Jupyter Notebook
-* HPC/Compute Canada
-* Python libraries: `numpy`, `pandas`, `matplotlib`, `scikit-learn`, `nilearn`, `seaborn`, `pyplot`, `pyplot`
-* `venv`
-
-***
-
-### Data
-
-The goal of this project was to compare different machine learning models and cross-validation methods and see how well each is able to predict autism from resting state fMRI data. We used the preprocessed open source [ABIDE database](http://fcon_1000.projects.nitrc.org/indi/abide/), which contains structural, functional, and phenotypic data of 539 individuals with autism and 573 typical controls from 20 different research sites.
-
-***
-
-### Deliverables
-
-By the end of The BrainHack School, we aimed to have the following:
-* `README.md` file
-* `requirements.txt` file that outlines the packages needed to run the script
-* Jupyter notebooks with code and explanations
-* GitHub repository documenting project workflow
-* Presentation showing project results
-
-## Project Results
-
-
-
-### Project Background
-
-Several studies have found an altered connectivity profile in the default mode network of subjects with Autism Spectrum Disorder, or ASD (Anderson, 2014). Based on these findings, resting state fMRI data have been used to predict autism by training a classifier on the multi-site ABIDE data set (Nielsen et al., 2013). This project's scientific aim is to replicate these findings and extends the literature by comparing the effects of differing cross-validation methods on various classification algorithms.
-
-### Becoming a Team
-The three of us joined forces when we realized that we shared many similar learning goals and interests. With such similar project ideas, we figured we would accomplish more working together by each taking on a different cross-validation methods to train various machine learning models.
-
-***
-
-### Team Project Management
-
-We all shared a common interest in making our project as reproducible as possible. This goal involved creating a transparent, collaborative workflow that could be tracked at any time by anyone. To achieve this objective, we utilized the many features that GitHub has to offer, all of which you can see in action at our shared repository [here](https://github.com/brainhack-school2020/abide-fmri).
-* **Branches:** used to work simultaneously on our own parts and then push changes to the master branch
-* **Pull requests:** created when making changes to the master branch
-* **Issues:** used to communicate with each other and keep track of tasks
-* **Tags:** used to keep issues organized
-* **Milestones:** used to keep our main goals in mind (Week 4 presentation and final deliverable)
-* **Projects:** used to track various aspects of our work
-
-***
-
-### Standardized Data Preparation and Jupyter Notebooks
-
-The data are processed in a standardized way using a Python script that prepares the data for the machine learning classifiers. Several Jupyter notebooks then implement different models and cross-validation techniques which are described in detail below.
-
-
-
-
-***
-
-### Tools, Technologies, and Libraries Learned
-
-Many of these contribute to open science practices!
-
-* **Jupyter Notebook:** Write code in a virtual environment
-* **Visual Studio Code:** Edit files such as `README.md`
-* **Python libraries:** `numpy`, `pandas`, `nilearn`, `matplotlib`, `plotly`, `seaborn`, `plotly`, `sklearn` (dimensionality reduction, test-train split, gridsearch, cross-validation methods, evaluate performance)
-* **Git:** Track file changes
-* **GitHub:** Organize team workflow and project
-* **Machine learning:** Apply ML concepts and tools to the neuroimaging field
-* **`venv`:** Make `requirement.txt` file for a reproducible virtual environment
-
-***
-
-### Deliverables
-
-#### Deliverable 1: Jupyter notebooks (x3)
-
-* ##### Leave-site-out cross-validation
-
-[*`leave-site-out-cv_classifier.ipynb`*](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/leave-site-out-cv_classifier.ipynb)
-
-This notebook contains code to run a linear support vector classification to predict autism from resting state data. It uses leave-group-out cross-validation using site as the group variable. The results give a good estimate of how stable the model is. While for most of the sites the prediction works above chance level, for some, autism is predicted only at or even below chance level.
-
-* ##### K-fold and leave-one-out cross-validation
-
-[*`kfold-leave-one-out-cv_classifier.ipynb`*](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/kfold-leave-one-out-cv_classifier.ipynb)
-
-This notebook contains the code to run linear support vector classification, k-nearest neighbors, decision tree, and random forest algorithms on the ABIDE dataset. The models are trained and evaluated using k-fold and leave-one out cross-validation methods. We obtain accuracy scores that represent how skilled the model is at predicting the labels of unseen data. Leave-one out cross validation gives more accurate predictions than kfold cross validation. The accuracy values range from 55.8% to 69.2%.
-
-* ##### Group k-folds cross-validation
-
-[*`group-kfolds-cv_classifier.ipynb`*](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/group-kfolds-cv_classifier.ipynb)
-
-This notebook implements a 10-fold iterator variant to split the data into non-overlapping groups. It then runs four classification algorithms on the data: linear support vector machines (`LinearSVC`), *k*-nearest neighbors (`KNeighborsClassifier`), decision tree (`DecisionTreeClassifier`), and random forest (`RandomForestClassifier`). The most accurate classifier, `LinearSVC`, has an average accuracy score for all models of 63.5%, while the least accurate classifier, `RandomForestClassifier`, has an average accuracy score for all models of 52.6%. This latter percentage is not far off from the average accuracy scores of the other two classifiers. These scores are not particularly impressive, and one reason could be the data that this model is being trained on. Each site had differing processes, methods, and amounts of data they collected, so the lack of similarity could suggest less widespread generalizability. Future work into optimizing the parameters would be the logical next step.
-
-##### Table 1: Group 10-fold cross validation average accuracy scores (percentages) per classifier algorithm
-| Algorithm | Average Score | Maximum Score | Minimum Score |
-| ----------|---------------|---------------|-------------- |
-| `LinearSVC` | 63.5% | 72.0% | 52.3% |
-| `KNeighborsClassifier` | 55.2% | 66.7% | 48.7% |
-| `DecisionTreeClassifier` | 54.3% | 61.6% | 44.9% |
-| `RandomForestClassifier` | 52.6% | 60.5% | 39.7% |
-
-##### What were the overall results?
-
-
-#### Deliverable 2: [`prepare_data.py`](https://github.com/brainhack-school2020/abide-fmri/blob/master/code/prepare_data.py) script
-
-This script
-
-* downloads the data
-* extracts the time series of 64 regions of interest defined by the BASC brain atlas
-* computes the correlations between time series for each participant
-* uses a principal component analysis for dimensionality reduction
-
-To fetch and prepare the data set you can call the `prepare_data.py` script like this:
-
-`./prepare_data.py data_dir output_dir`
-
-or alternatively:
-
-`python prepare_data.py data_dir output_dir`
-
-where
-* `data_dir` is the directory where you want to save the data or have it already saved and
-* `output_dir` is the directory where you want to store the outputs the script generates.
-
-The notebooks also call the `prepare_data` function from the preparation script.
-
-#### Deliverable 3: [`requirements.txt`](https://github.com/brainhack-school2020/abide-fmri/blob/master/requirements.txt) file
-
-This file increases reproducibility by helping to ensure that the scripts run correctly on any machine. To make sure that all scripts work correctly, you can create a virtual environment using pythons built-in library `venv`. To do this, follow these steps:
-
-1. Clone repo and navigate to folder in a shell
-2. Create a virtual environment in a folder of your choice: `python -m venv /path/to/folder`
-3. Activate it (bash command, see [here](https://docs.python.org/3/library/venv.html) how to activate in different shells): `source /path/to/folder/bin/activate`
-4. Install all necessary requirements from requirements file: `pip install -r requirements.txt`
-5. Create kernel for jupyter notebooks: `ipython kernel install --user --name=abide-ml`
-6. Open a jupyter notebook: `jupyter-notebook`, then click the notebook you want to run
-7. Select different kernel by clicking *Kernel -> Change Kernel -> abide-ml*
-8. Run the code!
-
-#### Deliverable 4: Data visualizations (Week 3)
-
-* [Emily's GitHub Repository](https://github.com/emilyemchen/bhs2020-dataviz)
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_age.html) for the website.
-
-
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_fiq.html) for the website.
-
-
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_viq.html) for the website.
-
-
-
-Click [here](https://emilyemchen.github.io/bhs2020-dataviz/abide_piq.html) for the website.
-
-
-
-* [Andréanne's GitHub Repository](https://github.com/brainhack-school2020/anproulx-fMRI-autism)
-
-Click [here](https://anproulx.github.io/publication_website/) for the website.
-
-
-
-Click [here](https://anproulx.github.io/cross_validation_plots/) for the website.
-
-
-
-* [Mikkel's GitHub Repository](https://github.com/brainhack-school2020/mschoettner_fMRI-ML)
-
-Click [here](https://mschoettner.github.io/brainhack_visualization/) for the website.
-
-
-
-#### Deliverable 5: Presentation (Week 4)
-
-The presentation slides can be viewed [here](https://www.canva.com/design/DAD-ByEQaXI/QLgHbYgnKd-xDWJXVnaGDA/view) on Canva, which is the platform we used to create the slides. A video of our presentation can be viewed on this project page. We presented our work to The BrainHack School on June 5, 2020 using the RISE integration in a Jupyter notebook, which can be found [here](https://github.com/brainhack-school2020/abide-fmri/tree/master/presentation).
-
-## Conclusion and Acknowledgement
-
-First and foremost, we would like to thank our Weeks 3 and 4 peer clinic mentor and co-organizer of the Brainhack School, Pierre Bellec. A special thank you as well to our Week 2 mentors Désirée Lussier-Lévesque, Alexa Pichet-Binette, and Sebastian Urchs.
-
-Thank you also to The BrainHack School leaders and co-organizers Jean-Baptiste Poline, Tristan Glatard, and Benjamin de Leener, as well as the instructors and mentors Karim Jerbi, Elizabeth DuPre, Ross Markello, Peer Herholz, Samuel Guay, Valerie Hayot-Sasson, Greg Kiar, Jake Vogel, and Agâh Karakuzu.
-
-## References
-
-1. Choosing appropriate estimators with `scikit-learn` https://scikit-learn.org/stable/tutorial/machine_learning_map/
-
-2. Benchmarking functional connectome-based predictive models for resting-state fMRI (for classification estimator inspiration) https://hal.inria.fr/hal-01824205
-
-3. Getting the data using `nilearn.datasets.fetch` https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_abide_pcp.html
-
-4. Python Data Science Handbook GitHub and website https://github.com/jakevdp/PythonDataScienceHandbook
-
-5. BrainHack School course materials and lectures https://github.com/neurodatascience/course-materials-2020
-
-**Scientific articles:**
-
-Anderson, J. S., Patel, V. B., Preedy, V. R., & Martin, C. R. (2014). Cortical underconnectivity hypothesis in autism: evidence from functional connectivity MRI. Comprehensive Guide to Autism, 1457, 1471.
-
-Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., ... & Anderson, J. S. (2013). Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in Human Neuroscience, 7, 599.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-14" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Functional Brain Activation During a Memory Encoding and Retrieval Task: Discovering Tools and Techniques for Analysis"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Stella Ruddy]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/Ruddy_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [task-related activation, fmri, memory, open-science]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to explore tools and techniques used to analyze fMRI brain activation at the first level using data from an open-access dataset. We produced a comprehensive Jupyter Notebook that provides a step-by-step guide to running the analyses, including applying the GLM, defining contrasts, and generating various brain activation maps."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "brain_cover.gif"
----
-
-
-I am a PhD student in clinical neuropsychology (research and intervention stream) at the Université de Montréal. I completed my undergraduate studies in Psychology with a concentration in Neuroscience. I am interested in investigating the neurobiological basis of cognitive training gains and whether it shares similar underlying neurobiological mechanisms with cognitive reserve. Brainhack School 2025 provides an exciting first opportunity for me to delve into neuroimaging analysis using task-based fMRI. Prior to Brainhack school, I had no experience in neuroimaging analyses nor coding.
-## Project definition
-
-### Background
-
-Studies have shown that there are numerous factors that might account for up to 40% of cases of Alzheimer’s disease, such as those pertaining to cognitive engagement and cognitive reserve (CR) proxies (Livingston et al., 2020). Thus, gaining a better understanding of cognitive interventions and protective mechanisms, such as CR, that may alter the cognitive aging trajectory to prevent or delay the onset of neurocognitive disorders in older age has become a priority. Shedding light on the underlying neurofunctional mechanisms put into action following cognitive interventions and the interplay with CR proxies will ultimately promote the development of better, more personalized, cognitive interventions and uncover whether they can be useful for the development of CR, even in older adults.
-
-Functional Magnetic Resonance Imaging (fMRI) represents an important technique for discerning functional changes over time and between populations. fMRI is the main technique used in previous research to investigate both the neurofunctional basis of CR and effects of cognitive training gains (Pappalettra et al., 2014), and has been shown to be sensitive to chronological age, thus qualifying it as a potential biomarker of aging and neurodegenerative disease (Belleville & Bherer, 2012).
-
-#### ENGAGE Study
-
-My doctoral project is on the ENGAGE study, which was a randomized controlled, double-blind preference trial with a comprehensive cohort design that aimed to test the efficacy and long-term impact of an intervention that combines cognitive training and cognitively stimulating leisure activities in older adults with subjective cognitive decline. More information can be found in the study protocol (Belleville et al., 2019). The objectives of my thesis will be to explore the neurofunctional basis of a multi-faceted cognitive training and cognitive stimulation intervention (ENGAGE) and the relationship between CR and task-related brain activation.
-
-## Brainhack Objectives
-The primary objective of my Brainhack School project was to analyze fMRI activation data from one open-access subject. This project consists of a first exposure and learning opportunity for neuroimaging analyses and coding, which will be useful for future analyses using data from the ENGAGE study.
-
-### Specific Objectives
-Skills development (coding, visualization, open science)
-Modeling (GLM, contrasts, activation maps)
-Conceptual notions pertaining to data handling (BIDS format, preprocessing tools)
-
-### Tools
-
-* GitHub: Sharing the project via a public repository
-* Jupyter Notebook/Python code: Code writing and documentation, narratives
-* Nilearn: GLM fitting (subsequent memory paradigm), statistical contrasts, and brain visualizations
-
-### Data
-We used raw data from an open-access dataset (OpenNeuro ds003789). We used snapshot 2.0.0, corresponding to Git commit 26d526316b558d23eea68db5cd48736950fa22ca, which follows the BIDS 1.0.1 specification and can be downloaded using Datalad. Though fMRIPrepped data is available for this dataset, it is not complete, in that it did the steps of head motion correction and coregistration, but not of spatial normalization to MNI space. Therefore, we were unable to do any second-level (group-level) analyses.
-
-This specific dataset was chosen because the task resembles that used in the ENGAGE study. The task used in the current study was composed of two phases: 1) encoding and 2) retrieval. In the first phase of the task, participants were asked to remember 9 lists of words, each presented 3 times. In the retrieval phase, which took place 24 hours later, the participants were presented with a series of words and indicated whether or not the word was presented in the lists shown on the previous day. We analyzed brain activations during memory encoding as participants were shown the lists for the first and third time (rep1 and rep3). The figure below shows a more detailed description of the task.
-
-### Deliverables
-- Markdown folder for project description
-- Creation of a GitHub repository to share my results
-- Comprehensive Jupyter Notebook with narratives
-- Memory-related brain activation maps
-- Bonus: interactive brain maps
-
-## Results
-The repository of this project, with the complete Jupyter Notebook and interactive map, can be found [here](https://github.com/brainhack-school2025/Ruddy_project).
-We created a Jupyter Notebook with narratives explaining step-by-step the analysis process, which can be found in my GitHub repository. The Notebook includes not only the code, but also descriptions of each conceptual step and provides some theoretical background to key concepts to help guide the reader.
-
-The steps include:
-
-* Importation of libraries, accessing the data, “preprocessing”
-* Familiarization with the data: display of anatomical and mean functional images, events files
-* Specify and apply the GLM, design matrix, expected BOLD signal
-* Definition and computation of the contrasts
-* Extraction of different brain activation maps (+ interactive map)
-* Extraction of predicted and observed time series
-* Exploration of statistical tests and next steps, reporting of results
-
-#### Example activation map
-The image below shows a z-map when we apply the contrast rep1 > rep3. Therefore, here, we see regions that are more active during the first presentation of the word list stimuli compared to the activity during the third presentation of the word lists. We also chose to look at specific coordinates based on the clusters table.
-
-
-
-These results remain exploratory and should be interpreted with caution as they are based on a single participant across nine runs and have not been corrected for multiple comparisons. In addition, the images have not been properly preprocessed. These preliminary results seem to align with the hypothesis that there would be more activation during rep1 than rep3 in regions involved in memory encoding, such as the Prefrontal Cortex, perhaps because the participant is generating memory strategies to memorize the words. When the list is presented for the third time, the subject may be more familiar with the word list, and therefore less effort is required to encode the information at that point.
-An interactive visualization of this map is available in my repo.
-
-#### A note on open science best practices
-Through this project, I developed a deeper appreciation for open science practices, many of which are underemphasized in formal training. I now better recognize their critical role in ensuring transparency, collaboration, and equitable access to scientific knowledge. I look forward to working to integrate these principles into my future work, whether through open-access tools, publicly shared code, or accessible community-oriented knowledge translation efforts.
-In line with best practices in open-science, we used the following tools:
-
-* GitHub: Sharing the project via a public repository, including the complete Jupyter Notebook, interactive image, and complete references to the version of the dataset used. More generally, by exploring this tool, I see potential for collaborations and sharing information/code/results with a larger audience.
-
-* Python: Learning to code using Python allows for the project to be more reproducible by automating the commands, as opposed to manually executing steps through graphical user interfaces, which are harder to track, share, and replicate.
-
-* Jupyter Notebook with narratives: The narratives provide context, which helps others follow along exactly what we did and to detect potential errors in my code or approach. This allows for full transparency and minimizes the "black box" science.
-
-* BIDS format: Though we did not directly work to organize the files in BIDS format, I became familiar with this structure, which I now see as being very important for standardization, compatibility with other tools and collaborators, transparency and reusability.
-
-## Conclusion
-Due to only using a limited amount of data from one subject that was not fully preprocessed, the results of this project are not significant in terms of scientific advancement. However, completing this project over the intensive course of four weeks was a great first learning opportunity and exposure to these tools. I look forward to continuing to build on my skills acquired during Brainhack School and apply them to my own projects.
-#### Next Steps
-Though this project focused on acquiring basic competencies for single-subject (first-level) fMRI activation analyses with open-access data, I also acquired some experience and exposure to a series of tools and theoretical concepts that I envision will be useful in the future when working with the ENGAGE study data.
-
-- Docker: Containerized environment for running neuroimaging tools (BIDS-validator, fMRIPrep)
-- BIDS-validator: Ensures raw data complies with BIDS format
-- fMRIPrep: Standardized preprocessing pipeline for fMRI data
-
-
-A heartfelt thanks to all those involved for this rich learning experience and growing opportunity.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-09" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Age-Dependent EEG patterns for Predicting Treatment Response in ADHD"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Tianyi/Amanda Liu, Ingrid Campbell]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/age_eeg_pattern
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [adhd, eeg, age, prediction]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project investigates whether there are age-dependent EEG patterns for individuals with ADHD and whether these patterns can predict neurofeedback treatment response. Using the ADHD samples from TDBrain database (n=204), we developed a random forest model to characterize age-related EEG biomarkers and assess treatment prediction across different age groups. Our model achieved AUC=0.865, identifying key EEG signatures including theta-beta ratios and frontal low-frequency patterns that vary with age and treatment response."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "eeg_adhd.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental and psychiatric disorder that exhibits significant age-related changes throughout development. Electroencephalography (EEG) has emerged as a powerful neuroimaging technique for extracting neural features and patterns associated with ADHD, such as theta-beta ratios and alpha peak frequency. While machine learning approaches have shown promise for age prediction using neural data, there remains a critical gap in developing treatments based on EEG patterns and age-specific considerations for individuals with ADHD.
-
-Current research has established that EEG biomarkers change across the lifespan, but the relationship between these developmental patterns and treatment response remains poorly understood. Therefore, this project aims to address the question: How can we use EEG patterns to predict treatment responses for individuals with ADHD across different age groups?
-
-### Tools
-
- * **Python**: `numpy`, `FOOOF`, `matplotlib` for data processing, feature extraction and visualization
- * **R**: `caret`, `randomForest`, `ggplot2` for machine learning and statistical analysis
- * **Jupyter notebooks**: interactive analysis
- * **GitHub**: version control and collaboration
-
-### Data
-
-[**TDBrain Database**](https://www.brainclinics.com/resources)
-
-We utilized the comprehensive TDBrain database, an extensive clinical EEG dataset containing:
-
-- **Sample size**: 1,274 participants collected over two decades
-- **Age range**: 5-89 years
-- **Data types**: Raw resting-state EEG recordings, NEO-FFI personality data, demographic information, and behavioral measures
-- **ADHD subset**: 204 ADHD participants, with 70 having treatment response data for neurofeedback therapy
-
-### Deliverables
-
-**[GitHub Repository](https://github.com/brainhack-school2025/age_eeg_pattern)**
-
-- Workflows in both Python and R
-- Documentation with figures and visualizations
-- Reproducible analysis pipeline
-
-### Results
-
-#### Dataset Characteristics
-
-While the ADHD sample contains 204 samples, only 70 individuals have neurofeedback treatment response data.
-
-| Age Group | Non-Responder (Number of sessions) | Responder (Number of sessions) |
-| --------- | ---------------------------------- | ------------------------------ |
-| Youngest | 14 | 40 |
-| Middle | 4 | 48 |
-| Oldest | 1 | 50 |
-
-The distribution of number of sessions for responder v.s. non-responder is very imbalanced.
-
-#### Feature selection
-
-
-
-Using bootstrap, we determined the maximum number of features to be used in the model is 60. The selected 60-feature set demonstrated high stability across bootstrap iterations, with minimal variance in feature rankings across different data samples. The above figure presents the top 15 features being selected by our model. Top tier precitors includes C3_delta_rel, CPz_fooof_alpha_cf and T8_theta_beta_ratio represents central region delta relative power, central-parietal alpha center frequency and right temporal theta-beta ratio. Notably, age emerged as a significant predictor, ranking 8th among all features, confirming the importance of developmental considerations in treatment prediction.
-
-#### Random forest classification results
-
-
-
-The AUC of our random forest model achieve 0.865, indicating strong predictive performance, although there is potential of overfitting due to imbalanced dataset and small sample size. The model is evaluated by out-of-bag bootstrap method.
-
-### Age-Related EEG feature
-
-We evaluated whether the selected features are correlated with age and has variabiltiy across age groups.
-
-
-
-The left panel shows age-EEG correlations for the top 10 features by correlation strength. Features like C4_beta_rel and Cz_low_beta_rel show significant correlations with age, indicating that these brain activity patterns change systematically across development. The right panel displays the most age-variable EEG features using coefficient of variation across age groups. F4_delta_abs shows the highest variability, followed by T8_foof_offset and C3_delta_rel. This suggests these features may be sensitive developmental markers that can play crucial role for age-specific treatment prediction.
-
-
-
-We then visualized the overlapping features identified by correlation and variability analysis. The left heatmap displays developmental patterns by response group, showing clear differences in EEG feature means across age groups between responders and non-responders. The right panel shows developmental trajectories, illustrating how EEG features change with age differently for responders versus non-responders. For example, the C3_delta_rel and C4_beta_rel features show divergent developmental patterns between groups. There is no significant visual difference between age groups, possibly due to small sample size. Additionally, due to imblanaced dataset, the pattern shown for non-responders may be not highly reliable. Further tests are needed for validate model accuracy in larger samples.
-
-### Tools Learned and Applied
-
-- **Machine Learning**: feature selection and random forest implementation using bootstrap
-- **EEG Analysis**: Frequency domain analysis and biomarker extraction
-- **Data Integration**: Merging multiple data sources and handling missing data
-
-## Conclusion
-
-This project demonstrates that age-dependent EEG patterns provide valuable information for predicting neurofeedback treatment response in ADHD. The Random Forest model achieved strong performance (AUC=0.865), with age emerging as an important predictor with several EEG features showing robust correlations with developmental stage. However, there is potential for overfit due the small sample size and imbalanced case-control sessions. There is need to validate findings in a larger dataset.
-
-## References
-
-1. van Dijk, H., van Wingen, G., Denys, D. et al. The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Sci Data 9, 333 (2022). https://doi-org.myaccess.library.utoronto.ca/10.1038/s41597-022-01409-z
-2. Arns, M., Conners, C. K., & Kraemer, H. C. (2012). A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis. Journal of Attention Disorders, 17(5), 374-383. https://doi-org.myaccess.library.utoronto.ca/10.1177/1087054712460087 (Original work published 2013)
-3. Clarke, A. R., Barry, R. J., Heaven, P. C. L., McCarthy, R., Selikowitz, M., & Byrne, M. K. (2008). EEG in adults with Attention-Deficit/Hyperactivity Disorder. International Journal of Psychophysiology, 70(3), 176-183.
-
-## Acknowledgments
-
-We thank the [Brainclinics Foundation](https://brainclinics.com/) for providing the [TDBrain dataset](https://brainclinics.com/resources/tdbrain-dataset/introduction/downloads) and the [BrainHack School](https://school.brainhackmtl.org/) community for tutorials and learning material. Special appreciation to our instructors, TA and participants who provided valuable feedback throughout the project development.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-
-date: "2025-06-15"
-
-title: "Decoding Depression via EEG Biomarkers: A Neurocomputational Approach using Machine and Deep Learning"
-
-names: [Wei-Xuan Chai]
-
-github_repo: https://github.com/ChaiWeiXuan/brainhack-EEG-depression-ml-dl
-
-website:
-
-tags: [EEG, depression, svm, eegnet]
-
-summary: "This project was conducted as part of Brainhack School 2025. It aimed to classify major depressive disorder (MDD) using temporal-domain EEG features (i.e., band power), applying both machine learning (SVM) and deep learning (EEGNet) models."
-
-image: "cover img.png"
-
----
-
-## Project definition
-
-### Background
-
-This project was inspired by a clinical case from Shanghai Ruijin Hospital, where Brain-Computer Interface (BCI) technology was applied to treat severe depression. That real case made me wonder about the potential of neural decoding in understanding and supporting mental health.
-
-As someone interested in both neuroscience and BCI, I believe it is essential to master machine learning and deep learning techniques—especially when working with high-dimensional signals like EEG or other neuroimaging data. This project is my personal attempt to explore how machine learning (SVM) and deep learning (EEGNet) models perform in classifying major depressive disorder (MDD) based on EEG band power features.
-
-
-### Tools
-
-The project will rely on the following technologies:
-
-1. Programming & Environment
-- Python
-- Jupyter Notebook: Used for data preprocessing and training the SVM machine learning model.
-- Google Colab: Used for training the EEGNet deep learning model.
-
-2. Libraries & Frameworks
-- scikit-learn, gridsearchCV: For machine learning models.
-- MNE: For EEG data processing and visualization.
-- numpy, pandas, matplotlib: For data manipulation and plotting.
-- tensorflow, keras: For deep learning models.
-
-3. External Models / Resources
-- Brainhack School Modules: Working with MNE-Python and EEG-BIDS [page](https://school-brainhack.github.io/modules/mne_python/)
-- Brainhack School Modules: Machine learning for neuroimaging [page](https://school-brainhack.github.io/modules/machine_learning_neuroimaging/)
-- EEGNet implementation from ARL (Army Research Laboratory) EEGModels Project [page](https://github.com/vlawhern/arl-eegmodels)
-
-
-### Data
-This project uses the MDD Patients and Healthy Controls EEG Data (New), downloaded from [page](https://figshare.com/articles/dataset/EEG_Data_New/4244171/2).
-- The dataset includes 30 Healthy Controls (H) and 34 Major Depressive Disorder (MDD) participants.
-- Each participant completed three conditions:
- - Eyes Closed (EC)
- - Eyes Open (EO)
- - P300 Task (TASK)
-
-For this analysis, we used:
-- Only the 5-minute EC EEG data
-- Successfully downloaded: 28 H and 30 MDD subjects
-
-
-### Deliverables
-
-At the end of this project, we will have:
-1. Preprocessing & Bandpower Feature.ipynb
-2. Machine Learning_SVM.ipynb
-3. Deep Learning_EEGNet.ipynb
-These deliverables reflect the full pipeline from raw EEG preprocessing to both traditional ML and DL models training.
-
-
-## Results
-
-### Progress overview
-
-This project started with the goal of integrating what I’ve previously learned—EEG preprocessing, machine learning, and deep learning—into a single end-to-end pipeline. While I had prior experience working with EEG data and individual modeling techniques, I had never tried building an entire classification workflow on my own, from data preparation to model comparison.
-
-I began by preprocessing the EEG data using MNE-Python and extracting temporal-domain features (band power). Then I implemented an SVM classifier, followed by a deep learning model based on EEGNet. Although the EEGNet part was more challenging due to model tuning and GPU limitations, I still managed to get a working prototype on Google Colab.
-
-Beyond modeling, I also learned how to structure a project repository, push updates to GitHub, and write clear documentation—sometimes after a few frustrating errors (and moments of panic). But overall, it was a fun and fulfilling learning experience, and a solid first attempt at decoding mental health signals using both machine learning and deep learning techniques.
-
-
-### Tools I learned during this project
-
-- Learned to preprocess EEG signals and extract bandpower features using MNE.
-- Applied classical machine learning models (SVM) with scikit-learn and gridsearchCV.
-- Implemented and trained a deep learning EEG model (EEGNet) with TensorFlow and Keras.
-- Developing, testing, and executing the entire analysis pipeline using Jupyter Notebooks and Google Colab as interactive environments
-
-
-### Results
-
-Preliminary results suggested that SVM achieved higher accuracy (> 0.95) compared to EEGNet (~0.8), possibly due to limited hyperparameter tuning and model optimization in this early-stage exploration. While deep learning provides promising end-to-end modeling capabilities, classical approaches like SVM may still offer superior performance when the dataset is small and features are carefully extracted.
-
-
-#### Deliverable 1: Preprocessing & Bandpower Feature
-
-An EEG preprocessing pipeline was implemented using MNE-Python, followed by the extraction of frequency-domain features (band power) for classification purposes.
-
-
-#### Deliverable 2: Machine Learning_SVM
-
-A Support Vector Machine (SVM) model was trained and evaluated based on the extracted band power features. Grid search with 10-fold cross-validation was used to tune key hyperparameters (e.g., C, gamma). Several trials were conducted, including the integration of feature selection and dimensionality reduction using PCA. However, these modifications did not improve the accuracy, suggesting that the original band power features already effectively captured discriminative patterns related to depression in the EEG data, or that such techniques may not be beneficial in this particular case.
-
-
-#### Deliverable 3: Deep Learning_EEGNet
-
-An EEGNet model [page](https://github.com/vlawhern/arl-eegmodels) was trained and evaluated using preprocessed EEG epoch data, without extracting band power features. The model was implemented on Google Colab with GPU support. Hyperparameter tuning was conducted using both Keras Tuner and manual for-loop implementations, each within a 10-fold cross-validation framework. Due to time constraints, only minimal tuning was performed, which may explain the lower performance (~80% accuracy). Nevertheless, this pipeline demonstrates how CNN-based architectures can be applied end-to-end to EEG data.
-
-
-
-## Conclusion and acknowledgement
-
-This project was my first attempt at an EEG decoding pipeline, from preprocessing, feature extraction, to model training with SVM and EEGNet. Though basic, it revealed skill gaps in computational neuroscience and BCI, guiding my future learning.
-
-I appreciate the supportive learning environment and resources from Brainhack School 2025, including video tutorials and coding notebooks. Special thanks to the researchers who shared the MDD EEG dataset on Figshare and the EEGNet developers for their open-source contribution. This project reflects the power of open science and community collaboration.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-08-06" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Classifying Neuropsychiatric Disorder Diagnoses Using Resting State BOLD fMRI Connectivity Data"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Peter Brotherwood]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/brotherwood_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [Machine-Learning, Connectivity, Diagnosis, Workflow]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Can functional connectivity data be used as a predictor for neuropsychiatric diagnosis? This project explores the usefulness of connectivity data in predicting ADHD, Bipolar Disorder, and Schizophrenia diagnoses using machine learning classification methods."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "connectivity_image.jpg"
----
-
-
-## About Me
-
-
- 
- 
-
-### Background: Calcium imaging
-
-The mouse completes the task with a miniatrue microscope (miniscope) implanted on its head, which shines an LED light onto an implanted lens to illuminate genetically modified neurons that fluoresce (i.e., light-up) whenever calcium is being used by the cell. In the same way that fMRI infers brain activity from blood-oxygen levels, calcium imaging infers neuronal activity from calcium concentration in the cell (indicating action potentials, graded potentials, synaptic potentials etc.)
-
-This is what the camera sees. A MATLAB script takes each frame and determines which groups of pixels are neurons and which are not and tags and counts them.
-
-
-
-Once tagged, each neurons's brightness level is measured to infer calcium activity. The raster plots generated here looks similar to what you would see in 'spiking' data for single-unit electrophysiology.
-
-
-
-(left; zoomed in, right; same but zoomed out) The y axis is each one individual neuron. The x-axis is time (in frames; ~30ms). A more yellow colour = stronger fluoresence measured = higher likelihood of neuronal activity.
-
-Big overarching question: How does neural activity change over time as the animal gets better at the memory task? Are there clusters of cells that activate during certain events? (Incorrect vs. correct choices, left vs. right vs middle response, etc.).
-
-
-For a detailed review of general tools and methodology, see: https://www.biorxiv.org/content/10.1101/2020.02.06.937573v1.full
-
-
-### Data
-
-Behavioural data is time-stamped for every action done by the animal (touching the screen, drinking reward, incorrect choice, correct choice, etc.)
-
-Calcium data is a matrix representing NumberofCells x TotalFrames (e.g., 100 cells and 30,000 frames is a 100 x 30000 matrix). Where each value in the matrix represents fluoresence level (inferred spiking probability) for a particular cell at a particular frame in time.
-
-Hence, the design of my data uses the activity of individual neurons as its features (x-values), the behavioural events as its dependent or predictor variables (y-values). A 'sample' here is defined as each individual attempt made by animal on the task.
-
-
-
-Note: Ones and zeros here denote the cell being 'on' or 'off' for sake of simplicity. In reality, the activity is defined as a transient of activity based on the change in fluoresence measured by the image analysis software.
-
-### Learning Goals
-
-The primary issue with my analysis design is that each cell's activity is its own time-series, which makes it difficult or impossible to use with traditional machine learning tools (at least, in its current format). Thus, my first goal is to pre-process my time series data into a meaningful format for cell-by-cell and population level analyses. My second goal is to use machine learning to predict and decode correct vs incorrect events from individual cell and population level activity.
-
-
-
-1. For single-cell analysis, I have the option of using the time series data of a single cell. However, treating each frame in the time series as its own independent variable is not realible; any slight jitter in the cell activity from trial to trial will make it impossible to find recurring patterns. To fix this, I will use a sliding window that takes the average of a few frames, effectively smoothing out the time series for each trial to allow a more liberal overlap of activity between trials.
-
-2. From the single-cell analysis, I have the option of taking the cell-by-cell predicted categories and inputting them into a random forest analysis to test whether neurons show any kind of 'democratic' coding for correct vs. incorrect attempts.
-
-3. For the population level analysis, I have the option of condensing my time series data into mean firing or total firing across the time series per cell, then using this overall activity score to predict behavioural events.
-
-4. Finally, I can also apply nilearn tools to compute a correlation matrix between cells, and using those correlation values as my features to predict behaioural events.
-
-
-### Tools
-
-* Python
-* Jupyter notebooks
-* nilearn
-* scikitlearn
-* MATLAB
-
-## Results
-
-### Single Cell - Sliding window analysis
-
-Below are histograms depicting the distribution of test scores (top) and 5-fold cross validation scores (bottom) for three machine learning classifiers tested on 109 individual cells with windowed (4 frames) time-series data. Training and test data was shuffled and re-tested 50 times, and counts of cells that scored >80% accuracy on each test are tabulated next to each classifier method.
-
-
-
-Adding +1 to the Cell ID, it appears that cells 29, 25, and 4 most frequently scored above 80% prediction accuracy for correct vs. incorrect trial attempts. Let's see what they look like:
-
-
-
-Here we can see visually that these cells do indeed differ significantly between correct and incorrect responses. These results were, in fact, already known. Several cells in the hippocampus are expected to light up whenever an animal receives a reward for a correct trial. Thus, the purpose of this test was just to validate that my machine learning tools were working properly.
-
-### Population level - 'democracy'
-
-Now then, what about population level activity? Is there something about the activity of all these cells, or groups of cells, that can indicate correct vs. incorrect response choices? One idea is to test whether hippocampal cells function as some kind of 'democracy'; where the predictions of individual neurons accounted together could somehow improve the overall accuracy of the machine learning tools.
-
-To do this, I took the predctions of each individual neuron from the training set of the linear SVC analysis and put it into a Random Forest classifier. Unfortunately, the results were quite poor (41.6% +/- 11% accuracy on cross validation). This isn't too surprising, as hippocampal cells aren't really expected to work 'democratically' to predict single, specific events.
-
-### Population level - Summed Activity
-
-Equally bad were the results of the summed time-series activity all 109 cells. This is again to be expected, as temporal dynamics cannot be ignored on the large timescales over which activity was averaged (over 2 seconds). Cells with distinct and specific activity in very narrow timeframes (left image) are treated the same as cells that fire sparsely, but the same amount overall, within the same time-frame (right image). However, this type of 'summed' activity might be more useful for more specific and narrow time windows of interest (<1 second) that are tied to specific, expected events. Accuracy might also improve by 'leaving out' features (i.e., cells) with inconsistent activity from the model. Indeed, not all cells would be expected to contribute equally (or at all) to the coding of a correct or incorrect trial.
-
-
-
-### Population level - Cell-to-Cell Correlations
-
-Another way to look at population level activity is through cell-to-cell correlations, similar to what is done fMRI connectivity analyses. Except here, each region of interest is an individual neuron. Adapting the script learned from the machine learning with nilearn tutorial, I generated a 109 x 109 correlation matrix for all cells, as well as a feature matrix of 5800+ correlations between pairs of cells' windowed time series data:
-
-
-
-Feeding these featrues into a few machine learning classifiers, we get some cool test results (+ cross validation)!
-
-* SVC (rbf): 78.8% (82.5 +/- 10%)
-* Decision tree: 82.6% (65 +/- 10%)
-* Random forest: 73.5% (72.5 +/- 9%)
-
-It appears that looking at correlated activity is a reasonable approach for understanding correct vs. incorrect responses in this task, when the animal is collecting reward. This again makes intuitive sense, as we know that large populations of cells fire together whenever the animal receives a reward, so highly correlated activity would be indicative of a 'reward' event (and thus, a 'correct' event), perhaps even more so than any single cell's activity.
-
-## Conclusions
-
-Here, we see that both single cell and population level analyses can be useful for modelling correct vs. incorrect performance on a reward-driven behavioural task. However, some methods seem to work better than others, and both the methods that do and do not work help us understand the functional dynamics of the hippocampal cells that govern certain aspects of reward-driven learning.
-
-## Future directions
-
-* Use machine learning to predict and decode other events in the population activity of the cells, such as trial and object types.
-* What does the 'connectome' of relevant correlations for specific events look like? How does it change over time?
-* How does prediction accuracy for each of these tests change over time? Does this reflect anything about how the hippocampus handles the learning of new information?
-* Create a polished machine learning pipeline to systematically decode different types of events in the behaviour and pick return a rank of the best models and their respective prediction accuracies and how they change over time.
-
-## Acknowledgements
-
-I'd like to thank the BrainHack team for putting together this wonderfully fun and fascinating course on neural data science! I've learned a lot about how other neuroscientists are tackling their analyses and how best to share my data with collaborators and labmates. Special thanks to Yann and Loic for helping me out with the machine learning aspects of my project.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-14" # Date you first upload your project.
-title: "2025 Brainhack School (Mini-)Project - Cognitive Dispersion and Its Neural Correlates"
-names: [Truc (Curt) Nguyen]
-github_repo: https://github.com/curt-1711/Curt_brainhack_project
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/PSY6983-2021/project_template), click `manage topics`.
-tags: [neuropsychology, fMRI, cognitive dispersion, default mode network]
-summary: "This mini-project for the 2025 Brainhack School is part of my PhD dissertation on Late-Life Cognitive Heterogeneity, where I examine the neural correlates of cognitive dispersion -- a measure of within-individual variability -- using neuropsychological and fMRI data from the Midnight Scan Club dataset (OpenNeuro ds000224)."
-image: "project_cover_20250617.jpg"
-
----
-
-
-
-## Project definition
-
-### Background
-
-Cognitive dispersion is a measure of intra-individual fluctuations
-across a range of neuropsychological tests, operationalized as the
-standard deviation of scores across multiple cognitive domains or tasks
-during one occasion. One of the most prevailing speculations regarding
-the mechanism of dispersion is the disruption of functional
-connectivity. However, these studies have primarily consisted of
-cross-sectional findings, i.e., scans that were acquired at a single
-time point, examined group-average brain, with RS-fMRI scan length
-lasting 5–6 minutes—an approach that might not fully capture functional
-brain organization at the individual level. While conventional fMRI
-studies have strived to increase the sample size, more recent works have
-directed attention towards *precision functional mapping*, i.e.,
-collecting large amounts of data at the individual level.
-
-Here I (a third year PhD student in Neuroscience at National Taiwan
-University / Academia Sinica as of June 2025) propose to use
-state-of-the-art precision functional mapping tools to determine brain
-activity patterns associated with cognitive dispersion. As a
-proof-of-concept (pilot) study, I will be using the neuropsychological
-and fMRI data from the Midnight Scan Club dataset (OpenNeuro ds000224)
-to examine the neural correlates of cognitive dispersion.
-
-### Tools
-
-- MATLAB;
-- CONN toolbox, an SPM-based software for the analysis of functional
- connectivity;
-- Utilities by the authors of the Midnight Scan Club dataset
- ([github](https://github.com/MidnightScanClub/MSCcodebase/tree/master));
-- Tutorials to run the *precision functional mapping* pipeline (Lynch
- *et al., Nature* 2024;
- [github](https://github.com/cjl2007/PFM-Depression/tree/main)) and
- the *multi-session hierarchical Bayesian modeling* (Kong *et al.,
- Cereb Cortex* 2019;
- [github](https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2019_MSHBM)).
-
-### Data
-
-The Midnight Scan Club dataset (Gordon *et al.*, Precision Functional
-Mapping of Individual Human Brains, *Neuron* 2017; OpenNeuro
-[ds000224](https://openneuro.org/datasets/ds000224/versions/1.0.4)).
-
-## Results
-
-### Progress overview
-
-During the three weeks that I had to work on this mini-project, I ran
-into multiple errors in MATLAB (details in the PDF file of my
-presentation). After hours (and hours) of troubleshooting and as the
-deadline to submit my project approached, I resorted to use the CONN
-toolbox for a quick analysis of the resting-state fMRI data from
-Midnight Scan participants.
-
-### Tools I learned during this project
-
-- **Human Connectome Workbench** to visualize the CIFTI files
- (derivatives).
-- **MATLAB** running code and troubleshooting.
-- **CONN toolbox** for fMRI data preprocessing and functional
- connectivity analysis.
-
-## Conclusion and acknowledgement
-
-Many, many thanks to the 2025 Brainhack School Organizers (Faculty and
-TAs)! I have learned so much from you and my peers this semester.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-18" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "CWAS4fMRI: a python package to perform Connectome Wide Association Study"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Clara El Khantour]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/elkhantour_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website: https://brainhack-school2025.github.io/elkhantour_project/
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [fmri, cwas, connectome, package]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "This project aimed to develop a BIDS App for performing Connectome-Wide Association Studies (CWAS) on fMRI connectivity matrices. The result is a GitHub repository that can be installed via pip, enabling analyses on BIDS-formatted connectomes. Integration tests ensure the pipeline runs reliably, and a dedicated website provides full documentation and example outputs."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "illuminated-neural-network-stockcake.jpg"
----
-
-
-## Project definition
-
-### Background
-
-The reproducibility crisis in neuroimaging has affected many research domains, and psychiatric research is no exception [1](https://doi.org/10.1016/j.bpsc.2022.12.006). Indeed, psychiatric studies using neuroimaging tools have long struggled with poor reproducibility, largely due to small sample sizes and heterogeneity in analysis methods.
-
-To address this issue, initiatives such as the ENIGMA consortium (Enhancing NeuroImaging Genetics through Meta-Analyses) were launched [2](https://doi.org/10.1007/s11682-013-9269-5). ENIGMA is a consortium comprising international research sites and organized into several Disease Working Groups that focus on most major psychiatric conditions. It promotes a collaborative framework: while data remain at local sites, analyses follow standardized protocols, enabling large-scale meta-analyses across cohorts.
-
-This approach has proven successful for structural and diffusion MRI, thanks to the development of open and reproducible protocols (https://enigma.ini.usc.edu/protocols/). In this context, Waller et al., 2020 developed HALFpipe [3](https://doi.org/10.1002/hbm.25829), an open-source software that facilitates the preprocessing and extraction of connectivity matrices from functional MRI data. However, no standard tools has yet been proposed by ENIGMA for statistical analyses of functional connectivity matrices.
-
-#### Objectives
-This project aims to support the broader collaborative effort by developing a dedicated BIDS-App for conducting Connectome-Wide Association Studies (CWAS) based on connectivity matrices.
-
-#### Objectives for BrainHack school
-Learn how to :
-1. Write scripts in python following the BIDS-app workflow
-2. Build a GitHub repository with automated tests with GitHub Actions
-3. Develop a sustainable and easy-to-use python package
-4. Produce interactive plots and interactive website ✨
-
-### Tools
-
-This project uses the following tools and standards to ensure reproducibility, openness, and long-term usability:
-- **Python scripts** : To write functions, modules and be able to test them
-- **Git & GitHub**:
- - *Public repo*: Enables version control and promotes open-source, collaborative development of the library.
- - *GitHub Actions*: Implements automated testing and continuous integration to maintain code quality during development (Git Actions).
-- **BIDS Ecosystem**: Ensures compatibility with the BIDS standard, following the workflow of a BIDS App [4](https://doi.org/10.1371/journal.pcbi.1005209).
- - For this package, we followed the [BEP-017 proposal](https://bids.neuroimaging.io/extensions/beps/bep_017.html)
-- **Python Packaging with uv**: Distributes the tool as an installable open-source Python library for easy integration and reuse.
-- Interactive documentation : Provides documentation and examples on the website.
- - **Jupyter Notebooks**
- - **Plots with Plotly**
- - **Website with MyST**
-
-### Data
-
-To test the integration of the pipeline, toy data have been generated using `numpy` and `pandas`.
-
-Local tests have been made base on [Giga-connectome outputs](https://giga-connectome.readthedocs.io/en/latest/).
-
-### Deliverables
-
-1. Python scripts following the BIDS-App workflow
-2. A python package ready to be used available on [GitHub](https://github.com/brainhack-school2025/elkhantour_project)
-3. A public [GitHub repository](https://github.com/brainhack-school2025/elkhantour_project) with integration test
-4. A [website](https://brainhack-school2025.github.io/elkhantour_project/) with interactive plots
-
-## Results
-
-### Progress overview
-
-During Brain Hack School 2025, I developed a functional and pip-installable Python package. I also set up a website with all the documentation you need to run the CWAS pipeline. This first version of the Python package is now open to feedback and collaboration.
-
-### Tools I learned during this project
-
-- **Python scripts** : All original scripts were refactored into modular Python files. Scripts are now organized by topic and function to improve readability and maintainability.
-
-- **Git, GitHub & Testing with pytest** : This project introduced me to advanced GitHub features, including GitHub Actions for continuous integration. I learned to use `pytest` to write and integrate tests that ensure the pipeline remains functional after updates.
-
-- **Python Packaging with uv** : I discovered `uv` during one of the BHS2025 modules in Montreal, and used it to build this package.
-
-- **BIDS Ecosystem** : This project helped me to understand the importance of the BIDS ecosystem and all the related BIDS-App. I discovered BIDS extension proposals (BEPs) such as those for derived data like connectivity matrices.
-
-- **Website with MyST** : I used MyST Markdown to build and publish a clean, structured documentation website.
-
-- **Plots with Plotly** : To create interactive visualizations, particularly for complex connectivity maps involving many brain regions.
-
-### Results
-
-#### Deliverable 1: BIDS workfow implementation
-This pipeline was created to follow the BIDS-App workflow as proposed by [Gorgolewski et al., 2017](https://doi.org/10.1371/journal.pcbi.1005209) for group level anlaysis.
-
-
-
-This pipeline takes as input a directory structured according to the [BEP-017 proposal](https://bids.neuroimaging.io/extensions/beps/bep_017.html). Both the input and output directories are defined via command-line arguments.
-
-#### Deliverable 2: Python package
-This package was initialize using [`uv`](https://github.com/astral-sh/uv).
-
-Thanks to this setup, the repository can be installed using using `pip install .`. This configuration ensures that:
-- All required dependencies are installed
-- The pipeline can be tested using `pytest`
-
-###### Quick start
-To run this pipeline, you simply need to follow the next 3 steps:
-
-1. Clone the repository
-```
-git clone https://github.com/brainhack-school2025/elkhantour_project.git
-```
-2. Install the package
-
-```
-pip install -e .
-```
-
-3. Run the pipeline using the command `cwas-rsfmri` followed by the flags.
-
-```bash
-cwas-rsfmri --bids_dir=bids_directory --output_dir=results --atlas_file=atlas.txt --atlas=example_atlas --phenotype_file=participants.tsv --group=diagnosis --case_id=NDD --control_id=HC --session=timepoint1 --task=task01 --run=01 --feature=denoiseSimple
-```
-
-#### Deliverable 3: Continuous integration with GitHub Action
-
-The tests were implemented using [pytest](https://docs.pytest.org/en/stable/).
-
-Since this package serves as a *glue* between already existing pipelines, the focus is on integration testing rather than unit testing. To support this, toy datasets are automatically generated and deleted during each pytest run to avoid unnecessary disk usage.
-
-For now, two integration tests are included:
-1. One without any optional arguments (`scan`, `sequence`, `medication`).
-2. One using all optional arguments
-
-These tests are automatically run on GitHub via GitHub Actions on every push to the `main` and `dev_test` branches, as well as on pull requests.
-
-#### Deliverable 4: MyST Website
-To provide complete documentation and show example figures generated by the pipeline, a website was built using MyST and is hosted on GitHub Pages.
-
-The documentation is available directly on the site https://brainhack-school2025.github.io/elkhantour_project/
-
-To visualize the interactive figure, please see: https://brainhack-school2025.github.io/elkhantour_project/interactive-plots
-
-
-
-#### Contribution to Open Science
-This project aims to:
-1. Promote __collaborative science__ through:
-- A public GitHub repository open to contributions
-- Continuous integration with GitHub Actions, which automatically run on new pull requests
-2. Ensure __reproductible pipeline and results__ by:
-- Providing a Python package that can be run with just three command-line commands
-- Following the BIDS standard for input organization, aligning with community efforts to standardize neuroimaging workflows
-3. Offer extensive __documentation__ using MyST markdown, a tool designed for open-source projects that supports clear, structured scientific communication and web-based publishing.
-
-## Conclusion and acknowledgement
-
-This project provides an easy-to-use and fast Python package for researchers working with fMRI connectivity matrices. It's fully open-source and welcomes collaboration from the community.
-
-The package was built using recent, open-source tools and follows principles established by the neuroimaging community to align with current standards in neuroimaging research.
-
-### Next steps
-- **Expand integration tests**: Improve existing tests and add failing test cases to ensure the pipeline handles incorrect inputs and edge cases properly.
-- **Publish to PyPi**: Once the pipeline is more stable and mature, release an official version to PyPI for easier installation and distribution.
-- **Docker & Apptainer**: Containerize the pipeline to ensure full environment reproducibility using Docker and Apptainer.
-
-### Brain Hack School presentation materials
-[Project description - Week 2](https://docs.google.com/presentation/d/1BFQEd32ZGSvIpQaBQh5KjRjrZ0RL78illSvqR80Dr_E/edit?usp=sharing).
-
-[Final presentation - Week 4](https://docs.google.com/presentation/d/1AT7jvhL63toRHIYBsFZYHyxpPp-cLphSkeC5kAkJMak/edit?usp=sharing).
-
-## Aknowledgements
-This library is based on code originally published by Dr. Clara A. Moreau & Dr. Sebastian Urchs.
-
-The original version of the scripts can be found here : https://github.com/claramoreau9/NeuropsychiatricCNVs_Connectivity
-
-Thanks to Sara & Cleo for their help to create the Myst website! I would like to thank all the Professors, TAs, speakers of Brain Hack School 2025 Montreal. Special thanks to Lune Bellec who was mentoring me over the past few weeks.
-
-## References
-1. Botvinik-Nezer R, Wager TD. Reproducibility in neuroimaging analysis: Challenges and solutions. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023;8: 780–788.
-2. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8: 153–182.
-3. Waller L, Erk S, Pozzi E, Toenders YJ, Haswell CC, Büttner M, et al. ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data. Hum Brain Mapp. 2022;43: 2727–2742.
-4. Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, et al. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol. 2017;13: e1005209.
\ No newline at end of file
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2020-06-12" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Diffusion MRI- From raw data to mapping brain connectomes."
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Michèle W. MacLean]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2020/mwmaclean-BHS2020
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [dMRI, data-management, preprocessing, tracking, data-visualization]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The focus of this project was to combine, use and present a set of tools to organize, preprocess, analyze and visualize diffusion MRI data. The overarching goal is to investigate the consequences of cortical blindness on structural connectivity using diffusion MRI. "
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "connectome_glassbrain.png"
----
-
-
-
-# Diffusion MRI- From raw data to mapping brain connectomes.
-## Organizing, preprocessing, analyzing and visualizing diffusion MRI data.
-### This project is part of the Brainhack Summer School 2020.
-
-### Contributors: Michèle W. MacLean & Brainhack School Members
-
-
-
-## Summary
-
-Hello! I'm currently a PhD student in Cognitive Neuroscience at l'Université de Montréal. My main focus is to study cortical visual impairment using MRI techniques. Damage to the primary visual areas can result in clinical blindness and prompt a cascade of dynamical structural and functional alterations of the neural networks at both the cortical and subcortical level. Following this damage, individuals can sometimes preserve the ability to non-consciously process visual information in their blind field, a striking phenomenon know as blindsight. My fascination resides in understanding how the brain can process visual information without conscious awareness.
-
-The overall goal of the current project was to combine, use and present the new tools learned during the BrainHack Summer School to organize, preprocess, analyze and visualize diffusion MRI data acquired at l'Unité de neuroimagerie fonctionnelle during my PhD.
-
-
-
-
-
-# Project definition
-
-## Background
-
-This project explored the steps for analyzing diffusion magnetic resonance imaging data starting from the raw data all the way to data visualization. For this, I used previously acquired data from l'Unité de Neuroimagerie Fonctionnelle in Montréal.
-
-**Project objectives during BHS 2020:**
-The first steps were to **1)** organize the MRI data in a BIDS friendly format and then **2)** preprocess the data. Then, I explored **3)** analyzing diffusion MRI (i.e. tracking) and **4)** performed data visualization.
-
-**Overarching goal:**
-The long term goal is to investigate the consequences of a primary visual cortex lesion on structural and functional connectivity using both diffusion MRI and resting state functional connectivity.
-
-## Tools
-This project will rely on the following:
-* **GitHub** for creating a repository & assembling all the resources.
-* **Bash terminal**
-* **Visual studio code** as a code editor
-* **BIDS** as a standard to organize the neuroimaging data
-* **Python**
-* **Docker container** to run preprocessing pipeline and basic tracking
-* **DIPY** for preprocessing and basic tracking of diffusion MRI
-* **Jupyter Notebook** and for data visualization
-* **Interactive data visualization tools**: Nilearn, Plotly, Numpy, Matplotlib, Seaborn, Widgets, etc.
-
-
-## Data
-MRI data was acquired with a high resolution 3 Tesla scanner (Siemens Trio system) and consists of a preliminary data set of 5 subjects, including 1 individual with cortical visual impairment with blindsight and 4 neurotypical controls. For each participant, raw structural MRI, resting state functional connectivity, fMRI and diffusion MRI data is available.
-
-Given the time course of the summer school, after the initial preprocessing of the data, I focused on working with the diffusion MRI data. This data set will allow to first become familiarized with the new neuroimaging tools learned during the BrainHack summer school. When I will acquire a larger data set, during the rest of my PhD, I plan to incorporate it to this project.
-
-
-
-The figure above is an example of an individual with cortical visual impairment to give you an idea of the type of data, where A) shows a T1-weighted anatomical scan with three different slice views showing the primary visual cortex removal in the left hemisphere and the destruction of the primary visual areas (V1) and B) the individual's visual field showing a symmetric loss across both eyes leading to a complete contralateral visual loss in the right visual field.
-
-As the dataset is not yet open access, feel free to send me a message if you have any questions and I'll be happy to answer!
-
-## Deliverables
-For the project:
-* The current markdown document, completed and revised.
-* Organization of MRI data into BIDS format
-* The diffusion MRI data preprocessed.
-* Basic tracking performed on the preprocessed diffusion MRI data with an output of streamlines & connectivity matrices.
-* Data visualization within a jupyter notebook using plotly, nilearn and matplotlib.
-* Requirements.txt for the Jupyter notebook
-
-For the course:
-* **Week 1 Deliverable:** [Assessement](https://github.com/mwmaclean/MacLean-M-QLSC612)
-* **Week 2 Deliverable:** [README file](https://github.com/brainhack-school2020/mwmaclean-BHS2020/blob/master/README.md)
-* **Week 3 Deliverable:** [Data visualization](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/data_visualization)
-* **Week 4 Deliverable:** [Presentation](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/presentation)
-
-## Methods
-1. **Data Organization/Management.**
-* Convert dicoms to a BIDS friendly dataset. [This tutorial](http://reproducibility.stanford.edu/bids-tutorial-series-part-1b/) served as a guideline.
-
-
-2. **Preprocessing diffusion MRI data.**
-* Running a preprocessing pipeline using [this docker image](https://hub.docker.com/r/gkiar/dwipreproc_fsl-5.0.11_minified)
-* [This python script](https://github.com/gkiar/stability/blob/master/code/preprocessing/preprocessing_pipeline.py) was used within the docker container.
-* Labels from Klein & Tourville (2012) were applied.
-
-3. **Creating a seed mask.**
-* [This python scricpt](https://github.com/gkiar/mask2boundary) was used to create a seed mask for each participant.
-
-4. **Tracking of diffusion weighted images (DWI)**
-* [This docker image](https://hub.docker.com/r/gkiar/dipy_tracking) was used to perform tracking of the diffusion data.
-* [This python script](https://github.com/gkiar/stability/blob/master/code/tractography/dipy/dipy_tracking.py) was used within the container. This script is based off of [this tutorial](https://dipy.org/documentation/1.0.0./examples_built/tracking_introduction_eudx/) for DIPY tracking.
-
-5. **Data visualization**
-* Data visualization was performed within a Jupyter notebook using Plotly, Nilearn, Matplotlib, Seaborn and more (see visualization examples with gif below).
-
-
-## Progress & Results
-
-### Data visualization
-1. Connectivity matrix
-2. Connectome projected on 3 views of a 2D glass brain
-3. Connectome projected on a 3D glass brain
-
-
-
-
-
-
-
-
-* The [jupyter notebook](https://github.com/brainhack-school2020/mwmaclean-BHS2020/blob/master/data_visualization/dMRI_data_visualization.ipynb) version of all three figures is interactive with a drop-down for each subject using widgets.
-* The connectivity matrices (.mat files) used for the data visualization can be found [here](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/data_visualization/connectivity_matrices)
-* The figures (saved as .png or .html) for each subject can also be found [here](https://github.com/brainhack-school2020/mwmaclean-BHS2020/tree/master/images)
-
-### Progress overview
-This project was initiated by Michèle MacLean May 19th 2020 as part of the BrainHack Summer School 2020.
-Organization of MRI data into BIDS format, preprocessing and basic tracking of the data is complete as well as data visualization for the deliverable for week 3. See the methods section for a detailed description. This will be an ongoing project that I will continue to work on after BrainHack Summer School. Feedback is welcome!
-
-#### Tools and skills I learned during this project
-* **Bash terminal**
-* **GitHub**
-* **Markdown**
-* **Visual Studio Code**
-* How to implement **BIDS**
-* **Python** scripts
-* **Docker container**
-* **DIPY** for preprocessing and basic tracking of diffusion MRI
-* **Jupyter Notebook**
-* Interactive figures in Jupyter Notebook using **data visualization libraries**: Nilearn, plotly & matplotlib, etc.
-* **Preprocessing diffusion MRI data**
-* **Tracking diffusion MRI data**
-* **Mapping connectomes**
-
-
-
-
-
-## Conclusion
-The BrainHack Summer School 2020 provided a theoretical framework as well as an extensive set of practical neural data analysis and visualization tools that were mainly all new to me. For the current project, my goal was to maximize using these new tools, to learn and apply the open science protocols in order to analyze data within my own doctoral project. Within the time frame of the summer school, I set out to perform all the steps starting from raw diffusion MRI data, which I previously acquired at l'Unité de neuroimagerie fonctionnelle, leading to data visualization. In particular, this dataset was organized into a BIDS friendly format, preprocessed with a python script within a docker container, basic tracking was also performed using another python script within a second container and finally data visualization was done using multiple libraries within a jupyter notebook. As a neuroscientist with a limited previous background in programming, I believe I sensibly improved my skills towards performing independently the whole process to analyze neuroimaging data. As I plan to continue working on this project with the new tools learned, my next immediate step will be to increase the number of subjects. Also, my next challenge will be to specifically account for brain injuries leading to cortical visual impairment during the analyses as this requires additional algorithmic tools and modifications to the preprocessing and analysis pipelines.
-
-
-## Acknowledgements
-Thanks to the wonderful BrainHack Summer School 2020 for this experience as well as all the excellent resources provided!
-I would like to thank my instructors Noor and Benjamin for their advice and for providing specific resources for this project. Special thanks to my instructor Greg who originally developed the python scripts and docker images I used and invested a lot of time to help and contribute to this project. I would also like to thank my new friend and coding partner Daniel and my friend Simon who was a great study buddy throughout the summer school!
-
-Be sure to check out the other cool projects from the BrainHack Summer School 2020 [here](https://school.brainhackmtl.org/project/)!
-
-## References
-1. Abraham, A., Pedregosa1, F., Eickenberg1, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics 8: 14. https://doi.org/10.3389/fninf.2014.00014 (for using nilearn)
-2. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I and Dipy Contributors (2014). DIPY, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, vol.8, no.8.
-3. Gorgolewski, K.J., Auer, T., Calhoun, V.D., Craddock, R.C., Das, S., Duff, E.P., Flandin, G., Ghosh, S.S., Glatard, T., Halchenko, Y.O., Handwerker, D.A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A., 2016. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3, 160044.
-4. Kiar, G. 
Unfiltered Cz Channel during 1 second epoch.
-
-
Filtered Cz Channel during 1 second epoch.
-
-
Unfiltered F3 Channel during 1 second epoch.
-
-
Filtered F3 Channel during 1 second epoch.
-
-
Power Spectral Density (PSD) plot for the unfiltered EEG.
-
-
Power Spectral Density (PSD) plot for the filtered EEG.
-
-
EEG cap sensor positions.
-
-##### Machine Learning
-
-
Feature Importance (Random Foreest)
-
-
Confusion Matrix (Random Forest)
-
-
Feature Importance (kNN)
-
-
Confusion Matrix (kNN)
-
-
Feature Importance (Logit)
-
-
Confusion Matrix (Logit)
-
-
Feature Importance (Multilayer Perceptron)
-
-
Confusion Matrix (Multilayer Perceptron)
-
-#### Discussion
-
-The full results can be viewed in the Jupyter notebook ml_eeg_classifier in our GitHub repository. We briefly summarise the accuracy rates attained by each model below.
-
-Random Forest: 0.8923478119142694
-
-Logistic Regression: 0.5955504848830576
-
-k-Nearest Neighbours: 0.9759595794963736
-
-Multilayer Perceptron: 0.6984760818189226
-
-A prima facie look will show that the k-Nearest Neighbours (kNN) classifier achieved the highest performance in accurately predicting whether the participant was looking at positive or negative feedback based on the EEG data. However, it is possible that the high accuracy rate may be due to overfitting, and more sophisticated evaluation metrics will need to be consulted (unfortunately, time limits prevent us from doing so here).
-
-Future directions for this project could involve exploring more advanced machine learning techniques, such as deep learning models, to capture complex patterns in the EEG data. Additionally, incorporating other modalities available in the multimodal dataset, such as eye-tracking or cursor movements, could potentially enhance the prediction accuracy and provide a more comprehensive understanding of the cognitive processes during language learning.
-
-## Conclusion and acknowledgement
-
-In summary, this project provides a starting point for applying machine learning methods to EEG data analysis in the context of language learning. The results highlight the potential of using EEG features to predict certain cognitive tasks, but further research is required to refine this (admittedly) very coarse implementation and address the aforementioned limitations.
-
-We humbly thank the Brainhack School Team for organising this fantastic learning journey, as well as all the instructors who developed the learning modules and corresponding video lectures/lessons. We would especially like to thank the Taiwan Hub team (National Taiwan University and National Central University) for all their support and encouragement.
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2022-07-05" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "An easy guide to not throwing your expensive computer out the window because you can't run a Python neuroimaging tool"
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Claudéric DeRoy]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2022/deroy_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [python, scripting, packaging, replicability]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "The goal of this project was to learn how to create code that would be easy to use for unexperienced users but also to be as more open as possible while also being replicable. So I took a code already written and scripted it, packaged it, made a Docker container for it, and finally created a guide on how to use it."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "python_code.jpg"
----
-
-
-# Project definition
-
-## Personnal Background
-Hello there! My name is Claudéric DeRoy. I have completed a Bachelor in Cognitive Neuroscience at Université de Montréal and also a part of a Bachelor degree in Computer Science, also at Université de Montréal. I am actually a master student in psychology in the NeCS lab under the supervision of Professor Sébastien Hétu. I essential study some computer programmes and pipelines for preprocessing electrodermal activity (EDA) to propose guidelines on how to signal process EDA to the scientific community.
-
-
-## Project Background
-The more theoretical background is from another [project](https://github.com/brainhack-school2022/Lajoie_project/blob/main/project_description.md) of the Brainhack School 2022. The background for this project is more of a practical one. Essentially, computer programmes or computer analysis pipelines for neuroimaging are quite common and can be pretty intimidating for people who are not used to programming in general, which corresponds to a lot of undergrad and graduate students in psychology. Fortunately, there are many tools that can make the installation of libraries, modules, virtual environments and the rest easier or at least less painful. This also helps on another level of research. In 2012, Gronenschild et al. obtained a difference in percentage of volume and cortical thickness using FreeSurfer on both a Macintosh and a Hewlett-Packard machine but also between two different versions of Mac OSX (OSX 10.5 and OSX 10.6). This demonstrates the importance of packaging and containerized programmes so that scripts are always run in the same environment, but also making those containers available so that the scientific community can use the same tools in the same environment, helping rule out the difference between software as a cause for reproducible failure.
-
-
-## Objectives/Goals
-The main goals of this project are to familiarise myself with scripting, packaging Python code, making Python scripts run more easily on high performance computers (HPC) for the average user, and containeraizing the whole thing. This will help make it easy for inexperienced users to get everything from my Github repository and execute the script either on HPC or their personal machine. So the main goals are :
-- Take the existing Python code for the seed to voxel correlation and make a script that will make it easy to run from the terminal.
-- Do the same thing but for the machine learning algorithm that will use the seed to voxel correlation to classify.
-- Use Git and Github to create version control of all the scripts.
-- Getting familiar with HPC, especially Calcul Canada (Alliance), using Singularity with Docker image or file to use them on Calcul Canada.
-- Containerize everything at the end so that the entire project can be easily installed on any computer or HPC.
-- Make an easy-to-use guide so people with little to no knowledge of computer science would be able to use and deploy the Docker container or execute the script.
-
-
-## Tools used
-- Python
-- Git/Github
-- Script and packaging with Python
-- Bash
-- HPC (Calcul Canada or Brainhack ORACLE)
-
-
-## Data
-I won't work with data, per se but more with code. However, I will use the other [project](https://github.com/brainhack-school2022/Lajoie_project/blob/main/project_description.md) data to make sure everything works correctly.
-
-
-## Deliverables
-I want to produce by the end of the project :
-- The script, packaging, and containerization of at least the seed to voxel correlation code.
-- In the best case scenario, I will do the same for the machine learning code.
-- Some sort of tutorial (either a markdown file or a video) to explain to someone who has no background in computer science how to install everything and run the code.
-
-
-# Results
-I do not have results in the more general term. As previously stated, my goal is to make an existing code more accessible, easy to use, open and replicatable. So, I will lay out what I accomplished in terms of these goals.
-
-## Accessibility
-I packaged everything I produced during this project. There are four important files : the `script.py`, `batch.json`, `seed_voxel.py` and `Dockerfile`. They are part of the packaging I did, which is available on TestPyPi (https://test.pypi.org/project/seed-to-voxel-neurok8050/). So, it is more accessible because anybody can install the package with a simple `pip install` (see the [`guide.md`](https://github.com/brainhack-school2022/deroy_project/blob/main/guide.md) for a full installation explication).
-
-## Ease of use
-The idea was to make the code as easy to use as possible. Often, when you have access to code that a researcher has used for a paper and you want to use it and have access to it, it is somewhat of a daunting task; you do not really know where to start or what to modify, because everything is "baked" into the code itself (variables, values, etc.). So my original idea was to use a `.txt` with all thes variables names and their values, so that the script could just parse it and plug the variables into the right places. The user would just have to modify the `.txt` file and would not have to play around in the source code (although he is more than welcome to do so). It turns out that `.txt` are not suited for such a task, but `.json` file are perfect for this purpose. That is where the `batch.json` comes from. Finally, when the user wants to run the `script.py` he only has to pass one argument (the `.json` file) instead of 20.
-
-## Openness
-This is fairly simple, everything is accessible on my GitHub repository. So feel absolutely free to `git clone` my repository and have fun with it. I really do not mind. Have fun and make it yours, that is the joy of programming.
-
-## Replicability
-Creating a Docker container (actually writing a Dockerfile) will help (in my humble opinion) to run the code more replicable. Like I said above, Gronenschild et al. (2012) have shown a certain degree of variability between different versions of the same OS and also between different versions of the same software. Using a Docker container constrains the version of software used during the execution of the code. This version constraint will be the same for every user that runs the code inside the Docker container. But also, in each run, the same user will technically have the same software version each time, thus ruling out the software as a potential point of source failure for replicated study.
-Also, the `.json` also helps for replicability. For example, if I run the code with an edited version of the `batch.json` by me, I can publish the article with my edited `batch.json` so that any researcher who is trying to replicate my finding will only have to run the code with the `batch.json` I provided in my article.
-
-
-## The tools I learned and used for this project
-
-- Scripting in Python (shebang is the rule)
-- Packaging Python code (setup.py, pyproject.toml) and uploading it to TestPyPi
-- Docker container (This was by far the most difficult thing)
-- High performance computer (I wanted to use Singularity to make the Docker container work on Compute Canada, but didn't have time to do it)
-
-# Conclusion
-
-To conclude, I managed to complete most of my objectives. Now I want to focus on deploying it and seeing if the users are capable of using the package, and make changes depending on the user experience with it. I did not package the machine learning algorithm part, but it is part of the next steps I want to take with this project, alongside adding the Singularity part for a good integration of the Dockerfile to HPC. I am really pleased with what I learned during Brainhack school. I have sort of a template for scripting, packaging, and containerizing all of my future projects.
-
-## Reference
-Gronenschild, E. H. B. M., Habets, P., Jacobs, H. I. L., Mengelers, R., Rozendaal, N., van Os, J., & Marcelis, M. (2012). The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements. PLoS ONE, 7(6), e38234. https://doi.org/10.1371/journal.pone.0038234
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-19" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "EEG Athlete Project: Brain Activity During Golf Performance"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Samantha Bjelis, Fardin Islam]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/brainhack-school2025/islam_project
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2023/JDaoust_project.git), click `manage topics`.
-# Please only lowercase letters
-tags: ["EEG", "sports neuroscience", "golf", "data analysis", "bootstrapping"]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Using EEG band power to investigate cognitive states during golf swings and correlate them with subjective performance ratings."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "project_image.png"
----
-
-## Project definition
----
-### Background
-
-Cognitive states such as focus, relaxation, and arousal are known to influence athletic performance, particularly in precision sports like golf. Electroencephalography (EEG) offers a non-invasive way to monitor neural activity and derive insights about cognitive engagement through frequency band analysis. Prior studies have associated increased alpha and theta power with focused attention and automaticity, while beta and gamma activity are linked to heightened alertness and motor preparation.
-
-The aim of this project is to explore how EEG-derived band power dynamics relate to subjective performance ratings of golf swings, as rated by the athletes themselves. This work contributes to a growing field of neurofeedback and cognitive training in sports.
-
-### Tools
-Python Libraries:
- MNE: EEG preprocessing and visualization
- numpy, pandas: Data manipulation
- matplotlib, seaborn: Plotting and statistical graphics
- scipy.stats: Correlation analysis
- sklearn: (optional for classification or clustering)
-
-Signal Processing: Welch’s method for power spectral density estimation.
-
-### Data
-Participants: Golf athletes performing standardized swing tasks.
-EEG Data: Multi-channel EEG signals recorded during golf swings.
-Subjective Ratings: Swing quality scores rated by the athletes on a Likert-type scale immediately post-swing.
-Electrode Layout: Standard 10-20 system with key channels such as Fz, Cz, Pz, etc.
-
-### Pipeline
-Preprocessing
-Bandpass filter: 1–50 Hz
-Artifact rejection (e.g., blink or muscle noise via thresholding or ICA)
-
-Feature Extraction
-Compute Relative Band Power for:
- Theta (4–8 Hz)
- Alpha (8–12 Hz)
- Beta (12–30 Hz)
- Gamma (30–45 Hz)
-Normalize power per epoch across channels
-
-Statistical Analysis
-Correlation analysis: Pearson’s r between each frequency band (per channel) and shot quality scores.
-Bootstrapping: Resample correlation coefficients to assess stability and confidence intervals, especially for alpha band (often hypothesized to relate to motor preparation).
-Topographical Maps: Visualize band power distributions across the scalp for high- vs. low-rated swings
-
-### Project deliverables
-At the end of this project, these files will be made available:
-1) To extract relative band power (Theta, Alpha, Beta, Gamma) from EEG recordings during golf swing preparation and execution.
-2) To assess the relationship between EEG features and subjective swing performance scores.
-3) To explore statistical and visual analysis methods (e.g., correlation, bootstrapping, topographical mapping) that may inform real-time cognitive training tools.
-
-## Results
-Correlation Between EEG Band Power and Shot Score
-Scatter plots were generated to assess linear relationships between relative EEG band power and subjective golf shot scores (rated out of 10).
-Key findings:
- Alpha band power showed a moderate positive correlation with shot scores (r = 0.43), suggesting increased alpha may relate to better perceived performance.
- Theta, Beta, and Gamma bands showed weak or no correlation with shot quality (r = -0.07, 0.00, and 0.14 respectively), indicating less consistent relationships.
-
-##### Figure 1. Correlation between EEG power bands and score
-
-
-EEG Topographical Maps
-Topographic visualizations of average relative EEG power for each frequency band (from Muse headset – 4 channels) reveal distinct spatial patterns:
- Alpha activity is more prominent over posterior regions, consistent with literature linking it to relaxed focus and motor readiness.
- Gamma activity showed asymmetric right-side prominence.
- Beta and Theta distributions were more uniformly diffuse, with Beta slightly higher at posterior sites.
-
-##### Figure 2. EEG Topographical Map for One Shot
-
-
-### Tools I learned during this project
-This project helped me build hands-on experience across several domains of neurodata analysis and scientific computing:
-MNE-Python: I learned to use the MNE library to preprocess EEG data, extract band power using Welch’s method, and visualize scalp topographies. This was my first time working with MNE, and it gave me a strong foundation in EEG signal workflows.
-Scipy.stats: I applied statistical functions such as Pearson correlation and bootstrapping to quantify relationships between brain activity and behavioral performance — gaining confidence in interpreting significance levels and confidence intervals.
-Git & GitHub Workflow: I practiced version control using Git and contributed to the BrainHack School repository via pull requests. I also learned how to structure markdown-based project documentation.
-
-## Conclusion and acknowledgement
-This preliminary analysis supports the potential of EEG-based monitoring for assessing cognitive states in sports performance. Alpha power, in particular, may serve as a target for real-time neurofeedback to enhance swing execution. Future work could involve real-time decoding models, athlete-specific baselines, and integration into wearable feedback systems.
-
-I would like to thanks all the Brainhack School organizators and all the crew. Mostly, thanks for this awesome opportunity!
-
-## References
-Crews, D. J., & Landers, D. M. (1993). Electroencephalographic measures of attentional patterns prior to the golf putt. Medicine & Science in Sports & Exercise, 25(1), 116–126. https://doi.org/10.1249/00005768-199301000-00018
-
-Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., ... & Hämäläinen, M. S. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7, 267. https://doi.org/10.3389/fnins.2013.00267
-
-Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017). Neurofeedback as supplementary training for optimizing athletes' performance: A systematic review with implications for future research. Neuroscience & Biobehavioral Reviews, 75, 419–432. https://doi.org/10.1016/j.neubiorev.2017.02.005
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-ZoneId=3
-ReferrerUrl=https://www.google.com/
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----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-20" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Automated detection of focal epilepsy based on brain morphometrics"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Arthur Toulouse]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/epilepsy-detection
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [MRI, PCA, Epilepsy, Visualization]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "epilepsy.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer.
-FreeSurfer enables the extraction of detailed cortical morphometric features such as thickness, surface area, curvature, and volume across predefined brain regions. These quantitative features can potentially reflect subtle structural anomalies associated with focal epilepsy. By using a mixed-age dataset of patients with focal epilepsy, we aim to build a machine learning model that predicts the anatomical region of the epileptic focus based on these morphometric biomarkers. This approach could contribute to more objective and automated presurgical evaluation workflows.
-
-
-### Objectives
-
- * To develop a machine learning model capable of classifying the anatomical region of seizure onset in focal epilepsy patients using cortical morphometric features extracted from T1-weighted MRI scans.
- * To evaluate the performance of different classifiers and identify the most informative brain features associated with focal epileptogenic zones.
-
-
-### Tools
-
- * GitHub – for version control and collaborative code management
- * DataLad – to manage and track neuroimaging datasets in a reproducible way
- * Python – for data processing, feature extraction, and model development
- * Bash – for automation and scripting of preprocessing pipelines
- * FreeSurfer – to extract cortical morphometric features from T1-weighted MRI scans
- * scikit-learn – for implementing and evaluating machine learning classification models
-
-### Data
-
-
-This study uses the Imaging Database for Epilepsy and Surgery ([IDEAS](https://pubmed.ncbi.nlm.nih.gov/39636622/)), an open-source dataset available on OpenNeuro. The dataset includes 442 pre-operative T1-weighted and FLAIR MRI scans from patients with focal epilepsy, along with 100 healthy control scans acquired on the same scanner for consistency. In addition to imaging, the dataset provides rich metadata such as age of seizure onset, sex, number of medications, scan and surgery timing, histopathological findings, and surgical outcomes. The additional data can be found [here](https://openneuro.org/datasets/ds005602/versions/1.0.0)
-
-
-
-
-### Deliverables
-
-At the end of this project, we will have a [GitHub repository](https://github.com/school-brainhack/epilepsy-detection) containing the code :
- * data.ipynb to process the data from freeSurfer
- * model.ipynb to run the classification models
- * the processed csv files obtained after running the data.ipynb for testing purposes
-
-## Results
-
-The first model was a binary classification task distinguishing between epileptic patients and healthy controls based on cortical morphometric features extracted from T1-weighted MRI using FreeSurfer. The classifier achieved promising results, with a receiver operating characteristic (ROC) curve showing a clear separation between the two groups. The area under the curve (AUC) was approximately 0.85, indicating that the model was able to effectively capture structural differences associated with epilepsy.
-
-
-
-In the second model, the task was extended to multiclass classification to predict the specific anatomical region of the epileptic focus using the full set of regions. However, the performance of this model was limited. This can be attributed to both class imbalance and the increased difficulty of distinguishing between many finely segmented areas in a relatively small dataset.
-
-
-
-To address this, a third model was developed where only regions with a significant number of patients are used. This simplification led to a noticeable improvement in classification performance.
-
-
-
-
-
-## Conclusion and acknowledgement
-
-This project demonstrates the feasibility of using morphometric features extracted from structural MRI to classify focal epilepsy and localize the seizure onset zone. While binary classification between epileptic and control subjects showed strong performance, multiclass classification across specific brain regions remains a challenging task.These results highlight both the potential and the limitations of current approaches, pointing toward the need for larger, balanced datasets and more advanced modeling techniques.
-
-I gratefully acknowledge the use of the IDEAS dataset (Imaging Database for Epilepsy And Surgery), an open-access resource available on OpenNeuro. This dataset was developed to support research in epilepsy diagnosis and surgical planning, and I thank the authors and contributors for making this valuable resource publicly available.
-Taylor PN, Wang Y, Simpson C, Janiukstyte V, Horsley J, Leiberg K, Little B, Clifford H, Adler S, Vos SB, Winston GP, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS. The Imaging Database for Epilepsy And Surgery (IDEAS). Epilepsia. 2025 Feb;66(2):471-481. doi: 10.1111/epi.18192. Epub 2024 Dec 5. PMID: 39636622; PMCID: PMC11827737.
-
-I also gratefully acknowledge the developers of FreeSurfer, whose open-source neuroimaging software made it possible to extract detailed cortical morphometric features essential to this analysis.
-
-Finaly, I would also like to sincerely thank the Brainhack School for providing this unique opportunity to explore applied neuroimaging and data science in a collaborative setting. Special thanks to Eva and Sebastian for their guidance, support, and insightful feedback throughout the project.
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diff --git a/content/en/project/epilepsy-detection/data.ipynb b/content/en/project/epilepsy-detection/data.ipynb
deleted file mode 100644
index e4cc8bf3..00000000
--- a/content/en/project/epilepsy-detection/data.ipynb
+++ /dev/null
@@ -1,584 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "c213b6e7",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import re\n",
- "import os\n",
- "from pathlib import Path\n",
- "import glob\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "85f36dc2",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Transform the age range in a single value\n",
- "def convert_age_range_to_midpoint(age_string):\n",
- " \n",
- " if pd.isna(age_string):\n",
- " return None\n",
- " \n",
- " numbers = re.findall(r'\\d+', str(age_string))\n",
- " \n",
- " if len(numbers) >= 2:\n",
- " min_age = int(numbers[0])\n",
- " max_age = int(numbers[1])\n",
- " return (min_age + max_age) / 2\n",
- " elif len(numbers) == 1:\n",
- " \n",
- " return float(numbers[0])\n",
- " else:\n",
- " \n",
- " return None\n",
- "\n",
- "# Transform the files Metadata_Controls_Release.csv and Metadata_Release_Anon.csv in the Subject folder in a single dataframe\n",
- "def create_demographics_dataframe():\n",
- " \n",
- " controls_df = pd.read_csv(r'.\\Subject\\Metadata_Controls_Release.csv')\n",
- " release_df = pd.read_csv(r'.\\Subject\\Metadata_Release_Anon.csv')\n",
- " \n",
- " \n",
- " controls_subset = controls_df[['ID', 'Sex', 'Binned_Age_at_Scan']].copy()\n",
- " release_subset = release_df[['ID', 'Sex', 'Binned_Age_at_Scan']].copy()\n",
- " \n",
- " \n",
- " controls_subset['Age'] = controls_subset['Binned_Age_at_Scan'].apply(convert_age_range_to_midpoint)\n",
- " release_subset['Age'] = release_subset['Binned_Age_at_Scan'].apply(convert_age_range_to_midpoint)\n",
- " \n",
- " \n",
- " controls_subset = controls_subset[['ID', 'Sex', 'Age']] #We only keep the features in all patients\n",
- " release_subset = release_subset[['ID', 'Sex', 'Age']]\n",
- " \n",
- " \n",
- " combined_df = pd.concat([controls_subset, release_subset], ignore_index=True)\n",
- " \n",
- " combined_df = combined_df.drop_duplicates(subset=['ID'], keep='first')\n",
- " combined_df = combined_df.sort_values('ID').reset_index(drop=True)\n",
- " \n",
- " return combined_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "91e67fe4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dataframe saved as 'combined_demographics.csv'\n"
- ]
- }
- ],
- "source": [
- "demographics_df = create_demographics_dataframe()\n",
- "\n",
- "demographics_df.to_csv('combined_demographics.csv', index=False)\n",
- "print(\"\\nDataframe saved as 'combined_demographics.csv'\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "ee60a35d",
- "metadata": {},
- "outputs": [],
- "source": [
- "#Read a FreeSurfer stats file and return a DataFrame\n",
- "def read_freesurfer_stats(filepath):\n",
- " \n",
- " with open(filepath, 'r') as file:\n",
- " lines = file.readlines()\n",
- " \n",
- " \n",
- " for i, line in enumerate(lines):\n",
- " if line.startswith('# ColHeaders'):\n",
- " headers = line.strip().split()[2:] \n",
- " data_start_idx = i + 1\n",
- " break\n",
- " else:\n",
- " raise ValueError(\"No column headers found in the file.\")\n",
- " \n",
- " \n",
- " data = []\n",
- " for line in lines[data_start_idx:]:\n",
- " if not line.startswith('#') and line.strip(): \n",
- " data.append(line.strip().split())\n",
- " \n",
- " df = pd.DataFrame(data, columns=headers)\n",
- " \n",
- " \n",
- " for col in df.columns:\n",
- " df[col] = pd.to_numeric(df[col], errors='ignore')\n",
- " \n",
- " return df\n",
- "\n",
- "def load_additional_data(csv_filepath, subject_id_column='Subject_ID'):\n",
- " \"\"\"\n",
- " Load additional patient data from CSV file\n",
- " \n",
- " Parameters:\n",
- " -----------\n",
- " csv_filepath : str\n",
- " Path to the CSV file containing additional patient data\n",
- " subject_id_column : str\n",
- " Name of the column containing subject IDs in the CSV\n",
- " \n",
- " Returns:\n",
- " --------\n",
- " pd.DataFrame\n",
- " DataFrame with additional patient data, indexed by subject ID\n",
- " \"\"\"\n",
- " try:\n",
- " # Read the CSV file\n",
- " additional_data = pd.read_csv(csv_filepath)\n",
- " \n",
- " # Convert subject ID column to string to match FreeSurfer data\n",
- " additional_data[subject_id_column] = additional_data[subject_id_column].astype(str)\n",
- " \n",
- " # Set subject ID as index\n",
- " additional_data.set_index(subject_id_column, inplace=True)\n",
- " \n",
- " print(f\"Successfully loaded additional data from: {csv_filepath}\")\n",
- " print(f\"Additional data shape: {additional_data.shape}\")\n",
- " print(f\"Additional data columns: {list(additional_data.columns)}\")\n",
- " \n",
- " return additional_data\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error loading additional data from {csv_filepath}: {str(e)}\")\n",
- " return None\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "1c014abe",
- "metadata": {},
- "outputs": [],
- "source": [
- "def process_lh_aparc_a2009s(base_dir):\n",
- " \n",
- " subject_base = os.path.join(base_dir, 'Subject')\n",
- " \n",
- " if not os.path.exists(subject_base):\n",
- " raise ValueError(f\"Subject directory not found: {subject_base}\")\n",
- " \n",
- " all_data = {}\n",
- " processed_subjects = []\n",
- " \n",
- " \n",
- " subject_dirs = [d for d in os.listdir(subject_base) \n",
- " if os.path.isdir(os.path.join(subject_base, d)) and d.isdigit()]\n",
- " \n",
- " print(f\"Found {len(subject_dirs)} subject directories: {sorted(subject_dirs)}\")\n",
- " \n",
- " for subject_id in subject_dirs:\n",
- " \n",
- " stats_path = os.path.join(subject_base, subject_id, 'stats', 'lh.aparc.a2009s.stats')\n",
- " \n",
- " if not os.path.exists(stats_path):\n",
- " print(f\"Warning: {stats_path} not found for subject {subject_id}\")\n",
- " continue\n",
- " \n",
- " try:\n",
- " \n",
- " df = read_freesurfer_stats(stats_path)\n",
- " \n",
- " # TO CHANGE TO INCLUDE LESS FEATURES\n",
- " measure_columns = ['SurfArea', 'GrayVol', 'ThickAvg', 'ThickStd', 'MeanCurv', 'GausCurv', 'FoldInd', 'CurvInd']\n",
- " \n",
- " \n",
- " available_measures = [col for col in measure_columns if col in df.columns]\n",
- " \n",
- " if not available_measures:\n",
- " print(f\"Warning: No standard measures found in {subject_id}\")\n",
- " continue\n",
- " \n",
- " \n",
- " subject_data = {}\n",
- " for _, row in df.iterrows():\n",
- " region = row['StructName'] \n",
- " for measure in available_measures:\n",
- " column_name = f\"{measure}_{region}\"\n",
- " subject_data[column_name] = row[measure]\n",
- " \n",
- " all_data[subject_id] = subject_data\n",
- " processed_subjects.append(subject_id)\n",
- " \n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error processing subject {subject_id}: {str(e)}\")\n",
- " continue\n",
- " \n",
- " if not all_data:\n",
- " raise ValueError(\"No data was successfully processed\")\n",
- " \n",
- "\n",
- " \n",
- " result_df = pd.DataFrame.from_dict(all_data, orient='index')\n",
- " result_df.index.name = 'Subject'\n",
- " \n",
- " \n",
- " result_df = result_df.reindex(sorted(result_df.index, key=lambda x: int(x)))\n",
- " \n",
- " \n",
- " def get_epileptic_status(subject_id):\n",
- " subject_num = int(subject_id)\n",
- " if 1 <= subject_num <= 463:\n",
- " return 'Epileptic'\n",
- " elif subject_num >= 4000:\n",
- " return 'Not Epileptic'\n",
- " else:\n",
- " return 'Unknown'\n",
- " \n",
- " result_df['Epileptic_Status'] = [get_epileptic_status(subj) for subj in result_df.index]\n",
- " \n",
- " cols = ['Epileptic_Status'] + [col for col in result_df.columns if col != 'Epileptic_Status']\n",
- " result_df = result_df[cols]\n",
- " \n",
- " other_cols = sorted([col for col in result_df.columns if col != 'Epileptic_Status'])\n",
- " result_df = result_df[['Epileptic_Status'] + other_cols]\n",
- " \n",
- " print(f\"\\nSuccessfully processed {len(processed_subjects)} subjects\")\n",
- " print(f\"Final dataset shape: {result_df.shape}\")\n",
- " \n",
- " return result_df\n",
- " \n",
- "def process_lh_aparc_a2009s_with_additional_data(base_dir, additional_csv_path=None, \n",
- " subject_id_column='Subject_ID'):\n",
- " \n",
- " \n",
- " \n",
- " freesurfer_data = process_lh_aparc_a2009s(base_dir)\n",
- " \n",
- " if freesurfer_data is None:\n",
- " print(\"Failed to process FreeSurfer data\")\n",
- " return None\n",
- " \n",
- " # If no additional CSV provided, return original data\n",
- " if additional_csv_path is None:\n",
- " print(\"No additional CSV provided, returning FreeSurfer data only\")\n",
- " return freesurfer_data\n",
- " \n",
- " \n",
- " additional_data = load_additional_data(additional_csv_path, subject_id_column)\n",
- " \n",
- " if additional_data is None:\n",
- " print(\"Failed to load additional data, returning FreeSurfer data only\")\n",
- " return freesurfer_data\n",
- " \n",
- " \n",
- " print(f\"FreeSurfer data subjects: {sorted(freesurfer_data.index.tolist(), key=int)}\")\n",
- " print(f\"Additional data subjects: {sorted(additional_data.index.tolist(), key=lambda x: int(x) if x.isdigit() else float('inf'))}\")\n",
- " \n",
- " \n",
- " merged_data = freesurfer_data.join(additional_data, how='left')\n",
- " \n",
- " \n",
- " freesurfer_subjects = set(freesurfer_data.index)\n",
- " additional_subjects = set(additional_data.index)\n",
- " \n",
- " matched_subjects = freesurfer_subjects.intersection(additional_subjects)\n",
- " freesurfer_only = freesurfer_subjects - additional_subjects\n",
- " additional_only = additional_subjects - freesurfer_subjects\n",
- " \n",
- " print(f\"\\nMerge Results:\")\n",
- " print(f\" Subjects with both FreeSurfer and additional data: {len(matched_subjects)}\")\n",
- " print(f\" Subjects with only FreeSurfer data: {len(freesurfer_only)}\")\n",
- " \n",
- " \n",
- " if freesurfer_only:\n",
- " print(f\" FreeSurfer-only subjects: {sorted(list(freesurfer_only), key=int)}\")\n",
- " \n",
- " if additional_only:\n",
- " print(f\" Additional-only subjects: {sorted(list(additional_only), key=lambda x: int(x) if x.isdigit() else float('inf'))}\")\n",
- " \n",
- " \n",
- " epileptic_col = ['Epileptic_Status']\n",
- " additional_cols = [col for col in additional_data.columns if col in merged_data.columns]\n",
- " freesurfer_cols = [col for col in merged_data.columns \n",
- " if col not in epileptic_col and col not in additional_cols]\n",
- " \n",
- " final_column_order = epileptic_col + additional_cols + sorted(freesurfer_cols)\n",
- " merged_data = merged_data[final_column_order]\n",
- " \n",
- " \n",
- " \n",
- " return merged_data\n",
- "\n",
- "\n",
- " \n",
- "def process_with_additional_data(): \n",
- " \n",
- " base_directory = r\"\"\n",
- " \n",
- " # Path to your additional CSV file \n",
- " additional_csv_path = r'.\\combined_demographics.csv' # UPDATE THIS PATH\n",
- " \n",
- " subject_id_column = 'ID' # UPDATE THIS IF THE COLUMN HAS A DIFFERENT NAME\n",
- " \n",
- " try:\n",
- " \n",
- " combined_data = process_lh_aparc_a2009s_with_additional_data(\n",
- " base_directory, \n",
- " additional_csv_path, \n",
- " subject_id_column\n",
- " )\n",
- " \n",
- " if combined_data is not None:\n",
- " \n",
- " \n",
- " epileptic_cols = [col for col in combined_data.columns if 'Epileptic' in col]\n",
- " additional_cols = [col for col in combined_data.columns \n",
- " if col not in epileptic_cols and not any(measure in col for measure in \n",
- " ['SurfArea', 'GrayVol', 'ThickAvg', 'ThickStd', 'MeanCurv', 'GausCurv', 'FoldInd', 'CurvInd'])]\n",
- " freesurfer_cols = [col for col in combined_data.columns \n",
- " if col not in epileptic_cols and col not in additional_cols]\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " output_file = 'lh.csv'\n",
- " combined_data.to_csv(output_file)\n",
- " print(f\"\\nCombined data saved to: {output_file}\")\n",
- " \n",
- " \n",
- " missing_summary = combined_data.isnull().sum()\n",
- " cols_with_missing = missing_summary[missing_summary > 0]\n",
- " \n",
- " if len(cols_with_missing) > 0:\n",
- " print(f\"\\nColumns with missing values:\")\n",
- " for col, count in cols_with_missing.items():\n",
- " percentage = (count / len(combined_data)) * 100\n",
- " print(f\" {col}: {count} missing ({percentage:.1f}%)\")\n",
- " else:\n",
- " print(f\"\\nNo missing values in the dataset!\")\n",
- " \n",
- " return combined_data\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error processing data: {str(e)}\")\n",
- " return None\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "82d12d1d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found 542 subject directories: ['1', '10', '100', '101', '102', '103', '104', '105', '106', '108', '109', '11', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '12', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '13', '130', '131', '132', '133', '134', '135', '136', '137', '138', '14', '141', '142', '143', '144', '145', '146', '147', '148', '149', '15', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '16', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '17', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '18', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '19', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '2', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '21', '210', '211', '213', '214', '215', '216', '217', '218', '219', '22', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '23', '230', '231', '232', '233', '234', '235', '236', '237', '238', '24', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '25', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '26', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '27', '270', '271', '273', '275', '276', '277', '278', '279', '28', '280', '281', '282', '283', '285', '286', '287', '288', '289', '29', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '3', '30', '300', '301', '302', '303', '304', '305', '306', '308', '309', '31', '310', '311', '312', '313', '314', '315', '316', '317', '319', '32', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '33', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '34', '340', '341', '343', '344', '345', '346', '347', '348', '349', '35', '350', '351', '352', '353', '354', '355', '356', '357', '358', '36', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '37', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '38', '380', '381', '382', '383', '384', '385', '387', '388', '389', '39', '390', '391', '392', '393', '394', '395', '396', '397', '399', '4', '40', '400', '4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009', '401', '4010', '4011', '4012', '4013', '4014', '4015', '4016', '4017', '4018', '4019', '402', '4020', '4021', '4022', '4023', '4024', '4025', '4026', '4027', '4028', '4029', '403', '4030', '4031', '4032', '4033', '4034', '4035', '4036', '4037', '4038', '4039', '404', '4040', '4041', '4042', '4043', '4044', '4045', '4046', '4047', '4048', '4049', '405', '4050', '4051', '4052', '4053', '4054', '4055', '4056', '4057', '4058', '4059', '406', '4060', '4061', '4062', '4063', '4064', '4065', '4066', '4067', '4068', '4069', '407', '4070', '4071', '4072', '4073', '4074', '4075', '4076', '4077', '4078', '4079', '408', '4080', '4081', '4082', '4083', '4084', '4085', '4086', '4087', '4088', '4089', '409', '4090', '4091', '4092', '4093', '4094', '4095', '4096', '4097', '4098', '4099', '41', '410', '4100', '411', '412', '413', '414', '415', '416', '417', '418', '419', '42', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '43', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '44', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '45', '450', '451', '452', '453', '454', '455', '459', '46', '460', '461', '462', '463', '47', '48', '49', '5', '50', '51', '53', '54', '55', '56', '57', '58', '59', '6', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '7', '70', '71', '72', '74', '75', '76', '77', '78', '79', '8', '81', '82', '83', '84', '85', '86', '87', '88', '89', '9', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99']\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\toulo\\AppData\\Local\\Temp\\ipykernel_18728\\3929357964.py:26: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
- " df[col] = pd.to_numeric(df[col], errors='ignore')\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Successfully processed 542 subjects\n",
- "Final dataset shape: (542, 593)\n",
- "Successfully loaded additional data from: .\\combined_demographics.csv\n",
- "Additional data shape: (542, 2)\n",
- "Additional data columns: ['Sex', 'Age']\n",
- "FreeSurfer data subjects: ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '74', '75', '76', '77', '78', '79', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '273', '275', '276', '277', '278', '279', '280', '281', '282', '283', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '300', '301', '302', '303', '304', '305', '306', '308', '309', '310', '311', '312', '313', '314', '315', '316', '317', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '343', '344', '345', '346', '347', '348', '349', '350', '351', '352', '353', '354', '355', '356', '357', '358', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '387', '388', '389', '390', '391', '392', '393', '394', '395', '396', '397', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '450', '451', '452', '453', '454', '455', '459', '460', '461', '462', '463', '4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009', '4010', '4011', '4012', '4013', '4014', '4015', '4016', '4017', '4018', '4019', '4020', '4021', '4022', '4023', '4024', '4025', '4026', '4027', '4028', '4029', '4030', '4031', '4032', '4033', '4034', '4035', '4036', '4037', '4038', '4039', '4040', '4041', '4042', '4043', '4044', '4045', '4046', '4047', '4048', '4049', '4050', '4051', '4052', '4053', '4054', '4055', '4056', '4057', '4058', '4059', '4060', '4061', '4062', '4063', '4064', '4065', '4066', '4067', '4068', '4069', '4070', '4071', '4072', '4073', '4074', '4075', '4076', '4077', '4078', '4079', '4080', '4081', '4082', '4083', '4084', '4085', '4086', '4087', '4088', '4089', '4090', '4091', '4092', '4093', '4094', '4095', '4096', '4097', '4098', '4099', '4100']\n",
- "Additional data subjects: ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '74', '75', '76', '77', '78', '79', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '273', '275', '276', '277', '278', '279', '280', '281', '282', '283', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294', '295', '296', '297', '298', '299', '300', '301', '302', '303', '304', '305', '306', '308', '309', '310', '311', '312', '313', '314', '315', '316', '317', '319', '320', '321', '322', '323', '324', '325', '326', '327', '328', '329', '330', '331', '332', '333', '334', '335', '336', '337', '338', '339', '340', '341', '343', '344', '345', '346', '347', '348', '349', '350', '351', '352', '353', '354', '355', '356', '357', '358', '360', '361', '362', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '379', '380', '381', '382', '383', '384', '385', '387', '388', '389', '390', '391', '392', '393', '394', '395', '396', '397', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '410', '411', '412', '413', '414', '415', '416', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '447', '448', '449', '450', '451', '452', '453', '454', '455', '459', '460', '461', '462', '463', '4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009', '4010', '4011', '4012', '4013', '4014', '4015', '4016', '4017', '4018', '4019', '4020', '4021', '4022', '4023', '4024', '4025', '4026', '4027', '4028', '4029', '4030', '4031', '4032', '4033', '4034', '4035', '4036', '4037', '4038', '4039', '4040', '4041', '4042', '4043', '4044', '4045', '4046', '4047', '4048', '4049', '4050', '4051', '4052', '4053', '4054', '4055', '4056', '4057', '4058', '4059', '4060', '4061', '4062', '4063', '4064', '4065', '4066', '4067', '4068', '4069', '4070', '4071', '4072', '4073', '4074', '4075', '4076', '4077', '4078', '4079', '4080', '4081', '4082', '4083', '4084', '4085', '4086', '4087', '4088', '4089', '4090', '4091', '4092', '4093', '4094', '4095', '4096', '4097', '4098', '4099', '4100']\n",
- "\n",
- "Merge Results:\n",
- " Subjects with both FreeSurfer and additional data: 542\n",
- " Subjects with only FreeSurfer data: 0\n",
- "\n",
- "Combined data saved to: lh.csv\n",
- "\n",
- "Columns with missing values:\n",
- " CurvInd_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " FoldInd_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " GausCurv_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " GrayVol_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " MeanCurv_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " SurfArea_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " ThickAvg_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " ThickStd_G_cingul-Post-ventral: 1 missing (0.2%)\n",
- " Epileptic_Status Sex Age CurvInd_G_Ins_lg_and_S_cent_ins \\\n",
- "Subject \n",
- "1 Epileptic M 22.0 0.8 \n",
- "2 Epileptic F 37.0 0.6 \n",
- "3 Epileptic M 27.0 1.4 \n",
- "4 Epileptic M 42.0 1.2 \n",
- "5 Epileptic F 47.0 0.7 \n",
- "... ... .. ... ... \n",
- "4096 Not Epileptic F 32.0 0.9 \n",
- "4097 Not Epileptic M 37.0 1.4 \n",
- "4098 Not Epileptic F 27.0 1.0 \n",
- "4099 Not Epileptic M 27.0 0.9 \n",
- "4100 Not Epileptic F 37.0 1.1 \n",
- "\n",
- " CurvInd_G_and_S_cingul-Ant CurvInd_G_and_S_cingul-Mid-Ant \\\n",
- "Subject \n",
- "1 2.4 0.9 \n",
- "2 2.4 1.1 \n",
- "3 2.1 0.7 \n",
- "4 1.9 0.9 \n",
- "5 2.6 1.1 \n",
- "... ... ... \n",
- "4096 3.0 1.1 \n",
- "4097 3.2 0.8 \n",
- "4098 1.7 1.1 \n",
- "4099 2.4 0.9 \n",
- "4100 1.8 0.9 \n",
- "\n",
- " CurvInd_G_and_S_cingul-Mid-Post CurvInd_G_and_S_frontomargin \\\n",
- "Subject \n",
- "1 1.7 2.0 \n",
- "2 1.3 1.2 \n",
- "3 0.9 1.1 \n",
- "4 0.9 1.7 \n",
- "5 0.9 1.9 \n",
- "... ... ... \n",
- "4096 1.1 1.2 \n",
- "4097 1.1 1.8 \n",
- "4098 1.1 1.5 \n",
- "4099 1.1 2.2 \n",
- "4100 0.9 1.8 \n",
- "\n",
- " CurvInd_G_and_S_occipital_inf CurvInd_G_and_S_paracentral ... \\\n",
- "Subject ... \n",
- "1 2.1 1.9 ... \n",
- "2 1.6 1.9 ... \n",
- "3 2.7 2.0 ... \n",
- "4 2.1 2.3 ... \n",
- "5 1.6 2.2 ... \n",
- "... ... ... ... \n",
- "4096 2.0 1.4 ... \n",
- "4097 1.7 2.7 ... \n",
- "4098 1.7 1.3 ... \n",
- "4099 1.5 1.4 ... \n",
- "4100 1.5 1.2 ... \n",
- "\n",
- " ThickStd_S_parieto_occipital ThickStd_S_pericallosal \\\n",
- "Subject \n",
- "1 0.598 0.556 \n",
- "2 0.515 0.579 \n",
- "3 0.545 0.725 \n",
- "4 0.650 0.611 \n",
- "5 0.533 0.641 \n",
- "... ... ... \n",
- "4096 0.409 0.602 \n",
- "4097 0.510 0.464 \n",
- "4098 0.476 0.613 \n",
- "4099 0.478 0.518 \n",
- "4100 0.462 0.638 \n",
- "\n",
- " ThickStd_S_postcentral ThickStd_S_precentral-inf-part \\\n",
- "Subject \n",
- "1 0.413 0.344 \n",
- "2 0.421 0.473 \n",
- "3 0.473 0.404 \n",
- "4 0.597 0.465 \n",
- "5 0.415 0.413 \n",
- "... ... ... \n",
- "4096 0.401 0.414 \n",
- "4097 0.559 0.392 \n",
- "4098 0.421 0.415 \n",
- "4099 0.497 0.354 \n",
- "4100 0.461 0.421 \n",
- "\n",
- " ThickStd_S_precentral-sup-part ThickStd_S_suborbital \\\n",
- "Subject \n",
- "1 0.341 0.810 \n",
- "2 0.396 0.721 \n",
- "3 0.407 0.604 \n",
- "4 0.541 0.680 \n",
- "5 0.374 0.809 \n",
- "... ... ... \n",
- "4096 0.413 0.514 \n",
- "4097 0.384 0.542 \n",
- "4098 0.396 0.795 \n",
- "4099 0.365 0.692 \n",
- "4100 0.458 0.637 \n",
- "\n",
- " ThickStd_S_subparietal ThickStd_S_temporal_inf \\\n",
- "Subject \n",
- "1 0.436 0.450 \n",
- "2 0.689 0.514 \n",
- "3 0.390 0.776 \n",
- "4 0.697 0.602 \n",
- "5 0.349 0.462 \n",
- "... ... ... \n",
- "4096 0.466 0.535 \n",
- "4097 0.517 0.370 \n",
- "4098 0.358 0.410 \n",
- "4099 0.485 0.461 \n",
- "4100 0.584 0.470 \n",
- "\n",
- " ThickStd_S_temporal_sup ThickStd_S_temporal_transverse \n",
- "Subject \n",
- "1 0.475 0.403 \n",
- "2 0.580 0.304 \n",
- "3 0.634 0.461 \n",
- "4 0.498 0.344 \n",
- "5 0.470 0.463 \n",
- "... ... ... \n",
- "4096 0.483 0.331 \n",
- "4097 0.514 0.761 \n",
- "4098 0.472 0.569 \n",
- "4099 0.418 0.383 \n",
- "4100 0.423 0.580 \n",
- "\n",
- "[542 rows x 595 columns]\n"
- ]
- }
- ],
- "source": [
- "data = process_with_additional_data()\n",
- "print(data)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "base",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/content/en/project/epilepsy-detection/distribution.jpg b/content/en/project/epilepsy-detection/distribution.jpg
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diff --git a/content/en/project/epilepsy-detection/epilepsy.jpg b/content/en/project/epilepsy-detection/epilepsy.jpg
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diff --git a/content/en/project/epilepsy-detection/epilepsy.jpg:Zone.Identifier b/content/en/project/epilepsy-detection/epilepsy.jpg:Zone.Identifier
deleted file mode 100644
index f3e01950..00000000
--- a/content/en/project/epilepsy-detection/epilepsy.jpg:Zone.Identifier
+++ /dev/null
@@ -1,4 +0,0 @@
-[ZoneTransfer]
-ZoneId=3
-ReferrerUrl=https://www.google.com/
-HostUrl=https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ8ccg49ypkUjs3omTRjb6xoGqvud-Kxroc4g&s
diff --git a/content/en/project/epilepsy-detection/index.md b/content/en/project/epilepsy-detection/index.md
deleted file mode 100644
index 263c51f0..00000000
--- a/content/en/project/epilepsy-detection/index.md
+++ /dev/null
@@ -1,93 +0,0 @@
----
-type: "project" # DON'T TOUCH THIS ! :)
-date: "2025-06-20" # Date you first upload your project.
-# Title of your project (we like creative title)
-title: "Automated detection of focal epilepsy based on brain morphometrics"
-
-# List the names of the collaborators within the [ ]. If alone, simple put your name within []
-names: [Arthur Toulouse]
-
-# Your project GitHub repository URL
-github_repo: https://github.com/school-brainhack/epilepsy-detection
-
-# If you are working on a project that has website, indicate the full url including "https://" below or leave it empty.
-website:
-
-# List +- 4 keywords that best describe your project within []. Note that the project summary also involves a number of key words. Those are listed on top of the [github repository](https://github.com/brainhack-school2020/project_template), click `manage topics`.
-# Please only lowercase letters
-tags: [MRI, PCA, Epilepsy, Visualization]
-
-# Summarize your project in < ~75 words. This description will appear at the top of your page and on the list page with other projects..
-
-summary: "Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer."
-
-# If you want to add a cover image (listpage and image in the right), add it to your directory and indicate the name
-# below with the extension.
-image: "epilepsy.jpg"
----
-
-
-## Project definition
-
-### Background
-
-Focal epilepsy is a type of epilepsy in which seizures originate from a specific region of the brain, often associated with structural abnormalities visible on neuroimaging. Accurate localization of the epileptogenic zone is essential for effective treatment planning, especially in cases considered for surgical intervention. In this project, we aim to classify the anatomical region of seizure onset using structural MRI data (T1-weighted images) processed with FreeSurfer.
-FreeSurfer enables the extraction of detailed cortical morphometric features such as thickness, surface area, curvature, and volume across predefined brain regions. These quantitative features can potentially reflect subtle structural anomalies associated with focal epilepsy. By using a mixed-age dataset of patients with focal epilepsy, we aim to build a machine learning model that predicts the anatomical region of the epileptic focus based on these morphometric biomarkers. This approach could contribute to more objective and automated presurgical evaluation workflows.
-
-
-### Objectives
-
- * To develop a machine learning model capable of classifying the anatomical region of seizure onset in focal epilepsy patients using cortical morphometric features extracted from T1-weighted MRI scans.
- * To evaluate the performance of different classifiers and identify the most informative brain features associated with focal epileptogenic zones.
-
-
-### Tools
-
- * GitHub – for version control and collaborative code management
- * DataLad – to manage and track neuroimaging datasets in a reproducible way
- * Python – for data processing, feature extraction, and model development
- * Bash – for automation and scripting of preprocessing pipelines
- * FreeSurfer – to extract cortical morphometric features from T1-weighted MRI scans
- * scikit-learn – for implementing and evaluating machine learning classification models
-
-### Data
-
-
-This study uses the Imaging Database for Epilepsy and Surgery ([IDEAS](https://pubmed.ncbi.nlm.nih.gov/39636622/)), an open-source dataset available on OpenNeuro. The dataset includes 442 pre-operative T1-weighted and FLAIR MRI scans from patients with focal epilepsy, along with 100 healthy control scans acquired on the same scanner for consistency. In addition to imaging, the dataset provides rich metadata such as age of seizure onset, sex, number of medications, scan and surgery timing, histopathological findings, and surgical outcomes. The additional data can be found [here](https://openneuro.org/datasets/ds005602/versions/1.0.0)
-
-
-
-
-### Deliverables
-
-At the end of this project, we will have a [GitHub repository](https://github.com/school-brainhack/epilepsy-detection) containing the code :
- * data.ipynb to process the data from freeSurfer
- * model.ipynb to run the classification models
- * the processed csv files obtained after running the data.ipynb for testing purposes
-
-## Results
-
-The first model was a binary classification task distinguishing between epileptic patients and healthy controls based on cortical morphometric features extracted from T1-weighted MRI using FreeSurfer. The classifier achieved promising results, with a receiver operating characteristic (ROC) curve showing a clear separation between the two groups. The area under the curve (AUC) was approximately 0.85, indicating that the model was able to effectively capture structural differences associated with epilepsy.
-
-
-
-In the second model, the task was extended to multiclass classification to predict the specific anatomical region of the epileptic focus using the full set of regions. However, the performance of this model was limited. This can be attributed to both class imbalance and the increased difficulty of distinguishing between many finely segmented areas in a relatively small dataset.
-
-
-
-To address this, a third model was developed where only regions with a significant number of patients are used. This simplification led to a noticeable improvement in classification performance.
-
-
-
-
-
-## Conclusion and acknowledgement
-
-This project demonstrates the feasibility of using morphometric features extracted from structural MRI to classify focal epilepsy and localize the seizure onset zone. While binary classification between epileptic and control subjects showed strong performance, multiclass classification across specific brain regions remains a challenging task.These results highlight both the potential and the limitations of current approaches, pointing toward the need for larger, balanced datasets and more advanced modeling techniques.
-
-I gratefully acknowledge the use of the IDEAS dataset (Imaging Database for Epilepsy And Surgery), an open-access resource available on OpenNeuro. This dataset was developed to support research in epilepsy diagnosis and surgical planning, and I thank the authors and contributors for making this valuable resource publicly available.
-Taylor PN, Wang Y, Simpson C, Janiukstyte V, Horsley J, Leiberg K, Little B, Clifford H, Adler S, Vos SB, Winston GP, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS. The Imaging Database for Epilepsy And Surgery (IDEAS). Epilepsia. 2025 Feb;66(2):471-481. doi: 10.1111/epi.18192. Epub 2024 Dec 5. PMID: 39636622; PMCID: PMC11827737.
-
-I also gratefully acknowledge the developers of FreeSurfer, whose open-source neuroimaging software made it possible to extract detailed cortical morphometric features essential to this analysis.
-
-Finaly, I would also like to sincerely thank the Brainhack School for providing this unique opportunity to explore applied neuroimaging and data science in a collaborative setting. Special thanks to Eva and Sebastian for their guidance, support, and insightful feedback throughout the project.
diff --git a/content/en/project/epilepsy-detection/lh.csv b/content/en/project/epilepsy-detection/lh.csv
deleted file mode 100644
index 1ed2b2a2..00000000
--- a/content/en/project/epilepsy-detection/lh.csv
+++ /dev/null
@@ -1,543 +0,0 @@
-Subject,Epileptic_Status,Sex,Age,CurvInd_G_Ins_lg_and_S_cent_ins,CurvInd_G_and_S_cingul-Ant,CurvInd_G_and_S_cingul-Mid-Ant,CurvInd_G_and_S_cingul-Mid-Post,CurvInd_G_and_S_frontomargin,CurvInd_G_and_S_occipital_inf,CurvInd_G_and_S_paracentral,CurvInd_G_and_S_subcentral,CurvInd_G_and_S_transv_frontopol,CurvInd_G_cingul-Post-dorsal,CurvInd_G_cingul-Post-ventral,CurvInd_G_cuneus,CurvInd_G_front_inf-Opercular,CurvInd_G_front_inf-Orbital,CurvInd_G_front_inf-Triangul,CurvInd_G_front_middle,CurvInd_G_front_sup,CurvInd_G_insular_short,CurvInd_G_oc-temp_lat-fusifor,CurvInd_G_oc-temp_med-Lingual,CurvInd_G_oc-temp_med-Parahip,CurvInd_G_occipital_middle,CurvInd_G_occipital_sup,CurvInd_G_orbital,CurvInd_G_pariet_inf-Angular,CurvInd_G_pariet_inf-Supramar,CurvInd_G_parietal_sup,CurvInd_G_postcentral,CurvInd_G_precentral,CurvInd_G_precuneus,CurvInd_G_rectus,CurvInd_G_subcallosal,CurvInd_G_temp_sup-G_T_transv,CurvInd_G_temp_sup-Lateral,CurvInd_G_temp_sup-Plan_polar,CurvInd_G_temp_sup-Plan_tempo,CurvInd_G_temporal_inf,CurvInd_G_temporal_middle,CurvInd_Lat_Fis-ant-Horizont,CurvInd_Lat_Fis-ant-Vertical,CurvInd_Lat_Fis-post,CurvInd_Pole_occipital,CurvInd_Pole_temporal,CurvInd_S_calcarine,CurvInd_S_central,CurvInd_S_cingul-Marginalis,CurvInd_S_circular_insula_ant,CurvInd_S_circular_insula_inf,CurvInd_S_circular_insula_sup,CurvInd_S_collat_transv_ant,CurvInd_S_collat_transv_post,CurvInd_S_front_inf,CurvInd_S_front_middle,CurvInd_S_front_sup,CurvInd_S_interm_prim-Jensen,CurvInd_S_intrapariet_and_P_trans,CurvInd_S_oc-temp_lat,CurvInd_S_oc-temp_med_and_Lingual,CurvInd_S_oc_middle_and_Lunatus,CurvInd_S_oc_sup_and_transversal,CurvInd_S_occipital_ant,CurvInd_S_orbital-H_Shaped,CurvInd_S_orbital_lateral,CurvInd_S_orbital_med-olfact,CurvInd_S_parieto_occipital,CurvInd_S_pericallosal,CurvInd_S_postcentral,CurvInd_S_precentral-inf-part,CurvInd_S_precentral-sup-part,CurvInd_S_suborbital,CurvInd_S_subparietal,CurvInd_S_temporal_inf,CurvInd_S_temporal_sup,CurvInd_S_temporal_transverse,FoldInd_G_Ins_lg_and_S_cent_ins,FoldInd_G_and_S_cingul-Ant,FoldInd_G_and_S_cingul-Mid-Ant,FoldInd_G_and_S_cingul-Mid-Post,FoldInd_G_and_S_frontomargin,FoldInd_G_and_S_occipital_inf,FoldInd_G_and_S_paracentral,FoldInd_G_and_S_subcentral,FoldInd_G_and_S_transv_frontopol,FoldInd_G_cingul-Post-dorsal,FoldInd_G_cingul-Post-ventral,FoldInd_G_cuneus,FoldInd_G_front_inf-Opercular,FoldInd_G_front_inf-Orbital,FoldInd_G_front_inf-Triangul,FoldInd_G_front_middle,FoldInd_G_front_sup,FoldInd_G_insular_short,FoldInd_G_oc-temp_lat-fusifor,FoldInd_G_oc-temp_med-Lingual,FoldInd_G_oc-temp_med-Parahip,FoldInd_G_occipital_middle,FoldInd_G_occipital_sup,FoldInd_G_orbital,FoldInd_G_pariet_inf-Angular,FoldInd_G_pariet_inf-Supramar,FoldInd_G_parietal_sup,FoldInd_G_postcentral,FoldInd_G_precentral,FoldInd_G_precuneus,FoldInd_G_rectus,FoldInd_G_subcallosal,FoldInd_G_temp_sup-G_T_transv,FoldInd_G_temp_sup-Lateral,FoldInd_G_temp_sup-Plan_polar,FoldInd_G_temp_sup-Plan_tempo,FoldInd_G_temporal_inf,FoldInd_G_temporal_middle,FoldInd_Lat_Fis-ant-Horizont,FoldInd_Lat_Fis-ant-Vertical,FoldInd_Lat_Fis-post,FoldInd_Pole_occipital,FoldInd_Pole_temporal,FoldInd_S_calcarine,FoldInd_S_central,FoldInd_S_cingul-Marginalis,FoldInd_S_circular_insula_ant,FoldInd_S_circular_insula_inf,FoldInd_S_circular_insula_sup,FoldInd_S_collat_transv_ant,FoldInd_S_collat_transv_post,FoldInd_S_front_inf,FoldInd_S_front_middle,FoldInd_S_front_sup,FoldInd_S_interm_prim-Jensen,FoldInd_S_intrapariet_and_P_trans,FoldInd_S_oc-temp_lat,FoldInd_S_oc-temp_med_and_Lingual,FoldInd_S_oc_middle_and_Lunatus,FoldInd_S_oc_sup_and_transversal,FoldInd_S_occipital_ant,FoldInd_S_orbital-H_Shaped,FoldInd_S_orbital_lateral,FoldInd_S_orbital_med-olfact,FoldInd_S_parieto_occipital,FoldInd_S_pericallosal,FoldInd_S_postcentral,FoldInd_S_precentral-inf-part,FoldInd_S_precentral-sup-part,FoldInd_S_suborbital,FoldInd_S_subparietal,FoldInd_S_temporal_inf,FoldInd_S_temporal_sup,FoldInd_S_temporal_transverse,GausCurv_G_Ins_lg_and_S_cent_ins,GausCurv_G_and_S_cingul-Ant,GausCurv_G_and_S_cingul-Mid-Ant,GausCurv_G_and_S_cingul-Mid-Post,GausCurv_G_and_S_frontomargin,GausCurv_G_and_S_occipital_inf,GausCurv_G_and_S_paracentral,GausCurv_G_and_S_subcentral,GausCurv_G_and_S_transv_frontopol,GausCurv_G_cingul-Post-dorsal,GausCurv_G_cingul-Post-ventral,GausCurv_G_cuneus,GausCurv_G_front_inf-Opercular,GausCurv_G_front_inf-Orbital,GausCurv_G_front_inf-Triangul,GausCurv_G_front_middle,GausCurv_G_front_sup,GausCurv_G_insular_short,GausCurv_G_oc-temp_lat-fusifor,GausCurv_G_oc-temp_med-Lingual,GausCurv_G_oc-temp_med-Parahip,GausCurv_G_occipital_middle,GausCurv_G_occipital_sup,GausCurv_G_orbital,GausCurv_G_pariet_inf-Angular,GausCurv_G_pariet_inf-Supramar,GausCurv_G_parietal_sup,GausCurv_G_postcentral,GausCurv_G_precentral,GausCurv_G_precuneus,GausCurv_G_rectus,GausCurv_G_subcallosal,GausCurv_G_temp_sup-G_T_transv,GausCurv_G_temp_sup-Lateral,GausCurv_G_temp_sup-Plan_polar,GausCurv_G_temp_sup-Plan_tempo,GausCurv_G_temporal_inf,GausCurv_G_temporal_middle,GausCurv_Lat_Fis-ant-Horizont,GausCurv_Lat_Fis-ant-Vertical,GausCurv_Lat_Fis-post,GausCurv_Pole_occipital,GausCurv_Pole_temporal,GausCurv_S_calcarine,GausCurv_S_central,GausCurv_S_cingul-Marginalis,GausCurv_S_circular_insula_ant,GausCurv_S_circular_insula_inf,GausCurv_S_circular_insula_sup,GausCurv_S_collat_transv_ant,GausCurv_S_collat_transv_post,GausCurv_S_front_inf,GausCurv_S_front_middle,GausCurv_S_front_sup,GausCurv_S_interm_prim-Jensen,GausCurv_S_intrapariet_and_P_trans,GausCurv_S_oc-temp_lat,GausCurv_S_oc-temp_med_and_Lingual,GausCurv_S_oc_middle_and_Lunatus,GausCurv_S_oc_sup_and_transversal,GausCurv_S_occipital_ant,GausCurv_S_orbital-H_Shaped,GausCurv_S_orbital_lateral,GausCurv_S_orbital_med-olfact,GausCurv_S_parieto_occipital,GausCurv_S_pericallosal,GausCurv_S_postcentral,GausCurv_S_precentral-inf-part,GausCurv_S_precentral-sup-part,GausCurv_S_suborbital,GausCurv_S_subparietal,GausCurv_S_temporal_inf,GausCurv_S_temporal_sup,GausCurv_S_temporal_transverse,GrayVol_G_Ins_lg_and_S_cent_ins,GrayVol_G_and_S_cingul-Ant,GrayVol_G_and_S_cingul-Mid-Ant,GrayVol_G_and_S_cingul-Mid-Post,GrayVol_G_and_S_frontomargin,GrayVol_G_and_S_occipital_inf,GrayVol_G_and_S_paracentral,GrayVol_G_and_S_subcentral,GrayVol_G_and_S_transv_frontopol,GrayVol_G_cingul-Post-dorsal,GrayVol_G_cingul-Post-ventral,GrayVol_G_cuneus,GrayVol_G_front_inf-Opercular,GrayVol_G_front_inf-Orbital,GrayVol_G_front_inf-Triangul,GrayVol_G_front_middle,GrayVol_G_front_sup,GrayVol_G_insular_short,GrayVol_G_oc-temp_lat-fusifor,GrayVol_G_oc-temp_med-Lingual,GrayVol_G_oc-temp_med-Parahip,GrayVol_G_occipital_middle,GrayVol_G_occipital_sup,GrayVol_G_orbital,GrayVol_G_pariet_inf-Angular,GrayVol_G_pariet_inf-Supramar,GrayVol_G_parietal_sup,GrayVol_G_postcentral,GrayVol_G_precentral,GrayVol_G_precuneus,GrayVol_G_rectus,GrayVol_G_subcallosal,GrayVol_G_temp_sup-G_T_transv,GrayVol_G_temp_sup-Lateral,GrayVol_G_temp_sup-Plan_polar,GrayVol_G_temp_sup-Plan_tempo,GrayVol_G_temporal_inf,GrayVol_G_temporal_middle,GrayVol_Lat_Fis-ant-Horizont,GrayVol_Lat_Fis-ant-Vertical,GrayVol_Lat_Fis-post,GrayVol_Pole_occipital,GrayVol_Pole_temporal,GrayVol_S_calcarine,GrayVol_S_central,GrayVol_S_cingul-Marginalis,GrayVol_S_circular_insula_ant,GrayVol_S_circular_insula_inf,GrayVol_S_circular_insula_sup,GrayVol_S_collat_transv_ant,GrayVol_S_collat_transv_post,GrayVol_S_front_inf,GrayVol_S_front_middle,GrayVol_S_front_sup,GrayVol_S_interm_prim-Jensen,GrayVol_S_intrapariet_and_P_trans,GrayVol_S_oc-temp_lat,GrayVol_S_oc-temp_med_and_Lingual,GrayVol_S_oc_middle_and_Lunatus,GrayVol_S_oc_sup_and_transversal,GrayVol_S_occipital_ant,GrayVol_S_orbital-H_Shaped,GrayVol_S_orbital_lateral,GrayVol_S_orbital_med-olfact,GrayVol_S_parieto_occipital,GrayVol_S_pericallosal,GrayVol_S_postcentral,GrayVol_S_precentral-inf-part,GrayVol_S_precentral-sup-part,GrayVol_S_suborbital,GrayVol_S_subparietal,GrayVol_S_temporal_inf,GrayVol_S_temporal_sup,GrayVol_S_temporal_transverse,MeanCurv_G_Ins_lg_and_S_cent_ins,MeanCurv_G_and_S_cingul-Ant,MeanCurv_G_and_S_cingul-Mid-Ant,MeanCurv_G_and_S_cingul-Mid-Post,MeanCurv_G_and_S_frontomargin,MeanCurv_G_and_S_occipital_inf,MeanCurv_G_and_S_paracentral,MeanCurv_G_and_S_subcentral,MeanCurv_G_and_S_transv_frontopol,MeanCurv_G_cingul-Post-dorsal,MeanCurv_G_cingul-Post-ventral,MeanCurv_G_cuneus,MeanCurv_G_front_inf-Opercular,MeanCurv_G_front_inf-Orbital,MeanCurv_G_front_inf-Triangul,MeanCurv_G_front_middle,MeanCurv_G_front_sup,MeanCurv_G_insular_short,MeanCurv_G_oc-temp_lat-fusifor,MeanCurv_G_oc-temp_med-Lingual,MeanCurv_G_oc-temp_med-Parahip,MeanCurv_G_occipital_middle,MeanCurv_G_occipital_sup,MeanCurv_G_orbital,MeanCurv_G_pariet_inf-Angular,MeanCurv_G_pariet_inf-Supramar,MeanCurv_G_parietal_sup,MeanCurv_G_postcentral,MeanCurv_G_precentral,MeanCurv_G_precuneus,MeanCurv_G_rectus,MeanCurv_G_subcallosal,MeanCurv_G_temp_sup-G_T_transv,MeanCurv_G_temp_sup-Lateral,MeanCurv_G_temp_sup-Plan_polar,MeanCurv_G_temp_sup-Plan_tempo,MeanCurv_G_temporal_inf,MeanCurv_G_temporal_middle,MeanCurv_Lat_Fis-ant-Horizont,MeanCurv_Lat_Fis-ant-Vertical,MeanCurv_Lat_Fis-post,MeanCurv_Pole_occipital,MeanCurv_Pole_temporal,MeanCurv_S_calcarine,MeanCurv_S_central,MeanCurv_S_cingul-Marginalis,MeanCurv_S_circular_insula_ant,MeanCurv_S_circular_insula_inf,MeanCurv_S_circular_insula_sup,MeanCurv_S_collat_transv_ant,MeanCurv_S_collat_transv_post,MeanCurv_S_front_inf,MeanCurv_S_front_middle,MeanCurv_S_front_sup,MeanCurv_S_interm_prim-Jensen,MeanCurv_S_intrapariet_and_P_trans,MeanCurv_S_oc-temp_lat,MeanCurv_S_oc-temp_med_and_Lingual,MeanCurv_S_oc_middle_and_Lunatus,MeanCurv_S_oc_sup_and_transversal,MeanCurv_S_occipital_ant,MeanCurv_S_orbital-H_Shaped,MeanCurv_S_orbital_lateral,MeanCurv_S_orbital_med-olfact,MeanCurv_S_parieto_occipital,MeanCurv_S_pericallosal,MeanCurv_S_postcentral,MeanCurv_S_precentral-inf-part,MeanCurv_S_precentral-sup-part,MeanCurv_S_suborbital,MeanCurv_S_subparietal,MeanCurv_S_temporal_inf,MeanCurv_S_temporal_sup,MeanCurv_S_temporal_transverse,SurfArea_G_Ins_lg_and_S_cent_ins,SurfArea_G_and_S_cingul-Ant,SurfArea_G_and_S_cingul-Mid-Ant,SurfArea_G_and_S_cingul-Mid-Post,SurfArea_G_and_S_frontomargin,SurfArea_G_and_S_occipital_inf,SurfArea_G_and_S_paracentral,SurfArea_G_and_S_subcentral,SurfArea_G_and_S_transv_frontopol,SurfArea_G_cingul-Post-dorsal,SurfArea_G_cingul-Post-ventral,SurfArea_G_cuneus,SurfArea_G_front_inf-Opercular,SurfArea_G_front_inf-Orbital,SurfArea_G_front_inf-Triangul,SurfArea_G_front_middle,SurfArea_G_front_sup,SurfArea_G_insular_short,SurfArea_G_oc-temp_lat-fusifor,SurfArea_G_oc-temp_med-Lingual,SurfArea_G_oc-temp_med-Parahip,SurfArea_G_occipital_middle,SurfArea_G_occipital_sup,SurfArea_G_orbital,SurfArea_G_pariet_inf-Angular,SurfArea_G_pariet_inf-Supramar,SurfArea_G_parietal_sup,SurfArea_G_postcentral,SurfArea_G_precentral,SurfArea_G_precuneus,SurfArea_G_rectus,SurfArea_G_subcallosal,SurfArea_G_temp_sup-G_T_transv,SurfArea_G_temp_sup-Lateral,SurfArea_G_temp_sup-Plan_polar,SurfArea_G_temp_sup-Plan_tempo,SurfArea_G_temporal_inf,SurfArea_G_temporal_middle,SurfArea_Lat_Fis-ant-Horizont,SurfArea_Lat_Fis-ant-Vertical,SurfArea_Lat_Fis-post,SurfArea_Pole_occipital,SurfArea_Pole_temporal,SurfArea_S_calcarine,SurfArea_S_central,SurfArea_S_cingul-Marginalis,SurfArea_S_circular_insula_ant,SurfArea_S_circular_insula_inf,SurfArea_S_circular_insula_sup,SurfArea_S_collat_transv_ant,SurfArea_S_collat_transv_post,SurfArea_S_front_inf,SurfArea_S_front_middle,SurfArea_S_front_sup,SurfArea_S_interm_prim-Jensen,SurfArea_S_intrapariet_and_P_trans,SurfArea_S_oc-temp_lat,SurfArea_S_oc-temp_med_and_Lingual,SurfArea_S_oc_middle_and_Lunatus,SurfArea_S_oc_sup_and_transversal,SurfArea_S_occipital_ant,SurfArea_S_orbital-H_Shaped,SurfArea_S_orbital_lateral,SurfArea_S_orbital_med-olfact,SurfArea_S_parieto_occipital,SurfArea_S_pericallosal,SurfArea_S_postcentral,SurfArea_S_precentral-inf-part,SurfArea_S_precentral-sup-part,SurfArea_S_suborbital,SurfArea_S_subparietal,SurfArea_S_temporal_inf,SurfArea_S_temporal_sup,SurfArea_S_temporal_transverse,ThickAvg_G_Ins_lg_and_S_cent_ins,ThickAvg_G_and_S_cingul-Ant,ThickAvg_G_and_S_cingul-Mid-Ant,ThickAvg_G_and_S_cingul-Mid-Post,ThickAvg_G_and_S_frontomargin,ThickAvg_G_and_S_occipital_inf,ThickAvg_G_and_S_paracentral,ThickAvg_G_and_S_subcentral,ThickAvg_G_and_S_transv_frontopol,ThickAvg_G_cingul-Post-dorsal,ThickAvg_G_cingul-Post-ventral,ThickAvg_G_cuneus,ThickAvg_G_front_inf-Opercular,ThickAvg_G_front_inf-Orbital,ThickAvg_G_front_inf-Triangul,ThickAvg_G_front_middle,ThickAvg_G_front_sup,ThickAvg_G_insular_short,ThickAvg_G_oc-temp_lat-fusifor,ThickAvg_G_oc-temp_med-Lingual,ThickAvg_G_oc-temp_med-Parahip,ThickAvg_G_occipital_middle,ThickAvg_G_occipital_sup,ThickAvg_G_orbital,ThickAvg_G_pariet_inf-Angular,ThickAvg_G_pariet_inf-Supramar,ThickAvg_G_parietal_sup,ThickAvg_G_postcentral,ThickAvg_G_precentral,ThickAvg_G_precuneus,ThickAvg_G_rectus,ThickAvg_G_subcallosal,ThickAvg_G_temp_sup-G_T_transv,ThickAvg_G_temp_sup-Lateral,ThickAvg_G_temp_sup-Plan_polar,ThickAvg_G_temp_sup-Plan_tempo,ThickAvg_G_temporal_inf,ThickAvg_G_temporal_middle,ThickAvg_Lat_Fis-ant-Horizont,ThickAvg_Lat_Fis-ant-Vertical,ThickAvg_Lat_Fis-post,ThickAvg_Pole_occipital,ThickAvg_Pole_temporal,ThickAvg_S_calcarine,ThickAvg_S_central,ThickAvg_S_cingul-Marginalis,ThickAvg_S_circular_insula_ant,ThickAvg_S_circular_insula_inf,ThickAvg_S_circular_insula_sup,ThickAvg_S_collat_transv_ant,ThickAvg_S_collat_transv_post,ThickAvg_S_front_inf,ThickAvg_S_front_middle,ThickAvg_S_front_sup,ThickAvg_S_interm_prim-Jensen,ThickAvg_S_intrapariet_and_P_trans,ThickAvg_S_oc-temp_lat,ThickAvg_S_oc-temp_med_and_Lingual,ThickAvg_S_oc_middle_and_Lunatus,ThickAvg_S_oc_sup_and_transversal,ThickAvg_S_occipital_ant,ThickAvg_S_orbital-H_Shaped,ThickAvg_S_orbital_lateral,ThickAvg_S_orbital_med-olfact,ThickAvg_S_parieto_occipital,ThickAvg_S_pericallosal,ThickAvg_S_postcentral,ThickAvg_S_precentral-inf-part,ThickAvg_S_precentral-sup-part,ThickAvg_S_suborbital,ThickAvg_S_subparietal,ThickAvg_S_temporal_inf,ThickAvg_S_temporal_sup,ThickAvg_S_temporal_transverse,ThickStd_G_Ins_lg_and_S_cent_ins,ThickStd_G_and_S_cingul-Ant,ThickStd_G_and_S_cingul-Mid-Ant,ThickStd_G_and_S_cingul-Mid-Post,ThickStd_G_and_S_frontomargin,ThickStd_G_and_S_occipital_inf,ThickStd_G_and_S_paracentral,ThickStd_G_and_S_subcentral,ThickStd_G_and_S_transv_frontopol,ThickStd_G_cingul-Post-dorsal,ThickStd_G_cingul-Post-ventral,ThickStd_G_cuneus,ThickStd_G_front_inf-Opercular,ThickStd_G_front_inf-Orbital,ThickStd_G_front_inf-Triangul,ThickStd_G_front_middle,ThickStd_G_front_sup,ThickStd_G_insular_short,ThickStd_G_oc-temp_lat-fusifor,ThickStd_G_oc-temp_med-Lingual,ThickStd_G_oc-temp_med-Parahip,ThickStd_G_occipital_middle,ThickStd_G_occipital_sup,ThickStd_G_orbital,ThickStd_G_pariet_inf-Angular,ThickStd_G_pariet_inf-Supramar,ThickStd_G_parietal_sup,ThickStd_G_postcentral,ThickStd_G_precentral,ThickStd_G_precuneus,ThickStd_G_rectus,ThickStd_G_subcallosal,ThickStd_G_temp_sup-G_T_transv,ThickStd_G_temp_sup-Lateral,ThickStd_G_temp_sup-Plan_polar,ThickStd_G_temp_sup-Plan_tempo,ThickStd_G_temporal_inf,ThickStd_G_temporal_middle,ThickStd_Lat_Fis-ant-Horizont,ThickStd_Lat_Fis-ant-Vertical,ThickStd_Lat_Fis-post,ThickStd_Pole_occipital,ThickStd_Pole_temporal,ThickStd_S_calcarine,ThickStd_S_central,ThickStd_S_cingul-Marginalis,ThickStd_S_circular_insula_ant,ThickStd_S_circular_insula_inf,ThickStd_S_circular_insula_sup,ThickStd_S_collat_transv_ant,ThickStd_S_collat_transv_post,ThickStd_S_front_inf,ThickStd_S_front_middle,ThickStd_S_front_sup,ThickStd_S_interm_prim-Jensen,ThickStd_S_intrapariet_and_P_trans,ThickStd_S_oc-temp_lat,ThickStd_S_oc-temp_med_and_Lingual,ThickStd_S_oc_middle_and_Lunatus,ThickStd_S_oc_sup_and_transversal,ThickStd_S_occipital_ant,ThickStd_S_orbital-H_Shaped,ThickStd_S_orbital_lateral,ThickStd_S_orbital_med-olfact,ThickStd_S_parieto_occipital,ThickStd_S_pericallosal,ThickStd_S_postcentral,ThickStd_S_precentral-inf-part,ThickStd_S_precentral-sup-part,ThickStd_S_suborbital,ThickStd_S_subparietal,ThickStd_S_temporal_inf,ThickStd_S_temporal_sup,ThickStd_S_temporal_transverse
-1,Epileptic,M,22.0,0.8,2.4,0.9,1.7,2.0,2.1,1.9,1.8,1.6,0.8,0.4,2.7,1.2,0.5,1.3,6.4,9.7,0.7,1.6,4.8,1.3,3.0,1.5,4.0,2.3,2.7,3.9,2.4,2.6,2.7,1.8,1.3,0.7,2.6,0.9,0.5,4.8,3.3,0.4,0.2,0.6,3.0,2.5,3.1,2.3,0.7,0.5,0.8,1.3,0.8,0.7,1.6,1.3,3.0,0.4,2.4,0.6,0.9,1.0,1.0,0.8,1.5,0.2,0.5,2.2,1.4,2.9,1.1,0.8,0.9,0.8,1.4,4.4,0.2,8,23,10,13,16,21,20,19,14,11,5.0,31,22,7,19,69,113,10,21,51,14,39,17,38,38,35,53,26,34,39,19,9,6,31,8,5,49,44,5,2,4,33,29,26,15,6,2,7,8,7,4,16,10,26,2,19,4,7,7,8,6,13,2,5,18,19,24,8,6,11,8,10,34,1,0.034,0.024,0.02,0.026,0.04,0.028,0.025,0.029,0.051,0.032,0.037,0.037,0.028,0.028,0.031,0.032,0.031,0.037,0.028,0.037,0.029,0.029,0.027,0.043,0.028,0.026,0.031,0.023,0.022,0.029,0.054,0.042,0.027,0.029,0.024,0.015,0.041,0.032,0.018,0.012,0.02,0.032,0.036,0.027,0.016,0.015,0.022,0.015,0.017,0.014,0.024,0.016,0.022,0.021,0.019,0.017,0.017,0.014,0.022,0.022,0.02,0.024,0.011,0.018,0.019,0.025,0.018,0.014,0.012,0.021,0.021,0.02,0.015,0.009,1560,5972,2242,2953,2392,4910,4005,3429,2050,2219,657.0,3207,3129,1360,2966,11882,22035,2275,4050,5920,4178,5916,2050,7153,6176,6670,7768,4736,7320,5980,2331,1375,1249,7631,2895,1402,8983,8375,730,492,1094,3523,6767,3617,3942,1697,839,2234,2393,2317,705,3552,1878,5716,652,3944,1296,2618,1399,1385,1622,3149,476,1017,4092,1216,5011,2709,1930,1834,1412,2253,10116,663,0.119,0.117,0.116,0.12,0.142,0.131,0.118,0.14,0.166,0.158,0.155,0.162,0.136,0.143,0.136,0.144,0.14,0.128,0.127,0.16,0.111,0.131,0.12,0.152,0.136,0.139,0.121,0.111,0.115,0.136,0.15,0.116,0.115,0.136,0.114,0.091,0.144,0.136,0.125,0.098,0.104,0.136,0.141,0.126,0.088,0.096,0.107,0.091,0.098,0.093,0.118,0.094,0.112,0.114,0.104,0.106,0.102,0.087,0.107,0.113,0.109,0.116,0.094,0.115,0.105,0.117,0.104,0.09,0.087,0.133,0.119,0.114,0.093,0.071,421,1757,788,968,853,1357,1282,1020,550,496,193.0,1448,969,324,779,3267,6090,473,986,2420,790,1678,968,1801,1648,1736,2478,1718,2031,1740,723,522,408,1647,629,493,2330,2051,290,250,507,1526,1390,1956,2259,779,311,905,1118,869,389,1615,890,2381,294,2095,562,1092,778,712,643,1071,269,472,1817,886,2480,1209,928,672,659,1063,4128,297,3.347,3.024,2.816,2.878,2.226,2.731,2.557,2.869,2.618,3.263,2.669,1.929,2.585,2.873,2.725,2.66,2.82,3.407,3.143,2.159,3.752,2.634,1.903,2.98,2.793,2.927,2.429,2.21,2.757,2.705,2.379,2.71,2.707,3.14,3.892,2.458,2.919,2.968,2.571,2.18,2.761,2.066,3.377,2.083,1.985,2.46,3.173,3.142,2.643,2.941,2.248,2.346,2.155,2.537,2.421,2.112,2.478,2.641,2.114,2.125,2.358,2.807,1.79,2.55,2.507,1.639,2.228,2.569,2.391,2.594,2.497,2.467,2.538,2.517,0.989,0.706,0.506,0.561,0.806,0.74,0.559,0.495,0.736,0.681,0.952,0.533,0.459,0.621,0.608,0.635,0.667,0.871,0.643,0.701,0.869,0.524,0.499,0.796,0.613,0.547,0.631,0.614,0.545,0.498,0.691,0.914,0.591,0.662,0.778,0.468,0.815,0.664,0.418,0.368,0.386,0.587,0.84,0.666,0.581,0.386,0.588,0.744,0.47,0.455,0.482,0.416,0.512,0.397,0.472,0.422,0.516,0.554,0.398,0.397,0.527,0.814,0.348,0.72,0.598,0.556,0.413,0.344,0.341,0.81,0.436,0.45,0.475,0.403
-2,Epileptic,F,37.0,0.6,2.4,1.1,1.3,1.2,1.6,1.9,1.4,1.4,0.4,0.4,3.7,1.2,0.5,1.1,4.8,6.8,1.0,2.2,4.9,0.9,2.7,2.0,3.5,3.3,1.8,2.9,2.0,2.3,2.4,2.3,2.1,0.4,2.6,0.4,1.2,2.7,2.2,0.1,0.3,1.1,3.3,2.9,3.4,2.4,0.6,0.3,0.9,1.5,0.8,0.5,1.4,1.4,2.1,0.2,2.3,0.6,1.5,1.7,1.7,0.6,1.5,0.3,0.6,1.7,1.0,2.8,1.0,1.1,0.5,1.2,1.0,3.6,0.3,5,18,7,10,12,16,15,13,10,5,5.0,30,15,5,14,51,59,7,20,41,7,30,23,36,38,23,28,18,20,25,22,14,4,29,2,9,26,23,2,2,7,31,20,24,14,4,1,6,8,5,3,10,12,13,1,19,5,8,10,13,3,10,2,5,13,15,23,7,8,3,7,6,30,3,0.04,0.033,0.028,0.03,0.03,0.031,0.032,0.034,0.051,0.033,0.05,0.037,0.029,0.028,0.026,0.033,0.033,0.037,0.04,0.039,0.029,0.034,0.034,0.043,0.035,0.022,0.027,0.031,0.026,0.028,0.05,0.074,0.025,0.035,0.018,0.02,0.037,0.032,0.013,0.019,0.028,0.036,0.048,0.028,0.019,0.017,0.016,0.022,0.022,0.026,0.024,0.019,0.035,0.02,0.027,0.019,0.018,0.02,0.032,0.025,0.018,0.027,0.021,0.025,0.022,0.03,0.028,0.016,0.023,0.019,0.029,0.018,0.019,0.021,1091,3498,1635,1866,1910,2090,2667,2340,1618,1014,382.0,3484,2566,982,2641,8506,14526,2038,3530,5263,2252,4289,2755,5407,5217,4377,5113,3167,5958,4922,2979,1000,841,5440,1392,2106,5300,4940,290,442,1348,3141,5313,2645,3258,1142,652,1539,2027,1051,524,2566,1511,3247,302,3706,1246,2134,1572,1863,920,2141,350,813,2490,930,3328,2099,1691,887,1224,1586,6983,444,0.128,0.127,0.113,0.128,0.136,0.121,0.111,0.138,0.162,0.128,0.148,0.14,0.13,0.135,0.123,0.139,0.127,0.141,0.141,0.149,0.097,0.132,0.123,0.147,0.124,0.117,0.112,0.111,0.106,0.13,0.168,0.153,0.105,0.15,0.084,0.108,0.129,0.135,0.094,0.104,0.124,0.135,0.167,0.127,0.099,0.102,0.086,0.099,0.111,0.121,0.123,0.099,0.141,0.1,0.15,0.105,0.101,0.105,0.129,0.123,0.105,0.125,0.117,0.125,0.108,0.135,0.124,0.095,0.106,0.105,0.138,0.103,0.099,0.113,291,1101,649,726,744,822,974,795,507,269,166.0,1560,798,273,715,2461,3745,401,1025,2211,570,1345,1092,1503,1605,1390,1823,1071,1467,1453,784,503,310,1311,418,835,1396,1292,213,256,618,1477,1020,1840,1918,565,308,698,1019,482,263,1171,727,1582,124,1959,603,1130,866,1019,438,845,231,373,1229,572,1657,900,823,419,554,806,2953,269,3.504,2.795,2.599,2.601,2.162,2.231,2.257,2.541,2.496,2.93,1.989,1.921,2.454,2.541,2.628,2.576,2.911,3.595,2.711,2.062,2.849,2.504,2.164,2.744,2.528,2.557,2.272,2.265,2.988,2.661,2.819,2.227,2.114,2.796,2.591,2.405,2.794,2.838,1.446,1.863,2.75,1.995,3.152,1.707,1.932,2.207,2.295,2.877,2.356,2.663,1.878,2.245,2.034,2.144,2.545,2.147,2.201,2.113,2.075,1.965,2.32,2.499,1.844,2.184,2.217,1.955,2.078,2.512,2.42,2.402,2.475,2.268,2.318,2.107,0.83,0.67,0.519,0.431,0.75,0.655,0.622,0.625,0.573,0.941,0.854,0.618,0.452,0.512,0.632,0.643,0.638,0.834,0.609,0.611,0.804,0.568,0.557,0.686,0.659,0.575,0.635,0.604,0.526,0.582,0.71,0.82,0.475,0.8,0.86,0.556,0.726,0.658,0.298,0.406,0.544,0.492,0.979,0.48,0.617,0.458,0.657,0.806,0.612,0.57,0.564,0.457,0.583,0.511,0.623,0.428,0.573,0.571,0.463,0.446,0.53,0.727,0.482,0.62,0.515,0.579,0.421,0.473,0.396,0.721,0.689,0.514,0.58,0.304
-3,Epileptic,M,27.0,1.4,2.1,0.7,0.9,1.1,2.7,2.0,2.2,1.0,0.9,0.6,3.5,1.8,0.6,1.3,6.9,9.9,0.9,3.0,8.0,0.7,2.7,2.3,2.6,4.2,4.6,3.1,3.2,2.8,4.0,1.1,1.1,1.3,4.0,1.4,1.3,3.1,3.2,0.1,0.5,1.7,3.9,3.1,3.9,3.2,0.7,0.5,1.4,1.4,0.8,0.6,1.9,1.4,2.6,0.5,2.4,1.0,3.0,0.8,1.6,0.8,1.1,0.2,0.6,1.8,0.5,2.9,2.3,0.8,1.0,1.5,1.2,4.0,0.8,11,20,6,9,11,22,15,18,12,9,5.0,34,18,5,10,75,81,7,34,59,5,31,22,38,50,49,37,32,25,44,18,8,15,35,9,12,33,33,2,3,8,34,29,31,18,5,2,7,7,6,4,16,10,17,4,22,8,17,6,12,6,10,2,4,16,8,20,14,5,12,9,8,30,6,0.041,0.019,0.01,0.017,0.028,0.03,0.027,0.026,0.037,0.029,0.037,0.035,0.032,0.023,0.025,0.03,0.026,0.027,0.029,0.047,0.014,0.03,0.03,0.034,0.03,0.03,0.026,0.027,0.023,0.031,0.032,0.061,0.036,0.034,0.036,0.024,0.028,0.03,0.013,0.024,0.024,0.039,0.033,0.027,0.018,0.015,0.018,0.016,0.014,0.02,0.027,0.016,0.018,0.017,0.025,0.017,0.021,0.026,0.018,0.022,0.022,0.02,0.013,0.024,0.019,0.031,0.018,0.02,0.013,0.025,0.024,0.018,0.017,0.026,2123,6014,2755,2581,2088,4435,4225,4842,2002,2058,1106.0,3494,3977,1303,3185,15168,25330,3331,7468,6174,3180,5386,3267,6355,9130,8964,6546,5340,7562,7514,2804,1017,1964,8047,2460,2901,9236,8252,459,909,2647,3921,9329,4097,4876,2112,1103,2455,3389,2086,567,4272,2777,6360,616,4747,2175,4331,1379,2310,1361,2442,523,893,3711,625,5070,4221,1780,1663,2340,2362,10159,1104,0.142,0.101,0.08,0.087,0.123,0.137,0.114,0.112,0.117,0.123,0.129,0.149,0.126,0.125,0.127,0.129,0.117,0.106,0.128,0.157,0.076,0.14,0.134,0.125,0.135,0.134,0.127,0.112,0.101,0.138,0.116,0.133,0.141,0.12,0.115,0.107,0.126,0.125,0.082,0.115,0.113,0.154,0.131,0.127,0.096,0.089,0.097,0.081,0.084,0.104,0.127,0.095,0.105,0.089,0.127,0.105,0.106,0.106,0.105,0.116,0.126,0.105,0.095,0.119,0.112,0.123,0.103,0.106,0.088,0.126,0.113,0.111,0.097,0.125,568,1840,979,929,693,1455,1226,1365,519,520,271.0,1678,1023,317,795,3846,6361,587,1823,2947,854,1555,1295,1499,2423,2602,2103,1831,1921,2269,622,348,608,1860,517,964,1863,1702,220,304,993,1738,1613,2357,2625,755,419,1246,1425,666,364,1886,1242,2486,292,2293,837,1953,711,1106,487,889,257,411,1583,264,2345,1702,824,632,1053,941,3654,474,3.743,2.981,2.692,2.926,2.613,2.614,2.771,2.965,2.833,3.186,3.247,1.864,2.943,2.811,2.977,2.94,3.18,3.988,3.06,1.888,2.799,2.715,2.182,3.038,2.81,2.919,2.441,2.404,2.98,2.656,3.106,2.926,2.551,3.112,3.599,2.775,3.558,3.391,2.436,2.849,3.175,2.024,3.769,1.918,2.125,2.691,3.311,2.27,2.801,3.59,1.897,2.359,2.409,2.699,2.495,2.298,2.781,2.523,2.277,2.383,2.854,2.612,2.503,2.314,2.621,2.498,2.393,2.931,2.5,2.7,2.664,2.966,2.979,2.318,0.719,0.573,0.612,0.49,0.64,0.639,0.587,0.641,0.542,0.493,0.769,0.465,0.541,0.544,0.587,0.578,0.6,0.803,0.773,0.695,0.909,0.473,0.608,0.656,0.55,0.627,0.442,0.552,0.448,0.597,0.744,1.155,0.743,0.764,0.972,0.447,0.736,0.667,0.457,0.583,0.608,0.624,0.667,0.671,0.656,0.687,0.497,1.122,0.618,0.744,0.33,0.575,0.628,0.521,0.48,0.348,0.475,0.511,0.382,0.384,0.51,0.825,0.418,0.92,0.545,0.725,0.473,0.404,0.407,0.604,0.39,0.776,0.634,0.461
-4,Epileptic,M,42.0,1.2,1.9,0.9,0.9,1.7,2.1,2.3,2.3,0.9,0.3,0.1,3.0,2.0,0.7,1.9,6.9,10.8,1.0,3.4,5.3,0.8,2.6,1.4,3.5,2.8,5.1,3.8,2.4,3.6,4.2,1.2,1.0,0.6,2.7,0.3,0.5,3.1,4.7,0.2,0.4,1.5,3.8,2.4,3.0,3.6,0.5,0.4,0.7,1.4,0.6,0.6,2.8,1.2,3.1,0.3,2.5,0.9,1.3,1.2,0.8,0.7,1.3,0.3,0.6,1.9,0.8,2.1,1.3,1.0,0.6,0.8,1.2,4.3,0.4,7,15,7,6,15,16,16,15,9,3,1.0,25,14,6,16,58,70,6,25,36,6,24,13,43,27,41,36,20,25,35,13,9,4,23,1,4,25,37,2,2,8,29,21,22,16,3,2,4,6,4,5,18,7,15,1,17,4,11,8,8,5,8,2,4,11,8,15,7,6,5,4,8,35,3,0.051,0.023,0.021,0.021,0.038,0.043,0.038,0.036,0.042,0.025,0.019,0.035,0.038,0.041,0.038,0.04,0.037,0.034,0.041,0.041,0.02,0.035,0.027,0.039,0.039,0.039,0.035,0.035,0.038,0.039,0.038,0.048,0.025,0.032,0.009,0.014,0.037,0.045,0.018,0.023,0.024,0.041,0.046,0.028,0.026,0.013,0.022,0.012,0.016,0.018,0.031,0.028,0.02,0.026,0.029,0.02,0.025,0.016,0.024,0.017,0.024,0.021,0.036,0.025,0.023,0.032,0.022,0.021,0.021,0.021,0.026,0.023,0.022,0.017,1786,5017,2529,1896,2328,2621,2706,3329,1904,1091,463.0,3343,3366,1103,3568,10786,19703,2691,3981,3440,2811,4249,1818,6282,4791,8313,6119,3677,5823,5982,2169,1149,1028,5007,1951,2019,4384,6492,365,895,1773,2964,4945,2694,3720,1287,747,2119,2769,1197,470,3559,1974,4420,442,3826,1246,2515,1298,1343,1113,2773,494,1013,2558,812,3587,2144,1645,928,1190,1988,8277,549,0.131,0.106,0.106,0.097,0.139,0.151,0.114,0.129,0.135,0.104,0.071,0.127,0.136,0.152,0.134,0.143,0.131,0.123,0.138,0.141,0.081,0.135,0.117,0.132,0.14,0.134,0.126,0.116,0.123,0.14,0.12,0.124,0.107,0.121,0.06,0.072,0.127,0.13,0.091,0.096,0.117,0.144,0.143,0.112,0.109,0.086,0.093,0.076,0.09,0.098,0.148,0.115,0.098,0.109,0.111,0.103,0.111,0.095,0.126,0.102,0.114,0.105,0.12,0.117,0.11,0.139,0.106,0.098,0.102,0.125,0.12,0.111,0.104,0.097,404,1294,704,678,764,942,966,1122,452,225,140.0,1373,920,270,872,3122,4914,494,1266,1991,674,1242,818,1505,1296,2303,1983,1279,1644,1765,542,380,360,1328,537,636,1308,1764,223,304,887,1447,995,1727,2101,595,308,803,1224,530,313,1632,913,1923,186,1850,580,1297,698,715,459,890,229,391,1263,373,1589,949,769,435,496,952,3526,332,3.956,3.062,3.165,2.755,2.498,2.573,2.316,2.64,2.715,3.47,2.691,2.082,2.789,2.754,3.024,2.635,3.179,3.895,2.629,1.591,3.032,2.566,2.023,3.055,2.888,2.853,2.449,2.211,2.713,2.66,2.672,2.954,2.271,2.675,3.267,2.82,2.612,2.738,2.116,3.117,2.374,1.89,3.294,1.789,2.093,2.304,3.046,3.025,2.663,2.866,1.981,2.256,2.306,2.466,2.449,2.346,2.797,2.194,2.285,2.146,2.467,2.949,2.435,2.675,2.273,2.418,2.387,2.611,2.595,2.422,2.817,2.51,2.494,2.041,0.92,0.82,0.789,0.611,0.773,0.751,0.556,0.549,0.778,0.481,0.761,0.521,0.603,0.534,0.637,0.593,0.779,0.743,0.701,0.515,0.829,0.531,0.442,0.872,0.639,0.75,0.545,0.615,0.601,0.528,0.841,0.966,0.421,0.659,0.953,0.5,0.702,0.743,0.59,0.785,0.436,0.701,0.897,0.591,0.54,0.575,0.607,0.952,0.574,0.571,0.402,0.485,0.496,0.465,0.476,0.609,0.621,0.574,0.35,0.472,0.543,0.779,0.319,1.281,0.65,0.611,0.597,0.465,0.541,0.68,0.697,0.602,0.498,0.344
-5,Epileptic,F,47.0,0.7,2.6,1.1,0.9,1.9,1.6,2.2,1.4,1.6,0.9,0.4,3.5,1.8,0.6,0.8,5.9,8.6,2.2,2.2,5.1,1.1,2.7,2.8,3.4,3.2,4.0,5.6,2.4,3.0,3.3,2.0,1.3,0.4,2.4,0.4,0.6,3.0,3.0,0.1,0.2,1.0,4.1,2.6,3.2,2.7,0.9,0.4,0.9,1.3,0.7,0.6,1.7,1.3,2.3,0.1,2.3,0.7,1.8,1.6,1.7,0.8,2.1,0.5,0.5,1.5,1.6,2.3,1.1,1.0,0.6,1.4,1.5,3.5,0.4,8,24,10,9,19,19,16,16,17,11,4.0,31,23,7,12,71,86,11,25,47,9,34,25,43,40,44,50,23,23,41,22,11,5,39,2,4,36,45,2,1,9,33,28,27,19,6,3,5,7,5,5,13,10,17,0,20,8,16,15,11,5,15,5,6,11,20,19,9,6,5,9,9,32,3,0.033,0.027,0.021,0.019,0.034,0.029,0.035,0.029,0.06,0.04,0.025,0.04,0.027,0.03,0.028,0.033,0.029,0.063,0.032,0.038,0.022,0.028,0.04,0.036,0.035,0.033,0.04,0.032,0.03,0.032,0.052,0.054,0.024,0.034,0.014,0.013,0.035,0.03,0.015,0.018,0.021,0.039,0.035,0.028,0.023,0.022,0.018,0.015,0.016,0.023,0.025,0.015,0.025,0.019,0.019,0.016,0.02,0.021,0.025,0.028,0.023,0.026,0.027,0.018,0.023,0.031,0.02,0.016,0.019,0.018,0.028,0.018,0.015,0.019,1545,4634,2165,2100,2072,2800,2792,3190,1820,1703,699.0,3340,3800,1139,2322,11210,19140,2603,3841,4783,3305,4885,2925,6611,5146,6869,6484,3530,5871,5934,2761,1262,1013,5018,1542,1986,5745,7678,324,367,1569,3154,5064,2801,2842,1401,888,1965,2223,1016,636,3566,1589,4465,102,4039,1197,3104,1712,1864,1017,2963,486,981,2051,1037,3469,2595,1674,1086,1436,2336,7751,441,0.127,0.132,0.119,0.099,0.146,0.134,0.136,0.132,0.182,0.161,0.122,0.161,0.141,0.142,0.132,0.143,0.13,0.17,0.127,0.151,0.101,0.126,0.142,0.138,0.131,0.15,0.128,0.109,0.106,0.147,0.13,0.121,0.105,0.145,0.074,0.08,0.141,0.147,0.105,0.105,0.118,0.138,0.155,0.131,0.11,0.112,0.103,0.089,0.092,0.121,0.139,0.094,0.125,0.1,0.126,0.101,0.118,0.115,0.124,0.124,0.115,0.122,0.138,0.11,0.112,0.134,0.106,0.096,0.1,0.117,0.129,0.101,0.092,0.13,429,1492,767,814,910,1028,1034,938,549,466,195.0,1502,1096,327,644,3144,4980,544,1163,2306,778,1470,1153,1745,1527,2065,2167,1287,1444,1787,799,523,281,1299,445,735,1633,1877,185,173,771,1662,1253,1821,1852,642,371,891,1110,481,342,1710,855,2007,48,2120,588,1419,1044,922,507,1154,294,527,1070,714,1847,1115,808,491,723,1207,3445,258,3.449,2.798,2.636,2.381,2.023,2.387,2.194,2.771,2.491,2.983,2.925,1.97,2.734,2.62,2.67,2.696,2.964,3.619,2.558,1.823,2.988,2.502,2.199,2.761,2.576,2.694,2.397,2.128,2.911,2.572,2.531,2.508,2.763,2.815,2.96,2.537,2.677,2.911,1.894,2.118,2.518,1.749,2.988,1.77,1.761,2.457,2.894,2.735,2.391,2.484,2.04,2.198,2.037,2.409,2.377,2.135,2.391,2.315,1.895,2.217,2.362,2.446,1.832,2.222,2.214,1.81,1.996,2.547,2.399,2.461,2.396,2.268,2.376,2.139,0.75,0.637,0.679,0.511,0.624,0.536,0.7,0.582,0.62,0.552,0.757,0.594,0.541,0.549,0.587,0.589,0.646,0.712,0.606,0.616,0.9,0.584,0.668,0.805,0.64,0.54,0.658,0.67,0.718,0.509,0.697,0.972,0.512,0.653,0.578,0.472,0.64,0.665,0.462,0.375,0.543,0.475,0.684,0.572,0.597,0.475,0.492,0.66,0.46,0.5,0.46,0.434,0.43,0.452,0.25,0.361,0.509,0.559,0.388,0.42,0.445,0.7,0.302,0.698,0.533,0.641,0.415,0.413,0.374,0.809,0.349,0.462,0.47,0.463
-6,Epileptic,F,22.0,1.3,3.3,1.4,1.2,2.6,2.2,3.6,2.8,1.8,1.0,0.4,4.0,2.4,0.6,1.6,8.1,17.1,1.1,3.4,4.7,1.5,4.9,2.6,7.0,6.6,6.1,7.5,4.6,5.7,4.1,3.4,1.5,0.6,3.1,0.8,0.9,5.9,4.2,0.2,0.2,1.5,4.1,4.7,3.3,4.3,1.1,0.6,1.0,1.9,1.0,0.8,2.0,2.3,4.2,0.4,3.2,1.2,2.3,1.4,1.4,0.8,1.9,0.3,1.2,2.1,1.6,3.6,2.7,1.4,0.7,1.6,1.8,5.2,0.5,11,25,10,9,20,21,27,26,13,13,4.0,39,22,6,16,74,121,9,45,45,11,52,24,56,71,67,67,35,45,52,23,10,5,31,5,7,62,54,1,1,9,37,32,25,20,6,3,6,9,9,7,12,15,27,3,23,8,20,9,11,5,12,1,10,18,19,25,19,7,6,11,15,40,3,0.053,0.032,0.023,0.023,0.05,0.038,0.047,0.038,0.058,0.048,0.023,0.042,0.041,0.04,0.051,0.058,0.049,0.047,0.043,0.04,0.032,0.048,0.041,0.073,0.051,0.038,0.052,0.049,0.051,0.036,0.075,0.054,0.027,0.034,0.022,0.019,0.05,0.037,0.017,0.018,0.029,0.043,0.061,0.027,0.033,0.019,0.028,0.014,0.023,0.021,0.028,0.024,0.035,0.036,0.034,0.021,0.026,0.026,0.028,0.022,0.03,0.037,0.032,0.037,0.023,0.031,0.028,0.031,0.034,0.025,0.035,0.021,0.022,0.026,1620,4979,2442,2669,3077,3450,3534,3692,2301,1749,804.0,3818,3689,1284,2505,9354,22294,2601,5609,5541,3066,5344,2424,6038,6051,8538,6414,4468,6373,6258,2394,1397,1138,6348,2339,2205,7792,8384,391,352,1504,3323,5448,3493,3272,1716,856,2713,2488,1582,746,2675,2826,4513,485,4080,1715,3734,1267,1792,879,2326,268,958,2931,1288,4125,2996,1557,1165,1153,2419,8438,509,0.147,0.124,0.108,0.109,0.148,0.139,0.137,0.153,0.171,0.17,0.117,0.17,0.15,0.148,0.163,0.164,0.151,0.141,0.137,0.148,0.116,0.146,0.135,0.18,0.167,0.141,0.148,0.139,0.151,0.149,0.172,0.127,0.112,0.134,0.099,0.101,0.154,0.144,0.09,0.103,0.117,0.149,0.168,0.12,0.12,0.096,0.109,0.079,0.106,0.113,0.14,0.106,0.131,0.129,0.147,0.111,0.109,0.123,0.13,0.119,0.124,0.135,0.128,0.125,0.116,0.135,0.121,0.118,0.119,0.12,0.151,0.12,0.105,0.13,457,1746,988,916,948,1114,1442,1255,569,440,291.0,1677,1105,299,728,2850,6457,503,1493,2271,825,1705,1126,1818,2166,2856,2644,1749,2037,2119,747,437,386,1619,615,779,2346,2143,224,176,850,1549,1437,1960,2089,849,361,1051,1140,730,434,1377,1172,2031,237,2233,770,1564,750,985,440,896,151,466,1498,763,2112,1378,741,512,640,1293,3876,286,3.414,2.485,2.407,2.801,2.499,2.459,2.113,2.49,2.757,3.016,2.543,1.99,2.595,2.842,2.563,2.388,2.62,3.7,2.805,2.038,2.797,2.401,1.952,2.491,2.244,2.433,2.059,2.097,2.403,2.297,2.31,3.058,2.373,2.722,3.208,2.519,2.571,2.857,1.926,2.118,2.271,1.865,2.727,1.955,1.801,2.194,2.886,2.973,2.634,2.455,1.972,2.15,2.044,2.271,2.397,2.019,2.457,2.476,1.945,1.989,2.406,2.478,1.96,2.221,2.051,2.079,2.108,2.296,2.292,2.404,2.125,2.253,2.314,2.272,0.829,0.665,0.592,0.608,0.831,0.63,0.539,0.579,0.831,0.687,0.533,0.487,0.475,0.499,0.507,0.679,0.69,0.86,0.701,0.627,0.736,0.645,0.46,0.759,0.654,0.606,0.558,0.592,0.651,0.609,0.754,1.028,0.527,0.739,0.825,0.489,0.759,0.593,0.389,0.334,0.478,0.692,0.841,0.661,0.471,0.56,0.54,0.901,0.585,0.574,0.414,0.48,0.694,0.501,0.439,0.432,0.467,0.546,0.399,0.377,0.433,0.601,0.419,0.811,0.523,0.608,0.454,0.491,0.554,0.724,0.444,0.482,0.492,0.382
-7,Epileptic,F,27.0,0.5,2.5,1.0,1.1,1.9,1.7,1.6,1.4,1.1,0.6,0.2,2.9,1.5,0.4,1.1,5.6,7.6,0.6,1.9,4.7,0.7,2.5,2.0,4.3,3.2,2.9,3.3,2.2,2.7,3.5,1.4,1.2,0.4,2.0,0.3,0.5,5.1,4.9,0.1,0.2,1.0,2.5,1.7,2.8,2.6,0.8,0.2,0.6,1.2,0.7,0.4,2.0,1.8,3.0,0.4,2.5,0.8,2.8,0.4,1.6,0.5,2.2,0.2,0.5,1.8,1.1,2.5,1.3,0.8,0.3,1.5,1.2,5.1,0.2,8,28,9,11,15,19,14,18,12,7,2.0,32,25,6,14,72,72,5,27,42,5,34,19,37,42,43,44,25,27,47,21,9,4,31,1,4,57,58,1,2,5,26,19,31,16,5,1,4,7,9,4,16,16,22,3,19,5,22,5,15,4,15,1,5,13,14,20,8,6,3,9,10,45,1,0.035,0.033,0.02,0.02,0.039,0.028,0.024,0.027,0.052,0.032,0.024,0.04,0.033,0.029,0.027,0.034,0.031,0.024,0.026,0.042,0.018,0.03,0.035,0.049,0.032,0.03,0.028,0.026,0.024,0.038,0.033,0.038,0.028,0.026,0.009,0.012,0.047,0.044,0.01,0.013,0.019,0.039,0.034,0.029,0.022,0.017,0.014,0.011,0.018,0.019,0.027,0.023,0.029,0.024,0.025,0.019,0.024,0.027,0.019,0.027,0.02,0.031,0.016,0.016,0.021,0.021,0.022,0.018,0.02,0.017,0.031,0.023,0.019,0.013,1305,4351,2460,2383,2434,2715,3619,2733,1584,1459,586.0,2828,3480,1126,3039,10822,16869,2012,4410,4193,2757,4390,2647,5567,6321,6067,6688,4356,6328,6909,2865,1446,887,4662,1535,1846,6094,7701,291,473,1617,2427,4728,2538,3410,1412,628,1817,2282,1712,547,3348,2089,4710,545,3950,1351,3199,613,1812,745,2708,374,905,2525,1339,3597,2441,1296,681,1699,1970,8649,539,0.122,0.14,0.11,0.11,0.15,0.138,0.116,0.13,0.2,0.129,0.094,0.168,0.14,0.144,0.128,0.15,0.132,0.104,0.127,0.158,0.075,0.135,0.151,0.157,0.142,0.143,0.135,0.118,0.115,0.162,0.138,0.117,0.104,0.128,0.059,0.086,0.163,0.166,0.076,0.112,0.102,0.142,0.142,0.119,0.105,0.1,0.078,0.078,0.099,0.114,0.125,0.113,0.128,0.119,0.128,0.108,0.118,0.122,0.121,0.132,0.114,0.133,0.099,0.107,0.117,0.106,0.115,0.099,0.107,0.122,0.132,0.127,0.106,0.086,358,1363,795,843,809,953,1021,948,432,353,149.0,1270,953,251,749,2866,4218,433,1222,1867,666,1353,961,1575,1764,1775,2040,1336,1751,1872,754,490,248,1206,428,666,1835,1792,149,240,790,1074,925,1580,1870,707,217,834,1052,640,278,1435,932,1941,256,1997,591,1602,338,985,390,1013,205,487,1198,747,1741,1055,632,300,821,881,3664,252,3.252,2.891,2.939,2.703,2.458,2.318,2.718,2.519,2.804,3.238,3.18,1.909,2.744,3.093,2.924,2.754,3.037,3.755,2.89,1.976,2.825,2.472,2.349,2.722,2.703,2.771,2.498,2.454,2.796,2.764,2.741,2.661,2.637,2.803,3.173,2.56,2.61,3.006,2.351,2.219,2.523,1.87,3.432,1.807,2.044,2.243,3.027,2.721,2.573,2.615,1.996,2.425,2.231,2.48,2.587,2.148,2.492,2.232,1.865,2.108,2.167,2.63,1.969,2.206,2.373,2.216,2.185,2.641,2.367,2.505,2.392,2.491,2.415,2.535,0.685,0.567,0.596,0.512,0.82,0.732,0.667,0.418,0.596,0.47,0.803,0.632,0.481,0.662,0.595,0.658,0.61,0.705,0.551,0.582,0.707,0.592,0.565,0.842,0.657,0.539,0.64,0.57,0.559,0.671,0.728,1.015,0.376,0.579,0.527,0.454,0.696,0.753,0.342,0.33,0.489,0.663,0.789,0.581,0.52,0.419,0.519,0.639,0.412,0.467,0.418,0.444,0.519,0.455,0.437,0.398,0.444,0.544,0.735,0.377,0.345,0.728,0.464,0.731,0.585,0.678,0.477,0.408,0.442,0.72,0.418,0.507,0.471,0.38
-8,Epileptic,F,42.0,0.5,1.9,0.7,1.5,1.3,3.1,1.3,1.6,1.1,0.9,0.5,3.1,1.8,0.7,1.9,4.0,7.9,0.6,2.1,4.8,0.9,2.8,1.3,3.0,3.1,3.5,3.6,2.4,2.9,3.0,1.4,1.1,0.3,1.9,0.5,0.3,3.6,2.9,0.2,0.3,1.1,2.8,1.8,2.7,3.2,0.5,0.4,0.8,1.3,0.7,0.7,2.3,1.0,2.4,0.1,2.5,0.8,1.8,1.0,1.2,0.5,1.7,0.3,0.3,1.8,1.6,2.2,1.2,1.2,0.4,0.7,1.1,3.9,0.4,5,21,8,11,14,24,11,15,12,7,3.0,23,18,7,16,41,73,6,24,40,6,29,13,31,37,31,35,21,23,34,16,9,3,24,2,3,41,32,1,2,6,22,17,20,18,4,2,6,7,7,6,16,9,18,1,20,6,15,5,9,4,13,2,3,15,20,18,7,7,3,5,7,31,2,0.031,0.027,0.015,0.03,0.039,0.046,0.023,0.031,0.049,0.036,0.035,0.037,0.031,0.039,0.035,0.036,0.034,0.031,0.034,0.042,0.022,0.04,0.023,0.045,0.029,0.037,0.032,0.03,0.03,0.038,0.041,0.049,0.026,0.03,0.016,0.013,0.039,0.04,0.015,0.022,0.03,0.041,0.036,0.027,0.027,0.012,0.026,0.015,0.018,0.02,0.032,0.025,0.026,0.023,0.018,0.02,0.027,0.025,0.031,0.022,0.021,0.038,0.029,0.017,0.023,0.028,0.022,0.022,0.023,0.016,0.023,0.025,0.021,0.022,1081,3491,1617,2201,1774,3142,2494,2417,1678,1261,713.0,2605,3193,1004,2993,6502,14106,1997,3481,4956,2980,3921,1751,4677,5350,4729,5140,2626,4667,4448,1879,1153,724,4597,1528,1053,5052,5046,311,498,1117,2195,4231,2781,2853,1347,698,1794,2179,1017,579,3514,1687,3880,228,3562,1013,2778,945,1438,1008,1749,545,740,2601,1438,2719,1942,1642,732,1062,1568,6539,514,0.11,0.129,0.102,0.126,0.151,0.174,0.1,0.148,0.173,0.138,0.134,0.141,0.13,0.154,0.136,0.14,0.137,0.111,0.136,0.153,0.087,0.149,0.12,0.156,0.134,0.158,0.133,0.113,0.116,0.15,0.146,0.131,0.108,0.135,0.079,0.081,0.121,0.139,0.093,0.118,0.126,0.149,0.14,0.119,0.116,0.085,0.116,0.093,0.096,0.12,0.141,0.118,0.124,0.113,0.117,0.11,0.135,0.122,0.124,0.113,0.093,0.147,0.131,0.113,0.124,0.132,0.111,0.107,0.112,0.105,0.124,0.117,0.108,0.121,311,1281,738,846,651,1123,879,810,478,369,200.0,1266,1027,313,888,1978,4076,394,1068,1948,686,1263,835,1251,1760,1486,1877,1228,1389,1491,520,393,207,1072,399,419,1643,1327,203,277,584,1122,895,1592,1844,695,271,831,1072,505,365,1504,675,1719,108,2005,494,1211,552,812,456,739,233,344,1198,838,1664,880,779,362,514,806,3005,226,3.395,2.497,2.108,2.554,2.308,2.445,2.401,2.657,2.659,2.726,2.647,1.832,2.538,2.497,2.622,2.526,2.783,3.775,2.697,2.111,3.148,2.441,1.855,2.757,2.474,2.653,2.241,1.815,2.654,2.421,2.68,2.671,2.623,2.904,3.331,2.203,2.335,2.842,1.766,1.953,2.3,1.791,3.299,1.946,1.826,2.089,3.163,2.644,2.528,2.247,1.815,2.289,2.337,2.295,2.297,1.918,2.411,2.397,2.083,2.001,2.107,2.593,2.431,2.491,2.436,2.028,1.825,2.523,2.165,2.2,2.371,2.419,2.344,2.657,0.634,0.453,0.427,0.5,0.63,0.735,0.545,0.449,0.497,0.435,0.961,0.467,0.444,0.475,0.475,0.576,0.568,0.749,0.573,0.683,0.736,0.463,0.477,0.561,0.546,0.508,0.441,0.43,0.54,0.513,0.686,0.94,0.408,0.672,0.778,0.462,0.675,0.624,0.265,0.382,0.503,0.546,0.745,0.665,0.586,0.515,0.572,0.675,0.531,0.398,0.471,0.546,0.555,0.534,0.47,0.394,0.425,0.434,0.339,0.397,0.434,0.563,0.493,0.974,0.572,0.675,0.396,0.39,0.477,0.696,0.45,0.555,0.504,0.482
-9,Epileptic,F,27.0,1.4,2.7,0.9,1.3,1.9,1.8,1.5,1.8,1.1,0.4,0.3,3.1,1.2,0.4,1.2,7.5,8.9,0.9,2.4,4.5,0.9,2.8,1.8,3.8,3.1,4.3,3.5,2.2,2.3,3.6,0.8,1.4,0.4,2.5,0.4,0.5,4.0,3.7,0.2,0.1,1.3,2.2,2.0,2.5,2.9,0.7,0.5,0.7,1.2,0.8,0.9,1.5,1.6,2.1,0.4,3.1,0.5,2.4,0.7,1.2,1.1,1.6,0.4,0.4,1.7,0.5,2.9,1.4,1.4,0.9,1.1,1.5,4.9,0.3,14,24,9,11,14,14,15,16,13,11,3.0,31,13,5,18,93,85,9,25,36,10,24,20,40,38,46,46,23,25,41,13,9,6,30,1,4,44,48,2,1,7,22,21,20,17,5,2,5,7,8,5,11,13,15,3,29,4,19,5,9,9,15,2,5,16,7,23,10,10,8,8,12,39,3,0.048,0.024,0.016,0.022,0.031,0.032,0.023,0.027,0.033,0.039,0.024,0.036,0.027,0.033,0.031,0.03,0.025,0.031,0.028,0.035,0.017,0.033,0.028,0.036,0.03,0.029,0.027,0.026,0.021,0.024,0.024,0.052,0.022,0.027,0.009,0.009,0.034,0.029,0.012,0.014,0.022,0.03,0.03,0.027,0.02,0.016,0.024,0.014,0.015,0.014,0.035,0.013,0.02,0.017,0.018,0.02,0.016,0.028,0.025,0.024,0.025,0.025,0.018,0.017,0.017,0.016,0.021,0.017,0.019,0.025,0.023,0.015,0.018,0.016,1620,5589,2662,2283,2422,2667,2821,3648,2047,795,493.0,2855,2957,1069,2858,15592,20107,2483,5161,4759,3856,4061,2660,6206,6268,9318,6320,4121,6519,6730,2038,990,901,6980,2003,2529,7788,9322,364,479,1806,2723,5091,2327,3639,1449,773,2087,2610,2350,799,3917,2672,4327,628,5058,1509,3787,820,1576,2027,2769,508,890,3274,861,4546,2768,2404,1435,1799,3109,10452,533,0.132,0.116,0.093,0.109,0.13,0.134,0.109,0.13,0.129,0.125,0.119,0.155,0.124,0.139,0.126,0.137,0.124,0.125,0.129,0.137,0.087,0.143,0.133,0.131,0.139,0.135,0.129,0.113,0.105,0.129,0.105,0.125,0.124,0.124,0.061,0.075,0.128,0.126,0.092,0.101,0.114,0.136,0.13,0.127,0.102,0.092,0.114,0.08,0.091,0.096,0.134,0.081,0.102,0.096,0.114,0.114,0.083,0.121,0.122,0.122,0.122,0.115,0.105,0.121,0.105,0.103,0.115,0.1,0.105,0.133,0.113,0.101,0.101,0.118,471,1900,937,860,873,965,1084,1108,598,229,150.0,1396,776,269,857,4374,5741,502,1381,2057,1071,1332,1051,1652,1868,2542,2167,1479,1772,2249,554,456,343,1612,546,893,1975,2226,222,149,845,1177,1134,1459,2195,712,327,882,1162,860,420,1928,1303,1930,303,2426,677,1578,499,858,815,1049,280,367,1613,442,2122,1222,1153,573,840,1468,4234,288,3.289,2.792,2.749,2.593,2.357,2.423,2.213,2.909,2.568,2.955,2.757,1.851,2.826,2.916,2.627,2.761,2.903,3.54,2.965,2.069,2.882,2.409,2.17,2.828,2.67,2.95,2.309,2.259,2.793,2.524,2.745,2.225,2.132,3.103,3.316,2.702,2.889,3.029,1.974,3.257,2.859,2.038,3.257,1.939,1.926,2.385,3.061,2.931,2.663,2.843,2.354,2.187,2.192,2.455,2.497,2.314,2.531,2.632,1.982,2.064,2.497,2.489,2.184,2.264,2.276,2.33,2.319,2.579,2.44,2.789,2.541,2.435,2.673,2.312,0.87,0.601,0.577,0.575,0.538,0.457,0.526,0.44,0.527,0.46,0.621,0.463,0.437,0.477,0.51,0.547,0.526,0.784,0.512,0.654,0.883,0.423,0.436,0.599,0.493,0.538,0.494,0.586,0.465,0.498,0.611,0.792,0.409,0.654,0.75,0.586,0.644,0.625,0.431,0.791,0.639,0.49,0.645,0.701,0.551,0.478,0.441,0.595,0.617,0.615,0.381,0.446,0.517,0.403,0.341,0.388,0.422,0.584,0.352,0.338,0.422,0.681,0.551,0.964,0.512,0.756,0.516,0.405,0.383,0.625,0.481,0.512,0.599,0.481
-10,Epileptic,M,22.0,0.6,2.4,0.9,1.0,1.8,2.2,1.6,1.6,1.0,0.6,0.4,3.0,1.4,0.4,1.2,4.5,7.2,2.1,1.8,4.6,1.4,2.7,1.6,4.3,2.1,3.5,3.2,2.3,2.1,2.8,2.2,2.3,0.6,2.1,0.5,0.6,3.3,3.1,0.3,0.1,1.0,3.1,2.5,2.6,2.5,0.5,0.7,1.1,1.3,1.5,0.7,2.1,1.6,2.5,0.2,2.6,0.5,1.8,1.9,0.8,0.5,1.7,0.4,0.6,1.9,0.9,2.5,1.2,0.8,1.1,1.0,1.9,4.3,0.4,4,24,8,12,17,20,14,17,12,9,4.0,30,18,7,15,56,71,11,24,46,18,32,20,52,33,43,38,26,21,41,23,22,7,28,3,4,35,41,3,1,7,27,22,20,15,4,3,5,7,12,5,16,13,20,1,18,4,14,14,7,3,11,5,4,14,15,20,7,4,9,6,14,35,2,0.027,0.019,0.015,0.02,0.033,0.036,0.025,0.027,0.031,0.028,0.033,0.04,0.025,0.037,0.027,0.026,0.025,0.055,0.024,0.038,0.025,0.034,0.03,0.044,0.027,0.03,0.026,0.03,0.025,0.033,0.053,0.06,0.03,0.033,0.017,0.014,0.035,0.031,0.02,0.016,0.021,0.038,0.033,0.027,0.017,0.011,0.026,0.015,0.016,0.03,0.022,0.018,0.021,0.018,0.018,0.016,0.014,0.02,0.028,0.018,0.014,0.026,0.019,0.018,0.018,0.024,0.017,0.015,0.013,0.023,0.019,0.024,0.019,0.02,1575,6030,2127,2177,2902,3151,2818,3303,2217,1407,972.0,3153,4388,1179,3222,12356,20696,2924,5006,5505,4338,4773,2765,8021,5714,8117,7228,4530,6594,5803,2568,1371,1214,5727,2222,1814,6937,8410,447,395,1644,3218,6607,2852,3907,1305,943,2641,2585,1737,661,4405,2580,5856,373,4522,1315,3243,1740,1384,850,2716,779,1140,2792,970,4277,2821,1599,1998,1485,4075,7987,552,0.099,0.112,0.096,0.108,0.13,0.15,0.1,0.128,0.144,0.137,0.127,0.164,0.12,0.142,0.121,0.127,0.119,0.151,0.121,0.151,0.103,0.137,0.145,0.148,0.125,0.135,0.118,0.117,0.108,0.143,0.155,0.138,0.118,0.132,0.082,0.09,0.138,0.135,0.116,0.093,0.103,0.129,0.132,0.12,0.094,0.085,0.113,0.083,0.087,0.127,0.122,0.095,0.11,0.097,0.116,0.096,0.09,0.097,0.135,0.101,0.093,0.114,0.135,0.106,0.108,0.12,0.101,0.087,0.087,0.125,0.105,0.112,0.106,0.101,447,2112,920,860,989,1123,1017,1037,537,376,224.0,1479,1072,294,886,3083,5207,593,1305,2325,1035,1537,1013,2113,1621,2181,2289,1542,1514,1890,834,701,331,1275,504,655,1772,1864,227,173,785,1507,1377,1546,2159,672,407,980,1178,794,443,1933,1130,2242,161,2415,623,1512,1021,749,467,1016,331,538,1469,589,2200,1200,780,717,804,1422,3470,306,3.528,2.629,2.24,2.394,2.494,2.302,2.321,2.722,2.835,2.745,3.212,1.881,3.137,2.873,2.761,2.865,3.005,3.794,2.935,2.008,2.916,2.398,2.215,2.822,2.66,2.835,2.386,2.327,3.099,2.419,2.328,1.973,2.74,3.159,3.812,2.529,2.827,3.147,2.229,2.44,2.63,1.861,3.36,2.083,2.031,2.059,2.96,3.561,2.66,2.404,1.88,2.331,2.257,2.527,2.553,2.024,2.337,2.382,1.857,1.984,2.069,2.567,2.261,2.57,2.057,2.032,2.014,2.669,2.262,2.657,2.069,2.767,2.404,2.335,0.835,0.615,0.518,0.487,0.734,0.631,0.676,0.482,0.688,0.577,0.776,0.409,0.476,0.54,0.561,0.683,0.704,0.777,0.51,0.661,0.957,0.517,0.528,0.763,0.607,0.546,0.611,0.576,0.614,0.53,0.746,0.848,0.373,0.619,0.653,0.502,0.827,0.68,0.559,0.482,0.553,0.511,0.726,0.617,0.624,0.458,0.439,0.825,0.432,0.505,0.368,0.547,0.442,0.525,0.389,0.44,0.352,0.492,0.377,0.373,0.32,0.681,0.49,0.614,0.464,0.63,0.458,0.428,0.428,0.767,0.47,0.72,0.512,0.294
-11,Epileptic,F,22.0,1.0,1.7,1.1,1.1,1.4,2.4,1.6,1.3,1.0,1.1,0.3,2.7,1.2,0.3,1.2,4.4,7.4,1.2,2.7,4.1,1.7,3.6,1.5,3.6,2.6,3.0,4.0,3.0,2.4,3.2,2.4,0.9,0.4,2.2,0.3,0.6,3.6,3.2,0.1,0.1,0.9,4.1,2.0,2.3,2.6,0.6,0.5,0.6,1.4,0.8,0.5,2.0,1.2,2.3,0.1,2.6,0.6,1.2,1.1,1.1,0.7,1.9,0.6,0.5,1.9,1.4,2.8,1.2,0.8,0.5,1.5,1.8,6.3,0.2,10,19,10,8,13,21,12,14,9,11,2.0,26,15,4,16,57,79,9,31,38,16,56,17,45,42,34,48,33,22,46,23,7,5,32,2,5,41,42,1,2,5,34,22,16,15,5,2,6,7,5,3,16,9,19,1,21,5,12,8,9,7,13,4,5,16,18,21,9,5,4,12,12,54,2,0.04,0.021,0.019,0.019,0.026,0.031,0.029,0.02,0.042,0.039,0.029,0.038,0.026,0.024,0.03,0.027,0.027,0.043,0.034,0.037,0.025,0.037,0.028,0.034,0.025,0.023,0.033,0.028,0.023,0.03,0.046,0.034,0.024,0.029,0.008,0.014,0.038,0.033,0.011,0.017,0.019,0.038,0.032,0.023,0.018,0.013,0.017,0.013,0.019,0.013,0.03,0.021,0.025,0.017,0.014,0.017,0.014,0.016,0.021,0.019,0.022,0.025,0.027,0.018,0.02,0.024,0.02,0.017,0.014,0.016,0.027,0.021,0.019,0.012,1680,4378,2594,2376,2147,3391,3252,3236,1210,1916,606.0,3176,3219,1038,2697,9959,18597,2532,4798,4916,5675,5708,2225,6773,6786,7451,6947,4966,5946,6569,2590,1102,1145,6242,2407,1862,6761,7939,238,446,1385,3365,5513,2880,4151,1253,885,1957,2221,1799,406,3476,2074,5295,508,4831,1160,3238,1530,1562,1259,3112,564,1091,2846,1662,4306,2488,1907,891,1940,2486,11945,471,0.14,0.114,0.108,0.101,0.127,0.13,0.113,0.115,0.151,0.157,0.1,0.151,0.118,0.113,0.133,0.128,0.129,0.146,0.144,0.141,0.11,0.143,0.129,0.137,0.119,0.119,0.137,0.115,0.105,0.145,0.148,0.11,0.11,0.145,0.057,0.084,0.131,0.142,0.079,0.107,0.107,0.138,0.136,0.115,0.095,0.096,0.102,0.083,0.099,0.087,0.129,0.111,0.119,0.095,0.093,0.098,0.096,0.103,0.108,0.114,0.117,0.118,0.137,0.109,0.116,0.116,0.107,0.097,0.087,0.109,0.129,0.106,0.108,0.104,428,1481,886,870,820,1249,965,1037,417,495,164.0,1317,919,249,719,2894,4931,513,1355,1981,1126,1757,919,1887,1968,2082,2300,1708,1590,2013,756,486,310,1419,571,632,1773,1882,123,205,719,1658,1125,1573,2266,663,365,883,1068,808,234,1512,783,2169,154,2377,591,1367,849,864,538,1175,342,546,1413,915,2143,1140,889,456,890,1262,5051,234,3.373,2.764,2.897,2.654,2.309,2.394,2.604,2.623,2.235,3.189,2.848,2.032,2.705,3.059,2.733,2.583,2.871,3.564,2.788,2.146,3.453,2.525,2.052,2.691,2.569,2.714,2.33,2.231,2.796,2.63,2.521,2.372,2.763,3.087,3.559,2.586,2.821,2.94,2.129,2.246,2.41,1.807,3.425,2.109,2.079,2.089,2.834,2.735,2.549,2.645,2.202,2.313,2.331,2.461,2.835,2.152,2.11,2.519,1.962,1.995,2.276,2.484,1.822,2.3,2.245,2.249,2.151,2.461,2.376,2.163,2.415,2.272,2.474,2.348,0.81,0.64,0.523,0.464,0.654,0.635,0.638,0.435,0.701,0.497,0.591,0.583,0.409,0.485,0.537,0.6,0.629,0.848,0.541,0.584,0.821,0.601,0.522,0.795,0.583,0.581,0.531,0.611,0.426,0.524,0.706,0.778,0.47,0.62,0.662,0.538,0.8,0.665,0.2,0.406,0.459,0.57,0.751,0.596,0.576,0.529,0.609,0.538,0.43,0.516,0.377,0.5,0.584,0.475,0.369,0.458,0.403,0.49,0.404,0.367,0.513,0.64,0.458,0.606,0.62,0.782,0.444,0.374,0.461,0.501,0.406,0.472,0.464,0.312
-12,Epileptic,F,27.0,0.9,1.8,1.0,1.5,1.8,1.8,1.7,1.9,1.0,0.7,0.2,2.8,1.0,0.5,0.7,4.6,9.3,0.6,2.2,4.9,0.9,2.9,1.1,3.1,2.4,2.8,3.3,2.7,2.5,4.0,1.5,0.6,0.5,2.2,0.4,0.7,3.6,2.6,0.1,0.1,1.2,2.6,2.3,2.6,2.7,0.7,0.4,0.7,1.2,0.7,0.4,1.1,0.9,2.7,0.4,2.1,0.5,1.5,1.1,0.8,0.5,1.6,0.3,0.4,1.9,0.8,3.4,1.1,0.8,0.5,1.2,0.7,4.5,0.3,8,21,10,13,19,18,14,20,10,6,2.0,22,12,7,7,53,90,6,25,38,11,31,12,33,35,37,42,29,21,42,21,4,4,24,3,6,43,31,1,1,8,24,25,21,17,5,2,6,7,6,4,8,6,21,3,19,4,12,6,6,3,12,2,3,14,11,32,10,6,4,7,8,46,3,0.037,0.021,0.016,0.026,0.033,0.033,0.026,0.026,0.037,0.032,0.022,0.035,0.027,0.026,0.024,0.031,0.029,0.024,0.029,0.04,0.019,0.035,0.022,0.033,0.028,0.025,0.024,0.026,0.026,0.033,0.032,0.028,0.025,0.027,0.015,0.018,0.038,0.025,0.01,0.02,0.026,0.036,0.034,0.032,0.021,0.016,0.02,0.013,0.016,0.012,0.025,0.02,0.02,0.023,0.016,0.019,0.014,0.019,0.03,0.019,0.024,0.027,0.019,0.017,0.023,0.03,0.02,0.018,0.016,0.02,0.025,0.016,0.02,0.016,1369,3946,1923,2537,2465,3130,3239,4266,1861,1394,552.0,2558,2535,1301,1848,9415,19687,1784,4007,4286,4200,4499,1875,5918,5315,6678,6690,4487,5467,6242,2971,871,984,5325,1943,1830,7814,6753,272,271,1350,2879,6373,2150,3467,1476,804,1878,2253,1931,604,1899,1641,4875,545,3580,1075,3129,1029,1170,752,2607,455,884,2753,614,5738,2342,1469,1075,1715,1707,8985,486,0.13,0.122,0.119,0.119,0.154,0.142,0.115,0.131,0.152,0.128,0.108,0.141,0.125,0.126,0.133,0.145,0.132,0.106,0.132,0.148,0.096,0.156,0.116,0.14,0.14,0.13,0.126,0.121,0.118,0.148,0.147,0.102,0.099,0.127,0.086,0.1,0.146,0.128,0.094,0.101,0.119,0.159,0.148,0.126,0.103,0.099,0.101,0.088,0.09,0.091,0.136,0.103,0.112,0.107,0.113,0.11,0.099,0.104,0.134,0.109,0.117,0.122,0.127,0.108,0.122,0.125,0.108,0.104,0.104,0.114,0.121,0.114,0.107,0.116,443,1390,804,962,852,1051,998,1256,464,367,186.0,1145,690,291,490,2517,5283,386,1199,1949,933,1332,753,1534,1540,2027,2268,1667,1558,2041,737,372,337,1337,494,600,1762,1690,146,121,766,1212,1219,1398,2043,673,340,847,1074,766,318,919,689,1967,307,1747,521,1357,525,649,370,971,215,408,1266,383,2823,1007,674,418,771,735,3701,271,2.996,2.661,2.39,2.743,2.522,2.544,2.755,2.786,2.81,3.099,2.534,1.98,2.76,2.995,2.878,2.849,2.99,3.615,2.738,1.958,3.372,2.581,2.137,2.996,2.661,2.679,2.349,2.244,2.692,2.526,2.926,2.391,2.253,2.886,3.401,2.773,3.235,2.889,2.097,2.785,2.302,2.145,3.4,1.749,1.971,2.437,2.996,2.738,2.548,2.682,2.056,2.373,2.549,2.541,2.182,2.117,2.374,2.672,2.325,2.051,2.647,2.816,2.425,2.557,2.59,1.931,2.141,2.552,2.566,2.999,2.621,2.54,2.553,2.249,0.728,0.516,0.623,0.516,0.617,0.482,0.645,0.485,0.673,0.423,0.631,0.53,0.394,0.397,0.454,0.501,0.554,0.701,0.584,0.553,0.765,0.462,0.523,0.545,0.499,0.46,0.476,0.495,0.651,0.49,0.598,0.87,0.509,0.599,0.697,0.502,0.698,0.569,0.316,0.465,0.529,0.627,0.78,0.658,0.62,0.442,0.506,0.775,0.453,0.605,0.458,0.505,0.473,0.556,0.291,0.369,0.414,0.629,0.475,0.382,0.392,0.611,0.356,0.744,0.49,0.631,0.392,0.366,0.434,0.723,0.385,0.5,0.482,0.359
-13,Epileptic,M,27.0,0.7,2.3,0.9,1.3,1.7,1.4,2.0,1.7,0.9,0.9,0.3,2.5,1.1,0.5,0.6,4.7,7.0,0.8,1.6,3.9,1.9,3.1,1.1,3.4,6.2,4.1,3.1,2.4,2.4,3.2,1.5,1.4,0.4,2.2,0.5,0.8,2.9,3.3,0.2,0.3,1.5,3.0,3.1,2.4,3.2,0.6,0.4,0.7,1.5,1.1,0.7,1.2,1.5,2.0,0.3,2.8,0.6,1.4,1.4,1.3,0.8,1.6,0.4,0.5,1.3,0.9,3.1,0.7,1.1,0.6,1.6,1.3,4.5,0.3,5,20,8,11,16,18,15,18,17,7,2.0,24,13,6,9,60,67,5,26,41,22,33,12,44,57,49,36,21,21,41,16,9,5,29,3,7,39,46,1,3,10,24,23,17,21,4,2,4,8,8,5,10,11,15,2,21,4,10,9,11,8,13,3,5,9,15,22,5,8,7,11,10,38,2,0.041,0.028,0.022,0.027,0.037,0.029,0.031,0.029,0.034,0.037,0.022,0.036,0.032,0.031,0.025,0.03,0.031,0.039,0.03,0.039,0.035,0.037,0.028,0.04,0.055,0.036,0.031,0.026,0.027,0.032,0.041,0.049,0.028,0.031,0.017,0.015,0.032,0.031,0.015,0.021,0.025,0.035,0.048,0.027,0.025,0.012,0.02,0.015,0.021,0.02,0.028,0.017,0.02,0.018,0.022,0.019,0.016,0.019,0.029,0.021,0.024,0.022,0.015,0.017,0.019,0.018,0.02,0.014,0.019,0.023,0.025,0.017,0.016,0.016,1574,3994,2042,2437,2054,2765,3054,3209,1829,1877,607.0,2125,2754,1214,1865,11520,15950,2234,4113,4499,4585,3753,1370,5922,5959,7782,5828,3456,5914,6384,2255,1250,960,6252,1749,2293,7490,7378,276,543,1803,2291,3738,2248,3097,1366,722,2088,2242,1754,542,2198,1927,4559,464,4041,1382,2406,1155,1731,1363,2711,532,959,1890,1146,5206,1763,2029,1252,1814,2299,9410,555,0.13,0.123,0.105,0.119,0.136,0.138,0.103,0.138,0.155,0.137,0.104,0.153,0.142,0.135,0.113,0.135,0.132,0.12,0.125,0.157,0.113,0.137,0.116,0.137,0.151,0.14,0.123,0.104,0.106,0.139,0.145,0.125,0.116,0.136,0.09,0.097,0.131,0.132,0.105,0.121,0.125,0.132,0.142,0.12,0.113,0.078,0.101,0.075,0.103,0.105,0.133,0.104,0.108,0.098,0.109,0.11,0.086,0.107,0.128,0.113,0.126,0.116,0.116,0.114,0.101,0.105,0.105,0.086,0.101,0.14,0.123,0.11,0.097,0.107,327,1264,663,842,839,897,1036,1009,557,442,175.0,1177,680,288,500,2928,4063,405,1144,1856,1103,1393,722,1719,1918,2149,1869,1396,1443,1850,656,438,298,1334,441,819,1763,1871,140,224,876,1262,964,1452,1964,723,316,751,1001,809,342,1074,1077,1833,211,2131,629,1086,721,920,515,1092,341,498,1056,797,2380,798,932,452,900,1130,4233,239,3.781,2.804,3.152,2.745,2.095,2.323,2.355,2.633,2.417,3.077,2.862,1.648,3.013,3.092,2.673,2.817,2.999,4.012,2.749,2.08,2.997,2.345,1.702,2.653,2.45,2.844,2.392,2.018,2.983,2.533,2.566,2.726,2.674,3.206,3.774,2.621,3.006,2.833,2.402,2.603,2.574,1.659,2.812,1.769,1.819,2.025,2.856,3.325,2.659,2.476,1.916,2.223,1.99,2.47,2.481,2.116,2.445,2.509,1.818,2.078,2.478,2.526,1.818,2.277,1.972,1.851,2.257,2.478,2.547,2.712,2.237,2.428,2.377,2.639,0.89,0.649,0.594,0.536,0.669,0.71,0.627,0.437,0.758,0.728,0.637,0.424,0.465,0.49,0.549,0.684,0.656,0.8,0.559,0.603,0.9,0.595,0.432,0.76,0.738,0.664,0.636,0.516,0.694,0.642,0.621,0.842,0.405,0.677,0.531,0.541,0.791,0.7,0.356,0.49,0.431,0.608,0.981,0.533,0.603,0.436,0.433,0.937,0.591,0.46,0.314,0.451,0.373,0.427,0.507,0.402,0.378,0.477,0.423,0.429,0.438,0.523,0.453,0.628,0.421,0.659,0.476,0.454,0.43,0.833,0.44,0.664,0.414,0.421
-14,Epileptic,F,22.0,0.6,2.5,1.5,0.9,1.7,1.7,1.4,1.7,1.0,0.7,0.3,3.1,1.7,0.5,1.0,6.1,8.7,0.6,2.3,3.6,1.8,2.9,1.7,2.7,3.7,3.2,4.1,3.1,2.8,3.1,0.9,1.4,0.5,2.7,0.4,1.4,3.8,2.9,0.1,0.1,1.0,2.7,2.6,1.8,2.5,0.8,0.4,0.8,1.3,1.0,0.6,1.5,1.4,2.7,0.4,1.6,0.9,1.3,0.9,1.2,0.8,1.1,0.3,0.3,2.2,1.0,2.0,1.8,0.8,0.4,0.8,1.4,4.9,0.4,7,23,18,9,17,17,14,19,11,6,4.0,26,16,4,7,67,79,6,20,28,13,29,19,34,47,37,52,32,28,40,14,12,4,33,2,15,44,33,1,1,6,22,31,15,17,7,2,5,7,9,4,12,12,21,4,14,8,12,6,11,6,9,2,4,18,12,20,12,6,5,6,13,43,2,0.026,0.02,0.019,0.018,0.033,0.027,0.019,0.026,0.036,0.04,0.033,0.034,0.028,0.03,0.025,0.03,0.027,0.025,0.031,0.031,0.028,0.031,0.027,0.031,0.033,0.023,0.029,0.027,0.024,0.027,0.022,0.049,0.021,0.026,0.009,0.019,0.029,0.027,0.013,0.013,0.018,0.036,0.035,0.021,0.019,0.019,0.015,0.012,0.015,0.016,0.03,0.017,0.018,0.02,0.013,0.014,0.025,0.013,0.022,0.019,0.035,0.021,0.017,0.014,0.021,0.027,0.016,0.019,0.014,0.012,0.024,0.02,0.02,0.02,1749,5635,3129,1890,2364,2917,3133,3507,1914,1040,559.0,2975,4417,1253,3030,12974,21512,2466,4150,3597,4293,4482,2507,5824,6855,7676,6619,5363,7139,5929,2027,1203,1084,6715,2263,3317,7867,7437,347,439,2052,2594,5920,2443,3925,1332,963,2340,2723,2174,638,2842,2748,5846,968,3267,1391,3342,1086,1781,765,2065,474,972,3627,957,3979,3594,1710,955,1112,2746,10632,493,0.109,0.109,0.116,0.105,0.137,0.132,0.101,0.125,0.144,0.161,0.131,0.14,0.125,0.112,0.108,0.135,0.125,0.108,0.12,0.119,0.105,0.141,0.133,0.132,0.143,0.121,0.136,0.123,0.11,0.137,0.104,0.139,0.103,0.127,0.07,0.097,0.123,0.122,0.087,0.096,0.094,0.138,0.135,0.105,0.103,0.113,0.098,0.076,0.089,0.099,0.139,0.101,0.097,0.097,0.093,0.094,0.129,0.084,0.111,0.108,0.144,0.109,0.11,0.104,0.113,0.122,0.102,0.106,0.097,0.1,0.123,0.109,0.106,0.11,452,1843,1160,772,828,1066,999,1120,509,290,185.0,1426,1048,285,682,3273,5354,507,1182,1772,1134,1397,987,1520,2059,2337,2356,1784,1838,1875,601,500,389,1609,601,1197,2225,1763,192,180,927,1212,1472,1372,2121,677,410,1059,1244,931,330,1393,1266,2227,500,1742,556,1647,651,962,358,788,261,442,1660,570,2030,1430,851,529,544,1231,3921,282,3.416,2.752,2.653,2.56,2.417,2.565,2.641,2.897,2.726,3.01,2.609,1.885,3.143,3.036,3.199,2.838,3.107,3.793,2.952,1.848,2.952,2.483,2.146,2.791,2.619,2.679,2.315,2.406,2.945,2.507,2.453,2.618,2.317,3.094,3.279,2.608,2.84,3.045,2.111,2.835,2.806,1.993,3.033,1.898,2.137,2.283,3.001,2.698,2.71,2.619,2.19,2.24,2.18,2.514,2.435,2.076,2.585,2.263,1.991,2.049,2.362,2.583,2.123,2.626,2.458,2.056,2.149,2.787,2.511,2.127,2.461,2.607,2.723,2.21,0.582,0.575,0.727,0.51,0.708,0.562,0.693,0.566,0.553,0.441,0.643,0.469,0.398,0.49,0.569,0.641,0.634,0.753,0.641,0.539,0.758,0.489,0.433,0.688,0.546,0.501,0.496,0.622,0.563,0.553,0.648,0.861,0.428,0.609,0.956,0.406,0.654,0.582,0.33,0.564,0.464,0.481,0.669,0.553,0.719,0.392,0.505,0.625,0.573,0.463,0.392,0.521,0.587,0.588,0.304,0.432,0.432,0.529,0.318,0.447,0.41,0.747,0.594,1.158,0.621,0.552,0.459,0.4,0.455,0.543,0.386,0.468,0.573,0.586
-15,Epileptic,F,32.0,1.3,1.9,1.1,1.3,1.3,2.2,1.7,1.3,1.1,0.8,0.3,3.5,1.3,0.6,1.8,4.8,8.1,0.7,2.2,4.3,1.1,2.9,2.5,2.7,3.5,3.3,3.7,2.8,2.5,3.3,1.5,1.4,0.7,2.4,0.5,0.5,3.1,3.2,0.2,0.1,1.3,2.4,2.2,2.8,2.5,0.6,0.4,1.1,1.4,0.8,0.7,2.0,1.7,2.4,0.1,2.7,0.6,1.9,0.9,1.8,0.7,1.5,0.4,0.7,1.5,1.8,2.7,1.4,1.0,0.6,0.9,1.2,5.7,0.5,9,16,10,11,11,16,11,11,10,6,2.0,29,15,6,18,45,69,5,16,33,11,31,19,28,36,30,32,26,20,31,13,8,4,20,3,4,30,34,2,1,9,20,16,20,15,6,2,6,8,7,6,14,15,19,1,22,5,15,7,14,5,10,3,5,11,13,20,12,7,6,6,11,44,3,0.053,0.026,0.024,0.027,0.039,0.032,0.033,0.031,0.049,0.032,0.035,0.042,0.031,0.038,0.034,0.031,0.033,0.033,0.037,0.04,0.032,0.031,0.032,0.036,0.034,0.034,0.035,0.036,0.028,0.033,0.044,0.062,0.035,0.032,0.02,0.018,0.035,0.035,0.016,0.014,0.028,0.033,0.045,0.031,0.021,0.017,0.019,0.02,0.02,0.024,0.033,0.024,0.025,0.023,0.013,0.021,0.02,0.023,0.034,0.027,0.023,0.031,0.023,0.024,0.023,0.046,0.024,0.021,0.02,0.026,0.026,0.019,0.024,0.029,1054,3186,1689,1811,1397,2789,2081,2184,1408,1135,490.0,2365,2503,799,2644,7896,14679,1600,3590,3548,3123,4234,2392,4278,5407,5332,4957,3818,5325,4432,2225,840,1000,4301,1446,1097,5014,6121,303,253,1220,1797,3800,2051,3115,1160,600,1842,1992,1297,566,2801,1894,3777,217,3259,1034,2555,774,1658,837,1885,422,871,1770,809,3057,2141,1516,954,1100,2119,8597,380,0.153,0.12,0.136,0.124,0.149,0.129,0.11,0.126,0.165,0.141,0.119,0.15,0.126,0.153,0.151,0.13,0.132,0.112,0.137,0.15,0.107,0.126,0.128,0.136,0.116,0.124,0.127,0.119,0.103,0.139,0.161,0.142,0.118,0.135,0.089,0.098,0.144,0.15,0.099,0.089,0.133,0.129,0.16,0.129,0.104,0.101,0.102,0.1,0.105,0.121,0.144,0.112,0.118,0.105,0.102,0.112,0.119,0.114,0.136,0.12,0.111,0.132,0.116,0.12,0.117,0.148,0.115,0.111,0.108,0.138,0.119,0.105,0.114,0.135,392,1164,685,814,594,1135,843,808,440,378,169.0,1405,790,242,826,2604,4274,379,1039,1730,786,1495,1134,1369,1723,1686,1914,1344,1460,1678,600,391,388,1175,424,482,1459,1579,211,143,675,1133,941,1442,1890,631,311,857,1010,521,338,1380,1070,1844,113,1960,499,1361,414,1050,450,758,265,441,1004,507,1762,1052,750,394,602,1029,3903,251,2.613,2.535,2.498,2.231,2.191,2.145,2.119,2.539,2.457,2.507,2.608,1.562,2.449,2.409,2.482,2.361,2.773,3.21,2.803,1.811,2.851,2.285,1.843,2.479,2.474,2.589,2.086,2.247,2.752,2.224,2.766,2.395,2.135,2.808,3.142,2.177,2.652,2.843,1.596,1.97,2.179,1.554,2.979,1.66,1.891,2.04,2.229,2.744,2.425,2.703,1.948,2.11,1.912,2.132,2.007,1.846,2.392,2.2,1.944,1.779,2.253,2.472,1.962,2.283,1.994,2.013,1.967,2.27,2.26,2.739,2.145,2.266,2.322,1.876,0.651,0.546,0.551,0.404,0.613,0.547,0.548,0.472,0.522,0.573,0.656,0.472,0.457,0.496,0.541,0.611,0.56,0.683,0.662,0.629,0.719,0.532,0.614,0.637,0.708,0.519,0.573,0.565,0.556,0.506,0.645,0.706,0.481,0.512,0.77,0.465,0.6,0.596,0.311,0.371,0.471,0.491,0.673,0.487,0.527,0.444,0.497,0.634,0.411,0.546,0.469,0.422,0.522,0.488,0.397,0.383,0.355,0.601,0.652,0.335,0.307,0.635,0.342,0.8,0.579,0.602,0.468,0.464,0.452,0.692,0.456,0.599,0.5,0.378
-16,Epileptic,M,57.0,0.8,2.5,1.0,1.8,2.0,2.0,1.6,1.6,0.9,1.0,0.4,3.3,1.2,0.5,0.9,5.8,7.8,1.4,2.9,4.8,0.7,2.8,3.5,3.1,4.2,4.2,3.5,2.3,2.6,2.7,1.7,1.4,0.3,2.1,0.2,1.2,3.5,2.4,0.1,0.2,1.3,3.1,1.9,3.4,2.9,0.5,0.4,0.7,1.4,0.6,0.6,1.5,1.3,2.2,0.2,2.8,0.9,1.9,1.7,1.8,0.3,1.7,0.3,0.5,2.1,0.9,2.7,1.1,1.1,0.3,1.2,1.3,4.1,0.4,6,22,8,14,16,20,15,17,11,10,5.0,34,15,6,12,64,82,11,38,43,8,33,31,38,36,49,46,23,25,37,21,11,5,26,1,11,36,39,1,2,11,24,24,27,17,4,2,5,7,6,4,13,11,21,1,25,6,16,12,16,2,13,1,5,20,19,22,9,7,4,10,12,38,3,0.037,0.024,0.016,0.03,0.039,0.026,0.025,0.032,0.038,0.036,0.032,0.029,0.029,0.025,0.021,0.034,0.028,0.035,0.033,0.042,0.02,0.032,0.033,0.038,0.042,0.029,0.027,0.027,0.025,0.029,0.043,0.044,0.023,0.03,0.014,0.018,0.037,0.029,0.012,0.014,0.023,0.037,0.032,0.027,0.02,0.009,0.018,0.014,0.018,0.022,0.027,0.018,0.02,0.021,0.017,0.019,0.021,0.021,0.03,0.024,0.014,0.027,0.024,0.018,0.021,0.032,0.02,0.018,0.019,0.016,0.026,0.022,0.018,0.024,1386,3973,2098,2587,2014,3231,3026,2472,1598,1598,836.0,3709,3094,1315,2687,9482,17377,2535,5513,4099,3204,4173,3102,5039,4914,8475,6128,4163,6240,4960,2440,1415,896,5408,1334,2338,6863,6105,345,572,1851,2505,4916,2840,3738,1371,653,1937,2094,1289,515,3069,1918,4531,256,4317,1318,2671,1522,2120,548,2526,343,809,3066,769,3916,2034,1759,759,1359,2058,7543,422,0.134,0.117,0.108,0.128,0.141,0.126,0.113,0.14,0.155,0.157,0.132,0.138,0.123,0.125,0.111,0.137,0.129,0.142,0.139,0.166,0.084,0.123,0.129,0.138,0.128,0.134,0.117,0.104,0.117,0.134,0.166,0.118,0.102,0.14,0.072,0.096,0.139,0.135,0.087,0.102,0.116,0.133,0.152,0.125,0.104,0.076,0.104,0.078,0.102,0.117,0.121,0.1,0.111,0.107,0.132,0.112,0.116,0.115,0.128,0.12,0.102,0.131,0.107,0.112,0.113,0.143,0.11,0.105,0.102,0.105,0.141,0.111,0.106,0.123,384,1530,864,985,901,1295,1094,914,465,454,257.0,1866,804,309,752,2891,4809,555,1594,2132,682,1512,1666,1503,1490,2612,2176,1422,1563,1799,730,537,281,1260,348,1033,1815,1564,203,286,902,1339,1126,2002,2175,751,324,833,1067,519,353,1356,1023,1982,142,2280,660,1401,840,1205,286,965,191,399,1624,554,2059,962,827,390,688,1013,3431,263,3.471,2.478,2.428,2.461,2.094,2.172,2.198,2.387,2.567,2.923,2.88,1.764,2.848,2.835,2.604,2.473,2.734,3.504,2.697,1.731,3.086,2.254,1.758,2.655,2.542,2.665,2.28,2.232,2.904,2.218,2.676,2.718,2.451,2.988,3.407,2.278,2.832,2.762,1.906,2.318,2.35,1.685,3.08,1.709,1.896,1.948,2.569,2.622,2.434,2.711,1.66,2.293,2.061,2.354,2.312,2.066,2.253,2.154,1.919,1.961,2.097,2.811,1.943,2.397,1.931,1.852,2.042,2.299,2.384,2.186,2.092,2.315,2.326,2.079,0.65,0.588,0.488,0.515,0.575,0.565,0.588,0.439,0.621,0.584,0.743,0.534,0.377,0.511,0.565,0.646,0.644,0.805,0.544,0.562,0.847,0.593,0.578,0.721,0.596,0.526,0.534,0.628,0.558,0.553,0.636,0.847,0.428,0.67,0.81,0.377,0.691,0.662,0.337,0.421,0.401,0.54,0.728,0.59,0.615,0.468,0.351,0.75,0.423,0.54,0.289,0.483,0.465,0.44,0.456,0.369,0.392,0.466,0.451,0.361,0.26,0.607,0.38,0.825,0.43,0.722,0.425,0.408,0.41,0.853,0.431,0.494,0.465,0.326
-17,Epileptic,F,32.0,0.6,1.8,1.1,1.3,1.5,2.3,1.4,1.0,1.4,0.7,0.3,2.0,1.9,0.6,1.6,4.3,6.6,0.5,1.9,3.5,0.6,2.5,1.2,2.4,2.7,3.3,2.8,2.0,2.2,3.3,1.3,1.2,0.5,2.4,0.3,0.3,2.6,2.9,0.1,0.1,0.8,2.4,1.3,1.7,2.3,0.6,0.2,0.7,1.0,0.5,0.8,1.8,1.1,1.5,0.1,1.9,0.4,1.8,1.1,0.8,0.5,0.6,0.3,0.3,1.3,0.8,2.1,1.6,0.8,0.3,0.9,0.9,3.5,0.3,5,15,11,12,11,18,12,12,11,6,3.0,17,21,7,13,44,59,4,24,33,5,32,14,27,33,36,35,20,22,33,21,6,6,25,2,2,23,39,1,1,4,22,17,13,13,4,1,5,5,4,5,13,9,11,1,14,4,17,7,6,3,5,2,3,12,12,15,11,6,3,6,7,30,3,0.026,0.022,0.019,0.021,0.043,0.032,0.026,0.023,0.045,0.034,0.032,0.03,0.039,0.04,0.034,0.032,0.025,0.025,0.027,0.034,0.013,0.036,0.033,0.035,0.037,0.034,0.029,0.026,0.026,0.034,0.055,0.045,0.019,0.03,0.015,0.009,0.03,0.033,0.012,0.012,0.018,0.032,0.03,0.025,0.019,0.019,0.012,0.012,0.013,0.016,0.031,0.02,0.02,0.014,0.017,0.019,0.01,0.021,0.028,0.022,0.019,0.014,0.023,0.013,0.021,0.027,0.02,0.019,0.017,0.013,0.03,0.02,0.016,0.017,1402,3777,2261,2445,1454,3488,2343,2968,1628,1160,605.0,2185,4036,990,3034,9248,16461,2113,4006,4039,2463,3985,1684,4958,5421,6716,5301,3820,5953,5308,1600,880,1325,6278,1692,1512,5908,6445,358,417,1426,2502,4363,2265,3228,1215,691,2026,2388,1711,620,3171,1618,4351,391,3181,1294,3221,1075,1162,1092,1536,400,842,2364,706,3341,3188,1795,843,1470,2408,8955,509,0.099,0.111,0.113,0.108,0.141,0.136,0.117,0.113,0.146,0.143,0.145,0.13,0.151,0.121,0.124,0.131,0.114,0.092,0.123,0.133,0.068,0.154,0.13,0.134,0.147,0.146,0.138,0.114,0.109,0.144,0.132,0.118,0.113,0.123,0.067,0.064,0.124,0.132,0.081,0.077,0.101,0.138,0.126,0.112,0.099,0.104,0.087,0.076,0.084,0.092,0.139,0.105,0.119,0.084,0.101,0.11,0.085,0.109,0.126,0.111,0.101,0.1,0.132,0.097,0.115,0.132,0.107,0.099,0.099,0.109,0.119,0.106,0.089,0.11,378,1217,852,952,594,1188,892,834,473,306,176.0,955,936,261,736,2360,4256,386,1188,1554,678,1204,679,1168,1432,1772,1823,1273,1512,1555,476,417,425,1302,381,541,1370,1432,216,158,664,1180,803,1169,1856,542,276,870,1103,521,399,1416,804,1801,116,1467,630,1475,625,612,446,547,179,337,1014,475,1531,1261,794,311,561,776,3649,282,3.225,2.811,2.656,2.547,2.238,2.475,2.267,3.107,2.533,3.129,2.851,1.941,3.194,2.624,2.863,2.787,3.054,3.83,2.814,2.153,2.718,2.518,2.095,2.93,2.801,2.989,2.413,2.392,2.925,2.638,2.434,2.165,2.562,3.207,3.538,2.578,3.125,3.042,1.963,2.965,2.676,1.933,3.287,2.088,1.981,2.448,2.907,2.842,2.677,3.283,2.001,2.278,1.979,2.446,3.613,2.407,2.299,2.398,1.996,2.13,2.626,2.618,2.246,2.653,2.712,2.046,2.305,2.748,2.496,3.319,2.672,3.109,2.53,2.202,0.782,0.826,0.696,0.451,0.532,0.55,0.477,0.596,0.598,0.598,0.815,0.482,0.644,0.643,0.608,0.551,0.62,0.852,0.591,0.688,0.666,0.453,0.389,0.743,0.548,0.67,0.484,0.561,0.473,0.596,0.617,0.888,0.379,0.664,0.892,0.558,0.772,0.827,0.375,0.401,0.645,0.483,0.844,0.762,0.593,0.578,0.579,0.604,0.525,0.732,0.416,0.548,0.637,0.513,0.973,0.567,0.507,0.521,0.348,0.369,0.4,1.096,0.54,1.298,0.739,0.75,0.478,0.42,0.571,0.84,0.676,1.098,0.533,0.56
-18,Epileptic,F,37.0,1.0,2.0,1.3,1.7,1.9,1.5,1.2,2.0,1.5,0.6,0.2,2.3,2.3,0.6,1.4,7.0,9.1,1.1,2.4,3.7,1.3,4.0,1.7,3.3,5.3,2.8,2.9,2.4,2.1,2.0,1.2,0.6,0.5,2.4,0.4,0.8,2.9,3.7,0.2,0.2,1.2,2.9,2.4,2.4,2.5,0.8,0.5,1.2,1.4,0.9,0.3,2.6,1.5,2.6,0.1,2.4,0.8,1.4,1.0,0.6,0.8,1.5,0.2,0.4,2.0,1.1,2.5,1.7,1.2,0.6,0.7,1.1,5.4,0.4,7,19,13,18,14,17,13,20,16,6,3.0,23,21,8,17,84,92,8,31,33,21,36,12,42,42,29,38,26,22,28,15,7,4,31,2,7,37,45,2,1,8,26,21,18,15,6,3,8,8,8,3,20,10,18,1,21,7,12,9,5,6,10,2,5,17,19,23,13,6,7,5,8,47,3,0.041,0.023,0.023,0.028,0.038,0.028,0.027,0.032,0.054,0.035,0.03,0.037,0.032,0.05,0.029,0.036,0.032,0.04,0.026,0.038,0.029,0.038,0.036,0.042,0.044,0.028,0.028,0.028,0.028,0.03,0.036,0.056,0.028,0.027,0.013,0.017,0.04,0.033,0.018,0.017,0.023,0.043,0.037,0.032,0.02,0.016,0.021,0.019,0.02,0.025,0.027,0.022,0.027,0.021,0.018,0.021,0.021,0.016,0.031,0.016,0.024,0.024,0.02,0.023,0.022,0.019,0.018,0.026,0.02,0.019,0.02,0.014,0.018,0.018,1424,4264,2740,2829,1734,2050,2545,2976,1798,1223,613.0,2622,3854,1023,3222,12729,19117,2113,5463,3504,3421,4037,1679,5860,5130,5626,5588,4090,5251,4302,2132,573,896,5581,1666,2315,5157,8052,381,405,1556,1898,5712,2492,3143,1486,818,2084,2355,1321,338,4184,1584,4394,354,3622,1612,3313,967,884,1008,2267,392,897,3031,1411,4543,2201,1681,971,1120,2637,9824,532,0.126,0.113,0.116,0.131,0.137,0.129,0.109,0.137,0.167,0.147,0.121,0.136,0.136,0.16,0.139,0.152,0.133,0.141,0.124,0.147,0.12,0.124,0.115,0.155,0.129,0.134,0.125,0.123,0.112,0.135,0.15,0.119,0.108,0.129,0.073,0.094,0.134,0.136,0.103,0.109,0.113,0.13,0.149,0.128,0.105,0.103,0.111,0.098,0.098,0.117,0.145,0.111,0.128,0.106,0.099,0.11,0.101,0.099,0.126,0.094,0.115,0.118,0.106,0.125,0.118,0.121,0.106,0.126,0.107,0.125,0.111,0.099,0.104,0.106,433,1519,965,1030,789,935,879,1102,517,344,162.0,1112,1197,298,840,3701,5057,487,1551,1513,796,1436,688,1626,1863,1748,1875,1401,1324,1346,584,264,298,1433,471,733,1400,1971,205,183,813,1112,1129,1354,1858,687,334,948,1098,583,199,1926,865,1983,154,1852,670,1465,582,529,552,918,207,380,1418,887,2186,973,832,478,543,1160,4511,275,3.283,2.645,2.683,2.606,1.882,2.049,2.386,2.309,2.584,2.761,2.965,2.009,2.619,2.65,2.734,2.603,2.994,3.291,2.795,2.012,3.091,2.256,2.002,2.747,2.262,2.639,2.409,2.311,2.896,2.523,2.707,2.305,2.36,2.785,3.111,2.611,2.719,2.919,2.117,2.551,2.368,1.59,3.416,2.059,1.928,2.36,2.765,2.699,2.638,2.436,1.992,2.331,1.956,2.397,2.686,2.19,2.327,2.413,1.953,1.807,2.248,2.319,1.976,2.593,2.245,1.923,2.204,2.396,2.328,2.236,2.35,2.376,2.326,2.247,0.882,0.588,0.498,0.577,0.648,0.504,0.547,0.627,0.656,0.616,0.627,0.524,0.48,0.424,0.572,0.569,0.593,0.963,0.601,0.659,0.741,0.639,0.636,0.674,0.641,0.566,0.456,0.52,0.465,0.452,0.646,0.948,0.337,0.625,0.737,0.555,0.84,0.74,0.382,0.517,0.489,0.571,0.753,0.628,0.54,0.534,0.372,0.908,0.544,0.558,0.552,0.446,0.443,0.433,0.409,0.389,0.475,0.511,0.364,0.39,0.358,0.691,0.386,0.605,0.477,0.659,0.459,0.505,0.459,0.73,0.397,0.521,0.498,0.537
-19,Epileptic,M,27.0,0.8,3.0,1.2,1.1,2.1,3.2,1.4,2.2,1.6,0.6,0.3,3.4,1.7,0.6,1.3,6.9,8.2,1.1,2.8,6.0,0.7,3.3,1.7,3.6,3.3,3.4,3.3,2.2,2.8,4.6,1.3,1.0,0.4,2.3,0.3,1.0,2.5,4.1,0.2,0.3,1.4,3.5,2.3,3.6,2.2,0.6,0.5,1.2,1.5,0.7,0.9,2.5,1.5,1.8,0.4,2.7,0.8,3.2,0.9,1.2,1.0,1.4,1.2,0.5,2.1,1.0,2.2,1.0,1.8,0.4,1.3,1.6,6.1,0.2,5,28,13,11,21,28,10,18,20,6,2.0,36,21,7,13,75,72,10,27,42,8,37,19,44,36,40,38,23,26,49,21,9,4,30,2,8,31,41,1,3,8,27,22,27,13,4,3,14,8,6,6,20,9,12,3,23,4,29,6,10,6,10,9,3,16,12,17,7,13,4,9,13,52,1,0.035,0.027,0.019,0.021,0.042,0.034,0.019,0.03,0.042,0.025,0.025,0.032,0.025,0.039,0.026,0.033,0.025,0.034,0.032,0.036,0.014,0.03,0.026,0.034,0.025,0.027,0.022,0.022,0.021,0.03,0.035,0.046,0.024,0.03,0.011,0.017,0.031,0.033,0.012,0.011,0.025,0.031,0.037,0.029,0.015,0.01,0.016,0.017,0.017,0.021,0.029,0.023,0.022,0.016,0.018,0.019,0.021,0.024,0.016,0.016,0.028,0.024,0.035,0.016,0.016,0.02,0.017,0.017,0.023,0.012,0.02,0.02,0.018,0.024,1567,6076,2747,2626,2435,5495,3316,4173,2242,1609,733.0,4314,4409,1420,3068,11744,19477,3024,5226,5413,3289,5957,2706,8216,7547,7900,7414,4207,7319,8066,2673,1360,1339,6206,1993,2475,6697,8152,608,944,1740,3832,5487,3653,4237,1746,1207,2377,2842,1455,979,4351,2460,4465,755,4986,1656,4551,1619,2122,1358,2407,1224,899,4304,1878,4178,2481,2921,866,1906,3201,13964,357,0.111,0.123,0.116,0.111,0.153,0.143,0.089,0.134,0.158,0.12,0.099,0.138,0.128,0.124,0.123,0.14,0.116,0.128,0.121,0.126,0.074,0.136,0.129,0.142,0.117,0.127,0.122,0.099,0.101,0.14,0.133,0.118,0.114,0.119,0.067,0.092,0.12,0.124,0.076,0.092,0.116,0.131,0.131,0.116,0.087,0.081,0.098,0.088,0.097,0.114,0.124,0.111,0.108,0.089,0.103,0.106,0.099,0.11,0.097,0.101,0.119,0.113,0.143,0.094,0.099,0.101,0.098,0.094,0.115,0.104,0.112,0.102,0.095,0.104,443,1708,1010,945,911,1584,1011,1236,654,395,207.0,1751,1185,348,834,3519,5222,583,1509,2626,895,1810,1079,1818,2159,2165,2464,1502,2009,2423,682,500,358,1495,518,880,1480,1936,231,416,880,1708,1107,2112,2351,867,501,966,1315,516,561,1816,1094,1768,356,2210,624,2033,844,1038,577,935,518,435,1917,880,1955,1055,1215,478,965,1317,5272,186,3.421,3.066,2.581,2.747,2.365,2.817,2.671,2.906,2.573,3.26,3.048,2.076,2.864,3.07,2.766,2.561,3.036,3.757,2.834,1.856,3.044,2.688,2.152,3.105,2.754,2.88,2.462,2.229,2.854,2.585,2.852,2.47,2.872,3.064,3.346,2.506,3.206,3.117,2.571,2.47,2.432,2.049,3.489,1.885,2.089,2.273,2.919,3.062,2.762,3.007,2.172,2.345,2.37,2.574,2.393,2.356,3.115,2.44,2.276,2.264,2.742,2.555,2.255,2.615,2.462,2.594,2.256,2.763,2.508,2.129,2.395,2.746,2.644,2.522,0.686,0.67,0.783,0.574,0.578,0.609,0.583,0.485,0.477,0.394,0.869,0.445,0.511,0.435,0.508,0.599,0.565,0.559,0.582,0.608,0.667,0.487,0.483,0.665,0.513,0.503,0.512,0.553,0.536,0.527,0.657,1.058,0.369,0.717,0.55,0.552,0.721,0.576,0.283,0.354,0.461,0.684,0.778,0.611,0.654,0.399,0.648,0.77,0.503,0.474,0.406,0.542,0.631,0.569,0.328,0.421,0.693,0.503,0.305,0.505,0.714,0.653,0.45,0.956,0.472,0.956,0.531,0.429,0.472,0.542,0.417,0.661,0.53,0.299
-21,Epileptic,M,27.0,1.1,3.5,1.1,1.7,1.3,2.7,2.1,2.8,1.9,1.1,0.3,3.8,2.8,0.4,1.4,6.8,10.0,0.9,2.7,6.3,0.9,3.7,1.9,3.1,4.3,4.3,3.8,3.3,3.3,5.1,1.3,0.8,0.7,3.3,0.5,0.6,5.6,5.3,0.2,0.1,1.3,3.3,2.3,2.9,3.7,0.7,0.5,0.8,1.4,0.7,0.9,2.9,1.0,2.6,0.2,3.1,1.1,3.0,1.1,2.0,1.1,1.1,0.4,0.4,2.5,1.4,3.8,1.5,1.4,0.5,1.2,2.1,5.5,0.4,6,31,11,15,15,26,18,27,16,12,4.0,41,26,8,14,78,90,10,28,52,8,41,21,39,54,47,47,36,26,60,27,6,7,35,3,5,60,56,2,1,8,36,24,22,25,5,4,6,7,6,6,23,8,17,1,26,9,26,9,14,9,7,3,4,19,18,33,12,8,5,7,13,41,3,0.038,0.028,0.017,0.025,0.033,0.029,0.026,0.03,0.049,0.041,0.041,0.035,0.028,0.032,0.028,0.03,0.027,0.026,0.028,0.04,0.017,0.032,0.027,0.032,0.032,0.029,0.026,0.024,0.022,0.032,0.037,0.038,0.027,0.033,0.016,0.014,0.036,0.035,0.011,0.012,0.028,0.031,0.032,0.023,0.022,0.013,0.018,0.012,0.015,0.02,0.025,0.022,0.013,0.016,0.02,0.016,0.019,0.022,0.019,0.024,0.024,0.02,0.019,0.018,0.02,0.02,0.021,0.016,0.021,0.017,0.019,0.023,0.018,0.016,1906,6589,3574,3268,2296,4550,3805,5756,2614,2197,725.0,4056,5739,1212,3163,15148,24407,3289,5147,7027,3481,6837,2706,7760,8147,9185,8112,6028,7857,9283,3096,1230,1463,7142,2238,1984,9159,10579,604,594,1795,3926,6805,3406,4304,1788,1056,2831,2843,1738,906,5841,2690,6353,268,5558,2150,4484,1631,2553,1467,2774,679,918,4179,2094,6169,4243,2315,1046,2028,2819,11778,639,0.121,0.113,0.093,0.123,0.124,0.135,0.112,0.121,0.147,0.138,0.124,0.14,0.128,0.144,0.131,0.127,0.123,0.108,0.115,0.138,0.079,0.142,0.134,0.13,0.144,0.13,0.125,0.106,0.097,0.14,0.129,0.113,0.119,0.135,0.084,0.085,0.128,0.133,0.08,0.076,0.122,0.141,0.129,0.109,0.108,0.086,0.1,0.081,0.088,0.097,0.137,0.106,0.084,0.092,0.115,0.102,0.097,0.102,0.104,0.113,0.116,0.102,0.106,0.105,0.109,0.11,0.109,0.096,0.1,0.11,0.108,0.112,0.101,0.106,513,1858,1017,1100,851,1602,1213,1705,692,532,197.0,1780,1638,295,875,3883,6058,634,1605,2727,906,1944,1208,1685,2350,2559,2612,2224,2087,2843,725,442,421,1675,534,706,2758,2601,314,213,737,1816,1329,2028,2600,828,450,1033,1302,669,482,2172,1267,2458,123,2785,972,2294,916,1281,754,855,360,402,1860,1215,2906,1507,1071,468,945,1424,4620,364,3.462,3.051,3.072,3.018,2.475,2.673,2.631,2.932,2.833,3.128,2.658,1.967,2.955,2.878,2.874,2.9,3.261,3.803,2.737,2.153,2.763,2.611,2.034,3.286,2.684,2.875,2.466,2.244,2.886,2.619,3.028,2.813,2.631,3.039,3.766,2.649,2.658,3.002,2.212,2.872,3.183,1.987,3.458,1.891,1.928,2.377,2.893,3.531,2.738,2.934,2.241,2.669,2.248,2.754,2.327,2.169,2.463,2.241,2.069,2.196,2.262,3.061,2.229,2.502,2.48,2.012,2.249,2.894,2.643,2.441,2.485,2.437,2.752,2.189,1.011,0.702,0.75,0.564,0.537,0.59,0.612,0.446,0.685,0.726,1.024,0.518,0.486,0.515,0.571,0.598,0.621,0.763,0.722,0.663,0.701,0.561,0.466,0.68,0.592,0.554,0.485,0.559,0.57,0.531,0.815,1.019,0.494,0.816,0.745,0.485,0.673,0.74,0.403,0.608,0.676,0.505,0.744,0.56,0.525,0.461,0.72,0.83,0.669,0.738,0.337,0.586,0.513,0.534,0.534,0.398,0.492,0.544,0.366,0.367,0.443,0.921,0.481,1.066,0.615,0.711,0.478,0.504,0.401,0.709,0.495,0.543,0.558,0.399
-22,Epileptic,F,32.0,0.9,2.2,0.6,0.8,1.8,2.2,1.1,1.3,1.1,0.7,0.2,1.9,1.5,0.4,1.6,5.3,7.8,0.6,2.3,3.9,0.7,2.2,2.0,3.2,2.7,2.7,3.1,2.4,1.8,2.3,1.1,0.9,0.4,1.6,0.4,0.5,2.8,2.5,0.1,0.1,0.8,2.3,1.6,2.1,2.2,0.7,0.4,0.5,0.9,0.9,0.6,1.4,1.1,2.8,0.3,1.8,0.4,1.1,0.5,0.8,0.2,1.3,0.2,0.4,1.4,1.1,1.8,1.2,0.7,1.0,0.5,0.6,2.1,0.3,7,29,7,6,17,22,8,13,12,7,2.0,20,17,4,16,51,68,6,27,36,8,20,20,34,36,29,37,23,18,29,11,6,5,24,2,3,28,33,1,1,4,21,15,18,14,7,2,5,5,7,4,10,7,21,3,13,4,10,4,6,1,9,2,6,11,13,14,9,6,12,4,6,19,2,0.043,0.031,0.016,0.022,0.044,0.046,0.023,0.026,0.043,0.043,0.029,0.03,0.035,0.043,0.044,0.034,0.03,0.031,0.03,0.037,0.019,0.038,0.033,0.039,0.036,0.031,0.031,0.03,0.021,0.028,0.032,0.044,0.028,0.025,0.016,0.013,0.04,0.036,0.012,0.014,0.019,0.038,0.028,0.024,0.018,0.018,0.02,0.013,0.016,0.03,0.029,0.018,0.021,0.025,0.021,0.02,0.018,0.017,0.018,0.022,0.02,0.024,0.02,0.017,0.02,0.029,0.021,0.02,0.02,0.031,0.019,0.024,0.013,0.021,1047,3725,1524,1761,1992,2659,1816,2839,1614,1091,629.0,2049,3088,747,2787,9346,15603,1832,3840,3833,2488,3323,2347,5282,4748,5295,4993,3708,4440,4356,1543,1171,951,4501,1487,1632,5479,5163,329,330,1267,2193,4612,1986,3152,1048,676,1685,1972,1096,481,3223,1490,4283,339,2738,1043,2380,672,1136,342,2467,504,735,2169,911,2797,2020,1327,1460,1002,1134,6209,466,0.144,0.135,0.106,0.102,0.164,0.18,0.099,0.131,0.16,0.151,0.109,0.137,0.149,0.16,0.162,0.143,0.127,0.113,0.137,0.149,0.083,0.16,0.139,0.144,0.163,0.146,0.137,0.117,0.095,0.136,0.123,0.107,0.126,0.117,0.078,0.079,0.148,0.15,0.113,0.089,0.1,0.148,0.141,0.127,0.096,0.11,0.104,0.083,0.088,0.132,0.14,0.092,0.109,0.117,0.139,0.107,0.106,0.101,0.106,0.114,0.108,0.115,0.114,0.124,0.106,0.14,0.108,0.113,0.104,0.138,0.101,0.117,0.084,0.133,306,1256,623,667,712,838,631,845,442,292,156.0,1027,864,183,686,2462,4231,336,1114,1668,693,998,937,1368,1300,1510,1828,1232,1348,1477,536,439,279,1052,376,562,1184,1253,150,142,578,1028,930,1277,1903,586,279,734,916,473,301,1353,733,1766,162,1390,490,1173,459,614,185,824,222,400,1087,507,1364,926,635,506,524,465,2594,209,3.455,2.914,2.369,2.575,2.413,2.6,2.415,2.939,2.694,2.884,3.282,1.75,2.773,2.88,2.979,2.797,2.928,3.666,2.727,1.939,2.863,2.464,2.117,2.749,2.648,2.799,2.168,2.34,2.54,2.369,2.242,2.765,2.546,2.988,3.385,2.476,3.279,2.98,2.448,2.654,2.9,1.871,3.456,1.738,1.851,1.953,3.036,2.777,2.587,2.745,1.879,2.406,2.271,2.52,2.197,2.139,2.509,2.361,1.707,1.946,2.415,2.731,2.389,2.132,2.091,2.276,2.197,2.61,2.322,3.014,2.184,2.563,2.359,3.017,0.912,0.659,0.579,0.582,0.52,0.618,0.587,0.563,0.627,0.533,0.732,0.457,0.504,0.525,0.66,0.582,0.603,0.756,0.54,0.526,0.683,0.578,0.539,0.851,0.634,0.539,0.594,0.651,0.595,0.534,0.746,0.881,0.5,0.629,0.842,0.488,0.752,0.606,0.391,0.44,0.673,0.485,0.773,0.581,0.557,0.406,0.418,0.811,0.458,0.589,0.517,0.496,0.478,0.57,0.453,0.403,0.481,0.555,0.358,0.43,0.347,0.774,0.744,0.716,0.544,0.64,0.501,0.486,0.504,0.663,0.463,0.52,0.512,0.499
-23,Epileptic,M,37.0,1.6,2.6,0.8,0.9,2.6,1.6,2.2,1.4,1.3,0.8,0.5,2.5,1.3,0.5,1.7,3.9,8.3,1.2,1.5,2.9,0.8,7.8,3.0,4.2,8.5,3.7,4.1,3.1,3.4,3.1,1.9,0.8,0.3,1.9,0.4,0.4,4.4,3.3,0.3,0.1,0.7,2.9,2.8,1.7,3.2,0.5,0.5,1.1,1.3,1.0,0.6,1.6,1.6,2.2,0.6,2.3,0.7,1.8,1.2,1.4,1.2,1.8,0.8,0.5,1.8,1.6,2.8,1.1,1.3,0.5,1.2,1.3,5.0,0.3,9,24,6,7,16,17,14,13,15,9,5.0,24,14,5,21,47,73,6,22,26,8,62,22,37,65,33,46,31,31,38,17,5,2,22,3,6,47,38,3,1,4,28,28,16,17,3,2,4,6,7,4,14,11,16,4,17,4,16,12,12,9,13,5,3,15,20,21,7,8,3,8,11,34,5,0.064,0.031,0.016,0.021,0.044,0.028,0.036,0.027,0.049,0.04,0.042,0.031,0.028,0.028,0.038,0.035,0.036,0.046,0.03,0.032,0.019,0.056,0.039,0.047,0.052,0.036,0.04,0.037,0.034,0.034,0.047,0.034,0.016,0.025,0.015,0.02,0.04,0.034,0.022,0.023,0.017,0.035,0.052,0.024,0.022,0.01,0.022,0.017,0.019,0.034,0.027,0.026,0.029,0.023,0.017,0.014,0.015,0.018,0.022,0.018,0.024,0.029,0.044,0.021,0.022,0.028,0.024,0.018,0.02,0.022,0.024,0.015,0.019,0.021,1526,4643,2075,2048,2763,3350,2556,3464,2226,1735,887.0,2676,3511,1476,2899,7376,17527,2380,3826,3733,2974,5460,2424,6631,5380,5595,5445,3570,6517,5885,2568,1151,784,5886,2096,1564,9218,6600,426,223,1374,2706,6776,1948,3785,1233,857,2218,2200,1678,747,2541,2159,4001,716,4539,1414,3656,1585,1980,1550,2726,620,895,2628,1496,3524,2054,2305,858,1432,2924,8949,591,0.148,0.135,0.102,0.11,0.151,0.134,0.108,0.125,0.18,0.154,0.168,0.134,0.118,0.13,0.15,0.144,0.137,0.13,0.133,0.126,0.09,0.155,0.14,0.152,0.144,0.124,0.129,0.116,0.118,0.141,0.148,0.112,0.084,0.122,0.08,0.075,0.146,0.131,0.135,0.133,0.096,0.13,0.182,0.117,0.107,0.08,0.1,0.088,0.095,0.137,0.129,0.117,0.121,0.112,0.109,0.087,0.1,0.101,0.126,0.105,0.107,0.125,0.17,0.117,0.112,0.121,0.115,0.102,0.104,0.121,0.123,0.091,0.091,0.151,385,1391,707,710,920,1035,889,890,501,390,204.0,1285,891,307,871,2129,4226,433,932,1604,634,2049,1176,1655,2357,1711,2101,1374,1592,1762,663,401,270,1151,464,439,1939,1598,191,84,631,1310,1182,1143,2123,616,334,831,1019,513,378,1158,949,1577,433,2305,634,1590,915,1098,744,971,263,392,1316,881,1783,877,1010,366,742,1323,3890,233,3.663,3.088,2.941,2.812,2.46,2.763,2.355,3.176,3.188,3.085,3.108,1.816,2.984,3.313,2.65,2.692,3.161,3.915,3.032,1.95,3.159,2.262,1.879,2.971,2.088,2.718,2.137,2.093,3.01,2.649,2.789,2.889,2.613,3.438,3.614,2.931,3.293,2.951,2.6,2.872,2.797,1.835,3.717,1.935,2.064,2.213,3.215,3.257,2.677,3.404,2.316,2.304,2.242,2.531,2.184,2.154,2.67,2.504,1.98,1.975,2.301,2.716,2.065,2.545,2.231,2.091,2.185,2.726,2.663,2.663,2.315,2.467,2.495,2.654,0.783,0.604,0.463,0.577,0.698,0.768,0.722,0.519,0.646,0.714,0.853,0.564,0.477,0.327,0.577,0.602,0.626,0.723,0.526,0.589,0.834,0.754,0.502,0.774,0.627,0.604,0.592,0.619,0.526,0.581,0.642,0.943,0.378,0.717,0.615,0.609,0.685,0.714,0.305,0.554,0.558,0.528,0.79,0.899,0.728,0.465,0.436,0.837,0.562,0.629,0.38,0.485,0.468,0.529,0.376,0.442,0.578,0.546,0.368,0.49,0.571,0.767,0.627,0.694,0.574,0.719,0.555,0.421,0.468,0.563,0.451,0.475,0.532,0.356
-24,Epileptic,F,47.0,0.9,3.3,1.2,1.7,2.5,2.0,2.4,1.4,1.7,1.0,0.3,3.8,1.8,0.8,0.9,6.8,10.5,1.4,2.1,4.8,1.1,2.6,2.0,5.1,3.4,3.5,4.4,2.9,4.5,4.0,2.3,2.3,0.4,3.1,0.5,0.7,5.1,3.7,0.1,0.1,1.0,4.0,3.7,2.5,3.8,1.0,0.6,1.0,1.7,1.1,0.7,2.1,1.7,3.2,0.3,3.2,0.8,2.2,1.0,1.4,0.4,1.8,0.3,0.8,1.7,1.2,3.4,1.6,1.2,0.6,1.1,1.3,3.9,0.4,7,25,7,8,16,15,18,13,12,8,2.0,29,16,5,7,56,78,11,22,39,8,22,17,41,32,37,42,25,30,38,19,23,4,28,3,4,46,37,1,1,6,32,27,18,17,8,3,5,7,8,3,15,14,20,2,21,6,17,8,12,3,11,2,6,12,11,20,10,6,3,8,8,26,4,0.046,0.037,0.025,0.031,0.05,0.04,0.047,0.03,0.062,0.044,0.029,0.044,0.036,0.052,0.036,0.04,0.045,0.056,0.043,0.047,0.033,0.037,0.035,0.066,0.039,0.041,0.04,0.039,0.045,0.04,0.065,0.071,0.027,0.043,0.023,0.018,0.057,0.044,0.021,0.018,0.024,0.046,0.066,0.029,0.028,0.022,0.036,0.016,0.024,0.032,0.028,0.028,0.039,0.029,0.017,0.023,0.02,0.027,0.026,0.025,0.021,0.033,0.031,0.03,0.021,0.028,0.026,0.024,0.022,0.026,0.029,0.024,0.021,0.024,1399,4591,1880,2209,1858,2893,2813,2514,1635,1552,619.0,2617,3038,1031,1742,10053,15466,1942,3266,3847,2118,3501,2054,5311,4765,5051,5659,3330,5661,5696,2342,925,753,5279,1435,1455,4996,6975,264,179,1091,2845,4778,2189,3233,1347,558,1796,1911,1003,549,2395,1755,4108,389,3772,1130,2850,1017,1516,607,2547,281,1099,2349,1204,3579,2136,1555,694,1248,1933,6993,394,0.14,0.136,0.112,0.117,0.146,0.146,0.137,0.135,0.173,0.152,0.112,0.162,0.136,0.157,0.135,0.148,0.147,0.15,0.148,0.157,0.106,0.138,0.126,0.168,0.148,0.154,0.128,0.126,0.131,0.147,0.16,0.164,0.105,0.162,0.09,0.096,0.159,0.159,0.115,0.115,0.11,0.153,0.169,0.121,0.117,0.118,0.116,0.087,0.107,0.116,0.125,0.119,0.138,0.118,0.109,0.11,0.108,0.115,0.13,0.126,0.1,0.13,0.144,0.134,0.108,0.115,0.113,0.112,0.101,0.132,0.127,0.117,0.103,0.132,427,1495,755,860,838,921,993,865,492,399,164.0,1390,909,262,490,3056,4479,520,1014,1688,591,1245,1003,1509,1499,1614,2166,1333,1761,1841,683,567,310,1361,426,546,1576,1732,133,101,643,1502,1182,1389,1989,697,269,911,998,513,347,1320,832,1882,212,2132,592,1388,589,865,322,898,135,443,1225,676,1970,1036,780,333,614,865,3073,245,2.89,2.835,2.46,2.517,2.13,2.679,2.288,2.53,2.7,3.026,2.99,1.751,2.614,2.782,2.651,2.523,2.695,2.867,2.62,1.999,2.63,2.308,1.812,2.687,2.527,2.449,2.106,2.027,2.549,2.35,2.486,1.841,2.017,2.787,2.842,2.359,2.279,2.999,2.171,1.709,2.116,1.751,3.039,1.806,1.838,2.143,2.45,2.367,2.306,2.153,1.816,2.059,2.209,2.149,2.198,1.871,2.029,2.127,1.967,1.883,2.021,2.719,2.207,2.512,2.158,2.126,1.98,2.192,2.19,2.352,2.341,2.302,2.333,2.01,0.981,0.63,0.543,0.622,0.585,0.619,0.675,0.614,0.598,0.6,0.98,0.451,0.511,0.407,0.487,0.553,0.641,0.657,0.744,0.619,0.807,0.493,0.485,0.879,0.603,0.578,0.515,0.513,0.587,0.679,0.802,0.768,0.446,0.641,0.768,0.558,0.829,0.678,0.444,0.362,0.457,0.497,0.823,0.577,0.532,0.548,0.348,0.678,0.572,0.687,0.461,0.37,0.521,0.499,0.366,0.425,0.584,0.629,0.427,0.404,0.399,0.677,0.418,0.919,0.508,0.732,0.463,0.442,0.426,1.019,0.428,0.6,0.557,0.359
-25,Epileptic,F,17.5,0.5,1.7,1.3,1.3,1.2,1.7,1.5,1.9,1.0,0.9,0.3,3.0,1.5,0.4,1.0,6.5,7.4,0.9,1.8,4.4,1.0,2.9,1.3,2.3,3.2,2.8,2.9,2.6,3.0,2.6,1.2,0.6,0.6,1.9,0.3,1.0,2.3,2.5,0.3,0.1,1.3,2.4,2.1,2.1,2.8,0.5,0.4,0.7,0.9,0.9,0.5,1.2,1.2,2.1,0.1,1.2,0.8,1.4,1.0,0.9,0.4,1.3,0.1,0.3,1.4,1.0,1.7,1.5,0.8,0.3,1.1,0.9,4.1,0.4,3,17,11,14,12,17,12,19,12,9,3.0,28,14,6,13,67,69,15,17,37,9,28,14,24,40,32,36,25,27,25,15,4,5,28,1,9,25,30,3,1,7,21,19,17,17,4,2,4,4,8,4,12,8,14,1,9,7,13,10,8,2,10,1,3,12,14,14,10,5,2,9,7,34,2,0.034,0.022,0.025,0.029,0.032,0.028,0.027,0.036,0.044,0.04,0.034,0.037,0.031,0.033,0.028,0.04,0.034,0.031,0.031,0.039,0.025,0.042,0.029,0.034,0.04,0.031,0.028,0.031,0.032,0.033,0.042,0.027,0.031,0.03,0.013,0.019,0.037,0.036,0.026,0.017,0.026,0.034,0.038,0.022,0.023,0.017,0.02,0.012,0.015,0.023,0.019,0.017,0.026,0.019,0.022,0.013,0.032,0.019,0.036,0.017,0.02,0.024,0.011,0.021,0.018,0.027,0.018,0.021,0.017,0.012,0.028,0.02,0.022,0.012,1299,4798,2618,2485,2466,3176,2750,4054,1554,1656,605.0,3152,3626,1005,2846,12600,18660,2381,3461,5019,3040,4795,2250,6493,6174,6598,5653,4289,6344,5262,2419,920,1361,5969,1724,2739,6366,6697,608,333,1932,2385,6512,2988,3387,1205,681,2027,2019,1766,639,2984,1653,5216,171,2943,1003,2792,1421,1768,735,2510,377,810,2768,929,3228,3080,1636,1015,1676,1680,9025,899,0.116,0.114,0.124,0.123,0.133,0.14,0.116,0.144,0.154,0.157,0.128,0.15,0.14,0.143,0.133,0.155,0.137,0.134,0.132,0.15,0.096,0.161,0.138,0.133,0.153,0.137,0.13,0.136,0.135,0.145,0.152,0.089,0.124,0.138,0.07,0.106,0.135,0.145,0.126,0.094,0.115,0.142,0.141,0.106,0.109,0.093,0.097,0.08,0.083,0.103,0.124,0.091,0.121,0.098,0.125,0.092,0.132,0.101,0.154,0.101,0.115,0.119,0.087,0.119,0.107,0.14,0.103,0.104,0.099,0.089,0.125,0.115,0.107,0.085,321,1236,781,811,699,1006,874,1065,369,372,175.0,1287,805,210,651,2924,4075,445,941,2094,715,1297,780,1287,1513,1560,1735,1352,1632,1354,557,371,364,1184,395,786,1145,1345,198,140,760,1098,1049,1586,1872,548,288,792,866,661,338,1249,711,1845,79,1369,358,1210,542,863,274,812,167,295,1186,549,1488,1108,730,337,620,668,3154,420,3.769,3.194,3.069,2.841,2.824,2.612,2.575,3.114,2.865,3.523,2.832,2.009,3.246,3.419,3.289,3.235,3.487,3.697,2.955,2.045,3.24,2.836,2.361,3.467,2.92,3.15,2.497,2.496,2.974,2.964,3.133,2.32,2.752,3.423,3.731,3.143,3.508,3.313,3.087,2.605,3.201,1.977,3.84,2.07,2.025,2.316,3.076,3.443,2.785,2.872,2.167,2.44,2.36,2.787,2.485,2.355,3.042,2.59,2.773,2.319,2.957,3.04,2.374,3.082,2.649,2.09,2.457,3.018,2.735,3.019,2.718,2.956,2.754,2.615,0.877,1.012,0.828,0.607,0.728,0.498,0.621,0.769,0.709,0.656,0.804,0.565,0.628,0.652,0.494,0.66,0.755,0.783,0.686,0.591,0.766,0.581,0.481,0.756,0.643,0.673,0.539,0.641,0.533,0.634,0.755,0.939,0.48,0.75,0.692,0.624,0.741,0.66,0.462,0.478,0.716,0.517,0.721,0.621,0.605,0.397,0.405,0.687,0.625,0.719,0.42,0.487,0.63,0.64,0.438,0.48,0.786,0.583,0.561,0.415,0.571,0.839,0.441,0.893,0.57,0.708,0.468,0.524,0.432,0.922,0.54,0.697,0.603,0.388
-26,Epileptic,F,27.0,0.5,2.4,0.5,1.0,1.6,2.6,1.5,1.9,1.0,0.9,0.5,3.5,1.5,0.6,1.7,6.7,8.8,0.7,1.7,5.2,1.4,2.9,1.6,2.6,2.6,4.0,2.6,2.3,2.8,3.3,0.8,1.0,0.4,3.0,0.4,0.9,3.4,3.9,0.2,0.2,0.9,2.6,2.5,3.0,2.8,0.6,0.4,0.8,1.1,1.1,0.8,2.7,1.4,2.7,0.7,2.1,0.8,1.6,1.3,0.8,0.4,1.4,0.5,0.3,1.3,1.8,2.7,1.5,0.9,0.4,1.4,1.5,4.6,0.4,3,30,8,10,19,23,13,20,17,11,7.0,31,21,7,18,84,95,6,26,44,10,36,15,39,36,41,36,24,25,46,14,8,4,36,2,8,37,48,2,1,5,27,21,25,20,6,2,5,7,9,7,24,10,21,9,17,7,18,10,8,4,14,3,4,10,20,24,11,6,6,10,11,42,3,0.023,0.022,0.01,0.02,0.036,0.036,0.023,0.03,0.034,0.033,0.032,0.032,0.026,0.027,0.03,0.033,0.026,0.031,0.029,0.032,0.035,0.031,0.031,0.03,0.029,0.031,0.023,0.025,0.026,0.033,0.027,0.042,0.025,0.03,0.016,0.018,0.034,0.036,0.012,0.017,0.02,0.029,0.044,0.023,0.022,0.025,0.018,0.014,0.014,0.026,0.027,0.024,0.023,0.018,0.041,0.016,0.02,0.017,0.02,0.017,0.02,0.024,0.031,0.015,0.016,0.033,0.022,0.019,0.016,0.016,0.026,0.022,0.017,0.023,1567,5246,2135,2127,2152,3496,2345,3502,2023,1703,725.0,3178,4107,1277,3262,12990,20259,2445,3861,5507,2279,4396,1912,6182,5274,7404,4988,3771,5692,5450,1722,1076,825,6166,1599,2296,6482,7760,457,464,1399,2780,5253,3553,3251,1063,871,1883,2488,1621,727,3914,2271,5800,570,3210,1409,3591,1652,1145,712,2611,569,687,2630,1082,3930,2733,1612,1182,1575,1931,9314,576,0.09,0.118,0.091,0.109,0.139,0.149,0.116,0.133,0.141,0.154,0.139,0.136,0.13,0.136,0.132,0.144,0.125,0.118,0.138,0.134,0.116,0.144,0.131,0.137,0.135,0.14,0.119,0.115,0.109,0.149,0.112,0.125,0.119,0.137,0.072,0.102,0.124,0.145,0.088,0.101,0.105,0.125,0.152,0.116,0.111,0.112,0.098,0.081,0.093,0.127,0.144,0.117,0.106,0.101,0.198,0.099,0.1,0.105,0.116,0.1,0.114,0.122,0.132,0.112,0.092,0.163,0.114,0.108,0.103,0.11,0.127,0.123,0.101,0.125,385,1771,821,816,873,1265,920,1118,566,483,234.0,1538,1077,319,898,3472,5607,404,1058,2505,609,1511,876,1691,1653,2206,1861,1413,1582,1768,519,428,277,1530,416,784,1713,1877,263,217,658,1381,1078,2175,2014,520,347,825,1115,683,448,1789,995,2438,214,1843,641,1546,957,758,366,1013,294,357,1320,657,1975,1205,785,550,796,1020,4151,248,3.813,2.73,2.466,2.57,2.203,2.504,2.194,2.738,2.572,2.918,2.656,1.899,2.907,2.853,2.662,2.793,2.866,4.201,2.889,1.918,2.868,2.277,1.888,2.725,2.504,2.744,2.161,2.155,2.689,2.549,2.458,2.6,2.549,2.898,3.048,2.666,2.916,3.073,1.958,2.564,2.852,1.833,3.399,1.876,1.838,2.207,2.882,2.794,2.65,2.745,1.954,2.222,2.308,2.423,2.648,1.968,2.636,2.394,2.009,1.699,2.188,2.546,2.279,2.053,2.13,2.262,2.125,2.592,2.448,2.347,2.214,2.317,2.488,2.791,0.922,0.552,0.458,0.482,0.582,0.54,0.6,0.431,0.626,0.388,0.788,0.5,0.439,0.498,0.484,0.541,0.599,0.69,0.452,0.674,0.958,0.49,0.504,0.572,0.477,0.476,0.485,0.501,0.51,0.489,0.573,0.76,0.404,0.601,0.782,0.55,0.779,0.595,0.491,0.429,0.595,0.536,0.885,0.635,0.528,0.355,0.592,0.74,0.584,0.614,0.331,0.503,0.543,0.524,0.55,0.368,0.435,0.56,0.385,0.385,0.462,0.718,0.367,0.821,0.454,0.607,0.412,0.429,0.409,0.639,0.403,0.526,0.504,0.52
-27,Epileptic,F,42.0,0.7,2.2,0.8,1.3,1.2,1.4,1.6,1.5,1.1,0.9,0.2,2.9,1.3,0.8,1.1,5.9,9.6,0.6,2.2,3.2,0.9,2.9,1.5,3.4,3.5,2.8,3.6,2.6,2.6,3.9,1.3,0.5,0.4,2.2,0.3,0.4,4.5,3.5,0.1,0.2,0.8,3.0,1.9,2.2,2.6,0.6,0.4,0.8,1.0,0.7,0.4,1.8,1.0,2.0,0.5,2.3,0.5,1.5,0.8,1.3,0.5,1.2,0.3,0.9,1.4,1.2,1.5,1.8,1.3,0.9,0.6,1.7,4.3,0.3,16,19,6,12,12,16,15,16,11,9,5.0,33,14,7,12,57,85,5,21,33,11,32,15,29,40,33,41,28,23,51,13,5,5,24,1,4,38,38,2,2,7,24,20,17,17,5,2,5,6,6,2,14,7,11,2,19,4,17,9,10,4,9,1,7,13,14,13,12,8,6,5,12,39,3,0.045,0.032,0.017,0.033,0.035,0.033,0.026,0.03,0.056,0.046,0.033,0.038,0.031,0.043,0.031,0.043,0.037,0.034,0.042,0.038,0.024,0.047,0.033,0.042,0.035,0.032,0.027,0.026,0.029,0.049,0.034,0.028,0.027,0.035,0.011,0.014,0.051,0.036,0.016,0.018,0.023,0.047,0.044,0.022,0.02,0.015,0.024,0.014,0.016,0.016,0.028,0.024,0.027,0.02,0.03,0.02,0.021,0.025,0.028,0.019,0.022,0.023,0.023,0.029,0.019,0.023,0.016,0.023,0.034,0.033,0.02,0.03,0.019,0.021,1161,4202,1578,1907,1910,2283,2498,2632,1403,1125,549.0,2949,2745,1144,2402,8741,15794,1905,3253,4322,3060,3918,2022,5294,6441,4837,5286,4030,4717,5569,1724,857,756,4677,1631,1256,5567,6387,222,650,1288,3050,5864,2636,2882,1171,595,1926,1898,1598,396,2410,1312,3339,539,3545,696,2575,1004,1945,968,2266,313,943,2419,1134,2718,2505,1126,1041,1062,2090,7916,550,0.113,0.131,0.105,0.138,0.134,0.15,0.113,0.14,0.177,0.177,0.131,0.152,0.133,0.173,0.138,0.161,0.145,0.12,0.149,0.154,0.105,0.161,0.133,0.141,0.152,0.149,0.137,0.123,0.106,0.171,0.123,0.089,0.133,0.134,0.067,0.09,0.153,0.133,0.102,0.106,0.119,0.162,0.167,0.113,0.104,0.097,0.123,0.085,0.092,0.104,0.138,0.117,0.117,0.103,0.146,0.109,0.107,0.125,0.147,0.106,0.112,0.12,0.127,0.129,0.112,0.121,0.099,0.109,0.133,0.149,0.117,0.119,0.107,0.141,299,1194,625,742,588,775,973,923,359,338,166.0,1288,801,287,651,2410,4658,350,846,1562,714,1138,842,1418,1808,1604,1989,1503,1449,1603,623,363,257,1139,424,506,1550,1724,146,230,599,1188,873,1571,1936,611,257,807,901,691,219,1204,627,1517,227,1765,367,1117,518,998,446,800,152,485,1174,720,1471,1198,625,389,517,925,3480,242,3.217,2.978,2.321,2.625,2.514,2.629,2.23,2.388,2.793,2.762,2.917,1.994,2.683,2.714,2.61,2.719,2.733,3.723,3.239,2.389,3.087,2.545,2.136,2.65,2.761,2.505,2.144,2.211,2.51,2.721,2.188,2.184,2.419,2.843,3.474,2.266,2.719,2.703,1.702,2.526,2.54,2.199,3.884,1.862,1.718,2.1,2.922,2.991,2.599,2.706,2.233,2.221,2.196,2.32,2.826,2.104,2.163,2.414,2.028,2.159,2.46,2.728,2.235,2.24,2.364,2.0,2.089,2.381,2.187,2.822,2.37,2.464,2.384,2.756,1.317,0.899,0.651,0.436,0.809,0.486,0.514,0.534,0.642,0.704,0.714,0.447,0.462,0.586,0.607,0.516,0.635,0.801,0.538,0.576,0.799,0.528,0.398,0.959,0.53,0.486,0.503,0.499,0.544,0.581,1.064,1.061,0.476,0.802,1.018,0.445,0.873,0.658,0.327,0.809,0.528,0.501,0.848,0.529,0.51,0.46,0.42,0.865,0.476,0.526,0.446,0.516,0.672,0.483,0.484,0.43,0.446,0.777,0.512,0.351,0.391,0.9,0.618,0.971,0.536,0.666,0.517,0.439,0.416,0.752,0.51,0.584,0.519,0.384
-28,Epileptic,M,22.0,1.0,2.5,1.0,1.6,1.6,3.0,2.2,2.1,1.5,0.7,0.3,3.7,1.5,0.9,2.1,10.0,13.4,1.0,2.4,7.0,1.2,3.3,2.1,3.5,3.3,3.4,3.1,4.3,5.8,3.2,1.4,1.4,0.5,3.4,0.5,0.7,3.5,3.3,0.4,0.2,1.0,3.9,2.6,6.4,4.0,0.5,0.5,1.2,1.3,1.2,1.0,2.2,2.4,3.8,0.6,2.3,1.1,2.4,1.3,1.1,0.9,1.6,0.2,0.3,2.5,1.0,3.4,1.5,1.5,0.5,0.7,1.7,3.8,0.4,8,26,9,17,19,27,17,16,14,7,4.0,35,17,11,21,94,113,10,19,57,10,33,23,35,37,32,31,39,36,34,24,10,5,37,2,6,41,38,3,1,5,34,30,47,22,5,2,7,7,14,6,15,15,21,4,17,10,16,11,9,7,18,3,3,17,12,24,9,11,4,6,13,27,4,0.038,0.026,0.012,0.027,0.032,0.038,0.031,0.032,0.05,0.03,0.026,0.034,0.032,0.033,0.046,0.043,0.036,0.031,0.032,0.041,0.025,0.036,0.031,0.035,0.034,0.029,0.026,0.033,0.038,0.033,0.043,0.044,0.018,0.033,0.019,0.019,0.036,0.034,0.027,0.014,0.019,0.038,0.047,0.036,0.022,0.013,0.017,0.016,0.016,0.036,0.039,0.02,0.025,0.023,0.024,0.018,0.03,0.022,0.026,0.02,0.028,0.024,0.021,0.017,0.028,0.025,0.024,0.018,0.021,0.019,0.02,0.021,0.017,0.019,1834,5417,2550,2645,2026,3347,2820,3441,1595,1416,728.0,3641,3424,1586,3114,12778,21007,2989,3726,5790,3657,4261,2579,6249,5395,6455,4656,4965,7069,5263,2158,1351,940,6691,1781,1802,7246,7201,510,519,1517,3992,7150,4286,4213,1077,954,2134,2540,1687,720,3562,2645,5896,635,3257,1539,3544,1244,1370,995,2367,476,783,2816,945,4176,3014,2416,1110,1092,2424,7882,465,0.141,0.117,0.09,0.116,0.138,0.156,0.108,0.134,0.142,0.139,0.108,0.143,0.135,0.136,0.16,0.144,0.13,0.12,0.133,0.151,0.096,0.138,0.139,0.135,0.131,0.127,0.113,0.124,0.115,0.127,0.137,0.134,0.098,0.139,0.082,0.092,0.137,0.134,0.116,0.087,0.099,0.14,0.155,0.147,0.105,0.086,0.092,0.088,0.089,0.136,0.152,0.1,0.12,0.106,0.125,0.099,0.13,0.106,0.127,0.108,0.13,0.12,0.122,0.109,0.119,0.117,0.113,0.098,0.109,0.111,0.113,0.114,0.096,0.105,475,1692,1081,1111,887,1360,1179,1108,565,368,244.0,1786,955,416,934,4021,6402,557,1197,2809,823,1478,1187,1721,1667,1935,1931,2060,2181,1686,630,587,401,1639,465,660,1767,1751,302,241,774,1690,1243,2851,2608,669,403,987,1225,566,397,1718,1347,2521,358,1914,690,1720,773,780,525,1037,219,299,1450,611,2220,1284,1149,463,597,1209,3525,358,3.724,2.87,2.334,2.412,2.193,2.268,2.041,2.829,2.235,2.828,2.539,1.814,2.833,2.535,2.619,2.439,2.639,3.829,2.608,1.885,3.272,2.328,1.971,2.696,2.552,2.793,2.017,2.029,2.54,2.444,2.564,2.504,1.916,2.89,3.31,2.418,2.978,2.961,2.004,2.178,2.511,2.029,3.425,1.774,1.819,1.847,2.828,2.619,2.579,2.916,1.864,2.211,2.106,2.4,2.137,1.898,2.583,2.382,1.79,1.877,2.184,2.219,2.095,2.545,2.004,2.047,2.046,2.64,2.39,2.727,2.105,2.438,2.423,1.609,0.694,0.692,0.421,0.663,0.446,0.55,0.587,0.53,0.541,0.464,0.903,0.514,0.51,0.563,0.537,0.654,0.681,0.766,0.712,0.6,0.823,0.539,0.475,0.655,0.538,0.564,0.498,0.523,0.625,0.52,0.609,0.965,0.383,0.703,0.805,0.581,0.726,0.637,0.438,0.408,0.567,0.61,0.865,0.519,0.571,0.375,0.418,0.542,0.443,0.672,0.431,0.499,0.504,0.536,0.341,0.398,0.518,0.546,0.434,0.357,0.663,0.562,0.493,0.971,0.482,0.572,0.537,0.431,0.525,0.571,0.496,0.565,0.555,0.367
-29,Epileptic,F,32.0,0.7,2.2,0.8,1.4,1.3,2.0,1.5,1.5,1.6,1.0,0.5,2.3,1.7,0.9,1.8,5.2,7.4,0.5,3.0,4.9,1.2,2.5,1.4,3.1,3.0,3.4,3.9,2.9,2.8,4.2,2.1,1.6,0.4,1.5,0.6,0.5,4.3,3.3,0.3,0.2,0.8,2.7,2.8,3.5,3.7,0.5,0.5,0.8,1.1,1.1,0.5,1.4,1.0,2.3,0.2,2.5,0.8,1.8,0.6,1.3,0.8,1.3,0.3,0.5,1.7,1.7,3.6,0.7,1.2,1.1,1.0,1.1,3.0,0.3,6,18,9,9,11,15,12,13,11,10,5.0,16,16,8,17,52,65,4,28,36,9,25,13,31,35,31,36,27,21,43,20,14,4,18,4,3,44,34,2,1,5,23,25,20,22,5,2,6,7,7,5,8,5,12,1,23,6,14,5,10,5,8,3,3,14,18,27,5,7,10,6,7,27,2,0.037,0.026,0.013,0.026,0.037,0.036,0.023,0.025,0.058,0.042,0.036,0.034,0.037,0.043,0.038,0.038,0.028,0.025,0.038,0.042,0.028,0.036,0.031,0.04,0.032,0.033,0.029,0.028,0.027,0.033,0.047,0.058,0.024,0.028,0.024,0.014,0.037,0.037,0.02,0.017,0.019,0.034,0.045,0.044,0.024,0.013,0.025,0.016,0.015,0.022,0.032,0.018,0.026,0.023,0.022,0.024,0.017,0.024,0.017,0.024,0.024,0.029,0.031,0.024,0.027,0.033,0.025,0.015,0.02,0.029,0.025,0.019,0.018,0.018,1214,4200,1770,2290,1855,2589,2611,3177,1958,1397,545.0,2083,3047,1405,3289,9323,15899,1963,4337,3442,2876,4326,1719,5547,5235,5751,5868,4806,5489,5742,2017,985,1024,3596,1568,1273,7382,6278,503,415,1506,2347,4842,2259,4007,1173,818,1863,2634,1412,398,2876,1445,3876,292,3249,1680,2727,971,1500,1067,1922,484,772,2263,1605,4044,1733,1990,1556,1334,2010,6879,382,0.12,0.116,0.113,0.116,0.133,0.136,0.095,0.122,0.163,0.157,0.162,0.141,0.138,0.146,0.148,0.149,0.122,0.089,0.139,0.136,0.103,0.143,0.121,0.137,0.137,0.138,0.121,0.127,0.116,0.144,0.126,0.157,0.115,0.128,0.099,0.085,0.14,0.142,0.11,0.1,0.101,0.139,0.151,0.149,0.114,0.089,0.12,0.093,0.088,0.117,0.137,0.094,0.115,0.1,0.143,0.118,0.104,0.118,0.11,0.123,0.123,0.127,0.143,0.11,0.121,0.14,0.122,0.092,0.103,0.136,0.121,0.099,0.096,0.113,346,1407,806,844,624,909,931,998,455,420,187.0,980,858,336,859,2443,4446,365,1336,1695,765,1239,796,1395,1613,1716,2029,1634,1625,2032,597,477,279,966,437,511,2066,1607,256,198,685,1275,1201,1322,2392,655,327,794,1128,658,283,1219,618,1654,117,1759,727,1307,531,839,480,720,208,406,1184,729,2100,806,939,598,673,945,2891,210,3.277,2.779,2.161,2.778,2.511,2.528,2.336,2.953,2.908,2.882,2.741,1.97,2.728,2.907,2.826,2.809,2.935,3.893,2.621,1.818,3.178,2.647,1.985,2.967,2.51,2.766,2.304,2.369,2.662,2.409,2.483,2.32,2.59,2.725,3.19,2.385,2.741,3.027,2.278,2.526,2.677,1.794,3.049,2.077,1.951,1.9,3.207,2.978,2.759,2.481,1.768,2.494,2.671,2.421,3.464,2.064,2.754,2.48,2.098,2.007,2.496,2.812,2.285,2.294,2.19,2.508,2.186,2.569,2.492,2.675,2.449,2.518,2.513,2.335,0.827,0.594,0.495,0.681,0.67,0.777,0.542,0.7,0.673,0.548,0.577,0.474,0.483,0.431,0.621,0.571,0.55,0.908,0.791,0.586,0.785,0.611,0.559,0.613,0.611,0.586,0.535,0.499,0.51,0.471,0.657,0.853,0.509,0.655,0.8,0.529,0.777,0.592,0.364,0.455,0.547,0.439,0.677,0.922,0.585,0.483,0.503,0.713,0.62,0.542,0.379,0.618,0.554,0.581,0.379,0.449,0.609,0.623,0.44,0.343,0.354,0.586,0.376,0.826,0.503,0.792,0.459,0.367,0.398,0.586,0.421,0.412,0.487,0.4
-30,Epileptic,F,57.0,1.1,2.2,1.6,1.8,1.8,2.4,2.0,1.8,1.4,0.6,0.5,2.5,1.6,0.5,1.2,5.8,9.5,1.5,2.6,3.9,1.2,2.8,1.9,2.9,3.1,3.5,3.6,2.2,3.5,2.6,1.2,0.4,0.5,2.6,0.6,0.7,4.9,3.3,0.2,0.1,1.1,3.6,2.8,3.3,2.9,0.8,0.4,1.1,1.4,1.0,0.8,2.1,1.4,4.1,0.4,2.3,1.1,1.9,1.6,1.0,0.7,1.8,0.5,0.4,2.0,1.2,2.5,2.0,1.1,0.6,0.7,1.6,4.1,0.4,12,18,14,13,19,17,15,15,15,5,4.0,21,14,5,13,56,72,14,25,30,8,26,17,34,29,30,38,17,31,28,16,4,4,27,4,6,46,33,1,1,4,24,27,23,14,5,2,7,6,7,4,14,12,30,2,17,7,14,10,7,5,11,3,3,13,16,16,10,6,4,5,10,28,3,0.048,0.027,0.03,0.034,0.05,0.042,0.032,0.037,0.055,0.035,0.033,0.031,0.032,0.036,0.029,0.039,0.037,0.05,0.039,0.039,0.024,0.049,0.038,0.038,0.039,0.042,0.038,0.028,0.031,0.037,0.041,0.02,0.029,0.033,0.019,0.022,0.049,0.037,0.015,0.014,0.022,0.047,0.04,0.043,0.021,0.02,0.02,0.017,0.022,0.023,0.033,0.023,0.026,0.028,0.017,0.027,0.026,0.025,0.036,0.021,0.028,0.033,0.038,0.018,0.026,0.025,0.025,0.023,0.017,0.022,0.023,0.03,0.02,0.025,1363,4295,2401,2144,2284,3090,2674,2638,2083,1016,691.0,2568,3115,1101,2355,9493,16591,2310,4476,3726,2821,4034,1922,5353,5114,4792,4855,3022,5926,4196,2414,910,694,5859,2050,1845,6693,5829,318,384,1291,2908,5388,1882,3419,1178,642,2156,2049,1845,540,3032,2642,6325,561,2504,1484,3089,1509,1251,780,2675,417,745,2461,1219,2786,3142,1856,925,883,1849,8183,429,0.133,0.121,0.127,0.125,0.15,0.153,0.127,0.141,0.157,0.139,0.14,0.126,0.131,0.137,0.138,0.144,0.135,0.143,0.141,0.134,0.096,0.157,0.131,0.136,0.149,0.148,0.135,0.102,0.117,0.141,0.145,0.084,0.122,0.138,0.086,0.095,0.143,0.138,0.093,0.082,0.103,0.153,0.136,0.147,0.1,0.098,0.1,0.082,0.101,0.109,0.14,0.11,0.117,0.113,0.102,0.117,0.111,0.117,0.135,0.111,0.122,0.13,0.126,0.11,0.116,0.122,0.114,0.104,0.096,0.115,0.116,0.13,0.098,0.121,446,1415,938,861,664,1049,983,908,515,282,213.0,1258,846,271,688,2755,4314,557,1266,1703,816,1124,906,1377,1376,1498,1729,1187,1763,1409,586,419,273,1320,538,599,1932,1556,189,216,653,1323,1297,1292,2040,630,294,977,944,803,340,1445,986,2442,329,1420,704,1317,735,683,410,886,205,358,1220,748,1552,1277,964,406,496,849,3324,226,3.07,2.76,2.327,2.525,2.661,2.543,2.286,2.804,2.811,2.857,2.788,1.82,2.829,2.695,2.568,2.605,2.923,2.946,2.73,1.874,2.985,2.61,1.932,2.858,2.731,2.686,2.252,2.05,2.559,2.448,2.983,2.198,2.18,3.207,3.44,2.627,2.637,2.771,2.03,1.947,2.419,1.99,3.102,1.756,1.877,2.036,2.794,2.674,2.71,2.497,2.084,2.324,2.275,2.48,2.049,2.013,2.461,2.477,2.291,2.014,2.248,2.802,2.147,2.189,2.267,1.901,2.003,2.877,2.216,2.424,2.076,2.532,2.541,2.277,0.75,0.529,0.598,0.517,0.741,0.626,0.488,0.666,0.672,0.327,0.884,0.442,0.429,0.64,0.509,0.554,0.628,0.919,0.513,0.597,0.704,0.611,0.515,0.584,0.603,0.611,0.447,0.424,0.494,0.481,0.736,1.121,0.441,0.706,0.781,0.599,0.68,0.652,0.341,0.431,0.563,0.483,0.699,0.651,0.536,0.436,0.443,0.77,0.531,0.481,0.411,0.505,0.68,0.6,0.294,0.417,0.494,0.575,0.445,0.418,0.487,0.81,0.435,0.7,0.496,0.8,0.485,0.769,0.449,0.65,0.474,0.569,0.506,0.499
-31,Epileptic,F,52.0,1.0,2.7,0.9,1.1,1.7,1.5,1.5,1.7,1.1,0.6,0.4,3.0,1.2,0.7,0.9,5.4,8.3,0.8,2.3,3.4,1.1,3.6,1.8,3.7,3.1,2.5,2.9,1.9,2.6,3.6,1.0,1.3,0.5,2.1,0.7,0.7,3.1,2.5,0.2,0.3,1.0,2.9,3.0,2.1,2.5,0.6,0.5,0.9,1.1,1.0,0.6,1.6,1.6,1.5,0.3,2.6,0.9,1.5,0.9,1.1,1.2,1.7,0.3,0.5,1.6,1.4,2.2,0.8,1.4,0.5,1.4,1.5,4.1,0.3,11,27,9,8,18,16,12,14,15,7,3.0,24,16,9,9,54,79,8,25,29,9,35,20,46,38,28,37,19,22,40,16,10,5,28,4,6,34,32,1,2,9,24,25,18,14,5,3,6,6,10,4,12,13,10,1,20,7,8,8,10,8,12,2,5,14,17,19,5,9,5,9,10,33,3,0.038,0.03,0.017,0.023,0.029,0.031,0.023,0.025,0.037,0.027,0.037,0.035,0.023,0.034,0.027,0.033,0.032,0.029,0.028,0.034,0.02,0.035,0.027,0.039,0.03,0.025,0.022,0.02,0.024,0.026,0.031,0.039,0.018,0.027,0.014,0.015,0.032,0.024,0.019,0.016,0.022,0.037,0.044,0.026,0.018,0.009,0.022,0.014,0.015,0.021,0.031,0.02,0.027,0.018,0.031,0.016,0.02,0.014,0.027,0.018,0.031,0.025,0.032,0.02,0.02,0.022,0.016,0.014,0.02,0.017,0.022,0.022,0.015,0.014,1494,4628,2074,2177,2778,2568,2382,2973,1855,1557,603.0,2741,3076,1476,1935,8696,15660,2356,4705,3777,2590,5420,2609,6211,5907,5682,6339,3630,5918,6063,1907,996,1341,4736,2239,1595,6652,6793,294,568,1598,2384,4393,2074,3702,1849,773,2292,2157,1987,598,2820,2479,3484,355,4491,1686,3194,1207,1807,1420,2565,379,844,2668,1529,4107,1668,2354,831,1753,2386,8962,544,0.134,0.124,0.101,0.106,0.13,0.134,0.098,0.118,0.145,0.114,0.124,0.137,0.119,0.15,0.119,0.133,0.126,0.118,0.126,0.135,0.076,0.146,0.123,0.151,0.139,0.118,0.11,0.098,0.099,0.128,0.117,0.12,0.091,0.116,0.071,0.084,0.117,0.109,0.114,0.1,0.122,0.146,0.131,0.123,0.094,0.075,0.118,0.082,0.091,0.11,0.141,0.105,0.113,0.092,0.134,0.098,0.109,0.085,0.128,0.109,0.144,0.126,0.127,0.117,0.116,0.115,0.096,0.086,0.106,0.107,0.114,0.112,0.091,0.092,499,1569,773,828,1028,918,898,996,577,412,189.0,1308,918,377,573,2708,4526,462,1340,1568,852,1762,1083,1835,1916,1706,2202,1386,1708,2082,591,491,420,1197,657,663,1705,1661,149,288,755,1211,1082,1334,2193,922,333,954,1040,817,331,1248,1110,1483,161,2438,773,1628,602,939,563,1007,203,485,1293,961,2125,838,1074,448,929,1155,4155,340,3.004,2.626,2.601,2.578,2.335,2.413,2.236,2.617,2.47,2.837,2.599,1.853,2.645,2.756,2.564,2.502,2.75,3.643,2.876,2.051,2.512,2.484,2.12,2.737,2.468,2.738,2.332,2.109,2.644,2.377,2.4,2.163,2.527,2.727,2.946,2.285,2.922,2.996,2.262,2.368,2.562,1.826,2.817,1.757,1.909,2.171,3.035,2.91,2.536,2.638,2.163,2.253,2.174,2.367,2.681,2.082,2.415,2.223,2.163,2.111,2.617,2.47,2.023,2.074,2.334,1.973,2.172,2.372,2.42,2.083,2.23,2.582,2.368,1.89,0.784,0.786,0.524,0.399,0.545,0.449,0.515,0.487,0.478,0.459,0.649,0.435,0.367,0.418,0.472,0.572,0.561,0.787,0.497,0.659,0.665,0.463,0.47,0.562,0.464,0.454,0.375,0.478,0.448,0.464,0.441,0.98,0.374,0.583,0.464,0.35,0.672,0.576,0.384,0.427,0.577,0.477,0.755,0.602,0.536,0.466,0.551,0.687,0.477,0.709,0.551,0.457,0.475,0.511,0.409,0.365,0.364,0.452,0.547,0.368,0.522,0.683,0.331,0.606,0.458,0.629,0.486,0.324,0.48,0.41,0.404,0.493,0.456,0.284
-32,Epileptic,F,32.0,0.9,2.7,1.0,1.6,1.7,2.0,1.5,1.3,0.9,0.7,0.4,2.5,1.3,0.5,1.5,5.3,7.9,1.7,2.5,4.4,1.8,2.9,1.5,4.1,4.3,3.9,4.3,3.1,3.8,3.2,1.6,0.5,0.4,2.0,0.5,0.9,4.7,2.9,0.4,0.2,1.1,2.8,2.0,1.9,2.4,0.7,0.5,0.7,1.4,0.8,0.7,1.7,1.1,3.0,0.7,2.6,1.3,1.6,1.2,1.0,0.5,1.5,0.2,0.6,1.7,1.1,2.2,1.3,0.8,0.5,1.2,1.1,3.8,0.4,8,29,8,14,14,24,14,15,10,6,4.0,23,16,9,19,66,78,12,29,44,18,35,15,55,50,42,50,27,32,35,22,6,4,24,3,9,52,40,2,1,6,29,18,14,11,5,2,4,8,7,6,19,9,23,5,20,9,11,10,7,4,12,1,7,15,16,19,9,5,5,9,8,32,2,0.038,0.026,0.019,0.031,0.035,0.03,0.029,0.027,0.032,0.034,0.04,0.038,0.027,0.042,0.024,0.029,0.026,0.042,0.03,0.039,0.033,0.037,0.031,0.044,0.044,0.032,0.035,0.035,0.035,0.029,0.035,0.045,0.023,0.028,0.015,0.019,0.045,0.032,0.021,0.017,0.019,0.039,0.028,0.023,0.017,0.012,0.018,0.01,0.019,0.014,0.037,0.018,0.023,0.017,0.027,0.017,0.027,0.018,0.021,0.018,0.015,0.023,0.02,0.019,0.02,0.019,0.017,0.015,0.015,0.017,0.025,0.016,0.017,0.014,1521,5718,2552,2400,2634,3644,3133,3099,2046,1558,792.0,2946,3575,1414,3938,13737,21270,2902,5467,5275,4159,4983,2455,6921,6361,8126,7762,3748,7200,6420,2612,971,1198,5973,2190,2514,7721,7615,534,442,1749,2860,6179,2444,3926,1617,997,2416,2345,1751,767,4207,1950,7038,744,4627,1400,2863,1442,1695,885,2762,369,1115,2718,1389,3914,2790,1811,832,1584,2242,8244,602,0.136,0.128,0.102,0.132,0.132,0.131,0.114,0.123,0.138,0.14,0.152,0.15,0.129,0.157,0.126,0.13,0.123,0.146,0.134,0.153,0.123,0.141,0.155,0.153,0.146,0.129,0.136,0.105,0.116,0.134,0.132,0.104,0.11,0.132,0.083,0.104,0.156,0.149,0.108,0.094,0.098,0.155,0.129,0.115,0.087,0.091,0.102,0.069,0.1,0.101,0.149,0.106,0.108,0.094,0.131,0.102,0.129,0.102,0.116,0.107,0.1,0.115,0.113,0.114,0.111,0.108,0.095,0.095,0.088,0.12,0.127,0.093,0.097,0.101,422,1789,912,898,872,1322,950,926,507,402,212.0,1231,946,324,1078,3456,5270,632,1547,2101,971,1603,883,1979,1962,2335,2358,1361,1709,1978,863,360,310,1276,584,799,2118,1753,277,219,877,1452,1300,1351,2092,810,430,955,1163,801,344,1705,859,2810,411,2284,680,1386,830,847,483,1040,180,545,1287,942,2152,1208,860,461,788,1103,3563,326,3.313,2.766,2.638,2.508,2.634,2.368,2.603,2.698,2.968,2.819,2.728,1.977,2.852,3.297,2.837,2.898,3.13,3.524,2.827,2.091,3.051,2.467,2.252,2.789,2.602,2.698,2.593,2.209,3.122,2.561,2.4,2.445,2.811,3.177,3.356,2.695,2.802,3.174,2.223,2.314,2.33,1.804,3.337,1.969,2.152,2.098,2.814,2.967,2.455,2.467,2.243,2.389,2.38,2.578,2.122,2.158,2.291,2.317,1.951,2.154,2.149,2.634,2.292,2.339,2.28,1.827,1.947,2.576,2.375,1.95,2.077,2.263,2.359,2.249,0.969,0.73,0.499,0.552,0.655,0.595,0.777,0.558,0.599,0.574,0.825,0.527,0.478,0.532,0.545,0.638,0.635,0.798,0.539,0.621,0.804,0.527,0.506,0.757,0.597,0.616,0.594,0.649,0.58,0.579,0.716,0.83,0.37,0.623,0.775,0.508,0.848,0.624,0.425,0.292,0.468,0.436,0.712,0.51,0.697,0.37,0.37,0.99,0.434,0.528,0.478,0.45,0.455,0.484,0.5,0.403,0.516,0.477,0.327,0.444,0.313,0.488,0.514,0.531,0.444,0.559,0.411,0.417,0.473,0.523,0.441,0.547,0.463,0.404
-33,Epileptic,M,37.0,0.6,2.5,0.8,1.1,1.8,2.6,1.6,1.4,1.1,0.8,0.4,2.9,1.7,0.8,2.3,6.2,9.0,1.1,3.1,4.7,1.9,2.7,2.0,3.2,3.8,4.0,4.0,2.4,3.1,3.3,1.6,1.2,0.6,2.2,0.4,1.1,4.2,3.5,0.3,0.3,1.3,3.5,2.5,3.8,2.8,0.7,0.6,1.0,1.6,1.0,0.7,2.1,1.3,2.2,0.7,2.4,1.0,2.0,1.0,1.3,0.6,1.3,0.4,0.3,2.2,1.9,2.3,1.5,0.8,0.5,1.7,2.0,4.6,0.3,4,20,7,9,20,25,16,13,12,8,4.0,29,16,10,21,62,80,7,28,41,18,28,20,30,48,37,44,24,23,40,21,8,5,24,2,7,41,41,2,1,8,27,25,29,16,5,3,5,8,10,4,17,8,14,6,21,6,18,8,10,5,10,4,4,18,15,17,10,4,6,11,14,34,4,0.029,0.026,0.012,0.021,0.034,0.038,0.023,0.03,0.043,0.041,0.041,0.032,0.034,0.025,0.039,0.036,0.028,0.035,0.039,0.038,0.033,0.033,0.03,0.034,0.029,0.033,0.028,0.028,0.028,0.029,0.04,0.05,0.028,0.029,0.012,0.026,0.035,0.034,0.027,0.023,0.025,0.039,0.034,0.033,0.021,0.01,0.022,0.014,0.02,0.023,0.032,0.021,0.021,0.02,0.022,0.015,0.02,0.023,0.025,0.021,0.032,0.024,0.025,0.015,0.02,0.036,0.019,0.022,0.014,0.022,0.028,0.021,0.021,0.027,1574,4697,2075,2207,2023,3197,2845,2752,1635,1516,607.0,2883,3429,1375,3248,10882,18218,2532,4949,4295,3174,4393,2251,5661,7325,6504,6450,3259,5977,5965,2319,956,678,4662,1410,1847,6346,6963,463,419,1447,2943,4361,2514,3439,1604,915,2458,2729,1603,520,3113,1719,3931,670,4477,1552,3075,1023,1515,533,2229,449,822,3147,1409,3383,2379,1443,939,1795,2473,8663,354,0.107,0.115,0.089,0.105,0.14,0.158,0.102,0.124,0.152,0.137,0.154,0.126,0.133,0.128,0.151,0.14,0.118,0.109,0.138,0.142,0.111,0.136,0.13,0.13,0.132,0.135,0.125,0.112,0.111,0.128,0.141,0.131,0.117,0.119,0.065,0.1,0.13,0.134,0.112,0.109,0.117,0.148,0.131,0.132,0.099,0.084,0.102,0.081,0.099,0.114,0.142,0.105,0.111,0.097,0.12,0.093,0.104,0.114,0.128,0.114,0.143,0.117,0.122,0.11,0.113,0.123,0.104,0.106,0.095,0.12,0.132,0.121,0.103,0.133,397,1559,878,832,882,1192,1102,865,494,395,164.0,1373,929,460,987,3065,5381,497,1442,2061,885,1393,1031,1552,2271,2040,2379,1334,1674,2015,683,475,357,1304,471,707,1942,1771,214,198,780,1400,1228,1685,2064,856,435,1040,1217,706,337,1651,944,1712,401,2495,760,1500,631,867,325,834,286,400,1571,757,1858,1113,709,405,896,1362,3734,223,3.577,2.827,2.335,2.568,2.164,2.49,2.199,2.787,2.448,3.129,3.123,1.863,2.737,2.278,2.637,2.669,2.756,3.785,2.741,1.858,2.922,2.515,1.953,2.704,2.495,2.543,2.199,1.992,2.727,2.425,2.651,2.186,1.674,2.645,2.81,2.345,2.549,2.925,2.256,2.294,2.278,1.925,2.735,1.689,1.882,2.06,2.627,2.844,2.7,2.376,2.084,2.022,2.018,2.443,2.168,1.956,2.378,2.248,2.008,1.966,2.027,2.629,1.756,2.212,2.146,2.231,1.969,2.452,2.356,2.678,2.382,2.301,2.486,1.994,0.899,0.61,0.481,0.555,0.549,0.468,0.533,0.534,0.563,0.48,0.747,0.493,0.497,0.578,0.496,0.531,0.605,0.666,0.557,0.609,0.933,0.508,0.491,0.682,0.526,0.484,0.498,0.437,0.449,0.523,0.708,1.002,0.288,0.519,0.77,0.501,0.593,0.597,0.399,0.411,0.499,0.488,0.696,0.537,0.585,0.473,0.692,1.052,0.561,0.436,0.491,0.433,0.564,0.533,0.341,0.362,0.426,0.519,0.322,0.412,0.435,0.761,0.432,0.901,0.563,0.775,0.482,0.405,0.477,0.849,0.579,0.447,0.641,0.424
-34,Epileptic,F,22.0,0.6,1.4,1.3,1.3,1.2,1.7,1.4,1.8,1.3,0.8,0.2,3.3,1.7,0.8,1.0,6.4,12.1,0.6,2.1,4.5,1.2,2.0,1.6,3.3,2.7,3.6,3.6,2.4,3.6,2.7,1.5,1.4,0.5,2.1,0.4,0.7,3.2,3.7,0.2,0.2,1.1,3.7,1.7,3.9,2.9,0.5,0.6,1.0,1.0,0.5,0.6,1.3,1.3,3.4,0.5,1.9,0.3,2.5,1.5,0.5,0.5,1.2,0.2,0.3,2.0,0.6,3.0,1.9,2.0,0.3,0.9,0.8,4.0,0.3,6,12,11,11,12,15,10,16,15,6,1.0,22,16,8,9,51,95,4,19,35,10,16,13,34,30,28,34,17,25,26,17,7,6,22,3,5,34,31,2,1,5,28,15,21,17,3,3,6,5,3,5,11,12,20,2,15,2,15,8,6,5,7,1,3,14,7,19,13,11,2,5,4,32,3,0.034,0.022,0.028,0.031,0.031,0.032,0.032,0.031,0.049,0.034,0.033,0.045,0.038,0.043,0.032,0.04,0.039,0.027,0.027,0.043,0.026,0.034,0.037,0.042,0.035,0.034,0.03,0.032,0.035,0.03,0.037,0.052,0.02,0.036,0.02,0.023,0.039,0.037,0.017,0.023,0.022,0.049,0.042,0.046,0.025,0.013,0.025,0.018,0.016,0.012,0.031,0.02,0.025,0.027,0.025,0.018,0.011,0.029,0.029,0.021,0.031,0.023,0.023,0.018,0.024,0.024,0.026,0.023,0.041,0.014,0.022,0.021,0.022,0.016,1375,3806,2295,2085,1693,2902,1972,2782,1348,1224,412.0,2450,3351,1526,1956,8524,15954,2188,4225,3526,3331,2630,1790,5090,4373,6370,5235,3033,5258,4583,2122,1116,1126,5413,1658,1801,5338,5910,428,525,1556,2495,3980,2385,3062,1044,932,2217,2223,1509,601,2510,2145,5345,464,3354,1394,3473,1429,835,900,2064,301,706,3103,862,3790,2955,1548,723,1293,1431,8412,457,0.131,0.109,0.125,0.127,0.124,0.132,0.119,0.13,0.143,0.134,0.096,0.147,0.135,0.143,0.13,0.128,0.131,0.104,0.113,0.148,0.099,0.132,0.139,0.132,0.144,0.134,0.124,0.117,0.122,0.129,0.142,0.123,0.105,0.13,0.096,0.096,0.139,0.141,0.111,0.11,0.107,0.158,0.128,0.147,0.116,0.091,0.117,0.098,0.085,0.082,0.143,0.1,0.105,0.107,0.128,0.1,0.069,0.109,0.133,0.121,0.117,0.103,0.128,0.112,0.117,0.117,0.12,0.115,0.153,0.09,0.115,0.107,0.103,0.117,371,1108,738,781,662,989,719,977,440,352,138.0,1100,911,339,534,2639,4959,354,1216,1824,832,963,756,1399,1303,1838,1949,1198,1682,1561,618,402,399,1093,379,562,1459,1599,189,191,740,1227,868,1474,1900,561,377,884,985,563,327,1164,914,2172,260,1606,490,1509,791,429,375,769,139,289,1393,469,2011,1380,767,320,628,635,3271,260,3.575,2.863,2.91,2.856,2.193,2.466,2.338,2.558,2.314,2.883,2.654,2.0,2.797,2.811,2.828,2.536,2.528,3.993,2.743,1.881,3.212,2.289,2.082,2.639,2.592,2.841,2.156,2.038,2.55,2.361,2.643,2.66,2.296,3.235,3.56,2.882,2.752,2.841,2.561,2.962,2.723,1.919,3.337,1.907,1.908,2.072,3.083,3.265,2.613,2.936,2.063,2.203,2.218,2.504,2.346,2.308,3.078,2.645,2.348,2.131,2.413,2.677,2.181,2.344,2.588,2.259,2.168,2.523,2.316,2.619,2.472,2.716,2.685,2.057,0.896,0.904,0.753,0.48,0.629,0.626,0.641,0.679,0.601,0.515,0.791,0.541,0.538,0.741,0.512,0.602,0.884,0.832,0.628,0.563,0.699,0.447,0.479,0.824,0.511,0.579,0.561,0.475,0.675,0.499,0.715,1.034,0.337,0.62,0.681,0.745,0.675,0.605,0.503,0.565,0.682,0.451,0.723,0.609,0.63,0.653,0.513,0.709,0.661,0.843,0.432,0.53,0.543,0.607,0.36,0.556,0.77,0.618,0.398,0.386,0.737,0.799,0.345,1.233,0.608,0.605,0.504,0.42,0.587,0.566,0.505,0.718,0.636,0.499
-35,Epileptic,F,32.0,0.7,2.5,0.9,1.3,1.4,1.3,1.7,1.4,1.2,1.0,0.5,3.0,1.7,0.7,1.6,5.7,9.9,1.0,1.6,3.1,1.3,2.4,2.4,2.6,5.0,4.5,2.9,2.8,2.1,2.8,1.9,1.2,0.9,3.2,0.3,1.3,2.7,2.5,0.4,0.4,1.2,2.8,2.3,1.8,2.7,0.7,0.4,1.2,1.4,0.7,0.6,2.0,1.5,2.3,0.5,2.1,0.6,1.8,0.6,0.9,0.5,1.4,0.4,0.4,2.0,1.2,2.9,1.2,0.9,0.4,0.9,1.3,5.3,0.4,6,23,7,12,14,15,13,14,12,12,4.0,32,20,7,17,65,96,8,20,34,15,26,21,32,54,44,35,24,21,34,22,9,9,32,1,7,27,33,3,3,7,19,27,16,16,5,2,7,7,5,6,14,14,15,3,17,4,13,6,6,4,10,2,3,14,18,22,8,5,4,5,8,50,2,0.035,0.023,0.014,0.024,0.036,0.031,0.027,0.023,0.04,0.039,0.046,0.035,0.032,0.04,0.038,0.035,0.03,0.036,0.026,0.031,0.027,0.032,0.042,0.033,0.051,0.029,0.024,0.031,0.024,0.031,0.054,0.045,0.031,0.036,0.011,0.029,0.031,0.029,0.029,0.024,0.022,0.04,0.043,0.024,0.021,0.015,0.018,0.016,0.016,0.019,0.027,0.023,0.024,0.016,0.017,0.015,0.016,0.017,0.019,0.018,0.02,0.027,0.023,0.02,0.021,0.021,0.022,0.017,0.014,0.012,0.026,0.022,0.022,0.016,1605,5191,2480,2600,1745,2586,3036,3156,1744,1736,485.0,3123,3991,860,2931,11218,20875,2236,4359,4971,4214,3534,1635,5927,4841,9175,6509,4466,5459,5271,2337,1052,1593,6424,1752,1767,5979,6996,538,828,1739,1971,5032,2137,3327,1372,789,2402,2649,1392,553,3079,2038,5031,690,4139,1137,3768,988,1525,705,2165,418,745,2997,1187,4337,2243,1902,768,1247,1857,9480,644,0.108,0.115,0.093,0.113,0.142,0.129,0.107,0.119,0.149,0.159,0.159,0.142,0.124,0.152,0.15,0.14,0.128,0.128,0.114,0.114,0.107,0.108,0.138,0.137,0.138,0.133,0.117,0.11,0.108,0.138,0.156,0.114,0.116,0.141,0.057,0.11,0.132,0.127,0.123,0.113,0.108,0.13,0.146,0.112,0.105,0.091,0.09,0.093,0.091,0.107,0.15,0.111,0.12,0.092,0.106,0.095,0.099,0.097,0.125,0.097,0.111,0.122,0.109,0.114,0.106,0.123,0.113,0.097,0.087,0.105,0.116,0.112,0.111,0.101,438,1832,945,981,680,795,998,1026,503,465,167.0,1300,1035,300,774,3185,5673,495,1153,1866,959,1011,881,1490,1583,2576,2034,1471,1423,1693,629,445,453,1550,476,662,1521,1651,252,295,817,975,1128,1214,1969,692,345,1033,1257,523,318,1444,999,2215,394,2115,575,1707,424,761,399,793,282,381,1472,851,2082,1042,864,452,596,874,4191,342,3.486,2.568,2.608,2.575,2.11,2.449,2.482,2.769,2.476,2.944,2.329,2.041,2.87,2.283,2.647,2.606,2.864,3.449,2.844,2.21,3.114,2.575,1.763,2.975,2.428,2.754,2.462,2.337,2.9,2.484,2.692,2.415,2.658,2.991,3.293,2.521,2.745,2.933,2.106,2.696,2.648,1.772,2.847,1.941,1.935,2.197,2.679,2.893,2.58,2.733,1.954,2.244,1.919,2.338,2.16,2.14,2.338,2.421,2.414,2.211,2.113,2.645,1.75,2.001,2.184,1.801,2.24,2.367,2.613,1.996,2.439,2.218,2.311,2.205,0.803,0.543,0.432,0.47,0.653,0.656,0.544,0.564,0.627,0.634,0.938,0.521,0.514,0.585,0.705,0.685,0.637,0.631,0.509,0.61,0.861,0.598,0.55,0.612,0.668,0.629,0.593,0.65,0.533,0.493,0.657,0.728,0.396,0.886,0.552,0.59,0.787,0.745,0.387,0.507,0.544,0.781,0.891,0.554,0.613,0.457,0.482,0.752,0.517,0.47,0.361,0.438,0.559,0.479,0.392,0.412,0.367,0.492,0.518,0.381,0.368,0.875,0.341,0.665,0.501,0.602,0.456,0.439,0.427,0.553,0.463,0.578,0.587,0.4
-36,Epileptic,M,32.0,0.8,2.3,0.7,1.6,1.2,1.9,1.8,1.7,0.8,1.2,0.3,2.7,1.6,0.6,1.0,6.6,9.7,1.5,3.0,5.5,0.9,3.3,1.8,2.6,3.7,2.7,2.4,2.7,2.6,2.6,1.2,1.5,0.5,2.4,0.4,0.7,3.8,4.0,0.1,0.1,1.1,3.1,3.4,2.9,2.9,1.0,0.5,0.7,1.2,0.7,0.5,1.6,1.4,2.6,0.3,2.6,0.7,1.4,1.6,1.0,0.7,1.8,0.3,0.5,2.0,1.5,2.5,1.4,1.1,0.7,0.8,0.9,5.5,0.3,7,23,8,13,12,17,14,16,8,11,4.0,22,15,7,10,66,83,11,30,42,12,37,20,28,44,34,29,24,22,32,16,9,5,30,3,6,44,42,2,1,7,25,29,21,16,8,2,5,7,8,5,14,10,16,2,25,5,10,13,10,6,16,3,4,15,19,21,12,6,5,6,9,44,3,0.036,0.019,0.012,0.027,0.027,0.031,0.025,0.024,0.032,0.036,0.022,0.034,0.025,0.029,0.026,0.03,0.028,0.042,0.032,0.041,0.02,0.03,0.033,0.027,0.031,0.026,0.026,0.026,0.025,0.029,0.037,0.041,0.019,0.029,0.013,0.016,0.034,0.032,0.01,0.017,0.02,0.037,0.043,0.022,0.021,0.019,0.019,0.012,0.015,0.019,0.034,0.017,0.019,0.018,0.015,0.02,0.014,0.014,0.026,0.02,0.016,0.025,0.017,0.017,0.024,0.025,0.015,0.017,0.017,0.019,0.02,0.017,0.02,0.011,1580,5330,2302,2548,1775,3028,2868,3827,1614,1763,991.0,2911,3857,1541,2657,14319,21514,2979,5454,5192,3182,5466,2690,6598,7262,6760,4565,4896,6461,4999,2489,1354,1222,7016,2591,2067,8458,9087,443,328,1756,3117,7449,3530,3216,1598,1034,2331,2422,1903,394,3317,2242,5231,704,3945,1920,3351,1710,1736,1445,2897,688,1091,2871,1553,4564,3298,2312,1336,1264,1732,10733,643,0.123,0.111,0.093,0.122,0.125,0.137,0.102,0.117,0.119,0.148,0.109,0.14,0.126,0.136,0.113,0.13,0.124,0.139,0.129,0.146,0.092,0.14,0.145,0.12,0.147,0.129,0.124,0.106,0.103,0.134,0.126,0.137,0.095,0.13,0.075,0.09,0.135,0.143,0.085,0.095,0.107,0.145,0.145,0.111,0.103,0.104,0.101,0.081,0.086,0.107,0.15,0.099,0.107,0.094,0.11,0.116,0.088,0.093,0.133,0.111,0.1,0.124,0.116,0.111,0.123,0.123,0.1,0.103,0.095,0.117,0.118,0.109,0.1,0.09,428,1888,956,979,758,1076,937,1204,472,531,249.0,1289,1018,327,697,3623,5697,574,1483,2206,713,1779,1000,1635,1932,1942,1534,1659,1619,1617,626,539,407,1465,584,706,2086,2136,241,165,831,1398,1473,1995,2019,837,418,934,1174,691,258,1551,1098,2169,326,2028,813,1488,969,866,669,1085,263,464,1300,926,2374,1328,1038,521,636,844,4416,352,3.452,2.525,2.375,2.595,2.274,2.436,2.569,2.769,2.763,2.893,2.894,2.028,2.884,3.115,2.878,2.92,3.055,3.615,3.027,2.071,3.288,2.474,2.445,3.077,2.862,2.824,2.328,2.294,2.958,2.519,3.001,2.565,2.365,3.23,3.681,2.707,3.056,3.148,2.198,2.343,2.72,2.019,3.369,2.115,1.794,2.096,2.977,2.984,2.571,2.787,1.822,2.325,2.29,2.611,2.676,2.247,2.647,2.633,2.09,2.062,2.317,2.626,2.734,2.709,2.309,1.984,2.111,2.743,2.676,2.839,2.338,2.466,2.584,2.379,0.669,0.687,0.563,0.472,0.488,0.48,0.636,0.514,0.606,0.588,0.887,0.526,0.485,0.505,0.537,0.634,0.57,0.915,0.631,0.7,0.762,0.44,0.468,0.608,0.566,0.505,0.449,0.609,0.591,0.458,0.643,0.928,0.363,0.798,0.706,0.586,0.672,0.559,0.223,0.348,0.535,0.646,0.833,0.681,0.522,0.372,0.401,0.702,0.437,0.714,0.41,0.548,0.517,0.432,0.411,0.405,0.497,0.668,0.428,0.44,0.394,0.594,0.603,0.879,0.575,0.529,0.387,0.38,0.449,0.62,0.413,0.491,0.477,0.323
-37,Epileptic,F,32.0,0.7,2.3,1.0,0.9,1.4,2.4,1.6,1.5,0.7,0.5,0.1,3.0,1.2,0.3,0.7,5.4,8.0,0.7,2.9,5.3,1.3,4.5,1.7,3.4,3.6,4.2,2.7,2.5,2.4,2.9,1.5,1.1,0.5,2.4,0.3,0.4,1.9,3.7,0.2,0.2,1.1,3.5,2.0,2.4,2.9,0.6,0.2,1.0,1.2,1.0,0.4,1.4,0.9,2.7,0.4,2.9,0.7,1.4,1.3,1.1,0.6,1.4,0.2,0.5,1.6,0.9,2.4,1.0,1.1,1.0,0.8,1.1,5.6,0.6,8,19,8,6,16,21,11,12,9,5,2.0,28,15,4,8,57,70,3,29,47,13,40,18,34,35,47,40,20,20,36,18,8,3,31,2,4,20,41,1,2,8,29,21,19,19,5,1,6,8,11,5,11,9,18,2,27,9,9,10,11,5,12,2,6,16,17,20,11,7,8,6,6,49,6,0.032,0.026,0.021,0.018,0.028,0.031,0.026,0.029,0.029,0.028,0.017,0.034,0.027,0.024,0.026,0.032,0.028,0.026,0.034,0.038,0.033,0.035,0.033,0.032,0.039,0.032,0.022,0.029,0.023,0.028,0.042,0.043,0.024,0.033,0.012,0.014,0.028,0.037,0.013,0.014,0.021,0.031,0.035,0.025,0.023,0.012,0.01,0.016,0.018,0.03,0.027,0.015,0.021,0.023,0.022,0.019,0.026,0.016,0.025,0.023,0.015,0.02,0.011,0.018,0.019,0.026,0.022,0.016,0.019,0.027,0.022,0.023,0.022,0.023,1196,4140,1948,1962,2003,2923,2420,2540,1323,1208,434.0,2550,2761,1002,2101,10062,16874,2007,4662,4954,3337,4391,2436,6330,4189,6769,6142,3075,5633,5763,2112,1432,793,5319,1594,1623,4869,7137,393,515,1554,2643,6464,2487,3022,1326,768,1931,2099,1762,460,2256,1630,4681,322,4434,1326,2761,1471,1255,1140,2912,294,888,2490,815,3500,2228,1473,1220,1201,1770,9213,714,0.128,0.122,0.115,0.094,0.123,0.133,0.108,0.124,0.141,0.128,0.088,0.148,0.128,0.108,0.106,0.135,0.126,0.101,0.132,0.15,0.115,0.119,0.115,0.125,0.114,0.129,0.112,0.11,0.092,0.138,0.168,0.114,0.093,0.129,0.061,0.082,0.129,0.144,0.094,0.101,0.109,0.129,0.144,0.119,0.11,0.091,0.069,0.095,0.104,0.137,0.137,0.097,0.11,0.109,0.123,0.109,0.134,0.096,0.12,0.123,0.097,0.114,0.103,0.117,0.117,0.127,0.112,0.104,0.102,0.135,0.119,0.114,0.103,0.147,394,1426,728,748,870,1192,874,841,423,308,139.0,1353,838,251,507,2845,4801,410,1403,2183,762,1656,823,1834,1346,2160,1999,1094,1424,1722,645,482,292,1305,464,595,1252,1702,185,238,819,1556,1107,1583,1976,666,347,925,982,578,308,1364,815,2000,183,2402,562,1346,830,749,597,1079,202,503,1306,535,1741,1047,820,506,596,753,4018,339,2.962,2.614,2.752,2.574,2.069,2.217,2.39,2.885,2.321,2.962,2.584,1.73,2.533,2.863,2.9,2.639,2.917,3.754,2.686,1.966,3.169,2.346,2.408,2.714,2.586,2.566,2.38,2.3,2.963,2.602,2.541,3.043,2.262,2.973,2.819,2.483,3.12,3.125,2.508,2.468,2.34,1.601,3.565,1.871,1.71,2.159,2.708,2.714,2.71,2.95,1.631,1.919,2.074,2.456,2.101,2.049,2.466,2.348,1.951,1.889,2.186,2.624,1.754,2.117,2.021,1.976,2.04,2.385,2.123,2.601,2.382,2.656,2.367,2.476,0.585,0.697,0.478,0.586,0.633,0.721,0.582,0.53,0.598,0.64,0.722,0.491,0.542,0.46,0.603,0.658,0.56,0.714,0.648,0.614,0.924,0.688,0.624,0.846,0.702,0.66,0.654,0.636,0.67,0.549,0.643,0.898,0.49,0.634,0.69,0.545,0.664,0.629,0.49,0.462,0.555,0.429,0.776,0.589,0.502,0.488,0.602,0.724,0.465,0.664,0.343,0.423,0.432,0.453,0.41,0.461,0.491,0.529,0.385,0.363,0.41,0.654,0.44,0.688,0.498,0.742,0.561,0.421,0.385,0.498,0.397,0.599,0.502,0.504
-38,Epileptic,M,32.0,1.0,2.7,1.1,0.8,3.2,2.7,2.1,1.6,1.8,0.7,0.4,3.3,1.5,0.5,1.5,8.0,9.2,1.4,2.4,5.0,1.1,3.1,2.5,6.7,7.6,4.8,4.2,2.1,4.3,3.2,2.5,2.1,0.6,2.2,0.2,1.3,8.3,4.4,0.2,0.4,1.7,4.3,4.8,3.7,2.5,1.0,0.5,0.7,1.2,1.8,0.7,1.8,1.3,3.6,0.8,1.8,1.3,2.1,0.9,1.1,0.4,2.1,0.5,0.5,2.3,1.9,1.3,1.5,2.2,0.6,1.3,1.4,4.6,0.4,7,26,14,8,25,20,21,18,13,6,4.0,29,22,6,17,86,92,10,30,46,9,34,26,62,65,36,45,24,38,37,18,12,4,30,2,11,61,46,1,3,9,28,41,30,18,9,2,4,7,10,5,13,10,24,5,17,9,15,7,7,4,18,4,4,19,20,8,11,12,6,10,10,38,2,0.044,0.025,0.019,0.014,0.047,0.034,0.04,0.026,0.054,0.034,0.038,0.038,0.029,0.031,0.032,0.037,0.031,0.052,0.036,0.045,0.02,0.039,0.037,0.058,0.051,0.037,0.043,0.035,0.035,0.029,0.061,0.062,0.032,0.03,0.011,0.025,0.055,0.037,0.013,0.02,0.027,0.04,0.061,0.038,0.021,0.024,0.023,0.012,0.017,0.033,0.027,0.019,0.017,0.022,0.033,0.022,0.021,0.022,0.02,0.019,0.02,0.03,0.029,0.017,0.024,0.032,0.019,0.017,0.028,0.018,0.024,0.017,0.018,0.019,1623,5468,2655,2542,1900,3194,2919,4576,1747,1578,768.0,2842,4516,1367,3537,14566,20104,2559,4984,5471,3322,4719,2521,5888,5451,6582,5037,3999,5477,6583,2178,1670,1045,6274,1759,2915,6411,7959,407,911,2042,2608,5112,2935,3307,2184,883,2333,2483,1753,703,3340,2346,6876,673,2816,2232,3761,1263,1794,959,3568,605,965,3302,1631,2111,4022,2353,872,1876,2733,9900,523,0.151,0.124,0.113,0.092,0.147,0.128,0.141,0.125,0.156,0.138,0.143,0.159,0.134,0.144,0.138,0.148,0.141,0.168,0.136,0.177,0.078,0.128,0.146,0.166,0.167,0.143,0.152,0.141,0.123,0.137,0.188,0.149,0.123,0.127,0.059,0.112,0.153,0.148,0.091,0.113,0.112,0.139,0.172,0.149,0.115,0.12,0.107,0.076,0.093,0.122,0.132,0.102,0.098,0.106,0.143,0.113,0.111,0.105,0.103,0.102,0.103,0.138,0.133,0.101,0.116,0.143,0.099,0.09,0.116,0.128,0.126,0.099,0.097,0.113,421,1759,983,905,992,1284,1165,1236,596,392,208.0,1555,1148,352,854,4136,5639,483,1409,2343,865,1535,1174,2055,2441,2121,1875,1193,2256,2077,802,612,303,1418,432,885,2499,2030,231,350,902,1619,1369,1748,1719,740,340,848,1125,876,389,1575,1224,2747,341,1377,936,1612,697,932,425,1255,308,512,1659,875,1092,1411,1285,460,873,1282,4063,257,3.643,2.906,2.607,2.595,1.801,2.195,2.043,3.017,2.312,3.401,3.215,1.671,2.918,2.785,2.953,2.597,2.76,3.762,2.88,2.037,2.756,2.406,1.929,2.277,1.992,2.614,2.267,2.606,2.14,2.577,2.186,2.659,2.668,3.14,3.451,2.87,2.204,2.91,2.061,2.484,2.674,1.588,2.769,1.979,2.209,2.464,3.016,3.176,2.68,2.485,2.211,2.23,2.108,2.444,2.496,2.245,2.75,2.681,2.068,2.144,2.485,2.847,1.999,2.171,2.168,2.068,2.2,2.799,1.968,1.904,2.482,2.541,2.56,2.754,0.879,0.671,0.49,0.66,0.604,0.656,0.6,0.571,0.855,0.522,0.932,0.46,0.548,0.414,0.649,0.76,0.663,0.716,0.639,0.725,0.711,0.677,0.502,0.835,0.618,0.762,0.525,0.53,0.629,0.578,0.701,0.998,0.454,0.688,0.651,0.484,0.852,0.89,0.363,0.539,0.512,0.437,0.964,0.783,0.452,0.713,0.489,0.879,0.399,0.823,0.395,0.439,0.518,0.545,0.452,0.404,0.582,0.511,0.34,0.416,0.341,0.754,0.529,0.77,0.533,0.797,0.394,0.501,0.536,0.61,0.391,0.536,0.51,0.507
-39,Epileptic,F,57.0,1.1,2.5,0.8,1.3,1.5,1.9,2.1,1.7,1.4,0.5,0.4,3.4,1.2,0.6,1.1,7.6,7.3,1.4,1.6,4.7,1.1,2.7,1.6,3.0,2.0,3.0,3.8,2.3,3.0,2.3,1.5,1.2,0.4,2.4,0.6,0.3,5.0,2.4,0.2,0.0,0.9,3.4,2.5,3.4,2.6,0.6,0.6,1.0,1.7,0.6,0.6,1.5,1.1,2.1,0.2,2.2,0.6,1.4,1.2,0.9,0.3,1.8,0.6,0.8,2.1,1.3,2.3,1.2,1.0,1.1,0.8,1.9,3.6,0.1,9,22,8,11,14,18,22,15,12,6,4.0,33,15,7,12,77,72,10,21,46,11,34,15,30,30,29,42,25,32,36,13,9,4,28,3,2,43,32,2,1,5,28,32,24,16,4,3,6,8,6,3,9,7,14,1,22,5,12,7,7,3,12,3,6,21,13,16,9,8,10,5,12,28,2,0.055,0.036,0.021,0.032,0.039,0.034,0.038,0.033,0.065,0.032,0.036,0.041,0.038,0.048,0.04,0.05,0.036,0.055,0.034,0.041,0.03,0.034,0.03,0.043,0.032,0.038,0.03,0.027,0.033,0.032,0.041,0.049,0.027,0.04,0.024,0.015,0.057,0.046,0.017,0.012,0.027,0.04,0.055,0.036,0.018,0.011,0.027,0.022,0.028,0.027,0.028,0.024,0.022,0.021,0.013,0.02,0.016,0.023,0.027,0.016,0.011,0.032,0.047,0.023,0.022,0.03,0.023,0.022,0.02,0.036,0.029,0.023,0.021,0.014,1067,3817,1585,2028,1618,2771,2582,2225,1710,1130,671.0,2930,2075,916,1732,8303,13572,1831,3794,4844,2357,4064,1784,3899,3810,4364,5129,3562,5221,3969,1531,923,845,4413,1720,1020,6245,4800,260,212,932,2729,4497,2541,3448,1394,739,1813,1699,1001,553,1641,1334,3436,369,3267,1147,2256,1093,1314,891,2138,398,845,3008,1044,2921,1815,1493,1164,1044,1964,6335,286,0.139,0.133,0.117,0.125,0.141,0.148,0.107,0.142,0.188,0.131,0.128,0.156,0.158,0.154,0.149,0.169,0.141,0.159,0.138,0.156,0.106,0.127,0.116,0.136,0.128,0.149,0.124,0.116,0.114,0.14,0.137,0.129,0.103,0.151,0.092,0.079,0.163,0.16,0.106,0.104,0.112,0.151,0.159,0.137,0.101,0.083,0.115,0.097,0.109,0.128,0.12,0.112,0.113,0.109,0.095,0.116,0.099,0.117,0.121,0.103,0.078,0.128,0.162,0.117,0.116,0.128,0.115,0.111,0.107,0.145,0.136,0.118,0.104,0.123,332,1319,599,728,705,989,963,838,435,299,201.0,1403,692,233,556,2588,3720,430,1004,2079,639,1293,860,1340,1191,1279,1992,1308,1512,1375,628,409,254,1082,473,380,1559,1202,166,106,524,1295,1105,1611,2126,755,330,774,889,386,330,887,769,1593,199,1725,631,1042,676,798,462,857,189,537,1587,565,1524,875,744,556,436,1102,2795,145,3.059,2.635,2.637,2.513,2.016,2.44,2.224,2.323,2.819,2.969,2.64,1.886,2.388,2.67,2.358,2.391,2.723,3.185,2.876,2.069,2.648,2.35,1.849,2.279,2.459,2.641,2.148,2.153,2.612,2.257,1.918,2.432,2.71,2.808,2.929,2.281,2.816,2.827,1.867,2.062,2.152,1.842,2.963,1.731,1.839,2.035,2.531,2.761,2.459,2.536,2.026,2.004,1.879,2.327,1.983,1.987,2.058,2.343,1.934,1.797,2.046,2.473,1.967,1.894,2.004,2.3,2.084,2.298,2.21,2.131,2.391,2.109,2.3,2.437,0.843,0.599,0.643,0.59,0.572,0.634,0.586,0.586,0.663,0.429,0.9,0.465,0.403,0.52,0.573,0.579,0.697,0.887,0.707,0.554,0.816,0.554,0.54,0.778,0.55,0.525,0.521,0.508,0.487,0.566,0.647,0.767,0.4,0.656,0.68,0.496,0.823,0.672,0.417,0.415,0.451,0.508,0.733,0.511,0.562,0.498,0.565,0.644,0.445,0.665,0.37,0.372,0.48,0.46,0.332,0.432,0.38,0.506,0.359,0.438,0.373,0.793,0.579,0.583,0.436,0.769,0.455,0.357,0.341,0.57,0.596,0.498,0.496,0.633
-40,Epileptic,F,42.0,1.1,2.2,1.0,1.3,1.5,2.6,1.7,1.4,1.9,0.6,0.3,2.4,1.6,0.4,1.5,4.7,8.8,1.2,2.2,4.3,1.2,2.1,1.2,4.2,2.6,3.0,2.4,2.1,2.0,2.5,1.6,1.3,0.6,1.9,0.3,0.7,1.9,3.1,0.2,0.2,1.0,3.1,2.1,2.9,2.9,0.5,0.2,1.0,1.4,0.8,0.7,1.5,0.9,2.5,0.1,1.7,0.8,1.8,0.9,0.8,0.6,2.0,0.3,0.3,1.4,1.4,2.3,1.2,0.5,0.8,0.6,0.8,4.0,0.3,9,21,13,11,12,27,15,14,19,7,4.0,24,16,7,20,60,81,10,26,39,11,26,14,42,33,36,30,18,19,31,20,10,6,23,2,8,23,32,2,2,7,30,24,26,16,4,2,6,8,7,3,13,7,19,1,17,8,12,10,6,4,16,2,5,13,19,21,7,5,16,5,6,37,2,0.039,0.025,0.018,0.025,0.036,0.033,0.021,0.023,0.059,0.029,0.027,0.031,0.026,0.023,0.026,0.028,0.027,0.034,0.024,0.039,0.02,0.027,0.024,0.04,0.028,0.027,0.023,0.023,0.02,0.025,0.035,0.064,0.019,0.022,0.01,0.015,0.026,0.03,0.012,0.02,0.02,0.034,0.032,0.028,0.025,0.012,0.012,0.015,0.016,0.017,0.032,0.018,0.015,0.018,0.016,0.015,0.02,0.019,0.018,0.015,0.023,0.032,0.018,0.014,0.017,0.021,0.022,0.017,0.011,0.021,0.018,0.014,0.017,0.017,1384,4192,2629,2353,1589,3827,3013,3054,1893,1212,700.0,2427,3760,915,3027,9897,18127,2646,4762,4255,4204,3710,1823,5555,4777,5894,4494,3343,4787,5079,2073,1104,1213,5275,1880,2138,4528,6194,372,523,1654,3161,5396,2571,3109,1352,789,2395,2610,1611,548,3263,1765,5329,278,3110,1362,3153,1213,1263,848,2602,428,754,2590,1341,3133,2304,1533,1271,1203,1849,9751,561,0.142,0.124,0.108,0.118,0.141,0.142,0.101,0.118,0.168,0.135,0.12,0.141,0.123,0.118,0.131,0.129,0.124,0.137,0.121,0.145,0.09,0.126,0.125,0.145,0.132,0.133,0.12,0.096,0.098,0.128,0.14,0.133,0.094,0.109,0.059,0.098,0.117,0.133,0.089,0.101,0.111,0.149,0.146,0.133,0.11,0.087,0.085,0.084,0.098,0.093,0.144,0.095,0.099,0.095,0.112,0.101,0.116,0.104,0.117,0.1,0.104,0.13,0.097,0.114,0.101,0.134,0.113,0.102,0.085,0.134,0.108,0.097,0.098,0.098,459,1438,933,885,713,1411,1051,971,573,328,224.0,1166,1061,266,926,2877,5262,529,1412,1905,993,1282,798,1676,1568,1854,1609,1239,1338,1614,676,396,397,1382,503,815,1244,1528,221,258,845,1485,1249,1699,1843,683,322,960,1210,665,296,1532,892,2311,139,1724,623,1485,731,726,476,1029,252,434,1301,890,1764,1028,739,615,573,868,3844,297,2.999,2.715,2.663,2.71,2.086,2.451,2.396,2.866,2.507,2.943,2.544,1.86,2.771,2.512,2.497,2.634,2.891,3.656,2.827,1.934,3.148,2.324,1.949,2.572,2.422,2.667,2.253,2.168,2.691,2.501,2.436,2.746,2.352,2.857,3.381,2.506,2.871,3.025,2.07,2.227,2.453,1.921,3.162,1.782,1.923,2.2,2.987,3.057,2.593,2.596,2.382,2.233,2.009,2.462,2.153,1.957,2.514,2.518,1.777,1.93,2.136,2.506,2.113,1.998,2.252,1.984,1.954,2.659,2.33,2.24,2.364,2.606,2.536,2.465,0.579,0.583,0.475,0.422,0.467,0.468,0.454,0.421,0.584,0.519,0.698,0.477,0.403,0.466,0.348,0.465,0.534,0.788,0.472,0.501,0.831,0.53,0.439,0.719,0.43,0.422,0.475,0.473,0.481,0.41,0.664,0.96,0.343,0.596,0.787,0.39,0.509,0.514,0.258,0.403,0.4,0.448,0.635,0.505,0.583,0.29,0.521,0.794,0.479,0.416,0.404,0.408,0.451,0.383,0.366,0.398,0.393,0.505,0.285,0.298,0.437,0.622,0.263,0.722,0.483,0.681,0.407,0.411,0.45,0.397,0.392,0.489,0.528,0.33
-41,Epileptic,M,17.5,0.5,1.8,1.1,1.3,2.0,2.3,2.0,1.4,1.2,0.5,0.4,3.6,1.4,0.7,1.3,7.2,10.4,0.6,3.0,5.4,1.9,3.5,2.0,3.6,3.3,4.5,3.3,3.1,3.7,3.4,1.9,0.6,0.6,3.2,0.3,0.8,3.1,4.4,0.2,0.2,1.0,3.4,2.1,3.4,2.6,0.7,0.4,0.8,1.2,0.5,0.7,1.5,1.2,2.9,0.4,2.1,0.5,2.8,1.8,1.2,0.7,1.3,0.2,0.9,2.0,1.1,2.4,2.2,1.4,0.4,0.5,1.1,4.1,0.4,4,14,15,11,21,19,17,15,19,6,3.0,26,13,10,14,84,90,5,26,33,18,29,19,48,37,36,25,28,29,27,18,5,3,31,2,6,34,45,1,3,5,24,24,20,14,4,2,6,6,5,4,10,10,20,2,17,3,21,13,8,4,8,1,7,14,11,17,12,8,3,3,10,29,3,0.027,0.02,0.017,0.026,0.041,0.031,0.032,0.024,0.048,0.036,0.03,0.04,0.03,0.035,0.032,0.034,0.035,0.023,0.038,0.04,0.029,0.035,0.032,0.041,0.033,0.035,0.035,0.031,0.028,0.041,0.045,0.037,0.043,0.032,0.012,0.02,0.036,0.033,0.015,0.019,0.019,0.04,0.039,0.034,0.018,0.014,0.019,0.014,0.015,0.013,0.035,0.015,0.024,0.019,0.022,0.019,0.015,0.029,0.03,0.021,0.018,0.024,0.016,0.044,0.023,0.024,0.023,0.027,0.019,0.018,0.018,0.018,0.016,0.017,1994,5320,3480,3016,2813,4193,3130,3795,2267,1313,956.0,3504,3743,1645,3167,15701,25177,2576,5385,3961,5958,5524,2760,7376,7419,8453,4992,5243,7887,6260,2779,1237,975,7516,2237,2563,6197,10769,375,774,1889,2662,5661,3033,4308,1446,813,2349,3047,1715,795,3819,2085,7427,558,3987,1679,3800,2160,2002,1391,2488,324,968,3614,1589,3640,2892,2865,1199,921,2695,11759,731,0.101,0.099,0.107,0.11,0.149,0.123,0.116,0.112,0.162,0.138,0.128,0.141,0.122,0.152,0.121,0.138,0.13,0.103,0.146,0.143,0.113,0.126,0.132,0.144,0.128,0.119,0.12,0.123,0.112,0.132,0.133,0.113,0.144,0.122,0.059,0.087,0.124,0.114,0.104,0.095,0.098,0.137,0.133,0.131,0.088,0.092,0.09,0.084,0.083,0.092,0.127,0.089,0.116,0.095,0.115,0.099,0.082,0.118,0.123,0.108,0.102,0.112,0.115,0.157,0.107,0.115,0.107,0.102,0.094,0.115,0.104,0.096,0.09,0.118,389,1407,1056,902,844,1325,1068,1001,530,304,217.0,1491,842,348,695,3480,5378,415,1300,2108,1073,1613,1157,1657,1786,2090,1640,1611,2036,1535,665,360,272,1521,498,788,1518,2029,195,242,773,1352,961,1685,2290,679,320,871,1154,629,379,1520,882,2663,254,1695,567,1715,964,923,559,799,136,340,1428,696,1665,1160,1127,333,431,1118,4158,331,4.222,3.116,2.973,3.157,2.379,2.653,2.466,3.365,3.2,3.236,3.598,2.062,3.335,3.102,3.247,3.153,3.524,4.183,3.175,1.698,3.61,2.659,2.141,2.901,3.045,3.177,2.395,2.472,2.991,2.895,2.75,3.211,2.784,3.372,3.478,3.011,2.87,3.374,2.283,3.0,3.386,1.829,3.579,2.139,2.172,2.418,3.03,3.459,3.16,2.975,2.432,2.703,2.422,2.851,2.752,2.462,3.411,2.455,2.446,2.402,3.027,2.808,2.976,2.705,2.764,2.531,2.513,2.966,2.988,3.166,2.689,2.695,3.06,3.149,0.775,0.854,0.684,0.547,0.801,0.578,0.687,0.672,0.725,0.546,0.689,0.68,0.502,0.517,0.512,0.663,0.65,0.769,0.714,0.517,0.811,0.623,0.565,0.937,0.592,0.639,0.536,0.787,0.616,0.738,1.03,0.935,0.627,0.648,0.906,0.714,0.825,0.803,0.332,0.569,0.573,0.482,0.847,0.744,0.621,0.545,0.504,0.779,0.704,0.64,0.547,0.616,0.474,0.598,0.338,0.592,0.756,0.775,0.597,0.559,0.528,0.826,0.535,0.947,0.724,0.79,0.616,0.572,0.556,0.99,0.575,0.611,0.697,0.553
-42,Epileptic,M,22.0,0.6,2.6,1.2,1.7,1.6,2.0,2.2,1.9,1.5,1.1,0.3,3.1,1.8,0.9,1.7,7.9,11.2,1.2,3.0,5.5,1.0,3.2,1.5,3.0,2.2,3.9,2.2,2.4,2.7,3.2,1.5,1.2,0.5,3.0,0.8,0.4,4.0,3.9,0.2,0.2,0.9,3.2,2.0,2.8,2.2,0.8,0.6,1.0,1.4,0.5,0.5,1.5,1.5,2.8,0.2,2.7,0.9,1.8,1.0,0.9,0.9,1.5,0.5,0.3,1.6,1.8,3.0,1.6,0.9,0.6,1.1,1.5,5.4,0.3,6,27,11,16,17,20,18,19,23,9,5.0,29,20,14,18,89,100,11,31,49,10,36,16,40,25,44,29,23,22,34,26,9,5,34,5,3,46,43,2,2,6,26,19,22,13,8,3,7,8,4,3,14,13,21,2,24,10,13,8,10,6,13,3,4,13,18,23,14,6,5,7,12,46,3,0.03,0.021,0.018,0.024,0.035,0.029,0.029,0.024,0.053,0.041,0.029,0.031,0.028,0.039,0.03,0.035,0.031,0.032,0.032,0.038,0.022,0.032,0.024,0.034,0.025,0.029,0.023,0.021,0.027,0.033,0.037,0.034,0.019,0.029,0.024,0.009,0.036,0.029,0.01,0.013,0.016,0.03,0.032,0.024,0.019,0.016,0.02,0.016,0.016,0.016,0.024,0.015,0.021,0.019,0.025,0.019,0.021,0.017,0.027,0.018,0.022,0.027,0.023,0.013,0.018,0.034,0.016,0.019,0.014,0.019,0.033,0.019,0.02,0.013,1548,5561,2608,3032,2176,3120,3392,3948,1829,1643,655.0,3358,3903,1694,3432,13246,20903,2672,5878,5165,3092,5652,2442,6140,4793,7493,5187,4600,5303,5249,2626,1524,1284,7098,1943,1974,9241,9794,435,517,1776,3412,6103,3042,2890,1570,1081,2427,2783,1399,638,3786,2355,5805,403,4154,1938,3770,1239,1578,1320,2451,687,832,2998,1313,5064,2977,1878,1060,1209,2415,11131,550,0.111,0.113,0.103,0.114,0.137,0.136,0.121,0.125,0.173,0.155,0.132,0.147,0.13,0.148,0.139,0.144,0.132,0.124,0.137,0.15,0.093,0.145,0.131,0.145,0.121,0.135,0.119,0.098,0.104,0.147,0.143,0.121,0.111,0.136,0.09,0.069,0.142,0.135,0.089,0.098,0.097,0.13,0.136,0.117,0.1,0.104,0.109,0.087,0.094,0.095,0.115,0.091,0.116,0.101,0.11,0.111,0.115,0.1,0.127,0.113,0.109,0.117,0.124,0.104,0.104,0.134,0.097,0.108,0.093,0.118,0.137,0.112,0.106,0.107,415,1990,1054,1246,805,1170,1205,1294,538,459,207.0,1524,1071,442,963,3986,6060,558,1483,2390,806,1734,1021,1580,1538,2315,1698,1671,1535,1656,720,559,406,1601,487,726,2152,2206,279,270,854,1496,1162,1832,1809,780,444,1010,1326,614,345,1710,1036,2394,139,2108,762,1657,668,830,638,874,361,437,1392,782,2593,1322,896,504,597,1122,4436,312,3.486,2.53,2.342,2.459,2.182,2.435,2.32,2.72,2.571,2.958,2.52,1.978,2.794,2.796,2.734,2.606,2.799,3.682,3.073,1.911,3.026,2.568,2.143,2.883,2.504,2.747,2.43,2.241,2.652,2.575,2.719,2.642,2.456,3.074,3.334,2.502,3.104,3.295,1.711,2.145,2.671,2.003,3.384,1.913,1.863,2.152,2.986,2.73,2.589,2.917,2.173,2.171,2.274,2.492,2.739,2.151,2.842,2.653,2.12,2.049,2.446,2.618,2.162,2.099,2.396,2.069,2.074,2.562,2.448,2.313,2.312,2.545,2.663,2.199,0.821,0.649,0.555,0.609,0.643,0.55,0.545,0.352,0.616,0.462,0.786,0.444,0.407,0.532,0.5,0.58,0.58,0.819,0.584,0.66,0.728,0.476,0.466,0.674,0.511,0.47,0.458,0.532,0.585,0.432,0.746,1.052,0.489,0.61,0.824,0.49,0.727,0.563,0.302,0.496,0.44,0.487,0.782,0.639,0.588,0.404,0.495,0.779,0.561,0.384,0.363,0.472,0.555,0.512,0.559,0.378,0.487,0.473,0.322,0.389,0.388,0.76,0.351,0.758,0.469,0.731,0.436,0.417,0.396,0.57,0.321,0.461,0.475,0.481
-43,Epileptic,F,47.0,1.2,2.2,0.7,1.9,1.7,2.8,1.9,1.7,1.9,0.6,0.2,2.5,1.8,0.5,1.1,6.9,11.1,1.2,2.7,4.8,0.5,2.1,1.5,3.8,3.6,3.7,2.6,3.0,2.7,3.1,1.0,0.8,0.6,3.6,0.3,1.1,5.0,4.0,0.1,0.1,1.1,2.4,2.2,1.8,2.4,0.9,0.4,1.1,1.5,0.7,0.9,1.7,1.3,3.6,0.7,1.8,0.7,3.4,1.0,1.0,0.7,1.5,0.3,0.3,2.0,0.6,2.8,1.5,1.3,0.6,1.1,2.4,5.2,0.4,8,19,6,23,14,22,14,17,17,6,2.0,23,19,5,13,67,97,8,23,37,4,24,14,35,44,42,29,31,22,33,11,5,7,36,2,12,53,40,1,1,7,22,23,14,15,6,2,7,7,6,6,13,9,31,5,17,6,16,10,9,5,10,2,3,14,5,21,9,8,6,9,16,42,4,0.044,0.025,0.011,0.033,0.035,0.035,0.03,0.032,0.053,0.035,0.02,0.036,0.037,0.028,0.038,0.039,0.036,0.04,0.036,0.041,0.012,0.031,0.026,0.044,0.03,0.033,0.028,0.031,0.024,0.034,0.031,0.038,0.023,0.036,0.016,0.024,0.044,0.035,0.013,0.019,0.025,0.031,0.038,0.025,0.018,0.021,0.02,0.016,0.019,0.024,0.031,0.024,0.023,0.028,0.015,0.019,0.02,0.029,0.023,0.018,0.031,0.031,0.025,0.015,0.027,0.028,0.021,0.024,0.018,0.022,0.026,0.026,0.023,0.021,1405,4116,1991,2406,2259,3271,2521,2883,1885,1090,465.0,2141,3292,863,2144,9120,17431,2213,3214,3234,2021,3127,1839,5316,6045,6156,4470,4197,5259,4954,1945,922,1237,6526,1364,2030,7204,6522,277,250,1335,2278,5231,1750,3118,1446,684,2278,2294,1217,693,2420,1940,5215,1118,2898,1204,2925,1210,1359,690,1869,309,678,2189,833,3660,1796,1895,883,1359,2526,8386,412,0.134,0.112,0.086,0.136,0.137,0.147,0.113,0.136,0.161,0.145,0.097,0.149,0.141,0.118,0.131,0.141,0.134,0.129,0.141,0.148,0.067,0.135,0.124,0.142,0.133,0.131,0.122,0.124,0.102,0.14,0.13,0.103,0.119,0.146,0.082,0.113,0.146,0.132,0.079,0.12,0.121,0.135,0.13,0.117,0.096,0.105,0.105,0.087,0.1,0.107,0.142,0.117,0.106,0.124,0.106,0.113,0.106,0.124,0.131,0.108,0.123,0.135,0.127,0.106,0.126,0.102,0.114,0.108,0.102,0.138,0.129,0.128,0.108,0.134,432,1505,853,986,865,1170,966,929,593,314,165.0,1046,967,265,599,2910,5255,472,1179,1770,617,1137,838,1466,1926,2069,1600,1565,1637,1582,527,363,403,1649,360,745,2020,1853,192,104,710,1203,1050,1212,2053,665,324,1075,1101,555,421,1096,989,2189,599,1545,583,1628,641,827,373,749,171,316,1128,398,1964,947,1001,387,668,1303,3789,240,3.049,2.534,2.338,2.442,2.292,2.38,2.176,2.795,2.354,3.013,2.628,1.898,2.758,2.303,2.573,2.359,2.616,3.66,2.296,1.691,2.573,2.257,1.903,2.77,2.416,2.474,2.248,2.135,2.467,2.455,2.788,2.613,2.578,3.02,3.219,2.35,2.745,2.656,1.664,2.81,2.404,1.71,3.267,1.627,1.717,2.552,2.602,2.519,2.629,2.592,1.955,2.155,2.049,2.234,2.357,2.093,2.483,2.154,2.002,1.857,2.357,2.546,1.93,2.448,2.151,2.301,2.068,2.202,2.115,2.574,2.313,2.314,2.36,1.883,0.75,0.636,0.535,0.524,0.604,0.492,0.582,0.524,0.59,0.508,0.776,0.505,0.507,0.704,0.608,0.59,0.619,0.751,0.647,0.53,0.648,0.528,0.457,0.74,0.462,0.623,0.476,0.502,0.561,0.504,0.654,0.989,0.437,0.624,0.72,0.499,0.701,0.627,0.405,0.522,0.569,0.457,0.789,0.526,0.508,0.666,0.514,0.537,0.515,0.657,0.526,0.554,0.634,0.575,0.513,0.416,0.454,0.599,0.572,0.337,0.478,0.742,0.396,1.139,0.517,0.661,0.47,0.452,0.577,0.922,0.45,0.539,0.515,0.311
-44,Epileptic,F,37.0,0.9,2.1,0.6,0.9,1.1,1.2,1.3,1.3,1.2,1.0,0.4,3.1,1.5,0.8,1.5,5.2,6.5,0.7,2.6,4.1,1.2,2.2,1.7,4.0,2.6,3.2,3.8,2.3,1.8,2.9,1.8,1.4,0.4,2.3,0.2,0.8,3.3,2.3,0.2,0.1,1.3,2.4,2.0,2.3,1.8,0.5,0.3,1.4,1.2,0.6,0.8,2.1,0.7,1.8,0.1,2.4,0.4,1.7,0.9,1.2,0.5,1.4,0.4,0.6,1.7,2.4,3.6,1.6,0.5,0.5,1.1,1.3,4.4,0.2,6,23,8,8,12,14,10,14,10,11,4.0,26,18,9,16,58,67,6,35,38,13,27,18,37,31,36,53,26,20,42,23,10,7,28,1,9,39,28,1,1,10,24,22,21,11,4,2,9,6,5,7,19,5,11,1,22,4,16,9,9,5,12,2,6,13,26,28,11,3,3,7,10,41,3,0.036,0.024,0.012,0.016,0.027,0.022,0.023,0.025,0.049,0.038,0.029,0.036,0.024,0.035,0.029,0.031,0.025,0.024,0.031,0.036,0.024,0.025,0.029,0.037,0.031,0.027,0.028,0.024,0.021,0.025,0.032,0.044,0.028,0.027,0.01,0.016,0.036,0.025,0.017,0.015,0.023,0.035,0.029,0.025,0.015,0.01,0.016,0.021,0.016,0.019,0.035,0.022,0.018,0.017,0.018,0.015,0.012,0.019,0.018,0.02,0.013,0.025,0.025,0.015,0.018,0.031,0.019,0.021,0.012,0.016,0.02,0.019,0.019,0.017,1339,4054,1716,1996,1844,3239,2456,2520,1483,1822,663.0,3194,3995,1485,3192,9585,15420,1908,4684,4260,3780,3636,2125,6303,4830,6605,7157,4028,4688,6246,3090,1471,997,5655,1648,2030,6634,5564,447,450,1566,2935,5956,2508,3418,1461,780,2350,2221,1150,709,3622,1068,3457,263,4114,1189,3648,1391,1473,1076,2220,392,1204,2810,1729,5301,2259,1187,989,1722,2262,8385,296,0.117,0.124,0.101,0.099,0.126,0.108,0.102,0.128,0.162,0.141,0.124,0.143,0.116,0.149,0.135,0.133,0.124,0.121,0.136,0.148,0.097,0.11,0.124,0.131,0.119,0.127,0.107,0.094,0.101,0.134,0.138,0.132,0.133,0.135,0.061,0.093,0.148,0.127,0.09,0.087,0.123,0.128,0.135,0.122,0.085,0.075,0.087,0.091,0.093,0.12,0.153,0.112,0.111,0.095,0.12,0.097,0.082,0.11,0.106,0.109,0.092,0.124,0.119,0.102,0.109,0.129,0.104,0.113,0.084,0.092,0.112,0.109,0.105,0.139,468,1478,762,812,719,977,809,898,442,507,244.0,1328,1098,357,872,2963,4331,443,1355,1840,934,1209,850,1923,1368,1924,2188,1526,1322,2007,992,545,276,1378,456,829,1688,1572,260,176,838,1107,1274,1519,1919,790,333,1005,1066,504,372,1652,559,1604,77,2282,607,1468,785,833,560,881,232,624,1365,1133,2660,1069,569,479,845,976,3666,165,2.986,2.666,2.387,2.309,2.26,2.547,2.432,2.553,2.647,2.782,2.587,1.963,2.818,3.037,2.786,2.503,2.786,3.543,2.727,2.021,2.877,2.401,2.128,2.617,2.636,2.733,2.49,2.132,2.66,2.402,2.525,2.711,2.782,2.996,3.115,2.316,2.966,2.773,2.037,2.659,2.363,2.098,3.401,1.918,1.934,1.976,2.916,3.027,2.559,2.674,2.144,2.228,2.002,2.313,3.096,1.98,2.258,2.556,1.882,1.935,2.033,2.587,1.95,2.228,2.159,1.883,2.072,2.408,2.283,2.467,2.262,2.482,2.396,2.261,0.765,0.459,0.435,0.489,0.637,0.699,0.62,0.481,0.54,0.552,0.735,0.606,0.409,0.364,0.544,0.586,0.558,0.629,0.636,0.693,0.819,0.558,0.563,0.743,0.577,0.492,0.63,0.578,0.548,0.637,0.541,0.934,0.359,0.545,0.458,0.449,0.621,0.566,0.41,0.395,0.469,0.803,0.667,0.738,0.618,0.484,0.322,0.791,0.425,0.426,0.514,0.426,0.564,0.521,0.509,0.386,0.329,0.543,0.454,0.351,0.373,0.602,0.41,0.722,0.605,0.817,0.492,0.384,0.511,0.373,0.455,0.543,0.451,0.618
-45,Epileptic,F,37.0,0.7,2.4,1.1,1.4,1.9,1.5,2.4,1.4,1.6,0.8,0.2,3.4,2.4,0.9,1.3,7.2,8.7,1.1,1.9,4.2,1.2,2.7,1.9,4.6,2.8,4.4,3.8,2.8,2.4,3.3,1.2,1.0,0.5,2.8,0.4,0.6,3.6,4.2,0.4,0.6,1.2,3.2,2.5,2.7,3.6,0.7,0.5,0.9,1.4,1.5,0.6,2.4,2.6,2.7,0.3,3.1,0.7,2.0,1.5,1.4,0.8,1.2,0.5,0.5,1.8,1.6,3.5,1.4,1.0,0.8,0.9,0.9,4.3,0.2,5,21,12,11,16,13,15,12,16,6,3.0,31,19,6,12,60,69,6,17,38,11,26,16,39,27,32,36,24,19,38,14,10,4,21,2,4,30,42,2,4,7,28,22,21,22,5,2,6,7,10,3,14,20,17,3,22,5,16,8,11,6,7,3,4,16,16,26,9,6,5,6,5,33,2,0.035,0.028,0.022,0.029,0.041,0.027,0.039,0.031,0.052,0.036,0.024,0.04,0.036,0.047,0.034,0.042,0.039,0.045,0.034,0.039,0.02,0.035,0.036,0.047,0.032,0.041,0.032,0.031,0.029,0.029,0.038,0.037,0.037,0.039,0.015,0.018,0.038,0.04,0.027,0.031,0.025,0.035,0.038,0.025,0.028,0.02,0.034,0.017,0.022,0.026,0.029,0.022,0.039,0.026,0.019,0.022,0.024,0.026,0.029,0.026,0.028,0.02,0.036,0.02,0.021,0.032,0.027,0.02,0.022,0.021,0.021,0.022,0.021,0.012,1151,4283,2044,2003,2578,2601,2725,2472,1855,1347,567.0,3231,3968,1046,2388,9279,14539,2156,3265,5067,3370,4183,2163,6002,4689,5833,5860,3625,4315,5637,2068,933,724,4968,1664,1501,5613,6852,537,684,1530,3175,5313,3193,3524,1213,581,1942,2251,2253,593,3586,3273,4092,502,4407,1052,3056,1536,1556,802,2576,492,783,2869,1318,4550,2200,1438,1613,1501,1623,7610,550,0.123,0.118,0.115,0.119,0.142,0.127,0.136,0.126,0.165,0.136,0.13,0.148,0.129,0.157,0.123,0.135,0.136,0.137,0.135,0.145,0.089,0.136,0.135,0.142,0.138,0.147,0.131,0.117,0.109,0.137,0.127,0.115,0.135,0.131,0.082,0.093,0.13,0.149,0.135,0.131,0.114,0.14,0.139,0.117,0.116,0.104,0.123,0.095,0.103,0.11,0.125,0.099,0.132,0.11,0.123,0.111,0.118,0.121,0.124,0.132,0.142,0.102,0.14,0.123,0.103,0.14,0.119,0.105,0.108,0.109,0.105,0.103,0.1,0.086,372,1424,848,799,859,887,982,767,501,346,183.0,1383,1161,308,731,2887,3773,389,977,1880,1002,1303,903,1703,1424,1834,2008,1498,1416,1862,543,418,251,1191,422,558,1520,1762,193,306,760,1459,1189,1797,1981,613,261,814,1002,918,347,1805,1199,1715,266,2217,501,1287,797,795,409,865,249,383,1381,789,2095,1073,674,597,697,711,3332,250,3.098,2.532,2.254,2.396,2.369,2.508,2.372,2.885,2.912,3.155,2.524,2.1,2.641,2.745,2.52,2.441,2.88,3.727,2.721,2.319,2.762,2.511,2.097,2.63,2.568,2.618,2.349,2.068,2.449,2.494,2.647,2.19,2.381,2.958,3.475,2.523,2.802,2.849,2.625,2.501,2.519,2.009,3.24,2.006,1.972,2.253,2.951,2.833,2.783,2.553,2.228,2.097,2.462,2.404,2.354,2.238,2.443,2.557,2.27,2.175,2.179,2.798,2.338,2.351,2.17,2.079,2.224,2.461,2.283,2.647,2.52,2.566,2.467,2.537,0.707,0.809,0.719,0.592,0.637,0.454,0.548,0.728,0.807,0.555,0.804,0.489,0.507,0.467,0.516,0.625,0.685,0.85,0.525,0.596,0.839,0.446,0.357,0.72,0.502,0.527,0.403,0.546,0.559,0.562,0.801,0.935,0.397,0.739,0.756,0.566,0.707,0.683,0.576,0.406,0.616,0.475,0.821,0.567,0.656,0.541,0.512,0.857,0.568,0.672,0.405,0.508,0.705,0.541,0.531,0.443,0.419,0.623,0.465,0.402,0.345,0.747,0.576,1.081,0.542,0.595,0.503,0.45,0.43,0.884,0.611,0.655,0.506,0.548
-46,Epileptic,F,32.0,0.8,2.1,1.1,0.8,1.2,2.8,2.4,1.2,0.9,0.8,0.4,3.4,1.1,0.5,1.0,5.3,8.1,1.6,2.6,4.2,1.3,4.5,3.2,2.8,3.8,2.3,4.7,1.8,2.7,3.3,2.0,0.6,0.5,2.7,0.4,0.8,4.5,3.3,0.1,0.2,0.9,6.8,3.2,2.9,2.4,0.6,0.4,0.7,1.1,1.1,0.7,1.2,1.1,2.2,0.3,2.3,0.8,1.9,1.4,0.8,0.5,1.1,0.2,0.6,1.5,1.0,2.1,1.2,1.0,0.5,0.9,1.5,4.1,0.3,5,22,11,9,12,29,21,13,14,9,7.0,37,12,6,11,68,98,9,28,40,11,39,23,25,48,26,42,24,23,39,19,5,4,30,2,8,40,40,1,1,5,43,28,19,14,4,1,4,6,8,4,10,7,18,2,20,5,12,10,6,3,9,1,3,12,16,16,10,7,3,6,10,31,3,0.03,0.024,0.019,0.021,0.03,0.036,0.043,0.023,0.045,0.03,0.049,0.043,0.03,0.026,0.02,0.032,0.034,0.046,0.03,0.039,0.027,0.048,0.05,0.036,0.037,0.028,0.038,0.028,0.029,0.038,0.052,0.045,0.021,0.031,0.017,0.024,0.041,0.034,0.011,0.014,0.018,0.053,0.055,0.027,0.018,0.014,0.018,0.01,0.018,0.02,0.026,0.014,0.019,0.017,0.016,0.019,0.022,0.02,0.026,0.014,0.015,0.022,0.017,0.027,0.019,0.02,0.02,0.016,0.017,0.016,0.02,0.022,0.016,0.025,1733,4700,2390,2312,1936,3790,2842,2900,2145,1595,476.0,4094,2701,1288,3029,11754,19616,2690,5795,5680,3674,3745,1984,5830,6313,5802,6045,4371,7265,5677,2456,763,1130,7368,1993,2095,6036,8401,427,424,1539,2680,5672,3399,3906,1210,804,2668,2128,1961,667,2999,1702,5257,538,3634,1572,3533,1662,1623,874,2177,344,1081,2605,1063,3159,2445,2037,796,1555,2429,9014,393,0.115,0.119,0.119,0.106,0.133,0.142,0.123,0.116,0.14,0.154,0.166,0.152,0.138,0.141,0.11,0.139,0.142,0.144,0.135,0.149,0.113,0.134,0.135,0.141,0.129,0.129,0.121,0.112,0.111,0.141,0.167,0.112,0.098,0.134,0.074,0.101,0.143,0.149,0.08,0.092,0.097,0.171,0.157,0.113,0.098,0.091,0.094,0.067,0.095,0.113,0.125,0.085,0.105,0.096,0.108,0.108,0.106,0.1,0.115,0.084,0.095,0.121,0.092,0.117,0.109,0.111,0.111,0.102,0.096,0.114,0.106,0.112,0.095,0.146,447,1488,843,751,699,1437,1028,862,522,449,191.0,1593,701,277,734,3142,4821,517,1414,2044,818,1468,1020,1404,1968,1558,2166,1391,1733,1707,707,317,402,1514,451,714,1755,1862,182,211,749,1739,1224,1747,1955,591,298,980,959,797,378,1361,885,2261,250,1836,647,1567,914,848,435,791,193,361,1230,735,1567,1117,928,364,740,1081,3801,207,3.579,2.938,2.594,2.755,2.242,2.283,2.291,2.863,2.946,2.792,2.251,2.193,2.922,3.155,3.043,2.778,3.009,3.748,3.083,2.295,3.191,2.099,1.869,3.131,2.524,2.856,2.358,2.432,3.108,2.537,2.571,2.438,2.457,3.315,3.558,2.719,2.722,3.19,2.547,2.147,2.598,1.556,3.225,2.242,2.249,2.205,3.295,3.235,2.892,2.809,2.16,2.226,2.13,2.34,2.442,2.19,2.593,2.628,2.051,2.149,2.234,2.676,1.972,3.473,2.384,1.75,2.256,2.458,2.431,2.551,2.316,2.603,2.518,2.333,0.95,0.675,0.755,0.599,0.762,0.625,0.617,0.44,0.753,0.713,0.913,0.544,0.481,0.47,0.512,0.598,0.715,0.864,0.542,0.685,0.808,0.647,0.554,0.747,0.636,0.588,0.542,0.596,0.507,0.572,0.795,0.777,0.457,0.755,0.662,0.399,0.91,0.6,0.466,0.394,0.441,0.502,1.108,0.593,0.652,0.463,0.602,0.665,0.424,0.593,0.328,0.437,0.474,0.529,0.634,0.343,0.433,0.514,0.387,0.35,0.359,0.706,0.561,0.548,0.441,0.465,0.449,0.383,0.446,0.897,0.435,0.649,0.523,0.536
-47,Epileptic,M,67.5,0.4,2.3,0.7,1.0,1.6,2.4,1.8,1.5,0.9,0.6,0.1,2.8,1.3,0.5,1.4,6.9,8.4,0.7,2.5,4.5,1.9,3.5,1.4,3.6,2.8,2.4,3.9,1.9,2.2,3.1,1.4,0.7,0.4,3.2,0.3,0.6,3.2,3.6,0.2,0.1,0.7,3.5,2.3,1.8,3.7,0.4,0.6,0.7,0.9,0.8,0.5,1.5,0.9,2.4,0.3,2.3,0.5,3.0,1.1,1.2,0.8,2.7,0.4,0.6,2.5,0.3,2.8,1.1,1.3,0.5,1.2,1.4,5.5,0.3,3,18,10,8,15,21,10,12,9,5,2.0,25,13,5,13,58,71,6,22,39,14,31,14,33,32,24,44,17,16,38,16,7,4,28,3,5,38,35,2,0,4,27,19,14,23,4,3,4,5,8,5,12,6,14,2,16,4,22,7,9,6,19,3,4,19,3,23,8,8,5,6,10,40,3,0.019,0.024,0.019,0.025,0.036,0.033,0.029,0.031,0.035,0.028,0.02,0.038,0.027,0.032,0.027,0.037,0.032,0.025,0.031,0.034,0.025,0.037,0.024,0.037,0.029,0.03,0.03,0.024,0.024,0.031,0.046,0.024,0.03,0.035,0.016,0.017,0.036,0.04,0.019,0.008,0.015,0.035,0.047,0.025,0.035,0.015,0.022,0.011,0.011,0.021,0.031,0.019,0.018,0.024,0.02,0.015,0.014,0.039,0.028,0.023,0.029,0.044,0.029,0.048,0.028,0.016,0.024,0.02,0.029,0.024,0.032,0.022,0.022,0.02,1431,5438,1614,1514,2548,3141,2359,2534,1936,902,354.0,2596,2668,1000,2403,10219,15856,2122,3099,3719,4463,4708,2236,6328,4650,4661,6196,2633,3963,5880,2642,1003,772,5248,1649,1684,7177,6171,312,276,1341,3060,5160,1783,2355,1062,997,2028,2092,1671,338,3247,1782,4506,348,4065,1212,2489,1311,1522,919,2460,643,857,2984,545,3570,2004,1297,858,1444,2123,9846,409,0.08,0.102,0.114,0.116,0.136,0.134,0.112,0.128,0.134,0.111,0.087,0.138,0.121,0.132,0.132,0.135,0.126,0.099,0.129,0.129,0.09,0.143,0.117,0.135,0.118,0.131,0.125,0.108,0.104,0.137,0.119,0.086,0.123,0.124,0.067,0.082,0.134,0.141,0.101,0.057,0.091,0.137,0.146,0.102,0.13,0.093,0.111,0.077,0.076,0.107,0.155,0.101,0.097,0.103,0.111,0.09,0.098,0.145,0.134,0.115,0.14,0.142,0.125,0.109,0.113,0.086,0.117,0.105,0.131,0.121,0.134,0.123,0.107,0.142,385,1554,674,664,771,1201,883,757,489,297,156.0,1133,732,247,795,2945,4105,450,1327,2012,1169,1508,885,1562,1556,1392,2101,1221,1237,1748,626,559,249,1179,477,596,1563,1590,182,127,663,1447,956,1251,1746,510,408,901,1053,673,276,1360,837,1695,201,2233,576,1302,605,770,448,984,267,392,1390,313,1798,888,739,411,642,954,3937,195,3.332,3.105,2.698,2.465,2.876,2.335,2.242,2.734,3.002,2.652,2.276,1.993,2.745,2.943,2.357,2.715,3.093,3.752,2.053,1.719,2.672,2.6,2.191,3.026,2.337,2.697,2.313,1.858,2.582,2.646,2.801,1.88,2.576,3.109,2.618,2.556,3.065,2.975,1.979,2.239,2.321,1.982,3.728,1.594,1.568,2.204,2.894,2.638,2.321,2.315,1.537,2.429,2.659,2.774,2.201,2.059,2.491,2.049,2.656,2.241,2.54,2.71,2.831,2.272,2.335,2.085,2.305,2.622,2.237,2.423,2.641,2.757,2.683,2.59,1.034,0.895,0.716,0.49,0.722,0.815,0.517,0.618,0.632,0.599,0.839,0.56,0.498,0.367,0.51,0.662,0.744,0.876,0.654,0.508,0.857,0.529,0.487,0.695,0.579,0.671,0.629,0.483,0.714,0.714,0.929,0.819,0.424,0.678,1.032,0.761,0.809,0.68,0.639,0.508,0.834,0.564,0.919,0.416,0.587,0.473,0.69,0.946,0.611,0.941,0.323,0.588,0.561,0.572,0.473,0.511,0.548,0.828,0.74,0.49,0.54,0.835,0.545,0.758,0.803,0.874,0.786,0.495,0.418,0.557,0.597,0.532,0.573,0.731
-48,Epileptic,M,47.0,0.4,1.6,0.8,1.4,1.4,2.0,1.4,1.7,1.0,0.9,0.1,3.3,1.2,1.1,1.3,7.8,8.9,1.0,2.2,4.1,0.8,2.3,1.0,4.1,3.0,4.2,2.8,2.4,3.3,3.5,1.7,0.9,0.7,3.0,0.2,0.8,3.9,3.5,0.3,0.2,1.2,2.4,2.5,2.2,2.8,0.6,0.6,0.9,1.2,0.5,1.1,2.2,1.4,2.6,0.4,2.6,1.0,3.1,1.0,0.8,0.7,2.2,0.4,0.6,1.5,0.7,2.9,1.3,1.0,0.7,0.9,1.9,5.9,0.5,4,18,7,12,16,18,11,14,8,7,1.0,28,12,11,13,69,82,8,26,30,9,27,9,36,32,44,32,24,24,41,19,8,5,29,1,7,38,41,4,1,7,19,29,22,17,5,2,6,6,6,4,19,9,18,2,19,9,23,8,7,5,15,3,4,15,9,23,8,6,12,7,15,43,3,0.022,0.021,0.014,0.029,0.032,0.032,0.024,0.03,0.042,0.039,0.011,0.039,0.029,0.039,0.03,0.038,0.029,0.029,0.036,0.044,0.017,0.032,0.026,0.037,0.03,0.031,0.027,0.026,0.027,0.03,0.048,0.031,0.031,0.036,0.007,0.014,0.038,0.035,0.026,0.014,0.024,0.034,0.04,0.029,0.019,0.013,0.02,0.013,0.014,0.016,0.036,0.023,0.026,0.022,0.02,0.023,0.032,0.036,0.03,0.02,0.034,0.037,0.026,0.022,0.021,0.017,0.022,0.021,0.018,0.024,0.025,0.027,0.023,0.023,1444,3562,1861,1924,2227,2879,1911,3329,1460,1117,436.0,2320,2968,1483,2538,10684,17406,2240,3994,2949,2294,3316,1553,6536,4817,7348,4890,3327,5661,5328,2525,1252,846,5527,1510,2235,7738,6911,489,506,1560,1994,5147,1946,3260,1357,974,2298,2462,1455,662,3143,1779,4266,411,3227,1564,2496,825,956,688,2703,543,975,1914,792,3795,2239,1865,1295,1431,2386,8679,581,0.088,0.108,0.093,0.123,0.135,0.132,0.099,0.129,0.141,0.142,0.058,0.164,0.12,0.154,0.123,0.141,0.121,0.114,0.134,0.144,0.088,0.14,0.12,0.147,0.135,0.138,0.12,0.109,0.097,0.14,0.145,0.101,0.141,0.139,0.046,0.081,0.149,0.139,0.127,0.083,0.109,0.14,0.145,0.131,0.101,0.093,0.106,0.079,0.089,0.112,0.199,0.112,0.115,0.111,0.109,0.113,0.135,0.123,0.134,0.123,0.138,0.135,0.116,0.119,0.119,0.094,0.109,0.101,0.098,0.137,0.13,0.129,0.116,0.114,420,1349,792,821,838,1043,871,995,419,361,188.0,1290,752,425,796,3380,5169,482,1098,1558,820,1171,687,1753,1711,2336,1879,1418,1675,1983,621,496,322,1367,476,804,1700,1828,239,249,754,1072,1235,1275,2182,746,415,1039,1167,564,639,1565,943,1887,263,1862,609,1401,543,619,364,966,320,435,1136,589,2152,1024,896,575,687,981,3874,311,3.287,2.571,2.185,2.43,2.326,2.41,1.899,2.984,2.77,2.767,2.204,1.753,2.951,2.633,2.531,2.474,2.761,3.772,2.753,1.715,2.313,2.275,1.965,2.809,2.32,2.721,2.136,1.915,2.597,2.301,2.831,2.279,2.155,2.934,2.887,2.633,3.117,2.783,2.232,2.292,2.424,1.695,2.959,1.702,1.689,1.95,2.935,2.662,2.53,2.687,1.36,2.128,2.223,2.3,1.982,1.897,2.721,1.946,1.798,1.76,2.082,2.71,1.883,2.58,1.906,1.654,1.906,2.429,2.213,2.323,2.451,2.624,2.348,2.43,0.84,0.51,0.572,0.478,0.632,0.812,0.493,0.495,0.579,0.394,0.803,0.471,0.4,0.619,0.687,0.594,0.672,0.563,0.781,0.642,0.897,0.527,0.489,0.741,0.521,0.599,0.525,0.534,0.574,0.582,0.686,1.045,0.37,0.565,0.392,0.544,0.834,0.594,0.488,0.447,0.772,0.516,0.856,0.785,0.585,0.529,0.428,0.798,0.581,0.358,0.351,0.467,0.624,0.651,0.299,0.505,0.6,0.716,0.37,0.379,0.486,0.877,0.311,0.883,0.398,0.582,0.456,0.496,0.441,0.507,0.448,0.783,0.558,0.401
-49,Epileptic,F,47.0,1.0,2.3,1.0,1.4,1.6,2.0,2.0,1.6,1.5,1.3,0.5,3.8,2.4,0.5,1.3,5.3,9.5,0.8,2.6,4.9,1.4,3.1,1.5,3.5,4.3,3.5,2.8,2.8,2.7,3.8,1.2,0.8,0.7,2.6,0.8,1.2,6.2,3.8,0.3,0.4,1.9,3.5,1.7,3.2,3.0,0.6,0.2,1.6,1.5,0.9,0.6,2.4,1.2,2.5,0.7,3.0,0.7,2.3,1.5,0.7,1.3,1.6,0.4,0.4,2.6,1.8,3.4,1.6,0.6,0.6,1.6,1.5,7.8,0.3,9,22,12,12,11,18,15,15,15,11,5.0,29,21,5,14,60,83,6,27,41,10,35,16,37,53,35,33,30,22,41,14,11,4,30,4,10,68,43,3,2,8,29,18,26,16,4,1,11,8,8,6,17,9,15,5,27,6,16,12,7,12,14,3,3,22,21,25,10,4,6,11,11,68,3,0.031,0.019,0.015,0.022,0.03,0.028,0.027,0.025,0.043,0.033,0.04,0.036,0.033,0.027,0.022,0.027,0.027,0.028,0.031,0.033,0.024,0.03,0.023,0.031,0.031,0.025,0.024,0.024,0.023,0.027,0.03,0.028,0.034,0.024,0.016,0.021,0.037,0.025,0.017,0.023,0.036,0.034,0.022,0.026,0.018,0.013,0.015,0.021,0.016,0.018,0.028,0.019,0.02,0.021,0.019,0.021,0.015,0.021,0.025,0.013,0.023,0.026,0.017,0.014,0.023,0.023,0.02,0.021,0.014,0.015,0.025,0.017,0.02,0.024,1833,4976,2511,2623,1884,3403,3260,3047,1877,1987,558.0,2995,4627,1130,3579,12092,18810,2186,4727,4877,2812,4956,2285,6550,7605,7256,5662,4983,6181,6426,1980,1671,747,6513,2570,2118,9572,9693,496,662,1261,3159,4755,2393,4084,1431,777,2753,2752,1731,638,4210,2082,4625,770,3978,1744,3143,1776,1268,2530,2284,403,968,3379,2391,4584,2348,1313,1126,1976,2928,14383,286,0.128,0.104,0.094,0.103,0.133,0.134,0.105,0.128,0.163,0.138,0.164,0.142,0.149,0.125,0.114,0.127,0.127,0.118,0.136,0.136,0.095,0.135,0.12,0.132,0.145,0.122,0.122,0.11,0.098,0.136,0.115,0.106,0.131,0.111,0.071,0.115,0.146,0.115,0.098,0.093,0.13,0.149,0.115,0.121,0.097,0.084,0.081,0.103,0.095,0.106,0.143,0.097,0.104,0.101,0.127,0.122,0.098,0.097,0.121,0.091,0.118,0.117,0.121,0.093,0.115,0.118,0.109,0.111,0.092,0.108,0.118,0.098,0.105,0.138,552,1870,1033,1079,778,1218,1082,1047,583,600,202.0,1589,1274,327,999,3455,5512,491,1355,2432,889,1761,1031,1872,2488,2369,2024,1827,1664,2295,661,648,303,1919,739,926,2907,2622,297,303,793,1617,1288,1819,2346,761,293,1147,1374,750,372,1952,1059,2034,443,2131,803,1512,945,783,1066,1033,264,534,1774,1321,2598,1140,616,606,1059,1463,6309,191,3.134,2.535,2.425,2.46,2.309,2.367,2.511,2.634,2.53,2.707,2.547,1.776,2.961,2.702,2.751,2.725,2.773,3.427,2.82,1.809,2.758,2.313,1.899,2.654,2.425,2.51,2.265,2.204,2.83,2.292,2.322,2.556,2.142,2.637,3.227,2.125,2.663,2.829,1.961,2.506,2.019,1.797,2.965,1.518,1.988,2.063,3.158,2.922,2.498,2.515,2.092,2.216,2.205,2.484,2.115,1.993,2.481,2.433,1.905,1.838,2.29,2.183,1.814,1.992,2.109,2.172,1.942,2.312,2.378,2.174,2.166,2.398,2.365,1.805,0.746,0.424,0.356,0.43,0.476,0.458,0.583,0.502,0.497,0.408,0.532,0.468,0.449,0.406,0.451,0.523,0.571,0.667,0.524,0.562,0.621,0.419,0.492,0.568,0.504,0.504,0.473,0.475,0.52,0.472,0.546,0.81,0.527,0.688,0.634,0.366,0.608,0.526,0.46,0.451,0.489,0.399,0.729,0.496,0.643,0.421,0.474,0.912,0.514,0.475,0.475,0.5,0.459,0.476,0.359,0.39,0.405,0.412,0.438,0.304,0.376,0.586,0.359,0.615,0.535,0.557,0.404,0.397,0.438,0.54,0.421,0.411,0.446,0.534
-50,Epileptic,F,52.0,0.6,1.3,0.9,0.9,1.4,1.5,1.5,1.2,0.7,0.2,0.1,2.3,1.1,0.5,1.1,4.1,6.9,0.5,1.8,3.8,0.5,1.9,0.9,2.4,2.3,2.5,2.4,2.2,1.6,2.8,1.0,0.6,0.5,1.8,0.2,0.3,1.9,1.9,0.2,0.1,0.8,2.0,1.9,2.4,1.9,0.5,0.4,0.6,0.9,0.4,0.6,1.6,1.1,1.8,0.3,1.8,0.6,1.6,0.9,0.8,0.3,0.9,0.5,0.5,1.7,0.8,1.9,0.7,0.8,0.3,0.7,0.7,2.5,0.2,5,15,8,8,15,14,10,11,8,3,1.0,17,12,6,9,47,60,3,21,34,4,19,10,22,29,25,30,20,14,30,15,6,6,23,1,2,23,25,2,1,5,17,22,18,11,4,2,4,5,4,5,13,7,16,2,14,5,11,8,6,4,6,3,4,13,7,12,4,4,3,4,6,16,2,0.038,0.022,0.019,0.024,0.037,0.036,0.026,0.029,0.036,0.031,0.016,0.042,0.03,0.035,0.041,0.034,0.029,0.025,0.03,0.038,0.012,0.033,0.026,0.039,0.036,0.028,0.027,0.031,0.023,0.03,0.04,0.049,0.025,0.025,0.012,0.011,0.037,0.031,0.029,0.02,0.019,0.037,0.044,0.038,0.019,0.015,0.023,0.014,0.014,0.017,0.035,0.025,0.025,0.02,0.018,0.017,0.026,0.021,0.029,0.023,0.023,0.022,0.039,0.035,0.021,0.027,0.025,0.019,0.015,0.024,0.026,0.018,0.017,0.015,1228,3599,2068,2002,1760,2204,1837,2262,1356,721,368.0,1247,2199,691,1679,7087,12031,1497,3647,3599,2008,2535,1298,4135,3877,4834,4252,2966,3776,4218,1591,894,1028,4596,1430,1403,4056,4535,261,226,1386,1680,3664,1716,2524,1037,708,1895,2064,1127,512,2172,1349,4277,448,3017,1091,2617,817,1008,536,1555,568,662,2153,744,2492,1272,1338,577,945,1722,5677,336,0.133,0.112,0.114,0.114,0.154,0.147,0.105,0.118,0.148,0.113,0.07,0.153,0.129,0.142,0.144,0.143,0.128,0.096,0.135,0.151,0.069,0.149,0.128,0.134,0.158,0.133,0.13,0.128,0.102,0.134,0.134,0.128,0.132,0.127,0.069,0.065,0.133,0.131,0.153,0.107,0.108,0.149,0.149,0.142,0.101,0.098,0.113,0.081,0.086,0.106,0.145,0.125,0.119,0.098,0.115,0.102,0.125,0.107,0.142,0.112,0.123,0.119,0.147,0.15,0.113,0.121,0.115,0.1,0.092,0.123,0.125,0.098,0.092,0.105,350,1083,684,697,617,745,779,746,351,182,117.0,789,636,189,471,2081,3668,293,956,1700,591,884,576,1076,1114,1452,1564,1048,1128,1543,476,290,347,1071,343,500,886,1031,115,109,640,841,783,1071,1588,557,306,816,946,343,314,966,707,1651,258,1642,369,1278,479,552,246,607,211,296,1181,368,1226,603,743,266,440,707,2373,189,3.253,2.952,2.744,2.908,2.504,2.568,2.0,2.715,2.825,3.225,2.731,1.492,2.741,2.652,2.736,2.552,2.627,3.48,3.11,1.871,2.656,2.358,1.973,2.843,2.733,2.772,2.184,2.227,2.556,2.289,2.554,2.942,2.643,2.976,3.349,2.505,3.168,2.991,2.623,2.323,2.543,1.895,3.055,1.885,1.806,2.033,2.887,2.907,2.623,2.883,1.871,2.188,2.123,2.43,2.224,2.041,3.071,2.431,1.834,1.979,2.333,2.617,2.563,2.411,2.048,2.535,2.164,2.547,2.206,2.417,2.611,2.934,2.556,2.352,0.815,0.788,0.762,0.571,0.653,0.472,0.511,0.542,0.489,0.405,0.731,0.382,0.434,0.413,0.482,0.528,0.576,0.858,0.65,0.674,0.682,0.385,0.407,0.623,0.511,0.573,0.453,0.514,0.483,0.504,0.621,1.014,0.568,0.686,0.708,0.442,0.803,0.625,0.383,0.309,0.701,0.52,0.805,0.75,0.484,0.579,0.487,0.852,0.506,0.916,0.418,0.437,0.48,0.547,0.29,0.425,0.803,0.558,0.335,0.387,0.33,0.603,0.477,0.817,0.565,0.631,0.463,0.381,0.367,0.545,0.547,0.816,0.512,0.46
-51,Epileptic,M,32.0,1.0,2.0,0.9,1.3,1.3,2.5,1.1,1.6,1.4,0.7,0.5,2.2,1.5,1.1,1.2,5.5,8.6,1.1,2.7,4.1,1.5,3.6,1.3,2.9,3.0,3.9,3.0,2.6,2.9,3.1,1.0,0.7,0.7,3.0,0.7,0.7,4.1,3.6,0.2,0.2,1.2,4.5,2.2,3.0,3.4,0.7,0.8,1.0,1.4,0.5,0.5,2.2,1.5,2.7,0.5,2.3,1.0,2.4,2.2,1.2,0.9,1.5,0.3,0.4,1.7,1.2,3.4,1.5,1.4,0.7,1.4,1.5,5.0,0.4,6,14,8,12,10,17,9,16,14,7,4.0,19,12,13,14,55,65,10,27,33,13,30,10,35,31,34,29,25,21,28,12,6,6,29,3,6,37,36,2,1,7,32,16,23,21,5,4,7,7,4,5,17,10,16,3,19,8,21,15,8,7,10,2,4,15,13,23,10,11,7,9,12,38,4,0.036,0.021,0.017,0.024,0.027,0.042,0.018,0.023,0.04,0.029,0.034,0.032,0.025,0.052,0.021,0.029,0.027,0.028,0.03,0.032,0.027,0.028,0.026,0.033,0.025,0.024,0.026,0.022,0.023,0.026,0.03,0.02,0.022,0.027,0.02,0.017,0.037,0.028,0.012,0.014,0.021,0.04,0.038,0.029,0.02,0.017,0.021,0.017,0.018,0.015,0.035,0.019,0.02,0.018,0.017,0.017,0.018,0.02,0.037,0.022,0.028,0.025,0.026,0.023,0.022,0.025,0.022,0.017,0.019,0.023,0.022,0.026,0.017,0.019,1640,4092,2259,2257,1580,2142,2297,3485,1714,1469,661.0,2268,3055,1458,2852,10888,18423,2939,4568,3272,3336,4456,2042,5042,5966,7887,4865,5419,6142,5406,1711,1139,1203,7529,2394,2200,7294,7630,418,457,1981,3445,5840,2399,4088,1172,1176,2673,2341,1392,427,3856,1786,5501,740,4033,1799,3221,1433,1561,1215,2312,471,704,2332,1321,4697,2887,2197,1339,1761,2579,10124,491,0.12,0.106,0.096,0.109,0.12,0.144,0.088,0.112,0.132,0.111,0.12,0.132,0.112,0.158,0.123,0.121,0.113,0.104,0.125,0.127,0.097,0.12,0.123,0.123,0.112,0.112,0.115,0.096,0.089,0.117,0.11,0.077,0.105,0.117,0.081,0.086,0.138,0.119,0.087,0.091,0.107,0.14,0.134,0.12,0.106,0.101,0.113,0.09,0.097,0.093,0.146,0.101,0.102,0.095,0.118,0.103,0.107,0.107,0.132,0.109,0.113,0.113,0.14,0.125,0.117,0.111,0.103,0.101,0.106,0.12,0.106,0.113,0.097,0.112,473,1378,878,948,803,969,783,1103,624,370,234.0,1101,887,365,884,3192,5044,624,1315,1976,909,1789,802,1479,1870,2484,1859,1782,1804,1819,541,577,458,1561,568,729,1835,1961,284,255,813,1675,1027,1511,2513,612,548,1082,1166,599,284,1879,1118,2351,396,2082,887,1822,987,816,599,924,199,408,1294,774,2305,1321,1159,509,954,1189,4567,320,3.281,2.649,2.528,2.435,1.871,2.272,2.422,2.773,2.258,3.198,2.611,1.86,2.616,2.811,2.604,2.534,2.943,3.552,2.784,1.606,2.977,2.134,2.199,2.584,2.466,2.698,2.171,2.381,2.709,2.436,2.422,1.97,2.193,3.32,3.588,2.735,2.935,2.818,1.585,2.118,2.957,1.901,3.745,1.75,1.825,2.145,2.806,3.041,2.401,2.66,1.739,2.086,1.8,2.474,2.405,2.174,2.456,2.028,1.632,2.094,2.233,2.605,2.485,2.037,2.048,1.984,2.177,2.435,2.167,2.691,2.024,2.501,2.445,1.77,0.886,0.872,0.619,0.543,0.47,0.596,0.614,0.513,0.425,0.527,0.66,0.481,0.447,0.539,0.439,0.582,0.627,0.838,0.764,0.431,0.836,0.554,0.497,0.735,0.551,0.719,0.513,0.493,0.593,0.525,0.63,0.825,0.439,0.687,0.724,0.466,0.674,0.588,0.338,0.471,0.773,0.545,0.787,0.572,0.534,0.518,0.628,0.871,0.568,0.518,0.422,0.52,0.575,0.604,0.469,0.621,0.582,0.57,0.326,0.418,0.588,0.668,0.774,0.896,0.613,0.734,0.536,0.4,0.413,0.728,0.441,0.526,0.52,0.451
-53,Epileptic,M,42.0,1.2,2.3,1.0,1.2,1.8,2.5,1.6,1.6,1.7,1.2,0.5,2.5,2.0,0.7,1.4,6.1,7.6,0.7,2.3,4.7,2.2,3.9,2.1,3.2,8.8,4.7,3.9,3.2,3.0,3.8,1.5,2.5,0.6,2.2,0.8,0.5,4.9,4.2,0.3,0.3,0.8,3.8,2.1,2.4,1.9,0.6,0.4,0.8,1.7,0.5,0.6,2.4,1.6,1.7,0.3,2.8,1.0,2.0,1.8,1.3,0.6,1.4,0.4,0.6,1.8,1.4,3.1,1.1,0.8,1.2,1.3,2.4,6.8,0.4,9,20,7,7,12,24,15,17,12,10,4.0,22,22,7,15,73,69,7,23,38,18,40,20,37,68,52,37,29,26,34,19,22,6,26,6,5,43,42,2,2,4,30,22,16,12,4,2,5,8,4,6,17,11,11,2,19,7,14,14,12,7,12,3,4,12,13,26,7,4,9,9,16,46,3,0.041,0.028,0.018,0.024,0.031,0.037,0.028,0.027,0.049,0.043,0.036,0.03,0.031,0.034,0.03,0.035,0.031,0.032,0.034,0.042,0.034,0.035,0.031,0.038,0.054,0.039,0.031,0.033,0.03,0.034,0.048,0.071,0.038,0.04,0.035,0.014,0.043,0.037,0.015,0.021,0.019,0.038,0.043,0.027,0.014,0.012,0.018,0.013,0.021,0.015,0.029,0.025,0.022,0.019,0.021,0.017,0.025,0.023,0.026,0.014,0.024,0.027,0.02,0.023,0.021,0.025,0.02,0.017,0.016,0.032,0.024,0.026,0.023,0.024,1699,4623,2327,1954,2761,3388,3058,3135,2398,2131,887.0,2836,4499,1482,2951,12563,17243,2227,4376,4513,5177,5049,2272,6451,6082,7506,6068,4322,5921,6071,3373,1373,997,5468,1638,1568,8129,8559,497,414,1217,3009,6065,2275,3530,1331,935,2298,2633,1559,624,3481,2288,3221,536,4129,1891,2948,1755,2097,1014,2624,599,1051,2385,1420,4524,2238,1571,2356,1895,3251,10541,557,0.136,0.124,0.102,0.112,0.135,0.118,0.107,0.133,0.169,0.155,0.147,0.131,0.134,0.139,0.133,0.137,0.131,0.122,0.129,0.158,0.129,0.112,0.124,0.146,0.152,0.132,0.105,0.103,0.101,0.138,0.16,0.179,0.118,0.149,0.121,0.081,0.156,0.148,0.099,0.114,0.096,0.146,0.154,0.126,0.086,0.086,0.103,0.081,0.103,0.088,0.143,0.115,0.115,0.097,0.107,0.096,0.124,0.118,0.125,0.096,0.118,0.124,0.11,0.119,0.108,0.108,0.109,0.093,0.091,0.131,0.123,0.124,0.112,0.125,422,1411,838,743,950,1226,923,1023,576,496,205.0,1338,1208,356,767,3368,4325,453,1321,1979,1099,1818,1088,1670,2344,2149,2078,1530,1455,1812,692,597,275,1201,413,595,1974,2027,270,207,628,1542,1035,1426,2046,709,339,889,1125,605,344,1550,1094,1425,227,2380,686,1395,1031,1335,560,939,337,423,1258,866,2300,952,727,664,906,1386,4584,267,3.642,2.999,2.919,2.681,2.501,2.393,2.566,2.673,3.069,3.196,2.928,1.847,2.873,2.941,2.831,2.772,3.094,3.837,2.646,1.964,3.117,2.165,1.855,2.89,2.205,2.838,2.326,2.265,3.011,2.539,3.293,2.615,2.722,3.075,3.178,2.358,3.023,3.054,2.104,2.318,2.345,1.765,3.451,1.828,1.966,2.161,3.496,3.373,2.788,2.795,1.937,2.356,2.211,2.517,2.284,1.941,2.789,2.422,1.927,1.765,2.176,2.733,1.89,2.616,2.035,2.024,2.093,2.579,2.591,3.357,2.304,2.552,2.465,2.706,0.861,0.694,0.607,0.577,0.853,0.707,0.704,0.532,0.595,0.633,0.917,0.535,0.528,0.465,0.621,0.653,0.627,0.824,0.657,0.607,0.862,0.648,0.622,0.7,0.719,0.61,0.566,0.614,0.493,0.668,0.759,0.816,0.429,0.774,0.789,0.528,0.76,0.71,0.511,0.476,0.536,0.589,1.052,0.688,0.635,0.519,0.659,0.851,0.705,0.448,0.584,0.482,0.553,0.533,0.764,0.466,0.517,0.559,0.528,0.478,0.572,0.793,0.466,0.738,0.713,0.634,0.535,0.463,0.507,0.907,0.634,0.523,0.616,0.369
-54,Epileptic,M,52.0,1.0,2.2,1.0,1.3,1.8,2.4,1.7,2.2,1.6,0.4,0.3,3.3,2.0,0.5,1.8,7.5,10.1,1.1,2.5,6.1,0.9,3.2,1.9,2.8,2.8,3.2,3.7,2.3,2.9,3.3,1.0,1.8,0.7,2.7,0.5,0.7,2.7,2.9,0.3,0.2,1.2,2.9,2.3,4.0,3.3,0.6,0.7,1.1,1.6,0.6,0.5,2.3,1.6,3.2,0.4,2.0,0.9,2.3,1.2,0.9,0.5,1.5,0.3,0.3,3.1,0.7,2.3,2.0,1.0,0.8,0.8,1.1,4.2,0.3,6,18,7,7,16,22,14,18,14,4,3.0,31,18,5,17,64,88,8,24,50,6,37,18,26,34,33,40,22,20,37,12,13,7,26,4,6,30,29,2,2,8,32,25,30,19,4,3,8,9,6,4,16,11,21,4,18,8,17,11,8,4,10,2,2,24,4,22,18,6,9,6,9,32,4,0.038,0.024,0.015,0.022,0.043,0.029,0.023,0.028,0.043,0.026,0.026,0.031,0.026,0.027,0.031,0.035,0.027,0.031,0.03,0.039,0.015,0.032,0.027,0.034,0.029,0.025,0.025,0.021,0.023,0.03,0.035,0.064,0.017,0.028,0.018,0.015,0.033,0.031,0.016,0.015,0.022,0.029,0.041,0.028,0.02,0.013,0.029,0.015,0.019,0.013,0.019,0.023,0.026,0.022,0.016,0.016,0.026,0.023,0.026,0.019,0.016,0.024,0.017,0.016,0.025,0.022,0.017,0.02,0.016,0.025,0.019,0.018,0.019,0.012,1345,3852,1690,2113,1995,3761,2864,3846,2104,957,933.0,3283,3932,998,3054,10129,18670,2530,4391,5179,3008,4798,2522,5636,4834,6301,6245,4190,5464,5415,1959,1449,1529,5527,1806,2076,5965,6180,439,502,1856,3648,6511,3903,4023,1302,739,2607,2626,1728,606,3175,2096,5401,629,3507,1158,3216,1443,1437,974,2244,304,942,3697,968,3883,3286,1546,1177,1524,1766,8311,699,0.132,0.103,0.094,0.102,0.129,0.136,0.09,0.125,0.142,0.113,0.11,0.139,0.119,0.121,0.133,0.127,0.115,0.13,0.126,0.144,0.073,0.148,0.127,0.121,0.133,0.115,0.119,0.103,0.095,0.141,0.126,0.142,0.091,0.123,0.08,0.089,0.129,0.126,0.093,0.098,0.107,0.136,0.142,0.123,0.101,0.084,0.119,0.09,0.102,0.092,0.108,0.111,0.123,0.102,0.122,0.101,0.121,0.113,0.122,0.103,0.103,0.119,0.106,0.098,0.124,0.082,0.095,0.108,0.095,0.119,0.102,0.112,0.101,0.091,455,1565,893,840,765,1404,1045,1353,634,295,256.0,1645,1321,300,930,3312,6080,553,1398,2533,842,1657,1106,1488,1611,2167,2365,1536,1804,1771,498,535,581,1544,502,760,1572,1591,276,274,902,1700,1104,2333,2556,722,376,1135,1278,699,380,1687,1026,2435,357,1886,611,1576,874,794,497,866,227,333,1963,413,2274,1583,866,537,728,943,3635,444,2.921,2.375,2.14,2.618,2.179,2.444,2.301,2.653,2.43,2.854,2.868,1.829,2.516,2.425,2.582,2.391,2.582,3.513,2.603,1.827,2.707,2.376,2.047,2.833,2.502,2.446,2.206,2.159,2.429,2.565,2.717,2.729,2.143,2.59,3.096,2.569,2.825,2.855,1.913,2.089,2.618,1.95,3.642,1.94,1.788,2.047,2.442,2.838,2.496,2.617,1.864,1.975,2.218,2.32,2.177,2.051,2.385,2.252,1.961,2.041,2.33,2.479,1.558,2.928,2.006,2.536,1.839,2.151,2.103,2.316,2.293,2.42,2.461,1.895,0.744,0.557,0.497,0.438,0.701,0.487,0.61,0.668,0.608,0.377,0.688,0.485,0.51,0.431,0.55,0.574,0.636,0.799,0.592,0.511,0.764,0.439,0.507,0.764,0.51,0.503,0.42,0.528,0.518,0.509,0.843,1.002,0.413,0.634,0.988,0.596,0.674,0.574,0.311,0.4,0.431,0.421,0.804,0.551,0.504,0.484,0.62,0.7,0.548,0.457,0.301,0.457,0.532,0.57,0.341,0.413,0.511,0.528,0.379,0.371,0.359,0.853,0.365,1.11,0.442,0.751,0.419,0.413,0.421,0.624,0.48,0.482,0.45,0.33
-55,Epileptic,M,37.0,1.8,2.4,0.7,0.9,1.5,2.8,1.2,1.8,1.9,0.7,0.2,2.9,1.2,0.7,1.2,6.2,7.6,0.9,2.3,6.0,1.5,3.3,2.1,3.0,2.6,3.6,3.9,1.9,4.0,3.5,0.9,0.8,0.8,2.5,0.8,0.5,4.2,3.1,0.2,0.2,1.0,3.3,2.9,3.5,2.7,0.5,0.4,1.0,1.2,0.9,0.5,2.5,1.6,2.0,0.3,2.2,0.8,1.4,1.6,1.2,0.5,1.6,0.4,0.4,1.9,0.9,2.6,2.0,0.9,0.5,1.5,1.7,3.6,0.4,10,20,13,9,13,26,10,17,15,6,3.0,25,12,8,11,59,62,7,25,42,10,31,14,28,27,35,38,19,30,36,11,88,8,23,3,3,37,30,2,2,6,28,26,26,17,4,2,6,6,8,2,19,12,12,2,17,6,14,10,11,6,9,2,3,14,8,19,16,5,3,10,11,29,4,0.063,0.025,0.013,0.018,0.029,0.034,0.02,0.028,0.049,0.034,0.023,0.034,0.025,0.034,0.036,0.034,0.029,0.031,0.038,0.043,0.025,0.034,0.033,0.037,0.029,0.027,0.032,0.021,0.029,0.033,0.039,0.047,0.026,0.031,0.039,0.014,0.04,0.033,0.013,0.015,0.021,0.035,0.048,0.029,0.019,0.009,0.023,0.014,0.017,0.019,0.025,0.028,0.026,0.021,0.025,0.016,0.029,0.02,0.032,0.02,0.02,0.03,0.021,0.03,0.025,0.041,0.016,0.021,0.019,0.02,0.029,0.02,0.014,0.019,1580,4680,1822,1988,2257,4002,2389,3294,2040,1333,604.0,2814,3023,1414,2223,10554,15608,2446,3527,4208,2656,5049,2385,5452,4634,7343,5836,3487,6572,6137,2376,910,1469,6726,1721,1795,6631,6122,355,359,1633,2932,5448,2654,3432,1413,620,2722,2356,1839,470,3342,2312,3971,468,3622,1082,2396,1454,1701,814,2109,590,727,2437,605,4579,3331,1555,864,1683,2201,8944,484,0.152,0.112,0.098,0.096,0.125,0.144,0.086,0.124,0.153,0.137,0.107,0.139,0.118,0.137,0.137,0.139,0.121,0.115,0.148,0.143,0.105,0.132,0.12,0.138,0.128,0.127,0.12,0.093,0.105,0.14,0.126,0.139,0.119,0.124,0.116,0.074,0.132,0.127,0.098,0.103,0.106,0.145,0.142,0.127,0.098,0.077,0.116,0.081,0.094,0.11,0.114,0.125,0.116,0.101,0.121,0.097,0.124,0.104,0.132,0.117,0.109,0.117,0.107,0.128,0.116,0.15,0.09,0.107,0.095,0.124,0.133,0.115,0.088,0.111,414,1574,896,787,837,1278,841,1140,645,363,202.0,1365,830,358,606,3034,4244,478,1124,2271,906,1617,938,1455,1440,2095,1983,1372,1853,1850,533,394,459,1303,365,619,1787,1538,223,222,761,1483,1244,1870,2176,800,282,1070,1065,795,275,1455,1160,1490,202,2018,521,1306,778,913,469,859,272,255,1218,340,2459,1509,714,367,805,1171,3922,308,3.48,2.735,2.155,2.557,2.307,2.585,2.308,2.659,2.475,2.892,2.627,1.842,2.772,2.805,2.688,2.662,2.915,3.862,2.606,1.736,2.545,2.5,2.186,2.822,2.549,2.885,2.288,2.063,2.74,2.557,3.11,2.324,2.558,3.349,3.807,2.592,2.831,2.958,1.834,1.884,2.635,1.882,2.941,1.629,1.766,1.902,2.861,2.909,2.649,2.548,2.083,2.186,2.011,2.564,2.724,1.99,2.322,2.077,2.145,2.031,1.948,2.359,2.082,3.04,2.105,2.099,2.011,2.405,2.497,2.641,2.378,2.395,2.375,1.93,0.768,0.704,0.441,0.621,0.651,0.634,0.71,0.53,0.584,0.401,0.66,0.549,0.485,0.348,0.573,0.64,0.633,0.784,0.71,0.547,0.753,0.571,0.528,0.65,0.524,0.662,0.549,0.523,0.629,0.55,0.702,0.993,0.472,0.705,0.869,0.471,0.753,0.681,0.321,0.38,0.767,0.514,0.966,0.477,0.52,0.495,0.668,0.809,0.569,0.571,0.464,0.554,0.533,0.722,0.688,0.399,0.565,0.589,0.447,0.49,0.484,0.693,0.993,1.096,0.537,0.57,0.417,0.505,0.531,0.929,0.551,0.642,0.518,0.406
-56,Epileptic,F,32.0,1.1,2.8,1.3,1.2,2.0,2.0,2.0,1.7,2.0,0.8,0.5,3.9,1.4,0.6,1.0,6.1,9.4,1.1,2.6,5.7,1.2,2.9,2.4,3.3,3.6,3.8,4.1,2.9,2.8,3.8,2.9,3.0,0.5,2.3,0.6,0.7,3.4,3.4,0.2,0.2,1.6,3.1,2.0,4.3,3.3,0.8,0.4,1.0,1.6,0.9,0.9,1.5,1.4,3.5,0.3,3.0,0.8,2.1,1.5,1.1,0.3,2.0,0.2,0.6,1.7,1.9,2.7,1.7,0.9,1.5,1.3,1.3,4.4,0.5,9,27,13,7,19,19,16,19,15,10,5.0,34,17,6,11,63,84,9,30,54,8,38,17,35,45,44,49,29,24,45,34,50,4,27,3,7,43,43,2,1,8,28,18,34,19,6,2,5,7,5,7,12,11,25,2,25,6,26,9,8,3,18,2,6,12,22,19,11,5,11,8,9,35,5,0.044,0.026,0.018,0.02,0.041,0.023,0.032,0.031,0.05,0.036,0.03,0.039,0.03,0.031,0.029,0.029,0.029,0.033,0.03,0.038,0.021,0.029,0.038,0.033,0.03,0.026,0.032,0.031,0.026,0.029,0.05,0.063,0.023,0.025,0.015,0.015,0.028,0.03,0.015,0.023,0.028,0.032,0.031,0.034,0.023,0.017,0.018,0.012,0.02,0.022,0.032,0.017,0.027,0.022,0.028,0.019,0.018,0.021,0.028,0.016,0.016,0.026,0.016,0.019,0.021,0.029,0.021,0.019,0.015,0.032,0.02,0.017,0.016,0.018,1819,4541,3041,2570,2433,3332,2839,3451,1915,1604,1074.0,3392,3173,1383,2127,10576,17471,2410,4900,5710,4186,5134,2422,5856,6464,8216,6250,4245,6046,6991,2543,2061,1052,6077,2157,1912,8181,7942,362,284,1563,3039,5329,3561,3829,1440,785,2286,2227,1564,906,2909,1903,5533,400,4838,1582,3639,1397,1765,823,3084,373,899,2326,2221,3831,3041,1714,1288,2088,2411,9574,732,0.136,0.124,0.108,0.096,0.151,0.12,0.115,0.127,0.159,0.14,0.134,0.148,0.139,0.137,0.131,0.131,0.127,0.131,0.137,0.145,0.091,0.124,0.13,0.133,0.131,0.133,0.125,0.105,0.099,0.127,0.161,0.147,0.097,0.124,0.079,0.094,0.127,0.138,0.108,0.107,0.115,0.125,0.125,0.132,0.107,0.098,0.097,0.078,0.102,0.108,0.134,0.1,0.124,0.109,0.135,0.103,0.101,0.124,0.119,0.1,0.084,0.124,0.108,0.115,0.11,0.125,0.108,0.105,0.088,0.135,0.1,0.102,0.094,0.123,519,1775,1155,905,893,1309,999,1057,603,421,281.0,1513,880,312,642,3341,5297,551,1368,2568,862,1642,962,1723,1960,2416,2165,1473,1632,2264,885,728,356,1472,506,731,2118,1989,196,159,888,1528,1161,2183,2240,711,323,1070,1112,652,467,1353,867,2515,195,2543,729,1641,889,958,414,1242,213,485,1181,1051,1984,1360,831,624,984,1120,4238,357,3.375,2.428,2.566,2.705,2.342,2.159,2.362,2.709,2.61,2.977,3.065,1.943,2.781,3.028,2.549,2.503,2.66,3.582,2.824,1.92,3.232,2.384,2.078,2.577,2.599,2.704,2.33,2.223,2.753,2.481,2.284,2.784,2.435,2.946,3.752,2.379,2.883,2.895,2.226,2.239,2.176,1.79,3.458,1.838,1.94,2.211,2.954,2.537,2.397,3.065,2.111,2.227,2.224,2.229,2.304,2.081,2.349,2.352,1.771,1.996,2.157,2.325,1.89,2.192,2.094,2.35,2.109,2.573,2.284,2.135,2.301,2.475,2.407,2.335,0.778,0.546,0.393,0.5,0.634,0.621,0.514,0.632,0.54,0.403,0.574,0.513,0.407,0.428,0.462,0.503,0.556,0.714,0.632,0.608,0.861,0.608,0.565,0.727,0.581,0.557,0.499,0.592,0.448,0.516,0.597,1.028,0.505,0.533,0.75,0.401,0.696,0.606,0.486,0.51,0.494,0.503,0.655,0.559,0.547,0.472,0.472,0.506,0.425,0.421,0.42,0.501,0.466,0.469,0.438,0.381,0.425,0.481,0.349,0.365,0.396,0.568,0.563,0.708,0.355,0.775,0.478,0.445,0.419,0.517,0.416,0.468,0.468,0.359
-57,Epileptic,M,42.0,1.8,2.7,0.8,1.3,1.5,1.8,1.7,2.5,1.6,0.8,0.2,3.6,1.5,0.6,1.8,7.0,10.2,0.7,3.1,5.0,1.9,3.2,1.9,3.3,3.3,3.3,3.6,3.4,3.9,2.8,1.5,1.2,0.6,4.3,0.6,1.0,3.8,4.9,0.3,0.1,1.1,2.9,2.5,3.1,3.7,0.5,0.4,1.0,1.3,0.8,0.8,2.6,1.3,3.1,0.5,2.7,1.1,1.4,0.7,0.7,0.9,1.1,0.3,0.4,2.6,1.3,3.9,1.2,1.6,1.1,1.3,1.2,5.6,0.3,12,26,9,13,11,15,13,22,9,11,3.0,30,13,8,14,67,86,6,31,46,17,29,17,35,37,32,38,32,30,33,12,9,5,36,4,8,35,50,1,1,6,29,26,24,22,5,2,8,7,6,6,15,6,21,4,23,8,12,6,6,7,7,3,3,19,18,29,8,10,11,11,10,55,3,0.065,0.028,0.016,0.02,0.041,0.029,0.032,0.036,0.054,0.033,0.024,0.036,0.035,0.039,0.036,0.044,0.039,0.025,0.04,0.04,0.029,0.036,0.033,0.047,0.034,0.033,0.034,0.035,0.037,0.027,0.034,0.052,0.036,0.039,0.021,0.024,0.04,0.037,0.014,0.011,0.022,0.036,0.045,0.029,0.028,0.011,0.023,0.019,0.015,0.018,0.032,0.025,0.023,0.026,0.017,0.018,0.022,0.016,0.025,0.016,0.024,0.026,0.024,0.017,0.024,0.022,0.022,0.019,0.026,0.038,0.026,0.017,0.019,0.018,1578,4630,1889,2419,1548,3192,2356,4068,1534,1623,550.0,3158,3526,1173,2998,12917,17392,2080,5412,5295,4155,4423,1848,5824,5889,6813,5596,4483,6469,5686,2481,1254,898,7851,2056,2051,6909,9280,474,467,1606,3020,5860,2750,3399,1471,789,2374,2733,1649,560,3886,1717,5463,565,4166,1635,3042,938,1258,1355,2223,524,773,3165,1081,4877,2064,2275,1356,1846,2200,11733,727,0.152,0.133,0.101,0.114,0.139,0.135,0.117,0.146,0.155,0.135,0.114,0.143,0.123,0.156,0.131,0.144,0.136,0.113,0.155,0.154,0.118,0.145,0.144,0.149,0.136,0.127,0.133,0.128,0.127,0.126,0.124,0.118,0.138,0.144,0.093,0.104,0.141,0.137,0.09,0.073,0.107,0.142,0.155,0.126,0.119,0.082,0.098,0.094,0.087,0.104,0.139,0.109,0.107,0.105,0.11,0.11,0.111,0.098,0.118,0.106,0.118,0.111,0.125,0.106,0.117,0.121,0.113,0.101,0.109,0.148,0.125,0.112,0.102,0.1,453,1585,822,969,679,1053,880,1282,475,462,182.0,1610,825,295,839,3115,4723,450,1450,2284,1003,1464,864,1497,1877,1888,1867,1735,1813,1773,673,481,320,1725,471,747,1701,2291,269,185,767,1342,1235,1726,2151,732,309,907,1225,713,374,1698,850,2164,343,2140,717,1356,484,681,621,694,222,395,1578,833,2605,969,1013,508,821,1084,4923,329,3.481,2.657,2.285,2.479,2.21,2.583,2.315,2.834,2.553,2.806,2.496,1.81,3.074,2.832,2.638,2.93,2.93,3.599,2.919,2.065,3.089,2.492,1.944,3.048,2.48,2.786,2.356,2.146,2.708,2.632,2.631,2.466,2.273,3.218,3.56,2.58,2.984,2.93,2.057,2.332,2.576,1.982,3.235,1.805,1.831,2.151,3.039,3.069,2.677,2.586,1.882,2.378,2.291,2.375,1.993,2.111,2.586,2.631,2.057,2.144,2.487,3.081,2.522,2.315,2.177,1.615,2.077,2.494,2.474,2.695,2.53,2.329,2.5,2.615,0.774,0.592,0.544,0.469,0.455,0.522,0.533,0.571,0.601,0.889,0.91,0.526,0.491,0.545,0.65,0.82,0.622,0.663,0.582,0.679,0.861,0.687,0.412,0.845,0.567,0.714,0.585,0.515,0.602,0.539,0.769,0.877,0.382,0.761,0.768,0.602,0.717,0.608,0.341,0.551,0.634,0.583,0.818,0.594,0.483,0.407,0.457,0.817,0.716,0.509,0.474,0.509,0.482,0.603,0.415,0.414,0.507,0.596,0.384,0.387,0.327,1.048,0.511,0.98,0.544,0.496,0.479,0.414,0.391,0.565,0.489,0.512,0.51,0.538
-58,Epileptic,M,32.0,1.2,2.6,0.8,0.8,1.1,2.6,1.7,1.9,1.0,0.9,0.3,2.9,1.5,0.5,1.3,7.0,9.5,0.8,2.8,3.7,1.0,2.8,2.1,3.0,3.6,4.4,2.5,4.4,3.2,3.3,1.1,2.1,0.5,2.9,0.5,1.0,3.9,4.0,0.1,0.2,1.5,3.4,2.2,2.7,3.8,0.6,0.6,0.7,1.1,0.8,0.8,1.9,1.0,3.2,0.4,1.5,0.9,1.7,0.7,1.1,1.3,1.2,0.6,0.2,2.4,0.8,3.0,1.6,1.2,0.6,1.0,1.4,5.8,0.4,8,25,6,6,11,24,17,19,16,6,2.0,31,17,8,14,82,89,7,30,41,12,42,20,38,49,46,33,42,28,44,13,20,6,33,4,7,43,46,1,1,11,31,24,21,20,3,3,5,6,6,6,13,6,22,3,13,5,14,6,8,13,9,4,3,19,6,21,12,6,6,6,11,60,3,0.039,0.029,0.012,0.019,0.031,0.03,0.028,0.029,0.041,0.036,0.026,0.035,0.03,0.038,0.033,0.038,0.032,0.032,0.029,0.033,0.021,0.034,0.032,0.034,0.037,0.038,0.033,0.033,0.029,0.036,0.034,0.07,0.025,0.033,0.018,0.016,0.038,0.035,0.017,0.017,0.024,0.036,0.03,0.03,0.023,0.014,0.021,0.011,0.015,0.024,0.031,0.019,0.021,0.025,0.014,0.016,0.02,0.016,0.017,0.02,0.031,0.023,0.034,0.017,0.022,0.022,0.025,0.019,0.017,0.02,0.027,0.021,0.023,0.013,1512,5525,2027,1890,1344,4168,2970,3894,1638,1342,776.0,3154,3430,1160,2787,12790,19507,2297,5862,5287,3527,4640,2895,5837,6288,7319,3604,6031,6705,5530,2210,1141,1288,6523,2208,2581,6264,7046,313,479,2177,3742,5119,3009,4539,1294,879,2392,2273,1380,788,3554,1682,6175,563,2570,1352,3621,1143,1525,1505,2264,630,834,3459,1174,3451,3058,1998,1228,1218,2439,9976,775,0.145,0.124,0.092,0.098,0.132,0.134,0.107,0.132,0.139,0.158,0.102,0.148,0.126,0.177,0.141,0.146,0.131,0.108,0.134,0.145,0.086,0.144,0.14,0.142,0.146,0.142,0.137,0.131,0.117,0.137,0.115,0.166,0.101,0.132,0.09,0.088,0.144,0.138,0.106,0.089,0.121,0.153,0.12,0.134,0.106,0.091,0.116,0.082,0.089,0.109,0.14,0.1,0.106,0.106,0.11,0.107,0.107,0.097,0.105,0.112,0.137,0.115,0.148,0.121,0.116,0.096,0.115,0.106,0.096,0.115,0.123,0.111,0.119,0.097,460,1610,835,698,560,1462,992,1152,462,327,192.0,1378,1002,273,728,3523,5204,504,1529,1935,906,1561,1129,1510,1903,2151,1389,2107,1857,1701,540,462,415,1594,541,922,1767,2030,165,212,942,1516,1323,1581,2533,598,392,991,1069,658,446,1554,749,2191,319,1358,605,1704,676,827,685,808,289,322,1644,498,1826,1215,929,457,616,1171,4255,412,3.269,2.851,2.375,2.677,2.215,2.443,2.502,2.827,2.576,3.27,3.287,1.97,2.775,2.929,2.901,2.72,2.905,3.549,3.051,2.26,2.914,2.319,2.247,2.846,2.563,2.667,2.224,2.243,2.78,2.536,2.78,2.587,2.424,3.04,3.583,2.666,2.758,2.64,2.111,2.686,2.666,2.156,2.865,2.211,2.033,2.436,2.639,3.036,2.592,2.627,2.147,2.384,2.251,2.678,2.008,2.097,2.447,2.401,1.981,2.093,2.168,2.43,2.36,2.196,2.225,2.407,2.074,2.675,2.471,2.93,2.317,2.464,2.393,2.434,0.721,0.765,0.686,0.517,0.587,0.605,0.549,0.588,0.5,0.488,0.774,0.414,0.467,0.513,0.464,0.545,0.662,0.786,0.494,0.636,0.756,0.48,0.381,0.62,0.524,0.599,0.648,0.564,0.459,0.578,0.819,1.074,0.353,0.622,0.664,0.7,0.518,0.62,0.363,0.362,0.625,0.552,0.796,0.62,0.505,0.504,0.476,0.642,0.57,0.453,0.378,0.516,0.594,0.569,0.494,0.401,0.409,0.443,0.261,0.399,0.419,0.753,0.522,0.959,0.498,0.84,0.548,0.439,0.434,0.995,0.38,0.509,0.495,0.245
-59,Epileptic,F,42.0,0.9,2.6,0.8,1.4,1.5,2.6,1.3,1.9,1.0,0.8,0.3,2.3,1.5,0.6,1.0,5.2,8.9,0.6,3.7,3.9,1.4,2.3,1.8,3.9,3.3,3.6,4.3,2.6,2.9,3.7,1.5,1.5,0.4,2.3,0.5,1.1,4.5,3.7,0.2,0.1,1.3,3.4,2.4,3.0,3.3,0.5,0.6,1.3,1.0,1.1,0.7,1.6,0.7,3.3,0.4,2.5,1.3,1.8,1.0,0.9,0.4,1.4,0.4,0.7,2.4,0.5,3.2,1.3,1.1,0.5,1.4,1.4,4.8,0.3,6,20,8,11,15,16,10,15,11,7,3.0,19,15,7,9,46,67,4,24,28,40,22,16,40,32,32,38,22,20,34,24,11,5,25,3,8,45,33,1,1,7,26,21,22,21,4,3,8,5,11,6,10,4,23,4,20,11,16,9,7,4,12,3,4,18,7,21,9,9,4,10,12,35,3,0.034,0.029,0.016,0.024,0.032,0.038,0.021,0.03,0.039,0.027,0.035,0.031,0.033,0.036,0.023,0.029,0.029,0.021,0.044,0.034,0.019,0.026,0.031,0.039,0.031,0.03,0.03,0.025,0.025,0.027,0.044,0.063,0.026,0.027,0.017,0.022,0.034,0.028,0.017,0.016,0.027,0.038,0.036,0.031,0.024,0.011,0.034,0.019,0.015,0.016,0.037,0.018,0.015,0.023,0.019,0.023,0.035,0.022,0.027,0.018,0.019,0.024,0.03,0.024,0.03,0.021,0.023,0.018,0.019,0.016,0.024,0.024,0.02,0.017,1535,4689,2274,2123,1989,2292,2207,3260,1601,1239,422.0,2256,2429,904,1912,8620,15802,1780,2594,2877,3513,4149,2396,5649,5523,6633,5779,3417,4856,5285,2082,1041,822,5049,1823,1836,6616,5709,373,367,1676,2802,4634,2080,3180,1351,717,2237,2174,2400,483,3147,1412,4982,626,3189,1224,2439,1140,1396,998,2014,431,741,2494,665,3865,2145,1629,1346,1696,2144,8411,448,0.123,0.119,0.095,0.111,0.132,0.138,0.09,0.136,0.143,0.116,0.132,0.127,0.139,0.154,0.126,0.119,0.119,0.09,0.143,0.128,0.095,0.123,0.13,0.145,0.133,0.125,0.127,0.108,0.096,0.118,0.14,0.154,0.145,0.122,0.081,0.1,0.13,0.118,0.106,0.098,0.117,0.147,0.144,0.14,0.119,0.08,0.128,0.098,0.089,0.099,0.16,0.089,0.093,0.114,0.12,0.118,0.145,0.114,0.127,0.104,0.099,0.126,0.143,0.118,0.129,0.11,0.119,0.111,0.104,0.106,0.126,0.124,0.107,0.127,481,1503,827,947,775,1104,896,940,473,413,178.0,1108,798,269,584,2738,4662,446,1285,1689,1087,1309,918,1646,1691,2035,2224,1459,1543,2042,575,422,331,1258,547,754,2126,1868,202,192,775,1384,1162,1472,2141,773,310,955,1053,1118,361,1392,743,2290,361,1756,624,1368,650,854,461,892,242,408,1358,410,2046,1049,909,490,887,1122,3821,245,3.072,2.945,2.543,2.245,2.241,2.056,2.161,3.18,2.574,2.507,2.261,1.792,2.443,2.476,2.481,2.498,2.742,3.247,1.92,1.608,2.742,2.352,2.18,2.697,2.573,2.739,2.16,1.971,2.485,2.201,2.621,2.6,2.141,2.9,3.072,2.301,2.582,2.459,2.096,1.976,2.595,1.863,3.095,1.644,1.722,1.875,2.788,2.9,2.445,2.367,1.73,2.342,2.141,2.292,2.098,1.986,2.186,2.041,1.937,1.785,2.317,2.266,2.017,2.11,2.046,2.022,2.209,2.326,1.948,2.989,2.281,2.293,2.462,2.389,1.08,0.731,0.784,0.566,0.539,0.449,0.671,0.726,0.588,0.583,0.519,0.523,0.458,0.33,0.44,0.554,0.641,0.851,0.576,0.439,0.587,0.511,0.551,0.621,0.535,0.627,0.549,0.552,0.622,0.562,0.727,1.125,0.551,0.695,0.633,0.571,0.598,0.599,0.358,0.347,0.804,0.467,0.676,0.556,0.561,0.5,0.476,0.834,0.638,0.589,0.432,0.69,0.586,0.596,0.367,0.515,0.452,0.69,0.507,0.405,0.732,0.612,0.393,0.951,0.795,0.499,0.624,0.671,0.623,0.932,0.548,0.463,0.508,0.594
-60,Epileptic,M,62.0,1.2,3.0,1.2,1.6,2.3,1.7,2.4,2.3,2.3,1.0,0.3,2.6,1.6,0.4,1.4,10.0,10.9,2.5,2.9,5.5,1.2,4.2,1.7,4.7,3.6,3.1,7.9,4.6,5.4,3.5,2.5,1.1,0.4,2.7,0.7,0.7,5.5,3.7,0.3,0.3,1.2,3.1,3.6,2.1,4.2,0.7,0.5,1.0,1.6,1.0,0.8,1.9,1.6,2.9,0.1,3.5,1.4,2.4,1.2,1.1,1.0,2.3,0.2,0.7,2.2,1.3,3.7,1.9,1.0,0.8,1.1,2.0,6.3,0.3,9,32,13,12,20,20,21,22,19,11,3.0,29,15,5,15,91,98,19,35,46,11,46,14,49,47,42,74,42,47,46,19,10,5,31,3,5,58,56,1,2,7,29,28,14,23,9,2,7,7,8,5,15,11,17,0,30,10,15,7,11,8,18,1,5,19,19,29,12,6,7,6,12,57,3,0.044,0.025,0.024,0.027,0.035,0.029,0.034,0.033,0.057,0.045,0.03,0.033,0.034,0.021,0.035,0.045,0.034,0.07,0.031,0.044,0.021,0.039,0.032,0.048,0.034,0.028,0.042,0.039,0.039,0.03,0.054,0.056,0.021,0.031,0.016,0.016,0.042,0.035,0.021,0.023,0.022,0.038,0.047,0.027,0.026,0.015,0.023,0.016,0.019,0.025,0.03,0.018,0.02,0.021,0.015,0.018,0.029,0.021,0.023,0.016,0.035,0.03,0.022,0.02,0.021,0.023,0.023,0.022,0.017,0.017,0.028,0.022,0.021,0.016,1722,5842,2337,2795,2772,3091,3406,3384,2112,1796,774.0,3197,3289,1033,2624,12431,20674,2621,6574,5591,3643,5508,2033,6506,5757,6830,6392,4883,7044,6512,2542,1249,904,6548,2295,2050,9235,10010,357,462,1533,3609,6690,2571,4362,1691,794,2428,2510,2136,846,3036,2353,5328,113,5025,1573,3577,1244,1799,885,2823,393,1169,2980,1557,5050,3076,1764,1157,1508,2903,10075,540,0.138,0.12,0.118,0.116,0.125,0.136,0.111,0.135,0.162,0.165,0.114,0.137,0.13,0.115,0.133,0.142,0.129,0.178,0.134,0.16,0.09,0.143,0.122,0.153,0.129,0.126,0.132,0.112,0.111,0.136,0.135,0.127,0.084,0.13,0.078,0.083,0.149,0.143,0.097,0.111,0.104,0.134,0.164,0.113,0.11,0.099,0.106,0.089,0.097,0.103,0.138,0.101,0.105,0.103,0.075,0.111,0.127,0.099,0.116,0.104,0.14,0.133,0.101,0.113,0.11,0.123,0.111,0.111,0.1,0.102,0.126,0.114,0.112,0.112,466,2054,851,991,1164,1104,1137,1157,712,449,189.0,1335,842,267,784,3665,5850,579,1702,2249,920,1702,911,1942,2060,2035,2721,1837,1931,2112,830,409,336,1488,583,741,2292,2252,210,208,805,1328,1269,1386,2558,814,335,947,1186,795,420,1597,1207,2212,46,2743,694,1756,791,1024,507,1143,214,551,1560,922,2593,1334,834,685,660,1357,4666,319,3.423,2.645,2.624,2.713,2.169,2.308,2.522,2.456,2.39,2.923,2.868,1.993,2.909,2.772,2.535,2.586,2.812,2.867,2.922,2.103,2.752,2.413,1.911,2.583,2.261,2.676,2.0,2.151,2.768,2.418,2.334,2.818,2.181,3.009,3.47,2.525,2.938,3.007,1.946,2.42,2.327,2.133,3.54,2.014,1.919,2.029,2.631,3.066,2.53,2.614,2.09,2.118,2.121,2.587,2.327,1.983,2.412,2.243,1.805,1.882,2.006,2.328,2.021,2.314,2.074,1.886,2.141,2.549,2.406,1.952,2.571,2.495,2.359,2.114,0.789,0.646,0.516,0.541,0.524,0.618,0.565,0.536,0.62,0.685,0.883,0.487,0.435,0.354,0.436,0.624,0.609,1.15,0.661,0.602,0.837,0.606,0.501,0.724,0.597,0.524,0.484,0.571,0.566,0.645,0.675,1.016,0.446,0.596,0.764,0.46,0.74,0.785,0.416,0.458,0.439,0.667,0.759,0.674,0.566,0.47,0.609,0.745,0.542,0.644,0.404,0.396,0.522,0.481,0.236,0.434,0.601,0.557,0.374,0.392,0.404,0.621,0.333,0.982,0.542,0.63,0.44,0.43,0.456,0.725,0.462,0.566,0.429,0.303
-61,Epileptic,M,47.0,1.0,2.6,0.9,1.4,2.0,1.9,1.6,1.6,1.6,0.7,0.3,2.4,1.8,0.5,1.3,5.1,11.0,1.3,1.8,3.8,1.2,5.2,1.9,4.3,7.8,3.8,4.2,1.9,2.5,3.0,1.7,1.1,0.5,2.2,0.6,0.9,4.8,3.0,0.2,0.1,1.2,2.5,2.4,1.7,2.3,0.6,0.7,1.0,1.7,0.7,0.5,2.6,1.4,2.6,0.5,3.1,0.5,1.7,1.0,0.7,0.6,1.7,0.4,0.5,1.8,1.3,2.8,1.5,0.8,1.0,0.9,1.5,6.5,0.4,8,20,7,10,19,19,14,18,17,7,4.0,20,16,8,13,57,96,14,19,32,9,48,15,47,58,43,45,21,20,37,22,8,5,27,4,9,55,38,1,1,7,18,23,14,11,4,3,5,8,6,3,20,10,19,3,23,4,15,5,7,6,15,4,5,14,18,22,9,5,12,5,9,52,3,0.043,0.025,0.018,0.025,0.038,0.029,0.029,0.031,0.044,0.036,0.032,0.034,0.03,0.038,0.029,0.033,0.035,0.053,0.035,0.043,0.028,0.059,0.039,0.049,0.053,0.033,0.038,0.025,0.026,0.032,0.04,0.043,0.021,0.03,0.025,0.021,0.038,0.031,0.02,0.016,0.023,0.042,0.039,0.023,0.018,0.013,0.023,0.015,0.022,0.02,0.028,0.026,0.023,0.024,0.014,0.017,0.015,0.023,0.025,0.018,0.017,0.026,0.029,0.021,0.02,0.026,0.022,0.023,0.017,0.023,0.025,0.015,0.025,0.017,1425,4721,1975,2368,2180,2911,2981,2998,2015,1379,616.0,2703,3360,1039,2317,8623,18250,2147,3777,3388,3154,2529,1493,6112,4495,6838,4899,3242,5354,4938,2524,1120,1261,5932,1691,2231,8888,7273,310,313,1498,1662,4745,2159,3266,1330,991,2319,2425,1425,407,3669,1658,3985,780,4722,1223,2674,880,1053,1517,2692,325,790,3044,1287,3829,2108,1475,1480,1138,2289,8763,530,0.139,0.119,0.103,0.115,0.149,0.129,0.106,0.134,0.146,0.146,0.133,0.126,0.132,0.151,0.133,0.137,0.135,0.157,0.132,0.147,0.104,0.151,0.12,0.143,0.156,0.135,0.126,0.103,0.107,0.139,0.151,0.134,0.091,0.135,0.104,0.102,0.143,0.137,0.099,0.096,0.111,0.129,0.148,0.117,0.095,0.085,0.113,0.087,0.104,0.113,0.141,0.117,0.118,0.111,0.104,0.1,0.098,0.122,0.115,0.104,0.098,0.124,0.147,0.119,0.105,0.13,0.112,0.107,0.095,0.128,0.111,0.103,0.111,0.107,467,1710,786,911,912,1025,1061,989,655,347,172.0,1068,1028,268,767,2797,5644,510,1025,1495,769,1201,747,1940,1987,2094,1861,1218,1455,1596,789,455,419,1397,420,709,2228,1782,173,172,791,968,1153,1222,1898,699,443,956,1181,557,234,1688,906,1811,429,2513,557,1261,526,568,654,1081,190,421,1457,801,1966,1054,749,683,609,1164,3901,325,3.107,2.673,2.581,2.57,2.19,2.304,2.341,2.553,2.353,2.895,2.783,2.028,2.594,2.745,2.373,2.454,2.592,3.118,2.808,1.946,3.101,1.855,1.827,2.521,1.989,2.662,2.129,2.11,2.725,2.478,2.567,2.517,2.378,3.053,3.643,2.725,2.846,2.958,2.183,2.017,2.301,1.486,3.153,1.943,1.948,2.146,2.727,2.997,2.476,2.732,2.087,2.242,2.055,2.254,2.321,2.042,2.439,2.501,1.955,2.124,2.373,2.546,2.206,2.251,2.281,1.906,2.073,2.332,2.24,2.372,2.244,2.277,2.451,2.053,0.699,0.507,0.389,0.428,0.507,0.655,0.569,0.476,0.486,0.536,0.69,0.499,0.501,0.446,0.41,0.489,0.578,0.758,0.522,0.619,0.841,0.729,0.761,0.566,0.578,0.535,0.513,0.477,0.488,0.619,0.553,0.833,0.401,0.565,0.567,0.485,0.728,0.714,0.239,0.293,0.334,0.747,0.699,0.548,0.535,0.466,0.342,0.698,0.443,0.531,0.365,0.37,0.38,0.362,0.309,0.401,0.441,0.46,0.371,0.357,0.47,0.578,0.419,0.616,0.468,0.506,0.439,0.392,0.44,0.66,0.36,0.467,0.49,0.391
-62,Epileptic,F,17.5,0.9,3.6,0.8,1.3,1.5,1.9,2.4,2.7,1.4,1.2,0.3,3.7,2.2,0.9,1.1,11.6,13.3,1.4,3.4,6.6,1.1,3.4,2.1,5.5,5.5,5.7,4.7,3.1,4.6,5.3,2.1,0.6,0.7,4.5,0.6,1.1,5.1,5.9,0.2,0.4,1.7,4.1,2.9,5.9,3.8,0.9,0.5,1.1,1.2,0.7,0.5,1.6,2.5,2.9,0.4,3.6,1.3,4.0,1.4,1.4,0.7,2.3,0.6,0.5,2.1,1.7,3.4,1.4,1.8,0.5,2.1,3.0,7.3,0.6,5,25,7,9,11,16,14,19,8,12,3.0,28,18,8,10,88,99,7,28,48,7,28,16,41,41,47,40,25,36,45,18,4,4,42,3,7,47,46,1,3,9,32,26,38,22,6,3,6,7,6,4,12,15,16,3,27,12,28,9,12,6,14,3,4,16,13,24,12,9,5,14,16,54,5,0.029,0.03,0.016,0.022,0.033,0.028,0.034,0.036,0.045,0.048,0.036,0.04,0.039,0.052,0.033,0.051,0.038,0.042,0.034,0.037,0.02,0.034,0.039,0.048,0.038,0.037,0.03,0.032,0.036,0.038,0.06,0.022,0.022,0.039,0.019,0.017,0.043,0.04,0.018,0.025,0.026,0.041,0.046,0.044,0.026,0.016,0.033,0.014,0.013,0.02,0.032,0.019,0.031,0.022,0.025,0.022,0.026,0.028,0.03,0.02,0.02,0.037,0.044,0.023,0.022,0.031,0.025,0.019,0.026,0.019,0.032,0.031,0.023,0.022,1933,6611,2441,2756,2620,2915,2697,4199,1888,1975,628.0,2841,4002,1592,2607,16542,24840,2614,5532,4284,2459,5204,2538,6559,7657,8371,6859,4132,8136,7177,2159,941,1239,8029,2328,2481,7292,8913,422,788,2320,3042,5826,2649,3945,1865,726,3249,3100,1616,566,3267,3545,5242,493,4816,2332,4291,1292,1960,1043,3038,738,872,3507,1716,3962,2677,2347,1111,2638,2936,11631,638,0.106,0.117,0.087,0.106,0.117,0.131,0.115,0.136,0.136,0.148,0.131,0.142,0.135,0.151,0.136,0.156,0.132,0.121,0.13,0.137,0.08,0.136,0.127,0.145,0.143,0.136,0.124,0.12,0.127,0.141,0.146,0.079,0.09,0.132,0.081,0.079,0.135,0.128,0.093,0.118,0.114,0.145,0.14,0.142,0.108,0.093,0.116,0.077,0.084,0.096,0.126,0.093,0.117,0.096,0.127,0.109,0.108,0.114,0.13,0.11,0.109,0.122,0.142,0.112,0.108,0.127,0.112,0.099,0.115,0.098,0.13,0.13,0.108,0.109,509,1999,876,935,796,1107,1035,1336,461,463,174.0,1481,1087,325,586,3869,6067,540,1569,2796,928,1651,988,1858,2403,2680,2600,1663,2203,2401,621,459,521,1909,558,970,2141,2395,217,269,1031,1530,1237,1980,2383,895,325,1197,1338,686,324,1457,1347,2189,242,2618,936,2197,770,1066,579,1058,254,431,1589,917,2126,1212,1087,536,1128,1542,5083,431,3.611,2.793,2.553,2.844,2.578,2.411,2.284,2.819,2.744,3.411,3.112,1.714,2.831,3.21,3.202,2.802,3.04,3.57,2.791,1.5,2.143,2.549,2.269,2.656,2.438,2.557,2.208,2.021,2.742,2.488,2.386,1.99,2.033,2.856,3.477,2.519,2.525,2.829,2.414,3.028,2.643,1.868,3.11,1.599,1.894,2.244,2.788,3.222,2.799,2.528,2.1,2.239,2.243,2.378,2.166,1.956,2.836,2.207,2.001,1.877,2.044,2.775,2.993,2.261,2.438,2.418,1.991,2.474,2.652,2.239,2.643,2.416,2.432,1.814,1.035,0.81,0.693,0.813,0.963,0.612,0.629,0.599,0.726,0.665,1.074,0.499,0.555,0.605,0.824,0.843,0.739,0.799,0.538,0.443,0.734,0.531,0.579,0.928,0.699,0.59,0.606,0.522,0.659,0.651,0.907,0.894,0.6,0.92,0.876,0.673,0.886,0.786,0.46,0.617,0.573,0.636,0.867,0.701,0.594,0.526,0.635,0.868,0.646,0.65,0.543,0.745,0.721,0.749,0.491,0.507,0.709,0.759,0.33,0.441,0.471,0.806,0.846,1.006,0.614,0.893,0.494,0.425,0.595,0.721,0.564,0.549,0.582,0.393
-63,Epileptic,M,27.0,0.8,2.4,0.7,1.2,1.3,2.2,1.5,2.0,0.9,0.9,0.3,2.9,1.9,0.6,1.4,6.3,9.6,0.8,2.1,4.7,1.1,3.7,2.0,2.9,3.2,3.9,3.0,3.0,3.4,4.1,1.1,0.9,0.7,1.9,0.9,1.0,5.1,3.0,0.2,0.3,1.2,3.0,2.2,2.9,2.8,0.9,0.4,0.7,1.3,0.9,0.5,1.8,1.7,3.0,0.4,2.7,0.9,2.2,1.2,1.3,0.8,1.6,0.4,0.5,1.6,1.4,2.6,1.7,1.3,0.5,1.1,1.4,3.3,0.4,8,23,10,13,16,28,13,18,13,10,3.0,32,21,6,18,84,96,6,25,44,11,42,22,38,42,39,42,30,31,48,13,7,7,26,8,7,50,35,2,3,6,27,30,22,17,7,2,4,6,9,4,15,12,21,2,24,7,24,7,11,9,16,3,5,16,19,22,12,8,5,7,9,29,2,0.033,0.021,0.012,0.023,0.029,0.03,0.025,0.032,0.035,0.034,0.04,0.032,0.038,0.027,0.029,0.033,0.029,0.027,0.031,0.034,0.022,0.031,0.03,0.036,0.035,0.034,0.025,0.029,0.029,0.031,0.033,0.04,0.031,0.029,0.029,0.021,0.04,0.038,0.013,0.02,0.021,0.031,0.039,0.026,0.023,0.016,0.024,0.012,0.014,0.024,0.028,0.017,0.021,0.021,0.026,0.017,0.023,0.019,0.02,0.018,0.026,0.028,0.017,0.019,0.018,0.025,0.019,0.022,0.019,0.015,0.023,0.022,0.015,0.015,1601,5910,2094,2569,2349,3972,2489,3952,1910,1662,697.0,3433,3799,1455,3404,12233,19900,2321,4272,5114,3206,6195,2569,5802,6012,7492,6310,4293,6788,6907,2253,925,1213,5364,2691,2390,8853,6814,357,785,1852,3215,7293,3312,3201,1935,780,2259,2679,2141,587,3869,2420,5675,581,4146,1466,3582,1573,1808,1556,2626,567,837,2773,1449,4410,3118,2182,870,1771,2041,8746,702,0.114,0.109,0.096,0.107,0.127,0.145,0.107,0.136,0.133,0.156,0.13,0.144,0.145,0.132,0.134,0.137,0.128,0.114,0.129,0.136,0.091,0.14,0.143,0.137,0.142,0.137,0.119,0.113,0.114,0.137,0.112,0.126,0.131,0.124,0.12,0.111,0.144,0.141,0.089,0.1,0.107,0.133,0.148,0.117,0.111,0.095,0.108,0.078,0.083,0.114,0.124,0.1,0.11,0.102,0.107,0.11,0.115,0.104,0.11,0.111,0.124,0.139,0.117,0.112,0.105,0.118,0.104,0.106,0.1,0.11,0.116,0.112,0.088,0.087,452,1930,811,943,859,1412,918,1114,540,426,180.0,1532,1014,350,888,3471,5542,439,1194,2273,796,1966,1129,1447,1795,2025,2239,1661,1929,2195,607,380,358,1235,525,773,2040,1537,202,286,794,1572,1281,1903,1923,885,293,857,1225,648,337,1648,1175,2378,272,2189,640,1909,884,1020,565,869,316,437,1422,938,2173,1335,1056,480,779,1010,3606,355,3.35,2.841,2.494,2.533,2.357,2.453,2.26,3.18,2.658,2.93,2.787,1.923,2.858,2.964,2.908,2.695,2.841,3.734,2.904,1.944,3.128,2.427,2.064,2.873,2.592,2.966,2.273,2.136,2.79,2.52,2.687,2.421,2.669,3.072,3.587,2.85,3.083,3.15,2.145,2.824,2.925,1.805,3.63,2.017,1.908,2.406,3.104,3.221,2.622,3.049,1.987,2.431,2.242,2.486,2.675,2.158,2.683,2.165,2.119,1.964,2.454,2.863,2.192,2.042,2.078,1.917,2.249,2.661,2.423,2.236,2.416,2.511,2.516,2.276,0.986,0.665,0.532,0.574,0.629,0.55,0.527,0.701,0.584,0.647,0.884,0.41,0.563,0.436,0.597,0.514,0.59,0.802,0.666,0.582,0.688,0.529,0.46,0.772,0.536,0.681,0.472,0.547,0.562,0.538,0.746,1.094,0.51,0.704,0.876,0.556,0.823,0.628,0.327,0.472,0.584,0.474,0.831,0.733,0.538,0.608,0.619,0.782,0.545,0.682,0.409,0.464,0.437,0.482,0.544,0.512,0.522,0.508,0.436,0.36,0.458,0.68,0.363,0.814,0.623,0.576,0.432,0.4,0.366,0.717,0.544,0.404,0.467,0.445
-64,Epileptic,M,32.0,0.6,2.8,1.1,1.0,1.1,1.6,2.0,1.7,0.6,0.9,0.1,3.4,1.1,0.6,1.2,5.4,8.1,0.9,2.6,4.7,1.9,2.9,2.5,3.4,2.7,3.1,3.2,2.0,2.2,2.7,1.3,0.9,0.6,2.2,0.5,0.8,4.3,3.1,0.1,0.1,1.0,2.9,2.2,4.2,2.6,0.6,0.5,1.0,1.1,1.0,0.7,1.9,1.4,2.6,0.3,2.7,0.5,1.8,1.1,1.3,0.4,1.1,0.2,0.4,2.2,1.2,2.5,1.3,1.1,0.8,0.8,1.7,4.1,0.3,4,28,9,8,11,13,16,16,10,7,1.0,25,13,8,11,62,79,7,23,34,19,31,21,41,35,31,36,22,21,27,19,6,7,25,3,8,44,34,1,1,5,22,23,32,17,4,2,7,6,9,5,17,10,19,2,24,5,16,9,9,2,11,2,4,17,15,21,11,9,7,6,11,29,3,0.035,0.03,0.02,0.022,0.028,0.031,0.026,0.029,0.028,0.04,0.029,0.039,0.031,0.03,0.033,0.031,0.029,0.033,0.035,0.039,0.029,0.034,0.033,0.038,0.026,0.03,0.029,0.023,0.021,0.03,0.036,0.053,0.032,0.029,0.015,0.021,0.032,0.031,0.018,0.015,0.022,0.034,0.032,0.04,0.021,0.013,0.024,0.016,0.016,0.019,0.04,0.019,0.023,0.02,0.026,0.018,0.026,0.023,0.025,0.024,0.017,0.021,0.026,0.017,0.027,0.029,0.02,0.017,0.016,0.028,0.028,0.025,0.017,0.021,1445,4369,1711,1731,1928,2047,2499,3019,1438,1159,398.0,2580,2452,1008,2188,9946,15316,2428,3551,3566,3618,3831,2191,5876,5270,5203,4837,2855,5066,4692,2147,710,752,5993,1920,1919,8103,6347,273,448,1354,2157,4552,2659,2929,1179,772,2250,2005,2196,466,3165,2036,4572,333,4270,1173,3124,1165,1451,724,2266,348,748,2512,1003,3797,2537,1902,1357,1068,2674,9446,503,0.113,0.129,0.127,0.106,0.125,0.126,0.101,0.129,0.138,0.155,0.082,0.142,0.122,0.141,0.14,0.135,0.133,0.109,0.132,0.139,0.111,0.132,0.134,0.139,0.115,0.13,0.116,0.099,0.098,0.128,0.146,0.128,0.154,0.12,0.084,0.092,0.13,0.134,0.089,0.094,0.109,0.141,0.134,0.155,0.103,0.091,0.099,0.092,0.091,0.101,0.146,0.107,0.111,0.108,0.138,0.105,0.115,0.107,0.127,0.121,0.092,0.113,0.119,0.11,0.125,0.139,0.103,0.1,0.099,0.127,0.125,0.12,0.094,0.124,359,1392,685,729,727,852,985,1047,434,338,124.0,1224,749,322,601,2832,4585,494,1097,1749,1026,1265,1102,1609,1645,1693,1805,1340,1589,1421,627,313,302,1278,456,607,2180,1604,137,186,673,1145,1166,1791,1931,684,345,950,978,823,298,1542,1025,2025,158,2292,491,1460,747,846,304,853,178,417,1331,620,2087,1136,978,465,523,1155,3806,227,3.785,2.747,2.205,2.334,2.233,2.213,2.171,2.609,2.419,2.803,2.813,1.9,2.719,2.615,2.708,2.593,2.698,3.531,2.511,1.812,2.955,2.436,1.815,2.632,2.48,2.6,2.187,1.8,2.492,2.541,2.629,2.225,2.171,3.291,3.532,2.792,2.823,3.064,2.047,2.671,2.446,1.778,2.889,1.841,1.732,1.877,2.871,2.907,2.474,2.681,1.788,2.111,2.111,2.384,2.648,1.995,2.726,2.369,1.927,1.911,2.529,2.448,2.271,2.156,2.075,2.154,1.884,2.42,2.208,2.907,2.346,2.749,2.687,2.811,0.782,0.718,0.62,0.494,0.651,0.512,0.545,0.506,0.559,0.386,0.811,0.585,0.537,0.473,0.547,0.49,0.512,1.026,0.574,0.565,0.805,0.487,0.461,0.587,0.518,0.49,0.543,0.461,0.434,0.672,0.773,0.825,0.411,0.645,0.624,0.573,0.628,0.617,0.373,0.843,0.618,0.489,0.628,0.612,0.457,0.455,0.555,0.665,0.563,0.634,0.351,0.472,0.518,0.447,0.6,0.439,0.503,0.838,0.365,0.356,0.416,0.809,0.362,0.942,0.545,0.668,0.401,0.452,0.33,0.88,0.457,0.471,0.744,0.79
-65,Epileptic,F,52.0,1.0,2.1,1.0,1.1,0.8,1.7,2.1,1.4,1.1,0.4,0.4,3.7,1.1,0.8,1.7,4.4,6.5,1.0,3.0,4.4,2.1,2.5,3.6,3.5,2.5,2.8,5.7,2.1,2.2,2.3,1.7,0.6,0.4,1.3,0.4,0.4,4.3,2.2,0.2,0.2,0.7,3.5,1.7,2.5,2.4,0.4,0.3,0.8,1.1,0.4,0.8,1.9,0.9,1.8,0.5,2.0,0.6,1.6,0.7,1.8,0.7,1.1,0.3,0.4,1.6,0.8,2.6,0.9,0.9,0.5,0.8,1.1,3.1,0.3,8,18,10,8,8,16,17,12,11,5,4.0,33,13,7,21,51,57,8,29,36,16,26,26,32,31,32,49,21,19,27,18,4,3,19,3,4,35,23,1,1,3,27,17,19,13,3,1,5,6,6,6,14,6,15,3,17,6,12,5,12,5,8,1,3,12,10,19,6,5,4,7,8,22,2,0.047,0.023,0.023,0.022,0.027,0.029,0.046,0.026,0.035,0.021,0.032,0.039,0.028,0.036,0.033,0.03,0.029,0.04,0.039,0.045,0.035,0.034,0.044,0.045,0.036,0.028,0.044,0.032,0.027,0.03,0.045,0.039,0.031,0.026,0.014,0.018,0.039,0.026,0.019,0.015,0.016,0.044,0.03,0.027,0.019,0.011,0.014,0.014,0.019,0.014,0.035,0.025,0.015,0.021,0.022,0.018,0.019,0.021,0.024,0.022,0.023,0.026,0.022,0.022,0.018,0.021,0.022,0.017,0.019,0.02,0.022,0.016,0.018,0.03,1172,4331,1811,1907,1727,2756,2381,2685,1790,1192,746.0,3307,2500,1359,3191,8504,14836,1987,4783,4165,3812,3428,2466,5693,4765,6297,6575,3342,5382,4704,2085,566,775,3625,1763,1224,5830,6267,363,470,1160,2219,5458,2563,3468,1120,693,1913,1956,1284,605,2545,1790,3947,603,3508,1247,2668,1081,2145,1089,1839,397,973,2626,944,3494,1430,1462,738,1285,2309,5818,366,0.136,0.114,0.124,0.114,0.119,0.128,0.122,0.124,0.132,0.117,0.115,0.149,0.126,0.153,0.149,0.131,0.124,0.133,0.135,0.15,0.113,0.125,0.143,0.159,0.139,0.121,0.141,0.102,0.098,0.133,0.146,0.117,0.088,0.118,0.075,0.088,0.142,0.115,0.096,0.096,0.09,0.142,0.132,0.127,0.098,0.081,0.089,0.086,0.096,0.091,0.155,0.123,0.092,0.103,0.116,0.1,0.116,0.108,0.11,0.115,0.109,0.122,0.113,0.117,0.107,0.109,0.114,0.104,0.101,0.138,0.123,0.096,0.098,0.145,396,1473,668,713,625,1010,806,831,555,312,214.0,1508,667,359,931,2539,3893,456,1407,1853,1034,1257,1238,1477,1369,1736,2274,1206,1376,1424,611,317,207,974,488,458,1648,1431,200,229,615,1173,1124,1479,1884,555,283,873,915,653,318,1205,867,1489,317,1796,516,1247,523,1153,571,665,202,337,1298,618,1758,715,734,328,562,1115,2576,145,2.958,2.831,2.777,2.518,2.478,2.365,2.39,2.737,2.498,2.891,2.705,1.971,2.773,2.733,2.665,2.607,2.939,3.364,2.705,1.963,2.962,2.204,1.881,2.883,2.745,2.804,2.38,2.188,2.906,2.568,2.557,1.908,2.916,2.777,3.226,2.451,2.652,3.168,2.236,2.387,2.437,1.794,3.345,1.954,2.105,2.255,2.779,2.773,2.612,2.174,1.941,2.181,2.368,2.58,2.371,2.18,2.352,2.356,2.302,2.062,2.151,2.576,2.161,3.172,2.265,1.89,2.202,2.213,2.366,2.734,2.531,2.497,2.491,2.927,0.595,0.557,0.465,0.526,0.484,0.464,0.673,0.417,0.503,0.607,0.65,0.488,0.392,0.467,0.429,0.522,0.589,0.933,0.607,0.533,0.837,0.485,0.592,0.578,0.486,0.497,0.605,0.55,0.482,0.497,0.699,0.818,0.348,0.481,0.636,0.423,0.646,0.519,0.323,0.481,0.431,0.501,0.678,0.509,0.561,0.384,0.505,0.563,0.387,0.428,0.404,0.44,0.377,0.445,0.369,0.38,0.494,0.456,0.493,0.372,0.352,0.626,0.293,0.623,0.392,0.58,0.444,0.461,0.411,0.5,0.483,0.438,0.372,0.403
-66,Epileptic,M,27.0,0.6,3.0,1.2,1.7,1.6,1.7,1.4,1.9,0.9,0.8,0.4,2.8,1.4,0.5,1.3,7.6,8.9,0.9,1.9,3.8,1.0,2.3,1.3,3.5,4.2,2.9,1.9,2.2,3.1,2.8,1.7,1.0,0.5,1.9,0.4,1.2,3.2,3.2,0.1,0.2,0.9,2.5,3.4,2.2,2.2,0.5,0.5,0.8,1.1,1.0,0.7,2.5,1.2,3.1,0.3,1.9,0.8,1.4,1.7,0.9,0.4,1.5,0.4,0.5,1.4,1.5,1.4,1.1,1.0,0.3,0.7,0.9,4.0,0.3,3,20,10,16,15,13,10,15,10,7,4.0,26,14,4,10,64,71,6,19,32,9,27,13,39,40,24,20,21,25,26,19,7,5,18,2,12,28,33,1,1,5,22,26,16,10,4,2,4,6,6,5,18,11,19,2,14,3,11,11,8,3,12,3,5,10,19,11,6,5,3,6,6,31,3,0.028,0.025,0.016,0.029,0.03,0.028,0.025,0.031,0.042,0.039,0.039,0.037,0.029,0.035,0.033,0.042,0.03,0.036,0.027,0.037,0.02,0.03,0.023,0.043,0.032,0.031,0.024,0.03,0.03,0.037,0.04,0.044,0.031,0.026,0.013,0.026,0.038,0.031,0.012,0.015,0.016,0.033,0.05,0.028,0.015,0.016,0.025,0.014,0.015,0.016,0.031,0.024,0.034,0.02,0.022,0.015,0.017,0.016,0.035,0.014,0.017,0.024,0.03,0.021,0.02,0.021,0.016,0.015,0.015,0.015,0.017,0.016,0.016,0.015,1560,6575,3004,2906,2394,3226,2750,3217,1771,1231,497.0,2919,3986,1311,2530,12330,19750,2053,4736,4491,3757,4780,2294,6568,7787,5516,4520,3958,6974,4745,2526,1343,914,6385,2336,2158,6939,8098,383,456,1686,3297,6596,2353,3712,1263,684,2352,2442,1692,608,4093,2038,6619,551,3671,1619,3275,1443,1666,916,2940,659,933,2288,1454,3071,2575,2060,944,1184,2438,9722,526,0.094,0.116,0.096,0.128,0.138,0.124,0.102,0.125,0.147,0.144,0.157,0.152,0.13,0.14,0.136,0.141,0.122,0.127,0.116,0.143,0.097,0.132,0.123,0.146,0.13,0.131,0.112,0.112,0.114,0.149,0.142,0.123,0.141,0.119,0.071,0.12,0.136,0.14,0.079,0.092,0.097,0.127,0.159,0.121,0.084,0.094,0.117,0.08,0.082,0.099,0.138,0.116,0.13,0.1,0.109,0.097,0.093,0.094,0.148,0.093,0.1,0.121,0.136,0.128,0.111,0.112,0.095,0.089,0.087,0.098,0.111,0.093,0.092,0.115,414,1859,1083,953,897,952,848,1038,467,335,160.0,1157,836,263,659,3200,4952,425,1140,1744,880,1368,846,1599,2274,1572,1301,1271,1760,1362,648,458,295,1231,510,742,1391,1679,194,188,738,1204,1235,1371,2079,586,260,907,1091,684,366,1629,708,2403,221,1809,623,1418,721,940,424,1013,228,396,1091,1057,1324,1046,961,377,575,880,3909,273,3.74,3.141,2.646,2.781,2.463,2.885,2.593,2.839,2.873,2.902,2.5,2.193,3.367,3.251,2.927,2.82,3.153,3.849,3.213,2.219,3.273,2.748,2.283,3.015,2.719,3.012,2.595,2.413,2.997,2.813,2.88,2.968,2.617,3.397,3.959,2.572,3.528,3.427,2.376,2.847,2.982,2.282,3.51,1.934,2.026,2.435,3.517,3.4,2.615,2.996,2.138,2.434,2.556,2.685,2.987,2.245,3.288,2.696,2.228,1.978,2.34,2.962,2.833,2.554,2.511,1.697,2.421,2.833,2.533,2.824,2.241,3.117,2.625,2.505,0.732,0.717,0.705,0.674,0.588,0.602,0.565,0.668,0.542,0.818,0.976,0.546,0.534,0.479,0.52,0.674,0.617,0.571,0.555,0.566,0.822,0.534,0.531,0.748,0.519,0.487,0.51,0.574,0.56,0.574,0.821,0.931,0.601,0.701,0.662,0.436,0.705,0.671,0.272,0.341,0.774,0.605,0.875,0.619,0.66,0.615,0.5,0.651,0.522,0.57,0.373,0.542,0.695,0.528,0.52,0.447,0.617,0.485,0.564,0.478,0.36,0.763,0.751,0.965,0.644,0.575,0.524,0.671,0.449,0.789,0.469,0.686,0.506,0.377
-67,Epileptic,F,42.0,0.8,1.8,0.9,1.1,2.0,1.6,1.3,2.4,1.4,0.6,0.2,2.2,1.5,0.7,1.2,4.7,8.0,1.6,1.1,3.9,2.5,2.9,1.8,3.4,2.8,3.0,2.9,2.3,2.0,3.0,1.6,1.0,0.5,2.0,0.4,0.6,3.0,2.4,0.2,0.2,1.1,2.9,2.3,1.2,2.3,0.7,0.5,0.8,1.4,0.7,0.5,1.6,1.4,2.7,0.2,2.1,0.5,1.4,1.1,0.9,0.6,1.6,0.3,0.4,1.6,0.8,1.5,1.5,1.0,0.7,0.7,1.5,4.4,0.4,6,15,6,8,16,17,14,22,13,8,2.0,26,17,9,17,58,88,14,19,38,22,33,17,40,36,36,44,25,21,41,22,12,7,31,3,4,37,39,1,1,8,29,27,10,13,5,2,4,7,5,3,12,12,23,1,15,4,13,10,7,8,13,2,4,11,14,14,13,6,6,6,9,40,3,0.04,0.025,0.019,0.023,0.047,0.031,0.035,0.036,0.062,0.042,0.027,0.035,0.032,0.046,0.041,0.035,0.036,0.062,0.028,0.041,0.05,0.048,0.041,0.044,0.036,0.03,0.035,0.029,0.029,0.04,0.049,0.057,0.025,0.033,0.02,0.013,0.045,0.034,0.017,0.015,0.021,0.043,0.05,0.019,0.018,0.019,0.022,0.013,0.018,0.02,0.024,0.021,0.026,0.025,0.035,0.016,0.027,0.02,0.025,0.018,0.031,0.031,0.025,0.02,0.021,0.021,0.017,0.022,0.02,0.024,0.021,0.021,0.022,0.015,1822,4153,2020,1997,2125,3418,2340,3599,1748,1112,489.0,3097,3371,1245,2376,9762,16075,1930,3545,4660,3348,4046,2060,5771,5950,6156,4978,3292,5027,5003,2304,1100,1357,5907,1356,1807,4582,6961,298,344,1561,2946,4214,1976,3219,1138,705,2463,2266,1185,641,2442,1950,4561,298,3443,876,2670,1380,1388,1129,2452,271,916,2423,877,2925,2342,1523,1197,997,2359,7149,616,0.125,0.118,0.107,0.119,0.161,0.143,0.107,0.144,0.196,0.169,0.127,0.137,0.131,0.164,0.15,0.137,0.145,0.159,0.137,0.153,0.152,0.149,0.125,0.158,0.133,0.135,0.123,0.114,0.104,0.142,0.154,0.141,0.105,0.133,0.088,0.082,0.142,0.145,0.113,0.09,0.109,0.146,0.168,0.108,0.096,0.095,0.112,0.076,0.097,0.107,0.121,0.115,0.129,0.114,0.136,0.098,0.118,0.114,0.115,0.099,0.127,0.137,0.139,0.108,0.112,0.105,0.094,0.115,0.097,0.143,0.12,0.111,0.106,0.097,400,1282,745,731,745,981,760,1103,470,299,154.0,1177,946,351,695,2818,4409,480,825,1736,846,1163,782,1575,1683,1892,1604,1296,1340,1480,695,421,383,1308,364,685,1283,1570,154,184,816,1290,1008,1072,1853,596,312,904,1054,500,312,1187,870,1948,102,1827,340,1160,721,738,435,878,158,357,1128,627,1413,1044,729,465,537,1019,3222,342,3.658,2.853,2.463,2.57,2.58,2.711,2.569,2.836,2.991,2.816,2.77,2.126,2.895,2.706,2.687,2.681,2.998,3.419,3.061,2.197,2.841,2.695,2.233,2.939,2.721,2.625,2.438,2.052,2.785,2.542,2.628,2.811,2.653,3.161,3.323,2.428,2.666,3.109,2.423,2.057,2.381,1.937,3.06,1.959,2.002,1.983,2.796,3.339,2.616,2.637,2.377,2.236,2.295,2.394,2.907,2.1,2.479,2.558,2.007,2.075,2.348,2.806,2.032,3.004,2.348,1.731,2.09,2.442,2.381,2.91,1.925,2.476,2.392,2.238,0.837,0.673,0.59,0.488,0.533,0.733,0.675,0.521,0.521,0.729,0.8,0.609,0.432,0.503,0.521,0.497,0.548,0.697,0.59,0.565,1.041,0.579,0.622,0.65,0.625,0.551,0.538,0.566,0.533,0.597,0.693,0.737,0.398,0.595,0.545,0.382,0.807,0.636,0.457,0.381,0.45,0.573,0.71,0.646,0.547,0.432,0.394,0.88,0.349,0.598,0.421,0.422,0.455,0.485,0.377,0.418,0.614,0.631,0.42,0.396,0.501,0.573,0.281,0.65,0.464,0.7,0.461,0.437,0.476,0.802,0.386,0.577,0.447,0.421
-68,Epileptic,F,42.0,0.9,1.8,0.6,1.4,1.4,1.7,1.6,1.1,0.8,0.8,0.2,2.0,1.0,0.3,1.0,5.3,7.1,0.7,2.3,4.1,1.6,2.3,1.4,2.9,2.8,3.8,2.9,2.5,2.9,2.7,1.6,1.2,0.5,2.5,0.4,0.7,2.8,3.4,0.1,0.2,1.2,2.6,2.0,2.6,2.8,0.5,0.4,0.8,1.0,0.5,0.4,1.1,1.2,2.5,0.3,1.8,0.9,1.4,0.9,1.0,0.8,1.0,0.2,0.3,1.5,1.1,2.3,1.1,1.1,0.9,0.8,1.2,4.4,0.4,4,14,6,13,10,13,11,11,8,6,3.0,17,11,5,11,45,61,7,23,32,13,26,13,25,33,38,29,24,25,28,13,17,4,22,1,4,24,33,1,2,7,23,16,15,16,5,2,5,5,5,3,8,8,12,2,17,4,12,7,8,8,7,1,4,13,11,15,7,6,6,6,9,38,4,0.041,0.027,0.015,0.026,0.04,0.027,0.023,0.028,0.036,0.037,0.029,0.029,0.027,0.026,0.034,0.039,0.026,0.029,0.032,0.036,0.026,0.031,0.028,0.045,0.03,0.029,0.029,0.028,0.03,0.027,0.05,0.059,0.018,0.036,0.015,0.017,0.035,0.035,0.015,0.016,0.025,0.032,0.037,0.032,0.021,0.01,0.022,0.018,0.015,0.021,0.02,0.022,0.03,0.022,0.02,0.015,0.024,0.018,0.022,0.018,0.032,0.024,0.026,0.02,0.022,0.03,0.02,0.019,0.02,0.033,0.021,0.021,0.019,0.016,1421,3011,1395,2286,1799,2255,2684,2336,1020,1176,453.0,2028,2414,856,2100,8060,15373,1787,4338,3890,3606,4053,1793,4281,5969,6763,4127,3582,5805,4486,1915,1004,964,4325,1368,1697,4984,6048,286,497,1503,1985,4422,1884,3641,1498,602,1650,2201,1212,569,1659,1475,3650,440,3312,1258,2853,1179,1495,755,1868,241,765,2050,1034,3487,1962,1798,1059,1202,1903,8927,511,0.124,0.114,0.093,0.115,0.138,0.124,0.099,0.121,0.123,0.134,0.106,0.126,0.117,0.115,0.129,0.129,0.119,0.117,0.126,0.142,0.092,0.134,0.12,0.135,0.135,0.135,0.119,0.109,0.111,0.13,0.141,0.133,0.099,0.124,0.07,0.098,0.12,0.134,0.085,0.116,0.115,0.137,0.147,0.122,0.103,0.086,0.11,0.089,0.086,0.092,0.114,0.11,0.117,0.1,0.108,0.096,0.107,0.097,0.113,0.104,0.136,0.114,0.121,0.107,0.11,0.136,0.098,0.1,0.101,0.14,0.122,0.111,0.099,0.109,389,1072,634,931,615,949,966,817,378,343,159.0,1045,680,236,569,2180,4410,378,1164,1784,1006,1278,814,1122,1718,2058,1602,1363,1609,1600,496,441,414,1143,406,622,1421,1691,179,210,764,1123,949,1216,2052,786,267,736,1019,528,308,857,668,1574,203,1940,553,1374,666,856,423,643,138,329,1127,547,1854,924,791,410,606,911,3943,342,3.25,2.43,2.102,2.449,2.484,2.129,2.349,2.673,2.183,2.959,2.53,1.737,2.765,2.627,2.607,2.741,2.72,3.498,2.911,1.935,2.766,2.481,1.996,2.78,2.684,2.635,2.075,2.095,2.788,2.306,2.609,2.411,1.904,2.681,2.908,2.56,2.641,2.765,1.905,2.634,2.626,1.677,3.314,1.799,2.072,2.056,2.865,2.706,2.546,2.688,2.046,2.185,2.168,2.547,2.568,1.898,2.979,2.39,2.064,1.947,2.168,2.775,1.919,2.616,2.006,2.336,2.17,2.339,2.688,2.775,2.279,2.572,2.4,1.933,0.924,0.756,0.541,0.401,0.717,0.466,0.564,0.509,0.633,0.499,0.524,0.453,0.465,0.481,0.646,0.636,0.664,0.876,0.596,0.528,0.781,0.422,0.408,0.765,0.558,0.575,0.491,0.51,0.596,0.487,0.764,0.971,0.436,0.714,1.11,0.546,0.723,0.601,0.433,0.631,0.573,0.383,0.674,0.768,0.604,0.502,0.623,0.757,0.51,0.55,0.406,0.412,0.593,0.627,0.596,0.412,0.407,0.625,0.391,0.386,0.471,0.776,0.503,0.717,0.494,0.532,0.587,0.51,0.56,0.648,0.499,0.488,0.461,0.437
-69,Epileptic,F,17.5,0.6,2.3,0.8,1.2,1.3,1.6,1.8,1.7,1.0,0.7,0.3,2.6,1.3,0.4,1.3,6.6,8.9,0.6,2.1,5.4,0.9,2.3,1.8,4.0,4.0,2.9,2.4,3.1,2.9,3.0,1.3,1.6,0.4,2.6,0.6,0.9,2.5,2.8,0.1,0.1,1.1,3.1,2.3,2.6,3.4,0.7,0.4,1.1,1.1,0.7,0.6,1.9,1.1,2.8,0.5,1.6,0.4,1.8,1.6,1.1,0.5,1.3,0.2,0.3,1.6,1.1,3.1,1.0,1.0,0.6,0.8,1.0,4.7,0.3,4,19,7,10,12,17,17,17,11,6,2.0,24,15,6,14,81,81,6,26,49,7,27,21,43,47,35,26,38,26,35,18,9,5,29,3,8,27,35,1,1,7,27,22,18,21,6,2,6,5,10,4,13,11,19,4,15,3,18,10,9,4,8,3,4,14,16,23,6,6,6,5,8,41,3,0.029,0.025,0.012,0.022,0.033,0.029,0.022,0.031,0.04,0.037,0.031,0.035,0.031,0.03,0.036,0.037,0.029,0.025,0.031,0.038,0.019,0.032,0.034,0.041,0.036,0.033,0.026,0.025,0.024,0.031,0.042,0.058,0.016,0.03,0.017,0.02,0.032,0.032,0.013,0.013,0.021,0.036,0.042,0.028,0.021,0.017,0.021,0.018,0.014,0.029,0.024,0.018,0.024,0.021,0.023,0.014,0.01,0.022,0.03,0.019,0.019,0.022,0.024,0.016,0.02,0.023,0.021,0.015,0.016,0.016,0.021,0.02,0.018,0.013,1531,5148,2740,2482,1969,2747,4056,4070,1853,1628,878.0,2412,3377,1376,2261,15154,22157,2594,4941,5611,2917,4335,2077,6636,7335,6434,4913,6117,7876,6017,2425,1366,1212,7242,1822,2929,7236,7400,312,645,1743,2870,5659,2893,4474,1602,735,2416,2420,1498,712,3570,2488,6066,622,3719,1407,3390,1483,1768,1101,2935,616,833,2474,1366,5092,2299,2320,1283,1458,1678,11186,508,0.107,0.116,0.091,0.109,0.127,0.14,0.103,0.129,0.139,0.138,0.124,0.147,0.129,0.14,0.153,0.146,0.129,0.109,0.148,0.154,0.095,0.143,0.154,0.154,0.152,0.139,0.113,0.123,0.112,0.137,0.143,0.138,0.096,0.133,0.088,0.097,0.128,0.14,0.089,0.077,0.108,0.141,0.156,0.122,0.101,0.1,0.103,0.091,0.084,0.137,0.131,0.1,0.109,0.104,0.13,0.09,0.073,0.11,0.134,0.106,0.109,0.105,0.119,0.119,0.11,0.121,0.111,0.092,0.091,0.1,0.103,0.118,0.099,0.11,351,1451,947,808,758,990,1231,1014,481,367,180.0,1290,798,275,603,3293,5372,424,1214,2502,768,1301,911,1580,1901,1772,1650,2022,1889,1678,537,530,393,1437,518,793,1453,1610,152,183,767,1400,1077,1501,2490,708,286,954,1054,494,345,1506,897,2303,316,1778,623,1499,815,891,367,910,204,389,1188,769,2262,965,984,528,649,747,4145,302,3.805,3.018,2.747,2.835,2.285,2.402,2.616,3.353,2.655,3.241,3.554,1.701,3.037,3.103,2.701,3.157,3.229,4.072,3.077,1.971,2.91,2.554,2.085,3.064,2.825,2.986,2.349,2.361,3.077,2.766,3.149,2.565,2.363,3.532,3.235,3.131,3.418,3.206,2.408,2.573,2.873,1.872,3.286,2.189,2.038,2.52,3.239,3.145,2.776,2.941,2.346,2.394,2.641,2.696,2.488,2.201,2.534,2.483,2.152,2.185,2.707,3.224,2.43,2.222,2.365,2.197,2.445,2.856,2.718,2.474,2.691,2.569,2.806,2.148,0.986,0.767,0.666,0.586,0.604,0.592,0.684,0.666,0.628,0.542,0.898,0.41,0.525,0.716,0.572,0.752,0.61,0.73,0.568,0.599,0.867,0.519,0.583,0.683,0.536,0.58,0.599,0.616,0.614,0.568,0.786,1.029,0.45,0.745,0.704,0.719,0.783,0.62,0.289,0.762,0.544,0.455,0.867,0.686,0.611,0.548,0.561,0.737,0.62,0.728,0.532,0.612,0.529,0.559,0.293,0.417,0.425,0.513,0.401,0.408,0.578,0.796,0.725,0.792,0.576,0.636,0.426,0.357,0.416,0.53,0.531,0.547,0.57,0.333
-70,Epileptic,M,47.0,1.7,2.8,1.2,0.9,2.1,2.3,2.9,2.0,1.9,0.8,0.5,3.6,1.8,0.5,2.3,5.4,9.2,1.1,2.6,4.5,1.5,3.1,2.5,3.6,4.2,3.5,5.3,2.6,2.8,3.2,2.0,0.7,0.5,2.5,0.7,0.8,4.1,3.0,0.2,0.1,1.1,4.4,2.4,3.2,2.7,1.1,0.6,1.2,1.5,0.9,0.5,2.8,1.5,2.9,0.5,3.2,0.5,1.5,1.3,1.6,1.0,1.7,0.7,0.7,2.3,1.5,2.7,2.0,1.2,1.0,1.2,1.3,3.9,0.4,11,28,15,7,17,21,22,22,15,9,5.0,35,15,6,24,54,68,11,31,43,15,45,22,32,45,38,55,25,22,36,19,24,5,27,3,9,41,38,2,1,6,38,22,27,16,9,3,9,9,6,4,19,10,21,3,26,4,11,6,13,10,11,4,5,23,18,19,12,8,9,9,12,31,4,0.053,0.027,0.019,0.021,0.039,0.032,0.041,0.029,0.049,0.033,0.035,0.032,0.028,0.024,0.036,0.032,0.038,0.034,0.031,0.033,0.025,0.029,0.028,0.043,0.03,0.028,0.034,0.025,0.027,0.03,0.061,0.104,0.024,0.029,0.019,0.018,0.042,0.029,0.011,0.015,0.02,0.048,0.036,0.026,0.02,0.018,0.026,0.018,0.018,0.014,0.028,0.023,0.022,0.024,0.027,0.018,0.014,0.016,0.019,0.022,0.026,0.031,0.033,0.025,0.025,0.033,0.018,0.027,0.025,0.026,0.02,0.021,0.015,0.018,1833,5066,2373,2017,2331,3455,2990,3635,2063,1638,660.0,3495,3351,1200,3342,10753,16794,2531,5371,4947,4591,4888,2594,5553,6378,7742,6339,4694,5651,6467,2150,1152,1206,7180,2546,2592,6823,8561,510,375,1452,3201,6514,3483,3752,1746,727,2623,2693,2027,450,4344,2123,4243,599,4892,1047,2881,1633,2093,1448,2788,763,980,3329,1122,4500,2328,1525,1580,1780,2456,9702,511,0.159,0.126,0.117,0.101,0.14,0.143,0.121,0.132,0.151,0.136,0.149,0.143,0.121,0.105,0.143,0.124,0.128,0.117,0.141,0.136,0.106,0.121,0.123,0.133,0.115,0.12,0.114,0.1,0.098,0.123,0.153,0.111,0.095,0.122,0.082,0.099,0.151,0.121,0.092,0.086,0.102,0.148,0.144,0.122,0.101,0.101,0.117,0.092,0.101,0.09,0.136,0.11,0.115,0.11,0.136,0.105,0.109,0.096,0.096,0.11,0.125,0.12,0.127,0.128,0.123,0.138,0.1,0.118,0.121,0.138,0.115,0.115,0.095,0.125,518,1652,983,715,878,1233,1042,1149,683,450,207.0,1704,1014,289,1024,2860,4393,550,1502,2231,1028,1584,1261,1532,1890,2187,2430,1546,1451,1961,704,471,372,1447,582,733,1702,1873,233,197,763,1550,1291,2158,2071,921,344,1142,1232,893,291,1928,1042,1977,256,2597,506,1414,961,1147,614,931,368,480,1624,717,2288,1175,725,542,854,1104,3900,255,3.335,2.807,2.457,2.635,2.498,2.57,2.39,2.786,2.339,2.914,2.613,1.842,2.726,2.956,2.574,2.805,2.911,3.54,2.932,1.961,3.207,2.371,1.849,2.742,2.644,2.841,2.267,2.375,2.936,2.591,2.36,2.326,2.656,3.454,3.588,2.999,3.026,3.264,2.42,2.164,2.473,1.877,3.488,1.834,2.037,2.06,2.606,2.933,2.779,2.685,1.827,2.327,2.362,2.298,2.729,2.128,2.188,2.289,1.992,2.019,2.383,3.138,2.488,2.438,2.13,1.83,2.195,2.394,2.354,2.974,2.251,2.521,2.74,2.561,0.796,0.58,0.416,0.582,0.548,0.679,0.633,0.567,0.77,0.685,0.742,0.466,0.414,0.434,0.526,0.538,0.672,0.732,0.616,0.566,0.749,0.652,0.512,0.871,0.635,0.547,0.537,0.55,0.526,0.571,0.72,1.085,0.399,0.581,0.698,0.676,0.88,0.669,0.448,0.458,0.472,0.529,0.756,0.674,0.666,0.51,0.438,0.545,0.519,0.401,0.35,0.499,0.439,0.477,0.558,0.413,0.407,0.584,0.464,0.497,0.562,0.741,0.737,0.836,0.556,0.566,0.502,0.395,0.425,0.911,0.487,0.566,0.437,0.423
-71,Epileptic,M,57.0,1.8,2.9,1.1,1.2,2.1,2.3,2.7,2.5,0.9,1.0,0.3,3.4,2.2,0.8,2.4,7.7,12.9,1.5,2.4,6.0,1.4,3.1,2.0,4.6,3.0,3.9,5.1,2.3,3.6,5.8,2.3,1.1,0.6,3.1,0.4,1.1,3.0,4.8,0.3,0.2,1.4,3.1,3.9,4.0,3.1,1.0,0.5,1.0,1.6,0.6,0.7,2.7,2.2,3.3,0.2,2.7,0.6,1.9,0.7,1.8,0.9,2.7,0.8,0.6,3.7,1.2,5.1,2.3,1.5,0.7,1.4,1.4,5.4,0.6,9,27,10,9,21,18,19,21,9,9,2.0,31,20,10,21,74,100,7,28,48,9,37,22,50,40,39,53,20,29,48,25,9,5,34,2,12,33,53,1,2,6,28,27,71,18,7,2,6,8,5,5,19,10,19,2,19,4,10,4,16,10,18,4,4,22,9,42,13,10,5,8,9,41,4,0.045,0.021,0.013,0.019,0.039,0.033,0.032,0.032,0.036,0.04,0.027,0.033,0.03,0.032,0.036,0.034,0.033,0.036,0.031,0.036,0.024,0.036,0.028,0.04,0.034,0.027,0.032,0.027,0.03,0.037,0.048,0.026,0.023,0.031,0.011,0.022,0.043,0.036,0.016,0.013,0.026,0.035,0.058,0.246,0.025,0.021,0.02,0.014,0.016,0.025,0.03,0.023,0.023,0.022,0.029,0.02,0.014,0.02,0.021,0.025,0.027,0.037,0.025,0.021,0.028,0.022,0.025,0.023,0.023,0.019,0.024,0.019,0.018,0.023,2102,6696,2826,2525,2491,2973,3032,3822,1630,1363,564.0,2960,3931,1506,3500,13843,22836,3339,5257,6865,2787,4654,2583,7717,5558,7325,8031,3950,6528,7667,2647,1755,1272,7073,2134,2702,6296,10245,453,552,1798,3274,5463,3207,3382,1501,940,2661,3257,1342,648,4496,3052,5800,337,3859,1438,3016,798,2040,1229,3023,768,935,4061,1644,7228,3228,1974,1184,1717,2230,10774,645,0.139,0.099,0.088,0.095,0.14,0.14,0.122,0.136,0.136,0.142,0.106,0.137,0.121,0.141,0.14,0.131,0.127,0.123,0.124,0.135,0.097,0.14,0.131,0.145,0.137,0.124,0.13,0.101,0.103,0.141,0.158,0.094,0.101,0.129,0.066,0.105,0.138,0.138,0.086,0.109,0.112,0.142,0.178,0.117,0.108,0.106,0.088,0.078,0.091,0.108,0.129,0.104,0.107,0.102,0.136,0.107,0.085,0.097,0.116,0.125,0.129,0.14,0.127,0.119,0.119,0.095,0.118,0.104,0.118,0.108,0.117,0.107,0.098,0.124,581,2287,1175,1000,989,1124,1241,1298,492,383,186.0,1558,1164,417,1131,3826,6415,576,1393,2697,863,1525,1121,2102,1705,2451,2889,1398,1888,2667,735,687,425,1582,567,862,1492,2367,254,280,821,1404,1159,1983,2104,767,388,1094,1511,457,397,2078,1332,2444,158,2059,689,1477,477,1201,556,1174,405,468,2041,843,3257,1463,972,614,886,1088,4568,384,3.506,2.678,2.471,2.577,2.286,2.243,2.104,2.525,2.5,2.985,2.604,1.77,2.633,2.58,2.501,2.687,2.813,4.116,2.943,2.122,2.566,2.405,1.918,2.819,2.415,2.515,2.191,2.198,2.568,2.341,2.629,2.39,2.428,3.048,3.266,2.657,3.08,2.999,2.03,2.064,2.713,2.04,3.198,1.855,1.834,2.196,2.852,2.996,2.552,3.073,2.154,2.228,2.479,2.449,2.503,2.028,2.33,2.308,1.865,1.802,2.07,2.532,2.313,2.28,2.133,2.174,2.097,2.322,2.204,2.37,2.196,2.366,2.488,2.099,0.684,0.532,0.433,0.434,0.522,0.556,0.459,0.684,0.55,0.439,0.618,0.488,0.426,0.449,0.422,0.57,0.62,0.66,0.684,0.64,0.831,0.514,0.527,0.565,0.607,0.557,0.502,0.539,0.598,0.574,0.674,0.973,0.495,0.668,0.576,0.503,0.664,0.74,0.375,0.473,0.633,0.452,0.776,0.652,0.484,0.513,0.523,0.68,0.566,0.549,0.444,0.503,0.699,0.482,0.563,0.405,0.482,0.543,0.39,0.388,0.484,0.607,0.588,0.731,0.61,0.642,0.513,0.433,0.469,0.525,0.541,0.476,0.623,0.48
-72,Epileptic,F,47.0,0.6,2.4,1.4,1.2,2.0,2.1,1.5,2.1,1.0,0.6,0.3,2.3,1.6,0.6,1.0,5.8,8.6,0.6,2.7,5.6,1.1,2.7,1.9,3.0,2.2,4.2,3.3,3.0,2.8,3.8,1.1,0.7,0.9,2.0,0.6,0.5,3.0,3.5,0.1,0.3,1.3,2.8,2.2,4.6,3.4,0.8,0.7,1.3,1.0,0.8,0.7,1.7,2.4,2.4,0.1,2.5,1.3,1.8,1.4,1.3,0.9,1.3,0.3,0.3,2.2,1.8,2.4,1.3,1.3,0.4,1.3,0.8,4.4,0.2,6,26,16,10,20,18,11,15,11,4,3.0,18,14,6,8,57,70,6,23,44,14,27,16,31,27,39,35,28,20,29,15,7,6,21,3,2,28,32,1,2,7,23,18,34,18,6,4,8,5,6,5,12,20,16,0,21,10,16,11,9,5,8,2,3,14,19,18,9,8,3,8,7,32,2,0.027,0.023,0.021,0.02,0.033,0.031,0.022,0.035,0.041,0.031,0.024,0.029,0.032,0.028,0.032,0.032,0.032,0.024,0.035,0.04,0.022,0.029,0.034,0.038,0.028,0.034,0.03,0.028,0.025,0.035,0.039,0.024,0.025,0.024,0.017,0.012,0.032,0.034,0.024,0.02,0.029,0.037,0.039,0.045,0.024,0.018,0.03,0.019,0.014,0.019,0.03,0.019,0.024,0.017,0.015,0.018,0.031,0.023,0.033,0.022,0.034,0.022,0.024,0.013,0.023,0.03,0.022,0.023,0.025,0.017,0.033,0.022,0.02,0.018,1301,4810,2831,2429,1984,2623,2325,3108,1412,1154,596.0,2207,2761,1025,1851,9968,16032,1996,3626,4358,2456,4340,2015,4909,4526,6154,5918,4622,5085,5358,1682,1226,1628,5834,1872,1748,5933,6584,154,454,1660,2277,4945,2235,3635,1344,884,2470,2265,1552,550,2830,3545,5171,152,4076,1310,2719,1315,1601,927,2069,449,696,3039,1955,3353,1837,1725,610,1522,1846,8832,467,0.117,0.115,0.117,0.109,0.134,0.134,0.098,0.132,0.137,0.119,0.106,0.125,0.13,0.126,0.119,0.13,0.127,0.106,0.129,0.142,0.088,0.127,0.133,0.134,0.129,0.136,0.124,0.105,0.102,0.142,0.135,0.095,0.109,0.113,0.091,0.075,0.121,0.139,0.119,0.12,0.117,0.142,0.132,0.156,0.107,0.1,0.11,0.103,0.081,0.111,0.134,0.104,0.121,0.096,0.076,0.112,0.13,0.109,0.14,0.115,0.129,0.116,0.115,0.102,0.112,0.145,0.114,0.113,0.116,0.107,0.132,0.117,0.103,0.11,417,1729,1042,947,887,1030,910,1021,413,313,193.0,1128,819,309,572,3055,4633,422,1329,2141,708,1476,881,1364,1379,2048,1947,1628,1512,1719,503,535,484,1336,488,565,1528,1702,111,197,787,1175,1052,1653,2253,703,412,1069,1069,673,344,1371,1542,2212,66,2096,738,1389,763,924,443,802,210,392,1486,871,1736,931,907,332,664,702,3547,223,3.194,2.636,2.614,2.557,1.982,2.172,2.217,2.835,2.649,3.127,2.412,1.769,2.688,2.38,2.552,2.558,2.797,3.45,2.357,1.863,2.802,2.378,2.011,2.721,2.668,2.6,2.37,2.296,2.605,2.651,2.435,2.283,2.815,3.14,3.255,2.878,2.937,2.959,1.644,2.871,2.665,1.805,3.379,1.636,1.814,2.18,2.739,2.972,2.535,2.769,1.735,2.277,2.247,2.398,2.197,2.219,2.181,2.197,1.985,1.934,2.422,2.539,2.453,2.126,2.308,2.605,2.117,2.285,2.198,2.086,2.584,2.962,2.688,2.557,0.686,0.534,0.585,0.472,0.535,0.461,0.627,0.754,0.619,0.466,0.596,0.441,0.445,0.485,0.46,0.478,0.483,0.661,0.666,0.525,0.681,0.507,0.453,0.639,0.483,0.459,0.485,0.555,0.516,0.524,0.579,0.901,0.488,0.563,0.527,0.461,0.703,0.589,0.266,0.524,0.699,0.534,0.673,0.601,0.559,0.487,0.487,0.761,0.573,0.635,0.348,0.391,0.494,0.455,0.231,0.583,0.46,0.486,0.435,0.344,0.43,0.723,0.731,1.017,0.645,0.996,0.437,0.391,0.388,0.387,0.801,0.684,0.484,0.756
-74,Epileptic,M,17.5,0.7,2.4,0.9,1.1,1.4,1.5,1.1,1.7,1.0,0.9,0.5,3.3,1.8,0.4,1.3,6.3,10.1,0.8,1.3,4.4,0.9,2.4,1.7,2.1,2.4,3.0,2.8,1.7,2.2,3.2,1.2,0.8,0.5,2.1,0.5,0.6,2.8,3.6,0.2,0.2,1.3,2.4,1.7,2.5,2.1,0.7,0.3,0.8,1.0,1.0,0.4,1.7,1.6,2.5,0.2,1.9,0.4,1.4,0.7,0.9,0.8,0.8,0.5,0.4,1.4,1.8,2.0,1.4,0.7,0.4,0.7,1.7,3.9,0.4,4,25,7,8,15,18,14,15,14,10,7.0,30,19,6,12,89,94,6,17,40,8,30,18,25,29,37,37,19,23,46,14,9,5,23,3,6,32,47,1,1,10,23,15,19,13,6,1,5,6,9,3,11,11,16,1,17,6,14,6,8,6,6,4,2,12,21,14,11,5,4,5,12,32,4,0.031,0.028,0.013,0.019,0.042,0.028,0.025,0.025,0.046,0.036,0.038,0.032,0.027,0.028,0.038,0.031,0.033,0.024,0.023,0.036,0.022,0.028,0.027,0.031,0.03,0.023,0.026,0.02,0.021,0.035,0.042,0.048,0.021,0.028,0.016,0.011,0.028,0.031,0.012,0.019,0.024,0.03,0.044,0.02,0.015,0.016,0.015,0.012,0.013,0.023,0.017,0.017,0.026,0.018,0.018,0.014,0.012,0.018,0.02,0.016,0.023,0.02,0.048,0.018,0.016,0.029,0.017,0.016,0.012,0.017,0.017,0.018,0.016,0.019,1562,4359,2499,2331,1748,2518,2590,3640,1358,1802,871.0,3332,4155,846,2694,13532,20771,2498,3593,4496,2974,4457,2451,4823,4827,7999,6399,3501,6699,5761,2183,963,1240,7139,2016,2563,6745,8492,453,378,1669,2796,3686,3682,3671,1436,745,2670,2175,1883,570,3301,1946,5383,342,3568,1066,3022,1126,1667,979,1323,423,767,2886,1431,3724,3088,1851,825,1591,2923,9413,556,0.108,0.126,0.094,0.102,0.146,0.136,0.113,0.124,0.16,0.146,0.145,0.142,0.127,0.119,0.141,0.137,0.139,0.102,0.113,0.153,0.078,0.136,0.135,0.134,0.13,0.12,0.122,0.098,0.102,0.142,0.14,0.134,0.104,0.12,0.087,0.08,0.115,0.139,0.086,0.086,0.122,0.132,0.126,0.107,0.089,0.095,0.092,0.076,0.084,0.111,0.107,0.096,0.119,0.096,0.117,0.097,0.098,0.101,0.109,0.104,0.124,0.116,0.163,0.092,0.098,0.116,0.101,0.1,0.086,0.126,0.103,0.106,0.096,0.117,384,1368,896,841,661,991,853,1096,413,410,255.0,1563,1062,216,682,3664,5127,480,945,2087,812,1399,978,1184,1319,2161,1905,1255,1747,1798,507,405,376,1332,459,869,1687,1916,260,173,794,1302,837,1983,2048,684,289,987,1066,730,344,1523,952,2147,156,1976,547,1401,649,863,473,550,190,316,1373,929,1783,1292,838,355,730,1421,3610,335,3.657,3.034,2.677,2.724,2.347,2.361,2.476,2.972,2.497,3.277,2.785,1.899,3.003,2.721,2.869,2.769,3.183,3.827,3.035,1.906,2.875,2.446,2.171,2.937,2.758,2.892,2.532,2.241,2.894,2.515,3.099,2.432,2.486,3.564,3.96,2.676,2.895,3.231,2.117,2.369,2.636,1.969,3.25,2.05,2.038,2.15,3.235,3.366,2.552,2.834,2.064,2.225,2.174,2.624,2.748,2.085,2.292,2.377,2.067,2.114,2.633,2.446,2.208,2.699,2.288,1.888,2.297,2.666,2.538,2.674,2.413,2.487,2.739,2.092,0.746,0.615,0.605,0.559,0.613,0.529,0.615,0.672,0.525,0.81,0.787,0.465,0.444,0.416,0.472,0.588,0.583,0.859,0.412,0.576,0.637,0.411,0.561,0.677,0.533,0.538,0.528,0.529,0.516,0.593,0.775,1.173,0.504,0.56,0.618,0.489,0.608,0.583,0.316,0.412,0.482,0.545,0.726,0.58,0.592,0.527,0.606,0.817,0.46,0.535,0.393,0.537,0.456,0.488,0.247,0.402,0.407,0.504,0.409,0.412,0.437,0.579,0.603,0.91,0.487,0.622,0.405,0.402,0.427,0.477,0.436,0.381,0.519,0.361
-75,Epileptic,F,42.0,0.5,2.5,1.1,1.5,2.6,2.4,2.8,1.8,1.1,0.9,0.3,3.5,1.3,0.4,1.0,7.0,13.5,0.8,2.5,4.8,2.6,2.8,1.3,3.9,2.9,2.6,2.9,4.2,6.1,2.7,2.0,1.3,0.8,3.1,0.7,0.5,5.1,2.8,0.1,0.1,0.9,3.7,3.1,2.1,2.8,1.0,0.3,0.9,1.3,0.7,1.2,1.7,0.9,2.7,0.3,2.0,1.0,2.5,0.9,0.8,0.7,1.7,0.3,0.5,2.1,1.5,2.7,1.7,1.6,0.5,1.0,1.5,3.8,0.3,5,21,12,11,22,19,20,16,8,5,2.0,26,14,4,8,71,92,8,24,36,18,29,12,54,29,25,29,31,39,27,16,10,7,27,3,4,43,30,1,1,6,33,24,20,15,8,2,6,7,6,7,12,7,19,1,15,8,20,7,7,6,11,2,5,19,18,20,13,10,6,7,11,26,3,0.032,0.024,0.022,0.028,0.049,0.029,0.042,0.032,0.047,0.033,0.027,0.038,0.029,0.029,0.026,0.034,0.038,0.031,0.031,0.041,0.04,0.034,0.027,0.049,0.041,0.028,0.032,0.035,0.043,0.03,0.047,0.061,0.041,0.036,0.022,0.015,0.05,0.03,0.014,0.012,0.02,0.037,0.05,0.027,0.019,0.023,0.015,0.014,0.018,0.023,0.04,0.022,0.025,0.018,0.025,0.018,0.023,0.026,0.02,0.02,0.022,0.031,0.039,0.029,0.022,0.028,0.022,0.021,0.023,0.023,0.024,0.02,0.018,0.022,1127,4568,2393,2134,2110,3718,2549,2959,1351,1355,575.0,2656,2431,951,2298,11381,17179,1956,4041,3961,3405,4573,1971,5207,4632,4963,4191,4656,5742,4436,2301,981,881,5916,1916,1333,6373,7029,258,350,1228,3258,5333,2111,3874,1339,743,2193,2006,1250,621,2486,1192,5084,303,2718,1335,3054,1007,1026,1089,2221,346,939,2650,1537,3805,3016,2139,991,1198,2138,7609,351,0.105,0.119,0.12,0.123,0.156,0.124,0.13,0.116,0.154,0.14,0.095,0.143,0.117,0.132,0.109,0.131,0.126,0.124,0.122,0.147,0.125,0.136,0.117,0.154,0.134,0.123,0.111,0.11,0.12,0.137,0.142,0.153,0.111,0.133,0.089,0.08,0.15,0.14,0.098,0.086,0.105,0.138,0.159,0.117,0.097,0.123,0.088,0.083,0.097,0.11,0.16,0.107,0.119,0.097,0.128,0.104,0.103,0.129,0.116,0.109,0.112,0.129,0.149,0.128,0.121,0.125,0.11,0.106,0.103,0.13,0.121,0.113,0.096,0.131,350,1590,880,893,914,1281,1026,1023,419,401,170.0,1465,750,234,609,3463,5568,409,1332,2028,1068,1431,815,1618,1316,1571,1577,1770,1870,1643,760,401,332,1404,470,538,1841,1655,172,192,685,1644,1264,1427,2197,684,294,930,1052,598,431,1299,642,2327,142,1608,726,1543,681,610,554,906,139,378,1446,815,1964,1355,1006,408,660,1095,3440,225,3.025,2.614,2.796,2.39,2.185,2.41,2.225,2.491,2.426,2.814,2.84,1.652,2.512,2.76,2.707,2.593,2.638,3.274,2.601,1.786,2.576,2.46,2.064,2.555,2.691,2.674,2.154,2.171,2.551,2.215,2.439,2.713,2.238,2.977,3.363,2.247,2.556,3.039,1.686,2.028,2.271,1.767,2.933,1.735,2.011,2.166,3.017,2.811,2.446,2.4,1.748,2.06,2.028,2.348,2.467,1.959,2.188,2.173,1.752,1.905,2.071,2.571,2.879,2.925,2.015,2.273,2.124,2.345,2.282,2.576,2.138,2.322,2.384,1.903,0.622,0.687,0.492,0.507,0.604,0.717,0.683,0.63,0.762,0.549,0.808,0.414,0.429,0.475,0.528,0.704,0.694,0.827,0.8,0.576,0.897,0.601,0.685,0.755,0.654,0.524,0.551,0.606,0.643,0.571,0.831,1.094,0.563,0.801,0.662,0.517,0.868,0.505,0.254,0.428,0.466,0.542,0.91,0.566,0.649,0.541,0.544,0.681,0.432,0.542,0.314,0.412,0.428,0.486,0.338,0.505,0.488,0.598,0.38,0.412,0.398,0.633,0.604,0.534,0.562,0.732,0.494,0.431,0.542,0.76,0.416,0.56,0.455,0.398
-76,Epileptic,F,27.0,1.0,2.8,1.3,1.6,1.3,1.3,1.5,1.2,0.9,0.6,0.3,2.4,1.1,0.5,1.3,5.1,6.5,0.9,2.1,3.6,1.3,3.0,1.5,2.9,3.3,2.3,3.3,2.0,2.4,3.3,1.8,1.5,0.3,1.8,0.3,0.5,3.6,2.3,0.2,0.1,0.9,2.3,3.0,1.6,2.6,0.7,0.4,0.6,1.0,0.6,0.3,1.8,1.3,2.4,0.2,2.1,0.7,1.1,0.8,0.5,0.4,1.2,0.2,0.8,0.6,1.0,2.0,1.3,0.7,0.5,0.9,1.2,4.4,0.3,6,29,13,15,17,11,14,12,10,7,3.0,19,16,6,15,56,69,7,26,30,15,33,14,31,43,29,38,22,23,36,21,13,3,26,2,5,39,30,2,1,6,15,36,12,11,5,2,5,5,5,3,12,7,17,2,18,7,8,5,4,3,8,2,5,6,17,12,9,4,5,5,8,42,2,0.048,0.031,0.025,0.031,0.034,0.025,0.026,0.027,0.038,0.028,0.034,0.039,0.029,0.039,0.033,0.035,0.029,0.043,0.027,0.044,0.031,0.041,0.037,0.039,0.037,0.024,0.03,0.03,0.032,0.029,0.056,0.051,0.02,0.025,0.014,0.015,0.04,0.028,0.018,0.01,0.017,0.039,0.055,0.025,0.019,0.014,0.021,0.012,0.016,0.018,0.023,0.019,0.026,0.02,0.013,0.017,0.019,0.016,0.028,0.015,0.024,0.025,0.013,0.028,0.012,0.02,0.021,0.02,0.016,0.019,0.02,0.019,0.018,0.026,1407,4234,2283,2375,2127,2174,2920,2912,1677,1607,532.0,1775,2991,896,2283,9619,14050,1995,4827,3203,3897,3976,1548,6119,6357,5809,6082,3755,5507,6153,2008,858,759,4701,1374,1650,6096,6638,414,356,1460,1658,4223,1785,3401,1334,714,1945,1954,1557,449,2817,1299,4099,390,3534,1198,2410,881,1093,789,1984,336,896,1650,1158,2814,2276,1455,1146,1316,1750,7786,441,0.129,0.139,0.13,0.137,0.148,0.122,0.111,0.122,0.16,0.143,0.136,0.174,0.124,0.167,0.156,0.148,0.132,0.152,0.127,0.174,0.121,0.133,0.128,0.156,0.141,0.131,0.127,0.115,0.122,0.138,0.184,0.129,0.089,0.118,0.073,0.089,0.151,0.13,0.121,0.078,0.104,0.138,0.185,0.116,0.094,0.096,0.105,0.071,0.087,0.102,0.123,0.1,0.122,0.099,0.108,0.102,0.109,0.096,0.114,0.092,0.109,0.121,0.098,0.128,0.088,0.116,0.1,0.102,0.095,0.127,0.105,0.112,0.103,0.144,383,1502,818,865,751,839,993,874,443,393,157.0,984,836,262,672,2762,4195,423,1297,1489,844,1215,733,1528,1745,1658,1994,1268,1421,1936,636,451,263,1137,414,574,1612,1487,168,171,707,977,1068,1030,1877,665,294,783,952,616,243,1385,713,1948,207,1897,592,1092,467,544,371,743,213,437,797,807,1492,1068,668,439,649,880,3673,143,3.491,2.614,2.664,2.535,2.233,2.251,2.402,2.799,2.582,2.87,3.041,1.68,2.699,2.684,2.507,2.552,2.603,3.482,2.974,2.026,3.319,2.459,1.722,2.919,2.729,2.671,2.296,2.337,2.862,2.505,2.451,1.832,2.524,2.843,2.929,2.454,2.796,3.028,2.888,2.194,2.641,1.572,2.823,2.082,2.075,2.139,2.857,2.876,2.525,2.619,2.294,2.077,2.005,2.115,2.243,2.03,2.287,2.466,2.033,2.263,2.442,2.621,1.774,2.144,2.237,1.782,2.004,2.308,2.435,2.573,2.32,2.341,2.319,3.13,0.922,0.6,0.535,0.575,0.754,0.455,0.542,0.534,0.725,0.69,0.711,0.456,0.499,0.467,0.626,0.627,0.645,0.816,0.552,0.644,0.833,0.8,0.558,0.659,0.733,0.633,0.641,0.561,0.492,0.592,0.646,0.813,0.369,0.55,0.73,0.389,0.702,0.685,0.328,0.384,0.461,0.5,0.823,0.732,0.646,0.55,0.656,0.74,0.392,0.631,0.42,0.488,0.498,0.418,0.356,0.408,0.405,0.436,0.601,0.375,0.34,0.708,0.41,0.729,0.52,0.76,0.44,0.426,0.424,0.915,0.461,0.555,0.427,0.694
-77,Epileptic,F,22.0,0.8,2.1,1.0,1.6,2.7,1.8,2.0,1.5,1.2,0.8,0.4,3.7,1.0,0.6,1.4,5.7,7.8,1.6,1.9,4.5,1.5,3.4,2.6,4.3,8.8,5.2,8.0,4.3,2.9,3.2,2.4,1.3,0.5,2.1,0.4,1.1,5.5,4.0,0.2,0.2,0.7,2.9,2.4,1.8,2.5,0.9,0.5,1.2,1.7,0.9,0.6,1.5,1.6,2.3,0.4,3.7,0.9,1.4,0.9,1.4,0.6,1.5,0.4,0.6,1.4,0.9,3.0,1.2,1.1,0.7,0.5,1.3,5.2,0.2,10,21,7,15,24,17,21,18,11,7,4.0,30,14,5,17,70,74,14,24,38,14,34,19,48,66,46,60,34,28,36,20,15,6,26,2,10,58,43,2,1,4,20,26,15,14,5,2,6,9,9,5,12,9,17,3,30,8,11,10,11,7,12,3,5,11,11,30,7,7,4,4,9,43,2,0.043,0.024,0.019,0.026,0.051,0.035,0.04,0.025,0.044,0.036,0.059,0.044,0.025,0.035,0.031,0.034,0.032,0.046,0.034,0.041,0.029,0.043,0.042,0.044,0.058,0.042,0.047,0.047,0.032,0.033,0.054,0.082,0.025,0.028,0.013,0.032,0.043,0.036,0.009,0.016,0.017,0.043,0.041,0.021,0.02,0.017,0.02,0.017,0.022,0.018,0.029,0.018,0.023,0.024,0.013,0.019,0.019,0.016,0.021,0.022,0.019,0.023,0.027,0.019,0.017,0.017,0.02,0.017,0.021,0.02,0.018,0.018,0.019,0.015,1582,4929,2551,3197,2870,2520,2728,3967,2601,1624,684.0,3210,3281,1542,3696,12970,20178,2818,4325,4209,4240,4009,2049,7888,5232,6541,6906,3654,6675,6039,2776,923,1230,6299,2409,1497,8526,9029,491,337,1251,2001,7328,2428,3411,1680,917,2452,2520,2051,695,3090,2192,4544,719,6052,1622,2932,1517,1721,1326,2897,593,1341,2397,1491,4817,2349,1843,1258,1106,2149,9845,484,0.142,0.114,0.098,0.127,0.157,0.148,0.129,0.122,0.171,0.152,0.161,0.176,0.13,0.156,0.129,0.143,0.134,0.14,0.138,0.168,0.113,0.149,0.139,0.154,0.172,0.142,0.145,0.137,0.122,0.141,0.143,0.146,0.115,0.126,0.064,0.102,0.144,0.152,0.088,0.099,0.093,0.144,0.159,0.103,0.093,0.099,0.099,0.082,0.103,0.106,0.136,0.096,0.108,0.112,0.098,0.106,0.111,0.093,0.115,0.114,0.11,0.123,0.133,0.102,0.103,0.106,0.103,0.094,0.102,0.1,0.104,0.105,0.099,0.114,436,1596,837,1065,945,943,1016,1147,590,369,154.0,1483,826,313,906,3382,4904,585,1136,1925,971,1476,1143,1976,2426,2296,2905,1591,1710,1725,865,354,398,1315,581,537,2318,2099,255,161,636,1173,1347,1440,2001,781,337,1003,1132,819,368,1447,1050,1733,437,2912,678,1391,765,983,622,1029,243,534,1156,827,2529,1036,890,549,501,1087,4314,237,3.289,2.786,2.955,2.785,2.488,2.326,2.231,2.797,3.205,3.182,3.076,1.927,3.049,3.386,2.99,2.815,3.133,3.396,2.951,1.993,3.133,2.223,1.681,2.962,1.938,2.361,2.026,1.923,2.861,2.733,2.435,2.47,2.488,3.327,3.581,2.383,2.657,3.083,2.133,2.303,2.494,1.593,3.467,1.889,1.934,2.34,3.057,2.987,2.791,2.509,2.263,2.269,2.413,2.473,1.941,2.207,2.382,2.34,2.082,1.984,2.275,2.901,2.564,2.828,2.344,2.16,2.0,2.509,2.394,2.56,2.428,2.369,2.356,2.531,0.836,0.598,0.535,0.553,0.707,0.484,0.672,0.558,0.648,0.742,0.766,0.608,0.549,0.444,0.52,0.622,0.685,1.27,0.61,0.615,0.74,0.614,0.478,0.818,0.704,0.727,0.675,0.569,0.67,0.687,0.889,1.0,0.435,0.562,0.588,0.564,0.871,0.756,0.405,0.44,0.406,0.522,0.849,0.65,0.585,0.502,0.675,0.613,0.473,0.614,0.264,0.425,0.402,0.548,0.379,0.441,0.488,0.459,0.538,0.372,0.484,0.555,0.466,0.762,0.428,0.751,0.444,0.498,0.415,0.563,0.465,0.457,0.496,0.393
-78,Epileptic,F,62.0,0.4,2.9,1.5,2.3,1.9,1.5,2.1,1.3,1.3,0.8,0.3,2.6,1.1,0.4,1.1,4.9,10.1,0.9,1.7,5.5,0.9,2.5,1.8,3.0,2.7,2.5,3.0,2.1,2.6,2.9,1.4,1.6,0.4,2.3,0.2,0.4,2.8,2.5,0.2,0.1,0.9,3.0,1.8,2.7,3.0,0.7,0.3,0.6,0.8,0.7,1.2,1.1,1.2,2.8,0.2,1.9,0.6,1.8,0.6,1.2,0.4,1.8,0.5,0.4,1.2,1.4,2.4,1.0,1.7,0.8,0.7,1.2,3.3,0.3,3,23,15,16,14,17,13,11,10,8,3.0,28,13,4,11,44,64,8,24,48,8,31,20,24,33,25,35,21,19,34,13,11,4,25,1,3,32,30,1,1,5,23,22,18,16,4,1,5,4,6,8,7,8,22,1,15,5,16,6,9,3,14,3,3,8,14,19,6,12,6,5,9,24,3,0.026,0.036,0.035,0.043,0.045,0.029,0.04,0.028,0.045,0.038,0.029,0.028,0.032,0.029,0.033,0.035,0.044,0.039,0.031,0.039,0.023,0.03,0.03,0.039,0.033,0.033,0.031,0.028,0.026,0.034,0.047,0.059,0.026,0.038,0.009,0.016,0.038,0.031,0.014,0.015,0.02,0.035,0.044,0.023,0.022,0.016,0.019,0.013,0.014,0.027,0.034,0.015,0.025,0.024,0.017,0.017,0.022,0.022,0.019,0.022,0.012,0.037,0.038,0.022,0.018,0.025,0.024,0.018,0.024,0.029,0.024,0.019,0.015,0.022,1072,4101,1966,2076,1716,2551,2354,2458,1545,1196,642.0,3162,2281,1003,2330,7733,12977,1896,3442,5734,2599,4164,2167,4890,4957,4546,4912,3392,4540,4471,1992,1005,778,4871,1502,1028,6489,5906,341,395,1213,2847,6278,3331,3154,1181,632,2044,1746,1154,1005,2171,1509,4955,201,3338,1043,2832,820,1465,926,2153,340,876,2263,1524,2898,1731,2211,821,989,1780,7260,394,0.091,0.137,0.139,0.129,0.137,0.143,0.13,0.121,0.152,0.157,0.113,0.129,0.141,0.122,0.141,0.132,0.14,0.143,0.14,0.144,0.095,0.14,0.132,0.138,0.151,0.14,0.13,0.117,0.091,0.145,0.138,0.149,0.115,0.13,0.065,0.078,0.142,0.128,0.098,0.088,0.103,0.123,0.143,0.104,0.108,0.092,0.094,0.081,0.083,0.118,0.155,0.088,0.115,0.112,0.124,0.1,0.109,0.122,0.124,0.118,0.082,0.138,0.169,0.105,0.097,0.11,0.124,0.098,0.117,0.132,0.12,0.118,0.09,0.134,285,1456,753,805,706,927,849,780,478,351,203.0,1379,660,218,604,2210,3805,379,1008,2312,632,1371,949,1297,1561,1356,1733,1120,1337,1564,548,430,259,1071,369,410,1390,1414,181,170,634,1325,915,1993,1976,634,265,792,858,365,506,1118,733,2021,114,1729,481,1352,503,830,483,811,180,368,1106,845,1461,798,1133,455,526,863,3233,205,3.376,2.638,2.587,2.454,2.314,2.379,2.298,2.721,2.82,2.839,2.799,1.936,2.68,3.179,2.875,2.669,2.856,3.586,2.813,2.073,3.009,2.305,2.059,2.856,2.511,2.725,2.218,2.34,2.62,2.308,2.633,2.515,2.541,3.01,3.433,2.23,3.146,2.924,2.198,2.288,2.314,1.898,3.653,1.827,1.809,2.035,2.889,2.851,2.396,3.134,2.071,2.166,2.288,2.387,2.18,2.071,2.45,2.327,1.859,1.995,2.22,2.85,2.293,2.666,2.137,2.438,2.142,2.248,2.132,2.147,2.135,2.347,2.437,2.197,0.609,0.799,0.63,0.592,0.64,0.525,0.568,0.623,0.635,0.479,0.589,0.471,0.447,0.558,0.533,0.584,0.599,0.757,0.53,0.563,0.862,0.44,0.409,0.681,0.483,0.491,0.503,0.548,0.44,0.479,0.841,1.004,0.329,0.619,0.782,0.596,0.821,0.689,0.361,0.606,0.424,0.464,0.932,0.517,0.478,0.328,0.477,0.972,0.502,0.841,0.565,0.427,0.572,0.496,0.231,0.416,0.666,0.462,0.415,0.385,0.492,0.657,0.418,0.586,0.484,0.893,0.569,0.496,0.389,0.697,0.405,0.566,0.54,0.421
-79,Epileptic,F,22.0,0.8,2.9,1.0,1.8,1.8,2.1,1.5,1.3,1.4,1.0,0.3,2.9,1.6,0.4,1.8,6.3,9.8,1.3,3.0,4.7,1.5,3.0,2.4,5.5,5.7,5.5,5.6,2.7,2.0,3.2,2.5,1.2,0.4,3.0,1.0,0.7,6.7,3.2,0.1,0.3,1.2,3.4,2.7,2.2,2.6,1.0,0.6,1.4,1.3,1.0,0.4,1.8,1.8,3.1,0.3,4.1,0.9,1.6,0.7,1.3,0.9,2.2,0.5,0.4,1.6,1.1,3.1,1.1,0.8,1.0,1.2,1.5,3.9,0.4,4,26,9,13,14,20,17,15,14,11,5.0,27,22,6,21,87,107,10,34,46,34,36,25,48,56,57,59,30,23,45,25,10,5,30,11,6,64,38,1,2,8,32,24,16,15,9,2,6,8,7,5,14,17,23,3,35,6,14,5,7,7,18,3,4,12,19,27,9,9,14,9,14,31,4,0.029,0.027,0.016,0.027,0.034,0.036,0.028,0.025,0.055,0.037,0.041,0.043,0.024,0.032,0.035,0.033,0.03,0.046,0.035,0.045,0.031,0.043,0.038,0.048,0.05,0.04,0.034,0.031,0.022,0.03,0.055,0.045,0.022,0.032,0.027,0.016,0.052,0.033,0.011,0.014,0.023,0.044,0.037,0.026,0.019,0.016,0.018,0.018,0.016,0.028,0.024,0.018,0.024,0.019,0.016,0.018,0.022,0.017,0.02,0.022,0.022,0.03,0.028,0.013,0.021,0.017,0.019,0.018,0.016,0.03,0.024,0.016,0.016,0.015,1795,5507,2155,2868,2402,2799,2664,2781,1604,1827,606.0,2829,4024,1168,3862,14236,22637,2565,5159,4339,3543,4243,2300,6551,5561,7637,7823,4190,6289,6972,2288,1092,1007,7757,2503,1886,7341,7564,404,591,1471,2563,6682,2261,3354,1715,919,2534,2619,1495,634,3478,2537,6244,441,6348,1445,3286,1005,1730,1152,3489,509,1216,2337,1376,5205,2256,2012,1800,1639,2685,8968,522,0.098,0.13,0.106,0.12,0.127,0.135,0.116,0.121,0.166,0.157,0.139,0.168,0.13,0.129,0.151,0.145,0.134,0.15,0.135,0.165,0.108,0.145,0.133,0.146,0.153,0.151,0.129,0.112,0.1,0.131,0.162,0.126,0.107,0.132,0.111,0.096,0.148,0.132,0.09,0.097,0.119,0.149,0.141,0.122,0.099,0.1,0.099,0.087,0.094,0.116,0.138,0.1,0.119,0.106,0.108,0.102,0.108,0.097,0.105,0.104,0.12,0.132,0.133,0.088,0.107,0.104,0.102,0.098,0.092,0.139,0.116,0.104,0.091,0.123,442,1708,937,1078,932,1130,1011,968,536,534,178.0,1274,1137,274,993,3757,5912,501,1463,1981,899,1388,1180,2019,2071,2486,2922,1595,1713,2160,773,517,318,1606,572,675,2496,1940,225,293,745,1357,1372,1352,2022,887,415,992,1204,673,306,1625,1208,2510,266,3452,683,1605,489,889,578,1156,265,566,1185,971,2653,1030,943,662,892,1333,3932,294,3.513,2.994,2.335,2.489,2.327,2.186,2.252,2.478,2.564,2.577,2.573,1.953,2.716,2.877,2.908,2.771,2.963,3.791,2.81,1.965,2.823,2.453,1.811,2.673,2.223,2.56,2.191,2.077,2.706,2.47,2.249,2.114,2.468,3.431,3.747,2.595,2.384,2.833,2.095,2.369,2.478,1.74,3.421,1.957,1.851,2.064,2.776,3.01,2.643,2.686,2.199,2.261,2.294,2.517,2.042,1.952,2.37,2.278,2.168,2.116,2.293,3.175,2.405,2.508,2.178,1.665,1.887,2.311,2.122,2.877,2.061,2.241,2.401,2.162,0.988,0.65,0.49,0.586,0.636,0.57,0.573,0.454,0.744,0.689,0.836,0.607,0.445,0.484,0.51,0.587,0.685,0.753,0.682,0.603,0.827,0.644,0.476,0.921,0.678,0.685,0.584,0.657,0.553,0.613,0.81,0.88,0.469,0.653,0.725,0.487,0.873,0.691,0.362,0.419,0.439,0.599,0.717,0.673,0.505,0.407,0.62,0.911,0.466,0.54,0.417,0.421,0.438,0.448,0.358,0.396,0.43,0.509,0.344,0.389,0.398,0.892,0.406,0.714,0.555,0.624,0.506,0.426,0.482,0.763,0.487,0.495,0.495,0.342
-81,Epileptic,M,32.0,1.2,2.8,1.1,1.2,2.7,2.7,1.8,2.4,1.6,0.7,0.3,3.8,2.3,0.7,1.8,8.9,12.0,1.5,2.8,4.9,1.5,8.8,3.7,5.3,9.0,6.6,4.6,3.0,3.3,4.2,1.3,1.0,0.8,3.4,1.2,0.7,3.3,5.4,0.4,0.2,1.5,5.3,3.6,2.2,3.9,0.9,0.7,1.2,2.0,0.6,0.6,3.4,2.1,3.0,0.5,2.6,0.8,3.0,1.6,1.9,1.1,2.5,0.4,0.8,2.2,1.1,3.1,2.4,1.2,0.5,1.0,1.8,6.4,0.6,8,26,8,10,26,32,18,20,19,7,3.0,34,27,9,20,90,108,11,34,49,12,68,28,52,62,64,62,33,29,50,18,9,7,41,6,4,40,58,3,1,9,43,29,17,19,6,4,7,9,6,5,23,15,19,3,19,6,23,10,16,7,20,4,5,18,15,26,15,7,6,6,11,45,4,0.038,0.027,0.019,0.022,0.043,0.032,0.03,0.032,0.048,0.039,0.037,0.037,0.039,0.035,0.038,0.047,0.037,0.041,0.035,0.036,0.029,0.051,0.043,0.045,0.054,0.045,0.036,0.035,0.031,0.039,0.031,0.036,0.028,0.034,0.022,0.013,0.036,0.04,0.024,0.02,0.024,0.044,0.054,0.022,0.024,0.017,0.026,0.018,0.02,0.018,0.022,0.032,0.029,0.022,0.021,0.016,0.022,0.024,0.026,0.022,0.019,0.03,0.026,0.022,0.019,0.019,0.022,0.029,0.018,0.018,0.021,0.023,0.021,0.019,1840,5169,2268,2490,2553,4225,3424,4385,2222,1578,761.0,3453,4400,1608,3340,12176,23118,3031,5441,5652,3445,5048,3409,7725,5985,7660,7113,5123,7429,7370,3182,1530,1143,7087,2375,2186,6872,10421,592,422,1702,2942,6061,2688,4121,1644,1039,2319,3091,1480,768,4322,2639,5430,663,4614,1179,4398,1711,2336,1664,3419,621,1076,3519,1562,5274,3337,2170,1015,1632,2553,10917,722,0.127,0.13,0.099,0.104,0.144,0.124,0.122,0.129,0.162,0.14,0.121,0.146,0.144,0.146,0.148,0.157,0.144,0.143,0.145,0.141,0.106,0.146,0.136,0.149,0.156,0.131,0.135,0.12,0.116,0.158,0.142,0.105,0.105,0.136,0.084,0.077,0.139,0.144,0.129,0.106,0.113,0.145,0.159,0.113,0.107,0.095,0.11,0.091,0.104,0.1,0.115,0.127,0.117,0.1,0.105,0.101,0.119,0.117,0.112,0.113,0.102,0.134,0.124,0.118,0.106,0.105,0.107,0.124,0.092,0.109,0.111,0.111,0.102,0.108,557,1722,874,884,1140,1432,1056,1410,765,345,188.0,1657,1256,416,868,3619,5874,617,1427,2415,889,2330,1331,2203,2416,2397,2383,1517,1829,2106,809,570,473,1661,649,795,1658,2279,232,195,935,1696,1187,1635,2447,806,462,965,1372,601,464,1855,1248,2229,359,2256,557,1995,1030,1296,793,1320,288,562,1672,821,2384,1291,1002,454,775,1165,4739,446,3.197,2.538,2.56,2.725,2.169,2.489,2.537,2.698,2.283,3.118,3.328,1.915,2.783,2.862,2.82,2.592,2.939,3.721,2.942,2.012,2.696,1.926,2.04,2.68,2.129,2.557,2.408,2.502,2.992,2.756,2.888,2.488,2.011,2.996,3.273,2.499,2.954,3.181,2.352,2.366,2.291,1.616,3.148,1.825,1.94,2.211,2.682,3.014,2.724,2.724,2.076,2.399,2.072,2.419,2.327,2.287,2.449,2.324,1.886,2.023,2.43,2.6,2.202,2.396,2.338,2.219,2.232,2.573,2.451,2.176,2.426,2.699,2.483,1.916,0.595,0.713,0.476,0.625,0.581,0.602,0.709,0.529,0.628,0.915,0.689,0.544,0.533,0.491,0.673,0.657,0.726,0.769,0.556,0.645,0.784,0.642,0.546,0.712,0.649,0.664,0.516,0.703,0.588,0.629,0.693,0.91,0.492,0.7,0.771,0.481,0.756,0.762,0.407,0.425,0.482,0.483,0.912,0.566,0.637,0.504,0.465,0.499,0.531,0.341,0.415,0.47,0.509,0.527,0.341,0.403,0.463,0.525,0.354,0.38,0.499,0.556,0.489,0.586,0.49,0.662,0.432,0.467,0.357,0.621,0.417,0.558,0.478,0.473
-82,Epileptic,F,22.0,1.0,3.2,0.9,1.1,1.5,2.0,1.1,1.8,1.5,0.8,0.3,2.4,1.6,0.5,1.6,5.8,8.1,0.6,1.9,4.4,0.8,3.1,1.4,3.5,4.4,3.7,3.5,2.2,2.4,3.5,0.9,0.8,0.4,2.5,0.3,0.3,3.4,4.1,0.2,0.1,0.9,3.6,2.9,2.3,2.8,0.4,0.3,0.6,1.2,0.7,0.6,1.9,1.1,2.2,0.2,2.5,0.7,1.4,0.9,0.9,1.0,1.2,0.4,0.7,1.5,1.2,2.5,1.0,1.0,0.4,1.1,1.0,5.0,0.2,8,31,8,8,15,27,9,18,13,10,4.0,27,26,6,18,63,80,7,23,46,7,35,14,40,57,44,38,24,22,38,17,6,6,33,2,2,41,46,1,1,5,37,26,18,20,3,1,4,6,7,4,14,8,16,1,19,7,12,6,7,10,9,4,6,14,17,23,9,7,6,7,8,40,2,0.039,0.032,0.02,0.024,0.032,0.03,0.02,0.025,0.048,0.028,0.037,0.032,0.035,0.03,0.04,0.037,0.035,0.03,0.026,0.041,0.019,0.036,0.031,0.044,0.035,0.033,0.031,0.024,0.026,0.03,0.029,0.041,0.026,0.036,0.015,0.008,0.039,0.033,0.011,0.016,0.021,0.036,0.04,0.024,0.02,0.008,0.019,0.011,0.016,0.016,0.027,0.025,0.022,0.023,0.024,0.019,0.018,0.018,0.023,0.017,0.026,0.026,0.024,0.023,0.017,0.024,0.02,0.018,0.018,0.02,0.023,0.017,0.017,0.012,1694,5222,2286,2133,2645,3643,2485,3977,2597,1806,630.0,3009,3361,1241,2811,10801,16217,2435,4436,5402,2920,5302,2067,5828,7998,7587,5918,4095,5693,6465,1931,653,949,5913,1517,1597,5521,7601,388,468,1382,3289,5400,2767,3779,1305,766,1972,2471,1520,620,2878,2013,3902,395,4407,1410,2779,1061,1436,1390,2277,504,924,2960,1245,4482,2323,1774,1162,1795,1998,11371,620,0.132,0.137,0.109,0.114,0.138,0.14,0.098,0.125,0.178,0.14,0.121,0.152,0.146,0.144,0.149,0.148,0.146,0.123,0.123,0.159,0.089,0.147,0.123,0.158,0.152,0.147,0.137,0.117,0.117,0.143,0.121,0.124,0.138,0.14,0.088,0.057,0.135,0.133,0.087,0.097,0.101,0.151,0.156,0.116,0.102,0.075,0.093,0.078,0.091,0.108,0.13,0.122,0.113,0.108,0.138,0.112,0.105,0.105,0.119,0.103,0.128,0.121,0.145,0.132,0.103,0.123,0.114,0.108,0.105,0.135,0.118,0.103,0.099,0.104,436,1591,738,750,863,1373,815,1177,600,453,177.0,1251,963,288,749,2815,4070,442,1182,1996,709,1572,806,1479,2272,2046,1847,1440,1511,1887,584,341,245,1304,361,603,1541,1868,217,201,672,1537,1211,1486,2205,655,279,852,1074,678,366,1206,874,1638,132,1998,673,1309,587,828,568,768,223,488,1346,777,2018,971,819,440,768,950,4459,299,3.487,3.068,2.782,2.821,2.544,2.361,2.519,2.775,3.162,3.01,2.603,2.109,2.771,3.137,2.674,2.788,3.176,4.095,2.973,2.327,3.034,2.608,2.174,2.939,2.673,3.001,2.49,2.323,2.794,2.705,2.443,2.073,2.727,3.211,3.524,2.422,2.685,2.978,2.178,2.396,2.62,1.91,3.249,2.098,1.93,2.199,3.503,2.86,2.751,2.576,2.044,2.391,2.452,2.498,3.459,2.346,2.474,2.4,2.107,2.011,2.479,2.908,2.256,2.14,2.275,1.958,2.27,2.668,2.583,2.751,2.709,2.331,2.707,2.7,0.895,0.755,0.637,0.548,0.677,0.498,0.523,0.548,0.559,0.499,0.945,0.513,0.501,0.55,0.638,0.652,0.563,0.695,0.522,0.555,0.653,0.503,0.326,0.836,0.552,0.501,0.517,0.614,0.571,0.536,0.645,0.796,0.381,0.622,0.801,0.389,0.671,0.56,0.446,0.495,0.382,0.51,0.645,0.669,0.53,0.417,0.475,0.708,0.499,0.512,0.422,0.554,0.57,0.526,0.432,0.486,0.345,0.466,0.392,0.349,0.451,0.708,0.509,0.865,0.596,0.666,0.507,0.376,0.42,0.924,0.436,0.434,0.607,0.46
-83,Epileptic,M,32.0,1.0,3.0,1.1,1.4,2.8,1.3,2.5,2.2,1.2,0.9,0.2,2.7,1.3,0.5,1.1,6.5,12.4,1.4,2.4,3.9,1.7,2.7,1.4,4.9,3.4,3.9,2.7,2.3,3.2,3.9,2.5,1.6,0.7,2.4,0.8,0.6,3.8,3.6,0.2,0.3,1.6,2.8,2.3,1.7,3.3,0.8,0.5,1.0,1.5,1.2,0.3,2.6,2.0,2.7,0.5,3.0,1.1,1.5,0.6,1.3,0.4,2.0,0.7,0.8,1.4,2.1,2.7,1.8,1.0,0.5,1.4,2.0,4.8,0.4,9,28,10,12,22,15,24,18,18,10,3.0,26,16,8,12,70,118,12,25,26,14,34,17,50,48,46,40,26,28,49,19,16,6,26,5,6,42,48,1,3,9,29,26,12,17,5,2,5,7,7,2,22,14,16,5,28,5,11,4,10,4,20,4,5,8,23,24,11,6,5,8,15,37,3,0.034,0.024,0.018,0.025,0.038,0.027,0.032,0.029,0.042,0.031,0.037,0.029,0.022,0.028,0.028,0.034,0.034,0.039,0.029,0.04,0.026,0.025,0.021,0.042,0.026,0.029,0.025,0.025,0.025,0.026,0.049,0.053,0.037,0.025,0.019,0.012,0.027,0.028,0.016,0.019,0.028,0.031,0.028,0.02,0.022,0.011,0.023,0.012,0.02,0.017,0.017,0.023,0.032,0.021,0.02,0.015,0.022,0.02,0.02,0.016,0.018,0.028,0.033,0.022,0.012,0.03,0.017,0.021,0.015,0.019,0.023,0.017,0.017,0.018,1908,5981,2984,3052,2950,3027,3684,4022,2294,2275,617.0,3296,4185,1333,2390,12350,24525,2766,5217,4255,4790,6881,3490,8122,8676,8439,6805,4815,7919,9211,2567,1499,1133,7142,3189,2311,9729,10254,287,634,1828,4018,7570,2586,4269,2097,803,2792,2640,2718,633,4449,2433,5129,833,6616,1456,3360,900,2372,1071,3503,848,1199,3446,1812,5573,3237,2001,846,1977,3286,11209,501,0.11,0.113,0.091,0.114,0.146,0.127,0.117,0.131,0.162,0.138,0.12,0.132,0.117,0.138,0.13,0.141,0.131,0.133,0.124,0.141,0.108,0.124,0.114,0.147,0.128,0.135,0.118,0.103,0.1,0.122,0.147,0.148,0.11,0.123,0.092,0.086,0.119,0.135,0.096,0.115,0.117,0.135,0.129,0.101,0.103,0.079,0.097,0.074,0.096,0.093,0.098,0.11,0.124,0.097,0.112,0.096,0.11,0.101,0.104,0.101,0.098,0.121,0.135,0.113,0.082,0.126,0.1,0.104,0.092,0.123,0.115,0.099,0.096,0.12,562,2062,1006,1026,1097,946,1259,1267,638,484,156.0,1425,998,330,661,3434,6283,608,1387,1730,1050,1723,1108,2085,2275,2352,2120,1666,1944,2626,811,609,355,1558,723,756,2319,2311,165,282,886,1416,1513,1464,2299,954,319,1125,1191,1005,311,1883,1055,2235,439,2935,657,1378,515,1192,414,1246,317,583,1578,1184,2552,1349,932,439,924,1762,4567,291,3.294,2.647,2.834,2.867,2.366,2.472,2.465,2.835,2.702,3.28,2.849,1.988,3.111,2.938,2.726,2.717,3.046,3.401,2.892,2.142,3.353,2.86,2.492,2.959,2.843,2.839,2.517,2.263,3.051,2.709,2.451,2.713,2.524,3.226,3.805,2.789,3.041,3.052,2.102,2.673,2.557,2.241,3.392,2.038,2.127,2.417,2.969,2.975,2.738,2.888,2.144,2.451,2.351,2.464,2.201,2.348,2.442,2.577,1.82,2.158,2.456,2.691,2.426,2.369,2.416,1.761,2.266,2.722,2.507,2.113,2.423,2.293,2.564,2.263,0.742,0.574,0.479,0.486,0.742,0.651,0.584,0.507,0.582,0.696,0.963,0.513,0.367,0.428,0.477,0.554,0.612,0.675,0.493,0.631,0.748,0.52,0.67,0.809,0.553,0.573,0.551,0.533,0.483,0.598,0.695,1.1,0.498,0.593,0.537,0.422,0.688,0.68,0.301,0.559,0.419,0.584,0.764,0.67,0.591,0.508,0.405,0.581,0.387,0.634,0.323,0.53,0.508,0.406,0.418,0.385,0.502,0.498,0.407,0.4,0.468,0.763,0.527,0.706,0.43,0.624,0.397,0.46,0.451,0.618,0.433,0.503,0.461,0.298
-84,Epileptic,F,22.0,0.6,2.1,0.9,1.0,2.2,2.0,1.6,1.3,1.3,0.6,0.4,2.7,1.0,0.4,1.3,4.1,5.8,0.6,1.8,4.3,1.1,3.6,2.5,3.1,2.9,2.5,6.0,1.8,3.2,2.7,1.6,0.6,0.5,2.4,0.3,0.7,4.4,2.9,0.1,0.3,1.0,3.9,2.5,1.9,2.1,0.5,0.3,0.6,1.2,0.8,0.5,2.5,1.0,2.3,0.3,3.6,0.7,1.0,1.2,1.4,1.0,1.3,0.6,0.6,2.0,1.0,3.6,0.7,1.1,0.5,0.7,1.7,3.8,0.3,4,20,6,8,18,21,12,15,18,6,4.0,30,16,7,19,52,59,4,27,45,10,38,22,33,38,28,48,22,22,35,18,7,6,30,3,8,45,40,1,2,6,32,24,17,13,4,1,4,6,5,5,21,9,19,2,31,5,11,11,10,8,9,4,4,18,17,46,3,6,4,6,12,32,3,0.032,0.023,0.021,0.021,0.041,0.034,0.034,0.026,0.044,0.029,0.034,0.039,0.025,0.024,0.025,0.031,0.028,0.024,0.029,0.041,0.019,0.041,0.042,0.039,0.036,0.026,0.043,0.029,0.033,0.028,0.04,0.055,0.027,0.032,0.017,0.013,0.046,0.032,0.01,0.013,0.018,0.042,0.036,0.024,0.018,0.014,0.016,0.011,0.016,0.016,0.021,0.024,0.028,0.022,0.013,0.021,0.019,0.013,0.022,0.021,0.029,0.022,0.026,0.022,0.021,0.022,0.03,0.013,0.016,0.018,0.015,0.021,0.016,0.011,1516,5097,2248,2089,2724,3274,2518,3429,2679,1361,843.0,3397,3623,1447,4169,9184,17192,2320,4535,5165,3709,4379,2436,6490,6106,6161,7255,3686,5871,6321,2485,773,1232,6233,1773,2241,7493,7386,506,658,1598,2431,6623,2965,2910,1298,813,2184,2172,1655,689,4441,1848,4875,550,4918,1339,3039,1405,1862,1217,2450,512,1101,3381,1074,3969,1671,1880,1101,1518,2637,8013,603,0.099,0.114,0.102,0.104,0.16,0.143,0.112,0.123,0.183,0.139,0.133,0.155,0.113,0.126,0.127,0.13,0.124,0.103,0.13,0.159,0.086,0.133,0.138,0.147,0.13,0.129,0.146,0.115,0.113,0.133,0.15,0.114,0.115,0.14,0.083,0.076,0.151,0.139,0.074,0.097,0.101,0.139,0.142,0.118,0.095,0.089,0.082,0.073,0.09,0.099,0.136,0.119,0.126,0.114,0.1,0.116,0.109,0.088,0.123,0.106,0.129,0.119,0.149,0.11,0.111,0.122,0.105,0.081,0.095,0.113,0.106,0.118,0.09,0.1,385,1538,725,752,869,1169,844,954,650,364,231.0,1381,951,298,1045,2594,4044,431,1243,1877,887,1623,1054,1628,1612,1684,2573,1264,1461,1889,761,309,349,1293,393,732,1891,1643,262,295,762,1449,1296,1447,1743,623,327,859,1039,710,348,1812,777,1788,255,2398,559,1322,856,957,587,822,274,446,1514,724,1928,779,919,428,682,1181,3434,309,3.651,2.982,2.804,2.634,2.616,2.424,2.363,2.97,3.092,2.971,2.796,2.112,2.871,3.137,2.971,2.724,3.198,3.89,2.935,2.27,2.974,2.225,2.145,2.957,2.729,2.879,2.337,2.313,2.995,2.68,2.617,2.505,2.681,3.377,3.772,2.637,2.886,3.076,2.188,2.514,2.751,1.621,3.497,2.194,1.865,2.15,2.844,3.129,2.558,2.964,2.144,2.348,2.255,2.594,2.604,2.274,2.605,2.555,1.919,2.091,2.363,3.143,1.935,3.046,2.41,1.85,2.033,2.353,2.414,2.832,2.455,2.489,2.447,2.21,0.892,0.604,0.615,0.47,0.669,0.572,0.615,0.556,0.806,0.591,0.896,0.465,0.498,0.663,0.563,0.597,0.645,0.674,0.506,0.655,0.895,0.623,0.725,0.797,0.727,0.562,0.564,0.558,0.674,0.577,0.779,0.794,0.416,0.636,0.673,0.588,0.865,0.724,0.318,0.459,0.418,0.456,0.819,0.555,0.513,0.469,0.445,0.88,0.459,0.808,0.468,0.523,0.545,0.519,0.438,0.45,0.456,0.467,0.357,0.47,0.381,0.733,0.456,0.787,0.491,0.545,0.603,0.441,0.373,0.634,0.399,0.574,0.403,0.46
-85,Epileptic,F,42.0,0.4,1.7,0.7,0.6,1.7,1.3,1.2,1.5,1.1,0.5,0.2,1.9,1.2,0.4,1.2,4.1,7.6,0.5,1.5,3.1,0.9,1.7,1.3,3.3,1.8,2.7,3.3,2.3,2.0,2.3,1.6,0.5,0.4,2.1,0.3,0.5,3.3,2.6,0.1,0.1,1.2,2.2,2.3,2.3,2.1,0.6,0.5,0.7,0.9,1.0,0.5,1.3,1.6,2.2,0.3,2.2,0.6,1.5,0.9,1.0,0.5,1.7,0.3,0.3,1.5,0.5,2.3,1.3,1.0,0.6,0.6,1.5,3.3,0.3,3,20,6,5,14,12,13,13,12,5,2.0,20,13,4,9,46,77,4,21,34,9,22,13,36,27,32,38,24,23,33,19,6,5,27,2,3,47,31,1,1,7,24,29,17,14,4,2,5,5,10,3,12,11,15,2,16,4,16,7,8,4,13,2,4,10,5,20,7,6,4,4,10,33,2,0.023,0.025,0.015,0.012,0.04,0.034,0.023,0.029,0.047,0.027,0.021,0.033,0.029,0.042,0.033,0.036,0.029,0.026,0.025,0.031,0.019,0.033,0.027,0.043,0.035,0.029,0.029,0.03,0.023,0.027,0.042,0.027,0.019,0.032,0.009,0.012,0.034,0.029,0.014,0.02,0.022,0.039,0.043,0.029,0.017,0.015,0.024,0.012,0.014,0.027,0.026,0.019,0.036,0.021,0.016,0.021,0.019,0.023,0.026,0.025,0.023,0.034,0.027,0.021,0.022,0.014,0.022,0.025,0.018,0.02,0.018,0.022,0.021,0.017,884,3343,1586,1380,2180,1765,2546,3196,1565,993,415.0,2132,2660,981,1995,7856,14763,1602,3612,4315,2300,3091,1786,5604,3889,5959,5350,3918,5005,4759,2117,649,939,5129,1614,1701,5992,6709,261,254,1613,2052,4523,1836,2943,1039,622,1910,1754,1305,487,2552,1738,3799,448,3007,1198,2612,833,1043,820,1915,405,692,1972,655,3269,1725,1538,1083,936,1819,5916,410,0.093,0.12,0.093,0.084,0.155,0.151,0.12,0.134,0.171,0.125,0.107,0.142,0.137,0.137,0.141,0.147,0.133,0.106,0.124,0.142,0.092,0.151,0.131,0.156,0.14,0.142,0.138,0.137,0.12,0.139,0.161,0.104,0.103,0.138,0.064,0.077,0.129,0.137,0.106,0.108,0.111,0.159,0.147,0.13,0.102,0.099,0.11,0.083,0.085,0.124,0.134,0.103,0.125,0.106,0.107,0.107,0.099,0.119,0.128,0.121,0.117,0.137,0.123,0.114,0.109,0.09,0.121,0.116,0.106,0.103,0.113,0.121,0.108,0.097,316,1195,758,668,716,672,823,911,432,296,148.0,886,758,224,571,2167,4335,358,913,1544,692,967,748,1418,1106,1681,1836,1246,1353,1507,589,384,316,1211,486,583,1736,1501,136,126,843,982,1025,1206,1695,548,283,843,892,675,280,1149,720,1745,266,1588,573,1198,518,579,362,813,193,322,967,484,1621,820,798,444,503,927,2671,230,2.948,2.516,2.105,2.133,2.459,2.432,2.484,2.991,2.735,2.576,2.826,2.048,2.709,3.046,2.537,2.667,2.692,3.466,3.004,2.281,2.615,2.46,2.081,2.918,2.657,2.842,2.346,2.434,2.727,2.475,2.656,1.79,2.368,2.97,2.788,2.633,2.649,3.144,2.084,2.138,2.278,1.868,3.068,1.734,1.959,2.063,2.665,2.744,2.339,2.274,2.13,2.311,2.462,2.295,2.145,2.142,2.465,2.379,1.95,2.027,2.426,2.358,2.325,2.423,2.17,1.557,2.215,2.366,2.291,2.542,2.149,2.362,2.42,2.055,0.534,0.595,0.537,0.365,0.599,0.518,0.591,0.511,0.452,0.389,0.62,0.369,0.584,0.456,0.45,0.556,0.593,0.529,0.587,0.645,0.794,0.467,0.493,0.685,0.438,0.476,0.417,0.505,0.481,0.528,0.745,0.772,0.319,0.779,0.711,0.552,0.795,0.652,0.396,0.372,0.479,0.457,0.792,0.529,0.467,0.472,0.45,0.731,0.436,0.558,0.342,0.424,0.775,0.468,0.367,0.512,0.462,0.505,0.383,0.341,0.481,0.591,0.412,0.828,0.585,0.512,0.432,0.426,0.409,0.498,0.421,0.46,0.544,0.361
-86,Epileptic,M,22.0,0.8,1.5,0.7,1.0,1.5,1.3,1.4,1.7,1.1,0.9,0.3,2.6,1.3,0.4,1.3,4.7,7.3,1.1,1.4,3.7,1.1,2.7,1.4,2.9,4.1,4.0,2.5,2.1,2.3,2.8,1.4,1.0,0.4,1.7,0.3,1.0,4.0,2.6,0.2,0.2,1.1,2.7,2.1,1.6,2.2,0.5,0.5,0.8,1.4,0.9,0.5,1.6,1.8,2.2,0.5,2.3,0.4,1.5,0.4,0.7,0.7,1.3,0.5,0.7,1.6,1.1,2.5,1.2,1.1,0.4,1.0,1.4,4.4,0.3,8,17,7,7,11,13,15,16,13,8,4.0,23,17,5,15,56,80,9,19,33,12,36,11,37,47,59,37,24,24,35,19,8,5,23,2,10,47,37,1,1,7,18,21,12,13,5,2,6,7,8,4,11,13,14,3,19,5,14,4,6,7,11,4,4,14,17,19,10,10,4,8,10,37,3,0.035,0.018,0.012,0.019,0.032,0.028,0.021,0.024,0.041,0.037,0.033,0.036,0.025,0.023,0.028,0.029,0.026,0.037,0.025,0.038,0.021,0.037,0.03,0.034,0.041,0.032,0.024,0.025,0.021,0.024,0.031,0.058,0.02,0.023,0.01,0.019,0.038,0.028,0.028,0.022,0.02,0.041,0.033,0.022,0.016,0.01,0.015,0.014,0.017,0.02,0.023,0.022,0.023,0.018,0.017,0.016,0.01,0.018,0.017,0.018,0.021,0.025,0.028,0.022,0.019,0.022,0.021,0.02,0.018,0.014,0.025,0.015,0.018,0.019,1617,4441,2308,2421,2046,2069,2714,3316,1766,1658,751.0,2360,3420,866,3033,10487,18519,2570,3809,3847,4266,3611,1573,6772,6035,7123,5439,3936,6733,6127,3025,959,982,5561,2038,2272,7835,7150,297,421,1520,2385,5798,2107,3305,1561,989,2256,2638,1770,569,2422,1958,3961,765,3834,1249,3073,705,1179,989,2046,596,989,2544,1278,3207,2246,2056,764,1568,2364,9243,335,0.121,0.105,0.088,0.094,0.136,0.139,0.095,0.123,0.153,0.146,0.124,0.149,0.128,0.117,0.138,0.135,0.125,0.129,0.124,0.155,0.097,0.141,0.118,0.145,0.135,0.133,0.113,0.11,0.105,0.121,0.138,0.129,0.099,0.126,0.066,0.11,0.158,0.132,0.118,0.091,0.108,0.131,0.143,0.11,0.09,0.08,0.097,0.089,0.095,0.106,0.123,0.106,0.12,0.094,0.098,0.096,0.089,0.107,0.106,0.109,0.122,0.12,0.134,0.1,0.109,0.119,0.11,0.107,0.107,0.1,0.115,0.101,0.102,0.131,453,1448,855,892,783,819,972,1111,521,422,211.0,1063,898,242,757,2902,4975,504,980,1695,928,1247,679,1573,1722,2199,1763,1431,1610,1839,835,387,343,1299,555,800,1981,1715,157,218,805,1049,1135,1212,2006,804,412,945,1172,733,332,1252,1105,1868,400,2135,638,1373,377,620,537,865,312,482,1250,805,1783,987,956,399,739,1223,3798,194,3.56,2.727,2.55,2.638,2.221,2.303,2.338,2.539,2.605,3.059,2.828,1.899,2.832,2.591,2.712,2.601,2.83,3.869,2.902,1.968,3.23,2.298,2.095,3.108,2.696,2.535,2.405,2.161,2.955,2.506,2.701,2.642,2.326,3.002,3.296,2.493,2.992,2.936,2.03,2.173,2.395,1.959,3.481,2.033,1.862,2.098,2.853,3.016,2.739,2.578,1.994,2.079,1.902,2.264,2.211,1.988,2.312,2.424,2.035,2.017,2.134,2.183,1.803,2.42,2.259,1.917,1.952,2.435,2.36,2.137,2.388,2.264,2.49,2.199,0.691,0.597,0.492,0.455,0.634,0.554,0.63,0.462,0.72,0.569,0.719,0.631,0.439,0.502,0.69,0.707,0.698,0.629,0.532,0.642,0.772,0.615,0.591,0.743,0.756,0.735,0.655,0.643,0.593,0.619,0.612,0.863,0.416,0.625,0.758,0.448,0.754,0.654,0.558,0.506,0.457,0.711,0.755,0.725,0.565,0.503,0.644,0.52,0.541,0.505,0.341,0.477,0.468,0.417,0.336,0.396,0.443,0.442,0.468,0.383,0.454,0.615,0.557,0.726,0.568,0.509,0.463,0.41,0.447,0.419,0.522,0.451,0.485,0.388
-87,Epileptic,F,52.0,0.8,1.8,1.2,0.8,1.1,1.4,1.4,1.1,1.4,0.3,0.2,2.3,1.0,0.7,1.1,5.2,7.9,1.1,2.2,3.6,1.0,2.4,2.0,3.1,2.3,3.5,2.9,1.9,1.6,3.1,1.6,1.5,0.4,1.6,0.2,0.5,2.9,2.9,0.1,0.1,1.0,2.9,1.8,1.9,2.2,0.5,0.7,0.7,0.9,0.9,0.5,1.3,1.2,2.0,0.2,2.3,0.4,1.2,1.0,1.0,0.6,1.1,0.3,0.3,1.5,0.6,2.0,0.8,0.8,0.3,1.1,0.8,3.9,0.2,6,16,13,8,9,17,14,12,13,4,2.0,22,13,9,13,48,78,7,25,34,7,25,22,33,29,38,36,22,20,38,17,12,4,23,2,3,40,40,1,1,6,25,17,16,15,3,4,4,6,6,4,12,9,14,1,19,3,10,7,7,5,9,2,3,10,7,16,5,5,4,10,7,32,2,0.041,0.024,0.027,0.015,0.042,0.028,0.024,0.023,0.055,0.021,0.027,0.034,0.024,0.036,0.032,0.037,0.032,0.051,0.034,0.038,0.025,0.039,0.037,0.038,0.027,0.037,0.03,0.025,0.02,0.029,0.044,0.058,0.022,0.028,0.016,0.011,0.037,0.031,0.016,0.012,0.022,0.036,0.037,0.025,0.019,0.01,0.037,0.014,0.016,0.02,0.039,0.019,0.036,0.019,0.027,0.017,0.021,0.02,0.026,0.021,0.027,0.025,0.032,0.015,0.022,0.033,0.021,0.015,0.016,0.023,0.026,0.017,0.017,0.02,1266,3376,1840,1877,1406,2095,2235,2409,1587,894,364.0,1934,2235,1158,2022,7457,13737,1809,3497,3445,2834,3492,2191,4509,4344,5865,4858,2861,4170,4720,1789,1440,658,3391,1233,1448,4516,5422,225,294,1280,2350,3527,1641,2775,1188,632,1764,1922,1333,335,2343,1788,3371,257,3800,638,2299,1023,1422,618,1715,458,711,2042,612,2693,1799,1267,711,1551,1542,7879,352,0.12,0.117,0.129,0.102,0.145,0.139,0.109,0.12,0.169,0.103,0.106,0.151,0.127,0.143,0.139,0.144,0.141,0.133,0.146,0.148,0.096,0.151,0.159,0.148,0.129,0.15,0.137,0.109,0.108,0.136,0.158,0.167,0.119,0.12,0.064,0.077,0.141,0.134,0.11,0.096,0.114,0.148,0.136,0.12,0.1,0.081,0.144,0.088,0.094,0.119,0.167,0.102,0.148,0.103,0.151,0.106,0.116,0.108,0.132,0.11,0.132,0.129,0.139,0.116,0.111,0.114,0.11,0.093,0.098,0.132,0.129,0.112,0.099,0.127,335,1189,699,736,528,848,884,807,488,242,129.0,1101,671,328,636,2240,4229,329,965,1600,651,1075,915,1405,1468,1730,1683,1152,1191,1710,519,540,244,913,367,567,1353,1470,127,160,666,1209,787,1187,1729,640,308,794,934,626,215,1121,592,1563,106,2050,306,1032,578,783,354,663,173,297,1073,287,1549,817,733,264,738,780,3440,176,3.322,2.699,2.773,2.638,2.36,2.267,2.149,2.626,2.462,3.077,2.56,1.678,2.556,2.719,2.485,2.56,2.623,3.83,2.918,1.909,3.208,2.559,2.149,2.639,2.37,2.694,2.247,1.986,2.612,2.278,2.757,2.918,2.232,2.68,2.872,2.338,2.666,2.774,1.969,2.065,2.291,1.835,3.273,1.603,1.824,2.045,2.84,2.632,2.382,2.679,1.871,2.236,2.593,2.28,2.635,2.028,2.498,2.507,2.033,2.064,2.083,2.767,2.335,2.338,2.166,2.202,1.906,2.463,2.02,3.051,2.353,2.35,2.432,2.513,1.121,0.514,0.549,0.41,0.575,0.61,0.477,0.513,0.529,0.397,0.798,0.475,0.39,0.521,0.467,0.516,0.57,0.834,0.557,0.609,0.85,0.417,0.463,0.727,0.535,0.518,0.47,0.443,0.463,0.496,0.813,0.956,0.543,0.48,0.648,0.361,0.612,0.514,0.377,0.424,0.569,0.422,0.693,0.678,0.526,0.471,0.622,0.951,0.517,0.533,0.426,0.48,0.606,0.513,0.452,0.442,0.41,0.531,0.368,0.41,0.294,0.889,0.686,1.095,0.512,0.66,0.416,0.397,0.373,0.871,0.492,0.385,0.465,0.38
-88,Epileptic,M,47.0,1.5,3.4,1.2,1.2,2.1,2.5,2.3,2.0,1.5,0.9,0.5,2.9,1.7,0.6,1.7,6.8,12.1,2.6,2.6,4.7,1.2,5.7,2.1,6.2,6.7,3.5,4.7,3.9,4.0,2.7,1.7,1.8,0.6,2.7,0.4,0.9,5.1,4.7,0.3,0.2,1.1,4.6,3.2,2.6,4.0,0.8,1.0,1.1,2.1,1.3,0.8,2.2,1.5,2.6,0.4,3.2,1.1,2.2,1.7,1.2,1.1,2.3,0.4,0.7,2.2,1.7,3.2,1.7,1.1,0.8,0.9,1.6,4.7,0.3,9,37,13,11,15,23,17,23,12,10,6.0,26,19,6,16,84,109,16,30,42,13,46,21,47,53,39,37,40,29,35,20,13,8,30,2,9,51,47,1,1,5,39,26,19,21,5,3,6,9,12,4,18,10,18,2,26,6,20,10,10,8,13,2,6,16,20,25,12,6,5,6,11,36,3,0.053,0.036,0.021,0.023,0.039,0.034,0.036,0.029,0.047,0.046,0.041,0.035,0.038,0.04,0.036,0.046,0.042,0.081,0.031,0.039,0.025,0.046,0.035,0.06,0.045,0.034,0.043,0.039,0.044,0.036,0.043,0.049,0.034,0.03,0.015,0.021,0.041,0.042,0.022,0.018,0.021,0.041,0.044,0.03,0.026,0.017,0.043,0.014,0.023,0.029,0.032,0.025,0.021,0.025,0.017,0.021,0.019,0.024,0.028,0.025,0.03,0.034,0.021,0.017,0.022,0.024,0.022,0.024,0.022,0.021,0.024,0.017,0.019,0.02,1553,4885,2122,2096,1761,3748,3097,3354,1739,1378,618.0,2941,3350,1037,2916,10350,17840,2387,5636,5026,2676,3975,1989,5920,6453,5967,4642,4587,5779,4846,1979,1348,1183,5362,1769,2081,8626,8189,358,373,1343,3007,6368,2344,3925,1252,763,2374,2523,1739,604,2893,2162,3775,427,4266,1728,3180,1532,1279,1232,2768,407,1212,2967,1545,4471,2317,1572,1238,1135,2577,9347,390,0.144,0.14,0.125,0.114,0.137,0.132,0.127,0.129,0.14,0.167,0.148,0.135,0.147,0.138,0.134,0.161,0.148,0.192,0.14,0.149,0.109,0.14,0.132,0.161,0.137,0.138,0.126,0.13,0.137,0.14,0.145,0.13,0.105,0.131,0.076,0.097,0.143,0.154,0.105,0.098,0.103,0.153,0.166,0.127,0.112,0.1,0.131,0.08,0.098,0.129,0.123,0.121,0.111,0.113,0.105,0.111,0.099,0.127,0.123,0.127,0.129,0.134,0.111,0.102,0.11,0.119,0.106,0.117,0.101,0.114,0.127,0.107,0.099,0.12,459,1694,860,819,832,1251,1113,1208,611,382,217.0,1303,943,257,848,3106,5259,539,1442,2162,766,1717,984,1877,2419,1875,1658,1865,1665,1596,697,574,378,1437,470,686,2042,2068,207,205,737,1755,1340,1386,2350,658,321,1138,1196,628,369,1376,1106,1775,249,2283,846,1461,926,759,616,1082,238,634,1601,947,2366,1141,754,493,535,1295,4045,210,3.299,2.755,2.497,2.469,1.98,2.406,2.342,2.419,2.273,2.893,2.26,1.92,2.787,2.767,2.573,2.477,2.598,3.383,3.023,1.997,2.675,2.002,1.847,2.437,2.195,2.638,2.285,1.975,2.641,2.425,2.201,2.542,2.428,2.708,3.152,2.536,2.971,2.801,2.039,2.018,2.246,1.636,3.285,1.884,1.863,2.059,2.709,2.477,2.561,2.811,2.0,2.156,1.982,2.181,2.049,2.003,2.417,2.394,1.893,1.886,2.19,2.584,1.83,2.176,2.072,2.088,1.978,2.362,2.207,2.361,2.274,2.314,2.403,2.063,0.755,0.767,0.52,0.457,0.511,0.748,0.626,0.505,0.553,0.722,0.797,0.557,0.456,0.485,0.506,0.623,0.692,0.807,0.576,0.606,0.792,0.592,0.498,0.734,0.679,0.483,0.66,0.548,0.534,0.683,0.697,0.912,0.411,0.631,0.737,0.602,0.712,0.748,0.412,0.426,0.433,0.461,0.834,0.533,0.544,0.462,0.43,0.723,0.492,0.511,0.469,0.463,0.497,0.455,0.265,0.427,0.454,0.518,0.459,0.49,0.5,0.755,0.412,0.742,0.518,0.754,0.446,0.445,0.477,0.939,0.501,0.442,0.509,0.369
-89,Epileptic,M,32.0,1.2,3.2,1.4,1.1,2.9,2.3,1.4,1.8,1.9,0.6,0.4,3.7,1.4,0.4,1.5,7.3,9.4,1.0,2.1,5.0,1.7,3.3,2.6,5.7,4.7,3.7,5.2,3.4,3.4,3.6,1.9,1.0,0.5,2.8,0.3,1.0,3.0,3.8,0.3,0.1,1.1,3.3,2.6,3.4,2.9,0.7,0.6,0.6,1.1,0.6,0.8,2.2,2.4,3.2,0.7,3.0,0.6,1.9,1.5,1.2,0.7,1.8,0.5,0.7,1.8,1.3,2.1,1.4,1.2,0.6,0.7,1.0,4.2,0.3,8,28,15,9,30,28,12,17,17,9,4.0,31,18,7,17,83,95,8,25,53,20,38,21,56,41,42,47,34,29,45,23,6,4,32,2,9,31,43,2,0,7,28,25,27,17,5,4,4,5,4,6,18,17,24,3,25,7,19,11,9,5,12,4,6,16,21,17,10,10,5,5,9,36,3,0.057,0.026,0.02,0.018,0.04,0.026,0.022,0.031,0.052,0.03,0.031,0.032,0.025,0.032,0.028,0.036,0.029,0.043,0.032,0.033,0.037,0.033,0.032,0.048,0.04,0.026,0.031,0.03,0.028,0.03,0.035,0.043,0.025,0.032,0.011,0.019,0.037,0.033,0.016,0.009,0.022,0.029,0.046,0.026,0.02,0.013,0.021,0.013,0.016,0.014,0.029,0.021,0.025,0.02,0.028,0.016,0.017,0.023,0.025,0.014,0.017,0.024,0.026,0.023,0.017,0.02,0.018,0.018,0.019,0.018,0.016,0.017,0.015,0.014,1179,6203,3266,2358,3078,4178,3011,3053,2114,1666,655.0,3727,3788,1280,3160,13137,21233,2299,4063,6512,3884,5247,2516,9058,5998,7737,7266,5070,6706,6829,3403,1094,906,7063,1575,2410,6714,7740,601,257,1617,2943,6151,3556,3575,1479,1094,2199,2267,1509,765,4297,3322,6688,741,5469,1309,3508,1715,2216,1067,3682,576,1127,3158,1626,3685,2822,2026,1209,1352,2293,11047,521,0.142,0.119,0.121,0.097,0.149,0.131,0.098,0.131,0.155,0.141,0.135,0.139,0.12,0.14,0.121,0.14,0.129,0.124,0.143,0.143,0.13,0.112,0.126,0.159,0.128,0.129,0.106,0.115,0.11,0.137,0.15,0.115,0.107,0.124,0.061,0.096,0.146,0.134,0.102,0.08,0.106,0.12,0.165,0.114,0.102,0.092,0.114,0.074,0.089,0.091,0.133,0.108,0.12,0.104,0.124,0.098,0.107,0.121,0.118,0.097,0.106,0.119,0.125,0.124,0.108,0.118,0.102,0.096,0.108,0.119,0.101,0.098,0.089,0.103,380,2031,1119,904,1153,1496,1009,960,647,421,201.0,1796,977,267,901,3469,5560,436,1168,2623,900,1758,1195,2097,1770,2225,2551,1815,1795,1957,909,484,315,1462,453,807,1550,1811,312,97,802,1711,1120,2205,2215,771,399,870,1047,640,465,1778,1517,2611,350,2826,618,1414,933,1198,593,1166,322,485,1516,991,1812,1247,1028,463,648,944,4576,274,3.063,2.9,2.679,2.544,2.392,2.295,2.479,2.787,2.514,3.131,2.789,1.823,2.854,3.239,2.665,2.76,2.905,3.652,2.75,2.125,3.233,2.316,1.908,3.118,2.713,2.733,2.345,2.251,2.786,2.68,2.696,2.287,2.318,3.264,3.076,2.556,2.973,2.987,2.332,2.603,2.542,1.536,3.557,1.801,1.822,2.105,3.292,2.911,2.767,2.545,1.874,2.408,2.44,2.544,2.497,2.104,2.18,2.444,2.063,2.028,2.24,3.046,2.137,2.587,2.251,1.904,2.156,2.553,2.273,2.641,2.33,2.632,2.568,2.343,0.967,0.627,0.553,0.49,0.789,0.82,0.673,0.481,0.795,0.805,1.002,0.487,0.5,0.651,0.574,0.665,0.695,0.719,0.579,0.684,0.76,0.568,0.62,0.896,0.633,0.56,0.565,0.629,0.574,0.588,0.701,0.991,0.403,0.714,0.561,0.594,0.83,0.65,0.513,0.393,0.381,0.493,0.948,0.509,0.542,0.511,0.409,0.66,0.441,0.558,0.476,0.519,0.493,0.513,0.403,0.405,0.457,0.674,0.418,0.439,0.291,0.916,0.432,0.894,0.518,0.701,0.46,0.421,0.453,0.94,0.448,0.545,0.464,0.457
-90,Epileptic,M,42.0,0.8,1.8,0.9,0.7,1.3,1.9,1.7,1.7,1.2,0.5,0.3,3.4,1.6,0.7,0.8,6.3,8.8,1.0,2.0,4.4,1.0,2.0,1.4,2.8,2.4,3.6,3.8,2.3,2.7,3.9,1.1,1.0,0.7,3.1,0.5,1.1,2.2,2.6,0.2,0.1,1.1,3.3,1.9,2.9,2.8,0.7,0.6,0.8,1.2,0.7,0.5,1.2,1.8,1.8,0.2,2.3,0.5,1.7,0.8,0.6,0.9,1.3,0.2,0.5,2.2,0.2,2.5,1.6,1.4,0.5,1.0,0.6,2.9,0.6,5,17,7,6,18,18,16,14,12,5,3.0,31,13,7,11,60,86,7,25,36,9,24,16,30,32,42,42,25,29,44,15,7,8,38,5,12,23,31,2,1,6,30,21,24,16,4,3,7,6,6,3,11,11,13,1,17,3,16,5,4,5,7,2,3,16,1,20,12,7,4,6,4,25,4,0.029,0.02,0.015,0.013,0.039,0.03,0.025,0.026,0.047,0.028,0.024,0.039,0.032,0.04,0.031,0.035,0.03,0.038,0.029,0.034,0.023,0.03,0.034,0.036,0.03,0.037,0.028,0.026,0.025,0.042,0.037,0.049,0.024,0.032,0.02,0.024,0.031,0.029,0.025,0.017,0.02,0.036,0.035,0.027,0.019,0.012,0.024,0.012,0.015,0.018,0.027,0.02,0.026,0.016,0.02,0.02,0.02,0.019,0.019,0.019,0.028,0.024,0.015,0.026,0.028,0.014,0.02,0.026,0.02,0.017,0.026,0.023,0.02,0.018,1344,4999,1863,1964,2184,2781,2927,3739,2087,1127,682.0,3585,3385,1136,1548,10627,21496,2387,4252,5173,2745,4191,2152,5613,4377,8158,6612,3652,6777,5781,2041,887,1424,7129,1667,2538,4790,5639,290,239,2206,3308,5599,3371,4076,1820,874,2567,2783,1343,558,2141,2879,5291,464,3657,915,3411,1155,1078,989,2432,376,816,3025,579,4200,2450,2126,864,1243,1385,6709,990,0.108,0.104,0.099,0.085,0.138,0.142,0.105,0.124,0.144,0.117,0.094,0.151,0.142,0.162,0.123,0.14,0.124,0.117,0.134,0.124,0.083,0.132,0.141,0.13,0.147,0.147,0.129,0.115,0.107,0.155,0.137,0.132,0.126,0.126,0.1,0.118,0.136,0.124,0.129,0.097,0.092,0.147,0.138,0.118,0.099,0.082,0.117,0.084,0.091,0.102,0.128,0.105,0.118,0.087,0.103,0.113,0.105,0.11,0.108,0.104,0.128,0.109,0.104,0.12,0.121,0.063,0.105,0.12,0.103,0.124,0.123,0.1,0.1,0.103,404,1409,811,779,660,1000,1045,1065,457,271,181.0,1460,864,296,462,2929,5171,413,1099,1933,779,1182,788,1345,1322,1908,2207,1356,1778,1720,490,376,495,1571,437,790,1082,1408,135,120,865,1442,1008,1915,2171,835,350,1100,1160,551,291,1031,1024,1902,184,1792,431,1521,628,520,455,780,206,321,1291,278,1882,976,1024,396,558,498,2611,469,3.229,3.207,2.464,2.566,2.432,2.45,2.317,3.124,2.85,3.311,3.149,2.07,2.929,2.841,2.514,2.728,3.135,4.15,3.085,2.233,2.818,2.634,2.274,2.911,2.695,3.209,2.34,2.183,2.843,2.623,2.9,2.499,2.353,3.099,3.214,2.83,3.154,2.892,2.47,2.086,3.203,1.992,3.595,2.003,2.133,2.446,3.108,2.923,2.898,2.634,2.345,2.331,2.414,2.656,2.796,2.287,2.517,2.54,2.286,2.252,2.754,2.984,2.121,2.539,2.338,2.302,2.313,2.57,2.519,2.408,2.667,2.774,2.57,2.619,0.843,0.773,0.784,0.493,0.722,0.563,0.574,0.845,0.732,0.366,0.674,0.506,0.483,0.322,0.522,0.649,0.665,0.802,0.479,0.672,0.811,0.493,0.517,0.743,0.531,0.636,0.519,0.522,0.497,0.588,0.78,1.054,0.346,0.719,0.547,0.607,0.803,0.594,0.447,0.467,0.822,0.531,0.816,0.773,0.648,0.748,0.581,0.569,0.622,0.623,0.364,0.404,0.656,0.632,0.386,0.501,0.49,0.568,0.341,0.366,0.38,0.797,0.35,1.211,0.534,0.744,0.573,0.562,0.409,0.649,0.54,0.626,0.501,0.633
-91,Epileptic,F,27.0,1.2,2.6,1.5,1.3,1.5,2.0,1.4,1.5,1.5,0.7,0.2,2.9,1.1,0.7,1.5,5.7,7.7,0.7,2.2,6.2,1.7,5.8,2.8,4.4,6.4,2.8,2.9,2.3,2.4,3.1,1.3,1.3,0.5,2.7,0.4,0.8,3.2,4.3,0.2,0.2,1.1,2.7,1.7,2.5,3.2,0.6,0.6,0.7,1.3,0.7,0.7,1.5,2.3,2.4,0.2,2.5,0.8,1.5,0.7,1.8,1.0,1.4,0.3,0.4,1.4,1.0,2.2,1.0,1.1,0.9,0.6,1.4,5.0,0.4,11,23,16,13,21,20,11,17,11,8,3.0,28,17,8,16,68,75,8,27,44,14,52,22,44,52,34,29,26,26,36,18,9,7,33,2,9,46,48,2,2,7,25,20,19,19,5,4,6,7,4,5,10,15,19,1,19,7,11,6,15,7,11,2,5,11,18,20,7,7,9,6,10,35,4,0.049,0.028,0.026,0.026,0.03,0.034,0.024,0.025,0.051,0.032,0.024,0.033,0.027,0.035,0.032,0.032,0.032,0.033,0.033,0.042,0.035,0.049,0.044,0.042,0.047,0.03,0.029,0.03,0.023,0.037,0.039,0.048,0.025,0.032,0.016,0.022,0.038,0.036,0.018,0.014,0.027,0.034,0.031,0.026,0.021,0.015,0.023,0.013,0.017,0.016,0.028,0.02,0.033,0.019,0.008,0.021,0.019,0.019,0.016,0.027,0.027,0.027,0.028,0.014,0.015,0.018,0.022,0.015,0.017,0.027,0.019,0.02,0.02,0.021,1629,4382,3210,2415,2542,2403,2934,3497,1905,1714,750.0,3082,2928,1411,3088,12070,17667,2418,4587,5389,4273,4027,2135,6740,6544,6363,4948,4694,7221,5299,2214,1224,1015,7666,1803,2199,7545,9310,312,404,1378,2334,5655,2617,4198,1237,868,2131,2377,1473,566,2677,2089,5047,698,3896,1581,2928,1135,1896,1353,2415,452,968,2866,1442,3561,2390,2102,1359,1273,2128,9130,944,0.157,0.13,0.123,0.119,0.141,0.136,0.107,0.122,0.16,0.15,0.117,0.144,0.131,0.147,0.153,0.142,0.139,0.126,0.131,0.158,0.116,0.144,0.151,0.146,0.146,0.141,0.125,0.118,0.112,0.145,0.144,0.126,0.143,0.146,0.078,0.102,0.147,0.153,0.134,0.111,0.122,0.119,0.13,0.121,0.103,0.097,0.117,0.086,0.096,0.096,0.125,0.102,0.135,0.105,0.068,0.111,0.107,0.108,0.104,0.128,0.126,0.119,0.129,0.101,0.093,0.116,0.111,0.091,0.097,0.135,0.114,0.111,0.103,0.122,440,1369,965,842,887,996,939,1048,477,400,172.0,1523,761,325,739,2940,4259,436,1161,2395,819,1700,994,1729,2124,1665,1643,1396,1685,1556,642,482,315,1384,418,651,1679,1913,130,213,644,1181,1026,1577,2302,595,365,877,1094,587,414,1211,977,1987,300,1805,720,1229,658,1070,561,908,192,486,1431,898,1635,1024,946,497,534,988,3787,337,3.469,2.798,3.008,2.778,2.24,2.136,2.447,2.838,2.681,3.252,2.992,1.801,2.909,2.965,2.937,2.848,3.112,3.867,3.008,1.936,3.51,2.034,1.933,2.805,2.419,2.958,2.35,2.462,3.085,2.591,2.553,2.627,2.769,3.534,3.665,2.782,3.081,3.206,2.514,2.175,2.792,1.824,3.551,1.931,2.092,2.32,2.951,3.024,2.653,2.818,1.731,2.414,2.124,2.506,2.259,2.33,2.494,2.721,2.01,1.982,2.649,2.641,2.295,2.328,2.214,1.873,2.353,2.648,2.576,2.711,2.371,2.417,2.57,3.042,0.812,0.598,0.67,0.766,0.735,0.747,0.679,0.601,0.791,0.734,0.728,0.63,0.431,0.471,0.644,0.726,0.679,0.783,0.607,0.708,0.838,0.689,0.631,0.714,0.831,0.677,0.692,0.728,0.53,0.706,0.715,0.959,0.361,0.665,0.715,0.532,0.76,0.811,0.325,0.422,0.485,0.609,0.784,0.622,0.568,0.536,0.48,0.647,0.471,0.588,0.377,0.428,0.509,0.525,0.567,0.46,0.429,0.503,0.386,0.37,0.385,0.745,0.561,0.75,0.54,0.604,0.503,0.434,0.455,0.702,0.542,0.555,0.51,0.46
-92,Epileptic,M,37.0,1.5,3.6,1.5,1.8,2.1,2.6,2.6,1.8,2.6,0.8,0.4,2.9,2.3,0.6,2.5,8.4,10.2,0.6,2.5,4.4,1.3,4.2,2.2,5.2,2.8,2.8,3.4,3.4,4.7,3.7,1.9,1.3,0.7,3.9,0.5,0.8,5.5,3.7,0.3,0.4,0.9,3.6,2.4,2.5,4.0,1.0,0.6,1.0,1.2,0.7,0.5,3.5,1.3,3.0,0.4,2.8,1.1,1.5,1.2,1.2,0.8,2.7,0.6,0.7,2.2,1.4,2.8,1.9,1.8,0.6,1.0,1.9,4.7,0.4,9,34,13,16,14,23,17,16,19,10,6.0,27,25,9,23,82,94,5,30,38,10,50,23,42,37,30,34,25,33,44,19,10,6,43,4,7,53,42,2,2,5,32,20,18,18,7,3,7,6,7,4,20,9,14,2,19,7,14,8,8,5,15,3,5,17,19,17,12,10,4,6,16,37,3,0.041,0.026,0.025,0.029,0.037,0.029,0.039,0.031,0.06,0.032,0.05,0.033,0.035,0.044,0.035,0.039,0.033,0.024,0.029,0.036,0.027,0.035,0.032,0.055,0.031,0.028,0.035,0.041,0.04,0.032,0.043,0.044,0.031,0.039,0.017,0.018,0.045,0.035,0.013,0.02,0.02,0.032,0.04,0.027,0.03,0.019,0.021,0.017,0.014,0.019,0.022,0.028,0.017,0.022,0.014,0.02,0.021,0.016,0.025,0.016,0.02,0.038,0.032,0.018,0.021,0.023,0.029,0.028,0.028,0.023,0.022,0.022,0.015,0.015,1803,6179,2403,2995,2227,4239,2986,3423,2170,1707,728.0,4189,5019,1389,5022,14236,21981,2284,5345,5228,3287,6148,3041,5788,6404,7368,7167,4197,6509,7401,2739,1325,1097,9028,2306,2237,9523,8661,582,667,1406,3506,5368,2848,3618,1756,1090,2746,2748,1831,709,4706,2098,4424,712,4333,1854,2813,1318,2043,1264,2954,564,1218,3479,1109,3158,2658,2140,1024,1544,3168,11564,881,0.12,0.126,0.127,0.126,0.123,0.126,0.124,0.128,0.173,0.141,0.146,0.132,0.141,0.139,0.14,0.136,0.128,0.102,0.127,0.141,0.106,0.13,0.118,0.147,0.133,0.125,0.122,0.121,0.125,0.136,0.131,0.113,0.113,0.148,0.08,0.093,0.15,0.132,0.084,0.107,0.101,0.132,0.138,0.119,0.114,0.103,0.105,0.088,0.087,0.115,0.103,0.113,0.098,0.1,0.092,0.106,0.106,0.092,0.118,0.095,0.108,0.132,0.139,0.1,0.11,0.121,0.118,0.115,0.121,0.112,0.113,0.116,0.09,0.098,541,2048,929,1068,972,1512,1018,993,695,427,207.0,1484,1238,306,1289,3823,5640,443,1459,2133,846,1871,1190,1837,1641,1753,1866,1340,1773,2058,759,584,307,1788,538,741,2299,1954,313,287,703,1678,1171,1535,2010,770,416,1035,1188,652,408,2127,1137,1953,387,2173,880,1477,776,1061,621,1144,288,639,1686,871,1549,1139,984,430,727,1342,4773,360,3.145,2.823,2.542,2.586,2.142,2.279,2.443,2.949,2.372,2.87,2.509,2.202,3.051,2.997,2.841,2.713,2.96,3.797,2.84,2.083,2.724,2.558,2.082,2.462,2.87,3.061,2.789,2.372,2.836,2.759,2.57,2.304,2.843,3.392,3.738,2.63,3.102,3.052,2.158,2.457,2.568,1.806,3.258,2.134,2.049,2.556,2.89,3.365,2.82,3.03,1.952,2.33,2.003,2.398,2.154,2.195,2.317,2.099,1.773,2.033,2.245,2.477,1.985,2.151,2.211,1.571,2.16,2.529,2.338,2.539,2.495,2.523,2.516,2.835,0.905,0.562,0.538,0.633,0.607,0.645,0.527,0.643,0.764,0.552,0.923,0.696,0.663,0.636,0.587,0.719,0.683,0.722,0.646,0.65,0.861,0.675,0.697,0.807,0.718,0.681,0.761,0.741,0.696,0.574,0.617,0.933,0.646,0.616,0.641,0.449,0.795,0.768,0.422,0.445,0.443,0.632,0.77,0.735,0.572,0.586,0.425,0.604,0.444,0.623,0.446,0.468,0.422,0.438,0.379,0.426,0.567,0.601,0.533,0.447,0.436,0.674,0.448,0.77,0.487,0.513,0.54,0.517,0.512,0.766,0.504,0.619,0.554,0.496
-93,Epileptic,F,47.0,0.8,3.1,1.0,1.8,2.0,2.4,2.1,1.9,1.3,1.2,0.6,3.4,2.2,0.4,1.5,8.6,10.9,0.9,2.4,4.3,1.0,6.9,2.8,4.6,4.7,4.2,4.2,3.7,2.9,4.7,1.5,1.5,1.0,3.0,0.5,0.8,3.7,3.5,0.2,0.3,1.9,3.8,3.0,2.6,3.7,1.0,0.4,0.8,1.7,1.1,0.8,2.6,1.5,2.8,0.4,3.2,0.8,1.6,1.5,1.8,1.2,1.4,0.3,0.7,3.3,1.5,3.0,1.5,1.7,0.7,1.3,1.3,6.6,0.4,8,27,8,19,15,28,26,22,17,16,7.0,28,28,7,18,98,115,5,30,35,9,72,25,61,50,53,86,46,26,49,17,12,10,36,2,7,45,47,2,2,10,29,26,17,24,6,2,5,7,7,7,19,11,20,2,27,8,13,9,17,13,12,1,5,53,20,20,10,11,7,8,10,61,4,0.032,0.029,0.016,0.028,0.036,0.033,0.034,0.028,0.04,0.041,0.047,0.039,0.037,0.03,0.03,0.038,0.034,0.036,0.026,0.035,0.023,0.043,0.034,0.044,0.038,0.028,0.043,0.033,0.027,0.039,0.033,0.052,0.029,0.031,0.016,0.012,0.039,0.033,0.014,0.02,0.029,0.036,0.058,0.022,0.026,0.019,0.015,0.014,0.021,0.029,0.029,0.022,0.025,0.02,0.022,0.021,0.015,0.017,0.02,0.019,0.023,0.024,0.021,0.015,0.028,0.023,0.021,0.02,0.02,0.022,0.023,0.016,0.019,0.016,1779,5855,2629,3251,1904,4046,3525,4339,1927,2398,901.0,3572,4450,1202,3620,14483,22367,2661,5751,4789,3478,7014,2431,7223,7366,9780,6652,6456,6527,6657,2413,1599,1718,8018,2010,2707,7469,9975,398,812,1955,2488,6552,3484,3907,1785,923,2523,2764,2030,934,3821,1909,4936,555,4487,1763,3583,1839,2618,1646,2647,323,1114,3709,1645,4448,2511,2587,1567,1790,2483,12611,773,0.123,0.119,0.096,0.128,0.147,0.127,0.139,0.131,0.156,0.161,0.149,0.143,0.135,0.129,0.123,0.146,0.135,0.118,0.121,0.137,0.1,0.121,0.132,0.15,0.126,0.129,0.132,0.132,0.116,0.154,0.141,0.135,0.109,0.128,0.076,0.075,0.145,0.138,0.091,0.108,0.119,0.132,0.164,0.107,0.113,0.105,0.096,0.085,0.093,0.117,0.137,0.106,0.121,0.104,0.135,0.114,0.094,0.097,0.109,0.108,0.122,0.113,0.117,0.107,0.115,0.111,0.109,0.109,0.105,0.121,0.112,0.104,0.105,0.099,432,1734,960,1103,834,1231,1160,1257,549,594,254.0,1441,1133,264,895,3813,5710,391,1507,1990,712,2549,1155,1810,2097,2502,2120,1946,1739,2031,674,630,517,1591,453,931,1754,1980,237,326,1017,1446,982,1821,2315,775,340,932,1145,612,464,1846,1014,2215,246,2297,799,1617,1056,1455,774,1008,197,585,1783,1067,2058,1066,1198,545,873,1155,5175,453,3.533,2.802,2.684,2.859,1.931,2.536,2.406,3.119,2.414,3.072,2.788,2.098,2.852,2.998,2.843,2.733,2.936,4.342,2.921,2.061,3.186,2.273,1.873,2.801,2.66,2.914,2.344,2.534,2.786,2.525,2.607,2.751,2.517,3.287,3.653,2.608,2.956,3.307,1.982,2.454,2.414,1.694,3.754,2.107,1.933,2.642,3.048,3.385,2.84,3.079,2.189,2.243,1.898,2.387,2.682,2.121,2.494,2.49,2.002,1.953,2.244,2.262,1.971,2.161,2.229,1.799,2.38,2.665,2.469,2.748,2.403,2.389,2.562,2.146,1.037,0.782,0.539,0.438,0.66,0.727,0.543,0.565,0.718,0.688,0.798,0.498,0.568,0.46,0.56,0.621,0.65,0.711,0.672,0.635,0.9,0.63,0.505,0.748,0.726,0.62,0.55,0.612,0.501,0.582,0.658,0.893,0.528,0.667,0.585,0.498,0.771,0.711,0.323,0.585,0.417,0.47,0.924,0.69,0.534,0.546,0.669,0.808,0.656,0.764,0.421,0.476,0.486,0.482,0.631,0.434,0.503,0.514,0.402,0.418,0.525,0.771,0.265,0.695,0.542,0.602,0.471,0.452,0.441,1.037,0.446,0.403,0.505,0.363
-94,Epileptic,F,47.0,0.9,3.0,0.9,1.2,1.2,2.1,1.3,1.7,1.3,0.8,0.5,2.8,1.3,0.7,1.4,5.5,7.1,0.9,1.8,4.4,0.8,3.6,1.7,2.3,3.3,2.9,2.5,2.5,2.3,3.4,1.3,1.5,0.7,2.7,0.4,0.3,3.4,3.0,0.2,0.2,1.0,3.9,2.0,2.9,2.6,0.5,0.5,1.3,1.1,1.0,0.6,1.4,1.0,2.2,0.5,2.7,0.6,2.3,1.4,0.7,0.6,1.0,0.2,0.2,2.0,1.1,2.2,1.4,0.7,0.4,0.8,1.3,4.1,0.3,9,28,7,10,12,17,11,17,11,9,6.0,26,15,9,14,56,63,8,19,35,6,43,16,23,38,35,28,24,21,39,16,9,6,31,3,4,37,33,1,1,7,29,23,23,15,4,2,9,5,7,6,8,8,13,3,22,6,21,10,6,4,6,2,2,15,17,18,8,4,4,5,9,35,4,0.047,0.03,0.015,0.022,0.032,0.032,0.025,0.027,0.039,0.031,0.039,0.033,0.027,0.036,0.028,0.032,0.026,0.031,0.031,0.038,0.015,0.038,0.028,0.039,0.033,0.029,0.026,0.028,0.024,0.034,0.035,0.045,0.025,0.029,0.014,0.011,0.037,0.032,0.018,0.016,0.019,0.04,0.034,0.025,0.021,0.012,0.024,0.015,0.015,0.022,0.021,0.016,0.017,0.019,0.018,0.02,0.014,0.022,0.028,0.017,0.019,0.023,0.017,0.014,0.022,0.02,0.02,0.019,0.015,0.014,0.023,0.017,0.019,0.017,1540,5062,2312,2417,1855,3015,2584,3350,2229,1583,682.0,2881,3281,1426,2992,11255,16444,2502,3145,3929,2213,5567,2102,5155,5692,6382,5140,4195,5944,5167,2101,1343,1287,7119,1455,1604,6930,7123,355,340,1549,3638,5006,2843,3434,1271,770,2846,2493,1673,691,3209,2167,4355,691,3727,1612,2968,1505,1234,810,1648,395,672,2992,1263,3717,2636,1490,750,1191,2521,8327,362,0.138,0.134,0.098,0.113,0.137,0.147,0.105,0.13,0.147,0.134,0.13,0.143,0.127,0.166,0.136,0.134,0.12,0.124,0.13,0.141,0.08,0.15,0.129,0.141,0.144,0.133,0.12,0.111,0.103,0.145,0.137,0.142,0.104,0.137,0.072,0.072,0.134,0.137,0.108,0.101,0.106,0.154,0.132,0.122,0.101,0.08,0.107,0.087,0.086,0.115,0.123,0.086,0.095,0.096,0.107,0.112,0.105,0.115,0.127,0.101,0.117,0.123,0.115,0.096,0.116,0.119,0.103,0.105,0.094,0.111,0.112,0.105,0.098,0.116,463,1642,927,914,680,1097,846,1085,556,448,242.0,1294,877,342,792,3075,4331,481,963,1908,740,1738,964,1159,1705,1856,1676,1368,1496,1685,610,570,470,1600,431,567,1748,1688,194,174,794,1545,1082,1810,1950,683,339,1262,1126,671,412,1389,967,1849,399,1990,644,1594,867,663,409,590,192,340,1404,886,1827,1118,699,413,596,1149,3530,274,3.298,2.961,2.485,2.501,2.379,2.602,2.54,2.749,2.835,2.898,2.465,2.011,2.888,3.028,2.891,2.75,2.971,3.756,2.735,1.811,2.481,2.573,1.963,3.18,2.532,2.808,2.434,2.342,2.964,2.436,2.48,2.378,2.159,3.224,3.218,2.559,3.036,3.131,1.953,2.304,2.383,2.1,3.395,1.804,1.982,2.003,2.742,2.622,2.526,2.897,1.984,2.41,2.252,2.415,2.039,2.066,2.686,2.082,2.027,2.057,2.231,2.765,2.234,2.272,2.232,1.796,2.208,2.717,2.442,2.182,2.401,2.709,2.449,1.618,0.863,0.593,0.584,0.523,0.604,0.521,0.578,0.565,0.753,0.646,0.779,0.525,0.547,0.496,0.51,0.551,0.59,0.593,0.505,0.545,0.625,0.573,0.477,0.61,0.549,0.738,0.476,0.575,0.504,0.56,0.739,0.991,0.652,0.591,0.713,0.447,0.684,0.6,0.418,0.45,0.613,0.482,0.722,0.619,0.603,0.468,0.537,0.587,0.568,0.537,0.418,0.487,0.554,0.49,0.407,0.433,0.564,0.458,0.38,0.376,0.357,0.685,0.442,1.039,0.526,0.553,0.48,0.492,0.498,0.622,0.407,0.613,0.534,0.445
-95,Epileptic,F,32.0,1.2,2.5,1.2,1.1,1.6,2.7,1.4,2.0,1.4,0.5,0.3,2.9,1.6,0.5,1.0,6.9,9.9,0.8,2.2,5.1,0.8,6.0,3.2,3.6,6.4,4.2,3.8,1.1,1.8,2.8,1.9,1.4,0.4,2.5,0.4,0.6,2.5,4.4,0.1,0.1,1.1,4.5,2.0,2.4,1.8,0.8,0.4,0.9,1.5,0.8,0.7,2.0,1.8,3.4,0.4,1.7,1.1,1.9,0.9,1.2,0.5,1.6,0.5,0.5,2.0,1.4,1.2,1.2,1.1,0.7,0.8,1.4,5.3,0.3,10,26,13,9,14,20,16,22,18,5,4.0,25,22,6,12,87,97,9,25,49,8,47,25,43,53,37,36,19,21,34,20,13,6,27,2,6,31,47,1,1,6,37,21,21,9,7,2,6,8,7,6,16,13,25,2,14,7,15,9,8,4,15,3,5,16,16,7,10,4,6,6,10,42,3,0.048,0.026,0.021,0.018,0.028,0.044,0.029,0.035,0.045,0.023,0.039,0.04,0.026,0.033,0.031,0.033,0.031,0.03,0.035,0.043,0.02,0.049,0.048,0.043,0.052,0.046,0.037,0.021,0.024,0.031,0.05,0.059,0.026,0.027,0.014,0.016,0.029,0.039,0.013,0.017,0.023,0.042,0.032,0.026,0.016,0.027,0.018,0.017,0.021,0.024,0.028,0.019,0.025,0.02,0.033,0.022,0.021,0.021,0.022,0.022,0.023,0.03,0.028,0.017,0.02,0.032,0.013,0.022,0.021,0.027,0.021,0.022,0.019,0.019,1569,4507,2571,2839,2370,2275,2877,3228,1972,1213,760.0,2618,4210,1144,2744,13724,20063,2221,4823,5387,3167,3171,1641,6232,4238,4472,4677,3222,3787,5061,2647,889,804,6706,1625,1705,7521,8197,383,389,1293,1948,5734,3293,3001,1509,712,2023,2215,1412,559,4105,2438,6918,284,2579,1539,3209,1046,1485,630,2188,510,874,3167,1259,2190,1853,1155,966,1144,2377,9155,778,0.135,0.12,0.115,0.1,0.136,0.138,0.125,0.153,0.17,0.121,0.108,0.153,0.119,0.148,0.145,0.137,0.13,0.128,0.134,0.172,0.082,0.154,0.164,0.159,0.165,0.15,0.138,0.108,0.099,0.14,0.167,0.152,0.13,0.126,0.082,0.095,0.127,0.15,0.093,0.086,0.116,0.149,0.149,0.121,0.09,0.126,0.094,0.089,0.106,0.123,0.138,0.099,0.11,0.101,0.133,0.114,0.115,0.107,0.118,0.108,0.111,0.143,0.14,0.111,0.109,0.141,0.089,0.112,0.098,0.141,0.118,0.113,0.104,0.117,448,1511,952,976,956,1011,901,1009,600,328,202.0,1202,1262,258,696,3731,5624,475,1141,2237,746,1639,1044,1571,1806,1441,1680,951,1380,1569,793,371,267,1343,411,604,1582,1968,186,161,664,1402,1131,1648,1589,496,311,841,1049,521,376,1726,1172,2836,157,1229,735,1458,715,827,333,892,253,450,1552,640,1157,910,766,427,588,1007,4081,290,3.337,2.745,2.676,2.713,2.115,2.1,2.459,2.703,2.495,2.911,2.892,1.86,2.703,2.935,2.829,2.751,2.726,3.608,3.13,2.027,3.079,1.806,1.599,2.875,2.106,2.518,2.312,2.516,2.156,2.425,2.602,2.358,2.533,3.207,3.396,2.483,3.237,2.995,2.387,2.387,2.52,1.36,3.452,2.15,2.115,2.616,2.839,2.839,2.59,2.71,1.857,2.45,2.161,2.482,2.166,2.201,2.367,2.421,1.791,2.037,2.046,2.315,2.034,2.263,2.179,2.432,2.109,2.093,1.76,2.505,2.372,2.608,2.454,2.612,0.741,0.52,0.426,0.649,0.689,0.66,0.567,0.545,0.668,0.463,0.819,0.486,0.386,0.531,0.583,0.632,0.686,0.766,0.701,0.741,0.846,0.582,0.446,0.757,0.721,0.678,0.719,0.52,0.57,0.718,0.705,0.889,0.435,0.664,0.683,0.506,0.757,0.858,0.409,0.347,0.415,0.509,0.725,0.611,0.365,0.671,0.408,0.805,0.442,0.439,0.402,0.482,0.449,0.435,0.641,0.377,0.465,0.468,0.337,0.401,0.386,0.589,0.417,0.653,0.529,0.628,0.47,0.62,0.449,0.749,0.374,0.5,0.465,0.526
-96,Epileptic,F,67.5,0.9,2.7,1.3,1.1,1.1,1.8,1.3,1.3,0.9,0.9,0.3,2.7,1.8,0.8,2.2,4.9,8.6,1.2,2.7,4.0,0.9,2.9,1.3,2.9,3.4,3.4,3.1,2.4,2.7,3.0,1.6,1.1,0.6,2.4,0.4,0.7,2.2,3.3,0.1,0.4,1.3,2.4,2.1,2.7,3.6,0.5,0.7,1.1,1.5,0.7,0.6,2.0,1.4,2.0,0.4,2.6,0.3,1.4,1.0,1.0,0.6,1.7,0.2,0.4,2.2,1.6,2.4,1.9,1.2,0.7,1.1,1.2,4.3,0.4,9,20,10,9,12,16,11,11,11,8,3.0,25,16,10,21,47,77,8,23,35,8,30,14,33,37,31,33,20,20,33,20,8,4,29,3,6,24,31,2,3,6,22,22,20,21,5,3,6,7,5,5,13,11,12,3,22,3,12,7,7,5,13,2,5,20,18,20,12,7,5,7,10,36,4,0.039,0.027,0.024,0.018,0.03,0.033,0.024,0.021,0.044,0.035,0.028,0.029,0.028,0.036,0.034,0.036,0.032,0.039,0.032,0.034,0.022,0.032,0.022,0.034,0.033,0.028,0.024,0.026,0.023,0.025,0.036,0.041,0.019,0.029,0.02,0.017,0.029,0.032,0.009,0.026,0.022,0.028,0.035,0.024,0.025,0.013,0.025,0.015,0.02,0.022,0.025,0.021,0.03,0.018,0.017,0.019,0.018,0.021,0.029,0.017,0.021,0.027,0.018,0.016,0.023,0.029,0.019,0.022,0.018,0.02,0.024,0.023,0.019,0.02,1594,4320,2213,2283,1599,2333,2187,2974,1307,1401,529.0,2529,3599,984,3056,7662,14846,2323,3606,3866,3215,4322,1952,5411,5519,5696,5182,3308,5695,5244,2381,1108,964,5445,1747,1629,5477,6120,408,549,1550,2891,6638,2580,3739,1275,872,2257,2224,1312,453,2943,1773,3858,529,3806,959,2329,942,1464,831,2478,279,938,2517,1465,3319,2884,1793,1142,1388,1899,7792,463,0.142,0.124,0.115,0.105,0.124,0.136,0.094,0.1,0.129,0.15,0.115,0.135,0.119,0.126,0.149,0.143,0.128,0.12,0.143,0.13,0.094,0.148,0.118,0.125,0.136,0.128,0.12,0.109,0.095,0.123,0.137,0.122,0.099,0.119,0.095,0.086,0.127,0.131,0.074,0.128,0.106,0.128,0.14,0.115,0.113,0.088,0.107,0.08,0.094,0.115,0.128,0.104,0.133,0.095,0.107,0.108,0.105,0.109,0.132,0.103,0.11,0.12,0.129,0.113,0.12,0.136,0.108,0.11,0.103,0.104,0.116,0.119,0.105,0.115,480,1559,855,889,647,959,783,977,398,399,187.0,1299,1156,304,1074,2419,4475,459,1245,1818,750,1480,860,1496,1921,1994,2057,1343,1619,1846,747,529,462,1367,430,708,1363,1645,274,292,834,1311,1054,1832,2253,686,405,1035,1097,477,329,1438,769,1711,310,2105,355,1173,565,838,432,939,167,418,1448,864,1872,1306,942,531,700,831,3559,329,3.197,2.606,2.578,2.631,2.33,2.204,2.363,2.601,2.505,3.154,2.568,1.731,2.552,2.375,2.322,2.493,2.768,3.739,2.53,1.901,3.237,2.385,1.978,2.751,2.337,2.512,2.095,2.122,2.782,2.367,2.448,2.127,1.788,2.786,3.412,2.195,2.931,2.774,1.719,2.475,2.312,1.938,3.703,1.615,1.886,2.038,2.688,2.672,2.439,3.08,1.581,2.206,2.159,2.435,2.09,1.957,2.835,2.289,2.031,1.99,2.244,2.708,1.846,2.106,1.854,2.027,1.894,2.545,2.242,2.506,2.254,2.583,2.408,1.774,0.796,0.531,0.405,0.417,0.646,0.471,0.568,0.502,0.686,0.515,0.813,0.472,0.36,0.449,0.47,0.535,0.547,0.735,0.659,0.523,0.749,0.475,0.504,0.573,0.52,0.481,0.464,0.474,0.5,0.535,0.667,0.99,0.363,0.79,0.738,0.418,0.754,0.647,0.295,0.394,0.432,0.459,0.969,0.519,0.545,0.416,0.562,0.731,0.574,0.509,0.299,0.487,0.625,0.561,0.325,0.404,0.693,0.763,0.472,0.357,0.56,0.787,0.337,0.967,0.604,0.623,0.434,0.402,0.342,0.355,0.558,0.611,0.503,0.364
-97,Epileptic,M,42.0,0.7,2.9,0.9,1.6,1.8,1.9,2.7,2.2,1.3,0.9,0.3,2.5,2.0,0.6,1.3,8.3,11.8,1.0,2.2,4.7,1.1,3.2,1.9,4.1,3.3,4.2,3.8,3.7,3.3,4.5,1.9,1.4,0.5,2.4,0.4,1.2,4.7,3.2,0.3,0.3,1.5,3.2,3.8,2.7,3.3,0.7,0.4,0.8,1.7,0.8,0.9,2.6,1.5,2.9,0.2,2.1,0.9,2.2,0.8,1.5,0.7,1.7,0.3,0.5,2.7,1.9,3.0,1.8,1.0,0.7,1.4,1.3,5.2,0.4,4,25,8,12,18,17,17,18,10,9,3.0,20,17,7,13,65,91,7,22,35,9,32,16,29,38,42,34,31,27,46,21,10,4,24,2,8,43,35,1,2,10,27,27,22,14,6,2,4,7,6,5,17,11,18,1,15,6,20,6,12,5,9,1,3,23,18,20,12,6,5,8,8,38,3,0.026,0.029,0.015,0.027,0.044,0.032,0.036,0.027,0.055,0.033,0.032,0.031,0.035,0.033,0.035,0.041,0.037,0.031,0.028,0.038,0.022,0.037,0.026,0.043,0.026,0.03,0.03,0.03,0.028,0.035,0.044,0.051,0.024,0.026,0.014,0.021,0.04,0.033,0.02,0.019,0.026,0.033,0.05,0.03,0.022,0.012,0.018,0.012,0.02,0.016,0.031,0.027,0.029,0.02,0.016,0.016,0.021,0.021,0.027,0.024,0.021,0.033,0.017,0.019,0.025,0.026,0.02,0.022,0.015,0.022,0.022,0.02,0.019,0.02,1833,5619,2646,2782,2287,2911,2819,4009,1926,1697,780.0,2588,3462,1491,2434,12138,19511,2703,4395,4159,3573,4854,2222,6832,6979,7772,6228,5089,6967,6775,2625,1358,847,6138,1519,2557,6923,7091,316,415,1846,2844,5998,2228,4107,1785,955,2159,2448,2031,737,3488,1790,5777,381,4053,1590,3801,848,1860,1077,2200,440,1083,3452,1739,4671,2907,2130,1247,2039,2228,10245,460,0.101,0.12,0.096,0.118,0.136,0.14,0.119,0.117,0.16,0.134,0.116,0.123,0.135,0.124,0.131,0.136,0.131,0.122,0.118,0.134,0.089,0.143,0.125,0.138,0.125,0.127,0.122,0.104,0.109,0.139,0.14,0.129,0.1,0.113,0.068,0.102,0.136,0.139,0.104,0.105,0.122,0.139,0.148,0.127,0.098,0.086,0.089,0.077,0.098,0.093,0.139,0.116,0.117,0.097,0.106,0.097,0.107,0.11,0.12,0.115,0.113,0.123,0.091,0.093,0.125,0.12,0.106,0.106,0.09,0.12,0.114,0.114,0.1,0.108,483,1678,902,997,784,1009,1130,1350,456,442,218.0,1312,1018,341,717,3457,5512,524,1302,2000,912,1470,1088,1650,2134,2284,2097,1904,1916,2180,762,569,319,1449,442,845,2002,1653,203,243,945,1403,1389,1423,2353,890,358,988,1200,810,443,1529,903,2323,177,2028,740,1790,551,1011,490,806,249,436,1819,1029,2318,1362,1031,514,992,1020,4397,304,3.679,3.112,2.655,2.817,2.43,2.546,2.215,2.568,2.87,2.955,2.884,1.775,2.65,2.878,2.555,2.587,2.769,3.666,2.686,1.837,2.849,2.552,1.861,2.946,2.614,2.813,2.338,2.122,2.716,2.464,2.541,2.434,2.145,2.912,3.042,2.791,2.708,3.163,1.904,2.052,2.478,1.843,2.97,1.839,2.029,2.303,3.087,2.66,2.529,2.624,2.123,2.335,2.052,2.548,2.313,2.197,2.579,2.372,1.76,2.031,2.418,2.714,1.968,2.769,2.077,2.05,2.26,2.488,2.475,2.493,2.422,2.744,2.536,1.878,0.699,0.852,0.67,0.402,0.657,0.687,0.593,0.513,0.525,0.567,0.718,0.486,0.443,0.604,0.55,0.637,0.652,0.707,0.514,0.547,0.793,0.485,0.399,0.762,0.582,0.599,0.639,0.615,0.685,0.486,0.734,0.976,0.457,0.665,0.775,0.549,0.717,0.553,0.408,0.38,0.555,0.649,0.82,0.82,0.585,0.476,0.631,0.573,0.437,0.543,0.565,0.507,0.58,0.541,0.397,0.473,0.479,0.529,0.41,0.353,0.436,0.667,0.499,1.004,0.554,0.629,0.493,0.527,0.395,0.877,0.462,0.442,0.523,0.399
-98,Epileptic,M,22.0,1.4,3.1,1.4,1.3,1.8,2.1,1.4,1.4,2.8,1.2,0.2,3.2,1.4,0.5,1.7,9.4,10.7,1.2,2.5,4.1,1.1,5.4,2.7,3.8,6.1,4.0,5.0,3.4,3.1,3.3,2.4,1.0,0.5,2.8,0.7,0.7,4.1,3.5,0.2,0.3,1.1,4.0,2.6,2.5,2.6,1.1,0.5,0.9,1.6,0.9,0.5,2.7,1.9,4.3,0.3,3.3,0.7,1.3,1.5,1.5,0.3,1.5,0.4,0.5,1.7,1.3,2.7,1.6,1.3,0.9,1.2,1.5,5.5,0.4,10,27,10,10,12,24,13,18,17,9,3.0,28,20,8,21,88,95,11,29,39,8,51,23,46,54,42,53,32,24,42,22,9,4,34,3,6,51,40,1,2,7,30,25,18,13,7,2,5,8,9,3,18,11,27,1,27,6,9,8,10,3,12,4,4,13,17,19,10,6,6,8,10,39,3,0.05,0.028,0.026,0.027,0.032,0.033,0.034,0.028,0.066,0.047,0.038,0.043,0.028,0.03,0.034,0.044,0.042,0.041,0.032,0.041,0.029,0.047,0.049,0.047,0.047,0.039,0.039,0.041,0.032,0.035,0.055,0.043,0.025,0.035,0.022,0.017,0.038,0.032,0.013,0.016,0.021,0.044,0.043,0.025,0.021,0.023,0.018,0.014,0.019,0.02,0.028,0.023,0.03,0.032,0.026,0.019,0.022,0.016,0.031,0.02,0.012,0.026,0.027,0.017,0.021,0.02,0.022,0.019,0.018,0.022,0.026,0.018,0.017,0.022,1676,5800,3087,2855,2449,3737,2856,3436,2022,1802,591.0,2653,4727,1520,4501,14422,18974,2945,5358,5512,3329,6144,2340,6719,7166,7119,7505,5312,6701,7402,3286,1233,1295,7398,2061,2320,9469,9903,549,792,1640,2625,5435,2989,3329,1431,994,2515,3014,2191,538,4430,2217,5843,396,5180,1590,2951,1428,2506,999,2829,590,1118,2915,1896,3948,3130,1874,1712,1632,2878,11199,487,0.155,0.116,0.111,0.118,0.13,0.149,0.127,0.127,0.175,0.158,0.134,0.166,0.123,0.143,0.131,0.146,0.146,0.143,0.133,0.16,0.106,0.151,0.143,0.159,0.153,0.129,0.135,0.13,0.118,0.128,0.153,0.108,0.099,0.127,0.087,0.091,0.146,0.135,0.081,0.085,0.105,0.143,0.151,0.118,0.099,0.105,0.096,0.076,0.096,0.113,0.138,0.107,0.125,0.121,0.105,0.104,0.104,0.091,0.127,0.103,0.084,0.121,0.131,0.099,0.108,0.107,0.108,0.098,0.095,0.112,0.116,0.096,0.096,0.121,508,1911,960,905,920,1173,902,1037,693,431,170.0,1264,1061,324,1016,3962,5021,539,1356,2045,676,1922,947,1714,2255,1990,2547,1646,1711,1978,850,497,387,1606,545,770,2116,1944,242,343,806,1404,1242,1615,1910,698,391,1042,1273,772,256,1967,1022,2279,178,2578,642,1375,762,1160,441,1004,286,505,1302,1039,1904,1304,938,694,781,1278,4626,269,3.254,2.808,2.911,2.841,2.43,2.627,2.432,2.964,2.373,3.079,2.686,1.891,3.104,3.253,3.227,2.685,2.84,4.054,3.017,2.274,3.253,2.558,2.158,2.945,2.512,2.819,2.297,2.51,2.869,2.759,2.773,2.382,2.659,3.083,3.252,2.711,3.198,3.372,2.588,2.711,2.574,1.773,2.979,2.076,1.944,2.138,3.24,3.057,2.809,2.944,2.293,2.428,2.279,2.464,2.415,2.215,2.64,2.455,2.123,2.303,2.493,2.9,2.17,2.669,2.473,2.187,2.3,2.792,2.439,2.55,2.411,2.439,2.597,2.298,0.983,0.509,0.551,0.647,0.718,0.674,0.649,0.459,0.843,0.808,0.975,0.514,0.534,0.624,0.588,0.751,0.733,0.87,0.667,0.735,0.809,0.817,0.572,0.72,0.826,0.678,0.652,0.68,0.55,0.615,0.785,0.938,0.424,0.682,0.54,0.507,0.839,0.723,0.452,0.325,0.565,0.594,0.765,0.572,0.543,0.433,0.572,0.76,0.595,0.475,0.526,0.439,0.63,0.565,0.4,0.457,0.546,0.436,0.421,0.504,0.543,0.663,0.399,0.755,0.547,0.685,0.445,0.408,0.434,0.666,0.516,0.562,0.477,0.45
-99,Epileptic,M,37.0,1.0,3.2,2.1,1.7,2.1,3.4,2.2,2.1,1.5,1.1,0.3,4.1,1.9,0.6,1.3,10.5,12.5,1.0,3.4,6.2,2.6,4.4,3.6,5.6,5.3,4.5,4.3,3.9,6.5,3.6,1.8,1.8,1.4,3.3,0.7,0.7,5.1,4.9,0.2,0.1,1.4,3.9,2.8,4.8,4.0,1.1,0.3,1.2,1.9,0.7,0.6,2.8,2.6,3.5,0.5,3.9,1.0,2.5,2.2,2.0,1.0,2.0,0.4,0.7,3.0,1.9,2.9,1.8,1.7,0.8,1.0,1.4,6.8,0.7,7,25,14,10,14,24,16,19,16,8,3.0,31,19,5,12,82,100,9,28,44,19,39,26,47,45,45,37,29,52,33,16,13,8,25,4,4,42,44,2,1,9,29,25,33,23,8,2,9,9,4,5,19,18,25,2,29,8,15,16,85,8,13,3,4,18,15,23,13,9,5,7,9,48,5,0.039,0.028,0.027,0.03,0.036,0.042,0.039,0.033,0.045,0.054,0.037,0.042,0.036,0.036,0.033,0.045,0.042,0.042,0.049,0.044,0.049,0.035,0.043,0.052,0.04,0.041,0.034,0.04,0.051,0.037,0.049,0.064,0.072,0.048,0.033,0.021,0.045,0.041,0.016,0.019,0.029,0.036,0.058,0.035,0.029,0.027,0.018,0.022,0.024,0.017,0.04,0.026,0.035,0.025,0.034,0.025,0.026,0.028,0.032,0.048,0.024,0.035,0.025,0.037,0.027,0.033,0.026,0.022,0.025,0.03,0.031,0.021,0.024,0.038,1077,5412,3359,2280,2299,2529,3044,3424,2082,1213,627.0,2868,3322,991,2485,11287,19578,1982,4040,4304,4479,3841,1799,5826,4528,5315,5218,3989,5532,5107,3166,1216,750,5330,1493,1452,7331,7181,328,343,1225,2250,4917,3184,3523,1244,782,1876,2107,1416,408,3491,2755,5419,368,4460,1493,2865,1666,1489,1309,2449,426,978,3327,1852,3595,2813,2076,1191,1011,1952,10768,538,0.129,0.124,0.111,0.117,0.125,0.143,0.129,0.128,0.148,0.169,0.117,0.145,0.129,0.123,0.131,0.137,0.137,0.144,0.158,0.15,0.134,0.123,0.15,0.158,0.121,0.14,0.112,0.125,0.131,0.133,0.16,0.15,0.169,0.149,0.112,0.094,0.146,0.137,0.104,0.105,0.125,0.142,0.169,0.132,0.12,0.119,0.096,0.108,0.113,0.091,0.162,0.118,0.124,0.114,0.129,0.111,0.117,0.113,0.137,0.126,0.109,0.131,0.119,0.128,0.116,0.116,0.112,0.11,0.11,0.13,0.133,0.114,0.111,0.149,392,1833,1314,944,999,1279,962,1234,647,405,206.0,1627,1041,270,704,3783,5451,473,1367,2322,1092,1780,1214,1828,2001,1913,2107,1569,1831,1828,704,522,343,1316,389,577,2024,2115,209,158,739,1647,1019,2219,2069,645,290,906,1099,649,268,1803,1346,2316,212,2505,676,1520,1085,1026,693,977,243,332,1739,889,1928,1302,993,447,541,965,4662,288,2.715,2.718,2.51,2.447,2.226,1.951,2.493,2.435,2.487,2.696,2.522,1.654,2.442,2.593,2.61,2.388,2.862,3.045,2.449,1.703,2.965,1.877,1.53,2.577,1.937,2.369,2.063,2.097,2.497,2.363,3.29,2.479,1.979,2.995,3.397,2.288,2.808,2.53,1.815,2.319,2.112,1.366,3.308,1.646,1.954,2.207,3.225,2.509,2.496,2.312,1.865,2.098,2.287,2.407,2.152,1.991,2.477,2.123,1.8,1.636,2.11,2.732,2.082,3.28,2.172,2.226,1.932,2.365,2.348,2.991,2.109,2.323,2.418,2.312,0.755,0.576,0.452,0.474,0.56,0.481,0.786,0.598,0.668,0.671,0.641,0.462,0.6,0.608,0.52,0.673,0.601,0.677,0.61,0.479,0.933,0.532,0.42,0.702,0.548,0.628,0.573,0.621,0.588,0.604,0.825,0.841,0.655,0.658,0.826,0.521,0.651,0.722,0.328,0.372,0.391,0.48,0.805,0.434,0.565,0.46,0.711,0.702,0.433,0.555,0.34,0.462,0.54,0.559,0.503,0.43,0.579,0.493,0.345,0.412,0.478,0.701,0.472,0.691,0.628,0.589,0.508,0.445,0.473,0.922,0.474,0.479,0.558,0.467
-100,Epileptic,M,27.0,0.6,2.2,1.0,1.3,1.6,2.0,1.8,1.9,1.1,0.6,0.4,2.9,2.0,0.7,1.1,7.1,7.7,0.6,1.8,5.2,0.8,2.1,2.0,3.0,3.6,4.6,2.6,2.5,2.6,2.4,1.2,1.0,0.8,3.0,0.5,1.2,3.3,3.3,0.4,0.3,1.5,2.7,3.0,2.8,2.7,0.9,0.5,1.2,1.1,0.7,0.9,1.9,1.4,3.0,0.6,3.3,0.6,2.0,1.2,0.9,0.9,0.9,0.4,0.4,3.0,2.1,1.9,1.9,1.0,0.3,1.1,1.2,6.5,0.5,6,24,11,13,22,18,16,16,11,6,4.0,29,21,8,14,75,70,5,22,45,8,21,20,32,35,50,30,23,26,27,20,18,7,34,3,9,43,42,3,2,9,25,29,20,14,6,3,6,7,5,6,13,12,20,2,25,4,16,7,8,5,7,2,3,18,16,14,14,7,3,8,10,56,4,0.025,0.023,0.018,0.024,0.029,0.029,0.026,0.028,0.039,0.028,0.037,0.03,0.033,0.033,0.029,0.032,0.027,0.028,0.028,0.041,0.017,0.03,0.029,0.03,0.033,0.031,0.027,0.031,0.026,0.03,0.038,0.051,0.025,0.028,0.018,0.02,0.036,0.031,0.023,0.02,0.027,0.03,0.038,0.032,0.02,0.02,0.02,0.017,0.015,0.017,0.032,0.021,0.023,0.021,0.025,0.028,0.028,0.021,0.031,0.018,0.026,0.02,0.018,0.017,0.026,0.04,0.023,0.023,0.019,0.014,0.03,0.023,0.022,0.015,1624,4693,2113,2326,2194,3052,2677,3579,1549,1218,721.0,2918,3553,1305,2196,12527,16365,2142,3725,5345,3509,3224,2158,5873,5655,8318,4737,3716,5951,3806,2105,816,1346,6950,1766,2406,5989,7014,542,546,1795,2501,6431,2466,3525,1329,836,2181,2442,1453,633,3192,2307,5140,590,3421,841,3441,1050,1258,1082,1815,591,822,3535,1289,2553,3159,1497,807,1270,1719,10815,852,0.105,0.115,0.107,0.108,0.13,0.128,0.118,0.12,0.14,0.132,0.119,0.13,0.136,0.142,0.151,0.139,0.126,0.1,0.109,0.135,0.076,0.132,0.136,0.132,0.137,0.132,0.126,0.122,0.113,0.131,0.121,0.127,0.101,0.134,0.084,0.106,0.144,0.132,0.124,0.114,0.127,0.13,0.152,0.132,0.104,0.107,0.108,0.087,0.09,0.105,0.144,0.107,0.114,0.107,0.122,0.131,0.13,0.1,0.119,0.105,0.115,0.108,0.115,0.103,0.115,0.142,0.116,0.117,0.104,0.118,0.13,0.123,0.113,0.093,436,1603,769,943,935,1144,1062,1150,496,353,209.0,1530,975,311,635,3450,4577,397,1102,2188,888,1186,1112,1557,1894,2588,1635,1305,1610,1297,641,332,486,1757,433,869,1703,1850,240,223,857,1296,1378,1476,1996,665,347,946,1123,647,428,1513,1031,2289,298,1848,380,1644,686,779,498,772,309,388,1769,773,1358,1331,788,360,612,862,4527,507,3.507,2.643,2.442,2.415,2.127,2.295,2.206,2.692,2.372,2.932,2.764,1.72,2.845,2.951,2.674,2.732,2.87,3.875,2.726,2.026,3.001,2.216,1.787,2.804,2.439,2.709,2.286,2.234,2.792,2.396,2.594,2.446,2.236,3.047,3.545,2.536,2.734,2.83,2.395,2.478,2.689,1.72,3.206,1.953,2.004,2.121,3.029,2.811,2.553,2.656,1.891,2.28,2.208,2.343,2.328,2.059,2.631,2.393,1.854,1.866,2.287,2.465,2.154,2.234,2.183,1.937,2.087,2.575,2.192,2.636,2.241,2.32,2.481,2.01,0.878,0.759,0.585,0.521,0.503,0.602,0.507,0.542,0.47,0.48,0.78,0.422,0.595,0.369,0.299,0.517,0.537,0.696,0.537,0.748,0.747,0.45,0.38,0.565,0.503,0.585,0.405,0.515,0.445,0.468,0.71,0.931,0.465,0.653,0.617,0.488,0.676,0.592,0.348,0.501,0.498,0.418,0.674,0.675,0.571,0.435,0.652,0.614,0.513,0.458,0.498,0.418,0.535,0.524,0.328,0.397,0.364,0.558,0.354,0.32,0.439,0.548,0.432,0.807,0.427,0.728,0.441,0.498,0.418,1.055,0.44,0.459,0.551,0.368
-101,Epileptic,F,22.0,0.8,2.8,1.2,1.7,2.4,2.2,1.9,1.9,1.4,0.8,0.3,3.6,1.5,0.6,1.2,6.5,10.3,1.0,2.6,5.6,1.0,3.2,1.6,3.3,3.6,3.1,3.7,2.6,3.2,4.2,1.2,0.9,0.7,2.6,0.4,0.6,4.2,2.9,0.2,0.2,1.0,3.4,2.5,3.0,2.5,1.0,0.4,1.1,1.3,0.6,0.6,1.7,1.7,2.9,0.2,2.9,1.0,1.3,1.3,1.3,0.6,1.2,0.3,0.3,1.8,1.6,3.0,1.6,1.0,0.5,1.6,1.4,4.8,0.4,7,26,10,14,23,26,17,17,26,7,5.0,30,17,10,13,74,94,9,32,49,9,36,21,39,41,34,45,24,27,48,19,6,7,26,2,4,51,36,2,1,5,33,28,18,14,6,2,6,6,6,6,13,15,23,1,25,9,10,10,12,4,10,2,4,15,13,24,12,6,5,10,14,37,3,0.034,0.025,0.017,0.024,0.04,0.03,0.026,0.028,0.044,0.035,0.034,0.035,0.032,0.034,0.028,0.032,0.033,0.035,0.033,0.039,0.018,0.038,0.031,0.042,0.029,0.035,0.029,0.026,0.026,0.031,0.038,0.042,0.027,0.03,0.014,0.014,0.041,0.027,0.014,0.016,0.019,0.034,0.048,0.03,0.018,0.014,0.021,0.015,0.018,0.018,0.021,0.016,0.023,0.02,0.027,0.021,0.023,0.016,0.023,0.023,0.021,0.025,0.03,0.015,0.023,0.022,0.021,0.018,0.016,0.013,0.027,0.02,0.017,0.02,1790,5890,3135,3291,2880,3705,3310,3504,2361,1611,736.0,3536,3374,1290,3404,12761,20649,2673,5258,5459,3428,5376,2793,6783,7116,6165,6402,4493,6987,7462,1936,1162,1285,7023,1893,1993,7241,8605,417,503,1632,3828,6139,2882,3925,2041,820,2627,2305,1693,693,3381,2892,5713,345,3840,1735,3131,1510,1499,1042,2444,365,746,2748,1865,4032,3127,2027,903,1950,2357,10783,570,0.12,0.112,0.101,0.116,0.148,0.141,0.106,0.123,0.159,0.134,0.123,0.134,0.126,0.135,0.123,0.137,0.137,0.124,0.134,0.146,0.081,0.15,0.144,0.147,0.131,0.14,0.133,0.111,0.112,0.136,0.12,0.117,0.122,0.128,0.078,0.085,0.143,0.124,0.084,0.087,0.102,0.143,0.157,0.13,0.097,0.09,0.098,0.081,0.09,0.104,0.133,0.094,0.118,0.104,0.138,0.113,0.127,0.091,0.128,0.128,0.099,0.114,0.137,0.109,0.118,0.105,0.109,0.101,0.094,0.1,0.125,0.112,0.099,0.116,464,1854,1130,1162,1085,1366,1113,1125,704,409,212.0,1540,897,333,814,3711,5463,495,1409,2353,835,1596,1049,1660,2145,1768,2220,1539,1869,2419,550,432,409,1403,463,670,1709,1854,265,251,756,1643,1140,1647,2152,999,315,1116,1097,582,421,1662,1269,2542,133,2054,713,1429,851,874,492,862,153,364,1306,1001,2193,1407,937,529,934,1066,4398,282,3.486,2.752,2.602,2.727,2.255,2.359,2.493,2.763,2.492,2.974,2.827,2.029,2.77,2.644,2.922,2.598,2.94,3.647,3.022,2.031,2.931,2.543,2.257,2.918,2.628,2.786,2.314,2.284,2.86,2.483,2.587,2.595,2.564,3.329,3.518,2.793,3.037,3.201,1.878,2.342,2.65,2.06,3.5,2.1,2.125,2.264,3.109,2.935,2.48,2.799,1.838,2.069,2.253,2.314,2.896,2.036,2.575,2.525,2.082,1.905,2.409,2.534,2.414,2.027,2.372,2.228,2.032,2.369,2.513,1.978,2.348,2.446,2.604,2.549,0.732,0.667,0.68,0.467,0.617,0.459,0.582,0.487,0.675,0.399,0.71,0.486,0.527,0.523,0.59,0.609,0.599,0.774,0.601,0.651,0.863,0.49,0.561,0.684,0.533,0.471,0.511,0.586,0.483,0.53,0.694,0.843,0.456,0.663,0.8,0.485,0.649,0.61,0.414,0.391,0.606,0.566,0.765,0.781,0.588,0.52,0.512,0.653,0.589,0.751,0.486,0.478,0.517,0.5,0.304,0.407,0.534,0.463,0.374,0.428,0.543,0.791,0.436,0.88,0.404,0.79,0.472,0.427,0.407,0.573,0.496,0.623,0.635,0.326
-102,Epileptic,F,32.0,1.1,1.9,0.9,1.5,1.7,1.6,1.8,1.5,1.6,0.6,0.3,2.7,1.4,0.4,1.3,5.1,7.8,1.4,4.0,4.2,1.8,2.8,1.6,5.0,3.0,2.6,2.6,3.0,2.3,2.2,1.9,1.6,0.6,2.1,0.4,0.9,3.3,4.5,0.2,0.2,0.8,2.5,2.5,1.9,2.3,0.7,0.4,0.8,1.6,0.5,0.7,1.9,1.1,2.7,0.3,2.1,0.7,1.7,0.9,1.0,0.7,1.7,0.4,0.6,1.5,1.1,2.2,1.2,0.7,0.5,0.7,1.3,4.4,0.2,7,20,8,9,17,18,13,17,15,6,3.0,27,19,7,14,58,74,11,28,41,20,38,15,49,45,31,34,30,20,31,24,22,8,24,2,9,29,47,1,1,4,25,24,14,13,5,2,5,8,4,4,13,10,17,2,15,9,15,6,7,4,13,2,3,10,16,18,11,5,6,4,12,38,2,0.044,0.024,0.017,0.028,0.045,0.038,0.033,0.028,0.057,0.046,0.037,0.045,0.03,0.031,0.025,0.036,0.028,0.04,0.056,0.049,0.046,0.041,0.039,0.059,0.044,0.028,0.033,0.036,0.03,0.028,0.044,0.054,0.034,0.029,0.016,0.025,0.055,0.043,0.015,0.017,0.019,0.038,0.053,0.028,0.022,0.019,0.018,0.013,0.018,0.017,0.042,0.022,0.024,0.023,0.016,0.017,0.024,0.023,0.025,0.021,0.019,0.031,0.027,0.019,0.02,0.022,0.021,0.021,0.017,0.022,0.023,0.025,0.023,0.024,1497,4054,1956,2548,1845,2468,2384,2950,2211,1222,511.0,2352,3477,1151,3060,10527,18410,2590,3948,4193,2984,4306,1972,5956,5141,5803,4463,3429,5319,4568,2651,1414,1077,6131,1713,1937,4270,7872,367,460,1208,2154,4807,1968,2919,1234,783,2481,2490,1142,534,2765,1928,4165,456,3042,1115,3050,821,1298,990,2474,358,1098,2133,1081,3187,1897,1453,950,921,2072,7545,430,0.142,0.116,0.111,0.121,0.165,0.156,0.12,0.133,0.184,0.155,0.144,0.158,0.136,0.134,0.128,0.148,0.13,0.151,0.159,0.162,0.131,0.146,0.129,0.17,0.144,0.131,0.137,0.128,0.121,0.135,0.142,0.126,0.138,0.135,0.082,0.12,0.146,0.163,0.089,0.094,0.093,0.144,0.156,0.121,0.099,0.107,0.088,0.077,0.094,0.099,0.143,0.107,0.124,0.108,0.105,0.106,0.125,0.113,0.124,0.103,0.099,0.141,0.136,0.099,0.11,0.111,0.107,0.119,0.094,0.123,0.114,0.127,0.107,0.13,446,1483,842,828,663,908,930,974,548,307,161.0,1226,996,293,828,2835,4964,573,1167,1680,905,1404,854,1700,1521,1719,1589,1376,1382,1446,788,532,380,1309,439,634,1246,1806,209,219,685,1167,1114,1223,1645,600,330,957,1227,533,269,1364,792,1796,247,1627,515,1367,505,742,479,890,191,478,1073,696,1664,919,712,420,446,877,3240,203,3.258,2.681,2.489,2.646,2.317,2.371,2.189,2.611,3.039,2.991,2.498,1.762,2.714,2.918,2.788,2.749,2.903,3.643,2.664,2.057,2.669,2.485,2.023,2.65,2.619,2.661,2.214,2.003,2.851,2.51,2.509,2.46,2.369,3.213,3.467,2.603,2.534,3.057,2.074,2.348,2.207,1.701,3.183,1.818,2.047,2.208,2.89,2.945,2.403,2.572,2.229,2.21,2.437,2.423,2.294,2.094,2.362,2.367,1.753,1.951,2.465,2.932,2.264,2.81,2.146,1.844,2.074,2.186,2.207,2.574,2.282,2.572,2.388,2.425,0.956,0.495,0.428,0.593,0.704,0.607,0.746,0.463,0.571,0.8,0.732,0.546,0.444,0.44,0.468,0.601,0.627,0.638,0.688,0.687,0.817,0.594,0.495,0.781,0.676,0.531,0.557,0.68,0.551,0.598,0.725,0.795,0.377,0.633,0.881,0.536,0.838,0.738,0.274,0.37,0.424,0.503,0.921,0.425,0.647,0.484,0.439,0.791,0.441,0.528,0.537,0.407,0.55,0.492,0.267,0.398,0.489,0.459,0.418,0.359,0.467,0.694,0.566,0.804,0.454,0.527,0.449,0.441,0.474,0.832,0.471,0.558,0.553,0.506
-103,Epileptic,M,27.0,1.4,1.6,0.9,1.2,1.6,1.7,1.5,1.2,1.5,0.6,0.4,2.6,1.0,0.4,0.6,4.7,7.0,0.8,2.8,3.9,2.9,2.0,1.8,4.0,3.7,3.2,3.6,2.2,2.6,2.1,2.9,2.7,0.4,1.8,0.2,0.7,2.4,2.6,0.1,0.1,1.2,2.1,2.0,2.9,2.2,0.6,0.5,0.8,1.1,0.7,0.4,1.3,1.4,2.0,0.1,1.3,1.0,1.5,0.9,1.0,0.7,0.9,0.1,0.8,2.2,1.0,2.4,1.0,0.8,0.3,0.7,1.2,4.0,0.3,11,14,7,10,13,19,13,14,15,8,3.0,25,11,5,8,56,64,9,34,40,32,28,16,40,43,42,33,22,24,26,27,17,5,25,1,7,26,35,1,1,9,23,26,24,15,4,2,5,6,5,3,9,11,15,1,12,5,13,10,7,5,7,1,6,16,17,20,8,7,3,6,9,33,3,0.051,0.019,0.018,0.025,0.033,0.025,0.024,0.023,0.051,0.035,0.03,0.038,0.024,0.03,0.023,0.031,0.025,0.047,0.033,0.04,0.058,0.029,0.032,0.043,0.04,0.028,0.04,0.028,0.023,0.024,0.059,0.059,0.025,0.025,0.011,0.011,0.033,0.028,0.013,0.013,0.025,0.033,0.041,0.032,0.019,0.014,0.024,0.014,0.016,0.022,0.026,0.018,0.021,0.017,0.028,0.015,0.022,0.017,0.025,0.018,0.018,0.02,0.017,0.023,0.02,0.02,0.018,0.015,0.016,0.011,0.02,0.021,0.016,0.014,1648,4507,2499,2250,1872,3402,2919,3482,2187,1483,823.0,3231,3340,1282,1929,11113,17975,2291,5597,5160,3197,4180,1998,5245,5687,7527,4441,4361,7005,5853,2012,1769,1216,6546,1583,2742,5306,7902,294,246,1574,2264,4287,2877,3203,1441,692,1987,1965,1435,715,2363,2091,4644,247,3257,1241,3208,1103,1452,1080,2188,231,1134,3243,1078,4565,2547,1969,927,1372,1978,9038,709,0.148,0.1,0.09,0.117,0.139,0.125,0.104,0.12,0.167,0.151,0.113,0.154,0.113,0.134,0.118,0.133,0.124,0.134,0.137,0.157,0.14,0.123,0.129,0.144,0.148,0.129,0.136,0.095,0.104,0.12,0.169,0.142,0.126,0.125,0.057,0.082,0.124,0.128,0.092,0.09,0.124,0.123,0.135,0.139,0.099,0.085,0.108,0.081,0.089,0.114,0.131,0.101,0.114,0.094,0.11,0.09,0.109,0.099,0.139,0.104,0.113,0.101,0.116,0.105,0.107,0.122,0.101,0.091,0.096,0.091,0.11,0.115,0.095,0.103,500,1385,822,814,759,1130,958,952,569,370,210.0,1257,747,265,485,2813,4510,459,1490,1878,1023,1238,848,1575,1483,2043,1452,1337,1624,1444,852,732,280,1307,401,961,1380,1643,170,123,779,1067,927,1538,1867,680,286,837,941,509,263,1063,997,1903,89,1544,588,1451,546,782,526,807,125,603,1643,758,2059,1115,851,391,624,915,3935,332,2.993,2.947,2.939,2.673,2.272,2.516,2.49,3.04,2.777,3.082,2.931,2.174,3.179,3.223,2.8,2.865,3.094,3.66,2.92,2.266,2.575,2.553,2.015,2.454,2.798,2.901,2.533,2.43,3.144,2.994,1.887,2.378,3.023,3.374,3.598,2.636,2.828,3.265,2.044,2.11,2.585,1.86,3.284,1.967,1.968,2.424,2.878,2.939,2.592,2.762,2.333,2.31,2.225,2.452,3.017,2.283,2.449,2.427,2.074,2.084,2.299,2.524,1.87,2.343,2.182,1.845,2.331,2.609,2.679,2.512,2.421,2.515,2.457,2.714,0.802,0.618,0.564,0.479,0.627,0.719,0.696,0.436,0.819,0.809,0.67,0.53,0.453,0.477,0.561,0.641,0.599,0.746,0.649,0.594,0.924,0.654,0.503,1.001,0.723,0.652,0.64,0.58,0.518,0.615,0.813,0.846,0.445,0.565,0.755,0.533,0.723,0.626,0.368,0.393,0.431,0.494,0.864,0.513,0.639,0.385,0.372,0.544,0.365,0.633,0.505,0.451,0.476,0.496,0.407,0.357,0.48,0.499,0.601,0.396,0.36,0.71,0.66,0.488,0.427,0.585,0.396,0.399,0.363,0.854,0.531,0.58,0.457,0.305
-104,Epileptic,M,17.5,0.8,2.3,0.8,1.2,1.8,2.2,1.4,1.5,1.1,0.8,0.3,2.5,1.7,0.5,1.6,6.1,9.4,0.8,2.0,4.3,1.1,3.2,1.8,3.1,4.2,3.3,3.1,3.4,2.4,3.9,1.6,1.2,0.6,2.2,0.4,0.6,2.4,2.7,0.1,0.2,0.9,2.7,2.0,2.7,3.0,0.6,0.3,0.8,1.1,0.6,0.4,3.0,1.5,2.9,0.5,2.3,1.0,2.1,0.7,1.0,0.9,1.4,0.4,0.3,1.5,1.1,1.9,1.3,0.6,0.6,1.8,1.4,3.9,0.4,8,24,8,11,16,16,11,13,17,8,3.0,21,17,6,16,68,104,5,22,32,9,33,18,32,47,36,35,32,23,47,23,9,6,25,2,6,32,32,1,2,5,22,21,18,17,5,1,5,5,6,3,24,12,22,3,18,7,15,5,8,7,10,2,4,10,11,15,9,4,7,11,11,28,3,0.034,0.021,0.011,0.02,0.035,0.039,0.02,0.03,0.043,0.035,0.026,0.034,0.031,0.031,0.026,0.034,0.027,0.023,0.026,0.037,0.019,0.034,0.029,0.035,0.036,0.026,0.024,0.027,0.023,0.031,0.051,0.037,0.022,0.024,0.012,0.014,0.025,0.026,0.013,0.014,0.016,0.031,0.035,0.028,0.02,0.014,0.014,0.013,0.014,0.015,0.032,0.024,0.026,0.023,0.018,0.017,0.02,0.021,0.018,0.017,0.025,0.027,0.022,0.017,0.023,0.024,0.018,0.018,0.011,0.018,0.031,0.016,0.016,0.017,1760,5445,2390,2787,3156,2678,3127,3019,2301,1849,800.0,2842,3856,1272,4484,12664,22988,2943,4697,3937,3966,5756,2565,6825,7206,7886,7291,5185,6774,7295,2777,1535,1142,7654,2135,2338,8681,9129,381,736,1940,3642,5831,2190,4543,1587,891,2764,2690,1708,375,5341,2437,5239,821,4653,1720,3923,953,1840,1133,2589,590,1002,2495,1630,3824,2764,1777,1349,2095,3086,11158,539,0.128,0.106,0.09,0.106,0.146,0.151,0.097,0.139,0.164,0.128,0.114,0.14,0.129,0.128,0.122,0.138,0.127,0.1,0.118,0.141,0.088,0.149,0.134,0.142,0.154,0.118,0.119,0.112,0.103,0.136,0.161,0.12,0.103,0.117,0.067,0.077,0.129,0.126,0.085,0.1,0.091,0.138,0.137,0.116,0.101,0.086,0.081,0.073,0.084,0.098,0.139,0.115,0.128,0.105,0.104,0.101,0.11,0.107,0.104,0.101,0.132,0.122,0.133,0.126,0.109,0.112,0.104,0.102,0.082,0.112,0.134,0.106,0.093,0.103,480,1839,1004,962,848,981,1029,878,527,420,213.0,1201,939,316,1027,3092,5969,531,1298,1781,936,1582,1006,1486,1953,1957,2173,1818,1567,2211,659,572,456,1451,521,697,1847,1801,209,309,834,1367,1041,1432,2339,781,346,1127,1163,636,196,1986,802,2142,411,2139,719,1687,573,923,540,898,252,358,1043,705,1752,1173,779,584,896,1339,3999,305,3.432,2.808,2.391,2.836,2.815,2.532,2.494,3.121,3.115,3.273,3.163,2.013,3.145,2.912,3.261,2.919,3.0,4.164,2.952,1.933,3.202,2.747,2.298,3.202,2.816,3.076,2.599,2.327,3.111,2.651,3.075,2.677,2.124,3.622,3.827,2.784,3.318,3.526,2.175,2.887,2.792,2.187,3.44,1.809,2.286,2.2,3.3,2.956,2.885,3.046,1.915,2.51,2.731,2.646,2.597,2.453,2.807,2.746,1.976,2.232,2.684,2.802,2.6,2.725,2.644,2.891,2.387,2.7,2.597,2.406,2.585,2.769,3.026,2.244,0.704,0.688,0.524,0.467,0.723,0.687,0.501,0.544,0.623,0.5,0.997,0.525,0.428,0.569,0.58,0.638,0.577,0.624,0.592,0.652,0.839,0.528,0.505,0.663,0.573,0.58,0.539,0.587,0.554,0.628,0.784,1.131,0.335,0.613,0.7,0.665,0.565,0.593,0.329,0.473,0.744,0.624,0.864,0.791,0.705,0.535,0.416,0.541,0.561,0.455,0.5,0.566,0.708,0.512,0.366,0.527,0.473,0.691,0.448,0.434,0.452,0.656,0.53,0.848,0.513,0.818,0.465,0.393,0.429,0.691,0.493,0.48,0.631,0.43
-105,Epileptic,F,37.0,0.4,2.8,1.0,1.3,1.4,1.7,1.3,1.8,1.2,0.9,0.2,2.4,1.5,0.4,1.2,4.6,6.6,0.9,1.6,3.2,0.7,3.2,1.9,3.0,2.9,3.2,2.4,2.7,2.2,2.2,1.5,0.6,0.3,2.0,0.2,0.9,2.6,2.4,0.2,0.1,1.0,3.1,1.9,2.4,2.7,0.6,0.4,0.8,1.1,0.6,0.7,1.8,0.9,2.7,0.4,2.4,0.6,1.5,1.1,1.4,0.9,1.5,0.3,0.5,1.4,1.2,2.5,1.6,1.0,0.4,0.9,1.2,3.6,0.2,5,28,14,10,18,17,13,16,13,9,2.0,25,16,4,13,57,65,6,19,31,9,36,17,32,36,34,29,26,20,28,18,7,4,25,2,8,30,29,1,1,5,25,20,16,17,3,2,5,8,4,7,12,5,18,3,21,4,16,8,12,7,12,1,5,12,16,23,11,6,4,7,10,28,2,0.027,0.026,0.017,0.026,0.028,0.031,0.018,0.029,0.04,0.041,0.025,0.031,0.028,0.018,0.03,0.028,0.023,0.032,0.031,0.029,0.017,0.034,0.035,0.032,0.03,0.032,0.021,0.028,0.02,0.024,0.032,0.037,0.019,0.023,0.01,0.023,0.032,0.027,0.012,0.015,0.022,0.036,0.03,0.026,0.022,0.011,0.017,0.012,0.015,0.024,0.026,0.019,0.02,0.02,0.017,0.017,0.024,0.018,0.031,0.025,0.028,0.024,0.02,0.021,0.019,0.028,0.021,0.02,0.016,0.016,0.03,0.02,0.017,0.017,1168,4800,2273,2089,1660,2656,2756,2874,1625,1260,345.0,2524,3486,1132,1943,9988,15784,1942,3405,4296,2790,4573,2183,5449,5414,5732,4918,3376,5378,4470,2014,1042,748,5613,1888,1916,5842,6066,367,323,1311,2597,4843,2235,2934,1364,753,2234,2370,1160,635,2748,1421,4750,537,3756,851,3172,916,1620,1135,2581,418,912,2227,907,3446,2776,1948,868,1346,2260,7846,455,0.113,0.13,0.118,0.113,0.139,0.148,0.09,0.139,0.159,0.159,0.109,0.149,0.128,0.102,0.141,0.133,0.118,0.118,0.148,0.136,0.081,0.147,0.145,0.137,0.137,0.139,0.111,0.119,0.097,0.127,0.124,0.105,0.113,0.121,0.059,0.108,0.136,0.131,0.09,0.097,0.108,0.147,0.142,0.117,0.108,0.076,0.106,0.082,0.094,0.11,0.147,0.105,0.111,0.104,0.106,0.109,0.119,0.107,0.145,0.127,0.141,0.118,0.104,0.128,0.109,0.132,0.118,0.106,0.096,0.133,0.133,0.115,0.101,0.124,317,1661,888,843,774,969,996,1012,529,383,143.0,1184,956,292,605,2728,4657,451,927,1720,701,1534,888,1581,1714,1792,1844,1406,1518,1455,647,445,261,1306,492,644,1449,1531,206,168,660,1283,1044,1346,1849,746,335,983,1120,479,414,1366,685,2143,327,2075,439,1438,553,879,535,1033,215,416,1129,628,1852,1200,940,322,593,949,3427,198,3.334,2.875,2.801,2.572,2.03,2.492,2.261,2.697,2.365,2.81,2.618,1.88,2.789,2.755,2.5,2.773,2.767,3.444,2.876,2.095,3.09,2.44,2.212,2.722,2.46,2.651,2.18,1.959,2.71,2.437,2.411,2.402,2.41,3.132,3.33,2.672,3.037,3.049,2.19,2.294,2.393,1.862,3.141,1.936,1.826,1.991,2.769,2.724,2.511,3.074,2.108,2.315,2.384,2.445,2.164,2.085,2.527,2.422,2.002,2.108,2.623,2.75,2.331,2.542,2.097,1.84,2.037,2.531,2.42,3.557,2.545,2.694,2.574,2.721,0.694,0.517,0.41,0.418,0.524,0.468,0.655,0.435,0.521,0.452,0.658,0.437,0.353,0.358,0.463,0.516,0.535,0.841,0.394,0.614,0.638,0.432,0.374,0.566,0.493,0.459,0.417,0.501,0.478,0.49,0.684,1.086,0.446,0.471,0.581,0.501,0.611,0.517,0.346,0.364,0.441,0.395,0.821,0.632,0.511,0.371,0.402,0.729,0.384,0.519,0.477,0.434,0.522,0.399,0.364,0.425,0.337,0.426,0.323,0.327,0.385,0.545,0.41,0.912,0.497,0.639,0.469,0.417,0.357,0.685,0.429,0.51,0.446,0.49
-106,Epileptic,F,22.0,0.4,1.7,0.9,1.0,2.0,1.9,1.6,1.8,1.4,0.7,0.2,2.6,1.6,0.9,1.5,7.0,7.6,1.2,2.5,4.7,2.0,2.6,1.4,2.9,2.1,3.0,2.4,2.7,2.1,3.1,1.6,1.5,0.8,2.8,0.6,0.4,3.4,4.2,0.2,0.0,0.8,2.7,3.5,2.2,2.3,0.8,0.6,0.6,1.1,1.7,0.6,1.7,1.1,2.1,0.1,1.2,0.6,2.0,0.9,1.1,0.8,1.2,0.1,0.3,1.1,0.5,2.0,0.9,0.9,0.4,0.8,1.9,4.9,0.1,4,15,8,7,16,19,17,14,16,6,3.0,26,18,8,13,75,68,6,25,42,18,31,15,30,25,30,33,25,18,38,15,7,5,29,3,3,39,39,1,0,3,22,27,19,13,6,3,4,6,14,4,11,6,15,1,9,4,13,5,7,6,9,1,3,9,7,19,6,6,3,4,13,40,1,0.026,0.021,0.017,0.022,0.039,0.042,0.024,0.037,0.054,0.032,0.024,0.038,0.033,0.045,0.042,0.036,0.029,0.042,0.036,0.044,0.035,0.04,0.03,0.035,0.033,0.031,0.028,0.031,0.024,0.029,0.048,0.049,0.031,0.034,0.02,0.009,0.035,0.039,0.013,0.029,0.015,0.042,0.053,0.028,0.019,0.017,0.027,0.014,0.017,0.04,0.027,0.017,0.019,0.022,0.027,0.015,0.012,0.021,0.026,0.023,0.028,0.027,0.016,0.02,0.016,0.025,0.019,0.014,0.016,0.017,0.024,0.024,0.02,0.01,1491,5236,1981,1926,2478,2810,3316,3115,2801,1260,527.0,2394,3610,1532,2578,13715,17198,1803,4329,4927,2761,4090,1847,6563,4065,6512,4952,3980,5118,5191,1965,997,1030,5998,2134,1915,5937,6980,431,76,1842,2434,5027,1968,3110,1284,900,1908,2541,1731,536,3200,2026,4396,198,2571,1710,3511,890,1222,1301,2173,296,900,1942,572,3595,2066,1833,1463,905,2919,9741,364,0.094,0.112,0.104,0.101,0.139,0.156,0.122,0.124,0.174,0.136,0.112,0.14,0.143,0.146,0.141,0.147,0.13,0.139,0.133,0.153,0.108,0.157,0.129,0.136,0.142,0.126,0.135,0.128,0.108,0.135,0.156,0.111,0.117,0.128,0.079,0.065,0.128,0.131,0.085,0.128,0.084,0.14,0.142,0.124,0.096,0.102,0.115,0.076,0.091,0.132,0.129,0.095,0.096,0.106,0.152,0.093,0.081,0.105,0.129,0.108,0.131,0.119,0.097,0.114,0.098,0.112,0.104,0.094,0.099,0.107,0.12,0.113,0.102,0.07,342,1258,738,706,809,869,1030,841,581,344,182.0,1103,892,326,666,3389,4407,376,1165,1874,834,1220,822,1482,1198,1737,1565,1396,1348,1749,543,399,375,1369,480,602,1746,1701,196,32,700,1018,1174,1191,1819,628,371,724,979,685,317,1525,856,1668,71,1252,704,1478,495,737,525,789,129,299,1081,339,1715,923,798,370,458,1250,3911,175,3.614,3.324,2.563,2.639,2.493,2.677,2.545,3.006,3.241,2.928,2.607,1.848,3.039,3.094,2.783,2.952,3.064,3.694,2.969,2.394,2.706,2.55,1.966,3.139,2.545,2.863,2.414,2.319,2.844,2.318,2.783,2.318,2.571,2.976,3.356,2.792,2.823,2.976,2.518,2.619,3.055,1.975,3.045,1.764,1.981,2.007,3.083,3.15,3.009,2.677,1.978,2.272,2.559,2.689,2.774,2.15,2.772,2.617,1.987,1.87,2.501,2.819,2.725,2.996,2.014,2.194,2.194,2.7,2.558,3.439,2.281,2.539,2.694,2.119,1.235,0.986,0.63,0.664,0.542,0.718,0.582,0.606,0.738,0.573,0.561,0.442,0.546,0.586,0.568,0.646,0.722,1.01,0.691,0.773,0.813,0.625,0.478,0.664,0.56,0.699,0.553,0.486,0.537,0.668,0.826,1.088,0.782,0.885,0.971,0.563,0.706,0.715,0.39,0.339,0.775,0.657,1.069,0.65,0.54,0.646,0.457,0.91,0.606,0.742,0.458,0.55,0.68,0.632,0.446,0.447,0.488,0.679,0.495,0.388,0.522,0.578,0.379,0.872,0.69,0.621,0.466,0.448,0.528,1.117,0.482,0.545,0.568,0.489
-108,Epileptic,F,32.0,1.2,2.3,0.8,1.9,1.4,1.7,1.3,1.6,1.4,0.9,0.2,2.8,1.7,0.4,1.3,6.5,8.3,1.1,2.7,3.4,1.1,2.7,2.1,3.5,3.4,3.9,3.4,3.0,2.5,3.5,1.4,1.7,0.5,2.9,0.5,0.6,3.8,3.2,0.2,0.4,1.0,3.1,1.5,2.3,2.3,0.9,0.3,0.7,1.5,0.9,0.4,1.7,1.8,3.4,0.2,2.2,1.0,1.6,1.0,0.7,0.4,1.4,0.4,0.6,2.1,1.5,2.4,0.9,0.8,0.4,0.7,1.3,5.0,0.3,9,21,6,14,16,16,12,17,17,13,5.0,28,20,6,15,72,79,9,30,33,17,36,22,37,46,45,48,31,23,38,19,16,6,33,3,6,45,39,1,2,9,28,18,20,16,6,2,5,7,8,4,14,15,28,2,19,7,14,10,6,3,14,5,5,15,23,22,8,4,4,5,12,42,4,0.046,0.025,0.016,0.027,0.033,0.028,0.021,0.026,0.041,0.036,0.03,0.033,0.032,0.027,0.028,0.031,0.029,0.039,0.035,0.037,0.024,0.03,0.036,0.036,0.035,0.025,0.022,0.027,0.024,0.032,0.036,0.055,0.016,0.032,0.014,0.012,0.031,0.03,0.013,0.023,0.018,0.037,0.032,0.023,0.017,0.019,0.016,0.011,0.017,0.02,0.026,0.019,0.027,0.021,0.017,0.019,0.021,0.015,0.019,0.015,0.013,0.021,0.024,0.018,0.019,0.023,0.017,0.014,0.014,0.016,0.02,0.015,0.018,0.011,1610,4847,2357,2955,2412,3246,3173,3504,2489,2041,417.0,3592,3839,1215,3091,12504,19646,2877,4706,4423,3632,5718,3072,6509,6177,9432,7955,5044,6732,6019,2999,1732,1436,7945,2572,2536,9379,7949,441,642,1662,3743,5034,2561,3565,1383,910,2527,2783,1970,595,2858,2064,6269,336,3591,1587,3361,1438,1506,824,2769,716,1024,3365,1392,4489,2391,1725,726,1072,2457,9859,732,0.146,0.119,0.096,0.13,0.139,0.136,0.101,0.126,0.174,0.158,0.13,0.153,0.144,0.127,0.132,0.137,0.132,0.138,0.131,0.145,0.094,0.135,0.143,0.143,0.133,0.118,0.118,0.117,0.103,0.15,0.131,0.142,0.092,0.135,0.069,0.075,0.133,0.135,0.083,0.114,0.105,0.142,0.127,0.118,0.098,0.113,0.095,0.074,0.095,0.112,0.115,0.106,0.124,0.112,0.123,0.107,0.106,0.094,0.116,0.098,0.097,0.117,0.127,0.116,0.111,0.134,0.103,0.089,0.086,0.117,0.105,0.1,0.103,0.089,465,1594,786,1059,858,1013,948,1119,687,518,164.0,1564,987,307,838,3601,5104,522,1359,1746,877,1548,1009,1785,1793,2823,2562,1752,1579,1844,742,578,527,1667,578,815,2190,1839,239,270,885,1476,969,1585,2074,630,362,968,1230,702,328,1419,1071,2604,200,1836,740,1630,810,775,389,1055,284,513,1566,954,2153,1059,807,391,604,1267,4170,445,3.319,2.73,2.794,2.762,2.269,2.641,2.717,2.692,2.796,2.972,2.146,2.014,2.953,2.76,2.615,2.56,2.979,3.952,2.746,2.168,3.019,2.813,2.487,2.803,2.66,2.697,2.42,2.243,3.131,2.526,2.798,3.018,2.231,3.246,3.578,2.771,3.021,2.992,2.042,2.719,2.322,2.197,3.447,1.864,1.978,2.375,2.81,3.077,2.815,2.997,2.117,2.242,2.066,2.505,2.06,2.18,2.615,2.394,2.085,2.2,2.189,2.478,2.094,2.467,2.43,1.858,2.215,2.435,2.471,1.958,2.094,2.254,2.46,2.028,0.876,0.658,0.524,0.485,0.887,0.723,0.75,0.439,0.71,0.527,0.488,0.554,0.504,0.584,0.639,0.654,0.677,0.69,0.612,0.635,0.941,0.551,0.689,0.758,0.599,0.631,0.634,0.627,0.576,0.641,0.705,0.915,0.514,0.611,0.784,0.455,0.804,0.675,0.461,0.334,0.433,0.593,0.871,0.579,0.636,0.393,0.637,0.859,0.51,0.54,0.372,0.418,0.539,0.474,0.468,0.442,0.414,0.533,0.37,0.367,0.534,0.684,0.652,0.707,0.57,0.601,0.467,0.416,0.406,0.538,0.436,0.488,0.463,0.274
-109,Epileptic,M,42.0,0.8,2.3,0.7,0.9,1.5,2.5,1.5,1.6,1.0,0.4,0.4,2.4,1.3,0.4,1.6,7.6,9.0,0.8,1.7,3.2,0.7,3.3,1.0,3.3,2.3,2.8,2.8,2.1,3.2,3.3,1.0,0.6,0.4,2.0,0.5,1.0,3.2,2.5,0.3,0.4,1.1,2.4,2.3,3.4,3.0,0.6,0.4,0.9,1.3,0.6,0.7,1.6,1.1,3.1,0.1,2.0,1.0,1.7,0.8,0.9,0.6,1.9,0.6,0.4,1.6,1.1,2.4,1.5,1.0,0.4,0.9,1.1,4.3,0.3,4,22,7,7,15,19,12,13,10,5,5.0,21,16,5,15,76,76,7,24,31,6,37,12,38,32,32,38,17,27,38,15,5,4,20,3,8,34,32,2,2,6,21,20,19,15,4,2,7,6,4,4,10,7,19,1,17,7,14,5,6,5,15,5,4,15,10,16,10,6,4,6,10,35,2,0.036,0.024,0.012,0.02,0.034,0.033,0.026,0.037,0.032,0.02,0.033,0.028,0.031,0.029,0.035,0.035,0.029,0.03,0.021,0.027,0.015,0.039,0.023,0.039,0.032,0.031,0.026,0.029,0.031,0.029,0.035,0.035,0.028,0.029,0.015,0.028,0.031,0.024,0.022,0.024,0.022,0.035,0.046,0.027,0.025,0.015,0.018,0.015,0.016,0.016,0.035,0.018,0.018,0.024,0.015,0.014,0.016,0.019,0.021,0.016,0.019,0.029,0.022,0.016,0.017,0.031,0.018,0.02,0.022,0.014,0.027,0.023,0.017,0.027,1347,4972,1977,1831,2001,3411,2819,3096,1921,1448,690.0,2899,3418,889,3060,12841,19388,2394,4805,4495,2696,5800,2455,5610,4881,6563,6344,3463,7014,6563,2150,752,817,5599,1751,1669,6793,8166,416,533,1663,2622,6046,3076,3533,1450,792,2184,2321,1577,862,3250,2147,5439,327,3818,2058,3044,1052,1756,1077,2537,689,759,3199,995,3793,2492,1681,1034,1302,2100,10946,423,0.104,0.111,0.091,0.104,0.124,0.14,0.109,0.131,0.133,0.113,0.143,0.119,0.128,0.124,0.131,0.134,0.123,0.115,0.104,0.116,0.082,0.153,0.112,0.13,0.139,0.131,0.124,0.106,0.115,0.139,0.113,0.1,0.111,0.118,0.074,0.12,0.131,0.116,0.114,0.113,0.106,0.132,0.149,0.109,0.106,0.088,0.107,0.092,0.09,0.091,0.131,0.092,0.099,0.104,0.098,0.092,0.095,0.098,0.108,0.098,0.109,0.131,0.12,0.108,0.104,0.126,0.096,0.103,0.112,0.095,0.115,0.121,0.091,0.136,357,1610,817,735,745,1182,843,832,550,348,199.0,1255,848,259,829,3686,4985,505,1302,1800,759,1510,824,1436,1348,1667,1938,1114,1645,1949,583,372,260,1147,500,587,1519,1745,204,234,729,1061,1044,1837,1912,686,322,931,1172,567,336,1430,979,2260,115,2038,942,1510,605,869,477,967,388,417,1466,538,1922,1158,712,454,643,861,4195,156,3.371,2.833,2.372,2.444,2.468,2.428,2.722,3.068,2.666,3.153,2.897,1.973,2.946,2.567,2.76,2.646,3.053,3.62,2.879,2.117,2.751,2.726,2.495,2.882,2.701,3.143,2.489,2.358,3.089,2.583,2.614,2.134,2.56,3.274,3.131,2.686,3.205,3.253,2.152,2.474,2.751,2.079,3.551,1.838,2.092,2.276,2.934,2.96,2.37,3.01,2.453,2.391,2.286,2.468,2.746,2.108,2.5,2.305,1.91,2.24,2.446,2.537,2.08,2.055,2.428,2.154,2.161,2.478,2.594,2.7,2.509,2.678,2.628,2.928,1.13,0.645,0.465,0.562,0.651,0.603,0.644,0.541,0.523,0.454,0.615,0.478,0.491,0.599,0.545,0.597,0.632,0.741,0.512,0.664,0.715,0.58,0.572,0.631,0.669,0.551,0.564,0.654,0.652,0.573,0.612,0.963,0.61,0.739,0.799,0.449,0.721,0.726,0.578,0.436,0.571,0.606,0.919,0.561,0.729,0.518,0.636,0.831,0.509,0.852,0.451,0.507,0.642,0.552,0.434,0.42,0.646,0.445,0.421,0.477,0.49,0.692,0.451,0.808,0.433,0.707,0.453,0.465,0.434,0.677,0.468,0.621,0.532,0.32
-110,Epileptic,M,37.0,0.8,1.7,0.8,1.5,1.3,1.5,1.6,1.7,1.2,0.6,0.4,2.4,1.5,0.5,1.2,6.7,9.0,0.7,2.0,5.8,1.7,2.3,2.0,2.3,3.0,2.5,2.0,2.9,2.8,3.2,1.7,0.8,0.6,3.1,0.6,0.9,3.8,2.8,0.2,0.1,0.9,3.7,2.5,3.6,3.8,0.7,0.6,1.0,1.1,1.0,0.5,1.7,1.6,2.3,0.2,1.8,0.6,1.3,1.0,0.9,0.7,1.3,0.7,0.4,1.9,1.0,2.9,1.3,1.4,0.7,1.5,1.5,4.8,0.3,6,14,7,14,12,14,12,15,8,5,4.0,16,13,6,11,63,70,7,15,37,15,26,16,26,34,24,23,24,20,34,20,27,5,30,4,7,42,29,1,1,5,28,23,20,20,5,3,6,6,8,3,12,11,17,2,17,5,10,9,7,7,9,4,3,14,10,21,9,10,7,8,10,40,3,0.043,0.02,0.017,0.023,0.03,0.028,0.025,0.031,0.047,0.034,0.034,0.033,0.027,0.032,0.035,0.031,0.028,0.025,0.029,0.052,0.027,0.033,0.032,0.028,0.026,0.026,0.021,0.026,0.023,0.031,0.041,0.066,0.031,0.038,0.021,0.022,0.034,0.027,0.014,0.01,0.02,0.045,0.033,0.044,0.028,0.023,0.026,0.016,0.017,0.015,0.028,0.022,0.023,0.021,0.017,0.019,0.021,0.02,0.022,0.016,0.022,0.023,0.03,0.021,0.023,0.03,0.023,0.019,0.02,0.023,0.035,0.026,0.019,0.026,1431,3868,1949,2572,1545,2737,2358,2874,1481,1078,608.0,2264,3359,1166,2105,11376,17808,2229,3223,3530,3148,4171,2372,5002,5528,5128,4392,4266,5493,5832,1792,891,866,5614,1849,1856,7410,5713,436,246,1347,2784,5497,2374,3428,1309,820,2075,2162,2188,502,2670,2173,4320,307,3169,1283,2904,1233,1572,1295,2442,735,759,2528,1104,3883,2238,1868,1111,1412,2232,10444,425,0.129,0.103,0.101,0.12,0.123,0.129,0.095,0.135,0.128,0.134,0.117,0.133,0.119,0.13,0.138,0.128,0.119,0.106,0.115,0.158,0.108,0.134,0.132,0.115,0.126,0.122,0.108,0.111,0.097,0.131,0.145,0.115,0.123,0.134,0.093,0.105,0.125,0.112,0.084,0.082,0.106,0.154,0.134,0.157,0.119,0.105,0.12,0.093,0.094,0.1,0.126,0.105,0.113,0.1,0.119,0.106,0.111,0.099,0.129,0.098,0.12,0.107,0.133,0.098,0.117,0.118,0.114,0.111,0.116,0.111,0.134,0.116,0.093,0.132,385,1426,815,1009,701,979,820,961,445,314,198.0,1063,978,261,624,3681,5200,481,964,1662,976,1252,1029,1412,1780,1619,1494,1545,1659,1780,600,404,318,1301,485,641,1946,1554,229,107,671,1283,1299,1433,2215,615,345,925,1049,954,279,1321,1146,1966,177,1636,500,1222,738,854,547,859,330,302,1248,585,1981,1049,1019,524,674,951,4268,221,3.542,2.638,2.456,2.474,2.105,2.492,2.371,2.784,2.494,3.023,2.458,1.988,2.798,2.935,2.463,2.473,2.846,3.502,2.595,2.001,2.764,2.626,2.109,2.668,2.494,2.611,2.318,2.236,2.595,2.545,2.351,2.185,2.229,3.033,3.159,2.651,2.775,2.823,2.173,2.593,2.426,2.121,3.116,1.989,1.793,2.361,3.053,2.879,2.487,2.589,2.206,2.109,2.044,2.223,2.185,2.151,2.59,2.739,1.961,2.031,2.547,2.589,2.343,2.463,2.311,2.29,2.229,2.476,2.199,2.369,2.572,2.813,2.516,2.367,0.87,0.668,0.712,0.457,0.525,0.533,0.602,0.599,0.572,0.452,0.662,0.425,0.464,0.489,0.601,0.519,0.627,0.743,0.665,0.674,0.757,0.532,0.483,0.671,0.46,0.459,0.431,0.487,0.528,0.557,0.603,0.894,0.501,0.675,0.786,0.572,0.721,0.682,0.423,0.308,0.501,0.609,0.674,0.633,0.545,0.491,0.81,0.704,0.675,0.454,0.315,0.532,0.568,0.607,0.257,0.363,0.434,0.48,0.349,0.417,0.439,0.75,0.686,1.126,0.426,0.638,0.45,0.47,0.461,0.639,0.394,0.732,0.528,0.488
-111,Epileptic,F,37.0,1.9,1.9,0.6,0.8,2.0,1.4,1.4,1.2,0.8,0.8,0.2,3.0,1.3,0.6,0.7,5.2,7.5,0.8,2.3,6.2,0.7,2.1,1.4,2.9,3.4,2.8,3.1,2.6,2.0,3.0,1.3,0.5,0.5,2.2,0.4,0.5,2.6,4.4,0.3,0.2,0.8,2.6,1.7,2.6,2.5,0.4,0.3,0.8,1.1,0.5,0.6,1.3,1.3,1.6,0.4,2.0,0.9,1.6,0.4,0.9,0.3,1.2,0.2,0.3,1.2,1.0,2.6,1.4,1.6,0.4,1.0,0.9,3.5,0.3,9,16,5,5,19,13,13,11,12,7,3.0,25,12,6,7,52,66,6,22,46,5,22,11,31,42,28,36,25,17,29,24,5,5,23,2,4,28,42,2,1,5,21,24,21,15,3,2,5,5,7,3,10,11,14,1,19,7,13,3,6,2,8,1,3,11,13,21,8,9,3,5,6,25,3,0.063,0.023,0.012,0.017,0.042,0.022,0.025,0.024,0.048,0.031,0.029,0.035,0.027,0.031,0.023,0.036,0.029,0.033,0.03,0.04,0.015,0.033,0.033,0.038,0.033,0.031,0.03,0.027,0.021,0.034,0.045,0.035,0.018,0.027,0.011,0.017,0.03,0.035,0.019,0.016,0.018,0.037,0.035,0.025,0.02,0.011,0.018,0.013,0.015,0.014,0.026,0.016,0.025,0.017,0.018,0.019,0.03,0.015,0.016,0.018,0.019,0.023,0.018,0.02,0.021,0.025,0.019,0.021,0.025,0.015,0.023,0.017,0.014,0.02,1503,4497,1896,1964,2256,2999,2702,3072,1369,1511,411.0,2585,3420,1301,2017,9595,16128,2609,4494,5058,2556,3862,1951,5772,6679,6286,5562,4148,5824,5540,2382,838,1437,5948,2188,1391,5244,8085,447,392,1441,2776,4539,2860,3568,1279,859,2534,2326,1304,577,2706,1801,4175,499,3453,1621,3665,814,1327,709,2076,393,719,2041,1251,4112,2522,1709,773,1500,2118,8887,499,0.172,0.105,0.079,0.092,0.14,0.123,0.11,0.114,0.134,0.135,0.118,0.141,0.125,0.128,0.105,0.132,0.122,0.112,0.121,0.142,0.079,0.139,0.127,0.125,0.141,0.134,0.136,0.11,0.095,0.137,0.133,0.106,0.1,0.117,0.069,0.086,0.117,0.142,0.122,0.098,0.102,0.145,0.118,0.116,0.1,0.085,0.096,0.073,0.089,0.096,0.118,0.09,0.113,0.088,0.104,0.107,0.123,0.096,0.103,0.102,0.101,0.109,0.102,0.116,0.107,0.141,0.108,0.107,0.126,0.106,0.111,0.102,0.086,0.128,457,1420,671,717,802,963,868,862,379,412,141.0,1179,800,340,528,2627,4099,432,1143,2399,665,1159,755,1341,1846,1573,1793,1449,1456,1520,533,365,435,1291,512,438,1451,1917,212,185,642,1123,940,1724,1998,615,313,918,1012,620,331,1334,861,1731,268,1695,566,1650,413,726,261,808,204,291,1015,672,2134,1047,904,341,689,873,3849,237,3.253,2.845,2.628,2.601,2.202,2.803,2.497,3.21,2.545,3.017,2.735,1.92,3.217,2.799,2.789,2.729,3.008,4.181,3.133,1.934,2.982,2.671,2.255,2.973,2.78,3.223,2.402,2.26,2.942,2.851,3.029,2.43,2.768,3.153,3.661,2.751,2.718,3.098,2.405,2.61,2.972,2.255,3.174,1.91,2.054,2.297,3.816,3.382,2.795,2.233,2.336,2.159,2.009,2.278,2.359,2.143,3.187,2.56,2.251,2.079,3.055,2.496,2.142,2.394,2.263,2.417,2.152,2.691,2.439,2.753,2.555,2.712,2.487,2.371,0.781,0.778,0.592,0.562,0.658,0.452,0.542,0.6,0.678,0.477,0.819,0.507,0.519,0.444,0.526,0.678,0.684,0.63,0.701,0.543,0.693,0.525,0.494,0.694,0.528,0.748,0.581,0.547,0.508,0.595,0.799,0.832,0.456,0.666,0.791,0.462,0.693,0.584,0.73,0.525,0.664,0.678,0.875,0.595,0.626,0.51,0.667,0.832,0.541,0.501,0.503,0.542,0.605,0.635,0.376,0.463,0.703,0.562,0.377,0.366,0.437,0.741,0.734,0.819,0.417,0.672,0.495,0.465,0.405,0.925,0.544,0.625,0.595,0.578
-112,Epileptic,M,22.0,0.9,3.0,1.9,1.2,1.9,1.9,3.0,1.9,2.2,1.2,0.9,4.5,1.8,0.5,1.9,8.5,15.0,1.8,4.3,5.2,2.5,3.6,2.0,5.3,5.7,6.2,5.2,3.9,6.7,4.1,2.5,2.7,0.9,4.6,1.1,0.8,5.7,4.7,0.3,0.2,1.3,4.0,4.4,2.4,3.0,0.7,0.4,1.0,1.9,1.9,0.6,2.0,1.6,3.6,0.2,3.7,1.4,2.5,1.2,1.2,1.2,1.9,0.4,0.8,3.4,1.0,3.5,1.4,1.3,0.7,1.1,2.2,7.2,0.5,8,22,13,8,15,17,18,19,19,8,6.0,32,23,4,20,84,123,13,34,40,16,38,18,41,50,57,45,33,45,45,21,15,9,68,8,6,46,55,2,2,6,30,39,16,15,6,2,6,11,10,4,16,13,24,1,25,9,14,8,8,9,12,2,4,22,10,24,11,7,6,5,14,59,3,0.035,0.03,0.024,0.024,0.039,0.041,0.047,0.028,0.058,0.057,0.076,0.05,0.031,0.038,0.04,0.038,0.042,0.052,0.058,0.049,0.051,0.042,0.037,0.054,0.042,0.041,0.041,0.039,0.049,0.042,0.059,0.068,0.052,0.046,0.043,0.02,0.062,0.038,0.02,0.013,0.027,0.047,0.065,0.028,0.021,0.015,0.018,0.016,0.023,0.048,0.026,0.023,0.024,0.021,0.035,0.02,0.027,0.021,0.04,0.022,0.031,0.035,0.021,0.025,0.031,0.024,0.024,0.016,0.018,0.019,0.022,0.033,0.021,0.022,1930,5387,3177,2400,2384,2729,2561,3132,2391,1545,482.0,3314,4088,850,3361,13061,22352,2729,5451,4155,3229,5144,2432,5308,7660,8861,6033,4019,6917,5864,2134,1481,1142,7185,1759,2055,5994,9818,311,561,1532,3344,6060,2386,3749,1656,744,2539,2601,1822,637,3214,2280,6830,299,5276,1894,3556,865,1500,1327,2385,579,1125,3498,1339,4801,2692,2712,1332,1348,2375,12919,721,0.129,0.129,0.113,0.106,0.146,0.147,0.137,0.121,0.176,0.174,0.184,0.169,0.139,0.112,0.147,0.137,0.14,0.162,0.154,0.161,0.143,0.149,0.125,0.158,0.135,0.138,0.129,0.117,0.135,0.156,0.15,0.151,0.15,0.151,0.131,0.09,0.161,0.155,0.108,0.088,0.102,0.148,0.185,0.113,0.09,0.091,0.089,0.081,0.105,0.144,0.118,0.109,0.112,0.1,0.148,0.101,0.12,0.102,0.132,0.107,0.134,0.137,0.111,0.108,0.13,0.104,0.102,0.094,0.091,0.104,0.108,0.132,0.106,0.103,489,1626,1159,847,903,938,1090,1116,726,369,204.0,1644,1054,244,930,3773,6377,581,1515,2043,886,1665,1048,1672,2460,2738,2304,1665,2091,1893,772,620,381,1808,524,704,1818,2408,205,285,814,1529,1405,1447,2264,774,323,1021,1252,710,359,1462,1055,2810,104,2759,837,1710,519,885,700,845,286,443,1623,671,2484,1304,1106,544,701,1090,5490,341,3.56,2.781,2.69,2.679,2.234,2.43,2.13,2.501,2.459,3.097,2.184,1.811,2.876,2.524,2.697,2.667,2.809,3.528,2.738,1.871,2.745,2.444,1.982,2.593,2.45,2.596,2.194,2.01,2.66,2.47,2.078,2.495,2.65,2.798,2.582,2.604,2.593,2.971,1.665,2.12,2.336,1.884,2.779,1.883,1.893,2.303,2.53,2.786,2.482,2.714,2.108,2.203,2.107,2.525,2.988,2.042,2.454,2.373,1.826,1.821,2.006,2.756,2.14,2.937,2.387,2.047,2.024,2.291,2.693,2.399,2.174,2.305,2.372,2.573,0.841,0.934,0.584,0.65,0.774,0.715,0.596,0.493,0.73,0.65,0.635,0.479,0.518,0.749,0.658,0.736,0.647,0.737,0.831,0.538,0.771,0.649,0.501,0.893,0.689,0.633,0.561,0.552,0.623,0.652,0.733,0.818,0.501,0.713,0.791,0.456,0.836,0.786,0.369,0.437,0.458,0.6,0.914,0.624,0.565,0.608,0.565,0.799,0.522,0.761,0.489,0.56,0.62,0.587,0.753,0.502,0.675,0.556,0.444,0.448,0.43,0.741,0.643,0.65,0.601,0.662,0.546,0.477,0.518,0.967,0.495,0.569,0.54,0.452
-113,Epileptic,M,17.5,0.6,2.4,1.5,1.8,1.6,2.6,1.2,1.7,0.9,0.7,0.3,3.6,1.9,0.7,1.0,6.4,9.8,0.6,2.6,5.6,1.6,7.5,3.0,3.2,9.0,3.1,3.7,3.2,2.6,2.7,1.8,1.6,0.3,2.1,0.6,0.5,3.4,5.6,0.2,0.2,0.9,3.5,2.0,2.8,2.4,0.5,0.4,0.6,1.3,1.3,0.6,1.8,1.5,3.2,0.6,3.0,0.9,2.1,1.5,1.8,1.3,1.9,0.4,0.6,2.3,1.5,3.5,0.9,1.0,1.0,1.0,1.9,7.0,0.2,6,20,16,16,17,30,12,20,13,8,4.0,38,26,8,13,72,101,5,28,55,16,59,25,39,70,37,45,43,24,35,18,12,4,32,4,5,42,63,2,1,4,34,22,22,15,4,2,4,6,10,5,14,10,22,4,28,6,20,10,15,7,12,4,5,20,23,31,7,7,7,8,15,61,1,0.031,0.022,0.033,0.032,0.037,0.032,0.024,0.031,0.042,0.033,0.032,0.039,0.033,0.034,0.031,0.037,0.036,0.032,0.034,0.037,0.032,0.046,0.037,0.04,0.048,0.031,0.038,0.034,0.025,0.028,0.041,0.062,0.022,0.025,0.015,0.013,0.036,0.037,0.013,0.014,0.02,0.035,0.038,0.024,0.018,0.012,0.017,0.01,0.018,0.021,0.022,0.022,0.023,0.025,0.02,0.021,0.021,0.022,0.027,0.022,0.023,0.032,0.026,0.02,0.023,0.021,0.021,0.016,0.017,0.029,0.019,0.02,0.018,0.013,1556,5368,2417,2705,2168,3576,2486,3540,1893,1684,729.0,3761,3721,1255,2334,10520,17903,2359,5673,6785,4363,4138,2128,6950,7315,6384,4891,5404,5648,5890,3101,1273,888,6841,2392,1733,7206,10774,384,460,1157,2734,6475,2935,3163,1180,868,2093,2220,1750,630,2836,1879,4843,593,4051,1328,3537,1239,2190,1406,2603,438,1071,3138,1739,5147,1998,1623,1601,1700,2689,13428,416,0.128,0.108,0.137,0.133,0.141,0.128,0.106,0.135,0.146,0.137,0.126,0.154,0.145,0.142,0.132,0.149,0.146,0.112,0.137,0.15,0.12,0.151,0.136,0.142,0.15,0.129,0.127,0.127,0.106,0.129,0.145,0.144,0.102,0.112,0.075,0.082,0.137,0.135,0.098,0.087,0.103,0.137,0.152,0.119,0.097,0.085,0.09,0.074,0.096,0.11,0.12,0.109,0.113,0.115,0.123,0.111,0.11,0.118,0.12,0.107,0.101,0.128,0.136,0.113,0.114,0.113,0.111,0.092,0.101,0.123,0.105,0.109,0.1,0.088,398,1682,855,999,872,1304,839,1144,594,429,213.0,1711,1094,332,617,3218,5167,391,1364,2869,954,2363,1216,1688,3140,1845,1970,1884,1670,1826,797,497,259,1594,626,606,1739,2578,213,216,602,1544,1218,1729,1991,618,365,840,1000,815,398,1338,1075,2146,356,2326,637,1632,879,1293,778,961,240,478,1555,1061,2616,991,854,602,796,1422,6135,192,3.694,2.782,2.744,2.501,2.119,2.338,2.375,2.678,2.407,2.973,2.826,1.918,2.592,2.767,2.7,2.477,2.643,4.071,3.028,2.023,3.257,1.72,1.727,3.074,2.043,2.741,2.104,2.287,2.58,2.449,2.886,2.681,2.827,2.998,3.322,2.466,3.042,2.921,2.098,2.142,2.431,1.645,3.332,1.946,1.812,2.128,2.832,2.98,2.732,2.451,1.899,2.262,1.936,2.366,2.005,1.88,2.303,2.351,1.634,1.874,1.999,2.651,2.063,2.58,2.26,1.865,2.108,2.268,2.208,2.761,2.257,2.209,2.302,2.529,0.931,0.651,0.63,0.711,0.637,0.631,0.56,0.484,0.646,0.779,0.792,0.604,0.55,0.406,0.586,0.562,0.647,0.724,0.698,0.648,0.886,0.518,0.534,0.655,0.548,0.599,0.614,0.527,0.516,0.701,0.756,1.017,0.501,0.682,0.591,0.475,0.823,0.827,0.443,0.454,0.481,0.516,0.876,0.568,0.508,0.492,0.514,0.715,0.479,0.487,0.518,0.475,0.432,0.465,0.363,0.432,0.489,0.559,0.311,0.386,0.459,0.681,0.577,0.783,0.558,0.707,0.457,0.433,0.4,0.891,0.435,0.448,0.472,0.308
-114,Epileptic,F,37.0,1.0,1.9,1.0,1.5,1.4,1.6,1.6,1.4,1.4,0.6,0.2,2.3,1.7,0.6,0.8,7.2,9.7,0.8,1.7,4.5,0.8,2.9,1.2,2.2,2.6,2.7,2.7,2.1,1.9,2.5,1.3,1.3,0.7,2.2,0.4,0.7,2.5,4.0,0.2,0.2,1.1,2.1,2.5,2.2,2.4,0.9,0.4,1.3,1.2,0.8,0.6,2.0,1.3,2.5,0.3,2.2,0.5,1.5,0.5,1.0,0.3,1.5,0.7,0.3,1.8,1.3,2.9,1.2,1.0,0.3,1.1,1.6,5.0,0.4,7,16,8,11,16,13,14,13,19,4,2.0,26,18,6,7,71,86,5,21,37,6,30,10,27,25,28,32,23,17,31,14,11,5,24,3,8,28,46,3,2,5,20,22,20,15,7,2,8,7,6,5,17,10,16,3,20,4,14,4,9,2,11,5,3,15,23,22,10,6,3,9,9,42,3,0.04,0.021,0.02,0.03,0.036,0.029,0.021,0.027,0.05,0.033,0.034,0.03,0.028,0.032,0.028,0.035,0.033,0.027,0.021,0.035,0.017,0.038,0.022,0.031,0.033,0.029,0.023,0.022,0.02,0.029,0.044,0.05,0.03,0.025,0.013,0.019,0.03,0.03,0.019,0.02,0.021,0.031,0.041,0.024,0.018,0.019,0.019,0.019,0.018,0.022,0.032,0.021,0.02,0.02,0.018,0.018,0.017,0.018,0.017,0.021,0.017,0.029,0.025,0.018,0.025,0.034,0.021,0.019,0.017,0.016,0.025,0.02,0.02,0.026,1485,4218,2079,2357,1926,2845,2667,2715,1938,1098,402.0,2158,3508,1256,1614,11530,17529,2286,4273,4188,2890,4471,1840,4701,4538,5310,5225,3694,4544,4278,2178,1160,938,5459,1987,1723,5638,7815,503,339,1688,2533,5476,2352,3616,1552,664,2178,2438,1303,581,3415,2294,4566,422,3660,1381,2883,757,1413,678,2029,645,819,2593,1090,4194,2309,1522,1131,1405,2574,8991,431,0.139,0.105,0.106,0.12,0.139,0.125,0.103,0.114,0.169,0.132,0.12,0.135,0.13,0.132,0.116,0.144,0.132,0.109,0.114,0.133,0.09,0.147,0.11,0.133,0.136,0.129,0.111,0.105,0.094,0.131,0.134,0.128,0.144,0.115,0.066,0.102,0.122,0.128,0.13,0.118,0.105,0.134,0.138,0.122,0.1,0.11,0.105,0.091,0.094,0.114,0.13,0.105,0.104,0.102,0.12,0.106,0.098,0.105,0.104,0.118,0.095,0.126,0.148,0.115,0.11,0.15,0.114,0.102,0.1,0.106,0.13,0.112,0.108,0.144,444,1323,777,878,719,1001,996,816,542,284,145.0,1140,1001,313,494,3221,4830,435,1183,1888,688,1320,786,1198,1415,1597,1915,1323,1332,1510,467,439,332,1242,469,649,1471,1867,211,159,707,1071,1189,1493,2110,752,305,909,1069,601,358,1634,1028,1964,241,1967,565,1372,506,779,337,795,329,340,1311,541,2111,1014,822,320,682,1179,3823,197,3.323,2.9,2.377,2.543,2.364,2.411,2.248,2.914,2.545,3.258,2.753,1.682,2.729,2.856,2.586,2.712,2.788,3.917,2.828,1.919,3.25,2.482,1.998,2.882,2.554,2.656,2.176,2.205,2.589,2.327,3.032,2.42,2.456,3.051,3.611,2.45,2.964,3.123,2.302,2.531,3.04,2.048,3.322,1.732,1.922,2.179,2.807,2.826,2.68,2.528,1.971,2.254,2.403,2.423,2.378,2.006,2.81,2.364,1.807,1.918,2.128,2.626,2.189,2.728,2.162,2.413,2.115,2.515,2.265,3.402,2.295,2.615,2.543,2.551,0.896,0.79,0.603,0.553,0.556,0.583,0.515,0.432,0.58,0.403,0.635,0.452,0.421,0.452,0.387,0.5,0.687,0.767,0.478,0.616,0.808,0.529,0.453,0.661,0.559,0.615,0.45,0.455,0.461,0.512,0.871,1.082,0.464,0.632,0.905,0.475,0.742,0.57,0.405,0.375,0.751,0.484,0.661,0.626,0.522,0.621,0.415,0.783,0.583,0.44,0.401,0.473,0.541,0.543,0.444,0.479,0.653,0.49,0.323,0.373,0.409,0.588,0.435,0.811,0.628,0.7,0.444,0.382,0.413,0.876,0.482,0.52,0.558,0.49
-115,Epileptic,F,42.0,0.8,2.6,0.9,1.4,2.1,3.1,1.3,1.6,1.4,1.4,0.2,3.4,1.3,0.7,1.5,5.6,7.8,0.6,2.5,5.3,0.9,3.3,1.6,2.1,3.8,2.3,3.9,2.4,3.1,3.7,1.5,1.3,0.5,1.9,0.4,0.5,3.3,2.8,0.1,0.2,0.8,3.2,2.0,3.1,3.1,0.5,0.3,1.0,1.1,1.4,0.9,1.9,1.1,2.0,0.1,2.8,1.2,1.8,1.3,1.3,0.6,1.0,0.4,0.6,1.9,1.2,2.9,1.2,1.1,0.5,1.3,1.6,3.8,0.3,6,22,11,11,20,24,12,14,17,13,2.0,25,13,7,12,60,68,3,20,37,8,35,14,28,35,22,40,23,24,40,17,10,5,18,2,3,26,31,2,2,4,31,17,24,18,3,1,9,6,9,6,14,8,12,1,23,9,14,10,11,5,14,3,5,14,13,24,9,8,5,10,11,28,2,0.037,0.026,0.015,0.025,0.041,0.03,0.018,0.029,0.043,0.036,0.022,0.035,0.023,0.038,0.029,0.031,0.03,0.023,0.031,0.04,0.019,0.029,0.027,0.03,0.032,0.025,0.03,0.024,0.024,0.03,0.035,0.051,0.034,0.022,0.014,0.016,0.032,0.025,0.01,0.014,0.02,0.032,0.025,0.028,0.022,0.008,0.017,0.017,0.014,0.027,0.036,0.02,0.025,0.02,0.041,0.019,0.029,0.023,0.022,0.022,0.025,0.019,0.032,0.027,0.024,0.031,0.021,0.018,0.02,0.017,0.026,0.023,0.015,0.014,1370,4386,2143,2179,2005,4450,2691,2877,1939,1887,525.0,2792,3031,995,2870,10394,14670,2214,3920,4573,3267,5546,2062,4722,6263,4892,5972,3966,5867,6084,2101,1059,836,4563,1539,1611,4966,6521,347,557,1399,3024,4217,2703,3683,1385,730,2073,2292,1630,745,3672,1523,3598,232,3869,1504,3150,1577,1573,965,2038,517,773,2520,1246,3845,2268,1951,1162,1597,2376,8686,481,0.121,0.121,0.107,0.121,0.151,0.136,0.095,0.14,0.141,0.151,0.101,0.137,0.113,0.142,0.124,0.131,0.132,0.086,0.134,0.137,0.082,0.131,0.124,0.121,0.144,0.124,0.132,0.111,0.11,0.134,0.126,0.15,0.137,0.114,0.075,0.074,0.124,0.122,0.079,0.085,0.092,0.141,0.111,0.122,0.106,0.072,0.095,0.089,0.087,0.114,0.134,0.1,0.118,0.101,0.141,0.112,0.118,0.112,0.115,0.129,0.105,0.117,0.137,0.136,0.115,0.13,0.118,0.107,0.104,0.12,0.124,0.114,0.09,0.109,369,1441,926,914,800,1573,1020,903,606,562,172.0,1449,914,282,849,3110,4250,444,1267,1995,785,1903,978,1359,1959,1533,2144,1514,1818,2088,575,451,284,1229,452,588,1574,1809,233,286,647,1596,1140,1854,2221,798,295,853,1072,787,452,1577,716,1586,94,2198,738,1353,915,852,496,817,243,372,1290,601,2103,1012,948,462,867,1146,3895,245,3.303,2.829,2.397,2.501,2.415,2.637,2.219,2.98,2.414,2.915,2.771,1.797,2.605,2.471,2.606,2.612,2.808,3.907,2.721,2.022,3.142,2.361,1.934,2.577,2.57,2.604,2.252,2.153,2.568,2.362,2.676,2.515,2.428,2.719,3.305,2.577,2.563,2.76,1.903,2.375,2.652,1.812,2.89,1.705,1.906,1.917,3.166,2.821,2.581,2.363,2.09,2.386,2.268,2.544,2.695,1.969,2.216,2.529,1.998,2.078,2.133,2.474,2.573,2.266,2.288,2.431,2.102,2.625,2.517,2.909,2.24,2.535,2.455,2.507,0.785,0.605,0.53,0.443,0.524,0.525,0.527,0.692,0.551,0.555,0.748,0.479,0.363,0.637,0.446,0.6,0.482,0.817,0.443,0.63,0.693,0.447,0.466,0.713,0.473,0.494,0.467,0.471,0.431,0.472,0.732,0.845,0.477,0.538,0.817,0.756,0.543,0.49,0.457,0.497,0.637,0.552,0.639,0.623,0.538,0.323,0.483,0.923,0.561,0.39,0.331,0.582,0.568,0.486,0.42,0.389,0.484,0.728,0.39,0.389,0.335,0.79,0.517,1.02,0.576,0.654,0.46,0.309,0.317,0.854,0.342,0.475,0.56,0.414
-116,Epileptic,M,27.0,0.9,2.4,1.2,1.4,1.7,1.6,2.6,1.8,1.5,1.2,0.4,2.8,1.3,0.5,1.7,5.3,10.2,1.1,1.9,4.7,1.2,3.7,2.1,3.7,4.3,3.2,4.2,3.1,2.7,3.7,2.4,1.4,0.5,2.6,0.6,0.6,3.6,2.7,0.3,0.2,1.1,2.6,2.2,2.8,3.4,0.8,0.5,0.7,1.3,0.9,0.6,2.2,1.7,3.2,0.3,2.7,0.9,1.8,1.0,0.9,0.6,1.7,0.3,0.5,1.7,1.0,2.5,1.2,1.2,0.6,1.2,1.4,4.6,0.3,6,19,13,9,14,15,18,15,15,13,3.0,24,17,8,16,53,76,7,23,41,14,43,21,35,44,34,41,28,22,38,15,12,5,30,4,4,43,34,2,1,6,23,20,20,16,4,2,4,6,5,5,13,12,22,1,22,5,12,8,7,3,13,2,3,11,15,22,7,7,4,8,10,33,3,0.041,0.028,0.024,0.031,0.039,0.029,0.05,0.03,0.047,0.045,0.038,0.038,0.028,0.03,0.036,0.038,0.046,0.045,0.03,0.044,0.034,0.039,0.04,0.04,0.04,0.035,0.042,0.039,0.034,0.04,0.057,0.072,0.034,0.033,0.02,0.015,0.034,0.031,0.019,0.021,0.025,0.029,0.04,0.026,0.025,0.02,0.018,0.011,0.017,0.02,0.031,0.023,0.025,0.028,0.029,0.02,0.033,0.019,0.022,0.015,0.017,0.03,0.019,0.025,0.02,0.024,0.024,0.022,0.019,0.021,0.019,0.016,0.016,0.022,1549,4674,2425,2183,2411,3394,2988,3018,2028,2238,802.0,2620,3281,1493,3379,9392,16430,2141,4817,5125,3279,4644,1855,6744,5603,5906,6201,4303,5599,6866,2634,851,867,5743,2233,1722,9465,7613,515,396,1249,2141,4754,2669,3130,1058,903,2214,2266,1805,676,2642,2296,5078,354,4114,1013,3258,1196,1638,930,2352,439,738,2581,1261,4221,1851,1965,1046,1852,2342,10148,282,0.135,0.122,0.124,0.117,0.143,0.125,0.141,0.127,0.184,0.165,0.123,0.141,0.126,0.123,0.141,0.145,0.153,0.131,0.126,0.161,0.106,0.127,0.146,0.144,0.139,0.135,0.141,0.123,0.113,0.145,0.16,0.164,0.11,0.142,0.09,0.084,0.134,0.141,0.099,0.107,0.106,0.129,0.134,0.126,0.108,0.099,0.092,0.074,0.09,0.101,0.139,0.11,0.114,0.115,0.137,0.107,0.134,0.097,0.117,0.093,0.097,0.126,0.107,0.111,0.108,0.13,0.107,0.099,0.092,0.118,0.107,0.106,0.092,0.137,412,1357,839,789,829,1010,1042,1043,561,513,187.0,1325,877,342,870,2677,4226,423,1226,1965,844,1511,878,1784,1760,1676,2129,1422,1513,1912,672,383,330,1310,535,628,2039,1681,262,209,708,1238,1073,1618,1881,561,387,864,1100,695,357,1390,1072,1990,138,2130,423,1498,711,922,450,985,270,348,1264,686,1717,869,932,484,961,1167,4308,204,3.503,2.866,2.563,2.504,2.39,2.573,2.351,2.551,2.651,3.189,3.038,1.745,2.796,3.107,2.7,2.566,2.869,3.705,3.034,2.137,2.876,2.46,1.92,2.749,2.502,2.744,2.275,2.246,2.801,2.716,2.669,2.44,2.161,3.027,3.684,2.457,3.163,3.115,2.1,1.993,2.316,1.648,3.036,1.919,1.882,1.968,2.903,2.875,2.376,2.814,1.967,1.956,2.062,2.293,2.986,2.093,2.441,2.428,1.913,1.953,2.45,2.17,1.792,2.301,2.326,2.245,2.409,2.341,2.313,2.305,2.173,2.307,2.454,1.691,0.928,0.702,0.729,0.549,0.766,0.752,0.676,0.492,0.718,0.657,1.0,0.471,0.409,0.447,0.588,0.581,0.699,0.791,0.667,0.803,0.763,0.629,0.569,0.679,0.708,0.644,0.604,0.7,0.537,0.642,0.809,0.966,0.49,0.592,0.635,0.537,0.79,0.68,0.355,0.31,0.5,0.46,0.83,0.624,0.589,0.501,0.516,0.697,0.413,0.648,0.471,0.407,0.511,0.588,0.368,0.43,0.529,0.509,0.411,0.399,0.376,0.667,0.403,0.503,0.52,0.694,0.966,0.442,0.45,0.537,0.432,0.515,0.481,0.336
-117,Epileptic,F,37.0,0.6,1.8,1.2,1.4,1.5,1.7,1.7,1.5,1.0,1.0,0.3,2.2,1.2,0.3,1.2,4.3,8.2,0.8,1.9,4.3,1.2,2.8,2.0,3.8,3.1,2.7,4.1,2.6,2.3,2.0,2.0,1.0,0.4,1.9,0.4,0.3,5.1,2.7,0.1,0.2,0.9,2.5,1.6,3.0,2.6,0.9,0.4,0.9,1.4,0.7,0.5,1.5,1.4,2.0,0.1,1.6,0.6,1.6,0.7,1.0,0.7,1.2,0.3,0.4,1.2,1.1,3.2,0.9,1.3,0.4,1.0,1.4,3.5,0.3,8,17,10,12,13,16,17,15,13,9,5.0,29,13,6,14,56,80,8,26,46,15,37,18,48,35,34,40,30,22,25,23,8,3,24,2,3,66,33,1,1,7,24,17,19,15,7,2,6,8,6,4,15,12,13,1,14,4,15,5,7,5,10,2,5,12,18,26,9,9,4,7,11,28,3,0.035,0.02,0.021,0.025,0.025,0.025,0.029,0.022,0.025,0.036,0.028,0.038,0.024,0.026,0.028,0.028,0.027,0.029,0.029,0.036,0.024,0.033,0.035,0.042,0.037,0.027,0.031,0.03,0.019,0.025,0.045,0.049,0.023,0.024,0.009,0.009,0.048,0.029,0.009,0.012,0.02,0.035,0.031,0.025,0.017,0.021,0.019,0.016,0.018,0.02,0.027,0.015,0.021,0.017,0.014,0.013,0.017,0.017,0.018,0.018,0.019,0.02,0.023,0.015,0.017,0.022,0.021,0.016,0.016,0.015,0.03,0.02,0.015,0.016,1424,4623,2347,2664,2642,3326,3025,3517,2313,1663,761.0,2972,3044,890,2907,10726,19531,2057,4850,5556,3410,4836,2473,5734,5050,6523,6047,4525,7104,4840,2258,1058,926,5604,2017,1655,7017,7462,409,464,1319,2763,4718,3427,3914,1255,669,1973,2225,1480,519,3292,2241,4599,255,3241,1172,3327,1064,1671,1158,2297,389,1138,2420,997,4805,2181,2555,706,1092,2698,7922,436,0.13,0.108,0.119,0.119,0.125,0.121,0.104,0.116,0.133,0.174,0.134,0.162,0.118,0.134,0.126,0.134,0.128,0.124,0.12,0.156,0.097,0.123,0.138,0.148,0.135,0.125,0.122,0.11,0.098,0.124,0.136,0.123,0.101,0.114,0.06,0.07,0.147,0.132,0.089,0.086,0.112,0.132,0.136,0.116,0.094,0.115,0.098,0.084,0.099,0.105,0.122,0.096,0.117,0.092,0.12,0.089,0.099,0.1,0.099,0.099,0.104,0.114,0.127,0.111,0.103,0.133,0.115,0.097,0.096,0.12,0.129,0.104,0.09,0.109,423,1603,890,1008,982,1102,1057,1157,618,479,223.0,1279,868,233,734,2926,5406,517,1270,2286,859,1688,955,1754,1547,1967,2133,1521,1837,1414,754,473,322,1339,560,635,2100,1706,210,237,724,1347,1026,1901,2301,656,307,837,1068,621,326,1568,1091,1954,129,1840,561,1632,658,839,599,927,196,475,1167,719,2364,984,1179,399,558,1224,3522,265,3.188,2.653,2.459,2.334,2.362,2.525,2.358,2.591,2.685,2.7,2.72,2.008,2.723,2.796,2.893,2.744,2.844,3.197,2.932,2.066,2.916,2.316,2.207,2.544,2.596,2.632,2.286,2.302,2.931,2.607,2.305,2.225,2.24,2.946,3.258,2.385,2.535,3.124,2.43,2.11,2.219,1.845,3.279,2.002,1.906,2.049,2.625,2.985,2.535,2.668,1.895,2.252,2.315,2.481,2.272,1.979,2.474,2.334,1.819,2.152,2.203,2.552,2.271,2.736,2.18,1.681,2.14,2.443,2.424,1.94,2.121,2.498,2.364,2.043,0.78,0.55,0.568,0.535,0.576,0.58,0.584,0.46,0.615,0.531,0.749,0.416,0.361,0.367,0.517,0.517,0.564,0.819,0.567,0.625,0.806,0.493,0.613,0.716,0.579,0.534,0.579,0.546,0.559,0.491,0.761,0.927,0.447,0.526,0.598,0.428,0.798,0.513,0.235,0.387,0.415,0.563,0.784,0.53,0.488,0.433,0.564,0.783,0.492,0.412,0.35,0.326,0.422,0.426,0.28,0.344,0.505,0.417,0.306,0.408,0.358,0.481,0.347,0.713,0.423,0.504,0.481,0.417,0.462,0.614,0.386,0.478,0.445,0.394
-118,Epileptic,M,37.0,0.5,2.4,1.4,1.4,2.2,2.1,1.7,1.8,1.5,0.7,0.4,3.4,1.6,0.5,1.4,8.7,8.8,1.0,2.6,6.3,1.0,4.0,2.3,3.6,3.6,5.0,3.7,3.0,3.4,4.8,1.6,1.3,0.6,2.4,0.5,0.9,5.7,3.9,0.1,0.3,1.2,3.7,2.5,2.9,3.3,0.8,0.6,0.8,1.2,0.9,0.9,2.0,1.4,2.5,0.2,2.9,1.5,1.9,1.1,1.8,0.9,1.9,0.5,0.4,2.5,1.0,3.3,1.3,2.0,0.8,1.3,1.7,4.2,0.3,3,21,23,14,17,19,17,15,12,11,3.0,37,18,5,12,82,78,6,28,49,8,42,21,41,44,48,46,29,29,54,25,10,6,23,2,9,50,43,1,2,6,33,22,21,17,5,2,5,6,8,6,12,9,15,1,23,11,12,7,13,7,15,3,3,19,9,26,8,13,6,8,13,29,2,0.021,0.02,0.016,0.023,0.037,0.03,0.02,0.032,0.05,0.042,0.032,0.036,0.026,0.029,0.033,0.038,0.031,0.031,0.028,0.043,0.021,0.039,0.029,0.04,0.038,0.036,0.027,0.025,0.024,0.037,0.039,0.044,0.036,0.032,0.014,0.019,0.046,0.038,0.013,0.016,0.025,0.039,0.044,0.025,0.021,0.012,0.021,0.014,0.014,0.024,0.035,0.02,0.02,0.023,0.023,0.018,0.029,0.016,0.024,0.021,0.025,0.03,0.035,0.019,0.026,0.017,0.022,0.017,0.019,0.02,0.025,0.025,0.018,0.023,1947,5354,3371,2564,2396,3289,3355,3352,1708,1981,792.0,3593,3907,1198,2695,13702,19645,2691,4868,5920,2353,6048,2938,6014,5830,8116,6732,4143,7480,7633,2651,1528,930,5640,1666,2167,6517,5263,401,495,1458,3388,4255,3385,4231,1796,878,2460,2722,1585,761,3736,2211,4390,320,4515,1639,3565,1279,2414,1194,2888,623,867,3260,1856,4732,2663,3595,1226,1753,1939,8701,530,0.084,0.104,0.098,0.109,0.14,0.132,0.093,0.129,0.153,0.146,0.13,0.145,0.123,0.136,0.138,0.148,0.13,0.113,0.128,0.146,0.087,0.155,0.134,0.147,0.159,0.148,0.128,0.111,0.1,0.15,0.154,0.129,0.158,0.121,0.076,0.094,0.149,0.139,0.094,0.11,0.118,0.138,0.146,0.11,0.101,0.087,0.109,0.084,0.082,0.107,0.132,0.097,0.111,0.102,0.132,0.106,0.13,0.095,0.116,0.113,0.121,0.127,0.137,0.106,0.119,0.093,0.109,0.095,0.103,0.124,0.12,0.133,0.097,0.103,421,1807,1383,1008,944,1139,1126,1039,531,444,209.0,1586,1050,291,730,3873,4975,503,1439,2445,713,1826,1269,1618,1722,2409,2366,1678,2043,2391,713,581,315,1189,443,800,2051,1722,186,250,735,1455,1017,1877,2447,916,393,903,1236,718,416,1673,1023,1823,137,2432,779,1697,728,1249,629,1018,237,415,1531,884,2452,1197,1580,539,817,1050,3634,222,3.741,2.652,2.476,2.535,2.372,2.395,2.463,2.766,2.605,3.397,3.228,1.969,2.95,2.951,2.809,2.705,3.135,3.854,2.748,2.041,2.664,2.54,2.044,2.856,2.625,2.735,2.293,2.025,2.822,2.505,2.795,2.455,2.625,3.107,3.19,2.443,2.527,2.381,2.487,2.371,2.404,2.019,2.835,2.068,2.011,2.16,2.861,3.242,2.588,2.379,2.145,2.408,2.344,2.562,2.596,2.084,2.418,2.437,2.081,2.168,2.129,2.705,2.495,2.48,2.179,2.431,2.099,2.528,2.503,2.408,2.401,2.184,2.459,2.687,1.165,0.606,0.551,0.491,0.52,0.55,0.558,0.51,0.527,0.64,0.897,0.551,0.515,0.45,0.517,0.584,0.574,0.997,0.529,0.618,0.783,0.541,0.438,0.704,0.563,0.582,0.485,0.485,0.594,0.609,0.603,1.105,0.627,0.654,0.471,0.541,0.774,0.577,0.359,0.343,0.589,0.517,0.847,0.715,0.684,0.461,0.533,0.889,0.545,0.626,0.468,0.516,0.606,0.586,0.502,0.473,0.563,0.499,0.407,0.378,0.303,0.729,0.543,1.044,0.548,0.809,0.471,0.333,0.415,0.647,0.505,0.594,0.636,0.379
-119,Epileptic,M,17.5,1.1,2.2,1.0,1.9,2.4,3.0,3.0,2.1,1.9,0.9,0.3,3.5,1.6,0.6,2.1,7.6,9.9,1.4,3.3,4.1,2.3,4.0,3.5,4.5,8.7,4.7,9.3,3.8,3.0,4.7,1.6,1.3,1.0,3.6,0.6,1.0,2.6,3.7,0.3,0.6,1.7,3.4,2.8,3.2,3.6,0.9,0.5,1.3,2.0,0.6,0.8,2.8,2.8,3.0,0.5,3.6,1.3,1.9,1.3,1.4,0.8,1.8,0.4,0.7,2.3,1.4,3.5,1.5,1.2,0.4,1.5,1.6,5.7,0.4,10,19,7,16,14,27,21,20,15,12,8.0,34,22,8,23,72,89,13,36,36,17,38,26,51,71,45,68,32,23,54,16,9,8,40,3,8,37,49,2,2,11,32,25,21,18,6,2,7,9,5,6,21,17,18,3,29,9,12,8,9,5,14,2,3,21,18,22,9,6,3,14,11,49,3,0.043,0.025,0.015,0.031,0.037,0.037,0.04,0.032,0.05,0.032,0.032,0.044,0.036,0.029,0.037,0.04,0.038,0.043,0.037,0.034,0.04,0.044,0.047,0.052,0.058,0.032,0.046,0.034,0.032,0.031,0.035,0.057,0.037,0.04,0.029,0.015,0.036,0.033,0.017,0.019,0.027,0.04,0.043,0.027,0.024,0.017,0.017,0.02,0.022,0.012,0.033,0.025,0.037,0.021,0.036,0.018,0.025,0.019,0.028,0.022,0.019,0.028,0.021,0.019,0.025,0.027,0.023,0.022,0.023,0.016,0.021,0.019,0.02,0.016,1927,5289,2426,3251,2465,3861,3385,4165,2031,2004,954.0,3017,4319,1488,4211,14608,22733,2901,6135,4835,4475,4576,2261,7468,6305,9134,8541,4698,7006,7646,3035,1296,1512,8451,1901,2666,6160,9999,613,921,2014,3139,5688,3604,3995,1894,965,2551,3229,1558,701,4710,2624,5705,575,6250,1575,3400,1310,1977,1202,2815,571,1035,3318,1361,5272,2562,1840,864,2910,2800,11151,749,0.135,0.113,0.099,0.129,0.135,0.143,0.131,0.134,0.143,0.146,0.111,0.149,0.13,0.127,0.141,0.141,0.134,0.139,0.142,0.134,0.128,0.135,0.144,0.151,0.158,0.121,0.139,0.117,0.118,0.129,0.129,0.146,0.123,0.153,0.097,0.09,0.13,0.142,0.089,0.1,0.117,0.14,0.153,0.121,0.103,0.094,0.088,0.092,0.102,0.086,0.134,0.113,0.139,0.098,0.129,0.101,0.116,0.102,0.133,0.115,0.106,0.118,0.105,0.098,0.122,0.124,0.103,0.104,0.103,0.098,0.11,0.102,0.101,0.101,529,1599,862,1054,1065,1404,1287,1253,709,516,251.0,1469,1102,361,1109,3639,5350,596,1693,2086,970,1490,1091,1917,2295,2623,3344,1840,1675,2737,804,454,497,1757,435,943,1513,2132,308,410,1014,1455,1227,1908,2338,830,379,1049,1340,663,423,2004,1208,2319,204,3011,750,1492,685,913,591,1047,297,517,1598,947,2459,1068,800,387,1231,1141,4574,396,3.421,3.067,2.872,2.929,2.116,2.356,2.232,2.831,2.262,2.984,2.892,1.798,2.993,2.956,2.82,2.851,3.212,3.642,2.876,1.974,3.199,2.37,1.917,2.811,2.26,2.704,2.11,2.094,2.992,2.358,2.682,2.751,2.455,3.202,3.644,2.685,2.893,3.153,2.318,2.496,2.462,1.806,2.951,2.217,2.012,2.534,2.983,2.986,2.985,2.467,2.01,2.385,2.166,2.582,3.078,2.259,2.254,2.476,2.318,2.354,2.391,2.569,2.306,2.473,2.144,1.783,2.231,2.775,2.77,2.703,2.458,2.427,2.527,2.386,0.777,0.591,0.485,0.739,0.65,0.678,0.764,0.498,0.817,0.747,0.584,0.565,0.486,0.525,0.529,0.736,0.696,0.735,0.553,0.686,0.872,0.71,0.574,0.77,0.805,0.704,0.683,0.629,0.633,0.563,0.718,1.036,0.466,0.763,0.738,0.446,0.863,0.809,0.386,0.551,0.441,0.718,1.012,0.737,0.635,0.432,0.597,0.624,0.491,0.597,0.303,0.558,0.577,0.48,0.679,0.453,0.509,0.571,0.388,0.48,0.401,0.746,0.418,0.876,0.535,0.558,0.427,0.399,0.591,0.677,0.525,0.524,0.513,0.38
-120,Epileptic,M,22.0,1.3,3.0,0.9,1.6,2.7,2.1,1.6,1.3,1.8,1.5,0.4,2.6,2.0,0.5,1.4,9.3,13.5,0.8,2.9,3.9,3.3,3.6,1.5,4.1,4.2,4.4,2.6,1.5,3.3,3.5,2.4,1.5,0.4,2.7,0.5,1.4,3.9,4.2,0.2,0.1,1.6,2.7,3.0,2.2,1.5,0.8,0.4,1.1,1.3,0.7,0.8,2.5,1.7,4.1,0.4,2.6,1.0,2.0,1.0,1.2,0.6,1.9,0.4,0.4,2.5,1.5,2.3,2.0,2.0,0.8,1.2,2.3,6.7,0.4,8,40,10,13,22,24,17,16,19,15,6.0,28,22,9,18,110,122,6,39,40,35,48,17,46,50,45,36,21,31,46,27,8,5,32,3,13,43,54,1,0,10,27,37,17,12,5,2,7,8,6,8,23,12,28,3,21,8,19,5,9,5,16,5,3,21,20,18,15,12,6,10,16,61,3,0.047,0.028,0.016,0.026,0.043,0.026,0.031,0.027,0.041,0.041,0.039,0.031,0.036,0.041,0.037,0.042,0.031,0.036,0.029,0.034,0.051,0.035,0.022,0.047,0.029,0.034,0.029,0.029,0.027,0.029,0.06,0.043,0.026,0.029,0.013,0.023,0.035,0.037,0.016,0.014,0.029,0.031,0.05,0.026,0.016,0.015,0.017,0.02,0.016,0.015,0.027,0.025,0.023,0.023,0.025,0.02,0.021,0.02,0.018,0.021,0.019,0.032,0.031,0.019,0.02,0.025,0.02,0.025,0.026,0.019,0.024,0.021,0.02,0.014,1368,5239,2542,3248,2718,3484,2520,2885,2346,2660,819.0,2757,3656,913,2834,14521,24356,1452,6897,5189,4219,5950,2453,5862,8614,6706,4501,3403,4640,7111,3005,1228,929,7928,2179,2839,8000,10415,447,213,1742,2976,5528,2551,2383,1400,719,2030,2432,1780,892,3800,2277,6348,725,4712,1891,3890,1254,1833,1101,2813,483,678,4139,1315,3200,3159,2457,1390,1815,3521,12626,1014,0.148,0.123,0.1,0.112,0.149,0.118,0.126,0.121,0.169,0.163,0.151,0.139,0.148,0.152,0.148,0.161,0.125,0.111,0.127,0.147,0.159,0.134,0.105,0.154,0.129,0.138,0.114,0.127,0.109,0.134,0.166,0.125,0.107,0.125,0.078,0.121,0.127,0.152,0.089,0.083,0.12,0.128,0.162,0.121,0.095,0.093,0.097,0.096,0.097,0.098,0.142,0.119,0.117,0.109,0.117,0.099,0.11,0.109,0.102,0.106,0.102,0.142,0.139,0.116,0.109,0.127,0.107,0.114,0.103,0.121,0.119,0.109,0.105,0.096,398,1764,858,1068,1053,1364,868,937,693,589,200.0,1386,1043,242,796,4054,7175,365,1845,1938,1113,1583,1018,1643,2382,2131,1716,1043,1939,2272,793,586,262,1713,569,930,1993,2194,236,100,910,1437,1201,1358,1434,741,313,908,1204,757,469,1777,1094,2739,303,2112,805,1703,805,933,551,964,264,368,2019,885,1538,1354,1278,564,832,1647,5369,380,2.761,2.65,2.608,2.846,2.085,2.178,2.303,2.631,2.444,3.287,3.002,1.776,2.641,2.613,2.68,2.508,2.616,3.05,2.88,2.193,2.789,2.722,2.051,2.593,2.64,2.486,2.202,2.468,1.971,2.504,2.729,2.324,2.812,3.155,3.022,2.649,2.815,3.224,2.22,2.147,2.341,1.804,3.049,2.053,1.722,2.032,2.545,2.832,2.415,2.608,2.052,2.061,2.124,2.308,2.42,2.229,2.593,2.443,1.808,2.112,2.146,2.597,1.816,2.021,2.259,1.903,2.121,2.205,2.062,2.665,2.453,2.529,2.503,2.985,0.934,0.665,0.545,0.665,0.69,0.677,0.589,0.574,0.64,0.691,0.621,0.437,0.646,0.676,0.618,0.738,0.703,0.833,0.585,0.824,0.887,0.65,0.559,0.716,0.671,0.653,0.552,0.568,0.651,0.597,0.831,0.939,0.524,0.664,0.768,0.608,0.756,0.621,0.346,0.426,0.509,0.553,0.699,0.673,0.572,0.544,0.589,0.59,0.631,0.437,0.47,0.577,0.594,0.594,0.874,0.549,0.409,0.592,0.348,0.421,0.384,0.811,0.514,0.564,0.577,0.563,0.458,0.63,0.707,0.966,0.49,0.459,0.495,0.436
-121,Epileptic,M,42.0,1.2,2.4,1.3,1.7,2.0,2.9,1.8,1.7,1.0,0.6,0.5,3.2,1.1,0.5,1.2,7.6,11.3,1.2,2.7,4.3,1.5,10.5,2.8,5.2,7.2,4.4,4.9,3.8,2.8,2.7,1.4,0.7,0.4,2.0,0.8,0.7,5.0,4.4,0.2,0.2,0.9,5.1,2.6,2.4,2.7,0.9,0.5,0.9,1.3,1.2,0.7,1.6,1.3,2.5,0.5,2.7,1.1,1.6,1.5,1.9,0.8,1.8,0.6,0.8,2.4,1.3,2.3,1.9,1.3,0.7,0.7,1.5,6.1,0.3,8,21,11,15,18,28,17,17,16,6,5.0,33,12,6,13,85,110,10,28,43,14,86,19,57,56,39,42,32,27,33,18,5,10,23,5,8,55,52,1,1,4,48,25,14,15,7,2,6,7,12,7,14,9,17,5,26,11,11,10,13,5,17,5,6,19,18,18,13,9,7,4,10,49,1,0.043,0.023,0.022,0.028,0.034,0.037,0.027,0.027,0.038,0.042,0.036,0.04,0.021,0.033,0.023,0.033,0.033,0.043,0.038,0.033,0.023,0.063,0.033,0.044,0.048,0.034,0.039,0.033,0.029,0.031,0.037,0.039,0.028,0.028,0.022,0.023,0.035,0.035,0.013,0.013,0.02,0.041,0.036,0.024,0.02,0.016,0.017,0.014,0.016,0.018,0.035,0.019,0.024,0.018,0.022,0.023,0.022,0.02,0.018,0.021,0.018,0.025,0.027,0.021,0.018,0.023,0.018,0.017,0.019,0.019,0.023,0.017,0.021,0.016,1760,5080,2893,3037,2681,3857,3447,3797,1894,1251,847.0,3525,3019,1378,2780,13976,22201,3113,4543,5532,5543,5287,2409,7713,5387,7393,5379,5033,6841,5254,2657,768,877,6195,2369,2043,10832,9578,437,530,1374,3262,7186,2872,3736,1564,1057,2447,2558,2458,864,3224,1829,4584,995,3955,2257,3149,2238,2030,1266,2975,666,1083,3963,1548,4366,3840,1836,1367,1152,3049,11756,480,0.141,0.119,0.104,0.124,0.139,0.134,0.119,0.123,0.151,0.137,0.147,0.168,0.104,0.139,0.123,0.139,0.137,0.129,0.148,0.144,0.094,0.153,0.126,0.15,0.147,0.12,0.13,0.103,0.115,0.131,0.142,0.119,0.112,0.123,0.094,0.108,0.136,0.139,0.079,0.086,0.096,0.153,0.145,0.111,0.103,0.1,0.094,0.083,0.091,0.098,0.153,0.103,0.117,0.097,0.113,0.107,0.116,0.103,0.106,0.106,0.099,0.128,0.129,0.12,0.109,0.118,0.097,0.093,0.107,0.107,0.115,0.097,0.105,0.09,504,1628,1035,1071,952,1237,1082,1122,493,301,226.0,1454,827,312,797,3911,6039,582,1258,2140,1205,2353,1148,2043,2140,2089,2055,1739,1703,1558,756,352,235,1274,555,592,2537,2277,236,285,669,1665,1434,1511,2048,785,411,1031,1121,1067,400,1449,880,2107,378,2107,800,1274,1175,1207,630,1179,369,530,1884,919,2032,1608,916,607,463,1371,4785,222,3.492,2.857,2.711,2.66,2.433,2.408,2.539,3.004,2.737,3.067,2.771,2.15,2.77,3.079,2.641,2.743,2.837,3.917,2.888,2.235,3.383,2.06,1.892,2.806,2.198,2.878,2.191,2.377,2.96,2.592,2.653,2.448,2.716,3.368,3.854,2.927,3.151,3.105,2.211,2.182,2.631,1.85,3.455,2.247,2.109,2.254,3.039,3.0,2.845,2.553,2.218,2.299,2.318,2.388,2.875,2.089,2.472,2.768,2.282,1.873,2.381,2.449,1.891,2.547,2.392,2.059,2.265,2.59,2.172,2.394,2.735,2.6,2.602,2.544,0.9,0.678,0.52,0.496,0.574,0.725,0.67,0.488,0.618,0.724,0.788,0.534,0.404,0.482,0.468,0.557,0.646,0.905,0.523,0.721,0.833,0.66,0.503,0.769,0.75,0.631,0.568,0.625,0.55,0.67,0.593,0.831,0.368,0.597,0.622,0.511,0.802,0.696,0.438,0.315,0.454,0.551,0.773,0.557,0.637,0.424,0.738,0.635,0.394,0.416,0.433,0.412,0.483,0.428,0.571,0.396,0.567,0.538,0.383,0.354,0.371,0.631,0.491,0.749,0.469,0.623,0.459,0.31,0.432,0.667,0.371,0.493,0.496,0.338
-122,Epileptic,F,57.0,1.5,1.9,0.8,1.0,2.5,3.1,1.5,1.4,3.1,0.4,0.5,3.2,1.2,0.5,2.0,7.2,9.1,1.0,2.4,5.1,1.7,3.0,3.8,5.7,5.1,2.5,5.4,3.7,3.4,3.7,2.2,1.1,0.3,2.1,0.3,1.0,3.0,3.5,0.3,0.2,0.9,5.3,2.0,3.7,3.8,0.7,0.4,0.9,1.4,0.6,0.5,2.2,1.9,2.6,0.2,2.5,0.6,1.6,1.0,1.1,0.2,1.4,0.3,0.6,1.8,1.2,2.9,1.1,0.9,0.8,0.8,0.6,5.0,0.4,14,15,6,8,15,29,13,11,22,7,5.0,24,15,6,21,61,80,7,29,143,11,26,22,40,38,27,35,26,24,36,21,9,3,25,1,9,37,33,3,2,5,44,21,19,22,4,1,5,7,4,4,19,12,17,1,18,4,13,7,9,2,10,3,5,14,17,20,6,6,6,7,5,38,3,0.067,0.03,0.021,0.024,0.046,0.044,0.029,0.033,0.071,0.032,0.045,0.046,0.032,0.036,0.047,0.047,0.037,0.044,0.035,0.119,0.037,0.04,0.064,0.06,0.051,0.035,0.048,0.045,0.037,0.04,0.059,0.046,0.02,0.031,0.016,0.024,0.049,0.046,0.023,0.021,0.025,0.064,0.041,0.037,0.029,0.014,0.021,0.017,0.02,0.023,0.027,0.025,0.034,0.026,0.017,0.018,0.02,0.023,0.03,0.023,0.016,0.028,0.022,0.019,0.029,0.028,0.032,0.022,0.019,0.027,0.023,0.025,0.024,0.022,1432,3263,1994,1765,1491,2940,2457,2311,1633,1007,476.0,2421,3143,1020,2959,8613,16436,2086,4825,3852,2287,2647,1424,4686,4661,4819,4283,3445,5486,5201,2019,1064,920,5423,1408,2147,4492,5881,590,545,1169,1724,5195,2001,3303,1327,700,1760,2304,948,641,3406,1805,3668,344,3760,915,2862,986,1426,436,2514,397,922,1896,975,2949,2000,1380,1327,1226,1171,8457,429,0.173,0.134,0.105,0.114,0.142,0.161,0.107,0.126,0.178,0.147,0.135,0.16,0.131,0.136,0.165,0.152,0.142,0.138,0.148,0.16,0.128,0.134,0.179,0.15,0.153,0.142,0.137,0.131,0.12,0.145,0.146,0.132,0.086,0.127,0.075,0.114,0.155,0.156,0.125,0.12,0.115,0.179,0.159,0.137,0.123,0.088,0.097,0.093,0.101,0.12,0.127,0.118,0.141,0.108,0.106,0.105,0.111,0.118,0.116,0.114,0.1,0.118,0.13,0.102,0.133,0.13,0.13,0.1,0.105,0.129,0.122,0.128,0.111,0.133,432,1073,676,690,809,1235,815,796,697,287,203.0,1200,797,253,814,2802,4395,403,1214,1762,687,1125,902,1573,1637,1415,1952,1345,1442,1672,645,414,292,1113,362,644,1247,1330,237,253,584,1197,959,1337,2096,668,271,764,1047,405,310,1511,882,1635,180,2026,434,1175,560,793,240,856,228,513,1019,678,1520,865,717,435,551,462,3433,235,3.079,2.877,2.866,2.611,1.976,2.103,2.365,2.639,1.952,2.723,2.064,1.723,2.907,2.932,2.707,2.402,2.924,3.864,2.974,1.864,2.667,2.024,1.625,2.36,2.333,2.738,1.889,2.114,2.758,2.424,2.246,2.475,2.507,3.192,3.277,2.803,2.699,3.068,2.694,2.345,2.544,1.425,3.538,1.74,1.801,2.2,3.164,2.849,2.808,2.854,2.058,2.384,2.397,2.453,2.323,2.156,2.453,2.704,2.122,2.107,2.072,3.182,1.917,2.279,2.109,1.85,2.274,2.671,2.227,3.098,2.381,2.614,2.667,2.137,1.02,0.652,0.481,0.475,0.53,0.699,0.721,0.678,0.585,0.727,0.86,0.535,0.49,0.505,0.551,0.673,0.681,0.83,0.742,0.778,0.952,0.64,0.488,0.914,0.791,0.626,0.562,0.612,0.654,0.705,0.798,0.82,0.468,0.684,0.847,0.511,0.752,0.768,0.399,0.305,0.416,0.456,0.805,0.488,0.549,0.488,0.472,0.654,0.45,0.457,0.561,0.409,0.512,0.499,0.38,0.452,0.466,0.562,0.451,0.536,0.396,0.658,0.49,0.593,0.572,0.617,0.507,0.356,0.383,0.744,0.576,0.731,0.52,0.435
-123,Epileptic,M,57.0,1.0,2.1,1.2,1.1,1.7,1.6,2.1,2.0,1.5,1.1,0.5,3.2,1.2,0.6,1.7,10.6,12.0,1.0,2.0,3.5,1.1,2.8,1.4,5.2,3.9,3.1,4.2,3.8,5.3,3.0,2.6,1.2,0.5,2.8,0.7,0.8,4.3,3.2,0.1,0.3,1.1,2.4,2.1,2.9,3.2,0.7,0.6,1.2,1.6,0.9,0.4,2.1,1.4,3.8,0.2,3.0,0.8,1.3,1.3,1.2,0.5,2.5,0.8,0.5,1.4,1.4,3.2,1.8,1.6,0.7,1.1,1.4,4.2,0.5,8,18,9,9,15,16,17,20,11,13,4.0,29,14,6,16,82,108,9,25,39,12,35,15,40,42,40,52,48,37,42,23,10,5,31,4,6,56,46,2,1,6,23,23,21,16,7,3,7,7,6,3,13,9,24,1,24,6,10,7,8,4,19,4,5,14,20,26,12,9,6,8,10,36,3,0.035,0.023,0.018,0.019,0.039,0.032,0.038,0.031,0.055,0.038,0.034,0.037,0.032,0.05,0.032,0.046,0.036,0.032,0.03,0.035,0.024,0.032,0.036,0.048,0.038,0.028,0.031,0.041,0.048,0.029,0.056,0.047,0.023,0.031,0.018,0.017,0.041,0.033,0.013,0.018,0.023,0.038,0.036,0.027,0.022,0.014,0.023,0.019,0.018,0.02,0.02,0.019,0.027,0.026,0.017,0.017,0.015,0.017,0.026,0.02,0.021,0.034,0.032,0.016,0.017,0.021,0.024,0.021,0.022,0.02,0.022,0.022,0.017,0.018,1866,4964,2682,2615,1848,3071,2969,4252,1840,2337,795.0,3030,2868,873,3435,11930,21748,2578,4756,4555,3912,5271,1566,5804,7483,6706,7276,3824,6588,6672,2384,1391,1408,7602,2815,2031,7851,7690,359,508,1413,2329,6537,2754,4162,1603,1011,2876,2696,1652,537,3888,1993,5392,370,5289,1652,2837,1310,1608,698,3233,568,894,2485,1522,4377,2775,2358,1461,1530,2028,8661,723,0.119,0.113,0.098,0.099,0.139,0.14,0.115,0.127,0.167,0.15,0.13,0.137,0.119,0.153,0.126,0.151,0.136,0.122,0.138,0.139,0.098,0.127,0.127,0.15,0.127,0.129,0.117,0.123,0.133,0.125,0.166,0.118,0.091,0.124,0.09,0.089,0.155,0.137,0.095,0.101,0.108,0.132,0.147,0.122,0.103,0.091,0.108,0.081,0.09,0.103,0.107,0.096,0.11,0.111,0.098,0.099,0.094,0.095,0.119,0.107,0.116,0.139,0.134,0.106,0.099,0.11,0.113,0.103,0.102,0.106,0.111,0.125,0.098,0.102,563,1635,1021,911,803,916,914,1248,529,594,249.0,1358,794,217,886,4079,5901,567,1244,1895,756,1529,763,1992,2032,2026,2566,1528,1772,1987,807,579,396,1573,633,734,2027,1936,205,241,756,1106,1264,1808,2232,800,452,1142,1260,719,298,1706,883,2432,160,2622,823,1273,756,882,360,1317,363,494,1175,1117,2286,1291,1049,650,791,937,3876,391,3.27,2.733,2.6,2.683,2.2,2.611,2.585,2.771,2.749,2.952,2.741,1.883,2.89,2.702,2.828,2.373,2.891,3.625,2.887,2.03,3.492,2.516,1.786,2.443,2.668,2.712,2.262,2.014,2.801,2.555,2.237,2.444,2.659,3.304,3.837,2.456,2.807,2.815,2.116,2.614,2.298,1.785,3.536,1.726,2.097,2.166,2.722,3.036,2.601,2.815,1.987,2.388,2.413,2.405,2.443,2.13,2.166,2.369,1.998,2.0,2.108,2.501,1.907,2.117,2.248,1.68,1.951,2.392,2.385,2.473,2.196,2.402,2.369,2.328,0.558,0.548,0.472,0.526,0.562,0.695,0.773,0.511,0.782,0.733,0.842,0.522,0.415,0.644,0.647,0.703,0.739,0.612,0.558,0.651,0.936,0.599,0.541,0.766,0.675,0.485,0.521,0.566,0.697,0.61,0.737,0.834,0.274,0.691,0.676,0.553,0.8,0.768,0.331,0.268,0.425,0.591,0.821,0.51,0.635,0.466,0.351,0.64,0.457,0.439,0.498,0.424,0.434,0.439,0.407,0.449,0.459,0.484,0.417,0.398,0.408,0.507,0.405,0.59,0.499,0.486,0.454,0.448,0.439,0.574,0.402,0.506,0.465,0.378
-124,Epileptic,M,27.0,0.6,2.5,0.7,1.5,2.1,1.5,1.7,1.8,1.3,0.8,0.2,2.8,1.6,0.4,1.2,5.9,9.0,0.7,1.3,3.1,1.1,4.2,2.2,3.6,5.6,2.7,2.6,1.8,2.1,1.8,1.1,1.6,0.3,1.8,0.3,1.1,4.2,2.7,0.1,0.2,0.7,2.8,2.1,2.0,2.4,1.0,0.4,0.7,1.0,0.8,0.5,1.7,1.3,2.3,0.4,1.9,1.0,2.0,1.2,1.2,0.5,1.4,0.3,0.3,1.3,0.8,1.4,1.3,0.9,0.4,1.0,1.2,4.5,0.3,6,25,6,12,19,14,19,19,16,7,3.0,28,16,4,13,58,84,7,19,32,11,40,17,35,48,29,23,22,21,21,13,12,4,24,2,12,45,33,1,2,4,22,24,15,17,9,2,4,6,7,3,11,9,17,3,14,6,14,8,9,3,16,3,4,9,11,11,12,5,5,8,9,41,2,0.029,0.027,0.017,0.025,0.031,0.027,0.023,0.03,0.035,0.043,0.024,0.036,0.029,0.021,0.026,0.03,0.026,0.029,0.026,0.042,0.022,0.036,0.037,0.038,0.047,0.028,0.032,0.026,0.021,0.029,0.028,0.076,0.027,0.026,0.013,0.023,0.04,0.027,0.017,0.017,0.015,0.039,0.032,0.027,0.021,0.03,0.018,0.013,0.016,0.019,0.023,0.02,0.022,0.017,0.02,0.019,0.027,0.03,0.023,0.02,0.019,0.027,0.024,0.018,0.018,0.023,0.019,0.02,0.018,0.013,0.026,0.016,0.018,0.013,1116,4262,2087,2452,2280,2757,3214,3111,2289,1089,629.0,2526,3201,1048,2540,11321,17722,1944,2952,3782,3099,4422,2198,5575,4861,4532,3145,3567,3513,3481,2135,788,766,4828,1460,2637,7445,7043,289,400,1328,2202,5136,1955,2983,1509,798,1739,1866,1496,423,2678,2373,5010,533,2644,1119,2322,1127,1385,705,2125,362,793,1951,1093,2038,2761,1098,665,1087,2415,8700,542,0.113,0.126,0.092,0.12,0.134,0.121,0.12,0.14,0.138,0.162,0.111,0.162,0.13,0.123,0.126,0.131,0.122,0.115,0.132,0.162,0.099,0.108,0.136,0.146,0.131,0.121,0.113,0.12,0.095,0.125,0.115,0.153,0.102,0.131,0.07,0.116,0.147,0.125,0.073,0.099,0.087,0.133,0.136,0.126,0.107,0.138,0.097,0.081,0.095,0.113,0.135,0.107,0.105,0.094,0.127,0.109,0.121,0.132,0.116,0.104,0.096,0.132,0.155,0.102,0.097,0.111,0.107,0.103,0.091,0.124,0.133,0.105,0.099,0.097,387,1630,765,956,1065,861,1169,1009,621,334,195.0,1275,911,250,745,3271,5568,453,825,1436,782,1436,959,1512,1721,1557,1414,1102,1474,1125,624,346,205,1159,407,834,1939,1633,127,210,678,1128,1232,1201,1751,571,331,834,926,664,258,1328,1121,2105,240,1414,563,1095,734,892,375,831,196,380,1079,577,1102,1027,770,391,548,1148,4131,269,3.063,2.581,2.63,2.497,1.938,2.495,2.202,2.699,2.672,2.828,2.522,1.736,2.651,2.958,2.606,2.59,2.581,3.373,2.652,2.125,3.024,2.429,1.955,2.815,2.301,2.417,1.957,2.453,1.972,2.473,2.591,2.63,2.663,2.992,3.026,2.67,2.902,2.968,2.613,2.238,2.406,1.782,3.116,1.814,1.891,2.342,2.792,2.585,2.498,2.483,2.039,2.177,2.149,2.367,2.616,2.045,2.347,2.451,1.763,1.733,2.019,2.504,1.921,2.379,2.07,2.143,2.004,2.544,1.654,1.977,2.288,2.42,2.289,2.535,0.823,0.503,0.478,0.568,0.577,0.773,0.583,0.441,0.551,0.451,0.746,0.51,0.415,0.488,0.503,0.639,0.643,0.564,0.551,0.716,0.82,0.595,0.615,0.63,0.717,0.677,0.516,0.492,0.627,0.65,0.56,0.753,0.428,0.503,0.674,0.517,0.681,0.57,0.276,0.337,0.414,0.511,0.586,0.567,0.466,0.628,0.538,0.544,0.424,0.466,0.44,0.438,0.5,0.536,0.473,0.407,0.454,0.435,0.327,0.327,0.472,0.665,0.389,0.616,0.515,0.676,0.434,0.726,0.52,0.427,0.37,0.439,0.404,0.46
-125,Epileptic,F,32.0,0.8,1.8,0.5,1.2,1.3,1.8,1.6,1.9,1.0,0.8,0.2,2.5,2.0,0.6,1.2,5.7,7.6,0.4,2.4,3.9,0.9,2.2,1.5,3.0,2.8,4.7,3.0,2.7,2.5,3.1,0.9,1.4,0.7,2.6,0.2,1.1,4.5,3.3,0.1,0.2,0.9,3.2,2.2,1.9,2.3,0.8,0.3,0.5,0.9,1.1,0.5,1.5,1.2,1.9,0.1,1.6,2.0,1.5,0.8,1.2,0.3,0.9,0.1,0.4,1.4,0.8,2.3,1.1,0.8,0.3,0.8,2.1,5.4,0.4,5,20,7,13,13,17,13,16,9,8,2.0,20,20,7,14,54,75,2,22,32,7,26,15,36,33,45,36,27,20,31,10,12,7,34,1,8,41,38,1,1,4,24,19,16,14,6,1,3,4,8,4,12,8,13,1,13,12,12,7,10,2,7,1,3,11,12,18,7,6,2,5,11,39,3,0.036,0.023,0.01,0.022,0.038,0.032,0.026,0.033,0.051,0.039,0.03,0.034,0.037,0.033,0.036,0.036,0.03,0.022,0.032,0.033,0.022,0.037,0.029,0.048,0.034,0.037,0.031,0.028,0.03,0.033,0.034,0.059,0.024,0.031,0.008,0.022,0.047,0.035,0.013,0.018,0.019,0.038,0.044,0.025,0.022,0.022,0.018,0.01,0.014,0.025,0.03,0.016,0.027,0.018,0.017,0.018,0.037,0.02,0.024,0.026,0.019,0.026,0.013,0.025,0.019,0.024,0.021,0.018,0.019,0.015,0.024,0.036,0.019,0.023,1685,4287,1818,2439,2092,3533,2641,3480,1683,1537,324.0,2531,4362,1162,2946,11073,17867,2002,4270,4110,2621,4038,2369,5174,5091,8588,4930,4327,4897,5195,1624,1165,1457,6668,1157,2237,5008,7084,256,379,2100,3021,3867,2240,2947,1673,623,2022,2182,2098,567,3596,1806,4773,263,2682,1666,2982,910,1346,520,1618,315,806,2847,809,3934,2269,2131,667,1455,2065,10754,548,0.124,0.111,0.093,0.116,0.14,0.137,0.109,0.131,0.157,0.146,0.098,0.13,0.14,0.143,0.126,0.145,0.131,0.092,0.13,0.132,0.096,0.148,0.121,0.153,0.141,0.148,0.139,0.125,0.122,0.141,0.136,0.144,0.125,0.127,0.059,0.108,0.135,0.13,0.083,0.09,0.09,0.135,0.143,0.115,0.108,0.115,0.097,0.068,0.084,0.107,0.141,0.093,0.112,0.094,0.132,0.105,0.138,0.104,0.131,0.125,0.102,0.12,0.105,0.12,0.102,0.13,0.107,0.099,0.098,0.092,0.115,0.138,0.095,0.12,406,1363,731,875,612,1052,888,966,369,368,114.0,1048,971,256,655,2726,4468,306,1180,1740,664,1176,867,1261,1467,2205,1611,1473,1281,1532,427,377,449,1476,337,723,1711,1567,145,159,772,1308,851,1323,1699,610,241,884,939,738,297,1518,743,1753,114,1390,812,1287,486,715,247,553,140,293,1242,530,1808,964,727,298,571,892,4232,279,3.67,2.741,2.4,2.532,2.531,2.796,2.434,3.085,3.028,3.369,2.664,2.057,3.248,2.828,2.994,2.891,3.084,4.209,2.798,2.016,3.275,2.551,2.231,2.967,2.617,3.1,2.368,2.301,2.91,2.628,2.828,2.743,2.79,3.169,2.69,2.877,2.65,3.248,2.012,2.74,3.168,2.075,3.536,1.904,1.963,2.605,3.293,2.658,2.665,2.984,2.365,2.313,2.43,2.763,2.045,2.085,2.412,2.547,2.036,2.055,2.459,3.116,2.23,2.751,2.604,1.889,2.292,2.661,3.016,2.374,2.739,2.713,2.651,2.345,1.081,0.71,0.588,0.545,0.714,0.621,0.551,0.757,0.744,0.52,0.779,0.531,0.603,0.586,0.668,0.576,0.673,0.802,0.564,0.603,0.839,0.569,0.48,0.599,0.694,0.732,0.489,0.512,0.725,0.706,0.738,0.99,0.583,0.619,0.552,0.661,0.795,0.563,0.398,0.437,0.869,0.733,0.785,0.676,0.714,0.652,0.475,0.753,0.669,0.685,0.584,0.649,0.631,0.649,0.62,0.403,0.398,0.638,0.418,0.424,0.451,0.892,0.458,1.018,0.762,0.634,0.546,0.482,0.576,0.557,0.554,0.63,0.59,0.535
-126,Epileptic,F,47.0,0.8,2.4,1.2,1.4,1.8,2.3,1.9,1.6,0.9,1.0,0.4,3.2,1.4,0.7,1.6,6.1,7.7,0.8,2.0,4.4,1.2,3.3,2.1,2.9,2.6,4.3,3.0,2.6,2.6,3.3,1.4,1.4,0.6,2.3,0.3,0.7,2.5,2.8,0.2,0.1,1.5,2.7,2.1,3.1,2.6,0.5,0.4,0.8,1.3,0.8,0.6,1.7,1.4,3.8,0.3,1.3,0.5,2.0,1.0,1.3,0.5,1.3,0.2,0.5,2.2,1.3,2.8,1.2,1.0,0.5,1.3,1.1,3.7,0.4,8,26,11,11,19,19,14,14,13,11,4.0,31,15,10,14,70,65,5,16,40,10,41,19,36,30,40,31,26,21,43,25,12,7,25,1,6,28,29,1,1,9,25,21,25,18,4,2,7,7,9,5,13,9,32,2,12,4,19,7,12,4,10,2,4,18,14,22,7,6,6,7,9,30,4,0.032,0.022,0.019,0.024,0.04,0.032,0.035,0.029,0.034,0.031,0.029,0.039,0.029,0.039,0.039,0.037,0.027,0.028,0.033,0.037,0.021,0.03,0.035,0.038,0.028,0.033,0.028,0.027,0.024,0.042,0.038,0.055,0.026,0.033,0.012,0.015,0.037,0.033,0.016,0.013,0.029,0.032,0.036,0.028,0.02,0.015,0.02,0.013,0.019,0.02,0.029,0.017,0.02,0.023,0.021,0.017,0.014,0.026,0.022,0.023,0.024,0.028,0.031,0.022,0.024,0.041,0.023,0.019,0.019,0.017,0.029,0.022,0.018,0.015,1587,4416,2211,2137,2437,3062,2178,2964,1585,1608,726.0,2846,3074,1158,2641,10810,16042,2064,2816,4290,3507,4794,2286,5200,4542,6303,4991,4182,5760,5270,2340,1402,1162,5337,1327,1819,4853,5652,297,359,1704,2540,4812,2923,3186,1043,679,1963,2278,1562,432,2774,2080,6688,315,2566,1127,2976,1321,1875,647,2214,271,871,2860,1191,3613,2378,1689,929,1339,1830,7838,527,0.125,0.119,0.11,0.113,0.139,0.141,0.133,0.127,0.147,0.147,0.125,0.16,0.14,0.12,0.145,0.143,0.12,0.12,0.129,0.139,0.102,0.147,0.142,0.138,0.139,0.145,0.131,0.118,0.106,0.141,0.146,0.149,0.134,0.135,0.074,0.09,0.132,0.131,0.09,0.108,0.123,0.141,0.142,0.127,0.104,0.091,0.117,0.088,0.098,0.107,0.146,0.102,0.108,0.111,0.133,0.097,0.091,0.129,0.117,0.12,0.118,0.122,0.142,0.125,0.121,0.129,0.118,0.103,0.097,0.104,0.131,0.116,0.093,0.106,469,1611,906,902,859,1151,883,1039,465,492,255.0,1379,827,311,729,3057,4611,426,972,1986,898,1815,1006,1397,1546,2165,1792,1568,1629,1777,652,486,383,1320,343,747,1299,1403,188,143,819,1317,1103,1762,2028,556,297,903,1097,680,283,1402,1029,2626,191,1298,550,1302,749,900,348,748,128,401,1411,547,1854,925,832,466,640,851,3357,363,3.412,2.663,2.535,2.356,2.41,2.384,2.037,2.534,2.514,2.825,2.582,1.882,2.976,2.54,2.716,2.645,2.803,3.578,2.502,1.898,3.04,2.284,2.06,2.813,2.365,2.459,2.247,2.132,2.672,2.397,2.694,2.729,2.292,2.897,3.198,2.364,2.828,2.979,1.896,2.57,2.639,1.81,3.095,1.912,1.799,1.972,3.078,2.607,2.532,2.51,1.769,2.108,2.237,2.413,2.113,2.073,2.442,2.569,2.102,2.123,2.248,2.983,2.15,2.583,2.216,2.41,2.194,2.847,2.464,2.21,2.5,2.573,2.433,1.809,0.808,0.492,0.505,0.447,0.612,0.59,0.564,0.552,0.464,0.582,0.625,0.403,0.542,0.735,0.64,0.555,0.539,0.645,0.514,0.528,0.603,0.424,0.379,0.594,0.497,0.467,0.463,0.526,0.502,0.552,0.689,0.974,0.497,0.702,0.487,0.495,0.712,0.479,0.274,0.612,0.637,0.459,0.79,0.686,0.526,0.508,0.411,0.518,0.468,0.487,0.341,0.558,0.503,0.503,0.323,0.483,0.433,0.749,0.282,0.346,0.359,0.757,0.547,0.972,0.476,0.75,0.441,0.472,0.384,0.459,0.374,0.473,0.522,0.299
-127,Epileptic,F,47.0,0.7,2.4,1.0,1.5,1.5,1.8,1.7,1.9,1.5,0.5,0.3,2.6,1.9,0.7,1.6,7.5,11.2,0.5,2.3,4.1,1.5,2.2,1.8,4.4,5.4,4.6,3.8,3.5,3.8,4.5,1.8,0.6,0.6,3.8,0.7,0.8,2.5,2.9,0.1,0.2,1.4,2.8,2.6,2.1,2.8,0.7,0.4,0.9,1.3,0.9,0.5,2.0,1.0,2.3,0.2,2.4,0.6,1.5,0.6,1.2,0.6,1.3,0.1,0.7,1.7,1.5,2.7,1.6,0.9,0.5,1.0,1.8,4.9,0.3,4,20,6,9,12,20,14,16,14,4,3.0,22,17,6,11,61,85,4,26,31,11,28,16,32,42,44,36,27,25,47,17,9,5,33,5,6,32,33,1,1,7,21,21,13,17,5,2,4,7,5,4,14,7,15,1,15,4,15,4,10,6,8,1,6,18,9,19,10,6,4,7,14,39,3,0.046,0.037,0.02,0.031,0.04,0.031,0.036,0.034,0.061,0.035,0.043,0.051,0.049,0.059,0.043,0.056,0.049,0.033,0.04,0.049,0.042,0.042,0.039,0.06,0.059,0.05,0.04,0.05,0.05,0.049,0.066,0.044,0.035,0.056,0.049,0.026,0.044,0.044,0.017,0.027,0.031,0.046,0.061,0.028,0.033,0.019,0.027,0.015,0.023,0.035,0.025,0.027,0.036,0.028,0.025,0.022,0.022,0.023,0.026,0.029,0.028,0.037,0.038,0.035,0.027,0.048,0.028,0.032,0.028,0.021,0.032,0.034,0.022,0.018,1129,3630,1540,1762,1465,2613,2236,2992,1466,1051,612.0,1719,2170,756,2143,7646,13717,1644,3864,3094,2307,3094,1805,4213,4169,5574,4928,2648,4456,4724,1744,647,794,4978,1263,1492,4757,4617,285,417,1229,2021,3784,1692,2086,1224,491,2010,1949,1226,545,2486,893,2554,224,2989,975,1912,572,1048,584,1577,175,675,1952,965,2448,1452,906,803,949,1597,7046,543,0.121,0.131,0.109,0.124,0.154,0.153,0.123,0.153,0.186,0.139,0.131,0.173,0.169,0.17,0.147,0.167,0.162,0.105,0.162,0.167,0.143,0.14,0.144,0.178,0.161,0.162,0.139,0.146,0.142,0.179,0.158,0.125,0.12,0.179,0.13,0.114,0.176,0.161,0.113,0.113,0.123,0.163,0.19,0.121,0.128,0.105,0.114,0.081,0.107,0.141,0.139,0.114,0.136,0.12,0.129,0.112,0.115,0.116,0.13,0.133,0.127,0.136,0.123,0.137,0.132,0.142,0.132,0.13,0.128,0.105,0.142,0.164,0.113,0.124,261,1234,661,739,614,903,900,936,485,246,155.0,881,759,214,671,2616,4313,310,1093,1334,663,996,742,1335,1670,1897,1743,1181,1333,1717,607,248,283,1292,319,514,1240,1350,139,173,691,936,860,1163,1431,610,231,794,884,406,307,1301,484,1352,113,1626,468,1092,370,632,357,619,101,339,1088,448,1428,836,519,362,528,742,3266,281,3.577,2.631,2.312,2.372,2.146,2.344,2.107,2.758,2.506,3.159,3.158,1.717,2.328,2.818,2.466,2.244,2.521,3.686,2.751,1.997,2.699,2.436,2.084,2.522,2.078,2.394,2.199,1.821,2.433,2.254,2.307,2.391,2.182,2.792,2.851,2.532,2.903,2.59,2.294,2.449,2.302,1.933,2.951,1.726,1.674,2.156,2.485,3.048,2.651,2.771,1.992,2.071,2.165,2.0,2.18,2.003,2.353,1.846,1.754,1.924,1.951,2.693,1.779,2.144,1.903,2.413,1.876,1.976,1.932,2.364,1.955,2.353,2.396,2.299,1.107,0.787,0.675,0.565,0.579,0.664,0.488,0.557,0.798,0.562,0.912,0.467,0.472,0.51,0.639,0.569,0.609,0.845,0.635,0.695,0.759,0.513,0.559,0.696,0.694,0.586,0.653,0.58,0.716,0.571,0.749,0.95,0.408,0.607,0.685,0.596,0.681,0.664,0.477,0.599,0.473,0.653,0.799,0.489,0.469,0.572,0.496,1.045,0.58,0.762,0.349,0.447,0.583,0.422,0.493,0.446,0.335,0.532,0.363,0.521,0.361,0.806,0.602,0.598,0.475,0.696,0.435,0.485,0.379,0.727,0.527,0.509,0.503,0.46
-128,Epileptic,F,17.5,0.7,2.4,0.7,1.2,1.0,1.9,1.6,1.4,1.1,0.6,0.3,2.9,1.8,0.6,1.7,6.8,7.5,0.6,2.6,5.8,1.0,3.5,1.7,2.8,2.7,2.4,2.6,2.8,2.4,3.1,0.9,0.7,0.4,2.0,0.5,0.5,3.8,4.0,0.1,0.1,0.9,3.8,2.4,2.4,2.9,0.6,0.4,0.8,0.9,0.6,0.4,2.2,2.6,2.2,0.3,1.9,0.6,1.8,1.0,1.5,0.6,1.2,0.3,0.3,1.6,1.2,2.2,1.3,1.0,0.4,1.1,2.1,4.3,0.1,4,23,6,10,11,18,16,13,12,6,3.0,24,18,6,18,74,66,4,28,39,6,41,16,31,35,27,31,29,23,34,12,6,3,29,3,6,43,44,1,0,4,30,19,18,15,5,2,4,5,8,5,14,20,14,2,14,4,14,7,9,4,9,2,3,10,14,18,10,6,3,7,14,35,0,0.033,0.024,0.012,0.027,0.026,0.033,0.025,0.027,0.047,0.032,0.029,0.034,0.032,0.043,0.032,0.039,0.027,0.024,0.038,0.045,0.019,0.035,0.036,0.035,0.03,0.028,0.027,0.029,0.023,0.031,0.026,0.028,0.024,0.024,0.014,0.019,0.032,0.033,0.014,0.019,0.018,0.034,0.042,0.026,0.019,0.014,0.02,0.011,0.011,0.019,0.024,0.018,0.035,0.022,0.024,0.019,0.014,0.019,0.021,0.021,0.018,0.021,0.034,0.014,0.022,0.033,0.02,0.019,0.019,0.015,0.026,0.025,0.017,0.011,1422,6509,2234,2488,1935,3277,2950,3988,2182,1304,668.0,3030,3938,1211,3192,13771,19899,2098,4458,4448,2968,5826,2057,6809,5630,6133,4718,4398,6151,6283,2347,1158,994,6100,2485,1528,8673,9729,356,326,1868,3388,5770,2586,3831,1556,801,2800,2528,1601,566,3986,2600,4554,341,3299,1633,3276,1458,1894,1168,2416,485,969,2571,1170,3415,2539,1683,949,1672,2965,11246,267,0.111,0.113,0.089,0.115,0.126,0.152,0.116,0.118,0.159,0.14,0.109,0.138,0.132,0.15,0.135,0.148,0.127,0.101,0.139,0.135,0.075,0.146,0.135,0.133,0.132,0.119,0.13,0.124,0.113,0.141,0.112,0.097,0.11,0.12,0.065,0.093,0.127,0.121,0.088,0.082,0.095,0.14,0.144,0.113,0.097,0.089,0.102,0.07,0.075,0.099,0.127,0.1,0.137,0.1,0.124,0.097,0.093,0.098,0.114,0.109,0.102,0.11,0.118,0.107,0.107,0.129,0.11,0.101,0.102,0.104,0.119,0.121,0.092,0.08,393,1701,735,809,641,1008,1107,954,445,323,168.0,1348,1063,241,864,3341,4596,380,1194,2020,825,1806,887,1441,1718,1568,1596,1627,1590,1796,577,420,282,1298,613,563,2052,1991,182,94,694,1793,991,1584,2235,736,304,944,1081,629,299,1726,1160,1723,189,1587,651,1522,734,1009,482,856,188,374,1114,650,1521,1062,835,377,668,1341,4210,100,3.263,3.227,2.821,2.984,2.453,2.817,2.23,3.567,3.214,3.342,2.762,2.03,3.005,3.232,2.679,2.959,3.354,3.626,2.898,1.922,2.843,2.567,2.106,3.254,2.556,3.061,2.376,2.239,2.831,2.729,2.799,2.612,3.173,3.11,3.497,2.617,3.139,3.576,2.374,3.567,3.168,1.799,3.748,1.85,1.975,2.37,3.273,3.35,2.766,2.8,2.065,2.422,2.282,2.58,2.498,2.213,2.854,2.579,2.258,2.158,2.778,2.588,2.957,2.875,2.542,2.303,2.358,2.549,2.566,2.82,2.741,2.691,2.759,2.58,1.047,0.782,0.679,0.619,0.786,0.658,0.608,0.765,0.728,0.592,0.839,0.642,0.578,0.443,0.721,0.731,0.69,0.868,0.66,0.597,0.691,0.617,0.455,0.707,0.517,0.641,0.488,0.544,0.486,0.644,0.708,1.09,0.658,0.854,0.813,0.571,0.772,0.745,0.36,0.662,0.812,0.404,0.758,0.694,0.581,0.5,0.602,0.923,0.665,0.534,0.6,0.584,0.609,0.691,0.557,0.481,0.466,0.572,0.571,0.388,0.576,0.825,0.496,1.04,0.658,0.708,0.536,0.455,0.446,1.038,0.654,0.584,0.631,0.493
-129,Epileptic,F,42.0,0.9,2.3,0.9,1.2,2.3,1.7,1.3,1.5,1.1,0.7,0.5,4.2,1.2,0.9,1.2,5.1,8.8,0.9,2.1,5.8,0.8,3.0,3.4,3.1,2.4,2.6,4.3,2.3,3.4,2.5,2.0,1.6,0.5,2.1,0.4,0.3,3.2,2.3,0.2,0.1,1.0,2.6,2.2,4.0,2.8,0.6,0.3,0.7,1.1,0.8,0.7,1.7,2.5,2.4,0.5,2.5,0.9,1.3,1.0,1.3,1.4,1.4,0.4,0.5,2.4,1.3,2.8,1.2,1.0,0.6,0.9,1.4,3.1,0.3,9,25,8,10,37,20,15,16,22,8,10.0,40,13,9,12,59,93,8,27,53,16,35,26,35,38,34,48,22,57,29,24,22,4,28,2,2,39,31,2,1,5,24,21,32,17,5,1,5,7,5,7,18,22,14,2,22,11,10,9,13,11,13,3,5,23,19,21,8,7,9,10,10,29,2,0.042,0.027,0.018,0.024,0.074,0.031,0.024,0.027,0.055,0.029,0.047,0.04,0.027,0.058,0.036,0.038,0.031,0.036,0.029,0.04,0.026,0.03,0.036,0.042,0.028,0.027,0.034,0.025,0.026,0.033,0.051,0.105,0.027,0.029,0.012,0.008,0.035,0.025,0.019,0.018,0.021,0.03,0.04,0.026,0.022,0.012,0.021,0.012,0.018,0.026,0.025,0.022,0.04,0.021,0.041,0.016,0.022,0.015,0.025,0.021,0.033,0.031,0.034,0.023,0.026,0.028,0.023,0.019,0.02,0.023,0.025,0.018,0.015,0.015,1365,3849,1820,2344,1864,2388,2505,2895,1634,1482,688.0,3461,2980,1216,2567,9222,17655,2011,4750,6232,2517,4316,2351,4782,5081,5884,5499,4293,6558,4861,2127,886,1081,5293,1876,1634,6616,6234,365,288,1414,1944,4713,3689,3380,1414,589,1813,1758,1238,733,2841,2603,3897,480,4434,1400,2712,996,1833,1183,2163,420,783,3118,1138,3597,2211,1745,1071,1456,2300,6642,484,0.136,0.134,0.107,0.108,0.168,0.13,0.103,0.128,0.161,0.134,0.157,0.163,0.124,0.195,0.144,0.146,0.134,0.136,0.137,0.153,0.094,0.135,0.135,0.144,0.12,0.135,0.113,0.107,0.109,0.13,0.167,0.166,0.106,0.142,0.073,0.067,0.142,0.136,0.115,0.11,0.107,0.127,0.154,0.127,0.106,0.089,0.093,0.082,0.102,0.117,0.137,0.116,0.148,0.104,0.143,0.101,0.129,0.102,0.131,0.119,0.138,0.132,0.138,0.127,0.126,0.139,0.119,0.104,0.105,0.138,0.134,0.101,0.095,0.106,445,1338,708,845,758,946,863,988,554,389,229.0,1837,831,281,654,2638,4982,437,1287,2666,667,1582,1241,1451,1440,1758,1894,1438,1720,1400,727,458,346,1200,479,581,1798,1520,167,148,707,1285,1074,2397,2047,723,213,811,886,510,381,1304,1090,1805,196,2279,651,1296,620,983,618,809,225,440,1505,717,1759,988,805,469,659,1194,3105,263,2.768,2.597,2.581,2.674,2.173,2.168,2.381,2.602,2.344,2.921,2.762,1.685,2.722,3.055,2.89,2.625,2.811,3.469,2.927,2.043,2.86,2.228,1.811,2.5,2.659,2.721,2.345,2.34,2.9,2.659,2.348,2.209,2.423,3.018,3.207,2.607,2.681,2.894,2.416,2.236,2.537,1.462,3.221,1.765,1.868,2.121,2.929,2.841,2.558,2.741,1.926,2.231,2.211,2.359,2.757,2.161,2.287,2.361,1.807,2.058,1.981,2.607,1.984,2.135,2.187,1.87,2.332,2.537,2.373,2.562,2.217,2.276,2.259,2.255,0.798,0.644,0.523,0.496,0.602,0.617,0.617,0.47,0.508,0.589,0.562,0.559,0.52,0.438,0.573,0.628,0.58,0.981,0.631,0.688,0.705,0.616,0.541,0.922,0.575,0.475,0.547,0.583,0.557,0.475,0.799,0.772,0.452,0.636,0.669,0.48,0.734,0.793,0.429,0.353,0.497,0.401,0.716,0.583,0.564,0.408,0.51,0.666,0.44,0.569,0.548,0.472,0.504,0.398,0.511,0.369,0.426,0.453,0.425,0.439,0.421,0.741,0.439,0.707,0.45,0.574,0.413,0.386,0.435,0.642,0.465,0.508,0.441,0.384
-130,Epileptic,M,37.0,0.9,2.8,1.5,1.6,1.7,2.9,1.5,2.3,1.3,1.0,0.4,3.6,1.6,0.7,1.1,6.9,8.7,1.0,2.6,5.5,1.7,3.6,1.7,3.8,3.5,5.0,4.2,3.2,3.1,3.2,1.8,1.5,0.7,3.1,0.4,0.8,4.2,3.6,0.3,0.2,1.4,4.1,2.4,3.6,3.6,1.1,0.7,1.0,1.5,0.6,1.1,1.6,2.3,3.0,0.8,2.8,0.8,2.7,0.9,1.0,1.3,1.7,0.5,0.3,2.0,1.7,3.5,1.3,1.3,0.5,1.1,1.3,6.1,0.3,5,26,16,9,17,25,13,24,14,8,3.0,30,19,8,13,73,78,5,25,41,14,41,18,39,43,55,48,31,30,37,21,8,7,37,3,8,42,39,3,2,8,36,27,25,22,8,3,7,8,4,7,13,18,25,5,24,4,18,7,9,11,13,3,4,15,17,30,8,9,5,6,10,51,3,0.033,0.026,0.026,0.033,0.026,0.035,0.022,0.027,0.04,0.036,0.033,0.035,0.027,0.035,0.03,0.031,0.031,0.036,0.035,0.04,0.03,0.035,0.027,0.04,0.028,0.026,0.03,0.025,0.027,0.03,0.045,0.044,0.022,0.037,0.012,0.02,0.037,0.03,0.018,0.019,0.023,0.043,0.037,0.03,0.023,0.024,0.023,0.016,0.017,0.015,0.031,0.019,0.026,0.021,0.024,0.019,0.022,0.027,0.019,0.018,0.035,0.024,0.02,0.015,0.022,0.03,0.019,0.016,0.018,0.014,0.026,0.018,0.024,0.017,1848,4917,2239,1987,2667,4221,2749,4803,2129,1793,655.0,3407,3834,1483,1674,12056,18170,2190,5307,4429,3731,5830,2140,6952,5592,9002,6563,5286,6854,5341,3058,1351,1373,6395,2233,2125,8268,7810,701,452,1865,3269,5882,2887,3663,1458,1078,2471,2802,1675,798,2953,3315,6002,808,4821,1693,3425,1400,1542,1671,3319,758,889,3022,1357,5888,2729,2238,1119,1239,2542,10235,382,0.114,0.121,0.129,0.122,0.121,0.146,0.103,0.124,0.146,0.131,0.134,0.142,0.129,0.144,0.143,0.138,0.127,0.126,0.135,0.141,0.113,0.148,0.125,0.143,0.13,0.123,0.135,0.112,0.112,0.135,0.149,0.125,0.11,0.146,0.069,0.098,0.144,0.132,0.106,0.122,0.106,0.157,0.143,0.129,0.109,0.117,0.111,0.087,0.094,0.1,0.135,0.1,0.124,0.107,0.124,0.108,0.101,0.12,0.113,0.114,0.139,0.117,0.107,0.105,0.107,0.123,0.107,0.09,0.098,0.101,0.122,0.106,0.112,0.119,440,1724,905,765,1066,1467,971,1545,577,465,206.0,1485,1020,350,593,3761,4808,428,1324,2271,1043,1860,1021,1794,1988,3073,2366,2041,1978,1891,709,526,512,1533,572,772,1902,2019,324,190,976,1524,1240,1882,2354,723,440,1005,1204,614,510,1414,1407,2397,451,2455,630,1617,770,816,684,1122,399,418,1436,816,2832,1225,1090,577,642,1171,4200,255,3.845,2.793,2.405,2.553,2.292,2.48,2.338,2.831,2.745,3.012,2.932,2.004,2.889,2.995,2.333,2.559,2.971,3.98,3.098,1.71,2.743,2.461,1.92,2.98,2.373,2.437,2.231,2.12,2.669,2.332,3.091,2.549,2.315,2.974,3.577,2.541,3.091,2.826,2.348,2.812,2.303,1.966,3.266,1.787,1.779,2.208,2.961,2.994,2.799,2.834,1.996,2.257,2.344,2.462,2.336,2.126,2.869,2.341,2.104,2.155,2.459,3.025,2.297,2.394,2.25,1.975,2.198,2.546,2.225,2.08,2.305,2.619,2.514,1.945,0.982,0.528,0.628,0.621,0.473,0.593,0.731,0.647,0.566,0.469,0.723,0.525,0.583,0.404,0.524,0.545,0.586,0.734,0.698,0.577,0.772,0.451,0.441,0.56,0.492,0.513,0.491,0.517,0.512,0.476,0.762,1.186,0.578,0.59,0.694,0.568,0.689,0.494,0.407,0.637,0.505,0.536,0.743,0.505,0.563,0.42,0.467,0.799,0.59,0.524,0.562,0.472,0.549,0.446,0.452,0.414,0.737,0.599,0.373,0.352,0.447,0.629,0.52,1.008,0.502,0.707,0.491,0.449,0.46,0.45,0.351,0.474,0.565,0.495
-131,Epileptic,M,22.0,1.6,2.4,1.0,1.2,1.8,2.5,2.5,2.1,1.0,0.9,0.4,2.3,2.0,0.3,1.5,7.0,10.7,1.1,2.9,5.2,1.3,3.4,1.7,4.0,3.3,3.8,3.3,2.9,3.3,3.9,1.4,1.9,0.5,2.4,0.6,0.9,2.7,3.6,0.2,0.2,1.1,2.8,2.2,2.2,3.0,0.7,0.4,1.1,1.4,0.7,0.9,2.6,1.6,2.4,0.3,2.6,0.9,1.8,1.4,0.8,0.5,1.4,0.4,0.2,2.0,1.4,3.8,2.0,0.9,0.5,0.8,1.4,4.9,0.3,11,21,7,10,15,23,19,19,10,10,3.0,23,17,4,13,68,87,10,30,43,14,42,16,35,39,38,38,24,27,42,20,12,8,29,3,10,44,41,2,1,7,30,24,17,20,5,2,6,7,6,5,18,11,16,2,25,5,14,9,7,4,13,2,2,16,16,30,15,5,4,5,12,38,3,0.046,0.018,0.012,0.02,0.03,0.031,0.033,0.028,0.032,0.032,0.039,0.03,0.031,0.022,0.03,0.033,0.028,0.033,0.03,0.036,0.025,0.032,0.033,0.036,0.025,0.026,0.025,0.025,0.027,0.036,0.04,0.056,0.027,0.025,0.014,0.019,0.029,0.031,0.017,0.012,0.02,0.038,0.038,0.021,0.022,0.015,0.018,0.016,0.014,0.014,0.028,0.019,0.018,0.017,0.018,0.018,0.02,0.017,0.021,0.014,0.02,0.023,0.016,0.012,0.017,0.031,0.021,0.021,0.017,0.021,0.02,0.016,0.016,0.015,2164,5949,2694,2519,2824,4130,3812,4432,1864,1815,852.0,3241,4698,1059,3411,15075,25795,3210,6021,5599,4847,7146,2297,7094,8381,8131,7266,5103,8301,7330,2313,1569,1363,7471,2478,2853,6772,9959,549,578,1891,3722,5868,3144,3833,1666,1062,2753,3115,1673,1144,5560,3335,6332,550,4866,1556,4169,1910,1639,1250,2951,700,912,3947,1554,6145,3937,1725,1223,1576,2903,12020,574,0.132,0.102,0.085,0.101,0.122,0.142,0.12,0.12,0.129,0.148,0.133,0.129,0.118,0.109,0.122,0.132,0.126,0.119,0.128,0.134,0.097,0.137,0.137,0.115,0.115,0.124,0.118,0.1,0.106,0.144,0.12,0.139,0.128,0.113,0.069,0.097,0.113,0.128,0.105,0.087,0.1,0.144,0.124,0.107,0.109,0.097,0.09,0.082,0.084,0.088,0.128,0.096,0.099,0.092,0.101,0.103,0.1,0.097,0.114,0.098,0.104,0.108,0.099,0.078,0.101,0.123,0.11,0.107,0.095,0.102,0.106,0.098,0.092,0.111,570,1969,1083,980,1007,1372,1191,1322,524,466,209.0,1283,1164,247,813,3870,6459,588,1598,2293,996,1976,909,1782,2146,2434,2251,1800,2039,2121,576,539,354,1647,639,863,1771,2148,191,244,893,1456,1241,1632,2227,746,385,1067,1362,756,497,2060,1344,2360,218,2300,660,1808,938,848,460,1049,343,419,1760,780,2818,1538,714,485,729,1400,4889,318,3.825,2.852,2.588,2.676,2.534,2.67,2.81,2.918,2.671,3.13,3.139,2.135,3.175,3.232,3.209,3.041,3.276,3.918,3.049,2.119,3.534,2.836,2.19,2.973,2.927,2.895,2.592,2.274,3.023,2.769,2.628,2.661,2.986,3.263,3.512,2.887,2.747,3.306,3.067,2.688,2.738,2.247,3.351,2.122,2.045,2.485,3.367,3.096,2.709,2.261,2.552,2.716,2.522,2.897,3.005,2.276,2.437,2.63,2.366,2.074,2.645,2.608,2.583,2.261,2.462,2.253,2.389,2.769,2.754,2.556,2.5,2.47,2.718,2.049,0.685,0.658,0.627,0.497,0.564,0.539,0.606,0.575,0.605,0.464,0.716,0.554,0.472,0.381,0.424,0.566,0.585,0.742,0.565,0.577,0.845,0.497,0.466,0.677,0.582,0.505,0.479,0.57,0.657,0.508,0.839,0.983,0.397,0.605,0.907,0.611,0.831,0.592,0.236,0.321,0.494,0.544,0.797,0.669,0.664,0.433,0.57,0.664,0.734,0.664,0.498,0.569,0.573,0.572,0.512,0.409,0.559,0.594,0.417,0.389,0.672,0.767,0.548,1.146,0.554,0.596,0.446,0.477,0.448,0.638,0.497,0.635,0.517,0.39
-132,Epileptic,M,47.0,1.1,3.5,2.2,2.3,1.7,3.2,2.4,3.2,1.2,1.4,0.6,2.6,3.0,1.2,4.6,8.7,13.3,1.0,4.4,6.2,1.6,4.1,1.5,5.2,4.5,6.4,5.6,6.0,6.6,5.5,1.5,1.5,0.9,4.3,1.1,2.1,6.2,5.4,0.4,0.4,2.5,4.8,3.1,5.3,6.0,1.3,0.9,1.3,2.1,1.4,1.5,3.7,2.1,4.9,0.7,3.3,1.3,3.1,1.3,1.8,0.8,2.1,1.3,0.3,3.1,1.7,6.4,2.1,2.8,0.7,2.7,2.5,8.5,0.7,8,31,18,17,14,25,16,25,10,12,4.0,20,24,11,30,63,84,8,34,40,13,36,15,40,44,51,46,40,36,42,20,9,7,34,9,16,54,46,2,2,14,39,25,28,35,8,4,22,11,11,8,26,14,30,6,27,9,19,10,15,7,13,7,3,19,16,47,12,17,8,14,13,55,4,0.031,0.027,0.026,0.03,0.028,0.036,0.039,0.031,0.036,0.037,0.043,0.034,0.032,0.04,0.045,0.035,0.036,0.03,0.037,0.039,0.024,0.031,0.03,0.041,0.028,0.032,0.033,0.039,0.037,0.034,0.033,0.036,0.029,0.032,0.02,0.026,0.037,0.034,0.021,0.018,0.035,0.04,0.034,0.043,0.036,0.025,0.027,0.019,0.019,0.023,0.055,0.025,0.025,0.025,0.016,0.021,0.02,0.021,0.025,0.029,0.019,0.037,0.045,0.018,0.034,0.024,0.029,0.022,0.03,0.018,0.031,0.023,0.023,0.037,1827,5799,3417,2912,2164,3701,2466,4116,1532,1984,675.0,2241,4981,1360,4687,12407,19801,2516,5875,4808,3866,6620,2320,6177,8118,9360,7957,5562,7108,6335,2361,1500,1251,7273,3560,3817,10404,10332,485,786,2267,4077,6326,3060,3680,1912,973,2749,3541,2543,604,5137,2710,6947,999,4838,2507,4952,1634,1864,1366,2456,670,984,2585,2132,6957,3088,2550,1506,2581,3028,15357,685,0.127,0.123,0.122,0.13,0.12,0.135,0.123,0.121,0.122,0.135,0.138,0.134,0.122,0.145,0.133,0.124,0.123,0.122,0.131,0.132,0.095,0.136,0.127,0.129,0.117,0.125,0.132,0.121,0.121,0.128,0.123,0.118,0.127,0.123,0.089,0.11,0.132,0.13,0.108,0.101,0.135,0.139,0.13,0.148,0.138,0.115,0.124,0.101,0.096,0.106,0.169,0.108,0.108,0.102,0.11,0.108,0.103,0.102,0.124,0.137,0.101,0.132,0.163,0.096,0.136,0.114,0.127,0.109,0.129,0.114,0.136,0.12,0.106,0.136,606,2081,1338,1271,1050,1482,1030,1642,589,650,226.0,1292,1574,517,1627,3924,5728,582,1908,2575,1119,2056,884,1851,2609,3253,2847,2375,2646,2634,706,691,552,2113,853,1280,2875,2704,290,375,1170,1945,1595,2013,2703,878,452,1196,1665,1004,457,2469,1344,3012,596,2555,1044,2296,921,1023,734,953,446,374,1521,1033,3589,1404,1484,641,1301,1478,5978,339,3.025,2.608,2.538,2.378,1.993,2.307,2.177,2.326,2.348,2.698,2.804,1.745,2.623,2.136,2.425,2.552,2.877,3.482,2.702,1.81,2.872,2.58,2.172,2.645,2.45,2.48,2.252,1.991,2.315,2.174,2.515,2.197,2.126,2.677,3.194,2.648,2.852,3.089,2.05,2.476,2.466,1.945,2.993,1.814,1.632,2.344,2.773,2.886,2.657,2.656,1.731,2.25,2.096,2.295,2.104,2.029,2.651,2.516,2.214,1.959,2.132,2.342,1.984,2.503,2.071,2.336,2.128,2.623,2.08,2.497,2.437,2.619,2.738,2.216,0.726,0.541,0.544,0.538,0.492,0.626,0.646,0.487,0.588,0.646,0.765,0.435,0.497,0.528,0.485,0.653,0.628,0.726,0.837,0.656,0.764,0.426,0.666,0.703,0.497,0.495,0.568,0.519,0.711,0.546,0.795,0.995,0.518,0.567,0.756,0.512,0.673,0.574,0.297,0.44,0.559,0.69,0.767,0.54,0.429,0.63,0.449,0.656,0.59,0.559,0.453,0.528,0.51,0.629,0.388,0.458,0.606,0.474,0.345,0.441,0.477,0.708,0.387,0.91,0.691,0.827,0.471,0.483,0.437,0.666,0.418,0.537,0.582,0.702
-133,Epileptic,M,37.0,0.8,2.1,0.8,1.3,1.4,1.9,1.5,2.3,1.0,1.0,0.3,2.6,2.0,0.4,1.5,5.6,9.7,1.0,2.3,3.7,2.8,2.5,1.6,2.9,2.9,4.9,2.8,2.9,2.8,3.2,1.3,2.1,0.5,2.7,0.6,0.4,3.6,3.2,0.1,0.3,0.9,2.5,2.7,3.1,2.4,1.1,0.3,1.1,1.4,0.7,0.8,2.4,1.0,2.4,0.6,3.4,0.5,1.4,0.8,1.0,0.5,1.4,0.5,0.3,1.5,1.8,2.7,1.3,1.9,0.6,0.8,1.5,4.8,0.4,7,21,10,11,14,17,13,19,12,11,4.0,26,21,7,22,64,98,10,28,31,22,31,16,30,37,51,34,31,26,40,18,14,5,28,3,3,41,38,1,2,5,22,27,20,16,9,2,9,8,6,4,18,7,16,4,26,4,10,6,7,3,11,2,3,9,21,24,11,11,5,6,13,39,3,0.026,0.017,0.01,0.02,0.026,0.027,0.022,0.028,0.035,0.031,0.032,0.034,0.029,0.024,0.03,0.029,0.024,0.027,0.031,0.034,0.04,0.031,0.027,0.028,0.03,0.03,0.021,0.026,0.021,0.029,0.03,0.055,0.023,0.027,0.019,0.012,0.034,0.028,0.014,0.018,0.019,0.028,0.038,0.031,0.018,0.019,0.013,0.016,0.014,0.014,0.028,0.02,0.016,0.015,0.027,0.018,0.013,0.017,0.022,0.019,0.016,0.023,0.024,0.015,0.02,0.025,0.019,0.016,0.021,0.019,0.019,0.02,0.017,0.017,1994,5211,2720,2803,2470,3442,2887,4394,1767,2222,542.0,2971,4432,1049,3082,12625,24153,3154,5247,5001,5318,5046,2754,7200,6524,9587,6168,4837,7444,6102,2618,1603,1075,7206,2199,1896,9002,8706,346,502,1626,3438,7442,2726,3743,2146,947,2473,3003,2014,672,4047,2039,6125,661,4984,1371,3152,1028,1591,920,2824,473,959,2593,1888,4696,3145,2865,1110,1634,2806,11111,590,0.112,0.109,0.095,0.106,0.129,0.132,0.102,0.129,0.136,0.145,0.136,0.152,0.127,0.142,0.144,0.131,0.117,0.119,0.134,0.13,0.122,0.152,0.135,0.129,0.137,0.134,0.113,0.111,0.094,0.139,0.125,0.14,0.113,0.128,0.092,0.079,0.138,0.133,0.079,0.106,0.1,0.128,0.146,0.13,0.096,0.102,0.088,0.089,0.086,0.091,0.129,0.109,0.098,0.087,0.125,0.111,0.091,0.099,0.119,0.1,0.104,0.112,0.122,0.097,0.101,0.123,0.109,0.1,0.111,0.112,0.113,0.115,0.098,0.113,534,1764,1168,1035,846,1185,1038,1336,524,527,174.0,1246,1257,273,890,3282,6615,640,1293,1807,1142,1483,986,1676,1789,2729,2245,1686,1996,1947,705,608,345,1537,521,655,1989,1893,178,248,782,1386,1379,1485,2107,917,396,1134,1408,760,385,1785,911,2366,342,2624,594,1311,534,825,423,965,257,428,1112,1029,2238,1297,1333,507,682,1201,4358,306,3.692,2.728,2.303,2.667,2.58,2.534,2.31,2.869,2.546,3.352,2.871,2.094,2.828,2.82,2.815,2.884,2.95,3.768,3.08,2.258,2.946,2.695,2.391,3.152,2.848,2.826,2.274,2.302,2.861,2.554,2.758,2.722,2.534,3.248,3.482,2.743,3.142,3.233,2.257,2.454,2.677,2.142,3.617,2.086,2.016,2.364,3.02,2.714,2.649,2.937,2.285,2.361,2.33,2.678,2.455,2.196,2.777,2.69,2.342,2.261,2.504,2.902,2.262,2.679,2.484,2.323,2.28,2.6,2.617,2.523,2.76,2.666,2.705,2.436,0.596,0.614,0.544,0.45,0.563,0.595,0.546,0.518,0.633,0.556,0.715,0.486,0.45,0.433,0.457,0.546,0.584,0.802,0.547,0.728,1.095,0.453,0.411,0.638,0.541,0.5,0.495,0.523,0.501,0.506,0.655,0.921,0.42,0.635,0.735,0.446,0.737,0.645,0.3,0.277,0.466,0.599,0.736,0.811,0.552,0.571,0.513,0.586,0.491,0.61,0.424,0.458,0.392,0.583,0.369,0.422,0.436,0.548,0.366,0.521,0.326,0.737,0.413,0.951,0.64,0.799,0.549,0.377,0.417,0.574,0.57,0.619,0.449,0.491
-134,Epileptic,M,27.0,0.9,2.2,1.4,1.6,1.3,1.9,2.2,2.0,1.0,1.6,0.4,2.7,1.7,0.5,1.2,4.5,10.9,1.1,2.6,5.1,1.5,2.8,1.1,3.5,4.3,4.7,4.0,3.0,2.4,3.7,1.4,1.0,0.4,2.3,0.4,0.4,3.0,3.9,0.2,0.2,1.3,3.2,2.3,3.0,2.7,0.7,0.4,1.0,1.8,0.9,0.6,2.0,1.2,2.6,0.3,2.5,0.7,1.8,1.6,1.1,1.1,1.6,0.2,0.9,1.5,1.6,3.5,1.2,1.3,0.7,1.2,1.2,5.1,0.3,11,19,10,11,16,20,14,19,10,12,4.0,26,23,5,14,56,103,10,29,45,10,31,12,42,44,55,44,28,24,40,16,9,5,27,2,3,37,47,2,1,6,30,24,22,16,6,2,5,10,11,6,13,8,17,3,21,7,14,12,8,8,14,1,6,12,19,31,8,10,5,6,12,37,2,0.042,0.023,0.022,0.026,0.025,0.025,0.023,0.028,0.049,0.048,0.037,0.033,0.028,0.025,0.029,0.033,0.03,0.041,0.032,0.036,0.03,0.031,0.024,0.035,0.03,0.03,0.029,0.028,0.024,0.032,0.028,0.037,0.029,0.033,0.02,0.011,0.023,0.035,0.013,0.019,0.023,0.035,0.044,0.026,0.018,0.013,0.017,0.02,0.02,0.027,0.021,0.024,0.019,0.017,0.012,0.017,0.016,0.02,0.028,0.019,0.027,0.025,0.021,0.021,0.017,0.027,0.018,0.017,0.018,0.021,0.027,0.021,0.02,0.015,1294,4251,2397,2396,1960,3056,3320,3377,1476,1916,835.0,2546,3674,1024,2522,9044,19740,2300,4600,5082,3448,3958,1725,6130,5832,8095,5260,4128,5561,6069,2626,1460,730,4858,1262,1480,7373,7145,366,372,1518,2349,5807,2838,3773,1384,769,2021,2659,1580,879,2691,1790,5486,617,3742,1450,2560,1299,1470,1163,2410,413,1103,2343,1685,5360,2222,1845,1058,1456,2265,8842,581,0.142,0.115,0.111,0.12,0.131,0.123,0.093,0.128,0.167,0.17,0.113,0.126,0.134,0.126,0.134,0.138,0.13,0.143,0.138,0.138,0.106,0.122,0.115,0.145,0.106,0.122,0.111,0.105,0.097,0.141,0.126,0.111,0.121,0.129,0.093,0.072,0.119,0.148,0.097,0.097,0.114,0.129,0.153,0.117,0.097,0.093,0.098,0.096,0.106,0.122,0.12,0.118,0.102,0.091,0.096,0.102,0.111,0.106,0.131,0.108,0.129,0.119,0.102,0.112,0.102,0.126,0.103,0.098,0.105,0.117,0.12,0.116,0.105,0.091,385,1608,970,959,814,1150,1262,1113,443,512,223.0,1226,1130,293,705,2497,5955,498,1350,2196,856,1377,763,1804,1859,2467,1998,1542,1575,1993,794,541,204,1215,351,584,1982,1915,236,187,808,1363,1101,1910,2236,768,339,794,1352,643,439,1317,925,2339,367,2095,745,1378,823,861,635,1006,199,635,1298,918,2821,985,1024,499,728,979,3997,288,3.305,2.516,2.474,2.456,2.149,2.24,2.209,2.556,2.472,2.924,2.872,1.802,2.529,2.532,2.585,2.657,2.627,3.574,2.725,1.96,2.833,2.258,1.926,2.66,2.479,2.635,2.133,2.175,2.719,2.409,2.5,2.633,2.837,2.686,3.153,2.325,2.83,2.879,1.768,2.121,2.26,1.602,3.267,1.675,1.86,1.929,2.805,2.957,2.437,2.389,1.89,2.265,2.081,2.346,2.037,1.888,2.324,2.158,1.698,1.838,2.105,2.396,2.406,2.139,1.892,2.073,1.961,2.344,1.968,2.306,2.207,2.519,2.369,2.265,0.715,0.567,0.48,0.46,0.549,0.737,0.563,0.53,0.609,0.721,0.851,0.43,0.484,0.431,0.534,0.515,0.573,0.671,0.506,0.62,0.729,0.643,0.452,0.5,0.647,0.592,0.565,0.625,0.543,0.579,0.572,0.896,0.348,0.627,0.711,0.488,0.642,0.609,0.327,0.35,0.49,0.491,0.757,0.484,0.567,0.487,0.597,0.988,0.425,0.603,0.509,0.451,0.418,0.504,0.328,0.426,0.463,0.524,0.505,0.36,0.395,0.576,0.464,0.688,0.476,0.669,0.513,0.419,0.397,0.624,0.57,0.511,0.499,0.411
-135,Epileptic,F,52.0,1.8,3.5,1.3,1.1,2.0,1.6,2.5,1.6,0.7,0.9,0.3,3.0,2.0,0.5,1.2,5.8,8.7,0.9,2.1,4.3,0.9,2.2,1.8,3.5,4.2,2.9,6.8,2.9,3.1,3.0,1.3,1.4,0.4,2.2,0.3,0.5,3.1,3.6,0.2,0.2,1.0,2.9,2.4,2.2,2.4,1.1,0.3,1.1,1.3,0.7,0.9,2.4,0.9,2.2,0.5,2.1,1.0,1.5,0.8,0.8,1.2,1.3,0.4,0.5,2.0,1.5,3.1,1.1,1.0,0.5,1.1,1.7,4.0,0.4,14,35,12,10,18,17,16,18,12,9,3.0,27,25,6,19,57,89,9,25,40,8,29,18,46,55,29,50,29,27,34,15,9,6,30,2,6,38,47,1,2,7,29,26,16,14,9,2,8,6,5,5,20,7,14,3,18,8,12,6,7,9,9,3,5,13,19,27,6,6,4,8,15,35,4,0.063,0.038,0.026,0.018,0.046,0.025,0.044,0.03,0.036,0.04,0.036,0.041,0.036,0.026,0.027,0.036,0.034,0.042,0.032,0.037,0.023,0.031,0.036,0.039,0.041,0.03,0.051,0.033,0.036,0.032,0.033,0.047,0.019,0.029,0.011,0.012,0.035,0.038,0.017,0.014,0.02,0.036,0.041,0.026,0.019,0.026,0.016,0.017,0.018,0.021,0.034,0.023,0.02,0.023,0.019,0.017,0.029,0.017,0.026,0.018,0.027,0.024,0.033,0.017,0.02,0.026,0.021,0.016,0.022,0.018,0.033,0.019,0.017,0.014,1384,4157,2219,2553,2002,3182,3555,3048,1348,1598,668.0,2710,3369,1198,2619,8832,16287,2000,4257,5044,3225,4105,2404,6467,6118,6272,5757,4790,5294,4835,2270,1188,857,5517,1533,1799,6522,7833,352,415,1365,2391,5511,2159,3379,1479,700,2062,2121,1503,621,3675,1590,3160,540,3690,1533,2734,926,1171,1203,2440,395,935,2788,1345,4165,2165,1477,971,1164,2587,8492,566,0.158,0.145,0.119,0.102,0.156,0.127,0.136,0.139,0.149,0.146,0.128,0.155,0.15,0.117,0.134,0.14,0.141,0.128,0.131,0.147,0.084,0.129,0.129,0.145,0.149,0.131,0.151,0.122,0.122,0.146,0.13,0.126,0.1,0.133,0.07,0.072,0.138,0.152,0.093,0.098,0.109,0.136,0.149,0.115,0.1,0.129,0.09,0.084,0.097,0.109,0.139,0.116,0.107,0.109,0.116,0.099,0.13,0.098,0.133,0.101,0.129,0.112,0.135,0.111,0.103,0.115,0.115,0.095,0.105,0.103,0.138,0.119,0.097,0.11,471,1398,820,908,794,1008,975,979,445,401,195.0,1235,1021,287,768,2613,4599,467,1130,2117,760,1158,909,1820,1729,1692,2089,1473,1483,1546,651,495,323,1287,427,667,1701,1879,203,258,715,1180,1146,1363,1980,673,324,971,1046,566,399,1646,789,1517,320,2013,641,1459,501,689,630,900,207,504,1494,897,2191,977,684,492,601,1328,3867,362,2.886,2.759,2.43,2.548,2.153,2.461,2.776,2.567,2.31,3.019,2.734,1.881,2.53,2.769,2.495,2.517,2.69,3.34,2.94,1.97,2.814,2.666,2.293,2.608,2.652,2.825,2.23,2.467,2.648,2.512,2.542,2.43,2.063,2.931,3.047,2.447,2.741,2.933,1.985,1.777,2.217,1.853,3.195,1.753,1.893,2.296,2.467,2.414,2.397,3.149,1.982,2.131,1.894,2.23,1.959,1.972,2.322,2.117,2.144,1.873,2.125,2.55,2.054,2.164,1.985,1.817,2.067,2.433,2.383,2.201,2.196,2.167,2.261,1.815,0.789,0.709,0.673,0.569,0.59,0.658,0.748,0.675,0.61,0.479,0.801,0.546,0.52,0.586,0.655,0.689,0.662,0.781,0.663,0.692,0.947,0.543,0.687,0.743,0.694,0.635,0.651,0.682,0.689,0.602,0.62,0.943,0.423,0.648,0.614,0.43,0.835,0.626,0.236,0.336,0.459,0.556,0.858,0.549,0.603,0.52,0.585,0.654,0.456,0.551,0.52,0.57,0.492,0.414,0.379,0.4,0.513,0.527,0.668,0.343,0.454,0.751,0.38,0.583,0.512,0.711,0.471,0.517,0.476,0.601,0.435,0.448,0.509,0.367
-136,Epileptic,M,32.0,0.7,2.3,0.6,0.9,1.5,1.8,1.2,1.7,1.2,0.7,0.2,2.5,1.4,0.6,1.7,5.2,6.7,0.8,2.1,4.7,1.6,2.9,1.7,3.5,2.9,2.6,3.0,2.4,2.9,2.7,1.4,0.7,0.4,2.2,0.5,0.7,3.4,2.9,0.1,0.2,0.9,3.3,2.9,2.4,3.2,0.4,0.3,0.9,1.4,0.9,0.7,2.9,1.0,1.9,0.2,2.6,1.1,1.8,1.0,0.9,1.0,1.8,0.3,0.4,1.2,0.9,2.8,1.3,0.9,0.4,0.7,2.4,3.4,0.2,5,19,7,11,16,16,10,15,14,6,3.0,23,15,6,17,61,62,7,22,35,13,30,17,35,34,27,34,21,25,40,17,5,4,23,3,8,38,30,1,2,6,26,22,17,18,3,2,5,7,6,5,20,7,12,3,23,6,12,7,7,7,12,2,3,12,13,23,11,7,4,6,13,24,3,0.029,0.023,0.012,0.019,0.032,0.029,0.023,0.027,0.042,0.029,0.028,0.032,0.028,0.026,0.027,0.03,0.027,0.03,0.03,0.039,0.028,0.034,0.025,0.037,0.03,0.028,0.025,0.024,0.025,0.024,0.042,0.03,0.016,0.027,0.014,0.025,0.033,0.03,0.01,0.015,0.021,0.039,0.037,0.027,0.026,0.011,0.016,0.014,0.015,0.019,0.028,0.029,0.022,0.019,0.014,0.019,0.019,0.026,0.025,0.014,0.028,0.029,0.019,0.018,0.017,0.034,0.021,0.017,0.018,0.015,0.019,0.026,0.016,0.015,1637,5262,1846,1957,2699,2826,2517,3449,2304,1472,627.0,2781,3196,1504,3339,11100,16688,2618,4462,4430,2681,4870,2289,6875,5756,5318,5519,4080,6560,6241,2329,1039,1157,5677,2278,1913,7649,7158,447,501,1418,2922,5810,2173,3194,1218,958,2241,3069,1931,612,3800,1949,3607,456,4135,2099,2380,1190,1657,1281,2635,571,855,2570,948,3947,2878,1841,1184,1338,3087,8718,425,0.113,0.111,0.091,0.111,0.135,0.141,0.102,0.125,0.159,0.14,0.113,0.134,0.128,0.125,0.134,0.141,0.122,0.111,0.131,0.143,0.099,0.146,0.125,0.14,0.135,0.138,0.123,0.114,0.114,0.127,0.124,0.094,0.092,0.127,0.068,0.108,0.133,0.129,0.075,0.087,0.108,0.144,0.132,0.124,0.114,0.08,0.095,0.084,0.093,0.102,0.135,0.131,0.102,0.101,0.119,0.111,0.099,0.116,0.116,0.095,0.128,0.124,0.112,0.111,0.102,0.13,0.113,0.101,0.1,0.11,0.107,0.114,0.09,0.114,432,1616,755,773,808,1081,824,1041,539,385,181.0,1303,866,370,961,2959,4278,465,1159,1981,802,1465,1051,1747,1685,1609,1940,1488,1885,1840,609,449,384,1301,618,527,1960,1689,234,297,645,1302,1310,1435,2068,600,361,906,1235,740,353,1525,760,1527,223,2049,844,1186,686,916,462,981,242,379,1197,519,2057,1256,825,431,630,1456,3454,253,3.502,2.905,2.433,2.594,2.776,2.439,2.517,2.942,3.068,3.268,2.939,1.901,2.832,2.991,2.681,2.716,3.131,3.747,2.896,1.908,2.738,2.527,1.986,2.992,2.719,2.783,2.335,2.198,2.71,2.682,2.685,2.22,2.496,3.17,3.413,3.047,2.875,3.147,2.295,1.99,2.828,1.984,3.425,1.842,1.819,2.07,3.32,3.141,2.864,2.866,2.127,2.528,2.774,2.444,2.376,2.195,2.925,2.312,2.069,2.032,2.552,2.922,2.538,2.522,2.309,2.208,2.159,2.527,2.471,2.979,2.534,2.619,2.691,2.004,0.921,0.75,0.552,0.504,0.599,0.495,0.529,0.607,0.595,0.498,0.719,0.51,0.37,0.604,0.453,0.61,0.661,0.889,0.551,0.633,0.834,0.566,0.382,0.597,0.519,0.554,0.459,0.475,0.579,0.472,0.782,0.969,0.493,0.628,0.768,0.525,0.69,0.58,0.469,0.37,0.538,0.535,0.767,0.635,0.55,0.442,0.53,0.599,0.591,0.484,0.461,0.524,0.633,0.655,0.308,0.43,0.625,0.439,0.452,0.387,0.454,0.715,0.725,1.11,0.567,0.61,0.404,0.359,0.423,0.871,0.351,0.561,0.522,0.358
-137,Epileptic,M,32.0,0.8,2.8,1.0,1.3,1.5,1.6,1.3,1.7,0.9,1.1,0.4,2.1,2.4,0.6,2.2,3.8,7.4,1.0,2.0,5.1,1.1,2.8,1.2,2.8,3.3,3.2,2.5,2.5,3.0,3.8,2.1,1.1,0.8,2.8,0.3,1.3,3.2,3.0,0.1,0.3,1.2,2.5,2.1,2.2,3.2,0.6,0.5,0.9,1.3,0.8,0.5,1.6,1.1,2.8,0.6,2.3,0.7,1.7,1.2,0.7,0.8,0.9,0.4,0.3,1.8,1.7,2.7,1.3,0.8,0.4,0.8,1.1,4.4,0.2,7,29,16,11,17,17,11,17,7,12,4.0,20,21,6,22,44,70,10,20,43,12,28,13,35,38,40,31,23,27,42,25,19,7,30,2,13,39,34,1,2,8,25,24,17,19,5,3,5,7,7,5,13,6,22,3,22,6,11,10,6,6,8,2,3,14,20,25,10,6,2,5,9,36,1,0.03,0.041,0.019,0.028,0.055,0.029,0.024,0.033,0.051,0.049,0.039,0.032,0.038,0.04,0.045,0.033,0.035,0.032,0.032,0.045,0.024,0.033,0.029,0.035,0.034,0.032,0.028,0.029,0.03,0.042,0.055,0.074,0.036,0.031,0.017,0.026,0.035,0.032,0.007,0.017,0.025,0.031,0.038,0.028,0.024,0.016,0.022,0.014,0.019,0.019,0.026,0.026,0.028,0.026,0.023,0.02,0.014,0.023,0.028,0.019,0.023,0.024,0.029,0.017,0.027,0.03,0.028,0.02,0.016,0.017,0.02,0.019,0.021,0.028,1636,4609,2197,2035,2726,2459,2004,3039,1372,1469,536.0,2099,3644,1114,3096,6636,13147,2512,3756,4294,3547,4163,1601,5864,5824,6198,4386,3068,5872,5142,2173,1223,737,6143,1911,2151,7362,7291,421,445,1613,2495,5288,2093,3236,1228,871,2255,2340,1493,533,2360,1230,4231,620,3571,1763,2895,1317,1001,1115,1967,407,814,2064,1353,3561,2407,1498,561,1266,2059,8874,339,0.129,0.132,0.121,0.127,0.189,0.144,0.105,0.139,0.167,0.173,0.144,0.138,0.142,0.155,0.159,0.14,0.14,0.126,0.128,0.156,0.102,0.149,0.134,0.145,0.145,0.143,0.132,0.124,0.125,0.166,0.156,0.138,0.16,0.136,0.078,0.122,0.153,0.136,0.078,0.106,0.119,0.139,0.157,0.121,0.113,0.107,0.113,0.084,0.096,0.105,0.139,0.127,0.132,0.12,0.125,0.119,0.099,0.108,0.13,0.112,0.115,0.116,0.133,0.11,0.131,0.139,0.131,0.112,0.104,0.085,0.113,0.117,0.106,0.1,457,1387,855,767,611,978,799,919,317,401,196.0,1006,1031,263,886,2088,3633,495,998,1924,796,1330,645,1379,1670,1868,1554,1317,1610,1601,613,427,315,1438,375,811,1576,1634,198,246,764,1219,1031,1334,2021,599,348,962,1003,598,327,970,546,1780,325,1814,770,1261,755,557,525,676,205,359,1062,828,1725,1051,734,345,633,818,3599,143,3.738,2.7,2.422,2.69,3.069,2.309,2.134,2.931,3.015,3.0,2.578,1.857,2.805,3.13,2.717,2.535,2.858,3.628,2.916,1.955,3.188,2.414,2.137,3.094,2.671,2.749,2.35,1.953,2.789,2.675,2.351,2.613,2.245,3.09,3.963,2.521,3.425,3.165,2.402,2.217,2.699,1.851,3.475,1.869,1.89,2.218,3.24,2.897,2.84,2.885,2.128,2.424,2.298,2.351,2.456,2.185,2.495,2.791,2.009,2.05,2.486,2.758,2.235,2.279,2.225,2.011,2.256,2.71,2.41,1.717,2.342,2.768,2.739,2.189,0.628,0.917,0.502,0.536,0.881,0.577,0.518,0.651,0.917,0.532,0.797,0.442,0.43,0.513,0.49,0.517,0.648,0.707,0.61,0.575,0.74,0.499,0.398,0.727,0.543,0.532,0.452,0.542,0.564,0.552,0.846,0.954,0.535,0.689,0.921,0.478,0.77,0.623,0.374,0.318,0.56,0.451,0.743,0.72,0.562,0.529,0.512,0.523,0.61,0.449,0.524,0.438,0.498,0.427,0.342,0.367,0.437,0.51,0.458,0.323,0.376,0.697,0.374,1.013,0.545,0.664,0.457,0.434,0.382,0.703,0.396,0.595,0.535,0.417
-138,Epileptic,M,17.5,0.6,2.0,0.8,1.1,1.3,1.7,2.0,1.8,0.8,0.6,0.3,2.2,1.8,0.7,1.5,6.2,10.0,0.6,2.9,4.3,2.5,3.8,2.4,3.7,3.6,4.6,3.1,3.5,3.1,3.6,1.1,1.5,0.5,2.7,0.4,1.1,4.9,3.6,0.4,0.2,1.1,4.7,3.0,5.6,2.9,0.6,0.5,1.2,1.2,1.0,0.5,2.7,1.9,2.4,0.3,1.8,0.8,2.5,1.1,1.3,0.5,1.2,0.6,0.6,1.5,0.6,2.8,0.8,0.9,0.4,1.4,2.1,4.5,0.3,3,20,6,8,21,15,16,18,7,6,2.0,19,18,8,14,70,101,5,23,30,20,35,20,43,36,37,31,30,25,35,13,10,4,23,3,7,49,32,4,1,6,35,26,41,19,4,3,7,6,8,4,22,20,14,2,13,7,20,7,9,4,6,3,4,10,8,22,5,6,4,8,11,37,2,0.025,0.024,0.017,0.024,0.04,0.028,0.03,0.028,0.036,0.033,0.033,0.033,0.029,0.036,0.036,0.035,0.036,0.027,0.035,0.038,0.041,0.035,0.039,0.041,0.034,0.035,0.031,0.032,0.026,0.032,0.04,0.054,0.037,0.037,0.016,0.034,0.033,0.037,0.024,0.019,0.024,0.049,0.046,0.068,0.023,0.013,0.024,0.021,0.016,0.021,0.036,0.026,0.04,0.016,0.018,0.016,0.024,0.034,0.024,0.019,0.017,0.021,0.026,0.028,0.02,0.024,0.027,0.014,0.017,0.014,0.039,0.019,0.017,0.029,1396,5003,2565,2355,2024,3115,3658,4093,1439,1566,462.0,2228,4023,1663,2781,12797,22038,2232,3888,3649,4114,6310,2650,7737,7122,6640,6512,4817,6381,6845,2344,1126,907,5637,2047,1341,12080,7106,538,434,1557,3021,7649,1582,3806,1427,904,2082,2652,2577,540,4284,3018,5521,497,4033,1539,2416,1737,2198,1379,2428,807,928,2952,782,4044,2098,2302,982,1644,3683,10995,594,0.093,0.11,0.097,0.111,0.144,0.128,0.12,0.123,0.139,0.112,0.114,0.127,0.128,0.146,0.136,0.132,0.134,0.102,0.129,0.135,0.126,0.145,0.138,0.14,0.128,0.124,0.12,0.123,0.11,0.129,0.136,0.143,0.128,0.127,0.079,0.098,0.128,0.12,0.14,0.128,0.106,0.145,0.158,0.172,0.107,0.09,0.109,0.099,0.091,0.102,0.139,0.118,0.14,0.092,0.103,0.09,0.108,0.132,0.12,0.106,0.09,0.106,0.129,0.129,0.102,0.116,0.117,0.086,0.094,0.107,0.14,0.099,0.096,0.113,405,1336,709,731,654,1060,987,1110,338,333,137.0,984,967,347,677,3050,4822,403,1325,1789,901,1823,1000,1575,1769,1987,1604,1648,1694,1753,478,472,239,1122,453,416,2442,1596,215,147,705,1438,1266,1179,2106,645,373,833,1060,819,270,1672,829,2140,247,1801,666,1289,787,1022,592,790,314,331,1206,429,1850,932,888,419,660,1629,4174,199,3.287,3.015,3.249,3.173,2.62,2.571,2.896,3.157,2.91,3.712,3.138,2.036,3.131,3.266,2.934,3.025,3.5,4.008,2.477,1.881,3.322,2.706,2.3,3.413,2.961,2.747,2.897,2.301,2.881,3.059,3.178,2.469,3.107,3.211,3.737,2.706,3.486,3.128,2.643,3.05,2.901,2.004,3.74,1.588,2.125,2.378,3.077,3.27,2.964,3.115,2.194,2.557,2.738,2.815,2.512,2.529,2.417,2.095,2.653,2.421,2.724,3.475,2.905,2.766,2.802,2.188,2.445,2.783,2.746,2.545,2.879,2.795,2.958,3.35,0.739,0.938,0.853,0.67,0.618,0.635,0.836,0.596,0.788,0.474,1.135,0.614,0.49,0.459,0.654,0.659,0.738,0.737,0.602,0.608,1.055,0.479,0.593,0.751,0.539,0.661,0.593,0.502,0.548,0.639,0.768,1.136,0.648,0.897,0.78,0.602,0.784,0.705,0.33,0.714,0.427,0.515,0.811,0.669,0.528,0.503,0.41,0.716,0.584,0.726,0.441,0.53,0.869,0.525,0.311,0.467,0.61,0.596,0.546,0.398,0.457,0.863,0.875,1.106,0.43,0.759,0.49,0.335,0.43,0.852,0.525,0.565,0.55,0.394
-141,Epileptic,M,32.0,1.3,2.8,1.4,1.9,1.7,1.8,2.1,2.0,1.6,0.8,0.5,4.1,1.6,0.7,1.9,6.2,10.0,1.0,2.3,6.0,3.3,3.2,2.4,5.5,2.6,3.2,3.8,2.9,3.0,4.0,2.0,1.6,0.5,1.6,0.5,0.5,4.3,4.1,0.4,0.4,1.2,3.7,2.6,3.6,3.6,0.7,0.5,0.7,1.6,1.4,1.1,3.1,1.4,2.5,0.3,2.6,1.2,2.2,1.2,1.0,0.6,2.2,0.4,0.7,2.0,1.5,2.7,1.6,1.0,0.6,1.2,1.3,4.6,0.3,11,34,14,18,16,25,16,18,18,8,5.0,43,19,11,23,63,98,7,29,60,29,39,25,44,40,39,42,27,26,38,29,12,5,25,2,5,43,49,2,2,7,35,23,31,21,5,2,5,9,9,9,23,13,19,2,23,11,16,11,9,6,18,3,5,15,23,20,14,7,6,10,10,44,3,0.047,0.024,0.021,0.026,0.025,0.025,0.03,0.029,0.04,0.032,0.035,0.035,0.027,0.03,0.029,0.032,0.031,0.036,0.028,0.037,0.045,0.031,0.028,0.047,0.023,0.025,0.027,0.029,0.029,0.032,0.043,0.049,0.024,0.022,0.013,0.012,0.033,0.032,0.018,0.02,0.026,0.037,0.045,0.027,0.024,0.014,0.02,0.012,0.017,0.032,0.03,0.026,0.025,0.021,0.011,0.018,0.025,0.02,0.027,0.017,0.019,0.032,0.029,0.016,0.02,0.019,0.022,0.017,0.018,0.014,0.019,0.012,0.016,0.016,1565,5955,3182,3437,2245,3588,3987,3519,2120,2059,966.0,4412,3858,1342,4029,12240,21966,2639,5125,6783,4081,5160,3254,6399,7348,8197,8193,5237,7318,8103,2749,1493,1035,5273,2201,1895,8143,9806,546,624,1633,3773,6041,4144,3868,1694,873,2375,2817,2201,996,4314,2320,4636,463,4413,1803,3821,1237,1729,1004,2888,478,1168,3288,1934,4036,3433,2169,1600,2388,2612,10584,575,0.142,0.117,0.114,0.133,0.118,0.135,0.111,0.132,0.146,0.137,0.128,0.148,0.128,0.127,0.134,0.132,0.133,0.131,0.13,0.151,0.131,0.135,0.13,0.151,0.123,0.121,0.124,0.114,0.112,0.135,0.124,0.131,0.128,0.106,0.063,0.085,0.141,0.139,0.092,0.096,0.111,0.123,0.146,0.122,0.109,0.087,0.096,0.079,0.098,0.118,0.141,0.122,0.114,0.105,0.106,0.105,0.126,0.099,0.138,0.1,0.112,0.13,0.138,0.103,0.109,0.115,0.113,0.102,0.099,0.104,0.116,0.094,0.095,0.108,523,1974,1088,1140,1019,1284,1224,1153,643,460,256.0,1909,1025,351,1082,3165,5631,514,1438,2768,1199,1653,1310,1870,1960,2157,2250,1603,1795,2171,808,600,346,1236,630,642,2040,2224,303,303,772,1723,1120,2298,2234,794,334,941,1267,744,564,1913,1036,1976,260,2200,804,1771,728,972,520,1134,266,614,1517,1271,1938,1427,899,611,923,1328,4635,249,2.936,2.729,2.779,2.841,2.098,2.334,2.583,2.784,2.477,3.391,2.734,2.043,2.835,2.72,2.805,2.79,2.977,3.75,2.76,2.085,2.723,2.44,2.113,2.568,2.744,2.955,2.709,2.485,3.092,2.875,2.541,2.561,2.639,2.962,3.181,2.583,2.865,3.138,1.933,2.459,2.594,1.995,3.515,2.002,1.992,2.381,2.993,3.065,2.709,2.713,1.999,2.41,2.251,2.575,2.13,2.219,2.391,2.495,1.952,1.916,2.15,2.495,1.962,2.359,2.355,1.746,2.277,2.593,2.761,2.637,2.673,2.287,2.422,2.582,0.801,0.541,0.542,0.577,0.533,0.619,0.75,0.416,0.562,0.95,0.925,0.574,0.447,0.497,0.565,0.654,0.679,0.701,0.597,0.712,0.862,0.597,0.592,0.824,0.678,0.569,0.687,0.649,0.537,0.622,0.703,1.014,0.473,0.546,0.444,0.534,0.765,0.673,0.494,0.423,0.531,0.6,0.995,0.712,0.625,0.489,0.485,0.761,0.402,1.021,0.336,0.448,0.468,0.398,0.272,0.364,0.461,0.533,0.406,0.358,0.345,0.714,0.394,0.641,0.457,0.642,0.431,0.359,0.47,0.898,0.58,0.397,0.447,0.48
-142,Epileptic,F,32.0,0.5,1.8,1.0,1.0,1.6,2.1,1.2,1.9,1.1,0.5,0.1,2.5,1.6,0.6,1.1,6.0,9.0,1.1,1.6,3.7,0.9,2.2,1.8,3.3,3.2,3.6,2.6,2.2,2.2,2.9,1.1,0.4,0.5,2.0,0.4,0.4,3.9,3.5,0.3,0.1,1.0,3.1,2.9,1.5,2.1,0.5,0.5,0.8,0.9,1.0,0.7,1.7,1.0,1.9,0.2,2.5,0.8,2.1,1.2,0.6,0.5,1.6,0.3,0.5,1.8,0.7,2.5,0.8,0.8,0.4,1.0,1.7,3.6,0.3,5,18,12,10,16,18,11,15,13,5,1.0,18,16,7,9,57,73,8,19,25,7,25,15,29,31,36,32,20,18,31,20,5,5,28,3,4,44,40,2,1,5,29,33,12,12,5,2,5,5,7,6,11,7,13,1,21,8,17,8,6,4,13,1,4,14,17,18,5,5,4,9,9,28,2,0.033,0.022,0.02,0.022,0.032,0.038,0.022,0.033,0.04,0.035,0.014,0.04,0.031,0.031,0.028,0.035,0.031,0.04,0.028,0.04,0.025,0.035,0.037,0.037,0.037,0.033,0.029,0.026,0.028,0.028,0.043,0.033,0.033,0.03,0.021,0.012,0.035,0.032,0.024,0.022,0.023,0.041,0.051,0.027,0.017,0.015,0.025,0.01,0.012,0.027,0.036,0.019,0.021,0.02,0.021,0.019,0.025,0.024,0.033,0.016,0.016,0.026,0.028,0.025,0.024,0.026,0.02,0.014,0.018,0.017,0.026,0.022,0.016,0.023,1356,4255,2124,1878,2275,3010,2361,3201,1657,841,300.0,2201,3608,1277,2117,10031,16968,2180,3382,3093,2076,3807,1997,5553,4517,7171,4916,3530,4492,4956,2296,582,1014,5717,1424,1634,6803,7156,570,358,1372,2853,4502,1333,2946,1259,786,2764,2344,1506,544,3017,1825,3960,335,3731,1304,2877,1213,974,1087,2439,403,806,2269,503,4147,1757,1149,864,1392,2359,8172,562,0.104,0.113,0.11,0.107,0.139,0.152,0.103,0.136,0.143,0.119,0.067,0.143,0.13,0.149,0.13,0.144,0.131,0.131,0.127,0.138,0.08,0.144,0.136,0.143,0.147,0.139,0.124,0.102,0.109,0.131,0.129,0.101,0.15,0.128,0.079,0.08,0.126,0.13,0.115,0.088,0.109,0.149,0.138,0.117,0.098,0.092,0.112,0.074,0.08,0.112,0.147,0.095,0.108,0.099,0.118,0.108,0.125,0.111,0.139,0.103,0.098,0.132,0.118,0.131,0.124,0.113,0.106,0.09,0.112,0.107,0.133,0.11,0.092,0.137,365,1346,842,757,839,974,807,966,495,276,101.0,950,996,302,595,2761,4716,403,975,1489,629,1187,787,1483,1374,1971,1603,1284,1216,1593,562,279,297,1194,343,600,1931,1747,238,127,668,1226,1161,905,1763,616,301,1016,1038,647,348,1388,853,1611,140,2024,591,1451,610,572,551,908,157,333,1132,313,2042,836,611,378,685,1131,3562,220,3.501,2.861,2.439,2.569,2.442,2.568,2.452,2.912,2.61,2.614,2.455,2.076,2.928,3.063,2.795,2.737,2.898,3.923,2.726,1.811,2.684,2.51,2.144,2.907,2.473,2.834,2.368,2.172,2.755,2.5,2.911,2.129,2.838,3.243,3.315,2.558,2.649,3.068,2.802,2.873,2.678,2.063,3.04,1.7,1.939,2.071,3.32,3.238,2.72,2.816,1.709,2.265,2.306,2.643,2.343,1.96,2.307,2.138,2.077,1.875,2.16,2.872,3.02,2.778,2.146,2.04,2.2,2.432,2.253,2.686,2.26,2.501,2.525,3.105,1.179,0.685,0.501,0.504,0.539,0.643,0.497,0.483,0.596,0.467,0.805,0.589,0.4,0.336,0.354,0.593,0.541,0.815,0.578,0.528,0.672,0.565,0.603,0.621,0.507,0.552,0.614,0.403,0.496,0.611,0.655,0.776,0.544,0.652,0.567,0.485,0.73,0.644,0.4,0.377,0.527,0.629,0.861,0.637,0.534,0.494,0.586,0.753,0.599,0.886,0.277,0.467,0.487,0.475,0.502,0.433,0.471,0.6,0.421,0.425,0.376,0.657,0.653,0.643,0.581,0.469,0.584,0.333,0.466,0.681,0.535,0.523,0.499,0.487
-143,Epileptic,F,32.0,0.8,2.2,1.8,1.1,1.2,1.9,1.9,2.0,1.0,0.4,0.3,2.8,1.8,0.4,1.3,7.1,9.6,1.1,3.3,5.1,1.0,2.0,1.1,3.8,3.6,5.0,3.6,2.6,3.1,3.2,1.2,1.1,0.5,3.0,0.3,1.0,3.2,3.4,0.2,0.1,1.7,3.1,2.0,3.3,3.3,1.0,0.6,0.7,1.3,0.5,0.6,1.8,1.4,2.6,0.6,3.2,1.1,2.3,1.0,1.3,0.6,1.4,0.2,0.5,1.9,0.4,2.8,1.4,1.3,0.4,1.6,1.0,5.8,0.4,4,22,22,9,12,16,18,16,11,7,2.0,21,15,7,12,64,72,10,29,41,7,20,13,34,40,42,37,20,21,34,17,14,5,23,2,10,34,36,1,1,9,25,17,25,24,7,3,5,7,5,4,17,9,16,4,25,11,16,6,11,4,9,1,4,16,7,22,9,9,4,11,9,38,3,0.029,0.02,0.025,0.022,0.027,0.032,0.024,0.029,0.043,0.03,0.027,0.035,0.03,0.025,0.036,0.033,0.031,0.034,0.032,0.039,0.014,0.031,0.024,0.038,0.031,0.033,0.03,0.025,0.025,0.029,0.028,0.047,0.023,0.032,0.011,0.025,0.033,0.036,0.013,0.017,0.029,0.035,0.037,0.038,0.026,0.022,0.026,0.011,0.016,0.01,0.037,0.019,0.02,0.02,0.022,0.022,0.037,0.027,0.033,0.025,0.022,0.021,0.02,0.015,0.023,0.016,0.022,0.023,0.022,0.015,0.029,0.017,0.023,0.027,1612,4863,2883,2130,1564,2401,2729,3117,1321,857,437.0,2485,3644,1112,2507,12353,17841,2267,5447,4222,3685,3025,1957,5009,5968,8085,5463,3250,5459,5457,1950,1008,1210,6072,2131,1972,5691,6978,341,392,1980,2759,5413,2195,3017,1422,908,2314,2600,1516,472,3237,2077,4835,707,4001,1734,2863,817,1317,821,2443,398,753,2654,674,3763,2028,1912,1103,1813,2062,9559,511,0.098,0.109,0.132,0.101,0.125,0.136,0.11,0.124,0.122,0.132,0.108,0.139,0.127,0.135,0.135,0.134,0.126,0.127,0.138,0.139,0.086,0.138,0.127,0.131,0.146,0.142,0.137,0.106,0.103,0.134,0.116,0.132,0.104,0.129,0.056,0.115,0.129,0.141,0.086,0.1,0.126,0.136,0.122,0.143,0.122,0.104,0.12,0.082,0.092,0.089,0.156,0.103,0.105,0.098,0.131,0.122,0.141,0.12,0.142,0.126,0.111,0.11,0.114,0.116,0.116,0.109,0.112,0.113,0.109,0.095,0.146,0.104,0.107,0.138,494,1674,1042,864,685,981,1107,1124,414,284,188.0,1118,1029,292,671,3616,4991,496,1663,1993,951,1063,802,1498,1904,2508,2042,1412,1703,1802,612,473,397,1499,523,701,1415,1701,202,150,891,1280,1035,1483,2092,744,403,979,1221,691,284,1599,1108,2180,391,2157,641,1485,496,811,415,912,202,453,1385,352,2087,1011,962,511,822,994,4030,225,3.148,2.571,2.614,2.515,2.012,2.205,2.182,2.49,2.368,2.785,2.446,1.999,2.864,2.634,2.799,2.643,3.002,3.535,2.684,1.936,3.069,2.327,2.087,2.482,2.58,2.693,2.241,1.924,2.559,2.456,2.394,2.253,2.572,2.947,3.596,2.568,2.916,2.875,1.931,2.473,2.722,1.907,3.34,1.721,1.653,2.091,2.856,2.949,2.56,2.372,2.162,2.138,1.993,2.345,2.335,2.148,2.642,2.256,2.012,1.936,2.319,2.632,2.214,1.953,2.181,2.319,1.962,2.388,2.247,2.368,2.419,2.544,2.54,2.893,1.048,0.925,0.825,0.446,0.579,0.452,0.534,0.634,0.501,0.524,0.823,0.609,0.435,0.523,0.578,0.511,0.595,0.888,0.558,0.528,0.75,0.477,0.445,0.667,0.438,0.522,0.438,0.44,0.536,0.48,0.661,0.962,0.397,0.718,0.933,0.498,0.788,0.703,0.263,0.745,0.621,0.467,0.873,0.59,0.448,0.452,0.545,0.746,0.649,0.684,0.33,0.465,0.533,0.623,0.41,0.375,0.511,0.545,0.564,0.355,0.332,0.868,0.337,0.916,0.478,0.771,0.405,0.472,0.46,1.023,0.429,0.641,0.611,0.679
-144,Epileptic,F,47.0,0.7,2.5,1.0,1.4,2.2,1.3,1.3,1.5,1.3,0.7,0.4,2.5,1.7,0.5,1.1,4.8,6.7,0.8,1.9,4.1,0.9,2.4,1.9,3.9,2.3,3.2,3.9,2.1,2.2,3.3,1.9,0.9,0.6,2.3,0.5,0.5,2.9,3.2,0.2,0.2,0.9,3.1,2.1,2.4,2.4,0.5,0.3,0.8,1.3,0.6,0.6,1.7,1.1,1.3,0.4,2.2,0.5,1.6,0.9,1.0,0.5,1.3,0.3,0.7,2.1,1.4,2.4,1.3,0.7,0.8,1.2,1.4,3.6,0.3,6,24,7,8,18,13,11,15,12,9,5.0,27,15,4,15,46,57,7,24,40,9,26,16,30,36,28,44,20,20,37,19,5,5,22,3,5,37,35,3,1,5,29,21,21,13,3,2,5,6,6,4,11,7,9,2,19,3,10,6,9,3,7,2,5,15,18,17,10,3,8,9,9,25,2,0.038,0.034,0.022,0.029,0.045,0.027,0.026,0.031,0.055,0.039,0.048,0.037,0.028,0.04,0.038,0.037,0.033,0.036,0.029,0.036,0.024,0.033,0.032,0.053,0.03,0.033,0.028,0.029,0.029,0.032,0.058,0.051,0.032,0.032,0.015,0.017,0.038,0.035,0.024,0.022,0.022,0.045,0.04,0.027,0.02,0.012,0.018,0.015,0.022,0.022,0.033,0.024,0.032,0.02,0.024,0.022,0.018,0.02,0.02,0.019,0.017,0.028,0.02,0.029,0.021,0.032,0.019,0.018,0.016,0.033,0.026,0.02,0.016,0.019,1456,4033,1905,1954,1805,2565,2543,2766,1673,1645,493.0,2680,3183,830,1959,7350,12260,2215,4123,5167,3558,4233,2107,5160,4957,5836,7000,3678,4375,5831,2130,896,867,5638,1910,1903,6124,6556,446,273,1286,2615,5331,2557,2960,1209,624,1943,1817,1409,521,2499,1254,2515,421,3270,988,2844,1140,1456,913,1960,300,761,3050,1108,3276,2538,1190,1028,1396,1853,7887,531,0.127,0.136,0.108,0.12,0.157,0.131,0.11,0.138,0.176,0.147,0.161,0.138,0.124,0.139,0.15,0.142,0.133,0.138,0.141,0.146,0.101,0.124,0.119,0.162,0.139,0.142,0.126,0.109,0.111,0.141,0.168,0.122,0.12,0.129,0.079,0.084,0.14,0.147,0.118,0.102,0.106,0.153,0.142,0.128,0.101,0.084,0.098,0.08,0.103,0.113,0.134,0.107,0.124,0.096,0.138,0.108,0.109,0.104,0.112,0.112,0.101,0.119,0.129,0.127,0.111,0.134,0.106,0.1,0.091,0.151,0.126,0.117,0.095,0.118,359,1294,699,750,777,813,867,853,448,383,157.0,1135,1019,233,579,2178,3530,415,1068,2048,733,1190,844,1333,1354,1625,2475,1133,1239,1797,592,294,259,1233,491,572,1512,1633,180,179,641,1161,1024,1512,1765,608,262,867,898,542,309,1224,596,1174,189,1632,474,1257,695,747,456,749,201,365,1473,707,1774,1098,619,436,767,938,3372,230,3.47,2.852,2.691,2.557,2.085,2.738,2.36,2.798,2.734,3.056,2.501,1.993,2.564,2.63,2.529,2.465,2.703,3.843,3.01,2.131,3.487,2.63,2.156,2.877,2.747,2.844,2.27,2.426,2.697,2.456,2.661,2.912,2.596,3.164,3.539,2.875,2.912,2.865,2.378,1.753,2.516,1.998,3.359,1.89,1.904,2.135,2.947,2.779,2.466,2.696,2.109,2.1,2.058,2.338,2.46,2.08,2.286,2.63,1.9,2.047,2.391,2.727,1.494,2.411,2.254,1.981,2.045,2.371,2.286,2.703,2.156,2.324,2.511,3.002,0.836,0.58,0.44,0.481,0.652,0.584,0.5,0.61,0.715,0.557,0.788,0.501,0.445,0.482,0.407,0.627,0.582,0.705,0.5,0.612,0.665,0.529,0.664,0.732,0.619,0.571,0.603,0.561,0.385,0.629,0.642,0.683,0.515,0.648,0.669,0.459,0.683,0.733,0.436,0.366,0.459,0.603,0.833,0.637,0.54,0.486,0.528,0.824,0.401,0.651,0.319,0.504,0.536,0.469,0.623,0.43,0.412,0.463,0.329,0.476,0.397,0.58,0.323,0.664,0.484,0.673,0.445,0.391,0.364,0.714,0.476,0.486,0.546,0.476
-145,Epileptic,F,47.0,1.6,2.9,0.9,1.1,1.3,2.7,1.5,1.5,1.7,0.7,0.4,2.2,2.3,0.9,2.0,7.0,9.4,0.9,2.4,3.9,1.1,2.8,1.8,3.6,4.1,4.3,4.3,2.8,3.1,3.5,0.9,0.8,0.7,3.0,1.0,0.4,2.5,2.8,0.2,0.5,1.1,2.9,2.3,3.8,3.4,0.7,0.4,0.9,1.2,0.5,0.5,2.1,1.8,3.0,0.1,3.4,0.4,2.0,1.4,1.2,0.6,1.9,0.4,0.2,2.1,2.0,2.9,1.2,1.4,1.1,1.1,1.1,4.6,0.4,13,32,10,12,13,22,10,13,12,7,3.0,17,17,8,17,71,80,7,25,32,8,30,15,35,46,39,46,25,27,39,18,6,6,33,8,3,28,33,2,2,6,28,19,22,18,5,2,7,6,7,6,13,11,21,1,27,3,11,10,10,5,15,3,3,18,27,23,8,11,11,6,7,31,3,0.056,0.025,0.014,0.02,0.028,0.033,0.027,0.027,0.048,0.03,0.029,0.032,0.037,0.034,0.034,0.035,0.03,0.033,0.026,0.034,0.018,0.028,0.029,0.036,0.03,0.038,0.028,0.027,0.026,0.03,0.026,0.031,0.026,0.029,0.033,0.012,0.037,0.029,0.02,0.028,0.023,0.035,0.041,0.038,0.023,0.018,0.021,0.014,0.017,0.017,0.033,0.018,0.029,0.023,0.017,0.021,0.012,0.021,0.031,0.023,0.023,0.029,0.031,0.014,0.023,0.043,0.021,0.019,0.019,0.021,0.022,0.022,0.018,0.025,1540,5248,2342,1977,1888,3386,2182,2664,2225,1364,555.0,2150,3855,1531,3530,12068,17133,2347,4267,3305,2966,4627,1826,5977,7237,6634,6997,3999,6115,5462,2167,971,1318,6868,2054,1498,5490,6680,425,558,1447,2525,4944,2473,3642,1177,805,2274,2289,1254,428,3851,2270,4994,175,4520,1187,3061,1306,1285,993,2607,471,740,2911,1141,3996,2223,2434,1351,1422,2067,8762,406,0.161,0.122,0.097,0.111,0.12,0.14,0.105,0.123,0.163,0.127,0.126,0.121,0.135,0.141,0.133,0.137,0.128,0.122,0.118,0.132,0.082,0.129,0.129,0.139,0.142,0.146,0.123,0.101,0.098,0.138,0.122,0.12,0.115,0.128,0.109,0.071,0.132,0.134,0.09,0.117,0.109,0.127,0.15,0.144,0.108,0.098,0.107,0.084,0.092,0.106,0.139,0.091,0.114,0.107,0.135,0.113,0.094,0.101,0.136,0.119,0.108,0.131,0.133,0.105,0.118,0.14,0.111,0.097,0.108,0.122,0.116,0.109,0.101,0.121,529,1993,1040,845,824,1318,839,908,565,411,194.0,1056,1109,381,966,3467,5072,451,1401,1811,953,1587,854,1589,2230,2079,2593,1529,1739,1911,631,474,472,1643,545,576,1304,1663,251,292,773,1331,1085,1558,2244,646,319,1036,1082,489,334,1943,1090,2222,79,2458,539,1489,744,811,473,1070,239,352,1498,708,2205,1012,1151,745,752,838,3865,259,2.919,2.423,2.19,2.323,2.094,2.319,2.225,2.635,2.782,2.768,2.506,1.869,2.732,2.76,2.699,2.577,2.663,3.569,2.59,1.708,2.646,2.315,1.915,2.783,2.577,2.725,2.198,2.125,2.727,2.413,2.584,2.188,2.361,2.972,2.827,2.472,3.193,2.836,2.022,2.074,2.348,1.751,3.086,1.861,1.835,1.991,2.873,2.669,2.558,2.613,1.518,2.058,2.115,2.402,2.321,2.045,2.441,2.391,2.026,1.807,2.293,2.379,2.152,2.195,2.038,1.901,1.907,2.501,2.346,1.994,2.17,2.619,2.49,1.754,0.828,0.702,0.582,0.54,0.573,0.685,0.536,0.463,0.618,0.477,0.852,0.458,0.419,0.559,0.509,0.57,0.548,0.845,0.519,0.517,0.698,0.534,0.542,0.697,0.546,0.465,0.472,0.465,0.574,0.435,0.576,0.887,0.415,0.599,0.88,0.436,0.663,0.566,0.427,0.389,0.444,0.443,0.84,0.682,0.546,0.463,0.782,0.679,0.521,0.515,0.435,0.411,0.503,0.488,0.618,0.368,0.419,0.512,0.394,0.4,0.52,0.734,0.526,0.926,0.487,0.723,0.461,0.48,0.504,0.466,0.519,0.684,0.484,0.407
-146,Epileptic,F,22.0,0.9,1.9,0.5,0.9,1.8,1.7,1.2,1.4,0.9,0.5,0.2,3.6,1.5,0.9,1.0,5.8,5.8,0.6,1.7,5.2,1.0,2.6,1.5,3.3,2.4,2.9,3.1,2.2,2.1,2.9,1.0,0.5,0.7,1.9,0.5,0.4,2.4,3.4,0.2,0.1,0.8,2.5,2.9,4.0,2.4,0.4,0.5,1.0,1.1,0.7,0.7,1.4,1.0,2.1,0.3,1.6,0.4,1.4,0.9,0.9,0.5,1.1,0.2,0.4,1.4,1.0,2.1,1.1,0.9,0.5,0.8,1.5,3.4,0.3,5,22,6,10,17,16,10,14,12,6,2.0,28,16,8,10,58,52,3,18,41,7,26,15,31,32,35,38,20,21,33,17,4,7,27,3,2,35,36,1,1,4,22,26,27,14,3,2,6,5,8,5,11,7,13,2,14,3,11,7,7,3,7,2,3,9,11,17,7,7,5,5,10,26,3,0.04,0.026,0.016,0.018,0.045,0.035,0.023,0.03,0.056,0.035,0.021,0.04,0.032,0.041,0.038,0.034,0.025,0.022,0.026,0.038,0.022,0.04,0.035,0.043,0.036,0.04,0.03,0.026,0.025,0.035,0.04,0.031,0.029,0.029,0.023,0.01,0.031,0.035,0.015,0.014,0.02,0.035,0.047,0.033,0.022,0.009,0.023,0.016,0.015,0.019,0.031,0.023,0.027,0.02,0.037,0.015,0.019,0.02,0.024,0.018,0.022,0.022,0.031,0.02,0.024,0.027,0.019,0.02,0.017,0.017,0.022,0.023,0.018,0.017,1346,4633,1578,1784,1704,2639,2300,2915,1291,1250,580.0,2896,3814,1846,2362,11363,15357,2181,3180,4296,2400,4033,1579,5262,4712,6406,4783,2820,5393,5084,1429,840,1324,5420,1360,1485,5815,7037,363,376,1421,2131,4925,3146,2813,1021,844,2155,2842,1256,512,3115,1406,4276,348,2982,959,2335,1290,1375,808,1822,329,733,2323,1004,3453,2288,2063,1002,1271,1973,8561,491,0.12,0.124,0.098,0.111,0.134,0.135,0.107,0.136,0.14,0.142,0.098,0.146,0.131,0.146,0.132,0.14,0.119,0.088,0.121,0.135,0.092,0.149,0.134,0.137,0.143,0.155,0.139,0.112,0.11,0.139,0.132,0.104,0.136,0.127,0.092,0.066,0.117,0.139,0.105,0.096,0.097,0.145,0.153,0.129,0.105,0.08,0.109,0.086,0.085,0.118,0.137,0.102,0.116,0.098,0.146,0.102,0.101,0.111,0.129,0.109,0.112,0.11,0.13,0.105,0.104,0.124,0.102,0.106,0.095,0.12,0.112,0.117,0.092,0.114,383,1208,576,710,660,881,810,848,340,296,145.0,1324,881,376,541,2956,3817,365,1038,2110,708,1092,731,1310,1350,1616,1709,1216,1359,1570,484,319,396,1220,351,548,1496,1624,180,131,662,1071,1062,2052,1740,578,360,932,1128,525,342,1174,632,1773,129,1616,423,1162,609,757,389,711,138,324,1016,572,1711,874,884,372,580,971,3256,246,3.261,3.193,2.781,2.528,2.205,2.543,2.297,3.036,2.596,3.207,2.978,1.987,3.066,3.243,3.06,2.691,3.122,3.836,2.575,1.827,2.857,2.748,1.906,2.856,2.735,3.037,2.255,1.911,2.898,2.507,2.224,2.672,2.722,2.941,3.188,2.396,2.783,3.058,2.185,2.447,2.675,1.822,3.226,1.773,1.869,1.912,3.083,2.807,2.872,2.592,1.891,2.581,2.227,2.584,3.014,2.051,2.495,2.43,2.201,2.019,2.517,2.857,2.983,2.554,2.203,2.212,2.163,2.865,2.588,2.606,2.61,2.393,2.689,2.704,1.287,1.095,0.737,0.452,0.616,0.648,0.561,0.654,0.654,0.446,1.04,0.616,0.648,0.499,0.605,0.668,0.667,1.016,0.631,0.581,0.669,0.648,0.42,0.681,0.622,0.568,0.49,0.428,0.528,0.685,0.814,0.888,0.394,0.665,0.627,0.509,0.659,0.617,0.397,0.713,0.638,0.469,0.696,0.622,0.507,0.367,0.609,0.776,0.625,0.51,0.508,0.624,0.664,0.517,0.5,0.408,0.47,0.51,0.728,0.366,0.549,0.534,0.565,1.064,0.728,0.719,0.52,0.464,0.477,0.902,0.543,0.461,0.605,0.377
-147,Epileptic,M,32.0,0.8,2.8,1.3,1.2,2.0,2.1,1.5,1.5,1.2,0.8,0.3,2.9,1.2,0.6,1.2,7.0,7.1,1.2,2.8,6.2,0.9,3.0,2.3,3.2,3.1,3.6,2.8,2.4,2.8,3.9,1.3,1.7,0.7,2.3,0.3,0.7,6.1,3.2,0.1,0.1,1.2,3.4,2.6,3.6,4.2,0.4,0.4,0.7,1.1,0.8,1.1,2.3,1.9,2.6,0.4,3.0,0.6,1.8,1.6,1.5,0.4,1.1,0.1,0.5,2.0,1.8,3.3,1.4,1.6,0.3,1.0,1.8,5.3,0.5,4,29,15,10,21,18,12,16,13,9,4.0,25,14,6,16,70,59,6,27,40,8,31,20,29,37,34,33,17,22,46,17,14,5,20,2,5,54,24,1,1,6,25,21,21,25,4,2,6,6,6,8,19,13,21,2,23,5,14,10,10,3,9,1,3,18,15,27,9,10,3,7,11,46,2,0.033,0.027,0.023,0.025,0.037,0.032,0.023,0.025,0.039,0.035,0.028,0.036,0.031,0.032,0.032,0.033,0.027,0.038,0.028,0.046,0.019,0.036,0.037,0.041,0.035,0.032,0.024,0.03,0.023,0.033,0.035,0.068,0.029,0.037,0.012,0.022,0.044,0.038,0.015,0.016,0.026,0.038,0.044,0.029,0.032,0.011,0.025,0.015,0.015,0.019,0.042,0.023,0.026,0.02,0.026,0.019,0.015,0.023,0.025,0.026,0.015,0.023,0.011,0.016,0.025,0.039,0.025,0.022,0.023,0.02,0.026,0.025,0.024,0.03,1515,5462,2272,2251,2665,3255,2043,3205,1582,1287,712.0,2723,2802,1152,2352,12122,15096,2020,4158,4303,3590,4239,2352,5954,5760,6457,5163,2525,4967,5857,2446,1194,944,4548,1678,1421,7442,4986,305,439,1490,2606,5693,2928,2780,1081,699,1877,2300,1487,745,3330,2582,5178,439,4069,1659,2659,1588,1586,615,2009,331,918,2318,1343,4249,2268,2036,900,1336,2248,9027,477,0.113,0.122,0.126,0.112,0.144,0.134,0.095,0.117,0.143,0.147,0.129,0.138,0.123,0.138,0.138,0.133,0.118,0.124,0.124,0.146,0.083,0.145,0.144,0.142,0.148,0.133,0.108,0.107,0.097,0.145,0.122,0.145,0.107,0.121,0.074,0.1,0.138,0.121,0.088,0.091,0.114,0.141,0.14,0.124,0.131,0.088,0.114,0.09,0.088,0.103,0.149,0.112,0.115,0.105,0.144,0.113,0.086,0.113,0.123,0.123,0.093,0.112,0.079,0.108,0.127,0.136,0.118,0.111,0.11,0.111,0.12,0.11,0.113,0.122,424,1675,859,787,928,1036,824,993,478,367,220.0,1312,735,296,723,3479,4099,463,1531,2003,825,1426,966,1415,1618,1886,1992,1110,1561,1880,624,436,354,1108,409,578,2157,1419,168,187,736,1211,1175,1966,2132,622,282,817,1057,700,440,1594,1171,2174,208,2293,755,1344,958,860,331,730,173,442,1265,735,2137,1011,1057,324,682,1172,3624,275,3.251,2.837,2.313,2.686,2.463,2.642,2.118,2.73,2.572,2.93,2.865,1.875,2.815,2.603,2.534,2.604,2.901,3.254,2.36,1.903,3.105,2.397,2.104,3.027,2.699,2.758,2.143,1.89,2.477,2.46,2.936,2.858,2.155,2.783,3.46,2.291,2.772,2.653,2.191,2.876,2.617,2.005,3.369,1.709,1.54,1.845,2.686,2.744,2.7,2.61,1.886,2.092,2.249,2.349,2.535,2.036,2.566,2.264,2.023,2.009,2.168,2.697,2.086,2.224,1.951,2.345,2.007,2.505,2.259,2.806,2.147,2.422,2.411,2.354,0.952,0.832,0.729,0.701,0.743,0.512,0.536,0.512,0.577,0.564,0.755,0.455,0.503,0.629,0.528,0.607,0.656,0.989,0.547,0.735,0.667,0.487,0.45,0.74,0.562,0.609,0.417,0.47,0.657,0.589,0.832,1.014,0.552,0.849,0.968,0.572,0.713,0.71,0.277,0.508,0.502,0.576,0.821,0.486,0.447,0.442,0.728,0.561,0.491,0.487,0.369,0.458,0.653,0.463,0.548,0.434,0.44,0.547,0.456,0.403,0.515,0.848,0.376,0.963,0.47,0.632,0.499,0.435,0.427,0.911,0.571,0.692,0.557,0.55
-148,Epileptic,M,42.0,0.8,2.6,1.2,1.4,2.3,1.8,2.2,2.8,1.3,1.3,0.2,2.6,2.4,1.0,2.0,6.1,11.4,1.6,3.2,4.3,1.7,2.4,1.4,4.7,4.3,5.1,4.5,3.8,3.8,4.3,1.6,0.7,0.9,4.0,1.0,1.3,5.9,4.3,0.6,0.5,1.5,3.3,3.6,2.6,3.9,1.0,0.8,1.3,2.0,1.1,0.7,2.9,1.7,3.0,0.3,2.8,0.7,1.5,1.2,1.1,0.4,1.4,0.6,0.6,1.6,1.1,2.9,1.6,1.5,0.8,1.0,1.4,5.5,0.6,7,23,8,9,19,17,16,21,13,11,2.0,24,22,7,17,57,101,13,26,33,15,26,15,35,48,48,45,33,28,53,14,6,6,34,6,11,45,40,5,2,9,25,34,18,19,7,4,7,9,10,3,19,12,21,2,22,6,10,8,7,3,8,3,7,11,12,19,10,11,4,7,10,40,4,0.041,0.025,0.02,0.028,0.045,0.033,0.037,0.044,0.047,0.046,0.034,0.031,0.05,0.059,0.051,0.043,0.042,0.052,0.043,0.038,0.032,0.032,0.022,0.05,0.046,0.036,0.039,0.039,0.036,0.042,0.051,0.04,0.038,0.047,0.031,0.024,0.055,0.046,0.045,0.032,0.029,0.038,0.056,0.023,0.026,0.022,0.03,0.02,0.025,0.027,0.031,0.026,0.026,0.024,0.024,0.023,0.026,0.017,0.023,0.023,0.017,0.032,0.028,0.048,0.02,0.022,0.027,0.022,0.024,0.027,0.02,0.024,0.027,0.025,1857,4911,2241,2248,2130,3343,2546,3370,1860,2032,561.0,3214,3541,1253,2390,8778,18808,2759,5293,4818,4383,4242,2657,6229,6998,8330,7100,5123,6524,7279,2759,765,1154,5894,1951,2732,7582,6736,557,471,1592,3237,5649,2887,3726,1424,797,2223,2380,1691,679,3302,1691,4109,636,3713,1109,2715,1515,1309,733,1944,585,899,2370,1343,3647,2474,1937,989,1624,1943,7458,577,0.115,0.113,0.108,0.117,0.149,0.13,0.119,0.142,0.151,0.167,0.108,0.132,0.136,0.153,0.146,0.14,0.14,0.146,0.151,0.137,0.115,0.132,0.115,0.15,0.165,0.141,0.145,0.121,0.118,0.158,0.163,0.097,0.112,0.149,0.124,0.103,0.163,0.157,0.141,0.117,0.118,0.133,0.182,0.114,0.11,0.114,0.121,0.096,0.109,0.118,0.135,0.108,0.118,0.113,0.1,0.115,0.119,0.096,0.112,0.116,0.103,0.121,0.109,0.153,0.106,0.108,0.114,0.104,0.107,0.125,0.111,0.111,0.116,0.128,460,1730,880,808,981,1054,1005,1223,569,537,144.0,1370,1139,341,769,2783,5143,583,1349,1908,1052,1287,1006,1626,1902,2621,2162,1591,1783,2152,619,332,375,1520,553,961,1853,1727,225,244,855,1318,1328,1729,2200,681,364,940,1193,660,324,1739,1120,1975,290,1976,499,1356,840,731,374,709,359,311,1212,777,1776,1202,960,439,786,996,3463,331,3.832,2.559,2.627,2.608,1.974,2.678,2.145,2.414,2.458,2.958,3.08,2.006,2.456,2.663,2.36,2.478,2.727,3.564,3.146,2.175,2.989,2.57,2.21,2.946,2.678,2.556,2.569,2.529,2.808,2.587,3.115,2.274,2.459,2.811,2.973,2.387,2.833,2.698,2.381,1.964,2.383,2.102,2.786,1.913,1.906,2.095,2.534,2.888,2.441,2.632,2.386,1.986,1.713,2.224,2.213,2.136,2.249,2.239,1.979,2.009,2.16,2.527,1.895,2.622,2.117,1.852,2.237,2.299,2.318,2.621,2.341,2.354,2.329,2.385,0.716,0.595,0.546,0.75,0.522,0.603,0.533,0.589,0.612,0.778,0.842,0.536,0.479,0.597,0.64,0.659,0.711,0.772,0.647,0.734,0.84,0.568,0.63,0.819,0.756,0.652,0.771,0.718,0.552,0.623,0.734,0.841,0.487,0.783,0.542,0.612,0.963,0.741,0.421,0.459,0.53,0.647,0.869,0.737,0.534,0.587,0.6,0.788,0.468,0.668,0.654,0.515,0.373,0.477,0.489,0.441,0.492,0.521,0.457,0.361,0.328,0.758,0.419,0.7,0.529,0.587,0.5,0.383,0.38,0.645,0.511,0.606,0.468,0.534
-149,Epileptic,F,27.0,0.5,2.7,0.7,1.5,1.6,1.8,1.4,1.2,1.3,0.6,0.4,2.5,1.8,0.7,1.5,6.2,9.4,0.7,2.3,3.5,1.1,2.4,1.4,3.4,3.6,3.1,2.4,2.1,2.6,3.3,1.1,1.7,0.4,2.6,0.4,0.9,3.7,2.8,0.2,0.2,0.9,2.4,2.0,2.1,2.6,0.5,0.4,0.8,1.1,1.0,0.7,1.3,1.0,2.3,0.4,2.2,0.4,1.2,0.9,0.9,0.4,1.2,0.4,0.5,1.0,0.6,2.1,1.6,0.7,0.4,0.9,1.5,3.5,0.3,3,26,8,13,26,15,12,12,17,6,3.0,17,19,9,15,70,87,5,27,25,7,24,11,43,43,28,31,19,22,35,13,9,5,28,3,8,35,30,2,1,4,19,18,13,16,4,2,5,6,7,4,11,8,15,3,18,4,9,7,8,3,9,2,4,8,13,14,11,5,4,5,10,24,2,0.025,0.025,0.012,0.03,0.037,0.038,0.019,0.024,0.052,0.026,0.049,0.037,0.037,0.045,0.037,0.035,0.028,0.034,0.029,0.032,0.022,0.037,0.03,0.045,0.038,0.032,0.026,0.026,0.023,0.038,0.038,0.046,0.031,0.034,0.023,0.023,0.04,0.034,0.018,0.014,0.018,0.041,0.036,0.024,0.02,0.01,0.015,0.015,0.014,0.04,0.035,0.018,0.023,0.017,0.018,0.019,0.013,0.015,0.03,0.018,0.015,0.024,0.034,0.022,0.018,0.025,0.02,0.019,0.013,0.012,0.027,0.028,0.016,0.018,1254,5584,2259,2361,2041,2555,2651,3208,1672,1242,352.0,2175,3200,1251,2615,11436,21169,2079,4749,3249,2964,3570,1829,5349,6093,6312,4429,3273,5767,4849,2418,1177,917,6574,1964,2380,5008,6207,406,308,1595,2581,5468,2007,3314,1403,933,2341,2515,1177,420,2581,1616,5204,661,3395,1194,2583,836,1318,665,2217,433,821,1899,741,3360,2759,1786,1159,1013,1454,8615,595,0.099,0.125,0.095,0.122,0.145,0.146,0.096,0.114,0.17,0.137,0.154,0.134,0.146,0.147,0.159,0.15,0.13,0.108,0.13,0.127,0.087,0.154,0.126,0.139,0.153,0.136,0.126,0.101,0.109,0.145,0.134,0.148,0.141,0.131,0.098,0.11,0.143,0.128,0.102,0.085,0.096,0.149,0.129,0.109,0.103,0.082,0.098,0.085,0.086,0.127,0.158,0.104,0.112,0.095,0.11,0.11,0.091,0.091,0.137,0.106,0.102,0.11,0.136,0.129,0.101,0.112,0.105,0.102,0.088,0.097,0.125,0.122,0.092,0.111,356,1749,942,919,745,862,1008,895,471,325,124.0,1040,919,298,744,3009,5544,386,1285,1501,702,1141,753,1316,1723,1700,1564,1206,1676,1560,520,515,295,1200,333,684,1553,1404,201,162,703,992,1013,1307,2043,750,397,880,1151,480,281,1302,715,2161,336,1805,550,1253,440,756,364,780,189,371,957,383,1684,1238,822,482,545,832,3322,249,3.252,2.906,2.394,2.758,2.287,2.58,2.319,3.117,2.448,2.987,2.603,1.867,2.785,2.877,2.668,2.735,2.936,3.732,3.029,1.915,3.184,2.449,2.147,2.912,2.764,3.036,2.255,2.153,2.672,2.503,3.118,2.5,2.558,3.333,3.889,3.001,2.634,3.051,2.259,1.901,2.664,2.117,3.348,1.79,1.876,2.151,2.983,3.12,2.639,2.887,1.836,2.136,2.248,2.541,2.521,2.147,2.352,2.309,2.182,1.99,2.09,2.705,2.232,2.33,2.077,2.223,2.26,2.505,2.524,2.612,2.357,2.129,2.715,2.963,0.752,0.645,0.536,0.419,0.6,0.477,0.574,0.696,0.62,0.421,0.649,0.442,0.509,0.492,0.514,0.587,0.629,0.801,0.676,0.614,0.747,0.485,0.411,0.687,0.48,0.73,0.477,0.501,0.541,0.597,0.802,0.889,0.519,0.754,0.712,0.547,0.738,0.656,0.392,0.347,0.686,0.673,0.747,0.577,0.566,0.346,0.626,0.781,0.588,0.474,0.493,0.444,0.583,0.522,0.308,0.452,0.475,0.496,0.404,0.329,0.431,0.776,0.351,0.817,0.659,0.652,0.487,0.356,0.378,0.676,0.446,0.455,0.696,0.355
-150,Epileptic,F,27.0,0.7,2.1,1.0,1.1,1.6,1.7,1.9,1.2,1.6,0.5,0.4,2.6,1.5,0.6,1.0,5.7,9.7,0.8,3.4,4.3,1.9,3.1,1.6,2.8,3.5,2.5,2.7,2.5,3.2,2.8,1.4,0.7,0.5,2.2,0.9,0.6,2.5,3.2,0.1,0.2,0.8,2.9,2.7,2.3,3.2,0.7,0.4,1.1,1.2,1.3,0.6,1.6,1.6,2.5,0.3,2.4,0.6,1.8,1.1,1.0,0.9,1.4,0.4,0.4,1.6,0.5,3.3,1.5,0.9,0.5,0.7,1.1,4.4,0.3,8,16,11,10,14,17,14,10,13,5,5.0,21,15,4,8,52,79,5,30,34,20,30,15,32,40,24,26,20,23,34,15,5,3,20,5,4,34,36,1,1,4,23,24,16,17,4,2,6,5,8,4,10,13,17,2,18,5,21,7,8,8,12,3,5,12,13,23,9,6,5,4,7,29,2,0.034,0.019,0.02,0.022,0.031,0.029,0.029,0.025,0.042,0.027,0.035,0.033,0.032,0.031,0.026,0.031,0.032,0.03,0.037,0.035,0.044,0.033,0.031,0.033,0.036,0.027,0.027,0.032,0.035,0.031,0.036,0.031,0.024,0.029,0.028,0.015,0.032,0.035,0.018,0.018,0.019,0.032,0.044,0.025,0.025,0.013,0.021,0.016,0.018,0.046,0.028,0.017,0.021,0.02,0.024,0.016,0.018,0.024,0.024,0.017,0.029,0.023,0.03,0.014,0.018,0.027,0.025,0.019,0.019,0.018,0.019,0.022,0.019,0.023,1342,5070,2428,2172,2256,2755,2894,2810,2050,1247,481.0,2786,3141,1144,2468,11188,18894,2106,5079,4120,2237,4801,1895,5159,6243,5287,4998,3425,5541,5511,2076,915,1032,6179,2154,1798,5576,6588,438,455,1424,2756,6672,2188,3211,1547,721,2492,2267,942,593,3149,2434,5032,372,4363,1255,3054,1301,1750,977,2527,601,1032,2688,598,4357,2514,1419,1028,1365,2068,8428,360,0.124,0.101,0.103,0.105,0.134,0.137,0.116,0.121,0.151,0.119,0.142,0.133,0.134,0.13,0.117,0.124,0.133,0.105,0.143,0.137,0.129,0.136,0.136,0.13,0.144,0.125,0.124,0.112,0.116,0.137,0.122,0.107,0.102,0.118,0.114,0.079,0.13,0.149,0.098,0.104,0.099,0.132,0.148,0.115,0.11,0.084,0.104,0.091,0.092,0.11,0.139,0.096,0.107,0.098,0.142,0.099,0.099,0.121,0.11,0.1,0.141,0.114,0.142,0.111,0.108,0.129,0.109,0.102,0.105,0.109,0.102,0.111,0.103,0.128,461,1685,835,880,869,1077,1047,820,597,301,191.0,1220,862,280,638,3241,4997,443,1522,1956,725,1533,864,1552,1897,1600,1607,1379,1675,1707,638,381,331,1187,550,614,1471,1722,175,189,706,1331,1384,1446,2000,720,287,1058,1011,478,322,1440,1278,2117,169,2226,583,1435,768,944,480,997,238,492,1256,364,2129,1169,709,466,637,808,3675,188,2.708,2.844,2.8,2.532,2.478,2.262,2.348,3.054,2.747,3.113,2.309,2.001,2.93,3.051,2.947,2.714,3.013,3.596,2.722,1.851,2.366,2.38,2.005,2.651,2.558,2.718,2.392,2.081,2.604,2.593,2.453,2.288,2.516,3.463,3.459,2.62,2.789,2.713,2.975,2.5,2.615,1.895,3.26,1.762,1.84,2.522,3.203,3.023,2.808,1.738,2.008,2.327,2.085,2.432,2.488,2.207,2.485,2.23,1.931,2.048,2.086,2.551,2.676,2.4,2.423,2.215,2.115,2.44,2.287,2.477,2.485,2.65,2.565,2.378,0.819,0.57,0.602,0.417,0.526,0.507,0.509,0.564,0.515,0.474,0.552,0.471,0.473,0.433,0.438,0.602,0.632,0.862,0.562,0.507,0.909,0.477,0.365,0.573,0.515,0.519,0.51,0.591,0.559,0.558,0.666,0.945,0.622,0.656,0.695,0.519,0.914,0.661,0.381,0.48,0.545,0.566,0.766,0.572,0.489,0.36,0.486,0.69,0.548,0.844,0.406,0.487,0.518,0.535,0.445,0.425,0.475,0.54,0.329,0.393,0.444,0.658,0.425,0.735,0.471,0.622,0.48,0.383,0.42,0.431,0.401,0.691,0.54,0.737
-151,Epileptic,F,32.0,1.0,1.5,0.8,1.2,1.9,1.3,3.3,1.5,1.4,0.9,0.4,2.9,1.0,0.5,0.7,4.9,11.1,0.9,7.6,6.6,3.6,5.5,2.6,4.1,13.2,4.4,8.4,2.7,3.3,3.4,1.5,1.5,0.7,2.0,0.6,0.4,4.7,3.1,0.1,0.2,1.7,4.6,2.1,4.1,2.8,0.5,0.3,0.8,1.4,0.6,0.4,1.4,1.8,2.6,0.1,3.1,0.6,3.0,1.0,1.4,0.7,1.7,0.3,0.8,1.0,1.7,2.0,0.8,0.8,0.6,0.9,1.3,5.2,0.3,8,12,6,8,19,14,26,16,15,7,3.0,21,13,5,10,67,104,8,107,61,24,50,22,42,114,47,91,27,26,46,18,11,5,22,3,3,48,37,1,1,9,27,19,27,16,4,2,4,6,5,3,10,19,19,1,24,5,39,8,9,9,15,4,5,6,24,16,6,4,6,7,9,46,2,0.04,0.023,0.016,0.023,0.038,0.027,0.045,0.024,0.049,0.038,0.038,0.036,0.022,0.033,0.028,0.029,0.037,0.031,0.079,0.051,0.051,0.048,0.042,0.04,0.067,0.033,0.047,0.028,0.032,0.032,0.046,0.046,0.051,0.028,0.022,0.01,0.048,0.029,0.011,0.014,0.032,0.056,0.034,0.042,0.023,0.013,0.016,0.015,0.02,0.011,0.02,0.018,0.026,0.02,0.029,0.017,0.024,0.033,0.023,0.022,0.024,0.026,0.019,0.019,0.014,0.032,0.016,0.013,0.014,0.021,0.026,0.017,0.02,0.014,1706,3995,2331,2381,2094,3291,2968,4637,1777,1193,618.0,2770,3826,1087,2489,12438,20691,2328,4026,5762,3596,5491,2337,6596,7240,7914,8588,5825,7645,6443,2447,1476,1104,6908,2210,2270,7925,9431,307,583,1673,1876,5858,2634,4222,1544,700,2151,2533,1685,644,3300,2373,6092,245,6165,1091,3373,1523,2183,1434,3328,473,1260,2396,1367,4676,2538,2088,1141,1784,2657,11178,751,0.127,0.112,0.098,0.114,0.127,0.128,0.126,0.117,0.169,0.156,0.139,0.141,0.108,0.125,0.121,0.135,0.132,0.12,0.182,0.161,0.13,0.144,0.136,0.137,0.166,0.136,0.138,0.12,0.116,0.142,0.157,0.124,0.113,0.128,0.095,0.067,0.155,0.139,0.09,0.085,0.119,0.169,0.141,0.157,0.106,0.084,0.093,0.083,0.1,0.087,0.112,0.097,0.122,0.101,0.101,0.105,0.125,0.13,0.128,0.108,0.124,0.121,0.125,0.109,0.093,0.137,0.095,0.088,0.09,0.111,0.113,0.107,0.101,0.097,448,1088,752,845,785,936,1084,1108,530,401,188.0,1264,836,241,545,2892,5006,493,1589,2117,987,1671,923,1677,2639,2110,2876,1627,1577,1888,625,622,287,1294,468,657,1826,1935,156,246,795,1158,1203,1616,1994,714,323,882,1049,762,346,1243,1078,2136,88,2800,459,1542,671,1028,503,1093,242,608,1030,854,1946,999,829,435,681,1101,4359,270,3.157,3.19,2.983,2.934,2.271,2.874,2.418,3.384,2.668,2.84,2.744,1.957,3.286,2.947,3.155,3.016,3.237,3.475,2.082,2.194,2.814,2.582,2.043,2.901,2.132,2.841,2.352,2.676,3.43,2.711,2.873,2.156,2.958,3.502,3.794,3.018,3.126,3.249,2.349,2.975,2.716,1.71,3.602,1.978,2.445,2.493,2.807,3.188,3.046,2.698,2.606,2.771,2.51,2.871,3.113,2.524,2.968,2.574,2.67,2.334,2.938,3.183,1.956,2.534,2.704,1.923,2.556,3.026,2.994,2.663,2.967,2.758,2.815,3.385,0.778,0.693,0.518,0.538,0.738,0.677,0.719,0.542,0.735,0.879,0.684,0.49,0.468,0.746,0.662,0.735,0.799,0.825,0.864,0.809,1.068,0.84,0.69,0.917,0.959,0.8,0.623,0.723,0.614,0.554,0.91,0.937,0.747,0.654,0.765,0.506,0.773,0.868,0.543,0.394,0.641,0.688,0.773,0.603,0.739,0.415,0.463,0.521,0.482,0.468,0.438,0.434,0.549,0.519,0.539,0.432,0.503,0.687,0.557,0.406,0.476,0.832,0.562,0.546,0.516,0.702,0.53,0.403,0.444,0.651,0.455,0.593,0.486,0.376
-152,Epileptic,F,22.0,1.0,1.9,0.8,0.9,2.0,1.7,1.0,1.5,1.2,0.6,0.3,2.7,1.3,0.5,1.6,5.2,7.9,0.8,2.2,4.9,2.3,2.5,1.5,2.7,3.5,3.1,4.9,1.9,2.8,3.3,1.3,1.0,0.4,2.5,0.2,0.7,2.6,3.4,0.1,0.2,0.9,2.3,1.8,1.9,2.6,0.6,0.2,0.7,1.2,1.1,0.6,2.0,1.3,2.3,0.4,2.4,0.9,3.8,1.3,0.9,0.7,1.0,0.2,0.4,1.7,0.8,2.1,1.3,0.9,0.4,1.1,1.4,4.1,0.3,8,17,7,6,19,17,10,16,17,5,3.0,25,13,6,19,58,83,5,29,45,13,30,15,31,42,34,48,23,23,41,15,8,4,32,2,7,35,37,1,1,5,22,14,15,16,4,1,5,6,8,7,16,9,14,3,17,4,29,8,6,6,7,1,3,16,10,17,8,6,3,7,10,31,2,0.038,0.021,0.016,0.018,0.04,0.029,0.021,0.027,0.044,0.034,0.029,0.028,0.035,0.033,0.036,0.03,0.028,0.027,0.025,0.038,0.03,0.035,0.029,0.038,0.036,0.026,0.032,0.024,0.024,0.027,0.037,0.036,0.021,0.027,0.009,0.017,0.032,0.029,0.012,0.016,0.018,0.026,0.033,0.027,0.019,0.011,0.012,0.012,0.015,0.024,0.024,0.02,0.019,0.02,0.014,0.016,0.019,0.032,0.027,0.015,0.025,0.024,0.018,0.015,0.019,0.021,0.019,0.018,0.017,0.013,0.022,0.022,0.015,0.016,1549,5141,2196,2126,2410,3021,2599,3545,2156,1432,497.0,2584,3516,1221,3399,11589,20100,2690,5512,5695,3588,4647,2337,5596,6557,7310,6262,4756,6700,7176,2015,1183,1136,7145,1983,2283,5866,7801,310,445,1594,2545,4004,2117,3695,1446,704,2214,2548,1591,795,3781,2521,4802,721,4202,1692,4000,1400,1700,958,2098,314,790,2838,1016,3589,2741,2024,796,1719,2042,10288,626,0.133,0.109,0.101,0.094,0.14,0.136,0.103,0.124,0.154,0.128,0.113,0.134,0.132,0.11,0.155,0.135,0.128,0.106,0.119,0.144,0.101,0.143,0.127,0.132,0.144,0.125,0.136,0.112,0.108,0.132,0.116,0.113,0.099,0.124,0.065,0.094,0.127,0.124,0.086,0.083,0.099,0.127,0.128,0.115,0.1,0.091,0.083,0.078,0.088,0.118,0.133,0.105,0.098,0.102,0.098,0.099,0.095,0.125,0.127,0.095,0.121,0.105,0.108,0.108,0.114,0.125,0.103,0.103,0.097,0.107,0.118,0.112,0.091,0.104,476,1476,762,768,796,1035,824,991,525,326,169.0,1409,772,244,813,2935,4909,480,1441,2229,1162,1334,899,1270,1727,1949,2204,1370,1681,2103,553,433,352,1551,498,660,1520,1804,189,184,708,1326,922,1203,2054,706,271,921,1112,655,441,1459,1036,1780,401,2045,670,1803,708,867,463,717,154,363,1337,530,1681,1082,860,369,785,978,3997,308,3.292,3.011,2.755,2.819,2.606,2.527,2.566,3.032,3.038,3.619,2.554,1.639,3.399,3.196,2.909,2.906,3.182,3.928,2.973,2.185,2.702,2.671,2.135,3.098,2.774,2.903,2.337,2.636,2.967,2.675,2.666,2.533,2.628,3.225,3.327,2.881,2.919,3.185,1.828,2.6,2.831,1.807,3.268,2.029,2.043,2.173,3.351,2.933,2.812,2.636,2.059,2.534,2.49,2.789,2.337,2.236,2.966,2.551,2.12,2.209,2.466,2.932,2.255,2.725,2.409,2.263,2.303,2.995,2.564,2.602,2.588,2.568,2.7,2.465,0.842,0.683,0.546,0.546,0.6,0.582,0.562,0.591,0.631,0.516,0.604,0.447,0.646,0.671,0.663,0.604,0.575,0.668,0.536,0.682,0.776,0.517,0.444,0.598,0.643,0.62,0.551,0.491,0.452,0.606,0.598,0.999,0.377,0.607,0.586,0.465,0.61,0.541,0.315,0.539,0.723,0.475,0.685,0.999,0.548,0.459,0.411,0.685,0.623,0.655,0.48,0.665,0.532,0.558,0.303,0.483,0.536,0.59,0.418,0.435,0.532,0.779,0.414,0.696,0.555,0.649,0.456,0.453,0.502,0.59,0.667,0.664,0.596,0.352
-153,Epileptic,F,42.0,0.7,2.5,0.7,1.2,1.4,2.0,2.4,1.8,0.9,0.8,0.2,2.8,1.4,0.6,1.0,6.3,7.3,1.2,2.7,4.9,1.5,6.2,1.8,3.2,6.8,3.1,4.1,2.8,3.4,2.9,2.2,1.3,0.4,2.7,0.6,0.6,3.6,4.1,0.3,0.2,1.0,4.7,3.6,2.5,3.5,0.9,0.4,1.0,1.5,1.4,0.2,1.8,1.2,2.9,0.3,2.9,1.2,1.6,1.3,1.9,0.4,2.1,0.6,0.9,1.9,1.0,2.8,1.1,1.2,0.6,0.9,1.6,5.5,0.2,8,29,6,9,15,20,18,16,10,7,3.0,25,16,7,11,68,62,12,28,44,20,63,20,39,62,36,48,31,27,39,21,10,5,29,4,7,35,42,3,2,6,36,35,19,22,6,2,7,12,11,2,15,9,23,2,21,7,13,8,13,4,14,3,5,18,15,22,8,8,6,7,15,39,2,0.035,0.026,0.017,0.024,0.025,0.032,0.034,0.029,0.036,0.028,0.026,0.035,0.028,0.035,0.026,0.032,0.026,0.041,0.029,0.039,0.032,0.046,0.027,0.039,0.047,0.033,0.038,0.028,0.029,0.027,0.056,0.044,0.023,0.036,0.019,0.021,0.044,0.04,0.022,0.012,0.021,0.041,0.048,0.025,0.022,0.017,0.019,0.016,0.02,0.025,0.019,0.017,0.018,0.018,0.026,0.016,0.021,0.018,0.023,0.023,0.014,0.029,0.031,0.03,0.026,0.021,0.02,0.017,0.019,0.018,0.025,0.022,0.019,0.014,1306,4621,1840,2215,2329,3055,2921,3544,1611,1609,721.0,2865,3340,1232,2320,12001,17888,2494,5629,4977,3945,4898,2852,5696,5993,5021,5761,5046,6791,6405,1820,1197,911,5567,1833,1791,5995,7771,425,625,1356,3420,5824,2742,4458,1598,721,2235,2527,1932,266,3465,2185,6027,247,5108,1770,3358,1311,2436,1113,2698,596,1144,2474,1265,4469,2193,1881,775,1336,2856,9679,413,0.132,0.123,0.1,0.115,0.121,0.127,0.107,0.134,0.145,0.139,0.118,0.148,0.129,0.134,0.112,0.133,0.117,0.157,0.124,0.152,0.107,0.135,0.121,0.144,0.136,0.135,0.116,0.107,0.106,0.121,0.154,0.131,0.124,0.121,0.082,0.09,0.128,0.139,0.134,0.099,0.109,0.137,0.161,0.123,0.105,0.1,0.093,0.089,0.092,0.104,0.125,0.097,0.105,0.1,0.133,0.096,0.103,0.097,0.115,0.109,0.083,0.132,0.134,0.131,0.142,0.117,0.107,0.096,0.104,0.128,0.122,0.114,0.101,0.106,458,1634,689,859,963,1208,1066,1073,499,440,204.0,1393,949,347,703,3602,4946,599,1584,2247,930,2063,1167,1686,2333,1695,2168,1752,1824,1993,694,506,317,1451,520,662,1744,1941,199,261,699,1906,1569,1623,2531,820,327,912,1210,874,162,1739,1048,2581,157,2650,849,1520,845,1240,522,1102,310,419,1157,774,2167,1013,909,438,639,1235,4363,185,2.871,2.501,2.529,2.548,2.18,2.288,2.267,2.662,2.494,2.772,2.931,1.849,2.72,2.668,2.519,2.558,2.904,3.068,2.79,1.996,2.982,2.043,2.08,2.638,2.243,2.499,2.212,2.286,2.799,2.498,2.143,2.243,2.529,2.756,2.808,2.422,2.692,2.906,2.202,2.329,2.336,1.619,2.611,1.969,2.017,2.177,2.58,2.624,2.494,2.378,2.039,2.097,2.158,2.391,1.926,2.119,2.358,2.451,1.87,2.188,2.259,2.423,2.18,3.266,2.378,1.924,2.191,2.5,2.297,1.896,2.268,2.416,2.373,2.666,1.081,0.536,0.485,0.53,0.545,0.547,0.599,0.538,0.629,0.608,0.573,0.439,0.489,0.384,0.482,0.525,0.559,0.814,0.511,0.576,0.834,0.588,0.55,0.666,0.643,0.598,0.487,0.51,0.549,0.546,0.635,0.778,0.461,0.71,0.687,0.515,0.842,0.701,0.397,0.626,0.451,0.604,0.88,0.613,0.5,0.38,0.483,0.909,0.507,0.71,0.22,0.398,0.442,0.43,0.383,0.388,0.482,0.421,0.443,0.369,0.385,0.54,0.468,0.7,0.497,0.694,0.485,0.465,0.416,0.505,0.552,0.498,0.46,0.457
-154,Epileptic,M,32.0,0.6,1.7,0.8,0.7,1.9,2.7,1.2,1.2,0.9,0.6,0.3,3.4,1.2,0.4,1.0,5.3,8.5,0.7,2.2,4.8,0.7,2.6,1.9,3.3,2.9,2.9,4.3,2.3,2.6,3.4,1.0,0.6,0.5,2.4,0.4,1.4,3.5,4.4,0.1,0.2,1.1,4.2,2.4,2.7,3.0,0.5,0.4,0.8,1.0,0.4,0.5,2.1,1.4,2.6,0.8,2.9,0.8,1.6,1.0,1.1,0.9,1.8,0.3,0.4,2.1,1.4,2.7,1.5,0.6,0.8,1.6,2.0,5.2,0.4,5,19,6,6,21,23,11,12,12,5,3.0,33,13,7,16,64,84,10,25,41,7,32,21,44,40,31,46,25,27,39,15,6,4,25,2,16,43,42,1,2,6,33,29,22,19,5,2,5,6,6,3,20,10,19,7,24,5,15,8,12,8,14,2,5,16,16,22,13,4,8,11,16,46,4,0.025,0.021,0.014,0.014,0.042,0.033,0.02,0.022,0.04,0.031,0.025,0.032,0.03,0.027,0.026,0.029,0.026,0.024,0.027,0.035,0.016,0.025,0.029,0.037,0.027,0.028,0.028,0.025,0.022,0.03,0.031,0.033,0.026,0.028,0.012,0.031,0.033,0.039,0.009,0.014,0.023,0.034,0.036,0.028,0.019,0.013,0.017,0.013,0.015,0.012,0.027,0.026,0.02,0.018,0.032,0.018,0.018,0.017,0.022,0.021,0.021,0.027,0.015,0.017,0.025,0.052,0.025,0.02,0.012,0.026,0.031,0.026,0.02,0.017,1549,4278,1938,1709,2569,3838,3011,3608,1449,1331,690.0,3036,2930,1522,2674,11429,20272,2812,5261,4161,2897,5037,2719,6550,6669,6369,7575,4174,7386,6345,2090,1133,898,6691,2233,2680,9459,8219,426,461,1331,3648,6048,2354,4354,1275,847,1993,2329,1464,548,3560,2226,5518,1073,5318,1772,3314,1397,1375,1610,2884,490,827,2782,926,3716,2757,1347,1879,1861,3165,11419,578,0.101,0.111,0.097,0.091,0.148,0.144,0.101,0.11,0.15,0.125,0.13,0.147,0.119,0.138,0.142,0.136,0.125,0.111,0.122,0.145,0.08,0.128,0.14,0.143,0.138,0.133,0.135,0.111,0.108,0.133,0.13,0.099,0.125,0.131,0.065,0.133,0.144,0.151,0.078,0.098,0.114,0.143,0.145,0.122,0.103,0.089,0.109,0.085,0.088,0.089,0.117,0.127,0.108,0.099,0.156,0.106,0.096,0.101,0.111,0.124,0.118,0.121,0.094,0.122,0.126,0.15,0.114,0.107,0.086,0.119,0.141,0.132,0.107,0.105,449,1465,804,684,909,1351,844,971,453,348,216.0,1629,758,361,727,3088,5311,542,1343,2157,687,1666,1113,1732,1859,1728,2397,1456,1753,2000,574,471,300,1356,508,781,1943,1952,212,252,675,1871,1326,1586,2362,666,352,883,1046,559,310,1446,1014,2276,501,2615,682,1586,751,818,643,1061,287,406,1317,490,1874,1237,650,599,799,1289,4310,313,3.376,2.706,2.396,2.453,2.339,2.503,2.736,3.075,2.417,3.285,3.044,1.743,2.885,3.024,2.833,2.739,3.036,3.8,3.093,1.751,3.015,2.423,2.113,2.906,2.732,2.918,2.457,2.241,3.031,2.501,2.641,2.47,2.339,3.384,3.614,2.933,3.316,3.053,2.134,2.345,2.679,1.804,3.121,1.772,2.122,2.066,3.087,2.819,2.833,2.665,2.017,2.414,2.234,2.559,2.603,2.262,3.046,2.37,2.193,1.785,2.456,2.67,2.043,2.37,2.425,2.306,2.096,2.461,2.5,2.906,2.629,2.791,2.794,2.253,0.566,0.709,0.578,0.481,0.6,0.535,0.631,0.578,0.584,0.489,0.866,0.54,0.502,0.528,0.465,0.633,0.553,0.743,0.679,0.479,0.742,0.411,0.555,0.65,0.54,0.553,0.56,0.58,0.563,0.539,0.645,0.862,0.533,0.629,0.828,0.563,0.651,0.687,0.461,0.357,0.531,0.524,0.727,0.86,0.605,0.427,0.48,0.433,0.572,0.469,0.461,0.479,0.518,0.444,0.41,0.396,0.511,0.502,0.262,0.416,0.404,0.58,0.438,0.867,0.518,0.51,0.473,0.359,0.386,0.835,0.515,0.408,0.564,0.583
-155,Epileptic,F,27.0,0.6,2.3,0.9,1.1,1.6,1.6,1.4,2.0,0.8,0.5,0.2,2.6,2.3,0.5,1.4,5.4,8.7,0.7,2.1,3.8,1.5,3.1,1.7,2.8,4.0,4.9,3.3,3.0,2.7,2.7,1.2,0.6,0.8,1.9,0.2,1.5,3.9,3.4,0.2,0.1,1.4,3.0,2.4,2.6,3.0,0.6,0.6,0.5,1.4,1.4,0.7,1.8,1.7,2.4,1.9,2.4,0.8,1.8,1.1,1.0,0.5,1.0,0.3,0.4,2.0,1.9,2.9,1.9,0.7,0.9,1.1,1.9,4.8,0.1,4,22,11,13,15,17,13,20,9,6,4.0,20,23,8,15,62,82,7,24,32,13,32,16,31,53,45,44,28,25,32,21,5,4,26,1,12,43,42,1,1,7,29,24,18,16,6,3,4,7,14,5,13,10,21,12,20,5,17,9,6,6,7,2,4,16,28,27,13,4,8,9,12,37,0,0.029,0.026,0.022,0.022,0.042,0.027,0.024,0.027,0.041,0.033,0.031,0.041,0.031,0.045,0.034,0.033,0.033,0.028,0.026,0.033,0.026,0.034,0.037,0.037,0.038,0.033,0.032,0.032,0.029,0.037,0.042,0.036,0.04,0.024,0.007,0.026,0.033,0.034,0.02,0.026,0.023,0.037,0.033,0.032,0.023,0.017,0.022,0.011,0.016,0.024,0.029,0.022,0.023,0.02,0.063,0.019,0.014,0.022,0.03,0.021,0.019,0.021,0.023,0.026,0.027,0.037,0.026,0.023,0.014,0.027,0.031,0.023,0.018,0.016,1931,4903,2143,2497,1750,2918,3371,4735,1462,1400,523.0,2277,5480,1106,3269,10912,17199,2808,5009,4550,4622,4859,2155,5986,7697,8849,5416,4309,5941,4835,2275,902,1038,5620,1740,2977,8078,8204,363,359,1853,2582,4650,2078,3391,1205,1223,1750,2859,2658,639,3190,2234,4927,713,3720,2105,3550,1067,1178,1366,1860,477,962,2736,1065,4391,3693,1401,995,1382,3005,9967,149,0.099,0.111,0.114,0.11,0.144,0.132,0.116,0.121,0.147,0.117,0.137,0.15,0.132,0.165,0.144,0.136,0.133,0.111,0.121,0.133,0.106,0.145,0.143,0.137,0.153,0.138,0.14,0.126,0.121,0.147,0.144,0.101,0.14,0.119,0.05,0.115,0.127,0.134,0.093,0.114,0.105,0.158,0.13,0.13,0.104,0.104,0.109,0.073,0.09,0.111,0.141,0.11,0.116,0.106,0.164,0.109,0.08,0.119,0.123,0.108,0.104,0.11,0.114,0.114,0.122,0.147,0.12,0.111,0.094,0.126,0.139,0.112,0.097,0.093,414,1569,748,912,656,1050,999,1439,413,363,173.0,1037,1250,254,755,2876,4621,455,1312,1853,1099,1503,809,1430,2066,2539,1824,1471,1567,1353,584,328,302,1244,507,908,2059,1853,186,109,886,1265,1192,1278,1961,572,379,738,1230,973,358,1350,1036,1922,386,1929,880,1399,654,693,521,690,215,359,1126,710,1931,1319,639,422,560,1248,4192,57,3.992,2.632,2.685,2.538,2.28,2.448,2.671,3.005,2.592,2.963,2.73,2.14,3.272,2.936,3.195,2.798,2.911,4.227,2.999,2.045,3.11,2.401,2.339,2.952,2.867,2.841,2.247,2.297,2.867,2.785,2.74,2.558,2.854,3.071,3.147,2.965,2.884,3.241,2.237,3.311,2.688,1.832,3.068,1.867,2.003,2.29,3.504,2.796,2.885,2.772,2.128,2.452,2.183,2.57,2.221,2.059,2.673,2.696,1.896,1.916,2.512,2.609,2.513,2.842,2.424,1.816,2.214,2.765,2.503,2.375,2.663,2.758,2.553,2.896,0.817,0.67,0.778,0.679,0.602,0.515,0.607,0.627,0.645,0.595,0.66,0.675,0.598,0.486,0.515,0.705,0.646,0.647,0.54,0.61,0.8,0.504,0.501,0.738,0.639,0.544,0.577,0.59,0.545,0.69,0.672,0.833,0.646,0.563,0.735,0.517,0.715,0.663,0.358,0.53,0.544,0.483,0.673,0.735,0.538,0.492,0.764,0.679,0.724,0.621,0.48,0.563,0.622,0.529,0.605,0.468,0.585,0.749,0.3,0.392,0.499,0.727,0.562,0.813,0.664,0.619,0.49,0.516,0.421,0.778,0.538,0.581,0.613,0.389
-156,Epileptic,M,32.0,0.9,2.6,1.7,1.4,1.7,2.1,1.5,2.0,1.0,0.7,0.3,2.4,2.1,0.5,2.4,5.4,7.9,1.0,2.3,4.1,0.8,1.8,1.5,3.3,2.4,3.1,3.2,2.3,2.0,3.9,1.0,1.3,0.3,2.0,0.4,0.4,4.2,3.4,0.2,0.3,1.5,3.1,1.9,2.2,2.3,0.7,0.7,1.1,1.4,0.7,0.4,2.6,1.9,2.9,0.2,2.8,0.8,1.3,0.5,1.0,0.6,1.7,0.5,0.3,1.1,1.2,2.3,1.0,0.9,0.5,1.0,1.4,3.3,0.3,5,26,16,11,16,20,12,16,11,6,4.0,21,18,7,23,59,66,8,29,40,9,19,16,30,31,31,39,25,17,41,13,10,3,25,2,2,42,35,1,2,10,25,21,18,12,5,4,7,8,6,2,19,12,19,2,22,5,10,4,8,4,14,3,3,11,18,21,6,6,5,9,10,27,3,0.032,0.022,0.02,0.021,0.032,0.028,0.021,0.031,0.032,0.032,0.025,0.03,0.03,0.02,0.029,0.025,0.025,0.034,0.028,0.031,0.017,0.032,0.025,0.03,0.029,0.026,0.024,0.021,0.02,0.028,0.026,0.04,0.021,0.023,0.01,0.009,0.038,0.035,0.014,0.017,0.028,0.033,0.027,0.027,0.017,0.016,0.021,0.015,0.015,0.02,0.022,0.019,0.018,0.019,0.019,0.016,0.023,0.014,0.018,0.023,0.014,0.028,0.024,0.013,0.016,0.028,0.017,0.015,0.016,0.015,0.019,0.02,0.015,0.019,1831,6014,3059,2594,1846,3561,2975,3130,1853,1491,645.0,2361,4101,1328,4783,13050,17018,2566,4617,5714,3275,3234,2349,7098,5004,6357,6565,4090,5073,6660,2278,1420,896,5361,1953,1460,7018,7291,509,653,1562,3395,4713,2869,3672,1405,1025,2201,2897,1370,478,4869,2967,6044,306,4334,1505,3143,753,1252,1091,2696,535,906,2264,1351,3965,2200,1566,1023,1632,2327,7565,380,0.108,0.108,0.116,0.109,0.147,0.125,0.1,0.126,0.136,0.122,0.126,0.136,0.128,0.122,0.133,0.121,0.118,0.129,0.128,0.14,0.084,0.125,0.132,0.13,0.139,0.124,0.118,0.102,0.095,0.135,0.109,0.117,0.092,0.127,0.065,0.063,0.129,0.135,0.084,0.094,0.126,0.148,0.126,0.116,0.092,0.095,0.113,0.085,0.091,0.117,0.114,0.098,0.099,0.1,0.141,0.098,0.107,0.087,0.104,0.127,0.093,0.127,0.123,0.099,0.101,0.131,0.102,0.088,0.098,0.104,0.116,0.117,0.092,0.117,488,1916,1219,979,815,1214,1038,1135,531,382,207.0,1153,1154,346,1338,3491,4956,527,1360,2134,889,1044,956,1812,1506,1871,2357,1597,1464,2199,661,562,316,1342,563,567,1960,1771,288,304,826,1440,1204,1559,2051,732,491,943,1349,595,277,2146,1546,2410,148,2429,581,1505,466,616,585,986,304,437,1139,677,2010,1066,805,534,803,1107,3418,226,3.62,2.914,2.438,2.559,2.287,2.438,2.426,2.586,2.648,3.193,2.66,1.81,2.804,2.699,2.681,2.814,2.859,3.56,2.732,2.254,2.875,2.425,2.166,2.865,2.59,2.81,2.295,2.126,2.659,2.517,2.572,2.53,2.362,2.956,3.241,2.473,2.625,3.077,2.164,2.378,2.418,2.164,3.053,2.056,2.059,2.092,2.65,2.74,2.579,2.733,2.159,2.323,2.278,2.539,2.68,2.029,2.749,2.333,1.91,2.14,2.259,2.86,2.163,2.41,2.201,2.333,2.081,2.52,2.185,2.388,2.276,2.522,2.397,1.865,0.819,0.608,0.522,0.517,0.439,0.533,0.592,0.408,0.478,0.422,0.819,0.45,0.389,0.46,0.479,0.459,0.545,0.784,0.451,0.657,0.762,0.517,0.362,0.569,0.48,0.438,0.456,0.679,0.488,0.497,0.61,0.934,0.316,0.655,0.71,0.438,0.743,0.55,0.313,0.459,0.385,0.515,0.643,0.534,0.632,0.494,0.364,0.704,0.671,0.435,0.429,0.591,0.421,0.445,0.616,0.412,0.591,0.485,0.305,0.326,0.33,0.603,0.327,1.061,0.517,0.681,0.543,0.374,0.433,0.506,0.448,0.514,0.468,0.442
-157,Epileptic,F,32.0,0.9,2.1,0.8,1.1,1.5,2.6,2.0,1.6,1.0,0.8,0.3,4.1,1.4,0.5,1.6,4.4,7.8,0.9,2.2,4.8,1.4,3.0,1.9,2.9,3.3,2.7,3.5,2.2,2.6,3.4,1.2,2.0,0.6,1.9,0.4,0.7,3.8,2.7,0.1,0.2,0.9,2.7,1.9,3.5,3.7,0.9,0.6,0.7,1.2,0.5,0.6,1.5,1.1,2.8,0.4,2.3,0.6,1.6,0.7,0.9,0.8,0.8,0.4,0.3,1.5,1.6,2.6,1.0,1.2,0.3,1.2,1.3,3.9,0.3,5,21,7,8,12,21,16,13,13,7,4.0,30,14,6,13,45,67,7,21,43,12,31,15,28,32,29,38,21,19,37,14,13,6,23,2,7,36,32,2,2,4,23,20,21,25,8,3,5,6,5,5,12,10,21,2,20,6,14,6,9,6,5,4,3,13,15,21,6,8,3,10,9,30,2,0.039,0.025,0.018,0.021,0.037,0.036,0.03,0.028,0.04,0.04,0.028,0.045,0.031,0.033,0.039,0.03,0.028,0.034,0.029,0.039,0.024,0.032,0.034,0.038,0.034,0.026,0.028,0.024,0.028,0.031,0.039,0.091,0.028,0.026,0.014,0.02,0.038,0.027,0.013,0.015,0.019,0.037,0.036,0.035,0.029,0.02,0.024,0.013,0.015,0.013,0.029,0.02,0.024,0.024,0.028,0.018,0.04,0.019,0.02,0.019,0.036,0.02,0.034,0.022,0.027,0.032,0.022,0.019,0.025,0.016,0.029,0.025,0.017,0.015,1370,3558,1783,2355,1445,3229,2558,2462,1538,1309,701.0,2570,2824,971,2602,8772,17063,2098,3930,4801,3171,4757,1884,4596,5530,5581,5506,3805,4735,5178,1780,1059,1075,4858,1669,1868,4812,6631,415,507,1418,2884,3800,2591,2652,1559,855,2342,2430,1210,606,2563,2002,4949,487,3728,817,3184,877,1456,691,1884,670,680,1892,1185,4229,1802,1213,772,1546,1580,8470,598,0.125,0.124,0.107,0.103,0.133,0.143,0.12,0.129,0.136,0.14,0.112,0.16,0.137,0.144,0.149,0.13,0.126,0.132,0.13,0.15,0.103,0.139,0.143,0.143,0.139,0.128,0.133,0.11,0.108,0.141,0.15,0.183,0.134,0.127,0.07,0.094,0.128,0.118,0.09,0.099,0.096,0.149,0.136,0.134,0.133,0.114,0.124,0.073,0.088,0.105,0.14,0.1,0.117,0.11,0.12,0.107,0.144,0.104,0.115,0.117,0.141,0.099,0.157,0.111,0.119,0.14,0.11,0.102,0.129,0.097,0.137,0.12,0.095,0.096,356,1279,709,816,623,1278,972,886,525,334,199.0,1436,808,271,709,2400,4601,434,1210,2093,920,1583,875,1259,1662,1742,2095,1379,1418,1735,499,477,357,1213,438,665,1620,1647,215,245,689,1252,1036,1623,1989,726,352,942,1132,546,339,1178,802,2035,258,1999,358,1463,552,739,413,628,217,286,1006,705,1984,835,761,360,706,803,3611,281,3.567,2.554,2.411,2.899,2.118,2.371,2.223,2.555,2.206,3.114,2.936,1.702,2.663,2.588,2.749,2.781,2.935,3.478,2.729,2.002,2.688,2.424,1.964,2.694,2.628,2.712,2.168,2.225,2.651,2.421,2.685,2.628,2.505,2.896,3.155,2.618,2.364,2.971,2.202,2.35,2.567,2.004,2.873,1.865,1.551,2.245,2.989,2.925,2.646,2.674,2.041,2.215,2.186,2.399,2.252,2.09,2.575,2.506,1.864,2.189,2.121,2.789,2.67,2.299,2.012,2.249,2.308,2.491,1.925,2.45,2.601,2.35,2.506,2.506,0.838,0.815,0.772,0.499,0.478,0.525,0.537,0.457,0.629,0.728,0.526,0.558,0.455,0.495,0.506,0.695,0.629,0.695,0.59,0.604,0.795,0.46,0.436,0.711,0.518,0.513,0.452,0.504,0.602,0.503,0.635,0.785,0.406,0.661,0.627,0.704,0.642,0.59,0.513,0.393,0.522,0.507,0.622,0.733,0.455,0.733,0.501,0.837,0.668,0.508,0.406,0.608,0.712,0.49,0.31,0.411,0.559,0.537,0.361,0.393,0.387,0.838,0.48,0.965,0.483,0.525,0.471,0.388,0.452,0.608,0.408,0.522,0.639,0.528
-158,Epileptic,M,22.0,1.2,2.1,1.1,1.5,2.1,2.1,3.0,2.2,1.8,0.9,0.4,3.0,1.8,0.4,1.1,5.5,9.5,0.9,2.7,4.9,1.4,4.0,2.2,3.7,4.3,4.0,4.6,4.0,3.0,3.6,1.6,0.8,0.4,1.9,0.4,0.7,3.7,3.4,0.2,0.1,1.1,3.6,2.1,3.2,3.1,0.7,0.6,0.9,1.2,0.8,0.8,2.1,1.9,2.6,0.3,2.6,0.6,1.8,1.0,1.3,1.1,1.9,0.5,0.8,1.8,1.0,2.7,1.8,1.4,0.6,0.8,1.7,5.1,0.3,7,21,11,11,17,21,20,20,17,11,4.0,33,20,7,16,70,106,8,30,46,13,51,24,40,50,44,48,35,32,44,22,7,3,25,2,5,37,56,1,1,7,35,19,21,16,4,3,4,6,7,4,19,16,17,2,17,4,19,7,10,8,16,4,6,10,12,20,11,7,5,6,14,34,3,0.046,0.023,0.024,0.026,0.038,0.03,0.04,0.033,0.049,0.047,0.053,0.04,0.031,0.036,0.029,0.035,0.033,0.035,0.04,0.044,0.029,0.043,0.039,0.041,0.039,0.03,0.04,0.039,0.036,0.036,0.043,0.041,0.033,0.027,0.01,0.016,0.04,0.035,0.016,0.01,0.022,0.037,0.035,0.03,0.021,0.014,0.018,0.014,0.018,0.021,0.028,0.022,0.023,0.018,0.017,0.016,0.017,0.024,0.019,0.019,0.031,0.025,0.019,0.023,0.019,0.018,0.018,0.021,0.02,0.017,0.022,0.018,0.02,0.024,1541,5172,2610,2684,2533,4206,3351,3739,1833,1685,667.0,3763,4092,1225,2499,11807,22423,2758,4806,5512,3940,6626,2777,6678,8002,8740,7228,4720,6689,6689,2749,875,727,6313,2312,1939,7281,8933,298,374,1381,4008,5674,3105,3901,1376,1020,2367,2417,1866,656,3741,3030,5331,591,4548,940,3105,1610,2184,1317,3514,768,1118,3027,1238,4626,2933,2098,1226,1186,2729,9321,397,0.134,0.116,0.109,0.111,0.129,0.133,0.119,0.14,0.148,0.166,0.139,0.137,0.131,0.142,0.125,0.137,0.132,0.126,0.151,0.154,0.109,0.148,0.133,0.137,0.138,0.133,0.132,0.124,0.116,0.145,0.142,0.104,0.104,0.122,0.063,0.088,0.136,0.149,0.097,0.093,0.112,0.135,0.13,0.124,0.103,0.084,0.093,0.077,0.086,0.113,0.121,0.117,0.112,0.096,0.104,0.093,0.103,0.123,0.102,0.104,0.128,0.123,0.103,0.121,0.1,0.11,0.099,0.11,0.104,0.12,0.107,0.109,0.099,0.132,413,1518,820,946,971,1230,1133,1146,578,391,177.0,1487,1029,299,702,3007,5571,508,1220,2322,929,1745,1031,1900,2128,2353,2304,1721,1622,1836,756,436,230,1325,542,629,1711,1934,149,161,709,1659,1169,1697,2219,694,464,908,1086,615,430,1568,1287,2192,274,2304,479,1368,822,1092,539,1226,416,575,1332,676,2330,1216,965,476,584,1418,3905,225,3.307,2.925,2.947,2.763,2.273,2.575,2.44,2.814,2.321,3.084,2.932,2.079,2.92,2.984,2.599,2.797,3.016,3.735,2.932,2.041,3.21,2.794,2.27,2.658,2.759,2.77,2.42,2.161,2.987,2.722,2.71,2.204,2.413,3.234,3.616,2.715,2.98,3.195,2.053,2.374,2.433,2.063,3.481,2.042,2.014,2.305,2.739,3.158,2.622,2.902,1.958,2.338,2.275,2.511,2.269,2.212,2.288,2.441,2.086,2.09,2.149,2.597,2.047,2.232,2.442,2.181,2.098,2.678,2.517,2.546,2.306,2.344,2.534,2.01,1.007,0.682,0.607,0.476,0.703,0.761,0.62,0.48,0.692,0.796,0.886,0.592,0.491,0.372,0.637,0.71,0.7,0.813,0.68,0.667,0.852,0.708,0.624,0.794,0.726,0.664,0.613,0.656,0.576,0.641,0.743,1.061,0.532,0.679,0.858,0.695,0.837,0.692,0.412,0.378,0.565,0.659,0.683,0.609,0.622,0.374,0.471,0.651,0.408,0.592,0.345,0.537,0.527,0.481,0.513,0.402,0.464,0.598,0.435,0.436,0.508,0.738,0.458,0.59,0.583,0.711,0.449,0.477,0.449,1.116,0.439,0.436,0.504,0.48
-159,Epileptic,M,27.0,2.0,2.9,1.1,1.2,1.3,1.7,1.5,1.4,1.1,0.8,0.5,3.6,1.7,0.6,1.7,7.0,10.3,0.9,2.1,4.9,2.0,3.4,3.2,4.6,6.6,3.5,5.5,3.0,3.2,4.9,1.8,1.4,0.4,2.5,0.4,0.7,3.2,3.5,0.2,0.4,1.0,3.2,2.7,3.8,2.2,0.7,0.7,0.9,1.4,0.6,0.6,2.8,1.3,2.7,0.6,2.8,0.6,1.9,1.7,1.1,0.4,1.3,0.9,0.8,1.8,1.3,2.4,1.3,1.1,0.8,1.1,1.7,3.7,0.3,10,28,12,13,14,20,17,16,11,11,5.0,34,21,7,21,76,97,11,24,47,19,41,31,41,64,34,52,29,28,55,21,12,4,27,2,10,36,39,2,3,5,29,25,32,13,6,3,5,7,5,6,21,10,18,4,26,5,17,10,9,3,10,5,5,15,17,19,9,6,12,7,12,28,2,0.057,0.026,0.02,0.024,0.03,0.025,0.023,0.027,0.034,0.036,0.031,0.044,0.03,0.038,0.03,0.038,0.034,0.035,0.033,0.043,0.034,0.043,0.047,0.044,0.05,0.03,0.033,0.03,0.03,0.041,0.045,0.058,0.023,0.032,0.012,0.013,0.036,0.034,0.016,0.018,0.022,0.042,0.037,0.03,0.017,0.012,0.02,0.013,0.018,0.013,0.026,0.026,0.024,0.02,0.015,0.018,0.021,0.023,0.026,0.02,0.012,0.027,0.032,0.026,0.02,0.028,0.017,0.017,0.016,0.032,0.019,0.02,0.015,0.018,1773,5655,2596,2707,2160,3776,2938,2577,2044,1867,1081.0,3522,3930,1029,3423,11584,19785,2689,3824,5594,4893,4132,2190,7040,6009,7324,6739,3804,6325,7031,3101,1546,945,6464,2143,2288,6965,7687,410,763,1481,2432,6175,3802,3075,1885,954,2411,2384,1482,684,3881,1885,4933,808,4662,1185,3281,1431,1493,903,2124,839,1072,2880,1557,3923,2296,1912,1135,1816,2809,8374,393,0.148,0.12,0.107,0.113,0.119,0.123,0.105,0.137,0.148,0.157,0.131,0.162,0.133,0.142,0.141,0.152,0.14,0.134,0.149,0.162,0.124,0.128,0.15,0.156,0.156,0.124,0.126,0.117,0.106,0.151,0.162,0.134,0.113,0.128,0.069,0.094,0.14,0.138,0.097,0.105,0.098,0.142,0.151,0.134,0.092,0.086,0.104,0.083,0.092,0.096,0.125,0.115,0.121,0.103,0.111,0.102,0.114,0.113,0.124,0.106,0.078,0.125,0.151,0.11,0.112,0.128,0.1,0.098,0.092,0.138,0.105,0.104,0.094,0.118,494,1963,907,954,822,1209,1033,929,581,451,298.0,1518,1171,296,1044,3549,5545,580,1048,2219,1089,1385,1132,1973,2285,2072,2649,1599,1749,2168,863,547,330,1410,520,770,1740,1823,250,406,710,1297,1261,2166,1914,913,460,984,1128,685,403,1862,863,2170,481,2384,480,1454,895,818,466,830,388,517,1407,789,2065,1102,924,451,874,1313,3630,201,3.428,2.728,2.77,2.693,2.23,2.491,2.345,2.431,2.574,3.184,3.022,1.989,2.671,2.497,2.523,2.524,2.771,3.681,2.789,2.131,3.187,2.337,1.807,2.746,2.214,2.8,2.209,2.031,2.707,2.508,2.749,2.752,2.432,3.195,3.571,2.561,2.907,3.033,1.89,2.175,2.628,1.689,3.323,2.019,1.811,2.303,2.501,2.97,2.543,2.445,1.987,2.174,2.152,2.279,2.027,2.046,2.48,2.452,1.839,1.945,2.194,2.463,2.026,2.471,2.135,1.976,2.007,2.358,2.321,2.507,2.391,2.475,2.514,2.401,0.846,0.545,0.615,0.533,0.653,0.694,0.618,0.507,0.65,0.624,0.83,0.59,0.395,0.412,0.453,0.558,0.704,0.746,0.549,0.649,0.829,0.619,0.592,0.743,0.624,0.514,0.581,0.565,0.58,0.681,0.67,1.049,0.334,0.583,0.789,0.573,0.803,0.61,0.323,0.391,0.388,0.474,0.833,0.642,0.534,0.461,0.443,0.696,0.398,0.4,0.461,0.412,0.482,0.494,0.509,0.42,0.474,0.513,0.371,0.403,0.342,0.79,0.611,0.768,0.472,0.686,0.464,0.394,0.462,0.72,0.412,0.48,0.461,0.402
-160,Epileptic,M,37.0,1.2,2.3,0.8,1.3,1.3,1.7,1.8,1.8,0.9,0.8,0.3,3.1,1.2,0.7,1.0,7.8,9.3,1.0,1.8,5.6,1.0,3.8,2.7,4.0,4.1,5.5,3.9,2.4,3.3,3.8,1.2,1.2,0.5,2.4,0.6,1.0,4.3,2.6,0.2,0.2,1.5,4.1,2.2,3.0,3.1,0.6,0.9,0.8,1.1,0.6,0.8,1.7,1.7,2.2,0.5,3.4,0.9,1.8,2.3,1.3,0.3,1.7,0.5,0.3,2.4,1.5,2.9,1.6,1.1,0.6,1.3,1.1,6.2,0.4,8,20,11,10,13,17,19,19,9,9,4.0,29,16,8,13,84,90,8,21,47,10,41,22,42,49,60,45,22,30,42,14,9,5,29,6,11,43,36,3,2,12,33,27,25,17,5,5,6,6,6,5,14,12,17,4,31,7,14,16,11,2,11,2,4,22,20,25,12,7,6,10,8,62,4,0.039,0.022,0.015,0.022,0.027,0.026,0.025,0.026,0.037,0.031,0.028,0.033,0.023,0.028,0.025,0.035,0.027,0.027,0.029,0.038,0.017,0.041,0.035,0.036,0.03,0.03,0.026,0.024,0.024,0.034,0.044,0.038,0.032,0.026,0.016,0.017,0.039,0.026,0.018,0.018,0.021,0.039,0.029,0.024,0.02,0.012,0.032,0.012,0.014,0.019,0.029,0.015,0.025,0.018,0.018,0.021,0.017,0.018,0.032,0.021,0.011,0.028,0.026,0.017,0.022,0.021,0.02,0.018,0.021,0.021,0.027,0.019,0.019,0.03,1707,4893,2269,2547,1962,3231,2541,3863,1301,1502,820.0,3147,3638,1448,2324,13292,19802,2914,4035,5048,3883,5403,2712,6443,6995,8815,7231,3206,6976,6001,2450,1704,942,5728,2739,2692,7780,7292,376,459,2101,3414,6545,3291,3771,1435,1147,2151,2637,1553,866,3334,2167,4368,824,4273,1498,3562,2001,1699,792,2245,593,718,3355,1953,4278,3524,1423,1374,1581,2020,11874,357,0.125,0.112,0.1,0.103,0.116,0.127,0.111,0.12,0.131,0.138,0.13,0.13,0.117,0.13,0.119,0.14,0.124,0.109,0.135,0.142,0.083,0.156,0.132,0.144,0.138,0.134,0.116,0.105,0.101,0.145,0.138,0.115,0.118,0.127,0.081,0.1,0.152,0.125,0.123,0.105,0.115,0.138,0.137,0.111,0.097,0.082,0.115,0.087,0.087,0.109,0.136,0.096,0.121,0.097,0.112,0.118,0.115,0.095,0.136,0.111,0.078,0.124,0.133,0.107,0.113,0.117,0.108,0.096,0.107,0.122,0.13,0.113,0.106,0.157,468,1575,928,966,803,1138,1060,1188,443,449,249.0,1496,927,409,653,3677,5422,544,1053,2329,940,1673,1181,1832,2234,2859,2546,1383,1941,1997,543,623,319,1487,637,988,1813,1766,200,227,1029,1634,1291,1935,2286,782,451,930,1158,572,417,1663,990,1942,448,2442,662,1625,1141,958,363,932,259,399,1624,1035,2295,1462,801,470,701,887,4958,202,3.48,2.867,2.381,2.55,2.286,2.498,2.103,2.897,2.396,2.832,2.85,1.881,2.98,2.831,2.747,2.785,2.915,3.949,2.956,1.894,2.903,2.503,2.069,2.707,2.385,2.587,2.261,1.933,2.79,2.358,3.007,2.632,2.475,2.84,3.557,2.506,3.227,3.044,1.967,2.433,2.505,1.892,3.392,1.903,1.909,1.964,3.129,2.987,2.849,2.847,2.283,2.165,2.3,2.403,2.086,2.032,2.606,2.416,2.032,1.972,2.354,2.357,2.533,1.989,2.163,2.195,1.982,2.667,2.102,2.913,2.36,2.806,2.457,2.149,0.567,0.542,0.532,0.528,0.488,0.435,0.576,0.529,0.601,0.571,0.655,0.546,0.455,0.391,0.399,0.514,0.674,0.686,0.671,0.574,0.872,0.556,0.559,0.614,0.556,0.486,0.548,0.514,0.564,0.608,0.844,0.915,0.408,0.573,0.694,0.568,0.689,0.573,0.406,0.505,0.616,0.536,0.823,0.634,0.604,0.627,0.543,0.636,0.505,0.711,0.429,0.455,0.502,0.507,0.382,0.474,0.53,0.52,0.412,0.404,0.517,0.727,0.453,0.868,0.554,0.664,0.473,0.352,0.477,0.77,0.552,0.48,0.488,0.571
-161,Epileptic,F,32.0,1.3,3.2,1.3,1.6,1.4,2.4,1.8,2.0,1.3,1.1,0.2,2.8,1.6,0.5,1.1,5.8,9.6,1.1,1.8,4.7,0.7,2.7,1.4,3.5,2.7,3.8,3.6,2.6,2.9,3.3,1.4,1.8,0.7,2.5,0.3,0.4,3.5,2.9,0.1,0.3,0.9,3.0,2.2,3.3,2.4,0.8,0.3,1.0,1.3,0.8,0.7,1.5,1.5,3.6,0.1,2.1,0.7,1.3,1.5,0.9,1.0,1.4,0.2,0.4,1.4,1.8,2.1,1.6,1.3,0.4,0.9,1.3,4.0,0.4,9,31,14,13,13,25,16,18,15,13,4.0,23,15,7,13,66,87,10,30,44,6,31,17,38,36,47,41,29,27,34,18,19,6,29,1,2,44,38,1,2,6,28,27,24,17,6,2,7,7,8,5,12,11,31,1,17,6,10,11,7,7,14,1,5,10,20,18,11,7,4,6,10,29,3,0.042,0.026,0.022,0.024,0.032,0.033,0.026,0.028,0.036,0.04,0.026,0.035,0.029,0.032,0.026,0.035,0.033,0.034,0.028,0.039,0.017,0.033,0.024,0.036,0.029,0.034,0.03,0.026,0.029,0.032,0.028,0.059,0.032,0.027,0.009,0.009,0.033,0.027,0.013,0.016,0.018,0.033,0.035,0.028,0.02,0.018,0.015,0.014,0.016,0.015,0.028,0.019,0.028,0.026,0.016,0.015,0.022,0.017,0.024,0.017,0.029,0.021,0.018,0.015,0.022,0.024,0.018,0.021,0.019,0.013,0.024,0.016,0.017,0.017,1751,5678,3115,2854,2165,3505,3166,4126,1926,1799,572.0,2850,3825,1152,2728,11184,19294,2719,3861,4425,2970,5341,2303,6570,6213,7115,5756,4573,6702,5790,2256,1347,1263,7004,2073,1679,8256,7141,389,546,1698,2682,5585,3393,3516,1417,837,2541,2679,1837,567,2840,2364,6467,250,4056,1396,2663,1949,1721,1049,2688,458,867,2357,2213,4395,2842,2200,927,1375,2402,8720,822,0.151,0.118,0.112,0.116,0.124,0.154,0.125,0.131,0.141,0.159,0.109,0.14,0.122,0.135,0.132,0.143,0.137,0.124,0.131,0.148,0.08,0.142,0.13,0.139,0.134,0.153,0.134,0.129,0.124,0.139,0.116,0.157,0.134,0.13,0.057,0.062,0.136,0.129,0.085,0.098,0.1,0.138,0.133,0.128,0.104,0.103,0.094,0.084,0.091,0.102,0.131,0.104,0.121,0.119,0.121,0.097,0.12,0.1,0.119,0.104,0.128,0.121,0.097,0.115,0.112,0.118,0.099,0.107,0.102,0.099,0.113,0.106,0.095,0.105,501,1862,1010,1048,779,1215,1122,1301,566,467,177.0,1240,971,297,719,3114,5059,541,1110,2062,713,1415,929,1680,1756,1937,1973,1555,1696,1696,657,529,358,1446,485,618,1927,1734,197,294,784,1389,1253,1919,1965,689,343,1057,1169,735,368,1260,996,2391,106,2062,549,1209,938,833,541,1012,186,467,1023,1113,1981,1191,984,483,665,1193,3561,310,3.202,2.737,2.872,2.674,2.423,2.538,2.353,2.829,2.446,3.13,2.715,2.031,2.948,2.758,2.76,2.713,2.948,3.574,2.877,1.903,3.218,2.748,2.12,2.94,2.702,2.95,2.33,2.315,2.914,2.733,2.561,2.599,2.814,3.266,3.628,2.565,3.074,2.902,2.104,2.297,2.662,1.824,3.083,2.073,2.058,2.28,3.023,3.075,2.765,2.574,1.966,2.353,2.287,2.598,2.387,2.133,2.863,2.646,2.244,2.254,2.494,2.659,2.353,2.053,2.556,2.374,2.292,2.694,2.724,2.283,2.322,2.314,2.641,3.107,0.707,0.676,0.682,0.532,0.604,0.518,0.495,0.49,0.703,0.534,0.839,0.534,0.381,0.616,0.534,0.63,0.652,0.805,0.644,0.579,0.785,0.542,0.511,0.685,0.541,0.482,0.458,0.602,0.472,0.499,0.588,0.903,0.577,0.623,0.51,0.442,0.767,0.632,0.592,0.391,0.489,0.557,0.797,0.761,0.607,0.509,0.488,0.558,0.554,0.536,0.56,0.482,0.522,0.561,0.54,0.445,0.526,0.458,0.434,0.419,0.463,0.658,0.592,0.896,0.623,0.802,0.529,0.403,0.377,0.55,0.463,0.467,0.51,0.601
-162,Epileptic,F,27.0,0.8,2.2,1.1,1.2,2.1,1.8,1.9,1.6,0.8,0.8,0.2,3.1,1.6,0.4,1.1,6.2,7.9,0.7,1.7,5.2,1.8,3.1,1.5,2.9,2.7,2.6,3.8,3.1,2.4,3.3,2.1,1.0,0.5,1.9,0.2,0.7,5.1,2.7,0.2,0.1,1.0,2.4,2.2,3.4,2.4,0.5,0.3,0.6,0.9,1.1,0.7,1.3,1.0,2.1,0.1,2.4,0.6,1.6,1.2,0.8,0.5,1.4,0.2,0.4,1.5,1.4,1.8,1.8,1.3,0.5,1.0,1.0,4.4,0.3,5,21,11,10,22,15,18,14,11,8,3.0,28,20,6,10,65,78,4,15,42,13,35,16,30,36,32,43,34,25,41,25,6,5,29,2,6,45,32,1,1,6,22,22,26,14,4,1,5,5,6,5,9,8,16,1,17,4,13,8,6,3,10,1,3,12,20,14,14,8,4,7,8,38,2,0.033,0.025,0.019,0.022,0.04,0.033,0.027,0.026,0.038,0.031,0.025,0.035,0.034,0.025,0.032,0.035,0.03,0.036,0.036,0.041,0.036,0.035,0.032,0.035,0.033,0.029,0.029,0.03,0.027,0.03,0.051,0.038,0.032,0.036,0.011,0.02,0.041,0.032,0.014,0.014,0.021,0.038,0.038,0.029,0.018,0.01,0.014,0.011,0.013,0.02,0.042,0.015,0.017,0.018,0.021,0.018,0.017,0.025,0.032,0.015,0.02,0.024,0.021,0.018,0.02,0.027,0.022,0.021,0.021,0.016,0.021,0.025,0.019,0.016,1544,4906,2502,2411,3082,2893,3550,3386,1978,1554,614.0,3100,3413,1167,2700,12026,17532,2001,3209,4998,3561,4814,2140,6258,6047,6288,6667,5029,5660,6842,2706,1282,907,5106,1539,1897,7204,6714,370,603,1516,2581,4503,2941,3603,1410,791,2017,2087,1956,598,3040,2139,4909,254,3927,1285,2565,1147,1465,853,2429,283,822,2741,1506,2859,2890,2151,1139,1582,1695,9127,497,0.12,0.119,0.104,0.109,0.15,0.144,0.121,0.124,0.137,0.14,0.116,0.146,0.134,0.113,0.132,0.143,0.129,0.107,0.127,0.148,0.119,0.15,0.137,0.137,0.144,0.131,0.13,0.136,0.122,0.133,0.16,0.12,0.131,0.134,0.068,0.099,0.154,0.132,0.082,0.097,0.11,0.145,0.13,0.122,0.096,0.081,0.09,0.08,0.082,0.105,0.151,0.093,0.092,0.094,0.109,0.1,0.101,0.117,0.144,0.098,0.099,0.118,0.122,0.112,0.104,0.14,0.112,0.115,0.104,0.105,0.113,0.115,0.102,0.112,414,1493,849,890,829,937,1172,1001,415,378,180.0,1374,865,271,619,3004,4505,373,898,2090,894,1476,835,1493,1631,1690,2223,1778,1567,1952,664,462,271,1068,378,612,1953,1474,215,185,688,1135,1142,1730,2051,698,329,882,1007,791,300,1338,924,1982,86,1933,557,1141,614,754,410,896,138,372,1265,819,1347,1304,942,475,717,746,3759,245,3.46,2.915,2.627,2.687,2.742,2.627,2.474,2.956,2.909,2.979,2.809,1.91,2.953,2.956,3.117,2.874,3.008,3.885,2.796,2.054,3.05,2.598,2.217,3.035,2.843,2.872,2.361,2.305,2.718,2.62,2.999,2.62,2.507,3.145,3.373,2.793,2.813,3.208,2.096,3.469,2.846,1.934,2.849,1.938,1.963,2.256,2.816,2.788,2.504,2.957,2.052,2.409,2.4,2.493,2.794,2.274,2.738,2.397,2.2,2.131,2.393,2.695,2.18,2.413,2.358,2.263,2.266,2.508,2.452,2.803,2.583,2.544,2.618,2.493,0.706,0.868,0.638,0.496,0.653,0.629,0.528,0.561,0.819,0.51,0.649,0.544,0.544,0.505,0.61,0.577,0.595,0.597,0.464,0.572,0.924,0.438,0.495,0.688,0.569,0.575,0.533,0.511,0.602,0.618,0.711,0.98,0.519,0.647,0.628,0.664,0.651,0.553,0.358,0.705,0.6,0.547,0.693,0.634,0.578,0.371,0.529,0.513,0.512,0.563,0.459,0.446,0.572,0.537,0.467,0.448,0.634,0.591,0.433,0.385,0.395,0.64,0.587,0.8,0.527,0.646,0.48,0.466,0.509,0.587,0.522,0.554,0.517,0.723
-163,Epileptic,F,27.0,1.1,2.5,1.7,1.1,1.1,1.8,1.2,2.4,0.9,0.6,0.2,1.9,1.9,0.6,1.1,6.4,8.8,0.9,1.8,4.2,1.1,2.6,1.3,4.1,2.9,1.8,1.8,1.7,2.0,2.0,1.2,0.7,0.3,2.1,0.3,0.4,3.8,3.2,0.2,0.2,1.0,2.2,3.0,3.0,2.3,0.9,0.4,1.0,1.2,1.1,0.5,1.7,1.8,3.6,0.0,1.7,0.6,1.6,0.7,0.5,0.8,1.0,0.4,0.4,1.0,1.1,1.6,1.4,0.7,0.6,0.9,1.8,4.1,0.3,6,22,16,9,11,13,13,20,9,8,3.0,18,18,7,13,57,78,8,22,36,12,28,12,29,32,20,22,18,22,21,17,4,3,25,3,2,41,36,1,1,6,22,20,24,15,9,2,7,7,9,5,13,14,31,0,15,4,13,6,6,7,8,3,4,8,13,12,8,6,8,6,14,31,2,0.051,0.028,0.029,0.017,0.027,0.036,0.027,0.033,0.05,0.034,0.036,0.034,0.031,0.031,0.03,0.034,0.035,0.032,0.027,0.04,0.023,0.031,0.034,0.051,0.037,0.024,0.021,0.026,0.022,0.027,0.042,0.03,0.022,0.027,0.015,0.014,0.034,0.032,0.014,0.022,0.027,0.03,0.041,0.033,0.017,0.029,0.022,0.021,0.017,0.028,0.028,0.018,0.026,0.023,0.029,0.018,0.015,0.022,0.019,0.013,0.024,0.025,0.032,0.022,0.018,0.025,0.02,0.021,0.02,0.024,0.024,0.022,0.018,0.015,1290,4013,2268,2103,1732,2046,1998,3343,1187,1240,452.0,1742,3566,1328,2725,10751,14112,2272,3921,4204,2443,3940,1615,4936,4192,4034,3484,3560,3231,3510,1651,708,636,4598,1556,893,6629,6369,351,380,1165,2215,3768,2211,3187,1483,653,1970,2459,1507,476,3063,2660,5914,107,2620,1288,2536,850,1024,1215,1666,496,677,1619,1190,2515,1635,967,1192,1202,2723,7095,388,0.133,0.111,0.111,0.096,0.129,0.146,0.126,0.146,0.148,0.138,0.141,0.147,0.14,0.144,0.126,0.128,0.12,0.12,0.126,0.16,0.098,0.142,0.147,0.136,0.155,0.122,0.116,0.122,0.106,0.13,0.145,0.104,0.107,0.125,0.07,0.077,0.131,0.139,0.083,0.107,0.117,0.138,0.132,0.136,0.097,0.144,0.099,0.093,0.091,0.119,0.132,0.1,0.126,0.114,0.15,0.105,0.09,0.115,0.107,0.089,0.114,0.117,0.131,0.127,0.105,0.12,0.104,0.105,0.103,0.135,0.117,0.12,0.099,0.103,364,1426,945,946,670,837,713,1137,382,325,137.0,915,1088,321,704,3204,4247,453,1060,1740,786,1339,617,1191,1332,1250,1388,1092,1357,1137,511,341,245,1167,455,430,1777,1683,242,151,580,1103,1153,1429,1848,539,286,867,1104,645,325,1565,1171,2550,46,1466,633,1220,591,620,643,704,223,325,867,656,1247,1057,604,492,597,1261,3292,208,3.315,2.573,2.432,2.239,2.186,2.283,2.28,2.577,2.421,2.851,2.85,1.711,2.699,2.81,2.804,2.612,2.624,3.653,2.888,2.119,2.693,2.333,2.193,2.881,2.515,2.664,2.081,2.463,1.953,2.461,2.473,2.091,2.271,2.779,3.086,2.023,2.888,2.935,1.654,2.935,2.528,1.804,2.672,1.752,1.917,2.65,2.629,2.769,2.657,2.514,1.79,2.112,2.21,2.305,2.49,1.942,2.433,2.412,1.726,1.8,2.106,2.357,2.384,2.261,2.037,2.138,2.142,1.839,1.775,2.709,2.335,2.367,2.326,2.16,0.892,0.645,0.535,0.593,0.724,0.436,0.663,0.455,0.448,0.468,0.666,0.365,0.522,0.346,0.662,0.648,0.652,0.814,0.533,0.716,0.808,0.555,0.409,0.632,0.574,0.657,0.58,0.546,0.521,0.516,0.66,0.81,0.556,0.612,0.651,0.449,0.734,0.646,0.337,0.38,0.674,0.5,0.845,0.601,0.45,0.612,0.661,0.878,0.609,0.561,0.363,0.481,0.546,0.57,0.276,0.463,0.477,0.494,0.361,0.351,0.443,0.685,0.648,0.699,0.558,0.718,0.528,0.477,0.543,0.801,0.547,0.498,0.577,0.44
-164,Epileptic,F,42.0,0.7,1.9,0.9,0.9,1.2,1.4,1.4,1.6,1.3,0.7,0.2,2.7,1.2,0.4,0.9,3.7,9.0,0.9,1.4,3.9,1.2,6.3,1.8,3.2,3.1,2.9,2.2,2.2,2.8,2.6,1.3,1.0,0.8,2.1,0.3,0.9,2.5,2.9,0.1,0.1,1.3,3.9,2.1,2.0,2.9,0.6,0.6,0.9,1.6,0.9,0.5,1.2,0.8,1.7,0.2,1.9,0.5,1.1,0.7,1.3,0.3,1.1,0.4,0.5,1.9,0.7,2.8,0.8,0.9,0.5,0.8,1.1,4.3,0.2,6,18,7,8,13,16,16,15,16,6,2.0,24,13,4,9,34,83,9,19,33,11,50,17,29,37,29,23,23,20,30,16,6,5,24,1,7,26,31,2,1,7,29,23,17,19,5,3,6,8,7,2,9,5,11,1,17,4,9,6,12,2,6,2,4,15,13,20,6,5,5,5,8,34,2,0.032,0.023,0.02,0.021,0.029,0.029,0.035,0.027,0.047,0.038,0.023,0.039,0.028,0.04,0.029,0.034,0.035,0.036,0.028,0.042,0.026,0.053,0.034,0.04,0.041,0.028,0.03,0.027,0.031,0.032,0.044,0.05,0.039,0.029,0.012,0.02,0.035,0.036,0.015,0.014,0.023,0.044,0.039,0.026,0.022,0.021,0.02,0.016,0.022,0.016,0.026,0.02,0.017,0.022,0.028,0.021,0.022,0.017,0.023,0.024,0.016,0.025,0.028,0.025,0.02,0.026,0.022,0.017,0.021,0.023,0.023,0.019,0.019,0.017,1629,3776,1692,2055,1626,3087,2228,3220,1978,1429,492.0,2794,2894,749,1869,6837,18426,2322,3782,3939,4174,3582,1922,5722,4303,7012,4018,4499,5480,5303,2250,831,1034,5318,1834,2380,5329,6465,460,400,1585,2320,5993,2142,3714,1067,815,2050,2153,1773,475,1906,1505,3002,280,3033,811,2383,1050,1742,685,1874,322,829,3015,723,4354,1694,1377,679,1068,1669,8419,399,0.117,0.124,0.113,0.102,0.136,0.128,0.126,0.117,0.177,0.149,0.083,0.142,0.121,0.139,0.137,0.145,0.143,0.142,0.129,0.148,0.105,0.144,0.117,0.15,0.141,0.125,0.132,0.115,0.121,0.137,0.134,0.12,0.134,0.126,0.068,0.103,0.141,0.132,0.089,0.091,0.109,0.147,0.144,0.131,0.107,0.117,0.109,0.091,0.106,0.097,0.119,0.105,0.103,0.104,0.14,0.109,0.119,0.102,0.104,0.098,0.083,0.114,0.136,0.125,0.109,0.123,0.111,0.098,0.103,0.139,0.115,0.112,0.101,0.112,390,1304,680,695,667,849,817,1049,529,334,157.0,1153,781,214,514,1893,4686,520,909,1622,890,1545,827,1374,1292,1812,1281,1455,1454,1463,531,350,282,1264,431,742,1279,1471,173,217,812,1253,1070,1240,2032,461,382,861,1063,737,262,884,722,1257,126,1492,384,1056,577,937,281,664,197,353,1482,484,2019,718,624,374,522,857,3561,213,3.642,2.535,2.579,2.714,1.936,2.876,2.186,2.629,2.7,3.154,2.579,2.028,2.781,2.809,2.667,2.69,2.971,3.406,3.107,2.11,3.338,2.065,2.17,3.087,2.517,2.94,2.425,2.409,2.913,2.79,2.743,2.466,2.742,3.025,3.501,2.871,2.914,3.016,2.497,1.975,2.446,1.683,3.466,2.025,2.098,2.285,2.55,3.028,2.456,2.509,2.266,2.141,1.933,2.43,2.557,2.135,2.378,2.513,2.026,1.94,2.702,2.548,1.864,2.414,2.252,1.909,2.339,2.487,2.784,1.982,2.505,2.329,2.374,2.493,0.815,0.834,0.426,0.543,0.759,0.546,0.58,0.556,0.633,0.597,0.831,0.577,0.484,0.553,0.472,0.705,0.675,0.7,0.583,0.59,0.938,0.726,0.491,0.687,0.794,0.611,0.648,0.575,0.632,0.634,0.748,0.692,0.383,0.575,0.749,0.575,0.81,0.759,0.33,0.448,0.477,0.613,0.93,0.621,0.581,0.489,0.565,0.779,0.444,0.789,0.456,0.54,0.61,0.52,0.435,0.417,0.413,0.668,0.356,0.513,0.439,0.748,0.49,0.825,0.45,0.549,0.494,0.541,0.494,0.479,0.422,0.612,0.535,0.527
-165,Epileptic,F,22.0,0.7,2.4,0.7,1.2,1.5,2.1,1.4,1.7,1.0,0.8,0.3,3.3,1.7,0.4,1.1,7.8,8.3,0.9,1.9,5.0,1.4,3.2,1.5,3.0,4.3,3.0,3.7,2.7,3.3,3.5,1.7,1.0,0.6,2.2,0.4,0.7,3.3,3.5,0.2,0.1,1.1,3.3,2.1,4.4,3.7,0.5,0.3,0.6,0.9,0.6,0.6,2.0,1.2,2.7,0.4,3.0,0.7,1.6,1.5,1.2,0.8,1.9,0.5,0.5,1.9,1.2,1.9,1.4,1.5,0.4,1.1,1.3,4.0,0.4,3,20,8,10,18,19,15,15,9,8,7.0,31,20,5,10,84,90,10,23,42,19,38,18,34,48,28,45,31,29,39,23,7,9,26,3,7,36,40,2,1,6,30,19,28,24,4,1,4,4,4,4,14,9,21,2,27,4,12,13,9,5,12,4,4,18,16,15,11,11,5,7,9,35,3,0.033,0.029,0.018,0.023,0.036,0.039,0.031,0.034,0.047,0.042,0.031,0.034,0.041,0.032,0.036,0.041,0.032,0.037,0.033,0.038,0.025,0.039,0.027,0.033,0.039,0.035,0.03,0.03,0.029,0.032,0.049,0.039,0.03,0.026,0.013,0.018,0.039,0.037,0.015,0.01,0.021,0.037,0.041,0.034,0.023,0.011,0.016,0.01,0.015,0.01,0.03,0.02,0.02,0.022,0.025,0.021,0.024,0.019,0.033,0.021,0.022,0.027,0.024,0.022,0.022,0.024,0.017,0.02,0.023,0.021,0.021,0.021,0.018,0.019,1601,4538,1962,1800,2463,3306,2471,3166,1881,1312,597.0,3289,3064,1386,2331,12401,18701,2507,4550,5161,5090,5178,2527,6757,6364,5725,6857,4577,6214,5952,2349,1378,1022,6137,2054,2287,7074,7154,463,373,1704,3735,5769,2905,4025,1228,917,2294,1996,1653,648,2746,2084,5290,649,4475,1074,3518,1513,1490,818,3145,533,845,2973,1040,3223,2577,2111,1393,1523,2125,8345,780,0.102,0.126,0.101,0.116,0.143,0.155,0.113,0.119,0.144,0.15,0.133,0.15,0.144,0.118,0.135,0.15,0.132,0.129,0.132,0.142,0.107,0.155,0.126,0.131,0.161,0.136,0.132,0.113,0.109,0.138,0.156,0.128,0.151,0.116,0.076,0.096,0.138,0.137,0.116,0.075,0.105,0.151,0.144,0.135,0.112,0.084,0.084,0.075,0.088,0.082,0.127,0.102,0.104,0.108,0.124,0.11,0.119,0.099,0.142,0.11,0.127,0.119,0.134,0.123,0.115,0.124,0.102,0.106,0.107,0.11,0.118,0.119,0.099,0.109,354,1375,743,769,809,966,852,929,438,372,202.0,1508,800,289,568,3559,4842,440,1062,2182,1079,1573,1021,1667,1840,1527,2182,1510,1805,1969,649,472,344,1316,471,754,1523,1656,201,187,757,1506,983,1879,2417,637,315,888,880,753,365,1397,1048,2200,265,2296,447,1502,785,867,440,1053,258,404,1461,721,1653,1150,1073,443,758,942,3508,299,3.834,2.879,2.394,2.324,2.517,2.699,2.328,2.797,2.832,2.858,2.414,1.919,2.901,3.258,2.932,2.643,2.98,3.969,3.118,2.045,3.241,2.521,2.081,2.995,2.632,2.858,2.398,2.321,2.693,2.43,2.777,2.637,2.572,3.162,3.78,2.716,3.279,2.972,2.819,2.129,2.804,2.117,3.641,1.785,1.864,2.046,3.404,2.923,2.763,2.554,2.194,2.123,2.065,2.345,2.954,2.134,2.804,2.635,2.098,1.947,2.243,2.726,1.983,2.148,2.184,1.847,2.192,2.365,2.252,3.124,2.307,2.614,2.478,3.145,0.977,0.707,0.59,0.521,0.798,0.616,0.616,0.699,0.667,0.411,0.819,0.495,0.573,0.435,0.52,0.631,0.649,0.608,0.582,0.574,0.766,0.51,0.51,0.796,0.729,0.692,0.493,0.642,0.709,0.514,0.782,1.083,0.516,0.672,0.664,0.565,0.774,0.633,0.435,0.474,0.655,0.5,0.74,0.6,0.589,0.535,0.553,0.84,0.608,0.486,0.346,0.438,0.471,0.507,0.713,0.484,0.533,0.595,0.403,0.487,0.428,0.716,0.55,0.922,0.529,0.584,0.583,0.488,0.488,0.754,0.474,0.584,0.572,0.472
-166,Epileptic,F,32.0,0.5,1.4,0.9,1.6,1.2,1.3,1.5,2.1,1.6,1.1,0.3,2.1,1.1,0.3,1.9,7.6,8.6,0.6,2.4,3.3,0.9,2.7,1.1,3.0,1.9,4.7,3.0,2.6,2.5,4.1,0.9,1.0,0.6,2.4,0.5,0.5,3.9,2.2,0.1,0.2,1.4,2.1,2.1,1.9,2.5,0.6,0.5,0.8,1.0,0.6,0.6,1.8,1.9,2.8,0.3,1.8,0.9,1.3,0.9,1.0,0.6,1.3,0.3,0.5,1.4,1.2,3.3,1.0,0.6,0.9,1.1,1.5,2.9,0.2,3,15,9,15,13,14,15,18,18,10,3.0,21,16,7,21,87,91,5,28,30,8,35,15,34,23,54,35,27,23,44,15,8,9,29,2,5,42,28,1,1,8,21,19,16,19,7,3,5,6,5,3,15,14,21,1,18,8,12,7,8,7,8,2,4,12,20,31,9,5,11,9,13,29,2,0.022,0.018,0.015,0.032,0.035,0.026,0.026,0.032,0.047,0.047,0.033,0.033,0.028,0.03,0.04,0.037,0.027,0.022,0.033,0.036,0.021,0.037,0.031,0.039,0.029,0.038,0.03,0.027,0.027,0.037,0.033,0.052,0.025,0.032,0.018,0.016,0.032,0.03,0.019,0.013,0.027,0.032,0.036,0.023,0.02,0.018,0.014,0.015,0.015,0.023,0.039,0.02,0.023,0.02,0.025,0.021,0.021,0.016,0.029,0.02,0.032,0.027,0.018,0.019,0.024,0.025,0.022,0.016,0.013,0.028,0.02,0.015,0.017,0.012,1522,3958,2177,2197,1519,1972,2738,3169,2069,1113,416.0,2018,3046,806,3681,13314,17935,2360,4125,3709,2717,4382,1461,5134,3684,7625,4798,4069,5403,6046,2136,771,977,4847,1611,1454,7031,5718,404,410,1704,2190,4133,2185,2959,1267,1030,1761,2485,1196,424,2884,2421,5660,236,2935,1498,2750,1050,1438,938,2021,501,815,1991,1136,4493,2090,1449,1306,1862,2871,6594,660,0.095,0.101,0.102,0.144,0.134,0.134,0.123,0.147,0.155,0.167,0.134,0.139,0.122,0.136,0.162,0.149,0.13,0.099,0.135,0.146,0.084,0.156,0.145,0.139,0.144,0.164,0.14,0.131,0.121,0.157,0.122,0.141,0.125,0.132,0.076,0.096,0.129,0.129,0.11,0.089,0.128,0.149,0.137,0.116,0.108,0.11,0.099,0.086,0.087,0.117,0.145,0.104,0.118,0.106,0.142,0.117,0.106,0.102,0.131,0.112,0.132,0.118,0.104,0.105,0.113,0.128,0.124,0.104,0.093,0.117,0.116,0.104,0.105,0.081,385,1278,813,842,601,766,898,1070,584,383,149.0,982,796,228,879,3658,5085,446,1262,1531,724,1420,666,1390,1146,2255,1790,1501,1475,1865,541,348,375,1284,466,548,1997,1389,154,207,828,1088,999,1409,1917,601,436,793,1109,518,265,1403,1222,2290,128,1446,760,1273,564,759,435,749,277,436,1002,837,2280,1000,675,504,794,1337,2858,363,3.627,2.888,2.621,2.611,2.096,2.246,2.512,2.59,2.506,2.521,2.848,1.83,2.829,2.582,2.931,2.722,2.802,3.617,2.637,2.062,2.949,2.449,2.0,2.637,2.46,2.817,2.194,2.193,2.78,2.66,2.769,2.366,2.131,2.881,3.118,2.588,2.802,3.1,2.646,2.191,2.68,1.854,3.167,1.73,1.733,2.227,2.908,2.53,2.664,2.854,2.116,2.076,2.149,2.538,2.148,2.122,2.284,2.398,2.178,2.185,2.405,2.783,1.945,2.259,2.135,1.638,2.157,2.449,2.476,2.658,2.625,2.364,2.4,2.2,1.018,0.698,0.616,0.542,0.631,0.471,0.57,0.464,0.611,0.718,1.149,0.524,0.466,0.665,0.52,0.637,0.593,1.084,0.602,0.614,0.765,0.443,0.357,0.702,0.5,0.481,0.468,0.49,0.493,0.546,0.771,0.803,0.415,0.575,0.741,0.543,0.583,0.667,0.543,0.521,0.505,0.493,0.626,0.49,0.494,0.426,0.622,0.788,0.547,0.477,0.407,0.591,0.483,0.559,0.235,0.402,0.404,0.497,0.343,0.362,0.437,0.816,0.594,0.75,0.563,0.529,0.423,0.422,0.352,0.778,0.564,0.531,0.471,0.572
-167,Epileptic,F,27.0,0.5,2.0,0.6,1.1,1.5,1.4,1.3,1.6,1.3,0.8,0.2,2.3,1.2,0.4,1.5,5.3,7.4,0.7,2.5,5.5,1.1,2.5,1.5,2.9,3.2,2.8,2.6,1.9,2.1,3.4,1.3,0.8,0.4,1.9,0.2,0.4,3.6,3.5,0.1,0.2,0.7,2.0,2.0,2.4,1.8,0.6,0.5,0.9,1.2,0.8,0.5,1.5,1.2,2.6,0.3,1.8,0.8,1.8,0.5,1.4,0.6,1.4,0.5,0.3,1.3,1.4,2.4,1.1,0.8,0.2,0.7,1.5,3.8,0.3,6,20,7,10,16,15,12,15,22,6,2.0,21,17,5,16,57,78,6,30,39,8,29,11,32,32,32,33,20,20,39,13,5,5,31,2,3,41,38,2,1,4,17,20,18,13,5,2,6,6,6,3,13,7,21,1,16,6,12,4,10,4,11,3,3,10,15,21,8,6,3,4,11,40,3,0.033,0.024,0.014,0.025,0.034,0.027,0.026,0.031,0.062,0.044,0.025,0.032,0.026,0.03,0.038,0.033,0.028,0.032,0.031,0.042,0.024,0.036,0.034,0.039,0.042,0.029,0.028,0.027,0.025,0.038,0.039,0.028,0.027,0.033,0.011,0.011,0.035,0.037,0.019,0.021,0.017,0.032,0.035,0.028,0.017,0.015,0.023,0.014,0.017,0.026,0.026,0.019,0.025,0.024,0.022,0.015,0.024,0.019,0.017,0.028,0.023,0.03,0.038,0.02,0.019,0.025,0.021,0.016,0.019,0.013,0.021,0.02,0.018,0.015,1417,4187,1880,1954,2123,2995,2149,2685,1656,1513,547.0,2199,3044,879,3016,10909,17041,2153,4679,4000,2730,4531,1743,6009,5331,6129,5366,3348,5031,5405,1878,972,795,5058,1314,1351,7142,7763,296,315,1274,2075,4568,2428,2698,1194,766,2421,2373,1427,442,2780,1808,4672,408,3299,1301,3382,909,1439,1000,2224,477,933,2090,1262,3755,2401,1547,682,1101,2835,9418,538,0.118,0.125,0.093,0.113,0.143,0.133,0.119,0.142,0.163,0.143,0.109,0.143,0.142,0.156,0.167,0.141,0.133,0.113,0.14,0.152,0.094,0.149,0.13,0.147,0.15,0.138,0.129,0.117,0.114,0.151,0.139,0.103,0.132,0.138,0.069,0.074,0.132,0.144,0.125,0.112,0.098,0.131,0.128,0.12,0.097,0.099,0.109,0.083,0.096,0.119,0.12,0.101,0.11,0.114,0.105,0.099,0.106,0.098,0.107,0.127,0.112,0.134,0.157,0.109,0.108,0.124,0.109,0.099,0.106,0.096,0.112,0.109,0.099,0.111,351,1285,677,759,716,932,768,891,467,342,148.0,1059,815,212,720,2699,4543,399,1285,2079,681,1264,693,1345,1411,1653,1646,1213,1294,1645,511,404,275,1108,358,500,1817,1721,141,140,626,989,1008,1415,1618,611,324,943,996,564,279,1261,772,1904,220,1767,554,1467,490,794,419,747,189,311,1037,768,1795,1024,676,319,499,1217,3571,256,3.553,3.017,2.532,2.638,2.483,2.736,2.329,2.635,2.581,3.307,2.822,1.871,2.917,2.895,2.964,2.854,2.906,3.813,2.774,1.759,3.1,2.634,2.127,3.098,2.684,2.88,2.443,2.212,2.893,2.574,2.702,2.256,2.298,3.124,3.289,2.447,2.871,3.179,2.188,2.362,2.561,1.912,3.243,1.887,1.888,2.18,2.984,3.15,2.927,2.847,1.963,2.321,2.555,2.428,2.219,2.083,2.574,2.664,1.994,2.058,2.512,3.099,2.441,2.924,2.274,2.153,2.26,2.621,2.453,2.488,2.568,2.657,2.668,2.552,0.659,0.5,0.642,0.55,0.704,0.646,0.563,0.503,0.591,0.718,0.786,0.623,0.447,0.529,0.607,0.617,0.595,0.835,0.644,0.498,0.81,0.504,0.533,0.712,0.713,0.523,0.55,0.514,0.526,0.566,0.651,0.932,0.422,0.645,0.687,0.463,0.746,0.655,0.318,0.512,0.525,0.506,0.748,0.755,0.622,0.527,0.499,0.83,0.604,0.52,0.378,0.467,0.651,0.478,0.357,0.531,0.476,0.598,0.386,0.394,0.472,0.609,0.595,1.0,0.51,0.636,0.527,0.468,0.453,0.688,0.592,0.607,0.577,0.489
-168,Epileptic,M,42.0,0.6,1.8,1.4,0.8,1.5,2.1,1.8,2.3,1.2,0.8,0.3,3.4,1.5,0.3,0.9,4.7,9.0,0.8,1.5,4.3,0.6,3.2,1.7,3.2,2.6,3.4,4.6,2.5,4.1,3.6,0.9,1.3,0.5,2.7,0.3,0.9,2.8,3.6,0.1,0.1,1.3,2.8,1.9,2.5,2.8,0.8,0.3,0.7,1.2,0.6,0.7,1.9,1.7,2.7,0.3,2.4,0.6,1.9,1.3,1.0,0.8,1.7,0.3,0.6,1.9,0.7,3.6,1.2,1.4,0.9,1.1,1.0,4.6,0.5,5,17,10,5,15,19,16,19,15,6,2.0,33,17,4,11,51,72,8,24,42,7,38,16,31,36,40,55,25,28,44,12,9,4,36,2,11,34,37,1,1,9,26,24,22,16,4,2,5,6,5,3,14,15,19,2,21,4,14,7,8,6,13,3,6,17,7,32,8,8,9,7,8,38,4,0.034,0.024,0.024,0.018,0.034,0.031,0.026,0.031,0.046,0.042,0.031,0.034,0.035,0.021,0.026,0.032,0.031,0.031,0.024,0.033,0.017,0.037,0.026,0.039,0.033,0.029,0.027,0.029,0.031,0.034,0.032,0.05,0.022,0.032,0.01,0.022,0.031,0.032,0.009,0.023,0.024,0.031,0.034,0.027,0.022,0.018,0.017,0.012,0.017,0.021,0.029,0.023,0.025,0.025,0.013,0.017,0.021,0.022,0.028,0.018,0.027,0.032,0.017,0.034,0.022,0.025,0.026,0.021,0.024,0.03,0.028,0.022,0.02,0.019,1427,3824,2120,1748,2127,3149,3220,3642,1707,1199,505.0,2836,3518,887,1938,8876,16380,2255,3552,4786,2893,4768,2184,5576,5125,7002,8376,3287,5841,5526,2092,1043,1016,6079,2042,2043,7098,6880,316,288,1693,2573,5662,2443,3019,1342,715,2309,2284,1616,680,2953,2622,3879,511,4264,1113,2708,1076,1587,821,2158,445,955,2632,766,3926,1840,1591,1279,1209,2226,7878,530,0.106,0.114,0.112,0.094,0.128,0.145,0.107,0.137,0.154,0.151,0.147,0.141,0.147,0.125,0.147,0.134,0.126,0.115,0.129,0.142,0.072,0.158,0.118,0.144,0.147,0.132,0.126,0.104,0.106,0.154,0.138,0.139,0.098,0.137,0.061,0.12,0.139,0.147,0.081,0.117,0.118,0.134,0.15,0.122,0.105,0.107,0.096,0.082,0.096,0.109,0.118,0.117,0.111,0.116,0.1,0.099,0.109,0.108,0.127,0.102,0.124,0.138,0.118,0.14,0.124,0.101,0.125,0.113,0.112,0.147,0.127,0.12,0.108,0.116,389,1247,856,689,853,1084,1021,1212,514,327,146.0,1575,882,222,583,2718,4732,393,977,2227,663,1483,969,1518,1470,2119,3020,1342,1756,1850,508,453,342,1538,535,738,1596,1671,156,137,784,1352,1048,1610,1933,611,292,873,1046,516,373,1226,1261,1718,287,2242,469,1353,657,889,483,891,271,338,1274,465,2197,829,888,525,554,819,3497,323,3.408,2.812,2.428,2.899,2.144,2.424,2.504,2.626,2.497,2.818,3.143,1.687,3.074,2.838,2.555,2.544,2.817,3.918,2.874,1.919,3.036,2.436,1.961,2.897,2.664,2.712,2.25,1.99,2.551,2.409,3.039,2.539,2.358,2.928,3.464,2.624,3.222,2.992,2.334,2.567,2.631,1.755,3.652,1.71,1.788,2.452,3.056,3.262,2.681,3.406,2.222,2.491,2.189,2.298,2.203,2.017,2.689,2.438,1.921,1.942,2.134,2.503,1.946,2.825,2.347,2.049,1.927,2.51,2.205,2.596,2.366,2.754,2.412,2.027,0.914,0.558,0.722,0.652,0.615,0.527,0.6,0.541,0.538,0.451,0.63,0.467,0.51,0.417,0.465,0.563,0.594,0.734,0.433,0.603,0.884,0.496,0.52,0.657,0.483,0.584,0.593,0.612,0.654,0.579,0.629,1.008,0.381,0.641,0.78,0.434,0.754,0.617,0.265,0.39,0.537,0.42,0.764,0.576,0.593,0.819,0.563,0.787,0.505,0.671,0.319,0.543,0.544,0.527,0.415,0.514,0.599,0.531,0.477,0.343,0.427,0.847,0.392,0.8,0.579,0.742,0.479,0.403,0.563,0.554,0.579,0.789,0.505,0.415
-169,Epileptic,M,42.0,1.1,2.6,1.4,2.1,2.5,2.0,2.3,2.1,2.1,0.6,0.3,4.2,2.8,0.5,2.3,7.0,11.9,1.2,2.6,5.4,1.1,3.3,2.5,4.3,4.7,2.1,1.9,2.9,4.2,2.3,2.1,0.9,0.6,3.1,0.6,1.3,4.9,4.6,0.4,0.6,1.2,3.4,3.0,3.7,3.4,1.0,0.5,1.2,2.1,0.9,0.7,3.9,2.7,3.8,0.5,1.4,1.3,2.1,1.5,1.4,0.8,2.2,0.3,0.6,3.0,1.2,2.2,2.1,1.7,0.5,0.6,1.9,5.9,0.4,9,25,10,18,21,21,25,19,21,6,7.0,39,26,10,29,75,104,10,27,59,7,40,24,47,52,21,24,33,42,28,24,7,6,36,4,17,44,57,2,3,8,29,30,25,27,8,3,8,10,7,5,28,19,23,3,11,10,18,9,12,9,19,3,5,26,18,17,17,9,5,5,15,50,5,0.039,0.023,0.022,0.029,0.039,0.026,0.04,0.027,0.052,0.03,0.04,0.039,0.026,0.027,0.035,0.031,0.031,0.041,0.032,0.036,0.022,0.032,0.036,0.042,0.041,0.024,0.025,0.034,0.032,0.027,0.042,0.044,0.031,0.034,0.019,0.022,0.039,0.034,0.016,0.023,0.025,0.034,0.043,0.029,0.023,0.029,0.017,0.017,0.021,0.024,0.025,0.027,0.026,0.023,0.032,0.015,0.023,0.017,0.03,0.022,0.019,0.031,0.014,0.018,0.025,0.028,0.023,0.024,0.029,0.014,0.028,0.021,0.021,0.021,1565,5045,2474,2967,2414,3298,2997,3485,2387,1098,838.0,3365,5895,1254,3973,12999,19710,2611,4852,5463,3075,5386,2455,6603,6194,4211,3318,4796,4800,3906,2734,1098,983,6018,2198,2664,7730,9309,634,670,1501,3264,6224,3347,4024,1121,1030,2593,3027,1527,655,5194,3816,6210,515,2487,2111,3686,1187,1690,1273,3039,509,1002,3315,1195,2834,2862,1206,1110,905,2789,10271,476,0.123,0.12,0.109,0.131,0.14,0.134,0.153,0.13,0.159,0.146,0.141,0.161,0.119,0.151,0.151,0.13,0.131,0.136,0.132,0.147,0.088,0.133,0.13,0.148,0.131,0.122,0.118,0.13,0.112,0.133,0.138,0.122,0.121,0.141,0.09,0.12,0.147,0.148,0.091,0.119,0.12,0.131,0.165,0.125,0.114,0.128,0.093,0.09,0.106,0.12,0.119,0.122,0.119,0.104,0.138,0.094,0.119,0.1,0.132,0.118,0.114,0.143,0.117,0.11,0.126,0.127,0.114,0.122,0.124,0.1,0.124,0.118,0.109,0.146,504,1788,973,1162,1001,1205,1105,1188,783,301,197.0,1740,1790,344,1200,3808,6307,548,1425,2638,769,1695,1133,1921,2000,1406,1314,1550,1970,1461,831,412,352,1533,584,988,2161,2419,346,364,766,1501,1379,2038,2294,608,442,1049,1435,621,413,2388,1624,2600,244,1359,905,1866,763,992,697,1172,291,517,1819,783,1459,1314,872,536,368,1399,4482,301,3.163,2.732,2.54,2.535,2.259,2.336,2.267,2.644,2.328,2.871,2.927,1.761,2.707,2.649,2.577,2.655,2.596,3.621,2.811,1.85,2.961,2.519,1.971,2.674,2.511,2.72,2.096,2.41,2.052,2.227,2.482,2.641,2.262,2.926,3.305,2.368,2.755,2.943,2.061,2.217,2.44,1.892,3.419,1.966,1.926,1.99,2.682,3.032,2.547,2.722,1.878,2.385,2.64,2.423,2.584,1.958,2.409,2.15,1.774,1.891,2.008,2.71,2.105,2.255,1.986,1.79,2.105,2.156,1.658,2.38,2.533,2.304,2.398,2.088,0.881,0.499,0.431,0.512,0.603,0.597,0.47,0.373,0.671,0.591,0.753,0.451,0.4,0.43,0.526,0.581,0.562,0.865,0.66,0.61,0.732,0.484,0.463,0.651,0.582,0.509,0.474,0.452,0.533,0.58,0.609,0.72,0.483,0.638,0.713,0.405,0.671,0.602,0.372,0.353,0.445,0.522,0.699,0.618,0.386,0.325,0.539,0.876,0.459,0.584,0.331,0.401,0.95,0.55,0.488,0.346,0.524,0.467,0.4,0.361,0.354,0.622,0.269,0.745,0.484,0.516,0.415,0.713,0.411,0.692,0.298,0.425,0.437,0.37
-170,Epileptic,M,47.0,1.2,2.4,0.7,1.6,2.2,1.6,3.0,2.0,1.6,0.7,0.3,3.5,1.5,0.5,1.1,6.4,14.2,1.2,1.8,3.5,1.6,4.6,2.0,5.0,8.2,5.9,4.9,4.2,4.4,3.5,1.7,0.8,0.5,3.4,0.4,1.2,3.1,3.4,0.2,0.3,1.3,3.0,2.3,2.3,4.4,0.9,0.9,1.1,1.7,0.9,0.8,1.5,1.2,3.4,0.9,2.5,1.0,1.8,1.7,1.0,0.9,2.3,0.5,0.7,2.5,1.2,4.1,1.3,1.8,0.8,1.0,1.5,5.5,0.5,10,18,5,13,16,16,21,18,19,6,5.0,30,14,6,11,62,102,11,20,30,11,45,17,55,65,54,42,31,33,36,18,7,4,34,3,11,32,39,2,2,8,27,21,20,23,5,5,6,9,11,6,12,9,21,5,21,8,12,12,7,6,15,5,5,19,21,28,8,11,6,7,10,51,4,0.042,0.027,0.016,0.022,0.037,0.026,0.05,0.027,0.044,0.03,0.035,0.041,0.026,0.026,0.031,0.035,0.049,0.041,0.028,0.035,0.03,0.039,0.033,0.044,0.044,0.029,0.035,0.032,0.034,0.031,0.042,0.037,0.025,0.034,0.019,0.023,0.034,0.026,0.021,0.025,0.022,0.038,0.042,0.026,0.029,0.014,0.027,0.017,0.02,0.029,0.029,0.02,0.021,0.026,0.019,0.016,0.023,0.016,0.027,0.019,0.022,0.031,0.028,0.022,0.024,0.021,0.023,0.021,0.026,0.024,0.022,0.021,0.019,0.02,1610,4198,2162,2942,2794,3474,3008,3941,2215,1620,652.0,2957,3199,875,2268,10439,19669,2541,4266,4105,3328,5222,2093,7555,6882,9799,6345,5330,7263,6580,3065,1053,1232,7312,1909,2576,6906,9574,366,386,1771,2665,5109,2344,4066,1534,1038,2612,2692,1993,721,2892,1861,4973,1143,4180,1541,3386,1564,1287,1156,2958,679,1063,3055,1240,5649,2232,2246,1288,1587,2552,12174,595,0.139,0.124,0.087,0.115,0.151,0.127,0.14,0.125,0.157,0.122,0.139,0.153,0.117,0.13,0.131,0.14,0.155,0.142,0.123,0.138,0.101,0.124,0.122,0.145,0.133,0.118,0.119,0.113,0.114,0.133,0.145,0.115,0.094,0.129,0.084,0.107,0.132,0.125,0.124,0.116,0.11,0.131,0.142,0.119,0.118,0.086,0.118,0.091,0.102,0.115,0.148,0.11,0.11,0.116,0.109,0.101,0.122,0.092,0.132,0.105,0.105,0.132,0.137,0.113,0.123,0.119,0.11,0.105,0.117,0.117,0.111,0.11,0.1,0.123,522,1438,755,1047,970,1092,1143,1250,658,414,179.0,1469,918,274,636,3281,5466,562,1147,1726,872,1711,827,2190,2359,3189,2135,1785,2006,2062,804,387,376,1732,462,935,1711,2153,212,200,913,1289,1085,1487,2332,828,507,1050,1259,639,378,1257,870,2185,650,2320,652,1658,975,770,568,1098,292,562,1564,939,2778,1038,1059,525,758,1144,4875,353,3.088,2.692,2.655,2.536,2.506,2.556,2.191,2.692,2.636,2.919,2.71,1.784,2.686,2.464,2.681,2.523,2.742,3.385,2.906,2.055,2.759,2.411,2.144,2.599,2.464,2.58,2.364,2.335,2.759,2.469,2.766,2.518,2.539,3.062,3.509,2.557,3.04,3.148,1.999,2.33,2.464,1.887,3.356,1.779,2.027,2.065,2.571,3.134,2.576,3.125,2.155,2.349,2.198,2.394,2.289,2.075,2.605,2.33,1.832,1.844,2.247,2.618,2.287,2.29,2.133,1.636,2.2,2.477,2.355,2.562,2.388,2.681,2.498,2.122,0.75,0.747,0.606,0.52,0.611,0.644,0.599,0.537,0.575,0.653,0.856,0.478,0.401,0.409,0.501,0.497,0.676,0.784,0.574,0.696,0.788,0.668,0.576,0.642,0.602,0.512,0.531,0.623,0.613,0.615,0.618,0.953,0.412,0.596,0.595,0.469,0.682,0.683,0.277,0.288,0.493,0.521,0.74,0.496,0.557,0.395,0.423,0.63,0.457,0.64,0.486,0.444,0.464,0.438,0.339,0.341,0.519,0.477,0.365,0.37,0.326,0.638,0.545,0.59,0.504,0.646,0.397,0.368,0.433,0.608,0.534,0.492,0.529,0.34
-171,Epileptic,F,37.0,0.9,2.5,0.7,1.1,1.7,2.0,2.5,1.3,1.1,0.7,0.3,3.2,1.5,0.6,1.8,4.7,7.9,0.7,2.6,3.9,1.1,2.5,1.9,3.6,4.1,4.5,6.2,2.8,3.2,2.4,1.8,1.3,0.6,2.1,0.5,0.7,3.1,1.8,0.2,0.3,1.4,3.3,2.0,2.3,2.6,0.5,0.3,0.7,1.2,0.9,0.3,2.4,0.8,1.9,0.4,2.2,1.0,1.1,0.8,0.9,0.4,1.9,0.3,0.7,2.3,1.2,2.2,0.9,1.0,0.8,0.8,1.1,3.8,0.3,7,24,5,7,17,19,19,14,13,7,4.0,32,17,8,22,55,74,6,28,33,15,26,22,37,45,50,50,25,29,32,20,10,6,28,2,6,41,26,1,3,11,25,22,19,16,3,1,5,6,6,2,21,5,14,2,19,8,9,9,7,3,12,2,5,18,17,17,5,8,8,7,9,33,3,0.048,0.032,0.019,0.024,0.033,0.035,0.04,0.028,0.043,0.035,0.025,0.033,0.028,0.032,0.036,0.039,0.032,0.028,0.035,0.041,0.026,0.037,0.032,0.04,0.051,0.029,0.041,0.032,0.034,0.027,0.052,0.06,0.025,0.029,0.015,0.015,0.036,0.027,0.018,0.016,0.026,0.039,0.038,0.028,0.021,0.01,0.016,0.012,0.017,0.015,0.016,0.029,0.024,0.02,0.024,0.015,0.026,0.016,0.021,0.016,0.028,0.031,0.025,0.034,0.024,0.024,0.018,0.016,0.016,0.025,0.02,0.021,0.018,0.012,1475,3925,1674,2083,1955,2824,2839,2748,1702,1254,589.0,3734,3298,1184,3162,7731,15739,2244,4438,3520,3409,3547,2128,6144,4501,8983,6553,3388,6206,5059,1995,942,1237,5871,1699,1866,6742,4463,411,589,1645,2419,6075,2265,3670,1233,835,2090,1944,2153,359,2548,1076,3357,514,4227,1353,2641,1159,1517,604,2687,423,866,3083,1030,3681,1874,2019,956,1232,1859,7431,685,0.138,0.131,0.101,0.108,0.144,0.139,0.135,0.121,0.154,0.151,0.132,0.138,0.125,0.138,0.146,0.156,0.139,0.122,0.142,0.15,0.102,0.129,0.119,0.146,0.147,0.132,0.128,0.106,0.113,0.127,0.15,0.147,0.102,0.125,0.077,0.094,0.143,0.122,0.095,0.118,0.12,0.139,0.151,0.128,0.099,0.084,0.088,0.082,0.095,0.1,0.111,0.134,0.118,0.101,0.136,0.097,0.13,0.097,0.123,0.092,0.117,0.133,0.137,0.134,0.119,0.126,0.1,0.092,0.098,0.147,0.114,0.123,0.102,0.088,388,1281,623,727,819,1000,954,879,481,341,196.0,1490,888,297,892,2308,4284,416,1229,1585,709,1007,965,1612,1401,2542,2283,1246,1513,1570,591,424,381,1317,448,699,1625,1146,235,288,865,1255,1017,1358,1987,642,341,894,932,856,230,1344,534,1471,216,2140,630,1179,654,844,249,992,222,405,1407,647,1893,839,934,469,633,777,3324,357,3.506,2.624,2.552,2.742,2.134,2.348,2.451,2.79,2.461,2.863,2.86,2.047,2.834,2.691,2.614,2.481,2.784,3.725,2.877,1.991,3.434,2.492,1.953,2.783,2.454,2.742,2.308,2.151,2.956,2.502,2.429,2.476,2.491,3.108,3.335,2.473,3.029,2.78,2.141,2.213,2.429,1.754,3.585,1.906,2.105,2.038,2.865,2.964,2.558,2.719,1.82,2.025,2.016,2.309,2.592,2.188,2.359,2.461,1.985,1.997,2.458,2.491,2.011,2.416,2.311,2.007,2.142,2.507,2.295,2.074,2.186,2.361,2.352,2.284,0.695,0.698,0.516,0.463,0.701,0.64,0.643,0.465,0.618,0.68,0.834,0.559,0.396,0.523,0.579,0.579,0.686,0.736,0.568,0.721,0.827,0.741,0.56,0.736,0.755,0.62,0.623,0.615,0.569,0.524,0.648,0.804,0.395,0.612,0.872,0.518,0.777,0.796,0.459,0.717,0.452,0.609,0.894,0.601,0.67,0.391,0.397,0.409,0.433,0.499,0.302,0.469,0.486,0.489,0.438,0.42,0.439,0.723,0.426,0.386,0.442,0.673,0.505,0.755,0.424,0.678,0.38,0.352,0.484,0.592,0.394,0.786,0.467,0.352
-172,Epileptic,F,22.0,0.7,2.4,1.2,1.2,1.7,3.1,2.3,1.3,1.6,0.6,0.5,2.3,1.2,0.4,1.5,4.4,8.6,1.1,2.5,3.7,1.7,3.0,1.5,3.4,6.6,3.1,3.4,2.9,2.5,3.3,1.7,0.7,0.8,1.8,0.6,0.5,2.9,2.7,0.2,0.2,0.9,3.4,2.1,1.7,2.6,0.6,0.4,1.1,1.7,0.8,0.7,1.6,1.4,1.3,0.3,2.3,0.8,1.2,0.8,0.9,0.8,1.5,0.4,0.7,1.5,2.2,2.5,1.2,1.0,0.5,1.0,1.1,3.2,0.2,6,19,7,7,17,26,15,13,16,7,5.0,28,17,5,21,51,78,10,27,40,15,33,14,35,61,42,45,33,29,36,17,7,7,25,3,5,36,34,1,2,6,29,20,13,15,5,2,5,9,6,4,11,13,9,2,18,6,9,7,8,5,11,2,4,12,32,19,10,8,4,6,9,28,4,0.035,0.031,0.021,0.023,0.033,0.05,0.031,0.022,0.05,0.026,0.045,0.04,0.026,0.025,0.029,0.032,0.035,0.041,0.042,0.037,0.028,0.039,0.029,0.04,0.047,0.028,0.024,0.032,0.025,0.03,0.045,0.041,0.035,0.028,0.022,0.016,0.026,0.026,0.014,0.014,0.02,0.045,0.038,0.025,0.019,0.017,0.018,0.016,0.02,0.014,0.029,0.018,0.023,0.022,0.02,0.02,0.018,0.02,0.022,0.019,0.018,0.024,0.023,0.031,0.023,0.039,0.019,0.017,0.017,0.02,0.023,0.016,0.016,0.02,1560,3842,2305,2200,2453,2963,3439,3196,1909,1550,589.0,2652,3603,1179,3240,8359,15330,2203,4191,4812,3910,3783,2448,6003,5778,7155,7615,4761,7312,6059,2710,933,1028,4862,1685,1326,7286,7344,326,498,1283,2661,5039,1918,3866,1327,811,2294,2453,1830,690,2803,1971,2809,596,3996,1564,2438,1095,1410,1045,2468,408,797,2189,1152,4294,2521,2157,1016,1475,2268,7809,327,0.11,0.125,0.1,0.104,0.142,0.132,0.119,0.114,0.172,0.118,0.165,0.161,0.116,0.124,0.144,0.141,0.138,0.144,0.156,0.162,0.125,0.121,0.106,0.141,0.14,0.125,0.114,0.118,0.109,0.144,0.153,0.118,0.146,0.125,0.087,0.097,0.123,0.119,0.099,0.092,0.103,0.148,0.142,0.113,0.096,0.097,0.093,0.086,0.105,0.093,0.125,0.099,0.117,0.102,0.086,0.106,0.104,0.104,0.119,0.105,0.098,0.118,0.129,0.134,0.114,0.147,0.105,0.101,0.096,0.11,0.112,0.101,0.093,0.144,425,1319,849,788,886,987,1154,965,601,388,204.0,1020,956,284,912,2404,4617,527,1132,1790,944,1312,746,1609,1978,2058,2335,1579,1896,1789,690,378,381,1191,507,443,1937,1743,200,257,675,1263,1126,1089,2073,626,319,909,1202,778,372,1349,962,1102,246,1904,645,1090,553,735,557,992,227,375,1047,756,2040,1064,955,435,683,1091,3273,207,3.421,2.679,2.533,2.769,2.268,2.52,2.493,2.736,2.398,3.017,2.54,2.264,2.901,2.882,2.745,2.603,2.675,3.344,2.947,2.237,3.226,2.363,2.524,2.837,2.436,2.826,2.541,2.396,2.906,2.61,2.974,2.453,2.285,2.957,2.955,2.534,2.814,2.969,1.884,2.343,2.373,1.765,3.229,2.052,2.173,2.231,3.142,3.055,2.601,2.569,2.357,2.337,2.058,2.605,2.535,2.289,2.562,2.444,2.357,2.12,2.248,2.588,2.148,2.439,2.261,1.992,2.315,2.707,2.49,2.511,2.5,2.465,2.521,1.907,0.742,0.802,0.565,0.426,0.695,0.805,0.557,0.48,0.641,0.556,0.694,0.551,0.418,0.44,0.444,0.575,0.597,0.668,0.556,0.598,0.689,0.622,0.668,0.594,0.634,0.594,0.603,0.561,0.522,0.559,0.653,0.846,0.438,0.608,0.586,0.589,0.726,0.632,0.391,0.319,0.549,0.668,0.874,0.568,0.613,0.488,0.739,0.757,0.494,0.564,0.347,0.458,0.499,0.838,0.537,0.465,0.519,0.611,0.483,0.365,0.31,0.546,0.396,0.6,0.447,0.596,0.43,0.358,0.643,1.003,0.501,0.475,0.454,0.346
-173,Epileptic,F,37.0,0.7,3.3,0.7,1.1,1.2,2.2,1.2,0.9,0.7,0.5,0.4,2.9,1.4,0.2,1.2,4.4,8.3,0.6,1.6,4.3,1.1,2.2,1.2,2.5,3.1,1.9,2.6,2.0,2.5,1.9,1.2,0.9,0.4,1.8,0.3,0.5,2.0,2.2,0.2,0.2,0.7,3.0,2.2,2.4,2.6,0.4,0.4,0.8,0.9,0.4,0.4,1.5,0.8,2.4,0.2,1.8,0.6,1.2,1.4,0.4,0.4,1.0,0.4,0.3,1.6,1.2,1.7,1.0,0.9,0.4,0.4,0.8,3.2,0.4,9,28,7,8,15,22,12,9,8,6,4.0,27,12,2,12,47,71,6,19,44,13,21,12,28,31,24,30,22,23,26,17,6,4,25,2,3,26,25,1,2,5,31,19,16,17,3,2,5,5,5,3,12,7,14,1,14,6,11,9,4,3,9,3,4,10,16,12,6,4,4,3,6,29,3,0.041,0.037,0.019,0.023,0.028,0.055,0.022,0.023,0.037,0.03,0.033,0.031,0.03,0.028,0.032,0.037,0.036,0.03,0.03,0.034,0.031,0.032,0.024,0.032,0.036,0.025,0.031,0.025,0.025,0.026,0.033,0.045,0.019,0.028,0.016,0.016,0.036,0.036,0.017,0.02,0.018,0.034,0.038,0.022,0.018,0.009,0.02,0.015,0.016,0.013,0.022,0.02,0.02,0.024,0.028,0.022,0.035,0.016,0.021,0.017,0.019,0.022,0.027,0.022,0.022,0.03,0.019,0.016,0.014,0.016,0.015,0.019,0.017,0.02,1285,4689,1756,2137,1844,3521,2397,2351,1624,1131,758.0,3382,2910,732,2453,8985,16419,2341,3784,5604,3417,4013,2165,5296,4803,4645,4848,3471,6104,3757,2239,1414,1208,4735,2122,1286,4876,5763,511,512,1268,3427,5234,2847,3481,1144,746,1788,1978,1119,446,2853,1463,3749,238,2645,1173,2763,2056,936,749,2219,538,882,2372,924,2991,2024,1820,701,804,1798,7177,507,0.132,0.14,0.106,0.104,0.13,0.151,0.107,0.113,0.143,0.141,0.126,0.136,0.129,0.114,0.147,0.144,0.144,0.122,0.13,0.146,0.12,0.145,0.12,0.135,0.148,0.129,0.148,0.111,0.115,0.126,0.129,0.122,0.096,0.134,0.072,0.086,0.142,0.144,0.102,0.114,0.104,0.146,0.149,0.104,0.1,0.08,0.108,0.083,0.088,0.104,0.125,0.104,0.104,0.103,0.176,0.113,0.137,0.101,0.114,0.12,0.111,0.112,0.141,0.125,0.109,0.136,0.099,0.096,0.089,0.128,0.102,0.118,0.099,0.116,362,1377,646,769,696,1111,873,761,367,285,210.0,1374,785,163,627,2320,4074,369,963,2088,703,1172,825,1312,1395,1233,1553,1213,1606,1231,572,443,398,1067,406,475,1148,1183,177,188,593,1459,971,1679,2008,601,286,784,906,458,249,1205,684,1554,96,1281,327,1261,1005,418,364,761,228,350,1063,566,1403,878,861,343,368,633,2907,294,3.184,3.184,2.691,2.931,2.303,2.751,2.365,2.755,3.071,3.146,2.755,2.123,2.785,3.167,2.963,2.938,3.191,4.079,3.084,2.256,3.388,2.684,2.264,2.959,2.741,2.973,2.497,2.28,2.917,2.4,2.857,3.063,2.415,3.023,3.565,2.434,3.144,3.343,2.798,2.926,2.788,2.113,3.719,1.898,2.004,2.15,3.229,2.694,2.708,2.637,2.153,2.522,2.378,2.608,2.82,2.348,2.972,2.53,2.429,2.323,2.721,2.813,2.497,2.723,2.346,2.07,2.38,2.639,2.664,2.419,2.426,3.069,2.695,2.229,0.876,0.703,0.518,0.432,0.611,0.551,0.51,0.493,0.719,0.646,0.783,0.442,0.45,0.487,0.41,0.553,0.523,0.761,0.591,0.579,0.884,0.457,0.544,0.658,0.438,0.51,0.532,0.509,0.475,0.499,0.646,0.925,0.367,0.652,0.985,0.372,0.748,0.605,0.435,0.485,0.509,0.443,0.718,0.557,0.594,0.336,0.473,0.601,0.54,0.398,0.333,0.518,0.475,0.422,0.628,0.41,0.657,0.593,0.337,0.411,0.382,0.777,0.432,0.899,0.364,0.549,0.478,0.409,0.367,0.539,0.541,0.878,0.433,0.337
-174,Epileptic,M,27.0,0.7,2.8,1.4,2.0,1.8,1.9,1.7,2.0,1.5,0.5,0.4,3.3,1.4,0.7,1.5,5.1,8.7,0.9,2.5,5.2,1.5,3.8,2.4,4.1,5.7,4.7,4.2,3.5,4.9,4.4,1.7,1.6,0.5,3.9,0.6,0.6,3.9,3.8,0.3,0.2,1.3,4.1,2.6,2.2,3.5,1.1,0.4,1.0,1.6,0.7,0.5,2.0,1.7,5.1,0.4,3.8,1.0,1.2,0.8,1.6,0.6,2.1,0.2,0.5,2.2,1.6,2.9,1.2,1.4,0.6,0.8,1.3,5.0,0.3,8,23,11,15,13,20,16,20,16,6,3.0,28,16,7,15,52,72,9,25,38,10,36,27,38,52,49,39,32,37,46,18,14,5,34,3,3,45,43,2,1,7,29,24,16,20,7,2,6,8,6,3,13,17,41,3,30,8,9,7,11,5,14,2,5,18,19,26,8,9,5,5,9,42,2,0.045,0.03,0.029,0.03,0.035,0.029,0.029,0.031,0.047,0.036,0.048,0.042,0.034,0.032,0.031,0.034,0.034,0.04,0.036,0.054,0.038,0.037,0.031,0.051,0.038,0.035,0.034,0.036,0.036,0.042,0.045,0.072,0.032,0.045,0.024,0.016,0.038,0.041,0.019,0.017,0.025,0.047,0.048,0.024,0.024,0.018,0.018,0.019,0.022,0.022,0.03,0.023,0.032,0.031,0.015,0.021,0.021,0.017,0.02,0.023,0.023,0.039,0.033,0.027,0.023,0.029,0.02,0.019,0.023,0.021,0.021,0.023,0.02,0.015,1329,4291,2303,2976,2263,2736,2771,3034,1889,1205,541.0,2663,2909,1150,3114,8559,16509,1954,4009,4259,2221,3901,2384,5166,5643,7608,4975,4179,6745,5865,2628,855,1062,5890,1651,1626,6816,6794,401,422,1388,2582,4786,2295,3840,1587,687,1825,2148,1325,380,2880,2193,7517,691,4634,1443,1999,944,1797,739,2197,308,832,2665,1273,4499,2296,2060,1308,1083,2078,8953,483,0.115,0.127,0.125,0.128,0.142,0.135,0.107,0.141,0.16,0.138,0.142,0.159,0.121,0.141,0.137,0.134,0.132,0.141,0.151,0.183,0.121,0.119,0.125,0.16,0.124,0.128,0.107,0.107,0.12,0.161,0.155,0.154,0.106,0.153,0.091,0.083,0.145,0.146,0.095,0.103,0.114,0.146,0.16,0.12,0.108,0.1,0.096,0.097,0.104,0.106,0.127,0.11,0.129,0.125,0.093,0.113,0.111,0.099,0.111,0.115,0.114,0.144,0.13,0.141,0.119,0.14,0.105,0.099,0.107,0.128,0.123,0.111,0.103,0.107,399,1506,763,1013,838,1031,919,1048,624,313,149.0,1288,776,338,831,2722,4357,448,1283,1721,643,1508,1195,1544,2259,2341,1970,1669,1850,1927,723,484,322,1481,450,600,1813,1772,254,215,751,1338,1160,1361,2227,869,296,813,1079,572,255,1316,947,2904,389,2697,738,1071,627,1058,403,864,167,361,1486,854,2379,1004,982,402,516,929,4101,278,3.155,2.779,2.913,2.69,2.249,2.308,2.444,2.57,2.42,3.093,2.877,1.804,2.707,2.434,2.842,2.497,2.914,3.127,2.55,2.139,2.615,2.122,1.813,2.654,2.118,2.652,2.056,2.074,2.812,2.421,2.703,2.031,2.53,2.799,3.083,2.493,2.771,2.874,1.831,2.275,2.314,1.793,2.847,1.903,1.989,2.081,2.521,2.827,2.403,2.214,1.784,2.211,2.158,2.427,2.068,1.947,2.385,2.126,1.802,1.871,2.064,2.473,2.166,2.251,1.934,1.859,1.943,2.552,2.39,3.332,2.301,2.646,2.292,2.193,0.91,0.599,0.634,0.638,0.717,0.562,0.736,0.493,0.64,0.718,0.689,0.486,0.509,0.524,0.595,0.56,0.644,0.772,0.601,0.527,0.667,0.543,0.515,0.571,0.639,0.534,0.565,0.578,0.652,0.584,0.81,0.757,0.404,0.663,0.656,0.506,0.644,0.641,0.376,0.322,0.482,0.542,0.797,0.59,0.624,0.537,0.536,0.766,0.512,0.643,0.284,0.543,0.544,0.55,0.423,0.485,0.411,0.441,0.354,0.378,0.375,0.697,0.31,0.652,0.599,0.552,0.425,0.552,0.495,0.917,0.499,0.454,0.47,0.383
-175,Epileptic,F,42.0,1.1,2.2,0.7,1.4,1.7,2.1,1.5,1.9,1.2,1.0,0.3,3.1,1.8,0.4,1.1,5.7,9.5,0.8,1.5,4.8,1.1,3.5,2.1,3.9,3.0,3.3,3.2,2.4,3.7,3.1,1.1,1.2,0.5,2.2,0.6,0.7,3.3,4.4,0.1,0.3,1.2,3.3,3.4,3.1,3.5,0.5,0.2,1.0,1.4,0.8,0.6,1.7,1.3,2.7,0.2,2.2,1.0,1.7,1.1,1.8,0.5,1.8,0.5,0.4,2.4,1.0,2.0,1.4,2.1,0.6,1.1,1.6,4.5,0.4,9,26,7,13,17,20,14,15,13,11,4.0,33,18,6,16,66,86,6,20,42,13,41,22,41,38,37,42,21,29,35,14,10,6,27,4,7,44,53,1,2,7,28,32,25,22,3,2,5,7,7,5,14,11,19,1,18,6,17,8,12,5,15,3,6,20,18,16,9,15,7,8,13,40,3,0.04,0.019,0.015,0.024,0.028,0.03,0.022,0.031,0.04,0.039,0.027,0.032,0.029,0.024,0.027,0.036,0.028,0.027,0.027,0.034,0.023,0.035,0.029,0.037,0.03,0.03,0.027,0.026,0.024,0.03,0.024,0.041,0.025,0.028,0.015,0.015,0.033,0.032,0.015,0.023,0.021,0.037,0.047,0.023,0.024,0.014,0.011,0.015,0.018,0.018,0.029,0.02,0.021,0.021,0.027,0.015,0.021,0.02,0.021,0.024,0.023,0.024,0.033,0.013,0.023,0.019,0.018,0.021,0.022,0.017,0.024,0.019,0.018,0.018,1529,4965,2219,2622,3069,3447,2806,3343,2111,1750,722.0,3647,3732,861,2299,11005,19697,2515,3704,5039,3876,5999,3010,6046,5426,6369,6066,3802,7807,5228,2190,1470,1200,5915,2312,2022,7345,10006,265,395,1733,3204,7115,4301,3900,1073,811,2070,2606,1996,686,2900,2208,4882,376,3934,1505,3609,1321,2397,759,3154,508,1035,3467,1193,3478,2608,3271,1005,1493,2730,9477,551,0.129,0.111,0.094,0.114,0.13,0.147,0.106,0.13,0.156,0.152,0.113,0.14,0.137,0.121,0.141,0.145,0.128,0.109,0.136,0.143,0.108,0.149,0.136,0.136,0.137,0.137,0.128,0.107,0.1,0.137,0.116,0.127,0.104,0.124,0.077,0.089,0.136,0.142,0.097,0.119,0.109,0.156,0.177,0.114,0.111,0.09,0.085,0.082,0.097,0.106,0.145,0.109,0.11,0.111,0.127,0.101,0.102,0.115,0.116,0.114,0.12,0.115,0.155,0.11,0.119,0.119,0.105,0.111,0.112,0.122,0.123,0.107,0.097,0.105,490,1800,818,955,1011,1205,958,1068,564,473,238.0,1570,1048,281,704,3026,5462,456,997,2273,827,1832,1214,1844,1723,1917,2071,1393,2210,1666,638,588,383,1396,601,785,1838,2323,151,218,859,1388,1283,2320,2261,582,334,934,1182,701,327,1349,957,1967,142,2061,651,1403,771,1136,408,1155,211,560,1595,850,1750,1053,1488,553,731,1264,4143,316,3.082,2.621,2.503,2.771,2.583,2.494,2.38,2.789,2.789,3.068,2.855,2.027,2.834,2.403,2.58,2.791,2.932,4.001,2.978,2.027,3.718,2.529,2.123,2.548,2.574,2.715,2.312,2.203,2.743,2.473,2.528,2.521,2.484,2.965,3.155,2.384,2.95,3.001,2.094,2.334,2.476,2.005,3.562,2.047,1.939,2.036,3.081,2.692,2.709,2.987,2.374,2.327,2.337,2.627,2.921,2.067,2.549,2.64,2.031,2.18,2.3,2.77,2.303,2.079,2.233,1.774,2.134,2.635,2.467,2.071,2.325,2.502,2.435,2.267,0.744,0.614,0.527,0.516,0.686,0.497,0.529,0.516,0.6,0.763,0.908,0.393,0.396,0.425,0.366,0.49,0.513,0.743,0.517,0.621,0.903,0.523,0.508,0.668,0.529,0.476,0.439,0.565,0.502,0.533,0.77,0.835,0.328,0.628,0.71,0.444,0.692,0.663,0.2,0.377,0.506,0.504,0.879,0.611,0.556,0.385,0.469,0.638,0.472,0.686,0.409,0.495,0.564,0.479,0.457,0.419,0.581,0.614,0.369,0.39,0.309,0.711,0.431,0.684,0.489,0.494,0.46,0.34,0.457,0.406,0.423,0.492,0.38,0.311
-176,Epileptic,F,22.0,0.9,2.6,1.1,1.3,1.3,2.2,2.0,1.4,0.8,0.7,0.2,2.4,1.8,0.5,1.1,5.4,10.9,1.0,3.0,4.0,1.2,3.3,1.4,2.6,3.5,2.7,3.8,2.2,3.3,3.3,1.5,1.0,0.5,2.9,0.8,0.8,3.1,2.9,0.1,0.2,1.0,3.7,3.3,2.8,3.3,0.6,0.6,1.0,1.3,1.6,0.7,1.8,1.0,2.6,0.2,2.7,0.8,1.6,1.1,1.0,0.5,1.5,0.2,0.3,1.8,0.8,2.1,1.3,1.1,0.4,1.0,1.5,5.2,0.4,6,23,12,10,12,18,15,12,10,6,3.0,21,16,6,8,56,93,6,25,33,8,37,12,28,36,27,41,21,21,37,18,8,4,23,4,7,28,31,1,1,5,24,22,20,20,4,3,6,7,9,5,13,8,17,1,22,6,15,9,9,6,12,2,3,15,7,21,10,7,4,9,10,39,4,0.043,0.023,0.019,0.022,0.03,0.035,0.025,0.026,0.033,0.031,0.022,0.029,0.032,0.029,0.03,0.034,0.034,0.035,0.034,0.034,0.025,0.03,0.024,0.029,0.032,0.033,0.027,0.024,0.026,0.029,0.037,0.03,0.02,0.031,0.029,0.025,0.032,0.031,0.013,0.024,0.021,0.044,0.048,0.028,0.022,0.017,0.023,0.015,0.019,0.037,0.035,0.018,0.021,0.02,0.027,0.018,0.013,0.02,0.023,0.017,0.021,0.026,0.018,0.018,0.021,0.021,0.021,0.02,0.021,0.017,0.024,0.022,0.018,0.019,1481,5101,2290,2209,2400,2506,2746,2492,1603,1138,479.0,2234,3238,1136,2224,10348,19575,2735,4098,3609,2583,5606,1893,6103,6131,4728,6007,3460,5477,5439,2489,1121,1031,5505,2055,1461,5644,6450,326,350,1408,2329,3181,2222,3734,1252,1013,2642,2361,1495,542,3535,1986,4948,301,4126,1892,2778,1485,1654,1156,2718,388,1096,3070,990,3313,2319,1677,873,1248,2385,10113,499,0.139,0.117,0.117,0.107,0.122,0.145,0.104,0.118,0.137,0.144,0.119,0.127,0.126,0.147,0.119,0.139,0.139,0.112,0.135,0.138,0.092,0.134,0.118,0.122,0.143,0.142,0.128,0.108,0.107,0.137,0.139,0.108,0.105,0.124,0.106,0.111,0.124,0.119,0.1,0.114,0.11,0.151,0.143,0.135,0.108,0.099,0.111,0.08,0.102,0.132,0.146,0.095,0.102,0.102,0.136,0.108,0.092,0.11,0.116,0.103,0.109,0.119,0.103,0.108,0.114,0.094,0.113,0.114,0.11,0.115,0.132,0.112,0.097,0.125,366,1838,896,869,728,1092,1093,852,437,329,191.0,1170,964,299,609,2765,5582,453,1387,1749,733,1746,840,1548,1915,1435,2198,1337,1800,1785,641,499,354,1419,458,540,1556,1617,155,138,674,1276,1073,1488,2324,630,403,1057,1012,713,288,1513,878,2072,117,2231,888,1356,840,930,501,929,194,349,1379,553,1664,1051,802,414,642,1111,4540,297,3.551,2.746,2.678,2.653,2.527,2.201,2.164,2.526,2.758,2.999,2.45,1.713,2.745,2.875,2.837,2.777,2.798,4.116,2.582,1.838,2.829,2.529,1.998,3.039,2.625,2.77,2.219,2.136,2.404,2.5,2.887,2.374,2.336,2.798,3.819,2.57,2.709,2.932,2.35,2.79,2.689,1.785,2.572,1.763,1.856,2.196,3.064,2.962,2.832,2.515,2.066,2.41,2.359,2.423,2.932,2.128,2.66,2.349,2.143,2.026,2.423,2.758,2.399,2.935,2.636,2.038,2.102,2.613,2.348,2.445,2.198,2.504,2.487,2.048,0.892,0.55,0.498,0.46,0.864,0.517,0.518,0.394,0.626,0.409,0.856,0.368,0.494,0.299,0.519,0.63,0.605,0.807,0.557,0.492,0.899,0.478,0.359,0.722,0.481,0.477,0.446,0.534,0.579,0.507,0.699,1.014,0.38,0.813,0.731,0.509,0.669,0.6,0.373,0.401,0.617,0.409,0.674,0.609,0.613,0.494,0.69,0.785,0.599,0.763,0.621,0.672,0.592,0.494,0.283,0.446,0.533,0.651,0.38,0.335,0.455,0.71,0.335,0.973,0.784,0.645,0.508,0.509,0.522,0.409,0.359,0.509,0.526,0.544
-177,Epileptic,F,27.0,0.6,1.7,0.7,1.1,0.9,2.3,1.4,1.4,1.0,0.5,0.4,2.4,1.5,0.5,0.8,6.2,7.9,0.7,2.4,5.2,1.4,3.0,1.8,2.8,2.7,3.9,2.9,2.1,2.5,2.5,1.3,1.2,0.6,2.3,0.3,0.3,4.0,3.6,0.3,0.1,0.9,2.2,2.2,3.1,2.5,0.5,0.5,1.1,1.0,0.5,0.6,1.4,1.2,1.7,0.3,2.2,0.7,1.3,0.6,1.0,0.5,1.2,0.2,0.3,1.2,1.2,2.5,1.2,0.8,0.3,1.1,1.0,4.0,0.3,5,17,9,13,14,16,10,13,14,5,4.0,22,15,9,10,61,71,6,24,40,8,30,16,37,33,36,34,17,18,31,14,8,5,26,2,2,39,38,2,1,5,22,17,19,16,4,2,6,6,4,5,8,8,11,1,18,5,8,5,8,4,9,1,3,10,15,16,9,6,3,8,8,27,2,0.025,0.022,0.016,0.023,0.024,0.03,0.023,0.031,0.042,0.026,0.026,0.033,0.032,0.027,0.022,0.035,0.029,0.027,0.029,0.038,0.023,0.035,0.032,0.034,0.034,0.037,0.031,0.026,0.023,0.029,0.04,0.049,0.029,0.027,0.011,0.008,0.032,0.031,0.021,0.009,0.019,0.033,0.036,0.026,0.022,0.014,0.021,0.015,0.015,0.016,0.026,0.015,0.02,0.018,0.025,0.018,0.02,0.016,0.022,0.018,0.027,0.023,0.019,0.013,0.018,0.032,0.021,0.018,0.018,0.012,0.024,0.019,0.017,0.015,1580,3743,1477,2210,1494,3136,2697,3121,1162,1174,738.0,2481,3057,1108,2249,10935,16463,2312,4609,5645,2393,4111,2130,5033,5335,6926,4886,3402,5791,4815,1462,1068,1120,5679,1935,1584,7094,8378,483,448,1556,2312,4120,3313,3099,1065,807,2375,2105,1143,686,2835,1649,4053,328,3398,1545,2710,949,1398,705,2138,276,668,2200,958,3585,2282,1716,895,1661,2123,8959,771,0.107,0.115,0.117,0.132,0.107,0.136,0.11,0.131,0.149,0.125,0.126,0.138,0.122,0.139,0.121,0.133,0.124,0.103,0.122,0.144,0.094,0.146,0.136,0.129,0.148,0.148,0.138,0.107,0.102,0.14,0.118,0.136,0.125,0.123,0.064,0.066,0.13,0.131,0.109,0.065,0.102,0.139,0.13,0.116,0.105,0.098,0.107,0.091,0.087,0.095,0.13,0.09,0.112,0.092,0.14,0.107,0.1,0.091,0.108,0.104,0.122,0.109,0.101,0.11,0.102,0.135,0.106,0.102,0.1,0.088,0.118,0.106,0.091,0.098,424,1238,702,815,685,1139,904,935,400,349,246.0,1128,867,298,618,2899,4569,441,1286,2210,741,1336,909,1312,1501,1770,1678,1248,1576,1476,445,444,349,1330,492,572,2015,1963,241,174,708,1150,1030,1822,1870,537,327,1041,1016,552,371,1262,889,1623,142,1842,653,1245,521,794,340,791,153,321,1063,617,1766,1029,767,411,742,903,3782,277,3.377,2.944,1.995,2.727,2.022,2.476,2.451,3.116,2.269,3.03,2.517,1.996,2.728,2.765,2.754,2.826,2.986,3.686,2.814,2.229,2.631,2.477,2.014,2.816,2.789,3.138,2.365,2.195,2.831,2.578,2.396,2.432,2.664,3.105,3.437,2.598,2.686,3.174,2.374,2.437,2.948,1.844,2.951,2.064,1.935,2.084,3.287,2.96,2.576,2.577,2.186,2.346,2.071,2.606,3.086,2.122,2.878,2.531,2.202,1.999,2.629,2.506,2.114,2.311,2.256,2.076,2.292,2.607,2.504,2.352,2.483,2.815,2.595,3.115,0.91,0.793,0.619,0.641,0.533,0.494,0.573,0.605,0.543,0.445,0.651,0.411,0.472,0.411,0.478,0.501,0.628,0.864,0.507,0.674,0.724,0.447,0.351,0.579,0.434,0.514,0.473,0.463,0.548,0.465,0.644,0.763,0.368,0.518,0.656,0.416,0.709,0.561,0.383,0.313,0.555,0.431,0.692,0.577,0.652,0.434,0.675,0.617,0.584,0.338,0.394,0.558,0.506,0.558,0.542,0.392,0.52,0.495,0.498,0.362,0.547,0.702,0.274,1.089,0.497,0.65,0.499,0.391,0.394,0.56,0.476,0.673,0.552,0.649
-178,Epileptic,M,27.0,1.0,1.9,1.0,1.5,1.6,1.7,1.3,1.4,0.6,0.8,0.3,2.6,1.5,0.7,2.0,5.0,8.3,0.6,2.0,5.5,1.3,2.7,1.6,3.8,3.1,3.7,2.4,1.8,2.6,2.5,1.2,0.5,0.6,3.1,0.4,0.9,2.7,3.1,0.2,0.3,1.3,3.0,2.0,3.3,2.6,0.5,0.4,1.1,1.1,0.6,0.7,3.0,1.4,2.3,0.2,2.0,0.7,1.4,1.0,1.3,0.7,1.2,0.5,0.4,1.6,0.9,2.4,1.5,1.0,0.7,1.0,1.0,5.0,0.2,6,18,7,10,17,17,10,16,8,8,4.0,25,15,8,22,51,77,4,24,43,16,27,16,42,37,39,31,20,23,29,21,7,6,31,3,9,30,38,2,2,10,27,17,24,16,4,2,7,6,5,5,29,10,15,2,20,5,12,7,11,6,12,3,4,13,15,20,9,6,9,7,7,47,1,0.039,0.023,0.02,0.028,0.044,0.026,0.019,0.024,0.041,0.035,0.025,0.031,0.029,0.04,0.035,0.029,0.028,0.021,0.027,0.041,0.022,0.036,0.027,0.035,0.037,0.028,0.027,0.018,0.024,0.028,0.036,0.025,0.032,0.029,0.011,0.02,0.031,0.028,0.012,0.012,0.024,0.035,0.03,0.027,0.019,0.011,0.019,0.017,0.016,0.012,0.033,0.022,0.026,0.017,0.022,0.02,0.015,0.017,0.023,0.031,0.02,0.023,0.026,0.014,0.019,0.019,0.019,0.019,0.016,0.021,0.026,0.018,0.022,0.016,1709,4492,2585,2287,2097,2522,2281,2686,1043,1235,739.0,2677,3332,1182,3591,9857,18758,2795,4590,3846,4167,3716,2156,7340,4732,7161,4530,3369,5960,4376,2561,1022,838,6256,2649,1949,6782,7249,501,740,1589,2885,5263,2932,3470,1250,834,2552,2362,1470,729,5941,1642,4920,386,2823,1611,2658,1271,1437,986,2708,708,1115,2638,1169,3570,2999,2115,1400,1319,2140,8874,283,0.116,0.116,0.097,0.122,0.148,0.134,0.089,0.124,0.128,0.153,0.123,0.148,0.123,0.167,0.15,0.128,0.128,0.089,0.126,0.16,0.112,0.149,0.137,0.142,0.162,0.134,0.123,0.1,0.103,0.124,0.134,0.101,0.148,0.124,0.077,0.104,0.134,0.13,0.088,0.085,0.122,0.147,0.133,0.123,0.096,0.084,0.099,0.087,0.092,0.096,0.133,0.115,0.133,0.094,0.111,0.122,0.095,0.105,0.108,0.132,0.117,0.116,0.123,0.104,0.102,0.128,0.107,0.101,0.094,0.121,0.124,0.101,0.113,0.104,383,1365,881,823,742,1054,880,942,325,382,213.0,1310,887,298,975,2781,4915,477,1264,2197,1005,1256,923,1789,1523,2152,1691,1432,1725,1500,684,387,305,1508,577,767,1573,1911,259,368,841,1268,1045,1881,2181,692,351,986,1048,631,372,2388,735,2180,165,1527,688,1315,720,717,523,913,339,503,1370,726,1928,1184,969,561,641,912,3822,154,3.913,2.904,2.954,2.721,2.332,2.244,2.2,2.571,2.52,2.726,2.887,1.838,2.863,3.018,2.826,2.685,3.02,4.044,2.83,1.703,3.153,2.462,2.051,3.054,2.524,2.701,2.231,1.946,2.75,2.329,2.862,2.344,2.592,2.917,3.76,2.348,3.182,2.783,2.196,2.359,2.367,1.977,3.461,1.778,1.837,2.065,3.049,3.146,2.849,2.588,2.047,2.292,2.361,2.558,2.367,2.022,2.622,2.339,2.082,2.163,2.127,2.641,2.373,2.488,2.12,2.007,2.082,2.933,2.622,2.69,2.391,2.616,2.382,2.281,0.828,0.615,0.535,0.584,0.657,0.536,0.579,0.629,0.469,0.661,1.017,0.508,0.49,0.512,0.505,0.57,0.557,0.782,0.601,0.46,0.627,0.401,0.444,0.769,0.495,0.548,0.476,0.516,0.422,0.563,0.548,0.931,0.598,0.653,0.905,0.522,0.704,0.646,0.38,0.372,0.507,0.545,0.675,0.46,0.563,0.343,0.603,0.847,0.48,0.397,0.456,0.555,0.541,0.438,0.566,0.326,0.606,0.47,0.356,0.385,0.308,0.783,0.719,1.128,0.404,0.769,0.469,0.464,0.37,0.53,0.532,0.65,0.406,0.519
-179,Epileptic,M,32.0,0.8,2.0,0.8,1.1,1.3,2.4,1.9,2.9,1.6,1.1,0.4,4.0,1.5,0.6,1.7,5.0,9.5,1.2,2.0,8.0,1.0,3.2,2.5,3.9,3.5,6.6,4.5,3.0,5.8,5.3,1.4,1.9,0.8,3.1,0.3,1.3,3.7,4.5,0.2,0.4,1.4,3.9,2.4,6.5,2.9,0.8,0.3,0.9,1.5,0.6,0.4,2.0,1.4,3.0,0.2,3.0,0.5,2.1,1.0,1.5,0.5,1.5,0.2,0.4,2.3,1.2,2.6,2.1,2.0,0.4,1.6,0.9,3.5,0.1,7,23,10,6,14,18,12,21,17,8,3.0,33,12,6,15,48,84,7,21,56,9,25,20,36,30,48,34,27,163,40,20,9,5,25,1,7,32,39,1,2,9,35,23,30,16,4,1,5,7,6,4,12,10,20,1,20,4,14,7,10,3,12,1,3,14,14,20,11,13,5,8,7,28,1,0.033,0.02,0.014,0.021,0.033,0.038,0.029,0.042,0.043,0.037,0.032,0.033,0.029,0.027,0.033,0.035,0.032,0.034,0.031,0.043,0.025,0.042,0.039,0.047,0.035,0.047,0.033,0.033,0.365,0.043,0.047,0.053,0.033,0.034,0.009,0.026,0.032,0.038,0.013,0.018,0.026,0.039,0.038,0.036,0.021,0.015,0.017,0.015,0.016,0.021,0.021,0.022,0.027,0.026,0.018,0.023,0.014,0.023,0.025,0.027,0.026,0.032,0.023,0.024,0.03,0.029,0.022,0.024,0.024,0.013,0.034,0.019,0.017,0.02,1765,5087,2281,2258,2122,3315,2664,3249,2263,1552,898.0,3429,3631,1387,3848,10371,19482,2958,3714,5773,3642,3920,2772,6938,4920,7842,5750,3108,6516,5867,2502,1146,1071,5423,1819,1944,8631,6682,440,559,1635,3740,7679,3643,3417,1482,870,2447,3202,1429,484,3514,2309,5000,429,3878,1404,3076,979,1500,610,2542,888,1045,2175,1060,3859,3329,2953,1078,1863,1870,8267,152,0.116,0.11,0.103,0.092,0.148,0.137,0.107,0.14,0.166,0.146,0.104,0.132,0.11,0.115,0.126,0.137,0.126,0.111,0.139,0.149,0.091,0.136,0.137,0.156,0.126,0.134,0.117,0.121,0.124,0.147,0.157,0.123,0.131,0.117,0.059,0.101,0.137,0.119,0.09,0.105,0.121,0.149,0.154,0.145,0.098,0.089,0.084,0.085,0.091,0.119,0.12,0.106,0.113,0.108,0.095,0.109,0.086,0.112,0.12,0.119,0.112,0.124,0.103,0.122,0.128,0.127,0.106,0.107,0.111,0.103,0.12,0.103,0.092,0.112,434,1512,978,825,745,1137,933,1135,590,464,224.0,1835,895,301,860,2430,5175,481,1157,2940,827,1321,1109,1469,1621,2167,2068,1463,2051,2059,582,401,339,1372,466,773,1797,1706,228,280,837,1689,1096,2571,2133,762,317,947,1293,499,319,1403,877,1868,190,2059,616,1511,608,855,296,871,189,353,1234,592,2002,1316,1299,484,783,775,3488,63,3.645,2.916,2.358,2.597,2.454,2.488,2.405,2.738,2.675,2.86,3.007,1.7,2.971,3.096,3.355,3.056,2.895,4.061,2.561,1.794,3.166,2.345,2.232,3.299,2.501,2.759,2.338,1.815,2.593,2.317,3.072,2.816,2.768,2.809,3.432,2.435,3.294,2.871,2.381,2.478,2.379,2.074,3.609,1.724,1.824,2.095,3.284,3.129,2.871,2.874,1.835,2.711,2.601,2.656,2.776,2.118,2.587,2.389,1.911,2.05,2.391,2.916,3.248,3.383,2.057,2.142,2.049,2.596,2.657,2.395,2.626,2.831,2.511,2.393,1.171,0.669,0.668,0.565,0.742,0.651,0.708,0.726,0.682,0.623,0.902,0.467,0.629,0.759,0.844,0.704,0.714,0.807,0.657,0.551,0.823,0.626,0.621,0.709,0.691,0.79,0.618,0.49,0.741,0.633,0.588,1.1,0.541,0.781,0.503,0.629,0.924,0.862,0.494,0.534,0.577,0.524,0.862,0.51,0.586,0.623,0.754,0.875,0.637,0.708,0.333,0.542,0.683,0.659,0.409,0.479,0.579,0.567,0.378,0.458,0.605,0.822,1.052,0.711,0.524,0.623,0.561,0.533,0.599,0.66,0.485,0.721,0.56,0.667
-180,Epileptic,M,32.0,0.6,2.6,1.1,0.8,1.2,2.8,1.7,1.9,1.3,0.4,0.2,3.5,1.6,0.5,1.5,5.4,8.6,0.9,2.6,6.3,1.1,3.3,1.5,3.3,2.8,2.2,3.2,3.4,2.1,3.9,0.9,0.4,0.7,2.1,0.8,0.5,3.8,3.0,0.2,0.3,1.3,3.6,2.5,3.2,2.5,0.6,0.5,0.8,1.2,0.6,1.4,2.0,2.0,2.4,0.2,3.0,0.6,2.4,1.3,1.3,0.7,1.4,0.4,0.4,1.7,0.6,2.4,1.3,1.2,0.6,1.0,1.4,4.8,0.3,6,25,11,6,13,23,15,15,19,5,2.0,24,15,6,15,56,77,6,24,42,11,37,16,31,39,27,38,27,20,40,15,5,5,23,8,4,43,35,2,1,6,29,23,23,14,4,2,4,6,4,9,14,14,16,1,28,5,14,13,10,4,13,3,4,13,7,20,10,8,6,7,11,38,3,0.029,0.025,0.014,0.018,0.027,0.039,0.025,0.035,0.041,0.021,0.021,0.038,0.034,0.029,0.033,0.029,0.032,0.031,0.031,0.04,0.017,0.03,0.029,0.035,0.029,0.03,0.026,0.03,0.024,0.036,0.029,0.022,0.03,0.026,0.028,0.016,0.036,0.029,0.019,0.016,0.025,0.037,0.035,0.034,0.019,0.011,0.02,0.011,0.015,0.018,0.059,0.019,0.022,0.022,0.025,0.02,0.021,0.022,0.027,0.021,0.025,0.023,0.027,0.018,0.022,0.025,0.021,0.019,0.021,0.021,0.022,0.018,0.019,0.022,1476,5068,2842,2018,2311,4116,2862,3574,2528,1189,572.0,2627,3725,1006,3091,11488,18514,2652,4178,4369,3038,5744,2007,6221,5628,4919,5804,3795,5211,5886,1975,1008,1301,6186,2275,1467,8013,7761,448,469,1756,3106,6676,2166,3424,1532,927,2506,2488,1459,656,3628,3372,5037,250,4190,1265,3675,1572,1854,883,2516,725,858,2582,963,3569,2632,1750,1297,1555,2971,10307,410,0.099,0.121,0.099,0.092,0.116,0.146,0.107,0.131,0.139,0.116,0.082,0.142,0.132,0.134,0.149,0.127,0.132,0.113,0.113,0.132,0.079,0.144,0.135,0.126,0.13,0.12,0.125,0.115,0.098,0.139,0.122,0.081,0.124,0.12,0.117,0.083,0.126,0.127,0.111,0.108,0.108,0.143,0.132,0.124,0.097,0.082,0.105,0.075,0.087,0.092,0.169,0.106,0.115,0.098,0.13,0.11,0.107,0.105,0.132,0.114,0.118,0.121,0.112,0.118,0.109,0.103,0.112,0.102,0.115,0.109,0.117,0.102,0.099,0.136,427,1624,1074,762,767,1288,1017,952,524,332,153.0,1295,940,286,737,3076,4681,473,1297,2391,844,1806,843,1503,1821,1336,2035,1512,1379,1888,505,399,356,1277,498,552,1904,1752,190,203,717,1408,1206,1508,2069,778,373,1032,1159,597,410,1633,1315,1888,114,2282,524,1760,824,1005,454,967,256,399,1263,407,1800,1085,844,516,753,1262,4114,201,3.225,2.804,2.53,2.62,2.55,2.76,2.353,3.051,3.091,2.858,3.021,1.845,3.101,2.536,2.945,2.787,3.077,3.93,2.72,1.7,2.842,2.543,2.126,3.041,2.46,2.886,2.298,2.016,2.824,2.538,2.711,2.342,3.11,3.162,3.643,2.473,3.048,3.133,2.361,2.571,2.806,2.046,3.48,1.679,1.925,2.164,3.145,2.864,2.579,3.048,1.826,2.462,2.493,2.691,2.425,1.997,2.718,2.537,2.019,2.101,2.537,2.687,2.565,2.324,2.297,2.536,2.126,2.507,2.433,2.724,2.488,2.616,2.619,2.199,0.857,0.674,0.755,0.42,0.526,0.681,0.604,0.658,0.651,0.437,0.699,0.493,0.583,0.606,0.573,0.555,0.603,0.713,0.575,0.59,0.853,0.526,0.423,0.657,0.545,0.653,0.474,0.534,0.568,0.535,0.741,1.125,0.496,0.793,0.706,0.555,0.79,0.778,0.453,0.445,0.913,0.649,0.883,0.621,0.579,0.472,0.528,0.521,0.52,0.492,0.6,0.452,0.577,0.563,0.368,0.392,0.339,0.649,0.438,0.353,0.343,0.834,0.576,0.859,0.643,0.67,0.451,0.366,0.404,0.622,0.412,0.507,0.591,0.33
-181,Epileptic,F,37.0,0.7,1.5,0.4,1.0,0.9,1.4,1.4,1.1,1.1,0.7,0.2,2.3,0.8,0.3,0.7,4.8,6.4,1.1,1.8,3.3,0.7,2.3,1.1,2.5,3.2,2.6,2.4,2.2,2.1,2.6,1.0,1.1,0.4,1.6,0.3,0.4,2.2,2.4,0.1,0.1,0.8,2.5,1.8,2.1,2.3,0.7,0.5,0.7,1.0,0.5,0.4,1.1,1.6,2.0,0.1,2.1,0.6,1.4,0.8,1.1,0.7,0.8,0.1,0.4,0.8,1.0,1.6,1.1,0.9,0.6,1.0,1.1,3.1,0.2,6,13,5,7,12,12,13,13,11,9,3.0,22,8,5,9,62,59,7,23,33,7,32,11,29,37,27,34,23,18,30,12,8,6,22,2,3,30,29,1,1,4,24,16,16,12,7,2,4,5,5,3,8,12,14,1,19,3,13,7,10,7,5,1,5,7,14,10,7,6,8,9,8,26,1,0.036,0.025,0.012,0.025,0.026,0.027,0.028,0.024,0.045,0.046,0.024,0.035,0.025,0.028,0.025,0.034,0.029,0.045,0.027,0.031,0.019,0.031,0.026,0.034,0.037,0.032,0.029,0.028,0.025,0.032,0.031,0.059,0.029,0.028,0.019,0.017,0.034,0.03,0.019,0.01,0.02,0.034,0.047,0.026,0.021,0.02,0.031,0.013,0.017,0.019,0.034,0.018,0.03,0.019,0.013,0.018,0.022,0.017,0.022,0.02,0.029,0.017,0.027,0.02,0.019,0.026,0.02,0.021,0.021,0.029,0.026,0.022,0.016,0.013,1191,2839,1379,1589,1337,2012,2050,2551,1489,1104,429.0,1952,2034,790,1760,9410,12700,1761,3716,3572,2565,3633,1660,4031,4995,4459,3965,3566,4025,4354,1562,872,781,4393,1449,1294,4810,5195,326,283,1200,2023,4274,1941,3067,1128,565,1633,1684,1115,335,2046,1803,3502,182,3113,1041,2614,939,1545,756,1629,301,841,1627,773,2406,1660,1211,981,1271,1494,6734,447,0.13,0.114,0.097,0.113,0.123,0.127,0.119,0.123,0.169,0.158,0.112,0.136,0.115,0.13,0.125,0.14,0.13,0.149,0.139,0.131,0.086,0.141,0.117,0.137,0.15,0.133,0.131,0.122,0.108,0.146,0.141,0.152,0.142,0.129,0.091,0.091,0.138,0.138,0.112,0.09,0.102,0.14,0.148,0.121,0.1,0.117,0.13,0.085,0.096,0.118,0.135,0.1,0.137,0.103,0.106,0.109,0.106,0.107,0.116,0.114,0.135,0.101,0.106,0.132,0.094,0.132,0.099,0.109,0.107,0.147,0.133,0.126,0.094,0.096,334,956,552,666,542,844,798,830,435,314,157.0,983,582,192,505,2715,3672,372,1024,1674,685,1289,707,1195,1561,1392,1507,1203,1184,1387,463,318,224,920,304,453,1140,1300,138,155,591,1124,758,1353,1681,551,235,738,828,423,208,994,825,1612,94,1766,475,1297,615,883,428,684,123,351,835,533,1280,834,638,341,612,760,3047,215,3.233,2.842,2.5,2.367,2.185,2.189,2.111,2.729,2.585,2.728,2.295,1.768,2.669,2.855,2.621,2.63,2.789,3.697,2.8,1.867,2.865,2.242,1.987,2.621,2.523,2.634,2.174,2.373,2.614,2.44,2.602,2.828,2.719,3.164,3.58,2.636,2.991,2.889,2.371,2.039,2.453,1.646,3.214,1.677,2.035,2.094,2.856,2.784,2.657,3.018,1.693,2.236,2.28,2.302,2.024,1.951,2.619,2.317,1.807,2.025,2.027,2.48,2.336,2.529,1.968,1.91,2.1,2.372,2.266,3.051,2.353,2.53,2.385,2.343,0.569,0.642,0.407,0.484,0.535,0.578,0.512,0.412,0.513,0.698,0.723,0.383,0.437,0.546,0.426,0.559,0.493,0.739,0.445,0.546,0.73,0.426,0.381,0.622,0.512,0.44,0.418,0.543,0.46,0.505,0.663,0.848,0.308,0.587,0.815,0.525,0.606,0.619,0.422,0.386,0.598,0.439,0.76,0.466,0.616,0.489,0.54,0.855,0.442,0.49,0.483,0.408,0.432,0.405,0.231,0.356,0.373,0.462,0.386,0.399,0.377,0.481,0.444,1.118,0.496,0.57,0.467,0.343,0.463,0.544,0.471,0.417,0.465,0.367
-182,Epileptic,F,27.0,0.7,2.6,0.6,0.7,2.0,2.6,1.4,1.6,0.9,0.5,0.2,3.0,1.3,0.2,1.0,5.1,6.4,0.6,2.4,4.7,1.5,5.4,2.9,3.8,5.8,3.5,4.4,2.2,2.3,3.1,1.2,0.9,0.4,2.1,0.3,0.8,4.2,3.1,0.2,0.2,1.1,3.6,2.3,2.6,2.5,0.5,0.2,0.6,1.1,0.6,0.9,1.6,1.1,2.0,0.3,2.4,0.9,1.7,1.7,1.3,0.8,1.5,0.3,0.5,2.0,0.7,2.5,1.9,0.6,1.0,1.1,1.6,5.6,0.3,5,25,6,8,20,25,12,22,13,6,3.0,27,17,4,17,72,64,6,26,48,22,41,21,39,38,37,51,26,19,45,14,7,4,25,2,10,43,37,1,1,6,31,22,23,14,3,1,4,6,6,5,14,8,14,2,20,6,13,13,12,7,15,3,5,16,8,18,16,4,15,7,12,42,2,0.033,0.028,0.014,0.02,0.035,0.04,0.028,0.034,0.039,0.026,0.024,0.04,0.03,0.032,0.034,0.034,0.029,0.029,0.034,0.044,0.025,0.049,0.04,0.043,0.054,0.034,0.032,0.029,0.026,0.029,0.034,0.044,0.028,0.027,0.015,0.019,0.04,0.031,0.012,0.015,0.022,0.042,0.046,0.03,0.019,0.013,0.013,0.013,0.018,0.017,0.034,0.018,0.021,0.019,0.013,0.018,0.019,0.022,0.037,0.021,0.024,0.026,0.015,0.017,0.018,0.047,0.022,0.023,0.015,0.022,0.022,0.021,0.02,0.018,1176,3964,1435,1599,2326,3027,2719,3174,1774,1268,579.0,2607,3018,685,2451,10185,14632,1837,4862,4902,4159,2921,2220,5655,4136,5779,5666,4437,5382,6075,2111,945,709,5718,1495,2036,6786,6704,371,332,1338,2062,5668,2098,3466,1159,613,1555,1832,1536,911,3156,1722,3388,417,4266,1433,3141,911,1898,1222,2488,458,839,3039,555,3295,2835,1160,1487,1463,2217,9008,558,0.114,0.128,0.098,0.099,0.153,0.135,0.114,0.148,0.151,0.12,0.102,0.16,0.13,0.118,0.146,0.148,0.13,0.115,0.133,0.161,0.116,0.152,0.151,0.149,0.148,0.134,0.126,0.116,0.107,0.143,0.127,0.118,0.097,0.127,0.069,0.101,0.144,0.127,0.095,0.101,0.111,0.151,0.158,0.13,0.102,0.083,0.08,0.083,0.097,0.1,0.135,0.099,0.114,0.1,0.099,0.1,0.104,0.109,0.151,0.108,0.111,0.131,0.114,0.113,0.105,0.15,0.112,0.121,0.093,0.133,0.113,0.116,0.097,0.108,378,1485,616,626,947,1192,890,1001,516,336,177.0,1350,931,169,677,2971,4095,438,1366,2149,974,1666,1115,1659,1768,1764,2238,1376,1328,1990,682,408,230,1340,432,707,1759,1789,211,172,705,1301,1152,1379,1929,580,275,763,915,653,429,1481,817,1642,258,2164,688,1312,677,1036,607,997,280,475,1607,272,1677,1278,578,716,772,1133,4090,227,3.146,2.629,2.335,2.523,2.181,2.169,2.483,2.728,2.562,2.957,2.721,1.751,2.654,2.868,2.715,2.566,2.769,3.308,2.764,2.002,3.146,1.669,1.803,2.629,2.047,2.634,2.062,2.387,2.917,2.466,2.317,2.39,2.482,3.07,3.148,2.505,2.762,2.663,2.048,2.174,2.309,1.58,3.264,1.799,1.986,2.138,2.506,2.485,2.468,2.453,2.173,2.201,2.081,2.165,2.042,2.087,2.336,2.5,1.618,2.058,2.131,2.437,1.89,1.981,2.037,2.335,2.095,2.341,2.191,2.213,2.154,2.17,2.272,2.778,0.695,0.526,0.48,0.488,0.65,0.755,0.548,0.496,0.61,0.422,0.521,0.552,0.495,0.427,0.527,0.634,0.597,0.664,0.548,0.561,0.793,0.482,0.563,0.757,0.659,0.588,0.631,0.589,0.574,0.635,0.664,0.729,0.455,0.685,0.814,0.578,0.781,0.749,0.277,0.489,0.516,0.508,0.84,0.639,0.641,0.479,0.454,0.575,0.425,0.626,0.432,0.469,0.445,0.452,0.251,0.449,0.397,0.458,0.283,0.463,0.466,0.721,0.308,0.584,0.513,0.671,0.475,0.39,0.337,0.607,0.459,0.44,0.477,0.457
-183,Epileptic,M,32.0,0.8,2.1,0.9,1.0,2.5,2.1,3.4,2.0,1.1,0.6,0.4,2.5,1.4,0.4,1.2,11.0,11.9,0.9,1.5,4.7,0.7,3.8,2.3,3.6,3.0,3.2,4.4,4.3,5.2,3.9,1.9,1.1,0.5,3.0,0.4,0.7,3.4,4.5,0.2,0.2,1.0,3.5,2.7,2.8,2.7,0.6,0.5,1.1,1.2,0.7,0.4,2.2,2.1,3.2,0.2,3.4,1.0,1.3,0.9,1.2,0.8,2.0,0.4,0.9,1.7,1.3,3.2,1.7,1.5,0.5,0.8,0.8,4.5,0.4,7,22,7,7,23,16,18,16,11,6,5.0,21,14,4,13,79,89,5,19,36,8,32,19,40,29,33,39,33,34,34,15,8,4,33,3,6,32,42,1,1,4,24,20,21,17,5,2,7,6,7,3,16,10,17,1,22,7,12,6,9,6,12,2,7,11,15,18,10,7,3,5,6,34,3,0.031,0.027,0.015,0.024,0.045,0.037,0.04,0.039,0.05,0.03,0.035,0.045,0.03,0.038,0.03,0.051,0.042,0.032,0.026,0.043,0.024,0.041,0.041,0.043,0.033,0.029,0.046,0.039,0.042,0.034,0.042,0.058,0.03,0.034,0.021,0.022,0.047,0.046,0.014,0.016,0.02,0.047,0.048,0.028,0.019,0.014,0.029,0.018,0.017,0.028,0.023,0.023,0.026,0.025,0.017,0.025,0.026,0.017,0.021,0.023,0.021,0.026,0.019,0.03,0.022,0.027,0.024,0.022,0.022,0.019,0.021,0.03,0.02,0.015,1657,5373,2190,2026,2528,2942,2637,3385,2013,1646,580.0,2698,3381,1049,2785,9961,16928,2449,3443,3690,2946,5002,2583,6711,5116,6660,4254,3886,6348,5660,2458,1066,941,7601,1683,2169,5891,7496,366,517,1560,2833,5965,2540,3687,1216,798,2186,2320,1282,437,3802,2387,5207,391,3742,1359,2500,991,1396,1318,2874,567,1011,2432,986,4152,3072,2350,1288,1290,1810,9003,768,0.123,0.118,0.1,0.105,0.15,0.146,0.133,0.135,0.157,0.124,0.16,0.147,0.12,0.128,0.128,0.152,0.137,0.126,0.116,0.152,0.088,0.132,0.141,0.144,0.129,0.124,0.125,0.127,0.124,0.135,0.143,0.139,0.107,0.134,0.085,0.1,0.154,0.15,0.103,0.085,0.099,0.149,0.161,0.125,0.097,0.094,0.105,0.092,0.088,0.132,0.123,0.104,0.118,0.103,0.103,0.113,0.125,0.098,0.111,0.102,0.103,0.12,0.113,0.131,0.106,0.135,0.106,0.1,0.103,0.097,0.109,0.138,0.1,0.098,458,1466,894,700,915,1023,1117,988,452,355,182.0,1077,818,246,658,3412,4739,443,1000,1754,676,1480,982,1690,1604,1902,1729,1581,1926,1795,715,390,259,1503,362,625,1356,1691,201,222,709,1228,1072,1642,2232,625,300,1017,1063,453,270,1609,1147,2131,184,1982,584,1305,638,855,583,1046,281,464,1228,681,2143,1288,1075,452,616,540,3506,384,3.396,2.972,2.493,2.704,2.25,2.541,2.193,2.83,3.139,3.225,2.915,2.126,3.0,3.049,3.176,2.424,2.912,3.923,2.649,1.883,3.151,2.485,2.236,2.909,2.566,2.753,2.043,2.067,2.594,2.502,2.622,2.537,2.669,3.437,3.527,2.981,3.084,3.087,2.003,2.844,2.704,1.977,3.456,1.821,1.934,2.105,3.164,2.837,2.636,2.968,1.883,2.389,2.38,2.65,2.623,1.972,2.448,2.201,1.861,1.798,2.388,2.508,2.195,2.322,2.043,1.898,2.186,2.658,2.548,2.911,2.412,3.365,2.62,2.375,0.624,0.88,0.61,0.487,0.683,0.787,0.695,0.587,0.763,0.581,0.701,0.565,0.521,0.555,0.53,0.654,0.774,0.773,0.678,0.56,0.756,0.698,0.742,0.785,0.73,0.64,0.555,0.666,0.778,0.686,0.829,0.883,0.645,0.732,0.873,0.801,0.753,0.855,0.338,0.366,0.691,0.623,0.806,0.697,0.664,0.571,0.649,0.624,0.533,0.746,0.375,0.524,0.598,0.623,0.51,0.566,0.508,0.539,0.461,0.428,0.514,0.811,0.5,0.934,0.623,0.63,0.633,0.585,0.557,0.717,0.515,0.805,0.714,0.446
-184,Epileptic,F,32.0,0.8,2.2,0.9,1.3,1.6,2.0,0.9,1.4,1.1,0.5,0.2,2.9,1.2,0.4,1.0,4.0,7.6,0.7,2.1,4.3,0.8,1.8,1.1,2.2,2.9,2.7,2.4,2.6,2.3,2.9,0.9,0.8,0.5,1.8,0.3,0.4,5.0,2.8,0.1,0.2,0.7,2.8,1.7,3.0,2.6,0.4,0.6,0.9,1.0,0.9,0.4,1.2,1.1,2.0,0.2,2.1,0.8,1.4,1.3,0.9,0.4,1.2,0.1,0.4,1.4,0.8,2.9,1.2,1.0,0.3,0.7,1.4,4.9,0.1,7,25,10,11,17,24,10,18,14,6,2.0,30,16,8,13,52,76,6,29,42,7,23,14,32,33,31,34,31,25,35,21,6,5,21,2,4,51,47,1,1,4,28,17,19,16,4,3,6,6,8,3,9,10,15,1,23,5,13,10,6,4,10,1,4,15,10,21,9,6,4,6,9,34,0,0.037,0.022,0.014,0.025,0.031,0.029,0.021,0.03,0.036,0.03,0.015,0.038,0.026,0.028,0.03,0.029,0.025,0.023,0.026,0.038,0.017,0.025,0.022,0.026,0.031,0.029,0.022,0.026,0.024,0.028,0.028,0.044,0.034,0.025,0.009,0.014,0.042,0.032,0.01,0.017,0.017,0.035,0.032,0.029,0.018,0.009,0.021,0.021,0.015,0.024,0.024,0.015,0.022,0.017,0.016,0.019,0.021,0.015,0.024,0.019,0.022,0.021,0.013,0.018,0.018,0.021,0.023,0.019,0.019,0.013,0.017,0.015,0.019,0.011,1401,5093,2063,2233,2204,3397,2050,2717,1639,1214,770.0,3042,3125,937,2433,9310,17477,2139,4435,4924,2760,3875,1753,5710,5761,5619,5528,4941,5638,5243,1940,879,719,5249,1948,1344,7313,7072,394,326,1093,3116,4300,2653,3903,1187,882,1582,1903,1335,470,2590,1949,4526,442,3845,1255,2994,1671,1351,736,2229,281,863,2651,968,3939,2286,1510,766,1424,2695,9300,332,0.131,0.116,0.107,0.116,0.138,0.137,0.099,0.143,0.142,0.141,0.09,0.16,0.125,0.143,0.134,0.133,0.121,0.103,0.13,0.148,0.078,0.127,0.124,0.125,0.14,0.135,0.12,0.125,0.119,0.138,0.121,0.116,0.145,0.127,0.064,0.087,0.136,0.133,0.084,0.095,0.107,0.155,0.119,0.123,0.098,0.077,0.107,0.104,0.093,0.105,0.13,0.096,0.107,0.094,0.107,0.109,0.114,0.094,0.123,0.107,0.115,0.116,0.112,0.118,0.112,0.111,0.117,0.102,0.103,0.117,0.11,0.104,0.101,0.068,368,1609,890,835,822,1204,735,917,476,300,215.0,1298,813,228,652,2423,4740,439,1280,1902,648,1147,804,1500,1565,1536,1716,1580,1454,1668,518,362,217,1128,470,449,1911,1646,199,171,553,1345,895,1607,2170,639,371,717,974,597,261,1205,863,1927,216,1933,556,1454,811,715,367,900,168,366,1273,529,1917,1067,730,355,663,1221,3779,123,3.688,2.916,2.356,2.534,2.313,2.432,2.28,2.743,2.497,3.0,3.134,2.057,2.909,2.868,2.798,2.847,2.918,3.6,2.863,2.171,2.881,2.521,1.983,2.842,2.792,2.828,2.434,2.428,2.848,2.494,2.745,2.601,2.816,3.285,3.571,2.729,2.914,3.112,2.276,2.304,2.561,2.004,3.36,1.902,2.043,2.007,2.926,2.89,2.402,2.662,2.076,2.215,2.15,2.434,2.385,2.135,2.578,2.413,2.113,2.06,2.314,2.509,1.987,2.493,2.204,2.118,2.285,2.546,2.359,2.692,2.322,2.492,2.615,2.743,0.752,0.751,0.568,0.462,0.606,0.59,0.536,0.49,0.569,0.499,0.604,0.399,0.342,0.432,0.476,0.488,0.487,0.683,0.451,0.569,0.75,0.528,0.43,0.557,0.609,0.511,0.44,0.564,0.438,0.549,0.598,1.036,0.4,0.513,0.739,0.522,0.716,0.658,0.303,0.486,0.488,0.473,0.708,0.584,0.59,0.497,0.477,0.51,0.416,0.511,0.377,0.4,0.533,0.47,0.491,0.436,0.389,0.481,0.452,0.368,0.382,0.564,0.243,0.96,0.478,0.702,0.456,0.415,0.452,0.607,0.486,0.522,0.548,0.501
-185,Epileptic,F,27.0,0.6,1.9,1.0,1.2,2.0,1.9,1.3,1.3,1.7,0.7,0.6,4.5,1.3,0.5,0.9,5.5,7.2,0.9,2.0,4.3,1.5,4.8,2.8,3.0,6.2,2.4,3.2,2.3,2.7,2.3,1.6,0.7,0.4,2.3,0.3,0.9,3.3,2.6,0.1,0.1,0.7,2.9,2.0,2.3,2.7,0.5,0.3,1.3,1.0,0.6,0.4,1.6,1.5,2.1,0.2,2.3,0.5,1.6,0.9,1.1,0.5,1.3,0.2,0.4,1.9,1.2,1.7,1.3,0.7,0.4,1.1,1.3,3.8,0.6,7,16,9,11,20,18,12,15,16,10,6.0,41,14,9,9,74,79,8,24,39,23,52,20,35,48,29,34,25,23,34,24,6,5,29,2,8,45,44,1,1,5,28,22,21,15,4,2,7,5,7,4,11,11,18,1,19,4,12,7,10,3,11,1,5,15,16,15,8,4,4,7,9,27,5,0.037,0.023,0.02,0.026,0.031,0.028,0.023,0.025,0.039,0.033,0.035,0.032,0.025,0.04,0.028,0.034,0.029,0.043,0.03,0.031,0.03,0.046,0.031,0.033,0.046,0.025,0.027,0.027,0.025,0.028,0.051,0.042,0.027,0.029,0.012,0.021,0.035,0.026,0.013,0.012,0.018,0.035,0.031,0.024,0.019,0.013,0.014,0.021,0.015,0.016,0.017,0.018,0.024,0.016,0.015,0.016,0.022,0.017,0.02,0.018,0.018,0.019,0.019,0.017,0.017,0.021,0.019,0.018,0.014,0.017,0.026,0.021,0.016,0.022,1567,4251,2195,2196,2931,3257,2699,3022,2212,1593,1014.0,4803,3483,1023,2198,11502,18317,2346,3793,5256,4712,4313,2346,6277,6564,5192,5413,4752,6988,5200,2288,863,946,6836,1880,2468,8026,7505,228,461,1539,2026,5239,3111,4093,996,915,2035,2171,1715,426,2889,2002,4632,452,3909,813,3004,1231,1846,703,2498,332,847,3442,1187,2992,2573,1779,1043,1281,2159,8419,761,0.115,0.113,0.111,0.117,0.143,0.12,0.11,0.122,0.156,0.135,0.141,0.131,0.118,0.173,0.122,0.143,0.128,0.126,0.136,0.135,0.119,0.128,0.119,0.13,0.136,0.128,0.114,0.119,0.11,0.127,0.173,0.106,0.121,0.126,0.063,0.101,0.146,0.131,0.095,0.086,0.095,0.139,0.149,0.109,0.096,0.097,0.091,0.096,0.087,0.092,0.118,0.102,0.119,0.1,0.091,0.101,0.119,0.102,0.108,0.103,0.103,0.11,0.124,0.123,0.102,0.125,0.109,0.095,0.087,0.105,0.13,0.112,0.092,0.141,409,1349,752,771,1037,1095,863,951,658,368,311.0,2072,821,251,576,2989,4662,428,1073,2200,944,1607,1287,1647,2043,1579,1902,1377,1691,1518,646,332,279,1380,466,810,1790,1781,97,221,689,1309,1080,1837,2210,534,370,942,1003,765,282,1345,1001,1950,219,2041,347,1394,736,1001,381,992,171,425,1688,789,1382,1132,784,462,601,992,3615,360,3.387,2.883,2.828,2.67,2.378,2.438,2.463,2.67,2.518,2.978,2.717,1.912,3.011,2.938,2.831,2.739,2.959,3.626,2.774,2.002,3.511,2.147,1.733,2.805,2.498,2.724,2.293,2.506,2.99,2.612,2.709,2.518,2.533,3.35,3.203,2.622,3.105,2.963,2.404,2.392,2.863,1.506,3.219,1.891,2.101,1.984,2.915,2.865,2.69,2.425,1.826,2.245,2.2,2.436,2.298,2.072,2.676,2.377,1.911,1.972,2.132,2.483,1.965,2.151,2.13,1.865,2.268,2.663,2.661,2.386,2.459,2.512,2.483,2.509,0.723,0.629,0.491,0.516,0.64,0.699,0.565,0.582,0.64,0.547,0.715,0.596,0.393,0.666,0.556,0.622,0.628,0.821,0.484,0.669,0.827,0.666,0.537,0.814,0.763,0.521,0.572,0.576,0.553,0.572,0.747,0.784,0.36,0.65,0.77,0.558,0.755,0.652,0.547,0.353,0.42,0.492,0.797,0.649,0.591,0.451,0.54,0.567,0.385,0.53,0.35,0.414,0.45,0.459,0.356,0.477,0.404,0.535,0.487,0.393,0.389,0.535,0.612,0.563,0.52,0.7,0.461,0.362,0.412,0.765,0.517,0.458,0.462,0.498
-186,Epileptic,F,27.0,0.4,2.8,1.4,1.5,1.4,2.6,1.5,1.8,1.3,0.7,0.3,2.7,1.4,0.9,1.5,5.9,7.6,0.9,2.2,3.8,0.6,2.4,1.8,2.9,2.9,3.5,4.2,2.0,2.9,4.1,1.4,0.8,0.4,2.9,0.3,0.5,3.5,3.3,0.2,0.2,0.9,2.4,1.9,1.9,1.9,0.7,0.4,0.8,1.2,0.5,0.6,1.6,1.0,2.0,0.3,2.4,0.5,1.4,0.9,1.4,0.3,1.0,0.2,0.7,1.6,1.7,2.2,0.9,1.1,0.6,1.4,1.7,3.7,0.3,3,25,20,12,12,22,12,16,12,10,4.0,23,16,10,16,61,70,6,27,35,5,28,17,30,35,34,43,21,24,36,14,6,5,31,2,3,32,39,2,1,5,25,19,15,12,7,2,4,6,6,3,12,10,15,3,18,3,11,6,9,2,5,2,3,11,17,18,6,6,5,11,12,23,3,0.023,0.029,0.023,0.026,0.034,0.039,0.028,0.034,0.045,0.036,0.032,0.035,0.03,0.04,0.043,0.045,0.032,0.034,0.034,0.035,0.014,0.037,0.031,0.038,0.035,0.036,0.035,0.027,0.03,0.037,0.045,0.031,0.026,0.037,0.017,0.014,0.039,0.041,0.017,0.022,0.018,0.035,0.039,0.027,0.016,0.022,0.021,0.011,0.015,0.026,0.023,0.022,0.028,0.021,0.02,0.018,0.019,0.017,0.03,0.022,0.021,0.026,0.028,0.027,0.022,0.026,0.02,0.015,0.019,0.023,0.036,0.035,0.018,0.014,1469,4853,2460,2503,1841,3288,2042,2810,1607,1329,532.0,2297,2740,1256,2476,8796,13847,2371,3881,3640,1842,3691,1862,5152,4428,5390,5266,2583,4361,4780,2611,991,1010,5621,1282,1583,5956,5272,368,326,1413,2427,4885,1811,2649,1194,688,2348,2368,1338,472,2294,1588,3594,321,3566,909,2558,849,1490,372,1735,352,718,2204,1616,3127,1751,1471,939,1382,1802,6940,514,0.089,0.122,0.131,0.114,0.148,0.163,0.112,0.137,0.169,0.152,0.128,0.141,0.128,0.163,0.145,0.167,0.132,0.108,0.135,0.139,0.075,0.15,0.13,0.155,0.147,0.146,0.134,0.113,0.114,0.144,0.149,0.098,0.127,0.144,0.076,0.08,0.137,0.146,0.105,0.132,0.096,0.144,0.143,0.118,0.095,0.115,0.103,0.074,0.091,0.103,0.127,0.109,0.127,0.103,0.14,0.099,0.104,0.101,0.133,0.115,0.12,0.119,0.114,0.133,0.107,0.115,0.107,0.097,0.097,0.12,0.147,0.147,0.096,0.1,335,1623,1012,902,686,1035,816,871,478,367,177.0,1131,800,355,665,2362,4130,431,1154,1744,599,1088,908,1241,1424,1634,1994,1132,1443,1732,556,400,315,1222,349,572,1472,1402,228,158,698,1143,1018,1182,1771,596,299,999,1127,477,332,1216,675,1609,206,1992,426,1336,453,886,221,597,184,314,1157,924,1793,868,834,439,665,893,3081,318,3.731,2.717,2.269,2.512,2.389,2.529,2.063,2.778,2.611,3.058,2.69,1.838,2.668,2.606,2.898,2.732,2.65,3.804,2.782,1.856,2.459,2.537,1.808,3.187,2.391,2.622,2.082,1.892,2.402,2.26,3.268,2.172,2.46,3.174,3.011,2.524,2.815,2.792,1.782,2.253,2.314,1.899,3.067,1.797,1.665,1.952,2.778,2.594,2.371,2.749,1.732,1.936,2.239,2.121,1.804,1.892,2.408,2.132,2.052,1.836,2.18,2.757,1.883,2.612,2.071,2.15,1.78,2.242,1.953,2.447,2.254,2.392,2.388,1.908,1.025,0.789,0.503,0.739,0.793,0.734,0.509,0.508,0.466,0.466,0.73,0.384,0.462,0.467,0.56,0.643,0.588,0.911,0.572,0.578,0.68,0.576,0.46,0.749,0.494,0.542,0.526,0.429,0.418,0.578,0.746,1.074,0.447,0.646,0.619,0.637,0.785,0.569,0.3,0.414,0.607,0.474,0.825,0.755,0.505,0.729,0.492,0.757,0.567,0.807,0.389,0.406,0.637,0.527,0.294,0.437,0.662,0.473,0.668,0.423,0.35,0.799,0.451,0.924,0.681,0.794,0.47,0.442,0.511,0.575,0.502,0.507,0.627,0.395
-187,Epileptic,F,27.0,0.9,1.5,0.8,1.6,1.4,1.3,2.4,1.0,1.0,0.5,0.3,3.0,1.6,0.4,1.0,6.9,9.3,0.6,1.6,3.0,1.0,2.3,1.3,2.6,2.9,3.1,2.7,3.0,3.2,3.4,1.4,1.0,0.6,1.7,0.2,0.8,3.4,2.5,0.1,0.1,0.8,3.0,1.8,3.1,4.2,0.6,0.4,0.7,0.8,1.1,0.7,1.2,1.0,2.3,0.3,2.1,0.6,1.4,0.6,1.1,0.4,1.3,0.4,0.2,2.2,0.7,2.8,0.8,1.3,0.8,0.5,1.5,3.8,0.2,6,12,8,13,13,13,18,10,10,5,1.0,23,13,6,9,69,61,4,18,24,10,25,12,25,29,31,27,23,20,27,17,6,5,17,2,6,33,23,2,1,4,22,17,19,24,5,2,7,5,11,3,8,6,14,1,18,5,13,5,9,4,7,2,3,16,8,20,7,9,7,3,11,29,1,0.046,0.021,0.018,0.031,0.04,0.03,0.036,0.022,0.054,0.024,0.035,0.036,0.031,0.037,0.031,0.038,0.035,0.03,0.034,0.03,0.017,0.034,0.023,0.037,0.041,0.037,0.027,0.033,0.031,0.041,0.042,0.054,0.03,0.027,0.012,0.026,0.036,0.031,0.013,0.021,0.019,0.042,0.03,0.038,0.031,0.012,0.022,0.016,0.014,0.019,0.028,0.017,0.021,0.023,0.016,0.02,0.018,0.019,0.026,0.025,0.022,0.028,0.029,0.023,0.022,0.017,0.024,0.018,0.024,0.027,0.017,0.024,0.019,0.015,1390,3485,2253,2458,1613,2079,2382,2501,1584,1020,436.0,2347,3376,728,2429,10313,14557,1859,3214,3326,4007,3891,2127,4725,4365,4663,4752,3522,4989,3731,1780,878,770,4673,1698,1501,6640,4950,281,318,1190,2287,4794,2102,3205,1447,716,1827,1942,2239,572,2355,1484,3793,418,3436,1273,2729,785,1236,772,1639,422,744,3126,1386,3694,1568,1702,1337,1087,2140,8013,493,0.136,0.105,0.106,0.131,0.131,0.136,0.132,0.107,0.152,0.125,0.104,0.133,0.131,0.118,0.109,0.134,0.125,0.099,0.139,0.123,0.085,0.138,0.113,0.119,0.148,0.131,0.12,0.114,0.113,0.127,0.154,0.125,0.124,0.12,0.069,0.105,0.134,0.119,0.09,0.108,0.107,0.141,0.13,0.145,0.124,0.085,0.107,0.097,0.088,0.108,0.133,0.095,0.1,0.099,0.108,0.108,0.106,0.104,0.133,0.13,0.11,0.117,0.128,0.118,0.115,0.101,0.111,0.109,0.118,0.144,0.104,0.126,0.099,0.098,380,1199,719,935,604,776,1036,752,390,315,137.0,1196,859,205,619,2972,3991,333,850,1501,934,1192,836,1273,1215,1431,1580,1331,1528,1298,517,363,250,1020,394,491,1649,1329,203,100,578,1131,954,1396,2202,756,274,773,960,844,324,1132,813,1755,206,1770,552,1226,354,693,365,672,209,267,1494,704,1990,723,911,432,452,1026,3304,180,3.456,2.711,2.838,2.659,2.22,2.472,2.142,2.826,2.785,3.128,3.033,1.764,2.894,2.472,2.812,2.567,2.946,3.722,2.998,1.979,3.265,2.544,2.171,2.741,2.716,2.704,2.427,2.138,2.569,2.31,2.836,2.552,2.62,2.992,3.609,2.696,3.082,2.757,1.558,3.501,2.562,1.946,3.216,1.746,1.737,2.188,3.141,2.735,2.512,2.741,2.274,2.168,2.043,2.303,2.367,2.179,2.688,2.453,2.484,1.962,2.597,2.533,2.071,3.081,2.396,2.449,2.068,2.331,2.291,3.483,2.626,2.428,2.62,3.031,0.875,0.745,0.732,0.477,0.63,0.616,0.664,0.653,0.687,0.605,0.966,0.486,0.705,0.531,0.824,0.674,0.679,0.722,0.544,0.697,0.795,0.535,0.462,0.748,0.579,0.825,0.58,0.662,0.705,0.633,0.844,0.766,0.445,0.588,0.605,0.481,0.78,0.573,0.294,0.424,0.597,0.539,0.808,0.627,0.452,0.504,0.504,0.906,0.458,0.563,0.366,0.572,0.54,0.603,0.473,0.487,0.584,0.639,0.686,0.324,0.61,0.56,0.599,0.99,0.663,0.758,0.468,0.498,0.375,0.538,0.524,0.603,0.571,0.435
-188,Epileptic,F,27.0,0.5,3.3,1.9,1.5,2.1,1.4,2.0,3.1,2.1,0.6,0.3,4.0,1.2,1.1,2.9,6.0,9.1,0.7,1.7,5.9,1.3,2.5,2.2,5.2,3.8,5.4,5.8,4.0,3.3,4.8,1.6,0.9,0.7,2.7,0.3,0.8,3.1,3.7,0.1,0.1,1.3,3.9,2.8,3.3,4.2,0.8,0.9,1.1,1.2,0.8,0.7,1.8,0.8,1.7,0.4,3.0,0.6,2.1,1.3,1.3,0.6,1.4,0.1,0.6,1.5,1.3,2.4,2.3,1.2,0.4,0.9,0.8,5.1,0.4,4,19,16,10,11,13,17,21,10,6,2.0,31,6,7,12,34,57,5,14,46,8,29,15,34,37,37,44,33,23,38,11,7,5,24,2,6,21,29,1,0,6,24,22,21,18,5,5,6,6,5,4,8,5,10,2,18,4,16,9,8,3,7,1,5,9,12,21,9,4,2,7,5,32,2,0.033,0.039,0.042,0.033,0.051,0.034,0.058,0.051,0.064,0.051,0.033,0.066,0.036,0.101,0.059,0.056,0.047,0.04,0.048,0.055,0.034,0.045,0.045,0.07,0.051,0.05,0.058,0.06,0.055,0.053,0.051,0.048,0.053,0.047,0.035,0.025,0.052,0.063,0.039,0.019,0.029,0.056,0.07,0.035,0.041,0.026,0.065,0.021,0.025,0.044,0.029,0.037,0.027,0.034,0.028,0.032,0.032,0.031,0.035,0.028,0.033,0.046,0.036,0.045,0.026,0.036,0.029,0.052,0.035,0.024,0.035,0.03,0.029,0.027,1484,4752,2108,1781,1658,1809,2002,3762,1425,1204,666.0,2800,2684,928,2769,7305,13235,1678,2381,4924,2270,3568,1857,3905,4318,4622,5664,3850,4761,4744,1817,1415,896,5310,1053,1353,4413,4848,136,145,1330,2042,4111,2576,2965,916,475,1596,1898,1300,423,1618,1319,2535,317,3082,787,2514,874,1191,687,1352,170,790,1912,1282,3122,1816,839,619,1358,1337,6972,565,0.112,0.136,0.132,0.133,0.16,0.14,0.156,0.154,0.16,0.145,0.118,0.185,0.122,0.219,0.176,0.159,0.141,0.133,0.157,0.172,0.123,0.148,0.151,0.176,0.159,0.157,0.157,0.164,0.148,0.162,0.151,0.102,0.133,0.143,0.098,0.109,0.163,0.175,0.099,0.107,0.121,0.162,0.188,0.141,0.131,0.112,0.149,0.097,0.111,0.158,0.144,0.122,0.105,0.114,0.111,0.129,0.139,0.128,0.148,0.125,0.121,0.146,0.11,0.148,0.116,0.128,0.121,0.165,0.107,0.108,0.138,0.12,0.12,0.125,329,1542,833,736,777,752,661,1099,527,252,170.0,1159,533,257,882,1820,3663,335,770,1990,615,1138,806,1357,1288,1755,2083,1419,1255,1552,570,400,268,1083,229,546,1056,1080,56,48,742,1239,742,1603,1638,504,179,771,788,350,350,768,588,895,164,1621,332,1265,666,770,318,544,98,271,978,642,1520,712,472,246,527,526,3042,253,3.621,2.558,2.411,2.492,1.974,2.01,2.365,2.57,2.066,3.334,3.167,1.998,3.025,2.391,2.273,2.46,2.592,3.579,2.382,2.018,2.669,2.328,1.9,2.323,2.428,2.18,2.162,2.119,2.66,2.28,2.482,2.687,2.631,2.932,3.101,2.334,2.954,3.027,2.287,3.351,2.245,1.564,3.292,1.8,2.163,1.896,2.485,2.53,2.894,2.834,1.455,2.095,2.145,2.537,1.858,2.037,2.267,2.054,1.528,1.698,2.025,2.801,1.871,2.397,2.003,2.591,2.066,2.387,2.121,2.453,2.306,2.297,2.251,2.523,0.859,0.865,0.647,0.454,0.647,0.697,0.699,0.644,0.813,0.61,0.899,0.583,0.819,0.752,0.726,0.907,0.811,0.929,0.516,0.673,0.788,0.695,0.538,0.73,0.876,0.656,0.609,0.593,0.824,0.695,0.712,1.0,0.458,0.808,0.773,0.594,0.868,0.836,0.413,0.909,0.518,0.474,0.86,0.618,0.663,0.502,0.715,0.867,0.68,0.986,0.295,0.48,0.681,0.804,0.414,0.598,0.755,0.509,0.293,0.416,0.603,0.848,0.285,0.696,0.677,0.819,0.512,0.589,0.446,0.972,0.653,0.754,0.606,0.543
-189,Epileptic,M,37.0,0.7,1.6,0.8,1.3,2.2,2.1,1.5,1.9,1.7,0.4,0.3,3.1,2.1,0.4,1.8,7.3,8.3,0.7,2.7,5.1,1.5,3.4,2.0,3.4,3.8,2.9,3.3,3.1,3.7,3.6,1.3,0.9,0.5,2.7,0.3,0.7,4.3,3.9,0.2,0.1,1.1,3.0,2.5,3.3,3.1,0.8,0.5,0.7,1.2,0.5,0.4,2.0,1.3,1.9,0.2,2.3,0.7,2.9,1.4,1.1,0.4,1.8,0.7,0.4,2.0,0.9,3.2,1.7,1.8,0.8,0.5,2.4,4.5,0.3,8,16,7,8,19,19,15,17,13,4,4.0,28,21,4,13,63,73,7,30,43,13,41,21,32,47,33,42,24,29,40,12,8,5,29,3,7,44,49,1,2,6,33,25,20,18,5,2,7,7,5,3,11,7,16,2,18,7,21,10,10,5,13,6,4,18,8,21,9,13,8,5,17,35,3,0.039,0.021,0.018,0.027,0.05,0.038,0.026,0.029,0.055,0.03,0.025,0.042,0.031,0.036,0.045,0.044,0.032,0.031,0.037,0.044,0.031,0.041,0.033,0.043,0.037,0.033,0.038,0.029,0.032,0.031,0.036,0.045,0.032,0.034,0.015,0.021,0.04,0.033,0.018,0.021,0.024,0.037,0.04,0.03,0.027,0.015,0.028,0.013,0.02,0.021,0.031,0.024,0.024,0.024,0.018,0.019,0.025,0.027,0.028,0.02,0.028,0.033,0.044,0.021,0.027,0.027,0.023,0.025,0.024,0.027,0.017,0.028,0.019,0.024,1327,3774,1751,2152,2162,2935,2393,3265,1935,947,689.0,2531,3514,946,2151,10735,15797,2212,3994,4576,2202,4663,2085,4690,6065,5166,4771,3527,5560,5414,1924,1075,883,5503,1846,1351,6100,7777,390,437,1277,3034,6287,2724,2803,1707,668,2001,1994,998,516,3255,1691,3857,314,3203,1201,3396,1344,1482,814,2546,950,791,2216,881,3696,2361,2306,1234,943,3120,7969,529,0.128,0.114,0.099,0.114,0.158,0.152,0.109,0.13,0.171,0.124,0.102,0.15,0.143,0.127,0.17,0.146,0.129,0.121,0.142,0.156,0.106,0.172,0.147,0.149,0.153,0.142,0.137,0.119,0.112,0.137,0.122,0.137,0.146,0.142,0.082,0.104,0.145,0.141,0.1,0.112,0.118,0.148,0.155,0.122,0.112,0.093,0.115,0.084,0.099,0.1,0.132,0.105,0.112,0.103,0.129,0.109,0.12,0.11,0.13,0.113,0.126,0.134,0.161,0.109,0.129,0.106,0.11,0.11,0.11,0.126,0.109,0.129,0.106,0.132,352,1226,691,791,749,1035,937,1079,535,251,198.0,1232,1097,226,685,2991,4559,412,1247,2006,753,1474,997,1424,1790,1647,1712,1563,1806,2028,605,381,286,1341,447,573,1904,2098,145,150,654,1463,1238,1658,1821,789,292,868,966,424,237,1412,801,1572,152,1784,566,1760,792,872,359,900,299,389,1175,462,2152,1033,1198,490,507,1337,3620,223,3.504,2.903,2.533,2.845,2.446,2.46,2.203,2.678,2.749,3.061,2.75,1.807,2.684,2.808,2.641,2.652,2.835,3.792,2.674,1.967,2.33,2.492,1.913,2.575,2.554,2.569,2.214,1.86,2.458,2.203,2.481,2.843,2.508,2.963,3.429,2.227,2.618,2.823,2.616,2.547,2.438,1.818,3.29,1.798,1.722,2.339,2.644,2.745,2.574,2.631,2.183,2.407,2.401,2.386,2.067,1.971,2.353,2.164,1.945,1.878,2.186,2.902,2.697,2.361,2.049,2.037,1.875,2.45,2.07,2.648,2.119,2.575,2.383,2.992,0.844,0.689,0.489,0.513,0.585,0.646,0.467,0.551,0.59,0.557,0.853,0.401,0.45,0.49,0.453,0.573,0.616,0.794,0.509,0.525,0.781,0.546,0.424,0.852,0.546,0.51,0.492,0.461,0.484,0.548,0.689,0.791,0.487,0.683,0.749,0.434,0.574,0.549,0.527,0.694,0.6,0.54,0.816,0.543,0.447,0.613,0.55,0.852,0.494,0.368,0.468,0.493,0.565,0.456,0.446,0.449,0.403,0.46,0.341,0.348,0.409,0.789,0.528,0.867,0.637,0.64,0.442,0.478,0.4,0.661,0.442,0.549,0.408,0.482
-190,Epileptic,F,42.0,0.7,2.1,1.2,1.3,1.6,2.8,1.6,1.7,1.1,0.9,0.4,2.6,1.6,0.3,1.2,7.3,8.8,1.0,1.7,5.8,1.6,4.1,1.7,2.6,4.6,2.9,2.9,2.5,2.5,3.7,1.6,0.6,0.6,3.1,0.4,1.0,2.6,3.3,0.2,0.1,1.1,2.8,2.0,3.1,2.9,0.9,0.4,0.9,1.1,0.6,0.6,1.7,1.3,2.8,0.4,2.6,0.8,2.1,1.4,1.0,0.7,0.9,0.3,0.3,2.3,1.6,1.9,1.4,1.5,0.4,0.8,1.6,4.3,0.1,8,21,10,10,14,23,14,12,14,9,4.0,21,17,5,13,69,77,10,21,46,14,42,19,30,46,29,33,26,23,43,23,6,4,33,2,10,31,43,2,1,6,27,17,23,19,6,2,7,6,5,5,11,7,21,3,19,8,19,10,8,5,7,2,3,19,19,17,10,10,3,6,9,33,1,0.034,0.023,0.019,0.023,0.037,0.04,0.024,0.029,0.038,0.03,0.028,0.033,0.029,0.037,0.033,0.037,0.03,0.041,0.027,0.042,0.026,0.041,0.026,0.035,0.034,0.031,0.024,0.027,0.025,0.035,0.041,0.031,0.03,0.031,0.011,0.029,0.03,0.03,0.024,0.023,0.024,0.037,0.034,0.031,0.022,0.021,0.017,0.015,0.017,0.012,0.032,0.02,0.022,0.02,0.019,0.022,0.021,0.023,0.026,0.019,0.021,0.02,0.027,0.016,0.025,0.029,0.02,0.019,0.022,0.013,0.021,0.024,0.018,0.014,1351,4744,2498,2370,1914,3686,2812,3326,1679,1564,796.0,2524,3769,662,2289,12008,18294,2393,3868,4642,3716,5759,2278,5262,6769,5374,5816,4204,5802,5733,2445,1006,925,5910,2015,1917,5065,7795,369,220,1653,3244,5434,2730,3679,1495,765,2174,2290,1523,626,2957,1874,5159,536,3632,1570,3310,1506,1516,1086,1753,399,744,3054,1391,3256,2280,1853,871,1407,2410,9240,430,0.118,0.114,0.112,0.107,0.136,0.159,0.115,0.12,0.144,0.143,0.125,0.128,0.128,0.162,0.139,0.152,0.132,0.135,0.118,0.146,0.103,0.147,0.129,0.135,0.145,0.135,0.122,0.13,0.111,0.151,0.141,0.105,0.117,0.129,0.063,0.114,0.121,0.126,0.126,0.12,0.109,0.148,0.121,0.127,0.103,0.111,0.1,0.082,0.09,0.09,0.149,0.1,0.105,0.102,0.122,0.116,0.109,0.119,0.123,0.111,0.103,0.106,0.123,0.106,0.117,0.139,0.106,0.109,0.119,0.103,0.104,0.112,0.097,0.075,408,1542,957,923,734,1268,1030,987,517,433,240.0,1221,1028,186,667,3412,4987,504,1113,2321,1012,1797,1009,1329,2353,1777,2030,1421,1608,1832,651,451,265,1527,512,685,1493,1987,148,104,724,1375,1138,1672,2147,683,325,969,1034,728,330,1431,869,2229,244,1841,670,1501,849,833,539,673,204,369,1494,733,1654,1038,984,406,690,1023,3983,174,3.09,2.624,2.42,2.578,2.255,2.573,2.273,2.935,2.591,2.935,2.675,1.84,2.895,2.674,2.67,2.665,2.913,3.457,2.735,1.791,2.968,2.481,1.982,2.826,2.33,2.506,2.311,2.351,2.766,2.39,2.816,2.269,2.977,2.838,3.407,2.363,2.677,2.849,2.262,2.577,2.728,2.109,3.236,1.886,1.917,2.286,2.987,2.669,2.675,2.405,2.15,2.204,2.459,2.302,2.513,2.21,2.486,2.515,2.053,2.054,2.435,2.536,2.225,2.128,2.182,2.247,2.189,2.265,2.365,2.412,2.223,2.778,2.442,2.636,0.647,0.755,0.693,0.459,0.554,0.633,0.531,0.656,0.592,0.32,0.795,0.464,0.55,0.483,0.519,0.553,0.574,0.762,0.623,0.548,0.741,0.528,0.419,0.66,0.513,0.589,0.49,0.564,0.466,0.59,0.735,0.929,0.6,0.691,0.597,0.62,0.528,0.657,0.689,0.423,0.723,0.541,0.772,0.86,0.553,0.476,0.584,0.624,0.562,0.366,0.499,0.536,0.607,0.453,0.469,0.414,0.493,0.646,0.376,0.333,0.532,0.856,0.312,0.842,0.62,0.729,0.463,0.409,0.346,0.473,0.496,0.673,0.543,0.669
-191,Epileptic,F,32.0,1.0,2.4,0.8,0.8,1.5,1.7,1.5,2.1,1.2,0.9,0.3,3.6,1.8,1.0,1.8,5.6,7.3,0.7,2.2,5.1,1.0,3.1,1.9,2.6,2.3,3.4,2.9,3.1,2.2,2.7,1.4,0.7,0.6,3.0,0.6,0.9,3.3,3.4,0.1,0.2,1.2,3.2,2.5,4.5,3.0,0.6,0.5,1.2,1.4,1.2,0.5,2.3,1.3,1.9,0.1,1.5,0.7,2.0,1.1,1.4,0.8,1.2,0.4,0.3,2.1,1.3,2.8,1.4,0.6,0.7,0.9,1.6,5.3,0.3,9,24,7,6,15,16,14,19,11,7,3.0,31,19,10,15,55,64,6,27,43,9,35,22,28,25,36,33,31,19,30,22,4,5,33,3,10,41,38,2,1,7,22,22,34,20,5,2,8,8,9,3,18,9,15,1,14,5,18,8,11,6,11,3,4,19,14,23,11,5,5,6,11,40,3,0.037,0.02,0.012,0.015,0.026,0.027,0.021,0.026,0.039,0.035,0.024,0.032,0.03,0.036,0.031,0.033,0.024,0.024,0.027,0.032,0.02,0.025,0.029,0.032,0.025,0.029,0.025,0.026,0.022,0.026,0.041,0.03,0.025,0.028,0.012,0.016,0.033,0.028,0.012,0.019,0.021,0.037,0.039,0.027,0.022,0.013,0.017,0.016,0.016,0.022,0.028,0.022,0.019,0.019,0.02,0.015,0.023,0.019,0.024,0.021,0.024,0.021,0.036,0.014,0.021,0.037,0.016,0.018,0.014,0.019,0.02,0.02,0.019,0.017,1515,5149,2020,1945,2071,3084,3055,4273,1825,1472,647.0,3701,4001,1538,3087,11378,16736,2368,4644,5103,3294,6098,2782,5958,5200,7277,5824,5445,5754,5497,1970,929,1226,7334,1925,2421,6209,8087,419,524,1740,3520,4116,4192,3740,1414,886,2376,2798,1814,594,3941,1811,3860,209,3144,1126,4116,1554,2366,1168,2017,418,813,3285,980,5610,3088,1283,889,1577,2471,10029,603,0.138,0.109,0.091,0.086,0.125,0.134,0.101,0.123,0.141,0.138,0.12,0.135,0.124,0.149,0.137,0.137,0.117,0.103,0.119,0.13,0.085,0.13,0.132,0.128,0.128,0.128,0.124,0.123,0.105,0.135,0.133,0.1,0.111,0.137,0.076,0.094,0.13,0.131,0.075,0.09,0.106,0.135,0.129,0.121,0.111,0.095,0.097,0.093,0.092,0.12,0.127,0.104,0.105,0.095,0.114,0.096,0.116,0.103,0.116,0.114,0.112,0.113,0.136,0.095,0.112,0.139,0.099,0.103,0.092,0.122,0.11,0.102,0.101,0.114,476,1845,937,789,857,1036,1056,1315,529,388,220.0,1700,1114,367,873,3009,4756,512,1389,2472,912,1921,1125,1512,1457,2112,1939,1864,1520,1627,595,397,423,1673,628,884,1799,1988,287,226,873,1384,1154,2585,2131,707,403,1073,1302,844,283,1821,1005,1738,78,1576,515,1885,809,1022,597,862,215,409,1553,528,2529,1257,651,487,750,1197,4336,321,3.199,2.626,2.11,2.412,2.061,2.677,2.36,2.803,2.551,3.066,2.739,1.985,2.804,2.807,2.68,2.812,2.881,3.693,2.753,1.836,2.922,2.583,2.189,2.83,2.742,2.926,2.458,2.381,2.8,2.69,2.453,2.256,2.273,3.269,2.877,2.564,2.734,2.982,1.657,2.548,2.608,2.195,2.838,1.822,2.012,2.2,2.691,2.82,2.505,2.593,2.513,2.157,2.029,2.319,2.853,2.112,2.633,2.481,2.179,2.269,2.404,2.285,2.382,2.294,2.221,2.225,2.316,2.67,2.236,2.238,2.497,2.325,2.485,2.401,0.88,0.521,0.64,0.534,0.57,0.385,0.585,0.516,0.726,0.4,0.719,0.534,0.5,0.71,0.533,0.609,0.589,0.757,0.577,0.531,0.784,0.492,0.474,0.7,0.608,0.528,0.59,0.613,0.577,0.471,0.675,0.898,0.418,0.526,0.491,0.411,0.647,0.525,0.356,0.424,0.442,0.527,0.708,0.667,0.621,0.522,0.494,0.542,0.549,0.579,0.388,0.523,0.45,0.504,0.652,0.416,0.443,0.626,0.428,0.491,0.446,0.692,0.432,1.028,0.506,0.656,0.496,0.471,0.414,0.779,0.332,0.464,0.452,0.283
-192,Epileptic,M,22.0,1.0,1.8,1.1,1.8,2.3,2.0,2.2,1.9,1.9,1.2,0.6,3.4,2.0,0.6,2.0,10.7,12.5,1.2,4.0,6.0,1.9,4.3,2.6,3.5,6.5,4.3,6.8,4.1,5.2,4.4,1.5,1.8,0.5,3.0,0.5,1.2,4.8,3.8,0.2,0.2,1.1,4.4,2.6,3.6,3.2,0.9,0.4,1.0,2.0,1.0,0.6,2.6,2.5,3.2,0.4,3.9,1.5,2.8,1.3,1.1,0.8,1.9,0.4,0.7,2.6,1.9,2.8,1.7,1.4,0.7,1.2,2.0,6.3,0.4,9,13,7,13,21,21,19,20,18,10,4.0,32,23,5,19,87,111,11,37,49,18,37,23,37,53,46,59,35,41,43,15,15,4,33,2,9,50,47,1,2,7,37,27,25,18,6,2,6,9,6,5,19,17,22,3,30,9,17,11,8,8,12,3,5,18,20,23,12,10,3,8,13,51,4,0.039,0.019,0.021,0.028,0.038,0.026,0.026,0.026,0.052,0.042,0.042,0.038,0.036,0.041,0.044,0.042,0.035,0.038,0.041,0.04,0.037,0.033,0.031,0.041,0.043,0.034,0.034,0.031,0.033,0.037,0.041,0.051,0.029,0.033,0.013,0.027,0.038,0.032,0.016,0.012,0.02,0.041,0.045,0.03,0.021,0.015,0.018,0.015,0.02,0.024,0.022,0.022,0.025,0.02,0.018,0.016,0.024,0.026,0.026,0.016,0.022,0.032,0.021,0.029,0.025,0.041,0.021,0.02,0.021,0.021,0.025,0.02,0.019,0.016,1856,5617,2592,3417,2610,3195,3187,3625,2347,1968,1048.0,3200,3974,1212,3508,13644,25126,2923,5512,6120,3213,4662,2838,7393,5819,6961,6861,4389,7528,7327,3249,1339,931,7201,1770,2275,11696,9071,480,539,1548,3031,6562,3078,4046,1686,831,2075,2843,2029,568,4368,2942,5736,640,6224,2166,3397,1246,1694,1138,2735,576,1119,2769,1371,3976,2923,2218,1064,1732,3086,11352,632,0.117,0.095,0.099,0.115,0.136,0.117,0.114,0.121,0.158,0.16,0.129,0.15,0.14,0.14,0.147,0.13,0.132,0.128,0.156,0.16,0.109,0.119,0.133,0.152,0.132,0.124,0.114,0.112,0.106,0.139,0.15,0.137,0.091,0.135,0.07,0.113,0.148,0.136,0.091,0.095,0.106,0.154,0.155,0.127,0.099,0.092,0.102,0.089,0.106,0.11,0.119,0.108,0.112,0.105,0.104,0.093,0.113,0.118,0.118,0.091,0.116,0.123,0.117,0.124,0.123,0.151,0.106,0.108,0.105,0.105,0.12,0.114,0.104,0.11,529,1641,877,1089,1075,1273,1279,1182,619,464,261.0,1568,1062,228,841,4151,6326,556,1650,2709,933,1816,1277,1697,2176,2250,2906,1818,2097,2199,710,603,330,1647,566,749,2299,2076,193,257,866,1690,1220,1928,2368,871,330,974,1375,656,398,1976,1485,2513,343,3623,1007,1770,834,1052,588,983,291,438,1479,743,2137,1260,1074,420,822,1478,5155,384,3.267,2.967,2.754,2.816,2.109,2.126,2.133,2.653,2.617,3.228,2.853,1.846,2.872,3.513,3.003,2.56,3.001,3.797,2.698,1.942,2.584,2.135,1.915,3.223,2.196,2.549,2.026,2.042,2.811,2.596,3.169,2.338,2.262,3.017,2.8,2.757,3.304,3.091,2.406,2.335,2.163,1.684,3.22,1.777,1.902,2.059,2.826,2.653,2.456,3.141,1.747,2.321,2.159,2.401,2.219,1.934,2.548,2.248,1.654,1.753,1.964,2.855,2.214,2.686,2.045,2.249,2.01,2.725,2.375,2.854,2.364,2.449,2.402,1.98,0.812,0.761,0.564,0.689,0.734,0.642,0.585,0.506,0.844,0.645,0.872,0.552,0.592,0.522,0.692,0.78,0.758,0.798,0.673,0.597,0.701,0.644,0.659,0.758,0.748,0.698,0.599,0.576,0.604,0.708,0.647,0.911,0.583,0.724,0.662,0.591,0.782,0.726,0.458,0.494,0.56,0.501,0.899,0.705,0.678,0.655,0.619,0.714,0.611,0.82,0.288,0.509,0.577,0.594,0.398,0.547,0.582,0.597,0.425,0.507,0.437,0.913,0.607,0.605,0.78,0.575,0.526,0.475,0.55,0.765,0.534,0.546,0.548,0.543
-193,Epileptic,F,42.0,0.7,3.0,0.8,0.8,2.1,3.1,2.1,1.5,1.2,0.5,0.1,2.9,1.5,0.6,1.6,6.0,8.6,0.7,2.6,5.0,1.6,3.0,1.4,3.3,3.2,3.5,2.5,2.8,2.6,4.2,1.4,1.3,0.5,2.8,0.5,1.1,3.8,3.4,0.1,0.0,1.1,3.1,2.5,2.8,2.9,0.7,0.3,1.0,1.1,1.0,0.7,2.4,1.4,2.4,0.2,1.6,0.8,1.8,1.3,1.0,0.7,1.1,0.4,0.3,2.0,0.5,2.6,1.4,0.9,0.4,0.7,2.0,4.4,0.3,6,23,7,7,31,21,16,14,16,6,2.0,29,15,7,19,70,82,4,28,39,16,31,17,33,38,30,27,27,23,42,23,8,5,27,4,7,46,42,1,1,8,27,20,24,18,4,1,5,5,8,5,19,7,16,1,12,4,15,11,8,5,6,2,3,14,5,20,9,6,4,5,17,29,2,0.039,0.032,0.016,0.02,0.046,0.05,0.031,0.033,0.054,0.03,0.029,0.034,0.033,0.031,0.034,0.038,0.038,0.031,0.035,0.038,0.028,0.04,0.028,0.042,0.044,0.03,0.038,0.031,0.028,0.045,0.05,0.061,0.026,0.032,0.018,0.028,0.041,0.033,0.014,0.018,0.024,0.041,0.04,0.03,0.023,0.016,0.018,0.015,0.015,0.018,0.031,0.025,0.023,0.025,0.027,0.023,0.022,0.021,0.029,0.024,0.023,0.02,0.026,0.018,0.022,0.026,0.023,0.02,0.021,0.015,0.027,0.027,0.021,0.023,1353,4706,1520,1622,2118,3628,2911,2812,1622,1066,353.0,2988,3119,1131,2664,10606,14770,1956,4493,4692,3670,4702,1908,4984,5376,5633,4054,4018,4721,5010,2104,693,1111,5822,1677,1725,6499,6692,222,219,1522,2902,4850,2688,3256,1039,670,2006,2250,1910,741,3027,2370,4074,312,2289,1347,3049,1362,1174,1040,2240,537,661,2701,523,3356,1939,1232,791,1015,2316,7849,439,0.117,0.128,0.098,0.094,0.154,0.169,0.128,0.138,0.156,0.118,0.096,0.136,0.128,0.144,0.154,0.141,0.136,0.106,0.143,0.141,0.105,0.148,0.133,0.14,0.155,0.119,0.139,0.124,0.117,0.156,0.142,0.137,0.112,0.121,0.086,0.108,0.145,0.13,0.089,0.079,0.115,0.154,0.14,0.121,0.11,0.098,0.085,0.089,0.089,0.106,0.134,0.11,0.108,0.108,0.122,0.121,0.096,0.112,0.137,0.121,0.109,0.102,0.121,0.11,0.114,0.111,0.115,0.103,0.11,0.108,0.125,0.118,0.102,0.11,388,1488,779,680,682,1079,1003,791,427,272,124.0,1369,838,325,782,2852,4002,362,1210,1933,962,1261,829,1277,1428,1798,1322,1418,1418,1569,544,336,327,1431,424,641,1823,1797,124,89,763,1243,1020,1665,1939,537,264,903,1000,801,357,1550,911,1643,127,1095,605,1365,705,665,490,789,273,298,1361,233,1692,1007,656,401,480,1128,3252,202,3.234,2.799,1.936,2.391,2.416,2.704,2.407,3.146,2.601,3.18,2.528,1.923,2.901,2.782,2.738,2.639,2.77,3.907,2.983,2.058,2.924,2.584,1.976,2.809,2.811,2.563,2.402,2.247,2.547,2.519,2.753,2.159,2.66,2.942,3.411,2.63,2.852,2.763,2.19,2.454,2.332,2.093,3.443,1.886,1.895,2.029,3.045,2.69,2.53,2.695,2.438,2.019,2.661,2.478,2.866,2.323,2.634,2.459,2.192,1.999,2.328,2.787,2.162,2.188,2.239,2.259,2.189,2.189,2.158,2.081,2.589,2.347,2.636,2.676,0.882,0.867,0.58,0.543,0.709,0.595,0.573,0.744,0.623,0.483,0.685,0.463,0.446,0.459,0.449,0.617,0.651,0.912,0.613,0.62,0.832,0.624,0.383,0.63,0.612,0.549,0.564,0.44,0.457,0.582,0.699,0.816,0.495,0.736,0.69,0.576,0.677,0.531,0.371,0.354,0.518,0.476,0.733,0.656,0.454,0.601,0.643,0.63,0.731,0.492,0.568,0.485,0.674,0.527,0.521,0.614,0.419,0.526,0.512,0.409,0.494,0.821,0.465,0.914,0.587,0.579,0.425,0.38,0.442,0.567,0.551,0.563,0.587,0.626
-194,Epileptic,F,37.0,0.6,2.0,0.6,0.9,1.5,1.8,1.2,1.5,0.9,0.7,0.3,3.2,1.8,0.4,1.4,5.6,8.9,0.8,2.8,4.6,1.0,2.9,1.3,2.8,2.3,4.1,2.5,2.7,2.4,2.6,1.1,0.7,0.5,1.7,0.3,0.3,3.3,2.8,0.1,0.1,0.8,3.2,2.6,3.3,2.5,0.8,0.3,1.1,1.4,0.6,0.4,1.6,1.8,2.7,0.4,1.9,1.1,1.6,0.9,0.7,0.7,1.4,0.2,0.3,1.8,1.8,2.7,1.6,1.2,0.5,0.8,1.6,4.5,0.3,6,22,8,6,15,21,11,14,9,10,4.0,33,16,5,17,58,82,5,31,42,12,35,14,33,29,40,28,30,23,33,14,4,6,21,4,2,38,35,1,1,5,26,29,23,15,7,1,7,7,4,3,13,15,20,2,17,7,12,5,7,6,12,3,4,16,18,18,13,10,8,5,12,37,2,0.028,0.02,0.011,0.018,0.035,0.025,0.018,0.027,0.036,0.032,0.024,0.033,0.035,0.027,0.032,0.035,0.028,0.026,0.032,0.033,0.02,0.03,0.021,0.033,0.025,0.036,0.025,0.026,0.022,0.026,0.028,0.027,0.027,0.024,0.012,0.007,0.036,0.031,0.013,0.016,0.017,0.03,0.042,0.028,0.017,0.014,0.014,0.019,0.017,0.013,0.023,0.018,0.024,0.02,0.03,0.017,0.028,0.017,0.021,0.017,0.021,0.026,0.026,0.016,0.018,0.028,0.02,0.022,0.02,0.018,0.021,0.022,0.017,0.015,1666,4729,1691,2102,1954,3153,2828,3395,1516,1496,619.0,3282,3437,829,2764,10722,19041,2645,5257,4978,3895,5132,2503,6468,5724,6556,4571,4936,6347,5354,2280,724,949,4989,1829,1365,6558,7254,360,296,1392,3241,6251,2827,3635,1714,743,2121,2681,1478,508,2814,2711,5238,396,3488,1333,3481,1226,1296,930,2176,631,886,2969,1353,4078,2789,2016,1084,1244,2328,9225,481,0.11,0.112,0.099,0.097,0.139,0.128,0.094,0.127,0.129,0.141,0.126,0.148,0.138,0.126,0.144,0.141,0.128,0.099,0.127,0.135,0.083,0.138,0.107,0.142,0.13,0.15,0.13,0.123,0.102,0.131,0.117,0.086,0.119,0.111,0.063,0.057,0.138,0.134,0.089,0.102,0.1,0.131,0.149,0.124,0.096,0.089,0.088,0.095,0.094,0.092,0.116,0.102,0.112,0.099,0.129,0.104,0.126,0.097,0.1,0.102,0.12,0.123,0.133,0.112,0.105,0.134,0.109,0.11,0.107,0.118,0.111,0.126,0.095,0.114,462,1590,713,792,797,1288,974,976,461,408,217.0,1563,918,233,770,2832,5370,480,1499,2214,947,1616,997,1559,1567,1861,1571,1641,1724,1728,582,397,302,1123,515,564,1662,1604,166,162,694,1576,1104,1809,2129,832,286,933,1226,601,279,1474,1186,2170,196,1649,664,1575,686,661,489,870,206,401,1492,849,2027,1223,955,476,606,1042,4100,219,3.422,2.747,2.303,2.588,2.173,2.235,2.366,2.833,2.544,2.989,2.656,1.882,2.819,2.718,2.729,2.798,2.864,4.018,2.852,1.982,3.099,2.438,2.13,2.951,2.701,2.977,2.326,2.404,2.802,2.459,2.788,1.828,2.535,2.965,2.989,2.365,2.926,3.126,2.393,1.924,2.492,1.891,3.445,1.756,1.882,2.127,3.095,2.902,2.651,2.897,2.056,2.108,2.278,2.442,2.524,2.228,2.364,2.571,2.002,2.139,2.369,2.431,2.979,2.397,2.184,2.101,2.26,2.417,2.28,2.421,2.336,2.619,2.379,2.788,0.744,0.588,0.491,0.518,0.528,0.436,0.531,0.591,0.484,0.659,0.712,0.502,0.522,0.441,0.504,0.582,0.545,0.845,0.554,0.579,0.687,0.538,0.473,0.614,0.503,0.506,0.527,0.509,0.473,0.595,0.643,0.785,0.387,0.609,0.781,0.425,0.733,0.637,0.277,0.39,0.515,0.433,0.791,0.633,0.543,0.492,0.53,0.715,0.549,0.462,0.428,0.388,0.58,0.468,0.32,0.407,0.469,0.487,0.375,0.487,0.392,0.572,0.547,0.839,0.662,0.582,0.456,0.442,0.552,0.679,0.557,0.653,0.534,0.44
-195,Epileptic,F,52.0,0.9,3.0,1.0,1.4,1.6,2.1,1.2,1.3,1.5,0.7,0.3,3.0,1.1,0.3,1.1,7.0,7.7,1.1,1.6,4.7,1.3,3.3,1.3,3.5,3.0,2.3,3.7,2.0,2.7,2.5,2.1,1.2,0.4,2.2,0.4,0.5,3.8,3.4,0.1,0.3,1.1,2.6,2.8,3.5,3.0,0.7,0.5,0.8,1.4,0.6,0.5,2.2,1.6,2.4,0.2,2.6,0.5,1.2,0.7,0.8,0.5,1.2,0.2,0.4,1.6,1.2,2.0,1.1,0.7,0.9,0.8,1.6,4.9,0.4,8,39,8,15,17,20,12,14,16,8,3.0,28,14,4,17,84,78,7,20,40,14,40,11,42,40,33,42,21,33,35,20,13,4,38,2,6,39,41,1,4,6,21,27,29,16,5,2,7,26,6,3,16,8,21,2,17,6,9,5,5,3,9,1,3,14,16,15,9,4,14,6,14,44,2,0.04,0.036,0.018,0.029,0.043,0.036,0.03,0.029,0.05,0.039,0.033,0.048,0.026,0.026,0.035,0.038,0.032,0.043,0.031,0.046,0.033,0.045,0.036,0.047,0.044,0.024,0.04,0.03,0.031,0.035,0.059,0.057,0.021,0.027,0.011,0.013,0.055,0.04,0.012,0.022,0.021,0.048,0.048,0.034,0.022,0.019,0.022,0.014,0.019,0.012,0.035,0.022,0.026,0.021,0.018,0.018,0.023,0.021,0.022,0.018,0.024,0.025,0.011,0.018,0.022,0.027,0.02,0.016,0.016,0.037,0.023,0.025,0.022,0.024,1510,4492,2211,2570,1999,3464,2493,2733,2033,1334,871.0,2401,3035,1005,2665,11796,16492,2312,3304,4677,3797,3802,1276,5198,4475,5350,5295,3073,5182,5062,2024,936,965,5461,1744,1776,3913,6746,277,708,1427,1645,5290,3375,3678,1188,665,1959,2187,1425,424,3051,1896,4235,454,3884,984,2711,1023,1283,729,1940,414,807,2430,1243,2763,2247,1300,956,1218,2608,7875,387,0.154,0.145,0.104,0.131,0.142,0.141,0.105,0.127,0.189,0.158,0.124,0.161,0.121,0.135,0.142,0.151,0.134,0.133,0.143,0.169,0.119,0.143,0.115,0.162,0.135,0.118,0.128,0.107,0.112,0.14,0.183,0.139,0.096,0.124,0.062,0.087,0.162,0.144,0.08,0.113,0.106,0.15,0.17,0.148,0.099,0.106,0.112,0.077,0.102,0.096,0.131,0.115,0.118,0.109,0.105,0.102,0.109,0.106,0.113,0.099,0.11,0.129,0.087,0.106,0.111,0.123,0.104,0.093,0.092,0.172,0.113,0.125,0.111,0.127,446,1548,797,875,740,1054,800,908,616,341,206.0,1101,840,247,705,3593,4626,443,909,1957,852,1354,606,1476,1445,1680,1980,1194,1604,1554,714,493,344,1353,517,713,1396,1804,164,304,761,905,1168,1758,2110,629,274,930,1086,710,244,1560,936,1873,200,2153,495,1041,542,662,357,756,221,353,1186,730,1561,1055,662,422,579,1008,3699,208,3.135,2.711,2.585,2.604,2.317,2.573,2.37,2.612,2.67,3.116,2.878,1.838,2.712,2.776,2.739,2.534,2.778,3.64,2.846,2.149,3.314,2.317,1.843,2.666,2.428,2.474,2.151,2.018,2.483,2.494,2.43,1.854,2.327,2.812,2.967,2.323,2.306,2.739,1.858,2.212,2.256,1.672,3.282,2.081,1.945,2.039,2.77,2.484,2.385,2.265,2.167,2.061,2.112,2.386,2.349,1.975,2.017,2.666,2.069,2.108,2.236,2.624,2.117,2.816,2.218,1.989,1.907,2.315,2.201,2.36,2.155,2.496,2.289,2.31,0.855,0.645,0.59,0.575,0.641,0.701,0.703,0.543,0.554,0.765,0.747,0.575,0.441,0.591,0.667,0.574,0.629,0.836,0.55,0.683,0.865,0.643,0.635,0.848,0.59,0.528,0.481,0.524,0.496,0.571,0.666,0.853,0.348,0.624,0.84,0.457,0.759,0.755,0.379,0.528,0.479,0.578,0.685,0.588,0.554,0.454,0.602,0.611,0.486,0.503,0.407,0.452,0.444,0.478,0.38,0.428,0.431,0.639,0.438,0.408,0.42,0.529,0.576,0.698,0.418,0.683,0.403,0.474,0.501,0.536,0.58,0.624,0.449,0.265
-196,Epileptic,F,37.0,0.7,2.3,1.2,0.9,1.5,1.6,1.5,1.5,0.9,0.6,0.3,3.3,1.2,0.5,1.2,4.4,8.2,0.5,2.0,4.7,1.1,3.6,1.9,3.0,4.5,4.0,2.2,2.1,1.7,2.5,2.3,0.9,0.5,3.0,0.3,0.8,2.6,3.8,0.2,0.3,0.8,3.8,2.4,3.3,2.4,0.7,0.4,0.7,1.4,0.7,0.6,1.2,1.4,2.4,0.4,2.6,0.8,1.3,1.7,0.9,0.4,1.7,0.5,0.5,1.7,0.8,2.4,1.0,1.0,0.7,1.2,1.0,5.3,0.3,5,19,9,9,15,20,15,16,13,8,4.0,29,19,8,17,51,81,4,28,41,11,39,16,36,53,47,29,25,18,28,20,7,7,36,2,9,27,51,1,2,5,32,23,24,14,6,2,4,8,9,5,9,12,17,2,22,6,10,9,7,3,17,4,5,11,13,18,5,5,7,9,6,53,2,0.035,0.027,0.021,0.02,0.038,0.026,0.03,0.029,0.044,0.038,0.038,0.039,0.031,0.034,0.031,0.033,0.034,0.027,0.037,0.036,0.031,0.038,0.035,0.039,0.047,0.037,0.029,0.031,0.023,0.028,0.062,0.062,0.029,0.038,0.018,0.023,0.048,0.049,0.019,0.015,0.019,0.043,0.048,0.027,0.018,0.015,0.018,0.012,0.021,0.018,0.026,0.017,0.029,0.022,0.023,0.026,0.03,0.016,0.029,0.021,0.022,0.029,0.037,0.021,0.018,0.028,0.021,0.017,0.017,0.029,0.028,0.019,0.026,0.021,1560,4661,2524,2404,2187,3314,3081,3193,1564,1713,634.0,3375,2885,1158,2557,8270,17416,2247,4467,5658,3630,5102,2192,5721,6104,6676,4788,4096,5057,5289,2519,1114,1138,6024,1534,1875,4916,6013,386,537,1389,2181,5840,3474,3725,1420,886,2125,2227,1522,614,2281,1637,4690,421,3268,1403,2586,1702,1241,971,2585,676,816,2861,1295,3708,1932,1858,1144,1426,1565,6921,480,0.115,0.12,0.108,0.1,0.16,0.131,0.121,0.136,0.16,0.14,0.144,0.144,0.138,0.155,0.136,0.144,0.144,0.105,0.143,0.135,0.122,0.14,0.125,0.138,0.157,0.15,0.128,0.124,0.109,0.134,0.159,0.132,0.127,0.158,0.083,0.119,0.153,0.169,0.1,0.105,0.102,0.155,0.168,0.119,0.097,0.095,0.096,0.074,0.104,0.114,0.13,0.096,0.126,0.112,0.109,0.131,0.137,0.102,0.124,0.11,0.11,0.133,0.141,0.119,0.105,0.106,0.112,0.092,0.089,0.135,0.134,0.118,0.118,0.13,370,1349,910,816,718,1062,929,994,465,342,151.0,1440,812,308,717,2376,4505,359,1080,2263,734,1540,818,1552,1778,1965,1505,1294,1309,1456,715,340,288,1331,356,583,1172,1524,195,252,664,1317,980,2009,1987,692,318,841,1029,570,357,1074,799,1836,237,1520,527,1228,905,688,374,953,278,394,1327,712,1758,877,866,416,646,708,3251,215,3.743,3.073,2.802,2.794,2.436,2.708,2.673,2.747,2.419,3.503,2.902,2.039,2.808,2.846,2.589,2.611,2.998,4.2,3.086,2.175,3.392,2.612,2.293,2.693,2.656,2.728,2.498,2.424,2.907,2.683,2.584,3.036,2.907,3.013,3.598,2.816,2.937,2.699,2.178,2.226,2.679,1.683,3.628,1.993,2.136,2.293,3.213,3.083,2.607,2.508,2.18,2.171,2.1,2.513,2.082,2.377,2.601,2.4,2.164,2.07,2.637,2.416,2.075,2.306,2.402,1.973,2.225,2.51,2.435,2.735,2.375,2.393,2.261,2.698,0.889,0.811,0.462,0.485,0.808,0.664,0.601,0.535,0.717,0.789,0.848,0.609,0.4,0.577,0.583,0.611,0.657,0.71,0.679,0.692,0.809,0.81,0.664,0.734,0.768,0.614,0.573,0.543,0.385,0.655,0.706,1.014,0.336,0.734,0.747,0.584,0.929,0.794,0.387,0.488,0.51,0.506,0.937,0.592,0.58,0.51,0.686,0.895,0.433,0.592,0.392,0.392,0.463,0.507,0.448,0.461,0.567,0.417,0.42,0.437,0.579,0.762,0.611,0.68,0.523,0.661,0.474,0.431,0.429,0.703,0.365,0.467,0.625,0.482
-197,Epileptic,F,57.0,1.0,2.3,0.9,0.9,1.4,1.7,1.8,1.9,1.3,0.7,0.4,2.7,2.2,0.6,1.9,5.2,8.7,1.0,2.4,5.7,1.7,2.8,2.9,2.6,2.5,4.4,3.8,2.8,4.0,2.9,1.7,0.7,0.7,3.6,0.5,0.8,2.5,3.0,0.2,0.2,1.4,2.9,2.6,2.2,2.6,0.4,0.3,0.9,1.4,0.5,0.7,2.4,1.7,2.9,0.2,2.7,0.6,1.7,0.9,0.8,0.4,1.1,0.4,0.4,1.8,1.0,2.8,1.5,1.2,0.6,0.9,1.0,4.8,0.3,7,24,11,7,14,14,14,13,10,6,3.0,23,14,4,12,45,67,8,31,31,13,25,23,26,23,33,36,24,33,32,19,6,6,25,2,6,34,32,1,1,8,26,21,15,17,3,1,6,7,5,4,14,11,19,1,22,4,14,9,7,3,7,2,3,12,9,20,11,7,5,7,7,37,3,0.044,0.026,0.015,0.021,0.035,0.036,0.03,0.041,0.046,0.04,0.034,0.036,0.04,0.033,0.042,0.032,0.032,0.036,0.033,0.052,0.031,0.035,0.037,0.033,0.034,0.036,0.029,0.033,0.039,0.029,0.042,0.037,0.024,0.039,0.014,0.02,0.032,0.037,0.018,0.029,0.028,0.04,0.046,0.025,0.021,0.011,0.021,0.016,0.021,0.015,0.031,0.027,0.026,0.024,0.024,0.021,0.023,0.023,0.029,0.026,0.016,0.022,0.023,0.018,0.024,0.028,0.021,0.023,0.021,0.02,0.024,0.021,0.021,0.026,1153,4203,2198,1596,2439,2143,1656,2301,1924,1123,697.0,2675,2807,949,2798,9792,13965,2495,3539,3287,2847,3699,2661,5146,3781,6572,5758,3234,4969,4374,2384,1286,1068,5499,2067,1592,5266,5739,329,405,1722,2536,5283,2350,3047,1011,680,2281,2454,1470,502,2973,2191,4847,171,3594,826,2539,1162,1077,698,2572,422,756,2753,992,3774,2446,1970,1165,1400,1679,8029,303,0.141,0.117,0.111,0.101,0.128,0.138,0.113,0.134,0.157,0.144,0.125,0.125,0.127,0.109,0.15,0.124,0.122,0.12,0.121,0.143,0.106,0.137,0.138,0.132,0.13,0.128,0.108,0.106,0.11,0.133,0.144,0.1,0.107,0.117,0.07,0.091,0.131,0.132,0.087,0.102,0.124,0.137,0.142,0.113,0.106,0.085,0.102,0.09,0.097,0.097,0.135,0.111,0.111,0.108,0.15,0.117,0.119,0.11,0.132,0.117,0.098,0.103,0.12,0.104,0.109,0.118,0.109,0.115,0.108,0.114,0.126,0.113,0.106,0.141,378,1410,914,682,724,796,789,833,503,326,190.0,1187,931,265,736,2752,4480,451,1168,1548,783,1356,1155,1325,1229,1967,2112,1371,1636,1604,673,445,462,1239,542,676,1346,1433,175,180,861,1202,1126,1383,1984,607,276,903,1103,526,350,1401,1013,1992,95,1893,394,1305,540,562,380,795,254,333,1290,571,2045,1097,840,466,642,693,3440,182,2.976,2.807,2.265,2.367,2.797,2.306,1.906,2.476,2.752,3.016,3.053,1.937,2.4,2.664,2.789,2.647,2.596,3.842,2.483,1.887,2.972,2.262,2.071,2.976,2.458,2.724,2.24,1.969,2.465,2.272,2.713,2.85,2.034,3.01,3.386,2.276,3.011,2.847,2.12,2.624,2.505,1.911,3.125,1.886,1.747,1.858,3.245,3.111,2.676,2.679,1.797,2.19,2.326,2.486,2.086,2.1,2.475,2.161,2.159,2.12,2.227,3.161,2.081,2.635,2.259,2.113,1.98,2.399,2.435,2.494,2.383,2.592,2.47,1.972,0.865,0.623,0.68,0.508,0.777,0.686,0.518,0.475,0.666,0.559,0.945,0.455,0.538,0.467,0.596,0.585,0.568,0.899,0.76,0.644,0.966,0.52,0.474,0.558,0.515,0.614,0.587,0.598,0.663,0.551,0.785,1.079,0.384,0.666,0.66,0.44,0.887,0.583,0.386,0.582,0.479,0.566,0.841,0.747,0.533,0.388,0.642,0.835,0.621,0.818,0.568,0.516,0.64,0.565,0.309,0.531,0.487,0.773,0.423,0.403,0.429,0.881,0.406,0.964,0.734,0.589,0.53,0.454,0.507,0.504,0.756,0.784,0.57,0.484
-198,Epileptic,M,32.0,2.1,2.4,1.3,2.1,2.6,2.3,2.7,2.0,1.3,1.3,0.4,3.4,2.2,0.8,1.4,5.7,9.7,1.4,2.8,6.7,1.5,4.0,2.0,5.4,5.3,3.6,5.1,3.1,5.2,5.2,4.1,1.1,1.0,3.3,1.0,0.7,4.0,6.7,0.3,0.2,1.3,4.0,1.3,3.2,3.1,1.1,0.6,0.9,1.5,0.7,0.9,1.9,1.1,2.5,0.2,3.2,1.1,2.2,1.2,1.7,0.9,1.8,0.7,0.8,2.7,1.3,2.2,1.9,1.7,0.8,1.4,1.0,5.4,0.3,48,20,13,14,23,21,26,17,13,10,5.0,34,22,8,16,57,90,14,35,67,13,46,20,50,52,42,57,28,43,53,36,6,11,37,6,7,31,64,2,1,7,36,11,26,17,7,3,5,8,4,7,14,9,16,1,23,8,15,9,12,8,13,4,6,25,16,20,14,10,6,9,7,45,4,0.085,0.027,0.02,0.031,0.049,0.03,0.046,0.032,0.047,0.052,0.035,0.041,0.031,0.04,0.034,0.039,0.035,0.047,0.032,0.054,0.038,0.048,0.032,0.05,0.041,0.035,0.039,0.039,0.035,0.042,0.068,0.039,0.035,0.043,0.043,0.018,0.056,0.057,0.019,0.017,0.024,0.047,0.062,0.028,0.021,0.019,0.018,0.021,0.02,0.028,0.029,0.019,0.022,0.021,0.016,0.021,0.029,0.021,0.023,0.022,0.026,0.028,0.041,0.031,0.025,0.021,0.021,0.024,0.02,0.031,0.028,0.027,0.02,0.017,1827,4742,2465,2800,2083,4097,2374,3486,2071,1723,701.0,2984,4474,1518,2612,10023,19087,2587,5552,6870,2859,4551,2223,6863,6614,6739,5691,3231,7400,6864,2865,911,1346,5406,1224,1695,3625,6753,416,419,1573,3422,698,3157,3763,1575,1137,1643,2510,1127,708,3078,1712,3948,412,4208,969,3307,1272,2015,980,2860,496,1108,3325,1279,3072,3140,2588,1381,1420,1231,9108,467,0.156,0.125,0.116,0.129,0.163,0.137,0.137,0.138,0.172,0.182,0.14,0.159,0.127,0.143,0.142,0.15,0.137,0.143,0.139,0.185,0.142,0.146,0.127,0.159,0.142,0.143,0.128,0.126,0.122,0.156,0.194,0.115,0.149,0.157,0.145,0.097,0.178,0.176,0.112,0.101,0.112,0.157,0.172,0.128,0.102,0.111,0.096,0.1,0.095,0.126,0.134,0.106,0.114,0.105,0.083,0.112,0.121,0.113,0.121,0.117,0.117,0.123,0.169,0.114,0.121,0.112,0.109,0.111,0.104,0.136,0.129,0.132,0.107,0.118,520,1437,967,1061,886,1304,1037,1142,562,466,231.0,1549,1341,426,733,2747,5254,586,1585,2581,676,1648,1133,1973,2248,1967,2552,1330,2227,2320,958,476,486,1397,415,686,1179,1920,204,174,847,1499,356,1852,2264,829,477,697,1153,420,481,1520,869,1813,229,2315,517,1576,784,1078,552,1039,253,492,1840,881,1741,1374,1275,474,802,597,4280,274,2.325,2.927,2.473,2.43,2.158,2.475,2.015,2.695,2.672,2.772,2.317,1.755,2.691,2.83,2.66,2.682,2.883,3.296,2.615,2.281,2.911,2.267,1.768,2.671,2.345,2.639,1.897,1.941,2.61,2.387,2.383,2.001,2.466,2.753,2.209,2.284,2.348,2.514,2.284,2.689,2.185,2.04,1.94,1.887,1.872,1.983,2.901,2.443,2.592,2.345,1.775,2.193,2.133,2.44,1.958,1.947,1.917,2.202,1.842,2.025,1.983,2.828,2.267,2.781,1.867,1.774,1.82,2.364,2.338,2.784,1.988,2.16,2.165,1.932,1.25,0.545,0.597,0.57,0.501,0.712,0.544,0.466,0.641,0.745,0.928,0.398,0.418,0.429,0.47,0.563,0.588,0.791,0.722,0.61,0.725,0.577,0.437,0.852,0.641,0.617,0.475,0.642,0.592,0.528,0.81,0.638,0.444,0.678,0.865,0.373,0.807,0.756,0.354,0.437,0.46,0.607,0.63,0.527,0.529,0.425,0.667,0.844,0.468,0.728,0.301,0.349,0.45,0.403,0.275,0.432,0.45,0.498,0.322,0.485,0.406,0.59,0.398,0.91,0.41,0.581,0.485,0.386,0.439,0.795,0.413,0.57,0.48,0.49
-199,Epileptic,F,37.0,0.8,2.0,1.1,1.2,2.0,2.2,2.0,1.3,1.3,0.6,0.4,2.7,1.2,0.5,0.9,5.2,7.4,1.3,2.1,5.0,1.6,6.0,3.6,3.9,11.8,4.0,7.1,2.8,2.8,3.6,1.5,0.8,0.5,2.7,0.4,1.0,4.4,2.7,0.2,0.1,1.0,4.4,2.6,2.4,2.9,0.7,0.4,0.9,1.2,0.7,0.6,1.2,1.2,2.5,0.7,2.7,0.6,1.4,2.0,1.6,0.6,2.4,0.4,1.0,1.3,1.3,2.6,0.6,1.1,0.6,0.9,1.4,6.6,0.2,8,17,10,9,19,24,18,15,15,8,5.0,26,12,6,10,67,69,12,27,51,14,46,24,52,73,40,58,30,25,38,20,8,4,33,2,11,47,34,1,1,5,33,39,16,17,5,2,5,6,6,3,10,10,20,3,20,6,11,16,11,4,18,2,7,11,19,25,4,8,9,7,10,47,1,0.048,0.022,0.021,0.024,0.035,0.033,0.034,0.024,0.044,0.033,0.04,0.038,0.024,0.026,0.034,0.033,0.031,0.05,0.034,0.039,0.031,0.05,0.044,0.046,0.067,0.035,0.045,0.034,0.03,0.034,0.036,0.05,0.033,0.03,0.013,0.026,0.042,0.028,0.017,0.016,0.021,0.051,0.053,0.024,0.021,0.016,0.02,0.014,0.02,0.015,0.029,0.018,0.026,0.022,0.028,0.015,0.02,0.02,0.05,0.022,0.016,0.032,0.038,0.031,0.022,0.027,0.018,0.012,0.019,0.024,0.021,0.02,0.022,0.01,1426,4470,2831,2273,2495,3713,3884,3512,2175,1391,462.0,2627,2945,1343,1811,11176,17868,2320,4235,5373,4283,4569,2242,6813,4702,6797,5671,4492,6782,6251,2280,1032,914,6421,1770,2153,7962,8123,368,293,1546,1856,6137,2334,4226,1667,771,2351,1938,1514,642,2297,1575,4680,663,4327,1339,2659,1150,2215,1033,3314,417,887,1914,884,4930,1778,1902,1041,1539,2204,9639,565,0.138,0.113,0.113,0.112,0.146,0.136,0.125,0.119,0.165,0.145,0.151,0.145,0.113,0.135,0.136,0.139,0.135,0.157,0.147,0.148,0.118,0.152,0.145,0.148,0.168,0.135,0.151,0.116,0.114,0.14,0.135,0.116,0.12,0.134,0.082,0.115,0.141,0.124,0.098,0.079,0.107,0.167,0.2,0.117,0.101,0.094,0.099,0.084,0.098,0.096,0.121,0.1,0.115,0.117,0.131,0.102,0.115,0.107,0.165,0.116,0.096,0.128,0.151,0.133,0.114,0.129,0.104,0.08,0.104,0.142,0.119,0.115,0.106,0.072,383,1446,841,800,976,1233,1071,992,550,356,164.0,1240,763,298,487,3014,4231,528,1061,2348,847,1840,1183,1766,2256,1911,2339,1419,1567,1867,686,373,261,1379,414,661,1845,1713,197,126,712,1346,1220,1507,2142,741,282,924,914,704,326,1117,781,1798,330,2386,552,1245,715,1092,550,1232,175,486,907,691,2276,809,870,396,679,1005,4332,221,3.18,2.9,2.929,2.809,2.209,2.439,2.745,2.919,2.761,3.114,2.388,1.878,2.855,2.958,2.733,2.717,3.145,3.343,3.001,1.903,3.377,2.133,1.75,2.759,1.858,2.876,2.05,2.358,3.062,2.577,2.444,2.724,2.811,3.204,3.692,2.905,2.938,3.203,2.127,2.578,2.627,1.437,3.442,1.848,2.206,2.391,3.069,3.214,2.655,2.463,2.232,2.189,2.279,2.451,2.545,1.982,2.554,2.491,1.831,2.205,2.289,2.599,2.712,2.179,2.212,1.648,2.231,2.51,2.463,2.626,2.398,2.478,2.498,2.614,0.687,0.577,0.675,0.619,0.688,0.819,0.7,0.526,0.676,0.696,0.731,0.669,0.442,0.472,0.461,0.694,0.653,0.843,0.518,0.699,0.951,0.85,0.528,0.778,0.695,0.639,0.629,0.688,0.75,0.668,0.699,0.963,0.614,0.639,0.625,0.455,0.793,0.682,0.346,0.264,0.516,0.44,0.87,0.679,0.673,0.473,0.547,0.725,0.517,0.508,0.356,0.443,0.45,0.543,0.507,0.434,0.483,0.569,0.511,0.406,0.36,0.718,0.695,0.648,0.588,0.517,0.455,0.379,0.504,0.711,0.59,0.478,0.546,0.519
-200,Epileptic,M,32.0,1.7,2.4,1.2,1.9,1.6,1.8,1.8,3.1,1.2,1.2,0.6,3.2,2.4,0.9,1.4,9.0,13.1,1.0,2.4,4.8,1.5,2.7,1.9,3.3,2.8,3.7,4.1,3.5,4.4,4.8,1.5,1.5,0.8,2.5,0.9,0.5,4.3,3.4,0.4,0.3,1.3,3.6,3.0,2.9,3.5,0.6,0.5,1.0,1.3,0.7,0.7,2.0,1.5,4.0,0.2,3.1,0.7,1.6,0.6,1.6,0.9,1.4,0.3,1.4,1.4,1.7,3.1,1.5,2.0,0.4,1.0,1.6,4.8,0.4,12,19,11,15,10,17,14,24,8,11,6.0,28,18,6,12,66,95,7,30,40,13,33,20,31,32,37,41,24,28,50,12,10,7,27,6,3,44,36,3,1,7,31,25,22,19,5,2,5,7,7,4,13,8,19,1,25,6,14,6,13,6,9,2,9,13,18,23,10,10,3,7,12,35,3,0.047,0.023,0.017,0.028,0.042,0.028,0.031,0.035,0.045,0.043,0.04,0.032,0.035,0.05,0.036,0.051,0.038,0.033,0.029,0.035,0.025,0.027,0.029,0.039,0.027,0.031,0.028,0.036,0.034,0.035,0.043,0.048,0.023,0.031,0.027,0.01,0.037,0.032,0.035,0.028,0.022,0.035,0.039,0.024,0.023,0.015,0.017,0.014,0.014,0.017,0.027,0.024,0.026,0.029,0.028,0.019,0.014,0.018,0.016,0.022,0.02,0.026,0.033,0.043,0.02,0.033,0.025,0.022,0.028,0.016,0.027,0.02,0.018,0.015,1725,6188,2585,2924,2359,3159,2938,4503,1852,1762,922.0,3835,4191,1218,2582,11989,22741,2620,5224,5756,3703,5797,3167,5107,5720,6526,7248,4347,7164,6746,2157,1181,1535,6430,2448,1960,8832,8502,435,277,1861,3392,6094,3853,4010,1316,913,2303,3062,1734,646,3160,1798,5721,228,4468,1963,3269,1381,2192,1259,1970,416,1166,2426,1560,3679,2527,2232,979,1643,2676,9799,666,0.141,0.109,0.098,0.121,0.136,0.121,0.115,0.135,0.144,0.171,0.147,0.137,0.129,0.156,0.142,0.153,0.131,0.121,0.124,0.139,0.1,0.124,0.134,0.134,0.131,0.136,0.118,0.126,0.116,0.149,0.14,0.118,0.11,0.132,0.107,0.068,0.132,0.136,0.142,0.127,0.106,0.135,0.146,0.117,0.104,0.092,0.09,0.082,0.086,0.105,0.115,0.11,0.114,0.11,0.157,0.108,0.09,0.102,0.098,0.118,0.108,0.115,0.132,0.148,0.108,0.137,0.118,0.109,0.119,0.095,0.117,0.113,0.094,0.1,523,1781,1021,1146,696,1128,983,1556,491,473,255.0,1524,1226,335,692,3250,6227,548,1391,2150,1047,1823,1186,1514,1776,2141,2624,1581,2146,2282,640,460,551,1448,598,741,2223,1950,211,141,917,1578,1329,1927,2410,690,388,997,1326,695,397,1365,863,2323,90,2437,856,1558,756,1103,595,715,170,505,1205,816,1888,1093,1085,418,706,1211,4208,383,3.297,3.211,2.635,2.47,2.86,2.448,2.457,2.541,2.872,3.187,2.853,2.18,2.89,2.83,2.934,2.791,2.968,3.679,3.083,2.241,2.7,2.495,2.22,2.642,2.494,2.611,2.208,2.194,2.665,2.415,2.585,2.715,2.286,3.217,3.465,2.45,2.937,3.244,2.286,2.268,2.649,1.97,3.182,2.182,1.868,2.077,2.796,2.771,2.7,2.564,2.077,2.496,2.428,2.538,2.728,2.051,2.557,2.401,2.125,2.168,2.358,2.993,2.857,2.585,2.196,2.168,2.065,2.654,2.441,2.73,2.569,2.605,2.486,2.209,0.736,0.682,0.554,0.543,0.696,0.476,0.686,0.625,0.779,0.473,0.595,0.492,0.61,0.609,0.498,0.689,0.742,0.65,0.626,0.588,0.89,0.504,0.637,0.74,0.49,0.43,0.544,0.543,0.551,0.502,0.734,1.0,0.506,0.665,0.815,0.399,0.667,0.584,0.434,0.415,0.464,0.592,0.791,0.621,0.485,0.534,0.597,0.614,0.605,0.662,0.547,0.514,0.494,0.521,0.24,0.428,0.356,0.485,0.388,0.341,0.424,0.796,0.451,0.905,0.472,0.644,0.477,0.459,0.456,0.635,0.581,0.437,0.528,0.261
-201,Epileptic,M,52.0,0.8,2.4,0.9,1.7,1.7,2.9,1.6,2.0,1.2,1.0,0.4,2.6,1.8,0.6,1.6,8.2,10.2,0.8,2.9,7.0,1.1,3.5,2.0,3.5,3.1,4.3,3.1,3.0,3.1,2.7,1.2,0.8,0.8,2.0,0.6,0.8,3.0,4.3,0.2,0.2,1.1,3.5,2.0,4.0,3.3,0.9,0.5,0.8,1.4,0.6,0.8,2.1,1.3,3.0,0.4,1.7,0.7,2.6,1.0,1.4,0.6,1.8,0.3,0.4,2.6,1.8,3.5,1.9,1.2,0.5,1.1,1.0,5.1,0.4,5,24,11,16,15,30,16,18,15,8,4.0,25,20,7,18,91,97,8,28,47,13,40,23,32,32,44,37,33,29,32,16,7,7,24,4,7,33,47,2,2,7,32,20,29,19,7,2,7,6,9,7,15,9,17,3,16,7,23,9,12,5,12,3,3,18,20,30,12,7,7,7,10,44,3,0.029,0.022,0.014,0.024,0.038,0.029,0.024,0.032,0.048,0.035,0.034,0.031,0.032,0.027,0.032,0.039,0.031,0.029,0.034,0.04,0.021,0.029,0.027,0.039,0.027,0.027,0.026,0.026,0.027,0.026,0.038,0.035,0.025,0.028,0.02,0.021,0.027,0.031,0.013,0.016,0.022,0.038,0.034,0.033,0.02,0.018,0.022,0.013,0.016,0.017,0.027,0.019,0.018,0.021,0.015,0.014,0.015,0.025,0.017,0.02,0.019,0.034,0.021,0.014,0.024,0.029,0.022,0.024,0.018,0.019,0.03,0.015,0.02,0.021,1582,4833,2422,2689,2211,3878,2352,3354,1847,1384,675.0,2434,3557,1072,3039,12502,17320,2310,5008,5617,3008,5764,2773,5566,5144,7114,4968,4401,5936,4818,2202,936,1165,5267,2000,1661,7376,8548,384,447,1565,3078,5729,2372,3943,1702,831,2277,2588,1992,700,3637,1928,4734,570,3275,1443,3172,1395,1929,930,2079,496,874,3206,1739,4486,2461,1907,844,1179,2236,9045,518,0.114,0.112,0.095,0.117,0.151,0.138,0.108,0.125,0.159,0.142,0.121,0.136,0.132,0.128,0.143,0.147,0.129,0.118,0.125,0.142,0.088,0.134,0.128,0.148,0.124,0.122,0.123,0.125,0.111,0.127,0.127,0.112,0.122,0.123,0.09,0.097,0.117,0.136,0.097,0.107,0.108,0.15,0.137,0.128,0.1,0.097,0.104,0.088,0.089,0.103,0.137,0.094,0.106,0.102,0.108,0.096,0.099,0.121,0.11,0.112,0.101,0.142,0.121,0.094,0.115,0.126,0.12,0.109,0.1,0.119,0.132,0.09,0.109,0.108,476,1783,1022,1154,792,1667,990,1260,517,426,207.0,1381,1084,327,965,3890,5591,515,1529,2611,897,1911,1239,1583,1741,2541,1981,1766,1799,1681,605,432,402,1318,524,676,1834,2348,237,211,814,1562,1202,1731,2440,820,371,986,1248,705,417,1871,1002,2033,339,1886,778,1738,885,1100,512,827,252,411,1688,980,2342,1231,948,517,619,1098,4065,317,3.199,2.514,2.224,2.346,2.357,2.112,2.127,2.511,2.67,2.787,2.666,1.621,2.545,2.445,2.535,2.431,2.526,3.24,2.627,1.892,2.781,2.336,1.906,2.701,2.375,2.409,2.101,2.034,2.582,2.333,2.653,2.102,2.293,2.813,3.336,2.313,2.958,2.75,1.874,2.443,2.289,1.768,3.156,1.497,1.792,2.17,2.686,2.868,2.495,2.543,1.914,2.064,2.021,2.349,2.045,1.936,2.138,2.109,1.826,1.949,2.055,2.395,2.246,2.395,2.087,2.001,2.032,2.214,2.231,1.94,2.382,2.255,2.446,2.104,0.786,0.545,0.541,0.529,0.531,0.508,0.529,0.516,0.632,0.377,0.704,0.397,0.549,0.441,0.449,0.61,0.587,0.829,0.694,0.586,0.727,0.453,0.496,0.602,0.439,0.462,0.468,0.501,0.477,0.503,0.692,1.013,0.342,0.742,0.826,0.399,0.713,0.625,0.492,0.451,0.52,0.542,0.851,0.588,0.532,0.641,0.406,0.8,0.544,0.835,0.42,0.427,0.592,0.546,0.296,0.45,0.393,0.531,0.365,0.418,0.547,0.601,0.471,0.795,0.503,0.765,0.606,0.384,0.504,0.584,0.493,0.46,0.593,0.489
-202,Epileptic,M,37.0,0.8,3.9,1.8,2.1,2.3,3.3,3.9,2.1,1.1,1.0,0.6,4.3,1.8,0.7,1.8,9.2,11.9,1.0,4.0,7.2,4.0,7.4,3.1,4.4,6.5,3.7,5.9,3.2,4.4,5.5,2.8,2.0,0.7,2.9,0.7,0.9,7.6,4.2,0.3,0.5,1.0,4.9,3.2,3.3,3.7,1.2,0.6,0.9,1.7,1.0,1.0,3.4,2.4,3.2,0.4,3.3,1.3,3.6,1.3,2.1,1.1,2.1,0.6,0.6,2.2,1.7,2.1,1.8,1.3,0.7,1.6,2.0,5.5,0.5,5,40,14,16,16,33,33,20,13,9,5.0,39,17,7,23,81,98,8,38,62,25,77,27,43,56,40,44,30,34,57,26,18,6,31,4,11,70,50,2,2,5,36,30,24,19,6,3,5,9,8,7,25,14,18,3,23,8,33,8,15,9,15,3,3,20,21,17,12,6,8,11,14,42,3,0.043,0.031,0.024,0.031,0.041,0.035,0.049,0.03,0.047,0.05,0.041,0.048,0.029,0.039,0.035,0.048,0.038,0.037,0.042,0.052,0.058,0.051,0.041,0.048,0.047,0.031,0.047,0.033,0.033,0.044,0.053,0.048,0.037,0.038,0.019,0.029,0.053,0.032,0.021,0.028,0.021,0.047,0.053,0.033,0.022,0.019,0.024,0.014,0.02,0.022,0.037,0.021,0.032,0.024,0.018,0.019,0.02,0.03,0.018,0.023,0.022,0.034,0.023,0.024,0.023,0.023,0.017,0.018,0.021,0.023,0.03,0.02,0.017,0.029,1838,7185,3686,3436,2507,4452,3323,4190,1967,1810,990.0,3551,4352,1213,3806,13724,20962,2760,6770,7179,2903,6931,2851,7686,6918,7844,5001,3847,7186,6999,3185,1891,887,6570,2433,1658,9264,10762,462,598,1373,3367,5610,3395,4412,1930,923,3026,2580,2320,863,5859,2917,4964,650,5115,2068,4609,1727,2970,1530,2616,669,1249,3025,2154,3663,3572,1654,946,1854,3170,12037,466,0.11,0.124,0.121,0.128,0.148,0.142,0.138,0.134,0.154,0.174,0.149,0.172,0.119,0.158,0.134,0.156,0.142,0.127,0.144,0.175,0.147,0.152,0.139,0.158,0.15,0.125,0.129,0.116,0.114,0.143,0.171,0.134,0.124,0.129,0.081,0.125,0.155,0.141,0.107,0.121,0.103,0.15,0.164,0.129,0.099,0.096,0.119,0.075,0.096,0.098,0.153,0.104,0.124,0.114,0.11,0.102,0.103,0.133,0.099,0.107,0.11,0.138,0.115,0.099,0.119,0.118,0.089,0.096,0.096,0.125,0.133,0.108,0.093,0.124,411,2208,1223,1140,972,1609,1355,1271,560,403,272.0,1629,1145,305,1123,3603,5527,473,1667,2741,1054,2573,1287,2036,2355,2343,1993,1642,2140,2446,996,745,348,1534,673,646,2523,2440,242,258,710,1704,1377,1809,2489,967,379,975,1168,815,444,2588,1201,1989,335,2593,958,2066,1039,1453,810,962,355,410,1541,1089,1996,1583,839,452,860,1473,5049,227,3.774,2.957,2.69,2.711,2.374,2.288,2.173,2.779,2.623,3.223,3.362,1.957,2.887,2.771,2.741,2.792,2.912,4.036,3.047,2.258,2.175,2.207,1.942,2.927,2.315,2.581,2.114,1.95,2.622,2.274,2.451,2.427,2.327,3.021,3.392,2.285,2.696,3.157,2.158,2.829,2.286,1.801,2.955,2.106,1.975,2.17,3.013,3.417,2.557,2.892,2.214,2.239,2.315,2.597,2.23,2.143,2.463,2.512,1.815,2.198,2.121,2.665,2.208,3.534,2.063,2.274,1.89,2.418,2.22,2.3,2.385,2.572,2.432,2.206,1.005,0.704,0.589,0.674,0.634,0.661,0.697,0.644,0.632,0.614,0.871,0.492,0.505,0.578,0.513,0.754,0.716,0.825,0.685,0.636,0.827,0.646,0.524,0.748,0.724,0.724,0.564,0.53,0.632,0.714,0.724,0.977,0.483,0.655,0.733,0.548,0.914,0.753,0.542,0.55,0.52,0.563,0.846,0.65,0.556,0.516,0.512,0.894,0.552,0.58,0.473,0.512,0.616,0.544,0.495,0.489,0.483,0.596,0.395,0.441,0.47,0.772,0.467,0.586,0.594,0.766,0.442,0.523,0.445,0.565,0.477,0.621,0.516,0.463
-203,Epileptic,F,57.0,0.9,2.1,0.8,0.8,2.1,1.4,1.4,1.5,1.2,0.5,0.2,2.5,1.7,0.5,1.3,5.1,6.5,0.8,2.1,4.0,0.8,2.3,1.3,3.8,2.5,3.1,2.4,2.3,2.3,3.4,1.5,1.5,0.3,1.6,0.3,0.7,2.3,4.3,0.2,0.2,1.1,2.3,2.0,1.9,2.4,0.4,0.6,0.9,1.4,0.7,0.6,1.7,1.0,1.4,0.4,2.1,0.4,1.5,0.4,0.9,0.6,1.6,0.2,0.4,1.5,0.2,2.6,1.2,1.3,0.5,1.2,1.7,3.1,0.4,6,15,7,5,17,13,11,13,12,5,3.0,22,15,6,10,49,54,8,23,31,5,23,11,37,28,33,28,23,20,39,21,8,2,20,2,6,29,42,1,1,6,22,16,34,15,3,3,5,7,7,4,11,7,9,1,19,5,15,5,8,5,13,1,5,12,2,19,9,12,3,7,12,24,2,0.045,0.028,0.019,0.02,0.053,0.033,0.024,0.032,0.053,0.03,0.028,0.035,0.035,0.031,0.037,0.043,0.029,0.036,0.03,0.036,0.019,0.027,0.026,0.043,0.034,0.032,0.027,0.027,0.025,0.032,0.036,0.066,0.022,0.027,0.013,0.023,0.029,0.036,0.017,0.018,0.024,0.029,0.037,0.026,0.021,0.01,0.023,0.017,0.019,0.017,0.041,0.024,0.02,0.018,0.034,0.02,0.02,0.02,0.017,0.019,0.016,0.026,0.02,0.024,0.018,0.025,0.023,0.022,0.019,0.02,0.03,0.02,0.018,0.03,1162,3572,1306,1438,2223,1817,2018,2663,1532,865,445.0,2145,2947,1198,2484,8263,12978,1989,3625,4114,2701,3201,1678,5342,4393,5932,3786,2916,4419,4702,2782,1068,573,3595,1212,1389,5432,7212,326,293,1269,2104,4194,1823,2791,1095,810,1556,2047,1550,510,2746,1477,3161,364,2590,884,2314,583,1290,842,2322,253,810,2367,461,3610,2044,1889,934,1151,2665,6118,287,0.129,0.113,0.102,0.097,0.153,0.147,0.098,0.127,0.165,0.133,0.109,0.141,0.138,0.128,0.14,0.145,0.116,0.12,0.126,0.141,0.075,0.128,0.113,0.145,0.141,0.147,0.123,0.108,0.107,0.146,0.152,0.149,0.1,0.117,0.076,0.1,0.112,0.136,0.099,0.123,0.12,0.133,0.142,0.117,0.104,0.081,0.111,0.089,0.101,0.113,0.147,0.108,0.113,0.087,0.144,0.117,0.123,0.11,0.11,0.102,0.102,0.125,0.123,0.124,0.109,0.084,0.111,0.107,0.105,0.118,0.132,0.107,0.097,0.136,370,1192,627,620,748,748,812,899,434,280,152.0,1029,833,266,620,2294,3611,402,1115,1752,757,1242,815,1516,1331,1685,1534,1251,1436,1636,740,394,216,979,365,535,1397,1856,188,135,645,1136,990,1276,1743,594,353,823,1006,641,267,1175,752,1389,158,1566,359,1304,412,733,491,937,133,363,1223,194,1782,873,1009,406,584,1261,2724,174,3.142,2.872,2.306,2.298,2.451,2.218,2.12,2.576,2.42,2.765,2.49,1.878,2.65,3.004,3.049,2.729,2.875,3.651,2.685,1.932,2.86,2.183,1.831,2.701,2.644,2.82,2.068,1.915,2.47,2.304,2.813,2.714,2.067,2.671,3.04,2.402,2.859,2.857,1.942,2.427,2.4,1.806,3.182,1.663,1.814,2.005,2.893,2.301,2.455,2.648,2.043,2.41,2.194,2.332,2.666,1.87,2.511,2.098,1.607,1.809,1.874,2.514,2.19,2.51,2.088,2.357,2.141,2.46,1.953,2.791,2.058,2.524,2.339,2.225,0.805,0.618,0.626,0.499,0.73,0.526,0.478,0.508,0.687,0.434,0.614,0.466,0.526,0.491,0.581,0.654,0.662,0.881,0.615,0.62,0.727,0.472,0.527,0.695,0.602,0.572,0.519,0.5,0.593,0.562,0.701,1.136,0.366,0.758,0.587,0.592,0.685,0.826,0.355,0.558,0.587,0.406,0.69,0.46,0.575,0.479,0.643,0.872,0.489,0.56,0.397,0.582,0.547,0.564,0.419,0.418,0.556,0.577,0.351,0.41,0.34,0.723,0.369,0.674,0.709,0.559,0.51,0.584,0.541,0.641,0.557,0.589,0.498,0.388
-204,Epileptic,F,32.0,1.2,2.7,0.8,0.9,1.4,2.4,1.8,1.5,0.9,0.9,0.3,2.8,1.5,0.2,1.0,6.5,9.0,0.6,3.6,4.6,1.7,3.0,1.9,3.0,3.6,4.2,5.0,2.9,2.7,4.9,1.3,1.2,0.6,2.9,0.4,1.3,3.5,3.3,0.2,0.1,1.1,2.8,2.1,2.6,3.7,1.0,0.3,1.5,1.2,0.6,0.7,1.4,1.2,2.0,0.2,2.3,0.5,1.9,1.0,1.1,0.7,0.9,0.5,0.3,3.4,1.0,2.8,1.2,1.1,0.5,1.1,1.6,4.5,0.4,11,22,9,9,12,19,15,14,11,6,3.0,24,15,6,14,65,81,7,33,34,12,35,17,41,46,43,54,25,26,45,16,8,5,29,2,12,44,36,2,1,8,26,19,23,27,9,2,13,6,6,5,8,14,15,3,19,5,14,7,9,5,8,4,2,30,11,22,9,10,4,8,10,34,3,0.047,0.027,0.012,0.016,0.041,0.031,0.026,0.031,0.041,0.034,0.031,0.036,0.029,0.018,0.028,0.038,0.029,0.028,0.038,0.035,0.033,0.031,0.03,0.044,0.032,0.03,0.03,0.027,0.027,0.035,0.042,0.05,0.028,0.032,0.014,0.025,0.046,0.038,0.018,0.013,0.022,0.033,0.039,0.026,0.028,0.02,0.016,0.022,0.016,0.015,0.029,0.016,0.025,0.019,0.016,0.016,0.021,0.02,0.023,0.021,0.03,0.025,0.037,0.016,0.028,0.026,0.022,0.02,0.022,0.019,0.028,0.028,0.018,0.02,1544,4692,2390,2294,1505,3270,2906,2781,1238,1382,465.0,2473,3273,683,1929,10230,16775,2604,5016,4310,2891,4604,2266,4189,5700,7293,7282,4097,5120,6084,1934,1171,1092,5192,1603,2737,4791,5868,394,441,1498,2680,3736,2956,3438,1583,738,2372,2542,1358,616,3265,1875,3545,545,3949,1282,3273,1271,1534,712,1393,613,615,4000,1154,4116,2237,1695,818,1488,1993,9513,444,0.147,0.124,0.089,0.097,0.143,0.143,0.11,0.13,0.126,0.135,0.129,0.146,0.132,0.117,0.124,0.138,0.128,0.102,0.134,0.134,0.104,0.148,0.137,0.145,0.143,0.135,0.135,0.126,0.123,0.15,0.14,0.142,0.113,0.13,0.072,0.109,0.139,0.143,0.097,0.091,0.114,0.138,0.137,0.121,0.123,0.115,0.098,0.1,0.093,0.094,0.131,0.09,0.113,0.096,0.115,0.1,0.098,0.104,0.12,0.117,0.137,0.107,0.151,0.106,0.135,0.116,0.111,0.105,0.116,0.105,0.123,0.139,0.1,0.111,443,1641,926,899,593,1253,1023,859,405,376,172.0,1228,855,226,624,3051,4919,474,1527,1905,871,1595,991,1170,1923,2273,2869,1586,1584,2216,515,461,380,1406,443,876,1397,1515,211,206,778,1309,918,1676,2178,814,294,1020,1113,640,394,1352,939,1788,280,2147,565,1619,715,816,366,601,240,293,1909,589,2027,997,862,407,696,881,3864,252,3.256,2.692,2.602,2.657,2.349,2.312,2.437,3.045,2.315,3.135,2.576,1.789,2.981,2.348,2.368,2.594,2.813,3.922,2.796,2.018,2.682,2.375,2.181,2.65,2.395,2.718,2.137,2.118,2.548,2.371,2.772,2.593,2.374,2.802,3.297,2.604,2.625,2.932,2.125,2.565,2.435,1.917,3.081,1.989,1.816,2.314,3.133,2.939,2.875,2.4,1.975,2.839,2.044,2.151,2.301,2.032,2.581,2.31,2.277,1.965,2.288,2.379,2.363,1.997,2.26,2.254,2.212,2.559,2.24,2.249,2.424,2.726,2.734,2.155,0.738,0.701,0.552,0.51,0.488,0.634,0.525,0.604,0.634,0.551,0.68,0.469,0.499,0.52,0.421,0.547,0.538,0.831,0.728,0.622,0.704,0.418,0.405,0.751,0.465,0.505,0.468,0.5,0.466,0.525,0.874,0.736,0.488,0.573,0.792,0.531,0.665,0.549,0.331,0.608,0.599,0.508,0.73,0.782,0.52,0.496,0.517,0.81,0.541,0.413,0.336,0.795,0.59,0.511,0.339,0.516,0.471,0.508,0.39,0.431,0.401,0.981,0.51,0.997,0.569,0.685,0.507,0.412,0.405,0.532,0.441,0.56,0.608,0.318
-205,Epileptic,F,27.0,0.6,1.6,1.4,1.7,1.8,1.7,3.0,1.4,1.4,0.9,0.7,4.3,1.5,0.7,1.6,9.5,14.2,1.7,2.8,5.2,1.9,2.6,1.6,4.4,3.1,4.7,4.6,3.4,8.1,3.6,1.7,1.1,0.7,2.8,0.7,0.9,4.8,2.6,0.2,0.3,1.7,3.0,2.4,4.2,3.5,0.9,0.3,1.0,1.5,0.9,0.7,2.8,1.6,3.0,0.4,3.0,0.9,2.9,1.0,1.3,0.4,1.6,0.2,0.7,2.8,1.1,2.9,1.8,2.3,0.8,0.9,1.3,4.0,0.3,4,8,9,9,14,17,16,12,12,9,4.0,41,13,6,17,81,116,9,23,45,15,23,17,40,28,39,42,24,52,37,13,6,9,26,5,5,42,26,1,1,10,28,18,19,16,4,1,4,6,4,3,14,10,14,1,16,6,20,7,7,2,13,1,4,16,11,21,9,9,4,5,6,23,2,0.048,0.026,0.031,0.032,0.048,0.045,0.048,0.038,0.057,0.06,0.087,0.058,0.038,0.057,0.037,0.062,0.058,0.071,0.055,0.054,0.064,0.058,0.048,0.059,0.045,0.049,0.047,0.049,0.067,0.048,0.061,0.052,0.048,0.072,0.061,0.028,0.063,0.056,0.017,0.024,0.035,0.045,0.072,0.037,0.029,0.023,0.027,0.024,0.027,0.026,0.038,0.027,0.032,0.026,0.029,0.028,0.029,0.046,0.031,0.032,0.024,0.036,0.027,0.044,0.034,0.035,0.03,0.028,0.042,0.036,0.027,0.045,0.03,0.025,1349,3831,2395,2598,2223,2400,2245,2477,2052,1416,579.0,3887,3668,1062,2896,11021,14488,2080,3933,5341,2308,3342,2293,5676,5359,6314,6675,2998,4583,6010,2113,827,1278,3751,782,1263,4630,4484,344,457,1641,3439,3705,3111,3294,1168,633,1652,1821,1063,618,3678,1849,4342,383,3256,1382,2546,1050,1397,697,2242,346,822,2747,1139,3262,2766,1831,1095,1260,1155,4975,392,0.111,0.105,0.117,0.122,0.15,0.149,0.14,0.122,0.167,0.171,0.163,0.168,0.125,0.17,0.133,0.165,0.158,0.182,0.155,0.162,0.152,0.168,0.136,0.163,0.16,0.143,0.147,0.136,0.165,0.153,0.179,0.13,0.156,0.185,0.143,0.115,0.157,0.165,0.087,0.104,0.133,0.158,0.175,0.126,0.114,0.106,0.104,0.093,0.103,0.117,0.142,0.111,0.126,0.102,0.121,0.119,0.118,0.137,0.122,0.122,0.124,0.117,0.146,0.134,0.122,0.129,0.118,0.102,0.14,0.141,0.115,0.141,0.119,0.117,284,1037,727,784,708,784,1021,755,413,337,191.0,1387,826,241,729,2997,4472,379,1094,2023,553,895,801,1425,1246,1842,2008,1246,1789,1613,515,314,337,909,200,537,1310,1029,175,202,737,1227,698,1810,1879,544,213,610,839,445,279,1628,808,1784,184,1629,601,1047,569,697,220,734,131,315,1366,646,1639,1136,838,368,563,459,2055,223,3.797,3.117,3.141,2.934,2.661,2.522,2.032,2.727,3.356,3.154,2.416,2.284,3.107,3.02,2.827,2.759,2.774,4.022,2.91,2.238,3.048,2.834,2.388,2.775,3.029,2.667,2.64,2.049,2.161,2.858,2.774,2.694,2.651,2.732,2.716,2.386,2.65,3.042,2.31,2.734,2.519,2.293,3.157,1.946,2.065,2.397,2.994,3.321,2.667,2.601,2.664,2.308,2.492,2.711,2.461,2.289,2.499,2.528,2.182,2.335,2.947,2.763,2.446,2.667,2.233,2.242,2.075,2.722,2.442,3.099,2.529,2.901,2.512,2.211,1.044,0.824,0.596,0.633,0.685,0.547,0.668,0.545,0.613,0.77,0.762,0.699,0.526,0.498,0.578,0.747,0.898,0.889,0.677,0.679,0.865,0.654,0.561,1.051,0.657,0.739,0.554,0.627,0.783,0.677,0.783,0.851,0.621,0.76,0.807,0.501,0.998,0.749,0.479,0.47,0.468,0.553,0.833,0.532,0.652,0.403,0.663,1.022,0.531,0.838,0.582,0.506,0.467,0.457,0.415,0.407,0.475,0.723,0.428,0.528,0.385,0.803,0.477,0.836,0.468,0.716,0.441,0.447,0.547,0.992,0.624,0.846,0.581,0.4
-206,Epileptic,F,42.0,0.8,2.3,1.0,1.2,1.1,2.4,1.4,1.9,0.8,0.9,0.3,2.3,1.2,0.4,1.1,6.5,6.8,0.8,2.4,5.9,0.5,3.3,1.7,2.7,2.5,4.0,2.9,2.0,2.6,3.9,1.5,1.1,0.7,2.4,0.2,0.6,2.5,2.3,0.1,0.1,1.1,3.4,1.9,3.7,3.0,0.7,0.5,1.5,1.0,0.4,0.8,1.5,1.4,2.6,0.4,2.4,0.6,1.8,0.8,1.6,0.4,0.9,0.4,0.3,1.2,1.5,2.7,1.1,1.4,0.4,1.6,1.0,3.6,0.4,10,19,9,8,15,20,19,17,10,8,4.0,17,10,4,10,56,58,7,23,37,4,27,20,26,29,33,35,19,21,37,14,8,8,25,2,4,27,27,1,1,8,32,20,22,17,5,3,9,7,5,5,11,8,20,2,22,5,14,6,12,3,7,2,3,9,16,23,8,10,3,13,8,32,3,0.05,0.024,0.016,0.025,0.033,0.032,0.019,0.03,0.031,0.032,0.02,0.032,0.033,0.033,0.036,0.036,0.027,0.032,0.029,0.041,0.012,0.037,0.034,0.033,0.03,0.036,0.025,0.022,0.025,0.032,0.042,0.041,0.019,0.029,0.012,0.013,0.031,0.033,0.014,0.018,0.026,0.039,0.033,0.043,0.022,0.014,0.026,0.02,0.014,0.016,0.039,0.024,0.019,0.022,0.036,0.021,0.023,0.02,0.023,0.027,0.016,0.02,0.024,0.017,0.018,0.026,0.021,0.017,0.018,0.018,0.031,0.021,0.02,0.014,1380,4107,2271,2158,1770,3170,3179,3387,1497,1503,560.0,2315,2573,753,2263,10493,14365,1811,4066,4889,2580,4586,2098,5187,4529,6111,4925,3426,5845,5728,1969,789,1392,4588,1146,1907,5247,4163,336,182,1652,3318,3399,2502,3568,1557,691,2227,2292,1087,479,2102,2296,5112,436,3501,1162,3064,1059,1816,669,1782,426,1015,1826,1233,4223,2157,2444,796,1892,1673,6605,707,0.135,0.109,0.1,0.111,0.129,0.145,0.1,0.136,0.131,0.131,0.098,0.124,0.137,0.15,0.122,0.133,0.12,0.128,0.123,0.143,0.063,0.148,0.152,0.125,0.135,0.146,0.125,0.096,0.111,0.134,0.136,0.125,0.113,0.13,0.078,0.082,0.134,0.133,0.098,0.116,0.124,0.146,0.124,0.149,0.107,0.095,0.123,0.094,0.09,0.103,0.16,0.106,0.104,0.107,0.141,0.118,0.11,0.1,0.113,0.133,0.105,0.11,0.113,0.111,0.103,0.139,0.108,0.097,0.107,0.107,0.139,0.113,0.106,0.089,430,1508,891,824,631,1203,1076,1092,442,442,217.0,1034,694,206,589,3079,4055,433,1296,2164,776,1441,918,1408,1430,1931,1932,1378,1593,1936,569,452,491,1238,314,706,1310,1152,169,88,762,1460,887,1519,2129,801,303,1033,1097,446,317,1057,1103,1991,153,1790,507,1561,569,883,335,714,263,349,1016,757,2114,981,1176,364,898,757,2940,383,3.281,2.556,2.46,2.591,2.389,2.47,2.444,2.801,2.583,2.775,2.342,1.99,2.815,2.743,2.693,2.565,2.798,3.303,2.559,1.993,2.605,2.51,2.043,2.785,2.549,2.66,2.184,2.04,2.835,2.403,2.594,1.849,2.289,2.685,3.142,2.622,2.997,2.705,2.108,2.345,2.799,1.942,2.761,2.025,1.918,2.161,2.826,2.701,2.603,2.755,2.025,2.231,2.334,2.386,3.105,2.134,2.55,2.386,2.189,2.404,2.494,2.52,2.095,3.165,2.016,1.976,2.091,2.342,2.464,2.413,2.47,2.569,2.415,2.298,0.672,0.718,0.638,0.45,0.633,0.59,0.614,0.578,0.472,0.44,0.546,0.5,0.552,0.275,0.603,0.588,0.591,0.829,0.678,0.652,0.688,0.528,0.409,0.671,0.479,0.58,0.493,0.511,0.528,0.508,0.584,0.745,0.474,0.618,0.507,0.45,0.7,0.588,0.334,0.477,0.514,0.588,0.783,0.719,0.614,0.503,0.645,0.673,0.538,0.708,0.357,0.494,0.701,0.496,0.296,0.443,0.521,0.51,0.49,0.389,0.385,0.664,0.404,0.925,0.376,0.625,0.579,0.493,0.488,0.718,0.408,0.604,0.496,0.384
-207,Epileptic,F,47.0,0.5,1.3,0.7,0.9,1.2,1.3,1.5,1.4,0.9,0.5,0.2,1.7,1.0,0.5,1.4,4.8,6.9,0.5,1.8,3.3,1.1,2.2,1.1,2.5,2.7,2.8,2.4,1.6,2.4,2.1,1.2,0.9,0.3,2.1,0.4,0.7,3.1,2.8,0.1,0.1,1.0,1.9,3.0,1.8,2.4,0.6,0.4,0.7,1.1,1.4,0.7,1.4,1.0,1.8,0.3,1.7,0.7,2.2,0.6,0.9,0.6,0.8,0.3,0.2,0.9,0.7,1.7,1.1,1.1,0.3,0.8,1.4,3.4,0.3,5,14,7,10,11,16,13,11,10,5,3.0,14,12,5,15,53,71,5,20,27,10,21,11,24,31,27,28,17,21,23,16,7,4,24,2,6,34,30,1,2,5,19,19,13,16,5,2,5,5,8,7,10,12,12,2,14,6,20,5,9,7,6,2,2,7,9,12,8,7,3,6,9,27,2,0.03,0.022,0.018,0.019,0.036,0.029,0.019,0.03,0.042,0.034,0.024,0.03,0.027,0.032,0.037,0.033,0.03,0.022,0.029,0.034,0.023,0.034,0.036,0.037,0.033,0.034,0.03,0.023,0.024,0.027,0.041,0.043,0.021,0.029,0.018,0.022,0.039,0.032,0.013,0.015,0.023,0.037,0.044,0.029,0.02,0.017,0.027,0.013,0.018,0.033,0.035,0.022,0.03,0.017,0.014,0.02,0.019,0.027,0.021,0.025,0.02,0.028,0.026,0.018,0.016,0.026,0.02,0.021,0.026,0.017,0.032,0.023,0.019,0.022,1198,2880,1652,1830,1800,2197,2996,2546,1263,849,466.0,1609,2499,663,2060,8000,14004,1939,3351,3105,2510,2990,1581,4102,4879,4976,3659,2966,5497,3681,1718,737,681,5225,1635,1559,5127,5134,395,389,1181,2677,4234,1447,2968,1095,555,2100,2008,1539,560,2105,1620,3180,555,2476,1108,2594,750,1006,1010,1404,402,658,1532,693,2556,1725,1319,796,984,1996,6915,483,0.122,0.113,0.104,0.103,0.132,0.146,0.095,0.131,0.162,0.134,0.11,0.138,0.124,0.143,0.155,0.145,0.138,0.11,0.135,0.135,0.085,0.16,0.146,0.141,0.145,0.142,0.139,0.113,0.113,0.14,0.16,0.107,0.112,0.132,0.076,0.106,0.142,0.129,0.098,0.1,0.111,0.154,0.136,0.127,0.106,0.1,0.112,0.079,0.094,0.123,0.15,0.107,0.128,0.096,0.099,0.11,0.1,0.126,0.112,0.127,0.112,0.122,0.124,0.103,0.099,0.123,0.108,0.111,0.118,0.121,0.136,0.119,0.099,0.105,323,994,682,818,587,826,978,786,357,248,127.0,844,743,226,635,2408,3895,363,1020,1556,810,948,564,1066,1427,1456,1350,1090,1480,1164,473,333,262,1140,369,502,1499,1401,160,189,656,933,1072,1027,1853,589,250,882,942,685,323,1076,682,1533,322,1371,592,1387,472,573,524,505,230,259,859,446,1334,832,690,329,423,941,3040,195,3.46,2.771,2.442,2.222,2.68,2.378,2.499,2.891,2.686,2.829,2.934,1.726,2.644,2.329,2.391,2.505,2.895,3.771,2.758,1.796,2.756,2.479,2.351,2.826,2.588,2.776,2.209,2.129,2.827,2.526,2.692,2.169,2.271,3.062,3.451,2.693,2.691,2.754,2.375,2.132,2.25,2.292,3.093,1.657,1.831,2.038,2.807,2.833,2.621,2.575,1.896,1.998,2.168,2.171,2.118,2.014,2.197,2.162,1.822,1.924,2.076,2.63,2.003,2.437,1.964,2.032,2.077,2.356,2.221,2.915,2.571,2.578,2.404,2.558,0.926,0.729,0.497,0.482,0.672,0.483,0.63,0.575,0.485,0.494,0.81,0.484,0.429,0.488,0.486,0.619,0.549,0.784,0.482,0.606,0.743,0.517,0.485,0.605,0.495,0.451,0.489,0.558,0.558,0.527,0.844,1.028,0.325,0.592,0.779,0.491,0.671,0.599,0.572,0.397,0.531,0.668,0.851,0.602,0.535,0.586,0.357,0.615,0.519,0.432,0.481,0.479,0.532,0.465,0.297,0.465,0.392,0.634,0.309,0.437,0.677,0.829,0.416,1.061,0.586,0.541,0.503,0.441,0.488,1.029,0.591,0.575,0.58,0.406
-208,Epileptic,F,17.5,1.1,2.8,1.0,1.0,1.9,1.1,1.5,1.7,1.6,0.8,0.3,2.5,1.4,0.3,1.1,6.5,9.1,1.0,2.1,4.6,1.3,3.2,2.4,3.0,3.1,3.3,2.8,2.6,2.2,3.3,2.0,0.9,0.4,2.1,0.4,0.6,3.5,4.4,0.1,0.1,1.0,3.3,1.9,2.2,2.4,0.6,0.4,0.7,1.2,1.0,0.6,1.6,1.2,2.5,0.2,2.4,0.9,1.1,1.8,1.2,0.9,2.0,0.3,0.5,1.5,1.0,2.9,1.2,0.9,0.6,1.2,1.1,4.4,0.3,9,26,10,11,19,13,17,16,17,10,4.0,26,19,5,15,77,101,7,27,50,13,45,24,33,40,39,37,33,24,47,19,6,3,29,2,4,41,58,1,1,6,33,25,19,16,6,2,4,6,10,4,14,8,19,1,23,9,12,14,10,7,17,2,5,14,18,24,11,7,5,11,7,38,2,0.039,0.029,0.02,0.02,0.035,0.02,0.027,0.025,0.051,0.035,0.029,0.037,0.026,0.026,0.029,0.035,0.031,0.038,0.026,0.035,0.028,0.033,0.032,0.037,0.028,0.028,0.029,0.027,0.024,0.032,0.052,0.037,0.024,0.027,0.011,0.013,0.036,0.035,0.014,0.021,0.019,0.038,0.034,0.021,0.017,0.015,0.021,0.011,0.017,0.016,0.023,0.015,0.017,0.019,0.017,0.015,0.04,0.016,0.033,0.018,0.024,0.027,0.027,0.016,0.018,0.022,0.022,0.019,0.018,0.025,0.025,0.017,0.018,0.019,1571,4759,2098,2173,2231,2437,2635,3330,2177,1685,538.0,2668,3955,972,2710,11949,19274,1993,4849,5137,3775,5703,2486,6050,7503,6851,6330,5015,6200,6530,2043,1028,799,5015,1792,1862,6562,9366,318,398,1571,2703,6189,2656,3687,1430,665,2019,2154,1729,603,3189,2023,4891,416,4557,1043,2696,1788,1790,1334,3160,397,879,2231,883,3999,2295,1340,1313,1852,2084,9384,364,0.132,0.124,0.117,0.105,0.137,0.112,0.107,0.119,0.18,0.15,0.127,0.142,0.129,0.133,0.143,0.145,0.137,0.132,0.136,0.144,0.121,0.136,0.122,0.129,0.126,0.13,0.118,0.121,0.106,0.145,0.149,0.104,0.098,0.123,0.063,0.082,0.131,0.154,0.09,0.099,0.1,0.127,0.144,0.111,0.098,0.092,0.103,0.077,0.094,0.105,0.127,0.098,0.104,0.101,0.112,0.101,0.144,0.103,0.141,0.107,0.119,0.124,0.123,0.109,0.11,0.131,0.118,0.103,0.107,0.125,0.128,0.1,0.1,0.132,419,1578,816,821,921,934,889,1182,566,447,175.0,1156,995,213,645,3060,5116,398,1332,2197,809,1634,1052,1559,1816,1876,1814,1529,1508,1850,629,382,248,1253,483,637,1720,2046,167,143,789,1280,1218,1633,2094,744,284,884,1006,810,362,1531,1033,2142,182,2420,452,1233,913,1022,547,1171,196,473,1199,640,1961,1023,718,461,810,1004,3956,183,3.41,2.661,2.504,2.543,2.024,2.198,2.4,2.523,2.669,2.88,2.587,1.941,2.966,3.026,2.864,2.744,2.845,3.745,2.816,2.05,3.5,2.548,2.079,2.795,2.936,2.843,2.554,2.404,2.956,2.662,2.359,2.615,2.426,2.901,3.252,2.631,2.805,3.113,2.151,2.886,2.585,1.829,3.247,1.843,1.946,2.073,2.725,2.745,2.629,2.436,2.015,2.11,2.004,2.279,2.873,2.057,2.35,2.367,1.983,1.934,2.457,2.485,2.113,2.083,2.044,1.859,2.234,2.426,2.178,2.579,2.316,2.483,2.531,2.537,0.674,0.647,0.5,0.452,0.714,0.557,0.603,0.531,0.719,0.724,0.69,0.54,0.54,0.421,0.588,0.655,0.656,0.62,0.55,0.626,0.737,0.646,0.548,0.846,0.63,0.54,0.612,0.59,0.53,0.582,0.716,0.839,0.356,0.6,0.67,0.441,0.679,0.718,0.323,0.623,0.484,0.551,0.82,0.565,0.6,0.53,0.552,0.725,0.405,0.38,0.498,0.47,0.489,0.499,0.477,0.469,0.417,0.507,0.494,0.406,0.473,0.765,0.322,0.763,0.618,0.539,0.525,0.449,0.405,0.666,0.65,0.519,0.464,0.42
-209,Epileptic,M,22.0,0.8,3.2,1.1,1.5,1.7,2.3,1.8,2.0,0.8,0.8,0.2,2.4,1.5,0.6,0.9,9.3,10.3,0.9,2.6,3.4,1.4,2.6,1.2,4.0,2.8,3.9,2.9,3.4,3.4,2.5,1.2,1.3,0.7,2.3,0.5,0.6,3.9,2.6,0.2,0.1,1.3,2.8,2.6,2.2,4.0,0.8,0.6,1.0,1.2,0.7,0.8,2.0,1.3,3.4,0.7,2.8,0.7,0.9,0.9,0.9,0.8,1.3,0.3,0.3,1.6,1.4,3.0,1.4,1.7,1.1,1.1,1.3,3.1,0.3,6,22,7,9,12,20,11,16,8,6,2.0,17,11,7,8,70,75,7,23,36,10,25,12,34,31,35,30,26,21,29,16,11,6,22,3,5,42,25,1,1,6,23,18,14,19,6,3,6,6,5,6,10,7,20,4,19,5,9,7,8,8,9,2,4,12,14,21,8,8,13,6,9,22,3,0.039,0.028,0.017,0.026,0.043,0.029,0.028,0.038,0.034,0.038,0.033,0.03,0.032,0.037,0.031,0.048,0.034,0.032,0.032,0.032,0.027,0.035,0.024,0.037,0.029,0.034,0.027,0.041,0.034,0.023,0.037,0.046,0.04,0.029,0.019,0.017,0.033,0.03,0.013,0.02,0.023,0.032,0.041,0.027,0.028,0.016,0.027,0.015,0.016,0.014,0.038,0.023,0.023,0.024,0.026,0.017,0.028,0.014,0.023,0.018,0.024,0.027,0.019,0.016,0.019,0.026,0.023,0.02,0.024,0.029,0.03,0.021,0.013,0.021,1347,5741,2890,2191,1844,3609,2567,3363,1404,1418,567.0,2077,3254,1283,2111,10755,17507,2585,4536,3695,3898,4466,1776,6135,6033,6970,5645,3385,5468,5566,1885,1437,1146,6314,2095,1705,8478,5460,425,369,1580,3044,5976,2431,3382,1314,907,2614,2294,1828,477,2819,2047,6154,851,4461,1090,2591,1060,1430,1077,2206,454,969,2472,1586,4028,2301,2112,1631,1215,2279,7890,448,0.121,0.119,0.093,0.114,0.129,0.13,0.099,0.133,0.126,0.142,0.111,0.129,0.118,0.144,0.121,0.137,0.124,0.114,0.131,0.13,0.103,0.132,0.108,0.136,0.119,0.12,0.11,0.116,0.106,0.121,0.138,0.129,0.118,0.119,0.081,0.086,0.127,0.126,0.085,0.091,0.111,0.134,0.135,0.118,0.117,0.095,0.119,0.087,0.086,0.088,0.155,0.102,0.106,0.105,0.144,0.099,0.115,0.088,0.123,0.106,0.123,0.117,0.106,0.109,0.106,0.116,0.106,0.099,0.107,0.141,0.119,0.107,0.082,0.115,402,1812,961,883,730,1303,902,981,444,364,153.0,1059,844,315,565,3312,4898,491,1351,1696,888,1287,783,1708,1760,2106,1899,1302,1489,1783,523,560,319,1259,495,632,1986,1464,242,153,817,1308,1128,1329,2106,738,379,1021,1103,720,336,1328,918,2290,368,2430,508,1204,609,828,574,814,268,374,1268,812,2123,1082,1063,618,636,1078,3597,247,3.44,2.843,2.791,2.573,2.279,2.426,2.425,2.918,2.438,3.025,2.938,1.754,2.919,2.872,2.704,2.618,2.938,3.621,2.781,1.932,3.389,2.686,2.002,2.727,2.692,2.765,2.336,2.13,2.727,2.491,2.713,2.658,2.681,3.346,3.723,2.384,3.02,2.789,1.976,2.636,2.449,2.009,3.483,2.165,1.861,2.055,3.006,3.113,2.528,2.583,1.644,2.184,2.461,2.704,2.916,2.05,2.459,2.419,2.084,1.96,2.202,2.478,2.005,2.625,2.173,2.33,2.065,2.476,2.41,2.543,2.331,2.482,2.335,2.31,0.634,0.693,0.616,0.507,0.522,0.614,0.622,0.575,0.543,0.435,0.65,0.427,0.486,0.465,0.575,0.642,0.663,0.773,0.635,0.522,0.775,0.548,0.501,0.662,0.581,0.524,0.483,0.519,0.544,0.465,0.697,1.068,0.616,0.787,0.629,0.62,0.706,0.644,0.313,0.642,0.563,0.63,0.83,0.817,0.58,0.44,0.68,0.69,0.641,0.688,0.353,0.492,0.625,0.594,0.526,0.513,0.635,0.581,0.387,0.397,0.392,0.812,0.275,1.038,0.607,0.587,0.518,0.387,0.422,0.542,0.478,0.491,0.625,0.545
-210,Epileptic,F,47.0,1.0,3.3,1.2,1.1,2.7,1.7,2.9,1.8,1.7,0.7,0.3,2.0,1.6,0.6,1.9,6.2,11.5,0.9,2.3,4.1,0.6,3.0,1.6,4.2,3.1,4.2,5.1,3.2,3.5,3.6,2.3,1.3,0.5,2.3,0.4,0.9,4.4,3.2,0.3,0.2,1.0,3.4,2.9,2.2,3.5,0.9,0.6,0.7,1.4,0.8,0.4,2.3,2.0,2.6,0.4,2.6,0.8,1.7,0.7,0.7,0.5,1.6,0.6,0.5,2.1,1.1,3.5,1.4,1.6,0.7,0.9,1.5,3.4,0.4,6,38,11,9,21,20,21,16,19,8,4.0,22,16,7,18,63,90,7,29,39,7,39,19,38,45,52,52,31,23,44,18,16,6,26,2,9,51,36,2,1,6,30,27,16,23,8,3,5,9,8,5,15,13,15,3,21,9,17,6,6,3,12,4,5,17,18,29,8,9,8,7,13,32,2,0.038,0.035,0.024,0.02,0.045,0.034,0.044,0.032,0.048,0.034,0.025,0.033,0.034,0.039,0.038,0.039,0.037,0.039,0.031,0.038,0.018,0.059,0.041,0.042,0.037,0.03,0.039,0.036,0.035,0.034,0.05,0.057,0.022,0.026,0.011,0.021,0.039,0.034,0.019,0.021,0.02,0.054,0.043,0.025,0.028,0.02,0.019,0.011,0.02,0.018,0.03,0.028,0.034,0.023,0.018,0.018,0.023,0.02,0.044,0.016,0.016,0.029,0.027,0.02,0.024,0.018,0.024,0.021,0.024,0.023,0.022,0.022,0.017,0.02,1737,4153,2211,2390,2409,2895,3445,3018,2051,1524,755.0,2532,3187,939,3192,9009,19752,2200,4339,4587,2601,2873,1849,5812,5530,8252,7347,3933,5697,6360,2376,917,1062,5652,1685,1864,7255,6680,363,334,1612,2385,5658,2316,3302,1594,923,2061,2439,1426,593,3258,1986,3938,502,4070,1374,2693,482,1070,757,2149,482,796,3238,1315,4881,2048,2327,750,1318,2661,7706,482,0.122,0.144,0.124,0.101,0.16,0.141,0.129,0.135,0.175,0.147,0.112,0.143,0.134,0.148,0.144,0.146,0.137,0.133,0.14,0.139,0.077,0.173,0.159,0.149,0.146,0.132,0.124,0.118,0.115,0.142,0.143,0.143,0.093,0.122,0.062,0.115,0.154,0.151,0.118,0.108,0.106,0.151,0.158,0.113,0.115,0.109,0.105,0.075,0.103,0.103,0.138,0.117,0.15,0.107,0.122,0.099,0.129,0.116,0.152,0.104,0.104,0.128,0.135,0.117,0.118,0.111,0.119,0.103,0.109,0.14,0.122,0.111,0.096,0.098,452,1593,776,901,962,948,1231,977,648,390,221.0,1041,890,279,945,2920,5475,471,1200,1913,709,932,688,1648,1564,2670,2215,1525,1459,2016,710,398,380,1373,500,643,1949,1641,186,165,779,1089,1259,1412,2004,771,392,976,1136,687,274,1409,874,1808,280,2214,643,1400,313,539,372,905,310,414,1420,869,2328,964,1009,442,661,1237,3324,286,3.369,2.407,2.619,2.508,2.019,2.433,2.31,2.707,2.264,2.958,2.677,1.984,2.802,2.499,2.549,2.398,2.837,3.576,2.81,2.012,2.664,2.294,2.157,2.692,2.648,2.565,2.508,2.114,2.89,2.473,2.388,2.54,2.225,2.928,2.982,2.559,2.797,2.907,2.206,2.208,2.591,1.842,3.122,1.855,1.884,2.145,2.772,2.554,2.542,2.314,2.134,2.237,2.083,2.304,2.232,2.064,2.387,2.223,1.672,2.133,2.606,2.202,1.769,2.258,2.327,1.865,2.139,2.452,2.462,1.854,2.231,2.441,2.401,2.018,0.824,0.554,0.67,0.499,0.736,0.761,0.621,0.439,0.731,0.735,0.951,0.572,0.41,0.42,0.574,0.613,0.697,0.871,0.535,0.606,0.693,0.651,0.734,0.664,0.621,0.536,0.683,0.602,0.569,0.601,0.74,0.669,0.373,0.634,0.668,0.503,0.714,0.7,0.432,0.427,0.571,0.573,0.775,0.606,0.595,0.469,0.493,0.678,0.503,0.354,0.404,0.475,0.551,0.509,0.234,0.387,0.471,0.475,0.494,0.539,0.645,0.628,0.337,0.75,0.526,0.584,0.503,0.394,0.436,0.501,0.437,0.475,0.441,0.323
-211,Epileptic,F,42.0,0.8,2.0,1.2,1.0,1.8,2.2,1.6,1.8,0.8,0.6,0.4,2.5,1.9,0.4,1.4,6.0,8.3,0.9,2.8,4.6,1.3,2.3,1.4,3.4,3.4,3.1,3.6,2.7,2.8,3.3,1.0,0.7,0.4,3.2,0.3,1.1,2.8,2.6,0.2,0.3,0.9,2.9,2.2,3.0,4.3,0.7,0.5,1.1,1.2,0.7,0.7,2.4,1.5,2.4,0.4,2.9,0.8,1.3,1.3,0.7,0.6,1.6,0.5,0.4,1.6,1.3,2.4,1.0,1.5,0.3,1.0,1.1,5.2,0.3,6,20,9,8,17,19,12,18,13,5,3.0,22,16,5,15,62,68,6,26,42,13,27,13,37,37,31,34,21,20,36,17,7,4,35,4,8,29,30,2,1,5,23,23,20,22,5,2,7,6,8,7,19,11,18,3,26,7,9,10,5,6,12,3,4,13,18,17,7,10,4,7,10,41,3,0.038,0.022,0.024,0.02,0.038,0.034,0.025,0.027,0.052,0.027,0.038,0.034,0.031,0.024,0.028,0.03,0.031,0.037,0.032,0.033,0.025,0.031,0.028,0.04,0.026,0.031,0.027,0.03,0.028,0.033,0.033,0.037,0.022,0.031,0.017,0.023,0.034,0.029,0.013,0.026,0.02,0.038,0.043,0.03,0.03,0.015,0.026,0.019,0.019,0.018,0.025,0.022,0.024,0.018,0.014,0.023,0.033,0.017,0.026,0.018,0.027,0.028,0.031,0.02,0.025,0.033,0.02,0.015,0.023,0.013,0.024,0.019,0.021,0.024,1194,4165,2216,1967,2002,2845,2472,2787,1156,1083,431.0,2280,3280,870,2453,10272,15068,1996,4496,4599,3138,3664,1733,5412,6000,5154,5364,3652,4683,4822,2162,922,720,5172,1488,1968,4894,5728,409,441,1284,2738,4757,2250,3071,1223,652,1628,1730,1368,827,3565,1676,4592,558,3609,952,2415,1096,993,922,2188,499,846,2181,933,3032,1819,1794,700,1335,1549,8857,355,0.129,0.115,0.112,0.096,0.153,0.138,0.106,0.133,0.168,0.123,0.147,0.137,0.14,0.128,0.131,0.137,0.132,0.136,0.137,0.131,0.106,0.138,0.13,0.145,0.125,0.126,0.119,0.114,0.11,0.14,0.135,0.109,0.126,0.131,0.085,0.109,0.133,0.13,0.084,0.104,0.108,0.144,0.139,0.124,0.127,0.1,0.122,0.101,0.097,0.115,0.129,0.112,0.116,0.1,0.11,0.118,0.15,0.091,0.126,0.097,0.121,0.128,0.127,0.12,0.114,0.135,0.112,0.098,0.113,0.111,0.122,0.118,0.106,0.144,353,1503,802,817,771,1116,978,1082,328,327,170.0,1133,994,249,787,3320,4413,390,1393,2148,818,1292,762,1518,1988,1725,2058,1376,1471,1762,576,412,298,1545,396,732,1358,1524,270,227,682,1262,1019,1520,2094,689,291,785,915,585,491,1744,953,2095,323,2085,427,1267,738,627,443,862,261,385,1136,580,1749,909,967,397,704,849,3991,182,3.192,2.589,2.676,2.475,2.165,2.262,2.208,2.381,2.543,2.707,2.343,1.739,2.668,2.62,2.399,2.479,2.767,3.456,2.723,1.865,2.969,2.313,1.907,2.751,2.372,2.468,2.133,2.13,2.476,2.268,2.822,2.196,2.1,2.539,3.048,2.466,2.804,2.768,1.758,2.288,2.281,1.885,3.1,1.722,1.728,1.954,2.628,2.691,2.217,2.387,1.906,2.093,1.858,2.208,2.121,1.945,2.467,2.206,1.712,1.755,2.021,2.467,2.172,2.197,2.111,1.949,1.933,2.256,2.12,1.915,2.222,2.112,2.372,2.452,0.565,0.558,0.517,0.432,0.694,0.506,0.454,0.554,0.54,0.293,0.653,0.403,0.393,0.455,0.43,0.483,0.509,0.889,0.652,0.516,0.715,0.443,0.503,0.52,0.434,0.508,0.527,0.478,0.51,0.471,0.649,0.793,0.426,0.567,0.932,0.5,0.628,0.485,0.378,0.35,0.551,0.605,0.83,0.53,0.433,0.417,0.305,0.676,0.496,0.768,0.423,0.464,0.47,0.451,0.374,0.418,0.59,0.481,0.263,0.337,0.338,0.698,0.466,0.882,0.492,0.605,0.387,0.351,0.398,0.422,0.485,0.484,0.476,0.333
-213,Epileptic,F,37.0,0.8,2.0,0.9,1.0,1.0,2.8,1.2,1.5,1.1,0.5,0.2,3.3,1.3,0.7,1.4,4.4,8.6,0.6,2.1,3.7,2.0,2.3,1.6,2.5,2.6,2.0,2.9,1.9,1.8,2.1,1.4,0.4,0.2,1.9,0.2,0.7,2.6,2.6,0.2,0.1,0.8,3.0,2.7,3.8,1.9,0.5,0.3,0.5,1.2,0.5,0.3,1.2,0.9,1.8,0.3,2.1,0.7,1.1,0.8,1.0,0.3,0.9,0.4,0.3,2.9,1.2,2.0,0.8,1.1,0.5,0.6,1.5,3.5,0.2,5,18,11,8,12,24,11,14,12,5,2.0,28,14,7,14,49,82,5,26,33,13,27,16,26,34,21,34,21,18,29,20,4,3,22,1,6,35,25,2,1,5,27,26,23,14,4,1,4,5,5,4,9,4,10,2,21,6,9,6,7,5,7,2,3,20,14,18,6,6,4,4,11,31,2,0.038,0.028,0.018,0.021,0.049,0.04,0.021,0.033,0.05,0.031,0.023,0.039,0.033,0.053,0.039,0.035,0.031,0.03,0.034,0.036,0.037,0.036,0.033,0.038,0.04,0.032,0.033,0.026,0.022,0.029,0.042,0.031,0.02,0.027,0.01,0.023,0.041,0.035,0.019,0.02,0.019,0.038,0.042,0.031,0.02,0.01,0.019,0.011,0.017,0.013,0.033,0.017,0.026,0.022,0.035,0.024,0.019,0.019,0.023,0.025,0.032,0.025,0.018,0.024,0.034,0.027,0.023,0.015,0.017,0.017,0.021,0.024,0.019,0.015,1351,3483,1818,1941,1451,2963,2241,2135,1646,922,690.0,2675,2736,976,2446,8662,15267,2029,3645,3929,2521,3366,1748,4196,4374,4248,4212,2919,3838,3778,1772,781,668,4222,1417,1515,4831,5067,370,279,1172,2581,2911,2786,2294,1095,589,1553,2122,1268,409,2064,1212,3323,322,2780,1115,2073,1024,1091,602,1394,519,758,2877,1116,2703,1706,1589,1006,791,1930,7081,386,0.121,0.12,0.114,0.109,0.141,0.158,0.106,0.143,0.172,0.13,0.105,0.152,0.142,0.154,0.127,0.14,0.139,0.106,0.139,0.149,0.124,0.148,0.132,0.14,0.156,0.138,0.135,0.121,0.11,0.13,0.156,0.096,0.108,0.121,0.055,0.107,0.131,0.129,0.119,0.107,0.105,0.156,0.129,0.128,0.103,0.082,0.093,0.078,0.09,0.099,0.132,0.1,0.118,0.101,0.161,0.128,0.111,0.108,0.12,0.134,0.14,0.124,0.124,0.127,0.137,0.135,0.119,0.094,0.107,0.118,0.118,0.126,0.1,0.107,362,1160,756,753,457,1137,880,792,384,258,183.0,1327,766,232,643,2301,4394,347,1048,1664,767,1167,808,1170,1246,1203,1597,1110,1220,1301,556,254,244,1020,391,488,1278,1248,162,124,588,1218,1025,1698,1602,663,242,751,1004,579,247,1073,476,1387,138,1362,520,1009,579,548,251,546,269,251,1357,649,1405,831,806,462,399,940,2998,184,3.42,2.766,2.414,2.534,2.594,2.448,2.181,2.59,2.969,3.159,2.968,1.874,2.738,2.726,2.65,2.779,2.78,3.734,2.748,1.988,2.811,2.382,1.954,2.655,2.7,2.793,2.212,2.093,2.45,2.274,2.479,2.655,2.331,2.915,3.096,2.716,2.799,2.857,2.239,2.471,2.521,1.927,2.32,1.886,1.628,1.795,2.849,2.489,2.536,2.4,1.972,2.143,2.64,2.542,2.639,2.309,2.542,2.364,2.114,2.188,2.307,2.635,2.113,2.967,2.179,2.145,2.056,2.383,2.468,2.552,2.212,2.505,2.472,2.584,0.854,0.606,0.654,0.457,0.757,0.513,0.473,0.584,0.55,0.557,0.731,0.504,0.427,0.763,0.588,0.608,0.504,0.926,0.494,0.596,0.714,0.481,0.426,0.596,0.441,0.474,0.502,0.529,0.478,0.538,0.656,1.165,0.394,0.593,0.617,0.61,0.653,0.563,0.285,0.447,0.454,0.504,0.923,0.689,0.493,0.423,0.75,0.729,0.568,0.385,0.411,0.483,0.728,0.479,0.623,0.483,0.452,0.525,0.443,0.573,0.362,0.592,0.331,0.707,0.487,0.536,0.391,0.385,0.436,0.513,0.705,0.517,0.479,0.28
-214,Epileptic,M,22.0,0.9,2.7,0.6,1.1,1.3,1.8,1.5,2.0,1.4,0.7,0.5,3.0,2.0,0.6,1.3,7.1,8.6,1.1,2.5,6.0,0.6,2.8,2.5,3.1,3.0,4.0,4.4,2.3,3.1,3.9,0.9,0.6,0.6,3.0,0.6,0.4,4.6,4.8,0.2,0.2,1.3,2.4,2.3,4.3,2.5,0.6,0.5,0.9,1.1,1.1,0.7,2.7,1.5,2.1,0.1,2.7,0.7,1.3,1.0,1.1,0.4,1.6,0.5,0.5,2.2,0.8,3.9,1.4,0.9,0.5,1.3,2.3,4.0,0.4,8,24,7,8,11,17,12,15,14,7,4.0,26,19,7,14,68,73,8,25,45,5,40,22,33,34,42,43,22,23,42,12,5,6,37,3,2,51,43,1,1,6,23,17,27,16,5,2,7,6,8,4,19,9,15,1,25,6,11,7,8,3,10,4,4,18,9,28,10,5,5,8,13,28,3,0.041,0.025,0.013,0.02,0.031,0.035,0.022,0.033,0.049,0.035,0.042,0.037,0.034,0.029,0.029,0.035,0.031,0.036,0.031,0.042,0.018,0.031,0.034,0.04,0.033,0.033,0.03,0.028,0.028,0.034,0.03,0.03,0.029,0.035,0.017,0.01,0.041,0.039,0.017,0.024,0.027,0.034,0.037,0.033,0.022,0.014,0.019,0.015,0.015,0.03,0.036,0.025,0.02,0.019,0.02,0.02,0.024,0.017,0.025,0.016,0.014,0.028,0.026,0.018,0.023,0.036,0.025,0.024,0.019,0.023,0.024,0.032,0.018,0.023,1333,4528,1498,2124,1938,2519,2693,3086,1815,1398,482.0,2583,4280,1430,2608,12279,16860,2687,4082,4858,2164,4991,2678,5337,5836,6653,6471,3256,5687,6449,1860,1033,1027,7209,2365,1394,5829,8233,303,402,1497,2863,4948,3340,2827,1247,843,2216,2118,1473,484,4454,2214,4605,147,3795,1293,2596,1129,1692,848,2375,531,745,3271,582,4781,2361,1483,1040,1771,2484,9222,545,0.134,0.125,0.094,0.101,0.122,0.139,0.102,0.137,0.16,0.118,0.171,0.138,0.136,0.144,0.142,0.141,0.129,0.124,0.13,0.146,0.081,0.145,0.133,0.14,0.136,0.144,0.133,0.109,0.113,0.146,0.117,0.096,0.123,0.136,0.071,0.066,0.135,0.142,0.096,0.111,0.119,0.142,0.141,0.136,0.108,0.094,0.099,0.088,0.09,0.135,0.143,0.116,0.109,0.095,0.153,0.113,0.115,0.098,0.12,0.099,0.092,0.125,0.129,0.119,0.117,0.135,0.123,0.111,0.105,0.121,0.122,0.134,0.094,0.121,423,1637,703,820,707,916,989,1072,539,363,177.0,1262,1091,335,790,3440,4737,520,1231,2235,569,1513,1241,1489,1750,2084,2375,1270,1619,2070,568,408,375,1473,600,533,1819,2037,186,171,695,1213,1097,1951,1861,654,355,919,1051,627,309,1795,1058,1956,59,2114,540,1243,635,978,423,849,289,354,1463,323,2417,1057,714,421,844,1158,3712,301,3.018,2.539,1.955,2.65,2.563,2.43,2.348,2.558,2.765,3.171,2.378,1.828,3.045,2.95,2.659,2.774,2.973,3.8,2.735,1.926,2.989,2.61,2.007,2.75,2.546,2.693,2.274,2.073,2.728,2.597,2.533,2.352,2.245,3.191,3.203,2.372,2.538,3.054,1.907,2.995,2.931,2.186,3.423,1.984,1.784,2.141,2.967,2.997,2.542,3.003,2.021,2.583,2.291,2.347,2.553,2.018,2.873,2.424,2.144,1.957,2.279,2.979,2.162,2.511,2.303,2.21,2.11,2.61,2.617,2.649,2.416,2.61,2.701,2.408,1.058,0.609,0.597,0.563,0.491,0.432,0.52,0.607,0.481,0.462,0.766,0.453,0.527,0.452,0.443,0.504,0.567,0.796,0.516,0.53,0.736,0.595,0.367,0.704,0.622,0.51,0.474,0.5,0.576,0.485,0.728,0.918,0.465,0.693,0.743,0.444,0.686,0.558,0.299,0.479,0.585,0.541,0.773,0.713,0.559,0.4,0.484,0.759,0.491,0.699,0.414,0.504,0.533,0.493,0.385,0.391,0.413,0.476,0.372,0.366,0.424,0.736,0.337,1.058,0.728,0.53,0.452,0.413,0.505,0.615,0.491,0.568,0.592,0.444
-215,Epileptic,F,37.0,0.8,3.1,1.0,1.4,2.0,1.7,2.3,2.3,1.4,0.7,0.4,4.7,1.6,0.9,1.6,8.0,11.6,1.1,2.4,5.0,2.0,3.4,2.3,4.8,3.9,5.0,4.9,3.9,4.0,3.8,3.0,2.1,0.4,2.8,0.6,0.8,5.1,4.1,0.1,0.2,1.2,2.8,4.7,3.2,3.6,0.7,0.5,1.0,1.4,1.2,0.7,2.6,1.1,2.9,0.3,3.5,0.6,1.7,0.8,1.5,0.5,1.7,0.3,0.6,2.7,1.3,3.2,1.6,1.2,0.5,0.8,1.7,4.7,0.3,5,29,9,15,20,17,15,19,15,6,5.0,39,22,12,63,83,89,7,26,36,12,36,23,53,41,56,48,32,34,41,23,17,5,32,3,9,53,49,1,2,9,24,36,20,20,5,2,7,8,8,5,20,6,20,2,28,4,10,6,11,4,10,3,5,23,17,24,11,6,4,6,12,37,2,0.038,0.035,0.021,0.027,0.054,0.037,0.041,0.037,0.058,0.034,0.032,0.052,0.031,0.054,0.07,0.049,0.047,0.044,0.037,0.05,0.051,0.043,0.035,0.063,0.041,0.043,0.042,0.042,0.04,0.043,0.055,0.071,0.03,0.042,0.028,0.019,0.051,0.041,0.027,0.022,0.024,0.044,0.071,0.03,0.026,0.015,0.022,0.016,0.021,0.027,0.037,0.026,0.028,0.031,0.018,0.026,0.019,0.019,0.026,0.026,0.023,0.029,0.03,0.022,0.024,0.024,0.027,0.023,0.021,0.017,0.026,0.022,0.02,0.02,1848,5337,2662,2773,2532,2515,3674,3689,2054,1400,979.0,3696,3367,1375,2613,10425,19333,2708,4929,4367,2837,4493,2727,6496,5921,7532,6709,4602,7129,5470,2958,1262,962,6218,1422,2012,7842,7076,278,453,1560,2240,5859,3169,3884,1264,782,2259,2227,1502,542,3894,1286,3946,520,3956,1348,2873,790,1753,847,2558,523,879,3750,1458,3760,2353,1702,822,1268,2147,8875,505,0.116,0.147,0.109,0.122,0.168,0.146,0.132,0.148,0.189,0.144,0.129,0.173,0.143,0.187,0.182,0.169,0.156,0.138,0.146,0.171,0.128,0.142,0.136,0.181,0.141,0.158,0.143,0.129,0.136,0.153,0.161,0.14,0.138,0.154,0.105,0.102,0.154,0.16,0.107,0.108,0.12,0.147,0.189,0.124,0.114,0.094,0.104,0.098,0.107,0.109,0.148,0.115,0.119,0.131,0.115,0.126,0.101,0.099,0.121,0.128,0.108,0.121,0.122,0.111,0.121,0.124,0.114,0.111,0.109,0.11,0.134,0.116,0.103,0.12,376,1456,790,975,717,876,1069,1114,482,328,231.0,1653,982,337,729,2951,4775,433,1259,1809,696,1378,1159,1726,1746,2174,2217,1682,1908,1724,877,460,273,1338,358,790,1992,1734,133,232,723,1111,1385,1783,2078,677,344,849,968,700,313,1700,647,1619,244,2036,526,1339,510,883,410,874,239,486,1781,825,1825,1098,806,390,542,1128,3563,208,3.892,2.867,2.834,2.675,2.607,2.392,2.7,2.92,2.851,3.051,3.097,1.996,2.578,2.811,2.7,2.643,2.901,4.119,3.001,2.082,2.87,2.422,2.049,2.6,2.579,2.745,2.361,2.153,2.803,2.335,2.435,2.448,2.995,3.106,3.3,2.403,2.806,2.784,2.481,2.177,2.785,1.79,2.902,1.992,2.102,2.194,2.72,3.327,2.862,2.418,2.181,2.2,2.131,2.404,2.271,2.144,2.596,2.421,1.82,2.154,2.17,2.599,2.251,2.069,2.219,2.167,2.19,2.445,2.426,2.349,2.593,2.377,2.503,2.874,0.988,0.871,0.827,0.526,0.809,0.595,0.659,0.618,0.751,0.644,0.812,0.561,0.482,0.605,0.622,0.666,0.772,0.864,0.583,0.75,0.818,0.651,0.642,0.809,0.627,0.518,0.675,0.621,0.606,0.699,0.871,1.054,0.462,0.742,0.602,0.45,0.865,0.73,0.302,0.52,0.607,0.521,0.972,0.651,0.651,0.451,0.496,0.743,0.565,0.476,0.559,0.496,0.495,0.469,0.432,0.404,0.705,0.506,0.315,0.441,0.418,0.856,0.707,0.603,0.588,0.623,0.448,0.396,0.432,0.546,0.592,0.47,0.55,0.448
-216,Epileptic,F,22.0,0.7,2.4,1.5,1.3,1.3,1.7,1.6,1.6,1.4,0.6,0.3,2.1,1.8,0.3,1.5,5.5,7.3,0.9,2.7,3.1,1.4,1.8,1.7,3.1,3.5,2.9,2.5,1.7,2.2,2.9,1.0,1.3,0.7,2.8,0.4,1.0,2.9,3.4,0.2,0.2,0.9,2.3,3.1,4.2,2.7,0.7,0.4,1.2,1.2,0.7,0.6,1.7,1.2,2.1,0.7,2.2,0.7,1.5,0.6,1.0,0.6,1.3,0.3,0.7,1.3,1.6,1.6,1.3,1.0,0.5,1.0,1.2,5.0,0.3,6,22,14,10,18,15,16,12,15,6,2.0,16,18,5,14,55,67,8,26,27,13,21,15,31,39,27,29,17,22,31,15,10,6,34,2,7,27,39,2,1,5,19,27,25,15,5,2,9,6,4,4,16,10,15,3,17,7,9,4,8,3,10,2,3,8,18,11,7,6,9,6,11,38,3,0.03,0.023,0.02,0.022,0.031,0.032,0.021,0.026,0.044,0.032,0.025,0.029,0.034,0.03,0.035,0.031,0.026,0.029,0.032,0.032,0.019,0.033,0.03,0.036,0.034,0.025,0.025,0.021,0.022,0.031,0.028,0.04,0.025,0.029,0.011,0.021,0.031,0.03,0.02,0.019,0.018,0.031,0.042,0.042,0.021,0.014,0.017,0.018,0.014,0.016,0.026,0.019,0.023,0.019,0.016,0.017,0.023,0.018,0.023,0.02,0.031,0.026,0.024,0.025,0.019,0.026,0.018,0.019,0.015,0.017,0.028,0.026,0.019,0.012,1653,5164,2835,2392,1599,2363,3103,3141,1792,1358,509.0,2224,3272,719,3227,9702,17154,2337,4747,3298,3639,3155,2088,5172,6944,7245,5014,3704,5665,5894,1393,1311,1561,6475,1976,2649,5674,7165,436,404,2007,2322,4443,2104,3823,1576,833,2566,2502,1651,543,3334,2075,4362,1442,4170,1550,3333,741,1316,671,2211,480,718,2325,1715,2887,2520,1932,939,1492,2159,9645,694,0.106,0.111,0.11,0.105,0.127,0.141,0.117,0.117,0.145,0.136,0.113,0.128,0.134,0.103,0.128,0.132,0.116,0.111,0.121,0.129,0.089,0.14,0.136,0.125,0.139,0.126,0.124,0.113,0.113,0.136,0.111,0.121,0.114,0.122,0.071,0.095,0.127,0.128,0.107,0.086,0.092,0.135,0.129,0.14,0.102,0.091,0.102,0.093,0.087,0.094,0.124,0.102,0.111,0.093,0.094,0.104,0.107,0.09,0.12,0.115,0.12,0.111,0.121,0.109,0.1,0.131,0.1,0.095,0.094,0.104,0.127,0.118,0.099,0.086,464,1749,1097,942,723,907,1120,1024,513,333,164.0,1039,890,229,763,2806,4413,520,1372,1534,1173,1006,837,1390,1741,1914,1624,1255,1618,1664,462,530,440,1558,586,810,1603,1915,201,204,840,1060,1176,1552,2047,775,349,1083,1208,727,345,1498,952,1900,578,1944,616,1558,416,712,350,810,202,400,1099,901,1343,1068,954,480,606,872,4201,394,3.233,2.85,2.441,2.581,1.986,2.37,2.297,2.712,2.502,3.385,2.684,1.915,2.798,2.439,2.868,2.617,2.999,3.496,2.835,1.892,2.638,2.455,2.148,2.653,2.994,3.039,2.389,2.329,2.726,2.775,2.171,2.5,2.71,2.989,3.082,2.987,2.772,2.793,2.233,2.278,2.963,1.909,2.849,1.631,2.168,2.251,2.945,2.763,2.412,2.81,2.075,2.179,2.046,2.329,3.231,2.374,2.885,2.456,2.045,2.07,2.364,2.534,2.614,2.023,2.339,2.2,2.428,2.614,2.469,2.035,2.751,2.688,2.413,2.109,0.826,0.711,0.544,0.588,0.561,0.48,0.501,0.53,0.625,0.473,0.795,0.431,0.494,0.497,0.665,0.571,0.617,0.685,0.531,0.539,0.766,0.454,0.433,0.711,0.527,0.542,0.472,0.582,0.374,0.55,0.585,0.772,0.358,0.66,0.562,0.575,0.688,0.751,0.377,0.366,0.697,0.466,0.611,0.611,0.6,0.56,0.588,0.687,0.624,0.479,0.423,0.599,0.533,0.603,0.743,0.495,0.453,0.468,0.378,0.372,0.48,0.77,0.458,0.943,0.601,0.781,0.55,0.518,0.403,0.607,0.472,0.683,0.526,0.408
-217,Epileptic,M,57.0,1.8,2.3,1.0,1.3,2.1,1.9,2.2,1.6,1.4,0.6,0.4,3.3,1.7,0.5,1.1,8.1,12.2,1.7,2.2,5.3,1.5,2.0,2.8,7.3,3.7,3.8,3.1,3.7,5.1,4.3,2.4,1.6,0.7,3.6,0.8,0.9,5.9,3.9,0.3,0.2,1.3,3.4,3.1,3.0,3.8,0.7,0.6,1.0,1.6,0.7,0.7,1.9,1.5,3.4,0.6,2.4,0.7,1.6,0.7,1.8,0.7,1.8,0.4,0.8,2.1,1.3,2.3,1.6,1.6,0.5,0.5,1.3,4.3,0.5,12,17,8,14,13,16,19,17,11,5,5.0,33,13,6,11,73,96,10,25,48,13,28,24,49,49,39,37,33,35,42,24,10,5,34,5,7,60,41,2,1,9,31,21,23,18,6,3,6,8,5,6,12,9,22,3,18,5,11,4,14,5,11,2,6,16,13,19,9,7,4,3,8,30,3,0.057,0.028,0.017,0.026,0.043,0.034,0.04,0.031,0.047,0.037,0.044,0.039,0.034,0.051,0.034,0.055,0.043,0.053,0.038,0.043,0.039,0.032,0.037,0.063,0.038,0.034,0.028,0.038,0.051,0.036,0.056,0.057,0.034,0.05,0.038,0.021,0.054,0.047,0.026,0.027,0.027,0.035,0.05,0.028,0.027,0.015,0.024,0.017,0.021,0.022,0.026,0.029,0.025,0.029,0.026,0.018,0.02,0.017,0.02,0.025,0.026,0.033,0.027,0.022,0.027,0.031,0.022,0.028,0.027,0.017,0.015,0.027,0.021,0.033,1281,4346,2357,2310,1607,2780,2812,3208,1547,1018,857.0,3088,2480,928,1984,9811,19187,2080,3986,4747,3240,3766,2482,5375,5489,6218,5133,4482,5323,6509,2348,1178,1096,5605,1379,2006,7098,5143,416,227,1439,2369,4555,2825,3673,1415,821,1850,2481,1283,637,2362,1904,4870,538,3724,1113,2582,819,1949,637,2357,409,949,2653,1738,3404,1741,1646,689,898,1669,7256,560,0.152,0.12,0.097,0.112,0.141,0.142,0.122,0.129,0.154,0.139,0.142,0.142,0.133,0.157,0.13,0.17,0.147,0.161,0.141,0.154,0.119,0.13,0.128,0.162,0.137,0.142,0.117,0.128,0.138,0.147,0.162,0.136,0.105,0.163,0.116,0.099,0.17,0.157,0.12,0.135,0.127,0.132,0.16,0.124,0.117,0.089,0.105,0.093,0.1,0.106,0.133,0.122,0.119,0.12,0.128,0.104,0.114,0.098,0.109,0.124,0.127,0.127,0.122,0.119,0.124,0.125,0.109,0.115,0.111,0.109,0.101,0.121,0.101,0.133,457,1392,892,846,786,944,1000,1032,526,277,195.0,1462,773,224,569,2990,5119,503,1167,2185,714,1184,1192,1952,1707,1970,1862,1683,1664,2036,782,522,286,1378,422,727,1971,1507,182,103,769,1384,1099,1778,2197,730,381,878,1142,575,416,1086,900,1981,298,2007,549,1375,528,1065,375,927,242,540,1287,671,1729,851,858,392,456,816,3274,269,2.971,2.852,2.545,2.605,2.041,2.404,2.255,2.671,2.23,3.181,3.058,1.836,2.552,2.584,2.627,2.459,2.837,3.094,2.74,1.89,3.116,2.443,1.884,2.226,2.542,2.578,2.257,2.164,2.434,2.5,2.3,2.354,2.703,2.866,2.759,2.522,2.733,2.626,2.341,2.42,2.291,1.605,2.917,1.814,1.919,2.162,2.476,2.486,2.575,2.629,1.786,2.283,2.395,2.394,2.288,2.041,2.326,2.123,1.775,1.983,2.044,2.593,2.117,2.05,2.301,2.544,1.973,2.334,2.236,2.121,2.029,2.27,2.366,2.709,0.645,0.648,0.562,0.565,0.536,0.673,0.579,0.553,0.576,0.564,0.756,0.462,0.491,0.646,0.522,0.607,0.709,0.732,0.601,0.615,0.791,0.612,0.501,0.756,0.591,0.53,0.529,0.566,0.603,0.561,0.77,0.714,0.389,0.624,0.699,0.538,0.755,0.642,0.264,0.315,0.407,0.389,0.768,0.619,0.564,0.504,0.481,0.586,0.538,0.551,0.374,0.487,0.485,0.483,0.479,0.374,0.402,0.407,0.323,0.44,0.436,0.596,0.361,0.614,0.568,0.789,0.463,0.406,0.476,0.704,0.428,0.556,0.422,0.528
-218,Epileptic,F,37.0,0.7,2.2,0.9,0.9,1.8,2.9,1.8,1.8,1.4,0.6,0.2,3.6,1.5,0.6,1.2,7.3,9.0,0.8,2.7,5.2,0.9,2.6,1.8,4.0,3.4,3.4,3.7,2.3,2.2,4.1,1.8,0.7,0.3,2.3,0.5,0.7,3.5,3.0,0.1,0.1,1.1,3.8,2.3,3.5,2.4,0.7,0.6,1.1,1.2,0.6,0.7,1.5,1.4,2.9,0.5,2.7,0.7,1.9,1.4,1.2,0.3,2.1,0.4,0.4,2.4,1.2,2.5,1.2,1.0,0.7,1.0,1.3,4.7,0.2,7,22,7,10,21,26,16,16,20,7,2.0,34,14,9,13,81,79,7,28,42,8,29,18,39,42,38,42,25,20,43,19,6,2,27,3,7,40,35,1,0,7,35,25,31,16,6,3,10,6,6,6,13,11,22,3,22,6,14,10,10,3,15,3,4,23,17,26,10,7,7,7,10,37,2,0.033,0.023,0.014,0.021,0.036,0.042,0.027,0.031,0.052,0.032,0.019,0.037,0.03,0.038,0.028,0.034,0.031,0.024,0.033,0.038,0.02,0.033,0.029,0.041,0.03,0.029,0.028,0.026,0.023,0.033,0.042,0.043,0.017,0.029,0.015,0.019,0.034,0.034,0.018,0.011,0.025,0.042,0.041,0.03,0.019,0.016,0.023,0.02,0.018,0.027,0.034,0.02,0.022,0.024,0.027,0.022,0.015,0.023,0.029,0.02,0.029,0.037,0.037,0.02,0.025,0.037,0.021,0.023,0.019,0.021,0.022,0.026,0.022,0.025,1156,4212,1996,1761,2323,3277,2202,2821,1646,1123,415.0,2600,2450,886,2176,10444,15180,2017,3817,3787,2925,3699,1932,5697,4886,5198,4815,2861,4212,5399,2124,825,567,4341,1948,1361,6082,5666,232,273,1347,3134,4768,2685,2729,1350,802,1682,2137,1226,551,2632,1807,4052,515,3082,1471,2651,1299,1443,473,2086,328,804,2647,809,3257,1948,1576,931,1218,1741,7311,206,0.128,0.113,0.091,0.108,0.146,0.164,0.106,0.135,0.184,0.127,0.096,0.146,0.12,0.158,0.132,0.146,0.134,0.108,0.138,0.149,0.09,0.149,0.134,0.151,0.139,0.134,0.128,0.117,0.099,0.138,0.154,0.129,0.093,0.13,0.078,0.11,0.151,0.154,0.117,0.078,0.118,0.168,0.16,0.127,0.102,0.098,0.118,0.096,0.095,0.117,0.141,0.102,0.116,0.118,0.139,0.117,0.095,0.113,0.133,0.114,0.116,0.14,0.152,0.106,0.131,0.156,0.118,0.114,0.102,0.12,0.12,0.127,0.112,0.13,411,1590,915,771,936,1288,911,932,512,330,173.0,1419,857,305,750,3728,4823,445,1404,2104,693,1269,901,1717,1885,1911,2074,1310,1363,2069,706,366,263,1173,525,654,1783,1595,152,121,708,1481,1120,1811,1886,698,397,896,1040,445,381,1378,984,1966,240,1875,730,1418,776,860,253,851,190,427,1468,511,1833,908,863,472,704,886,3450,117,2.699,2.544,2.209,2.237,2.196,2.352,2.038,2.652,2.508,2.738,2.161,1.75,2.344,2.266,2.315,2.268,2.567,3.45,2.478,1.66,2.952,2.304,1.951,2.575,2.219,2.348,1.944,1.872,2.433,2.25,2.483,2.521,1.833,2.683,3.107,2.068,2.743,2.758,1.78,2.14,2.278,1.987,3.024,1.692,1.62,2.079,2.45,2.279,2.444,2.739,1.88,2.037,1.862,2.139,2.544,1.787,2.397,2.18,2.031,1.888,2.125,2.443,1.973,2.103,2.002,2.12,1.794,2.22,2.049,2.197,2.049,2.468,2.344,2.133,0.696,0.524,0.463,0.399,0.491,0.596,0.496,0.628,0.563,0.464,0.699,0.505,0.401,0.479,0.439,0.495,0.559,0.788,0.699,0.438,0.85,0.41,0.401,0.641,0.468,0.599,0.435,0.432,0.402,0.462,0.641,1.061,0.349,0.714,0.635,0.419,0.559,0.531,0.319,0.376,0.613,0.547,0.741,0.665,0.421,0.491,0.478,0.678,0.467,0.451,0.405,0.452,0.426,0.442,0.618,0.43,0.443,0.565,0.301,0.362,0.312,0.698,0.368,0.82,0.537,0.637,0.394,0.425,0.38,0.594,0.436,0.436,0.492,0.431
-219,Epileptic,M,47.0,0.6,3.4,1.5,1.2,2.1,2.4,2.3,1.9,2.2,0.5,0.5,3.6,1.7,0.6,1.9,7.6,13.9,1.0,2.5,7.6,1.6,4.2,2.1,5.1,5.0,4.4,4.5,3.8,4.2,5.3,1.9,1.3,0.7,2.9,0.5,0.6,5.6,6.1,0.3,0.3,1.3,3.6,3.1,5.8,3.5,1.3,0.7,0.7,1.3,1.0,0.5,3.0,1.8,3.5,0.2,3.6,0.9,1.7,1.3,1.3,1.6,1.7,0.4,0.5,1.8,1.3,3.9,1.5,1.3,0.6,1.9,2.3,6.8,0.3,5,27,11,8,15,21,15,16,15,6,4.0,28,19,5,16,60,95,9,24,56,15,41,18,45,48,38,39,33,27,43,22,12,5,27,3,4,48,52,2,1,8,24,29,39,20,8,3,4,8,8,4,18,13,20,1,24,7,14,8,8,15,14,4,4,11,11,27,9,8,4,11,15,43,2,0.032,0.027,0.021,0.021,0.04,0.035,0.031,0.032,0.059,0.031,0.045,0.039,0.033,0.036,0.032,0.041,0.041,0.046,0.033,0.041,0.026,0.043,0.031,0.047,0.039,0.035,0.036,0.035,0.034,0.038,0.045,0.038,0.038,0.037,0.017,0.016,0.042,0.04,0.024,0.03,0.032,0.044,0.048,0.037,0.026,0.022,0.029,0.012,0.019,0.019,0.025,0.025,0.033,0.025,0.024,0.022,0.024,0.018,0.032,0.024,0.033,0.03,0.032,0.016,0.02,0.024,0.028,0.022,0.027,0.023,0.033,0.026,0.021,0.016,1615,6022,3066,2348,1994,3181,2689,3321,1918,1055,622.0,3096,3206,1165,2822,10946,18786,2274,4159,6097,4578,5699,2209,6827,7548,6781,5823,4227,6087,6070,2765,1915,766,7026,2108,1488,7704,8741,398,389,1330,2783,6125,4645,3203,1763,928,2117,2326,2228,587,3736,2726,5007,405,4210,1756,3573,1200,1546,2213,2489,611,865,2606,1482,4244,2619,1473,853,1790,2637,11819,569,0.111,0.12,0.101,0.1,0.144,0.143,0.12,0.136,0.16,0.127,0.134,0.136,0.139,0.134,0.136,0.143,0.139,0.112,0.129,0.145,0.101,0.147,0.129,0.148,0.138,0.142,0.122,0.126,0.115,0.139,0.129,0.116,0.131,0.134,0.084,0.084,0.132,0.132,0.097,0.107,0.118,0.146,0.149,0.14,0.117,0.111,0.113,0.077,0.097,0.098,0.121,0.111,0.124,0.108,0.123,0.107,0.114,0.092,0.131,0.117,0.139,0.125,0.142,0.103,0.101,0.101,0.116,0.103,0.117,0.134,0.13,0.121,0.102,0.094,352,2039,1219,843,921,1132,1159,1081,653,318,215.0,1516,899,291,922,3157,5696,408,1296,2993,1077,1840,1012,1968,2381,2214,2174,1705,1905,2330,791,716,265,1474,497,592,2347,2634,226,186,701,1304,1223,2707,2122,829,385,876,1097,933,360,1740,1048,2175,176,2430,708,1620,685,817,837,929,249,489,1315,729,2409,1180,781,385,899,1371,5168,278,3.581,2.699,2.448,2.941,2.149,2.5,2.164,2.965,2.49,2.722,2.519,1.827,2.797,2.849,2.542,2.727,2.859,3.733,2.696,1.911,3.249,2.479,2.029,2.641,2.461,2.623,2.217,2.073,2.517,2.224,2.611,2.594,2.346,3.263,3.572,2.339,2.648,2.649,2.034,2.454,2.29,2.019,3.138,1.986,1.75,2.386,2.85,2.965,2.523,2.66,1.913,2.199,2.444,2.45,2.87,1.947,2.728,2.39,2.128,2.113,2.422,2.565,2.718,2.021,2.167,2.146,1.98,2.497,2.209,2.602,2.232,2.412,2.399,2.455,0.851,0.657,0.637,0.66,0.416,0.591,0.512,0.659,0.484,0.455,0.6,0.444,0.495,0.596,0.492,0.623,0.639,0.845,0.579,0.541,0.884,0.626,0.4,0.653,0.568,0.551,0.606,0.509,0.611,0.494,0.759,0.897,0.411,0.87,0.776,0.53,0.673,0.568,0.463,0.677,0.515,0.566,0.961,0.64,0.456,0.733,0.614,0.722,0.652,0.547,0.438,0.569,0.704,0.597,0.444,0.451,0.438,0.624,0.485,0.416,0.559,0.747,0.505,0.745,0.677,0.58,0.428,0.504,0.472,0.677,0.493,0.469,0.612,0.451
-220,Epileptic,M,42.0,0.9,3.9,1.9,1.9,1.9,2.1,5.2,2.0,1.6,0.8,0.5,3.5,1.6,0.4,1.2,8.9,18.0,1.2,3.7,6.3,1.8,3.2,2.4,4.6,3.6,3.1,7.1,5.3,6.9,4.2,1.4,1.4,0.5,3.4,0.6,1.0,5.4,4.0,0.2,0.3,1.1,4.0,3.5,2.7,4.9,1.3,0.4,1.8,1.7,0.9,0.7,2.9,1.8,4.5,0.1,3.5,1.1,3.0,1.1,1.5,1.1,2.1,0.6,1.0,2.5,2.2,3.3,3.0,2.0,0.7,1.1,1.2,6.7,0.5,6,27,12,11,15,20,40,15,12,6,4.0,29,15,3,11,68,122,8,29,52,11,29,22,39,34,28,52,35,46,39,13,11,4,31,3,7,41,41,1,1,5,33,30,16,22,6,2,9,7,6,4,19,11,28,0,21,7,18,8,8,8,14,3,6,18,15,19,17,10,7,6,6,43,4,0.043,0.038,0.034,0.032,0.039,0.043,0.059,0.034,0.061,0.04,0.05,0.043,0.032,0.033,0.029,0.053,0.055,0.055,0.057,0.049,0.04,0.04,0.041,0.057,0.045,0.039,0.054,0.047,0.05,0.048,0.045,0.057,0.032,0.049,0.033,0.027,0.056,0.042,0.018,0.021,0.024,0.055,0.077,0.034,0.031,0.024,0.029,0.025,0.025,0.035,0.026,0.029,0.031,0.037,0.011,0.025,0.026,0.029,0.027,0.027,0.025,0.036,0.028,0.053,0.034,0.038,0.029,0.027,0.032,0.025,0.028,0.022,0.025,0.035,1562,5493,2963,2709,2668,2811,3313,3340,1185,1307,609.0,3339,3213,946,2371,8951,16853,2141,3573,5246,2793,4615,2590,5530,5813,4825,6638,4083,6715,4932,2234,1192,802,5071,1085,1416,5171,7644,290,439,1361,3253,4685,2171,4247,1437,612,2440,1925,1342,643,3920,2054,4736,326,3969,1639,3186,1177,1458,1538,2810,506,1121,2286,1600,3563,3812,2173,877,1223,1797,9430,490,0.122,0.135,0.122,0.114,0.13,0.146,0.158,0.126,0.155,0.143,0.13,0.138,0.118,0.122,0.123,0.144,0.151,0.152,0.158,0.153,0.118,0.152,0.147,0.162,0.14,0.134,0.14,0.129,0.137,0.158,0.14,0.137,0.109,0.148,0.118,0.104,0.147,0.153,0.098,0.088,0.108,0.152,0.201,0.122,0.117,0.109,0.102,0.096,0.105,0.128,0.124,0.121,0.116,0.123,0.069,0.116,0.103,0.118,0.122,0.117,0.106,0.13,0.133,0.148,0.13,0.125,0.116,0.111,0.114,0.13,0.127,0.106,0.109,0.146,381,1758,968,994,932,1033,1445,1023,474,364,216.0,1470,889,218,708,2957,5815,432,1282,2473,806,1367,1043,1572,1579,1455,2334,1771,2274,1667,562,501,252,1240,333,627,1686,1818,169,205,711,1412,950,1370,2494,759,259,995,956,460,406,1644,1009,2186,134,2067,804,1653,705,761,710,981,300,369,1214,858,1821,1737,1020,503,638,812,4282,228,3.476,2.826,2.698,2.648,2.316,2.299,2.035,2.854,2.059,2.857,2.512,1.927,2.832,3.059,2.631,2.403,2.497,3.434,2.33,1.873,2.709,2.595,2.119,2.816,2.685,2.668,2.284,1.961,2.45,2.417,2.833,2.552,2.574,2.817,2.849,2.142,2.395,2.972,2.068,2.498,2.373,1.94,3.138,1.778,1.994,2.116,2.592,2.845,2.371,2.56,1.892,2.315,2.133,2.28,2.301,2.145,2.307,2.182,1.967,2.031,2.176,2.84,1.957,3.283,1.994,2.356,2.125,2.289,2.464,2.132,2.276,2.63,2.283,2.497,0.917,0.659,0.706,0.502,0.737,0.542,0.627,0.674,0.592,0.447,0.614,0.447,0.4,0.544,0.525,0.614,0.737,0.814,0.619,0.51,0.824,0.607,0.642,0.758,0.663,0.569,0.644,0.584,0.673,0.518,0.782,1.081,0.411,0.747,0.537,0.471,0.819,0.707,0.377,0.3,0.459,0.587,0.877,0.549,0.609,0.443,0.466,0.743,0.529,0.853,0.43,0.532,0.49,0.461,0.349,0.477,0.479,0.539,0.393,0.416,0.488,0.785,0.45,0.721,0.499,0.784,0.437,0.473,0.458,0.722,0.593,0.535,0.459,0.457
-221,Epileptic,M,32.0,1.4,2.6,2.0,1.9,1.9,1.6,2.5,1.9,2.1,1.2,0.4,3.5,1.9,0.7,1.5,8.3,11.4,1.6,2.7,5.1,2.2,3.6,2.1,4.8,5.1,5.1,6.0,3.9,3.9,4.0,2.0,0.8,0.7,2.9,0.8,1.3,3.3,3.7,0.2,0.3,1.3,3.2,3.8,2.3,3.9,1.0,0.8,1.3,1.8,1.0,0.7,2.4,1.7,2.6,0.3,2.8,1.2,2.0,1.0,1.5,0.5,2.1,0.8,0.8,1.5,1.4,3.1,1.7,1.2,1.2,1.9,2.0,4.4,0.4,12,27,15,17,16,18,17,20,17,11,4.0,34,20,9,19,84,103,10,30,51,17,47,23,55,45,56,55,35,33,45,23,6,5,29,3,11,39,51,1,2,8,29,35,18,20,7,4,7,8,7,5,18,13,18,2,21,9,15,6,9,3,17,8,6,12,19,26,9,6,16,12,14,31,3,0.049,0.025,0.033,0.03,0.037,0.029,0.04,0.032,0.055,0.051,0.038,0.042,0.039,0.044,0.031,0.043,0.036,0.054,0.038,0.044,0.04,0.033,0.03,0.052,0.041,0.033,0.046,0.04,0.037,0.032,0.056,0.049,0.03,0.038,0.021,0.022,0.037,0.038,0.017,0.02,0.024,0.037,0.061,0.024,0.027,0.016,0.028,0.02,0.021,0.022,0.031,0.023,0.025,0.021,0.009,0.017,0.02,0.021,0.02,0.023,0.021,0.03,0.033,0.024,0.021,0.028,0.021,0.018,0.02,0.029,0.027,0.026,0.02,0.018,2125,5556,3176,3028,2282,3943,2842,3685,1891,1863,826.0,3546,3754,1352,3586,12916,21402,2888,4991,5834,4502,6748,3776,6810,6752,9483,7236,4612,7593,7961,2414,1021,1278,6778,1954,2724,7103,9228,228,477,1642,3465,5899,2933,3772,1921,927,2315,2515,1790,559,3973,2055,4628,869,4681,1858,3540,1486,1848,725,3039,870,933,2183,1511,4879,3143,1879,1704,2520,2485,7812,557,0.146,0.12,0.13,0.126,0.135,0.13,0.118,0.128,0.165,0.159,0.144,0.162,0.131,0.143,0.13,0.147,0.137,0.157,0.139,0.159,0.128,0.139,0.144,0.154,0.139,0.137,0.137,0.116,0.115,0.133,0.165,0.114,0.11,0.132,0.092,0.107,0.142,0.151,0.105,0.115,0.117,0.137,0.172,0.11,0.112,0.095,0.116,0.094,0.102,0.106,0.141,0.108,0.12,0.104,0.082,0.101,0.112,0.107,0.102,0.116,0.114,0.133,0.147,0.125,0.113,0.125,0.108,0.096,0.096,0.128,0.125,0.127,0.101,0.099,560,1785,1132,1110,976,1009,1097,1262,666,501,212.0,1538,1066,367,957,3565,5831,568,1264,2421,1107,1999,1240,1933,1999,2843,2340,1619,1906,2410,764,391,429,1449,567,965,1793,2000,112,227,822,1515,1377,1751,2254,911,413,981,1193,770,318,1776,1074,2079,441,2472,847,1589,834,940,369,1151,396,543,1138,884,2448,1345,910,756,1081,1177,3477,326,3.352,2.801,2.745,2.632,2.013,2.749,2.21,2.611,2.319,2.993,3.041,1.993,2.652,2.628,2.738,2.675,2.829,3.711,2.984,2.107,2.986,2.586,2.434,2.62,2.564,2.662,2.373,2.218,2.944,2.591,2.421,2.637,2.355,3.146,2.999,2.598,2.907,3.242,2.219,2.28,2.463,1.972,3.057,1.849,1.925,2.22,2.677,3.022,2.668,2.512,2.154,2.287,1.976,2.387,2.345,2.085,2.529,2.478,1.992,2.064,2.261,2.55,2.268,1.998,2.124,2.007,2.151,2.656,2.343,2.299,2.532,2.399,2.462,2.117,0.86,0.7,0.46,0.503,0.621,0.855,0.591,0.463,0.685,0.703,0.856,0.466,0.533,0.503,0.653,0.678,0.661,0.739,0.636,0.701,0.853,0.653,0.647,0.816,0.724,0.609,0.645,0.712,0.66,0.586,0.726,0.925,0.439,0.656,0.741,0.45,0.684,0.638,0.37,0.488,0.503,0.623,0.801,0.683,0.534,0.442,0.462,0.809,0.404,0.631,0.337,0.456,0.445,0.462,0.368,0.402,0.445,0.566,0.367,0.397,0.335,0.663,0.707,0.571,0.557,0.719,0.411,0.421,0.385,0.603,0.521,0.53,0.455,0.317
-222,Epileptic,F,37.0,1.0,2.2,0.5,0.9,1.5,1.5,1.0,1.4,1.1,0.6,0.2,2.5,1.4,0.5,1.1,5.5,8.2,0.6,2.7,4.9,2.7,2.3,1.6,3.1,3.0,3.2,3.0,1.7,2.0,3.8,0.9,1.7,0.5,1.9,0.2,0.7,2.4,2.1,0.2,0.1,0.9,1.9,1.8,2.3,2.2,0.6,0.4,0.8,1.0,0.6,0.7,1.9,1.3,1.9,0.4,2.6,0.4,1.9,0.7,0.9,0.2,1.1,0.1,0.5,1.7,1.0,2.2,1.5,1.1,0.4,1.3,0.8,3.5,0.2,6,22,6,6,13,13,10,12,12,5,2.0,22,15,6,14,63,77,5,21,37,14,27,15,33,38,29,39,15,23,40,13,10,5,17,1,7,22,29,1,1,4,17,16,16,14,4,2,5,5,5,4,17,11,11,3,21,3,12,4,6,3,10,1,3,13,10,16,11,8,4,8,7,30,2,0.043,0.026,0.012,0.018,0.034,0.028,0.018,0.026,0.052,0.023,0.03,0.033,0.027,0.036,0.026,0.033,0.029,0.021,0.034,0.037,0.031,0.032,0.028,0.039,0.028,0.026,0.028,0.024,0.018,0.032,0.032,0.054,0.029,0.024,0.018,0.022,0.032,0.029,0.017,0.011,0.02,0.031,0.034,0.022,0.018,0.012,0.021,0.014,0.016,0.011,0.039,0.019,0.025,0.019,0.016,0.017,0.028,0.021,0.02,0.018,0.02,0.025,0.016,0.029,0.032,0.027,0.023,0.022,0.018,0.013,0.023,0.018,0.017,0.015,1214,4056,1804,2261,1689,2424,2509,2630,1245,1484,464.0,2398,3060,1003,2775,9721,16991,1973,4491,4629,3989,4023,1553,4699,5573,6039,5611,2948,5669,6201,1593,1212,960,5253,1118,1751,5054,5391,382,372,1485,2178,4309,2857,3261,1510,721,2188,1918,1424,604,3386,1742,3582,653,4791,823,3213,929,1410,718,1852,346,546,1860,1056,2851,2344,1922,688,1971,1394,7904,488,0.146,0.121,0.089,0.096,0.14,0.133,0.096,0.124,0.179,0.114,0.099,0.129,0.121,0.144,0.126,0.137,0.13,0.109,0.129,0.134,0.108,0.136,0.129,0.133,0.132,0.127,0.132,0.101,0.095,0.133,0.126,0.147,0.135,0.116,0.08,0.101,0.128,0.12,0.095,0.089,0.103,0.142,0.133,0.109,0.098,0.082,0.115,0.08,0.096,0.087,0.142,0.108,0.122,0.099,0.098,0.105,0.099,0.11,0.109,0.103,0.119,0.118,0.092,0.116,0.135,0.131,0.118,0.111,0.105,0.11,0.117,0.116,0.098,0.097,339,1434,658,777,698,885,812,865,390,349,134.0,1101,831,250,731,2823,4880,417,1242,2001,1124,1240,804,1231,1783,1894,1973,1065,1553,2027,468,500,320,1198,256,610,1224,1332,222,196,702,986,921,1642,2015,770,302,923,928,691,288,1563,898,1562,398,2378,370,1440,516,762,287,726,188,293,902,533,1497,1130,1009,409,894,605,3401,233,3.225,2.641,2.464,2.742,2.168,2.474,2.477,2.703,2.474,3.337,2.707,1.906,2.743,2.739,2.756,2.61,2.766,3.567,2.812,2.063,2.802,2.479,1.798,2.783,2.463,2.687,2.258,2.186,2.729,2.515,2.475,2.497,2.418,3.045,3.804,2.441,3.083,2.906,1.996,2.342,2.833,1.931,3.405,2.035,1.826,2.157,2.965,2.74,2.526,2.386,2.21,2.135,1.995,2.472,2.002,2.27,2.422,2.54,2.111,2.103,2.453,2.47,1.898,1.886,2.152,2.361,2.075,2.345,2.229,1.918,2.555,2.578,2.477,2.446,0.749,0.537,0.579,0.626,0.489,0.397,0.48,0.433,0.524,0.406,0.923,0.449,0.412,0.452,0.463,0.511,0.581,0.795,0.539,0.686,0.955,0.456,0.386,0.66,0.445,0.491,0.478,0.517,0.449,0.508,0.545,0.942,0.45,0.688,0.543,0.534,0.629,0.603,0.31,0.392,0.663,0.407,0.676,0.63,0.492,0.483,0.547,0.721,0.485,0.429,0.404,0.421,0.478,0.43,0.325,0.614,0.417,0.562,0.383,0.393,0.381,0.67,0.359,0.84,0.591,0.748,0.521,0.414,0.423,0.322,0.49,0.496,0.502,0.303
-223,Epileptic,M,32.0,1.1,2.5,1.2,0.8,1.7,2.3,1.6,1.8,1.5,0.5,0.3,2.9,1.3,0.8,1.1,6.6,9.8,1.0,1.9,4.4,0.8,1.8,2.1,3.4,3.8,2.9,3.5,2.3,2.6,4.5,2.2,0.5,0.4,2.6,0.5,0.8,3.7,3.3,0.2,0.3,1.0,3.2,2.2,2.8,2.7,0.6,0.6,0.8,1.0,0.9,1.0,1.6,1.4,3.1,0.4,2.5,0.6,2.0,1.1,0.9,0.3,2.0,0.4,0.6,1.9,0.5,2.1,1.4,1.2,0.4,1.3,1.5,4.1,0.3,9,22,12,6,17,21,14,18,8,5,3.0,25,11,8,11,61,72,8,28,34,6,19,17,31,42,34,37,20,20,36,21,4,4,28,3,6,36,35,2,1,4,26,20,20,15,4,2,4,5,6,6,10,10,17,2,18,3,17,6,7,2,14,2,4,12,7,18,8,8,4,8,10,33,2,0.044,0.023,0.017,0.017,0.037,0.032,0.028,0.032,0.046,0.026,0.029,0.038,0.026,0.04,0.029,0.036,0.038,0.038,0.029,0.037,0.015,0.029,0.036,0.037,0.035,0.029,0.026,0.026,0.026,0.035,0.046,0.023,0.026,0.031,0.018,0.017,0.036,0.03,0.017,0.022,0.02,0.037,0.036,0.025,0.022,0.013,0.022,0.012,0.015,0.027,0.035,0.023,0.025,0.025,0.018,0.019,0.013,0.022,0.025,0.019,0.013,0.027,0.024,0.022,0.024,0.027,0.02,0.019,0.02,0.012,0.024,0.022,0.018,0.019,1514,5510,2657,1952,2527,3833,2292,3238,1650,1178,659.0,2707,3172,1227,2441,10998,16355,2322,4333,4428,3128,3489,2614,6640,6464,6437,5960,3262,5124,6343,2429,1013,899,6521,2070,2128,6359,7707,374,443,1458,2816,5458,2568,2821,1291,993,2394,2262,1284,685,2529,2306,4508,573,3408,1445,3147,1045,1268,688,3177,597,874,2259,846,3082,2619,1609,1169,1694,2128,9411,375,0.144,0.117,0.104,0.093,0.145,0.139,0.113,0.141,0.133,0.133,0.109,0.147,0.116,0.145,0.127,0.139,0.137,0.123,0.125,0.141,0.073,0.128,0.135,0.132,0.146,0.137,0.114,0.112,0.102,0.143,0.14,0.078,0.125,0.135,0.087,0.094,0.128,0.126,0.118,0.103,0.104,0.138,0.133,0.12,0.106,0.089,0.102,0.079,0.085,0.12,0.143,0.105,0.115,0.109,0.111,0.101,0.09,0.108,0.116,0.103,0.087,0.128,0.111,0.119,0.111,0.102,0.108,0.1,0.105,0.093,0.121,0.111,0.095,0.117,430,1749,1074,721,879,1327,977,1037,489,322,199.0,1327,860,348,664,3149,4626,467,1224,2078,797,1142,1025,1734,1869,1835,2210,1331,1671,2154,702,405,283,1464,495,723,1794,1888,188,216,686,1402,1160,1611,1847,654,403,942,1003,544,454,1157,969,1994,284,1978,662,1625,667,759,362,1127,271,435,1204,358,1661,1176,882,515,786,1052,3892,188,3.191,2.986,2.324,2.762,2.639,2.474,2.053,2.96,2.741,3.074,2.846,1.893,2.84,2.866,2.77,2.747,2.97,3.617,2.872,1.894,2.823,2.374,2.259,2.967,2.65,2.884,2.227,2.057,2.424,2.455,2.598,2.392,2.566,3.155,3.814,2.637,2.722,3.059,2.348,2.508,2.582,1.863,3.267,1.913,1.75,2.19,3.355,3.177,2.803,2.999,1.957,2.285,2.362,2.394,2.351,1.995,2.686,2.28,1.942,1.914,2.137,2.84,2.5,2.43,2.157,2.434,2.097,2.562,2.089,2.524,2.293,2.441,2.534,2.375,1.144,0.621,0.67,0.632,0.607,0.619,0.506,0.539,0.551,0.326,0.817,0.481,0.432,0.522,0.465,0.521,0.573,0.862,0.552,0.563,0.835,0.511,0.466,0.619,0.535,0.561,0.486,0.533,0.625,0.694,0.696,1.128,0.495,0.653,0.55,0.489,0.694,0.597,0.452,0.372,0.515,0.554,0.646,0.667,0.44,0.49,0.368,0.784,0.42,0.67,0.388,0.538,0.498,0.508,0.489,0.517,0.46,0.593,0.356,0.377,0.307,0.717,0.44,0.736,0.644,0.615,0.509,0.415,0.406,0.532,0.576,0.507,0.593,0.504
-224,Epileptic,F,37.0,2.7,1.6,1.0,1.2,1.7,1.9,1.6,1.6,1.2,0.9,0.3,2.3,1.4,0.4,0.9,5.3,7.6,1.3,1.3,2.6,1.1,3.1,2.1,2.7,3.1,2.5,3.0,2.8,2.8,3.1,1.2,1.9,0.4,1.5,1.6,0.4,1.9,2.7,0.1,0.1,1.0,3.1,1.2,1.5,3.0,0.6,0.5,0.8,1.1,0.6,0.4,1.4,2.2,2.6,0.2,2.4,0.5,1.1,0.7,0.7,0.6,1.0,0.2,0.6,1.6,0.9,2.9,1.3,0.9,0.4,1.2,0.7,3.8,0.3,18,14,10,10,17,27,14,15,14,10,5.0,24,14,7,9,59,82,16,20,25,12,37,21,37,38,32,37,32,26,37,18,14,4,29,21,2,25,36,1,1,5,32,15,11,15,5,2,6,6,7,3,9,17,16,1,35,4,9,5,6,3,9,1,6,12,16,20,7,6,4,7,6,26,2,0.078,0.023,0.02,0.023,0.029,0.036,0.031,0.03,0.03,0.035,0.035,0.037,0.031,0.038,0.025,0.036,0.03,0.049,0.03,0.032,0.028,0.038,0.038,0.044,0.031,0.027,0.031,0.032,0.032,0.033,0.039,0.076,0.035,0.027,0.043,0.013,0.032,0.038,0.016,0.013,0.022,0.038,0.048,0.022,0.021,0.017,0.022,0.013,0.016,0.032,0.018,0.019,0.035,0.022,0.021,0.019,0.017,0.016,0.018,0.013,0.018,0.027,0.028,0.03,0.02,0.016,0.021,0.021,0.019,0.02,0.024,0.021,0.019,0.012,1879,3652,2076,2216,2378,3025,2705,2999,2527,1946,580.0,2572,3113,968,2709,10288,17476,2525,3310,4025,3268,4893,2192,5987,7760,6555,7037,4818,7019,6453,2135,886,794,5240,1403,1298,5045,5733,345,405,1434,2028,1714,2277,3776,1333,819,2188,2096,1577,514,2357,2399,4344,250,4520,1124,2742,985,1519,1048,1894,293,863,2478,1268,4796,2096,1991,868,1917,1693,7826,579,0.188,0.116,0.114,0.116,0.132,0.146,0.124,0.127,0.133,0.144,0.142,0.151,0.134,0.156,0.114,0.14,0.128,0.161,0.128,0.124,0.098,0.132,0.121,0.159,0.13,0.123,0.131,0.116,0.119,0.142,0.143,0.146,0.118,0.118,0.14,0.075,0.145,0.14,0.104,0.084,0.111,0.14,0.146,0.115,0.102,0.09,0.106,0.082,0.087,0.13,0.108,0.097,0.139,0.103,0.126,0.106,0.095,0.101,0.105,0.091,0.101,0.123,0.114,0.122,0.109,0.109,0.104,0.102,0.091,0.123,0.114,0.113,0.102,0.09,633,1240,745,804,878,999,871,941,708,485,218.0,1066,831,248,668,2760,4751,572,937,1349,710,1352,925,1436,1789,1697,1779,1578,1646,1832,640,421,229,1195,579,463,1125,1397,176,180,695,1273,509,1176,2107,605,299,916,1046,380,313,1126,1056,1905,98,2121,527,1132,586,810,517,681,153,438,1260,897,2143,932,829,398,851,585,3124,251,2.641,2.706,2.614,2.593,2.222,2.438,2.433,2.628,2.574,2.962,2.284,2.075,2.804,2.791,2.866,2.731,2.866,3.213,2.773,2.34,3.102,2.629,2.003,2.947,2.997,2.931,2.748,2.341,3.087,2.706,2.478,2.287,2.683,2.968,2.188,2.638,3.163,2.968,1.953,2.279,2.521,1.557,2.82,2.056,2.056,2.217,3.023,2.775,2.459,2.821,1.889,2.258,2.122,2.368,2.418,2.299,2.31,2.486,1.862,2.002,2.452,2.44,2.174,2.165,2.163,1.739,2.394,2.421,2.727,2.428,2.515,2.805,2.656,2.605,0.922,0.571,0.516,0.557,0.657,0.545,0.715,0.531,0.76,0.777,0.835,0.506,0.413,0.515,0.504,0.623,0.607,1.096,0.633,0.662,1.008,0.652,0.558,0.722,0.72,0.619,0.735,0.66,0.593,0.547,0.577,0.73,0.487,0.602,0.691,0.445,0.769,0.76,0.419,0.298,0.453,0.427,0.999,0.637,0.563,0.458,0.716,0.794,0.442,0.821,0.487,0.382,0.516,0.508,0.635,0.496,0.442,0.553,0.363,0.386,0.338,0.689,0.451,0.66,0.462,0.481,0.479,0.409,0.495,0.685,0.493,0.58,0.51,0.481
-225,Epileptic,M,22.0,0.8,3.0,0.9,1.2,1.8,2.5,2.0,1.4,2.6,0.7,0.2,2.3,1.2,0.5,1.4,4.7,9.2,0.9,2.3,5.3,6.3,3.2,2.1,5.2,5.9,4.2,3.6,3.7,3.3,3.8,2.8,1.1,0.8,3.2,0.9,0.9,7.7,6.7,0.1,0.3,1.1,4.4,3.9,2.6,2.7,0.7,0.3,1.0,1.2,1.7,0.7,2.2,1.5,2.4,0.5,2.8,1.3,2.4,1.3,1.3,1.1,1.8,0.3,0.9,1.9,1.6,2.4,1.2,1.1,1.0,1.4,2.1,6.7,0.3,8,26,8,11,16,20,16,14,16,8,4.0,30,13,7,18,59,88,11,28,53,50,46,24,50,64,38,39,35,26,39,29,12,9,34,7,7,66,57,1,1,6,34,31,20,16,5,2,6,6,10,5,19,15,17,3,24,8,16,15,11,9,14,2,8,17,18,27,9,7,6,10,13,55,3,0.039,0.03,0.021,0.023,0.033,0.038,0.029,0.026,0.067,0.038,0.037,0.04,0.026,0.036,0.034,0.034,0.039,0.041,0.033,0.038,0.06,0.036,0.035,0.048,0.046,0.037,0.03,0.039,0.032,0.034,0.053,0.053,0.032,0.034,0.032,0.021,0.053,0.044,0.011,0.016,0.02,0.043,0.047,0.024,0.021,0.015,0.016,0.017,0.017,0.021,0.023,0.023,0.023,0.022,0.02,0.019,0.026,0.028,0.021,0.023,0.025,0.026,0.023,0.022,0.023,0.026,0.017,0.016,0.018,0.03,0.029,0.024,0.02,0.018,1548,6044,2391,2618,1946,3226,3585,3687,1181,1421,664.0,2294,3682,1188,3082,10738,18524,2335,4761,6296,4475,5311,2091,6124,6385,6221,6374,4505,7020,6581,2199,1420,1541,7158,2511,1916,5596,9549,309,509,1533,2208,5108,2843,3669,1496,758,2549,2380,2237,712,3510,2277,4181,676,4323,1890,3310,1555,1592,1163,3153,406,1165,2557,1508,4549,2546,2072,1285,1760,2940,12295,531,0.122,0.134,0.104,0.112,0.119,0.142,0.12,0.116,0.194,0.149,0.127,0.165,0.117,0.146,0.14,0.134,0.143,0.134,0.125,0.155,0.159,0.141,0.129,0.144,0.153,0.134,0.126,0.115,0.121,0.146,0.152,0.128,0.13,0.129,0.094,0.098,0.155,0.141,0.095,0.093,0.105,0.15,0.139,0.122,0.101,0.093,0.095,0.083,0.089,0.102,0.12,0.118,0.123,0.105,0.108,0.109,0.106,0.115,0.127,0.125,0.125,0.119,0.133,0.119,0.12,0.117,0.1,0.097,0.101,0.133,0.137,0.11,0.103,0.115,434,1723,765,869,903,1193,1196,996,549,357,182.0,1202,884,336,828,2890,4564,486,1330,2550,1686,1649,999,1930,2358,2010,2213,1817,1775,1997,918,519,495,1659,546,669,2398,2613,155,247,785,1453,1291,1723,2101,721,313,1000,1047,1132,448,1612,1042,1816,386,2222,907,1495,962,910,662,1177,194,680,1344,923,2296,1120,927,495,754,1402,5353,300,3.392,3.207,2.947,2.884,2.017,2.316,2.458,2.986,1.931,3.063,2.943,1.711,3.078,2.753,2.882,2.734,3.037,3.598,2.799,2.111,2.383,2.494,1.877,2.427,2.288,2.551,2.318,2.086,2.936,2.519,1.869,2.82,2.472,3.025,3.764,2.439,2.04,2.833,2.319,2.648,2.52,1.487,2.867,1.841,2.008,2.306,3.158,3.043,2.815,2.512,1.901,2.315,2.247,2.426,2.169,2.083,2.345,2.421,1.757,1.94,2.035,2.632,2.158,2.138,1.982,1.889,2.113,2.642,2.435,2.891,2.471,2.431,2.434,2.015,0.656,0.825,0.572,0.63,0.552,0.677,0.603,0.544,0.721,0.591,0.689,0.489,0.557,0.466,0.519,0.625,0.694,0.859,0.654,0.68,0.859,0.65,0.446,0.934,0.615,0.636,0.611,0.536,0.555,0.584,0.831,1.022,0.563,0.654,0.705,0.502,0.826,0.805,0.341,0.348,0.468,0.442,0.994,0.676,0.692,0.395,0.444,0.634,0.412,0.534,0.452,0.47,0.481,0.412,0.372,0.397,0.488,0.544,0.481,0.362,0.438,0.597,0.422,0.59,0.521,0.68,0.452,0.421,0.457,0.687,0.453,0.468,0.439,0.368
-226,Epileptic,F,17.5,0.5,1.9,0.6,1.0,1.2,1.6,1.5,1.5,1.0,0.7,0.4,2.6,1.2,0.7,1.2,5.2,7.2,0.8,2.5,4.9,1.1,2.8,1.5,3.2,3.8,3.2,5.0,2.9,2.8,3.8,1.6,2.0,0.6,2.2,0.4,0.5,2.3,2.3,0.2,0.3,0.8,3.0,2.3,2.7,3.3,0.8,0.6,0.7,1.2,1.1,0.9,1.6,0.8,3.3,0.2,2.5,0.8,1.9,0.7,1.0,0.6,1.2,0.3,0.6,1.3,0.8,2.9,1.4,1.5,0.4,1.2,1.5,3.6,0.3,3,14,5,6,12,15,13,12,12,7,4.0,23,16,8,12,49,72,6,24,38,9,28,13,29,46,35,54,32,22,45,14,16,4,21,2,3,28,29,1,2,5,27,20,24,19,5,2,5,6,8,5,12,6,27,1,19,8,14,6,10,3,10,3,4,10,10,21,11,12,4,7,11,27,1,0.025,0.024,0.014,0.018,0.03,0.029,0.022,0.028,0.039,0.042,0.034,0.037,0.031,0.033,0.035,0.033,0.028,0.03,0.035,0.041,0.022,0.037,0.031,0.039,0.036,0.032,0.034,0.029,0.024,0.034,0.055,0.071,0.032,0.028,0.013,0.014,0.029,0.032,0.018,0.018,0.019,0.041,0.044,0.029,0.025,0.012,0.025,0.013,0.016,0.023,0.049,0.015,0.019,0.023,0.022,0.016,0.023,0.02,0.023,0.022,0.021,0.022,0.022,0.034,0.019,0.025,0.023,0.023,0.02,0.018,0.032,0.026,0.018,0.019,1588,4324,1834,2243,1737,2679,2632,3114,1686,1442,509.0,2860,3705,1538,2509,10560,17521,2287,3827,3740,3087,4550,1778,6572,6688,6838,7122,4351,5713,6225,1778,1132,907,5525,1799,1819,6181,7232,338,538,1366,2789,4455,2267,3350,1764,892,2252,2674,1703,398,3472,1644,6818,322,4473,1360,3049,1128,1651,807,2610,581,954,2411,903,3784,2043,2627,965,1468,2389,8401,431,0.097,0.106,0.09,0.095,0.121,0.138,0.102,0.122,0.138,0.151,0.135,0.145,0.138,0.142,0.137,0.132,0.122,0.1,0.127,0.143,0.091,0.147,0.139,0.142,0.14,0.133,0.136,0.121,0.103,0.14,0.149,0.178,0.12,0.123,0.06,0.078,0.122,0.123,0.117,0.101,0.099,0.155,0.124,0.118,0.11,0.087,0.109,0.076,0.088,0.115,0.172,0.092,0.097,0.111,0.117,0.099,0.124,0.104,0.117,0.108,0.117,0.119,0.12,0.135,0.103,0.116,0.116,0.102,0.109,0.121,0.138,0.118,0.091,0.114,351,1213,637,821,665,926,1000,920,498,337,165.0,1085,778,380,581,2576,4342,434,1226,1821,828,1234,733,1422,1858,1705,2433,1551,1656,1933,449,441,274,1189,517,522,1412,1370,139,240,642,1178,906,1446,2137,872,369,883,1045,726,274,1573,753,2372,150,2225,536,1544,613,850,370,822,256,298,1071,511,1880,916,1214,326,552,999,3196,181,3.806,3.4,2.566,2.725,2.156,2.581,2.308,3.109,2.463,3.486,2.908,2.169,3.43,3.007,3.036,2.944,3.183,4.003,2.723,1.842,2.987,2.684,2.117,3.234,2.677,2.977,2.377,2.266,2.649,2.528,2.859,2.61,2.804,3.248,3.155,2.93,3.116,3.554,2.565,2.625,2.751,2.165,3.552,1.74,1.801,2.161,3.332,3.223,3.07,2.616,1.75,2.243,2.338,2.746,2.676,2.179,2.827,2.36,2.24,2.139,2.975,2.969,2.386,3.566,2.484,2.243,2.244,2.52,2.436,2.942,2.877,2.954,2.792,2.781,0.957,0.757,0.614,0.523,0.731,0.693,0.551,0.785,0.619,0.65,0.797,0.55,0.569,0.438,0.57,0.724,0.632,0.868,0.73,0.535,0.769,0.6,0.394,0.643,0.597,0.626,0.52,0.548,0.449,0.628,0.832,1.154,0.497,0.581,0.489,0.864,0.723,0.677,0.331,0.698,0.738,0.562,0.7,0.62,0.551,0.525,0.548,0.955,0.567,0.517,0.355,0.638,0.657,0.635,0.358,0.58,0.56,0.523,0.387,0.513,0.51,0.914,0.476,0.965,0.632,0.698,0.504,0.464,0.412,0.897,0.663,0.573,0.687,0.492
-227,Epileptic,F,42.0,0.6,2.5,1.1,1.1,1.5,1.3,1.7,1.9,1.6,0.6,0.2,3.3,1.2,0.8,1.1,6.2,9.0,0.7,2.3,5.8,0.8,2.7,1.6,2.4,2.4,3.3,3.3,2.6,3.3,2.8,1.5,0.9,0.5,3.1,0.3,0.3,3.5,3.0,0.1,0.2,0.9,2.5,1.9,3.5,2.7,0.8,0.4,0.7,1.0,1.1,0.7,2.0,1.0,2.0,0.4,1.8,1.1,2.0,0.6,1.4,0.6,0.8,0.2,0.3,1.8,2.1,3.1,1.2,1.3,0.5,1.1,1.4,2.3,0.3,3,20,13,12,17,15,17,16,16,9,2.0,29,13,9,11,69,82,5,30,48,8,27,17,25,28,29,37,27,26,32,14,5,6,35,1,2,38,27,2,1,5,26,20,24,20,6,2,5,5,6,5,16,6,16,3,16,7,22,5,13,4,6,1,3,16,19,26,8,10,5,10,11,17,2,0.032,0.026,0.015,0.021,0.043,0.023,0.024,0.034,0.051,0.031,0.022,0.037,0.025,0.057,0.026,0.04,0.031,0.029,0.031,0.042,0.018,0.038,0.029,0.033,0.037,0.031,0.029,0.029,0.029,0.029,0.047,0.032,0.031,0.047,0.016,0.01,0.032,0.033,0.011,0.018,0.02,0.031,0.039,0.03,0.024,0.019,0.021,0.014,0.015,0.031,0.031,0.027,0.022,0.02,0.033,0.018,0.028,0.021,0.02,0.023,0.025,0.02,0.018,0.021,0.024,0.038,0.021,0.02,0.023,0.027,0.029,0.025,0.014,0.018,1422,4159,2150,2294,1670,2487,2798,3131,2091,1266,562.0,3024,2735,810,2520,8496,14923,1939,3459,5844,2063,3620,2381,4393,3699,5740,4866,3721,5125,5000,1989,1004,1059,4689,1323,1508,5520,6048,385,419,1633,2556,3817,3440,3043,1398,729,2083,2304,1201,613,2939,1435,3650,356,2995,1326,3034,1011,1863,775,1821,310,744,2442,1290,4485,1843,1850,1111,1338,1734,6295,470,0.115,0.122,0.113,0.114,0.152,0.122,0.115,0.142,0.162,0.154,0.123,0.153,0.122,0.136,0.122,0.143,0.136,0.111,0.133,0.155,0.079,0.154,0.14,0.129,0.149,0.132,0.137,0.131,0.12,0.137,0.151,0.098,0.126,0.158,0.083,0.069,0.129,0.127,0.084,0.089,0.102,0.147,0.139,0.125,0.118,0.11,0.111,0.086,0.087,0.129,0.147,0.12,0.111,0.107,0.159,0.108,0.131,0.11,0.121,0.128,0.12,0.108,0.115,0.127,0.12,0.154,0.116,0.107,0.119,0.132,0.142,0.13,0.087,0.115,341,1385,1009,929,641,927,1050,944,539,398,176.0,1399,840,252,710,2496,4644,388,1138,2247,658,1178,909,1192,1184,1697,1813,1469,1677,1642,499,437,291,1094,313,568,1729,1496,232,199,710,1239,862,2042,1977,687,323,809,1004,549,370,1333,684,1745,169,1606,583,1567,526,988,382,686,170,294,1228,815,2235,923,925,323,623,895,2640,228,3.763,2.671,2.089,2.575,2.362,2.441,2.322,2.806,2.706,2.807,2.753,1.948,2.602,2.363,2.651,2.566,2.642,3.903,2.528,2.301,2.449,2.419,2.248,2.682,2.471,2.792,2.206,2.069,2.528,2.447,2.978,2.277,2.791,2.812,3.116,2.548,2.565,2.924,1.97,2.596,2.911,1.937,3.273,1.944,1.782,2.307,2.851,3.026,2.735,2.692,2.135,2.208,2.199,2.214,2.437,2.172,2.502,2.214,2.181,2.228,2.644,2.713,1.973,2.849,2.241,2.142,2.235,2.313,2.337,3.328,2.351,2.44,2.548,2.737,1.081,0.656,0.711,0.489,0.555,0.503,0.51,0.72,0.654,0.445,0.896,0.479,0.422,0.5,0.479,0.552,0.498,0.714,0.602,0.683,0.59,0.407,0.353,0.592,0.475,0.687,0.43,0.449,0.518,0.464,0.786,0.992,0.356,0.627,0.751,0.426,0.612,0.58,0.378,0.308,0.831,0.442,0.712,0.566,0.525,0.453,0.484,0.701,0.756,0.609,0.335,0.517,0.604,0.524,0.379,0.4,0.471,0.471,0.364,0.318,0.56,0.606,0.265,0.909,0.587,0.688,0.437,0.41,0.405,0.918,0.43,0.559,0.585,0.287
-228,Epileptic,M,47.0,1.0,2.9,1.3,1.8,2.0,2.0,2.3,1.6,2.4,0.5,0.5,3.1,1.8,0.7,1.6,6.8,8.6,1.1,2.6,6.0,1.0,3.3,2.2,6.5,2.8,3.3,3.4,2.3,3.2,3.7,2.1,1.2,0.8,2.9,0.5,0.7,4.2,3.5,0.2,0.3,1.2,3.2,2.2,2.7,3.6,1.1,0.5,0.7,1.6,1.1,0.8,3.1,1.7,2.6,0.2,2.2,1.1,1.7,1.6,1.1,0.7,1.9,0.6,0.5,1.6,1.5,2.1,1.7,1.4,1.0,1.1,2.0,4.3,0.6,8,27,14,14,16,21,18,15,20,7,4.0,28,17,6,13,56,66,12,34,56,8,35,19,57,36,32,36,19,23,43,18,7,5,36,4,7,45,41,2,1,8,30,22,23,19,10,3,4,8,8,6,21,13,14,1,16,8,16,11,8,6,18,3,4,14,23,17,14,8,9,8,15,33,5,0.04,0.027,0.023,0.028,0.035,0.025,0.03,0.023,0.066,0.028,0.031,0.037,0.032,0.033,0.032,0.038,0.031,0.04,0.03,0.04,0.021,0.034,0.03,0.057,0.028,0.027,0.03,0.025,0.027,0.032,0.049,0.038,0.038,0.032,0.015,0.019,0.036,0.032,0.024,0.019,0.025,0.029,0.035,0.025,0.024,0.021,0.022,0.012,0.02,0.026,0.029,0.026,0.032,0.023,0.024,0.014,0.017,0.021,0.023,0.018,0.018,0.026,0.031,0.018,0.02,0.025,0.019,0.019,0.02,0.032,0.021,0.02,0.016,0.026,1469,4967,2587,2607,1994,3028,2933,3661,1707,1505,793.0,3501,3734,1294,2454,9128,17046,2435,6078,6487,3765,4938,2377,6794,5924,5986,5180,3532,6392,6340,2123,1287,755,6589,2278,1544,7651,8519,325,404,1283,2951,5594,3371,3865,1404,875,2131,2499,1742,774,3851,2265,3874,247,3561,2024,3112,1544,1525,1099,3416,483,931,2026,1523,3379,3282,1844,1085,1476,2652,9280,763,0.125,0.129,0.117,0.127,0.129,0.126,0.11,0.109,0.166,0.136,0.117,0.144,0.127,0.139,0.126,0.135,0.122,0.145,0.135,0.162,0.092,0.13,0.118,0.163,0.126,0.12,0.123,0.101,0.099,0.138,0.136,0.119,0.139,0.141,0.083,0.11,0.134,0.135,0.141,0.104,0.125,0.123,0.134,0.121,0.105,0.112,0.111,0.076,0.101,0.118,0.138,0.117,0.132,0.103,0.123,0.093,0.1,0.115,0.12,0.098,0.102,0.125,0.124,0.114,0.117,0.116,0.106,0.104,0.101,0.149,0.115,0.115,0.096,0.141,449,1704,926,1010,943,1231,1151,1140,657,403,268.0,1406,1056,312,791,2890,4691,565,1597,2499,760,1612,1132,2119,1707,1969,1908,1299,1730,2018,720,528,294,1584,610,533,2109,2019,159,228,692,1581,1234,1843,2257,826,343,893,1162,697,428,1926,945,1782,128,2088,955,1476,985,900,635,1275,286,460,1155,1045,1708,1386,992,549,823,1443,4038,336,3.202,2.751,2.72,2.443,2.072,2.117,2.176,2.676,2.235,2.747,2.513,2.04,2.767,2.907,2.502,2.509,2.88,3.459,2.829,2.164,3.155,2.424,1.899,2.592,2.611,2.516,2.213,2.15,2.837,2.409,2.247,2.674,2.316,3.037,3.218,2.567,2.727,3.031,2.545,2.038,2.525,1.727,3.376,2.041,1.952,1.826,3.008,2.958,2.669,2.917,1.872,2.201,2.405,2.361,2.068,1.895,2.365,2.306,1.779,1.814,2.0,2.65,2.01,2.298,1.99,1.78,2.048,2.528,2.156,2.24,2.14,2.276,2.477,2.473,0.672,0.538,0.405,0.585,0.507,0.54,0.598,0.437,0.574,0.689,0.718,0.552,0.422,0.462,0.513,0.588,0.629,0.767,0.652,0.665,0.841,0.627,0.47,0.739,0.662,0.546,0.537,0.607,0.574,0.59,0.806,0.893,0.454,0.59,0.707,0.448,0.771,0.583,0.506,0.451,0.631,0.475,0.742,0.559,0.578,0.367,0.513,0.746,0.479,0.595,0.54,0.406,0.604,0.455,0.469,0.42,0.464,0.548,0.401,0.371,0.414,0.608,0.42,0.812,0.481,0.67,0.451,0.46,0.465,0.48,0.45,0.49,0.443,0.41
-229,Epileptic,F,47.0,1.5,2.5,1.5,1.6,1.8,1.4,1.6,1.3,0.8,0.6,0.2,1.9,1.6,0.5,1.1,6.2,5.8,0.7,2.5,3.4,0.8,3.4,1.3,2.8,3.0,2.3,2.5,1.9,1.8,2.0,2.0,1.1,0.5,1.9,0.2,0.9,2.4,2.0,0.2,0.1,0.8,2.4,2.4,1.4,2.0,0.8,0.4,1.0,1.2,0.5,0.7,2.2,1.3,2.9,0.3,2.5,0.6,1.4,0.7,0.9,0.2,1.3,0.4,0.2,1.7,1.6,1.9,1.3,0.5,0.4,0.9,0.8,3.0,0.2,12,23,10,10,16,13,14,13,9,6,3.0,23,17,6,13,73,57,5,27,39,12,33,15,26,35,24,30,21,18,28,24,8,5,24,1,8,31,21,1,1,6,23,19,15,12,6,2,4,6,5,5,17,8,23,2,17,7,14,4,8,3,11,2,2,15,15,14,10,3,3,6,7,26,3,0.052,0.025,0.026,0.03,0.044,0.031,0.021,0.024,0.042,0.034,0.028,0.027,0.029,0.025,0.023,0.033,0.024,0.028,0.033,0.031,0.024,0.038,0.028,0.031,0.034,0.023,0.022,0.019,0.02,0.022,0.054,0.045,0.023,0.025,0.006,0.023,0.038,0.028,0.018,0.011,0.019,0.035,0.05,0.019,0.014,0.019,0.02,0.015,0.015,0.013,0.029,0.022,0.023,0.022,0.018,0.018,0.018,0.016,0.021,0.019,0.011,0.025,0.023,0.012,0.022,0.029,0.018,0.018,0.011,0.02,0.022,0.017,0.015,0.018,1661,4912,2742,2414,1853,2525,3069,2967,1138,1342,564.0,2263,3167,1293,2442,11228,13863,2215,4299,4124,3413,4864,2066,5967,5535,5148,5543,3488,4609,4749,2568,1182,834,5148,1565,1868,5323,4789,360,472,1451,2397,4536,1881,3398,1311,728,1958,2424,1532,658,3509,1909,5385,465,3927,1125,3227,884,1423,766,2368,507,802,2513,1551,3524,2594,1324,957,1207,1598,8187,324,0.154,0.12,0.119,0.122,0.15,0.138,0.111,0.12,0.15,0.137,0.117,0.136,0.135,0.134,0.121,0.146,0.124,0.115,0.149,0.14,0.101,0.156,0.141,0.138,0.147,0.121,0.111,0.096,0.097,0.122,0.151,0.136,0.14,0.124,0.049,0.106,0.14,0.126,0.088,0.082,0.103,0.151,0.151,0.112,0.085,0.107,0.116,0.073,0.091,0.093,0.14,0.109,0.113,0.112,0.118,0.104,0.117,0.101,0.122,0.107,0.087,0.119,0.117,0.102,0.123,0.128,0.098,0.102,0.085,0.111,0.119,0.108,0.09,0.126,443,1638,855,858,739,830,1048,903,356,340,181.0,1120,825,310,773,3270,4008,407,1261,1767,737,1457,797,1439,1656,1604,1836,1302,1384,1517,623,458,330,1199,493,649,1230,1185,200,227,715,1185,843,1186,2087,656,296,870,1093,563,381,1616,875,2170,244,2027,474,1373,486,791,374,847,264,303,1162,783,1723,1209,686,415,586,744,3325,183,3.463,2.761,2.939,2.747,2.355,2.607,2.393,2.872,2.428,3.032,2.652,1.748,2.856,2.929,2.42,2.629,2.704,3.812,2.871,2.06,3.379,2.556,2.262,2.965,2.698,2.655,2.38,2.137,2.542,2.454,2.985,2.607,2.147,3.026,2.861,2.518,3.126,2.93,2.066,2.287,2.631,1.863,3.562,1.846,1.826,2.408,3.169,2.68,2.645,2.87,2.107,2.245,2.342,2.542,2.375,2.198,2.294,2.491,2.033,2.011,2.174,2.608,2.169,2.397,2.4,2.276,2.2,2.421,2.204,2.546,2.329,2.467,2.621,2.223,0.68,0.723,0.819,0.441,0.471,0.405,0.538,0.53,0.486,0.43,0.552,0.476,0.417,0.437,0.498,0.579,0.571,0.771,0.605,0.646,0.843,0.511,0.53,0.615,0.501,0.624,0.465,0.502,0.485,0.458,0.849,1.023,0.407,0.611,0.914,0.487,0.734,0.602,0.391,0.56,0.6,0.533,0.71,0.623,0.511,0.596,0.36,0.752,0.57,0.709,0.345,0.513,0.554,0.56,0.318,0.458,0.378,0.648,0.358,0.385,0.311,0.678,0.405,0.988,0.487,0.828,0.449,0.471,0.387,0.547,0.468,0.538,0.479,0.358
-230,Epileptic,M,27.0,0.7,2.5,1.1,1.3,1.8,2.8,1.7,1.5,1.2,0.8,0.3,3.4,1.3,0.7,1.1,5.1,7.8,0.6,2.6,7.1,1.6,4.0,2.0,4.1,4.2,3.9,3.6,3.6,2.7,2.5,1.0,1.2,0.4,2.0,0.4,0.5,3.6,2.9,0.2,0.2,0.9,4.9,4.3,4.5,2.3,0.8,0.4,0.9,1.3,0.7,0.6,1.6,1.4,2.0,0.5,2.3,0.8,1.8,1.6,1.0,0.5,0.9,0.3,0.6,2.9,0.9,2.6,0.9,0.9,0.5,0.8,1.8,3.5,0.3,6,21,10,10,15,25,12,12,12,6,4.0,34,14,7,11,49,60,5,26,57,14,39,21,36,38,38,45,29,22,28,12,11,4,22,2,6,38,30,1,1,5,47,33,30,15,6,2,5,7,6,4,10,9,11,4,18,6,14,13,8,4,7,3,4,25,8,21,6,5,4,5,15,25,3,0.045,0.026,0.024,0.028,0.039,0.04,0.028,0.029,0.045,0.035,0.029,0.035,0.032,0.038,0.032,0.034,0.03,0.024,0.036,0.044,0.031,0.039,0.034,0.042,0.043,0.036,0.029,0.04,0.033,0.026,0.035,0.055,0.031,0.034,0.016,0.02,0.044,0.033,0.017,0.015,0.022,0.041,0.056,0.033,0.019,0.023,0.016,0.016,0.02,0.025,0.031,0.02,0.023,0.02,0.02,0.019,0.03,0.02,0.025,0.024,0.019,0.021,0.016,0.024,0.026,0.031,0.021,0.016,0.019,0.017,0.022,0.027,0.019,0.016,1084,4665,2347,2080,2222,3396,2710,2841,2106,1249,606.0,3099,2544,1222,2487,11195,16750,2264,4439,5498,3492,4255,2070,6381,4330,5951,6284,4128,5407,4846,2186,977,822,5006,1532,1505,5827,7076,245,288,1095,3352,6966,3313,3170,1150,769,1791,1883,1296,581,2741,1948,4272,691,3517,1095,2944,1483,1148,845,1976,456,1111,3647,803,3949,2108,1558,979,1008,2485,6954,618,0.111,0.123,0.113,0.116,0.135,0.156,0.111,0.129,0.173,0.142,0.095,0.153,0.129,0.135,0.124,0.136,0.124,0.103,0.152,0.169,0.111,0.132,0.144,0.149,0.12,0.138,0.116,0.123,0.113,0.127,0.136,0.13,0.115,0.134,0.073,0.103,0.164,0.144,0.116,0.103,0.105,0.15,0.182,0.14,0.099,0.115,0.092,0.088,0.104,0.119,0.13,0.102,0.11,0.094,0.13,0.103,0.136,0.108,0.123,0.116,0.113,0.109,0.126,0.114,0.122,0.121,0.109,0.092,0.103,0.106,0.119,0.133,0.099,0.108,318,1584,802,808,765,1150,934,907,542,391,205.0,1676,761,323,643,2690,4371,461,1255,2801,892,1573,1031,1648,1490,1890,2197,1485,1283,1594,603,424,238,1140,442,496,1410,1537,144,144,619,1902,1374,2194,1869,563,328,826,964,478,353,1271,936,1598,312,1857,484,1470,984,674,418,717,263,414,1828,478,1993,890,717,431,520,992,2928,259,3.25,2.972,2.843,2.665,2.453,2.418,2.398,2.734,2.929,2.562,2.476,1.728,2.622,2.762,2.785,3.07,3.14,3.466,2.716,1.765,2.99,2.208,1.895,2.918,2.329,2.582,2.296,2.245,3.118,2.44,2.734,2.434,2.723,3.132,2.836,2.513,2.901,3.352,1.954,2.258,2.282,1.608,3.175,1.826,1.917,2.324,2.567,2.752,2.456,2.7,1.989,2.258,2.389,2.653,2.42,2.1,2.624,2.332,1.727,1.85,2.549,2.831,2.131,2.816,2.06,2.049,2.166,2.676,2.555,2.402,2.251,2.62,2.484,2.535,0.925,0.621,0.553,0.558,0.73,0.731,0.696,0.518,0.606,0.458,0.608,0.557,0.541,0.51,0.614,0.633,0.587,0.844,0.665,0.551,0.83,0.696,0.548,0.748,0.706,0.688,0.674,0.643,0.633,0.583,0.71,0.711,0.431,0.733,0.622,0.65,0.843,0.715,0.46,0.465,0.506,0.533,0.849,0.695,0.622,0.558,0.659,0.641,0.529,0.599,0.409,0.461,0.492,0.549,0.745,0.416,0.54,0.517,0.393,0.412,0.371,0.789,0.31,0.616,0.532,0.589,0.491,0.465,0.427,0.733,0.469,0.716,0.521,0.575
-231,Epileptic,M,22.0,1.7,2.9,1.3,1.7,2.3,2.0,1.5,1.8,1.7,1.0,0.5,3.6,1.5,0.8,1.7,7.7,10.0,1.4,2.7,4.5,1.1,5.0,2.1,3.9,5.6,4.8,4.0,3.9,2.8,3.3,1.8,1.5,0.9,2.7,0.7,1.0,6.0,3.7,0.2,0.3,1.2,4.6,3.3,3.8,2.7,1.1,0.5,1.0,1.8,0.8,0.6,1.6,1.7,3.2,0.4,2.6,1.2,2.0,1.3,1.5,1.0,2.1,0.5,0.6,2.5,2.0,3.5,1.2,1.3,1.3,1.7,1.7,5.1,0.5,11,25,12,12,19,21,14,19,16,13,5.0,35,21,11,20,84,94,10,30,45,8,51,22,49,58,58,40,35,27,32,16,11,7,30,4,11,56,40,2,2,9,38,35,29,17,9,3,5,10,8,5,15,14,26,3,19,9,16,12,12,7,14,5,5,21,21,25,8,9,13,10,12,42,4,0.054,0.026,0.026,0.027,0.038,0.03,0.025,0.031,0.043,0.037,0.031,0.037,0.032,0.04,0.032,0.035,0.035,0.046,0.03,0.037,0.027,0.038,0.026,0.037,0.043,0.036,0.034,0.031,0.025,0.03,0.042,0.05,0.034,0.03,0.018,0.018,0.045,0.035,0.018,0.015,0.022,0.038,0.054,0.028,0.018,0.022,0.019,0.015,0.021,0.027,0.027,0.018,0.026,0.022,0.017,0.019,0.018,0.021,0.021,0.02,0.022,0.034,0.026,0.022,0.023,0.027,0.022,0.018,0.021,0.04,0.025,0.026,0.019,0.016,1656,5392,2660,3012,2785,3204,3047,3481,2305,2021,1124.0,3812,3495,1262,3174,13071,19458,2609,5636,5373,3360,5754,2663,7160,6590,7588,5529,4594,7220,5984,2479,1378,1249,5868,2133,2485,9217,7047,354,505,1695,3829,5863,4067,3761,1780,1054,2358,2972,1690,621,3095,2736,5722,494,4046,2099,3384,1973,2111,1430,2370,666,832,3569,2227,4549,2359,2120,1561,2221,2579,9744,842,0.161,0.115,0.121,0.114,0.142,0.12,0.105,0.127,0.159,0.149,0.12,0.154,0.126,0.144,0.131,0.134,0.138,0.142,0.127,0.154,0.091,0.127,0.12,0.145,0.135,0.129,0.121,0.112,0.103,0.127,0.143,0.129,0.102,0.13,0.082,0.098,0.159,0.148,0.111,0.105,0.105,0.135,0.188,0.124,0.098,0.111,0.098,0.082,0.104,0.115,0.127,0.105,0.116,0.108,0.117,0.102,0.106,0.109,0.111,0.104,0.104,0.132,0.126,0.119,0.121,0.124,0.113,0.095,0.106,0.166,0.117,0.119,0.105,0.099,536,1805,902,1038,1055,1240,1000,1167,693,576,337.0,1746,897,368,859,3855,5036,527,1509,2271,787,2069,1312,1917,2139,2407,2007,1851,1720,1898,678,557,476,1496,611,916,2276,1791,187,256,896,1951,1266,2263,2170,836,427,997,1327,588,360,1540,1186,2374,297,2078,960,1561,970,1226,686,1027,359,448,1705,1151,2342,1089,960,602,1021,1055,4173,502,3.066,2.888,2.738,2.644,2.242,2.275,2.417,2.609,2.519,2.896,2.916,1.948,2.847,2.504,2.627,2.571,2.952,3.519,2.833,2.048,2.907,2.269,1.863,2.752,2.409,2.577,2.255,2.069,3.111,2.466,2.712,2.876,2.112,2.783,3.114,2.587,3.06,2.888,2.079,2.322,2.295,1.784,3.125,2.061,1.976,2.099,2.893,3.054,2.729,3.042,2.262,2.141,2.238,2.357,2.001,2.093,2.525,2.329,2.057,1.914,2.257,2.198,2.134,2.2,2.183,2.245,2.175,2.522,2.491,2.675,2.373,2.558,2.416,2.1,0.701,0.652,0.565,0.692,0.641,0.543,0.65,0.549,0.687,0.523,0.619,0.458,0.532,0.57,0.676,0.662,0.645,0.646,0.635,0.543,0.713,0.612,0.54,0.673,0.688,0.548,0.595,0.553,0.553,0.661,0.797,0.958,0.445,0.706,0.953,0.6,0.733,0.616,0.408,0.48,0.518,0.474,0.868,0.61,0.598,0.573,0.745,0.684,0.528,0.549,0.697,0.466,0.595,0.552,0.338,0.486,0.476,0.522,0.487,0.425,0.469,0.698,0.588,0.665,0.521,0.893,0.468,0.461,0.462,0.922,0.511,0.598,0.543,0.348
-232,Epileptic,F,37.0,0.8,1.3,0.6,1.3,1.4,2.3,1.4,1.6,1.2,0.7,0.3,3.0,1.3,0.5,1.3,5.4,8.5,1.0,2.1,4.5,0.5,2.1,2.4,4.3,2.7,3.5,3.3,2.8,2.8,3.8,1.2,0.8,0.6,2.0,0.2,1.5,2.7,2.5,0.3,0.2,1.4,2.9,2.2,2.7,3.3,0.7,0.3,0.9,1.4,0.5,0.6,1.3,1.8,2.7,0.2,2.5,0.5,1.3,1.1,1.0,0.5,1.1,0.2,0.5,1.5,0.7,2.2,0.9,1.1,0.4,0.8,1.0,3.2,0.4,7,11,5,11,18,17,12,14,16,6,3.0,25,16,7,15,53,81,9,23,35,5,22,20,42,31,35,37,25,20,39,17,6,4,21,1,17,29,28,3,1,8,25,20,24,24,5,2,6,7,4,6,9,16,21,2,20,6,12,8,10,4,9,2,4,13,13,17,7,7,5,5,8,28,2,0.034,0.017,0.011,0.026,0.029,0.037,0.022,0.029,0.038,0.03,0.025,0.034,0.032,0.032,0.033,0.033,0.029,0.033,0.029,0.033,0.011,0.029,0.029,0.036,0.033,0.03,0.025,0.028,0.024,0.032,0.033,0.031,0.021,0.026,0.007,0.027,0.03,0.026,0.019,0.018,0.026,0.032,0.034,0.029,0.027,0.015,0.017,0.016,0.018,0.011,0.03,0.018,0.026,0.021,0.02,0.019,0.018,0.018,0.03,0.026,0.024,0.021,0.017,0.019,0.021,0.03,0.021,0.017,0.02,0.015,0.024,0.02,0.019,0.017,1388,3782,1875,2108,2558,2773,2494,2723,2059,1197,530.0,2628,3275,1319,2793,9994,18180,2805,3777,3743,3008,3631,2823,6227,4987,6654,6271,4090,5099,5605,2111,988,1324,5620,2025,2159,6074,6601,553,461,1764,3053,6204,2510,2933,1269,762,2233,2541,1459,481,2641,2570,5883,420,3872,1073,2345,1233,1194,664,2117,328,848,2338,919,3271,1992,1701,834,1092,1975,6790,531,0.113,0.095,0.081,0.115,0.129,0.15,0.096,0.128,0.143,0.138,0.101,0.131,0.137,0.134,0.148,0.142,0.128,0.124,0.127,0.128,0.07,0.14,0.124,0.142,0.15,0.136,0.119,0.112,0.102,0.142,0.125,0.091,0.088,0.116,0.056,0.125,0.132,0.125,0.118,0.105,0.123,0.138,0.137,0.128,0.13,0.099,0.093,0.095,0.102,0.091,0.157,0.099,0.125,0.107,0.119,0.109,0.109,0.105,0.128,0.131,0.12,0.109,0.125,0.111,0.111,0.115,0.117,0.102,0.109,0.124,0.122,0.108,0.1,0.102,424,1254,755,855,864,1066,891,888,587,340,194.0,1398,840,318,738,2841,4871,527,1212,2032,782,1120,1285,1803,1441,2011,2254,1443,1515,1912,604,494,425,1263,528,761,1487,1477,232,193,814,1339,1131,1749,2082,653,310,975,1165,652,354,1183,1084,2292,154,1998,524,1206,671,654,339,893,175,458,1251,440,1566,892,871,433,546,858,2794,283,3.16,2.697,2.378,2.634,2.443,2.435,2.418,2.855,2.534,2.984,2.457,1.751,3.004,3.111,2.802,2.685,2.978,3.745,2.641,1.65,3.051,2.46,2.021,2.694,2.708,2.777,2.286,2.355,2.673,2.397,2.528,2.133,2.46,3.18,3.457,2.644,3.026,3.16,2.631,2.933,2.953,2.01,3.629,1.772,1.649,2.114,3.374,2.89,2.666,2.476,1.787,2.402,2.254,2.592,2.666,2.195,2.247,2.287,2.167,1.985,2.588,2.436,2.112,2.037,2.059,2.267,2.347,2.57,2.297,2.418,2.472,2.705,2.637,2.326,0.674,0.702,0.615,0.447,0.711,0.53,0.567,0.595,0.67,0.438,0.807,0.498,0.545,0.465,0.426,0.543,0.635,0.789,0.619,0.516,0.968,0.448,0.483,0.627,0.477,0.581,0.513,0.599,0.696,0.531,0.683,0.656,0.55,0.61,0.845,0.448,0.711,0.608,0.46,0.443,0.476,0.634,0.791,0.68,0.518,0.552,0.553,0.648,0.534,0.68,0.39,0.474,0.58,0.608,0.6,0.383,0.436,0.554,0.347,0.449,0.413,0.687,0.534,0.813,0.542,0.591,0.563,0.408,0.464,0.539,0.413,0.588,0.489,0.439
-233,Epileptic,M,52.0,0.7,2.0,1.4,1.5,1.6,2.1,1.2,1.8,1.2,1.0,0.5,3.9,1.4,0.5,0.8,5.3,8.1,0.9,1.6,5.8,0.9,2.4,1.7,3.3,3.0,3.3,3.3,2.1,2.3,2.6,1.3,1.3,0.3,1.7,0.3,0.5,3.5,3.6,0.1,0.2,0.7,1.8,1.8,3.8,2.1,0.6,0.4,1.0,1.1,0.4,0.6,2.2,1.4,2.5,0.3,3.0,0.4,1.9,0.6,1.6,0.9,1.8,0.3,0.3,1.8,1.8,2.4,1.7,1.0,0.6,1.0,1.4,4.0,0.3,4,27,18,14,13,20,12,17,14,8,4.0,40,13,6,8,52,87,9,19,54,8,30,17,38,40,36,39,20,19,34,16,13,3,22,2,5,41,40,1,1,3,22,20,28,13,4,2,7,6,5,5,16,10,14,2,22,3,16,7,14,9,11,3,4,14,23,19,13,5,6,8,9,36,2,0.034,0.023,0.018,0.026,0.042,0.033,0.023,0.033,0.045,0.044,0.036,0.033,0.032,0.028,0.022,0.034,0.03,0.039,0.028,0.038,0.02,0.031,0.024,0.037,0.03,0.031,0.027,0.029,0.024,0.029,0.039,0.046,0.024,0.027,0.009,0.016,0.039,0.033,0.014,0.014,0.016,0.026,0.035,0.027,0.018,0.018,0.02,0.019,0.016,0.012,0.031,0.025,0.03,0.022,0.041,0.016,0.016,0.024,0.019,0.021,0.03,0.029,0.026,0.018,0.02,0.027,0.024,0.024,0.016,0.02,0.024,0.021,0.015,0.02,1276,4344,2814,2453,1700,2831,2152,2881,1765,1415,778.0,3559,2661,1017,2059,9772,15682,2088,3730,6194,3511,4137,2237,6112,6140,5817,5692,2749,5195,4781,1850,1572,632,4917,1956,1425,6943,7620,324,399,1102,1938,5427,3972,2768,1041,762,1890,2190,1485,636,2639,1871,3830,389,4840,954,2867,725,1849,1004,2355,453,786,2783,1580,3183,2656,1771,821,1269,2436,9329,396,0.12,0.115,0.112,0.12,0.14,0.15,0.113,0.141,0.152,0.16,0.142,0.138,0.134,0.146,0.115,0.137,0.135,0.136,0.13,0.149,0.094,0.137,0.122,0.141,0.134,0.14,0.124,0.11,0.099,0.141,0.136,0.142,0.101,0.117,0.066,0.093,0.149,0.146,0.09,0.082,0.09,0.132,0.142,0.122,0.098,0.1,0.109,0.102,0.092,0.088,0.136,0.12,0.128,0.104,0.154,0.102,0.087,0.121,0.119,0.121,0.133,0.124,0.129,0.116,0.109,0.128,0.117,0.115,0.089,0.12,0.122,0.104,0.093,0.114,341,1589,1151,905,700,1097,787,948,486,374,237.0,1765,737,283,597,2741,4584,428,1010,2486,732,1400,994,1578,1854,1729,1961,1073,1479,1561,548,552,224,1144,504,528,1540,1851,207,214,594,1141,1041,2285,1833,537,319,775,993,574,365,1316,801,1718,149,2598,403,1339,482,1012,506,894,226,362,1392,990,1690,1211,901,461,680,1076,4053,187,3.595,2.643,2.333,2.488,2.13,2.287,2.314,2.613,2.736,2.908,2.837,1.795,2.754,2.795,2.681,2.7,2.69,3.873,2.92,2.149,3.426,2.324,1.948,2.895,2.572,2.751,2.279,2.063,2.634,2.456,2.469,2.691,2.33,3.033,3.503,2.527,3.09,2.961,1.842,2.178,2.44,1.57,3.466,1.94,1.719,2.051,2.972,3.011,2.594,2.473,2.124,2.15,2.301,2.372,3.142,2.047,2.622,2.541,1.65,1.995,2.308,2.706,2.391,2.082,2.124,1.895,2.005,2.403,2.313,1.991,2.114,2.511,2.445,2.595,0.713,0.437,0.496,0.557,0.62,0.451,0.502,0.517,0.59,0.663,0.664,0.441,0.454,0.415,0.425,0.551,0.562,0.746,0.53,0.607,0.771,0.508,0.482,0.607,0.523,0.442,0.45,0.458,0.518,0.522,0.574,0.849,0.319,0.642,0.857,0.503,0.78,0.605,0.267,0.434,0.628,0.443,0.815,0.551,0.442,0.397,0.473,0.737,0.496,0.583,0.319,0.427,0.44,0.385,0.37,0.398,0.49,0.554,0.344,0.46,0.345,0.647,0.432,0.892,0.484,0.823,0.443,0.388,0.319,0.484,0.398,0.468,0.409,0.401
-234,Epileptic,F,47.0,0.6,2.1,0.9,1.2,1.4,2.1,1.8,1.3,1.4,0.6,0.4,2.4,0.9,0.6,1.4,5.3,10.2,1.5,1.9,3.9,1.3,2.2,1.0,2.6,6.5,3.8,3.9,2.4,3.9,3.5,1.4,1.0,0.4,3.0,0.5,1.0,3.3,3.1,0.2,0.2,1.1,3.0,2.8,1.8,2.5,0.7,0.7,0.8,1.1,0.8,0.2,1.8,0.9,2.5,0.5,2.8,0.8,1.2,0.3,0.8,0.6,1.6,0.1,0.7,2.3,1.7,2.7,1.5,1.0,0.4,0.9,1.3,3.7,0.1,6,21,9,13,11,21,13,14,17,8,5.0,20,13,8,13,52,89,15,21,37,21,21,9,32,42,35,35,20,30,33,16,11,5,32,4,11,38,34,2,1,8,20,31,14,15,5,3,6,7,9,2,12,9,18,3,22,8,10,3,5,3,11,1,6,21,23,17,12,5,3,6,11,28,1,0.041,0.025,0.021,0.027,0.031,0.028,0.028,0.028,0.048,0.041,0.039,0.038,0.026,0.036,0.038,0.037,0.046,0.063,0.032,0.036,0.027,0.031,0.026,0.038,0.046,0.033,0.032,0.026,0.042,0.039,0.039,0.061,0.033,0.038,0.02,0.02,0.034,0.036,0.017,0.018,0.024,0.047,0.042,0.028,0.02,0.017,0.028,0.02,0.016,0.021,0.021,0.026,0.023,0.024,0.022,0.024,0.019,0.017,0.022,0.02,0.029,0.028,0.015,0.019,0.033,0.022,0.025,0.024,0.018,0.02,0.025,0.019,0.02,0.018,1525,3927,1935,2050,1974,3134,2595,2492,1713,1338,719.0,2153,2576,1099,2525,8049,16003,2124,3385,4003,3769,2265,1165,5568,3768,4754,4989,3331,5033,4674,2278,716,818,5322,2162,1802,6827,5792,355,398,1400,1428,7152,1708,3010,1196,858,1843,2147,1943,354,2078,1406,3952,542,3350,1685,2353,457,982,468,2274,229,1019,2432,1484,2866,2019,1422,743,1072,2002,6330,344,0.114,0.118,0.111,0.134,0.136,0.118,0.114,0.136,0.167,0.154,0.128,0.141,0.129,0.162,0.135,0.145,0.164,0.171,0.133,0.152,0.105,0.115,0.119,0.143,0.153,0.133,0.123,0.108,0.132,0.152,0.139,0.142,0.146,0.138,0.085,0.106,0.136,0.131,0.103,0.101,0.112,0.166,0.153,0.12,0.1,0.101,0.124,0.089,0.092,0.115,0.121,0.11,0.104,0.105,0.119,0.12,0.124,0.104,0.113,0.109,0.125,0.126,0.091,0.108,0.138,0.106,0.119,0.112,0.1,0.133,0.122,0.105,0.104,0.127,377,1407,769,797,790,1202,1005,832,592,362,222.0,1110,735,343,682,2589,4436,455,1057,1849,869,966,590,1525,1991,1792,2116,1309,1613,1644,681,331,256,1434,540,770,1846,1431,206,199,712,1040,1381,1108,1907,666,361,767,1045,649,202,1085,696,1601,351,1846,656,1192,245,556,281,885,163,543,1254,1102,1582,988,768,323,563,1100,2918,135,3.488,2.542,2.349,2.35,2.05,2.18,2.06,2.563,2.265,2.833,2.672,1.665,2.614,2.574,2.598,2.343,2.602,3.459,2.573,1.856,3.177,1.992,1.793,2.69,1.829,2.282,2.002,2.052,2.451,2.311,2.592,2.196,2.526,2.652,3.265,2.209,2.801,2.913,1.847,2.132,2.282,1.359,3.396,1.792,1.793,1.955,2.763,2.669,2.359,2.889,2.075,1.994,2.072,2.267,1.862,2.02,2.497,2.323,2.025,1.971,2.011,2.403,1.514,2.25,2.029,1.613,2.0,2.172,2.078,2.41,2.02,2.279,2.34,2.372,0.856,0.72,0.615,0.528,0.69,0.564,0.566,0.573,0.603,0.548,0.796,0.593,0.569,0.523,0.745,0.647,0.779,0.903,0.485,0.545,0.83,0.633,0.494,0.728,0.514,0.624,0.533,0.587,0.563,0.583,0.576,0.837,0.629,0.774,0.951,0.556,0.869,0.626,0.411,0.501,0.484,0.61,0.826,0.642,0.551,0.514,0.419,0.835,0.583,0.665,0.393,0.458,0.51,0.59,0.391,0.433,0.536,0.436,0.785,0.34,0.357,0.682,0.289,0.634,0.457,0.484,0.494,0.548,0.45,0.949,0.425,0.368,0.452,0.35
-235,Epileptic,M,52.0,0.5,2.1,1.0,1.0,1.2,1.9,1.1,1.7,0.9,0.9,0.4,3.0,1.6,0.3,1.3,4.3,8.5,0.9,1.8,4.3,0.6,2.1,1.8,2.6,2.3,3.4,2.8,2.0,2.1,2.7,1.7,1.2,0.6,2.4,0.4,0.6,3.6,3.3,0.1,0.2,1.3,2.9,2.5,2.5,2.3,0.6,0.5,1.0,1.2,0.7,0.3,2.1,0.9,2.9,0.2,2.6,0.9,1.2,1.1,0.9,0.5,1.3,0.3,0.4,1.5,2.1,2.4,0.9,0.9,0.5,0.8,1.2,4.8,0.3,4,19,7,7,13,21,13,13,10,10,7.0,33,17,5,12,43,76,8,22,42,8,22,17,26,31,32,39,19,17,29,17,11,6,27,2,5,39,34,1,1,8,31,22,21,12,3,3,6,6,8,3,18,8,19,2,20,7,12,10,8,4,10,3,5,12,24,20,6,4,5,6,10,34,3,0.03,0.022,0.017,0.019,0.031,0.03,0.019,0.032,0.034,0.044,0.039,0.033,0.029,0.024,0.027,0.033,0.029,0.033,0.029,0.032,0.017,0.031,0.028,0.035,0.03,0.028,0.028,0.022,0.026,0.027,0.038,0.045,0.028,0.03,0.014,0.015,0.033,0.032,0.012,0.018,0.023,0.038,0.039,0.024,0.018,0.012,0.026,0.014,0.016,0.018,0.018,0.024,0.021,0.024,0.026,0.017,0.026,0.015,0.027,0.018,0.025,0.029,0.013,0.016,0.018,0.029,0.02,0.015,0.017,0.022,0.02,0.021,0.019,0.014,1340,4865,2495,2266,1727,3314,2823,2674,1779,1503,535.0,3523,3248,1007,2456,8366,17223,2357,3643,4951,2929,3724,2354,4622,4987,6762,5157,3216,4751,5483,1962,1247,976,6115,1720,1868,7032,7144,350,409,1674,3342,5149,2923,3240,1697,760,2418,2323,1651,500,3423,1726,4792,339,4296,1358,2721,1285,1261,642,1965,546,860,2634,1554,4053,1735,1435,834,1109,2301,9056,511,0.105,0.109,0.096,0.1,0.127,0.14,0.099,0.13,0.155,0.155,0.139,0.154,0.137,0.117,0.128,0.13,0.124,0.125,0.138,0.146,0.077,0.14,0.138,0.132,0.143,0.121,0.131,0.097,0.092,0.129,0.132,0.123,0.113,0.127,0.073,0.089,0.136,0.142,0.093,0.093,0.11,0.151,0.156,0.106,0.096,0.079,0.123,0.081,0.088,0.109,0.11,0.112,0.105,0.109,0.142,0.104,0.128,0.098,0.129,0.112,0.119,0.123,0.097,0.102,0.103,0.121,0.104,0.09,0.091,0.121,0.113,0.113,0.102,0.102,341,1526,857,838,699,1185,917,914,503,410,205.0,1439,949,250,753,2348,4738,453,1049,2043,702,1161,1011,1336,1474,1987,1814,1220,1311,1763,714,532,353,1366,446,613,1937,1683,179,204,817,1387,1143,1736,1887,808,305,966,1073,668,281,1495,783,1924,126,2212,548,1298,659,675,361,744,345,488,1286,1059,1935,846,775,359,572,1067,3873,281,3.633,2.808,2.747,2.625,2.277,2.348,2.554,2.763,2.619,2.905,2.264,2.111,2.705,2.812,2.508,2.636,2.826,3.712,2.852,2.085,3.037,2.509,2.081,2.6,2.673,2.761,2.287,2.094,2.704,2.456,2.114,2.503,2.334,3.105,3.299,2.797,2.69,3.092,2.204,2.269,2.593,2.082,3.212,1.849,1.939,2.262,2.962,2.919,2.589,2.633,2.165,2.309,2.331,2.466,2.938,2.103,2.697,2.201,2.07,2.039,2.411,2.645,1.843,2.057,2.221,1.783,2.063,2.304,2.158,2.61,2.301,2.47,2.505,2.131,0.668,0.653,0.573,0.516,0.473,0.553,0.635,0.479,0.581,0.736,0.856,0.467,0.414,0.576,0.499,0.515,0.616,0.696,0.567,0.536,0.74,0.479,0.481,0.762,0.552,0.517,0.455,0.563,0.498,0.558,0.681,1.041,0.364,0.658,0.61,0.61,0.671,0.612,0.442,0.463,0.495,0.527,0.719,0.579,0.556,0.554,0.688,0.759,0.386,0.673,0.32,0.456,0.531,0.5,0.529,0.398,0.472,0.471,0.368,0.315,0.451,0.804,0.299,0.739,0.506,0.52,0.503,0.325,0.417,0.532,0.512,0.636,0.448,0.349
-236,Epileptic,F,17.5,0.6,1.7,0.9,0.9,1.1,2.3,1.2,1.9,1.0,0.8,0.2,3.2,2.2,0.6,1.4,5.4,8.6,0.9,2.2,6.6,0.9,3.1,1.5,3.2,2.7,4.2,3.1,2.6,2.4,3.4,1.0,0.9,0.7,2.8,0.4,1.0,2.7,3.1,0.2,0.4,1.0,3.0,2.0,3.0,2.5,0.5,0.6,0.9,1.1,0.6,0.5,2.0,1.2,2.3,0.5,2.3,0.9,1.9,0.9,0.7,0.5,1.3,0.4,0.5,2.2,1.0,2.4,1.1,0.6,0.4,1.0,0.7,3.7,0.5,3,14,9,10,12,20,11,15,9,7,2.0,26,22,7,17,54,77,8,22,44,8,28,16,29,29,41,33,23,18,38,11,6,8,25,2,9,29,34,1,2,5,25,15,22,17,5,2,8,7,6,4,15,9,15,4,19,6,14,6,7,4,9,3,3,16,12,22,9,4,4,9,7,26,4,0.026,0.022,0.018,0.018,0.032,0.035,0.023,0.029,0.039,0.031,0.026,0.034,0.035,0.033,0.031,0.036,0.029,0.03,0.03,0.043,0.018,0.033,0.029,0.036,0.03,0.036,0.026,0.032,0.024,0.031,0.039,0.035,0.027,0.032,0.014,0.025,0.035,0.032,0.019,0.018,0.018,0.031,0.04,0.029,0.024,0.016,0.022,0.016,0.014,0.017,0.031,0.021,0.022,0.02,0.016,0.02,0.021,0.024,0.022,0.018,0.026,0.028,0.019,0.023,0.024,0.022,0.021,0.02,0.012,0.013,0.021,0.013,0.017,0.024,1649,4037,2120,1983,1612,3247,2256,3654,1559,1422,473.0,2771,4619,1331,3224,11188,17819,2267,3622,4252,2535,4453,1891,6243,5686,7301,5489,3073,5503,5556,2037,1053,1408,6331,1502,2337,5361,6407,308,701,1688,3071,4882,2483,2756,1140,893,2082,2677,1400,493,3774,1984,4470,869,3441,1459,2808,1373,1157,599,1820,513,962,2944,1132,3555,1925,1511,776,1606,1962,8596,548,0.09,0.106,0.101,0.098,0.129,0.141,0.097,0.127,0.114,0.13,0.102,0.131,0.146,0.137,0.13,0.137,0.127,0.118,0.133,0.146,0.088,0.146,0.143,0.136,0.131,0.151,0.132,0.125,0.107,0.132,0.135,0.095,0.127,0.13,0.073,0.103,0.133,0.132,0.096,0.104,0.098,0.135,0.136,0.127,0.113,0.096,0.105,0.094,0.086,0.108,0.127,0.104,0.109,0.1,0.106,0.114,0.111,0.114,0.106,0.117,0.127,0.124,0.107,0.111,0.113,0.13,0.113,0.104,0.089,0.104,0.118,0.094,0.095,0.126,439,1296,791,758,612,1162,759,1082,420,381,139.0,1361,1090,330,835,2690,4823,486,1139,2390,717,1464,825,1513,1540,2088,1868,1252,1463,1817,495,413,463,1417,480,773,1292,1504,170,312,773,1431,897,1686,1723,552,367,968,1211,496,318,1542,910,1801,463,1718,616,1351,702,588,327,681,333,386,1365,643,1811,903,681,387,760,856,3311,293,3.446,2.847,2.575,2.583,2.297,2.46,2.424,3.162,2.597,3.07,3.272,1.822,3.097,2.842,2.794,2.926,3.043,3.653,2.653,1.693,2.946,2.428,2.06,2.94,2.735,2.893,2.409,2.025,2.879,2.517,2.852,2.359,2.457,3.164,2.738,2.665,3.021,2.969,2.179,2.654,2.835,1.915,3.643,1.731,1.876,2.172,3.216,2.709,2.713,3.257,2.037,2.502,2.267,2.546,2.355,2.244,2.58,2.549,2.287,2.172,2.408,2.632,1.9,3.045,2.366,2.207,2.154,2.444,2.652,2.3,2.329,2.554,2.791,2.251,0.764,0.777,0.591,0.557,0.594,0.604,0.558,0.614,0.578,0.478,0.836,0.494,0.507,0.726,0.501,0.645,0.565,0.612,0.584,0.487,0.609,0.524,0.524,0.726,0.549,0.523,0.472,0.443,0.551,0.585,0.888,0.896,0.383,0.539,0.606,0.52,0.692,0.595,0.301,0.461,0.605,0.474,0.835,0.653,0.575,0.537,0.456,0.699,0.464,0.664,0.386,0.505,0.593,0.577,0.287,0.387,0.436,0.635,0.41,0.476,0.269,0.713,0.359,0.944,0.648,0.834,0.465,0.389,0.473,0.696,0.509,0.665,0.578,0.624
-237,Epileptic,F,37.0,0.9,2.8,1.7,1.9,1.8,1.6,2.0,1.4,1.4,0.6,0.4,2.0,1.1,0.4,1.2,5.5,7.6,0.7,2.2,3.9,2.1,4.2,2.7,3.5,4.0,2.6,3.9,3.2,1.8,2.8,1.8,1.4,0.5,2.1,0.8,0.6,4.0,2.8,0.2,0.1,1.1,3.1,3.7,1.7,1.8,0.6,0.4,0.9,1.5,0.8,0.5,1.9,2.0,3.1,0.5,2.1,0.6,1.5,1.3,1.5,0.7,2.3,0.1,0.8,1.5,2.1,2.6,1.1,0.7,0.6,0.9,0.9,5.4,0.4,7,24,12,14,16,18,18,13,12,5,4.0,15,15,4,14,53,67,6,23,33,16,41,20,36,40,29,38,41,20,30,22,15,5,24,4,5,38,32,2,1,7,24,25,13,12,5,2,5,8,5,4,13,17,24,2,16,4,12,11,10,5,14,1,7,9,17,18,6,4,7,7,7,40,4,0.037,0.024,0.023,0.028,0.035,0.032,0.029,0.026,0.036,0.033,0.037,0.034,0.024,0.034,0.026,0.027,0.029,0.033,0.035,0.036,0.031,0.038,0.037,0.036,0.036,0.025,0.029,0.028,0.019,0.031,0.038,0.049,0.021,0.025,0.021,0.016,0.034,0.026,0.016,0.027,0.025,0.034,0.055,0.026,0.015,0.012,0.02,0.014,0.019,0.014,0.021,0.02,0.029,0.02,0.034,0.015,0.015,0.019,0.024,0.017,0.019,0.033,0.013,0.023,0.018,0.03,0.019,0.016,0.015,0.022,0.022,0.017,0.019,0.021,1521,4557,2940,2741,2240,2410,2546,2747,2250,1469,461.0,2068,2991,753,2647,10762,16401,1675,4269,4163,4571,4644,1917,5037,5918,5813,6731,4269,5496,5339,2644,1098,972,5792,2065,1584,7064,6931,422,303,1144,2125,4588,1849,3303,1564,549,2249,2188,1945,597,2732,2724,5808,460,3766,1373,2815,1280,2055,1043,2578,390,863,2248,1786,3813,2230,1525,917,1406,2014,8982,385,0.127,0.116,0.11,0.125,0.146,0.129,0.112,0.13,0.147,0.125,0.126,0.123,0.112,0.131,0.123,0.121,0.121,0.132,0.136,0.146,0.122,0.118,0.129,0.136,0.116,0.121,0.114,0.101,0.091,0.134,0.157,0.149,0.097,0.126,0.1,0.088,0.145,0.134,0.109,0.112,0.113,0.144,0.164,0.116,0.086,0.087,0.104,0.079,0.099,0.091,0.125,0.103,0.125,0.106,0.141,0.092,0.089,0.105,0.12,0.099,0.106,0.133,0.087,0.116,0.097,0.13,0.101,0.09,0.083,0.131,0.116,0.1,0.102,0.131,431,1827,1124,1150,894,879,984,920,703,358,183.0,988,780,213,733,3181,4579,469,1239,1861,1134,1535,1081,1722,1804,1799,2180,1573,1376,1668,855,476,347,1364,577,575,1917,1761,215,140,665,1276,1139,1173,1901,800,291,1032,1111,804,361,1449,1260,2513,210,2154,659,1320,826,1245,542,1099,174,508,1247,982,2073,1033,744,461,728,892,4144,243,3.116,2.369,2.403,2.381,2.149,2.324,2.17,2.52,2.524,3.122,2.194,1.789,2.827,2.51,2.637,2.577,2.804,3.035,2.691,1.95,2.975,2.334,1.704,2.332,2.555,2.601,2.366,2.171,2.961,2.491,2.523,2.305,2.242,3.008,3.127,2.43,2.835,2.904,2.284,2.436,2.187,1.573,2.855,1.799,1.904,2.008,2.289,2.621,2.331,2.736,1.889,2.022,2.027,2.214,2.566,1.89,2.237,2.417,1.748,1.795,2.213,2.38,2.289,1.925,2.028,2.167,2.016,2.383,2.393,2.211,1.998,2.474,2.349,1.963,0.698,0.501,0.582,0.566,0.71,0.524,0.62,0.499,0.676,0.559,0.532,0.543,0.444,0.562,0.574,0.666,0.613,0.83,0.652,0.502,0.71,0.553,0.481,0.61,0.692,0.597,0.621,0.579,0.634,0.568,0.691,0.7,0.402,0.574,0.575,0.661,0.713,0.537,0.543,0.456,0.508,0.523,0.877,0.466,0.592,0.504,0.502,0.733,0.438,0.515,0.375,0.457,0.559,0.578,0.603,0.474,0.432,0.542,0.385,0.436,0.429,0.516,0.72,0.496,0.498,0.581,0.442,0.492,0.46,0.574,0.553,0.617,0.527,0.444
-238,Epileptic,M,42.0,1.7,2.4,1.5,1.4,2.4,1.6,1.7,1.9,1.8,0.7,0.5,2.8,1.3,0.6,1.2,5.8,9.2,1.6,3.6,4.3,2.7,4.6,2.5,4.6,5.3,2.9,4.7,2.7,3.4,3.5,1.2,1.6,0.7,2.1,0.8,0.7,4.5,2.7,0.2,0.2,1.0,2.9,3.1,2.7,2.4,0.8,0.5,1.3,1.7,0.9,0.6,2.3,2.2,2.6,0.4,2.4,0.7,2.3,1.3,2.0,0.9,1.6,0.8,0.8,1.5,1.2,2.1,1.5,1.3,0.7,0.9,1.9,5.5,0.4,15,21,13,7,19,17,11,17,13,7,4.0,22,15,8,13,63,78,10,30,40,19,48,21,36,49,32,44,23,31,38,15,10,5,20,5,7,46,29,1,1,7,25,25,19,15,5,2,6,9,6,6,19,15,18,4,16,5,19,11,19,6,11,5,6,10,15,16,10,9,5,6,12,37,3,0.063,0.028,0.031,0.027,0.048,0.031,0.027,0.031,0.057,0.035,0.039,0.033,0.03,0.035,0.03,0.039,0.036,0.055,0.04,0.033,0.045,0.042,0.036,0.049,0.042,0.034,0.039,0.027,0.029,0.03,0.043,0.059,0.042,0.029,0.033,0.02,0.046,0.032,0.022,0.014,0.023,0.03,0.046,0.025,0.018,0.014,0.021,0.021,0.024,0.019,0.023,0.03,0.034,0.024,0.038,0.017,0.015,0.028,0.024,0.029,0.032,0.026,0.046,0.024,0.02,0.03,0.018,0.021,0.024,0.022,0.021,0.023,0.02,0.021,1373,5344,2388,2092,2880,2607,2253,3104,2065,1408,510.0,2675,2680,1194,2284,9909,16446,2224,4594,4630,4629,4656,2268,6141,5775,4926,4754,3346,6354,5503,2082,1057,935,5344,2148,1631,6181,5872,337,405,1343,2567,6126,2628,3615,1488,824,2199,2281,1698,551,2965,2270,3957,396,3530,1184,3486,1334,1893,815,2447,635,880,2259,1350,3347,2518,1707,1162,1273,2224,8898,452,0.166,0.122,0.124,0.113,0.166,0.135,0.099,0.137,0.172,0.143,0.132,0.13,0.134,0.147,0.143,0.143,0.135,0.168,0.141,0.142,0.144,0.118,0.129,0.147,0.126,0.13,0.117,0.103,0.1,0.129,0.141,0.146,0.133,0.126,0.113,0.099,0.148,0.131,0.123,0.103,0.109,0.126,0.167,0.112,0.091,0.089,0.1,0.095,0.106,0.101,0.134,0.129,0.135,0.113,0.158,0.098,0.091,0.126,0.12,0.12,0.128,0.123,0.167,0.122,0.103,0.118,0.093,0.106,0.12,0.123,0.112,0.119,0.107,0.125,475,1561,916,768,950,885,848,1023,549,353,189.0,1261,821,329,704,2943,4547,520,1416,2052,1052,1734,1023,1643,2025,1573,1945,1443,1753,1996,648,498,287,1192,474,606,1844,1473,171,209,697,1430,1278,1722,2181,783,340,915,1087,702,363,1281,1035,1720,201,1971,666,1411,863,1092,410,946,282,474,1180,737,1855,1157,855,491,688,1118,3967,228,2.779,3.032,2.596,2.576,2.567,2.568,2.267,2.711,2.714,2.981,2.42,1.871,2.604,2.778,2.376,2.594,2.933,3.374,2.628,1.927,3.237,2.299,2.025,2.795,2.369,2.614,2.081,1.987,2.748,2.289,2.489,2.349,2.667,3.14,3.489,2.433,2.692,3.009,2.27,2.097,2.554,1.627,3.185,1.825,1.901,2.102,2.971,2.919,2.637,2.66,1.918,2.312,2.363,2.389,2.2,2.028,1.999,2.346,1.741,2.001,2.22,2.638,2.061,2.243,2.086,2.074,1.977,2.46,2.455,2.718,2.144,2.444,2.433,2.318,0.76,0.573,0.579,0.552,0.823,0.711,0.655,0.512,0.578,0.607,0.7,0.503,0.5,0.589,0.564,0.621,0.553,0.769,0.711,0.604,0.896,0.56,0.638,0.731,0.618,0.552,0.506,0.561,0.434,0.607,0.715,0.927,0.465,0.673,0.717,0.499,0.825,0.67,0.522,0.377,0.452,0.533,0.945,0.667,0.562,0.453,0.671,0.597,0.44,0.563,0.377,0.511,0.598,0.499,0.557,0.437,0.368,0.638,0.429,0.581,0.502,0.661,0.594,0.523,0.426,0.668,0.411,0.371,0.47,0.761,0.488,0.675,0.525,0.389
-240,Epileptic,M,27.0,0.8,2.3,0.9,1.7,1.3,2.1,2.0,1.5,1.3,0.8,0.4,3.5,2.4,0.5,1.6,6.5,10.3,0.8,3.1,4.1,1.2,2.5,1.3,3.0,2.8,3.8,3.8,3.3,2.4,4.0,1.1,1.5,0.4,1.9,0.3,1.3,2.8,3.1,0.2,0.4,1.1,2.8,1.7,2.9,2.4,1.0,0.6,1.0,1.3,0.6,0.7,2.2,1.3,2.0,0.3,2.5,1.2,2.8,1.1,0.9,0.5,1.7,0.4,0.8,1.7,1.8,3.5,1.2,1.4,0.5,0.7,1.1,4.1,0.3,8,19,8,19,15,20,19,14,17,7,6.0,31,30,7,19,73,103,6,33,37,18,25,19,34,36,36,44,39,27,46,21,10,6,26,3,12,30,37,2,2,6,28,21,25,14,9,3,6,7,6,4,14,12,12,2,22,9,18,11,7,3,9,3,6,15,16,28,8,10,4,5,9,32,2,0.037,0.025,0.015,0.027,0.034,0.033,0.026,0.03,0.042,0.034,0.032,0.037,0.036,0.031,0.038,0.04,0.031,0.033,0.035,0.037,0.026,0.032,0.024,0.04,0.03,0.034,0.029,0.029,0.024,0.036,0.044,0.082,0.024,0.025,0.02,0.028,0.032,0.033,0.019,0.023,0.02,0.031,0.034,0.028,0.016,0.017,0.024,0.014,0.018,0.017,0.028,0.023,0.03,0.019,0.016,0.017,0.026,0.029,0.025,0.019,0.019,0.032,0.029,0.039,0.019,0.039,0.023,0.018,0.024,0.021,0.018,0.019,0.018,0.014,1537,4869,2459,3202,1977,3317,3757,3260,2197,1489,729.0,3368,4620,1002,3427,11487,20991,2535,5331,3884,3275,3454,2231,6476,6397,7000,7294,5523,5715,6889,3402,734,1082,6595,1618,2185,6729,8005,454,477,1671,2719,6492,2635,3598,1992,964,2527,2643,1695,680,3675,2095,3889,666,4431,1832,2794,1233,1342,875,2175,484,888,3211,1239,5316,2462,1785,1034,1425,2392,9305,692,0.133,0.113,0.097,0.127,0.149,0.143,0.119,0.133,0.161,0.136,0.124,0.151,0.156,0.151,0.152,0.154,0.138,0.117,0.139,0.144,0.105,0.138,0.129,0.152,0.139,0.137,0.132,0.122,0.11,0.149,0.141,0.164,0.127,0.117,0.077,0.125,0.132,0.136,0.124,0.118,0.11,0.138,0.139,0.125,0.092,0.101,0.114,0.087,0.094,0.099,0.127,0.109,0.132,0.095,0.099,0.111,0.12,0.125,0.143,0.114,0.115,0.132,0.143,0.136,0.113,0.149,0.119,0.101,0.118,0.108,0.1,0.106,0.099,0.089,432,1455,905,1114,635,1114,1259,903,547,367,205.0,1518,1255,249,860,2965,5553,440,1571,1837,849,1206,958,1451,1703,1832,2342,1951,1650,1949,590,374,326,1240,364,725,1498,1747,217,232,720,1453,1020,1713,2142,941,399,1014,1108,658,378,1544,795,1705,302,2044,728,1479,708,700,391,758,210,371,1383,670,2377,1052,888,414,668,927,3589,318,3.553,2.897,2.64,2.847,2.507,2.547,2.538,3.097,2.73,3.165,3.018,1.904,2.793,2.762,2.848,2.791,2.868,3.853,2.85,1.814,2.932,2.306,2.005,3.174,2.802,2.981,2.519,2.294,2.641,2.738,3.709,2.181,2.655,3.306,3.486,2.78,3.037,3.183,2.193,2.65,2.953,1.761,3.604,1.766,1.911,2.366,2.929,3.03,2.831,2.721,2.208,2.411,2.402,2.51,2.596,2.3,2.603,2.107,1.978,2.148,2.613,2.813,2.506,2.768,2.501,2.389,2.335,2.647,2.307,2.898,2.528,2.658,2.788,2.711,0.945,0.82,0.585,0.567,0.745,0.567,0.556,0.527,0.75,0.49,0.66,0.506,0.514,0.552,0.648,0.669,0.651,0.859,0.634,0.595,0.704,0.463,0.575,0.712,0.579,0.645,0.505,0.537,0.503,0.553,0.786,0.894,0.421,0.702,0.763,0.534,0.825,0.651,0.489,0.327,0.659,0.626,0.93,0.612,0.536,0.494,0.683,0.894,0.543,0.694,0.374,0.629,0.597,0.461,0.366,0.428,0.481,0.582,0.349,0.467,0.355,0.813,0.595,0.917,0.622,0.651,0.523,0.453,0.402,0.878,0.54,0.769,0.601,0.539
-241,Epileptic,F,27.0,1.3,1.8,1.0,1.4,1.2,3.0,1.4,1.4,1.1,1.0,0.4,3.0,1.4,0.8,1.1,6.5,8.9,0.7,3.3,6.2,1.6,2.9,1.4,2.9,3.8,2.7,3.1,2.0,2.3,2.4,1.4,1.9,0.6,2.6,0.3,0.5,4.1,2.9,0.3,0.4,0.7,3.4,2.4,3.2,3.3,0.7,0.6,0.9,1.2,1.6,0.9,1.8,1.8,1.9,0.2,2.9,1.3,2.1,1.0,1.1,1.0,1.6,0.4,0.4,1.9,1.6,2.9,1.3,0.9,0.4,0.9,1.9,4.8,0.5,8,19,9,12,17,21,14,13,13,9,3.0,22,16,9,10,64,76,5,26,46,8,30,12,35,36,25,36,19,18,30,16,10,3,28,2,6,44,30,2,2,4,26,21,21,18,4,3,8,6,9,6,12,14,12,1,25,6,13,7,8,7,10,2,3,13,16,22,9,6,4,5,13,32,4,0.047,0.018,0.017,0.027,0.027,0.043,0.022,0.029,0.034,0.035,0.033,0.033,0.032,0.049,0.03,0.031,0.033,0.026,0.039,0.039,0.027,0.031,0.027,0.032,0.029,0.028,0.024,0.025,0.023,0.028,0.038,0.051,0.026,0.027,0.011,0.021,0.032,0.026,0.016,0.027,0.015,0.031,0.035,0.033,0.025,0.014,0.021,0.015,0.016,0.051,0.049,0.019,0.023,0.017,0.013,0.018,0.021,0.021,0.031,0.021,0.023,0.025,0.023,0.017,0.019,0.028,0.022,0.018,0.02,0.017,0.024,0.018,0.018,0.03,1732,5282,2858,2653,2148,2682,2967,3160,1704,1652,702.0,2817,3197,1480,2686,12715,17865,2314,4860,4875,3219,4656,2001,6705,6227,5809,5951,3876,5297,5425,2165,1036,996,6815,2024,1281,8448,7285,484,695,1700,2897,5211,2946,3297,1381,1109,2771,2645,1101,525,3772,2690,4590,385,5568,2471,3624,1103,1517,1831,2575,586,772,3412,1891,4236,2965,1554,1035,1314,3438,11001,623,0.109,0.097,0.092,0.109,0.117,0.143,0.101,0.12,0.126,0.139,0.131,0.121,0.13,0.154,0.114,0.124,0.131,0.096,0.129,0.128,0.096,0.129,0.119,0.126,0.124,0.118,0.115,0.108,0.098,0.123,0.114,0.137,0.091,0.122,0.071,0.103,0.125,0.107,0.106,0.12,0.091,0.121,0.12,0.132,0.113,0.092,0.103,0.08,0.086,0.161,0.137,0.098,0.111,0.093,0.099,0.102,0.099,0.108,0.129,0.111,0.112,0.109,0.107,0.092,0.101,0.12,0.11,0.096,0.109,0.098,0.115,0.103,0.094,0.128,438,1603,936,932,767,1180,955,839,517,432,221.0,1268,808,305,663,3426,4373,456,1366,2337,811,1393,793,1460,1866,1552,2132,1247,1440,1547,518,522,329,1564,480,476,2052,1751,217,262,703,1396,1113,1697,2040,690,431,1021,1116,494,335,1521,1116,1802,169,2593,960,1666,603,781,759,903,283,374,1569,872,1923,1178,677,485,605,1563,4121,292,3.637,3.028,2.761,2.93,2.54,2.223,2.593,3.102,2.567,3.127,3.018,1.935,2.95,3.343,2.944,2.795,3.161,3.993,2.806,1.897,3.272,2.613,2.243,3.187,2.737,2.929,2.265,2.429,2.76,2.707,2.774,2.091,2.628,3.155,3.702,2.429,3.043,3.127,2.63,2.969,2.878,1.856,3.484,2.066,1.875,2.352,3.143,3.278,2.781,2.966,1.744,2.578,2.331,2.688,2.357,2.327,3.121,2.58,2.159,2.166,2.557,2.771,2.481,2.298,2.445,2.428,2.46,2.944,2.565,2.43,2.622,2.643,2.819,2.317,0.843,0.672,0.717,0.451,0.637,0.608,0.567,0.769,0.651,0.582,0.74,0.48,0.408,0.513,0.554,0.633,0.627,0.769,0.847,0.499,0.79,0.508,0.484,0.809,0.502,0.598,0.464,0.504,0.596,0.526,0.664,0.863,0.516,0.688,0.814,0.446,0.792,0.544,0.307,0.471,0.887,0.479,0.811,0.72,0.528,0.592,0.49,0.762,0.72,0.634,0.402,0.498,0.685,0.669,0.333,0.443,0.571,0.596,0.423,0.38,0.514,0.842,0.431,0.596,0.571,0.872,0.543,0.546,0.502,0.57,0.363,0.481,0.644,0.325
-242,Epileptic,M,52.0,0.9,3.1,1.0,0.9,2.2,1.6,2.1,1.1,1.2,0.6,0.5,2.5,1.8,0.5,0.7,5.1,7.8,1.2,2.1,3.9,1.5,2.5,2.7,4.7,3.2,2.3,4.0,3.5,2.1,3.6,2.7,0.8,0.7,2.7,0.4,0.4,5.4,3.3,0.1,0.3,0.8,2.2,2.7,2.7,2.4,0.7,0.8,1.1,1.0,1.8,0.7,1.6,1.2,2.5,0.4,2.0,1.1,1.5,0.8,1.3,0.9,1.8,0.4,0.7,1.4,1.8,2.8,0.9,1.2,1.2,1.3,1.5,4.1,0.1,7,32,7,7,17,15,21,12,11,7,4.0,25,20,6,10,54,79,9,21,38,12,27,22,40,44,25,47,33,19,30,19,11,7,30,3,5,50,36,1,2,4,22,30,24,17,5,3,6,5,13,5,11,8,20,3,16,8,14,5,9,8,10,3,6,10,18,22,7,7,11,8,10,36,1,0.049,0.032,0.02,0.019,0.04,0.035,0.034,0.027,0.044,0.035,0.049,0.037,0.037,0.03,0.022,0.032,0.033,0.036,0.034,0.038,0.026,0.035,0.038,0.048,0.032,0.025,0.04,0.038,0.028,0.039,0.065,0.043,0.034,0.035,0.021,0.019,0.051,0.032,0.014,0.02,0.018,0.036,0.045,0.03,0.018,0.016,0.026,0.014,0.015,0.027,0.033,0.018,0.021,0.023,0.03,0.016,0.027,0.023,0.024,0.024,0.031,0.03,0.03,0.025,0.018,0.032,0.02,0.015,0.019,0.026,0.024,0.023,0.016,0.01,1353,5093,1928,1960,2421,2395,2329,2345,1790,1436,670.0,2254,3130,1104,2149,8500,14875,2318,3968,3697,3659,3711,1821,5927,5797,5326,4807,3236,4225,5181,2199,976,887,5531,1619,1369,7800,6797,320,618,1207,2217,6936,2910,2884,1148,993,2508,1987,1921,425,3046,1755,4180,507,3253,1252,2429,750,1537,1055,2431,446,928,2259,1103,4041,2605,1719,1628,1561,1910,9434,523,0.131,0.134,0.107,0.099,0.143,0.151,0.112,0.109,0.164,0.14,0.138,0.142,0.146,0.143,0.111,0.144,0.132,0.131,0.136,0.155,0.113,0.135,0.147,0.173,0.129,0.13,0.137,0.121,0.099,0.138,0.172,0.112,0.144,0.139,0.101,0.09,0.163,0.133,0.094,0.101,0.095,0.144,0.165,0.128,0.098,0.096,0.128,0.072,0.084,0.113,0.136,0.095,0.11,0.112,0.126,0.097,0.121,0.111,0.121,0.114,0.139,0.125,0.132,0.116,0.102,0.131,0.106,0.08,0.097,0.126,0.118,0.123,0.091,0.066,429,1788,714,739,942,812,969,810,562,373,219.0,1196,951,289,617,2776,4547,520,1105,1773,946,1273,1026,1750,1856,1586,1902,1463,1301,1716,799,478,289,1337,401,474,2028,1866,203,276,654,1073,1430,1695,1965,605,397,1131,982,912,329,1374,817,1897,278,1852,675,1169,468,866,456,951,211,443,1179,725,2212,1050,873,699,789,958,4140,214,3.045,2.722,2.579,2.461,2.228,2.315,2.102,2.468,2.397,3.006,2.622,1.674,2.581,2.694,2.584,2.362,2.543,3.437,2.848,1.849,3.015,2.353,1.646,2.607,2.446,2.679,2.06,1.859,2.5,2.459,2.212,2.149,2.595,2.96,3.483,2.404,2.834,2.836,1.798,2.211,2.212,1.802,3.4,1.864,1.639,1.93,3.124,2.584,2.476,2.071,1.695,2.197,2.247,2.163,2.008,1.912,2.185,2.154,1.884,1.904,2.038,2.544,2.403,2.326,2.053,1.974,1.897,2.75,2.058,2.465,2.154,2.294,2.365,2.406,0.711,0.587,0.614,0.511,0.661,0.68,0.617,0.605,0.66,0.552,0.796,0.391,0.537,0.502,0.532,0.592,0.65,0.744,0.682,0.536,0.83,0.552,0.438,0.765,0.58,0.552,0.572,0.527,0.549,0.58,0.711,0.75,0.577,0.659,0.612,0.542,0.866,0.577,0.319,0.47,0.504,0.587,0.701,0.587,0.53,0.568,0.564,0.847,0.472,0.66,0.369,0.551,0.549,0.56,0.445,0.457,0.397,0.588,0.422,0.453,0.545,0.64,0.533,0.772,0.549,0.742,0.523,0.783,0.551,0.773,0.486,0.493,0.468,0.398
-243,Epileptic,F,57.0,1.0,2.5,1.0,1.1,2.0,1.9,1.2,2.2,1.5,0.9,0.3,3.6,2.0,0.8,2.0,7.1,8.8,1.2,3.2,7.2,1.4,2.5,1.7,3.5,2.6,3.9,3.2,2.6,2.3,4.4,1.2,1.0,0.5,2.4,0.3,0.7,3.8,3.7,0.3,0.2,1.5,3.4,2.5,2.7,2.9,0.6,0.4,1.2,1.2,1.6,0.9,2.2,1.8,2.2,0.5,2.5,0.7,2.7,1.5,1.2,0.9,1.5,0.4,0.4,2.0,1.7,2.4,1.1,1.4,0.4,1.1,2.0,4.9,0.4,6,24,9,11,14,16,11,16,13,8,3.0,32,18,8,17,62,69,9,27,53,281,30,15,35,30,39,41,26,16,42,14,6,5,25,1,7,38,41,2,2,7,35,15,21,16,4,2,7,7,11,7,17,12,17,2,21,6,18,9,9,6,10,3,3,16,14,18,7,8,5,8,13,35,2,0.038,0.027,0.015,0.025,0.037,0.035,0.02,0.035,0.042,0.037,0.03,0.033,0.034,0.027,0.03,0.034,0.033,0.043,0.034,0.043,0.067,0.029,0.03,0.04,0.027,0.033,0.025,0.026,0.022,0.037,0.031,0.032,0.024,0.026,0.01,0.02,0.038,0.031,0.015,0.019,0.026,0.035,0.041,0.029,0.023,0.013,0.021,0.016,0.015,0.035,0.039,0.023,0.027,0.022,0.029,0.021,0.016,0.025,0.028,0.021,0.024,0.028,0.028,0.018,0.023,0.034,0.017,0.021,0.022,0.019,0.026,0.027,0.022,0.022,1315,4517,2310,2018,2233,2449,2290,2831,1813,1351,504.0,3140,3198,1347,2969,9738,14881,2029,4460,5020,2061,4180,2338,4850,4516,6843,5545,3461,4512,5405,1899,816,981,5058,1569,1829,7101,6963,464,294,1753,2794,3203,2200,3186,1475,694,2088,2611,1374,513,3206,2301,3772,368,3469,1663,2935,1279,1474,1094,2228,516,697,2805,1258,3987,1884,1780,823,1291,2329,8713,480,0.123,0.121,0.101,0.109,0.137,0.145,0.092,0.133,0.15,0.139,0.117,0.133,0.133,0.126,0.131,0.132,0.123,0.135,0.122,0.131,0.103,0.131,0.13,0.141,0.126,0.132,0.123,0.113,0.087,0.142,0.122,0.105,0.128,0.11,0.054,0.096,0.12,0.122,0.088,0.118,0.119,0.139,0.139,0.124,0.112,0.088,0.113,0.091,0.091,0.133,0.148,0.11,0.125,0.11,0.136,0.111,0.093,0.117,0.132,0.116,0.12,0.124,0.148,0.111,0.115,0.127,0.103,0.104,0.114,0.127,0.125,0.131,0.104,0.119,411,1523,977,755,907,944,862,1031,571,398,166.0,1594,1029,400,1038,3378,4298,441,1472,2374,785,1394,972,1463,1428,2078,1997,1459,1284,1935,598,428,363,1321,517,665,1839,1839,278,177,806,1468,941,1556,2023,736,320,988,1240,768,389,1586,1138,1733,231,1944,734,1697,796,886,589,823,247,366,1454,757,2122,931,903,389,697,1241,3769,237,3.108,2.791,2.394,2.62,2.302,2.32,2.244,2.518,2.504,2.879,2.415,1.756,2.552,2.431,2.316,2.395,2.777,3.454,2.569,1.877,2.234,2.307,2.028,2.556,2.408,2.746,2.208,2.014,2.702,2.279,2.521,2.06,2.386,2.742,2.725,2.513,2.848,2.836,1.969,2.12,2.59,1.707,2.87,1.616,1.784,2.164,2.63,2.665,2.481,2.247,1.559,2.073,2.259,2.209,1.975,1.96,2.52,2.066,1.869,1.82,2.223,2.625,2.228,2.281,2.154,2.174,2.01,2.436,2.254,2.416,2.156,2.32,2.548,2.573,0.677,0.667,0.453,0.507,0.526,0.567,0.53,0.61,0.52,0.378,0.954,0.433,0.511,0.452,0.488,0.514,0.496,0.734,0.686,0.596,0.947,0.512,0.499,0.572,0.425,0.658,0.447,0.441,0.534,0.503,0.56,0.9,0.354,0.493,0.67,0.473,0.758,0.592,0.244,0.428,0.597,0.5,0.735,0.578,0.505,0.628,0.468,0.535,0.618,0.574,0.318,0.495,0.502,0.441,0.387,0.42,0.344,0.519,0.471,0.382,0.484,0.812,0.546,0.762,0.635,0.765,0.453,0.382,0.502,0.724,0.47,0.524,0.572,0.403
-244,Epileptic,M,37.0,0.5,1.8,0.9,0.8,1.1,1.7,1.2,1.3,0.5,0.8,0.4,2.3,1.4,0.4,1.3,4.2,6.3,0.9,1.8,3.4,1.0,2.4,1.4,2.5,2.8,3.3,2.2,1.9,2.3,3.5,0.9,0.8,0.6,2.6,0.6,0.5,3.5,2.5,0.1,0.2,1.1,2.7,1.9,1.5,2.4,0.9,0.4,0.8,1.0,1.0,0.4,1.1,0.8,1.6,0.3,2.1,0.5,2.1,1.1,0.8,0.7,0.9,0.3,0.3,1.5,1.5,2.3,1.2,0.5,0.3,0.9,1.3,5.1,0.4,3,21,13,8,10,19,11,14,9,8,5.0,21,13,6,13,42,61,7,24,29,10,30,12,30,30,39,27,21,22,34,13,4,5,28,2,6,38,25,1,1,7,26,14,13,16,7,2,5,5,9,2,8,8,11,1,17,4,18,8,7,8,5,2,3,11,19,20,11,3,4,6,9,43,4,0.026,0.02,0.014,0.015,0.028,0.026,0.024,0.022,0.039,0.029,0.042,0.031,0.029,0.028,0.035,0.028,0.025,0.031,0.024,0.033,0.022,0.031,0.029,0.034,0.032,0.031,0.026,0.023,0.023,0.031,0.029,0.026,0.019,0.028,0.017,0.015,0.034,0.029,0.014,0.017,0.019,0.032,0.036,0.021,0.018,0.021,0.021,0.012,0.015,0.022,0.027,0.015,0.016,0.015,0.015,0.017,0.011,0.023,0.024,0.017,0.022,0.02,0.026,0.013,0.021,0.034,0.017,0.019,0.012,0.011,0.023,0.017,0.019,0.016,1255,4145,2349,2057,1825,3222,2326,3122,1093,1604,767.0,2955,3136,1013,2740,9488,15818,2402,4332,3842,3213,4621,2129,5469,5852,6663,4944,3972,6105,5731,2292,1132,1309,7250,1757,2155,7407,6231,425,475,1900,3650,4466,2031,3820,1223,727,2799,2405,1891,525,2976,2132,4443,335,3900,1438,3197,1561,1543,1561,1910,469,764,2540,1371,4457,2543,1156,843,1352,2395,10964,677,0.098,0.108,0.106,0.095,0.121,0.135,0.11,0.114,0.136,0.144,0.152,0.138,0.142,0.142,0.138,0.132,0.118,0.12,0.117,0.13,0.097,0.139,0.126,0.14,0.13,0.137,0.116,0.112,0.109,0.137,0.127,0.101,0.102,0.12,0.085,0.072,0.128,0.117,0.092,0.087,0.099,0.135,0.129,0.111,0.096,0.11,0.108,0.073,0.087,0.115,0.121,0.09,0.098,0.086,0.1,0.103,0.085,0.12,0.119,0.099,0.11,0.105,0.107,0.103,0.105,0.122,0.1,0.104,0.086,0.102,0.115,0.106,0.099,0.099,351,1350,952,800,676,1153,827,972,337,414,204.0,1208,794,276,678,2422,4152,443,1339,1632,764,1337,796,1327,1569,1877,1576,1317,1626,1847,560,467,462,1435,476,717,1759,1517,178,207,863,1339,982,1157,2143,620,296,1063,1016,776,259,1221,850,1732,189,1821,670,1433,740,752,579,665,222,338,1166,758,2066,1074,637,414,632,1108,4228,403,3.586,2.945,2.496,2.5,2.478,2.403,2.353,2.731,2.463,3.172,3.076,2.136,3.016,2.808,3.007,2.954,3.024,3.827,2.68,2.016,3.105,2.667,2.259,3.068,2.842,2.783,2.44,2.324,2.821,2.467,2.87,2.573,2.311,3.427,3.588,2.602,3.076,3.005,2.612,2.611,2.76,2.18,3.71,1.909,2.046,2.126,3.198,3.055,2.823,2.682,2.56,2.724,2.455,2.728,2.091,2.321,2.554,2.378,2.368,2.264,2.506,3.032,2.403,2.542,2.276,2.246,2.213,2.607,2.218,2.328,2.498,2.669,2.675,2.098,0.624,0.479,0.423,0.555,0.56,0.592,0.543,0.497,0.66,0.507,0.643,0.584,0.442,0.465,0.537,0.606,0.536,0.749,0.421,0.615,0.779,0.526,0.558,0.613,0.634,0.579,0.507,0.548,0.488,0.603,0.651,1.048,0.435,0.688,0.685,0.49,0.631,0.645,0.294,0.696,0.506,0.66,0.676,0.816,0.662,0.565,0.45,0.647,0.477,0.421,0.478,0.579,0.512,0.511,0.39,0.455,0.445,0.515,0.433,0.463,0.563,0.62,0.337,1.141,0.567,0.57,0.487,0.534,0.419,0.587,0.482,0.522,0.553,0.38
-245,Epileptic,F,42.0,0.5,2.8,1.2,1.1,1.9,1.7,2.3,1.4,1.4,0.8,0.2,2.8,1.2,0.5,0.8,6.3,8.4,0.5,1.7,4.3,0.7,2.6,1.5,3.3,2.7,2.2,2.3,3.0,2.8,2.8,1.3,0.6,0.4,1.9,0.3,0.7,2.2,2.3,0.2,0.2,1.0,2.6,1.4,2.3,3.8,0.5,0.3,0.6,0.9,0.5,0.4,1.5,1.2,2.4,0.1,2.0,0.7,1.7,1.0,1.0,0.5,1.4,0.3,0.3,1.4,1.0,1.6,1.4,1.5,0.5,0.7,0.8,3.7,0.1,3,28,16,11,12,17,16,13,12,7,3.0,24,12,5,9,64,70,5,19,47,9,32,16,37,30,23,29,26,22,33,15,4,3,24,2,6,30,34,2,2,6,24,20,20,21,5,2,4,5,6,4,10,8,17,1,16,6,14,7,8,3,12,3,3,13,13,15,9,9,8,5,7,26,1,0.029,0.032,0.02,0.023,0.036,0.028,0.034,0.023,0.045,0.035,0.023,0.037,0.023,0.031,0.021,0.031,0.032,0.028,0.031,0.036,0.019,0.032,0.028,0.037,0.03,0.027,0.028,0.035,0.026,0.035,0.045,0.036,0.025,0.027,0.012,0.025,0.027,0.027,0.014,0.02,0.023,0.031,0.043,0.024,0.025,0.014,0.02,0.013,0.015,0.016,0.026,0.019,0.019,0.023,0.017,0.02,0.019,0.021,0.021,0.018,0.02,0.023,0.033,0.019,0.018,0.028,0.016,0.019,0.023,0.022,0.022,0.021,0.016,0.016,1513,4295,2446,2017,2355,2800,2737,2880,1701,1222,521.0,2770,2855,977,2396,12754,16375,2127,3399,5578,2995,4494,2085,6173,5826,4389,4349,4236,6133,4531,1796,977,782,5012,1563,1800,5287,6165,409,474,1385,2681,5281,3063,4045,1266,738,1557,1930,1265,437,2845,2013,4875,226,3350,1235,2839,1096,1592,845,2592,368,854,2486,930,3134,2398,1944,787,1329,1769,8384,391,0.102,0.132,0.116,0.114,0.128,0.14,0.116,0.12,0.159,0.143,0.101,0.147,0.123,0.136,0.124,0.132,0.127,0.103,0.137,0.152,0.088,0.141,0.139,0.147,0.135,0.129,0.135,0.121,0.107,0.147,0.136,0.111,0.107,0.125,0.067,0.121,0.132,0.132,0.095,0.107,0.116,0.134,0.144,0.116,0.109,0.096,0.115,0.082,0.086,0.113,0.132,0.099,0.107,0.105,0.103,0.108,0.109,0.111,0.116,0.107,0.106,0.118,0.133,0.11,0.108,0.138,0.101,0.103,0.114,0.143,0.113,0.112,0.094,0.124,316,1449,1045,820,829,1044,1091,950,493,331,159.0,1191,796,238,609,3513,4476,384,960,2065,652,1436,864,1594,1610,1322,1550,1469,1694,1450,534,292,248,1101,391,508,1378,1446,212,214,630,1332,773,1561,2345,612,271,680,906,497,287,1283,949,1921,96,1515,612,1271,675,858,426,927,189,336,1190,551,1526,1101,976,366,579,739,3423,133,4.085,2.729,2.308,2.439,2.337,2.34,2.223,2.777,2.609,2.928,2.588,2.049,2.786,2.998,2.912,2.713,2.882,3.951,2.763,2.263,3.146,2.45,2.08,2.834,2.748,2.691,2.278,2.314,2.78,2.534,2.431,3.046,2.762,3.098,3.08,3.064,2.825,2.96,2.151,2.735,2.79,1.821,3.762,2.155,1.971,2.259,3.054,2.853,2.618,2.606,1.942,2.376,2.345,2.604,2.402,2.366,2.583,2.43,1.857,2.066,2.44,2.641,2.082,2.533,2.289,2.202,2.136,2.597,2.299,2.254,2.667,2.657,2.632,3.28,0.705,0.614,0.54,0.484,0.596,0.524,0.582,0.553,0.459,0.492,0.753,0.406,0.396,0.427,0.477,0.618,0.557,0.766,0.548,0.64,0.839,0.413,0.38,0.647,0.568,0.534,0.5,0.632,0.49,0.466,0.827,1.057,0.417,0.679,0.995,0.578,0.593,0.574,0.296,0.393,0.505,0.475,0.8,0.564,0.618,0.386,0.433,0.822,0.464,0.498,0.345,0.442,0.629,0.484,0.44,0.515,0.429,0.408,0.312,0.315,0.37,0.752,0.41,0.987,0.496,0.621,0.393,0.405,0.375,0.708,0.47,0.56,0.573,0.545
-246,Epileptic,M,37.0,1.1,2.3,0.7,1.2,1.3,2.3,1.5,1.8,0.7,0.9,0.2,2.6,1.7,0.5,1.4,5.9,8.3,0.7,3.2,5.2,0.8,4.1,1.8,3.0,2.7,3.7,3.0,2.1,2.7,2.8,1.6,0.8,0.7,2.2,0.5,0.5,2.2,3.5,0.2,0.3,1.7,4.1,2.0,3.3,2.6,1.0,0.4,0.7,1.3,0.6,0.8,2.1,1.4,2.5,0.3,2.6,0.9,2.3,1.2,1.3,0.5,1.6,0.3,0.4,2.0,0.8,2.9,1.2,0.9,0.4,0.9,1.0,4.2,0.2,9,18,6,9,12,22,14,17,9,9,2.0,26,15,7,13,57,74,7,31,42,7,45,18,34,34,34,32,20,19,33,20,9,7,25,3,3,25,42,1,2,8,31,22,19,16,7,2,5,6,6,5,18,8,17,2,19,5,18,8,11,4,10,2,3,16,9,26,7,5,3,6,8,31,1,0.037,0.025,0.011,0.019,0.029,0.028,0.021,0.029,0.04,0.034,0.024,0.031,0.024,0.031,0.027,0.031,0.027,0.023,0.034,0.041,0.015,0.037,0.026,0.034,0.025,0.032,0.024,0.023,0.025,0.025,0.044,0.029,0.03,0.026,0.018,0.01,0.024,0.031,0.016,0.018,0.027,0.038,0.031,0.043,0.021,0.016,0.016,0.01,0.014,0.013,0.039,0.021,0.023,0.02,0.022,0.018,0.016,0.026,0.024,0.022,0.022,0.027,0.02,0.016,0.021,0.022,0.021,0.016,0.017,0.017,0.021,0.023,0.018,0.017,1724,4809,2426,2752,2119,3411,3391,3085,1540,1590,608.0,2664,4467,1150,3214,11838,19799,2372,4845,3833,3009,5980,2469,6856,6006,7357,6199,3644,5635,5683,2661,1243,1206,5307,1769,2056,6345,7767,419,471,2214,3130,5465,2162,3203,2208,1007,2921,3288,1745,514,3932,2413,5651,387,4340,2138,3224,1497,1837,780,2460,473,914,2935,1240,5010,2856,1726,830,1644,2090,9347,402,0.135,0.105,0.087,0.098,0.123,0.139,0.097,0.132,0.122,0.139,0.09,0.139,0.119,0.126,0.122,0.133,0.123,0.102,0.13,0.13,0.076,0.154,0.125,0.137,0.131,0.132,0.113,0.107,0.106,0.129,0.131,0.092,0.114,0.127,0.082,0.073,0.113,0.134,0.095,0.109,0.119,0.147,0.123,0.147,0.105,0.094,0.09,0.073,0.082,0.094,0.133,0.112,0.104,0.1,0.133,0.103,0.098,0.116,0.123,0.112,0.113,0.123,0.122,0.105,0.115,0.103,0.112,0.094,0.096,0.111,0.116,0.106,0.096,0.102,526,1557,912,1004,745,1334,1071,1109,389,445,186.0,1300,1119,298,901,3141,5115,491,1476,1966,965,1803,1048,1642,1772,2027,2137,1417,1525,1864,607,481,432,1361,474,675,1575,1953,179,213,892,1509,1131,1213,1956,978,375,1155,1396,714,359,1777,1055,2072,208,2180,800,1648,848,1001,396,879,204,447,1461,554,2297,1162,806,380,747,899,3872,202,3.314,2.93,2.627,2.831,2.43,2.344,2.534,2.599,2.724,3.065,3.137,1.904,3.046,2.82,2.787,2.779,3.01,3.713,2.674,1.773,2.662,2.585,2.053,2.906,2.613,2.893,2.34,2.102,2.867,2.464,2.968,2.283,2.322,2.846,3.465,2.761,2.957,2.933,2.697,2.675,2.652,1.946,3.22,1.984,1.895,2.525,3.26,3.148,2.853,2.569,1.783,2.472,2.5,2.705,2.335,2.215,3.049,2.309,2.057,2.039,2.295,2.629,2.508,2.497,2.23,2.403,2.285,2.824,2.585,2.463,2.593,2.721,2.637,2.323,0.778,0.643,0.493,0.472,0.588,0.574,0.563,0.528,0.643,0.488,0.766,0.46,0.52,0.4,0.4,0.571,0.591,0.664,0.652,0.575,0.74,0.569,0.439,0.636,0.465,0.541,0.488,0.541,0.565,0.491,0.682,1.071,0.48,0.597,0.606,0.454,0.802,0.6,0.485,0.386,0.626,0.561,0.831,0.809,0.656,0.572,0.4,0.626,0.484,0.713,0.37,0.539,0.632,0.517,0.326,0.467,0.756,0.658,0.487,0.397,0.441,0.609,0.477,0.698,0.604,0.601,0.516,0.424,0.424,0.709,0.447,0.723,0.558,0.43
-247,Epileptic,F,37.0,0.8,2.7,1.0,1.1,1.0,1.7,1.7,1.8,1.2,0.6,0.4,3.2,1.6,1.0,1.8,4.9,7.2,0.6,1.5,3.3,1.3,2.8,1.3,3.8,2.8,3.2,2.9,2.4,2.7,2.9,1.0,1.9,0.7,1.7,0.4,0.9,4.3,3.0,0.1,0.0,0.9,2.8,2.2,2.0,2.2,0.8,0.3,0.7,1.0,0.8,0.5,2.4,1.4,1.6,0.4,1.8,0.5,1.4,0.7,0.5,0.5,1.2,0.2,0.4,1.7,1.4,1.4,1.3,0.8,0.4,0.8,1.4,3.1,0.4,5,27,11,10,11,16,16,17,11,7,5.0,27,18,9,22,55,72,3,17,26,9,33,14,41,35,34,30,25,23,34,16,16,8,24,3,7,45,33,1,0,5,24,21,18,14,5,1,4,5,5,4,19,17,10,2,15,4,28,4,5,4,11,1,4,20,13,12,9,7,5,5,9,22,2,0.038,0.026,0.014,0.024,0.025,0.031,0.027,0.034,0.049,0.039,0.039,0.042,0.028,0.042,0.028,0.031,0.028,0.025,0.026,0.037,0.025,0.037,0.025,0.044,0.034,0.03,0.027,0.028,0.024,0.028,0.032,0.063,0.033,0.026,0.018,0.022,0.038,0.038,0.012,0.004,0.018,0.039,0.04,0.032,0.02,0.018,0.019,0.013,0.016,0.031,0.028,0.021,0.03,0.017,0.019,0.019,0.023,0.017,0.017,0.014,0.018,0.021,0.034,0.013,0.018,0.032,0.017,0.019,0.019,0.012,0.03,0.025,0.015,0.022,1231,5482,2296,2286,1681,2602,2768,3367,1770,1285,668.0,2695,4081,1569,3805,9670,16883,2068,2827,3712,2642,4833,1983,5130,5773,6599,5352,3991,5506,4991,2128,1421,1369,5509,1746,1928,7341,6912,476,266,1489,2813,4561,1851,2941,1423,692,2150,2124,1326,548,3682,2494,3527,489,3192,1191,2810,1074,1109,972,2403,374,853,3073,1139,2812,2439,1657,904,1128,2007,7891,530,0.131,0.116,0.097,0.115,0.121,0.141,0.114,0.141,0.169,0.141,0.152,0.161,0.128,0.153,0.139,0.129,0.129,0.096,0.111,0.134,0.094,0.147,0.128,0.142,0.144,0.134,0.128,0.119,0.102,0.138,0.112,0.141,0.146,0.123,0.085,0.101,0.139,0.14,0.086,0.04,0.1,0.155,0.139,0.129,0.101,0.105,0.089,0.081,0.089,0.118,0.125,0.108,0.128,0.093,0.122,0.108,0.115,0.091,0.094,0.091,0.11,0.119,0.127,0.101,0.112,0.128,0.097,0.101,0.1,0.099,0.124,0.12,0.09,0.122,359,1755,1025,740,702,946,1007,971,435,275,197.0,1240,1050,371,1137,2801,4370,396,875,1551,787,1286,873,1358,1494,1917,1714,1325,1552,1660,581,470,368,1110,366,598,1814,1504,221,88,682,1184,991,1103,1772,645,255,844,935,477,308,1746,847,1564,252,1518,419,1483,635,657,430,845,153,427,1490,580,1276,1105,725,494,474,909,3039,220,3.345,2.785,2.152,2.84,2.141,2.453,2.282,3.275,2.771,3.425,2.776,1.952,2.931,3.053,2.727,2.651,2.96,4.014,2.615,2.067,2.669,2.721,2.028,2.741,2.85,2.929,2.469,2.442,2.753,2.464,2.552,2.825,3.043,3.339,3.863,2.986,2.957,3.284,2.58,2.703,2.784,2.041,3.264,1.993,1.904,2.624,3.522,3.196,2.829,3.262,2.359,2.221,2.453,2.462,2.246,2.309,3.289,2.147,2.002,1.869,2.403,3.075,2.297,2.271,2.279,2.47,2.4,2.62,2.538,2.13,2.692,2.74,2.687,3.068,0.889,0.585,0.723,0.596,0.507,0.462,0.608,0.581,0.645,0.407,0.665,0.524,0.463,0.514,0.389,0.572,0.552,0.689,0.566,0.506,0.872,0.566,0.495,0.756,0.566,0.594,0.542,0.61,0.55,0.551,0.666,1.214,0.649,0.624,0.71,0.592,0.712,0.674,0.532,0.494,0.51,0.442,0.768,0.724,0.612,0.58,0.442,0.822,0.545,0.723,0.405,0.517,0.611,0.41,0.33,0.494,0.784,0.506,0.419,0.299,0.302,0.717,0.502,0.808,0.538,0.785,0.435,0.364,0.398,0.408,0.418,0.624,0.627,0.649
-248,Epileptic,M,27.0,0.5,2.6,0.9,1.0,1.3,2.3,1.5,1.6,0.6,0.6,0.3,3.4,1.4,0.7,2.1,6.8,7.9,0.7,1.8,6.6,1.6,2.4,1.7,2.9,2.7,3.4,4.6,3.2,3.7,3.7,1.1,2.6,0.6,2.3,0.4,0.4,5.2,3.5,0.2,0.2,0.9,3.3,2.4,4.4,4.3,1.2,0.4,0.6,1.1,0.8,1.1,1.9,1.2,2.2,0.4,2.8,0.7,1.6,1.0,1.3,0.7,0.9,0.6,0.7,1.7,0.6,3.1,1.9,1.4,0.5,0.8,1.9,3.9,0.2,3,22,9,8,11,24,12,14,7,8,3.0,29,16,7,19,70,75,5,21,46,17,27,13,29,37,29,57,28,29,39,14,15,4,26,2,4,53,32,1,1,4,23,20,32,25,10,2,4,5,6,7,18,7,14,3,22,7,13,7,12,6,6,3,4,12,7,23,12,9,4,5,10,32,2,0.022,0.027,0.015,0.018,0.035,0.029,0.022,0.025,0.037,0.035,0.031,0.034,0.029,0.03,0.035,0.035,0.026,0.026,0.027,0.038,0.025,0.032,0.026,0.042,0.034,0.031,0.027,0.031,0.03,0.032,0.035,0.074,0.029,0.031,0.014,0.014,0.045,0.035,0.014,0.016,0.017,0.032,0.038,0.038,0.029,0.026,0.022,0.012,0.014,0.014,0.032,0.021,0.019,0.018,0.019,0.017,0.02,0.019,0.021,0.023,0.024,0.022,0.029,0.027,0.022,0.028,0.021,0.021,0.022,0.021,0.021,0.023,0.018,0.023,1541,5111,2507,2469,1707,4443,2982,3735,1153,1368,686.0,3127,3899,1459,3757,12020,18585,2457,4113,6773,3938,4097,2415,6267,5649,7534,8538,5049,7132,6488,2569,1349,1097,6586,1796,1521,7987,7076,422,531,1937,3524,5523,2579,3973,1904,758,2027,2832,1988,850,3633,1948,5055,453,5223,1170,3903,1368,2027,1043,1829,599,878,3044,707,5075,3477,2226,870,1504,2645,9115,847,0.095,0.118,0.095,0.093,0.143,0.134,0.109,0.125,0.138,0.13,0.103,0.135,0.136,0.131,0.152,0.14,0.124,0.106,0.13,0.14,0.108,0.135,0.11,0.141,0.146,0.125,0.126,0.118,0.116,0.135,0.132,0.194,0.137,0.112,0.078,0.077,0.141,0.121,0.078,0.09,0.092,0.123,0.144,0.139,0.119,0.125,0.109,0.083,0.086,0.096,0.144,0.107,0.1,0.094,0.127,0.099,0.117,0.093,0.11,0.12,0.123,0.108,0.138,0.136,0.105,0.122,0.111,0.101,0.109,0.117,0.106,0.113,0.098,0.1,364,1598,961,847,614,1376,1010,1015,345,347,201.0,1457,892,327,933,3306,4883,435,1088,2768,1086,1222,1013,1336,1524,1861,2786,1673,1919,2003,487,546,286,1205,411,522,2041,1614,243,181,776,1542,1075,1774,2357,794,289,773,1147,868,527,1558,931,1936,255,2552,558,1559,681,926,453,665,273,351,1269,336,2294,1374,1031,340,725,1259,3570,201,3.903,2.933,2.651,2.842,2.716,2.736,2.472,3.276,2.593,3.236,2.888,1.954,3.298,3.018,3.076,2.704,3.048,4.024,2.979,2.081,2.965,2.626,2.096,3.308,2.825,3.169,2.454,2.405,2.833,2.604,3.27,2.472,3.474,3.498,3.834,2.675,2.835,3.203,2.119,3.18,3.099,2.069,3.364,1.765,1.991,2.544,3.462,3.238,2.929,2.631,2.183,2.355,2.469,2.679,2.299,2.335,2.252,2.698,2.313,2.364,2.664,3.014,2.728,2.606,2.683,2.347,2.377,2.68,2.632,2.801,2.537,2.656,2.709,4.472,0.756,0.792,0.619,0.643,0.557,0.56,0.449,0.597,0.613,0.532,0.72,0.571,0.547,0.446,0.584,0.572,0.626,0.747,0.571,0.775,0.757,0.485,0.478,0.694,0.526,0.696,0.499,0.551,0.561,0.54,0.937,1.14,0.738,0.612,0.723,0.555,0.731,0.645,0.279,0.66,0.893,0.565,0.846,0.776,0.592,0.518,0.435,0.766,0.66,0.5,0.402,0.508,0.569,0.51,0.44,0.426,0.552,0.511,0.368,0.416,0.427,0.691,0.417,1.071,0.464,0.484,0.542,0.367,0.497,0.43,0.618,0.489,0.498,0.532
-249,Epileptic,M,27.0,1.3,2.5,1.1,1.9,1.1,2.8,2.1,1.9,1.0,1.1,0.4,3.1,1.5,0.5,1.8,6.3,9.1,0.7,2.1,6.2,1.0,2.1,1.3,3.1,3.5,4.0,3.4,2.7,2.9,3.8,0.8,1.5,0.8,3.0,0.4,0.6,4.5,5.2,0.1,0.3,1.0,3.9,1.6,3.1,3.3,0.8,0.4,1.3,1.4,0.7,0.7,2.3,1.7,3.0,0.6,2.8,1.0,2.4,0.8,0.7,0.9,1.2,0.5,0.4,1.7,0.9,3.1,1.4,1.1,1.2,1.4,2.1,4.8,0.4,9,21,12,16,13,23,18,16,9,10,5.0,22,15,8,19,64,75,8,24,47,10,23,12,34,41,37,35,24,25,42,11,11,8,29,3,5,49,55,2,2,5,29,17,21,21,7,2,9,8,5,5,18,11,18,4,22,7,17,6,6,5,9,5,4,15,11,28,10,10,17,9,15,47,3,0.039,0.025,0.018,0.027,0.027,0.033,0.028,0.03,0.039,0.036,0.028,0.04,0.026,0.028,0.033,0.032,0.026,0.025,0.027,0.042,0.018,0.032,0.033,0.03,0.03,0.028,0.025,0.03,0.024,0.029,0.031,0.047,0.031,0.025,0.015,0.015,0.034,0.031,0.013,0.017,0.02,0.043,0.028,0.034,0.026,0.015,0.015,0.015,0.014,0.015,0.022,0.019,0.022,0.021,0.019,0.019,0.022,0.021,0.031,0.022,0.024,0.021,0.026,0.018,0.025,0.022,0.023,0.021,0.019,0.027,0.025,0.019,0.018,0.021,1732,4985,2449,2799,1838,3818,2968,3681,1488,1666,546.0,2200,4099,1229,3376,11660,18946,2859,4705,5412,3126,3469,1703,5926,6478,7822,5999,4117,5911,5989,1523,1344,1787,8220,1926,2185,8838,9905,388,608,1763,2898,4735,2364,3031,1408,924,3005,2904,1642,680,3892,2277,4895,735,4101,1811,3901,743,901,991,2369,547,801,2339,1106,4326,2393,1902,1599,1848,3346,9825,714,0.137,0.115,0.106,0.128,0.123,0.143,0.122,0.138,0.133,0.147,0.126,0.149,0.127,0.124,0.137,0.142,0.121,0.112,0.123,0.145,0.081,0.135,0.142,0.124,0.135,0.128,0.126,0.12,0.112,0.14,0.118,0.123,0.128,0.121,0.087,0.084,0.136,0.124,0.092,0.104,0.103,0.15,0.127,0.139,0.117,0.101,0.089,0.09,0.088,0.097,0.124,0.101,0.111,0.105,0.124,0.111,0.115,0.094,0.135,0.117,0.121,0.106,0.136,0.12,0.118,0.105,0.12,0.111,0.109,0.14,0.126,0.104,0.104,0.116,551,1715,1037,1141,802,1457,1181,1082,464,461,192.0,1159,1024,358,981,3272,5516,587,1337,2401,957,1180,652,1698,2038,2355,2250,1538,1828,2296,462,515,478,1876,454,739,2412,2825,232,313,789,1426,1072,1494,1994,750,378,1219,1414,691,461,1912,1133,2216,415,2114,755,1981,459,506,523,892,343,416,1218,616,2180,1084,909,711,891,1565,4343,330,3.153,2.56,2.387,2.573,2.115,2.32,2.226,3.091,2.558,3.02,2.353,1.777,3.092,2.519,2.718,2.798,2.894,3.661,2.796,2.0,2.665,2.331,2.287,2.697,2.549,2.775,2.283,2.225,2.581,2.284,2.466,2.496,2.853,3.224,3.674,2.73,2.941,2.644,1.971,2.259,2.833,1.866,3.181,1.927,1.803,2.132,3.17,3.05,2.556,2.813,1.841,2.172,2.248,2.502,2.238,2.163,3.03,2.296,1.924,1.978,2.378,2.625,2.006,2.176,2.195,2.063,2.138,2.589,2.336,2.299,2.432,2.552,2.381,2.952,0.894,0.671,0.476,0.463,0.562,0.559,0.516,0.493,0.512,0.403,0.879,0.519,0.453,0.554,0.542,0.48,0.513,0.815,0.549,0.594,0.735,0.467,0.429,0.745,0.477,0.478,0.417,0.583,0.646,0.468,0.592,1.024,0.521,0.707,0.652,0.424,0.661,0.686,0.44,0.357,0.497,0.55,0.741,0.59,0.588,0.394,0.401,0.692,0.53,0.62,0.393,0.483,0.528,0.472,0.317,0.401,0.501,0.552,0.325,0.336,0.487,0.866,0.519,0.865,0.557,0.645,0.422,0.338,0.353,0.613,0.45,0.509,0.495,0.713
-250,Epileptic,F,37.0,0.9,2.5,1.6,1.2,1.7,1.9,1.8,1.7,1.2,0.7,0.2,3.0,1.8,0.9,1.2,8.2,8.9,0.7,2.5,5.2,0.5,2.5,1.5,3.1,3.0,3.0,2.8,2.9,3.1,3.7,1.3,1.4,0.7,3.2,0.4,1.0,3.7,3.8,0.2,0.2,1.1,2.9,2.1,2.4,3.3,0.6,0.4,0.9,1.2,0.7,0.7,1.8,1.7,2.3,0.3,1.8,0.6,1.0,1.4,0.9,0.8,1.9,0.2,0.5,1.9,2.5,3.6,1.6,1.3,0.5,0.9,1.2,4.2,0.4,8,25,14,9,18,18,16,15,11,6,4.0,27,17,11,11,76,86,6,24,42,4,29,16,34,39,33,33,31,23,34,24,13,6,34,2,7,40,42,2,1,7,23,18,20,22,5,2,6,7,8,5,16,11,20,1,16,5,10,11,7,7,14,1,5,18,22,29,11,9,6,6,8,35,5,0.041,0.023,0.02,0.022,0.031,0.039,0.026,0.025,0.033,0.036,0.029,0.033,0.027,0.04,0.035,0.039,0.029,0.027,0.033,0.039,0.013,0.035,0.03,0.031,0.032,0.03,0.03,0.023,0.024,0.031,0.036,0.042,0.03,0.034,0.014,0.023,0.036,0.033,0.015,0.017,0.024,0.038,0.036,0.028,0.02,0.014,0.02,0.016,0.017,0.031,0.034,0.02,0.023,0.017,0.02,0.022,0.015,0.016,0.031,0.018,0.02,0.03,0.015,0.017,0.021,0.034,0.023,0.024,0.021,0.016,0.02,0.021,0.018,0.026,1433,4342,2803,2153,2420,2385,2323,3010,1668,1163,551.0,2462,3617,1310,2281,10746,15601,1847,3606,4125,2298,4032,2055,5344,5150,5060,4325,4673,5847,4881,1842,1570,930,5920,1513,1801,6621,7160,396,352,1400,2071,5096,2042,3765,1302,762,1970,2198,1271,531,2615,2297,4492,334,2565,1546,2228,1345,1254,1260,2157,272,717,2772,1589,4208,2100,1710,840,1416,1741,8060,601,0.143,0.117,0.114,0.106,0.134,0.153,0.106,0.125,0.139,0.134,0.123,0.14,0.13,0.163,0.125,0.151,0.127,0.114,0.138,0.152,0.074,0.146,0.132,0.138,0.148,0.137,0.132,0.111,0.098,0.132,0.143,0.137,0.142,0.14,0.078,0.104,0.136,0.136,0.097,0.099,0.114,0.148,0.135,0.122,0.102,0.095,0.112,0.086,0.095,0.139,0.139,0.105,0.116,0.098,0.119,0.113,0.095,0.099,0.133,0.105,0.11,0.135,0.105,0.125,0.113,0.128,0.122,0.111,0.106,0.102,0.109,0.116,0.101,0.143,435,1757,1218,867,991,900,1033,1073,540,327,193.0,1420,1087,365,707,3616,5120,393,1192,2043,579,1268,853,1660,1599,1728,1586,1833,1786,1888,618,657,354,1519,439,581,1816,1862,225,189,702,1166,1047,1420,2480,730,349,953,1091,477,330,1502,1169,2183,192,1375,720,1157,773,720,644,918,185,425,1534,977,2386,1073,1002,477,747,848,3645,309,3.319,2.337,2.203,2.358,2.173,2.326,1.952,2.537,2.365,2.814,2.433,1.616,2.679,2.658,2.456,2.354,2.448,3.614,2.488,1.869,2.996,2.443,2.06,2.514,2.525,2.416,2.215,2.083,2.528,2.174,2.398,2.38,2.205,2.895,3.067,2.712,2.8,2.753,2.098,2.27,2.505,1.697,3.511,1.66,1.655,1.963,2.746,2.425,2.362,2.744,1.94,1.914,2.005,2.11,2.329,1.909,2.416,2.209,2.05,1.982,2.245,2.395,1.702,2.109,1.989,1.959,1.946,2.077,2.004,1.946,2.244,2.402,2.265,2.496,0.661,0.43,0.562,0.513,0.54,0.496,0.501,0.47,0.496,0.523,0.505,0.407,0.508,0.435,0.439,0.525,0.497,0.722,0.572,0.524,0.68,0.47,0.427,0.582,0.522,0.537,0.465,0.508,0.522,0.425,0.502,0.905,0.378,0.672,0.442,0.532,0.709,0.663,0.395,0.48,0.552,0.42,0.683,0.654,0.45,0.471,0.364,0.625,0.547,0.568,0.302,0.375,0.423,0.421,0.405,0.383,0.388,0.412,0.365,0.345,0.405,0.634,0.351,0.748,0.546,0.535,0.411,0.402,0.436,0.509,0.433,0.503,0.5,0.431
-251,Epileptic,F,52.0,0.5,1.1,0.4,0.9,1.1,1.8,1.3,1.6,1.1,0.5,0.2,2.3,1.3,0.8,0.9,5.5,7.3,0.6,1.8,4.2,1.2,3.0,1.3,3.0,2.9,3.3,2.9,2.5,3.1,3.1,1.3,1.1,0.5,2.8,0.3,1.0,3.0,3.0,0.2,0.1,1.1,2.7,1.9,2.6,2.8,0.7,0.9,1.0,1.0,0.6,0.4,1.3,1.1,2.2,0.1,2.8,1.1,2.0,1.1,0.9,0.5,0.9,0.3,0.6,1.2,1.1,2.9,1.9,0.9,0.3,1.8,1.1,4.4,0.4,4,11,6,6,15,14,10,14,14,5,2.0,19,15,10,10,55,67,5,14,29,25,27,13,35,32,35,34,24,25,41,22,13,4,24,1,10,34,31,2,1,7,21,17,19,15,8,5,6,5,5,2,8,7,18,1,21,7,15,7,7,4,9,2,3,9,11,23,14,8,2,13,7,34,3,0.024,0.014,0.009,0.019,0.033,0.032,0.019,0.027,0.041,0.026,0.028,0.037,0.03,0.027,0.021,0.032,0.025,0.026,0.036,0.038,0.024,0.035,0.035,0.037,0.033,0.028,0.025,0.025,0.025,0.028,0.043,0.034,0.02,0.033,0.011,0.021,0.032,0.034,0.019,0.007,0.021,0.036,0.034,0.022,0.018,0.016,0.032,0.013,0.013,0.019,0.026,0.014,0.021,0.02,0.015,0.022,0.032,0.022,0.031,0.029,0.024,0.019,0.014,0.025,0.027,0.03,0.021,0.025,0.02,0.013,0.031,0.022,0.022,0.016,1677,3806,1648,1932,2237,3239,2650,3565,1757,1512,527.0,2362,3149,1857,2759,12695,18875,2581,2831,3770,3186,4439,1556,5479,5314,5957,6065,4159,6320,5632,2126,1835,1172,5884,1540,2471,5138,6223,337,301,1822,2674,5052,2841,4374,1630,947,2412,2419,1546,354,3354,1820,4680,351,3528,1559,3097,1116,932,724,2023,530,724,1869,1163,4538,2682,1836,696,2539,1517,8099,668,0.089,0.093,0.088,0.095,0.136,0.138,0.093,0.131,0.154,0.121,0.106,0.14,0.126,0.135,0.098,0.134,0.117,0.092,0.131,0.131,0.101,0.143,0.144,0.139,0.143,0.126,0.121,0.104,0.103,0.128,0.137,0.107,0.101,0.124,0.056,0.106,0.129,0.13,0.132,0.057,0.103,0.145,0.133,0.112,0.098,0.101,0.133,0.085,0.084,0.098,0.132,0.083,0.105,0.096,0.087,0.12,0.129,0.108,0.131,0.126,0.127,0.105,0.105,0.115,0.122,0.116,0.112,0.113,0.1,0.091,0.133,0.113,0.108,0.103,390,1314,630,725,720,957,878,1000,480,369,156.0,936,861,483,718,3013,4901,397,830,1751,841,1378,631,1433,1554,1771,1952,1431,1737,2033,620,608,362,1302,424,873,1556,1419,164,149,845,1181,876,1802,2256,679,413,973,1109,566,226,1465,846,1934,168,1833,480,1469,630,501,319,782,263,391,802,565,2187,1157,799,362,998,737,3317,375,3.511,2.816,2.476,2.682,2.428,2.799,2.537,3.084,2.685,3.245,2.804,2.181,2.744,2.818,2.818,3.057,3.051,4.211,2.821,1.866,3.076,2.46,2.187,2.845,2.748,2.688,2.438,2.266,2.756,2.21,2.525,2.668,2.488,3.153,3.254,2.648,2.628,2.989,2.296,2.066,2.755,1.993,3.704,1.84,2.186,2.186,2.945,3.001,2.603,2.753,2.227,2.357,2.217,2.453,2.434,2.16,2.913,2.426,2.082,1.891,2.553,2.57,2.299,2.161,2.368,2.381,2.25,2.538,2.515,2.266,2.54,2.448,2.66,2.365,1.103,0.556,0.559,0.569,0.688,0.623,0.683,0.599,0.71,0.635,0.937,0.484,0.619,0.673,0.544,0.704,0.64,0.727,0.773,0.614,0.721,0.544,0.464,0.586,0.538,0.564,0.46,0.556,0.556,0.634,0.675,1.11,0.44,0.676,0.796,0.47,0.625,0.586,0.299,0.523,0.603,0.506,0.856,0.529,0.674,0.723,0.541,0.763,0.553,0.763,0.459,0.647,0.659,0.53,0.429,0.452,0.652,0.789,0.363,0.398,0.401,0.652,0.385,0.822,0.666,0.797,0.504,0.515,0.431,0.453,0.771,0.67,0.506,0.417
-252,Epileptic,M,17.5,0.6,2.6,0.7,1.1,1.6,2.4,1.6,2.0,1.4,0.7,0.3,2.9,1.4,0.6,1.2,8.0,9.5,1.1,2.7,5.4,1.6,3.7,1.3,2.9,3.5,3.8,2.8,2.6,3.0,3.1,1.0,1.1,0.7,3.5,0.4,0.6,4.9,5.8,0.4,0.1,0.9,3.1,2.9,3.0,2.5,0.6,0.4,0.8,1.0,0.8,0.7,1.5,1.1,2.6,0.5,1.9,0.8,1.4,0.8,1.1,0.4,1.4,0.3,0.5,1.7,1.5,2.0,1.1,1.0,0.5,0.9,2.5,6.0,0.2,5,18,7,11,20,24,16,17,13,6,4.0,25,14,8,15,78,98,10,27,47,13,51,16,30,49,37,33,29,30,37,15,8,7,36,3,6,43,46,4,0,4,31,30,18,16,4,2,6,4,6,4,14,12,18,3,14,4,11,6,10,3,13,2,5,13,16,13,10,7,5,6,20,41,2,0.035,0.026,0.014,0.022,0.035,0.034,0.024,0.027,0.05,0.034,0.023,0.032,0.029,0.037,0.038,0.039,0.032,0.034,0.035,0.039,0.028,0.036,0.027,0.038,0.033,0.033,0.028,0.025,0.031,0.031,0.033,0.045,0.038,0.037,0.013,0.022,0.039,0.035,0.031,0.012,0.019,0.033,0.047,0.026,0.016,0.011,0.017,0.011,0.013,0.015,0.03,0.023,0.026,0.019,0.02,0.018,0.016,0.02,0.021,0.018,0.016,0.026,0.012,0.031,0.019,0.023,0.015,0.015,0.016,0.016,0.02,0.026,0.019,0.017,1724,5707,2361,2501,2570,3487,2523,4411,2094,1390,710.0,2790,3648,1317,2279,12568,20053,2521,5014,5218,3912,5234,1818,7028,6853,6611,4850,4865,5824,5874,2345,935,1028,6274,2482,1865,7471,8293,494,244,1550,2780,5875,2743,3897,1731,772,2474,2241,1903,686,3193,2754,5905,821,3104,2011,2835,1015,1663,869,2710,491,1096,2559,1954,3698,2641,2105,1265,1601,3157,10965,632,0.105,0.124,0.093,0.109,0.155,0.142,0.115,0.116,0.174,0.139,0.117,0.134,0.136,0.16,0.155,0.153,0.134,0.13,0.143,0.147,0.114,0.153,0.127,0.14,0.141,0.132,0.121,0.113,0.121,0.143,0.138,0.116,0.164,0.127,0.081,0.097,0.131,0.122,0.144,0.075,0.1,0.139,0.16,0.117,0.09,0.08,0.088,0.076,0.077,0.093,0.133,0.107,0.11,0.099,0.106,0.101,0.084,0.108,0.11,0.115,0.097,0.122,0.096,0.134,0.114,0.11,0.096,0.093,0.095,0.116,0.113,0.119,0.094,0.086,367,1566,821,840,781,1350,986,1333,531,354,224.0,1378,882,331,651,3529,5136,524,1340,2323,997,1809,846,1500,2001,1965,1793,1652,1689,1710,542,442,319,1552,552,621,2170,2496,214,106,680,1425,1245,1718,2365,772,326,961,1015,834,373,1195,929,2211,417,1575,834,1313,667,922,402,899,286,394,1238,1035,1861,1155,935,521,770,1655,4841,262,3.637,3.007,2.648,2.845,2.723,2.298,2.133,2.847,2.831,3.13,2.677,1.832,2.961,2.804,2.396,2.655,2.979,3.534,2.949,1.974,3.029,2.253,1.834,3.254,2.597,2.72,2.181,2.264,2.644,2.639,3.096,2.105,2.982,2.729,3.551,2.598,2.631,2.506,2.552,2.453,2.738,1.794,3.047,1.826,1.847,2.434,2.909,3.012,2.688,2.515,2.065,2.482,2.519,2.743,2.192,2.142,2.689,2.341,1.659,1.94,2.157,2.829,2.045,2.976,2.265,2.334,2.186,2.582,2.496,2.502,2.291,2.331,2.381,2.601,0.776,0.74,0.573,0.541,0.646,0.583,0.583,0.7,0.651,0.552,0.692,0.447,0.556,0.587,0.781,0.665,0.689,0.634,0.576,0.635,0.909,0.598,0.499,0.739,0.584,0.64,0.507,0.568,0.477,0.581,0.63,1.011,0.644,0.746,0.819,0.588,0.828,0.746,0.329,0.597,0.808,0.406,0.9,0.746,0.577,0.509,0.573,0.752,0.609,0.481,0.467,0.52,0.691,0.646,0.396,0.632,0.571,0.505,0.388,0.449,0.596,0.769,0.534,0.884,0.672,0.851,0.581,0.433,0.508,0.673,0.588,0.636,0.568,0.709
-253,Epileptic,M,27.0,1.4,3.6,1.2,1.5,2.1,2.0,1.8,1.4,1.9,0.9,0.7,3.8,1.1,0.5,1.8,7.0,9.7,1.2,2.2,6.1,1.9,3.1,2.4,5.3,3.8,3.4,5.0,3.5,4.1,2.9,1.6,1.2,0.6,3.0,0.7,0.7,3.7,3.3,0.5,0.2,1.0,3.9,2.5,3.9,4.3,0.7,0.4,1.1,1.4,0.8,0.8,2.0,2.0,3.0,0.5,3.0,0.8,2.0,1.3,1.7,1.0,2.6,0.4,0.7,2.6,2.0,2.8,1.4,1.5,0.6,0.8,1.2,4.7,0.4,12,33,12,15,25,20,15,13,23,11,9.0,43,14,7,19,76,93,10,25,65,13,40,21,55,47,42,58,32,34,44,27,8,4,33,4,6,43,44,5,2,5,31,29,30,26,5,2,5,7,8,5,15,13,21,3,22,6,17,12,15,7,25,2,6,21,23,22,10,10,6,6,9,35,4,0.046,0.027,0.021,0.025,0.034,0.029,0.023,0.023,0.051,0.031,0.039,0.031,0.026,0.028,0.032,0.03,0.03,0.039,0.027,0.035,0.026,0.035,0.034,0.04,0.033,0.025,0.028,0.028,0.028,0.025,0.035,0.044,0.023,0.03,0.019,0.016,0.032,0.03,0.025,0.016,0.022,0.032,0.035,0.026,0.026,0.012,0.016,0.014,0.017,0.013,0.023,0.018,0.022,0.02,0.027,0.017,0.021,0.021,0.025,0.022,0.02,0.032,0.021,0.018,0.024,0.024,0.02,0.018,0.019,0.017,0.021,0.016,0.018,0.018,1632,5373,2722,2685,3125,3843,3543,3464,1879,1852,953.0,4043,3434,1252,3405,13506,19669,2743,4400,6233,5537,4677,2140,7767,6158,7490,8913,5809,9395,6222,3068,1483,1215,6948,2458,1846,7958,9017,730,520,1492,2837,6416,5015,4732,1537,1006,2393,2512,2034,723,3718,2690,6228,694,5008,1380,3658,1355,2113,1286,3634,449,1118,3865,2107,4285,2983,2638,969,1475,2461,9671,534,0.142,0.128,0.11,0.12,0.127,0.128,0.101,0.111,0.145,0.134,0.142,0.144,0.107,0.123,0.131,0.122,0.13,0.133,0.123,0.142,0.108,0.112,0.12,0.137,0.125,0.119,0.115,0.108,0.108,0.125,0.138,0.117,0.092,0.13,0.082,0.092,0.133,0.131,0.127,0.097,0.101,0.134,0.153,0.116,0.112,0.086,0.089,0.078,0.092,0.098,0.122,0.097,0.109,0.098,0.132,0.099,0.115,0.109,0.12,0.113,0.11,0.136,0.115,0.105,0.113,0.13,0.11,0.093,0.098,0.122,0.11,0.099,0.094,0.116,556,2002,993,1073,1218,1227,1194,1025,734,535,291.0,1952,918,296,963,3852,5418,630,1377,3032,1242,1725,1251,2359,2022,2307,2991,1974,2478,2066,914,586,393,1699,659,697,2188,2091,330,258,737,1726,1399,2594,2674,806,410,1088,1185,943,460,1815,1430,2475,272,2658,640,1624,870,1249,686,1420,252,625,1926,1206,2147,1298,1221,514,698,1162,4188,334,2.919,2.623,2.756,2.55,2.229,2.444,2.445,2.838,2.115,2.775,2.634,1.84,2.894,2.887,2.709,2.642,2.831,3.29,2.593,1.798,3.331,2.176,1.627,2.638,2.424,2.611,2.339,2.319,2.923,2.373,2.562,2.491,2.373,2.996,3.207,2.393,2.75,3.15,2.266,2.422,2.459,1.527,3.196,2.074,2.001,2.104,2.878,2.684,2.72,2.564,1.823,2.181,2.079,2.543,2.445,2.07,2.286,2.42,1.802,1.888,2.146,2.468,1.895,2.053,2.133,2.113,2.18,2.569,2.337,2.132,2.387,2.476,2.442,1.994,0.738,0.42,0.45,0.466,0.596,0.667,0.642,0.555,0.579,0.578,0.606,0.509,0.416,0.478,0.578,0.678,0.601,0.828,0.506,0.588,0.772,0.662,0.444,0.755,0.666,0.552,0.608,0.6,0.565,0.522,0.51,0.766,0.49,0.616,0.49,0.507,0.636,0.629,0.356,0.408,0.505,0.486,0.862,0.515,0.606,0.397,0.391,0.47,0.459,0.399,0.381,0.424,0.431,0.497,0.72,0.348,0.386,0.437,0.395,0.394,0.414,0.681,0.433,0.596,0.421,0.864,0.425,0.335,0.405,0.6,0.422,0.415,0.454,0.387
-254,Epileptic,F,17.5,1.3,2.3,1.0,1.2,1.3,1.8,1.2,1.7,1.1,0.5,0.1,2.6,1.2,0.6,1.0,4.6,8.0,0.6,2.5,4.1,1.5,2.4,1.3,2.4,1.9,4.2,3.5,2.5,2.7,2.7,1.4,0.4,0.5,2.2,0.3,0.3,3.1,3.3,0.1,0.1,0.8,2.7,2.1,3.0,2.3,0.6,0.5,0.9,0.9,0.7,0.6,1.1,0.9,2.3,0.2,2.5,0.8,1.6,0.7,0.7,0.8,1.2,0.2,0.3,1.2,1.8,2.2,1.0,0.9,0.4,0.8,1.8,3.9,0.3,9,21,15,13,15,18,13,20,12,5,2.0,23,11,9,10,48,79,3,26,33,17,30,12,32,29,45,41,29,29,33,16,3,5,28,2,2,33,42,1,1,5,25,26,18,15,5,2,5,5,5,3,8,7,19,2,20,6,12,6,5,8,7,1,3,9,16,18,6,9,3,5,11,28,3,0.046,0.023,0.017,0.021,0.033,0.035,0.026,0.029,0.041,0.036,0.043,0.03,0.031,0.036,0.023,0.034,0.027,0.024,0.036,0.034,0.026,0.032,0.029,0.032,0.024,0.034,0.029,0.027,0.027,0.028,0.037,0.027,0.019,0.029,0.011,0.008,0.032,0.032,0.012,0.013,0.017,0.038,0.04,0.032,0.017,0.013,0.019,0.015,0.011,0.012,0.02,0.014,0.023,0.019,0.029,0.017,0.023,0.019,0.023,0.013,0.032,0.021,0.021,0.019,0.016,0.027,0.019,0.016,0.018,0.014,0.024,0.024,0.016,0.017,1821,5380,2843,3239,2277,3273,2386,3655,2417,1462,500.0,2559,3367,1383,2779,11871,19675,2317,4534,4233,4349,4817,1808,6916,5449,7955,7255,4045,6431,5618,2457,693,1278,6743,1935,1676,7487,7866,370,334,1595,2509,6173,2498,3635,1394,984,1963,2671,1930,770,2702,1610,4884,401,4254,1830,3238,848,1424,925,2809,377,791,2590,1877,3755,2349,2184,1317,1302,2580,9781,511,0.146,0.111,0.1,0.116,0.134,0.149,0.096,0.141,0.148,0.121,0.093,0.126,0.126,0.137,0.108,0.129,0.126,0.091,0.133,0.14,0.104,0.138,0.13,0.129,0.118,0.143,0.133,0.123,0.125,0.132,0.138,0.087,0.106,0.122,0.063,0.064,0.122,0.129,0.093,0.104,0.099,0.15,0.152,0.131,0.094,0.095,0.102,0.086,0.077,0.089,0.111,0.087,0.108,0.102,0.16,0.102,0.109,0.097,0.125,0.097,0.123,0.108,0.105,0.124,0.095,0.136,0.108,0.088,0.101,0.108,0.122,0.119,0.09,0.114,429,1519,968,937,718,941,752,1049,485,304,128.0,1217,747,295,662,2409,4878,409,1207,1858,1029,1393,759,1450,1445,2021,2064,1454,1676,1509,565,258,406,1264,469,572,1575,1786,158,115,698,1203,1097,1508,2067,660,348,850,1086,780,410,1250,653,1994,124,2154,617,1439,445,717,421,824,176,310,1179,892,1909,923,903,431,540,1186,3766,278,3.479,3.066,2.772,3.12,2.489,2.844,2.562,3.088,3.091,3.667,2.952,1.828,3.352,3.21,2.896,3.136,3.115,3.777,3.004,1.938,3.197,2.611,2.119,3.282,2.831,3.107,2.621,2.23,2.91,2.872,2.987,2.443,2.554,3.455,3.491,2.665,3.336,3.154,2.579,3.228,2.835,1.912,3.52,1.921,2.041,2.375,3.507,2.997,2.903,2.715,2.316,2.283,2.38,2.434,3.849,2.223,2.995,2.623,2.098,2.266,2.499,3.179,2.13,2.465,2.472,2.471,2.235,2.707,2.482,2.98,2.914,2.633,2.823,2.423,0.986,0.661,0.598,0.599,0.678,0.539,0.604,0.582,0.866,0.401,0.83,0.512,0.556,0.45,0.586,0.75,0.66,0.94,0.657,0.597,0.751,0.556,0.522,0.678,0.498,0.527,0.515,0.55,0.489,0.586,0.805,1.252,0.465,0.633,0.877,0.488,0.736,0.695,0.26,0.491,0.648,0.475,0.804,0.653,0.593,0.53,0.602,0.53,0.637,0.526,0.361,0.548,0.676,0.622,0.506,0.498,0.578,0.54,0.41,0.377,0.611,0.919,0.446,0.881,0.525,0.755,0.483,0.477,0.528,1.14,0.512,0.529,0.56,0.451
-255,Epileptic,F,22.0,0.4,1.4,0.6,0.9,1.5,1.3,1.5,1.3,0.9,0.7,0.3,2.2,1.3,0.5,1.1,6.4,7.3,0.7,1.9,3.4,0.9,3.0,1.6,3.0,4.0,2.8,3.2,2.1,2.4,2.9,0.9,1.6,0.4,2.6,0.2,0.7,3.5,2.9,0.1,0.3,0.9,3.6,2.0,2.9,2.2,0.7,0.5,0.7,1.0,0.5,0.5,1.5,1.4,2.1,0.2,2.4,1.0,1.5,0.9,1.1,0.7,1.4,0.1,0.3,1.4,1.7,2.5,0.8,0.9,0.4,0.9,1.2,4.3,0.1,3,19,7,8,16,16,14,16,10,7,5.0,26,18,6,11,66,79,6,25,35,12,34,15,37,49,29,40,21,22,30,17,16,4,27,2,7,50,35,1,2,5,33,26,23,14,5,2,6,5,5,3,12,12,19,1,22,6,16,6,9,4,12,1,5,12,19,21,5,8,6,6,11,35,2,0.024,0.018,0.012,0.018,0.035,0.029,0.022,0.03,0.042,0.031,0.025,0.033,0.031,0.036,0.028,0.037,0.025,0.028,0.027,0.031,0.02,0.036,0.028,0.033,0.032,0.026,0.03,0.028,0.023,0.026,0.028,0.061,0.032,0.035,0.01,0.021,0.033,0.034,0.016,0.021,0.019,0.043,0.034,0.032,0.02,0.015,0.021,0.015,0.016,0.01,0.038,0.017,0.028,0.017,0.034,0.018,0.022,0.022,0.026,0.021,0.022,0.024,0.013,0.015,0.019,0.036,0.02,0.014,0.015,0.014,0.025,0.015,0.017,0.017,1218,3809,2035,2008,1835,2361,2922,2679,1240,1399,680.0,2144,3437,1015,2459,11468,19316,2144,3933,3698,3665,4403,2201,6091,7247,5701,5601,3307,6116,5141,1553,1182,759,5528,1471,1855,7524,6774,255,602,1363,3064,5956,2208,2809,1265,794,2096,2089,1273,415,3108,2143,5285,302,3767,1612,2572,1000,1569,1089,2511,367,706,2351,847,4264,2112,2074,1204,1356,2267,9270,323,0.087,0.109,0.094,0.097,0.137,0.144,0.108,0.146,0.126,0.129,0.133,0.154,0.139,0.13,0.133,0.148,0.123,0.116,0.137,0.141,0.102,0.153,0.13,0.133,0.144,0.131,0.139,0.121,0.108,0.127,0.116,0.148,0.144,0.152,0.073,0.11,0.14,0.139,0.1,0.117,0.108,0.171,0.14,0.127,0.101,0.098,0.107,0.086,0.09,0.09,0.165,0.096,0.126,0.094,0.128,0.108,0.105,0.115,0.13,0.109,0.107,0.12,0.103,0.118,0.105,0.158,0.11,0.088,0.097,0.11,0.119,0.109,0.097,0.139,331,1334,740,792,737,902,974,838,387,356,221.0,1133,843,245,628,2922,4677,387,1107,1707,857,1440,907,1567,2107,1731,1866,1242,1507,1572,523,496,233,1177,388,606,1848,1576,141,233,650,1436,1158,1448,1677,638,328,872,973,580,221,1385,866,2130,135,2016,685,1269,537,875,467,926,176,401,1151,585,1976,901,920,508,616,1152,3798,125,3.431,2.744,2.505,2.545,2.084,2.347,2.49,2.781,2.431,3.149,2.682,1.745,3.028,2.826,2.919,2.914,3.211,3.873,2.865,1.905,3.237,2.471,2.05,2.828,2.604,2.703,2.329,2.119,2.953,2.571,2.243,2.519,2.589,3.286,3.252,2.768,2.995,3.033,2.046,2.557,2.697,1.952,3.388,1.838,1.899,2.277,3.115,2.92,2.631,2.487,2.256,2.369,2.265,2.491,2.397,2.056,2.697,2.321,2.292,2.023,2.504,2.657,2.379,1.954,2.299,1.857,2.197,2.685,2.503,2.641,2.633,2.455,2.556,2.899,0.642,0.551,0.696,0.403,0.557,0.507,0.647,0.42,0.479,0.547,0.696,0.409,0.48,0.554,0.556,0.64,0.634,0.657,0.529,0.535,0.841,0.493,0.461,0.706,0.571,0.588,0.595,0.504,0.513,0.529,0.614,0.843,0.662,0.708,0.63,0.699,0.684,0.619,0.363,0.529,0.528,0.506,0.666,0.668,0.542,0.432,0.605,0.79,0.549,0.423,0.376,0.543,0.6,0.523,0.456,0.42,0.522,0.557,0.39,0.333,0.44,0.657,0.436,0.897,0.627,0.62,0.509,0.511,0.451,0.64,0.489,0.438,0.498,0.636
-256,Epileptic,M,32.0,0.6,2.7,1.0,1.2,1.4,1.6,1.6,2.5,0.9,0.7,0.6,3.2,1.6,0.6,0.9,5.8,7.6,0.9,2.1,3.2,0.7,2.2,1.6,3.8,3.4,4.5,2.9,2.4,2.7,3.2,0.9,1.2,0.7,2.7,0.6,1.5,4.1,3.6,0.2,0.3,2.0,2.4,2.4,2.2,2.7,0.5,0.6,1.0,1.2,0.5,0.4,1.9,1.4,2.8,0.1,2.1,0.6,1.7,0.4,0.7,0.5,1.1,0.4,0.4,1.5,1.7,2.5,1.5,0.9,0.6,1.1,0.8,3.8,0.3,4,31,12,11,17,16,13,23,9,7,6.0,28,15,8,11,70,80,8,19,25,7,21,16,40,40,47,34,26,23,42,12,12,5,29,3,12,39,39,2,2,7,24,23,21,16,4,3,7,8,4,3,15,10,23,1,20,3,17,3,7,5,8,3,4,13,22,24,11,7,7,9,7,32,2,0.03,0.024,0.018,0.022,0.025,0.025,0.026,0.026,0.03,0.034,0.043,0.035,0.027,0.038,0.026,0.026,0.025,0.033,0.029,0.033,0.018,0.033,0.03,0.033,0.033,0.032,0.026,0.024,0.024,0.033,0.021,0.044,0.03,0.025,0.017,0.023,0.042,0.033,0.014,0.021,0.019,0.032,0.033,0.026,0.02,0.011,0.018,0.016,0.015,0.017,0.024,0.022,0.018,0.019,0.023,0.014,0.02,0.016,0.016,0.017,0.027,0.017,0.014,0.016,0.016,0.028,0.019,0.017,0.016,0.014,0.021,0.015,0.016,0.011,1907,5643,2463,2585,2647,2910,3029,5385,1870,1669,965.0,3307,4441,1271,2099,14097,19738,2392,4129,3499,2582,4381,2688,7871,7044,9072,5631,4800,7282,6191,2120,1687,1481,8311,2490,3522,7763,9029,448,437,2350,3069,7694,2558,3682,1453,1130,2360,2969,1500,605,4053,2499,6615,214,4637,1336,3900,800,1444,804,2999,596,1176,2916,1361,4578,3647,2067,1609,2026,2497,9297,1074,0.104,0.124,0.113,0.114,0.124,0.128,0.112,0.124,0.115,0.132,0.118,0.155,0.127,0.134,0.117,0.122,0.115,0.11,0.12,0.131,0.079,0.142,0.135,0.133,0.138,0.131,0.125,0.113,0.103,0.144,0.114,0.124,0.121,0.12,0.082,0.104,0.153,0.142,0.098,0.111,0.091,0.145,0.142,0.126,0.1,0.083,0.102,0.083,0.082,0.094,0.117,0.111,0.104,0.095,0.104,0.098,0.103,0.092,0.102,0.103,0.131,0.097,0.102,0.096,0.098,0.124,0.108,0.096,0.098,0.115,0.118,0.105,0.097,0.068,408,1787,903,885,978,1041,993,1664,565,424,272.0,1436,1058,302,652,3430,5121,460,1135,1596,737,1128,987,1929,1924,2581,1895,1526,1719,1765,630,576,369,1699,508,1099,1666,1942,232,193,1421,1303,1302,1442,2044,675,468,853,1259,518,318,1435,1101,2556,88,2252,500,1845,406,719,340,970,320,513,1437,916,2111,1424,855,638,872,950,3760,437,3.898,2.807,2.817,2.911,2.475,2.59,2.51,2.832,2.607,3.132,2.68,1.909,3.175,3.004,2.612,3.017,3.084,3.879,2.904,1.9,2.729,2.862,2.364,3.057,2.791,2.652,2.403,2.42,3.117,2.765,2.543,2.785,2.951,3.461,3.812,2.761,3.269,3.337,2.127,2.832,1.869,2.086,3.645,1.959,2.057,2.413,3.121,3.312,2.876,3.46,2.411,2.755,2.381,2.712,2.487,2.315,3.098,2.36,2.355,2.258,2.843,3.078,2.313,2.43,2.305,1.825,2.245,2.915,2.69,2.816,2.632,2.975,2.683,2.72,0.818,0.651,0.506,0.52,0.499,0.359,0.69,0.573,0.585,0.64,0.803,0.62,0.502,0.537,0.473,0.551,0.555,0.647,0.859,0.555,0.685,0.516,0.598,0.662,0.493,0.818,0.5,0.572,0.544,0.516,0.604,1.036,0.773,0.651,0.728,0.736,0.685,0.681,0.259,0.541,0.746,0.603,0.763,0.611,0.626,0.492,0.48,0.829,0.481,0.857,0.374,0.605,0.495,0.478,0.527,0.349,0.603,0.515,0.359,0.299,0.344,0.766,0.398,1.159,0.532,0.589,0.404,0.364,0.378,0.452,0.375,0.589,0.506,0.579
-257,Epileptic,F,22.0,1.0,2.2,0.8,1.3,3.4,1.4,1.5,1.6,1.4,0.9,0.3,3.5,2.1,0.3,1.1,6.8,9.4,1.1,2.2,5.6,1.9,2.1,2.3,3.8,6.2,2.9,4.0,2.5,2.8,2.8,2.3,1.8,0.5,2.8,0.5,0.9,5.1,3.7,0.1,0.1,1.0,3.6,2.8,3.5,3.1,0.6,0.4,1.0,1.2,0.6,0.7,1.4,1.8,2.5,0.4,2.3,0.8,1.2,1.5,1.0,0.4,2.0,0.4,0.8,2.0,1.0,3.5,1.0,0.9,0.7,0.9,1.6,5.0,0.2,8,20,8,13,32,16,13,18,15,12,5.0,31,22,8,19,79,91,8,28,55,16,27,20,39,58,34,48,29,25,31,18,10,4,31,2,8,61,44,1,1,6,33,26,23,15,8,2,5,7,5,5,10,12,17,3,21,5,10,9,9,4,15,2,6,15,16,33,10,5,5,6,11,41,2,0.051,0.03,0.018,0.026,0.053,0.027,0.025,0.027,0.054,0.04,0.039,0.037,0.035,0.032,0.038,0.037,0.036,0.046,0.036,0.038,0.033,0.034,0.035,0.042,0.045,0.028,0.034,0.026,0.03,0.031,0.057,0.047,0.034,0.037,0.015,0.019,0.043,0.037,0.02,0.018,0.025,0.037,0.04,0.032,0.021,0.019,0.023,0.017,0.019,0.013,0.027,0.018,0.026,0.022,0.012,0.02,0.015,0.016,0.026,0.022,0.015,0.028,0.024,0.027,0.02,0.02,0.022,0.019,0.014,0.02,0.026,0.019,0.018,0.017,1529,4515,2094,2533,1875,2728,3107,3701,1634,1636,668.0,3616,2986,840,2387,11378,18108,2342,4451,5973,4857,3934,2055,5721,7129,6785,6892,5073,6223,5070,2081,1286,743,5876,1894,2017,9129,7847,279,273,1238,3105,5793,3385,3710,1196,683,1942,2266,1730,617,2551,2332,4385,792,3997,1591,2861,1543,1425,601,3156,404,835,3410,1365,5410,2117,1758,1032,1073,2770,10064,393,0.134,0.127,0.109,0.119,0.162,0.126,0.113,0.124,0.161,0.152,0.136,0.143,0.133,0.154,0.142,0.15,0.144,0.137,0.143,0.152,0.12,0.119,0.117,0.135,0.138,0.119,0.131,0.106,0.116,0.135,0.143,0.13,0.118,0.138,0.081,0.105,0.148,0.144,0.095,0.105,0.115,0.132,0.145,0.129,0.1,0.109,0.104,0.087,0.093,0.089,0.144,0.097,0.122,0.109,0.094,0.11,0.095,0.1,0.122,0.112,0.103,0.127,0.128,0.133,0.113,0.112,0.107,0.101,0.088,0.124,0.132,0.103,0.102,0.128,426,1309,727,879,907,940,967,1050,510,417,180.0,1550,981,225,619,3115,4702,448,1176,2512,1026,1131,1012,1573,2214,1855,2203,1697,1578,1540,651,493,228,1316,497,694,2236,1853,133,134,639,1343,1297,1835,2191,556,285,847,1062,756,368,1259,1076,1882,456,1988,759,1260,844,730,332,1132,216,481,1527,815,2491,921,833,445,512,1279,4239,169,3.44,3.049,2.831,2.767,1.922,2.431,2.56,2.891,2.35,2.969,2.714,1.994,2.48,2.786,2.717,2.608,2.949,3.721,2.855,2.053,3.344,2.606,1.863,2.651,2.497,2.806,2.374,2.336,2.868,2.608,2.395,2.811,2.569,3.025,3.467,2.504,2.958,2.968,2.486,2.191,2.513,2.055,3.198,1.994,1.907,2.467,2.951,2.807,2.514,2.526,1.917,2.171,2.172,2.451,2.087,2.129,2.459,2.63,2.109,2.081,1.937,2.769,1.958,2.196,2.401,2.06,2.228,2.461,2.433,2.534,2.334,2.413,2.426,2.885,0.763,0.717,0.444,0.523,0.602,0.644,0.564,0.486,0.796,0.465,0.716,0.537,0.422,0.488,0.637,0.643,0.628,0.741,0.553,0.58,0.759,0.615,0.538,0.822,0.694,0.604,0.645,0.638,0.635,0.539,0.859,0.886,0.442,0.644,0.846,0.473,0.741,0.68,0.385,0.401,0.511,0.596,0.752,0.623,0.582,0.467,0.371,0.667,0.465,0.414,0.294,0.419,0.505,0.458,0.263,0.442,0.442,0.488,0.483,0.458,0.375,0.698,0.494,0.737,0.555,0.594,0.559,0.367,0.42,0.988,0.43,0.444,0.477,0.451
-258,Epileptic,F,52.0,0.5,2.0,1.0,1.3,1.3,2.0,2.1,1.8,1.3,0.6,0.3,2.9,1.2,0.5,1.6,5.2,8.6,0.8,1.7,4.3,0.6,2.1,1.4,2.7,2.4,3.0,2.9,2.0,2.7,2.7,1.1,0.9,0.7,2.0,0.4,0.6,2.8,3.6,0.3,0.2,1.0,2.6,2.2,2.0,2.6,0.8,0.3,0.8,1.3,0.7,0.4,1.6,1.6,2.2,0.2,2.4,0.6,1.4,0.8,0.7,0.6,1.1,0.4,0.5,1.2,1.7,2.4,1.2,1.2,0.8,0.6,1.0,5.0,0.3,3,16,14,12,13,14,18,17,12,5,6.0,21,15,7,16,60,78,6,19,38,5,27,14,35,29,36,34,18,21,30,22,7,7,27,3,5,30,41,4,1,6,24,20,17,14,5,2,6,6,5,3,12,12,14,1,17,5,9,6,5,4,8,3,3,8,21,15,7,7,8,6,9,46,3,0.026,0.027,0.021,0.025,0.039,0.036,0.035,0.032,0.043,0.032,0.032,0.035,0.031,0.028,0.038,0.038,0.034,0.03,0.031,0.037,0.013,0.033,0.026,0.042,0.031,0.031,0.029,0.026,0.026,0.032,0.042,0.043,0.032,0.031,0.013,0.018,0.031,0.038,0.026,0.014,0.023,0.034,0.041,0.029,0.022,0.02,0.021,0.016,0.017,0.03,0.024,0.018,0.029,0.021,0.016,0.02,0.022,0.019,0.024,0.016,0.023,0.026,0.05,0.025,0.018,0.034,0.018,0.019,0.025,0.031,0.022,0.021,0.021,0.015,1610,4208,2200,2381,1889,3533,2755,3575,2127,1177,565.0,2412,3013,1043,3206,10514,16383,2481,4018,4781,3210,4084,1863,5319,4845,6750,5081,3649,5684,4561,2287,681,1182,5841,1836,1702,6081,7452,578,459,1402,2543,6035,1736,2931,1477,692,2031,2390,1372,576,3193,1960,4374,323,3397,1365,2968,990,1258,972,2088,452,706,2183,1213,3904,2209,1758,1463,1334,2351,10357,524,0.092,0.119,0.115,0.118,0.143,0.14,0.132,0.137,0.167,0.138,0.149,0.142,0.126,0.134,0.16,0.144,0.139,0.115,0.128,0.147,0.079,0.145,0.117,0.151,0.137,0.136,0.126,0.101,0.103,0.145,0.148,0.125,0.141,0.125,0.072,0.085,0.136,0.156,0.136,0.093,0.115,0.14,0.153,0.125,0.105,0.103,0.103,0.089,0.095,0.117,0.116,0.099,0.13,0.104,0.104,0.108,0.122,0.099,0.127,0.1,0.111,0.123,0.159,0.109,0.102,0.143,0.1,0.1,0.113,0.141,0.121,0.112,0.109,0.111,380,1297,792,919,662,975,975,989,470,297,185.0,1156,791,332,823,2671,4429,423,903,1874,656,1104,782,1329,1369,1725,1663,1225,1474,1363,550,281,378,1188,478,543,1444,1669,220,202,671,1155,1000,1179,1805,713,274,852,1064,442,320,1403,865,1645,170,1773,439,1235,519,667,405,668,157,338,1049,777,1972,1016,784,475,533,949,3905,272,3.778,2.927,2.553,2.573,2.605,3.149,2.378,3.118,3.038,3.311,2.58,1.872,2.872,2.449,2.915,2.856,2.896,4.0,3.377,2.209,3.527,2.769,2.085,3.016,2.709,3.055,2.375,2.328,2.841,2.644,3.043,2.311,2.553,3.279,3.173,2.787,3.092,3.156,2.447,2.35,2.765,1.953,3.745,1.631,1.834,2.26,2.96,2.815,2.749,3.558,2.233,2.376,2.476,2.6,2.079,2.192,3.092,2.735,2.217,2.141,2.768,3.035,2.932,2.351,2.291,1.999,2.195,2.563,2.496,3.076,2.75,2.58,2.729,2.382,0.969,0.672,0.578,0.515,0.498,0.681,0.533,0.568,0.581,0.624,0.759,0.54,0.539,0.638,0.7,0.631,0.57,0.826,0.482,0.727,0.685,0.568,0.487,0.651,0.556,0.58,0.489,0.587,0.521,0.573,0.728,0.927,0.336,0.662,0.75,0.665,0.75,0.705,0.572,0.504,0.52,0.618,0.776,0.742,0.545,0.567,0.64,0.663,0.527,0.87,0.527,0.511,0.573,0.523,0.329,0.416,0.569,0.502,0.451,0.305,0.453,0.814,0.638,0.898,0.562,0.653,0.441,0.452,0.433,0.863,0.628,0.603,0.549,0.452
-259,Epileptic,M,47.0,0.9,2.0,1.2,1.0,1.5,1.7,1.6,1.7,1.2,0.6,0.2,1.9,1.8,0.6,1.5,4.8,6.9,0.8,1.6,4.5,0.5,3.3,1.4,2.9,2.7,3.3,2.0,2.4,2.5,3.1,1.4,1.2,0.7,2.2,0.1,0.5,3.8,3.1,0.2,0.1,0.9,2.7,1.4,2.2,2.7,0.9,0.5,0.8,1.2,0.6,0.6,1.7,0.9,2.0,0.3,1.3,0.7,1.1,0.9,0.9,0.6,1.4,0.6,0.4,1.8,1.6,2.2,1.6,0.9,0.6,0.7,1.5,4.2,0.3,9,20,10,12,18,21,16,15,14,6,2.0,20,18,6,15,60,69,9,23,39,12,32,15,34,32,34,27,22,23,36,15,9,6,28,1,4,48,30,2,1,6,23,19,19,15,8,3,6,7,7,6,13,7,12,2,13,5,8,5,8,5,15,2,6,16,13,17,10,8,5,4,13,33,2,0.039,0.022,0.021,0.015,0.031,0.029,0.02,0.03,0.03,0.039,0.029,0.027,0.03,0.028,0.032,0.028,0.024,0.035,0.025,0.035,0.015,0.036,0.028,0.03,0.03,0.032,0.02,0.023,0.022,0.031,0.029,0.05,0.035,0.029,0.007,0.013,0.034,0.032,0.02,0.012,0.02,0.033,0.03,0.025,0.021,0.023,0.02,0.017,0.016,0.021,0.029,0.016,0.017,0.016,0.035,0.014,0.018,0.012,0.02,0.017,0.017,0.02,0.024,0.016,0.025,0.036,0.017,0.019,0.015,0.024,0.018,0.025,0.019,0.018,1455,4501,2435,2350,2292,2681,2915,3078,2242,937,531.0,2423,3642,966,2682,10037,16658,2323,3518,4665,2727,4377,1688,5800,5277,5897,4441,4415,6075,5104,2167,1318,986,4815,1434,1723,7362,6524,430,439,1376,2722,5078,2380,3840,1372,898,1805,2471,1412,684,3377,1556,4160,376,2809,1368,2577,1060,1320,1127,2766,590,933,2401,1073,3615,2871,2015,1057,1189,2683,7969,371,0.14,0.114,0.105,0.106,0.138,0.147,0.101,0.126,0.142,0.152,0.113,0.135,0.132,0.119,0.14,0.132,0.119,0.13,0.125,0.139,0.087,0.149,0.118,0.128,0.141,0.145,0.111,0.103,0.099,0.146,0.124,0.125,0.139,0.129,0.051,0.084,0.153,0.136,0.119,0.087,0.112,0.141,0.136,0.118,0.101,0.121,0.111,0.094,0.093,0.115,0.145,0.096,0.104,0.091,0.141,0.096,0.104,0.085,0.112,0.106,0.098,0.117,0.125,0.117,0.124,0.133,0.104,0.103,0.099,0.136,0.105,0.126,0.102,0.111,449,1523,915,984,853,1090,974,951,614,288,175.0,1067,970,280,787,2791,4660,439,1030,2040,659,1443,808,1734,1495,1831,1629,1425,1641,1614,695,495,312,1181,409,533,1843,1545,198,223,661,1199,917,1498,2110,635,366,808,1119,524,335,1628,822,1841,170,1458,625,1311,666,764,502,1091,299,485,1176,651,1827,1327,932,430,596,1067,3446,217,3.183,2.715,2.552,2.382,2.365,2.181,2.404,2.83,2.776,2.92,2.768,1.978,2.737,2.521,2.666,2.744,2.938,3.745,2.691,1.917,2.949,2.346,1.884,2.623,2.625,2.719,2.307,2.421,2.796,2.56,2.377,2.539,2.494,2.91,2.95,2.765,3.02,3.041,2.195,2.182,2.72,2.013,3.745,1.783,2.071,2.335,3.176,2.858,2.719,2.982,2.083,2.204,2.261,2.449,2.429,2.012,2.526,2.316,1.868,1.93,2.281,2.483,2.393,2.106,2.143,1.961,2.166,2.51,2.471,2.871,2.218,2.869,2.446,2.078,0.786,0.668,0.617,0.524,0.551,0.471,0.537,0.471,0.415,0.591,0.772,0.377,0.46,0.442,0.61,0.499,0.536,0.705,0.477,0.634,0.767,0.459,0.344,0.743,0.483,0.524,0.411,0.52,0.52,0.482,0.744,0.804,0.354,0.53,0.885,0.529,0.662,0.574,0.504,0.427,0.501,0.438,0.827,0.557,0.619,0.495,0.312,0.775,0.459,0.57,0.487,0.449,0.46,0.439,0.384,0.428,0.437,0.345,0.371,0.378,0.486,0.6,0.398,0.945,0.558,0.729,0.346,0.348,0.386,0.626,0.486,0.665,0.543,0.494
-260,Epileptic,F,32.0,0.7,2.2,0.6,1.1,1.2,1.5,1.6,2.0,1.4,0.7,0.2,2.5,1.5,0.6,1.2,5.0,6.7,1.0,1.7,4.1,1.0,2.6,1.3,2.7,3.6,3.9,2.9,2.3,3.0,3.0,1.4,1.6,0.6,2.3,0.3,0.5,3.9,2.6,0.2,0.3,1.0,2.0,1.6,2.2,2.9,0.7,0.3,2.1,1.4,0.6,0.6,1.8,1.1,3.0,0.2,2.0,0.4,1.9,0.9,1.0,0.5,1.3,0.4,0.3,1.7,1.0,2.3,1.2,1.1,0.4,1.3,1.4,4.0,0.3,6,20,5,7,14,17,10,20,13,6,3.0,25,15,6,9,53,55,6,19,35,11,27,13,30,36,37,36,26,21,35,21,7,7,32,2,5,40,34,2,2,7,17,19,17,17,7,2,10,7,5,5,11,7,21,1,14,3,13,7,9,4,11,3,4,12,14,16,8,7,3,11,10,30,3,0.033,0.022,0.014,0.025,0.036,0.023,0.022,0.031,0.038,0.029,0.024,0.029,0.027,0.029,0.027,0.031,0.025,0.032,0.026,0.033,0.022,0.027,0.026,0.031,0.034,0.033,0.027,0.027,0.027,0.027,0.033,0.071,0.021,0.028,0.016,0.014,0.037,0.029,0.014,0.015,0.02,0.026,0.029,0.023,0.02,0.017,0.013,0.027,0.017,0.012,0.029,0.02,0.019,0.022,0.017,0.015,0.014,0.023,0.025,0.018,0.02,0.022,0.03,0.015,0.022,0.035,0.019,0.018,0.021,0.014,0.029,0.023,0.016,0.014,1404,4623,1907,1855,1949,3509,3025,3447,1637,1425,472.0,2527,3527,1044,2680,9877,15524,2208,3685,4143,3171,4153,1909,5597,5933,6336,5742,4003,6173,5490,2397,922,1361,6971,1679,1990,6856,7569,395,473,1646,2396,4438,2731,3624,1361,1030,2710,2408,1684,522,3002,1906,5324,225,3668,1084,3337,1187,1595,957,2749,525,805,2588,682,3660,2351,1521,844,1821,2592,9055,418,0.124,0.112,0.089,0.103,0.144,0.121,0.097,0.145,0.142,0.126,0.119,0.127,0.122,0.123,0.122,0.138,0.119,0.119,0.12,0.133,0.098,0.131,0.121,0.128,0.14,0.14,0.127,0.111,0.109,0.134,0.13,0.179,0.101,0.134,0.081,0.08,0.136,0.137,0.1,0.108,0.111,0.12,0.114,0.114,0.101,0.099,0.083,0.105,0.093,0.086,0.135,0.102,0.099,0.101,0.117,0.098,0.088,0.108,0.124,0.106,0.109,0.108,0.132,0.115,0.109,0.133,0.105,0.099,0.119,0.09,0.141,0.113,0.097,0.118,396,1617,700,701,680,1079,959,1113,493,405,172.0,1240,965,294,667,2719,4241,460,1086,1929,865,1479,779,1507,1805,1991,1920,1410,1601,1835,696,371,406,1481,399,662,1883,1675,229,236,779,1104,1074,1503,2173,657,390,1082,1182,711,346,1375,900,2130,131,1883,470,1389,555,882,407,968,281,381,1246,419,1888,1078,703,446,781,1049,3782,243,3.481,2.725,2.724,2.456,2.429,2.733,2.55,2.763,2.52,2.834,2.343,1.762,2.81,2.589,2.864,2.758,2.911,3.685,2.81,1.874,3.018,2.371,2.115,2.835,2.561,2.668,2.387,2.27,2.914,2.438,2.606,2.683,2.646,3.32,3.691,2.644,2.851,3.133,1.87,2.298,2.621,1.934,2.95,2.054,1.899,2.172,3.609,3.217,2.563,2.672,1.987,2.429,2.328,2.545,1.999,2.194,2.649,2.699,2.206,2.038,2.738,2.719,2.122,2.195,2.356,1.916,2.185,2.523,2.547,2.149,2.507,2.698,2.674,2.229,0.843,0.638,0.673,0.59,0.554,0.645,0.612,0.423,0.601,0.441,0.672,0.492,0.387,0.448,0.545,0.572,0.643,0.777,0.567,0.576,0.781,0.422,0.455,0.609,0.509,0.495,0.476,0.558,0.643,0.508,0.804,0.831,0.466,0.597,0.63,0.472,0.723,0.61,0.372,0.468,0.4,0.483,0.818,0.603,0.583,0.491,0.527,0.861,0.507,0.49,0.367,0.492,0.497,0.561,0.45,0.469,0.394,0.517,0.495,0.367,0.343,0.776,0.432,0.948,0.468,0.556,0.415,0.382,0.437,0.594,0.412,0.456,0.488,0.27
-261,Epileptic,M,42.0,0.7,1.8,0.9,1.1,1.5,3.0,1.3,1.4,0.9,0.8,0.2,2.4,1.3,0.6,1.2,4.9,6.6,0.8,2.0,3.8,1.5,2.5,1.6,3.0,2.9,3.1,2.6,2.6,3.4,3.2,1.4,1.1,0.4,2.4,0.4,0.9,3.3,3.1,0.2,0.3,1.3,3.5,2.5,2.2,3.3,0.9,0.4,0.7,1.2,0.7,0.8,2.0,2.5,2.2,0.2,2.3,0.5,2.6,1.4,0.7,0.8,1.1,0.4,0.4,1.8,1.0,2.7,1.1,1.8,0.7,0.8,0.9,4.9,0.3,5,21,9,15,13,28,13,15,10,9,3.0,22,12,9,12,51,63,10,31,41,16,25,14,37,35,38,32,23,23,39,20,8,4,27,3,6,29,37,2,2,8,31,19,18,20,6,2,4,6,8,6,16,21,17,1,23,3,20,9,6,5,12,2,5,16,15,19,7,10,4,6,6,45,3,0.03,0.022,0.016,0.025,0.036,0.041,0.023,0.025,0.04,0.04,0.029,0.035,0.028,0.032,0.027,0.033,0.029,0.03,0.027,0.034,0.024,0.034,0.035,0.038,0.032,0.027,0.026,0.03,0.032,0.033,0.034,0.031,0.022,0.03,0.016,0.019,0.041,0.033,0.012,0.019,0.024,0.036,0.043,0.032,0.025,0.017,0.022,0.011,0.016,0.022,0.043,0.024,0.029,0.017,0.021,0.019,0.016,0.028,0.032,0.017,0.027,0.023,0.023,0.018,0.028,0.018,0.025,0.021,0.023,0.024,0.024,0.019,0.019,0.028,1445,3900,2122,2191,1723,3410,2398,3267,1326,1491,491.0,2024,3230,1210,2479,8951,13712,2353,4517,4063,4142,3550,1483,4849,4800,6502,4385,3259,5185,4690,2430,1485,905,6112,1794,1902,6541,6207,345,486,1462,2262,5356,1917,3267,1294,743,2718,2219,1600,643,3132,3570,4833,257,3474,1282,3076,1078,1134,859,2092,440,971,2097,1464,2983,1608,2409,955,1174,1778,9404,328,0.112,0.123,0.102,0.112,0.144,0.167,0.1,0.115,0.153,0.152,0.113,0.156,0.118,0.133,0.124,0.139,0.13,0.131,0.135,0.154,0.095,0.138,0.14,0.151,0.144,0.134,0.127,0.114,0.111,0.144,0.14,0.104,0.101,0.132,0.084,0.099,0.148,0.145,0.092,0.116,0.119,0.149,0.154,0.139,0.113,0.11,0.114,0.074,0.088,0.112,0.172,0.118,0.133,0.091,0.112,0.113,0.087,0.119,0.143,0.101,0.125,0.12,0.119,0.119,0.136,0.121,0.123,0.109,0.109,0.121,0.123,0.108,0.106,0.147,446,1301,799,888,724,1345,865,1034,435,399,148.0,1035,840,318,742,2702,3880,439,1319,1832,1050,1268,707,1428,1513,1979,1720,1296,1502,1601,654,572,347,1333,445,699,1390,1615,210,216,797,1423,1046,1085,2073,703,312,994,1071,553,324,1381,1404,2052,123,1913,482,1606,695,672,436,830,263,445,1066,896,1624,799,1162,414,547,806,4113,171,3.405,2.896,2.54,2.415,2.344,2.398,2.283,2.697,2.534,2.933,2.506,1.812,2.878,2.716,2.563,2.622,2.843,3.681,2.788,1.98,2.906,2.271,1.899,2.782,2.497,2.637,2.094,2.047,2.664,2.463,2.84,2.546,2.255,3.246,3.683,2.557,3.244,2.915,1.95,2.676,2.278,1.535,3.708,1.88,1.832,2.01,3.081,3.212,2.486,3.067,2.348,2.315,2.535,2.422,2.386,2.101,2.922,2.197,1.879,1.884,2.313,2.689,1.978,2.647,2.324,1.95,2.07,2.464,2.453,2.838,2.458,2.49,2.403,2.138,0.675,0.608,0.472,0.474,0.463,0.489,0.576,0.538,0.525,0.713,0.802,0.5,0.392,0.347,0.419,0.498,0.494,0.694,0.643,0.592,0.798,0.472,0.403,0.622,0.522,0.508,0.472,0.525,0.503,0.549,0.546,1.091,0.541,0.533,0.735,0.519,0.787,0.582,0.391,0.415,0.454,0.352,0.577,0.642,0.518,0.369,0.438,0.85,0.549,0.676,0.522,0.469,0.576,0.438,0.472,0.446,0.842,0.55,0.481,0.382,0.395,0.653,0.377,0.834,0.478,0.651,0.506,0.393,0.453,0.829,0.487,0.593,0.552,0.782
-262,Epileptic,F,27.0,0.8,1.7,0.7,1.2,1.6,2.7,1.7,1.2,0.9,0.7,0.2,2.1,1.2,0.3,1.3,4.8,8.9,0.9,1.5,4.6,2.4,3.5,2.6,4.3,2.8,2.4,3.7,3.0,2.9,2.6,1.9,1.2,0.4,2.0,0.4,0.5,4.2,2.9,0.1,0.1,0.8,3.4,2.9,1.6,2.4,0.7,0.5,0.9,1.2,0.8,0.6,2.3,1.6,2.3,0.3,2.3,0.7,1.4,1.5,1.0,0.7,1.4,0.2,0.5,1.5,1.0,2.0,1.0,1.5,0.7,0.9,1.5,4.0,0.2,10,17,6,10,15,30,17,14,11,6,2.0,21,12,3,13,69,77,6,16,41,23,38,19,38,34,25,31,30,26,30,27,8,5,24,3,5,35,37,0,1,5,28,28,12,14,4,2,5,6,5,5,17,12,17,2,18,5,12,13,8,6,8,2,4,11,16,12,7,12,6,6,13,30,2,0.038,0.024,0.018,0.026,0.036,0.04,0.034,0.03,0.036,0.039,0.031,0.042,0.025,0.035,0.028,0.034,0.032,0.033,0.032,0.046,0.043,0.038,0.051,0.041,0.04,0.033,0.039,0.036,0.032,0.033,0.046,0.047,0.026,0.028,0.018,0.019,0.051,0.034,0.01,0.015,0.023,0.047,0.054,0.021,0.019,0.016,0.023,0.019,0.019,0.013,0.027,0.024,0.023,0.021,0.02,0.019,0.025,0.02,0.026,0.019,0.02,0.025,0.013,0.019,0.026,0.023,0.022,0.018,0.021,0.023,0.024,0.021,0.017,0.026,1510,4063,2175,2157,1741,3690,2554,2184,2158,1029,650.0,2349,3052,769,2872,10415,17470,2508,3556,4093,4107,5025,1829,6662,4456,4486,4830,3445,5658,5226,2266,937,741,5402,1703,1425,5711,6986,394,416,1040,1961,5111,2285,3127,1197,893,1952,1933,1786,577,3559,2131,4471,437,3484,1204,2870,1442,1492,1017,2501,556,918,2092,912,2713,2079,2357,1174,1319,1826,8340,291,0.145,0.115,0.094,0.116,0.124,0.151,0.119,0.139,0.146,0.151,0.11,0.157,0.121,0.13,0.138,0.146,0.137,0.128,0.135,0.174,0.131,0.139,0.159,0.148,0.132,0.14,0.123,0.121,0.118,0.143,0.145,0.125,0.137,0.122,0.07,0.102,0.144,0.145,0.07,0.095,0.11,0.151,0.168,0.108,0.097,0.091,0.097,0.08,0.093,0.087,0.126,0.11,0.122,0.106,0.102,0.106,0.12,0.108,0.135,0.11,0.114,0.116,0.1,0.107,0.12,0.132,0.104,0.097,0.114,0.12,0.125,0.12,0.098,0.153,439,1250,725,813,808,1367,951,790,526,298,186.0,945,742,173,756,2879,4776,436,950,1792,1145,1630,876,1763,1456,1367,1630,1337,1554,1522,764,387,257,1201,435,465,1609,1683,145,200,553,1201,1141,1240,1884,644,363,785,920,796,368,1563,1106,1861,240,1786,499,1187,876,769,506,887,265,422,949,575,1412,907,1112,497,529,1043,3454,151,3.364,3.006,2.792,2.527,1.886,2.371,2.274,2.485,2.806,2.595,2.883,2.152,3.065,3.085,2.932,2.697,2.894,3.808,2.982,2.005,2.831,2.391,1.946,2.842,2.514,2.593,2.399,2.078,2.748,2.732,2.326,2.344,2.593,3.118,3.433,2.475,2.631,3.018,2.718,2.477,2.357,1.574,3.173,2.103,1.903,2.065,2.881,2.987,2.609,2.488,1.983,2.413,2.124,2.327,2.122,2.169,2.524,2.47,1.901,2.109,2.241,2.883,2.252,2.539,2.277,1.979,1.959,2.685,2.143,2.632,2.486,2.063,2.54,2.203,0.921,0.599,0.494,0.599,0.616,0.579,0.642,0.477,0.751,0.799,0.825,0.517,0.357,0.554,0.479,0.57,0.704,0.892,0.522,0.623,0.923,0.64,0.701,0.722,0.644,0.539,0.592,0.582,0.562,0.596,0.704,0.861,0.371,0.571,0.706,0.539,0.803,0.792,0.349,0.522,0.561,0.445,0.805,0.518,0.564,0.501,0.457,0.671,0.407,0.485,0.278,0.408,0.498,0.489,0.452,0.43,0.651,0.568,0.399,0.424,0.364,0.58,0.381,0.687,0.431,0.622,0.52,0.431,0.53,0.601,0.526,0.552,0.45,0.479
-263,Epileptic,F,27.0,0.6,1.5,0.4,1.0,1.2,1.8,1.3,1.2,1.2,0.2,0.3,3.4,1.2,0.8,1.0,4.2,6.5,0.6,3.2,4.7,0.6,2.9,1.8,2.7,2.7,2.8,2.6,1.9,2.2,3.0,0.9,0.7,0.7,2.3,0.7,0.6,3.3,2.2,0.1,0.3,1.0,2.5,2.5,3.1,2.7,0.4,0.5,0.7,1.0,0.6,0.6,1.2,0.6,1.9,0.2,2.2,0.8,1.6,1.1,1.3,0.6,0.9,0.1,0.3,2.1,1.6,2.4,1.0,0.7,0.4,1.1,1.0,3.1,0.3,3,16,13,11,14,16,10,12,13,4,3.0,25,13,6,10,46,66,4,30,40,7,28,17,27,32,28,30,19,22,32,10,4,7,26,3,4,35,24,2,2,5,24,18,25,14,3,2,5,6,8,6,10,6,12,1,15,7,15,7,9,3,6,2,3,17,35,18,7,5,4,8,9,22,2,0.035,0.023,0.018,0.024,0.037,0.03,0.021,0.03,0.051,0.026,0.029,0.041,0.023,0.039,0.029,0.03,0.027,0.022,0.034,0.034,0.015,0.037,0.03,0.039,0.029,0.032,0.024,0.025,0.024,0.036,0.041,0.04,0.037,0.031,0.028,0.015,0.036,0.028,0.018,0.026,0.025,0.029,0.036,0.029,0.022,0.012,0.027,0.015,0.016,0.019,0.038,0.017,0.02,0.017,0.03,0.02,0.027,0.018,0.029,0.027,0.03,0.027,0.021,0.023,0.028,0.089,0.024,0.018,0.014,0.019,0.03,0.021,0.018,0.023,1108,3049,864,1441,2425,2594,2609,2429,1694,455,461.0,2909,3266,1651,2899,8834,15878,1968,4184,4300,1929,4562,2076,4789,5531,5632,5153,3546,5523,4946,2183,532,1186,5947,1717,1672,5561,4801,261,492,1380,2739,4789,2626,3259,1021,773,1902,1790,1306,510,2436,1647,4206,187,3497,1071,2852,1077,1531,771,1521,484,802,2461,262,3400,1976,1737,1013,1476,1744,6687,364,0.104,0.123,0.099,0.119,0.133,0.144,0.098,0.14,0.147,0.117,0.11,0.151,0.124,0.148,0.125,0.134,0.129,0.092,0.129,0.133,0.078,0.157,0.145,0.144,0.138,0.144,0.123,0.109,0.114,0.141,0.126,0.102,0.146,0.13,0.101,0.085,0.134,0.123,0.095,0.116,0.117,0.132,0.129,0.121,0.103,0.084,0.115,0.087,0.094,0.122,0.129,0.098,0.099,0.094,0.138,0.106,0.124,0.101,0.125,0.13,0.12,0.129,0.139,0.131,0.132,0.105,0.118,0.102,0.089,0.115,0.13,0.118,0.098,0.115,324,1023,530,673,674,966,843,715,459,182,177.0,1310,823,311,593,2284,4001,411,1502,2041,641,1403,903,1172,1563,1529,1728,1188,1415,1497,416,240,347,1222,430,582,1497,1259,140,216,619,1370,1170,1675,1864,568,308,819,861,570,337,1192,638,1630,73,1733,522,1556,589,758,368,545,123,306,1234,162,1583,819,814,366,627,824,2725,212,3.219,2.774,2.01,2.339,2.692,2.435,2.587,2.881,2.698,2.58,2.478,2.014,2.935,3.332,3.339,2.915,3.107,3.693,2.404,1.885,2.605,2.566,2.092,2.896,2.693,3.031,2.356,2.276,2.932,2.688,3.417,2.282,2.698,3.137,3.331,2.758,2.751,2.889,2.22,2.837,3.105,1.841,3.011,1.808,2.017,1.95,3.253,3.035,2.587,2.545,1.705,2.225,2.495,2.587,3.143,2.301,2.399,2.197,2.159,2.374,2.669,2.993,2.867,2.652,2.297,1.627,2.3,2.76,2.48,2.866,2.635,2.602,2.68,2.157,0.83,0.815,0.432,0.538,0.821,0.584,0.638,0.489,0.644,0.528,0.831,0.497,0.394,0.749,0.557,0.517,0.613,0.807,0.599,0.569,0.758,0.599,0.449,0.753,0.505,0.55,0.472,0.594,0.464,0.616,0.782,0.779,0.422,0.637,0.695,0.482,0.673,0.539,0.393,0.443,0.536,0.598,0.712,0.574,0.651,0.305,0.643,0.644,0.422,0.51,0.284,0.514,0.686,0.542,0.485,0.529,0.396,0.698,0.521,0.382,0.474,0.697,0.983,0.968,0.54,0.798,0.541,0.425,0.44,0.78,0.572,0.603,0.571,0.569
-264,Epileptic,F,47.0,0.6,2.0,0.7,0.8,0.8,1.8,1.2,1.1,0.7,0.3,0.2,2.8,1.0,0.6,0.6,3.5,6.3,0.5,1.5,2.9,1.0,2.4,1.0,2.1,2.1,2.2,2.0,1.6,2.2,1.8,1.1,0.9,0.4,1.8,0.2,0.4,1.7,2.0,0.1,0.1,0.8,2.7,0.9,1.7,2.3,0.4,0.2,0.7,0.9,0.4,0.5,1.1,1.1,2.0,0.3,1.7,0.5,1.4,0.7,0.8,0.5,1.0,0.1,0.3,1.3,1.1,1.9,1.1,0.9,0.7,0.9,0.9,2.3,0.3,4,20,9,7,10,14,10,10,12,4,2.0,20,10,6,5,39,59,4,15,27,10,20,10,23,30,21,22,15,20,27,17,7,5,20,2,4,23,23,1,1,5,21,13,14,15,3,2,5,5,4,4,8,11,16,2,16,4,12,7,8,3,8,1,3,9,14,17,9,7,6,7,7,14,3,0.032,0.029,0.016,0.02,0.018,0.032,0.022,0.021,0.048,0.027,0.03,0.038,0.034,0.033,0.032,0.031,0.03,0.03,0.032,0.034,0.023,0.033,0.024,0.032,0.028,0.026,0.022,0.021,0.022,0.025,0.04,0.036,0.027,0.026,0.012,0.012,0.039,0.034,0.017,0.02,0.02,0.044,0.032,0.025,0.018,0.01,0.014,0.014,0.016,0.019,0.029,0.017,0.024,0.022,0.012,0.016,0.021,0.021,0.027,0.019,0.026,0.022,0.017,0.018,0.021,0.031,0.02,0.019,0.02,0.024,0.019,0.023,0.013,0.021,1098,3741,1817,1873,1777,2652,2300,2981,1248,913,484.0,2015,1782,935,1101,7191,12725,1492,2709,3072,2761,3827,1585,4456,4808,4887,3942,3176,5195,3920,1508,1137,684,4440,1253,1539,4117,4841,335,304,1292,1935,3597,1777,3225,1229,642,1506,1776,875,456,1967,1668,3502,472,2808,951,2427,928,1218,692,1965,325,646,1991,1035,3033,2168,1360,1014,1166,1458,6233,535,0.12,0.128,0.117,0.102,0.106,0.149,0.092,0.107,0.145,0.13,0.125,0.146,0.133,0.149,0.12,0.13,0.132,0.101,0.133,0.138,0.1,0.136,0.115,0.137,0.134,0.124,0.111,0.099,0.103,0.132,0.135,0.131,0.141,0.127,0.072,0.078,0.138,0.136,0.109,0.109,0.106,0.153,0.126,0.11,0.099,0.082,0.093,0.084,0.091,0.115,0.134,0.099,0.121,0.112,0.096,0.1,0.117,0.115,0.129,0.108,0.126,0.111,0.1,0.116,0.111,0.142,0.111,0.102,0.113,0.13,0.116,0.124,0.082,0.135,289,1203,702,713,667,918,777,860,359,249,144.0,991,502,234,321,2034,3618,279,753,1397,640,1095,639,1163,1401,1392,1498,1045,1391,1254,520,421,256,908,291,518,945,1133,147,118,603,988,611,1162,1921,640,249,684,812,338,273,1031,760,1514,305,1612,402,1147,485,701,320,743,128,321,982,519,1605,985,707,430,586,626,2717,201,3.312,2.819,2.451,2.573,2.363,2.578,2.432,2.879,2.574,3.268,2.864,1.814,2.986,2.864,2.575,2.648,2.874,3.968,2.804,1.893,3.267,2.643,2.131,2.891,2.613,2.847,2.197,2.335,2.807,2.526,2.336,2.524,2.397,3.249,3.53,2.62,3.147,3.08,2.392,2.368,2.66,1.768,3.282,1.693,1.909,2.049,3.039,2.614,2.632,2.688,2.098,2.183,2.092,2.277,1.923,1.919,2.948,2.348,2.194,1.908,2.713,2.625,2.925,2.229,2.17,2.619,2.059,2.406,2.23,2.598,2.094,2.945,2.52,3.043,0.815,0.714,0.535,0.518,0.543,0.672,0.488,0.614,0.467,0.353,0.826,0.47,0.504,0.345,0.42,0.511,0.532,0.682,0.538,0.614,0.801,0.476,0.357,0.58,0.51,0.601,0.502,0.448,0.457,0.498,0.62,0.942,0.545,0.503,0.612,0.489,0.784,0.486,0.59,0.611,0.768,0.505,0.811,0.601,0.538,0.441,0.688,0.603,0.521,0.542,0.482,0.444,0.529,0.46,0.263,0.39,0.639,0.599,0.596,0.47,0.661,0.594,0.466,0.751,0.515,0.671,0.465,0.506,0.462,0.598,0.502,0.551,0.534,0.458
-265,Epileptic,F,17.5,0.5,2.2,1.1,1.1,1.6,1.3,1.5,1.5,1.6,0.6,0.3,3.2,1.2,0.4,1.3,6.1,8.3,0.5,1.9,4.7,1.5,3.1,1.6,3.6,2.8,3.2,3.8,1.8,2.6,3.8,1.5,2.4,0.5,2.2,0.4,0.3,3.5,2.9,0.2,0.3,0.8,2.5,2.2,3.1,2.2,0.9,0.4,0.9,1.1,0.7,0.5,1.7,1.6,2.8,0.3,2.5,0.5,1.5,0.9,1.0,0.6,1.6,0.1,0.5,1.5,1.4,1.9,1.4,1.0,0.4,0.6,1.8,3.0,0.2,5,22,10,8,16,14,18,16,13,8,4.0,31,18,7,19,69,85,6,24,43,14,38,17,42,35,36,49,20,26,46,19,18,5,30,2,2,41,35,3,1,5,28,24,20,17,7,2,6,6,7,3,10,9,20,1,21,3,11,8,7,5,15,1,5,13,18,14,10,7,4,4,15,25,2,0.031,0.02,0.018,0.021,0.03,0.031,0.02,0.029,0.045,0.028,0.021,0.036,0.03,0.031,0.027,0.032,0.029,0.023,0.035,0.035,0.028,0.037,0.031,0.041,0.028,0.031,0.029,0.019,0.024,0.028,0.034,0.076,0.026,0.027,0.014,0.01,0.036,0.03,0.014,0.016,0.018,0.032,0.038,0.025,0.018,0.018,0.015,0.018,0.014,0.016,0.024,0.018,0.025,0.021,0.022,0.016,0.023,0.021,0.02,0.018,0.021,0.023,0.012,0.014,0.018,0.026,0.016,0.019,0.017,0.013,0.023,0.022,0.015,0.012,1377,5825,2737,2535,2725,2379,3862,3219,1536,1393,653.0,3148,3658,1029,3088,13516,19128,2052,3212,5567,3326,4919,2216,6504,5882,7007,6786,3999,6332,6741,2271,1809,951,4850,1385,1494,5694,6930,517,634,1451,2793,4066,3321,3169,1728,1001,1792,2478,1438,537,3248,2173,5686,453,4925,979,2919,1255,1701,1068,3185,296,1004,2707,1384,4039,2748,2014,1279,1031,2597,7592,580,0.1,0.106,0.099,0.098,0.128,0.143,0.108,0.131,0.134,0.131,0.12,0.154,0.119,0.146,0.14,0.138,0.134,0.101,0.151,0.142,0.105,0.157,0.136,0.133,0.133,0.131,0.139,0.097,0.108,0.134,0.13,0.174,0.12,0.124,0.073,0.059,0.136,0.124,0.099,0.095,0.101,0.144,0.153,0.118,0.1,0.099,0.093,0.092,0.088,0.108,0.135,0.09,0.108,0.107,0.126,0.097,0.116,0.105,0.122,0.106,0.104,0.117,0.102,0.105,0.103,0.128,0.091,0.099,0.107,0.098,0.113,0.115,0.086,0.093,348,1746,879,894,894,784,1217,931,470,349,213.0,1350,928,219,851,3322,4795,416,985,2113,916,1408,868,1696,1645,1844,2185,1393,1620,2161,657,659,274,1246,452,558,1600,1643,238,268,693,1300,1093,1856,1923,762,409,828,1104,688,307,1388,924,2193,195,2475,354,1278,668,842,469,1092,153,516,1309,789,1917,1136,870,497,474,1275,3283,252,3.443,2.952,2.849,2.801,2.409,2.665,2.545,3.014,2.478,2.976,2.614,2.01,3.002,3.287,2.703,2.993,3.168,3.713,2.668,2.214,2.816,2.625,2.154,2.754,2.737,2.941,2.398,2.285,2.897,2.456,2.54,2.847,2.692,2.784,2.788,2.547,2.648,3.062,2.346,2.67,2.847,1.966,2.988,2.008,1.885,2.498,3.059,2.596,2.789,2.463,1.959,2.403,2.438,2.608,2.698,2.258,2.838,2.501,2.236,2.195,2.448,2.921,2.092,2.337,2.24,2.182,2.263,2.609,2.626,2.773,2.601,2.403,2.459,2.847,0.955,0.756,0.559,0.463,0.724,0.491,0.586,0.453,0.833,0.448,0.836,0.544,0.445,0.378,0.518,0.634,0.663,0.73,0.44,0.767,0.616,0.491,0.462,0.794,0.626,0.528,0.534,0.569,0.557,0.549,0.8,0.954,0.479,0.623,0.587,0.512,0.646,0.563,0.371,0.525,0.536,0.449,0.496,0.63,0.613,0.617,0.418,0.797,0.526,0.367,0.364,0.478,0.566,0.537,0.487,0.383,0.622,0.672,0.344,0.401,0.431,0.73,0.356,0.494,0.511,0.629,0.404,0.459,0.427,0.688,0.559,0.415,0.514,0.496
-266,Epileptic,F,17.5,0.7,2.3,0.7,1.3,1.2,1.6,1.5,1.4,0.9,0.5,0.2,2.7,1.5,0.4,1.5,6.0,8.7,0.8,3.1,4.8,0.9,3.4,1.8,3.4,2.8,3.1,4.1,2.4,2.4,3.3,1.0,0.8,0.3,2.1,0.4,0.4,2.6,2.7,0.2,0.1,1.0,2.6,1.7,2.6,2.5,0.8,0.4,1.0,1.2,0.7,1.0,1.7,1.1,2.0,0.2,5.3,0.8,2.4,1.1,1.3,0.5,1.3,0.2,0.4,1.6,1.0,3.0,1.1,0.8,0.4,1.1,0.9,3.6,0.3,6,21,9,13,14,19,14,17,13,7,3.0,30,20,3,21,70,86,7,31,40,7,39,20,38,36,39,46,25,23,41,17,9,4,27,2,5,34,34,2,1,6,25,17,18,17,5,3,6,6,7,5,13,8,14,1,33,5,16,7,11,5,9,2,4,14,13,24,10,6,4,5,7,31,3,0.036,0.024,0.013,0.022,0.029,0.032,0.023,0.025,0.046,0.03,0.025,0.031,0.033,0.034,0.039,0.035,0.029,0.03,0.037,0.041,0.024,0.04,0.029,0.041,0.033,0.03,0.032,0.026,0.027,0.036,0.034,0.054,0.012,0.03,0.015,0.017,0.035,0.031,0.016,0.015,0.02,0.035,0.036,0.03,0.02,0.016,0.02,0.019,0.017,0.02,0.036,0.02,0.022,0.018,0.023,0.028,0.021,0.025,0.022,0.021,0.021,0.025,0.02,0.02,0.017,0.023,0.021,0.018,0.019,0.02,0.025,0.019,0.019,0.022,1466,4580,2145,2470,2000,2635,2957,3460,1813,1545,661.0,3332,3513,975,3062,12068,18969,2104,5347,5156,2403,4946,2434,5971,6050,6668,7038,3568,5148,5932,2138,1024,1078,4916,1792,1312,5623,6478,445,399,1534,2909,4852,2578,3156,1624,767,1992,2360,1325,832,3306,1991,4017,481,5212,1517,3428,1347,1863,823,2343,399,793,3271,1700,4478,2196,1340,824,1328,1352,6990,388,0.128,0.113,0.1,0.111,0.126,0.152,0.11,0.124,0.149,0.128,0.124,0.141,0.143,0.133,0.156,0.148,0.134,0.118,0.144,0.139,0.096,0.158,0.134,0.154,0.15,0.141,0.13,0.112,0.122,0.142,0.141,0.135,0.079,0.134,0.078,0.099,0.144,0.14,0.102,0.101,0.113,0.144,0.145,0.12,0.102,0.096,0.11,0.092,0.094,0.121,0.151,0.107,0.109,0.102,0.089,0.129,0.103,0.114,0.113,0.116,0.129,0.119,0.105,0.129,0.105,0.126,0.112,0.112,0.112,0.12,0.116,0.112,0.102,0.122,403,1535,798,929,695,946,991,990,462,325,178.0,1336,944,231,779,3069,5102,410,1517,2064,616,1468,964,1523,1649,1854,2098,1397,1392,1770,512,322,387,1159,452,494,1374,1562,205,174,716,1253,922,1462,1971,774,336,833,1051,480,402,1425,841,1747,185,2684,644,1617,722,964,396,821,204,327,1518,699,2085,941,645,340,634,731,3069,241,3.538,2.919,2.67,2.67,2.418,2.514,2.429,2.816,2.702,3.401,3.194,2.114,2.911,3.213,2.919,2.902,2.995,3.637,2.788,2.14,2.793,2.577,2.2,2.806,2.727,2.894,2.565,2.111,2.752,2.69,2.822,3.006,2.343,2.92,3.497,2.382,2.871,2.856,2.609,2.602,2.702,1.988,3.49,2.06,1.823,2.334,2.892,2.911,2.752,2.685,2.225,2.482,2.612,2.532,2.491,2.136,2.606,2.439,2.219,2.147,2.388,2.798,2.265,2.641,2.372,2.812,2.151,2.561,2.464,2.684,2.519,2.389,2.528,1.898,0.89,0.541,0.395,0.481,0.719,0.554,0.483,0.585,0.716,0.442,0.906,0.56,0.456,0.507,0.694,0.632,0.587,0.724,0.551,0.602,0.909,0.434,0.487,0.739,0.533,0.604,0.51,0.503,0.543,0.52,0.58,0.989,0.405,0.654,0.639,0.467,0.755,0.645,0.442,0.505,0.572,0.48,0.773,0.871,0.516,0.385,0.469,0.688,0.516,0.524,0.509,0.438,0.61,0.408,0.486,0.471,0.458,0.615,0.352,0.447,0.456,0.626,0.345,0.891,0.488,0.708,0.487,0.473,0.479,0.698,0.394,0.476,0.488,0.317
-267,Epileptic,M,22.0,0.9,2.6,1.0,1.5,1.1,2.3,1.9,2.1,0.6,1.2,0.6,2.2,2.3,0.6,1.5,8.1,8.1,1.1,2.8,5.3,1.1,3.3,1.3,3.7,2.5,3.4,2.7,2.7,3.3,3.7,1.3,1.1,0.5,2.2,0.2,0.7,4.3,3.1,0.2,0.2,1.4,3.5,2.8,1.9,3.4,0.7,0.5,1.1,1.4,0.5,0.7,2.2,2.1,2.3,0.3,2.1,1.3,1.6,1.6,0.9,1.1,1.2,0.3,0.6,1.7,1.5,3.5,1.4,1.4,0.6,1.3,1.4,4.3,0.3,5,23,13,15,11,23,16,17,8,9,6.0,21,28,8,17,76,75,7,32,43,10,38,15,39,38,37,35,25,28,41,19,8,5,27,1,5,55,38,2,1,7,29,36,18,18,5,3,7,6,5,5,14,17,15,2,18,11,13,14,9,7,11,3,4,15,15,26,8,10,5,10,11,34,3,0.033,0.027,0.016,0.024,0.023,0.031,0.024,0.036,0.033,0.041,0.04,0.025,0.03,0.032,0.024,0.035,0.029,0.03,0.039,0.034,0.022,0.03,0.023,0.034,0.031,0.031,0.023,0.029,0.026,0.026,0.03,0.041,0.026,0.026,0.012,0.013,0.039,0.033,0.022,0.019,0.024,0.038,0.043,0.021,0.024,0.012,0.018,0.019,0.015,0.016,0.023,0.019,0.024,0.017,0.015,0.017,0.022,0.017,0.024,0.014,0.021,0.024,0.013,0.022,0.019,0.03,0.024,0.019,0.024,0.018,0.024,0.017,0.016,0.016,1542,5284,2673,2606,1664,3612,3428,3470,1018,2047,901.0,2971,5141,1263,3963,13578,18166,2959,5382,5934,3783,5886,2221,7723,5065,7520,5731,4404,7349,7297,2177,1281,1093,6068,1125,2191,7870,7772,417,303,1940,3101,5035,2707,4007,1660,1142,2346,3000,1331,634,4785,2762,4948,439,3590,2476,3848,2082,1922,1729,2461,676,945,3301,1556,4541,2785,2357,1316,1804,3208,10110,806,0.124,0.116,0.094,0.116,0.111,0.139,0.101,0.142,0.122,0.144,0.158,0.12,0.134,0.14,0.11,0.132,0.122,0.122,0.144,0.128,0.094,0.128,0.122,0.138,0.132,0.129,0.11,0.111,0.102,0.119,0.12,0.109,0.116,0.123,0.062,0.078,0.143,0.132,0.107,0.133,0.111,0.137,0.158,0.105,0.106,0.086,0.1,0.089,0.085,0.106,0.134,0.099,0.109,0.091,0.111,0.097,0.115,0.091,0.12,0.086,0.108,0.108,0.088,0.1,0.105,0.119,0.114,0.101,0.11,0.107,0.118,0.102,0.092,0.105,411,1637,927,1018,802,1341,1124,964,324,504,264.0,1278,1323,320,1044,3796,4585,542,1352,2497,823,1871,895,1762,1550,1985,1953,1501,1840,2273,631,500,318,1372,345,807,1950,1822,199,124,894,1383,1212,1502,2264,802,454,985,1285,476,410,1823,1360,2056,224,1846,942,1717,995,964,795,916,403,440,1526,781,2192,1138,1037,521,859,1330,4111,346,3.658,2.844,2.598,2.594,1.983,2.452,2.536,3.048,2.493,3.113,2.892,1.986,2.982,2.885,2.854,2.746,3.098,4.118,2.987,2.03,3.417,2.498,2.111,3.134,2.524,3.026,2.368,2.321,3.067,2.54,2.511,2.345,2.638,3.121,2.903,2.487,3.012,3.131,2.378,2.903,2.775,1.952,3.086,1.961,2.039,2.2,3.138,2.827,2.824,3.0,2.001,2.566,1.952,2.555,2.544,2.114,2.724,2.577,2.067,2.144,2.686,2.507,2.051,2.593,2.343,2.375,2.352,2.734,2.603,2.678,2.34,2.706,2.549,2.662,0.682,0.752,0.665,0.46,0.484,0.572,0.709,0.51,0.546,0.51,0.936,0.445,0.55,0.467,0.59,0.62,0.609,0.602,0.615,0.693,0.808,0.613,0.57,0.633,0.449,0.507,0.47,0.563,0.587,0.613,0.603,1.013,0.627,0.593,0.647,0.528,0.629,0.682,0.327,0.545,0.513,0.581,0.783,0.548,0.69,0.482,0.661,0.709,0.562,0.823,0.537,0.539,0.591,0.505,0.376,0.427,0.454,0.609,0.453,0.438,0.367,0.62,0.317,1.07,0.541,0.754,0.536,0.423,0.422,0.771,0.561,0.538,0.544,0.561
-268,Epileptic,M,62.0,0.9,2.9,1.1,1.1,1.5,2.5,1.3,2.1,1.5,0.6,0.3,2.7,1.7,0.8,1.5,7.6,9.5,1.3,2.5,4.6,2.0,2.9,1.9,4.1,3.0,3.0,3.1,3.1,2.3,2.6,1.4,1.2,0.3,3.3,0.7,0.5,5.6,2.9,0.3,0.2,1.2,3.2,4.6,3.0,3.3,0.6,0.6,0.8,1.1,1.5,0.6,2.2,1.9,2.9,0.2,1.8,1.4,1.8,1.1,1.5,0.9,1.7,0.5,0.5,2.6,1.5,2.1,1.4,1.6,1.1,0.9,2.2,5.4,0.4,7,31,13,8,15,24,10,19,15,5,5.0,24,15,9,14,80,84,7,27,35,17,34,15,52,35,31,33,30,23,29,18,6,4,39,4,3,50,38,3,1,7,27,32,22,18,4,3,5,5,10,5,16,12,20,1,16,11,13,8,11,7,15,3,5,22,18,15,10,13,11,6,18,44,2,0.035,0.027,0.021,0.019,0.027,0.033,0.022,0.029,0.039,0.029,0.036,0.029,0.029,0.039,0.032,0.031,0.029,0.039,0.027,0.031,0.031,0.029,0.025,0.041,0.026,0.027,0.023,0.025,0.018,0.026,0.034,0.032,0.013,0.035,0.022,0.011,0.04,0.031,0.021,0.018,0.022,0.033,0.059,0.023,0.019,0.012,0.022,0.015,0.013,0.028,0.034,0.018,0.02,0.019,0.039,0.014,0.033,0.021,0.021,0.022,0.024,0.025,0.021,0.017,0.023,0.025,0.016,0.018,0.022,0.031,0.023,0.026,0.017,0.02,1799,5746,2531,2258,2424,4083,2130,3763,2184,1381,731.0,2838,3293,1573,2909,13161,19126,2922,4960,5213,3765,4851,2736,7204,5845,5768,4590,4771,6276,5106,2461,1593,833,6927,1869,1750,6680,7015,464,417,1619,3282,5882,2646,4314,1494,899,2104,2448,2223,519,4301,2893,5393,291,3387,2009,3117,1215,1840,1336,2958,686,1045,3301,1408,3609,2347,2229,1650,1351,3279,11073,518,0.123,0.119,0.117,0.095,0.123,0.144,0.089,0.122,0.153,0.13,0.139,0.13,0.132,0.15,0.132,0.133,0.123,0.112,0.124,0.125,0.11,0.136,0.11,0.152,0.118,0.118,0.107,0.102,0.085,0.119,0.13,0.105,0.079,0.123,0.088,0.068,0.133,0.112,0.13,0.089,0.106,0.133,0.17,0.111,0.094,0.081,0.109,0.079,0.083,0.12,0.144,0.1,0.105,0.101,0.148,0.096,0.136,0.099,0.113,0.114,0.11,0.123,0.12,0.11,0.115,0.114,0.097,0.103,0.115,0.146,0.115,0.12,0.095,0.102,411,1697,918,880,903,1290,812,1205,648,370,223.0,1421,892,423,838,3974,5461,487,1552,2267,1008,1604,1076,1904,2033,1832,1819,1774,1829,1679,721,575,360,1456,489,694,2200,1744,216,205,860,1426,1523,1848,2580,776,425,930,1084,891,302,1980,1365,2465,124,1940,740,1541,775,1007,576,1060,331,512,1757,905,1910,1159,1118,644,666,1458,4920,309,3.833,2.928,2.62,2.452,2.379,2.534,2.227,2.866,2.665,3.013,2.747,1.778,2.939,2.946,2.72,2.579,2.851,4.104,2.641,1.93,2.808,2.411,2.148,2.943,2.338,2.537,2.069,2.182,2.638,2.412,2.7,2.553,1.907,2.934,3.312,2.365,2.517,2.851,2.289,2.488,2.19,1.987,2.816,1.623,1.838,1.988,2.669,2.709,2.673,2.682,1.986,2.268,2.377,2.255,2.633,1.905,2.604,2.29,1.804,2.077,2.348,2.544,2.158,2.367,2.008,2.046,2.028,2.332,2.223,2.587,2.325,2.342,2.283,1.981,0.879,0.536,0.636,0.553,0.477,0.776,0.633,0.693,0.496,0.912,0.85,0.55,0.462,0.479,0.397,0.526,0.619,0.795,0.702,0.667,1.084,0.494,0.581,0.542,0.495,0.598,0.492,0.616,0.579,0.59,0.551,0.899,0.438,0.852,0.824,0.584,0.759,0.656,0.445,0.337,0.545,0.68,1.102,0.609,0.612,0.625,0.517,0.896,0.594,0.771,0.462,0.52,0.566,0.444,0.565,0.458,0.606,0.602,0.46,0.359,0.472,0.71,0.47,0.788,0.527,0.886,0.531,0.547,0.531,0.455,0.464,0.621,0.542,0.464
-269,Epileptic,F,22.0,1.3,2.9,1.1,1.6,3.0,2.0,2.1,2.6,1.5,0.9,0.9,3.4,2.9,1.6,3.4,13.6,14.8,1.1,2.6,6.5,2.1,5.4,3.1,7.6,6.2,5.6,6.4,4.9,5.8,5.9,1.9,1.5,0.9,5.3,1.1,1.9,7.2,7.6,0.6,0.6,1.8,4.7,4.5,4.8,4.5,0.8,1.2,1.7,2.2,1.2,0.6,5.7,2.9,4.7,0.4,3.1,0.9,2.1,1.7,1.5,1.3,2.4,1.1,0.7,2.5,1.1,3.1,4.0,2.0,0.7,1.1,2.6,9.7,0.5,10,20,6,10,17,16,13,15,12,9,5.0,22,18,11,22,78,102,8,18,51,16,41,16,58,35,33,42,29,28,43,18,10,6,40,6,11,52,55,2,2,11,32,29,28,23,5,5,9,10,7,4,29,14,24,1,19,7,12,12,9,9,14,5,5,18,12,15,17,9,4,7,13,56,4,0.043,0.033,0.021,0.032,0.045,0.037,0.029,0.044,0.056,0.047,0.05,0.047,0.043,0.077,0.044,0.056,0.045,0.04,0.047,0.053,0.054,0.047,0.043,0.058,0.047,0.044,0.043,0.038,0.041,0.042,0.056,0.063,0.04,0.047,0.025,0.031,0.054,0.051,0.032,0.034,0.028,0.047,0.065,0.043,0.028,0.016,0.034,0.027,0.028,0.028,0.038,0.038,0.037,0.032,0.049,0.021,0.029,0.028,0.031,0.026,0.031,0.031,0.043,0.037,0.031,0.024,0.025,0.041,0.028,0.025,0.026,0.03,0.033,0.023,1691,4263,2209,2568,2010,3185,2618,2913,1169,1587,901.0,2204,3018,1068,3378,10607,21787,2541,3234,5471,3364,4863,1193,7402,4288,4621,4448,3457,4582,5583,2362,985,1035,4628,2532,2192,9202,6314,557,531,2002,2529,6364,2905,3719,1277,1292,1958,2354,1768,353,4559,2507,5325,216,4256,1124,2645,1469,1822,1540,3506,736,922,2509,1170,4155,2681,1971,750,1421,2932,9215,497,0.145,0.113,0.101,0.122,0.136,0.119,0.109,0.13,0.141,0.156,0.136,0.151,0.138,0.168,0.139,0.152,0.135,0.111,0.149,0.166,0.136,0.134,0.144,0.156,0.139,0.132,0.137,0.13,0.126,0.142,0.164,0.153,0.122,0.151,0.095,0.113,0.16,0.14,0.116,0.128,0.12,0.163,0.16,0.151,0.117,0.097,0.123,0.107,0.112,0.112,0.149,0.122,0.13,0.119,0.19,0.101,0.13,0.112,0.131,0.114,0.118,0.119,0.145,0.13,0.122,0.114,0.1,0.134,0.118,0.122,0.12,0.115,0.118,0.115,547,1523,808,853,1076,1060,1207,1048,525,423,273.0,1160,1113,420,1372,4017,6107,628,1032,2462,852,1770,962,2459,1886,2005,2294,1819,2059,2208,649,393,418,1708,730,1004,2492,2426,289,267,1056,1572,1384,1823,2446,761,580,963,1138,729,269,2554,1290,2464,115,2398,562,1312,926,944,736,1311,417,402,1290,725,2044,1450,1052,394,696,1415,4736,323,2.786,2.501,2.488,2.783,1.878,2.417,1.962,2.42,1.987,2.872,2.655,1.665,2.281,2.075,2.128,2.253,2.944,3.111,2.517,1.928,2.894,2.257,1.296,2.46,2.057,2.081,1.79,1.755,1.974,1.99,2.529,2.49,2.124,2.236,3.084,2.239,2.737,2.22,2.24,2.388,2.242,1.601,3.074,1.896,1.735,1.916,2.687,2.383,2.525,2.569,1.777,1.993,2.265,2.335,2.631,2.024,2.349,2.306,1.899,2.212,2.414,2.556,2.198,2.329,2.146,1.953,2.185,2.248,2.292,2.13,2.325,2.344,2.131,1.957,0.785,0.746,0.701,0.63,0.539,0.678,0.593,0.658,0.594,0.732,0.839,0.586,0.735,0.543,0.67,0.676,0.825,0.81,0.524,0.594,0.878,0.534,0.595,0.757,0.623,0.671,0.634,0.483,0.678,0.731,0.651,0.921,0.478,0.748,0.911,0.684,0.753,0.652,0.6,0.462,0.393,0.583,0.971,0.51,0.425,0.561,0.574,0.555,0.625,0.598,0.363,0.516,0.525,0.552,0.559,0.417,0.485,0.569,0.325,0.506,0.499,0.697,0.399,0.797,0.678,0.524,0.583,0.582,0.469,0.655,0.515,0.596,0.482,0.357
-270,Epileptic,F,17.5,0.3,2.4,0.8,1.2,1.2,2.2,1.8,1.4,0.9,0.6,0.2,2.5,1.3,0.4,1.7,4.5,8.4,0.5,3.1,4.7,1.0,3.1,1.6,2.5,2.7,2.4,3.0,2.9,2.3,4.0,2.0,0.7,0.7,2.7,0.4,0.6,2.8,3.2,0.1,0.2,1.2,2.7,2.3,2.1,2.4,0.7,0.3,0.7,0.9,0.6,0.8,1.8,0.9,2.1,0.3,1.8,1.2,1.5,1.0,1.2,0.7,1.0,0.3,0.6,1.9,1.2,2.0,1.1,1.3,0.4,1.0,1.1,3.9,0.2,2,18,6,8,9,19,15,12,11,7,2.0,23,11,6,15,47,75,3,26,34,10,38,14,24,38,26,36,28,22,38,22,5,6,32,2,6,30,32,1,1,7,26,26,12,15,5,1,4,4,6,7,13,6,13,2,13,10,12,6,9,5,9,2,5,14,12,14,9,7,3,6,7,33,2,0.016,0.028,0.017,0.022,0.036,0.034,0.033,0.025,0.04,0.03,0.052,0.039,0.039,0.035,0.044,0.035,0.031,0.027,0.038,0.038,0.027,0.034,0.03,0.032,0.03,0.031,0.028,0.03,0.024,0.04,0.058,0.036,0.026,0.037,0.025,0.016,0.035,0.037,0.013,0.016,0.022,0.035,0.059,0.026,0.02,0.018,0.02,0.014,0.012,0.015,0.035,0.024,0.024,0.021,0.017,0.017,0.035,0.019,0.021,0.024,0.024,0.022,0.027,0.039,0.021,0.026,0.019,0.018,0.021,0.015,0.023,0.028,0.017,0.017,1623,4855,2850,3028,1952,3480,2801,3709,1915,1736,369.0,2348,3463,1290,4256,10391,22553,1756,4455,3626,2302,5539,2478,5930,6651,6247,6009,4716,5812,5700,2524,782,1321,6488,1108,1974,5683,6466,348,404,1781,2713,5076,2148,2878,1387,663,2099,2326,1539,698,2422,1775,4305,482,3804,1674,2601,1309,1791,1103,1853,499,735,3135,1188,3374,2897,2085,1156,1389,1813,9130,505,0.067,0.117,0.098,0.101,0.141,0.144,0.128,0.119,0.143,0.13,0.125,0.152,0.135,0.125,0.164,0.142,0.13,0.086,0.135,0.136,0.095,0.145,0.13,0.133,0.135,0.129,0.131,0.12,0.104,0.157,0.169,0.111,0.134,0.14,0.089,0.084,0.131,0.127,0.089,0.113,0.101,0.139,0.153,0.114,0.107,0.112,0.095,0.076,0.079,0.103,0.15,0.117,0.114,0.107,0.114,0.099,0.13,0.099,0.105,0.114,0.107,0.118,0.129,0.162,0.112,0.12,0.1,0.099,0.102,0.107,0.119,0.113,0.094,0.119,310,1312,776,845,577,1107,916,911,469,329,90.0,1036,649,262,717,2234,4779,326,1340,1848,628,1580,880,1287,1645,1453,1784,1451,1518,1549,539,338,435,1257,348,586,1291,1429,168,136,752,1229,1018,1282,1770,573,277,774,990,652,359,1062,661,1550,229,1655,618,1307,721,860,502,688,190,256,1400,695,1598,947,938,365,624,698,3589,243,3.762,3.35,3.123,3.047,2.723,2.728,2.345,3.259,2.885,3.607,3.227,1.965,3.552,3.222,3.749,3.13,3.443,3.736,2.67,1.784,2.906,2.649,2.339,3.001,2.964,3.147,2.537,2.46,2.856,2.76,3.239,2.33,2.536,3.314,2.665,2.781,3.148,3.289,2.525,3.007,2.753,2.025,3.382,1.868,1.89,2.624,2.956,3.215,2.898,2.672,2.144,2.518,2.473,2.78,2.64,2.503,2.605,2.303,2.075,2.212,2.588,2.609,2.657,2.911,2.597,2.156,2.328,2.791,2.674,3.014,2.639,2.871,2.728,2.714,1.102,0.793,0.733,0.707,0.839,0.644,0.609,0.7,0.621,0.442,0.545,0.587,0.739,0.646,0.64,0.695,0.759,0.755,0.849,0.574,0.758,0.54,0.443,0.788,0.555,0.621,0.637,0.699,0.6,0.611,0.808,0.854,0.513,0.663,0.848,0.559,0.733,0.78,0.422,0.453,0.586,0.451,0.95,0.692,0.508,0.706,0.563,0.911,0.551,0.498,0.445,0.618,0.647,0.516,0.302,0.54,0.544,0.598,0.486,0.499,0.467,0.723,0.7,0.688,0.714,0.705,0.478,0.543,0.415,0.684,0.493,0.695,0.626,0.393
-271,Epileptic,F,32.0,0.7,2.6,1.3,1.5,1.6,1.8,1.5,2.1,1.0,0.7,0.4,2.5,1.4,0.7,1.3,6.0,7.7,0.7,1.8,3.3,1.2,3.2,2.0,3.4,3.2,2.4,3.1,2.8,2.5,2.8,1.6,1.0,0.3,2.7,0.6,0.5,3.6,3.2,0.1,0.3,1.0,3.2,2.7,2.3,3.6,0.6,0.7,0.9,1.3,0.6,0.7,2.0,1.6,2.5,0.3,2.0,0.7,1.3,0.9,0.9,0.7,2.4,0.3,0.5,1.7,2.1,2.4,1.1,1.3,0.6,0.9,1.2,5.7,0.3,7,20,16,10,19,16,12,19,12,7,5.0,28,18,8,19,64,80,11,21,34,12,40,19,38,47,25,36,25,21,31,15,6,3,30,4,5,41,40,2,2,5,31,31,18,23,5,5,7,8,5,6,16,15,21,2,17,4,15,7,8,7,24,2,6,18,23,20,8,11,5,6,10,50,3,0.029,0.022,0.018,0.026,0.026,0.027,0.02,0.033,0.029,0.031,0.035,0.026,0.022,0.03,0.029,0.027,0.026,0.024,0.024,0.031,0.025,0.027,0.026,0.029,0.027,0.027,0.023,0.022,0.021,0.028,0.027,0.037,0.02,0.03,0.017,0.014,0.034,0.03,0.01,0.018,0.022,0.029,0.031,0.026,0.027,0.015,0.023,0.014,0.015,0.012,0.032,0.021,0.022,0.02,0.023,0.016,0.016,0.017,0.024,0.015,0.022,0.028,0.019,0.013,0.018,0.03,0.018,0.016,0.02,0.021,0.024,0.017,0.019,0.012,1643,5215,2768,2514,2745,2767,2473,3228,1885,1357,622.0,2394,3598,1326,2859,11589,16709,2316,3881,3759,3726,5342,2476,6060,6074,5239,5587,4729,5184,4484,2333,1436,726,7005,2684,1881,6972,7593,423,518,1408,3183,6965,2239,3298,1346,1139,2162,2642,1534,591,3634,3153,5313,277,3799,1430,2894,1107,1398,1220,2990,476,1092,2666,1742,3990,2317,2111,915,1273,2232,10225,583,0.119,0.111,0.112,0.116,0.128,0.13,0.095,0.136,0.138,0.138,0.153,0.136,0.117,0.143,0.134,0.13,0.126,0.118,0.125,0.142,0.11,0.13,0.125,0.124,0.139,0.128,0.119,0.101,0.086,0.133,0.115,0.104,0.11,0.133,0.093,0.082,0.136,0.134,0.086,0.106,0.11,0.134,0.141,0.118,0.116,0.095,0.122,0.093,0.095,0.088,0.138,0.109,0.112,0.106,0.141,0.102,0.089,0.109,0.117,0.099,0.112,0.133,0.112,0.103,0.108,0.137,0.105,0.094,0.111,0.121,0.123,0.116,0.108,0.108,477,1834,1083,930,1062,1016,949,1137,577,355,201.0,1401,1028,331,816,3522,4863,504,1118,1682,885,1933,1128,1950,2121,1507,2057,1699,1532,1663,865,461,263,1530,614,620,1787,1865,250,276,692,1610,1579,1402,2136,679,426,974,1299,704,383,1577,1339,2170,164,1907,670,1318,648,871,514,1291,252,607,1474,999,1942,1110,1040,444,634,1056,4531,275,3.228,2.715,2.462,2.6,2.318,2.273,2.233,2.649,2.479,2.887,2.673,1.608,2.681,2.758,2.687,2.629,2.753,3.416,2.817,1.933,3.135,2.232,1.909,2.479,2.334,2.832,2.211,2.254,2.599,2.303,2.167,2.844,2.284,3.291,3.599,2.755,2.969,3.02,1.945,2.397,2.637,1.801,3.225,1.849,1.758,2.173,3.05,2.836,2.543,2.566,1.962,2.496,2.383,2.6,2.343,2.137,2.52,2.409,1.89,1.791,2.237,2.326,2.157,2.022,2.009,1.979,2.216,2.481,2.234,2.514,2.504,2.445,2.379,2.485,0.478,0.7,0.498,0.521,0.566,0.488,0.534,0.516,0.484,0.514,0.815,0.347,0.426,0.377,0.406,0.588,0.513,0.797,0.498,0.675,0.772,0.502,0.465,0.882,0.457,0.534,0.486,0.574,0.585,0.418,0.789,0.982,0.452,0.635,0.663,0.561,0.652,0.591,0.277,0.287,0.464,0.476,0.636,0.642,0.497,0.546,0.366,0.511,0.537,0.388,0.346,0.457,0.513,0.389,0.365,0.437,0.487,0.545,0.368,0.347,0.464,0.653,0.429,0.747,0.449,0.721,0.49,0.379,0.495,0.719,0.353,0.398,0.52,0.604
-273,Epileptic,F,22.0,0.7,2.1,0.8,1.3,1.4,1.5,1.8,0.9,0.8,0.5,0.3,3.3,0.9,0.5,0.9,4.9,6.3,0.7,1.9,5.1,2.6,2.2,2.1,3.5,3.1,2.6,3.2,1.8,1.9,3.3,2.5,1.3,0.4,1.7,0.3,0.4,2.7,2.7,0.2,0.1,0.8,3.1,2.9,2.2,1.7,0.7,0.2,0.8,1.2,0.8,0.6,1.2,1.8,2.1,0.4,2.0,0.7,1.3,1.0,1.2,0.5,1.3,0.4,0.5,1.9,0.8,2.1,0.8,0.8,0.7,0.8,1.5,4.0,0.2,5,20,8,10,17,16,14,13,9,6,3.0,35,14,6,11,64,62,5,24,46,22,31,20,41,41,34,37,19,20,45,22,12,6,23,2,3,36,45,2,1,4,32,21,18,10,6,1,5,7,8,5,9,14,21,2,17,7,13,10,10,4,12,2,5,19,13,15,6,4,6,5,13,33,2,0.034,0.027,0.021,0.026,0.029,0.023,0.029,0.023,0.038,0.031,0.041,0.038,0.025,0.028,0.032,0.033,0.031,0.032,0.028,0.041,0.045,0.028,0.031,0.04,0.033,0.028,0.031,0.024,0.024,0.037,0.062,0.065,0.028,0.026,0.011,0.014,0.03,0.032,0.014,0.012,0.019,0.036,0.046,0.02,0.016,0.016,0.013,0.015,0.018,0.023,0.025,0.019,0.024,0.018,0.016,0.015,0.02,0.016,0.026,0.019,0.017,0.022,0.022,0.015,0.021,0.018,0.019,0.013,0.014,0.023,0.018,0.021,0.017,0.013,1344,4129,2059,2475,2549,3102,3294,2812,1702,1446,751.0,3592,2937,1480,2653,10982,16546,2220,4586,4150,3830,4482,2807,5518,7331,6970,6685,4012,5568,6962,2321,806,982,5334,1823,1593,8136,8459,430,341,1328,3176,4392,2767,2731,1487,739,1821,2045,1656,557,2250,2169,5299,546,3816,1487,2631,1492,1814,944,2408,374,890,2623,1017,3564,2043,1517,1361,1402,2808,9054,437,0.11,0.132,0.111,0.115,0.133,0.123,0.124,0.125,0.154,0.129,0.135,0.151,0.121,0.138,0.132,0.137,0.133,0.109,0.119,0.152,0.131,0.131,0.118,0.147,0.132,0.124,0.128,0.104,0.116,0.152,0.168,0.137,0.115,0.127,0.065,0.069,0.13,0.141,0.095,0.088,0.101,0.127,0.138,0.114,0.089,0.097,0.074,0.091,0.094,0.117,0.134,0.103,0.117,0.106,0.104,0.097,0.109,0.092,0.127,0.108,0.1,0.113,0.141,0.11,0.118,0.112,0.1,0.088,0.085,0.117,0.111,0.112,0.097,0.122,364,1235,661,845,891,1004,1012,761,427,290,166.0,1614,746,283,611,2844,3764,403,1177,2088,989,1333,1060,1549,1645,1704,1844,1200,1296,1809,696,362,265,1084,434,466,1665,1772,219,175,648,1414,1040,1704,1606,704,278,787,913,628,362,1042,1071,1853,319,2013,636,1348,613,918,485,898,204,477,1278,684,1716,842,767,468,624,1214,3609,220,3.424,2.817,2.858,2.717,2.276,2.477,2.611,3.119,2.953,3.439,3.144,1.931,2.874,3.316,2.937,2.762,3.195,4.027,3.014,1.788,2.828,2.603,2.209,2.554,3.052,2.997,2.673,2.466,2.997,2.919,2.557,2.365,2.783,3.273,3.655,2.837,3.212,3.32,2.112,2.263,2.571,1.962,3.001,1.89,1.939,2.266,3.048,3.043,2.719,2.846,1.934,2.263,2.121,2.522,2.082,2.097,2.656,2.235,2.284,2.075,2.129,2.48,1.78,2.25,2.278,1.787,2.244,2.644,2.323,2.828,2.411,2.628,2.643,2.545,0.794,0.773,0.566,0.566,0.677,0.66,0.574,0.547,0.659,0.51,0.678,0.611,0.473,0.573,0.684,0.727,0.74,0.79,0.545,0.584,0.917,0.655,0.634,0.908,0.762,0.652,0.679,0.718,0.672,0.638,0.79,0.77,0.401,0.677,0.834,0.584,0.768,0.689,0.456,0.426,0.399,0.563,1.159,0.65,0.518,0.561,0.464,0.636,0.485,0.481,0.352,0.472,0.441,0.597,0.341,0.372,0.416,0.462,0.665,0.383,0.374,0.72,0.534,0.558,0.477,0.605,0.457,0.469,0.396,0.714,0.487,0.484,0.501,0.326
-275,Epileptic,M,17.5,1.0,2.9,0.7,1.0,1.5,1.6,1.7,2.0,1.2,0.6,0.3,2.2,1.0,0.5,0.7,6.3,9.8,0.9,2.4,4.8,1.4,2.3,1.4,3.7,4.1,4.2,3.2,2.7,3.2,3.1,1.5,0.9,0.6,2.4,0.7,0.8,3.9,4.3,0.1,0.2,0.9,2.5,2.8,2.0,3.3,0.6,0.5,0.8,1.2,1.2,0.7,1.4,1.8,3.1,0.2,2.4,0.7,1.5,0.5,0.8,0.6,1.6,0.3,0.6,2.0,1.8,2.9,1.2,1.2,0.5,0.9,1.5,6.2,0.3,8,28,9,11,16,15,13,18,13,7,4.0,22,12,6,8,79,93,12,32,41,14,29,16,42,52,43,40,29,29,39,16,9,6,30,4,6,41,53,1,1,6,25,24,17,19,4,2,6,6,10,3,12,11,21,1,22,4,13,4,7,6,15,2,5,17,22,23,11,8,6,6,12,68,3,0.045,0.019,0.012,0.021,0.03,0.027,0.023,0.026,0.037,0.024,0.029,0.029,0.023,0.034,0.02,0.029,0.027,0.027,0.029,0.034,0.029,0.029,0.024,0.032,0.034,0.026,0.026,0.022,0.025,0.029,0.035,0.042,0.024,0.025,0.022,0.017,0.034,0.031,0.009,0.019,0.021,0.033,0.046,0.018,0.02,0.012,0.016,0.015,0.013,0.026,0.025,0.016,0.02,0.017,0.04,0.02,0.015,0.016,0.017,0.018,0.018,0.02,0.02,0.02,0.019,0.025,0.018,0.013,0.018,0.015,0.016,0.017,0.018,0.012,1526,6086,2617,2414,2568,3263,3630,4760,2144,1815,594.0,2705,2788,1136,2052,15153,25209,2674,6013,6667,3634,4298,2646,7214,8146,9152,6293,6068,7787,7042,2289,1490,1161,7778,2154,1992,7864,10617,331,254,1317,3053,5323,3333,4288,1520,1044,2313,2634,1851,631,3119,2504,7404,265,4179,1582,3916,905,1249,1205,3092,457,959,3739,1888,5920,3381,2651,1623,1823,3105,13122,620,0.137,0.103,0.093,0.109,0.112,0.129,0.107,0.123,0.148,0.127,0.122,0.134,0.108,0.142,0.099,0.134,0.128,0.123,0.137,0.135,0.104,0.138,0.129,0.13,0.15,0.131,0.13,0.114,0.109,0.138,0.133,0.113,0.098,0.121,0.088,0.088,0.134,0.131,0.089,0.117,0.107,0.145,0.139,0.1,0.099,0.081,0.092,0.086,0.083,0.118,0.118,0.094,0.108,0.095,0.142,0.108,0.09,0.093,0.107,0.105,0.105,0.113,0.092,0.114,0.104,0.123,0.103,0.088,0.101,0.108,0.099,0.104,0.104,0.093,447,2114,1004,862,912,1052,1078,1310,573,387,176.0,1143,749,249,548,3810,6159,511,1554,2288,914,1258,903,1961,2134,2574,2096,1989,1930,1954,715,466,378,1522,579,711,2029,2341,189,142,656,1233,1094,1831,2503,743,460,933,1239,766,370,1500,1239,2886,89,1948,642,1661,480,682,588,1204,235,510,1735,1023,2528,1447,1091,537,841,1274,5327,328,3.267,2.671,2.488,2.696,2.379,2.688,2.686,3.072,2.709,3.263,2.798,2.033,2.839,3.042,2.794,2.981,3.235,3.704,2.977,2.361,3.119,2.561,2.314,2.722,2.808,2.946,2.389,2.443,3.035,2.794,2.506,2.785,2.449,3.413,3.546,2.599,2.944,3.108,2.068,2.388,2.615,2.176,3.249,2.095,1.97,2.28,2.847,3.061,2.607,2.957,2.039,2.251,2.294,2.638,2.948,2.32,2.837,2.606,2.111,2.023,2.304,2.525,2.189,2.207,2.353,2.127,2.492,2.512,2.807,2.997,2.613,2.614,2.581,2.297,1.08,0.577,0.673,0.454,0.625,0.614,0.714,0.451,0.691,0.48,1.175,0.499,0.497,0.497,0.532,0.62,0.642,0.792,0.492,0.726,0.847,0.471,0.554,0.855,0.605,0.554,0.551,0.54,0.591,0.682,0.897,0.956,0.366,0.741,0.824,0.474,0.78,0.689,0.225,0.367,0.5,0.54,0.9,0.738,0.508,0.371,0.517,0.617,0.479,0.469,0.365,0.474,0.436,0.461,0.734,0.4,0.378,0.525,0.386,0.318,0.34,0.642,0.302,0.882,0.527,0.875,0.433,0.385,0.454,0.863,0.393,0.507,0.474,0.372
-276,Epileptic,M,27.0,0.7,1.9,1.1,1.0,1.3,2.5,2.0,1.4,0.9,0.6,0.2,3.2,1.5,0.7,1.1,5.7,7.7,0.6,3.3,6.6,1.7,3.7,1.7,2.5,3.2,3.0,3.3,2.8,2.9,4.0,0.7,0.6,0.6,2.8,0.3,0.6,4.5,3.4,0.2,0.1,1.0,2.6,2.6,2.7,3.3,0.5,0.4,0.9,1.1,1.3,0.9,2.2,1.3,3.5,0.3,2.2,1.3,1.9,0.9,1.1,0.8,1.1,0.5,0.5,2.1,1.1,2.4,1.4,1.3,0.4,1.5,1.5,5.3,0.3,6,18,16,8,13,19,13,13,10,5,2.0,21,14,7,10,54,63,5,23,40,14,37,14,26,29,30,29,21,20,38,12,4,6,30,2,5,38,37,1,1,5,23,20,21,17,4,2,6,5,9,6,16,9,22,1,13,9,12,6,9,5,7,3,5,15,12,17,7,7,4,9,11,35,3,0.033,0.025,0.026,0.023,0.025,0.036,0.031,0.029,0.039,0.034,0.025,0.046,0.031,0.036,0.037,0.036,0.032,0.03,0.038,0.048,0.037,0.039,0.034,0.033,0.039,0.032,0.033,0.03,0.031,0.044,0.028,0.035,0.026,0.031,0.018,0.019,0.035,0.035,0.016,0.027,0.022,0.029,0.051,0.033,0.028,0.014,0.024,0.014,0.016,0.031,0.04,0.029,0.025,0.023,0.034,0.02,0.031,0.02,0.024,0.022,0.024,0.021,0.027,0.029,0.026,0.038,0.022,0.025,0.024,0.018,0.034,0.02,0.02,0.019,1255,3996,2087,2036,1748,3335,2123,2876,1350,1149,636.0,2228,3103,1384,1944,8729,15248,1909,3446,4016,2559,5271,2291,5315,4976,6716,4884,3726,5035,4922,1854,701,1198,7137,1327,1549,8214,7790,305,167,1595,3030,4255,1994,3155,1160,616,2599,2371,1651,394,2867,1430,5537,314,3138,1799,2611,1135,1358,991,2082,768,832,2881,785,3395,1933,1777,931,1650,2723,10765,396,0.123,0.122,0.134,0.099,0.116,0.138,0.115,0.123,0.148,0.119,0.102,0.155,0.131,0.137,0.146,0.139,0.121,0.103,0.127,0.15,0.112,0.151,0.132,0.131,0.139,0.131,0.133,0.119,0.114,0.152,0.118,0.098,0.116,0.124,0.087,0.086,0.137,0.135,0.105,0.14,0.103,0.135,0.148,0.13,0.116,0.091,0.108,0.087,0.086,0.121,0.169,0.111,0.118,0.106,0.127,0.099,0.125,0.104,0.116,0.117,0.122,0.115,0.123,0.133,0.116,0.124,0.11,0.114,0.114,0.102,0.138,0.116,0.099,0.119,331,1226,692,715,730,1155,870,865,417,292,198.0,1045,792,299,548,2657,4024,382,1206,2150,676,1622,811,1253,1440,1712,1680,1410,1405,1607,492,331,402,1464,297,543,2069,1826,131,80,730,1324,969,1391,1887,548,256,1061,1053,710,321,1361,787,2409,152,1688,730,1385,647,764,541,736,328,310,1384,384,1724,900,855,357,704,1132,4280,214,3.55,2.815,2.897,2.737,2.153,2.593,2.127,2.942,2.405,3.133,2.962,1.999,3.041,3.236,2.782,2.636,2.933,3.489,2.376,1.693,2.85,2.53,2.385,3.003,2.626,3.101,2.334,2.222,2.731,2.591,2.653,2.093,2.496,3.179,3.796,2.523,2.964,2.995,2.436,2.579,2.808,2.055,3.082,1.691,1.954,2.307,2.95,2.878,2.743,2.505,1.603,2.216,1.937,2.377,2.409,2.134,2.543,2.251,2.062,2.016,2.238,2.762,2.633,2.966,2.224,2.176,2.151,2.527,2.499,2.794,2.661,2.788,2.641,2.102,0.726,0.719,0.72,0.617,0.526,0.623,0.606,0.635,0.624,0.56,1.079,0.448,0.499,0.481,0.569,0.539,0.623,0.975,0.784,0.682,0.928,0.443,0.571,0.716,0.673,0.562,0.506,0.525,0.552,0.683,0.782,0.755,0.41,0.763,0.611,0.632,0.697,0.629,0.445,0.4,0.479,0.765,0.894,0.609,0.538,0.547,0.575,0.694,0.591,0.577,0.394,0.495,0.453,0.438,0.51,0.408,0.489,0.437,0.421,0.311,0.457,0.871,0.82,1.257,0.513,0.63,0.484,0.406,0.376,0.895,0.608,0.654,0.529,0.489
-277,Epileptic,M,22.0,0.5,3.2,0.9,1.4,2.2,3.1,2.4,1.6,1.1,0.8,0.3,3.7,2.0,0.8,1.4,5.8,8.7,2.2,2.9,5.8,2.5,7.6,3.7,3.4,7.9,5.7,9.8,3.5,3.9,6.4,2.0,1.3,0.5,3.0,0.6,0.5,6.7,5.4,0.4,0.4,1.3,3.8,2.8,3.5,2.5,0.8,0.4,0.8,2.2,0.9,0.9,1.9,1.4,2.4,0.4,3.6,1.2,1.7,1.4,1.6,0.4,1.4,0.3,0.6,2.3,1.0,4.4,1.4,1.2,0.4,0.8,1.7,4.6,0.3,5,28,8,9,20,33,21,18,14,7,4.0,34,27,11,15,68,84,16,33,49,22,54,27,35,52,62,75,31,31,66,18,11,6,32,4,2,68,61,2,3,7,37,26,21,14,6,2,5,12,5,5,16,10,17,2,26,10,11,9,11,3,12,3,4,18,12,33,9,7,4,5,12,44,3,0.028,0.026,0.016,0.023,0.038,0.041,0.042,0.027,0.035,0.037,0.047,0.045,0.036,0.033,0.026,0.034,0.032,0.061,0.041,0.046,0.055,0.068,0.047,0.04,0.054,0.045,0.047,0.038,0.037,0.049,0.048,0.049,0.025,0.033,0.017,0.01,0.057,0.043,0.022,0.016,0.021,0.041,0.051,0.031,0.02,0.016,0.019,0.012,0.024,0.019,0.028,0.021,0.022,0.019,0.031,0.017,0.028,0.019,0.024,0.023,0.011,0.027,0.022,0.019,0.029,0.02,0.026,0.021,0.017,0.013,0.02,0.016,0.018,0.011,1916,6227,2170,2773,3132,4545,2928,3972,2561,1245,813.0,3693,5038,2039,4122,14094,22480,3263,5173,6239,3397,4037,2318,7405,4593,9303,8601,5109,6530,7109,2976,1372,1404,7229,2744,1905,7629,11549,477,718,1727,2687,7219,3070,3183,1823,794,2533,2980,2173,786,4135,2382,5953,397,5780,1797,3109,1591,2059,960,2734,757,1095,3296,1367,5459,2353,1962,869,1584,3140,10343,755,0.095,0.123,0.104,0.118,0.148,0.152,0.129,0.124,0.145,0.157,0.164,0.177,0.149,0.139,0.12,0.141,0.137,0.181,0.169,0.168,0.138,0.173,0.15,0.147,0.17,0.146,0.146,0.122,0.127,0.164,0.152,0.129,0.105,0.138,0.088,0.065,0.154,0.147,0.112,0.104,0.104,0.14,0.154,0.13,0.103,0.093,0.099,0.076,0.107,0.093,0.123,0.105,0.106,0.099,0.138,0.1,0.13,0.104,0.114,0.119,0.078,0.126,0.125,0.107,0.121,0.102,0.12,0.111,0.096,0.112,0.111,0.101,0.096,0.09,432,1961,846,877,1042,1525,1135,1195,639,330,182.0,1614,1267,426,1000,3490,5496,653,1364,2479,865,1951,1315,1706,2443,2877,3692,1815,1849,2512,780,496,421,1623,623,714,2056,2589,262,357,911,1558,1350,1750,1962,830,306,968,1343,664,473,1667,1063,2232,216,3074,776,1329,969,1034,527,897,253,479,1389,750,2659,1041,1001,411,642,1493,4471,411,3.841,2.924,2.505,2.8,2.587,2.396,2.171,2.781,2.842,3.187,3.044,2.061,2.987,3.198,3.031,2.911,3.152,3.721,2.909,2.189,2.784,1.908,1.725,3.195,1.792,2.563,2.044,2.219,2.71,2.266,2.958,2.583,2.567,3.135,3.692,2.485,2.688,3.202,2.247,2.167,2.289,1.658,3.411,2.011,1.844,2.317,3.11,3.235,2.731,3.47,2.043,2.466,2.41,2.655,2.208,2.034,2.435,2.56,1.949,2.2,1.977,2.963,2.661,2.903,2.355,2.108,2.112,2.442,2.296,2.49,2.366,2.413,2.39,2.28,0.842,0.622,0.565,0.666,0.72,0.659,0.7,0.625,0.708,0.603,0.883,0.584,0.567,0.778,0.56,0.687,0.676,0.702,0.808,0.718,0.947,0.671,0.554,0.712,0.679,0.687,0.651,0.731,0.744,0.744,0.611,1.054,0.408,0.738,0.605,0.467,0.819,0.899,0.435,0.481,0.447,0.532,0.938,0.761,0.592,0.485,0.398,0.804,0.489,1.009,0.444,0.479,0.486,0.504,0.289,0.454,0.557,0.725,0.39,0.397,0.332,0.782,0.672,0.526,0.582,0.623,0.451,0.454,0.422,0.606,0.68,0.496,0.516,0.282
-278,Epileptic,F,27.0,0.8,2.3,1.4,1.2,1.8,2.3,1.9,1.9,1.6,0.9,0.3,2.5,1.1,0.5,1.9,6.1,9.9,0.8,1.8,4.6,1.0,3.1,1.7,2.5,2.7,3.4,2.9,2.9,2.5,2.4,1.5,0.7,0.6,2.9,0.2,0.7,2.6,3.0,0.2,0.2,1.0,2.8,1.7,2.7,2.5,0.8,0.6,1.4,1.2,0.6,0.5,1.8,1.7,2.5,0.2,1.9,0.6,1.3,1.2,1.0,0.5,1.5,0.4,0.4,1.8,1.3,2.4,1.1,1.3,0.5,1.0,1.1,5.0,0.3,8,23,16,9,15,20,16,15,14,7,3.0,26,14,8,21,65,88,9,22,39,9,30,16,35,32,37,37,35,23,29,22,5,6,33,1,9,25,42,3,2,6,25,18,18,14,5,3,7,7,7,4,14,10,18,2,12,3,10,6,7,3,15,3,4,12,14,18,7,8,5,6,7,44,2,0.032,0.023,0.019,0.022,0.03,0.03,0.032,0.029,0.04,0.04,0.03,0.035,0.025,0.024,0.033,0.034,0.031,0.024,0.028,0.042,0.025,0.039,0.032,0.03,0.03,0.028,0.028,0.028,0.024,0.032,0.034,0.042,0.034,0.03,0.009,0.021,0.031,0.031,0.014,0.013,0.02,0.034,0.03,0.028,0.019,0.018,0.019,0.021,0.014,0.022,0.023,0.023,0.023,0.019,0.011,0.016,0.035,0.013,0.028,0.026,0.015,0.026,0.015,0.017,0.02,0.025,0.017,0.017,0.019,0.019,0.026,0.023,0.02,0.017,1845,5077,3053,2303,2600,4022,2735,3488,2748,1555,711.0,2901,3383,1228,3403,11679,20507,2749,4516,4836,3180,4010,2348,6406,5817,6874,5727,4853,6325,4882,2704,967,914,7141,1699,2118,6932,7146,491,612,1499,3027,5707,2624,3663,1444,970,2147,2712,1669,536,3174,2540,4842,669,3574,1156,3390,1332,1294,934,2736,752,835,3130,1462,4301,2012,2130,1092,1308,2404,10383,423,0.127,0.113,0.114,0.106,0.136,0.137,0.125,0.136,0.157,0.149,0.128,0.147,0.122,0.13,0.147,0.145,0.133,0.116,0.123,0.144,0.097,0.151,0.15,0.129,0.134,0.132,0.127,0.116,0.107,0.14,0.121,0.106,0.152,0.134,0.058,0.108,0.131,0.136,0.099,0.094,0.108,0.146,0.126,0.123,0.099,0.099,0.103,0.09,0.085,0.121,0.129,0.11,0.112,0.102,0.086,0.101,0.116,0.089,0.127,0.124,0.1,0.116,0.098,0.105,0.105,0.114,0.102,0.096,0.102,0.113,0.121,0.119,0.108,0.113,494,1679,1148,875,879,1249,971,996,673,393,211.0,1230,838,355,957,3165,5380,538,1122,1868,712,1314,852,1588,1607,2028,1876,1642,1628,1449,702,345,318,1547,419,621,1439,1692,260,267,725,1357,1101,1523,2031,680,439,981,1269,515,316,1327,1090,1997,314,1759,407,1506,645,619,462,971,401,419,1374,781,2151,938,1020,458,568,753,3995,210,3.501,2.786,2.605,2.579,2.565,2.699,2.484,3.101,3.035,3.267,2.858,2.095,3.081,2.584,2.711,2.798,3.016,3.669,3.167,2.198,3.211,2.508,2.363,3.065,2.767,2.833,2.4,2.299,2.929,2.684,2.822,2.778,2.613,3.381,3.442,2.819,3.542,3.049,2.068,2.472,2.665,2.072,3.527,1.964,2.046,2.474,2.69,2.646,2.55,2.963,2.102,2.414,2.532,2.505,2.29,2.222,3.043,2.595,2.36,2.085,2.418,2.725,2.203,2.199,2.492,2.392,2.149,2.457,2.411,2.778,2.505,3.04,2.732,2.432,0.69,0.602,0.617,0.504,0.538,0.551,0.529,0.411,0.607,0.669,0.689,0.451,0.446,0.402,0.575,0.619,0.567,0.791,0.634,0.624,0.798,0.446,0.416,0.485,0.541,0.555,0.526,0.564,0.471,0.568,0.703,0.888,0.459,0.537,0.733,0.507,0.72,0.645,0.374,0.346,0.656,0.518,0.773,0.569,0.63,0.463,0.618,0.496,0.498,0.581,0.481,0.54,0.579,0.474,0.386,0.394,0.797,0.469,0.368,0.444,0.339,0.592,0.48,0.889,0.535,0.712,0.438,0.449,0.431,0.774,0.486,0.772,0.528,0.469
-279,Epileptic,M,37.0,1.1,3.4,1.0,1.4,2.2,1.6,1.9,2.2,0.9,0.9,0.4,3.4,2.4,0.5,1.7,6.0,11.5,1.5,1.9,5.2,1.8,3.7,1.6,4.0,4.6,3.6,4.4,3.5,2.7,3.5,1.5,1.2,0.6,2.6,0.6,0.4,4.6,3.7,0.3,0.3,1.1,3.2,2.5,3.1,3.0,0.9,0.7,0.8,1.6,0.8,0.5,2.5,2.2,2.9,0.3,3.5,0.9,2.4,1.0,1.0,0.5,2.4,0.5,0.9,2.9,1.5,3.1,1.2,0.9,1.0,1.4,1.5,5.8,0.4,11,37,8,10,20,19,15,19,12,11,5.0,41,23,6,20,80,107,28,29,49,18,42,19,42,48,48,55,37,26,52,21,10,8,32,3,3,50,49,2,2,7,29,28,24,18,7,3,5,9,5,6,21,17,20,3,27,7,20,5,8,7,17,3,6,23,21,25,10,6,12,9,12,47,3,0.037,0.026,0.016,0.022,0.036,0.031,0.026,0.031,0.033,0.034,0.039,0.042,0.028,0.047,0.038,0.032,0.032,0.048,0.027,0.036,0.036,0.038,0.029,0.04,0.041,0.029,0.03,0.029,0.025,0.033,0.037,0.042,0.022,0.033,0.012,0.008,0.047,0.035,0.018,0.017,0.02,0.038,0.04,0.023,0.018,0.017,0.02,0.01,0.016,0.018,0.029,0.02,0.024,0.018,0.015,0.016,0.019,0.022,0.021,0.017,0.016,0.032,0.021,0.021,0.022,0.02,0.021,0.015,0.014,0.022,0.027,0.021,0.019,0.015,1808,6528,2749,2850,3152,3330,2813,3906,2118,1890,865.0,3352,5146,1206,2857,12444,22368,3156,4511,5919,3778,5353,2665,7559,6405,7514,7668,4831,6214,6787,2255,1100,1613,7232,2579,1958,6736,8972,411,409,1526,3343,5820,4316,4451,1529,1165,3212,3100,1587,895,4432,2796,6075,698,5851,1316,3643,1220,1746,1312,3059,555,1202,4305,1627,4193,2659,1891,1857,1504,2458,11228,725,0.13,0.119,0.089,0.11,0.148,0.127,0.099,0.136,0.12,0.153,0.144,0.146,0.115,0.138,0.133,0.134,0.13,0.153,0.114,0.143,0.103,0.134,0.123,0.143,0.135,0.126,0.119,0.108,0.099,0.144,0.125,0.11,0.1,0.131,0.069,0.064,0.152,0.143,0.117,0.099,0.104,0.146,0.161,0.106,0.094,0.101,0.101,0.069,0.09,0.091,0.134,0.105,0.118,0.097,0.092,0.097,0.112,0.101,0.105,0.101,0.099,0.139,0.12,0.101,0.116,0.102,0.111,0.091,0.09,0.108,0.134,0.117,0.099,0.094,579,2315,1051,1033,1111,1052,1093,1301,647,507,253.0,1624,1589,308,919,3878,6801,598,1334,2614,992,1747,1041,2029,2224,2518,2914,1975,1827,2303,846,537,488,1581,714,763,1938,2190,206,229,800,1505,1376,2373,2594,776,490,1195,1465,688,388,2141,1416,2622,327,3077,662,1884,732,913,517,1212,322,566,2093,1185,2178,1267,981,856,790,1125,4792,415,3.041,2.627,2.515,2.525,2.488,2.6,2.193,2.621,2.491,2.94,2.556,1.789,2.597,2.904,2.487,2.508,2.631,3.669,2.713,1.999,2.699,2.465,2.147,2.871,2.361,2.448,2.194,1.999,2.624,2.382,2.087,2.023,2.533,3.233,3.234,2.395,2.525,2.988,2.237,2.158,2.37,1.961,3.058,1.943,1.935,2.153,2.772,3.167,2.521,2.716,2.259,2.202,2.111,2.333,2.353,2.051,2.436,2.148,1.94,2.046,2.332,2.589,2.002,2.443,2.156,1.629,2.092,2.336,2.165,2.29,2.068,2.489,2.437,2.19,0.85,0.603,0.492,0.529,0.691,0.727,0.541,0.457,0.544,0.491,0.907,0.462,0.486,0.652,0.521,0.508,0.614,0.925,0.633,0.608,0.898,0.571,0.48,0.689,0.56,0.557,0.491,0.523,0.441,0.527,0.674,0.852,0.393,0.614,0.68,0.408,0.92,0.714,0.385,0.488,0.414,0.59,0.731,0.585,0.559,0.537,0.554,0.9,0.528,0.59,0.449,0.455,0.45,0.43,0.415,0.41,0.561,0.502,0.414,0.37,0.548,0.631,0.499,0.76,0.395,0.673,0.466,0.361,0.416,0.514,0.407,0.55,0.49,0.328
-280,Epileptic,F,27.0,0.7,1.5,0.6,1.0,1.4,1.9,1.4,1.3,0.8,0.6,0.4,2.9,1.9,0.5,1.0,5.3,8.1,0.6,2.1,5.8,0.7,2.7,1.7,2.3,2.9,3.4,3.3,1.9,2.4,3.6,1.1,1.3,0.5,2.0,0.3,0.4,3.1,2.8,0.1,0.1,0.9,4.3,2.0,2.9,2.3,0.6,0.3,0.6,0.9,0.4,0.8,1.7,1.4,2.5,0.2,4.0,0.6,2.2,1.2,1.0,0.4,1.1,0.2,0.3,1.7,1.2,2.6,1.2,1.1,0.3,0.8,0.7,4.3,0.2,5,15,6,7,35,19,13,14,9,7,3.0,25,17,5,11,66,79,6,25,47,5,34,20,30,37,33,36,21,20,43,24,12,5,28,2,4,45,37,1,2,5,42,24,23,17,6,2,5,6,5,6,15,10,18,1,30,5,16,9,7,4,9,2,2,12,14,23,10,7,4,6,7,33,2,0.034,0.019,0.014,0.02,0.033,0.033,0.026,0.028,0.038,0.034,0.029,0.033,0.036,0.024,0.023,0.031,0.027,0.025,0.035,0.039,0.017,0.035,0.027,0.027,0.033,0.036,0.026,0.023,0.025,0.033,0.033,0.055,0.034,0.029,0.012,0.014,0.037,0.031,0.014,0.02,0.02,0.038,0.038,0.028,0.019,0.014,0.015,0.014,0.015,0.016,0.028,0.018,0.022,0.019,0.02,0.023,0.021,0.024,0.026,0.021,0.02,0.022,0.016,0.015,0.024,0.027,0.022,0.02,0.02,0.011,0.022,0.019,0.021,0.019,1256,3474,1789,2062,1920,3076,2744,2063,1243,1221,637.0,2794,3153,940,2688,11075,17463,2122,3948,4882,2833,4744,1940,5299,5077,5060,5890,2796,4774,6239,1967,1174,703,4798,1475,1369,5403,6127,404,595,1386,3048,4821,2599,2604,1465,820,1825,2049,1046,750,3230,1889,4774,212,4779,1206,3030,1353,1310,816,2031,438,778,2330,927,3187,1834,1916,823,1258,1798,8189,479,0.111,0.103,0.091,0.102,0.131,0.14,0.106,0.132,0.151,0.137,0.123,0.137,0.147,0.118,0.11,0.141,0.127,0.09,0.143,0.146,0.081,0.145,0.14,0.132,0.144,0.147,0.118,0.108,0.104,0.14,0.129,0.152,0.15,0.135,0.071,0.086,0.143,0.141,0.085,0.098,0.112,0.153,0.151,0.117,0.108,0.094,0.086,0.089,0.088,0.103,0.138,0.102,0.114,0.103,0.134,0.118,0.105,0.116,0.129,0.12,0.117,0.112,0.104,0.092,0.116,0.122,0.121,0.11,0.104,0.088,0.121,0.104,0.104,0.094,325,1326,702,754,775,1065,911,808,407,329,209.0,1442,856,248,777,3032,5032,450,1017,2391,695,1393,943,1381,1637,1624,2161,1202,1408,1956,559,467,245,1192,422,522,1444,1563,202,196,631,1756,955,1669,1788,715,344,794,962,448,430,1528,927,2001,126,2488,500,1477,713,714,345,830,254,333,1131,580,1802,907,873,451,596,751,3391,235,3.317,2.531,2.422,2.613,2.07,2.431,2.453,2.427,2.352,2.971,2.676,1.759,2.874,2.611,2.609,2.742,2.818,3.519,3.05,1.832,2.965,2.585,1.853,2.867,2.5,2.547,2.29,1.915,2.727,2.492,2.593,2.486,2.263,2.886,3.205,2.36,2.774,2.844,2.226,3.177,2.728,1.692,3.391,1.795,1.678,2.269,2.785,2.783,2.632,2.5,2.383,2.21,2.33,2.467,2.097,2.18,2.581,2.404,2.263,2.098,2.715,2.409,1.913,2.507,2.252,2.01,1.968,2.345,2.577,2.023,2.49,2.556,2.568,2.373,0.748,0.537,0.552,0.617,0.563,0.816,0.569,0.442,0.459,0.499,0.772,0.454,0.499,0.484,0.451,0.561,0.567,0.793,0.516,0.537,0.933,0.601,0.428,0.658,0.478,0.543,0.461,0.436,0.496,0.565,0.589,0.886,0.337,0.64,0.87,0.54,0.653,0.552,0.356,0.678,0.491,0.451,0.834,0.65,0.441,0.424,0.543,0.704,0.464,0.473,0.671,0.474,0.444,0.407,0.406,0.362,0.608,0.547,0.584,0.399,0.389,0.588,0.335,1.03,0.514,0.475,0.422,0.418,0.415,0.433,0.512,0.629,0.5,0.417
-281,Epileptic,M,37.0,1.2,2.4,1.0,1.6,1.3,2.2,1.5,2.1,1.0,0.8,0.2,3.1,1.5,0.5,1.0,6.9,9.3,1.0,2.2,4.0,1.5,2.5,1.7,3.6,2.2,3.0,3.1,2.0,3.0,4.3,1.4,1.0,0.7,3.4,0.5,0.9,2.9,2.8,0.1,0.2,1.3,2.4,3.3,2.4,2.9,0.6,0.3,1.0,1.2,0.7,0.4,2.0,1.7,2.5,0.1,2.8,0.5,1.3,1.1,1.2,0.9,1.6,0.3,1.2,2.3,1.4,2.5,1.3,1.4,0.6,1.2,1.1,4.6,0.4,7,22,7,12,12,19,13,17,10,7,3.0,31,14,5,11,67,77,8,21,37,10,32,17,35,29,35,32,18,27,43,13,5,7,38,2,9,34,36,2,1,9,22,32,21,17,4,2,5,6,6,4,14,12,17,0,24,3,11,8,10,9,13,2,11,16,18,21,9,7,7,8,6,39,3,0.043,0.026,0.02,0.024,0.027,0.033,0.023,0.03,0.038,0.042,0.031,0.033,0.03,0.027,0.025,0.034,0.03,0.034,0.038,0.039,0.024,0.036,0.032,0.036,0.032,0.03,0.03,0.022,0.024,0.038,0.029,0.049,0.02,0.031,0.012,0.019,0.038,0.032,0.01,0.017,0.024,0.032,0.041,0.022,0.021,0.013,0.016,0.014,0.016,0.018,0.022,0.022,0.024,0.02,0.014,0.022,0.022,0.018,0.027,0.021,0.027,0.027,0.03,0.031,0.023,0.026,0.023,0.028,0.018,0.018,0.028,0.015,0.02,0.016,1918,5202,1989,2620,2285,3481,2395,3814,1532,1611,642.0,3277,3236,1264,2653,11737,17705,2125,4333,4432,4382,4307,1965,5999,4124,6414,5006,3967,6445,6623,2632,1236,1431,7864,2660,2250,6401,6996,345,520,1537,2457,7335,2743,3844,1437,714,2309,2476,1333,468,3105,2563,4862,220,3771,952,2437,1077,1441,975,2758,482,1129,3053,1248,3396,2365,2182,1065,1471,2043,8886,756,0.138,0.114,0.106,0.116,0.121,0.145,0.098,0.13,0.127,0.146,0.105,0.136,0.133,0.12,0.12,0.136,0.126,0.125,0.132,0.142,0.102,0.147,0.131,0.129,0.145,0.134,0.126,0.094,0.093,0.15,0.118,0.137,0.093,0.128,0.066,0.1,0.137,0.139,0.093,0.083,0.119,0.132,0.147,0.112,0.103,0.09,0.082,0.083,0.088,0.114,0.122,0.112,0.111,0.105,0.085,0.117,0.103,0.101,0.13,0.111,0.134,0.123,0.126,0.133,0.11,0.117,0.112,0.112,0.099,0.123,0.13,0.1,0.102,0.085,492,1683,764,998,829,1131,934,1139,458,371,184.0,1507,830,307,727,3401,4997,451,1138,1786,911,1335,921,1736,1342,1889,1570,1267,1713,1949,732,378,472,1868,647,783,1507,1747,211,221,794,1214,1421,1684,2154,702,301,938,1097,535,294,1429,1137,1978,83,1961,413,1159,663,850,553,937,203,494,1547,872,1703,874,1036,490,671,921,3721,421,3.677,2.786,2.593,2.476,2.489,2.531,2.247,3.001,2.781,3.285,2.786,1.895,2.894,2.902,2.823,2.731,2.883,3.428,3.04,2.104,3.274,2.473,1.9,2.7,2.543,2.745,2.468,2.419,2.903,2.647,2.686,3.096,2.365,3.138,3.562,2.607,3.173,3.073,1.961,2.637,2.483,1.886,3.441,1.781,2.036,2.199,2.812,3.005,2.571,2.73,1.907,2.32,2.378,2.539,2.385,2.092,2.664,2.37,1.891,1.967,2.126,3.054,2.756,2.275,2.104,1.779,2.119,2.631,2.509,2.632,2.541,2.625,2.485,2.129,0.658,0.601,0.584,0.533,0.57,0.591,0.55,0.652,0.624,0.805,0.826,0.509,0.454,0.525,0.504,0.472,0.569,0.733,0.569,0.572,0.852,0.546,0.511,0.861,0.505,0.552,0.547,0.654,0.603,0.646,0.798,0.918,0.337,0.67,0.871,0.521,0.665,0.606,0.36,0.347,0.396,0.493,0.708,0.56,0.598,0.468,0.453,0.616,0.633,0.622,0.418,0.438,0.417,0.518,0.264,0.43,0.433,0.678,0.355,0.362,0.375,0.833,0.411,0.818,0.531,0.621,0.482,0.557,0.411,0.782,0.6,0.492,0.487,0.331
-282,Epileptic,M,22.0,1.6,2.8,1.7,1.5,1.6,1.8,1.6,1.7,1.2,0.9,0.3,3.3,1.3,0.3,1.4,6.3,8.6,1.1,3.4,5.5,1.2,2.6,1.4,4.0,3.2,3.8,2.6,2.7,3.4,3.0,1.2,0.7,0.5,2.4,0.5,0.8,2.5,3.0,0.1,0.2,1.2,2.6,2.7,3.7,3.4,0.7,0.5,1.0,1.4,0.5,0.6,2.5,2.0,2.7,0.5,2.9,0.6,1.5,1.3,1.6,0.8,1.6,0.4,0.4,2.1,1.7,2.8,1.4,1.1,0.8,0.9,1.3,5.2,0.4,11,34,16,11,15,16,13,16,15,9,4.0,26,16,6,17,70,77,8,41,44,9,30,16,41,40,38,27,23,23,39,17,6,5,26,2,7,27,37,1,2,8,22,23,24,19,5,2,25,7,6,3,23,16,19,3,24,5,13,9,12,6,13,2,5,15,18,23,9,6,6,6,10,41,4,0.057,0.025,0.018,0.025,0.032,0.032,0.023,0.025,0.033,0.039,0.038,0.038,0.022,0.021,0.025,0.028,0.027,0.034,0.033,0.044,0.026,0.03,0.025,0.036,0.029,0.027,0.025,0.025,0.026,0.03,0.031,0.041,0.026,0.026,0.014,0.018,0.037,0.032,0.01,0.014,0.021,0.032,0.04,0.03,0.021,0.013,0.017,0.018,0.016,0.018,0.036,0.016,0.024,0.02,0.02,0.019,0.018,0.018,0.028,0.022,0.028,0.021,0.014,0.016,0.023,0.024,0.02,0.017,0.015,0.019,0.021,0.016,0.017,0.021,1914,5832,3976,2576,2528,2611,2867,3755,2413,1479,631.0,2974,3687,1099,3334,13126,19856,2621,6949,5608,3748,4872,2056,6981,7378,8196,5540,4204,6644,5435,2512,1360,946,6395,1881,2475,6786,8411,350,688,1665,2361,5629,3225,4254,1517,843,2421,2603,1798,455,4967,3093,5163,675,4306,1309,3124,1195,1907,923,2977,554,924,2931,1978,4447,2833,2262,1101,1414,2241,11282,772,0.153,0.122,0.106,0.113,0.134,0.147,0.103,0.12,0.148,0.152,0.146,0.139,0.119,0.127,0.123,0.131,0.123,0.118,0.149,0.155,0.099,0.133,0.122,0.132,0.127,0.126,0.114,0.107,0.102,0.138,0.13,0.098,0.119,0.124,0.073,0.09,0.129,0.138,0.088,0.096,0.109,0.129,0.15,0.134,0.101,0.094,0.098,0.083,0.092,0.1,0.129,0.1,0.114,0.104,0.108,0.113,0.105,0.107,0.129,0.114,0.137,0.118,0.108,0.111,0.116,0.115,0.106,0.091,0.093,0.116,0.115,0.104,0.1,0.13,541,1891,1490,929,921,967,1082,1095,677,388,179.0,1343,951,290,943,3566,5305,493,1790,2161,840,1485,910,1893,1912,2352,1820,1646,1807,1776,645,460,316,1352,558,770,1377,1737,171,323,866,1269,1124,1997,2454,748,363,1029,1145,618,282,2333,1460,2109,369,2219,514,1365,697,1060,456,1115,316,455,1355,1016,2251,1312,988,585,654,1071,4676,322,3.346,2.785,2.605,2.641,2.491,2.579,2.266,3.062,2.797,2.959,2.555,1.971,2.978,2.83,2.73,2.8,3.032,3.944,3.032,2.171,3.377,2.516,1.923,2.893,2.853,2.821,2.396,2.106,2.846,2.474,2.693,2.651,2.444,3.363,3.244,2.774,3.311,3.424,2.136,2.502,2.458,1.742,3.259,1.877,2.011,2.244,2.834,2.898,2.709,2.806,2.086,2.239,2.32,2.578,2.303,2.191,2.782,2.585,2.034,2.042,2.726,2.713,2.056,2.519,2.351,2.259,2.179,2.526,2.59,2.22,2.507,2.438,2.623,2.741,1.109,0.509,0.535,0.423,0.505,0.415,0.594,0.481,0.517,0.699,0.923,0.516,0.392,0.35,0.428,0.493,0.496,0.754,0.491,0.641,0.923,0.517,0.533,0.566,0.571,0.569,0.549,0.559,0.552,0.554,0.626,1.053,0.543,0.502,0.542,0.626,0.751,0.55,0.341,0.442,0.455,0.473,0.782,0.524,0.593,0.475,0.6,0.645,0.472,0.677,0.393,0.491,0.443,0.474,0.344,0.478,0.686,0.644,0.425,0.366,0.363,0.569,0.46,0.842,0.472,0.841,0.432,0.341,0.443,0.511,0.424,0.441,0.581,0.611
-283,Epileptic,F,17.5,1.2,3.0,0.9,0.9,1.9,2.5,1.6,1.4,1.2,1.3,0.3,2.6,1.5,0.3,1.5,6.1,8.8,0.9,2.0,4.4,1.3,3.1,1.3,2.8,2.9,3.4,3.9,2.6,2.4,3.2,1.5,0.8,0.7,2.3,0.4,0.3,3.2,3.6,0.2,0.1,0.9,2.7,2.1,2.2,2.7,1.1,0.4,0.8,1.5,0.4,0.5,2.1,1.1,3.6,0.4,2.3,1.0,1.3,1.2,0.9,0.7,1.6,0.4,0.4,1.6,1.2,2.8,1.3,1.0,0.2,1.6,1.2,4.1,0.4,8,29,9,7,20,19,14,14,15,12,4.0,26,17,7,19,67,87,7,30,45,11,43,15,47,37,40,48,29,21,39,21,6,8,26,3,2,39,44,3,1,7,27,18,17,16,6,2,5,8,4,5,21,12,24,2,19,6,13,11,8,7,12,3,3,12,16,26,8,7,3,9,8,34,4,0.045,0.024,0.014,0.017,0.035,0.033,0.024,0.023,0.041,0.041,0.032,0.031,0.026,0.02,0.029,0.03,0.028,0.028,0.027,0.034,0.025,0.033,0.031,0.033,0.025,0.029,0.028,0.024,0.023,0.029,0.037,0.037,0.032,0.027,0.019,0.007,0.032,0.03,0.015,0.018,0.019,0.033,0.039,0.024,0.021,0.02,0.016,0.013,0.017,0.013,0.022,0.021,0.021,0.022,0.017,0.015,0.018,0.017,0.022,0.014,0.018,0.031,0.022,0.014,0.018,0.032,0.021,0.017,0.015,0.01,0.028,0.016,0.016,0.021,1585,5629,2384,2229,3077,3231,3070,3506,2144,1834,786.0,2663,3914,881,3214,12764,19321,2508,4582,5432,3315,5173,1763,6116,6433,7020,6885,4730,6068,5809,2331,1212,1244,6405,1746,1619,6992,9023,632,461,1733,3303,5048,2778,3517,1476,808,2313,2812,1177,527,4326,2106,5647,474,4785,1985,3192,1392,1631,1228,2393,556,941,2636,1235,4693,2442,2165,711,1713,2387,9336,496,0.132,0.121,0.1,0.095,0.142,0.138,0.105,0.119,0.166,0.175,0.116,0.141,0.124,0.119,0.142,0.134,0.13,0.11,0.139,0.144,0.098,0.145,0.137,0.147,0.124,0.137,0.133,0.111,0.095,0.142,0.123,0.115,0.145,0.127,0.078,0.058,0.139,0.134,0.093,0.073,0.109,0.143,0.14,0.113,0.105,0.109,0.089,0.081,0.098,0.096,0.13,0.109,0.116,0.112,0.118,0.095,0.098,0.102,0.127,0.097,0.111,0.128,0.117,0.095,0.108,0.122,0.114,0.102,0.097,0.095,0.131,0.102,0.098,0.132,467,1838,979,797,992,1152,987,1028,623,500,241.0,1218,974,280,896,3419,5305,499,1317,2114,881,1667,732,1567,1985,1943,2343,1672,1535,1826,762,481,393,1317,458,593,1615,2046,244,179,784,1356,1025,1475,2017,701,334,971,1252,514,346,1897,909,2380,270,2451,854,1391,813,947,587,829,309,441,1281,689,2286,1084,995,393,821,1060,3874,294,3.046,2.718,2.417,2.736,2.582,2.369,2.573,2.941,2.771,3.112,2.771,1.904,3.143,2.443,2.691,2.804,2.921,3.671,2.846,2.223,2.778,2.475,2.044,2.92,2.542,2.923,2.329,2.254,2.947,2.573,2.349,2.569,2.708,3.311,3.358,2.44,3.049,3.102,2.396,2.621,2.735,2.038,3.259,2.142,2.016,2.322,2.835,3.012,2.793,2.666,1.748,2.337,2.344,2.554,2.261,2.095,2.79,2.443,1.987,1.889,2.196,2.957,1.985,2.205,2.173,2.081,2.226,2.651,2.658,2.057,2.496,2.57,2.581,2.13,0.922,0.649,0.573,0.536,0.804,0.485,0.539,0.431,0.623,0.542,0.77,0.463,0.58,0.504,0.491,0.562,0.516,0.768,0.462,0.595,0.727,0.526,0.475,0.757,0.49,0.482,0.532,0.618,0.591,0.482,0.634,0.731,0.678,0.541,0.4,0.514,0.677,0.673,0.416,0.473,0.507,0.551,0.887,0.751,0.639,0.445,0.608,0.548,0.658,0.491,0.381,0.499,0.442,0.475,0.31,0.37,0.495,0.596,0.349,0.356,0.473,0.743,0.438,0.847,0.6,0.721,0.456,0.451,0.524,0.396,0.51,0.568,0.493,0.532
-285,Epileptic,F,22.0,0.7,2.6,0.6,1.9,1.6,3.4,1.3,2.2,1.0,0.9,1.3,2.9,1.3,0.7,1.0,5.9,8.7,0.7,2.7,5.4,0.5,2.8,1.2,3.6,2.7,4.6,3.3,2.4,2.9,3.4,1.1,2.1,0.5,2.2,0.4,0.3,3.9,3.3,0.2,0.2,1.1,4.5,2.0,2.2,2.3,0.9,0.4,0.7,1.1,0.7,1.2,1.3,1.5,2.7,0.4,2.0,0.8,1.6,1.1,0.7,0.6,1.3,0.4,0.5,1.5,1.1,2.7,1.7,1.0,0.4,0.8,1.4,3.5,0.3,7,29,7,12,13,26,13,19,11,9,17.0,28,14,7,8,59,79,5,27,43,4,30,16,41,34,44,40,21,22,33,18,13,6,27,2,2,38,35,1,1,7,34,15,18,18,6,2,5,6,7,8,9,12,20,2,17,7,17,9,6,5,9,2,4,11,15,20,12,6,5,6,12,30,2,0.033,0.023,0.011,0.032,0.03,0.039,0.021,0.025,0.033,0.036,0.092,0.032,0.027,0.036,0.028,0.03,0.028,0.028,0.033,0.038,0.012,0.034,0.028,0.034,0.029,0.034,0.028,0.026,0.025,0.031,0.032,0.05,0.019,0.027,0.014,0.008,0.038,0.031,0.02,0.022,0.023,0.043,0.04,0.026,0.02,0.018,0.022,0.013,0.015,0.018,0.041,0.018,0.022,0.02,0.016,0.016,0.018,0.019,0.025,0.016,0.015,0.023,0.033,0.015,0.019,0.03,0.022,0.02,0.018,0.014,0.021,0.022,0.018,0.016,1279,5078,2000,2546,2374,3807,2816,4056,1579,1383,600.0,2725,3305,1128,2083,10861,18443,2465,3982,4480,2287,4341,2033,5989,4675,7766,5755,4085,6126,5674,1988,1378,1170,4661,1582,1535,6606,7148,333,437,1681,3261,3618,2443,2798,1531,704,2270,2441,1522,657,2759,2531,4971,521,3392,1925,2851,1228,1154,1079,2271,449,870,2669,1112,4252,3209,1712,835,1454,2480,7391,488,0.122,0.112,0.09,0.131,0.129,0.154,0.106,0.123,0.144,0.149,0.154,0.14,0.127,0.149,0.137,0.134,0.13,0.098,0.127,0.135,0.064,0.144,0.131,0.133,0.139,0.144,0.134,0.12,0.115,0.139,0.114,0.153,0.102,0.121,0.067,0.06,0.13,0.13,0.106,0.103,0.11,0.152,0.137,0.119,0.11,0.102,0.107,0.082,0.092,0.098,0.154,0.099,0.11,0.108,0.104,0.103,0.099,0.107,0.125,0.103,0.1,0.111,0.147,0.1,0.108,0.116,0.118,0.105,0.104,0.102,0.115,0.11,0.096,0.1,411,1910,760,931,834,1393,948,1366,503,405,226.0,1288,887,288,562,3086,5220,455,1330,2107,696,1395,826,1738,1539,2337,2120,1416,1775,1896,654,576,429,1243,475,618,1816,1700,154,182,803,1598,842,1422,1856,725,308,928,1087,656,491,1297,1128,2053,303,1885,841,1538,742,678,568,881,211,488,1230,564,1950,1321,825,510,649,1078,3312,293,3.163,2.482,2.432,2.621,2.461,2.347,2.362,2.707,2.401,2.845,2.092,1.866,2.849,2.911,2.829,2.733,2.959,3.795,2.463,1.838,2.611,2.448,2.05,2.668,2.464,2.641,2.269,2.348,2.693,2.457,2.248,2.524,2.204,2.765,3.235,2.336,2.78,3.09,2.831,2.645,2.591,1.933,3.202,1.952,1.692,2.319,2.977,2.858,2.771,2.556,1.703,2.456,2.339,2.5,2.211,2.072,2.556,2.163,1.968,1.987,2.244,2.56,2.517,2.196,2.476,2.163,2.409,2.71,2.397,2.002,2.607,2.558,2.383,2.147,0.925,0.562,0.536,0.514,0.763,0.494,0.491,0.58,0.541,0.383,0.976,0.408,0.401,0.324,0.424,0.534,0.582,0.842,0.55,0.685,0.648,0.479,0.429,0.576,0.487,0.505,0.424,0.49,0.529,0.451,0.433,0.842,0.368,0.519,0.486,0.402,0.704,0.56,0.358,0.453,0.577,0.577,0.864,0.704,0.476,0.636,0.47,0.795,0.697,0.483,0.346,0.376,0.554,0.516,0.28,0.356,0.417,0.652,0.394,0.363,0.375,0.595,0.545,0.631,0.612,0.638,0.477,0.456,0.439,0.371,0.463,0.587,0.467,0.313
-286,Epileptic,F,27.0,0.5,2.5,1.4,1.2,1.6,1.7,1.6,1.1,1.0,0.7,0.2,3.0,1.2,0.4,1.0,5.6,7.6,0.7,1.8,4.5,1.2,3.6,2.8,3.0,2.6,1.7,2.5,2.0,2.3,2.8,1.6,0.9,0.4,2.0,0.5,0.4,3.3,2.5,0.1,0.2,0.5,3.2,2.1,2.2,2.4,0.6,0.3,0.8,1.1,0.7,0.6,1.5,1.9,2.4,0.4,1.8,1.0,1.7,0.8,1.3,0.4,1.5,0.2,0.5,1.7,1.0,1.5,1.1,0.9,0.5,0.7,1.1,3.2,0.2,5,24,17,11,18,16,13,13,13,8,2.0,29,14,6,12,62,76,6,24,46,14,44,20,37,38,22,39,24,24,30,14,5,5,25,3,5,50,33,1,2,3,22,24,20,14,4,1,5,6,6,4,10,16,22,2,14,7,14,6,10,3,10,1,5,15,13,12,6,6,5,5,7,26,2,0.036,0.03,0.03,0.029,0.037,0.028,0.031,0.025,0.047,0.038,0.023,0.039,0.033,0.04,0.038,0.039,0.036,0.04,0.027,0.042,0.025,0.042,0.046,0.042,0.041,0.026,0.033,0.029,0.028,0.032,0.046,0.047,0.019,0.032,0.024,0.016,0.036,0.031,0.014,0.021,0.014,0.04,0.035,0.027,0.019,0.016,0.021,0.014,0.019,0.014,0.022,0.02,0.032,0.022,0.02,0.016,0.026,0.023,0.016,0.019,0.015,0.025,0.022,0.026,0.017,0.021,0.017,0.017,0.016,0.02,0.023,0.017,0.014,0.012,1309,4519,2586,2487,2657,2714,2541,3043,1799,1471,632.0,3575,2798,1042,2120,9820,15595,1971,4361,5781,4296,4814,2115,5544,5339,4502,4808,3983,6086,5531,1892,635,1029,5640,1599,1226,7773,5768,260,562,995,2210,5341,2581,3225,1158,721,1850,1776,1603,759,2309,2302,4351,466,3036,1311,2936,1129,1893,996,2358,364,767,3192,1258,2694,2177,1664,696,1124,1983,8336,479,0.111,0.128,0.132,0.122,0.148,0.123,0.119,0.125,0.186,0.167,0.094,0.149,0.141,0.153,0.162,0.156,0.146,0.144,0.135,0.166,0.114,0.147,0.146,0.156,0.143,0.132,0.136,0.122,0.118,0.15,0.151,0.122,0.11,0.142,0.104,0.08,0.153,0.143,0.111,0.117,0.088,0.139,0.156,0.114,0.097,0.099,0.099,0.089,0.098,0.095,0.124,0.109,0.141,0.108,0.12,0.102,0.127,0.118,0.102,0.108,0.093,0.125,0.11,0.137,0.102,0.108,0.094,0.097,0.095,0.127,0.107,0.105,0.086,0.107,351,1458,924,801,769,974,866,803,424,323,160.0,1454,739,233,588,2796,4046,381,1181,2135,931,1602,1037,1416,1494,1237,1549,1308,1518,1599,562,288,298,1142,395,399,1721,1447,144,203,531,1307,1133,1452,1869,554,246,789,848,678,353,1135,968,1893,252,1559,548,1221,704,960,481,865,166,368,1506,798,1373,944,837,377,526,846,3571,231,3.402,2.758,2.694,2.779,2.625,2.379,2.383,2.922,2.844,3.271,2.958,2.042,2.901,3.06,2.722,2.617,2.859,3.726,2.897,2.287,3.285,2.362,1.921,2.866,2.666,2.784,2.42,2.306,2.948,2.593,2.461,2.355,2.704,3.251,3.45,2.615,3.23,2.933,1.977,2.391,2.345,1.542,3.189,1.977,1.94,2.346,3.062,2.951,2.631,2.638,2.227,2.184,2.269,2.202,2.204,2.15,2.836,2.556,1.883,2.205,2.333,2.584,2.08,2.184,2.402,1.904,2.129,2.591,2.356,2.049,2.4,2.455,2.368,2.34,0.885,0.698,0.615,0.626,0.781,0.765,0.633,0.549,0.716,0.769,0.704,0.591,0.438,0.526,0.48,0.641,0.691,0.738,0.536,0.699,0.896,0.803,0.585,0.768,0.642,0.62,0.663,0.578,0.493,0.664,0.694,0.728,0.382,0.631,0.657,0.574,0.839,0.728,0.296,0.599,0.493,0.543,0.857,0.658,0.577,0.584,0.494,0.709,0.442,0.631,0.591,0.455,0.546,0.475,0.263,0.422,0.436,0.619,0.395,0.374,0.419,0.799,0.515,0.615,0.506,0.752,0.373,0.407,0.436,0.73,0.529,0.526,0.502,0.306
-287,Epileptic,M,37.0,0.4,2.1,1.1,1.6,1.9,2.7,2.2,1.7,1.0,0.5,0.3,3.2,1.2,0.4,1.3,6.5,7.7,0.5,2.3,3.8,1.0,2.9,1.5,3.6,2.9,3.6,3.3,2.3,2.7,3.2,1.5,1.9,1.1,2.8,0.4,0.5,3.2,3.0,0.1,0.2,1.3,3.4,2.8,2.2,3.0,0.7,0.4,1.0,1.3,0.5,0.5,1.9,2.5,2.3,0.2,2.2,1.0,2.2,1.4,0.8,0.7,1.4,0.4,0.4,1.5,1.2,3.1,1.3,1.0,0.5,0.9,1.1,3.5,0.3,3,28,9,14,13,30,17,17,11,5,3.0,33,18,6,17,69,72,5,31,36,11,36,16,48,33,39,35,26,23,33,18,12,15,36,6,4,41,30,1,2,8,33,22,17,18,5,2,6,7,9,3,17,16,18,2,22,8,15,11,7,7,12,2,6,11,16,26,7,7,5,5,8,28,3,0.02,0.026,0.019,0.027,0.034,0.028,0.031,0.028,0.039,0.026,0.03,0.031,0.029,0.033,0.026,0.036,0.028,0.021,0.031,0.034,0.02,0.029,0.025,0.037,0.029,0.028,0.028,0.025,0.023,0.035,0.033,0.062,0.032,0.031,0.015,0.01,0.034,0.028,0.011,0.017,0.025,0.03,0.039,0.023,0.023,0.018,0.023,0.014,0.016,0.016,0.027,0.02,0.028,0.022,0.013,0.019,0.021,0.023,0.023,0.014,0.018,0.022,0.019,0.016,0.019,0.026,0.023,0.019,0.015,0.013,0.023,0.014,0.014,0.015,1465,4733,2362,2689,2574,4555,3058,3904,1844,1103,544.0,3662,3485,1105,3236,12211,18133,2339,5531,5226,2707,5181,2656,6518,5796,8121,5799,4254,6702,5949,2406,1194,2150,6906,2251,1991,7812,7866,355,620,1635,3749,6471,2594,3355,1474,745,2243,2525,1489,496,3804,2875,4723,356,3762,1599,3508,1743,1675,1277,2733,594,928,2995,940,4270,2321,2103,1091,1326,2613,8687,568,0.077,0.126,0.105,0.129,0.133,0.139,0.129,0.131,0.149,0.125,0.12,0.144,0.139,0.122,0.13,0.143,0.127,0.112,0.132,0.142,0.095,0.138,0.121,0.142,0.138,0.135,0.13,0.115,0.107,0.147,0.11,0.151,0.166,0.14,0.101,0.076,0.14,0.128,0.082,0.112,0.117,0.137,0.152,0.115,0.108,0.104,0.101,0.084,0.092,0.114,0.119,0.109,0.13,0.109,0.102,0.109,0.113,0.107,0.124,0.1,0.102,0.112,0.113,0.119,0.102,0.14,0.117,0.102,0.097,0.109,0.113,0.102,0.089,0.119,446,1478,876,968,886,1599,1051,1110,477,287,174.0,1588,906,263,836,3214,4609,436,1441,1940,867,1715,1017,1833,1608,2203,1905,1401,1679,1630,732,440,617,1447,513,622,1784,1664,215,246,788,1742,1173,1441,2026,631,298,993,1204,539,281,1625,1304,1805,213,1842,759,1671,929,882,660,1001,303,480,1317,673,2119,1037,955,488,610,1074,3708,283,3.092,2.919,2.581,2.671,2.517,2.474,2.383,3.073,2.808,3.214,2.462,2.103,3.04,2.956,2.839,2.881,3.18,3.782,2.966,2.202,2.48,2.392,2.282,2.685,2.686,2.898,2.421,2.397,2.944,2.8,2.571,2.69,3.025,3.399,3.822,2.795,3.071,3.212,1.857,2.802,2.5,1.927,3.636,1.975,1.951,2.379,3.395,2.868,2.557,2.625,1.937,2.389,2.4,2.597,2.144,2.117,2.498,2.366,2.135,2.163,2.141,2.852,2.124,2.177,2.523,1.857,2.182,2.623,2.556,2.391,2.543,2.646,2.468,2.299,0.645,0.689,0.682,0.542,0.682,0.518,0.636,0.497,0.583,0.402,0.725,0.592,0.392,0.483,0.523,0.52,0.583,0.768,0.58,0.662,0.817,0.535,0.473,0.741,0.549,0.544,0.517,0.569,0.653,0.593,0.789,0.9,0.546,0.569,0.636,0.479,0.78,0.787,0.318,0.583,0.597,0.521,0.864,0.633,0.564,0.474,0.493,0.753,0.496,0.652,0.38,0.468,0.492,0.426,0.277,0.4,0.541,0.448,0.387,0.415,0.393,0.7,0.362,0.816,0.52,0.604,0.526,0.444,0.379,0.687,0.469,0.522,0.493,0.293
-288,Epileptic,F,57.0,0.8,2.0,1.0,1.0,1.1,1.4,1.3,1.5,1.0,0.7,0.4,2.4,1.3,0.4,1.2,4.9,7.4,0.8,2.1,3.3,2.1,2.5,1.3,3.4,3.1,2.8,3.9,1.8,2.1,3.3,1.6,1.2,0.7,2.1,0.4,0.9,3.7,2.7,0.2,0.2,0.8,2.5,1.4,1.6,2.0,0.5,0.4,0.9,1.2,0.4,0.5,1.7,1.0,2.6,0.4,1.9,0.5,2.0,1.1,1.0,0.5,1.4,0.2,0.7,1.2,1.4,2.2,1.0,0.7,0.7,1.6,1.4,3.3,0.1,5,18,10,7,10,10,10,13,9,8,4.0,21,12,5,11,44,60,6,20,36,21,25,12,34,34,29,37,16,15,32,17,8,5,21,4,7,32,27,2,1,5,22,17,13,11,4,2,6,6,4,4,11,6,16,3,17,4,12,6,8,4,12,1,6,12,16,17,7,5,6,10,11,25,1,0.034,0.023,0.016,0.018,0.027,0.037,0.02,0.027,0.036,0.031,0.038,0.032,0.03,0.022,0.026,0.031,0.028,0.028,0.027,0.027,0.042,0.03,0.026,0.036,0.03,0.026,0.026,0.019,0.021,0.03,0.037,0.048,0.039,0.031,0.026,0.022,0.036,0.03,0.017,0.021,0.017,0.037,0.047,0.022,0.016,0.011,0.018,0.017,0.015,0.019,0.029,0.024,0.019,0.024,0.011,0.015,0.022,0.022,0.029,0.019,0.023,0.024,0.019,0.034,0.018,0.026,0.017,0.018,0.016,0.019,0.024,0.026,0.017,0.008,1116,4184,2183,2012,1823,1872,2573,2970,1466,1302,482.0,2407,2466,811,2391,8731,13912,1734,3447,3831,4498,3905,1732,5693,5793,5432,6403,3072,4802,5115,2327,1305,867,5380,1214,1804,5445,5026,367,270,1413,2201,2427,2061,3221,1422,795,2000,2071,1164,376,2676,1874,4380,660,3595,1098,2439,894,1372,716,2347,373,737,2365,1688,3600,1800,1415,1118,1824,1764,7358,359,0.118,0.114,0.101,0.096,0.125,0.138,0.096,0.123,0.13,0.134,0.128,0.144,0.128,0.13,0.126,0.135,0.125,0.121,0.127,0.121,0.142,0.142,0.125,0.147,0.143,0.129,0.129,0.092,0.093,0.133,0.139,0.123,0.155,0.129,0.12,0.108,0.13,0.129,0.094,0.098,0.099,0.143,0.13,0.108,0.091,0.077,0.1,0.086,0.088,0.089,0.138,0.107,0.09,0.109,0.096,0.102,0.104,0.108,0.126,0.105,0.12,0.117,0.116,0.138,0.102,0.12,0.096,0.096,0.095,0.114,0.121,0.126,0.094,0.064,323,1409,920,882,726,668,937,953,441,396,201.0,1021,751,234,714,2516,4270,385,1079,1697,795,1319,801,1579,1791,1817,2369,1266,1315,1777,720,468,257,1132,267,619,1635,1465,216,142,687,1061,588,1241,1986,735,359,901,1095,468,237,1205,865,1763,389,1884,479,1259,567,738,382,956,179,361,1165,769,1974,911,644,534,977,892,3228,171,3.218,2.784,2.371,2.324,2.284,2.539,2.351,2.65,2.511,2.844,2.124,2.01,2.582,2.452,2.597,2.722,2.662,3.433,2.576,1.899,3.202,2.378,1.991,2.749,2.523,2.526,2.231,1.991,2.713,2.41,2.578,2.745,2.969,3.151,3.028,2.426,2.59,2.72,2.016,2.205,2.436,1.879,2.784,1.868,1.849,2.062,2.735,2.576,2.283,2.43,1.817,2.335,2.348,2.51,2.023,2.084,2.579,2.071,2.043,2.07,2.159,2.352,2.502,2.329,2.081,2.495,1.963,2.277,2.491,2.339,2.139,2.461,2.429,2.386,0.534,0.563,0.499,0.445,0.485,0.62,0.47,0.412,0.514,0.438,0.634,0.483,0.413,0.353,0.439,0.485,0.534,0.639,0.621,0.55,1.222,0.416,0.354,0.584,0.418,0.499,0.457,0.448,0.488,0.467,0.577,0.835,0.381,0.688,0.846,0.535,0.703,0.534,0.487,0.323,0.549,0.44,0.993,0.633,0.569,0.48,0.432,0.773,0.42,1.109,0.465,0.473,0.759,0.464,0.303,0.387,0.499,0.795,0.302,0.334,0.526,0.654,0.374,0.612,0.733,0.716,0.404,0.438,0.421,0.59,0.488,0.456,0.549,0.421
-289,Epileptic,F,22.0,0.8,2.3,0.8,1.5,1.4,2.8,2.0,3.0,1.4,0.9,0.4,3.0,2.1,0.7,1.7,6.4,9.5,1.2,2.3,5.2,1.0,3.5,1.9,4.2,3.2,5.5,4.4,2.8,2.8,4.6,1.4,1.0,0.6,3.3,0.4,0.7,4.1,4.0,0.3,0.2,1.4,4.4,2.3,3.6,2.6,0.8,0.5,1.0,1.5,0.9,0.8,2.8,2.0,2.5,0.4,3.0,1.0,1.9,0.9,1.1,0.6,1.2,0.6,0.5,2.5,2.0,3.6,1.6,0.8,0.5,1.0,1.1,5.1,0.6,10,24,8,14,15,27,15,27,13,10,5.0,25,21,11,15,68,91,10,30,44,10,40,18,46,36,65,55,29,30,54,22,10,6,38,3,6,49,48,3,1,9,36,24,27,15,5,3,7,8,5,5,24,13,18,2,26,10,16,8,8,3,8,5,3,17,19,31,14,5,6,7,8,44,3,0.026,0.022,0.013,0.022,0.029,0.026,0.024,0.026,0.041,0.035,0.034,0.03,0.024,0.034,0.032,0.03,0.026,0.032,0.024,0.035,0.018,0.033,0.028,0.038,0.03,0.027,0.025,0.024,0.021,0.031,0.035,0.041,0.018,0.028,0.009,0.012,0.035,0.03,0.017,0.017,0.023,0.037,0.038,0.027,0.018,0.014,0.017,0.014,0.015,0.021,0.032,0.019,0.022,0.016,0.032,0.019,0.023,0.019,0.018,0.02,0.019,0.021,0.021,0.02,0.024,0.029,0.02,0.018,0.014,0.018,0.018,0.022,0.02,0.017,1749,4940,2260,2859,1762,4090,3166,5447,1580,1722,702.0,3005,5170,1264,2949,12973,20447,2933,4941,5412,3889,5602,2801,6424,6249,11310,8124,4941,7021,7112,1881,1086,1376,7210,2804,2414,7665,9104,627,385,2067,3448,5278,3055,3767,1750,970,2519,3075,1656,791,5138,3226,5411,508,4576,1789,3470,1412,1580,1136,1963,853,779,3619,1507,5841,3415,1816,1091,1853,2200,9752,899,0.117,0.109,0.088,0.115,0.125,0.13,0.105,0.121,0.145,0.131,0.139,0.131,0.113,0.147,0.13,0.124,0.123,0.121,0.125,0.137,0.085,0.141,0.124,0.136,0.133,0.129,0.12,0.105,0.102,0.143,0.131,0.121,0.083,0.128,0.062,0.081,0.145,0.136,0.104,0.119,0.111,0.144,0.137,0.121,0.096,0.091,0.101,0.085,0.091,0.106,0.139,0.106,0.106,0.09,0.129,0.107,0.111,0.1,0.111,0.107,0.094,0.108,0.116,0.109,0.115,0.138,0.111,0.101,0.089,0.108,0.105,0.105,0.105,0.097,523,1710,963,1088,783,1720,1167,1856,551,478,237.0,1507,1446,386,861,3674,6072,629,1453,2316,958,1762,1180,1749,1826,3335,2865,1860,1937,2512,629,444,476,1777,722,864,1965,2204,245,180,1043,1718,1117,1992,2222,861,412,1067,1455,622,437,2311,1458,2403,208,2495,771,1653,798,896,538,836,465,389,1568,1061,2870,1375,845,538,894,970,4179,495,3.296,2.546,2.303,2.494,2.042,2.254,2.373,2.644,2.345,2.975,2.437,1.782,2.807,2.52,2.693,2.663,2.788,3.677,2.731,2.034,3.0,2.529,2.11,2.657,2.729,2.757,2.28,2.151,2.859,2.363,2.25,2.427,2.305,2.922,3.31,2.702,2.825,3.03,2.513,2.411,2.566,1.86,3.091,1.778,1.939,2.254,2.845,3.071,2.494,2.911,2.394,2.241,2.229,2.371,2.729,2.054,2.552,2.363,2.085,2.004,2.396,2.283,1.829,2.247,2.586,1.796,2.244,2.453,2.28,2.009,2.382,2.495,2.469,2.313,0.523,0.626,0.578,0.47,0.531,0.458,0.658,0.601,0.476,0.727,0.802,0.494,0.461,0.498,0.519,0.548,0.594,0.724,0.545,0.704,0.783,0.463,0.514,0.643,0.531,0.55,0.486,0.529,0.568,0.467,0.612,0.99,0.448,0.696,0.906,0.467,0.69,0.648,0.458,0.389,0.46,0.476,0.725,0.643,0.659,0.509,0.655,0.586,0.648,0.527,0.426,0.509,0.55,0.494,0.478,0.383,0.532,0.501,0.418,0.371,0.545,0.605,0.482,0.852,0.518,0.547,0.469,0.527,0.449,0.717,0.579,0.515,0.438,0.333
-290,Epileptic,M,47.0,1.0,2.2,1.3,1.1,1.5,2.0,1.6,1.9,1.2,0.7,0.3,2.5,1.3,0.5,1.6,7.2,8.2,1.2,2.4,3.6,0.9,3.1,1.1,3.2,3.0,4.2,2.9,2.2,2.3,3.7,1.4,0.7,0.5,2.3,0.2,0.5,2.1,4.5,0.2,0.2,1.3,3.4,2.7,2.2,2.3,0.8,0.6,0.9,1.4,0.9,0.8,2.2,1.0,2.9,0.4,2.6,0.7,2.0,0.6,1.1,0.6,1.6,0.3,0.5,1.9,1.5,2.7,1.7,1.1,0.7,1.1,0.7,4.7,0.3,8,17,17,8,13,16,12,16,13,7,4.0,25,15,5,13,66,70,12,19,27,9,36,10,47,38,44,33,24,21,37,18,6,5,30,1,4,20,46,1,1,8,26,29,15,14,5,3,6,8,6,5,12,7,24,3,20,6,13,5,9,3,14,2,4,13,16,18,13,7,6,7,6,39,4,0.033,0.026,0.023,0.018,0.029,0.03,0.025,0.03,0.042,0.03,0.037,0.035,0.031,0.033,0.039,0.035,0.03,0.041,0.034,0.039,0.019,0.034,0.021,0.038,0.029,0.032,0.028,0.025,0.024,0.035,0.044,0.034,0.021,0.028,0.007,0.013,0.028,0.036,0.019,0.023,0.023,0.039,0.042,0.025,0.018,0.017,0.022,0.013,0.021,0.021,0.044,0.022,0.017,0.022,0.017,0.016,0.035,0.022,0.019,0.017,0.016,0.024,0.015,0.02,0.023,0.036,0.025,0.021,0.021,0.021,0.031,0.018,0.019,0.014,1659,4632,2390,2330,2242,3223,2890,3427,1758,1366,659.0,2637,3294,978,2741,12579,16762,2549,3850,3382,3372,4579,1889,6698,6301,7057,4646,3418,5618,5848,2493,1126,1139,5169,1780,1581,5019,8859,381,383,1675,2797,5617,2291,3286,1551,932,2374,2556,1644,561,3550,1748,5744,590,4743,1234,2943,899,1855,1126,2490,463,966,2498,1127,3124,2744,1610,1353,1522,2183,9541,505,0.134,0.119,0.119,0.099,0.124,0.134,0.109,0.128,0.163,0.138,0.134,0.142,0.13,0.134,0.147,0.138,0.125,0.141,0.136,0.137,0.091,0.144,0.106,0.139,0.131,0.136,0.122,0.108,0.102,0.148,0.146,0.113,0.101,0.121,0.052,0.081,0.12,0.144,0.098,0.105,0.115,0.14,0.151,0.113,0.093,0.089,0.105,0.081,0.1,0.108,0.159,0.104,0.102,0.108,0.114,0.095,0.131,0.109,0.109,0.098,0.098,0.122,0.111,0.112,0.116,0.129,0.116,0.107,0.109,0.118,0.118,0.103,0.104,0.097,490,1406,946,868,879,1161,960,1101,523,390,191.0,1174,844,236,711,3524,4670,527,1125,1452,812,1532,739,1780,1788,2230,1677,1376,1546,1818,687,458,369,1376,521,613,1238,2067,187,168,846,1330,1267,1392,1975,762,413,1051,1104,627,324,1499,908,2273,332,2431,403,1424,523,931,472,1083,242,434,1236,595,1645,1237,798,589,669,719,3883,358,3.31,2.892,2.579,2.743,2.363,2.472,2.394,2.793,2.741,2.976,2.938,1.983,3.043,2.84,2.9,2.773,2.904,3.151,2.929,2.036,3.153,2.424,2.127,2.904,2.702,2.608,2.217,2.035,2.72,2.582,2.689,2.43,2.479,2.711,3.118,2.427,2.953,2.978,2.354,2.871,2.452,1.922,3.078,1.89,1.903,2.214,2.898,2.801,2.826,2.932,2.143,2.569,2.197,2.489,2.406,2.092,3.074,2.265,2.037,2.057,2.626,2.305,2.289,2.455,2.255,2.332,2.086,2.479,2.226,2.571,2.539,2.924,2.553,1.64,0.627,0.581,0.62,0.537,0.585,0.567,0.551,0.544,0.497,0.538,0.746,0.513,0.444,0.38,0.425,0.548,0.574,0.906,0.666,0.62,0.807,0.691,0.427,0.609,0.57,0.537,0.464,0.512,0.536,0.53,0.568,0.892,0.342,0.668,0.864,0.428,0.751,0.682,0.472,0.469,0.582,0.456,0.758,0.71,0.542,0.546,0.539,0.595,0.473,0.668,0.588,0.478,0.51,0.484,0.354,0.394,0.56,0.691,0.382,0.378,0.704,0.657,0.331,0.799,0.63,0.649,0.434,0.414,0.449,0.674,0.471,0.821,0.543,0.304
-291,Epileptic,F,22.0,0.9,1.9,1.0,1.1,1.6,1.6,1.3,1.8,1.3,0.9,0.5,3.1,2.1,0.6,1.2,4.8,7.7,0.9,2.4,3.5,1.0,1.8,1.5,3.6,2.1,3.7,2.5,1.9,2.6,2.7,1.4,0.8,0.6,2.8,0.3,0.6,3.5,2.4,0.1,0.3,1.1,3.4,2.7,2.9,2.2,0.5,0.4,0.9,1.2,0.8,0.7,2.5,1.6,4.2,0.2,2.2,0.7,1.8,1.0,1.2,0.5,1.4,0.3,0.3,2.3,1.2,1.7,1.0,1.4,0.5,0.9,0.9,3.4,0.4,7,15,11,9,16,16,11,19,12,10,4.0,26,22,9,14,49,73,5,24,27,9,23,16,36,24,40,31,21,23,31,19,6,7,33,2,6,39,24,2,2,6,30,26,24,11,4,2,5,6,7,7,18,13,30,1,18,5,11,8,10,3,11,3,3,21,18,12,6,11,4,6,6,28,4,0.035,0.019,0.019,0.018,0.031,0.026,0.02,0.026,0.042,0.034,0.044,0.034,0.032,0.037,0.029,0.03,0.026,0.027,0.033,0.029,0.021,0.029,0.03,0.038,0.027,0.03,0.024,0.024,0.023,0.028,0.049,0.031,0.021,0.029,0.011,0.012,0.034,0.025,0.011,0.02,0.018,0.034,0.043,0.026,0.017,0.01,0.018,0.012,0.014,0.016,0.035,0.026,0.021,0.022,0.015,0.017,0.02,0.019,0.023,0.024,0.028,0.023,0.027,0.023,0.024,0.023,0.017,0.015,0.021,0.015,0.016,0.017,0.016,0.013,1693,4575,2383,2585,2635,3345,2953,3431,1882,1981,720.0,2682,4216,1393,3054,12271,21071,2586,4379,3815,3305,3843,2417,6721,4511,8126,5572,3727,7073,5943,2360,941,1287,7838,1581,2539,8063,7615,349,836,1766,2992,5471,2642,3893,1345,832,2678,2727,1743,564,3679,3741,7911,381,3363,1178,3196,1260,1456,674,2426,568,733,3499,1195,3449,2264,2552,1458,1917,2353,9030,681,0.11,0.103,0.106,0.098,0.126,0.135,0.092,0.128,0.139,0.136,0.152,0.141,0.136,0.153,0.13,0.126,0.118,0.109,0.133,0.118,0.096,0.14,0.143,0.144,0.129,0.137,0.118,0.103,0.105,0.126,0.145,0.114,0.103,0.128,0.065,0.077,0.145,0.122,0.093,0.1,0.101,0.142,0.145,0.121,0.084,0.08,0.097,0.074,0.083,0.104,0.151,0.11,0.099,0.106,0.091,0.11,0.105,0.096,0.119,0.119,0.115,0.115,0.121,0.119,0.121,0.119,0.092,0.095,0.106,0.114,0.103,0.1,0.092,0.093,448,1478,878,923,914,1071,984,1157,524,464,214.0,1383,1080,350,781,2951,5109,457,1245,1725,783,1080,863,1603,1317,2087,1830,1308,1764,1718,482,426,458,1582,406,791,1880,1519,218,284,851,1578,1165,1708,2185,700,350,1099,1219,674,367,1599,1354,2999,175,1802,539,1483,704,753,296,891,239,305,1586,851,1620,970,1110,439,851,899,3394,428,3.411,2.855,2.618,2.632,2.457,2.629,2.485,2.612,2.609,3.128,2.719,1.74,3.01,2.884,2.771,2.92,3.146,3.936,2.76,1.909,3.336,2.651,2.328,2.957,2.741,3.032,2.369,2.217,2.943,2.684,3.351,2.239,2.264,3.346,3.282,2.774,3.274,3.458,1.666,3.034,2.551,1.779,3.329,1.777,1.984,2.184,2.813,2.877,2.583,2.686,1.739,2.359,2.529,2.47,2.465,2.14,2.659,2.381,2.116,2.083,2.437,2.546,2.671,2.72,2.343,1.704,2.293,2.641,2.433,3.48,2.596,2.886,2.724,1.887,0.851,0.697,0.57,0.506,0.607,0.561,0.578,0.489,0.633,0.587,0.676,0.445,0.507,0.709,0.578,0.675,0.69,0.726,0.63,0.639,0.764,0.543,0.486,0.744,0.585,0.661,0.569,0.483,0.587,0.504,0.984,1.073,0.367,0.64,0.406,0.484,0.701,0.546,0.328,0.57,0.517,0.46,0.767,0.555,0.662,0.417,0.67,0.675,0.596,0.701,0.297,0.585,0.735,0.562,0.533,0.49,0.37,0.607,0.396,0.421,0.38,0.741,0.566,0.886,0.499,0.534,0.51,0.398,0.511,0.942,0.555,0.69,0.628,0.341
-292,Epileptic,M,17.5,0.9,3.4,1.2,2.0,1.6,2.4,4.0,2.7,1.6,0.6,0.5,3.2,1.6,0.4,2.3,7.9,10.4,1.8,2.9,5.2,1.9,5.0,2.3,4.4,6.1,8.4,5.5,4.7,5.4,3.5,2.7,1.6,0.8,3.6,0.9,1.4,5.1,3.7,0.2,0.2,1.4,3.3,3.2,2.9,3.7,0.9,0.6,1.0,2.0,1.3,0.8,1.8,2.7,3.2,0.6,2.6,1.5,1.9,1.2,1.1,0.9,1.9,0.5,0.5,2.4,1.4,3.5,2.2,1.4,1.2,1.1,1.0,6.8,0.4,10,35,10,16,19,21,25,26,18,8,8.0,35,17,9,31,95,111,21,27,44,20,56,23,44,62,76,65,51,46,41,25,18,6,40,5,14,54,48,2,2,8,36,35,18,21,7,15,6,9,9,4,14,20,23,3,21,10,14,10,7,7,15,3,4,26,19,34,18,10,13,9,8,57,3,0.036,0.028,0.018,0.027,0.034,0.033,0.045,0.029,0.042,0.024,0.042,0.041,0.028,0.028,0.036,0.036,0.031,0.055,0.031,0.043,0.031,0.043,0.032,0.043,0.047,0.04,0.039,0.036,0.036,0.03,0.056,0.046,0.031,0.034,0.021,0.023,0.038,0.031,0.015,0.013,0.02,0.04,0.048,0.026,0.019,0.016,0.042,0.014,0.018,0.021,0.035,0.017,0.025,0.018,0.025,0.016,0.024,0.022,0.024,0.017,0.021,0.032,0.024,0.014,0.02,0.02,0.023,0.021,0.017,0.022,0.024,0.014,0.019,0.017,2137,7211,3207,4000,2791,3613,3592,5563,2830,1962,1046.0,3188,4731,1227,4593,17118,27052,3613,5661,5427,5513,7675,2773,8514,7017,9335,7605,6613,9096,7861,2453,2099,1387,8284,3079,3277,10378,10991,417,713,2094,3689,7190,3337,5456,2022,1170,2929,3792,2460,664,4001,3612,7534,605,4905,2474,3825,1889,2168,1453,3491,718,1751,4272,2153,4944,4033,2817,1911,2150,2618,14021,731,0.116,0.121,0.101,0.122,0.133,0.13,0.129,0.126,0.155,0.122,0.145,0.162,0.122,0.121,0.156,0.144,0.13,0.153,0.137,0.157,0.116,0.141,0.14,0.145,0.14,0.137,0.127,0.122,0.125,0.131,0.142,0.117,0.103,0.131,0.082,0.11,0.135,0.13,0.092,0.087,0.103,0.145,0.164,0.117,0.099,0.092,0.074,0.083,0.093,0.096,0.128,0.096,0.116,0.097,0.121,0.097,0.111,0.108,0.108,0.095,0.107,0.128,0.123,0.08,0.112,0.101,0.111,0.108,0.099,0.119,0.116,0.088,0.099,0.103,537,2135,1038,1269,952,1303,1375,1631,752,462,347.0,1499,1132,337,1102,4352,6680,682,1460,2221,1205,2242,1225,2077,2389,3251,2661,2490,2588,2271,802,639,461,1874,676,1078,2473,2245,246,326,1043,1603,1498,1801,2823,932,448,1106,1636,973,397,1704,1616,2895,313,2392,1008,1563,980,1022,660,1036,320,582,2014,1260,2472,1673,1235,801,842,1183,5665,346,3.739,2.9,2.999,2.916,2.415,2.366,2.288,2.969,2.82,3.147,2.382,1.871,3.104,2.625,2.903,2.85,3.097,3.77,2.994,2.141,3.411,2.556,1.95,2.981,2.345,2.402,2.339,2.202,2.72,2.767,2.322,2.891,2.432,3.13,3.707,2.634,2.944,3.359,2.028,2.221,2.57,1.964,3.278,2.129,2.226,2.369,2.568,3.336,2.849,2.829,2.101,2.354,2.328,2.575,2.303,2.228,2.656,2.718,2.121,2.277,2.368,3.135,2.61,3.359,2.268,1.917,2.087,2.409,2.587,2.493,2.681,2.525,2.608,2.55,0.808,0.686,0.625,0.659,0.724,0.537,0.681,0.542,0.615,0.689,0.812,0.492,0.491,0.52,0.746,0.606,0.73,0.932,0.509,0.645,0.879,0.672,0.507,0.803,0.749,0.669,0.521,0.607,0.712,0.541,0.88,1.182,0.422,0.642,0.735,0.675,0.943,0.637,0.336,0.452,0.55,0.574,0.856,0.679,0.629,0.48,0.876,0.712,0.615,0.648,0.382,0.476,0.507,0.499,0.343,0.405,0.49,0.577,0.429,0.396,0.421,0.891,0.458,0.843,0.537,0.742,0.47,0.557,0.447,0.568,0.582,0.549,0.488,0.456
-293,Epileptic,M,52.0,1.1,2.4,0.8,0.9,0.9,2.3,1.4,1.6,0.8,0.7,0.4,3.2,1.8,0.7,1.3,6.3,7.9,1.6,2.1,4.0,1.5,2.8,2.0,2.7,2.5,4.1,2.9,1.7,2.0,3.7,1.5,0.8,0.4,2.1,0.3,0.5,2.2,3.2,0.2,0.3,1.5,2.8,2.7,2.5,2.0,0.6,0.6,1.1,1.3,0.6,0.5,1.4,1.8,3.1,0.4,2.3,0.6,1.6,1.0,0.9,0.6,1.3,0.3,0.3,1.6,1.7,2.4,1.8,1.0,0.5,1.2,0.9,3.3,0.4,11,23,8,7,12,19,12,15,10,6,3.0,32,19,9,12,70,72,8,23,34,16,35,19,38,32,47,35,18,20,41,29,9,4,25,2,4,28,36,2,2,8,26,21,21,11,5,3,7,8,6,4,12,15,25,2,21,7,13,7,8,4,12,2,4,12,18,18,13,7,6,10,8,27,4,0.04,0.029,0.015,0.017,0.022,0.031,0.019,0.025,0.035,0.031,0.033,0.032,0.034,0.032,0.027,0.027,0.026,0.041,0.029,0.029,0.027,0.033,0.026,0.03,0.03,0.029,0.023,0.018,0.019,0.03,0.032,0.031,0.02,0.026,0.012,0.011,0.027,0.031,0.014,0.017,0.026,0.031,0.045,0.022,0.015,0.011,0.022,0.019,0.017,0.011,0.027,0.016,0.023,0.02,0.026,0.013,0.018,0.021,0.017,0.017,0.021,0.022,0.016,0.012,0.021,0.035,0.015,0.024,0.016,0.017,0.025,0.016,0.015,0.023,1641,4287,2188,2150,1589,3260,2781,3108,1504,1512,691.0,3392,3656,1286,2974,11208,16269,2841,4599,4870,4319,5207,2869,6120,5108,8243,6446,3166,5022,6404,2339,1342,1155,5767,1519,2470,5749,7685,550,568,1775,3123,5318,2803,3350,1704,982,2387,2815,2019,574,3040,2608,5837,391,4359,1395,2749,1505,1630,951,2307,536,802,2652,1458,4723,2541,1679,1155,1744,2489,8392,438,0.131,0.123,0.093,0.092,0.111,0.146,0.098,0.123,0.129,0.127,0.122,0.141,0.138,0.154,0.121,0.13,0.121,0.125,0.137,0.124,0.114,0.145,0.129,0.133,0.136,0.14,0.119,0.091,0.092,0.142,0.141,0.106,0.095,0.12,0.073,0.074,0.115,0.135,0.083,0.11,0.118,0.151,0.167,0.112,0.087,0.084,0.109,0.094,0.092,0.094,0.136,0.097,0.117,0.106,0.119,0.099,0.107,0.117,0.105,0.107,0.119,0.119,0.1,0.103,0.109,0.132,0.093,0.114,0.104,0.114,0.126,0.093,0.09,0.146,504,1414,850,833,711,1201,1009,1013,456,407,210.0,1564,961,320,792,3699,4978,561,1221,2128,896,1547,1155,1610,1522,2419,2122,1337,1457,2092,731,537,323,1292,479,803,1478,1796,280,265,850,1384,1058,1819,2091,837,429,1021,1233,772,305,1462,1234,2601,206,2357,582,1233,835,867,427,896,269,432,1301,783,2446,1245,878,544,850,1015,3701,238,3.059,2.783,2.457,2.497,2.135,2.505,2.359,2.717,2.556,3.102,2.836,1.918,2.942,2.891,2.894,2.436,2.664,3.777,2.909,1.983,3.285,2.593,2.207,2.857,2.63,2.803,2.394,1.949,2.637,2.495,2.493,2.547,2.692,3.226,2.798,2.82,2.859,3.081,2.156,2.411,2.588,1.953,3.353,1.791,1.818,2.214,2.857,2.772,2.731,2.687,2.175,2.166,2.245,2.306,2.463,2.1,2.405,2.501,2.123,2.111,2.385,2.478,2.096,2.048,2.296,2.292,2.059,2.336,2.223,2.478,2.395,2.665,2.449,2.381,0.819,0.491,0.568,0.495,0.443,0.462,0.534,0.465,0.424,0.403,0.804,0.491,0.406,0.353,0.398,0.441,0.498,0.75,0.513,0.64,0.868,0.482,0.497,0.565,0.478,0.502,0.411,0.479,0.373,0.487,0.564,0.904,0.471,0.481,0.739,0.459,0.711,0.679,0.327,0.479,0.48,0.506,0.835,0.597,0.567,0.43,0.458,0.732,0.635,0.58,0.432,0.396,0.504,0.408,0.372,0.38,0.5,0.488,0.312,0.403,0.408,0.519,0.382,0.846,0.467,0.901,0.406,0.4,0.439,0.476,0.401,0.794,0.45,0.681
-294,Epileptic,M,37.0,0.9,2.1,1.4,0.9,1.2,2.4,1.3,2.3,1.0,0.8,0.3,2.5,1.9,0.6,1.1,5.8,10.5,1.2,1.9,4.7,0.7,1.3,1.0,3.1,3.1,3.6,4.2,2.1,3.1,2.3,1.2,0.5,0.5,2.4,0.6,0.5,3.9,2.6,0.2,0.1,0.9,2.8,2.4,2.8,2.3,0.5,0.4,0.8,1.3,0.6,0.7,1.8,1.1,2.0,0.3,2.4,0.8,2.0,0.6,1.1,0.2,1.7,0.2,0.4,1.4,0.9,2.3,1.3,1.6,0.5,0.9,0.8,3.4,0.4,7,19,14,11,13,21,10,22,11,8,2.0,19,19,6,11,59,83,11,23,38,5,15,10,31,31,38,44,19,28,29,15,4,5,26,3,3,37,31,2,2,6,26,25,21,15,4,1,5,6,7,5,11,7,13,2,23,5,18,4,9,2,10,2,4,10,21,20,9,11,4,6,6,30,2,0.041,0.024,0.026,0.019,0.033,0.036,0.023,0.032,0.042,0.035,0.034,0.031,0.04,0.034,0.034,0.034,0.039,0.046,0.027,0.033,0.018,0.029,0.021,0.036,0.044,0.035,0.035,0.028,0.032,0.033,0.037,0.027,0.021,0.035,0.023,0.016,0.047,0.037,0.017,0.017,0.02,0.038,0.042,0.024,0.019,0.011,0.02,0.016,0.017,0.025,0.029,0.023,0.025,0.022,0.016,0.02,0.026,0.024,0.017,0.027,0.013,0.028,0.022,0.018,0.019,0.036,0.018,0.02,0.021,0.019,0.027,0.017,0.023,0.021,1574,5090,2662,1912,2562,3560,2133,3889,1994,1344,620.0,2682,3296,1644,2608,11526,18854,2772,4629,4866,2449,2725,1917,6375,5097,7180,5313,3590,5484,4309,2419,1058,1163,5896,1942,1630,6778,6155,456,357,1424,3249,6445,2844,2680,1270,716,2152,2249,1185,777,2982,1611,3800,553,3044,1213,3113,865,1296,523,2751,575,874,2253,837,3946,2254,2304,1087,1083,1591,6137,503,0.127,0.105,0.119,0.097,0.143,0.158,0.098,0.154,0.155,0.141,0.108,0.125,0.141,0.152,0.148,0.145,0.143,0.141,0.125,0.133,0.074,0.126,0.106,0.142,0.173,0.143,0.134,0.111,0.113,0.138,0.13,0.082,0.099,0.147,0.094,0.083,0.163,0.154,0.111,0.118,0.106,0.147,0.157,0.121,0.099,0.081,0.098,0.085,0.09,0.13,0.128,0.108,0.119,0.098,0.103,0.113,0.117,0.119,0.103,0.132,0.091,0.126,0.124,0.119,0.104,0.154,0.102,0.11,0.111,0.117,0.13,0.114,0.113,0.108,391,1562,957,760,712,1087,828,1234,460,375,179.0,1240,935,364,631,3001,4679,487,1167,2220,706,859,747,1512,1391,2083,2001,1202,1602,1471,684,434,371,1211,483,520,1533,1322,186,134,740,1232,1171,1667,1756,664,274,898,1044,427,409,1273,724,1564,262,1790,495,1412,498,620,251,869,187,385,1084,393,1958,964,1092,394,556,613,2469,273,3.545,3.122,2.733,2.664,2.92,2.802,2.22,2.976,3.347,3.001,2.972,1.893,2.808,3.389,3.069,2.888,3.169,3.838,3.075,1.968,2.746,2.499,2.248,3.268,2.927,2.897,2.154,2.358,2.634,2.368,2.702,2.294,2.466,3.383,3.396,2.672,3.261,3.281,2.745,2.499,2.521,2.271,3.517,1.94,1.735,2.144,3.39,2.933,2.684,2.945,2.33,2.526,2.332,2.601,2.726,1.964,2.745,2.591,2.177,2.298,2.37,3.412,3.167,2.817,2.362,2.392,2.101,2.716,2.315,3.18,2.277,2.73,2.635,2.467,0.969,0.73,0.615,0.429,0.786,0.533,0.589,0.572,0.743,0.421,0.802,0.458,0.477,0.606,0.69,0.626,0.735,0.965,0.562,0.636,0.822,0.427,0.427,0.841,0.697,0.564,0.634,0.749,0.697,0.616,0.732,1.161,0.366,0.736,0.892,0.392,0.863,0.78,0.424,0.409,0.446,0.676,0.853,0.677,0.5,0.44,0.504,0.54,0.499,0.633,0.433,0.364,0.618,0.567,0.473,0.47,0.404,0.6,0.45,0.443,0.432,0.583,0.864,0.676,0.542,0.651,0.454,0.458,0.473,0.96,0.453,0.563,0.514,0.373
-295,Epileptic,M,47.0,0.9,1.8,1.0,0.9,1.3,2.4,1.6,1.9,1.5,0.8,0.2,4.2,1.8,0.5,1.4,5.8,10.1,0.5,2.5,4.9,0.7,3.1,1.9,3.5,4.3,4.8,5.3,2.7,3.0,3.5,2.1,1.0,0.5,3.2,1.2,1.0,4.1,2.9,0.4,0.3,1.3,3.4,2.9,3.3,2.8,0.8,0.5,1.0,1.5,0.5,0.6,1.3,1.1,2.2,0.4,2.8,0.7,2.0,1.1,1.3,0.5,0.8,0.6,0.9,2.8,1.2,3.6,1.4,1.4,0.8,1.0,1.2,3.8,0.4,7,15,7,6,14,15,12,15,10,6,3.0,34,15,5,11,48,72,4,27,37,7,24,17,39,39,43,47,22,20,33,22,7,4,33,8,8,30,28,2,2,8,29,25,25,16,5,2,5,7,3,4,11,7,12,2,20,7,17,7,10,5,4,3,5,20,14,23,7,7,12,7,8,27,3,0.035,0.027,0.019,0.024,0.043,0.045,0.035,0.037,0.06,0.043,0.029,0.044,0.036,0.035,0.042,0.049,0.04,0.022,0.041,0.039,0.019,0.042,0.039,0.052,0.043,0.039,0.042,0.037,0.037,0.037,0.067,0.042,0.03,0.042,0.034,0.023,0.05,0.039,0.028,0.023,0.028,0.058,0.061,0.032,0.026,0.017,0.028,0.019,0.019,0.023,0.043,0.031,0.033,0.029,0.024,0.021,0.018,0.021,0.033,0.028,0.021,0.024,0.048,0.041,0.032,0.038,0.033,0.025,0.024,0.036,0.026,0.025,0.021,0.025,1481,2967,1866,1593,1623,2142,2246,2744,1814,979,560.0,3436,3368,963,2220,8099,15037,1977,3446,4026,2372,3575,2554,4712,5937,7434,7023,3264,4572,4627,1386,1223,984,4854,2064,1896,4949,5350,298,369,1394,3807,4017,2783,2531,1274,619,1693,2197,1005,449,2071,1690,2958,378,3499,1345,3066,1045,1254,703,1153,534,683,2556,768,3884,1918,1760,1208,1125,1677,6793,485,0.122,0.112,0.109,0.11,0.147,0.14,0.112,0.134,0.158,0.157,0.097,0.153,0.128,0.135,0.142,0.149,0.137,0.092,0.132,0.132,0.087,0.127,0.139,0.151,0.145,0.14,0.143,0.121,0.121,0.137,0.169,0.107,0.105,0.142,0.119,0.104,0.148,0.137,0.128,0.108,0.122,0.163,0.146,0.131,0.115,0.097,0.112,0.097,0.097,0.109,0.15,0.122,0.118,0.11,0.12,0.111,0.103,0.108,0.134,0.125,0.107,0.103,0.151,0.123,0.136,0.142,0.126,0.107,0.113,0.149,0.13,0.121,0.1,0.143,433,1114,746,639,578,832,806,965,435,305,188.0,1658,960,277,656,2179,4263,444,1048,1937,619,1176,1016,1291,1745,2295,2270,1284,1354,1753,485,515,297,1152,519,760,1372,1277,192,201,776,1239,862,1732,1694,668,269,817,1136,404,285,841,641,1319,246,2044,634,1567,579,733,407,564,242,342,1451,484,1935,893,849,452,622,775,3126,249,3.222,2.328,2.55,2.406,2.227,2.171,2.319,2.599,2.694,2.729,2.549,1.814,2.656,2.574,2.571,2.683,2.747,3.459,2.593,1.821,2.781,2.336,2.103,2.644,2.474,2.596,2.328,2.059,2.571,2.139,2.14,2.124,2.392,2.963,3.035,2.22,2.654,2.761,1.769,2.152,2.256,2.381,2.914,1.792,1.746,2.118,2.548,2.467,2.287,2.643,1.906,2.277,2.395,2.233,1.938,1.924,2.271,2.138,1.989,1.859,1.919,2.026,2.242,2.039,1.909,1.905,2.056,2.271,2.333,2.437,2.309,2.443,2.249,2.497,0.6,0.743,0.575,0.54,0.691,0.656,0.529,0.549,0.772,0.6,0.711,0.461,0.494,0.588,0.68,0.699,0.64,0.671,0.568,0.508,0.752,0.586,0.529,0.68,0.618,0.667,0.617,0.558,0.599,0.551,0.736,0.904,0.667,0.657,0.868,0.541,0.677,0.749,0.382,0.402,0.506,0.724,0.855,0.638,0.483,0.636,0.505,0.701,0.544,0.65,0.392,0.539,0.62,0.573,0.312,0.366,0.502,0.561,0.485,0.361,0.348,0.56,0.523,0.562,0.492,0.512,0.441,0.454,0.499,0.815,0.532,0.66,0.568,0.591
-296,Epileptic,M,27.0,0.7,1.9,0.6,1.1,1.8,1.7,1.4,1.6,1.0,0.7,0.3,2.5,1.2,0.9,1.6,5.4,8.3,0.8,1.5,4.4,0.6,2.7,1.7,3.4,2.5,3.6,3.4,2.6,2.3,3.1,1.3,0.7,0.7,2.5,0.3,0.5,3.5,3.2,0.2,0.1,0.9,2.3,2.1,1.8,2.3,0.7,0.6,0.7,1.3,0.5,0.6,1.5,1.2,2.1,0.2,1.5,0.8,1.3,0.7,0.9,0.5,1.2,0.8,0.4,1.7,1.2,2.0,1.1,1.2,0.4,0.8,0.6,4.0,0.3,7,21,6,8,18,19,13,15,13,7,4.0,20,13,11,16,55,75,7,17,33,6,32,18,40,32,35,39,30,24,34,21,7,7,36,2,6,32,34,1,1,7,20,25,14,15,6,3,5,7,5,4,11,9,17,1,12,6,13,5,8,3,8,5,3,13,16,17,8,7,4,5,7,31,3,0.038,0.028,0.016,0.023,0.043,0.033,0.028,0.032,0.058,0.043,0.054,0.043,0.029,0.039,0.051,0.038,0.031,0.03,0.03,0.043,0.017,0.044,0.039,0.042,0.031,0.039,0.032,0.034,0.03,0.039,0.039,0.044,0.031,0.031,0.01,0.015,0.046,0.033,0.016,0.013,0.021,0.037,0.045,0.027,0.021,0.017,0.02,0.015,0.018,0.017,0.029,0.025,0.028,0.025,0.031,0.014,0.019,0.019,0.022,0.022,0.02,0.028,0.047,0.021,0.024,0.022,0.018,0.021,0.023,0.014,0.024,0.017,0.019,0.017,1307,3883,1655,2072,1973,2714,2121,2513,1813,1308,419.0,1803,2807,1465,2627,9167,15413,2206,2808,3245,2274,4018,1813,6350,4443,5976,5227,3094,4977,4904,2272,653,854,4694,1550,1512,5483,7032,353,361,1285,2116,5293,1958,2880,1227,869,1793,2338,1455,547,1966,1537,3571,255,2904,1251,2068,842,1116,798,1837,613,749,2210,1325,2900,1903,1485,855,1134,1864,7776,366,0.125,0.129,0.101,0.11,0.172,0.16,0.116,0.135,0.149,0.141,0.153,0.16,0.121,0.155,0.147,0.154,0.133,0.119,0.123,0.156,0.08,0.149,0.145,0.159,0.141,0.153,0.134,0.13,0.116,0.149,0.124,0.129,0.155,0.131,0.062,0.093,0.167,0.143,0.103,0.078,0.114,0.141,0.164,0.12,0.105,0.109,0.115,0.09,0.095,0.1,0.136,0.122,0.129,0.117,0.155,0.094,0.105,0.106,0.111,0.129,0.116,0.126,0.171,0.109,0.12,0.109,0.108,0.108,0.114,0.103,0.123,0.102,0.102,0.125,386,1216,609,738,685,960,754,883,423,329,137.0,882,766,387,695,2433,4536,434,880,1596,600,1169,734,1354,1379,1629,1861,1229,1343,1571,603,299,326,1159,470,589,1224,1554,194,138,662,941,962,1153,1764,641,422,812,1130,467,297,925,698,1557,79,1624,583,1155,506,638,372,666,257,351,1098,778,1676,828,803,410,566,716,3267,210,3.344,2.882,2.485,2.665,2.391,2.459,2.307,2.464,2.752,2.953,2.691,1.785,2.761,2.666,2.705,2.706,2.687,3.536,2.528,1.812,2.941,2.458,2.097,3.162,2.439,2.758,2.259,2.02,2.748,2.49,2.765,2.203,2.336,2.697,2.902,2.42,3.2,3.074,1.968,2.557,2.426,1.987,3.514,1.941,1.851,2.074,2.526,2.756,2.533,2.782,2.018,2.217,2.078,2.298,2.735,2.001,2.455,2.102,1.926,1.956,2.461,2.593,2.332,2.418,2.277,2.103,1.904,2.419,2.114,2.531,2.317,2.707,2.522,2.186,0.717,0.668,0.522,0.631,0.645,0.605,0.565,0.447,0.724,0.693,0.734,0.428,0.392,0.531,0.637,0.565,0.644,0.693,0.526,0.521,0.864,0.647,0.485,0.68,0.538,0.635,0.485,0.454,0.422,0.571,0.484,0.947,0.331,0.613,0.541,0.449,0.683,0.634,0.258,0.523,0.528,0.441,0.866,0.648,0.533,0.497,0.458,0.761,0.543,0.671,0.41,0.44,0.572,0.547,0.342,0.406,0.526,0.539,0.4,0.356,0.439,0.648,0.618,0.761,0.573,0.616,0.463,0.469,0.415,0.646,0.402,0.719,0.529,0.439
-297,Epileptic,F,42.0,0.8,2.6,0.7,1.2,2.3,2.8,1.6,1.6,1.3,0.7,0.2,2.2,1.4,0.7,1.1,7.7,11.1,0.8,2.4,3.0,1.2,4.2,1.1,4.2,3.7,4.2,3.5,2.5,2.6,3.5,2.0,1.3,0.4,2.3,0.4,0.5,5.0,3.1,0.2,0.2,1.3,4.1,2.4,2.6,3.1,0.6,0.4,0.7,1.4,1.3,0.9,1.8,1.1,2.2,0.3,2.8,0.7,2.3,1.2,0.5,0.9,1.6,0.5,0.5,2.2,2.3,2.0,1.1,1.1,0.6,1.1,2.1,4.5,0.4,7,24,9,8,16,25,14,14,13,8,3.0,22,14,7,15,89,102,5,29,27,8,49,10,42,42,46,43,20,24,36,17,18,5,27,3,3,50,41,2,2,8,32,19,21,18,4,2,6,7,8,8,14,10,13,2,23,6,17,7,4,8,12,3,4,18,20,16,7,8,6,11,14,37,3,0.037,0.021,0.013,0.02,0.037,0.03,0.022,0.027,0.048,0.036,0.031,0.029,0.028,0.042,0.027,0.037,0.03,0.031,0.028,0.029,0.027,0.036,0.022,0.044,0.031,0.032,0.029,0.026,0.021,0.026,0.043,0.043,0.021,0.029,0.013,0.009,0.034,0.03,0.014,0.017,0.025,0.037,0.04,0.03,0.02,0.01,0.016,0.013,0.018,0.026,0.032,0.02,0.02,0.016,0.018,0.019,0.025,0.019,0.018,0.014,0.029,0.026,0.028,0.017,0.021,0.053,0.017,0.016,0.016,0.018,0.021,0.019,0.02,0.018,1435,5103,1833,2496,1979,3798,2719,3176,1562,1447,577.0,2554,3192,1249,2430,12131,20663,2386,4838,3689,3099,6612,1840,5760,6215,7588,5954,4211,6530,6651,1982,1295,1137,6107,2130,2140,8136,6950,537,395,1790,3580,3902,2153,4144,1518,839,2223,2543,1812,875,2990,1918,4772,332,3885,1322,3888,1726,1107,1294,2432,521,898,3253,1077,3751,2190,2149,1158,1618,3084,8210,562,0.11,0.112,0.099,0.096,0.136,0.138,0.103,0.125,0.154,0.14,0.114,0.137,0.124,0.15,0.139,0.149,0.132,0.106,0.123,0.123,0.093,0.155,0.106,0.159,0.146,0.139,0.138,0.105,0.094,0.122,0.134,0.115,0.11,0.126,0.068,0.068,0.123,0.136,0.087,0.107,0.12,0.137,0.129,0.135,0.1,0.082,0.093,0.084,0.094,0.111,0.153,0.1,0.111,0.09,0.123,0.111,0.112,0.101,0.103,0.088,0.131,0.122,0.133,0.109,0.117,0.166,0.095,0.096,0.097,0.105,0.114,0.115,0.107,0.119,398,1989,867,933,875,1572,992,997,442,372,170.0,1184,909,316,709,3669,5834,418,1469,1627,760,2079,776,1666,2017,2271,2047,1468,1756,2156,745,522,354,1355,551,783,2414,1849,225,214,834,1634,1079,1396,2318,743,346,891,1142,822,442,1435,970,2081,203,2160,597,1915,944,567,525,885,262,479,1594,624,1943,1035,1029,519,824,1506,3600,325,3.307,2.523,2.308,2.62,1.942,2.258,2.321,2.869,2.762,3.089,2.839,1.925,2.767,2.929,2.614,2.538,2.792,4.165,2.679,1.954,3.089,2.477,2.111,2.503,2.435,2.811,2.305,2.321,2.869,2.45,2.176,2.502,2.499,3.008,3.127,2.623,2.67,2.893,2.415,2.22,2.75,1.976,2.996,1.73,2.011,2.197,2.91,3.073,2.594,2.635,2.096,2.163,1.973,2.401,1.994,2.023,2.537,2.283,2.034,2.089,2.445,2.712,2.109,2.321,2.265,2.116,2.091,2.431,2.433,2.411,2.322,2.562,2.479,2.311,1.018,0.508,0.559,0.529,0.528,0.456,0.537,0.462,0.593,0.467,0.75,0.4,0.413,0.515,0.457,0.592,0.658,0.692,0.556,0.608,0.817,0.485,0.463,0.737,0.571,0.452,0.534,0.484,0.517,0.551,0.715,0.92,0.402,0.693,0.815,0.374,0.641,0.505,0.359,0.504,0.49,0.477,0.774,0.66,0.599,0.438,0.521,0.869,0.493,0.528,0.487,0.46,0.52,0.53,0.298,0.428,0.429,0.504,0.402,0.322,0.349,0.696,0.536,0.905,0.574,0.661,0.431,0.391,0.5,0.77,0.394,0.451,0.48,0.438
-298,Epileptic,F,52.0,0.7,2.4,1.0,1.1,1.5,1.6,1.3,1.5,1.3,0.6,0.3,2.6,1.5,0.6,1.3,5.1,7.6,0.8,2.9,4.5,1.4,2.0,1.5,3.0,2.2,2.8,2.5,2.1,2.2,1.8,1.2,0.9,0.5,1.9,0.4,1.0,2.4,2.1,0.2,0.1,1.3,2.9,2.4,4.9,4.4,0.5,0.7,0.8,1.2,0.8,0.9,1.5,1.3,2.2,0.2,3.0,1.3,2.2,0.7,1.1,0.3,1.1,0.3,0.3,1.6,1.0,2.5,1.3,0.7,0.7,0.5,1.3,4.5,0.4,7,20,11,8,16,14,12,13,17,5,5.0,23,14,7,11,57,64,7,23,37,9,21,14,34,27,27,26,17,17,27,19,8,4,20,2,11,32,25,1,1,8,23,23,31,25,4,4,5,6,7,7,13,11,15,1,24,9,18,5,9,2,8,2,4,13,15,19,7,5,7,4,10,38,4,0.032,0.029,0.02,0.021,0.032,0.027,0.021,0.031,0.05,0.035,0.032,0.03,0.027,0.033,0.037,0.03,0.028,0.028,0.038,0.037,0.024,0.034,0.029,0.033,0.028,0.024,0.022,0.027,0.023,0.021,0.039,0.039,0.017,0.023,0.013,0.02,0.035,0.028,0.015,0.017,0.025,0.032,0.04,0.039,0.034,0.011,0.022,0.014,0.017,0.019,0.043,0.019,0.025,0.021,0.012,0.021,0.038,0.021,0.027,0.024,0.024,0.019,0.019,0.014,0.019,0.033,0.024,0.021,0.015,0.02,0.019,0.021,0.022,0.015,1496,3875,1978,1918,2126,2890,2261,2454,1840,954,382.0,2538,3179,981,2116,9477,15227,2195,3105,3781,2842,3234,1931,5404,4085,5932,4743,2853,4761,4055,1813,1220,1096,5485,1978,2317,5197,4579,343,253,1537,2796,5420,2481,2916,1130,917,2052,2100,1486,460,2624,2003,3839,491,3872,1349,2781,824,1291,624,2036,295,734,2503,742,2940,2010,1424,1158,1024,2058,7608,492,0.12,0.128,0.115,0.11,0.138,0.125,0.099,0.13,0.153,0.13,0.125,0.127,0.121,0.126,0.146,0.134,0.124,0.113,0.138,0.136,0.097,0.137,0.12,0.131,0.139,0.127,0.115,0.113,0.102,0.116,0.121,0.106,0.091,0.113,0.074,0.099,0.128,0.121,0.1,0.123,0.117,0.135,0.145,0.147,0.135,0.09,0.124,0.079,0.093,0.117,0.172,0.103,0.119,0.105,0.076,0.116,0.142,0.113,0.13,0.125,0.111,0.107,0.123,0.114,0.108,0.148,0.117,0.1,0.096,0.126,0.113,0.121,0.11,0.106,425,1270,804,798,876,946,886,868,551,276,175.0,1187,926,276,604,2769,4446,496,1235,1852,894,1037,867,1565,1353,1839,1731,1153,1447,1372,571,503,450,1255,433,873,1272,1156,203,136,816,1321,1057,1900,2053,634,440,880,1092,661,372,1314,903,1767,248,2117,609,1642,433,665,307,814,167,411,1262,460,1652,974,711,529,445,909,3230,345,3.336,2.807,2.481,2.504,2.217,2.712,2.245,2.615,2.572,3.01,2.056,1.918,2.698,2.585,2.726,2.654,2.786,3.41,2.145,1.91,2.683,2.477,2.0,2.65,2.419,2.791,2.251,2.056,2.617,2.358,2.397,2.399,1.994,3.045,3.785,2.311,3.009,2.805,1.927,2.066,2.327,1.881,3.433,1.559,1.665,2.02,2.738,2.875,2.364,2.602,1.67,2.066,2.133,2.353,2.037,2.095,2.397,1.956,2.228,2.058,2.592,2.509,2.019,2.112,2.236,2.124,1.984,2.458,2.352,2.656,2.68,2.63,2.413,1.744,0.779,0.712,0.691,0.439,0.494,0.812,0.521,0.498,0.52,0.328,0.563,0.53,0.433,0.357,0.474,0.482,0.543,0.856,0.595,0.589,0.604,0.473,0.566,0.6,0.475,0.47,0.442,0.457,0.46,0.473,0.579,0.945,0.362,0.72,0.944,0.509,0.863,0.573,0.373,0.382,0.399,0.541,0.782,0.463,0.512,0.375,0.38,0.806,0.425,0.506,0.402,0.495,0.559,0.391,0.355,0.45,0.437,0.549,0.429,0.371,0.326,0.673,0.36,0.804,0.537,0.481,0.478,0.472,0.367,0.767,0.59,0.738,0.476,0.295
-299,Epileptic,M,32.0,1.0,3.2,1.2,1.4,1.3,2.3,2.0,1.7,1.0,1.0,0.4,3.0,1.3,0.5,1.8,7.3,9.5,0.9,2.3,5.3,2.2,4.0,1.6,4.5,3.0,3.5,3.6,3.2,2.5,3.1,2.0,2.4,0.7,3.0,1.0,1.0,9.2,5.1,0.3,0.4,1.3,3.6,3.1,3.1,3.3,0.7,0.5,1.1,1.4,0.7,0.6,1.9,1.9,3.0,0.3,3.1,1.6,1.4,1.5,1.3,1.2,2.0,0.5,0.6,1.6,1.3,2.9,2.0,0.9,0.5,1.2,2.6,4.7,0.3,8,33,9,13,15,24,18,21,12,10,4.0,33,19,8,19,91,97,9,28,47,25,46,21,48,54,40,44,35,25,43,22,20,7,33,6,9,75,65,2,2,6,38,31,20,17,4,2,8,7,6,5,19,14,22,2,23,12,9,9,9,9,17,4,5,14,20,24,15,5,5,8,17,35,2,0.045,0.028,0.019,0.026,0.033,0.033,0.036,0.03,0.033,0.035,0.033,0.034,0.024,0.026,0.033,0.031,0.031,0.033,0.031,0.04,0.035,0.039,0.026,0.036,0.032,0.027,0.034,0.031,0.021,0.033,0.043,0.058,0.036,0.031,0.025,0.018,0.057,0.04,0.023,0.017,0.022,0.041,0.044,0.029,0.021,0.014,0.016,0.015,0.016,0.012,0.021,0.017,0.022,0.018,0.022,0.018,0.029,0.015,0.029,0.019,0.022,0.029,0.028,0.017,0.015,0.018,0.022,0.019,0.015,0.017,0.021,0.025,0.016,0.014,1578,6025,2688,2736,2548,3951,2880,4285,2016,2096,840.0,3109,4053,1423,3820,16069,23557,2948,4553,6103,5452,5676,2958,7399,7208,7845,5340,5262,7782,7045,2303,2014,1379,7839,2690,2430,9723,9858,393,741,1633,3917,6863,2731,4346,1438,1051,2693,2752,2283,709,4180,3231,6608,404,4694,2331,3365,1372,2030,1782,3249,579,1393,3102,1702,4851,3720,1757,935,1713,3215,10877,688,0.109,0.125,0.107,0.117,0.131,0.127,0.118,0.14,0.144,0.154,0.133,0.151,0.124,0.131,0.136,0.134,0.134,0.135,0.135,0.153,0.127,0.126,0.126,0.143,0.129,0.128,0.119,0.119,0.106,0.138,0.134,0.139,0.133,0.135,0.116,0.093,0.165,0.15,0.094,0.108,0.108,0.148,0.143,0.122,0.101,0.087,0.093,0.089,0.093,0.081,0.123,0.1,0.11,0.099,0.127,0.106,0.128,0.092,0.116,0.105,0.117,0.134,0.122,0.097,0.094,0.106,0.113,0.103,0.088,0.105,0.111,0.119,0.088,0.097,510,1920,948,954,891,1416,1059,1161,605,524,242.0,1606,1041,353,951,4548,5916,531,1318,2607,1267,1920,1169,2055,2039,2282,2111,1831,1900,2042,822,796,371,1633,643,952,2745,2462,214,320,837,1705,1508,1755,2324,726,442,1084,1282,990,438,1901,1402,2644,192,2419,924,1411,824,1049,828,1139,328,639,1582,1130,2225,1572,850,488,873,1504,4681,296,2.967,2.822,2.691,2.552,2.59,2.364,2.197,3.097,2.702,2.94,2.892,1.746,2.852,3.014,2.891,2.725,2.997,3.844,2.843,2.088,3.218,2.311,2.15,2.75,2.685,2.726,2.124,2.236,3.004,2.561,2.221,2.461,2.911,3.214,3.645,2.355,2.633,2.943,1.988,2.463,2.431,2.005,3.215,1.819,2.119,2.137,2.697,2.967,2.434,2.558,1.888,2.278,2.332,2.528,2.402,2.141,2.541,2.523,1.832,2.137,2.161,2.882,2.258,2.497,2.085,1.72,2.221,2.496,2.405,2.048,2.102,2.483,2.419,2.769,0.922,0.627,0.587,0.728,0.683,0.718,0.636,0.561,0.56,0.883,0.681,0.484,0.481,0.499,0.572,0.575,0.707,0.703,0.501,0.665,0.945,0.658,0.505,0.691,0.612,0.61,0.487,0.629,0.545,0.638,0.675,0.949,0.607,0.722,0.789,0.53,0.867,0.617,0.42,0.373,0.449,0.592,0.786,0.632,0.607,0.401,0.532,0.783,0.569,0.474,0.382,0.398,0.509,0.489,0.432,0.414,0.497,0.47,0.417,0.342,0.436,0.736,0.383,0.72,0.486,0.766,0.473,0.491,0.476,0.719,0.476,0.547,0.442,0.474
-300,Epileptic,M,37.0,0.8,2.2,1.2,1.4,1.2,3.0,2.1,2.3,1.1,0.4,0.4,3.6,2.6,0.7,2.0,7.5,9.0,0.9,2.7,4.8,0.7,3.3,1.8,3.7,3.2,4.2,3.3,3.1,3.7,3.3,1.6,0.8,0.4,2.6,0.6,1.0,4.5,3.9,0.2,0.4,1.2,4.4,3.1,4.1,3.8,1.0,0.5,1.0,1.5,0.6,0.8,2.9,1.2,2.0,0.2,2.6,1.4,2.1,0.9,0.8,1.2,1.5,0.7,0.4,3.2,0.9,3.6,1.7,1.4,0.9,0.9,2.5,5.6,0.2,8,19,10,13,10,25,13,18,9,4,3.0,33,20,8,19,63,72,9,29,37,5,33,16,36,27,37,33,27,28,28,19,7,3,26,4,9,49,38,2,2,6,35,23,31,22,8,3,6,8,7,6,21,8,14,1,20,9,17,7,6,10,10,5,4,25,13,27,11,10,9,6,15,45,1,0.026,0.021,0.016,0.022,0.034,0.03,0.026,0.025,0.046,0.021,0.029,0.03,0.036,0.046,0.035,0.038,0.028,0.027,0.025,0.031,0.014,0.031,0.028,0.037,0.034,0.031,0.023,0.026,0.026,0.029,0.032,0.031,0.02,0.029,0.015,0.023,0.036,0.032,0.02,0.021,0.023,0.034,0.041,0.029,0.022,0.018,0.017,0.016,0.014,0.014,0.03,0.022,0.027,0.017,0.016,0.021,0.023,0.018,0.018,0.016,0.026,0.031,0.029,0.015,0.029,0.021,0.02,0.019,0.023,0.028,0.022,0.027,0.02,0.017,1617,5395,2859,2956,1544,4810,2840,4334,1841,1036,595.0,3703,4673,1026,3291,12188,17834,2892,6247,5842,3270,5395,2032,6990,4442,6788,5861,5250,6668,4919,2155,1327,957,6099,2199,1962,8101,8736,315,577,1707,4056,6246,3454,4282,1537,995,2180,3231,1822,687,5310,1907,4156,270,3162,2080,3450,1305,1039,1712,2453,839,990,3427,973,5364,2839,1769,1122,1277,3012,10855,341,0.115,0.105,0.095,0.106,0.133,0.135,0.095,0.128,0.132,0.1,0.117,0.126,0.146,0.155,0.148,0.137,0.119,0.117,0.11,0.126,0.078,0.131,0.118,0.146,0.133,0.139,0.108,0.102,0.1,0.118,0.121,0.099,0.096,0.124,0.086,0.124,0.126,0.135,0.103,0.121,0.105,0.133,0.148,0.133,0.106,0.101,0.102,0.091,0.087,0.095,0.135,0.106,0.114,0.091,0.109,0.112,0.109,0.105,0.106,0.105,0.122,0.125,0.135,0.103,0.132,0.12,0.105,0.102,0.118,0.13,0.115,0.12,0.105,0.097,500,1692,1096,1093,573,1647,1133,1429,473,280,226.0,1823,1251,279,985,3250,5159,610,1770,2391,739,1818,1020,1716,1549,2166,2287,1850,2018,1869,731,482,289,1538,590,646,2155,2084,213,274,828,1954,1256,2303,2569,813,433,961,1449,711,467,2194,815,1959,166,1861,970,1818,791,655,765,825,376,473,1775,592,2769,1352,960,470,640,1464,4439,173,3.116,2.825,2.53,2.587,2.478,2.451,2.111,2.682,2.77,2.995,2.502,1.841,2.999,2.774,2.781,2.819,2.805,3.652,2.916,1.959,3.111,2.5,1.846,3.089,2.385,2.605,2.136,2.289,2.614,2.245,2.274,2.39,2.957,2.884,3.501,2.844,2.856,3.058,1.718,2.487,2.594,1.977,3.385,1.777,1.894,2.098,2.87,2.806,2.748,2.684,1.914,2.344,2.464,2.307,1.934,1.933,2.573,2.229,1.995,1.771,2.451,2.982,2.36,2.302,2.214,1.921,2.014,2.421,2.181,2.497,2.331,2.408,2.614,2.336,0.649,0.672,0.54,0.589,0.793,0.783,0.648,0.475,0.694,0.397,0.746,0.418,0.398,0.516,0.682,0.679,0.567,0.779,0.658,0.801,0.835,0.542,0.52,0.623,0.57,0.515,0.56,0.57,0.565,0.583,0.656,1.137,0.403,0.682,0.813,0.523,0.671,0.672,0.365,0.338,0.594,0.457,0.703,0.516,0.595,0.536,0.617,0.568,0.61,0.473,0.279,0.649,0.657,0.573,0.462,0.51,0.476,0.49,0.402,0.35,0.502,0.843,0.506,0.939,0.483,0.67,0.547,0.445,0.431,0.529,0.528,0.478,0.518,0.469
-301,Epileptic,F,32.0,1.1,3.1,1.9,1.9,3.2,1.9,1.9,2.1,2.5,1.1,0.3,3.0,1.9,0.8,1.2,6.6,10.5,1.2,2.5,4.8,1.5,2.7,1.4,4.0,3.9,6.1,3.7,3.0,2.6,3.8,1.3,2.0,0.8,4.8,0.5,1.2,4.0,3.7,0.2,0.3,1.7,2.6,3.5,3.5,2.8,0.8,0.6,0.8,1.5,1.1,0.7,2.8,2.2,2.9,0.4,3.0,0.9,2.1,0.9,0.9,0.5,2.1,0.2,0.6,2.1,1.9,3.9,1.9,0.7,0.6,1.3,1.8,6.1,0.5,9,28,21,15,22,18,15,18,20,10,5.0,31,20,10,18,72,96,7,30,44,20,30,15,36,46,66,47,28,26,45,14,14,8,52,3,13,37,46,1,3,12,23,21,23,19,6,4,6,7,11,5,18,16,20,3,27,6,19,6,6,4,11,1,6,16,21,31,14,6,7,7,13,51,3,0.036,0.025,0.022,0.029,0.05,0.03,0.032,0.032,0.055,0.04,0.032,0.034,0.034,0.034,0.031,0.035,0.035,0.035,0.03,0.038,0.029,0.046,0.029,0.041,0.038,0.03,0.03,0.029,0.026,0.031,0.041,0.049,0.023,0.039,0.016,0.017,0.037,0.036,0.012,0.028,0.025,0.034,0.045,0.029,0.022,0.016,0.024,0.013,0.018,0.028,0.035,0.026,0.027,0.021,0.014,0.023,0.015,0.021,0.029,0.02,0.022,0.027,0.033,0.025,0.025,0.025,0.02,0.021,0.016,0.022,0.027,0.027,0.023,0.015,2093,5582,3337,2778,2694,3044,2526,3567,2439,1931,508.0,3030,3809,1764,2708,12210,19312,2862,5016,4958,3212,4657,2014,6803,6740,11904,6017,4061,5609,6822,2136,1709,1757,9491,2364,3426,8131,7556,592,492,2152,3439,4372,3139,3312,1349,1105,1928,2804,1575,678,3001,2976,5429,554,3777,1766,3019,855,1173,1012,3328,251,960,2608,2172,5408,3054,1159,1651,1541,2408,10995,918,0.126,0.116,0.122,0.125,0.152,0.138,0.125,0.13,0.168,0.148,0.126,0.148,0.13,0.131,0.124,0.135,0.137,0.125,0.126,0.141,0.105,0.153,0.127,0.137,0.154,0.131,0.14,0.118,0.113,0.144,0.128,0.144,0.11,0.147,0.08,0.1,0.127,0.131,0.079,0.136,0.114,0.143,0.136,0.126,0.105,0.101,0.111,0.079,0.095,0.103,0.152,0.121,0.116,0.106,0.103,0.119,0.092,0.1,0.131,0.103,0.107,0.122,0.124,0.125,0.116,0.118,0.109,0.11,0.095,0.119,0.123,0.125,0.108,0.088,535,1888,1270,1013,1047,1064,918,1105,667,477,187.0,1431,1083,392,749,3343,5243,537,1417,2127,758,1178,880,1731,1902,3730,2211,1588,1653,2059,634,643,566,2230,547,1184,1982,1986,306,155,1114,1286,1123,1944,2011,675,423,907,1258,672,343,1376,1367,2308,337,2056,850,1501,473,667,393,1100,123,463,1289,1161,2811,1346,616,531,731,1050,4458,486,3.333,2.632,2.436,2.595,2.143,2.407,2.333,2.677,2.529,3.054,2.273,1.864,2.833,2.924,2.656,2.576,2.826,3.631,2.937,2.01,3.052,2.773,1.963,2.662,2.555,2.635,2.217,2.095,2.546,2.652,2.484,2.644,2.412,3.175,3.558,2.584,3.008,2.864,2.299,3.0,2.346,2.137,3.039,1.89,1.862,2.093,3.07,2.552,2.748,2.613,2.229,2.137,2.126,2.298,1.935,2.025,2.331,2.234,1.902,1.962,2.634,2.808,2.29,2.291,2.157,2.106,2.037,2.408,1.853,2.755,2.412,2.481,2.466,2.322,0.905,0.813,0.582,0.55,0.631,0.589,0.557,0.616,0.671,0.529,0.917,0.445,0.667,0.649,0.655,0.673,0.667,0.927,0.594,0.51,0.684,0.72,0.474,0.949,0.707,0.551,0.507,0.465,0.544,0.581,0.928,1.239,0.533,0.668,0.933,0.514,0.703,0.622,0.394,0.397,0.525,0.654,1.002,0.629,0.54,0.613,0.634,0.672,0.563,0.749,0.369,0.591,0.613,0.607,0.423,0.419,0.416,0.593,0.426,0.383,0.51,0.833,0.78,1.255,0.697,0.795,0.455,0.538,0.43,1.016,0.54,0.644,0.556,0.711
-302,Epileptic,M,37.0,1.6,3.2,1.4,1.8,3.5,3.2,3.3,3.6,2.7,1.0,0.5,3.7,2.3,1.4,1.9,13.1,19.4,1.5,2.8,6.2,2.4,7.0,2.5,8.0,8.5,10.9,7.9,4.1,6.3,6.1,2.8,0.7,1.5,5.9,1.2,3.3,7.1,6.5,0.3,0.3,1.7,3.9,3.5,5.0,5.7,1.0,0.9,2.2,1.9,1.2,1.1,3.7,2.1,6.8,1.2,5.1,1.6,1.7,2.3,2.1,1.0,3.1,0.5,0.7,2.5,1.7,3.5,2.1,3.2,0.9,1.5,2.7,6.8,0.8,19,30,9,14,19,25,17,22,21,13,5.0,31,20,9,15,79,124,14,31,49,26,41,18,61,52,61,54,26,31,54,25,11,9,39,7,18,49,50,1,2,8,31,32,35,25,6,4,10,9,7,7,20,12,30,9,29,9,11,17,11,6,20,4,5,15,21,21,10,19,6,9,13,39,4,0.057,0.029,0.023,0.032,0.043,0.042,0.043,0.05,0.064,0.042,0.042,0.039,0.041,0.061,0.05,0.048,0.049,0.047,0.033,0.039,0.048,0.056,0.036,0.057,0.055,0.053,0.048,0.032,0.042,0.043,0.064,0.05,0.049,0.055,0.031,0.046,0.054,0.055,0.022,0.02,0.03,0.034,0.053,0.036,0.038,0.026,0.025,0.032,0.027,0.029,0.041,0.034,0.031,0.033,0.029,0.03,0.022,0.021,0.049,0.029,0.033,0.041,0.025,0.033,0.033,0.029,0.026,0.029,0.036,0.027,0.025,0.025,0.024,0.044,1465,5182,2610,2538,2190,2365,1996,2772,1553,1631,559.0,2517,2741,907,1509,7752,14663,2291,5746,5246,3396,2835,1722,6135,4211,6559,5391,2946,3677,6113,2408,1133,1120,5014,2253,2305,6438,6929,355,555,1751,2274,4598,2792,3103,1326,1087,2249,2315,1525,866,3521,2205,5420,1067,4979,2044,2652,1032,1748,839,3121,337,768,2118,1217,4037,1868,2064,1043,1881,2844,9291,419,0.151,0.121,0.114,0.127,0.146,0.14,0.129,0.147,0.168,0.147,0.149,0.139,0.135,0.147,0.136,0.146,0.147,0.153,0.121,0.137,0.139,0.164,0.133,0.155,0.164,0.153,0.138,0.124,0.13,0.13,0.152,0.122,0.14,0.143,0.106,0.142,0.147,0.137,0.1,0.104,0.117,0.133,0.146,0.146,0.146,0.113,0.115,0.119,0.117,0.122,0.148,0.122,0.118,0.125,0.133,0.121,0.1,0.107,0.177,0.126,0.139,0.14,0.142,0.123,0.132,0.127,0.111,0.126,0.147,0.124,0.116,0.115,0.108,0.157,507,1796,958,1018,1217,1171,1061,1163,667,454,238.0,1582,922,338,630,3664,6103,576,1652,2660,939,1744,1112,2301,2149,3054,2406,1736,2005,2458,857,389,456,1543,693,1041,1982,1970,178,244,901,1707,1344,2303,2388,688,538,1066,1069,675,428,1808,1229,2863,640,2676,1013,1292,848,1084,508,1304,258,404,1199,965,2122,1041,1341,529,947,1487,4204,282,2.736,2.67,2.679,2.439,1.951,1.945,1.719,2.282,2.053,2.783,2.08,1.482,2.493,2.171,2.019,1.941,2.243,3.25,2.781,1.772,2.837,1.577,1.502,2.273,1.843,2.143,2.03,1.544,1.668,2.14,2.163,2.629,2.165,2.546,3.051,2.196,2.649,2.712,2.214,2.454,2.455,1.31,2.681,1.453,1.542,2.255,2.59,2.585,2.683,2.473,2.136,2.176,2.124,2.188,2.189,2.13,2.305,2.293,1.53,1.945,2.205,2.645,1.688,2.104,2.009,1.564,2.187,2.118,1.86,2.263,2.315,2.392,2.487,1.989,0.845,0.437,0.464,0.452,0.466,0.723,0.501,0.626,0.628,0.636,0.745,0.509,0.652,0.602,0.586,0.544,0.698,0.708,0.625,0.669,0.735,0.552,0.498,0.733,0.583,0.641,0.513,0.38,0.511,0.572,0.654,0.872,0.611,0.838,0.654,0.662,0.884,0.931,0.36,0.351,0.438,0.394,0.81,0.462,0.362,0.511,0.274,0.723,0.442,0.443,0.589,0.432,0.43,0.476,0.366,0.397,0.421,0.463,0.428,0.532,0.46,0.493,0.392,0.515,0.528,0.463,0.493,0.437,0.431,0.454,0.358,0.562,0.543,0.293
-303,Epileptic,F,37.0,0.9,2.3,0.9,1.3,1.0,1.7,1.6,1.4,1.1,0.7,0.3,3.2,1.6,0.5,1.6,5.2,8.6,0.7,2.3,5.5,1.5,2.7,1.2,3.4,2.9,3.2,3.1,2.5,2.3,3.2,1.3,1.1,0.6,2.6,0.3,1.1,2.6,3.1,0.1,0.2,1.1,2.2,2.0,2.7,2.9,0.7,0.4,1.6,1.0,0.5,0.8,2.4,1.0,2.7,0.5,2.2,1.0,1.2,0.8,1.3,0.5,1.2,0.3,0.2,1.5,1.3,3.1,1.4,1.6,0.4,0.6,1.1,4.2,0.4,7,17,10,10,9,10,12,13,8,6,4.0,26,15,6,18,52,79,8,22,39,16,29,16,29,32,34,32,20,18,32,14,8,6,28,2,9,26,35,1,1,8,20,21,21,21,5,2,9,5,5,5,17,6,17,3,21,6,9,7,11,4,11,3,3,12,11,22,11,9,5,4,8,42,3,0.038,0.024,0.017,0.024,0.027,0.034,0.021,0.025,0.042,0.042,0.032,0.033,0.033,0.031,0.031,0.03,0.029,0.03,0.029,0.039,0.027,0.029,0.024,0.036,0.028,0.029,0.027,0.027,0.024,0.033,0.036,0.047,0.023,0.029,0.011,0.026,0.032,0.035,0.016,0.017,0.023,0.029,0.035,0.026,0.023,0.013,0.019,0.019,0.014,0.011,0.042,0.023,0.031,0.021,0.02,0.019,0.024,0.019,0.02,0.023,0.027,0.025,0.022,0.015,0.02,0.033,0.025,0.024,0.025,0.017,0.02,0.022,0.018,0.016,1724,4567,2526,2361,1581,1833,2955,3238,1729,1314,579.0,2884,3497,896,3232,10061,18129,2417,3729,3857,3557,5118,1817,5858,5831,6768,5528,3963,5762,5204,1910,1169,1199,6670,1892,2150,5666,6152,312,506,1531,2067,5343,2425,3229,1549,755,2611,2259,1552,478,3409,1268,4704,696,3609,1919,2569,1268,1459,745,1988,469,798,2705,1301,3597,2341,1949,867,1244,1906,9605,562,0.115,0.113,0.099,0.113,0.125,0.13,0.095,0.12,0.151,0.132,0.117,0.139,0.131,0.14,0.134,0.133,0.127,0.111,0.133,0.14,0.109,0.131,0.125,0.138,0.132,0.135,0.121,0.109,0.099,0.141,0.125,0.14,0.119,0.13,0.072,0.122,0.132,0.151,0.088,0.091,0.123,0.12,0.137,0.134,0.109,0.089,0.094,0.09,0.084,0.095,0.156,0.106,0.117,0.103,0.109,0.109,0.105,0.1,0.112,0.121,0.122,0.119,0.132,0.107,0.107,0.128,0.117,0.117,0.111,0.122,0.113,0.128,0.094,0.11,448,1583,907,899,658,753,1094,939,461,308,183.0,1454,925,239,905,2805,4927,418,1249,2136,932,1508,872,1528,1759,1893,1908,1347,1460,1705,615,469,377,1352,478,706,1320,1526,201,204,746,1166,1065,1627,2102,787,309,1122,1088,638,329,1738,602,2154,362,1938,768,1228,708,886,335,795,238,331,1240,679,1995,1003,1013,432,554,757,4136,296,3.48,2.727,2.793,2.699,2.266,2.204,2.354,2.958,2.712,3.37,2.393,1.836,2.868,2.677,2.66,2.672,3.018,3.814,2.483,1.698,3.007,2.563,1.823,2.789,2.545,2.803,2.351,2.286,2.948,2.49,2.336,2.664,2.548,3.374,3.62,2.709,3.113,3.0,1.841,3.069,2.768,1.679,3.352,1.758,1.774,2.23,3.001,2.886,2.443,2.675,1.903,2.087,2.25,2.363,2.479,1.995,2.674,2.407,2.056,1.869,2.369,2.35,2.393,2.361,2.361,2.324,2.043,2.44,2.107,2.385,2.678,2.717,2.482,2.337,0.708,0.554,0.526,0.597,0.541,0.605,0.651,0.444,0.737,0.569,0.795,0.536,0.626,0.545,0.622,0.64,0.571,0.804,0.614,0.533,0.777,0.538,0.498,0.689,0.548,0.594,0.608,0.707,0.664,0.541,0.689,0.795,0.395,0.6,0.715,0.561,0.793,0.614,0.358,0.618,0.558,0.503,0.797,0.631,0.577,0.593,0.451,0.684,0.465,0.513,0.403,0.492,0.587,0.612,0.346,0.503,0.617,0.454,0.431,0.453,0.504,0.775,0.532,0.945,0.635,0.895,0.522,0.436,0.521,0.725,0.469,0.765,0.564,0.497
-304,Epileptic,F,27.0,1.3,1.8,0.7,1.3,1.7,2.0,1.5,1.6,1.5,0.8,0.4,3.5,1.5,0.6,1.6,7.5,9.8,0.8,2.9,4.4,1.3,2.4,2.1,4.0,3.2,2.9,4.1,2.2,2.7,2.9,1.6,1.2,0.6,2.7,0.3,0.4,3.9,2.8,0.2,0.3,1.1,3.7,2.0,2.4,2.5,0.9,0.7,0.7,1.6,0.9,0.7,2.0,1.5,2.8,0.1,2.7,0.7,1.6,1.2,0.8,0.5,1.4,0.2,0.5,2.3,0.9,2.0,1.5,0.9,0.7,0.7,1.7,4.0,0.6,11,14,7,12,18,21,13,22,10,10,4.0,31,19,10,21,82,87,5,42,40,11,35,26,43,39,32,43,23,24,36,19,10,7,31,1,4,43,34,2,2,8,32,27,19,16,8,4,5,8,9,4,16,10,17,1,24,5,13,9,8,4,11,2,5,22,19,18,12,6,6,6,11,34,7,0.041,0.022,0.011,0.027,0.033,0.03,0.029,0.027,0.052,0.037,0.03,0.041,0.029,0.032,0.031,0.036,0.031,0.031,0.036,0.04,0.026,0.032,0.04,0.042,0.038,0.025,0.032,0.028,0.024,0.03,0.042,0.041,0.032,0.032,0.01,0.012,0.045,0.032,0.016,0.019,0.019,0.041,0.031,0.026,0.02,0.017,0.025,0.012,0.018,0.017,0.038,0.019,0.023,0.021,0.017,0.02,0.013,0.018,0.025,0.019,0.016,0.023,0.015,0.014,0.023,0.018,0.018,0.019,0.018,0.021,0.019,0.02,0.018,0.024,1672,4257,1930,2373,2369,3313,2821,3867,1852,1546,934.0,3310,3787,1393,3586,14342,20977,2468,5193,4841,4643,4822,2726,7025,5558,7179,6159,3824,6929,5894,2444,1482,1169,6434,1973,1688,7293,7844,503,477,1790,2810,6419,2638,3303,1501,1095,2159,2775,2423,502,3671,1999,5120,194,4553,1430,2993,1434,1238,1049,2616,404,1015,3553,1264,4094,3148,1615,1299,1541,2895,8646,870,0.142,0.11,0.092,0.119,0.127,0.131,0.113,0.139,0.158,0.143,0.127,0.166,0.133,0.143,0.144,0.15,0.133,0.1,0.144,0.167,0.102,0.125,0.143,0.148,0.139,0.125,0.139,0.114,0.109,0.134,0.136,0.12,0.137,0.139,0.058,0.085,0.152,0.137,0.095,0.124,0.106,0.148,0.138,0.115,0.1,0.103,0.115,0.079,0.096,0.1,0.159,0.107,0.123,0.099,0.129,0.109,0.092,0.109,0.121,0.115,0.103,0.115,0.113,0.101,0.121,0.124,0.102,0.106,0.103,0.119,0.109,0.111,0.1,0.141,495,1350,784,876,933,1213,884,1102,509,416,254.0,1501,985,340,986,3881,5577,469,1516,1950,1013,1520,1098,1924,1586,1950,2156,1388,1756,1744,800,555,316,1414,473,593,1840,1734,251,235,910,1492,1255,1576,1921,780,427,918,1232,887,278,1703,931,2035,76,2222,745,1380,747,679,456,955,224,568,1669,860,1809,1213,777,520,631,1273,3557,398,3.187,2.785,2.29,2.357,2.145,2.341,2.508,2.979,2.603,2.876,2.796,1.902,2.883,2.846,2.691,2.705,2.889,4.078,2.777,2.064,3.186,2.421,2.104,2.719,2.605,2.884,2.345,2.14,2.921,2.64,2.454,2.717,2.741,3.106,3.569,2.559,2.881,3.131,2.301,2.547,2.346,1.737,3.261,1.892,1.955,2.11,2.831,2.96,2.711,2.541,2.012,2.27,2.203,2.714,2.738,2.199,2.137,2.44,1.968,2.003,2.205,2.692,1.909,2.133,2.212,1.741,2.171,2.673,2.402,2.558,2.498,2.495,2.517,2.418,0.806,0.756,0.518,0.663,0.718,0.571,0.679,0.534,0.833,0.712,0.652,0.585,0.471,0.43,0.583,0.697,0.683,0.726,0.599,0.699,0.772,0.609,0.611,0.868,0.708,0.695,0.681,0.614,0.562,0.607,0.621,0.849,0.358,0.646,0.668,0.454,0.852,0.673,0.38,0.405,0.511,0.54,0.751,0.553,0.593,0.382,0.72,0.644,0.418,0.558,0.357,0.418,0.413,0.584,0.45,0.442,0.398,0.525,0.518,0.4,0.498,0.64,0.321,0.669,0.46,0.582,0.57,0.439,0.431,0.683,0.594,0.455,0.459,0.435
-305,Epileptic,F,32.0,1.1,2.2,1.1,1.4,1.5,1.8,1.8,1.4,1.1,0.5,0.2,2.9,1.1,0.4,1.4,6.4,8.1,0.6,2.7,4.6,1.1,3.0,1.9,2.1,2.4,2.1,3.3,2.0,3.3,3.7,1.0,0.9,0.3,2.0,0.3,0.7,4.1,3.1,0.1,0.1,0.6,3.3,1.9,2.7,2.1,0.9,0.4,0.7,1.0,1.1,0.8,2.1,1.2,2.3,0.2,2.5,0.6,1.6,0.9,1.8,0.6,1.3,0.3,0.4,1.9,1.3,2.0,1.0,1.3,0.4,0.7,1.2,3.3,0.4,8,19,12,10,15,14,13,13,14,5,2.0,20,11,6,14,67,68,6,27,35,10,24,19,28,25,22,30,18,25,33,17,5,2,18,2,6,35,31,2,1,3,26,18,20,12,6,2,4,5,8,6,17,10,18,2,19,5,13,6,11,6,11,2,4,16,15,16,7,9,5,4,9,23,3,0.044,0.025,0.021,0.029,0.027,0.034,0.029,0.03,0.047,0.038,0.029,0.036,0.026,0.025,0.029,0.037,0.034,0.032,0.031,0.04,0.027,0.038,0.034,0.031,0.033,0.03,0.025,0.025,0.029,0.04,0.029,0.039,0.016,0.025,0.012,0.025,0.043,0.035,0.008,0.011,0.016,0.044,0.035,0.035,0.018,0.024,0.021,0.013,0.018,0.028,0.035,0.026,0.023,0.022,0.014,0.024,0.027,0.017,0.029,0.029,0.026,0.021,0.02,0.019,0.027,0.029,0.018,0.017,0.019,0.018,0.022,0.017,0.017,0.024,1113,4058,1989,1902,1885,2276,2598,2734,1711,1101,462.0,2219,2325,1017,2797,9830,15020,1753,4239,3455,2474,3881,2240,4843,4505,4454,5163,2809,5576,4836,1961,708,725,4322,1376,1162,5483,6172,394,441,1177,2082,4950,1832,2786,1016,767,1925,1953,936,553,3242,1898,3888,278,3134,1003,2928,947,1616,777,2374,360,766,2466,1172,2809,1857,1779,867,855,2200,6915,445,0.157,0.117,0.123,0.116,0.127,0.137,0.112,0.129,0.153,0.14,0.101,0.134,0.118,0.128,0.129,0.139,0.135,0.115,0.134,0.142,0.098,0.148,0.14,0.126,0.135,0.124,0.11,0.105,0.114,0.145,0.129,0.126,0.087,0.11,0.066,0.098,0.131,0.136,0.079,0.091,0.089,0.154,0.131,0.135,0.095,0.116,0.109,0.083,0.095,0.135,0.154,0.12,0.115,0.111,0.122,0.112,0.107,0.101,0.125,0.131,0.129,0.116,0.127,0.123,0.12,0.124,0.105,0.101,0.106,0.122,0.127,0.108,0.093,0.138,369,1305,821,756,853,943,873,797,501,285,147.0,1199,681,267,755,2902,4167,360,1421,1857,716,1195,942,1334,1308,1327,1926,1203,1609,1658,530,341,233,1111,458,462,1644,1467,256,247,585,1195,1008,1278,1725,622,276,860,836,592,351,1449,958,1711,150,1797,454,1509,487,956,415,898,211,366,1195,704,1694,870,939,403,435,1057,3212,208,3.029,2.809,2.423,2.504,1.965,2.13,2.44,3.017,2.484,2.954,2.608,1.707,2.61,2.653,2.698,2.57,2.885,3.74,2.597,1.716,2.678,2.539,2.235,2.704,2.713,2.734,2.16,1.915,2.692,2.393,2.665,2.113,2.602,2.784,2.772,2.206,2.621,3.012,1.851,1.9,2.443,1.692,3.421,1.726,1.816,1.911,3.418,2.787,2.714,1.905,1.847,2.181,1.922,2.239,2.023,1.983,2.183,2.234,2.296,1.932,2.203,2.463,1.958,2.178,2.366,2.117,1.829,2.402,2.245,2.283,2.332,2.513,2.378,2.211,0.666,0.651,0.515,0.536,0.586,0.547,0.65,0.757,0.67,0.527,0.644,0.405,0.523,0.49,0.513,0.625,0.59,0.684,0.783,0.55,0.885,0.584,0.525,0.747,0.759,0.695,0.553,0.505,0.57,0.522,0.665,1.034,0.394,0.618,0.921,0.617,0.813,0.66,0.376,0.409,0.757,0.493,0.732,0.568,0.588,0.434,0.583,0.758,0.628,0.474,0.333,0.552,0.517,0.564,0.483,0.49,0.57,0.499,0.511,0.366,0.477,0.605,0.417,0.981,0.676,0.861,0.435,0.534,0.473,0.734,0.563,0.495,0.628,0.45
-306,Epileptic,F,47.0,0.8,2.7,1.4,1.9,1.5,1.5,1.9,1.8,1.1,0.5,0.3,3.2,1.3,0.4,1.0,4.7,8.7,0.9,1.6,4.5,1.5,2.7,2.8,3.3,3.3,3.8,3.3,1.9,2.6,2.3,1.7,1.4,0.5,3.0,0.3,0.6,3.1,2.0,0.2,0.3,1.2,3.1,1.9,2.2,2.8,0.5,0.4,0.8,1.6,0.5,0.5,1.3,1.2,2.8,0.3,2.0,0.6,1.2,0.9,1.7,0.7,1.8,0.3,1.5,1.6,1.3,2.1,1.5,0.8,0.4,1.1,1.0,3.7,0.3,4,28,9,16,14,17,15,17,12,8,5.0,31,16,7,12,58,83,8,23,37,20,33,23,38,37,45,33,22,24,31,24,8,7,35,3,4,41,31,1,2,8,28,16,15,15,3,2,4,9,5,3,12,11,21,2,14,4,10,7,15,4,18,2,8,14,19,18,9,4,4,7,8,30,4,0.035,0.031,0.023,0.03,0.027,0.028,0.032,0.028,0.049,0.027,0.027,0.034,0.026,0.028,0.027,0.028,0.034,0.04,0.028,0.039,0.029,0.037,0.034,0.034,0.037,0.032,0.031,0.023,0.027,0.03,0.046,0.055,0.021,0.031,0.014,0.015,0.035,0.022,0.012,0.019,0.022,0.034,0.036,0.024,0.02,0.011,0.013,0.012,0.023,0.013,0.023,0.018,0.026,0.021,0.03,0.015,0.022,0.017,0.02,0.025,0.02,0.029,0.031,0.04,0.016,0.019,0.02,0.018,0.016,0.015,0.025,0.019,0.015,0.011,1442,4324,2330,2668,2408,2938,2859,3255,1402,1222,591.0,3184,3263,924,2492,9323,15731,1891,3475,4457,3699,3622,2839,5749,4662,6846,5503,3846,5501,5065,2197,964,1155,6167,1649,1788,5540,6417,391,521,1483,2671,4306,2307,3478,1211,810,2562,2388,1119,522,2359,1992,5115,348,3382,933,2312,1121,2002,881,2421,272,1013,2919,1395,3272,2186,1430,611,1343,1877,7747,569,0.112,0.137,0.114,0.131,0.124,0.137,0.114,0.142,0.146,0.14,0.138,0.139,0.118,0.13,0.119,0.137,0.142,0.146,0.13,0.15,0.122,0.125,0.122,0.136,0.113,0.136,0.121,0.106,0.111,0.129,0.149,0.15,0.092,0.141,0.074,0.076,0.152,0.117,0.088,0.099,0.109,0.137,0.144,0.117,0.099,0.077,0.086,0.074,0.101,0.098,0.117,0.106,0.118,0.106,0.16,0.096,0.119,0.101,0.107,0.119,0.11,0.132,0.154,0.137,0.097,0.109,0.106,0.103,0.096,0.11,0.121,0.118,0.091,0.094,375,1516,869,985,873,909,939,1046,446,327,170.0,1496,892,252,723,2889,4497,411,939,2011,859,1140,1206,1796,1287,2074,1846,1277,1498,1521,719,442,385,1520,433,695,1522,1521,224,255,809,1334,1017,1436,2026,670,391,997,1114,577,309,1196,852,2166,153,1888,467,1177,723,1004,470,1000,144,530,1540,1033,1713,1090,714,360,677,824,3646,381,3.522,2.689,2.585,2.54,2.34,2.61,2.48,2.708,2.399,2.712,2.684,1.817,2.801,2.67,2.566,2.491,2.668,3.397,2.782,1.946,3.07,2.552,2.034,2.525,2.734,2.668,2.347,2.323,2.76,2.535,2.391,2.579,2.297,3.0,3.158,2.343,2.728,2.974,2.029,2.464,2.232,1.791,2.981,1.816,1.936,1.963,2.447,2.971,2.567,2.213,2.076,2.154,2.171,2.357,2.605,2.004,2.202,2.168,1.774,2.107,2.282,2.298,2.111,2.255,2.065,1.643,2.039,2.376,2.21,1.921,2.139,2.447,2.262,1.793,0.844,0.564,0.533,0.538,0.66,0.657,0.571,0.411,0.601,0.608,0.722,0.517,0.371,0.368,0.471,0.565,0.621,0.728,0.521,0.556,0.847,0.57,0.601,0.647,0.648,0.501,0.54,0.532,0.498,0.542,0.555,0.686,0.356,0.581,0.588,0.391,0.66,0.656,0.301,0.577,0.464,0.56,0.744,0.532,0.577,0.336,0.414,1.073,0.505,0.323,0.395,0.435,0.524,0.476,0.413,0.409,0.37,0.465,0.385,0.467,0.493,0.579,0.519,0.676,0.474,0.576,0.396,0.508,0.413,0.476,0.481,0.643,0.449,0.337
-308,Epileptic,F,22.0,1.2,2.8,1.3,1.3,1.9,1.9,2.1,2.1,1.0,0.7,0.4,3.3,1.6,0.6,1.5,5.0,8.9,0.8,1.7,4.4,1.2,3.9,3.3,4.4,2.8,3.6,3.8,2.5,2.6,3.2,2.3,1.6,0.6,3.0,0.4,0.5,4.0,3.2,0.3,0.5,1.1,4.5,3.1,3.4,2.6,0.8,0.5,0.9,1.5,1.3,0.5,2.0,1.7,2.3,0.4,2.8,1.3,1.6,1.2,2.2,0.7,1.4,0.2,0.5,1.9,1.0,2.8,0.9,1.0,0.4,0.9,2.5,5.0,0.3,10,23,9,9,14,26,18,22,12,7,6.0,31,24,7,14,55,87,6,25,56,14,47,26,42,33,49,44,25,23,42,21,13,8,38,2,3,45,41,1,3,7,33,29,29,17,6,2,5,7,10,4,15,15,15,2,21,9,14,8,15,4,10,3,3,12,16,20,5,5,4,8,19,43,3,0.048,0.029,0.024,0.027,0.038,0.033,0.035,0.033,0.039,0.037,0.037,0.039,0.03,0.036,0.031,0.032,0.029,0.032,0.03,0.045,0.03,0.035,0.041,0.045,0.04,0.032,0.04,0.037,0.034,0.029,0.05,0.057,0.036,0.043,0.019,0.01,0.049,0.031,0.017,0.017,0.022,0.045,0.049,0.032,0.022,0.022,0.016,0.014,0.021,0.024,0.03,0.019,0.028,0.022,0.018,0.018,0.025,0.022,0.023,0.031,0.021,0.026,0.029,0.018,0.021,0.019,0.02,0.012,0.018,0.015,0.022,0.033,0.02,0.015,1453,5338,2532,2170,2679,3145,3454,3460,2137,1182,861.0,3834,4431,1792,3610,11423,22267,2299,4326,5923,3179,5515,2189,7820,5587,7409,7315,4400,5716,6612,2573,1153,1311,6846,2105,1999,6613,7532,508,942,1576,2384,5470,3344,3156,1197,940,2174,2434,1850,669,3921,2364,4840,443,4245,1361,3204,1461,2349,950,2620,477,1059,2625,1207,4153,1933,1807,667,1582,2464,10361,571,0.133,0.126,0.115,0.115,0.133,0.141,0.128,0.149,0.15,0.15,0.151,0.155,0.127,0.143,0.125,0.129,0.132,0.114,0.129,0.163,0.114,0.138,0.144,0.165,0.141,0.146,0.136,0.121,0.128,0.134,0.142,0.148,0.143,0.163,0.079,0.076,0.151,0.129,0.093,0.111,0.111,0.152,0.164,0.139,0.107,0.114,0.087,0.088,0.097,0.109,0.12,0.105,0.13,0.101,0.119,0.108,0.119,0.116,0.113,0.117,0.107,0.116,0.138,0.094,0.11,0.109,0.104,0.085,0.095,0.103,0.121,0.132,0.103,0.12,429,1603,856,851,947,1266,1151,1081,558,367,250.0,1608,1145,368,887,3029,5511,476,1157,2198,777,1934,1244,1833,1585,2327,2131,1523,1479,2244,814,457,327,1525,447,736,1687,2017,257,355,769,1525,1272,1837,1815,640,394,863,1070,813,345,1686,1003,1826,224,2231,681,1297,809,1214,473,874,187,436,1294,809,2051,940,782,329,725,1158,4225,288,3.08,3.009,2.737,2.333,2.566,2.26,2.431,2.864,2.93,2.468,2.712,2.089,2.982,3.331,2.924,2.802,3.095,3.702,2.924,2.263,3.115,2.29,1.675,3.17,2.723,2.613,2.5,2.223,2.889,2.36,2.354,2.344,2.925,3.216,3.649,2.504,2.764,2.678,2.354,2.654,2.596,1.553,3.01,2.048,1.969,1.868,2.838,3.135,2.793,2.239,1.933,2.375,2.308,2.694,2.079,2.058,2.267,2.603,2.02,2.12,2.293,3.007,2.497,2.729,2.134,1.909,2.144,2.369,2.559,2.215,2.126,2.198,2.44,2.47,0.961,0.707,0.568,0.618,0.594,0.627,0.632,0.562,0.565,0.561,0.624,0.444,0.493,0.637,0.672,0.592,0.656,0.736,0.606,0.59,0.628,0.651,0.501,0.805,0.69,0.6,0.632,0.65,0.613,0.537,0.846,0.971,0.491,0.65,0.662,0.428,0.918,0.74,0.536,0.414,0.409,0.407,0.821,0.5,0.515,0.475,0.623,0.84,0.509,0.617,0.492,0.507,0.514,0.634,0.533,0.422,0.495,0.507,0.434,0.427,0.439,0.617,0.429,0.735,0.44,0.583,0.44,0.443,0.459,0.748,0.518,0.626,0.564,0.513
-309,Epileptic,M,47.0,0.7,3.4,1.2,1.2,2.0,2.2,2.3,1.6,1.5,0.5,0.3,2.8,1.0,0.5,1.2,7.6,9.5,0.8,2.2,4.9,1.5,2.5,1.2,3.5,2.8,4.8,3.9,2.9,3.7,3.3,1.9,1.5,0.4,3.5,0.5,0.6,4.5,4.4,0.1,0.3,1.1,3.2,3.4,2.9,3.8,0.7,0.5,0.9,1.3,0.8,0.7,1.8,2.2,3.3,0.3,1.9,1.1,2.3,1.5,1.6,0.7,1.7,0.5,0.3,1.8,1.8,3.9,1.9,0.9,0.4,0.7,1.5,4.4,0.3,6,30,11,14,17,18,16,17,13,4,3.0,24,9,5,9,63,67,8,23,42,9,26,12,32,30,44,38,28,29,34,21,9,2,35,3,5,45,41,1,2,6,25,23,25,20,4,2,5,7,7,5,13,17,20,2,14,10,20,8,11,6,11,2,2,16,17,32,12,5,3,5,10,37,2,0.03,0.03,0.018,0.023,0.036,0.032,0.03,0.026,0.04,0.036,0.02,0.034,0.028,0.029,0.027,0.034,0.035,0.029,0.033,0.034,0.021,0.032,0.024,0.04,0.032,0.033,0.031,0.027,0.028,0.03,0.045,0.051,0.021,0.034,0.021,0.016,0.038,0.037,0.016,0.018,0.022,0.036,0.054,0.025,0.022,0.018,0.028,0.013,0.019,0.016,0.024,0.024,0.028,0.026,0.028,0.016,0.026,0.021,0.031,0.027,0.026,0.031,0.022,0.019,0.019,0.036,0.021,0.021,0.015,0.016,0.021,0.022,0.017,0.025,1511,5654,2390,2253,2335,3072,3204,3262,2113,913,608.0,2906,2369,941,2335,12311,15904,2499,3714,5115,3265,4292,1894,6133,4814,7880,5701,4925,7134,5464,2354,1648,763,6266,1767,2011,7048,8063,286,459,1578,3032,5052,2830,4321,1342,735,2533,2477,1946,785,2974,3361,5310,474,3538,2014,4088,1327,1652,909,2517,463,875,3434,1045,5763,2932,1757,872,1272,2362,9310,328,0.128,0.115,0.107,0.122,0.132,0.144,0.119,0.127,0.147,0.125,0.093,0.139,0.117,0.143,0.119,0.134,0.133,0.125,0.128,0.138,0.085,0.135,0.117,0.135,0.147,0.138,0.131,0.118,0.106,0.133,0.161,0.133,0.093,0.13,0.096,0.086,0.136,0.138,0.087,0.093,0.104,0.142,0.148,0.118,0.106,0.099,0.114,0.079,0.1,0.099,0.119,0.105,0.117,0.113,0.129,0.097,0.12,0.105,0.128,0.129,0.137,0.124,0.127,0.096,0.105,0.137,0.11,0.105,0.087,0.1,0.108,0.106,0.096,0.122,420,1833,901,849,909,1094,1141,1019,632,267,231.0,1265,697,286,632,3554,4335,480,1176,2265,1013,1383,856,1599,1487,2496,2098,1814,2055,1709,605,566,319,1618,451,705,2069,2079,174,244,804,1400,1257,1784,2517,616,287,1037,1038,819,444,1347,1499,2078,201,1842,816,1867,736,882,418,825,257,323,1619,706,2945,1382,839,389,582,1156,4117,172,3.685,2.715,2.42,2.547,2.36,2.525,2.378,2.837,2.679,2.988,2.264,2.038,2.644,2.655,2.828,2.692,2.941,3.736,2.565,1.986,2.701,2.461,2.014,2.86,2.555,2.684,2.268,2.193,2.704,2.583,2.814,2.776,2.104,2.772,3.34,2.652,2.63,2.96,2.009,2.289,2.54,2.014,3.06,1.803,1.971,2.347,3.236,2.928,2.971,2.451,2.3,2.351,2.361,2.634,2.652,2.154,2.725,2.408,2.278,2.109,2.274,2.815,2.406,2.691,2.284,1.951,2.134,2.472,2.326,2.557,2.48,2.433,2.44,2.369,0.702,0.673,0.658,0.536,0.578,0.516,0.561,0.542,0.488,0.42,0.675,0.462,0.429,0.397,0.439,0.537,0.667,0.715,0.561,0.614,0.692,0.44,0.433,0.617,0.447,0.437,0.407,0.461,0.477,0.562,0.83,1.099,0.389,0.688,0.717,0.585,0.727,0.576,0.418,0.454,0.484,0.443,0.761,0.621,0.502,0.569,0.569,0.728,0.611,0.493,0.354,0.487,0.598,0.537,0.395,0.356,0.632,0.497,0.307,0.408,0.444,0.732,0.58,0.973,0.548,0.691,0.458,0.396,0.41,0.556,0.563,0.592,0.569,0.366
-310,Epileptic,M,37.0,1.1,2.0,1.1,1.2,2.9,1.6,2.0,1.7,1.2,0.6,0.2,2.7,1.7,0.7,1.9,7.3,10.6,0.8,1.6,4.1,2.0,2.7,2.2,4.7,4.0,6.6,5.8,4.0,6.7,2.6,1.9,1.7,0.7,3.1,0.6,1.2,3.4,4.7,0.3,0.2,1.0,3.1,2.2,2.3,2.8,0.7,0.4,0.8,1.5,0.7,0.4,2.2,1.1,2.7,0.3,2.0,1.1,1.6,1.3,0.9,0.2,1.8,0.2,0.8,2.2,1.4,2.6,1.4,1.2,0.7,0.6,1.2,4.5,0.4,7,15,7,8,23,14,16,19,8,5,2.0,24,16,8,16,63,81,6,16,31,11,26,18,36,39,57,48,33,37,28,17,14,4,21,3,13,30,40,2,1,5,26,19,18,16,5,2,5,8,6,2,17,7,16,2,16,8,10,9,7,3,9,2,6,14,13,17,8,7,5,4,9,32,2,0.045,0.027,0.023,0.027,0.066,0.034,0.036,0.034,0.06,0.038,0.032,0.036,0.036,0.063,0.05,0.048,0.043,0.042,0.028,0.035,0.038,0.04,0.039,0.062,0.045,0.053,0.048,0.043,0.052,0.038,0.062,0.066,0.037,0.04,0.026,0.036,0.04,0.046,0.022,0.015,0.022,0.033,0.057,0.023,0.019,0.017,0.024,0.021,0.022,0.028,0.018,0.028,0.028,0.024,0.017,0.015,0.029,0.022,0.034,0.016,0.02,0.042,0.021,0.036,0.025,0.039,0.021,0.022,0.02,0.026,0.022,0.026,0.018,0.02,1313,4261,1637,1947,2251,1932,2223,2538,1452,1146,545.0,2528,2614,812,1685,8682,15659,1802,3597,4082,3207,3328,2125,5436,4171,5742,4713,3937,5748,4425,2008,966,793,4705,1290,1893,6067,5517,411,405,1323,2998,3677,2162,3926,1127,727,1626,2084,1250,464,2739,1326,3784,459,3230,1268,2588,997,1527,665,1969,406,973,2430,1090,4177,2281,1839,1166,880,1572,8672,450,0.139,0.11,0.121,0.115,0.17,0.124,0.114,0.127,0.156,0.152,0.129,0.14,0.121,0.154,0.148,0.149,0.14,0.135,0.124,0.14,0.123,0.126,0.136,0.163,0.127,0.155,0.127,0.116,0.126,0.137,0.152,0.158,0.101,0.124,0.098,0.127,0.146,0.137,0.141,0.089,0.104,0.133,0.166,0.12,0.099,0.099,0.104,0.105,0.111,0.134,0.11,0.127,0.12,0.106,0.113,0.099,0.132,0.102,0.132,0.094,0.102,0.127,0.11,0.131,0.119,0.137,0.104,0.104,0.099,0.121,0.118,0.116,0.101,0.1,394,1278,672,748,802,738,993,971,400,276,125.0,1240,855,238,669,2674,4355,344,921,1798,748,1034,811,1387,1412,2022,1864,1396,1669,1368,658,497,278,1101,373,640,1539,1595,166,220,686,1447,741,1493,2246,662,297,720,1011,436,314,1322,698,1790,254,1913,568,1252,602,869,253,726,194,412,1320,595,2012,947,947,449,422,745,3757,251,3.17,2.905,2.56,2.514,2.442,2.326,1.986,2.362,2.723,3.316,3.2,1.773,2.403,2.403,2.112,2.529,2.78,3.602,2.98,1.977,2.849,2.469,2.082,2.868,2.243,2.421,2.107,2.285,2.725,2.504,2.235,2.149,2.425,2.867,2.836,2.7,2.919,2.591,2.707,2.093,2.382,1.805,2.968,1.684,1.97,1.839,2.598,2.853,2.567,2.866,1.813,2.208,2.269,2.284,2.344,1.942,2.518,2.306,1.955,1.979,2.403,2.71,2.339,2.665,1.97,2.247,2.252,2.824,2.312,2.8,2.289,2.389,2.474,2.023,0.644,0.827,0.539,0.627,0.754,0.699,0.5,0.534,0.603,0.567,0.637,0.52,0.502,0.849,0.648,0.746,0.659,0.821,0.534,0.604,0.695,0.72,0.743,0.862,0.702,0.674,0.612,0.672,0.695,0.814,0.767,0.745,0.467,0.919,0.741,0.648,0.773,0.707,0.708,0.435,0.53,0.806,0.819,0.523,0.686,0.537,0.666,0.658,0.6,0.631,0.502,0.478,0.538,0.493,0.414,0.48,0.488,0.505,0.537,0.563,0.584,0.721,0.639,0.737,0.477,0.79,0.485,0.508,0.444,0.887,0.636,0.589,0.612,0.631
-311,Epileptic,F,22.0,0.8,1.8,0.8,1.3,1.7,1.3,1.1,1.5,1.4,0.4,0.2,2.7,1.2,0.7,1.5,4.5,7.6,1.1,2.1,4.7,1.0,2.7,1.4,3.5,1.8,2.8,2.3,1.8,2.0,2.1,0.8,1.1,0.5,2.1,0.5,0.6,2.6,2.7,0.2,0.2,1.0,2.7,1.8,2.1,2.2,0.4,0.5,0.9,1.2,0.6,0.6,1.6,1.7,2.5,0.3,2.6,1.1,1.5,1.2,0.7,0.7,1.2,0.4,0.5,1.8,1.1,1.7,0.7,0.7,0.8,1.0,1.2,2.6,0.2,4,18,7,9,18,17,10,13,17,5,3.0,27,17,9,20,54,81,10,27,49,9,32,16,37,25,30,27,19,23,27,16,7,5,23,3,7,34,33,1,2,5,29,17,18,14,4,3,6,6,5,4,13,13,24,2,24,10,10,9,8,5,10,2,5,15,13,16,4,5,7,6,7,19,2,0.031,0.025,0.017,0.025,0.031,0.025,0.017,0.025,0.04,0.028,0.02,0.029,0.027,0.039,0.031,0.033,0.027,0.03,0.029,0.036,0.018,0.028,0.023,0.035,0.024,0.027,0.022,0.023,0.021,0.025,0.026,0.058,0.025,0.026,0.012,0.018,0.03,0.028,0.015,0.014,0.018,0.034,0.03,0.023,0.018,0.009,0.02,0.015,0.015,0.012,0.026,0.019,0.022,0.021,0.02,0.018,0.03,0.019,0.026,0.017,0.018,0.022,0.03,0.021,0.02,0.025,0.017,0.012,0.013,0.021,0.032,0.018,0.013,0.019,1540,4332,1993,2327,2238,2704,2760,3327,2867,1031,643.0,3441,3485,1490,3846,10262,20196,2984,4296,5374,3164,4832,2463,7237,5183,6553,4883,3439,5771,5244,2225,958,1113,6462,2194,2246,6522,7568,408,632,1660,3092,5428,3213,3407,1140,1108,2494,2674,1552,608,3441,2666,5447,497,4291,1878,2900,1344,1244,1083,2427,594,960,3162,1042,3237,1955,1679,1916,1371,2019,7806,640,0.123,0.111,0.106,0.119,0.131,0.128,0.1,0.118,0.142,0.136,0.111,0.128,0.123,0.118,0.145,0.135,0.129,0.119,0.132,0.143,0.099,0.132,0.116,0.138,0.118,0.131,0.118,0.116,0.112,0.126,0.115,0.133,0.138,0.123,0.072,0.1,0.131,0.134,0.097,0.101,0.1,0.146,0.128,0.106,0.097,0.083,0.106,0.082,0.09,0.098,0.127,0.109,0.112,0.111,0.136,0.109,0.119,0.102,0.126,0.09,0.109,0.11,0.134,0.114,0.108,0.13,0.102,0.078,0.091,0.118,0.128,0.111,0.085,0.121,410,1275,679,797,898,936,904,962,673,258,200.0,1409,890,320,916,2620,4783,559,1200,2160,794,1566,905,1644,1371,1704,1573,1163,1555,1361,547,381,315,1311,629,620,1562,1559,201,276,736,1313,1092,1677,1892,585,401,961,1177,648,343,1324,1177,1990,179,2081,671,1269,763,713,506,890,207,412,1486,631,1632,851,818,607,526,901,3030,212,3.64,2.984,2.822,2.99,2.179,2.415,2.474,3.025,2.892,3.14,2.717,2.023,3.02,3.096,3.034,2.917,3.235,3.832,2.759,2.08,3.103,2.508,2.237,3.135,2.992,3.082,2.47,2.3,2.808,2.9,2.977,2.466,2.85,3.361,3.249,3.314,3.099,3.405,2.404,2.582,2.832,2.124,3.532,2.074,2.128,2.185,3.323,3.165,2.755,2.721,2.165,2.525,2.326,2.591,2.977,2.224,2.672,2.656,2.053,1.948,2.583,2.774,2.48,2.538,2.304,2.178,2.202,2.774,2.559,3.222,2.998,2.956,2.762,3.622,0.894,0.667,0.694,0.578,0.641,0.496,0.522,0.553,0.724,0.507,0.748,0.522,0.473,0.658,0.623,0.647,0.683,0.804,0.513,0.59,0.816,0.564,0.439,0.621,0.55,0.629,0.487,0.596,0.395,0.541,0.719,1.091,0.53,0.572,0.663,0.515,0.695,0.575,0.428,0.49,0.527,0.555,0.695,0.7,0.643,0.436,0.664,0.841,0.471,0.572,0.377,0.58,0.595,0.526,0.741,0.427,0.429,0.55,0.391,0.387,0.384,0.658,0.705,0.902,0.483,0.53,0.439,0.4,0.461,0.864,0.471,0.67,0.543,0.827
-312,Epileptic,M,57.0,0.7,2.7,1.4,1.6,2.0,2.9,4.4,2.2,1.2,0.9,0.4,3.2,1.7,0.7,1.4,7.9,16.8,1.2,3.0,5.0,1.8,6.0,3.1,7.0,7.4,5.5,7.4,4.2,4.6,6.1,3.0,1.1,0.9,3.5,1.2,0.8,3.3,4.0,0.3,0.3,1.2,4.3,2.7,2.6,4.2,1.4,0.7,0.9,1.5,0.8,0.7,3.0,3.1,4.3,0.4,4.0,0.9,1.6,1.5,1.8,0.7,2.7,0.3,0.6,1.9,1.2,4.4,2.2,2.3,0.9,1.8,1.4,7.3,0.2,5,18,8,11,15,26,27,19,9,6,3.0,28,15,5,17,60,116,8,25,44,19,42,23,53,49,49,68,33,35,52,25,21,7,33,7,6,37,43,2,1,6,37,19,19,18,6,3,4,7,4,4,18,22,23,3,24,4,10,10,12,5,15,2,4,15,8,27,13,13,8,9,10,49,3,0.041,0.038,0.031,0.032,0.052,0.053,0.069,0.039,0.072,0.045,0.038,0.047,0.04,0.07,0.045,0.062,0.061,0.053,0.044,0.056,0.042,0.056,0.047,0.075,0.056,0.046,0.056,0.053,0.053,0.056,0.069,0.055,0.053,0.051,0.048,0.024,0.044,0.046,0.021,0.023,0.027,0.045,0.063,0.039,0.037,0.029,0.037,0.019,0.024,0.02,0.032,0.034,0.052,0.039,0.019,0.024,0.021,0.023,0.028,0.032,0.03,0.043,0.025,0.026,0.023,0.027,0.038,0.032,0.041,0.027,0.04,0.029,0.028,0.018,1387,3847,2094,2223,1810,3102,3193,2797,1107,1292,629.0,2382,2967,890,2376,7434,17112,2509,4321,4213,4065,2932,2412,6150,5622,6588,6297,3285,4876,6681,2511,810,734,4892,1429,1483,5994,6507,360,475,1338,3171,3063,1996,2709,1325,859,1683,1925,1374,656,3111,2356,4047,523,4517,979,2453,1315,1633,584,2526,405,910,2409,1235,3531,2034,2019,937,1442,1638,7675,203,0.128,0.133,0.119,0.12,0.165,0.177,0.173,0.148,0.177,0.145,0.12,0.169,0.137,0.174,0.159,0.176,0.169,0.147,0.156,0.191,0.132,0.17,0.148,0.199,0.162,0.164,0.163,0.152,0.153,0.169,0.192,0.131,0.135,0.168,0.136,0.105,0.159,0.159,0.11,0.102,0.109,0.154,0.198,0.14,0.13,0.122,0.123,0.092,0.102,0.107,0.154,0.126,0.173,0.135,0.119,0.112,0.111,0.114,0.123,0.128,0.138,0.146,0.123,0.118,0.115,0.104,0.13,0.129,0.138,0.131,0.147,0.124,0.122,0.124,290,1321,798,841,778,1072,1234,1065,398,349,172.0,1204,864,235,702,2615,5442,446,1214,1830,871,1664,1122,1753,2253,2174,2572,1385,1702,2311,834,327,282,1292,430,606,1561,1732,201,224,728,1609,870,1158,1717,677,296,697,913,602,309,1482,964,1895,274,2437,536,1180,846,930,342,967,214,400,1267,599,1898,1008,970,517,742,849,3710,174,3.695,2.705,2.643,2.613,2.323,2.401,2.038,2.34,2.187,2.935,3.024,1.731,2.609,2.785,2.589,2.26,2.446,3.757,2.69,2.012,2.948,1.653,1.895,2.616,2.135,2.491,2.035,1.947,2.32,2.329,2.397,2.309,2.093,2.615,2.754,2.188,2.801,2.718,2.02,2.558,2.196,1.796,2.681,1.976,1.811,2.215,3.085,2.904,2.525,2.184,2.106,2.222,2.229,2.235,2.172,2.039,2.132,2.263,1.832,1.967,2.276,2.579,2.083,2.248,2.072,2.346,1.901,2.381,2.171,1.981,2.273,2.322,2.248,1.448,1.033,0.625,0.449,0.534,0.539,0.684,0.613,0.529,0.518,0.509,0.642,0.502,0.459,0.639,0.645,0.571,0.69,0.818,0.608,0.692,0.941,0.462,0.456,0.792,0.586,0.637,0.547,0.605,0.637,0.63,0.706,1.105,0.556,0.673,0.805,0.505,0.691,0.671,0.524,0.436,0.445,0.498,0.788,0.707,0.444,0.486,0.687,1.101,0.468,0.675,0.506,0.539,0.588,0.479,0.359,0.44,0.407,0.609,0.391,0.418,0.433,0.675,0.459,0.599,0.547,0.778,0.415,0.46,0.513,0.526,0.481,0.504,0.483,0.311
-313,Epileptic,F,37.0,1.7,3.2,1.2,1.6,2.4,2.0,1.9,1.7,1.7,0.7,0.4,2.7,1.4,0.6,1.8,6.1,10.8,1.0,2.9,4.3,1.8,4.7,1.3,4.1,3.2,3.6,3.4,2.9,3.1,3.0,2.6,1.2,0.7,3.0,0.7,0.6,5.4,4.5,0.1,0.3,1.2,4.2,3.2,2.1,3.2,1.0,0.6,1.4,1.5,1.3,0.4,2.3,1.6,2.3,0.4,2.1,0.8,1.3,0.9,0.7,0.6,2.0,0.7,0.5,2.5,1.4,2.9,1.6,0.9,0.7,0.9,1.6,5.5,0.5,11,31,11,11,23,19,17,17,18,10,5.0,23,19,8,23,72,109,7,30,43,20,43,14,44,36,46,45,37,34,41,23,18,6,35,4,4,61,47,1,2,8,33,29,16,21,6,3,7,7,10,3,17,12,16,1,20,7,10,6,5,5,18,6,5,19,14,23,10,4,5,5,11,51,4,0.05,0.026,0.018,0.026,0.046,0.027,0.029,0.024,0.045,0.047,0.037,0.04,0.029,0.035,0.034,0.037,0.036,0.032,0.031,0.037,0.031,0.047,0.032,0.044,0.033,0.034,0.029,0.032,0.033,0.034,0.049,0.044,0.028,0.031,0.019,0.013,0.053,0.032,0.013,0.017,0.023,0.042,0.046,0.025,0.02,0.02,0.02,0.021,0.017,0.018,0.031,0.022,0.022,0.02,0.023,0.018,0.022,0.017,0.018,0.018,0.015,0.032,0.041,0.018,0.027,0.025,0.02,0.019,0.015,0.017,0.022,0.017,0.018,0.023,1824,6489,2902,2551,2465,3734,2659,3715,2209,1693,741.0,2820,3539,1314,3461,11356,20319,2723,5457,5190,4508,4252,1828,7040,5323,6824,5970,5261,6488,5922,2875,1257,1332,7732,2801,1796,7509,9229,389,598,1507,2629,6147,2568,4219,1576,898,2570,2815,2332,552,3415,2695,4590,437,3928,1692,3066,1070,1212,1412,3071,743,1006,3062,1639,4548,2798,1452,1260,1189,2761,11526,630,0.148,0.121,0.099,0.116,0.158,0.121,0.107,0.117,0.165,0.148,0.143,0.151,0.133,0.152,0.145,0.143,0.138,0.112,0.129,0.148,0.118,0.136,0.124,0.159,0.126,0.13,0.121,0.127,0.112,0.142,0.139,0.129,0.103,0.136,0.088,0.073,0.142,0.126,0.085,0.109,0.116,0.152,0.169,0.12,0.105,0.11,0.102,0.096,0.088,0.097,0.124,0.109,0.108,0.104,0.115,0.102,0.106,0.093,0.103,0.103,0.086,0.137,0.158,0.115,0.118,0.106,0.107,0.103,0.088,0.122,0.116,0.104,0.1,0.12,543,2142,1098,925,925,1276,1027,1230,618,402,204.0,1195,967,317,967,3275,5525,483,1598,2081,1051,1552,708,1896,1616,2126,2064,1732,1825,1817,838,492,381,1651,652,666,1950,2277,214,301,799,1470,1327,1406,2385,725,371,997,1270,1009,269,1583,1102,1879,195,1911,736,1357,706,625,648,1078,313,482,1502,850,2213,1214,773,561,605,1344,4899,335,3.258,2.785,2.636,2.533,2.41,2.348,2.15,2.707,2.631,3.127,2.865,2.044,2.74,2.932,2.875,2.626,2.852,3.864,2.779,2.153,3.216,2.282,2.224,2.905,2.595,2.599,2.317,2.344,2.721,2.606,2.572,2.417,2.633,3.297,3.708,2.423,2.802,2.966,2.119,2.331,2.318,1.655,3.194,2.109,2.003,2.228,2.943,3.305,2.735,2.625,2.156,2.219,2.407,2.49,2.368,2.176,2.442,2.456,1.849,2.226,2.281,2.772,2.46,2.422,2.232,2.078,2.214,2.396,2.205,2.587,2.297,2.468,2.511,2.269,0.788,0.54,0.585,0.547,0.5,0.609,0.52,0.571,0.636,0.559,1.05,0.458,0.581,0.501,0.503,0.593,0.628,0.747,0.622,0.699,0.819,0.621,0.467,0.668,0.608,0.627,0.486,0.516,0.593,0.586,0.767,0.85,0.42,0.703,0.832,0.537,0.893,0.661,0.475,0.497,0.417,0.53,0.961,0.632,0.51,0.618,0.495,0.68,0.453,0.655,0.504,0.431,0.597,0.512,0.354,0.391,0.517,0.51,0.289,0.381,0.36,0.588,0.513,0.515,0.486,0.669,0.439,0.549,0.342,0.599,0.432,0.553,0.493,0.549
-314,Epileptic,M,27.0,1.0,2.2,1.0,1.1,1.3,2.2,1.2,1.7,1.0,0.9,0.3,2.4,1.8,0.7,1.2,6.2,10.1,1.1,2.7,5.7,1.4,3.8,1.4,2.8,3.6,3.8,3.5,2.3,2.6,2.8,1.0,1.0,0.4,3.5,0.3,0.9,3.0,2.3,0.2,0.2,1.2,4.1,2.8,2.9,2.7,0.5,0.6,1.2,1.3,0.6,0.2,1.8,0.9,1.9,0.2,2.4,0.7,2.1,1.8,0.7,0.4,1.4,0.4,0.4,1.8,1.3,3.2,1.7,1.0,0.6,1.3,0.8,4.8,0.3,8,24,11,11,16,20,11,18,15,8,3.0,20,20,8,14,66,91,8,31,45,13,37,15,32,35,39,36,23,23,36,14,9,5,40,2,12,35,35,2,1,7,36,25,22,16,4,2,7,7,7,3,17,9,12,1,20,4,17,9,5,4,13,2,3,17,19,23,15,8,6,12,7,39,3,0.036,0.023,0.02,0.021,0.031,0.026,0.021,0.03,0.044,0.03,0.026,0.028,0.033,0.037,0.03,0.036,0.032,0.033,0.03,0.038,0.025,0.039,0.025,0.032,0.028,0.029,0.029,0.024,0.026,0.034,0.027,0.044,0.025,0.034,0.013,0.018,0.029,0.026,0.012,0.012,0.02,0.037,0.05,0.027,0.02,0.009,0.017,0.02,0.016,0.013,0.015,0.021,0.019,0.016,0.012,0.022,0.02,0.021,0.025,0.019,0.028,0.024,0.023,0.014,0.022,0.025,0.025,0.018,0.018,0.02,0.034,0.014,0.017,0.016,1817,5559,2609,2457,2326,3447,2858,3334,2253,1652,695.0,2941,4165,1681,3226,10961,21942,3007,5523,5451,4082,5181,2249,6718,6198,8813,5507,4312,6166,5399,2427,1206,1026,7328,1873,2746,7237,6407,518,567,1884,3543,5676,3068,3434,1403,1038,2329,2754,1905,469,3502,2362,4341,388,3813,1525,3716,1888,1182,936,2671,500,750,3096,1529,3875,3634,1771,1329,1897,2042,10809,500,0.14,0.115,0.103,0.109,0.127,0.13,0.089,0.144,0.159,0.136,0.119,0.129,0.145,0.154,0.13,0.145,0.134,0.117,0.138,0.142,0.104,0.15,0.13,0.13,0.127,0.124,0.133,0.111,0.111,0.138,0.108,0.131,0.135,0.144,0.077,0.095,0.126,0.122,0.078,0.083,0.108,0.141,0.165,0.119,0.104,0.074,0.094,0.095,0.094,0.086,0.111,0.113,0.101,0.089,0.094,0.113,0.098,0.11,0.119,0.112,0.117,0.117,0.117,0.111,0.114,0.134,0.12,0.105,0.104,0.11,0.143,0.099,0.096,0.116,490,1643,798,895,750,1368,880,1027,502,442,188.0,1316,1029,355,793,3005,5449,527,1461,2513,1020,1577,837,1556,1923,2275,1907,1420,1601,1583,614,439,309,1682,459,882,1717,1558,290,281,820,1756,1059,1753,2016,714,442,958,1223,767,280,1491,902,1834,181,1735,634,1616,1030,571,302,900,257,390,1338,783,1964,1467,824,546,756,927,4255,252,3.509,3.149,3.033,2.707,2.565,2.29,2.556,2.994,3.022,3.015,3.325,2.036,3.046,3.194,2.97,2.818,3.29,4.048,3.015,1.91,3.195,2.565,2.227,3.063,2.636,3.115,2.399,2.348,2.91,2.665,2.823,2.564,2.559,3.151,3.635,2.8,3.029,2.929,2.087,2.348,2.946,1.902,3.704,2.049,2.003,2.148,3.015,3.063,2.72,2.586,2.118,2.427,2.473,2.596,2.459,2.39,2.785,2.57,2.121,2.306,3.029,2.897,2.33,2.361,2.423,2.326,2.264,2.626,2.453,2.784,2.597,2.478,2.756,2.604,0.839,0.649,0.746,0.583,0.626,0.422,0.649,0.596,0.732,0.45,0.836,0.504,0.504,0.399,0.619,0.603,0.614,0.647,0.538,0.625,0.923,0.47,0.533,0.689,0.485,0.681,0.527,0.608,0.598,0.591,0.633,0.944,0.466,0.725,0.596,0.459,0.656,0.53,0.431,0.342,0.536,0.549,0.616,0.679,0.608,0.352,0.434,0.688,0.617,0.503,0.471,0.468,0.607,0.473,0.276,0.453,0.391,0.567,0.396,0.451,0.709,0.754,0.391,0.74,0.546,0.869,0.481,0.442,0.396,0.614,0.426,0.508,0.525,0.328
-315,Epileptic,F,42.0,1.0,2.0,0.8,1.5,1.7,2.4,1.4,1.5,1.0,0.7,0.3,3.0,1.7,0.6,0.6,6.8,7.2,0.6,3.0,5.7,1.4,3.0,1.6,2.9,2.6,3.6,3.0,2.1,2.1,3.1,1.2,1.6,1.1,3.3,0.8,0.4,2.9,4.3,0.2,0.1,0.9,2.3,2.7,2.9,2.1,0.6,0.5,0.9,1.0,1.4,0.2,1.2,1.9,2.9,0.3,1.8,0.6,1.2,1.2,1.0,0.5,1.4,0.3,0.8,1.4,0.9,2.1,1.0,0.7,0.2,1.0,1.4,4.7,0.3,7,18,11,10,16,21,14,16,12,8,4.0,29,16,9,11,80,68,4,35,46,11,38,17,31,38,39,40,23,24,40,14,14,10,27,3,2,34,42,2,1,5,22,23,29,11,4,2,5,5,10,2,12,19,23,2,17,4,9,8,8,4,12,2,8,12,11,14,7,5,2,6,11,35,3,0.042,0.025,0.021,0.032,0.034,0.042,0.022,0.029,0.047,0.042,0.031,0.04,0.032,0.036,0.034,0.04,0.034,0.029,0.039,0.046,0.029,0.037,0.032,0.043,0.028,0.037,0.029,0.026,0.025,0.029,0.05,0.071,0.041,0.033,0.033,0.01,0.037,0.037,0.017,0.021,0.018,0.034,0.053,0.035,0.017,0.019,0.028,0.013,0.014,0.04,0.023,0.019,0.026,0.026,0.027,0.015,0.02,0.018,0.035,0.016,0.02,0.027,0.028,0.048,0.016,0.032,0.02,0.015,0.016,0.011,0.027,0.027,0.019,0.02,1787,5319,1974,2533,2366,3210,2860,3691,2535,1625,611.0,2930,2901,1553,1976,11873,17277,1925,4924,5964,2502,4406,1911,5344,6114,7175,5531,4246,5436,5814,2615,736,1504,5664,1270,1653,5836,6004,386,321,1802,2200,4345,2807,3548,1112,729,2323,2267,1065,536,2572,3291,6040,304,3998,1466,2398,1217,1666,908,2425,484,827,2620,861,3334,2642,1288,784,1210,1842,9139,596,0.14,0.114,0.124,0.123,0.136,0.151,0.107,0.126,0.152,0.156,0.141,0.147,0.139,0.169,0.127,0.151,0.131,0.107,0.147,0.156,0.105,0.156,0.133,0.146,0.124,0.14,0.127,0.114,0.116,0.146,0.148,0.157,0.164,0.116,0.101,0.061,0.135,0.129,0.098,0.099,0.095,0.143,0.166,0.134,0.088,0.098,0.117,0.074,0.084,0.143,0.107,0.098,0.114,0.116,0.141,0.093,0.099,0.104,0.147,0.101,0.115,0.126,0.145,0.179,0.1,0.124,0.11,0.093,0.097,0.097,0.125,0.132,0.097,0.129,424,1267,653,795,809,1020,994,962,459,371,186.0,1288,809,311,404,3066,4042,350,1235,1975,734,1455,850,1197,1715,1778,1848,1327,1462,1698,481,354,378,1357,356,582,1488,1755,185,110,726,1092,969,1526,1953,522,269,929,1002,575,222,1112,1224,2041,150,1955,603,1098,552,942,406,775,161,255,1290,459,1531,1042,696,240,563,747,3876,254,3.784,3.2,2.95,2.914,2.457,2.607,2.296,3.27,3.398,3.318,2.675,2.022,2.864,3.153,3.137,2.75,3.256,3.646,3.073,2.372,2.685,2.476,1.953,3.261,2.647,2.979,2.321,2.478,2.751,2.607,3.684,2.125,3.275,2.927,3.077,2.56,2.944,2.516,2.487,2.917,2.841,1.813,3.01,2.037,2.094,2.192,3.196,2.824,2.714,2.181,2.271,2.409,2.396,2.667,2.452,2.181,2.758,2.412,2.208,1.997,2.182,2.912,3.061,3.017,2.377,2.327,2.4,2.852,2.192,2.871,2.541,2.437,2.527,2.885,1.164,0.928,0.84,0.662,0.631,0.642,0.508,0.824,0.998,0.546,0.887,0.51,0.582,0.621,0.713,0.679,0.719,0.93,0.633,0.68,0.79,0.519,0.398,0.803,0.584,0.744,0.549,0.632,0.508,0.591,0.771,1.127,0.591,0.664,0.762,0.514,0.955,0.643,0.491,0.533,0.871,0.467,0.85,0.71,0.643,0.815,0.609,0.875,0.57,0.538,0.501,0.538,0.622,0.585,0.456,0.654,0.614,0.583,0.646,0.396,0.451,0.804,0.817,0.962,0.741,0.647,0.663,0.743,0.465,1.073,0.495,0.584,0.614,0.513
-316,Epileptic,M,42.0,1.3,2.5,1.0,1.4,1.1,1.7,1.6,2.2,0.9,0.8,0.4,2.7,1.3,0.8,1.9,7.8,11.2,0.9,2.5,4.6,0.8,3.3,1.5,3.6,2.5,3.8,3.5,2.6,3.3,3.4,1.4,0.8,0.8,2.5,0.6,0.9,4.0,3.6,0.3,0.2,1.3,2.8,2.0,2.5,3.5,0.6,0.7,1.2,1.6,0.8,0.7,2.2,1.9,3.5,0.4,2.0,1.0,1.5,1.3,1.0,0.9,1.4,0.6,0.4,2.4,1.6,2.0,1.7,0.8,0.5,1.3,1.6,4.4,0.4,10,22,7,11,9,17,12,19,9,9,4.0,25,12,10,20,71,78,10,28,40,8,39,18,42,31,34,36,20,24,37,22,6,5,28,2,8,45,40,5,1,8,24,23,19,20,4,3,5,10,10,5,17,13,20,2,17,9,11,10,9,6,10,4,4,19,23,13,10,6,4,8,13,34,4,0.041,0.024,0.012,0.024,0.031,0.028,0.021,0.025,0.032,0.03,0.03,0.027,0.022,0.033,0.026,0.033,0.031,0.029,0.03,0.032,0.021,0.032,0.022,0.037,0.03,0.026,0.026,0.021,0.022,0.032,0.038,0.029,0.03,0.024,0.015,0.019,0.038,0.033,0.022,0.013,0.023,0.036,0.038,0.02,0.019,0.01,0.021,0.014,0.016,0.018,0.028,0.017,0.024,0.021,0.019,0.018,0.025,0.015,0.025,0.016,0.02,0.026,0.022,0.016,0.021,0.019,0.017,0.017,0.014,0.02,0.024,0.02,0.017,0.019,1652,5061,2355,2374,1684,3347,3008,4448,1770,1685,654.0,3439,3495,1307,3481,13522,19519,2934,5240,5718,2849,5720,2648,6484,4726,7711,6310,4942,7337,5872,2573,1114,1207,6210,2237,2496,10517,8566,649,464,1745,3120,7639,2870,5057,1515,1165,3087,3076,2284,612,4106,2421,6034,456,3443,1702,3348,1882,1803,1231,2268,746,897,3540,1865,3863,3320,1967,963,1538,2582,9637,751,0.148,0.117,0.093,0.112,0.123,0.137,0.089,0.119,0.124,0.132,0.13,0.123,0.103,0.138,0.128,0.133,0.125,0.115,0.13,0.127,0.077,0.14,0.118,0.134,0.126,0.127,0.118,0.093,0.09,0.138,0.14,0.11,0.106,0.116,0.072,0.099,0.145,0.135,0.116,0.096,0.113,0.143,0.138,0.109,0.098,0.077,0.104,0.081,0.094,0.111,0.142,0.096,0.119,0.1,0.125,0.107,0.12,0.094,0.12,0.102,0.115,0.121,0.122,0.106,0.108,0.111,0.089,0.098,0.089,0.105,0.122,0.115,0.095,0.112,536,1718,995,966,625,1056,1044,1434,484,501,259.0,1520,1009,383,1147,3943,5830,533,1437,2269,746,1685,1138,1706,1444,2322,2153,1714,2182,1803,701,490,349,1537,591,800,2167,1895,336,232,849,1251,1211,1880,2847,840,473,1117,1408,850,360,2038,1188,2576,240,1692,682,1575,878,1028,607,834,429,438,1765,1248,1961,1447,905,456,795,1261,4037,401,3.256,2.764,2.237,2.391,2.528,2.646,2.394,2.698,2.816,2.652,2.29,1.963,2.752,2.523,2.547,2.704,2.771,3.935,2.996,2.179,3.067,2.669,2.049,2.85,2.584,2.823,2.4,2.367,2.639,2.589,2.816,2.237,2.64,2.946,3.491,2.95,3.437,3.195,2.066,2.405,2.634,2.21,3.867,1.733,2.012,1.908,3.095,3.427,2.654,2.875,2.173,2.155,2.241,2.481,2.437,2.256,2.528,2.445,2.165,1.911,2.234,2.511,1.999,2.214,2.175,1.731,2.144,2.63,2.38,2.378,2.156,2.419,2.476,2.325,0.788,0.635,0.518,0.52,0.814,0.563,0.53,0.492,0.837,0.61,0.689,0.455,0.318,0.535,0.422,0.536,0.515,0.87,0.55,0.57,0.717,0.599,0.437,0.611,0.561,0.455,0.569,0.497,0.494,0.472,0.617,1.007,0.512,0.543,0.738,0.475,0.803,0.696,0.448,0.423,0.453,0.487,0.756,0.484,0.563,0.392,0.611,0.814,0.621,0.574,0.338,0.492,0.426,0.475,0.84,0.478,0.546,0.428,0.44,0.382,0.366,0.659,0.363,0.918,0.369,0.736,0.368,0.493,0.502,0.531,0.46,0.465,0.529,0.46
-317,Epileptic,F,42.0,0.5,2.5,0.7,1.1,1.8,2.4,1.2,1.1,1.7,0.6,0.2,2.4,1.3,0.7,1.1,5.2,6.9,0.8,1.5,4.1,1.0,2.7,1.1,3.2,2.1,2.6,3.0,2.0,1.9,3.0,1.1,2.2,0.3,2.5,0.2,0.8,3.0,2.8,0.1,0.4,1.1,2.6,1.8,2.8,2.4,0.8,0.5,0.8,1.2,0.9,1.0,1.3,1.5,2.1,0.1,2.2,0.6,1.6,0.6,0.8,0.6,1.5,0.2,0.2,1.9,1.5,2.0,1.3,0.7,0.5,0.8,1.3,4.3,0.3,5,27,7,9,19,18,11,11,15,6,2.0,22,17,7,12,56,63,8,16,39,10,29,10,40,26,29,33,19,18,33,14,16,3,24,1,6,40,31,2,4,7,23,16,25,16,6,2,6,6,10,6,11,10,13,0,16,4,11,5,8,7,14,2,3,16,17,16,10,5,6,6,11,32,2,0.03,0.026,0.014,0.024,0.035,0.037,0.02,0.024,0.047,0.033,0.026,0.027,0.027,0.031,0.024,0.032,0.026,0.033,0.027,0.035,0.026,0.034,0.025,0.038,0.026,0.03,0.024,0.019,0.018,0.032,0.036,0.058,0.018,0.026,0.008,0.019,0.027,0.029,0.014,0.018,0.025,0.031,0.032,0.029,0.017,0.019,0.019,0.015,0.017,0.023,0.037,0.015,0.026,0.016,0.011,0.02,0.014,0.022,0.021,0.022,0.027,0.027,0.02,0.018,0.024,0.034,0.018,0.018,0.014,0.022,0.019,0.018,0.019,0.016,1016,5083,1892,1856,2302,3015,2295,2586,2120,1381,393.0,2208,2991,1149,2463,9540,15788,2158,3395,3531,3565,4143,1667,5691,4114,5044,5467,3766,5579,4832,1841,1324,743,5312,2061,1909,7174,6101,337,667,1258,2536,4275,2042,3433,1362,811,1749,2027,1497,779,2940,2020,4529,211,2734,1394,2684,1047,1146,1104,2176,287,728,2481,989,3447,2559,1480,905,1216,2589,8659,571,0.123,0.129,0.095,0.114,0.147,0.156,0.101,0.11,0.158,0.14,0.116,0.127,0.131,0.141,0.135,0.138,0.125,0.127,0.115,0.143,0.092,0.145,0.117,0.135,0.127,0.138,0.119,0.095,0.09,0.147,0.134,0.159,0.108,0.123,0.055,0.103,0.116,0.126,0.093,0.124,0.119,0.134,0.127,0.12,0.096,0.112,0.11,0.088,0.105,0.116,0.147,0.095,0.121,0.093,0.081,0.108,0.09,0.11,0.11,0.122,0.118,0.121,0.135,0.112,0.12,0.149,0.098,0.101,0.093,0.115,0.113,0.102,0.1,0.108,297,1679,714,752,876,1076,811,824,552,358,143.0,1220,890,321,732,2856,4359,453,878,1943,852,1376,700,1488,1443,1546,2038,1399,1553,1593,511,544,282,1331,534,677,1957,1525,196,321,685,1231,938,1572,2117,628,358,825,968,637,434,1388,901,1956,101,1639,639,1206,578,565,538,941,136,308,1231,679,1787,1136,777,490,647,1143,3604,254,3.255,2.801,2.464,2.528,2.278,2.525,2.342,2.697,2.608,3.067,2.52,1.659,2.68,2.587,2.53,2.559,2.951,3.542,2.949,1.697,3.348,2.377,2.06,2.804,2.377,2.678,2.245,2.19,2.745,2.486,2.611,2.546,2.162,2.947,3.456,2.601,2.767,2.979,2.008,2.274,2.318,1.895,3.339,1.503,1.839,2.377,2.926,2.591,2.586,2.458,2.215,2.262,2.245,2.464,2.096,1.92,2.743,2.614,2.145,2.173,2.128,2.346,2.289,2.263,2.185,1.926,2.1,2.452,2.288,2.207,2.239,2.501,2.499,2.519,0.511,0.469,0.464,0.431,0.704,0.62,0.614,0.466,0.79,0.547,0.65,0.438,0.465,0.531,0.491,0.57,0.546,0.716,0.483,0.494,0.744,0.439,0.374,0.629,0.502,0.522,0.475,0.486,0.464,0.478,0.613,0.9,0.428,0.543,0.774,0.567,0.646,0.624,0.303,0.553,0.475,0.492,0.789,0.521,0.534,0.342,0.466,0.63,0.48,0.609,0.339,0.414,0.573,0.441,0.293,0.388,0.408,0.576,0.369,0.381,0.39,0.559,0.387,0.718,0.68,0.527,0.453,0.465,0.381,0.427,0.466,0.606,0.494,0.569
-319,Epileptic,M,32.0,1.2,3.3,1.7,1.6,2.0,2.0,4.3,2.0,1.3,1.2,0.5,2.6,3.1,0.6,1.1,7.0,14.0,2.2,2.6,4.8,1.9,3.2,2.0,3.8,4.6,3.7,4.8,2.9,3.2,4.5,2.2,0.8,0.6,3.1,0.8,0.7,3.8,4.4,0.3,0.2,1.3,2.8,3.7,2.7,3.0,1.3,0.8,1.1,2.0,0.9,0.5,2.5,2.2,4.3,0.4,4.0,1.2,1.7,1.3,1.4,0.7,2.0,0.4,0.8,1.9,1.4,3.5,1.8,1.6,0.8,1.0,1.4,5.3,0.4,8,25,15,14,16,20,30,18,15,12,4.0,25,23,7,12,71,119,17,29,48,15,43,20,39,45,38,53,28,28,44,21,6,6,34,4,6,41,53,2,1,6,30,27,18,14,9,5,6,10,5,3,15,17,23,2,29,9,14,8,10,4,16,2,5,13,19,25,12,9,5,7,7,34,4,0.049,0.036,0.038,0.03,0.037,0.029,0.055,0.033,0.042,0.05,0.031,0.039,0.045,0.03,0.031,0.043,0.051,0.06,0.039,0.044,0.042,0.031,0.034,0.043,0.043,0.036,0.038,0.034,0.039,0.047,0.062,0.038,0.04,0.035,0.018,0.016,0.046,0.04,0.018,0.02,0.025,0.034,0.055,0.028,0.023,0.031,0.029,0.019,0.026,0.019,0.023,0.026,0.033,0.028,0.015,0.023,0.024,0.027,0.021,0.02,0.02,0.028,0.028,0.028,0.028,0.025,0.026,0.025,0.024,0.028,0.026,0.019,0.02,0.021,1497,5504,2980,2818,2358,3406,3837,3068,1846,1887,972.0,2933,4343,1139,2255,11111,21381,2831,4032,5973,3834,6752,3068,6275,5626,6606,7203,4106,6006,5944,2137,1402,902,6571,2500,1962,7157,8055,434,330,1371,3906,5514,3032,3271,1753,1074,2230,2269,1516,590,3087,3004,6003,568,4869,1497,2706,1885,2370,1036,2851,347,1001,2632,1626,4473,3001,2378,1173,1230,2188,9763,535,0.146,0.13,0.135,0.126,0.133,0.136,0.163,0.138,0.155,0.171,0.124,0.153,0.152,0.141,0.128,0.148,0.156,0.177,0.155,0.164,0.127,0.128,0.137,0.144,0.134,0.129,0.128,0.117,0.132,0.161,0.155,0.11,0.147,0.133,0.076,0.089,0.139,0.145,0.095,0.093,0.114,0.141,0.16,0.119,0.103,0.125,0.115,0.092,0.108,0.093,0.109,0.114,0.133,0.111,0.101,0.111,0.114,0.117,0.104,0.105,0.101,0.126,0.122,0.122,0.121,0.123,0.113,0.107,0.104,0.137,0.121,0.107,0.098,0.122,408,1704,897,939,888,1199,1326,1061,569,480,292.0,1243,1217,271,660,3324,5633,669,1256,2138,857,1915,1045,1765,1906,1899,2414,1583,1645,1812,727,396,312,1514,703,668,1849,2003,266,165,697,1496,1215,1617,1978,735,434,911,1130,750,356,1596,1192,2584,300,2666,757,1212,993,1090,531,1108,201,453,1145,989,2250,1251,1092,483,623,976,4244,283,3.303,2.603,2.813,2.627,2.263,2.388,2.35,2.522,2.506,2.936,2.637,2.023,2.797,2.755,2.543,2.544,2.803,3.403,2.509,2.27,3.017,2.657,2.407,2.663,2.345,2.721,2.27,2.085,2.754,2.506,2.209,3.197,2.368,2.979,3.213,2.498,2.731,2.885,1.953,2.131,2.431,2.106,2.87,2.221,1.896,2.436,2.769,2.788,2.422,2.259,2.019,2.17,2.28,2.428,2.174,2.059,2.17,2.409,2.141,2.328,2.149,2.511,2.072,2.385,2.269,1.873,2.103,2.381,2.498,2.285,2.24,2.477,2.469,2.467,0.93,0.73,0.824,0.618,0.558,0.552,0.643,0.441,0.481,0.62,0.775,0.505,0.45,0.523,0.526,0.542,0.698,0.801,0.601,0.67,1.005,0.525,0.536,0.754,0.652,0.648,0.562,0.511,0.503,0.59,0.664,1.121,0.511,0.659,0.902,0.685,0.726,0.616,0.324,0.351,0.564,0.614,0.941,0.687,0.496,0.47,0.596,0.868,0.497,0.643,0.343,0.436,0.594,0.471,0.395,0.466,0.405,0.482,0.427,0.427,0.395,0.657,0.349,0.83,0.447,0.702,0.417,0.432,0.493,0.691,0.39,0.498,0.47,0.541
-320,Epileptic,F,32.0,0.6,1.8,0.7,1.2,1.9,1.9,1.5,1.5,0.7,0.7,0.4,3.4,1.1,0.4,1.1,6.9,9.0,0.9,2.1,4.6,1.9,4.9,1.8,2.9,5.6,2.5,3.0,2.5,2.9,3.1,1.1,1.3,0.5,1.9,0.3,0.6,6.8,2.6,0.1,0.3,0.9,5.0,2.2,2.3,2.3,0.6,0.3,0.7,1.4,0.7,0.8,2.5,1.2,2.4,0.4,2.6,1.0,1.8,1.5,0.7,0.5,1.2,0.6,0.5,1.6,1.2,2.7,1.3,1.2,0.3,1.0,1.2,4.6,0.2,6,18,6,10,23,20,15,15,11,8,4.0,33,16,5,18,80,88,8,28,42,16,53,18,36,52,32,42,24,28,42,10,9,5,27,1,7,65,40,1,2,5,36,21,18,11,4,2,5,6,6,6,21,11,16,3,20,7,17,10,6,5,9,3,4,15,15,22,9,7,3,11,9,37,3,0.031,0.022,0.016,0.023,0.037,0.025,0.027,0.031,0.042,0.032,0.038,0.034,0.025,0.023,0.027,0.03,0.028,0.034,0.025,0.037,0.038,0.041,0.03,0.035,0.048,0.029,0.027,0.024,0.03,0.029,0.039,0.046,0.028,0.028,0.01,0.018,0.048,0.028,0.02,0.017,0.019,0.042,0.037,0.025,0.017,0.013,0.019,0.012,0.017,0.014,0.024,0.02,0.022,0.017,0.017,0.017,0.016,0.024,0.023,0.016,0.016,0.026,0.032,0.031,0.02,0.024,0.018,0.019,0.017,0.014,0.019,0.017,0.017,0.018,1541,4157,2316,2517,2110,4153,2904,3173,1269,1685,774.0,3456,3362,1278,3341,16033,22042,2342,5610,5621,3777,5345,2082,6570,7209,5809,6741,3963,7012,6974,2090,1296,999,6008,1791,1895,8619,7230,330,632,1466,3062,5937,2894,3557,1566,720,2323,2450,1696,813,4038,2211,5143,684,4513,2415,3258,1828,1514,948,2292,527,1159,2805,1285,5134,2563,2116,807,1811,2419,9224,544,0.111,0.112,0.093,0.118,0.139,0.115,0.108,0.123,0.134,0.138,0.137,0.141,0.123,0.121,0.127,0.132,0.125,0.127,0.123,0.137,0.122,0.131,0.118,0.15,0.143,0.125,0.116,0.094,0.1,0.131,0.138,0.125,0.122,0.114,0.061,0.089,0.139,0.132,0.095,0.096,0.102,0.133,0.146,0.122,0.091,0.083,0.103,0.076,0.092,0.103,0.121,0.108,0.108,0.096,0.114,0.102,0.098,0.11,0.111,0.094,0.092,0.12,0.131,0.134,0.109,0.118,0.094,0.101,0.099,0.1,0.11,0.097,0.097,0.11,414,1344,756,875,811,1458,993,921,363,436,243.0,1705,840,272,852,4179,5650,464,1468,2293,903,1893,977,1587,2076,1696,2277,1619,1751,1986,579,522,285,1295,506,615,2529,1811,156,278,699,1779,1129,1525,1930,761,292,903,1114,696,507,1847,998,2157,342,2249,893,1408,1075,695,515,818,274,286,1344,724,2463,1105,1001,382,925,1126,3997,235,3.596,2.692,2.752,2.649,2.237,2.431,2.379,2.92,2.547,2.898,2.885,1.835,3.088,3.199,2.799,2.839,2.964,3.834,2.905,2.079,3.052,2.264,1.983,3.269,2.62,2.815,2.406,2.002,2.941,2.67,2.87,2.437,2.752,3.222,3.308,2.562,2.516,2.875,2.147,2.574,2.55,1.616,3.632,2.09,2.143,2.153,3.1,3.244,2.782,2.483,1.924,2.285,2.286,2.516,2.367,2.173,2.644,2.483,1.934,2.309,2.02,2.863,2.31,3.591,2.072,1.911,2.109,2.706,2.332,2.237,2.089,2.518,2.433,2.634,0.872,0.794,0.562,0.593,0.655,0.603,0.618,0.491,0.578,0.614,0.529,0.48,0.385,0.582,0.513,0.579,0.706,0.677,0.66,0.661,0.935,0.614,0.479,0.594,0.754,0.606,0.509,0.522,0.467,0.541,0.773,0.906,0.421,0.665,0.588,0.615,0.878,0.779,0.447,0.372,0.538,0.558,0.765,0.639,0.593,0.466,0.541,0.715,0.398,0.494,0.35,0.472,0.511,0.472,0.383,0.402,0.523,0.534,0.41,0.4,0.356,0.583,0.57,0.55,0.483,0.759,0.54,0.47,0.488,0.591,0.488,0.55,0.553,0.414
-321,Epileptic,M,22.0,0.7,2.1,1.2,1.6,2.4,2.8,1.6,1.8,0.7,0.8,0.4,3.1,2.1,0.9,1.0,7.3,9.5,1.1,3.0,3.4,1.6,3.3,1.7,3.6,4.2,4.4,4.2,3.0,4.0,3.2,1.1,1.4,0.5,2.3,0.5,0.4,3.3,3.1,0.3,0.2,0.9,2.8,2.6,2.5,4.5,0.7,0.6,0.7,1.2,0.7,0.8,1.8,1.4,3.5,0.5,3.8,0.9,1.4,1.1,1.3,1.0,1.7,0.3,0.6,1.7,1.1,4.8,1.6,2.0,0.6,1.3,1.2,4.7,0.2,6,15,12,11,18,25,12,13,8,8,5.0,26,18,9,10,63,81,8,30,31,16,35,15,36,47,44,39,25,31,34,13,12,4,22,3,3,33,34,2,1,5,27,21,19,24,5,3,5,6,6,7,12,12,20,4,28,9,12,9,11,7,12,2,5,15,12,33,9,12,4,6,8,38,3,0.03,0.022,0.021,0.027,0.045,0.032,0.024,0.027,0.036,0.034,0.035,0.033,0.031,0.031,0.03,0.036,0.029,0.031,0.03,0.031,0.023,0.031,0.028,0.038,0.031,0.033,0.027,0.023,0.029,0.028,0.028,0.059,0.027,0.025,0.016,0.009,0.033,0.027,0.016,0.023,0.019,0.033,0.04,0.03,0.029,0.016,0.02,0.014,0.016,0.015,0.032,0.02,0.027,0.02,0.022,0.017,0.019,0.016,0.022,0.02,0.021,0.026,0.025,0.027,0.019,0.026,0.031,0.023,0.029,0.019,0.025,0.016,0.017,0.016,1694,5137,2691,2494,2999,4074,3180,3960,1262,1636,725.0,2887,4399,1724,2603,11549,20287,2424,6949,4246,4332,5616,2290,6856,7756,7670,7741,4809,7572,6287,2434,1248,1007,6387,1934,1936,7791,8617,434,295,1704,3278,5827,2379,4014,1691,1107,2299,2506,1898,679,3636,2426,6540,1055,6258,1913,3408,1335,1837,1647,2786,524,1094,2715,1313,4836,2608,2135,947,1745,2586,11922,355,0.125,0.104,0.107,0.117,0.134,0.146,0.103,0.119,0.126,0.14,0.126,0.129,0.124,0.137,0.127,0.11,0.11,0.117,0.126,0.122,0.099,0.13,0.123,0.128,0.133,0.134,0.115,0.101,0.104,0.128,0.124,0.151,0.11,0.119,0.078,0.066,0.127,0.121,0.105,0.11,0.1,0.128,0.135,0.13,0.119,0.096,0.103,0.081,0.087,0.097,0.149,0.099,0.112,0.09,0.111,0.101,0.105,0.094,0.118,0.107,0.108,0.116,0.118,0.124,0.108,0.119,0.128,0.111,0.113,0.117,0.116,0.103,0.093,0.095,443,1575,874,949,908,1501,1016,1059,335,413,227.0,1382,1074,398,626,3013,4953,510,1668,1756,1023,1776,1007,1658,2369,2150,2553,1785,1987,1864,646,443,346,1363,479,688,1700,1892,192,128,783,1389,1061,1374,2436,735,453,969,1130,698,411,1521,979,2580,350,3191,833,1443,768,984,775,1025,266,422,1343,652,2447,1089,964,451,796,1095,4558,199,3.437,3.01,2.927,2.649,2.575,2.563,2.703,3.066,2.773,3.278,2.581,1.842,3.144,3.043,3.064,2.876,3.233,3.555,3.293,2.015,2.978,2.496,1.992,2.958,2.624,2.909,2.404,2.186,2.937,2.636,2.792,2.791,2.45,3.148,3.427,2.505,3.279,3.26,2.616,2.78,2.588,2.042,3.372,1.989,1.928,2.531,3.063,3.07,2.82,2.65,1.994,2.654,2.472,2.675,2.663,2.231,2.489,2.506,2.02,2.096,2.429,2.835,2.146,2.799,2.338,2.479,2.142,2.802,2.591,2.623,2.642,2.814,2.717,2.091,0.729,0.738,0.512,0.531,0.846,0.566,0.659,0.633,0.644,0.488,0.798,0.463,0.553,0.461,0.56,0.704,0.674,0.629,0.717,0.712,0.954,0.481,0.509,0.829,0.547,0.589,0.48,0.675,0.718,0.502,0.703,1.0,0.437,0.671,0.748,0.522,0.68,0.687,0.408,0.584,0.658,0.526,0.937,0.573,0.615,0.516,0.544,0.673,0.642,0.684,0.273,0.573,0.784,0.56,0.698,0.42,0.45,0.725,0.342,0.422,0.366,0.692,0.328,1.155,0.602,0.536,0.511,0.452,0.465,0.645,0.463,0.799,0.666,0.491
-322,Epileptic,M,42.0,0.8,2.4,0.7,1.7,1.7,2.2,1.5,2.0,1.3,0.9,0.2,3.3,1.7,0.7,1.2,6.0,7.1,0.7,2.7,3.9,0.5,3.1,1.9,2.7,2.6,3.5,3.2,2.7,2.9,3.1,1.3,1.0,0.7,3.0,0.7,1.1,2.7,3.5,0.2,0.2,1.4,3.2,1.7,3.3,3.6,0.5,0.3,0.9,1.2,0.5,0.7,1.7,1.6,2.6,0.5,2.0,0.7,2.1,1.6,1.2,0.5,0.9,0.3,0.7,2.7,1.2,3.2,1.0,1.0,0.4,0.8,1.1,4.4,0.3,7,25,8,13,18,18,11,15,15,9,2.0,30,15,8,14,66,61,6,31,37,4,35,17,32,35,38,35,26,22,36,12,7,5,28,4,8,30,38,2,1,8,31,16,26,19,4,2,7,6,8,5,13,13,20,3,16,4,19,11,10,3,6,3,6,23,14,28,6,7,4,5,8,34,3,0.037,0.026,0.013,0.029,0.039,0.035,0.021,0.031,0.043,0.037,0.027,0.033,0.033,0.028,0.029,0.028,0.026,0.026,0.029,0.03,0.012,0.034,0.029,0.033,0.027,0.029,0.022,0.027,0.023,0.03,0.037,0.049,0.036,0.032,0.02,0.021,0.034,0.035,0.014,0.013,0.026,0.035,0.025,0.032,0.025,0.013,0.017,0.014,0.015,0.02,0.027,0.021,0.022,0.019,0.014,0.014,0.018,0.022,0.03,0.018,0.016,0.018,0.022,0.036,0.022,0.025,0.023,0.014,0.019,0.017,0.018,0.02,0.019,0.026,1720,5156,2106,2180,1907,3065,2950,3433,1744,1549,543.0,2977,3362,1319,2651,12909,15655,2564,5148,4992,2349,4935,2551,5101,5321,7256,6018,4028,6448,5337,2238,1274,965,6950,2610,2482,5639,7042,426,552,1627,3124,4669,2767,4107,1295,778,2416,2664,1420,602,3167,2662,5556,745,3559,1325,3188,1406,1930,873,2317,428,802,3470,1170,4241,2528,1916,662,1267,2035,8853,454,0.117,0.122,0.097,0.123,0.132,0.147,0.101,0.124,0.146,0.149,0.093,0.147,0.129,0.141,0.128,0.128,0.115,0.107,0.123,0.122,0.073,0.143,0.131,0.129,0.124,0.131,0.11,0.108,0.098,0.129,0.133,0.132,0.133,0.13,0.095,0.105,0.124,0.141,0.081,0.097,0.121,0.143,0.116,0.13,0.11,0.086,0.094,0.086,0.087,0.098,0.138,0.103,0.108,0.098,0.093,0.092,0.098,0.11,0.135,0.109,0.097,0.095,0.118,0.146,0.111,0.121,0.118,0.085,0.098,0.122,0.104,0.109,0.098,0.142,476,1565,798,880,762,1079,1015,1075,576,427,158.0,1451,907,348,680,3554,4313,458,1578,2061,658,1551,1024,1411,1643,2158,2368,1580,1753,1831,553,430,322,1605,550,824,1427,1646,242,245,802,1438,1151,1790,2253,654,324,998,1236,560,380,1368,1251,2256,438,2013,591,1611,830,1040,424,765,236,364,1868,658,2162,1091,863,345,678,958,3831,204,3.306,2.966,2.412,2.624,2.186,2.373,2.402,2.78,2.39,2.826,2.937,1.822,2.857,2.74,2.879,2.777,2.93,3.888,2.776,2.026,2.873,2.447,2.11,2.724,2.499,2.764,2.123,2.108,2.814,2.371,2.924,2.972,2.44,3.136,3.959,2.677,2.908,3.034,2.061,2.617,2.616,1.889,3.115,1.777,2.106,2.199,2.979,2.781,2.611,2.71,1.863,2.55,2.205,2.577,2.115,1.92,2.663,2.205,2.066,2.049,2.194,3.031,2.044,2.365,2.047,2.117,2.142,2.732,2.652,2.324,2.263,2.531,2.487,2.76,0.986,0.903,0.851,0.509,0.545,0.475,0.575,0.755,0.617,0.425,0.641,0.408,0.411,0.373,0.455,0.514,0.54,0.862,0.657,0.558,0.645,0.462,0.402,0.653,0.481,0.488,0.491,0.481,0.549,0.486,0.915,0.812,0.428,0.591,0.786,0.53,0.772,0.718,0.322,0.409,0.569,0.492,0.769,0.574,0.586,0.496,0.542,0.785,0.685,0.821,0.353,0.409,0.587,0.539,0.283,0.397,0.386,0.507,0.431,0.358,0.382,0.96,0.338,0.959,0.564,0.711,0.444,0.433,0.363,0.552,0.557,0.659,0.547,0.462
-323,Epileptic,M,32.0,0.6,1.4,0.7,0.7,1.7,2.4,1.1,2.0,1.3,1.0,0.7,3.2,1.7,0.4,1.6,5.5,9.0,0.8,2.4,4.9,1.4,3.1,2.1,3.1,3.2,2.3,3.0,2.6,2.4,3.6,1.2,0.8,0.5,2.3,0.6,1.0,2.8,2.7,0.1,0.3,1.1,3.0,3.4,2.7,2.7,0.7,0.6,0.6,1.3,0.9,0.8,2.2,2.0,2.4,0.1,2.4,0.8,2.4,1.4,1.3,0.5,1.4,0.4,0.5,1.5,1.0,2.5,1.3,1.5,0.4,0.8,1.1,4.9,0.3,5,13,6,6,11,24,10,18,12,10,5.0,26,20,8,13,61,89,6,33,48,15,34,17,31,38,27,36,25,21,43,13,11,4,29,4,8,33,34,1,2,6,28,23,22,16,4,3,5,7,6,7,17,15,15,1,19,6,25,9,13,4,11,3,4,19,12,21,8,10,4,6,8,38,2,0.031,0.021,0.011,0.015,0.033,0.031,0.018,0.033,0.043,0.036,0.039,0.031,0.027,0.032,0.03,0.035,0.028,0.027,0.032,0.035,0.028,0.031,0.032,0.035,0.036,0.023,0.027,0.028,0.022,0.031,0.029,0.052,0.027,0.031,0.018,0.024,0.028,0.034,0.013,0.019,0.022,0.027,0.049,0.026,0.021,0.013,0.024,0.013,0.016,0.019,0.03,0.024,0.029,0.019,0.025,0.018,0.022,0.022,0.026,0.021,0.02,0.026,0.023,0.015,0.022,0.021,0.022,0.016,0.021,0.015,0.02,0.019,0.02,0.019,1626,4387,2141,2079,2084,3305,2206,3423,1938,1638,874.0,3521,4392,1050,3227,10500,19582,2528,5551,5739,3852,5321,2759,5776,6114,5860,5547,4205,6197,5460,2361,1274,849,5364,1798,2092,5733,6776,336,518,1669,2543,5338,2933,3055,1472,960,1693,2639,1684,691,3836,2526,4236,300,3602,1452,3896,1344,1786,1002,2722,602,1023,2426,1345,4023,2734,2299,1285,1555,2024,9240,410,0.105,0.103,0.087,0.084,0.129,0.142,0.093,0.139,0.155,0.139,0.131,0.123,0.125,0.151,0.132,0.141,0.125,0.103,0.134,0.137,0.098,0.146,0.125,0.139,0.145,0.125,0.12,0.112,0.098,0.15,0.123,0.115,0.114,0.133,0.084,0.114,0.121,0.134,0.087,0.118,0.11,0.125,0.144,0.112,0.102,0.088,0.11,0.074,0.088,0.103,0.144,0.119,0.134,0.099,0.124,0.107,0.107,0.127,0.121,0.116,0.109,0.127,0.12,0.106,0.112,0.103,0.113,0.095,0.104,0.106,0.112,0.102,0.104,0.102,388,1239,838,808,809,1291,860,1100,507,452,272.0,1526,1191,271,794,2956,5195,494,1407,2392,931,1620,973,1531,1678,1672,1940,1462,1678,1867,697,400,310,1273,497,707,1504,1518,194,239,781,1569,1153,1789,1967,752,377,823,1188,692,421,1479,1025,1884,113,1882,593,1674,818,945,401,924,281,491,1193,635,1936,1162,1109,450,679,947,3713,238,3.62,3.046,2.453,2.557,2.443,2.239,2.2,2.881,2.818,2.975,2.695,2.006,2.906,2.852,2.959,2.77,2.961,3.757,2.991,1.999,2.91,2.484,2.269,2.943,2.763,2.767,2.331,2.285,2.766,2.396,2.666,2.93,2.194,2.975,2.667,2.703,2.776,3.293,1.975,2.506,2.671,1.549,3.079,1.887,1.779,2.144,3.085,2.421,2.638,2.806,1.959,2.494,2.476,2.47,2.729,2.117,2.542,2.43,1.885,2.06,2.512,2.97,2.495,2.588,2.129,2.438,2.222,2.699,2.469,3.148,2.412,2.571,2.556,2.129,0.832,0.725,0.542,0.405,0.637,0.556,0.529,0.442,0.539,0.394,0.72,0.599,0.433,0.41,0.608,0.532,0.606,0.816,0.652,0.584,0.939,0.53,0.704,0.799,0.539,0.464,0.496,0.633,0.536,0.5,0.83,1.231,0.376,0.619,0.806,0.505,0.711,0.516,0.417,0.592,0.454,0.492,0.913,0.743,0.478,0.4,0.511,0.582,0.55,0.583,0.509,0.54,0.536,0.468,0.517,0.456,0.597,0.607,0.421,0.372,0.676,0.729,0.565,0.675,0.471,0.663,0.426,0.495,0.414,0.86,0.635,0.506,0.485,0.324
-324,Epileptic,M,52.0,0.8,1.5,0.7,1.2,1.5,2.2,2.2,2.1,1.3,0.9,0.3,2.3,1.8,0.7,2.0,5.1,8.8,0.9,2.3,3.8,1.2,2.5,1.6,3.1,3.3,3.4,2.9,2.8,2.4,3.0,1.2,1.4,0.4,2.9,0.3,0.8,3.2,3.6,0.3,0.2,1.0,2.6,2.3,2.2,2.7,0.7,0.3,1.0,1.4,0.5,0.7,2.7,1.3,2.0,0.3,2.1,1.0,1.4,1.2,1.0,0.6,1.7,0.5,0.4,1.5,1.4,2.6,1.7,1.0,0.5,0.8,1.2,4.5,0.3,9,17,7,9,14,22,17,18,17,7,3.0,24,23,8,21,59,89,11,32,42,25,28,14,36,39,39,32,30,25,38,19,9,5,33,2,6,36,44,3,1,8,21,24,17,15,5,2,7,8,4,5,24,7,14,1,18,10,13,10,8,6,13,4,4,12,17,21,11,7,6,5,11,35,2,0.033,0.018,0.011,0.023,0.04,0.033,0.027,0.028,0.038,0.041,0.039,0.027,0.027,0.029,0.031,0.035,0.027,0.033,0.033,0.032,0.022,0.033,0.028,0.038,0.032,0.028,0.022,0.023,0.022,0.028,0.035,0.042,0.023,0.037,0.015,0.019,0.033,0.036,0.016,0.012,0.023,0.033,0.037,0.02,0.017,0.014,0.015,0.016,0.016,0.009,0.034,0.025,0.02,0.017,0.036,0.014,0.024,0.017,0.025,0.018,0.021,0.03,0.023,0.015,0.019,0.024,0.018,0.019,0.018,0.016,0.024,0.02,0.02,0.019,1653,4362,2197,2477,1820,3580,3185,3435,2280,1599,496.0,2672,4076,1258,3592,9272,19322,2369,4370,4558,3477,4126,2026,5528,5408,6915,5238,4955,6040,5715,2677,1346,929,6376,1697,1893,6872,7574,525,380,1500,2482,5904,2483,4315,1626,770,2363,2752,1589,589,4177,1845,4430,290,4269,1234,2765,1406,1838,1102,2459,604,811,2624,1114,4501,3040,1955,1492,1225,1930,8736,498,0.124,0.101,0.091,0.109,0.147,0.142,0.108,0.134,0.142,0.146,0.133,0.124,0.126,0.136,0.142,0.148,0.123,0.128,0.151,0.132,0.099,0.14,0.117,0.142,0.132,0.127,0.097,0.111,0.102,0.132,0.139,0.138,0.12,0.134,0.065,0.101,0.144,0.142,0.112,0.093,0.115,0.132,0.145,0.108,0.089,0.088,0.09,0.087,0.094,0.082,0.144,0.125,0.104,0.091,0.141,0.092,0.126,0.105,0.128,0.096,0.118,0.133,0.127,0.115,0.104,0.116,0.104,0.103,0.102,0.117,0.115,0.125,0.104,0.111,492,1401,885,896,717,1168,1167,1178,709,353,162.0,1228,1248,350,1136,2745,5402,526,1220,1859,803,1278,853,1525,1758,2061,2009,1778,1660,1796,650,571,316,1363,457,672,1612,1727,256,186,718,1196,1163,1559,2443,798,343,1015,1263,671,328,1761,891,1875,124,2262,646,1293,798,970,490,904,342,418,1238,811,2201,1329,857,535,573,892,3547,225,3.156,2.757,2.449,2.603,2.254,2.569,2.252,2.662,2.473,3.165,2.703,1.877,2.617,2.597,2.556,2.628,2.798,3.492,2.897,2.078,3.306,2.522,2.112,2.742,2.508,2.694,2.157,2.251,2.747,2.463,3.044,2.427,2.535,3.187,3.081,2.6,2.964,3.084,2.021,2.295,2.791,1.868,3.498,1.791,1.977,2.18,2.757,2.907,2.605,2.533,2.092,2.371,2.276,2.458,2.679,2.092,2.36,2.239,2.14,2.052,2.46,2.701,2.043,2.337,2.319,1.685,2.201,2.571,2.534,2.889,2.39,2.495,2.597,2.634,0.78,0.56,0.471,0.586,0.565,0.542,0.548,0.586,0.555,0.788,0.777,0.496,0.481,0.51,0.558,0.54,0.579,0.681,0.561,0.62,0.666,0.548,0.449,0.592,0.569,0.486,0.488,0.591,0.52,0.478,0.719,0.867,0.514,0.666,0.558,0.685,0.714,0.652,0.403,0.407,0.485,0.461,0.728,0.662,0.645,0.488,0.519,0.537,0.469,0.515,0.458,0.466,0.482,0.487,0.281,0.478,0.465,0.505,0.359,0.397,0.392,0.666,0.428,0.697,0.584,0.539,0.503,0.442,0.489,0.906,0.58,0.334,0.591,0.623
-325,Epileptic,F,32.0,0.8,3.2,1.2,1.4,1.6,1.7,2.8,1.5,2.1,0.8,0.4,2.7,2.4,0.5,1.4,5.8,9.2,1.3,2.9,5.3,0.8,3.5,2.2,5.3,4.9,2.8,5.1,3.0,4.3,3.9,2.0,1.3,0.3,2.0,0.5,0.4,4.0,2.7,0.1,0.3,1.0,3.2,2.4,4.6,3.0,0.8,0.5,0.7,1.4,0.7,0.5,2.6,1.7,2.2,0.3,3.0,1.0,2.3,1.3,1.5,1.2,1.2,0.2,0.6,2.3,1.5,2.1,2.0,1.2,0.5,0.9,1.7,4.5,0.3,9,30,9,10,14,21,16,16,21,10,6.0,28,30,8,15,58,81,10,29,47,7,45,23,47,58,30,59,29,31,47,27,11,5,29,2,3,53,33,1,2,5,28,22,33,16,5,2,4,7,9,4,17,14,14,2,26,6,18,9,12,7,9,2,5,18,18,15,15,6,5,8,14,36,3,0.043,0.033,0.026,0.029,0.032,0.023,0.042,0.027,0.072,0.042,0.034,0.034,0.034,0.041,0.037,0.041,0.039,0.047,0.034,0.039,0.024,0.032,0.034,0.049,0.037,0.03,0.037,0.033,0.04,0.038,0.052,0.043,0.016,0.024,0.012,0.01,0.041,0.029,0.015,0.025,0.022,0.034,0.042,0.033,0.024,0.019,0.02,0.013,0.021,0.026,0.017,0.024,0.035,0.024,0.022,0.019,0.022,0.021,0.024,0.023,0.033,0.02,0.028,0.019,0.026,0.026,0.022,0.022,0.02,0.017,0.022,0.024,0.019,0.014,1353,4749,2325,2391,1619,3888,3011,3454,1809,1422,862.0,3300,4174,1034,2775,8934,16826,2663,5720,6253,3036,5832,2715,6269,6837,5345,6508,3925,5759,6092,2215,1655,1003,6005,2132,1701,7840,7094,322,473,1246,3301,5565,3512,3350,1348,791,2003,2302,1510,592,2975,2023,3187,520,4106,1469,3814,1562,2122,931,2224,335,878,2903,1390,2820,3148,1700,786,1348,2085,8669,563,0.141,0.136,0.107,0.122,0.14,0.12,0.121,0.129,0.193,0.162,0.141,0.144,0.151,0.157,0.146,0.148,0.145,0.145,0.136,0.148,0.093,0.134,0.126,0.155,0.14,0.134,0.133,0.127,0.129,0.155,0.148,0.126,0.08,0.13,0.069,0.066,0.149,0.134,0.108,0.114,0.109,0.118,0.15,0.144,0.106,0.101,0.092,0.071,0.098,0.111,0.116,0.118,0.141,0.111,0.114,0.109,0.112,0.113,0.114,0.115,0.133,0.106,0.113,0.112,0.122,0.128,0.108,0.108,0.101,0.11,0.124,0.129,0.099,0.11,350,1632,770,821,753,1347,1014,977,539,357,233.0,1356,1197,292,702,2409,4244,501,1351,2464,647,1742,1091,1838,2181,1559,2426,1472,1662,1878,711,572,357,1267,589,588,1846,1530,176,215,663,1323,1067,2155,2001,653,383,845,1061,562,365,1564,824,1486,251,2280,662,1657,883,1002,477,895,185,491,1393,876,1473,1389,878,456,643,991,3733,321,3.463,2.651,2.848,2.714,1.979,2.456,2.444,2.883,2.376,3.088,2.869,2.051,2.693,2.656,2.807,2.564,2.975,3.768,3.064,2.061,3.322,2.64,2.166,2.557,2.462,2.641,2.14,2.127,2.654,2.491,2.297,2.602,2.259,3.047,3.077,2.503,3.014,3.137,1.936,2.369,2.371,2.16,3.347,1.896,1.873,2.29,2.496,2.838,2.529,2.74,1.864,2.093,2.188,2.265,2.284,2.001,2.427,2.563,1.949,2.157,2.338,2.467,1.92,2.263,2.157,1.953,2.05,2.288,2.291,2.109,2.394,2.434,2.51,2.048,0.766,0.563,0.659,0.54,0.638,0.751,0.702,0.539,0.609,0.637,0.673,0.601,0.488,0.603,0.546,0.713,0.701,0.729,0.768,0.685,0.714,0.515,0.648,0.804,0.663,0.617,0.578,0.602,0.712,0.634,0.604,1.024,0.409,0.733,0.731,0.542,0.858,0.697,0.338,0.483,0.489,0.677,0.82,0.656,0.6,0.426,0.347,0.719,0.473,0.491,0.474,0.457,0.59,0.466,0.404,0.419,0.583,0.549,0.403,0.456,0.459,0.692,0.372,0.655,0.468,0.68,0.427,0.436,0.494,0.489,0.438,0.521,0.507,0.384
-326,Epileptic,F,22.0,0.5,2.5,1.0,0.8,1.6,2.1,1.8,1.7,0.8,0.7,0.4,3.3,1.5,0.9,1.4,6.7,11.2,0.8,1.6,5.0,0.8,2.6,2.3,2.8,2.4,3.3,2.8,2.6,3.1,4.0,0.9,1.3,0.4,2.4,0.3,0.7,3.4,3.3,0.2,0.2,1.0,2.9,1.8,3.1,2.4,0.7,0.4,0.8,1.1,0.6,0.9,1.8,1.4,3.0,0.3,1.9,0.5,1.7,1.1,1.7,0.6,1.0,0.2,0.3,1.7,0.3,2.7,1.1,1.6,1.1,1.4,1.3,4.2,0.3,5,15,7,7,10,18,13,15,9,10,7.0,29,16,7,9,55,84,5,18,45,7,25,17,28,23,29,24,25,24,35,12,10,4,23,2,4,37,36,2,1,5,24,15,23,17,5,2,5,6,6,5,14,8,22,3,17,4,13,11,14,4,6,1,3,18,4,23,9,10,8,11,12,31,3,0.029,0.026,0.026,0.02,0.036,0.03,0.027,0.026,0.042,0.06,0.035,0.034,0.025,0.037,0.028,0.035,0.036,0.029,0.023,0.034,0.018,0.026,0.031,0.038,0.026,0.027,0.027,0.028,0.024,0.038,0.034,0.049,0.017,0.029,0.011,0.015,0.032,0.029,0.019,0.02,0.022,0.03,0.033,0.028,0.017,0.018,0.018,0.015,0.015,0.023,0.035,0.02,0.024,0.021,0.017,0.021,0.018,0.02,0.023,0.023,0.019,0.026,0.022,0.017,0.022,0.099,0.021,0.015,0.021,0.028,0.028,0.022,0.017,0.018,1342,4878,1734,1412,2157,2963,2361,3655,1389,706,696.0,3677,4042,1665,3547,12429,19255,2916,4039,5962,3248,4554,2510,6660,4449,7033,4659,4041,6299,5872,1676,1216,1112,6065,1818,1870,7862,8676,278,454,1529,3071,6132,2801,3158,1174,1005,2098,2657,1506,706,3390,1885,6273,580,2856,1093,3335,1265,1946,931,2168,478,954,2803,73,4111,2821,2540,1530,1706,2177,9215,471,0.104,0.109,0.111,0.101,0.127,0.141,0.111,0.116,0.132,0.19,0.125,0.132,0.109,0.15,0.112,0.12,0.123,0.106,0.118,0.137,0.092,0.124,0.126,0.119,0.116,0.119,0.108,0.105,0.099,0.132,0.117,0.14,0.091,0.118,0.057,0.082,0.125,0.124,0.116,0.089,0.097,0.129,0.124,0.123,0.098,0.096,0.092,0.087,0.084,0.099,0.144,0.101,0.104,0.095,0.103,0.112,0.1,0.106,0.127,0.118,0.097,0.102,0.09,0.107,0.118,0.24,0.116,0.092,0.107,0.129,0.131,0.114,0.094,0.122,345,1531,749,600,720,1117,952,1149,400,255,176.0,1586,1029,377,716,3063,5077,439,1071,2302,737,1551,1106,1293,1477,1908,1652,1387,1718,1715,441,439,360,1267,495,665,1764,1889,148,197,733,1465,914,1811,2047,677,343,783,1122,462,397,1511,929,2605,278,1549,492,1445,759,1136,522,677,237,356,1376,37,2023,1155,1180,590,824,1087,3710,241,3.581,3.007,2.841,2.661,2.652,2.536,2.172,2.932,2.566,2.498,2.924,2.021,3.013,3.145,3.405,2.942,3.053,4.085,2.949,2.188,3.23,2.391,2.048,3.41,2.463,2.853,2.4,2.354,2.805,2.658,2.695,2.757,2.55,3.267,3.26,2.602,3.067,3.208,2.457,2.481,2.561,1.902,4.073,1.811,1.805,2.037,3.398,3.377,2.886,3.563,2.405,2.525,2.239,2.5,2.329,2.048,2.617,2.695,1.931,1.975,2.196,3.043,2.235,2.5,2.32,2.369,2.254,2.739,2.752,2.73,2.281,2.415,2.696,2.526,0.711,0.687,0.558,0.519,0.692,0.517,0.545,0.761,0.724,0.685,0.836,0.5,0.537,0.721,0.74,0.779,0.75,0.784,0.45,0.612,0.906,0.444,0.443,0.921,0.603,0.644,0.581,0.666,0.71,0.68,0.737,0.997,0.438,0.704,0.928,0.496,0.835,0.611,0.42,0.491,0.655,0.536,0.728,0.552,0.518,0.518,0.659,0.851,0.715,0.69,0.48,0.518,0.672,0.554,0.347,0.397,0.392,0.54,0.353,0.388,0.383,0.962,0.572,1.023,0.57,0.35,0.496,0.617,0.589,0.543,0.407,0.505,0.6,0.376
-327,Epileptic,F,27.0,0.7,2.2,0.7,1.1,1.3,1.8,1.2,1.9,1.3,0.6,0.4,1.9,1.6,0.4,1.1,5.5,8.6,0.8,2.6,4.2,0.8,2.5,1.3,2.0,2.5,3.8,3.7,2.1,2.3,3.3,1.2,0.4,0.5,2.2,0.2,0.4,2.5,2.5,0.1,0.2,1.2,3.4,1.6,3.4,2.3,0.4,0.5,0.9,1.0,0.7,0.9,1.5,0.8,1.9,0.1,2.0,0.6,1.7,1.5,1.0,0.3,0.9,0.2,0.3,1.6,1.1,1.4,1.3,1.0,0.6,0.8,1.3,4.0,0.4,5,18,6,10,13,14,11,15,20,6,4.0,19,14,5,11,60,75,6,26,39,6,25,10,20,34,38,43,18,18,33,26,3,6,24,1,3,27,24,1,1,8,29,20,24,15,4,2,5,6,6,6,11,5,14,1,17,5,12,12,8,4,6,2,2,13,16,12,9,7,5,7,8,40,2,0.037,0.029,0.014,0.024,0.044,0.029,0.021,0.033,0.051,0.03,0.032,0.03,0.032,0.029,0.03,0.037,0.029,0.028,0.03,0.03,0.021,0.033,0.03,0.032,0.028,0.031,0.029,0.029,0.026,0.034,0.033,0.025,0.015,0.028,0.01,0.013,0.032,0.036,0.012,0.016,0.021,0.043,0.038,0.028,0.02,0.011,0.025,0.015,0.015,0.019,0.029,0.023,0.026,0.017,0.009,0.015,0.022,0.019,0.032,0.026,0.025,0.02,0.023,0.015,0.019,0.028,0.017,0.021,0.016,0.027,0.02,0.025,0.02,0.015,1371,4387,1922,2232,1820,2527,2083,3346,1627,1268,612.0,2417,3238,987,2288,10498,17103,2167,4025,4831,1963,3866,1625,4464,5119,6313,6238,2983,5054,5591,2250,745,1062,4894,1517,1578,4439,4876,356,355,1629,2345,4481,3134,3061,1074,722,2214,2079,1163,652,2530,1516,4393,293,3787,1007,3014,1572,1235,571,1768,457,705,2930,1058,2492,2069,1697,917,1148,1773,7483,683,0.127,0.117,0.098,0.12,0.153,0.124,0.095,0.141,0.159,0.135,0.131,0.129,0.133,0.111,0.127,0.139,0.127,0.106,0.13,0.123,0.083,0.136,0.131,0.118,0.133,0.138,0.132,0.12,0.107,0.135,0.131,0.096,0.089,0.118,0.059,0.08,0.124,0.125,0.07,0.094,0.119,0.152,0.14,0.121,0.1,0.09,0.112,0.086,0.093,0.107,0.136,0.109,0.111,0.094,0.074,0.101,0.106,0.102,0.139,0.123,0.128,0.107,0.113,0.104,0.107,0.143,0.095,0.108,0.102,0.134,0.109,0.119,0.105,0.085,354,1362,709,779,532,1014,759,1001,467,331,217.0,994,816,269,613,2618,4665,463,1335,2050,613,1265,663,1109,1582,1994,2199,1141,1413,1687,574,306,375,1242,410,545,1269,1166,193,193,783,1209,870,1841,1861,582,304,870,1001,542,404,1186,581,1776,137,1992,479,1415,794,602,266,694,185,347,1305,607,1406,920,862,350,627,828,3272,382,3.67,2.954,2.691,2.768,2.634,2.205,2.255,2.925,2.589,2.985,2.441,2.048,2.953,2.641,2.747,2.867,2.947,3.551,2.589,2.036,2.712,2.506,2.15,2.803,2.627,2.632,2.27,2.086,2.692,2.562,2.873,2.255,2.195,2.762,3.103,2.556,2.673,2.943,1.977,2.227,2.619,1.865,3.412,1.893,1.858,1.991,3.125,3.114,2.576,2.438,1.945,2.264,2.465,2.442,1.991,2.187,2.414,2.427,2.31,2.211,2.397,2.558,2.237,2.107,2.355,2.138,1.918,2.562,2.28,3.054,2.149,2.7,2.361,2.203,0.874,0.609,0.607,0.531,0.715,0.597,0.475,0.682,0.547,0.379,0.545,0.416,0.401,0.571,0.602,0.595,0.575,0.857,0.617,0.593,0.61,0.428,0.427,0.678,0.595,0.595,0.493,0.482,0.488,0.694,0.647,1.002,0.332,0.706,0.863,0.502,0.533,0.614,0.371,0.611,0.639,0.495,0.765,0.568,0.567,0.49,0.505,0.965,0.468,0.374,0.342,0.437,0.784,0.628,0.396,0.476,0.485,0.605,0.387,0.41,0.435,0.626,0.803,0.95,0.534,0.496,0.406,0.412,0.409,0.588,0.425,0.454,0.526,0.25
-328,Epileptic,M,52.0,0.9,2.7,1.0,1.4,1.7,2.8,3.1,1.9,1.3,1.0,0.2,3.3,1.5,0.4,1.1,6.3,9.9,2.0,3.0,5.2,1.3,4.7,1.4,3.3,6.6,4.9,4.9,3.5,3.7,4.8,1.9,1.0,0.5,2.9,0.7,1.6,6.7,4.2,0.3,0.2,1.1,4.3,2.2,2.4,3.5,1.0,0.4,1.4,1.6,1.0,0.9,1.9,2.5,2.9,0.8,3.7,1.2,2.2,1.2,1.6,0.6,1.8,0.4,0.6,1.7,1.3,3.4,1.6,0.9,0.6,1.4,2.0,6.8,0.1,8,29,7,12,13,31,24,21,20,8,3.0,33,16,7,17,82,121,13,31,52,11,45,17,34,79,50,53,32,33,61,21,9,5,37,7,16,67,53,2,1,6,38,20,15,20,7,2,10,6,6,5,16,21,20,8,32,8,17,11,12,5,11,2,5,13,16,27,12,5,6,11,13,61,1,0.04,0.027,0.02,0.027,0.029,0.034,0.041,0.031,0.044,0.045,0.028,0.038,0.03,0.034,0.036,0.035,0.036,0.055,0.037,0.037,0.028,0.049,0.029,0.035,0.051,0.037,0.04,0.032,0.035,0.035,0.051,0.043,0.028,0.033,0.02,0.028,0.047,0.04,0.016,0.024,0.023,0.046,0.042,0.027,0.023,0.018,0.017,0.017,0.019,0.029,0.028,0.019,0.029,0.022,0.028,0.023,0.022,0.022,0.025,0.025,0.021,0.031,0.015,0.019,0.018,0.022,0.022,0.019,0.017,0.02,0.027,0.024,0.022,0.014,1744,5036,2394,2542,2398,4491,2618,3358,1935,1595,751.0,2976,3324,1011,2086,11635,18131,2353,5341,5811,3614,4125,2191,6137,7142,7175,5699,3765,5859,6686,2759,927,896,6802,2313,2649,7561,8994,477,327,1500,3397,5914,2647,3658,1534,766,2871,2449,1365,775,3432,2674,4817,879,4648,1727,3660,1461,1810,875,2717,663,941,2701,2006,4449,2770,1558,965,1586,2885,11082,376,0.123,0.126,0.098,0.117,0.125,0.134,0.124,0.132,0.164,0.165,0.112,0.145,0.133,0.126,0.138,0.141,0.139,0.155,0.138,0.145,0.107,0.148,0.128,0.132,0.145,0.136,0.127,0.111,0.118,0.139,0.17,0.123,0.116,0.128,0.097,0.112,0.152,0.146,0.097,0.096,0.1,0.151,0.148,0.111,0.104,0.099,0.094,0.088,0.095,0.111,0.132,0.101,0.139,0.104,0.117,0.115,0.109,0.116,0.119,0.122,0.096,0.131,0.093,0.112,0.098,0.117,0.108,0.1,0.094,0.129,0.129,0.121,0.109,0.102,480,1838,845,949,873,1670,1238,1195,686,413,188.0,1505,903,285,726,3724,5626,542,1489,2712,903,1629,969,1651,2650,2525,2328,1733,1805,2550,829,436,315,1737,594,903,2490,2256,258,150,832,1666,1200,1570,2196,843,338,1198,1120,551,457,1690,1344,2161,543,2563,839,1629,822,1011,503,942,385,457,1460,855,2460,1255,734,483,850,1320,4997,163,3.115,2.534,2.66,2.543,2.404,2.237,1.872,2.458,2.265,2.902,3.002,1.697,2.813,2.716,2.336,2.46,2.511,3.425,2.683,1.904,2.89,2.067,1.987,2.757,2.198,2.357,2.01,1.82,2.558,2.144,2.703,2.311,2.468,2.794,3.359,2.449,2.316,2.9,1.979,2.468,2.207,1.817,3.296,1.936,1.883,1.898,2.755,2.839,2.569,2.565,1.986,2.05,2.107,2.305,2.038,1.947,2.392,2.421,1.934,1.923,1.989,2.833,1.996,2.443,1.997,2.591,1.881,2.342,2.339,2.093,1.958,2.424,2.35,2.387,0.689,0.649,0.71,0.55,0.553,0.621,0.513,0.52,0.556,0.66,0.876,0.464,0.428,0.419,0.43,0.494,0.623,0.76,0.639,0.559,0.81,0.579,0.526,0.742,0.676,0.587,0.537,0.508,0.521,0.582,0.652,0.809,0.421,0.691,0.518,0.604,0.699,0.617,0.396,0.476,0.449,0.511,0.809,0.665,0.587,0.437,0.382,0.664,0.522,0.536,0.373,0.447,0.424,0.461,0.383,0.401,0.426,0.476,0.424,0.418,0.366,0.796,0.306,0.708,0.604,0.918,0.401,0.455,0.446,0.613,0.43,0.536,0.493,0.384
-329,Epileptic,M,57.0,1.1,2.8,1.1,1.2,3.2,2.0,1.8,2.4,1.2,0.9,0.4,3.1,1.3,0.7,1.2,6.6,10.2,1.1,2.5,3.6,1.4,3.0,2.1,4.5,3.5,3.8,4.1,2.7,3.6,3.7,3.0,1.5,0.6,3.9,0.4,1.2,7.5,4.2,0.2,0.4,1.4,3.6,2.9,2.7,3.3,0.7,0.6,0.8,1.6,1.5,0.4,2.2,2.0,3.2,0.4,3.0,1.2,2.3,1.4,1.1,0.7,2.1,0.6,1.0,2.0,1.6,2.6,1.4,1.6,1.1,1.8,2.2,5.5,0.5,7,32,10,10,24,22,15,19,12,12,5.0,30,15,10,17,66,92,8,28,35,15,34,22,50,52,50,49,30,36,47,28,13,6,36,3,13,67,62,2,3,8,27,30,18,17,3,3,4,9,11,4,18,20,23,2,25,7,20,13,8,5,15,4,8,16,23,21,9,11,11,12,18,52,4,0.04,0.024,0.018,0.023,0.052,0.03,0.03,0.032,0.042,0.036,0.044,0.037,0.025,0.033,0.029,0.031,0.033,0.04,0.031,0.034,0.031,0.032,0.032,0.046,0.034,0.032,0.029,0.032,0.035,0.031,0.059,0.061,0.036,0.042,0.02,0.023,0.06,0.037,0.021,0.022,0.028,0.037,0.048,0.026,0.021,0.011,0.018,0.013,0.018,0.042,0.018,0.025,0.023,0.025,0.021,0.019,0.025,0.02,0.031,0.019,0.018,0.026,0.028,0.042,0.019,0.027,0.02,0.02,0.022,0.023,0.028,0.021,0.018,0.022,1658,5970,2444,2337,2835,3809,2680,3915,1861,1623,564.0,2862,3225,1572,2339,11485,19849,2316,5143,3814,3188,4840,2743,6769,6559,7592,7052,4199,6333,5945,2641,1272,945,6087,1583,2298,7491,9009,423,597,1330,2858,4105,2627,3696,1500,997,2362,2721,1502,528,3420,3755,5578,405,4629,1628,3232,1292,1698,1056,3635,588,1051,3085,1218,3723,2093,2199,1621,1942,2760,10173,688,0.132,0.128,0.104,0.111,0.17,0.135,0.101,0.133,0.156,0.157,0.147,0.136,0.121,0.126,0.133,0.13,0.131,0.142,0.134,0.125,0.108,0.12,0.131,0.145,0.125,0.141,0.118,0.105,0.108,0.126,0.157,0.139,0.133,0.136,0.084,0.115,0.158,0.149,0.108,0.12,0.128,0.13,0.142,0.119,0.106,0.076,0.09,0.083,0.095,0.151,0.12,0.114,0.111,0.112,0.117,0.103,0.111,0.113,0.132,0.101,0.1,0.122,0.125,0.125,0.101,0.132,0.105,0.103,0.108,0.123,0.131,0.116,0.104,0.114,453,2056,1010,875,1067,1254,1075,1262,573,463,212.0,1461,879,458,781,3691,5745,521,1382,1863,883,1583,1129,2072,2203,2345,2726,1677,1871,2234,937,594,296,1610,446,846,2606,2413,194,278,724,1502,1264,1618,2322,839,494,904,1283,639,367,1625,1631,2275,261,2592,806,1698,762,938,618,1328,343,471,1593,908,2151,1062,1137,765,1031,1443,4634,342,3.549,2.66,2.37,2.582,2.42,2.355,2.108,2.665,2.476,2.611,2.31,1.761,2.821,2.485,2.381,2.505,2.726,3.521,2.871,1.803,2.836,2.391,2.099,2.581,2.355,2.505,2.089,2.002,2.589,2.164,2.16,2.307,2.529,2.783,3.211,2.345,2.263,2.703,2.499,2.38,2.294,1.664,2.62,1.815,1.807,1.946,2.444,3.212,2.531,2.426,1.71,2.174,2.277,2.399,1.937,1.904,2.246,2.058,1.791,1.984,1.89,2.62,2.036,2.376,2.096,1.733,1.806,2.217,2.152,2.265,1.907,2.202,2.277,2.132,0.688,0.531,0.463,0.438,0.617,0.73,0.583,0.477,0.627,0.7,0.674,0.426,0.411,0.592,0.465,0.521,0.62,0.599,0.579,0.514,0.787,0.621,0.445,0.737,0.571,0.64,0.506,0.575,0.591,0.534,0.813,0.833,0.479,0.689,0.49,0.517,0.737,0.617,0.306,0.328,0.425,0.528,0.706,0.622,0.514,0.386,0.491,0.851,0.476,0.559,0.356,0.352,0.438,0.452,0.343,0.358,0.515,0.43,0.51,0.393,0.372,0.686,0.437,0.562,0.471,0.604,0.394,0.418,0.44,0.505,0.447,0.452,0.505,0.513
-330,Epileptic,F,42.0,0.7,2.1,0.8,1.2,2.2,1.5,3.5,1.3,1.7,0.6,0.4,2.9,1.8,0.5,1.8,7.1,11.8,0.9,2.1,4.4,1.1,3.5,2.1,3.6,3.8,2.4,4.1,3.3,3.2,4.8,1.9,1.0,0.5,3.3,0.6,0.3,3.7,3.0,0.4,0.2,0.9,4.4,3.1,2.2,4.5,0.7,0.5,0.8,1.4,0.5,0.4,2.2,1.2,3.1,0.2,2.9,1.1,1.3,1.3,1.4,0.8,1.6,0.3,0.4,1.7,1.0,3.1,1.3,1.4,1.1,0.7,1.1,4.6,0.3,5,12,6,7,10,17,19,12,14,6,3.0,23,17,6,14,66,88,5,20,37,8,32,17,36,47,25,37,27,26,43,20,6,3,29,3,2,37,29,1,1,4,25,28,15,21,4,2,4,8,4,3,11,7,17,2,18,7,9,8,10,5,10,1,3,11,13,18,6,6,7,4,7,36,2,0.041,0.028,0.021,0.023,0.049,0.038,0.053,0.028,0.073,0.04,0.038,0.047,0.048,0.041,0.046,0.056,0.053,0.043,0.038,0.047,0.03,0.043,0.035,0.06,0.042,0.033,0.04,0.046,0.042,0.051,0.057,0.049,0.036,0.048,0.028,0.01,0.046,0.04,0.024,0.026,0.024,0.055,0.066,0.027,0.032,0.017,0.026,0.014,0.022,0.015,0.027,0.028,0.034,0.031,0.013,0.021,0.026,0.021,0.028,0.023,0.03,0.033,0.029,0.03,0.023,0.023,0.029,0.024,0.025,0.054,0.022,0.023,0.021,0.025,1518,3610,2171,2194,1732,2110,2686,2522,2116,1297,802.0,2860,2917,974,2301,9355,17397,2213,3365,4587,3477,4658,2131,5288,5891,4767,5485,3466,4812,5587,2167,860,736,4494,1220,1098,5470,6566,348,408,1024,2034,4784,2245,3297,1365,592,1926,1941,1153,412,2585,1397,4005,380,3936,1540,2186,1056,1742,1001,2296,285,771,2086,987,3273,1567,1557,919,886,1449,7510,408,0.129,0.115,0.097,0.101,0.142,0.141,0.148,0.134,0.22,0.149,0.131,0.161,0.149,0.139,0.158,0.169,0.164,0.121,0.136,0.169,0.111,0.141,0.122,0.17,0.145,0.134,0.13,0.141,0.13,0.164,0.177,0.123,0.12,0.156,0.093,0.061,0.156,0.149,0.099,0.097,0.101,0.165,0.195,0.109,0.118,0.093,0.108,0.077,0.1,0.09,0.134,0.112,0.125,0.118,0.102,0.101,0.124,0.108,0.123,0.11,0.115,0.127,0.138,0.122,0.11,0.111,0.119,0.105,0.109,0.145,0.116,0.114,0.101,0.119,351,1185,717,835,713,836,1095,807,540,322,192.0,1267,822,279,737,2735,4627,405,1052,1794,748,1548,1077,1400,1862,1417,2095,1407,1561,1835,649,376,249,1207,380,431,1681,1546,225,177,572,1214,1039,1303,2102,661,274,766,990,587,239,1277,686,1742,235,2157,616,1007,683,969,470,778,143,290,1110,655,1785,830,806,346,468,721,3426,195,3.741,2.791,2.817,2.67,2.17,2.264,2.06,2.695,2.918,2.825,2.977,1.976,2.593,2.536,2.395,2.456,2.77,3.802,2.607,2.165,3.276,2.336,1.763,2.817,2.472,2.631,2.134,1.996,2.337,2.41,2.646,2.428,2.435,2.741,2.961,2.31,2.417,3.046,1.825,2.414,2.151,1.613,3.055,1.891,1.814,2.172,2.521,3.022,2.389,2.334,2.144,2.131,2.089,2.231,1.934,1.97,2.299,2.361,1.796,1.923,2.276,2.987,2.187,2.66,2.031,1.873,1.932,2.123,2.249,2.488,2.056,2.291,2.275,2.585,0.957,0.698,0.54,0.447,0.553,0.581,0.64,0.562,0.657,0.703,0.702,0.527,0.603,0.562,0.525,0.716,0.726,0.884,0.593,0.655,0.734,0.626,0.49,0.801,0.59,0.619,0.554,0.534,0.536,0.649,0.769,0.886,0.397,0.571,0.887,0.477,0.771,0.631,0.33,0.468,0.497,0.44,0.975,0.576,0.432,0.559,0.469,0.936,0.464,0.404,0.454,0.434,0.538,0.538,0.31,0.405,0.533,0.507,0.4,0.472,0.465,0.804,0.525,0.63,0.45,0.717,0.464,0.429,0.403,0.812,0.456,0.376,0.499,0.453
-331,Epileptic,F,42.0,0.8,1.9,0.8,1.4,1.3,1.5,1.3,1.2,1.3,0.5,0.3,2.0,1.4,0.7,0.8,5.5,6.4,0.6,2.5,4.0,1.2,2.6,1.3,2.5,2.6,1.8,2.0,2.1,2.3,2.6,1.0,1.2,1.0,2.4,0.2,1.1,2.2,2.4,0.2,0.2,0.9,2.6,2.2,2.4,2.8,0.4,0.5,0.7,1.2,0.5,0.6,1.5,1.8,2.9,0.2,1.7,0.7,1.4,0.7,0.7,0.5,0.8,0.4,0.8,1.5,1.6,2.0,1.0,1.1,0.3,0.9,1.2,3.6,0.1,4,18,10,12,12,13,13,11,11,5,3.0,20,14,9,11,55,63,5,24,35,12,29,13,25,25,22,22,21,19,31,12,8,8,26,1,8,27,31,1,1,5,24,15,17,15,3,2,6,6,6,3,12,15,24,1,15,5,13,5,6,5,5,2,8,11,16,20,7,7,4,6,8,30,1,0.041,0.024,0.016,0.024,0.034,0.031,0.023,0.026,0.053,0.029,0.024,0.031,0.028,0.034,0.031,0.036,0.028,0.026,0.036,0.033,0.025,0.042,0.036,0.034,0.033,0.027,0.024,0.027,0.025,0.033,0.036,0.044,0.067,0.029,0.009,0.033,0.028,0.028,0.028,0.017,0.022,0.037,0.039,0.023,0.023,0.009,0.021,0.015,0.019,0.013,0.032,0.022,0.031,0.027,0.035,0.021,0.023,0.018,0.024,0.02,0.02,0.021,0.022,0.048,0.027,0.037,0.02,0.017,0.022,0.017,0.024,0.02,0.017,0.013,1432,4050,2250,2456,1721,2332,2169,2582,1693,994,496.0,2563,2893,1151,1769,8529,13810,2133,3738,4126,3222,3848,1608,4574,3900,3960,4016,3375,5042,4015,1674,1544,888,5088,1845,1665,4772,5737,254,414,1370,2780,3776,2849,3076,1162,702,1869,2175,1286,523,2579,2509,5115,284,2598,1380,2483,815,1046,813,1750,428,622,2021,931,2999,2050,1532,823,1004,2050,8233,398,0.12,0.113,0.102,0.121,0.131,0.127,0.116,0.123,0.147,0.113,0.118,0.141,0.131,0.15,0.127,0.143,0.122,0.105,0.137,0.133,0.103,0.155,0.14,0.136,0.14,0.123,0.12,0.115,0.108,0.148,0.119,0.129,0.19,0.138,0.06,0.123,0.119,0.13,0.144,0.101,0.11,0.147,0.134,0.109,0.107,0.078,0.112,0.072,0.096,0.098,0.123,0.112,0.123,0.119,0.153,0.118,0.103,0.105,0.121,0.114,0.111,0.104,0.109,0.168,0.119,0.152,0.109,0.098,0.118,0.116,0.124,0.114,0.095,0.095,340,1328,846,922,631,839,862,752,432,283,193.0,1007,757,336,532,2601,3869,388,1111,1857,838,1175,601,1175,1261,1291,1411,1235,1365,1299,436,439,280,1273,443,573,1387,1512,116,200,641,1169,957,1650,1819,651,326,872,952,581,312,1158,965,1762,102,1266,550,1222,481,558,425,592,244,326,949,635,1642,940,770,354,550,936,3519,155,3.575,2.817,2.556,2.668,2.354,2.491,2.166,3.003,2.86,2.842,2.333,2.088,2.903,2.602,2.593,2.593,2.896,3.732,2.757,1.903,2.898,2.518,2.183,2.806,2.456,2.505,2.319,2.193,2.803,2.505,2.763,2.989,2.684,2.855,3.559,2.532,2.661,2.853,2.748,2.398,2.636,2.024,3.07,1.927,1.941,1.914,2.715,2.456,2.742,2.352,2.142,2.341,2.292,2.541,2.955,2.259,2.864,2.23,1.933,2.037,2.138,2.838,2.147,2.161,2.223,1.942,2.018,2.499,2.459,2.427,2.203,2.817,2.469,2.953,0.718,0.583,0.553,0.483,0.579,0.525,0.435,0.659,0.587,0.346,0.634,0.482,0.434,0.448,0.353,0.559,0.636,0.819,0.625,0.589,0.82,0.514,0.502,0.625,0.433,0.572,0.399,0.454,0.44,0.58,0.606,1.101,0.554,0.62,0.897,0.438,0.571,0.488,0.304,0.363,0.777,0.521,0.68,0.645,0.609,0.414,0.485,0.726,0.484,0.485,0.275,0.449,0.681,0.639,0.627,0.466,0.636,0.517,0.431,0.417,0.374,0.852,0.315,0.67,0.567,0.504,0.41,0.355,0.399,0.697,0.424,0.599,0.563,0.54
-332,Epileptic,F,27.0,0.6,1.6,0.7,1.0,1.6,2.0,1.3,1.1,1.1,0.7,0.2,2.7,1.6,0.4,1.0,4.6,5.7,0.5,1.9,5.8,0.8,2.4,1.4,2.5,3.2,3.7,3.1,1.7,1.7,3.4,1.1,0.7,0.3,2.0,0.2,0.4,3.2,2.8,0.1,0.1,0.8,2.7,1.9,3.0,1.6,0.5,0.4,0.9,1.0,1.0,0.8,1.2,0.7,1.8,0.1,2.3,0.5,2.0,0.9,1.0,0.5,0.9,0.3,0.4,1.1,1.9,1.8,0.9,0.7,0.3,1.0,1.6,4.9,0.2,7,17,8,8,13,15,12,10,11,6,3.0,24,14,6,11,48,61,3,20,44,8,21,11,27,39,40,43,20,15,37,12,8,4,21,2,4,35,30,1,1,4,25,18,22,13,3,2,7,5,9,5,7,6,11,1,20,5,17,7,8,3,6,3,4,8,16,13,6,6,3,6,9,37,2,0.032,0.022,0.015,0.024,0.044,0.042,0.03,0.028,0.049,0.035,0.025,0.037,0.04,0.034,0.031,0.036,0.027,0.022,0.029,0.041,0.021,0.042,0.034,0.034,0.04,0.038,0.03,0.025,0.023,0.037,0.044,0.053,0.025,0.031,0.011,0.014,0.041,0.035,0.013,0.016,0.021,0.035,0.032,0.028,0.018,0.013,0.022,0.017,0.017,0.021,0.041,0.017,0.026,0.02,0.021,0.02,0.015,0.03,0.033,0.021,0.023,0.022,0.035,0.026,0.018,0.048,0.017,0.017,0.017,0.017,0.022,0.031,0.024,0.019,1138,3444,1488,1737,1824,2794,2072,2156,1850,1306,450.0,2223,3004,890,2176,8852,13254,1836,3429,3757,2484,3326,1759,4536,5266,6234,5312,2579,3890,5462,2022,566,587,4536,1656,1334,4754,5915,229,311,1158,2680,4614,2727,2033,1116,653,1704,1842,1568,580,2302,1205,3171,291,3279,1231,2430,945,1317,862,1584,465,817,1900,980,2915,1693,1358,1067,1470,1490,7668,304,0.128,0.118,0.109,0.114,0.154,0.155,0.111,0.129,0.161,0.13,0.126,0.145,0.15,0.146,0.141,0.145,0.127,0.1,0.126,0.15,0.094,0.155,0.13,0.146,0.157,0.151,0.127,0.111,0.107,0.147,0.138,0.155,0.132,0.13,0.07,0.086,0.129,0.135,0.107,0.097,0.108,0.142,0.136,0.12,0.097,0.087,0.108,0.095,0.096,0.119,0.161,0.098,0.122,0.106,0.128,0.116,0.087,0.13,0.137,0.112,0.112,0.11,0.14,0.139,0.097,0.17,0.096,0.103,0.105,0.11,0.115,0.134,0.114,0.113,314,1142,655,669,563,901,730,686,403,320,124.0,1126,732,232,548,2212,3586,358,1004,2201,619,955,676,1158,1523,1756,1931,1064,1109,1596,472,242,223,987,394,463,1517,1451,134,96,612,1249,1016,1787,1378,549,281,782,862,706,326,1056,485,1408,123,1756,488,1206,525,758,343,607,169,325,973,539,1640,793,630,316,719,794,3132,145,3.302,2.817,2.205,2.522,2.548,2.692,2.288,2.778,3.213,3.404,3.263,1.771,3.137,2.825,2.85,2.979,2.958,3.858,2.723,1.627,3.089,2.568,2.125,2.908,2.624,2.757,2.158,1.953,2.687,2.614,3.029,2.403,2.226,3.148,3.635,2.549,2.584,2.884,1.887,3.38,2.349,1.972,3.299,1.786,1.675,2.204,2.817,2.737,2.623,2.501,2.337,2.42,2.33,2.558,2.559,2.068,2.433,2.298,2.068,1.923,2.578,2.67,2.556,2.719,2.076,2.387,1.955,2.513,2.399,3.86,2.307,2.341,2.603,2.385,0.565,0.726,0.597,0.565,0.753,0.659,0.55,0.607,0.658,0.505,0.979,0.472,0.499,0.385,0.606,0.586,0.531,0.684,0.575,0.476,0.837,0.549,0.482,0.571,0.746,0.617,0.564,0.457,0.512,0.697,0.639,0.882,0.461,0.724,0.905,0.432,0.668,0.743,0.337,0.631,0.506,0.691,0.675,0.616,0.492,0.483,0.493,0.775,0.456,0.461,0.534,0.475,0.701,0.469,0.382,0.45,0.481,0.736,0.403,0.354,0.552,0.79,0.532,0.816,0.488,0.707,0.422,0.346,0.419,0.78,0.514,0.549,0.576,0.419
-333,Epileptic,M,52.0,1.1,3.3,1.6,2.0,2.3,3.4,2.5,2.8,2.2,0.5,0.3,4.0,2.2,0.8,1.5,11.8,13.3,1.6,2.6,5.2,1.4,4.0,2.2,3.5,2.9,2.1,2.2,2.3,4.2,2.4,2.2,1.2,0.4,2.9,0.6,0.9,6.6,3.8,0.2,0.2,1.0,4.7,2.9,4.2,2.9,1.0,0.5,0.9,1.8,1.3,0.7,2.8,2.3,5.1,0.4,1.5,0.7,1.9,1.2,1.4,0.7,1.9,0.5,0.8,2.6,1.5,1.6,3.9,1.6,0.8,0.6,1.7,4.9,0.5,8,21,11,16,20,29,24,23,16,5,5.0,41,27,7,13,95,99,11,29,47,10,45,26,31,36,23,40,27,35,27,20,13,4,30,4,6,60,42,2,1,5,41,27,30,18,8,2,7,9,10,4,21,16,32,3,11,5,16,7,12,5,13,4,7,17,19,13,30,9,4,4,11,38,3,0.036,0.029,0.022,0.022,0.04,0.039,0.032,0.028,0.054,0.028,0.028,0.039,0.029,0.047,0.032,0.04,0.033,0.047,0.028,0.032,0.026,0.035,0.03,0.037,0.033,0.026,0.024,0.023,0.026,0.025,0.05,0.047,0.025,0.029,0.014,0.021,0.046,0.028,0.015,0.02,0.018,0.039,0.041,0.026,0.017,0.025,0.02,0.015,0.017,0.019,0.025,0.02,0.024,0.022,0.012,0.016,0.012,0.019,0.017,0.02,0.02,0.027,0.024,0.027,0.021,0.022,0.015,0.024,0.023,0.022,0.02,0.018,0.016,0.019,1934,6788,3345,3640,2557,3500,3981,4714,1964,1205,636.0,4583,5405,1252,3041,15253,23235,2823,5271,6450,3715,5761,2964,7189,5989,4803,4246,5427,5603,5105,2437,1187,896,6710,2800,2396,8755,9418,403,397,1533,4169,6702,4578,4753,1539,984,2499,3060,2278,624,4693,3093,9033,714,3107,1810,3852,1889,2207,1052,2963,640,1295,3930,1696,3098,6163,1740,1223,982,2906,10077,664,0.109,0.115,0.096,0.112,0.141,0.14,0.12,0.114,0.149,0.13,0.127,0.141,0.114,0.135,0.118,0.129,0.125,0.145,0.126,0.125,0.104,0.116,0.139,0.133,0.113,0.106,0.124,0.102,0.093,0.121,0.136,0.125,0.09,0.117,0.076,0.093,0.139,0.123,0.094,0.098,0.093,0.141,0.148,0.114,0.091,0.115,0.099,0.081,0.092,0.106,0.123,0.101,0.113,0.103,0.091,0.093,0.082,0.101,0.095,0.113,0.1,0.124,0.123,0.112,0.107,0.109,0.093,0.107,0.105,0.115,0.108,0.101,0.089,0.114,556,1988,1265,1442,1000,1465,1374,1596,659,306,208.0,1896,1521,317,853,4915,6803,602,1605,2693,905,1824,1311,1780,1798,1489,1538,1746,2534,1710,728,546,292,1655,693,779,2627,2222,221,192,786,2010,1429,2736,2588,683,402,1072,1458,1075,409,2352,1505,3558,445,1386,916,1786,1051,1080,560,1100,327,517,1836,1144,1616,2541,1048,535,479,1439,4748,336,3.406,2.774,2.469,2.362,2.366,2.155,2.335,2.559,2.407,2.917,2.472,2.046,2.785,2.763,2.734,2.53,2.742,3.463,2.668,2.08,2.936,2.461,1.942,2.995,2.616,2.69,2.317,2.442,1.988,2.409,2.384,2.276,2.493,2.969,3.109,2.632,2.597,3.077,2.112,2.432,2.332,1.906,3.303,1.938,2.012,2.154,3.026,2.81,2.599,2.429,1.84,2.089,2.129,2.484,2.038,2.322,2.203,2.331,2.032,2.235,2.102,2.828,2.234,2.757,2.203,1.732,2.096,2.366,1.904,2.352,2.043,2.401,2.281,2.493,0.621,0.801,0.505,0.523,0.65,0.557,0.647,0.597,0.719,0.605,0.7,0.445,0.438,0.498,0.555,0.624,0.642,0.691,0.56,0.609,0.83,0.594,0.547,0.685,0.593,0.604,0.504,0.497,0.657,0.636,0.866,0.777,0.397,0.63,0.817,0.541,0.9,0.69,0.425,0.466,0.408,0.505,0.889,0.542,0.399,0.473,0.595,0.744,0.466,0.591,0.308,0.435,0.464,0.57,0.309,0.431,0.396,0.55,0.355,0.361,0.492,0.626,0.365,0.815,0.535,0.584,0.375,0.564,0.582,0.705,0.441,0.478,0.434,0.356
-334,Epileptic,M,42.0,0.9,2.2,0.9,1.4,1.5,1.4,1.3,1.9,1.5,1.0,0.3,2.8,1.3,0.6,1.8,4.8,9.1,0.8,1.8,5.0,1.3,3.2,1.4,4.5,2.9,3.3,3.0,2.6,2.3,3.0,1.1,1.6,0.5,2.2,0.2,0.9,3.4,3.5,0.2,0.1,1.0,3.4,2.4,2.3,2.2,0.7,0.4,0.6,1.2,0.6,0.8,2.0,1.1,2.1,0.3,2.1,0.5,1.5,0.9,1.1,1.4,1.7,0.5,0.4,1.6,1.9,2.6,0.8,1.1,0.6,0.8,1.7,4.2,0.1,10,20,11,12,15,15,13,15,16,12,5.0,28,16,8,19,52,83,6,23,47,18,34,14,49,39,39,36,27,20,37,16,13,3,27,2,7,39,41,2,1,6,31,29,18,13,4,2,4,6,5,6,13,7,13,2,19,3,14,7,8,14,12,4,3,14,22,21,6,7,6,7,15,37,1,0.037,0.02,0.016,0.023,0.033,0.031,0.024,0.03,0.045,0.043,0.028,0.035,0.025,0.033,0.03,0.031,0.028,0.027,0.028,0.036,0.023,0.036,0.028,0.042,0.035,0.028,0.029,0.024,0.022,0.033,0.03,0.05,0.028,0.025,0.008,0.022,0.033,0.031,0.013,0.02,0.021,0.04,0.037,0.025,0.015,0.012,0.024,0.012,0.015,0.016,0.03,0.018,0.019,0.017,0.015,0.018,0.016,0.02,0.023,0.017,0.03,0.026,0.024,0.015,0.019,0.029,0.021,0.014,0.018,0.021,0.025,0.021,0.017,0.016,1752,4997,2258,2620,2680,2524,2677,3386,2377,1748,729.0,3080,3700,1241,3724,11467,21313,3082,4082,5517,4543,5136,1947,8064,5552,6497,5568,3503,5726,5622,2748,1682,890,5971,2256,2194,9146,8974,470,308,1548,3566,6663,2655,3913,1800,858,2298,2538,1773,728,4038,2465,4895,573,3331,1251,3222,1362,1653,1901,2943,837,1162,2388,1429,3490,2029,1918,1617,1170,3041,8974,263,0.126,0.108,0.115,0.12,0.135,0.147,0.091,0.123,0.162,0.177,0.13,0.149,0.119,0.149,0.138,0.125,0.122,0.113,0.136,0.147,0.094,0.151,0.132,0.154,0.154,0.133,0.123,0.095,0.098,0.142,0.129,0.143,0.096,0.112,0.054,0.108,0.137,0.129,0.089,0.095,0.104,0.151,0.15,0.118,0.09,0.083,0.099,0.073,0.089,0.089,0.146,0.099,0.102,0.093,0.107,0.105,0.093,0.102,0.125,0.102,0.131,0.122,0.124,0.101,0.108,0.12,0.111,0.091,0.101,0.119,0.134,0.11,0.097,0.093,488,1659,854,921,898,920,871,1056,584,492,229.0,1253,910,306,983,2848,5397,448,1024,2235,1038,1492,848,1878,1556,1990,1842,1482,1534,1721,675,654,253,1344,565,732,1891,2053,273,148,754,1479,1214,1525,2129,858,311,880,1126,662,364,1684,901,1960,303,1707,528,1429,687,875,803,1019,349,432,1147,991,1923,913,911,553,558,1368,3917,109,3.46,2.885,2.536,2.727,2.565,2.507,2.525,2.908,2.93,2.998,2.414,2.105,3.041,2.929,2.912,2.935,3.065,3.982,3.026,2.107,3.102,2.61,2.061,3.188,2.746,2.637,2.381,1.944,2.811,2.536,2.918,2.554,3.287,2.994,3.392,2.74,3.154,3.031,1.973,2.337,2.556,2.07,3.585,1.92,2.08,2.269,3.284,3.088,2.742,2.901,2.086,2.512,2.674,2.69,2.368,2.101,2.775,2.443,2.291,2.087,2.429,2.812,2.482,2.906,2.275,1.839,1.981,2.606,2.429,2.898,2.446,2.553,2.507,2.807,0.861,0.644,0.555,0.667,0.592,0.441,0.45,0.69,0.612,0.737,0.977,0.474,0.433,0.403,0.492,0.614,0.676,0.731,0.499,0.74,0.792,0.581,0.503,0.7,0.469,0.618,0.562,0.519,0.513,0.595,0.677,1.124,0.856,0.682,0.842,0.469,0.801,0.623,0.273,0.32,0.515,0.48,0.741,0.588,0.708,0.441,0.649,0.725,0.553,0.706,0.436,0.486,0.74,0.448,0.346,0.427,0.504,0.408,0.534,0.445,0.452,0.77,0.483,0.929,0.582,0.548,0.446,0.393,0.437,0.785,0.648,0.575,0.476,0.42
-335,Epileptic,M,27.0,0.8,3.1,1.1,1.3,1.6,1.3,4.1,1.6,1.0,1.1,0.4,2.9,1.2,0.5,0.8,5.6,10.8,1.6,1.9,5.1,1.3,3.1,2.2,4.8,4.3,2.9,4.8,4.6,2.4,5.4,2.5,1.7,0.4,2.5,0.6,0.7,3.4,3.2,0.2,0.1,0.9,2.7,2.8,3.0,2.6,0.7,0.5,0.7,1.2,0.8,0.3,1.3,2.6,3.3,0.3,2.2,0.8,1.3,1.1,1.0,0.6,1.6,0.5,0.7,2.0,1.3,2.9,1.3,1.0,0.6,1.1,1.5,5.2,0.1,7,28,8,11,19,14,34,18,11,9,5.0,24,13,6,12,69,82,11,28,43,13,37,19,59,47,34,56,41,23,51,25,11,5,32,5,9,39,44,2,1,4,22,28,24,12,4,2,4,6,6,4,10,21,24,2,18,6,13,7,8,6,13,4,6,14,18,19,9,6,6,7,11,43,1,0.04,0.034,0.02,0.028,0.037,0.027,0.066,0.029,0.039,0.044,0.037,0.039,0.03,0.031,0.025,0.033,0.045,0.057,0.029,0.046,0.028,0.035,0.034,0.052,0.041,0.027,0.042,0.042,0.026,0.056,0.071,0.062,0.024,0.031,0.021,0.021,0.033,0.03,0.017,0.015,0.018,0.037,0.05,0.028,0.019,0.014,0.02,0.012,0.017,0.015,0.017,0.017,0.033,0.026,0.013,0.015,0.017,0.016,0.022,0.017,0.017,0.023,0.018,0.028,0.029,0.022,0.02,0.019,0.021,0.021,0.026,0.018,0.02,0.009,1417,4818,2300,2306,2643,2358,3116,3495,1759,1825,705.0,2829,2643,1177,1899,11180,18098,2540,4795,4680,4443,5149,1975,6234,6160,7059,6385,4423,6283,6012,2241,1219,910,6198,1838,1908,8547,8180,386,353,1374,2222,5119,2967,3477,1362,766,2448,2007,1799,452,2386,2364,5188,565,3615,1575,2691,1172,1500,1105,2765,658,792,2146,1189,4506,2219,1332,989,1330,2397,8998,366,0.132,0.135,0.108,0.123,0.15,0.129,0.162,0.135,0.164,0.16,0.149,0.147,0.13,0.138,0.139,0.138,0.151,0.165,0.137,0.165,0.113,0.126,0.125,0.148,0.143,0.131,0.145,0.13,0.112,0.17,0.189,0.135,0.113,0.137,0.1,0.114,0.141,0.14,0.12,0.086,0.094,0.129,0.156,0.13,0.094,0.091,0.105,0.074,0.092,0.096,0.119,0.102,0.127,0.11,0.099,0.101,0.101,0.104,0.112,0.107,0.105,0.117,0.108,0.14,0.129,0.121,0.101,0.103,0.103,0.124,0.123,0.106,0.109,0.061,411,1493,827,813,864,795,1260,1010,468,443,219.0,1307,711,293,553,3318,4550,545,1199,2017,944,1582,1008,1752,1956,1909,2283,1729,1534,1990,665,444,273,1410,446,655,1860,1894,165,161,716,1148,1167,1781,1995,691,315,922,957,758,241,1180,1112,2184,295,2001,715,1266,750,860,563,1067,408,388,1153,860,2263,1041,706,423,645,1206,3973,152,3.285,2.836,2.754,2.6,2.514,2.329,2.07,2.727,2.634,3.109,2.468,1.872,2.851,2.922,2.428,2.545,2.855,3.491,2.964,1.99,3.291,2.529,1.794,2.676,2.433,2.788,2.141,2.064,2.967,2.352,2.498,2.68,2.658,2.927,3.464,2.47,3.136,3.055,2.503,1.958,2.38,1.688,3.275,1.894,1.997,2.126,2.853,3.13,2.599,2.716,1.811,2.154,2.138,2.255,2.063,1.938,2.484,2.236,1.787,1.893,2.039,2.615,1.872,2.238,2.073,1.727,2.115,2.444,2.201,2.404,2.348,2.398,2.426,2.344,0.739,0.71,0.585,0.488,0.785,0.798,0.615,0.588,0.751,0.7,0.728,0.45,0.55,0.514,0.627,0.635,0.77,0.786,0.59,0.707,0.829,0.682,0.489,0.869,0.724,0.63,0.608,0.548,0.518,0.717,0.747,1.018,0.354,0.68,0.659,0.532,0.72,0.683,0.464,0.414,0.424,0.464,0.843,0.524,0.547,0.353,0.587,0.963,0.423,0.549,0.641,0.489,0.482,0.532,0.403,0.417,0.413,0.485,0.326,0.436,0.461,0.696,0.431,0.639,0.555,0.672,0.448,0.443,0.448,0.771,0.432,0.464,0.466,0.612
-336,Epileptic,M,37.0,0.7,2.1,0.7,1.2,1.7,1.3,1.5,1.6,1.3,0.6,0.5,4.1,1.4,0.7,1.1,5.0,8.3,0.7,2.4,5.3,0.9,2.5,2.2,3.9,2.7,2.9,2.7,2.3,2.9,2.5,1.4,0.9,0.4,2.3,0.4,0.8,2.2,2.9,0.2,0.3,0.9,3.1,2.1,4.0,2.2,0.2,0.3,0.6,1.2,0.7,0.4,3.2,1.2,3.0,0.3,1.8,0.8,1.7,1.1,0.9,0.6,2.3,0.6,0.4,2.3,1.6,2.0,1.8,1.2,1.2,0.6,0.9,4.0,0.3,6,19,6,11,17,12,11,15,15,8,5.0,40,18,9,11,55,75,7,25,50,11,29,22,41,34,30,32,21,23,32,16,7,4,27,2,8,24,33,2,2,6,31,17,29,14,3,1,4,6,5,3,25,12,19,2,14,6,15,9,8,5,17,4,5,20,19,19,12,9,11,5,6,32,2,0.029,0.021,0.012,0.022,0.026,0.025,0.025,0.022,0.031,0.022,0.033,0.03,0.025,0.028,0.024,0.025,0.026,0.031,0.031,0.031,0.024,0.028,0.027,0.038,0.025,0.027,0.024,0.022,0.021,0.024,0.03,0.049,0.025,0.022,0.014,0.019,0.026,0.024,0.011,0.021,0.02,0.028,0.033,0.026,0.015,0.007,0.014,0.01,0.017,0.013,0.021,0.024,0.019,0.017,0.022,0.015,0.018,0.019,0.019,0.021,0.021,0.026,0.028,0.018,0.021,0.024,0.015,0.019,0.016,0.024,0.011,0.018,0.015,0.012,1485,5379,2591,2178,2677,2489,2075,3138,2396,1395,706.0,4777,3528,1455,2373,9454,18723,2297,4639,6390,3692,4650,2750,6705,5812,5963,5219,3598,5846,5234,2617,1221,773,6551,1975,1814,5502,7849,433,529,1427,3861,6289,4132,3814,1050,816,2020,2319,1806,515,4375,2296,5799,301,2802,1557,3611,1607,1165,1116,3283,658,850,3256,2386,3285,3375,2012,1363,1424,2017,9579,644,0.127,0.108,0.081,0.114,0.126,0.12,0.099,0.115,0.131,0.118,0.135,0.14,0.121,0.138,0.121,0.114,0.115,0.116,0.136,0.139,0.091,0.126,0.136,0.135,0.127,0.124,0.116,0.09,0.088,0.125,0.123,0.106,0.108,0.12,0.079,0.103,0.119,0.12,0.085,0.124,0.103,0.135,0.139,0.125,0.088,0.071,0.086,0.071,0.093,0.092,0.113,0.118,0.109,0.087,0.118,0.101,0.096,0.109,0.11,0.112,0.108,0.127,0.134,0.113,0.119,0.121,0.094,0.099,0.099,0.135,0.091,0.101,0.093,0.087,437,1640,936,870,1156,881,758,1094,694,459,276.0,2169,1022,365,725,2911,5011,415,1297,2728,871,1475,1253,1828,1775,1727,1876,1415,1763,1731,780,430,270,1554,422,682,1366,1814,243,209,705,1735,1151,2386,2202,572,306,868,1027,738,299,2035,1054,2590,177,1627,761,1435,909,684,427,1273,331,424,1609,1014,1992,1531,1014,612,773,778,4078,283,3.287,3.031,2.807,2.582,2.111,2.469,2.31,2.615,2.516,2.683,2.412,1.928,2.64,2.886,2.641,2.549,2.919,3.801,2.844,2.05,3.249,2.467,1.957,2.795,2.551,2.793,2.258,2.072,2.603,2.456,2.537,2.554,2.408,2.979,3.704,2.503,2.959,3.023,2.101,2.722,2.509,1.951,3.763,1.937,1.98,1.952,3.42,2.824,2.781,2.725,2.135,2.243,2.207,2.309,2.055,1.95,2.293,2.46,2.072,1.883,2.522,2.62,2.277,2.154,2.172,2.465,1.801,2.494,2.301,2.488,2.078,2.566,2.501,2.575,0.752,0.574,0.423,0.443,0.563,0.551,0.511,0.448,0.534,0.431,0.448,0.461,0.366,0.459,0.495,0.521,0.571,0.724,0.52,0.622,0.742,0.429,0.454,0.857,0.492,0.546,0.498,0.634,0.513,0.439,0.821,1.19,0.468,0.663,0.638,0.481,0.697,0.642,0.398,0.589,0.426,0.462,0.76,0.473,0.67,0.398,0.347,0.64,0.451,0.518,0.28,0.398,0.381,0.461,0.311,0.375,0.482,0.594,0.37,0.371,0.565,0.604,0.374,0.768,0.491,0.549,0.47,0.344,0.373,0.603,0.337,0.651,0.428,0.38
-337,Epileptic,F,47.0,0.8,3.0,1.1,1.4,1.8,2.0,1.4,1.6,1.5,1.0,0.3,2.7,1.7,0.4,1.2,5.7,8.3,1.0,1.7,4.0,1.1,3.1,1.4,2.4,3.0,4.3,2.3,2.0,2.4,3.0,1.4,1.2,0.6,2.4,0.3,0.4,2.6,3.0,0.1,0.2,0.9,2.3,2.2,2.5,2.5,0.6,0.3,1.0,1.2,0.5,0.6,2.1,1.3,2.5,0.2,2.3,0.4,1.6,0.7,0.9,0.7,1.0,0.1,0.3,1.0,2.1,2.5,1.3,1.1,0.5,1.1,1.5,3.6,0.3,5,28,14,15,20,19,12,16,16,10,6.0,24,15,3,13,59,80,10,25,36,12,33,16,27,34,46,33,19,20,32,18,11,8,29,1,3,32,43,1,2,6,23,23,20,15,5,1,7,6,5,5,18,11,17,2,17,2,17,6,7,6,7,1,3,8,22,22,10,6,7,7,9,28,3,0.039,0.024,0.015,0.023,0.031,0.034,0.022,0.029,0.037,0.033,0.038,0.029,0.029,0.026,0.032,0.028,0.028,0.036,0.032,0.032,0.022,0.038,0.027,0.036,0.031,0.035,0.026,0.025,0.024,0.031,0.04,0.065,0.024,0.029,0.008,0.011,0.032,0.029,0.011,0.015,0.019,0.029,0.038,0.025,0.02,0.016,0.021,0.015,0.016,0.008,0.027,0.021,0.021,0.022,0.035,0.015,0.017,0.02,0.022,0.017,0.024,0.018,0.012,0.018,0.016,0.028,0.023,0.022,0.019,0.016,0.022,0.023,0.017,0.017,1472,5139,3070,2743,2519,3141,2747,2686,2081,1772,666.0,2830,3525,1109,2691,11608,16861,2565,3745,4732,3051,4337,2115,5755,6014,7480,5030,3232,5373,5406,2192,980,1289,6482,1953,1694,5705,7252,256,430,1599,2226,5475,3127,3446,1423,778,2402,2694,1606,641,3318,2572,4752,267,4149,842,3150,915,1528,1012,2365,438,764,2119,2386,3454,2422,1933,1114,1642,2217,7614,424,0.107,0.117,0.099,0.121,0.14,0.155,0.108,0.136,0.147,0.145,0.15,0.128,0.128,0.111,0.133,0.129,0.129,0.135,0.129,0.135,0.094,0.156,0.129,0.127,0.142,0.148,0.128,0.115,0.111,0.141,0.135,0.155,0.119,0.123,0.05,0.07,0.126,0.139,0.084,0.109,0.107,0.13,0.142,0.112,0.103,0.085,0.096,0.081,0.093,0.085,0.129,0.111,0.108,0.11,0.151,0.097,0.102,0.11,0.12,0.105,0.129,0.102,0.089,0.12,0.097,0.123,0.123,0.106,0.102,0.115,0.121,0.119,0.094,0.127,404,1865,1130,997,923,1041,932,880,593,466,193.0,1279,941,256,728,3306,4866,438,1007,1843,839,1317,868,1295,1703,2155,1697,1190,1428,1611,603,366,427,1336,579,637,1465,1743,140,225,723,1168,1126,1798,1928,693,265,990,1169,760,374,1603,1127,1893,92,2196,354,1428,554,814,503,804,201,309,990,1143,1721,1005,875,446,748,921,3336,241,3.372,2.488,2.553,2.634,2.297,2.641,2.414,2.777,2.54,2.986,2.788,1.932,2.882,2.953,2.717,2.66,2.8,3.89,2.898,2.141,2.984,2.477,2.131,3.048,2.68,2.847,2.371,2.141,2.827,2.616,2.67,2.645,2.538,3.284,3.079,2.489,2.883,3.067,2.061,2.161,2.702,1.739,3.445,2.011,2.06,2.222,3.432,3.024,2.735,2.364,2.057,2.215,2.335,2.601,2.83,2.101,2.674,2.367,1.818,2.093,2.481,2.836,2.388,2.532,2.248,2.357,2.224,2.562,2.589,2.585,2.447,2.779,2.471,2.009,0.863,0.71,0.498,0.476,0.648,0.487,0.535,0.508,0.607,0.57,0.606,0.558,0.451,0.6,0.516,0.573,0.563,0.734,0.52,0.709,0.825,0.505,0.503,0.736,0.464,0.567,0.394,0.484,0.518,0.569,0.894,1.018,0.409,0.533,0.645,0.407,0.767,0.615,0.286,0.409,0.416,0.396,0.704,0.698,0.57,0.437,0.814,0.796,0.53,0.555,0.398,0.47,0.649,0.491,0.366,0.457,0.512,0.573,0.35,0.429,0.345,0.805,0.334,0.977,0.49,1.093,0.498,0.389,0.458,0.818,0.45,0.522,0.505,0.551
-338,Epileptic,M,17.5,0.5,1.6,0.7,0.9,2.7,2.4,2.8,1.6,2.1,0.6,0.5,4.1,2.8,0.4,1.8,10.2,10.3,0.7,2.8,4.1,1.9,3.3,1.5,4.0,4.8,5.4,3.9,4.1,5.5,3.1,1.1,1.1,0.4,4.0,0.2,1.1,3.5,3.5,0.1,0.1,0.8,3.7,3.1,2.6,3.7,1.0,0.6,0.6,1.0,0.6,0.3,1.2,0.7,3.3,0.6,2.6,1.2,1.5,1.5,1.0,0.4,0.9,0.2,0.4,1.8,0.9,2.4,1.3,1.3,0.6,0.4,0.8,3.6,0.3,3,11,6,6,16,17,17,13,16,5,4.0,28,22,6,14,95,82,4,20,33,14,27,10,31,37,41,31,28,40,28,10,5,4,36,2,9,35,42,1,1,5,28,28,15,19,8,4,3,5,4,2,7,4,25,3,15,11,12,8,6,2,9,1,3,11,8,17,10,6,2,3,6,22,4,0.029,0.023,0.017,0.021,0.063,0.044,0.037,0.03,0.075,0.035,0.045,0.06,0.052,0.042,0.037,0.06,0.042,0.035,0.041,0.049,0.036,0.047,0.041,0.052,0.048,0.049,0.05,0.05,0.049,0.044,0.048,0.038,0.016,0.048,0.013,0.026,0.05,0.041,0.025,0.017,0.018,0.052,0.07,0.035,0.03,0.029,0.029,0.01,0.015,0.013,0.038,0.016,0.018,0.032,0.019,0.029,0.041,0.025,0.033,0.024,0.021,0.038,0.028,0.024,0.026,0.033,0.028,0.022,0.019,0.023,0.02,0.023,0.018,0.017,1963,4515,1903,2595,2396,3177,3327,3471,2314,1108,884.0,3781,3880,1116,3559,14645,22742,2934,4327,5750,4096,4578,2224,4785,7238,7280,6004,4744,6421,5326,1414,1150,1212,6065,1940,2525,6241,8000,349,355,1596,3773,5357,2720,3238,1551,873,1973,2690,1480,539,3111,1954,5793,903,3239,2050,2852,1432,1687,670,1518,297,824,2596,1120,3426,2268,2392,803,982,1948,7737,635,0.1,0.092,0.086,0.093,0.161,0.151,0.123,0.118,0.199,0.129,0.144,0.18,0.143,0.158,0.131,0.167,0.134,0.106,0.137,0.155,0.118,0.144,0.124,0.15,0.155,0.145,0.15,0.146,0.136,0.147,0.132,0.114,0.091,0.13,0.065,0.106,0.149,0.145,0.099,0.095,0.099,0.152,0.174,0.123,0.122,0.126,0.11,0.075,0.082,0.09,0.133,0.085,0.086,0.113,0.105,0.113,0.144,0.105,0.133,0.112,0.102,0.126,0.11,0.119,0.113,0.128,0.114,0.11,0.095,0.107,0.108,0.1,0.093,0.126,328,1179,710,742,728,1020,1171,949,513,276,208.0,1113,944,201,789,3118,4547,366,1120,1607,948,1187,728,1333,1673,1970,1437,1503,1912,1386,412,425,380,1389,463,774,1376,1563,120,131,685,1266,864,1268,1936,663,299,774,1039,634,174,1236,717,1925,447,1430,574,1260,753,663,309,539,145,254,1200,451,1424,964,999,309,362,746,3210,290,4.057,3.065,2.678,2.854,2.785,2.825,2.252,3.065,3.038,3.05,3.416,2.528,2.983,3.065,2.984,2.945,3.425,4.315,2.95,2.546,3.195,2.812,2.512,2.651,2.895,2.692,2.733,2.415,2.683,2.68,2.638,2.409,2.65,2.858,3.444,2.742,3.135,3.226,2.679,2.435,2.746,2.212,3.644,2.348,1.957,2.45,3.139,3.021,2.785,2.508,2.733,2.485,2.676,2.554,2.518,2.401,2.817,2.548,2.473,2.701,2.525,2.638,2.181,3.606,2.476,2.65,2.55,2.616,2.598,3.369,3.108,2.7,2.556,2.532,0.832,1.024,0.726,0.815,0.941,0.757,0.725,0.932,0.849,0.534,0.639,0.562,0.694,0.703,0.758,0.847,0.93,0.632,0.574,0.74,0.774,0.594,0.525,0.848,0.781,0.847,0.695,0.741,0.767,0.657,0.639,0.906,0.657,0.93,0.772,0.637,0.866,0.871,0.744,0.632,0.695,0.721,0.89,0.835,0.553,0.733,0.769,0.731,0.844,0.532,0.711,0.837,0.848,0.933,0.496,0.54,0.569,0.623,0.56,0.749,0.476,0.73,0.473,0.743,0.585,0.769,0.768,0.526,0.647,0.761,1.041,0.748,0.642,0.453
-339,Epileptic,M,32.0,0.8,3.0,1.6,1.9,2.4,1.9,2.7,2.2,1.6,1.2,0.5,4.5,1.4,0.7,1.2,9.8,13.2,1.6,3.0,7.7,1.7,3.7,1.9,5.8,3.2,4.7,4.9,5.3,5.6,3.6,1.7,0.5,0.5,3.2,0.9,1.1,4.9,5.3,0.2,0.3,1.4,3.4,4.1,4.7,4.1,1.0,0.4,1.2,1.8,1.0,0.8,3.6,2.7,6.0,0.8,2.2,1.0,2.0,1.8,1.4,0.7,2.2,0.4,0.8,2.4,1.5,3.9,1.7,1.4,0.9,0.8,2.1,6.4,0.4,7,22,11,15,18,20,18,21,17,10,5.0,42,18,6,17,83,100,8,27,66,12,39,20,54,33,46,46,36,41,37,18,4,3,31,5,8,41,47,1,2,7,35,34,33,18,8,2,6,8,6,6,27,24,33,6,15,7,16,15,9,6,17,2,7,19,19,26,10,6,8,5,16,54,2,0.036,0.028,0.02,0.035,0.044,0.028,0.044,0.029,0.047,0.049,0.035,0.042,0.03,0.041,0.025,0.039,0.04,0.05,0.035,0.044,0.039,0.033,0.03,0.049,0.03,0.036,0.036,0.041,0.045,0.035,0.044,0.035,0.036,0.035,0.027,0.022,0.046,0.04,0.015,0.017,0.03,0.037,0.051,0.029,0.025,0.023,0.017,0.019,0.022,0.018,0.029,0.035,0.034,0.033,0.031,0.021,0.025,0.023,0.025,0.025,0.021,0.032,0.02,0.028,0.025,0.03,0.022,0.024,0.018,0.025,0.022,0.023,0.021,0.022,1659,5707,2878,2678,2705,3608,2508,3646,2242,1555,821.0,4671,2897,1341,2930,13064,18244,2709,5024,7017,3111,6145,3253,6443,6158,7863,6811,4706,6133,5993,2473,804,861,6479,2333,2295,6230,9675,439,587,1532,3421,7804,4393,4057,1357,740,2280,2589,1672,677,4296,3352,6931,1002,3121,1413,3079,2122,1725,941,3280,450,954,3566,1331,5329,2284,2251,1398,1217,3776,11800,480,0.123,0.117,0.112,0.144,0.149,0.12,0.129,0.13,0.169,0.173,0.125,0.163,0.12,0.133,0.126,0.136,0.135,0.127,0.14,0.159,0.118,0.138,0.136,0.153,0.129,0.134,0.128,0.129,0.128,0.149,0.137,0.106,0.098,0.132,0.088,0.108,0.142,0.148,0.091,0.092,0.115,0.139,0.17,0.121,0.108,0.117,0.086,0.084,0.103,0.096,0.135,0.127,0.122,0.123,0.144,0.106,0.111,0.109,0.123,0.115,0.113,0.137,0.119,0.125,0.119,0.123,0.105,0.103,0.09,0.136,0.102,0.12,0.105,0.109,410,1832,1102,980,1005,1274,998,1265,670,455,268.0,1889,844,326,901,4364,5554,496,1503,3026,791,2044,1176,1999,1870,2440,2427,1995,1912,1809,746,288,310,1706,631,771,1885,2394,258,318,782,1701,1664,2606,2359,676,335,920,1156,815,433,1729,1437,2728,472,1584,668,1523,1157,877,561,1203,291,480,1648,910,2694,1080,1078,566,629,1409,4984,242,3.049,2.746,2.479,2.597,2.346,2.378,2.272,2.571,2.671,2.823,2.528,2.084,2.665,2.876,2.485,2.482,2.743,3.503,2.762,2.072,2.948,2.444,2.372,2.569,2.621,2.617,2.318,2.025,2.579,2.649,2.46,2.714,2.345,2.856,3.106,2.697,2.546,2.983,1.97,2.156,2.431,1.867,3.132,1.867,1.971,2.188,2.532,2.975,2.66,2.345,1.968,2.349,2.36,2.573,2.398,2.139,2.292,2.251,1.995,2.105,1.916,2.753,1.766,2.347,2.294,1.764,2.109,2.329,2.521,2.574,2.181,2.603,2.38,2.31,1.001,0.829,0.515,0.533,0.694,0.668,0.516,0.613,0.462,0.613,0.778,0.515,0.412,0.535,0.464,0.566,0.724,1.124,0.537,0.661,0.679,0.601,0.523,0.798,0.619,0.602,0.613,0.555,0.674,0.508,0.739,0.887,0.452,0.612,0.813,0.542,0.897,0.73,0.542,0.356,0.499,0.537,0.943,0.562,0.576,0.441,0.438,0.816,0.522,0.495,0.431,0.59,0.499,0.556,0.557,0.354,0.442,0.445,0.386,0.38,0.39,0.653,0.405,0.673,0.453,0.611,0.437,0.436,0.372,0.801,0.53,0.666,0.468,0.484
-340,Epileptic,F,52.0,0.8,1.6,1.0,1.2,1.5,2.2,1.8,1.4,1.4,0.6,0.2,2.8,1.5,0.4,0.9,5.5,8.0,0.4,2.1,4.6,1.8,2.7,1.6,2.7,3.4,2.9,3.1,2.6,2.2,3.0,1.4,1.1,0.4,2.5,0.3,0.9,2.6,3.1,0.1,0.1,1.0,2.9,1.2,2.2,2.8,0.6,0.4,1.1,1.1,0.9,0.8,1.9,1.8,1.7,0.6,2.6,1.1,2.4,1.0,1.2,0.7,1.6,0.2,0.5,1.8,0.6,2.4,1.2,1.2,0.9,1.1,1.3,5.3,0.5,7,14,13,8,11,16,14,13,9,5,2.0,21,14,3,7,45,57,3,21,38,10,25,15,26,37,31,35,20,18,29,14,8,4,22,2,6,23,28,1,1,7,21,12,16,18,5,2,8,5,6,5,15,12,11,3,19,7,15,7,10,5,10,1,4,14,9,22,9,6,6,6,10,49,5,0.046,0.03,0.025,0.029,0.036,0.038,0.034,0.029,0.055,0.031,0.027,0.034,0.035,0.026,0.03,0.039,0.038,0.022,0.033,0.038,0.029,0.03,0.03,0.038,0.033,0.031,0.029,0.029,0.023,0.03,0.058,0.07,0.018,0.034,0.019,0.025,0.03,0.029,0.014,0.018,0.022,0.036,0.026,0.035,0.025,0.018,0.022,0.018,0.019,0.031,0.03,0.028,0.032,0.023,0.018,0.022,0.025,0.024,0.032,0.022,0.024,0.031,0.019,0.024,0.024,0.028,0.02,0.021,0.026,0.031,0.029,0.026,0.022,0.029,993,2626,1579,1521,1961,2607,1912,2706,1444,881,429.0,2190,2635,805,1648,8246,12491,1700,3505,4085,2525,3999,2036,4551,5248,4796,4835,3005,4673,4558,1424,763,1006,4422,987,1535,4678,6013,208,275,1346,2730,2759,1626,2554,937,635,1966,1756,1193,579,2237,2046,2716,709,3374,1554,3183,848,1381,892,1990,279,795,2090,511,3362,1912,1332,1061,1174,1727,8333,322,0.136,0.111,0.124,0.118,0.133,0.147,0.114,0.13,0.151,0.129,0.085,0.137,0.14,0.12,0.126,0.134,0.129,0.098,0.123,0.149,0.093,0.135,0.132,0.129,0.137,0.135,0.129,0.116,0.107,0.127,0.155,0.136,0.081,0.132,0.078,0.107,0.113,0.121,0.122,0.101,0.113,0.134,0.108,0.132,0.114,0.1,0.11,0.094,0.097,0.108,0.129,0.122,0.125,0.105,0.113,0.111,0.116,0.111,0.134,0.113,0.117,0.129,0.12,0.119,0.122,0.109,0.108,0.11,0.114,0.131,0.122,0.119,0.111,0.14,331,966,691,692,761,988,773,865,408,311,161.0,1228,756,204,488,2487,3552,329,1011,1927,892,1402,943,1299,1832,1614,1847,1276,1431,1631,470,300,338,1205,296,571,1417,1719,109,154,706,1184,738,1137,1810,541,268,936,839,499,425,1169,930,1240,394,1882,725,1579,540,859,454,798,145,288,1158,333,1938,945,699,460,603,897,3978,244,3.005,2.714,2.273,2.29,2.466,2.323,2.206,2.83,2.608,2.555,2.344,1.645,2.727,2.848,2.447,2.581,2.915,3.922,2.718,1.93,2.463,2.36,1.931,2.705,2.316,2.458,2.143,1.95,2.57,2.334,2.357,2.542,2.419,2.703,2.858,2.413,2.572,2.654,2.136,1.983,2.442,2.03,2.726,1.684,1.643,1.877,2.85,2.579,2.539,2.52,1.703,2.007,2.385,2.352,2.332,1.974,2.454,2.383,1.948,1.769,2.228,2.693,1.99,2.786,2.056,1.867,1.937,2.349,2.319,2.6,2.31,2.288,2.271,1.497,0.989,0.657,0.418,0.396,0.547,0.629,0.55,0.702,0.724,0.455,0.64,0.44,0.501,0.429,0.558,0.701,0.604,0.747,0.631,0.535,0.757,0.497,0.531,0.701,0.492,0.491,0.576,0.469,0.489,0.513,0.551,0.985,0.482,0.631,0.666,0.526,0.766,0.532,0.263,0.321,0.526,0.583,0.885,0.61,0.411,0.429,0.731,0.75,0.525,0.856,0.324,0.462,0.567,0.487,0.362,0.432,0.656,0.635,0.309,0.364,0.51,0.711,0.49,0.673,0.548,0.628,0.407,0.501,0.396,0.609,0.491,0.579,0.543,0.451
-341,Epileptic,M,37.0,0.7,3.4,1.1,2.0,2.7,1.7,2.1,2.9,1.2,0.8,0.4,1.6,1.9,0.7,1.6,14.5,12.3,1.1,3.7,5.0,1.4,3.4,2.3,5.3,4.7,7.2,5.8,6.3,8.6,4.7,2.0,1.6,0.7,3.4,0.6,1.2,4.3,3.4,0.3,0.4,1.5,3.1,3.5,3.9,4.0,1.3,0.7,1.3,2.3,1.0,0.8,3.0,2.3,3.2,0.9,1.9,1.2,2.2,0.6,2.1,0.7,2.8,0.4,0.7,1.8,1.6,4.4,2.1,1.6,0.7,1.1,1.9,5.5,0.5,8,28,7,13,18,17,15,30,15,9,4.0,19,26,8,15,112,98,10,33,49,10,39,24,46,50,63,55,46,53,44,19,15,6,27,3,10,42,41,2,2,8,31,40,27,20,7,3,7,11,7,4,20,16,21,7,13,7,19,8,17,6,19,2,5,19,20,31,12,8,6,7,12,39,3,0.034,0.027,0.019,0.03,0.042,0.028,0.034,0.036,0.054,0.045,0.036,0.03,0.033,0.041,0.03,0.048,0.038,0.044,0.04,0.037,0.028,0.032,0.031,0.048,0.04,0.045,0.044,0.045,0.053,0.039,0.046,0.067,0.042,0.033,0.017,0.027,0.038,0.035,0.019,0.02,0.025,0.034,0.057,0.033,0.026,0.025,0.02,0.018,0.024,0.03,0.027,0.023,0.03,0.022,0.036,0.017,0.021,0.024,0.017,0.026,0.023,0.036,0.021,0.027,0.025,0.036,0.026,0.025,0.025,0.02,0.023,0.022,0.019,0.022,1645,5718,2636,2710,2412,2387,3140,4302,1753,1497,614.0,2404,4467,1520,3157,13068,20958,2504,5567,5076,3635,4994,2891,7085,6545,7505,5521,5367,6671,6556,2762,1233,1103,6507,2023,2686,8391,8191,533,671,1810,2468,5223,3105,3916,1728,1290,2395,3090,1769,641,4331,2590,5096,868,3035,1766,3005,1220,2021,991,3440,465,916,2639,1482,5438,3335,2330,1427,1650,2553,9466,753,0.121,0.12,0.097,0.121,0.142,0.129,0.108,0.133,0.151,0.151,0.132,0.135,0.134,0.143,0.124,0.14,0.131,0.138,0.144,0.148,0.101,0.121,0.117,0.147,0.133,0.15,0.144,0.129,0.132,0.15,0.153,0.135,0.122,0.124,0.084,0.107,0.141,0.132,0.102,0.109,0.119,0.132,0.168,0.137,0.111,0.117,0.101,0.099,0.11,0.125,0.127,0.112,0.127,0.105,0.13,0.098,0.104,0.119,0.102,0.13,0.117,0.136,0.107,0.127,0.123,0.144,0.117,0.106,0.106,0.118,0.114,0.114,0.1,0.12,472,1928,910,1036,1047,1052,1051,1425,521,389,194.0,1004,1105,384,866,4518,5687,515,1635,2246,893,1567,1107,2117,1973,2665,2197,2097,2028,2099,732,511,331,1571,529,843,1950,1759,237,323,876,1450,1226,1858,2356,760,508,1043,1406,633,414,2070,1340,2364,444,1631,890,1513,679,1134,531,1293,277,415,1313,724,2663,1393,1022,543,761,1269,4373,324,3.275,2.563,2.89,2.478,2.216,2.002,2.472,2.676,2.474,3.135,2.787,2.0,2.969,2.928,2.773,2.428,2.871,3.728,2.701,1.988,2.903,2.421,2.229,2.686,2.545,2.431,2.13,2.144,2.717,2.473,2.793,2.449,2.675,2.919,3.102,2.779,2.979,3.312,2.411,2.383,2.678,1.639,2.832,1.934,1.926,2.463,2.926,2.889,2.585,2.757,1.807,2.37,2.276,2.352,2.159,2.022,2.295,2.13,2.018,2.023,2.082,2.751,1.878,2.489,2.033,2.386,2.239,2.627,2.481,2.639,2.516,2.41,2.295,2.819,0.717,0.685,0.48,0.582,0.486,0.473,0.73,0.557,0.596,0.391,0.587,0.429,0.522,0.483,0.661,0.696,0.671,0.781,0.565,0.562,0.701,0.607,0.534,0.705,0.612,0.635,0.592,0.59,0.744,0.611,0.674,0.721,0.489,0.842,0.703,0.503,0.792,0.775,0.419,0.473,0.512,0.432,0.893,0.549,0.568,0.525,0.766,0.844,0.564,0.582,0.337,0.469,0.479,0.454,0.425,0.405,0.461,0.533,0.427,0.429,0.374,0.711,0.411,0.56,0.508,0.686,0.458,0.481,0.462,0.865,0.421,0.519,0.481,0.429
-343,Epileptic,F,27.0,1.0,2.5,1.0,1.3,1.3,1.7,1.7,1.9,1.2,0.9,0.2,4.4,1.4,0.4,1.0,6.7,8.5,1.1,1.9,4.2,2.3,4.4,2.8,4.2,3.8,2.9,5.0,2.6,2.3,3.3,1.5,1.0,0.6,2.7,0.7,0.4,6.2,3.0,0.2,0.1,0.9,5.5,3.2,3.4,3.0,0.7,0.6,0.9,1.3,0.9,0.6,2.1,1.6,3.0,0.2,2.7,0.7,1.4,1.4,1.2,0.8,1.4,0.5,0.5,2.0,1.2,2.3,1.1,1.2,0.6,1.3,1.9,4.8,0.5,7,23,7,12,13,18,16,18,16,14,5.0,40,17,5,13,88,89,8,27,48,21,56,35,42,47,34,51,35,24,47,15,6,9,34,6,4,64,46,2,1,5,51,33,26,18,5,4,6,8,7,4,20,11,22,1,20,5,12,10,9,8,13,4,5,18,18,19,7,6,5,10,14,49,4,0.038,0.024,0.016,0.021,0.033,0.028,0.027,0.027,0.036,0.04,0.026,0.048,0.027,0.024,0.024,0.032,0.029,0.04,0.028,0.037,0.032,0.042,0.048,0.04,0.035,0.032,0.035,0.037,0.024,0.028,0.042,0.047,0.034,0.035,0.023,0.011,0.051,0.034,0.012,0.015,0.022,0.067,0.044,0.028,0.019,0.012,0.027,0.011,0.017,0.015,0.029,0.02,0.023,0.019,0.028,0.017,0.016,0.016,0.028,0.019,0.019,0.025,0.027,0.025,0.022,0.02,0.02,0.016,0.015,0.018,0.024,0.019,0.019,0.022,2000,5320,2979,2945,2437,2931,3610,4138,2213,2234,897.0,3573,4152,1339,2735,14630,21610,2498,5159,6036,6021,5865,2385,6855,7997,5538,8507,4556,7002,7839,2252,1136,1315,7123,2125,1455,8536,7508,438,463,1311,1873,7007,4530,4364,1733,1061,2797,2646,2375,535,4116,2373,5776,253,4648,1365,3579,1429,1872,1358,2827,856,1276,3315,1665,3849,2212,2143,1180,1915,2691,10074,879,0.126,0.119,0.087,0.111,0.131,0.126,0.105,0.127,0.156,0.144,0.115,0.168,0.13,0.118,0.119,0.14,0.129,0.123,0.131,0.153,0.111,0.147,0.155,0.146,0.134,0.136,0.127,0.118,0.103,0.132,0.142,0.112,0.153,0.137,0.11,0.08,0.151,0.128,0.087,0.082,0.105,0.161,0.153,0.123,0.098,0.085,0.13,0.077,0.092,0.089,0.121,0.102,0.11,0.103,0.15,0.099,0.109,0.095,0.126,0.103,0.113,0.119,0.145,0.113,0.114,0.101,0.108,0.097,0.091,0.113,0.119,0.108,0.103,0.124,456,1847,995,1058,825,1152,1251,1183,619,566,252.0,1623,1012,291,769,4126,5679,526,1390,2482,1372,2033,1066,1851,2385,1757,2778,1599,1802,2446,730,472,368,1546,498,528,2368,2059,292,207,647,1138,1533,2314,2344,884,376,1098,1173,996,327,1906,1184,2379,81,2379,652,1421,845,969,692,1025,309,462,1507,1080,1883,981,1014,544,907,1400,4394,362,3.714,2.739,2.845,2.653,2.419,2.263,2.395,3.035,2.605,2.974,2.9,1.919,3.011,2.961,2.629,2.7,2.927,3.574,2.894,2.096,3.363,2.331,1.92,2.813,2.62,2.627,2.435,2.254,2.872,2.564,2.448,2.374,2.824,3.257,3.784,2.276,2.68,2.729,1.799,2.453,2.661,1.427,3.254,2.154,2.121,2.002,3.324,3.074,2.759,2.673,2.019,2.205,2.224,2.479,3.167,2.109,2.35,2.611,1.963,2.085,2.124,2.835,2.634,2.994,2.294,1.77,2.123,2.562,2.438,2.243,2.31,2.166,2.388,2.664,0.996,0.704,0.522,0.577,0.664,0.541,0.669,0.549,0.647,0.622,0.679,0.486,0.641,0.551,0.635,0.601,0.654,0.701,0.515,0.6,0.868,0.716,0.666,0.876,0.667,0.582,0.613,0.642,0.601,0.518,0.821,0.95,0.435,0.721,0.631,0.563,0.975,0.682,0.456,0.305,0.472,0.655,0.947,0.591,0.644,0.49,0.623,0.788,0.485,0.495,0.424,0.501,0.492,0.462,0.351,0.451,0.454,0.629,0.37,0.388,0.37,0.774,0.656,1.197,0.453,0.676,0.564,0.392,0.483,0.737,0.463,0.509,0.463,0.49
-344,Epileptic,M,47.0,1.1,2.7,0.8,1.3,1.8,2.5,1.6,1.9,1.8,0.6,0.3,2.9,1.4,0.4,1.2,5.8,9.5,0.8,2.4,4.3,1.5,3.1,1.7,4.3,2.6,3.6,2.1,2.3,3.2,2.9,1.9,1.0,0.6,2.4,0.5,1.2,3.0,2.8,0.2,0.3,1.4,3.1,1.9,2.4,3.2,0.8,0.6,1.2,1.1,0.8,0.9,2.7,1.1,3.2,0.4,2.1,0.7,1.6,1.3,1.1,0.7,1.7,0.4,0.4,1.4,1.6,2.1,1.7,1.1,0.5,0.7,1.6,5.4,0.4,8,25,8,10,19,23,16,18,20,7,3.0,30,16,7,13,66,87,9,25,39,16,37,19,48,34,44,29,22,28,37,25,8,4,29,3,12,37,36,2,1,10,31,24,22,17,5,3,7,7,9,5,19,8,18,2,15,5,14,10,8,6,13,2,4,14,19,18,13,5,4,6,10,48,5,0.042,0.025,0.017,0.021,0.029,0.031,0.025,0.027,0.048,0.03,0.027,0.028,0.024,0.026,0.027,0.03,0.028,0.026,0.026,0.032,0.02,0.034,0.029,0.034,0.028,0.03,0.023,0.024,0.026,0.027,0.047,0.042,0.022,0.025,0.013,0.021,0.034,0.03,0.015,0.018,0.022,0.032,0.028,0.024,0.023,0.012,0.018,0.017,0.015,0.011,0.027,0.02,0.017,0.023,0.021,0.02,0.025,0.019,0.022,0.018,0.026,0.022,0.025,0.015,0.017,0.03,0.019,0.019,0.017,0.021,0.02,0.02,0.019,0.015,1683,5203,2123,2286,2578,4317,2624,3696,1976,1362,673.0,3392,3688,1483,2520,11822,20352,3008,5065,4456,5789,5345,2746,8264,5274,7263,4496,4027,6989,5596,2798,1285,1321,6612,2369,2721,6618,6715,319,547,1849,3409,4919,3024,3876,1669,1052,2902,2418,2638,758,4099,2043,5809,577,2883,1395,3574,1739,1596,980,3157,635,1142,2818,1377,3490,3074,1891,1108,1242,2297,11199,710,0.113,0.119,0.095,0.109,0.139,0.141,0.105,0.129,0.175,0.134,0.118,0.135,0.121,0.131,0.137,0.133,0.123,0.106,0.121,0.134,0.1,0.143,0.137,0.14,0.139,0.141,0.122,0.118,0.101,0.131,0.149,0.115,0.101,0.123,0.072,0.108,0.132,0.128,0.119,0.097,0.115,0.138,0.131,0.12,0.107,0.087,0.098,0.086,0.089,0.086,0.14,0.106,0.102,0.099,0.122,0.109,0.117,0.103,0.12,0.105,0.119,0.117,0.12,0.104,0.107,0.119,0.107,0.11,0.093,0.108,0.112,0.114,0.104,0.109,530,1807,868,952,1007,1358,940,1127,648,366,193.0,1620,1028,352,733,3345,5704,560,1483,2121,1292,1652,1056,2159,1669,2207,1517,1450,1814,1839,790,417,439,1562,626,938,1625,1499,173,210,926,1557,1183,1788,2205,812,472,1103,1125,1153,429,1986,918,2343,225,1548,535,1528,896,928,480,1243,266,487,1346,870,1737,1366,919,470,635,1089,4698,442,3.071,2.743,2.521,2.406,2.305,2.661,2.362,2.875,2.469,2.906,2.8,1.844,2.79,2.978,2.665,2.711,2.929,3.733,2.869,1.797,3.332,2.556,2.215,2.955,2.508,2.747,2.384,2.21,2.913,2.436,2.729,2.822,2.362,3.005,3.456,2.68,3.033,3.098,2.14,2.774,2.678,1.977,3.009,1.903,2.025,2.345,2.73,3.059,2.594,2.614,2.059,2.136,2.261,2.57,2.937,2.131,2.907,2.423,2.155,1.943,2.29,2.608,2.377,2.439,2.246,1.984,2.209,2.579,2.371,2.558,2.292,2.594,2.516,1.973,0.893,0.593,0.482,0.397,0.496,0.621,0.565,0.398,0.499,0.465,0.794,0.396,0.478,0.469,0.467,0.516,0.551,0.678,0.508,0.524,0.7,0.438,0.463,0.629,0.471,0.525,0.382,0.498,0.481,0.531,0.665,1.155,0.421,0.659,0.485,0.432,0.852,0.532,0.367,0.434,0.495,0.511,0.905,0.627,0.61,0.399,0.578,0.691,0.52,0.35,0.325,0.394,0.414,0.467,0.503,0.322,0.675,0.637,0.341,0.32,0.385,0.636,0.467,1.001,0.468,0.619,0.434,0.487,0.432,0.574,0.377,0.677,0.488,0.483
-345,Epileptic,F,27.0,1.1,2.2,0.5,1.5,1.0,3.4,1.3,1.8,1.3,0.7,0.3,2.7,1.6,0.6,0.8,8.1,7.8,0.8,2.5,3.8,1.1,3.8,1.3,3.9,2.7,2.5,3.0,2.5,2.1,3.1,1.1,0.5,0.5,2.4,0.3,0.4,3.3,3.3,0.2,0.1,0.9,4.0,1.6,2.1,2.4,0.5,0.4,0.9,1.1,0.5,1.0,1.5,1.1,2.7,0.1,1.5,0.9,1.9,0.9,0.9,0.7,1.2,0.4,0.4,1.4,1.1,2.2,1.1,0.8,0.3,0.7,1.2,3.3,0.3,7,21,7,15,10,24,11,16,13,6,2.0,20,13,5,8,67,62,6,23,29,6,33,14,34,29,25,36,19,18,33,16,13,3,23,1,3,28,34,2,1,5,32,12,14,13,4,2,5,5,5,6,10,6,15,1,12,7,16,6,7,5,8,2,3,10,12,14,7,4,4,5,8,25,3,0.042,0.023,0.011,0.028,0.037,0.04,0.026,0.031,0.045,0.033,0.035,0.034,0.03,0.035,0.024,0.045,0.033,0.029,0.033,0.035,0.022,0.038,0.03,0.042,0.032,0.028,0.029,0.03,0.027,0.031,0.032,0.026,0.025,0.028,0.011,0.013,0.036,0.035,0.012,0.014,0.018,0.038,0.034,0.029,0.023,0.014,0.025,0.015,0.017,0.016,0.041,0.02,0.021,0.026,0.031,0.015,0.026,0.019,0.022,0.019,0.024,0.025,0.025,0.024,0.019,0.023,0.022,0.019,0.019,0.012,0.023,0.025,0.016,0.015,1541,4895,1939,2163,1611,3460,2391,3073,2124,1092,536.0,2565,3139,1194,1732,12025,15195,2658,4505,3691,2667,5109,1829,6204,4952,5808,5393,3562,4920,5222,1809,922,970,6500,1803,1689,5156,6603,464,272,1423,3175,4473,2199,2659,1179,602,2219,2130,1423,497,2897,1865,4292,146,2987,1600,3256,1426,1416,886,2485,579,748,2306,1413,3124,2225,1531,718,961,2015,8415,445,0.13,0.115,0.086,0.139,0.122,0.139,0.105,0.135,0.168,0.139,0.114,0.135,0.128,0.13,0.117,0.152,0.127,0.109,0.123,0.126,0.085,0.158,0.13,0.151,0.138,0.125,0.133,0.111,0.111,0.136,0.114,0.086,0.105,0.131,0.057,0.069,0.118,0.123,0.102,0.096,0.1,0.143,0.118,0.12,0.106,0.084,0.11,0.079,0.091,0.089,0.152,0.1,0.103,0.109,0.146,0.093,0.111,0.105,0.106,0.109,0.121,0.114,0.129,0.13,0.101,0.115,0.105,0.104,0.107,0.106,0.122,0.114,0.09,0.096,433,1564,713,834,571,1393,797,1007,490,327,144.0,1262,821,299,551,3003,4001,463,1247,1729,732,1577,808,1498,1484,1565,1766,1243,1273,1707,571,388,349,1258,490,584,1450,1490,190,162,729,1580,845,1223,1674,595,268,934,959,517,355,1285,852,1753,64,1659,594,1491,732,774,481,786,229,342,1172,723,1600,946,638,419,496,877,3418,267,3.385,2.809,2.496,2.544,2.207,2.27,2.362,2.814,2.764,2.786,3.087,1.776,2.901,2.916,2.468,2.872,2.947,3.899,2.877,1.884,2.84,2.525,2.057,3.088,2.634,2.843,2.364,2.246,2.874,2.47,2.324,2.234,2.142,3.396,3.313,2.659,2.844,3.158,2.402,1.895,2.562,1.867,3.604,2.033,1.803,2.196,2.871,2.943,2.81,3.023,1.853,2.399,2.292,2.388,2.882,2.016,2.671,2.314,2.239,2.073,2.223,2.862,3.266,2.417,2.292,2.502,2.182,2.763,2.733,2.141,2.362,2.725,2.671,2.105,0.833,0.773,0.686,0.479,0.728,0.578,0.678,0.679,0.722,0.486,0.769,0.425,0.533,0.429,0.503,0.592,0.674,0.789,0.679,0.661,0.753,0.469,0.422,0.651,0.609,0.556,0.452,0.491,0.564,0.577,0.595,0.898,0.483,0.693,0.628,0.699,0.783,0.689,0.274,0.566,0.518,0.452,0.875,0.777,0.546,0.686,0.525,0.687,0.607,0.689,0.398,0.621,0.671,0.606,0.258,0.446,0.615,0.66,0.512,0.406,0.408,0.735,0.658,0.888,0.741,0.865,0.532,0.467,0.522,0.653,0.429,0.647,0.574,0.367
-346,Epileptic,M,27.0,0.6,3.0,1.1,1.4,2.2,2.1,1.8,2.1,1.5,0.8,0.4,2.6,1.5,0.7,1.1,7.2,9.7,0.9,2.5,4.6,1.1,4.6,1.5,5.5,8.0,3.0,4.0,2.5,3.3,2.7,1.9,2.2,0.5,2.5,0.7,0.5,3.9,3.3,0.2,0.2,1.0,3.3,2.2,2.8,2.5,0.7,0.5,0.9,1.5,1.1,0.6,2.7,1.9,2.4,0.4,2.2,0.6,1.6,1.6,1.3,0.5,2.0,0.5,0.6,2.1,1.4,2.7,1.6,0.9,0.4,0.8,1.8,5.0,0.4,5,30,11,13,18,23,16,21,23,12,4.0,30,20,9,17,77,93,9,35,44,11,46,17,54,61,34,46,26,27,53,21,15,5,28,3,5,48,44,1,1,6,43,24,22,16,6,2,5,8,10,5,21,15,21,3,17,5,14,10,9,3,15,5,5,20,19,20,12,7,4,5,17,48,4,0.028,0.026,0.016,0.023,0.041,0.026,0.028,0.025,0.044,0.038,0.031,0.034,0.027,0.032,0.03,0.033,0.03,0.032,0.034,0.035,0.026,0.038,0.029,0.047,0.051,0.026,0.034,0.026,0.03,0.029,0.043,0.057,0.028,0.026,0.014,0.015,0.03,0.027,0.014,0.016,0.023,0.038,0.032,0.026,0.019,0.015,0.019,0.015,0.019,0.018,0.02,0.022,0.025,0.018,0.014,0.015,0.013,0.016,0.022,0.019,0.019,0.026,0.024,0.018,0.02,0.02,0.018,0.019,0.017,0.012,0.02,0.021,0.015,0.018,1644,5740,2995,3016,2488,4359,3174,4236,2204,1890,805.0,3423,3906,1499,2571,14103,21738,2777,5300,5908,4244,5392,2015,7452,6171,7615,6239,4774,6697,6122,2754,1977,961,7783,3121,1709,9302,9867,364,402,1338,2992,7350,2948,3404,1610,932,2386,2734,2422,778,4544,2807,5466,742,4179,1432,3955,1913,1721,751,3163,667,1217,3756,1806,4220,2951,1786,913,1532,3209,13309,688,0.109,0.119,0.096,0.111,0.148,0.122,0.119,0.125,0.17,0.15,0.105,0.149,0.123,0.128,0.125,0.136,0.127,0.127,0.132,0.143,0.102,0.127,0.114,0.145,0.148,0.117,0.126,0.104,0.109,0.135,0.139,0.138,0.1,0.118,0.076,0.078,0.132,0.131,0.1,0.108,0.107,0.13,0.128,0.124,0.1,0.098,0.091,0.082,0.1,0.101,0.111,0.111,0.12,0.093,0.1,0.095,0.09,0.098,0.115,0.103,0.121,0.127,0.118,0.097,0.106,0.112,0.099,0.105,0.1,0.101,0.103,0.108,0.088,0.127,440,1984,1023,1025,906,1425,1059,1346,672,463,222.0,1390,1045,377,729,3867,5799,534,1510,2318,872,1894,841,2174,2322,2032,2147,1460,1661,1795,839,698,336,1605,687,584,2401,2157,186,146,706,1484,1363,1705,2030,749,400,984,1192,961,443,1940,1249,2265,432,2164,656,1705,1047,950,349,1177,352,632,1736,1128,2225,1241,822,461,718,1488,5368,307,3.473,2.69,2.863,2.742,2.337,2.542,2.454,2.765,2.468,3.054,2.96,2.184,2.817,2.906,2.733,2.751,2.895,3.935,2.786,2.176,3.453,2.319,2.016,2.643,2.313,2.843,2.323,2.461,2.984,2.683,2.487,2.778,2.367,3.379,3.948,2.616,2.868,3.204,2.19,2.929,2.397,1.84,3.55,1.965,1.868,2.355,2.908,2.976,2.908,2.559,2.024,2.393,2.276,2.544,2.097,2.136,2.557,2.672,2.133,2.031,2.341,2.736,2.105,2.373,2.279,1.812,2.081,2.648,2.437,2.269,2.443,2.487,2.566,2.737,0.73,0.554,0.425,0.522,0.746,0.573,0.582,0.513,0.69,0.726,0.703,0.516,0.549,0.492,0.529,0.647,0.683,0.7,0.599,0.661,0.743,0.685,0.553,0.826,0.662,0.623,0.637,0.646,0.567,0.518,0.661,0.884,0.449,0.676,0.694,0.503,0.784,0.619,0.259,0.444,0.458,0.612,0.883,0.612,0.557,0.379,0.521,0.533,0.456,0.671,0.409,0.531,0.452,0.422,0.317,0.403,0.372,0.476,0.448,0.411,0.48,0.701,0.505,0.748,0.494,0.767,0.469,0.529,0.434,0.462,0.423,0.505,0.473,0.424
-347,Epileptic,F,52.0,0.8,2.7,1.4,1.1,1.7,2.5,1.6,1.5,1.1,0.9,0.5,2.8,1.5,0.7,1.4,6.4,11.1,1.8,1.9,4.6,1.5,7.1,2.6,3.6,3.6,3.1,3.9,2.7,2.8,2.8,2.1,1.1,0.4,2.0,0.4,0.6,4.1,2.8,0.2,0.2,1.0,6.7,3.0,3.5,2.6,0.8,0.4,1.7,1.8,0.9,0.5,1.6,1.2,2.6,1.2,2.8,1.4,1.9,3.1,1.2,0.7,1.5,0.5,0.6,1.5,1.4,2.8,1.4,1.2,0.7,0.8,1.0,4.4,0.4,7,26,15,9,13,20,13,16,13,9,3.0,27,18,8,17,67,82,11,19,40,12,52,20,39,38,31,33,20,22,33,22,7,6,23,2,5,34,31,1,2,7,47,26,29,13,5,2,9,10,6,3,13,9,17,7,26,10,12,16,11,6,11,3,5,15,13,22,10,7,4,7,8,38,4,0.042,0.028,0.025,0.024,0.038,0.034,0.03,0.03,0.044,0.039,0.036,0.034,0.032,0.036,0.038,0.04,0.042,0.049,0.027,0.034,0.035,0.048,0.039,0.047,0.034,0.025,0.027,0.026,0.03,0.033,0.055,0.044,0.028,0.03,0.018,0.017,0.046,0.035,0.016,0.016,0.022,0.049,0.044,0.028,0.019,0.015,0.016,0.026,0.022,0.023,0.034,0.019,0.019,0.023,0.034,0.017,0.035,0.021,0.042,0.022,0.025,0.029,0.029,0.021,0.022,0.037,0.021,0.022,0.023,0.024,0.026,0.02,0.021,0.024,1416,4415,2205,1971,1661,2916,2678,2812,2028,1429,598.0,2724,3248,1141,2390,10618,16385,2337,3550,4551,3116,4074,1706,5075,4395,6249,4962,3682,5169,4259,2613,918,720,4660,1430,1662,6413,5966,350,508,1314,3101,5587,3016,3063,1564,749,2022,2640,1607,431,2736,1766,4114,858,4516,1968,3140,1808,1447,782,2360,511,869,2261,963,3882,1957,1460,898,1155,1616,7766,618,0.128,0.127,0.111,0.11,0.134,0.122,0.11,0.132,0.154,0.162,0.133,0.146,0.128,0.147,0.144,0.145,0.144,0.151,0.123,0.141,0.108,0.149,0.141,0.148,0.117,0.107,0.097,0.098,0.11,0.135,0.16,0.136,0.104,0.129,0.076,0.086,0.14,0.135,0.09,0.106,0.115,0.177,0.157,0.121,0.1,0.096,0.095,0.11,0.109,0.102,0.127,0.102,0.107,0.104,0.151,0.101,0.133,0.108,0.16,0.108,0.125,0.123,0.143,0.115,0.118,0.131,0.107,0.109,0.111,0.119,0.123,0.108,0.104,0.13,431,1600,920,782,745,1210,935,930,589,396,220.0,1329,922,309,729,3057,4685,528,1088,2070,739,1844,973,1541,1561,1921,1967,1411,1464,1412,744,452,234,1249,406,664,1719,1442,212,287,699,1906,1209,2025,1883,770,348,976,1235,625,250,1350,975,1937,519,2544,676,1435,1026,810,467,856,252,461,1116,580,2087,951,776,461,557,833,3618,278,3.1,2.637,2.403,2.621,1.972,2.289,2.364,2.631,2.614,2.906,2.483,1.943,2.624,2.651,2.388,2.559,2.678,3.59,2.721,1.955,3.134,2.028,1.638,2.618,2.311,2.604,2.109,2.08,2.673,2.346,2.583,2.295,2.399,2.845,3.17,2.375,2.843,2.961,1.84,1.981,2.349,1.73,3.186,1.722,1.836,2.244,2.698,2.637,2.624,2.6,2.074,2.135,1.934,2.241,2.123,1.969,2.704,2.437,2.162,1.935,2.05,2.711,2.18,2.043,2.104,1.95,2.041,2.399,2.131,2.122,2.227,2.2,2.334,2.702,0.729,0.46,0.43,0.501,0.577,0.512,0.608,0.406,0.613,0.532,0.779,0.529,0.491,0.564,0.572,0.608,0.635,0.697,0.469,0.547,0.772,0.605,0.478,0.691,0.569,0.48,0.451,0.529,0.542,0.5,0.645,0.831,0.502,0.469,0.752,0.365,0.944,0.624,0.42,0.33,0.436,0.574,0.813,0.565,0.557,0.433,0.448,0.557,0.389,0.554,0.257,0.394,0.488,0.519,0.334,0.381,0.691,0.488,0.548,0.41,0.408,0.614,0.555,0.591,0.432,0.631,0.464,0.359,0.434,0.513,0.412,0.452,0.382,0.452
-348,Epileptic,M,27.0,1.0,2.3,1.0,1.2,2.0,2.8,1.5,2.0,1.6,0.6,0.3,3.2,1.9,0.7,1.8,4.9,9.4,1.0,2.5,5.2,1.6,3.3,3.1,5.1,3.6,4.2,4.2,2.5,2.4,3.6,1.2,1.8,0.6,3.9,0.6,1.3,2.6,2.4,0.3,0.3,1.2,4.8,3.2,3.0,2.5,0.8,0.5,0.7,1.5,0.9,0.6,1.9,1.4,2.5,0.3,2.5,0.7,1.5,1.7,1.2,0.8,2.2,0.6,0.7,2.8,1.3,2.5,1.1,1.2,0.9,1.1,0.8,4.7,0.4,9,23,7,10,16,29,13,22,18,8,4.0,34,22,10,21,60,101,9,32,47,14,49,23,50,44,54,57,30,27,41,15,9,6,48,7,13,40,34,2,2,8,45,29,24,16,6,3,5,9,13,5,17,12,20,2,23,6,10,14,11,7,21,4,6,27,17,20,9,8,9,9,7,45,3,0.039,0.028,0.019,0.025,0.041,0.033,0.023,0.034,0.056,0.032,0.045,0.036,0.032,0.037,0.036,0.033,0.033,0.039,0.027,0.042,0.035,0.037,0.037,0.051,0.044,0.04,0.033,0.033,0.026,0.031,0.033,0.058,0.034,0.044,0.024,0.018,0.032,0.031,0.015,0.02,0.024,0.04,0.046,0.028,0.019,0.022,0.019,0.012,0.018,0.021,0.027,0.024,0.034,0.022,0.013,0.019,0.021,0.023,0.026,0.022,0.021,0.033,0.037,0.027,0.025,0.021,0.021,0.018,0.02,0.027,0.027,0.021,0.024,0.02,1461,4843,2258,2297,1843,4906,2778,3464,1830,1377,521.0,3028,3813,1328,2834,9466,17249,2294,4882,5226,4083,4767,2827,6444,4928,6845,6505,3514,5375,5947,2319,1240,919,5706,2253,2560,6301,5776,551,562,1602,3205,5367,2697,3248,1134,1067,1975,2728,1763,654,2965,1640,4106,448,3757,1335,2587,1599,1450,840,3082,406,1040,3794,1439,3374,2407,1676,1224,1227,1617,6955,506,0.14,0.128,0.106,0.121,0.153,0.136,0.107,0.149,0.178,0.154,0.149,0.151,0.14,0.147,0.152,0.138,0.14,0.137,0.126,0.156,0.128,0.137,0.134,0.165,0.149,0.155,0.132,0.123,0.117,0.137,0.13,0.128,0.112,0.166,0.098,0.098,0.136,0.142,0.085,0.113,0.117,0.147,0.15,0.12,0.102,0.114,0.1,0.082,0.1,0.115,0.13,0.114,0.132,0.107,0.102,0.109,0.121,0.112,0.13,0.113,0.123,0.138,0.155,0.118,0.118,0.108,0.107,0.102,0.106,0.152,0.126,0.108,0.115,0.113,437,1466,792,797,844,1372,978,1125,619,351,150.0,1502,1122,372,892,2872,5011,453,1523,2234,846,1723,1171,2037,1723,2125,2307,1447,1488,2015,657,449,307,1481,546,1104,1497,1547,319,284,809,1664,1313,1798,1944,586,413,793,1232,735,361,1387,815,1946,276,2018,514,1129,1003,768,468,1167,244,513,1872,910,1944,1020,859,528,676,690,3097,298,3.393,2.841,2.648,2.639,1.988,2.618,2.325,2.762,2.29,2.804,2.719,1.742,2.624,2.611,2.479,2.491,2.67,3.597,2.601,2.009,3.382,2.242,2.079,2.536,2.305,2.579,2.287,1.96,2.672,2.353,2.633,2.891,2.369,2.827,3.465,2.142,3.03,2.736,1.941,2.225,2.506,1.744,2.947,1.744,1.914,2.159,3.069,3.071,2.608,2.46,1.932,2.214,2.116,2.267,1.905,1.987,2.45,2.365,1.882,1.993,2.215,2.516,1.809,2.455,2.031,1.803,1.871,2.443,2.295,2.518,2.14,2.367,2.288,2.097,0.829,0.61,0.536,0.518,0.59,0.649,0.611,0.397,0.631,0.599,0.778,0.543,0.486,0.499,0.473,0.576,0.634,0.846,0.56,0.676,0.841,0.525,0.571,0.746,0.594,0.562,0.529,0.472,0.441,0.54,0.697,1.076,0.385,0.715,0.729,0.358,0.722,0.744,0.339,0.407,0.473,0.523,0.855,0.605,0.575,0.465,0.485,0.882,0.478,0.464,0.41,0.415,0.493,0.4,0.331,0.408,0.502,0.515,0.349,0.376,0.435,0.637,0.459,0.652,0.465,0.667,0.416,0.435,0.459,0.835,0.496,0.427,0.487,0.446
-349,Epileptic,M,27.0,0.6,2.5,1.0,1.0,1.2,3.2,1.6,1.8,1.0,0.9,0.2,3.3,1.7,0.7,1.2,6.1,9.9,0.8,3.3,5.4,1.5,2.8,1.4,2.6,3.5,3.6,3.8,3.0,2.2,3.1,1.5,1.2,1.0,3.1,0.5,0.5,4.3,2.7,0.2,0.3,1.1,2.9,2.6,2.9,2.5,0.7,0.6,1.1,1.3,0.9,0.7,1.8,1.2,2.0,0.3,2.8,0.9,1.8,0.5,0.9,0.8,1.4,0.2,0.4,2.1,0.8,3.6,0.7,1.1,0.4,1.4,1.9,4.6,0.2,5,21,8,8,12,27,14,16,11,10,2.0,29,20,10,12,68,107,8,34,45,10,29,18,31,48,36,43,32,23,37,24,13,10,33,3,5,45,31,2,2,5,22,27,21,17,5,3,6,7,16,5,16,8,15,2,27,6,17,5,8,7,11,2,4,17,10,30,6,9,4,9,15,43,2,0.027,0.025,0.017,0.019,0.039,0.041,0.022,0.031,0.037,0.036,0.029,0.033,0.032,0.036,0.028,0.039,0.033,0.031,0.035,0.038,0.023,0.034,0.023,0.04,0.034,0.03,0.031,0.031,0.025,0.036,0.039,0.053,0.047,0.035,0.015,0.015,0.039,0.032,0.024,0.017,0.021,0.037,0.039,0.027,0.02,0.019,0.024,0.016,0.017,0.023,0.032,0.02,0.022,0.02,0.014,0.02,0.018,0.019,0.023,0.02,0.027,0.028,0.024,0.02,0.025,0.022,0.022,0.012,0.025,0.02,0.034,0.025,0.02,0.015,1544,4898,2547,1972,1487,3774,2947,3886,1784,1459,528.0,3103,3243,1269,2662,10205,17693,2597,5472,4271,3307,3665,2076,4753,6599,6764,5300,4159,4347,5368,2207,1260,1334,7134,2161,1716,10013,6977,330,807,1762,2541,6800,3017,2801,1341,851,2442,2662,1839,534,3124,1810,4165,500,4087,2075,2908,633,1161,1176,2033,430,892,3111,1238,4919,1933,1546,954,1446,2600,10019,504,0.111,0.116,0.103,0.104,0.143,0.159,0.119,0.128,0.149,0.16,0.115,0.136,0.146,0.151,0.134,0.151,0.139,0.113,0.145,0.138,0.093,0.149,0.125,0.142,0.148,0.13,0.139,0.12,0.116,0.138,0.146,0.135,0.197,0.152,0.084,0.093,0.143,0.129,0.135,0.083,0.105,0.144,0.148,0.12,0.107,0.107,0.115,0.088,0.093,0.109,0.137,0.111,0.112,0.099,0.101,0.116,0.1,0.103,0.122,0.11,0.13,0.132,0.119,0.119,0.12,0.111,0.112,0.088,0.125,0.111,0.145,0.129,0.103,0.103,429,1614,952,802,576,1362,1037,1062,481,411,157.0,1533,924,362,703,2837,4949,493,1637,2314,961,1351,945,1279,1981,1955,2106,1631,1417,1680,677,463,365,1620,513,579,2082,1698,153,288,728,1207,1250,1752,1888,642,354,1006,1156,712,355,1378,829,1859,286,2161,748,1659,387,735,547,776,180,397,1412,542,2574,910,728,402,709,1128,3863,245,3.473,2.886,2.712,2.5,2.379,2.407,2.275,3.177,2.886,3.201,3.14,1.809,2.755,2.568,2.891,2.706,2.914,3.831,2.708,1.643,2.808,2.3,1.892,2.784,2.521,2.798,2.132,2.132,2.463,2.553,2.728,2.545,3.077,3.16,3.669,2.627,3.281,3.097,2.387,2.909,2.946,1.87,3.632,1.926,1.713,2.208,3.025,3.043,2.797,2.673,1.912,2.192,2.512,2.375,2.353,1.966,3.191,1.975,1.806,1.853,2.285,2.644,2.751,2.543,2.487,2.504,2.02,2.456,2.223,2.761,2.276,2.643,2.631,2.505,0.953,0.617,0.574,0.556,0.494,0.573,0.563,0.71,0.648,0.532,0.711,0.465,0.44,0.547,0.515,0.541,0.64,0.771,0.632,0.602,0.661,0.454,0.451,0.599,0.527,0.59,0.504,0.569,0.478,0.584,0.619,0.997,0.781,0.673,0.688,0.612,0.807,0.629,0.559,0.543,0.646,0.695,0.761,0.746,0.427,0.516,0.547,0.568,0.515,0.878,0.487,0.513,0.578,0.472,0.385,0.401,0.781,0.558,0.37,0.331,0.486,0.629,0.821,0.871,0.845,0.606,0.468,0.508,0.397,0.68,0.506,0.581,0.689,0.813
-350,Epileptic,F,42.0,0.9,2.0,0.9,0.9,2.1,2.5,1.9,1.4,1.7,0.8,0.2,3.8,2.0,1.0,2.1,8.2,10.7,0.9,2.3,6.2,0.9,3.2,2.1,6.4,3.1,3.6,3.9,3.1,3.7,3.8,1.4,0.5,0.8,2.5,0.3,0.3,4.0,3.5,0.3,0.5,0.7,3.3,2.5,4.7,4.0,0.5,0.4,0.8,1.1,0.9,0.9,2.2,1.5,3.9,0.1,3.6,1.2,2.0,1.0,1.4,0.6,1.9,0.8,0.7,2.3,1.4,2.8,1.9,1.1,0.8,1.0,1.4,3.3,0.4,6,19,7,9,14,18,12,15,11,7,3.0,27,17,13,13,68,70,6,19,46,9,27,15,129,31,31,32,23,25,35,17,4,6,24,2,2,40,32,1,1,5,24,24,34,20,4,2,6,5,7,4,13,9,21,0,25,8,14,8,10,4,11,4,4,16,18,19,14,7,5,6,13,23,4,0.036,0.027,0.016,0.021,0.051,0.034,0.031,0.028,0.07,0.032,0.023,0.043,0.039,0.062,0.05,0.051,0.039,0.03,0.032,0.041,0.014,0.035,0.033,0.063,0.035,0.038,0.033,0.035,0.033,0.033,0.05,0.025,0.03,0.031,0.012,0.011,0.036,0.037,0.027,0.041,0.019,0.041,0.051,0.039,0.026,0.012,0.023,0.013,0.016,0.021,0.036,0.022,0.028,0.033,0.028,0.026,0.031,0.021,0.025,0.024,0.026,0.028,0.035,0.025,0.025,0.033,0.024,0.022,0.022,0.034,0.024,0.02,0.016,0.021,1464,4407,2148,2212,2433,3429,2856,3087,1903,1260,499.0,2583,3424,1071,2196,10489,17097,2245,3899,5352,3639,5140,2511,6817,5253,5701,5514,4278,5699,5914,2145,748,1229,5567,1806,1155,8326,6567,437,358,1442,2694,5234,2577,3972,1224,877,2342,2226,1752,612,3166,1795,5006,92,4005,1826,3529,1144,1660,986,2438,733,1151,2779,872,3656,3056,1941,1258,1471,2264,7431,455,0.125,0.114,0.101,0.102,0.138,0.133,0.101,0.117,0.157,0.136,0.106,0.147,0.134,0.173,0.131,0.15,0.13,0.104,0.126,0.15,0.073,0.138,0.12,0.166,0.136,0.142,0.126,0.116,0.116,0.126,0.157,0.085,0.121,0.126,0.068,0.064,0.139,0.14,0.096,0.141,0.101,0.139,0.146,0.152,0.115,0.083,0.1,0.08,0.089,0.106,0.139,0.101,0.115,0.117,0.137,0.123,0.123,0.106,0.128,0.117,0.113,0.12,0.135,0.123,0.121,0.152,0.109,0.108,0.104,0.137,0.115,0.113,0.093,0.142,393,1446,790,807,800,1261,983,906,482,384,214.0,1350,956,349,685,2968,4648,472,1181,2440,943,1485,1046,1800,1615,1628,2033,1514,1815,2054,576,343,408,1274,513,475,2061,1624,201,164,649,1373,1053,1830,2356,710,318,1005,1022,703,359,1563,890,2003,41,2187,687,1683,651,919,433,996,320,423,1463,559,1896,1441,858,459,697,1047,3292,287,3.47,2.569,2.488,2.591,2.643,2.48,2.4,3.098,2.79,2.718,2.337,1.733,2.814,2.277,2.482,2.605,2.887,3.533,2.705,1.973,3.0,2.619,2.103,2.735,2.51,2.852,2.182,2.225,2.473,2.337,2.806,2.214,2.456,3.124,3.071,2.284,2.937,2.982,2.549,2.665,2.804,1.876,3.256,1.686,1.955,1.815,3.314,2.888,2.716,2.804,1.969,2.273,2.206,2.487,2.616,2.052,2.852,2.478,2.086,2.032,2.607,2.438,2.279,3.016,2.103,2.143,2.197,2.345,2.48,2.704,2.476,2.49,2.511,2.104,1.245,0.713,0.635,0.524,0.684,0.587,0.561,0.648,0.794,0.471,0.807,0.496,0.541,0.604,0.557,0.654,0.72,0.957,0.684,0.659,0.761,0.7,0.49,0.827,0.553,0.668,0.544,0.616,0.595,0.68,0.735,1.164,0.461,0.817,0.703,0.405,0.796,0.706,0.506,0.44,0.612,0.503,0.956,0.573,0.526,0.425,0.577,0.648,0.497,0.518,0.433,0.561,0.553,0.596,0.493,0.453,0.669,0.526,0.464,0.46,0.559,0.555,0.462,0.799,0.505,0.547,0.639,0.358,0.564,0.694,0.536,0.527,0.442,0.564
-351,Epileptic,M,22.0,0.6,2.0,1.0,1.2,1.9,1.5,1.6,1.7,1.7,0.6,0.3,3.6,1.1,0.5,0.7,5.8,7.0,0.8,1.6,4.7,1.3,2.9,2.1,2.8,2.5,3.4,2.7,2.3,3.5,3.1,1.6,0.4,0.4,2.7,0.7,0.3,3.1,3.7,0.1,0.1,1.0,2.6,3.3,2.4,2.2,0.5,0.3,1.1,1.1,0.8,0.8,1.5,1.4,2.6,0.1,2.3,0.4,1.1,1.1,1.4,0.7,1.3,0.2,0.5,1.5,1.2,2.6,1.3,1.1,0.3,0.6,0.7,4.7,0.2,7,17,10,10,14,13,13,14,14,6,4.0,33,10,6,7,50,61,6,22,36,14,37,20,26,28,32,31,20,26,32,14,3,5,24,4,3,27,32,2,1,6,22,22,20,14,4,2,8,6,7,6,12,12,18,1,20,3,11,7,13,6,9,1,6,12,14,21,12,9,2,4,5,38,3,0.036,0.023,0.02,0.022,0.036,0.042,0.024,0.028,0.036,0.041,0.041,0.036,0.026,0.034,0.031,0.031,0.025,0.034,0.031,0.035,0.031,0.035,0.024,0.032,0.023,0.031,0.026,0.025,0.025,0.036,0.034,0.026,0.023,0.035,0.029,0.012,0.038,0.037,0.013,0.025,0.021,0.032,0.053,0.025,0.017,0.011,0.019,0.017,0.016,0.016,0.033,0.02,0.027,0.021,0.021,0.017,0.013,0.018,0.03,0.021,0.028,0.02,0.015,0.014,0.016,0.025,0.023,0.015,0.02,0.014,0.021,0.02,0.02,0.013,1152,4611,2421,2505,2699,2055,2393,3017,1923,1232,467.0,3422,2960,1194,1759,10685,16569,2056,3378,4315,2830,5259,2563,5727,5547,6474,5193,3338,6335,5293,2052,775,694,5084,1956,1136,5657,5402,337,197,1248,2363,4140,2284,3175,1375,672,1837,2132,1465,607,2795,2200,4628,307,3662,1229,2162,919,1716,894,2973,287,936,2776,1163,3352,3032,1531,960,1283,1243,8736,485,0.127,0.118,0.111,0.107,0.137,0.154,0.102,0.13,0.153,0.136,0.15,0.145,0.12,0.147,0.133,0.137,0.118,0.116,0.142,0.14,0.112,0.138,0.113,0.122,0.119,0.128,0.119,0.11,0.104,0.15,0.118,0.083,0.114,0.133,0.109,0.073,0.133,0.135,0.1,0.146,0.111,0.135,0.153,0.118,0.092,0.081,0.095,0.103,0.092,0.094,0.146,0.108,0.119,0.105,0.111,0.097,0.083,0.106,0.126,0.114,0.124,0.102,0.123,0.101,0.099,0.12,0.115,0.094,0.107,0.101,0.112,0.112,0.097,0.105,348,1372,845,919,890,726,979,984,688,303,166.0,1609,754,249,428,2860,4367,392,1083,2053,919,1500,1265,1475,1586,1968,1746,1345,1983,1549,737,370,263,1220,440,472,1398,1550,168,76,669,1229,1125,1476,1983,690,261,858,1026,740,359,1189,1009,1967,131,2095,580,1113,586,1061,426,1002,146,551,1393,702,1809,1377,877,331,516,583,3910,236,3.116,2.968,2.827,2.508,2.569,2.415,2.106,2.671,2.263,3.108,2.449,1.874,2.911,3.168,2.96,2.852,2.998,3.748,2.592,1.868,2.615,2.484,1.815,2.863,2.653,2.701,2.298,2.033,2.593,2.659,2.152,2.009,2.074,2.825,3.515,2.216,2.94,2.522,2.255,3.037,2.322,1.782,2.676,1.748,1.81,2.117,2.857,2.659,2.515,1.97,1.866,2.316,2.166,2.416,2.512,1.881,2.315,2.076,1.794,1.747,2.246,2.741,2.191,2.01,2.296,2.164,1.976,2.345,1.997,3.194,2.643,2.237,2.259,2.43,0.72,0.778,0.634,0.578,0.756,0.714,0.551,0.528,0.605,0.493,0.859,0.476,0.576,0.441,0.691,0.632,0.688,0.83,0.655,0.501,0.921,0.675,0.556,1.049,0.713,0.612,0.551,0.53,0.734,0.712,1.071,0.925,0.428,0.733,0.859,0.455,0.868,0.751,0.426,0.523,0.567,0.481,0.953,0.527,0.587,0.562,0.814,0.655,0.503,0.694,0.438,0.526,0.55,0.577,0.674,0.492,0.721,0.527,0.476,0.489,0.623,0.841,0.477,0.641,0.647,0.605,0.526,0.477,0.501,0.64,0.709,0.66,0.618,0.456
-352,Epileptic,M,57.0,1.5,2.8,1.2,1.1,1.2,1.6,1.3,1.5,1.3,0.7,0.4,2.6,1.6,0.5,1.3,4.7,7.3,0.8,2.1,4.7,1.3,2.9,1.9,3.1,4.6,3.7,4.7,4.4,3.9,2.9,1.4,0.9,0.8,2.5,0.5,0.6,3.0,2.6,0.3,0.3,1.2,3.0,3.1,2.8,2.4,0.7,0.4,1.0,1.4,0.6,0.5,2.2,0.6,2.3,0.2,3.3,0.7,1.6,1.2,0.9,0.7,1.7,0.3,1.0,1.6,1.6,2.6,1.2,1.4,0.6,0.8,1.8,3.8,0.3,9,23,10,7,12,16,12,14,13,6,3.0,25,16,6,12,51,65,6,16,40,8,29,15,31,36,38,43,37,29,28,18,7,8,25,2,6,32,28,2,2,7,27,26,21,14,5,2,5,6,5,3,16,4,18,1,26,5,13,9,8,6,12,2,6,11,19,21,9,8,6,5,13,30,4,0.052,0.032,0.027,0.025,0.025,0.034,0.027,0.026,0.048,0.039,0.04,0.035,0.032,0.038,0.037,0.03,0.031,0.033,0.037,0.041,0.028,0.03,0.034,0.035,0.045,0.034,0.033,0.038,0.035,0.032,0.042,0.046,0.04,0.036,0.022,0.014,0.035,0.03,0.019,0.017,0.025,0.037,0.049,0.028,0.017,0.017,0.017,0.02,0.019,0.016,0.024,0.027,0.021,0.023,0.017,0.019,0.025,0.02,0.024,0.023,0.022,0.032,0.024,0.043,0.022,0.038,0.019,0.021,0.019,0.019,0.026,0.027,0.017,0.02,1228,4103,2223,1856,2419,2265,2109,3013,1813,1248,450.0,2216,3045,1027,2596,8470,15211,1806,3433,4314,2767,3271,1558,5545,3725,6210,5586,4447,5458,4829,2668,986,1184,5407,1413,1587,6471,5671,367,496,1258,2276,7003,2147,3834,1344,802,1780,2246,1287,451,2822,1141,3931,350,4242,921,2406,1114,1208,683,2259,406,785,2101,1248,4165,2419,2202,937,1048,2285,7492,332,0.167,0.135,0.11,0.109,0.117,0.135,0.097,0.12,0.166,0.144,0.14,0.142,0.14,0.138,0.138,0.12,0.121,0.119,0.139,0.15,0.097,0.102,0.124,0.143,0.116,0.119,0.103,0.12,0.111,0.136,0.155,0.134,0.128,0.143,0.093,0.086,0.143,0.141,0.122,0.119,0.113,0.14,0.172,0.121,0.089,0.093,0.102,0.096,0.094,0.098,0.128,0.122,0.103,0.111,0.089,0.106,0.127,0.111,0.113,0.103,0.12,0.125,0.124,0.161,0.109,0.136,0.101,0.102,0.096,0.138,0.12,0.123,0.096,0.132,428,1465,755,736,905,837,791,1007,525,334,149.0,1219,839,244,667,2613,3927,407,938,2061,734,1189,859,1544,1380,1979,2065,1658,1456,1479,669,448,349,1209,393,671,1495,1458,204,193,713,1319,1311,1529,2136,692,325,775,1070,577,284,1313,574,1630,196,2475,424,1244,766,710,446,900,225,386,1106,649,2136,914,1036,455,547,1074,3663,239,3.048,2.675,2.796,2.508,2.323,2.33,2.35,2.674,2.622,2.754,2.352,1.64,2.829,2.832,2.92,2.506,3.013,3.483,2.759,1.807,2.663,2.229,1.701,2.889,2.259,2.496,2.199,2.191,2.902,2.609,2.869,2.281,2.548,3.1,3.281,2.285,3.058,2.818,2.103,2.436,2.113,1.607,3.538,1.634,2.012,2.051,2.925,2.824,2.535,2.446,1.751,2.188,2.089,2.488,1.939,1.912,2.515,2.222,1.72,1.902,1.794,2.594,2.004,2.358,2.102,2.362,2.087,2.558,2.483,2.19,2.117,2.418,2.172,1.81,0.649,0.579,0.71,0.53,0.68,0.629,0.567,0.542,0.623,0.684,0.96,0.44,0.503,0.612,0.565,0.706,0.6,0.652,0.735,0.568,0.722,0.624,0.546,0.617,0.619,0.563,0.628,0.621,0.572,0.566,0.546,0.932,0.432,0.661,0.587,0.497,0.676,0.686,0.346,0.488,0.45,0.483,0.719,0.511,0.641,0.559,0.563,0.785,0.557,0.497,0.451,0.514,0.525,0.515,0.451,0.507,0.38,0.573,0.419,0.551,0.431,0.734,0.396,0.625,0.603,0.903,0.468,0.586,0.493,0.572,0.588,0.485,0.488,0.436
-353,Epileptic,F,52.0,1.3,1.6,0.6,0.8,1.6,1.0,1.4,1.2,0.7,0.6,0.2,3.1,1.4,0.5,0.8,4.3,6.5,0.5,2.0,5.2,1.4,2.4,2.2,2.8,2.7,3.5,3.7,3.3,3.1,2.9,1.2,1.0,0.4,2.3,0.3,0.8,2.7,2.4,0.1,0.1,1.2,2.9,2.5,3.1,2.5,0.6,0.3,1.1,1.3,0.9,0.3,1.7,0.9,2.1,0.7,2.1,0.5,1.9,0.7,1.5,0.2,1.5,0.2,1.0,1.4,0.5,2.2,0.9,0.9,0.3,1.1,1.1,3.8,0.2,7,14,6,6,14,10,11,11,8,4,2.0,27,14,5,9,43,59,3,18,39,10,19,21,27,23,34,38,26,24,29,11,6,3,21,2,8,26,25,2,1,8,22,23,24,16,6,1,7,7,6,2,13,10,13,3,17,5,14,6,9,2,11,2,8,13,5,18,9,7,3,9,7,30,1,0.069,0.026,0.018,0.02,0.04,0.024,0.026,0.031,0.044,0.033,0.021,0.042,0.033,0.035,0.032,0.044,0.031,0.027,0.037,0.042,0.035,0.041,0.03,0.044,0.038,0.034,0.03,0.035,0.032,0.034,0.043,0.052,0.032,0.037,0.025,0.023,0.047,0.035,0.014,0.017,0.028,0.038,0.066,0.032,0.021,0.019,0.02,0.024,0.023,0.035,0.031,0.022,0.027,0.027,0.058,0.021,0.016,0.03,0.021,0.026,0.024,0.03,0.028,0.043,0.023,0.024,0.022,0.015,0.025,0.016,0.031,0.031,0.026,0.015,896,2805,1340,1505,1920,1259,1697,2120,1058,935,504.0,2151,1995,857,1361,6269,12099,1192,2940,3797,1730,2278,2054,3461,2474,4544,3911,3118,3985,3806,1667,527,462,3577,761,1364,3405,4043,237,252,1038,1597,3856,2184,2614,887,492,1258,1746,927,248,2452,1203,2709,345,2783,650,2340,617,1474,415,1811,317,673,1893,466,2916,2004,1329,641,1120,1279,4755,278,0.16,0.126,0.112,0.107,0.169,0.121,0.098,0.118,0.152,0.132,0.097,0.153,0.138,0.149,0.137,0.154,0.132,0.109,0.159,0.17,0.131,0.125,0.133,0.146,0.123,0.132,0.118,0.126,0.104,0.148,0.147,0.152,0.112,0.134,0.101,0.112,0.152,0.14,0.116,0.114,0.133,0.139,0.184,0.139,0.105,0.115,0.097,0.107,0.109,0.133,0.108,0.118,0.142,0.119,0.171,0.109,0.115,0.134,0.117,0.119,0.098,0.132,0.137,0.144,0.12,0.118,0.109,0.097,0.114,0.106,0.137,0.135,0.122,0.099,289,989,515,613,673,632,691,685,341,289,183.0,1129,739,239,443,1855,3413,287,935,1879,599,841,1042,1146,922,1584,1716,1380,1307,1324,508,332,182,945,235,552,1079,1058,154,128,612,1066,812,1587,1764,511,214,655,863,422,170,1196,581,1312,195,1665,426,1077,466,802,187,732,165,399,1046,277,1560,924,631,279,604,619,2326,186,2.778,2.601,2.618,2.333,2.356,1.821,2.078,2.586,2.342,2.682,2.499,1.71,2.319,2.604,2.395,2.529,2.818,3.249,2.466,1.819,2.36,2.196,1.866,2.473,2.268,2.428,1.943,2.024,2.452,2.282,2.428,1.979,2.117,2.692,2.592,2.345,2.457,2.737,1.639,2.08,2.178,1.41,3.071,1.659,1.657,1.889,2.731,2.163,2.482,2.497,1.625,2.22,2.043,2.192,2.103,1.851,1.755,2.218,1.675,2.077,2.141,2.696,2.016,2.082,1.893,1.842,1.988,2.308,2.205,2.324,1.98,2.427,2.218,1.566,0.767,0.71,0.541,0.547,0.655,0.571,0.527,0.564,0.565,0.518,0.786,0.528,0.531,0.413,0.461,0.635,0.643,0.714,0.669,0.52,0.768,0.614,0.509,0.804,0.59,0.59,0.553,0.529,0.512,0.555,0.81,0.588,0.438,0.592,0.586,0.589,0.728,0.75,0.337,0.334,0.57,0.649,0.743,0.473,0.485,0.505,0.377,0.961,0.432,0.499,0.263,0.448,0.475,0.528,0.458,0.486,0.357,0.565,0.304,0.536,0.512,0.686,0.371,0.591,0.59,0.431,0.471,0.378,0.43,0.859,0.549,0.618,0.481,0.34
-354,Epileptic,F,27.0,0.8,2.0,1.2,1.4,1.6,1.5,1.2,2.2,0.9,1.1,0.6,3.2,1.5,0.5,1.0,6.0,8.2,0.8,2.4,5.1,2.1,5.3,3.8,3.5,5.9,4.3,4.2,2.5,2.5,3.5,1.1,1.2,0.5,2.3,0.3,0.5,3.0,3.5,0.2,0.2,1.1,4.7,2.7,2.6,2.8,0.5,0.7,0.9,1.3,0.6,0.6,1.3,1.3,2.0,0.4,2.8,1.0,2.3,2.7,1.5,0.3,2.2,0.4,0.5,2.1,1.0,1.8,1.3,1.1,1.1,1.3,1.2,4.6,0.3,7,15,14,16,17,17,13,22,12,14,6.0,30,18,6,12,76,91,7,29,48,13,47,32,42,47,36,54,28,27,49,18,6,8,44,2,5,47,39,1,1,7,42,28,22,20,5,3,7,8,6,6,11,10,18,2,25,8,16,21,14,3,16,3,5,19,16,18,11,8,14,8,10,34,3,0.036,0.024,0.026,0.032,0.035,0.024,0.026,0.029,0.037,0.045,0.047,0.036,0.034,0.027,0.029,0.036,0.031,0.032,0.032,0.039,0.05,0.044,0.051,0.04,0.05,0.039,0.033,0.029,0.027,0.034,0.035,0.053,0.034,0.041,0.018,0.014,0.035,0.036,0.017,0.026,0.023,0.052,0.056,0.025,0.021,0.014,0.025,0.016,0.016,0.023,0.029,0.02,0.024,0.017,0.029,0.02,0.025,0.027,0.035,0.022,0.014,0.034,0.029,0.023,0.025,0.025,0.016,0.018,0.018,0.035,0.029,0.021,0.022,0.021,1298,3983,2313,2182,2131,3187,3022,3531,1522,1556,629.0,2957,3190,1128,1807,9781,16753,2103,4243,4546,2656,3602,2741,6015,5313,5434,6096,4536,5917,6184,2160,753,830,5386,1372,1659,6274,6935,366,398,1372,2169,6483,2335,3360,1206,794,1893,2478,1161,502,2313,1834,4355,413,3811,1243,3202,1497,1850,568,2755,369,789,2596,1022,3502,2549,1855,1492,1538,1886,7515,390,0.122,0.116,0.132,0.133,0.144,0.1,0.12,0.128,0.161,0.166,0.167,0.155,0.139,0.134,0.15,0.152,0.137,0.122,0.143,0.151,0.121,0.137,0.147,0.148,0.154,0.138,0.128,0.121,0.116,0.149,0.135,0.134,0.132,0.149,0.082,0.085,0.145,0.152,0.099,0.098,0.112,0.167,0.191,0.126,0.107,0.089,0.132,0.1,0.097,0.129,0.141,0.106,0.117,0.103,0.166,0.12,0.125,0.125,0.147,0.12,0.095,0.147,0.129,0.127,0.122,0.131,0.107,0.103,0.108,0.153,0.128,0.118,0.107,0.132,355,1327,811,780,794,978,835,1270,506,406,211.0,1429,899,274,558,3042,4674,440,1206,2234,657,1694,1175,1637,1855,1697,2177,1465,1604,1807,586,384,263,1203,358,558,1675,1564,194,155,720,1419,995,1631,2015,608,363,802,1175,425,342,1118,884,1958,183,2033,595,1363,1076,1062,326,1007,237,409,1319,665,1662,1124,858,547,735,862,3250,189,3.333,2.837,2.712,2.668,2.323,2.557,2.687,2.499,2.384,2.832,2.394,1.819,2.746,2.796,2.399,2.51,2.802,3.378,2.781,1.839,2.904,1.92,2.035,2.752,2.272,2.691,2.241,2.332,2.727,2.536,2.658,2.186,2.612,2.998,3.079,2.539,2.815,3.04,2.125,2.38,2.365,1.578,3.758,1.692,1.879,2.146,2.69,2.82,2.609,2.949,1.928,2.153,2.178,2.364,2.718,2.094,2.442,2.562,1.698,1.901,2.067,2.643,1.886,2.202,2.208,1.864,2.222,2.481,2.448,2.751,2.411,2.386,2.466,2.616,0.821,0.662,0.435,0.55,0.589,0.704,0.744,0.397,0.513,0.712,0.853,0.572,0.484,0.371,0.577,0.524,0.615,0.823,0.63,0.559,0.891,0.575,0.591,0.665,0.75,0.519,0.626,0.575,0.462,0.698,0.575,0.74,0.328,0.631,0.866,0.58,0.662,0.666,0.353,0.491,0.478,0.476,0.81,0.515,0.559,0.424,0.414,0.597,0.399,0.61,0.326,0.432,0.528,0.379,0.425,0.437,0.457,0.505,0.389,0.414,0.357,0.77,0.433,0.699,0.672,0.561,0.487,0.37,0.374,0.834,0.418,0.568,0.485,0.372
-355,Epileptic,F,22.0,0.9,2.0,0.5,1.1,1.7,1.7,1.3,1.5,1.2,0.6,0.4,2.5,1.3,0.6,1.7,5.2,8.1,0.7,2.3,3.5,1.2,2.8,1.5,3.2,2.3,3.3,2.4,1.9,2.0,3.0,1.3,1.1,0.4,1.8,0.1,0.5,2.7,2.5,0.3,0.2,1.0,3.0,1.8,2.7,1.9,0.6,0.3,0.9,1.2,0.6,0.5,1.7,1.3,2.5,0.1,2.3,0.5,1.1,1.1,1.0,0.5,1.0,0.3,0.3,1.4,0.9,2.1,0.9,0.9,0.3,0.8,0.8,3.0,0.3,8,22,6,10,14,19,12,13,13,6,6.0,26,17,7,18,64,83,3,27,35,13,32,16,40,32,38,36,19,20,33,16,7,3,21,1,4,28,32,1,1,6,33,20,16,13,5,2,6,6,5,4,14,10,16,1,23,5,9,8,9,6,6,3,3,16,13,15,5,7,2,5,6,25,3,0.036,0.025,0.011,0.023,0.04,0.034,0.019,0.029,0.044,0.032,0.035,0.033,0.028,0.029,0.033,0.031,0.028,0.026,0.031,0.031,0.027,0.035,0.028,0.042,0.03,0.033,0.024,0.023,0.023,0.028,0.031,0.035,0.027,0.031,0.008,0.013,0.034,0.033,0.018,0.022,0.022,0.039,0.039,0.029,0.016,0.012,0.015,0.017,0.017,0.02,0.028,0.018,0.023,0.02,0.014,0.017,0.024,0.015,0.025,0.019,0.02,0.025,0.024,0.016,0.021,0.022,0.018,0.016,0.018,0.013,0.023,0.016,0.016,0.021,1465,4189,1687,2448,2096,2176,2689,2600,2073,1200,591.0,2678,3472,1211,3701,10426,18761,2026,4675,4056,3437,4431,1910,5361,4294,6927,5813,3475,5360,5719,2010,1203,804,4232,1477,1695,5463,6083,459,357,1446,2803,4693,2234,3005,1389,777,2019,2341,1217,587,2981,1944,4922,316,3486,1098,2607,1172,1435,1047,2219,478,798,2270,910,3446,1937,1964,775,1099,1700,7021,377,0.124,0.133,0.095,0.112,0.15,0.153,0.099,0.133,0.142,0.136,0.135,0.143,0.134,0.145,0.138,0.139,0.129,0.102,0.139,0.136,0.1,0.159,0.133,0.151,0.146,0.143,0.12,0.103,0.106,0.14,0.116,0.103,0.114,0.134,0.049,0.086,0.132,0.14,0.089,0.104,0.12,0.167,0.142,0.121,0.096,0.091,0.092,0.089,0.094,0.101,0.137,0.104,0.118,0.101,0.105,0.106,0.111,0.095,0.129,0.112,0.103,0.11,0.124,0.096,0.125,0.121,0.104,0.091,0.101,0.083,0.121,0.107,0.096,0.131,387,1248,674,872,724,958,963,861,573,308,170.0,1191,871,289,982,2858,4991,398,1264,1692,782,1366,842,1436,1267,1868,1877,1158,1340,1702,726,463,236,946,404,567,1364,1363,257,170,658,1342,924,1341,1734,696,313,839,1049,539,248,1401,848,2004,159,1929,466,1221,661,806,479,704,229,395,1095,621,1680,833,810,359,530,734,3013,213,3.387,3.13,2.384,2.671,2.413,2.118,2.334,2.731,2.667,2.999,2.977,1.924,2.964,2.883,2.76,2.741,2.957,3.805,2.95,2.043,3.063,2.501,1.959,2.775,2.599,2.881,2.398,2.332,2.949,2.555,2.138,2.559,2.597,3.076,3.123,2.556,2.822,3.045,2.058,2.651,2.912,1.883,3.079,1.885,1.977,2.144,3.118,3.135,2.774,2.227,2.171,2.263,2.28,2.756,2.08,2.055,2.41,2.322,2.035,2.035,2.171,2.94,2.391,2.327,2.285,1.853,2.305,2.747,2.691,2.325,2.382,2.52,2.526,2.268,0.487,0.609,0.394,0.593,0.647,0.495,0.448,0.394,0.603,0.489,0.934,0.444,0.409,0.398,0.556,0.592,0.579,0.729,0.606,0.623,0.795,0.489,0.439,0.762,0.46,0.512,0.508,0.516,0.517,0.557,0.691,0.795,0.398,0.522,0.831,0.624,0.733,0.551,0.443,0.402,0.631,0.453,1.031,0.706,0.597,0.526,0.452,0.708,0.438,0.605,0.48,0.486,0.506,0.53,0.279,0.411,0.414,0.578,0.478,0.487,0.405,0.761,0.472,1.012,0.569,0.651,0.48,0.365,0.48,0.569,0.457,0.505,0.546,0.493
-356,Epileptic,F,37.0,1.0,1.7,1.4,1.3,1.4,2.0,1.8,1.2,1.2,0.5,0.2,2.5,1.6,0.7,1.1,5.6,8.9,0.6,3.0,5.1,0.8,2.8,1.4,3.0,3.4,3.4,3.4,2.5,3.1,2.8,1.2,1.3,0.6,2.3,0.3,0.4,2.9,3.1,0.2,0.3,0.8,3.1,1.7,3.2,3.1,1.2,0.4,0.8,1.0,0.7,0.7,1.5,0.9,2.2,0.1,1.6,0.6,1.3,0.9,0.8,0.8,0.9,0.3,0.3,1.3,1.6,2.3,1.5,1.0,0.4,1.0,0.8,3.7,0.2,7,17,14,13,20,17,18,13,10,7,3.0,26,16,9,13,61,74,5,32,39,7,29,14,29,34,29,36,23,28,32,17,10,3,29,2,4,33,35,2,2,5,25,21,21,19,12,2,5,5,5,4,13,9,14,1,12,5,11,8,7,6,7,5,3,12,18,19,12,8,6,8,7,26,2,0.04,0.023,0.023,0.024,0.038,0.039,0.025,0.025,0.049,0.024,0.023,0.038,0.034,0.043,0.037,0.037,0.034,0.027,0.031,0.04,0.019,0.038,0.03,0.035,0.039,0.039,0.029,0.029,0.029,0.032,0.037,0.05,0.036,0.032,0.013,0.021,0.04,0.032,0.02,0.019,0.021,0.041,0.03,0.032,0.025,0.028,0.019,0.013,0.014,0.021,0.033,0.017,0.023,0.021,0.026,0.019,0.024,0.019,0.026,0.018,0.036,0.021,0.037,0.023,0.022,0.036,0.025,0.018,0.021,0.016,0.029,0.021,0.021,0.023,1393,4225,2596,2455,1756,2959,2766,2748,1532,1213,607.0,2018,2843,1309,2094,9751,17066,2460,4379,4413,2823,4105,1934,4695,4497,4747,4918,3307,5436,4568,1896,1069,884,5024,1646,1041,4698,5599,256,644,1278,2268,4309,2559,3033,1529,881,2050,2256,1141,667,2822,2085,4258,180,2739,1085,2625,1141,1149,789,1763,422,771,2299,1279,3012,2836,1683,1133,1234,1446,7943,382,0.143,0.113,0.118,0.112,0.149,0.148,0.122,0.123,0.158,0.133,0.115,0.159,0.141,0.158,0.134,0.146,0.13,0.101,0.133,0.148,0.088,0.15,0.132,0.137,0.156,0.157,0.138,0.128,0.124,0.138,0.15,0.158,0.114,0.133,0.076,0.093,0.132,0.126,0.116,0.102,0.11,0.158,0.128,0.133,0.113,0.141,0.1,0.081,0.089,0.109,0.142,0.106,0.107,0.101,0.127,0.104,0.12,0.104,0.127,0.112,0.144,0.117,0.13,0.108,0.114,0.154,0.118,0.104,0.113,0.115,0.134,0.112,0.098,0.127,410,1272,904,934,658,952,1017,869,378,333,191.0,1102,806,321,583,2653,4501,402,1386,1892,726,1262,803,1345,1454,1458,1940,1272,1670,1509,495,422,270,1164,397,400,1337,1502,155,230,600,1153,1055,1613,2011,704,324,873,1036,568,378,1333,725,1735,90,1328,489,1232,607,639,388,679,152,260,1064,691,1497,1281,772,461,581,657,3041,162,3.422,2.991,2.842,2.588,2.211,2.795,2.384,2.887,2.775,3.054,2.61,1.72,2.751,3.014,2.648,2.707,3.017,4.103,2.64,1.951,2.99,2.496,2.124,2.649,2.477,2.741,2.174,2.083,2.625,2.482,2.912,2.535,2.687,3.013,3.503,2.385,2.74,2.814,1.838,2.96,2.729,1.881,3.104,1.858,1.727,2.439,3.233,2.826,2.608,2.613,2.263,2.249,2.345,2.457,2.257,2.225,2.489,2.467,2.153,1.964,2.525,2.617,2.335,2.901,2.362,2.372,2.259,2.576,2.542,2.554,2.614,2.432,2.782,2.743,0.763,0.697,0.579,0.456,0.611,0.693,0.566,0.604,0.645,0.51,0.817,0.47,0.545,0.61,0.611,0.711,0.618,0.721,0.56,0.618,0.838,0.582,0.417,0.63,0.541,0.497,0.473,0.483,0.467,0.471,0.625,0.948,0.408,0.72,0.849,0.36,0.728,0.567,0.332,0.705,0.684,0.422,0.599,0.7,0.53,0.468,0.641,0.668,0.646,0.484,0.505,0.513,0.714,0.612,0.244,0.53,0.561,0.579,0.455,0.411,0.422,0.723,0.512,0.864,0.715,0.662,0.515,0.486,0.44,0.638,0.447,0.617,0.695,0.547
-357,Epileptic,M,27.0,1.0,1.8,0.8,0.8,2.6,2.4,1.0,1.3,0.7,0.1,,1.5,1.1,0.6,0.5,1.9,4.7,2.0,0.7,0.8,1.8,3.2,1.9,3.4,5.0,0.7,2.1,1.9,1.4,2.1,1.3,0.6,0.2,3.9,0.8,0.7,11.6,9.2,0.3,0.0,1.8,3.2,3.5,0.7,1.9,1.8,0.5,1.3,0.4,0.0,0.1,1.6,0.6,0.9,0.1,2.4,0.5,0.2,0.3,2.1,0.8,2.7,0.0,0.4,1.6,0.6,2.1,0.8,0.6,0.4,1.6,2.3,9.7,0.1,9,13,8,9,17,29,8,11,11,1,,9,10,5,6,21,49,22,9,7,12,29,12,31,50,4,16,17,13,8,25,5,2,35,7,3,107,60,2,0,6,27,33,4,11,11,5,9,5,0,1,9,6,7,1,25,2,1,4,13,3,19,1,4,11,9,22,5,5,3,6,15,68,0,0.033,0.028,0.024,0.027,0.064,0.045,0.038,0.045,0.062,0.038,,0.06,0.045,0.056,0.036,0.044,0.036,0.05,0.043,0.047,0.039,0.041,0.057,0.047,0.052,0.016,0.03,0.041,0.033,0.059,0.036,0.034,0.039,0.057,0.029,0.039,0.089,0.079,0.04,0.017,0.059,0.049,0.045,0.055,0.031,0.064,0.036,0.029,0.025,0.028,0.019,0.037,0.039,0.028,0.028,0.034,0.042,0.028,0.029,0.039,0.032,0.056,0.025,0.019,0.057,0.023,0.036,0.036,0.035,0.032,0.084,0.054,0.056,0.023,1562,3809,1613,1695,2363,1162,1833,1962,1650,256,,980,1746,706,1118,3596,10271,2264,813,674,3318,3188,900,4497,3708,1465,2585,2305,2596,1660,2509,1084,532,4071,1975,730,5424,5058,209,219,827,2236,9228,664,2366,1118,890,2303,941,15,154,1364,1408,1716,281,2213,268,159,718,1494,566,2847,184,972,1264,1355,2006,742,1354,943,788,897,5065,324,0.133,0.129,0.124,0.137,0.235,0.124,0.159,0.147,0.191,0.154,,0.217,0.121,0.181,0.152,0.164,0.153,0.165,0.124,0.152,0.13,0.144,0.213,0.164,0.18,0.09,0.125,0.15,0.135,0.196,0.157,0.136,0.1,0.175,0.108,0.126,0.212,0.207,0.232,0.087,0.155,0.172,0.166,0.184,0.134,0.169,0.166,0.126,0.101,0.123,0.091,0.161,0.159,0.134,0.105,0.139,0.138,0.091,0.111,0.177,0.115,0.206,0.222,0.122,0.194,0.135,0.133,0.145,0.153,0.131,0.304,0.151,0.151,0.089,504,1102,535,584,812,707,521,583,331,63,,422,450,193,291,884,2469,722,336,328,911,1293,567,1292,1763,656,1223,886,813,588,650,326,135,1252,649,290,2143,2049,110,47,337,1106,1614,257,1027,417,282,796,392,12,87,642,372,642,116,1173,188,102,337,961,432,900,55,398,455,484,981,358,368,335,370,758,2664,65,2.555,3.135,2.857,2.908,2.79,1.459,2.866,2.389,3.413,3.411,,2.494,2.538,2.841,2.897,3.278,3.373,2.48,1.759,1.934,2.98,2.048,1.746,2.84,1.961,2.095,1.935,2.24,2.719,2.948,2.751,2.921,3.48,2.473,3.084,2.909,1.814,1.745,2.562,3.406,3.313,1.703,3.497,2.711,2.505,2.682,2.578,3.061,2.373,1.634,1.8,2.69,3.058,2.856,3.223,1.705,1.229,1.429,2.091,1.689,1.526,2.862,3.142,2.482,2.622,3.004,1.852,2.67,3.649,2.927,2.583,1.347,1.924,3.915,0.45,0.628,0.448,0.491,0.592,0.506,0.534,0.835,0.602,0.674,,0.707,0.843,0.651,0.499,0.55,0.574,0.634,0.706,0.625,1.021,0.646,0.407,0.697,0.605,0.943,0.704,0.521,0.537,0.475,0.641,1.309,0.48,0.79,0.896,0.797,0.919,0.904,0.552,1.034,0.943,0.749,0.823,0.737,0.479,1.085,0.689,0.468,0.796,0.302,0.563,0.601,0.557,0.465,0.481,0.592,0.517,0.315,0.633,0.455,0.485,0.857,0.27,0.563,0.616,0.646,0.541,0.378,0.427,0.749,0.641,0.662,0.936,0.563
-358,Epileptic,M,57.0,0.8,2.9,1.0,1.4,2.1,2.3,3.0,1.7,1.0,1.1,0.4,4.0,1.2,0.7,1.3,8.2,12.2,1.4,2.8,6.7,2.5,4.9,2.1,4.6,4.8,3.7,6.7,2.7,5.3,4.1,2.0,1.3,0.5,2.4,0.7,0.5,4.9,3.1,0.2,0.3,1.2,5.0,2.7,5.3,3.5,0.8,0.7,1.0,1.4,0.9,1.0,2.4,1.4,3.4,0.5,3.5,0.8,2.1,1.1,1.3,0.7,1.6,0.3,0.5,2.4,1.2,3.3,1.7,1.7,0.7,1.1,1.5,4.8,0.3,6,20,7,8,19,22,22,17,10,11,3.0,38,23,6,14,73,103,10,34,51,17,43,17,46,44,42,56,27,40,48,21,9,4,30,4,3,48,39,1,2,6,41,23,27,17,4,3,6,8,8,6,15,8,20,2,26,4,19,6,8,4,12,2,4,17,16,24,10,11,6,6,11,34,2,0.034,0.03,0.017,0.024,0.044,0.032,0.044,0.027,0.038,0.049,0.034,0.047,0.032,0.036,0.033,0.046,0.043,0.044,0.038,0.051,0.039,0.053,0.032,0.046,0.045,0.031,0.041,0.03,0.042,0.037,0.055,0.042,0.035,0.03,0.023,0.012,0.052,0.036,0.017,0.018,0.024,0.044,0.051,0.041,0.02,0.013,0.025,0.015,0.018,0.019,0.04,0.02,0.024,0.025,0.022,0.017,0.015,0.025,0.02,0.018,0.023,0.027,0.016,0.029,0.023,0.024,0.022,0.02,0.024,0.023,0.021,0.021,0.019,0.01,2013,4593,2044,2373,2492,3784,2649,3611,1455,1719,665.0,3578,3036,1459,2678,10614,18888,2788,6068,6492,5723,4412,2466,7869,5424,7308,5757,2882,5827,5978,3129,1579,843,6077,1996,1603,6757,7538,350,548,1437,4383,5979,3679,4073,1532,945,2287,2118,1585,734,3649,1548,4619,553,5274,1547,3843,1525,2089,786,2706,441,1315,3095,1307,4554,2607,2154,975,1506,2559,9054,687,0.117,0.129,0.099,0.106,0.149,0.136,0.129,0.119,0.142,0.178,0.127,0.167,0.132,0.15,0.133,0.14,0.142,0.147,0.139,0.171,0.127,0.154,0.123,0.162,0.13,0.128,0.123,0.105,0.13,0.142,0.18,0.117,0.115,0.126,0.09,0.071,0.152,0.143,0.104,0.118,0.11,0.151,0.164,0.16,0.097,0.084,0.106,0.081,0.099,0.097,0.146,0.095,0.11,0.108,0.114,0.096,0.092,0.116,0.097,0.098,0.114,0.12,0.1,0.118,0.117,0.107,0.103,0.103,0.107,0.128,0.107,0.11,0.096,0.086,540,1581,865,912,970,1370,1176,1205,518,451,208.0,1676,894,355,731,3605,5966,603,1533,2582,1225,1704,1074,2065,1900,2395,2646,1427,2101,2136,823,599,245,1424,573,623,1894,1755,199,263,728,1941,1174,2225,2515,865,444,981,1135,799,443,1758,846,2152,306,2976,765,1556,863,1065,446,991,249,395,1524,837,2400,1286,1127,489,814,1234,4042,344,3.198,2.638,2.383,2.441,2.293,2.459,2.002,2.5,2.294,2.843,2.903,1.913,2.644,3.078,2.756,2.338,2.542,3.587,2.884,2.107,3.211,2.159,1.967,2.883,2.325,2.527,1.843,1.714,2.228,2.244,2.708,2.353,2.551,2.988,3.097,2.392,2.601,2.955,1.996,2.13,2.372,1.944,3.221,1.943,1.819,1.928,2.36,2.699,2.173,2.226,1.969,2.191,2.04,2.235,2.197,1.906,2.126,2.43,2.047,2.178,1.954,2.675,1.921,3.332,2.175,1.999,1.846,2.193,2.039,2.073,2.049,2.268,2.342,2.233,0.917,0.564,0.464,0.519,0.561,0.689,0.692,0.563,0.546,0.611,0.648,0.525,0.565,0.414,0.436,0.6,0.621,0.741,0.765,0.709,0.867,0.58,0.523,0.842,0.588,0.553,0.569,0.544,0.638,0.688,0.752,0.863,0.511,0.611,0.854,0.408,0.865,0.609,0.232,0.399,0.492,0.678,0.871,0.66,0.576,0.508,0.538,0.808,0.449,0.456,0.47,0.378,0.461,0.457,0.383,0.498,0.493,0.614,0.388,0.463,0.427,0.802,0.421,0.743,0.497,0.758,0.557,0.466,0.505,0.521,0.496,0.546,0.507,0.457
-360,Epileptic,M,32.0,0.7,2.0,0.9,1.1,1.5,2.6,2.6,1.4,0.5,0.5,0.1,3.5,1.6,0.5,1.5,6.2,12.0,1.2,2.7,5.7,0.9,3.6,1.9,2.8,3.3,3.1,3.8,3.3,3.7,3.6,1.3,0.9,0.5,2.3,0.7,0.7,3.2,2.6,0.1,0.2,1.1,3.2,2.7,2.7,4.5,0.6,0.5,0.9,1.1,0.7,0.5,3.3,1.0,2.6,0.2,2.7,0.5,2.1,1.2,1.2,0.4,1.1,0.3,0.4,2.9,1.5,2.8,2.1,1.4,0.6,1.4,0.8,4.3,0.5,7,19,11,7,14,21,17,14,6,5,2.0,31,14,5,14,56,92,8,26,43,9,34,15,30,40,25,39,28,24,40,19,5,3,27,4,5,33,29,1,1,7,29,34,20,25,5,2,6,6,6,5,24,10,15,2,18,4,16,10,10,4,8,2,4,17,14,22,14,8,5,12,6,37,5,0.026,0.02,0.014,0.019,0.04,0.031,0.031,0.026,0.029,0.032,0.023,0.032,0.028,0.03,0.03,0.033,0.034,0.036,0.034,0.034,0.018,0.038,0.026,0.033,0.029,0.033,0.031,0.028,0.029,0.034,0.038,0.04,0.026,0.028,0.019,0.017,0.035,0.027,0.013,0.016,0.027,0.032,0.034,0.028,0.032,0.013,0.024,0.013,0.015,0.016,0.025,0.028,0.029,0.02,0.026,0.017,0.014,0.022,0.024,0.02,0.017,0.022,0.012,0.02,0.026,0.026,0.022,0.024,0.028,0.021,0.027,0.017,0.016,0.031,1557,5055,2735,2447,1754,3743,2550,3124,1346,1337,615.0,3134,3583,1118,2756,11370,21255,2752,4458,5348,3317,5112,2276,6468,6990,5160,6147,4019,6022,5985,2502,769,822,5997,2316,1416,6565,7139,387,486,1612,3231,5978,2388,3330,1504,901,2304,2360,1528,729,4785,1911,5074,310,4185,1192,3432,1460,1698,1130,2156,544,898,3370,1444,4107,3276,1614,1329,2363,2494,9432,478,0.122,0.107,0.096,0.095,0.131,0.134,0.114,0.125,0.112,0.115,0.084,0.131,0.118,0.131,0.137,0.132,0.127,0.119,0.14,0.129,0.09,0.145,0.12,0.137,0.127,0.131,0.129,0.106,0.1,0.128,0.132,0.106,0.118,0.127,0.082,0.09,0.126,0.119,0.087,0.092,0.12,0.131,0.138,0.129,0.123,0.093,0.105,0.085,0.088,0.102,0.121,0.121,0.116,0.098,0.138,0.1,0.094,0.112,0.126,0.111,0.095,0.111,0.1,0.106,0.12,0.12,0.109,0.111,0.118,0.114,0.126,0.088,0.096,0.148,485,1612,911,923,678,1423,1116,968,377,345,162.0,1613,940,267,761,3039,5856,582,1364,2583,908,1652,1032,1478,2015,1548,2055,1683,1879,1857,605,368,324,1355,619,558,1536,1597,198,241,697,1572,1307,1600,2236,725,358,1016,1099,606,419,1888,714,1974,133,2270,579,1669,741,919,505,739,320,393,1679,796,2028,1464,859,482,913,939,4052,232,3.139,2.795,2.874,2.667,2.413,2.393,2.038,2.916,2.685,3.333,2.894,1.809,2.91,3.123,2.785,2.852,2.989,3.669,2.829,1.873,2.931,2.507,1.94,3.168,2.694,2.814,2.412,2.022,2.567,2.62,3.028,2.245,2.297,3.022,3.404,2.315,3.074,3.183,2.343,2.56,2.802,1.81,3.352,1.756,1.762,2.264,3.213,2.79,2.71,2.687,2.094,2.499,2.697,2.715,2.621,2.1,2.609,2.352,2.129,2.072,2.405,2.718,2.105,2.465,2.298,2.352,2.158,2.61,2.277,2.846,2.825,2.715,2.516,2.147,0.62,0.681,0.76,0.501,0.477,0.496,0.519,0.65,0.514,0.585,0.77,0.537,0.383,0.516,0.593,0.577,0.598,0.705,0.556,0.569,0.662,0.48,0.505,0.693,0.505,0.566,0.52,0.57,0.552,0.601,0.81,0.924,0.621,0.726,0.725,0.371,0.762,0.675,0.398,0.358,0.644,0.438,0.729,0.687,0.536,0.62,0.551,0.651,0.564,0.768,0.548,0.442,0.69,0.51,0.447,0.404,0.598,0.54,0.444,0.435,0.442,0.697,0.38,0.999,0.62,0.744,0.458,0.454,0.444,0.695,0.565,0.874,0.472,0.433
-361,Epileptic,F,42.0,1.0,2.1,1.0,1.3,1.6,1.7,1.7,1.4,1.7,0.9,0.3,2.2,1.5,0.6,1.2,5.2,8.2,0.6,1.9,3.7,1.5,2.7,1.2,2.7,2.9,4.4,2.5,1.6,2.1,3.2,1.3,1.0,0.3,1.8,0.3,0.8,3.6,2.8,0.1,0.1,1.4,2.7,1.9,2.2,1.8,0.5,0.4,0.9,1.1,1.0,0.5,1.7,1.1,2.6,0.1,2.2,0.6,1.3,0.7,0.8,0.3,1.6,0.1,0.5,1.4,1.7,2.3,1.2,0.8,0.9,1.0,1.1,3.6,0.4,9,20,9,9,15,16,15,16,15,8,4.0,22,18,7,12,60,76,6,23,38,14,26,13,35,39,45,40,17,22,41,20,7,4,24,2,9,32,38,2,1,7,24,20,21,10,4,2,6,5,7,3,13,8,19,1,18,4,13,4,7,2,10,1,4,14,17,18,12,6,9,6,9,25,3,0.042,0.023,0.015,0.024,0.033,0.037,0.025,0.025,0.051,0.033,0.03,0.028,0.031,0.035,0.036,0.033,0.03,0.031,0.033,0.035,0.029,0.037,0.024,0.036,0.03,0.033,0.024,0.019,0.024,0.029,0.046,0.035,0.021,0.028,0.014,0.019,0.039,0.038,0.012,0.013,0.026,0.042,0.036,0.023,0.014,0.011,0.017,0.016,0.017,0.02,0.019,0.021,0.028,0.021,0.02,0.016,0.029,0.019,0.022,0.017,0.02,0.027,0.02,0.02,0.023,0.037,0.018,0.016,0.015,0.026,0.02,0.019,0.017,0.015,1621,4934,2315,2258,2283,2962,3007,3195,2357,1417,475.0,2164,3653,1300,2557,10423,17310,2105,4235,4489,3868,3911,2015,5557,5927,8076,5425,3251,5503,6437,2244,1243,924,5267,1656,2139,5501,6422,403,301,1694,2600,4962,2379,3208,1478,834,1977,2107,1605,700,2499,1600,5298,266,3901,914,3064,990,1399,441,2624,281,748,2484,1360,4270,2975,1863,1632,1577,1898,7324,813,0.142,0.112,0.102,0.111,0.147,0.156,0.11,0.128,0.162,0.152,0.149,0.137,0.133,0.143,0.142,0.145,0.127,0.112,0.133,0.149,0.117,0.162,0.113,0.14,0.147,0.15,0.124,0.101,0.106,0.133,0.151,0.116,0.115,0.123,0.071,0.094,0.136,0.149,0.091,0.122,0.12,0.16,0.143,0.121,0.084,0.081,0.096,0.093,0.091,0.112,0.108,0.107,0.117,0.105,0.115,0.105,0.137,0.105,0.11,0.111,0.115,0.12,0.115,0.125,0.111,0.142,0.101,0.103,0.093,0.126,0.112,0.109,0.097,0.12,444,1568,944,805,775,887,1053,1047,552,417,176.0,1109,937,306,666,2721,4666,398,1125,1726,878,1128,830,1380,1631,2238,1881,1185,1474,1961,543,509,257,1145,398,739,1521,1446,209,127,744,1087,979,1545,1930,731,318,867,985,715,387,1232,688,2086,95,2039,386,1201,525,746,194,890,112,356,1166,722,2058,1228,855,546,701,921,3088,330,3.464,2.925,2.393,2.75,2.451,2.994,2.298,2.693,2.911,2.87,2.39,1.751,2.977,2.967,3.025,2.805,2.925,3.68,2.987,2.257,3.116,2.743,2.063,3.044,2.783,2.942,2.309,2.187,2.824,2.578,2.922,2.401,2.823,3.078,3.452,2.779,2.77,3.184,2.214,2.671,2.922,2.121,3.308,1.759,1.892,2.164,3.256,2.913,2.66,2.517,2.222,2.185,2.471,2.597,3.369,2.189,3.01,2.639,2.341,2.114,2.565,2.948,2.431,2.106,2.259,2.218,2.213,2.544,2.427,2.904,2.569,2.369,2.593,3.144,0.736,0.545,0.614,0.575,0.707,0.778,0.54,0.436,0.746,0.57,0.672,0.378,0.444,0.536,0.512,0.554,0.595,0.828,0.626,0.557,0.718,0.543,0.446,0.694,0.589,0.544,0.446,0.493,0.458,0.517,0.944,0.762,0.484,0.684,0.595,0.428,0.629,0.607,0.399,0.435,0.505,0.48,0.995,0.564,0.595,0.429,0.401,0.694,0.499,0.414,0.296,0.473,0.453,0.522,0.692,0.445,0.506,0.515,0.3,0.389,0.463,0.898,0.491,0.909,0.533,0.727,0.477,0.384,0.464,0.553,0.586,0.587,0.464,0.55
-362,Epileptic,F,47.0,1.2,2.2,1.0,1.1,1.4,1.9,1.6,1.8,1.3,0.7,0.3,2.7,2.2,0.6,1.2,5.8,8.3,1.1,2.5,3.6,0.9,2.5,1.0,2.8,2.8,4.3,2.8,1.9,2.5,3.1,0.9,0.8,0.5,3.0,0.7,0.6,4.1,3.7,0.1,0.1,1.3,2.5,2.0,1.8,2.6,1.0,0.5,0.8,1.0,0.6,0.8,1.4,1.3,3.2,0.3,2.2,0.8,1.7,0.5,0.7,0.7,1.6,0.9,0.4,1.6,1.1,2.6,1.3,0.7,1.1,1.2,1.0,4.6,0.4,5,24,9,9,16,20,15,18,13,7,4.0,25,19,9,14,63,82,9,28,38,7,30,13,42,42,47,40,20,26,40,16,6,4,34,3,4,43,42,2,1,9,21,18,15,14,8,3,5,6,7,4,10,10,21,2,17,6,17,7,7,5,12,8,4,12,17,17,9,5,11,8,8,39,3,0.04,0.023,0.02,0.018,0.033,0.03,0.022,0.031,0.051,0.03,0.059,0.034,0.037,0.044,0.03,0.031,0.03,0.045,0.032,0.031,0.016,0.031,0.023,0.034,0.032,0.028,0.025,0.024,0.029,0.029,0.024,0.034,0.019,0.029,0.021,0.014,0.035,0.035,0.01,0.015,0.023,0.032,0.032,0.024,0.02,0.016,0.026,0.013,0.014,0.024,0.032,0.014,0.027,0.022,0.012,0.015,0.017,0.019,0.016,0.015,0.018,0.026,0.044,0.013,0.016,0.019,0.022,0.019,0.012,0.026,0.025,0.019,0.016,0.021,1680,5552,2567,2445,1837,3027,3318,3000,1740,1558,565.0,2756,3684,1138,3113,11643,18528,2425,4841,4682,2775,4355,2058,6283,6161,8729,6022,3571,6043,6287,2666,1474,1416,7961,2049,2140,7958,8501,412,632,1887,2984,5228,2353,3298,1818,814,2919,2272,1473,656,3275,1843,6340,711,4488,1759,3369,1146,1611,1382,2675,756,924,3138,1449,3631,2337,1784,1631,1622,2439,11848,570,0.108,0.116,0.107,0.101,0.141,0.15,0.113,0.131,0.157,0.142,0.11,0.149,0.152,0.161,0.133,0.141,0.134,0.149,0.133,0.132,0.071,0.144,0.123,0.141,0.135,0.134,0.121,0.112,0.119,0.137,0.114,0.104,0.099,0.129,0.094,0.086,0.148,0.143,0.098,0.088,0.113,0.141,0.138,0.117,0.104,0.11,0.115,0.078,0.085,0.109,0.139,0.089,0.122,0.108,0.09,0.097,0.107,0.106,0.116,0.09,0.105,0.129,0.181,0.098,0.092,0.121,0.115,0.103,0.089,0.134,0.121,0.121,0.096,0.119,414,1711,845,916,753,1127,1177,973,505,384,176.0,1220,1005,277,787,3182,4834,489,1329,1957,823,1354,820,1638,1707,2479,2044,1251,1516,1879,696,527,432,1730,477,715,2081,1923,202,204,855,1233,1125,1335,1892,874,330,987,1062,536,357,1487,854,2458,360,2143,753,1623,491,810,604,932,320,474,1550,886,1728,1063,777,640,764,823,4564,298,3.643,2.852,2.824,2.636,2.276,2.471,2.38,2.702,2.662,3.223,2.811,1.976,2.951,2.905,2.912,2.811,3.07,3.73,2.993,2.033,2.668,2.542,2.181,2.941,2.796,2.912,2.395,2.278,2.985,2.656,2.767,2.714,2.706,3.223,3.632,2.768,3.072,3.147,2.437,2.76,2.73,2.083,3.385,1.983,2.011,2.307,3.073,3.343,2.638,2.799,2.231,2.324,2.373,2.705,2.297,2.271,2.552,2.418,2.183,2.245,2.585,2.815,2.405,2.324,2.169,1.991,2.354,2.56,2.501,2.725,2.598,3.211,2.605,2.55,0.794,0.579,0.563,0.371,0.444,0.546,0.623,0.603,0.462,0.452,0.885,0.518,0.466,0.387,0.612,0.489,0.543,0.717,0.617,0.58,0.737,0.524,0.414,0.734,0.554,0.477,0.448,0.517,0.536,0.488,0.609,1.194,0.385,0.622,0.677,0.448,0.787,0.717,0.263,0.631,0.525,0.565,0.716,0.743,0.628,0.477,0.748,0.874,0.471,0.824,0.448,0.499,0.449,0.491,0.289,0.45,0.473,0.596,0.45,0.297,0.473,0.74,0.553,0.77,0.499,0.679,0.433,0.454,0.411,0.484,0.403,0.784,0.515,0.548
-363,Epileptic,M,37.0,0.7,2.2,0.9,1.4,1.8,1.8,1.6,1.8,1.5,0.5,0.2,2.4,1.5,0.4,1.9,6.0,9.5,1.1,2.1,4.2,1.0,3.2,1.8,3.1,3.4,3.4,3.5,2.5,3.4,3.4,1.4,1.4,0.8,2.5,0.2,1.1,3.4,4.0,0.1,0.2,1.1,3.2,2.4,2.3,3.3,1.0,0.5,1.9,1.3,0.7,0.7,2.1,1.9,2.5,0.4,1.8,0.7,1.6,1.0,1.3,0.5,0.9,0.3,0.3,1.7,1.0,2.3,1.5,1.1,0.6,0.9,1.4,4.4,0.3,6,24,9,11,21,16,14,19,17,5,2.0,24,15,6,20,61,89,9,27,33,8,38,19,32,42,36,34,24,26,37,13,13,8,29,2,10,35,42,1,1,6,27,22,16,19,7,2,9,7,6,5,15,15,15,2,17,5,14,8,10,5,6,2,4,12,11,20,10,6,7,5,8,32,2,0.034,0.022,0.015,0.024,0.035,0.031,0.027,0.027,0.047,0.033,0.023,0.03,0.027,0.03,0.031,0.034,0.03,0.034,0.033,0.036,0.02,0.037,0.029,0.032,0.038,0.028,0.028,0.03,0.026,0.035,0.033,0.06,0.034,0.027,0.013,0.021,0.041,0.031,0.014,0.015,0.02,0.039,0.04,0.026,0.023,0.023,0.023,0.022,0.016,0.016,0.03,0.026,0.033,0.021,0.017,0.017,0.017,0.019,0.027,0.028,0.018,0.018,0.015,0.014,0.019,0.02,0.018,0.021,0.018,0.023,0.029,0.023,0.019,0.024,1384,4708,2186,2546,2287,2443,2686,3698,2130,1199,599.0,2642,3265,1033,3353,10532,19400,2615,4018,3874,2864,4293,2256,5400,5383,6547,5760,3927,6809,5028,1901,1315,1210,5998,1347,2401,4479,8169,353,482,1679,3034,4936,2324,3816,1346,719,2505,2623,1242,596,3330,2439,4413,654,3259,1148,2857,1144,1367,1029,2035,382,807,2638,1465,3863,2523,1757,1278,1034,1883,8739,455,0.119,0.117,0.095,0.11,0.141,0.14,0.114,0.133,0.159,0.126,0.105,0.133,0.122,0.127,0.142,0.143,0.13,0.12,0.134,0.131,0.096,0.149,0.135,0.129,0.152,0.127,0.131,0.124,0.114,0.147,0.118,0.144,0.146,0.122,0.069,0.105,0.132,0.127,0.088,0.093,0.101,0.15,0.139,0.119,0.107,0.11,0.107,0.101,0.091,0.104,0.127,0.112,0.127,0.102,0.105,0.107,0.101,0.105,0.123,0.127,0.101,0.094,0.107,0.1,0.105,0.105,0.107,0.104,0.1,0.118,0.13,0.109,0.098,0.106,396,1660,842,977,926,934,954,1175,574,311,177.0,1243,940,260,983,3135,5480,514,1099,1917,806,1481,992,1585,1662,1992,2081,1397,1970,1687,630,547,384,1526,352,852,1502,2064,209,258,843,1326,1080,1428,2193,687,320,1102,1176,584,346,1384,1021,1809,336,1686,600,1382,661,764,471,796,231,429,1317,702,1982,1137,851,468,481,929,3823,230,3.526,2.651,2.514,2.571,2.22,2.321,2.309,2.89,2.742,3.136,2.874,1.919,2.731,2.792,2.594,2.606,2.826,3.65,2.892,1.829,2.804,2.354,2.019,2.559,2.525,2.752,2.256,2.226,2.703,2.473,2.357,2.408,2.508,2.885,3.35,2.578,2.499,3.02,2.008,2.143,2.43,2.09,3.158,1.905,1.965,2.269,2.855,2.787,2.608,2.296,2.105,2.364,2.21,2.562,2.364,2.128,2.267,2.315,2.007,1.957,2.181,2.595,1.902,1.913,2.282,2.407,2.131,2.546,2.427,2.908,2.631,2.588,2.47,2.344,0.782,0.718,0.475,0.412,0.553,0.434,0.483,0.507,0.659,0.447,0.707,0.524,0.402,0.463,0.483,0.565,0.587,0.815,0.518,0.466,0.841,0.54,0.377,0.634,0.549,0.527,0.41,0.495,0.462,0.454,0.831,0.933,0.443,0.621,0.541,0.523,0.632,0.629,0.35,0.384,0.419,0.46,0.649,0.733,0.523,0.391,0.321,0.694,0.556,0.424,0.432,0.527,0.566,0.556,0.526,0.44,0.433,0.489,0.344,0.386,0.483,0.624,0.311,0.897,0.489,0.674,0.457,0.419,0.406,0.982,0.489,0.564,0.496,0.44
-364,Epileptic,F,52.0,0.9,2.8,0.9,1.1,1.7,2.0,2.7,2.4,1.8,0.7,0.3,3.2,1.5,0.5,0.9,6.9,13.4,0.9,2.8,4.0,2.0,4.7,2.3,3.5,8.0,3.0,3.9,3.7,4.0,3.9,2.0,1.0,0.6,2.7,0.4,0.7,4.5,2.7,0.1,0.1,1.1,3.1,3.3,2.8,4.0,0.8,0.4,1.2,1.4,0.6,0.5,1.6,1.7,2.5,0.6,2.6,0.6,1.9,1.5,1.0,1.0,1.3,0.3,0.5,2.5,1.3,2.8,2.0,1.6,0.8,0.7,1.6,5.6,0.4,8,23,7,7,16,18,19,18,16,7,4.0,30,16,4,11,63,112,8,31,35,15,55,22,35,81,33,38,26,27,41,19,8,6,32,2,7,50,35,1,1,6,27,35,25,23,5,2,8,6,6,3,9,13,16,4,21,4,13,13,9,8,9,2,5,19,16,19,13,9,10,4,12,51,4,0.038,0.029,0.019,0.022,0.035,0.033,0.041,0.032,0.049,0.031,0.033,0.036,0.035,0.025,0.032,0.047,0.04,0.037,0.035,0.034,0.033,0.04,0.033,0.041,0.055,0.029,0.032,0.037,0.034,0.036,0.051,0.046,0.027,0.032,0.017,0.02,0.039,0.029,0.012,0.016,0.022,0.034,0.054,0.029,0.028,0.017,0.016,0.02,0.02,0.012,0.022,0.022,0.031,0.021,0.03,0.02,0.012,0.02,0.023,0.018,0.018,0.023,0.024,0.015,0.026,0.035,0.018,0.024,0.024,0.03,0.022,0.017,0.017,0.019,1739,4737,1907,2167,2074,3001,2637,4049,2020,1359,654.0,3131,2864,1077,2299,8971,20456,2142,5257,4813,4073,5766,2921,5205,7175,6599,6241,4312,6883,5714,1931,1035,1117,5653,1825,1992,8724,7276,326,310,1654,2779,5455,2668,3633,1436,839,2312,2211,1690,607,2241,1459,3632,524,4190,1427,3385,1621,1370,2000,2458,327,811,3197,1028,4638,2687,2140,1093,1145,2720,11989,477,0.126,0.124,0.099,0.102,0.13,0.128,0.118,0.127,0.158,0.144,0.119,0.149,0.133,0.108,0.131,0.151,0.144,0.141,0.14,0.142,0.117,0.126,0.136,0.14,0.142,0.126,0.128,0.114,0.116,0.148,0.144,0.11,0.104,0.13,0.075,0.093,0.151,0.129,0.085,0.096,0.106,0.127,0.16,0.132,0.119,0.098,0.09,0.095,0.099,0.088,0.111,0.103,0.137,0.108,0.142,0.111,0.088,0.111,0.118,0.114,0.099,0.113,0.116,0.105,0.124,0.122,0.101,0.112,0.11,0.14,0.114,0.103,0.099,0.122,463,1629,798,830,846,1052,963,1202,673,330,198.0,1522,887,284,553,2689,5793,439,1336,1936,995,1880,1119,1577,2266,1889,1950,1592,1876,1852,608,445,398,1433,437,631,2181,1688,192,171,746,1278,1294,1583,2251,718,364,936,1039,765,352,1121,834,1808,275,2058,667,1397,876,739,846,913,184,457,1584,565,2274,1248,1042,471,547,1328,4962,294,3.494,2.532,2.363,2.665,1.904,2.421,2.308,2.842,2.289,3.151,2.72,1.882,2.532,2.627,2.852,2.423,2.784,3.687,2.978,2.091,3.069,2.47,2.225,2.558,2.498,2.796,2.484,2.186,2.759,2.462,2.341,2.362,2.285,2.871,3.593,2.683,3.022,3.068,2.061,1.976,2.751,1.954,2.978,1.964,1.866,2.381,2.833,2.882,2.676,2.406,2.181,2.167,1.867,2.198,2.301,2.228,2.398,2.668,1.964,2.158,2.418,2.83,1.872,2.092,2.245,2.102,2.188,2.247,2.35,2.316,2.529,2.412,2.533,2.025,0.915,0.724,0.578,0.486,0.738,0.565,0.642,0.505,0.779,0.497,0.883,0.516,0.544,0.608,0.713,0.7,0.711,0.735,0.473,0.672,0.867,0.555,0.517,0.765,0.707,0.601,0.612,0.608,0.607,0.588,0.639,0.792,0.452,0.619,0.737,0.528,0.716,0.648,0.312,0.453,0.599,0.483,0.76,0.598,0.532,0.522,0.5,0.596,0.407,0.439,0.364,0.446,0.421,0.465,0.428,0.454,0.402,0.6,0.452,0.348,0.626,0.769,0.627,0.516,0.468,0.595,0.487,0.402,0.401,0.801,0.451,0.43,0.475,0.389
-365,Epileptic,F,47.0,0.7,1.9,0.9,1.3,2.1,1.5,1.9,1.5,1.2,0.7,0.4,2.7,1.8,0.3,1.1,4.4,8.0,1.6,2.2,4.0,1.0,1.7,2.1,4.0,5.3,3.0,3.3,2.0,2.7,2.9,2.3,0.8,0.3,2.1,0.6,0.8,4.1,3.6,0.2,0.2,0.9,2.1,2.0,2.5,2.5,0.6,0.3,0.8,1.4,0.6,0.4,1.6,1.3,3.7,0.5,2.4,0.7,1.4,0.6,1.3,0.8,1.9,0.5,0.6,1.6,1.1,2.3,1.1,0.6,0.3,0.8,1.2,4.4,0.3,6,22,7,10,17,16,14,17,13,8,6.0,32,18,5,16,56,79,14,28,36,11,25,23,40,48,31,36,21,23,34,23,5,3,25,3,6,52,41,1,2,5,23,22,21,12,5,2,4,7,4,3,11,8,30,3,21,6,10,4,9,6,16,4,5,13,17,17,8,3,3,6,8,39,2,0.037,0.025,0.017,0.027,0.043,0.031,0.033,0.031,0.038,0.034,0.028,0.04,0.032,0.026,0.031,0.029,0.03,0.046,0.029,0.039,0.024,0.027,0.037,0.042,0.04,0.034,0.033,0.027,0.03,0.031,0.048,0.038,0.032,0.03,0.019,0.023,0.035,0.034,0.014,0.017,0.021,0.033,0.032,0.022,0.022,0.019,0.013,0.012,0.021,0.011,0.027,0.021,0.019,0.024,0.024,0.021,0.026,0.02,0.018,0.019,0.022,0.027,0.02,0.021,0.02,0.024,0.017,0.018,0.013,0.013,0.021,0.014,0.018,0.024,1607,4208,1929,2191,2491,2911,2585,2744,2016,1497,755.0,3016,3871,974,2670,9983,17127,2644,4944,4582,3634,4444,3051,6458,6950,5792,5674,3082,4873,5966,2703,797,781,6029,1842,1805,8505,8038,364,395,1340,2526,6775,3234,2755,1063,859,2542,2045,1639,376,2494,2134,6654,690,3684,963,2845,914,1991,1080,3028,539,1082,2473,1010,3699,2216,1121,838,1239,2567,9139,416,0.109,0.123,0.105,0.12,0.144,0.144,0.122,0.139,0.153,0.16,0.146,0.152,0.14,0.126,0.139,0.132,0.132,0.162,0.13,0.145,0.103,0.126,0.126,0.144,0.143,0.136,0.121,0.107,0.117,0.135,0.149,0.103,0.104,0.126,0.088,0.103,0.133,0.144,0.091,0.112,0.104,0.139,0.139,0.113,0.105,0.109,0.092,0.072,0.103,0.091,0.138,0.107,0.106,0.116,0.117,0.116,0.117,0.102,0.109,0.101,0.11,0.134,0.129,0.117,0.115,0.109,0.099,0.096,0.088,0.091,0.108,0.093,0.106,0.128,445,1450,845,819,905,946,915,907,564,402,261.0,1257,1019,233,738,2936,5027,600,1311,2020,769,1274,1199,1702,2343,1682,1923,1258,1433,1798,801,438,227,1348,510,645,2124,1902,194,214,652,1139,1307,1916,1665,540,360,992,998,716,225,1160,1004,2650,369,1904,450,1242,506,1031,566,1084,307,489,1182,732,2023,998,567,388,655,1157,3708,184,3.466,2.729,2.373,2.471,2.634,2.555,2.283,2.602,2.756,2.941,2.685,2.055,2.891,2.886,2.746,2.646,2.758,3.243,3.054,2.016,3.383,2.62,2.164,2.837,2.492,2.809,2.313,2.001,2.609,2.613,2.551,1.99,2.753,3.2,3.238,2.666,2.951,3.134,2.157,1.989,2.54,1.924,3.557,1.946,1.886,2.026,2.88,3.179,2.508,2.634,2.079,2.248,2.341,2.415,2.315,2.066,2.4,2.606,2.065,2.098,2.297,2.812,2.107,2.654,2.266,1.765,1.963,2.481,2.118,2.506,2.195,2.509,2.547,2.654,1.068,0.512,0.492,0.547,0.532,0.658,0.614,0.506,0.536,0.72,0.838,0.532,0.487,0.529,0.486,0.474,0.493,0.913,0.49,0.643,0.813,0.533,0.501,0.675,0.593,0.649,0.653,0.576,0.589,0.562,0.748,0.825,0.421,0.592,0.543,0.427,0.795,0.581,0.296,0.535,0.386,0.563,0.623,0.584,0.487,0.452,0.516,0.796,0.353,0.401,0.453,0.406,0.428,0.427,0.519,0.417,0.436,0.611,0.353,0.358,0.412,0.607,0.363,0.496,0.46,0.552,0.433,0.414,0.403,0.698,0.362,0.501,0.435,0.36
-366,Epileptic,M,32.0,0.9,2.3,0.7,0.9,1.4,2.2,1.8,2.4,1.4,0.7,0.4,3.0,1.9,0.6,1.6,7.0,10.5,0.5,2.7,5.5,4.3,3.2,1.5,3.4,3.4,4.3,3.4,2.4,3.0,4.5,1.6,1.6,0.8,3.9,0.4,1.9,4.0,3.5,0.2,0.3,1.1,3.6,2.2,5.5,3.8,0.7,0.4,0.9,1.0,0.7,1.8,2.3,1.3,2.8,0.7,2.8,1.1,6.6,1.5,1.0,2.1,1.5,0.5,0.8,2.0,0.4,2.4,1.7,1.6,0.3,1.0,2.4,6.2,0.3,6,24,6,6,16,16,13,19,14,6,3.0,27,20,8,17,76,89,3,14,31,33,28,13,40,35,39,38,23,27,45,25,9,7,36,2,13,35,30,2,2,7,29,21,32,21,5,2,5,5,5,6,17,6,20,3,22,5,33,10,8,9,12,4,5,13,7,22,11,10,3,7,12,46,2,0.029,0.022,0.011,0.016,0.03,0.039,0.025,0.03,0.04,0.031,0.044,0.029,0.029,0.027,0.029,0.031,0.029,0.016,0.039,0.046,0.045,0.035,0.028,0.039,0.024,0.028,0.026,0.024,0.024,0.036,0.036,0.057,0.026,0.035,0.012,0.029,0.042,0.03,0.016,0.018,0.018,0.033,0.039,0.038,0.024,0.015,0.017,0.013,0.011,0.017,0.066,0.02,0.016,0.019,0.028,0.017,0.032,0.053,0.024,0.018,0.072,0.023,0.026,0.028,0.021,0.017,0.017,0.018,0.021,0.011,0.022,0.038,0.026,0.014,2237,6275,2148,2489,2491,2484,3123,4815,2162,1577,492.0,3278,4212,1416,3811,15662,25592,2756,2411,3688,5029,5419,2038,6559,6738,7604,7131,4265,7295,6959,2917,1052,1690,7138,2093,2856,7471,6259,476,912,2456,4373,6889,3726,4193,1885,951,2830,3160,1815,688,4521,2502,6298,658,5430,1359,3470,2190,1863,956,2735,926,910,3413,851,4430,3478,2524,949,1951,2212,9725,648,0.095,0.1,0.084,0.09,0.125,0.142,0.106,0.117,0.147,0.101,0.136,0.125,0.114,0.132,0.122,0.124,0.121,0.069,0.131,0.137,0.134,0.13,0.123,0.126,0.107,0.107,0.115,0.101,0.092,0.129,0.117,0.146,0.096,0.103,0.062,0.102,0.128,0.11,0.098,0.083,0.095,0.13,0.127,0.129,0.112,0.091,0.093,0.076,0.074,0.1,0.202,0.103,0.09,0.099,0.117,0.1,0.106,0.172,0.117,0.103,0.246,0.115,0.122,0.119,0.106,0.103,0.1,0.102,0.111,0.086,0.108,0.137,0.11,0.089,492,1737,907,859,805,968,1003,1230,602,333,162.0,1571,1073,363,904,3751,6090,468,1025,1974,1650,1468,842,1485,2053,2299,2139,1454,1763,2064,654,478,459,1466,527,878,1620,1579,232,356,1014,1685,1095,2167,2385,787,354,1084,1315,691,480,1923,1156,2432,357,2728,598,1878,1015,911,610,983,326,403,1442,463,2161,1448,1134,500,834,1016,4015,321,3.868,2.925,2.464,2.804,2.517,2.426,2.55,3.439,2.661,3.553,2.668,1.821,3.018,2.8,3.101,2.99,3.244,4.051,2.166,1.796,2.816,2.807,2.14,3.046,2.556,2.789,2.468,2.296,2.987,2.654,2.981,2.3,2.698,3.281,3.4,2.951,3.264,2.944,2.598,2.695,2.975,2.271,3.636,1.917,2.018,2.64,3.557,3.15,2.827,2.797,1.983,2.477,2.479,2.836,2.264,2.256,2.628,2.041,2.533,2.256,2.102,2.538,2.518,2.414,2.627,2.217,2.201,2.785,2.555,2.036,2.826,2.696,2.68,2.435,1.099,0.785,0.605,0.683,0.674,0.69,0.706,0.635,0.674,0.628,0.974,0.496,0.566,0.615,0.602,0.555,0.683,0.802,0.628,0.688,0.923,0.497,0.456,0.814,0.515,0.518,0.501,0.549,0.498,0.563,0.74,0.926,0.666,0.882,0.72,0.578,0.876,0.791,0.378,0.719,0.694,0.634,0.873,0.63,0.594,0.781,0.618,0.662,0.916,0.62,0.447,0.59,0.528,0.545,0.395,0.494,0.743,0.743,0.467,0.44,0.613,0.849,0.716,1.0,0.7,0.778,0.468,0.471,0.494,0.642,0.419,0.676,0.621,0.402
-367,Epileptic,F,27.0,0.9,3.4,0.9,1.1,1.9,2.0,1.5,1.9,0.8,0.7,0.2,3.0,1.8,0.7,0.9,6.3,9.0,0.9,2.8,4.2,1.8,2.3,1.5,2.7,3.2,2.9,3.2,3.1,2.8,3.2,0.8,0.9,0.6,2.5,0.5,0.5,2.3,3.2,0.2,0.1,0.8,2.4,2.0,3.0,2.9,0.8,0.3,0.8,1.4,0.4,1.0,1.8,1.9,2.8,0.4,2.1,1.1,1.4,0.9,1.1,0.7,1.5,0.2,0.4,1.5,1.7,2.9,1.1,0.7,0.3,1.0,1.1,4.6,0.3,7,34,8,10,22,18,13,18,11,8,3.0,25,18,8,10,75,84,7,28,37,17,25,13,34,40,31,32,32,19,36,12,10,6,32,5,7,26,39,1,1,4,23,25,24,19,6,2,7,8,4,6,15,15,22,2,16,8,11,7,9,7,12,3,5,10,22,23,9,5,4,6,10,42,3,0.037,0.027,0.014,0.02,0.034,0.026,0.021,0.024,0.035,0.027,0.023,0.035,0.028,0.034,0.02,0.029,0.028,0.033,0.031,0.034,0.028,0.028,0.025,0.028,0.031,0.029,0.026,0.025,0.023,0.032,0.024,0.028,0.03,0.029,0.026,0.018,0.029,0.027,0.015,0.013,0.018,0.029,0.035,0.029,0.021,0.019,0.015,0.016,0.017,0.013,0.037,0.017,0.026,0.017,0.022,0.017,0.023,0.018,0.022,0.018,0.022,0.024,0.015,0.013,0.018,0.025,0.017,0.018,0.013,0.012,0.019,0.018,0.018,0.014,1446,5246,2765,2231,2469,3096,3349,3721,1590,1549,525.0,2712,4162,1265,1981,12524,18811,2286,5106,4267,4394,4127,2347,5467,5928,5671,5350,5248,5988,5131,2047,1310,1018,6117,1228,1873,4784,7158,321,221,1413,2507,4809,2631,3944,1155,911,2189,2630,1138,601,3216,2196,5779,408,3534,1653,2797,1058,1550,1353,2714,419,891,2393,1639,4630,2225,1970,687,1437,2284,9786,638,0.134,0.123,0.088,0.103,0.144,0.136,0.105,0.118,0.128,0.123,0.106,0.136,0.131,0.143,0.11,0.135,0.128,0.121,0.132,0.14,0.099,0.129,0.118,0.127,0.138,0.134,0.114,0.119,0.099,0.14,0.104,0.116,0.131,0.128,0.089,0.087,0.126,0.134,0.101,0.083,0.094,0.136,0.146,0.126,0.102,0.108,0.085,0.096,0.096,0.085,0.15,0.101,0.119,0.103,0.139,0.105,0.12,0.1,0.117,0.104,0.117,0.117,0.101,0.103,0.096,0.128,0.104,0.105,0.085,0.111,0.106,0.113,0.104,0.119,387,1879,1035,877,1016,1260,1067,1305,489,404,166.0,1356,1141,362,672,3753,5312,478,1486,1992,1061,1330,884,1653,1841,1826,1915,1874,1676,1851,585,564,341,1449,394,623,1305,1942,159,122,710,1218,1118,1727,2318,640,387,886,1213,507,397,1696,1125,2518,231,1864,741,1324,657,935,530,995,247,508,1261,986,2356,1000,907,370,741,987,4283,269,3.403,2.56,2.496,2.513,2.261,2.258,2.591,2.612,2.52,2.804,2.608,1.825,2.893,2.636,2.321,2.599,2.912,3.611,2.831,1.933,3.032,2.422,2.219,2.599,2.498,2.638,2.259,2.272,2.777,2.315,2.483,2.309,2.479,2.907,2.449,2.577,2.769,2.744,2.465,2.121,2.606,1.883,2.906,1.738,1.931,1.965,2.957,3.099,2.615,2.499,1.865,2.115,2.055,2.374,2.36,2.098,2.603,2.457,1.815,1.873,2.381,2.643,1.761,2.042,2.128,2.076,2.122,2.417,2.587,1.993,2.321,2.548,2.435,2.889,1.061,0.622,0.627,0.397,0.561,0.519,0.642,0.617,0.622,0.487,0.652,0.455,0.464,0.361,0.447,0.485,0.557,0.756,0.602,0.539,0.81,0.475,0.526,0.677,0.477,0.52,0.532,0.563,0.541,0.502,0.66,0.9,0.414,0.637,0.604,0.5,0.74,0.548,0.261,0.469,0.597,0.534,0.729,0.533,0.59,0.497,0.348,0.803,0.517,0.522,0.418,0.44,0.472,0.473,0.43,0.464,0.491,0.486,0.339,0.375,0.496,0.715,0.382,0.901,0.481,0.728,0.425,0.491,0.467,0.395,0.476,0.525,0.484,0.375
-368,Epileptic,M,42.0,0.8,2.4,0.6,1.1,1.4,1.7,1.3,1.6,1.3,0.7,0.4,2.2,1.4,0.6,1.0,4.8,7.6,0.6,2.3,4.8,1.4,2.8,1.6,2.8,2.4,3.6,3.3,2.1,2.1,4.2,1.2,2.7,0.6,2.1,0.2,0.8,3.8,2.1,0.1,0.1,0.8,4.1,2.2,2.8,2.5,0.6,0.4,0.7,1.1,1.0,0.7,1.7,1.6,3.0,0.2,2.9,0.6,2.2,1.0,1.3,0.5,1.1,0.3,0.5,1.2,1.4,3.0,1.4,0.5,0.3,1.1,2.0,3.5,0.3,4,29,8,8,11,13,11,14,8,6,5.0,15,15,6,12,44,70,5,19,31,9,30,15,31,29,32,34,17,22,38,13,14,4,23,1,6,40,27,1,1,5,29,20,18,17,5,2,4,5,9,4,12,13,24,1,22,4,16,9,13,4,8,2,4,9,13,22,10,4,4,8,12,30,2,0.048,0.03,0.014,0.026,0.033,0.034,0.023,0.037,0.045,0.045,0.037,0.03,0.03,0.029,0.027,0.033,0.03,0.03,0.036,0.049,0.027,0.033,0.034,0.032,0.031,0.033,0.026,0.023,0.024,0.033,0.039,0.072,0.031,0.026,0.01,0.021,0.041,0.026,0.014,0.014,0.019,0.043,0.037,0.039,0.021,0.014,0.022,0.015,0.017,0.02,0.033,0.021,0.032,0.027,0.035,0.02,0.016,0.032,0.024,0.022,0.02,0.021,0.025,0.028,0.022,0.038,0.026,0.017,0.014,0.013,0.029,0.031,0.019,0.016,1181,4345,1565,1938,2125,2426,1958,2947,1615,1120,616.0,2319,3010,1127,2601,8749,14671,1801,3106,3312,2038,3986,2425,5016,4335,5952,5484,3008,4683,5998,2115,1544,993,4267,1077,1983,5180,4901,337,399,1692,2993,3493,2019,3211,1206,744,1594,2093,1428,547,2644,2205,4890,147,4106,1192,2086,1139,1646,897,2345,412,705,2108,1043,3626,3102,1119,710,1333,2458,7579,554,0.13,0.134,0.109,0.121,0.136,0.129,0.097,0.138,0.151,0.165,0.161,0.13,0.127,0.143,0.12,0.13,0.132,0.11,0.126,0.151,0.095,0.143,0.134,0.13,0.132,0.127,0.12,0.111,0.106,0.137,0.132,0.175,0.112,0.115,0.056,0.096,0.129,0.118,0.097,0.091,0.091,0.162,0.137,0.144,0.102,0.099,0.111,0.085,0.092,0.118,0.141,0.106,0.134,0.113,0.152,0.108,0.101,0.124,0.129,0.127,0.117,0.114,0.134,0.139,0.107,0.154,0.118,0.099,0.09,0.12,0.13,0.133,0.1,0.097,297,1299,678,727,690,865,776,813,414,279,201.0,1027,837,304,675,2286,4190,338,1003,1636,741,1355,887,1406,1305,1733,2029,1228,1417,1928,551,556,305,1159,379,674,1620,1311,195,193,752,1397,928,1225,1950,640,308,746,952,686,343,1272,869,1871,74,2227,608,1180,669,896,391,813,187,339,1011,521,1891,1260,559,376,638,1082,3204,230,3.786,2.93,2.231,2.693,2.666,2.405,2.143,3.012,2.798,3.297,2.552,1.98,2.751,2.612,2.837,2.778,2.759,3.737,2.502,1.86,2.363,2.395,2.329,2.78,2.666,2.771,2.244,2.012,2.551,2.413,2.717,2.816,2.731,2.675,2.409,2.786,2.577,2.829,2.118,2.488,2.723,1.983,2.795,1.953,1.903,2.134,2.886,2.506,2.78,2.338,1.959,2.311,2.292,2.484,2.458,2.078,2.365,2.064,1.879,2.095,2.425,2.723,2.481,2.324,2.281,2.512,2.082,2.797,2.397,2.085,2.506,2.763,2.525,2.924,0.916,0.617,0.597,0.557,0.624,0.725,0.56,0.682,0.519,0.611,0.841,0.476,0.367,0.524,0.532,0.573,0.59,0.717,0.58,0.62,0.754,0.527,0.56,0.611,0.51,0.632,0.521,0.512,0.494,0.552,0.811,1.194,0.704,0.507,0.726,0.566,0.718,0.527,0.274,0.49,0.587,0.622,0.678,0.686,0.552,0.505,0.475,0.725,0.67,0.513,0.653,0.525,0.658,0.52,0.4,0.525,0.536,0.581,0.373,0.433,0.49,0.811,0.586,0.997,0.58,0.746,0.506,0.453,0.426,0.495,0.543,0.522,0.536,0.669
-369,Epileptic,F,37.0,1.2,1.9,1.3,0.8,2.3,2.2,1.4,1.3,1.5,0.7,0.2,4.0,1.3,0.5,1.6,5.9,8.4,0.9,2.8,5.4,2.0,3.2,2.0,3.6,2.4,2.8,2.8,2.0,3.1,2.6,1.5,2.1,0.4,1.9,0.5,0.5,3.2,3.2,0.2,0.2,1.0,3.5,2.4,3.4,2.5,0.5,0.4,1.6,1.7,0.7,0.5,1.6,1.1,2.7,0.2,2.5,0.7,1.8,1.5,1.3,1.0,1.2,0.7,0.8,2.5,1.4,2.7,1.4,0.8,0.5,0.7,1.5,5.0,0.3,8,17,13,5,17,21,11,10,13,5,2.0,34,15,6,15,51,66,7,25,45,15,37,22,37,30,25,28,20,21,29,13,14,3,19,2,4,29,37,2,2,6,31,21,30,13,4,2,10,8,5,4,12,7,20,1,19,5,12,10,10,6,8,4,6,21,17,18,9,5,4,5,12,34,2,0.041,0.023,0.024,0.02,0.042,0.027,0.027,0.032,0.041,0.038,0.028,0.037,0.027,0.04,0.034,0.036,0.032,0.033,0.033,0.037,0.043,0.029,0.033,0.045,0.028,0.029,0.033,0.027,0.03,0.034,0.039,0.07,0.024,0.026,0.019,0.017,0.036,0.03,0.015,0.021,0.026,0.032,0.043,0.03,0.021,0.015,0.018,0.026,0.021,0.014,0.024,0.024,0.023,0.025,0.044,0.02,0.016,0.022,0.026,0.021,0.026,0.028,0.047,0.026,0.026,0.033,0.026,0.023,0.018,0.019,0.025,0.019,0.021,0.026,1532,4097,2261,1442,2210,2669,2137,2389,1943,1178,624.0,2914,2668,985,3061,8755,15796,2312,4919,5562,3476,4778,2049,5290,4860,5016,4473,3481,5364,4144,2139,899,843,5487,1800,1310,6072,7661,368,532,1140,2311,5451,2867,2825,988,762,2087,2436,1644,443,2217,1453,4514,244,3299,1454,2957,1162,1675,1047,1761,401,754,3031,1087,2991,1802,1218,1067,1023,2180,8542,472,0.142,0.11,0.117,0.095,0.145,0.119,0.1,0.12,0.149,0.135,0.087,0.145,0.119,0.135,0.135,0.138,0.127,0.117,0.124,0.147,0.13,0.116,0.144,0.136,0.113,0.12,0.118,0.098,0.1,0.139,0.147,0.163,0.102,0.116,0.077,0.087,0.124,0.128,0.09,0.109,0.115,0.131,0.143,0.132,0.101,0.099,0.097,0.105,0.104,0.094,0.111,0.113,0.106,0.113,0.149,0.104,0.098,0.112,0.12,0.111,0.12,0.115,0.158,0.134,0.126,0.134,0.118,0.114,0.098,0.104,0.12,0.114,0.104,0.112,494,1397,895,662,938,1275,759,768,649,302,173.0,1655,770,290,822,2770,4577,465,1483,2363,858,1735,1043,1536,1461,1440,1581,1206,1365,1406,650,452,291,1194,527,485,1541,1884,226,257,615,1619,1138,1985,1793,521,331,895,1189,702,338,1143,859,1891,101,1950,657,1314,839,976,600,734,193,419,1613,626,1624,938,647,421,523,1079,3905,201,2.985,2.756,2.54,2.056,2.223,1.912,2.337,2.607,2.408,3.073,2.865,1.645,2.574,2.475,2.792,2.442,2.779,3.479,2.63,2.023,3.085,2.269,1.747,2.715,2.545,2.751,2.265,2.239,2.909,2.362,2.628,2.291,2.696,3.167,2.96,2.389,3.0,2.931,2.075,2.335,2.269,1.423,3.208,1.708,1.811,2.055,2.846,2.967,2.509,2.668,1.433,2.109,1.872,2.287,2.676,1.902,2.493,2.648,1.6,1.911,2.223,2.505,2.28,2.148,1.976,2.085,1.996,2.177,2.115,2.722,2.246,2.431,2.335,2.616,0.869,0.614,0.426,0.541,0.672,0.539,0.663,0.526,0.624,0.427,0.785,0.425,0.504,0.649,0.639,0.696,0.682,0.89,0.549,0.704,0.74,0.559,0.46,0.656,0.629,0.533,0.664,0.641,0.635,0.636,0.754,0.758,0.433,0.624,0.672,0.474,0.685,0.646,0.623,0.549,0.445,0.382,0.824,0.549,0.575,0.6,0.697,0.723,0.572,0.589,0.274,0.435,0.476,0.586,0.288,0.452,0.399,0.689,0.398,0.412,0.457,0.547,0.366,0.366,0.457,0.674,0.496,0.437,0.48,0.9,0.489,0.505,0.55,0.542
-370,Epileptic,M,37.0,1.1,1.7,1.4,1.4,2.1,2.0,1.8,1.8,0.9,0.9,0.4,3.4,1.1,0.6,1.7,6.8,9.6,1.2,2.8,4.5,0.8,3.7,2.0,3.9,8.4,5.9,4.8,3.3,2.8,2.8,1.4,1.2,0.7,2.7,1.0,1.6,4.4,5.3,0.5,0.1,1.2,3.3,2.8,3.5,2.2,0.9,0.5,1.1,1.7,0.7,0.7,1.8,1.1,2.5,0.6,3.0,0.9,2.2,1.0,1.6,0.9,2.2,0.2,0.6,2.2,1.2,3.0,1.1,0.9,0.7,0.9,1.5,6.6,0.4,8,14,11,11,20,18,17,20,8,10,5.0,30,15,5,15,67,85,10,26,40,8,35,18,39,60,50,48,28,31,33,14,10,6,32,8,13,40,44,3,1,9,27,30,23,14,5,2,6,10,5,6,13,6,17,4,22,7,16,8,10,7,12,1,5,19,12,29,7,6,7,8,10,50,3,0.039,0.024,0.03,0.023,0.043,0.041,0.024,0.031,0.042,0.04,0.034,0.038,0.023,0.035,0.033,0.039,0.034,0.039,0.038,0.044,0.025,0.043,0.034,0.048,0.05,0.034,0.028,0.029,0.027,0.03,0.042,0.053,0.025,0.034,0.036,0.027,0.043,0.043,0.035,0.012,0.02,0.041,0.039,0.031,0.015,0.015,0.021,0.015,0.025,0.02,0.035,0.022,0.024,0.022,0.016,0.019,0.024,0.02,0.024,0.028,0.02,0.036,0.021,0.019,0.023,0.024,0.017,0.019,0.015,0.025,0.023,0.019,0.022,0.024,1374,3552,2075,2367,2162,2065,2783,3254,1401,1515,631.0,2420,2566,1002,2563,10081,16920,2205,4382,3891,2430,3950,2301,5403,5307,7665,5466,3563,5946,4712,2428,1026,1250,5458,1555,2508,6361,6752,570,471,1579,2461,6191,2429,3594,1464,759,2395,2193,1275,561,2620,1460,3457,754,3935,1281,3097,963,1413,1089,2058,271,961,3032,1236,4733,1962,1756,1186,1150,2489,9383,406,0.126,0.116,0.128,0.115,0.159,0.148,0.097,0.131,0.156,0.156,0.132,0.149,0.116,0.129,0.129,0.145,0.135,0.148,0.146,0.16,0.093,0.139,0.138,0.15,0.141,0.121,0.102,0.099,0.101,0.13,0.147,0.125,0.103,0.134,0.109,0.114,0.153,0.14,0.122,0.08,0.106,0.154,0.153,0.131,0.088,0.089,0.099,0.088,0.114,0.103,0.15,0.11,0.11,0.114,0.102,0.109,0.12,0.107,0.123,0.124,0.114,0.138,0.109,0.107,0.11,0.113,0.103,0.098,0.09,0.128,0.117,0.11,0.112,0.125,460,1220,752,947,899,931,1049,1094,415,435,235.0,1308,804,313,833,3137,4908,519,1262,1814,694,1412,1012,1649,2257,2651,2234,1588,1716,1628,723,453,404,1395,495,984,1737,1887,241,254,885,1308,1411,1600,2126,846,363,1045,1077,578,379,1391,756,1654,418,2336,623,1673,641,854,600,889,168,490,1639,728,2668,895,904,485,643,1184,4450,270,3.001,2.731,2.756,2.476,2.124,2.079,2.206,2.529,2.459,2.784,2.457,1.676,2.526,2.524,2.38,2.503,2.759,3.422,2.79,1.843,2.611,2.297,1.992,2.562,2.108,2.432,2.045,1.877,2.677,2.264,2.564,2.245,2.465,2.855,2.742,2.278,2.746,2.64,2.129,2.133,2.174,1.653,2.945,1.747,1.883,1.868,2.715,2.79,2.575,2.488,1.799,2.015,2.015,2.28,2.168,1.86,2.283,2.021,1.764,1.758,2.124,2.337,1.785,2.279,1.971,2.057,1.881,2.573,2.189,2.637,2.11,2.453,2.222,1.849,0.656,0.522,0.523,0.507,0.545,0.466,0.531,0.499,0.562,0.484,0.637,0.369,0.374,0.581,0.507,0.561,0.549,0.604,0.573,0.558,0.658,0.636,0.46,0.608,0.556,0.56,0.532,0.475,0.458,0.592,0.666,0.766,0.593,0.592,0.774,0.496,0.695,0.751,0.481,0.381,0.484,0.471,0.811,0.502,0.56,0.405,0.476,0.773,0.437,0.437,0.376,0.347,0.472,0.438,0.37,0.438,0.43,0.485,0.354,0.362,0.437,0.568,0.373,0.689,0.449,0.659,0.434,0.406,0.437,0.686,0.477,0.429,0.484,0.39
-371,Epileptic,F,27.0,1.5,2.8,1.0,1.4,1.1,2.6,1.4,1.4,1.0,0.8,0.2,2.7,1.3,0.4,1.0,5.6,6.6,0.8,2.5,3.7,1.6,2.6,1.7,2.5,2.7,2.8,3.8,1.6,2.5,3.6,0.9,0.8,0.5,2.0,0.4,0.3,3.8,2.6,0.1,0.2,0.8,3.1,4.0,1.6,2.4,0.9,0.4,0.7,0.9,0.7,0.7,1.4,1.4,1.9,0.2,2.3,0.4,1.1,0.8,1.2,0.4,1.5,0.2,0.4,1.2,1.5,2.4,1.1,1.0,0.5,1.1,1.6,3.6,0.4,12,28,10,14,14,25,12,15,14,9,3.0,25,15,6,11,61,67,8,23,33,11,33,15,33,31,33,47,20,24,44,15,6,6,24,4,2,53,29,1,1,5,32,29,43,15,7,2,5,5,6,4,12,11,15,2,19,4,11,7,8,3,16,2,4,9,20,19,10,6,3,7,11,27,4,0.05,0.024,0.017,0.027,0.027,0.029,0.026,0.024,0.037,0.038,0.036,0.039,0.025,0.03,0.026,0.03,0.028,0.034,0.033,0.038,0.023,0.032,0.028,0.029,0.029,0.028,0.027,0.024,0.024,0.028,0.027,0.035,0.027,0.024,0.012,0.011,0.036,0.026,0.012,0.022,0.022,0.035,0.053,0.023,0.019,0.017,0.019,0.01,0.014,0.014,0.032,0.017,0.022,0.019,0.012,0.016,0.022,0.013,0.021,0.022,0.017,0.022,0.013,0.016,0.016,0.023,0.02,0.016,0.021,0.015,0.024,0.025,0.015,0.017,1600,5010,2679,2366,1777,4209,2230,3307,1762,1500,403.0,2367,3501,879,2503,11351,16027,2181,3803,3714,4200,4505,2491,6467,5465,6393,6930,2997,6234,6215,2233,1319,1077,6318,2388,1238,7051,7272,342,399,1339,2664,4320,2033,3655,1310,936,2583,2218,1857,743,2795,2025,3868,548,4390,1127,3177,990,1501,653,2836,422,1027,2293,1571,3912,2538,1633,1016,1579,2461,8942,801,0.149,0.117,0.099,0.126,0.129,0.139,0.112,0.124,0.132,0.137,0.133,0.151,0.116,0.138,0.126,0.139,0.126,0.127,0.133,0.15,0.088,0.139,0.124,0.128,0.136,0.133,0.134,0.112,0.119,0.136,0.116,0.11,0.126,0.114,0.083,0.068,0.122,0.125,0.088,0.097,0.109,0.146,0.146,0.111,0.099,0.108,0.105,0.075,0.085,0.097,0.134,0.097,0.116,0.1,0.107,0.097,0.12,0.086,0.12,0.119,0.1,0.122,0.119,0.109,0.099,0.12,0.105,0.099,0.105,0.119,0.117,0.12,0.09,0.134,458,1754,905,921,717,1424,833,982,546,357,131.0,1145,854,206,601,3137,4102,424,1174,1507,1040,1415,885,1603,1552,1752,2420,1150,1611,2125,587,444,336,1293,555,442,1870,1575,195,180,620,1356,1134,1248,1967,729,345,1023,1014,726,380,1293,959,1645,238,2182,395,1465,595,806,342,1065,204,439,1142,937,1873,1108,743,383,753,1037,3767,310,3.444,2.708,2.703,2.56,2.27,2.498,2.326,2.916,2.509,3.151,2.463,1.87,3.007,2.953,3.107,2.702,3.027,3.688,2.765,2.101,3.052,2.408,2.343,3.121,2.654,2.924,2.323,2.113,2.946,2.461,2.734,2.778,2.779,3.313,3.799,2.555,2.803,3.289,2.061,2.714,2.729,1.822,3.056,1.803,2.13,1.958,3.306,2.905,2.716,2.856,2.408,2.353,2.33,2.505,2.343,2.251,2.709,2.435,1.854,2.039,2.39,2.674,2.428,2.89,2.306,1.986,2.155,2.667,2.594,2.984,2.549,2.776,2.584,2.937,0.742,0.48,0.604,0.512,0.449,0.566,0.547,0.525,0.517,0.825,0.807,0.487,0.449,0.49,0.435,0.573,0.586,0.726,0.584,0.604,0.69,0.468,0.436,0.613,0.477,0.53,0.425,0.501,0.395,0.484,0.559,0.915,0.368,0.665,0.698,0.513,0.687,0.63,0.213,0.288,0.559,0.482,0.956,0.562,0.6,0.495,0.512,0.753,0.425,0.488,0.5,0.383,0.441,0.403,0.306,0.442,0.59,0.437,0.362,0.407,0.477,0.744,0.497,0.864,0.558,0.654,0.434,0.425,0.439,0.783,0.38,0.537,0.511,0.326
-372,Epileptic,M,52.0,0.5,2.0,0.8,1.2,1.1,1.7,1.2,1.5,1.3,0.6,0.3,2.2,1.8,0.7,1.8,5.1,7.8,0.5,2.3,3.3,0.8,2.6,1.4,2.1,2.8,2.8,1.5,2.6,2.6,1.8,0.8,0.6,0.7,3.0,0.3,0.4,2.0,3.3,0.2,0.2,1.1,2.7,1.8,1.8,2.4,0.5,0.4,0.6,0.9,0.4,0.4,2.9,1.1,1.6,0.3,1.6,0.8,1.7,1.3,0.6,0.9,1.0,0.2,0.6,1.9,1.4,2.4,1.8,0.7,0.5,1.1,1.0,3.4,0.2,5,20,6,11,10,13,10,11,10,5,3.0,20,15,6,16,42,65,5,24,26,6,26,12,20,28,25,18,21,20,18,9,3,6,26,1,3,26,34,1,1,5,23,19,16,14,3,2,4,4,5,4,19,7,11,3,14,5,14,10,6,5,5,2,3,17,10,16,12,4,5,7,8,24,2,0.029,0.028,0.015,0.027,0.031,0.035,0.023,0.027,0.05,0.048,0.033,0.031,0.03,0.044,0.045,0.037,0.033,0.028,0.033,0.033,0.021,0.035,0.029,0.039,0.034,0.034,0.021,0.032,0.028,0.027,0.04,0.04,0.028,0.037,0.015,0.016,0.032,0.043,0.018,0.017,0.021,0.036,0.043,0.023,0.02,0.014,0.027,0.012,0.014,0.022,0.023,0.025,0.023,0.016,0.025,0.017,0.023,0.022,0.034,0.02,0.035,0.028,0.034,0.035,0.023,0.034,0.023,0.024,0.015,0.025,0.038,0.026,0.018,0.018,1180,4046,1935,1789,1536,2084,1845,3091,1781,1023,721.0,2240,3504,932,2343,7717,14624,1902,3873,3809,2519,3590,1882,4068,4639,5332,2829,3243,5163,3296,2095,563,1251,5342,1533,1262,3790,6267,279,357,1551,2601,6020,1996,3005,942,632,1857,1883,992,447,4179,1664,3672,368,2579,1325,2542,1190,916,785,1448,302,815,2471,1128,3188,2850,1180,878,1049,1544,6779,356,0.107,0.122,0.095,0.119,0.128,0.137,0.091,0.107,0.167,0.142,0.136,0.136,0.121,0.165,0.151,0.13,0.122,0.101,0.128,0.125,0.093,0.145,0.123,0.137,0.137,0.137,0.108,0.125,0.112,0.118,0.129,0.11,0.119,0.131,0.067,0.073,0.131,0.153,0.093,0.1,0.108,0.136,0.148,0.11,0.1,0.09,0.113,0.073,0.082,0.118,0.124,0.111,0.106,0.087,0.13,0.099,0.101,0.105,0.14,0.106,0.141,0.113,0.101,0.124,0.121,0.121,0.109,0.112,0.089,0.115,0.154,0.121,0.092,0.112,326,1297,761,760,607,875,739,930,426,272,164.0,1046,1031,239,720,2263,4000,324,1120,1588,596,1188,773,985,1321,1484,1183,1241,1438,1052,417,259,343,1168,382,478,1026,1423,175,219,693,1161,847,1271,1906,509,246,747,920,394,314,1762,788,1594,223,1428,559,1261,642,571,402,529,126,231,1288,559,1605,1294,641,333,434,662,3091,187,3.545,2.693,2.562,2.395,2.349,2.16,2.09,2.903,2.91,3.349,3.111,1.883,2.615,2.583,2.647,2.555,2.778,3.997,2.781,2.016,2.874,2.351,2.102,2.931,2.597,2.924,1.94,2.098,2.719,2.455,3.265,2.121,3.141,3.078,3.43,2.266,2.701,3.057,1.801,1.989,2.753,1.994,3.614,1.739,1.835,1.981,2.855,2.72,2.397,2.613,1.746,2.149,2.054,2.324,2.14,1.918,2.436,2.114,1.992,1.816,2.23,2.41,2.406,2.969,2.074,2.39,2.101,2.349,2.135,2.528,2.523,2.561,2.358,2.36,0.785,0.767,0.652,0.56,0.669,0.614,0.548,0.754,0.643,0.521,0.979,0.475,0.543,0.598,0.556,0.663,0.624,0.75,0.619,0.655,0.885,0.586,0.419,0.816,0.648,0.762,0.563,0.481,0.515,0.487,0.755,0.985,1.011,0.725,1.061,0.445,0.63,0.668,0.328,0.367,0.853,0.553,0.885,0.694,0.518,0.777,0.533,0.911,0.475,0.839,0.537,0.722,0.504,0.618,0.397,0.529,0.622,0.56,0.485,0.391,0.447,0.812,0.627,1.039,0.613,0.726,0.531,0.455,0.459,0.69,0.59,0.564,0.675,0.568
-373,Epileptic,M,27.0,0.5,2.4,0.9,1.0,1.6,1.6,1.6,2.0,1.6,0.6,0.2,3.6,2.0,0.6,1.4,6.9,7.8,0.5,2.9,5.4,0.9,2.1,1.5,3.1,3.0,4.0,3.0,3.4,2.8,3.0,1.0,1.7,0.8,2.2,0.3,0.7,4.3,3.3,0.1,0.1,1.3,2.7,2.5,2.7,3.0,0.7,0.4,0.8,1.1,0.7,0.6,2.5,1.3,3.2,0.6,3.2,0.8,1.6,1.1,0.8,0.4,1.4,0.7,0.3,2.3,1.2,2.6,1.2,1.6,0.7,0.9,1.3,4.7,0.3,4,25,8,11,17,15,15,18,20,7,3.0,29,20,6,14,80,72,6,37,42,13,23,15,40,37,44,39,29,23,36,19,12,7,27,2,5,47,42,1,1,9,22,26,23,20,5,2,5,6,7,4,18,11,23,4,26,5,13,8,6,4,10,3,3,16,17,24,9,12,12,7,10,42,3,0.023,0.024,0.014,0.019,0.029,0.026,0.027,0.024,0.035,0.031,0.022,0.038,0.029,0.03,0.026,0.03,0.025,0.024,0.029,0.036,0.019,0.028,0.027,0.029,0.029,0.027,0.024,0.029,0.023,0.028,0.031,0.054,0.031,0.025,0.014,0.014,0.032,0.035,0.014,0.007,0.024,0.027,0.035,0.027,0.02,0.012,0.016,0.011,0.016,0.016,0.023,0.019,0.019,0.02,0.02,0.021,0.015,0.018,0.025,0.017,0.018,0.025,0.024,0.015,0.021,0.028,0.019,0.018,0.02,0.02,0.023,0.018,0.019,0.016,1384,5518,2096,2364,2584,3123,2606,4226,2128,1474,596.0,3542,4849,1285,3549,14534,20164,2262,6289,5961,3577,3748,2106,7297,5909,8597,5902,4754,6436,6163,2031,1263,1417,6402,1808,2276,8698,7512,349,540,1523,3784,6806,3276,3697,1709,878,2275,2310,1816,780,4944,2640,6174,735,4258,1840,3393,1154,1186,941,2793,723,976,3733,935,4806,2691,2794,1453,1426,2328,9631,388,0.105,0.118,0.101,0.103,0.119,0.122,0.119,0.127,0.138,0.141,0.12,0.145,0.128,0.132,0.12,0.129,0.115,0.11,0.134,0.143,0.087,0.129,0.127,0.13,0.133,0.121,0.123,0.111,0.1,0.134,0.118,0.145,0.133,0.12,0.07,0.084,0.133,0.141,0.09,0.064,0.12,0.118,0.149,0.121,0.102,0.085,0.103,0.084,0.091,0.105,0.118,0.103,0.1,0.103,0.109,0.119,0.091,0.099,0.118,0.106,0.109,0.108,0.118,0.1,0.108,0.133,0.107,0.096,0.11,0.13,0.113,0.102,0.105,0.126,367,1598,880,887,966,1075,973,1244,721,372,170.0,1429,1191,290,861,3736,5164,430,1654,2391,916,1178,870,1701,1728,2490,2021,1717,1809,1841,559,501,443,1417,402,786,2163,1759,183,188,796,1486,1324,1740,2276,785,326,969,994,646,393,2041,1218,2441,418,2134,792,1493,663,658,385,961,402,367,1652,676,2121,1018,1238,567,681,1169,4059,216,3.605,2.959,2.349,2.723,2.282,2.519,2.372,2.965,2.302,3.098,2.918,2.046,3.169,3.172,3.082,2.94,3.211,3.965,2.99,2.113,3.067,2.551,2.132,3.187,2.717,2.815,2.376,2.242,2.714,2.656,2.705,2.655,2.727,3.321,3.704,2.773,2.945,3.097,2.279,2.807,2.477,1.984,3.383,2.129,1.87,2.415,3.325,3.043,3.011,3.107,2.151,2.547,2.308,2.678,2.293,2.286,2.791,2.558,2.075,2.046,2.318,2.784,2.233,2.749,2.511,1.836,2.41,2.806,2.566,2.616,2.495,2.485,2.565,2.301,0.618,0.702,0.69,0.393,0.623,0.569,0.634,0.532,0.509,0.753,0.872,0.529,0.505,0.456,0.538,0.534,0.621,0.581,0.59,0.661,0.73,0.441,0.478,0.723,0.441,0.531,0.467,0.595,0.682,0.536,0.725,1.007,0.45,0.558,0.719,0.532,0.664,0.542,0.346,0.331,0.473,0.85,0.758,0.657,0.545,0.437,0.553,0.604,0.46,0.718,0.423,0.525,0.512,0.439,0.269,0.431,0.597,0.514,0.348,0.362,0.535,0.9,0.397,1.208,0.598,0.508,0.531,0.425,0.331,0.969,0.436,0.466,0.511,0.499
-374,Epileptic,M,27.0,0.7,2.7,1.2,1.6,1.8,2.8,1.5,1.7,1.0,1.4,0.3,3.3,1.4,0.6,2.0,4.7,8.1,1.3,2.1,5.5,1.4,4.7,3.0,4.1,5.7,4.8,3.8,2.2,2.6,3.5,1.2,1.7,0.5,2.6,0.4,0.6,4.6,3.7,0.2,0.2,1.1,4.1,2.7,3.3,2.6,0.8,0.3,1.3,1.5,0.8,0.7,2.5,1.2,2.3,0.3,3.0,1.0,1.7,1.4,1.5,0.9,2.4,0.6,0.7,1.8,1.4,2.7,1.6,1.0,0.8,1.5,1.6,5.5,0.3,7,24,15,13,18,21,15,19,15,14,9.0,29,17,7,21,62,81,13,27,48,14,35,26,44,45,45,47,25,27,47,16,8,6,28,2,7,56,37,1,2,6,30,32,25,17,6,2,9,8,6,5,22,8,16,1,25,11,16,11,10,7,20,5,9,13,22,22,13,8,6,12,12,37,3,0.03,0.027,0.019,0.029,0.027,0.031,0.024,0.027,0.036,0.039,0.037,0.035,0.024,0.032,0.031,0.032,0.029,0.04,0.029,0.037,0.022,0.045,0.042,0.039,0.042,0.035,0.029,0.026,0.026,0.031,0.031,0.049,0.033,0.03,0.012,0.013,0.035,0.028,0.011,0.014,0.021,0.03,0.036,0.028,0.018,0.023,0.016,0.017,0.018,0.018,0.022,0.022,0.016,0.022,0.023,0.018,0.018,0.019,0.026,0.02,0.022,0.028,0.022,0.025,0.026,0.018,0.021,0.02,0.019,0.02,0.025,0.019,0.017,0.017,1547,4424,2559,2616,2362,3879,2747,3684,1967,2483,671.0,2994,3588,1244,3957,10337,18404,2672,5209,5884,4148,3954,2293,7205,6352,7808,6796,4193,7649,6336,2493,1002,880,6864,2253,2174,8184,9238,393,597,1449,2528,6829,2872,3841,1210,816,2410,2481,1854,709,4053,2220,3623,442,4764,1961,3451,1432,1860,1242,3063,794,1025,1898,1598,3952,2945,1817,1408,1934,2356,11526,569,0.117,0.133,0.122,0.124,0.132,0.117,0.113,0.131,0.142,0.155,0.122,0.14,0.115,0.139,0.131,0.136,0.132,0.145,0.132,0.143,0.102,0.137,0.137,0.146,0.134,0.143,0.124,0.106,0.112,0.144,0.135,0.127,0.124,0.127,0.065,0.088,0.133,0.124,0.083,0.109,0.112,0.124,0.156,0.122,0.097,0.116,0.095,0.088,0.1,0.111,0.116,0.116,0.096,0.11,0.104,0.106,0.105,0.109,0.122,0.109,0.113,0.134,0.123,0.122,0.109,0.108,0.108,0.107,0.107,0.113,0.124,0.107,0.091,0.123,473,1533,959,966,1062,1315,1037,1145,547,624,201.0,1475,1032,316,1080,2860,4976,560,1283,2518,1042,1441,1108,1861,2204,2181,2361,1440,1810,1995,751,467,292,1487,564,759,2457,2041,202,260,764,1693,1470,1846,2202,596,334,1100,1148,653,492,1819,1104,1686,222,2493,868,1518,883,1015,656,1281,425,508,979,1134,2063,1268,852,635,968,1207,4871,273,3.333,2.69,2.52,2.533,1.976,2.482,2.267,2.764,2.605,2.914,2.848,1.771,2.723,2.788,2.775,2.674,2.834,3.527,3.019,2.005,2.927,2.328,1.923,2.909,2.356,2.769,2.255,2.203,2.956,2.504,2.587,2.362,2.56,3.227,3.452,2.563,2.518,3.138,2.111,2.618,2.492,1.441,3.351,1.741,1.971,2.151,2.808,2.675,2.738,2.918,1.88,2.172,2.18,2.383,2.353,2.062,2.388,2.499,1.947,2.073,2.215,2.246,1.954,2.234,2.062,1.672,2.067,2.524,2.358,2.276,2.258,2.274,2.549,2.381,0.756,0.618,0.503,0.601,0.588,0.814,0.524,0.585,0.601,0.685,0.781,0.562,0.598,0.566,0.564,0.7,0.612,0.752,0.596,0.689,0.911,0.571,0.476,0.78,0.653,0.625,0.513,0.631,0.68,0.604,0.563,0.854,0.435,0.596,0.692,0.474,0.723,0.692,0.492,0.446,0.573,0.457,0.691,0.534,0.541,0.445,0.559,0.519,0.436,0.62,0.43,0.54,0.46,0.471,0.484,0.407,0.425,0.476,0.376,0.417,0.319,0.599,0.48,0.572,0.478,0.635,0.427,0.419,0.434,0.694,0.381,0.394,0.455,0.345
-375,Epileptic,F,27.0,0.7,1.7,1.4,1.9,1.4,1.6,1.4,1.6,1.0,0.5,0.2,1.6,1.0,0.6,0.9,6.0,8.4,1.0,2.5,4.3,1.9,3.3,1.5,3.2,3.0,3.7,2.8,2.5,2.1,2.8,1.0,1.4,0.5,2.8,0.3,0.3,5.0,3.4,0.1,0.2,1.0,2.7,2.9,1.8,2.8,0.9,0.4,0.9,1.4,0.9,0.7,1.3,1.3,2.7,0.3,2.1,1.2,2.1,1.1,1.0,1.1,1.6,0.3,0.7,1.4,1.2,2.3,1.3,1.0,0.5,0.5,2.2,4.2,0.4,28,16,15,16,16,13,13,14,9,5,3.0,13,11,5,10,65,287,7,24,46,11,39,14,30,39,37,38,25,23,33,15,7,6,30,2,2,44,35,1,1,5,23,20,14,17,7,2,6,8,7,5,12,10,19,2,19,8,17,8,7,9,13,3,3,13,15,21,8,5,4,4,15,28,2,0.029,0.02,0.018,0.029,0.029,0.025,0.02,0.023,0.038,0.032,0.025,0.028,0.028,0.031,0.033,0.039,0.036,0.036,0.034,0.037,0.033,0.033,0.03,0.034,0.03,0.032,0.027,0.027,0.021,0.028,0.031,0.062,0.033,0.03,0.01,0.012,0.038,0.031,0.011,0.016,0.021,0.039,0.044,0.027,0.018,0.019,0.022,0.015,0.021,0.027,0.031,0.016,0.028,0.02,0.016,0.017,0.022,0.024,0.026,0.02,0.026,0.024,0.025,0.026,0.019,0.027,0.016,0.022,0.017,0.018,0.016,0.025,0.017,0.017,1559,4813,3145,3011,2258,2414,2519,3467,2062,1148,497.0,1894,2643,1036,1978,9706,16777,2466,4812,4975,3019,5383,1954,6021,6012,6503,5480,4326,5642,4908,2271,783,1033,6166,1702,1597,7772,7168,326,418,1576,2253,4225,2036,4023,1497,720,2174,2289,1754,537,2607,1992,5598,496,3678,1956,3480,1027,1486,1503,2619,451,872,2484,1422,4091,2184,1668,1105,884,2497,9676,738,0.109,0.102,0.106,0.126,0.124,0.123,0.106,0.116,0.148,0.124,0.123,0.131,0.118,0.132,0.128,0.152,0.134,0.133,0.129,0.145,0.103,0.143,0.128,0.13,0.135,0.136,0.127,0.119,0.106,0.135,0.121,0.149,0.144,0.133,0.062,0.065,0.127,0.125,0.082,0.124,0.104,0.15,0.132,0.116,0.097,0.107,0.11,0.085,0.09,0.109,0.142,0.095,0.112,0.097,0.113,0.104,0.113,0.102,0.122,0.107,0.123,0.114,0.131,0.127,0.105,0.115,0.097,0.113,0.097,0.113,0.106,0.127,0.095,0.103,443,1444,1196,1075,822,971,922,1046,463,292,161.0,832,690,285,558,2705,4504,469,1345,1957,842,1764,834,1577,1834,1937,1989,1509,1541,1634,589,351,315,1402,481,571,2159,1860,163,172,766,1115,1151,1076,2360,764,286,951,1062,741,356,1317,923,2249,274,1931,840,1552,653,831,692,1027,234,358,1191,730,2158,959,850,438,487,1248,3791,326,3.274,3.075,2.607,2.613,2.264,2.249,2.245,2.834,2.862,3.05,2.63,1.934,2.859,2.665,2.656,2.624,2.922,3.776,2.833,2.158,2.909,2.381,2.034,2.795,2.562,2.77,2.212,2.254,2.774,2.434,2.742,2.538,2.592,3.14,3.113,2.547,2.743,2.892,2.404,2.699,2.57,1.842,2.946,2.072,1.908,2.162,3.054,2.705,2.501,2.871,1.885,2.072,2.182,2.388,2.35,2.046,2.401,2.575,1.769,1.899,2.266,2.413,2.091,2.572,2.107,2.278,1.944,2.521,2.339,2.925,2.072,2.4,2.611,2.583,1.046,0.633,0.456,0.561,0.574,0.552,0.558,0.639,0.716,0.471,0.809,0.403,0.575,0.471,0.551,0.551,0.626,0.724,0.524,0.596,0.745,0.48,0.422,0.63,0.548,0.46,0.514,0.495,0.475,0.583,0.737,0.927,0.403,0.519,0.533,0.496,0.761,0.7,0.326,0.71,0.535,0.417,0.684,0.764,0.603,0.484,0.497,0.659,0.596,0.568,0.445,0.505,0.599,0.475,0.351,0.442,0.553,0.721,0.331,0.445,0.406,0.666,0.408,0.806,0.659,0.688,0.442,0.44,0.389,0.811,0.486,0.499,0.643,0.71
-376,Epileptic,F,32.0,1.5,2.6,0.6,0.5,1.6,2.1,1.4,1.3,1.3,0.6,0.2,2.7,1.1,0.7,1.6,7.2,7.2,0.7,1.9,4.6,0.7,2.3,1.4,3.2,2.1,3.4,3.4,2.0,2.0,3.1,1.3,0.5,0.3,2.5,0.4,0.3,3.8,3.1,0.2,0.2,0.8,2.5,1.7,2.5,1.6,0.4,0.4,0.6,1.1,0.7,0.5,1.3,1.6,1.4,0.2,2.1,0.6,1.3,1.0,0.8,0.4,1.4,0.5,0.3,1.5,1.2,2.2,1.1,0.9,0.4,1.1,1.3,3.8,0.2,10,28,7,5,16,20,13,14,14,6,4.0,25,12,9,11,71,67,4,23,40,8,30,16,31,28,32,40,22,19,37,19,4,4,27,2,3,46,35,1,2,5,23,24,16,11,4,2,4,5,6,3,8,10,13,1,14,4,10,6,6,4,10,3,3,14,9,17,9,6,3,6,8,33,2,0.05,0.031,0.014,0.013,0.045,0.033,0.027,0.037,0.047,0.031,0.022,0.039,0.031,0.038,0.049,0.045,0.03,0.033,0.032,0.039,0.021,0.037,0.033,0.041,0.031,0.042,0.029,0.027,0.024,0.036,0.034,0.043,0.023,0.037,0.014,0.011,0.036,0.032,0.021,0.023,0.02,0.032,0.031,0.027,0.016,0.013,0.017,0.011,0.017,0.035,0.028,0.021,0.028,0.016,0.019,0.021,0.017,0.022,0.028,0.022,0.016,0.03,0.044,0.018,0.022,0.043,0.026,0.021,0.017,0.02,0.029,0.023,0.02,0.017,1364,4372,1572,1553,2050,3415,2417,2903,2150,1258,493.0,2555,2499,1412,2129,10927,15314,1980,3613,4963,3021,4128,1932,5560,4437,5866,5768,3293,4604,5168,2065,832,933,5293,1798,1615,7718,6269,257,601,1389,2693,5212,3029,2572,1186,824,1836,2189,914,464,2364,1977,3995,271,2822,1325,2570,1048,1281,937,2360,405,694,2176,593,3108,2001,1497,835,1332,2319,7345,330,0.139,0.134,0.102,0.087,0.15,0.155,0.124,0.141,0.174,0.146,0.12,0.153,0.134,0.157,0.162,0.16,0.13,0.102,0.14,0.155,0.093,0.152,0.148,0.154,0.144,0.161,0.139,0.125,0.109,0.153,0.138,0.104,0.113,0.146,0.08,0.071,0.148,0.134,0.119,0.113,0.111,0.135,0.142,0.114,0.094,0.086,0.09,0.077,0.094,0.131,0.123,0.101,0.127,0.094,0.135,0.11,0.098,0.106,0.128,0.117,0.099,0.132,0.153,0.11,0.118,0.141,0.125,0.109,0.098,0.111,0.128,0.113,0.103,0.112,358,1402,723,644,670,1074,806,773,514,326,169.0,1151,704,352,558,2925,4127,390,1042,2013,625,1243,727,1353,1214,1460,2030,1148,1316,1608,557,261,312,1094,423,544,1866,1569,140,174,602,1288,1084,1614,1545,548,334,798,923,407,280,1064,870,1599,141,1401,579,1147,573,600,462,776,175,318,1053,374,1376,866,741,325,557,976,2990,190,3.527,2.809,2.299,2.334,2.565,2.548,2.666,3.372,2.93,3.029,2.422,1.966,2.7,3.073,2.961,2.769,2.937,3.737,2.827,2.178,3.343,2.564,2.251,3.061,2.773,3.219,2.335,2.255,2.72,2.589,2.795,2.794,2.327,3.293,3.637,2.618,3.114,2.968,2.332,3.005,2.901,1.893,3.565,2.049,1.854,2.26,3.186,2.679,2.808,2.726,2.053,2.291,2.378,2.431,2.356,2.379,2.769,2.616,2.322,2.302,2.324,2.826,2.29,2.31,2.181,2.046,2.443,2.547,2.419,2.679,2.605,2.808,2.532,2.249,0.898,0.78,0.596,0.412,0.637,0.589,0.644,0.83,0.702,0.564,0.655,0.555,0.517,0.453,0.606,0.629,0.603,0.871,0.482,0.596,0.828,0.589,0.366,0.653,0.546,0.753,0.472,0.562,0.445,0.615,0.719,1.039,0.497,0.708,0.786,0.552,0.631,0.663,0.628,0.486,0.775,0.477,0.673,0.689,0.509,0.618,0.602,0.681,0.6,0.592,0.362,0.567,0.615,0.593,0.346,0.456,0.434,0.525,0.455,0.32,0.331,0.739,0.656,0.917,0.568,0.494,0.6,0.539,0.382,0.817,0.595,0.508,0.542,0.643
-377,Epileptic,F,32.0,0.7,2.3,1.3,0.9,1.9,1.7,1.4,1.5,1.1,0.8,0.2,2.5,1.9,0.3,1.2,4.1,8.3,1.1,1.9,4.2,0.5,2.7,1.5,3.5,2.8,2.9,4.7,2.1,3.6,3.8,1.3,0.9,0.5,1.7,0.2,0.8,3.2,2.9,0.2,0.3,1.0,2.9,2.5,2.0,3.4,0.5,0.3,0.9,1.4,0.7,0.6,1.7,1.1,1.7,0.1,3.0,0.4,1.4,1.1,1.1,0.7,1.9,0.3,0.4,1.7,1.0,1.9,1.6,1.4,1.0,0.7,1.0,3.6,0.6,6,24,13,9,16,18,12,27,9,7,4.0,25,20,7,12,48,75,8,22,38,5,33,14,43,36,32,48,19,30,41,18,8,5,25,2,8,37,32,2,2,7,25,24,16,22,5,2,7,9,7,4,14,9,11,1,25,5,10,8,11,8,14,1,5,17,14,20,13,10,9,6,9,32,5,0.037,0.022,0.02,0.019,0.029,0.027,0.022,0.025,0.039,0.031,0.019,0.034,0.033,0.028,0.03,0.028,0.026,0.036,0.026,0.037,0.013,0.034,0.026,0.032,0.032,0.031,0.03,0.024,0.024,0.029,0.035,0.039,0.03,0.024,0.008,0.017,0.031,0.031,0.013,0.02,0.022,0.038,0.03,0.023,0.025,0.012,0.014,0.016,0.02,0.017,0.034,0.021,0.02,0.017,0.024,0.018,0.021,0.018,0.021,0.018,0.024,0.024,0.021,0.017,0.02,0.034,0.018,0.019,0.019,0.021,0.018,0.013,0.019,0.029,1620,4782,2075,1863,2710,2532,2607,3055,1829,1431,499.0,2734,3668,877,2224,8828,17203,2302,3804,5014,2328,4166,1882,5930,4905,4986,7323,3455,7213,6404,1969,905,915,4481,2123,1878,6920,6710,592,545,1432,2550,5811,2604,3474,1511,698,1997,2583,1761,512,2919,2015,3714,173,4754,1020,2799,1301,1671,1038,3233,393,786,2720,838,3360,3222,2499,1627,1521,2402,7275,566,0.123,0.117,0.123,0.1,0.13,0.144,0.103,0.127,0.152,0.136,0.129,0.144,0.133,0.147,0.14,0.131,0.126,0.13,0.131,0.144,0.074,0.144,0.122,0.133,0.144,0.139,0.136,0.112,0.108,0.141,0.132,0.12,0.123,0.126,0.05,0.096,0.126,0.141,0.1,0.107,0.115,0.15,0.14,0.115,0.114,0.085,0.094,0.087,0.106,0.109,0.134,0.112,0.113,0.09,0.15,0.106,0.113,0.1,0.108,0.114,0.13,0.115,0.117,0.12,0.115,0.14,0.11,0.104,0.106,0.119,0.109,0.09,0.107,0.156,431,1670,904,782,1040,961,950,1054,457,386,187.0,1213,1085,265,657,2441,5137,485,1127,2021,617,1448,886,1856,1611,1774,2572,1295,2106,2121,679,425,321,1170,579,698,1796,1638,220,239,710,1195,1325,1420,2244,742,318,914,1128,619,309,1332,905,1726,59,2498,407,1271,797,939,475,1282,186,435,1313,447,1696,1364,1117,667,658,1208,3085,317,3.622,2.711,2.265,2.332,2.25,2.378,2.301,2.646,2.815,3.018,2.435,1.983,2.694,2.72,2.706,2.726,2.841,3.681,2.735,2.145,2.947,2.334,1.914,2.553,2.457,2.566,2.346,2.116,2.707,2.497,2.337,2.263,2.317,2.789,3.221,2.513,2.868,3.04,2.451,2.575,2.637,1.929,3.164,2.056,1.796,2.227,2.992,2.732,2.851,3.058,2.004,2.295,2.264,2.351,2.819,2.164,2.583,2.52,1.873,1.997,2.294,2.62,2.57,1.933,2.291,2.328,2.122,2.6,2.609,2.566,2.541,2.345,2.513,2.003,0.785,0.564,0.548,0.493,0.585,0.371,0.609,0.503,0.525,0.38,0.727,0.532,0.483,0.388,0.334,0.469,0.502,0.643,0.408,0.609,0.789,0.393,0.428,0.66,0.472,0.382,0.443,0.531,0.61,0.438,0.717,0.808,0.423,0.597,0.745,0.46,0.704,0.537,0.381,0.43,0.346,0.439,0.742,0.559,0.57,0.394,0.424,0.637,0.443,0.547,0.362,0.548,0.469,0.392,0.324,0.359,0.403,0.63,0.322,0.339,0.325,0.637,0.475,0.79,0.481,0.522,0.392,0.401,0.378,0.783,0.414,0.456,0.453,0.445
-378,Epileptic,M,17.5,0.7,2.8,1.1,1.2,1.3,1.7,1.8,2.1,0.8,0.8,0.2,2.0,2.3,0.6,1.9,5.8,9.2,1.1,1.9,3.5,3.1,2.6,1.6,4.0,4.1,3.4,4.1,3.0,4.1,4.5,1.2,0.4,0.6,3.0,1.0,0.4,2.7,3.2,0.3,0.2,1.1,2.8,2.3,1.7,2.8,0.8,0.6,0.9,1.0,0.9,0.6,1.6,1.1,2.7,0.4,2.7,0.6,1.7,0.9,1.0,0.5,1.4,0.7,0.6,1.2,1.0,2.8,1.8,0.8,0.4,1.6,1.0,3.6,0.3,3,25,10,9,14,17,13,18,10,8,3.0,19,21,7,20,65,82,6,18,31,37,23,13,36,42,33,42,25,34,42,15,4,5,27,7,4,28,38,2,2,6,25,20,15,18,6,2,5,5,7,4,10,7,22,2,21,5,20,6,7,3,10,4,6,10,11,23,13,6,3,10,8,29,3,0.033,0.028,0.026,0.026,0.032,0.034,0.028,0.031,0.043,0.047,0.032,0.034,0.035,0.036,0.04,0.037,0.031,0.039,0.033,0.037,0.047,0.034,0.033,0.044,0.038,0.03,0.028,0.031,0.032,0.041,0.036,0.033,0.022,0.041,0.04,0.01,0.036,0.037,0.026,0.017,0.022,0.033,0.048,0.032,0.021,0.021,0.026,0.014,0.015,0.019,0.034,0.019,0.021,0.022,0.026,0.019,0.019,0.022,0.026,0.02,0.032,0.024,0.045,0.031,0.022,0.022,0.022,0.019,0.016,0.013,0.031,0.02,0.016,0.013,1279,5894,2416,2546,2141,2749,2801,4318,1830,1528,648.0,2011,4549,1013,3300,11413,20133,2310,3748,4068,3040,4487,2236,7099,6766,7064,7494,4392,7717,6302,2567,925,1257,6193,1757,2055,6802,7983,390,463,1674,2514,4934,1794,3652,1463,839,2494,2483,1947,611,3148,2005,5398,636,4531,1186,3222,1121,1472,470,2685,579,897,1900,1399,4442,3697,1858,891,1900,1705,8881,484,0.105,0.123,0.114,0.11,0.136,0.148,0.118,0.134,0.139,0.168,0.15,0.147,0.14,0.158,0.162,0.143,0.134,0.121,0.139,0.139,0.128,0.139,0.133,0.144,0.137,0.122,0.124,0.124,0.127,0.162,0.134,0.093,0.106,0.14,0.117,0.07,0.14,0.14,0.118,0.11,0.111,0.145,0.156,0.12,0.101,0.112,0.114,0.081,0.086,0.104,0.127,0.101,0.106,0.112,0.117,0.101,0.105,0.108,0.119,0.109,0.133,0.117,0.152,0.121,0.114,0.105,0.111,0.1,0.096,0.094,0.133,0.115,0.097,0.095,296,1601,710,744,752,907,977,1162,436,324,142.0,966,1111,263,807,2711,4975,474,932,1574,929,1218,800,1682,1827,1798,2540,1491,1968,1778,598,324,407,1141,464,649,1337,1617,186,214,715,1253,938,948,2072,613,330,840,979,690,328,1285,846,2069,209,2210,547,1430,582,760,264,887,216,368,910,743,1990,1485,763,443,761,765,3387,280,3.613,3.285,3.197,3.157,2.447,2.614,2.461,3.219,3.03,3.565,3.758,1.914,3.179,2.781,3.051,3.045,3.298,3.468,3.02,2.139,2.72,2.766,2.504,3.138,2.807,3.2,2.389,2.354,2.926,2.838,3.026,2.562,2.407,3.414,3.118,2.798,3.401,3.387,2.669,2.666,3.275,1.914,3.519,2.19,2.063,2.769,3.149,3.585,3.177,3.078,2.369,2.72,2.674,2.702,3.066,2.313,2.604,2.395,2.391,2.209,2.392,3.126,2.793,2.947,2.395,2.325,2.375,2.789,2.712,2.328,2.887,2.661,2.874,2.094,1.2,0.747,0.639,0.657,0.571,0.607,0.591,0.549,0.665,0.714,0.785,0.485,0.394,0.66,0.585,0.607,0.619,1.198,0.584,0.733,1.102,0.537,0.467,0.725,0.639,0.681,0.597,0.698,0.655,0.698,0.828,1.037,0.536,0.885,1.015,0.401,0.731,0.641,0.396,0.479,0.475,0.531,0.944,0.743,0.529,0.552,0.52,0.778,0.505,0.718,0.407,0.53,0.483,0.411,0.691,0.459,0.618,0.747,0.463,0.44,0.388,0.872,0.555,0.895,0.468,0.749,0.467,0.404,0.424,0.394,0.687,0.57,0.485,0.437
-379,Epileptic,F,52.0,0.8,2.7,1.0,1.3,2.0,3.0,1.5,1.2,1.2,0.6,0.4,2.5,1.3,0.6,1.2,5.5,9.9,0.5,1.4,3.9,2.0,2.7,1.1,3.6,2.8,3.8,3.3,2.8,2.8,4.0,1.5,1.0,0.7,2.8,0.6,0.5,2.5,2.8,0.1,0.2,0.9,3.4,4.4,1.9,2.9,0.6,0.5,1.2,1.1,0.9,0.6,1.6,1.4,2.6,0.2,1.8,0.4,1.4,1.1,0.8,0.5,1.2,0.3,0.4,1.3,1.4,2.9,1.3,1.2,0.3,0.9,1.2,3.9,0.5,5,28,9,11,21,24,15,11,13,7,4.0,25,13,6,13,62,89,7,20,36,14,29,12,42,32,39,37,24,20,42,18,8,7,31,3,5,29,30,2,1,5,33,26,17,19,5,2,6,5,7,4,14,9,17,1,18,4,14,7,7,4,8,2,5,13,14,21,10,8,5,8,9,36,3,0.038,0.03,0.014,0.026,0.038,0.033,0.022,0.026,0.045,0.025,0.036,0.027,0.027,0.031,0.032,0.037,0.029,0.026,0.022,0.031,0.035,0.033,0.024,0.041,0.032,0.04,0.031,0.027,0.024,0.033,0.043,0.041,0.03,0.029,0.025,0.014,0.039,0.036,0.012,0.012,0.019,0.037,0.069,0.026,0.022,0.013,0.027,0.022,0.015,0.027,0.029,0.02,0.031,0.023,0.026,0.016,0.022,0.016,0.027,0.016,0.019,0.02,0.023,0.017,0.014,0.037,0.024,0.023,0.019,0.015,0.021,0.019,0.017,0.019,1765,4763,2074,1970,2006,4329,2744,2695,1759,1310,630.0,2845,3025,1308,2604,9715,18853,1939,3514,4559,2908,4111,1996,4961,4888,7079,5580,4461,5737,5812,2128,1119,1222,5912,1667,1653,4640,5733,380,502,1770,3152,3517,2353,3629,1303,788,2132,2078,1073,632,3023,1816,4487,229,3098,1012,3279,1160,1352,874,2541,370,786,2819,1080,4084,2325,2052,850,1377,1584,8697,897,0.114,0.127,0.104,0.125,0.144,0.132,0.109,0.12,0.155,0.131,0.145,0.122,0.124,0.121,0.132,0.152,0.13,0.098,0.124,0.123,0.113,0.137,0.118,0.145,0.147,0.153,0.132,0.118,0.103,0.143,0.127,0.125,0.143,0.129,0.085,0.084,0.129,0.128,0.091,0.081,0.104,0.155,0.164,0.123,0.106,0.087,0.113,0.093,0.091,0.126,0.135,0.105,0.13,0.109,0.113,0.104,0.1,0.1,0.12,0.099,0.108,0.102,0.139,0.121,0.089,0.139,0.119,0.111,0.109,0.111,0.122,0.117,0.098,0.105,419,1598,887,753,849,1320,1018,861,488,376,184.0,1302,859,359,713,2717,5369,363,988,1977,882,1243,795,1556,1442,1878,1904,1546,1628,2041,607,451,379,1483,410,598,1308,1436,199,243,719,1422,1120,1388,2147,686,322,848,959,510,358,1401,723,1936,105,1766,467,1497,655,766,460,896,170,416,1507,568,1845,1024,978,413,627,877,3587,361,3.652,2.608,2.237,2.757,1.98,2.528,2.249,2.82,2.744,3.072,3.109,1.914,2.694,2.741,2.707,2.81,2.888,3.752,2.825,1.989,2.759,2.405,2.145,2.469,2.601,3.094,2.325,2.339,2.706,2.363,2.614,2.531,2.64,2.981,3.518,2.505,2.649,2.847,2.114,2.435,2.884,1.987,2.678,1.98,1.955,1.996,3.193,2.952,2.47,2.359,2.182,2.277,2.567,2.433,2.338,1.954,2.449,2.4,2.124,1.933,2.449,2.939,2.383,2.208,2.107,2.262,2.504,2.574,2.433,2.368,2.292,2.258,2.417,2.923,1.103,0.712,0.752,0.541,0.571,0.637,0.494,0.544,0.668,0.609,0.936,0.431,0.355,0.531,0.552,0.59,0.614,0.799,0.399,0.544,0.831,0.539,0.49,0.756,0.596,0.611,0.538,0.466,0.442,0.599,0.761,0.809,0.562,0.585,0.621,0.507,0.658,0.637,0.511,0.38,0.756,0.511,0.664,0.641,0.657,0.517,0.453,0.865,0.684,0.558,0.475,0.496,0.706,0.468,0.518,0.468,0.448,0.559,0.444,0.311,0.448,0.762,0.709,0.646,0.585,0.696,0.584,0.357,0.339,0.468,0.536,0.436,0.609,0.57
-380,Epileptic,M,52.0,1.6,2.4,0.8,1.2,1.3,1.9,1.5,1.6,0.9,0.6,0.2,3.2,1.7,0.7,1.4,5.0,8.5,1.2,1.9,5.0,1.2,2.7,1.4,2.8,2.7,2.8,2.0,2.4,2.0,2.5,1.0,1.2,0.6,2.2,0.4,1.1,3.3,2.7,0.3,0.3,1.0,2.8,2.9,3.8,2.5,0.8,0.2,0.9,1.6,0.5,0.6,2.0,1.2,2.6,0.3,2.0,0.8,1.4,1.3,0.6,0.8,1.5,0.4,0.4,1.8,1.3,2.1,1.4,1.3,0.4,0.9,1.3,4.6,0.2,10,25,8,9,16,17,13,14,11,5,2.0,31,17,7,16,56,76,9,21,37,9,29,12,29,26,25,28,24,18,27,16,8,4,24,2,9,37,29,3,2,6,26,26,22,16,6,1,6,8,4,4,16,8,20,2,15,7,12,10,6,7,12,2,4,13,15,18,11,8,4,7,10,33,3,0.045,0.026,0.016,0.022,0.029,0.032,0.026,0.031,0.032,0.027,0.02,0.034,0.027,0.03,0.023,0.033,0.028,0.039,0.033,0.036,0.025,0.034,0.026,0.031,0.033,0.03,0.023,0.024,0.02,0.025,0.027,0.049,0.025,0.026,0.014,0.027,0.038,0.031,0.019,0.013,0.021,0.034,0.039,0.034,0.017,0.019,0.012,0.015,0.019,0.012,0.032,0.019,0.02,0.021,0.015,0.017,0.023,0.018,0.024,0.014,0.027,0.026,0.021,0.021,0.02,0.034,0.022,0.02,0.018,0.014,0.024,0.025,0.021,0.015,1436,4175,2253,2177,2104,2654,2188,2638,1577,1072,538.0,2656,3372,1389,2914,9051,16213,2465,3321,4356,3643,4090,1897,5334,4820,4821,4482,4014,4867,4361,1749,1190,1048,5575,2204,2000,6571,6127,573,698,1276,3041,6323,2220,3551,1494,752,2350,2640,1451,447,3002,1677,4698,466,3336,1305,2371,1451,1110,1197,2349,513,1021,2758,924,3173,2282,1924,821,1192,1799,7940,357,0.133,0.129,0.104,0.109,0.135,0.143,0.111,0.138,0.144,0.132,0.093,0.14,0.13,0.122,0.128,0.147,0.131,0.139,0.133,0.129,0.096,0.143,0.119,0.132,0.141,0.134,0.117,0.11,0.092,0.127,0.12,0.134,0.105,0.123,0.072,0.115,0.142,0.136,0.121,0.085,0.111,0.144,0.157,0.133,0.096,0.097,0.075,0.084,0.104,0.085,0.136,0.106,0.108,0.106,0.117,0.102,0.119,0.101,0.123,0.094,0.136,0.125,0.126,0.122,0.11,0.148,0.117,0.111,0.101,0.121,0.115,0.131,0.109,0.104,424,1460,863,856,811,1020,854,871,482,313,179.0,1416,1018,375,924,2650,4965,492,967,2062,719,1322,895,1492,1424,1590,1577,1455,1466,1582,528,467,351,1279,547,711,1519,1445,265,394,696,1266,1209,1632,2132,739,304,969,1143,627,305,1541,902,2080,253,1743,606,1232,807,676,533,911,241,370,1372,526,1560,1088,1031,425,611,765,3455,231,3.364,2.795,2.603,2.558,2.284,2.274,2.244,2.702,2.599,2.786,2.518,1.712,2.653,2.718,2.447,2.629,2.698,3.625,2.784,1.808,3.366,2.441,1.838,2.786,2.622,2.596,2.267,2.218,2.592,2.315,2.506,2.731,2.426,3.07,3.431,2.468,3.098,2.999,2.327,2.164,2.333,2.075,3.617,1.639,1.855,2.193,3.066,2.993,2.783,2.553,2.052,2.094,2.076,2.367,2.316,2.139,2.46,2.209,1.956,1.825,2.414,2.481,2.494,3.038,2.195,2.132,2.187,2.425,2.176,2.207,2.275,2.704,2.504,1.714,0.725,0.551,0.418,0.418,0.539,0.491,0.518,0.541,0.433,0.33,0.677,0.431,0.383,0.56,0.363,0.455,0.465,0.83,0.707,0.638,0.818,0.581,0.46,0.601,0.45,0.468,0.418,0.531,0.436,0.416,0.532,0.983,0.374,0.586,0.797,0.425,0.709,0.641,0.344,0.4,0.511,0.61,0.766,0.607,0.567,0.422,0.568,0.793,0.614,0.574,0.449,0.341,0.382,0.387,0.35,0.491,0.381,0.585,0.353,0.407,0.6,0.484,0.667,0.846,0.585,0.544,0.469,0.33,0.473,0.485,0.398,0.586,0.509,0.303
-381,Epileptic,F,42.0,0.7,2.6,1.5,1.2,1.1,2.2,1.3,1.2,1.4,0.8,0.3,3.4,1.9,0.6,1.3,5.4,7.3,1.2,3.5,5.3,1.8,2.5,1.8,2.6,2.4,2.7,2.9,3.1,3.1,3.0,0.9,0.9,0.4,2.8,0.7,0.8,2.6,3.0,0.1,0.2,1.3,3.2,2.1,3.7,2.9,0.7,0.4,0.9,1.6,0.8,0.5,1.5,1.1,2.4,0.4,2.6,0.7,3.1,1.2,1.2,0.7,1.6,0.3,0.3,1.5,1.0,2.0,0.9,1.0,0.4,0.8,1.2,5.5,0.4,4,22,15,8,11,18,10,11,12,7,2.0,32,19,5,13,57,65,8,26,38,11,23,18,23,32,29,34,29,29,30,11,6,4,26,3,5,30,28,1,1,8,24,19,26,19,5,3,5,9,7,3,11,7,20,3,22,6,22,9,9,6,13,2,2,14,9,15,7,6,3,6,9,40,4,0.035,0.03,0.031,0.024,0.032,0.034,0.023,0.025,0.038,0.037,0.027,0.034,0.034,0.031,0.028,0.03,0.029,0.041,0.036,0.041,0.029,0.031,0.027,0.032,0.031,0.026,0.026,0.027,0.027,0.031,0.037,0.07,0.027,0.033,0.02,0.02,0.037,0.039,0.013,0.019,0.026,0.038,0.037,0.031,0.02,0.017,0.022,0.015,0.023,0.025,0.034,0.021,0.021,0.018,0.016,0.021,0.024,0.033,0.029,0.027,0.024,0.031,0.015,0.023,0.02,0.025,0.019,0.015,0.017,0.019,0.024,0.02,0.023,0.019,1137,4152,1945,1637,1685,2542,2278,2501,1974,1288,516.0,2780,2733,885,2254,9726,14587,2000,4311,4034,3264,3283,2074,4332,3791,5133,5036,4324,5627,4283,2476,464,839,5165,1835,1558,4982,4954,257,296,1494,2696,4908,2999,3628,1096,621,2062,2134,1163,289,2468,1655,4989,532,3456,1198,2736,1061,1207,1050,1973,485,691,1904,926,2863,1860,1657,1352,1128,1615,8094,509,0.103,0.125,0.132,0.114,0.128,0.14,0.109,0.111,0.147,0.15,0.1,0.133,0.136,0.121,0.132,0.133,0.123,0.13,0.13,0.138,0.099,0.129,0.125,0.12,0.131,0.119,0.121,0.122,0.106,0.134,0.12,0.146,0.095,0.131,0.093,0.089,0.139,0.14,0.088,0.109,0.118,0.141,0.152,0.128,0.107,0.103,0.113,0.09,0.12,0.135,0.14,0.108,0.109,0.1,0.105,0.117,0.11,0.135,0.132,0.129,0.113,0.14,0.106,0.117,0.108,0.127,0.106,0.097,0.094,0.106,0.123,0.122,0.113,0.121,371,1475,812,753,618,1030,854,863,570,352,200.0,1409,833,278,704,2949,4248,455,1518,2043,880,1281,992,1231,1315,1682,1835,1626,1662,1585,493,269,305,1350,498,650,1319,1344,164,180,810,1255,1066,1961,2127,597,291,886,1083,502,232,1237,832,2217,334,1971,546,1602,725,678,540,842,294,246,1231,514,1640,898,870,365,582,847,3781,292,2.932,2.571,2.467,2.135,2.373,2.283,2.214,2.473,2.523,2.943,2.556,1.78,2.567,2.36,2.433,2.512,2.654,3.189,2.356,1.792,2.932,2.137,1.933,2.656,2.293,2.491,2.174,2.188,2.608,2.249,3.384,2.078,2.209,2.796,3.179,2.221,2.706,2.718,1.921,1.753,2.229,1.939,3.364,1.738,1.931,2.023,2.516,2.822,2.334,2.691,1.592,2.2,2.211,2.239,2.0,2.022,2.395,1.947,1.83,1.916,2.252,2.356,1.97,2.614,1.688,2.108,1.89,2.283,2.224,3.404,2.207,2.391,2.306,2.219,1.053,0.712,0.533,0.432,0.585,0.499,0.519,0.406,0.625,0.412,0.651,0.459,0.442,0.458,0.512,0.553,0.577,0.911,0.561,0.551,0.787,0.455,0.432,0.566,0.447,0.472,0.569,0.495,0.513,0.552,0.774,0.748,0.408,0.655,0.888,0.399,0.608,0.599,0.428,0.407,0.468,0.532,0.714,0.497,0.573,0.436,0.414,0.804,0.439,0.563,0.284,0.416,0.645,0.431,0.45,0.455,0.343,0.566,0.37,0.392,0.552,0.561,0.266,0.72,0.469,0.646,0.46,0.473,0.476,1.127,0.472,0.492,0.508,0.507
-382,Epileptic,M,32.0,0.5,1.9,0.8,1.0,1.9,2.6,1.6,1.8,1.1,0.9,0.3,2.7,1.7,0.6,2.0,5.5,9.8,1.2,2.3,4.1,1.3,2.4,2.3,3.1,2.8,3.1,3.2,3.2,2.7,3.3,1.4,0.4,0.5,2.3,0.5,0.5,3.4,2.8,0.2,0.1,0.9,3.1,1.8,3.5,2.5,0.7,0.5,0.8,1.1,0.8,0.9,2.5,1.5,2.3,0.3,2.8,0.5,1.9,1.1,1.2,0.3,1.3,0.4,0.7,2.0,1.2,2.6,2.1,0.7,0.7,1.4,1.2,4.8,0.3,5,18,7,10,21,21,15,16,13,6,3.0,22,17,10,22,61,78,9,23,31,12,24,18,40,29,30,32,27,21,33,20,4,6,25,3,6,39,33,2,1,7,25,21,23,15,5,3,5,6,6,6,18,9,16,3,22,4,16,8,9,2,11,3,5,21,13,22,15,6,6,9,10,43,3,0.024,0.022,0.012,0.022,0.035,0.037,0.024,0.023,0.034,0.041,0.026,0.035,0.029,0.035,0.033,0.033,0.032,0.036,0.032,0.033,0.025,0.032,0.033,0.032,0.03,0.026,0.027,0.027,0.021,0.03,0.036,0.022,0.02,0.031,0.014,0.019,0.034,0.031,0.018,0.019,0.019,0.037,0.037,0.026,0.018,0.015,0.031,0.012,0.014,0.02,0.034,0.021,0.025,0.019,0.023,0.022,0.015,0.019,0.033,0.022,0.016,0.023,0.029,0.028,0.018,0.025,0.021,0.017,0.014,0.024,0.039,0.021,0.023,0.018,1259,5210,2157,2623,2956,3758,2504,3675,2300,1151,786.0,2520,3898,1161,3427,10608,18983,2467,3844,3286,3057,4148,2742,5943,4985,6196,5302,3732,5333,4858,2665,1023,1182,5694,1994,1441,7644,6442,358,388,1568,4063,5564,3122,3674,1490,774,2542,2540,1702,609,4040,2209,4484,438,3848,1170,2918,1042,1554,543,2433,479,909,3306,1678,3846,4176,1297,1590,1307,1850,8072,387,0.108,0.107,0.095,0.104,0.133,0.151,0.091,0.113,0.139,0.149,0.114,0.129,0.126,0.157,0.149,0.127,0.124,0.12,0.133,0.128,0.092,0.139,0.127,0.138,0.134,0.115,0.112,0.097,0.087,0.121,0.147,0.077,0.11,0.131,0.076,0.08,0.137,0.13,0.117,0.111,0.112,0.14,0.14,0.118,0.095,0.092,0.133,0.084,0.085,0.101,0.147,0.109,0.111,0.1,0.118,0.118,0.095,0.097,0.136,0.111,0.09,0.116,0.125,0.133,0.103,0.116,0.109,0.099,0.097,0.131,0.155,0.121,0.112,0.115,394,1489,856,854,984,1124,920,1175,549,333,193.0,1132,1075,324,949,2898,4958,496,1116,1769,904,1256,1049,1732,1581,1960,1826,1517,1644,1606,658,408,378,1202,504,502,1666,1519,181,145,716,1369,1057,1985,2116,768,329,995,1104,625,370,1804,977,1987,230,1962,516,1589,594,836,304,871,236,443,1648,749,1884,1757,676,497,579,879,3383,251,3.181,3.126,2.452,2.948,2.467,2.697,2.282,2.804,2.95,2.907,3.002,1.968,2.858,2.703,2.787,2.759,3.014,3.616,2.81,1.673,2.749,2.6,2.262,2.698,2.569,2.654,2.351,2.055,2.607,2.365,2.976,2.359,2.465,3.162,3.498,2.424,3.032,2.959,2.427,2.928,2.861,2.31,3.504,1.832,1.983,2.155,2.951,3.259,2.711,3.072,1.961,2.338,2.457,2.445,2.289,2.163,2.63,2.089,1.973,1.964,2.182,2.795,2.462,2.481,1.975,2.266,2.085,2.583,2.241,3.219,2.427,2.59,2.407,1.978,1.013,0.715,0.521,0.788,0.628,0.677,0.524,0.641,0.727,0.78,0.765,0.54,0.442,0.49,0.625,0.549,0.672,0.955,0.658,0.461,0.89,0.575,0.52,0.613,0.514,0.542,0.585,0.584,0.592,0.618,0.725,1.068,0.435,0.805,0.759,0.449,0.696,0.742,0.509,0.458,0.537,0.761,0.844,0.65,0.607,0.582,0.529,0.78,0.621,0.619,0.45,0.49,0.471,0.489,0.66,0.588,0.451,0.642,0.373,0.451,0.543,0.756,0.498,0.839,0.49,0.839,0.499,0.421,0.463,0.77,0.511,0.629,0.581,0.3
-383,Epileptic,M,22.0,1.1,2.7,0.8,1.3,1.4,1.7,2.0,1.9,1.8,1.0,0.3,4.3,1.3,0.5,1.4,6.1,10.9,1.1,2.5,5.4,1.6,2.8,2.4,3.3,3.2,3.5,7.1,3.4,3.1,3.3,1.9,1.4,0.8,2.6,0.4,1.3,3.1,2.8,0.1,0.1,1.4,3.4,2.1,5.7,3.6,0.8,0.5,1.0,1.6,1.0,0.7,1.4,1.6,3.2,0.1,2.5,0.8,1.6,0.8,1.4,0.5,1.9,0.3,0.5,2.8,1.4,2.6,1.6,1.1,1.0,1.1,1.5,4.2,0.6,11,28,6,10,14,18,18,26,18,10,5.0,41,16,7,21,70,123,11,33,52,13,33,27,35,46,42,75,36,29,42,24,14,5,34,2,14,38,39,1,1,9,33,24,42,21,7,2,6,8,11,4,10,16,28,1,20,6,10,6,10,5,12,3,5,22,23,24,14,7,10,7,11,38,3,0.045,0.025,0.014,0.022,0.029,0.032,0.03,0.03,0.044,0.041,0.042,0.046,0.03,0.032,0.032,0.034,0.031,0.036,0.033,0.044,0.028,0.037,0.039,0.037,0.037,0.03,0.045,0.039,0.029,0.031,0.046,0.048,0.026,0.03,0.011,0.022,0.029,0.037,0.012,0.014,0.024,0.046,0.032,0.04,0.026,0.019,0.018,0.015,0.021,0.021,0.032,0.018,0.025,0.022,0.022,0.019,0.015,0.021,0.027,0.023,0.019,0.027,0.021,0.02,0.022,0.024,0.018,0.021,0.016,0.024,0.031,0.016,0.021,0.023,1796,5615,2395,2589,2643,3425,3371,4047,2041,1945,866.0,3723,3543,1179,3166,12414,25559,2895,4816,5749,4477,3986,2550,6464,6180,8277,7338,5151,7157,6980,2648,1969,1294,6647,2824,2714,7453,6684,446,483,1811,2701,6627,4126,3686,1622,991,2668,2521,2273,602,2879,2237,6247,213,3945,1462,2685,891,1703,979,3121,375,1051,4209,1339,4986,2769,2290,1752,1340,2529,8268,749,0.135,0.116,0.095,0.105,0.127,0.137,0.117,0.143,0.166,0.152,0.123,0.183,0.129,0.134,0.153,0.143,0.132,0.132,0.14,0.165,0.099,0.142,0.145,0.137,0.141,0.131,0.149,0.138,0.119,0.137,0.151,0.124,0.105,0.136,0.065,0.123,0.127,0.141,0.084,0.084,0.107,0.151,0.134,0.154,0.11,0.103,0.098,0.082,0.095,0.114,0.14,0.099,0.118,0.109,0.133,0.108,0.097,0.103,0.14,0.118,0.097,0.12,0.128,0.114,0.116,0.121,0.104,0.111,0.094,0.125,0.133,0.103,0.106,0.107,536,1842,866,901,871,947,1161,1224,708,414,242.0,1679,842,291,805,3168,6639,593,1379,2410,1010,1383,1084,1684,1721,2235,2556,1804,1903,2088,812,633,470,1523,691,935,1837,1557,208,225,930,1431,1379,2390,2231,726,394,993,1188,804,339,1237,1078,2548,75,2018,772,1251,469,938,447,1053,212,473,1872,958,2274,1181,1028,619,602,1236,3342,363,3.328,2.771,2.696,2.741,2.554,2.63,2.451,2.782,2.321,3.526,2.69,1.975,3.055,2.903,2.983,2.866,2.975,3.642,2.785,2.053,3.243,2.288,2.08,2.855,2.762,2.843,2.297,2.289,2.797,2.602,2.471,2.961,2.263,3.15,3.481,2.693,3.012,3.067,2.449,2.571,2.499,1.708,3.467,1.931,1.911,2.593,2.663,3.379,2.551,2.86,2.159,2.434,2.181,2.467,2.724,2.187,2.227,2.505,1.883,1.921,2.274,2.857,1.892,2.514,2.46,1.602,2.333,2.607,2.566,2.599,2.578,2.437,2.529,2.552,0.79,0.675,0.5,0.442,0.686,0.779,0.68,0.508,0.638,0.554,0.656,0.534,0.443,0.509,0.456,0.658,0.683,0.829,0.571,0.727,0.826,0.585,0.495,0.804,0.612,0.602,0.645,0.618,0.692,0.604,0.756,0.883,0.467,0.7,0.78,0.479,0.69,0.639,0.285,0.428,0.457,0.532,0.599,0.695,0.619,0.461,0.685,0.824,0.486,0.565,0.28,0.462,0.388,0.473,0.529,0.427,0.471,0.47,0.557,0.39,0.412,0.693,0.36,0.815,0.498,0.577,0.47,0.386,0.426,0.76,0.467,0.533,0.508,0.404
-384,Epileptic,M,42.0,0.4,2.2,0.7,0.9,1.6,2.0,1.6,1.4,1.0,0.7,0.3,2.5,1.4,0.6,1.0,5.9,9.3,1.0,2.5,5.7,0.7,3.1,1.5,2.7,4.2,4.0,4.5,2.7,2.8,3.8,1.5,1.1,0.4,2.0,0.4,0.5,2.9,3.5,0.2,0.1,1.0,3.0,2.4,2.4,3.1,0.6,0.5,0.8,1.1,0.3,0.8,1.7,1.7,2.3,0.4,3.4,1.0,1.7,1.0,1.0,0.7,1.8,0.2,0.4,1.8,1.0,4.6,0.9,0.8,0.7,1.1,1.0,5.7,0.4,3,24,8,10,16,17,13,14,10,8,3.0,19,13,8,12,58,78,7,24,41,9,31,13,36,44,41,43,23,21,41,19,7,4,22,2,5,31,41,2,1,7,21,23,16,21,4,3,5,6,4,6,14,12,14,3,25,9,16,8,9,5,13,2,4,11,11,38,7,6,9,8,10,43,3,0.022,0.024,0.014,0.02,0.033,0.032,0.022,0.031,0.04,0.033,0.035,0.036,0.027,0.036,0.032,0.034,0.03,0.04,0.03,0.039,0.016,0.032,0.032,0.033,0.034,0.036,0.035,0.034,0.028,0.037,0.043,0.04,0.032,0.03,0.012,0.022,0.032,0.038,0.016,0.017,0.021,0.038,0.037,0.03,0.028,0.016,0.02,0.016,0.016,0.014,0.039,0.019,0.03,0.021,0.03,0.022,0.023,0.02,0.024,0.022,0.024,0.031,0.022,0.018,0.032,0.031,0.031,0.016,0.019,0.026,0.027,0.018,0.022,0.017,1545,4673,1694,2081,2314,2742,2564,3253,1420,1353,634.0,2308,3105,1268,2397,11404,18146,2201,4835,4841,2941,4586,1771,6185,6974,6964,5743,3303,5316,5666,2372,1637,742,5674,1908,1505,6760,6522,352,253,1569,2420,5807,2372,2529,1304,902,1906,2193,1277,482,3179,2623,5101,445,4774,1756,2968,1070,1502,853,2386,442,884,2023,845,4382,2028,1427,1080,1647,2381,10877,655,0.09,0.122,0.102,0.102,0.137,0.137,0.108,0.136,0.142,0.135,0.131,0.14,0.134,0.147,0.131,0.139,0.127,0.135,0.132,0.152,0.088,0.139,0.135,0.141,0.143,0.146,0.136,0.137,0.121,0.15,0.144,0.118,0.14,0.125,0.078,0.109,0.136,0.151,0.118,0.096,0.113,0.136,0.145,0.119,0.121,0.096,0.11,0.09,0.089,0.09,0.153,0.107,0.125,0.103,0.127,0.111,0.118,0.102,0.12,0.118,0.124,0.133,0.112,0.113,0.123,0.124,0.133,0.098,0.106,0.141,0.124,0.109,0.109,0.101,359,1541,692,780,820,1051,1007,890,476,357,193.0,1034,799,311,634,3070,5127,447,1271,2244,726,1475,782,1545,2088,1956,2198,1313,1467,1849,589,532,252,1187,459,469,1593,1669,188,110,704,1106,1094,1353,1805,592,357,795,990,474,342,1420,1016,1815,232,2380,689,1481,661,799,466,845,201,385,947,505,2218,900,707,454,716,985,4300,299,3.767,2.84,2.345,2.574,2.374,2.43,2.177,3.177,2.392,3.039,2.651,1.94,3.006,2.951,2.761,2.774,2.933,3.653,3.009,1.913,2.958,2.424,2.008,2.984,2.604,2.95,2.235,2.051,2.767,2.424,2.963,2.905,2.389,3.295,3.553,2.847,3.179,2.889,2.132,2.499,2.835,2.022,3.464,2.065,1.654,2.413,3.057,3.068,2.793,2.802,1.938,2.42,2.556,2.921,2.328,2.288,2.655,2.366,1.952,2.055,2.075,3.031,2.348,2.466,2.355,2.149,2.185,2.73,2.465,2.729,2.74,2.545,2.677,2.521,0.815,0.518,0.763,0.519,0.671,0.67,0.65,0.597,0.562,0.502,0.584,0.463,0.491,0.466,0.667,0.647,0.678,0.807,0.604,0.694,0.849,0.553,0.469,0.706,0.546,0.695,0.532,0.585,0.622,0.602,0.615,0.992,0.482,0.66,0.662,0.607,0.766,0.596,0.376,0.405,0.603,0.643,0.796,0.888,0.515,0.478,0.687,0.71,0.529,0.672,0.506,0.534,0.581,0.577,0.494,0.49,0.442,0.527,0.49,0.467,0.425,0.754,0.532,1.06,0.568,0.649,0.539,0.417,0.475,0.514,0.549,0.547,0.508,0.424
-385,Epileptic,F,57.0,0.3,1.8,0.7,1.7,1.5,1.7,1.6,1.6,1.0,0.8,0.3,2.7,1.2,0.7,1.1,4.0,9.1,0.4,2.1,4.6,0.8,2.5,1.2,2.4,2.8,2.3,3.0,2.5,2.8,2.5,1.1,2.3,0.5,1.7,0.3,0.7,2.1,2.8,0.2,0.2,0.7,2.7,2.3,2.8,3.0,0.6,0.6,0.6,1.5,0.8,0.5,1.1,0.9,2.0,0.3,2.3,0.6,1.8,1.4,0.9,0.7,1.0,0.4,0.8,1.1,0.4,2.8,1.4,1.1,0.6,0.9,1.2,3.3,0.3,2,19,11,15,16,13,13,13,11,7,2.0,19,11,8,11,44,75,3,25,36,8,23,11,27,30,22,29,22,21,27,17,9,6,19,2,6,26,31,1,1,4,20,22,20,21,5,3,5,7,7,5,6,6,11,3,16,3,14,8,7,5,5,2,5,10,4,23,11,7,5,5,9,22,2,0.017,0.023,0.015,0.03,0.033,0.03,0.026,0.032,0.041,0.032,0.03,0.043,0.028,0.036,0.032,0.028,0.031,0.017,0.025,0.036,0.017,0.032,0.031,0.03,0.029,0.029,0.031,0.028,0.024,0.03,0.038,0.079,0.032,0.028,0.011,0.022,0.031,0.031,0.014,0.015,0.016,0.039,0.036,0.032,0.025,0.012,0.025,0.01,0.022,0.028,0.032,0.015,0.023,0.019,0.022,0.02,0.016,0.021,0.032,0.019,0.028,0.02,0.025,0.033,0.021,0.016,0.025,0.019,0.02,0.017,0.028,0.022,0.017,0.018,1210,3964,1402,2314,2186,2191,2947,2832,1923,988,390.0,2083,2767,1326,2083,8524,16874,1809,4246,4161,2596,3717,1723,5551,5064,5131,4824,3446,5563,4599,1672,697,860,4735,1593,1440,4689,5613,353,410,1287,2455,5429,1944,3162,1519,829,1905,2214,1106,350,2424,1500,3746,547,3341,1347,2780,1152,1345,832,2095,554,682,1718,686,3769,2797,1993,962,1269,1727,7258,553,0.07,0.115,0.119,0.125,0.139,0.137,0.114,0.134,0.159,0.15,0.127,0.15,0.122,0.156,0.136,0.129,0.125,0.087,0.111,0.133,0.095,0.142,0.125,0.127,0.138,0.128,0.132,0.115,0.104,0.129,0.123,0.162,0.153,0.13,0.07,0.112,0.117,0.135,0.102,0.098,0.097,0.145,0.137,0.136,0.114,0.089,0.113,0.077,0.097,0.128,0.14,0.088,0.104,0.1,0.147,0.111,0.084,0.116,0.135,0.107,0.13,0.105,0.122,0.141,0.107,0.09,0.118,0.106,0.104,0.113,0.131,0.123,0.094,0.108,375,1383,700,948,842,893,959,848,495,323,140.0,1012,734,320,596,2391,4637,383,1312,1968,722,1208,661,1332,1638,1404,1638,1357,1635,1438,489,390,312,1053,433,473,1280,1356,184,188,642,1178,1132,1378,2036,734,385,894,1042,496,234,1120,680,1641,217,1702,597,1397,702,697,422,768,279,346,886,427,1902,1186,826,461,518,884,3093,229,3.11,2.774,2.039,2.399,2.353,2.189,2.527,2.962,2.793,2.696,2.477,1.886,2.837,2.871,2.728,2.713,2.98,3.603,2.674,1.86,2.773,2.441,2.307,2.969,2.527,2.92,2.449,2.055,2.63,2.602,2.511,2.035,2.258,3.172,3.192,2.653,2.663,2.923,2.251,2.386,2.552,1.878,3.322,1.612,1.803,2.27,2.613,2.614,2.463,2.541,1.661,2.368,2.355,2.561,3.019,2.236,2.559,2.292,1.982,2.194,2.208,2.729,2.308,2.119,2.221,2.008,2.095,2.511,2.536,2.325,2.665,2.39,2.514,2.807,0.679,0.73,0.489,0.629,0.588,0.445,0.56,0.651,0.571,0.379,0.758,0.542,0.335,0.449,0.412,0.475,0.53,0.92,0.553,0.555,0.845,0.433,0.48,0.577,0.452,0.57,0.447,0.5,0.588,0.457,0.66,0.841,0.519,0.681,0.604,0.502,0.801,0.558,0.269,0.553,0.642,0.439,0.785,0.54,0.607,0.454,0.506,0.645,0.557,0.536,0.265,0.439,0.476,0.464,0.588,0.401,0.77,0.573,0.357,0.453,0.381,0.769,0.378,0.785,0.538,0.796,0.468,0.399,0.452,0.732,0.663,0.538,0.509,0.405
-387,Epileptic,F,32.0,1.3,1.7,1.2,0.9,1.4,2.2,1.5,2.0,0.7,0.8,0.3,2.8,1.7,0.5,1.2,5.6,8.0,1.0,1.9,4.8,0.9,2.0,2.4,3.5,2.3,3.6,3.7,2.8,2.5,3.3,1.6,2.0,0.5,2.8,0.8,0.5,3.5,2.9,0.3,0.3,1.0,3.1,2.0,2.6,2.7,0.6,0.5,0.9,1.4,0.7,0.6,2.4,1.4,2.3,0.5,2.8,0.8,1.3,0.7,1.5,0.9,1.8,0.5,0.7,1.7,1.5,2.7,1.0,1.5,1.0,1.3,1.5,4.4,0.2,9,15,13,8,18,22,11,19,11,8,4.0,26,19,6,14,62,75,9,23,43,8,27,22,44,33,40,43,30,25,39,22,13,6,35,3,5,38,40,2,2,7,27,19,20,15,4,3,5,9,8,5,16,12,16,3,26,7,10,6,11,8,15,3,5,18,21,23,6,9,6,9,11,41,2,0.049,0.019,0.022,0.02,0.03,0.029,0.025,0.026,0.033,0.03,0.035,0.035,0.025,0.026,0.027,0.031,0.03,0.036,0.023,0.043,0.021,0.025,0.027,0.033,0.024,0.029,0.028,0.029,0.025,0.026,0.041,0.064,0.019,0.03,0.026,0.011,0.03,0.032,0.019,0.021,0.022,0.034,0.034,0.026,0.02,0.012,0.014,0.015,0.016,0.023,0.026,0.022,0.024,0.017,0.018,0.021,0.019,0.017,0.018,0.022,0.022,0.026,0.028,0.019,0.021,0.024,0.02,0.015,0.023,0.028,0.024,0.019,0.018,0.011,1707,4208,2401,2084,2278,3136,2557,3734,1221,1679,771.0,2507,3952,1165,2625,10164,16400,2073,4666,4231,2686,4118,3424,6571,6372,7352,6574,4819,5939,6720,2540,1404,1430,5151,1616,1867,7312,7120,382,471,1521,2275,5410,2617,3241,1373,1040,2076,2413,1544,717,3973,1737,4618,633,3911,1261,2614,1005,1795,1603,2711,480,981,2810,1713,3679,2064,1961,1139,1807,2729,9608,626,0.148,0.099,0.114,0.097,0.141,0.136,0.098,0.131,0.132,0.132,0.123,0.141,0.129,0.129,0.13,0.141,0.132,0.129,0.107,0.164,0.089,0.115,0.11,0.139,0.121,0.132,0.128,0.111,0.102,0.131,0.143,0.15,0.098,0.126,0.099,0.079,0.136,0.14,0.105,0.105,0.108,0.133,0.145,0.123,0.101,0.084,0.093,0.082,0.097,0.124,0.118,0.108,0.124,0.098,0.117,0.115,0.109,0.099,0.103,0.114,0.119,0.123,0.122,0.109,0.117,0.118,0.106,0.089,0.111,0.136,0.115,0.104,0.099,0.087,476,1438,911,777,834,1169,887,1225,480,442,199.0,1368,1155,319,841,3170,4777,495,1256,1991,666,1200,1258,1899,1735,2165,2318,1686,1554,2134,802,562,435,1394,433,700,1945,1781,222,251,775,1356,1145,1616,2010,725,452,860,1198,560,353,1747,889,2085,316,2046,657,1218,567,999,635,1137,295,544,1376,988,2129,958,949,486,874,1231,3966,340,3.562,2.688,2.528,2.606,2.165,2.351,2.419,2.746,2.061,2.975,2.853,1.708,2.68,2.688,2.478,2.466,2.755,3.396,2.757,1.903,2.944,2.582,2.281,2.572,2.753,2.689,2.221,2.22,2.814,2.537,2.44,2.547,2.497,2.709,3.281,2.423,2.791,2.861,1.948,2.095,2.514,1.631,3.175,1.862,1.872,2.023,2.649,2.762,2.429,2.693,2.081,2.197,1.879,2.285,2.194,2.068,2.203,2.406,1.929,1.919,2.55,2.281,1.854,2.168,2.141,2.156,1.928,2.384,2.466,2.304,2.299,2.484,2.448,2.303,0.814,0.505,0.377,0.421,0.715,0.568,0.498,0.49,0.606,0.563,0.822,0.51,0.462,0.369,0.445,0.608,0.553,0.912,0.55,0.573,0.699,0.485,0.578,0.661,0.659,0.504,0.635,0.566,0.558,0.471,0.536,0.819,0.365,0.63,0.649,0.426,0.686,0.665,0.436,0.314,0.441,0.389,0.809,0.609,0.557,0.42,0.498,0.825,0.445,0.595,0.529,0.49,0.445,0.468,0.572,0.326,0.373,0.462,0.393,0.339,0.637,0.613,0.419,0.591,0.479,0.826,0.41,0.404,0.349,0.643,0.399,0.448,0.435,0.319
-388,Epileptic,F,27.0,0.9,2.3,1.0,1.2,1.3,2.2,1.4,2.0,1.0,0.7,0.2,2.8,1.6,0.4,1.8,6.9,9.0,1.2,3.4,4.7,1.2,3.0,1.9,2.7,3.0,3.4,2.6,2.5,2.9,3.2,1.3,1.2,0.7,2.7,0.3,0.5,4.8,4.5,0.1,0.1,1.5,2.5,2.6,2.0,3.5,0.7,0.4,0.9,1.5,1.0,0.5,1.8,1.8,2.4,0.5,2.8,1.1,2.2,1.2,0.8,1.0,1.7,0.5,0.3,2.0,1.1,3.1,1.4,1.4,0.3,1.3,2.3,4.0,0.3,10,22,10,9,9,17,11,16,10,8,2.0,28,15,5,21,67,77,9,30,40,9,38,17,32,37,33,30,26,21,42,19,11,8,32,2,5,58,44,2,1,8,23,26,21,20,6,2,7,8,10,4,15,18,17,3,24,7,20,11,6,10,14,4,3,17,15,25,11,11,4,8,16,30,4,0.035,0.022,0.016,0.019,0.028,0.026,0.024,0.031,0.033,0.028,0.021,0.033,0.025,0.022,0.03,0.031,0.027,0.041,0.034,0.037,0.021,0.03,0.025,0.029,0.028,0.029,0.022,0.023,0.024,0.029,0.037,0.045,0.024,0.032,0.011,0.013,0.037,0.03,0.01,0.016,0.029,0.029,0.04,0.029,0.023,0.014,0.021,0.013,0.017,0.024,0.02,0.014,0.024,0.019,0.024,0.016,0.023,0.021,0.022,0.013,0.019,0.031,0.028,0.014,0.019,0.021,0.018,0.02,0.023,0.012,0.021,0.031,0.016,0.017,1715,5338,2411,2417,1928,3480,2831,3621,1517,1791,623.0,2773,3869,1102,3122,12325,18205,2365,4717,4140,2382,6057,2364,5964,6183,6541,5537,5077,6136,6405,2123,1419,1368,5552,1517,1692,8189,7216,369,274,1760,3147,5011,2307,3842,1767,766,2216,3143,1824,639,3650,3063,4842,640,5192,1994,3185,1748,1965,1808,2253,578,879,3319,1961,5164,2363,2008,867,2004,2554,8368,511,0.122,0.115,0.101,0.1,0.123,0.125,0.105,0.134,0.125,0.125,0.102,0.14,0.125,0.127,0.14,0.138,0.129,0.134,0.137,0.141,0.084,0.143,0.128,0.129,0.135,0.133,0.116,0.113,0.104,0.131,0.118,0.143,0.129,0.135,0.074,0.079,0.128,0.126,0.081,0.092,0.119,0.136,0.134,0.13,0.109,0.092,0.103,0.086,0.096,0.107,0.119,0.089,0.127,0.1,0.121,0.098,0.105,0.102,0.124,0.081,0.101,0.138,0.138,0.093,0.108,0.11,0.106,0.112,0.117,0.094,0.115,0.129,0.092,0.093,534,1768,931,990,731,1343,899,1138,477,454,196.0,1346,1078,296,975,3736,5392,501,1631,2072,878,1824,1106,1623,1817,2020,1882,1765,1792,1971,652,546,447,1355,456,631,2379,2364,212,133,828,1363,1197,1257,2261,855,338,1080,1376,817,398,1962,1251,2115,346,2562,896,1827,877,1054,823,909,279,452,1741,929,2588,1142,1016,490,945,1298,3811,323,2.949,2.84,2.501,2.423,2.463,2.3,2.586,2.756,2.573,3.144,3.05,1.803,2.791,2.728,2.558,2.655,2.746,3.496,2.417,1.819,2.32,2.525,1.997,2.78,2.63,2.726,2.31,2.344,2.696,2.602,2.399,2.538,2.438,2.994,3.092,2.434,2.717,2.543,1.988,2.321,2.525,2.115,3.224,2.028,1.931,2.186,2.783,2.529,2.733,2.437,2.003,1.999,2.401,2.411,2.406,2.112,2.691,2.035,2.257,2.088,2.312,2.546,2.597,2.176,2.119,2.382,2.18,2.343,2.279,2.001,2.56,2.301,2.325,1.817,0.839,0.53,0.508,0.459,0.595,0.541,0.49,0.554,0.791,0.505,0.788,0.455,0.501,0.541,0.456,0.499,0.525,0.771,0.612,0.604,0.73,0.492,0.438,0.545,0.456,0.585,0.485,0.488,0.5,0.573,0.636,1.121,0.436,0.636,0.815,0.474,0.76,0.605,0.317,0.285,0.578,0.535,0.826,0.893,0.617,0.604,0.53,0.616,0.674,0.574,0.31,0.488,0.664,0.603,0.513,0.471,0.561,0.614,0.361,0.515,0.63,0.629,0.481,1.067,0.651,0.825,0.49,0.38,0.477,0.527,0.524,0.636,0.557,0.611
-389,Epileptic,M,32.0,0.9,3.3,1.6,1.9,1.4,2.8,1.7,2.0,1.0,1.2,0.4,3.6,1.7,0.6,1.7,4.9,9.2,1.2,2.8,4.8,0.9,4.5,3.6,3.3,5.1,5.1,5.2,3.3,2.7,3.4,1.7,1.1,0.8,2.7,0.4,0.8,3.0,3.0,0.3,0.3,1.2,3.6,2.6,2.8,2.3,0.9,0.6,1.0,1.7,0.7,0.9,2.9,2.3,2.7,0.3,2.7,1.0,2.3,1.1,1.7,0.5,2.1,0.2,0.5,2.6,2.4,3.3,1.5,1.4,0.5,1.0,1.5,5.1,0.2,8,67,17,17,16,27,18,22,9,14,5.0,38,17,9,22,58,89,10,35,43,8,49,31,36,52,58,56,33,28,44,19,14,8,36,2,9,40,42,2,2,10,38,28,22,15,7,3,6,9,10,5,26,18,21,2,23,9,23,12,12,6,16,1,5,26,26,27,13,11,5,9,13,49,2,0.04,0.035,0.024,0.026,0.028,0.028,0.027,0.03,0.039,0.047,0.031,0.04,0.031,0.024,0.036,0.031,0.035,0.041,0.031,0.038,0.022,0.04,0.041,0.038,0.044,0.036,0.038,0.029,0.023,0.03,0.037,0.062,0.032,0.031,0.011,0.018,0.029,0.033,0.017,0.022,0.021,0.034,0.038,0.025,0.018,0.017,0.028,0.015,0.02,0.022,0.033,0.023,0.03,0.024,0.015,0.017,0.024,0.021,0.023,0.022,0.018,0.031,0.016,0.016,0.021,0.042,0.02,0.018,0.02,0.023,0.021,0.016,0.02,0.01,1554,5414,2952,3149,2326,5104,3275,3895,1647,1909,1032.0,3443,3390,1440,3564,8256,16595,2580,6135,5658,3137,4957,3056,6097,5954,8581,5956,4422,6580,6120,2660,1111,1216,6982,2343,2420,7973,8316,456,506,1946,3978,5753,3309,3414,1782,912,2765,2673,1725,688,4606,3006,4557,505,4357,1502,3358,1503,2125,1280,2758,391,1013,4505,1104,5149,2878,2338,1098,1844,2852,9351,629,0.128,0.137,0.125,0.128,0.143,0.132,0.12,0.133,0.159,0.176,0.125,0.171,0.125,0.128,0.152,0.143,0.143,0.148,0.134,0.147,0.087,0.129,0.136,0.149,0.136,0.146,0.133,0.118,0.109,0.132,0.15,0.134,0.121,0.134,0.065,0.101,0.128,0.15,0.094,0.131,0.115,0.135,0.155,0.113,0.099,0.097,0.108,0.077,0.103,0.11,0.127,0.117,0.132,0.111,0.11,0.1,0.131,0.096,0.114,0.115,0.108,0.14,0.096,0.113,0.107,0.15,0.105,0.101,0.107,0.121,0.107,0.105,0.104,0.093,459,1817,1063,1133,886,1649,1130,1258,438,502,273.0,1592,1038,379,1008,2887,4787,532,1581,2377,740,1841,1335,1743,1914,2451,2279,1747,1862,2116,805,431,362,1628,586,837,1847,1750,270,209,907,1699,1185,1837,1969,850,393,1015,1198,636,429,2121,1222,1960,275,2398,696,1692,890,1149,522,1019,206,525,2096,808,2626,1293,1075,454,915,1365,4141,336,3.429,2.807,2.52,2.663,2.505,2.561,2.314,2.684,2.793,3.086,2.796,1.89,2.615,2.74,2.774,2.317,2.728,3.676,3.048,2.035,2.976,2.217,2.069,2.677,2.487,2.702,2.157,2.048,2.714,2.361,2.631,2.456,2.638,3.001,3.625,2.732,2.998,3.265,1.966,2.657,2.787,1.996,3.341,2.023,1.95,2.289,2.786,3.179,2.738,3.092,1.998,2.193,2.452,2.374,2.069,1.998,2.409,2.213,1.963,2.055,2.328,2.7,2.057,2.391,2.249,1.718,2.063,2.485,2.346,2.506,2.251,2.411,2.433,2.336,0.837,0.543,0.566,0.503,0.529,0.65,0.652,0.6,0.558,0.681,0.716,0.541,0.551,0.494,0.512,0.62,0.611,0.785,0.605,0.645,0.846,0.56,0.573,0.737,0.615,0.642,0.523,0.569,0.503,0.537,0.582,0.988,0.409,0.639,0.756,0.461,0.748,0.687,0.405,0.444,0.542,0.643,0.766,0.544,0.622,0.589,0.484,1.022,0.583,0.557,0.38,0.475,0.585,0.496,0.418,0.44,0.462,0.555,0.364,0.508,0.498,0.597,0.401,0.561,0.57,0.587,0.574,0.384,0.442,0.692,0.507,0.436,0.449,0.274
-390,Epileptic,M,22.0,0.8,2.1,1.1,1.3,1.1,2.3,1.6,1.8,0.8,0.7,0.3,3.3,1.6,0.7,1.8,5.8,8.9,1.2,1.9,4.8,1.7,2.6,1.6,3.4,2.3,4.0,2.2,2.4,2.9,2.7,1.5,1.1,0.5,2.5,0.5,0.9,2.8,3.5,0.3,0.3,1.0,3.5,2.3,2.7,2.9,0.5,0.7,1.0,1.4,0.8,0.7,2.7,1.2,3.6,0.3,2.4,0.6,1.6,1.0,0.9,0.4,1.2,0.4,0.6,1.8,1.5,2.2,1.1,0.9,0.5,0.7,0.8,4.6,0.4,6,15,10,10,11,18,12,15,9,8,3.0,36,17,8,18,64,84,10,25,40,16,33,16,38,31,38,29,21,25,39,25,8,4,28,3,7,32,38,2,2,6,30,22,23,18,4,2,6,6,5,5,21,9,24,2,20,6,14,8,7,3,9,2,4,15,19,19,7,5,4,5,6,40,3,0.034,0.022,0.017,0.027,0.03,0.033,0.026,0.031,0.037,0.038,0.033,0.03,0.027,0.031,0.032,0.03,0.029,0.034,0.029,0.038,0.032,0.03,0.027,0.04,0.027,0.029,0.025,0.026,0.028,0.028,0.042,0.046,0.026,0.035,0.019,0.02,0.035,0.029,0.019,0.013,0.021,0.033,0.041,0.022,0.022,0.012,0.025,0.014,0.018,0.014,0.03,0.023,0.023,0.022,0.024,0.02,0.02,0.022,0.021,0.016,0.015,0.022,0.022,0.022,0.022,0.023,0.024,0.017,0.013,0.023,0.019,0.014,0.017,0.019,1814,4926,2823,2445,1791,3401,2636,3171,1497,1375,724.0,4016,3970,1794,4065,12410,19596,3152,3993,5017,4802,4967,2282,5810,5292,7555,4488,3808,6704,5230,2290,993,892,5822,1484,2140,7114,8753,421,736,1448,2869,5471,3797,3920,1243,990,2242,2276,1840,619,4056,1827,6687,411,3376,1242,3257,1204,1488,866,2354,508,765,3030,1240,3598,2504,1888,954,1138,2440,9671,617,0.105,0.112,0.095,0.117,0.125,0.135,0.102,0.137,0.135,0.154,0.127,0.138,0.122,0.137,0.131,0.133,0.129,0.126,0.138,0.139,0.111,0.137,0.125,0.139,0.132,0.135,0.113,0.109,0.112,0.132,0.135,0.13,0.102,0.128,0.09,0.111,0.135,0.129,0.089,0.082,0.109,0.13,0.154,0.112,0.107,0.087,0.103,0.087,0.096,0.085,0.138,0.11,0.116,0.106,0.128,0.116,0.117,0.111,0.117,0.095,0.098,0.11,0.126,0.122,0.118,0.119,0.111,0.098,0.083,0.122,0.117,0.09,0.099,0.116,459,1407,969,857,650,1204,922,1014,368,357,167.0,1789,1026,382,1019,3200,4994,592,1161,2051,993,1516,1022,1512,1553,2196,1568,1334,1665,1761,637,464,289,1321,375,708,1511,1973,258,365,759,1481,1053,2131,2116,647,374,996,1080,814,379,1787,776,2495,171,1757,533,1299,710,822,433,813,248,363,1364,885,1672,1035,902,348,534,953,4185,292,3.515,3.049,2.739,2.82,2.331,2.46,2.41,2.747,2.816,2.868,3.076,1.948,2.871,3.046,2.977,2.773,3.048,3.961,2.715,2.084,3.395,2.472,2.035,2.874,2.601,2.789,2.291,2.237,3.023,2.375,2.66,2.295,2.415,2.982,3.497,2.785,3.266,3.082,1.966,2.413,2.389,1.769,3.622,2.051,2.103,2.17,3.097,2.835,2.683,2.704,2.08,2.364,2.599,2.726,2.768,2.07,2.561,2.55,1.977,1.995,2.347,2.775,2.451,2.227,2.332,1.684,2.207,2.773,2.495,2.714,2.497,2.87,2.414,2.637,0.915,0.769,0.613,0.604,0.615,0.533,0.626,0.515,0.672,0.606,0.893,0.51,0.375,0.611,0.485,0.618,0.65,0.687,0.633,0.626,0.794,0.508,0.48,0.668,0.528,0.564,0.527,0.606,0.528,0.562,0.832,0.942,0.387,0.64,0.771,0.471,0.759,0.649,0.44,0.329,0.364,0.451,0.635,0.644,0.583,0.404,0.492,0.545,0.427,0.48,0.408,0.547,0.577,0.553,0.434,0.458,0.353,0.74,0.349,0.418,0.415,0.803,0.476,0.94,0.549,0.487,0.512,0.416,0.418,0.617,0.596,0.736,0.483,0.402
-391,Epileptic,M,42.0,1.2,3.7,1.4,2.1,2.3,1.8,2.6,2.1,1.1,1.4,0.4,3.4,1.5,0.8,1.8,9.8,12.7,1.3,2.4,5.6,1.7,3.5,1.6,4.0,2.7,4.3,4.3,3.7,4.1,2.7,1.6,1.1,0.6,2.7,0.7,0.6,4.0,4.0,0.3,0.3,1.2,3.8,1.9,3.3,4.0,1.0,0.7,1.4,1.5,0.8,0.3,2.7,1.7,3.6,0.1,3.4,1.1,1.8,1.2,1.1,0.8,2.2,0.5,0.4,2.7,1.3,3.9,2.0,1.5,1.1,1.2,1.8,5.4,0.4,11,36,13,15,22,14,18,17,11,11,5.0,31,16,8,15,85,98,11,25,46,16,34,15,40,32,41,44,29,28,27,25,8,5,25,4,4,44,40,3,1,7,30,21,26,22,7,4,8,8,5,2,18,10,21,1,22,6,14,8,9,7,17,4,3,19,11,28,12,7,14,11,11,42,4,0.038,0.031,0.021,0.031,0.044,0.033,0.032,0.031,0.034,0.047,0.041,0.034,0.03,0.034,0.032,0.041,0.037,0.039,0.036,0.04,0.023,0.033,0.029,0.037,0.028,0.032,0.027,0.034,0.032,0.026,0.041,0.033,0.023,0.031,0.016,0.016,0.035,0.033,0.021,0.02,0.025,0.041,0.038,0.03,0.024,0.022,0.025,0.02,0.018,0.013,0.033,0.024,0.026,0.024,0.014,0.022,0.023,0.02,0.025,0.018,0.02,0.035,0.025,0.017,0.024,0.032,0.022,0.025,0.025,0.025,0.033,0.02,0.018,0.017,2122,5816,3037,2595,2533,3029,2656,3829,1926,1838,701.0,3407,3248,1265,3313,14733,21391,3019,4654,5847,6309,6299,2150,7743,5331,7596,7822,4064,6757,5037,2409,1555,1165,6321,2720,1956,9149,9529,515,423,1487,3641,5649,3289,4292,1494,1097,2481,2671,1983,341,4432,2143,6432,417,4779,2013,3218,1488,1860,1313,3026,682,1004,3648,1405,5501,2885,1851,1700,1445,2842,11079,522,0.126,0.128,0.104,0.122,0.142,0.127,0.125,0.132,0.141,0.17,0.132,0.131,0.131,0.134,0.123,0.141,0.132,0.13,0.151,0.137,0.103,0.127,0.125,0.136,0.123,0.131,0.117,0.109,0.107,0.125,0.156,0.106,0.102,0.121,0.084,0.078,0.133,0.133,0.109,0.113,0.113,0.142,0.132,0.127,0.107,0.105,0.117,0.09,0.094,0.083,0.127,0.106,0.113,0.102,0.109,0.106,0.1,0.106,0.116,0.102,0.113,0.132,0.121,0.107,0.112,0.122,0.105,0.116,0.11,0.125,0.131,0.105,0.099,0.109,584,2041,1141,1118,962,1073,1189,1154,560,503,191.0,1565,913,374,964,4211,6142,628,1255,2365,1316,1802,935,2007,1703,2229,2634,1681,1972,1695,637,606,411,1429,683,668,2044,2219,250,210,769,1560,1165,1916,2541,748,450,1073,1245,886,169,1919,998,2497,150,2508,834,1444,793,972,648,1106,331,447,1800,724,2910,1302,902,739,711,1345,4739,332,3.418,2.653,2.689,2.366,2.411,2.415,2.033,2.947,2.567,2.872,2.802,1.916,2.817,2.624,2.654,2.69,2.815,3.601,3.002,2.04,3.412,2.715,2.034,2.826,2.593,2.76,2.362,2.023,2.652,2.458,2.721,2.386,2.335,3.136,3.543,2.782,3.176,3.141,2.241,2.512,2.403,1.983,3.241,1.866,1.953,2.243,3.045,2.883,2.677,2.4,2.268,2.431,2.305,2.633,2.564,2.143,2.767,2.542,2.233,2.148,2.214,2.697,2.271,2.357,2.222,2.203,2.057,2.562,2.463,2.218,2.295,2.603,2.536,1.919,0.659,0.622,0.51,0.472,0.565,0.586,0.434,0.56,0.66,0.446,0.624,0.494,0.45,0.53,0.489,0.618,0.642,0.636,0.551,0.684,0.849,0.466,0.462,0.67,0.521,0.537,0.461,0.49,0.601,0.498,0.747,1.132,0.368,0.639,0.864,0.481,0.708,0.633,0.528,0.402,0.45,0.582,0.81,0.511,0.572,0.436,0.58,0.616,0.616,0.468,0.382,0.589,0.58,0.628,0.384,0.396,0.56,0.711,0.434,0.427,0.347,0.77,0.378,0.953,0.492,0.644,0.396,0.446,0.385,0.58,0.377,0.544,0.448,0.431
-392,Epileptic,M,52.0,0.5,2.2,0.7,1.1,2.0,1.8,1.5,1.8,1.2,0.6,0.3,3.3,1.5,0.8,1.5,5.5,8.2,0.8,3.1,3.9,1.1,2.9,2.1,4.1,2.9,2.9,3.1,2.5,2.6,3.9,1.0,1.5,0.6,2.9,0.3,0.9,3.6,2.7,0.1,0.3,1.3,3.2,3.0,2.8,2.9,0.5,0.4,1.0,1.3,0.9,0.7,1.8,1.2,2.4,0.4,2.4,1.2,3.2,1.1,1.5,0.5,1.5,0.6,0.4,3.0,1.8,2.4,2.0,0.8,0.4,1.2,1.4,3.4,0.4,5,18,8,12,18,18,12,18,13,5,2.0,27,15,8,15,53,74,8,32,41,11,31,18,41,36,31,34,26,21,43,12,10,5,34,2,7,48,32,1,2,6,29,30,23,16,4,2,5,6,8,6,13,9,14,3,20,10,24,7,11,5,9,3,5,23,19,18,12,7,5,8,10,27,3,0.025,0.024,0.015,0.024,0.035,0.031,0.023,0.032,0.038,0.031,0.032,0.032,0.027,0.037,0.03,0.031,0.028,0.023,0.039,0.031,0.018,0.034,0.029,0.038,0.028,0.025,0.026,0.026,0.023,0.028,0.029,0.042,0.03,0.03,0.013,0.019,0.032,0.031,0.009,0.022,0.024,0.032,0.042,0.026,0.02,0.012,0.023,0.014,0.017,0.033,0.027,0.021,0.019,0.022,0.022,0.02,0.024,0.028,0.022,0.024,0.018,0.026,0.023,0.019,0.027,0.044,0.018,0.027,0.02,0.012,0.029,0.022,0.017,0.018,1491,4148,1968,2045,2352,2799,2347,3360,2174,1211,596.0,2910,3021,1298,2640,9088,16305,2676,5127,3937,3462,4335,2427,6173,5510,6428,5440,3695,4539,5919,1959,1957,1203,6001,1827,2141,7251,6460,434,497,1472,3625,6806,2557,3521,1161,824,1995,2211,1433,602,2598,1891,3652,587,3420,1567,3245,1210,1574,922,2378,636,869,3263,1070,3791,2313,1238,784,1384,1821,7705,433,0.099,0.113,0.099,0.117,0.145,0.143,0.099,0.133,0.152,0.129,0.115,0.132,0.13,0.15,0.138,0.132,0.125,0.111,0.156,0.144,0.086,0.154,0.127,0.146,0.131,0.119,0.124,0.103,0.086,0.133,0.12,0.111,0.105,0.14,0.072,0.103,0.141,0.135,0.077,0.104,0.109,0.142,0.167,0.124,0.102,0.087,0.1,0.079,0.092,0.129,0.133,0.102,0.106,0.106,0.115,0.111,0.128,0.122,0.111,0.126,0.104,0.116,0.118,0.113,0.129,0.145,0.106,0.116,0.109,0.108,0.131,0.119,0.098,0.118,398,1418,719,830,979,1036,906,1062,594,326,200.0,1506,885,353,846,2825,4677,547,1406,1971,931,1398,1033,1937,1931,1970,1932,1433,1412,2169,645,629,405,1603,426,783,1919,1601,193,279,777,1594,1353,1714,2079,653,363,945,1066,533,399,1433,948,1651,262,1840,733,1752,740,915,461,923,376,426,1668,742,1951,1151,663,449,693,948,3174,283,3.408,2.781,2.528,2.409,2.305,2.334,2.191,2.799,2.723,3.186,2.775,1.722,2.643,2.745,2.481,2.562,2.918,3.719,2.926,1.744,2.706,2.429,2.07,2.452,2.395,2.639,2.227,2.095,2.519,2.267,2.291,2.795,2.307,2.861,3.302,2.588,2.858,2.969,2.429,2.065,2.434,1.997,3.321,1.698,1.922,1.862,2.837,2.599,2.566,2.829,1.76,1.98,2.03,2.447,2.477,2.031,2.341,2.085,1.966,1.92,2.211,2.604,1.905,2.428,2.048,1.873,2.127,2.327,2.127,2.029,2.365,2.285,2.518,1.985,0.785,0.653,0.523,0.515,0.557,0.545,0.584,0.555,0.474,0.546,0.664,0.441,0.455,0.404,0.464,0.454,0.54,0.808,0.541,0.643,0.823,0.468,0.633,0.726,0.475,0.467,0.472,0.516,0.512,0.569,0.699,1.053,0.581,0.643,0.774,0.529,0.741,0.644,0.296,0.572,0.409,0.527,0.869,0.606,0.6,0.374,0.584,0.786,0.556,0.603,0.308,0.387,0.504,0.432,0.558,0.508,0.421,0.566,0.412,0.435,0.46,0.635,0.453,0.827,0.496,0.609,0.485,0.421,0.527,0.354,0.447,0.474,0.517,0.559
-393,Epileptic,F,17.5,1.0,2.3,0.6,0.7,1.4,1.8,1.7,1.5,0.7,0.4,0.1,2.5,1.6,0.5,1.3,5.2,7.0,0.6,2.1,4.1,1.0,2.3,1.2,1.8,2.5,2.4,2.3,2.0,3.1,2.6,1.0,1.1,0.6,2.3,0.9,0.7,2.3,1.9,0.1,0.2,0.6,2.5,1.9,2.3,2.8,0.5,0.6,1.0,0.9,0.6,0.6,1.4,0.8,1.6,0.4,2.4,0.6,1.4,0.9,0.8,0.8,0.8,0.2,0.3,1.6,1.0,2.3,1.2,1.0,0.3,0.7,1.0,2.9,0.3,7,19,5,6,14,13,12,12,10,4,1.0,21,16,6,12,44,58,5,19,37,8,25,12,24,29,20,27,17,22,31,15,7,4,23,7,5,25,24,1,1,3,22,14,15,16,5,2,6,5,8,3,8,4,10,2,19,4,11,5,6,8,5,1,3,12,11,18,7,5,3,4,7,19,3,0.043,0.026,0.015,0.015,0.039,0.037,0.027,0.031,0.038,0.024,0.018,0.035,0.038,0.035,0.03,0.038,0.028,0.023,0.029,0.035,0.022,0.033,0.028,0.028,0.026,0.029,0.026,0.028,0.035,0.028,0.041,0.056,0.023,0.029,0.023,0.019,0.035,0.027,0.01,0.014,0.017,0.033,0.038,0.027,0.026,0.01,0.03,0.017,0.015,0.018,0.03,0.016,0.019,0.017,0.023,0.019,0.015,0.021,0.022,0.017,0.029,0.018,0.015,0.018,0.02,0.033,0.022,0.02,0.017,0.014,0.027,0.019,0.015,0.018,1339,4733,1903,1869,1946,2352,2643,2596,1683,1063,510.0,2692,3241,1105,2666,10072,17450,2471,4311,4621,2713,3617,1867,4632,5637,5251,4925,3322,5650,5301,2011,794,1053,5896,1815,1547,4839,5766,324,515,1478,2600,4113,2508,2795,1411,695,2289,2068,1447,580,2740,1194,3707,439,3702,1147,2919,1090,1316,911,1547,288,709,2567,916,3167,2499,1889,677,1302,1944,7665,431,0.137,0.117,0.089,0.089,0.129,0.138,0.117,0.136,0.144,0.119,0.079,0.141,0.135,0.128,0.13,0.14,0.125,0.103,0.122,0.134,0.089,0.141,0.126,0.118,0.136,0.123,0.114,0.106,0.119,0.134,0.141,0.131,0.112,0.128,0.088,0.092,0.119,0.121,0.07,0.083,0.089,0.137,0.128,0.117,0.115,0.083,0.127,0.081,0.089,0.101,0.127,0.092,0.109,0.09,0.135,0.105,0.099,0.107,0.111,0.101,0.137,0.096,0.116,0.113,0.106,0.139,0.115,0.099,0.094,0.105,0.118,0.105,0.084,0.101,426,1369,606,705,631,847,856,815,377,272,162.0,1182,845,245,730,2351,4197,420,1153,1802,770,1193,740,1152,1549,1481,1563,1175,1539,1621,472,356,363,1257,510,570,1211,1227,142,258,642,1219,911,1366,1743,694,279,895,884,609,300,1260,582,1510,227,1924,554,1207,617,698,439,657,153,296,1233,454,1589,996,844,359,505,843,3112,258,2.974,3.143,2.942,2.578,2.489,2.481,2.687,2.937,2.944,3.421,2.652,1.992,2.856,3.158,2.835,3.086,3.279,3.898,2.937,2.142,3.009,2.274,2.189,2.868,2.817,2.829,2.485,2.223,2.874,2.664,2.826,2.496,2.38,3.306,3.158,2.445,2.873,3.211,2.353,2.469,2.956,1.914,3.217,2.082,1.861,2.314,3.145,3.003,2.779,2.491,2.101,2.302,2.224,2.674,2.483,2.186,2.553,2.755,2.172,2.219,2.337,2.479,2.003,2.398,2.391,2.448,2.278,2.747,2.625,2.281,2.865,2.688,2.721,1.927,1.225,0.702,0.718,0.502,0.64,0.58,0.542,0.531,0.618,0.532,0.742,0.443,0.498,0.563,0.522,0.639,0.585,0.838,0.618,0.626,0.803,0.515,0.434,0.708,0.468,0.532,0.486,0.569,0.585,0.524,0.741,0.977,0.402,0.641,0.822,0.38,0.707,0.595,0.292,0.369,0.612,0.43,0.673,0.694,0.641,0.521,0.617,0.855,0.542,0.48,0.442,0.508,0.542,0.545,0.36,0.467,0.543,0.754,0.449,0.444,0.354,0.58,0.274,1.008,0.569,0.637,0.471,0.438,0.477,0.446,0.523,0.485,0.587,0.399
-394,Epileptic,F,22.0,0.7,2.7,0.8,1.0,1.2,2.4,1.3,2.0,0.9,0.7,0.4,2.9,1.7,0.6,1.4,5.3,9.7,0.9,2.8,5.3,1.1,3.5,1.4,2.9,3.2,3.1,2.5,1.8,2.4,2.2,1.5,1.0,0.4,1.6,0.3,0.4,4.0,2.4,0.2,0.1,1.2,2.8,2.4,3.6,2.4,0.5,0.4,1.0,1.3,0.7,0.8,1.7,1.1,1.9,0.4,1.5,1.0,1.9,1.2,1.0,0.6,1.0,0.5,0.4,2.0,1.2,2.3,1.8,0.8,0.5,0.6,1.2,3.4,0.2,6,19,7,8,10,19,12,15,9,8,4.0,23,15,5,12,51,78,6,22,40,8,31,15,23,29,29,24,18,16,24,13,9,3,16,2,2,40,22,2,1,8,26,19,28,17,4,2,6,7,6,7,13,7,12,1,13,9,14,8,8,5,7,2,3,19,11,21,13,6,6,5,9,29,2,0.031,0.027,0.015,0.023,0.034,0.035,0.02,0.031,0.045,0.04,0.032,0.036,0.029,0.036,0.036,0.034,0.035,0.028,0.039,0.041,0.023,0.034,0.023,0.042,0.038,0.031,0.023,0.021,0.026,0.03,0.04,0.049,0.016,0.024,0.011,0.011,0.039,0.031,0.033,0.016,0.023,0.035,0.04,0.034,0.018,0.01,0.023,0.015,0.017,0.017,0.049,0.019,0.024,0.018,0.048,0.015,0.024,0.027,0.028,0.02,0.024,0.025,0.034,0.018,0.022,0.033,0.02,0.022,0.016,0.021,0.02,0.025,0.017,0.013,1219,3960,1607,1664,1693,2855,1917,2788,1123,913,629.0,2360,2998,907,2914,8371,13877,2033,3603,4142,3218,4682,1502,4613,4160,4587,3879,2745,3888,3506,1475,632,637,3659,1446,1157,6325,4672,304,387,1446,2329,5730,2320,2573,1166,691,2220,2203,1417,434,2617,1335,3623,302,2551,1485,2589,1179,1382,812,1951,473,724,2449,1284,2930,2500,1192,737,884,2103,6409,291,0.118,0.111,0.092,0.112,0.132,0.14,0.099,0.133,0.136,0.146,0.119,0.141,0.125,0.139,0.129,0.126,0.123,0.113,0.148,0.15,0.087,0.14,0.127,0.141,0.148,0.137,0.112,0.102,0.105,0.13,0.135,0.152,0.085,0.108,0.065,0.065,0.133,0.123,0.127,0.089,0.118,0.142,0.141,0.145,0.098,0.086,0.108,0.088,0.094,0.098,0.181,0.1,0.116,0.089,0.164,0.093,0.126,0.122,0.128,0.105,0.123,0.12,0.144,0.1,0.129,0.119,0.112,0.115,0.099,0.113,0.128,0.114,0.1,0.095,404,1547,764,739,614,1156,921,1041,336,336,205.0,1248,878,296,722,2640,4269,445,1215,1975,823,1561,871,1129,1354,1555,1623,1265,1352,1237,594,334,332,1051,465,544,1793,1323,125,174,763,1271,1026,1688,2011,660,302,1015,1114,638,323,1377,736,1729,105,1561,641,1246,713,786,419,625,224,378,1328,617,1844,1221,705,443,503,979,3145,213,2.993,2.397,2.14,2.258,2.353,2.247,1.858,2.457,2.519,2.462,2.482,1.826,2.644,2.389,2.774,2.396,2.594,3.458,2.473,1.844,3.071,2.396,1.688,2.965,2.473,2.555,2.033,1.824,2.278,2.353,2.02,2.084,1.676,2.554,2.734,2.057,2.738,2.66,2.698,2.361,2.277,1.753,3.472,1.601,1.465,1.969,2.878,2.718,2.355,2.41,1.745,1.924,1.92,2.194,3.379,1.821,2.466,2.322,1.926,2.012,2.191,2.836,2.301,2.21,2.039,2.346,1.777,2.356,1.948,2.094,2.038,2.342,2.217,1.752,0.787,0.629,0.47,0.491,0.737,0.544,0.449,0.518,0.736,0.38,0.604,0.383,0.522,0.65,0.813,0.669,0.737,1.052,0.583,0.606,0.829,0.412,0.357,0.774,0.512,0.522,0.48,0.457,0.55,0.404,0.697,1.053,0.421,0.788,1.056,0.538,0.855,0.648,0.477,0.415,0.544,0.375,0.851,0.391,0.34,0.519,0.482,0.992,0.547,0.662,0.325,0.548,0.525,0.58,0.389,0.444,0.546,0.765,0.395,0.37,0.346,0.831,0.838,0.931,0.432,0.647,0.511,0.536,0.529,0.744,0.489,0.592,0.627,0.422
-395,Epileptic,F,27.0,1.0,2.1,0.8,0.8,2.1,2.1,1.5,1.4,1.2,0.8,0.3,3.6,1.2,0.5,1.3,5.0,8.7,0.5,1.6,4.6,1.4,2.8,3.1,2.9,3.6,3.6,2.8,2.2,2.8,3.5,1.1,1.8,0.6,2.3,0.5,0.8,3.4,2.9,0.3,0.2,1.1,2.7,1.8,1.9,2.2,0.8,0.4,1.0,1.3,0.7,0.6,1.8,1.5,2.3,0.4,2.9,0.6,1.6,1.0,1.5,0.4,1.4,0.7,0.5,2.1,1.0,2.7,1.5,1.1,0.4,1.4,1.4,5.3,0.4,7,28,9,9,19,18,13,16,15,8,3.0,35,15,7,14,62,92,6,24,41,23,40,33,38,49,42,38,22,26,41,13,12,6,28,4,6,40,40,2,1,6,27,21,13,12,7,3,6,7,7,5,12,9,16,2,22,4,18,7,13,4,13,5,5,18,18,18,11,8,4,9,10,47,3,0.039,0.024,0.014,0.017,0.04,0.033,0.028,0.025,0.043,0.044,0.039,0.045,0.031,0.031,0.027,0.031,0.03,0.024,0.035,0.045,0.025,0.037,0.041,0.035,0.034,0.028,0.032,0.027,0.031,0.032,0.035,0.06,0.026,0.027,0.016,0.016,0.038,0.03,0.019,0.017,0.022,0.035,0.032,0.022,0.018,0.016,0.019,0.015,0.018,0.014,0.029,0.023,0.029,0.021,0.019,0.02,0.017,0.022,0.027,0.022,0.02,0.026,0.034,0.021,0.021,0.02,0.022,0.021,0.022,0.014,0.031,0.02,0.02,0.018,1567,3948,2158,2285,2256,2833,2767,3102,1623,1465,578.0,3455,2921,1130,2968,9770,17622,1813,3584,4343,4419,4324,2491,5482,6538,7818,5563,3985,5706,5811,1978,1546,1155,6689,1784,2449,7008,7746,413,458,1527,2877,5147,2617,3179,1429,762,2196,2282,1723,584,2361,1356,3561,584,3703,1181,3126,985,2096,697,2154,432,838,2935,1174,3647,2412,1677,602,1464,2330,9898,697,0.138,0.122,0.102,0.1,0.159,0.148,0.125,0.13,0.165,0.141,0.124,0.158,0.144,0.152,0.131,0.139,0.135,0.119,0.153,0.161,0.111,0.138,0.139,0.136,0.139,0.133,0.134,0.108,0.117,0.144,0.143,0.123,0.125,0.126,0.086,0.086,0.133,0.134,0.114,0.1,0.104,0.143,0.136,0.11,0.092,0.102,0.103,0.088,0.095,0.097,0.134,0.113,0.129,0.1,0.096,0.111,0.101,0.106,0.122,0.115,0.117,0.125,0.161,0.121,0.115,0.114,0.106,0.109,0.108,0.115,0.135,0.104,0.108,0.113,467,1579,877,810,891,998,924,969,496,371,158.0,1363,840,288,780,3000,5288,462,946,1883,1059,1382,1288,1570,2019,2401,1709,1439,1515,1884,612,546,422,1472,457,818,1796,1947,212,238,807,1226,1142,1361,1845,743,329,951,1094,790,333,1234,821,1747,332,2182,542,1435,604,1122,365,986,266,439,1507,795,1923,1153,836,393,695,1191,4287,352,3.418,2.324,2.41,2.514,2.045,2.515,2.395,2.724,2.462,2.906,2.867,2.093,2.737,2.854,2.778,2.449,2.592,3.221,2.916,2.019,2.994,2.412,1.848,2.586,2.509,2.6,2.458,2.204,2.782,2.409,2.36,2.835,2.226,3.277,3.5,2.725,2.806,2.926,2.115,2.167,2.41,1.951,3.237,2.216,1.972,2.163,2.835,2.864,2.519,2.562,2.033,1.998,1.802,2.121,1.996,1.922,2.587,2.408,1.943,2.043,2.31,2.266,1.912,2.244,2.112,1.85,2.103,2.294,2.219,1.682,2.339,2.428,2.411,2.53,0.753,0.568,0.521,0.472,0.667,0.585,0.537,0.422,0.667,0.601,0.717,0.674,0.507,0.382,0.555,0.667,0.712,0.615,0.436,0.667,0.736,0.544,0.491,0.774,0.599,0.525,0.604,0.564,0.534,0.551,0.678,0.886,0.46,0.485,0.579,0.45,0.697,0.543,0.474,0.461,0.497,0.577,0.688,0.632,0.546,0.563,0.496,0.603,0.414,0.431,0.481,0.388,0.427,0.487,0.417,0.391,0.324,0.487,0.313,0.449,0.342,0.615,0.395,0.628,0.451,0.585,0.41,0.428,0.464,0.438,0.415,0.469,0.442,0.503
-396,Epileptic,F,22.0,0.6,2.5,0.9,1.4,1.5,1.9,1.3,1.7,1.2,0.6,0.4,3.5,1.8,0.6,1.4,5.3,9.8,0.7,1.5,4.5,1.4,3.0,1.3,2.9,3.1,4.1,3.6,2.3,2.4,3.1,1.0,1.8,0.5,2.6,0.3,0.3,2.9,3.5,0.2,0.3,1.1,3.7,2.3,3.1,2.8,0.7,0.4,1.0,1.1,0.7,0.7,1.4,1.4,2.5,0.1,2.8,0.4,2.1,1.1,1.2,0.5,1.1,0.2,0.5,1.6,1.1,2.7,1.0,1.3,0.4,1.0,1.4,5.0,0.4,5,25,9,13,22,16,11,16,12,6,2.0,26,18,6,17,67,91,6,18,34,12,31,15,44,39,43,48,24,20,35,16,12,4,29,3,2,38,47,1,2,6,27,22,27,16,5,2,7,6,6,4,9,11,18,2,22,3,18,10,10,4,9,1,4,11,15,23,8,8,4,7,10,43,3,0.028,0.028,0.016,0.021,0.034,0.031,0.021,0.027,0.037,0.026,0.035,0.04,0.03,0.034,0.029,0.035,0.029,0.025,0.027,0.038,0.024,0.033,0.027,0.037,0.032,0.033,0.03,0.022,0.021,0.03,0.033,0.049,0.022,0.031,0.012,0.008,0.035,0.032,0.014,0.014,0.02,0.04,0.034,0.034,0.022,0.013,0.017,0.019,0.018,0.014,0.027,0.016,0.024,0.019,0.016,0.017,0.016,0.024,0.027,0.024,0.023,0.021,0.031,0.018,0.019,0.023,0.018,0.015,0.018,0.013,0.025,0.021,0.019,0.017,1507,5578,2447,2834,2389,3070,3027,3669,2121,1375,670.0,2933,3937,1229,3466,11019,21677,2866,4129,3542,3843,4936,2403,5585,6192,8329,7324,4255,6492,5995,2159,1525,1200,6179,1925,1583,6357,8712,363,946,1631,3159,5570,2389,3225,1694,801,2111,2479,1735,621,2986,2044,5559,572,4425,1168,3601,1428,1640,815,2198,290,906,2884,1305,4445,2536,2466,837,1519,2149,9776,507,0.107,0.116,0.097,0.114,0.133,0.139,0.109,0.124,0.139,0.118,0.104,0.15,0.136,0.14,0.132,0.138,0.131,0.107,0.123,0.14,0.103,0.145,0.135,0.136,0.139,0.145,0.135,0.112,0.096,0.137,0.119,0.128,0.099,0.13,0.072,0.058,0.134,0.13,0.083,0.085,0.107,0.144,0.134,0.135,0.102,0.086,0.098,0.09,0.094,0.093,0.127,0.091,0.122,0.106,0.104,0.106,0.097,0.115,0.132,0.122,0.121,0.102,0.152,0.118,0.103,0.118,0.103,0.095,0.103,0.109,0.122,0.115,0.098,0.114,401,1602,881,1013,875,1038,960,1069,620,353,159.0,1365,1074,310,887,2753,5634,428,1008,1947,941,1490,833,1466,1755,2271,2253,1555,1623,1863,524,578,363,1296,514,609,1603,2003,212,367,729,1439,1133,1553,1975,809,330,871,1029,735,372,1346,891,2079,175,2373,473,1531,709,823,396,848,102,434,1263,764,2246,1041,1045,414,624,1043,4202,299,3.598,3.021,2.631,2.628,2.362,2.64,2.5,3.015,2.469,3.04,3.236,1.886,2.896,2.722,2.79,2.896,3.112,4.07,3.126,1.688,3.042,2.557,2.351,2.796,2.659,2.956,2.484,2.2,3.033,2.669,2.742,2.69,2.684,3.157,3.211,2.391,2.966,3.025,2.069,2.69,2.896,2.019,3.424,1.797,1.921,2.305,3.05,2.824,2.859,2.686,2.178,2.326,2.325,2.677,2.765,2.135,2.747,2.523,2.201,2.191,2.416,2.507,2.596,2.191,2.482,2.106,2.149,2.669,2.901,2.149,2.866,2.594,2.457,2.162,0.892,0.682,0.569,0.595,0.677,0.556,0.595,0.63,0.652,0.383,0.757,0.546,0.629,0.493,0.556,0.596,0.587,0.745,0.565,0.457,0.866,0.531,0.5,0.777,0.52,0.515,0.51,0.465,0.553,0.491,0.833,0.892,0.499,0.718,0.691,0.489,0.599,0.78,0.259,0.705,0.577,0.531,0.628,0.67,0.638,0.479,0.509,0.771,0.655,0.478,0.321,0.616,0.627,0.515,0.441,0.392,0.411,0.563,0.337,0.389,0.406,0.773,0.493,1.178,0.596,0.783,0.411,0.393,0.408,0.76,0.583,0.467,0.513,0.609
-397,Epileptic,F,47.0,0.8,1.9,1.0,1.4,1.9,2.5,1.4,1.9,0.7,0.5,0.3,2.9,1.1,0.6,1.7,7.4,10.1,0.9,2.2,4.1,0.8,3.0,1.5,3.1,2.7,4.4,4.3,2.3,2.5,4.0,1.0,1.2,0.3,2.7,0.3,0.6,3.5,3.8,0.1,0.1,1.4,3.3,1.9,2.7,3.0,1.0,0.5,0.8,1.1,1.1,0.7,1.4,1.2,3.5,0.4,2.6,0.8,2.0,1.3,1.1,0.6,1.3,0.3,0.4,2.1,1.0,2.8,1.7,1.2,0.6,1.0,1.8,4.6,0.5,5,18,8,13,16,22,14,15,12,6,3.0,28,11,7,18,79,85,8,26,36,7,37,16,34,34,45,44,24,21,40,14,10,4,25,2,8,42,41,1,1,7,30,21,20,19,7,4,5,6,8,4,14,8,27,3,25,8,15,10,10,4,9,4,4,17,13,20,15,6,6,7,14,39,3,0.046,0.021,0.017,0.03,0.041,0.034,0.024,0.031,0.036,0.03,0.033,0.03,0.025,0.034,0.039,0.035,0.028,0.036,0.029,0.033,0.016,0.033,0.029,0.038,0.035,0.033,0.031,0.023,0.025,0.034,0.031,0.063,0.016,0.033,0.011,0.014,0.031,0.032,0.014,0.016,0.025,0.034,0.032,0.034,0.024,0.017,0.025,0.014,0.017,0.019,0.029,0.021,0.027,0.027,0.014,0.021,0.022,0.025,0.024,0.024,0.02,0.025,0.026,0.018,0.025,0.028,0.024,0.021,0.019,0.016,0.025,0.028,0.02,0.021,1182,3549,1970,2201,2162,3212,2190,3054,1143,1155,526.0,2722,2456,909,2439,10860,18335,1906,4651,4176,2664,4231,1970,5155,4392,6822,6096,3489,5028,5245,1803,940,844,4906,1654,2016,5856,6238,196,365,1588,2952,3915,2234,3072,1504,740,1907,2004,1554,586,2365,1532,5175,577,3384,1322,3427,1315,1140,763,2070,611,814,2640,912,3307,2806,1731,1046,1349,1805,7664,630,0.123,0.11,0.106,0.127,0.157,0.149,0.115,0.129,0.135,0.139,0.122,0.133,0.127,0.146,0.165,0.15,0.13,0.13,0.135,0.138,0.083,0.153,0.133,0.154,0.15,0.151,0.138,0.112,0.104,0.144,0.126,0.158,0.109,0.129,0.066,0.089,0.126,0.13,0.112,0.102,0.117,0.147,0.131,0.138,0.113,0.105,0.125,0.087,0.092,0.112,0.127,0.103,0.126,0.12,0.11,0.121,0.111,0.114,0.125,0.129,0.106,0.116,0.135,0.104,0.119,0.122,0.122,0.119,0.106,0.116,0.123,0.121,0.108,0.133,339,1446,865,893,769,1327,818,1000,402,350,177.0,1362,788,328,777,3385,5578,434,1300,1939,812,1515,863,1382,1514,2294,2347,1426,1646,1938,576,383,345,1325,484,739,1843,1884,121,196,754,1530,1067,1380,1981,747,334,777,992,794,351,1249,742,2287,364,1953,670,1474,777,712,436,793,250,413,1383,544,1768,1286,851,574,653,974,3598,296,3.172,2.447,2.306,2.516,2.448,2.296,2.283,2.77,2.331,2.836,2.473,1.823,2.554,2.307,2.435,2.482,2.711,3.317,2.917,1.93,2.705,2.308,2.007,2.833,2.331,2.539,2.164,2.016,2.459,2.259,2.38,2.481,2.105,2.699,3.016,2.6,2.625,2.547,1.8,2.241,2.672,1.815,2.869,1.829,1.773,2.208,2.818,2.922,2.419,2.304,2.108,2.054,2.206,2.413,2.033,1.944,2.202,2.541,2.054,1.808,2.042,2.544,2.467,2.461,2.202,1.888,2.129,2.495,2.446,2.134,2.389,2.216,2.332,2.796,1.061,0.448,0.569,0.457,0.786,0.403,0.508,0.566,0.441,0.381,0.75,0.382,0.373,0.381,0.469,0.487,0.532,0.766,0.611,0.549,0.77,0.42,0.344,0.562,0.483,0.505,0.471,0.432,0.466,0.495,0.543,0.83,0.318,0.527,0.746,0.675,0.553,0.484,0.388,0.573,0.638,0.536,0.679,0.69,0.491,0.606,0.423,0.87,0.493,0.481,0.338,0.447,0.582,0.467,0.255,0.436,0.384,0.649,0.386,0.328,0.468,0.784,0.502,0.84,0.637,0.661,0.46,0.414,0.409,0.462,0.558,0.61,0.486,0.548
-399,Epileptic,F,37.0,0.6,2.2,1.3,1.1,1.5,1.4,1.4,1.5,1.4,0.8,0.3,2.3,1.2,0.5,0.7,5.1,7.1,0.8,2.1,4.5,0.9,2.4,1.9,2.7,2.4,3.9,3.4,2.0,2.1,3.1,1.2,1.5,0.5,1.8,0.3,0.7,3.2,2.2,0.1,0.0,1.2,3.0,1.8,2.0,2.0,0.7,0.4,0.7,1.3,0.9,0.5,1.4,2.0,2.2,0.4,2.3,0.6,1.9,1.2,1.3,0.6,1.7,0.3,0.4,1.6,1.4,2.1,0.8,1.4,0.4,1.2,1.3,3.2,0.3,4,20,12,8,20,16,11,16,15,7,4.0,26,13,8,9,57,79,5,28,41,7,29,18,35,30,47,42,21,21,41,14,8,5,25,2,7,41,36,1,0,7,23,26,20,13,6,2,5,7,10,4,12,17,22,2,19,4,17,10,11,4,13,2,4,12,21,15,7,8,4,9,9,27,2,0.031,0.024,0.024,0.023,0.029,0.028,0.025,0.029,0.037,0.037,0.031,0.033,0.021,0.033,0.022,0.025,0.028,0.03,0.028,0.037,0.019,0.035,0.034,0.032,0.031,0.029,0.028,0.029,0.024,0.027,0.036,0.051,0.023,0.028,0.015,0.018,0.034,0.025,0.009,0.015,0.022,0.033,0.031,0.023,0.018,0.018,0.018,0.012,0.018,0.024,0.03,0.015,0.027,0.017,0.027,0.017,0.021,0.021,0.025,0.021,0.022,0.032,0.023,0.018,0.017,0.023,0.017,0.018,0.019,0.017,0.027,0.021,0.018,0.02,1414,4241,2439,2328,2099,2914,2589,2848,1987,1519,612.0,2385,3115,1077,1579,10809,15652,2058,5135,4512,3648,3246,1906,5732,4745,8180,7003,2829,5405,6279,2235,921,1157,5171,1406,1880,7565,6217,439,130,1558,2539,5647,2081,3129,1259,811,1972,2344,1732,534,2611,2463,5271,352,3749,967,3196,1011,1706,701,2306,346,801,2847,1283,3793,1757,2098,608,1648,1985,6731,369,0.113,0.12,0.115,0.109,0.136,0.126,0.111,0.137,0.155,0.143,0.136,0.154,0.113,0.138,0.114,0.123,0.129,0.109,0.135,0.145,0.09,0.117,0.119,0.144,0.122,0.133,0.122,0.104,0.099,0.13,0.136,0.143,0.114,0.133,0.088,0.093,0.143,0.124,0.074,0.08,0.11,0.126,0.144,0.119,0.096,0.101,0.105,0.082,0.097,0.123,0.14,0.1,0.126,0.098,0.128,0.1,0.106,0.114,0.126,0.11,0.118,0.136,0.12,0.123,0.1,0.125,0.095,0.102,0.103,0.117,0.133,0.113,0.097,0.13,335,1485,887,770,844,867,809,903,578,354,187.0,1109,845,296,536,3323,4264,399,1180,2102,769,1006,841,1478,1273,2319,2231,1118,1320,1938,577,465,343,1134,304,669,1700,1459,206,60,841,1347,1173,1376,1705,584,347,825,1120,712,296,1397,1170,2052,194,2071,460,1503,699,939,372,886,219,377,1438,869,1957,786,1062,368,750,999,3004,202,3.772,2.716,2.771,2.682,2.123,2.676,2.518,2.668,2.576,3.144,2.617,1.849,2.899,2.709,2.347,2.531,2.835,3.812,3.249,1.883,3.268,2.438,2.104,2.973,2.723,2.728,2.406,1.997,2.934,2.524,2.878,2.306,2.787,3.087,3.641,2.511,3.158,2.888,2.255,2.385,2.201,1.702,3.438,1.683,2.069,2.253,2.973,2.869,2.515,2.78,1.994,2.084,2.055,2.445,2.229,2.02,2.364,2.436,1.704,1.964,2.368,2.482,1.773,2.293,2.159,1.843,2.083,2.388,2.44,2.08,2.395,2.309,2.405,2.213,0.674,0.572,0.579,0.609,0.732,0.768,0.498,0.54,0.522,0.581,0.905,0.543,0.505,0.436,0.491,0.584,0.58,0.667,0.722,0.683,0.735,0.575,0.58,0.619,0.659,0.675,0.583,0.566,0.561,0.619,0.557,0.771,0.6,0.65,0.632,0.502,0.689,0.682,0.227,0.442,0.423,0.597,0.737,0.556,0.688,0.575,0.405,0.708,0.535,0.542,0.392,0.377,0.475,0.438,0.39,0.439,0.567,0.637,0.285,0.422,0.372,0.578,0.396,0.54,0.536,0.796,0.467,0.348,0.389,0.444,0.51,0.464,0.456,0.391
-400,Epileptic,F,42.0,0.6,1.5,0.7,1.0,0.9,1.8,1.3,1.3,0.8,0.7,0.2,3.2,1.2,0.4,0.9,3.2,7.5,0.5,1.4,3.1,0.6,2.6,2.1,2.3,2.5,2.9,2.3,2.6,2.3,2.1,0.7,0.8,0.4,1.8,0.3,0.7,2.5,2.6,0.1,0.1,0.8,2.0,1.8,2.8,2.6,0.5,0.4,0.7,0.9,0.4,0.6,1.4,1.1,1.4,0.0,2.3,0.4,1.1,0.7,1.3,0.5,0.8,0.2,0.5,1.7,1.1,2.4,1.1,0.6,0.4,0.7,1.0,4.1,0.3,4,12,6,6,12,14,11,11,9,7,3.0,25,11,4,9,33,65,5,16,27,5,23,18,25,26,29,22,24,16,19,13,6,4,23,1,6,24,34,1,1,4,19,18,16,17,4,2,5,6,7,6,12,12,12,0,19,4,9,6,10,5,6,1,4,12,13,15,9,4,5,5,8,35,3,0.034,0.026,0.016,0.024,0.034,0.032,0.026,0.025,0.036,0.034,0.029,0.033,0.029,0.035,0.034,0.027,0.033,0.027,0.024,0.031,0.015,0.032,0.036,0.036,0.03,0.033,0.028,0.027,0.024,0.028,0.031,0.054,0.022,0.026,0.015,0.018,0.033,0.034,0.018,0.016,0.019,0.029,0.035,0.03,0.02,0.016,0.022,0.012,0.016,0.011,0.044,0.025,0.03,0.016,0.011,0.02,0.015,0.016,0.023,0.027,0.031,0.023,0.023,0.028,0.02,0.048,0.021,0.019,0.014,0.021,0.027,0.019,0.019,0.02,1098,2842,1430,1688,1527,2145,1832,2593,1696,1179,472.0,2519,2224,988,1698,6451,13280,1679,2957,3171,3004,3831,2175,4472,4714,5288,3521,3992,4534,3552,1330,782,878,4354,1384,1368,4881,5502,227,243,1249,2460,4452,1805,3107,1023,737,1940,1836,1167,312,2238,1721,3239,122,3208,851,1988,787,1160,1098,1499,243,686,2251,733,3398,2087,1245,810,875,2159,7813,371,0.13,0.113,0.104,0.102,0.132,0.142,0.108,0.117,0.132,0.139,0.11,0.139,0.123,0.12,0.139,0.119,0.125,0.104,0.119,0.135,0.081,0.131,0.138,0.138,0.13,0.136,0.116,0.116,0.104,0.122,0.128,0.13,0.111,0.125,0.076,0.098,0.131,0.138,0.083,0.105,0.102,0.131,0.15,0.121,0.107,0.095,0.102,0.087,0.095,0.108,0.175,0.113,0.123,0.088,0.076,0.106,0.102,0.101,0.113,0.128,0.12,0.118,0.126,0.13,0.105,0.144,0.107,0.107,0.091,0.125,0.129,0.114,0.103,0.126,305,1001,603,651,538,907,759,832,398,325,157.0,1300,662,231,455,1926,3788,345,927,1492,647,1306,926,1111,1449,1574,1301,1476,1351,1264,435,299,311,1106,367,574,1262,1420,134,135,639,1120,913,1323,1991,535,288,858,883,533,243,961,705,1511,55,1757,459,1093,543,769,398,595,115,318,1258,410,1808,938,649,364,465,798,3482,216,3.399,2.648,2.25,2.595,2.386,2.273,2.142,2.701,2.772,2.987,2.543,1.801,2.612,2.808,2.625,2.512,2.728,3.409,2.494,1.793,3.182,2.333,1.979,2.862,2.481,2.689,2.211,2.187,2.584,2.288,2.404,2.519,2.285,2.812,3.18,2.253,3.007,2.8,1.931,1.97,2.538,1.884,3.194,1.56,1.801,2.083,2.952,2.753,2.479,2.373,1.767,2.312,2.389,2.287,2.097,1.87,2.033,2.119,1.674,1.745,2.413,2.649,2.173,2.266,2.046,2.059,2.135,2.474,2.239,2.597,2.255,2.943,2.384,2.089,0.675,0.608,0.602,0.64,0.627,0.627,0.561,0.583,0.699,0.516,0.654,0.572,0.453,0.7,0.595,0.557,0.682,0.847,0.559,0.611,0.747,0.565,0.668,0.798,0.608,0.59,0.565,0.523,0.564,0.566,0.703,0.972,0.362,0.636,1.118,0.467,0.709,0.665,0.293,0.409,0.509,0.734,0.782,0.644,0.524,0.563,0.618,0.846,0.521,0.439,0.419,0.519,0.598,0.529,0.213,0.513,0.404,0.401,0.296,0.315,0.621,0.656,0.694,0.944,0.616,0.714,0.564,0.516,0.447,0.709,0.505,0.737,0.45,0.647
-401,Epileptic,M,42.0,0.5,2.1,0.6,1.2,1.2,2.1,1.6,1.4,1.2,0.9,0.3,2.6,1.4,0.6,1.2,4.4,9.2,0.7,1.6,4.8,1.0,2.5,2.3,2.6,2.5,2.3,2.4,2.3,2.9,2.4,1.6,1.2,0.4,1.6,0.3,0.4,2.6,2.2,0.2,0.1,0.6,2.5,1.2,3.1,2.5,0.6,0.5,0.8,1.1,0.5,0.9,1.5,1.1,2.3,0.1,2.5,0.4,1.5,1.0,0.8,1.3,1.4,0.2,0.5,0.9,1.3,2.9,1.0,1.1,0.4,1.0,1.3,4.8,0.3,5,19,5,9,13,18,11,12,11,8,3.0,24,15,7,16,43,82,6,20,42,7,28,19,32,28,22,30,20,19,29,14,7,4,18,2,5,27,23,1,1,4,17,15,20,16,4,2,5,6,5,7,11,8,17,1,23,5,12,7,7,10,10,1,4,8,15,22,7,7,5,7,10,34,2,0.025,0.024,0.012,0.022,0.029,0.03,0.025,0.028,0.039,0.039,0.025,0.033,0.029,0.032,0.028,0.031,0.03,0.03,0.026,0.033,0.017,0.03,0.032,0.034,0.023,0.027,0.022,0.025,0.028,0.027,0.039,0.041,0.034,0.025,0.012,0.015,0.033,0.029,0.023,0.014,0.017,0.033,0.029,0.03,0.021,0.015,0.02,0.014,0.015,0.016,0.03,0.017,0.025,0.022,0.021,0.018,0.014,0.018,0.022,0.017,0.027,0.026,0.015,0.02,0.015,0.025,0.022,0.017,0.02,0.017,0.023,0.018,0.018,0.023,1574,4885,2283,2245,1863,3169,2707,2527,1567,1362,654.0,2579,3365,1104,2808,9198,19201,2817,4015,5360,3272,4346,2021,5748,5895,4953,5342,3556,5172,5334,2198,1330,647,5061,1537,1258,6917,6718,304,412,1145,2015,4525,2688,3300,1346,908,1961,2484,1126,731,2860,1460,4537,278,4004,1130,3220,1244,1412,1957,2488,425,903,1992,1328,4135,2188,1770,1058,1514,2394,10294,355,0.106,0.112,0.084,0.108,0.132,0.139,0.092,0.129,0.148,0.152,0.116,0.138,0.122,0.145,0.131,0.137,0.127,0.114,0.118,0.133,0.081,0.135,0.136,0.128,0.117,0.123,0.115,0.101,0.1,0.125,0.118,0.11,0.138,0.112,0.062,0.08,0.13,0.124,0.121,0.105,0.097,0.123,0.128,0.125,0.106,0.093,0.107,0.087,0.088,0.099,0.145,0.097,0.12,0.108,0.134,0.109,0.095,0.1,0.114,0.1,0.124,0.113,0.087,0.105,0.098,0.119,0.105,0.101,0.108,0.106,0.116,0.106,0.097,0.123,379,1412,751,880,715,1083,883,796,504,370,194.0,1205,855,298,764,2419,4915,439,999,2255,787,1357,974,1430,1731,1370,1731,1358,1422,1614,598,490,216,1069,411,450,1388,1292,154,187,568,1115,840,1595,2001,714,360,805,1104,460,451,1369,683,1756,101,2066,524,1419,692,785,755,851,222,414,1007,838,2132,915,789,413,719,1027,3970,164,3.832,2.983,2.741,2.642,2.365,2.452,2.533,2.777,2.491,3.077,2.845,1.909,2.938,2.735,2.85,2.939,3.109,4.363,3.119,2.043,3.137,2.512,1.933,3.016,2.61,2.949,2.356,2.123,2.812,2.622,2.748,2.668,2.544,3.332,3.409,2.334,3.297,3.455,2.197,2.542,2.564,1.748,3.836,1.863,1.909,2.141,3.182,3.198,2.742,2.808,2.074,2.368,2.256,2.644,2.924,2.123,2.525,2.601,2.177,2.064,2.605,2.835,2.038,2.589,2.134,2.022,2.196,2.727,2.408,2.661,2.531,2.758,2.718,2.761,0.735,0.687,0.648,0.467,0.566,0.545,0.671,0.565,0.398,0.629,0.8,0.453,0.528,0.427,0.447,0.562,0.616,0.542,0.5,0.666,0.617,0.495,0.469,0.579,0.536,0.617,0.558,0.463,0.577,0.561,0.719,0.991,0.529,0.693,0.724,0.476,0.743,0.666,0.295,0.386,0.619,0.489,0.808,0.514,0.596,0.564,0.641,0.842,0.545,0.574,0.518,0.439,0.416,0.493,0.307,0.445,0.433,0.508,0.465,0.385,0.473,0.84,0.314,0.756,0.473,0.674,0.561,0.462,0.562,0.872,0.354,0.619,0.6,0.553
-402,Epileptic,F,22.0,1.1,2.0,0.7,0.9,1.6,1.8,1.7,1.9,1.4,1.1,0.2,3.3,1.5,0.4,1.5,6.9,11.3,1.1,1.9,4.1,1.2,2.6,2.0,3.4,2.6,4.5,3.9,2.4,3.8,2.9,1.7,1.3,0.6,2.7,0.5,0.5,2.8,3.1,0.3,0.2,1.3,3.2,2.7,2.5,2.8,0.8,0.4,1.1,1.1,0.6,1.0,1.9,2.0,2.9,0.2,2.6,0.6,1.3,1.5,0.9,0.6,1.4,0.5,0.5,1.4,1.6,3.1,1.9,1.3,0.4,1.0,1.0,4.5,0.3,10,16,8,7,12,21,11,15,14,8,3.0,28,12,4,15,59,66,8,23,44,12,33,19,30,25,45,36,18,28,36,15,10,5,27,3,5,27,26,3,1,8,31,20,22,17,5,2,7,6,5,6,12,14,18,1,21,5,12,11,10,4,9,4,5,10,16,20,9,9,3,8,7,48,3,0.04,0.023,0.017,0.018,0.032,0.026,0.028,0.029,0.053,0.037,0.021,0.037,0.026,0.029,0.027,0.038,0.038,0.037,0.025,0.031,0.023,0.033,0.03,0.04,0.033,0.035,0.026,0.033,0.031,0.03,0.05,0.055,0.024,0.033,0.017,0.014,0.035,0.03,0.025,0.015,0.022,0.03,0.041,0.023,0.022,0.015,0.018,0.017,0.015,0.011,0.032,0.021,0.03,0.026,0.019,0.016,0.024,0.015,0.034,0.023,0.019,0.023,0.035,0.017,0.023,0.029,0.026,0.026,0.025,0.013,0.025,0.02,0.021,0.013,1694,4056,1872,1973,2098,3298,2212,3232,1983,1744,554.0,3389,3951,916,3268,11787,17036,2303,4136,5586,4331,4199,2528,4645,4447,8310,6594,3102,5513,5689,1613,1171,1345,4740,1475,1625,5380,6942,425,598,1857,3020,6149,2837,3127,1360,742,2046,2560,1481,807,3477,2670,4505,312,4607,1066,3482,1461,1337,921,2505,586,1091,2108,1334,3820,2558,1828,1006,1494,1816,8882,699,0.128,0.113,0.113,0.096,0.127,0.134,0.104,0.123,0.157,0.15,0.114,0.147,0.107,0.109,0.125,0.137,0.132,0.129,0.12,0.145,0.091,0.14,0.138,0.129,0.133,0.133,0.123,0.109,0.112,0.128,0.129,0.134,0.104,0.137,0.076,0.09,0.136,0.124,0.136,0.083,0.114,0.137,0.154,0.119,0.105,0.096,0.099,0.096,0.089,0.082,0.138,0.103,0.123,0.112,0.111,0.101,0.113,0.097,0.137,0.12,0.117,0.105,0.126,0.108,0.114,0.141,0.113,0.108,0.117,0.094,0.119,0.114,0.112,0.091,491,1398,689,772,827,1254,844,1080,586,461,182.0,1406,990,267,894,3268,4671,513,1300,2145,993,1395,1067,1469,1334,2354,2345,1171,1717,1754,607,423,423,1415,462,606,1288,1675,198,269,914,1542,1124,1762,1965,686,307,960,1168,701,468,1566,1191,1915,146,2407,493,1466,788,699,447,938,265,506,1020,819,1941,1180,898,412,735,766,3610,396,3.252,2.807,2.631,2.439,2.312,2.402,2.249,2.696,2.53,3.017,2.605,2.074,2.943,2.65,2.808,2.725,2.924,3.484,2.599,2.237,3.222,2.392,2.122,2.382,2.652,2.858,2.337,2.122,2.559,2.499,2.098,2.977,2.469,2.553,2.83,2.46,2.909,3.03,2.515,2.704,2.634,1.865,3.301,1.823,1.84,2.214,3.125,2.688,2.751,2.386,2.138,2.425,2.251,2.53,2.485,2.148,2.48,2.583,2.066,2.046,2.689,2.703,2.283,2.42,2.328,2.187,2.162,2.501,2.341,2.625,2.391,2.81,2.441,2.079,0.722,0.589,0.583,0.578,0.712,0.485,0.525,0.507,0.639,0.559,0.793,0.464,0.606,0.521,0.544,0.673,0.707,0.74,0.526,0.559,0.772,0.467,0.422,0.887,0.488,0.527,0.471,0.636,0.617,0.598,0.675,0.959,0.474,0.617,0.958,0.453,0.782,0.659,0.345,0.411,0.388,0.538,0.968,0.555,0.508,0.457,0.524,0.88,0.596,0.674,0.435,0.481,0.474,0.563,0.375,0.358,0.606,0.527,0.324,0.34,0.562,0.797,0.431,0.952,0.474,0.611,0.422,0.453,0.395,0.585,0.486,0.69,0.546,0.328
-403,Epileptic,F,22.0,1.2,2.0,0.8,1.0,1.4,2.2,1.4,1.8,1.3,0.9,0.2,3.3,1.7,0.6,0.9,5.5,8.6,1.3,2.4,5.1,0.8,3.0,1.8,2.6,3.0,3.6,3.5,2.6,2.7,3.1,1.8,1.8,0.4,2.5,0.3,0.7,3.2,3.4,0.3,0.3,1.1,2.9,1.8,3.7,3.1,0.9,0.5,1.2,1.2,0.6,0.9,1.8,1.7,1.9,0.5,2.5,0.8,2.1,1.1,1.1,0.4,1.2,0.3,0.5,2.0,1.3,2.9,1.7,0.8,0.2,1.6,1.1,5.0,0.1,8,21,8,10,14,17,15,14,16,8,3.0,26,18,7,9,60,76,8,26,38,7,33,19,34,36,36,44,25,24,31,23,9,5,30,3,5,41,42,1,2,6,24,17,24,20,7,2,9,6,6,6,15,14,14,3,20,5,18,8,11,3,9,2,5,16,16,22,14,5,3,10,9,41,1,0.042,0.02,0.012,0.018,0.03,0.034,0.022,0.026,0.034,0.038,0.025,0.034,0.026,0.027,0.027,0.032,0.024,0.037,0.029,0.036,0.016,0.034,0.029,0.029,0.027,0.031,0.026,0.027,0.024,0.029,0.036,0.055,0.022,0.029,0.014,0.016,0.028,0.028,0.017,0.016,0.02,0.033,0.03,0.032,0.024,0.019,0.019,0.019,0.015,0.015,0.033,0.019,0.024,0.015,0.024,0.019,0.019,0.022,0.023,0.02,0.02,0.02,0.027,0.02,0.024,0.026,0.024,0.017,0.015,0.009,0.034,0.019,0.022,0.008,1808,5384,1941,2262,1778,3235,2967,3569,1810,1478,637.0,2888,4400,1678,2016,10944,20834,2472,4704,4352,3093,4486,1993,5283,5724,7122,6149,4096,6356,5021,2291,1091,788,5770,1663,2309,6871,8054,492,723,1874,2439,4264,2945,3449,1637,1010,2262,2669,1478,701,3277,2715,4849,701,3928,1499,3326,1369,1559,676,2388,510,973,3065,1199,4168,3657,1837,854,2130,2146,9919,471,0.124,0.105,0.096,0.105,0.132,0.143,0.111,0.13,0.135,0.146,0.1,0.141,0.123,0.115,0.121,0.133,0.117,0.131,0.134,0.13,0.086,0.147,0.133,0.125,0.139,0.142,0.135,0.118,0.104,0.132,0.124,0.152,0.126,0.13,0.074,0.091,0.118,0.132,0.093,0.102,0.106,0.131,0.119,0.127,0.114,0.098,0.107,0.092,0.085,0.098,0.14,0.103,0.114,0.09,0.115,0.109,0.095,0.114,0.122,0.118,0.115,0.108,0.119,0.104,0.119,0.134,0.116,0.103,0.095,0.082,0.129,0.109,0.104,0.069,465,1777,871,851,758,1109,980,1090,607,410,196.0,1424,1149,377,551,2906,5512,486,1315,2066,751,1476,997,1457,1819,2011,2157,1403,1679,1676,721,515,274,1390,453,660,1953,2012,274,261,820,1245,1121,1873,2084,798,357,973,1217,620,420,1498,1138,2007,341,2007,660,1549,788,882,328,893,217,450,1382,722,1919,1493,813,412,805,992,4033,198,3.571,2.767,2.361,2.647,2.175,2.62,2.502,2.964,2.329,3.285,2.807,1.818,2.971,3.021,2.879,2.829,3.059,3.696,2.958,1.866,3.001,2.398,1.894,2.746,2.525,2.925,2.307,2.317,2.9,2.471,2.618,2.318,2.521,2.973,3.447,3.092,2.787,2.886,2.214,2.875,2.822,1.797,3.112,1.769,1.906,2.225,3.481,2.803,2.65,2.656,2.096,2.288,2.419,2.595,2.592,2.217,2.562,2.485,2.099,1.941,2.337,2.729,2.515,2.292,2.463,2.109,2.343,2.77,2.706,2.333,2.909,2.62,2.674,2.538,0.887,0.612,0.512,0.551,0.554,0.499,0.542,0.532,0.554,0.622,0.697,0.551,0.515,0.382,0.57,0.58,0.557,0.872,0.539,0.568,0.699,0.386,0.441,0.602,0.442,0.49,0.458,0.475,0.499,0.526,0.801,0.807,0.387,0.666,0.582,0.649,0.631,0.718,0.32,0.425,0.583,0.458,0.556,0.716,0.534,0.541,0.63,0.656,0.72,0.55,0.511,0.613,0.679,0.516,0.62,0.414,0.527,0.585,0.325,0.404,0.549,0.643,0.681,0.979,0.531,0.643,0.457,0.46,0.372,0.64,0.647,0.53,0.599,0.502
-404,Epileptic,M,32.0,1.1,3.0,1.0,0.9,1.2,2.0,1.8,2.8,1.0,1.1,0.5,3.3,2.1,0.5,0.9,7.0,10.8,1.0,2.4,5.5,1.2,3.8,1.8,4.0,3.0,4.3,3.0,2.4,3.7,4.4,2.6,1.4,0.6,2.4,0.8,0.6,3.9,3.8,0.2,0.2,1.2,3.4,3.1,3.6,3.0,0.9,0.7,1.0,1.4,1.0,0.8,2.1,1.5,3.3,0.2,3.2,0.9,1.7,1.2,1.2,0.5,1.6,0.4,1.1,2.5,1.7,3.7,1.7,1.6,0.4,1.1,1.3,5.4,0.4,7,30,7,6,15,19,16,19,13,14,7.0,37,22,6,10,77,95,7,26,51,13,46,19,43,38,42,39,23,26,47,28,13,6,31,6,2,46,46,1,1,8,33,31,29,19,6,4,7,9,6,5,13,11,21,1,26,6,15,9,9,4,16,4,10,21,16,28,11,8,5,8,10,43,3,0.039,0.024,0.012,0.018,0.03,0.026,0.026,0.03,0.036,0.033,0.027,0.038,0.028,0.027,0.029,0.037,0.027,0.029,0.032,0.041,0.021,0.034,0.028,0.035,0.031,0.032,0.028,0.029,0.034,0.035,0.052,0.038,0.026,0.035,0.021,0.012,0.033,0.034,0.01,0.022,0.021,0.035,0.037,0.028,0.023,0.02,0.018,0.012,0.014,0.014,0.029,0.02,0.029,0.021,0.016,0.019,0.017,0.019,0.021,0.021,0.016,0.023,0.026,0.026,0.02,0.03,0.025,0.021,0.024,0.012,0.019,0.016,0.019,0.012,1863,7047,2300,2332,1818,3854,3236,4586,2230,2249,1126.0,3705,5189,1422,2333,13271,24932,2902,4848,6822,4966,5920,2829,7853,6348,7968,5844,3956,6547,7149,2079,2049,1498,5971,2849,1830,9343,8551,443,376,1988,3161,7680,4049,3518,1453,1330,2858,3481,2434,810,3739,2275,5715,360,4879,1653,3991,1558,1909,786,2879,658,1177,3834,1720,5080,2578,2171,982,1816,2595,11068,848,0.119,0.115,0.095,0.093,0.12,0.125,0.115,0.127,0.14,0.151,0.126,0.159,0.125,0.13,0.131,0.148,0.121,0.112,0.131,0.155,0.104,0.152,0.139,0.138,0.143,0.139,0.13,0.116,0.121,0.147,0.149,0.118,0.11,0.145,0.098,0.077,0.129,0.144,0.088,0.108,0.11,0.152,0.149,0.125,0.107,0.108,0.099,0.081,0.084,0.091,0.126,0.097,0.126,0.103,0.093,0.106,0.1,0.108,0.115,0.106,0.109,0.117,0.124,0.124,0.114,0.122,0.117,0.111,0.11,0.098,0.11,0.101,0.104,0.093,502,2157,1005,860,783,1254,1088,1436,567,594,357.0,1583,1426,373,592,3488,6865,570,1362,2413,1131,1956,1079,2038,1865,2199,2032,1461,1801,2239,779,692,456,1391,656,646,2265,2083,200,170,839,1539,1586,2123,1991,696,534,1132,1450,987,458,1780,931,2482,149,2476,768,1520,904,972,427,1104,294,645,1850,850,2368,1093,994,545,875,1253,4434,467,3.54,2.913,2.454,2.525,2.252,2.591,2.462,2.818,2.823,3.099,2.841,2.052,2.985,2.883,2.962,2.885,2.989,3.949,2.899,2.337,3.499,2.398,2.258,2.814,2.606,2.964,2.347,2.167,2.88,2.58,2.092,2.891,2.518,3.064,3.74,2.648,3.16,3.045,2.436,2.486,2.974,1.845,3.452,2.116,2.037,2.412,3.022,3.12,2.712,2.823,2.231,2.391,2.542,2.503,2.355,2.094,2.576,2.799,2.004,2.147,2.243,2.575,2.38,2.302,2.204,2.085,2.153,2.482,2.594,2.193,2.402,2.53,2.553,2.309,0.642,0.68,0.633,0.586,0.495,0.483,0.631,0.531,0.67,0.483,0.803,0.506,0.541,0.442,0.49,0.566,0.529,0.765,0.553,0.673,0.758,0.486,0.477,0.871,0.517,0.499,0.433,0.459,0.505,0.53,0.719,0.925,0.393,0.763,0.704,0.443,0.642,0.695,0.253,0.354,0.588,0.501,0.738,0.6,0.564,0.487,0.516,0.718,0.517,0.628,0.317,0.47,0.62,0.478,0.379,0.364,0.451,0.554,0.366,0.345,0.276,0.676,0.536,0.811,0.49,0.714,0.502,0.442,0.445,0.353,0.422,0.543,0.495,0.385
-405,Epileptic,M,22.0,0.8,2.1,0.6,1.1,1.8,2.0,1.6,1.8,1.2,1.0,0.3,2.4,2.1,0.6,1.2,6.8,8.6,1.1,3.0,5.8,0.8,3.9,1.9,2.9,3.0,3.4,3.8,2.7,2.7,3.5,1.2,0.7,0.6,3.8,0.6,0.7,6.0,6.6,0.2,0.2,1.0,3.4,2.8,4.6,2.2,0.6,0.8,0.8,1.2,1.0,0.6,1.5,1.4,3.3,0.1,2.5,1.0,1.9,0.8,1.8,0.7,0.9,0.3,0.9,1.9,1.1,2.3,3.0,0.9,0.5,1.0,2.4,5.1,0.3,5,17,6,9,30,16,11,13,15,10,3.0,17,23,9,12,61,80,8,28,37,6,40,17,30,31,30,38,24,25,43,17,7,5,31,3,5,59,59,2,1,6,28,23,27,13,5,3,14,5,6,4,12,12,44,1,18,6,14,7,15,6,6,2,5,18,9,18,19,5,5,7,11,34,4,0.032,0.024,0.011,0.025,0.038,0.034,0.025,0.033,0.043,0.047,0.038,0.029,0.042,0.039,0.033,0.035,0.03,0.036,0.038,0.043,0.02,0.036,0.032,0.032,0.028,0.029,0.027,0.031,0.026,0.035,0.042,0.029,0.024,0.037,0.02,0.013,0.043,0.043,0.015,0.017,0.02,0.039,0.052,0.05,0.017,0.015,0.025,0.011,0.016,0.025,0.027,0.018,0.022,0.028,0.026,0.017,0.028,0.02,0.017,0.027,0.02,0.021,0.019,0.028,0.026,0.026,0.022,0.036,0.017,0.019,0.022,0.028,0.017,0.016,1707,5549,1869,2344,2184,2928,2758,3625,2296,1555,501.0,2266,4085,1450,3002,12746,20572,2697,4889,4202,2590,6400,2187,6302,6134,6917,6502,3116,6307,5700,2262,1205,1143,7721,1937,2221,9113,10299,366,405,2005,2853,4849,2434,3327,1636,1165,2770,2762,1569,644,3059,3098,5263,213,4524,1628,3150,1138,2284,1547,1912,427,827,2856,1296,3689,2860,1917,1131,1491,3066,13142,431,0.116,0.105,0.088,0.109,0.129,0.13,0.103,0.117,0.142,0.159,0.132,0.122,0.134,0.145,0.127,0.127,0.127,0.125,0.128,0.144,0.087,0.139,0.126,0.128,0.113,0.113,0.121,0.109,0.101,0.134,0.122,0.092,0.115,0.125,0.092,0.069,0.134,0.13,0.09,0.099,0.098,0.136,0.131,0.157,0.095,0.098,0.115,0.08,0.084,0.114,0.126,0.101,0.108,0.101,0.135,0.101,0.111,0.104,0.117,0.12,0.106,0.105,0.115,0.125,0.117,0.117,0.103,0.132,0.099,0.116,0.118,0.126,0.089,0.123,462,1557,770,807,820,1046,916,932,558,397,163.0,1108,997,332,710,3330,4917,495,1343,2139,668,1782,983,1560,1721,1824,2148,1216,1577,1791,533,492,415,1562,473,820,2354,2481,210,169,793,1302,1023,1624,1978,678,439,1127,1141,627,391,1430,1130,2067,72,2223,603,1566,668,1182,618,727,236,428,1320,633,1806,1263,834,463,713,1369,4794,271,3.628,2.984,2.34,2.767,2.153,2.511,2.457,3.071,2.785,3.152,2.75,1.846,2.994,2.928,2.934,2.801,3.063,3.837,2.824,1.813,2.881,2.739,1.98,2.815,2.726,2.955,2.339,2.02,2.888,2.474,2.84,2.391,2.172,3.142,3.649,2.401,2.91,3.055,2.056,2.638,2.964,1.977,3.272,1.851,1.941,2.55,3.181,2.975,2.695,2.765,2.051,2.202,2.408,2.584,2.971,2.291,3.052,2.398,1.928,2.103,2.759,2.462,2.118,2.275,2.216,2.342,2.104,2.566,2.552,2.752,2.421,2.845,2.877,1.967,0.876,0.896,0.561,0.582,0.652,0.521,0.668,0.742,0.864,0.446,0.648,0.463,0.637,0.546,0.714,0.7,0.756,0.82,0.848,0.635,0.838,0.705,0.527,0.757,0.629,0.594,0.617,0.538,0.585,0.647,0.781,1.171,0.512,0.808,0.735,0.65,0.799,0.802,0.517,0.644,0.836,0.581,0.837,0.757,0.668,0.72,0.636,0.794,0.745,0.517,0.508,0.574,0.688,0.671,0.616,0.523,0.629,0.56,0.348,0.467,0.594,0.591,0.348,0.926,0.628,0.674,0.512,0.598,0.548,0.972,0.527,0.568,0.634,0.475
-406,Epileptic,F,32.0,0.6,3.6,1.7,1.3,2.3,1.8,1.6,1.6,1.0,0.7,0.4,3.2,1.5,0.5,1.3,6.3,9.3,1.2,1.8,4.4,1.5,3.1,1.5,3.9,3.8,2.5,5.1,2.9,2.9,3.8,2.5,0.8,0.7,1.8,0.5,0.4,3.1,2.5,0.2,0.2,0.9,3.9,2.4,2.9,3.0,0.7,0.4,1.1,1.8,0.9,0.6,2.1,1.6,2.1,0.2,2.6,1.0,1.7,1.0,1.1,0.6,2.4,0.5,0.5,1.8,1.4,2.8,1.7,1.0,0.7,1.0,1.4,4.0,0.3,7,31,14,12,17,18,13,17,12,12,5.0,30,17,5,17,82,90,10,26,43,17,34,16,44,42,30,51,31,24,42,36,6,6,21,3,3,46,37,1,1,6,29,28,25,18,5,2,6,11,11,4,19,11,14,1,22,9,15,7,11,4,16,4,5,14,22,24,13,8,6,8,12,32,3,0.033,0.034,0.03,0.021,0.043,0.026,0.027,0.024,0.041,0.034,0.039,0.035,0.034,0.031,0.03,0.036,0.033,0.046,0.024,0.037,0.025,0.032,0.03,0.044,0.033,0.022,0.033,0.029,0.027,0.032,0.049,0.055,0.032,0.027,0.019,0.013,0.033,0.028,0.011,0.017,0.019,0.039,0.037,0.027,0.025,0.013,0.027,0.017,0.022,0.015,0.022,0.021,0.028,0.019,0.016,0.018,0.02,0.02,0.027,0.021,0.021,0.031,0.029,0.017,0.021,0.021,0.02,0.019,0.021,0.022,0.019,0.015,0.017,0.019,1440,5079,2864,2642,2104,3775,3059,3828,1798,1627,571.0,3186,3573,1141,3062,11970,18784,2298,4708,4658,4625,5410,2398,6202,7466,6362,7749,4843,6355,7555,2625,737,1058,5752,2124,1385,7245,7186,430,452,1337,2446,5930,2287,3110,1513,657,2195,2465,2009,619,3245,1656,4069,336,4853,1745,3181,1130,1658,1107,2763,663,921,3036,1547,4607,3322,2024,1228,1580,2131,8825,478,0.116,0.14,0.123,0.106,0.138,0.12,0.109,0.118,0.153,0.154,0.146,0.136,0.135,0.153,0.145,0.15,0.136,0.143,0.121,0.146,0.111,0.125,0.118,0.149,0.12,0.119,0.13,0.116,0.113,0.142,0.157,0.13,0.11,0.127,0.085,0.083,0.134,0.136,0.082,0.09,0.108,0.132,0.15,0.13,0.113,0.094,0.113,0.09,0.105,0.103,0.126,0.109,0.133,0.097,0.109,0.105,0.115,0.105,0.13,0.115,0.105,0.136,0.149,0.113,0.113,0.116,0.111,0.1,0.104,0.122,0.116,0.109,0.099,0.118,414,1687,986,1006,858,1224,1024,1168,507,443,190.0,1636,939,267,825,3289,5237,466,1359,2155,1077,1564,932,1816,2111,1903,2657,1647,1783,2197,765,311,342,1211,461,493,1941,1640,228,240,694,1435,1314,1614,1909,768,257,985,1191,887,371,1657,865,1830,156,2213,797,1407,608,879,473,1143,290,475,1425,1045,2243,1427,837,513,739,1175,3679,226,3.254,2.728,2.806,2.609,2.033,2.445,2.381,2.784,2.474,2.907,2.492,1.749,2.833,3.004,2.756,2.65,2.828,3.438,2.885,1.907,3.341,2.687,2.095,2.591,2.755,2.664,2.379,2.347,2.763,2.679,2.527,2.401,2.449,3.194,3.599,2.666,2.768,3.045,2.325,2.189,2.432,1.676,3.123,1.651,1.88,2.178,2.934,2.695,2.574,2.531,2.057,2.114,2.017,2.365,2.343,2.313,2.424,2.389,2.139,2.072,2.493,2.423,2.001,2.176,2.466,1.664,2.249,2.476,2.482,2.514,2.566,2.16,2.57,2.491,0.713,0.647,0.676,0.415,0.714,0.73,0.626,0.413,0.628,0.68,0.965,0.521,0.424,0.489,0.516,0.639,0.628,1.018,0.523,0.626,0.901,0.542,0.619,0.707,0.614,0.517,0.63,0.562,0.631,0.573,0.734,1.024,0.477,0.655,0.75,0.471,0.749,0.625,0.39,0.299,0.404,0.493,0.783,0.565,0.577,0.401,0.654,0.807,0.527,0.406,0.597,0.451,0.44,0.418,0.23,0.428,0.373,0.456,0.497,0.327,0.388,0.704,0.567,0.558,0.475,0.677,0.36,0.348,0.45,0.757,0.449,0.442,0.448,0.531
-407,Epileptic,F,17.5,0.8,2.4,0.8,1.0,1.1,2.3,1.1,1.8,0.7,0.9,0.3,2.8,1.7,0.7,1.5,4.9,8.0,0.6,3.8,4.4,1.6,2.3,1.6,3.2,3.2,3.8,3.3,2.3,2.3,3.5,0.9,0.9,0.5,2.7,0.4,0.6,3.2,3.5,0.1,0.1,1.0,2.9,2.4,3.4,3.0,1.1,0.5,1.0,0.8,1.1,1.7,2.3,1.1,2.2,0.2,2.3,1.0,2.3,0.8,0.9,0.4,1.0,0.2,0.2,1.8,0.4,2.4,1.6,1.1,0.4,0.8,2.0,6.3,0.5,7,20,9,8,8,15,8,14,8,8,2.0,21,12,7,14,49,65,3,33,28,11,28,15,32,34,29,34,19,17,32,12,6,5,24,2,4,35,35,1,1,5,20,21,25,18,7,3,6,4,8,13,16,13,14,1,19,8,13,5,6,2,7,2,2,13,7,18,10,7,4,6,12,40,4,0.032,0.029,0.016,0.018,0.028,0.041,0.021,0.031,0.04,0.036,0.021,0.038,0.031,0.048,0.036,0.03,0.029,0.023,0.035,0.044,0.03,0.034,0.035,0.035,0.036,0.035,0.03,0.027,0.022,0.032,0.035,0.041,0.021,0.031,0.015,0.018,0.03,0.032,0.021,0.017,0.021,0.032,0.039,0.037,0.026,0.024,0.024,0.016,0.01,0.018,0.087,0.024,0.028,0.019,0.025,0.019,0.028,0.024,0.023,0.021,0.017,0.021,0.015,0.019,0.021,0.016,0.024,0.021,0.027,0.019,0.023,0.03,0.028,0.028,1452,5053,2210,2436,1497,2923,2543,3153,1319,1237,483.0,2618,3132,1282,2857,9750,15969,1818,4266,3026,2387,3790,1973,5294,4984,6698,5429,3204,4802,5153,1370,794,1357,5735,1593,1759,6733,6754,304,364,1896,2771,3220,2207,3041,1845,796,2356,2404,1954,475,4406,2041,4567,330,3779,1548,2934,1157,1221,831,2040,321,636,2371,653,3358,2730,1518,1131,1255,2401,9347,592,0.13,0.113,0.104,0.094,0.113,0.13,0.094,0.125,0.118,0.131,0.116,0.142,0.101,0.136,0.13,0.12,0.123,0.085,0.127,0.137,0.099,0.136,0.138,0.126,0.141,0.129,0.122,0.12,0.1,0.129,0.118,0.121,0.098,0.114,0.07,0.077,0.118,0.126,0.102,0.072,0.098,0.125,0.12,0.139,0.115,0.107,0.111,0.086,0.071,0.105,0.241,0.111,0.114,0.096,0.122,0.109,0.103,0.109,0.11,0.112,0.1,0.112,0.121,0.102,0.107,0.104,0.115,0.108,0.116,0.108,0.118,0.129,0.111,0.111,407,1352,863,858,606,962,783,971,347,385,160.0,1130,914,282,661,2699,4351,403,1679,1490,839,1155,830,1540,1505,1773,1885,1257,1503,1724,388,390,424,1226,466,594,1715,1655,156,157,756,1275,991,1485,1891,716,341,1002,1163,900,402,1586,757,1913,107,1932,710,1503,582,646,381,724,161,259,1294,406,1687,1172,721,413,590,1015,3750,306,3.395,3.226,2.334,2.757,2.212,2.706,2.639,2.995,2.619,2.737,2.769,2.062,2.765,2.819,3.043,2.689,3.017,3.45,2.216,1.886,2.463,2.528,2.064,2.617,2.6,2.987,2.234,2.086,2.508,2.551,2.534,2.21,2.544,3.042,3.037,2.657,2.911,3.033,2.004,2.296,3.025,1.961,2.661,1.758,1.878,2.806,2.889,2.873,2.364,2.426,1.595,2.679,2.315,2.529,2.555,2.166,2.451,2.358,2.437,2.081,2.654,2.777,1.981,2.424,2.077,1.989,2.075,2.67,2.394,2.973,2.444,2.778,2.575,2.041,1.088,0.907,0.837,0.745,0.598,0.565,0.528,0.64,0.587,0.471,0.708,0.481,0.581,0.629,0.672,0.625,0.678,0.913,0.639,0.64,0.73,0.533,0.544,0.669,0.534,0.546,0.537,0.448,0.564,0.555,0.658,0.859,0.389,0.679,0.539,0.715,0.798,0.677,0.472,0.523,0.807,0.485,0.524,0.593,0.577,0.847,0.652,0.819,0.754,0.511,0.506,0.594,0.704,0.583,0.652,0.487,0.685,0.613,0.448,0.403,0.398,0.932,0.736,0.769,0.485,0.724,0.43,0.609,0.363,0.892,0.516,0.703,0.678,0.374
-408,Epileptic,F,37.0,0.6,2.0,1.1,0.9,1.8,1.5,1.6,1.2,0.9,0.4,0.2,2.1,1.2,0.5,0.9,4.9,6.4,0.6,1.3,3.7,1.5,2.5,2.6,3.1,2.9,2.0,5.2,1.9,2.1,2.5,2.1,1.6,0.5,2.3,0.3,0.8,2.7,2.5,0.1,0.2,0.8,3.4,1.8,1.7,1.9,0.5,0.3,0.7,0.9,0.6,0.5,1.4,1.1,1.7,0.0,2.2,0.9,1.3,0.8,1.4,0.5,1.6,0.3,0.4,1.1,1.4,1.9,0.9,1.0,1.3,0.8,1.7,3.0,0.1,8,21,10,6,18,15,16,14,12,5,3.0,19,12,6,11,57,68,5,18,36,13,31,22,31,33,26,47,20,22,34,21,12,5,27,2,8,34,29,1,1,6,27,18,14,11,4,1,4,5,5,3,10,10,11,0,20,8,10,6,10,5,11,2,5,9,21,16,6,8,13,6,15,27,0,0.033,0.022,0.02,0.018,0.031,0.028,0.025,0.028,0.03,0.025,0.023,0.035,0.024,0.029,0.024,0.03,0.027,0.027,0.023,0.036,0.025,0.029,0.042,0.037,0.032,0.028,0.034,0.024,0.025,0.028,0.049,0.055,0.029,0.029,0.014,0.02,0.033,0.032,0.012,0.016,0.019,0.039,0.033,0.022,0.014,0.011,0.015,0.011,0.015,0.012,0.022,0.017,0.022,0.016,0.018,0.015,0.024,0.017,0.024,0.023,0.02,0.023,0.023,0.017,0.015,0.039,0.017,0.014,0.018,0.035,0.019,0.018,0.012,0.007,1291,4352,2683,2151,2317,2626,3362,2901,2020,1387,517.0,2643,3393,1137,2328,9723,17663,2067,4068,4647,4165,5193,2347,5846,5611,5577,6868,3983,6597,6058,2210,1206,777,6138,1570,1783,7109,5810,348,349,1280,3080,4233,2126,3529,1248,691,1925,1944,1807,528,2731,1702,3561,162,4109,1337,2939,1029,1821,894,2707,418,780,2112,1080,3387,2356,1792,1406,1215,2854,8531,345,0.126,0.115,0.106,0.096,0.148,0.129,0.099,0.122,0.146,0.119,0.115,0.148,0.122,0.143,0.127,0.139,0.125,0.11,0.113,0.154,0.107,0.12,0.135,0.125,0.13,0.133,0.115,0.113,0.102,0.129,0.149,0.138,0.141,0.137,0.081,0.107,0.133,0.128,0.087,0.089,0.109,0.147,0.135,0.112,0.089,0.085,0.092,0.079,0.087,0.095,0.117,0.096,0.113,0.091,0.083,0.096,0.129,0.098,0.116,0.115,0.098,0.111,0.117,0.116,0.1,0.143,0.101,0.084,0.105,0.153,0.11,0.118,0.082,0.059,431,1518,857,783,923,966,1047,892,578,326,164.0,1111,857,257,641,2848,4288,406,1036,1919,1031,1371,954,1592,1578,1446,2314,1232,1414,1626,685,514,261,1340,383,664,1588,1431,194,183,631,1250,1000,1293,1940,677,306,879,907,766,333,1356,816,1624,38,2229,628,1344,569,953,442,1061,216,468,1040,537,1661,986,786,555,632,1257,3782,154,3.118,2.674,2.933,2.729,2.199,2.348,2.668,2.684,2.575,3.142,2.645,2.076,3.064,2.923,2.662,2.583,3.077,3.631,2.872,2.082,3.029,2.742,2.204,2.725,2.65,2.909,2.37,2.41,3.231,2.787,2.449,2.652,2.578,3.205,3.371,2.425,3.031,2.831,2.188,2.291,2.449,2.111,3.143,1.918,2.016,2.142,2.733,2.572,2.666,2.555,1.87,2.2,2.202,2.343,3.624,2.057,2.293,2.385,2.015,2.06,2.157,2.577,2.037,2.045,2.281,2.421,2.157,2.641,2.489,2.542,2.236,2.365,2.308,2.204,0.615,0.62,0.56,0.46,0.642,0.472,0.67,0.483,0.586,0.418,0.848,0.678,0.415,0.499,0.486,0.676,0.648,0.603,0.604,0.584,0.834,0.574,0.731,0.895,0.682,0.581,0.635,0.723,0.61,0.564,0.854,0.867,0.408,0.66,0.575,0.492,0.852,0.782,0.344,0.35,0.447,0.798,0.78,0.566,0.643,0.385,0.478,0.596,0.419,0.452,0.431,0.421,0.408,0.392,0.333,0.408,0.462,0.519,0.428,0.39,0.366,0.524,0.542,0.537,0.415,0.591,0.458,0.456,0.523,0.58,0.419,0.635,0.465,0.392
-409,Epileptic,M,17.5,1.4,2.1,0.8,1.2,2.4,1.8,1.5,1.7,1.0,1.1,0.5,3.1,1.1,0.5,1.2,6.2,8.2,1.0,1.4,5.4,1.1,2.5,2.1,3.7,4.8,5.1,7.1,2.1,3.6,4.4,1.4,1.3,0.4,2.8,0.4,1.1,5.8,4.2,0.2,0.2,1.2,4.2,2.5,2.3,1.9,0.7,0.4,0.8,1.3,0.7,0.7,2.0,2.2,3.1,0.5,2.3,1.0,1.3,1.0,1.0,0.5,1.8,0.2,0.7,2.1,0.9,2.4,1.4,1.0,0.9,0.8,1.3,5.4,0.3,9,23,7,11,16,20,14,18,12,11,5.0,32,17,8,12,77,90,8,20,50,21,29,21,46,54,68,63,24,32,59,65,8,7,34,3,11,62,51,2,1,10,30,29,19,13,4,3,5,6,5,6,18,16,28,3,17,7,12,9,7,3,14,1,6,19,17,17,12,7,6,7,9,50,3,0.05,0.026,0.018,0.024,0.038,0.034,0.03,0.025,0.037,0.057,0.034,0.036,0.027,0.031,0.029,0.033,0.029,0.036,0.027,0.043,0.038,0.037,0.039,0.035,0.044,0.03,0.042,0.027,0.033,0.04,0.049,0.068,0.022,0.034,0.014,0.02,0.058,0.033,0.014,0.013,0.022,0.041,0.036,0.022,0.015,0.014,0.021,0.014,0.018,0.013,0.032,0.021,0.034,0.02,0.041,0.012,0.022,0.019,0.022,0.02,0.02,0.027,0.015,0.02,0.023,0.017,0.02,0.016,0.014,0.023,0.024,0.017,0.021,0.013,1894,4185,2204,2363,2560,2800,2972,3524,1876,1773,784.0,3153,3454,1411,2590,13566,19609,2510,3862,5127,3678,3906,1892,6937,6721,9835,7054,3453,6754,6329,1973,873,1060,6664,2058,2560,7505,9807,426,441,1869,3062,5746,3519,3138,1358,776,2088,2387,1633,709,3567,2657,6249,470,4436,1300,2429,1185,1532,876,2921,385,917,3046,1354,3475,3038,2022,1504,1152,2476,10133,566,0.155,0.125,0.104,0.119,0.142,0.143,0.118,0.124,0.145,0.186,0.139,0.146,0.125,0.148,0.132,0.151,0.129,0.126,0.125,0.165,0.125,0.14,0.138,0.142,0.151,0.142,0.146,0.11,0.124,0.163,0.143,0.13,0.109,0.148,0.07,0.115,0.17,0.143,0.101,0.1,0.115,0.139,0.139,0.111,0.09,0.094,0.107,0.085,0.094,0.093,0.144,0.111,0.128,0.109,0.169,0.091,0.117,0.111,0.117,0.109,0.111,0.125,0.107,0.116,0.118,0.103,0.103,0.099,0.093,0.116,0.122,0.102,0.111,0.113,471,1382,737,852,982,999,950,1123,520,409,216.0,1542,873,347,688,3470,5320,511,989,2295,787,1314,912,1947,2018,2902,2800,1368,1700,2122,656,358,334,1532,509,897,2137,2250,211,198,905,1572,1312,1831,1847,700,323,861,1078,717,384,1564,1171,2553,185,2428,645,1204,712,784,355,1107,204,503,1482,884,1822,1331,983,549,557,1127,4222,307,3.755,2.775,2.838,2.594,2.232,2.277,2.448,2.799,2.5,3.096,2.675,1.801,2.931,2.913,2.643,2.806,2.782,3.63,2.879,1.942,3.296,2.388,1.995,2.616,2.641,2.658,2.078,1.992,2.874,2.309,2.255,2.591,2.465,2.927,3.46,2.739,2.596,3.01,2.2,2.204,2.597,1.775,3.046,2.068,1.948,2.119,2.83,2.932,2.687,2.768,2.195,2.402,2.206,2.445,3.154,2.071,2.551,2.32,1.943,2.219,2.444,2.509,2.029,2.274,2.235,1.833,2.135,2.624,2.422,2.566,2.434,2.472,2.515,2.422,0.786,0.656,0.613,0.552,0.748,0.741,0.686,0.529,0.785,0.787,0.66,0.531,0.494,0.431,0.603,0.697,0.667,0.712,0.512,0.56,0.83,0.727,0.535,0.827,0.695,0.691,0.642,0.664,0.619,0.69,0.785,0.936,0.233,0.732,0.667,0.585,0.808,0.773,0.53,0.483,0.55,0.499,0.796,0.552,0.628,0.524,0.466,0.753,0.471,0.383,0.302,0.521,0.575,0.532,0.975,0.492,0.583,0.482,0.389,0.513,0.549,0.726,0.336,0.554,0.506,0.522,0.5,0.463,0.419,0.922,0.463,0.513,0.463,0.239
-410,Epileptic,M,52.0,0.8,2.2,0.8,1.1,1.3,2.1,1.5,2.1,1.4,0.8,0.2,2.8,1.5,0.5,1.3,5.7,8.6,0.9,2.9,3.8,0.7,2.8,1.7,3.5,3.3,3.8,3.3,2.7,2.4,3.5,1.4,1.6,0.7,3.1,0.3,0.9,5.0,4.3,0.1,0.1,1.3,2.7,2.1,2.6,2.4,0.7,0.3,1.4,1.2,0.6,0.6,1.5,1.7,2.8,0.3,2.1,1.1,1.8,1.0,1.0,0.5,1.3,0.4,0.4,2.0,1.4,3.5,0.9,1.0,0.7,1.0,1.3,5.6,0.4,6,24,11,10,13,16,13,18,12,8,2.0,26,13,6,14,63,78,8,30,31,6,31,15,37,37,41,40,27,22,35,19,12,8,34,2,6,52,46,2,1,7,23,24,21,13,5,2,8,7,6,4,13,13,17,2,17,8,14,8,9,5,12,2,5,19,13,29,6,5,7,8,11,40,3,0.031,0.023,0.015,0.019,0.027,0.042,0.024,0.032,0.037,0.034,0.033,0.033,0.027,0.023,0.035,0.035,0.029,0.034,0.03,0.035,0.016,0.028,0.027,0.036,0.029,0.031,0.027,0.026,0.026,0.03,0.03,0.049,0.027,0.034,0.013,0.017,0.038,0.033,0.014,0.013,0.026,0.03,0.031,0.027,0.02,0.015,0.013,0.02,0.014,0.013,0.038,0.019,0.027,0.022,0.02,0.016,0.021,0.02,0.024,0.017,0.027,0.021,0.024,0.014,0.021,0.025,0.023,0.014,0.019,0.021,0.022,0.016,0.02,0.015,1867,4822,2230,2374,2521,2634,2580,3026,2359,1287,599.0,2952,3544,1066,2149,10589,17185,2499,5312,4148,3087,4933,2517,6679,6375,7278,5749,4132,4953,5606,2820,1153,1384,7302,1634,2112,9889,8793,280,377,1532,3332,5865,2322,3196,1530,893,2318,2534,2070,344,2634,2509,4657,424,3720,2108,3162,1144,1504,813,2387,371,1081,3199,1558,4436,1818,1467,1628,1486,2524,10500,615,0.113,0.107,0.099,0.101,0.123,0.143,0.102,0.132,0.137,0.143,0.108,0.135,0.12,0.121,0.143,0.139,0.127,0.121,0.135,0.137,0.083,0.131,0.13,0.134,0.129,0.136,0.123,0.108,0.102,0.133,0.133,0.126,0.119,0.135,0.075,0.087,0.138,0.131,0.1,0.096,0.114,0.126,0.125,0.119,0.099,0.094,0.088,0.088,0.087,0.093,0.156,0.102,0.12,0.105,0.124,0.094,0.105,0.108,0.122,0.105,0.126,0.11,0.115,0.106,0.116,0.12,0.117,0.094,0.1,0.131,0.118,0.104,0.103,0.101,452,1631,860,929,924,932,973,1136,634,368,168.0,1327,944,316,664,2930,4936,518,1515,1765,674,1554,925,1755,1921,2195,2096,1524,1435,1878,774,530,429,1661,394,778,2461,2226,189,183,796,1366,1348,1494,1924,748,373,1009,1212,779,265,1300,1070,2066,199,2108,834,1496,695,862,381,973,221,495,1560,854,2318,923,733,535,750,1238,4417,363,3.859,2.772,2.474,2.477,2.42,2.538,2.236,2.451,2.763,2.941,2.902,1.96,2.884,2.421,2.482,2.729,2.785,3.684,2.829,2.008,3.287,2.449,2.204,2.844,2.559,2.68,2.194,2.178,2.665,2.465,2.833,2.286,2.494,3.134,3.539,2.507,3.018,2.978,1.847,2.384,2.438,2.019,3.196,1.793,1.913,2.255,2.943,2.673,2.659,2.698,1.756,2.129,2.315,2.439,2.396,1.951,2.814,2.511,1.901,1.973,2.461,2.418,2.057,2.486,2.338,2.331,2.137,2.325,2.352,3.29,2.292,2.452,2.563,2.129,0.753,0.591,0.536,0.487,0.579,0.576,0.532,0.583,0.695,0.462,0.521,0.514,0.435,0.526,0.539,0.598,0.549,0.705,0.688,0.604,0.674,0.496,0.51,0.629,0.51,0.484,0.447,0.568,0.591,0.441,0.714,0.758,0.423,0.694,0.691,0.536,0.635,0.62,0.569,0.383,0.43,0.767,0.732,0.516,0.549,0.524,0.374,0.669,0.447,0.45,0.362,0.446,0.619,0.5,0.455,0.381,0.711,0.493,0.382,0.372,0.46,0.562,0.482,0.797,0.597,0.53,0.49,0.408,0.355,0.94,0.436,0.448,0.54,0.321
-411,Epileptic,F,47.0,1.0,2.4,1.5,0.9,1.7,1.5,1.3,1.3,1.2,0.9,0.2,3.6,1.5,0.6,0.9,6.0,8.3,1.1,1.9,4.8,1.4,3.5,1.8,3.4,3.1,2.4,2.1,2.1,2.4,2.5,1.2,0.7,0.4,1.8,0.5,1.2,3.4,4.0,0.2,0.2,1.1,3.6,2.6,3.3,2.2,0.5,0.4,0.8,1.3,1.0,0.4,1.8,1.8,2.5,0.4,2.4,0.6,1.5,1.0,1.1,0.7,1.6,0.4,0.5,1.7,1.1,2.1,1.3,1.1,0.8,1.1,1.9,5.3,0.3,9,22,12,7,19,15,13,13,11,8,3.0,34,16,7,11,62,84,9,18,46,16,35,22,40,46,25,27,21,22,36,16,16,5,21,3,19,38,44,2,2,6,32,21,24,13,4,3,5,7,8,4,12,12,18,2,17,5,12,6,10,5,11,4,4,14,15,21,10,8,10,9,13,46,3,0.038,0.028,0.028,0.017,0.035,0.026,0.024,0.024,0.04,0.039,0.021,0.036,0.03,0.026,0.029,0.036,0.028,0.04,0.027,0.038,0.029,0.038,0.031,0.037,0.03,0.023,0.026,0.029,0.024,0.03,0.037,0.041,0.022,0.024,0.013,0.024,0.03,0.03,0.015,0.02,0.024,0.039,0.041,0.026,0.018,0.011,0.018,0.015,0.02,0.02,0.02,0.022,0.036,0.021,0.018,0.016,0.017,0.02,0.019,0.022,0.022,0.027,0.033,0.021,0.019,0.022,0.018,0.019,0.019,0.027,0.024,0.021,0.016,0.017,1489,3433,2251,2160,2074,2877,2658,2726,1491,1575,899.0,3330,3060,1408,1897,9238,15528,2158,4023,5580,3567,4077,2138,6187,5921,5771,4261,3151,5894,5100,2138,1035,794,5712,2187,2327,7927,9756,389,525,1395,2117,5754,3264,3238,1229,705,1608,2034,1819,541,2819,1942,4942,562,3768,1219,2747,1206,1305,895,2351,569,805,2604,1152,3740,2200,1532,1015,1616,2847,11216,396,0.121,0.138,0.124,0.092,0.15,0.119,0.111,0.116,0.146,0.154,0.111,0.144,0.129,0.126,0.126,0.147,0.124,0.141,0.122,0.146,0.11,0.113,0.124,0.145,0.124,0.112,0.117,0.108,0.106,0.139,0.134,0.123,0.094,0.116,0.072,0.12,0.125,0.129,0.108,0.103,0.107,0.146,0.157,0.116,0.098,0.079,0.108,0.084,0.1,0.109,0.117,0.106,0.136,0.104,0.11,0.097,0.108,0.107,0.106,0.123,0.116,0.122,0.126,0.113,0.103,0.117,0.104,0.107,0.104,0.142,0.119,0.107,0.094,0.124,439,1266,842,790,822,942,896,916,503,386,237.0,1622,895,342,522,2930,4788,455,1112,2165,846,1423,1013,1734,1846,1748,1509,1229,1657,1643,606,429,283,1308,567,794,1914,2228,170,222,718,1403,1112,2043,1867,631,339,754,978,753,315,1369,810,1921,292,2063,519,1224,743,769,472,897,273,423,1375,770,1909,1045,866,475,798,1436,5069,251,3.213,2.705,2.746,2.49,2.257,2.515,2.418,2.63,2.259,3.13,3.075,1.847,2.633,2.899,2.675,2.403,2.625,3.484,2.843,2.102,3.16,2.258,1.867,2.78,2.477,2.624,2.266,2.083,2.672,2.393,2.581,2.518,2.231,3.081,3.427,2.73,2.94,3.148,2.174,2.392,2.317,1.536,3.486,1.772,1.974,2.094,2.616,2.7,2.553,2.574,2.103,2.213,2.493,2.389,2.213,1.97,2.531,2.42,1.825,1.853,2.125,2.725,2.175,2.201,2.025,1.968,2.05,2.3,2.147,2.435,2.177,2.399,2.369,2.043,0.745,0.631,0.483,0.572,0.646,0.667,0.656,0.448,0.692,0.745,0.685,0.453,0.379,0.544,0.485,0.649,0.572,0.822,0.687,0.698,0.872,0.606,0.421,0.688,0.629,0.496,0.511,0.468,0.445,0.626,0.507,0.859,0.311,0.515,0.748,0.406,0.685,0.613,0.475,0.496,0.401,0.403,0.748,0.462,0.609,0.479,0.63,0.576,0.469,0.62,0.406,0.411,0.888,0.623,0.382,0.393,0.406,0.427,0.42,0.369,0.471,0.532,0.419,0.64,0.475,0.745,0.438,0.374,0.371,0.651,0.485,0.465,0.46,0.338
-412,Epileptic,F,52.0,0.7,1.9,0.7,0.8,1.5,1.6,1.0,1.3,1.0,0.4,0.2,2.4,1.4,0.4,1.6,4.6,8.2,0.9,2.8,3.8,0.7,2.5,1.0,2.9,2.4,3.5,2.8,2.6,2.0,3.4,1.1,1.7,0.5,1.9,0.3,0.3,3.1,2.6,0.1,0.2,1.0,2.9,2.0,2.1,2.6,0.5,0.3,0.8,1.1,1.0,0.6,1.4,1.2,2.0,0.1,2.3,0.4,1.3,1.4,1.3,0.4,1.2,0.3,0.3,1.1,1.0,2.4,1.0,0.8,0.7,1.1,1.1,3.6,0.5,9,20,7,9,15,17,9,13,12,5,2.0,17,14,4,15,46,76,6,29,33,5,28,11,32,27,36,38,24,23,39,14,10,5,23,1,2,34,26,1,1,8,25,18,18,16,4,2,6,6,10,5,11,9,14,0,20,4,11,11,13,3,9,2,3,9,11,18,7,4,7,6,8,34,3,0.031,0.021,0.015,0.016,0.036,0.027,0.02,0.026,0.039,0.025,0.02,0.037,0.04,0.029,0.038,0.036,0.029,0.033,0.034,0.035,0.016,0.032,0.029,0.035,0.028,0.029,0.024,0.027,0.024,0.032,0.029,0.049,0.019,0.026,0.011,0.008,0.035,0.032,0.021,0.017,0.021,0.033,0.034,0.028,0.023,0.012,0.015,0.012,0.014,0.02,0.031,0.018,0.023,0.02,0.022,0.018,0.016,0.015,0.029,0.022,0.024,0.023,0.024,0.012,0.023,0.031,0.021,0.015,0.017,0.023,0.027,0.021,0.019,0.021,1427,4478,1760,1811,2017,2888,2515,2397,1643,1004,518.0,1940,2685,866,2919,8642,15737,2235,4131,3497,2042,3919,1527,5469,4638,6669,5533,4190,5117,5505,1843,1138,992,4977,1505,1211,5394,4828,258,425,1476,2747,4220,1764,3240,1018,730,2230,2210,1937,573,2697,1978,4054,152,3320,959,2860,1434,1553,553,2025,470,691,1841,880,3640,2161,1269,1069,1265,1666,7444,649,0.118,0.109,0.098,0.108,0.135,0.132,0.096,0.128,0.149,0.125,0.094,0.141,0.147,0.135,0.148,0.144,0.129,0.116,0.14,0.134,0.073,0.14,0.129,0.132,0.135,0.14,0.125,0.117,0.118,0.142,0.129,0.149,0.099,0.122,0.064,0.061,0.133,0.123,0.101,0.117,0.115,0.137,0.119,0.122,0.104,0.091,0.088,0.072,0.088,0.109,0.128,0.102,0.118,0.101,0.119,0.109,0.098,0.095,0.132,0.122,0.12,0.114,0.122,0.099,0.104,0.128,0.11,0.097,0.098,0.123,0.118,0.119,0.106,0.102,450,1393,673,742,683,1064,824,864,493,309,158.0,954,730,213,744,2365,4593,435,1240,1625,694,1310,657,1445,1505,2025,1939,1378,1443,1814,532,504,400,1124,420,519,1539,1326,131,180,746,1311,1004,1212,1811,580,310,1001,1096,809,341,1237,820,1619,67,1834,399,1357,760,877,307,808,193,383,865,430,1803,1007,636,442,681,790,3053,353,2.788,2.823,2.528,2.327,2.448,2.364,2.49,2.597,2.545,2.715,2.724,1.848,2.886,2.916,2.871,2.728,2.726,3.717,2.785,1.855,2.486,2.411,1.996,2.744,2.447,2.685,2.292,2.354,2.769,2.462,2.661,2.395,2.008,3.094,3.161,2.208,2.698,2.735,2.129,2.702,2.47,1.913,2.998,1.733,2.026,1.902,2.99,2.745,2.396,2.59,1.957,2.227,2.438,2.486,2.179,2.036,2.417,2.317,2.227,1.98,2.137,2.505,2.645,2.086,2.331,2.234,2.217,2.441,2.271,2.598,2.241,2.574,2.528,2.316,0.637,0.683,0.727,0.415,0.659,0.455,0.495,0.55,0.603,0.405,0.638,0.473,0.505,0.509,0.578,0.558,0.655,0.927,0.527,0.608,0.727,0.381,0.42,0.593,0.44,0.487,0.47,0.475,0.464,0.479,0.771,0.947,0.398,0.599,0.702,0.43,0.599,0.594,0.256,0.449,0.498,0.49,0.806,0.776,0.516,0.405,0.427,0.583,0.485,0.493,0.411,0.537,0.629,0.608,0.3,0.414,0.469,0.407,0.35,0.433,0.367,0.594,0.45,0.9,0.587,0.612,0.475,0.466,0.393,0.699,0.382,0.56,0.588,0.533
-413,Epileptic,F,47.0,0.6,2.3,1.0,1.1,1.9,1.5,1.5,2.1,1.4,0.7,0.2,3.5,1.7,0.6,1.6,5.6,9.2,1.0,2.2,4.7,1.4,2.4,2.3,2.7,3.4,3.6,2.8,2.9,3.1,3.1,1.1,0.9,0.6,2.6,0.4,1.1,3.0,3.5,0.2,0.3,1.2,2.7,2.2,2.7,2.9,0.8,0.4,1.0,1.0,0.6,0.8,2.5,1.0,2.6,0.5,2.5,0.8,2.7,1.2,1.0,0.4,1.6,0.6,0.3,2.1,1.4,2.6,2.2,1.7,0.6,0.7,1.8,4.2,0.5,5,18,9,7,17,14,13,17,17,6,2.0,32,16,6,19,56,71,7,22,37,10,27,21,26,40,34,34,26,24,38,16,5,5,25,2,9,31,34,2,2,6,30,22,20,18,4,2,6,6,4,6,20,7,14,3,19,6,20,7,9,2,13,4,3,18,21,20,18,12,4,4,10,34,3,0.033,0.025,0.019,0.025,0.037,0.03,0.028,0.027,0.053,0.032,0.019,0.037,0.028,0.037,0.031,0.036,0.033,0.041,0.033,0.037,0.026,0.04,0.033,0.033,0.033,0.032,0.029,0.032,0.029,0.036,0.039,0.034,0.027,0.034,0.013,0.024,0.031,0.033,0.015,0.018,0.023,0.038,0.039,0.03,0.024,0.018,0.019,0.016,0.016,0.014,0.034,0.022,0.029,0.022,0.027,0.019,0.031,0.028,0.032,0.025,0.026,0.028,0.035,0.017,0.022,0.028,0.022,0.024,0.022,0.019,0.02,0.031,0.02,0.023,1447,4438,2163,2268,2143,3066,2398,3763,1754,1160,505.0,2945,3359,1115,3148,9942,17350,2517,4569,4570,3325,3650,2478,5505,5829,6442,5160,4071,5496,4764,2544,1255,1002,5542,1784,2277,6465,7017,330,507,1532,2889,5248,2463,2858,1412,711,2411,2138,1755,812,3949,1400,4125,568,3650,1306,3832,1110,1401,593,2878,459,894,3011,1228,3342,3135,2410,1042,1130,2026,8434,544,0.112,0.119,0.105,0.1,0.14,0.135,0.115,0.123,0.162,0.129,0.087,0.15,0.123,0.141,0.136,0.143,0.133,0.128,0.137,0.139,0.097,0.16,0.147,0.13,0.155,0.141,0.134,0.131,0.121,0.15,0.126,0.099,0.128,0.123,0.073,0.11,0.121,0.125,0.091,0.113,0.108,0.159,0.142,0.117,0.113,0.095,0.103,0.086,0.091,0.086,0.145,0.11,0.124,0.105,0.118,0.104,0.118,0.121,0.127,0.127,0.125,0.127,0.144,0.095,0.118,0.125,0.119,0.117,0.119,0.113,0.105,0.128,0.101,0.113,319,1467,813,767,792,895,893,1195,524,335,163.0,1560,1095,292,898,2877,4767,421,1157,2059,941,1180,1118,1523,1747,1968,1788,1457,1761,1530,608,451,358,1324,460,745,1693,1766,233,269,759,1296,1098,1512,1881,668,286,918,975,685,356,1818,603,1893,317,2019,526,1639,602,694,241,960,241,386,1431,652,1771,1513,1162,459,566,932,3494,299,3.866,2.799,2.645,2.897,2.303,2.915,2.339,2.856,2.721,3.088,2.78,1.761,2.533,2.636,2.706,2.712,3.001,3.987,3.022,1.916,2.933,2.594,2.018,2.936,2.599,2.802,2.265,2.259,2.533,2.526,2.877,2.65,2.273,2.868,3.475,2.825,2.885,2.872,1.627,2.343,2.528,1.921,3.263,1.839,1.766,2.27,3.133,3.042,2.723,2.828,2.345,2.246,2.402,2.443,2.318,2.08,2.873,2.619,2.268,2.22,2.929,3.309,2.101,2.403,2.271,2.185,2.185,2.366,2.292,2.477,2.235,2.56,2.641,2.268,0.882,0.527,0.601,0.424,0.586,0.691,0.543,0.641,0.551,0.442,0.482,0.475,0.419,0.505,0.642,0.465,0.55,0.808,0.59,0.653,0.925,0.564,0.435,0.554,0.506,0.553,0.459,0.513,0.517,0.486,0.801,1.064,0.405,0.66,0.869,0.437,0.692,0.646,0.341,0.283,0.51,0.534,0.742,0.703,0.461,0.535,0.455,0.927,0.557,0.543,0.571,0.511,0.45,0.423,0.298,0.361,0.545,0.74,0.358,0.41,0.322,0.842,0.404,1.036,0.487,0.879,0.449,0.362,0.396,0.612,0.639,0.543,0.538,0.385
-414,Epileptic,M,22.0,0.7,2.7,0.8,1.5,2.0,1.5,1.6,1.8,1.6,0.7,0.4,3.4,1.3,0.6,1.8,6.8,8.7,1.1,1.8,4.2,2.2,4.6,1.8,3.4,6.2,5.8,3.0,1.9,2.6,2.1,1.1,1.3,0.5,2.0,0.6,1.0,4.3,3.7,0.1,0.3,1.4,4.4,2.0,2.6,2.6,0.9,0.6,1.0,1.3,0.7,0.6,2.0,1.8,3.0,0.6,2.1,1.2,3.4,1.2,0.9,0.6,1.7,0.4,0.5,2.1,1.2,1.8,1.6,1.0,0.5,1.1,1.7,3.9,0.4,7,30,7,15,22,17,15,18,19,9,7.0,28,17,6,21,80,93,10,25,36,13,42,15,50,63,69,42,22,27,29,16,9,6,26,3,13,48,50,2,2,10,31,20,19,17,7,3,7,8,8,6,14,14,25,4,17,9,26,8,8,5,15,2,5,16,20,14,12,7,6,9,13,32,3,0.035,0.022,0.012,0.029,0.032,0.027,0.026,0.025,0.035,0.035,0.039,0.037,0.024,0.035,0.03,0.029,0.027,0.04,0.026,0.036,0.039,0.042,0.033,0.039,0.05,0.03,0.028,0.02,0.022,0.024,0.028,0.05,0.028,0.024,0.02,0.019,0.034,0.031,0.015,0.015,0.027,0.044,0.034,0.023,0.02,0.02,0.022,0.014,0.017,0.025,0.032,0.018,0.021,0.019,0.016,0.015,0.016,0.028,0.02,0.021,0.018,0.027,0.017,0.014,0.018,0.021,0.015,0.014,0.02,0.018,0.026,0.019,0.016,0.024,1417,5218,2634,2461,2656,2463,3143,4052,2203,1768,716.0,3233,3865,900,3670,15724,21926,2286,4816,5278,3916,3958,1623,6024,5259,9871,5824,4452,7485,5243,2582,1384,958,6317,2313,2463,8604,9128,422,500,1635,2243,5534,3100,3289,1481,906,2561,2520,1688,680,3806,2697,6218,1087,4133,2466,4762,1543,1243,1112,2574,534,1207,3459,1265,3930,3738,1717,860,1428,3036,8736,394,0.122,0.115,0.084,0.128,0.14,0.116,0.111,0.126,0.156,0.135,0.15,0.144,0.12,0.141,0.131,0.129,0.124,0.137,0.123,0.142,0.12,0.117,0.118,0.139,0.132,0.124,0.113,0.093,0.104,0.125,0.107,0.122,0.111,0.122,0.077,0.112,0.133,0.138,0.117,0.104,0.128,0.144,0.141,0.114,0.102,0.101,0.112,0.089,0.094,0.102,0.146,0.101,0.116,0.103,0.097,0.097,0.099,0.131,0.103,0.114,0.1,0.129,0.094,0.094,0.102,0.117,0.092,0.09,0.104,0.126,0.13,0.112,0.096,0.145,424,1974,938,957,1076,995,993,1242,721,415,213.0,1470,987,274,1055,4087,5703,494,1283,1901,944,1468,887,1757,1917,3250,1870,1423,1906,1539,754,450,350,1389,559,893,2278,2123,187,260,853,1455,1123,1810,2029,700,378,991,1201,584,353,1699,1276,2646,553,2122,1027,2036,946,681,544,1026,302,587,1729,920,1871,1614,794,447,704,1399,3800,225,3.322,2.54,2.737,2.563,2.133,2.183,2.517,2.776,2.429,3.063,2.415,1.887,2.899,2.515,2.583,2.799,2.895,3.566,2.861,2.155,2.991,2.189,1.65,2.645,2.269,2.549,2.409,2.424,2.938,2.589,2.525,2.829,2.276,3.121,3.609,2.596,2.877,3.074,2.407,2.201,2.449,1.517,3.389,1.958,1.84,2.334,2.815,3.092,2.624,2.787,2.141,2.379,2.245,2.461,2.39,2.104,2.485,2.559,1.856,1.986,2.19,2.594,1.984,2.333,2.167,1.672,2.203,2.617,2.467,2.263,2.377,2.449,2.476,2.068,0.881,0.497,0.407,0.512,0.719,0.518,0.631,0.523,0.673,0.723,0.976,0.564,0.405,0.722,0.631,0.667,0.701,0.796,0.557,0.856,1.0,0.684,0.574,0.788,0.662,0.604,0.634,0.683,0.594,0.591,0.584,1.019,0.519,0.524,0.787,0.571,0.758,0.685,0.543,0.422,0.48,0.484,0.75,0.504,0.552,0.442,0.455,0.759,0.414,0.696,0.52,0.448,0.403,0.431,0.299,0.44,0.525,0.812,0.415,0.41,0.474,0.659,0.418,0.855,0.447,0.437,0.425,0.393,0.434,0.526,0.357,0.522,0.447,0.432
-415,Epileptic,M,32.0,0.9,2.1,0.9,1.5,2.2,1.6,1.8,1.9,1.1,0.7,0.3,2.9,1.2,0.7,1.3,6.2,9.0,0.8,3.0,4.4,1.1,2.3,1.2,3.2,3.3,2.7,3.3,2.5,2.4,3.2,0.9,1.7,1.1,2.6,0.2,1.2,2.3,3.3,0.1,0.2,1.0,3.7,2.4,1.7,2.4,0.8,0.4,0.7,1.0,2.9,0.8,1.8,2.4,4.4,0.3,2.0,0.8,1.0,1.2,1.5,0.5,1.9,0.5,0.4,1.3,0.9,2.4,1.6,0.7,0.6,0.9,0.6,4.6,0.3,6,22,10,15,14,14,16,19,10,8,1.0,28,14,7,14,61,80,6,46,46,7,26,11,37,42,28,37,27,23,33,11,12,11,33,2,14,22,37,1,2,8,29,26,13,16,5,2,5,5,17,6,13,20,35,3,17,7,11,7,11,5,16,4,5,12,15,19,9,5,4,6,4,34,2,0.038,0.027,0.016,0.023,0.037,0.028,0.022,0.03,0.029,0.039,0.032,0.035,0.031,0.035,0.03,0.032,0.027,0.026,0.033,0.039,0.03,0.037,0.026,0.037,0.028,0.025,0.029,0.028,0.022,0.032,0.026,0.046,0.033,0.029,0.009,0.029,0.045,0.035,0.009,0.018,0.022,0.038,0.038,0.024,0.018,0.014,0.024,0.013,0.014,0.064,0.027,0.021,0.025,0.023,0.015,0.016,0.03,0.023,0.029,0.027,0.038,0.025,0.022,0.014,0.018,0.022,0.02,0.022,0.017,0.02,0.024,0.013,0.02,0.019,1598,5404,2625,2865,1719,2850,2895,4068,1470,1515,368.0,2938,3098,1281,3057,11601,21059,2392,5133,5334,2394,3374,1787,5905,6885,6807,5961,4120,6572,5574,1837,1477,1590,7009,1833,2259,3154,6774,407,447,1565,2964,5607,2773,3688,1651,727,2438,2268,1152,825,2867,3816,8630,528,3679,991,1815,1132,1512,531,2993,723,962,2409,1048,3649,2520,1587,1454,1478,1922,8751,483,0.111,0.117,0.103,0.122,0.145,0.122,0.112,0.131,0.116,0.149,0.114,0.149,0.129,0.142,0.131,0.133,0.12,0.102,0.132,0.15,0.116,0.154,0.109,0.136,0.132,0.125,0.128,0.113,0.1,0.138,0.115,0.133,0.148,0.142,0.057,0.12,0.114,0.135,0.082,0.112,0.112,0.148,0.147,0.107,0.099,0.093,0.116,0.081,0.088,0.183,0.133,0.105,0.121,0.109,0.114,0.103,0.116,0.117,0.134,0.129,0.143,0.128,0.116,0.113,0.103,0.132,0.106,0.107,0.099,0.11,0.124,0.092,0.106,0.114,457,1463,847,1010,819,989,1155,1147,462,368,135.0,1274,773,278,768,3247,5351,483,1421,1962,557,1121,732,1713,2145,1869,1967,1446,1672,1748,564,601,516,1501,463,715,1070,1636,214,212,772,1442,1121,1256,2063,819,266,1005,1049,781,475,1347,1571,3243,290,1881,458,791,662,816,283,1119,367,456,1160,687,1911,1117,714,470,616,702,3502,269,3.397,3.185,2.75,2.599,2.176,2.41,2.171,2.968,2.527,3.226,2.366,2.02,2.997,3.022,2.916,2.718,3.067,3.72,2.697,2.239,3.017,2.44,2.064,2.644,2.607,2.913,2.443,2.223,2.914,2.52,2.533,2.572,2.569,3.345,3.459,2.696,2.244,2.853,2.158,2.557,2.615,1.904,3.275,2.236,2.01,2.231,3.407,2.871,2.666,1.8,1.985,2.333,2.351,2.598,2.315,2.167,2.273,2.385,1.976,2.015,2.155,2.673,2.182,2.487,2.296,2.072,2.052,2.713,2.484,3.366,2.606,2.309,2.6,2.232,0.874,0.734,0.739,0.644,0.569,0.525,0.467,0.609,0.645,0.585,1.017,0.486,0.463,0.591,0.461,0.554,0.598,0.839,0.712,0.629,0.892,0.521,0.463,0.677,0.581,0.577,0.524,0.59,0.569,0.582,0.608,0.998,0.338,0.52,0.772,0.521,0.85,0.782,0.32,0.534,0.59,0.502,0.858,0.812,0.593,0.458,0.397,0.699,0.451,0.855,0.4,0.479,0.518,0.513,0.305,0.4,0.687,0.633,0.466,0.4,0.34,0.623,0.513,0.771,0.641,0.605,0.536,0.38,0.396,0.813,0.549,0.708,0.641,0.303
-416,Epileptic,M,47.0,0.6,2.5,0.7,1.0,1.6,1.8,1.7,2.0,1.7,0.4,0.3,3.4,1.8,0.6,1.4,5.9,10.0,0.8,2.1,5.0,1.5,3.2,1.6,4.1,2.6,4.3,3.0,2.3,2.5,3.8,1.9,1.4,0.5,2.3,0.5,0.5,2.8,2.5,0.2,0.3,1.1,3.2,2.7,2.4,4.0,0.7,0.7,1.3,1.7,0.5,0.7,1.5,1.4,2.3,0.3,2.9,1.1,1.6,1.3,0.9,1.1,2.0,0.7,0.7,2.9,1.2,2.4,0.8,1.0,0.4,1.0,1.3,4.6,0.5,8,24,8,7,14,17,12,21,17,5,3.0,30,17,8,16,65,87,7,23,47,18,32,15,39,33,47,32,23,20,45,19,8,5,23,5,4,29,31,2,2,7,27,30,20,23,5,3,7,10,4,4,12,10,16,2,22,6,18,11,9,8,15,6,6,25,12,19,5,7,4,6,10,36,4,0.025,0.022,0.011,0.018,0.032,0.03,0.025,0.031,0.04,0.025,0.025,0.038,0.032,0.028,0.033,0.029,0.028,0.025,0.028,0.039,0.025,0.038,0.029,0.036,0.03,0.028,0.025,0.023,0.026,0.033,0.036,0.049,0.021,0.028,0.02,0.012,0.032,0.031,0.015,0.03,0.018,0.034,0.036,0.027,0.026,0.013,0.019,0.017,0.02,0.011,0.023,0.016,0.022,0.02,0.025,0.019,0.024,0.02,0.033,0.021,0.033,0.027,0.023,0.026,0.027,0.027,0.019,0.014,0.02,0.012,0.023,0.023,0.019,0.024,1481,4747,1965,2124,2195,2296,2762,2835,1768,1330,640.0,2613,3305,1330,2553,10948,18740,2933,4598,4430,4499,4001,2009,6769,4642,7679,5457,3311,4139,6024,2342,1224,1072,5243,2377,1669,6386,5838,341,353,1675,2850,6147,2418,2923,1500,1129,2622,2819,1352,641,2989,1872,4266,329,3957,1621,3219,1124,1163,859,3058,793,820,3146,1165,3702,1655,1559,954,1289,1956,8328,427,0.101,0.116,0.093,0.099,0.139,0.141,0.102,0.131,0.13,0.111,0.123,0.153,0.134,0.124,0.131,0.133,0.124,0.102,0.128,0.156,0.109,0.16,0.135,0.131,0.137,0.127,0.125,0.101,0.1,0.151,0.142,0.116,0.09,0.122,0.082,0.083,0.138,0.137,0.109,0.134,0.105,0.145,0.144,0.117,0.121,0.088,0.098,0.09,0.107,0.091,0.118,0.091,0.113,0.103,0.126,0.109,0.11,0.119,0.152,0.122,0.144,0.123,0.129,0.121,0.131,0.121,0.107,0.09,0.103,0.101,0.12,0.129,0.103,0.116,440,1748,864,848,887,977,953,1105,570,353,211.0,1355,950,390,836,3357,5677,560,1272,2045,1082,1410,929,1986,1578,2606,1941,1299,1284,2000,895,480,385,1319,518,739,1487,1461,180,182,897,1422,1373,1522,2144,752,509,1120,1303,601,409,1556,882,1939,181,2233,685,1337,650,644,525,1173,445,465,1676,665,1990,887,777,516,677,901,3646,283,3.104,2.587,2.263,2.514,2.284,2.146,2.379,2.306,2.391,3.028,2.669,1.775,2.622,2.622,2.413,2.586,2.76,3.856,2.882,1.941,3.139,2.337,1.949,2.586,2.382,2.429,2.261,2.082,2.515,2.464,2.151,2.666,2.185,2.927,3.605,2.24,3.036,2.931,2.251,2.178,2.388,1.849,3.231,1.846,1.575,2.2,2.843,2.83,2.701,2.367,1.927,2.143,2.34,2.457,2.161,1.984,2.725,2.516,1.978,1.932,2.12,2.634,2.081,2.081,2.054,2.058,2.06,2.167,2.266,2.114,2.332,2.511,2.391,1.94,0.751,0.538,0.517,0.365,0.464,0.336,0.601,0.595,0.452,0.408,0.662,0.427,0.454,0.377,0.427,0.521,0.496,0.843,0.568,0.562,0.723,0.442,0.394,0.833,0.504,0.509,0.4,0.554,0.687,0.464,0.738,0.922,0.402,0.596,0.833,0.501,0.706,0.543,0.372,0.341,0.499,0.422,0.697,0.648,0.542,0.417,0.544,0.91,0.628,0.498,0.312,0.402,0.562,0.402,0.283,0.408,0.529,0.6,0.498,0.32,0.35,0.601,0.315,0.805,0.51,0.653,0.418,0.353,0.407,0.497,0.398,0.443,0.474,0.343
-417,Epileptic,F,52.0,0.9,2.0,0.7,1.2,1.4,1.7,1.3,1.6,1.1,0.7,0.5,3.7,1.3,0.5,1.1,4.8,8.1,1.6,2.0,4.9,1.2,2.4,3.0,3.7,2.2,3.2,2.8,2.3,2.8,2.2,1.6,1.1,0.4,2.3,0.4,0.4,3.7,3.1,0.1,0.2,1.2,2.9,2.4,3.2,2.7,0.4,0.6,2.6,1.5,0.9,0.7,1.4,1.9,3.7,0.2,2.4,0.6,1.7,1.0,1.1,0.4,1.4,0.5,0.4,2.6,1.4,2.9,0.8,1.0,0.8,0.6,1.6,4.3,0.4,9,20,7,9,16,20,10,15,13,8,4.0,35,12,6,14,51,80,12,24,48,10,31,30,42,26,33,36,22,24,30,23,9,4,28,1,2,45,44,1,2,6,25,24,32,15,3,3,61,7,9,4,11,15,26,1,18,4,14,9,10,3,16,4,4,21,17,22,4,6,10,4,17,33,4,0.038,0.023,0.016,0.023,0.025,0.029,0.024,0.03,0.039,0.029,0.028,0.034,0.025,0.029,0.029,0.035,0.029,0.049,0.028,0.036,0.025,0.034,0.035,0.043,0.027,0.027,0.028,0.028,0.03,0.026,0.046,0.058,0.029,0.03,0.009,0.011,0.032,0.032,0.014,0.011,0.026,0.032,0.036,0.022,0.022,0.011,0.018,0.03,0.02,0.024,0.027,0.018,0.027,0.023,0.018,0.017,0.017,0.022,0.02,0.02,0.016,0.024,0.018,0.02,0.024,0.022,0.02,0.016,0.02,0.026,0.017,0.022,0.019,0.024,1586,3711,1829,2051,2081,2849,2321,2894,1504,1566,722.0,3680,2737,1109,2348,8409,17024,2454,4729,5893,3017,3986,2874,5566,4606,6047,6268,3518,4922,4900,2400,1003,769,4886,2065,1315,7450,7483,308,448,1259,2965,5688,4009,3546,1178,965,1924,2053,1434,532,2361,2099,5962,262,4007,982,2999,1396,1754,705,2235,587,855,3432,1154,4757,1631,1482,1032,1142,2533,7627,551,0.131,0.119,0.093,0.104,0.126,0.139,0.093,0.125,0.163,0.136,0.125,0.138,0.12,0.135,0.13,0.141,0.129,0.158,0.126,0.138,0.101,0.136,0.137,0.156,0.123,0.127,0.123,0.104,0.107,0.126,0.163,0.128,0.104,0.132,0.059,0.07,0.143,0.14,0.103,0.089,0.119,0.121,0.149,0.112,0.105,0.077,0.098,0.1,0.101,0.129,0.137,0.099,0.129,0.112,0.118,0.103,0.102,0.113,0.114,0.113,0.101,0.134,0.115,0.126,0.116,0.116,0.105,0.086,0.104,0.144,0.101,0.131,0.104,0.138,480,1351,741,823,878,1060,724,959,511,440,260.0,1680,796,340,741,2585,4871,598,1231,2280,768,1193,1273,1614,1353,1959,1954,1325,1405,1560,603,434,239,1240,602,460,2063,1821,156,238,663,1285,1247,2285,1931,597,467,892,1097,571,334,1253,1080,2416,146,2048,532,1354,748,888,374,916,402,341,1653,868,2199,712,762,501,573,1126,3373,263,3.151,2.553,2.342,2.383,2.088,2.351,2.586,2.637,2.253,2.848,2.435,1.881,2.661,2.474,2.436,2.463,2.739,3.217,2.839,2.112,3.022,2.581,1.957,2.716,2.613,2.545,2.458,2.139,2.688,2.5,2.908,2.474,2.454,2.763,3.131,2.633,2.736,2.981,1.984,2.27,2.514,1.985,3.253,1.991,2.098,2.129,2.471,2.805,2.266,2.789,2.041,1.982,1.98,2.31,1.986,2.144,2.242,2.506,2.07,2.174,2.215,2.372,1.626,2.62,2.171,1.722,2.13,2.531,2.249,2.154,2.137,2.452,2.434,2.536,0.865,0.604,0.468,0.428,0.631,0.601,0.606,0.442,0.686,0.644,0.629,0.502,0.352,0.536,0.426,0.604,0.614,0.858,0.51,0.708,0.85,0.48,0.569,0.58,0.576,0.547,0.515,0.59,0.514,0.452,0.775,0.873,0.469,0.58,0.653,0.547,0.642,0.584,0.503,0.36,0.471,0.631,0.68,0.585,0.688,0.393,0.572,0.859,0.471,0.575,0.418,0.38,0.437,0.523,0.392,0.426,0.34,0.481,0.413,0.374,0.389,0.674,0.401,0.595,0.421,0.525,0.491,0.434,0.382,0.763,0.386,0.603,0.448,0.4
-418,Epileptic,F,42.0,0.9,2.4,0.6,1.0,1.6,2.1,1.9,1.4,0.9,0.4,0.3,2.6,1.9,0.5,1.7,5.7,8.3,0.4,1.7,3.9,0.5,2.3,1.5,2.8,2.7,2.8,3.0,2.5,2.2,3.1,1.2,0.2,0.7,2.8,0.4,0.7,2.9,2.8,0.2,0.2,1.0,2.7,1.7,1.8,2.1,0.5,0.3,0.8,1.2,0.9,0.4,1.4,1.3,2.3,0.2,1.7,0.5,1.8,0.6,1.0,0.6,1.4,0.2,0.4,1.9,0.8,3.1,1.0,1.0,0.4,1.1,1.5,4.5,0.5,7,25,7,7,12,20,16,12,11,5,3.0,23,19,7,20,53,74,3,20,34,5,27,14,25,28,30,34,19,17,31,18,3,5,30,2,7,34,34,1,1,8,25,16,14,13,3,1,5,6,8,2,9,8,15,1,14,3,16,4,8,3,7,2,3,16,9,23,8,6,5,8,11,42,5,0.038,0.026,0.012,0.021,0.033,0.033,0.028,0.026,0.038,0.024,0.023,0.034,0.034,0.037,0.04,0.035,0.028,0.018,0.025,0.038,0.011,0.034,0.027,0.036,0.031,0.031,0.029,0.026,0.025,0.031,0.033,0.017,0.037,0.036,0.013,0.019,0.04,0.042,0.017,0.021,0.02,0.034,0.036,0.025,0.02,0.011,0.019,0.013,0.016,0.039,0.033,0.017,0.02,0.022,0.024,0.015,0.02,0.025,0.024,0.021,0.026,0.029,0.016,0.013,0.024,0.034,0.022,0.016,0.02,0.015,0.027,0.029,0.026,0.029,1418,4792,1685,2013,1844,3430,2677,2880,2005,1067,606.0,2420,3374,1106,3155,10596,17350,1779,3222,3048,1617,3844,2041,4830,4872,5326,5677,3642,4583,5477,2210,580,948,4970,1491,1452,4229,5252,380,371,1663,2765,3658,1863,2748,1379,816,2100,2374,1003,348,2823,2416,4530,271,3176,780,2410,692,1279,666,2151,324,714,2320,711,4344,2086,1617,933,1395,1684,7114,733,0.125,0.12,0.094,0.1,0.132,0.132,0.12,0.128,0.128,0.128,0.109,0.14,0.132,0.126,0.149,0.141,0.125,0.091,0.12,0.145,0.072,0.145,0.124,0.131,0.135,0.131,0.122,0.106,0.1,0.139,0.132,0.075,0.126,0.13,0.072,0.095,0.134,0.135,0.103,0.104,0.112,0.152,0.134,0.115,0.101,0.076,0.091,0.078,0.092,0.149,0.128,0.094,0.104,0.105,0.145,0.097,0.108,0.114,0.115,0.113,0.114,0.124,0.111,0.09,0.119,0.134,0.111,0.099,0.101,0.117,0.128,0.141,0.116,0.128,373,1531,722,796,713,1134,1022,904,482,300,193.0,1168,970,265,771,2958,4697,342,1075,1672,641,1156,860,1311,1547,1675,1858,1297,1228,1682,681,265,281,1326,411,605,1400,1304,187,157,791,1253,875,1233,1681,665,317,955,1102,454,208,1356,903,1826,97,1799,407,1268,458,761,370,750,190,394,1244,388,2203,928,788,456,637,820,3083,356,3.222,2.841,2.258,2.535,2.275,2.513,2.259,2.874,2.923,2.938,2.692,1.846,2.691,2.898,3.004,2.745,2.915,3.636,2.453,1.68,2.066,2.553,2.072,2.829,2.53,2.738,2.345,2.269,2.839,2.553,2.463,2.167,2.811,2.743,3.011,2.272,2.372,2.781,2.275,2.46,2.548,1.97,3.131,1.736,1.848,2.22,3.236,2.738,2.536,2.653,1.889,2.287,2.476,2.609,3.332,1.953,2.192,2.085,1.695,1.857,2.041,3.083,1.94,2.142,2.109,2.29,2.065,2.478,2.296,2.421,2.603,2.387,2.328,2.173,0.859,0.678,0.63,0.471,0.593,0.697,0.563,0.511,0.646,0.458,0.901,0.376,0.577,0.566,0.636,0.615,0.592,0.774,0.537,0.427,0.615,0.578,0.471,0.664,0.534,0.481,0.539,0.5,0.631,0.543,0.602,0.962,0.593,0.637,0.539,0.53,0.694,0.608,0.487,0.388,0.666,0.471,0.712,0.651,0.581,0.464,0.438,0.614,0.528,0.552,0.342,0.522,0.727,0.554,0.415,0.441,0.407,0.451,0.427,0.362,0.449,0.566,0.261,1.008,0.622,0.589,0.457,0.351,0.47,0.577,0.489,0.582,0.576,0.632
-419,Epileptic,M,32.0,0.6,2.2,0.9,1.2,1.3,1.8,1.4,1.6,1.1,0.7,0.2,2.0,1.2,0.6,1.5,6.0,8.0,0.8,1.4,3.7,0.5,2.8,1.1,3.5,2.8,3.2,3.2,2.9,2.2,3.5,1.3,1.2,0.7,2.8,0.3,0.9,3.6,3.7,0.1,0.2,1.0,3.4,2.4,1.6,2.3,0.8,0.4,0.8,1.2,0.8,0.7,1.8,1.1,2.3,0.5,1.9,0.6,1.0,1.3,1.1,0.6,1.4,0.4,0.3,1.4,0.6,3.1,1.6,0.8,0.5,1.3,1.6,4.9,0.4,4,23,8,12,12,18,15,16,16,7,3.0,21,16,7,18,68,77,7,18,38,7,32,11,33,40,38,42,30,23,44,18,9,5,37,2,9,41,46,1,1,5,26,27,13,13,6,2,5,7,7,6,14,8,15,4,19,4,10,12,8,5,13,3,3,9,8,25,11,5,5,9,13,40,3,0.028,0.025,0.016,0.022,0.028,0.034,0.024,0.025,0.04,0.028,0.022,0.033,0.026,0.033,0.035,0.035,0.027,0.028,0.023,0.037,0.015,0.031,0.024,0.037,0.032,0.03,0.027,0.028,0.023,0.031,0.041,0.051,0.035,0.032,0.013,0.02,0.035,0.035,0.015,0.016,0.02,0.039,0.039,0.021,0.016,0.022,0.019,0.013,0.014,0.019,0.032,0.021,0.019,0.02,0.023,0.016,0.021,0.015,0.027,0.019,0.022,0.025,0.023,0.017,0.02,0.028,0.021,0.021,0.014,0.015,0.025,0.022,0.019,0.017,1486,5049,2359,2162,2204,2795,3048,3849,2256,1447,574.0,2292,3691,1503,3204,11688,19582,2600,3592,4252,3645,5002,2338,6507,5580,6476,6062,4564,6441,6362,2456,1340,1102,6959,2015,2455,8436,8959,400,487,1452,3027,6975,2344,3761,1410,794,2364,2732,1690,508,3160,2141,4541,665,3409,1074,2827,1404,1592,1035,2569,529,1169,2408,455,5119,2950,1698,1027,1829,2735,9771,661,0.106,0.109,0.097,0.117,0.134,0.149,0.109,0.12,0.15,0.122,0.112,0.143,0.12,0.13,0.147,0.141,0.124,0.115,0.121,0.15,0.076,0.139,0.112,0.139,0.141,0.138,0.13,0.115,0.11,0.143,0.151,0.134,0.12,0.147,0.076,0.102,0.137,0.146,0.095,0.093,0.104,0.149,0.148,0.103,0.089,0.104,0.105,0.084,0.088,0.114,0.144,0.106,0.111,0.099,0.132,0.104,0.118,0.096,0.133,0.106,0.13,0.122,0.132,0.106,0.104,0.118,0.111,0.107,0.092,0.121,0.119,0.124,0.102,0.103,374,1563,841,862,752,875,996,1107,586,379,190.0,962,884,323,826,3032,4884,464,976,1639,739,1473,784,1601,1615,1860,2091,1627,1544,1973,609,399,321,1492,463,741,1821,1953,194,217,714,1337,1269,1335,2103,613,323,930,1186,643,327,1332,932,1872,303,1768,452,1194,796,851,415,940,256,369,1046,319,2313,1197,758,468,898,1136,3982,340,3.713,3.027,2.804,2.528,2.499,2.628,2.48,2.908,2.933,3.072,2.761,2.035,3.102,3.166,2.941,2.883,3.128,4.177,2.922,2.238,3.453,2.545,2.436,3.021,2.619,2.883,2.346,2.237,3.023,2.5,2.868,3.041,2.6,3.224,3.654,2.884,3.228,3.23,2.214,2.662,2.661,1.969,3.602,2.029,2.072,2.283,3.103,3.167,2.751,2.784,1.996,2.485,2.4,2.613,2.511,2.094,2.904,2.62,1.976,2.052,2.7,2.773,2.353,2.927,2.46,1.801,2.385,2.878,2.62,2.573,2.455,2.817,2.624,2.485,0.833,0.627,0.575,0.525,0.737,0.578,0.683,0.459,0.688,0.518,0.639,0.469,0.384,0.633,0.516,0.607,0.599,0.646,0.599,0.686,0.754,0.526,0.487,0.667,0.539,0.61,0.495,0.583,0.55,0.564,0.581,1.032,0.529,0.611,0.728,0.527,0.775,0.616,0.358,0.412,0.484,0.557,0.819,0.614,0.641,0.533,0.746,0.72,0.491,0.456,0.45,0.503,0.36,0.489,0.472,0.38,0.482,0.441,0.398,0.448,0.547,0.719,0.535,0.964,0.577,0.685,0.423,0.393,0.428,0.675,0.462,0.484,0.473,0.465
-420,Epileptic,M,42.0,1.1,2.5,1.0,1.1,1.3,1.9,1.3,1.9,1.1,0.7,0.2,3.2,1.9,0.5,1.1,5.6,9.5,1.4,1.8,4.2,1.8,2.7,1.6,3.6,2.9,4.3,1.8,2.2,3.6,2.5,1.2,1.3,0.4,3.0,0.3,0.6,3.6,3.5,0.1,0.1,1.1,2.7,2.7,3.1,3.1,0.7,0.2,0.9,1.4,0.6,0.6,1.5,2.2,3.4,0.3,2.0,0.5,1.7,0.7,1.3,0.5,1.0,0.2,1.0,1.3,1.5,2.4,1.5,1.2,0.3,1.1,1.4,3.9,0.3,8,22,7,9,10,21,12,14,10,7,3.0,24,17,7,11,64,92,15,24,38,16,30,17,38,39,50,26,25,28,28,14,12,3,36,2,6,38,41,1,1,7,23,26,18,16,5,1,5,7,6,4,11,22,22,2,14,4,17,5,12,2,8,2,7,9,19,20,10,8,4,7,12,26,3,0.041,0.027,0.017,0.021,0.036,0.035,0.024,0.036,0.046,0.034,0.031,0.039,0.039,0.034,0.029,0.037,0.035,0.044,0.029,0.039,0.027,0.032,0.034,0.042,0.037,0.04,0.025,0.032,0.032,0.029,0.041,0.067,0.025,0.036,0.013,0.017,0.04,0.037,0.013,0.015,0.026,0.036,0.048,0.027,0.023,0.015,0.014,0.012,0.021,0.015,0.024,0.02,0.037,0.026,0.015,0.018,0.017,0.022,0.022,0.02,0.015,0.026,0.017,0.032,0.018,0.027,0.025,0.021,0.019,0.012,0.026,0.023,0.016,0.021,1637,4905,2407,2533,1737,3610,2480,3292,1499,1700,761.0,2799,3503,1179,2536,11017,19584,2718,4357,5126,4893,5359,2141,6362,6436,7032,3827,3305,6574,5009,2635,983,821,7053,2057,1916,6772,7775,361,387,1658,2700,5594,2872,3537,1378,670,2701,2107,1513,564,2685,3378,5789,437,3215,1230,2851,947,1778,1087,2380,357,1419,2147,1431,3326,2841,1880,919,1569,2480,10132,506,0.128,0.122,0.102,0.103,0.141,0.144,0.102,0.139,0.159,0.128,0.113,0.142,0.149,0.151,0.129,0.14,0.139,0.149,0.128,0.144,0.108,0.137,0.143,0.151,0.143,0.15,0.115,0.127,0.126,0.135,0.128,0.16,0.112,0.138,0.071,0.102,0.145,0.138,0.087,0.092,0.111,0.149,0.15,0.118,0.105,0.094,0.09,0.073,0.1,0.1,0.129,0.097,0.145,0.11,0.109,0.106,0.101,0.109,0.117,0.116,0.091,0.113,0.116,0.125,0.101,0.13,0.122,0.104,0.103,0.092,0.12,0.126,0.09,0.134,434,1502,862,873,577,1051,904,908,378,389,226.0,1299,927,253,638,2863,4909,575,1038,1956,1110,1590,840,1448,1691,1933,1293,1236,1776,1551,585,388,293,1474,449,624,1577,1691,191,177,707,1155,1109,1637,2051,716,274,1036,1004,694,318,1227,1081,2214,240,1647,535,1415,515,993,453,712,188,596,1063,786,1483,1194,940,385,646,980,3897,219,3.442,3.103,2.696,2.788,2.648,2.789,2.301,3.024,2.909,3.158,2.808,1.963,2.82,3.231,2.954,2.785,3.038,3.394,3.147,2.139,3.051,2.607,2.16,3.204,2.82,2.774,2.319,2.135,2.817,2.542,3.148,2.587,2.351,3.378,3.774,2.872,3.152,3.336,2.196,2.419,2.689,1.964,3.338,2.031,2.021,2.175,2.949,3.174,2.643,2.519,2.209,2.355,2.669,2.638,2.12,2.163,2.645,2.295,2.119,1.973,2.53,3.075,2.11,2.625,2.202,2.175,2.328,2.596,2.415,2.522,2.559,2.867,2.727,2.611,0.777,0.625,0.514,0.535,0.66,0.65,0.467,0.538,0.534,0.603,0.628,0.45,0.545,0.472,0.534,0.66,0.612,1.028,0.7,0.71,0.799,0.561,0.458,0.66,0.65,0.579,0.464,0.562,0.507,0.503,0.744,1.039,0.488,0.586,0.697,0.822,0.758,0.605,0.494,0.378,0.541,0.488,0.881,0.607,0.532,0.488,0.521,0.816,0.608,0.488,0.64,0.474,0.714,0.566,0.301,0.445,0.468,0.496,0.403,0.377,0.522,0.694,0.424,0.966,0.432,0.587,0.557,0.414,0.409,0.532,0.495,0.605,0.499,0.34
-421,Epileptic,F,22.0,0.7,2.5,1.1,1.5,1.4,1.6,1.5,1.4,1.5,0.4,0.2,2.5,1.8,0.6,1.7,6.7,9.1,0.7,1.5,3.7,0.8,2.0,1.5,3.6,2.1,2.8,3.1,2.3,2.1,2.3,1.6,1.1,0.7,2.6,0.3,0.9,3.1,2.5,0.1,0.1,1.1,2.3,1.5,2.9,3.2,0.5,0.5,0.9,1.2,0.7,0.5,2.5,1.5,2.6,0.1,2.3,0.5,1.0,0.4,0.9,0.5,1.5,0.2,0.4,1.7,1.0,1.7,1.6,1.3,1.1,0.6,0.8,3.4,0.3,4,20,9,14,17,20,13,13,18,5,2.0,27,20,7,22,74,100,5,24,35,8,27,13,40,29,37,35,22,20,30,16,9,10,33,2,8,38,29,2,2,18,21,14,23,22,6,2,7,6,4,3,20,12,20,1,18,5,9,5,6,3,11,2,6,18,13,15,12,11,12,6,9,28,3,0.031,0.025,0.019,0.025,0.035,0.026,0.023,0.025,0.047,0.025,0.033,0.034,0.032,0.038,0.031,0.033,0.031,0.032,0.023,0.036,0.019,0.034,0.033,0.038,0.032,0.028,0.027,0.026,0.02,0.031,0.042,0.054,0.031,0.036,0.02,0.018,0.038,0.029,0.011,0.015,0.026,0.032,0.038,0.026,0.024,0.015,0.02,0.015,0.018,0.02,0.03,0.024,0.029,0.019,0.012,0.021,0.026,0.013,0.022,0.025,0.02,0.026,0.025,0.02,0.019,0.022,0.019,0.022,0.017,0.024,0.027,0.026,0.018,0.011,1238,5196,2750,2687,2607,2847,2342,2500,2112,1149,409.0,2263,3788,1052,3369,11234,18283,2244,3857,3491,2627,2778,1742,6021,4033,5455,4658,3269,5369,4076,2228,880,1165,4835,969,2019,5239,5050,442,452,1438,2634,4161,2748,2974,1283,802,1899,2146,1053,471,3349,2024,4692,389,3285,1167,2450,815,1053,806,2327,281,788,2839,1195,2648,2511,2423,1489,1031,1344,6861,494,0.1,0.119,0.101,0.125,0.14,0.14,0.111,0.122,0.158,0.115,0.118,0.149,0.13,0.154,0.153,0.145,0.139,0.118,0.127,0.148,0.09,0.153,0.142,0.145,0.146,0.137,0.133,0.116,0.1,0.146,0.137,0.148,0.132,0.147,0.089,0.092,0.153,0.125,0.082,0.099,0.113,0.144,0.157,0.115,0.11,0.099,0.108,0.09,0.093,0.111,0.142,0.121,0.135,0.105,0.083,0.116,0.105,0.087,0.124,0.12,0.105,0.119,0.143,0.123,0.121,0.126,0.108,0.119,0.109,0.131,0.116,0.128,0.1,0.109,342,1641,933,993,828,1065,930,914,538,308,111.0,1190,1020,277,979,3553,5293,393,1080,1683,657,1019,705,1638,1242,1595,1730,1294,1635,1372,644,364,413,1293,285,713,1406,1330,244,216,745,1163,746,1725,1971,650,341,876,973,492,244,1652,826,2164,185,1749,511,1188,369,562,363,929,135,404,1412,640,1404,1183,1132,674,464,624,2941,286,3.262,2.93,2.75,2.619,2.436,2.402,2.151,2.496,2.742,2.866,3.004,1.713,2.774,2.835,2.588,2.514,2.754,3.886,2.793,1.896,3.039,2.263,2.189,2.732,2.567,2.714,2.157,2.068,2.559,2.392,2.518,2.633,2.292,2.76,2.717,2.542,2.891,2.798,1.981,2.477,2.425,1.986,3.356,1.82,1.722,2.2,3.052,2.688,2.588,2.6,2.428,2.154,2.317,2.321,2.09,2.078,2.473,2.269,2.063,2.051,2.365,2.417,2.296,2.113,2.137,2.248,2.031,2.368,2.358,2.323,2.467,2.384,2.423,2.203,0.914,0.705,0.599,0.533,0.674,0.567,0.467,0.477,0.616,0.481,0.944,0.401,0.488,0.517,0.486,0.557,0.541,0.767,0.462,0.519,0.647,0.451,0.358,0.742,0.525,0.543,0.506,0.489,0.517,0.508,0.689,0.992,0.418,0.517,0.476,0.525,0.703,0.51,0.32,0.357,0.522,0.439,0.859,0.621,0.51,0.538,0.33,0.686,0.405,0.505,0.307,0.431,0.573,0.383,0.246,0.421,0.399,0.493,0.401,0.31,0.483,0.646,0.426,1.007,0.56,0.734,0.395,0.385,0.371,0.508,0.506,0.597,0.437,0.393
-422,Epileptic,M,42.0,1.7,3.0,1.2,1.3,3.4,1.7,3.8,2.2,2.3,0.9,0.5,3.7,2.6,0.4,1.7,13.2,18.8,1.8,2.4,5.5,1.4,4.9,2.3,5.1,5.7,4.4,6.2,5.5,6.4,5.5,1.9,1.3,0.5,3.4,0.4,0.5,3.8,4.4,0.3,0.4,1.1,4.4,2.7,3.5,4.4,1.1,0.5,1.0,1.6,1.0,0.9,3.2,2.6,5.1,0.2,3.1,1.1,2.7,1.6,0.9,1.2,2.5,0.6,0.7,2.7,1.8,3.7,1.8,1.8,0.7,1.1,2.3,5.1,0.4,16,27,9,10,24,17,28,21,14,8,4.0,33,21,6,19,98,132,13,29,46,11,43,17,41,57,46,52,39,41,49,24,10,3,33,2,3,39,48,2,2,6,35,26,26,24,6,3,7,7,12,5,23,18,30,1,24,7,21,13,7,12,19,3,5,23,21,25,11,10,6,7,17,41,3,0.047,0.03,0.015,0.022,0.052,0.022,0.053,0.033,0.056,0.037,0.036,0.04,0.037,0.035,0.037,0.051,0.048,0.045,0.032,0.041,0.022,0.046,0.038,0.051,0.045,0.033,0.038,0.047,0.049,0.039,0.059,0.054,0.026,0.033,0.012,0.011,0.031,0.033,0.022,0.027,0.022,0.039,0.036,0.03,0.033,0.024,0.022,0.018,0.019,0.019,0.034,0.026,0.031,0.031,0.016,0.023,0.02,0.024,0.026,0.015,0.022,0.035,0.039,0.023,0.025,0.031,0.025,0.022,0.029,0.022,0.021,0.022,0.019,0.024,1878,5209,2982,2447,2066,2833,3427,3626,1541,1760,844.0,3017,4502,794,3118,13467,22076,2608,5188,5499,3792,4658,2036,5566,5925,8325,6704,4960,6177,7592,2228,1347,943,6684,2181,1772,8830,9566,368,559,1532,3208,5896,3238,3476,1582,886,1729,2786,2724,743,4611,2463,5950,396,4198,1834,3884,1579,1576,1722,2662,482,904,3156,1577,4974,2834,2111,1012,1751,3676,10000,417,0.149,0.128,0.093,0.108,0.157,0.115,0.144,0.133,0.149,0.13,0.133,0.147,0.14,0.15,0.136,0.15,0.148,0.149,0.128,0.15,0.093,0.125,0.12,0.144,0.145,0.135,0.126,0.137,0.135,0.145,0.155,0.125,0.107,0.129,0.064,0.067,0.136,0.133,0.093,0.114,0.11,0.13,0.133,0.128,0.127,0.109,0.098,0.091,0.093,0.106,0.141,0.114,0.124,0.12,0.087,0.113,0.105,0.116,0.108,0.093,0.111,0.134,0.146,0.111,0.121,0.13,0.115,0.106,0.126,0.111,0.104,0.118,0.098,0.13,586,1776,1133,912,1040,1186,1217,1261,641,431,233.0,1573,1241,219,887,4351,6342,658,1440,2290,1055,1589,939,1795,2117,2412,2596,1893,2026,2396,733,513,335,1684,633,626,2048,2292,237,260,759,1572,1343,1881,2177,741,370,831,1238,862,376,2126,1328,2579,175,2110,838,1808,940,855,810,1096,273,494,1670,891,2274,1197,969,470,856,1578,4355,243,3.019,2.583,2.675,2.726,1.859,2.126,2.391,2.605,2.12,3.218,3.15,1.763,2.892,2.559,2.631,2.472,2.814,3.184,2.793,2.079,2.782,2.288,1.96,2.384,2.337,2.763,2.177,2.205,2.508,2.475,2.355,2.454,2.269,2.887,3.114,2.509,3.107,2.947,1.788,2.446,2.608,1.85,3.119,1.934,1.802,2.359,2.899,2.692,2.793,3.044,2.03,2.307,2.044,2.402,2.38,2.188,2.588,2.408,1.813,2.0,2.119,2.232,2.048,2.047,2.111,2.024,2.333,2.435,2.414,2.331,2.4,2.485,2.397,2.206,0.655,0.633,0.454,0.434,0.484,0.528,0.659,0.507,0.568,0.475,0.686,0.424,0.459,0.475,0.606,0.676,0.725,0.845,0.588,0.707,0.843,0.561,0.494,0.733,0.624,0.53,0.549,0.579,0.636,0.608,0.634,0.833,0.451,0.641,0.709,0.445,0.622,0.709,0.37,0.419,0.489,0.508,0.804,0.729,0.492,0.443,0.533,0.528,0.476,0.74,0.489,0.436,0.505,0.497,0.401,0.426,0.454,0.529,0.364,0.434,0.412,0.606,0.387,0.709,0.501,0.675,0.482,0.367,0.495,0.572,0.431,0.57,0.461,0.404
-423,Epileptic,M,27.0,0.8,2.7,1.0,1.8,1.6,2.5,2.1,1.7,1.1,0.4,0.4,2.1,1.5,0.4,0.9,8.6,9.3,0.5,3.1,4.0,1.5,3.2,1.3,3.7,3.6,3.8,5.5,3.1,2.8,3.8,1.6,0.9,0.4,2.5,0.4,0.6,3.8,3.1,0.1,0.1,1.2,2.4,3.1,2.4,3.0,1.1,0.3,0.8,1.3,0.7,0.4,1.7,1.5,3.5,0.1,2.1,1.0,1.9,1.1,0.7,1.1,2.1,0.4,0.5,1.7,1.4,2.4,2.1,0.8,0.7,1.4,1.9,6.2,0.3,7,26,8,16,15,25,17,15,9,6,5.0,18,14,4,12,90,88,4,28,35,13,35,13,39,46,41,52,34,29,41,19,6,3,29,2,5,40,37,2,1,7,23,29,20,19,9,2,5,6,6,3,14,11,25,1,17,9,13,8,6,10,20,3,5,14,18,22,15,5,8,8,12,51,2,0.032,0.028,0.017,0.03,0.03,0.031,0.032,0.027,0.041,0.029,0.035,0.034,0.03,0.019,0.025,0.035,0.03,0.022,0.037,0.036,0.023,0.031,0.027,0.034,0.033,0.032,0.036,0.027,0.024,0.033,0.04,0.035,0.024,0.028,0.013,0.017,0.036,0.029,0.01,0.015,0.026,0.038,0.052,0.032,0.02,0.021,0.014,0.012,0.015,0.014,0.023,0.019,0.025,0.023,0.028,0.018,0.016,0.019,0.028,0.015,0.029,0.028,0.019,0.02,0.021,0.028,0.018,0.023,0.012,0.019,0.026,0.024,0.018,0.022,1602,5544,2565,2737,2494,3811,3197,3820,1882,1355,731.0,2143,3485,1174,2607,14421,20526,2301,5812,4536,4334,6004,2051,7483,6257,7061,7095,4927,7178,6395,2273,1119,914,6408,2148,1802,9140,8536,379,780,1550,3030,6593,1903,4041,1762,861,2599,2746,2116,467,3603,2526,6375,209,3685,2244,3626,1138,1511,1353,3409,760,991,2439,1330,3915,3360,1799,1671,1830,3004,13151,297,0.103,0.123,0.1,0.136,0.134,0.142,0.13,0.128,0.141,0.131,0.152,0.151,0.134,0.125,0.129,0.145,0.13,0.092,0.147,0.151,0.101,0.142,0.125,0.139,0.149,0.14,0.135,0.116,0.106,0.141,0.148,0.104,0.097,0.125,0.068,0.095,0.146,0.137,0.081,0.084,0.114,0.154,0.181,0.137,0.101,0.115,0.088,0.082,0.089,0.094,0.116,0.1,0.117,0.109,0.141,0.101,0.096,0.099,0.125,0.092,0.132,0.127,0.114,0.122,0.114,0.121,0.102,0.111,0.085,0.122,0.127,0.12,0.103,0.143,426,1618,862,1017,918,1383,1072,1012,502,337,215.0,1031,870,268,681,4105,5422,391,1410,1852,1049,1817,809,1798,1920,1996,2324,1704,1820,1953,625,437,298,1398,546,604,1852,1875,207,188,723,1112,1088,1214,2272,810,327,966,1171,686,277,1574,1032,2479,99,1889,885,1593,633,797,567,1203,350,400,1201,830,2025,1429,901,639,829,1189,5313,166,3.469,3.063,2.811,2.698,2.373,2.463,2.412,3.265,2.616,3.174,2.658,1.829,2.986,3.017,2.807,2.732,3.021,4.039,3.284,2.032,3.069,2.645,2.153,2.993,2.598,2.889,2.438,2.334,2.887,2.633,2.766,2.496,2.549,3.231,3.462,2.698,3.301,3.26,2.209,3.2,2.797,2.389,3.658,1.84,2.036,2.414,3.226,3.301,2.814,2.948,2.056,2.421,2.416,2.641,2.145,2.12,2.535,2.628,2.122,2.125,2.448,2.749,2.586,2.584,2.299,2.099,2.119,2.657,2.3,2.67,2.615,2.907,2.622,2.283,0.922,0.642,0.587,0.502,0.618,0.562,0.586,0.541,0.689,0.624,0.918,0.443,0.508,0.519,0.567,0.628,0.581,0.581,0.607,0.62,0.943,0.459,0.649,0.661,0.5,0.682,0.593,0.604,0.596,0.555,0.659,0.838,0.427,0.64,0.862,0.529,0.769,0.55,0.362,0.76,0.564,0.684,1.029,0.716,0.523,0.538,0.529,0.528,0.578,0.564,0.313,0.454,0.58,0.538,0.464,0.385,0.435,0.54,0.423,0.345,0.4,0.823,0.591,0.746,0.525,0.669,0.461,0.439,0.43,0.671,0.435,0.568,0.537,0.592
-424,Epileptic,M,32.0,0.9,2.8,0.6,0.9,1.6,1.6,1.3,1.6,1.3,0.9,0.3,2.6,1.3,0.5,0.7,4.8,7.5,0.8,1.7,4.0,2.2,3.8,1.7,2.5,3.6,3.2,2.6,2.1,2.0,2.6,1.4,1.7,0.3,1.8,0.4,0.6,3.7,2.5,0.2,0.1,1.2,3.6,3.0,2.3,2.9,0.4,0.4,1.3,1.1,0.9,0.6,1.0,1.6,2.3,0.3,2.0,0.7,1.3,1.1,0.9,0.6,1.5,0.4,0.4,1.6,1.9,2.7,0.6,0.7,0.9,1.2,1.4,5.5,0.3,8,28,5,7,18,17,13,16,16,8,5.0,27,18,6,9,58,72,8,24,40,23,44,19,29,49,34,33,24,22,38,16,13,5,20,3,7,42,35,1,1,7,33,28,19,16,3,3,8,6,7,5,8,11,17,3,14,5,10,9,9,4,14,2,5,13,20,24,5,5,8,8,9,56,3,0.038,0.027,0.013,0.019,0.035,0.023,0.027,0.025,0.043,0.028,0.033,0.037,0.029,0.029,0.027,0.029,0.027,0.045,0.03,0.033,0.04,0.035,0.027,0.028,0.027,0.022,0.026,0.025,0.021,0.027,0.04,0.056,0.019,0.022,0.016,0.013,0.032,0.027,0.013,0.014,0.023,0.035,0.045,0.024,0.02,0.011,0.022,0.018,0.02,0.018,0.03,0.016,0.027,0.018,0.012,0.014,0.019,0.016,0.022,0.015,0.021,0.027,0.032,0.018,0.02,0.031,0.018,0.011,0.016,0.024,0.019,0.014,0.016,0.017,1374,4544,1864,2096,2319,3493,3134,3546,1754,1818,732.0,2971,3053,1206,1623,9188,16794,1954,3790,4846,5484,5910,2405,5253,7455,7923,5728,4523,6011,5729,1873,1389,857,5976,1896,2071,9099,7172,341,386,1581,3508,6252,2636,3927,1179,871,2269,1917,2039,607,2254,1843,4906,581,3593,1242,2857,1294,1620,839,2623,366,751,2588,1214,4676,1655,1421,1599,1813,2628,12089,551,0.133,0.127,0.089,0.097,0.137,0.124,0.106,0.127,0.152,0.126,0.14,0.146,0.138,0.144,0.123,0.133,0.124,0.146,0.136,0.142,0.131,0.118,0.12,0.127,0.119,0.119,0.11,0.104,0.102,0.132,0.141,0.136,0.118,0.124,0.079,0.086,0.139,0.129,0.1,0.09,0.111,0.135,0.158,0.119,0.1,0.083,0.111,0.09,0.091,0.099,0.136,0.094,0.128,0.1,0.095,0.088,0.103,0.102,0.124,0.098,0.109,0.121,0.145,0.123,0.111,0.13,0.104,0.083,0.091,0.14,0.106,0.092,0.098,0.127,399,1561,709,781,838,1157,986,1091,630,468,216.0,1326,869,279,510,2729,4656,398,1060,2128,1121,1848,979,1603,2360,2366,1951,1448,1540,1847,617,557,267,1267,460,737,2067,1757,168,184,837,1576,1319,1488,2115,608,344,988,899,772,355,1082,887,2033,357,1988,573,1295,740,968,454,1026,178,393,1290,847,2281,799,722,573,937,1344,5248,276,3.305,2.841,2.575,2.571,2.289,2.383,2.526,2.821,2.287,2.987,2.534,1.968,2.759,2.977,2.484,2.594,2.834,3.515,2.825,1.963,3.547,2.503,2.107,2.634,2.449,2.693,2.349,2.373,2.921,2.523,2.322,2.74,2.658,3.332,3.542,2.462,3.015,2.979,2.37,2.237,2.417,1.882,3.249,2.049,2.133,2.085,2.743,2.894,2.622,2.697,2.058,2.181,2.189,2.491,1.951,1.963,2.616,2.414,1.9,1.801,2.186,2.519,2.122,2.202,2.203,1.843,2.109,2.315,2.232,2.923,2.229,2.34,2.398,2.383,0.731,0.618,0.516,0.542,0.621,0.72,0.623,0.458,0.565,0.473,0.895,0.488,0.522,0.354,0.499,0.518,0.599,0.919,0.527,0.61,0.844,0.588,0.595,0.753,0.627,0.522,0.514,0.624,0.562,0.519,0.596,0.815,0.337,0.524,0.566,0.416,0.814,0.595,0.339,0.434,0.477,0.548,0.864,0.553,0.621,0.404,0.598,0.582,0.406,0.653,0.411,0.445,0.496,0.453,0.281,0.352,0.365,0.607,0.435,0.391,0.364,0.584,0.583,0.702,0.526,0.551,0.442,0.338,0.506,0.977,0.439,0.386,0.547,0.437
-425,Epileptic,M,32.0,0.5,2.0,1.6,1.5,1.5,2.2,2.6,1.2,1.5,0.9,0.3,3.4,1.5,0.2,1.9,6.8,10.0,0.9,3.0,5.8,0.9,2.6,1.9,3.6,2.5,5.0,4.5,2.2,3.7,3.6,1.1,1.8,0.7,2.7,0.3,0.9,3.0,3.9,0.2,0.3,1.5,4.1,1.8,3.2,3.6,0.8,0.6,0.7,1.3,0.6,0.7,1.9,2.2,3.2,0.3,3.0,0.8,2.3,1.3,0.9,0.6,1.5,0.3,0.5,2.3,1.4,3.2,2.0,1.0,1.0,1.3,1.1,4.1,0.3,4,17,12,15,14,18,19,11,17,10,3.0,26,17,3,22,71,79,9,28,43,8,26,14,41,30,53,45,19,27,36,16,14,5,32,1,7,28,40,1,2,10,31,22,20,16,6,2,4,6,5,4,13,20,18,2,24,7,16,8,6,4,10,2,5,15,16,23,15,7,9,8,8,27,2,0.027,0.023,0.026,0.025,0.028,0.03,0.037,0.023,0.04,0.039,0.04,0.032,0.028,0.026,0.03,0.03,0.031,0.034,0.032,0.038,0.024,0.035,0.031,0.037,0.028,0.033,0.033,0.024,0.028,0.034,0.024,0.052,0.029,0.032,0.012,0.021,0.037,0.04,0.021,0.015,0.029,0.037,0.039,0.029,0.023,0.015,0.019,0.012,0.016,0.022,0.033,0.016,0.025,0.02,0.014,0.023,0.026,0.023,0.026,0.021,0.026,0.022,0.014,0.015,0.023,0.031,0.021,0.02,0.021,0.027,0.027,0.022,0.02,0.017,1533,4774,2681,2811,2383,3364,2624,3337,2129,1525,733.0,3546,3415,675,4224,14109,21447,2613,5309,5135,3116,4294,2523,6673,5235,9342,7338,3719,6997,6459,3066,1589,1150,6143,1304,2408,5780,8248,489,643,1705,4371,6172,2824,4233,1522,992,2077,2716,1380,608,3811,3021,5998,463,4407,1521,3237,1569,1237,618,2730,521,1212,3442,1258,5033,3428,1736,1725,1644,1869,7279,602,0.105,0.113,0.116,0.116,0.128,0.132,0.12,0.11,0.154,0.171,0.125,0.128,0.133,0.113,0.139,0.132,0.125,0.131,0.132,0.141,0.097,0.14,0.12,0.138,0.127,0.136,0.127,0.093,0.101,0.137,0.116,0.14,0.113,0.132,0.071,0.098,0.136,0.149,0.106,0.097,0.124,0.131,0.144,0.123,0.101,0.096,0.097,0.076,0.089,0.103,0.139,0.092,0.108,0.101,0.105,0.114,0.125,0.106,0.121,0.108,0.116,0.112,0.101,0.105,0.106,0.126,0.104,0.107,0.099,0.12,0.12,0.109,0.099,0.102,410,1542,963,982,874,1226,1015,1009,634,436,175.0,1681,930,157,1087,3759,5336,505,1489,2437,693,1308,1000,1738,1540,2771,2365,1426,1886,1999,760,583,352,1419,378,741,1448,1777,175,317,844,1706,1002,1755,2319,767,407,878,1172,472,298,1798,1384,2400,260,2200,591,1636,838,654,344,990,284,561,1590,796,2446,1483,771,611,775,797,3193,307,3.57,2.92,2.711,2.804,2.57,2.493,2.179,2.945,2.74,2.984,3.215,1.869,2.859,3.004,2.897,2.88,3.216,3.837,2.879,1.818,3.148,2.652,2.173,2.964,2.74,2.75,2.516,2.128,2.854,2.529,2.932,2.665,2.508,3.006,3.0,2.915,2.955,3.275,2.978,2.453,2.603,2.173,3.798,1.88,2.121,2.247,3.155,2.979,2.792,2.91,2.098,2.379,2.396,2.764,2.036,2.242,2.375,2.352,2.188,2.167,2.157,2.871,2.047,2.726,2.413,1.915,2.165,2.569,2.497,3.154,2.527,2.509,2.548,2.497,0.951,0.733,0.568,0.511,0.506,0.647,0.574,0.447,0.488,0.666,0.773,0.506,0.574,0.479,0.468,0.521,0.598,0.668,0.607,0.674,0.715,0.426,0.629,0.619,0.502,0.58,0.541,0.523,0.602,0.532,0.598,0.998,0.413,0.64,0.569,0.523,0.857,0.602,0.252,0.345,0.423,0.687,0.764,0.604,0.652,0.474,0.472,0.829,0.542,0.588,0.542,0.404,0.432,0.41,0.288,0.358,0.47,0.522,0.371,0.423,0.484,0.524,0.306,0.807,0.476,0.738,0.47,0.443,0.455,0.665,0.395,0.771,0.425,0.394
-426,Epileptic,M,47.0,0.7,2.9,0.9,1.5,2.0,2.1,1.6,1.9,1.5,0.5,0.3,3.8,2.4,0.6,1.5,8.8,10.8,1.3,2.2,3.7,1.4,2.3,2.1,3.7,2.3,4.7,2.4,3.2,4.5,3.5,1.1,0.5,0.5,3.2,0.5,0.7,1.9,3.7,0.1,0.3,1.1,3.2,2.8,2.7,2.9,0.5,0.7,0.8,1.3,0.4,0.6,2.4,1.2,2.3,0.1,2.3,0.8,2.9,1.2,1.2,0.3,1.4,0.8,0.2,2.3,1.9,2.7,2.0,1.0,0.6,1.0,0.9,5.2,0.5,4,24,10,14,19,18,11,13,13,5,3.0,31,20,6,13,65,81,8,21,30,9,21,19,31,23,41,30,33,35,36,12,6,5,26,3,5,19,36,1,2,8,26,21,22,19,5,4,4,5,3,4,14,8,11,1,18,6,21,10,8,2,9,6,2,15,16,20,10,7,5,6,6,38,3,0.033,0.029,0.018,0.025,0.04,0.037,0.023,0.029,0.051,0.032,0.03,0.038,0.04,0.035,0.04,0.041,0.035,0.041,0.035,0.031,0.022,0.034,0.033,0.038,0.026,0.031,0.024,0.029,0.035,0.033,0.029,0.025,0.02,0.037,0.018,0.018,0.037,0.036,0.018,0.021,0.025,0.038,0.04,0.027,0.02,0.013,0.026,0.013,0.016,0.016,0.035,0.026,0.023,0.021,0.013,0.017,0.023,0.028,0.032,0.022,0.017,0.023,0.035,0.016,0.024,0.038,0.019,0.026,0.02,0.023,0.025,0.026,0.023,0.02,1689,5067,1966,2462,2479,3089,3088,3249,1958,1232,576.0,3162,4071,1063,2613,13553,20041,2261,4055,4262,2767,3629,2517,6401,4735,8141,5243,4753,6387,5528,2440,1267,1290,6420,1895,1751,5222,7211,269,454,1903,3447,6304,2301,3917,1443,966,2648,2701,1125,457,3845,2159,4817,457,3862,1091,3319,1185,1613,597,2303,849,832,3499,1315,4773,3105,1968,824,1206,1555,9407,563,0.092,0.129,0.11,0.124,0.142,0.151,0.095,0.12,0.148,0.122,0.122,0.143,0.14,0.135,0.143,0.137,0.135,0.12,0.136,0.122,0.096,0.144,0.145,0.138,0.113,0.124,0.104,0.109,0.101,0.141,0.122,0.088,0.094,0.136,0.08,0.088,0.136,0.143,0.094,0.112,0.112,0.144,0.141,0.117,0.1,0.091,0.123,0.078,0.089,0.097,0.143,0.11,0.104,0.095,0.084,0.102,0.123,0.121,0.139,0.11,0.105,0.109,0.151,0.107,0.117,0.136,0.101,0.111,0.111,0.124,0.128,0.127,0.108,0.113,411,1520,792,949,871,988,923,969,553,311,181.0,1514,1017,262,712,3608,5039,459,1083,1773,752,1101,1030,1533,1335,2214,1729,1761,1684,1739,580,399,404,1411,483,617,980,1638,138,239,750,1322,1169,1612,2253,702,421,991,1140,378,297,1525,926,1750,231,2008,472,1635,629,838,261,877,361,296,1472,764,2300,1207,810,364,593,519,3678,324,3.715,3.219,2.375,2.606,2.493,2.701,2.646,2.881,2.601,3.162,3.092,1.858,3.077,2.863,2.726,2.822,3.166,3.889,2.886,1.987,2.696,2.534,2.169,3.096,2.685,2.799,2.346,2.196,2.882,2.578,3.051,2.844,2.555,3.301,3.355,2.557,3.611,3.201,2.233,2.222,2.969,2.253,3.676,1.616,1.973,2.297,2.99,3.025,2.904,3.326,1.979,2.475,2.512,3.003,2.314,2.152,2.527,2.221,2.132,2.171,2.589,2.593,2.298,3.033,2.696,2.081,2.25,2.88,2.716,2.428,2.544,3.229,2.654,2.18,1.141,0.781,0.656,0.532,0.58,0.59,0.816,0.759,0.682,0.412,0.989,0.537,0.571,0.515,0.528,0.586,0.658,0.806,0.761,0.665,0.961,0.473,0.501,0.746,0.53,0.779,0.461,0.601,0.67,0.54,0.698,1.142,0.464,0.634,0.694,0.47,0.845,0.734,0.275,0.513,0.67,0.791,0.761,0.528,0.74,0.448,0.648,0.799,0.542,0.769,0.681,0.709,0.54,0.778,0.39,0.442,0.557,0.603,0.413,0.403,0.28,0.683,0.66,0.811,0.719,0.715,0.536,0.438,0.629,0.808,0.519,0.772,0.57,0.389
-427,Epileptic,F,42.0,0.7,2.0,0.7,1.2,1.1,1.4,1.3,1.4,0.7,0.6,0.4,2.7,1.4,0.5,1.0,5.9,7.5,0.7,1.9,3.9,0.6,2.5,1.5,3.3,2.3,3.0,3.1,2.0,2.2,3.2,1.0,0.9,0.5,2.0,0.4,0.5,3.3,3.2,0.1,0.1,0.8,2.1,1.5,1.9,2.3,0.4,0.3,0.8,1.1,1.0,0.7,1.2,1.4,1.9,0.4,2.3,0.7,1.5,0.9,0.9,0.5,1.0,0.3,0.2,2.1,0.2,2.9,0.9,0.7,0.6,1.2,1.4,2.9,0.2,4,19,7,9,14,13,13,11,9,6,3.0,22,11,6,12,55,70,5,27,32,5,24,14,36,31,28,37,20,18,34,15,8,7,25,2,4,32,31,1,1,5,20,18,16,14,4,2,4,6,10,5,9,10,14,3,16,5,12,8,8,3,7,2,3,17,3,24,6,6,6,8,12,22,2,0.032,0.025,0.013,0.023,0.028,0.031,0.025,0.03,0.03,0.025,0.03,0.031,0.031,0.032,0.033,0.033,0.028,0.029,0.032,0.035,0.015,0.033,0.028,0.037,0.03,0.027,0.026,0.023,0.026,0.03,0.031,0.031,0.034,0.027,0.016,0.016,0.03,0.028,0.013,0.02,0.021,0.031,0.031,0.023,0.018,0.01,0.015,0.013,0.016,0.025,0.036,0.021,0.026,0.02,0.017,0.019,0.019,0.02,0.026,0.021,0.021,0.022,0.025,0.018,0.023,0.021,0.021,0.019,0.018,0.022,0.026,0.016,0.014,0.016,1388,3809,1790,2006,1777,2148,2489,2934,1504,1034,459.0,2703,2859,812,2100,10298,14441,2233,3983,4079,2266,3344,2291,5562,4625,6618,5430,3651,4373,5303,1848,1073,926,4860,1591,1619,6621,6844,258,267,1317,2480,3861,2266,3043,1156,842,2217,2139,1744,530,2243,2174,3630,702,3373,1297,2848,1019,1262,739,1917,388,836,2919,158,4798,1712,1377,1076,1442,2482,6934,481,0.117,0.116,0.095,0.104,0.121,0.147,0.11,0.129,0.125,0.106,0.109,0.13,0.136,0.141,0.135,0.138,0.13,0.097,0.125,0.132,0.073,0.145,0.12,0.135,0.14,0.127,0.131,0.104,0.111,0.133,0.127,0.114,0.172,0.122,0.08,0.081,0.114,0.118,0.095,0.105,0.102,0.133,0.121,0.113,0.102,0.083,0.089,0.08,0.09,0.123,0.148,0.102,0.118,0.101,0.107,0.107,0.104,0.107,0.121,0.109,0.106,0.105,0.116,0.108,0.113,0.1,0.11,0.101,0.107,0.131,0.122,0.103,0.083,0.104,355,1333,806,787,731,742,895,859,410,344,199.0,1241,791,239,570,2966,4186,414,1054,1712,674,1150,879,1425,1398,1797,1916,1362,1273,1789,498,435,289,1125,415,557,1795,1728,157,112,617,1058,918,1380,1878,643,368,896,1022,706,318,1005,917,1613,381,1851,585,1316,571,693,357,714,192,328,1467,121,2198,787,625,432,689,1241,3250,206,3.517,2.557,2.202,2.485,2.203,2.553,2.244,3.104,2.687,2.651,2.394,1.941,2.848,2.624,2.72,2.652,2.73,3.734,2.856,2.02,2.76,2.292,2.234,2.895,2.522,2.921,2.282,2.182,2.705,2.454,2.72,2.412,2.756,3.055,3.388,2.623,2.775,2.847,1.864,3.024,2.571,2.056,3.094,1.836,1.903,1.869,2.861,3.136,2.494,2.836,2.041,2.24,2.342,2.36,2.279,2.052,2.604,2.487,1.93,1.928,2.602,2.775,2.386,2.646,2.214,1.639,2.317,2.505,2.415,2.831,2.403,2.305,2.274,2.791,0.815,0.658,0.557,0.6,0.523,0.563,0.439,0.743,0.685,0.437,0.834,0.455,0.428,0.377,0.638,0.556,0.563,0.853,0.513,0.607,0.583,0.469,0.471,0.565,0.423,0.608,0.45,0.488,0.454,0.548,0.63,0.99,0.463,0.589,0.678,0.582,0.754,0.532,0.31,0.681,0.708,0.517,0.665,0.609,0.477,0.458,0.522,0.955,0.521,0.58,0.419,0.557,0.552,0.491,0.283,0.382,0.417,0.485,0.325,0.398,0.422,0.797,0.261,1.324,0.498,0.385,0.535,0.469,0.392,0.844,0.483,0.481,0.484,0.434
-428,Epileptic,M,52.0,1.0,2.3,1.3,1.8,3.3,2.4,2.1,2.2,1.4,1.7,0.6,2.7,2.9,0.6,2.6,10.5,12.7,1.3,2.4,4.3,1.0,6.2,1.6,5.6,9.2,5.2,6.6,5.2,4.9,3.9,1.8,1.3,0.7,1.9,0.4,0.3,3.1,3.8,0.3,0.2,1.2,2.5,2.9,2.5,3.5,1.0,0.7,1.0,1.5,1.0,0.3,2.3,2.1,2.7,0.9,3.5,1.3,1.9,0.6,1.0,0.8,1.7,0.6,0.7,2.3,2.1,3.8,1.3,1.0,0.7,1.1,2.2,5.3,0.3,9,22,8,15,24,20,15,22,14,14,5.0,26,22,8,29,76,92,8,31,41,7,42,13,49,63,48,51,42,31,43,15,10,6,24,3,3,36,47,2,1,5,22,30,19,19,7,3,6,7,7,2,19,12,16,5,21,8,12,4,8,5,11,5,3,16,24,25,8,6,5,7,15,41,3,0.033,0.028,0.023,0.03,0.048,0.038,0.036,0.036,0.048,0.042,0.042,0.034,0.035,0.038,0.055,0.05,0.041,0.04,0.034,0.035,0.021,0.055,0.031,0.052,0.048,0.033,0.039,0.041,0.045,0.031,0.04,0.049,0.025,0.025,0.012,0.009,0.029,0.028,0.019,0.022,0.021,0.037,0.041,0.03,0.024,0.016,0.024,0.014,0.016,0.021,0.014,0.025,0.028,0.021,0.029,0.019,0.021,0.018,0.02,0.016,0.015,0.03,0.031,0.023,0.019,0.033,0.021,0.021,0.017,0.022,0.024,0.026,0.015,0.017,1642,4697,2789,2804,2306,3103,3187,3749,1707,2016,909.0,2683,4726,1130,2692,9824,18208,2629,6121,4946,3905,3276,1130,7086,6298,8098,5355,4505,5327,6558,2745,1248,1102,4718,2005,1418,8287,10559,417,251,1514,2696,6408,2155,3370,1951,1068,2290,2943,1744,517,3446,2165,4384,873,4821,1973,3321,701,1444,1276,2863,543,1027,3315,1307,5206,2119,2007,930,1522,2879,11572,490,0.129,0.117,0.106,0.122,0.147,0.143,0.117,0.126,0.143,0.16,0.141,0.128,0.128,0.141,0.152,0.149,0.141,0.126,0.133,0.146,0.084,0.15,0.125,0.169,0.14,0.127,0.127,0.126,0.132,0.125,0.124,0.123,0.116,0.11,0.064,0.06,0.121,0.128,0.097,0.116,0.099,0.133,0.156,0.131,0.104,0.088,0.11,0.083,0.086,0.111,0.101,0.106,0.116,0.101,0.13,0.097,0.102,0.097,0.109,0.097,0.092,0.119,0.142,0.101,0.1,0.139,0.1,0.099,0.092,0.121,0.115,0.115,0.089,0.105,536,1487,884,1104,1100,1014,1032,1166,576,643,267.0,1207,1364,288,848,3441,5414,547,1309,2090,894,1505,765,1976,2868,2670,2513,1914,1566,2229,797,476,410,1371,578,637,2008,2344,242,135,802,1182,1286,1353,2075,977,434,1085,1370,704,300,1787,1205,1929,466,2775,932,1601,480,913,756,997,318,501,1729,962,2873,994,930,461,791,1403,5353,303,2.987,2.819,2.769,2.561,2.086,2.565,2.511,2.798,2.285,2.588,2.718,1.875,2.838,2.707,2.49,2.33,2.727,3.759,3.287,2.035,3.124,1.94,1.442,2.579,1.983,2.488,1.856,2.008,2.718,2.338,2.476,2.412,2.224,2.624,3.243,2.177,3.01,3.21,2.034,2.032,2.36,1.854,3.399,1.82,1.89,2.186,2.755,2.532,2.569,2.79,2.065,2.064,2.081,2.488,2.279,1.89,2.333,2.259,1.705,1.744,1.843,2.608,1.836,2.337,2.097,1.724,1.933,2.263,2.453,2.38,2.161,2.457,2.319,2.027,0.718,0.683,0.68,0.531,0.534,0.827,0.763,0.629,0.655,0.603,0.882,0.658,0.547,0.555,0.575,0.723,0.72,0.721,0.657,0.665,0.959,0.639,0.505,0.782,0.598,0.61,0.528,0.55,0.635,0.632,0.577,0.996,0.453,0.618,0.678,0.413,0.741,0.675,0.406,0.351,0.451,0.693,0.786,0.652,0.636,0.542,0.651,0.679,0.583,0.673,0.347,0.447,0.42,0.511,0.625,0.496,0.535,0.514,0.36,0.354,0.412,0.68,0.441,0.87,0.546,0.59,0.51,0.468,0.572,0.768,0.422,0.513,0.512,0.502
-429,Epileptic,M,27.0,1.3,3.2,1.6,1.5,1.3,2.4,3.3,2.6,1.1,0.8,0.3,4.0,1.9,0.8,1.5,7.8,9.7,1.5,2.4,5.2,1.2,5.8,3.2,4.5,6.5,6.4,7.1,4.5,3.7,5.1,3.0,1.1,1.1,3.3,1.1,0.7,6.8,5.4,0.2,0.2,1.6,4.1,3.3,4.2,3.0,1.1,0.4,1.2,1.7,1.1,0.9,1.9,1.7,3.6,0.3,4.5,1.1,2.0,1.8,1.3,1.3,1.7,0.4,0.5,2.0,1.1,3.9,1.4,0.9,0.4,1.0,2.1,5.8,0.4,7,26,15,12,16,23,29,25,10,8,4.0,39,27,12,18,83,98,11,28,47,15,54,27,44,58,76,68,45,35,52,27,10,12,42,9,5,67,61,1,1,11,36,32,28,18,7,2,9,8,11,4,14,12,26,2,42,11,15,13,9,9,12,3,5,19,12,28,9,8,3,7,12,47,4,0.049,0.026,0.02,0.027,0.037,0.041,0.035,0.033,0.038,0.039,0.037,0.05,0.036,0.038,0.036,0.042,0.034,0.05,0.035,0.043,0.033,0.054,0.045,0.046,0.055,0.042,0.045,0.042,0.04,0.037,0.055,0.043,0.044,0.037,0.024,0.018,0.054,0.04,0.016,0.016,0.027,0.046,0.046,0.033,0.023,0.018,0.021,0.016,0.019,0.033,0.033,0.022,0.028,0.024,0.017,0.021,0.02,0.02,0.028,0.02,0.031,0.028,0.026,0.016,0.023,0.02,0.022,0.022,0.017,0.013,0.025,0.023,0.023,0.022,1834,6403,3820,3102,2413,3284,4028,3920,1916,1589,855.0,3822,4429,1729,3218,13133,20466,2992,5019,6044,3556,4803,2520,6606,5348,9308,7058,3982,5451,7654,2786,1477,1540,7923,3291,1855,9753,9757,426,455,1638,3025,6448,3816,3312,1953,840,3078,2672,1766,684,2980,2071,6181,562,5884,2246,3334,1693,1730,1073,2943,611,1090,2921,1589,5384,2128,1848,1120,1725,3066,10081,1020,0.148,0.125,0.104,0.117,0.15,0.16,0.132,0.144,0.17,0.169,0.153,0.178,0.144,0.148,0.148,0.148,0.14,0.138,0.134,0.161,0.104,0.163,0.152,0.161,0.155,0.156,0.14,0.137,0.137,0.154,0.172,0.112,0.178,0.149,0.12,0.102,0.166,0.163,0.094,0.116,0.125,0.155,0.174,0.137,0.107,0.107,0.101,0.093,0.099,0.106,0.136,0.111,0.118,0.108,0.103,0.112,0.108,0.104,0.128,0.106,0.131,0.128,0.137,0.098,0.115,0.107,0.109,0.111,0.094,0.089,0.117,0.12,0.111,0.115,464,2075,1347,1052,746,1122,1563,1384,553,392,223.0,1668,1181,414,856,3830,5643,559,1308,2340,858,1898,1317,1862,2169,2945,3120,1917,1728,2485,931,502,463,1635,689,664,2556,2376,210,186,786,1566,1364,2024,2007,900,335,1112,1204,771,416,1390,975,2625,289,3313,875,1659,1010,990,612,969,286,549,1426,778,2735,974,931,485,727,1335,4080,388,3.586,2.892,2.717,2.609,2.804,2.362,2.213,2.576,2.913,3.064,3.072,1.991,2.91,2.939,2.956,2.625,2.899,3.844,2.917,2.126,2.915,2.103,1.784,2.748,2.043,2.516,1.918,1.819,2.436,2.444,2.445,2.613,2.81,3.214,3.691,2.442,2.724,2.918,2.432,2.559,2.474,1.74,3.283,2.091,1.886,2.092,3.044,3.463,2.64,2.471,1.924,2.3,2.26,2.382,2.144,1.899,2.663,2.275,1.932,1.937,2.158,3.135,2.5,2.28,2.177,2.303,2.1,2.429,2.119,2.691,2.36,2.659,2.487,2.902,0.901,0.526,0.45,0.645,0.608,0.582,0.643,0.697,0.541,0.59,0.677,0.576,0.464,0.468,0.439,0.544,0.602,0.873,0.592,0.675,0.87,0.643,0.49,0.775,0.529,0.654,0.478,0.487,0.614,0.625,0.778,1.022,0.529,0.739,0.851,0.485,0.901,0.761,0.393,0.419,0.707,0.449,0.808,0.818,0.465,0.554,0.495,0.8,0.472,0.583,0.458,0.45,0.562,0.443,0.465,0.402,0.643,0.502,0.387,0.394,0.448,0.731,0.587,0.566,0.573,0.781,0.439,0.405,0.4,0.47,0.597,0.549,0.554,0.701
-430,Epileptic,M,37.0,1.3,3.4,1.2,1.5,2.1,3.0,2.4,2.9,2.3,1.1,0.3,3.3,3.1,0.6,2.7,8.9,12.7,1.8,4.2,6.5,2.5,3.5,2.1,5.4,3.7,5.5,5.0,3.1,4.3,4.6,2.4,1.4,0.7,4.0,0.6,0.9,4.5,3.9,0.4,0.6,1.5,4.1,3.5,5.4,3.2,0.7,0.8,1.3,1.5,0.9,0.6,3.7,2.8,3.0,0.5,4.3,0.7,3.7,1.6,1.4,0.9,1.9,0.6,0.7,3.3,1.2,3.5,1.9,1.8,1.0,1.3,1.4,5.4,0.5,10,25,7,10,20,19,15,20,20,9,4.0,30,20,7,18,70,93,8,30,44,16,34,18,40,35,48,44,22,25,42,21,11,4,29,3,5,46,37,3,2,9,30,23,38,20,5,4,8,8,6,6,24,17,18,2,29,6,24,9,10,7,12,2,4,25,13,25,11,10,6,8,11,33,4,0.047,0.03,0.02,0.025,0.044,0.039,0.032,0.036,0.057,0.042,0.03,0.031,0.046,0.049,0.049,0.039,0.04,0.046,0.042,0.04,0.038,0.035,0.031,0.056,0.035,0.04,0.032,0.03,0.034,0.036,0.062,0.042,0.019,0.046,0.019,0.019,0.044,0.042,0.019,0.036,0.027,0.041,0.066,0.04,0.024,0.012,0.029,0.018,0.017,0.018,0.037,0.029,0.034,0.025,0.028,0.024,0.018,0.036,0.028,0.024,0.021,0.03,0.027,0.023,0.029,0.022,0.024,0.026,0.03,0.033,0.034,0.026,0.023,0.019,1629,5586,2478,2206,2554,3348,2316,3160,2406,1592,709.0,3067,3079,1100,2918,11469,18030,3080,4716,4439,3262,5407,2236,6344,5959,7506,6231,3345,5250,6113,2688,1916,1384,5494,1518,1823,6828,6371,478,670,1896,3255,4714,2766,2902,1543,1041,2690,2927,1753,449,4814,3459,4672,475,4833,1832,2747,1499,1643,1573,2656,506,939,3728,1602,4136,2448,1828,1192,1357,2303,8776,664,0.149,0.12,0.101,0.11,0.143,0.144,0.116,0.136,0.166,0.145,0.099,0.131,0.142,0.14,0.152,0.136,0.135,0.12,0.15,0.139,0.116,0.136,0.13,0.155,0.131,0.143,0.125,0.112,0.118,0.133,0.17,0.113,0.088,0.142,0.083,0.092,0.15,0.136,0.106,0.107,0.12,0.145,0.154,0.145,0.11,0.083,0.123,0.093,0.095,0.101,0.171,0.12,0.126,0.109,0.135,0.119,0.101,0.144,0.121,0.121,0.115,0.121,0.126,0.123,0.132,0.111,0.115,0.118,0.122,0.131,0.138,0.122,0.106,0.1,535,2022,912,1015,918,1335,1109,1275,772,433,216.0,1581,1053,276,952,3909,5590,569,1659,2417,1012,1759,1104,1745,1983,2553,2646,1479,1869,2170,727,662,551,1392,542,766,1669,1706,284,302,931,1554,1012,2027,2091,903,430,1150,1356,760,328,2175,1426,2019,256,2679,715,1704,914,875,716,1066,298,433,1918,820,2362,1207,1004,514,727,1017,3807,412,3.183,2.641,2.715,2.298,2.213,2.334,1.937,2.333,2.369,3.041,2.607,1.753,2.38,2.533,2.342,2.38,2.646,3.896,2.364,1.702,2.695,2.426,1.888,2.736,2.453,2.514,2.01,1.908,2.288,2.29,2.674,2.637,2.101,2.854,2.643,2.397,2.838,2.76,1.952,2.381,2.507,1.886,2.974,1.626,1.59,1.848,2.756,2.814,2.669,2.414,1.757,2.299,2.372,2.34,2.437,1.98,2.617,1.889,1.983,2.085,2.246,2.503,1.986,2.465,2.212,2.245,1.947,2.362,2.11,2.48,2.286,2.585,2.516,2.08,0.881,0.571,0.662,0.568,0.67,0.568,0.483,0.552,0.549,0.609,0.8,0.451,0.518,0.679,0.601,0.585,0.605,0.835,0.721,0.554,1.009,0.494,0.427,0.817,0.521,0.598,0.498,0.472,0.67,0.525,0.712,1.118,0.563,0.617,0.777,0.446,0.71,0.602,0.361,0.492,0.553,0.536,0.831,0.609,0.44,0.466,0.731,0.704,0.617,0.554,0.355,0.473,0.628,0.541,0.427,0.453,0.564,0.66,0.391,0.375,0.414,0.799,0.361,1.054,0.612,0.813,0.5,0.589,0.461,0.51,0.478,0.649,0.625,0.581
-431,Epileptic,M,37.0,1.5,2.6,1.1,1.5,2.1,2.3,2.0,1.8,2.5,0.9,0.6,3.7,2.2,0.7,2.1,7.0,8.9,1.3,3.0,5.0,3.2,4.4,2.8,4.2,4.9,3.7,6.1,3.1,2.8,3.0,2.4,1.5,0.9,3.3,0.9,1.0,5.4,2.8,0.2,0.2,1.3,3.0,3.1,2.6,2.8,0.7,0.7,1.1,1.7,1.0,0.8,1.9,3.0,2.4,0.4,2.9,1.5,2.5,1.8,2.2,0.6,2.0,1.1,0.7,2.1,1.7,3.5,1.3,1.5,1.1,0.9,1.8,7.4,0.5,12,28,12,14,22,27,23,20,23,12,7.0,36,27,7,19,77,95,13,37,45,21,53,28,40,48,44,72,35,33,42,24,16,9,43,8,10,60,42,1,1,9,30,32,23,19,6,4,8,9,9,7,18,27,15,3,26,13,22,12,17,6,13,5,6,19,23,36,12,10,9,7,15,65,3,0.044,0.025,0.021,0.026,0.04,0.03,0.027,0.029,0.055,0.036,0.039,0.036,0.036,0.051,0.04,0.035,0.028,0.044,0.029,0.039,0.05,0.035,0.03,0.042,0.037,0.03,0.035,0.028,0.025,0.033,0.048,0.044,0.028,0.032,0.026,0.02,0.036,0.027,0.03,0.016,0.02,0.032,0.035,0.022,0.019,0.017,0.023,0.016,0.019,0.024,0.029,0.021,0.032,0.017,0.019,0.019,0.028,0.025,0.028,0.03,0.016,0.029,0.044,0.018,0.021,0.025,0.021,0.02,0.022,0.02,0.022,0.018,0.024,0.016,1589,5030,2157,2635,2073,3166,3277,3222,1890,1700,615.0,3401,4007,635,2529,11151,18097,2609,5242,4449,3864,5485,2444,4909,5198,6096,7465,4289,6007,5491,2309,1717,1287,7176,2413,2176,8235,7456,232,332,1767,2860,6861,2993,3722,1389,871,2648,2625,2112,815,3077,3716,4371,577,4195,1757,3596,1620,2038,1082,2692,820,1013,2959,1526,5169,2277,1970,1450,1289,2497,10468,791,0.126,0.116,0.113,0.117,0.146,0.142,0.115,0.137,0.171,0.175,0.143,0.143,0.142,0.146,0.152,0.145,0.129,0.144,0.136,0.151,0.135,0.139,0.121,0.142,0.128,0.125,0.127,0.112,0.103,0.146,0.145,0.132,0.115,0.142,0.114,0.109,0.146,0.13,0.113,0.099,0.111,0.129,0.141,0.111,0.102,0.096,0.117,0.093,0.105,0.109,0.14,0.116,0.141,0.096,0.114,0.108,0.131,0.122,0.126,0.124,0.101,0.128,0.162,0.114,0.109,0.119,0.117,0.113,0.105,0.117,0.116,0.112,0.116,0.102,547,1883,819,1007,1074,1384,1276,1134,756,446,261.0,1611,1234,284,901,3622,5531,555,1729,2186,1057,2024,1372,1814,1993,2132,2950,1759,1843,1813,893,686,485,1865,631,857,2516,2047,126,188,945,1508,1612,1869,2298,733,400,1123,1258,837,457,1542,1561,2169,332,2384,804,1625,978,1147,641,1064,403,593,1629,1062,2638,1111,1073,729,713,1391,4844,410,2.934,2.454,2.41,2.446,1.795,2.115,2.13,2.576,2.122,3.01,2.015,1.849,2.573,1.839,2.241,2.365,2.536,3.446,2.434,1.861,2.906,2.302,1.72,2.169,2.156,2.381,2.061,2.002,2.491,2.34,2.055,2.486,2.183,2.846,3.269,2.291,2.545,2.651,1.828,2.028,2.328,1.68,3.008,1.835,1.798,1.957,2.679,2.8,2.552,2.833,2.054,2.074,2.244,2.09,2.041,1.891,2.36,2.332,1.908,1.854,1.885,2.432,1.981,2.064,1.948,1.77,1.931,2.206,2.129,2.083,2.044,2.064,2.225,2.297,0.666,0.465,0.514,0.6,0.479,0.585,0.588,0.493,0.644,0.779,0.761,0.559,0.534,0.69,0.709,0.594,0.629,0.841,0.569,0.636,0.812,0.607,0.489,0.825,0.562,0.522,0.582,0.557,0.535,0.581,0.777,0.807,0.371,0.581,0.564,0.46,0.726,0.677,0.438,0.388,0.536,0.571,0.726,0.539,0.586,0.453,0.598,0.735,0.5,0.673,0.508,0.454,0.561,0.448,0.373,0.419,0.435,0.63,0.399,0.458,0.374,0.693,1.001,0.724,0.477,0.62,0.45,0.33,0.454,0.591,0.449,0.424,0.414,0.413
-432,Epileptic,M,27.0,0.8,1.8,0.9,0.8,1.5,2.2,1.7,1.9,0.9,0.5,0.4,2.8,1.7,0.5,1.2,5.9,7.4,0.6,2.4,4.1,1.8,2.9,2.2,2.8,3.1,5.4,3.0,4.0,2.3,2.3,1.5,1.1,0.5,2.3,0.6,0.7,4.8,3.3,0.1,0.2,0.8,3.3,2.7,3.2,2.2,0.7,0.3,0.9,1.1,1.1,0.7,1.2,1.1,1.9,0.5,2.8,0.6,1.2,0.5,1.2,0.4,1.4,0.3,0.3,1.5,1.0,1.4,1.5,0.9,0.4,0.9,2.0,4.3,0.6,5,17,6,7,13,19,14,15,10,5,5.0,26,17,6,12,62,69,4,24,38,26,32,21,29,41,41,37,33,23,36,19,8,5,25,4,7,42,38,1,1,4,27,20,25,11,5,1,6,5,10,4,12,9,14,4,21,8,10,5,8,4,12,2,3,12,16,11,9,4,4,5,13,32,4,0.032,0.02,0.017,0.017,0.027,0.029,0.025,0.027,0.038,0.023,0.036,0.031,0.027,0.028,0.031,0.032,0.025,0.025,0.034,0.029,0.027,0.031,0.028,0.027,0.031,0.044,0.025,0.043,0.021,0.023,0.035,0.039,0.038,0.028,0.012,0.022,0.037,0.03,0.01,0.017,0.014,0.031,0.036,0.023,0.015,0.017,0.014,0.017,0.013,0.016,0.031,0.013,0.014,0.018,0.02,0.017,0.019,0.016,0.016,0.02,0.029,0.024,0.014,0.014,0.017,0.036,0.014,0.017,0.011,0.015,0.018,0.023,0.016,0.031,1794,5166,2903,2300,2765,3870,3391,4273,1827,1660,744.0,3924,5121,1625,2861,13797,20865,2571,4770,5588,3985,5018,3143,8093,7203,6360,6890,4885,7944,6890,2690,1116,789,7380,2467,2296,8430,9449,372,448,1685,3604,5975,3987,4424,1452,945,2038,2761,2419,675,3536,2460,5266,840,4764,1506,2966,1023,2052,588,2970,665,811,3270,1073,3468,3623,2311,1394,1705,2700,10632,784,0.106,0.108,0.089,0.089,0.115,0.132,0.104,0.126,0.134,0.118,0.133,0.129,0.12,0.126,0.138,0.124,0.117,0.102,0.137,0.126,0.107,0.133,0.124,0.126,0.131,0.14,0.111,0.113,0.096,0.121,0.128,0.114,0.154,0.121,0.082,0.104,0.126,0.127,0.094,0.093,0.086,0.12,0.135,0.111,0.087,0.105,0.079,0.091,0.079,0.103,0.131,0.083,0.098,0.089,0.125,0.099,0.107,0.094,0.102,0.103,0.121,0.106,0.105,0.103,0.1,0.135,0.087,0.09,0.077,0.107,0.102,0.117,0.093,0.151,441,1440,844,765,851,1288,972,1078,502,344,207.0,1344,1129,308,657,3018,4764,424,1255,2158,984,1523,1145,1660,1782,1787,1986,1387,1758,1816,625,443,267,1372,641,569,2189,1847,185,206,713,1590,1267,2007,2100,621,322,830,1138,1013,363,1452,1096,1887,337,2262,572,1370,567,989,295,993,279,344,1368,536,1534,1338,1005,470,737,1253,3941,295,3.49,3.184,3.29,2.835,2.618,2.706,2.784,3.285,2.83,3.653,2.942,2.347,3.367,3.506,3.114,3.245,3.424,3.974,2.899,2.172,3.059,2.498,2.379,3.455,3.063,2.966,2.611,2.677,3.282,2.889,3.086,2.444,2.595,3.508,3.527,3.386,2.903,3.562,2.403,2.784,2.916,1.975,3.42,2.16,2.417,2.53,3.865,3.251,3.035,2.763,2.219,2.547,2.531,2.863,3.039,2.326,2.641,2.499,1.885,2.316,2.179,3.193,2.501,2.419,2.649,2.326,2.438,2.899,2.879,3.467,2.728,2.624,2.799,3.126,0.776,0.708,0.773,0.51,0.602,0.562,0.789,0.577,0.561,0.432,0.672,0.768,0.559,0.526,0.539,0.643,0.696,0.86,0.535,0.647,0.817,0.659,0.662,0.608,0.696,0.763,0.609,0.685,0.573,0.623,0.716,0.944,0.561,0.659,0.636,0.635,0.693,0.566,0.399,0.34,0.611,0.667,0.799,0.659,0.703,0.507,0.578,0.682,0.463,0.565,0.436,0.535,0.462,0.566,0.518,0.421,0.431,0.517,0.403,0.383,0.363,0.504,0.647,1.038,0.506,0.627,0.524,0.505,0.452,0.854,0.473,0.489,0.59,0.504
-433,Epileptic,F,62.0,0.3,2.8,1.0,1.4,1.6,2.3,1.5,1.7,1.0,0.7,0.2,3.1,1.7,0.5,1.1,5.3,8.4,0.8,2.1,4.4,1.5,2.7,1.8,3.9,3.6,3.4,3.1,2.8,3.1,3.3,2.0,1.0,0.6,2.2,0.3,0.3,2.7,3.2,0.1,0.2,1.0,3.2,2.5,3.0,2.9,0.8,0.7,0.7,1.2,0.6,0.6,2.2,1.9,2.3,0.0,2.3,0.7,1.8,0.6,1.7,1.1,1.6,0.3,0.5,1.3,1.3,2.8,1.7,1.1,0.4,1.4,0.7,4.5,0.3,3,29,10,9,14,25,14,16,15,6,3.0,29,17,6,12,53,75,5,29,37,14,25,20,39,35,36,43,31,28,33,19,7,5,28,2,3,32,40,2,1,5,30,23,22,17,6,4,4,7,5,6,18,13,14,0,20,5,14,4,14,8,13,2,5,11,15,24,11,8,5,10,7,45,2,0.022,0.024,0.019,0.024,0.039,0.038,0.022,0.03,0.04,0.045,0.027,0.036,0.033,0.048,0.032,0.036,0.03,0.034,0.028,0.037,0.036,0.034,0.033,0.044,0.038,0.034,0.029,0.028,0.027,0.031,0.049,0.045,0.027,0.033,0.021,0.013,0.037,0.039,0.014,0.023,0.024,0.04,0.055,0.03,0.021,0.015,0.032,0.012,0.02,0.025,0.035,0.024,0.03,0.02,0.01,0.021,0.021,0.023,0.028,0.028,0.027,0.025,0.021,0.026,0.022,0.028,0.021,0.023,0.017,0.02,0.03,0.025,0.022,0.016,1313,4687,1983,2107,2083,3133,2797,2929,1479,1242,504.0,2875,3056,834,2283,8467,15953,1802,4140,3780,3041,3837,1974,5849,4530,4881,4885,4426,5649,4948,2207,1111,1005,5012,1027,1171,5775,5430,348,350,1194,2826,4215,2504,3097,1620,826,2047,1915,1101,697,2978,2388,4308,160,3083,1196,2652,707,1620,1124,2844,409,780,1867,1140,3779,2337,1761,737,1532,1203,8176,553,0.088,0.118,0.115,0.108,0.145,0.161,0.106,0.147,0.168,0.138,0.136,0.147,0.138,0.168,0.141,0.145,0.129,0.12,0.136,0.145,0.132,0.146,0.142,0.156,0.158,0.153,0.133,0.125,0.112,0.14,0.158,0.121,0.129,0.135,0.083,0.083,0.148,0.154,0.094,0.096,0.118,0.158,0.183,0.122,0.101,0.097,0.144,0.075,0.101,0.116,0.142,0.114,0.12,0.098,0.076,0.114,0.098,0.116,0.124,0.137,0.135,0.121,0.132,0.13,0.114,0.126,0.114,0.113,0.098,0.114,0.132,0.134,0.107,0.117,292,1747,817,859,798,1083,1008,920,442,331,167.0,1343,905,216,674,2560,4655,327,1292,1829,720,1336,905,1673,1503,1596,1927,1719,1840,1795,677,389,349,1146,305,455,1407,1558,186,174,584,1340,837,1709,2097,790,371,877,921,417,367,1500,1017,1852,63,1655,525,1259,359,869,554,1043,219,380,1017,677,2081,1153,929,347,779,502,3424,251,3.635,2.557,2.322,2.333,2.201,2.38,2.298,2.924,2.533,3.019,2.682,1.908,2.575,2.747,2.626,2.581,2.742,3.857,2.549,1.831,3.077,2.367,1.94,2.656,2.48,2.601,2.117,2.091,2.426,2.231,2.478,2.789,2.501,2.92,2.811,2.387,2.918,2.745,2.036,2.314,2.667,1.918,3.296,1.708,1.643,2.246,2.72,2.809,2.413,3.107,2.027,2.067,2.209,2.432,2.173,2.018,2.547,2.333,2.176,2.12,2.446,2.735,1.921,2.126,2.0,2.085,2.03,2.301,2.197,2.275,2.071,2.473,2.471,2.467,1.143,0.568,0.731,0.489,0.669,0.551,0.472,0.541,0.654,0.453,0.763,0.504,0.403,0.417,0.54,0.498,0.539,0.837,0.505,0.518,0.765,0.435,0.408,0.759,0.481,0.559,0.395,0.492,0.536,0.492,0.826,1.105,0.457,0.649,0.626,0.464,0.703,0.596,0.368,0.353,0.603,0.481,0.878,0.562,0.478,0.581,0.537,1.019,0.465,0.599,0.373,0.405,0.555,0.372,0.326,0.389,0.549,0.486,0.337,0.388,0.405,0.669,0.412,0.694,0.407,0.669,0.403,0.383,0.41,0.461,0.483,0.446,0.534,0.48
-434,Epileptic,M,37.0,1.4,4.0,1.4,2.1,2.8,2.8,2.8,2.6,1.0,0.9,0.5,3.5,2.1,0.8,2.4,7.9,14.4,1.5,2.7,4.3,1.5,3.9,2.6,4.7,6.2,5.1,6.9,4.0,5.0,4.1,2.2,1.4,0.9,3.4,1.3,0.7,4.4,4.5,0.4,0.3,1.5,4.2,2.8,3.2,3.8,1.5,0.3,1.8,1.5,1.3,0.5,4.0,1.6,4.1,0.3,2.8,1.4,2.1,2.1,1.7,1.6,2.6,0.6,0.6,2.6,2.1,2.8,2.1,2.1,0.5,1.0,2.3,7.2,0.4,9,41,13,16,26,33,26,27,14,12,6.0,38,26,10,28,93,143,10,35,48,12,62,31,52,60,57,61,36,43,50,24,8,8,40,10,5,54,58,5,2,9,37,31,27,19,12,2,11,8,11,3,32,13,28,2,21,10,20,17,13,13,22,5,5,24,24,23,15,13,8,6,21,64,4,0.042,0.027,0.02,0.031,0.037,0.034,0.035,0.03,0.047,0.042,0.046,0.035,0.031,0.033,0.03,0.033,0.033,0.043,0.031,0.035,0.025,0.033,0.038,0.043,0.047,0.033,0.042,0.035,0.037,0.034,0.038,0.042,0.025,0.028,0.027,0.015,0.034,0.03,0.024,0.016,0.025,0.042,0.033,0.026,0.026,0.025,0.019,0.02,0.015,0.025,0.026,0.025,0.026,0.023,0.022,0.02,0.023,0.016,0.028,0.02,0.027,0.031,0.025,0.016,0.02,0.03,0.02,0.023,0.027,0.018,0.023,0.02,0.02,0.018,1954,6434,3141,3172,3028,3754,3387,5041,1776,1701,634.0,3781,5064,1306,4877,14227,25873,2791,5297,5709,4492,6989,3242,7071,6326,9014,6239,4612,7950,6027,2714,1674,1746,8119,3871,2030,9238,11386,719,667,2053,3702,7128,3650,4174,2163,751,3558,3261,2304,633,6233,2301,6497,344,3664,1998,4019,1775,2364,1743,3484,764,1204,3789,1522,3777,3076,2487,1067,1414,3733,12297,632,0.131,0.122,0.103,0.129,0.145,0.152,0.121,0.135,0.14,0.146,0.142,0.147,0.122,0.148,0.127,0.137,0.136,0.127,0.126,0.14,0.095,0.138,0.133,0.149,0.144,0.139,0.137,0.118,0.121,0.154,0.145,0.121,0.104,0.13,0.113,0.085,0.139,0.138,0.124,0.107,0.113,0.137,0.142,0.123,0.114,0.117,0.091,0.098,0.09,0.125,0.121,0.117,0.12,0.112,0.121,0.11,0.109,0.099,0.137,0.105,0.136,0.14,0.127,0.102,0.115,0.133,0.111,0.118,0.122,0.129,0.116,0.109,0.108,0.111,563,2299,1180,1148,1191,1451,1318,1461,535,429,192.0,1731,1292,329,1378,4159,7388,574,1606,2327,1040,2310,1293,2067,2239,2755,2322,1804,2198,2073,847,604,644,1870,805,767,2364,2661,339,299,1023,1569,1519,2035,2332,973,294,1292,1443,859,327,2663,1105,2741,153,2095,841,2033,1111,1421,906,1303,429,605,1882,1013,2130,1349,1202,489,707,1837,5567,369,3.403,2.609,2.628,2.727,2.21,2.191,2.275,3.076,2.416,2.996,2.561,1.895,3.008,2.822,2.731,2.634,2.799,3.599,2.705,2.044,3.126,2.296,2.168,2.674,2.287,2.661,2.182,2.109,2.781,2.386,2.487,2.615,2.354,3.031,3.757,2.441,2.91,3.01,2.216,2.504,2.483,2.034,3.397,1.99,2.049,2.244,3.019,3.387,2.769,3.238,2.065,2.358,2.138,2.468,2.406,1.968,2.453,2.211,1.652,1.788,2.222,2.602,2.096,2.279,2.134,1.829,1.993,2.483,2.406,2.229,2.265,2.315,2.36,2.069,0.751,0.556,0.452,0.52,0.672,0.535,0.534,0.5,0.691,0.616,0.746,0.514,0.509,0.649,0.598,0.613,0.583,0.782,0.633,0.693,0.779,0.597,0.531,0.772,0.645,0.607,0.575,0.58,0.634,0.518,0.701,1.113,0.481,0.682,0.848,0.418,0.761,0.654,0.523,0.365,0.517,0.56,0.887,0.589,0.588,0.47,0.483,0.726,0.51,0.589,0.331,0.496,0.455,0.496,0.65,0.38,0.564,0.536,0.437,0.387,0.51,0.658,0.423,0.681,0.51,0.612,0.435,0.384,0.423,0.711,0.497,0.414,0.456,0.35
-435,Epileptic,M,22.0,0.7,2.7,1.7,1.9,2.3,2.2,1.6,1.6,1.9,1.0,0.6,3.0,1.2,0.5,1.4,7.3,9.2,0.9,2.3,5.8,1.2,2.6,1.9,3.5,3.2,2.2,3.8,3.1,3.1,3.1,1.7,1.6,0.6,1.9,0.4,0.4,2.7,3.3,0.1,0.1,0.9,3.0,2.4,4.6,2.4,0.7,0.5,1.3,1.1,0.4,1.1,2.1,2.2,2.4,0.4,2.6,0.8,1.3,1.4,1.1,0.8,1.8,0.4,0.4,1.4,1.3,2.9,1.0,1.4,0.5,0.9,1.3,4.3,0.3,9,26,23,14,18,24,13,15,20,10,6.0,27,12,8,14,79,86,8,29,50,12,32,18,35,38,28,46,29,27,41,26,13,5,24,3,3,31,43,1,1,7,31,21,30,16,5,3,9,6,4,7,16,16,20,4,24,10,11,10,10,9,14,3,4,12,19,21,8,10,4,6,10,37,4,0.033,0.022,0.018,0.03,0.033,0.028,0.024,0.023,0.046,0.035,0.038,0.033,0.023,0.03,0.029,0.031,0.029,0.028,0.024,0.037,0.023,0.031,0.024,0.033,0.034,0.021,0.024,0.024,0.023,0.031,0.031,0.064,0.024,0.022,0.009,0.012,0.032,0.034,0.009,0.017,0.018,0.028,0.036,0.033,0.017,0.014,0.015,0.021,0.016,0.01,0.036,0.019,0.023,0.016,0.03,0.017,0.025,0.014,0.023,0.018,0.022,0.031,0.018,0.013,0.02,0.018,0.02,0.015,0.019,0.015,0.02,0.013,0.016,0.013,1363,5828,4059,3118,2716,3640,2613,3423,2484,1667,652.0,3244,2941,1211,2804,13611,19247,2565,5188,5899,4038,4983,2703,6602,5694,6078,6814,4792,6516,5729,2978,1262,1171,6423,2414,1673,6418,8421,514,381,1447,3166,6139,3892,3736,1363,988,2091,2255,1199,816,3409,3302,6001,434,4195,1489,2979,1618,1698,1197,2348,662,1153,2463,2044,3949,2365,2278,1054,1261,2788,10236,511,0.115,0.111,0.113,0.123,0.142,0.133,0.094,0.116,0.163,0.142,0.134,0.143,0.116,0.134,0.136,0.134,0.121,0.124,0.127,0.15,0.11,0.146,0.12,0.13,0.143,0.116,0.12,0.111,0.097,0.142,0.132,0.158,0.115,0.114,0.069,0.072,0.132,0.144,0.084,0.089,0.107,0.143,0.138,0.14,0.096,0.091,0.097,0.098,0.088,0.085,0.146,0.107,0.119,0.093,0.147,0.104,0.118,0.094,0.117,0.115,0.121,0.128,0.1,0.099,0.113,0.109,0.112,0.094,0.108,0.103,0.116,0.102,0.095,0.104,412,1993,1489,1086,1090,1331,988,1136,758,457,228.0,1454,837,309,825,3870,5268,508,1452,2499,908,1512,1182,1869,1683,1797,2492,1773,1804,1763,868,535,393,1410,617,607,1517,1858,242,163,761,1643,1216,2290,2130,722,433,936,1089,597,494,1634,1511,2509,218,2360,626,1448,951,881,646,973,351,554,1151,1283,2084,1132,1139,493,654,1315,4303,317,3.216,2.7,2.488,2.616,2.153,2.367,2.255,2.695,2.499,2.841,2.332,1.949,2.833,2.796,2.664,2.703,2.921,3.535,2.939,2.018,3.153,2.541,2.01,2.668,2.643,2.728,2.207,2.216,2.775,2.563,2.553,2.597,2.38,3.1,3.362,2.459,3.005,3.252,2.283,2.513,2.379,1.773,3.348,1.929,2.0,2.148,2.884,2.719,2.573,2.382,2.116,2.235,2.298,2.491,2.147,1.956,2.7,2.336,1.946,2.007,2.272,2.511,2.056,2.229,2.234,1.888,2.134,2.522,2.325,2.406,2.283,2.4,2.502,2.01,0.568,0.534,0.571,0.612,0.631,0.511,0.542,0.579,0.663,0.464,0.804,0.44,0.446,0.427,0.481,0.58,0.587,0.806,0.622,0.664,0.735,0.488,0.447,0.724,0.517,0.448,0.464,0.602,0.471,0.463,0.721,0.867,0.34,0.68,0.695,0.431,0.762,0.602,0.51,0.458,0.432,0.454,0.885,0.592,0.567,0.452,0.484,0.571,0.447,0.401,0.414,0.441,0.487,0.38,0.45,0.409,0.737,0.437,0.38,0.433,0.378,0.561,0.486,0.757,0.683,0.768,0.465,0.352,0.384,0.772,0.439,0.507,0.509,0.346
-436,Epileptic,M,27.0,0.9,2.4,0.6,1.1,1.8,1.2,1.1,2.1,1.3,0.8,0.2,1.9,1.7,0.6,0.8,4.7,7.8,1.1,1.4,4.5,0.9,2.5,1.0,4.1,3.0,2.7,2.1,2.1,1.9,2.7,1.1,1.0,0.3,2.4,0.4,0.3,3.5,2.9,0.1,0.2,0.9,3.1,1.8,2.0,2.0,0.5,0.8,1.0,1.2,0.9,0.6,1.8,1.4,3.8,0.1,2.4,0.9,1.7,1.5,0.9,0.9,1.7,0.3,0.4,1.0,1.6,2.5,1.3,0.5,0.4,0.8,1.1,4.0,0.3,8,29,9,9,16,13,12,19,16,8,4.0,15,19,7,9,54,80,10,19,37,9,31,11,42,38,34,26,23,17,34,17,8,3,29,3,3,44,36,2,2,7,26,17,15,13,4,5,6,7,8,5,15,11,30,1,27,6,12,13,7,10,17,2,5,7,17,23,8,5,4,8,8,32,3,0.032,0.022,0.013,0.022,0.029,0.024,0.016,0.033,0.032,0.028,0.025,0.026,0.029,0.029,0.026,0.028,0.024,0.032,0.02,0.035,0.015,0.028,0.025,0.037,0.027,0.028,0.021,0.019,0.02,0.022,0.025,0.037,0.015,0.026,0.012,0.011,0.03,0.025,0.01,0.014,0.018,0.036,0.027,0.028,0.015,0.01,0.022,0.016,0.015,0.023,0.032,0.019,0.022,0.022,0.018,0.018,0.018,0.018,0.028,0.015,0.02,0.024,0.019,0.014,0.015,0.029,0.018,0.02,0.012,0.012,0.021,0.017,0.016,0.014,1705,5089,1910,2378,2741,2575,3029,3612,2392,1435,733.0,2266,3841,1456,2262,10942,19780,2720,4310,5065,2780,4451,1699,6888,6430,5878,5272,4741,5025,6220,2342,1353,1216,6285,2268,1370,8891,8125,443,474,1586,3141,4958,1967,3505,1567,1180,2253,2581,1550,551,3291,2664,6791,232,4820,1865,2842,1503,1353,1527,2758,479,964,2100,1396,4440,2534,1617,1034,1644,2288,9645,521,0.126,0.118,0.103,0.108,0.133,0.132,0.091,0.135,0.136,0.147,0.126,0.119,0.128,0.158,0.121,0.126,0.115,0.127,0.108,0.145,0.081,0.131,0.125,0.141,0.136,0.133,0.119,0.102,0.095,0.128,0.112,0.106,0.076,0.127,0.074,0.068,0.138,0.119,0.094,0.096,0.11,0.15,0.128,0.12,0.088,0.08,0.114,0.086,0.09,0.12,0.147,0.108,0.109,0.114,0.122,0.111,0.101,0.092,0.132,0.1,0.113,0.121,0.113,0.106,0.094,0.134,0.107,0.098,0.096,0.1,0.11,0.106,0.095,0.108,508,1727,743,876,1055,839,1019,1178,660,433,213.0,1029,1102,304,590,2892,5319,575,1133,2100,837,1512,714,1898,1948,1779,1644,1582,1320,1932,765,523,351,1497,542,488,2065,1983,233,198,786,1435,1131,1178,2025,796,540,966,1214,648,340,1506,1123,2762,95,2276,811,1413,861,803,765,1149,253,498,1012,810,2194,1064,712,514,775,1009,3929,304,3.282,2.835,2.527,2.704,2.429,2.559,2.481,2.716,2.8,2.915,2.686,1.959,2.703,3.23,2.881,2.795,2.987,3.52,2.886,2.129,2.415,2.321,2.027,2.642,2.606,2.696,2.491,2.368,2.843,2.574,2.336,2.434,2.712,3.031,3.646,2.483,3.093,3.023,2.137,2.527,2.576,1.973,3.126,1.904,2.0,2.274,2.695,2.89,2.684,2.565,2.244,2.359,2.424,2.526,2.753,2.347,2.7,2.333,1.951,1.891,2.317,2.335,2.264,2.129,2.389,2.138,2.316,2.636,2.602,2.405,2.442,2.668,2.606,2.188,0.717,0.678,0.635,0.409,0.457,0.636,0.618,0.472,0.48,0.409,0.81,0.465,0.504,0.432,0.459,0.512,0.595,0.725,0.471,0.648,0.769,0.436,0.37,0.754,0.526,0.45,0.554,0.611,0.491,0.442,0.735,0.885,0.385,0.655,0.677,0.424,0.68,0.608,0.324,0.418,0.47,0.465,0.72,0.773,0.595,0.304,0.587,0.531,0.474,0.488,0.437,0.465,0.521,0.484,0.461,0.384,0.47,0.492,0.382,0.327,0.397,0.5,0.363,0.922,0.515,0.621,0.484,0.45,0.496,0.663,0.381,0.456,0.47,0.409
-437,Epileptic,F,27.0,0.5,2.2,0.9,1.1,1.3,1.8,1.4,1.3,1.3,0.7,0.3,3.3,1.3,0.6,1.5,5.6,8.0,0.6,3.0,4.1,0.7,2.7,1.6,2.9,2.0,2.9,3.3,1.5,2.5,2.6,1.3,0.6,0.4,2.8,0.2,0.9,1.9,3.1,0.2,0.1,1.4,3.0,1.9,2.4,2.7,0.7,0.3,1.4,1.2,0.6,0.6,1.4,1.2,2.0,0.3,2.7,0.8,1.9,1.2,1.2,0.7,2.1,0.3,0.5,1.9,1.3,2.2,0.9,1.1,0.8,0.7,0.9,4.2,0.4,3,19,10,10,13,18,11,12,11,7,4.0,26,15,7,14,56,75,4,28,31,5,26,18,41,26,34,34,15,24,34,17,7,6,32,2,8,31,38,1,1,8,27,20,20,14,5,2,7,6,4,4,18,7,14,1,22,5,13,8,12,5,14,2,4,14,16,18,7,8,8,7,9,32,4,0.032,0.027,0.017,0.02,0.034,0.032,0.021,0.026,0.051,0.042,0.031,0.032,0.029,0.033,0.031,0.035,0.031,0.025,0.033,0.036,0.016,0.035,0.027,0.033,0.034,0.033,0.03,0.022,0.02,0.025,0.037,0.04,0.016,0.035,0.012,0.022,0.028,0.033,0.019,0.01,0.023,0.036,0.036,0.026,0.022,0.015,0.015,0.021,0.018,0.021,0.045,0.018,0.024,0.018,0.035,0.022,0.034,0.019,0.029,0.025,0.024,0.032,0.023,0.024,0.021,0.026,0.022,0.015,0.017,0.028,0.023,0.024,0.023,0.016,1138,4289,2061,2078,1954,2939,2794,2540,1847,1261,577.0,2829,2744,1265,2634,9596,15795,1742,4934,3287,2507,4249,1931,5818,3685,5128,5337,2830,6420,5066,1960,665,1055,5761,1325,2152,4026,6845,392,407,1778,2750,3955,2165,3225,1235,801,2495,2098,1211,348,2588,1896,3874,187,3365,1092,2823,1176,1332,944,2183,376,854,2818,1080,3242,1981,2006,1244,1243,1879,7187,542,0.105,0.12,0.11,0.109,0.142,0.143,0.093,0.124,0.164,0.148,0.126,0.138,0.13,0.131,0.143,0.144,0.134,0.099,0.129,0.134,0.077,0.148,0.134,0.138,0.142,0.137,0.135,0.104,0.104,0.124,0.136,0.102,0.101,0.146,0.075,0.104,0.13,0.138,0.097,0.087,0.121,0.146,0.142,0.116,0.105,0.095,0.085,0.092,0.099,0.105,0.156,0.104,0.112,0.105,0.152,0.122,0.124,0.1,0.131,0.129,0.119,0.135,0.127,0.113,0.108,0.128,0.115,0.096,0.1,0.144,0.12,0.123,0.114,0.112,331,1303,798,835,698,1044,910,847,444,344,183.0,1592,786,302,729,2795,4384,372,1482,1703,660,1349,968,1578,1210,1638,1804,1120,1820,1755,621,351,401,1326,352,704,1292,1697,207,229,900,1335,931,1528,1901,667,334,956,994,446,232,1309,835,1789,95,1807,484,1479,679,777,457,943,214,439,1443,755,1621,957,918,461,572,710,2965,363,3.392,3.009,2.415,2.468,2.375,2.378,2.574,2.717,2.998,2.735,2.548,1.651,2.633,2.821,2.71,2.578,2.896,3.656,2.71,1.714,2.906,2.468,1.831,2.769,2.396,2.52,2.42,2.009,2.748,2.292,2.464,2.047,2.174,3.211,3.263,2.574,2.604,2.907,2.197,2.069,2.468,1.931,3.081,1.616,1.926,2.043,2.761,3.091,2.582,2.908,1.682,2.045,2.409,2.319,2.372,2.105,2.667,2.163,1.927,1.909,2.337,2.319,1.882,2.348,2.07,1.807,2.087,2.388,2.507,2.931,2.326,2.636,2.569,1.763,0.891,0.742,0.612,0.51,0.616,0.668,0.501,0.506,0.57,0.612,0.668,0.412,0.403,0.625,0.477,0.574,0.619,0.721,0.617,0.638,0.614,0.514,0.428,0.599,0.593,0.515,0.456,0.568,0.443,0.521,0.587,0.947,0.453,0.55,0.537,0.722,0.689,0.596,0.381,0.376,0.505,0.581,0.731,0.538,0.551,0.51,0.618,0.732,0.522,0.587,0.378,0.454,0.595,0.461,0.256,0.424,0.36,0.527,0.361,0.459,0.386,0.608,0.535,0.752,0.524,0.569,0.498,0.381,0.411,0.48,0.584,0.685,0.593,0.391
-438,Epileptic,M,42.0,1.1,2.4,1.0,1.1,3.0,1.9,2.3,1.6,2.3,0.8,0.4,3.4,1.7,0.6,1.5,5.8,14.1,1.6,2.1,4.6,2.4,5.1,2.7,5.0,4.1,3.3,5.6,3.3,3.2,2.1,3.1,2.0,0.5,2.8,1.1,0.6,6.1,3.7,0.2,0.3,1.1,4.3,4.3,2.9,2.5,1.0,0.5,1.0,1.6,1.1,0.8,1.8,1.3,3.2,0.3,2.7,1.0,1.6,2.2,1.2,1.0,2.1,0.5,0.7,3.3,1.5,3.3,1.0,1.2,0.5,0.7,1.4,5.1,0.3,8,22,8,8,25,22,13,16,18,8,4.0,33,22,6,18,66,116,11,27,42,21,59,23,39,43,40,49,31,28,29,26,17,4,28,9,5,54,45,2,2,8,44,39,22,16,8,2,5,8,9,6,12,12,21,2,22,8,17,17,10,9,13,5,5,28,19,26,6,6,4,6,12,44,3,0.047,0.022,0.015,0.021,0.048,0.027,0.034,0.03,0.058,0.036,0.03,0.034,0.031,0.035,0.032,0.033,0.036,0.044,0.029,0.035,0.035,0.033,0.038,0.044,0.035,0.026,0.041,0.031,0.029,0.025,0.051,0.068,0.023,0.033,0.026,0.015,0.053,0.033,0.018,0.014,0.022,0.034,0.051,0.026,0.02,0.019,0.025,0.015,0.019,0.017,0.027,0.02,0.026,0.023,0.019,0.021,0.017,0.019,0.029,0.025,0.021,0.034,0.029,0.019,0.028,0.026,0.021,0.015,0.016,0.017,0.022,0.017,0.019,0.014,1445,4886,2496,2206,2244,3143,2674,2825,1786,1306,948.0,3459,3432,1227,2913,9595,21933,2607,4652,4893,5649,6841,2433,5702,6296,6594,5377,4336,6200,4760,2621,1588,974,6669,3398,1645,7613,8323,432,592,1355,3325,4574,2741,3241,1491,738,2156,2469,2012,714,2868,1815,4870,519,3378,1553,3095,1769,1311,1492,2625,660,1082,3430,1488,4263,2178,1967,836,1071,2758,9429,491,0.138,0.112,0.092,0.104,0.143,0.124,0.119,0.125,0.164,0.158,0.123,0.134,0.12,0.127,0.133,0.134,0.138,0.143,0.134,0.142,0.121,0.132,0.142,0.141,0.131,0.124,0.13,0.103,0.099,0.124,0.171,0.14,0.097,0.134,0.107,0.087,0.153,0.144,0.109,0.104,0.114,0.134,0.146,0.125,0.098,0.11,0.113,0.085,0.099,0.097,0.139,0.104,0.125,0.109,0.1,0.117,0.101,0.108,0.135,0.122,0.109,0.129,0.136,0.107,0.134,0.124,0.113,0.09,0.091,0.098,0.12,0.106,0.106,0.1,430,1802,930,785,1082,1325,987,1018,658,355,230.0,1784,1029,319,869,3269,6690,589,1337,2273,1300,2559,1116,1828,2118,2205,2180,1680,1711,1538,996,587,357,1528,730,687,2228,2176,209,320,753,1877,1467,1715,1961,759,292,935,1178,944,417,1416,901,2237,234,1942,765,1499,1189,797,779,1066,368,600,1858,934,2334,1026,994,479,547,1409,4226,324,3.448,2.518,2.579,2.657,1.928,2.136,2.214,2.429,2.17,2.91,3.053,1.734,2.599,2.687,2.544,2.351,2.611,3.729,2.722,1.874,3.204,2.19,1.924,2.478,2.37,2.44,2.111,2.11,2.739,2.392,2.193,2.669,2.154,3.089,3.81,2.292,2.56,2.81,2.299,2.097,2.259,1.699,2.536,1.851,1.832,2.104,2.938,2.822,2.507,2.574,1.917,2.129,2.031,2.231,2.441,1.934,2.165,2.267,1.755,1.772,2.156,2.619,1.938,2.243,2.044,1.945,2.035,2.424,2.228,1.86,2.235,2.298,2.345,1.866,0.722,0.491,0.576,0.486,0.486,0.541,0.629,0.442,0.602,0.578,0.614,0.458,0.404,0.571,0.53,0.5,0.627,0.922,0.609,0.589,0.878,0.573,0.552,0.777,0.727,0.519,0.513,0.491,0.508,0.592,0.63,1.029,0.31,0.59,0.855,0.409,0.784,0.698,0.371,0.387,0.533,0.517,0.879,0.638,0.538,0.502,0.839,0.756,0.433,0.442,0.358,0.431,0.427,0.464,0.404,0.392,0.393,0.516,0.377,0.358,0.313,0.563,0.355,0.686,0.541,0.622,0.448,0.384,0.392,0.436,0.457,0.43,0.413,0.307
-439,Epileptic,M,32.0,0.9,1.9,0.9,1.4,2.0,2.4,1.6,1.1,0.9,0.7,0.3,2.4,1.7,0.4,1.5,7.0,7.2,0.9,2.9,3.9,1.1,3.1,1.7,3.4,3.5,1.9,2.9,2.1,2.3,3.1,1.4,1.3,0.3,2.2,1.1,0.9,3.5,3.0,0.3,0.2,0.8,3.4,2.1,2.3,2.4,0.7,0.3,0.9,1.3,1.1,0.6,1.7,1.1,2.2,0.4,2.2,0.9,2.0,1.2,1.1,0.7,1.4,0.2,0.7,1.4,1.9,2.0,1.1,1.0,0.5,1.1,1.2,5.2,0.2,9,18,7,10,17,23,13,11,10,6,2.0,21,20,5,16,73,62,6,34,34,12,34,17,32,36,19,27,22,22,31,15,10,4,22,7,9,35,31,2,2,4,27,21,18,12,4,2,6,7,8,5,12,8,14,2,15,6,12,11,8,4,14,2,7,8,22,17,6,5,5,6,10,33,1,0.035,0.019,0.017,0.025,0.033,0.025,0.026,0.023,0.034,0.033,0.03,0.031,0.03,0.032,0.025,0.032,0.027,0.035,0.024,0.034,0.024,0.026,0.026,0.036,0.029,0.025,0.028,0.025,0.02,0.027,0.029,0.059,0.027,0.029,0.031,0.025,0.03,0.031,0.017,0.022,0.016,0.036,0.047,0.027,0.015,0.012,0.017,0.017,0.017,0.033,0.022,0.018,0.019,0.014,0.024,0.017,0.018,0.02,0.022,0.014,0.02,0.02,0.025,0.02,0.014,0.037,0.017,0.017,0.016,0.015,0.021,0.018,0.017,0.014,1481,4452,1975,2306,2653,3965,2798,3106,1618,1515,673.0,2822,3622,732,3340,12936,18028,2416,6881,5244,2983,6424,2685,5513,7205,4983,5597,4435,7167,6693,2421,1153,877,6530,2555,2272,7982,7288,521,454,1284,2704,5118,2511,4416,1548,631,2082,2404,2064,688,3548,2008,6333,440,4286,1680,2887,1664,2173,1294,2898,383,1117,2811,1201,3966,2300,2054,1083,1722,2543,11086,361,0.12,0.108,0.105,0.119,0.116,0.113,0.103,0.111,0.141,0.128,0.11,0.127,0.13,0.091,0.099,0.114,0.113,0.121,0.115,0.135,0.088,0.111,0.118,0.121,0.122,0.12,0.112,0.098,0.089,0.126,0.116,0.131,0.109,0.119,0.126,0.114,0.133,0.132,0.096,0.104,0.097,0.124,0.146,0.119,0.087,0.083,0.091,0.09,0.09,0.124,0.133,0.096,0.104,0.083,0.131,0.095,0.103,0.1,0.118,0.085,0.101,0.114,0.105,0.106,0.087,0.136,0.096,0.092,0.086,0.097,0.112,0.107,0.095,0.098,446,1591,819,902,944,1543,922,922,441,379,218.0,1299,992,217,947,3413,4529,468,1994,1966,787,1841,1110,1696,1981,1328,1733,1242,1538,1818,821,486,254,1300,571,644,1873,1748,272,219,661,1392,951,1463,2273,786,282,824,1148,585,374,1648,1067,2453,211,2134,763,1463,882,1217,604,1152,171,589,1346,785,1791,1032,955,461,806,1102,4637,175,3.249,2.734,2.565,2.546,2.423,2.248,2.524,2.904,2.751,3.139,2.546,1.951,2.692,2.54,2.687,2.833,3.14,3.756,2.751,2.205,2.705,2.57,2.06,2.575,2.692,2.979,2.483,2.656,3.38,2.835,2.252,2.498,2.882,3.266,3.12,2.951,3.028,3.036,2.402,2.318,2.474,1.83,3.367,1.967,2.232,2.155,2.687,3.165,2.577,3.158,1.907,2.293,2.194,2.671,2.265,2.236,2.473,2.264,2.119,1.995,2.375,2.644,2.453,2.138,2.355,1.999,2.3,2.634,2.549,2.573,2.448,2.669,2.614,2.336,0.648,0.667,0.479,0.577,0.663,0.611,0.675,0.575,0.7,0.479,0.652,0.615,0.498,0.463,0.59,0.679,0.67,0.859,0.669,0.664,0.768,0.646,0.631,0.69,0.606,0.556,0.521,0.567,0.533,0.49,0.698,0.775,0.489,0.667,0.903,0.678,0.738,0.553,0.593,0.487,0.499,0.482,0.843,0.665,0.708,0.536,0.636,0.552,0.601,0.833,0.509,0.454,0.451,0.555,0.663,0.5,0.394,0.581,0.47,0.458,0.535,0.624,0.504,0.725,0.535,0.547,0.496,0.431,0.421,0.771,0.519,0.527,0.543,0.255
-440,Epileptic,F,52.0,0.6,2.3,0.8,1.5,1.5,1.7,1.6,1.3,1.3,0.9,0.4,2.5,1.7,0.6,1.0,4.9,7.4,0.6,2.0,3.5,1.4,5.2,1.9,3.7,4.0,2.6,2.7,2.0,2.2,2.3,0.9,1.2,0.4,1.8,0.4,0.5,3.7,2.7,0.1,0.2,1.1,3.6,2.0,2.5,2.6,0.8,0.4,0.7,1.2,0.6,0.6,1.8,1.1,2.5,0.4,2.4,0.8,1.6,1.1,1.2,0.4,1.3,0.2,0.5,1.8,1.1,2.2,1.3,1.0,0.5,0.7,1.7,4.6,0.3,5,28,6,14,18,17,20,15,13,11,7.0,26,18,8,16,71,84,6,22,40,12,62,23,40,40,33,33,21,25,32,14,10,4,24,2,4,58,42,1,1,9,35,25,20,17,6,2,5,6,5,3,14,9,19,2,21,7,12,11,9,4,12,1,5,13,18,19,9,6,5,5,14,40,2,0.029,0.023,0.014,0.024,0.032,0.025,0.024,0.027,0.047,0.043,0.037,0.032,0.03,0.034,0.027,0.029,0.028,0.026,0.031,0.033,0.027,0.047,0.03,0.047,0.042,0.026,0.027,0.021,0.022,0.028,0.031,0.051,0.02,0.025,0.011,0.014,0.036,0.029,0.018,0.013,0.021,0.038,0.03,0.023,0.019,0.016,0.018,0.013,0.018,0.014,0.029,0.018,0.024,0.019,0.019,0.017,0.019,0.018,0.024,0.018,0.014,0.026,0.013,0.02,0.019,0.024,0.021,0.017,0.016,0.016,0.025,0.019,0.02,0.017,1348,4846,2001,2752,1959,3228,2820,2913,1764,1498,616.0,3127,3384,1154,2299,10146,16439,2028,4446,5019,4169,4305,2683,5091,5177,6130,4749,4183,6326,5012,2110,1315,1117,5101,1894,1638,8031,7484,315,551,1607,2823,5780,3218,3747,1447,745,1862,2156,1266,524,3251,1631,4359,494,3902,1473,3235,1279,1556,819,2201,349,835,2885,1190,3368,2256,1960,859,1057,3068,8962,488,0.105,0.112,0.096,0.123,0.141,0.131,0.108,0.131,0.16,0.171,0.148,0.128,0.133,0.142,0.127,0.135,0.129,0.109,0.134,0.146,0.111,0.136,0.11,0.149,0.129,0.122,0.11,0.103,0.102,0.126,0.11,0.13,0.096,0.123,0.067,0.076,0.141,0.131,0.083,0.085,0.111,0.137,0.134,0.117,0.1,0.102,0.093,0.083,0.101,0.099,0.141,0.101,0.115,0.107,0.119,0.106,0.111,0.104,0.114,0.106,0.097,0.124,0.085,0.124,0.109,0.127,0.108,0.103,0.093,0.111,0.121,0.117,0.101,0.102,379,1749,779,1030,842,1056,1045,940,610,410,202.0,1357,894,316,713,3109,4881,452,1122,1920,878,1627,1094,1486,1766,1754,1657,1411,1584,1529,620,443,329,1212,544,570,2070,1719,178,287,807,1522,1266,1774,2159,755,315,820,989,598,295,1599,822,2023,281,2027,674,1431,757,894,448,888,194,416,1432,815,1786,1112,909,508,504,1338,3856,260,3.29,2.589,2.476,2.505,2.184,2.56,2.245,2.768,2.266,2.846,2.702,1.942,2.82,2.582,2.424,2.471,2.615,3.482,3.021,2.174,3.369,2.202,2.177,2.672,2.383,2.689,2.32,2.267,2.891,2.48,2.462,2.85,2.547,2.995,3.132,2.587,2.834,3.052,2.068,2.284,2.411,1.683,3.296,1.976,1.972,2.11,2.783,2.812,2.669,2.497,1.979,2.162,1.971,2.257,2.149,2.072,2.5,2.534,1.801,1.917,2.242,2.466,1.922,2.416,2.184,1.804,2.045,2.466,2.443,1.97,2.288,2.44,2.46,2.379,0.529,0.456,0.433,0.509,0.562,0.567,0.596,0.547,0.586,0.521,0.694,0.489,0.379,0.587,0.488,0.539,0.612,0.81,0.601,0.685,0.726,0.623,0.636,0.68,0.57,0.562,0.596,0.563,0.526,0.594,0.573,0.899,0.368,0.613,0.482,0.441,0.75,0.593,0.328,0.288,0.407,0.48,0.727,0.65,0.64,0.448,0.471,0.548,0.398,0.432,0.387,0.375,0.436,0.382,0.36,0.433,0.397,0.515,0.35,0.398,0.281,0.613,0.236,0.68,0.496,0.424,0.497,0.429,0.389,0.519,0.436,0.508,0.479,0.303
-441,Epileptic,F,32.0,0.6,2.5,0.7,1.0,1.8,2.1,1.7,1.3,1.9,0.6,0.3,2.3,1.5,1.3,1.0,6.7,7.5,0.8,2.3,2.8,2.3,2.2,1.5,3.9,2.0,2.4,2.6,1.8,2.2,2.3,2.0,0.9,0.7,2.5,1.0,0.8,6.2,3.2,0.2,0.2,0.9,2.6,3.7,1.4,2.0,0.6,0.7,0.7,1.2,1.5,0.3,1.6,1.7,2.7,0.4,2.1,0.8,1.5,0.8,0.8,0.4,1.6,0.4,0.3,1.7,1.3,2.2,1.4,1.2,0.4,0.6,1.6,3.2,0.4,5,26,6,10,17,16,14,11,22,8,5.0,21,13,13,12,74,80,5,31,30,13,33,12,44,28,30,40,21,22,35,25,8,9,29,5,7,57,32,2,1,5,25,28,12,11,4,6,5,6,12,3,13,12,22,2,19,4,9,5,6,6,10,3,4,11,17,15,11,9,4,5,11,25,5,0.035,0.022,0.013,0.02,0.033,0.033,0.027,0.023,0.058,0.034,0.034,0.03,0.038,0.055,0.035,0.04,0.028,0.032,0.041,0.034,0.036,0.032,0.027,0.037,0.03,0.026,0.022,0.02,0.025,0.031,0.043,0.035,0.03,0.03,0.029,0.021,0.043,0.036,0.017,0.021,0.017,0.036,0.049,0.021,0.015,0.012,0.026,0.013,0.016,0.031,0.027,0.026,0.029,0.024,0.025,0.016,0.019,0.018,0.022,0.019,0.027,0.027,0.029,0.014,0.021,0.027,0.017,0.019,0.017,0.012,0.022,0.018,0.015,0.02,1650,6534,2332,2305,3548,2935,3157,3208,2490,1377,522.0,2575,2833,2012,2564,10854,19140,2792,5135,3704,3555,4475,2044,8114,4997,5892,6491,4192,6037,4965,3110,1461,1211,8185,1908,1711,9342,7053,375,317,1425,3085,5810,1887,3308,1418,1223,2447,2697,2266,389,3103,2921,5546,422,3645,1695,3111,1081,1375,701,3041,487,1003,2854,1126,3878,2283,2122,991,1085,2492,8363,807,0.109,0.115,0.089,0.105,0.137,0.148,0.11,0.12,0.18,0.157,0.136,0.133,0.142,0.213,0.135,0.152,0.129,0.108,0.14,0.143,0.109,0.147,0.115,0.146,0.134,0.132,0.117,0.098,0.108,0.137,0.147,0.112,0.147,0.126,0.088,0.104,0.138,0.12,0.12,0.1,0.101,0.151,0.149,0.107,0.087,0.088,0.123,0.078,0.092,0.121,0.133,0.113,0.12,0.112,0.152,0.105,0.097,0.1,0.121,0.106,0.124,0.115,0.159,0.113,0.106,0.132,0.099,0.109,0.107,0.096,0.125,0.111,0.089,0.122,365,1948,835,824,993,1005,981,892,610,320,177.0,1129,713,446,618,2931,4849,410,1227,1357,928,1292,820,1835,1269,1585,2073,1385,1460,1505,727,503,377,1301,504,584,2298,1666,187,163,718,1191,1232,1119,1963,685,480,932,1066,901,212,1173,1078,1949,168,1874,672,1275,559,653,302,1016,178,423,1264,667,1901,1085,979,499,485,1214,3199,346,3.917,3.023,2.672,2.694,2.834,2.556,2.636,3.08,2.92,3.2,2.726,1.918,2.968,3.11,2.984,2.764,3.129,4.346,3.275,2.257,3.007,2.691,2.184,3.261,2.935,2.953,2.464,2.346,3.043,2.588,2.866,2.618,2.728,3.619,3.288,2.66,2.996,3.079,2.279,2.328,2.587,2.203,3.213,1.924,1.948,2.228,3.027,3.18,2.933,3.008,2.216,2.537,2.737,2.589,3.223,2.135,2.996,2.778,2.219,2.255,2.598,2.814,2.654,2.632,2.457,2.357,2.282,2.404,2.405,2.249,2.497,2.579,2.769,2.746,1.028,0.636,0.495,0.437,0.817,0.506,0.563,0.545,0.63,0.637,0.733,0.442,0.436,0.773,0.528,0.627,0.615,0.598,0.52,0.685,0.803,0.46,0.55,0.67,0.681,0.598,0.514,0.603,0.501,0.526,0.712,1.127,0.521,0.62,0.952,0.476,0.888,0.714,0.307,0.426,0.566,0.594,1.092,0.6,0.685,0.423,0.728,0.735,0.584,0.652,0.413,0.536,0.596,0.607,0.409,0.439,0.493,0.597,0.37,0.414,0.498,0.683,0.509,0.907,0.729,0.74,0.504,0.42,0.531,0.446,0.601,0.5,0.494,0.657
-442,Epileptic,F,42.0,0.6,2.9,1.0,1.6,1.7,1.8,1.7,1.9,1.2,0.6,0.5,2.1,2.1,0.7,1.4,5.9,11.9,0.9,1.7,4.1,1.1,2.4,1.3,3.5,2.5,3.0,2.9,2.0,2.3,3.4,1.5,1.0,0.5,2.8,0.3,0.9,3.3,3.7,0.2,0.2,0.8,2.1,2.1,1.7,1.9,0.8,0.3,0.8,1.2,0.9,0.5,1.9,1.5,2.3,0.2,1.9,0.6,1.5,0.6,0.7,0.6,1.7,0.5,0.4,1.4,1.5,2.2,0.8,0.7,0.6,1.2,1.4,4.2,0.4,5,22,9,13,13,16,13,15,9,6,6.0,20,20,7,11,52,111,5,24,31,10,26,12,32,33,35,38,20,23,39,16,8,4,25,2,8,39,43,1,1,5,18,22,15,11,6,1,5,6,6,3,12,9,15,1,15,3,11,5,6,3,14,3,4,10,16,15,6,4,7,6,10,38,4,0.029,0.026,0.019,0.028,0.035,0.036,0.028,0.031,0.049,0.038,0.037,0.031,0.041,0.039,0.033,0.034,0.033,0.032,0.031,0.039,0.021,0.035,0.028,0.042,0.037,0.031,0.027,0.023,0.024,0.032,0.042,0.044,0.019,0.032,0.01,0.021,0.044,0.034,0.016,0.022,0.018,0.037,0.037,0.027,0.017,0.022,0.018,0.013,0.016,0.019,0.036,0.02,0.029,0.022,0.022,0.019,0.015,0.021,0.02,0.017,0.029,0.032,0.032,0.02,0.022,0.027,0.019,0.014,0.013,0.028,0.026,0.021,0.018,0.02,1366,4601,2207,2471,2302,2667,2663,3189,2463,1292,676.0,2111,3638,1304,2717,10333,20195,2068,4400,3829,3217,4113,1711,5811,4384,5242,5037,3838,5067,5514,2484,1043,1079,6040,1552,1906,7466,8625,356,435,1583,2445,5544,1806,2886,1234,687,2106,2470,1672,426,3432,1877,4293,283,2686,1203,2583,936,1081,626,2418,497,689,2103,1470,3226,1659,1630,867,1573,2267,8964,631,0.105,0.113,0.098,0.125,0.135,0.158,0.116,0.138,0.135,0.141,0.142,0.144,0.151,0.138,0.134,0.136,0.132,0.108,0.13,0.148,0.09,0.136,0.128,0.145,0.144,0.137,0.124,0.108,0.11,0.147,0.141,0.113,0.096,0.136,0.059,0.115,0.149,0.143,0.095,0.096,0.099,0.145,0.138,0.123,0.095,0.109,0.088,0.087,0.09,0.104,0.149,0.1,0.114,0.098,0.129,0.113,0.091,0.112,0.117,0.099,0.124,0.133,0.136,0.118,0.108,0.127,0.101,0.097,0.089,0.128,0.113,0.118,0.098,0.12,443,1772,848,887,765,837,916,1015,499,322,182.0,1046,959,285,698,2910,6207,416,1044,1616,796,1231,739,1531,1283,1693,1832,1315,1494,1814,611,420,411,1407,481,695,1636,2014,192,163,729,969,1155,1164,1750,651,274,907,1108,681,245,1566,833,1870,127,1465,548,1108,491,645,353,924,267,376,1030,871,1775,816,787,413,732,1015,3701,355,3.205,2.531,2.447,2.694,2.465,2.744,2.366,2.81,2.933,3.099,2.934,1.848,2.958,3.034,2.864,2.638,2.585,3.808,3.198,2.087,3.166,2.488,2.002,2.907,2.527,2.553,2.16,2.253,2.624,2.437,2.787,2.399,2.22,3.021,2.872,2.481,3.164,3.078,2.188,2.735,2.818,2.173,3.172,1.736,1.855,2.083,3.263,2.84,2.679,2.875,2.304,2.27,2.325,2.302,2.388,2.027,2.652,2.766,2.147,1.903,2.208,2.641,2.073,2.158,2.284,2.158,2.041,2.273,2.306,2.377,2.612,2.628,2.549,2.232,0.731,0.557,0.626,0.648,0.665,0.555,0.529,0.449,0.852,0.415,0.842,0.555,0.51,0.562,0.459,0.613,0.703,0.788,0.484,0.652,0.818,0.583,0.447,0.74,0.536,0.499,0.56,0.528,0.593,0.586,0.8,1.04,0.405,0.633,0.95,0.464,0.776,0.616,0.44,0.439,0.51,0.552,0.741,0.809,0.512,0.462,0.42,0.586,0.444,0.502,0.368,0.513,0.76,0.532,0.405,0.521,0.372,0.503,0.403,0.35,0.306,0.69,0.411,0.846,0.519,0.662,0.485,0.384,0.43,0.656,0.549,0.543,0.553,0.556
-443,Epileptic,M,22.0,1.2,3.5,1.7,1.6,1.8,2.0,3.0,2.8,1.8,1.1,0.9,3.3,2.3,0.8,2.4,11.3,13.0,1.5,2.2,4.3,1.4,3.1,2.3,5.8,3.6,7.2,5.0,3.9,4.5,3.6,3.0,1.5,0.6,4.2,1.0,0.7,4.3,5.3,0.3,0.2,1.4,3.5,4.1,3.2,4.0,1.0,0.7,1.1,1.8,1.3,0.8,3.9,2.4,3.8,0.4,2.5,1.0,2.3,1.2,1.4,0.4,2.5,0.5,0.6,2.4,1.5,4.9,1.3,1.3,0.6,1.1,1.2,5.2,0.4,9,31,12,11,18,20,18,29,14,9,9.0,26,31,9,24,87,88,11,28,41,11,33,20,49,44,65,49,36,31,43,19,8,7,46,10,7,44,64,1,2,7,29,34,22,20,5,4,6,9,10,4,30,13,23,2,22,5,19,9,10,5,17,3,5,18,18,39,8,9,5,6,9,40,3,0.045,0.03,0.026,0.032,0.04,0.032,0.047,0.037,0.059,0.046,0.049,0.033,0.038,0.043,0.044,0.057,0.045,0.043,0.031,0.043,0.031,0.037,0.033,0.053,0.034,0.036,0.037,0.041,0.044,0.035,0.06,0.049,0.033,0.048,0.047,0.014,0.044,0.044,0.016,0.018,0.022,0.034,0.052,0.03,0.027,0.02,0.024,0.017,0.018,0.024,0.032,0.028,0.036,0.025,0.014,0.02,0.019,0.024,0.023,0.025,0.019,0.039,0.026,0.02,0.024,0.026,0.027,0.018,0.025,0.021,0.021,0.02,0.022,0.019,1512,6910,3012,2746,2761,3030,2748,4805,2421,2096,939.0,3278,4718,1232,3895,12952,20604,2669,4390,4794,3595,3797,2280,7113,6456,11621,6824,4953,6848,6535,2170,1538,1286,6896,2181,2601,7032,8725,504,508,1797,2718,6382,2679,3917,1476,1042,2140,2919,1790,559,5502,2175,5252,669,3570,1387,3789,1225,1473,838,3273,567,881,3196,1405,6137,2453,1725,1208,1650,2152,8413,567,0.147,0.133,0.119,0.129,0.146,0.14,0.135,0.149,0.182,0.159,0.156,0.136,0.145,0.162,0.158,0.18,0.146,0.14,0.138,0.167,0.109,0.135,0.129,0.16,0.14,0.138,0.136,0.139,0.139,0.143,0.171,0.124,0.119,0.176,0.143,0.091,0.159,0.155,0.102,0.103,0.103,0.131,0.169,0.131,0.109,0.099,0.111,0.092,0.095,0.114,0.139,0.123,0.132,0.113,0.091,0.112,0.098,0.118,0.116,0.116,0.117,0.14,0.12,0.107,0.113,0.122,0.12,0.093,0.115,0.114,0.107,0.11,0.108,0.119,448,1913,957,861,968,1032,1079,1482,654,437,264.0,1643,1362,345,1098,3662,5375,571,1234,2066,786,1316,1139,2036,1861,3441,2500,1820,1943,2052,799,508,399,1677,546,885,1830,2245,248,240,918,1513,1469,1673,2313,738,439,904,1360,737,361,2432,1064,2361,353,2007,730,1618,798,855,385,1066,305,497,1657,854,3107,1118,858,435,791,1017,3599,315,3.333,3.088,3.061,2.776,2.243,2.347,2.183,2.634,2.722,3.505,3.056,1.78,2.722,2.652,2.552,2.568,2.856,3.616,2.766,1.96,3.162,2.313,1.828,2.568,2.594,2.652,2.248,2.11,2.644,2.495,2.092,2.819,2.517,2.894,3.335,2.599,2.825,2.799,2.244,2.25,2.426,1.705,3.191,1.882,1.939,2.215,2.688,3.011,2.579,2.545,1.97,2.176,2.074,2.288,2.24,1.984,2.157,2.527,1.781,1.86,2.221,2.722,1.962,2.028,2.076,1.956,2.041,2.385,2.258,2.535,2.321,2.262,2.34,2.288,0.737,0.795,0.629,0.726,0.696,0.711,0.641,0.554,0.777,0.861,0.905,0.518,0.646,0.575,0.647,0.747,0.776,0.805,0.589,0.688,0.894,0.63,0.526,0.892,0.726,0.677,0.673,0.69,0.694,0.66,0.721,1.059,0.387,0.759,0.838,0.496,0.839,0.799,0.514,0.521,0.507,0.481,0.768,0.647,0.554,0.476,0.431,0.794,0.557,0.577,0.394,0.536,0.533,0.507,0.341,0.433,0.496,0.647,0.361,0.485,0.576,0.808,0.426,0.714,0.533,0.58,0.485,0.463,0.532,0.977,0.53,0.535,0.527,0.455
-444,Epileptic,F,27.0,0.6,3.0,1.4,1.1,1.9,1.6,1.7,1.7,1.1,1.0,0.3,2.4,1.3,0.6,1.0,5.9,8.7,1.0,1.7,4.6,0.5,2.0,1.4,4.0,3.0,4.4,2.5,2.5,2.5,4.5,1.4,1.0,0.7,2.2,0.2,0.7,3.3,3.6,0.1,0.1,1.0,2.4,2.5,2.1,2.2,0.8,0.5,0.6,1.1,0.6,0.4,1.4,1.6,3.1,0.3,2.4,0.3,1.2,0.8,0.8,0.5,1.4,0.2,0.5,1.7,1.3,3.3,1.1,1.1,0.5,1.3,0.9,3.3,0.2,6,29,13,12,19,16,17,17,14,11,6.0,28,17,7,13,67,84,11,22,49,5,27,19,45,37,52,34,27,23,53,17,9,7,29,1,7,45,40,2,1,7,26,26,19,14,5,3,4,6,7,4,13,12,21,2,27,3,14,7,7,5,13,1,5,17,18,30,9,7,6,12,8,27,3,0.027,0.027,0.022,0.021,0.036,0.026,0.022,0.031,0.035,0.037,0.023,0.03,0.028,0.031,0.034,0.029,0.029,0.035,0.026,0.033,0.012,0.027,0.028,0.036,0.035,0.033,0.025,0.024,0.022,0.031,0.034,0.04,0.03,0.034,0.01,0.02,0.032,0.038,0.012,0.017,0.022,0.031,0.043,0.018,0.016,0.015,0.019,0.011,0.016,0.017,0.02,0.016,0.022,0.022,0.019,0.018,0.011,0.016,0.027,0.016,0.019,0.019,0.017,0.018,0.02,0.022,0.022,0.016,0.02,0.016,0.021,0.017,0.017,0.014,1389,5101,2692,2081,2586,1987,2806,3151,2019,1660,652.0,2852,3045,1071,2078,10713,17448,2482,3736,5754,2571,3106,1870,6106,5229,7463,4979,4319,5328,7178,2061,1232,938,4701,1510,1585,7291,6339,314,326,1430,2250,5348,3282,3367,1590,926,1886,2133,1373,470,2886,2378,5357,377,3828,904,2803,788,1156,865,2839,309,984,2613,1346,4320,2484,1547,1120,2023,1820,6951,337,0.118,0.13,0.115,0.106,0.146,0.132,0.114,0.132,0.154,0.144,0.134,0.148,0.135,0.144,0.143,0.134,0.13,0.123,0.138,0.144,0.071,0.135,0.141,0.144,0.153,0.148,0.124,0.115,0.097,0.144,0.122,0.12,0.132,0.136,0.058,0.107,0.14,0.142,0.095,0.098,0.113,0.139,0.154,0.103,0.093,0.089,0.104,0.08,0.095,0.105,0.12,0.102,0.112,0.108,0.127,0.108,0.089,0.109,0.124,0.101,0.108,0.111,0.113,0.109,0.116,0.119,0.117,0.1,0.106,0.117,0.121,0.11,0.094,0.117,391,1727,993,884,941,966,1031,1052,575,473,211.0,1303,856,286,603,3278,4747,497,1081,2374,640,1213,826,1755,1572,2317,1816,1526,1489,2483,679,441,341,1130,394,644,1868,1565,192,160,724,1158,1037,1875,2063,804,435,826,999,607,323,1367,1166,2388,203,2145,481,1343,490,752,479,1113,185,517,1384,886,2303,1113,835,507,1038,887,3175,197,3.547,2.833,2.603,2.297,2.334,1.892,2.27,2.719,2.544,2.827,2.733,1.884,2.721,2.653,2.644,2.548,2.931,3.502,2.714,2.092,3.01,2.033,1.939,2.677,2.536,2.636,2.199,2.245,2.685,2.355,2.374,2.704,2.349,2.811,3.249,2.375,2.893,2.841,1.838,2.414,2.472,1.754,3.257,1.89,1.79,2.0,2.67,2.775,2.649,2.511,1.749,2.159,2.081,2.396,2.251,1.853,2.247,2.305,1.771,1.717,2.09,2.524,1.98,2.236,2.029,1.854,2.065,2.624,2.179,2.394,2.154,2.33,2.415,2.092,0.759,0.592,0.541,0.414,0.653,0.407,0.493,0.477,0.617,0.658,0.586,0.427,0.427,0.463,0.522,0.504,0.562,0.768,0.553,0.618,0.864,0.499,0.506,0.668,0.542,0.502,0.493,0.487,0.467,0.487,0.592,0.887,0.39,0.666,0.66,0.436,0.712,0.671,0.344,0.501,0.527,0.483,0.87,0.671,0.534,0.528,0.53,0.713,0.588,0.569,0.364,0.635,0.381,0.444,0.465,0.399,0.432,0.535,0.359,0.397,0.412,0.564,0.317,0.758,0.587,0.579,0.508,0.479,0.441,0.449,0.508,0.396,0.42,0.488
-445,Epileptic,F,37.0,0.3,1.7,1.1,0.9,1.2,1.9,1.1,1.3,0.9,0.4,0.1,2.2,1.5,0.4,1.0,4.5,6.8,0.7,1.2,3.9,0.8,2.3,1.5,2.4,2.6,3.2,2.7,2.0,1.7,2.6,1.3,0.7,0.4,1.9,0.2,0.3,3.4,2.4,0.3,0.2,1.0,2.5,2.5,2.2,2.0,0.6,0.3,0.6,0.9,0.7,0.4,1.3,0.9,2.9,0.3,1.8,0.8,1.4,0.7,0.7,0.5,0.8,0.2,0.3,1.7,0.5,2.3,0.8,0.9,0.4,1.0,1.1,4.2,0.3,2,17,11,8,10,20,11,15,9,5,2.0,19,15,4,10,49,64,7,14,34,8,21,12,30,31,33,30,18,16,29,15,8,4,25,1,3,39,30,3,1,6,22,26,18,15,5,2,4,6,7,4,10,6,20,2,16,6,18,5,6,3,8,1,4,14,7,20,5,5,4,6,10,32,2,0.021,0.022,0.022,0.018,0.028,0.03,0.02,0.028,0.036,0.023,0.018,0.029,0.027,0.038,0.03,0.031,0.025,0.03,0.023,0.035,0.022,0.033,0.029,0.033,0.033,0.033,0.027,0.023,0.02,0.023,0.034,0.046,0.03,0.026,0.01,0.009,0.037,0.033,0.021,0.017,0.024,0.03,0.05,0.03,0.018,0.016,0.018,0.013,0.016,0.026,0.019,0.019,0.023,0.023,0.032,0.016,0.02,0.02,0.024,0.018,0.023,0.021,0.019,0.015,0.02,0.028,0.021,0.015,0.014,0.019,0.021,0.019,0.019,0.018,1166,4173,2159,2071,2185,2738,2267,2747,1656,1125,472.0,2233,3367,960,2166,9450,15788,1923,3161,4040,2195,3343,1747,4616,4704,5968,5018,3405,4741,5340,1996,1201,776,4581,1256,1477,5420,5960,465,484,1284,2440,3577,2044,2983,1282,655,1578,1955,1256,435,2431,1339,5042,371,3170,1264,2692,742,1075,575,1707,316,756,2492,471,3467,1877,2056,837,1435,1634,7524,401,0.08,0.112,0.119,0.095,0.136,0.14,0.104,0.139,0.153,0.114,0.081,0.125,0.132,0.133,0.137,0.145,0.126,0.124,0.115,0.144,0.08,0.148,0.12,0.135,0.151,0.145,0.124,0.109,0.099,0.122,0.135,0.12,0.13,0.138,0.067,0.066,0.143,0.141,0.124,0.086,0.121,0.128,0.152,0.132,0.098,0.101,0.111,0.081,0.093,0.128,0.123,0.105,0.11,0.114,0.147,0.105,0.115,0.128,0.131,0.116,0.128,0.114,0.112,0.122,0.112,0.114,0.113,0.094,0.09,0.128,0.116,0.124,0.103,0.122,277,1280,796,774,698,1102,784,851,429,279,147.0,1121,909,208,548,2535,4341,415,860,1713,614,1080,782,1275,1394,1776,1670,1195,1277,1690,535,422,251,1056,329,553,1510,1407,179,205,650,1227,946,1301,1789,604,289,727,907,549,298,1113,672,1990,194,1591,618,1173,452,635,315,685,165,336,1274,266,1837,790,901,347,694,855,3245,197,3.741,3.041,2.815,2.601,2.736,2.269,2.36,2.912,2.978,3.119,2.735,1.808,2.864,3.253,2.948,2.828,2.994,3.638,2.863,1.984,2.813,2.394,1.931,2.772,2.594,2.756,2.386,2.258,2.784,2.527,2.771,2.845,2.539,2.962,3.243,2.357,2.655,3.097,2.58,2.546,2.656,1.791,2.873,1.765,1.928,2.205,2.986,2.587,2.697,2.845,1.725,2.462,2.371,2.67,2.305,2.179,2.524,2.5,1.911,2.017,2.29,2.644,2.103,2.264,2.26,1.998,2.118,2.569,2.68,2.939,2.521,2.39,2.514,2.824,0.833,0.657,0.534,0.399,0.693,0.496,0.545,0.444,0.416,0.362,0.701,0.479,0.419,0.463,0.364,0.513,0.526,0.744,0.529,0.594,0.745,0.541,0.492,0.618,0.508,0.515,0.529,0.52,0.473,0.542,0.605,1.099,0.389,0.629,0.564,0.469,0.745,0.498,0.335,0.349,0.496,0.451,0.786,0.702,0.582,0.564,0.411,0.689,0.424,0.602,0.298,0.421,0.417,0.41,0.375,0.406,0.448,0.558,0.351,0.312,0.321,0.495,0.328,0.966,0.52,0.581,0.451,0.363,0.342,0.491,0.421,0.463,0.494,0.367
-446,Epileptic,F,47.0,1.1,2.0,1.1,0.7,1.0,1.4,1.8,1.2,0.6,0.4,0.3,2.6,1.3,0.4,0.7,4.4,7.5,1.0,1.7,3.7,0.6,2.6,2.3,2.4,2.9,4.1,4.3,2.5,2.2,1.9,1.0,0.9,0.6,2.2,0.3,0.8,2.2,2.2,0.1,0.1,0.9,3.8,1.7,3.1,2.9,0.6,0.3,0.4,1.0,0.6,0.7,1.1,1.4,1.6,0.3,2.8,0.8,1.2,0.3,0.6,0.6,1.6,0.3,0.4,1.4,0.2,5.6,0.7,0.8,0.7,0.6,1.1,2.8,0.2,9,17,12,5,11,9,13,13,7,5,2.0,16,13,6,7,45,81,6,21,27,6,20,18,28,31,43,42,22,24,18,13,7,5,26,3,7,27,33,2,1,6,27,17,27,22,5,1,4,6,6,5,9,14,14,2,22,7,11,3,4,5,11,3,4,9,4,39,6,6,9,4,8,22,2,0.049,0.028,0.025,0.015,0.024,0.041,0.023,0.024,0.033,0.027,0.03,0.05,0.031,0.029,0.036,0.036,0.03,0.036,0.036,0.042,0.017,0.042,0.052,0.032,0.034,0.031,0.03,0.024,0.024,0.025,0.035,0.045,0.03,0.027,0.012,0.021,0.036,0.027,0.016,0.014,0.018,0.048,0.037,0.037,0.019,0.017,0.018,0.01,0.014,0.016,0.065,0.024,0.039,0.019,0.014,0.026,0.02,0.019,0.02,0.05,0.042,0.028,0.035,0.016,0.026,0.015,0.027,0.021,0.019,0.024,0.017,0.02,0.016,0.015,1469,3825,2091,1653,1798,1116,3307,2897,1223,803,377.0,1631,2311,821,1407,7107,15213,2267,2787,2415,2326,3194,1682,5281,4675,7506,5382,5026,5047,3382,2098,951,797,4517,1316,1741,4705,6150,292,177,1652,2920,4981,2321,5000,1323,663,1720,2194,1202,263,2030,1768,3297,534,3445,1268,2066,818,568,1032,2277,376,807,1940,537,5649,1374,1328,1216,952,2019,6230,449,0.147,0.125,0.111,0.09,0.122,0.136,0.109,0.121,0.137,0.123,0.108,0.154,0.137,0.126,0.145,0.151,0.139,0.125,0.143,0.148,0.087,0.143,0.187,0.137,0.151,0.143,0.13,0.109,0.12,0.125,0.135,0.119,0.14,0.13,0.079,0.103,0.122,0.134,0.11,0.112,0.102,0.145,0.152,0.143,0.108,0.097,0.1,0.07,0.09,0.112,0.184,0.106,0.142,0.107,0.099,0.124,0.118,0.111,0.068,0.15,0.137,0.13,0.158,0.12,0.128,0.099,0.123,0.107,0.111,0.128,0.109,0.12,0.094,0.089,407,1296,759,665,678,564,1117,893,340,251,175.0,745,713,213,413,2178,4332,413,887,1265,573,898,688,1338,1474,2349,2151,1593,1522,1153,495,386,291,1146,392,675,1099,1425,164,95,792,1093,891,1390,2386,539,253,788,1044,517,194,801,680,1386,297,1760,590,1062,275,262,256,881,175,373,778,328,3109,613,659,508,498,765,2802,236,3.23,2.635,2.672,2.509,2.259,2.021,2.517,2.694,2.7,2.729,2.398,2.044,2.69,2.696,2.615,2.577,2.907,3.779,2.54,1.794,3.034,2.473,2.213,3.002,2.55,2.758,2.106,2.49,2.602,2.519,2.932,2.469,2.312,2.841,2.97,2.431,3.045,3.06,2.033,1.945,2.688,2.141,3.558,1.902,2.277,2.422,3.187,2.6,2.454,2.798,1.596,2.236,2.439,2.34,2.174,2.122,2.355,2.237,2.897,2.155,3.154,2.565,2.692,2.335,2.507,2.078,1.888,2.325,2.398,2.461,2.253,2.756,2.388,2.249,0.984,0.587,0.607,0.458,0.719,0.475,0.52,0.48,0.587,0.512,0.772,0.669,0.476,0.522,0.359,0.584,0.536,0.925,0.583,0.67,0.726,1.005,0.596,0.633,0.507,0.533,0.586,0.428,0.481,0.634,0.706,0.975,0.449,0.666,0.591,0.523,0.795,0.796,0.431,0.42,0.558,0.864,0.762,0.763,0.425,0.414,0.57,0.803,0.686,0.541,0.393,0.705,0.567,0.531,0.475,0.454,0.47,0.724,1.07,0.769,0.635,0.701,0.522,0.926,0.587,0.656,0.48,0.439,0.395,0.644,0.468,0.821,0.516,0.406
-447,Epileptic,F,67.5,1.6,3.4,1.0,1.0,2.5,3.0,1.3,1.7,1.1,0.5,0.2,2.6,1.4,0.5,1.4,6.2,9.7,1.8,2.7,3.8,1.3,6.1,1.9,4.6,2.8,3.9,2.8,2.3,2.2,2.0,1.7,1.8,0.5,3.5,0.4,1.1,5.3,5.6,0.2,0.2,1.1,3.7,3.4,3.2,2.8,0.4,0.5,1.3,2.1,0.7,0.6,2.1,1.7,2.0,0.0,2.4,0.9,1.9,1.3,1.3,0.6,1.8,0.3,0.7,1.8,1.4,2.7,1.4,0.6,0.6,0.5,1.5,5.4,0.4,10,24,7,8,17,25,11,14,11,5,2.0,26,14,5,13,64,85,11,30,37,15,54,18,54,29,44,28,21,19,24,19,9,5,32,3,11,44,49,1,1,6,31,25,18,16,3,3,6,8,6,5,13,11,12,0,16,6,18,12,12,4,10,3,7,13,16,18,8,4,4,4,11,36,4,0.069,0.039,0.023,0.031,0.046,0.035,0.025,0.037,0.049,0.049,0.022,0.033,0.034,0.036,0.037,0.045,0.043,0.054,0.039,0.041,0.035,0.04,0.031,0.056,0.038,0.042,0.037,0.036,0.029,0.034,0.057,0.09,0.026,0.048,0.029,0.034,0.047,0.052,0.026,0.021,0.026,0.04,0.07,0.03,0.022,0.011,0.033,0.019,0.035,0.021,0.028,0.034,0.026,0.022,0.004,0.024,0.022,0.026,0.022,0.031,0.019,0.038,0.043,0.035,0.022,0.029,0.031,0.022,0.022,0.023,0.026,0.028,0.028,0.02,1276,4080,2026,1564,2339,3544,2222,2359,1559,913,539.0,2595,2774,661,1644,8507,14642,2150,3665,3211,2608,5462,1860,4243,3129,4982,4001,3116,4604,3574,2108,624,784,4980,1052,1471,5097,4661,229,297,1260,2447,4353,2452,3110,906,645,2126,1662,1385,476,2201,1834,3149,7,2878,1318,2280,1523,1388,913,1922,363,831,2308,1196,2885,2014,1122,890,883,1742,5469,451,0.161,0.121,0.105,0.122,0.155,0.126,0.099,0.135,0.147,0.157,0.094,0.129,0.126,0.129,0.132,0.148,0.145,0.164,0.14,0.142,0.114,0.134,0.122,0.14,0.123,0.139,0.113,0.105,0.103,0.138,0.148,0.153,0.096,0.129,0.104,0.108,0.143,0.14,0.112,0.101,0.113,0.141,0.199,0.126,0.102,0.082,0.112,0.089,0.123,0.111,0.143,0.129,0.117,0.103,0.047,0.115,0.105,0.119,0.114,0.118,0.099,0.13,0.166,0.137,0.106,0.13,0.119,0.107,0.104,0.11,0.118,0.115,0.118,0.12,361,1392,644,574,845,1347,841,822,440,233,162.0,1242,734,203,597,2477,3879,482,1309,1610,657,2197,931,1283,1189,1674,1375,1053,1274,1096,592,286,295,1180,303,606,1712,1845,148,161,659,1388,851,1640,1898,534,282,902,855,554,346,1062,961,1458,5,1540,680,1278,976,736,537,756,133,355,1270,773,1502,979,489,430,383,932,3058,322,3.336,2.688,2.913,2.486,2.401,2.219,2.194,2.544,2.695,3.071,2.835,1.81,2.813,2.418,2.209,2.525,2.854,3.085,2.302,1.742,2.883,2.104,1.885,2.603,2.222,2.43,2.345,2.25,2.718,2.47,2.506,2.18,2.123,2.8,2.703,2.264,2.314,2.016,1.897,2.094,2.335,1.531,3.125,1.683,1.821,1.857,2.736,2.806,2.501,2.689,1.554,2.221,1.888,2.349,1.379,2.027,2.238,1.898,1.726,1.945,2.002,2.514,2.776,2.432,1.923,2.067,2.142,2.331,2.453,2.342,2.553,2.206,1.916,1.728,0.852,0.742,0.656,0.755,0.681,0.688,0.536,0.536,0.536,0.696,0.639,0.439,0.472,0.692,0.599,0.654,0.684,0.963,0.601,0.523,0.851,0.675,0.501,0.815,0.519,0.634,0.604,0.54,0.426,0.641,0.883,0.897,0.458,0.678,0.625,0.522,0.78,0.69,0.473,0.487,0.576,0.59,0.977,0.492,0.581,0.411,0.579,0.912,0.545,0.562,0.331,0.45,0.538,0.466,0.119,0.498,0.481,0.575,0.393,0.543,0.409,0.638,0.819,0.83,0.484,0.742,0.549,0.39,0.4,0.677,0.754,0.533,0.462,0.332
-448,Epileptic,F,37.0,0.7,1.4,0.9,1.1,1.2,2.1,1.4,1.2,1.2,0.8,0.2,2.4,1.0,0.5,1.0,3.6,6.8,0.7,2.4,3.5,1.5,2.7,1.2,2.8,2.2,2.9,2.7,1.5,2.0,2.8,1.2,0.7,0.5,2.0,0.3,0.6,3.6,2.7,0.2,0.1,1.0,2.7,2.7,2.2,2.4,0.9,0.5,0.6,1.2,0.9,0.6,0.9,1.7,2.9,0.2,2.0,0.8,2.1,0.7,0.6,0.6,0.5,0.1,0.4,1.4,1.1,2.4,0.6,0.8,0.3,1.7,1.7,3.4,0.2,4,13,9,8,10,19,13,13,20,7,2.0,21,10,6,14,38,65,5,22,27,10,28,11,37,33,27,33,16,18,34,16,6,4,25,2,7,33,29,3,1,5,24,18,18,13,10,3,4,6,6,4,8,13,25,1,18,6,13,5,4,3,3,1,3,10,11,16,4,7,3,17,9,27,1,0.041,0.025,0.022,0.027,0.046,0.033,0.026,0.027,0.048,0.035,0.028,0.037,0.025,0.027,0.031,0.035,0.031,0.024,0.037,0.039,0.027,0.041,0.034,0.045,0.03,0.035,0.03,0.022,0.026,0.031,0.043,0.038,0.029,0.032,0.013,0.024,0.037,0.033,0.016,0.02,0.019,0.036,0.041,0.036,0.022,0.024,0.019,0.013,0.018,0.036,0.045,0.018,0.029,0.025,0.023,0.021,0.03,0.021,0.019,0.016,0.021,0.016,0.015,0.019,0.024,0.031,0.024,0.015,0.018,0.014,0.032,0.025,0.017,0.017,1396,3795,1784,1932,1471,3026,2759,2466,1887,1637,450.0,1957,2382,1053,2342,7104,14923,2237,3406,2376,2361,3492,1699,4687,4459,5422,5101,2799,4396,5274,1960,776,830,3892,1626,1574,6064,5046,512,508,1509,2380,3637,1457,2881,1664,879,1928,2341,1206,486,1778,2250,4534,250,3066,1184,2914,1017,1016,961,1448,212,879,2215,891,2746,1261,1731,736,1791,1786,7352,526,0.105,0.113,0.113,0.117,0.15,0.144,0.119,0.13,0.152,0.129,0.095,0.138,0.115,0.118,0.126,0.134,0.133,0.092,0.119,0.138,0.1,0.152,0.124,0.147,0.137,0.142,0.137,0.105,0.113,0.132,0.124,0.115,0.127,0.116,0.065,0.1,0.132,0.121,0.105,0.101,0.104,0.148,0.122,0.135,0.11,0.108,0.108,0.081,0.094,0.133,0.15,0.1,0.133,0.117,0.138,0.115,0.125,0.101,0.112,0.101,0.106,0.095,0.119,0.106,0.118,0.134,0.116,0.095,0.108,0.1,0.136,0.13,0.094,0.092,310,999,629,678,465,1011,864,814,485,369,119.0,926,692,306,647,1837,3794,413,1028,1399,824,1113,611,1083,1299,1339,1598,1004,1227,1613,470,387,279,969,406,514,1685,1327,206,154,694,1100,969,1021,1706,680,369,781,1003,442,272,819,841,1947,122,1489,495,1512,585,554,429,440,119,357,942,512,1501,589,706,309,887,903,3236,201,3.829,3.201,2.541,2.651,2.553,2.554,2.587,2.811,2.67,3.423,2.822,1.916,2.647,2.386,2.672,2.773,2.974,3.805,2.695,1.621,2.627,2.44,2.232,2.962,2.647,3.097,2.529,2.174,2.745,2.644,2.832,2.163,2.533,2.773,3.304,2.694,2.927,2.799,2.294,2.968,2.754,1.944,2.861,1.609,1.962,2.236,3.012,3.076,2.837,3.097,2.096,2.29,2.369,2.368,2.679,2.263,2.877,2.345,1.972,2.021,2.771,3.447,1.956,2.672,2.456,2.248,2.112,2.563,2.56,2.747,2.185,2.553,2.344,3.026,0.824,0.71,0.711,0.516,0.758,0.541,0.488,0.503,0.693,0.553,0.842,0.514,0.424,0.647,0.698,0.622,0.628,0.865,0.758,0.453,0.691,0.515,0.464,0.793,0.519,0.623,0.495,0.518,0.473,0.664,0.664,0.916,0.471,0.598,0.786,0.701,0.733,0.589,0.433,0.814,0.798,0.409,0.732,0.639,0.539,0.592,0.625,0.902,0.65,0.675,0.376,0.467,0.781,0.549,0.284,0.495,0.47,0.562,0.402,0.336,0.678,0.792,0.461,0.941,0.675,0.682,0.451,0.436,0.64,0.759,0.435,0.65,0.476,0.445
-449,Epileptic,F,67.5,0.8,2.3,0.7,1.4,1.6,1.8,1.7,1.6,1.2,0.5,0.2,3.0,1.3,0.5,1.1,6.4,9.0,0.7,2.4,3.5,1.2,2.8,1.1,3.6,2.2,3.1,2.0,2.7,2.3,3.1,1.3,0.9,0.3,2.0,0.5,0.4,3.0,2.7,0.2,0.2,1.3,2.8,2.4,2.4,2.3,0.6,0.4,1.0,1.0,0.7,0.6,1.9,1.0,2.2,0.2,2.1,0.4,1.6,0.8,0.8,0.2,1.4,0.3,0.3,1.8,0.7,3.0,1.4,1.4,0.7,1.3,1.0,3.0,0.3,7,21,7,11,13,18,13,13,16,6,2.0,27,15,6,13,68,76,6,24,31,14,33,13,34,29,30,20,28,20,34,14,6,3,20,3,4,29,27,2,1,7,25,24,21,14,4,2,6,6,5,4,14,9,16,2,19,5,15,6,5,2,10,4,4,12,10,27,9,10,6,7,9,24,2,0.028,0.024,0.013,0.023,0.035,0.029,0.023,0.027,0.039,0.028,0.028,0.036,0.029,0.033,0.027,0.036,0.031,0.028,0.035,0.034,0.025,0.035,0.027,0.043,0.028,0.032,0.023,0.027,0.022,0.03,0.036,0.035,0.014,0.027,0.015,0.015,0.037,0.033,0.015,0.016,0.025,0.036,0.036,0.025,0.017,0.011,0.017,0.015,0.013,0.024,0.032,0.021,0.022,0.021,0.032,0.021,0.014,0.027,0.021,0.014,0.02,0.028,0.034,0.015,0.02,0.02,0.023,0.02,0.023,0.022,0.033,0.02,0.016,0.018,1308,4097,2326,2221,2028,2349,2343,2616,1874,1046,444.0,2532,2717,933,2173,10329,15873,1727,3602,3318,3149,3772,1733,4858,4368,4588,3802,3775,5147,5007,2043,880,841,4162,1658,1200,5451,5298,385,427,1349,2564,5188,2508,3426,1382,786,2087,2244,1450,397,2988,1823,4186,310,3374,1100,2150,868,1310,432,1878,341,823,2782,949,3639,2251,1762,896,1479,1636,5820,431,0.115,0.122,0.085,0.109,0.149,0.152,0.095,0.112,0.154,0.132,0.107,0.147,0.135,0.147,0.135,0.148,0.132,0.111,0.145,0.136,0.106,0.153,0.123,0.144,0.133,0.135,0.111,0.115,0.096,0.141,0.142,0.102,0.083,0.122,0.074,0.087,0.138,0.134,0.115,0.087,0.121,0.143,0.145,0.117,0.092,0.079,0.098,0.081,0.086,0.102,0.143,0.11,0.106,0.105,0.132,0.111,0.093,0.13,0.123,0.092,0.099,0.128,0.164,0.109,0.106,0.102,0.121,0.104,0.117,0.131,0.134,0.119,0.094,0.102,442,1445,872,940,758,960,1006,943,543,313,155.0,1282,773,275,690,3124,4689,414,1142,1608,835,1329,764,1490,1345,1640,1400,1555,1467,1688,594,412,269,1059,486,497,1372,1362,212,213,730,1142,1194,1668,2118,780,347,977,1113,517,285,1415,883,1820,123,1622,579,1061,562,763,245,794,166,428,1341,497,2032,1082,947,493,640,811,2933,225,2.778,2.753,2.563,2.331,2.26,2.345,2.031,2.414,2.569,3.044,2.596,1.803,2.718,2.578,2.434,2.57,2.702,3.235,2.733,1.846,2.991,2.347,1.939,2.543,2.527,2.489,2.188,2.032,2.649,2.448,2.558,2.111,2.503,2.678,3.011,2.275,2.912,2.823,2.078,2.365,2.313,2.055,3.118,1.687,1.781,1.922,2.767,2.569,2.437,2.855,1.751,2.145,2.128,2.327,3.079,2.091,2.084,2.238,1.766,1.877,1.929,2.337,2.15,2.228,2.289,2.221,1.939,2.325,2.178,2.117,2.564,2.291,2.206,2.37,0.706,0.604,0.568,0.45,0.662,0.439,0.497,0.511,0.648,0.499,0.93,0.375,0.418,0.432,0.464,0.537,0.552,0.674,0.66,0.445,0.712,0.502,0.435,0.683,0.589,0.453,0.478,0.511,0.47,0.56,0.759,0.792,0.544,0.684,0.716,0.374,0.592,0.567,0.524,0.403,0.574,0.484,0.726,0.519,0.579,0.513,0.487,0.623,0.426,0.803,0.359,0.472,0.523,0.481,0.337,0.395,0.335,0.672,0.334,0.389,0.367,0.569,0.354,0.942,0.579,0.737,0.422,0.591,0.389,0.582,0.671,0.513,0.387,0.319
-450,Epileptic,M,27.0,1.6,2.9,1.4,1.7,2.6,2.4,2.8,2.3,1.7,1.7,0.6,4.3,2.0,1.1,1.8,9.6,14.3,2.7,2.5,6.1,2.5,4.4,2.7,4.7,5.2,6.1,4.4,5.4,4.3,5.1,2.1,1.2,0.8,3.1,0.8,1.3,3.6,3.2,0.2,0.3,1.4,4.1,3.6,4.5,3.8,1.4,0.4,0.7,1.3,0.9,0.5,2.6,1.7,4.4,0.3,3.2,1.0,3.0,1.3,1.5,1.0,1.7,0.3,0.8,3.4,1.3,3.3,2.0,1.8,0.6,1.9,2.3,5.3,0.4,13,31,13,16,17,21,23,24,13,16,10.0,39,25,10,23,103,125,15,29,50,24,45,26,51,64,71,55,58,38,59,30,8,10,33,6,12,46,43,1,2,9,37,37,35,24,14,3,4,7,7,3,17,11,36,2,25,10,29,8,12,6,14,2,6,31,23,28,13,12,7,15,15,43,3,0.052,0.029,0.016,0.032,0.049,0.036,0.046,0.028,0.058,0.053,0.042,0.048,0.039,0.069,0.043,0.047,0.039,0.054,0.032,0.043,0.041,0.048,0.045,0.052,0.048,0.034,0.035,0.041,0.038,0.045,0.065,0.04,0.037,0.037,0.036,0.019,0.04,0.033,0.016,0.021,0.023,0.043,0.056,0.029,0.026,0.036,0.021,0.01,0.018,0.014,0.03,0.028,0.027,0.026,0.013,0.02,0.021,0.027,0.022,0.023,0.025,0.032,0.028,0.027,0.032,0.025,0.025,0.027,0.032,0.02,0.029,0.023,0.02,0.02,1717,5737,3066,2859,2288,2908,3029,4642,2188,2582,928.0,3889,3665,1295,3349,14637,25990,3758,4863,5726,5224,5263,2844,6679,7670,11288,6081,5713,7510,6969,2362,1488,1326,7965,2079,2809,8589,8401,334,487,1980,3292,6996,4263,3786,1712,968,2232,2330,2367,358,3343,2101,6904,537,4496,2537,4913,1520,1811,1230,2758,595,1222,3127,1191,4918,2733,1868,1067,2603,3016,9436,580,0.16,0.127,0.102,0.136,0.155,0.133,0.149,0.134,0.178,0.166,0.168,0.177,0.151,0.167,0.163,0.162,0.144,0.16,0.139,0.164,0.14,0.149,0.158,0.173,0.169,0.141,0.14,0.139,0.136,0.165,0.154,0.121,0.141,0.14,0.105,0.108,0.154,0.137,0.105,0.114,0.113,0.153,0.176,0.125,0.115,0.132,0.103,0.072,0.095,0.092,0.137,0.118,0.123,0.116,0.096,0.109,0.114,0.129,0.11,0.12,0.123,0.124,0.113,0.115,0.145,0.12,0.118,0.123,0.129,0.11,0.133,0.119,0.107,0.119,549,1787,1270,1010,807,1214,1158,1437,668,611,278.0,1776,1016,309,860,4131,6678,839,1456,2665,1169,1780,1192,1899,2152,3366,2338,2334,2104,2232,790,512,406,1644,492,1092,1972,1827,197,230,985,1669,1347,2566,2243,669,356,907,1086,971,254,1597,1021,2837,308,2405,757,1916,886,988,621,1028,237,540,1514,876,2301,1184,957,519,1108,1409,3986,324,2.811,2.739,2.507,2.554,2.299,2.262,2.183,2.72,2.343,3.089,2.642,1.883,2.73,2.865,2.835,2.567,2.865,3.306,2.687,1.813,3.209,2.347,2.025,2.574,2.553,2.547,2.066,1.988,2.647,2.423,2.268,2.703,2.682,3.07,3.206,2.361,3.022,3.097,1.999,2.352,2.417,1.798,3.269,1.828,1.893,2.336,2.872,2.828,2.697,2.681,1.622,2.212,2.202,2.385,1.986,1.989,2.615,2.453,1.909,1.932,2.328,2.349,2.224,2.301,2.092,1.642,2.131,2.55,2.186,1.936,2.411,2.411,2.447,2.231,1.188,0.723,0.404,0.712,0.655,0.589,0.603,0.558,0.759,0.746,0.883,0.556,0.552,0.607,0.6,0.633,0.719,1.048,0.545,0.666,0.79,0.648,0.684,0.812,0.802,0.651,0.581,0.646,0.593,0.687,0.686,1.041,0.434,0.709,0.833,0.554,0.806,0.684,0.433,0.442,0.51,0.533,0.875,0.615,0.555,0.55,0.889,0.733,0.476,0.462,0.257,0.488,0.535,0.479,0.331,0.47,0.657,0.582,0.341,0.434,0.372,0.737,0.591,0.846,0.551,0.581,0.519,0.583,0.466,0.7,0.53,0.459,0.476,0.501
-451,Epileptic,F,42.0,0.9,1.3,0.7,0.8,1.1,1.6,1.3,1.4,1.0,0.6,0.2,2.9,1.2,0.5,0.9,6.1,8.6,0.8,1.7,4.0,0.7,2.3,1.5,2.6,2.1,2.9,4.1,2.0,2.4,2.6,0.8,0.5,0.5,2.0,0.3,0.6,2.7,2.5,0.1,0.1,1.0,3.4,1.9,2.5,2.4,0.5,0.3,0.8,1.3,0.5,0.3,1.4,1.8,2.4,0.2,2.6,0.7,1.3,1.3,0.9,0.7,1.3,0.3,0.3,2.3,1.5,2.1,1.7,0.8,0.6,1.3,1.2,4.9,0.2,7,16,6,6,11,13,12,15,13,7,1.0,23,11,6,11,56,74,6,19,37,8,26,13,30,28,29,40,18,23,30,12,6,4,23,2,6,27,27,2,1,7,30,17,22,15,4,1,5,7,5,2,12,14,22,1,19,6,10,8,8,4,10,1,3,17,16,21,13,6,6,9,8,41,3,0.041,0.021,0.015,0.018,0.028,0.031,0.02,0.026,0.043,0.035,0.024,0.036,0.03,0.031,0.026,0.036,0.03,0.03,0.028,0.035,0.018,0.028,0.027,0.038,0.026,0.034,0.03,0.026,0.025,0.027,0.038,0.048,0.019,0.025,0.01,0.018,0.032,0.029,0.018,0.013,0.019,0.036,0.039,0.028,0.021,0.012,0.016,0.013,0.018,0.016,0.042,0.023,0.027,0.026,0.025,0.021,0.026,0.017,0.033,0.018,0.037,0.029,0.028,0.018,0.025,0.032,0.021,0.023,0.02,0.025,0.033,0.026,0.023,0.016,1289,3184,1656,1794,1663,2725,2249,2559,1432,995,387.0,2592,2558,774,1994,8632,15412,2079,2953,3874,2233,3661,1843,4154,4465,4815,5831,3117,4983,4448,1732,713,997,4657,1477,1496,5321,4917,293,401,1459,2754,3526,2127,3238,1305,691,2133,2126,1273,334,2229,2313,4012,150,3799,1199,2418,1131,1244,578,1831,254,687,3052,986,3134,2394,1246,918,1463,1778,8359,413,0.141,0.111,0.102,0.097,0.13,0.132,0.098,0.136,0.156,0.157,0.085,0.141,0.131,0.15,0.124,0.142,0.13,0.114,0.123,0.141,0.082,0.135,0.121,0.139,0.13,0.145,0.131,0.11,0.107,0.129,0.127,0.125,0.095,0.125,0.068,0.096,0.129,0.125,0.095,0.085,0.113,0.145,0.133,0.13,0.101,0.083,0.094,0.089,0.099,0.101,0.149,0.111,0.128,0.122,0.15,0.113,0.134,0.099,0.129,0.11,0.147,0.131,0.134,0.122,0.119,0.136,0.118,0.118,0.112,0.151,0.145,0.131,0.116,0.101,358,1156,687,757,700,890,806,891,442,298,147.0,1215,696,246,616,2748,4574,451,968,1791,624,1250,868,1289,1489,1453,2233,1124,1430,1542,411,281,363,1148,395,550,1445,1367,191,202,735,1372,902,1537,1914,688,296,892,1055,539,159,1062,1078,1654,76,1904,443,1284,668,731,319,715,143,307,1501,654,1591,1152,616,384,632,787,3513,260,3.42,2.586,2.357,2.443,2.239,2.805,2.312,2.733,2.628,2.854,2.55,1.896,2.786,2.408,2.544,2.472,2.761,3.563,2.686,1.893,2.771,2.358,1.916,2.537,2.419,2.852,2.19,2.215,2.698,2.331,2.922,2.542,2.232,2.943,3.326,2.547,2.919,2.715,1.875,2.314,2.525,1.808,3.064,1.626,1.897,2.071,2.816,2.879,2.497,2.644,2.624,2.157,2.13,2.303,2.776,2.225,2.839,2.204,2.168,1.947,2.283,2.436,2.003,2.243,2.141,1.915,2.061,2.361,2.304,2.599,2.571,2.668,2.462,1.882,0.68,0.525,0.486,0.376,0.456,0.581,0.546,0.469,0.697,0.408,0.918,0.453,0.437,0.505,0.42,0.571,0.562,0.743,0.557,0.586,0.64,0.465,0.362,0.529,0.464,0.465,0.562,0.555,0.49,0.554,0.612,0.85,0.475,0.565,0.584,0.433,0.673,0.501,0.358,0.357,0.548,0.437,0.708,0.589,0.622,0.407,0.505,0.714,0.56,0.419,0.437,0.454,0.531,0.539,0.42,0.451,0.589,0.439,0.343,0.388,0.447,0.79,0.424,0.955,0.536,0.669,0.501,0.404,0.471,0.551,0.584,0.526,0.515,0.445
-452,Epileptic,F,37.0,1.1,2.6,1.0,1.3,1.7,2.7,1.5,1.8,1.3,0.7,0.4,3.4,2.6,0.6,1.9,6.0,11.0,0.8,2.6,5.1,0.7,4.0,2.2,3.7,4.1,3.9,4.2,3.2,3.1,4.3,1.1,1.0,0.4,2.4,0.5,0.5,3.8,4.2,0.2,0.3,1.2,2.9,2.3,3.2,2.7,1.2,0.6,0.9,1.4,1.0,1.0,2.8,1.2,2.5,0.3,2.7,1.2,2.1,1.5,1.4,0.8,2.0,0.4,0.5,2.8,1.4,3.6,1.8,1.7,0.5,1.1,1.7,6.2,0.4,8,23,7,10,17,23,15,18,13,7,5.0,31,27,10,18,67,99,6,28,49,6,43,21,42,49,47,46,33,24,47,18,7,4,27,3,6,41,48,2,3,8,26,24,22,16,8,3,7,8,9,6,20,9,15,3,20,9,15,9,14,7,17,2,4,25,16,30,9,14,6,6,13,56,3,0.039,0.023,0.014,0.021,0.029,0.037,0.019,0.028,0.039,0.042,0.031,0.033,0.036,0.027,0.037,0.033,0.029,0.026,0.03,0.033,0.015,0.037,0.029,0.036,0.036,0.026,0.028,0.028,0.024,0.032,0.029,0.032,0.018,0.031,0.014,0.014,0.033,0.034,0.015,0.026,0.021,0.039,0.039,0.027,0.02,0.018,0.019,0.013,0.016,0.029,0.033,0.027,0.021,0.022,0.017,0.018,0.036,0.024,0.03,0.02,0.021,0.027,0.026,0.017,0.024,0.033,0.024,0.023,0.023,0.015,0.025,0.019,0.021,0.014,1565,5228,2412,2410,2301,3380,2717,3407,1853,1205,624.0,2817,4031,1523,2947,10833,20550,2559,4517,5557,2287,5456,2419,6858,6410,7455,5982,4163,6310,6157,2356,1313,986,5998,1872,1815,6887,7852,463,501,1479,2653,6026,3550,3323,1802,1031,2485,2607,1519,762,3812,1855,3759,392,3698,1178,2735,1307,1808,989,2986,480,858,3076,1005,4162,2343,2300,1001,1396,2685,10116,550,0.127,0.112,0.086,0.103,0.137,0.15,0.098,0.126,0.145,0.14,0.141,0.139,0.149,0.143,0.15,0.137,0.126,0.104,0.132,0.141,0.081,0.147,0.128,0.14,0.152,0.128,0.127,0.115,0.091,0.14,0.126,0.106,0.089,0.127,0.071,0.08,0.137,0.139,0.099,0.141,0.107,0.139,0.156,0.118,0.099,0.103,0.105,0.084,0.092,0.131,0.143,0.117,0.112,0.111,0.13,0.106,0.137,0.107,0.133,0.122,0.121,0.128,0.125,0.115,0.12,0.129,0.123,0.107,0.115,0.111,0.113,0.107,0.111,0.109,502,1837,979,987,943,1204,1098,1149,538,340,226.0,1489,1221,372,853,3021,6231,491,1362,2389,693,1918,1120,1854,2096,2524,2333,1670,1715,2254,676,530,349,1390,507,658,1880,2151,234,193,850,1230,1222,2043,2037,911,448,1041,1299,562,460,1775,939,1748,255,2124,580,1475,776,1066,511,1169,240,459,1710,676,2223,1096,1159,528,677,1358,4839,326,3.051,2.66,2.406,2.384,2.133,2.284,2.106,2.648,2.539,2.872,2.118,1.732,2.652,2.999,2.571,2.683,2.682,3.815,2.725,2.002,2.58,2.251,1.88,2.771,2.425,2.5,2.132,2.041,2.721,2.234,2.589,2.48,2.241,2.957,3.336,2.439,2.668,2.789,2.184,2.63,2.097,1.888,3.315,1.909,1.848,2.159,2.866,2.783,2.395,2.816,2.073,2.225,2.137,2.257,1.848,1.856,2.417,2.105,1.834,1.83,2.095,2.505,2.357,2.168,1.854,1.875,1.966,2.37,2.063,2.353,2.23,2.293,2.222,2.135,0.795,0.54,0.596,0.476,0.544,0.532,0.529,0.485,0.62,0.515,0.647,0.381,0.482,0.374,0.616,0.629,0.604,0.76,0.508,0.548,0.707,0.461,0.489,0.628,0.471,0.469,0.481,0.532,0.629,0.439,0.656,0.97,0.453,0.626,0.615,0.51,0.627,0.545,0.314,0.642,0.448,0.481,0.736,0.578,0.564,0.562,0.42,0.788,0.429,0.512,0.54,0.501,0.568,0.531,0.289,0.444,0.543,0.468,0.384,0.392,0.388,0.605,0.45,0.758,0.441,0.581,0.513,0.462,0.467,0.666,0.524,0.467,0.482,0.362
-453,Epileptic,M,37.0,1.5,3.3,1.2,1.8,1.7,2.3,2.5,1.9,1.8,0.8,0.4,3.7,1.6,0.7,1.5,7.2,12.7,0.8,2.0,5.1,2.1,5.2,3.3,4.9,5.9,5.3,5.6,3.3,2.7,4.1,2.1,1.0,0.7,2.6,0.5,0.8,3.7,3.2,0.1,0.2,1.6,5.0,3.1,3.6,3.7,1.2,0.4,1.6,1.3,0.7,0.6,3.4,1.6,2.0,0.3,3.3,1.1,1.9,1.5,1.6,0.7,2.1,0.5,0.6,2.7,1.8,2.9,1.6,1.2,0.8,0.9,1.4,6.8,0.4,8,27,10,14,14,21,18,19,19,9,3.0,33,19,9,17,76,106,7,20,43,17,44,28,52,44,54,57,33,24,47,24,8,5,30,5,7,44,39,1,1,12,41,31,31,22,8,2,9,7,7,5,30,13,18,2,24,9,17,12,11,5,16,5,4,20,25,26,13,7,11,5,9,49,4,0.047,0.029,0.017,0.024,0.035,0.03,0.042,0.03,0.052,0.029,0.032,0.036,0.026,0.03,0.031,0.035,0.042,0.03,0.036,0.045,0.031,0.041,0.034,0.047,0.044,0.029,0.031,0.033,0.027,0.034,0.051,0.046,0.029,0.032,0.022,0.015,0.038,0.031,0.011,0.022,0.028,0.043,0.044,0.027,0.023,0.019,0.017,0.021,0.016,0.014,0.025,0.023,0.027,0.02,0.019,0.017,0.024,0.022,0.028,0.023,0.024,0.031,0.032,0.023,0.025,0.023,0.018,0.02,0.022,0.022,0.021,0.019,0.024,0.034,1641,5440,2664,2995,2299,3483,2815,3531,2008,1747,726.0,3570,3954,1558,2921,11590,17679,2151,3536,4627,5093,3621,2582,6586,5220,8471,7439,4328,6183,6272,2578,999,1006,5867,1609,1879,7011,7763,378,385,1879,2921,6377,3471,3869,1780,742,2527,2311,1884,578,5072,2123,4164,382,5293,1603,2718,1387,1658,1060,2607,537,769,3454,1653,4572,2744,1582,1133,1263,2202,8866,351,0.119,0.126,0.097,0.115,0.135,0.128,0.131,0.135,0.182,0.133,0.125,0.154,0.122,0.142,0.133,0.133,0.15,0.118,0.136,0.167,0.122,0.128,0.13,0.163,0.132,0.129,0.114,0.116,0.104,0.136,0.156,0.113,0.103,0.142,0.094,0.086,0.154,0.139,0.087,0.11,0.13,0.149,0.179,0.121,0.109,0.104,0.094,0.102,0.096,0.092,0.119,0.12,0.113,0.104,0.11,0.103,0.124,0.116,0.122,0.106,0.099,0.133,0.133,0.115,0.119,0.121,0.1,0.103,0.107,0.121,0.11,0.107,0.111,0.154,479,1916,1022,1134,899,1213,1113,1113,671,446,203.0,1700,1077,391,857,3703,5580,496,1010,2104,1089,1636,1478,1927,1822,2704,3027,1648,1673,2258,723,436,360,1416,400,818,1799,1835,210,187,972,1699,1395,2130,2314,880,324,1142,1128,784,371,2354,1112,1801,217,2747,696,1359,922,996,528,1115,308,427,1715,1139,2478,1327,837,641,648,1022,4157,218,3.252,2.654,2.495,2.546,2.295,2.391,2.163,2.645,2.436,2.952,2.996,1.89,2.784,2.773,2.66,2.406,2.539,3.415,2.747,1.906,3.312,1.944,1.635,2.676,2.38,2.53,2.047,2.118,2.769,2.254,2.597,2.367,2.31,2.997,3.312,2.283,2.936,3.071,2.08,2.328,2.367,1.616,3.303,1.886,1.929,2.228,2.76,2.923,2.516,2.564,1.85,2.098,2.011,2.278,2.142,2.141,2.497,2.262,1.764,1.83,2.236,2.414,2.105,1.915,2.146,1.78,1.915,2.278,2.225,1.969,2.314,2.49,2.293,2.02,0.545,0.539,0.51,0.585,0.576,0.578,0.531,0.477,0.608,0.706,1.142,0.497,0.432,0.506,0.549,0.608,0.569,0.831,0.584,0.686,0.882,0.584,0.508,0.695,0.626,0.551,0.522,0.596,0.5,0.63,0.634,0.776,0.48,0.627,0.613,0.489,0.628,0.658,0.352,0.415,0.447,0.525,0.722,0.466,0.597,0.508,0.456,0.614,0.416,0.361,0.352,0.446,0.46,0.459,0.307,0.41,0.463,0.576,0.452,0.43,0.354,0.664,0.383,0.519,0.531,0.744,0.526,0.363,0.422,0.47,0.384,0.418,0.418,0.439
-454,Epileptic,M,37.0,0.4,0.7,0.5,0.8,1.3,0.9,1.2,3.7,0.7,0.6,1.0,2.1,2.0,0.6,1.0,6.6,7.0,1.5,2.4,4.4,0.8,3.9,1.0,3.8,3.1,7.8,3.3,3.9,5.5,3.1,0.5,0.8,0.6,3.4,0.5,0.7,2.6,4.4,0.6,0.1,1.6,2.8,3.3,2.0,3.4,0.6,0.4,0.7,0.8,0.6,0.4,2.3,1.3,1.1,0.2,2.8,0.9,1.2,1.0,0.5,1.0,0.6,0.1,0.2,1.3,1.1,4.9,1.9,1.9,0.3,1.7,1.2,8.3,0.3,4,6,5,5,7,6,14,30,5,7,4.0,13,12,4,8,297,58,9,21,34,7,23,7,29,18,56,23,30,40,30,3,4,5,32,6,3,21,39,1,1,8,16,25,11,16,4,2,4,5,6,2,16,8,7,1,19,6,11,6,4,5,3,1,1,12,10,24,10,8,1,8,7,40,2,0.029,0.014,0.013,0.022,0.059,0.032,0.043,0.047,0.039,0.03,0.058,0.045,0.045,0.052,0.094,0.168,0.036,0.055,0.034,0.053,0.023,0.055,0.044,0.065,0.05,0.071,0.038,0.05,0.056,0.053,0.025,0.032,0.038,0.058,0.043,0.025,0.042,0.057,0.057,0.029,0.031,0.052,0.051,0.034,0.037,0.026,0.033,0.012,0.014,0.028,0.027,0.037,0.038,0.02,0.045,0.048,0.022,0.025,0.04,0.037,0.038,0.029,0.051,0.019,0.041,0.032,0.054,0.032,0.04,0.025,0.042,0.035,0.043,0.027,1269,2056,1434,1810,950,2492,2549,3256,1152,804,690.0,2813,2772,766,1966,6597,16229,1655,2977,4813,2086,4687,1520,3965,3986,4579,4618,3479,5915,4686,1507,875,1093,3099,1301,1110,3414,3622,273,386,2014,2955,2918,2097,3070,1435,788,2154,2533,909,398,2865,1776,3110,274,2367,984,1986,1081,983,968,908,192,578,1694,1191,3653,2491,1866,818,1360,1634,6449,625,0.087,0.094,0.099,0.102,0.17,0.102,0.138,0.142,0.135,0.129,0.184,0.135,0.141,0.181,0.127,0.146,0.115,0.147,0.132,0.146,0.101,0.156,0.141,0.169,0.14,0.178,0.129,0.151,0.138,0.152,0.105,0.102,0.128,0.159,0.127,0.098,0.133,0.153,0.144,0.096,0.126,0.155,0.158,0.133,0.124,0.104,0.111,0.084,0.087,0.136,0.132,0.128,0.136,0.096,0.138,0.138,0.12,0.128,0.136,0.118,0.133,0.109,0.137,0.078,0.124,0.125,0.148,0.119,0.132,0.101,0.155,0.136,0.146,0.111,307,759,505,547,342,592,595,1117,288,360,294.0,813,720,188,401,1924,3277,391,1012,1504,655,1196,438,948,1189,1765,1412,1327,1690,1200,347,365,297,968,279,463,1080,1231,153,117,846,1010,960,1131,1633,444,241,856,1047,473,241,1003,549,1096,73,1180,608,960,494,368,442,361,84,187,719,587,1477,1004,846,180,628,586,3192,244,3.704,2.404,2.43,3.018,2.426,2.939,2.859,2.485,2.708,2.227,2.561,2.482,2.867,2.536,2.894,2.497,3.244,3.494,2.367,2.52,2.732,2.628,2.556,2.821,2.579,2.344,2.361,1.964,2.579,2.718,3.265,2.439,2.806,2.214,3.365,2.306,2.567,2.27,2.401,3.066,2.949,2.353,2.43,2.228,2.238,2.69,3.944,2.996,2.97,2.299,2.277,2.475,2.885,2.882,3.341,2.096,1.977,2.311,2.612,2.535,2.093,2.518,2.22,2.862,2.221,2.303,2.271,2.622,2.558,3.687,2.492,2.917,2.28,3.028,0.988,0.962,0.913,0.771,0.531,0.956,1.012,1.112,0.827,0.63,0.677,0.695,0.819,0.765,0.915,0.773,1.059,0.853,0.665,0.802,0.726,0.817,0.793,1.131,0.696,0.973,0.826,0.846,0.829,0.79,1.068,1.097,0.708,0.937,0.712,0.669,1.016,1.053,0.569,0.793,0.99,0.601,0.912,0.628,0.593,0.859,0.683,0.909,0.756,0.53,0.458,0.707,0.751,0.924,0.727,0.539,0.424,0.633,0.849,0.697,0.734,0.725,0.522,1.016,0.618,0.566,0.692,0.749,0.712,1.135,0.718,1.17,0.701,0.831
-455,Epileptic,F,42.0,1.1,2.1,0.8,0.8,1.4,1.9,1.4,1.4,1.3,0.7,0.3,2.3,1.6,0.5,1.1,5.6,7.9,1.5,1.6,3.4,1.6,2.2,1.2,3.4,3.9,2.7,3.2,2.6,3.3,4.1,1.2,1.5,0.5,2.1,0.3,0.7,3.2,3.1,0.2,0.3,0.9,2.1,2.2,2.0,2.8,0.5,0.4,0.8,1.4,0.9,0.6,1.6,1.4,2.1,0.5,2.2,0.5,1.8,0.6,1.1,0.5,1.5,0.1,0.7,1.2,1.1,2.6,1.1,1.3,0.5,1.3,1.4,3.4,0.4,7,18,6,7,16,18,11,13,13,7,3.0,22,16,5,12,52,65,10,19,28,13,25,10,34,35,24,31,19,22,42,17,12,3,20,2,5,37,29,1,1,5,18,19,17,17,3,2,6,7,7,4,10,10,13,3,16,4,16,5,8,6,9,1,5,8,12,15,6,7,5,9,11,25,3,0.045,0.026,0.017,0.019,0.033,0.032,0.027,0.031,0.043,0.034,0.031,0.028,0.03,0.034,0.029,0.035,0.03,0.057,0.025,0.031,0.028,0.034,0.028,0.04,0.034,0.027,0.029,0.031,0.029,0.032,0.043,0.049,0.024,0.027,0.013,0.019,0.037,0.032,0.016,0.021,0.02,0.03,0.042,0.024,0.022,0.01,0.019,0.015,0.018,0.026,0.025,0.02,0.022,0.02,0.03,0.015,0.014,0.021,0.019,0.017,0.023,0.03,0.023,0.032,0.019,0.031,0.019,0.017,0.019,0.025,0.027,0.024,0.016,0.024,1674,4109,1807,1633,2242,2799,2288,2400,1595,1396,555.0,2191,3297,1068,2760,11111,16701,2192,4088,3751,3234,3684,1916,5533,6575,5341,5806,3622,5582,6453,2385,1187,1003,4607,1514,1752,5495,6368,345,451,1316,2146,4206,2093,3342,1244,731,1994,2383,1238,489,2653,2410,4368,640,3935,1002,2869,780,1789,1093,2148,262,892,1885,1038,3878,2083,1656,854,1925,2171,8661,462,0.124,0.121,0.104,0.101,0.139,0.145,0.103,0.138,0.156,0.135,0.124,0.129,0.135,0.126,0.125,0.129,0.125,0.18,0.109,0.123,0.109,0.142,0.112,0.137,0.137,0.122,0.122,0.115,0.103,0.133,0.166,0.137,0.112,0.112,0.083,0.088,0.138,0.121,0.09,0.101,0.107,0.129,0.151,0.124,0.106,0.075,0.095,0.081,0.102,0.125,0.144,0.103,0.105,0.092,0.136,0.092,0.094,0.114,0.12,0.1,0.12,0.122,0.107,0.139,0.108,0.141,0.097,0.09,0.099,0.122,0.123,0.12,0.089,0.119,437,1283,694,688,769,1027,863,813,483,372,166.0,1044,935,279,684,2826,4527,434,1134,1620,927,1182,705,1469,1947,1613,1915,1285,1582,2068,522,460,313,1130,381,602,1654,1559,202,229,658,1007,964,1351,2042,697,317,871,1083,572,310,1253,984,1755,300,2112,519,1451,467,968,425,773,86,326,964,530,1939,1002,950,398,851,974,3547,228,3.437,2.884,2.548,2.395,2.347,2.417,2.284,2.92,2.539,3.404,3.074,1.814,2.783,2.755,2.905,2.812,2.887,3.477,2.914,2.002,2.817,2.526,2.216,2.879,2.637,2.744,2.364,2.253,2.638,2.497,3.1,2.44,2.645,2.78,3.485,2.641,2.597,3.077,1.985,2.402,2.598,1.958,3.218,1.763,1.859,1.953,2.901,2.659,2.73,2.392,2.003,2.25,2.352,2.469,2.755,2.063,2.401,2.279,1.927,2.147,2.34,2.78,2.643,2.825,2.224,2.576,2.165,2.475,2.051,2.47,2.414,2.501,2.685,2.608,0.893,0.681,0.514,0.439,0.695,0.583,0.597,0.632,0.568,0.681,0.612,0.379,0.406,0.694,0.546,0.735,0.659,0.801,0.546,0.575,0.73,0.503,0.434,0.682,0.543,0.52,0.517,0.516,0.588,0.507,0.839,0.917,0.458,0.668,0.745,0.547,0.617,0.593,0.296,0.374,0.481,0.487,0.771,0.589,0.545,0.4,0.477,0.653,0.488,0.486,0.388,0.586,0.637,0.463,0.265,0.404,0.665,0.592,0.462,0.5,0.534,0.673,1.008,0.637,0.59,0.79,0.448,0.451,0.334,0.73,0.645,0.727,0.635,0.303
-459,Epileptic,M,17.5,0.8,2.1,0.9,0.7,1.7,5.8,2.3,1.5,1.1,0.4,0.4,2.7,1.0,0.5,0.6,4.3,8.1,0.8,3.4,3.7,1.4,3.4,3.0,4.2,2.2,5.1,5.5,3.3,2.2,2.6,1.1,1.0,0.5,2.6,0.4,0.8,3.7,2.7,0.1,0.1,1.2,5.2,4.0,2.2,3.5,1.1,0.6,1.0,1.1,0.6,0.8,1.3,1.1,2.2,0.3,2.3,1.3,1.4,1.2,1.3,0.7,1.7,0.2,0.5,2.1,1.5,3.7,1.1,1.0,0.4,0.7,1.7,6.0,0.6,6,25,8,9,15,45,18,18,9,4,4.0,19,12,7,6,48,67,6,33,25,16,27,21,42,25,42,47,25,24,23,16,8,6,29,2,8,44,38,1,1,8,34,28,18,24,7,3,5,6,6,5,13,6,15,2,16,8,11,12,12,5,13,2,5,17,16,21,10,6,4,6,11,45,5,0.034,0.025,0.017,0.016,0.035,0.069,0.029,0.022,0.044,0.025,0.032,0.046,0.025,0.042,0.04,0.038,0.032,0.026,0.038,0.052,0.025,0.031,0.055,0.038,0.03,0.032,0.035,0.03,0.025,0.029,0.024,0.045,0.026,0.03,0.015,0.018,0.031,0.027,0.012,0.01,0.022,0.058,0.048,0.037,0.018,0.026,0.02,0.014,0.014,0.012,0.061,0.025,0.02,0.02,0.026,0.02,0.034,0.02,0.029,0.036,0.029,0.025,0.018,0.015,0.031,0.024,0.022,0.022,0.021,0.014,0.016,0.023,0.025,0.025,1461,4717,2087,1909,2934,3385,4289,4474,1426,1074,348.0,1582,2555,1207,1304,8958,17909,2388,5729,2020,4271,3803,1769,7194,3800,9215,5211,6258,6349,3937,2715,1186,1224,7259,1932,2363,7800,7075,363,263,1952,2836,7750,1899,6730,1838,956,2824,2429,1712,379,2494,1934,4522,407,2927,1602,2230,936,676,631,3609,515,983,2291,1535,4919,1825,1940,1006,1150,2318,8255,817,0.108,0.118,0.105,0.096,0.135,0.177,0.11,0.119,0.138,0.122,0.136,0.151,0.122,0.171,0.149,0.147,0.129,0.114,0.148,0.172,0.1,0.124,0.188,0.14,0.119,0.122,0.124,0.108,0.113,0.117,0.103,0.128,0.138,0.134,0.077,0.099,0.138,0.121,0.092,0.066,0.112,0.178,0.156,0.143,0.102,0.113,0.113,0.084,0.089,0.097,0.162,0.119,0.103,0.101,0.128,0.107,0.125,0.096,0.142,0.147,0.138,0.119,0.11,0.112,0.136,0.122,0.102,0.117,0.103,0.103,0.11,0.118,0.118,0.132,444,1525,792,745,860,1886,1234,1177,407,290,175.0,920,720,267,316,2182,4369,476,1584,1120,1006,1598,890,1766,1168,2631,2399,1691,1612,1472,763,450,370,1528,463,829,2078,1848,155,133,894,1454,1474,985,2919,735,391,1042,1097,750,264,915,796,1784,184,1764,675,1151,723,564,399,1095,202,522,1156,827,2662,773,735,489,619,1143,3651,350,3.158,2.664,2.459,2.387,2.64,1.661,2.804,3.104,2.648,3.13,2.206,1.678,2.86,3.271,3.208,2.907,3.098,3.613,2.852,1.802,3.176,1.938,1.763,2.916,2.568,2.898,1.896,2.868,2.973,2.251,2.647,2.626,2.757,3.441,3.978,2.672,2.8,2.848,2.357,2.238,2.569,1.612,3.458,2.071,2.473,2.571,3.151,3.209,2.652,2.527,1.511,2.501,2.56,2.583,2.63,1.82,2.517,2.088,1.454,1.345,1.727,2.94,2.728,2.235,1.854,2.379,2.031,2.215,2.686,2.389,2.029,2.38,2.426,2.684,0.951,0.785,0.76,0.467,0.601,0.62,0.63,0.556,0.562,0.442,0.736,0.531,0.547,0.378,0.625,0.63,0.758,0.95,0.773,0.609,0.846,0.618,0.624,0.769,0.686,0.627,0.552,0.725,0.542,0.605,0.798,0.899,0.374,0.662,0.582,0.52,0.794,0.66,0.327,0.39,0.587,0.574,0.819,0.838,0.509,0.542,0.574,0.733,0.469,0.463,0.336,0.605,0.709,0.588,0.438,0.508,0.69,0.714,0.378,0.302,0.471,0.786,0.504,0.788,0.643,0.796,0.565,0.579,0.607,0.552,0.552,0.587,0.639,0.404
-460,Epileptic,M,47.0,0.8,1.9,0.8,1.5,2.7,2.6,2.4,2.2,1.2,0.8,0.4,2.8,2.3,0.8,1.9,7.2,11.0,0.9,2.7,4.4,1.1,3.0,1.4,4.5,4.5,3.5,3.7,4.0,3.6,3.4,1.5,1.9,0.5,3.5,0.5,0.9,3.7,4.1,0.2,0.5,1.0,2.5,3.1,2.4,3.4,0.7,0.5,0.7,1.6,0.4,1.0,1.9,2.0,3.4,0.5,2.0,0.7,2.5,0.8,1.3,1.0,1.5,0.3,0.5,2.0,1.1,2.9,1.6,1.4,0.7,1.4,1.4,6.0,0.3,6,15,7,10,18,22,14,16,8,7,3.0,24,20,6,12,58,84,5,24,35,8,34,12,40,40,29,32,31,24,30,14,10,4,24,4,7,38,40,2,2,5,20,24,16,16,5,2,5,8,5,7,11,12,19,3,13,4,44,6,10,8,9,2,4,16,10,17,8,6,5,8,11,44,2,0.036,0.025,0.018,0.029,0.062,0.036,0.035,0.036,0.051,0.037,0.032,0.033,0.043,0.044,0.043,0.05,0.043,0.035,0.036,0.037,0.023,0.038,0.028,0.048,0.042,0.039,0.036,0.044,0.042,0.036,0.043,0.065,0.034,0.048,0.024,0.03,0.041,0.038,0.018,0.028,0.02,0.031,0.057,0.03,0.027,0.017,0.022,0.013,0.02,0.023,0.04,0.025,0.033,0.029,0.029,0.016,0.017,0.03,0.018,0.025,0.025,0.029,0.026,0.024,0.023,0.031,0.021,0.023,0.034,0.024,0.031,0.022,0.022,0.025,1322,3825,1835,1887,2467,3723,2379,2582,1487,1293,492.0,2393,3211,1194,2428,9756,14777,2308,4266,4140,2196,4436,1745,6196,6461,4701,4777,3673,4091,5134,2554,1058,654,4824,1416,1689,6642,8049,427,368,1375,2353,4825,1988,3055,1189,801,1683,2617,1183,838,2502,2240,3929,463,3550,1490,2606,1091,1445,1624,2416,365,876,2703,708,3544,2100,1509,1296,1503,2442,10054,443,0.114,0.115,0.1,0.121,0.175,0.145,0.116,0.132,0.14,0.138,0.106,0.142,0.149,0.142,0.137,0.154,0.14,0.117,0.14,0.137,0.093,0.142,0.113,0.15,0.144,0.134,0.118,0.135,0.126,0.139,0.141,0.157,0.129,0.139,0.101,0.107,0.145,0.138,0.101,0.122,0.103,0.126,0.16,0.124,0.113,0.102,0.1,0.088,0.1,0.104,0.156,0.109,0.125,0.119,0.123,0.092,0.099,0.131,0.113,0.119,0.12,0.116,0.134,0.139,0.118,0.123,0.106,0.108,0.12,0.12,0.132,0.121,0.108,0.132,379,1257,722,837,813,1278,971,1031,438,382,185.0,1226,983,327,750,2647,4658,443,1288,1844,741,1431,789,1683,1976,1560,1748,1514,1414,1626,631,445,219,1261,428,555,1553,1975,215,224,708,1227,989,1331,2027,614,338,817,1187,414,432,1232,1031,1749,286,1879,651,1462,681,850,669,860,189,376,1401,461,2023,1023,680,465,730,1058,4419,185,3.387,2.665,2.508,2.258,2.669,2.579,2.169,2.385,2.566,2.877,2.442,1.82,2.519,2.718,2.451,2.671,2.676,3.871,2.809,1.988,2.394,2.443,1.92,2.886,2.605,2.482,2.152,2.008,2.322,2.623,2.903,2.45,2.625,2.823,3.04,2.654,3.05,2.921,2.349,2.211,2.418,1.8,3.253,1.808,1.743,2.041,2.782,2.466,2.664,3.097,2.266,2.188,2.369,2.326,2.035,2.069,2.492,2.049,1.815,1.969,2.505,2.992,2.613,2.57,2.215,1.85,1.995,2.341,2.576,2.98,2.436,2.769,2.473,2.916,0.653,0.752,0.551,0.469,0.533,0.65,0.517,0.579,0.549,0.508,0.677,0.481,0.542,0.621,0.476,0.691,0.61,0.788,0.798,0.691,0.769,0.563,0.409,0.79,0.553,0.583,0.514,0.52,0.558,0.54,0.799,0.875,0.475,0.779,0.819,0.567,0.667,0.654,0.452,0.463,0.843,0.463,0.854,0.672,0.468,0.558,0.663,0.696,0.597,0.923,0.527,0.452,0.501,0.492,0.338,0.553,0.643,0.702,0.417,0.391,0.534,0.731,0.47,0.882,0.575,0.579,0.536,0.462,0.506,0.881,0.46,0.588,0.502,0.435
-461,Epileptic,M,42.0,1.0,1.5,1.0,0.8,2.1,2.1,1.9,1.4,0.6,0.5,0.4,3.5,1.4,1.1,1.4,6.0,10.1,0.7,1.7,6.0,1.4,2.9,2.1,5.2,2.8,4.3,4.1,2.6,3.0,4.1,2.1,0.8,0.8,3.8,0.6,0.7,5.3,4.6,0.3,0.3,1.0,3.6,3.5,3.0,2.5,0.5,0.8,1.0,1.3,1.1,0.6,1.9,3.0,2.4,0.2,2.3,0.6,2.3,1.0,1.3,0.5,1.8,0.4,0.7,1.4,1.0,2.5,1.3,1.0,0.6,1.3,2.1,4.2,0.4,5,10,10,6,17,17,12,11,5,5,4.0,25,13,11,12,52,75,4,13,44,15,31,19,39,27,39,43,21,32,33,18,6,6,27,3,4,49,38,2,2,6,28,23,22,13,4,3,5,5,8,3,11,15,13,1,17,4,20,7,12,5,12,2,3,11,8,14,8,4,4,9,10,27,4,0.03,0.02,0.019,0.017,0.06,0.034,0.029,0.031,0.046,0.028,0.034,0.035,0.03,0.045,0.037,0.041,0.04,0.027,0.029,0.034,0.027,0.032,0.032,0.051,0.033,0.04,0.034,0.029,0.032,0.035,0.047,0.039,0.029,0.041,0.019,0.021,0.052,0.038,0.017,0.025,0.024,0.035,0.047,0.026,0.02,0.012,0.031,0.015,0.016,0.026,0.025,0.023,0.043,0.023,0.035,0.02,0.025,0.027,0.027,0.022,0.03,0.036,0.028,0.026,0.02,0.027,0.02,0.025,0.018,0.023,0.025,0.034,0.019,0.024,1723,4140,2337,1995,2013,3299,2633,2751,1176,1231,647.0,3257,2879,1831,2396,9792,17384,2347,2439,5632,3434,4893,2477,6028,5103,7344,6271,3365,4627,6577,2763,1324,1105,5505,1698,1421,7099,6186,476,387,1677,2929,4464,3562,3123,1094,1026,2449,2732,1507,633,2796,2545,3989,248,3363,1036,3459,1163,1827,627,2177,427,935,2570,1024,3758,1951,1659,1409,2035,1990,8200,397,0.103,0.097,0.1,0.086,0.147,0.135,0.107,0.11,0.109,0.117,0.122,0.138,0.114,0.138,0.112,0.127,0.124,0.098,0.114,0.125,0.106,0.132,0.133,0.135,0.134,0.132,0.118,0.107,0.11,0.126,0.145,0.115,0.118,0.122,0.081,0.092,0.143,0.12,0.1,0.115,0.103,0.135,0.132,0.119,0.101,0.083,0.114,0.087,0.09,0.121,0.115,0.1,0.119,0.096,0.146,0.104,0.116,0.128,0.124,0.116,0.12,0.122,0.115,0.109,0.104,0.11,0.097,0.114,0.097,0.11,0.113,0.134,0.098,0.115,520,1153,822,720,707,1044,1030,803,269,330,221.0,1480,828,459,676,2479,4367,443,867,2492,947,1435,1055,1740,1362,2059,2144,1313,1333,1914,708,424,391,1348,509,522,1955,1638,198,189,746,1475,1257,1937,1983,627,426,1023,1137,724,342,1384,995,1663,97,1828,432,1497,633,926,338,869,248,382,1226,551,1865,881,797,450,895,990,3478,255,2.978,2.957,2.626,2.692,2.336,2.611,2.166,3.041,2.97,3.079,2.709,1.955,2.654,2.932,2.639,2.76,3.014,3.73,2.428,1.979,2.888,2.506,2.074,2.555,2.672,2.786,2.308,2.077,2.617,2.588,2.702,2.963,2.344,2.732,3.029,2.51,2.751,2.789,2.528,2.497,2.715,1.959,2.792,2.098,1.839,1.923,3.045,2.945,2.98,2.558,2.407,2.158,2.262,2.609,2.644,2.056,2.765,2.65,2.313,2.09,2.38,2.53,2.073,2.839,2.246,2.183,2.204,2.537,2.514,3.126,2.388,2.633,2.654,1.986,1.2,0.909,0.679,0.672,0.632,0.601,0.594,0.687,0.79,0.541,0.458,0.494,0.564,0.766,0.606,0.778,0.749,0.985,0.538,0.562,0.882,0.503,0.407,0.85,0.62,0.668,0.578,0.543,0.571,0.68,1.104,1.202,0.495,0.816,1.12,0.662,0.864,0.815,0.442,0.701,0.757,0.581,0.848,0.662,0.516,0.488,0.791,0.946,0.731,0.697,0.541,0.584,0.64,0.692,0.525,0.538,0.384,0.718,0.524,0.378,0.482,0.78,0.521,1.083,0.572,0.681,0.554,0.527,0.528,0.822,0.582,0.643,0.657,0.409
-462,Epileptic,F,17.5,0.9,2.0,0.7,1.0,1.6,1.8,1.3,1.4,0.9,0.6,0.2,2.6,1.4,0.5,1.4,5.7,7.7,0.6,2.6,4.6,1.1,2.9,1.5,3.4,3.9,3.5,2.7,2.5,2.3,3.2,1.5,0.5,0.5,3.2,0.4,0.8,3.3,2.9,0.1,0.2,0.9,2.7,2.4,4.7,2.2,0.5,0.4,1.1,1.1,0.6,0.8,1.8,1.7,2.8,0.4,2.4,1.1,1.2,0.8,1.0,0.6,0.9,0.3,0.4,1.7,1.5,3.0,1.2,0.9,0.7,0.9,1.5,4.7,0.1,8,18,8,11,16,14,11,15,13,7,2.0,26,16,6,14,58,73,6,31,44,14,27,17,34,47,33,28,21,18,34,23,4,4,33,2,8,35,36,1,2,5,26,28,27,14,5,2,8,6,5,5,13,12,17,3,22,8,12,7,8,5,9,2,4,14,20,25,8,6,10,6,10,39,1,0.035,0.019,0.011,0.021,0.028,0.031,0.022,0.026,0.041,0.024,0.02,0.031,0.024,0.026,0.026,0.029,0.026,0.024,0.03,0.031,0.017,0.036,0.027,0.032,0.03,0.028,0.027,0.025,0.021,0.027,0.036,0.023,0.035,0.032,0.011,0.025,0.035,0.026,0.011,0.018,0.02,0.031,0.036,0.037,0.019,0.014,0.018,0.019,0.013,0.012,0.038,0.016,0.02,0.02,0.014,0.021,0.028,0.015,0.023,0.018,0.03,0.018,0.017,0.015,0.023,0.032,0.021,0.018,0.017,0.022,0.021,0.023,0.019,0.021,1434,4579,1851,2037,2620,2516,2198,3051,1714,1592,537.0,2782,4010,1272,3241,13409,20237,2178,5030,4664,3558,3857,2167,6722,6775,6564,5333,3753,5878,5617,1976,836,912,6382,1967,1864,5830,7495,390,651,1422,2673,4868,2780,2854,1213,953,2133,2744,1517,536,4321,3395,5651,943,3649,1485,2845,854,1313,680,2185,518,834,2581,1052,4971,2600,1928,1380,1461,2306,9248,520,0.144,0.112,0.095,0.111,0.133,0.141,0.109,0.125,0.141,0.116,0.105,0.129,0.121,0.123,0.126,0.131,0.123,0.095,0.137,0.135,0.095,0.142,0.137,0.136,0.144,0.125,0.128,0.117,0.098,0.13,0.133,0.079,0.131,0.126,0.06,0.101,0.135,0.124,0.092,0.103,0.109,0.137,0.142,0.143,0.096,0.098,0.094,0.089,0.085,0.098,0.152,0.095,0.105,0.096,0.097,0.114,0.126,0.093,0.128,0.111,0.146,0.101,0.102,0.1,0.112,0.141,0.115,0.097,0.095,0.128,0.112,0.118,0.104,0.073,476,1597,804,819,882,1039,874,961,507,399,191.0,1344,957,324,887,3402,4982,444,1428,2393,990,1318,950,1699,2103,2046,1716,1456,1535,1959,628,393,261,1456,553,600,1598,1798,196,243,666,1361,1170,1870,1928,671,358,909,1249,659,321,1791,1357,2279,454,1833,665,1418,521,840,329,890,286,448,1224,674,2216,1091,879,540,677,1068,3930,141,3.036,2.706,2.236,2.585,2.522,2.195,2.141,2.81,2.477,3.049,2.637,1.831,3.15,2.928,2.849,2.965,3.273,3.624,2.921,1.77,2.883,2.337,1.966,2.929,2.594,2.645,2.437,2.125,2.841,2.415,2.324,1.948,2.773,3.003,3.196,2.55,2.788,3.073,2.355,2.988,2.573,1.818,3.002,1.737,1.665,1.924,3.417,2.821,2.671,2.705,2.352,2.562,2.465,2.464,2.485,2.143,2.583,2.343,1.919,1.752,2.315,2.428,2.117,2.3,2.295,1.985,2.283,2.795,2.445,2.721,2.64,2.651,2.536,3.14,0.929,0.739,0.641,0.541,0.699,0.411,0.618,0.472,0.59,0.532,1.003,0.528,0.458,0.469,0.553,0.591,0.659,0.779,0.644,0.53,0.755,0.478,0.455,0.643,0.508,0.584,0.514,0.547,0.564,0.624,0.616,0.865,0.512,0.648,0.577,0.658,0.637,0.536,0.459,0.512,0.557,0.425,0.716,0.681,0.445,0.492,0.534,0.766,0.513,0.432,0.528,0.546,0.674,0.542,0.37,0.518,0.493,0.508,0.347,0.339,0.484,0.78,0.535,0.997,0.575,0.629,0.576,0.431,0.495,0.509,0.408,0.498,0.547,0.948
-463,Epileptic,F,27.0,0.9,2.2,1.1,1.1,1.2,2.7,1.7,1.3,1.3,0.6,0.3,3.1,1.7,1.1,1.0,5.2,8.1,0.6,2.2,2.9,1.3,3.3,1.3,2.6,4.4,2.7,3.5,2.3,2.6,3.4,1.0,0.5,0.3,2.4,0.3,0.9,3.6,2.6,0.1,0.3,0.7,3.7,1.8,3.1,2.7,0.8,0.4,1.0,1.2,0.7,0.4,1.3,1.1,2.4,0.4,2.1,0.8,1.6,1.1,1.1,0.9,1.0,0.2,0.2,1.7,0.5,2.2,1.4,0.8,0.3,0.8,1.3,5.5,0.3,7,22,9,9,14,22,15,15,14,6,3.0,21,17,11,9,56,72,6,25,21,10,32,15,35,49,29,41,25,24,38,16,4,4,25,2,8,37,35,1,3,5,30,19,39,18,7,2,7,7,7,3,9,10,16,3,18,8,14,9,9,7,8,1,3,20,8,21,12,8,4,6,9,55,2,0.046,0.025,0.02,0.023,0.031,0.038,0.028,0.024,0.056,0.03,0.039,0.044,0.028,0.057,0.031,0.037,0.031,0.03,0.03,0.035,0.022,0.034,0.026,0.037,0.035,0.032,0.032,0.026,0.026,0.034,0.033,0.031,0.023,0.024,0.014,0.026,0.037,0.029,0.012,0.022,0.017,0.045,0.029,0.035,0.022,0.022,0.019,0.015,0.019,0.013,0.027,0.015,0.024,0.021,0.019,0.022,0.023,0.02,0.027,0.025,0.033,0.019,0.019,0.017,0.024,0.02,0.018,0.016,0.019,0.018,0.023,0.026,0.021,0.018,1286,4248,2170,2040,1577,2646,2589,2598,1349,1158,494.0,2044,3415,870,1624,8913,16412,1784,3862,2612,3319,4591,1902,4151,6389,5145,4898,3489,5057,5085,1665,1089,710,5208,1373,1771,5526,5562,345,625,1124,2369,4627,2436,3289,1205,671,2228,2264,1924,481,2862,1589,4505,624,3362,1186,2607,1108,1297,1329,1656,375,716,2601,806,3889,3323,1533,777,1376,1692,10028,471,0.126,0.125,0.106,0.113,0.139,0.151,0.127,0.128,0.16,0.133,0.129,0.161,0.126,0.171,0.125,0.139,0.13,0.127,0.136,0.13,0.095,0.143,0.129,0.135,0.152,0.143,0.145,0.13,0.118,0.141,0.126,0.1,0.137,0.121,0.082,0.116,0.136,0.13,0.092,0.122,0.107,0.17,0.134,0.135,0.106,0.113,0.1,0.092,0.099,0.09,0.126,0.089,0.118,0.101,0.111,0.112,0.128,0.103,0.13,0.119,0.144,0.107,0.114,0.109,0.124,0.115,0.106,0.098,0.11,0.118,0.11,0.122,0.11,0.115,339,1424,857,788,637,1170,935,888,378,306,169.0,1012,942,281,487,2451,4459,363,1138,1236,964,1565,826,1256,2113,1556,1895,1430,1606,1686,475,365,236,1342,342,554,1672,1524,170,222,576,1226,1018,1418,1978,592,309,929,985,831,254,1303,721,1822,284,1665,586,1305,681,720,437,763,159,326,1319,398,1914,1389,740,313,627,799,4432,201,3.434,2.766,2.648,2.664,2.081,2.193,2.333,2.594,2.537,3.055,2.632,1.898,2.858,2.319,2.468,2.731,3.006,3.79,2.858,1.807,2.76,2.425,2.009,2.488,2.436,2.739,2.187,2.085,2.5,2.468,2.566,2.925,2.522,2.945,3.466,2.518,2.592,2.858,2.233,2.823,2.506,1.898,3.451,2.003,1.877,2.417,2.718,2.953,2.894,2.629,2.209,2.288,2.04,2.576,2.727,2.17,2.402,2.262,1.98,1.978,2.651,2.226,2.116,2.061,2.134,2.268,2.192,2.55,2.358,2.715,2.67,2.53,2.469,2.609,0.773,0.602,0.608,0.434,0.594,0.513,0.474,0.441,0.609,0.542,0.617,0.446,0.519,0.443,0.632,0.726,0.528,0.643,0.515,0.56,0.768,0.447,0.42,0.667,0.5,0.483,0.373,0.442,0.429,0.559,0.78,1.053,0.374,0.523,0.696,0.476,0.6,0.55,0.382,0.614,0.447,0.396,0.66,0.736,0.577,0.517,0.651,0.684,0.462,0.48,0.39,0.606,0.532,0.627,0.358,0.494,0.504,0.729,0.311,0.377,0.637,0.667,0.394,0.857,0.536,0.527,0.407,0.465,0.365,0.716,0.717,0.608,0.479,0.359
-4001,Not Epileptic,F,37.0,0.6,2.2,1.6,1.8,2.3,1.7,2.2,1.3,1.5,0.9,0.2,3.0,1.2,0.4,1.1,4.6,9.0,0.8,1.7,4.2,1.6,2.8,1.9,4.8,4.6,2.5,2.9,2.4,2.8,2.8,2.1,1.2,0.3,1.8,0.5,0.7,3.4,3.0,0.2,0.1,0.8,3.4,2.6,2.5,2.7,0.8,0.4,0.9,1.1,0.6,0.6,2.1,1.3,2.6,0.3,2.2,1.1,1.0,1.7,1.1,0.5,1.8,0.3,0.6,1.9,1.6,2.1,1.3,1.0,0.6,1.3,1.6,3.2,0.3,6,20,15,13,16,17,18,14,14,9,5.0,28,16,5,12,59,91,7,29,40,15,32,17,47,45,32,41,29,25,29,36,11,4,22,2,5,48,35,1,1,5,27,25,18,17,7,2,5,6,6,5,16,11,16,2,17,7,10,12,8,4,17,2,6,17,23,16,10,6,6,10,10,23,2,0.029,0.022,0.022,0.025,0.034,0.031,0.026,0.021,0.055,0.037,0.027,0.031,0.026,0.018,0.027,0.026,0.029,0.024,0.025,0.036,0.026,0.03,0.03,0.042,0.039,0.023,0.03,0.024,0.025,0.028,0.046,0.046,0.02,0.025,0.016,0.016,0.03,0.029,0.012,0.015,0.015,0.036,0.031,0.025,0.018,0.016,0.017,0.013,0.014,0.011,0.026,0.022,0.022,0.021,0.012,0.015,0.024,0.012,0.028,0.021,0.019,0.023,0.027,0.015,0.018,0.022,0.017,0.018,0.015,0.016,0.026,0.019,0.014,0.015,1561,4983,3381,3397,2026,2606,3533,3483,1717,1469,663.0,3685,3666,1333,2725,11513,19835,2087,4692,4670,4467,5322,2309,6212,6569,6717,5987,5855,7363,5874,2182,997,1000,5858,1889,2184,8281,7815,382,342,1584,2652,6165,2982,4270,1773,836,2121,2552,1621,752,3331,2119,4866,586,4571,1361,2912,1838,1673,1074,3325,388,1143,3678,1694,4100,2705,2131,1146,1839,2739,8026,709,0.112,0.115,0.118,0.114,0.135,0.136,0.108,0.117,0.153,0.149,0.107,0.128,0.126,0.106,0.124,0.133,0.131,0.111,0.121,0.141,0.114,0.116,0.109,0.139,0.146,0.119,0.12,0.117,0.101,0.131,0.124,0.123,0.103,0.125,0.081,0.089,0.132,0.136,0.082,0.1,0.093,0.126,0.135,0.111,0.098,0.091,0.095,0.081,0.086,0.096,0.128,0.111,0.112,0.106,0.092,0.093,0.124,0.087,0.121,0.106,0.101,0.118,0.121,0.107,0.102,0.135,0.1,0.098,0.087,0.109,0.133,0.101,0.09,0.102,441,1688,1162,1137,888,935,1173,1054,504,387,166.0,1498,938,307,671,2966,5369,476,1194,1936,999,1478,969,1908,1851,1909,1926,1731,1764,1609,628,462,270,1196,471,658,2055,1806,225,158,770,1435,1325,1658,2352,811,356,918,1141,669,351,1461,1040,1930,330,2201,632,1409,1013,848,446,1267,206,618,1677,1036,1866,1161,974,561,752,1262,3290,303,3.387,2.825,2.85,2.791,2.097,2.359,2.497,2.855,2.505,2.998,3.043,2.083,2.94,2.977,2.936,2.844,2.905,3.461,2.938,2.036,3.207,2.704,2.159,2.493,2.731,2.813,2.457,2.557,3.114,2.768,2.429,2.219,2.696,3.317,3.487,2.933,2.899,3.139,1.938,2.489,2.605,1.677,3.342,2.029,2.064,2.45,2.974,2.803,2.77,2.623,2.168,2.323,2.133,2.645,2.114,2.275,2.599,2.385,2.129,2.186,2.541,2.616,2.211,2.141,2.441,1.968,2.386,2.568,2.61,2.271,2.564,2.594,2.636,2.827,0.704,0.51,0.579,0.488,0.704,0.595,0.541,0.488,0.642,0.803,0.782,0.559,0.474,0.485,0.528,0.599,0.584,0.622,0.615,0.685,0.748,0.544,0.619,0.769,0.729,0.524,0.513,0.6,0.462,0.477,0.676,0.827,0.567,0.597,0.667,0.445,0.749,0.577,0.378,0.464,0.526,0.496,0.75,0.533,0.595,0.538,0.386,0.661,0.52,0.396,0.441,0.506,0.434,0.473,0.281,0.445,0.43,0.43,0.384,0.422,0.436,0.62,0.425,0.608,0.448,0.727,0.428,0.407,0.368,0.759,0.583,0.527,0.443,0.468
-4002,Not Epileptic,M,47.0,1.0,2.2,0.7,1.0,1.4,2.6,2.4,1.7,0.7,0.6,0.5,3.0,1.7,0.8,0.9,6.3,9.6,0.8,2.9,5.4,0.9,2.2,1.5,3.0,3.5,4.3,3.6,3.6,3.2,3.0,1.3,1.0,0.5,2.7,0.5,1.5,4.3,3.7,0.3,0.3,1.7,2.9,2.3,2.9,3.8,0.7,0.4,0.9,1.3,1.4,0.8,1.7,1.2,2.9,0.4,2.5,1.0,1.3,1.1,0.9,0.7,1.5,0.2,0.4,2.0,0.4,3.9,2.3,1.1,0.6,1.2,1.4,4.6,0.5,9,21,7,8,11,21,17,13,7,7,5.0,27,17,8,10,59,90,9,35,41,11,27,15,30,37,37,37,32,24,37,16,7,4,27,2,13,40,36,3,2,12,26,21,21,21,5,2,5,7,17,6,11,8,18,2,18,7,10,8,7,6,13,1,3,15,3,31,16,7,5,9,11,32,4,0.033,0.022,0.012,0.017,0.027,0.029,0.023,0.022,0.032,0.026,0.028,0.032,0.033,0.033,0.027,0.031,0.025,0.029,0.025,0.037,0.02,0.026,0.023,0.029,0.028,0.027,0.028,0.028,0.024,0.026,0.032,0.044,0.023,0.027,0.011,0.025,0.029,0.029,0.02,0.021,0.029,0.031,0.031,0.023,0.022,0.013,0.02,0.012,0.017,0.024,0.026,0.016,0.018,0.022,0.021,0.018,0.017,0.014,0.022,0.017,0.02,0.023,0.016,0.018,0.022,0.024,0.021,0.024,0.015,0.016,0.025,0.016,0.016,0.019,1532,5122,2019,2425,2603,4514,4461,3914,1434,1651,870.0,3569,3836,1700,2307,12814,22447,2675,6770,6109,4024,4578,2522,6989,7197,8770,6087,5266,7673,6555,2365,1082,1116,7463,2536,2981,8713,8638,505,596,1788,3087,6188,3525,4771,1562,826,2639,2535,2428,821,3342,2297,5137,701,4242,1955,3440,1421,1539,1349,2698,566,925,3147,608,6197,3893,2340,1022,1886,2636,10162,538,0.129,0.108,0.085,0.089,0.117,0.131,0.1,0.107,0.119,0.114,0.133,0.128,0.131,0.146,0.117,0.129,0.113,0.119,0.122,0.14,0.092,0.129,0.118,0.125,0.134,0.124,0.116,0.112,0.097,0.122,0.116,0.109,0.113,0.119,0.063,0.113,0.118,0.123,0.128,0.124,0.128,0.138,0.118,0.109,0.104,0.085,0.098,0.075,0.095,0.109,0.12,0.093,0.1,0.106,0.106,0.102,0.097,0.089,0.112,0.107,0.106,0.115,0.09,0.102,0.114,0.092,0.109,0.113,0.091,0.105,0.126,0.103,0.092,0.119,508,1718,821,943,824,1518,1455,1209,423,435,266.0,1497,1028,403,623,3457,6380,530,1872,2301,955,1440,1025,1756,2003,2634,2103,2006,2120,2105,645,457,369,1630,631,1040,2443,2071,227,280,944,1500,1349,2018,2647,772,327,1090,1106,942,473,1558,1005,2154,305,2056,863,1514,794,811,624,1059,240,385,1503,308,2923,1643,1049,565,821,1292,4368,343,3.097,2.768,2.417,2.637,2.684,2.538,2.645,2.943,2.599,3.14,2.724,2.048,2.902,3.163,2.846,2.86,2.962,3.55,2.929,2.263,3.208,2.452,2.108,3.012,2.807,2.816,2.403,2.105,2.768,2.551,2.524,2.465,2.391,3.258,3.415,2.693,2.825,3.072,2.524,2.491,2.34,1.899,3.341,2.043,2.069,2.21,2.885,3.049,2.837,2.613,2.177,2.369,2.44,2.573,2.711,2.343,2.538,2.568,2.048,2.107,2.435,2.514,2.728,2.364,2.297,2.201,2.28,2.566,2.574,2.109,2.7,2.459,2.48,2.003,0.683,0.516,0.518,0.423,0.714,0.516,0.602,0.475,0.522,0.416,0.736,0.419,0.476,0.326,0.352,0.513,0.533,0.831,0.497,0.676,0.799,0.421,0.484,0.57,0.441,0.532,0.5,0.537,0.479,0.582,0.67,0.927,0.52,0.562,0.811,0.45,0.668,0.617,0.325,0.37,0.445,0.419,0.696,0.569,0.586,0.442,0.513,0.844,0.499,0.639,0.391,0.351,0.479,0.434,0.453,0.36,0.351,0.494,0.383,0.357,0.45,0.599,0.411,0.893,0.447,0.756,0.468,0.39,0.469,0.405,0.462,0.463,0.499,0.314
-4003,Not Epileptic,F,32.0,0.7,1.7,0.8,1.0,1.7,1.5,1.2,1.3,0.9,0.6,0.2,3.6,1.2,0.3,0.6,4.6,7.6,1.6,2.1,6.1,0.9,2.6,1.6,2.7,3.5,3.1,3.3,2.6,2.5,2.3,1.5,1.1,0.5,2.2,0.4,0.6,3.1,2.8,0.2,0.1,1.0,4.2,2.1,3.3,2.3,0.6,0.3,1.7,1.1,0.6,0.6,2.3,0.8,2.3,0.4,2.2,0.6,1.2,1.5,1.2,0.5,1.4,0.4,0.5,1.9,1.3,1.9,1.4,1.0,0.5,0.7,1.3,4.1,0.5,5,13,7,7,13,18,12,10,10,7,2.0,27,13,3,7,54,66,12,24,58,7,34,18,28,39,29,41,21,21,32,16,8,5,23,2,5,35,32,2,1,7,39,21,26,14,5,2,12,5,6,6,18,5,16,2,15,7,11,9,9,4,11,3,5,14,16,14,9,6,5,5,9,35,5,0.046,0.019,0.016,0.021,0.036,0.023,0.023,0.027,0.034,0.028,0.025,0.038,0.029,0.029,0.021,0.029,0.028,0.055,0.029,0.04,0.023,0.031,0.033,0.035,0.046,0.032,0.033,0.029,0.027,0.026,0.047,0.062,0.028,0.027,0.013,0.014,0.034,0.031,0.016,0.017,0.022,0.038,0.04,0.026,0.016,0.011,0.023,0.026,0.019,0.024,0.022,0.021,0.019,0.019,0.033,0.018,0.012,0.014,0.023,0.019,0.015,0.026,0.02,0.023,0.019,0.025,0.019,0.02,0.016,0.017,0.024,0.019,0.018,0.029,1078,4460,2265,2288,2137,3179,2573,2669,1685,1339,534.0,3354,2989,829,1907,10144,18363,1894,4765,5666,3280,4450,2048,5501,4792,6251,6097,4326,6542,5009,2330,651,891,6494,2114,1821,6153,7841,392,315,1224,2975,5257,3238,3854,1494,615,1866,1731,1367,748,3818,1455,4986,395,3334,1411,3223,1813,1849,1104,2121,559,780,2959,1098,3386,2399,1807,972,1098,2579,9106,660,0.126,0.107,0.102,0.1,0.136,0.129,0.096,0.119,0.143,0.13,0.097,0.148,0.123,0.119,0.109,0.133,0.123,0.174,0.124,0.153,0.091,0.125,0.132,0.137,0.131,0.132,0.129,0.099,0.104,0.134,0.135,0.145,0.124,0.124,0.073,0.078,0.136,0.131,0.099,0.086,0.117,0.142,0.145,0.122,0.093,0.087,0.101,0.1,0.102,0.12,0.119,0.107,0.106,0.099,0.162,0.102,0.095,0.093,0.112,0.11,0.095,0.124,0.12,0.135,0.107,0.135,0.097,0.11,0.101,0.11,0.123,0.11,0.096,0.151,301,1344,758,825,826,1063,832,827,455,360,149.0,1531,754,187,486,2714,4535,464,1300,2566,692,1370,875,1386,1429,1621,1736,1359,1564,1496,598,325,307,1364,530,617,1525,1712,176,151,672,1543,1039,1991,2076,722,245,825,835,494,433,1606,674,1930,150,1751,614,1491,961,898,508,824,311,399,1489,700,1692,1023,888,468,458,1094,3770,280,3.287,2.965,3.006,2.673,2.19,2.496,2.502,2.835,2.609,2.936,2.847,2.012,2.823,3.08,2.796,2.791,3.134,3.267,2.92,1.964,3.269,2.51,2.048,2.983,2.598,2.903,2.651,2.384,3.037,2.613,2.687,2.166,2.651,3.274,3.418,2.626,3.048,3.271,2.36,2.442,2.429,1.775,3.387,1.868,2.104,2.382,3.11,2.806,2.647,2.829,1.968,2.328,2.213,2.658,2.788,2.176,2.284,2.375,2.258,2.312,2.385,2.475,2.001,2.207,2.19,1.843,2.279,2.749,2.586,2.37,2.687,2.593,2.537,2.688,0.767,0.793,0.398,0.49,0.751,0.53,0.556,0.405,0.744,0.53,0.713,0.624,0.433,0.437,0.524,0.571,0.584,0.754,0.558,0.596,0.745,0.558,0.491,0.772,0.667,0.579,0.667,0.616,0.488,0.527,0.713,0.958,0.472,0.536,0.721,0.476,0.751,0.515,0.44,0.244,0.414,0.472,0.761,0.626,0.648,0.425,0.495,0.552,0.381,0.591,0.336,0.519,0.528,0.458,0.51,0.355,0.413,0.414,0.368,0.428,0.356,0.662,0.531,0.734,0.488,0.547,0.432,0.425,0.398,0.518,0.503,0.573,0.445,0.51
-4004,Not Epileptic,F,37.0,0.7,2.7,1.2,1.2,1.5,1.9,1.3,1.6,0.7,1.1,0.4,2.4,1.0,0.6,1.3,6.2,8.4,0.8,2.4,3.5,1.0,2.7,1.5,2.8,2.7,3.3,2.9,2.2,2.3,2.5,1.8,0.9,0.4,2.0,0.4,0.7,4.3,3.3,0.2,0.1,0.8,2.5,2.4,2.0,1.7,0.6,0.3,0.8,1.3,1.5,0.7,1.3,1.2,2.1,0.3,2.7,0.9,1.8,1.0,0.8,0.5,1.2,0.6,0.3,1.8,1.6,2.6,1.3,0.9,0.9,1.2,1.4,4.6,0.4,6,25,15,8,11,17,12,17,10,11,5.0,21,12,9,16,66,77,5,20,31,8,25,14,31,30,32,35,24,22,32,21,5,3,24,2,5,40,34,2,1,5,25,20,17,11,4,1,6,6,8,6,13,9,16,2,20,5,21,6,7,3,10,5,3,14,17,21,11,8,9,8,12,37,3,0.033,0.028,0.025,0.023,0.034,0.028,0.022,0.027,0.032,0.04,0.038,0.027,0.025,0.031,0.029,0.033,0.031,0.027,0.037,0.031,0.023,0.034,0.023,0.03,0.036,0.031,0.023,0.024,0.023,0.026,0.041,0.039,0.022,0.022,0.011,0.023,0.039,0.028,0.012,0.011,0.019,0.032,0.036,0.02,0.014,0.013,0.017,0.014,0.017,0.035,0.028,0.018,0.02,0.017,0.017,0.017,0.018,0.022,0.021,0.014,0.016,0.02,0.032,0.015,0.019,0.027,0.019,0.019,0.022,0.024,0.033,0.019,0.019,0.019,1620,5019,2209,2068,2089,3367,2610,3002,1506,1517,692.0,3024,3018,1240,2917,11169,16689,2473,3728,4339,2380,4252,2317,5555,4863,6512,5954,3467,5337,5374,2380,1012,938,5426,1901,1643,7633,7380,323,401,1383,2903,5055,2765,3071,1255,757,1963,2333,1196,632,2412,1990,4572,469,4622,1458,3189,1182,1299,761,2495,673,797,2888,1194,3957,2273,1595,1152,1378,2537,9197,452,0.128,0.132,0.13,0.114,0.134,0.132,0.108,0.137,0.14,0.162,0.131,0.122,0.114,0.137,0.133,0.144,0.132,0.116,0.125,0.131,0.091,0.136,0.115,0.129,0.149,0.139,0.116,0.11,0.114,0.132,0.139,0.123,0.11,0.113,0.069,0.101,0.13,0.123,0.097,0.089,0.1,0.142,0.133,0.107,0.085,0.084,0.092,0.083,0.097,0.133,0.146,0.1,0.112,0.1,0.102,0.097,0.103,0.107,0.117,0.098,0.104,0.109,0.157,0.094,0.107,0.127,0.103,0.108,0.11,0.13,0.133,0.112,0.098,0.117,414,1500,800,818,699,1109,937,1018,441,461,214.0,1334,789,319,792,3228,4864,423,1160,1741,721,1308,945,1545,1447,1777,2112,1409,1604,1744,617,372,301,1330,489,561,1934,1820,212,215,627,1252,1208,1644,1845,660,325,882,1061,593,386,1222,932,1957,252,2366,688,1469,692,786,367,909,262,378,1444,868,4004,1094,751,492,650,1260,3958,256,3.568,2.968,2.706,2.573,2.549,2.645,2.342,2.721,2.673,2.71,2.94,1.951,2.871,2.776,2.766,2.648,2.871,4.149,2.795,2.112,2.596,2.479,2.11,2.795,2.61,2.986,2.297,2.025,2.659,2.457,2.791,2.743,2.491,2.901,3.455,2.55,2.884,2.974,1.786,2.3,2.78,2.029,3.052,1.909,1.921,2.04,3.012,2.604,2.699,2.263,2.134,2.078,2.197,2.427,2.251,2.017,2.609,2.386,1.974,1.843,2.22,2.645,2.619,2.558,2.134,1.76,2.12,2.361,2.343,2.682,2.305,2.439,2.476,2.432,0.738,0.735,0.618,0.457,0.773,0.443,0.478,0.606,0.552,0.554,0.652,0.39,0.427,0.564,0.509,0.551,0.602,0.656,0.707,0.552,0.719,0.485,0.49,0.62,0.609,0.576,0.441,0.502,0.521,0.473,0.6,0.942,0.371,0.609,0.786,0.509,0.782,0.547,0.409,0.341,0.651,0.497,0.803,0.628,0.568,0.418,0.538,0.679,0.459,0.474,0.393,0.391,0.427,0.463,0.401,0.408,0.567,0.623,0.408,0.413,0.47,0.69,0.779,0.933,0.447,0.562,0.525,0.402,0.468,0.635,0.445,0.423,0.418,0.396
-4005,Not Epileptic,M,37.0,1.3,2.5,1.1,1.6,2.4,2.4,2.0,1.6,2.8,1.0,0.4,4.2,1.8,0.7,1.8,8.3,12.6,1.0,3.1,6.5,1.1,5.1,2.3,5.3,2.8,3.8,5.0,4.7,3.5,4.2,2.6,2.0,0.6,2.8,0.9,0.6,5.8,3.7,0.2,0.2,1.1,4.8,2.9,4.3,4.0,0.8,0.6,1.0,1.7,0.9,0.7,2.2,2.2,4.3,0.1,3.3,1.1,2.6,1.8,1.7,0.5,2.1,0.4,0.6,2.9,2.2,3.3,2.1,1.3,0.9,1.5,2.1,4.5,0.4,10,23,10,12,20,24,18,17,20,10,3.0,42,20,9,21,82,126,11,30,59,12,55,28,51,36,46,55,47,31,53,25,15,6,36,5,4,58,47,2,1,8,47,34,33,22,7,3,7,8,8,5,16,21,28,1,29,14,25,9,13,8,19,2,4,26,25,28,16,9,8,8,20,36,4,0.039,0.02,0.012,0.023,0.036,0.029,0.025,0.024,0.042,0.037,0.034,0.039,0.027,0.029,0.029,0.037,0.029,0.033,0.036,0.04,0.024,0.035,0.031,0.043,0.029,0.032,0.037,0.03,0.025,0.034,0.051,0.047,0.021,0.028,0.018,0.013,0.042,0.029,0.012,0.013,0.022,0.035,0.042,0.029,0.022,0.014,0.022,0.017,0.017,0.021,0.027,0.019,0.03,0.025,0.018,0.025,0.024,0.022,0.025,0.023,0.019,0.034,0.026,0.019,0.024,0.031,0.021,0.021,0.021,0.018,0.027,0.019,0.019,0.017,1932,6045,3106,3077,2461,4214,3627,3595,2272,2109,777.0,4071,3903,1440,3589,13086,25987,2920,4752,6712,3422,7521,3460,7037,5210,6447,7082,6854,8535,7808,2650,1931,1240,6357,2855,1656,10209,8673,500,587,1414,4602,6493,4227,4533,1838,941,2240,2856,1845,730,3922,3068,6533,334,4120,1735,3626,1939,2072,1126,2692,429,1001,4258,2045,4688,3742,1976,1286,1807,3495,8996,553,0.13,0.109,0.095,0.109,0.145,0.131,0.106,0.12,0.125,0.139,0.121,0.16,0.123,0.122,0.136,0.132,0.12,0.116,0.134,0.153,0.087,0.145,0.138,0.151,0.14,0.137,0.12,0.113,0.102,0.137,0.162,0.133,0.094,0.129,0.087,0.079,0.148,0.126,0.081,0.094,0.111,0.147,0.151,0.13,0.109,0.091,0.109,0.093,0.096,0.104,0.117,0.1,0.126,0.11,0.089,0.124,0.125,0.119,0.113,0.119,0.092,0.139,0.127,0.106,0.118,0.136,0.114,0.106,0.103,0.123,0.124,0.115,0.102,0.111,554,2050,1256,1072,1032,1508,1245,1209,845,474,233.0,1793,1064,354,1051,3724,7281,642,1495,2855,860,2241,1342,1980,1572,2028,2544,2399,2214,2461,788,699,434,1619,788,633,2479,2250,301,252,762,2036,1402,2400,2612,896,386,967,1347,773,460,1892,1346,2806,129,2140,850,1882,1072,1137,550,1071,239,485,1975,1006,2336,1588,980,642,870,1694,3918,353,3.493,2.583,2.489,2.734,2.304,2.317,2.404,2.629,2.373,3.264,3.054,1.934,2.9,2.765,2.685,2.727,2.797,3.548,2.673,2.039,2.901,2.519,2.144,2.679,2.553,2.609,2.259,2.293,2.936,2.46,2.583,2.693,2.277,2.884,3.438,2.452,2.976,2.785,1.862,2.616,2.377,1.991,3.333,1.957,1.974,2.306,3.17,2.85,2.613,2.699,1.977,2.239,2.162,2.466,2.173,2.121,2.191,2.213,2.057,1.886,2.274,2.424,1.925,2.486,2.205,2.188,2.187,2.533,2.269,2.396,2.432,2.397,2.436,1.987,0.761,0.633,0.474,0.593,0.549,0.641,0.692,0.466,0.614,0.534,0.702,0.502,0.449,0.469,0.526,0.608,0.652,0.771,0.67,0.574,0.89,0.525,0.631,0.674,0.569,0.503,0.546,0.505,0.521,0.604,0.856,0.961,0.36,0.564,0.707,0.54,0.864,0.688,0.359,0.471,0.348,0.522,0.716,0.492,0.529,0.561,0.511,0.581,0.466,0.477,0.351,0.427,0.497,0.455,0.351,0.39,0.465,0.502,0.363,0.352,0.55,0.693,0.334,0.767,0.517,0.893,0.456,0.342,0.437,0.706,0.549,0.506,0.422,0.393
-4006,Not Epileptic,F,27.0,1.0,2.2,1.0,1.2,2.3,1.2,1.2,1.8,1.2,0.7,0.2,1.9,1.5,0.5,1.2,7.6,8.5,0.9,2.8,4.1,1.2,2.7,1.2,4.6,4.0,3.3,3.2,1.7,2.4,3.1,1.7,1.2,0.4,2.7,0.3,0.6,3.2,3.1,0.2,0.1,1.0,3.3,2.0,2.0,2.0,0.5,0.6,0.9,1.5,1.1,0.5,1.7,1.3,2.2,0.4,2.1,0.9,1.7,0.7,0.9,0.7,2.0,0.2,0.8,1.7,1.3,1.7,1.4,1.0,0.4,0.9,1.4,5.0,0.2,13,21,9,13,15,14,12,16,15,8,3.0,24,20,6,25,81,95,10,41,40,12,32,14,45,50,42,49,22,23,40,16,10,5,36,2,4,41,46,1,1,7,27,20,15,11,4,3,6,7,9,3,15,9,16,1,23,6,15,6,9,7,17,2,6,17,15,17,13,5,4,6,11,41,3,0.044,0.026,0.021,0.024,0.044,0.024,0.022,0.032,0.043,0.043,0.036,0.034,0.039,0.035,0.03,0.047,0.034,0.037,0.039,0.039,0.027,0.036,0.029,0.051,0.042,0.03,0.036,0.026,0.028,0.028,0.043,0.052,0.02,0.033,0.013,0.015,0.039,0.034,0.013,0.015,0.018,0.039,0.039,0.03,0.015,0.01,0.023,0.022,0.021,0.034,0.026,0.024,0.028,0.02,0.022,0.017,0.02,0.021,0.018,0.019,0.019,0.03,0.024,0.019,0.018,0.021,0.018,0.022,0.015,0.021,0.019,0.019,0.02,0.013,1737,4764,2676,2456,2222,2456,2581,3196,1802,1235,506.0,2349,3489,1187,2343,10035,17944,2396,6206,4466,3081,4112,1510,5559,5879,7412,4850,3133,5711,6224,2103,1274,1161,6440,1897,1959,6925,8068,448,391,1580,2847,5716,2032,3136,1498,781,2302,2274,1289,469,2297,1756,4294,434,3655,1451,3186,1033,1353,1208,2785,260,1077,2581,1332,3149,2424,1775,875,1372,2400,9076,559,0.146,0.126,0.1,0.112,0.147,0.118,0.105,0.139,0.166,0.169,0.137,0.14,0.15,0.134,0.157,0.176,0.143,0.12,0.154,0.166,0.105,0.138,0.123,0.157,0.147,0.143,0.131,0.115,0.118,0.139,0.128,0.134,0.093,0.145,0.062,0.086,0.148,0.141,0.08,0.089,0.102,0.149,0.146,0.123,0.088,0.078,0.115,0.082,0.103,0.14,0.12,0.113,0.128,0.101,0.116,0.103,0.114,0.11,0.111,0.115,0.11,0.124,0.127,0.109,0.112,0.109,0.104,0.116,0.089,0.126,0.104,0.106,0.107,0.1,457,1405,836,883,859,863,936,1040,573,300,145.0,1111,933,281,744,3058,4920,483,1461,1982,744,1311,763,1730,1830,2084,1856,1178,1484,1904,690,471,374,1410,447,650,1708,1836,246,187,800,1280,1054,1217,1864,756,351,926,1026,499,280,1126,815,1801,223,1958,660,1433,597,693,585,1081,159,615,1376,833,1546,1079,870,323,674,1111,3914,305,3.531,3.01,3.025,2.695,2.176,2.357,2.335,2.798,2.456,3.214,2.811,1.853,2.855,2.892,2.44,2.546,2.865,3.908,3.214,2.03,2.776,2.395,1.822,2.475,2.499,2.755,2.116,2.161,2.868,2.575,2.305,2.692,2.484,3.125,3.354,2.664,2.913,3.03,2.133,2.172,2.459,1.95,3.598,1.92,1.891,2.211,2.691,3.069,2.668,3.165,2.033,2.101,2.174,2.544,2.242,1.965,2.437,2.513,1.994,2.189,2.332,2.515,2.017,2.253,2.052,2.039,2.123,2.502,2.338,2.713,2.284,2.276,2.455,2.274,0.769,0.658,0.54,0.477,0.649,0.545,0.512,0.647,0.547,0.486,0.586,0.423,0.469,0.488,0.524,0.629,0.579,0.71,0.637,0.612,0.953,0.622,0.382,0.925,0.729,0.573,0.501,0.544,0.456,0.661,0.735,0.794,0.328,0.655,0.946,0.494,0.838,0.743,0.336,0.524,0.479,0.612,0.644,0.661,0.657,0.419,0.511,0.829,0.444,0.576,0.462,0.403,0.485,0.481,0.386,0.426,0.486,0.496,0.402,0.342,0.373,0.653,0.374,0.56,0.502,0.625,0.455,0.404,0.384,0.842,0.438,0.501,0.526,0.333
-4007,Not Epileptic,F,55.0,0.6,2.0,1.1,0.7,1.6,1.4,2.0,1.4,1.1,0.6,0.2,3.3,1.1,0.4,1.3,3.7,9.2,0.9,2.0,4.4,1.4,2.2,2.6,4.2,2.2,2.7,4.7,3.2,3.8,2.1,1.4,1.9,0.4,1.9,0.5,0.8,2.7,2.6,0.3,0.2,0.9,2.4,2.4,2.7,2.4,0.8,0.4,1.1,1.2,0.8,0.7,1.7,1.7,2.8,0.4,2.7,0.9,1.3,0.9,1.0,0.6,1.5,0.4,0.6,1.3,0.9,2.2,0.9,1.2,0.4,0.4,1.3,3.4,0.3,7,19,12,7,11,14,17,15,14,7,3.0,29,13,6,17,42,74,6,22,41,13,24,24,35,33,27,42,23,24,26,17,17,4,25,2,10,35,32,1,2,5,25,26,19,15,6,2,7,6,8,4,12,14,19,2,21,6,13,8,7,5,9,2,5,10,15,14,6,7,5,4,13,31,2,0.036,0.027,0.026,0.017,0.035,0.031,0.032,0.03,0.048,0.028,0.021,0.043,0.027,0.032,0.033,0.035,0.038,0.038,0.032,0.037,0.026,0.034,0.036,0.05,0.037,0.03,0.036,0.035,0.036,0.03,0.046,0.057,0.029,0.026,0.015,0.022,0.041,0.03,0.024,0.013,0.021,0.035,0.039,0.026,0.02,0.016,0.017,0.018,0.019,0.017,0.033,0.025,0.027,0.027,0.022,0.024,0.029,0.023,0.024,0.021,0.027,0.032,0.028,0.015,0.02,0.022,0.021,0.015,0.02,0.021,0.021,0.021,0.017,0.018,1183,3310,1815,1766,1840,2430,2550,2570,2205,1296,625.0,2983,2706,962,2722,7486,16611,1983,3711,4836,4422,4105,2610,4785,4745,5492,5770,3348,4901,4615,1555,1426,768,4821,1526,2102,5807,6552,328,552,1377,2594,5648,2580,3215,1308,647,1788,1819,1670,476,2527,2197,3951,414,3437,989,2372,1009,1316,741,1958,450,894,2201,1046,3261,1784,1830,624,799,2055,7890,524,0.131,0.126,0.131,0.098,0.137,0.133,0.111,0.14,0.162,0.149,0.108,0.154,0.118,0.138,0.134,0.128,0.134,0.127,0.139,0.156,0.103,0.136,0.135,0.145,0.14,0.135,0.12,0.104,0.109,0.126,0.135,0.148,0.106,0.12,0.073,0.106,0.143,0.139,0.101,0.109,0.102,0.149,0.148,0.12,0.1,0.102,0.091,0.093,0.097,0.111,0.136,0.118,0.13,0.113,0.129,0.122,0.125,0.121,0.126,0.112,0.125,0.119,0.115,0.104,0.102,0.119,0.102,0.094,0.102,0.135,0.108,0.124,0.097,0.116,340,1224,678,638,762,765,942,849,532,308,182.0,1330,762,250,796,2140,4393,438,1117,2090,952,1056,1041,1485,1213,1463,1950,1162,1345,1268,563,572,247,1238,452,691,1439,1516,196,242,726,1111,1205,1671,1887,698,307,829,921,713,304,1096,991,1725,211,1736,498,1010,579,740,374,744,225,530,1152,623,1643,805,896,349,360,999,3256,252,3.218,2.683,2.715,2.639,2.122,2.569,2.348,2.698,2.83,3.073,2.752,1.901,2.722,2.798,2.575,2.696,2.929,3.456,2.723,1.992,3.356,2.796,2.163,2.507,2.853,2.919,2.368,2.263,2.785,2.675,2.143,2.561,2.4,2.855,3.067,2.789,2.902,3.029,1.919,2.438,2.322,2.087,3.232,1.796,1.924,2.111,2.636,2.683,2.434,2.55,2.01,2.405,2.131,2.304,2.27,2.221,2.119,2.587,1.985,1.942,2.579,2.584,2.192,2.136,2.053,2.071,2.172,2.611,2.445,1.993,2.292,2.343,2.423,2.54,0.732,0.54,0.517,0.509,0.691,0.591,0.673,0.49,0.736,0.613,0.65,0.59,0.413,0.533,0.574,0.584,0.694,0.76,0.682,0.657,0.859,0.508,0.59,0.869,0.682,0.535,0.605,0.612,0.707,0.644,0.51,0.938,0.59,0.587,0.648,0.524,0.644,0.662,0.265,0.483,0.449,0.632,0.81,0.547,0.654,0.49,0.429,0.809,0.41,0.456,0.36,0.561,0.485,0.448,0.45,0.439,0.482,0.714,0.454,0.337,0.359,0.601,0.412,0.567,0.45,0.707,0.407,0.403,0.495,0.635,0.562,0.489,0.511,0.559
-4008,Not Epileptic,F,47.0,0.5,1.6,1.1,0.8,1.6,1.9,1.0,1.3,0.9,0.5,0.2,3.2,1.1,0.6,1.2,5.1,8.0,0.9,2.1,4.9,1.6,2.8,1.8,2.3,3.3,4.5,3.5,1.7,2.8,4.0,1.1,0.5,0.5,1.9,0.5,0.4,3.0,3.5,0.1,0.1,1.0,2.7,3.3,4.1,3.0,0.6,0.4,0.6,1.2,1.3,0.7,1.3,1.0,2.9,0.5,2.5,0.4,1.7,0.5,1.4,0.5,1.3,0.2,0.5,1.3,0.8,2.2,2.2,0.9,0.6,1.2,1.9,3.3,0.2,5,16,8,7,13,17,8,11,11,6,2.0,26,11,4,10,52,66,7,20,37,13,26,17,27,34,36,37,15,22,43,13,7,4,24,4,3,33,37,1,2,5,23,23,21,20,5,2,4,5,10,3,11,9,24,2,18,3,14,4,9,4,9,1,3,11,9,19,16,6,5,8,10,24,1,0.03,0.02,0.023,0.018,0.033,0.037,0.019,0.024,0.035,0.024,0.027,0.033,0.023,0.045,0.03,0.031,0.029,0.038,0.032,0.039,0.033,0.035,0.027,0.032,0.032,0.033,0.028,0.023,0.023,0.031,0.031,0.024,0.035,0.025,0.015,0.01,0.033,0.032,0.014,0.013,0.022,0.035,0.041,0.039,0.024,0.013,0.019,0.011,0.02,0.024,0.027,0.017,0.026,0.02,0.018,0.02,0.011,0.02,0.023,0.025,0.02,0.027,0.025,0.025,0.019,0.022,0.019,0.023,0.02,0.021,0.029,0.026,0.015,0.009,1190,3669,2279,1798,1916,2561,2279,2950,1544,1213,392.0,2902,2736,822,2304,8812,15893,1857,3150,4496,2594,4106,2533,4698,6326,7667,6024,3114,5581,5760,2029,772,852,4603,1830,1576,5462,6782,229,408,1674,2650,4840,2518,3164,1221,632,1993,1973,1934,583,2358,2089,5639,718,3922,818,3117,728,1715,990,1819,378,696,2162,1065,3602,3475,1565,1173,1223,2393,7438,644,0.118,0.101,0.11,0.099,0.123,0.143,0.093,0.111,0.145,0.117,0.103,0.143,0.124,0.109,0.124,0.126,0.126,0.137,0.129,0.14,0.105,0.145,0.126,0.124,0.142,0.137,0.129,0.106,0.112,0.137,0.122,0.101,0.156,0.111,0.077,0.073,0.112,0.135,0.094,0.087,0.105,0.147,0.126,0.146,0.113,0.095,0.105,0.073,0.101,0.116,0.126,0.101,0.102,0.108,0.112,0.109,0.092,0.103,0.122,0.115,0.113,0.126,0.117,0.123,0.101,0.112,0.108,0.111,0.1,0.131,0.13,0.116,0.092,0.066,310,1259,767,703,709,890,785,917,430,344,144.0,1367,750,213,567,2597,4303,420,1030,1906,814,1233,1055,1219,1863,2253,2123,1153,1708,2133,527,396,236,1173,524,551,1609,1707,139,236,754,1189,1218,1668,2058,675,264,852,911,897,321,1262,777,2228,354,2017,425,1347,360,923,443,763,138,310,1132,555,1845,1614,790,450,682,1157,3369,289,3.514,2.834,2.815,2.659,2.336,2.549,2.362,2.921,2.763,2.721,2.37,1.933,2.759,2.604,2.868,2.586,2.976,3.643,2.619,2.036,2.826,2.563,2.106,2.867,2.675,2.84,2.253,2.131,2.566,2.317,2.773,2.019,3.082,2.777,2.935,2.677,2.654,3.013,1.899,1.968,2.702,2.019,3.133,1.833,1.816,2.041,2.809,2.817,2.684,2.594,2.302,1.995,2.625,2.524,2.654,2.271,2.158,2.445,2.424,2.165,2.508,2.607,2.624,2.511,2.125,2.244,2.071,2.32,2.355,2.847,2.235,2.643,2.373,2.455,0.602,0.585,0.637,0.503,0.632,0.503,0.565,0.647,0.503,0.377,0.771,0.401,0.34,0.664,0.563,0.629,0.601,0.815,0.429,0.515,0.797,0.409,0.402,0.538,0.444,0.463,0.466,0.483,0.471,0.502,0.904,0.866,0.665,0.6,0.821,0.549,0.649,0.542,0.328,0.33,0.603,0.593,0.738,0.696,0.56,0.404,0.789,0.761,0.574,0.502,0.338,0.478,0.676,0.549,0.364,0.355,0.329,0.395,0.45,0.314,0.314,0.438,0.729,0.597,0.473,0.672,0.354,0.462,0.395,0.847,0.531,0.442,0.573,0.651
-4009,Not Epileptic,F,22.0,0.7,2.6,0.8,1.4,1.2,2.8,1.2,1.7,1.3,0.9,0.6,2.9,1.6,0.5,1.5,5.0,8.5,1.3,2.2,5.9,1.5,3.7,2.3,4.3,4.8,4.2,3.7,2.8,2.7,3.3,1.8,1.8,0.7,2.1,0.5,0.5,3.2,3.5,0.2,0.2,1.1,3.0,2.0,2.5,2.9,0.9,0.6,0.9,1.4,0.8,0.9,2.7,1.0,2.1,0.3,2.4,0.7,1.5,1.3,1.0,1.0,1.6,0.4,0.7,2.3,1.6,2.6,0.8,0.8,0.5,1.2,1.1,5.5,0.3,12,26,7,11,13,24,13,20,15,10,5.0,29,21,7,22,62,91,10,22,55,14,47,23,43,56,46,56,33,24,37,17,15,7,27,3,4,36,40,1,1,8,28,25,19,18,8,3,6,8,5,6,24,8,14,2,20,4,15,11,8,10,13,3,5,18,20,21,6,6,4,8,9,53,3,0.037,0.025,0.013,0.026,0.031,0.034,0.021,0.027,0.045,0.035,0.043,0.035,0.029,0.027,0.026,0.028,0.03,0.046,0.033,0.046,0.025,0.036,0.045,0.04,0.04,0.026,0.031,0.032,0.025,0.026,0.04,0.051,0.036,0.029,0.014,0.013,0.039,0.034,0.013,0.022,0.022,0.035,0.037,0.023,0.022,0.019,0.026,0.015,0.018,0.02,0.034,0.022,0.021,0.018,0.016,0.016,0.02,0.017,0.027,0.016,0.027,0.024,0.022,0.019,0.024,0.023,0.017,0.014,0.017,0.013,0.023,0.02,0.021,0.023,1562,6162,2347,2643,2224,4198,3075,4398,2453,1994,762.0,2805,4002,1490,3789,12224,21030,2719,4223,5884,5194,5375,2095,6812,7179,10596,7356,4862,7295,6254,2159,1622,1108,6467,1995,2076,6303,8809,412,417,1612,2322,5412,3160,3716,1881,896,2455,2472,1645,679,4847,1915,4262,570,4586,1090,3336,1423,1697,1208,2875,565,1021,3205,1775,5057,2024,1888,783,2073,2079,10618,417,0.133,0.115,0.098,0.112,0.127,0.147,0.112,0.133,0.154,0.139,0.159,0.152,0.135,0.135,0.133,0.135,0.137,0.167,0.128,0.172,0.103,0.139,0.134,0.143,0.133,0.122,0.13,0.113,0.111,0.126,0.137,0.131,0.14,0.129,0.076,0.081,0.14,0.15,0.082,0.1,0.113,0.131,0.14,0.111,0.106,0.104,0.115,0.085,0.098,0.101,0.138,0.11,0.104,0.094,0.098,0.101,0.111,0.098,0.126,0.096,0.128,0.116,0.116,0.104,0.122,0.114,0.096,0.095,0.097,0.103,0.115,0.105,0.105,0.134,471,1825,851,892,747,1378,923,1179,576,504,210.0,1378,1059,335,1089,3126,5062,540,1165,2565,1044,1751,913,1858,1984,2909,2437,1644,1728,2123,721,600,341,1345,507,712,1542,1821,248,200,823,1348,1146,1773,2107,748,379,938,1137,662,419,2089,850,1781,295,2252,474,1540,772,885,594,1081,292,553,1469,1076,2346,901,828,495,846,942,4232,208,2.971,2.934,2.782,2.847,2.348,2.509,2.654,3.107,2.84,3.075,2.777,1.826,2.852,2.899,2.632,2.809,3.085,3.505,2.9,2.026,3.417,2.483,2.021,2.638,2.704,2.798,2.359,2.348,3.11,2.42,2.266,2.793,2.602,3.173,3.448,2.668,2.961,3.324,1.943,2.64,2.658,1.632,3.306,1.911,2.027,2.423,2.817,3.173,2.779,2.8,1.943,2.231,2.329,2.523,2.415,2.31,2.728,2.378,1.993,2.188,2.475,2.609,2.194,2.205,2.482,2.012,2.306,2.514,2.602,1.879,2.462,2.458,2.549,2.522,0.814,0.652,0.479,0.65,0.9,0.652,0.706,0.496,0.829,0.594,0.886,0.557,0.576,0.487,0.616,0.684,0.673,1.095,0.539,0.6,0.837,0.725,0.549,0.857,0.774,0.615,0.688,0.609,0.619,0.596,0.803,0.833,0.435,0.663,0.819,0.47,0.788,0.802,0.422,0.375,0.482,0.463,0.75,0.706,0.696,0.447,0.655,0.741,0.443,0.417,0.42,0.526,0.468,0.43,0.346,0.426,0.451,0.47,0.539,0.406,0.42,0.767,0.452,0.594,0.525,0.732,0.398,0.374,0.498,0.633,0.432,0.477,0.484,0.561
-4010,Not Epileptic,M,37.0,0.6,2.1,1.3,1.4,1.9,2.5,1.4,2.2,1.6,0.9,0.2,2.9,1.6,0.3,2.0,5.9,7.6,1.3,3.4,3.8,0.9,2.6,2.4,3.2,4.2,4.7,3.0,2.4,2.5,3.3,1.2,1.1,0.8,2.7,0.3,0.7,4.3,4.0,0.2,0.5,1.5,3.8,2.3,3.4,2.7,0.6,0.4,0.7,1.4,1.3,0.8,2.8,0.9,1.7,0.4,2.5,1.2,2.3,1.2,1.6,0.5,1.5,0.7,0.4,2.4,1.7,2.5,1.1,1.3,0.7,1.1,2.7,4.8,0.3,4,25,14,13,16,20,12,19,14,7,3.0,24,17,4,20,67,76,10,28,31,5,30,22,38,42,55,36,25,23,38,19,9,7,27,2,5,44,40,2,3,11,24,22,25,15,5,2,5,7,11,7,19,8,11,1,21,9,15,8,13,4,9,5,4,16,16,25,6,10,9,7,18,40,4,0.026,0.025,0.02,0.027,0.037,0.033,0.019,0.028,0.044,0.042,0.021,0.031,0.024,0.023,0.036,0.032,0.026,0.042,0.035,0.033,0.014,0.028,0.033,0.04,0.034,0.031,0.026,0.023,0.022,0.032,0.039,0.041,0.029,0.028,0.01,0.013,0.037,0.034,0.021,0.024,0.028,0.041,0.041,0.038,0.018,0.013,0.023,0.011,0.016,0.027,0.043,0.025,0.021,0.015,0.046,0.015,0.032,0.02,0.036,0.022,0.027,0.026,0.036,0.019,0.024,0.029,0.019,0.014,0.022,0.024,0.024,0.03,0.018,0.014,1529,4442,2483,2664,2119,3248,2633,3599,2346,1521,461.0,2660,3730,978,3284,11189,16850,2233,4785,3329,2390,4229,2547,5423,6071,8135,5480,3842,5827,5719,2120,1227,1187,6112,1659,2251,6995,7747,367,660,1888,2524,5149,1798,3800,1441,697,2272,2801,1795,492,4163,1668,4208,484,4177,1079,3415,823,1844,841,2473,678,746,2789,1427,4501,2466,2051,1188,1539,2616,9803,423,0.101,0.12,0.115,0.115,0.141,0.135,0.089,0.127,0.149,0.146,0.116,0.129,0.118,0.11,0.152,0.137,0.12,0.132,0.127,0.128,0.066,0.135,0.149,0.15,0.135,0.132,0.112,0.099,0.089,0.132,0.135,0.125,0.123,0.117,0.069,0.078,0.119,0.127,0.118,0.118,0.116,0.154,0.132,0.143,0.097,0.089,0.112,0.078,0.093,0.119,0.158,0.118,0.107,0.083,0.165,0.101,0.138,0.098,0.14,0.115,0.111,0.113,0.139,0.119,0.12,0.132,0.105,0.089,0.113,0.134,0.119,0.13,0.103,0.111,371,1448,893,961,824,1260,944,1196,642,402,176.0,1380,1087,281,978,3167,4827,482,1518,1745,805,1438,1130,1405,1866,2651,1956,1471,1614,1912,557,454,440,1420,458,832,1958,1947,179,343,932,1250,1057,1347,2256,726,309,922,1200,835,356,1789,821,1840,148,2297,617,1764,546,1076,386,769,324,356,1443,855,2207,1128,940,466,717,1370,4133,286,3.555,2.821,2.654,2.628,2.264,2.287,2.39,2.723,2.634,3.053,2.458,1.747,2.776,2.444,2.59,2.628,2.827,3.587,2.658,1.75,2.523,2.422,2.061,2.859,2.541,2.591,2.229,2.118,2.741,2.357,2.829,2.515,2.223,3.008,3.257,2.566,2.74,2.851,2.153,2.219,2.51,1.961,3.378,1.625,1.902,2.182,2.821,2.859,2.73,2.487,1.646,2.383,2.206,2.382,3.414,2.075,2.08,2.203,1.724,1.976,2.621,2.992,2.245,2.376,2.131,2.096,2.084,2.552,2.334,2.547,2.41,2.323,2.618,1.828,0.913,0.737,0.608,0.576,0.592,0.53,0.607,0.549,0.667,0.534,0.644,0.414,0.514,0.685,0.433,0.501,0.562,0.767,0.696,0.476,0.667,0.476,0.476,0.69,0.495,0.487,0.482,0.514,0.5,0.523,0.802,1.089,0.462,0.633,0.75,0.536,0.729,0.599,0.401,0.441,0.521,0.52,0.791,0.687,0.573,0.611,0.408,0.978,0.602,0.485,0.279,0.558,0.622,0.477,0.298,0.441,0.354,0.56,0.372,0.436,0.299,0.872,0.522,0.894,0.511,0.768,0.536,0.416,0.515,0.765,0.558,0.555,0.587,0.383
-4011,Not Epileptic,M,32.0,0.6,2.4,0.9,1.2,1.6,1.7,1.2,1.6,2.1,0.7,0.3,2.9,1.7,0.5,1.4,5.6,8.7,1.0,1.4,5.4,0.8,2.7,1.9,4.1,3.1,3.0,3.4,2.7,2.5,2.1,1.5,1.2,0.4,2.9,0.4,0.6,2.9,4.6,0.1,0.1,1.0,2.7,1.5,2.9,2.2,0.5,0.5,0.9,1.5,0.7,0.7,1.7,1.3,2.0,0.2,2.4,0.6,1.2,1.1,1.4,0.7,1.5,0.4,0.5,2.3,1.4,2.3,1.2,0.7,1.1,1.2,1.5,4.2,0.4,6,25,8,13,16,17,13,18,19,8,4.0,27,22,6,17,66,89,10,18,51,9,35,20,48,43,33,48,29,25,32,15,9,4,31,3,6,31,48,1,2,7,28,16,22,14,4,2,5,9,6,4,14,10,16,2,21,4,11,8,10,8,12,3,6,16,20,22,10,4,12,8,13,33,4,0.031,0.023,0.017,0.023,0.03,0.03,0.019,0.025,0.043,0.03,0.032,0.037,0.034,0.026,0.031,0.031,0.029,0.036,0.026,0.042,0.022,0.03,0.035,0.045,0.032,0.027,0.029,0.029,0.024,0.027,0.047,0.038,0.019,0.03,0.013,0.012,0.028,0.032,0.011,0.02,0.022,0.033,0.028,0.027,0.017,0.01,0.022,0.013,0.018,0.017,0.027,0.017,0.025,0.017,0.028,0.017,0.013,0.015,0.022,0.024,0.026,0.022,0.034,0.016,0.021,0.024,0.019,0.019,0.011,0.029,0.025,0.016,0.017,0.019,1736,4901,2517,2295,2114,3525,3055,3852,2444,1645,600.0,3285,3290,1192,2716,11157,19330,2806,3932,6039,3289,5087,2152,5654,6383,6279,6024,3762,6117,5229,2144,1490,1003,6957,2188,1878,7764,9753,280,357,1339,3038,4619,3166,3693,1354,713,2234,2566,1751,686,3014,1661,4202,428,4337,1306,2613,1379,1693,1033,2688,521,954,3181,1228,4277,2415,1745,1875,1447,3266,8882,470,0.114,0.117,0.095,0.106,0.134,0.137,0.096,0.123,0.171,0.139,0.122,0.14,0.15,0.144,0.148,0.142,0.135,0.138,0.125,0.155,0.089,0.13,0.131,0.149,0.131,0.128,0.124,0.11,0.112,0.136,0.154,0.116,0.101,0.135,0.085,0.083,0.121,0.151,0.096,0.117,0.117,0.131,0.122,0.119,0.092,0.082,0.102,0.079,0.105,0.092,0.118,0.102,0.114,0.097,0.134,0.105,0.092,0.098,0.111,0.122,0.125,0.117,0.132,0.114,0.109,0.13,0.103,0.106,0.081,0.15,0.127,0.103,0.1,0.122,437,1775,844,881,891,959,931,1172,729,374,165.0,1350,948,324,804,3091,5107,553,961,2296,703,1509,918,1641,1781,1814,2074,1375,1573,1480,552,587,349,1463,521,749,1775,2262,164,166,724,1223,960,1745,1981,709,330,984,1181,645,415,1463,838,1860,125,2104,610,1231,802,893,469,1035,231,506,1567,863,1989,1017,835,635,698,1425,3865,285,3.785,2.544,2.743,2.587,2.114,2.764,2.579,2.826,2.696,3.077,3.074,2.094,2.775,2.721,2.554,2.684,2.932,3.812,3.024,2.271,3.305,2.562,2.111,2.65,2.753,2.81,2.327,2.168,2.93,2.657,2.73,2.558,2.245,3.155,3.744,2.414,3.201,3.107,2.002,2.535,2.43,2.084,3.449,2.053,2.128,2.105,2.643,2.818,2.67,2.858,2.048,2.237,2.131,2.353,2.918,2.313,2.512,2.434,2.021,2.051,2.197,2.554,2.655,2.295,2.173,1.717,2.201,2.572,2.476,3.078,2.406,2.591,2.529,2.108,0.693,0.603,0.469,0.412,0.693,0.773,0.617,0.413,0.604,0.576,0.676,0.445,0.439,0.587,0.571,0.645,0.62,0.737,0.564,0.599,0.732,0.502,0.508,0.871,0.467,0.522,0.527,0.506,0.427,0.592,0.729,0.913,0.443,0.636,0.818,0.41,0.804,0.656,0.267,0.512,0.494,0.658,0.767,0.646,0.656,0.37,0.477,0.596,0.426,0.529,0.409,0.444,0.491,0.468,1.02,0.434,0.415,0.411,0.328,0.426,0.511,0.675,0.462,0.783,0.515,0.707,0.456,0.385,0.4,0.797,0.42,0.498,0.452,0.546
-4012,Not Epileptic,M,42.0,0.8,2.0,1.1,1.4,2.6,2.5,1.7,2.1,2.1,1.1,0.5,3.3,1.8,0.9,1.0,6.9,9.0,0.9,1.8,6.2,1.4,6.0,3.6,7.5,5.7,3.8,6.7,3.0,5.2,3.6,2.5,1.7,0.4,2.7,0.8,0.8,4.0,3.5,0.2,0.3,1.2,4.1,3.1,2.3,3.5,0.7,0.7,1.2,1.3,1.4,0.6,3.7,1.7,3.5,0.3,2.6,0.9,1.7,1.6,0.9,1.1,2.7,0.3,0.9,2.5,1.5,3.2,1.8,1.6,0.8,1.0,1.6,4.5,0.2,6,16,12,10,20,25,12,21,13,10,6.0,30,17,9,9,59,72,5,24,53,7,56,34,49,49,41,61,25,35,35,17,12,3,28,4,8,43,37,2,1,8,36,21,19,21,6,2,7,7,13,5,25,11,24,1,16,8,15,9,7,8,17,2,6,17,20,24,11,9,5,6,13,33,2,0.039,0.023,0.021,0.025,0.05,0.033,0.038,0.032,0.051,0.037,0.039,0.04,0.04,0.05,0.037,0.045,0.039,0.031,0.03,0.052,0.026,0.045,0.051,0.067,0.044,0.041,0.054,0.036,0.042,0.041,0.051,0.049,0.025,0.034,0.027,0.018,0.041,0.031,0.013,0.027,0.024,0.037,0.049,0.027,0.024,0.016,0.023,0.019,0.019,0.03,0.041,0.029,0.031,0.029,0.023,0.017,0.019,0.022,0.027,0.017,0.031,0.044,0.035,0.025,0.029,0.025,0.025,0.024,0.028,0.026,0.024,0.02,0.018,0.015,1350,4638,2286,2300,2636,3067,2258,3874,1687,1938,867.0,3251,3317,1085,2049,8648,15284,1944,4044,4542,2990,6477,2229,5004,5691,6467,5287,2975,6151,5593,2424,1362,738,7178,1917,1909,7799,7942,299,347,1480,2907,5095,2279,3657,1323,766,2298,2256,1943,718,4040,1738,4642,423,3860,1363,2773,1457,1451,1035,2266,249,931,2368,1337,3581,2299,1573,1116,1362,2345,8470,423,0.131,0.109,0.111,0.12,0.149,0.144,0.116,0.144,0.161,0.154,0.147,0.156,0.127,0.161,0.131,0.151,0.139,0.113,0.132,0.169,0.1,0.16,0.166,0.173,0.142,0.147,0.14,0.123,0.119,0.158,0.148,0.147,0.105,0.135,0.108,0.105,0.157,0.136,0.099,0.131,0.117,0.143,0.15,0.128,0.108,0.1,0.106,0.097,0.094,0.138,0.156,0.126,0.123,0.123,0.125,0.096,0.113,0.118,0.119,0.093,0.134,0.144,0.144,0.117,0.129,0.128,0.116,0.113,0.119,0.126,0.113,0.121,0.099,0.102,396,1509,910,869,1015,1309,787,1166,611,477,243.0,1536,954,287,587,2828,4181,448,1085,2207,872,2216,1155,1972,2078,1877,2059,1360,1815,1659,864,582,244,1422,478,741,1827,1854,202,148,781,1658,1118,1423,2248,660,392,963,1054,746,301,1970,896,2013,151,2170,687,1305,942,902,607,1076,154,540,1263,926,2003,1151,844,430,695,1148,3885,202,3.143,2.781,2.533,2.484,2.244,2.048,2.351,2.845,2.309,3.014,2.825,1.867,2.718,2.695,2.537,2.377,2.845,3.448,2.777,1.84,2.645,2.409,1.774,2.212,2.233,2.647,2.065,1.852,2.608,2.584,2.271,2.413,2.511,3.393,3.641,2.308,3.059,2.981,1.691,2.675,2.363,1.664,3.467,1.773,1.884,2.166,2.467,2.954,2.592,2.587,2.119,2.044,2.089,2.355,2.725,1.96,2.068,2.264,1.694,1.791,1.97,2.316,1.739,2.15,2.077,1.851,1.868,2.385,2.236,2.779,2.221,2.365,2.286,2.377,0.882,0.809,0.448,0.532,0.689,0.616,0.604,0.534,0.653,0.602,0.862,0.589,0.52,0.668,0.646,0.655,0.689,0.824,0.568,0.578,0.78,0.847,0.503,0.705,0.709,0.668,0.67,0.515,0.621,0.639,0.805,0.806,0.402,0.8,0.744,0.414,0.794,0.806,0.394,0.373,0.511,0.483,0.835,0.626,0.566,0.454,0.321,0.836,0.52,0.547,0.486,0.555,0.519,0.509,0.734,0.427,0.357,0.518,0.361,0.304,0.485,0.713,0.418,0.61,0.47,0.702,0.473,0.461,0.381,0.8,0.479,0.522,0.532,0.36
-4013,Not Epileptic,M,20.0,0.7,2.9,1.0,1.1,2.6,2.2,1.4,2.0,2.3,1.0,0.3,3.0,1.3,0.4,1.2,7.1,9.6,0.9,2.8,5.4,1.2,3.8,1.8,3.3,3.3,3.4,4.0,2.7,2.8,3.3,1.5,2.2,0.4,2.5,0.5,0.9,3.9,4.7,0.1,0.2,1.0,3.3,2.2,2.9,2.3,0.5,0.3,1.0,1.3,0.8,0.8,1.7,2.6,2.4,0.4,2.4,0.9,1.7,1.1,1.2,0.7,2.4,0.3,0.7,1.7,1.3,2.8,1.9,0.8,0.5,1.4,1.5,6.1,0.3,6,28,7,9,23,27,15,18,23,12,5.0,31,18,6,17,92,110,6,37,49,13,48,19,32,45,42,48,34,31,47,17,29,5,37,3,7,48,54,1,1,6,31,24,19,14,4,1,6,7,6,6,14,25,17,2,23,7,13,8,9,8,20,2,6,16,18,23,18,6,4,11,13,54,3,0.033,0.027,0.017,0.021,0.038,0.03,0.023,0.028,0.058,0.032,0.033,0.032,0.027,0.024,0.028,0.036,0.03,0.031,0.032,0.039,0.025,0.035,0.026,0.034,0.028,0.026,0.031,0.024,0.022,0.027,0.037,0.077,0.025,0.028,0.011,0.015,0.037,0.034,0.012,0.019,0.021,0.035,0.033,0.029,0.016,0.011,0.016,0.016,0.018,0.016,0.029,0.019,0.027,0.018,0.018,0.017,0.023,0.018,0.021,0.016,0.017,0.031,0.024,0.017,0.016,0.022,0.019,0.017,0.015,0.018,0.026,0.017,0.02,0.014,1633,5731,2563,2456,3070,3810,3206,4535,2485,2291,870.0,3275,3402,1212,2918,14214,21670,2476,5810,5983,4497,6403,2482,6860,8016,8961,6630,6705,8812,7540,2590,1618,1034,7829,2613,2818,8884,12037,361,557,1663,2906,6714,2826,3908,1427,749,2276,2449,2050,706,3176,3096,5064,524,4093,1512,3213,1351,2218,1322,3349,417,1034,3328,1597,4984,4593,1728,904,2021,3186,12137,562,0.111,0.128,0.096,0.107,0.143,0.135,0.109,0.133,0.179,0.141,0.136,0.147,0.129,0.129,0.135,0.144,0.135,0.119,0.14,0.154,0.098,0.136,0.118,0.123,0.125,0.126,0.122,0.116,0.107,0.127,0.131,0.148,0.103,0.128,0.073,0.091,0.142,0.151,0.084,0.089,0.103,0.13,0.124,0.122,0.09,0.082,0.087,0.081,0.096,0.093,0.14,0.099,0.131,0.095,0.111,0.108,0.117,0.101,0.112,0.101,0.104,0.136,0.13,0.113,0.107,0.116,0.104,0.095,0.091,0.117,0.123,0.099,0.104,0.112,418,1726,883,811,1138,1347,1001,1187,789,516,219.0,1670,860,277,746,3951,5963,474,1543,2574,973,1871,1068,1864,1979,2189,2266,1908,2125,2048,715,595,280,1702,648,931,2215,2515,201,249,777,1495,1386,1611,2209,679,300,832,1057,839,422,1489,1472,2123,286,2097,670,1480,804,1091,592,1252,227,565,1542,959,2361,1856,820,434,903,1445,5021,301,3.564,3.038,2.848,2.941,2.224,2.329,2.532,3.172,2.429,3.319,3.167,1.812,2.908,2.992,2.811,2.741,2.94,3.813,3.013,2.039,3.109,2.591,2.043,2.736,2.988,3.041,2.4,2.521,3.08,2.733,2.677,2.739,2.745,3.257,3.606,2.735,2.939,3.311,1.999,2.555,2.849,1.793,3.346,2.036,1.968,2.333,3.198,3.196,2.899,2.794,2.038,2.321,2.132,2.542,2.179,2.178,2.619,2.582,1.982,2.22,2.345,2.615,1.806,2.346,2.44,1.967,2.313,2.658,2.382,2.373,2.526,2.581,2.573,2.341,0.985,0.53,0.442,0.538,0.665,0.668,0.551,0.527,0.605,0.715,0.663,0.523,0.522,0.421,0.557,0.613,0.61,0.734,0.542,0.636,0.897,0.581,0.48,0.802,0.651,0.61,0.581,0.689,0.551,0.639,0.679,1.0,0.319,0.607,0.659,0.603,0.84,0.679,0.388,0.442,0.553,0.468,0.791,0.719,0.609,0.427,0.309,0.631,0.488,0.385,0.408,0.411,0.439,0.407,0.342,0.394,0.66,0.518,0.369,0.398,0.432,0.581,0.479,0.628,0.475,0.65,0.469,0.363,0.439,0.765,0.433,0.436,0.518,0.352
-4014,Not Epileptic,M,37.0,0.7,2.1,0.8,1.3,1.4,1.8,1.9,1.3,1.1,0.7,0.3,2.9,1.2,0.4,1.5,6.1,8.8,0.9,2.2,5.0,1.2,2.6,2.3,2.8,3.0,2.9,3.3,2.1,2.7,2.8,1.2,1.1,0.3,2.5,0.3,0.5,3.7,3.4,0.2,0.1,1.0,2.9,2.0,2.1,2.8,0.9,0.4,0.8,0.8,0.6,0.7,2.1,1.3,2.7,0.4,2.8,0.9,1.7,0.5,1.0,0.8,1.2,0.3,0.4,1.7,1.6,3.0,1.3,0.8,0.4,0.8,1.6,3.6,0.3,5,21,7,12,11,17,14,11,10,6,3.0,20,12,6,15,58,72,9,23,38,6,27,19,28,31,33,31,17,20,28,18,5,4,29,2,4,38,38,2,1,4,27,17,18,18,7,3,5,5,5,5,17,9,17,3,23,5,14,4,9,7,8,3,4,13,15,25,7,5,4,7,12,27,2,0.032,0.02,0.015,0.021,0.031,0.024,0.028,0.023,0.044,0.032,0.029,0.029,0.026,0.021,0.027,0.027,0.028,0.031,0.026,0.036,0.021,0.029,0.031,0.031,0.027,0.024,0.03,0.024,0.023,0.033,0.042,0.034,0.023,0.028,0.011,0.013,0.03,0.031,0.013,0.018,0.02,0.031,0.033,0.027,0.02,0.02,0.022,0.012,0.011,0.021,0.029,0.02,0.019,0.018,0.013,0.016,0.018,0.017,0.019,0.018,0.016,0.021,0.015,0.02,0.018,0.033,0.02,0.017,0.016,0.018,0.019,0.02,0.015,0.015,1675,4903,2542,2654,1975,3232,3977,3282,1859,1439,781.0,2987,3181,1095,3373,13548,20597,2246,5392,5449,3169,5270,2752,5595,6244,7544,6265,3906,7097,5181,2038,891,1076,7128,2184,1691,8403,8338,363,438,1665,3002,4819,2148,3838,1658,849,2344,2263,1695,755,4385,2348,6526,736,5571,2315,3538,961,1719,2152,2798,601,857,2961,1116,4928,2346,1746,880,1557,3075,10048,588,0.104,0.115,0.09,0.113,0.124,0.128,0.113,0.107,0.137,0.13,0.113,0.118,0.125,0.119,0.131,0.121,0.127,0.127,0.122,0.138,0.08,0.135,0.135,0.132,0.129,0.118,0.131,0.103,0.096,0.141,0.144,0.127,0.112,0.124,0.063,0.078,0.125,0.135,0.102,0.08,0.094,0.138,0.13,0.124,0.101,0.101,0.108,0.078,0.08,0.097,0.124,0.103,0.102,0.095,0.106,0.101,0.088,0.094,0.115,0.11,0.096,0.107,0.106,0.123,0.103,0.134,0.108,0.092,0.088,0.116,0.114,0.111,0.086,0.102,415,1582,861,970,787,1150,1107,883,514,374,197.0,1446,779,300,929,3412,5204,458,1372,2270,846,1452,1164,1496,1807,2116,1826,1399,1714,1487,573,419,314,1480,543,587,2075,1821,178,185,739,1426,1025,1354,2201,717,312,963,1026,603,434,1806,1108,2387,404,2713,803,1700,448,876,791,881,366,342,1426,664,2216,1103,768,367,662,1336,3988,262,3.694,2.976,2.938,2.749,2.323,2.424,2.965,3.101,2.663,3.406,3.07,1.817,3.053,2.616,2.797,2.886,3.205,3.666,3.168,2.051,2.9,2.876,2.101,2.82,2.722,2.871,2.569,2.262,2.944,2.827,2.61,2.272,2.794,3.305,3.497,2.833,2.955,3.297,2.257,2.549,2.776,1.902,3.299,1.823,2.04,2.516,3.154,2.955,2.681,3.352,2.161,2.513,2.51,2.898,2.369,2.295,3.237,2.465,2.316,2.222,2.817,3.239,1.882,2.611,2.331,2.152,2.253,2.519,2.515,2.652,2.845,2.726,2.742,2.92,0.824,0.592,0.735,0.529,0.57,0.514,0.808,0.496,0.625,0.612,1.032,0.593,0.391,0.499,0.51,0.579,0.623,0.895,0.595,0.706,0.816,0.56,0.565,0.681,0.57,0.605,0.611,0.578,0.686,0.672,0.79,1.012,0.401,0.638,0.83,0.603,0.825,0.466,0.448,0.45,0.545,0.496,0.887,0.62,0.657,0.448,0.648,0.749,0.365,0.919,0.528,0.509,0.528,0.529,0.355,0.511,0.597,0.603,0.404,0.395,0.434,0.961,0.394,0.976,0.661,0.769,0.486,0.429,0.402,1.014,0.453,0.62,0.478,0.365
-4015,Not Epileptic,F,47.0,1.0,2.5,1.1,1.3,2.2,1.8,2.0,1.4,2.1,0.7,0.5,3.2,1.2,0.3,1.1,5.7,8.1,0.7,1.9,4.2,1.3,2.2,2.1,3.0,4.6,3.3,2.9,1.8,2.5,2.6,2.0,0.8,0.5,2.2,0.6,0.6,3.9,3.9,0.2,0.2,0.7,3.8,2.1,2.2,2.8,0.6,0.5,0.7,1.2,0.5,0.4,2.0,1.3,1.8,0.6,2.5,1.2,1.3,0.9,0.8,0.9,1.4,0.3,0.5,1.5,1.3,1.6,1.1,1.1,0.4,1.1,1.7,5.0,0.2,9,21,8,10,19,20,15,11,19,10,5.0,31,15,4,12,61,85,11,25,32,14,30,19,33,56,39,35,19,22,33,17,6,3,26,3,8,36,48,1,1,3,30,23,18,18,4,3,4,7,4,2,17,9,14,4,21,10,9,7,5,9,12,2,6,11,17,12,6,8,4,6,14,47,1,0.044,0.026,0.025,0.026,0.041,0.032,0.034,0.027,0.075,0.035,0.035,0.035,0.025,0.04,0.028,0.039,0.034,0.037,0.036,0.044,0.026,0.026,0.036,0.035,0.036,0.029,0.026,0.023,0.025,0.029,0.052,0.037,0.029,0.027,0.023,0.019,0.039,0.03,0.021,0.017,0.019,0.039,0.034,0.022,0.021,0.014,0.018,0.013,0.022,0.015,0.019,0.024,0.026,0.019,0.016,0.02,0.02,0.017,0.021,0.017,0.023,0.025,0.023,0.018,0.015,0.021,0.017,0.017,0.022,0.02,0.025,0.025,0.02,0.015,1491,5004,2805,2546,2507,3425,3085,3056,1869,1713,840.0,3898,3467,680,2317,9790,17889,2254,4388,3681,3813,4949,2577,5547,8498,7016,6792,4749,6394,6081,2544,909,888,6298,1440,2085,7007,9240,258,451,1270,2928,6054,3063,3829,1365,728,2006,1959,1365,571,3151,1544,3570,904,4296,2282,3061,1280,1274,1313,2412,318,898,2998,1457,3029,2372,1663,732,1566,2401,10236,432,0.14,0.124,0.103,0.116,0.152,0.142,0.116,0.126,0.2,0.157,0.135,0.148,0.128,0.155,0.134,0.155,0.143,0.135,0.143,0.165,0.112,0.129,0.123,0.138,0.147,0.136,0.126,0.107,0.106,0.139,0.147,0.111,0.123,0.126,0.092,0.1,0.135,0.133,0.119,0.085,0.092,0.134,0.141,0.11,0.101,0.087,0.111,0.081,0.102,0.097,0.117,0.127,0.123,0.104,0.113,0.109,0.11,0.094,0.119,0.099,0.114,0.124,0.111,0.115,0.096,0.11,0.098,0.095,0.111,0.151,0.119,0.122,0.104,0.096,414,1511,787,873,952,1111,991,877,592,421,216.0,1670,832,185,633,2813,4566,448,1152,1616,871,1324,4015,1556,2247,2048,1919,1336,1542,1636,808,431,274,1347,367,621,1758,2460,123,225,589,1486,1296,1652,2131,650,324,884,889,553,252,1371,793,1582,491,2042,915,1288,677,669,614,916,199,481,1392,963,1458,1003,803,301,681,1114,4071,188,3.286,3.02,3.121,2.826,2.304,2.516,2.553,3.054,2.476,3.166,2.8,2.004,3.005,2.735,2.725,2.685,3.057,3.758,3.121,2.031,3.158,2.715,2.14,2.74,2.855,2.8,2.697,2.634,2.981,2.819,2.543,2.278,2.652,3.158,3.177,2.778,2.988,2.889,2.248,2.556,2.814,1.868,3.377,2.132,2.037,2.438,2.628,2.783,2.776,2.826,2.638,2.415,2.057,2.502,2.235,2.36,2.508,2.619,2.171,2.131,2.253,2.795,1.598,2.12,2.328,1.856,2.288,2.757,2.478,2.461,2.631,2.444,2.61,2.656,0.695,0.589,0.603,0.5,0.68,0.602,0.574,0.436,0.69,0.742,0.809,0.565,0.433,0.649,0.528,0.544,0.564,0.786,0.573,0.625,0.813,0.567,0.659,0.884,0.616,0.584,0.586,0.674,0.585,0.607,0.95,0.866,0.649,0.693,0.62,0.442,0.848,0.568,0.57,0.293,0.489,0.561,0.808,0.69,0.557,0.377,0.892,0.502,0.436,0.421,0.562,0.425,0.504,0.396,0.408,0.394,0.47,0.495,0.37,0.368,0.393,0.531,0.364,0.642,0.528,0.51,0.374,0.423,0.374,1.003,0.436,0.589,0.399,0.443
-4016,Not Epileptic,M,22.0,0.8,2.9,0.7,1.6,1.7,2.1,1.9,1.7,1.8,0.8,0.5,2.5,1.6,0.5,1.3,8.5,10.1,0.8,2.7,5.0,1.4,2.9,1.5,3.0,2.9,3.0,2.9,2.8,2.8,3.2,1.4,2.0,0.6,4.1,0.4,1.2,3.5,4.2,0.2,0.2,1.3,3.0,2.3,1.9,3.6,0.7,0.3,0.9,1.3,0.7,0.8,2.2,1.3,2.6,0.3,2.2,0.6,1.4,0.9,1.1,0.7,1.7,0.1,0.5,2.0,0.9,4.6,1.6,0.8,0.5,1.2,1.5,6.4,0.3,7,30,8,10,22,18,15,15,18,7,6.0,24,17,7,17,84,93,8,32,39,16,33,16,31,38,34,29,26,21,34,26,13,7,40,3,11,38,39,2,1,7,23,25,17,25,5,2,8,6,6,5,15,8,17,2,17,4,13,7,9,6,14,1,5,15,12,32,12,6,4,8,14,53,2,0.031,0.028,0.011,0.025,0.038,0.036,0.025,0.03,0.052,0.033,0.035,0.034,0.028,0.026,0.029,0.037,0.028,0.028,0.029,0.044,0.026,0.032,0.035,0.037,0.03,0.026,0.032,0.027,0.025,0.034,0.037,0.064,0.025,0.038,0.016,0.023,0.03,0.036,0.012,0.012,0.023,0.043,0.038,0.024,0.027,0.015,0.016,0.013,0.015,0.015,0.039,0.021,0.022,0.019,0.02,0.02,0.016,0.016,0.027,0.022,0.023,0.032,0.013,0.017,0.019,0.021,0.029,0.021,0.016,0.017,0.028,0.02,0.02,0.012,1900,5721,2186,2710,2041,2695,2890,3459,1970,1441,596.0,2426,4119,1131,3156,13499,22391,2640,5171,3482,3738,4753,2227,5618,6304,7087,4880,3673,6365,5208,2420,1082,1261,7681,1769,2584,7072,8299,432,659,1742,2812,5542,2186,3584,1366,781,2521,2718,1378,749,3591,1938,5439,505,3213,1201,2991,880,1549,1039,2586,329,945,2895,1112,4742,2425,1749,1042,1600,2388,11702,752,0.113,0.119,0.085,0.109,0.14,0.147,0.115,0.123,0.161,0.143,0.141,0.144,0.137,0.13,0.139,0.147,0.128,0.107,0.13,0.155,0.106,0.137,0.14,0.126,0.134,0.126,0.131,0.117,0.108,0.141,0.128,0.166,0.138,0.142,0.092,0.111,0.126,0.135,0.079,0.089,0.113,0.147,0.139,0.121,0.121,0.101,0.088,0.09,0.087,0.104,0.145,0.104,0.105,0.096,0.117,0.112,0.099,0.099,0.129,0.116,0.112,0.131,0.096,0.114,0.104,0.117,0.13,0.111,0.097,0.106,0.126,0.113,0.106,0.08,480,1715,960,991,765,963,1066,972,598,375,203.0,1187,1035,312,874,3750,5834,503,1496,1813,889,1550,807,1433,1760,1869,1585,1620,1644,1710,668,481,386,1766,380,880,1836,1884,249,278,813,1140,1073,1280,2253,688,326,1109,1259,619,341,1649,985,2302,245,1654,600,1454,553,817,525,883,181,438,1446,676,2378,1105,816,485,676,1128,4935,330,3.656,2.784,2.176,2.761,2.181,2.525,2.298,3.083,2.4,3.132,2.512,1.806,3.061,2.509,2.74,2.74,3.045,3.751,2.768,1.734,3.103,2.376,2.235,2.845,2.685,3.051,2.439,1.924,2.89,2.491,2.645,2.466,2.838,3.125,3.746,2.638,2.909,3.031,2.078,2.571,2.73,2.084,3.397,1.934,1.874,2.232,2.823,2.786,2.637,2.593,2.503,2.263,1.951,2.515,2.518,2.155,2.39,2.385,1.908,2.11,2.344,2.649,2.135,2.273,2.27,2.081,2.235,2.515,2.494,2.237,2.698,2.507,2.548,2.563,0.773,0.806,0.61,0.581,0.669,0.436,0.616,0.591,0.731,0.436,0.801,0.537,0.471,0.451,0.586,0.573,0.596,0.696,0.535,0.517,0.795,0.444,0.523,0.752,0.517,0.606,0.488,0.481,0.67,0.585,0.761,0.762,0.542,0.629,0.877,0.541,0.605,0.602,0.302,0.377,0.523,0.717,0.698,0.708,0.652,0.606,0.498,0.733,0.557,0.457,0.433,0.502,0.535,0.521,0.438,0.435,0.372,0.555,0.38,0.42,0.312,0.778,0.402,0.94,0.537,0.584,0.442,0.407,0.473,0.581,0.554,0.508,0.477,0.534
-4017,Not Epileptic,F,47.0,0.8,2.8,1.4,1.5,2.0,2.1,2.3,1.6,1.8,1.5,0.4,3.4,1.4,0.5,1.1,8.0,9.8,1.2,2.9,4.1,1.5,2.8,3.3,6.5,3.7,3.9,3.9,2.6,4.7,4.0,1.3,1.2,0.7,2.6,0.4,0.8,3.6,3.6,0.2,0.1,1.0,4.1,2.5,2.6,3.2,0.8,0.5,1.4,1.2,0.9,0.7,1.8,1.5,3.1,0.5,3.1,0.9,2.0,1.1,1.5,0.6,2.3,0.3,0.5,1.9,1.8,3.5,1.3,1.5,0.6,1.0,1.9,5.1,0.3,7,26,13,14,15,22,16,15,19,10,3.0,29,16,5,9,88,94,10,35,34,14,32,26,59,39,44,45,23,29,45,17,8,7,33,3,7,43,48,2,1,6,36,31,23,17,5,2,10,6,8,6,11,12,22,4,25,10,19,10,12,5,21,2,5,16,24,33,8,9,6,9,16,41,4,0.038,0.025,0.02,0.027,0.039,0.027,0.035,0.026,0.058,0.054,0.029,0.04,0.031,0.032,0.034,0.035,0.036,0.041,0.036,0.037,0.024,0.036,0.039,0.057,0.037,0.027,0.026,0.03,0.039,0.032,0.036,0.045,0.024,0.034,0.015,0.02,0.039,0.035,0.017,0.016,0.02,0.038,0.043,0.025,0.025,0.014,0.019,0.024,0.019,0.019,0.033,0.02,0.023,0.023,0.013,0.02,0.026,0.017,0.028,0.026,0.021,0.036,0.03,0.019,0.02,0.029,0.023,0.018,0.024,0.019,0.026,0.021,0.02,0.018,1094,5117,3143,2606,1784,4000,2933,3322,2086,1837,756.0,3207,2970,1134,2123,12681,17847,2017,5566,3580,4322,4337,2579,5620,6674,7815,7734,3395,6080,7036,2171,1197,1517,5871,2143,2097,6118,8392,431,355,1528,3661,6161,2355,3262,1419,746,2263,2147,1587,670,3051,1941,5196,971,4813,1054,3721,1142,1575,905,2788,383,1056,3096,1842,4428,2127,1747,838,1427,2798,9531,463,0.125,0.122,0.104,0.126,0.132,0.136,0.108,0.118,0.156,0.165,0.119,0.15,0.129,0.111,0.133,0.14,0.139,0.125,0.145,0.145,0.107,0.127,0.132,0.164,0.133,0.131,0.117,0.102,0.115,0.144,0.13,0.121,0.105,0.139,0.077,0.095,0.137,0.15,0.102,0.096,0.106,0.15,0.145,0.123,0.112,0.09,0.101,0.104,0.096,0.105,0.143,0.098,0.115,0.107,0.09,0.109,0.127,0.106,0.125,0.129,0.123,0.133,0.117,0.108,0.11,0.131,0.111,0.1,0.117,0.13,0.12,0.119,0.104,0.115,378,1757,1143,983,866,1291,1029,1029,675,443,194.0,1509,825,277,524,4056,4934,424,1463,1871,1057,1264,1238,1982,1799,2359,2598,1371,1607,2262,640,477,503,1458,514,662,1749,2003,182,166,755,1661,1289,1616,1952,754,335,902,990,724,339,1433,1052,2275,548,2359,536,1744,680,890,387,1207,223,520,1527,958,2309,1028,854,488,697,1274,4135,293,2.968,2.697,2.637,2.611,1.916,2.523,2.454,2.74,2.38,3.358,2.898,1.769,2.723,2.793,2.86,2.456,2.791,3.573,2.972,1.743,3.075,2.513,1.877,2.318,2.77,2.742,2.395,2.046,2.859,2.48,2.334,2.596,2.5,3.017,3.552,2.663,2.694,3.034,2.258,2.362,2.452,1.882,3.357,1.655,1.875,2.112,2.72,2.986,2.586,2.396,1.975,2.263,1.971,2.335,2.243,2.216,2.235,2.3,1.909,1.937,2.459,2.34,1.833,2.345,2.15,2.261,2.089,2.35,2.296,2.12,2.406,2.544,2.466,1.843,0.626,0.624,0.492,0.573,0.558,0.686,0.712,0.392,0.637,0.658,0.613,0.674,0.482,0.543,0.55,0.583,0.606,0.846,0.655,0.661,0.812,0.553,0.606,0.708,0.667,0.499,0.536,0.558,0.693,0.562,0.751,0.914,0.394,0.469,0.72,0.509,0.724,0.536,0.451,0.403,0.511,0.657,0.754,0.561,0.574,0.409,0.614,0.896,0.45,0.38,0.522,0.446,0.398,0.45,0.332,0.416,0.464,0.57,0.416,0.433,0.493,0.549,0.435,0.649,0.517,0.655,0.437,0.438,0.516,0.523,0.341,0.429,0.438,0.469
-4018,Not Epileptic,M,55.0,1.2,3.5,1.9,1.7,2.1,2.1,3.0,1.8,3.0,1.5,0.5,2.6,1.5,0.4,1.3,6.4,15.4,1.3,3.1,4.0,2.6,4.2,2.1,5.6,5.2,3.6,4.4,3.3,5.1,5.5,1.7,1.6,0.6,2.8,0.5,1.6,5.9,4.2,0.3,0.3,1.2,2.9,3.5,2.2,4.0,0.8,0.7,0.9,1.7,1.0,0.7,1.7,2.0,3.7,1.1,2.5,1.0,1.5,1.0,1.7,1.0,1.6,0.3,0.7,2.3,2.7,2.7,1.6,0.9,0.5,1.1,1.9,4.9,0.3,10,32,16,14,14,24,23,17,16,14,5.0,25,16,6,14,71,126,10,31,40,21,45,22,56,67,44,55,28,47,55,20,10,5,40,3,17,60,55,2,4,7,34,31,18,20,5,3,5,9,8,5,14,13,33,7,18,7,13,10,12,7,14,2,6,16,29,18,14,5,4,8,16,38,3,0.043,0.03,0.031,0.025,0.038,0.032,0.046,0.029,0.062,0.049,0.041,0.036,0.031,0.031,0.032,0.041,0.043,0.044,0.037,0.033,0.036,0.04,0.033,0.048,0.044,0.028,0.033,0.032,0.037,0.036,0.042,0.048,0.032,0.034,0.017,0.02,0.042,0.036,0.013,0.025,0.024,0.04,0.045,0.025,0.025,0.017,0.018,0.015,0.021,0.015,0.024,0.021,0.029,0.024,0.026,0.017,0.021,0.018,0.021,0.025,0.022,0.026,0.015,0.021,0.02,0.031,0.018,0.021,0.019,0.014,0.016,0.02,0.017,0.015,1999,5843,3498,3388,2059,3618,3174,3230,1623,2201,658.0,2915,2947,989,2747,10457,21729,2500,5022,5802,6168,5883,2790,6723,8387,7863,7470,5071,7436,8402,2829,1733,1038,6683,2188,3507,9450,10054,541,767,1562,3414,6697,2388,4307,1611,1153,2359,2696,2451,644,2708,2156,6636,1011,4569,1529,3528,1436,1842,1165,3105,372,1081,3242,2553,4884,2971,1488,896,2153,2771,10991,618,0.134,0.126,0.113,0.11,0.13,0.129,0.119,0.137,0.167,0.161,0.147,0.146,0.133,0.135,0.127,0.142,0.147,0.149,0.142,0.144,0.127,0.139,0.126,0.143,0.148,0.13,0.123,0.106,0.122,0.14,0.142,0.135,0.116,0.129,0.09,0.108,0.143,0.142,0.093,0.113,0.11,0.131,0.15,0.12,0.109,0.097,0.105,0.082,0.1,0.091,0.117,0.113,0.124,0.113,0.12,0.095,0.112,0.092,0.11,0.123,0.115,0.114,0.111,0.109,0.11,0.124,0.094,0.106,0.102,0.1,0.104,0.114,0.093,0.107,463,2016,1156,1119,861,1189,1079,1083,605,540,206.0,1257,799,255,739,2999,6219,542,1257,2099,1268,1696,974,2020,2420,2369,2619,1634,2106,2691,844,626,301,1445,475,1233,2505,2362,265,263,759,1274,1471,1406,2464,742,500,920,1206,994,398,1327,1075,2682,577,2218,746,1551,796,967,663,1122,231,573,1599,1423,2319,1269,731,510,989,1422,4591,329,3.541,2.704,2.84,2.833,2.275,2.447,2.465,2.677,2.389,3.119,2.509,1.951,2.899,2.747,2.73,2.614,2.739,3.295,2.998,2.28,3.465,2.6,2.291,2.557,2.65,2.659,2.299,2.382,2.74,2.526,2.609,2.791,2.551,3.045,3.476,2.506,2.784,2.902,2.311,2.571,2.549,2.089,3.206,1.903,2.014,2.364,2.814,3.179,2.67,2.758,1.957,2.265,2.172,2.463,2.205,2.27,2.234,2.446,1.855,2.062,2.112,2.781,1.717,2.275,2.216,1.997,2.266,2.546,2.242,2.05,2.418,2.237,2.425,2.404,0.863,0.596,0.645,0.542,0.622,0.705,0.78,0.467,0.672,0.966,0.918,0.522,0.524,0.433,0.566,0.6,0.741,0.838,0.559,0.672,0.851,0.67,0.587,0.66,0.663,0.49,0.565,0.575,0.513,0.644,0.637,0.928,0.651,0.685,1.046,0.494,0.886,0.775,0.306,0.637,0.455,0.663,0.814,0.62,0.6,0.489,0.395,0.778,0.567,0.568,0.366,0.427,0.432,0.501,0.402,0.391,0.427,0.478,0.56,0.369,0.339,0.555,0.333,0.783,0.56,0.841,0.415,0.344,0.484,0.446,0.48,0.463,0.464,0.432
-4019,Not Epileptic,F,47.0,0.5,3.0,1.3,1.4,1.7,1.7,2.2,1.3,1.0,0.5,0.2,2.9,1.3,0.4,1.5,6.4,8.9,0.9,2.4,4.6,1.5,2.5,1.6,4.2,2.4,2.7,4.6,3.2,4.0,3.9,2.2,1.6,0.3,2.4,0.3,0.7,4.7,3.1,0.2,0.2,1.1,3.1,2.1,2.9,3.5,0.9,0.2,0.7,1.1,0.8,0.6,2.3,1.4,2.9,0.1,2.5,0.9,1.6,1.2,1.2,0.7,1.6,0.6,0.6,1.5,1.8,1.9,1.5,1.0,0.7,0.9,1.7,4.0,0.3,6,33,12,8,15,17,19,13,11,5,3.0,28,15,5,16,71,83,6,27,43,11,33,16,36,29,31,42,31,33,38,24,12,4,27,2,6,47,35,1,1,6,27,18,21,19,7,1,4,6,6,5,19,8,24,1,24,7,11,9,9,8,14,3,5,10,16,15,12,6,6,7,12,29,3,0.033,0.03,0.022,0.023,0.04,0.026,0.028,0.023,0.039,0.034,0.021,0.034,0.033,0.032,0.035,0.035,0.028,0.035,0.036,0.041,0.027,0.026,0.025,0.041,0.03,0.028,0.04,0.031,0.029,0.042,0.048,0.051,0.022,0.034,0.01,0.015,0.042,0.034,0.018,0.017,0.021,0.034,0.034,0.031,0.021,0.017,0.013,0.012,0.017,0.02,0.03,0.024,0.021,0.019,0.029,0.022,0.02,0.022,0.022,0.018,0.017,0.027,0.026,0.021,0.019,0.033,0.02,0.019,0.014,0.026,0.026,0.021,0.018,0.018,1277,4637,2667,2572,2106,2826,3362,3233,1824,1176,643.0,2587,2841,877,2623,11398,19191,1750,4626,4840,4043,5290,1834,5548,4742,5505,4397,5180,6705,5285,2399,1342,951,6013,1553,2136,7503,7123,280,416,1577,2323,4878,2239,4393,1547,643,1790,2096,1532,563,3208,1571,5695,175,3947,1521,2678,1338,1787,1112,2583,578,856,2226,1031,3292,2902,2136,983,1317,2359,7789,348,0.123,0.135,0.113,0.106,0.159,0.132,0.101,0.116,0.156,0.136,0.101,0.152,0.135,0.127,0.145,0.141,0.127,0.131,0.151,0.151,0.103,0.117,0.118,0.141,0.135,0.141,0.127,0.12,0.097,0.145,0.145,0.137,0.102,0.148,0.068,0.089,0.151,0.141,0.105,0.106,0.104,0.135,0.139,0.129,0.103,0.097,0.087,0.084,0.093,0.104,0.131,0.115,0.115,0.106,0.128,0.115,0.108,0.111,0.116,0.1,0.112,0.126,0.12,0.123,0.106,0.14,0.1,0.102,0.09,0.145,0.118,0.113,0.1,0.134,376,1615,928,938,758,1021,1139,999,480,274,182.0,1361,822,230,758,3406,5178,406,1228,2030,921,1620,937,1684,1420,1596,1903,1676,1765,1576,751,567,289,1259,418,719,2054,1759,169,206,778,1326,1043,1511,2455,782,279,790,974,690,346,1603,897,2469,72,1863,738,1208,842,984,636,1043,355,477,1139,628,1494,1261,1001,423,642,1247,3443,223,3.467,2.674,2.768,2.754,2.411,2.267,2.472,2.783,2.858,3.134,2.682,1.738,2.702,2.707,2.659,2.546,2.929,3.508,2.969,2.023,3.221,2.441,1.814,2.554,2.696,2.8,2.086,2.44,2.905,2.565,2.352,2.543,2.504,3.317,3.339,2.629,2.684,2.866,1.949,2.128,2.639,1.683,3.435,1.798,2.013,2.241,2.905,2.823,2.615,2.595,1.906,2.075,2.011,2.365,2.708,2.213,2.286,2.625,1.812,1.988,2.076,2.314,1.92,2.21,2.276,2.164,2.343,2.556,2.471,2.588,2.323,2.169,2.442,1.832,0.649,0.715,0.534,0.446,0.689,0.659,0.767,0.568,0.561,0.439,0.72,0.486,0.44,0.432,0.518,0.619,0.605,0.787,0.538,0.657,0.842,0.595,0.397,0.771,0.648,0.458,0.506,0.533,0.546,0.601,0.753,0.814,0.469,0.588,0.744,0.541,0.81,0.615,0.397,0.363,0.437,0.453,0.699,0.493,0.595,0.402,0.503,0.495,0.498,0.455,0.414,0.438,0.368,0.441,0.428,0.363,0.37,0.449,0.372,0.409,0.356,0.728,0.381,0.638,0.477,0.699,0.41,0.327,0.507,0.872,0.405,0.474,0.467,0.396
-4020,Not Epileptic,F,37.0,0.9,2.3,1.0,0.9,1.3,1.6,1.8,1.8,1.2,0.4,0.3,2.8,1.7,0.8,1.5,6.5,10.3,1.0,2.1,4.2,1.9,6.4,2.4,3.0,6.6,4.0,4.2,2.2,2.1,3.9,1.3,0.7,0.6,2.9,0.5,0.6,4.9,3.9,0.3,0.1,1.2,3.9,2.8,2.0,2.2,0.7,0.6,1.3,1.5,0.8,0.5,2.6,1.9,2.7,0.3,2.7,1.0,1.9,1.2,1.2,0.6,1.8,0.4,0.5,2.4,1.4,3.3,0.9,0.9,0.6,0.7,1.6,4.7,0.7,9,22,8,9,14,19,17,18,13,5,3.0,25,20,8,18,74,98,10,24,43,17,53,20,36,56,48,50,22,20,45,16,5,7,32,4,4,54,50,1,1,7,33,29,16,17,6,2,8,8,6,3,19,16,19,1,23,8,19,8,9,6,13,3,5,21,19,28,8,6,5,6,11,49,6,0.038,0.025,0.017,0.018,0.031,0.031,0.031,0.027,0.037,0.024,0.034,0.034,0.033,0.039,0.038,0.035,0.036,0.038,0.034,0.037,0.028,0.051,0.038,0.042,0.048,0.034,0.034,0.023,0.026,0.031,0.038,0.042,0.039,0.035,0.021,0.015,0.045,0.032,0.016,0.021,0.023,0.049,0.045,0.027,0.023,0.015,0.025,0.017,0.019,0.012,0.026,0.029,0.027,0.026,0.025,0.019,0.02,0.022,0.022,0.022,0.019,0.026,0.03,0.017,0.019,0.027,0.022,0.017,0.022,0.021,0.017,0.022,0.018,0.024,1461,4614,2390,2098,2057,3032,3382,3404,2054,1360,535.0,3371,3924,1341,2813,11208,19528,2131,4052,4319,5402,5079,1785,5700,5287,7516,6509,4238,5022,6994,2488,751,904,6838,2036,1881,8830,8865,399,179,1664,2465,6369,2408,2281,1524,672,2338,2176,2189,479,3090,2002,4234,346,4034,1573,3384,1536,1763,1093,2678,534,831,3859,1282,4529,1872,1444,1043,1512,2185,9632,871,0.122,0.119,0.096,0.093,0.133,0.13,0.13,0.128,0.157,0.121,0.116,0.147,0.137,0.161,0.15,0.151,0.143,0.142,0.139,0.161,0.118,0.146,0.14,0.145,0.145,0.135,0.133,0.113,0.11,0.14,0.127,0.111,0.149,0.141,0.093,0.086,0.17,0.137,0.096,0.119,0.108,0.158,0.158,0.122,0.11,0.098,0.111,0.096,0.1,0.083,0.121,0.125,0.126,0.111,0.126,0.112,0.112,0.117,0.107,0.108,0.105,0.124,0.153,0.115,0.113,0.131,0.11,0.1,0.104,0.124,0.107,0.119,0.097,0.135,495,1586,900,809,829,998,1097,1176,559,350,166.0,1367,1048,336,885,3358,5410,515,1155,1987,1170,1872,925,1530,2134,2252,2271,1538,1365,2288,685,352,331,1441,484,687,2099,2224,230,80,864,1210,1290,1262,1607,710,328,1020,1100,941,297,1554,1024,1892,145,2157,761,1397,892,899,515,995,261,431,1875,791,2401,921,695,474,700,1050,4350,409,2.82,2.695,2.712,2.426,2.172,2.563,2.456,2.606,2.771,3.016,2.645,2.055,2.85,2.889,2.495,2.576,2.842,3.189,2.79,1.906,3.524,2.209,1.821,2.84,2.119,2.703,2.274,2.226,2.732,2.484,2.623,2.273,2.487,3.142,3.433,2.621,3.122,2.92,2.013,2.327,2.504,1.885,3.22,2.102,1.598,2.352,2.473,2.706,2.424,2.617,2.021,2.196,2.025,2.327,2.469,2.054,2.288,2.404,1.916,2.178,2.308,2.761,2.022,2.121,2.238,1.965,2.049,2.292,2.421,2.362,2.437,2.419,2.434,2.22,0.626,0.615,0.474,0.615,0.718,0.553,0.577,0.468,0.647,0.524,0.647,0.573,0.464,0.408,0.573,0.524,0.618,0.69,0.499,0.483,0.961,0.715,0.534,0.746,0.648,0.537,0.611,0.481,0.538,0.592,0.687,0.964,0.457,0.63,0.754,0.439,0.762,0.662,0.31,0.387,0.57,0.627,0.873,0.596,0.43,0.45,0.531,0.612,0.539,0.51,0.37,0.451,0.465,0.454,0.73,0.366,0.431,0.635,0.341,0.366,0.467,0.7,0.48,0.741,0.584,0.589,0.398,0.443,0.386,0.871,0.444,0.593,0.449,0.46
-4021,Not Epileptic,F,32.0,0.7,2.2,0.5,1.0,1.2,1.9,1.3,1.7,1.4,0.9,1.0,3.7,1.7,0.4,1.7,5.4,8.2,1.2,1.9,4.5,1.7,2.9,1.0,2.7,2.5,4.0,2.4,3.0,2.1,2.9,1.1,1.1,0.5,2.2,0.4,1.2,3.8,2.5,0.2,0.3,1.4,3.0,2.5,2.3,2.6,0.8,0.6,0.8,1.1,1.1,0.6,2.2,1.5,3.3,0.4,2.3,1.4,2.3,0.8,0.9,0.8,0.9,0.3,0.6,1.2,1.3,2.7,1.9,0.9,0.4,1.0,1.5,5.1,0.4,6,21,6,9,14,13,10,11,15,7,5.0,24,13,6,12,57,67,6,19,34,16,31,11,26,31,35,24,26,18,27,13,8,5,26,2,12,34,27,1,2,9,28,19,20,16,8,2,6,6,12,5,14,10,24,2,18,10,16,6,7,6,11,2,4,9,13,21,13,7,4,7,10,40,3,0.033,0.023,0.009,0.018,0.027,0.035,0.023,0.028,0.044,0.034,0.061,0.04,0.026,0.023,0.029,0.033,0.026,0.038,0.031,0.037,0.024,0.034,0.029,0.031,0.031,0.03,0.027,0.027,0.022,0.03,0.028,0.045,0.023,0.024,0.013,0.026,0.034,0.025,0.017,0.021,0.023,0.032,0.036,0.025,0.021,0.017,0.023,0.014,0.013,0.021,0.031,0.024,0.027,0.024,0.021,0.022,0.043,0.029,0.023,0.021,0.03,0.018,0.021,0.02,0.016,0.029,0.026,0.026,0.018,0.01,0.028,0.019,0.021,0.017,1743,5011,2023,2033,1420,2474,2736,3759,2207,1433,673.0,3041,4113,1351,3707,10620,19089,2669,3259,4750,3158,4300,1743,5348,5059,8263,4232,4365,5392,4709,2096,734,1251,5282,1814,2624,5272,6136,432,692,2445,3089,4218,2697,3555,1469,1042,2577,2786,1843,717,3201,2468,5628,537,3123,1199,3033,1047,1533,1213,1791,511,965,2552,1182,3822,2635,1772,1108,1495,2624,8613,585,0.116,0.113,0.079,0.101,0.111,0.139,0.108,0.11,0.134,0.127,0.15,0.15,0.118,0.116,0.111,0.129,0.118,0.122,0.112,0.134,0.091,0.143,0.128,0.115,0.136,0.129,0.129,0.121,0.106,0.13,0.108,0.151,0.103,0.107,0.064,0.111,0.116,0.103,0.084,0.084,0.115,0.141,0.122,0.118,0.106,0.103,0.106,0.078,0.082,0.099,0.137,0.109,0.105,0.102,0.117,0.12,0.143,0.115,0.115,0.106,0.135,0.103,0.106,0.113,0.09,0.138,0.119,0.119,0.099,0.085,0.124,0.099,0.104,0.099,396,1576,812,876,628,869,833,1053,530,426,207.0,1311,1012,316,885,2774,4901,524,1181,1947,1099,1444,636,1454,1445,2194,1397,1580,1529,1510,558,403,404,1354,534,815,1642,1604,239,269,990,1488,1132,1543,1961,724,388,1019,1213,891,341,1462,979,2199,263,1663,557,1391,589,672,440,796,234,400,1252,637,1772,1122,855,560,618,1245,3843,304,3.757,2.807,2.385,2.291,2.02,2.608,2.733,3.11,2.929,3.11,2.878,2.035,3.045,3.009,2.924,2.757,3.124,3.836,2.475,2.146,2.581,2.405,2.367,2.746,2.736,3.052,2.346,2.248,2.793,2.547,2.705,1.993,2.513,2.73,3.093,2.991,2.551,2.849,2.221,2.966,3.264,1.92,3.054,1.988,2.094,2.034,3.57,3.015,2.863,2.381,2.422,2.388,2.159,2.58,2.705,2.221,2.418,2.452,2.03,2.257,2.613,2.332,2.363,2.765,2.2,2.253,2.29,2.628,2.501,2.236,2.583,2.498,2.404,2.281,1.109,0.772,0.525,0.583,0.672,0.524,0.688,0.62,0.674,0.625,0.758,0.554,0.416,0.447,0.798,0.702,0.653,0.967,0.576,0.698,0.784,0.506,0.472,0.73,0.43,0.552,0.467,0.487,0.443,0.533,0.64,0.807,0.433,0.548,0.658,0.593,0.66,0.65,0.547,0.795,0.562,0.563,0.768,0.679,0.566,0.528,0.472,0.91,0.8,0.736,0.513,0.718,0.711,0.559,0.421,0.552,0.536,0.539,0.366,0.558,0.371,0.652,0.686,1.198,0.533,0.681,0.455,0.449,0.535,0.774,0.522,0.514,0.518,0.421
-4022,Not Epileptic,M,32.0,0.7,2.1,1.0,1.1,1.7,1.4,1.6,1.6,1.0,0.8,0.5,2.6,1.4,0.5,1.2,5.4,7.1,0.7,2.8,6.0,1.2,2.7,1.4,2.8,3.0,2.9,2.6,2.4,2.0,3.2,0.8,1.0,0.2,3.1,0.3,0.5,4.1,3.2,0.2,0.1,0.7,2.6,2.0,3.0,2.4,0.7,0.4,1.3,1.0,1.1,0.5,2.0,1.3,2.5,0.4,2.0,0.7,1.9,0.8,0.8,0.9,1.1,0.3,0.3,1.2,1.0,2.4,1.2,0.9,0.8,0.8,1.5,4.1,0.2,4,20,12,10,17,15,15,14,10,7,4.0,21,15,5,12,52,78,7,31,44,9,31,15,33,33,26,30,26,17,30,15,9,4,30,4,4,42,35,1,1,4,25,18,23,13,6,1,6,6,8,5,18,12,19,2,17,5,17,5,6,6,10,3,3,9,15,16,10,7,14,5,13,27,2,0.026,0.02,0.016,0.021,0.028,0.021,0.021,0.028,0.037,0.03,0.046,0.03,0.024,0.029,0.027,0.029,0.024,0.023,0.03,0.038,0.02,0.029,0.028,0.033,0.029,0.03,0.023,0.023,0.021,0.029,0.022,0.035,0.019,0.034,0.023,0.015,0.032,0.031,0.015,0.013,0.016,0.03,0.036,0.033,0.02,0.013,0.016,0.017,0.013,0.022,0.027,0.02,0.021,0.017,0.02,0.02,0.018,0.017,0.02,0.019,0.031,0.021,0.024,0.013,0.017,0.021,0.02,0.017,0.016,0.019,0.02,0.019,0.016,0.015,1688,5032,2813,2228,1997,2804,2952,3019,1439,1486,745.0,2669,3871,980,2697,10964,18159,2439,5152,5383,3113,4911,2005,5622,6229,6143,5171,4188,5745,5644,1770,1294,940,6767,1636,1639,8046,7496,323,400,1344,3288,5339,2578,3471,1655,774,2305,2330,1889,477,3390,2233,6077,666,3264,1601,3952,1013,1228,921,2026,466,821,2238,1376,3373,2549,1802,1602,1207,2300,9397,624,0.101,0.102,0.11,0.102,0.13,0.116,0.105,0.13,0.131,0.135,0.127,0.129,0.127,0.119,0.127,0.132,0.12,0.095,0.13,0.14,0.089,0.128,0.133,0.131,0.129,0.132,0.12,0.114,0.101,0.13,0.104,0.104,0.098,0.144,0.089,0.081,0.12,0.133,0.08,0.09,0.093,0.134,0.139,0.137,0.096,0.09,0.084,0.089,0.082,0.106,0.141,0.109,0.116,0.092,0.106,0.113,0.091,0.099,0.113,0.102,0.134,0.107,0.129,0.093,0.098,0.122,0.107,0.099,0.099,0.115,0.111,0.117,0.09,0.098,458,1647,985,876,894,1091,1059,995,496,412,205.0,1237,1023,260,740,2908,4813,492,1548,2380,909,1491,826,1414,1770,1713,1815,1607,1481,1718,522,513,264,1386,395,550,2121,1704,209,164,630,1324,1052,1551,1971,808,329,964,1180,880,327,1550,1014,2391,308,1518,681,1879,595,656,468,829,217,384,1140,848,1786,1053,837,646,574,1176,3816,202,3.471,2.77,2.702,2.535,2.063,2.33,2.357,2.773,2.229,2.821,2.791,1.968,3.043,2.611,2.787,2.86,2.967,3.781,2.777,1.965,2.748,2.502,2.081,2.891,2.741,2.856,2.357,2.121,2.916,2.615,2.45,2.333,3.026,3.329,3.525,2.565,2.795,3.103,1.764,2.875,2.563,2.015,3.561,1.914,2.058,2.226,2.781,2.93,2.423,2.676,1.709,2.213,2.2,2.513,2.499,2.267,2.631,2.401,1.924,2.191,2.346,2.39,2.373,2.113,2.229,2.224,2.215,2.658,2.415,2.475,2.359,2.352,2.553,3.229,0.789,0.669,0.722,0.521,0.516,0.527,0.569,0.526,0.544,0.454,0.849,0.523,0.554,0.457,0.53,0.576,0.624,0.652,0.657,0.669,0.858,0.516,0.531,0.743,0.529,0.513,0.5,0.555,0.514,0.616,0.639,1.037,0.407,0.696,0.888,0.497,0.748,0.655,0.292,0.662,0.634,0.659,0.808,0.723,0.638,0.536,0.513,0.638,0.537,0.707,0.34,0.537,0.512,0.546,0.429,0.459,0.526,0.492,0.352,0.448,0.631,0.703,0.543,1.058,0.584,0.699,0.459,0.437,0.478,0.683,0.549,0.5,0.565,0.537
-4023,Not Epileptic,F,42.0,0.7,2.6,1.0,0.9,2.3,1.5,1.4,1.5,1.8,0.6,0.4,3.1,1.2,0.6,1.1,4.9,8.0,0.8,1.7,4.2,1.0,2.6,1.3,4.4,2.8,3.0,3.3,1.9,2.6,2.6,1.3,1.4,0.7,2.4,0.3,0.5,3.5,3.4,0.1,0.2,1.3,3.1,2.3,2.7,3.4,0.7,0.5,0.9,1.3,0.7,0.7,1.3,1.4,1.8,0.4,2.4,1.0,1.1,1.1,0.8,0.4,1.7,0.5,0.5,1.8,0.9,2.1,0.8,0.9,0.5,1.1,1.3,3.4,0.4,5,37,8,8,19,18,13,16,15,8,4.0,31,13,8,11,56,73,7,21,44,9,30,16,49,36,36,39,23,23,33,17,14,6,31,2,5,39,42,1,1,10,31,23,21,19,5,3,6,7,8,4,14,15,13,2,23,9,10,8,6,4,16,4,6,14,11,17,5,6,7,8,10,26,3,0.039,0.03,0.021,0.019,0.032,0.023,0.022,0.027,0.043,0.025,0.036,0.034,0.025,0.028,0.029,0.03,0.028,0.029,0.025,0.039,0.018,0.03,0.025,0.041,0.034,0.026,0.03,0.025,0.024,0.025,0.035,0.056,0.021,0.027,0.013,0.012,0.037,0.036,0.014,0.014,0.024,0.034,0.037,0.032,0.025,0.016,0.022,0.017,0.019,0.022,0.031,0.02,0.024,0.018,0.024,0.018,0.023,0.015,0.024,0.018,0.012,0.027,0.027,0.016,0.02,0.03,0.019,0.014,0.018,0.018,0.028,0.023,0.018,0.015,1218,4379,1825,2105,2800,3141,3453,3024,2170,1457,606.0,3205,3495,1345,1909,9846,18234,2296,4231,4843,3451,4109,2231,6198,5409,6791,6024,4045,6640,6000,2704,1219,1226,6178,1770,1920,6398,7076,233,424,1756,3561,5629,2199,3606,1467,761,1994,2158,1232,563,2615,2229,4010,561,4059,1474,2564,1145,1350,866,2685,588,888,2778,751,3145,1880,1671,1015,1623,1803,7416,601,0.117,0.14,0.104,0.095,0.13,0.127,0.102,0.132,0.152,0.128,0.138,0.151,0.118,0.138,0.132,0.131,0.123,0.119,0.121,0.162,0.091,0.134,0.104,0.141,0.144,0.129,0.121,0.101,0.111,0.133,0.124,0.131,0.093,0.136,0.068,0.076,0.15,0.15,0.102,0.093,0.117,0.136,0.139,0.126,0.108,0.097,0.108,0.09,0.103,0.109,0.132,0.106,0.129,0.096,0.113,0.105,0.119,0.099,0.123,0.102,0.09,0.125,0.139,0.112,0.116,0.132,0.109,0.086,0.101,0.124,0.125,0.129,0.099,0.106,391,1552,726,793,1140,998,1054,943,702,361,190.0,1478,769,382,563,2687,4862,461,1116,1887,752,1321,853,1827,1504,1960,1855,1264,1577,1635,722,506,431,1433,477,747,1726,1699,131,221,869,1435,1131,1403,2078,707,357,891,984,534,347,1191,1013,1663,260,2073,683,1168,690,740,491,1083,283,511,1329,464,1608,862,794,455,739,833,3126,382,3.256,2.657,2.611,2.58,2.169,2.437,2.62,2.817,2.485,3.012,2.71,1.836,3.078,2.686,2.734,2.676,2.938,3.705,2.853,2.17,3.16,2.397,2.171,2.58,2.79,2.665,2.402,2.393,3.057,2.647,2.68,2.426,2.227,3.051,3.302,2.436,2.804,3.006,1.989,2.29,2.542,2.028,3.32,1.783,1.966,2.243,2.819,2.783,2.729,2.773,2.159,2.278,2.144,2.467,2.531,2.076,2.386,2.353,1.915,1.966,2.0,2.577,2.238,2.086,2.283,1.981,2.16,2.494,2.515,2.474,2.362,2.572,2.506,1.967,0.691,0.51,0.479,0.476,0.653,0.604,0.725,0.45,0.668,0.442,0.726,0.504,0.546,0.461,0.551,0.665,0.647,0.653,0.572,0.772,0.8,0.589,0.593,0.79,0.566,0.596,0.706,0.593,0.556,0.672,0.739,0.955,0.42,0.6,0.674,0.384,0.646,0.653,0.297,0.321,0.455,0.561,0.802,0.533,0.632,0.397,0.408,0.602,0.415,0.47,0.416,0.42,0.536,0.461,0.544,0.45,0.488,0.368,0.347,0.382,0.332,0.615,0.551,0.521,0.478,0.546,0.484,0.42,0.468,0.693,0.487,0.465,0.474,0.277
-4024,Not Epileptic,M,32.0,1.1,2.8,1.1,1.4,2.2,2.2,2.9,1.6,1.0,0.9,0.4,3.3,1.5,0.8,1.2,6.0,8.7,0.8,2.4,4.6,1.3,3.1,2.4,4.9,3.8,3.3,3.9,3.4,4.9,4.3,2.8,1.5,0.7,2.4,0.5,0.6,3.4,3.4,0.2,0.4,1.1,3.4,2.8,2.6,3.8,1.1,0.6,1.0,1.5,0.7,0.9,2.1,1.8,2.5,0.4,2.5,0.8,1.7,1.3,1.2,1.0,2.5,0.6,0.9,2.1,0.9,2.4,1.7,1.3,0.7,1.3,1.3,4.7,0.4,8,25,11,12,22,21,28,17,10,9,4.0,30,17,9,14,67,81,8,32,46,10,41,24,47,51,39,42,28,36,51,27,14,6,27,2,3,38,42,2,2,6,31,27,19,20,7,3,6,6,6,5,20,15,21,2,21,8,12,9,9,6,20,4,8,17,15,20,13,9,8,10,11,36,3,0.039,0.029,0.024,0.025,0.047,0.031,0.041,0.029,0.039,0.036,0.03,0.037,0.026,0.033,0.029,0.036,0.032,0.035,0.033,0.038,0.025,0.031,0.033,0.042,0.042,0.029,0.032,0.033,0.042,0.034,0.046,0.049,0.03,0.028,0.011,0.015,0.035,0.032,0.013,0.021,0.023,0.033,0.047,0.027,0.027,0.021,0.021,0.019,0.019,0.02,0.033,0.024,0.027,0.02,0.022,0.018,0.022,0.018,0.025,0.019,0.028,0.031,0.028,0.018,0.022,0.018,0.021,0.021,0.02,0.019,0.021,0.016,0.018,0.018,1780,4071,2141,2678,1983,3372,3370,3468,1455,2018,807.0,3314,3863,1779,2828,10905,17902,2358,4844,5435,3460,5409,2608,6620,6478,7816,6150,4500,7007,7753,2338,1491,1358,7339,2451,1957,7400,8351,458,621,1448,2846,5238,2628,3634,1514,1089,2020,2192,1988,762,3594,2047,5418,425,3936,1375,3276,1426,1906,1066,3917,678,1390,3122,1329,3737,2796,1890,1311,2042,2627,9028,566,0.131,0.128,0.122,0.121,0.144,0.147,0.127,0.138,0.137,0.151,0.141,0.143,0.121,0.147,0.134,0.14,0.133,0.137,0.146,0.158,0.094,0.139,0.129,0.142,0.149,0.132,0.125,0.113,0.123,0.149,0.14,0.135,0.11,0.129,0.062,0.081,0.134,0.141,0.093,0.104,0.105,0.134,0.15,0.12,0.111,0.109,0.113,0.094,0.101,0.108,0.139,0.107,0.12,0.103,0.12,0.112,0.108,0.101,0.121,0.102,0.124,0.129,0.127,0.104,0.113,0.105,0.111,0.109,0.109,0.122,0.119,0.1,0.101,0.11,493,1362,784,899,911,1144,1237,1022,461,450,217.0,1581,985,429,763,3178,4851,464,1308,2248,799,1585,1212,2090,1886,2105,2110,1549,1843,2307,913,597,450,1477,659,669,1898,1966,252,279,772,1579,1186,1657,2229,765,424,865,1065,684,386,1656,1090,2211,237,2035,679,1545,829,1029,541,1465,361,780,1486,869,1869,1226,999,584,934,1295,3974,325,3.291,2.727,2.701,2.787,1.988,2.35,2.252,2.928,2.39,3.036,2.91,1.831,2.953,2.919,2.765,2.573,2.837,3.683,2.836,2.074,2.991,2.554,1.917,2.339,2.662,2.867,2.353,2.263,2.779,2.566,2.069,2.485,2.434,3.278,3.299,2.59,2.849,3.009,2.113,2.54,2.462,1.635,3.234,1.843,1.853,2.167,2.867,2.851,2.552,2.69,2.133,2.192,1.985,2.407,2.123,2.158,2.205,2.393,1.947,1.956,2.196,2.599,2.057,2.179,2.245,1.868,2.162,2.546,2.278,2.414,2.2,2.404,2.424,2.129,0.757,0.561,0.495,0.624,0.554,0.691,0.532,0.535,0.788,0.788,0.751,0.599,0.49,0.463,0.505,0.667,0.644,0.752,0.574,0.704,0.819,0.527,0.617,0.793,0.614,0.594,0.628,0.659,0.662,0.652,0.657,0.831,0.411,0.744,0.83,0.431,0.834,0.657,0.284,0.383,0.455,0.464,0.737,0.715,0.588,0.459,0.68,0.517,0.493,0.661,0.408,0.467,0.426,0.455,0.499,0.456,0.47,0.476,0.433,0.435,0.466,0.681,0.377,0.66,0.508,0.663,0.478,0.434,0.492,0.589,0.479,0.486,0.438,0.246
-4025,Not Epileptic,F,55.0,1.2,3.2,1.4,1.4,2.8,1.6,1.7,1.9,2.2,1.0,0.3,2.9,2.2,0.7,1.2,5.9,12.1,1.8,2.9,4.0,0.9,2.9,2.3,4.2,3.0,4.2,4.6,2.4,4.7,2.9,1.9,1.4,0.5,2.4,0.7,0.4,4.5,4.1,0.2,0.2,1.1,3.1,3.9,2.4,3.2,0.6,0.6,0.9,1.5,1.0,0.5,1.6,1.1,3.2,0.3,3.1,0.5,2.0,1.1,1.0,0.3,2.1,0.4,0.8,1.8,1.6,3.2,1.4,1.5,0.7,0.7,2.1,4.5,0.4,7,30,13,12,16,16,12,16,15,12,2.0,22,16,6,11,63,96,11,28,35,7,29,18,35,34,37,46,23,32,32,19,9,5,27,3,2,38,47,1,1,5,25,27,17,14,3,2,5,7,9,4,8,7,16,1,18,5,14,8,8,3,10,2,6,11,14,18,8,6,5,4,15,32,3,0.055,0.034,0.024,0.026,0.052,0.027,0.033,0.037,0.054,0.055,0.022,0.032,0.044,0.074,0.042,0.044,0.045,0.063,0.044,0.036,0.021,0.037,0.033,0.06,0.042,0.039,0.042,0.032,0.05,0.041,0.045,0.045,0.028,0.038,0.026,0.013,0.052,0.05,0.021,0.022,0.026,0.039,0.059,0.029,0.025,0.012,0.031,0.017,0.023,0.023,0.039,0.018,0.023,0.029,0.02,0.02,0.02,0.022,0.028,0.019,0.02,0.036,0.037,0.027,0.025,0.025,0.024,0.024,0.027,0.025,0.025,0.026,0.021,0.026,1479,4173,2296,2310,1475,2805,2287,3149,1274,1275,647.0,2729,3142,793,1952,8657,14599,2502,4214,4368,2757,3921,2065,4105,5434,6514,5612,2901,4667,4390,2318,1325,845,5948,1812,1361,5929,7673,239,405,1163,2330,5648,2132,2808,1275,602,2249,1877,1510,425,2705,1384,3701,379,3922,1525,3349,906,1586,625,2051,331,875,2134,2010,3794,1999,1391,622,1044,2827,7937,406,0.148,0.137,0.117,0.113,0.148,0.124,0.106,0.137,0.158,0.174,0.116,0.126,0.14,0.191,0.137,0.147,0.149,0.162,0.154,0.141,0.09,0.129,0.119,0.158,0.141,0.149,0.143,0.112,0.14,0.15,0.143,0.121,0.101,0.136,0.102,0.071,0.152,0.167,0.118,0.116,0.106,0.14,0.177,0.124,0.112,0.082,0.12,0.084,0.105,0.11,0.153,0.093,0.107,0.117,0.111,0.108,0.105,0.116,0.136,0.104,0.101,0.131,0.144,0.121,0.12,0.11,0.106,0.11,0.109,0.124,0.113,0.128,0.104,0.136,387,1687,968,902,727,974,848,926,622,371,187.0,1338,854,236,529,2569,4773,473,1138,1797,721,1222,944,1399,1476,1901,1897,1111,1419,1405,724,580,281,1182,440,498,1534,1646,145,177,623,1211,1120,1400,1861,693,275,898,940,620,241,1272,738,1695,208,2093,485,1468,579,848,304,843,160,489,1057,958,2023,917,736,403,504,1237,3404,253,3.254,2.397,2.408,2.405,1.984,2.409,2.212,2.788,1.865,2.687,3.037,1.798,2.856,2.497,2.66,2.518,2.487,3.428,2.82,2.095,2.848,2.522,1.952,2.323,2.606,2.66,2.247,2.075,2.593,2.383,2.379,2.353,2.29,3.102,3.366,2.463,2.644,3.109,1.867,2.209,2.332,1.733,3.253,1.671,1.733,1.985,2.588,2.782,2.316,2.629,2.027,2.265,1.977,2.167,2.055,2.046,2.563,2.386,1.852,1.968,2.395,2.662,2.054,2.079,2.22,2.488,1.928,2.375,2.084,1.792,2.15,2.401,2.415,2.053,0.662,0.63,0.476,0.468,0.545,0.62,0.624,0.679,0.5,0.591,0.879,0.495,0.542,0.522,0.589,0.602,0.634,0.886,0.616,0.621,0.761,0.603,0.477,0.746,0.607,0.702,0.574,0.523,0.593,0.71,0.673,0.96,0.381,0.667,0.773,0.459,0.797,0.729,0.29,0.56,0.5,0.498,0.825,0.654,0.476,0.504,0.348,0.886,0.517,0.521,0.419,0.537,0.477,0.504,0.329,0.471,0.603,0.572,0.389,0.481,0.488,0.732,0.354,0.696,0.552,0.965,0.498,0.585,0.489,0.511,0.613,0.524,0.504,0.472
-4026,Not Epileptic,F,22.0,0.7,2.6,0.9,1.2,1.9,1.9,1.8,1.2,1.4,0.9,0.4,3.2,1.6,0.7,1.4,6.2,8.2,0.7,1.9,4.7,1.6,3.1,1.7,3.7,3.4,3.0,6.0,2.9,2.6,3.1,3.2,0.9,0.4,3.1,1.0,0.6,3.6,3.4,0.1,0.2,0.9,3.0,2.0,3.3,2.5,0.5,0.3,0.6,1.2,0.8,0.5,2.0,1.4,2.0,0.2,3.4,0.8,1.3,0.7,1.3,0.8,1.8,0.2,0.5,1.9,1.4,1.8,1.2,1.3,0.5,0.9,1.6,6.3,0.3,7,23,7,10,17,20,16,14,15,11,5.0,31,19,8,22,83,86,5,25,47,13,49,24,37,44,37,58,33,26,44,29,7,6,32,10,4,52,45,1,2,5,30,24,26,16,5,2,4,6,8,5,16,9,16,1,27,7,9,7,14,6,17,2,6,19,20,15,11,10,4,7,12,54,3,0.041,0.027,0.016,0.019,0.036,0.028,0.032,0.022,0.044,0.04,0.03,0.042,0.033,0.037,0.031,0.033,0.03,0.028,0.03,0.037,0.031,0.031,0.028,0.038,0.032,0.025,0.045,0.029,0.025,0.03,0.05,0.044,0.022,0.038,0.027,0.013,0.034,0.031,0.01,0.016,0.019,0.038,0.033,0.027,0.018,0.012,0.014,0.011,0.018,0.022,0.019,0.018,0.024,0.019,0.016,0.022,0.021,0.015,0.019,0.023,0.023,0.026,0.015,0.016,0.021,0.022,0.017,0.019,0.02,0.012,0.024,0.018,0.019,0.014,1584,5217,2313,2867,2390,4021,4022,3556,1902,1759,968.0,3168,4236,1123,3711,13891,21478,2465,4053,5784,4361,6299,2937,7037,7421,8378,6883,4701,7853,7164,3065,1437,1222,7276,1967,2227,9211,9838,457,501,1597,2846,7151,3032,3803,1538,747,1999,2405,1762,536,3991,1790,4538,476,4909,1439,3350,1362,1998,1135,3282,328,1163,3404,1364,3302,2496,2653,1237,1474,3112,13202,567,0.125,0.126,0.104,0.106,0.142,0.128,0.112,0.111,0.154,0.162,0.133,0.164,0.144,0.146,0.137,0.15,0.137,0.109,0.139,0.151,0.117,0.141,0.131,0.144,0.133,0.131,0.142,0.116,0.106,0.14,0.158,0.118,0.116,0.152,0.104,0.082,0.145,0.137,0.072,0.1,0.099,0.141,0.135,0.129,0.096,0.093,0.089,0.08,0.093,0.114,0.132,0.105,0.123,0.099,0.106,0.114,0.107,0.091,0.111,0.119,0.126,0.125,0.109,0.092,0.111,0.13,0.099,0.104,0.103,0.095,0.123,0.109,0.102,0.111,419,1594,844,962,922,1235,1164,1018,573,412,246.0,1526,955,315,966,3544,5021,432,1145,2359,833,1847,1091,1903,1958,2054,2408,1550,1777,2091,986,405,341,1471,491,726,2129,2103,247,236,732,1409,1350,1855,2148,661,308,813,1029,663,338,1763,870,1853,195,2359,659,1410,718,1011,535,1147,176,584,1547,855,1590,1056,1078,563,670,1257,5141,291,3.5,2.869,2.719,2.831,2.283,2.611,2.748,2.955,2.393,3.213,2.957,1.933,3.219,2.595,2.799,2.851,3.14,3.894,2.875,2.11,3.498,2.563,2.182,2.765,2.773,3.013,2.315,2.333,3.098,2.712,2.266,3.126,2.694,3.284,3.338,2.764,3.139,3.262,2.133,2.422,2.751,1.866,3.627,1.921,2.035,2.598,2.923,3.2,2.853,2.822,2.034,2.38,2.106,2.601,2.75,2.29,2.464,2.593,2.096,2.24,2.563,2.632,1.869,2.207,2.448,1.954,2.39,2.717,2.6,2.182,2.585,2.655,2.592,2.327,0.769,0.699,0.432,0.514,0.727,0.588,0.652,0.516,0.684,0.629,0.931,0.481,0.463,0.646,0.585,0.658,0.657,0.814,0.61,0.665,0.812,0.616,0.591,0.861,0.701,0.543,0.65,0.668,0.579,0.563,0.785,0.796,0.492,0.646,0.646,0.438,0.774,0.785,0.363,0.409,0.418,0.554,0.633,0.639,0.585,0.545,0.378,0.665,0.49,0.445,0.366,0.494,0.454,0.437,0.572,0.398,0.318,0.488,0.406,0.335,0.412,0.748,0.463,0.565,0.456,0.587,0.464,0.393,0.369,0.703,0.413,0.527,0.482,0.303
-4027,Not Epileptic,F,32.0,0.9,1.6,0.8,1.3,2.7,1.7,1.7,1.2,1.2,0.7,0.2,3.3,1.3,0.3,1.0,5.5,9.0,1.1,2.0,5.5,1.1,2.6,1.9,3.2,2.8,2.9,3.0,2.7,2.5,3.3,1.8,2.0,0.6,2.3,0.8,0.7,3.7,3.7,0.1,0.2,1.0,3.0,2.6,2.9,2.7,0.6,0.5,0.8,1.2,0.7,0.9,1.7,1.9,2.4,0.2,2.2,0.8,1.1,1.4,1.4,0.9,1.6,0.2,0.4,2.2,1.1,3.0,1.0,0.8,0.4,0.9,1.4,3.6,0.1,9,14,6,15,24,16,14,13,14,6,2.0,29,13,4,9,71,94,8,29,48,12,38,26,41,40,35,37,33,25,36,21,15,5,31,3,5,49,42,1,2,5,27,28,22,17,5,3,4,7,5,5,14,15,20,2,19,6,10,9,10,7,16,2,4,18,18,25,10,5,4,6,11,27,1,0.043,0.019,0.013,0.022,0.039,0.028,0.026,0.021,0.039,0.034,0.025,0.036,0.028,0.023,0.027,0.032,0.031,0.037,0.028,0.038,0.023,0.031,0.028,0.038,0.029,0.033,0.027,0.031,0.025,0.034,0.045,0.057,0.029,0.026,0.024,0.019,0.036,0.03,0.019,0.014,0.023,0.03,0.039,0.028,0.019,0.016,0.016,0.012,0.02,0.017,0.028,0.019,0.022,0.019,0.017,0.018,0.019,0.015,0.026,0.022,0.022,0.03,0.02,0.013,0.025,0.022,0.02,0.014,0.015,0.013,0.023,0.015,0.013,0.012,1482,4133,2115,2675,3012,3016,3096,3267,1855,1343,681.0,3452,3039,932,2393,11322,19066,2755,4683,6055,3536,5541,2699,6767,6999,6331,6064,4257,6516,5643,2296,1228,1213,6718,1764,1285,7292,8808,258,411,1284,2129,6399,2807,3872,1293,878,2345,2099,1454,730,3036,2619,4899,321,3778,1340,2818,1515,1959,1392,2264,372,957,3065,1112,4538,2576,1607,781,1216,2376,9254,507,0.135,0.107,0.095,0.115,0.147,0.125,0.113,0.118,0.142,0.143,0.102,0.145,0.122,0.11,0.119,0.14,0.135,0.141,0.135,0.148,0.106,0.123,0.121,0.148,0.134,0.139,0.128,0.112,0.105,0.146,0.155,0.145,0.113,0.127,0.092,0.099,0.142,0.138,0.107,0.122,0.11,0.13,0.16,0.129,0.099,0.093,0.095,0.082,0.102,0.103,0.132,0.107,0.123,0.104,0.125,0.109,0.104,0.097,0.117,0.112,0.109,0.132,0.118,0.095,0.124,0.12,0.109,0.091,0.091,0.101,0.12,0.102,0.086,0.085,417,1346,797,916,1158,1029,986,895,575,333,170.0,1632,800,226,615,3265,5060,535,1266,2508,800,1564,1140,1721,1862,1760,1962,1509,1665,1654,731,550,317,1450,437,517,1891,2130,111,196,648,1405,1307,1687,2171,650,412,929,961,642,447,1442,1180,2070,168,1818,662,1309,864,974,595,939,186,521,1387,813,2325,1135,730,397,597,1221,3967,211,3.376,2.961,2.771,2.659,2.198,2.536,2.554,2.906,2.498,2.992,3.015,1.938,2.767,2.973,2.793,2.609,2.912,3.728,2.846,2.087,3.194,2.581,1.945,2.855,2.831,2.9,2.454,2.247,2.899,2.677,2.433,2.373,2.989,3.176,3.445,2.433,2.85,2.923,2.412,2.407,2.567,1.451,3.31,1.937,2.02,2.306,2.65,3.032,2.69,2.62,2.143,2.268,2.21,2.443,2.282,2.215,2.398,2.469,2.001,2.222,2.425,2.277,2.257,2.111,2.292,1.726,2.135,2.5,2.446,2.31,2.235,2.256,2.438,2.685,0.857,0.59,0.375,0.537,0.655,0.611,0.516,0.499,0.535,0.717,0.744,0.526,0.501,0.437,0.512,0.577,0.583,0.755,0.536,0.621,0.833,0.657,0.64,0.745,0.627,0.582,0.564,0.603,0.557,0.568,0.579,0.965,0.477,0.709,0.684,0.407,0.711,0.639,0.467,0.401,0.593,0.498,0.782,0.653,0.62,0.462,0.575,0.83,0.529,0.471,0.509,0.399,0.462,0.439,0.309,0.367,0.36,0.488,0.419,0.352,0.428,0.692,0.383,0.664,0.559,0.557,0.412,0.377,0.406,0.531,0.476,0.422,0.45,0.495
-4028,Not Epileptic,M,42.0,0.8,2.3,0.9,1.8,1.4,2.9,1.3,2.0,0.8,0.8,0.4,2.6,1.8,0.6,1.4,6.3,8.4,1.4,2.5,5.1,1.4,3.3,2.8,5.4,3.1,5.1,4.8,3.6,2.8,4.2,1.5,1.0,0.7,2.7,0.4,1.0,3.3,4.0,0.1,0.2,1.3,3.6,2.5,2.7,2.5,0.5,0.6,1.1,1.6,0.6,0.7,1.3,1.6,2.9,0.7,2.2,1.0,1.4,1.0,1.0,0.5,2.5,0.4,0.4,1.7,1.8,2.8,1.0,1.2,1.5,2.0,1.4,5.7,0.5,8,24,8,15,13,27,11,22,16,9,4.0,26,20,11,17,75,76,20,29,47,12,39,26,80,41,53,51,34,23,49,19,7,6,32,2,9,40,49,1,2,9,32,25,19,15,4,3,6,9,5,5,10,12,22,4,17,10,12,7,9,4,18,3,5,15,20,22,7,8,17,15,10,52,4,0.028,0.021,0.016,0.027,0.023,0.032,0.027,0.027,0.027,0.036,0.029,0.03,0.029,0.029,0.026,0.028,0.03,0.041,0.026,0.038,0.023,0.029,0.037,0.053,0.031,0.029,0.032,0.028,0.025,0.028,0.035,0.041,0.024,0.024,0.011,0.015,0.032,0.034,0.008,0.014,0.024,0.036,0.031,0.019,0.018,0.011,0.02,0.015,0.02,0.013,0.024,0.016,0.021,0.02,0.018,0.012,0.023,0.014,0.019,0.017,0.013,0.03,0.017,0.016,0.02,0.022,0.019,0.015,0.019,0.026,0.025,0.017,0.018,0.021,1779,5500,2530,3267,2302,4935,3175,4279,2023,2125,899.0,3086,3969,1462,2719,13328,19507,3115,6100,6709,4873,5820,2647,7605,6007,11529,8245,6986,6206,8390,2774,929,1447,8241,2153,3503,7221,9268,485,417,1753,3260,6806,3560,4097,1367,1011,2847,2688,1939,871,2771,2280,6008,1127,5318,1549,3740,1368,2102,1090,2956,547,930,3027,1906,5084,2301,2265,2029,2637,2346,11954,769,0.112,0.115,0.097,0.123,0.109,0.136,0.105,0.121,0.127,0.13,0.118,0.127,0.129,0.155,0.138,0.132,0.127,0.149,0.121,0.145,0.096,0.127,0.128,0.149,0.122,0.129,0.118,0.113,0.107,0.131,0.133,0.118,0.107,0.118,0.069,0.083,0.142,0.139,0.081,0.097,0.112,0.123,0.139,0.107,0.096,0.088,0.101,0.081,0.104,0.088,0.12,0.098,0.113,0.101,0.1,0.087,0.124,0.09,0.106,0.1,0.089,0.129,0.113,0.107,0.108,0.113,0.106,0.093,0.1,0.145,0.126,0.109,0.1,0.117,529,1889,876,1055,1066,1515,887,1228,559,463,219.0,1313,1060,431,806,3923,4932,721,1585,2385,1064,1684,1048,2033,1718,2872,2348,1934,1637,2502,774,425,438,1797,571,1016,1770,2057,229,242,851,1555,1432,1842,2255,631,441,1154,1225,771,455,1298,1117,2399,525,2615,704,1599,780,992,540,1264,327,493,1496,1210,2273,1009,1036,831,1129,1098,4882,411,3.438,2.731,2.813,2.843,2.075,2.612,2.812,2.926,2.657,3.52,3.301,2.035,2.817,2.511,2.572,2.643,3.035,3.315,2.984,2.317,3.427,2.649,2.263,2.792,2.636,3.077,2.632,2.654,2.886,2.62,2.64,2.267,2.523,3.215,3.37,3.088,2.954,3.183,2.246,1.891,2.714,1.913,3.296,2.282,2.106,2.242,2.924,3.095,2.773,2.8,2.192,2.305,2.158,2.562,2.427,2.333,2.488,2.551,2.03,2.291,2.345,2.313,1.894,2.147,2.204,1.825,2.457,2.616,2.536,2.492,2.46,2.496,2.606,2.336,0.716,0.5,0.539,0.648,0.461,0.653,0.76,0.524,0.441,0.965,0.804,0.586,0.422,0.609,0.459,0.56,0.64,0.735,0.513,0.655,0.777,0.546,0.598,0.764,0.607,0.654,0.639,0.716,0.518,0.657,0.607,0.852,0.606,0.595,0.646,0.507,0.792,0.647,0.385,0.39,0.383,0.573,0.808,0.67,0.599,0.518,0.463,0.578,0.418,0.45,0.326,0.419,0.425,0.438,0.346,0.42,0.34,0.394,0.341,0.409,0.349,0.663,0.463,0.759,0.519,0.719,0.431,0.401,0.376,0.864,0.472,0.603,0.466,0.447
-4029,Not Epileptic,M,55.0,1.1,2.2,1.1,1.3,2.2,1.6,1.5,1.8,1.4,1.0,0.7,3.0,1.6,0.5,1.5,4.4,9.4,2.2,2.0,5.5,1.2,3.1,1.4,4.2,2.9,2.5,4.3,3.2,3.0,3.1,2.3,1.3,0.5,2.7,0.7,0.6,2.9,2.5,0.2,0.3,0.9,3.4,2.2,3.5,2.9,0.4,0.6,1.2,1.4,0.7,0.5,2.2,1.1,2.2,0.4,2.0,0.3,1.2,1.3,1.0,0.6,1.4,0.3,0.6,1.6,1.3,2.8,1.2,1.0,0.5,0.8,1.3,4.3,0.3,11,23,9,18,15,15,12,16,14,10,9.0,33,17,4,19,48,83,16,37,52,10,43,13,43,37,26,39,26,27,37,22,10,6,26,6,5,35,33,2,2,5,29,19,26,14,3,4,7,7,6,4,16,6,17,2,17,3,11,10,8,6,11,3,6,14,18,27,9,6,4,5,10,39,4,0.057,0.022,0.025,0.025,0.033,0.023,0.025,0.027,0.049,0.033,0.038,0.035,0.025,0.032,0.031,0.03,0.034,0.056,0.028,0.037,0.022,0.031,0.024,0.035,0.029,0.026,0.033,0.031,0.026,0.027,0.046,0.047,0.026,0.027,0.026,0.016,0.029,0.029,0.016,0.025,0.021,0.03,0.033,0.027,0.021,0.01,0.021,0.016,0.021,0.015,0.018,0.024,0.021,0.02,0.037,0.015,0.015,0.016,0.021,0.015,0.023,0.024,0.013,0.018,0.021,0.024,0.022,0.016,0.018,0.018,0.019,0.019,0.017,0.019,1391,5055,2140,2551,2437,3474,2811,3449,1794,1818,834.0,3233,3582,889,3194,9163,16831,2964,3766,5933,3969,5859,1808,6492,6035,5342,5354,3883,6235,6934,2758,1052,1089,6569,1726,1675,6504,7262,441,428,1187,3077,5828,3549,3894,1312,844,2526,2092,1643,724,2991,1545,4178,399,3911,955,2708,1758,1857,780,2759,586,1062,2289,1314,4099,2413,2053,1079,1398,2220,9327,496,0.157,0.117,0.11,0.119,0.125,0.114,0.092,0.129,0.162,0.149,0.152,0.143,0.125,0.137,0.138,0.13,0.13,0.171,0.138,0.148,0.099,0.123,0.109,0.131,0.124,0.122,0.113,0.106,0.104,0.127,0.147,0.123,0.106,0.131,0.093,0.088,0.128,0.138,0.097,0.108,0.1,0.12,0.137,0.118,0.096,0.075,0.109,0.085,0.098,0.095,0.108,0.116,0.115,0.099,0.13,0.095,0.09,0.107,0.113,0.097,0.116,0.111,0.102,0.108,0.117,0.114,0.112,0.095,0.092,0.108,0.107,0.099,0.094,0.113,454,1642,748,902,939,1190,930,1088,495,465,272.0,1568,1008,245,889,2578,4621,656,1152,2630,907,1839,831,1905,1748,1565,1831,1457,1645,2094,856,532,351,1504,493,588,1717,1679,224,215,670,1636,1234,2049,2119,669,415,1068,1002,772,436,1472,730,1865,177,1997,394,1207,982,969,447,1028,350,582,1184,872,2052,1152,898,478,663,1126,4136,264,2.724,2.875,2.68,2.559,2.331,2.429,2.479,2.696,2.671,3.028,2.554,1.827,2.805,2.683,2.773,2.689,2.923,3.478,2.642,1.957,3.209,2.445,1.929,2.611,2.635,2.729,2.389,2.152,2.876,2.534,2.503,2.125,2.499,3.09,3.377,2.523,2.828,3.07,2.107,2.508,2.292,1.743,3.448,1.937,2.098,2.188,2.514,2.757,2.604,2.391,1.928,2.167,2.394,2.418,2.364,2.177,2.36,2.431,2.109,2.112,1.913,2.715,1.985,2.118,2.17,1.862,2.106,2.35,2.535,2.507,2.345,2.323,2.367,2.232,0.773,0.471,0.497,0.564,0.607,0.61,0.646,0.442,0.681,0.594,0.706,0.474,0.475,0.44,0.472,0.548,0.589,0.811,0.465,0.593,0.842,0.568,0.482,0.699,0.536,0.45,0.542,0.55,0.531,0.584,0.764,0.692,0.348,0.583,0.663,0.449,0.644,0.616,0.321,0.392,0.374,0.447,0.709,0.539,0.656,0.361,0.472,0.678,0.37,0.407,0.422,0.403,0.525,0.386,0.471,0.379,0.45,0.497,0.36,0.365,0.382,0.56,0.391,0.581,0.426,0.54,0.437,0.317,0.398,0.655,0.471,0.42,0.424,0.432
-4030,Not Epileptic,F,55.0,0.8,2.5,1.4,1.3,1.6,1.6,1.4,1.5,1.4,1.0,0.2,4.0,1.4,0.6,1.9,4.9,7.9,0.7,2.1,5.5,1.4,3.4,2.2,4.4,2.0,3.2,3.8,2.8,3.4,3.6,3.6,1.6,0.8,2.2,0.5,0.3,3.6,3.5,0.3,0.2,1.0,4.2,2.2,3.0,2.6,0.6,0.4,0.7,1.5,0.7,0.4,2.3,1.4,2.1,0.0,2.6,0.7,2.0,1.5,1.0,0.8,1.9,0.4,1.0,1.9,1.3,2.4,1.3,0.9,0.6,0.8,1.3,4.1,0.4,6,22,12,12,15,23,15,16,15,9,4.0,37,16,6,21,58,71,4,22,52,16,37,19,41,30,35,49,31,27,55,27,10,7,26,2,2,39,41,2,2,6,40,22,29,17,4,2,4,8,7,3,18,8,12,0,23,8,19,12,8,9,14,3,6,17,19,17,8,4,6,7,10,31,3,0.038,0.029,0.025,0.024,0.034,0.029,0.029,0.028,0.059,0.031,0.024,0.043,0.029,0.032,0.039,0.033,0.035,0.034,0.029,0.039,0.029,0.034,0.034,0.044,0.027,0.035,0.029,0.032,0.034,0.033,0.069,0.052,0.036,0.027,0.013,0.01,0.038,0.035,0.019,0.02,0.024,0.041,0.038,0.024,0.02,0.012,0.018,0.015,0.022,0.012,0.018,0.022,0.024,0.023,0.012,0.02,0.028,0.023,0.026,0.022,0.03,0.031,0.024,0.03,0.021,0.026,0.022,0.018,0.015,0.022,0.024,0.019,0.019,0.021,1424,3987,2243,2325,1616,2927,2574,2651,1556,1725,592.0,3586,2963,1065,3319,10123,14688,1664,4215,6084,4208,4755,2080,5529,4943,5688,6861,3587,5789,6572,1919,1317,965,5048,1709,1285,5813,6542,514,465,1178,3146,5227,3550,3443,1325,730,1345,1938,1765,786,3417,1482,3262,64,4099,942,3658,1646,1424,1249,2591,436,829,2958,1747,3524,2313,1690,1088,1312,2121,8225,496,0.126,0.127,0.118,0.113,0.132,0.14,0.101,0.128,0.172,0.154,0.104,0.163,0.131,0.13,0.155,0.138,0.137,0.12,0.14,0.155,0.12,0.129,0.134,0.144,0.117,0.144,0.119,0.108,0.115,0.142,0.177,0.117,0.139,0.121,0.069,0.066,0.139,0.142,0.114,0.11,0.111,0.146,0.14,0.117,0.101,0.082,0.096,0.084,0.109,0.091,0.107,0.112,0.118,0.106,0.085,0.11,0.135,0.121,0.123,0.116,0.124,0.127,0.137,0.126,0.114,0.126,0.107,0.098,0.087,0.138,0.119,0.109,0.099,0.123,374,1432,870,870,779,930,849,877,474,431,203.0,1612,857,290,923,2708,3983,347,1147,2404,944,1668,924,1728,1489,1620,2403,1376,1503,2143,723,498,371,1222,485,490,1626,1779,259,228,648,1618,1177,2081,2024,689,331,689,985,819,325,1688,795,1448,24,2046,466,1511,938,732,504,1060,257,459,1519,922,1739,1028,836,449,614,1066,3455,245,3.31,2.569,2.529,2.53,1.883,2.568,2.454,2.621,2.446,2.971,2.635,1.957,2.621,2.621,2.69,2.687,2.808,3.502,2.818,2.133,3.289,2.283,2.051,2.513,2.64,2.808,2.297,2.07,2.86,2.392,2.014,2.613,2.388,2.853,3.224,2.444,2.716,2.732,2.205,2.262,2.287,1.749,3.165,1.882,1.943,2.108,2.708,2.293,2.374,2.486,2.282,2.178,2.054,2.422,2.247,2.199,2.232,2.627,2.002,2.068,2.299,2.566,1.932,2.124,2.1,2.253,2.164,2.579,2.271,2.528,2.44,2.289,2.451,2.43,0.993,0.484,0.483,0.462,0.56,0.651,0.573,0.489,0.578,0.605,0.761,0.549,0.454,0.398,0.464,0.639,0.627,0.772,0.62,0.672,0.804,0.55,0.489,0.818,0.52,0.495,0.606,0.628,0.603,0.57,0.761,0.845,0.518,0.636,1.008,0.512,0.732,0.651,0.39,0.409,0.501,0.56,0.65,0.536,0.634,0.362,0.461,0.658,0.492,0.458,0.529,0.462,0.417,0.421,0.183,0.342,0.36,0.608,0.439,0.371,0.424,0.668,0.476,0.671,0.46,0.789,0.448,0.382,0.403,0.85,0.392,0.48,0.5,0.35
-4031,Not Epileptic,M,42.0,1.0,2.5,1.0,1.0,1.4,1.7,1.8,2.3,1.0,0.7,0.5,3.0,2.1,0.7,1.4,6.0,11.0,1.1,2.8,5.7,1.2,2.9,2.0,3.7,2.3,3.2,3.6,3.7,3.2,3.7,1.9,0.7,0.6,2.4,0.6,0.9,3.4,3.5,0.2,0.2,1.3,4.0,2.5,4.2,3.5,0.7,0.6,0.9,1.6,0.7,1.0,2.4,1.3,2.7,0.4,2.6,1.6,3.0,0.9,1.0,0.8,1.8,0.4,0.5,2.4,1.1,3.4,1.3,1.5,0.9,1.1,1.5,4.1,0.5,7,27,8,7,16,14,17,20,11,6,5.0,28,25,10,14,67,103,7,24,43,12,30,22,42,25,36,42,32,25,41,22,5,5,27,4,8,42,33,2,2,7,27,23,29,24,6,3,6,9,5,5,17,11,17,3,22,11,21,7,7,6,19,2,4,21,17,30,12,14,9,8,11,33,4,0.036,0.023,0.015,0.019,0.027,0.029,0.025,0.033,0.049,0.029,0.039,0.038,0.029,0.032,0.032,0.034,0.032,0.032,0.032,0.037,0.026,0.036,0.028,0.039,0.032,0.027,0.026,0.03,0.023,0.03,0.046,0.035,0.029,0.031,0.017,0.019,0.035,0.032,0.009,0.014,0.026,0.036,0.033,0.028,0.023,0.014,0.02,0.015,0.018,0.013,0.053,0.022,0.033,0.02,0.018,0.02,0.024,0.032,0.022,0.017,0.025,0.029,0.029,0.02,0.026,0.028,0.02,0.017,0.022,0.023,0.025,0.024,0.018,0.029,1415,4923,1970,2159,2251,2666,2557,3298,1717,1499,608.0,2955,4283,1450,2891,10116,18921,2787,5011,5107,3398,4160,2479,6635,4435,6666,6047,4250,5528,6123,2670,1073,1020,6261,2048,2276,7531,6630,422,594,1551,3463,5687,3346,3523,1491,1031,2507,2619,1553,535,3432,1584,4221,471,3645,2313,3859,1094,1746,1222,2577,379,814,2804,1343,4861,2756,2118,1615,1613,2253,8060,440,0.123,0.123,0.099,0.094,0.124,0.13,0.105,0.145,0.164,0.123,0.129,0.15,0.14,0.147,0.128,0.142,0.137,0.114,0.13,0.145,0.1,0.15,0.137,0.151,0.13,0.119,0.126,0.107,0.096,0.127,0.145,0.101,0.117,0.132,0.092,0.098,0.13,0.122,0.089,0.082,0.118,0.142,0.139,0.125,0.111,0.096,0.11,0.089,0.101,0.092,0.17,0.118,0.132,0.102,0.12,0.111,0.115,0.135,0.117,0.103,0.118,0.135,0.132,0.11,0.127,0.132,0.108,0.101,0.118,0.115,0.125,0.124,0.1,0.149,421,1732,875,837,909,1052,1053,1173,423,377,226.0,1330,1264,391,794,3101,5881,508,1406,2454,899,1324,1097,1808,1254,2059,2302,1800,1811,2188,697,440,359,1375,534,780,1851,1753,255,309,733,1592,1243,2201,2282,754,431,989,1195,705,306,1645,748,2007,274,1956,1003,1559,671,821,479,999,176,396,1491,674,2640,1252,1100,654,737,1049,3463,271,3.054,2.736,2.22,2.582,2.121,2.27,2.091,2.638,2.902,3.058,2.418,1.963,2.701,2.723,2.708,2.511,2.625,3.99,2.881,1.851,3.073,2.538,2.021,2.817,2.703,2.643,2.172,1.952,2.385,2.29,2.665,2.319,2.302,3.115,3.507,2.616,2.987,2.83,1.9,2.311,2.774,1.936,3.303,1.825,1.74,2.161,3.086,3.322,2.781,2.526,1.899,2.306,2.228,2.347,2.076,2.083,2.794,2.82,1.97,2.268,2.567,2.44,2.252,2.437,2.088,2.337,1.997,2.334,2.105,2.698,2.425,2.603,2.457,2.14,0.937,0.545,0.452,0.502,0.581,0.645,0.439,0.471,0.623,0.433,0.755,0.542,0.511,0.479,0.493,0.561,0.546,0.84,0.602,0.632,0.741,0.524,0.564,0.561,0.602,0.556,0.503,0.523,0.457,0.567,0.733,0.983,0.509,0.647,0.817,0.532,0.697,0.668,0.284,0.289,0.563,0.618,0.708,0.726,0.477,0.38,0.523,0.799,0.507,0.356,0.398,0.508,0.459,0.423,0.322,0.414,0.545,0.719,0.406,0.359,0.458,0.71,0.332,0.845,0.437,0.693,0.417,0.437,0.517,0.725,0.522,0.521,0.487,0.508
-4032,Not Epileptic,M,27.0,0.7,2.7,0.9,1.5,1.3,1.7,2.3,1.7,1.0,0.6,0.4,3.2,2.0,0.4,1.6,9.6,10.6,1.0,2.4,6.7,1.1,2.7,1.7,2.6,3.8,3.4,2.7,2.5,3.6,2.6,1.2,0.7,0.6,2.4,0.5,1.0,3.2,3.4,0.2,0.3,1.0,3.6,2.3,3.0,3.7,0.7,0.4,1.8,1.3,0.7,0.5,1.9,1.1,3.0,0.1,2.5,0.7,1.6,1.3,1.0,0.7,1.7,0.4,0.3,2.1,1.1,2.0,2.7,1.2,0.8,1.0,1.3,4.7,0.5,10,21,9,14,13,17,16,16,11,8,5.0,30,21,6,15,94,89,10,30,48,18,33,18,29,39,39,31,24,27,31,17,5,4,26,2,7,38,42,1,2,5,31,25,21,22,5,2,9,6,5,4,16,8,17,1,67,5,12,8,8,7,11,3,4,16,11,15,16,7,7,7,11,39,5,0.038,0.027,0.013,0.023,0.032,0.028,0.03,0.028,0.052,0.031,0.026,0.03,0.033,0.021,0.026,0.041,0.031,0.029,0.034,0.046,0.024,0.027,0.028,0.027,0.032,0.034,0.027,0.026,0.031,0.026,0.033,0.043,0.025,0.026,0.017,0.032,0.038,0.03,0.012,0.018,0.021,0.034,0.037,0.025,0.024,0.011,0.022,0.026,0.017,0.011,0.023,0.019,0.023,0.019,0.012,0.019,0.014,0.018,0.021,0.018,0.015,0.026,0.026,0.017,0.022,0.027,0.018,0.023,0.018,0.021,0.021,0.017,0.016,0.028,1496,5466,2678,3111,2098,2541,3077,3496,1782,1433,920.0,3826,4035,1188,3510,15216,22648,2718,5504,5475,3637,5705,2314,5996,7158,6301,5432,4897,7434,5856,2646,835,1207,5786,1716,1487,6330,8149,286,640,1498,3737,5188,3469,4071,1842,817,2460,2432,1826,545,4030,2006,6033,322,4339,1620,3303,1674,1684,1666,2755,630,829,3258,1332,4007,3678,2464,1765,1690,2371,11396,488,0.119,0.114,0.086,0.107,0.134,0.14,0.12,0.13,0.157,0.131,0.123,0.14,0.14,0.115,0.13,0.154,0.126,0.121,0.131,0.148,0.09,0.132,0.134,0.122,0.134,0.139,0.129,0.118,0.112,0.128,0.129,0.103,0.114,0.113,0.076,0.122,0.129,0.129,0.091,0.099,0.105,0.144,0.14,0.117,0.112,0.085,0.109,0.102,0.091,0.088,0.125,0.096,0.105,0.099,0.089,0.109,0.09,0.093,0.107,0.103,0.098,0.114,0.125,0.106,0.115,0.121,0.104,0.116,0.093,0.126,0.107,0.108,0.093,0.136,447,1662,1034,1119,758,999,1070,1067,474,377,257.0,1634,1026,312,918,3875,5762,532,1384,2390,1013,1760,1009,1567,2085,1735,1650,1526,1957,1654,598,305,388,1440,454,551,1668,1902,209,260,735,1659,1113,1919,2324,850,313,1112,1119,795,347,1731,843,2398,117,2067,735,1555,934,834,785,968,241,391,1430,641,1693,1530,1042,568,781,1167,4619,273,2.989,2.974,2.424,2.698,2.479,2.308,2.507,2.914,2.84,3.125,3.167,1.991,2.979,2.739,2.81,2.961,3.138,3.424,2.963,2.001,2.795,2.511,2.002,2.821,2.719,2.901,2.562,2.561,2.908,2.723,3.071,2.695,2.626,3.006,3.044,2.527,2.814,3.051,1.651,2.826,2.756,2.028,3.046,2.001,1.984,2.418,3.102,2.775,2.727,2.462,1.924,2.33,2.316,2.686,2.551,2.372,2.398,2.473,2.005,2.266,2.225,2.635,2.464,2.113,2.499,2.303,2.631,2.761,2.838,3.202,2.57,2.408,2.557,2.032,0.586,0.709,0.665,0.442,0.644,0.469,0.581,0.605,0.468,0.376,0.532,0.517,0.597,0.6,0.462,0.694,0.688,0.64,0.528,0.575,0.761,0.407,0.501,0.725,0.536,0.6,0.457,0.632,0.551,0.535,0.716,0.977,0.613,0.646,0.695,0.429,0.66,0.605,0.386,0.505,0.471,0.578,0.813,0.579,0.52,0.486,0.587,0.685,0.364,0.586,0.39,0.627,0.524,0.497,0.38,0.541,0.387,0.516,0.369,0.324,0.372,0.819,0.78,0.839,0.487,0.732,0.518,0.475,0.43,0.805,0.457,0.41,0.514,0.449
-4033,Not Epileptic,F,52.0,0.8,1.6,0.8,0.9,1.8,2.9,2.7,1.6,1.7,0.6,0.1,2.5,1.6,0.6,1.5,4.9,8.3,0.6,2.9,3.0,1.9,2.8,1.5,3.3,2.7,3.6,2.3,2.4,3.7,2.6,1.3,1.1,0.2,2.2,0.6,0.6,3.5,3.0,0.1,0.5,0.8,2.9,3.4,2.4,3.8,0.9,0.4,0.7,1.2,0.9,0.8,1.5,1.1,2.9,0.4,1.7,0.8,2.0,1.2,1.2,0.6,1.7,0.2,0.6,1.9,1.1,2.3,1.8,1.3,0.8,0.9,1.6,4.4,0.4,5,11,7,7,13,21,14,13,11,5,2.0,17,16,4,10,40,57,5,30,30,11,26,13,25,29,33,24,24,25,26,16,7,3,21,5,6,38,29,1,3,4,27,26,17,20,7,2,5,6,13,4,8,5,17,2,14,6,15,7,7,4,12,2,4,14,11,19,10,8,6,8,11,32,2,0.037,0.023,0.019,0.023,0.042,0.035,0.042,0.03,0.076,0.032,0.022,0.033,0.035,0.048,0.043,0.045,0.039,0.031,0.038,0.03,0.03,0.034,0.028,0.049,0.035,0.036,0.027,0.029,0.035,0.032,0.046,0.042,0.019,0.033,0.025,0.019,0.038,0.032,0.017,0.031,0.019,0.042,0.067,0.024,0.028,0.026,0.022,0.013,0.017,0.028,0.033,0.023,0.029,0.028,0.032,0.017,0.019,0.023,0.028,0.022,0.022,0.033,0.029,0.028,0.02,0.026,0.024,0.028,0.027,0.029,0.024,0.025,0.02,0.03,1253,3681,1971,1855,1600,3660,2421,3194,1446,1076,531.0,2630,3134,741,2521,8211,13303,1948,4530,3746,3447,4565,2147,4751,4658,5925,4221,3805,5095,4118,1880,756,692,4589,1733,1524,5418,5934,302,655,1338,2619,3793,2471,3459,1055,697,2028,2281,1433,778,2275,1402,4360,311,3248,1365,3181,1304,1607,963,1880,434,890,3083,1200,3500,2238,1941,1349,1169,2346,8823,390,0.113,0.106,0.112,0.102,0.149,0.135,0.138,0.122,0.177,0.139,0.086,0.128,0.133,0.134,0.137,0.142,0.126,0.103,0.129,0.121,0.102,0.133,0.116,0.13,0.134,0.138,0.123,0.124,0.128,0.126,0.13,0.117,0.103,0.121,0.088,0.09,0.122,0.121,0.094,0.124,0.1,0.144,0.16,0.111,0.119,0.117,0.103,0.079,0.092,0.108,0.126,0.099,0.111,0.116,0.158,0.1,0.099,0.111,0.122,0.106,0.105,0.133,0.129,0.125,0.108,0.127,0.115,0.124,0.114,0.121,0.128,0.115,0.097,0.14,364,1073,654,677,666,1367,888,944,404,290,168.0,1161,845,207,611,1953,3522,334,1380,1727,950,1376,847,1243,1425,1743,1405,1388,1624,1387,496,380,249,1127,450,567,1558,1405,159,299,664,1199,1014,1500,2099,507,278,825,1024,638,369,1076,593,1665,162,1594,663,1451,721,838,445,831,173,349,1502,656,1655,990,839,450,581,1078,3638,168,3.399,2.924,2.953,2.653,2.188,2.468,2.324,3.095,2.643,3.234,2.714,1.992,2.785,2.663,2.953,2.9,2.931,3.873,2.803,1.896,2.887,2.606,2.169,2.826,2.622,2.77,2.374,2.235,2.617,2.384,2.814,2.037,2.271,2.898,3.553,2.491,2.565,3.038,2.114,2.362,2.529,1.926,2.899,1.838,1.897,2.03,3.042,2.979,2.608,2.505,2.11,2.183,2.384,2.367,2.287,2.193,2.403,2.472,2.195,2.134,2.379,2.293,2.692,2.55,2.207,2.434,2.233,2.597,2.537,3.268,2.228,2.594,2.551,2.697,1.151,0.843,0.621,0.56,0.497,0.484,0.488,0.613,0.535,0.503,0.72,0.445,0.544,0.49,0.627,0.668,0.727,0.846,0.64,0.461,0.734,0.413,0.456,0.634,0.57,0.615,0.562,0.47,0.592,0.544,0.716,0.968,0.423,0.757,0.841,0.523,0.709,0.527,0.54,0.487,0.593,0.527,0.695,0.533,0.562,0.547,0.493,0.908,0.601,0.509,0.446,0.539,0.796,0.701,0.441,0.447,0.341,0.627,0.367,0.287,0.465,0.599,0.47,0.886,0.586,0.76,0.451,0.569,0.492,0.676,0.727,0.714,0.501,0.697
-4034,Not Epileptic,M,55.0,0.6,1.7,0.8,1.1,1.3,1.4,1.5,1.9,0.8,0.6,0.3,2.7,1.7,0.5,1.3,5.0,9.0,0.8,1.8,3.3,0.7,2.2,1.7,3.7,3.3,2.7,2.6,2.2,2.7,3.0,1.2,1.0,0.6,2.1,0.4,0.9,2.4,3.0,0.2,0.2,1.1,3.2,1.8,2.3,2.5,0.6,0.5,0.9,1.6,0.5,0.5,1.9,1.5,2.2,0.7,2.3,0.4,1.6,0.9,0.8,0.5,2.1,0.2,0.6,1.9,1.1,2.2,1.2,0.9,0.7,0.9,1.0,3.5,0.3,7,19,6,8,14,17,15,19,14,7,5.0,25,22,7,15,59,97,5,27,30,6,33,15,38,43,31,38,25,29,36,17,8,8,30,2,11,32,36,2,1,7,26,21,20,15,6,3,6,7,4,4,12,9,17,7,16,2,18,6,6,3,19,1,5,15,14,24,10,6,5,5,7,60,2,0.032,0.023,0.016,0.022,0.03,0.023,0.03,0.034,0.031,0.035,0.031,0.032,0.028,0.034,0.032,0.035,0.031,0.031,0.026,0.035,0.015,0.029,0.032,0.039,0.031,0.025,0.025,0.027,0.028,0.031,0.039,0.036,0.027,0.027,0.013,0.024,0.04,0.03,0.02,0.022,0.022,0.037,0.035,0.023,0.019,0.013,0.02,0.015,0.019,0.021,0.018,0.021,0.026,0.02,0.025,0.015,0.016,0.017,0.021,0.014,0.022,0.029,0.013,0.018,0.022,0.022,0.018,0.018,0.017,0.02,0.022,0.018,0.018,0.017,1596,3909,2074,2004,2068,3260,2527,3215,1627,1442,770.0,3263,3845,1009,2453,9421,18289,2164,4710,4499,3088,4737,2020,6163,6927,6441,5345,3777,5810,5424,2190,1169,1244,5998,2229,2196,5467,7577,451,265,1431,2737,5231,2500,3513,1412,876,2546,2600,993,689,2888,1539,3783,759,4360,997,3217,1030,1599,708,3334,322,989,2579,1362,3887,2397,1545,1231,1337,1982,8190,465,0.115,0.118,0.093,0.105,0.133,0.119,0.099,0.143,0.143,0.137,0.129,0.134,0.129,0.142,0.147,0.138,0.133,0.121,0.118,0.141,0.077,0.124,0.121,0.14,0.136,0.123,0.115,0.105,0.106,0.146,0.13,0.103,0.126,0.136,0.069,0.112,0.14,0.152,0.111,0.134,0.114,0.133,0.138,0.12,0.1,0.092,0.108,0.088,0.097,0.104,0.115,0.107,0.121,0.104,0.103,0.092,0.086,0.11,0.118,0.097,0.103,0.135,0.095,0.106,0.113,0.106,0.098,0.109,0.096,0.111,0.114,0.107,0.097,0.116,444,1262,741,759,820,1101,934,1016,568,337,201.0,1340,1147,249,726,2692,5255,397,1308,1643,730,1298,914,1728,1962,1947,1905,1400,1639,1590,648,466,396,1378,522,702,1316,1673,192,123,715,1255,1034,1522,2000,696,385,925,1176,446,385,1339,778,1753,419,2216,456,1396,609,828,367,1211,185,508,1307,800,2032,1031,788,510,631,874,3501,248,3.375,2.717,2.586,2.531,2.206,2.465,2.257,2.85,2.254,2.97,2.779,2.042,2.643,2.875,2.598,2.567,2.641,3.871,2.854,2.27,2.945,2.651,2.002,2.723,2.693,2.633,2.208,2.122,2.693,2.539,2.47,2.502,2.628,3.062,3.45,2.731,3.148,3.186,2.527,2.349,2.619,1.926,3.476,1.806,1.967,2.131,2.78,3.302,2.621,2.785,1.941,2.287,2.152,2.297,2.19,2.145,2.509,2.454,1.953,2.085,2.378,2.688,2.025,2.311,2.111,1.955,1.995,2.437,2.224,2.561,2.323,2.561,2.424,2.341,0.837,0.729,0.516,0.529,0.573,0.624,0.665,0.57,0.612,0.668,1.087,0.499,0.468,0.347,0.482,0.577,0.644,0.738,0.715,0.657,0.712,0.569,0.435,0.863,0.643,0.557,0.558,0.544,0.493,0.695,0.749,0.778,0.359,0.597,0.717,0.398,0.711,0.587,0.432,0.518,0.443,0.536,0.639,0.56,0.577,0.425,0.528,0.823,0.499,0.468,0.323,0.455,0.504,0.414,0.346,0.411,0.454,0.465,0.41,0.386,0.339,0.757,0.227,0.762,0.48,0.676,0.458,0.439,0.461,0.712,0.415,0.549,0.415,0.307
-4035,Not Epileptic,F,52.0,1.5,2.0,1.0,1.1,1.8,1.7,3.5,2.1,1.6,0.8,0.4,3.1,1.5,0.8,1.3,6.9,11.3,0.9,2.2,4.1,1.8,2.4,2.7,6.7,3.3,3.8,6.3,3.5,3.9,3.6,1.5,1.1,0.6,2.4,0.4,0.6,5.3,3.5,0.2,0.4,1.4,2.7,3.4,2.3,4.5,1.1,1.0,1.0,1.7,0.8,0.8,2.1,1.3,3.8,0.4,3.6,0.7,2.3,1.1,1.5,0.8,1.9,0.3,0.8,2.3,1.5,3.9,2.0,1.6,0.5,1.2,1.4,4.5,0.4,13,17,7,7,15,16,22,18,11,10,5.0,32,14,5,11,58,92,6,19,37,14,28,24,41,41,41,50,29,27,38,13,8,4,25,5,4,48,90,1,4,7,25,29,21,25,8,5,6,9,8,5,14,8,20,2,26,6,24,10,11,7,14,2,7,17,18,27,14,9,6,8,10,33,3,0.05,0.026,0.02,0.021,0.037,0.031,0.045,0.03,0.055,0.035,0.037,0.034,0.032,0.055,0.033,0.038,0.038,0.032,0.038,0.034,0.039,0.026,0.034,0.054,0.033,0.029,0.041,0.033,0.035,0.033,0.041,0.044,0.026,0.03,0.02,0.018,0.051,0.035,0.02,0.027,0.026,0.032,0.052,0.021,0.029,0.021,0.044,0.018,0.021,0.023,0.027,0.024,0.028,0.031,0.014,0.02,0.021,0.029,0.024,0.021,0.026,0.032,0.019,0.026,0.021,0.025,0.026,0.025,0.029,0.02,0.029,0.018,0.018,0.024,1537,3620,1919,1990,1971,3421,3642,3633,1335,1634,570.0,3205,3042,963,2114,10076,17805,2266,3201,4868,3562,4457,3261,5550,6208,7832,6098,4130,6474,6103,1992,1064,954,5199,1307,1617,6371,7800,344,584,1484,2448,5865,2713,3745,1455,695,1930,2300,1412,589,2761,1474,4031,543,4795,1336,3249,993,2056,982,2981,354,847,3368,1300,4523,2691,1873,942,1427,2372,8668,408,0.14,0.109,0.105,0.102,0.127,0.131,0.126,0.124,0.15,0.135,0.147,0.138,0.127,0.139,0.125,0.139,0.134,0.11,0.138,0.135,0.122,0.117,0.124,0.154,0.14,0.127,0.128,0.111,0.112,0.136,0.143,0.118,0.097,0.123,0.086,0.094,0.151,0.127,0.107,0.138,0.113,0.13,0.161,0.112,0.117,0.107,0.135,0.09,0.105,0.117,0.137,0.113,0.126,0.115,0.102,0.111,0.117,0.137,0.138,0.106,0.124,0.12,0.101,0.131,0.108,0.122,0.114,0.114,0.119,0.113,0.122,0.107,0.097,0.127,536,1424,764,785,897,973,1233,1205,500,418,203.0,1478,915,254,642,3176,5251,449,1029,2045,836,1472,1196,2009,1817,2297,2351,1670,1755,1962,594,485,293,1320,413,567,1897,1803,170,247,796,1229,1311,1743,2375,753,352,897,1139,613,386,1459,713,2008,337,2503,493,1433,650,1125,487,1050,243,523,1661,897,2346,1293,878,449,721,1171,3860,208,2.96,2.559,2.615,2.511,1.991,2.734,2.338,2.556,2.147,2.873,2.273,1.875,2.633,2.75,2.579,2.451,2.753,3.687,2.746,1.989,3.26,2.359,2.226,2.326,2.605,2.674,2.139,2.046,2.794,2.468,2.42,2.308,2.662,2.696,2.797,2.679,2.53,3.079,2.253,2.481,2.27,1.837,3.158,1.771,1.856,1.971,2.396,2.559,2.559,2.578,1.976,2.075,2.267,2.189,1.917,2.108,2.658,2.318,1.8,1.973,2.177,2.777,1.734,2.017,2.179,1.795,1.994,2.411,2.288,2.346,2.258,2.311,2.349,2.532,0.788,0.546,0.45,0.464,0.53,0.839,0.683,0.524,0.679,0.75,0.94,0.567,0.482,0.575,0.546,0.543,0.664,0.758,0.579,0.69,0.867,0.532,0.735,0.795,0.556,0.675,0.579,0.548,0.698,0.627,0.751,0.918,0.446,0.685,0.622,0.429,0.816,0.705,0.403,0.39,0.461,0.575,0.845,0.606,0.61,0.437,0.516,0.615,0.519,0.558,0.484,0.446,0.479,0.499,0.367,0.42,0.756,0.719,0.405,0.408,0.545,0.653,0.423,0.527,0.573,0.584,0.452,0.479,0.384,0.7,0.391,0.686,0.51,0.668
-4036,Not Epileptic,M,27.0,1.5,2.3,1.0,1.3,1.9,2.6,1.9,2.0,1.2,0.8,0.4,3.4,1.9,0.6,1.6,6.5,10.1,1.3,2.4,5.4,1.9,2.3,2.2,2.8,3.3,3.6,3.1,3.8,4.3,3.2,2.2,0.9,0.3,3.4,0.5,1.4,3.9,3.3,0.2,0.3,0.9,3.9,3.0,4.3,3.8,0.6,0.5,0.8,1.2,1.0,0.7,3.0,0.8,2.8,0.5,4.4,0.9,1.2,1.4,1.3,0.5,1.9,0.6,0.5,2.9,1.5,4.4,1.2,1.1,0.7,0.7,1.6,4.0,0.3,11,18,8,11,13,22,13,19,9,9,3.0,27,19,9,14,55,75,9,28,40,14,23,19,25,43,33,31,33,29,36,21,6,4,31,3,12,35,34,2,2,5,30,25,26,20,4,2,5,6,9,4,22,6,15,4,29,6,10,8,11,4,10,4,3,25,12,38,6,6,6,5,9,29,2,0.044,0.024,0.019,0.025,0.041,0.032,0.03,0.035,0.05,0.035,0.024,0.038,0.031,0.044,0.034,0.042,0.038,0.046,0.03,0.04,0.033,0.033,0.03,0.035,0.03,0.032,0.029,0.037,0.039,0.036,0.052,0.04,0.028,0.037,0.019,0.029,0.04,0.035,0.014,0.018,0.02,0.04,0.05,0.031,0.033,0.012,0.022,0.014,0.015,0.02,0.034,0.027,0.02,0.024,0.02,0.03,0.021,0.017,0.029,0.028,0.019,0.036,0.029,0.023,0.027,0.032,0.029,0.019,0.024,0.026,0.024,0.021,0.018,0.02,1755,4956,2894,2523,2481,3723,2881,3236,1446,1596,642.0,3062,4181,1333,3474,11637,19076,2584,5177,5049,3871,4018,2830,6420,6798,7512,5576,5018,6732,5252,2655,1123,789,6741,2029,2602,5549,6829,341,660,1504,2910,5774,4161,3006,1454,941,1721,2496,1760,592,5030,1627,4564,830,4689,1487,2755,1574,1494,963,2323,669,1040,3607,1454,5313,2291,1482,1644,1080,2009,9294,643,0.147,0.106,0.094,0.11,0.138,0.135,0.108,0.138,0.138,0.136,0.112,0.143,0.126,0.142,0.131,0.138,0.132,0.125,0.128,0.138,0.105,0.137,0.135,0.13,0.14,0.124,0.123,0.12,0.122,0.144,0.153,0.107,0.143,0.132,0.082,0.112,0.126,0.124,0.097,0.1,0.103,0.146,0.135,0.127,0.126,0.087,0.098,0.087,0.088,0.102,0.128,0.109,0.1,0.108,0.115,0.135,0.099,0.098,0.121,0.135,0.101,0.121,0.139,0.113,0.128,0.135,0.12,0.098,0.116,0.125,0.125,0.115,0.094,0.107,541,1562,895,907,824,1337,1066,1013,430,388,241.0,1477,1110,280,873,2766,4638,528,1430,2138,1015,1260,1131,1454,1888,1922,1840,1777,1678,1622,651,459,220,1432,490,815,1682,1631,202,317,678,1484,1206,2259,1884,719,329,806,1150,730,324,1893,749,1848,401,2250,696,1226,807,776,446,793,285,328,1759,655,2436,976,684,512,488,1045,3619,234,3.049,2.796,2.871,2.829,2.529,2.55,2.282,2.924,2.501,3.357,2.726,1.88,2.91,3.125,2.915,2.953,3.174,3.584,2.91,1.994,3.007,2.491,2.086,3.219,2.875,3.089,2.463,2.25,2.897,2.651,2.9,2.572,2.885,3.323,3.299,2.857,2.604,3.098,1.989,2.511,2.941,1.867,3.099,2.211,1.887,2.299,3.576,2.667,2.718,2.484,2.221,2.617,2.321,2.626,2.764,2.334,2.553,2.562,2.213,2.19,2.39,2.886,2.454,3.097,2.085,2.574,2.277,2.722,2.638,3.098,2.613,2.389,2.712,3.048,0.832,0.902,0.748,0.578,0.765,0.496,0.723,0.524,0.535,0.429,0.833,0.592,0.487,0.604,0.624,0.674,0.7,0.83,0.605,0.736,0.77,0.458,0.549,0.813,0.549,0.649,0.481,0.549,0.791,0.543,0.816,0.949,0.436,0.807,0.82,0.646,0.695,0.616,0.317,0.347,0.679,0.528,0.829,0.701,0.676,0.43,0.44,0.562,0.483,0.617,0.407,0.544,0.423,0.411,0.357,0.461,0.577,0.487,0.363,0.474,0.373,0.87,0.662,0.941,0.446,0.789,0.531,0.406,0.45,0.797,0.39,0.627,0.533,0.897
-4037,Not Epileptic,F,32.0,0.9,1.8,1.1,1.1,1.6,2.3,1.5,1.9,1.0,0.4,0.2,3.3,1.5,0.5,0.7,5.5,10.1,1.2,1.8,5.3,1.3,3.4,2.3,2.5,3.4,2.7,2.5,2.1,2.4,3.1,2.2,1.5,0.5,2.3,0.6,0.4,2.8,3.1,0.2,0.2,1.1,3.7,2.1,2.9,2.6,0.8,0.4,1.3,1.4,0.8,0.6,1.0,1.3,2.7,0.5,2.5,0.9,1.5,1.0,1.4,0.8,1.3,0.3,0.6,1.9,1.5,2.4,0.9,1.5,0.5,0.8,1.1,4.6,0.4,9,16,7,8,17,28,13,21,11,6,3.0,33,16,7,12,85,103,9,24,45,11,42,24,33,39,31,32,23,25,38,29,11,7,26,4,4,35,40,2,2,7,31,20,25,18,5,2,7,8,6,5,9,11,22,2,22,8,13,7,11,5,13,2,5,14,17,18,8,10,4,5,10,38,4,0.041,0.021,0.019,0.02,0.031,0.026,0.02,0.028,0.039,0.027,0.025,0.033,0.026,0.038,0.026,0.033,0.03,0.04,0.026,0.039,0.029,0.035,0.031,0.033,0.031,0.023,0.023,0.022,0.025,0.027,0.043,0.062,0.024,0.025,0.018,0.012,0.029,0.027,0.018,0.016,0.022,0.038,0.036,0.026,0.018,0.013,0.015,0.019,0.017,0.014,0.022,0.012,0.021,0.019,0.023,0.02,0.025,0.018,0.025,0.021,0.024,0.022,0.017,0.018,0.018,0.029,0.016,0.013,0.022,0.015,0.019,0.013,0.019,0.019,1441,4218,2501,2821,2304,4831,3298,3562,1639,1477,701.0,3654,3513,950,1698,10488,20751,2343,4618,5276,3802,5296,3100,5128,6550,6543,6244,4553,5506,6559,2976,1013,1239,6561,1895,1758,6668,8891,399,358,1634,2903,5080,2916,3791,1775,803,2828,2599,1777,828,2459,1950,5251,527,3901,1311,3281,1309,1865,1025,2318,384,896,3330,1414,4569,2302,2078,863,1399,2511,9476,639,0.13,0.108,0.103,0.099,0.13,0.136,0.101,0.127,0.159,0.118,0.117,0.142,0.129,0.131,0.134,0.145,0.133,0.13,0.124,0.157,0.099,0.134,0.114,0.126,0.126,0.12,0.11,0.108,0.115,0.128,0.174,0.162,0.111,0.119,0.094,0.072,0.126,0.128,0.108,0.11,0.104,0.13,0.137,0.129,0.103,0.092,0.086,0.094,0.098,0.101,0.122,0.091,0.117,0.102,0.12,0.115,0.133,0.106,0.115,0.117,0.118,0.12,0.105,0.115,0.101,0.127,0.099,0.093,0.114,0.109,0.107,0.097,0.103,0.129,450,1417,855,933,902,1398,1037,1194,481,312,191.0,1706,945,275,511,3255,5784,522,1189,2216,820,1611,1093,1434,1813,1845,1843,1499,1622,1918,859,421,331,1464,497,610,1674,1990,186,191,806,1380,1121,1724,2156,843,350,1146,1228,796,440,1182,989,2301,292,1885,558,1398,668,988,504,938,247,465,1563,746,2145,1056,1004,475,676,1212,3901,274,2.902,2.723,2.779,3.041,2.214,2.691,2.541,2.627,2.52,3.493,2.851,1.919,2.775,2.582,2.459,2.482,2.828,3.539,3.042,2.023,3.419,2.584,2.431,2.724,2.738,2.773,2.607,2.39,2.641,2.58,2.654,2.4,2.863,3.128,3.472,2.559,2.977,3.141,2.423,2.195,2.585,1.939,3.325,1.951,1.999,2.443,2.905,3.041,2.607,2.533,2.214,2.126,2.063,2.375,2.215,2.24,2.48,2.723,2.189,2.109,2.529,2.27,1.679,2.239,2.373,2.23,2.328,2.482,2.446,2.114,2.437,2.337,2.672,2.648,0.549,0.73,0.51,0.409,0.634,0.672,0.674,0.516,0.563,0.485,0.885,0.553,0.414,0.431,0.483,0.504,0.627,0.93,0.577,0.716,0.689,0.53,0.648,0.588,0.571,0.545,0.578,0.499,0.443,0.583,0.582,0.985,0.389,0.541,0.546,0.473,0.645,0.555,0.415,0.415,0.493,0.559,0.651,0.609,0.528,0.507,0.408,0.753,0.4,0.43,0.445,0.47,0.512,0.38,0.373,0.415,0.544,0.615,0.393,0.412,0.486,0.583,0.305,0.677,0.461,0.735,0.381,0.398,0.366,0.495,0.475,0.456,0.476,0.464
-4038,Not Epileptic,F,22.0,1.4,2.6,1.0,1.3,1.9,1.7,2.4,1.5,1.2,0.6,0.2,3.1,1.6,0.5,0.9,5.7,7.7,1.2,2.1,4.0,0.8,3.0,1.4,4.4,2.5,2.5,3.3,2.2,3.0,2.9,2.8,0.9,0.3,1.8,0.5,0.7,3.6,2.7,0.1,0.2,1.0,2.9,2.0,2.2,2.8,0.7,0.6,0.8,1.3,0.7,0.5,1.5,1.6,2.6,0.4,1.7,0.9,1.3,1.2,0.8,0.3,1.9,0.3,0.6,1.5,1.1,2.3,1.2,1.2,0.4,0.9,1.4,3.4,0.2,9,23,8,10,15,16,19,15,9,6,3.0,27,15,4,12,59,66,10,24,37,7,37,16,46,33,25,37,20,25,37,23,5,3,22,3,7,41,37,1,1,5,27,19,21,18,5,3,5,7,6,5,14,9,17,2,14,6,10,14,7,2,33,2,6,9,15,16,9,8,3,5,10,25,2,0.048,0.028,0.02,0.024,0.036,0.029,0.034,0.03,0.038,0.026,0.024,0.031,0.034,0.025,0.023,0.032,0.031,0.04,0.034,0.037,0.017,0.032,0.024,0.047,0.027,0.027,0.029,0.028,0.03,0.032,0.056,0.037,0.022,0.023,0.016,0.024,0.035,0.029,0.011,0.016,0.022,0.034,0.032,0.027,0.022,0.013,0.022,0.015,0.019,0.019,0.022,0.023,0.026,0.023,0.022,0.015,0.025,0.015,0.026,0.013,0.014,0.034,0.017,0.016,0.02,0.024,0.02,0.017,0.019,0.016,0.025,0.02,0.016,0.014,1565,4474,2107,2165,2218,2912,2972,2798,1820,1234,653.0,2801,3164,1144,2420,11613,16849,2346,3976,4398,3094,4465,2166,6473,5717,5381,5130,3817,6728,5685,2057,1124,832,5663,2104,1710,8667,7565,290,393,1335,2099,5783,2245,3392,1586,822,1836,2176,1641,498,2360,1980,4371,410,3308,1397,2792,1325,1694,604,2536,423,1005,2328,959,3663,2483,1789,945,1244,2252,7234,502,0.139,0.131,0.113,0.113,0.139,0.125,0.124,0.126,0.143,0.139,0.107,0.135,0.139,0.128,0.126,0.142,0.13,0.15,0.131,0.146,0.089,0.13,0.111,0.149,0.126,0.124,0.115,0.108,0.115,0.138,0.154,0.103,0.101,0.12,0.081,0.098,0.14,0.142,0.083,0.096,0.109,0.133,0.132,0.13,0.106,0.095,0.105,0.084,0.101,0.105,0.121,0.116,0.111,0.11,0.121,0.095,0.121,0.097,0.142,0.09,0.094,0.131,0.109,0.104,0.104,0.129,0.101,0.096,0.105,0.099,0.12,0.115,0.096,0.102,470,1481,744,828,894,1012,1143,953,508,332,179.0,1510,832,290,700,3088,4420,529,1073,1858,743,1474,897,1761,1488,1563,1653,1170,1702,1712,755,412,279,1242,530,601,1974,1684,183,219,673,1271,1170,1420,2013,762,368,834,1035,621,345,1131,1035,1826,203,1721,601,1331,785,903,320,1028,268,532,1106,651,1772,1097,848,413,568,1083,3204,227,3.157,2.764,2.899,2.598,2.053,2.63,2.211,2.626,2.506,2.892,2.761,1.708,2.778,2.761,2.575,2.737,2.975,3.489,2.92,2.0,2.957,2.372,2.087,2.654,2.855,2.764,2.416,2.453,2.829,2.643,2.107,2.669,2.446,3.098,3.489,2.496,3.172,3.193,1.961,2.148,2.536,1.601,3.518,1.885,1.892,2.305,2.727,2.912,2.578,2.954,1.818,2.166,2.132,2.32,2.503,2.128,2.757,2.478,1.984,2.07,2.065,2.444,1.699,2.221,2.405,1.977,2.141,2.508,2.354,2.352,2.638,2.468,2.465,2.63,0.654,0.73,0.56,0.452,0.749,0.631,0.602,0.54,0.753,0.56,0.766,0.508,0.468,0.574,0.549,0.739,0.658,0.743,0.528,0.704,0.791,0.573,0.545,0.79,0.588,0.605,0.671,0.7,0.632,0.613,0.617,0.844,0.393,0.608,0.64,0.517,0.74,0.62,0.412,0.348,0.481,0.402,0.683,0.646,0.547,0.443,0.447,0.477,0.444,0.623,0.405,0.411,0.496,0.583,0.443,0.41,0.469,0.374,0.397,0.365,0.335,0.609,0.467,0.55,0.508,0.643,0.41,0.385,0.394,0.714,0.377,0.491,0.437,0.452
-4039,Not Epileptic,F,27.0,0.8,1.7,0.6,0.9,1.1,1.9,1.3,2.0,1.1,0.5,0.1,2.5,1.5,0.5,0.9,5.5,8.0,0.7,2.2,3.9,1.0,1.9,1.0,2.1,2.5,2.7,3.0,2.3,2.7,3.4,1.0,1.0,0.6,3.2,0.3,0.9,2.9,2.7,0.1,0.1,1.0,2.7,3.0,2.2,3.0,0.8,0.3,1.5,1.3,1.6,0.7,1.7,1.4,2.3,0.2,2.0,0.7,2.1,0.6,0.6,0.3,0.9,0.5,0.6,1.4,1.2,2.6,1.0,1.8,0.5,1.0,1.2,3.5,0.5,7,13,5,7,13,21,11,18,11,5,1.0,22,14,8,11,53,73,6,24,35,8,23,10,28,33,20,32,22,22,34,14,6,6,37,2,8,25,28,1,1,5,21,23,16,17,6,1,9,7,8,3,15,9,15,2,14,6,16,3,4,2,7,2,4,10,10,16,7,9,5,7,8,27,3,0.035,0.021,0.014,0.022,0.03,0.03,0.023,0.032,0.048,0.028,0.021,0.037,0.031,0.039,0.032,0.037,0.03,0.03,0.029,0.037,0.023,0.03,0.028,0.035,0.027,0.036,0.027,0.029,0.027,0.037,0.038,0.043,0.027,0.041,0.018,0.021,0.041,0.032,0.018,0.016,0.022,0.037,0.045,0.026,0.023,0.019,0.015,0.022,0.018,0.036,0.027,0.025,0.029,0.022,0.015,0.017,0.03,0.022,0.021,0.016,0.02,0.02,0.038,0.025,0.021,0.043,0.026,0.018,0.024,0.017,0.026,0.018,0.017,0.019,1420,4187,1992,2050,1968,2969,2473,3332,1595,1232,365.0,2568,2945,1157,2111,10379,18842,2278,4472,3993,2586,4016,1752,5125,5892,5224,5145,3828,6191,5279,2372,931,1208,6697,1678,2461,3156,6062,281,340,1621,2652,5271,1905,3445,1307,728,2465,2459,1497,632,2881,1961,4338,461,3370,1366,3276,816,1321,688,1889,527,827,2201,777,3287,2271,2323,977,1463,2357,8317,865,0.123,0.105,0.087,0.103,0.127,0.15,0.102,0.135,0.155,0.127,0.078,0.151,0.134,0.155,0.131,0.141,0.13,0.113,0.122,0.137,0.094,0.131,0.126,0.134,0.13,0.132,0.121,0.12,0.112,0.147,0.121,0.11,0.126,0.142,0.095,0.101,0.126,0.132,0.097,0.104,0.109,0.142,0.153,0.117,0.107,0.104,0.091,0.097,0.098,0.144,0.133,0.112,0.114,0.105,0.116,0.1,0.121,0.114,0.113,0.094,0.092,0.109,0.143,0.146,0.116,0.146,0.119,0.101,0.115,0.117,0.124,0.096,0.093,0.115,402,1355,697,741,689,1048,850,1135,396,324,113.0,1055,770,262,556,2648,4589,410,1152,1849,719,1099,632,1179,1541,1350,1711,1352,1626,1564,460,412,357,1424,336,661,1192,1390,155,183,704,1201,1174,1282,2022,649,293,983,1032,727,379,1159,848,1758,217,1819,460,1482,440,652,299,704,205,338,1032,371,1541,916,1046,464,634,1047,3352,344,3.314,3.004,2.692,2.793,2.218,2.395,2.413,2.657,2.692,3.213,2.918,2.113,2.949,2.995,2.655,2.798,3.077,3.913,3.115,1.896,2.986,2.762,2.41,3.159,2.742,3.075,2.404,2.287,2.831,2.677,3.241,2.262,2.722,3.096,3.69,3.029,2.276,3.028,1.994,2.111,2.925,1.982,3.172,1.769,1.955,2.066,3.05,3.194,2.831,2.531,2.217,2.42,2.254,2.517,2.492,2.063,2.691,2.548,2.221,2.238,2.763,2.684,2.618,2.545,2.351,2.382,2.345,2.829,2.648,2.317,2.445,2.589,2.626,3.384,0.97,0.534,0.578,0.669,0.737,0.545,0.592,0.742,0.725,0.591,0.807,0.668,0.436,0.581,0.597,0.722,0.676,0.943,0.693,0.572,0.719,0.468,0.421,0.654,0.629,0.652,0.552,0.586,0.522,0.581,0.781,0.896,0.518,0.733,0.916,0.597,0.749,0.629,0.306,0.435,0.553,0.575,0.88,0.741,0.571,0.45,0.453,0.82,0.604,0.59,0.444,0.528,0.611,0.583,0.546,0.469,0.598,0.633,0.319,0.346,0.512,0.691,0.608,0.884,0.6,0.662,0.529,0.503,0.562,0.747,0.497,0.552,0.523,0.495
-4040,Not Epileptic,F,55.0,0.6,2.0,0.5,0.8,1.2,2.2,1.3,1.5,1.1,0.6,0.6,3.0,1.7,0.2,1.7,4.7,6.9,0.9,2.1,4.5,0.7,2.6,1.3,2.6,2.9,2.8,3.1,1.9,2.4,3.1,1.1,0.7,0.4,2.4,0.3,0.3,4.1,3.8,0.1,0.1,0.9,2.8,2.0,2.9,2.1,0.4,0.3,0.8,0.9,0.8,0.6,2.3,0.9,2.2,0.1,2.0,0.7,1.6,1.1,0.8,0.6,1.1,0.5,0.3,1.4,1.5,2.2,1.0,0.9,0.5,1.1,1.6,5.4,0.4,5,24,7,7,12,16,12,12,11,5,4.0,24,15,4,15,44,62,7,21,35,7,28,11,25,26,27,30,16,18,27,15,5,4,22,2,2,38,37,2,1,4,22,18,18,15,3,2,4,5,6,5,17,6,13,0,16,4,14,8,8,5,8,2,3,10,15,16,7,5,5,7,10,40,3,0.028,0.023,0.012,0.019,0.03,0.036,0.026,0.03,0.036,0.037,0.04,0.038,0.03,0.018,0.033,0.031,0.025,0.03,0.034,0.036,0.018,0.034,0.028,0.033,0.031,0.034,0.032,0.028,0.026,0.036,0.032,0.029,0.017,0.031,0.011,0.011,0.036,0.036,0.015,0.016,0.019,0.033,0.031,0.03,0.019,0.011,0.015,0.013,0.012,0.02,0.033,0.023,0.016,0.021,0.01,0.021,0.018,0.022,0.026,0.017,0.032,0.021,0.021,0.015,0.019,0.034,0.023,0.019,0.017,0.021,0.033,0.023,0.02,0.019,1658,3968,1556,1635,1899,3191,2214,2696,1623,1045,662.0,2979,3470,738,2605,8643,15207,2117,3265,4545,2959,4316,1526,5232,4230,5138,4383,3087,4930,4275,1974,705,1025,5294,1431,1306,5870,6489,395,344,1381,2630,4238,2384,2600,1070,753,2079,2546,1256,422,3748,1599,4239,183,3092,1151,2517,1156,1192,658,2446,506,975,2088,843,2678,1814,1289,936,1224,1964,8729,461,0.098,0.113,0.093,0.1,0.121,0.13,0.106,0.119,0.136,0.125,0.152,0.144,0.123,0.125,0.15,0.131,0.121,0.108,0.124,0.137,0.085,0.139,0.117,0.128,0.13,0.133,0.127,0.111,0.103,0.144,0.134,0.107,0.109,0.118,0.073,0.066,0.126,0.12,0.092,0.092,0.094,0.134,0.126,0.119,0.103,0.087,0.093,0.075,0.079,0.114,0.155,0.11,0.092,0.097,0.082,0.116,0.095,0.112,0.129,0.098,0.13,0.107,0.111,0.109,0.104,0.134,0.116,0.108,0.102,0.119,0.14,0.107,0.103,0.098,377,1458,661,709,745,993,786,845,450,286,219.0,1149,882,215,789,2338,4279,409,1041,1973,702,1372,745,1268,1454,1473,1636,1026,1311,1391,543,373,349,1244,440,513,1863,1751,180,153,701,1199,1035,1451,1692,550,322,905,1159,648,269,1609,824,1852,88,1525,564,1268,650,781,350,829,284,347,1079,585,1440,839,702,401,524,990,3923,300,3.641,2.464,2.303,2.221,2.276,2.692,2.308,2.693,2.478,3.052,2.673,2.174,2.932,2.635,2.743,2.698,2.789,3.982,2.532,2.004,3.007,2.526,1.886,3.033,2.41,2.803,2.236,2.287,2.747,2.405,2.749,1.965,2.318,3.007,2.948,2.34,2.504,2.752,2.304,2.339,2.256,1.945,3.082,1.801,1.755,2.23,2.847,2.655,2.507,2.432,1.838,2.376,2.218,2.338,1.969,2.246,2.639,2.271,1.964,1.692,2.141,2.86,2.052,3.294,2.079,1.947,1.989,2.43,2.174,2.657,2.535,2.396,2.381,1.882,0.671,0.653,0.567,0.524,0.504,0.843,0.549,0.678,0.737,0.493,0.712,0.577,0.509,0.444,0.498,0.523,0.622,0.799,0.597,0.533,0.809,0.562,0.475,0.581,0.534,0.748,0.586,0.629,0.607,0.668,0.67,0.796,0.28,0.728,0.829,0.53,0.753,0.845,0.381,0.599,0.781,0.658,0.606,0.596,0.447,0.643,0.517,0.873,0.669,0.339,0.403,0.526,0.569,0.531,0.234,0.637,0.573,0.691,0.352,0.375,0.381,0.821,0.441,0.829,0.591,0.65,0.459,0.502,0.415,0.482,0.611,0.754,0.55,0.453
-4041,Not Epileptic,F,52.0,0.9,2.3,1.4,0.8,1.6,1.6,2.0,1.3,1.0,0.7,0.3,2.4,1.9,0.5,1.2,5.4,9.2,0.9,2.7,3.4,1.7,3.9,2.5,3.8,3.9,2.7,3.0,3.4,4.8,3.5,1.9,1.3,0.6,2.0,0.6,0.4,4.8,2.2,0.2,0.2,1.2,3.5,2.4,2.1,4.6,0.7,0.4,1.0,1.5,0.8,0.8,2.3,1.3,1.9,0.4,2.4,1.2,1.8,2.0,0.9,0.7,1.5,0.3,0.6,1.7,1.3,2.8,1.5,0.9,0.6,0.7,1.3,3.4,0.3,7,26,10,6,16,16,18,14,9,6,4.0,23,19,5,13,60,76,6,30,26,14,42,17,39,42,29,37,25,34,36,25,9,6,22,3,6,50,35,1,2,8,29,19,16,23,4,2,5,6,7,6,16,9,14,2,20,14,12,14,8,6,14,2,6,11,17,21,9,7,7,6,10,28,2,0.037,0.024,0.026,0.015,0.028,0.026,0.033,0.027,0.039,0.042,0.032,0.035,0.028,0.027,0.029,0.032,0.033,0.035,0.034,0.04,0.029,0.035,0.046,0.041,0.038,0.028,0.028,0.029,0.039,0.029,0.053,0.049,0.025,0.026,0.023,0.014,0.038,0.027,0.015,0.024,0.027,0.039,0.038,0.024,0.03,0.013,0.016,0.016,0.021,0.021,0.029,0.024,0.024,0.02,0.024,0.017,0.025,0.021,0.025,0.019,0.018,0.022,0.015,0.02,0.02,0.023,0.021,0.02,0.02,0.02,0.017,0.016,0.015,0.018,1536,4592,2435,2092,2186,3321,3054,2915,1657,1245,701.0,2837,4009,1107,2885,10752,18306,2287,5029,3206,3877,5649,2062,6547,6684,5778,6515,4134,6747,7181,2424,1294,1208,5749,1835,1869,9207,7037,417,361,1403,2852,5660,2408,3814,1455,724,2125,1946,1435,705,3493,1634,3846,419,4597,1609,2832,2190,1447,1290,2921,413,787,2631,1197,4120,2433,1519,957,1342,2503,8538,493,0.114,0.124,0.124,0.092,0.129,0.124,0.114,0.133,0.148,0.15,0.122,0.138,0.126,0.126,0.125,0.142,0.137,0.119,0.143,0.142,0.117,0.116,0.138,0.142,0.12,0.134,0.121,0.106,0.121,0.132,0.155,0.125,0.108,0.125,0.087,0.08,0.145,0.139,0.095,0.115,0.129,0.131,0.137,0.114,0.121,0.09,0.09,0.089,0.1,0.112,0.143,0.115,0.115,0.099,0.145,0.102,0.125,0.112,0.119,0.102,0.103,0.119,0.112,0.127,0.105,0.115,0.105,0.106,0.105,0.132,0.103,0.1,0.093,0.12,423,1601,804,728,900,1027,911,858,452,320,203.0,1182,1103,274,739,2873,4814,442,1397,1466,931,1598,827,1761,1756,1680,1899,1567,1807,2076,771,530,365,1262,480,635,2266,1622,232,159,672,1377,1207,1400,2299,733,315,911,964,625,353,1565,868,1559,185,2202,817,1329,1221,769,637,1054,245,423,1287,867,2121,1109,738,495,638,1199,3535,212,3.521,2.587,2.862,2.668,2.173,2.494,2.697,2.807,2.632,3.016,2.566,2.052,2.851,2.863,2.947,2.745,2.998,3.933,2.864,1.877,3.087,2.651,2.171,2.816,2.885,2.755,2.61,2.154,2.779,2.697,2.438,2.557,2.708,3.173,3.528,2.573,2.947,3.077,2.05,2.524,2.374,1.811,3.463,1.962,1.913,2.281,2.774,2.869,2.503,2.558,2.046,2.42,2.056,2.544,2.584,2.311,2.185,2.364,2.083,2.096,2.341,2.634,1.966,2.21,2.353,1.753,2.196,2.536,2.459,2.089,2.415,2.362,2.578,3.22,0.759,0.558,0.76,0.526,0.617,0.768,0.668,0.501,0.673,0.992,0.837,0.578,0.526,0.386,0.55,0.653,0.681,0.728,0.593,0.622,0.8,0.653,0.625,0.774,0.582,0.569,0.571,0.588,0.713,0.605,0.673,0.644,0.396,0.629,0.759,0.471,0.806,0.619,0.381,0.468,0.394,0.614,0.673,0.573,0.571,0.441,0.506,0.782,0.519,0.406,0.581,0.51,0.493,0.455,0.484,0.39,0.322,0.641,0.441,0.387,0.46,0.644,0.43,0.597,0.422,0.501,0.422,0.463,0.448,0.577,0.461,0.522,0.424,0.502
-4042,Not Epileptic,M,22.0,0.9,1.9,0.9,1.0,1.7,3.1,1.9,2.5,2.7,0.8,0.3,3.5,1.7,0.5,1.3,7.9,11.4,1.1,3.1,4.1,2.7,5.2,3.6,4.8,5.8,4.0,6.1,2.9,3.4,3.3,2.6,0.6,0.5,2.9,0.6,0.5,3.5,4.0,0.2,0.3,1.1,2.9,3.6,2.5,3.0,0.8,0.6,0.9,1.3,1.3,0.7,2.2,1.5,2.7,0.5,2.7,0.5,2.2,1.4,1.3,0.6,1.1,0.4,0.4,1.9,1.0,2.9,1.7,1.0,0.5,1.0,1.5,5.6,0.3,8,15,7,8,12,34,16,23,18,9,2.0,30,19,6,13,73,89,9,34,36,22,49,28,40,48,40,59,28,31,44,18,5,4,29,4,5,45,49,2,3,6,31,27,17,15,5,3,4,6,11,6,16,8,20,3,20,5,24,10,10,4,6,2,4,15,12,21,12,6,6,6,10,42,2,0.044,0.025,0.018,0.022,0.038,0.033,0.034,0.04,0.073,0.039,0.035,0.046,0.042,0.038,0.044,0.047,0.042,0.041,0.037,0.041,0.049,0.048,0.052,0.055,0.049,0.034,0.048,0.037,0.039,0.036,0.061,0.033,0.035,0.047,0.033,0.016,0.04,0.041,0.029,0.023,0.023,0.034,0.058,0.025,0.023,0.019,0.027,0.015,0.016,0.032,0.029,0.03,0.029,0.026,0.023,0.018,0.014,0.024,0.023,0.019,0.023,0.027,0.031,0.022,0.022,0.026,0.025,0.021,0.016,0.026,0.027,0.023,0.022,0.02,1660,3666,1832,2109,2298,4276,2711,3407,2000,1660,661.0,3070,3373,930,2549,10525,18006,2146,4473,4167,3267,5081,2508,6132,5399,7708,5810,4057,5971,6121,2086,749,733,5247,1351,1760,6357,8120,288,682,1456,2294,5151,2800,3428,1319,848,2011,2286,1555,811,2895,1560,3891,579,4412,1252,3293,1609,1903,615,1840,362,837,2685,1039,3978,2853,1601,855,1472,1954,9694,354,0.131,0.115,0.102,0.101,0.142,0.136,0.123,0.151,0.193,0.161,0.127,0.16,0.147,0.164,0.157,0.161,0.145,0.132,0.148,0.155,0.134,0.133,0.15,0.174,0.146,0.146,0.145,0.127,0.127,0.149,0.183,0.097,0.113,0.151,0.126,0.09,0.15,0.148,0.141,0.115,0.114,0.133,0.187,0.117,0.104,0.104,0.111,0.081,0.084,0.118,0.139,0.129,0.12,0.115,0.117,0.105,0.098,0.127,0.111,0.11,0.122,0.11,0.124,0.108,0.12,0.111,0.119,0.105,0.094,0.135,0.132,0.117,0.102,0.13,412,1293,766,744,723,1584,946,1209,606,378,181.0,1311,942,248,625,3091,4910,481,1434,1820,961,1730,1051,1635,1944,2052,2139,1458,1707,1883,648,378,249,1226,352,583,1666,1834,147,232,709,1371,1176,1625,1914,604,356,894,1097,674,383,1236,750,1847,322,2206,561,1580,875,1008,364,646,214,370,1247,590,1848,1295,805,379,602,1018,4150,169,3.58,2.626,2.409,2.738,2.363,2.281,2.33,2.592,2.291,3.063,3.408,2.034,2.743,2.909,2.848,2.486,2.753,3.321,2.432,1.921,2.603,2.343,2.05,2.744,2.346,2.791,2.218,2.2,2.698,2.555,2.33,2.108,2.43,2.843,3.106,2.682,2.707,3.062,2.283,2.78,2.399,1.603,2.844,1.921,2.043,2.542,2.813,2.694,2.558,2.336,2.211,2.441,2.315,2.216,2.174,2.243,2.165,2.16,2.04,2.01,2.123,2.504,1.818,2.318,2.335,2.013,2.277,2.41,2.276,2.488,2.615,2.183,2.436,2.645,0.725,0.639,0.571,0.575,0.828,0.646,0.489,0.58,0.782,0.603,0.696,0.516,0.515,0.511,0.626,0.661,0.663,0.568,0.671,0.719,0.824,0.657,0.594,0.691,0.653,0.646,0.594,0.584,0.584,0.582,0.798,0.839,0.454,0.714,0.741,0.619,0.809,0.756,0.53,0.7,0.55,0.408,0.845,0.586,0.671,0.723,0.467,0.514,0.486,0.609,0.621,0.525,0.512,0.565,0.366,0.448,0.431,0.556,0.459,0.439,0.373,0.845,0.362,0.879,0.568,0.652,0.523,0.455,0.443,1.018,0.638,0.514,0.52,0.521
-4043,Not Epileptic,M,47.0,1.0,2.8,1.1,1.6,2.9,1.8,1.4,1.6,1.7,0.7,0.3,3.3,1.2,0.7,1.1,5.8,9.7,0.9,2.9,5.0,1.7,3.1,1.8,3.2,3.1,2.7,2.2,2.0,2.4,2.7,2.0,1.8,0.5,1.8,0.5,0.5,3.7,3.1,0.1,0.2,0.7,4.2,2.1,4.2,2.5,0.8,0.3,0.7,1.5,0.8,0.8,2.4,1.9,2.6,0.5,2.2,0.7,1.3,1.4,0.8,0.6,1.5,0.3,0.4,1.4,1.4,2.4,1.4,0.9,0.7,1.0,1.9,4.0,0.1,9,28,10,12,22,20,14,37,12,9,3.0,31,14,11,19,79,103,10,35,49,12,39,17,39,42,33,29,27,25,33,19,14,6,23,3,7,44,44,1,1,4,44,25,27,14,8,2,5,9,6,6,21,19,22,5,19,5,11,9,5,5,13,2,5,11,21,28,11,5,5,7,12,30,1,0.046,0.027,0.019,0.026,0.048,0.025,0.024,0.031,0.059,0.037,0.031,0.035,0.027,0.035,0.028,0.032,0.034,0.039,0.032,0.043,0.031,0.034,0.037,0.037,0.03,0.032,0.022,0.026,0.022,0.035,0.047,0.068,0.031,0.025,0.016,0.016,0.042,0.036,0.013,0.019,0.016,0.045,0.034,0.027,0.018,0.014,0.017,0.013,0.019,0.013,0.036,0.024,0.026,0.02,0.027,0.017,0.018,0.015,0.023,0.015,0.022,0.025,0.021,0.019,0.017,0.024,0.023,0.021,0.015,0.019,0.023,0.019,0.016,0.007,1710,5450,2658,3135,2998,3660,2700,3489,1834,1643,849.0,4209,3220,1315,2392,11609,20458,2199,6057,5975,4316,6191,2436,5635,6826,6075,5377,4506,7042,5281,1945,1153,892,5877,1955,1680,7839,8310,339,447,1224,3468,5959,4367,3842,1603,739,1965,2658,1970,669,3519,2491,4367,762,4063,1226,2874,1683,1475,971,2934,525,906,2813,1504,3823,2412,1982,942,1487,2845,9196,511,0.14,0.124,0.108,0.117,0.154,0.127,0.108,0.143,0.167,0.157,0.105,0.149,0.128,0.156,0.133,0.146,0.141,0.136,0.128,0.17,0.109,0.133,0.127,0.142,0.141,0.135,0.117,0.106,0.1,0.142,0.142,0.163,0.128,0.118,0.078,0.09,0.14,0.151,0.083,0.084,0.094,0.156,0.141,0.127,0.091,0.095,0.097,0.082,0.099,0.086,0.154,0.118,0.129,0.11,0.138,0.1,0.108,0.097,0.12,0.093,0.109,0.118,0.117,0.117,0.097,0.124,0.111,0.111,0.089,0.105,0.12,0.115,0.095,0.057,494,1770,902,1053,1037,1179,918,1088,553,411,217.0,1659,831,292,677,3387,5437,462,1627,2211,957,1694,973,1575,1911,1767,1686,1508,1719,1464,710,559,317,1232,480,568,1969,1769,206,250,628,1700,1186,2252,2240,794,316,869,1231,856,333,1650,1130,1996,317,2028,548,1331,912,782,446,1046,233,463,1342,939,1879,1101,916,488,613,1326,3750,220,3.28,2.75,2.953,2.739,2.423,2.498,2.358,2.829,2.458,3.157,2.937,2.094,2.815,2.982,2.634,2.604,2.9,3.706,2.866,2.22,3.335,2.714,2.144,2.718,2.721,2.747,2.448,2.278,3.025,2.728,2.066,2.289,2.453,3.246,3.392,2.537,2.955,3.248,1.806,2.193,2.49,1.831,3.342,2.081,1.896,2.249,2.7,2.884,2.472,2.601,2.192,2.19,2.205,2.364,2.514,2.149,2.572,2.447,2.053,1.993,2.326,2.638,2.197,2.241,2.319,1.998,2.102,2.489,2.492,2.245,2.555,2.494,2.539,2.506,0.96,0.605,0.572,0.592,0.72,0.629,0.592,0.51,0.807,0.663,0.875,0.648,0.48,0.606,0.614,0.589,0.635,0.817,0.658,0.816,0.902,0.682,0.638,0.86,0.649,0.61,0.623,0.659,0.5,0.621,0.751,0.774,0.429,0.63,0.765,0.477,0.741,0.672,0.348,0.427,0.481,0.571,0.714,0.532,0.601,0.45,0.613,0.647,0.526,0.457,0.551,0.425,0.464,0.468,0.485,0.452,0.337,0.606,0.429,0.354,0.495,0.683,0.426,0.771,0.386,0.73,0.474,0.42,0.392,0.464,0.398,0.414,0.469,0.315
-4044,Not Epileptic,M,32.0,0.7,3.0,0.7,1.0,1.1,3.0,1.5,2.2,1.0,1.0,0.6,3.8,1.8,0.7,2.0,7.9,9.4,0.6,2.1,5.6,1.0,4.9,2.1,3.5,2.9,3.5,3.6,3.5,3.6,2.9,1.7,1.3,1.1,3.7,0.6,0.8,4.9,4.8,0.2,0.0,0.9,4.3,2.5,3.1,2.7,1.0,0.4,1.0,1.2,0.6,0.5,2.4,1.8,2.9,0.1,2.4,1.0,2.2,1.5,1.7,1.1,1.7,0.3,0.3,2.9,1.5,3.3,1.1,1.2,0.7,0.6,1.6,5.8,0.4,5,28,7,8,10,26,14,17,9,9,7.0,30,20,7,17,72,79,4,25,46,8,57,25,47,35,35,40,27,25,35,22,11,9,35,4,7,43,46,1,0,6,36,26,24,15,9,2,6,7,6,4,18,12,22,1,18,9,14,13,14,11,12,2,3,21,16,24,7,7,5,6,12,44,4,0.03,0.025,0.012,0.022,0.023,0.032,0.025,0.034,0.035,0.037,0.042,0.035,0.028,0.032,0.029,0.04,0.029,0.021,0.027,0.037,0.018,0.037,0.026,0.037,0.027,0.027,0.026,0.03,0.031,0.029,0.048,0.045,0.036,0.035,0.024,0.018,0.037,0.032,0.013,0.012,0.022,0.038,0.035,0.024,0.021,0.021,0.016,0.014,0.014,0.011,0.025,0.018,0.026,0.022,0.025,0.016,0.022,0.021,0.026,0.023,0.025,0.031,0.015,0.015,0.026,0.034,0.022,0.016,0.02,0.018,0.017,0.021,0.02,0.026,1518,6330,1937,2369,2052,4503,2657,3590,1817,1643,840.0,3834,4105,1541,3626,13812,20208,2243,5006,5911,3100,7233,3062,6577,5939,6478,6848,4123,6150,5497,2248,1424,1446,8083,1905,1947,7646,8646,506,125,1482,3737,6086,3729,3349,1662,1045,2574,2560,1927,538,4243,2272,5536,362,3727,1974,3991,1875,2054,1657,2591,619,850,3810,949,4187,2337,1950,1425,1287,2681,10958,479,0.109,0.113,0.094,0.101,0.109,0.135,0.104,0.135,0.123,0.149,0.146,0.138,0.135,0.131,0.128,0.141,0.118,0.1,0.118,0.137,0.091,0.146,0.131,0.139,0.126,0.132,0.115,0.111,0.114,0.132,0.133,0.113,0.143,0.137,0.108,0.094,0.131,0.128,0.086,0.07,0.108,0.145,0.135,0.112,0.105,0.113,0.088,0.082,0.088,0.086,0.129,0.102,0.113,0.101,0.117,0.103,0.105,0.103,0.123,0.121,0.116,0.121,0.091,0.098,0.128,0.133,0.114,0.094,0.101,0.11,0.107,0.106,0.1,0.126,418,2106,797,853,788,1672,965,1147,530,429,265.0,1742,1156,373,1157,3689,5665,408,1469,2426,892,2299,1333,1757,1792,2101,2345,1657,1762,1759,643,514,481,1868,406,771,2224,2375,259,51,706,1791,1414,2106,1990,760,389,1053,1162,793,335,2013,1139,2188,115,2069,805,1697,972,1117,749,936,297,372,1700,648,2199,1043,914,540,566,1361,4644,278,3.485,2.592,2.339,2.735,2.376,2.445,2.265,2.748,2.602,3.085,2.568,1.946,2.88,2.992,2.547,2.818,2.845,3.89,2.767,2.093,2.794,2.43,1.987,2.881,2.524,2.522,2.305,2.041,2.71,2.469,2.609,2.621,2.426,3.055,4.074,2.477,2.801,2.816,2.373,2.293,2.731,1.914,3.115,1.964,1.959,2.316,3.261,2.909,2.601,2.601,2.026,2.278,2.204,2.639,3.128,2.076,2.423,2.577,2.067,2.012,2.373,2.521,2.219,2.532,2.412,1.825,2.078,2.618,2.553,2.786,2.352,2.377,2.509,2.23,0.76,0.718,0.506,0.524,0.587,0.5,0.514,0.582,0.562,0.803,0.673,0.508,0.488,0.452,0.544,0.683,0.672,0.607,0.545,0.724,0.781,0.526,0.547,0.721,0.489,0.548,0.485,0.49,0.632,0.624,0.708,0.933,0.53,0.732,0.596,0.463,0.752,0.546,0.48,0.334,0.64,0.533,0.838,0.597,0.61,0.577,0.445,0.852,0.529,0.443,0.372,0.522,0.533,0.494,0.624,0.468,0.415,0.501,0.462,0.385,0.392,0.776,0.639,1.226,0.549,0.552,0.48,0.453,0.406,0.694,0.555,0.473,0.48,0.395
-4045,Not Epileptic,F,22.0,1.2,2.7,1.2,1.5,2.8,3.7,2.2,2.2,2.6,0.8,0.2,3.9,2.1,0.8,2.5,10.4,8.7,0.9,2.9,4.6,3.5,5.4,2.4,7.5,10.1,10.9,9.4,4.4,4.2,3.0,2.3,2.6,0.6,4.4,0.6,1.4,7.4,5.5,0.3,0.2,0.9,4.5,2.4,2.8,3.2,1.0,0.6,0.8,1.4,0.9,0.6,2.3,1.7,2.8,0.4,5.3,0.8,1.5,1.5,2.0,1.6,2.2,0.4,0.7,2.4,1.1,3.2,1.6,0.8,0.5,1.1,2.1,5.3,0.4,9,19,10,14,22,34,20,19,24,11,3.0,35,22,12,19,100,94,7,34,49,25,53,22,75,72,74,87,36,43,43,21,19,6,45,4,12,64,59,2,1,7,42,27,22,20,7,3,5,8,8,5,18,15,21,2,38,6,11,11,21,15,20,3,6,18,18,29,14,5,3,9,16,37,3,0.042,0.027,0.019,0.025,0.036,0.038,0.029,0.033,0.061,0.037,0.029,0.042,0.025,0.04,0.044,0.042,0.027,0.037,0.032,0.04,0.042,0.047,0.035,0.053,0.053,0.052,0.046,0.038,0.031,0.032,0.041,0.056,0.025,0.04,0.013,0.019,0.049,0.039,0.014,0.017,0.018,0.041,0.032,0.026,0.023,0.019,0.018,0.012,0.018,0.013,0.029,0.019,0.023,0.018,0.016,0.025,0.017,0.02,0.023,0.026,0.037,0.027,0.018,0.018,0.02,0.019,0.019,0.017,0.013,0.017,0.026,0.019,0.017,0.015,2100,6708,2706,3009,2044,3267,4019,3754,1391,1803,762.0,3899,5568,1853,3283,14134,22796,2406,5786,5829,4866,4289,2400,6633,4657,7824,5975,5517,7663,6637,2798,2506,1566,7694,2830,2951,7976,9856,449,509,1774,2973,6787,3297,3987,1676,1200,2910,2885,2338,821,4457,2592,6149,678,6037,2088,3481,1710,2160,1693,3429,633,1309,4130,1958,6120,3817,2148,871,1928,3655,12204,664,0.126,0.119,0.113,0.125,0.145,0.143,0.119,0.115,0.174,0.168,0.106,0.171,0.124,0.15,0.141,0.142,0.126,0.13,0.131,0.162,0.133,0.151,0.116,0.152,0.161,0.157,0.139,0.122,0.105,0.136,0.129,0.138,0.116,0.125,0.078,0.097,0.142,0.14,0.123,0.101,0.108,0.133,0.128,0.126,0.113,0.107,0.105,0.081,0.101,0.092,0.145,0.105,0.112,0.1,0.098,0.119,0.096,0.099,0.112,0.128,0.118,0.127,0.117,0.103,0.109,0.116,0.105,0.093,0.087,0.108,0.126,0.111,0.094,0.113,535,1854,920,985,1210,1497,1304,1123,656,446,181.0,1645,1397,447,895,4091,5819,456,1598,2192,1265,1704,1150,2334,2739,3197,3133,1877,2005,1937,931,831,395,1657,702,1136,2319,2379,223,237,817,1532,1487,1748,2217,749,496,1021,1230,1087,350,2057,1292,2525,364,3094,740,1478,1052,1131,668,1282,324,652,1919,1022,2763,1536,922,407,701,1693,4842,352,3.555,3.102,2.953,2.874,1.692,2.11,2.518,2.983,1.969,3.204,3.102,2.117,2.986,3.054,2.845,2.593,3.034,3.834,2.856,2.3,2.831,2.158,1.845,2.278,1.661,2.24,1.748,2.371,2.961,2.686,2.276,2.726,2.921,3.24,3.575,2.584,2.6,3.0,2.179,2.427,2.804,1.781,3.425,2.117,2.096,2.545,3.111,3.451,2.921,2.672,2.403,2.44,2.175,2.614,2.286,2.226,2.804,2.69,1.796,2.15,2.513,2.635,2.236,2.312,2.36,2.254,2.396,2.566,2.704,2.358,2.649,2.589,2.734,2.444,0.935,0.655,0.504,0.497,0.489,0.607,0.621,0.555,0.668,0.661,0.721,0.54,0.52,0.652,0.75,0.779,0.744,0.648,0.663,0.621,1.061,0.714,0.592,0.852,0.686,0.778,0.702,0.687,0.681,0.599,0.735,0.988,0.458,0.714,0.594,0.549,0.972,0.821,0.484,0.429,0.493,0.608,0.711,0.676,0.561,0.533,0.583,0.766,0.468,0.519,0.452,0.41,0.498,0.437,0.349,0.507,0.584,0.464,0.384,0.44,0.588,0.647,0.644,0.714,0.545,0.712,0.453,0.395,0.396,0.705,0.471,0.523,0.487,0.468
-4046,Not Epileptic,F,37.0,0.8,2.8,0.9,1.7,1.6,1.6,1.9,1.4,1.1,0.7,0.4,3.9,1.1,0.4,1.3,5.0,8.2,0.8,1.8,3.8,1.0,3.3,2.7,3.6,3.3,3.5,3.9,3.4,3.2,4.1,2.3,1.0,0.4,1.9,0.4,0.6,2.9,3.3,0.2,0.2,1.0,2.8,2.0,3.0,4.2,0.9,0.4,0.8,1.1,0.6,0.8,1.6,1.3,2.8,0.2,3.0,0.6,1.4,0.8,2.0,0.7,1.9,0.5,0.8,2.1,1.5,3.0,1.1,1.3,0.6,1.0,1.7,5.4,0.4,6,29,7,16,17,16,15,12,13,7,4.0,40,15,4,16,59,80,10,24,41,10,31,26,46,41,36,42,30,30,44,25,9,2,22,3,5,33,45,1,2,6,26,20,26,25,7,3,5,6,6,5,12,9,20,2,26,7,13,8,17,5,14,5,7,17,21,21,8,7,6,7,14,40,2,0.041,0.025,0.017,0.028,0.037,0.026,0.028,0.025,0.04,0.033,0.036,0.039,0.025,0.029,0.034,0.031,0.028,0.032,0.029,0.034,0.024,0.031,0.038,0.042,0.03,0.028,0.03,0.032,0.029,0.033,0.049,0.062,0.024,0.027,0.015,0.017,0.033,0.032,0.015,0.017,0.022,0.036,0.035,0.031,0.028,0.016,0.016,0.017,0.017,0.013,0.036,0.02,0.025,0.024,0.016,0.019,0.027,0.02,0.029,0.027,0.025,0.029,0.03,0.026,0.022,0.034,0.022,0.02,0.02,0.025,0.022,0.024,0.019,0.019,1214,4724,2209,2960,1986,2412,3135,3214,1871,1535,685.0,2928,3190,993,2497,9778,18512,1949,3997,4320,3463,4885,2318,5466,7206,6763,7018,4810,7130,6375,2590,991,811,5669,1776,1691,6396,7820,351,302,1436,2353,4711,2620,4248,1723,762,1635,1990,1557,596,2636,1659,5043,402,4666,1106,2601,920,1991,730,2303,674,998,2798,1051,4265,2357,2139,921,1537,2633,10041,642,0.131,0.123,0.102,0.128,0.15,0.122,0.119,0.11,0.152,0.138,0.139,0.143,0.124,0.13,0.142,0.137,0.126,0.141,0.134,0.153,0.107,0.127,0.13,0.147,0.125,0.136,0.129,0.115,0.112,0.141,0.145,0.128,0.101,0.125,0.087,0.094,0.136,0.145,0.09,0.129,0.111,0.132,0.143,0.133,0.121,0.097,0.108,0.093,0.091,0.099,0.138,0.106,0.112,0.111,0.113,0.11,0.124,0.111,0.135,0.134,0.13,0.139,0.151,0.124,0.119,0.151,0.113,0.103,0.104,0.125,0.114,0.124,0.105,0.11,390,1687,821,1068,762,888,1081,945,515,375,207.0,1572,788,224,681,2656,4933,451,1075,1832,805,1505,1074,1586,1902,1941,2164,1576,1747,2087,688,405,267,1178,442,610,1504,1896,175,160,707,1199,992,1617,2358,836,335,742,980,717,363,1253,835,1967,216,2389,507,1214,562,1118,420,967,280,512,1444,707,2077,992,989,505,734,1230,4272,277,3.186,2.602,2.746,2.761,2.22,2.266,2.377,2.701,2.634,3.104,2.828,1.714,2.929,3.005,2.756,2.703,2.96,3.17,2.914,2.047,3.181,2.509,1.969,2.588,2.81,2.786,2.458,2.402,3.091,2.443,2.554,2.598,2.622,3.255,3.503,2.704,3.058,3.025,2.251,2.318,2.634,1.764,3.243,1.915,2.099,2.289,2.863,2.772,2.512,2.498,2.135,2.187,2.09,2.515,2.262,2.164,2.303,2.411,1.896,1.997,2.214,2.541,2.18,2.253,2.192,1.895,2.294,2.723,2.618,1.949,2.504,2.556,2.572,2.654,0.62,0.552,0.422,0.521,0.618,0.598,0.52,0.573,0.723,0.431,0.546,0.438,0.5,0.372,0.529,0.619,0.63,0.603,0.466,0.633,0.778,0.564,0.478,0.71,0.606,0.524,0.527,0.58,0.519,0.529,0.655,0.882,0.441,0.533,0.774,0.511,0.674,0.611,0.369,0.44,0.467,0.622,0.745,0.524,0.613,0.401,0.464,0.616,0.425,0.375,0.42,0.566,0.476,0.491,0.314,0.397,0.283,0.521,0.39,0.304,0.346,0.701,0.651,0.676,0.474,0.656,0.397,0.433,0.448,0.525,0.382,0.392,0.436,0.428
-4047,Not Epileptic,F,42.0,0.7,1.9,1.1,1.2,2.0,1.8,1.7,1.7,1.1,0.9,0.2,3.2,1.4,0.5,1.2,6.8,10.1,0.9,3.2,4.9,1.4,4.0,1.9,3.2,4.4,4.2,3.9,2.7,3.0,4.3,1.5,0.9,0.6,4.8,0.4,0.6,3.9,2.8,0.1,0.1,1.0,3.4,3.3,2.7,3.3,1.0,0.3,0.9,1.0,0.5,0.8,1.5,1.5,2.3,0.2,3.6,0.7,1.8,1.3,1.5,0.7,1.7,0.3,0.5,1.8,1.5,3.1,1.1,1.0,0.9,0.9,1.6,4.6,0.3,7,22,13,10,17,18,16,14,11,9,2.0,27,15,6,14,68,94,6,35,44,11,43,18,36,42,45,53,25,26,50,16,4,6,42,2,4,44,36,1,1,6,28,24,19,20,7,2,4,5,5,5,12,11,17,1,29,5,13,11,15,6,12,2,5,15,17,24,8,6,10,5,11,38,2,0.033,0.024,0.023,0.022,0.037,0.029,0.029,0.031,0.029,0.038,0.027,0.033,0.032,0.04,0.036,0.033,0.035,0.033,0.047,0.039,0.024,0.038,0.028,0.043,0.043,0.04,0.031,0.032,0.032,0.035,0.044,0.035,0.034,0.05,0.02,0.019,0.043,0.032,0.01,0.016,0.025,0.034,0.049,0.024,0.023,0.019,0.02,0.015,0.016,0.012,0.033,0.022,0.021,0.019,0.035,0.025,0.018,0.018,0.025,0.021,0.018,0.026,0.027,0.02,0.023,0.032,0.021,0.017,0.016,0.026,0.02,0.025,0.019,0.021,1584,4389,2376,2504,2411,2991,3077,3019,2028,1898,701.0,2957,3142,946,2084,11763,19819,2236,4746,5350,4030,5135,2163,5071,5864,6803,6974,4843,6417,7144,2213,775,1089,7568,1309,1653,7436,7232,272,381,1318,2487,5055,3057,4055,1690,631,1946,1928,1160,568,2667,2111,4930,262,4280,1196,3508,1233,2027,1309,2661,345,842,2708,974,4526,2220,1777,1324,1297,1972,9317,509,0.113,0.118,0.117,0.104,0.141,0.123,0.101,0.135,0.143,0.141,0.116,0.135,0.14,0.14,0.152,0.14,0.136,0.123,0.172,0.156,0.105,0.136,0.114,0.146,0.145,0.156,0.118,0.115,0.114,0.148,0.155,0.098,0.119,0.175,0.074,0.098,0.14,0.138,0.103,0.1,0.116,0.131,0.149,0.114,0.106,0.096,0.099,0.083,0.084,0.1,0.137,0.106,0.112,0.096,0.151,0.122,0.115,0.111,0.122,0.119,0.102,0.123,0.117,0.114,0.115,0.13,0.109,0.096,0.098,0.14,0.115,0.124,0.101,0.127,414,1338,812,912,900,1097,1031,908,567,450,159.0,1488,815,249,601,3318,5227,414,1198,2143,983,1579,1061,1344,1596,1865,2207,1499,1641,2176,614,349,284,1616,350,523,1849,1661,166,174,637,1403,1171,1694,2099,809,271,854,932,611,371,1242,1101,1991,85,2166,565,1480,785,1062,620,962,188,400,1275,663,2248,981,895,522,630,953,3729,235,3.496,2.913,2.835,2.58,2.268,2.296,2.441,2.945,2.555,3.181,3.405,1.765,2.829,2.606,2.652,2.641,2.846,3.656,3.046,2.118,3.06,2.465,1.831,2.882,2.763,2.856,2.449,2.455,2.873,2.512,2.647,2.194,2.89,3.247,3.238,2.841,2.865,3.099,1.759,2.374,2.726,1.646,3.091,2.139,2.17,2.114,2.829,2.91,2.633,2.198,1.962,2.353,2.116,2.454,2.928,2.238,2.425,2.644,1.841,2.141,2.388,2.772,1.958,2.185,2.215,1.878,2.137,2.63,2.463,2.652,2.318,2.441,2.64,2.748,0.831,0.692,0.571,0.562,0.708,0.68,0.567,0.404,0.703,0.656,0.689,0.653,0.456,0.634,0.474,0.691,0.717,0.735,0.643,0.736,0.817,0.649,0.552,0.843,0.674,0.595,0.652,0.636,0.631,0.67,0.66,0.742,0.34,0.675,0.811,0.49,0.753,0.618,0.362,0.302,0.477,0.588,0.938,0.731,0.593,0.503,0.444,0.606,0.422,0.388,0.483,0.415,0.471,0.513,0.43,0.453,0.338,0.5,0.336,0.409,0.495,0.683,0.496,0.616,0.525,0.656,0.457,0.404,0.398,0.66,0.448,0.46,0.474,0.571
-4048,Not Epileptic,M,42.0,1.1,3.4,1.9,1.7,2.2,2.3,2.7,1.3,2.4,1.3,0.4,4.6,2.4,0.7,2.3,7.9,15.1,1.5,3.5,6.6,2.1,4.2,3.1,4.3,7.9,4.0,5.7,4.6,6.3,5.0,1.7,1.5,0.7,3.5,0.9,1.0,3.2,4.2,0.2,0.5,1.3,4.2,3.2,4.5,4.1,1.1,0.5,1.1,1.8,0.9,0.9,2.8,2.1,2.7,0.6,3.2,0.7,2.2,1.5,1.9,0.8,2.3,0.5,0.4,3.2,1.4,3.9,1.8,1.7,0.8,1.0,1.5,6.2,0.4,8,25,17,13,20,21,20,11,21,10,5.0,43,23,7,24,81,133,13,40,56,17,44,21,41,68,41,54,35,39,50,21,11,7,36,4,6,43,50,2,2,7,38,31,31,22,6,2,7,8,9,7,20,15,16,3,24,6,18,9,16,6,23,3,4,22,17,26,10,9,5,6,12,58,4,0.034,0.025,0.022,0.029,0.046,0.032,0.033,0.024,0.058,0.042,0.035,0.04,0.04,0.029,0.036,0.041,0.042,0.049,0.037,0.044,0.036,0.038,0.04,0.045,0.051,0.032,0.041,0.042,0.047,0.041,0.042,0.052,0.026,0.037,0.023,0.018,0.039,0.037,0.018,0.02,0.025,0.036,0.055,0.036,0.025,0.018,0.021,0.015,0.02,0.024,0.04,0.024,0.026,0.021,0.021,0.02,0.018,0.024,0.024,0.025,0.02,0.039,0.023,0.024,0.03,0.022,0.025,0.021,0.023,0.024,0.026,0.022,0.021,0.016,1659,6176,3794,3001,2659,3253,3718,2485,2288,1811,753.0,3329,4193,1330,3693,12408,22068,2892,5398,5512,4078,5388,2266,5494,7049,7524,6367,4268,6696,6202,2368,1580,1407,7493,2466,2284,5679,8848,323,734,1638,3060,6868,3149,4195,1842,743,2581,2962,1453,602,3774,2484,4729,744,4661,1241,3429,1388,2023,1155,2677,486,802,2950,1948,4720,2576,2364,915,1472,2342,11312,796,0.126,0.111,0.107,0.115,0.15,0.136,0.116,0.113,0.175,0.156,0.127,0.153,0.145,0.127,0.139,0.14,0.143,0.153,0.152,0.157,0.125,0.136,0.137,0.15,0.149,0.128,0.138,0.121,0.123,0.16,0.137,0.12,0.096,0.134,0.097,0.094,0.146,0.149,0.098,0.096,0.11,0.141,0.168,0.139,0.113,0.098,0.105,0.084,0.099,0.113,0.151,0.111,0.119,0.1,0.104,0.107,0.104,0.119,0.12,0.126,0.108,0.143,0.12,0.125,0.128,0.107,0.114,0.102,0.107,0.132,0.12,0.11,0.107,0.102,481,2124,1399,1086,857,1170,1345,871,679,486,228.0,1922,1106,389,1096,3589,6310,591,1597,2636,943,1837,1147,1676,2460,2245,2454,1695,1986,2107,665,546,475,1687,606,848,1531,2243,202,367,817,1721,1288,2201,2425,883,343,1133,1344,625,390,1852,1302,2120,415,2479,612,1619,865,1147,622,994,295,354,1629,1085,2477,1240,1102,437,715,1126,4996,452,3.382,2.525,2.586,2.688,2.433,2.335,2.328,2.524,2.412,2.897,2.666,1.623,2.859,2.463,2.559,2.59,2.762,3.572,2.752,1.819,3.16,2.262,1.812,2.55,2.344,2.687,2.136,2.039,2.653,2.392,2.593,2.999,2.355,2.971,3.514,2.538,2.71,2.857,1.872,2.418,2.471,1.69,3.484,1.652,1.975,2.257,2.671,2.926,2.639,2.635,1.963,2.165,2.057,2.412,2.178,2.034,2.206,2.292,1.802,1.952,2.169,2.385,1.717,2.48,2.037,2.013,2.066,2.409,2.393,2.065,2.47,2.468,2.393,2.17,0.737,0.619,0.548,0.635,0.719,0.628,0.652,0.581,0.615,0.802,0.85,0.423,0.526,0.524,0.459,0.582,0.62,0.624,0.588,0.613,0.878,0.561,0.503,0.639,0.552,0.464,0.528,0.506,0.615,0.508,0.705,0.957,0.388,0.68,0.82,0.504,0.603,0.636,0.275,0.375,0.54,0.523,0.842,0.482,0.651,0.487,0.566,0.81,0.584,0.405,0.328,0.55,0.497,0.429,0.361,0.442,0.398,0.572,0.387,0.389,0.452,0.68,0.452,0.818,0.529,0.699,0.457,0.409,0.469,0.712,0.492,0.436,0.401,0.292
-4049,Not Epileptic,F,52.0,1.4,2.1,0.7,1.1,1.3,1.3,1.1,1.5,0.9,0.7,0.2,3.1,1.5,0.4,1.2,6.4,7.4,0.7,2.5,4.6,1.3,2.0,1.5,3.4,2.9,3.2,3.7,2.6,2.5,4.3,1.0,1.1,0.5,2.4,0.3,1.1,3.6,3.7,0.1,0.2,1.1,2.4,2.1,3.5,3.6,0.6,0.2,1.0,0.9,1.3,0.7,1.3,1.2,3.5,0.5,2.3,0.6,1.8,0.9,1.5,0.7,1.3,0.3,0.3,1.7,0.9,2.2,1.6,1.0,0.6,1.4,1.8,3.3,0.2,7,16,6,8,12,12,8,11,10,6,2.0,22,13,4,12,52,69,4,21,33,10,19,13,34,30,30,36,23,19,34,14,6,3,22,1,9,34,33,1,1,6,22,22,26,22,5,2,6,6,9,5,11,10,23,3,19,5,16,7,10,6,9,3,3,14,13,15,12,8,7,9,12,25,2,0.047,0.022,0.012,0.021,0.028,0.023,0.02,0.023,0.038,0.027,0.017,0.032,0.027,0.029,0.028,0.035,0.024,0.027,0.027,0.032,0.024,0.027,0.026,0.036,0.03,0.028,0.026,0.028,0.023,0.034,0.035,0.035,0.022,0.027,0.009,0.023,0.039,0.032,0.021,0.028,0.023,0.03,0.039,0.034,0.026,0.012,0.014,0.016,0.013,0.036,0.03,0.017,0.025,0.022,0.022,0.018,0.021,0.022,0.026,0.023,0.023,0.025,0.019,0.016,0.02,0.028,0.02,0.024,0.021,0.017,0.028,0.024,0.015,0.018,1568,3915,2116,2096,2047,2108,2108,2780,1408,1328,460.0,2417,2874,769,2144,9566,16392,1843,3979,4194,2479,3479,1978,6059,4666,5506,5859,3612,4553,5445,1664,923,988,5329,1759,2249,4907,6581,240,297,1749,2492,3836,2254,2943,1399,716,2110,2232,1376,665,2548,1925,5780,546,3632,1182,2837,993,1600,955,2140,419,901,2418,1003,3067,2074,1719,1110,1668,2306,7795,549,0.132,0.108,0.087,0.103,0.129,0.116,0.083,0.113,0.129,0.125,0.082,0.129,0.119,0.133,0.128,0.131,0.116,0.104,0.118,0.128,0.079,0.117,0.115,0.13,0.128,0.127,0.12,0.111,0.096,0.138,0.136,0.109,0.098,0.116,0.061,0.105,0.129,0.125,0.089,0.111,0.106,0.129,0.133,0.14,0.12,0.093,0.095,0.082,0.084,0.134,0.126,0.098,0.112,0.109,0.127,0.103,0.104,0.111,0.123,0.115,0.115,0.114,0.12,0.115,0.108,0.113,0.107,0.124,0.11,0.113,0.124,0.122,0.09,0.102,441,1477,846,853,718,899,828,933,423,388,200.0,1283,869,228,652,2912,4835,389,1342,2086,790,1120,865,1521,1535,1739,2186,1332,1502,1833,483,449,332,1324,458,761,1465,1826,123,166,780,1241,1045,1679,2127,762,317,938,1116,600,342,1394,891,2562,283,1999,519,1518,595,950,490,849,256,354,1295,560,1635,1034,825,536,868,1162,3463,207,3.298,2.482,2.486,2.514,2.258,2.194,2.138,2.772,2.394,2.81,2.211,1.769,2.592,2.558,2.534,2.498,2.748,3.442,2.482,1.819,2.61,2.39,1.984,2.754,2.448,2.654,2.197,2.227,2.478,2.435,2.456,2.014,2.697,2.935,3.504,2.768,2.57,2.814,2.248,2.09,2.797,1.895,2.848,1.607,1.597,1.938,2.616,2.816,2.374,2.7,1.928,1.982,2.168,2.244,2.471,2.032,2.522,2.236,1.976,1.895,2.105,2.534,1.995,2.571,2.145,2.127,2.08,2.316,2.266,2.271,2.256,2.416,2.443,3.117,0.974,0.569,0.492,0.575,0.684,0.519,0.539,0.524,0.582,0.439,0.779,0.518,0.417,0.517,0.397,0.561,0.569,0.721,0.578,0.537,0.835,0.357,0.477,0.781,0.444,0.589,0.442,0.571,0.595,0.489,0.676,0.936,0.549,0.69,0.732,0.496,0.667,0.677,0.461,0.376,0.562,0.481,0.737,0.509,0.418,0.606,0.405,0.674,0.544,0.476,0.629,0.518,0.573,0.541,0.42,0.523,0.374,0.49,0.336,0.381,0.352,0.66,0.432,0.944,0.557,0.614,0.499,0.441,0.504,0.464,0.408,0.432,0.581,0.732
-4050,Not Epileptic,M,52.0,0.8,2.6,0.9,1.2,1.5,2.1,3.1,2.2,1.1,0.8,0.6,2.7,1.7,0.3,1.5,4.7,7.3,2.1,2.4,3.4,1.6,4.4,2.2,4.2,4.0,2.8,3.6,3.5,3.4,2.3,1.9,1.2,0.7,2.2,0.3,0.7,3.8,4.0,0.2,0.4,0.8,3.9,2.8,2.2,2.0,0.9,0.5,0.9,1.2,1.0,0.9,2.6,1.8,1.7,0.5,2.4,0.7,1.9,1.1,0.8,0.5,1.7,1.0,0.7,1.8,1.4,2.8,1.8,1.2,0.7,0.9,1.8,3.6,0.3,6,31,10,11,16,23,18,21,16,9,5.0,21,21,5,20,63,69,17,30,32,16,44,18,44,49,36,42,32,30,32,22,10,9,26,1,8,57,51,2,2,4,33,44,16,10,8,4,6,6,8,6,23,10,11,3,20,3,13,9,7,4,15,9,6,15,17,23,15,11,7,7,13,32,3,0.036,0.028,0.017,0.025,0.032,0.027,0.04,0.032,0.032,0.038,0.047,0.034,0.029,0.027,0.027,0.032,0.026,0.052,0.029,0.037,0.027,0.042,0.039,0.041,0.038,0.028,0.035,0.032,0.028,0.027,0.041,0.044,0.025,0.027,0.009,0.018,0.032,0.036,0.013,0.022,0.019,0.034,0.04,0.025,0.016,0.023,0.026,0.012,0.016,0.02,0.035,0.022,0.025,0.014,0.022,0.019,0.021,0.02,0.021,0.015,0.024,0.028,0.033,0.016,0.02,0.026,0.019,0.019,0.021,0.019,0.021,0.021,0.015,0.014,1581,4798,2009,2314,1987,4477,3161,3317,1868,1568,713.0,2488,3998,1037,2814,10728,17616,2648,4540,3060,4435,5206,2888,5768,6775,6717,6463,4724,6641,5261,2165,1196,1229,6367,1700,1760,8224,8067,455,477,1239,3095,5750,1969,3437,1521,694,2207,2276,1885,703,3852,1982,4025,652,4014,1061,3090,1363,1342,665,2555,872,1001,3027,993,4528,3351,2039,1083,1513,2488,7567,510,0.118,0.133,0.108,0.116,0.129,0.125,0.138,0.144,0.154,0.145,0.139,0.135,0.131,0.132,0.138,0.138,0.124,0.161,0.136,0.157,0.112,0.135,0.145,0.149,0.135,0.129,0.124,0.122,0.104,0.13,0.139,0.119,0.116,0.128,0.06,0.102,0.145,0.149,0.102,0.125,0.097,0.136,0.133,0.12,0.087,0.121,0.129,0.083,0.089,0.107,0.143,0.117,0.116,0.086,0.114,0.104,0.105,0.104,0.118,0.094,0.123,0.12,0.162,0.105,0.109,0.123,0.105,0.106,0.115,0.123,0.117,0.118,0.096,0.105,472,1668,800,825,796,1364,1034,1150,553,419,207.0,1266,1022,275,945,2907,4763,670,1462,1543,1025,1753,988,1863,1922,1900,1908,1579,1827,1585,846,484,418,1369,533,615,2176,2051,216,250,673,1584,1338,1334,1969,588,319,1006,1097,758,409,1904,1035,1804,350,2002,506,1494,768,785,385,1033,460,591,1473,815,2272,1442,962,507,672,1313,3595,274,3.392,2.77,2.509,2.696,2.259,2.629,2.436,2.522,2.468,2.903,2.537,1.814,2.883,2.74,2.36,2.716,2.849,2.945,2.66,1.801,3.192,2.386,2.237,2.391,2.629,2.777,2.607,2.371,2.732,2.652,2.049,2.619,2.436,3.153,2.929,2.393,2.854,2.835,2.335,2.481,2.31,1.742,3.159,1.72,1.957,2.472,2.7,2.737,2.519,2.654,2.066,2.091,2.2,2.435,2.258,2.118,2.436,2.299,1.982,1.904,2.007,2.53,1.991,2.039,2.217,1.508,2.168,2.469,2.32,2.396,2.462,2.261,2.283,2.249,0.706,0.63,0.43,0.481,0.581,0.682,0.68,0.551,0.694,0.56,0.885,0.518,0.519,0.566,0.522,0.607,0.609,1.003,0.546,0.619,0.807,0.679,0.764,0.827,0.712,0.551,0.655,0.635,0.587,0.591,0.639,0.969,0.391,0.688,0.458,0.505,0.813,0.682,0.389,0.416,0.381,0.639,0.851,0.638,0.662,0.576,0.694,0.552,0.356,0.517,0.395,0.428,0.376,0.372,0.379,0.39,0.391,0.622,0.388,0.357,0.368,0.674,0.431,0.592,0.495,0.48,0.456,0.388,0.454,0.541,0.446,0.482,0.371,0.4
-4051,Not Epileptic,F,42.0,1.0,1.8,0.7,1.1,1.1,1.3,1.4,1.4,2.1,0.5,0.3,1.8,1.2,0.3,1.5,4.2,7.2,1.0,1.6,3.7,1.3,3.3,1.6,2.6,2.5,2.7,3.2,1.9,1.9,2.6,1.4,0.8,0.4,1.7,0.4,0.4,4.0,2.8,0.2,0.2,1.0,3.8,1.5,1.8,2.1,0.5,0.2,0.6,1.2,0.6,0.4,2.1,1.8,2.2,0.3,2.4,0.6,1.1,1.3,1.2,0.7,1.5,0.6,0.7,1.2,1.0,2.0,1.3,0.6,0.5,0.7,1.2,3.9,0.4,9,17,6,9,10,14,14,14,18,6,3.0,15,14,6,16,47,68,9,17,34,13,37,15,35,33,31,35,19,19,37,15,7,5,21,2,3,41,38,1,2,5,29,15,12,10,4,1,5,6,5,3,18,13,18,2,19,6,7,10,9,6,15,4,6,11,14,17,10,3,4,6,8,32,3,0.049,0.024,0.014,0.022,0.03,0.029,0.022,0.03,0.054,0.028,0.031,0.033,0.027,0.02,0.032,0.03,0.027,0.04,0.026,0.034,0.025,0.034,0.035,0.034,0.028,0.029,0.03,0.023,0.026,0.025,0.033,0.045,0.029,0.023,0.013,0.012,0.038,0.032,0.015,0.013,0.021,0.037,0.029,0.022,0.018,0.01,0.014,0.012,0.018,0.011,0.02,0.024,0.028,0.02,0.017,0.016,0.023,0.015,0.025,0.023,0.021,0.026,0.034,0.02,0.017,0.026,0.018,0.02,0.015,0.016,0.019,0.018,0.016,0.018,1187,3723,1872,2252,1550,2980,3230,2787,1712,1332,635.0,2219,2859,891,2591,8460,16479,2175,3942,4410,4061,4915,2179,5412,5755,5763,5080,3700,4722,5962,2273,1131,812,5034,1950,1563,7130,6578,335,563,1400,2511,4856,2092,3191,1315,649,1943,1997,1970,610,2948,1872,4173,331,4153,1122,2574,1485,1561,1243,2195,458,994,2104,993,3824,2045,1235,1014,1149,2013,9284,608,0.147,0.121,0.094,0.109,0.135,0.12,0.109,0.13,0.164,0.131,0.132,0.135,0.124,0.116,0.134,0.132,0.126,0.125,0.121,0.133,0.115,0.122,0.117,0.138,0.121,0.137,0.123,0.094,0.111,0.125,0.139,0.118,0.108,0.112,0.071,0.083,0.135,0.143,0.083,0.091,0.108,0.133,0.119,0.112,0.095,0.08,0.081,0.082,0.097,0.085,0.106,0.122,0.137,0.104,0.109,0.101,0.125,0.091,0.122,0.109,0.106,0.126,0.161,0.124,0.102,0.133,0.099,0.113,0.091,0.109,0.11,0.105,0.091,0.111,422,1276,708,823,690,841,984,858,565,341,178.0,925,820,259,756,2418,4537,486,1015,1854,912,1388,757,1422,1624,1615,1681,1201,1178,1904,668,385,272,1141,549,593,1840,1614,208,249,670,1295,973,1257,1740,676,279,804,982,806,322,1370,982,1837,191,2109,447,1165,821,797,608,961,264,503,1053,567,1857,987,589,460,588,993,3829,262,2.924,2.68,2.644,2.668,1.991,2.79,2.606,2.809,2.391,2.956,2.906,2.017,2.704,2.498,2.663,2.621,2.86,3.295,2.96,2.015,3.212,2.703,2.393,2.805,2.685,2.881,2.396,2.361,2.966,2.548,2.55,3.003,2.4,3.091,3.218,2.451,2.826,3.018,1.827,2.643,2.571,1.708,3.5,1.912,2.139,2.164,2.765,2.917,2.52,2.621,2.22,2.234,2.113,2.404,2.055,2.189,2.565,2.567,2.043,2.089,2.239,2.346,2.04,2.416,2.141,2.309,2.215,2.394,2.408,2.411,2.341,2.363,2.484,2.498,0.702,0.647,0.4,0.429,0.662,0.653,0.466,0.356,0.667,0.555,0.75,0.619,0.399,0.433,0.391,0.571,0.518,0.731,0.522,0.591,0.826,0.578,0.617,0.669,0.664,0.448,0.555,0.533,0.41,0.484,0.614,0.874,0.45,0.561,0.657,0.473,0.855,0.703,0.339,0.368,0.469,0.572,0.732,0.594,0.6,0.371,0.426,0.811,0.36,0.402,0.444,0.405,0.433,0.415,0.357,0.375,0.437,0.438,0.448,0.387,0.384,0.601,0.542,0.762,0.533,0.62,0.421,0.372,0.364,0.67,0.427,0.545,0.479,0.599
-4052,Not Epileptic,F,22.0,0.6,2.0,0.9,1.5,1.7,1.4,1.4,1.5,1.5,0.7,0.3,3.0,2.1,0.5,1.5,5.1,9.4,1.1,2.0,3.9,1.0,2.4,2.4,3.6,2.4,2.2,2.9,2.5,2.8,2.5,1.3,1.3,0.5,2.4,0.4,0.6,4.1,2.7,0.2,0.3,0.9,3.2,2.6,2.1,2.9,0.7,0.4,0.9,1.7,1.0,0.6,2.2,1.5,3.1,0.3,2.2,1.0,1.8,1.1,1.1,0.5,1.7,0.5,0.6,2.1,1.2,2.3,1.5,0.9,1.0,1.1,1.7,3.5,0.3,6,23,8,14,17,17,15,18,13,7,4.0,29,26,6,16,62,98,11,24,40,11,32,20,35,33,28,32,29,24,32,16,10,6,26,3,5,52,39,1,2,6,32,19,19,20,5,2,6,9,7,4,18,10,24,2,21,7,16,8,8,4,14,4,5,18,16,22,12,5,14,7,10,27,3,0.029,0.021,0.013,0.024,0.035,0.026,0.023,0.026,0.054,0.029,0.033,0.03,0.031,0.026,0.031,0.031,0.03,0.036,0.03,0.038,0.024,0.029,0.036,0.039,0.029,0.024,0.023,0.03,0.025,0.028,0.037,0.043,0.026,0.027,0.012,0.015,0.034,0.029,0.013,0.018,0.021,0.035,0.034,0.028,0.02,0.017,0.015,0.014,0.019,0.027,0.023,0.02,0.028,0.022,0.027,0.019,0.022,0.021,0.024,0.019,0.021,0.029,0.03,0.017,0.021,0.018,0.018,0.023,0.015,0.027,0.027,0.022,0.016,0.018,1504,4533,2224,2699,2360,3155,2987,3281,2276,1550,564.0,3109,4285,1082,3149,11161,20954,2490,4438,4245,2972,4690,2430,5335,5588,5337,5949,4860,6449,5202,2431,1130,1161,6624,1935,1897,8885,7780,313,480,1308,2419,5976,2115,3629,1235,697,2224,2801,1610,706,3389,1775,4897,379,3803,1410,3124,1236,1589,672,2566,613,966,3282,1365,4344,2310,2001,1496,1365,2329,7793,389,0.105,0.119,0.097,0.119,0.144,0.129,0.101,0.127,0.177,0.129,0.137,0.134,0.145,0.122,0.14,0.142,0.134,0.138,0.135,0.153,0.09,0.126,0.131,0.138,0.125,0.123,0.112,0.112,0.109,0.135,0.137,0.12,0.109,0.136,0.073,0.081,0.136,0.139,0.094,0.104,0.107,0.136,0.133,0.125,0.105,0.105,0.095,0.089,0.103,0.126,0.117,0.107,0.124,0.112,0.138,0.111,0.115,0.116,0.129,0.11,0.112,0.133,0.14,0.102,0.111,0.11,0.106,0.115,0.088,0.138,0.124,0.111,0.096,0.12,419,1637,870,1017,873,1060,919,1047,564,415,174.0,1523,1285,272,882,2918,5749,541,1144,1808,749,1390,1031,1541,1466,1518,2023,1454,1686,1493,659,513,377,1366,520,654,2319,1796,169,245,670,1246,1219,1305,2054,603,357,907,1246,611,406,1633,888,2140,153,1790,637,1359,678,798,359,917,263,530,1608,894,2008,1047,886,620,592,1154,3342,243,3.47,2.638,2.613,2.636,2.307,2.484,2.59,2.764,3.019,2.843,2.468,1.846,2.661,2.766,2.715,2.807,2.879,3.56,2.929,2.012,2.846,2.633,2.143,2.671,2.943,2.864,2.432,2.538,2.902,2.68,2.666,2.361,2.487,3.31,3.274,2.642,2.809,3.127,2.117,2.18,2.592,1.8,3.345,1.839,1.992,2.384,2.531,2.927,2.786,3.103,2.17,2.224,2.372,2.441,2.655,2.292,2.608,2.494,2.287,2.144,2.294,2.788,2.503,2.28,2.305,1.886,2.282,2.508,2.62,2.478,2.499,2.43,2.593,2.026,0.703,0.505,0.407,0.466,0.576,0.524,0.463,0.509,0.544,0.708,0.822,0.535,0.435,0.562,0.494,0.516,0.582,0.805,0.619,0.612,0.777,0.549,0.471,0.743,0.603,0.547,0.589,0.701,0.471,0.467,0.626,0.906,0.441,0.562,0.74,0.451,0.742,0.585,0.407,0.346,0.465,0.553,0.72,0.598,0.573,0.405,0.623,0.668,0.399,0.499,0.38,0.47,0.435,0.409,0.566,0.38,0.425,0.525,0.385,0.407,0.294,0.717,0.737,0.658,0.474,0.619,0.44,0.38,0.362,0.686,0.392,0.464,0.38,0.458
-4053,Not Epileptic,M,55.0,0.9,2.5,1.0,1.7,2.7,1.4,2.5,2.1,1.7,1.1,0.7,2.8,1.5,0.7,1.3,6.7,10.3,1.3,2.5,5.0,2.8,2.8,1.9,4.0,3.1,3.8,4.9,4.3,4.3,2.5,1.6,1.8,0.6,2.2,0.4,0.7,5.0,3.1,0.4,0.1,1.4,3.3,2.1,3.2,4.8,0.9,0.9,1.0,1.4,0.9,0.7,2.8,1.4,2.6,0.4,2.2,0.6,1.6,0.9,1.0,0.7,2.2,0.4,0.6,2.4,1.9,3.6,1.6,1.6,0.5,1.0,1.4,4.1,0.4,6,24,9,15,15,13,21,17,16,10,6.0,30,17,10,14,65,89,11,29,45,20,41,22,40,38,38,52,40,29,32,15,15,4,27,2,4,56,39,2,1,9,29,26,28,27,7,5,6,8,10,5,21,10,16,3,17,8,12,7,9,7,15,2,5,20,25,27,11,10,5,7,11,32,4,0.037,0.024,0.016,0.03,0.041,0.024,0.031,0.027,0.049,0.039,0.053,0.025,0.025,0.044,0.029,0.038,0.031,0.039,0.031,0.032,0.04,0.033,0.026,0.041,0.031,0.031,0.035,0.034,0.031,0.023,0.04,0.056,0.025,0.024,0.013,0.013,0.038,0.031,0.023,0.013,0.026,0.039,0.033,0.029,0.029,0.02,0.031,0.015,0.018,0.018,0.028,0.028,0.024,0.021,0.014,0.018,0.014,0.019,0.016,0.018,0.023,0.032,0.017,0.021,0.021,0.029,0.02,0.021,0.022,0.02,0.025,0.016,0.016,0.02,1491,4306,2158,2419,1882,2974,3082,3580,1874,1751,812.0,3477,3147,1157,2434,11102,18909,2298,4614,5479,4288,5457,2726,5699,5512,7311,6581,5221,7202,5913,2229,1443,1035,5684,1611,1997,7853,6888,464,343,1415,3031,5921,2678,4173,1412,932,1894,2161,1782,714,3732,1563,3799,697,3733,1258,3022,1333,1698,1000,2611,501,878,3442,1413,5348,2624,2315,805,1447,2450,8764,451,0.111,0.121,0.103,0.127,0.131,0.117,0.109,0.122,0.149,0.142,0.156,0.128,0.119,0.166,0.128,0.136,0.125,0.142,0.134,0.139,0.132,0.133,0.128,0.149,0.12,0.125,0.119,0.116,0.109,0.116,0.144,0.149,0.095,0.121,0.067,0.082,0.142,0.133,0.116,0.095,0.119,0.138,0.143,0.132,0.115,0.109,0.135,0.083,0.098,0.117,0.125,0.126,0.111,0.103,0.101,0.098,0.101,0.106,0.104,0.108,0.122,0.135,0.1,0.114,0.11,0.122,0.107,0.108,0.108,0.116,0.119,0.102,0.094,0.117,457,1749,926,956,939,1001,1145,1270,594,527,260.0,1649,1011,341,848,3291,5579,531,1421,2598,1101,1511,1104,1814,1649,2172,2368,1913,1961,1857,717,632,342,1486,490,742,2233,1807,248,177,829,1346,1308,1800,2569,706,423,943,1160,771,427,1643,915,1917,385,1864,677,1389,815,892,535,1071,307,479,1790,1094,2688,1198,1054,465,691,1269,3889,296,3.238,2.328,2.355,2.391,1.896,2.412,2.318,2.489,2.442,2.681,2.401,1.863,2.464,2.669,2.299,2.523,2.732,3.277,2.575,1.822,2.994,2.655,2.081,2.433,2.594,2.681,2.283,2.205,2.84,2.52,2.433,2.393,2.432,2.764,2.988,2.52,2.693,2.749,2.062,2.27,2.05,1.918,3.247,1.65,1.833,2.231,2.59,2.42,2.338,2.336,2.002,2.247,1.88,2.197,2.131,2.163,2.066,2.443,1.833,2.08,2.132,2.398,1.805,2.183,2.049,1.609,2.198,2.448,2.372,2.034,2.316,2.352,2.452,1.885,0.634,0.485,0.51,0.514,0.507,0.666,0.527,0.506,0.654,0.658,1.053,0.509,0.387,0.33,0.47,0.65,0.587,0.721,0.585,0.616,0.816,0.57,0.543,0.72,0.626,0.572,0.529,0.632,0.549,0.569,0.589,0.856,0.415,0.668,0.77,0.451,0.67,0.665,0.476,0.278,0.444,0.604,0.732,0.657,0.57,0.473,0.447,0.656,0.42,0.533,0.411,0.504,0.469,0.446,0.33,0.448,0.363,0.519,0.346,0.353,0.353,0.522,0.312,0.561,0.551,0.556,0.425,0.435,0.475,0.518,0.451,0.444,0.423,0.435
-4054,Not Epileptic,F,20.0,0.6,1.3,0.7,0.9,1.4,3.0,1.7,1.3,1.0,0.4,0.3,3.1,1.1,0.5,1.1,5.8,7.5,0.5,3.9,4.8,1.7,2.3,1.6,2.2,2.8,3.6,4.1,2.2,2.3,2.1,1.6,0.8,0.5,2.5,0.4,1.1,3.1,3.6,0.1,0.1,0.8,3.3,2.4,3.0,2.0,0.6,0.3,0.7,0.8,0.9,0.8,1.5,0.8,2.1,0.5,2.3,0.7,1.5,0.7,0.6,0.8,0.8,0.1,0.5,1.8,0.8,2.1,1.1,1.1,0.3,0.5,1.4,3.8,0.4,4,12,6,10,10,21,13,13,11,4,3.0,22,13,5,9,62,62,4,29,31,14,26,17,25,30,32,40,19,21,25,16,7,5,24,3,9,37,38,1,0,4,33,22,24,12,4,2,4,4,8,4,13,5,17,3,19,5,9,4,5,4,5,1,3,15,8,16,9,7,2,4,9,28,3,0.029,0.017,0.015,0.022,0.044,0.039,0.026,0.031,0.055,0.028,0.034,0.036,0.031,0.031,0.03,0.036,0.032,0.021,0.038,0.039,0.03,0.034,0.029,0.035,0.036,0.036,0.033,0.026,0.027,0.027,0.053,0.035,0.028,0.031,0.02,0.031,0.035,0.035,0.014,0.008,0.017,0.033,0.037,0.025,0.021,0.013,0.015,0.012,0.013,0.019,0.039,0.019,0.021,0.019,0.025,0.025,0.015,0.018,0.023,0.021,0.026,0.021,0.016,0.026,0.023,0.024,0.019,0.017,0.024,0.015,0.015,0.023,0.018,0.022,1499,4911,2370,2357,1579,3111,2805,3142,1749,959,593.0,2833,3554,1130,3529,13308,18392,2360,4630,3093,3212,4038,2334,4871,5837,6394,6625,3889,5780,4772,2454,965,1105,5859,1698,2219,5779,6622,372,325,1896,3027,5294,3204,2855,1338,885,2068,2240,1765,396,3391,1856,5126,525,3355,1625,2990,957,818,1062,1808,312,773,3296,1125,3770,2413,1617,921,1105,2029,8703,465,0.097,0.097,0.096,0.101,0.137,0.141,0.106,0.123,0.148,0.121,0.113,0.133,0.122,0.123,0.098,0.135,0.122,0.085,0.122,0.137,0.103,0.15,0.138,0.128,0.141,0.134,0.136,0.103,0.111,0.116,0.141,0.121,0.121,0.121,0.074,0.113,0.122,0.124,0.082,0.054,0.091,0.135,0.126,0.118,0.097,0.092,0.096,0.08,0.078,0.108,0.141,0.104,0.102,0.096,0.138,0.118,0.094,0.095,0.115,0.111,0.118,0.105,0.101,0.13,0.112,0.116,0.102,0.103,0.117,0.1,0.099,0.108,0.093,0.121,351,1231,687,757,504,1296,875,839,361,256,157.0,1271,735,231,603,2812,3914,402,1633,1808,845,1168,948,1113,1465,1714,2121,1288,1413,1340,491,415,363,1272,394,627,1606,1735,182,125,723,1464,1179,1923,1670,608,311,834,972,766,294,1327,734,1883,264,1490,699,1430,503,488,432,557,144,294,1329,545,1775,997,727,325,509,1015,3522,271,3.789,3.253,3.075,2.904,2.382,2.363,2.582,3.214,2.883,3.193,3.047,1.957,3.34,3.111,3.298,3.22,3.437,3.931,2.436,1.673,3.026,2.683,2.177,2.93,2.953,2.927,2.437,2.303,2.996,2.656,3.261,2.351,2.551,3.026,3.52,2.837,2.746,2.767,2.376,2.518,3.145,1.902,3.083,1.852,1.941,2.392,3.526,3.218,2.801,2.484,1.719,2.478,2.591,2.54,2.685,2.388,2.793,2.5,2.31,1.901,3.216,3.254,2.36,3.235,2.573,2.436,2.364,2.962,2.598,3.143,2.489,2.378,2.704,2.077,0.888,0.866,0.902,0.598,0.716,0.631,0.719,0.859,0.804,0.45,1.017,0.62,0.717,0.521,0.84,0.753,0.721,0.767,0.702,0.516,0.858,0.424,0.556,0.773,0.683,0.601,0.589,0.618,0.51,0.718,0.788,1.003,0.485,0.719,0.63,0.75,0.765,0.6,0.421,0.793,0.825,0.532,0.741,0.741,0.591,0.68,0.508,0.795,0.684,0.622,0.461,0.688,0.761,0.557,0.362,0.465,0.751,0.502,0.396,0.382,0.81,0.929,0.496,0.934,0.679,0.607,0.577,0.59,0.394,0.839,0.494,0.639,0.566,0.441
-4055,Not Epileptic,F,52.0,1.1,2.8,1.0,1.4,1.4,1.5,1.4,1.5,1.1,0.9,0.2,2.1,1.3,0.4,1.2,5.2,7.0,0.6,2.1,3.8,0.8,2.3,2.3,3.2,3.9,2.6,4.6,2.5,2.3,3.5,1.0,1.2,0.6,1.9,0.5,0.6,3.4,3.3,0.1,0.3,0.9,2.7,2.1,2.0,3.6,0.7,0.3,1.1,1.2,0.6,0.6,1.7,1.5,1.8,0.4,3.3,0.8,1.6,1.0,1.3,0.6,1.8,0.4,0.6,1.4,1.4,2.3,1.4,0.9,0.7,1.0,1.6,3.6,0.3,12,29,8,11,15,15,11,12,12,7,3.0,21,13,3,12,53,63,4,23,35,7,33,24,41,38,29,48,24,22,38,13,10,5,22,3,6,42,37,1,2,6,25,21,15,23,5,1,7,6,4,5,11,11,15,2,25,6,12,8,13,4,15,3,7,16,21,19,11,6,5,8,11,31,3,0.046,0.028,0.02,0.026,0.032,0.033,0.021,0.029,0.04,0.036,0.034,0.029,0.026,0.026,0.027,0.032,0.031,0.029,0.03,0.035,0.019,0.026,0.03,0.037,0.033,0.028,0.033,0.029,0.024,0.027,0.028,0.046,0.031,0.024,0.014,0.016,0.033,0.029,0.016,0.022,0.02,0.035,0.035,0.026,0.027,0.012,0.017,0.021,0.02,0.021,0.029,0.018,0.028,0.021,0.014,0.02,0.024,0.017,0.024,0.02,0.017,0.03,0.026,0.017,0.017,0.025,0.019,0.02,0.018,0.024,0.019,0.021,0.015,0.022,1348,4748,2218,2426,1807,2410,3187,2950,1705,1435,436.0,2068,3011,878,2410,8985,14910,1940,4052,3805,2877,4615,2444,5608,5910,5929,7024,4068,5575,7222,2217,1329,854,5555,2041,1666,7263,7548,268,478,1298,2083,5589,2095,3531,1588,712,1950,1912,1138,621,3069,1676,3772,751,5135,1440,3043,1247,2021,1063,2357,438,895,2576,1160,3730,2736,1635,754,1637,2269,8981,529,0.15,0.132,0.109,0.116,0.14,0.135,0.101,0.126,0.166,0.144,0.118,0.127,0.121,0.129,0.123,0.141,0.129,0.107,0.131,0.143,0.081,0.115,0.119,0.143,0.114,0.125,0.121,0.1,0.104,0.127,0.117,0.128,0.122,0.122,0.077,0.088,0.133,0.137,0.099,0.106,0.11,0.125,0.135,0.12,0.121,0.091,0.093,0.087,0.099,0.114,0.149,0.098,0.131,0.105,0.092,0.11,0.125,0.095,0.122,0.11,0.105,0.128,0.156,0.119,0.108,0.124,0.108,0.104,0.097,0.125,0.119,0.118,0.094,0.127,465,1676,813,883,817,883,958,835,502,372,144.0,1074,820,217,647,2629,3848,408,1139,1693,698,1259,1145,1613,1783,1624,2267,1310,1377,2226,638,480,297,1238,543,510,1889,1803,163,209,686,1107,1143,1305,2108,769,295,925,913,511,327,1432,811,1549,392,2508,558,1494,682,1038,439,947,209,517,1322,872,1871,1149,748,426,768,1151,3703,223,2.852,2.599,2.639,2.671,2.018,2.467,2.581,2.983,2.753,2.922,2.602,1.673,2.848,2.901,2.758,2.683,3.045,3.761,2.87,1.976,2.93,2.664,1.964,2.708,2.656,2.946,2.505,2.408,3.024,2.548,2.536,2.599,2.643,3.198,3.509,2.87,2.876,3.113,2.045,2.695,2.338,1.736,3.453,1.869,1.912,2.345,2.906,2.586,2.564,2.686,2.068,2.334,2.27,2.537,2.161,2.297,2.649,2.311,2.098,2.149,2.321,2.463,2.075,2.163,2.142,1.661,2.186,2.551,2.557,2.103,2.361,2.424,2.59,2.533,0.574,0.509,0.555,0.469,0.509,0.467,0.574,0.315,0.439,0.553,0.627,0.543,0.357,0.39,0.44,0.47,0.561,0.775,0.533,0.601,0.754,0.427,0.579,0.765,0.642,0.529,0.562,0.624,0.497,0.504,0.599,0.839,0.583,0.483,0.642,0.427,0.677,0.585,0.484,0.487,0.442,0.464,0.738,0.581,0.578,0.39,0.545,0.526,0.38,0.435,0.38,0.463,0.363,0.498,0.388,0.484,0.358,0.411,0.312,0.383,0.491,0.625,0.5,0.869,0.499,0.47,0.451,0.464,0.407,0.484,0.514,0.438,0.443,0.492
-4056,Not Epileptic,F,47.0,0.6,1.7,1.0,0.8,2.0,2.3,1.6,1.5,1.0,0.6,0.2,2.5,1.8,0.6,0.9,4.3,8.8,0.7,2.9,4.2,1.6,2.1,1.2,2.5,2.1,3.3,2.6,2.3,2.7,3.3,1.2,1.1,0.7,2.2,0.6,0.6,2.3,4.3,0.4,0.1,0.9,2.1,2.1,2.8,3.1,0.5,0.5,0.8,0.9,0.9,0.7,1.1,1.7,2.0,0.5,2.3,0.9,1.7,0.6,0.7,0.9,0.8,0.3,0.4,2.0,1.2,2.4,1.2,1.0,0.3,0.9,1.7,4.0,0.2,4,13,8,7,17,22,13,12,12,5,2.0,18,18,7,11,42,68,6,25,33,12,25,13,29,25,34,26,23,22,31,14,8,6,24,4,5,28,49,2,1,5,19,21,21,19,4,3,5,5,7,5,9,20,14,2,16,4,15,5,6,7,5,2,4,14,12,18,8,7,3,6,13,29,2,0.033,0.023,0.017,0.019,0.033,0.031,0.027,0.028,0.044,0.03,0.019,0.03,0.036,0.03,0.034,0.03,0.03,0.028,0.032,0.034,0.026,0.03,0.025,0.034,0.029,0.032,0.026,0.025,0.027,0.03,0.036,0.048,0.023,0.026,0.021,0.018,0.033,0.034,0.024,0.017,0.021,0.032,0.037,0.028,0.025,0.013,0.022,0.013,0.014,0.018,0.035,0.018,0.029,0.019,0.021,0.02,0.028,0.021,0.021,0.017,0.027,0.018,0.037,0.018,0.028,0.032,0.023,0.02,0.018,0.02,0.027,0.028,0.02,0.014,1269,3974,2557,2107,2438,3226,2506,2921,1648,1144,502.0,2265,3400,1297,1875,8509,16767,2433,4380,3491,3293,4221,1760,5304,4235,7126,4629,3907,5668,5104,1728,923,1316,6100,1912,1650,4900,8581,516,303,1498,2368,4519,2518,3255,1153,835,2183,2343,1560,438,2452,2841,4569,568,3711,1204,2544,764,1046,1283,1529,324,745,2470,883,3653,2308,1673,716,1128,2554,9069,505,0.104,0.107,0.088,0.093,0.139,0.14,0.104,0.128,0.151,0.126,0.097,0.122,0.134,0.146,0.143,0.128,0.115,0.102,0.134,0.124,0.1,0.138,0.131,0.131,0.133,0.134,0.122,0.112,0.116,0.129,0.127,0.131,0.102,0.119,0.078,0.085,0.122,0.139,0.125,0.111,0.1,0.133,0.131,0.122,0.108,0.084,0.115,0.078,0.082,0.101,0.138,0.096,0.113,0.094,0.115,0.105,0.115,0.11,0.122,0.101,0.125,0.102,0.16,0.12,0.12,0.131,0.113,0.106,0.104,0.123,0.127,0.135,0.096,0.099,324,1218,751,697,913,1177,905,887,459,324,173.0,1198,862,307,534,2301,4260,419,1404,1935,909,1220,793,1290,1265,1896,1607,1339,1593,1805,510,438,440,1331,495,562,1326,2147,206,129,669,1014,1055,1616,2020,659,329,901,1005,807,306,1069,1139,1721,307,1761,529,1307,461,655,542,591,138,353,1252,533,1705,966,882,286,539,1074,3596,227,3.687,3.024,3.041,2.724,2.363,2.457,2.267,2.914,2.737,3.008,2.689,1.678,2.977,3.159,2.852,2.759,3.111,3.898,2.678,1.647,2.805,2.609,1.928,2.936,2.522,2.949,2.289,2.288,2.725,2.352,2.553,2.401,2.498,3.155,3.353,2.556,2.791,2.883,2.838,2.618,2.889,1.991,3.231,1.743,1.876,1.827,3.259,2.829,2.755,2.34,1.894,2.45,2.408,2.722,2.342,2.304,2.764,2.216,2.005,1.88,2.545,2.668,2.404,2.23,2.236,2.281,2.381,2.734,2.314,2.995,2.484,2.872,2.689,2.699,1.007,0.59,0.78,0.86,0.641,0.603,0.614,0.74,0.538,0.536,0.877,0.45,0.5,0.604,0.548,0.579,0.717,0.804,0.714,0.535,0.782,0.477,0.513,0.75,0.532,0.551,0.524,0.61,0.664,0.621,0.648,0.892,0.544,0.599,0.956,0.598,0.79,0.657,0.703,0.479,0.716,0.654,0.794,0.63,0.669,0.48,0.639,0.888,0.681,0.404,0.442,0.415,0.616,0.538,0.353,0.622,0.516,0.557,0.428,0.434,0.504,0.692,0.447,0.877,0.557,0.713,0.539,0.449,0.434,0.792,0.54,0.569,0.628,0.504
-4057,Not Epileptic,F,55.0,0.5,2.2,0.9,1.4,2.0,1.6,1.5,1.9,1.0,0.6,0.4,2.7,1.2,0.8,0.8,6.1,9.0,1.2,1.9,3.7,1.1,2.3,1.9,3.4,2.5,3.5,4.2,3.6,2.8,3.0,2.2,1.1,0.4,2.7,0.4,0.8,4.3,2.6,0.1,0.1,1.1,3.4,2.3,2.0,2.7,0.6,0.6,1.2,1.3,1.2,0.4,1.7,1.5,2.5,0.3,1.8,0.5,1.2,1.0,1.1,0.4,2.5,0.6,0.6,1.9,1.0,2.3,1.4,1.5,0.4,0.9,1.0,4.9,0.3,7,19,8,13,19,21,15,18,11,8,3.0,28,15,7,12,76,81,7,24,44,14,34,20,40,34,42,49,34,28,33,18,8,5,29,3,7,49,36,1,1,7,34,22,16,15,5,3,8,7,10,3,14,9,21,1,15,4,11,11,11,3,21,5,5,20,17,19,13,10,4,5,9,43,3,0.032,0.025,0.016,0.029,0.041,0.025,0.026,0.028,0.038,0.029,0.033,0.034,0.028,0.036,0.024,0.034,0.031,0.042,0.027,0.034,0.021,0.027,0.035,0.036,0.03,0.033,0.034,0.04,0.029,0.026,0.051,0.038,0.021,0.031,0.014,0.019,0.039,0.032,0.016,0.012,0.021,0.038,0.03,0.024,0.018,0.015,0.025,0.018,0.019,0.018,0.018,0.018,0.022,0.018,0.024,0.014,0.018,0.017,0.023,0.02,0.026,0.033,0.033,0.018,0.022,0.02,0.019,0.019,0.02,0.016,0.019,0.013,0.021,0.02,1305,4557,2175,2201,2279,3186,3149,3411,1735,1598,517.0,3096,3230,1308,2321,11297,17231,2182,4514,4983,4535,5015,2041,6055,5411,7121,6415,4565,6380,6457,2275,1559,1218,5874,1848,1795,7951,6621,267,380,1476,2499,6623,2330,3916,1286,782,2424,2085,2466,590,3014,1748,5161,386,3533,933,2841,1774,1607,509,3053,828,942,2740,1205,3733,2466,2239,725,1562,1973,9235,444,0.11,0.12,0.095,0.133,0.145,0.136,0.105,0.13,0.15,0.131,0.118,0.154,0.131,0.152,0.118,0.148,0.138,0.141,0.121,0.153,0.099,0.129,0.136,0.134,0.124,0.133,0.14,0.125,0.108,0.133,0.158,0.105,0.096,0.138,0.075,0.1,0.155,0.144,0.108,0.087,0.112,0.141,0.125,0.117,0.096,0.098,0.116,0.095,0.101,0.103,0.096,0.098,0.119,0.102,0.128,0.096,0.118,0.101,0.132,0.111,0.121,0.133,0.133,0.103,0.119,0.124,0.104,0.111,0.107,0.097,0.103,0.099,0.106,0.125,349,1493,839,852,895,1103,997,1169,478,392,190.0,1395,918,346,682,3437,5098,454,1237,2011,1036,1484,1000,1889,1505,1982,2135,1621,1647,1939,780,565,432,1416,472,675,2083,1634,138,209,756,1421,1417,1413,2203,650,347,977,971,1033,375,1551,949,2284,171,1864,436,1252,718,863,279,1270,336,514,1475,781,1923,1071,1081,412,712,1081,3858,219,3.554,2.729,2.574,2.557,2.215,2.536,2.532,2.632,2.681,2.994,2.58,1.955,2.724,2.933,2.544,2.483,2.64,3.493,2.894,2.167,3.29,2.569,1.862,2.452,2.636,2.781,2.332,2.198,2.855,2.637,2.301,2.811,2.471,2.973,3.307,2.481,2.774,2.873,2.367,2.064,2.438,1.669,3.34,1.863,2.003,2.139,2.85,3.051,2.581,2.479,1.871,2.081,1.952,2.326,2.44,2.087,2.353,2.453,2.326,2.074,2.216,2.399,2.208,2.173,2.015,1.827,2.106,2.483,2.324,2.048,2.533,2.164,2.404,2.426,0.858,0.56,0.473,0.48,0.743,0.622,0.617,0.463,0.649,0.606,0.626,0.663,0.486,0.421,0.483,0.638,0.621,0.744,0.618,0.617,0.854,0.639,0.525,0.761,0.694,0.623,0.699,0.724,0.627,0.611,0.778,0.878,0.372,0.607,0.515,0.473,0.817,0.659,0.411,0.42,0.479,0.481,0.758,0.542,0.603,0.447,0.475,0.807,0.476,0.511,0.364,0.414,0.459,0.414,0.594,0.406,0.492,0.563,0.594,0.315,0.338,0.622,0.548,0.709,0.572,0.515,0.422,0.445,0.45,0.543,0.339,0.466,0.478,0.466
-4058,Not Epileptic,M,52.0,1.2,3.4,1.5,1.2,2.6,2.5,2.2,2.2,2.1,0.8,0.2,3.7,1.7,0.6,1.5,6.5,9.8,0.9,2.8,5.4,1.5,3.6,2.1,4.7,3.3,3.6,5.1,3.2,3.5,4.0,1.5,1.6,0.6,2.3,0.4,0.8,4.6,3.2,0.2,0.2,1.3,4.3,2.8,2.6,3.4,1.7,0.6,1.3,1.5,0.9,0.9,2.5,1.4,2.7,0.4,2.7,0.9,1.6,1.3,1.5,0.6,2.5,0.3,0.7,2.0,1.8,2.5,1.4,1.3,0.4,1.1,1.7,4.1,0.4,7,32,15,11,23,24,20,18,19,11,3.0,37,22,7,16,74,93,11,33,54,12,44,26,49,41,43,61,28,30,41,20,13,5,27,3,6,49,38,2,2,8,43,29,20,18,15,2,7,8,7,6,19,12,18,2,24,6,15,7,11,5,21,3,6,18,19,20,9,8,5,14,13,29,3,0.044,0.031,0.024,0.025,0.037,0.028,0.035,0.034,0.051,0.037,0.027,0.036,0.031,0.03,0.024,0.036,0.033,0.04,0.033,0.039,0.029,0.033,0.028,0.042,0.03,0.029,0.033,0.035,0.03,0.03,0.032,0.048,0.024,0.029,0.019,0.014,0.037,0.031,0.014,0.018,0.024,0.039,0.032,0.023,0.026,0.031,0.024,0.016,0.02,0.016,0.029,0.026,0.025,0.022,0.03,0.017,0.022,0.018,0.022,0.024,0.027,0.031,0.017,0.017,0.018,0.028,0.021,0.019,0.021,0.014,0.025,0.017,0.017,0.02,1523,5094,2329,2147,2433,4529,3116,3005,1713,1527,591.0,3682,3686,1115,2816,9925,18052,2359,5143,5889,4707,5864,2577,6622,5923,7273,6688,3774,6473,6468,2486,1450,1168,5698,1973,2342,8746,7818,472,495,1673,3430,6965,2740,3656,1842,791,2738,2239,1933,797,3416,1740,4056,456,4243,1504,3287,1365,1657,1016,3125,414,1100,3037,1457,3580,2315,1896,794,1623,2985,8436,484,0.132,0.133,0.129,0.115,0.142,0.131,0.117,0.134,0.155,0.159,0.123,0.148,0.132,0.135,0.126,0.146,0.136,0.137,0.145,0.151,0.105,0.13,0.131,0.141,0.127,0.135,0.122,0.124,0.105,0.141,0.136,0.126,0.104,0.133,0.088,0.084,0.145,0.133,0.091,0.108,0.117,0.141,0.133,0.119,0.112,0.129,0.105,0.086,0.101,0.1,0.147,0.118,0.128,0.111,0.125,0.106,0.113,0.106,0.11,0.12,0.129,0.136,0.113,0.111,0.109,0.123,0.109,0.104,0.105,0.104,0.127,0.102,0.096,0.111,444,1803,891,801,1142,1444,1109,990,624,412,181.0,1710,1096,292,932,3145,5203,513,1487,2631,985,1885,1203,1972,1911,2175,2579,1397,1766,2165,798,585,372,1349,450,820,2276,1828,259,241,832,1720,1466,1765,2048,847,358,1141,1102,801,412,1637,914,1897,233,2326,658,1545,805,958,374,1268,262,588,1556,877,1772,1095,950,474,805,1379,3614,301,3.274,2.561,2.54,2.652,1.972,2.383,2.373,2.68,2.192,2.925,2.874,1.866,2.667,2.697,2.376,2.477,2.726,3.563,2.78,1.939,3.387,2.443,1.934,2.622,2.449,2.667,2.095,2.175,2.813,2.386,2.457,2.56,2.438,3.024,3.703,2.619,2.878,3.108,2.07,2.536,2.505,1.845,3.364,1.723,2.034,2.123,2.651,2.797,2.517,2.573,2.074,2.175,2.038,2.361,2.395,1.965,2.452,2.397,1.883,1.914,2.606,2.445,1.853,2.284,2.134,2.121,2.081,2.47,2.3,1.997,2.141,2.48,2.493,1.974,0.857,0.618,0.537,0.475,0.508,0.758,0.579,0.424,0.618,0.692,0.734,0.55,0.441,0.351,0.525,0.502,0.595,0.711,0.592,0.578,0.922,0.558,0.484,0.837,0.553,0.558,0.521,0.582,0.489,0.642,0.573,0.843,0.315,0.623,0.97,0.422,0.785,0.546,0.341,0.554,0.525,0.482,0.685,0.548,0.614,0.519,0.45,0.877,0.511,0.473,0.429,0.429,0.458,0.437,0.372,0.416,0.377,0.492,0.41,0.392,0.575,0.541,0.337,0.7,0.526,0.728,0.503,0.371,0.436,0.504,0.513,0.439,0.431,0.321
-4059,Not Epileptic,M,37.0,1.1,3.6,1.1,1.7,2.1,2.0,3.3,1.6,0.9,1.3,0.5,3.0,1.9,0.5,1.6,7.1,10.1,0.9,2.1,5.0,1.6,2.8,2.0,5.5,6.7,3.5,3.6,4.6,4.7,2.8,1.7,1.4,0.8,2.5,0.5,0.7,5.3,3.5,0.2,0.4,1.1,3.2,2.3,3.0,3.0,1.0,0.6,1.0,1.8,0.8,0.6,2.2,1.6,3.0,0.2,3.4,0.7,1.9,0.8,1.1,0.8,2.8,0.6,0.8,1.6,1.9,2.8,1.3,1.8,0.6,1.2,1.9,6.7,0.4,9,30,11,16,18,20,25,21,10,16,6.0,25,24,6,18,72,91,8,24,50,13,41,17,53,60,41,38,36,38,33,16,10,7,30,3,7,57,52,2,2,6,29,23,20,16,6,3,7,10,5,4,17,13,21,2,27,7,12,7,8,9,22,3,6,14,21,23,9,10,5,11,15,54,4,0.038,0.035,0.016,0.022,0.039,0.028,0.037,0.025,0.038,0.038,0.038,0.031,0.027,0.034,0.032,0.033,0.03,0.032,0.031,0.037,0.026,0.031,0.036,0.045,0.043,0.032,0.029,0.038,0.035,0.028,0.042,0.04,0.03,0.031,0.014,0.015,0.041,0.024,0.015,0.017,0.022,0.033,0.033,0.023,0.019,0.018,0.023,0.017,0.02,0.012,0.026,0.021,0.023,0.022,0.017,0.017,0.026,0.02,0.021,0.019,0.024,0.035,0.021,0.021,0.018,0.023,0.018,0.016,0.022,0.018,0.021,0.019,0.019,0.021,1659,5584,3023,3577,2598,3340,3352,3934,1566,2104,864.0,2937,4218,869,3284,13447,21870,2653,5070,5936,5456,5316,1954,6898,7192,6777,6306,4578,7189,6378,2962,1551,1251,6994,2163,2230,9208,9111,335,595,1663,3118,6751,3407,4198,1905,918,2283,2784,1952,464,3716,2143,5003,361,4725,1215,3425,1041,1316,1405,2987,635,1113,2206,1860,4695,2497,2498,920,2025,3596,12706,494,0.131,0.137,0.094,0.115,0.139,0.131,0.119,0.129,0.154,0.165,0.141,0.131,0.124,0.128,0.139,0.136,0.129,0.124,0.131,0.161,0.108,0.128,0.133,0.148,0.142,0.134,0.115,0.12,0.108,0.129,0.143,0.118,0.114,0.132,0.08,0.086,0.147,0.125,0.101,0.103,0.105,0.132,0.132,0.111,0.097,0.098,0.112,0.095,0.106,0.087,0.134,0.107,0.112,0.106,0.126,0.107,0.109,0.102,0.116,0.112,0.123,0.144,0.109,0.115,0.111,0.116,0.099,0.096,0.108,0.117,0.117,0.107,0.105,0.129,517,1749,1062,1236,975,1223,1216,1172,452,600,235.0,1427,1219,250,957,3733,5756,559,1218,2394,1084,1658,850,2140,2475,2006,2087,1819,1902,1893,750,578,401,1479,561,729,2344,2496,185,311,807,1416,1380,2004,2341,868,371,945,1250,919,314,1763,1141,2211,164,2707,518,1498,682,819,539,1296,403,592,1177,1197,2411,1115,1177,484,889,1561,5183,292,3.215,2.788,2.644,2.687,2.271,2.297,2.297,2.847,2.529,2.82,2.823,1.78,2.707,2.423,2.548,2.608,2.875,3.738,3.161,2.129,3.438,2.43,1.996,2.462,2.309,2.727,2.35,2.117,2.889,2.523,2.764,2.666,2.502,3.207,3.566,2.692,2.896,2.724,2.175,2.298,2.641,1.898,3.435,1.921,2.033,2.256,3.124,2.879,2.718,2.581,1.808,2.246,2.011,2.343,2.463,1.952,2.51,2.648,1.69,1.852,2.445,2.197,1.83,2.207,2.037,1.806,2.065,2.396,2.436,2.107,2.383,2.449,2.559,2.189,0.77,0.733,0.562,0.525,0.633,0.632,0.636,0.479,0.553,0.76,0.81,0.491,0.586,0.612,0.552,0.711,0.659,0.638,0.604,0.736,0.799,0.616,0.585,0.783,0.745,0.548,0.684,0.632,0.677,0.604,0.709,0.813,0.503,0.674,0.664,0.529,0.803,0.691,0.456,0.412,0.602,0.637,0.779,0.536,0.649,0.563,0.454,0.698,0.429,0.416,0.439,0.396,0.453,0.501,0.722,0.469,0.507,0.499,0.335,0.384,0.577,0.666,0.389,0.834,0.457,0.542,0.45,0.389,0.444,0.629,0.49,0.561,0.466,0.572
-4060,Not Epileptic,F,47.0,0.7,2.2,0.9,1.4,1.5,1.6,1.6,1.6,0.8,1.1,0.2,3.2,1.6,0.5,1.3,6.6,8.9,1.0,2.4,5.7,1.1,2.6,1.5,2.7,3.1,3.7,3.1,2.8,2.3,3.6,1.1,0.8,0.5,1.8,0.4,0.3,3.8,4.0,0.2,0.3,0.9,2.7,2.4,2.2,2.4,0.5,0.6,0.9,1.2,1.4,0.8,1.5,1.3,2.6,0.4,1.7,1.1,1.5,0.4,0.8,0.6,0.9,0.3,0.2,1.5,1.2,3.4,0.6,1.0,0.4,1.0,1.4,4.2,0.3,8,23,9,11,16,16,15,16,7,9,4.0,25,18,7,14,65,84,8,26,41,9,25,17,37,30,35,36,29,19,39,17,5,5,22,3,2,37,40,2,2,5,24,20,18,14,4,3,5,7,11,6,12,8,16,2,16,9,13,5,9,4,7,2,4,14,29,27,5,7,4,7,11,32,3,0.027,0.024,0.016,0.022,0.032,0.035,0.024,0.031,0.033,0.038,0.024,0.039,0.027,0.027,0.036,0.032,0.029,0.027,0.036,0.041,0.022,0.036,0.033,0.029,0.032,0.035,0.024,0.027,0.021,0.032,0.029,0.032,0.028,0.025,0.013,0.009,0.034,0.037,0.014,0.019,0.023,0.035,0.032,0.024,0.017,0.013,0.018,0.015,0.018,0.023,0.031,0.016,0.024,0.018,0.019,0.013,0.022,0.019,0.016,0.018,0.027,0.019,0.015,0.011,0.02,0.026,0.02,0.012,0.018,0.014,0.023,0.021,0.017,0.016,1460,4841,2090,2445,2220,2705,2631,3342,1444,1948,627.0,2502,3338,1281,2443,10988,18342,2518,4177,4964,3481,3532,2035,5788,5625,6565,5346,4198,5404,6008,2473,827,894,5340,1907,1532,7789,7960,321,436,1469,2628,5287,2293,3602,1199,1142,2009,2326,2534,664,3420,1758,4849,462,3131,1726,2884,833,1266,725,2183,451,812,2456,1173,4953,1860,1929,1063,1845,2478,9228,397,0.122,0.121,0.104,0.109,0.136,0.144,0.107,0.137,0.129,0.146,0.121,0.139,0.13,0.136,0.141,0.143,0.135,0.115,0.142,0.141,0.088,0.155,0.14,0.133,0.141,0.147,0.116,0.116,0.1,0.145,0.123,0.097,0.135,0.128,0.076,0.067,0.132,0.142,0.114,0.101,0.105,0.149,0.122,0.113,0.095,0.089,0.102,0.083,0.097,0.102,0.146,0.091,0.115,0.101,0.123,0.09,0.116,0.107,0.107,0.108,0.13,0.107,0.106,0.109,0.112,0.136,0.111,0.085,0.103,0.104,0.117,0.113,0.095,0.127,474,1587,852,928,831,919,908,1004,385,501,180.0,1229,1094,354,710,3303,5229,577,1192,2292,833,1194,776,1614,1602,1899,2081,1568,1516,1877,603,437,314,1175,521,555,2022,1914,175,194,687,1166,1250,1488,2039,631,483,896,1045,922,400,1549,844,2126,229,1797,777,1362,379,750,360,778,253,401,1250,697,2522,877,891,496,775,1137,3998,195,3.071,2.819,2.446,2.572,2.414,2.625,2.461,2.958,2.778,3.203,2.924,1.765,2.563,2.728,2.759,2.618,2.851,3.458,2.894,1.87,3.108,2.441,2.272,2.776,2.647,2.822,2.094,2.169,2.723,2.635,2.92,2.021,2.379,3.175,3.299,2.437,2.917,3.045,2.166,2.236,2.711,2.0,3.197,1.712,2.016,2.079,3.1,2.738,2.789,2.686,1.957,2.304,2.223,2.499,2.442,1.828,2.609,2.518,2.059,1.842,2.439,2.804,1.898,2.281,2.161,2.182,2.164,2.476,2.582,2.69,2.737,2.64,2.455,2.86,0.708,0.521,0.532,0.468,0.589,0.377,0.625,0.526,0.557,0.626,0.872,0.553,0.521,0.456,0.4,0.536,0.527,0.708,0.567,0.568,0.923,0.568,0.519,0.575,0.509,0.544,0.541,0.503,0.523,0.624,0.703,0.868,0.394,0.585,0.627,0.51,0.666,0.564,0.263,0.342,0.565,0.474,0.619,0.544,0.639,0.454,0.547,0.65,0.493,0.597,0.389,0.57,0.484,0.495,0.584,0.423,0.418,0.606,0.438,0.349,0.306,0.733,0.468,0.792,0.736,0.649,0.566,0.487,0.465,0.56,0.465,0.44,0.548,0.637
-4061,Not Epileptic,M,22.0,1.2,2.8,1.2,1.7,2.5,1.8,2.5,2.1,1.4,0.7,0.5,3.4,1.9,0.5,1.3,7.0,13.8,1.4,2.8,6.7,2.3,2.4,3.2,4.0,4.2,4.1,4.3,3.3,3.2,5.2,1.4,1.9,0.7,3.0,0.7,1.3,4.4,5.4,0.2,0.2,1.4,4.5,2.4,3.4,3.4,0.7,0.5,1.1,1.9,1.2,0.8,1.9,1.9,3.3,0.6,3.4,0.7,2.0,1.0,0.6,0.9,2.0,0.4,0.7,2.7,1.5,2.6,1.5,0.9,0.8,1.0,1.8,4.5,0.4,13,29,11,13,19,21,39,22,14,8,5.0,30,24,9,17,81,130,11,30,62,19,32,34,43,51,47,48,36,30,57,14,14,7,38,4,12,47,58,2,2,8,39,25,27,21,5,3,6,10,8,6,17,16,28,5,25,5,17,5,5,9,20,2,5,23,18,23,10,7,7,8,13,37,3,0.04,0.026,0.019,0.024,0.038,0.023,0.03,0.027,0.04,0.033,0.032,0.034,0.033,0.032,0.027,0.031,0.039,0.043,0.034,0.041,0.035,0.029,0.044,0.036,0.042,0.029,0.03,0.03,0.027,0.04,0.035,0.055,0.031,0.038,0.013,0.02,0.039,0.038,0.015,0.02,0.022,0.041,0.034,0.026,0.022,0.013,0.018,0.016,0.02,0.022,0.03,0.02,0.026,0.022,0.024,0.018,0.015,0.023,0.019,0.017,0.021,0.027,0.025,0.018,0.021,0.019,0.018,0.019,0.018,0.018,0.018,0.017,0.017,0.013,1955,5458,3000,2971,2241,3787,4126,4081,2091,1616,1255.0,3349,3867,1233,3572,13632,22276,2614,6172,6710,5394,4669,2318,7576,6315,8523,7535,5497,6774,7479,2267,1530,1404,6877,2844,3348,7067,10086,427,457,2005,3420,6105,4069,4130,1723,1173,2429,3183,1979,928,3479,2448,6397,757,5933,1814,3415,1337,1163,1239,3106,432,1244,4350,2399,5745,2622,1596,1439,1694,3208,9849,691,0.137,0.12,0.1,0.112,0.143,0.114,0.123,0.131,0.138,0.136,0.123,0.131,0.14,0.15,0.126,0.132,0.15,0.137,0.131,0.156,0.114,0.111,0.14,0.141,0.137,0.128,0.121,0.12,0.113,0.145,0.128,0.148,0.115,0.141,0.078,0.1,0.137,0.148,0.09,0.107,0.107,0.14,0.143,0.115,0.107,0.088,0.092,0.09,0.1,0.111,0.136,0.104,0.125,0.112,0.125,0.101,0.089,0.113,0.098,0.104,0.117,0.121,0.118,0.1,0.115,0.107,0.095,0.101,0.101,0.127,0.111,0.1,0.096,0.104,576,1745,1004,1121,982,1310,1370,1345,639,403,281.0,1546,1034,318,905,4045,6089,578,1441,2898,1172,1388,1036,2013,1714,2489,2471,1924,1877,2258,677,591,392,1597,741,1051,2053,2460,250,209,968,1615,1325,2159,2330,816,472,1019,1393,807,390,1479,1189,2372,394,2906,778,1536,751,612,569,1195,251,636,1993,1286,2465,1145,793,653,854,1542,4085,372,3.294,2.788,2.74,2.582,2.078,2.355,2.437,2.641,2.438,3.044,3.182,1.877,2.822,2.786,2.839,2.592,2.804,3.483,3.187,1.968,3.243,2.473,2.062,2.774,2.788,2.799,2.388,2.292,2.759,2.546,2.43,2.62,2.73,3.058,3.432,2.806,2.582,3.013,1.944,2.555,2.743,1.846,3.395,2.055,2.068,2.306,2.904,3.026,2.733,2.794,2.487,2.443,2.121,2.572,2.324,2.262,2.731,2.513,2.02,2.135,2.21,2.4,2.008,2.344,2.315,2.15,2.303,2.494,2.445,2.393,2.339,2.407,2.567,2.33,0.695,0.602,0.589,0.509,0.669,0.678,0.61,0.469,0.751,0.567,0.851,0.561,0.467,0.499,0.54,0.615,0.724,0.706,0.513,0.676,0.814,0.561,0.566,0.729,0.663,0.55,0.55,0.596,0.491,0.536,0.701,1.015,0.429,0.579,0.621,0.54,0.803,0.81,0.346,0.508,0.582,0.596,0.65,0.608,0.59,0.463,0.591,0.691,0.531,0.407,0.579,0.479,0.487,0.555,0.428,0.399,0.358,0.488,0.405,0.372,0.502,0.724,0.331,0.681,0.503,0.571,0.475,0.4,0.416,0.879,0.493,0.515,0.442,0.288
-4062,Not Epileptic,F,22.0,0.8,2.6,1.4,1.4,1.1,2.1,1.8,1.5,1.6,0.5,0.3,3.4,1.7,0.4,1.1,6.1,7.2,0.9,1.4,4.7,1.3,4.3,2.3,5.1,2.3,4.3,2.4,2.1,2.6,2.1,2.2,1.5,0.6,2.2,0.5,0.9,3.5,2.8,0.2,0.2,1.3,4.1,1.8,2.4,2.6,0.5,0.7,1.0,1.4,0.9,0.5,2.5,1.6,2.8,0.2,2.3,0.7,1.8,1.6,1.1,0.4,1.9,0.5,0.6,1.6,1.2,2.1,1.1,0.9,0.6,0.7,1.5,4.3,0.3,9,27,18,13,13,23,17,16,16,9,4.0,32,23,6,14,84,70,7,21,40,13,57,23,58,35,53,38,23,24,35,19,12,7,32,3,8,37,36,1,2,11,36,17,22,17,4,4,7,8,4,3,23,15,20,1,22,6,11,12,10,3,14,5,5,12,18,18,10,6,5,5,11,41,3,0.041,0.025,0.024,0.021,0.022,0.034,0.03,0.026,0.047,0.034,0.025,0.035,0.031,0.028,0.028,0.031,0.026,0.032,0.027,0.048,0.025,0.044,0.037,0.044,0.03,0.031,0.026,0.026,0.023,0.026,0.043,0.056,0.022,0.027,0.013,0.018,0.035,0.026,0.017,0.018,0.023,0.038,0.031,0.022,0.02,0.011,0.026,0.017,0.018,0.022,0.021,0.02,0.027,0.02,0.02,0.017,0.015,0.023,0.027,0.021,0.013,0.024,0.026,0.015,0.018,0.018,0.018,0.018,0.016,0.019,0.017,0.018,0.019,0.013,1456,5227,2861,2609,1944,3301,3008,3316,1841,1397,674.0,3195,3940,1187,2664,13004,17891,2151,3667,4465,3474,4619,2234,6431,5521,8685,5598,4178,6772,5281,2648,1301,1416,6020,2013,2099,7040,8164,374,561,1967,2768,5316,2796,3565,1371,906,2189,2444,1465,531,4019,1893,5127,454,4502,1473,2678,2006,1835,757,3123,486,1146,3014,1941,3862,2783,1875,992,1254,2749,8508,599,0.144,0.12,0.117,0.113,0.119,0.151,0.117,0.128,0.159,0.143,0.122,0.149,0.133,0.142,0.137,0.141,0.124,0.131,0.133,0.168,0.11,0.15,0.139,0.148,0.133,0.136,0.118,0.112,0.108,0.124,0.146,0.135,0.109,0.128,0.073,0.102,0.132,0.13,0.095,0.113,0.125,0.144,0.125,0.114,0.1,0.08,0.12,0.09,0.096,0.1,0.118,0.107,0.124,0.107,0.11,0.109,0.1,0.109,0.116,0.115,0.087,0.12,0.134,0.099,0.102,0.111,0.098,0.097,0.09,0.115,0.117,0.104,0.105,0.1,466,1737,1058,954,926,1077,1032,1001,639,353,183.0,1616,1186,291,721,3688,4849,458,955,1920,825,1581,1006,2013,1463,2618,1738,1407,1716,1620,819,520,453,1438,503,817,1841,1948,196,221,878,1585,1084,1756,2101,686,398,965,1140,645,323,2048,945,2181,145,2085,663,1247,1022,837,431,1191,277,613,1441,1043,1932,1078,909,463,590,1246,3735,333,3.129,2.563,2.715,2.517,1.894,2.549,2.423,2.749,2.25,2.974,2.544,1.82,2.685,2.956,2.73,2.614,2.871,3.5,2.833,2.049,3.057,2.313,2.027,2.355,2.819,2.641,2.515,2.271,2.948,2.549,2.333,2.59,2.576,2.94,3.37,2.431,2.748,2.992,2.172,2.593,2.817,1.696,3.516,1.788,1.957,2.234,2.861,2.732,2.689,2.555,1.87,2.07,2.051,2.433,2.779,2.278,2.384,2.395,2.184,2.369,2.088,2.552,1.914,2.163,2.289,2.057,2.167,2.656,2.4,2.276,2.306,2.433,2.383,2.11,0.818,0.646,0.424,0.62,0.578,0.623,0.574,0.45,0.695,0.702,1.027,0.572,0.432,0.383,0.545,0.604,0.639,0.764,0.613,0.555,0.764,0.66,0.463,0.809,0.598,0.559,0.549,0.611,0.528,0.548,0.871,0.92,0.342,0.604,0.672,0.449,0.787,0.732,0.404,0.307,0.517,0.459,0.681,0.556,0.631,0.412,0.701,0.645,0.433,0.566,0.432,0.416,0.468,0.482,0.521,0.41,0.472,0.472,0.38,0.354,0.474,0.743,0.557,0.581,0.441,0.669,0.455,0.473,0.417,0.871,0.435,0.464,0.456,0.357
-4063,Not Epileptic,M,52.0,1.2,2.8,1.4,1.6,2.2,2.0,3.4,2.0,1.7,0.7,0.8,3.1,1.2,0.5,1.4,7.1,12.6,1.0,2.6,4.7,1.1,2.8,1.9,3.7,3.3,4.7,3.0,2.9,3.3,3.2,2.3,1.0,0.5,2.4,0.6,0.8,2.9,3.0,0.2,0.1,1.3,4.0,2.1,3.3,4.5,0.7,0.5,1.4,1.6,1.2,0.7,2.5,2.0,3.8,0.4,3.2,0.7,1.6,1.5,1.8,0.9,1.6,0.5,0.8,1.5,1.9,3.0,1.6,1.0,0.9,1.2,1.4,5.3,0.3,10,23,14,13,19,21,24,20,13,8,8.0,27,15,5,14,83,117,11,32,41,12,33,20,37,43,50,35,26,31,37,22,10,5,30,5,4,36,41,2,1,9,37,25,22,21,4,3,8,8,10,5,17,14,29,3,30,8,18,12,13,8,11,3,7,11,19,24,13,5,7,7,11,42,3,0.04,0.027,0.023,0.032,0.039,0.034,0.046,0.025,0.051,0.04,0.051,0.031,0.026,0.04,0.031,0.035,0.04,0.036,0.031,0.04,0.031,0.03,0.027,0.041,0.029,0.028,0.03,0.029,0.033,0.032,0.049,0.039,0.028,0.031,0.019,0.016,0.027,0.029,0.015,0.013,0.032,0.044,0.034,0.029,0.029,0.014,0.03,0.019,0.019,0.024,0.036,0.028,0.026,0.026,0.015,0.02,0.013,0.02,0.026,0.022,0.023,0.031,0.023,0.023,0.02,0.032,0.024,0.021,0.017,0.026,0.024,0.017,0.019,0.017,1501,4463,1876,2221,2049,2774,2584,3791,1680,1365,788.0,3144,3150,893,2071,11576,17188,2078,4914,4272,3052,4913,2121,5499,6345,8888,4791,3689,6257,5325,2245,1168,742,5203,2020,1673,6884,7707,422,236,1251,3565,5609,2883,4192,1458,764,2084,2219,1626,433,2672,2211,5094,653,4481,1567,2616,1176,1900,1016,2217,506,1026,2346,1336,3940,2283,1692,1286,1449,1991,8560,433,0.133,0.118,0.103,0.127,0.137,0.142,0.127,0.124,0.155,0.146,0.156,0.128,0.113,0.148,0.128,0.131,0.138,0.142,0.129,0.145,0.101,0.129,0.114,0.143,0.124,0.126,0.112,0.109,0.108,0.133,0.156,0.116,0.108,0.132,0.094,0.078,0.117,0.131,0.096,0.096,0.12,0.157,0.139,0.123,0.118,0.086,0.109,0.097,0.098,0.122,0.151,0.121,0.119,0.118,0.098,0.111,0.089,0.119,0.128,0.113,0.119,0.133,0.133,0.113,0.103,0.133,0.114,0.117,0.094,0.12,0.117,0.106,0.103,0.116,521,1779,912,890,1032,985,1138,1327,581,377,280.0,1546,894,226,676,3631,5719,553,1500,2027,770,1528,1077,1701,1912,2823,1790,1393,1724,1774,768,576,283,1365,577,693,1810,1836,222,106,781,1515,1247,1862,2337,774,352,1051,1236,780,301,1454,1176,2462,395,2412,863,1369,856,1183,609,851,276,563,1197,899,2086,1127,812,571,733,1126,4228,275,2.763,2.307,2.214,2.395,1.862,2.256,2.034,2.436,2.251,2.801,2.226,1.751,2.799,2.708,2.372,2.456,2.45,3.022,2.636,1.819,2.987,2.366,1.785,2.444,2.519,2.544,2.171,2.102,2.707,2.397,2.343,2.073,2.084,2.751,3.019,2.272,2.741,2.916,2.178,2.265,1.91,1.915,3.28,1.742,2.029,2.07,2.482,2.359,2.111,2.333,1.7,1.995,2.017,2.099,1.892,1.962,1.926,2.151,1.607,1.724,1.892,2.7,1.944,2.084,2.139,1.715,2.0,2.188,2.335,2.161,2.126,2.08,2.141,1.917,0.621,0.572,0.406,0.494,0.454,0.668,0.509,0.57,0.616,0.779,0.847,0.525,0.448,0.513,0.511,0.578,0.563,0.66,0.596,0.551,0.715,0.663,0.499,0.802,0.624,0.695,0.461,0.53,0.515,0.555,0.673,0.862,0.513,0.594,0.54,0.516,0.765,0.739,0.35,0.863,0.383,0.699,0.632,0.498,0.572,0.487,0.593,0.631,0.506,0.64,0.337,0.372,0.434,0.524,0.327,0.398,0.364,0.472,0.261,0.374,0.384,0.822,0.532,0.706,0.502,0.556,0.487,0.421,0.405,0.636,0.461,0.442,0.451,0.587
-4064,Not Epileptic,M,55.0,1.1,2.1,0.8,1.1,1.6,2.0,2.4,2.0,0.9,1.0,0.5,3.0,1.9,0.6,2.1,5.4,10.2,0.9,2.2,4.7,1.0,3.5,2.6,4.4,6.4,4.3,4.7,3.4,4.1,3.7,1.2,1.4,0.5,2.6,0.3,1.2,5.6,3.2,0.4,0.1,1.2,3.8,2.3,2.5,3.1,0.7,0.5,1.0,1.5,1.0,0.6,2.7,1.9,2.9,0.5,3.0,0.6,1.4,1.3,1.2,0.6,2.0,0.5,0.7,1.9,1.7,2.5,1.4,1.1,1.1,1.1,1.1,5.1,0.4,9,20,8,8,12,18,17,18,10,10,6.0,31,16,9,24,60,102,8,31,42,7,37,23,45,58,44,53,29,31,51,17,10,5,29,1,13,55,42,2,1,9,47,25,18,19,5,3,7,9,6,3,22,14,21,2,22,5,12,8,8,4,17,3,7,14,20,24,10,6,10,7,9,50,2,0.042,0.02,0.015,0.021,0.033,0.034,0.034,0.032,0.033,0.035,0.041,0.034,0.036,0.029,0.033,0.03,0.03,0.034,0.027,0.04,0.019,0.035,0.033,0.039,0.042,0.029,0.033,0.033,0.032,0.034,0.035,0.049,0.021,0.025,0.006,0.018,0.032,0.031,0.026,0.016,0.023,0.039,0.032,0.027,0.024,0.016,0.018,0.018,0.017,0.022,0.038,0.022,0.025,0.02,0.017,0.019,0.015,0.014,0.026,0.019,0.021,0.026,0.027,0.017,0.021,0.027,0.02,0.018,0.016,0.02,0.021,0.017,0.017,0.02,1519,5099,2212,2414,1896,2448,3644,3351,1759,1883,772.0,3196,3892,1323,4081,11573,21488,2507,5332,4005,3500,5781,2492,6983,7268,8113,7005,4371,7360,6337,2629,1277,1179,6067,2183,3134,10501,8335,475,294,1824,3686,6287,2775,3961,1526,1004,2421,2910,1703,460,4084,2058,5836,624,4725,1289,3107,1385,1965,995,3436,489,1265,2852,1375,4045,2711,2163,1644,1764,2437,11311,504,0.127,0.109,0.093,0.103,0.132,0.14,0.113,0.132,0.129,0.137,0.131,0.137,0.13,0.147,0.145,0.132,0.131,0.136,0.13,0.159,0.082,0.126,0.124,0.139,0.137,0.127,0.121,0.113,0.113,0.153,0.13,0.135,0.097,0.119,0.049,0.092,0.132,0.141,0.119,0.118,0.114,0.136,0.135,0.119,0.106,0.092,0.102,0.084,0.097,0.11,0.153,0.113,0.117,0.103,0.108,0.105,0.102,0.089,0.118,0.097,0.108,0.125,0.132,0.104,0.107,0.129,0.11,0.099,0.094,0.124,0.112,0.099,0.099,0.099,498,1745,813,848,748,1033,1134,1110,488,507,237.0,1385,966,350,1155,3251,5892,495,1440,1980,842,1556,1111,1959,2309,2488,2336,1526,1904,2019,748,499,368,1604,628,1139,2701,1969,245,128,821,1566,1354,1548,2047,725,398,939,1292,697,230,1914,1077,2353,339,2352,616,1492,792,992,466,1222,287,643,1381,894,2003,1133,1003,728,834,1094,4661,280,3.185,2.721,2.702,2.727,2.346,2.265,2.563,2.743,2.694,3.04,2.754,1.925,3.083,2.765,2.733,2.644,2.885,3.66,2.898,1.858,2.96,2.742,1.957,2.725,2.612,2.64,2.412,2.186,2.878,2.555,2.626,2.535,2.466,2.874,3.053,2.457,2.974,3.058,2.245,2.593,2.647,2.011,3.342,1.939,2.156,2.266,2.953,3.211,2.62,3.002,2.124,2.254,2.122,2.465,2.25,2.256,2.455,2.231,2.039,2.14,2.348,2.768,2.016,2.309,2.315,1.874,2.142,2.576,2.511,2.593,2.408,2.607,2.455,2.17,0.828,0.469,0.492,0.508,0.569,0.469,0.656,0.426,0.528,0.612,0.701,0.589,0.473,0.509,0.523,0.601,0.626,0.657,0.57,0.565,0.785,0.54,0.574,0.737,0.661,0.632,0.638,0.764,0.594,0.505,0.704,0.883,0.467,0.59,0.51,0.42,0.812,0.65,0.376,0.444,0.476,0.609,0.749,0.615,0.619,0.42,0.605,0.793,0.433,0.557,0.4,0.428,0.404,0.478,0.437,0.358,0.369,0.481,0.409,0.411,0.443,0.663,0.505,0.672,0.462,0.753,0.398,0.396,0.425,0.56,0.45,0.441,0.415,0.341
-4065,Not Epileptic,F,22.0,0.9,3.0,1.1,1.1,1.1,2.8,2.0,1.8,1.5,0.7,0.3,3.0,2.2,0.5,1.7,9.0,11.4,0.9,2.4,4.3,1.1,3.6,3.0,3.6,2.8,4.0,3.2,3.1,3.5,4.6,1.9,1.0,0.7,3.4,0.5,0.7,5.3,4.3,0.1,0.4,1.1,3.7,2.4,2.1,3.5,0.8,0.8,1.7,1.1,1.2,0.6,3.4,1.6,2.7,0.5,2.4,0.8,1.5,1.1,1.3,0.6,1.9,0.5,0.4,2.8,1.5,3.1,1.5,0.9,0.6,1.5,2.8,4.3,0.4,6,28,11,12,12,28,15,16,18,11,3.0,28,22,9,22,97,104,6,29,36,8,46,26,39,35,36,33,31,28,47,26,5,9,33,4,6,48,50,2,2,7,35,20,23,22,5,3,14,6,13,4,25,11,18,3,20,5,12,10,13,3,18,3,5,21,18,27,10,7,4,10,19,35,3,0.032,0.026,0.013,0.019,0.031,0.03,0.028,0.031,0.042,0.029,0.042,0.039,0.036,0.03,0.033,0.036,0.029,0.026,0.029,0.04,0.017,0.033,0.041,0.037,0.033,0.034,0.028,0.028,0.029,0.039,0.046,0.038,0.029,0.037,0.017,0.017,0.04,0.03,0.011,0.018,0.02,0.038,0.037,0.023,0.024,0.018,0.026,0.02,0.014,0.035,0.026,0.025,0.019,0.018,0.019,0.019,0.023,0.016,0.026,0.03,0.021,0.028,0.024,0.017,0.025,0.03,0.02,0.019,0.016,0.023,0.034,0.025,0.017,0.016,1708,6494,2902,2306,1941,4403,3290,3832,1972,1374,313.0,3574,4650,1354,3364,15608,24071,3029,4797,4005,2731,5525,3235,7193,4972,7213,5623,5188,7025,6097,2722,774,1629,7272,2113,2051,7753,8968,422,741,1647,4104,5306,2480,3669,1475,1038,2826,2449,1389,642,4883,3050,6344,596,3397,1527,3131,1560,1634,1152,3015,633,957,4226,1041,5111,2551,1862,1283,1771,3837,8797,559,0.123,0.114,0.091,0.104,0.122,0.145,0.114,0.132,0.146,0.141,0.126,0.154,0.141,0.123,0.143,0.148,0.125,0.106,0.123,0.145,0.084,0.144,0.152,0.133,0.144,0.145,0.126,0.125,0.118,0.161,0.142,0.11,0.123,0.153,0.09,0.087,0.147,0.126,0.079,0.111,0.109,0.155,0.13,0.123,0.114,0.101,0.112,0.1,0.087,0.123,0.128,0.119,0.108,0.096,0.117,0.11,0.109,0.093,0.127,0.13,0.092,0.129,0.118,0.111,0.122,0.14,0.109,0.101,0.097,0.111,0.135,0.122,0.098,0.1,498,2026,1278,939,677,1609,1112,1047,602,411,152.0,1364,1192,355,909,4459,6610,570,1503,1865,827,1922,1168,1878,1526,2018,1888,1816,2026,2017,683,372,468,1604,517,688,2260,2409,237,323,777,1725,1222,1437,2230,719,453,1177,1153,596,355,2068,1285,2499,320,1796,575,1581,795,759,524,1075,301,475,1889,670,2496,1212,852,425,714,1608,3919,315,3.337,2.897,2.278,2.402,2.351,2.433,2.441,3.22,2.614,2.893,2.136,2.271,2.983,2.717,2.858,2.712,3.014,3.86,2.648,1.907,2.597,2.305,2.318,2.895,2.473,2.777,2.375,2.293,2.709,2.523,2.737,2.22,2.676,3.395,3.509,2.588,2.846,2.803,2.213,2.736,2.664,2.144,3.172,1.831,1.889,2.237,2.947,2.909,2.538,2.576,2.274,2.356,2.558,2.658,2.228,2.102,2.862,2.252,2.08,2.141,2.242,2.831,2.307,2.263,2.488,2.059,2.179,2.584,2.347,3.2,2.595,2.601,2.424,2.178,0.725,0.613,0.494,0.496,0.619,0.468,0.494,0.655,0.568,0.612,0.775,0.493,0.483,0.612,0.55,0.524,0.562,0.747,0.502,0.524,0.691,0.543,0.472,0.69,0.584,0.581,0.494,0.524,0.501,0.59,0.867,0.863,0.494,0.579,0.572,0.626,0.615,0.621,0.388,0.454,0.598,0.442,0.698,0.608,0.498,0.382,0.396,0.799,0.443,0.723,0.379,0.508,0.575,0.532,0.283,0.42,0.631,0.449,0.383,0.428,0.409,0.786,0.615,0.824,0.695,0.626,0.452,0.468,0.571,0.765,0.662,0.563,0.543,0.508
-4066,Not Epileptic,F,22.0,0.8,2.0,0.9,1.4,2.2,1.5,1.7,1.4,1.1,0.6,0.3,3.0,1.4,0.5,1.0,5.0,7.4,0.7,2.2,3.8,1.4,2.5,2.8,4.0,6.0,2.7,4.3,2.4,2.7,3.2,1.9,0.7,0.6,1.9,0.4,0.7,3.2,2.3,0.1,0.3,1.0,4.4,2.8,2.4,2.9,0.6,0.5,0.7,1.3,0.7,0.4,1.9,1.0,2.0,0.3,2.9,0.5,1.0,0.9,1.0,0.4,1.7,0.5,0.6,2.3,1.1,2.5,1.4,0.8,0.7,1.1,1.2,3.9,0.3,7,18,8,10,21,15,15,13,13,8,3.0,24,16,7,11,58,75,5,25,29,16,31,22,42,63,33,52,25,22,37,17,5,6,29,2,6,33,37,1,3,6,33,26,15,14,4,2,5,7,7,3,16,11,15,1,24,5,10,6,7,3,12,3,4,18,16,17,10,5,3,7,8,35,2,0.036,0.026,0.018,0.026,0.045,0.028,0.027,0.025,0.039,0.03,0.035,0.033,0.031,0.035,0.032,0.032,0.029,0.03,0.027,0.04,0.028,0.032,0.04,0.05,0.047,0.03,0.035,0.035,0.032,0.029,0.042,0.046,0.031,0.031,0.015,0.018,0.039,0.03,0.016,0.021,0.021,0.043,0.041,0.028,0.023,0.013,0.017,0.013,0.018,0.013,0.018,0.027,0.02,0.02,0.017,0.019,0.024,0.013,0.018,0.016,0.019,0.029,0.032,0.017,0.025,0.025,0.02,0.02,0.016,0.019,0.025,0.018,0.019,0.024,1638,3973,2358,2530,2151,2908,3658,3277,1631,1560,901.0,2855,3262,1002,2548,10277,18840,1991,5185,3995,4919,5829,2593,5345,6517,6165,7962,4066,5965,7100,2918,1057,1075,4995,1751,1983,6644,7023,265,566,1498,3549,5627,2298,3499,1605,800,2082,2368,1839,575,3201,1900,4219,522,4821,1029,2683,1373,1794,644,2398,395,971,3150,1402,3941,2389,1435,944,1605,1932,8441,480,0.121,0.122,0.104,0.113,0.153,0.127,0.102,0.118,0.162,0.13,0.125,0.134,0.131,0.148,0.14,0.132,0.129,0.11,0.125,0.147,0.113,0.123,0.13,0.154,0.147,0.129,0.13,0.103,0.112,0.128,0.149,0.1,0.122,0.135,0.082,0.091,0.135,0.138,0.095,0.129,0.105,0.143,0.153,0.123,0.105,0.085,0.099,0.081,0.096,0.096,0.126,0.118,0.118,0.102,0.109,0.107,0.118,0.091,0.103,0.099,0.107,0.13,0.151,0.108,0.116,0.12,0.102,0.103,0.099,0.1,0.113,0.108,0.096,0.132,413,1283,787,874,862,931,1074,890,483,349,188.0,1384,847,257,617,2734,4504,435,1305,1640,959,1398,1079,1505,1981,1652,2298,1239,1377,1855,778,397,284,1103,424,626,1511,1466,159,241,718,1546,1136,1372,1921,751,350,871,1071,830,318,1385,888,1682,216,2340,463,1251,710,891,307,874,228,482,1562,743,1931,1054,692,451,772,949,3353,220,3.56,2.813,2.709,2.776,2.008,2.556,2.614,3.018,2.474,3.305,3.576,1.806,2.874,2.778,2.906,2.677,3.135,3.496,3.008,2.007,3.536,2.949,2.073,2.72,2.592,2.884,2.624,2.443,3.129,2.882,2.607,2.727,2.814,3.108,3.432,2.863,3.022,3.327,1.842,2.593,2.633,1.944,3.246,2.016,2.12,2.408,2.769,2.915,2.74,2.579,2.066,2.294,1.892,2.488,2.819,2.282,2.264,2.28,2.237,2.209,2.278,2.577,1.821,2.326,2.274,2.262,2.194,2.468,2.412,2.204,2.494,2.443,2.628,2.756,0.849,0.646,0.493,0.536,0.782,0.678,0.718,0.459,0.813,0.418,0.847,0.494,0.511,0.481,0.546,0.758,0.688,0.764,0.522,0.7,0.84,0.618,0.677,0.716,0.749,0.611,0.685,0.744,0.554,0.568,0.754,1.011,0.455,0.628,0.69,0.493,0.813,0.647,0.408,0.848,0.418,0.685,0.865,0.569,0.634,0.416,0.606,0.806,0.418,0.393,0.301,0.479,0.612,0.55,0.577,0.402,0.409,0.569,0.394,0.414,0.361,0.77,0.466,0.467,0.528,0.74,0.419,0.448,0.516,0.594,0.341,0.557,0.457,0.519
-4067,Not Epileptic,F,37.0,0.8,2.4,0.7,1.3,1.6,1.8,1.4,1.6,1.5,0.8,0.8,2.8,1.2,0.5,1.0,7.3,7.5,1.0,2.4,4.8,1.1,2.9,2.0,3.9,2.6,3.2,3.7,2.9,2.4,2.6,1.3,1.5,0.6,2.4,0.5,0.8,3.2,3.2,0.2,0.2,1.0,3.6,2.4,3.6,2.7,0.6,0.5,1.0,1.3,0.9,0.6,1.9,1.1,2.7,0.4,2.1,0.5,1.2,1.1,1.3,0.7,1.8,0.5,0.7,2.4,1.4,2.3,0.9,1.1,0.7,1.2,1.4,3.8,0.3,8,27,6,11,13,21,13,17,13,9,7.0,28,15,9,12,72,78,11,29,46,13,36,22,43,36,40,44,33,22,29,14,12,5,33,4,7,45,46,2,1,6,34,25,25,17,4,2,8,7,8,5,12,6,22,3,20,4,12,9,11,6,12,3,5,21,19,24,8,9,5,10,12,30,3,0.035,0.028,0.016,0.024,0.035,0.03,0.025,0.029,0.045,0.035,0.047,0.04,0.025,0.038,0.032,0.043,0.028,0.038,0.032,0.039,0.028,0.033,0.029,0.041,0.029,0.027,0.027,0.029,0.021,0.029,0.036,0.058,0.023,0.031,0.015,0.016,0.033,0.035,0.018,0.02,0.021,0.042,0.038,0.034,0.018,0.013,0.016,0.017,0.021,0.021,0.026,0.022,0.024,0.022,0.026,0.015,0.015,0.018,0.024,0.02,0.026,0.028,0.032,0.023,0.025,0.028,0.016,0.016,0.018,0.023,0.03,0.019,0.017,0.015,1370,4541,1905,2550,2027,3314,2764,3379,1960,1357,749.0,2742,2761,1029,2066,10702,18343,2223,4488,5197,3681,4928,2592,5903,5828,6454,6845,4726,7246,4955,2559,1088,1252,6523,2158,2199,6989,6920,369,321,1330,2646,5825,2791,3735,1244,931,2200,1950,1721,649,2689,1361,4395,489,4223,1251,2437,1259,1875,869,2722,522,993,2980,1191,4265,2091,1937,1281,1498,2204,7968,337,0.138,0.123,0.094,0.117,0.146,0.134,0.11,0.13,0.168,0.155,0.145,0.161,0.126,0.153,0.145,0.157,0.126,0.151,0.141,0.164,0.115,0.133,0.116,0.151,0.129,0.142,0.12,0.12,0.103,0.134,0.136,0.134,0.105,0.143,0.086,0.092,0.142,0.147,0.105,0.107,0.115,0.145,0.136,0.139,0.098,0.089,0.095,0.089,0.102,0.108,0.131,0.112,0.116,0.111,0.134,0.097,0.087,0.105,0.117,0.111,0.125,0.127,0.148,0.118,0.122,0.133,0.102,0.096,0.104,0.136,0.139,0.112,0.097,0.132,436,1623,687,855,770,1081,902,1072,552,396,221.0,1316,808,285,597,3144,4833,525,1273,2208,817,1530,1105,1708,1580,1943,2249,1670,1792,1540,696,481,427,1496,570,756,1853,1825,195,186,726,1256,1249,1675,2218,630,418,941,964,680,372,1301,725,1956,234,2244,617,1145,751,965,469,1005,255,473,1565,909,2156,971,962,465,677,1174,3575,213,3.264,2.579,2.649,2.767,2.16,2.505,2.569,2.73,2.626,2.911,2.772,1.846,2.644,2.689,2.736,2.559,2.93,3.312,2.813,2.106,3.144,2.495,2.044,2.66,2.772,2.705,2.355,2.245,2.94,2.552,2.717,2.43,2.41,3.115,3.343,2.557,2.755,2.757,2.263,1.865,2.358,1.894,3.384,1.968,1.94,2.127,2.787,2.79,2.555,2.703,2.186,2.13,1.982,2.298,2.445,2.089,2.24,2.331,1.885,2.146,2.201,2.713,2.158,2.457,2.075,1.62,2.133,2.447,2.461,2.967,2.396,2.102,2.362,2.103,0.659,0.648,0.492,0.465,0.668,0.599,0.576,0.452,0.801,0.677,0.694,0.51,0.369,0.515,0.643,0.638,0.634,0.668,0.521,0.546,0.831,0.526,0.638,0.758,0.611,0.55,0.633,0.598,0.509,0.537,0.692,0.796,0.388,0.663,0.841,0.525,0.762,0.636,0.368,0.383,0.571,0.513,0.738,0.599,0.603,0.44,0.438,0.71,0.386,0.578,0.307,0.433,0.478,0.475,0.575,0.372,0.436,0.455,0.318,0.365,0.313,0.82,0.766,0.741,0.467,0.42,0.462,0.335,0.447,0.784,0.565,0.473,0.397,0.284
-4068,Not Epileptic,M,47.0,1.6,2.6,1.0,1.6,2.1,2.3,1.8,2.1,0.9,1.3,0.3,3.0,1.4,0.5,1.3,6.6,9.7,1.2,3.3,6.0,2.2,3.6,1.8,3.2,3.7,4.5,3.4,3.9,4.2,3.6,1.6,0.9,0.5,3.5,1.1,1.2,5.5,5.0,0.1,0.2,1.4,3.2,2.7,3.8,3.5,0.7,0.4,1.3,1.2,0.7,0.9,1.9,1.3,3.1,0.3,2.5,0.9,2.3,1.3,1.0,0.6,2.0,0.3,0.6,2.2,2.3,3.1,1.7,0.9,0.8,1.3,2.6,8.0,0.4,9,21,10,11,17,20,15,18,9,11,4.0,25,14,5,15,64,87,8,25,45,18,39,16,32,38,41,31,29,28,34,16,8,5,33,15,9,42,48,2,2,8,29,23,28,21,5,2,6,6,6,5,15,9,23,2,22,7,14,11,9,4,12,3,5,18,22,23,12,6,8,8,15,69,4,0.05,0.024,0.023,0.032,0.043,0.035,0.023,0.029,0.037,0.04,0.027,0.034,0.023,0.023,0.03,0.039,0.03,0.037,0.039,0.039,0.032,0.031,0.027,0.036,0.029,0.039,0.029,0.026,0.029,0.028,0.039,0.03,0.024,0.032,0.03,0.023,0.044,0.035,0.013,0.015,0.026,0.038,0.039,0.03,0.02,0.016,0.019,0.017,0.017,0.01,0.036,0.022,0.024,0.023,0.03,0.024,0.02,0.029,0.025,0.02,0.016,0.029,0.02,0.019,0.024,0.038,0.019,0.019,0.014,0.021,0.026,0.029,0.02,0.018,1875,5105,2121,2208,2679,2706,2948,3716,2160,1951,629.0,2817,3374,970,2660,10652,19082,2727,3673,4708,3927,6261,2504,6432,5652,5774,5306,5091,6744,6241,2858,1209,1155,8382,2355,2267,6722,8752,333,516,1679,3179,5548,2405,4506,1378,866,2906,2379,2072,490,3000,2078,5794,287,3393,1341,2391,1521,1558,1418,3044,483,987,2870,1619,4511,3192,1681,1358,1862,3433,15583,448,0.15,0.113,0.109,0.128,0.144,0.145,0.098,0.121,0.143,0.152,0.123,0.132,0.111,0.12,0.139,0.148,0.129,0.124,0.138,0.139,0.123,0.142,0.124,0.127,0.112,0.136,0.121,0.101,0.101,0.128,0.125,0.097,0.117,0.128,0.097,0.095,0.131,0.126,0.106,0.086,0.12,0.145,0.143,0.129,0.098,0.099,0.092,0.083,0.09,0.083,0.156,0.108,0.113,0.107,0.132,0.115,0.113,0.118,0.134,0.108,0.086,0.12,0.109,0.106,0.12,0.137,0.106,0.103,0.094,0.111,0.111,0.121,0.105,0.118,503,1720,808,856,829,1117,1164,1167,466,520,230.0,1382,983,278,716,2955,5501,472,1454,2310,1085,1969,1042,1639,1845,1895,1863,1968,1950,2037,731,549,394,1740,551,759,1995,2323,184,271,774,1430,1216,1788,2629,692,348,1087,1108,939,363,1354,917,2270,132,1720,685,1246,816,863,620,1100,269,560,1426,963,2404,1442,915,612,871,1517,6483,267,3.512,2.627,2.503,2.511,2.623,2.185,2.154,2.874,3.094,3.063,2.473,1.832,2.665,2.588,2.805,2.697,2.781,3.918,2.218,1.886,2.889,2.543,2.096,2.869,2.409,2.659,2.371,2.164,2.705,2.386,2.768,2.175,2.454,3.319,3.676,2.71,2.659,2.827,1.859,2.415,2.818,1.948,3.195,1.59,1.944,2.168,3.045,3.19,2.606,2.468,1.799,2.277,2.293,2.494,2.847,2.135,2.211,2.085,2.243,2.03,2.682,2.619,2.1,2.079,2.215,2.035,2.088,2.502,2.231,2.247,2.406,2.65,2.526,2.118,0.974,0.66,0.698,0.556,0.732,0.575,0.663,0.496,0.749,0.538,0.808,0.508,0.415,0.511,0.489,0.611,0.674,0.673,0.587,0.633,0.843,0.496,0.474,0.598,0.531,0.624,0.521,0.653,0.661,0.558,0.726,0.931,0.575,0.611,0.814,0.561,0.701,0.597,0.458,0.408,0.671,0.502,0.773,0.554,0.638,0.496,0.476,0.932,0.448,0.36,0.37,0.488,0.532,0.566,0.361,0.46,0.53,0.489,0.543,0.376,0.461,0.679,0.358,0.552,0.644,0.732,0.51,0.456,0.452,0.544,0.529,0.488,0.505,0.591
-4069,Not Epileptic,F,52.0,0.6,2.2,0.9,0.9,1.3,1.8,1.6,1.4,1.1,0.4,0.3,3.1,1.2,0.4,0.8,5.5,7.4,0.8,1.8,5.4,1.1,2.8,2.1,4.7,3.1,3.1,2.8,2.6,3.0,3.1,1.6,1.6,0.8,2.1,0.5,0.5,3.5,2.8,0.1,0.2,1.0,3.7,2.0,2.8,3.5,0.6,0.4,0.9,1.1,0.6,0.5,1.8,1.2,2.2,0.2,2.2,1.0,1.3,1.1,1.2,0.5,1.8,0.2,0.6,2.3,1.3,2.0,1.1,1.4,0.4,1.1,1.4,4.7,0.3,7,20,7,8,12,18,14,13,11,5,4.0,27,15,4,11,69,68,8,21,54,8,33,17,44,33,27,28,22,23,38,22,11,8,21,3,3,45,38,1,2,5,27,18,19,20,4,2,5,6,5,5,23,8,18,1,18,7,12,8,10,3,12,2,5,16,14,15,8,8,4,7,11,36,2,0.034,0.029,0.018,0.022,0.029,0.029,0.03,0.028,0.043,0.027,0.027,0.044,0.027,0.034,0.03,0.036,0.031,0.033,0.033,0.048,0.026,0.038,0.046,0.048,0.041,0.037,0.033,0.033,0.034,0.035,0.039,0.047,0.04,0.03,0.021,0.015,0.034,0.032,0.014,0.017,0.025,0.052,0.037,0.03,0.028,0.012,0.021,0.017,0.017,0.016,0.029,0.03,0.023,0.022,0.028,0.021,0.027,0.022,0.027,0.026,0.023,0.033,0.023,0.018,0.024,0.038,0.022,0.019,0.023,0.025,0.024,0.018,0.021,0.02,1147,3484,1865,1902,1758,2761,2547,2735,1384,1186,633.0,2162,2940,682,1436,9213,15080,2039,3189,4225,2973,3431,1228,5034,4644,4916,4154,3387,5167,5258,2379,1391,1020,5114,1501,1478,6789,5870,241,468,1049,1451,4705,2440,3428,1199,741,1765,1832,1171,394,1797,1359,3610,264,2840,1010,2047,932,1337,695,2166,264,1000,2640,920,2308,2020,1783,746,1304,2178,7930,356,0.116,0.128,0.103,0.109,0.133,0.135,0.111,0.125,0.168,0.119,0.13,0.151,0.133,0.142,0.134,0.139,0.133,0.123,0.142,0.167,0.096,0.133,0.142,0.146,0.134,0.137,0.124,0.114,0.108,0.148,0.138,0.13,0.155,0.134,0.096,0.079,0.14,0.135,0.092,0.112,0.118,0.165,0.145,0.126,0.114,0.084,0.102,0.092,0.097,0.106,0.143,0.123,0.119,0.113,0.122,0.117,0.13,0.113,0.132,0.117,0.107,0.136,0.116,0.101,0.12,0.14,0.11,0.1,0.115,0.127,0.12,0.111,0.109,0.136,391,1255,746,720,737,969,868,865,426,292,205.0,1058,784,188,419,2584,3937,432,851,1982,710,1265,708,1567,1245,1308,1424,1164,1367,1609,757,545,340,1104,390,526,1762,1541,151,180,580,1000,904,1470,2068,675,327,791,934,554,269,1061,813,1620,131,1513,530,1076,600,767,388,841,156,545,1422,496,1388,930,847,303,698,1087,3542,180,2.833,2.626,2.453,2.605,1.998,2.48,2.371,2.653,2.498,3.021,2.718,1.817,2.816,2.682,2.471,2.55,2.87,3.384,2.83,1.892,3.149,2.195,1.648,2.53,2.764,2.785,2.278,2.248,2.807,2.476,2.357,2.488,2.443,3.144,3.555,2.448,2.91,2.851,1.725,2.34,2.21,1.483,3.632,1.855,1.92,1.981,2.902,2.783,2.462,2.518,1.893,1.812,1.805,2.216,2.073,2.042,2.297,2.19,1.852,2.001,2.186,2.631,1.997,2.161,2.024,2.182,1.886,2.265,2.456,2.907,2.1,2.29,2.431,2.548,0.751,0.729,0.545,0.521,0.658,0.645,0.682,0.494,0.701,0.406,0.628,0.51,0.482,0.386,0.559,0.68,0.633,0.596,0.582,0.571,0.735,0.608,0.408,0.81,0.678,0.61,0.595,0.641,0.592,0.565,0.613,0.723,0.424,0.603,0.64,0.401,0.697,0.629,0.386,0.465,0.42,0.409,0.685,0.482,0.585,0.378,0.343,0.644,0.42,0.351,0.443,0.519,0.385,0.504,0.532,0.348,0.314,0.394,0.344,0.321,0.296,0.658,0.386,0.668,0.502,0.536,0.413,0.308,0.457,0.624,0.367,0.482,0.421,0.348
-4070,Not Epileptic,F,37.0,0.5,2.3,0.9,0.9,1.4,2.0,1.0,1.4,1.1,0.5,0.4,2.4,1.5,0.7,1.1,5.7,6.8,0.9,2.3,4.3,1.5,2.4,1.4,2.8,2.5,2.9,3.3,2.0,2.1,2.8,1.2,0.4,0.4,2.3,0.3,1.0,3.9,2.2,0.2,0.1,0.8,2.7,1.6,1.7,2.5,0.6,0.4,0.9,1.0,0.6,0.5,2.2,1.0,2.4,0.1,2.3,0.9,1.4,0.7,0.9,0.7,1.5,0.3,0.4,0.8,1.6,2.1,1.5,1.2,0.5,0.7,2.2,3.2,0.2,5,28,9,7,12,19,12,14,12,5,3.0,22,15,7,13,61,58,8,23,36,12,29,14,29,31,31,39,22,18,35,17,4,5,25,2,10,40,28,2,1,4,22,19,13,13,4,1,5,5,5,3,16,6,20,1,17,6,10,5,7,5,15,3,4,7,15,18,10,7,4,5,13,28,2,0.026,0.025,0.015,0.019,0.032,0.031,0.02,0.028,0.049,0.029,0.03,0.03,0.026,0.031,0.024,0.034,0.028,0.037,0.035,0.036,0.029,0.034,0.026,0.039,0.031,0.033,0.03,0.026,0.024,0.032,0.036,0.026,0.029,0.027,0.013,0.03,0.038,0.027,0.014,0.015,0.018,0.039,0.034,0.021,0.021,0.015,0.02,0.016,0.013,0.014,0.025,0.022,0.02,0.023,0.023,0.018,0.024,0.016,0.019,0.019,0.023,0.028,0.028,0.016,0.016,0.04,0.02,0.024,0.022,0.016,0.019,0.032,0.014,0.016,1470,4311,2055,1931,2309,3106,2319,3015,1651,1264,641.0,2879,3721,1306,3195,10851,16129,2326,4264,5194,3389,4788,1878,5763,4899,5408,6308,3451,5319,5640,2215,711,845,5055,1505,1892,7072,6230,408,319,1432,3040,4816,2261,3071,1427,799,1899,2218,1512,586,3537,1713,4780,199,3763,994,3074,1101,1503,1023,2492,498,886,1845,1038,3391,2252,1634,1105,1151,2388,8521,438,0.107,0.125,0.103,0.098,0.137,0.143,0.109,0.126,0.165,0.124,0.132,0.134,0.132,0.156,0.122,0.139,0.125,0.122,0.149,0.133,0.1,0.143,0.127,0.145,0.14,0.15,0.137,0.123,0.108,0.137,0.126,0.089,0.142,0.126,0.076,0.116,0.133,0.117,0.085,0.095,0.102,0.152,0.139,0.103,0.103,0.091,0.099,0.086,0.085,0.098,0.126,0.107,0.103,0.111,0.128,0.099,0.126,0.099,0.11,0.103,0.115,0.132,0.145,0.108,0.095,0.146,0.105,0.116,0.11,0.114,0.115,0.135,0.087,0.106,394,1472,847,715,745,1012,778,920,444,305,199.0,1209,975,350,838,2957,4099,445,1171,1976,885,1364,852,1373,1444,1585,2037,1219,1434,1726,646,324,269,1201,387,596,1869,1445,241,141,625,1166,995,1273,1802,623,299,838,1023,679,311,1620,759,1866,91,1925,482,1312,588,752,489,827,207,392,882,555,1694,970,845,460,552,1078,3497,175,3.699,2.734,2.36,2.593,2.771,2.704,2.43,2.979,2.795,3.165,2.842,2.006,2.971,2.694,2.988,2.845,3.197,3.753,2.898,2.2,3.027,2.739,2.01,3.208,2.64,2.945,2.442,2.182,2.853,2.705,2.672,2.066,2.691,2.966,3.544,2.675,2.795,3.043,2.041,2.693,2.835,2.297,3.354,1.966,1.93,2.427,3.141,2.793,2.632,2.544,2.511,2.244,2.4,2.448,2.353,2.204,2.518,2.625,2.245,2.221,2.524,3.026,2.634,2.694,2.237,2.288,2.198,2.597,2.403,2.758,2.455,2.647,2.566,3.026,0.83,0.483,0.591,0.524,0.672,0.516,0.481,0.529,0.611,0.503,0.982,0.421,0.517,0.603,0.647,0.549,0.544,0.837,0.542,0.576,0.743,0.525,0.474,0.65,0.54,0.699,0.506,0.523,0.494,0.56,0.625,0.889,0.41,0.687,0.613,0.6,0.701,0.587,0.387,0.357,0.535,0.539,0.696,0.666,0.507,0.664,0.602,0.69,0.471,0.405,0.477,0.453,0.706,0.584,0.424,0.344,0.504,0.514,0.324,0.343,0.453,0.739,0.322,1.013,0.504,0.598,0.493,0.467,0.393,0.59,0.421,0.493,0.518,0.426
-4071,Not Epileptic,F,22.0,0.6,2.0,1.0,0.9,1.5,1.6,1.6,2.0,1.0,0.8,0.1,2.7,1.6,0.5,1.8,5.9,7.4,0.5,1.9,4.7,1.3,2.6,1.8,2.7,2.7,4.6,3.1,2.4,2.6,4.1,0.7,0.5,0.3,2.3,0.2,0.4,3.3,3.1,0.1,0.1,1.0,2.8,2.7,3.1,2.1,0.8,0.3,0.7,0.9,0.9,0.6,1.3,1.0,1.6,0.2,1.7,0.7,1.6,1.2,0.9,0.6,1.3,0.2,0.3,1.9,1.1,2.5,1.7,0.7,0.6,0.8,1.3,4.4,0.3,4,17,13,9,18,18,14,17,12,8,2.0,21,15,5,13,67,69,2,18,39,17,23,14,37,30,44,37,22,21,46,13,3,3,28,1,3,41,43,1,1,6,23,18,23,13,7,1,5,5,6,4,12,8,10,2,13,5,15,9,7,4,10,1,4,14,14,17,12,5,4,6,9,41,2,0.03,0.024,0.017,0.021,0.032,0.03,0.024,0.032,0.033,0.038,0.023,0.041,0.027,0.024,0.035,0.033,0.028,0.02,0.03,0.034,0.028,0.033,0.036,0.033,0.031,0.042,0.025,0.026,0.024,0.032,0.03,0.023,0.019,0.026,0.009,0.011,0.027,0.034,0.009,0.013,0.02,0.036,0.041,0.028,0.018,0.018,0.014,0.013,0.012,0.015,0.028,0.015,0.019,0.015,0.021,0.015,0.025,0.019,0.027,0.022,0.026,0.023,0.016,0.015,0.022,0.019,0.019,0.019,0.016,0.016,0.021,0.019,0.018,0.013,1161,5195,2457,2185,2092,2976,3276,3811,2009,1444,395.0,2331,4381,1223,3522,13326,20140,2376,3303,4939,3150,4472,2286,5905,5847,8440,6021,4461,6628,6914,1736,969,942,6492,1729,2021,7988,8347,450,663,1828,2818,4771,3068,3156,1410,923,2256,2896,2042,555,3497,1677,4089,361,3252,1349,3580,1326,1487,742,2669,304,769,2761,1161,4169,3637,1720,1131,1707,2417,9149,736,0.112,0.107,0.109,0.103,0.128,0.135,0.115,0.131,0.133,0.143,0.116,0.145,0.133,0.111,0.143,0.139,0.125,0.087,0.118,0.127,0.102,0.141,0.138,0.135,0.138,0.153,0.125,0.119,0.111,0.137,0.11,0.085,0.108,0.126,0.06,0.078,0.123,0.134,0.073,0.08,0.104,0.142,0.122,0.122,0.096,0.107,0.085,0.081,0.081,0.098,0.138,0.092,0.115,0.086,0.129,0.094,0.109,0.099,0.131,0.108,0.125,0.11,0.109,0.118,0.112,0.112,0.099,0.101,0.091,0.102,0.109,0.117,0.098,0.096,333,1414,917,794,807,976,1002,1076,519,334,129.0,1074,1021,283,811,3174,4590,392,986,2132,862,1256,814,1458,1517,2021,2037,1452,1738,2188,462,356,292,1451,416,665,2088,1834,244,260,779,1226,1115,1699,1771,678,313,921,1117,845,308,1548,752,1697,140,1675,549,1578,703,726,386,907,154,344,1320,782,1958,1390,753,471,665,1018,3964,295,3.198,3.276,2.476,2.72,2.166,2.636,2.625,3.159,2.747,3.179,2.772,1.92,3.191,2.998,3.122,3.037,3.387,3.962,2.62,2.011,2.857,2.703,2.515,2.882,2.888,3.102,2.34,2.439,2.939,2.603,2.709,2.37,2.54,3.191,3.484,2.76,2.96,3.222,2.152,2.988,2.924,2.034,3.28,2.116,2.032,2.223,3.698,3.058,3.086,2.651,2.102,2.353,2.27,2.676,2.807,2.25,2.867,2.632,2.294,2.24,2.447,2.827,2.255,2.585,2.321,1.957,2.343,2.85,2.651,2.612,2.834,2.804,2.449,3.023,0.837,0.695,0.735,0.543,0.652,0.655,0.516,0.742,0.724,0.665,0.846,0.46,0.561,0.443,0.592,0.681,0.668,0.813,0.694,0.62,0.838,0.535,0.429,0.681,0.652,0.69,0.518,0.561,0.608,0.613,0.764,1.107,0.624,0.614,0.624,0.477,0.664,0.648,0.311,0.392,0.624,0.489,0.743,0.608,0.564,0.559,0.774,0.65,0.78,0.522,0.363,0.441,0.586,0.568,0.578,0.471,0.43,0.53,0.386,0.385,0.382,0.845,0.308,0.898,0.587,0.685,0.516,0.462,0.384,0.724,0.498,0.512,0.535,0.55
-4072,Not Epileptic,F,42.0,0.6,2.6,0.9,1.3,1.5,2.4,2.3,1.7,1.5,0.9,0.6,2.7,2.2,0.4,1.5,5.1,8.7,1.0,2.4,5.4,1.9,3.3,1.8,2.7,3.4,3.7,3.5,2.9,3.6,3.7,1.7,1.0,0.6,2.1,0.4,0.7,4.3,3.1,0.3,0.3,1.1,3.4,2.9,3.2,3.4,1.1,0.3,0.8,1.3,0.9,0.7,2.3,1.1,2.2,0.6,2.6,0.8,1.7,1.3,0.8,0.5,1.3,0.2,0.2,1.6,1.5,2.8,1.6,1.1,0.9,1.3,0.9,3.9,0.3,4,27,8,11,15,22,16,14,14,8,4.0,26,17,7,17,48,75,10,26,40,14,34,17,29,42,33,36,28,26,38,23,7,6,21,3,5,48,34,3,2,7,27,24,23,21,9,2,5,8,8,5,16,8,15,4,20,7,13,10,8,4,10,2,3,13,16,24,11,7,8,8,8,32,3,0.028,0.022,0.016,0.021,0.038,0.035,0.03,0.025,0.049,0.036,0.042,0.039,0.035,0.031,0.038,0.037,0.029,0.032,0.034,0.039,0.031,0.037,0.032,0.038,0.034,0.032,0.028,0.033,0.029,0.032,0.049,0.035,0.025,0.031,0.015,0.021,0.037,0.029,0.02,0.026,0.024,0.038,0.039,0.03,0.025,0.022,0.016,0.012,0.017,0.02,0.037,0.026,0.029,0.021,0.033,0.022,0.029,0.021,0.026,0.017,0.029,0.026,0.015,0.014,0.024,0.028,0.024,0.025,0.021,0.023,0.032,0.017,0.019,0.02,1370,5085,2277,2548,1560,2775,2840,3029,1350,1445,648.0,2258,3614,740,2348,6742,14829,1919,3880,3982,3615,4189,1768,4849,5454,5795,5450,3579,5871,5175,2118,1141,964,4057,1369,1633,5508,5994,404,508,1380,2881,4672,2434,3407,1400,642,1832,2084,1718,586,3178,1531,3413,474,3642,1038,2798,1311,1194,710,1761,368,790,1854,1361,3722,2351,1672,1213,1469,1721,7481,399,0.097,0.118,0.095,0.105,0.142,0.156,0.122,0.124,0.167,0.141,0.143,0.15,0.144,0.137,0.152,0.151,0.134,0.136,0.136,0.15,0.107,0.158,0.138,0.141,0.144,0.14,0.124,0.129,0.121,0.148,0.166,0.134,0.125,0.122,0.089,0.093,0.136,0.131,0.116,0.113,0.118,0.147,0.143,0.127,0.117,0.12,0.097,0.082,0.099,0.106,0.141,0.124,0.135,0.104,0.152,0.115,0.129,0.112,0.129,0.107,0.128,0.128,0.106,0.108,0.124,0.125,0.122,0.117,0.103,0.12,0.133,0.112,0.098,0.133,398,1871,881,1047,681,1168,1091,1036,489,426,237.0,1136,1057,263,748,2277,4707,453,1170,2014,985,1478,858,1373,1818,1976,2016,1424,1779,1817,618,475,332,1066,376,553,1834,1745,188,233,729,1381,1243,1699,2069,744,286,888,1040,798,321,1395,619,1585,252,1870,526,1329,784,711,304,713,198,318,1025,805,1833,1049,843,568,714,853,3498,231,3.364,2.632,2.462,2.5,1.99,2.197,2.246,2.636,2.247,2.797,2.418,1.76,2.679,2.171,2.413,2.37,2.579,3.339,2.751,1.841,2.938,2.293,1.926,2.637,2.387,2.452,2.192,2.073,2.646,2.351,2.698,2.507,2.411,2.763,3.275,2.595,2.402,2.776,2.03,2.087,2.36,1.923,2.889,1.725,1.872,2.16,2.956,2.408,2.39,2.431,2.12,2.163,2.217,2.24,2.352,2.126,2.383,2.434,2.008,1.944,2.226,2.489,1.717,2.29,2.003,2.063,2.137,2.385,2.358,2.357,2.432,2.372,2.31,2.288,0.676,0.563,0.543,0.453,0.539,0.443,0.545,0.575,0.509,0.419,0.55,0.463,0.555,0.548,0.583,0.545,0.515,0.892,0.641,0.541,0.758,0.461,0.433,0.636,0.512,0.47,0.541,0.468,0.51,0.46,0.648,0.912,0.47,0.594,0.609,0.548,0.558,0.568,0.442,0.468,0.511,0.495,0.625,0.564,0.513,0.528,0.52,0.478,0.471,0.533,0.413,0.569,0.61,0.484,0.426,0.514,0.535,0.668,0.365,0.358,0.376,0.751,0.339,0.952,0.509,0.66,0.508,0.53,0.478,0.409,0.443,0.574,0.52,0.506
-4073,Not Epileptic,F,42.0,0.8,1.8,1.0,1.3,2.2,1.3,2.0,1.3,1.3,0.8,0.2,2.4,1.3,0.6,1.5,5.0,7.3,0.7,2.6,3.3,1.1,1.8,2.1,3.6,2.3,2.8,3.1,3.0,2.3,2.5,1.2,0.7,0.3,1.9,0.3,0.7,3.2,2.6,0.2,0.4,1.1,3.6,2.0,2.2,3.6,0.6,0.4,0.7,1.2,0.8,0.5,2.2,1.0,1.8,0.3,2.0,0.7,1.3,1.1,0.7,0.4,1.2,0.6,0.7,1.8,0.9,2.3,1.1,0.6,0.6,0.8,1.3,3.6,0.3,6,17,10,9,18,13,14,15,14,7,4.0,22,15,7,18,58,63,6,32,28,10,24,18,34,34,33,34,25,20,26,16,6,3,23,2,6,41,36,1,2,7,28,23,17,20,5,2,5,6,9,4,17,8,12,2,14,5,14,9,7,3,10,5,6,18,17,16,6,3,6,6,10,29,2,0.043,0.026,0.025,0.028,0.043,0.027,0.036,0.03,0.043,0.036,0.037,0.04,0.032,0.032,0.033,0.035,0.032,0.034,0.03,0.041,0.022,0.031,0.041,0.045,0.03,0.032,0.036,0.04,0.031,0.033,0.044,0.053,0.03,0.03,0.014,0.021,0.033,0.034,0.018,0.024,0.025,0.048,0.038,0.03,0.031,0.017,0.023,0.015,0.021,0.017,0.036,0.029,0.029,0.023,0.013,0.021,0.014,0.02,0.026,0.028,0.018,0.025,0.04,0.024,0.027,0.026,0.023,0.022,0.017,0.022,0.027,0.023,0.017,0.019,1269,3825,1848,2001,1921,2274,2381,2597,1740,1353,421.0,2249,3278,1199,3264,9271,15753,1912,4481,3461,3921,3444,2142,5104,5083,5992,4477,3820,4698,4285,2345,605,646,4864,1543,1657,6836,6120,300,563,1242,2437,6165,1633,2873,990,600,1639,1984,1667,501,3165,1339,3085,519,2905,1371,2833,1088,1044,793,2053,483,921,2135,944,3205,1649,1008,1258,1084,1725,7805,542,0.136,0.121,0.126,0.12,0.15,0.124,0.124,0.138,0.162,0.151,0.131,0.157,0.142,0.144,0.146,0.149,0.132,0.131,0.135,0.161,0.099,0.131,0.151,0.139,0.139,0.143,0.137,0.125,0.114,0.14,0.141,0.126,0.119,0.14,0.076,0.115,0.137,0.149,0.103,0.118,0.12,0.145,0.15,0.137,0.128,0.104,0.111,0.086,0.1,0.115,0.159,0.13,0.122,0.104,0.099,0.109,0.091,0.117,0.13,0.134,0.103,0.129,0.166,0.119,0.132,0.145,0.112,0.111,0.102,0.134,0.12,0.116,0.098,0.111,361,1190,673,753,855,821,807,842,533,353,147.0,1120,805,283,872,2734,4137,381,1387,1488,880,1035,851,1464,1414,1705,1454,1292,1300,1269,633,269,225,1151,389,532,1842,1510,190,256,679,1200,1144,1129,1768,502,253,750,890,681,234,1379,608,1363,284,1416,686,1153,612,490,360,763,262,493,1175,601,1627,754,456,419,539,836,3324,225,3.341,2.912,2.772,2.636,2.072,2.395,2.385,2.808,2.711,2.917,2.378,1.779,2.95,2.908,2.733,2.654,3.045,3.781,2.751,2.071,3.404,2.509,2.204,2.688,2.737,2.832,2.437,2.293,2.772,2.636,2.796,2.246,2.426,2.987,3.363,2.823,2.916,3.021,1.875,2.744,2.349,1.824,3.598,1.728,1.853,2.108,2.74,2.734,2.693,2.638,2.11,2.316,2.369,2.331,2.261,2.254,2.352,2.534,1.975,2.201,2.282,2.657,2.143,2.177,2.033,1.901,2.265,2.595,2.491,3.243,2.439,2.309,2.458,2.67,0.718,0.558,0.368,0.439,0.547,0.491,0.635,0.481,0.503,0.696,0.764,0.509,0.534,0.578,0.521,0.605,0.552,0.824,0.63,0.638,0.746,0.592,0.675,0.803,0.619,0.547,0.631,0.555,0.621,0.515,0.658,0.826,0.405,0.617,0.643,0.46,0.7,0.655,0.318,0.436,0.464,0.517,0.76,0.527,0.588,0.402,0.453,0.631,0.458,0.51,0.45,0.434,0.42,0.437,0.423,0.452,0.41,0.594,0.398,0.413,0.434,0.588,0.467,0.459,0.536,0.55,0.436,0.344,0.407,0.863,0.351,0.566,0.401,0.53
-4074,Not Epileptic,M,27.0,1.1,2.3,1.0,1.4,1.8,2.1,1.9,1.7,1.2,0.7,0.3,3.7,1.4,0.7,1.8,5.7,10.4,0.8,2.3,4.4,1.4,3.4,2.1,6.0,2.7,3.3,3.9,3.4,2.6,3.6,1.6,1.4,0.4,2.3,0.6,0.4,3.1,3.4,0.2,0.2,1.1,3.0,2.5,3.5,3.4,0.7,0.3,1.1,1.2,0.9,0.6,2.3,1.8,2.1,0.2,3.6,0.6,2.5,1.1,2.0,0.7,2.3,0.4,0.6,2.9,2.1,3.1,2.3,0.8,0.5,1.5,1.8,4.5,0.4,9,21,9,9,19,21,17,15,14,8,4.0,36,17,9,25,65,102,6,28,47,16,46,21,60,35,37,52,32,20,44,21,10,4,28,4,2,36,45,1,1,7,36,23,26,19,5,2,7,6,10,3,20,18,14,1,27,6,20,8,16,5,18,2,5,24,24,25,15,7,4,10,11,36,4,0.037,0.021,0.013,0.021,0.029,0.027,0.027,0.022,0.038,0.033,0.034,0.033,0.026,0.038,0.031,0.028,0.03,0.032,0.029,0.03,0.025,0.03,0.027,0.047,0.028,0.027,0.026,0.032,0.02,0.028,0.035,0.042,0.024,0.024,0.013,0.007,0.029,0.027,0.015,0.014,0.025,0.028,0.035,0.023,0.024,0.014,0.014,0.018,0.016,0.017,0.021,0.022,0.025,0.017,0.019,0.019,0.018,0.024,0.018,0.022,0.023,0.03,0.023,0.015,0.021,0.029,0.024,0.022,0.018,0.017,0.028,0.019,0.017,0.019,1630,5194,2471,3011,2512,3547,3289,3744,1954,1894,733.0,4190,3343,1333,3963,11626,22885,2638,4549,6512,4761,6428,3543,6938,6719,7242,7844,4846,7405,8107,3151,1664,1134,7198,2805,1915,7762,9059,404,561,1641,3695,6046,4277,3827,1570,855,2410,2458,2303,626,3820,3525,4071,248,5404,1321,3794,1475,2853,1161,3187,577,1134,4492,1631,4180,4165,1754,804,2092,3039,9584,540,0.136,0.112,0.099,0.102,0.131,0.132,0.097,0.119,0.144,0.14,0.116,0.13,0.126,0.142,0.143,0.129,0.128,0.108,0.135,0.133,0.11,0.133,0.101,0.143,0.126,0.127,0.123,0.112,0.096,0.127,0.129,0.129,0.084,0.121,0.083,0.055,0.125,0.135,0.096,0.086,0.115,0.128,0.136,0.111,0.108,0.091,0.09,0.09,0.09,0.106,0.11,0.111,0.117,0.095,0.122,0.108,0.1,0.117,0.101,0.112,0.108,0.129,0.107,0.107,0.112,0.136,0.116,0.11,0.102,0.111,0.122,0.102,0.096,0.134,494,1667,984,1006,969,1239,1048,1138,514,392,201.0,1670,871,308,1056,3193,5715,470,1286,2376,1048,1938,1277,2077,1720,1995,2495,1624,1847,2226,807,566,331,1458,686,730,1861,1973,204,258,741,1678,1262,2214,2174,752,354,1008,1125,844,402,1706,1201,1857,125,2730,630,1721,938,1344,492,1180,285,589,2083,967,2021,1582,781,439,871,1444,4073,298,3.256,2.814,2.523,2.922,2.315,2.364,2.617,2.984,2.755,3.508,3.014,2.077,3.003,2.976,2.848,2.799,3.116,3.934,2.862,2.229,3.276,2.553,2.331,2.598,2.879,2.912,2.449,2.339,3.057,2.731,2.855,2.847,2.627,3.315,3.684,2.515,3.023,3.169,2.24,2.339,2.734,1.951,3.418,2.034,2.012,2.285,2.886,3.011,2.759,2.9,1.806,2.307,2.503,2.464,2.068,2.254,2.359,2.393,1.77,2.177,2.285,2.745,2.174,2.227,2.286,2.039,2.233,2.628,2.55,2.2,2.62,2.477,2.583,2.375,0.581,0.534,0.419,0.526,0.647,0.613,0.559,0.56,0.614,0.659,0.578,0.58,0.41,0.591,0.55,0.601,0.631,0.747,0.549,0.616,0.826,0.627,0.686,0.768,0.565,0.596,0.605,0.645,0.592,0.52,0.666,1.087,0.493,0.676,0.646,0.388,0.775,0.638,0.312,0.468,0.483,0.516,0.684,0.602,0.643,0.529,0.431,0.684,0.446,0.481,0.363,0.374,0.696,0.354,0.553,0.455,0.375,0.499,0.418,0.444,0.477,0.662,0.477,0.733,0.483,0.724,0.403,0.451,0.372,0.516,0.504,0.477,0.424,0.398
-4075,Not Epileptic,F,22.0,0.6,2.2,0.6,0.9,1.9,1.9,2.1,1.3,1.4,0.7,0.2,3.0,1.5,0.8,1.7,5.9,10.1,0.8,4.3,5.1,1.9,3.7,1.9,3.6,3.3,4.7,4.9,3.3,3.7,4.6,1.4,0.8,0.6,3.2,0.3,0.9,5.7,5.9,0.2,0.2,1.0,3.8,4.2,3.3,3.0,0.9,0.4,0.7,0.9,1.2,0.8,1.4,1.8,2.1,0.2,2.7,0.9,1.5,1.1,0.9,0.6,1.0,0.2,0.6,2.2,1.4,3.2,1.4,0.9,0.4,0.7,1.7,3.6,0.3,4,20,5,8,14,16,14,11,9,6,2.0,24,13,6,16,52,72,5,28,40,23,29,14,33,25,36,39,23,25,30,17,7,5,27,2,6,49,51,1,1,4,25,33,21,17,7,2,4,4,9,5,10,13,13,1,18,7,13,8,7,5,6,1,4,14,13,22,7,6,3,6,12,26,2,0.029,0.028,0.011,0.021,0.05,0.042,0.036,0.031,0.052,0.037,0.031,0.04,0.034,0.049,0.046,0.048,0.041,0.033,0.048,0.041,0.034,0.05,0.038,0.054,0.038,0.048,0.039,0.041,0.035,0.042,0.049,0.045,0.029,0.039,0.013,0.022,0.047,0.044,0.015,0.019,0.024,0.049,0.055,0.027,0.026,0.026,0.02,0.012,0.015,0.026,0.043,0.021,0.03,0.024,0.031,0.026,0.02,0.019,0.037,0.019,0.026,0.027,0.027,0.04,0.026,0.032,0.029,0.024,0.021,0.018,0.02,0.018,0.018,0.026,1500,3855,1856,2199,2269,3102,2257,2781,1805,1180,507.0,2727,3647,1347,2662,10679,17018,2324,4011,4755,3198,4693,1693,5263,5135,6495,5667,3161,5530,5387,1804,928,979,6103,1497,1981,6541,7790,262,443,1536,2591,4611,2897,2889,1481,691,1961,2227,1658,489,2616,2190,4208,270,3503,1439,2999,1063,1239,1224,1995,442,815,2979,1041,4005,2045,1451,810,1423,2577,8266,505,0.11,0.122,0.087,0.098,0.156,0.135,0.121,0.119,0.145,0.138,0.118,0.137,0.14,0.138,0.155,0.143,0.13,0.119,0.154,0.142,0.112,0.164,0.14,0.149,0.136,0.153,0.129,0.128,0.137,0.137,0.15,0.131,0.118,0.141,0.068,0.097,0.142,0.142,0.1,0.109,0.106,0.146,0.147,0.119,0.117,0.111,0.1,0.079,0.088,0.128,0.166,0.099,0.118,0.101,0.116,0.12,0.107,0.105,0.146,0.112,0.109,0.116,0.11,0.127,0.116,0.134,0.121,0.108,0.109,0.106,0.11,0.108,0.096,0.13,356,1302,741,765,653,911,901,798,411,299,150.0,1290,787,290,721,2292,4233,434,1520,2103,938,1314,826,1284,1529,1773,2070,1285,1626,1924,536,369,340,1392,406,635,2160,2289,175,186,677,1249,1315,1894,1822,663,280,874,939,731,318,1167,914,1617,117,1724,722,1504,572,708,444,604,151,315,1363,670,1866,899,708,336,661,1475,3470,217,3.771,2.722,2.362,2.819,2.402,2.745,2.084,2.911,2.953,3.309,3.112,1.892,3.143,2.973,2.606,2.956,2.926,3.729,2.388,2.008,2.694,2.608,1.956,2.73,2.604,2.834,2.265,1.999,2.564,2.305,2.508,2.703,2.349,3.002,3.064,2.762,2.384,2.548,1.747,2.724,2.799,1.945,2.559,1.74,1.87,2.19,3.121,2.581,2.983,2.639,1.924,2.182,2.178,2.426,2.484,2.343,2.271,2.337,2.131,2.022,2.436,2.73,2.543,2.51,2.475,2.062,2.238,2.506,2.401,2.515,2.415,2.265,2.599,2.653,0.93,0.691,0.53,0.609,0.852,0.705,0.587,0.76,0.848,0.535,0.929,0.463,0.769,0.71,0.775,0.859,0.753,0.755,0.755,0.619,0.891,0.736,0.475,0.837,0.65,0.656,0.537,0.55,0.623,0.695,0.887,0.799,0.53,0.778,1.032,0.595,0.852,0.737,0.398,0.613,0.763,0.56,0.96,0.406,0.567,0.486,0.654,0.84,0.545,0.652,0.431,0.767,0.748,0.706,0.39,0.591,0.49,0.535,0.555,0.471,0.55,0.888,0.859,0.853,0.705,0.597,0.475,0.538,0.615,0.733,0.622,0.508,0.623,0.771
-4076,Not Epileptic,M,52.0,0.7,2.1,0.9,1.1,1.6,2.0,2.2,1.4,1.5,0.5,0.2,2.7,1.0,0.5,1.1,6.2,8.9,0.9,2.4,6.6,1.4,3.7,2.1,3.0,2.8,3.4,2.4,2.2,3.0,2.8,1.5,1.7,0.3,2.2,0.3,0.6,2.9,3.3,0.2,0.2,1.2,4.0,2.3,5.2,3.5,0.7,0.6,0.9,1.2,0.9,0.7,2.3,1.3,2.6,0.2,2.5,0.7,1.7,2.1,1.0,0.9,1.7,0.5,0.5,2.6,1.3,2.6,1.1,1.8,0.7,1.5,1.6,4.1,0.3,7,20,6,7,14,16,17,11,13,5,2.0,24,9,6,11,58,70,8,23,44,13,40,15,28,33,32,24,20,23,32,15,10,3,21,2,4,27,34,1,1,6,31,21,34,17,5,3,5,6,9,6,16,10,13,1,19,7,15,15,8,7,14,2,4,19,12,18,7,11,6,10,13,31,3,0.03,0.023,0.015,0.02,0.029,0.034,0.033,0.026,0.041,0.028,0.02,0.029,0.022,0.031,0.023,0.033,0.03,0.033,0.029,0.038,0.023,0.029,0.029,0.034,0.027,0.03,0.024,0.022,0.026,0.026,0.041,0.053,0.021,0.028,0.014,0.015,0.036,0.028,0.012,0.014,0.027,0.033,0.035,0.039,0.023,0.016,0.024,0.014,0.019,0.027,0.032,0.025,0.028,0.024,0.027,0.021,0.02,0.019,0.026,0.019,0.016,0.029,0.028,0.019,0.025,0.029,0.018,0.016,0.031,0.023,0.029,0.021,0.016,0.017,1526,4613,1993,2331,2853,3308,2730,2986,2324,1224,573.0,2899,3161,1363,2665,10682,17847,2369,4436,5398,4129,6600,2385,6208,5674,6574,5120,3834,5971,5456,2431,1141,911,6928,1855,1805,5347,7303,442,541,1268,3861,5950,3018,3841,1392,912,2242,2137,1339,618,3027,1826,3890,390,3780,1160,3085,2325,1437,1725,2591,415,880,3309,1170,4254,2246,1725,1357,1772,2402,9759,479,0.125,0.112,0.099,0.102,0.116,0.138,0.102,0.114,0.147,0.126,0.093,0.127,0.111,0.131,0.121,0.14,0.125,0.131,0.128,0.136,0.1,0.127,0.119,0.13,0.123,0.134,0.11,0.097,0.104,0.131,0.132,0.136,0.106,0.119,0.072,0.085,0.126,0.12,0.082,0.086,0.119,0.134,0.131,0.151,0.108,0.091,0.112,0.08,0.098,0.129,0.14,0.116,0.118,0.108,0.137,0.11,0.106,0.108,0.125,0.101,0.09,0.126,0.13,0.116,0.117,0.117,0.1,0.095,0.128,0.113,0.135,0.117,0.092,0.113,396,1511,857,857,1000,1036,1025,950,669,364,197.0,1537,770,320,754,3250,5101,487,1331,2592,1028,2186,1056,1534,1739,1958,1681,1460,1750,1825,598,562,322,1342,439,573,1482,1872,234,248,682,1867,1234,2214,2305,733,394,1046,985,585,387,1487,857,1661,132,1871,611,1543,1268,807,864,972,216,415,1647,660,2195,1025,934,532,781,1268,4066,227,3.337,2.911,2.527,2.638,2.425,2.724,2.207,2.781,2.621,3.231,2.687,1.73,3.023,2.985,2.731,2.605,2.847,3.629,2.787,1.895,3.138,2.38,1.97,2.937,2.543,2.787,2.428,2.126,2.663,2.405,2.815,2.199,2.384,3.526,3.503,2.829,2.73,2.859,2.147,2.742,2.443,1.869,3.362,1.663,1.921,2.075,2.743,2.682,2.728,2.526,1.91,2.264,2.34,2.437,3.434,2.231,2.15,2.283,2.118,1.97,2.177,2.735,2.392,2.343,2.164,2.118,2.131,2.514,2.181,2.924,2.458,2.292,2.535,2.453,0.614,0.571,0.376,0.489,0.599,0.59,0.592,0.557,0.552,0.453,0.802,0.437,0.37,0.392,0.519,0.498,0.569,0.689,0.58,0.636,0.781,0.498,0.514,0.676,0.545,0.535,0.523,0.545,0.561,0.578,0.694,0.916,0.72,0.601,0.77,0.581,0.734,0.614,0.337,0.431,0.505,0.604,0.771,0.602,0.588,0.441,0.609,0.589,0.524,0.52,0.458,0.411,0.493,0.548,0.479,0.477,0.43,0.573,0.482,0.397,0.4,0.716,0.361,0.687,0.583,0.663,0.482,0.414,0.518,0.89,0.405,0.391,0.577,0.561
-4077,Not Epileptic,F,42.0,1.1,1.9,1.0,0.9,1.2,1.6,1.5,1.8,1.5,0.8,0.3,2.6,1.4,0.4,1.5,5.9,8.6,1.1,2.3,2.9,1.1,3.1,2.0,2.8,3.2,2.8,3.0,2.9,3.5,2.9,2.1,1.1,0.7,2.0,0.4,0.9,2.8,3.6,0.2,0.3,1.1,3.3,1.7,1.7,3.2,0.7,0.4,0.8,1.3,0.7,0.4,2.4,1.2,2.6,0.2,2.1,0.9,1.4,0.9,1.7,0.5,1.5,0.6,0.6,2.1,1.2,2.7,1.8,0.8,1.0,1.0,1.2,3.9,0.3,7,18,8,8,12,18,16,16,14,8,5.0,23,19,6,19,66,77,10,29,31,9,40,19,33,37,37,39,29,28,38,21,9,11,29,3,8,34,45,1,3,5,32,18,17,18,8,2,5,6,4,3,17,8,17,2,17,7,10,7,12,4,15,3,6,16,17,18,14,6,8,7,8,31,3,0.042,0.023,0.02,0.022,0.032,0.023,0.029,0.032,0.052,0.029,0.036,0.031,0.032,0.027,0.032,0.036,0.031,0.036,0.026,0.03,0.026,0.029,0.031,0.036,0.034,0.031,0.03,0.031,0.034,0.03,0.046,0.047,0.028,0.031,0.012,0.016,0.033,0.037,0.018,0.021,0.02,0.034,0.031,0.022,0.024,0.019,0.015,0.013,0.017,0.018,0.024,0.024,0.028,0.021,0.02,0.016,0.033,0.013,0.017,0.021,0.022,0.023,0.055,0.018,0.022,0.019,0.021,0.021,0.017,0.032,0.018,0.019,0.018,0.014,1614,4176,2546,2397,2015,4212,3449,3491,2064,1859,747.0,3597,3374,954,3220,11737,19155,2337,5546,4650,3930,6894,3494,5704,6372,6539,6174,5599,6936,6617,2804,1157,1446,5743,2076,2285,6150,8228,337,603,1744,4233,5573,2171,3700,1329,775,2023,2371,1385,561,3078,1591,4701,330,3771,1202,3518,1509,2275,704,2620,419,1017,3327,1455,4021,3056,1684,1194,1850,2279,7670,710,0.127,0.112,0.1,0.099,0.131,0.122,0.109,0.136,0.177,0.131,0.131,0.146,0.144,0.134,0.139,0.149,0.131,0.136,0.12,0.142,0.1,0.125,0.137,0.133,0.129,0.14,0.128,0.122,0.118,0.14,0.165,0.12,0.116,0.129,0.066,0.099,0.13,0.149,0.095,0.117,0.101,0.128,0.127,0.12,0.108,0.113,0.093,0.082,0.091,0.103,0.123,0.119,0.121,0.101,0.132,0.104,0.137,0.089,0.102,0.11,0.116,0.122,0.21,0.111,0.112,0.111,0.115,0.108,0.093,0.163,0.104,0.102,0.1,0.089,476,1380,854,823,715,1179,983,1022,514,442,213.0,1462,967,247,947,3108,4952,523,1453,1708,841,1768,1079,1619,1721,1859,1903,1693,1833,1806,779,434,452,1280,546,843,1544,1909,194,270,819,1625,1054,1346,2122,619,341,860,1107,537,247,1527,763,2007,157,1951,449,1603,792,1144,379,1011,183,535,1510,959,1794,1334,742,466,857,1050,3394,361,3.478,2.666,2.777,2.742,2.409,2.66,2.777,2.933,3.183,3.234,2.882,2.145,2.688,2.729,2.631,2.777,3.054,3.565,2.918,2.346,3.269,2.788,2.549,2.794,2.715,2.77,2.474,2.531,2.873,2.711,2.778,2.686,2.592,3.059,3.329,2.589,2.86,3.103,1.843,2.292,2.749,2.195,3.472,1.812,1.986,2.281,2.822,2.887,2.638,3.034,2.612,2.134,2.178,2.482,2.623,2.135,2.792,2.509,2.069,2.167,2.364,2.612,2.038,2.233,2.459,1.855,2.416,2.434,2.546,2.612,2.453,2.409,2.373,2.381,0.819,0.573,0.544,0.467,0.697,0.786,0.684,0.499,0.679,0.649,0.818,0.57,0.48,0.63,0.543,0.668,0.608,0.709,0.572,0.73,0.978,0.58,0.622,0.726,0.62,0.537,0.619,0.656,0.654,0.667,0.572,0.966,0.329,0.608,0.72,0.528,0.784,0.666,0.297,0.457,0.609,0.645,0.72,0.564,0.584,0.412,0.399,0.774,0.443,0.474,0.509,0.477,0.443,0.423,0.288,0.462,0.625,0.42,0.409,0.351,0.357,0.87,0.682,0.592,0.518,0.547,0.512,0.372,0.424,0.712,0.467,0.489,0.45,0.369
-4078,Not Epileptic,M,27.0,0.7,1.8,0.8,1.1,1.8,1.8,1.1,1.2,1.3,0.7,0.2,3.1,1.3,0.8,1.3,5.2,7.8,0.9,2.8,5.2,1.5,2.4,1.7,4.0,3.0,2.9,5.7,2.4,2.6,2.9,2.1,1.3,0.4,2.0,0.3,0.4,3.4,3.2,0.2,0.3,1.0,3.4,2.4,3.5,2.2,0.4,0.4,0.9,1.4,0.8,0.7,2.0,2.2,2.6,0.4,2.7,0.6,1.9,1.4,1.0,1.3,1.6,0.4,0.7,2.2,1.6,2.8,1.0,0.8,0.5,1.1,1.6,4.3,0.4,5,17,7,8,17,21,12,15,16,10,4.0,29,17,10,16,64,84,8,32,52,19,34,18,48,43,37,58,26,31,36,20,10,5,30,1,2,38,40,2,2,6,33,27,31,12,3,2,6,8,6,4,17,15,21,3,22,5,20,14,9,12,15,2,5,16,20,21,7,5,4,7,11,31,3,0.034,0.023,0.016,0.023,0.034,0.028,0.022,0.026,0.048,0.029,0.038,0.035,0.03,0.026,0.024,0.027,0.028,0.037,0.032,0.038,0.028,0.029,0.03,0.04,0.028,0.026,0.036,0.027,0.03,0.026,0.048,0.047,0.03,0.029,0.009,0.012,0.033,0.034,0.015,0.014,0.021,0.036,0.038,0.029,0.015,0.011,0.017,0.016,0.017,0.022,0.026,0.023,0.029,0.02,0.011,0.016,0.02,0.018,0.023,0.019,0.028,0.024,0.025,0.019,0.023,0.022,0.02,0.016,0.015,0.015,0.024,0.019,0.018,0.015,1374,4714,2363,2643,2473,3418,3588,3062,2176,1948,715.0,3458,3496,1884,4172,12224,20752,2584,6085,6655,3845,5215,2199,7188,7631,8028,9113,4931,7217,7105,2836,1174,1055,6143,1864,1767,7237,9407,607,591,1548,4333,6443,4251,4003,1284,972,1827,2764,1524,838,3761,2707,5827,794,4703,1615,4446,1973,1435,2153,3307,452,1188,3310,1583,4794,2296,1594,1262,1474,2755,9063,612,0.119,0.107,0.1,0.107,0.148,0.136,0.095,0.124,0.147,0.149,0.126,0.138,0.138,0.124,0.122,0.134,0.128,0.137,0.125,0.156,0.104,0.133,0.122,0.146,0.13,0.126,0.139,0.105,0.118,0.124,0.138,0.13,0.12,0.131,0.052,0.066,0.134,0.137,0.089,0.107,0.102,0.14,0.147,0.125,0.091,0.082,0.089,0.087,0.097,0.106,0.12,0.11,0.124,0.097,0.085,0.099,0.102,0.1,0.128,0.103,0.122,0.119,0.124,0.109,0.118,0.107,0.104,0.094,0.095,0.094,0.121,0.105,0.097,0.108,382,1442,816,812,869,1163,911,903,597,431,189.0,1485,848,442,1002,3385,5047,522,1609,2537,869,1476,932,1832,1885,2083,2868,1594,1618,1995,857,481,302,1360,536,557,1797,1958,315,267,784,1711,1318,2124,2107,576,375,827,1192,581,442,1537,1114,2227,441,2422,633,1881,953,815,728,1156,232,554,1452,1087,2202,951,699,542,703,1270,3699,324,3.386,3.09,2.901,2.895,2.407,2.491,2.919,2.916,2.752,3.19,2.716,2.029,2.973,3.002,2.954,2.739,3.1,3.684,2.902,2.209,3.024,2.69,2.0,2.885,2.926,2.813,2.463,2.361,3.142,2.656,2.553,2.393,2.689,3.148,3.063,2.813,2.929,3.347,2.27,2.721,2.43,2.233,3.451,2.092,2.177,2.383,3.065,2.802,2.842,2.866,2.19,2.51,2.5,2.602,2.247,2.171,2.538,2.543,2.13,1.953,2.427,2.649,2.149,2.455,2.439,1.743,2.38,2.66,2.567,2.815,2.389,2.651,2.612,2.362,0.833,0.535,0.513,0.599,0.732,0.622,0.714,0.571,0.706,0.783,0.865,0.555,0.548,0.454,0.61,0.654,0.705,0.947,0.67,0.653,0.755,0.518,0.51,0.928,0.647,0.711,0.634,0.651,0.538,0.696,0.82,0.947,0.399,0.706,0.776,0.443,0.722,0.712,0.416,0.871,0.451,0.662,0.786,0.601,0.701,0.475,0.559,0.6,0.476,0.559,0.323,0.456,0.546,0.454,0.292,0.458,0.582,0.464,0.421,0.337,0.559,0.702,0.444,0.71,0.53,0.654,0.456,0.337,0.608,0.608,0.573,0.612,0.514,0.327
-4079,Not Epileptic,F,47.0,1.0,2.5,1.3,1.8,2.2,1.8,1.3,1.5,1.2,0.8,0.4,2.0,1.1,0.3,1.0,5.9,8.3,0.8,1.6,4.1,0.9,3.0,1.3,3.1,3.0,2.7,4.2,2.4,2.7,2.2,1.5,0.8,0.5,2.5,0.4,0.6,2.9,2.5,0.1,0.2,1.0,3.1,1.7,2.0,2.1,0.9,0.4,0.8,1.1,1.0,0.5,2.1,1.5,3.1,0.3,2.1,0.7,1.2,1.0,0.9,0.6,2.0,0.4,0.6,1.5,1.6,3.3,1.4,1.2,1.0,1.1,1.0,5.3,0.4,7,25,13,12,19,16,18,19,13,7,4.0,22,14,6,14,65,84,8,18,40,8,35,17,44,35,38,51,28,31,33,16,6,6,28,3,6,35,39,1,1,6,35,21,16,11,7,2,6,5,10,3,17,13,24,2,17,7,10,7,8,4,17,2,4,11,25,32,10,7,11,7,7,46,4,0.048,0.023,0.022,0.031,0.043,0.033,0.025,0.031,0.037,0.046,0.036,0.037,0.03,0.019,0.033,0.035,0.036,0.032,0.03,0.04,0.022,0.039,0.034,0.042,0.039,0.03,0.036,0.032,0.034,0.032,0.042,0.033,0.029,0.031,0.014,0.02,0.038,0.03,0.012,0.02,0.021,0.04,0.035,0.03,0.017,0.022,0.024,0.013,0.017,0.024,0.021,0.021,0.031,0.03,0.027,0.019,0.015,0.016,0.025,0.022,0.025,0.033,0.021,0.021,0.02,0.022,0.024,0.021,0.019,0.023,0.031,0.016,0.02,0.021,1295,5011,2519,2654,2449,3288,2781,3316,1978,1502,714.0,2394,2703,930,2538,10439,16405,1961,3949,5038,3408,4526,2143,6233,4210,5639,5107,3655,5677,4546,2200,1271,922,6939,1708,1546,6366,7153,401,387,1251,3210,4643,2109,3251,1198,700,2606,2033,1951,485,3119,1527,4065,342,2882,1436,2779,1040,1140,696,2679,361,892,2353,2291,4945,2434,1780,1574,1016,1854,9413,875,0.141,0.114,0.108,0.126,0.156,0.154,0.108,0.143,0.159,0.159,0.154,0.15,0.133,0.119,0.128,0.142,0.141,0.12,0.126,0.158,0.089,0.147,0.121,0.141,0.145,0.141,0.129,0.118,0.114,0.144,0.144,0.105,0.139,0.135,0.078,0.104,0.137,0.13,0.088,0.099,0.109,0.152,0.14,0.12,0.091,0.122,0.11,0.082,0.091,0.125,0.113,0.111,0.139,0.128,0.136,0.11,0.099,0.098,0.132,0.126,0.116,0.136,0.12,0.107,0.11,0.125,0.115,0.103,0.103,0.135,0.135,0.104,0.107,0.138,356,1746,954,928,917,894,958,1004,569,335,202.0,1046,758,216,690,3055,4481,418,926,1869,709,1377,774,1597,1286,1699,2132,1302,1495,1408,637,457,300,1452,427,547,1661,1669,207,182,667,1387,972,1273,1876,617,289,944,964,638,312,1536,766,1776,173,1614,709,1248,613,666,398,1009,202,451,1143,1072,2402,1107,888,642,563,939,4047,330,3.512,2.599,2.551,2.644,2.427,2.683,2.267,2.764,2.617,3.198,2.944,1.948,2.734,2.851,2.656,2.554,2.74,3.59,3.134,2.216,3.221,2.517,2.229,3.028,2.432,2.607,2.003,2.194,2.744,2.506,2.622,2.814,2.558,3.26,3.538,2.363,2.927,3.052,2.373,2.28,2.362,1.978,3.512,1.848,1.952,2.107,2.851,3.159,2.545,2.924,1.923,2.178,2.169,2.422,2.066,1.949,2.349,2.505,1.858,1.796,2.17,2.819,2.028,2.36,2.246,2.279,2.083,2.371,2.281,2.603,2.148,2.165,2.348,3.066,0.734,0.473,0.446,0.52,0.696,0.773,0.548,0.561,0.606,0.541,0.911,0.48,0.483,0.558,0.611,0.616,0.674,0.699,0.582,0.576,0.829,0.59,0.663,0.68,0.694,0.585,0.534,0.516,0.564,0.664,0.526,0.885,0.416,0.758,0.556,0.461,0.708,0.695,0.328,0.431,0.427,0.595,0.722,0.602,0.598,0.4,0.453,0.871,0.43,0.494,0.458,0.365,0.412,0.495,0.487,0.365,0.416,0.385,0.433,0.392,0.331,0.67,0.271,0.579,0.567,0.572,0.474,0.386,0.389,0.519,0.397,0.528,0.504,0.56
-4080,Not Epileptic,F,37.0,0.6,2.2,1.1,1.2,1.2,2.0,1.3,1.6,1.0,0.4,0.3,2.2,1.1,0.5,1.0,5.0,6.5,0.7,2.1,5.1,1.4,2.6,1.0,2.9,1.9,3.0,2.5,2.1,2.2,2.9,1.9,1.4,0.5,2.0,0.5,0.4,2.8,2.4,0.0,0.1,1.1,2.2,2.4,2.9,2.8,0.5,0.3,0.6,1.2,0.8,0.5,2.2,0.9,2.0,0.1,1.5,0.8,1.3,1.3,0.9,0.8,1.2,0.3,0.6,1.4,1.3,2.1,1.5,0.6,0.6,0.8,1.5,3.1,0.4,7,24,9,9,18,19,13,18,12,5,4.0,26,13,7,16,62,69,8,24,48,11,32,11,36,25,38,34,28,23,38,20,10,5,29,3,3,36,34,1,1,11,20,24,20,18,4,2,4,7,7,4,19,7,13,0,12,6,14,8,7,9,11,2,5,12,14,18,14,4,6,6,15,28,5,0.038,0.028,0.024,0.026,0.031,0.03,0.026,0.025,0.043,0.028,0.032,0.035,0.022,0.036,0.03,0.035,0.029,0.038,0.033,0.044,0.029,0.036,0.029,0.038,0.033,0.029,0.026,0.029,0.028,0.034,0.049,0.058,0.029,0.03,0.017,0.012,0.037,0.028,0.013,0.012,0.024,0.027,0.04,0.032,0.022,0.014,0.014,0.013,0.017,0.015,0.022,0.023,0.025,0.017,0.042,0.016,0.022,0.022,0.028,0.034,0.021,0.026,0.036,0.03,0.017,0.026,0.017,0.019,0.012,0.025,0.021,0.021,0.016,0.019,1498,4275,2281,1981,1950,3088,2424,3637,1567,1065,749.0,2390,3105,1145,2329,9239,15146,2022,3569,5357,4091,2987,1275,5840,3619,6540,4662,4140,5140,5066,1959,921,904,5715,1880,1138,7129,5605,232,321,1523,1853,5653,2518,3193,1189,726,1868,2017,2004,610,3532,1317,3770,88,2746,1239,2890,1153,896,1259,1979,390,691,2286,1158,3871,2889,1444,998,1200,2189,6864,760,0.13,0.128,0.12,0.122,0.139,0.132,0.117,0.127,0.163,0.125,0.115,0.153,0.111,0.162,0.147,0.149,0.13,0.129,0.157,0.165,0.11,0.124,0.108,0.156,0.127,0.139,0.125,0.125,0.114,0.148,0.167,0.139,0.124,0.13,0.084,0.078,0.145,0.127,0.094,0.099,0.124,0.118,0.156,0.135,0.115,0.096,0.092,0.078,0.098,0.097,0.122,0.113,0.121,0.098,0.204,0.101,0.115,0.113,0.129,0.119,0.11,0.128,0.144,0.154,0.103,0.11,0.103,0.109,0.085,0.129,0.118,0.119,0.094,0.138,392,1373,749,751,780,1075,872,1056,431,257,201.0,1129,858,280,659,2703,4066,372,987,2224,858,1154,614,1448,1094,1925,1727,1314,1358,1527,593,470,294,1192,467,468,1742,1440,127,175,749,1146,1166,1482,1815,559,290,812,980,773,338,1554,637,1684,25,1346,577,1150,715,439,612,742,178,342,1207,715,1855,1264,680,466,597,1110,3002,304,3.605,2.781,2.932,2.523,2.12,2.292,2.315,2.865,2.759,2.999,3.016,1.807,2.747,2.934,2.616,2.622,2.872,3.727,2.872,2.022,3.482,2.12,1.819,2.953,2.554,2.729,2.225,2.422,2.781,2.56,2.466,2.194,2.617,3.15,3.372,2.379,2.968,2.756,2.054,2.146,2.588,1.511,3.402,1.905,1.941,2.268,2.831,2.858,2.595,2.688,2.094,2.241,2.26,2.471,3.696,2.301,2.525,2.635,1.905,2.062,2.201,2.771,2.325,2.304,2.064,2.034,2.208,2.606,2.375,2.329,2.449,2.234,2.37,2.606,0.791,0.646,0.629,0.59,0.611,0.62,0.528,0.483,0.574,0.384,0.851,0.574,0.395,0.648,0.555,0.542,0.554,0.738,0.485,0.686,0.808,0.552,0.461,0.739,0.558,0.545,0.524,0.506,0.502,0.601,0.791,0.839,0.431,0.689,0.689,0.455,0.683,0.644,0.461,0.409,0.477,0.44,0.743,0.567,0.505,0.532,0.505,0.699,0.4,0.503,0.398,0.451,0.408,0.385,0.295,0.436,0.47,0.529,0.374,0.44,0.401,0.556,0.526,0.681,0.466,0.624,0.429,0.423,0.415,0.545,0.604,0.473,0.452,0.358
-4081,Not Epileptic,F,27.0,1.1,2.1,0.7,0.9,1.8,3.1,1.7,2.0,1.7,0.9,0.3,2.7,1.1,0.5,1.6,4.9,7.6,0.6,3.5,5.5,1.4,2.6,2.0,2.6,4.0,3.9,2.8,3.3,3.9,3.1,1.8,0.8,0.5,2.6,0.8,1.4,4.9,4.2,0.1,0.1,1.4,2.5,3.6,3.5,3.1,0.4,0.4,0.8,1.0,1.0,1.1,1.7,1.3,2.5,1.1,2.3,0.7,1.2,0.7,0.9,0.5,0.9,0.4,0.3,1.3,1.0,2.6,1.4,1.4,0.6,0.8,1.9,3.7,0.3,6,18,6,7,14,19,17,14,17,7,3.0,23,10,5,15,53,63,4,25,39,12,23,18,25,35,38,28,28,25,32,21,7,5,28,3,12,48,44,1,1,6,21,26,25,18,4,2,5,5,7,7,14,6,15,50,17,5,11,4,7,2,6,2,3,10,12,19,9,11,6,6,16,29,3,0.043,0.034,0.017,0.022,0.043,0.037,0.033,0.037,0.062,0.04,0.022,0.033,0.028,0.03,0.038,0.035,0.032,0.025,0.039,0.038,0.026,0.033,0.029,0.034,0.037,0.038,0.03,0.036,0.032,0.039,0.056,0.049,0.019,0.031,0.02,0.03,0.039,0.036,0.016,0.016,0.025,0.029,0.049,0.031,0.025,0.017,0.023,0.013,0.016,0.018,0.032,0.021,0.019,0.021,0.029,0.021,0.019,0.018,0.024,0.02,0.018,0.02,0.03,0.02,0.022,0.029,0.024,0.02,0.024,0.022,0.02,0.021,0.017,0.015,1554,4771,1793,1934,2525,3671,2654,3332,2132,1381,659.0,2678,2846,956,2860,11250,17643,2598,3949,4690,2787,3880,2421,6148,6070,6903,5090,3812,5911,5097,1968,835,1249,6309,2215,2478,6869,7630,319,422,1918,2365,4896,3206,3253,1092,818,2370,2585,2344,847,3291,2427,5031,1104,3337,1595,2661,836,1228,822,2037,499,777,2034,1078,3539,2208,1949,1427,1435,2886,8143,594,0.135,0.124,0.093,0.103,0.148,0.138,0.126,0.128,0.19,0.14,0.111,0.127,0.12,0.129,0.143,0.136,0.135,0.094,0.139,0.139,0.104,0.136,0.128,0.135,0.144,0.119,0.132,0.121,0.108,0.151,0.14,0.128,0.096,0.122,0.08,0.102,0.126,0.128,0.089,0.092,0.111,0.126,0.141,0.123,0.115,0.096,0.11,0.083,0.083,0.094,0.151,0.103,0.098,0.1,0.126,0.111,0.093,0.099,0.12,0.111,0.104,0.104,0.129,0.1,0.109,0.135,0.118,0.1,0.123,0.121,0.104,0.108,0.091,0.09,419,1202,647,700,725,1306,928,913,499,356,209.0,1314,725,262,774,2635,4158,401,1371,2261,830,1276,1102,1393,1729,1769,1644,1397,1789,1516,539,335,420,1501,660,771,2049,1904,170,198,780,1264,1328,1932,1989,478,297,912,1026,902,504,1384,1012,1912,599,1745,681,1290,429,716,388,730,212,315,1044,615,1756,1105,944,450,692,1394,3553,337,3.332,3.323,2.42,2.727,2.565,2.512,2.396,3.097,2.932,3.082,2.902,1.807,2.893,2.552,2.671,3.013,3.291,4.139,2.484,1.878,2.878,2.371,1.995,3.007,2.68,2.925,2.391,2.168,2.545,2.749,2.713,2.485,2.286,2.925,3.102,2.786,2.544,2.914,1.923,2.813,2.922,1.732,2.824,1.929,1.86,2.381,3.427,3.048,2.913,2.826,2.233,2.369,2.469,2.554,2.359,2.196,2.814,2.404,2.214,1.934,2.677,2.607,2.686,2.509,2.056,2.239,2.285,2.355,2.219,3.37,2.344,2.472,2.418,2.103,1.127,0.817,0.691,0.664,0.773,0.595,0.576,0.797,0.595,0.498,0.683,0.52,0.49,0.66,0.596,0.701,0.648,0.871,0.652,0.585,0.825,0.48,0.421,0.76,0.616,0.852,0.544,0.608,0.504,0.676,0.754,1.082,0.485,0.751,0.901,0.714,0.717,0.688,0.586,0.795,0.529,0.433,0.79,0.64,0.497,0.812,0.671,0.811,0.701,0.668,0.366,0.624,0.76,0.718,0.348,0.498,0.721,0.59,0.449,0.393,0.461,0.788,0.669,1.173,0.516,0.563,0.556,0.556,0.554,0.727,0.637,0.62,0.598,0.47
-4082,Not Epileptic,F,52.0,0.9,2.4,0.9,1.2,1.4,2.0,1.5,1.5,1.4,0.5,0.3,2.4,1.4,0.2,1.3,4.9,8.0,0.9,1.9,4.4,1.0,2.4,1.4,3.4,3.3,2.6,4.2,1.7,1.9,3.1,1.7,1.1,0.4,1.8,0.4,0.6,3.0,2.8,0.2,0.3,1.1,3.7,1.7,1.9,2.5,0.6,0.3,0.6,1.2,0.7,0.5,1.4,1.4,2.0,0.3,2.3,0.6,1.6,1.3,1.2,0.5,1.1,0.2,0.5,2.0,1.0,2.1,1.1,1.1,0.4,0.8,1.5,3.7,0.2,7,26,10,12,10,22,14,13,17,6,3.0,25,13,4,17,58,71,8,25,37,8,27,12,40,39,32,42,17,19,40,37,9,4,23,2,4,34,32,1,2,7,29,19,18,16,4,2,4,8,7,5,10,11,13,2,18,6,14,11,10,5,9,1,6,16,18,17,10,6,4,6,10,33,3,0.036,0.023,0.018,0.025,0.029,0.032,0.023,0.03,0.038,0.026,0.037,0.032,0.029,0.024,0.033,0.032,0.028,0.035,0.033,0.037,0.022,0.03,0.028,0.04,0.036,0.028,0.039,0.022,0.024,0.031,0.039,0.047,0.021,0.024,0.013,0.013,0.031,0.028,0.017,0.021,0.023,0.035,0.035,0.026,0.02,0.012,0.016,0.011,0.016,0.02,0.029,0.02,0.023,0.02,0.012,0.017,0.018,0.022,0.028,0.023,0.022,0.02,0.015,0.014,0.024,0.021,0.021,0.022,0.016,0.019,0.023,0.022,0.018,0.012,1278,4221,2033,2296,1694,2663,3256,2529,2027,1328,583.0,2409,2972,707,2273,9743,16745,2150,3636,4226,3114,3369,1995,5465,5207,5874,4919,4090,5519,5750,2637,1013,917,5283,1510,1827,5293,6267,285,503,1260,2207,5007,1706,3155,1286,694,1967,2103,1482,524,2149,1926,3787,607,3324,998,2579,1243,1582,830,2065,428,910,2371,917,2994,1979,1978,673,1282,2193,7284,322,0.128,0.122,0.114,0.12,0.125,0.147,0.106,0.13,0.149,0.13,0.121,0.152,0.142,0.111,0.154,0.142,0.122,0.131,0.142,0.15,0.099,0.121,0.118,0.143,0.138,0.13,0.131,0.101,0.098,0.145,0.122,0.107,0.095,0.122,0.071,0.083,0.127,0.138,0.107,0.108,0.113,0.134,0.135,0.128,0.107,0.084,0.093,0.075,0.098,0.112,0.132,0.106,0.12,0.098,0.095,0.103,0.113,0.117,0.126,0.118,0.122,0.107,0.093,0.105,0.123,0.126,0.115,0.108,0.096,0.112,0.118,0.116,0.101,0.123,425,1585,820,818,792,1063,993,873,738,351,176.0,1310,818,206,669,2707,4856,501,1085,2015,725,1187,814,1651,1614,1719,1908,1222,1427,1783,794,374,351,1268,442,658,1608,1630,162,250,720,1417,1036,1188,1890,682,309,870,1074,601,338,1131,958,1752,309,1924,492,1241,785,795,374,884,243,542,1267,738,1575,931,934,370,588,1071,3312,212,3.003,2.529,2.359,2.598,2.07,2.229,2.568,2.514,2.296,2.891,2.62,1.677,2.751,2.523,2.595,2.682,2.724,3.396,2.712,1.826,3.254,2.239,2.083,2.535,2.501,2.803,2.133,2.493,2.841,2.551,2.417,2.567,2.198,3.017,2.986,2.515,2.466,2.968,2.019,2.086,2.257,1.519,3.316,1.704,1.877,2.036,2.812,2.797,2.449,2.609,1.875,2.066,2.187,2.41,2.398,2.006,2.341,2.338,1.879,2.208,2.419,2.378,2.013,1.947,2.165,1.551,2.197,2.325,2.371,2.132,2.516,2.352,2.381,1.995,0.659,0.563,0.52,0.614,0.447,0.523,0.688,0.436,0.58,0.601,0.833,0.585,0.407,0.447,0.428,0.545,0.584,0.766,0.537,0.627,0.683,0.517,0.585,0.654,0.604,0.466,0.545,0.661,0.585,0.542,0.608,0.827,0.376,0.575,0.492,0.331,0.663,0.578,0.28,0.435,0.417,0.433,0.787,0.59,0.562,0.391,0.428,0.618,0.404,0.586,0.378,0.347,0.414,0.342,0.264,0.395,0.349,0.523,0.317,0.456,0.436,0.447,0.36,0.409,0.483,0.47,0.361,0.352,0.356,0.577,0.351,0.476,0.375,0.372
-4083,Not Epileptic,M,37.0,0.6,2.3,1.3,1.0,1.7,1.6,2.2,1.7,1.6,1.6,0.2,2.8,1.5,0.8,0.9,6.0,8.1,0.8,2.2,4.4,1.5,2.3,2.4,4.4,4.3,2.9,5.2,2.4,2.9,3.3,3.5,1.9,0.4,2.9,0.3,0.9,4.2,3.5,0.2,0.2,0.9,2.9,1.9,2.1,2.7,0.7,0.4,0.7,1.2,0.8,0.6,2.1,1.1,2.3,0.5,2.4,0.8,1.1,0.8,1.0,0.6,1.8,0.2,0.8,1.8,1.4,2.5,1.2,0.9,0.9,1.7,1.4,4.9,0.2,7,25,10,8,21,19,16,14,15,13,3.0,23,15,12,13,66,71,6,26,35,13,31,18,52,43,33,52,24,24,37,38,13,4,35,2,10,49,42,1,1,5,22,17,15,16,6,2,4,7,6,3,21,8,18,4,20,6,9,9,6,6,13,1,7,16,20,20,8,5,10,10,10,35,2,0.031,0.021,0.021,0.022,0.031,0.03,0.038,0.03,0.047,0.047,0.039,0.04,0.025,0.033,0.025,0.029,0.028,0.03,0.032,0.04,0.03,0.027,0.036,0.042,0.036,0.028,0.036,0.027,0.028,0.029,0.058,0.056,0.027,0.031,0.008,0.024,0.042,0.03,0.013,0.021,0.022,0.04,0.031,0.022,0.019,0.019,0.015,0.012,0.016,0.016,0.023,0.023,0.015,0.017,0.014,0.015,0.022,0.015,0.02,0.015,0.026,0.025,0.012,0.023,0.019,0.019,0.018,0.019,0.021,0.023,0.032,0.02,0.017,0.012,1558,5301,2971,2377,2406,2927,3857,3358,2008,1986,519.0,3056,3736,1745,2415,13983,18204,2285,4774,4919,4507,4839,2594,6626,6131,6521,7041,4661,6641,6685,2757,1339,988,7492,2145,2523,7421,9080,330,373,1398,2802,5957,2736,3741,1289,808,2187,2436,1802,597,3952,2259,5443,1027,4650,1183,2902,1240,1882,887,3170,335,1112,2959,1881,4295,2005,1563,1571,1982,2220,9406,633,0.118,0.111,0.105,0.103,0.138,0.138,0.115,0.131,0.149,0.168,0.125,0.147,0.118,0.157,0.118,0.123,0.124,0.103,0.127,0.154,0.116,0.13,0.122,0.148,0.134,0.128,0.133,0.111,0.107,0.134,0.167,0.142,0.122,0.136,0.059,0.111,0.145,0.139,0.106,0.118,0.107,0.134,0.125,0.116,0.098,0.1,0.089,0.078,0.091,0.098,0.125,0.109,0.101,0.096,0.096,0.097,0.115,0.093,0.124,0.092,0.124,0.121,0.095,0.123,0.107,0.113,0.106,0.102,0.099,0.135,0.133,0.115,0.096,0.088,434,1837,977,797,969,980,1064,933,610,550,158.0,1154,1005,445,627,3501,4618,450,1198,1755,937,1361,986,1960,1931,1841,2500,1490,1570,1908,895,537,278,1633,536,751,1934,2062,158,144,681,1135,1153,1492,2095,620,348,975,1141,784,347,1717,1109,2201,548,2317,551,1300,578,979,427,1117,200,510,1488,1110,2120,995,698,614,867,1066,4043,274,3.403,2.738,2.849,2.755,2.218,2.406,2.712,3.062,2.569,2.872,2.622,2.191,2.935,2.896,2.802,2.858,3.076,3.821,2.985,2.33,3.309,2.646,2.216,2.704,2.507,2.87,2.283,2.426,3.023,2.695,2.407,2.665,2.689,3.333,3.497,2.961,2.886,3.109,2.367,2.699,2.531,2.053,3.554,2.097,2.038,2.176,2.95,2.73,2.606,2.619,2.147,2.322,2.228,2.639,2.267,2.197,2.215,2.556,2.212,2.107,2.183,2.92,1.854,2.402,2.143,1.997,2.176,2.38,2.45,2.7,2.489,2.564,2.455,2.569,0.643,0.536,0.56,0.52,0.634,0.653,0.75,0.521,0.626,0.835,0.866,0.671,0.491,0.426,0.481,0.7,0.584,0.701,0.589,0.74,0.884,0.481,0.59,0.708,0.666,0.484,0.628,0.604,0.672,0.668,0.832,0.945,0.518,0.67,0.493,0.481,0.799,0.605,0.346,0.316,0.45,0.703,0.655,0.554,0.569,0.463,0.459,0.545,0.48,0.448,0.552,0.416,0.412,0.418,0.35,0.412,0.433,0.406,0.567,0.376,0.322,0.682,0.342,0.693,0.533,0.764,0.424,0.339,0.448,0.593,0.545,0.627,0.449,0.5
-4084,Not Epileptic,F,55.0,1.3,2.3,1.2,1.1,2.1,1.5,1.5,1.7,1.5,0.6,0.2,3.7,1.1,0.4,1.5,5.8,10.0,0.8,2.6,4.8,1.8,3.4,3.4,5.2,3.9,2.5,3.9,3.2,2.6,2.3,3.2,0.8,0.3,2.1,0.2,0.7,6.5,3.9,0.2,0.2,1.2,2.6,2.4,3.2,3.0,0.6,0.5,0.7,1.4,0.8,0.6,1.9,2.2,2.6,0.2,2.6,1.0,1.5,1.3,1.8,0.8,2.0,0.2,0.4,2.0,1.1,2.4,1.1,0.9,0.4,0.8,1.4,4.9,0.3,11,24,9,9,14,23,13,17,13,6,3.0,38,12,6,22,68,105,7,34,48,60,46,22,55,42,33,41,30,25,32,24,8,4,29,2,6,49,46,1,1,6,23,23,25,18,5,2,5,6,8,5,17,15,17,1,21,8,10,11,12,6,16,3,4,18,16,20,9,7,4,5,11,37,3,0.055,0.027,0.024,0.023,0.039,0.025,0.027,0.028,0.046,0.036,0.032,0.038,0.024,0.03,0.037,0.042,0.039,0.039,0.039,0.036,0.036,0.035,0.044,0.052,0.039,0.029,0.038,0.033,0.025,0.031,0.065,0.033,0.021,0.028,0.011,0.016,0.051,0.037,0.017,0.014,0.024,0.03,0.045,0.024,0.021,0.012,0.022,0.015,0.022,0.023,0.029,0.023,0.034,0.024,0.032,0.016,0.023,0.019,0.034,0.02,0.021,0.03,0.028,0.016,0.019,0.024,0.019,0.015,0.021,0.022,0.025,0.02,0.02,0.014,1572,4009,2072,2224,1783,2842,2770,2976,1285,1164,646.0,3497,2822,868,2847,8522,16505,1989,4441,5843,3490,4497,2070,5579,4691,5394,4564,4462,5549,4646,1760,1211,895,5769,1650,1697,5726,7797,360,410,1302,1901,3633,3531,3820,1295,756,1687,1835,1412,517,2700,1895,3662,201,4277,1542,2648,1428,2250,1162,2752,433,891,3293,1084,3950,2144,1452,801,1024,2325,8241,533,0.177,0.124,0.117,0.111,0.143,0.118,0.095,0.122,0.16,0.147,0.118,0.15,0.114,0.142,0.143,0.158,0.144,0.127,0.146,0.152,0.127,0.126,0.129,0.149,0.144,0.128,0.122,0.108,0.104,0.131,0.162,0.109,0.099,0.124,0.058,0.092,0.155,0.146,0.095,0.094,0.109,0.132,0.131,0.122,0.102,0.087,0.103,0.083,0.108,0.11,0.145,0.114,0.14,0.117,0.159,0.1,0.112,0.104,0.124,0.108,0.112,0.137,0.126,0.096,0.106,0.118,0.105,0.096,0.107,0.127,0.124,0.105,0.103,0.11,432,1473,826,811,865,1155,936,1065,558,315,177.0,1832,856,257,851,2819,5066,437,1233,2357,742,1681,1149,1911,1846,1724,1862,1657,1599,1647,795,469,319,1365,507,662,2022,2085,224,218,715,1238,1009,2131,2240,693,340,780,902,647,333,1454,936,1710,87,2396,761,1215,695,1257,589,1082,202,495,1665,755,1909,1050,735,379,509,1162,3821,323,3.506,2.513,2.455,2.497,1.886,2.064,2.418,2.435,1.945,2.714,2.778,1.73,2.58,2.46,2.542,2.39,2.573,3.556,2.83,2.131,3.292,2.222,1.624,2.241,2.132,2.506,2.025,2.174,2.679,2.259,1.821,2.531,2.224,2.981,3.006,2.427,2.35,2.74,1.813,2.066,2.302,1.536,2.779,1.825,1.901,2.121,2.531,2.478,2.521,2.495,1.85,2.051,2.008,2.336,2.422,2.014,2.355,2.553,2.102,1.971,2.227,2.573,2.026,2.112,2.081,1.891,2.115,2.262,2.264,2.067,2.186,2.231,2.359,2.057,0.865,0.59,0.471,0.493,0.545,0.511,0.662,0.441,0.598,0.742,0.806,0.5,0.41,0.455,0.498,0.518,0.688,0.757,0.762,0.644,0.771,0.626,0.47,0.764,0.547,0.542,0.445,0.557,0.483,0.518,0.633,0.748,0.403,0.608,0.642,0.476,0.724,0.578,0.324,0.38,0.481,0.494,0.791,0.531,0.594,0.45,0.385,0.725,0.477,0.658,0.405,0.422,0.435,0.411,0.4,0.395,0.405,0.451,0.601,0.347,0.402,0.694,0.501,0.581,0.466,0.706,0.44,0.37,0.426,0.708,0.404,0.392,0.416,0.328
-4085,Not Epileptic,F,52.0,0.8,2.4,0.9,1.4,2.0,1.6,1.8,1.9,1.2,0.6,0.3,2.3,1.7,0.5,1.7,4.8,9.7,1.0,2.6,4.8,1.2,2.6,2.1,3.9,3.3,4.2,4.4,4.0,3.3,3.4,1.2,2.3,0.6,1.9,0.6,0.6,4.2,2.6,0.3,0.3,1.2,2.2,3.3,2.2,3.5,1.0,0.7,1.1,1.3,0.8,0.6,3.0,1.7,2.9,0.2,2.6,0.9,1.3,1.0,1.4,0.6,1.5,0.4,0.6,1.6,1.5,3.7,1.6,1.4,0.8,1.3,1.3,4.2,0.3,7,22,7,9,16,17,15,16,11,7,2.0,24,16,6,18,58,94,8,32,48,12,34,22,39,41,47,42,37,28,42,15,21,8,28,3,5,50,32,2,1,8,24,29,21,22,8,3,8,7,7,4,21,14,18,2,21,4,15,6,11,5,15,3,5,16,20,28,11,8,8,8,9,32,3,0.038,0.022,0.015,0.023,0.038,0.027,0.028,0.028,0.037,0.027,0.035,0.029,0.033,0.025,0.034,0.03,0.03,0.032,0.029,0.042,0.027,0.029,0.034,0.038,0.027,0.034,0.032,0.034,0.029,0.025,0.03,0.066,0.027,0.023,0.02,0.012,0.038,0.028,0.017,0.02,0.027,0.039,0.047,0.023,0.023,0.019,0.027,0.018,0.016,0.02,0.022,0.024,0.024,0.022,0.018,0.017,0.022,0.014,0.028,0.021,0.023,0.023,0.02,0.015,0.018,0.032,0.024,0.022,0.023,0.02,0.027,0.017,0.017,0.015,1354,4893,2070,2425,1978,2682,3165,3186,1495,1518,546.0,2304,3355,1160,2838,9797,20285,2525,5292,4130,3423,4481,2554,6182,6459,7212,6521,5045,6406,6476,2353,1706,1091,5841,1875,1962,7564,6925,490,483,1314,2474,5298,2426,3807,1470,841,2188,2228,1391,577,4097,2344,4926,483,4005,1201,2965,935,1594,926,2523,475,1060,2549,1366,4859,2353,1911,1207,1549,1951,8918,576,0.133,0.116,0.095,0.105,0.142,0.133,0.107,0.129,0.141,0.135,0.119,0.134,0.134,0.124,0.144,0.131,0.127,0.119,0.131,0.161,0.103,0.13,0.136,0.129,0.117,0.144,0.122,0.113,0.099,0.124,0.117,0.159,0.129,0.112,0.09,0.077,0.147,0.136,0.087,0.086,0.128,0.143,0.15,0.12,0.113,0.109,0.121,0.093,0.094,0.107,0.125,0.111,0.117,0.104,0.104,0.103,0.107,0.096,0.135,0.113,0.109,0.119,0.115,0.098,0.109,0.135,0.119,0.109,0.107,0.11,0.124,0.111,0.094,0.101,395,1745,926,941,833,963,1028,1099,466,366,149.0,1212,962,289,884,2785,5649,506,1461,1939,823,1399,997,1772,2058,2228,2190,1839,1639,2280,744,658,373,1426,448,750,1969,1680,296,254,702,1061,1289,1524,2260,769,377,977,1098,646,372,1996,1148,2140,209,2184,575,1398,537,988,422,1085,302,621,1411,762,2402,1124,889,612,740,1021,3871,287,3.305,2.558,2.349,2.551,2.128,2.289,2.467,2.565,2.416,3.181,2.873,1.709,2.697,2.841,2.457,2.623,2.777,3.774,2.87,1.939,3.001,2.474,2.243,2.623,2.538,2.678,2.348,2.17,2.832,2.343,2.435,2.751,2.379,2.842,3.666,2.354,2.762,2.934,1.889,2.209,2.288,2.001,3.009,1.73,1.888,2.152,2.867,2.711,2.451,2.377,1.962,2.114,2.011,2.417,2.453,2.01,2.362,2.176,1.898,1.794,2.176,2.337,1.761,2.122,1.975,2.111,2.214,2.491,2.417,2.144,2.356,2.208,2.389,2.331,0.815,0.501,0.348,0.46,0.635,0.691,0.636,0.54,0.631,0.491,0.873,0.474,0.388,0.335,0.496,0.601,0.609,0.728,0.627,0.666,0.765,0.522,0.495,0.764,0.585,0.49,0.631,0.624,0.59,0.543,0.513,0.752,0.456,0.636,0.824,0.471,0.76,0.728,0.346,0.39,0.377,0.551,0.711,0.656,0.639,0.513,0.478,0.896,0.442,0.4,0.35,0.499,0.449,0.46,0.611,0.409,0.516,0.454,0.522,0.336,0.508,0.626,0.43,0.564,0.539,0.615,0.474,0.525,0.493,0.593,0.395,0.44,0.444,0.335
-4086,Not Epileptic,M,55.0,0.9,2.6,1.1,1.5,2.2,1.8,1.4,1.9,1.4,0.4,0.2,3.7,1.4,0.7,0.7,5.8,8.6,0.8,1.9,5.0,1.7,2.9,2.0,2.6,3.2,3.9,2.7,2.8,2.4,2.9,1.4,1.0,0.5,2.8,0.5,0.7,4.3,3.2,0.1,0.1,1.4,3.0,2.2,2.9,3.7,0.4,0.5,0.9,1.1,1.3,0.3,1.9,1.2,2.4,0.2,3.0,0.8,2.4,0.9,0.9,0.7,1.4,0.4,0.4,2.1,2.2,2.6,1.3,0.8,0.8,0.7,1.6,4.1,0.5,6,29,17,14,16,15,9,17,14,3,3.0,33,14,7,10,64,79,9,21,39,12,35,20,34,37,40,33,25,21,30,17,7,5,30,2,6,50,36,2,1,9,29,20,21,23,4,3,6,7,10,4,16,9,16,1,25,8,22,7,7,8,11,3,4,14,25,21,9,7,7,5,14,36,4,0.037,0.027,0.017,0.023,0.036,0.035,0.026,0.027,0.046,0.033,0.027,0.037,0.026,0.03,0.024,0.032,0.031,0.029,0.027,0.035,0.027,0.038,0.032,0.033,0.032,0.038,0.026,0.026,0.023,0.028,0.038,0.034,0.023,0.03,0.016,0.016,0.037,0.031,0.016,0.011,0.026,0.038,0.033,0.025,0.025,0.011,0.019,0.015,0.016,0.028,0.017,0.023,0.021,0.021,0.02,0.024,0.018,0.03,0.029,0.021,0.024,0.025,0.02,0.017,0.024,0.038,0.023,0.019,0.016,0.02,0.022,0.018,0.018,0.018,1153,4114,2304,2201,2428,2510,2467,3520,1881,893,619.0,3275,2691,1082,1625,10486,15435,2003,4137,4465,2972,4376,2043,5676,5106,5831,4731,4184,5832,4823,2305,1296,1023,5778,1644,1869,6434,6722,289,253,1428,3120,5126,2950,3180,1139,792,2100,2078,1386,467,2732,1876,4015,290,3234,1449,3195,981,1086,1016,2197,521,621,2788,1336,3407,2226,1544,1259,955,2372,8131,495,0.126,0.131,0.117,0.121,0.145,0.135,0.097,0.128,0.162,0.107,0.125,0.152,0.127,0.147,0.122,0.139,0.137,0.133,0.12,0.129,0.113,0.16,0.141,0.135,0.141,0.15,0.13,0.111,0.098,0.128,0.136,0.102,0.102,0.135,0.073,0.088,0.139,0.129,0.107,0.096,0.115,0.154,0.135,0.113,0.112,0.079,0.106,0.084,0.091,0.134,0.122,0.118,0.105,0.106,0.139,0.115,0.1,0.127,0.142,0.113,0.128,0.119,0.115,0.111,0.118,0.139,0.116,0.108,0.099,0.126,0.118,0.112,0.102,0.109,439,1543,1008,946,912,940,842,1105,553,250,175.0,1604,910,341,549,3182,4679,422,1181,2162,850,1449,995,1465,1740,1865,1669,1572,1648,1650,672,496,359,1504,557,657,2052,1784,177,150,728,1357,1191,1876,2115,622,366,924,1075,714,288,1302,931,1770,119,1842,748,1477,507,632,515,882,299,395,1379,855,1743,1047,770,574,533,1297,3653,333,2.841,2.582,2.264,2.311,2.378,2.353,2.356,2.72,2.543,2.96,2.714,1.853,2.444,2.463,2.325,2.601,2.741,3.247,2.65,1.789,2.73,2.48,1.85,2.862,2.383,2.647,2.267,2.167,2.691,2.39,2.65,2.357,2.208,2.905,2.747,2.593,2.584,2.744,1.782,1.841,2.482,2.093,3.223,1.742,1.7,2.063,2.605,2.734,2.378,2.356,2.001,2.193,2.174,2.32,2.694,1.976,2.279,2.3,2.189,1.951,2.213,2.576,2.125,1.765,2.235,1.929,2.027,2.457,2.161,2.36,2.171,2.184,2.439,1.749,0.77,0.571,0.577,0.445,0.54,0.588,0.588,0.649,0.527,0.535,0.747,0.483,0.374,0.391,0.522,0.475,0.541,0.771,0.543,0.549,0.82,0.521,0.476,0.619,0.5,0.484,0.452,0.57,0.507,0.432,0.703,0.825,0.334,0.615,0.812,0.557,0.609,0.51,0.38,0.509,0.467,0.485,0.63,0.564,0.45,0.339,0.491,0.872,0.488,0.415,0.499,0.401,0.457,0.504,0.425,0.397,0.411,0.642,0.434,0.375,0.305,0.689,0.465,0.668,0.535,0.649,0.394,0.345,0.394,0.539,0.512,0.42,0.48,0.337
-4087,Not Epileptic,F,37.0,0.8,2.6,1.0,0.9,2.0,1.2,1.4,1.2,1.1,1.0,0.3,2.1,1.4,0.5,1.5,4.5,7.1,0.9,1.3,3.6,1.3,2.9,2.4,3.1,2.6,3.6,3.6,2.0,2.5,4.2,1.4,1.4,0.5,1.9,0.4,0.4,3.3,3.4,0.1,0.1,1.1,3.0,2.7,1.9,2.8,0.7,0.3,1.5,1.2,0.9,0.5,2.7,1.5,1.6,0.4,3.0,0.4,1.8,1.1,1.3,1.1,2.2,0.4,0.5,1.3,1.8,2.5,1.4,0.8,1.2,1.3,1.8,3.7,0.3,6,23,8,10,18,12,12,13,13,11,4.0,22,16,7,19,53,75,9,17,30,11,29,17,34,40,39,48,20,23,50,20,11,5,26,4,3,47,38,1,1,7,23,26,14,16,6,2,11,6,8,3,20,11,12,2,22,3,14,9,9,11,19,3,5,9,23,19,10,4,9,10,14,33,3,0.031,0.026,0.019,0.016,0.032,0.024,0.023,0.021,0.033,0.037,0.028,0.035,0.022,0.022,0.025,0.027,0.026,0.037,0.028,0.034,0.023,0.027,0.034,0.03,0.029,0.028,0.028,0.025,0.026,0.033,0.037,0.056,0.023,0.023,0.017,0.011,0.031,0.029,0.01,0.016,0.022,0.039,0.038,0.022,0.022,0.016,0.012,0.019,0.017,0.022,0.02,0.022,0.02,0.018,0.015,0.016,0.01,0.021,0.022,0.019,0.022,0.028,0.022,0.019,0.02,0.028,0.021,0.018,0.015,0.029,0.024,0.021,0.014,0.014,1434,4876,2524,2418,2462,2271,3006,2928,2062,1783,619.0,2175,4263,1288,4026,10971,18897,2105,3886,3655,4634,6328,2836,6739,6672,7945,7780,3844,6749,7759,2436,1208,1233,6072,1651,1645,8238,8492,392,587,1543,2820,6503,1970,3750,1763,796,2722,2343,2063,602,4082,2267,4060,590,5275,1242,3039,1403,1989,1774,3188,552,952,2110,1364,4027,2439,1843,1589,1907,3138,9840,520,0.12,0.123,0.108,0.098,0.131,0.12,0.096,0.113,0.142,0.152,0.128,0.146,0.116,0.121,0.123,0.129,0.126,0.131,0.118,0.14,0.102,0.121,0.115,0.13,0.13,0.135,0.123,0.099,0.099,0.147,0.144,0.145,0.098,0.121,0.089,0.071,0.137,0.135,0.082,0.087,0.108,0.129,0.151,0.118,0.109,0.091,0.086,0.104,0.095,0.107,0.11,0.113,0.113,0.091,0.102,0.097,0.083,0.111,0.119,0.103,0.122,0.132,0.121,0.115,0.105,0.142,0.106,0.103,0.092,0.134,0.122,0.114,0.088,0.119,415,1618,800,891,969,812,964,903,533,438,207.0,1075,1023,325,1086,2752,4403,496,885,1684,976,1534,1100,1768,1732,2128,2372,1178,1505,2221,684,464,364,1377,407,593,2005,1980,219,251,774,1072,1229,1322,2000,794,351,1137,1038,701,352,1845,1066,1611,308,2641,573,1350,785,1010,706,1214,285,495,988,900,1856,1123,832,619,884,1379,4175,282,3.318,2.815,3.003,2.696,2.275,2.523,2.559,2.879,2.783,3.084,2.596,1.81,3.108,2.864,2.931,2.899,3.262,3.311,3.069,1.975,3.302,2.961,2.171,2.832,2.897,2.94,2.547,2.518,3.18,2.723,2.631,2.843,2.74,3.099,3.597,2.444,2.998,3.098,1.996,2.828,2.483,2.105,3.296,1.788,2.179,2.449,2.711,2.889,2.858,2.876,2.121,2.281,2.342,2.638,2.211,2.26,2.6,2.496,2.179,2.223,2.348,2.455,2.255,2.141,2.295,1.872,2.291,2.529,2.699,2.55,2.533,2.558,2.512,2.375,0.514,0.423,0.549,0.444,0.569,0.43,0.705,0.498,0.677,0.811,1.023,0.586,0.43,0.436,0.571,0.635,0.596,0.721,0.518,0.688,0.886,0.652,0.715,0.745,0.651,0.559,0.648,0.669,0.582,0.596,0.604,0.816,0.428,0.657,0.631,0.525,0.647,0.63,0.401,0.408,0.482,0.754,0.869,0.638,0.671,0.459,0.507,0.578,0.445,0.592,0.343,0.454,0.422,0.45,0.291,0.39,0.347,0.456,0.327,0.346,0.415,0.61,0.631,0.799,0.485,0.612,0.416,0.362,0.322,0.739,0.412,0.453,0.42,0.446
-4088,Not Epileptic,F,52.0,0.9,2.4,1.4,1.7,1.3,1.5,3.3,1.4,1.2,0.9,0.3,2.6,1.3,0.6,1.3,5.4,9.1,0.9,2.1,2.9,1.1,2.9,2.6,3.5,4.3,4.0,5.1,2.7,3.1,3.6,1.7,1.0,0.5,2.0,0.4,0.6,4.7,3.3,0.2,0.2,1.1,3.2,2.0,1.6,2.8,0.8,0.4,0.9,1.2,0.7,0.4,1.6,1.4,2.1,0.3,2.9,0.6,2.1,0.8,1.0,0.6,1.6,0.3,0.4,1.7,1.3,2.4,0.7,0.8,0.7,1.4,1.3,4.3,0.3,8,30,12,11,14,19,19,16,17,10,3.0,26,11,6,14,59,92,9,20,32,9,34,28,34,49,44,49,29,32,45,18,7,4,23,2,4,54,47,2,2,7,27,20,14,16,9,2,5,5,7,3,12,9,16,2,23,4,17,5,8,4,11,2,4,15,21,20,4,5,6,11,8,37,2,0.041,0.028,0.024,0.028,0.029,0.026,0.047,0.024,0.049,0.034,0.029,0.036,0.028,0.041,0.035,0.036,0.037,0.046,0.036,0.03,0.022,0.033,0.037,0.039,0.043,0.031,0.039,0.035,0.033,0.032,0.043,0.049,0.026,0.026,0.016,0.014,0.04,0.035,0.017,0.023,0.024,0.039,0.033,0.024,0.021,0.018,0.017,0.015,0.018,0.025,0.016,0.017,0.024,0.019,0.017,0.017,0.02,0.024,0.022,0.018,0.023,0.03,0.014,0.017,0.021,0.028,0.021,0.011,0.016,0.022,0.023,0.02,0.019,0.023,1574,4283,2667,2569,1871,2930,2447,3188,1736,1715,831.0,3011,3254,1096,2630,9209,15635,2065,4180,4220,3334,4760,2901,5425,6239,7140,7749,4005,6023,6738,2322,939,810,5627,1766,1925,8946,7191,403,431,1431,2800,5808,2168,3631,1454,715,2053,2273,1148,604,2757,1665,4051,335,4504,960,3030,885,1669,687,2483,504,799,2652,1399,3977,1835,1482,1097,1811,2246,8034,442,0.133,0.133,0.116,0.119,0.132,0.131,0.136,0.117,0.168,0.141,0.117,0.136,0.114,0.148,0.131,0.143,0.139,0.137,0.138,0.135,0.088,0.144,0.13,0.141,0.147,0.139,0.129,0.114,0.112,0.141,0.133,0.126,0.095,0.13,0.079,0.082,0.155,0.149,0.104,0.104,0.116,0.143,0.134,0.114,0.101,0.108,0.098,0.085,0.092,0.117,0.113,0.096,0.118,0.102,0.111,0.105,0.102,0.112,0.114,0.109,0.116,0.12,0.096,0.099,0.114,0.132,0.109,0.078,0.097,0.116,0.12,0.113,0.102,0.127,430,1474,976,903,753,983,988,949,533,442,222.0,1252,797,267,722,2594,4507,423,1048,1630,824,1352,1191,1542,1773,2149,2231,1342,1492,2020,713,362,259,1230,462,676,2053,1839,210,200,732,1184,1130,1274,2071,716,315,896,993,474,361,1414,893,1758,205,2378,469,1431,536,851,380,878,306,419,1334,700,1789,937,739,492,965,995,3609,198,3.408,2.852,2.68,2.667,2.201,2.31,2.132,2.679,2.444,2.905,2.986,1.945,2.99,2.952,2.709,2.596,2.723,3.737,2.983,2.147,2.786,2.586,2.123,2.625,2.669,2.629,2.596,2.296,2.936,2.524,2.465,2.457,2.664,3.103,3.413,2.615,3.114,2.822,2.199,2.444,2.525,2.05,3.632,1.931,1.958,2.054,2.785,2.735,2.766,2.583,1.844,2.096,2.034,2.379,2.076,2.129,2.383,2.322,1.832,2.197,2.341,2.748,1.917,2.112,2.135,2.292,2.212,2.292,2.29,2.343,2.148,2.274,2.353,2.593,0.698,0.609,0.577,0.501,0.62,0.735,0.604,0.6,0.627,0.545,0.706,0.563,0.505,0.387,0.52,0.537,0.628,0.751,0.485,0.627,0.842,0.647,0.481,0.714,0.678,0.549,0.637,0.665,0.621,0.613,0.606,0.836,0.418,0.625,0.585,0.421,0.786,0.65,0.332,0.38,0.431,0.563,0.72,0.76,0.601,0.428,0.494,0.63,0.551,0.612,0.355,0.392,0.428,0.473,0.289,0.447,0.45,0.489,0.303,0.346,0.373,0.566,0.335,0.755,0.537,0.883,0.455,0.277,0.34,0.741,0.474,0.564,0.43,0.502
-4089,Not Epileptic,M,55.0,1.1,2.8,0.8,1.3,2.6,2.5,2.1,1.8,1.8,0.7,0.3,3.6,2.6,0.3,1.5,6.5,8.5,1.1,2.5,4.4,1.4,2.6,1.9,4.4,2.6,2.9,3.3,2.2,2.7,3.5,1.3,1.1,0.5,2.4,0.3,1.0,3.8,3.4,0.3,0.4,1.1,2.9,2.7,3.4,3.0,0.6,0.4,1.2,1.4,1.0,0.7,2.3,1.6,2.2,0.4,3.1,0.7,1.5,1.4,1.1,0.6,2.1,0.3,0.7,1.8,1.6,2.6,2.0,0.7,1.2,0.8,1.1,3.3,0.5,7,23,9,11,19,27,18,15,17,8,4.0,34,27,5,15,79,80,9,28,49,9,33,18,45,35,35,40,24,28,42,16,9,6,32,2,10,42,39,4,3,7,26,26,24,16,4,2,6,7,8,4,17,11,13,3,27,5,10,8,7,4,18,3,6,16,17,17,14,4,11,5,8,23,4,0.045,0.026,0.015,0.025,0.039,0.031,0.031,0.027,0.043,0.04,0.032,0.034,0.033,0.027,0.029,0.04,0.031,0.038,0.035,0.034,0.031,0.034,0.03,0.044,0.033,0.032,0.029,0.026,0.031,0.034,0.038,0.045,0.033,0.031,0.013,0.023,0.041,0.034,0.024,0.025,0.023,0.029,0.037,0.024,0.022,0.012,0.018,0.017,0.018,0.022,0.024,0.023,0.027,0.024,0.03,0.021,0.022,0.017,0.025,0.017,0.025,0.029,0.023,0.02,0.019,0.027,0.023,0.022,0.019,0.036,0.025,0.016,0.016,0.031,1701,5364,1999,2428,2306,3956,3438,3349,2220,1461,723.0,3976,5439,953,2899,11553,17542,2557,5074,6276,3316,5055,2826,6464,5840,6255,5877,3678,6106,6332,1970,1569,967,6760,1864,2047,7537,8066,625,548,1599,2679,7143,3622,3682,1387,838,2364,2643,1470,661,3213,1829,3379,382,4755,1138,3128,1575,1797,769,3056,294,899,3236,1148,3867,3403,1314,1334,1255,2086,7886,638,0.122,0.122,0.102,0.115,0.137,0.149,0.113,0.13,0.143,0.145,0.128,0.126,0.135,0.128,0.136,0.155,0.128,0.132,0.142,0.142,0.11,0.13,0.114,0.147,0.135,0.137,0.122,0.119,0.123,0.138,0.125,0.115,0.114,0.124,0.066,0.12,0.147,0.135,0.119,0.12,0.115,0.119,0.145,0.119,0.107,0.082,0.095,0.092,0.094,0.109,0.124,0.118,0.124,0.109,0.143,0.119,0.116,0.102,0.118,0.101,0.12,0.131,0.125,0.122,0.11,0.124,0.113,0.111,0.1,0.15,0.119,0.102,0.092,0.154,439,1695,783,857,962,1344,1094,1013,710,315,220.0,1689,1343,261,806,3028,4518,530,1254,2304,729,1342,1070,1794,1473,1674,1908,1265,1496,1837,678,513,257,1316,443,682,1729,1664,282,272,740,1368,1318,2094,1973,676,336,966,1143,599,416,1541,870,1465,187,2127,503,1381,834,896,381,1147,194,516,1503,818,1695,1377,592,496,579,1040,3251,237,3.578,2.984,2.673,2.683,2.109,2.507,2.437,2.924,2.388,3.279,2.814,1.995,3.084,2.618,2.65,2.776,2.921,3.58,3.027,2.317,3.109,2.793,2.257,2.702,2.868,2.945,2.465,2.316,2.955,2.654,2.3,3.068,2.758,3.238,3.478,2.815,2.947,3.143,2.109,2.585,2.715,1.811,3.564,1.912,2.103,2.137,2.988,2.969,2.834,2.52,1.895,2.3,2.217,2.44,2.219,2.372,2.495,2.588,2.098,2.138,2.554,2.71,1.877,2.088,2.302,1.702,2.355,2.577,2.411,2.807,2.449,2.349,2.655,2.989,0.777,0.609,0.424,0.586,0.627,0.578,0.657,0.49,0.61,0.621,0.804,0.508,0.477,0.468,0.534,0.667,0.665,0.84,0.546,0.8,0.748,0.576,0.556,0.842,0.621,0.54,0.547,0.551,0.555,0.585,0.591,0.85,0.52,0.756,0.712,0.526,0.791,0.692,0.489,0.473,0.455,0.48,0.825,0.547,0.548,0.416,0.676,0.855,0.466,0.617,0.488,0.46,0.55,0.467,0.335,0.38,0.497,0.424,0.397,0.424,0.37,0.673,0.283,0.683,0.53,0.577,0.511,0.451,0.479,0.675,0.462,0.472,0.463,0.523
-4090,Not Epileptic,M,52.0,0.9,2.9,1.2,1.4,2.7,1.7,2.9,1.6,1.6,0.9,0.3,3.4,0.8,0.7,0.9,5.7,9.2,0.9,2.5,5.2,1.4,3.2,2.0,3.7,3.4,2.9,5.8,3.3,4.5,3.9,2.2,1.8,0.3,2.7,0.6,0.4,4.3,3.7,0.1,0.1,0.9,3.8,2.7,3.2,4.1,0.7,0.5,1.4,1.1,0.9,0.5,1.9,1.9,2.4,0.3,2.1,1.0,1.5,1.5,1.8,0.5,2.4,0.4,0.6,2.3,1.7,2.9,1.7,1.2,0.6,1.1,1.3,5.6,0.3,7,28,12,13,23,15,21,14,13,6,4.0,38,9,5,10,59,78,8,29,46,13,43,22,36,39,29,52,26,32,43,18,12,3,31,4,3,43,40,1,1,4,42,24,23,24,5,2,11,7,5,3,16,16,14,1,17,8,13,9,14,4,17,2,5,21,23,22,12,7,6,8,10,38,2,0.027,0.022,0.018,0.026,0.035,0.023,0.036,0.023,0.034,0.031,0.029,0.034,0.024,0.034,0.024,0.027,0.027,0.027,0.03,0.037,0.024,0.027,0.026,0.036,0.032,0.025,0.035,0.03,0.031,0.032,0.043,0.05,0.018,0.024,0.014,0.01,0.041,0.03,0.014,0.012,0.018,0.037,0.036,0.025,0.025,0.013,0.017,0.016,0.018,0.015,0.017,0.017,0.019,0.019,0.024,0.014,0.018,0.015,0.026,0.021,0.02,0.031,0.024,0.019,0.019,0.021,0.019,0.018,0.019,0.019,0.023,0.016,0.018,0.015,1773,5775,2684,2677,2770,3580,3276,3367,2180,1591,640.0,4422,2389,1176,2091,13007,22159,2502,4967,6064,4725,6698,3399,6617,6197,6437,7097,4537,6478,7480,2778,1563,814,7311,2872,1466,7415,8206,260,332,1402,3775,7153,3390,4222,1775,859,2644,1929,1772,611,3821,3180,4683,255,4226,1858,3420,1527,2435,866,3160,480,1067,3453,1496,5393,3416,2003,881,1758,2438,11264,723,0.111,0.114,0.102,0.122,0.14,0.121,0.129,0.108,0.135,0.118,0.132,0.145,0.114,0.142,0.121,0.117,0.119,0.125,0.124,0.145,0.107,0.136,0.12,0.134,0.122,0.118,0.106,0.101,0.108,0.131,0.142,0.137,0.092,0.127,0.076,0.075,0.142,0.139,0.096,0.076,0.097,0.142,0.141,0.12,0.111,0.085,0.093,0.081,0.094,0.098,0.103,0.097,0.109,0.097,0.133,0.087,0.109,0.093,0.12,0.114,0.101,0.131,0.107,0.112,0.109,0.116,0.103,0.097,0.102,0.121,0.116,0.102,0.095,0.098,511,2132,1078,967,1198,1242,1348,1124,671,448,217.0,1769,653,304,604,3567,5781,533,1501,2329,1117,1998,1178,1859,1909,1857,2379,1679,2070,2134,811,616,279,1728,754,564,1932,2059,144,155,744,1673,1468,1982,2534,816,378,1153,1016,743,418,1800,1553,1905,130,2181,839,1644,909,1289,431,1257,271,485,1820,1189,2506,1485,891,487,796,1176,4822,312,3.245,2.631,2.467,2.628,2.174,2.293,2.158,2.52,2.544,2.745,2.413,2.095,2.84,2.86,2.625,2.764,3.023,3.702,2.665,2.193,3.165,2.592,2.398,2.681,2.582,2.728,2.464,2.166,2.529,2.709,2.711,2.713,2.475,3.156,3.331,2.384,2.868,2.855,2.058,2.021,2.33,1.921,3.515,1.877,1.903,2.345,2.609,2.714,2.299,2.637,1.733,2.149,2.232,2.57,2.414,2.108,2.118,2.228,1.839,2.048,2.056,2.516,1.971,2.441,2.062,1.559,2.258,2.41,2.58,2.016,2.339,2.243,2.44,2.598,0.724,0.485,0.492,0.44,0.518,0.706,0.637,0.53,0.574,0.482,0.675,0.536,0.445,0.515,0.434,0.548,0.668,0.757,0.545,0.562,0.787,0.541,0.628,0.793,0.689,0.575,0.621,0.626,0.683,0.591,0.516,1.001,0.325,0.57,0.589,0.4,0.757,0.698,0.311,0.415,0.401,0.502,0.692,0.545,0.646,0.415,0.497,0.655,0.455,0.437,0.27,0.455,0.406,0.491,0.311,0.388,0.605,0.531,0.425,0.366,0.508,0.611,0.324,0.91,0.453,0.478,0.369,0.417,0.465,0.408,0.543,0.676,0.47,0.362
-4091,Not Epileptic,F,32.0,0.6,2.9,1.2,1.0,1.8,1.9,1.7,2.1,1.4,1.1,0.4,2.8,1.6,0.7,1.1,5.9,9.5,0.7,2.4,4.3,1.2,2.1,1.5,3.4,3.7,2.9,4.1,2.7,3.0,4.3,1.8,2.6,0.8,2.4,0.5,0.8,3.9,3.2,0.2,0.2,1.4,2.9,2.4,2.2,4.1,1.0,0.4,1.0,1.5,1.2,0.6,2.0,1.9,2.7,0.4,3.3,0.7,1.7,1.1,1.1,0.4,1.6,0.4,0.6,1.9,1.4,3.6,1.3,1.2,1.0,1.3,1.2,4.3,0.4,7,27,11,9,14,17,13,21,15,11,4.0,28,16,7,13,73,96,8,28,41,9,26,20,40,47,34,52,29,26,49,24,21,7,31,3,8,47,34,2,1,8,28,23,19,25,8,2,7,8,13,5,18,13,20,3,29,4,15,8,10,3,17,2,5,18,21,33,11,9,9,8,9,36,3,0.027,0.023,0.016,0.015,0.031,0.03,0.027,0.026,0.042,0.031,0.031,0.032,0.027,0.033,0.034,0.028,0.027,0.03,0.025,0.033,0.021,0.028,0.028,0.036,0.026,0.022,0.027,0.024,0.022,0.035,0.048,0.071,0.027,0.028,0.014,0.018,0.033,0.029,0.013,0.014,0.027,0.036,0.033,0.022,0.026,0.019,0.017,0.013,0.018,0.024,0.022,0.022,0.022,0.018,0.014,0.02,0.016,0.016,0.024,0.018,0.017,0.021,0.022,0.018,0.019,0.017,0.022,0.018,0.021,0.019,0.024,0.017,0.016,0.017,1694,5805,2776,2727,2375,3803,3351,4969,2019,2476,746.0,3098,3935,1577,2463,13770,21612,2171,5438,5427,3696,4672,2338,6076,8474,7991,7933,5632,8023,7359,2347,1566,1572,5441,2609,2311,8337,8550,472,559,1790,3233,6888,2818,4253,1719,761,2572,2852,2500,674,3044,2644,5164,740,4561,1476,3923,1111,1688,784,3206,360,1052,2980,1786,5014,2869,2031,1604,1694,2281,10285,694,0.109,0.121,0.108,0.09,0.12,0.129,0.106,0.115,0.161,0.135,0.127,0.145,0.122,0.123,0.141,0.129,0.125,0.13,0.123,0.134,0.095,0.133,0.14,0.134,0.13,0.118,0.127,0.103,0.101,0.152,0.147,0.15,0.105,0.137,0.073,0.096,0.129,0.13,0.094,0.098,0.116,0.135,0.139,0.118,0.118,0.105,0.09,0.085,0.094,0.111,0.124,0.113,0.113,0.101,0.093,0.112,0.093,0.096,0.123,0.11,0.104,0.116,0.138,0.107,0.116,0.11,0.117,0.097,0.111,0.125,0.118,0.099,0.095,0.101,485,2049,1074,996,881,1206,1048,1559,600,614,222.0,1452,1002,377,655,3677,6017,474,1651,2209,903,1257,956,1842,2360,2255,2667,1781,2026,2294,753,610,470,1376,630,789,2355,1931,257,228,857,1415,1443,1556,2469,832,340,1048,1280,888,405,1463,1330,2306,381,2559,689,1791,707,913,386,1249,195,557,1480,1231,2441,1337,943,720,847,1165,4145,375,3.452,2.74,2.57,2.635,2.347,2.575,2.581,2.756,2.602,2.956,2.643,1.823,2.953,3.019,2.858,2.82,2.869,3.653,2.82,2.087,3.021,2.656,2.041,2.636,2.757,2.787,2.318,2.446,2.976,2.523,2.447,2.654,2.565,2.979,3.581,2.571,2.756,3.163,2.059,2.512,2.659,1.957,3.399,2.024,2.017,2.329,2.745,3.181,2.776,2.925,1.968,2.247,2.195,2.478,2.24,1.981,2.449,2.421,1.759,1.891,2.362,2.589,1.975,2.183,2.262,1.704,2.192,2.429,2.508,2.359,2.339,2.383,2.5,2.298,0.82,0.525,0.433,0.39,0.619,0.594,0.6,0.518,0.657,0.688,0.896,0.442,0.504,0.481,0.462,0.572,0.557,0.725,0.452,0.612,0.787,0.477,0.492,0.8,0.602,0.568,0.59,0.596,0.536,0.575,0.74,0.848,0.423,0.624,0.581,0.455,0.715,0.59,0.437,0.525,0.464,0.56,0.799,0.686,0.572,0.413,0.418,0.7,0.458,0.545,0.317,0.456,0.441,0.383,0.343,0.404,0.365,0.443,0.362,0.372,0.356,0.733,0.619,0.879,0.543,0.679,0.454,0.352,0.415,0.591,0.444,0.412,0.527,0.275
-4092,Not Epileptic,F,20.0,1.3,2.9,1.8,0.9,2.8,2.0,1.6,2.2,1.3,0.8,0.2,3.5,2.0,0.5,1.3,5.9,10.6,0.6,2.3,4.9,0.8,4.5,2.2,4.1,6.5,2.1,2.3,1.7,2.1,3.0,1.4,1.8,0.5,2.1,0.3,0.5,4.1,3.3,0.2,0.3,0.9,3.5,1.7,3.3,1.8,0.6,0.3,0.8,1.5,0.6,0.6,2.3,1.8,2.8,0.2,1.6,0.6,1.4,0.7,1.5,0.7,1.5,0.5,0.6,1.9,1.2,1.2,1.6,0.9,0.4,0.9,1.9,5.4,0.3,8,23,12,7,23,19,16,23,13,10,3.0,29,25,7,16,69,92,6,28,43,7,52,23,43,63,25,27,24,19,37,24,13,5,27,2,4,42,46,1,1,5,25,15,20,10,4,1,4,7,5,4,22,11,17,2,15,4,13,6,12,5,14,3,5,13,17,9,11,4,3,5,15,41,2,0.047,0.028,0.023,0.018,0.049,0.032,0.041,0.029,0.038,0.032,0.031,0.036,0.028,0.026,0.025,0.031,0.03,0.025,0.032,0.039,0.019,0.041,0.037,0.043,0.049,0.026,0.029,0.03,0.023,0.032,0.044,0.058,0.028,0.032,0.025,0.012,0.044,0.034,0.014,0.013,0.02,0.038,0.031,0.032,0.016,0.014,0.012,0.013,0.02,0.026,0.026,0.021,0.025,0.018,0.019,0.019,0.015,0.014,0.019,0.021,0.019,0.025,0.017,0.019,0.017,0.027,0.015,0.015,0.021,0.01,0.018,0.021,0.017,0.02,1915,5865,3275,2341,2332,3587,2753,4904,2443,1652,702.0,3519,5053,1442,3682,13052,24458,2677,5346,5419,3500,6312,2811,5928,7088,5294,5018,4418,4738,5992,2276,1267,984,6902,1224,1884,8261,9433,509,714,1450,2480,5313,3090,3435,1588,774,2410,2720,1091,809,4402,2893,6012,342,3247,1418,3436,1288,1801,1525,3017,711,999,3849,1381,2273,4549,1193,845,1529,3003,12010,462,0.131,0.125,0.113,0.094,0.152,0.13,0.128,0.129,0.154,0.141,0.119,0.142,0.122,0.124,0.123,0.136,0.129,0.102,0.135,0.147,0.083,0.125,0.131,0.143,0.148,0.118,0.121,0.125,0.091,0.148,0.128,0.139,0.126,0.124,0.095,0.074,0.144,0.134,0.085,0.089,0.108,0.138,0.128,0.129,0.087,0.087,0.074,0.075,0.096,0.12,0.124,0.11,0.116,0.094,0.13,0.105,0.092,0.093,0.11,0.112,0.092,0.117,0.122,0.112,0.095,0.12,0.094,0.085,0.1,0.088,0.101,0.119,0.095,0.129,443,1694,1079,810,890,1179,761,1343,595,413,173.0,1627,1240,313,885,3331,5955,481,1323,2093,714,1834,995,1755,2137,1378,1439,1076,1601,1712,673,519,276,1288,291,613,1818,1901,263,331,676,1295,967,1709,1655,657,315,926,1147,435,412,1867,1068,2409,168,1428,649,1494,645,1019,693,1034,306,518,1702,704,1194,1718,692,512,714,1243,4765,221,3.691,3.007,2.98,2.889,2.287,2.499,2.66,2.973,2.934,3.3,3.06,1.921,3.072,2.966,2.912,2.882,3.083,3.815,3.067,2.209,3.201,2.593,2.327,2.644,2.542,2.984,2.592,2.878,2.334,2.691,2.45,2.566,2.752,3.634,3.62,2.711,3.031,3.277,2.33,2.541,2.736,1.817,3.567,2.022,2.399,2.632,2.856,3.154,2.931,2.953,2.36,2.431,2.493,2.714,2.347,2.289,2.661,2.577,2.182,2.082,2.644,2.649,2.249,2.323,2.527,2.508,2.123,2.638,2.093,1.936,2.484,2.655,2.67,2.733,0.935,0.61,0.598,0.449,0.68,0.663,0.595,0.533,0.756,0.568,0.577,0.61,0.474,0.629,0.696,0.679,0.674,0.915,0.558,0.687,0.762,0.685,0.577,0.807,0.828,0.634,0.741,0.512,0.608,0.559,0.749,0.857,0.559,0.584,0.534,0.505,0.88,0.736,0.502,0.401,0.465,0.555,0.733,0.67,0.393,0.41,0.468,0.678,0.47,0.468,0.368,0.484,0.615,0.395,0.247,0.553,0.366,0.404,0.42,0.455,0.556,0.69,0.581,0.633,0.558,0.753,0.46,0.614,0.545,0.515,0.388,0.536,0.498,0.45
-4093,Not Epileptic,F,42.0,0.6,2.3,0.9,1.1,1.9,1.9,1.5,2.0,0.8,0.6,0.3,3.7,1.8,0.4,1.2,5.6,6.7,0.9,2.0,5.6,0.6,3.3,2.0,2.8,3.3,3.4,3.5,3.1,2.4,2.6,1.1,0.7,0.6,2.8,0.3,1.0,3.7,3.2,0.1,0.1,1.1,2.3,2.0,2.2,3.0,0.6,0.3,1.1,1.0,0.7,0.9,1.8,1.3,2.0,0.5,2.2,0.8,1.5,1.1,0.9,0.7,1.4,0.3,0.3,1.9,1.3,3.0,0.9,0.9,1.0,1.0,1.4,4.0,0.3,6,21,13,12,17,22,16,17,11,7,4.0,29,21,4,12,58,65,6,27,47,6,27,18,30,42,40,40,25,22,35,19,6,9,28,2,7,41,41,1,1,6,22,18,18,19,4,2,7,6,6,5,13,12,13,3,20,7,12,9,6,6,12,3,3,18,12,23,7,7,10,7,10,30,3,0.034,0.024,0.015,0.02,0.036,0.035,0.026,0.03,0.034,0.032,0.03,0.042,0.034,0.029,0.029,0.03,0.023,0.034,0.027,0.038,0.016,0.037,0.034,0.031,0.033,0.03,0.032,0.029,0.024,0.029,0.038,0.03,0.027,0.03,0.016,0.023,0.038,0.03,0.01,0.015,0.022,0.029,0.034,0.026,0.025,0.013,0.018,0.016,0.014,0.024,0.028,0.023,0.023,0.018,0.02,0.021,0.021,0.018,0.03,0.018,0.027,0.022,0.026,0.015,0.02,0.038,0.022,0.017,0.016,0.023,0.03,0.021,0.017,0.019,1324,4663,2326,2460,2316,3184,2650,3195,1364,1265,737.0,2896,3677,899,2501,10302,16749,2386,4236,5328,1878,3861,2099,5805,6160,6953,5073,4087,5222,4717,1819,1023,1464,6380,1592,2197,6471,7150,350,389,1575,2669,4578,2348,3094,1216,742,2449,2489,1407,720,2866,2153,4260,535,3277,1427,2953,1155,1344,945,2665,523,748,3084,1255,4240,1973,1861,1664,1367,2316,8675,457,0.116,0.114,0.103,0.11,0.144,0.147,0.13,0.139,0.126,0.139,0.121,0.154,0.142,0.129,0.13,0.134,0.118,0.118,0.13,0.143,0.079,0.147,0.142,0.124,0.151,0.133,0.144,0.121,0.109,0.131,0.141,0.099,0.138,0.131,0.091,0.105,0.137,0.136,0.089,0.095,0.109,0.131,0.12,0.119,0.11,0.085,0.1,0.087,0.084,0.104,0.129,0.109,0.106,0.094,0.116,0.114,0.118,0.103,0.141,0.103,0.133,0.111,0.116,0.12,0.114,0.134,0.115,0.097,0.098,0.123,0.126,0.109,0.095,0.119,376,1578,970,919,896,1131,931,1057,507,352,221.0,1285,1028,216,691,3043,4646,414,1268,2379,572,1371,953,1531,1839,2036,1826,1520,1524,1581,522,428,396,1466,363,696,1731,1826,183,197,729,1231,1082,1398,1990,645,337,1034,1079,621,436,1317,949,1811,319,1638,625,1401,548,707,444,990,227,295,1547,539,2097,883,893,636,579,1104,3687,230,3.14,2.648,2.394,2.705,2.298,2.43,2.335,2.794,2.269,3.139,2.763,1.961,2.838,2.89,2.678,2.573,2.926,4.039,2.723,2.012,2.685,2.375,1.952,2.886,2.66,2.749,2.308,2.183,2.632,2.467,2.652,2.467,2.854,3.158,3.578,2.863,2.831,2.897,2.194,2.237,2.644,1.922,3.213,1.936,1.809,2.199,2.868,2.915,2.668,2.777,2.135,2.332,2.186,2.442,2.051,2.227,2.751,2.447,2.177,2.056,2.334,2.55,2.523,2.428,2.271,2.414,2.344,2.47,2.498,2.494,2.604,2.497,2.565,2.716,0.897,0.712,0.622,0.479,0.604,0.445,0.583,0.574,0.518,0.508,0.738,0.522,0.441,0.382,0.497,0.576,0.508,0.682,0.489,0.62,0.732,0.498,0.43,0.684,0.493,0.598,0.433,0.528,0.451,0.472,0.588,0.882,0.504,0.682,0.485,0.626,0.706,0.673,0.202,0.486,0.486,0.47,0.75,0.683,0.493,0.483,0.414,0.83,0.579,0.382,0.45,0.576,0.621,0.471,0.351,0.373,0.413,0.5,0.429,0.352,0.356,0.797,0.599,1.015,0.451,0.723,0.561,0.411,0.398,0.626,0.499,0.554,0.62,0.449
-4094,Not Epileptic,F,27.0,0.5,1.7,1.3,1.1,1.3,1.6,1.6,1.5,1.4,0.7,0.2,2.6,1.7,0.5,1.0,6.5,8.6,0.8,2.7,5.9,0.8,2.9,1.9,2.6,3.1,2.7,4.0,2.6,3.1,3.3,0.7,0.9,0.5,1.9,0.4,0.7,4.9,3.8,0.2,0.2,0.8,2.7,1.8,2.3,3.5,0.8,0.3,0.6,0.8,0.8,0.5,2.3,0.8,1.6,0.5,2.1,2.0,1.5,1.1,0.7,1.2,1.1,0.3,0.3,1.6,1.0,2.4,1.5,1.1,0.3,0.6,2.4,4.0,0.3,4,16,14,9,16,12,15,14,18,7,2.0,27,16,5,8,69,75,8,27,38,5,33,18,33,40,34,45,28,27,38,10,6,6,23,3,6,42,44,2,1,5,23,20,20,21,6,2,4,5,5,3,19,7,11,4,17,12,10,9,6,7,8,2,3,12,10,18,13,7,3,4,15,33,3,0.024,0.02,0.024,0.023,0.039,0.03,0.026,0.026,0.06,0.036,0.024,0.031,0.031,0.032,0.033,0.039,0.032,0.028,0.03,0.045,0.015,0.042,0.033,0.034,0.032,0.029,0.031,0.029,0.026,0.04,0.025,0.039,0.026,0.028,0.015,0.023,0.036,0.029,0.014,0.013,0.021,0.033,0.028,0.022,0.025,0.02,0.015,0.01,0.014,0.029,0.036,0.025,0.026,0.018,0.029,0.018,0.028,0.015,0.027,0.014,0.029,0.022,0.019,0.016,0.019,0.026,0.017,0.02,0.019,0.013,0.023,0.025,0.019,0.015,1346,4036,2441,2146,1650,2269,2744,2992,1643,1400,500.0,2973,3443,1023,2113,10860,18343,2142,4404,4412,2995,4519,2076,5054,6242,6001,7433,4203,7090,5026,1497,827,1030,3851,1840,1490,7761,8467,359,411,1762,2933,4357,3187,3542,1154,803,2554,2062,1145,325,3832,1413,3642,655,3529,2471,3456,1408,1356,1333,1903,499,754,2988,1216,4217,2986,2027,601,950,3252,7711,553,0.101,0.109,0.113,0.115,0.128,0.127,0.118,0.119,0.156,0.14,0.105,0.131,0.127,0.118,0.124,0.143,0.133,0.118,0.124,0.142,0.074,0.147,0.148,0.128,0.14,0.134,0.141,0.127,0.12,0.141,0.102,0.124,0.128,0.118,0.076,0.118,0.119,0.122,0.11,0.088,0.099,0.139,0.127,0.111,0.113,0.109,0.094,0.073,0.086,0.113,0.13,0.118,0.111,0.091,0.132,0.103,0.123,0.09,0.137,0.098,0.12,0.105,0.123,0.104,0.098,0.117,0.096,0.103,0.105,0.11,0.118,0.117,0.101,0.099,366,1379,805,751,641,834,1053,956,393,338,136.0,1301,904,260,548,2741,4449,445,1400,1962,776,1346,891,1283,1735,1719,2287,1478,1879,1539,454,377,363,1085,473,458,2177,2170,162,230,660,1267,1051,1666,2157,537,288,967,930,542,223,1491,586,1535,299,1767,1038,1594,647,735,644,758,189,348,1328,654,2136,1316,901,326,427,1448,3417,327,3.243,2.669,2.723,2.805,2.225,2.481,2.182,2.761,2.713,3.371,3.078,1.979,2.82,2.701,2.873,2.759,3.126,3.399,2.636,2.015,3.036,2.602,2.101,2.743,2.629,2.795,2.509,2.282,2.876,2.537,2.447,2.299,2.248,2.619,3.38,3.068,2.715,2.899,2.466,2.208,3.496,2.112,3.252,2.024,1.897,2.497,3.389,3.19,2.649,2.934,1.906,2.459,2.273,2.592,2.521,2.192,2.708,2.494,2.606,2.055,2.503,2.497,2.712,2.391,2.484,2.445,2.138,2.62,2.688,2.083,2.377,2.565,2.426,2.113,0.816,0.737,0.773,0.57,0.578,0.5,0.583,0.594,0.647,0.488,0.811,0.472,0.452,0.446,0.483,0.649,0.706,0.778,0.593,0.56,0.731,0.508,0.421,0.746,0.507,0.504,0.656,0.542,0.503,0.573,0.731,0.957,0.366,0.667,0.555,0.517,0.863,0.647,0.27,0.347,0.805,0.53,0.637,0.683,0.564,0.577,0.645,0.866,0.585,0.59,0.352,0.649,0.585,0.489,0.475,0.402,0.472,0.529,0.546,0.39,0.387,0.667,0.701,1.1,0.592,0.814,0.471,0.362,0.406,0.607,0.479,0.591,0.422,0.272
-4095,Not Epileptic,F,32.0,0.7,2.2,1.2,1.1,1.7,2.2,1.3,1.1,1.1,0.6,0.2,2.8,1.0,0.5,1.3,4.8,7.4,0.7,1.5,4.1,1.5,3.8,1.6,3.9,3.4,3.0,2.7,2.4,2.1,2.0,2.4,2.2,0.3,2.2,0.3,0.6,3.1,3.2,0.1,0.2,1.0,2.6,2.4,2.3,2.2,0.6,0.5,0.7,1.2,0.9,0.5,1.7,1.3,2.1,0.5,2.3,0.9,1.5,1.1,1.0,0.5,1.3,0.3,0.7,1.5,1.3,2.1,0.7,1.2,0.4,0.8,1.1,3.5,0.3,6,27,13,8,15,19,10,13,9,6,4.0,24,14,7,15,56,72,7,20,36,12,36,14,42,35,35,34,25,20,27,24,19,3,25,2,7,33,42,2,1,5,24,21,15,13,4,3,5,6,9,4,13,10,17,3,23,6,12,9,9,3,9,2,6,11,19,16,5,7,4,7,10,27,2,0.03,0.024,0.017,0.019,0.029,0.033,0.027,0.023,0.051,0.031,0.028,0.041,0.02,0.031,0.025,0.027,0.027,0.026,0.021,0.037,0.03,0.042,0.027,0.041,0.034,0.025,0.023,0.025,0.021,0.021,0.046,0.061,0.022,0.024,0.011,0.018,0.03,0.025,0.015,0.015,0.02,0.033,0.031,0.024,0.016,0.012,0.021,0.011,0.017,0.025,0.023,0.016,0.022,0.018,0.021,0.017,0.019,0.02,0.02,0.02,0.015,0.02,0.016,0.02,0.018,0.021,0.017,0.013,0.018,0.012,0.02,0.013,0.015,0.018,1612,4436,2919,2526,2290,3627,2910,2815,1490,1550,778.0,2846,3488,1185,3176,11831,18799,2298,4810,4840,3842,4539,2499,6590,6374,7386,6932,4973,6453,5501,2420,1666,849,6415,2036,2123,8299,9392,381,533,1454,2432,6820,2692,3706,1617,772,2118,2345,1465,699,3602,1881,4795,666,3867,1578,2610,1262,1279,911,2436,451,953,2642,1773,3863,2143,2213,736,1333,2971,9196,486,0.116,0.119,0.106,0.098,0.132,0.133,0.103,0.123,0.142,0.127,0.119,0.157,0.118,0.153,0.123,0.131,0.128,0.116,0.124,0.144,0.124,0.134,0.113,0.145,0.127,0.128,0.123,0.108,0.102,0.12,0.144,0.132,0.097,0.122,0.062,0.097,0.128,0.128,0.092,0.081,0.104,0.132,0.136,0.116,0.093,0.083,0.116,0.078,0.091,0.119,0.121,0.094,0.119,0.097,0.113,0.107,0.1,0.113,0.113,0.118,0.093,0.107,0.104,0.11,0.103,0.115,0.099,0.081,0.097,0.104,0.117,0.086,0.091,0.124,439,1488,1070,863,956,1146,855,857,407,381,213.0,1190,873,306,823,3035,4564,474,1143,1887,851,1382,804,1783,1585,2044,1885,1532,1441,1544,786,661,282,1387,545,628,1818,2183,209,259,715,1156,1340,1452,2054,740,337,894,1044,621,319,1618,905,1981,360,1977,719,1245,757,728,442,990,257,545,1310,955,1883,920,971,392,624,1417,3717,207,3.44,2.705,2.626,2.784,2.024,2.724,2.727,2.86,2.617,3.178,2.546,2.076,2.956,2.842,2.795,2.856,3.086,3.644,3.085,2.163,3.357,2.544,2.456,2.727,2.9,2.858,2.701,2.51,3.192,2.704,2.276,2.517,2.454,3.304,3.384,2.883,3.102,3.082,2.024,2.602,2.61,1.933,3.471,2.134,2.069,2.386,2.823,2.824,2.8,2.562,2.324,2.29,2.206,2.57,2.239,2.194,2.562,2.374,1.93,1.959,2.323,2.451,1.863,2.157,2.245,2.117,2.258,2.767,2.584,2.162,2.428,2.342,2.58,2.689,0.748,0.584,0.452,0.494,0.605,0.599,0.671,0.439,0.747,0.722,0.956,0.605,0.439,0.514,0.51,0.699,0.626,0.678,0.543,0.599,0.739,0.622,0.657,0.867,0.675,0.615,0.663,0.586,0.544,0.505,0.791,0.867,0.422,0.591,0.574,0.534,0.855,0.688,0.394,0.325,0.455,0.574,0.82,0.649,0.62,0.452,0.487,0.657,0.493,0.698,0.375,0.466,0.481,0.449,0.455,0.371,0.351,0.426,0.348,0.377,0.391,0.639,0.34,0.561,0.501,0.807,0.385,0.382,0.407,0.451,0.434,0.479,0.485,0.519
-4096,Not Epileptic,F,32.0,0.9,3.0,1.1,1.1,1.2,2.0,1.4,1.4,1.5,0.7,0.4,3.0,1.5,0.5,0.8,4.7,8.3,0.8,2.0,5.1,1.3,2.6,1.6,3.0,3.2,3.8,2.7,1.8,2.4,2.6,2.0,1.2,0.4,2.3,0.2,0.3,2.6,2.7,0.1,0.1,1.0,3.1,1.8,2.8,2.0,0.6,0.4,0.6,1.2,0.7,0.7,2.1,1.7,2.0,0.3,2.4,0.6,1.7,1.5,1.4,0.4,1.6,0.1,0.5,1.6,1.1,2.4,1.6,1.2,0.5,0.9,1.3,4.5,0.3,8,26,9,12,15,27,14,15,17,7,5.0,30,19,7,9,56,90,9,32,45,20,33,19,39,48,48,35,21,25,32,25,7,5,26,1,3,33,36,2,1,7,29,23,25,13,4,2,4,6,5,5,17,15,14,2,20,5,14,9,12,3,13,1,5,13,16,17,11,8,7,9,11,35,4,0.035,0.032,0.02,0.022,0.027,0.033,0.022,0.023,0.046,0.036,0.039,0.036,0.028,0.03,0.029,0.03,0.027,0.034,0.031,0.037,0.03,0.027,0.032,0.033,0.03,0.027,0.025,0.024,0.023,0.029,0.052,0.048,0.022,0.028,0.008,0.008,0.027,0.027,0.014,0.013,0.021,0.032,0.036,0.029,0.016,0.013,0.015,0.011,0.016,0.013,0.023,0.024,0.026,0.017,0.019,0.018,0.02,0.019,0.025,0.019,0.013,0.026,0.014,0.017,0.02,0.019,0.019,0.025,0.019,0.017,0.026,0.018,0.017,0.016,1546,4752,2238,2327,1940,3184,3056,3039,1891,1574,586.0,3321,3664,1081,1783,9017,18912,2182,3784,5321,3483,5431,1683,6177,6796,8172,5923,3571,6378,5751,2344,963,974,4803,1678,1678,7427,7435,325,383,1540,2577,5192,2772,3423,1507,914,1977,2368,1727,709,3490,2134,4339,510,3775,1045,3109,1519,2106,885,2737,285,933,2657,1272,3765,2386,2251,840,1458,1914,9036,468,0.132,0.138,0.11,0.113,0.134,0.154,0.108,0.12,0.165,0.14,0.148,0.152,0.13,0.153,0.128,0.134,0.125,0.126,0.134,0.145,0.108,0.126,0.14,0.135,0.135,0.127,0.115,0.103,0.104,0.132,0.139,0.126,0.103,0.123,0.054,0.066,0.131,0.132,0.1,0.092,0.109,0.128,0.138,0.14,0.092,0.086,0.089,0.072,0.089,0.094,0.126,0.121,0.128,0.096,0.116,0.1,0.108,0.108,0.113,0.108,0.091,0.127,0.102,0.12,0.112,0.11,0.102,0.118,0.102,0.123,0.129,0.105,0.099,0.116,447,1601,860,852,748,1108,995,947,562,396,184.0,1355,1020,261,509,2871,5354,447,1075,2211,769,1593,904,1711,2072,2469,1851,1259,1647,1667,740,401,332,1149,472,677,1786,1756,183,197,776,1265,1051,1536,1946,724,379,839,1120,754,437,1386,1038,1889,250,2047,509,1456,922,1156,435,1053,170,463,1235,828,1865,4096,1000,417,585,1033,3971,296,3.195,2.701,2.598,2.668,2.127,2.428,2.488,2.73,2.574,3.049,2.65,2.104,2.744,2.848,2.666,2.464,2.801,3.586,2.756,2.057,3.346,2.521,1.708,2.741,2.559,2.633,2.463,2.245,2.92,2.728,2.308,2.698,2.429,2.932,3.227,2.243,2.965,2.928,2.026,2.218,2.419,1.805,3.432,1.951,1.999,2.297,2.891,2.854,2.566,2.586,1.853,2.333,2.078,2.399,2.437,1.995,2.39,2.452,1.843,2.015,2.16,2.642,1.765,2.411,2.366,1.889,2.231,2.624,2.486,2.223,2.379,2.213,2.432,1.896,0.858,0.574,0.468,0.441,0.707,0.628,0.525,0.452,0.593,0.658,0.733,0.45,0.464,0.348,0.507,0.562,0.567,0.737,0.507,0.577,0.787,0.498,0.463,0.631,0.611,0.594,0.518,0.528,0.495,0.524,0.796,1.056,0.427,0.552,0.538,0.444,0.734,0.683,0.438,0.315,0.42,0.422,0.659,0.533,0.614,0.442,0.327,0.552,0.439,0.438,0.468,0.491,0.442,0.396,0.33,0.369,0.381,0.652,0.352,0.405,0.339,0.656,0.282,0.479,0.409,0.602,0.401,0.414,0.413,0.514,0.466,0.535,0.483,0.331
-4097,Not Epileptic,M,37.0,1.4,3.2,0.8,1.1,1.8,1.7,2.7,1.6,1.2,0.8,0.4,3.6,1.5,0.3,0.9,5.7,7.6,1.5,1.9,4.6,1.8,2.3,3.2,4.3,3.8,2.5,2.3,3.5,3.7,1.6,2.7,2.7,0.5,1.9,0.5,0.6,4.0,3.5,0.3,0.2,1.2,3.6,2.2,3.1,3.3,0.6,0.3,0.8,1.2,0.8,0.4,1.7,1.5,2.4,0.6,1.9,0.7,1.6,1.1,0.9,0.4,1.6,0.4,0.6,1.7,1.1,3.1,1.3,1.2,0.7,0.6,1.4,4.9,0.3,12,28,8,7,18,19,16,14,14,8,5.0,35,17,5,11,70,74,10,26,46,15,29,24,47,39,32,25,28,30,25,31,21,4,26,2,4,42,43,4,2,7,35,21,25,18,4,2,5,6,6,3,13,12,16,3,17,5,13,8,7,3,11,2,5,15,15,24,9,7,7,4,9,40,4,0.052,0.031,0.017,0.021,0.029,0.028,0.037,0.027,0.038,0.037,0.035,0.044,0.025,0.019,0.027,0.027,0.027,0.048,0.029,0.041,0.034,0.033,0.05,0.042,0.037,0.025,0.027,0.037,0.03,0.024,0.047,0.055,0.02,0.023,0.014,0.017,0.036,0.037,0.029,0.014,0.026,0.039,0.034,0.027,0.023,0.013,0.019,0.013,0.02,0.016,0.028,0.02,0.022,0.017,0.016,0.016,0.015,0.019,0.022,0.023,0.012,0.023,0.016,0.016,0.018,0.02,0.026,0.016,0.015,0.018,0.016,0.018,0.02,0.017,1589,5395,2236,2353,2712,2868,3119,3251,2172,1491,831.0,3347,4511,1033,2246,14520,20899,2853,4573,4930,4694,4286,1874,6629,5969,6022,4406,3544,7547,4374,2560,2089,1084,6603,2214,1537,7996,8851,565,525,1165,2778,6353,3057,3969,1202,812,2197,2391,1879,481,3447,1968,5626,956,3511,1550,2946,1368,1127,843,2652,399,1158,2833,1230,4443,2954,2513,1460,1000,2246,9245,454,0.141,0.14,0.109,0.104,0.137,0.136,0.119,0.123,0.144,0.15,0.136,0.154,0.123,0.117,0.12,0.118,0.121,0.142,0.13,0.153,0.115,0.133,0.151,0.143,0.131,0.122,0.111,0.11,0.101,0.12,0.154,0.138,0.088,0.115,0.063,0.083,0.138,0.142,0.139,0.095,0.12,0.142,0.137,0.128,0.103,0.093,0.089,0.075,0.091,0.097,0.125,0.105,0.114,0.09,0.097,0.094,0.09,0.105,0.112,0.121,0.092,0.12,0.116,0.098,0.11,0.113,0.114,0.092,0.087,0.109,0.102,0.106,0.107,0.127,473,1670,744,825,964,997,968,1012,558,336,235.0,1544,962,279,562,3718,4829,501,1124,2113,1071,1318,953,1842,1646,1768,1424,1417,1779,1234,835,792,404,1383,537,554,1880,1883,205,228,691,1431,1176,1839,2263,613,314,929,1046,790,250,1448,1020,2213,512,1810,721,1356,805,582,412,1014,239,608,1452,838,1962,1225,1129,584,485,1110,3707,272,2.974,2.97,2.764,2.681,2.441,2.453,2.59,2.803,2.794,3.124,2.842,1.883,3.286,2.783,2.833,2.85,3.224,3.779,2.941,2.033,3.16,2.479,1.818,2.668,2.759,2.622,2.429,2.028,3.135,2.675,2.315,2.543,2.221,3.247,3.66,2.532,3.064,3.299,2.54,2.511,2.115,1.808,3.557,1.868,2.011,2.232,2.965,2.813,2.774,2.533,2.11,2.449,2.033,2.544,2.352,2.13,2.505,2.404,1.991,2.141,2.267,2.596,1.85,2.319,2.148,1.799,2.188,2.733,2.593,2.627,2.243,2.453,2.557,2.086,0.743,0.748,0.615,0.533,0.663,0.578,0.688,0.534,0.694,0.715,0.828,0.495,0.525,0.771,0.702,0.639,0.721,0.836,0.556,0.615,0.871,0.672,0.521,0.88,0.75,0.537,0.723,0.577,0.691,0.623,0.773,0.858,0.436,0.704,0.756,0.479,0.814,0.638,0.498,0.415,0.406,0.586,0.849,0.57,0.751,0.383,0.846,0.661,0.476,0.485,0.495,0.488,0.419,0.467,0.395,0.382,0.378,0.534,0.385,0.428,0.353,0.606,0.36,0.522,0.51,0.464,0.559,0.392,0.384,0.542,0.517,0.37,0.514,0.761
-4098,Not Epileptic,F,27.0,1.0,1.7,1.1,1.1,1.5,1.7,1.3,1.2,1.5,0.5,0.2,2.5,1.2,0.3,1.3,4.7,6.7,0.8,1.5,3.4,1.2,2.1,2.5,4.7,2.9,3.1,3.0,2.6,2.1,2.2,1.1,0.9,0.6,1.7,0.4,0.7,3.1,2.4,0.2,0.2,0.9,5.0,2.1,1.8,2.5,0.6,0.3,0.9,1.1,0.7,0.6,1.7,1.4,3.1,0.5,1.8,0.8,1.5,1.0,1.3,0.6,1.5,0.7,0.6,1.5,1.2,2.1,1.1,1.2,0.6,0.7,1.1,3.4,0.4,6,19,14,9,20,21,11,13,17,6,2.0,26,13,4,15,59,69,5,22,36,11,28,25,43,37,39,37,24,22,27,15,7,5,21,3,7,43,30,1,1,6,39,20,16,14,6,2,4,6,5,3,14,11,25,2,13,7,13,6,10,4,11,5,6,15,16,15,7,9,6,4,10,27,5,0.048,0.025,0.021,0.023,0.032,0.028,0.024,0.026,0.052,0.025,0.028,0.036,0.024,0.029,0.028,0.031,0.028,0.034,0.025,0.035,0.025,0.029,0.04,0.046,0.03,0.028,0.027,0.032,0.023,0.025,0.035,0.052,0.036,0.023,0.016,0.018,0.036,0.034,0.016,0.014,0.021,0.051,0.038,0.024,0.023,0.018,0.014,0.016,0.02,0.016,0.027,0.023,0.021,0.022,0.025,0.014,0.027,0.019,0.022,0.02,0.022,0.026,0.032,0.021,0.018,0.031,0.019,0.016,0.024,0.02,0.023,0.018,0.017,0.018,1245,3685,2319,2073,1702,3269,2754,2463,1762,1334,630.0,2722,3097,747,2612,9168,16794,2096,3620,3744,3638,4407,1816,5424,5229,6889,5982,3700,5153,5112,2067,1130,859,5721,1920,1843,6246,5879,386,520,1258,2311,4796,2173,2769,1150,676,1921,1828,1591,534,2433,1801,5505,509,3533,995,2711,1211,2048,730,2561,653,988,2594,893,3593,2290,1551,1099,896,1858,7035,753,0.144,0.122,0.121,0.113,0.138,0.144,0.102,0.126,0.155,0.136,0.113,0.14,0.117,0.123,0.14,0.13,0.123,0.115,0.128,0.143,0.113,0.123,0.132,0.142,0.123,0.132,0.12,0.1,0.104,0.122,0.127,0.12,0.117,0.12,0.082,0.095,0.151,0.142,0.104,0.086,0.111,0.159,0.135,0.12,0.106,0.096,0.089,0.083,0.103,0.095,0.14,0.113,0.12,0.112,0.121,0.089,0.136,0.107,0.11,0.109,0.12,0.116,0.163,0.111,0.11,0.155,0.103,0.097,0.114,0.119,0.113,0.117,0.098,0.144,332,1192,806,750,842,1038,788,768,528,308,160.0,1138,831,195,718,2698,4128,393,950,1643,796,1203,947,1580,1519,1832,1765,1164,1309,1509,609,403,269,1147,424,607,1536,1340,179,272,685,1258,1068,1204,1696,569,309,857,877,660,294,1178,1040,2223,245,1863,452,1242,681,1000,393,945,304,528,1252,585,1743,994,770,422,451,955,2982,295,3.566,2.811,2.68,2.613,1.808,2.599,2.746,2.831,2.416,3.087,2.972,2.017,2.819,2.799,2.702,2.518,3.072,3.875,2.924,1.956,3.299,2.69,1.784,2.528,2.634,2.9,2.516,2.404,2.903,2.55,2.493,2.77,2.65,3.317,3.632,2.682,2.785,2.95,2.244,2.257,2.188,1.797,3.169,2.052,1.839,2.324,2.602,2.801,2.518,2.687,2.174,2.192,1.809,2.422,2.455,2.141,2.548,2.487,2.08,2.26,2.161,2.595,2.032,2.296,2.289,2.102,2.245,2.549,2.29,2.482,2.394,2.287,2.422,2.75,0.761,0.68,0.45,0.5,0.587,0.618,0.502,0.485,0.69,0.458,0.524,0.528,0.387,0.567,0.56,0.625,0.654,0.848,0.475,0.543,0.744,0.501,0.429,0.791,0.666,0.544,0.573,0.6,0.487,0.612,0.59,0.978,0.523,0.68,0.772,0.415,0.755,0.681,0.407,0.352,0.413,0.546,0.79,0.529,0.557,0.388,0.421,0.644,0.396,0.473,0.353,0.472,0.469,0.482,0.37,0.405,0.341,0.555,0.341,0.462,0.364,0.751,0.567,0.635,0.476,0.613,0.421,0.415,0.396,0.795,0.358,0.41,0.472,0.569
-4099,Not Epileptic,M,27.0,0.9,2.4,0.9,1.1,2.2,1.5,1.4,1.7,1.1,0.9,0.4,3.1,1.6,0.5,1.2,6.3,8.5,0.8,2.5,3.8,1.3,2.7,2.2,3.2,3.1,2.9,3.5,2.4,2.7,3.3,2.0,1.0,0.5,2.2,0.5,0.9,3.3,3.6,0.1,0.4,1.1,2.6,2.4,2.9,3.6,0.9,0.5,0.8,1.5,0.7,0.6,1.7,1.8,1.9,0.2,2.3,0.4,1.4,1.1,1.5,0.7,1.9,0.3,0.6,2.3,1.2,2.5,1.1,1.0,0.7,0.9,1.4,4.9,0.3,8,27,8,9,19,17,15,18,18,8,4.0,28,19,8,15,72,83,10,31,39,13,33,18,37,43,35,31,31,27,38,18,6,5,29,3,8,40,46,2,3,6,25,28,25,25,6,3,5,7,6,4,14,18,15,2,19,3,13,8,15,8,16,3,5,17,15,21,7,7,5,5,9,40,2,0.037,0.021,0.014,0.022,0.037,0.026,0.022,0.023,0.032,0.031,0.035,0.029,0.025,0.03,0.026,0.03,0.027,0.026,0.029,0.042,0.022,0.03,0.031,0.035,0.032,0.027,0.033,0.022,0.025,0.034,0.044,0.041,0.018,0.025,0.019,0.019,0.034,0.034,0.01,0.02,0.023,0.029,0.033,0.028,0.024,0.017,0.022,0.012,0.018,0.012,0.025,0.017,0.026,0.016,0.027,0.018,0.016,0.018,0.024,0.021,0.029,0.031,0.019,0.023,0.023,0.022,0.023,0.016,0.016,0.018,0.025,0.017,0.018,0.014,1702,5624,2207,2496,2538,2720,3367,3507,2076,1832,891.0,3815,4367,1102,2853,13379,19231,2641,5041,4384,4662,4683,2708,6238,6048,6260,4929,5498,7017,5849,2630,1008,1301,7184,1800,2163,7155,9374,537,745,1479,2316,5921,3128,4066,1806,947,2772,2500,1899,723,3535,2728,4490,414,3801,1079,3181,1298,2051,1117,2708,430,996,3307,1086,3978,2318,1907,997,1302,2355,9403,513,0.132,0.114,0.1,0.106,0.136,0.133,0.111,0.124,0.147,0.131,0.122,0.133,0.127,0.152,0.127,0.138,0.127,0.121,0.133,0.164,0.098,0.129,0.12,0.141,0.124,0.138,0.122,0.115,0.108,0.15,0.154,0.107,0.1,0.125,0.087,0.107,0.133,0.144,0.079,0.116,0.112,0.128,0.147,0.124,0.114,0.101,0.11,0.074,0.096,0.088,0.129,0.099,0.126,0.094,0.137,0.104,0.091,0.102,0.12,0.124,0.118,0.14,0.122,0.121,0.118,0.117,0.115,0.096,0.097,0.12,0.123,0.103,0.103,0.105,497,1906,923,861,1041,1022,1088,1204,630,472,223.0,1661,1109,284,804,3656,5273,542,1551,1811,1068,1532,1172,1665,1740,1862,1509,1776,1741,1731,740,457,419,1507,450,750,1856,2078,297,361,701,1315,1373,1759,2316,821,366,1105,1183,809,369,1622,1220,1889,146,1908,473,1334,696,1086,513,984,259,458,1516,787,1799,1037,944,518,602,1178,4083,269,3.321,2.703,2.551,2.798,2.26,2.341,2.457,2.634,2.419,3.009,3.127,1.968,2.927,2.704,2.604,2.777,2.914,3.73,2.759,2.141,3.172,2.439,2.01,2.896,2.7,2.809,2.574,2.435,2.963,2.698,2.616,2.272,2.486,3.144,3.591,2.689,2.883,3.088,2.034,2.312,2.768,1.658,3.264,2.09,1.998,2.437,3.124,3.046,2.608,2.467,2.232,2.278,2.278,2.453,2.769,2.197,2.337,2.468,2.06,2.081,2.214,2.782,1.938,2.426,2.418,1.632,2.322,2.6,2.501,2.132,2.436,2.414,2.498,2.451,0.834,0.534,0.378,0.469,0.627,0.565,0.598,0.515,0.643,0.528,0.863,0.554,0.487,0.523,0.565,0.595,0.558,0.666,0.492,0.601,0.933,0.568,0.608,0.681,0.567,0.424,0.603,0.631,0.544,0.544,0.853,0.786,0.399,0.62,0.731,0.418,0.679,0.646,0.418,0.479,0.495,0.413,0.673,0.652,0.596,0.4,0.474,0.697,0.375,0.404,0.523,0.45,0.498,0.399,0.533,0.401,0.425,0.596,0.437,0.381,0.384,0.752,0.381,0.722,0.478,0.518,0.497,0.354,0.365,0.692,0.485,0.461,0.418,0.383
-4100,Not Epileptic,F,37.0,1.1,1.8,0.9,0.9,1.8,1.5,1.2,1.4,1.1,1.0,0.4,3.1,1.0,0.6,1.1,4.7,8.0,0.8,2.2,4.6,1.1,2.8,2.2,3.8,3.1,2.4,3.2,2.6,2.4,3.5,1.8,1.2,0.5,2.2,0.3,0.3,3.7,3.4,0.2,0.2,0.9,2.4,2.5,3.0,2.2,0.6,0.5,0.6,1.4,1.0,0.8,1.5,1.3,2.2,0.2,2.6,1.2,1.4,1.1,1.4,1.2,1.3,0.5,0.6,1.4,0.9,2.4,1.0,1.0,0.7,1.0,1.2,3.6,0.3,9,18,7,9,17,22,10,17,14,14,4.0,28,17,7,14,53,78,9,25,49,10,31,19,37,33,33,35,23,17,45,19,8,4,26,2,3,46,43,2,2,6,26,27,22,13,4,3,3,8,8,6,13,12,18,1,22,10,10,8,12,12,12,2,5,13,17,20,6,6,7,9,10,29,3,0.045,0.026,0.025,0.023,0.035,0.026,0.023,0.026,0.041,0.049,0.033,0.038,0.027,0.028,0.034,0.037,0.033,0.046,0.035,0.041,0.027,0.032,0.037,0.043,0.038,0.027,0.034,0.032,0.028,0.038,0.057,0.051,0.027,0.03,0.012,0.012,0.035,0.036,0.017,0.019,0.024,0.033,0.036,0.032,0.021,0.016,0.02,0.012,0.021,0.023,0.028,0.021,0.021,0.024,0.023,0.021,0.033,0.022,0.029,0.025,0.031,0.03,0.03,0.022,0.019,0.025,0.021,0.02,0.022,0.026,0.029,0.022,0.018,0.02,1516,3647,1710,1767,2342,2870,2483,2862,1818,1328,645.0,2588,2483,1144,2393,8153,15180,2155,3841,4654,3755,4288,1964,5435,4420,5285,5016,3111,4945,4799,1910,1333,736,4230,1693,1044,6571,6992,370,350,1007,1801,5863,2707,2497,1124,842,1571,1903,1661,690,2327,1924,3626,263,3410,1354,2308,1120,1489,1107,2030,428,970,1988,807,3314,1731,1408,1031,1257,1872,6845,484,0.144,0.12,0.115,0.114,0.147,0.14,0.103,0.133,0.152,0.174,0.146,0.146,0.135,0.136,0.131,0.145,0.135,0.14,0.148,0.159,0.109,0.134,0.126,0.149,0.13,0.128,0.121,0.115,0.099,0.156,0.16,0.124,0.118,0.125,0.071,0.071,0.157,0.146,0.094,0.114,0.121,0.132,0.146,0.138,0.104,0.095,0.112,0.077,0.107,0.12,0.143,0.11,0.112,0.118,0.114,0.117,0.133,0.111,0.129,0.124,0.145,0.13,0.133,0.12,0.107,0.131,0.111,0.098,0.108,0.145,0.132,0.116,0.101,0.131,409,1226,612,677,805,1057,794,917,537,384,197.0,1320,712,313,663,2210,4163,407,1089,2031,798,1390,917,1547,1314,1472,1729,1180,1278,1662,628,466,277,1158,491,434,1821,1684,213,199,553,1147,1232,1610,1609,555,358,732,952,654,374,1095,939,1553,120,1801,603,1085,640,847,566,765,250,441,1094,589,1746,768,679,449,588,925,2977,218,3.486,2.682,2.701,2.556,2.342,2.297,2.527,2.598,2.507,2.733,2.505,1.748,2.687,2.604,2.611,2.729,2.869,3.759,2.807,1.989,3.398,2.44,1.961,2.692,2.58,2.762,2.279,2.119,2.862,2.355,2.381,2.836,2.28,2.742,3.087,2.16,2.834,3.014,1.902,2.043,2.308,1.52,3.368,1.852,1.791,2.168,2.912,2.585,2.434,2.755,1.936,2.196,1.993,2.479,2.504,2.099,2.418,2.48,1.834,1.977,2.267,2.643,1.951,2.611,1.919,1.817,2.03,2.595,2.421,2.357,2.273,2.565,2.442,2.445,0.794,0.714,0.484,0.52,0.725,0.603,0.66,0.45,0.592,0.769,0.593,0.482,0.334,0.409,0.544,0.611,0.574,0.697,0.543,0.545,0.845,0.717,0.514,0.776,0.672,0.606,0.59,0.563,0.566,0.654,0.71,0.888,0.31,0.567,0.88,0.422,0.621,0.633,0.419,0.333,0.608,0.414,0.784,0.486,0.565,0.447,0.432,0.722,0.377,0.553,0.424,0.401,0.52,0.41,0.393,0.403,0.461,0.476,0.469,0.355,0.324,0.836,0.336,0.762,0.462,0.638,0.461,0.421,0.458,0.637,0.584,0.47,0.423,0.58
diff --git a/content/en/project/epilepsy-detection/model.ipynb b/content/en/project/epilepsy-detection/model.ipynb
deleted file mode 100644
index 5d9f8482..00000000
--- a/content/en/project/epilepsy-detection/model.ipynb
+++ /dev/null
@@ -1,2527 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 84,
- "id": "df02d505",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import os\n",
- "from pathlib import Path\n",
- "import glob\n",
- "import numpy as np\n",
- "import seaborn as sns\n",
- "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
- "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
- "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.svm import SVC\n",
- "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
- "from sklearn.feature_selection import SelectKBest, f_classif\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "from sklearn.decomposition import PCA\n",
- "import matplotlib.pyplot as plt\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d3240cdc",
- "metadata": {},
- "source": [
- "# Model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4a065acf",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "import pandas as pd\n",
- "df = pd.read_csv(r'.\\lh.csv')\n",
- "\n",
- "df = df.drop('Subject', axis=1)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7324d92c",
- "metadata": {},
- "source": [
- "## Preprocessing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 86,
- "id": "0a4e8b59",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "def preprocess_data(df):\n",
- " \n",
- " target_candidates = [col for col in df.columns if 'epilep' in col.lower() or 'target' in col.lower() or 'label' in col.lower()]\n",
- " \n",
- " if not target_candidates:\n",
- " print(\"No target column found. Please specify the target column name.\")\n",
- " \n",
- " return None, None, None\n",
- " \n",
- " target_col = target_candidates[0]\n",
- " print(f\"Using '{target_col}' as target variable\")\n",
- " \n",
- " \n",
- " X = df.drop(columns=[target_col])\n",
- " y = df[target_col]\n",
- " \n",
- " \n",
- " if y.dtype == 'object':\n",
- " le = LabelEncoder()\n",
- " y = le.fit_transform(y)\n",
- " print(f\"Target classes: {le.classes_}\")\n",
- " \n",
- " \n",
- " numeric_cols = X.select_dtypes(include=[np.number]).columns\n",
- " X = X[numeric_cols]\n",
- " \n",
- " print(f\"Features shape: {X.shape}\")\n",
- " print(f\"Target distribution:\")\n",
- " print(pd.Series(y).value_counts())\n",
- " \n",
- " return X, y, target_col"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "id": "9c8aaa6c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using 'Epileptic_Status' as target variable\n",
- "Target classes: ['Epileptic' 'Not Epileptic']\n",
- "Features shape: (542, 593)\n",
- "Target distribution:\n",
- "0 442\n",
- "1 100\n",
- "Name: count, dtype: int64\n"
- ]
- }
- ],
- "source": [
- "X, y, target_col = preprocess_data(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 88,
- "id": "4c13b5e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "def feature_engineering(X, y):\n",
- " \"\"\"Perform feature engineering and selection\"\"\"\n",
- " \n",
- " X = X.fillna(X.median())\n",
- " \n",
- " # Remove constant features\n",
- " constant_features = X.columns[X.var() == 0]\n",
- " if len(constant_features) > 0:\n",
- " print(f\"Removing {len(constant_features)} constant features\")\n",
- " X = X.drop(columns=constant_features)\n",
- " \n",
- " \n",
- " selector = SelectKBest(score_func=f_classif, k=min(50, X.shape[1]))\n",
- " X_selected = selector.fit_transform(X, y)\n",
- " selected_features = X.columns[selector.get_support()]\n",
- " \n",
- " print(f\"Selected {len(selected_features)} features out of {X.shape[1]}\")\n",
- " \n",
- " return pd.DataFrame(X_selected, columns=selected_features), selected_features"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "67ed1a59",
- "metadata": {},
- "source": [
- "# PCA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 89,
- "id": "3105e3c7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Explained variance ratio:\n",
- "PC1: 0.1373 (13.73%)\n",
- "PC2: 0.0796 (7.96%)\n",
- "PC3: 0.0495 (4.95%)\n",
- "PC4: 0.0374 (3.74%)\n",
- "PC5: 0.0168 (1.68%)\n",
- "PC6: 0.0156 (1.56%)\n",
- "PC7: 0.0141 (1.41%)\n",
- "PC8: 0.0135 (1.35%)\n",
- "PC9: 0.0130 (1.30%)\n",
- "PC10: 0.0125 (1.25%)\n",
- "\n",
- "Cumulative explained variance:\n",
- "PC1-PC1: 0.1373 (13.73%)\n",
- "PC1-PC2: 0.2169 (21.69%)\n",
- "PC1-PC3: 0.2664 (26.64%)\n",
- "PC1-PC4: 0.3038 (30.38%)\n",
- "PC1-PC5: 0.3206 (32.06%)\n",
- "PC1-PC6: 0.3362 (33.62%)\n",
- "PC1-PC7: 0.3502 (35.02%)\n",
- "PC1-PC8: 0.3637 (36.37%)\n",
- "PC1-PC9: 0.3767 (37.67%)\n",
- "PC1-PC10: 0.3892 (38.92%)\n"
- ]
- },
- {
- "data": {
- "image/png": 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ms2HXrl3Q6XQ4evQo9Hq9R/tWSE9PxyeffILPP/8cRUVFmDhxIqZMmYIPPvigxu3LyspQVlbm+rmoqMir1yMiIiKi2rW6JPqlS5fw0EMPITs7G0ajEb1798bXX3+NwYMHAyj/w8JqteKJJ55AQUEBrrvuOnzzzTcwGAx+jpyIiIiI2iOFQoENGzZg0qRJWLNmDZKTk3HzzTdj9OjR6N27t1djLVy40PV7LwDk5uYCAMLCwtCxY0fX8vXr12Po0KEIDg4GANx+++1Yv369q9q8wpgxYzBhwgTXzw899BD+/ve/Y9y4cQDKK9wXLVqEmTNnupLoM2bMcG3ftWtXLFq0CI8//rhHSfQTJ06ge/fubstmzJiBtWvXAgCCgoKQmZlZ5xh6vd5VyV35mAFAp9Nh7dq1rjYu77zzDqxWK9577z3odDoAwOrVqzF8+HAsW7bM1fIxODgYq1evhlwux5VXXolhw4Zh+/btmDRpEkJCQiCXy2EwGKq9Xm3OnTuHUaNGuT0p4K3S0lKkpqYiOjoaALBq1SoMGzYMr732Wo1xLFmyBAsWLPD6dYiIiIiofq2uncu6deuQkZGBsrIy5OTk4Ntvv3X7Q0IQBMyfPx/Z2dkoLS3Fd999h6SkJD9GTERE1H6JooTTuWb8fr4Qp3PNEEXJ3yER+cWoUaNw4cIFbNmyBUOGDMHOnTuRnJzsUf/vyvr161fvNk6nE6mpqRg7dqxr2dixY5Gamgqn01nneL/++isWLlzoqp7X6/WYNGkSsrOzUVJSAqC8anvw4MGIioqCwWDAww8/jLy8PFgsFo+OoWq1+ezZs3HgwAHMnTvX47YwtenVq5dbH/Rjx46hT58+rgQ6ANxwww0QRRHHjx93LevZsyfkcrnr58jISOTk5DQ4junTp2Px4sW44YYbMG/ePBw8eNDrMTp37uxKoANA//79q8Vd2axZs2AymVz/zp8/3+D4iYiIiMhdq6tEJyIiotbhcJYJm/ZnIj3HjDK7CLVShvhwPUYlRyMpyujv8IianUajweDBgzF48GDMnTsXjz76KObNm4fdu3cDKJ/np0JFP+6qKieDa7N161ZkZWXh/vvvd1vudDrxzTffYOjQobWOJ4oiFixYgJEjR9YY/9mzZ3HHHXdg8uTJWLRoEUJCQrBnzx5MnDix1pgrS0hIwB9//OG2LCwsDGFhYQgPD693//pUPR5JkmptEVN5edUJVQVBgCiKDY7j0UcfxZAhQ/Dll1/im2++wZIlS/Daa69h2rRpkMlkbu81UPv7XVO8tR2PWq1uVKsgIiIiIqpdq6tEJyIiopbvcJYJK7efxKFME4K0KsR20CFIq8KhzPLlh7NM/g6RyO8SExNhsVhcfcwr9/euPMmot9atW4fRo0fjwIEDbv8efPDBGicYrSw5ORnHjx9HfHx8tX8ymQz79u2Dw+HAa6+9hpSUFFxxxRW4cOGCx7E98MADOH78OD777LMGH583EhMTceDAAbcq+b1790Imk7kmHvWESqWqVsVfn5iYGEyePBlpaWn429/+hnfeeQdA+U2D4uJit5hqer/PnTvndm5/+OEHr+MmIiIiIt9gJToRERH5lChK2LQ/E/kWG+LD9a6qSb1GgXi1Huk5ZqTtz0JiZCBksvonESRq7fLy8nDvvfdiwoQJ6N27NwwGA/bt24eXX34ZI0aMgFarRUpKCpYuXYrY2FhcvnwZL7zwQoNeKzc3F59//jm2bNlSraXhuHHjMGzYMOTm5roS91XNnTsXd955J2JiYnDvvfdCJpPh4MGDOHToEBYvXoxu3brB4XBg1apVGD58OPbu3Ys1a9Z4HN/o0aORlpaG0aNHY9asWRgyZAgiIiJw9uxZbNy40a2lii88+OCDmDdvHsaNG4f58+cjNzcX06ZNw0MPPeTqh+6J2NhY7Nq1C6NHj4ZarUaHDh3q3H7GjBkYOnQorrjiChQUFOC///0vevToAQC47rrrEBAQgOeffx7Tpk3Dzz//XGNbH41Gg3HjxuHVV19FUVERpk+fjvvuu8/jvuxERERE5DusRCciIiKfysizID3HjEijtlrbAUEQEGnU4mROMTLyPOufTNTa6fV6XHfddVixYgUGDBiApKQkzJkzB5MmTcLq1asBlE8Earfb0a9fPzz55JPVJgD1VMUEmoMGDaq2buDAgTAYDHj//fdr3X/IkCH44osvsG3bNlxzzTVISUnB8uXL0aVLFwBA3759sXz5cixbtgxJSUn44IMPsGTJEo/jEwQBGzduxOuvv46vvvoKgwYNQvfu3TFhwgTExMRgz5493h90HQICArB161bk5+fjmmuuwT333INBgwa5zrunFi5ciIyMDHTr1q3WGxCVOZ1OTJkyBT169MDtt9+O7t27uyZeDQkJwb/+9S989dVX6NWrFz766CPMnz+/2hjx8fEYOXIk7rjjDtx2221ISkryaPJWIiIiIvI9QarakI9QVFQEo9EIk8mEwMBAf4dDRETUqvx+vhAvfnkMsR10kNdQae4UJWRctmD2sB7oExPU/AGSz/B3JqKmMX/+fGze37BuvgABAABJREFUvLlRbX0qPp8xMz6BTB3gu+CIiIiI6pGxdJi/Q/CYp3/TsBKdiIiIfMqgUUCtlMFqq7l/sNXmhFopg0HDrnJERERERETU8jGJTkRERD4VG6pDfLge2SYrqj7wJkkSsk1WJIQbEBuq81OERNRUhg4dCr1eX+O/l156yeNxahtDr9dj9+7dTXgEnvHVcRIRERFR68ASMCIiIvIpmUzAqORoZBVYXb3RtSo5rDYnsk1WhOhUGJkcxUlFidqgtWvXwmq11rguJCTE43HqamMSFRXlbVg+56vjrM38+fNr7JNORERERP7BJDoRERH5XFKUEdMHJWDT/kyk55hxqUiEWilD7+ggjEyOQlKU0d8hElET8FWCOz4+3ifjNJWWkMgnIiIioubDJDoRERE1iaQoIxIjA5GRZ0FxqQMGjQKxoTpWoBMREREREVGrwiQ6ERERNRmZTEBcmN7fYRARERERERE1GCcWJSIiIiIiIiIiIiKqBZPoRERERERERERERES1YBKdiIiIiIiIiIiIiKgWTKITEREREREREREREdWCSXQiIiIiIiIiIiIiolowiU5EREREREREREREVAuFvwMgIiIiIiKipnF4wRAEBgb6OwwiIiKiVo2V6EREREREREREREREtWASnYiIiIiIiIiIiIioFkyiExERERERERERERHVgkl0IiIiIiIiIiIiIqJaMIlORERERERERERERFQLJtGJiIiIiIiIiIiIiGrBJDoRERERERERERERUS2YRCciIiIiIiIiIiIiqgWT6EREREREREREREREtVD4OwCitkgUJWTkWVBc6oBBo0BsqA4ymeDvsIiIiIionUmatxUydYC/wyAiIqIWJGPpMH+H0OowiU7kY4ezTNi0PxPpOWaU2UWolTLEh+sxKjkaSVFGf4dHREREREREREREXmASnciHDmeZsHL7SeRbbIg0aqE1ymG1OXEo04SsAiumD0pgIp2IiIiIiIiIiKgVYU90Ih8RRQmb9mci32JDfLgeeo0CcpkAvUaB+HA98i02pO3PgihK/g6ViIiIiIiIiIiIPMQkOpGPZORZkJ5jRqRRC0Fw738uCAIijVqczClGRp7FTxESERERERERERGRt5hEJ/KR4lIHyuwitCp5jeu1KjnK7CKKSx3NHBkRERERERERERE1FJPoRD5i0CigVspgtTlrXG+1OaFWymDQcCoCIiIiIiIiIiKi1oJJdCIfiQ3VIT5cj2yTFZLk3vdckiRkm6xICDcgNlTnpwiJiIiIiIiIiIjIW0yiE/mITCZgVHI0QnQqpOeYYS51wClKMJc6kJ5jRohOhZHJUZDJhPoHIyIiIiIiIiIiohaBfSWIfCgpyojpgxKwaX8m0nPMuFQkQq2UoXd0EEYmRyEpyujvEImonRJFCRl5FhSXOmDQKBAbquNNPSIiIiIiIiIPMIlO5GNJUUYkRgYyWUVELcbhLJPr5l6ZvfzmXny4HqOSo3lzj4iIiIiIiKgeTKITNQGZTEBcmN7fYRAR4XCWCSu3n0S+xYZIoxZaoxxWmxOHMk3IKrBi+qAEJtKJiIiIiIiI6sCe6ERERG2UKErYtD8T+RYb4sP10GsUkMsE6DUKxIfrkW+xIW1/FkRRqn8wIiIiIiIionaKSXQiIqI2KiPPgvQcMyKNWgiCe0spQRAQadTiZE4xMvIsfoqQqP2ZP38++vbt6+8w2qzx48fj7rvvbrLxd+7cCUEQUFhYWOs2GzZsQFBQUJ3jVL0OmjpuIiIiImocJtGJiIjaqOJSB8rsIrQqeY3rtSo5yuwiiksdzRwZUcNdvHgRTz75JOLj46HRaBAREYEbb7wRa9asQUlJSZO97qZNmyCXy3Hu3Lka11955ZWYPn26z1+3qKgIc+bMQc+ePaHVahEaGoprrrkGL7/8MgoKCjwaIzY2Fq+//rrPY2uPrr/+emRnZ8NoZBssIiIiovaEPdGJiIjaKINGAbVSBqvNCb2m+n/yrTYn1EoZDDWsI2qJTp8+jRtuuAFBQUF46aWX0KtXLzgcDpw4cQLr169Hp06dcNddd1Xbz263Q6lUNuq177rrLoSGhiI1NRVz5sxxW7d3714cP34cGzdubNRrVJWfn48bb7wRRUVFWLRoEa6++mqoVCqkp6fjww8/xIcffogpU6b49DVbKl+8h76gUqnQsWNHf4dBRERERM2MlehERERtVGyoDvHhemSbrJAk977nkiQh22RFQrgBsaE6P0VI5J0nnngCCoUC+/btw3333YcePXqgV69eGDVqFL788ksMHz4cQHm7ojVr1mDEiBHQ6XRYvHgxnE4nJk6ciK5du0Kr1aJ79+74xz/+4Rp7165dUCqVuHjxottr/u1vf8OAAQOgVCrx0EMPYcOGDdU+T+vXr8fVV1+NPn364Ny5cxgxYgT0ej0CAwNx33334dKlSw063ueffx7nzp3DTz/9hEceeQS9e/fGlVdeiTvvvBMffvghnnjiiXrHuOWWW3D27Fk89dRTEATBrbXT999/jwEDBkCr1SImJgbTp0+HxfJne6fY2FgsXrwYDz/8MPR6Pbp06YLPPvsMubm5rmPs1asX9u3b59qnopXJ5s2bccUVV0Cj0WDw4ME4f/68W1xvvvkmunXrBpVKhe7du+P99993W9+Q99Bbt9xyC6ZNm4YZM2YgODgYERERePvtt2GxWPDII4/AYDCgW7du+M9//uPap6Z2Lhs2bEDnzp0REBCA//u//0NeXl6111q6dCkiIiJgMBgwceJElJaW1hmbJEl4+eWXERcXB61Wiz59+uDf//53g4+ViIiIiBqHSXQiIqI2SiYTMCo5GiE6FdJzzDCXOuAUJZhLHUjPMSNEp8LI5CjIZEL9gxH5WV5eHr755htMmTIFOl3NN34qJ4jnzZuHESNG4NChQ5gwYQJEUUR0dDQ++eQTHD16FHPnzsXzzz+PTz75BAAwYMAAxMXFuSVzHQ4H/vWvf+GRRx4BAEycOBGnT5/Gd99959rGYrHgk08+wcSJEyFJEu6++27k5+fju+++w7Zt23Dq1Cncf//9Xh+vKIrYuHEjxo4di6ioqHqPtzZpaWmIjo7GwoULkZ2djezsbADAoUOHMGTIEIwcORIHDx7Exo0bsWfPHkydOtVt/xUrVuCGG27Ab7/9hmHDhuGhhx7Cww8/jLFjx2L//v2Ij4/Hww8/7HZjoaSkBC+++CJSU1Oxd+9eFBUVYfTo0a71n376KZ588kn87W9/w+HDh/HYY4/hkUcewY4dO9xe29v3sCFSU1PRoUMH/Pzzz5g2bRoef/xx3Hvvvbj++uuxf/9+DBkyBA899FCtrYJ++uknTJgwAU888QQOHDiAgQMHYvHixW7bfPLJJ5g3bx5efPFF7Nu3D5GRkXjjjTfqjOuFF17Au+++izfffBNHjhzBU089hbFjx7pde0RERETUfASpaikNoaioCEajESaTCYGBgf4Oh4iIqFEOZ5mwaX8m0nPMKLOLUCtlSAg3YGRyFJKi2NeXGq45f2f66aefkJKSgrS0NPzf//2fa3mHDh1cVb1TpkzBsmXLIAgCZsyYgRUrVtQ55pQpU3Dp0iVXhe/LL7+MDRs24OjRowCAzz77DGPHjsXFixddifuUlBR0794dqampAIB3330XU6ZMwYULF/DLL79g6NChOHPmDGJiYgAAR48eRc+ePfHzzz/jmmuuwfz587F582YcOHCgztguXbqEjh07Yvny5Xjqqadcy6+++mocP34cADB8+HB89NFH9Z672NhYzJgxAzNmzHAte/jhh6HVavHWW2+5lu3Zswc333wzLBYLNBoNYmNjcdNNN7luLFy8eBGRkZGYM2cOFi5cCAD48ccf0b9/f2RnZ6Njx47YsGEDHnnkEfz444+47rrrAAB//PEHevTogZ9++gnXXnstbrjhBvTs2RNvv/2267Xvu+8+WCwWfPnllwDQ4Pdw/PjxKCwsxObNm+s9L7fccgucTid2794NAHA6nTAajRg5ciTee+89t2P+4YcfkJKSgp07d2LgwIEoKChAUFAQxowZg4KCArdq9dGjR+Prr792Vatff/316NOnD958803XNikpKSgtLXVdB5Xjtlgs6NChA/773/+if//+rn0effRRlJSU4MMPP6zxeMrKylBWVub6uaioCDExMYiZ8Qlk6oB6zwcRERG1HxlLh/k7hBbD079pWIlORETUxiVFGTFnWCIW3NUTs4f1wIK7euKFYT2YQKdWqWr19c8//4wDBw6gZ8+ebgnEfv36Vdt3zZo16NevH8LCwqDX6/HOO++4TRQ6fvx4pKen48cffwRQ3qblvvvuc6t8nzhxIv7973+juLjYtc3IkSMRFBSEY8eOlSct/5dAB4DExETXOl8c76effooDBw5gyJAhsFqtDRoTAH799Vds2LABer3e9W/IkCEQRRFnzpxxbde7d2/X/4+IiAAA9OrVq9qynJwc1zKFQuF2/q+88kq3c3Ds2DHccMMNbvHccMMN1c5RQ95Db1U+PrlcjtDQ0HqPr7Jjx465JboBVPvZk20qO3r0KEpLSzF48GC39+e9997DqVOnat1vyZIlMBqNrn+Vr0MiIiIiahzOJEZERNQOyGQC4sL0/g6DqMHi4+MhCAL++OMPt+VxcXEAAK1W67a8asuXTz75BE899RRee+019O/fHwaDAa+88gp++ukn1zbh4eEYPnw43n33XcTFxeGrr77Czp073cYZPXo0nnrqKWzcuBG33HIL9uzZ46rKliSpxhYrtS2vS1hYGIKCgqodb+fOnQEABoPBrS+3t0RRxGOPPYbp06dXW1fxGgDcJvOsOIaalomi6DZGTcdbeVnV9TWdo4a8h96qOlmpIAgeHV/luH2t4rW+/PLLaq181Gp1rfvNmjULTz/9tOvnikp0IiIiImo8JtGJiIiIqMULDQ3F4MGDsXr1akybNq3Wvui12b17N66//nq3yThrqup99NFHMXr0aERHR6Nbt27VKqYNBgPuvfdevPvuuzh9+jTi4uJwyy23ACivOj937hzOnz/v1s7FZDKhR48eXsUrk8lw33334V//+hfmzJlTa190T6hUKjidTrdlycnJOHLkCOLj4xs8bm0cDgf27duHa6+9FgBw/PhxFBYW4sorrwQA9OjRA3v27MHDDz/s2uf777+v9xx5+h42p8TERNeTCxWq/tyjRw/8+OOPbsdbdZuqY6rVapw7dw4333yzx7Go1eo6k+xERERE1HBs50JERERErcIbb7wBh8OBfv36YePGjTh27BiOHz+Of/3rX/jjjz8gl8tr3Tc+Ph779u3D1q1bceLECcyZMwe//PJLte2GDBkCo9GIxYsXuyYUrWrixIn4/vvv8eabb2LChAmuauVbb70VvXv3xoMPPoj9+/fj559/xsMPP4ybb765xtYk9XnppZcQFRWF6667DuvXr8fBgwdx6tQpfPrpp/jhhx/qPN7KYmNjsWvXLmRlZeHy5csAgOeeew4//PADpkyZggMHDuDkyZPYsmULpk2b5nWcVSmVSkybNg0//fQT9u/fj0ceeQQpKSmupPqzzz6LDRs2YM2aNTh58iSWL1+OtLQ0PPPMM3WO6+l72JymT5+Or7/+Gi+//DJOnDiB1atX4+uvv3bb5sknn8T69euxfv16nDhxAvPmzcORI0dqHdNgMOCZZ57BU089hdTUVJw6dQq//fYb/vnPf7p68RMRERFR82ISnagSUZRwOteM388X4nSuGaLIeXeJiIhaim7duuG3337DrbfeilmzZqFPnz7o168fVq1ahWeeeQaLFi2qdd/Jkydj5MiRuP/++3HdddchLy/PraK5gkwmw/jx4+F0Ot0qhyu78cYb0b17dxQVFWHcuHGu5YIgYPPmzQgODsaAAQNw6623Ii4uDhs3bmzQ8YaGhroS8a+88gquvfZa9OrVC/Pnz8f999+Pd955x6NxFi5ciIyMDHTr1g1hYWEAynuBf/fddzh58iRuuukmXHXVVZgzZw4iIyMbFGtlAQEBeO655zBmzBj0798fWq0WH3/8sWv93XffjX/84x945ZVX0LNnT7z11lt49913XRX9tfH0PWxOKSkpWLt2LVatWoW+ffvim2++wQsvvOC2zf3334+5c+fiueeew9VXX42zZ8/i8ccfr3PcRYsWYe7cuViyZAl69OiBIUOG4PPPP0fXrl2b8nCIiIiIqBaC1BSN/Fo5T2dlpbblcJYJm/ZnIj3HjDK7CLVShvhwPUYlR3PyPSIiohq01d+ZJk2ahEuXLmHLli3+DqXV2bBhA2bMmNGofu3kGxWfz5gZn0CmDvB3OERERNSCZCwd5u8QWgxP/6ZhT3QilCfQV24/iXyLDZFGLbRGOaw2Jw5lmpBVYMX0QQlMpBMREbVxJpMJv/zyCz744AN89tln/g6HiIiIiIhaCLZzoXZPFCVs2p+JfIsN8eF66DUKyGUC9BoF4sP1yLfYkLY/i61diIiI2rgRI0bgrrvuwmOPPYbBgwc3+evp9fpa/+3evdujMXbv3l3nOO3VuXPn6jwv586d83eIRERERNSKsBKd2r2MPAvSc8yINGpdE4NVEAQBkUYtTuYUIyPPgriw9vvHKBERUVu3c+fOZn29AwcO1LouKirKozH69etX5zjNbfz48Rg/fry/w0CnTp3qPC+dOnVqvmCIiIiIqNVjEp3aveJSB8rsIrRGeY3rtSo5LhWJKC51NHNkVBNRlJCRZ0FxqQMGjQKxoTrIZEL9OxIREbUw8fHxjR5Dq9X6ZJy2RqFQ8LwQERERkc8wiU7tnkGjgFopg9XmhF5T/SNhtTmhVspgqGEdNS9O/kpERERERERERM2NPdGp3YsN1SE+XI9skxWS5N73XJIkZJusSAg3IDZU56cICfhz8tdDmSYEaVWI7aBDkFaFQ5nlyw9nmfwdIhERERERERERtUFMolO7J5MJGJUcjRCdCuk5ZphLHXCKEsylDqTnmBGiU2FkchRbhvgRJ38lIiIiIiIiIiJ/YRKdCEBSlBHTByWgV7QRhVYbMi5bUGi1oXd0EKYPSmCrED/zZvJXIiIiIiIiIiIiX2KTZ6L/SYoyIjEykJNWtkCc/JWIiIiIiIiIiPyFSXSiSmQyAXFhen+HQVVw8lciIiIiIiIiIvIXtnMhohaPk78SEREREREREZG/MIlORC0eJ38lIiIiIiIiIiJ/YRKdiFoFTv5KRERERERERET+wAbCRNRqcPJXIiIiIu8cXjAEgYGB/g6DiIiIqFVjEp2IWhVO/kpERERERERERM2J7VyIiIiIiIiIiIiIiGrBJDoRERERERERERERUS2YRCciIiIiIiIiIiIiqgWT6EREREREREREREREtWASnYiIiIiIiIiIiIioFkyiExERERERERERERHVgkl0IiIiIiIiIiIiIqJaKPwdABERERERETWNpHlbIVMH+DsMIiIiagEylg7zdwitVqurRF+yZAmuueYaGAwGhIeH4+6778bx48fdtpEkCfPnz0enTp2g1Wpxyy234MiRI36KmIiIiIiIiIiIiIhaq1aXRP/uu+8wZcoU/Pjjj9i2bRscDgduu+02WCwW1zYvv/wyli9fjtWrV+OXX35Bx44dMXjwYBQXF/sxciIiIiIiIiIiIiJqbVpdO5evv/7a7ed3330X4eHh+PXXXzFgwABIkoTXX38ds2fPxsiRIwEAqampiIiIwIcffojHHnvMH2ETERERERERERERUSvU6irRqzKZTACAkJAQAMCZM2dw8eJF3Hbbba5t1Go1br75Znz//fd+iZGIiIiIiIiIiIiIWqdWV4lemSRJePrpp3HjjTciKSkJAHDx4kUAQEREhNu2EREROHv2bI3jlJWVoayszPVzUVFRE0VMRERERERERERERK1Jq65Enzp1Kg4ePIiPPvqo2jpBENx+liSp2rIKS5YsgdFodP2LiYlpkniJiAgQRQmnc834/XwhTueaIYqSv0MiIiIiIiIiIqpVq61EnzZtGrZs2YJdu3YhOjratbxjx44AyivSIyMjXctzcnKqVadXmDVrFp5++mnXz0VFRUykExE1gcNZJmzan4n0HDPK7CLUShniw/UYlRyNpCijv8MjIiIiIiIiIqqm1VWiS5KEqVOnIi0tDf/973/RtWtXt/Vdu3ZFx44dsW3bNtcym82G7777Dtdff32NY6rVagQGBrr9IyIi3zqcZcLK7SdxKNOEIK0KsR10CNKqcCizfPnhLJO/QyQiIiIiIiIiqqbVVaJPmTIFH374IT777DMYDAZXD3Sj0QitVgtBEDBjxgy89NJLSEhIQEJCAl566SUEBARgzJgxfo6eiKh9EkUJm/ZnIt9iQ3y43tVeS69RIF6tR3qOGWn7s5AYGQiZrObWW0RERERERERE/tDqkuhvvvkmAOCWW25xW/7uu+9i/PjxAICZM2fCarXiiSeeQEFBAa677jp88803MBgMzRwtEREBQEaeBek5ZkQatdXmpxAEAZFGLU7mFCMjz4K4ML2foiQiIiIiIiIiqq7VJdElqf4J6ARBwPz58zF//vymD4iIWgRRlJCRZ0FxqQMGjQKxoTpWNLcgxaUOlNlFaI3yGtdrVXJcKhJRXOpo5siIiIiIiIiIiOrW6pLoRERVcbLKls+gUUCtlMFqc0Kvqf6fHqvNCbVSBkMN64iIiIiIiIiI/KnVTSxKRFQZJ6tsHWJDdYgP1yPbZK32RJEkScg2WZEQbkBsqM5PERIRERERERER1YxJdCJqtapOVqnXKCCXCeWTVYbrkW+xIW1/FkSx/jZQ1LRkMgGjkqMRolMhPccMc6kDTlGCudSB9BwzQnQqjEyOYgseImqz3n77bcTExEAmk+H111/3dzjNZv78+YiIiIAgCNi8ebO/w/GJ2NjYet/D+o43IyMDgiDgwIEDAICdO3dCEAQUFhb6LE4iIiIi8h0m0Ymo1fJmskryv6QoI6YPSkCvaCMKrTZkXLag0GpD7+ggTB+UwNY7ROR3OTk5eOyxx9C5c2eo1Wp07NgRQ4YMwQ8//NCocYuKijB16lQ899xzyMrKwl//+lfXOqvViuDgYISEhMBqtTb2EHxGkiS888476N+/PwIDA6HX69GzZ088+eSTSE9P92iMY8eOYcGCBXjrrbeQnZ2NoUOHNmnMniS3feGXX35xew+JiIiIqO1j81kiarU4WWXrkxRlRGJkICeBJaIWadSoUbDb7UhNTUVcXBwuXbqE7du3Iz8/v0HjSZIEp9OJc+fOwW63Y9iwYYiMjHTbZtOmTUhKSoIkSUhLS8ODDz5Y55h2ux1KpbJB8XhKkiSMGTMGmzdvxvPPP48VK1YgPDwcZ86cwbZt27B48WJs2LCh3nFOnToFABgxYkS1m90VbDYbVCqVL8NvcmFhYf4OgYiIiIiaGSvRiajVqjxZZU04WWXLJJMJiAvTo09MEOLC9Eyge0kUJZzONeP384U4nWtmuyIiHyksLMSePXuwbNkyDBw4EF26dMG1116LWbNmYdiwYdXab1TsIwgCdu7cCeDPlhxbt25Fv379oFar8f7776NXr14AgLi4OAiCgIyMDNcY69atw9ixYzF27FisW7euWlyCIGDNmjUYMWIEdDodFi9eDAD4/PPPcfXVV0Oj0SAuLg4LFiyAw/HnTePly5ejV69e0Ol0iImJwRNPPAGz2ezRudi4cSM+/vhjbNy4EXPmzEFKSgri4uIwaNAgLF26FO+++269Y8yfPx/Dhw8HAMhkMlcSffz48bj77ruxZMkSdOrUCVdccQUA4NChQ/jLX/4CrVaL0NBQ/PWvf3WLt2K/V199FZGRkQgNDcWUKVNgt9sBALfccgvOnj2Lp556CoIg1Jq0r2zDhg0ICgrCF198ge7duyMgIAD33HMPLBYLUlNTERsbi+DgYEybNg1O55+/a1SteD958iQGDBgAjUaDxMREbNu2rdpr/fzzz7jqqqug0WjQr18//Pbbb/XG9/3332PAgAHQarWIiYnB9OnTYbHw6ToiIiIif2BmiYh8ThSlZqk0rpis8lCmCfFqvdsfzBWTVfaODuJkldRmHM4yYdP+TKTnmFFmF6FWyhAfrseo5Gi2wyFqJL1eD71ej82bNyMlJQVqtbrBY82cOROvvvoq4uLioNFo8O233+LWW2/Fzz//jJiYGFcl86lTp/DDDz8gLS0NkiRhxowZOH36NOLi4tzGmzdvHpYsWYIVK1ZALpdj69atGDt2LFauXImbbroJp06dcrUXmTdvHoDyxPXKlSsRGxuLM2fO4IknnsDMmTPxxhtv1Bv/Rx99hO7du+Ouu+6qcb0nCepnnnkGsbGxeOSRR5Cdne22bvv27QgMDMS2bdsgSRJKSkpw++23IyUlBb/88gtycnLw6KOPYurUqW4V7zt27EBkZCR27NiB9PR03H///ejbty8mTZqEtLQ09OnTB3/9618xadKkeuOrUFJSgpUrV+Ljjz9GcXExRo4ciZEjRyIoKAhfffUVTp8+jVGjRuHGG2/E/fffX21/URQxcuRIdOjQAT/++COKioowY8YMt20sFgvuvPNO/OUvf8G//vUvnDlzBk8++WSdcR06dAhDhgzBokWLsG7dOuTm5mLq1KmYOnVqrTcxysrKUFZW5vq5qKjI4/NARERERHVjEp2IfKo5k3wVk1VmFVhdvdG1KjmsNieyTVZOVkltyuEsE1ZuP4l8i638WjeWX+uHMk3IKrCyrzxRIykUCmzYsAGTJk3CmjVrkJycjJtvvhmjR49G7969vRpr4cKFGDx4sOvn3NxcAOVtQDp27Ohavn79egwdOhTBwcEAgNtvvx3r1693VZtXGDNmDCZMmOD6+aGHHsLf//53jBs3DkB5hfuiRYswc+ZMVxK9ciK3a9euWLRoER5//HGPkugnTpxA9+7d3ZbNmDEDa9euBQAEBQUhMzOzzjH0ej2CgoIAwO2YAUCn02Ht2rWuNi7vvPMOrFYr3nvvPeh05Te+V69ejeHDh2PZsmWIiIgAAAQHB2P16tWQy+W48sorMWzYMGzfvh2TJk1CSEgI5HI5DAZDtderi91ux5tvvolu3boBAO655x68//77uHTpEvR6PRITEzFw4EDs2LGjxiT6t99+i2PHjiEjIwPR0dEAgJdeesmt//sHH3wAp9OJ9evXIyAgAD179kRmZiYef/zxWuN65ZVXMGbMGNf7mJCQgJUrV+Lmm2/Gm2++CY1GU22fJUuWYMGCBR4fOxERERF5ju1ciMhn7SEqknyHMk0I0qoQ20GHIK0KhzLLlx/OMvk4ck5WSe2jvYkoSti0PxP5Fhviw/XQaxSQywToNQrEh+uRb7EhbX9Wmzx2ouY0atQoXLhwAVu2bMGQIUOwc+dOJCcne9T/u7J+/frVu43T6URqairGjh3rWjZ27Fikpqa6tQ6pabxff/0VCxcudFXP6/V6TJo0CdnZ2SgpKQFQXrU9ePBgREVFwWAw4OGHH0ZeXp7H7UCqVpvPnj0bBw4cwNy5cz1uC1ObXr16ufVBP3bsGPr06eNKoAPADTfcAFEUcfz4cdeynj17Qi7/cx6UyMhI5OTkNCqWgIAAVwIdACIiIhAbGwu9Xu+2rLbXOXbsGDp37uxKoANA//79q23Tp08fBAQE1LpNVb/++is2bNjg9h4PGTIEoijizJkzNe4za9YsmEwm17/z58/X+RpERERE5DlWohO1c76qHK+a5Kv441uvUSBerUd6jhlp+7OQGBno88pwTlbZfrWX9iYZeRbX0xZVE1uCICDSqMXJnGJk5FkQF6avZRQi8oRGo8HgwYMxePBgzJ07F48++ijmzZuH3bt3AyhvF1ahoh93VZWTwbXZunUrsrKyqlU3O51OfPPNN26VzFXHE0URCxYswMiRI2uM/+zZs7jjjjswefJkLFq0CCEhIdizZw8mTpxYa8yVJSQk4I8//nBbFhYWhrCwMISHh9e7f32qHo8kSbW2iKm8vOqEqoIgQBTFRsVS05jevE7l66Hy9vVtUx9RFPHYY49h+vTp1dZ17ty5xn3UanWj2hARERERUe1YiU7VtIeqTirny8pxb5J8TYGTVbY//njywV+KSx0os4vQquQ1rteq5CiziyguddS4nogaLjExERaLxdXHvHJ/78qTjHpr3bp1GD16NA4cOOD278EHH6xxgtHKkpOTcfz4ccTHx1f7J5PJsG/fPjgcDrz22mtISUnBFVdcgQsXLngc2wMPPIDjx4/js88+a/DxeSMxMREHDhxwq5Lfu3cvZDKZa+JRT6hUqmpV/E0tMTER586dczu/P/zwQ7Vtfv/9d1itVteyH3/8sc5xk5OTceTIkRrf48pV/ERERETUPFiJTm7aS1Un+b5y3JXkM9ae5LtUxCQf+YY/n3zwB4NGAbVSBqvNCb2m+n+6rTYn1EoZDDWsIyLP5OXl4d5778WECRPQu3dvGAwG7Nu3Dy+//DJGjBgBrVaLlJQULF26FLGxsbh8+TJeeOGFBr1Wbm4uPv/8c2zZsgVJSUlu68aNG4dhw4YhNzfXlbivau7cubjzzjsRExODe++9FzKZDAcPHsShQ4ewePFidOvWDQ6HA6tWrcLw4cOxd+9erFmzxuP4Ro8ejbS0NIwePRqzZs3CkCFDEBERgbNnz2Ljxo1uLVV84cEHH8S8efMwbtw4zJ8/H7m5uZg2bRoeeughVz90T8TGxmLXrl0YPXo01Go1OnTo4NM4a3Lrrbeie/fuePjhh/Haa6+hqKgIs2fPdttmzJgxmD17NiZOnIgXXngBGRkZePXVV+sc97nnnkNKSgqmTJmCSZMmQafT4dixY9i2bRtWrVrVlIdERERERDVgJTq5tKeqTvJ95XjlJF9NmOQjX/L3kw/NLTZUh/hwPbJN1mptASRJQrbJioRwA2JD628hQUQ10+v1uO6667BixQoMGDAASUlJmDNnDiZNmoTVq1cDKJ8I1G63o1+/fnjyySerTQDqqYoJNAcNGlRt3cCBA2EwGPD+++/Xuv+QIUPwxRdfYNu2bbjmmmuQkpKC5cuXo0uXLgCAvn37Yvny5Vi2bBmSkpLwwQcfYMmSJR7HJwgCNm7ciNdffx1fffUVBg0ahO7du2PChAmIiYnBnj17vD/oOgQEBGDr1q3Iz8/HNddcg3vuuQeDBg1ynXdPLVy4EBkZGejWrVutNyB8TSaT4dNPP0VZWRmuvfZaPProo3jxxRfdttHr9fj8889x9OhRXHXVVZg9ezaWLVtW57i9e/fGd999h5MnT+Kmm27CVVddhTlz5iAyMrIpD4eIiIiIaiFIDWnS18YVFRXBaDTCZDIhMDDQ3+E0C1GUsOjLoziUaXKr6gTKEzTpOWb0jg7CC8N6tImqTgJ+P1+IF788htgOOshreE+dooSMyxbMHtYDfWKC6h2P1xA1J19fv61BxY3OfIsNkUYttCo5rDYnsk1WhOhUnEiX/KI9/s5E1FpUfD5jZnwCmTqg/h2IiIiozctYOszfIbQ4nv5Nw0p0AtD+qjrJ95XjMpmAUcnRCNGpkJ5jhrnUAacowVzqQHqOGSE6FUYmRzGBTj7RHp98SIoyYvqgBPSKNqLQakPGZQsKrTb0jg5iAp2IiIiIiIioCbWd7AI1CvtZtz8V7SEOZZoQr65eOZ5tsqJ3dJBX7SEqknwVffUvFZX31e8dHYSRyVFM8pHPNMX12xokRRmRGBmIjDwLiksdMGgUiA3V8eYUEXll6NCh2L17d43rnn/+eTz//PMejaPX62td95///Ac33XRTg+LzFV8dJxERERERk+gEgJPWtUcVleNZBVbXUwhV20M0pHKcST5qDk11/bYGMpmAuLDaE1dERPVZu3YtrFZrjetCQkI8HufAgQO1rouKivI2LJ/z1XESERERETEjSgDab1Vne9dUleNM8lFz4JMPREQN46sEd3x8vE/GaSotIZFPRERERG0Dk+gEoH1XdbZ3rByn1ozXLxERERERERE1NSbRyYVVne0XK8epNeP1S0RERERERERNiUl0csOqTiIiIiIiIiIiIqI/MYlO1bCqk4iIiIiIiIiIiKiczN8BEBERERERERERERG1VEyiExERERERERERERHVgkl0IiIiIiIiIiIiIqJaMIlORERERERERERERFQLTixKRERERETURh1eMASBgYH+DoOIiIioVWMlOhERERERERERERFRLViJTkRE7YIoSsjIs6C41AGDRoHYUB1kMsHfYRERERERERFRC8ckOhG5MMlIbdXhLBM27c9Eeo4ZZXYRaqUM8eF6jEqORlKU0d/hEREREREREVELxiQ6EQHwb5KRyXtqSoezTFi5/STyLTZEGrXQGuWw2pw4lGlCVoEV0wclMJFORERERERERLViEp2I/JpkZIUwNSVRlLBpfybyLTbEh+shCOU3Z/QaBeLVeqTnmJG2PwuJkYG8cUNERERERERENeLEokTtXNUko16jgFwmlCcZw/XIt9iQtj8Loij5/LUrkveHMk0I0qoQ20GHIK0KhzLLlx/OMvn8Nal9ycizID3HjEij1pVAryAIAiKNWpzMKUZGnsVPERIRERERERFRS8ckOlE7568koz+T99R+FJc6UGYXoVXJa1yvVclRZhdRXOpo5siIiIiIiIiIqLVgEp2onfNXkrEhyXtRlHA614zfzxfidK65VSfY29KxtGQGjQJqpQxWm7PG9VabE2qlDAYNu5sRERERERERUc2YNSBq5yonGfU1JBKbKsnoSt4ba0/eXyr6M3nflnqnt6VjaeliQ3WID9fjUKYJ8Wq92w0bSZKQbbKid3QQYkN1foySiIiIiIiIiFoyJtGJ2jl/JRm9Sd77c+JTX2tLx9IayGQCRiVHI6vA6nryQasqP+fZJitCdCqMTI7ipKJERNRmJc3bCpk6wN9hEBERUQuQsXSYv0NotdjOhaidq0gyhuhUSM8xw1zqgFOUYC51ID3H7FWS0ZsWJRXJ+2yTFZLkvl1F8j4h3IDOwQFtpnc6+8D7R1KUEdMHJaBXtBGFVhsyLltQaLWhd3QQb1oQERERERERUb1YiU5EriRjRYuRS0XlLUZ6RwdhZHKUR0lGb1uUeFohfK6gxOPe6XFhep+dk6bgTR/4ln4srU1SlBGJkYHIyLOguNQBg0aB2FAdK9CJiIiIiIiIqF5MohMRgMYlGRvaosST5P3v5wu96p3eknnbB558SyYTeHOCiIiIiIiIiLzGJDpRGyCKkk8qbBuSZKzaoqSiwlqvUSBerUd6jhlp+7OQGBlYY0z1Je/9NfFpU2hLx0JERERERERE1F4wU0PUynnbRsXXfNGipK7kvb8mPm0KbelYiIiIiIiIiIjaiwYn0cvKyvDzzz8jIyMDJSUlCAsLw1VXXYWuXbv6Mj4iqkND26j4UlO3KPG0d3pr6G3dlo6FiIiIiIiIiKi98DqJ/v3332PVqlXYvHkzbDYbgoKCoNVqkZ+fj7KyMsTFxeGvf/0rJk+eDIPB0BQxExEa30bFV5qjRYkvJj5tKVr7sfiqdRARERERERERUWvhVVZrxIgR+OWXXzBmzBhs3boV/fr1Q0BAgGv96dOnsXv3bnz00UdYvnw53nvvPQwePNjnQRORb9qo+EJztShpzMSnLU1rPZaK1kEnLxXDZHVALgPiwvQYf30sekcH+Ts8IiIiIiIiIqIm4VUS/bbbbsP/+3//DyqVqsb1cXFxiIuLw7hx43DkyBFcuHDBJ0ESUXVN3UbFU83ZoqQhE5+2VK3tWCpaB2UWlMBqc6LE7oTdIeF0rgX7MvLxt9u6Y0TfKH+HSURERERERETkc14l0adMmeLxtj179kTPnj29DoiIPNMcbVQ81dpblFDdKloHZRaUwGS1o8whIkClgE4lwCGKyDfb8No3JxDXQYderEgnIiIiIiIiojbGJ9m1w4cP47vvvoPT6cT111+Pfv36+WJYIqpDc7VR8VRrbVFC9cvIs+DkpWJYbU6UOUQEapSu600plyNYp0JBiQ0bvs/AK/f04XtORERERERERG2KrLED/POf/8SgQYPw3XffYceOHRg0aBBefPFFX8RGRHWoaKMSolMhPccMc6kDTlGCudSB9ByzT9uoeBNTXJgefWKCEBemZzK1jSgudcBkdaDE7kSASlGtB79SLoNSJsOpXAsy8ix+ipKIiIiIiIiIqGl4XYmemZmJ6Oho18+rV6/GkSNH0KFDBwDADz/8gLvuuguzZ8/2XZREVCO2UaHmYNAoIJcBdocEnar6jRGHKEEpFyCKUpP34CciIiIiIiIiam5eV6IPGjQI//jHPyBJEgAgNDQUW7duRVlZGYqLi/Htt98iLCzM54ESUc2SooyYMywRC+7qidnDemDBXT3xwrAeTKCTz8SG6hAXpodDFOEQxSprJZTYHAhQKWDUKpulBz8RUWO8/fbbiImJgUwmw+uvv+7vcMjHNmzYgKCgoCZ9DUEQsHnz5lrXZ2RkQBAEHDhwoNZtdu7cCUEQUFhYCKB54iYiIiKihvM6if7LL7/gjz/+wHXXXYfffvsNb7/9NpYvXw6tVougoCBs3LgRqampTRErEdWCbVSoKclkAsZfH4ugACXyzTbYHE5IkgS7U4TJaodaLoNWJUNChKHZevC3J6Io4XSuGb+fL8TpXDNEUfJ3SERNLicnB4899hg6d+4MtVqNjh07YsiQIfjhhx8aNW5RURGmTp2K5557DllZWfjrX//qWme1WhEcHIyQkBBYrdbGHoLPSJKEd955B/3790dgYCD0ej169uyJJ598Eunp6R6NMX78eNx9991NG2g7kp2djaFDh/o7DCIiIiJqRl6XDAYGBuLNN9/E3r17MX78eNx6663YvXs3nE4nnE4nKyiIiNqg3tFB+Ntt3fHaN8dRUGKHUiaDUi7AoFZCq5IhOjigzh78oihx0tkGOJxlcrVrKrOXt2uKD9djVHI0nzahNm3UqFGw2+1ITU1FXFwcLl26hO3btyM/P79B40mSBKfTiXPnzsFut2PYsGGIjIx022bTpk1ISkqCJElIS0vDgw8+WOeYdrsdSqWyQfF4SpIkjBkzBps3b8bzzz+PFStWIDw8HGfOnMG2bduwePFibNiwoUljaClsNhtUKpW/wwAAdOzY0d8hEBEREVEza/DEojfccAP27dsHo9GIq666Crt27WICnYioDRvRNwr/HJOM23pEoHNoACICNYgO1iIlrgOmD0qoNal7OMuERV8exbwtR/Dil8cwb8sRLPryKA5nmZr5CFqXw1kmrNx+EocyTQjSqhDbQYcgrQqHMsuX8/xRW1VYWIg9e/Zg2bJlGDhwILp06YJrr70Ws2bNwrBhw2pslVFYWAhBELBz504Af7bK2Lp1K/r16we1Wo33338fvXr1AgDExcVBEARkZGS4xli3bh3Gjh2LsWPHYt26ddXiEgQBa9aswYgRI6DT6bB48WIAwOeff46rr74aGo0GcXFxWLBgARyOP+eHWL58OXr16gWdToeYmBg88cQTMJvNHp2LjRs34uOPP8bGjRsxZ84cpKSkIC4uDoMGDcLSpUvx7rvv1jvG/PnzkZqais8++wyCILidp6ysLNx///0IDg5GaGgoRowY4XZOKirYX3rpJURERCAoKMh1fM8++yxCQkIQHR2N9evXu/apeH8+/vhjXH/99dBoNOjZs6frNSt89913uPbaa6FWqxEZGYm///3vbuftlltuwdSpU/H000+jQ4cOGDx4cKPPZ03npm/fvli/fj06d+4MvV6Pxx9/HE6nEy+//DI6duyI8PBwvPjii277VW3n8vPPP+Oqq66CRqNBv3798Ntvv1V7ra+++gpXXHEFtFotBg4c6Haea1PftUVEREREzcfrSnSHw4F33nkHR48eRZ8+fTB79myMHj0ajz32GDZs2IBVq1axOoOIqI3qFR2EV+7t43FVeUUiON9iQ6RRC61RDqvNiUOZJmQVWOtMvrdnoihh0/5M5FtsiA/XQxDKz69eo0C8Wo/0HDPS9mchMTKQFf314FMQrY9er4der8fmzZuRkpICtVrd4LFmzpyJV199FXFxcdBoNPj2229x66234ueff0ZMTIxrHp9Tp07hhx9+QFpaGiRJwowZM3D69GnExcW5jTdv3jwsWbIEK1asgFwux9atWzF27FisXLkSN910E06dOuVqETNv3jwAgEwmw8qVKxEbG4szZ87giSeewMyZM/HGG2/UG/9HH32E7t2746677qpxfcV3Q12eeeYZHDt2DEVFRa6ke0hICEpKSjBw4EDcdNNN2LVrFxQKBRYvXozbb78dBw8edFV9//e//0V0dDR27dqFvXv3YuLEifjhhx8wYMAA/PTTT9i4cSMmT56MwYMHIyYmxvW6zz77LF5//XUkJiZi+fLluOuuu3DmzBmEhoYiKysLd9xxB8aPH4/33nsPf/zxByZNmgSNRoP58+e7xkhNTcXjjz+OvXv3uuZjasz5rMmpU6fwn//8B19//TVOnTqFe+65B2fOnMEVV1yB7777Dt9//z0mTJiAQYMGISUlpdr+FosFd955J/7yl7/gX//6F86cOYMnn3zSbZvz589j5MiRmDx5Mh5//HHs27cPf/vb3+qMy5Nrq6qysjKUlZW5fi4qKvL2dBARERFRLbyuRJ80aRJWrVoFnU6Hd999F0899RSuuOIK7NixA0OGDEH//v3x5ptvNkWsRETUAnjag79qIlivUUAuE8oTweF65FtsSNufxR7fNcjIsyA9x4xIo7ZakkwQBEQatTiZU4yMPIufImwd+BRE66RQKLBhwwakpqYiKCgIN9xwA55//nkcPHjQ67EWLlyIwYMHo1u3boiKikJoaCgAICwsDB07doRcLgcArF+/HkOHDnX1RL/99tvdqqsrjBkzBhMmTEBcXBy6dOmCF198EX//+98xbtw4xMXFYfDgwVi0aBHeeust1z4zZszAwIED0bVrV/zlL3/BokWL8Mknn3gU/4kTJ9C9e3e3ZTNmzHDdaIiOjq53DL1eD61W6+ot37FjR6hUKnz88ceQyWRYu3YtevXqhR49euDdd9/FuXPn3KrGQ0JCsHLlSnTv3h0TJkxA9+7dUVJSgueffx4JCQmYNWsWVCoV9u7d6/a6U6dOxahRo9CjRw+8+eabMBqNrgr/N954AzExMVi9ejWuvPJK3H333ViwYAFee+01iJUmsI6Pj8fLL7+M7t2748orr2z0+ayJKIpYv349EhMTMXz4cAwcOBDHjx/H66+/ju7du+ORRx5B9+7dq1XSV/jggw/gdDqxfv169OzZE3feeSeeffZZt23efPNNxMXFYcWKFejevTsefPBBjB8/vs64PLm2qlqyZAmMRqPrX+WbGkRERETUOF4n0Tdv3oxNmzZh6dKl+Pbbb/Hll1+61j366KP46aefsHv3bp8GSURErQ8TwQ1XXOpAmV2EViWvcb1WJUeZXURxKR/rrw3b4bRuo0aNwoULF7BlyxYMGTIEO3fuRHJystf9v/v161fvNk6nE6mpqRg7dqxr2dixY5Gamgqn01nneL/++isWLlzoSmrr9XpMmjQJ2dnZKCkpAQDs2LEDgwcPRlRUFAwGAx5++GHk5eXBYvHsu6/q9+fs2bNx4MABzJ07t8FtTCpiT09Ph8FgcMUeEhKC0tJSnDp1yrVdz549IZP9+SdDRESEqy0OAMjlcoSGhiInJ8dt/P79+7v+v0KhQL9+/XDs2DEAwLFjx9C/f3+3Y7vhhhtgNpuRmZnpWlbT+9fY81lVbGwsDAaD2/ElJiZWO+aqx1fh2LFj6NOnDwICAlzLKh97xTYpKSlux1t1m6o8ubaqmjVrFkwmk+vf+fPn63wNIiIiIvKc1+1cwsPD8c0336Bbt27Yvn27q6Kn8voPP/zQZwESEVHr5EoEG2tPBF8qYiK4JgaNAmqlDFabE3pN9f9UW21OqJUyGGpYV5/20N6E7XDaBo1Gg8GDB2Pw4MGYO3cuHn30UcybN89VrFHR3gMon+SzJjqdrt7X2bp1q6s3eGVOpxPffPMNhg4dWut4oihiwYIFGDlyZI3xnz17FnfccQcmT56MRYsWISQkBHv27MHEiRNrjbmyhIQE/PHHH27LwsLCEBYWhvDw8Hr3r4soirj66qvxwQcfVFtX0eYGQLXJUwVBqHFZ5Qry2lR8FiVJqnZzoOL9rLy86vlu7PmsSWOPr/J1WBtPtqmqvmurJmq1ulHtj4iIiIiodl7/9b169WqMHTsWTz/9NCIjIxv1+CQREZVri4nNhiSC2+J5aIjYUB3iw/U4lGlCvFrvllSSJAnZJit6RwchNrT+BGFlh7NM2LQ/E+k5ZpTZRaiVMsSH6zEqObpN9ab35imIuDC9n6IkbyUmJmLz5s2uBG92djauuuoqAHCbZNRb69atw+jRozF79my35UuXLsW6devckuhVJScn4/jx44iPj69x/b59++BwOPDaa6+5Kpu9+d35gQcewJgxY/DZZ59hxIgRHu9XlUqlqlZVn5ycjI0bNyI8PByBgYENHrs2P/74IwYMGACgfE6lX3/9FVOnTgVQ/l5u2rTJLZn+/fffw2AwICoqqtYxG3s+m0JiYiLef/99WK1WaLVaAOXHXnWbyhOR1rRNVfVdW0RERETUvLxOog8ePBgXL17E5cuX3apUiIioYdpqYtPbRHBbPQ8NIZMJGJUcjawCqysZrFWVT8qabbIiRKfCyOQor24wtKdJXvkUROuWl5eHe++9FxMmTEDv3r1hMBiwb98+vPzyyxgxYgS0Wi1SUlKwdOlSxMbG4vLly3jhhRca9Fq5ubn4/PPPsWXLFiQlJbmtGzduHIYNG4bc3Nxaf+edO3cu7rzzTsTExODee++FTCbDwYMHcejQISxevBjdunWDw+HAqlWrMHz4cOzduxdr1qzxOL7Ro0cjLS0No0ePxqxZszBkyBBERETg7Nmz2Lhxo6une31iY2OxdetWHD9+HKGhoTAajXjwwQfxyiuvYMSIEVi4cCGio6Nx7tw5pKWl4dlnn/Wo33pd/vnPfyIhIQE9evTAihUrUFBQgAkTJgAAnnjiCbz++uuYNm0apk6diuPHj2PevHl4+umn3dqoVNXY89kUxowZg9mzZ2PixIl44YUXkJGRgVdffdVtm8mTJ+O1117D008/jcceewy//vprva2J6ru2iIiIiKh5ed0THSiv4goLC4PT6cSlS5eQk5NTrbqFiIjq15b7NlckgkN0KqTnmGEudcApSjCXOpCeY3ZLBLfl89BQSVFGTB+UgF7RRhRabci4bEGh1Ybe0UFeJ7zb2ySvlZ+CqElj2uFQ09Pr9bjuuuuwYsUKDBgwAElJSZgzZw4mTZqE1atXAyifCNRut6Nfv3548sknG5xUfO+996DT6TBo0KBq6wYOHAiDwYD333+/1v2HDBmCL774Atu2bcM111yDlJQULF++HF26dAEA9O3bF8uXL8eyZcuQlJSEDz74AEuWLPE4PkEQsHHjRrz++uv46quvMGjQINcEnzExMdizZ49H40yaNAndu3dHv379EBYWhr179yIgIAC7du1C586dMXLkSPTo0QMTJkyA1Wr1SWX60qVLsWzZMvTp0we7d+/GZ599hg4dOgAAoqKi8NVXX+Hnn39Gnz59MHnyZFcSui6NPZ9NQa/X4/PPP8fRo0dx1VVXYfbs2Vi2bJnbNp07d8amTZvw+eefo0+fPlizZg1eeumlOset79oiIiIiouYlSA1o0vfpp5/i1VdfdT1SCfw5YdCzzz6Lu+++29dxNquioiIYjUaYTKYmebyViAgoT2wu+vJoeaV2ePVK7fQcM3pHB+GFYT1adUuTmirME8INGJkchaQoY4s9Dy2ltYwv4jida8a8LUcQpFXV2FrHXOpAodWGBXf1bBPtTVrqNdUW8XcmqiojIwNdu3bFb7/9hr59+/o7nHat4vMZM+MTyNQB9e9AREREbV7G0mH+DqHF8fRvGq9LsN566y1Mnz4dEyZMwLPPPouIiAhIkoScnBxs3boVo0ePxqpVqzBp0qRGHQARUVvXXvo2J0UZkRgZWGsiuCWeh5bUWkYmExp93O2tvUlTtMMhIiIiIiKi9svrJPorr7yCN954AxMnTqy27u6778Y111yDF198kUl0IqJ6tKfEZl2J4JZ2Htpi7/CGTPLa2lW0w6m4GXKpqPxmSO/oINdTEET+NnToUOzevbvGdc8//zyef/55j8bR62u/0faf//wHN910U4Pia+169uyJs2fP1rjurbfewoMPPtjMERERERFRa+X1X8tZWVm48cYba11//fXX48KFC40KioioPWiPic2atKTzULV3eEVlvF6jQLxaj/QcM9L2ZyExMrBVVTF7O8lrW1HfUxBE/rZ27VpYrdYa14WEhHg8zoEDB2pdFxUV5W1YjRIbG4sGdItsEl999RXsdnuN6yIiIpo5GiIiIiJqzbzOSPTs2RNvv/02XnvttRrXv/POO+jZs2ejAyMiauvaa2KzqpZ0HlpiaxlfaM/tTXzRDoeoqfgqwR0fH++TcdoaTsJJRERERL7idRL9tddew7Bhw/D111/jtttuQ0REBARBwMWLF7Ft2zacPXsWX331VVPESkTUprTnxGZlLek8tLTWMr7E9iZEREREREREDeN1Ev3mm2/G4cOH8eabb+LHH3/ExYsXAQAdO3bEnXfeicmTJyM2NtbXcRIRtUlMbJZrKeehJbWWaQpsb0JERERERETkvQZlAWJjY7Fs2TJfx0JELZgoSky8NZH6Epvt5dy3hARvS2ot01TY3oSIiIiIiIjIO62zlI6ImtXhLJOrQrjMXl4hHB+ux6jk6HZTKd3Uaktstrdz7+8Eb0tqLUNERERERERELYPPk+jjxo3D+fPn8d///tfXQxNRHZqqWvlwlgkrt59EvsVWnlA0licUD2WakFVgxfRBCW0ymdsS8Nz7R0tpLUNERERERERELYPPk+hRUVGQyWS+HpaI6tBU1cqiKGHT/kzkW2yID/+ztYVeo0C8Wo/0HDPS9mchMTKQlbk+xnPvX4mRgdBc1xknLpkBSLgiwoC4DnqeayIiIiIiIqJ2yOdJ9JdeesnXQxJRHZqyWjkjz+JqaVG5NzQACIKASKMWJ3OKkZFnYY9lH+O595/21kKHiIiIiIiIiOrGknGiVqxqtbJeo4BcJpRXK4frkW+xIW1/FkRRatD4xaUOlNlFaFXyGtdrVXKU2UUUlzoacxhUA557/6i4KXUo04QgrQqxHXQI0qpwKLN8+eEsk79DJCIiIiIiIqJm1qBK9FWrVmHfvn0YNmwY7rvvPrz//vtYsmQJRFHEyJEjsXDhQigUnLOUqKk1dbWyQaOAWimD1eaEXlP9M221OaFWymCoYR01Ds9982MLHSIiaosOLxiCwMBAf4dBRERE1Kp5XYm+aNEizJ49GxaLBU8++SSWLVuGp556Cg8++CDGjRuHtWvXYtGiRU0RKxFV0dTVyrGhOsSH65FtskKS3KvZJUlCtsmKhHADYkN1DRrfU6Io4XSuGb+fL8TpXHODK+tbk5Zy7tsTb25KEREREREREVH74XUJ44YNG7BhwwaMHDkSv//+O66++mqkpqbiwQcfBABceeWVmDlzJhYsWODzYAFg165deOWVV/Drr78iOzsbn376Ke6++27XekmSsGDBArz99tsoKCjAddddh3/+85/o2bNnk8RD5E9NXa0skwkYlRyNrAKrK7moVZX3XM82WRGiU2FkclSTVuW21/7ULeHctzeum1LG2m9KXSpiCx0iIiIiIiKi9sbrSvTs7Gz069cPANCnTx/IZDL07dvXtT45ORkXLlzwWYBVWSwW9OnTB6tXr65x/csvv4zly5dj9erV+OWXX9CxY0cMHjwYxcXFTRYTkb80R7VyUpQR0wcloFe0EYVWGzIuW1BotaF3dFCjJi31RHvvT+3Pc98eVb4pVRO20CEiIiIiIiJqn7zOBHTs2BFHjx5F586dcfLkSTidThw9etRV6X3kyBGEh4f7PNAKQ4cOxdChQ2tcJ0kSXn/9dcyePRsjR44EAKSmpiIiIgIffvghHnvssSaLi8gfmqtaOSnKiMTIQGTkWVBc6oBBo0BsqK5Jq6DZn7qcP859e1VxU+pQpgnxar1bS5eKm1K9o4PYQoeIiIiIiIionfE6iT5mzBg8/PDDGDFiBLZv347nnnsOzzzzDPLy8iAIAl588UXcc889TRFrvc6cOYOLFy/itttucy1Tq9W4+eab8f3339eaRC8rK0NZWZnr56KioiaPlchXKqqVK1qeXCoqb3nSOzoII5OjfFatLJMJDZqctKGaetLU1qQpz70oSkzQ/w9b6BARERERERFRTbxOoi9YsABarRY//vgjHnvsMTz33HPo3bs3Zs6ciZKSEgwfPtxvE4tevHgRABAREeG2PCIiAmfPnq11vyVLljRZD3ei5tAWq5XZn7rptdd+83VprptSRERERERERNR6eJ1El8vlmD17ttuy0aNHY/To0T4LqrGqVq1KklRtWWWzZs3C008/7fq5qKgIMTExTRYfUVNo7krxptbUk6a2dxX95vMttvKKa2N5xfWhTBOyCqztuud6W7wpRUREREREREQN16ayTx07dgRQXpEeGRnpWp6Tk1OtOr0ytVoNtVrd5PERkefYn7rpWq2w33z92tpNKSIiIiIiIiJqOJk3G0+ePBnnz5/3aNuNGzfigw8+aFBQDdW1a1d07NgR27Ztcy2z2Wz47rvvcP311zdrLETUOBX9qUN0KqTnmGEudcApSjCXOpCeY27z/akPZ5mw6MujmLflCF788hjmbTmCRV8exeEsU6PH9qbfPBERERERERFRe+dVJXpYWBiSkpJw/fXX46677kK/fv3QqVMnaDQaFBQU4OjRo9izZw8+/vhjREVF4e233/Z5wGazGenp6a6fz5w5gwMHDiAkJASdO3fGjBkz8NJLLyEhIQEJCQl46aWXEBAQgDFjxvg8FiJq2okp22t/6qZutcJ+80RERO1H0rytkKkD/B0GERER+UHG0mH+DqHN8CqJvmjRIkybNg3r1q3DmjVrcPjwYbf1BoMBt956K9auXYvbbrvNp4FW2LdvHwYOHOj6uaKX+bhx47BhwwbMnDkTVqsVTzzxBAoKCnDdddfhm2++gcFgaJJ4iNqz5piYsr31p26OVivsN9/2NOXNLCIiIiIiIqL2TpAkSWrozoWFhTh79iysVis6dOiAbt261TmBZ2tRVFQEo9EIk8mEwMBAf4dD1CJVq5ZWlVdLZ5usCNGp2vXElI1xOteMeVuOIEirqjHBbS51oNBqw4K7eja4Z7coSlj05dHyfvPh1fvNp+eY0Ts6CC8M68FEbCvQHDeziGrD35mIWq6Kz2fMjE9YiU5ERNROsRK9fp7+TdOoMsOgoCAEBQU1ZggiaoU4MWXTaY5WKxX95rMKrK7e6FVvgrTlfvNtSVO3/iEiIiIiIiIiLycWJSICODFlVaIo4XSuGb+fL8TpXDNEscEP+Li1WqmJr1qtVPSb7xVtRKHVhozLFhRabegdHcTEaytR9WaWXqOAXCaU38wK1yPfYkPa/qxGXY/1vb6vrnsiIiIiIiKilowNb4nIa5yY8k++bqURG6pDfLi+vNWKunqrlWyTFb2jgxAbqmt07O2t33xb483NrIa2/qkNW8gQERERERFRe8IkOhF5jRNTlmtMK43aJoJs7lYrMpng8wQrNY+G3sxq7CSkbCFDRERERERE7U3bznARUZOoq1paFEWcuWxG1w56iJIEUZTaZGVzY/rC11fFW9FqpWKbS0Xl2/SODsLI5CgmKAlADTezJAnFZQ7YHSKUChkECdVuZjW2gpzzIRAREREREVF7xCQ6USvQ2MpRX6utWvqiqRR/XCyCU5QgAVjw+dE22+KhtlYakiShuNQBrVKO3zMLcfqyGfHhBtd6T6t4W1KrFVGUcPqyGScumQFIuCLCgLgOeiZJ/azyzaxQhwpn8iwosjrgFCXIBEAC0D8u1NX6p7Zr7+D5Qpy4WIxRV0ejb0xQndeZP1vIEBEREREREflLg5Lov//+Oz7//HOEhITgvvvuQ4cOHVzrioqKMGPGDKxfv95nQRK1Zy2193DVaunTuTZcLCqFQiagZ6dAdDRq23SLh5paaeRbbDhz2YwiqwMOUYTNIeIf357EYzd3Q1KU0esq3pbQauVwlglv7zqFfWcLYP5fWxC9RoF+XULw1wFxbeo9bW0qbmYdyy7CT2fyIQiAXq2AUi7AUuaAKAGXikpxNLsIiZGBNV57Nmd5u5eTOWak51pwRbge8RG1f79wPgQiIiIiIiJqj2Te7vDNN9/g2muvxccff4xly5ahR48e2LFjh2u91WpFamqqT4MkaiqiKOF0rhm/ny/E6VwzRFHyd0huKipHD2WaEKRVIbaDDkFaFQ5lli8/nGXya3xJUUbMGZaIecMTERemQ2SQBgO7hyEqOABymVCeHA7XI99iQ9r+rBZ3fhujcisNoDyBfjjLhHyLDSqFDFqlHCqFDGfyLK73ypsq3pbgcJYJi784il0nLqPU5oRRq4RRq4TV5sSuE7lY/OVRv1+D3mrpn3lvJUYGItyghkohg1ohQ6ldhN0pIcygxnVdQ+AQJaTtz8Lpy+Zq157rmi2xQa8ubwejkAt1fr9Uve6rai/zIRAREREREVH74vVfufPnz8czzzyDF198EZIk4dVXX8Vdd92F//f//h9uv/32poiRqEm01ArvCq2l97BMJkAmCCi02tE1VA9B5n5vrq22eKjcSqObSoczl80oczgRqFFCEACT1YFQnRpJkYFIz7UgbX8WhveJLK/iDZShuNTu6l1tUCsAQWhRVbyiKOHfv57H6csWKOTC/46r/DoLUchQWGLDmVwLNv2a6fdr0FMt/TPfEBl5FhSU2HFtbAgkAHanCKW8PIktCAJUCjlO5hTjxKVitwpySZLcrlmgvMpcJS8/J7V9v9Q1H4IkScg2WdE7OsjVQoaIiIiIiIioLfA6iX7kyBG8//77AMqTY88++yyio6Nxzz334KOPPsK1117r8yCJfM3TvtT+1Jp6DzdFi4eW1ge+JildQ3D0QhH2nytAQYkdWqUcDlFCic0BjUKOrh10EGQy13tVZO0Au1PEzxn5KLWLcIoS5DIBgVoF4jrooZTLWkwVb0aeBYeyiiBKEgJUiirXoACdWokSmwOH/ldh7+9rsD6t4TPfEBWfvQCjAvIaPh8Vnz1AcJuEtLjUgSKrw/Xe2p0i5DKhfELSOr5fapsPwWpzIttkRYhOhZHJUS3us0pERERERETUGF63c1Gr1SgsLHRb9sADD2DdunUYPXo0Pv30U1/FRtQkqlZ46zWKFtl6xGS1w1RiR6nNgeJSOyC5x6NVyVFmbxlVy75u8XA4y4RFXxzFzH8fxKxPD2Hmvw9i0Rctp3XI4SwTFn15FP/66RzMpQ4UlNhRZLWjuNQOm0Msr0CPMiJYpwLw53t12VyGPEsZLheXQSkXYNAooFLIkG+x4VBmIU7lFiMh3NAiqniLSx2u91NRQ0JU8b+ZK0vszhZxDdaltXzmG8LTz94VEXrEh+uRbbJCkiTYneU3ccrf2/IbP0atsvypCNT9/VIxH0KvaCMKrTZkXLag0GpD7+igVnszgqg1ePvttxETEwOZTIbXX3/d3+GQHwiCgM2bN/s7DCIiIqJ2yetyx759+2LHjh24+uqr3Zbff//9EEUR48aN81lwRE2hNVR4H84y4f0fz+JcQQkyC6xQKWSuauWKxGxL6j3syxYPh7NMWPzlUZzOtUCUJEACIADn8ktw7GIRXhiW6NckXdWK5kijFmFFauzLyIdcJkNCuB7RwVqg0jmw2pxQK2TYeyoPerUCTlGC1e6EIAiQywQEqOTIN9sgl8vwf1d1ahFVvAaNAlpV+ZMFDlGCUu4ek0OUAAEIUMpbxDVYl9bwmfdWxZMaJqsdYQY1zl62ICHCUOtnL66D3q2C3KBRQiYDSu1O2Jyi68mJiuu2vu+XpCgjEiMDW/zTIkTNLScnB3PmzMF//vMfXLp0CcHBwejTpw/mz5+P/v37N3jcoqIiTJ06FcuXL8eoUaNgNP7530Gr1YpOnTpBEARkZWVBq9X64lAaTZIkrF27FuvXr8eRI0cgiiK6dOmCW2+9FdOmTUN8fLy/Q2xysbGxmDFjBmbMmOHvUIiIiIiokbyuRH/88ceRlZVV47oHHngAqampGDBgQKMDI2oqrtYjqtpbj/izwrsiSXv2sgXB2vJEl1IuuCYBLLDYXMmxllK1XNHiIUSnQnqOGeZSB5yiBHOpA+k5Zo9bPIiihLd3ncYf2cVwiiICVAoEapUIUCngFEX8kV2Md3ad9lvFcG0VzR2NGnQK0qLM7sSlolK3fSreq/BANXKKStEtzICkqCCE6FSwOUSYSx2wOSSEBWoQqlNBp24ZCenYUB16RQVCJggosTkguT0JIcFSZodcENArytgirsG6tPTPvLcqnoSYt+UIlnz1B87kWpBrLsPBzMI6P3uVK8jtTicgAcVlDoQEqNyenPD0+0UmExAXpkefmCDEhemZQCcCMGrUKPz+++9ITU3FiRMnsGXLFtxyyy3Iz89v0HiSJMHhcODcuXOw2+0YNmwYIiMjERAQ4Npm06ZNSEpKQmJiItLS0uod0263NygWb0iShDFjxmD69Om444478M033+DgwYNYuXIltFotFi9e3GSvbbPZmmzspuB0OiGKor/DICIiIqJ6eJ1E/7//+z+sWLGi1vUPPPAAduzY0aigiJqSr1uP+FLlJG1ChAFXdAyEWiGH1e5EgEoOq92B4xeLvUpMNxdftHg4fdmMfWfzIQhAUIAKSnl5f2alXIagABUEAfjlbD5OXzY3wxFVV1tFsyAI6Bqmh06jRHZRKS4WlVZLZN4Y3wE2hwStSo4QnQrJnYNxdZdgXNU5CFd3CUa/LsFQymUtJpErkwm45+oYxHXQweGUUFhig83hhM3hRL7FBqcIdA3TYdTV0S3mGqxNS/7Me6viJtuhTBOCtCrEdtAhOjgAerUC5jIHMgtK6vzsJUUZMWdYIhaOSMLfh16J3tFGBGqVUMplDbrxRUR/KiwsxJ49e7Bs2TIMHDgQXbp0wbXXXotZs2Zh2LBhyMjIgCAIOHDggNs+giBg586dAICdO3dCEARs3boV/fr1g1qtxvvvv49evXoBAOLi4iAIAjIyMlxjrFu3DmPHjsXYsWOxbt26anEJgoA1a9ZgxIgR0Ol0rgT2559/jquvvhoajQZxcXFYsGABHI4//xu0fPly9OrVCzqdDjExMXjiiSdgNnv239+NGzfi448/xsaNGzFnzhykpKQgLi4OgwYNwtKlS/Huu+96NM748eNx9913Y8GCBQgPD0dgYCAee+wxt0T5LbfcgqlTp+Lpp59Ghw4dMHjwYADA0aNHcccdd0Cv1yMiIgIPPfQQLl++7NpPFEUsW7YM8fHxUKvV6Ny5M1588UXX+qysLNx///0IDg5GaGgoRowY4XbeK2J79dVXERkZidDQUEyZMsV1k+KWW27B2bNn8dRTT0EQBNfvDRs2bEBQUBC++OILJCYmQq1W4+zZs/jll18wePBgdOjQAUajETfffDP279/v0XkiIiIioqbndRK9oKAAq1atQlFRUbV1JpOp1nVELUVF65GK3sCV+bPCWxQl7D6Zi9/OFcKgUUAAEKIrrxAtr1qWIEkCCqw2dAkNaLbew6Io4XSuGb+fL8TpXHOdVeAVCboFd/XE7GE9sOCunnhhWA8kRRk9GufEpWKYSx3QqxUAqibvhPJEYakDJy4V+/YgPVRXRXOIToXe0UYEKOUoKLFXS2T2iQlyS+QKgoBArRKhejUCtUqU2kWvE7nevDcNkRRlxAt3JmLAFR2gUcnL+/Rb7dCq5Lj5ijC/t9bxVEv9zHurrt7uvaODEG7QIC5Mh1l3XOn22auqooL8/5KjMWtoD/Y2J/IRvV4PvV6PzZs3o6ysrFFjzZw5E0uWLMGxY8dw22234dtvvwUA/Pzzz8jOzkZMTAwA4NSpU/jhhx9w33334b777sP333+P06dPVxtv3rx5GDFiBA4dOoQJEyZg69atGDt2LKZPn46jR4/irbfewoYNG9ySyDKZDCtXrsThw4eRmpqK//73v5g5c6ZH8X/00Ufo3r077rrrrhrXV22tVZft27fj2LFj2LFjBz766CN8+umnWLBggds2qampUCgU2Lt3L9566y1kZ2fj5ptvRt++fbFv3z58/fXXuHTpEu677z7XPrNmzcKyZcswZ84cHD16FB9++CEiIiIAACUlJRg4cCD0ej127dqFPXv2QK/X4/bbb3dL4O/YsQOnTp3Cjh07kJqaig0bNmDDhg0AgLS0NERHR2PhwoXIzs5Gdna2a7+SkhIsWbIEa9euxZEjRxAeHo7i4mKMGzcOu3fvxo8//oiEhATccccdKC72z+88REREROTO67K71atX4+DBg5g2bVq1dUajEbt370ZRURFmz57tkwCJfK2i9UhFb+BIoxZalRxWmxPZJqtfKjAPZ5mwaX8mfjtXgPQcC3QqObIKrYjroEeIToXggODyBK7DiUtFZRib0qVZElwVcaXnmFH2vyRvfHh5b+XaXr8iQdewcTw95/6pjq1c0ayvIdmtUciREKHHpJviEBSgcusTLYqSz/rGAw17bxoiKcqI1++/Cqcvm3HikhmAhCsiDIjr0Hrad3jzma/oNd4S+3x70ts9p7gMRq3S497u7G1O5DsKhQIbNmzApEmTsGbNGiQnJ+Pmm2/G6NGj0bt3b6/GWrhwoauiGgByc3MBAGFhYejYsaNr+fr16zF06FAEBwcDAG6//XasX7++WruUMWPGYMKECa6fH3roIfz97393zWUUFxeHRYsWYebMmZg3bx4AuPXx7tq1KxYtWoTHH38cb7zxRr3xnzhxAt27d3dbNmPGDKxduxYAEBQUhMzMzHrHAQCVSoX169cjICAAPXv2xMKFC/Hss89i0aJFkMnK64Hi4+Px8ssvu/aZO3cukpOT8dJLL7mWrV+/HjExMThx4gQiIyPxj3/8A6tXr3adg27duuHGG28EAHz88ceQyWRYu3at6/v23XffRVBQEHbu3InbbrsNABAcHIzVq1dDLpfjyiuvxLBhw7B9+3ZMmjQJISEhkMvlMBgMbu8ZUN5S54033kCfPn1cy/7yl7+4bfPWW28hODgY3333He68806PzlVZWZnbDRwWNhERERH5jteV6Js2bcLkyZNrXf/YY4/h3//+d6OCImpqvmg94iuV2zMEB6ihU8shl7n3QK+oWtYqFQgKUMKoVTZrXBVtI4K0KhzKLF9+OMvk03FEUYJWKYNKIUOR1Q5Jcu8PKkkSLGUO6DUKXBGhd+3TlJXYVXlS0XxFRCBuSgir1ifaV33jAd+9N56SyQTEhxtwR69I3NGrE+LDDa0uyerJZ75yr/EXvzyGeVuOYNGXR31+PhuqqXq7t+Xe5s39HUE0atQoXLhwAVu2bMGQIUOwc+dOJCcnu6qTPdWvX796t3E6nUhNTcXYsWNdy8aOHYvU1FQ4ne7tq6qO9+uvv2LhwoWu6nm9Xo9JkyYhOzsbJSUlAMqrrAcPHoyoqCgYDAY8/PDDyMvLg8Vi8egYqt7smz17Ng4cOIC5c+d63BYGAPr06ePWA75///4wm804f/58nce3Y8cOt+O78sorAZRX7x87dgxlZWUYNGhQja/566+/Ij09HQaDwbV/SEgISktLcerUKdd2PXv2hFz+53dyZGQkcnJy6j0mlUpV7cZKTk4OJk+ejCuuuAJGoxFGoxFmsxnnzp2rd7wKS5Ysce1rNBpdTywQERERUeN5XYl+6tQpJCQk1Lo+ISHB7ZdLopaqJVRgVm3PAABZhUrkW2wI1ChQVOrAmcsWBAcoIQEeVys3tpq2alwVfwjrNQrEq/VIzzEjbX8WEiMD6xzXk3E2/ZqJzPwSfHEoG9mFVjidIiw2J8pMpQjSKqFVKeAUJZTYHBAl4JouIYjroPe6EtsXFcaNfYqhIpFbEfelovK4e0cHYWRylEc3b3z13rRHdX3mK25M5Fts5e+rUY6SMgd+OZOPoxeKMOGGrhicGOHXc1rfkxCtqbd7c2iupzWIqtJoNBg8eDAGDx6MuXPn4tFHH8W8efOwe/duAHC7CVvbJJ86Xf1PJW3dutXVt7syp9OJb775BkOHDq11PFEUsWDBAowcObLG+M+ePYs77rgDkydPxqJFixASEoI9e/Zg4sSJHk1MmpCQgD/++MNtWVhYGMLCwhAeHl7v/p6onKSv6fiGDx+OZcuWVdsvMjKyxpY3Vfe/+uqr8cEHH1RbFxYW5vr/SqV7UYMgCB5NEqrVVn+iaPz48cjNzcXrr7+OLl26QK1Wo3///l5NlDpr1iw8/fTTrp+LioqYSCciIiLyEa//0pbL5bhw4QI6d+5c4/oLFy64Hq0kaulqaj3SnGpqz9C1gx6WMhOKSh1QyWUoLLHhYlEpiksdHlUr+yJxdPqyGQczTQhQyl3Jxor4KtpGnMwpRkaepc7zV1/7iQCVAl8cvIBPf8uCQ5SgUcqg1yrhlACrzYF8iw0BdhEKuQCZIODKjnpMGhCHo9lF1RKeVpsThzJNyCqwVnuawJfJtMYmwht788aTlh6evDftVU2f+ZpuTORbbDhz2Ywiqx0ZeSVY8MUR/Hg6D6Ou9l8CtuJJCF+1BGrLaropUtd3BFFTSkxMxObNm13J1+zsbFx11VUA4DbJqLfWrVuH0aNHV2uhuHTpUqxbt84tiV5VcnIyjh8/jvj4+BrX79u3Dw6HA6+99prr9/pPPvnE49geeOABjBkzBp999hlGjBjh8X41+f3332G1WqHVagEAP/74I/R6PaKjo2vdJzk5GZs2bUJsbCwUiup/7iQkJECr1WL79u149NFHa9x/48aNrslMG0qlUlV7KqA2u3fvxhtvvIE77rgDAHD+/Hm3iVA9oVaroVarvY6TiIiIiOrndRL9qquuwubNm5GSklLj+k8//dT1hwER1c3VnsH456PAFZOJnrlshslqh8XmREGJHcmdg+tN0voicXQ4y4S3vjuFk5eKoVLIoJDJEKhVoOv/+rMD5W0jLhW5t42oqdK7puOrkG+xIT2nGIUldqgVMhi1CkgQYLU5oVHKYdQokF9ih0wmoEuIDr2jjRh1dTQSIwOx6MujHldiN0UyrbGJ8MbcvKnrnAI1vzdUt6o3JipaKZU5nAhQKRAsl8Fqd+KXs/nIKvRfArYlzufQEvFpDfKXvLw83HvvvZgwYQJ69+4Ng8GAffv24eWXX8aIESOg1WqRkpKCpUuXIjY2FpcvX8YLL7zQoNfKzc3F559/ji1btiApKclt3bhx4zBs2DDk5ua6VU1XNnfuXNx5552IiYnBvffeC5lMhoMHD+LQoUNYvHgxunXrBofDgVWrVmH48OHYu3cv1qxZ43F8o0ePRlpaGkaPHo1Zs2ZhyJAhiIiIwNmzZ7Fx40a3Fij1sdlsmDhxIl544QWcPXsW8+bNw9SpU+ss2pkyZQreeecdPPDAA3j22WfRoUMHpKen4+OPP8Y777wDjUaD5557DjNnzoRKpcINN9yA3NxcHDlyBBMnTsSDDz6IV155BSNGjMDChQsRHR2Nc+fOIS0tDc8++2ydCfzKYmNjsWvXLowePRpqtRodOnSoddv4+Hi8//776NevH4qKivDss8+6bhwQERERkf95XTI+depUvPbaa1i9erVbZYXT6cSqVauwYsUKTJkyxadBErVVldszVBaiUyG5czASI42ID9fhqVsT8MKwHnUm7qomjvQaBeQyoTxxFK5HvsWGtP1ZdfYErkg4n8mzQKWQQauUQ6WQuZKK+ZbyR4qrto2orZf0RZO1xuOTJMlV5esQJdhECXkWOwpK7LA5nLA5RGjVClwTG4LOIVpMHxSPOXcmIinK6FUlti/OSW381Ue6tmumQlts6dHUfa0r9xqvuDbLHE4EapRQymVQymUQIKBToKZR14wvtKT5HFoqb74jiHxJr9fjuuuuw4oVKzBgwAAkJSVhzpw5mDRpElavXg2gfHJLu92Ofv364cknn6w2Aain3nvvPeh0uhp7eg8cOBAGgwHvv/9+rfsPGTIEX3zxBbZt24ZrrrkGKSkpWL58Obp06QIA6Nu3L5YvX45ly5YhKSkJH3zwAZYsWeJxfIIgYOPGjXj99dfx1VdfYdCgQejevTsmTJiAmJgY7Nmzx+OxBg0ahISEBAwYMAD33Xcfhg8fjvnz59e5T6dOnbB37144nU4MGTIESUlJePLJJ2E0Gl3J9zlz5uBvf/sb5s6dix49euD+++939TMPCAjArl270LlzZ4wcORI9evTAhAkTYLVavapMX7hwITIyMtCtW7dab2hUWL9+PQoKCnDVVVfhoYcewvTp033W+oaIiIiIGk+Qqs6O54HZs2djyZIlMBgMiIuLgyAIOHXqFMxmM5599lksXbq0KWJtNkVFRTAajTCZTI16hJMIqLsXt8MhYmbaQRy7UISuYToEapSupI8kSUjPMaN3dBBeGNaj3iTt6Vwz5m05giCtqsZ+yeZSBwqtNiy4q2eNVdCiKGHRl0fLW0WE6bD/fCHyLTYYtUpIElBUakeoToW+MUE4lWtxxVWttUqlythgnQpapQwXCkvdKkKLrHb8eDoPhSU2SFJ59bRMJkCSAIdThCAAASoFru0agjyzDbOH9UCfmCAAwO/nC/Hil8cQ20EHeQ3nxClKyLhswexhPWDQKBp1Tloit/cpvHpLD2+umdagOfpaV/7siJKEX88WQKUoT54DgN0pwuYQ0S82GAKEFnHN+KLHf1vlzXdExfcKNRx/Z6KmNH78eBQWFmLz5s3+DqVVqvh8xsz4BDJ1QP07EBERUZuTsXSYv0No8Tz9m6ZBpYovvvgiRowYgQ8++ADp6emQJAkDBgzAmDFjcO211zY4aKK2pq4EIABs2p+JM7kWZBeVIqvQilCdCvERBmgU8vIkdIAS13UNwaEsU72Jssa2+XCr3pTJENdBj5IyE0xWOwJUCmiVcuRZbDhyoQidgrQYmRzlOoa62iYEBGkRHKB0az9hKrGhyGqHAEChkEEQBAgABAFQKWQosztRYnPAbLVXq6r2ZnLFttj6pD219GiuvtaVe40HByjhFCUoXOevfFLbUJ0aBrUCTgkt4prx93wOLRknYCUiIiIiIiJfa/BfkNdeey0T5kR1qCsBeCy7CJAAhyghOjgAYXo1TuQU47LFhoIz+ejaQYfOIQGQAPzrp7MeVeBWThzp1OUTgtqdIpTy8mRRfYmjqgnn4P/1Zj992YwiqwMOpwibU0TXDno8dnNc+bpcc71tE/JLbBh7XRf8eCbPNRGnxeaAIBNg1Chhd4oos4tQKmSoGEEul8HhFHGxuBQ3JYS7TZTozeSKGXmWNplMa+zkpq1Bc/a1rnxj4kKhFRIk2J0iBEFAic0BtUKGcIMa+RYbbE4RakXru2baE07AStT0hg4dit27d9e47vnnn8fzzz/v0Th6fe03A//zn/80KDYiIiIioqbgdRagpKQEzz77LDZv3gy73Y5bb70VK1eurHOiHKL2ps4EoEqH/x7PBQAM7B4GmUwGvUaB/noVikrtOJ1rQYhOCYvNgcISu8cVuBWJo59O58HhlFBU6oBTlCCXCQjUKKCQC0iJ61Br4qim6s1gnQpXBwSjuMwBU4kNVruIJ2+NR3y4AYDn1e8djRrMGZboaj9xvsCCRZ8fg1OSoFcrYHfaYXeIUMhlACQ4nCIkCQgOqF5V7U0ldltOpjV2ctOWzpu+1t5UZNfWBqXixsS/fz2Pb45eQkGJHTqVHDpV+WfhZI4ZDqeIMqeImGAtzGWt5+mF9qY9Pa1B5C9r166F1WqtcV1ISIjH4xw4cKDWdVFRUbjpppu8DY2IiIiIqEl4nUSfN28eNmzYgAcffBAajQYfffQRHn/8cfy///f/miI+olaprgSg2eaE+L+pCMxlTgRq/ze/ryAgUKtCtzAB+88XIESnQlIno8cVuDKZgL4xQfji9wuw2pwI1CqhVctR5hBxodAKrUqOPjHGWhNHtSacBQF6tQIXTaXoHR2EuA5/Jiy9aZtQuf2EQaNApyAtLphKUeYQEahRoMTmQJlDhFOUIBOAoAAlJt7Ytcaqak8rsdt6Mq0tt/RoilY89fVXr7gx0T8uFOv3ZiDPXIbiUgccogi1Qg4IcCXVV/83nRN5tmDt4WkNIn+KioryyTjx8fE+GYeIiIiIqKl5nURPS0vDunXrMHr0aADA2LFjccMNN8DpdEIurznZQdTe1JUAtDtEQAIglE9YWJVTkmAudaBbB51XFbiiKOHA+UKEGdSwO0UUlzphLnNCLhPQKUgLhVzA7+dNGN67U41J44YknBta6R0bqkPfzkEoq1Q1r1bIoVIAAUo5lAoZBiSEYXBix2pxVlQSO0UJD17XGQBgKXPWWond3pNprXUCSl/3tfa0v7pMJmBIUiQ6BWnx3KaDsNqdUCtkECUgVKdCbAe9q8e/r9rJUNNo609rEBERERERUfPxOol+/vx5t0crr732WigUCly4cAExMTE+DY6otaorAahUyFDR/Fspl1Xb1/y/ylq9Rlnj2LVV4FZUv3cLM0CvlqO4zAG7o7zXuEGtgLnMWW/7C28Tzg2t9K68X565DFHBAZDLAKcIFFltCNWrMerqaLf9RFHCtqOX8PnBC8g2lUIGQKOSuyqJ66rIbopkWmtITtdXed2S+bIVT0P6q+vUCoToVIgI1EIpF1xzC1Ts29B2MtS82vLTGkRERERERNR8vE6iO51OqFQq90EUCjgc7A9LVKGuBKBeJYesIomndq9UlyQJBSU26DUKKISaE7K1VeC6Vb8LAgxVkvCetr/wNuHc0ErvqvtZysr36xMTXG2/w1kmvL3rNPamX0aZQ4RGKUNwgAqRKkWdfeIr82UyrTUkpz2tvG6pfNmKpyH91YtLHbA5JHQKUkNew2s0pJ0MEREREREREbVOXifRJUnC+PHjoVarXctKS0sxefJk6HR/VgSmpaX5JkKiFqi+KuT6EoBxYTpAAk7lWqqtizRqEBemx4VCK+IrVb4CdVfg+rL9hbcJ57oS73WdK08S9oezTPjH9pP4/XwhRElCuEEFhwgUlNhQYnMiqVMg8iy2ZmutcTjLhH98ewLZplIEB6gQqldBLggtKjndkMrrlshXrXga0l/d1+1kfK01PAlBRERERERE1FZ4/df/uHHjqi0bO3asT4Ihag08rUKuLwEIoM51K7ef9KoC15ftLxqipsS7J+eqroR9RTI422SFAMCgUUIQZFDKgUCNEkWldmTkWZAQbmjS1hoVCUuT1Y5V/z2Jw5kmyGQCLhWVQS4TEKhVoGuorlmT+XVpSOV1S+WLVjzeJsRFUYIoSTBqlThz2YyenQIhk/3Zeqm+z1NTJ7hbw5MQRERERERERG2J10n0d999tyniIGoVvG2RUV8CsK513lbg+rL9hT/OVU0qksHBWiUumcqgqBS7IAgIUClgsjrglCSU2b1rreFporNywvJiYSnO5FkglwkIDlDCoFHAIUrIt9hgKXOiW5i+RSSnG1J53ZI1thWPNzeYKr/f+RYbLppKkVNchis7BqKjUVPv56mpE9ytvU0PERERERERUWvkn+fQiVqhhrbIqCsBWNe6hlTg+qL9hS+qaH3VTqQiGRyqV0EuE+AQJSjlf24vlwlwihLMpQ6vWmt4muisnLDsGKhBhsNSvkKSUFTqgEIug1ohd1XFZ5usCNIq/Z6cbumtSJqbpzeYjmYXuSWoI41ahOpU+ONiMY5cMCHPXIYQvarWz1NTJ7jbSpseIiIiIiIiotamfWRQqF3zVWsFf7TIaEgFbmPaX/iqitZX56oiGawQylum5FtsMGqVAMrHdIoS5LLy/ujXdg31qFWNp4nOqgnL4lIHrHYn5DIBiv8l74tL7VDrZa6q+IISGwK1Sr8np/3d2qclSooyYtpf4rFhbwZOXbZAFMvbtfSKMiIlLgR2p4h//XC2WoI6KjgAnYwaHM4uQlyoDtNvTUBcB321z1NzJLjbUpseIiIiIiIiotaESXRq03zZWqE1tchoSPK9sVW0lW9WZBWW+ORcVU4Gdw3VoaTMCZPVjgCVAnIBMJfaoZDLEGnUeNSqxptEZ0aeBemXzDBoFCiw2GCxOSAAUCsE2BwSFPLy/7U7y6vj5TKg1C6ik1Hr9+R0S2vt0xIczjIh7bcsXCoudd18USpkuGwuxb9+OofCEjvOF5QgSKtEB70awTqVa19BJkPXUD0KrDbIBKHG89YcCe7W9B1ERERERERE1JYwiU5+1ZQT8Pm6tUJbbpHR2CraqjcrnP+rdtYoZYgKDqi2vafnqnIyOM9iQ1yYDhdMVhSW2FFqF6FWyNA/LhSTBsR59F56k+g8cL4QJ3KKAQkQpfIK7hK7E1qVHE7RCbtTAgA4RREAyqvSFTLc2TvSZ9dwYz4fvmjt01ZU/S7oFBSAiyYr9p8tAAD0jg5CRKAaWQVWFJfacTjLhKQoo1sivb4EdXMkuH3xHdQU37lNPZEqERE1zuEFQxAYGOjvMIiIiIhatdaX7aM2QRQlbDt6CZ8fvIBsUylkADQquc8m4GuK1grN1SKjqRNSNY3fmCramm5WlJQ5cD6/BAczTdAq5QjRq13be3uuqiaDQwLUCNKq0MmowbDekRic2NHj8+NpovPA+UJs+jUTVpsTOpUcSoUMNocIhyiiqMSJYJ0KJTYRNqcIq02EUi5BJZchJS4UgxMjPIqlPr54iqIxrX3aihq/CyQJF4tKIZeVX9+XiqzoFq6HSiGDSiGgxObEmcsWBAcogf99HupLUDfHTbbGfgc1xaSnTT2RKhEREREREVFLwCQ6NbvDWSa8ves09qZfRplDhEYpQ3CACpEqhc8m4GuK1grN0SKjqRNStY3fK8rYoCra2m5WGLRKXBUThB9P52Hf2XwkdgyEXquEXBBwsajU63Plq2SwR4lOhQx70i+j1O5EkFaJHHMZBACSBAgon9y00GqHTqVAqF6NmOAAFFrt6GTUYtKAOJ8kqH3xFEXVmyW9ooztKnleoabvguIyB4qsDujUSgCAyeoAJLj67muVcpisdhSXOWDQKD1KUDfHTbbGfAc1xaSnTT2RKjUcnw4gIiIiIiLyLSbRqVkdzjLhH9tP4vfzhRAlCeEGFRxi+cSQJTYnkjoFIs9ia/QEfE3VWqEpW2Q0dUKqrvFPXCyG3Sl6XUVb180KCAL0GiVyzWX4LdMEmVD+JMA1XUI8ar9SUxKosZMlepLo7BIagEtFpdCplThfYIUolrdtUcplAACnTIDdIcEul6BXKyEIAq6NDfVZixRfPEXB6uA/1fRdYHeIcIoSFDIBEsonqHWIErp20MNSZoLV7oQoAqU2JwQIHt0ka64+9A35DmqKJ3OaYyJVahh+/omIiIiIiHyPSXRqNhVJl2yTFQIAg0YJQZBBKQcCNUoUldqRkWdBQrih0RPwNWVrhaZokdGQhJQ3lYb1jX/yUjHKHCKyTVavqmhru1mRb7HhcJYJZQ4n9Co5rowMhFwACqx2lNid9Z6Pw1km/PvX8ziUVQSrrbwPea+oQNxzdUyjkkCeJDpvjO+AD386h0JrGURJQphBDXOZAzaHBEmSoJCVV6T3iDTg+Tt6wKhV1njuG1oJ2tinKFgd7K6m7wKlQga5rPypAgCQywQo5TIEapVIijLixMUiFFjtyCkugzFArPcmWcV77RQljEqOwo+n85Ge23R96L39DmqKJ3OaYyJV8h4//0RERERERE2DSXRqNhVJl2CtEpdMZVBUSvgIgoAAlQImqwNOSUKZvXET8DV1awWZTGh0YqhykrWwxIb0S54npDypNKw6/slLxbWO3ykoAJkFJVArZV5V0daUoJQkCWcum1HmcEKrlMPulBCiU8GgUSJKkjyapHTxF0dx+rIFoiS5lp/PL8Ef2cV44c7ERiWB6qvkDVDJ8a+fzqGgxAaVXAYBAgK1SkAqb+kiShJsThGSBBi1yloT2Q2tBG3MUxSsDq6upu8Cg1qBQK0CeeYyCIKAUJ3KdUMtOECJEJ0KyV1CMDalc603SSrU9F53C9Nh7HVd0NGoabJWGt58BzXFkznNMZEqeYeffyIiIiIioqbDJDo1m4qkS6he5aoCVcr//ENeLhPgFCWYSx1eV4nXVPXbHK0VGqpq4s3mdCLbVIo+UUE1Vs5XTkh5UmkIoMr4IrILregdo4C+ho+9ViWHUi7DPcnROJhl8rhNRE0JyqJSO/ItNsgFwFzmQJhBDYO6/DXrq1AVRQlv7zqFPy4WQyEvv7Gi+N+1UmJz4I+LxXhn12msuL9vo967uip5RVFCgEoGU4kd5R1cBAiCAJVCgEGthM0pIjhABZkg1JggbGwlaGOeovBXdXBL7r9c29MHHQM1yCkqAyAhIlALUQKsZQ5km6wI1avxyA2x9d7wqO29PpxVhAuFpZg+KKFFVGE3xZM5zTGRKnmHTwcQERERERE1Hf51S82mIumiEATXBH5GrRJA+R/7TlGCXFbeH/3arqEeV4nXVfXbVP3LG6OmxFtucRlO51pwMLMQfTsHI1inctunIiGlU8vxwU/n6qw0fGfXaZTYnShwG78Up3LNOJhpQt+YIITUMn6fmCCM6BvlcUK0aoIyQKXAmctmFJbYy1ufyGUos4soKLG7jqmmCtWKJOzR7CL8eDofMqG8xU/F8SnlAgI1ShSW2PDL2XycvmxGfLihUe9DbZW8R7OLUGR1lPfKlgDV/270lNpFWG1lCApQItKogQRUSxD6ohK0MU9R+KM6uDX0X67t6YObrwiDBAkFJXZkXLZ49f3Qmqp+m+LJnOaYSJW8w6cDiIiIiIiImg6T6NRsKidduobqUFLmhMlqR4BKUV61XGqHQi5DpFHjcZW4J1W/c4Yltpgq2doSbxGBanQM1OBCoRWnc824OiAY+N+6ygkpAHVWGnYM1OCXs/kI0amQ1Mno2qZjoAYdA9XILizFmctmBAcEu9ZVTXh526qmIkH59q5T2HPysqvnuVopR6BGAYutvHo+KcqIYJ2qWoVq5STshUIr8ixl0CrksDlFqBV/JoMEQYBOrYDJaseJS38m0Wurgm5IdXTF+6OQCegUpEFOcRmc/+ubLRMACIBGIUOJzVljgtAXlaCNmaCyWnWwJKG4zAG7Q4RSIYMgwafVwa2p/3JtTx8A8Evv+ubUFJOeNtdEquQ5Ph1ARERERETUdPiXFDWbykmXPIsNcWE6XDBZUVhiR6ldhFohQ/+4UEwaEOdR4s3jStBhgX5PYlU4fbm8GjxAKXcl7QShvF1I1zA9TKUOZBeV4mJRKcIMmmoJKUuZs85KQ6dU3g6nWwede2JPENAtzIBiqwMXTaW4VFSGMIPaZwmvxMhASChPHmoVMpRIIuwOESU2BwI1KpQ6nDhz2YIgrcItYV81CetwSjibV4Iyp4gCiw3BOpVbIv1P5YntQ5mF2LA3A6cuWyCKEoxaJeIj9OgbE4QD5wu9ro6uSIx2CgpAB4MGTrEQVpsTKrkMSoUMTlFCgdWOmNCAGs+XrypB6+vbXtsxVL5RFepQ4UyeBUVWB5yiBJlQftb6x3n+lEddWlMldoXabhA15PuhtVX9NvSaau4xqeH4dAAR1SZp3lbI1AH+DoOIiIj8IGPpMH+H0GYwiU7NqmrSJSRAjSCtCp2MGgzrHYnBiR09Tri1xErQuqqfD2eZ8NZ3p3DyUjFUChkUMhkCtQp07aBHiE6FEJ0KvaONOHi+EAUldljKnNUSUqdzzXVWGpr/l7DTa5TlC6pUIidFG3Eoy4SCkjJYyhw+S3htO3oJ36fnQZQkGLQqBKhF5FtsKLGJsDvLEKhRIs9ShsPZRehk1GJkchQAVEvCOiURKoUMDqcIhyihuNQBtV6G8pY/EsxlDug1ClwRYcBnB7Lw2jfHUVBih1Img1Je3o89q9CKL36/gDCDGt3CDF5VR1dOjOr/P3tvHiZXWaf9f85a+9Jb0kmnszRJgBASFkEQxQ1EREVFcUNFGXyRcRxHZ3RcGGVgxGVYBv0p4wjK6DgvXIKOio4DzCsKsq8JYUs6W+9r7VVnfX5/nKqi9y2dpMHnc11eM91Vdc5znvOcanJ/7+f+qjqb29LsHiqQK7tUHB9Vhaih8e5pxPjFdILOlNs+HbVC1TO9OR7cPYKiQDykY2gKRcvFF9Cfq7CjN3fAAudSfP4OJS9F1+9C1tThOKZkYcjdARKJRCKRSCQSiURy8Fg6/7qX/NmwWKLLUnOCzpQNDXD93S/Qky1j6ioRQ0NRFEaKNkUriDppjJmEdY0Ny+Nc/JoO0lFz0tzM5jQcLdnEwzq6ojBatOmsCsBB3rxC2FBZkYrwN2dsmPL4C8H3Bb96qgfL9VmWMFEUFUNTaYop5C2Hku2RrTiYmkpHU4yPv/aIekFgogibDBs0x0P0ZcsIIbAcD9v1URSFouXg+oL1LXGe68/zz//zHKOFwK1uaCquH4jsZTsQixtiJrFQMM/xsM56M8b23hzfv2cXnzpjAx3N8UnXPVEYbYyZNEQbyFdcHM/H8QSO57G1PT3lXCy2E3S+0ToQ7ApYlgjROVhEVYIsd01VaEmEWNsUY7hoL4pDfNbnz1DZO+zw2N5RgJedsPpSdf0uZE0djmNKFobcHSCRSCQSiUQikUgkBwcpoksOC4shuiwlJ+hM2dBdo2UihspI0WbziiS269ebqibDBrmKw56hAulIui68vWZDy5SC42xOwxWpMB0tcXYO5MmWHCzPJ2rq6KqC4/kM5a26wFzLWD9QOocK7BkqoqsKFccnYgbh4SFDw9RVoqZHyXZpb4jyqTM21LPMpxJhFUXh6BVJKo5HruLgej65ioOCguUGsSpDeYvLf/k0mZJDY9TErMa9GJpC1NTIlgPBPld2yFdckhGjXlAYKdrsGy7Rna2wZVVqUrzLVMKooigkIwZCCHYOFGYURhfLCbqQPPcae4aLjJYcTl7biAAcz8fQ1Hp0kKlri+IQn+n5Gy3aPNuXI1N2uPn+vfzXkz0HrdnogczVgSBdv5KlitwdIJFIJBKJRCKRSCSLjxTRJS9ZlooTdLZs6O09WUaKNie0N6CoKh3NcUpWtt5UNWJoDBdtnu7JsTIdmVV4m81pKITgL3/6GNmyQ2PcRFMVXF9QdjxSUYN4SOMXj/eweWXqgEWV7d1ZbrhnF3uHizieoGi5REyVZCTIMlcUhbCukq9AR3OMjuYXRdvpRNjGmMnxqxt4vi/HUNEmYugULJeIqXHMihTxsE53towvIFdx0HW1npvu+6AqCp7vY7sCp5qtvr07S8X1iBjB+6KGNmW8y2IIo/Nxgk4lAO/ozU27o2EuAnStOBFN6WhTjHOxdmhM9/yNFm22dWXIlh1akmGOak1QcfyD0mx0pt0fh8JxK12/kqWK3B0gkUgkEolEIpFIJIuLFNElc+ZwOT6nY6k4QWfLhm6IGOwbLuGJoBlmQ8wM4kyqUSuu52N7Puua4/yf186tqepMTsPOwQJNsRCaolB2fDzfRVMVmmIma5vjmJq6KE7kmvu+J1MmZGhEzSCTvJaD3hgz0VSVQsUhpKucs2XFuHsxUxGkIWoQNlSWJUJkSw6+LzBUlf58BccPoSoKhqZMyk1XVQVVAc8HRQnc6S8MFKi4HqmIgeOJaha9QVtDZMrml4shjM7FCTqVANwQNRjIW7iemLSjYa4C9MHcoTHxO+Bdx7eNf/4MlWf7cmTLDqmowcblCXRNJa6pUzYbPZDvlJl2fyy2WD8T0vUrkUgkEolEIpFIJBLJyx8pokvmxOF2fE4nti0FJ+hs2dC1Jp+Fiks6agKBkH5itIG85ZIt2ZQdn78+Y3096mQuTOc0zFdcDE3lFWsbKdnepDgPzxcH7EQe674/ZmUS2wsiahpjJgXLpWR79cgaXVM5taOJMze1Thr/dEWQXYN5hgrB51GgKW7WM+QzJQcF0FQVT/hYjofjCQwtENZVRcHy/bpInCu7RE0dIaBkuzTFzPpcTNf8cjGE0ZmcoFMJwCXL5YHOEWzX55XrGusCeG1Hw1SC/1QcrB0a030HnLNlBU/sz7BzoMDeYYdM1YG+cXmCxphZ//zE+S7Z3oK/U2bb/THXuVospOtXIpFIJBKJRCKRSCSSlzdSRJfMyuF2fM4m4B9uJ+hszl+92thytGTT1jDGra4oxEM6fdkKW1alx0WdLMZ4Ko5PMmJMen0xsuLHuu9VVWVdc5yilcVyPZJhg4ihUXa8+r24+PSOKe/HVEUQUw/el4oYNMVMRooOhqYG2eRhg2zZxhegq0F8i+0JbMdDU4MYF18IQrpKxAhy0V3Px9AUchWXkKGxtvlF0XWmaJODJYxOJwALQFUCB/2e4VIgQFdfm0nwn2rci71DY7bvgE++YT3xkM5je0e5+f69HLk8TtnxGS5Y4wo4tfl+cn+G327vm7aHwLtPaKM1FZn2WZ5t98dc5+rlwFLbISSRSCQSiUQikUgkEsnLESmiS2bkcDs+5yrgH04n6KzO31yFk9Y0UrLdBYmaU4lkwLTC2aHIip/ovm+sRtTsrkXU+AIh4OjWJB9/7REzFlkmFkGe3D/KDfd0Vt3tDgXbxfF8UlGDkK4RCxkULRddVfEF+Pi4QpAtObi+T0sizHtPWsVQwWZbVxbb88GhHmcz1h19KJvP1phOAHY8H8+HeEgnW3bIWy6J8ItFkPlkmS/mDo25fAf84vEevnzO0QD8x4P7eHTvaDVKSKCpCsmIzrpqlJCpK9z7wtCUx2tyTR7fn+HpnixtqQhhU5vSnT7b7o+IobJ32OGxvaMAswrLL1Uh+nDvEJJIJBKJRCKRSCQSieTPBSmiS2bkUDk+pxOKl1Jkw3TMxfl78ekd9euZj6g5XW42KIyW7CmFs0ORFT+V+74xZtIQbSBfccmVHUqOx6fO2DCviJpdgwV+9lh30BQ1ZmJoKrbnU3Y8vKKo/05VFNY2R9k9VCIW0miOh9FUOKIlwUdetYYtq9L4vqBzqMB1d73AnqEix6xMoqpq/VyLVVCYrwA7nQBsaGq9EajnCxzXH/f6dIL/TFFHi7FDYz7fAUXLZbhoMVKwaYyb6KqG6wtGijZFK0MqYnD0ihQDucqk441Wm+s6no8vFJoTIXRVnXLHy0y7P0aLNs/25ciUHW6+fy//9WTPjMLyUhCix97DWChYF0XLm/GeHe4dQhKJRCKRSCQSiUQikfw5IUV0yYzM6vichzt2OqYTsU5Z1/iSiWyYq/N3PqLmVCJZX7bCH54fQgjBhuUJ0lEDz4en9mfGCWcHOyt+Ore7oigkwjr9uQpb5xhRU7///QWeH8hTrLgIIXA8gakrpKMGI0WB7fpkyw7piIFAULQ8tq5K8e4T22lNhSfNp6oqrF+W4JLXHsH1d7/ArsHiuIJCT6ZE2NDY3Bbck4UIzAsRYKcTgBNhnWREZygfRKAY+uyC/2znX+gOjbGibnemNKfvgGzZ4ZdP9hAP6XieoGR7RE0FXVWIGBqjRRtNVXnVEY3c8nAXEXPM8URQ8Ki4HumoQaHi4fmCdHTqgtl062+0aLOtK0O2mst+VGuCiuNPKywvBSF67D0cKdiMlm0AGqImjTFzyvV0uHcISSQSiUQikUgkEolE8ueGFNElMxILafhC0JspkYqaJEJ6PacZ5u+OnchMItaOnhyFisuKVGTKsS2GgL+YzMX5O1dRc6xIdkRLjILlMVq02DdSRAiB5Xrs6M0RMzU0VSUZ1ik73jjhbCFO5Lnet8Vyu4+9/4mwDgKSEYORks1I0UJTIaRrJMI6ubJL2XZxPZ+GmMlJaxs578TZ3cJTFRQcz8dyfXwB//eh/fz88e55u48nrt1wUmWoYPPArmGe78vz+bOPYsuq9KTPzVSAWNcUYyBnBT+LwJE+3ZweLAF4ojDvVQX8sKHS1hCd9P7ad0Cu7LBzoMARLQkcz6ezGu1Trka6tCTCpKMGzfHQpCJC3nLrDWA9HzRVwdDU+rxMLJhNuf4MlWf7cmTLDqmowcblCXRNJa6pUwrLS0GIHnsPo6bOaNmmYnsIQBE2TTFzyvspM+ElEolEIpFIJBKJRCI5tEgRXTIt27uz/OzR/XRnSmRLLmFDJRkxOHJ5gsZ4aMHu2BqziVjbe7KMlm1KlkviIDXIXGzm6/ydTrSuiWRRU+fx/RlyZRfb9clVnKABJSAQhA0NRVEYKQUu38f3jY4TzuYznvm6qg/U7T7x/o8Wg4ahMUOjKWYymLcYrLqyPSGC/HMBUUPjo69ay0WvnrpZ6VSMLSg8uT/Dzx7rQlOVceL/fMTnSWMvOTzXnyNXdvF8n+5Mmb+/bRvfOO9Yjp0gpM9UgBgu2hzVmmBZMsxoyaY/b005pwdLAJ5KmC9ZLvtHSjzVlSViaDTGQ2PmwWf3UIF1zXHylkvF9oikNOKqzonRBvKWi+P6GLpK1NDYM1wiGTEmFREct5qfrgSCelPMHPdcT1Uwm7j+9g47ZKoO9I3L4hiawkjBQteqTWQNjSe7MnQOFVi/LHHYheiJhbLH92ewXZ901ERRIFt26M9ZHN+eYudgcdz9PBQ7hCQSiUQikUgkEolEIpG8yNJRHyVLipqY1jVaQlVUXCHIVVxyFZfBvMWRyxOoqnJA7tjZRKx1jTFGijZ7hotsbksdlAaZh5OZRGvPF/VoB9v1iZo6KoKMLxAACihCQQgwdZVk2CBXcejJlMmWnSnPN5PLfKGu5gPJ3Z54/w09yAR3fUHY0EmEfUZLNr7voaoKCsG1NsRMHt4zyqlH5ObltK7FgPz4gb1Yjj8n8bk2Z9myQ67skIwYpCIGvhD1sY+WHLZ3Z7Fcj6ipo6sahuOxf7TE1//7Wb5w9tGTxjlbAWK2OZ307AgxTrBekQzPWwCeTphPRAyOb0/z4O4Rntif4eS1jURCOn3ZCs/25fCqa/JH95XpyZaJmDptDRFQlHGNUQsVl5AeONa3tKV4vj9fvwZVVRAIsmWHSEhnbfP4hrjTFczGrr/H9o5y8/17aU2E2D1cJFd2qTgetuuDAqam4gvBv9z1Av/ntUfg+eKwCtFj72HB8upO/Np1R82gwWzB9iYJ+jNlwsPSLDBKJBKJRCKRSCQSiUTyUkb+C/swMt+GhIdyXLc91kXXaIls2cFyfRqjBiXbxXKDJo87enO8adNyPv7aIxbsjp3VTRnSaYiaxEL6QWuQebiYTbR+5wlt9WiHwJmq4HiBGBi40MHzA/cuCBRFwdRUSo5HbgoRfSbBftOK5AG5mheauz3x/idCQSb4SNEmGTZwPA9VUarNRBVKtkdLIsQJ7elJzty5Mh/3ccn2uO2xLp7Yl6EnW8ZyfUKaysp0hFUNEUYKNq3JMM/157Bcj2TYqB8zbGjYrs9IwZ52nLMVIGaa07FzN1q069EpXjU6JR7WCOnavATgmeamMR5iy6o0Owfz9OYqlG2PvlwFXVU4ZmWS1lSEsuWyb6TEU10ZIoY6zrEuhGDXYB6Af/tjJ7YrqpE6Hl2jJQxNxdRVXE+weUWSxpg57rMzFczGrr//eHAf27qzuEKgq0FTWlcEWxh83yds6OweLnL93S9w3glth1WIHnsPMyUbzxfoY9aIriqUqw1mU1FznKA/XSQQzD5fS/XvjkRyqPj+97/PFVdcQXd3N9dccw2f/vSnD/eQXrL8/ve/5/Wvfz2jo6Ok0+nDOhZFUfj5z3/OO97xjkN63te97nUcd9xxXHfddYf0vBKJRCKRSCSSQ48U0Q8TC2lIeKjYM1zkhf48ZdvDcv26OBgL6TiewHI8CpaLrqtsWpEc97n5xCPMxU3ZGDO54JVreGD38EFpkHk4mEux4c4d/QgROHxrU1nTuTzx4rFGSzYlRyURMrBcj5CukZwQfTOlYG+5PLxnhB09Oc45tpUX+gskwjqjRRtDV+vZ9wcz1mLS/VcUOprjlKwsoyWbiuOjqQqaqmC5PjFTp6M5jqKqCx7TXGMwntif4b+399E1WmKkYOP6PlFDw/Z8erIVchWHTMkhYmqTHMQAri/QNZXW1MyO8IUWIGpz15ct0zlYpFJ3wQdO/uGCjaoo9GUrbG1fnLlpTYUp2y4fetUafv5YN2XX46jlcZKRoMgTH+NYf7zqWI+GdMq2x67BPIN5i5ZEiIZoaFJz1/NOXEXM1PnZo/sZLtqYujbvgtnqhigVxyVXcUhHDXIVF8/zCekqKME6UxQ4pjXJrqEiD3SOcERLjO3duXkL0YvB2PVvaC/uwjCq8TNutSBi6OokQX+hPQmW8t8diWQsAwMDXHbZZfz2t7+lv7+fhoYGtm7dyle/+lVOPfXUBR83l8vxyU9+kmuuuYbzzjuPVOrFdV8ul1m5ciWKotDd3U0kMnU/lkONEIIf/OAH3HTTTTz99NP4vs+aNWs444wz+Ku/+ivWr19/uIc4Z/71X/+V7373u+zcuRPDMFi3bh3ve9/7+PznP3/Ax+7t7aWhoWERRnnweNOb3sTdd9/NfffdxymnnDLvzx+uQoFEIpFIJBKJJECK6IeBg9UQcLHIV1yyZZeS400QBxUMTUFXFRxP0DlYHCcOzjend65uyjM3LefMTcsX1T15ON2Y44oNQL7i1GM4EiGdFakInYNFYiEdFY9s2SFqBgUMMUZAVwEUKDs+ZdsiEdZZmQqTGiOiTyXY15zL2ZLDHrvEc315LNclYuhoaiDmJSOBYN0QMw9arMVU978hZrK5LcXTPVmyZQdTCyJVmmIh1jXHaKg6lBc6plkLN5aLJ3x+9WQPg3kLp+pkTkdNQCEsBLmKg6ZoqArsGiygqQpRdeyaF5Rsl8aoSVhX2TtS5tm+/KKusbVNMda3xLljWy+e79fHB6CrQWNOVVF4oHOYMzctn9N551LUcn3Br5/s4fH9GVQUHtuXJRnRWdccpzFmVh3rKXYOFujNVdAUJRCxgZZE4GYfWzTasDzBzoEC27tzfPmco2lriHDbY1280J9nz7CLpsIRLQk+8qo1s34n3v1sP0MFG9eHgbyN7wtUFVwfhPAxNBVVUSg6QTzKzsECF7xyDT2ZymHZ6TJ2/R/REhu3C0NRoGS7NMVCxE2NnYPFSYL+fHsSLPW/OxLJWM477zwcx+Hmm2+mo6OD/v5+7r77bkZGRhZ0PCEEnuexb98+HMfhnHPOYcWKFePec9ttt7F582aEENx+++188IMfnPGYjuNgGJP7tSwmQgg+8IEP8Itf/IIvfvGLXHvttSxbtozdu3dz5513cuWVV/KjH/3ooI5hsbjxxhv5zGc+w/XXX89rX/taLMviqaeeYseOHYty/NbW1kU5zsFi37593H///Xzyk5/kxhtvXJCILpFIJBKJRCI5vKiHewB/bkwUNeNhvRq/oLN+WZyRYhD/4Pti9oMdJBJhHU0Fxx0fL1Cj5pb0fTFOxBwrwk3FdG7KxpjJzoEChUoQR1GouOwcKNAYM3nn8SvZM1xkW3cWgGPbUnS0xA9I2NreneWKO3bwlV8+zT/d8Qxf+eXTXHHHDrZXz3GwqRUbLMfj0X2jPLJnlMf2ZXhkzyiP7hul4nj4viAe0jliWSBO2q5HrmwDgSNdUwAlaLSpKaAoAoHguPbxQttEwb5rpMRj+0YZzFuEdJWQrlKwHCqOoGC5mHoQqzFStNnenWW0aB+0WIvp7r+hqaQiBsmwwdErEpy0rpETVqcDAV0I8hWH3kwJXwhioakLNtNREy57s2WEGP+MjRQsHtwzwv6RMo/sGaE7U6Y7U0FXVWoCtaIoRE2dXMVjTVMMIaBie1QcDyGCiJJs2UElcM8/vHeUrtESN927e1HXmKoqnNLRGOSRC3A8gV89f67iEDI0jmpNsnOwwJ7h4gHPTS2OZbhos3+kjIpCKqKPWysjxWB9tqYirEpHuejVa/nSOUfzF69ZR2PM5IiWxKw7VDa3pXjX8W0sT4TRVAXfh4FchZ8/3j3j3G3vznLTfXsoOz4NUYOQrlQjj8ByPXQtiAVSq01MI6aG5fi0psJ86o0bOHZVikzZZs9QkUzZZsuq9EEXlceu/12DRVqTEUxdJVOyGSnahDSV5ckQOweL0wr6m9tSXHbOJi5/+zF86Zyjufztx/DlcyZn8L8U/u5Ipsb3BZ2DBZ7cn6FzsPBncY8ymQz33nsv3/jGN3j961/PmjVrOPnkk/nCF77AOeecw549e1AUhSeeeGLcZxRF4fe//z0QxIwoisLvfvc7XvGKVxAKhfjxj3/MscceC0BHRweKorBnz576MW688UYuuOACLrjgAm688cZJ41IUhRtuuIFzzz2XWCzGlVdeCcCvfvUrTjzxRMLhMB0dHVx++eW47ov/bXTNNddw7LHHEovFaG9v59JLL6VQKMxpLm655Rb+7//9v9xyyy1cdtllnHLKKXR0dPDGN76Rr3/96/zwhz+c03EefvhhzjzzTJqbm0mlUrz2ta/lsccem3R9P/jBD3jnO99JNBplw4YN/PKXvxz3nt/85jds3LiRSCTC61//+nHzNxu/+tWvOP/887noootYv349xxxzDO9///u54oor5nyMm266iWOOOYZQKMSKFSv45Cc/OW78v/jFLwDqa+T222/n9a9/PdFolK1bt3L//fePO96//du/0d7eTjQa5Z3vfCfXXHPNuFiaCy+8cJLr+9Of/jSve93r5jzmGj/84Q9561vfyic+8QluueUWisXxf5tf97rX8alPfYrPfe5zNDY20trayle/+tX662vXrgXgne98J4qi1H+WSCQSiUQikRw6pBP9EDPfyJPDwdqmGB0tcToHi7i+j6FNdtkmQkGDxbHC6kJyemdyU25tT3H7492LGj2wFNyYibCO4/k81ZXBFWJcDMdI0SZXcmhNR+hoibFvuMQJ7Wn6chbburMkwlCwHGxXYGoqybCGpqpYro9CIKyOFdrqgr3u8Vx/jq7RMrYbxKQULQ/X8/EJhHnHE2RKFq2pCKmwwUjJZkdPlmTE4KS1jYsSazFxB8CmFckp7/9JaxvpaLHoyVSIV6Nlxjroi7ZHMqLzkwf28u4T2+d8zybGYLQmw3hC0Jcts3OgiKoEWeT7R0toSpDbn6s46JpCSA+eA01V8HxBOmqwMh2mbHsMFYMGsLqmEjN1yo5H0Q6KAsuTYVYkw/NaY3PZKdGaitCaDGN7gSO/lofeFDNZ2xwnFTHYM1Scs1t/poiQnkyJguUSD+msa44xWnLwBBjai01t9wwVaIg21IsuR7Um6WiJ8+T+DLYriJiz71DZ3p3l2/+7k5GiTXtDtH7+meauJhAXbZeYqREyNExDxctZgMBHqTrzmTIepaMlvuDmuAfKxO+/hojJKEExIh01ETBrdNVcIoEW8++OzFQ/dPy5xu/E43Hi8Ti/+MUvOOWUUwiFQrN/aBo+97nP8c///M90dHQQDoe56667OOOMM3jooYdob2+npaUFgF27dnH//fdz++23I4Tg05/+NJ2dnXR0dIw73le+8hWuuuoqrr32WjRN43e/+x0XXHAB119/Pa95zWvYtWsXH//4x+vvBVBVleuvv561a9eye/duLr30Uj73uc/x3e9+d9bx/+d//idHHnkkb3/726d8feLzPB35fJ6PfOQjXH/99QBcffXVvOUtb+GFF14gkUjU33f55ZfzzW9+k29961t8+9vf5oMf/CB79+6lsbGR/fv38653vYtLLrmET3ziEzzyyCN89rOfndP5IXCK33PPPezdu5c1a9bM+XM1vve97/GZz3yGr3/965x99tlks1nuu+++GT/zpS99iX/+539mw4YNfOlLX+L9738/O3fuRNd17rvvPi655BK+8Y1v8Pa3v5277rqLyy67bN7jmgtCCH74wx/y//1//x9HHXUUGzdu5NZbb+WjH/3ouPfdfPPNfOYzn+HBBx/k/vvv58ILL+S0007jzDPP5OGHH2bZsmX88Ic/5M1vfjOaNvXfVMuysCyr/nMulzso1ySRSCQSiUTy54gU0Q8x8408ORyoqsKFr1rLI3tGGCnYNMRMDE3F9QMBPaSpREyVDcsT44TVheb0TtVgsWC5fKcqpi2W2D3fxqcHynRi0+qGKJYb5MovS4ZQlGBDiKEpJMM6AzkLx/X58Klr+O7/28XOwSIRQ0UBVEXB84PGoq4vGC27GKpKU8wgZASNJDsHC/VzjRXsLc/Hr+4isD0fv3octXpu2xOUbJ/hgoUvBBUncFUnKi4dLRY7enMHJNzMJAhdds6mSXO1ozfH9Xe/wM6BAlFTY9dAgZLj4fsQMVRWpiJs68rSk6nMaz3UhMvv/2EXj+wdpVBxqTjB7onWVJhYSEdXVVQlmBe3uuMiFA8c6TWx2vOhKR7iA69czU8e2MtIwWZ5MkT3aJlMKThe2NToaIkRjxisD+tzWmNzFc4SYZ3GuEkqbCAAxwsiSxLhIIKpUHHnvYNguqLW2uY4voBVDVHiIa0ePZKKGHV3frYcFBwGcta4YtlcYmJChkospPEfD+578fkE8paL4/osT4boz1lTzl1NIF7XGAuauVYjUUKGiuX4GKqC7frB85YITxmPstBs+sVg4vdfbXdF0fIWTaRerL87f66i7uFgKRR8Dxe6rvOjH/2Iiy++mBtuuIETTjiB1772tbzvfe9jy5Yt8zrWP/7jP3LmmWfWfx4cHASgpaVlXPzHTTfdxNlnn13P1H7zm9/MTTfdVHeb1/jABz7Axz72sfrPH/rQh/j7v/97PvKRjwCBw/2KK67gc5/7XF1EH9u4dN26dVxxxRV84hOfmJOI/vzzz3PkkUeO+92nP/1pfvCDHwCQTqfp6uqa9ThveMMbxv38r//6rzQ0NHDPPffw1re+tf77Cy+8kPe///0AfO1rX+Pb3/42Dz30EG9+85v53ve+R0dHB9deey2KonDkkUeybds2vvGNb8x6fgiKCu9617tYu3YtGzdu5NRTT+Utb3kL7373u1HV2TfGXnnllXz2s5/lr//6r+u/O+mkk2b8zN/+7d9yzjnnAEGB4JhjjmHnzp0cddRRfPvb3+bss8/mb//2bwHYuHEjf/rTn/j1r389p+uZD3fddRelUomzzjoLoL7bYaKIvmXLlvq62bBhA9/5zne4++67OfPMM+sFn3Q6PWN0zVVXXcXll1++6NcgkUgkEolEIpFxLoec+UaeHC62rErz2TcdSWPcJFMOmihajhc40KMGqxqi0wriC4lHqIlYW6txJD9/vHvRowfm48Y8UGaKjNk3WiKka8TCQfNDx/PHRHG4xMMGph44fGtzWXZ8yo7HaMlGVRWWxUOsSIVpippomsJw0aYvb3Hzn/bwdz97ik/938f53fY+VqUidcE+YgSPu+sLEC82KhWAqamEq9nVubJL0fJQFAVTV+lojtGTqXD93S/MGkcyXfRATRDa1pUlHTFZ2xwjHTHZ1hX8fkdvbtz93zNcxPMF553QxuaVQSzJaNnGcn0cz8fxBF2jZfIVl67R0oLWQ9nxaYyZbFweNLltjoco2R67hwqYelBoMDQFBbBdr5pJHxSSkhGdXNlmw7IEZ21q5QtnH80pRzTRn7PYO1Ki5HjYno/rCXYPFRkt2nNaY7PN09j5r+386MtVSIR1muIhklVRu7bzY8OyxJx2EIy9b1FT40tnHz0uIuSCU1ZjaGrgJq82gA3rGtmyg+P5qArYrs/uKaJHZouJqY0TqD+fmZIzLuro0b0ZhgoWj+8bnTR3dYE4pNfHlas4RAwdVVXq60VXlFnjUQ4XY7//1i9LsH5Zgq3t6QOOrqqxGH935rM2X0osxbgUGb8TZKL39PTwy1/+krPOOovf//73nHDCCfPO/37FK14x63s8z+Pmm2/mggsuqP/uggsu4Oabb8bzxj8zE4/36KOP8o//+I9193w8Hufiiy+mt7eXUqkEwP/7f/+PM888k7a2NhKJBB/+8IcZHh6eFOcxHRP/e+VLX/oSTzzxBP/wD/8w51iYgYEBLrnkEjZu3EgqlSKVSlEoFNi3b9+4940tUsRiMRKJBAMDAwA888wznHLKKePGM58mrytWrOD+++9n27ZtfOpTn8JxHD7ykY/w5je/Gd/3Zx1/T08Pb3zjG+d8vonXU8vAr13Pc889x8knnzzu/RN/ni9f+9rXxq2F2vzeeOONvPe970XXg+/Y97///Tz44IM899xz0463NubaeOfKF77wBbLZbP1/+/fvP4ArkkgkEolEIpGMRTrRDzELiTw5XJx7XBsdzTF+dN8edg0V8X1BKmKwYXlixniBqZzl83FTHqzIm0O1C2A2B+HZm1sxNJUtq1LsHS6SK4+P4ljdFCNbcshXXLa2p9m0IsnOwTz/598fpT9XYXkyXHdtCcCtOFiuT0RRqDgevbkKz/XluX/XMMetSuH5gljYoOx4+ELgeQJFCT4LQdq3QEFTQVGC/zXFTDRVxfcFK9MREnNwUU/nUn3X8W3cPqYoMtUOgNse7SJsqDzVleXenUMM5CrYriBkqLTETEKaStjQESLIiq/tjBgp2WiqWhdX57IeagLVaNFm88oUo0WbvcNlwoZG2AgE2JipY2oqridQFA/b9SnZwbrQVRVdVWiKh+pC7Oa2FL4QPLpnFE1VSEcMIqZej+gpWVk2t6VIRoxp19h8d0osdOfHXO/beSesYmt7GoDOwcI4N3mtAWznUIFc2cV2fXwEm1am+Ohpa8d9N8x1nEXLq0cP7ejNUXG9cVFH+YpDpuzwxP7MuPs8ViCeOK6QruKLoGiUihlzikdZTJZK9MmB/t051Lt4DhVL1Vn/Uoh9OxSEw2HOPPNMzjzzTP7hH/6Bv/iLv+ArX/kKf/zjHwHGFeUcx5nyGLHY7P8t9bvf/Y7u7m7e+973jvu953n8z//8D2efffa0x/N9n8svv5x3vetdU45/7969vOUtb+GSSy7hiiuuoLGxkXvvvZeLLrpo2jGPZcOGDTz77LPjftfS0kJLSwvLli2b9fM1LrzwQgYHB7nuuutYs2YNoVCIU089Fdu2x71vYqNURVHqAvfEIuhC2bx5M5s3b+Yv//Ivuffee3nNa17DPffcw+tf//ppPxOJRBZ0rrHXU3uWxl7PxOdr4jWqqjrpdzPdt0suuYTzzz+//vPKlSsZGRnhF7/4BY7j8L3vfa/+mud53HTTTeOc/DPN/1wJhUIHFIEkkUgkEolEIpkeKaIfYhZL+DpUHLsqzbfes3XeQtCBxCMcLLF7rrESB7ILYC5i0707hzB1hbCuccLqBvJVN3otiqNYcRkVgu5MuT7fuqqSjpqUbY+85dbFxWzJCVy21YiWoaJNImyQDMNoyeHx/Rl8AUe2JupNQn0EGkE+tCoEng++8PH8QFDXVBVdVbE9n6aYWY8HmUm4malw8Hx/npLlsaohOqUgFDV1frejjwd3D9OTreD7gqaYyfrlCcK6xo7eHN2ZMlFTpzFm1o8RxN8Eedw9mTLZ8uyCBEDnUIFtXVkihkrBctG1IDPbrTY1jZo6luuzflmcgXyFgbyF7wts1ydiaKxIhTl+dQPvPH4lUVPj8X2jZMs2P3uki7LjkowYGLqGoigYmkIqYpAtO+weKrJxWXzaNbYQ4WymngJzEYrnGhkxlQjbEDM5MdpAruLQOVhk08ok33jXsej65A1Ocxln52CBkK7y/ECeiuuRihjUGroamkIspJMpOdy3c5h3HDfZ6V4bW21cecvFdjx6shU2tib40ClrSEWMQyZkLyWB9kD/7rwcRd2lHJfyUoh9Oxxs2rSJX/ziF/VYi97eXo4//niAcU1G58uNN97I+973Pr70pS+N+/3Xv/51brzxxnEi+kROOOEEnnvuOdavXz/l64888giu63L11VfXi9+33nrrnMf2/ve/nw984AP813/9F+eee+6cPzeRP/7xj3z3u9/lLW95CwD79+9naGhoXseozf9YHnjggQWPqXZMYFZXfiKRYO3atdx9990ziu3z4aijjuKhhx4a97tHHnlk3M8tLS1s37593O+eeOKJSWJ3jcbGRhobG8f97j/+4z9YtWrVpLm7++67ueqqq/inf/qnukN9NgzDmLQ7QiKRSCQSiURy6JAi+mHgQIWvQ82hzgs+WGL3fNyYC3WQzkVsGshZLEuG2TdcYv2yOMnIi/8YGylYPL4/g64p/PDe3YRNjfXL4hzblqq619PsGQnc6wXXx/J8wrqKACzXJ2poGJpanUeDouXguD4DeYuT1zaQCBs81Z0BETQm9YXA930qjl/PAVeAsuMRDemsbX5xnqYTbmYrHDy5P1N/bSIjRZudg3lyJQcEaAokowZ5y2VHT5DBvjwV5oXBAo7nM7GHmqIomJpKyfHIzUFE396d5YZ7dvF8fx5TU9E1lWREJ6SrFG2XVMSoNw6NmhrHt6d5uifH2qYY735FG6mISSpiULBcbn+8myf2ZejJlCk7HhXHI2JqKChY1aiYYC6CQkGmZLN7pMjJa5umdPwuVDhb6M6P+bqLpxNh+3MW7Y1RPnrauikF9LmOc21TjGWJEE90ZUiPEdCBaoyOR1M8RH+uPE6snW5sCgojJYeV6QgfO23dIf1eXYoC7YH83Xm5ibpL3Vl/KAq+S5nh4WHe85738LGPfYwtW7aQSCR45JFH+OY3v8m5555LJBLhlFNO4etf/zpr165laGiIL3/5yws61+DgIL/61a/45S9/yebNm8e99pGPfIRzzjmHwcHBunA/kX/4h3/grW99K+3t7bznPe9BVVWeeuoptm3bxpVXXskRRxyB67p8+9vf5m1vexv33XcfN9xww5zH9773vY/bb7+d973vfXzhC1/grLPOYvny5ezdu5dbbrll2uaSE1m/fj0//vGPecUrXkEul+Pv/u7v5u3uvuSSS7j66qv5zGc+w//5P/+HRx99dF7xOp/4xCdYuXIlb3jDG1i1ahW9vb1ceeWVtLS0zCkW5qtf/SqXXHIJy5Yt4+yzzyafz3PffffxV3/1V/O6jhp/9Vd/xemnn84111zD2972Nv73f/+X3/72t+P+2+0Nb3gD3/rWt/j3f/93Tj31VH7yk5+wffv2evFmLtx44428+93vnrS+1qxZw+c//3nuuOOOORdIaoWE0047jVAoVM/wl0gkEolEIpEcGmQm+mFic1uKy87ZNC53+MvnHL3kBPTDwVwzlOcbeVMT2xpjJjsHChQqQYxKoeKyc6BQd2Pu6M3V88yv/PWOcRnjs2XQ1sUmc3qxyXJ9Xr2+edI4ukdLPLh7BNv1Wd+SYF1LvJ45fNujXTieT8jQOHF1A69Y28DG5XHipk46ZtbjYDTtxUdaVxVURSUVMRguWOQtlw3L46xIRdA0FcfzsZzAfq4o4Pk+rg8CQUvcZPPKFI0xs3686YSb2QoHrakwluczVBi/bVwIwe6hApXqcW3PJxYyMDSNZNjAcj32DBUCsVtVsdwgZ3w8Asv1COnauGLEVNSEzT1DRUw9yPc2dZWRok3ZcfEFDBdsipaDqoLjCXYNFlmZjnDJ647gdUcu5/jVDZRsj+/8704e7BymJ1PG8vxqw9dgLK4vqDgeI0W7nncvhKBoe8RMfVrH74HkVo/N1J4uR3ti9nPnUGFePQIW2u9gruNUVYVXb2hGUxTKjjehV4BDyAgKSrYrpiwkHOjYFoulnGe90L87L5VeHnPlUPbHWAgH62/gS4V4PM4rX/lKrr32Wk4//XQ2b97MZZddxsUXX8x3vvMdIGgE6jgOr3jFK/jrv/7rSQ1A58q///u/E4vFpszafv3rX08ikeDHP/7xtJ8/66yz+PWvf82dd97JSSedxCmnnMI111zDmjVrADjuuOO45ppr+MY3vsHmzZv5j//4D6666qo5j09RFG655Rauu+46fvOb3/DGN76RI488ko997GO0t7dz7733zuk4N910E6Ojoxx//PF86EMf4lOf+tS84mAAVq9ezW233cavfvUrtm7dyg033MDXvva1OX/+jDPO4IEHHuA973kPGzdu5LzzziMcDnP33XfT1NQ06+c/8pGPcN111/Hd736XY445hre+9a288MIL87qGsZx22mnccMMNXHPNNWzdupX//u//5m/+5m8Ih8P195x11llcdtllfO5zn+Okk04in8/z4Q9/eM7nePTRR3nyySc577zzJr2WSCR405vexI033jjn41199dXceeedtLe3z0vIl0gkEolEIpEsDopYrJDDlxG5XI5UKkU2myWZTB7u4fxZMsnJOSF64ECEsaliFjYsC3Legfp5o6ZOb7bMaMmm4viEdJXT1jfz8dM7pj1352CBr/zyadIRc0oHYaHikinbXP72YyjZ3rhxdGVKuJ7g+PY0jfEX8yyFELzQn6fs+ESrznRFUchXHB7ZM4ovRHW8Ki2JMDUHr+P52K7PEc0xnu7LsSIZZv2yBBXX47E9I4yWHRQgHTWImBr5skvJ9jA0lVevb6IpER43hp0DBbasSvPlc44eJ34+uT/DP93xDGubY2hTiLee53PPC4M0Rk22tqfrolWu7PDo3hEs1ycRNijbXj06Zuz4j2xNsK07S76aVZ6MGONyshWgIWbyN2duZNOKZF1YGut4Xt0Q5Z9++wzburIc0RLj8ao7Phk2sF2foaJVDYkXOD6ENIWNyxOcsKZxnEvX9wVX3LGDp/ZnGCxYDOSDz4nqeAEihlrdDaAQNlS8amHC1FW+8tZNnLV5xZRrp3bsbV3Zce7YqeZ/4vVN5zyv7ah4Yn9mUtZ8OmLQOVjkmLbUuPsmRCBSW27gMv/Hc4/h+NUNk455MHK+OwcLfPbWJ8mUbMqOXy8OpSLBrghTU+vPz1S7Y5ZCBvmk7wAhyFsujutj6CqKgEzFmfYaliLzWZtLJYpsJmb9zvIFe4aKfOmco+t9AQ41B/Nv4GIj/5tJ8nLh4osv5tlnn63n7r8cqD2f7Z++FTUUPdzDkUgkEolEchjY8/VzDvcQljxz/TfNS8M2Jvmz42BG3kwXKwFwxR07GCnaNMVMtvfksKrNDWsZ4w92DlN2PP56GgFjPpExqqrUx/FsX44b793DimSYRGRyY6mV6ShdoyVCusr2niwNEYNYSCdsqAzkKmhqEBnyYgSGoGS7NMVCNMZM1jXFWNcSYzBvYTk+qqoQ0oP8b1VRcD1oTYVpjJo805fnia4sJ6/ViIb0WXOTZ40ecHxWpiJETW1c3Eau7FCwPFJhnTWNUV4YKFRzyYPj12JVTE0hFdaxHI9UVMdyfPKuh+X6eJ6PJ6Bgu/zjr56mLR2lvTECKIyW7HqRpCURYvdgkVUNUVRVZV1znKKVZaRoU3G9uis4aurENYVESCcW1nnn8SvH3eeag1VRFPpzFkIITF1DUQKh0fUFZccnYupoisKRrUlMTaE3W+GktY2cual12nU519zqHb25OWVt14pFT+zLsHu4OClrfvdwgb5chaZ4iLaGYFv/SNFm94RmoT+5fy+GptaPfTDjndY2xThudZqn9mdYngrjeqLeKwCoi7XTOXAPdfTUVIyNPhkt2vUmp7WCQDysEdK1l0z0Cbz0ennMxkshLuWlFvsmkbwU+ed//mfOPPNMYrEYv/3tb7n55pv57ne/e7iHJZFIJBKJRCJZokgRXbJkWWjW81yYSmzrHAziLVqTYZ4fyGO5HsmwURfCE2EDywlEo+nycucrNtXGka+4aIpCNDT1IxkxNWzPJ67qjBRt9g2XAAjpgdDjeLXGl4GoXLJdwrrG2qYovbkKx69u4ItnH8W+0VJdsG9NhEBRxjU1VRQFQ1fZOVigN1dBU5RZhZu5FA6OX93AO45fyc8f72Znf4G9ww62FzjrO5bFWJWO0J+vMFK06w0la6KjUY1eaYqHaI4ZWK6ga7QEQuD5AlVVSIUNPF+wb7jICwMFdFVhy6oUa5tjlG2PZ3py9OYqtMRDxMNBg9LNK5M8uHskOI+i4AlBKmJw1IokDVGDnQMFfv54D8esTNXvV77iUrE9+nMVPF8QNoIceQBTV/FdD8+HouUSC+n4vmDUclmZjnDeiatmXbuzCWfAnLK2x7pYR0v2lFnzm1ckGchZPNuXY0UqRKbssr07Wy0caTieoCFssnekxPV3v3BInK9jn5+BnFV/forW1M/PUnCeT6Qm0PZly3QOFqlUC3G13RPDBRtVUejLVtjafliHOi9eTqLufIqdh5OD+TdQsjQ4++yzp3U9f/GLX+SLX/zinI4Tj09fPPztb3/La17zmgWNbz4c6LUcjmt46KGH+OY3v0k+n6ejo4Prr7+ev/iLv1j080gkEolEIpFIXh5IEV2ypFmos3Qh4lrNQeqFBLmyS9TUx4kruqpQ8AS6ovBUV4bOoQLrlyUmHWchYtNszsi+bJm+bAUFOKG9AU8EWe6jJZuYqRE2dLb3ZBks2IQNlYaoyYpUmOGiXRcedV0dJ9jHwsaUUQatqQgVx+fDr1qNqqiAYOPyBB3N8WnndrbCwTuPX0kspLN5ZZKu0RK5ikBXFXRV4bm+AlFDp6M5TsnKki07RAyNsuORCBv0ZyusaoiytT3FHU/1snOwiFuNTlGAVMQgGTERwqcnWwEgpGv05yza0hHiIY1lyRD7Rkrs6MtyWqyZou1RtFw0FZrjJr4AzxNsbkuSjAQ58GNzkWtrMBHW8YGC5aKrSpDjUp1CTVUwNQ2bIM+74viUHI+t1fu+aUWSzsHCrGtyLjslZmqGeNTyRD2Te3kyRNdouZo1r5IMq+QqDnuGCjSsbuCo1gRP9+TY3p2lZHtYrkfE0CjZHhFD58jWBOlqQeFQNVqc6/MzVSzTVI78+bAYovzaphjrW+Lcsa0Xz/dJR01qi0RXg3WiKgoPdA5z5qblLylB9OUi6r6UnPVLYXeF5ODxgx/8gHK5POVrjY2Ncz7OE088Me1rbW1t8x3WgjjQazkc13DrrbcelONKJBKJRCKRSF6eSBFd8rJjoeJaTciuNfrUxwgoluuRKQXu6b3DJTwhuO6uF7jktUdMecz5ik0zOiN9n2f78miqwjErk6hq0Dw0HTVpa4iwc6BAR0uM972ynd881UtvtoKqKAiYUrifS5SB7Xr88vEeBgoWng+piM6G5QmOa0/zxP7MlHM7lfB5bFuKlekw3/9DJ7sGC/RkK/i+oDFqsLIhiqGp7Bwo8EDnMFvb0xy9IskLA3mGiza6opCOGGxtb6gL6KqiEDM1VOXFOIyi7RHSvWDOqvnkIV0jW3boGi3Tn6+QLTm4vqAnU+E323oxdQ3PFxRtl7CuomkqyxNhkuEXo3QipkZ/zh8Xu7G2KcaKVJhne3OYuort+hgK9fslhCBqaHgiyLb/yts30dEcrzerneuanGmnxGzNEO/bNVR/n+1649ayogSxP9myS77i0pqKMFy0aUmEeGJ/BhUFxxM0xUKsa47REJu+oHAwme35mZQXPY0jfz4sliivqgqndDTyyyd7ECJoUKupQc52yXYJGRpHNMfZOVg4ZPO5mLxcRN2Xk7Ne8tJlscTh9evXL8pxDoQDvZalcA0SiUQikUgkEslMSBFd8rLiQMS1mpD98O4RNJV6PrfleowU7SAuxdSIh3XKjseeoSL/ctfzvPvEdlpT4UlC33zEppmckbuHC3i+qMaKqOM+VxNPdw4W+NCpazjrfa2zCvezRRls78kwmLfpHCqiqyqGrlCoaHRnyvz6yR5aEiGOaElMObeXnbOpfv6+bIVfP9XNL5/swXI8XCEQQhA3dfryFr05i6ipYeoqJdvlmd4c65rjtDdEOXFNA69e38zW9nS9KehI0WZlKkx/zsLUFYqWh64puF7QuDFmarWLQFHAcjye68/jCxHk2gvBUMGmYHmYnk8qbFBSlGozVUHU1Bgp2hi6SqKaBT8xF1lVFd62ZSX37xrGcj0EUHF9NFXBr8bPKIpCRNe44JTVrF+WmHFNPt+f590nrGJre3pWR+/YrO2pqIn+tdz7SEpDEIxpqqx52/Wq165w+oZmhgsWrYkwIVMjEdJhzLqYqqBwsJnu+fF9UXfaz+TIn49rfrFF+dZUhNZkGNsL5qwWTdQUM1nbHCcVMdgzVHxJ5aK/HHm5OOslEolEIpFIJBKJRHLweVmL6N/97nf51re+RW9vL8cccwzXXXfdIcmFlBweDlRcqwnZXSMlBvIWhYpDKmIEDnTXx9RVklUBvSlmsjId5on9WZ7uzbEqHT3gOInpnJEdTTGEHzT+nIqxAudchPuZBPudAzn6qpEojYkQuqri+oFIXbaDBpwNMZNYSENRlMlze06SjpY427uz/OzR/TzZlUUIQTpqMFSw8QRkyk49RsbzfRJREyEEtid449EtnLmpdZyQNdaBXROFhai5vxV0lWoefFVcVoLXLTeIfGmsuqlHHQ9NhZChYbs+uYqDriigqXhC8Fx/nq7REpqqkghrGJrKKR3Nk3KRz9y0nJ8+uI+H9gzj+T6eD64XZLNHNJWQoXFqRxNnbmqddk06nk+2bNPXb7Gzv8CG5XE2LE/MuHbm2gyxJRGqvy8R0klG9ElZ874QvNBfYKRkEzF17niqj+GiTUPUpDk8eZ0thUaLNWrNXWdz5M/V5X0wRPlEWKcxbpIKGwiY1HegUHGXzHz+ufNycdZLJBKJRCKRSCQSieTgos7+lpcmt9xyC5/+9Kf50pe+xOOPP85rXvMazj77bPbt23e4hyaZI74v6Bws8OT+DJ2DBXxfzPj++Yhr07G5LcVfn7GRUzoaURSF/ryF5XqEjUBAt1yfkKHRGAvxdE8e1/OxXZ+muEk6YrKtK3C0bu/OLuiaN7eluOycTVz+9mP40jlHc/nbj+FTZ2ygMW5Str0pP7MQgbMm2B+7KkWmbLNnqMhoycLxBKqi0BwPYWiBUG5oKpGq8KyqCtmSM85BO3Fua6JkECsD8XAg3gohUABfgBBBI07HA1BIRYLGoE935yY5QesO7KpDOhnRsT0fU1OCbHQlcNCrigAlMFCX7GB8ieq5bTfIKA8bOsviIZrjIWIhnRXpcL1Bqev5hPSgUWhvpsJg3mJre2qScLqjN4fleYR0jaip0xI3aI4ZxAwVX8C6pigXn96BqipTrsnRos327iyjJafq+AZD02ZdO7UdBL3ZMkKMfxZqzRA3LEtw2hHNL74P6GiOE67G29iuR67sULY9BvMVoobG1rYUK1JhXE/wVFeWkYI17bEPd6NFGL8epiJialjO3F3zi/G9MZHaverLVUiEdZriIZKRoEnxUptPiUQikUgkEolEIpFIJLPzsrXBXXPNNVx00UX8xV/8BQDXXXcdv/vd7/je977HVVdddZhHJ5mNheQTzzXuYjZxbXNbiuveezx37ujjJw/s5fH9WcKGCig0xQzWNMXYM1zEcj1SEYOCFTi049GFO1fHMtEZ6ftixviV3myZLavS8xbkJkYZZEo21975PCE9cGGPJXB+gycEtufjVBt71hg7t3uGi7zQnyeka1TcwIGrqICi4Hs+qgJ+9ZhCiKA4oiqEDZWebGWSg3iiA7vWgNT1q7EtVcd5ruwQ1lQsT2C7QQ61qgQu4HzFRVEUUhEdVVVRFVFtLhpkhisE8T3ZikvM1FnZEEFXFZ7cn+VtW1bW72OtQOB6gleua2T3cJFcOYjriJjBnLWmImxakQSmWJNC0DlUoFJdO76AQsXF0BTWL5t97ZyyrpEdPTm292RZ1xgjUo2dGdsMUdfVSTsNxmbNW66Priq0paN0tMTruefHt6d5cPcIT+zPcPLaximPfTBjLuba1HOujvy5FpUW43tjIc12l0rjSolEIpFIJBKJRCKRSCSz87IU0W3b5tFHH+Xv//7vx/3+TW96E3/6058O06gkc2Wh+cSLKa6pqsJZm1dwxLI4n79tG1FDIxkxSIR18hWXXNklaup4IsiYNvRAQF1InMRcxrLYgtxE0e/YthTburP4AgxtfIZ2bQyqEuR+q1V3+ljGzu2T+zM831/A8XwKFZeS5RI2tODzArSqc9wXAkVRUNTAOd4QNVEVZZJYOTHDvSFmsrktxTO9OfosF9cTCIJIFV0VhAwNVYGyE2TZR02dhqiJpjpoqlrPuK9FsIQNDV8IHC8Ql9cvi7OqIULR8ibdx7Gu5XhYpzFmkrdcHNfH0FUUASMlu/6ZiWsyb724doJolSBP3dDUGdfO2KJSoeIyWrYZqcavNMbMSc0QJ0YDWY5Pe0OUjcsT7Bku0pYOMrvH5p43xkNsWZVm52Ce3lzQnPZQNVqcT9Fstkz/+RaVDvR7Y6axT9ds95SOJrzqThuZwS2RSCQSiUQikUgkEsnS52Upog8NDeF5HsuXLx/3++XLl9PX1zfp/ZZlYVkvRhjkcrmDPkbJ1BxIPvFii2sQRGFsWZViW1eWtoYg7sHxfDxfoKuQq7g0xUJBLEeVg9GEcbq89IUInNOJfqesayQVMchVHAqWW8/QhkBY11WFsuOTCOvjBMWxc1uwXH70pz2MlmwQAt8HD3B9t9p0E7y6qx1CukLZ9gjrGitSYQRMEiunKiJ4vqDiBMdMGFrdSe54PrbrETI0EIEA2tEc48jWBI/vzwQNYh0Ppxrd4lXd7MKHqKmjKjCYr7CqITLlfZzkWlaUamRMgOcL+vNW/TMT16Tj1tZOEOtRsl2aYmb9mqc658Si0opUhJIVOP5jIZ0LXrmGMzctn/Q8TNU0MVt2uOo3z9KSGC+g12hNhSnbLh999Tra0pFD0mhxvkWzxS4qHcj3xlzGPr7ZbpkHOkf4yYN757zDRiKRSCQSiUQikUgkEsnh52UpoteYmG8rqs7XiVx11VVcfvnlh2pYkhk4kKaBB8OxPdUxNVVBIBgtOcRMnXXNsXGC5MFqwjiVKDpfgXMm0a9rtExDzGC4qOG4PtmyQ9TU0dVq4UAITF0lYqgULW/S3L7z+JX82x93s3e4hKqAUFTCuoLl+vi+wPFE4EIniHJBgKmpNMVCrG2KMly0pxUrxxUR+gs8P5Cn7Pi0NUSwXEGu7OALP3C7E0j/jTGDoYLDjt4ciXAQwzNStCnYHoamEA/pZMoOtheI2smwEWS+l92gSWvViT32Ps7XtTxx/STCgVBfcTxsL8jXX9v8onA78fPTFZUSEYPNbSl2DhR4cPcIZ25aPmkstfOPfU46Bwuzjj9sahzVmliUXRTTRbTUfp8tO/zk/r3zLpotZlFpod8bcy74jWm2e9tj3fPeYSORSCQSiUQikUgkEonk8POyFNGbm5vRNG2S63xgYGCSOx3gC1/4Ap/5zGfqP+dyOdrb2w/6OCWTOdB84sUU16Y7ZsX2MHUVzxMcszJZz5SGA8sonwsTRdH5MBfRL5qO0JaOAFCyPcq2S9YRuL5POmrygVe2M1J0ppzbsKHyyN4RFAVa4mFGSzaeLzA1FRcf1xd4AnRVIRbS6WiO0pqKoCsKvbkKjTGTdxy/ctoiQa2I8McXBrn2rudpiIaImCqP7R3F832EAENXEQIcT5CO6rTEFUZKNs/151jTGKMxZlK0PKJmEOGiqQq+EDRGDUKGhi+CRrFD+QpFx+OkNY3j7uNCXMsTCwAoULBcWlNh1jXHaayun6k+fyBFpak4GLs1pmO6HQ/Htad5Yn+GnQMFMiWH/aMl0hGD5nho3LM02/WNLSplyw65skMyYgT31hfzKi4t5HtjPvdmbVNswTtsJBKJRCKRSCQSiUQikRx+XpYiummanHjiidx55528853vrP/+zjvv5Nxzz530/lAoRCgUOpRDlEzDYuSaL4Zje7Zj9mXL/OyxboaLNqauvSSaBs5F9Bsp2Xzw5NXcuaOfbT1ZMmWBJwQRQ6M1GWak6PCu49uIhfRxcwtw8/27yZYcEmEdU1dpiJnkKw62K1AV0NXAhf72rStJRQx2DhYYLth1sXJre4qfP949Zbb02Lkv2h6GqtKSCJEp2dhu4HLXNTUIn1FebFgaCenEPUFTLMRHX72OmKnx/Xt24Yrgul1P0JspUXF9XN+lUHGwPJ9t3TkipsZws8WO3lxdRJ3NtdwQNXjluka2dWfHrbux6+eJ/Rlue7SLiuNhakGkzHRrZ7Ga5dY4GLs1pmK6HQ8Pdg7z6yd7aEmEOKIlQUhX6R4tk684bO/OsrktNU5In+36VFWhZHv88smeGfPU59K0dL7fG/O5N4tdDJFIJBKJZD5sv/wsksnk4R6GRCKRSCQSyUual6WIDvCZz3yGD33oQ7ziFa/g1FNP5fvf/z779u3jkksuOdxDk8zAYjllD8SxPZdjbm1P09YQHedc9YVgZSrMOVtWsGnF0vmHSk1AfGzvKNmSw4pkeMr3RUyNziGbX2/rpWukRG+mgicEDVGDTa1JwqY+Lnpia3saeNFxfP+uYSqOh+uLIDs9pNMcD+F4gaDt+T5F2+OMTct58zGt48TKouXy7f/dOWXUxTO9OZYlQoyWHCwnmOeebJmIGWSzKwrVJqXBdQgRCJOqquD5AkNX0DWVtnQEVYHRskPXaJmQrqKpKiFdQ7g+w0UL1w8ia1amw6xMR+nJVLj+7hfGRW1M51puS0cQMG3edW39dLTE2bg8MSfX81yLSrGQRudgYU7i78HYrTGWaXc8hDQcz6dsB2skFgp2A5i6iqkHYvjuoSINUaMejzSXpp6zZZIDc25aOp/vjfkU/Ba7GCKRSCQSiUQikUgkEonk0PKyFdHf+973Mjw8zD/+4z/S29vL5s2b+c1vfsOaNWsO99AkM3ConLKLQc25eueOfn71VA+92Qq92Qo/eXAfD+wembZZ4FxcsYvF2EiNWnRGruJwVOv4GBqAvmyZvmwFhKBUFQAjhkbZ8XimL8/mthTrl42PntjRmxsjYobYO1ysNv0Mmnc2Rk1ChoZQBZmSSzyss3F5fJxY6fuCK+7YMWXURZNr8uDuEToHi5y8tpFoSqdsuewbKfFUV4aT1qYJGyqZksB2PPSqsztkaPXGr2FdQ1Xgyf2j/HHnMABRQ8PxfQwFipZL0XZRFWiKmRzZmmRVtYmsEGLKqI3pdiaMzjHveq6u57kUldrSEf7jgX3sHJxdJJ7v+RfCdK7rvOWSr3gkIwa5au58IqyTjOiMFG0ihka27JC3XBJhY9ai2Vziif7tD52UHG/O92U+zKfgt2e4eMA7bF6uHMrvQ4lEIpFIJBKJRCKRSBbKy/pf7JdeeimXXnrp4R6GZJ4cbKfsXJmLuLOjN1cX8laOEfynE+mmy4meSfBcKE91ZfjGb59lpGjTmopw5PI4hYrDYN5im5vh2FXpupAufJ9n+/JoqsLqpiiP78sSNXUMTcXUVbJlh91DRU5Yna5HT3QOFbjt0S56smVWJsMYehCx0pe1QAhcH3IVh5QCZcfDF3DSmkY6msc7faeNuhCC3cNFFIWgUSmgqQrxiMHx7Wnu2zXMH14YClzuAjxPYHkeqgKxsM5I0cHxfEq2h+163HBPJ64vOL49zerGGJ1DBXJlF88X2J4gZmocv7qBpviL0U71qI3+PH98YZB01By3Fjpa4vi+4BePd9NbnQdB0EB1trzrubieZysq6apCf75Cd6Y8b5H4YOzWgGliToQgU7KpuB5xM3CkO56Poiisa45TtLLBGvGhYnsoKLMWzWaLSGlNhnl47wiNMZPNK1OLlkM+9nvhlHVNdI2UZi34Hcos+vlew+EUrg/l96FEIpFIJBKJRCKRSCQHwstaRJe8dDmYTtm5MBdxZy5O2LEi3VyiJw7kmscKY72ZMv9y9/N0ZyqEdJXRkkMyotOaimB7PtmSw3N9eU5a20DZ8dk9XMDzBcesTOH54PkCvX5ehaip113CUVOnP+dz944BfrejD9v16c9aaKpCSFeJhzRKjofwBWXHQ1MVDE3lqNY4F5/eMel6pou6yFsuubJLPKRTcQLRtYaiKGgKFKt565oCXjXn3BeQLdnoapCRnoqadLTEeL4/j+cLnu4JMs5PXN1A3nIZyls805snbKoYmjppXiuOx/MDea6963lMTcPUFZYlw7x6fTPHtad5ri8/aR6SEZ2O5jgNMfOA866nKyod25ZiuGjTkykvqWaVE2NORos2nUMFRoo2RculZLnoWvC6EME6a2+I0DVapmC5DOQtUlF/1qLZbBEpnhAUKi5HNMcWLYd8qu+FhqhJxNQYLdnTFvyW0g6bpSJcz+X7UArpEolEIpFIJBKJRCJZKkgRXbJkOVhO2dmYKO6EkypDBZsHdg3zfF+ez599FFtWpefVLHBtU2xO0RONMXNesRxjx1wTxkaKNt2jZYq2SzpikAgHIvRAziJbduhoitGnVRgt2zzblycVNehoiiF8aE2FKVoumqrg+gJDC8apqwplX+C4PmWCqJY7tveSK7s0RA0MTcX1BUU7EEiXhQ2KlkPZ8VmeDHNqRxPnnTj1dUyXLe24fpBpril1IR4AIegcKlBxPTQVUhEDTVHxfJ+K41JxAxc8wqejJca6lgRCCBQUGqI6uYobZG+vTpMIGwCEh4o4rhgn1AOMFm22dWUoOx4N0RC6prCzP89TXVn+99kBlidDZEoO+YpDUyxUn4eRok3JynJMWxJVURgu2jzbl1twIWiqopIvBJf/aseSa1Y51nXd5Jo83ZOj4npETR3L8Sg7Pr7v82xvjn0jJSzXr947j2XJMB951RqOX90w61zNlkleqOaLx6v3eCLzzSGfTvTtyZRpiJlc8MrVtKYiMzYuPdw7bJaKcD3fAqREIpFIJBKJRCKRSCSHGymiSyRjmCjujJYcnuvPVWM/fLozZf7+tm1847xj8QVzbhY4m+AeNXXu3TnEinSYdU3xWcWtsa7zcXncyTC92TKeLxAicOuWHQ/PB9/3KVpguT4ntqcZKNh8+NQ1nLCmoS7Ilm1vXE51MmygKIGgrqkKuhbEbFiuh0LQLFJRFBRFwdAUUhGDbNkhbKgc0ZJitOTyN2ds4DUbWqYVw6aLujB0FbWaWd6SCNXzovOWy0jRxq9eY8HyUPBQFAVT10gYgYAaNXXWL0uQiprkyk61MMA4V30ibJAI6YEruGija+PjZHYN5ilYLisbIpiawvaeHJbrkY4aFC2XnkwFz/PxBDiej6lr9XkYLto8uHsEXVWwXZ8b793Dw3tGF+z4nVhUenJ/5qA2q1xo5EfNdd01Wubx/RkczycdNfB80FQVXRXomkKm7JC3XRqjJo4QxEydkK7yu6f7ObJ1dvF0toiU0ZJNPKyjK1MfZz455HMRfR/cPcqXz2mdcdyHc4fNUhKu51OAPByFVIlEIpFIJBKJRCKRSCYiRXSJZAxjxZ3RksP27ixW1UWrqxqG47F/tMTX//tZPnzKmjk3C5wpeqKWiWy5PiuT4fqxZoqFqblZK7ZHd7aM6wVZ30KBQiUQwi3Xw/J8HA/ChoZuaHi+oGi5PNmVob0xxglrGuq53nVBclm8nlOdqzhETY2i5ZIMm/TnLEKGiuurpCNmtVmkQzpioKoq9eiXkoOpq5y8tmlGAR2mj7pQRJCD7otAMK2JbY4buJZdHxQFdCU4hqgWNYI5DV5z/SDjZWxhIBHS8aquegjOETU1fGHSn62gKSoRU2MwX6EvZxEPG6xtirF7qEjJdokaGhAI9nnLpjGik6m4ZMrBXCmKiuX6WNXmqjHTYHkyzIpkeFEdv7M5sQ+kWeWBRn5sbkvx7hPaeLoniy8UCpUg1md5MkRD1OTZvjwVfFzPx/Z8muNh1jXHSEeNOYu5s0WkrEiF6WiJB3E3Yf2AcsgXU/Q9XDtslpJwPVsUz4EWgCQSiUQikUgkEolEIllsJgcASyR/xtTEnbChsnuogOV6JMNBXImiKIQNjZCmMlKweaBzhCNaYvRmywghxh2nJtJtWJZgbVNsnOA51TlHSzZhQ8U0xotKE8WtWhzDtq4s6YhJcyKE7QZ54U/35BguWHi+IGxooATCMlW9TCHIDtcUhaLlYbs+qxuiwIuCZGPMZOdAAVNT2bQySSKkkyk5eALSUYMtq9Kcsq6R3kyFZ/py5CsuJdujN1ehaLn4QiCEoGh7xEx9zjnPtaiLY1elyJRt9gwVyZQdjm1LsaohQne2TKHsVJuAenWx3NRUtOq9UdXADe8JgS+C661FwNQaWIZ0jWzZQSBQVYVCxWXnQIFVDVE++6aNbGlP188/UrQxtaDR6nDBpitTpmR7DBdtBvMWubKD7ws0LSgoCAGjJQfH88iVbTw/mHxTD0TTRMRg/bI4I0Wb2x/rxvfHrxnfF3QOFnhyf4bOwcKk1ydSc2LPZf1NZKZzTVxja5tjpCMm27qC32/vzs56P2vNXlNhg00rkhzXnuLENQ31xq1hQ6UlbhIPGxzVmuSE1UGT24nrfTamXjc2W1al+eszNvLx0zvqa7pQCZrI1u75fHLI66KvOb3oazlLW/RdStcw0/chHFgBSCKRSCQSiUQikUgkkoOB/BeqRDKGmrgzVLDJlR1MTcV2/bpA6/oCXVNpTYXZOVjggleuoSdTmbVZ4EzRE7brUXF8VqTCJEKTH8maKzNbdvjlkz3j4hiGC9a4rO++XAVNDZphKgRCsu8HjmxFUfBF0HAxFTYwdZV9o6W663RiZrPl+LQ1RDhhTSOnrW/iuPY0BcvlG//9LCXHIxHSaYiahPTAhT1asqk4GroWNNb82GlrZ3QtT4wL2bQiyaZzNrFnuMgT+zPcu3OIgVylKk7bjBRtGqJmUGzQVYTnMUlmVpTgupVAOI+HXhQMG2Mmm1cmeXx/Bk2FrtEymgpHtCT4yKvWsGVVmrdtWcme4SJP7s/w2+297B0u8WxvjrLj4fqCkKZg6BpCBPfN9wWW65MIG1QcjURYp2QHud+KomCqCke2JmmMmdXhTe34XYjze6HNKmc616YVyQOO/Kgdf1tXlu5MmcGCRWPMZF1zcDzH8/F8CBsqYRTSUSO4YRPW+1zF3NkiUhYjh/xguv4PFUvpGmaL4pnPLgGJRCKRSCQSiUQikUgOBUv3X/wSyQJYaI5zjZq48/tnB8iUHWoqbS3zW1UVlifCtMRD7Bku0ZoKz0mkm1nwrBDSVVakwuPExBo1cStXdibFMRiaOi7ru2z7hE2N4YKN670YV1Jzq6uKQjysc9zqNLmyO0monEmQ9H3BFXfswHJ8WpMhRksOEVMjFjKImhqjJYdESCcR1jl5XRNnbmqddp5nEnIB/nt7X7354cp0lJIV5MrHQjqvP2oZZdtjMG+RLbtUXB+jeo/dak562NBoS0fYNVgcN9fDRZu1TRFURWWkaIMC/bkKP3+8G1VR2NyWomR7/HZ7HyNFh1REpz9XCWZRgOMJNDXIh1cUUFQoWEHES9jQ2LwyRa7ssK07i6oqtMRNVjVExl372KJIzQ3+s8e6sBx/3s0eZ2tWuWlFks7BQv1eFiyX7/zvzmkbS553QtsBRX6Ma1yZDJMp2wwXggJI0cqyuS1VXbPBvC1LTC4cLUTMnSkiZTFyyF8Oou9SuoaFFoAkEolEsjA2f+V3qKHo4R6GRCKRSCSSQ8ier59zuIfwskOK6JKXDQea4wyBuHNce5pfPN6N4/oYuoquqvhCULI9dFWhKW5Srh4/FtJQFYW3b11JruyQjBikIsaUIt10gudJaxsZLtr0ZIJYjunErWTEmJQjnAhpRAyV0ZJDPBRknidCBr2ZCl41F9yoZoa7XvB/j16eIGLoVQf15K+A6QTJsZnKzfEQ27uzZMtONS9eIWJoZMoO7Y1RzjtxVV14nyhebu/J8o3fPstI0aY1FaG1KUzF8dnWlaVrpETE1Cc5oRMRg81tKXYOFHi+L0/E1GhriGDoFpmSje35CBHsEkhFDFoSIf7y9et5aM/IuLmOmTrP9ecp2i6GqmJoQdPPkaJF92iZv3rDem5/vLt+/oih0ZezQAg0FTwfLNdDV4N1kQhp5CoOQ0WL5ckIsZBO2fHwgYiusa4lMUmMLttBVvqPH9jLQK7CC/0FSo5HazJEczxEXNWJhzSWJ0N0Dhb54X27+ca7tqDrU6dvTScS7+jNccUdO158HnSVoaKF7fp0NMfwhUBVxrvM73iql4rtLSireqrGletbElTsLGXHpWy7dA4W2Lgsji+C3Pq1TdFxhaODJeYeaA75y0H0XWrXMFsB6EB7BkgkEolEIpFIJBKJRLKYSBFd8rJgnAN2nm7esfi+4In9GVqTIYSAouUifK+ah66iKgqD+Qq269OWjvCTB/aya7A4SbSfToiaSfC8/u4XZhS3oqY2Lo5htGjTOVQgV80lL1guuqagF8DUVVzPD7LBVQVVUYmFFFQFhqui89b2hnkJlWObAcZVnc1tqeD8ZZeyHwiyEVPnvBODosVURY101GB7d5bhgk1ID8T/ZERnXXOc9cvibO/OMlKyObG9YUondNTU+dOuQWxPULZ9oqZKSzxEyNAoVFxsL2g6Oly0uWNbL+dsWcEHX7maouXRkynxtd8+S6Hi0BAzMTQV1xcULLfaZLTEj/60h/5cpe7EjpgaUVPD8xUsV+CLIIokpAcxJKqiULRdkmGDdMRg73CJkK7SXnWfN0SNcdcghGDXYJ6C5aEqkIyYoEAipNcb2a5uijJUsMiVXWzXpyfbzyd++ihnb17Bce3pKQs0E0XiqZ6HzsECOwcKKMBwwSZsaPW5b4yZrEhF6MmWEbCgyI+pGlc2xMz6OhkuWPRkymiqwrFtKQqWy3DRxtS1l4Qg/VIRfWfajbPUrmExdglIJBKJRCKRSCQSiURyKJAiuuQlz1QOWJhfjnONmhC4flmS1Y0eD+8ZpWA7CCFwXIEA9gyXWNesoOcUujPleYv2U7li5yJu+b6oxzE0uSZP9+SouB6xkE5IVxkuWnieYDBvEw/rrGqIkq84eEIQNXXCuorl+vRlK7S0peYtVE7MVE5HDTYsi5Otxt7omoLnw3Ht6UmxHm5I0Jct89jeUSzXpyFqkAgbuL4YF/XREDXZN1LCFZObao4UbZ7pzZIpOYQNDc/3yZZ9chUXhSDCRRAI1bbr8/vnBrl/1zCnrW/iL17TwZ3PDJAtOTTGTQwtcFobmkIqYpAtO5Rtn10DBTwBK9PR6usqIV3D1HVAoWy75CouYUPDdgUCn4aYyWVv3cSRyxOTYlOmKooULJd4SGfD8gQjRRvfF5iGSlhRyVUcnurKEjZUoqaOosBI0eVPO4d5eM8o65piHLc6PePuiqmeh9Gizb6RUtAIVVXwhcDQlHFzn4oERYFlyRC92fK8Iz/GFlnG0hAzWSdiWK5PxbFwfR/b9VmeDAEKoyX7sIu5c2Wpi75z2Y2z1K7hQHcJSCQSiUQikUgkEolEciiQIrrkJc9UDtgac8lxHstYIdD2fDRVQZ1wTOH7DBUsdFVhy6r0AYn2Y5lN3KrFMXSNlnl8fwbH80lHDTwfbM+nKR6iJW7yfH+RVNjglI5GsmW37havuZ/HusXnw9hM5SbXZPdwkVzZxfODqBNfwKkdTaxuiPJPv32GkaJNU8zkuYE8ubJDtuxgu4E7vmR7JMIGhqaSDBvkKg57hgqsaw7E2ULFIR01X5xzIeoCOgSCflLRyZYcSo5XjQdxCekahq6RDBtoCmTLDg90jjCUtxgq2PUYlvEEDveS7WLoCtGq4B0PB/nuyUgQL5MMG0RMHVVROHplEkNV6M1WOGltI2dtap10r6cqiqxpiuL5glUNURRFoWx7FG2vXghwfB8hIB4K4fuCTCkQ2ZMxA9v1yZScWQs1k54HIegcKuB4PqauVpt7CkAZN/cbliUIGSpv27KyPu4VqQjhaqPdvqpL/B3Hr5xyXU/XuHKkaLO9J0fZdomYGhuWJdBVlZ5MmYaowQWvXENrKjxnMfdA+x7MlenOs1RF3/nsxlmq1yCRSCQSiUQikUgkEslSRYrokpc80zlga8yU4zyRmhBYslx2DxUQiMBJ7YMvBL4QWI6L7fiUHX/S5+cr2k8l1M30mc1tKd59QhtPd2dxPMiUHDRVoSlmsrY5jqEpdGcqFO1ANG+ImZwYbSBfjSyxPR/XExzXnp51LiZSE/Gf6cnx4O4RVAViIR1DUyhYLkJAf77C3c/2s3OgQNTU6m55UwuEa0NXsRyfsuNRtF3iIaMe05Itu1QcP4iqKTu0jcmHz5UdhvIWPhAzVXRNwfeDrHTH8/FEcH90LZgLqBU2DGzXoy9nkS07GLqC6wcu7LHoqkLBE0QNjY6WOPuGS3Un9rrmOEUrS67i4PmC5niIhKnTm6uwMh3hvBODZqhjG3iubYpNWRTJlh2u+s2zREwtiOMZLOCLYIeDFujdCAGjRRtdU3E8QdQMXOmGJig7HsuTIfpz1rSFmonPQ95yyZVdEmEDXwgqdhBP5AuBoqjVuXfYM1ysNoRdTltDhNse6+KJfRl6MmUszyekB81TxzZhHctUjSuFEOweKmC5HooCTbEQ6YgBilIvOD24e4Qvn3P0nITwxeh7MBe2d2f52aP72dado2x7REyNY9uSvPvE9iXpkl/M3TgSiUQikUgkEolEIpFIJiNF9CXMoXJcvtSZzgFbY6Yc54nUhMCHd4+QqzbNVFUVUwUQZMsO8bBJoeJSsl3yFZdkZHzu9VxF+4mCoKkrLEuGefX65mmzrwGKtofr+6hK0OiSqoCsEGRrp6MGfVkL2/WAQLBMhA2EEOwcKLBlVZrVDdFJou9c1tamFUmWJcN0DhVRgIoTuPWXJcKsbYoGWeTV5pSjZZuK65GqNkQVIhDRPc/H8aFQCWJNQEFTFTzfpz9X4aQ1jZQcb1wUSn/OwnL9ahSJwmDeRlTFZ1cINEXB8am6zF+8juC40BA1GS5aRAyDUjXDfOyuBcfzcXyfI5bF+cir1o6LYklFDDqaYzzdm8X3BQLBaNmuR48A4xt4ThB2xxZFOgcLwVq1XHYN5ik5HomQTt5ycX2BqDaD9YTAcTxMXQ1y01HQVSj7AtcTMxZqJj4Pjuvj+QJdDdaB7fq4flAQEtX/FSyPNU16PeJnc1sKIQTP9+dpjJm0psK0xEOUqw1gp3LCT9W40vV9Roo2ni+ImXqw06A67/MtOC1W34PZ2N6d5cpf76BzqIg/JlZo/0iJZ3vzfPmtm5ackL6Yu3EkEolEIpFIJBKJRCKRTEaK6EuUQ+W4fDkwlQO2xmw5zhOpCYE7enLsGS7RoKkIIXB9Qcl2CesaaxqjPN+fx3EFjjfZjT4X0X6iIFgxPHb253mqK8v/Pjswbfb19u4stz3aFTi2QzphQ8P1Rb0p5ea2FCtTEUaLDr3ZCmFDn9S0cWt7in/67TMLWlt7houMlmxeubYRoYDj+hi6SiKkg6Jg6ho92TIVxyNTCooQEERgBM5k0DUVT/hYrkfZ9ggbGpbjYbk+jTGTi0/vABgXhVK03fp9dT0fXQtiSTxf4Hjg+IGgPrEQ4FUzwFsSJl2jGoamIAiyx6OmXhfvR4s2jfEQF75qLceuSo+LYukctBkt24R1jVhMJxbSWZ4I887jVwLMS9itrdU/PD9IT6ZMTaOtZbkHISvUY1hSYYOQHjjK3eq1GLo6Y6Fm4vNg6CqaGjjwzWrGe0gJinT5iotAkIzofOy0dfWx+r7g9se7sRyfre1jIos0dUZn88Rs/+GCXc0/D3NES5yGmDlurHMtOB0qp7XvC77/h10825dH14IdEnp17kq2y7N9ef7tD51c+97jllRBczF340gkEolEIpFIJBKJRCKZjBTRlyCHynH5cmEqB+xE4Xg+TTQ3t6X42GnruPzXT1N2vLrbuikWYl1zjIaowf7REuWijT4hFmQuov1EQXC05LCjJ4fleqSjBkXLnTL7uva5iuPRmgwzUrIJGxqGptabY3YOFkhGDF69vpmGmMGuweK4po1b21Pc8VTvgtfWi2JdIEBPJGJqqIpCQ9Rgz3CJRCj4vaEpmLpCxfZAUYgYGq7nUbI9ilUX9prGKJ8/+6j6+cdGoQwXK/zVT5+g4niEQlrda66pgYs9yPgOIlEcz8f3BYoCZcejMWZScXxSUYN4SKfiBOJ9yfFwXIHr+zTGQ3z2TRs5dlW6vgY2rUhy544+brpvD4oK6xpjREI6Zdtj70iJ6/93JxFDnZewq6oKx7Wn+cXj3ViuT6gqcPsCXA9UIKSrJMM6I0VnjKc+EHGbYqHAuV5x8YSgO1OaNju//jwkw8TDGsMFG01ViIV1Nq9IYugqtuPRU3X/n7lpef1sB+JsHhtj82xfnpvu3R2MYcKODZj7LpEpxyNEPaYoEdZ5of/AndadQwUe2TuKqjBut4KhBfnxmZLNw3tH6BwqsH5ZYsHnWWwWczdODbkTSiKRSCQSiUQikUgkkheRIvoSQ2bbLoyJDtixwvG7Tmibd9HhzE3LeaBzmIf3jrAyGcY0tLrbWghB1NTwhUl/toKmqPMS7ccKgkA9M7om2sVCyrjs69se7SJsqDxfdaqvSEVoTgiK3dlxjmpTU+nNVWhJhrj49I5JedxjG34udG3NVaw78+hl7OjNV+NvDDQ1EM7LtgdCYGgqvlDxReDmD+kaRyyPj2viOrb54c6BPFFTw3Z9bMfD0DWqZm2oRtkIYKhg1UXpwPWuoKsKfdkKEVMnZgZjTkWM6rigoznOmZuWsTIdRNyMFQsf2D2C5ws2r0xNmq/tPVlGijYntDdMEpqDuTJ4bN8If3xhkNdsaEFVFXxf8MT+DE0xk4rjIQS4nqjedw0hlGruvoemKVQ8H8P1KDseYV1jXXOMkaLN4/sz6JrCj+7bO+VOgonPQ0gPihuqonBEc5xU1KRse4yUHFamglz3sff8QJ3NtXu3tinGw3tGAld8WF/wLpGJ4xkt2vWGuZ4vUBVAgSf2Zw5IRH++v0Ch4pKKGFMWD2KhID/++f6lJaIv5m4ckDuhJBKJRCKRSCQSiUQimYgU0ZcYMtt2PPNxQ07VyHGh7klVVTjvxFV0Z8pV17aOJ6BsufRmy6xqiHLOlhU8sT8zb9F+rCCYrwRNH6PmiwKjrir17OuoqfG7HX081Z2l7Hh0jZTIlR06WuJsbkuxe6yQqEJIUzl5TSNRMxAbJ+ZxH+jaWt0QZVk8xI7eHB0tMZJho55xPVas+8DJa3hk7ygPdI5gux6eH7jGV6QiWI7HUNFGAWIhleXJMCtSYXozFa6/+4Up3fBFy2NZIhzMn+UGMTrVAHFNUwmbQc64L0BUz6WrYLs+g3mbdNRgS1sS1xf0ZlxUQ+FDr1xNPGRwf+cQP31o/ySxMGpqM85XQ8Rg33AJb0xuNsBI0Wb3UIFs2aFoeVx71wv8/vnBccc8ujUoVAzkKph6EE1Ta75asj0EgoQeOOwzZYfmWIj1y+KUbI+nujIAHNWaojUVmXYnwcTnoS9b4f7OIXYNFtkzVJxxvS6Ws3kxdon4viBTsrE9j8G8hakp9Ya1tbiViuNRsFxue7SLjcsTByD0itnfMq/3HRoWczeO3AklkUgkEolEIpFIJBLJZKSIvsSQ2bYvshA35Fj38oEyF3f727asrIuUsVBwz4qWN8nRPJaxAqXjvdj0sUYt+7pkB8fJVVyOaNZYlggxkKswXLAoWi4dLXHWNEaxvcC5vH+0RMXxuOvZAR7ZNzpprg50bdXuR+dQkd5sha5MmaaYyYZlCcKGNk6s03WVj59+RFXEq9AQNYmHdVTg4T0jREyNTSsSRAwdU1cxdZWVqTA7B4tTuuETYZ3GuElTPERftsxIycatzltj1KBgeQghOHJZgsFiMD9FK8hR11UwdZW9IyVyZRfX87GyFf6//7eLloSJ5zOlWHj25tYZ5yseDuJJChWXdDTI+h4p2mzvzmK5HqamEjM1GqLGFMfUaY6F6B4tU7QdVEVBUYI4F0/AxuVxLn3deiqOx70vDDGQt8iUbLqzZUK6ynHtaRrjoeo4Zo6PqT0PW9uDHRZzKTItprP5QHaJ1L8D+gv0Zit0DhapDbcpHgKCnSG259OaClNxvAPaqbNxeYJ4WKdguTTq45vUgqBgucTDOhuXLx0Xeo3F2I0jd0JJJBKJRCKRSCQSiUQyNVJEX2IcjGzblyJLxQ05m7u9JlJu787yHw/um5PgP1agXJYMVTO9fUDBFz4l26M5btKfr1ByPOIhjWTEIBnWaYwFETIjJZvMvlFipoYvoOJ4+L6gNR3hqNYEFcefNFcHsrbG3o9VDVGaEyF29ucZLgYZ0euaYhy/umGcWLe5LcVfn7FxXJNJTwh0XWVDY5TRksPecrne/DMZ0WlNhqd0w4+ds+NXp8lbLtmSU70H0Lc/S0s8zMbWBBtJ0JursK0rS0gPImNGijYVxyMRNoiaGhXHo2u0xGDe4lVHNNXnIx7WWW/G2N6b41dP9uL5PmXLnTLPW1cU4mGd0ZJNW8PEaB6dXCXIMG9NhlmehJ0DBe7dOYSpB/Eye0dKGJqKqoDl+jieqDcaLVgeD+0Z4bwTVnHucW2z5ovPdSfBXItMB6PPwHx3iUz8DtjalubR/aOMFm1MTaVkewCUbJeQobG2KUZI1w5op05Hc5xXrGnkD88PkinZxEJGvbFo0XIQAk5a00hH89LcBTSXeZ5pd4/cCSWRSCQSiUQikUgkEsnUvLyV2Jcgi51t+1JkqbkhZxMe5yv4jxUo+7MVVAUGCxZCUBeUs2WHiuOjoJCMGIGwrSg0x0LsGy7h+AJNEYR0g5GSQ8UJnM+tyTC6phLX1ElztdC1NdX9iKPTFDPJVRx2DwaC2hfPPgpVVegcLNQFuk0rkmw6Z1NdtNs/WuS6u15g/3AJVwjiIZ2oqeH6gdBdrLikY+YkN/zYOdvWnR3XGNRyPVxfsCxp1htNup6PqkBYVxkoWLi+j6kbGFoQnq2pSjWQQ7BnuERjzARFqWdtjxRt9g2XCBsq+0fLHF9zflebWdYacr5iTQNl2+OF/jy6pjJUsNCV4P5FDJ11zTFQFBQCt/tAzqIlEeK+ncP4QtAYM7Fdn+GiRa2IomsqCHhqf2bc+slXXFRFIRKa+mt7sXepLHafgfnsEplyzYV1Ni6L8/i+UVxfMJCroCpKNfdeZc9wkTVNMSxn4XOgqgofP72DgXzgei/ZbpDcooCuqmxYHuPi0zuWtAt74jz7vmDnQJ7n+wvsHynyXF+BwbwVNLadUOyTO6EkEolEIpFIJBKJRCKZGimiLzEW2wH6UuSl5IZcqOBfEyi//4dd7Bwo4LgCRQkabirAcMEJXNsq5MoqXaNl2tJhhooWYUPD8H0qrk+u4uL6QXa6rqqMFC3WNEVRFGXKuVrI2prufiiKQipisn6ZykC+wt3P9vPA7pFp3fjbu7P87zOD7Bsp4/sCQwsagCZCOiFDIxUxGCnaiJJdj8YZy+a2FOdsWcHV//McoyUHFQVNhXhYI1t22dadJWIUUavNXwuWGwjero8QkC05lCyPiKnhVy3ftUaRuYpDruzyXF8ex/eJGiqer5COmPRmKzzQOUxbQ4R8xaVQFepDhkZHc4zjVqe546le9vUXKNkuuqoQNXVWN0ZpiJn18dcEyKOWJ/nD80P4QuD6glzFwfMFgkCojZsaJdvl6JUJBnJWff0cjl0qi9lnYD5Mt+aa4iFiIYN8xcFHoSFqEA/ruH4QpZMtu6xMhQ9oDja3pfjyOZu47dEutnVnKTkeUUPj2LYU55340mqsub07y/f/sItH9o6SLTlYro+iQEs8xOaVKUKGNq7YJ3dCSSSShfLVr36VX/ziFzzxxBOH7JylUokPfehD3HnnneTzeUZHR0mn04fs/BKJRCKRSCSSPy/kv4SXIIvtAH2p8VJyQx6I4L9pRZKmWIj2pihxU2PfSJnhoo3nB4I6AjwfBvMWoyWHzpiJ5fokq1EeJdulLR1h/0iZdNTA9QXZsku+4tbfM3GuFrK25nI/Ogdtvvv7XdhukE29oilKeUykzDlbVnDHU730ZMpBprUCqqJgOUEufGPUDBpszjDXvi94Yn+GWEhHUaBQ8RACHFfgej6eAFUJGpAWq+K5X/1sLZLD9gIRPMggF/h+kKe9rTvLUMHGdn00VaFQcasGZAVVCZqZPtObR1GCpqUxU2dNY5SdAwXu3zVMcyLEMSuSvDCQR68WB/aOlEhEjMDlzosCZHtTlNZUGMv1yJQcSrZb7ZGq4CtQsD0UPEYK9rj1c7h2qUzlIJ9rw9/p3jfb57Nlh2zJIaypQZPVULATI27qeELg+UHBydA1FEXF0CAZ1hnIWdgxk9UN0QO65sNVPFhMtndnufLXO3i2L4+CCDL3lcBYP1SweaIrwwmrG1i/7MVi3xfPPurPfieURHK46Ovr46qrruKOO+6gq6uLVCrFhg0buOCCC/jwhz9MNHpg32vTcdttt3H++eeze/duVq9ePen1o446ije96U1cf/31i3reXC7Ht771LW6//XY6OzuJRqN0dHTwnve8h4svvpiGhoZZj3HzzTfzxz/+kT/96U80NzeTSh28/z7es2cP69at4/HHH+e44447aOeRSCQSiUQikSxdpIi+RHk5iDgL5aXkhjwQwX/PcJGdgwXWNcWJhzRGSg5lx8PQ1Ko7OZCAQ7qK6wuGijZU3emWK2iIGbQ1ROjPWfVmpJ4vqvnqAVPN1XzX1mz3ozdTYvdwEQWIGBqjJYfuSJmO5jjrl8V5oT/Pv97TSdTUWJkK050pY7l+PbrG9QWZskNIV4iYGumISdHyppyvJ/ZlGC3YuEIQC+noqsJAPojCUYCK41N2XMqOi6pCdQqDwgSgVEVEFIGqKowUbZSqc73W4NWpCvK6CpoKJSdwsgsgHdEJGzq2G7j3FUWhYLnEQjpb2lKMlm1GinY9E33PUIGGaCAE1ATIjcvjNMZM0mGD/ZkSmZKDpoKpqShK0FjUcX12DxVJRIx6PMlCd6nMRfCeqygOc2/4O937jmtP88T+zIw7Fn78wF72jZboGi1j6irJiE5HcxxdU9CqES6eEPhCIKqO/pLtEg8FjWr3jZYOeJfKYjYpPtT4vuBnj+6nc6iIrimEDZ3hQpAlr6oKluuTrzh0DhU4cXVDvVizb7T0Z78TSiI5HHR2dnLaaaeRTqf52te+xrHHHovrujz//PPcdNNNrFy5kre//e2TPuc4DoYxuWfHfHj7299OU1MTN998M5dddtm41+677z6ee+45brnllgM6x0RGRkZ49atfTS6X44orruDEE0/ENE127tzJT3/6U37605/yl3/5l7MeZ9euXRx99NFs3rx52vfYto1pmtO+LpFIJBKJRCKRzBX1cA9AMj01EWdre5qOlvifjXBRc9z2ZsuIWqfFKjU35IZliSXhhhwrME/FTIJ/XYA3NfKWS67skggbWK4HAsJGIKq6vkDXVIQvcDzBQN6mWH3/zoECIV2lZLt4fuCiNrTgsfZ9n91DBdIRE18ErmvfF3QOFtjWnQXg2LbUrGtrpvsxUrB4Yn8W1/NJRQySEQNTVxkp2mzvzpIpOSQjJj3ZMomwjmlohHSNZNggZKj4IrintuuTDBsc0ZKgMW5OOV/ZskNPtozjB+cytKC44PmCsKGhKcGxSrZP2fHRVZWIEcxFIJxTzbYOMrRTYR1XgCsEoer7XF/gi8Cxq1azzesCPGC7goihETY0hks2fbkKni/oyVZ4aPcIzfEQYV0jV3ExNZVM2aE/Z7FzoFAXIGvFhZ5smUzJQYGgwaiqgBK4tCOmiicEOwcKmLpSn4/aToJjV6XIlG32DBXJlG22rEpP2Wx3e3eWK+7YwVd++TT/dMczfOWXT3PFHTvYXr3/c33P2Pdef/cLbOvKko6YrG2OkY6YbOsKfl/7zHTve7BzmCt/vYMHO4en/Px/PdHN9Xe/wN6hIg0RA1UFQ1Pq62moYFWLNTqxkI7vC/KVYNdBUyzEsavSGJq6JHapHE72DBfZ1p3DF4KoqQdFIBG40SFYb0IEETh5yyViavVizXzXmEQiOXAuvfRSdF3nkUce4fzzz+foo4/m2GOP5bzzzuOOO+7gbW97GxDsWLrhhhs499xzicViXHnllXiex0UXXcS6deuIRCIceeSR/Mu//Ev92H/4wx8wDIO+vr5x5/zsZz/L6aefjmEYfOhDH+JHP/rRpL/xN910EyeeeCJbt25l3759nHvuucTjcZLJJOeffz79/f0Lut4vfvGL7Nu3jwcffJCPfvSjbNmyhaOOOoq3vvWt/PSnP+XSSy+d9Rive93ruPrqq/nDH/6Aoii87nWvA2Dt2rVceeWVXHjhhaRSKS6++GIgcNwfc8wxhEIh1q5dy9VXXz3ueGvXruVrX/saH/vYx0gkEqxevZrvf//79dfXrVsHwPHHHz/ufDPx+9//npNPPplYLEY6nea0005j7969AFx44YW84x3vGPf+T3/60+OO+7rXvY5PfvKTfPKTnySdTtPU1MSXv/zlSfdJIpFIJBKJRHJoOPxWXolkAi+lXPgDidgYK8A7VWd2ICiLanPJamSFpuB4gdu22uOQhqhB2NQZKdpBNAkwUrBpSYaJmhrdo2We7cvVs7Yv/9UOGqIGoDBasmd0EE9k2vthuTy+P4MgcIWHDQ1FUTA0hVTEIFt22D1UpL0hgusJdEUhEdJJRoJxN8VCgQjuBaL3EcviZErOtPOVKweZzlFDq85C4LgVQqBWoz0c16ctFaYnWwlc80LQm62gqsEc6aqCooDjCSqOwBjzs+cHzmZdVTB1FQFUHA9DU/CU4IyuLyhWs9aDAkBwTE/AaMnB8QSrm6IMFSyyJYei7TFasjhhdeO4uJzzTljF8315Roo2RnWngaKA6wUO+WTERFVguGBxwuqGcfMx150Ec2l4C8y5Ke5c8/+PWp6Y8n2xkIbrCcq2h+sJ4iEtiGgZ8/l/vWcXEUNjw/IEoyWH7d1Zyo5H1NQoWi5do2Uszydm6mxdlcLQVRzXx9BVEiGdguVRcb0lsUvlcJKvuPXCnq4q9big4P9SF9NdX+C4PmXGF/v+nHdCSSSHmuHhYf7nf/6Hr33ta8RiU5sDxv63xVe+8hWuuuoqrr32WjRNw/d9Vq1axa233kpzczN/+tOf+PjHP86KFSs4//zzOf300+no6ODHP/4xf/d3fweA67r85Cc/4etf/zoAF110Eddccw333HNPXcQtFovceuutfPOb30QIwTve8Q5isRj33HMPruty6aWX8t73vpff//7387pe3/e55ZZbuOCCC2hra5v1eqfj9ttv5+///u/Zvn07t99++zi3+be+9S0uu+wyvvzlLwPw6KOPcv755/PVr36V9773vfzpT3/i0ksvpampiQsvvLD+uauvvporrriCL37xi/zsZz/jE5/4BKeffjpHHXUUDz30ECeffDJ33XUXxxxzzKzudtd1ecc73sHFF1/Mf/7nf2LbNg899NCcrm0sN998MxdddBEPPvggjzzyCB//+MdZs2ZNvTgwEcuysCyr/nMul5vX+SQSiUQikUgk0/PnrTRIliyHKxd+PrEWcGCC/1gBfnkyhKYGYnnNYeRWHdZNUYO+vIWmKqQieuBg93x0z8fUFAqWh64qNMRN0hGDHT05+nIVdFXhmJVJWlMR+rIV/vD8EABbVqVY2xybViydiqnuhy8EuqZwVGuSrtEybrVZaEDQXDNbdkhFdFQFRss2pqGyLBEmU3IYLdkkwgaqqqCq0JetsDIdmXa+khGDkKZiez5hIVAUJRDHq+7tWjxMSFfRqrnbvghE6caoScX1savFCgjc3ZqmoCtw5IokuwYKDBVswoaKQMH1gsx1AGriI1CwXXxfYKoK5WpxQ1GCKJqK6zFcsDmhvYH+fIXRksPfnLGB12xomdRY9rwTV7FzsIDj+hRsD8v1CRuBS19VFIqWg6oqnLa+adJ8zBY1MhfB+7ZHuxCIOTfFnWv+/327hqZ8X77ikqvm9ecqQSEiETbqn0+EdXYOFji+vQFFUWiMmWxuS7F7qECu7CJEkFW/PBHC1FUaYqbM7J6GRFgnYgYRU7Xn0tRVLMfDUEBUi1C6GkTjTDVvL+U4G4nkpcTOnTsRQnDkkUeO+31zczOVSgWAv/zLv+Qb3/gGAB/4wAf42Mc+Nu69l19+ef3/X7duHX/605+49dZbOf/884FAJP/hD39YF9HvuOMOSqVS/fVNmzbxyle+kh/+8Id1Ef3WW2/F8zze//73c9ddd/HUU0+xe/du2tvbAfjxj3/MMcccw8MPP8xJJ5005+sdHBwkk8lMut4TTzyR5557DoC3ve1t/Od//ueMx2lsbCQajWKaJq2treNee8Mb3sDf/u3f1n/+4Ac/yBvf+MZ6XM3GjRvZsWMH3/rWt8aJ6G95y1vqLvjPf/7zXHvttfz+97/nqKOOoqWlBYCmpqZJ55uKXC5HNpvlrW99K0cccQQARx999Kyfm0h7ezvXXnstiqJw5JFHsm3bNq699tppRfSrrrpq3HqQSCQSiUQikSweUkSXLFkOtRtyrlnPU41zIYL/WAG+P2cRNlQyJRuBwPYCgSsR0nFFkHMeMTViIR3XD0TSoYKNX41oMDWVS05ZwxuOWsa/3PUCigqbVyRRVBUhBH25MroWZH/35yza0pFpxdKZrrN2P7Jlhyf2Z7j14f00x02yZZvRkhOM16c+rrLt8nx/HtcXPN2T4+meHIqi1PPbK/lAIGiImZy0tpHzTpx+rlMRg5XpCD3ZCrmKQ9TU0dQgu7zsBFE2APtGS5Rsj1w5GIOmKISMYO5sL8iCboyaHL0iwUN7RoPzR002LEswUhymaHsoEDjNCSJc1DFNGV0vcLDXdgbYnkBVBJmSja6qDBUs8pZDvuJywuqGSQJ6jePa02xclkDXFMqOR1+uQsX26s1Nk2GTdNTguPb0tPdkOuYieG/rziKAlXNsijvX/P/BvDXl+xwvKGBEQhoFK9h9MRZNUXA9gTYmZKwxZtIQbQjO7Xr05ywufNUafvd0/5LfpXI4WdsU49i2JPtHSpRsl2TYIBHScTwfxxP4wkdVIB7S6c9Zct4kkiXAxO/hhx56CN/3+eAHPzjOWfyKV7xi0mdvuOEGfvCDH7B3717K5TK2bY9rfnnhhRfy5S9/mQceeIBTTjmFm266ifPPP3+c8/2iiy7i05/+NN/5zndIJBLcdNNNvOtd7yKdTvPMM8/Q3t5eF9AhEN5rr81HRJ/uen/+859j2zaf//znKZfL8z7eWCbO0TPPPMO555477nennXYa1113HZ7noWnB36stW7aMG19raysDAwMLGkNjYyMXXnghZ511FmeeeSZnnHEG559/PitWrJjXcU455ZRxc3Xqqady9dVXjxv3WL7whS/wmc98pv5zLpcbd98kEolEIpFIJAtHiuiSJc2hckPOJfpiNiF9IYL/WAH+iX0ZMiWnGrkgSEdMVFUhV3ZQCAT1XNnF9XxCukI8bFbF3iAX+u5nBljTGCNTdljXFEdRAzUyXwny06Nm8Lhny07dBTxWLO0cKqAqyozjV1WFku3xyyd7grnJlBksWERNHdcT9OYq1QDyIGvc8wRRU2N5MkR31a0exEsoxEI6JdslpGt89FVruejVHbNmsx+3Oo3VOYzrCXIVF88XmLqG6wsqjo+uBZnwyXCQy+75AkWDbNkmFTGoOB7xsMHG5QkgaDiqKgoxQ8MJBeK16wn8CedWqwp6bZeA54NVFYEVAgFYUcB2PSqOx47ePBuWxWcUJtc2xVi/vBoFtCzOuqYYecvFcYPr6M9VWNMUI1t26BwszKuANBfBu+QE2fs1x/JU7xnbFHeuDX9bEqEp32dowQ4Bq1okMPTxLTm86s4Gb8LkK4pCMmJQqCikoz7Hr27gyNbkId+l8lJCVRXefWI7z/bmebYvT6ZkEwvpJEI6mbKNLyBkaDRETTlvEslhZv369SiK8v+zd+fhUZV3+8Dvs8w+k8lOQhJMQhAEBARccLeIqBSxtSpVVF6tO1rbV3Gryqaixd2q1aJQ7av2V9FaXHDFfRcECiJbWEIgIctMZj/L8/tjkjF7BsgK9+e6uDQzZ8485zyHkHzPd+4HP/74Y5PHi4uLAQAOh6PJ480jX/75z3/iD3/4Ax544AGMGzcOHo8Hf/7zn/HVV18ltsnOzsbkyZPx3HPPobi4GG+++WaLGJapU6fiD3/4A15++WWcfPLJ+PTTTzFnzhwADWsqtPz3p63H25OVlYXU1NQWxztgwAAAgMfjQW1t7V7ts7nm56i1cbaWK958kdb4J92a/0SQvOeeew7XX3893n77bbz88sv405/+hHfffRfHHHMM5Pomh8Y0Tdvn92pgs9lgs9n2ez9ERERE1BKL6HTQSzbruaNO7X0t+DcuwK/cXoulq3Zi5bZa+CM67BYZXmf8r2lEM6AZJiyKhFSnLTFOzTDhtgHBmI6lq8pbFE8bOoDV+rGH63OQGzisCjbvieHh9zbAF9Z+7sLPcuOY4nTkeB2Jovracv/PNxtS7KgNx1AViMEf1uJFWQhIkCBMM1EMNUwT5bWRxAKdhhAwDECKGchPdUBVZJT7fu6ya+/8NnTuVwWiyEtzQpGBmmAMq8tqAcTnsioYg0WRkem2waJKqArEENFMCKEhw21DtseG9bvrUBWMAQKQJYEPf6qAKsvxfHMl3iWvKhJcVhUhTUdMF7AqMrwOFVXBGGJG/BdfGYCsxKNjYnr82E3Eu6mv+0XJXmfNOy0KKqM6fqoIIaIZ0E2Be9/8MelPRTRIpuDttCgQ9f/fXlG8ISc72fz/4wZmYvlPlS2289hVpNhV7KwNo3+qAx6b2uT1dREd/b12+MMx9EuxtRvVIssSM7s7MDzPiz/9ciie/ngTvt1aA184XpxJd1kxNNeLSSNyMaogleftALW30WTUczIyMjBhwgQ8/vjjuO6669rMRW/LJ598gmOPPbbJYpybNm1qsd3vfvc7TJ06Ffn5+Rg4cCCOO+64Js97PB6ce+65eO6557B582YUFxcnol2GDh2Kbdu2Yfv27Ymu5rVr18Ln8+11RIksyzjvvPPwwgsv4I477mgzF70zDR06FJ9++mmTxz7//HMceuihrXZzt6YhA90wWl9Ivi1HHHEEjjjiCNx6660YN24c/u///g/HHHMMsrKysGbNmibbrly5skUh/8svv2zx9aBBg5IeNxERERF1HhbR6aCXbNZzQ6xFV2gowBdnuXH2qDy8u3YXlq4qR7kvUr/wZRhRzYDdIsNlszQap0AopiPDZUNRugs7fWFIiBdAXTYFdREdwagOAQHdNAFILbqAd/nC2OWLQAJQlOmGw6tgly+MN1aX4/UfdiInxY50txUDM13YXh3CTl8ExVmu+E2GLA/CsVrsCURhmoDTqsBljcd1mJoBWUJ97Ek83x2IF/UlxCNoSrI9UGQ5cX4LM1ztFn6aR+dU1MWwrSoEU0hIdapw21RohkBUNyDLwGE5KYgZAmU1IRRmOFFRF8X63XUwTYFMtw0l2W5ENRM/7KjBnnAMUv0CpPE/EgQAl9UCi+u8jHsAAIX/SURBVGJCAMjy2BHW4t3ZiozEYqqmGY/cMQWQ4bSin9uG6mAMP2yvbXEczYtb1/2iBEtWlGHltlrs9IURihqIGgYssgy7KqOknx12VUn6UxFA04L3QKsrHp9imLAoMtw2BeW+MA7P88IUJr7bWotcrx1WVYHHrtYvQNkyXzzZ/H9VldvcTlXi2fFqfZa/w6ogFNVRWhWEy6biV0fk4est1R1GtbBAmJzheV48fP4R2LwngJ92BwAIHNrPg+JMN8/XAWxfo8mo5zzxxBM47rjjMHbsWMyaNQsjRoyALMv45ptv8OOPP2LMmDFtvrakpAR///vfsWzZMhQVFeH555/HN998g6KioibbTZw4EV6vF/PmzUt0mDd32WWX4YQTTsDatWtx4403Jn7WOPXUUzFixAhceOGFePjhhxMLi5500kmtxst05J577sHy5ctx9NFHY86cORg7dixcLhdWrVqFL774AsOHD9/rfbbnf//3f3HkkUdi7ty5OP/88/HFF1/g8ccfxxNPPJH0PrKzs+FwOPD2228jPz8fdrsdXm/bf5+2bNmCp59+GmeddRb69++P9evX46effsLFF18MIJ7b/uc//xl///vfMW7cOLzwwgtYs2YNjjjiiCb72b59O/74xz/iyiuvxPfff4/HHnsMDzzwwL6dCCIiIiLaLyyi00Ev2aznhliLribLEiYOz8WEoTmJQuEuXwQLP92MteV1cFrji4/qZryAblcVFGW64LCpkAHkeO3YVFmXiDzRDROhmIFAVIdNlZGT8nMXsDBN/LirDkr9IqSyLKMmGMPmyiAM04QQQMwwASHw+qqdqAvrcFgV1IQ0pDhUFGe6UZzpRk1IgxAmwpoBqyIjzWVBzB9/nUWRE9EniixBkRVENQNR3YBumHDbLdjtN7Fyey2e/3Jrh4Wfhs79Zf/dhfvf/hFafct7OGbAFAIeuwUumxW+sIbSqhBG5nsRjOq48uSB+MdX2xDVTRRluZBSH2dTHYzBIscL5jZZQobHCgggGDOgyjIG53iQm2LD+t0BTDtmAP7fN9vx/Q4f4jXIeIFBkuILnMoAZEnCxsogHnpvA6yKDFMI5Hrt+OWIXPRPdWDJirIWxzgi34ufdtUh3WmFKmsIxwC33YK6qI61O/0YnudFSfbefSrinNH5WLfTjw/XV8Js9JFxWZJQnBmPxnl/XQXKfRGUVoVgt8hIc1qR63UgFNNbzckempuCc0bn1d/gCUOWpFajVNpaJ+CY4kyMLPBi5fZabKwIYHNlDDXhWPxaBPDxhj1Ic1rrr7FYq1EtrRUIB2a5MK44EzleO4vqzciyhJJsD0qyPT09FOoG+xtNRj1j4MCBWLFiBe655x7ceuut2LFjB2w2G4YOHYobb7yxSZd5c1dddRVWrlyJ888/H5Ik4be//S2uueYavPXWW022k2UZ06dPxz333JMo5DZ3/PHHY/DgwdiwYQMuueSSxOOSJOG1117DddddhxNPPBGyLOP000/HY489tk/Hm5GRga+//hr33Xcf/vznP2PLli2QZRmDBg3C+eefjxtuuGGf9tuW0aNH45///CfuvPNOzJ07F7m5uZgzZ06TRUU7oqoqHn30UcyZMwd33nknTjjhhBaROI05nU78+OOPWLx4MaqqqpCbm4sZM2bgyiuvBBC/qXHHHXdg5syZiEQiuPTSS3HxxRdj9erVTfZz8cUXIxwO46ijjoKiKLjuuutwxRVX7MtpICIiIqL9JInWQgEPcn6/H16vFz6fDykpKT09HOpimysDuOv1/yLVYW011iIQ0VEbjmH2WcO6JZ+9NaYp8MKXW/HoBxtgmAKKJEFVZHgdFhRlupDmsibGeeKgTDz98WaEYwZSHBbYVBmBqI7qYAySJGFEvhcl2R6EYwa2VAVQXhvBsP5e5KU5ACHw3bYaVAfjGeKaIRCM6lBlCRFNRzBmwmGRkeayJQr4+ekObKoIwqZKCMYMDOufAkWW8MWmaqhKvLM7rBmwqTIs9atG6qaAbggcW5IBj82CHTUhOK0KorqJnBQ7DCEQiOioCcWQ67Xj96ce2qTws6bMh3vfXIfVZT7YVRl1UT3REa7IEtJcVsiShJhu4rBcD3RT4HfHF+Fvn25pMs9CCHy/rQaVdVGE6veRnWKHRYlnlfojGjJc8UVHfRENvzu+CI9+sBGbKgMwDBO6+XPWqlWNx8HURTRYFRlDclNQG46hNqQhoplQpPgNmXSXFQOzPE26rCvqInDbVBRluvDd1lpY689V4zEcMSANwaiR9LW4psyHeW+sxebKYLyILgDUL7SanWKDy6pCN+OZ9Tt94cQ4baqM40sycfmJxS3OeUPxOhIzYALI9doxeUR/TBjar9WidVsd46Yp8O7aXXj2s1IEYzqK0uM3gRrOR5rLit+MzmsSJSTLUssCoVXBLl8EP+7ywzAFcrx2pLus7Lqlg5JpCsx9Y21inYXmkUgbKwIYkZ+KP006rFNvMvFnpr7j8ssvx+7du/H666/39FAoCSeffDJGjRqFhx9+eJ/30fD3s+CGf0K2OTtvcERERNTrlc6f1NND6DOS/Z2Gneh00Es267kh1qK7JYqXuwNQZAkRzYDHacUhmS7kpzqARvEbh+d5sbM2giyPDZphoi5iIBA1oMgy8lIdCER17PJHoMoybBYZxRkuCDPevQ4AddHGi5BKUOR4t7tVlZHisCKqRxN54F6HBb6wht3+KBRZii9WqCrwOqzQdAOKjPos9nj3syHqv+EIEb8RIAMWCSj3hePxKxKQ6bbhp4o6+MN6YpuKuiie+XgzHjp/VKIA25Bhb1NluGwqooZAVDNgUSRoRjxfO8NlRcgwscsXwbiBmUhxWBDVTNhTZPjDGjTDRFQ34Q9riRiYsGYk9iNJEpxWtb6jPYijijKQ4rBABpDltqEmFEOqRYEQ8W5fVQb2BGLQTYE0p1p/XCacVhUeG7DTF0EopkMGoKWbcMsq3HYV/UQ8o12RZWh60/z6n8egoy6iw2VTk/pURMM50g2BXwzOQiBmQNNNWFQZLouCD3+qBBDFKYOzINdfG3VRHTHNwE5/pD43++d/ONrqbi33hfHK9zuQl+ZotWDd3joBX26phmEKDO/vbXUdgq+21OBPk3KaxOA0X7ugOhjDpsoATCFgCoGYbiLVbumTXbeMqKH91Ruiyah38vl8+Oabb/CPf/wD//73v3t6OEREREREfRKL6HTQSzbruScKWs2LlyPzUrFqRy2qgzFEjXjOuF1VEuM8pjgDL3y1FQOzPHDbFNRF9UTx1GNTURfRUe6PYPpxh2BITgpMITD7P2sTi0s2LuJqholQTIduCqRaVVgUGVZVRihmwDBMWBQVTquKcEyHXZWxJxBD/7R453AgCjitKoIxHVFdwKrGu6qjmgFIEmQJsKkyyuui8DosMEwFLpsFa3b6EdXjkTCKEo9YiekGPt24B++u3YWJw3MThaIcrwM1IQ2GADw2FZphQjMEZFlCVDMQiGqIGibS3fH5c1oVaIaJb0urEdbix2kKgUB9p71misTCpKH6Ln5ZAgJRA4dkqIl92K0Kcq0qQjEDYc2A06pCkSWENQOhmAFVlqAoEqK6Ca/DAkBKRM4osoywbmLLniDSnBZAkqAbAhZZRiimI1bfSa+bAhZFqn9NfKFTzTBbLPbZlibFNFmGx/5zBn5dREvEuwSiBlIcMiBJ8NgtgN0Cm0XFxspAotC2LwvvdlQQ3pdiX/PXCCGwZU8AUd1Ait0C3YzfPBES9ir6pjdghjV1ht4WTUa9x5QpU/D111/jyiuvxIQJE7r8/dzutm/SvPXWWzjhhBM63Mcnn3yCM844o83nA4HAPo2tM3XGcRIRERFR38EiOhHaznBunvXcnVorXrrtKkYNSMOmyjrs8kexanstBvVzJ8ZpmOLnIkpDYbQRp02FIknIS3UmCqSNu/AtajzDu6IuCt0wE4XlQESDIktwWhVENAP+qA4BwKrKiOkCDqscXzBSlhCMGnBa4otUhupz2O0WBaYQ8Wx0ISAgwWlTceQh6RiR78X/fbUN5b4wglEdpjARjBqJmBRVjse/vLGqHBOG5iQKRTkZdqQ4VFQHY0ixW5DutMa7qXUDuikQjBoozHDhltOHYHieF6t31KIqGEV1IIZ0txWqrMAf1hDTTcQAqIoElzV+3iK6gWidCadVQYpDxaXHFWF4nrfJ+RrePwVbqoKJrnlTxBdMzXBbYZoi0c3fMJcQAqiPffGFNdRFdXjsFlhUOd5BrwtYZSlxTA0FeKO+sK4qUtKfimivmKbpZiLaRavPu298s8VpaVpo29uCd0cFYdMU+HGXH1XBGNw2NXFeGmut2Nf8mOoiP39qouE6CZsCmm5CsvedrltmWFNn8dhV2Cxy4qZoc8nehKMDT3vZ3V1h5cqVbT6Xl5eX1D7Gjh3b7n56g844zvZ097wRERERUfv4mxRRvYYFK3tLpEJbxcs0lxVjnenY5Y+gJqTh8hOKccKgLMiyhM2VgbaLKEKgsi6CmGGiNhSDaYoWXfgOq4yIpsczvGUJFkWCKQFRI/5aAQmyJEHTTezRYpAkwKrIGD0gFScempVYMHK330Sa0wpTAC6rjByvE4oM6PX7cdstuPS4IkwY2g+lVUG88NU2VAaiiOgGIABVkeu7jeMLmwohEt3RDYWiiGaiKNONYNQHf0SD06oizWlBKCohEDMwOMeDWWcNw+H5qTBNgSUryuC2qTAMgVDMgMMCRHUDEgAT8UJ3aooVNlWGZpjwheI3Dk4bmoPxQ7KxuTKAuoiOY4oysKM6hKpgDIdmexL57bv8YQhIyEmxY0dNJBHJAsQ/7dBQKLYoMqKaGS9mI95F77AqCAdjUFUZxZluhKI++MIaHBYFYc2Ax27Bbl8EGW5bUp+KcNniNy3Ka0PwOq2JhWTrojqCMR26aUKSJFT4I/hpd139oqzxrveGBUYbCm17093aUUF40ohcrNxei1U7fNhRHUJFfXRMcaYbaS5rYp+tFfuaFwg1o2n0jV5/s8Giyi3G1VvtS5c/UVt6ezQZHTxKSkr2ex8Oh6NT9tOVevv4iIiIiKhzsYhOB7XWYid6S9dqu8VLSUKWx45g1ECq05oosLVVRKkJxhLd606Lgmc+2YzlP1UmuoNn/KIEiz4rxccbKmGIeDFSliWkOiwIxXSEY/GudEkSyPbY4bDICGsGfCENbruKi8YdglEFaZg8on+T8xmI6nh1RRk2VgQQjMa7ko8uzmzS3V+Y4UJuig2rttdClgCrRYH082ECAGQJqArG4AtrGJmf+vMxZrsxPM+LLXsC9R3h8ZzzogwXZtcX0AFg854AVu/wIcNlRYbLht11EVQHYojoJhRFgly/vLJpCpgCiOfBx988L9WOu99a16SzOs1phcOqoCakJR47viQLVcEYNu8JQJbQJJJFlRs1W4umxV4BwGlVYAordvsi6J/qxGG5KdhQUYeqYAyqFJ+HkQVpSX0qYk2ZD//6bjt2+sLwh3W4rArslvg1FNVNRGI6fPWF5bqIH5LUkGWvQpYk7KmLwhRAIBrfJtnuVpdNwT++2tZmQXjVjlo88M5PyPbYkJtihz+soSoQRVUgilDUwPA8L9Jc1jaLfc2vbYsiN4q+AUIxHRkuW+KGQV/oumWGNXWm3hxNRkRERERE1Nf13uoCURfr7TnE+/LR/NaKKBHNwOodtQhEdbjtFhye74VdVVp0B2+tDiKimbCrKpxWBYoMaEY8x9sQBgTixWCLEl8kVDcF0t1WeB0WvL6yHCPyUltdSHJ4f2+73f2yLOGoogws++9umIgvRtrArO80dljVxCKgrR3jyPxU7AnEsKu+UHTzGUMSBfQ1ZT489dEm/LS7DlZFhqrISHGoyE9zwKgWcNtUmELEc+b1eK66IkvIcNtgmALvrauAYYomndU7a8NIc1kx7egByPE6Ese1ttyPR97fgAp/FHURDalOCwwzXuBNsVsghEBtSEOWxw6nRUEgoqPcF0Z+mjMxDw3XY0GaE2MOScPxJZkYWZCa1KciGneCl2R5sLGyDoGIjupgDEC8Qz2kGYnis24K2NR4ZnvDQq1epwVum4LXVuzE8P7epLtbAbRZEG64XmtCMRyelwKPw4LiLDdCMQNR3UAwpmNTZQBDZA/K67vTmxf7ms97ToodHruKqkAUkgQ4LCqKMl1NFtrt7V23zLCmztYbo8mIiIiIiIgOBCyi00GpL+QQ7+tH85sUUXYH8FNFHcKagf5pDhRluKAqMqK6gewUG7ZUBhLdwU6LArsazzYPawZsUvymAiCwpswPzTAQM+L52XZVQYbLisJMN6yK3G63bGuF9eYOz/fCaYsXlcOGASDeua0qMlJsCkwB2BQZKQ5Ly2OsCCBaXygaN7Bpl3vDPO+sDcOqynBYFEiShOpgDD4pHk0jSRIUSUKK3YLD+qfAqsiwKDIgBFbsqEUwpmN4f2+rURtfbanBnyblJIq9w/O8+P34QXj64834bOMeVNbFEvEouV4H9gQiCER1pDotKK0KtShuNe/k35s4odaiQRwWGV9tqQak+POBqA5VkZFmV1ET0hKFdFUCdBOwWSQM6++FTVWazGky3a3BqNFmQbguoiOkGbDIMnQjfpMk3WVNfIqgOhjDbn8EXoel3WJf83m31sf+qLKE4iwXUhyWxI2JvtB1ywxr6gq9LZqMiIiIiIjoQMDfzOmg01dyiNv8aH5Ux5bqIFxWFUcXpbX62oYiyicbKvHQez8hzWmDRZGaLIQpS0BYM2CYAofnpcSLkfVFyRS7Bf6Ihsq6CA5Jd8KiyHDbFEQ0E4NzPEitz8yWpPjCl/vaLdsQp/P15moYJhILRCqyBAnxrvTakAaP3YL+aY76xTabHmNbhSLTFPFYk9owcrx2BKI66iIavI5493xtKJboEhdCINNtR26KPdHJvKbMBwAoSnftVdTG8DwvHj5/FN5duxv/WbUT5b4IZAACAscUZ+LsI/rDbVNbHXMyNxza0lo0iKrIsKkyHFYbYroBf1iH26YiGDOh13f8CxNQrBI8VhWqLMGiyC06oJPpbm0vj18zTGh6PN6mIcYGiBfS05xp8IU1bK0K4dLji3D6sJx2/941n/ddvjC+3Fwdz8zfE+xTXbfMsKausj/fS4iIiIiIiKglFtHpoNOXcoibFy8374mhJhSP5hAm8MJX2/DllupWI2hkWUKq0wqrokBVJPx3px9R3YCzvlga1gyEtfhCntXBGAozXEhxqKgOxuB1WOC0qvCFdcTqC+6hmIEsjw0F6c4m521fu2UTcTr13fIx3YAsAxZZhiniRcT44qImdMPEyHxvi2Jie4Wid9fuwjtrdyOmm9jtj8IUAhHNhGFGkeKwwmlVURfRENUFFElCvxQbDAGEo/FOZpdNhQDgsLVyXEJAN01UBWP4cZe/1YiaicNzEgundkc3aGvRIJphxhd3tcgwTAFDCPjDGiDFb1JIUnxRVd0QCEV12CwKNMNsdU47umnRXkFYVSTopgmv4+fM8gbxGycyMtxWDMnxJHV+Gs/7yIJUTBia0ye7bplhTURERERERNQ3sIhOB52+lkPcULx8d+1uPPvZFkiIFyydNrXDCBqPXYVVlbBxdx2iuoEUuyVR3GyIMjGFiV3+SHxR1Uw3QlEffGENDosC3TRh6CYEAFPE37czumUbx+l47CoggBSHBf6whphuwGWzxLuXDbO+W9zA9uoQ1pb7k+ouXlPmw7OflcIf1pHmtMCiyNBNAcMU0AwTwageLyALYHRBKrxOK2pCsSadzEcXpeOFr7a26KyuqV88tDoYQ0w3sfDTUnxTWtPmjYzuuhHTWjSIRZFhCoHKuiiierygHjMErArq515AkuLbaYYJ6CZUGW3OaePjaW1R3rYKwrt9EaQ6LXBYZQgAjUvCndFx3Ze7bplhTURERERERNT7sYhOB52+mkP85ZYqGKbA8LzW87lbi6ApzHAhO8WOVTt8SHVamhTAZSlewLSpCsIxE3VRHWn1OdWNi8Qh3cS44gzs9kdQFYzBqir71S3bPE6nJhiDKYAUuwU2VUZlIAp/RIOEeByJ0xqPmNnpi+DR9zd0mFffsP9gTIfLGs9AlyQJFkVCussKf0SDx2ZBXqodYd3EHZOHojjT3aIg3HDOG3dW1wRjWFPmQ1jTIQTQLyUeAbO3WfqtFaAbx9Dsy3OtdYJrhomIZiCqmxBCJOZdNwQgAULEu9EbnjOFwNbqEPJSne3OaXuL8rZWEB5ZkIaRBV68saqcHdetYIY1ERERERERUe/Wu6qERN2gL+YQ72sEjSxLOL4kEx/8WIFgVIfLFl+EUTcFQjE9sYimbpjQdBMAkOayYrQjFWvK/SjOcOH6UwehONONteX+TumWbX4sFlWGUj8mu0WBTZZhmibSnRbYrfFvUTHdRHGWC7v90Q7z6hv2X5TuQkw3E/E0QLyY7rSqCGs6asMajirKQHGmu81O5iad1Sl2bKysQzCmQ5El2K0KirPc8DgscNvVpLP02ytAA9in54bneVtEg+Sk2LFlTzCeLy8BEiRIkoAAEp8skCXAqsjQjHhHuiJJGJjpxhUnDWxzThs+RVAViMfi2BwyDBNYtb02cSPhjklDWy0ID8xys+O6DX25m56IiIiIiIjoQMciOh10+mIO8f5E0IwqSEVRhgs1oRgimomwKaDIEjLddmS4rNi0J4hQTEfMMGGYInEe+nsduOKkgSjJ9gBIvlu2vW7p1o7FY1MTWewOqwKtfnwWVYFFkeALa8hw2ZBit0CW5A7z6n/ev9oknqYhC14IgUDUwCEZaofz3DhqY/UOHyr8UVhVGRkuKwoz3Uh3WQEkn6XfOMYm1+uAwxtfKPab0mp8s6Uaihy/ydE/1Rl/rj6uZ91OP1DfQZ54XStRPo3Hu2qHD7v9EVhVGWlOKzTDxJ5ADAICphBQpPi4XTYVHpsFqU4LZFnC9acOSsx583ndvCeApz7ahE2VASgSUFYbgVE/Xyl2FWHNiN9ImJTS6jnoyx3XHV3XREREvdWa2RORkpLS08MgIiIi6tNYRKeDUl/LId6fCJrCDBdGDUjF6h0+9EuxQTcELKoMT/3CmbXhGDJcVuiGaJIJ3tp56Khbtr0u64Z9tXYs/Tx21IY01Ia0eFFWied1+8Ia7KqCokwXIElJ5dU33n/jeBp/WEfYFBAQSHGouPS4oqTmuaHw+/Z/d+HxDzbikAwnvA5Li08EdDS25jE2DfEwm/cE4AtpqA7GAAkoTHci02PCLatw21UMtLrw4fpKSABOGZwFSZYBtB3l8/N4y/H4h5twSLoTqQ4LhBD4cks1akKxxNwHowYG9/MgP82BTZVBjMhPRXFmy/ltmNfVO3xYV+6HZpjxhWsdFnjsKnRToDoUgyLLWLGtpt0bCX2x4zqZ67oBi+1EREREREREBx4W0emg1Ze6YvcngqZx5/1ufzTReR+IxjvO89OcmPGLErht6n6dh1a7rFvplm58LBm6FVuqgvCHdWiGiZhuQDfjed2mKZDhsqEo04W0+o7vZPLqm5+rNJcVY5xpqIvqiGkGdvojOPKQdEwY2i/pY5NlCUNyPMhwW6HKcosCejJjax5j05CvHtENWBUZsgwISNgTjCFU5sPwPC/SXVYEogbM+szyQMyAxy4n9tlWB3x8vCnIcMXHi/pc+CE5KU3e02aRYVUVbKoMtvkJjMbzalclGGZ8LMIU8Ed0qIoMm6ogxW6BP6JhZ20YvrCW9Lnt7ZK9rhu2TbbYTkRERERERER9B4vodFDrK12xextB07wbdmhuSpd23rfosgZQF9Wh6Sb6pdhaZJmfMzof68r9+GpLNSQJcNtUWBQVEgBT6FBlGYP6uVGQ5oyvfInk8+rbOlcSJFSHNPT3OnDOmPy9jqFJ9kbGgDQnNlcGWuynSYyNiEejRHQDXocFUc0E6jPKHRYFUd1A6Z4A0pxp0Ayz/k2QyK1vrK0O+NbG29CZv6myDrv8UTgtCjTDaPM6aD6vu/wRmCK+4KsiS9B0E3URDTZ3/MaCVZER0gz4D5AiemufHgBa/xTA2nJ/0sV2IiIiIiIiIupbWEQn6iOSjaBprxu2rQUf91fjLuvakJaIT2nIy7ZbmsZ8DM1NQbbHhs2VQcgSENFMKLKELI8Nh2a78d/yOvx3px+qLCPLY0NEM/cqr35v43qS6SBO5kbGyAIv7n5rXav7aRwzIyDgD+twWlUAUvx46gu0iizDqUioCsawvToEVanvPJcAiyqjubY64Nsar0WR4XVYkZ1ix29G52NkQWqb10Hz7nmrLEGRAcMwocoKVEVGTBfQDAGLAkR1I96V7rAke+n0asku6Lt5TyDpYntv/KQLEREREREREbWPRXSiXqitruiOImj2JnqiMzV0WUdVA2vL/YjoRmIhT90UqItoqA1rWLm9FsVZbpRWBVET0nDkIWkIxAxENAN2iwKbKqG0KgRFkRCM6vhua3X9Qp42HJ6fikuOPaTd8Tc/b7efcRi21YTavWmwN+esveL8yAIv3lhV3uZ+ZvyiJNEZnu60wDAF1PqxqHKihg7TNFEX1RDWTPx3px82NX4uHFYFbmvThWU76s7f3+z/5ovAWlQFTqsaX4hWj9/4EEIgppsIxUxYZBm5Xju8B0gRPdkFfX/aHUiq2N5eVjwRERERERER9V4sotMBq68u8NdRV3RbETR7Ez3R2efBY1dhU2X8VFGXiCgB4u9hUSS4bCpqQxo+21iFs0floS6iozoQw7bqEAIRHUIAkiQQ0UxYlPqualPAapERjBrY5Y/AWRnAqyvKIEtSq8Xf9s7byILUFueqtCoIX1jD819uRVUgikH9PEmds9ZuZAxIc+Lut9a1e+5fW7ETvz4iD2U1Yez0hSEgoBkmJElCKKYjxW6BZpioDMQACMhSvIM/qptQZBlCAKvKfBiY5Wk3yqe5xuP1hTX4wxpSHBY4rQpMU7R7LTRfBNZjV5HhtsLwC5jCREwXMISAbppId1qhKhKOGJDWbtxOX5Lsgr6ASKrY3t6CuERERERERETUe7GITgekvrrA3/50kicbPdEV3bCFGS5ke2xYuaMWqY0K6EC8WzoUM5DhtmG3P4zSqiC+3lKNTZUB6KaALKE+C13AFBJMIaDEJIQ0AwYAr0NFVDfhC2ltnod9XfzRF9KwrSaENIcFNSEN6fULmHZ0zprfyNhcGcCG3XXw2C2oDsYSNwKk+gU9G/bjsqnxzvDvdmDZ2l2oCWlw2xRkuKwozHDhx111CMV0mCagyIAQQLbHjsIMJ7bXhABIqA3FOuwob+0GUihm4PUfdu7V34nWctWLMt0IRg1EdQOAiVSHBQOzXaiLGEnH7XSHzriJlmwO/qH9PEkV29tbEJeIiIiIiIiIei/+Rk8HnK6INOmOrvb97SRPNnqiK7phZVnC8YMy8f6PFQhrBiRJgiJLMEyBUEyHzaKgJNsNX0jD99tq8PcvSmHUF9BtqgLDFNA0A5IkoOkCVVosXlzXDNQY8Vx1f0TD0P6eFouU7s/ij3ZFxo6aMOqiOtaU+TA8z5sopIv6DuuqQAw/7qprd85/2F6LDbsDgIT6AriEFIeKokw30l3WJud+ZEEqhuam4JjiDDz72RYEozoKM1wwhEAwpsNlVWFRZRRnuJDhscFjUwFJglVVUBOK4ncnFCHVaW03nqb5DaQ0pxUV/gh0U+zV34nWctW9DgsGZrnx4y5//GaBwwLdRKctUtsZOusmWrIL+hZnupMqth8oHfpEREREREREBxsW0emA0hWRJt3V1b6/neTJRk90VTfsyIJUFGW6UBuKIayZiUVFM1xWFGa6YVVkRFQDy/67C76whgyXFf6onog0gQSYAhD1+1NlCVaLAiEAzTARM0xU1Re/G5+H/Vn8UUDAqsqwKBLCmoHSPQGkOdNQE9KwZU8A1cEYYrqJZz/dgm9Kq1ud8zVlPvzr+x0IaQY8NhUuuwLdFKgOxhCMxgvzVkVucu5lWcLE4TnIS3Mkrq2q+vfql2JHcZa7SVc80HATRCDVaW0RT9N4LC1uIEV1fLm5CjHdxNFF6YlrI9m/E23lqk86PBfHFGcgx2vvVXFJnX0TLdlc+WSK7b3h/BARERERERHR3mMRnQ4onR1p0p1d7fvbSZ5s9ERXdcMWZrgwakAqVm2vRT+vHbohfs43B7CxIoBDMpzYWBGAKstw2lSoqoy6iIaIZgKNCugS4kV0CfEFNy2KhKhuYrc/igFpzibnobXzJoRAXSReoFdkCZGY0erijx6bihSHiupgDE6rAl9Yx46aMLbsCSKqGzBMgX4pduSm2Fud84abNlHNRE6KDTUhDQ6rAosiI8VugT8SL8Z7bCpGFrTMCm+cV/7jLj8WflqK3BQ7PK0szNnRTZC2biCJ+rgcWYr//UhzWRPPJft3oqMFbXuLrloXIJnj399FXImIiLrK8LuWQbY5e3oYRERE1MVK50/q6SEc0FhEpwNKZ0aadHdX+/50kjcU5g/P8+KnXXXYsLsO/VOd3doN2zj6osIfTXTjBqM/v//xJZn4aXcAFlWCbgrYVAU2t4yYbmJPIIqIZiYK6WgoAgPQDQG7RUE4pqMyEG1yHpqft+pgDFv2BOAP6zBMkeg2314dbHltSBKKM90IRX0IRDQYpoSfdtchqhlQZMBljUeXuB0WlNjVFnPe+KZNptuGNWU++MIanFY13kmvyNjliyArz4tfj84DEM9Pb16ILc5yozDDhW9Ka7B6hw/u+jz1BsncBGnrBpKmmzAF4LKp8IV11EU0SJIETTdhUWU4LU3/TrR1k6etBW17k65cF6Dx8bd1jvrKzQYiIiIiIiIi2jssotMBpTMjTbq7q/26X5R02El+SIYTvrCGzZWBRHGueWFeM0xEdQM7akKw1MeIdFc3bEfduE6rAq9DRSCioC6qI8VugVSf9+2xWRDRogDii2oapoCE+H9lWUKK3YKoZmCXL4JxAzMTxeTGHfgZuhVrdvoR1Y36QjZQE9JgGAIf/7QHmmG2uDbSXFYMSHdi9U4fQjENddF4prnLqmJAuhNp9bEqrc1545s2blnF8DwvNtcX8MOmgCzFC/hHFqWjrCaMf323HZsqg63GAiWbv91WQbatG0gWVYZS/5qobmB1mQ8xXSTiduz1mekeu9pnF+Rt0B3rAnR0jvrCzQYiIiIiIiIi2jssotMBpTMjTbq7q/3VFTvxqyPyWi2ibqqsQyAa76y+980fE4W7UQWpeGNVeYvCfLkvDJtFxm9G52NkQWq3dsO2141rmgKD+nniWeOGCX8k3rWtyBKU+tOsSoDLqiKsmYjpJuwWGSkOC4QQiBom0t1Ni8kNxecd1SGs2F4L3TDhdVhgCMAfiS/UOax/CqqCMUR1AztrQxjUz5OYg+pgDFurgxCmQIbLiqhuwmlRoJkCW6tD8DgsiXzy5nPe/KZNmsuKMc401EV1VAWi2F4dQjRqYOmqnXj+i61QZAlDclJQmOlqNRZofyJB2rqB1BBZU+6LIKab9dtaoMoSNMPEnrooorqJ//xQhq+31CCiGfFPMXRCdFF36+p1Aboi3omIiIiIiIiIej8W0emAsr/dvI31RFe726a2KKJqholA1IDbpiI/7eeIllXba/H+ut1w21SMyE9ttTC/usyPKaO6f0HDtrpxG88PEEI4ZiCkGdB0Ad004bIpyHRbMbS/F9WBGHb5I4hoBmK6iahuYkCaE7ecPqRFoXJ4nhe/GVOA/5b7YQoJgahRv6ipDUWZLqS5rLCqCnbUhGC3KIm5sFtk/LTLD19Ig9dhQXGWGxsqArCoMhyyBH9ESyw2KklSizlv9aaNJEE3BLZVheALa8jy2CGEgCkEJAFsqgzAYVWQ7rI2iQUa0s+DbTUhGKbAhUcPAAAEo0bSkSBt3kCSJBRmOFFaFb9R4LZZocjxOJ26iA4BwBfW8NePNkOR49dizDDhltX9zhLvbl25LkBX5a0TERERERERUe/HIjodcDprgb+e6mofWZCa6OT2hTU8/+VWyBKadE+77Sr6ee3YUBGAIsloXrLb3/znrtR4fjbsroMvrEORgYFZHhw/KANvrd6VyFQvSHdiTyCGXfU3QG4+YwgOz09tdb85XjvyU53IcFthmgIWVYbHpiay1RsW/DxnTD5Wl/mwsSKA2ioNNfWF7sE5HqQ5LdhdF0F1MAavwwKntSFHPN5R33zOW71pY5Hx4y4/fGENXqcF+ekObNgdSHR/Ny/M53odWLGtBjOXrEJlXbRFREiyc9feDaSdvggcqgKXTYVmABFNhykENMOERZFgVWXUhjS4bSqqQzEEy3wYnudFev0ipL31WmoumZtoRxelYXWZb6/zyrsyb52IiIiIiIiIejcW0emA1BkL/PVkV3tDJ/fmygAq66Lon+psUbjTDQFVlhGK6aiL6vDYLU2e74z8567S3vwMyvb8nDldfwNk3MDMpONMVFmG29n2OR5VkIqzR+WhtCqI77fW4O9fbMWQHA8URQaAxEKjvrAGh0VBVDdQVhOCLgT6ex0t5rz5TZutVRpqwxqyUuw4tJ8HEPH8cVWWIElSk8J8isOCiGZgS1UQEc1ASbZnvyJC2rqBVJzhgjCBof1TEIoZ0HQDP1UEAAikOq2IxAyYAlAVGXaL0qLQ39a11NYCmz2prXOQl+qAgMALX23bp7z37shbJyIiIiIiIqLeiUV0OmB1xgJ/Pd3V3l7hzqLIsKgSNF1Aq8+6bmx/85+7Wlvzs683QPbmHDd+73//sBNhzYS7voie5rJieJ4X68r92F0XgWaY2FARgNdpQXHmz/PTvIB8+xmHYVtNCN9vrcHi+sK8qsioi2iJ+BSLIkGRJRhmvAscQmBDRR1MU6Aoy5W4wbI/ESGtnT9TCMz+z1pENBMpDgvqIkBEM+GyWQBIEABkKf7f1gr9rV1LvXkR0ubnYJcvgn99tx01Ia3DLPO2bgx0dd46EREREREREfVe/G2fqAM92dXeXuHOY1fhtCjYE4tBVZq+bn/zn3vC/nY178s5bq/wrhkGZAD9PHaMyPdClWXsrI3g0fc3YNKIXKzcXttqAXn0IWn49w87EakvzDcs7NkQEWOYAooMRHUT26pDqKyLIstjQ0qzTxLsT0RI8xsUpimaHKemm4nueCEEYoYJp01BTDfgtCpNCv2tXUt9YYHNhnNgmgKvrSxDTUjrMMt8bbm/zRsDQ3NTuixvnYiIiIiIiIh6NxbRiZLQU13t7RV5gXiERJqwYrc/ClmS9zlupqd1Vlfz3p7j9jLN/WG9vis9FWkuK4B44XXVjlosWLYeXocFaU4rMtxWKJKUKCDP+EVJizlriIipDcWgGQKyJGFNWW08WsUQMIRATUhDev37NGgcEbI/NxmaH6fHrkKWEF+01TBhsyg4NM2JbdXxxVCtigxZBjRDYGNFoMm11NcW2Ew2y/zdtbsTx9XWjYHOinciIiIiIiIior6FRXSibrS3Xe0ddVfnpzmbdEXva9xMT+rsrua9PccdZZo3L2zXBGOoDERRF9GxszYCqyohxWFBUYYLVcEYXluxE78+Iq/JnKU4LCjOcmHNTh/CmgFVluGyKeiXYkNtSEcwomP1jloUZ7nhrF8A1WNXExEhu3xhvLaybL9uMjQ5zt0BQAICUR05XjuKMt1Id1mR4rBgU2UddvmjcFoUaIbR4lrqawtsJptl/p9VOzu8MfCnSYd1SrwTEREREREREfUtLKITdbO97WpPprt68oj+vW6Bx2S029VsdWFNuR9Pf7QJ1586CMWZ7r3qvN7bc9xQeG+ead7Yj7vqsLsuClMAwZgORZKgGRIimolg1MDALDc2VNTBZVNbzpkqIyfFjjSniYGZLlgtCtxWFd9vr8FuXwTVoRhqt9XAZVWgyDJS7CpUJb7Q6r++L0NNkjcZ2utYb3ycK7fX4pXvdiCiGbAqMgxTwKLI8DqsyE6x4zej8zGyILXFtdTXFthMJsvcFALlvgj6J3FjoDPinYiIiIiIiIiob2ERnagP6Khw1xlxMz2hra7mmmAMm/cEUB2MYVtVCGW+CEbke7t00crmi41GGi02CgBVgSjW7/LDFIAEwKrIkCQJmmFCNw0AQLkvjFSHBb6wBq/DgrNG9oc/rCHFYYE/rOGZTzYjzWlrUszNdNmwrSoEzRRQJAGbaoEpgJ21YTisCrLcdgRjelLRKcnE4jQcZ3GWG4f28+x1V3VfW2AzmQVn+3vtKPdF4LAmd2Ogr/59IyIiIiIiIqJ90zuqHETUoQOxcNdaV3NNMIY1ZT5EdAMOS/xxpyrjm9JqrN3px6XHFWLC0Jx96vxNJle8taKrEAI/7a6DZggAgCpLUOpfZ1Hl+oU6TdSE4ou8vvDFVlQEok0K2SPyvIjpokmhVgiBPcEo7BYFFtNERDcRiBmwqwr6pzpgCoENlXUYXZDWYYd0KGbsdSzOvnRVD0hzItttw9pyP4qzXPEFUevH1hsX2ExmwdlJI3Lxwlfb+syNASLaO08//TTmzp2LsrIyPPjgg7jhhht6ekhE+6SwsBA33HADr2EiIiKiHiB3vAkRUddo3NUMABACm/cEENENeB2W+gI2sL02jEp/FP/d6cfspWsxZ+l/sabMt1fvtabMh7lvrMVdr/8Xd7+xDne9/l/MfWNti/00FF3TnBasKfNhR3UIO2vDqAnFAACKBEiNiswSAFWRoRkmAhEd1UENW6tDSHVYUZjpQqrDitU7fPjX9zugGebPx4r4TQR/WEeKw4I0lw1ehwXD+3sxtjANYw5JQ67XjkBEhyFEq8fksCqIaiZ8Ya1JLI7brkKRpXjHerYb1cEYlnxfBtNsuZ+GmzMjC1JRnNV+ZM6aMh/ufmsdNu8JotwXwccb9uCLzVXYUxdFIKK3ugjp5soAfthei82VgVbfvzs0RCIdnu9FbTiG0j1B1IZjGJGfiuvHD8KEoTkoyXaj3BeGaHauG24MDMr29JobA0QHooqKClx55ZUYMGAAbDYbcnJyMHHiRHzxxRf7tV+/348ZM2bg5ptvRllZGa644orEc+FwGGlpaUhPT0c4HN7fQ+g0Qgg888wzGDduHFJSUuB2uzFs2DD8/ve/x8aNG5Pax/Tp03H22Wd37UD7uFmzZmHUqFH7/Prly5dDkiTU1tZ22piIiIiIqPdiWx0R9ZjmXd+BaLyo7LSqEALwhzVohglAwGWzwKrKCGsGvi2twc7aSNKLju7L4qUOq4rqUAzbqkMwTIGYXr8gqN2CSMxATDehKjIkKV7wiOkCigJ4bEqb0StR3cDO2hAG9fMkomAMU0CVAX9ER4bLhoI0R6Kz2223AAACER2pzqYLnAI/d0j7w1qXL/bZ+BzmpzmR6bFh4+46VAVj+GZrNYoyXDhiQFoiCiaZaJnu1FHXfUfd6g03Boioa5xzzjnQNA2LFy9GcXExdu/ejffffx/V1dX7tD8hBAzDwLZt26BpGiZNmoTc3Nwm27zyyisYPnw4hBBYsmQJLrzwwnb3qWkaLBbLPo0nWUIIXHDBBXjttddw22234aGHHkJ2dja2bNmCd999F/PmzcOiRYu6dAzUvRquVVXlr2VEREREvRk70YmoxzR0fae7rNhYEYAvFINumBBCwB+JF9BlWYK9PtZFVSRIkJDrtbfbXd1Y88VL2+vSNk2BZWt2Ye7StdhcGcDo/FQcV5KJITkeWNV4wdwwBFKdFtgs8cU4Nd2EbpiQZcBjs+CwnJQ2C9k2VYHdomBjRQCBiA5FliAgUBPSYFcVFGW6EgV0AFCl+DhrQrF2O6RTHJZ4LE47md5Rbd8X+2ztHGa6bTi6OAPHD8pEboodxVlu3HbGkEQB/dH3N2D1Dl+LjvxH39+w158i6Cztdd131K2+r4X/9rrxe0unPlFPq62txaeffor77rsPp5xyCg455BAcddRRuPXWWzFp0iSUlpZCkiSsXLmyyWskScLy5csB/NwVvGzZMowdOxY2mw3PP/88Dj/8cABAcXExJElCaWlpYh8LFy7EtGnTMG3aNCxcuLDFuCRJwlNPPYUpU6bA5XJh3rx5AID//Oc/GDNmDOx2O4qLizF79mzo+s/fXx988EEcfvjhcLlcKCgowDXXXINAIJDUuXj55Zfx0ksv4eWXX8Ydd9yBY445BsXFxRg/fjzmz5+P5557rsN9zJo1C4sXL8a///1vSJLU5DyVlZXh/PPPR1paGjIyMjBlypQm56Shg/2ee+5Bv379kJqamji+m266Cenp6cjPz8ezzz6beE3D/Lz00ks49thjYbfbMWzYsMR7dqSmpgYXXnghsrKy4HA4MGjQoMRxttbtvXLlyiZzuWjRIqSmpuK1117DoYceCrvdjgkTJmD79u1JvX9bXnjhBYwdOxYejwc5OTm44IILUFFRkTjmU045BQCQlhaPXJs+fTqA+L/P999/P4qLi+FwODBy5Ej861//Suy3tWv1k08+waZNmzBlyhT069cPbrcbRx55JN577739OgYiIiIi6jxseSCiHtVQvHzl+x1YvcOHmGECWnzhzoCpwzAFqgIxSJIEVQasqgKrqiDXqybVXd3W4qVA0y7td9fuxpebq7Bs7S74wzrcNgUxw0RRphuD+nlQF9GxvToEzTAR1SV4HRYIAWi6AX9UR6rdiky3DQ5b699WHVYFFkXGOWPysbrMh40VAURiBqyqDMMQGNY/BWmun7vNhRAo90dw5CHpCMX0djuknValSxf7bOscSpIEr8OKkmwZFXURbKsJoTDD1aTg3tFiqL3JvmTEt6e9bnwAvapTn6gnud1uuN1uvPbaazjmmGNgs9n2eV8zZ87EggULUFxcDLvdjvfeew+nnnoqvv76axQUFCArKwsAsGnTJnzxxRdYsmQJhBC44YYbsHnzZhQXFzfZ31133YV7770XDz30EBRFwbJlyzBt2jQ8+uijOOGEE7Bp06ZERMxdd90FAJBlGY8++igKCwuxZcsWXHPNNZg5cyaeeOKJDsf/4osvYvDgwTjrrLNafb75v2OtufHGG7Fu3Tr4/f5EMTo9PR2hUAinnHIKTjjhBHz88cdQVRXz5s3D6aefjlWrVsFqjf8b9MEHHyA/Px8ff/wxPvvsM1x22WX44osvcOKJJ+Krr77Cyy+/jKuuugoTJkxAQUFB4n1vuukmPPzwwxg6dCgefPBBnHXWWdiyZQsyMjLaHe8dd9yBtWvX4q233kJmZiY2bty41/E6oVAId999NxYvXgyr1YprrrkGU6dOxWeffbZX+2ksFoth7ty5GDx4MCoqKvCHP/wB06dPx5tvvomCggK88sorOOecc7B+/XqkpKTA4XAAAP70pz9hyZIlePLJJzFo0CB8/PHHmDZtGrKysnDSSScl9t/4Wk1NTcWOHTtw5plnYt68ebDb7Vi8eDEmT56M9evXY8CAAft8HERERETUOVhEJ6Ie11C83LwngIff24B15f54V7opYFPlRL52WDNhCkAzTKQ6rdjt77i7urXFSxtzWBVs3hPDs59tQTCmI6abSHPG89irgzEEoz4Mz/OiKMsNX0RHXSTeNR7VTWi6gG6ayHTbcdExA/Dxhj0dFrJHFaTi7FF5iULtLl8Y//q+DFXBGKyq0qJIfvmJ8YJOQ8F1tz9ecB2Rn5qITjFN0WIx1AadsdhnMuewYS6SvWmxP9EyXamzFvBtL0Jo3U4/IAG6IZKOFyI6kKmqikWLFuHyyy/HU089hdGjR+Okk07C1KlTMWLEiL3a15w5czBhwoTE15WVlQCArKws5OTkJB5/9tlnccYZZyAtLQ0AcPrpp+PZZ59NdJs3uOCCC3DppZcmvr7oootwyy234JJLLgEQ73CfO3cuZs6cmSiiN170saioCHPnzsXVV1+dVBH9p59+wuDBg5s8dsMNN+Bvf/sbACSKre1xu91wOByIRqNNjvmFF16ALMv429/+lvj+/NxzzyE1NRXLly/HaaedBiBecH/00UchyzIGDx6M+++/H6FQCLfddhsA4NZbb8X8+fPx2WefYerUqYn9z5gxA+eccw4A4Mknn8Tbb7+NhQsXYubMme2Od9u2bTjiiCMwduxYAPHFM/eWpml4/PHHcfTRRwMAFi9ejMMOOwxff/01jjrqqL3eH4Am815cXIxHH30URx11FAKBANxuN9LT0wEA2dnZSE1NBQAEg0E8+OCD+OCDDzBu3LjEaz/99FP89a9/bVJEb36tZmRkYOTIkYmv582bh1dffRWvv/46ZsyYkdSYo9EootFo4mu/37/3B05ERERErWKcCxH1CrIsoSTbgytPLIZmmAhpBpT671DCFDBMAasqw6JI2FoVRCiqJ9Vd3WLx0mbCUR01oRiCUR39U+yQIMGiyLAoMlLsFkR1A6V7AkhzWjAi3wuPTUWG24psjx0DMhyYMDQHj19wBC47vrjp4pRCoC6ioToQhT8ca7I4ZeNYkYnDc/H7DmJEhud5ccekoZh91jDcPukwzD5rGP406bBEobV5LE4gEu/gb22xz33R4Tls1OmeKLh3UbRMX9BehNDALBc27wliS2UQJVmuvVoEluhAds4552Dnzp14/fXXMXHiRCxfvhyjR4/e6/zvhkJsewzDwOLFizFt2rTEY9OmTcPixYthGE2/zzXf33fffYc5c+Ykuufdbjcuv/xylJeXIxQKAQA+/PBDTJgwAXl5efB4PLj44otRVVWFYDCY1DE0vwF5++23Y+XKlbjzzjuTjoVpzXfffYeNGzfC4/Ekxp6eno5IJIJNmzYlths2bBhk+edfEfr165eIxQEARVGQkZGRiDZp0FA0BuI3RsaOHYt169Z1OK6rr74aL730EkaNGoWZM2fi888/3+tja3i/BkOGDEFqairWrVuHbdu2NZmve+65J6l9rlixAlOmTMEhhxwCj8eDk08+GUC86N+WtWvXIhKJYMKECU3e8+9//3uTcwy0vLaCwSBmzpyJoUOHIjU1FW63Gz/++GO779fcvffeC6/Xm/jT+JMCRERERLR/2IlORL2Ky6Yiw2WDIknYXRdFVDdhUSTYLAo8NhWyLMEX1lBaFcRRRRkddlc3X7y0eZf2lupgYjtIgCJL0E0BixLPkXVaVfjCOuoiOuyqgkH93Lj8hGKkOq1tLk65akctQjED4ZgBzRDQzHh3+8gCb6uF7GRiRDrqkG4ci9NWx/q+6ugcNu50L60Kdmm0TF/QXjd+IGrArM+3D8QMeOw/F6r6Qqc+UVdqyLKeMGEC7rzzTvzud7/DXXfdhU8++QQAmqwNoWlaq/twuTr+xM2yZcsS2eCNGYaBd955B2eccUab+zNNE7Nnz8avf/3rVse/detWnHnmmbjqqqswd+5cpKen49NPP8Vll13W5pgbGzRoEH788ccmj2VlZSErKwvZ2dkdvr49pmlizJgx+Mc//tHiuYaYGwAtFk+VJKnVx0zT7PA9k4mfOeOMM7B161a88cYbeO+99zB+/Hhce+21WLBgQaKYn8zct/ZekiShf//+TfL0GzrI2xMMBnHaaafhtNNOwwsvvICsrCxs27YNEydORCwWa/N1DefkjTfeQF5eXpPnmscUNb+2brrpJixbtgwLFixASUkJHA4HfvOb37T7fs3deuut+OMf/5j42u/3s5BORERE1EkO3CoGEfVJdREdFkXG2MJ07PJFsH5XHTTThNumwqLI0AwTgaiBQzLUpLqrG7q0y2rCreaKu6wqhAk4bSoUCUhxqKgOxuB1WABIUGQJhikQ0w3UhDSMyE/FCYOy2iyGTxqRiwfeWY/akAZVlmFRJXitNjgsMt5YVY6BWe5WC9qdESPS2ZnejcfW3jls3OlemOHCwCwXvi2tQa7XDquqwGNXIUlSp0TL9AXtxd9oRn3RSQCa3rIA1Tgah+hgN3ToULz22muJAm95eTmOOOIIAGhSFN1bCxcuxNSpU3H77bc3eXz+/PlYuHBhkyJ6c6NHj8b69etRUlLS6vPffvstdF3HAw88kCgA//Of/0x6bL/97W9xwQUX4N///jemTJmS9Ouas1qtLbrqR48ejZdffhnZ2dlISUnZ53235csvv8SJJ54IANB1Hd99913SMSRZWVmYPn06pk+fjhNOOAE33XQTFixY0GTuG6J3Wpt7Xdfx7bffJqJb1q9fj9raWgwZMgSqqrY5X2358ccfsWfPHsyfPz9RhP7222+bbNOQId/4PA8dOhQ2mw3btm1rEt2SjE8++QTTp0/Hr371KwBAIBBosuhrMmw2236tKUBEREREbetzRfS7774bb7zxBlauXAmr1Yra2toW22zbtg3XXnstPvjgAzgcDlxwwQVYsGBB4oddIuq9GqJDIpqJ/HQnXDYVm/cE4A/riGg6BARSHCouPa4o6e7q9rq0jy5KwwtfbUt0ThdnuhGK+uALa3BaVQghYAoTpVVBpLtsOLqo7Q420xRYub0WWW4bhud5oRsCFuXnruvuWFSzszK9m0u2031tuR/VQQ3lvghKq0KwW2SkOa3I9ToQiun7HS3TFzSOv2nejW9pyCiSAIvaMlHtYOjUJ2quqqoK5557Li699FKMGDECHo8H3377Le6//35MmTIFDocDxxxzDObPn4/CwkLs2bMHf/rTn/bpvSorK/Gf//wHr7/+OoYPH97kuUsuuQSTJk1CZWVlk87sxu6880788pe/REFBAc4991zIsoxVq1Zh9erVmDdvHgYOHAhd1/HYY49h8uTJ+Oyzz/DUU08lPb6pU6diyZIlmDp1Km699VZMnDgR/fr1w9atW/Hyyy9DUVqPymqusLAQy5Ytw/r165GRkQGv14sLL7wQf/7znzFlyhTMmTMH+fn52LZtG5YsWYKbbroJ+fn5SY+zNX/5y18waNAgHHbYYXjooYdQU1PTJFe8LXfeeSfGjBmDYcOGIRqNYunSpTjssMMAACUlJSgoKMCsWbMwb948bNiwAQ888ECLfVgsFlx33XV49NFHYbFYMGPGDBxzzDEd5qGHw+EWRXm3240BAwbAarXisccew1VXXYU1a9Zg7ty5TbY75JBDIEkSli5dijPPPBMOhwMejwc33ngj/vCHP8A0TRx//PHw+/34/PPP4Xa7E1n6rSkpKcGSJUswefJkSJKEO+64I6lufyIiIiLqHn3ut/RYLIZzzz0X48aNw8KFC1s8bxgGJk2ahKysLHz66aeoqqrCJZdcAiEEHnvssR4YMRHtjebRIWkuK8Y401AX1RHTDOz0R3DkIemYMLTfXu23rS5tAPhyS3WT9xue58XmPQH4Qhr8ER2SBASjBqyqjhe+2oovt1ThnNH5LYr4DTEe/VOdrUaZ9PWojo463Rsvpjmsfwp2+sKoDcUL6tXBGI4vycTlJxYf8Itmthd/47YpkCUJEgB3s9z4g6VTn6g5t9uNo48+Gg899BA2bdoETdNQUFCAyy+/PLGY5bPPPotLL70UY8eOTSx22bAQ5t74+9//DpfLhfHjx7d47pRTToHH48Hzzz/fJBKjsYkTJ2Lp0qWYM2cO7r//flgsFgwZMgS/+93vAACjRo3Cgw8+iPvuuw+33norTjzxRNx77724+OKLkxqfJEl4+eWX8cwzz+C5557D/fffD03TkJ+fj/Hjx+PBBx9Maj+XX345li9fjrFjxyIQCODDDz/EySefjI8//hg333wzfv3rX6Ourg55eXkYP358p3Smz58/H/fddx9WrFiBgQMH4t///jcyMzM7fJ3VasWtt96K0tJSOBwOnHDCCXjppZcAxIvjL774Iq6++mqMHDkSRx55JObNm4dzzz23yT6cTiduvvlmXHDBBdixYweOP/54PPvssx2+908//ZT4dEODk046CcuXL8eiRYtw22234dFHH8Xo0aOxYMECnHXWWYnt8vLyMHv2bNxyyy34n//5H1x88cVYtGgR5s6di+zsbNx7773YvHkzUlNTMXr06MS13JaHHnoIl156KY499lhkZmbi5ptv5sKgRERERL2IJBqHDPYhixYtwg033NCiE/2tt97CL3/5S2zfvh39+/cHALz00kuYPn06Kioqkvolwe/3w+v1wufzdcnHXYmoKdMUTQqzgaiOxz/YiOpgrNXokIYFNztL4+Jv4v2iOtbu8qM6GEOGy4rDclLgsKntjuOH7bW4+411KMx0QWml09owBUr3BHH7pMMwsiC108bfG5imwNw31sYLx9n1hWMhWtz8uOOXQw/oLvQGrV5T9deOKkuABOiG6Jbrm6gr8WcmKi0tRVFREVasWIFRo0Z1+/u39TsB/fz3s+CGf0K2OXt6OERERNTFSudP6ukh9EnJ/k7T5zrRO/LFF19g+PDhiQI6EO8aikaj+O6773DKKaf04OiIqLk1Zb5EREhUi0eElGS7MWlELlZur+30RTJbMzzPixm/KMGiz0uxuTIAwwRS7ApsqoJcrx0j8lMT3cRuu4oSm7vVaJb2YjyAzo/qaH7zoTPyz/dVq4tpShI8dgtgt8BmUbGxMtBnu/D3VkfxNwC6ZBFYIiIiIiIiIup8B1wRfdeuXejXr2nMQ1paGqxWK3bt2tXqa6LRKKLRaOJrfnSSqHu06Nb1xjtyV+/woawmjOt+UQKXTe3yIvGaMh9eXVGGSn8UpgkosgSXVUUwZqIgzdkkjgOIf9y+tWiW9mI8Ojuqo62bD63FzHSH9hbTBA7OBTM7ir/pikVgiah3O+OMM/DJJ5+0+txtt93WYeRHA7e77ZuRb731Fk444YR9Gl9XuOqqq/DCCy+0+ty0adP2KjOeiIiIiKin9Ioi+qxZszB79ux2t/nmm28wduzYpPbXvOAFxAtYrT0OAPfee2+H709EyUumQ9o0BV75fgeqg7Gf4z/QtNP71RU78adJh3VpYbF5IT831YFwzMDGijqU+yPIdtta7SpvrSgsyxLOGZ2Psppwoiu7eVRHZyyquabMh0fe+wnlvgjSnFZkuK1QJClx86En4kC6uwu/r2hvodeuWgSWiHqvv/3tbwiHw60+l57e9sLVzTVfDLOxvLy8vR3WfiksLER76ZBz5szBjTfe2OpznREBNH36dEyfPn2/90NERERE1J5eUc2YMWMGpk6d2u42hYWFSe0rJycHX331VZPHampqoGlaiw71BrfeemuTBaT8fj8KCgqSej+ivqarI0CS7ZBuNf6jXlud3p2tvUJ+UZYLZbVhbKgIIMNtBZqNsa2icEcxHvtb3DZNgac/3oQfdvggS8BufxSKLCHFoaIow4WqYKxFzEx36M4ufCKivqqzCtwlJSWdsp/ukJ2djezs7J4eBhERERHRfukVRfTMzExkZmZ2yr7GjRuHu+++G+Xl5cjNzQUAvPPOO7DZbBgzZkyrr7HZbLDZbJ3y/kS9WVdHgHQUz9K4Q7o3xH+0V8hPsVuQ4bJiTzAKf0RDisOaeK6jonBHMR774921u/DZxioIIeC2W6DKEnRToDoYQzBqYGCWu8tvPrSmu7rwiYiIiIiIiIi6W68oou+Nbdu2obq6Gtu2bYNhGImPs5aUlMDtduO0007D0KFDcdFFF+HPf/4zqqurceONN+Lyyy/vlI+MEvVVe1Pg7khr3ewAOoxnadwh3RviP9or5EuShJJ+HtRsqcbmyiBKsuW9Kgp3RVSHaQosXVWOqG4iy22FLMsAAIsiIcVugT+iodwXRqrD0iPZ413dhU9ERERERERE1BP6XBH9zjvvxOLFixNfH3HEEQCADz/8ECeffDIURcEbb7yBa665BscddxwcDgcuuOACLFiwoKeGTNTjkskfTzYCpK1u9mOKMvYqnqU3xH90VMi3qwqKMl0oznShIhDt8aJwaVUQ5b4I7BYZhgDkRs9JkgSnVUVNKIYUh6XHsse7sgufiIiIiIiIiKgn9Lki+qJFi7Bo0aJ2txkwYACWLl3aPQMi6gM6K3+8vW72tTv9CER15Hodrb62eTxLb4j/SKaQf8SANNx2xhBsqwn1eFG4LqJDliSkOi2oDWnwOiwAfh6HIgMRzUR/r6NHs8e5YCYRERERERERHUj6XBGdiPZeZ+SPd9TNvqbMh5pQDKGoDkiAppuwqDI8NhWQpFbjWXo6/iPZQr6qyr2iKNzQOd/f60AkZsIX1uC0qolc9LqIBpsq45cjctn5TURERERERETUSVhEJzoIdEb+eEfd7IUZLuz2R/DF5iqosgRTAIosIcWhoijDhapgrNV4lp6O/+jpQv7eaNw5P6x/CrZUBeEP6wibArIEWBUZxxRnYMLQfj09VCIiIiIiIiKiAwaL6EQHgc7IH++omz1qmAhrZnyfigSXLf7tpbIuigp/FENyPW3Gs/R0/EdPF/KT1bhzvioYw+BsD3QhEIhoqAlr6O914PITi7tk3K0tJtvbzg8RERERERERUVdgEZ3oINAZ+ePtdbMLIbBxdx0kCRiZ70VFXRT+sA7DFLCpMkwB9PPYMTQ3pasPdZ/1dCE/Wc075xsWdz2qMKPLOufbWkz2nNH5vapTn7oHb6gQERERERHRwYZFdKKDxP7GlrTXze6PaKgKxpDpsqEgzYn8NCfqIjo0w4RFkSEBqA7FOly4lJLTnZ3z7S0mW1YTxvXjB7W4dlhkPXDxhgoREREREREdjFhEJzqI7E/xtb1u9i2VQciyhEHZbkCSIAFIcVgSrzVMgYq6aLsLl9Le6Y7O+fYWkx1odeG/O/3460eb8ftTS1Cc6YYsS91SZGWRvmfsyw0VIiLqeWtmT0RKSu/9NCARERFRX8AiOtFBZn+Kr211sw/t74XdosBmaT0vPZmFS6n3aWsx2epgDFv2BFAdjGFbdQjlvjAOz/diVEEq3lhV3qVFVnZC94z2bqiU2NzYWBHAku/LMDQ3hTc0iIiIiIiI6IDDihYR7ZXWutkHpDlx91vr9mvhUup9WltMtjoYw5oyH6K6AYdFAQTgsMhYtb0W76/bDbdNxYj81C4psrITuue0dUMFACRJQq7XgQ0VdYxsIiIiIiIiogOS3NMDIKK+p6GbfWRBKoqz3FBVGeeMzke6y4qNFQEEIvFFRQMRHRsrAkktXEq9T+PFZIH4DZEtewKI6gZS7BZIkgRVkeF1WtHPa0dtSEM4ZqL5LDcvsu6L5p3QbrsKRZbiRfpsN6qDMSz5vgymKfbzqKk1iRsq1tY/beKwKohqJiObiIiIiIiI6IDEIjoRdYqGqJfD872oDcdQuieI2nAMI/JT2SHcRzUsJlvuC0MIgbqIDn9Yh9OqQpKAUEyH12GBx6ZCNwRUWUYopqMu2rKQur9F1r3phKbO1/yGSnOMbCIiIiIiIqIDGX/bJaJOsz8Ll1Lv03wxWYdFgW6asAgJvrAOu6qgKNMFSBIsigyLKkHTBTTdbLGv/S2ythYt05jDqmC3n53QXaXhhgojm4iIiIiIiOhgxCI6EXWq/Vm4tCeZpmDxvxWNF5NdtcOHWH2BPMNlQ1GmC2kuK4B4p7LTomBPLAZVaXreOqPI2rgT2t1KIZ6d0F2r+Q2VXK8DDms8k77cF2ZkExERERERER3QWG0gooPemjIfXvl+BzZWBBDVTNgsMkqy3ThndD5jaPDzJww27wngkfc2YEtVEMNzUyDJTRPBHFYFacKK3f4oZEnu1CIrO6F7XuMbKhsrAtjtj/9dGZGfil+PzuPfFSIiIiIiIjpgsYhORAe1NWU+PPr+BlQHY/HuWm+88Lt6hw9lNWHmudeTZQkl2R5cedJAPPr+BmysDLboRs5Pc2LSiFys3F7b6UVWdkL3DoxsIiIiIiIiooMRi+hEdNAyTYFXvt+B6mAMJdk/dze77SpKbG5srAhgyfdlGJqbwiJhvWS6kSeP6N8lRVZ2QvcOfTWyiYjoYDX8rmWQbc6eHgYRERF1stL5k3p6CAcVFtGJ6KBVWhVMdDU3jgcBAEmSkOt1YENFHUqrgiwaNtJRN3JXFlnZCU1ERERERERE3Y1FdCI6aNVFdEQ1Ew6v0urzDquC3X4TdRG9m0fW+/VkNzI7oYmIiIiIiIioO7GITkQHLY9dhc0iIxwz4La3/HYYjhmwWWR4WnmuK5imYIc1EREREREREVEvwyI6ER20CjNcKMl2Y/UOH0ps7iaRLkIIlPvCGJGfisIMV5ePZU2ZL5H1HdXiWd8l2W6cMzqfWd9ERERERERERD2IRXQiOmjJsoRzRuejrCacyEZ3WBWEYwbKfWGku6z49ei8Lu8GX1Pmw6Pvb0B1MBYfgzc+htU7fCirCeP68YP2qpDOjnYiIiIiIiIios7DIjoRHdSG53lx/fhBiS7w3f54F/iI/FT8enRel3eBm6bAK9/vQHUwhpLsn7vh3XYVJTY3NlYEsOT7MgzNTUmqEM6OdiIiIiIiIiKizsUiOhEd9IbneTE0N6VHurdLq4KJLvjGcTIAIEkScr0ObKioQ2lVsMPFNDu7o52IiIiIiIiIiFhEJyICEI926ahI3RXqIjqimgmHV2n1eYdVwW6/ibqI3u5+OrujnYiIiIiIiIiI4uSeHgAR0cHMY1dhs8gIx4xWnw/HDNgsMjz29u957k1HO3U+0xTYXBnAD9trsbkyANMUPT0kIiIiIiIiIuok7EQnIupBhRkulGS7sXqHDyU2d5MCuBAC5b4wRuSnojDD1e5+OqujnfYec+iJiIiIiIiIDmzsRCci6kGyLOGc0flId1mxsSKAQESHYQoEIjo2VgSQ7rLi16PzOoxg6ayOdto7DTn0q3f4kOqwojDThVSHFat3xB9fU+br6SESERERERER0X5iEZ2IqIcNz/Pi+vGDcHi+F7XhGEr3BFEbjmFEfmrSi4E2dLSX+8IQommUSENH+6BsT4cd7ZS85jn0brsKRZbiOfTZblQHY1jyfRmjXYiIiIiIiIj6OLYkEhH1AsPzvBiam4LSqiDqIjo8dhWFGa6kFwFt6GgvqwknstEdVgXhmIFyXzjpjnZK3t7k0PfEorVERERERERE1DnYiU5E1EvIsoTiLDdGFqSiOMu91wXvzuhop+QlcuitbefQRzXm0BNR8p5++mkUFBRAlmU8/PDDPT0c6mUKCwt5XRARERH1EBbRiYgOIMPzvLhj0lDMPmsYbp90GGafNQx/mnQYC+hdgDn0RAeniooKXHnllRgwYABsNhtycnIwceJEfPHFF/u1X7/fjxkzZuDmm29GWVkZrrjiisRz4XAYaWlpSE9PRzgc3t9D6DRCCDzzzDMYN24cUlJS4Ha7MWzYMPz+97/Hxo0be3p4AIDS0lJIkoSVK1d2+3uffPLJuOGGG7r9fYmIiIio87GITkR0gNnfjnZKDnPoiQ5O55xzDn744QcsXrwYP/30E15//XWcfPLJqK6u3qf9CSGg6zq2bdsGTdMwadIk5Obmwul0JrZ55ZVXMHz4cAwdOhRLlizpcJ+apu3TWPaGEAIXXHABrr/+epx55pl45513sGrVKjz66KNwOByYN29el4/hQNAw/0RERETUu7GITkREtA8acujTXVZsrAggENFhmAKBiI6NFQHm0BMdgGpra/Hpp5/ivvvuwymnnIJDDjkERx11FG699VZMmjSp1a7n2tpaSJKE5cuXAwCWL18OSZKwbNkyjB07FjabDc8//zwOP/xwAEBxcTEkSUJpaWliHwsXLsS0adMwbdo0LFy4sMW4JEnCU089hSlTpsDlciUK2P/5z38wZswY2O12FBcXY/bs2U0Ktg8++CAOP/xwuFwuFBQU4JprrkEgEEjqXLz88st46aWX8PLLL+OOO+7AMcccg+LiYowfPx7z58/Hc889l9R+pk+fjrPPPhuzZ89GdnY2UlJScOWVVyIWiyW2iUajuP7665GdnQ273Y7jjz8e33zzTeL5mpoaXHjhhcjKyoLD4cCgQYMS719UVAQAOOKIIyBJEk4++eTE65599lkMGzYMNpsNubm5mDFjRuI5n8+HK664IjGmX/ziF/jhhx8Sz8+aNQujRo3C888/j8LCQni9XkydOhV1dXWJ4/roo4/wyCOPQJKkxJy2Nv+ffPIJNm3ahClTpqBfv35wu9048sgj8d577yV1DomIiIio67GITkREtI+YQ090cHG73XC73XjttdcQjUb3a18zZ87Evffei3Xr1uG0005LFEy//vprlJeXo6CgAACwadMmfPHFFzjvvPNw3nnn4fPPP8fmzZtb7O+uu+7ClClTsHr1alx66aVYtmwZpk2bhuuvvx5r167FX//6VyxatAh333134jWyLOPRRx/FmjVrsHjxYnzwwQeYOXNmUuN/8cUXMXjwYJx11lmtPt98weX2vP/++1i3bh0+/PBDvPjii3j11Vcxe/bsJufqlVdeweLFi/H999+jpKQEEydOTHT/33HHHVi7di3eeustrFu3Dk8++SQyMzMBxM8nALz33nsoLy9PdPI/+eSTuPbaa3HFFVdg9erVeP3111FSUgIg3h0+adIk7Nq1C2+++Sa+++47jB49GuPHj2/yiYNNmzbhtddew9KlS7F06VJ89NFHmD9/PgDgkUcewbhx43D55ZejvLy8yZw2HFPD/I8YMQKBQABnnnkm3nvvPaxYsQITJ07E5MmTsW3btqTPYzQahd/vb/KHiIiIiDoHg1qJiIj2w/A8L4bmpqC0Koi6iA6PXUVhhosd6EQHIFVVsWjRIlx++eV46qmnMHr0aJx00kmYOnUqRowYsVf7mjNnDiZMmJD4urKyEgCQlZWFnJycxOPPPvsszjjjDKSlpQEATj/9dDz77LMt4lIuuOACXHrppYmvL7roItxyyy245JJLAMQ73OfOnYuZM2firrvuAoAmed1FRUWYO3curr76ajzxxBMdjv+nn37C4MGDmzx2ww034G9/+xsAIDU1FTt27OhwPwBgtVrx7LPPwul0YtiwYZgzZw5uuukmzJ07F+FwGE8++SQWLVqEM844AwDwzDPP4N1338XChQtx0003Ydu2bTjiiCMwduxYAPEFOBtkZWUBADIyMpqc13nz5uF///d/8fvf/z7x2JFHHgkA+PDDD7F69WpUVFTAZrMBABYsWIDXXnsN//rXvxJ59aZpYtGiRfB4PADi5/z999/H3XffDa/XC6vVCqfT2eR9GzSf/4yMDIwcObLJ+F599VW8/vrrTTrk23Pvvfc2uflARERERJ2HnehERET7iTn0RAePc845Bzt37sTrr7+OiRMnYvny5Rg9ejQWLVq0V/tpKPi2xzAMLF68GNOmTUs8Nm3aNCxevBiG0XRR4+b7++677zBnzpxE97zb7U50RYdCIQDxYvGECROQl5cHj8eDiy++GFVVVQgGg0kdQ/Nu89tvvx0rV67EnXfemXQsDACMHDmySQb8uHHjEAgEsH37dmzatAmapuG4445LPG+xWHDUUUdh3bp1AICrr74aL730EkaNGoWZM2fi888/b/f9KioqsHPnTowfP77V57/77jsEAgFkZGQ0OX9btmzBpk2bEtsVFhYmCugAkJubi4qKiqSOufl8BYNBzJw5E0OHDkVqaircbjd+/PHHvepEv/XWW+Hz+RJ/tm/fnvRriYiIiKh97EQnIiIiItoLdrsdEyZMwIQJE3DnnXfid7/7He666y588sknANBkseG2Fvl0uTpedHjZsmUoKyvD+eef3+RxwzDwzjvvJDqzW9ufaZqYPXs2fv3rX7c6/q1bt+LMM8/EVVddhblz5yI9PR2ffvopLrvssqQWJh00aBB+/PHHJo9lZWUhKysL2dnZHb4+GZIkJc5l84K9ECLx2BlnnIGtW7fijTfewHvvvYfx48fj2muvxYIFC1rdr8PhaPd9TdNEbm5uIse+sdTU1MT/WyyWFuM1TbOjwwLQcr5uuukmLFu2DAsWLEBJSQkcDgd+85vfNMmG74jNZkt0zhMRERFR52InOhERERHRfhg6dCiCwWAiOqS8vDzxXONFRvfWwoULMXXqVKxcubLJnwsvvLDVBUYbGz16NNavX4+SkpIWf2RZxrfffgtd1/HAAw/gmGOOwaGHHoqdO3cmPbbf/va3WL9+Pf7973/v8/E1+OGHHxAOhxNff/nll3C73cjPz0dJSQmsVis+/fTTxPOapuHbb7/FYYcdlngsKysL06dPxwsvvICHH34YTz/9NIB4VAyAJp37Ho8HhYWFeP/991sdz+jRo7Fr1y6oqtri3DVkrSfDarW2+MRAWz755BNMnz4dv/rVr3D44YcjJyenyeKyRERERNSz2IlORERERJSEqqoqnHvuubj00ksxYsQIeDwefPvtt7j//vsxZcoUOBwOHHPMMZg/fz4KCwuxZ88e/OlPf9qn96qsrMR//vMfvP766xg+fHiT5y655BJMmjQJlZWVicJ9c3feeSd++ctfoqCgAOeeey5kWcaqVauwevVqzJs3DwMHDoSu63jssccwefJkfPbZZ3jqqaeSHt/UqVOxZMkSTJ06FbfeeismTpyIfv36YevWrXj55ZehKErS+4rFYrjsssvwpz/9CVu3bsVdd92FGTNmQJZluFwuXH311bjpppuQnp6OAQMG4P7770coFMJll12WONYxY8Zg2LBhiEajWLp0aaLAnp2dDYfDgbfffhv5+fmw2+3wer2YNWsWrrrqKmRnZ+OMM85AXV0dPvvsM1x33XU49dRTMW7cOJx99tm47777MHjwYOzcuRNvvvkmzj777KSieIB43MtXX32F0tJSuN1upKent7ltSUkJlixZgsmTJ0OSJNxxxx1Jd7UTERERUddjJzoRERERURLcbjeOPvpoPPTQQzjxxBMxfPhw3HHHHbj88svx+OOPA4gvBKppGsaOHYvf//73LRYATdbf//53uFyuVnO7TznlFHg8Hjz//PNtvn7ixIlYunQp3n33XRx55JE45phj8OCDD+KQQw4BAIwaNQoPPvgg7rvvPgwfPhz/+Mc/cO+99yY9PkmS8PLLL+Phhx/Gm2++ifHjx2Pw4MG49NJLUVBQ0KRzvCPjx4/HoEGDcOKJJ+K8887D5MmTMWvWrMTz8+fPxznnnIOLLroIo0ePxsaNG7Fs2bLEYqtWqxW33norRowYgRNPPBGKouCll14CEF8M9tFHH8Vf//pX9O/fH1OmTAEQvxHx8MMP44knnsCwYcPwy1/+Ehs2bEgc25tvvokTTzwRl156KQ499FBMnToVpaWl6NevX9LHdeONN0JRFAwdOhRZWVnt5ps/9NBDSEtLw7HHHovJkydj4sSJGD16dNLvRURERERdSxKNQxsJAOD3++H1euHz+ZCSktLTwyEion1kmgKlVUHURXR47CoKM1xc9JOoE/FnJtpf06dPR21tLV577bWeHsoBp+HvZ8EN/4Rsc3b8AiIiIupTSudP6ukhHBCS/Z2GcS5ERHRAWlPmwyvf78DGigCimgmbRUZJthvnjM7H8DxvTw+PiIiIiIiIiPoIxrkQEdEBZ02ZD4++vwGrd/iQ6rCiMNOFVIcVq3fEH19T5uvpIRIR9WpnnHEG3G53q3/uueeepPfT1j7cbjc++eSTLjwCIiIiIqLOw050IiI6oJimwCvf70B1MIaSbDckKR7f4rarKLG5sbEigCXfl2FobgqjXYiI2vC3v/0N4XC41efaWyCzuZUrV7b5XF5eHk444YS9HRoRERERUbdjEZ2IiA4opVVBbKwIINfrSBTQG0iShFyvAxsq6lBaFURxlruHRklE1Lvl5eV1yn5KSko6ZT9ERERERD2JcS5ERHRAqYvoiGomHFal1ecdVgVRzURdRO/mkRERERERERFRX8QiOhERHVA8dhU2i4xwzGj1+XDMgM0iw2Pnh7GIiIiIiIiIqGMsohMR0QGlMMOFkmw3yn1hCCGaPCeEQLkvjEHZHhRmuHpohERERERERETUl7ANj4iok5mmQGlVEHURHR67isIMFxew7EayLOGc0fkoqwknstEdVgXhmIFyXxjpLit+PTqPc0JERERERERESWERnYioE60p8+GV73dgY0UAUc2EzSKjJNuNc0bnY3iet6eHd9AYnufF9eMHJeZitz8+FyPyU/Hr0XmcCyIiIiIiIiJKGovoRESdZE2ZD4++vwHVwVi8+9kb735evcOHspowrh8/iMXbbjQ8z4uhuSn8VAARERERERER7RcW0YmIOoFpCrzy/Q5UB2MoyXZDkuKFWrddRYnNjY0VASz5vgxDc1NYxO1GsiyhOMvd08MgIiIiIiIioj6MC4sSEXWC0qpgIn+7oYDeQJIk5Hod2FBRh9KqYA+NkIiIiIiIiIiI9gU70YmIOkFdREdUM+HwKq0+77Aq2O03URfRu3lkREREdDBbM3siUlJSenoYRERERH0aO9GJiDqBx67CZpERjhmtPh+OGbBZZHjsvHdJRERERERERNSXsIhORNQJCjNcKMl2o9wXhhCiyXNCCJT7whiU7UFhhquHRkhERERERERERPuCRXQiok4gyxLOGZ2PdJcVGysCCER0GKZAIKJjY0UA6S4rfj06j4uKEhERERERERH1MSyiExF1kuF5Xlw/fhAOz/eiNhxD6Z4gasMxjMhPxfXjB2F4nrenh0hERERERERERHuJ4bxERJ1oeJ4XQ3NTUFoVRF1Eh8euojDDxQ50IiIiIiIiIqI+ikV0IqJOJssSirPcPT0MIiIiIiIiIiLqBIxzISIiIiIiIiIiIiJqA4voRERERERERERERERtYBGdiIiIiIiIiIiIiKgNLKITEREREREREREREbWBRXQiIiIiIiIiIiIiojawiE5ERERERERERERE1AYW0YmIiIiIiIiIiIiI2sAiOhERERERERERERFRG1hEJyIiIiIiIiIiIiJqA4voRERERERERERERERtYBGdiIiIiIiIiIiIiKgNLKITEREREREREREREbWBRXQiIiIiIiIiIiIiojawiE5ERERERERERERE1AYW0YmIiIiIiIiIiIiI2sAiOhERERERERERERFRG9SeHkBvJIQAAPj9/h4eCREREVHv1fCzUsPPTkTUe/B3GiIiIqKOJfs7DYvorairqwMAFBQU9PBIiIiIiHq/uro6eL3enh4GETVSVVUFgL/TEBERESWjo99pJMHWoRZM08TOnTvh8XggSVJPD6dH+P1+FBQUYPv27UhJSenp4RzUOBe9A+ehd+A89B6ci96hp+dBCIG6ujr0798fssyUQKLepLa2Fmlpadi2bRtvch2kevrfCOp5vAYObpx/4jWQnGR/p2EneitkWUZ+fn5PD6NXSElJ4V+0XoJz0TtwHnoHzkPvwbnoHXpyHlicI+qdGn4J9Hq9/D59kOO/1cRr4ODG+SdeAx1L5ncatgwREREREREREREREbWBRXQiIiIiIiIiIiIiojawiE6tstlsuOuuu2Cz2Xp6KAc9zkXvwHnoHTgPvQfnonfgPBBRW/j9gXgNEK+Bgxvnn3gNdC4uLEpERERERERERERE1AZ2ohMRERERERERERERtYFFdCIiIiIiIiIiIiKiNrCITkRERERERERERETUBhbRqYnS0lJcdtllKCoqgsPhwMCBA3HXXXchFos12W7btm2YPHkyXC4XMjMzcf3117fYhvbP3XffjWOPPRZOpxOpqamtbsN56B5PPPEEioqKYLfbMWbMGHzyySc9PaQD3scff4zJkyejf//+kCQJr732WpPnhRCYNWsW+vfvD4fDgZNPPhn//e9/e2awB7B7770XRx55JDweD7Kzs3H22Wdj/fr1TbbhXHSPJ598EiNGjEBKSgpSUlIwbtw4vPXWW4nnOQ9EB769/Xnko48+wpgxY2C321FcXIynnnqqxTavvPIKhg4dCpvNhqFDh+LVV1/tquFTJ+jsa+CZZ57BCSecgLS0NKSlpeHUU0/F119/3ZWHQPupK74PNHjppZcgSRLOPvvsTh41daauuAZqa2tx7bXXIjc3F3a7HYcddhjefPPNrjoE2k9dcQ08/PDDGDx4MBwOBwoKCvCHP/wBkUikqw6h7xJEjbz11lti+vTpYtmyZWLTpk3i3//+t8jOzhb/+7//m9hG13UxfPhwccopp4jvv/9evPvuu6J///5ixowZPTjyA8+dd94pHnzwQfHHP/5ReL3eFs9zHrrHSy+9JCwWi3jmmWfE2rVrxe9//3vhcrnE1q1be3poB7Q333xT3H777eKVV14RAMSrr77a5Pn58+cLj8cjXnnlFbF69Wpx/vnni9zcXOH3+3tmwAeoiRMniueee06sWbNGrFy5UkyaNEkMGDBABAKBxDaci+7x+uuvizfeeEOsX79erF+/Xtx2223CYrGINWvWCCE4D0QHur39eWTz5s3C6XSK3//+92Lt2rXimWeeERaLRfzrX/9KbPP5558LRVHEPffcI9atWyfuueceoaqq+PLLL7vrsGgvdMU1cMEFF4i//OUvYsWKFWLdunXif/7nf4TX6xU7duzorsOivdAV10CD0tJSkZeXJ0444QQxZcqULj4S2lddcQ1Eo1ExduxYceaZZ4pPP/1UlJaWik8++USsXLmyuw6L9kJXXAMvvPCCsNls4h//+IfYsmWLWLZsmcjNzRU33HBDdx1Wn8EiOnXo/vvvF0VFRYmv33zzTSHLsigrK0s89uKLLwqbzSZ8Pl9PDPGA9txzz7VaROc8dI+jjjpKXHXVVU0eGzJkiLjlllt6aEQHn+ZFdNM0RU5Ojpg/f37isUgkIrxer3jqqad6YIQHj4qKCgFAfPTRR0IIzkVPS0tLE3/72984D0QHgb39eWTmzJliyJAhTR678sorxTHHHJP4+rzzzhOnn356k20mTpwopk6d2kmjps7UFddAc7quC4/HIxYvXrz/A6ZO11XXgK7r4rjjjhN/+9vfxCWXXMIiei/WFdfAk08+KYqLi0UsFuv8AVOn64pr4NprrxW/+MUvmmzzxz/+URx//PGdNOoDB+NcqEM+nw/p6emJr7/44gsMHz4c/fv3Tzw2ceJERKNRfPfddz0xxIMS56HrxWIxfPfddzjttNOaPH7aaafh888/76FR0ZYtW7Br164m82Kz2XDSSSdxXrqYz+cDgMS/CZyLnmEYBl566SUEg0GMGzeO80B0gNuXn0e++OKLFttPnDgR3377LTRNa3cbft/ofbrqGmguFApB07Qmv/tR79CV18CcOXOQlZWFyy67rPMHTp2mq66B119/HePGjcO1116Lfv36Yfjw4bjnnntgGEbXHAjts666Bo4//nh89913iTivzZs3480338SkSZO64Cj6NrWnB0C926ZNm/DYY4/hgQceSDy2a9cu9OvXr8l2aWlpsFqt2LVrV3cP8aDFeeh6e/bsgWEYLc5zv379eI57UMO5b21etm7d2hNDOigIIfDHP/4Rxx9/PIYPHw6Ac9HdVq9ejXHjxiESicDtduPVV1/F0KFDEz80cx6IDkz78vNIaz8n9uvXD7quY8+ePcjNzW1zG/6M0/t01TXQ3C233IK8vDyceuqpnTd46hRddQ189tlnWLhwIVauXNlVQ6dO0lXXwObNm/HBBx/gwgsvxJtvvokNGzbg2muvha7ruPPOO7vseGjvddU1MHXqVFRWVuL444+HEAK6ruPqq6/GLbfc0mXH0lexE/0gMWvWLEiS1O6fb7/9tslrdu7cidNPPx3nnnsufve73zV5TpKkFu8hhGj1cfrZvsxDezgP3aP5+eQ57h04L91rxowZWLVqFV588cUWz3EuusfgwYOxcuVKfPnll7j66qtxySWXYO3atYnnOQ9EB7a9/Tve2vbNH+f3jb6lK66BBvfffz9efPFFLFmyBHa7vRNGS12hM6+Buro6TJs2Dc888wwyMzM7f7DUJTr7+4BpmsjOzsbTTz+NMWPGYOrUqbj99tvx5JNPdvLIqbN09jWwfPly3H333XjiiSfw/fffY8mSJVi6dCnmzp3bySPv+9iJfpCYMWMGpk6d2u42hYWFif/fuXMnTjnlFIwbNw5PP/10k+1ycnLw1VdfNXmspqYGmqa1uMNFTe3tPLSH89D1MjMzoShKi7u6FRUVPMc9KCcnB0D8rnrjLirOS9e57rrr8Prrr+Pjjz9Gfn5+4nHORfeyWq0oKSkBAIwdOxbffPMNHnnkEdx8880AOA9EB6p9+XkkJyen1e1VVUVGRka72/D7Ru/TVddAgwULFuCee+7Be++9hxEjRnTu4KlTdMU18N///helpaWYPHly4nnTNAEAqqpi/fr1GDhwYCcfCe2rrvo+kJubC4vFAkVREtscdthh2LVrF2KxGKxWaycfCe2rrroG7rjjDlx00UWJ5tnDDz8cwWAQV1xxBW6//XbIMvuvG/BMHCQyMzMxZMiQdv80dByUlZXh5JNPxujRo/Hcc8+1+Aszbtw4rFmzBuXl5YnH3nnnHdhsNowZM6Zbj6uv2Zt56AjnoetZrVaMGTMG7777bpPH3333XRx77LE9NCoqKipCTk5Ok3mJxWL46KOPOC+dTAiBGTNmYMmSJfjggw9QVFTU5HnORc8SQiAajXIeiA5w+/LzyLhx41ps/84772Ds2LGwWCztbsPvG71PV10DAPDnP/8Zc+fOxdtvv42xY8d2/uCpU3TFNTBkyBCsXr0aK1euTPw566yzcMopp2DlypUoKCjosuOhvddV3weOO+44bNy4MXEDBQB++ukn5ObmsoDey3TVNRAKhVrU/RRFgRAi0bVO9bpxEVPqA8rKykRJSYn4xS9+IXbs2CHKy8sTfxroui6GDx8uxo8fL77//nvx3nvvifz8fDFjxoweHPmBZ+vWrWLFihVi9uzZwu12ixUrVogVK1aIuro6IQTnobu89NJLwmKxiIULF4q1a9eKG264QbhcLlFaWtrTQzug1dXVJa55AOLBBx8UK1asEFu3bhVCCDF//nzh9XrFkiVLxOrVq8Vvf/tbkZubK/x+fw+P/MBy9dVXC6/XK5YvX97k34NQKJTYhnPRPW699Vbx8ccfiy1btohVq1aJ2267TciyLN555x0hBOeB6EDX0c8jt9xyi7jooosS22/evFk4nU7xhz/8Qaxdu1YsXLhQWCwW8a9//SuxzWeffSYURRHz588X69atE/Pnzxeqqoovv/yy24+POtYV18B9990nrFar+Ne//tXk3/mG3zeod+mKa6C5Sy65REyZMqWrD4X2UVdcA9u2bRNut1vMmDFDrF+/XixdulRkZ2eLefPmdfvxUce64hq46667hMfjES+++KLYvHmzeOedd8TAgQPFeeed1+3H19uxiE5NPPfccwJAq38a27p1q5g0aZJwOBwiPT1dzJgxQ0QikR4a9YHpkksuaXUePvzww8Q2nIfu8Ze//EUccsghwmq1itGjR4uPPvqop4d0wPvwww9bvf4vueQSIYQQpmmKu+66S+Tk5AibzSZOPPFEsXr16p4d9AGorX8PnnvuucQ2nIvucemllya+D2VlZYnx48cnCuhCcB6IDgbt/TxyySWXiJNOOqnJ9suXLxdHHHGEsFqtorCwUDz55JMt9vn//t//E4MHDxYWi0UMGTJEvPLKK119GLQfOvsaOOSQQ1r9d/6uu+7qhqOhfdEV3wcaYxG99+uKa+Dzzz8XRx99tLDZbKK4uFjcfffdQtf1rj4U2kedfQ1omiZmzZolBg4cKOx2uygoKBDXXHONqKmp6Yaj6VskIdibT0RERERERERERETUGmaiExERERERERERERG1gUV0IiIiIiIiIiIiIqI2sIhORERERERERERERNQGFtGJiIiIiIiIiIiIiNrAIjoRERERERERERERURtYRCciIiIiIiIiIiIiagOL6EREREREREREREREbWARnYiIiIiIiIiIiIioDSyiExH1EnfccQeuuOKKnh7GPrvxxhtx/fXX9/QwiIiIiIh6peXLl0OSJNTW1nbZe5x88sm44YYbumz/fdGsWbMwatSoxNfTp0/H2Wef3S3vRUQHDhbRiYgQ/0FKkiRIkgSLxYLi4mLceOONCAaDTbZ75ZVXcPLJJ8Pr9cLtdmPEiBGYM2cOqqurAQDl5eW44IILMHjwYMiynPQPsLt378YjjzyC2267LfHYxx9/jMmTJ6N///6QJAmvvfZai9fNmjULQ4YMgcvlQlpaGk499VR89dVX7b7XySefnDjWxn8mTZqU2OYf//gHCgoKkJ6ejptuuqnJ60tLS3HooYfC7/c3eXzmzJl47rnnsGXLlqSOmYiIiIjoQPP5559DURScfvrpPT2UpJSWlkKSJKxcubJT9rdr1y5cd911KC4uhs1mQ0FBASZPnoz333+/U/bfYG9uFtx4442d/v4AWv0dravei4h6HovoRET1Tj/9dJSXl2Pz5s2YN28ennjiCdx4442J52+//Xacf/75OPLII/HWW29hzZo1eOCBB/DDDz/g+eefBwBEo1FkZWXh9ttvx8iRI5N+74ULF2LcuHEoLCxMPBYMBjFy5Eg8/vjjbb7u0EMPxeOPP47Vq1fj008/RWFhIU477TRUVla2+ZolS5agvLw88WfNmjVQFAXnnnsuAGDPnj343e9+hwULFmDZsmVYvHgx3njjjcTrr776asyfPx8pKSlN9pudnY3TTjsNTz31VNLHTURERER0IHn22Wdx3XXX4dNPP8W2bdt6ejjdqrS0FGPGjMEHH3yA+++/H6tXr8bbb7+NU045Bddee223j0cIAV3X4Xa7kZGR0S3v2Z3vRUTdi0V0IqJ6NpsNOTk5KCgowAUXXIALL7ww0Vnw9ddf45577sEDDzyAP//5zzj22GNRWFiICRMm4JVXXsEll1wCACgsLMQjjzyCiy++GF6vN+n3fumll3DWWWc1eeyMM87AvHnz8Otf/7rN111wwQU49dRTUVxcjGHDhuHBBx+E3+/HqlWr2nxNeno6cnJyEn/effddOJ3ORBF98+bN8Hq9iRsGp5xyCtauXQsA+L//+z9YrdY2x3TWWWfhxRdfTPq4iYiIiIgOFMFgEP/85z9x9dVX45e//CUWLVrU6nafffYZRo4cCbvdjqOPPhqrV69OPLd161ZMnjwZaWlpcLlcGDZsGN58883E8x999BGOOuoo2Gw25Obm4pZbboGu622OqbVu6dTU1MTYioqKAABHHHEEJEnCySefnNjuueeew2GHHQa73Y4hQ4bgiSeeaPf4r7nmGkiShK+//hq/+c1vcOihh2LYsGH44x//iC+//DKx3bZt2zBlyhS43W6kpKTgvPPOw+7duxPPN0SiPP/88ygsLITX68XUqVNRV1cHIP4p4o8++giPPPJI4lO1paWlibicZcuWYezYsbDZbPjkk0/ajFiZPXs2srOzkZKSgiuvvBKxWCzxXGFhIR5++OEm248aNQqzZs1KPA8Av/rVryBJUuLr5u9lmibmzJmD/Px82Gw2jBo1Cm+//Xbi+YZPAixZsgSnnHIKnE4nRo4ciS+++KLdc01E3Y9FdCKiNjgcDmiaBiAeb+J2u3HNNde0um1qauo+v09NTQ3WrFmDsWPH7vM+ACAWi+Hpp5+G1+vd6y74qVOnwuVyAQAGDRqEUCiEFStWoLq6Gt988w1GjBiB6upq3Hnnne12xh911FHYvn07tm7dul/HQkRERETU17z88ssYPHgwBg8ejGnTpuG5556DEKLFdjfddBMWLFiAb775BtnZ2TjrrLMSv3dce+21iEaj+Pjjj7F69Wrcd999cLvdAICysjKceeaZOPLII/HDDz/gySefxMKFCzFv3rx9HvPXX38NAHjvvfdQXl6OJUuWAACeeeYZ3H777bj77ruxbt063HPPPbjjjjuwePHiVvdTXV2Nt99+G9dee23i94rGGn5fEkLg7LPPRnV1NT766CO8++672LRpE84///wm22/atAmvvfYali5diqVLl+Kjjz7C/PnzAQCPPPIIxo0bh8svvzzx6dqCgoLEa2fOnIl7770X69atw4gRI1od7/vvv49169bhww8/xIsvvohXX30Vs2fPTvq8ffPNNwDiNxrKy8sTXzf3yCOP4IEHHsCCBQuwatUqTJw4EWeddRY2bNjQZLvbb78dN954I1auXIlDDz0Uv/3tb9u9OUJE3U/t6QEQEfVGX3/9Nf7v//4P48ePBwBs2LABxcXFsFgsnf5eW7duhRAC/fv336fXL126FFOnTkUoFEJubi7effddZGZmJvXar7/+GmvWrMHChQsTj6WlpWHx4sW4+OKLEQ6HcfHFF2PixIm49NJLcd1112HLli2JH/RnzZqF3/zmN4nX5uXlAYh3VBxyyCH7dDxERERERH3RwoULMW3aNADxqMhAIID3338fp556apPt7rrrLkyYMAEAsHjxYuTn5+PVV1/Feeedh23btuGcc87B4YcfDgAoLi5OvO6JJ55AQUEBHn/8cUiShCFDhmDnzp24+eabceedd0KW975PMisrCwCQkZGBnJycxONz587FAw88kPgEalFREdauXYu//vWviU/hNrZx40YIITBkyJB23++9997DqlWrsGXLlkTh+/nnn8ewYcPwzTff4MgjjwQQ7+BetGgRPB4PAOCiiy7C+++/j7vvvhterxdWqxVOp7PJmBvMmTMncX7bYrVa8eyzz8LpdGLYsGGYM2cObrrpJsydOzep89hw3lJTU1sdQ4MFCxbg5ptvxtSpUwEA9913Hz788EM8/PDD+Mtf/pLY7sYbb0ysUTV79mwMGzYMGzdu7PB8ElH3YRGdiKje0qVL4Xa7oes6NE3DlClT8NhjjwGId0xIktQl7xsOhwEAdrt9n15/yimnYOXKldizZw+eeeYZnHfeefjqq6+QnZ3d4WsXLlyI4cOH46ijjmry+K9+9Sv86le/Sny9fPlyrF69Go8//jhKSkrw4osvIicnB0cddRROPPHExHs5HA4AQCgU2qdjISIiIiLqi9avX4+vv/460cmtqirOP/98PPvssy2K6OPGjUv8f3p6OgYPHox169YBAK6//npcffXVeOedd3DqqafinHPOSXRTr1u3DuPGjWvye8lxxx2HQCCAHTt2YMCAAZ1yLJWVldi+fTsuu+wyXH755YnHdV1vM7KyoeO+o9+Z1q1bh4KCgiad40OHDkVqairWrVuXKKIXFhYmCugAkJubi4qKiqTGn8wnfEeOHAmn05n4ety4cQgEAti+fXunNQP5/X7s3LkTxx13XJPHjzvuOPzwww9NHmvcMZ+bmwsAqKioYBH9/7d3byFRdW0cwP9j45iMTqiZh/EQqFMpqESWWDkgUhhlChlUmBd6EWYpinnRYcqIriIvSisZlEDKLuYiO0hRBolgJI1lB8s0KyvoMBlamjM+70Wv+2vUUev7eN/q+/9gLvZaa6+91p6bvZ9Z6xmiXwjTuRAR/W0sGN3Z2YmhoSFYLBYlOGwwGPD06VNlm+X/0tiqcZvN9lPna7VaREZGIjExEWazGWq12mlluSufP3/G2bNnkZeXN2W74eFh5Ofn4+TJk+jq6oLdbofRaMSCBQtgMBjQ2tqqtP3w4QOA/6zMICIiIiL6f2A2m2G326HX66FWq6FWq1FVVQWLxTKj5/yx4HNeXh66u7uRnZ2Ne/fuYcmSJVMu7JkueK1SqSaklJnunWZ0dBTAt5QuVqtV+XR0dDjlNv9eVFQUVCqV8mOAK64WJ40vH78DWKVSKeOazmTpZGZqbAxubm4/fN+m63PMZPfg+/mO1c10vkT0z2AQnYjob2PB6PDw8AkPbZs3b8bAwIDLP9P5+PHjT183IiICOp1O+fPO/5aIYHh4eNp2586dw/DwsLLl1JWDBw8iLS0NixcvhsPhcMrNNzIyAofDoRx3dHTA3d0dMTExPz8BIiIiIqLfiN1ux+nTp3HkyBGnoHN7ezvCw8NRV1fn1P77QLTNZsPjx4+dVhyHhoZi27ZtsFgsKCkpQXV1NYBvK7ZbWlqcgrstLS3w9vZW0iqO5+/vj9evXyvHT548cdo1qtFoAMDpmT4gIAB6vR7d3d2IjIx0+oz9Eel4vr6+WL16NY4fP47BwcEJ9WPvS9HR0Xj+/DlevHih1D148AD9/f1YtGjRpH1PRqPROI35R7W3tys7goFv34mXlxdCQkIATLxvnz59Qk9Pj1Mf7u7uU45Bp9MhODgYzc3NTuUtLS0/NFci+jUwnQsR0QwsW7YMu3btQklJCfr6+pCZmYng4GB0dXXhxIkTWLFiBQoLCwEAVqsVADAwMIC3b9/CarVCo9EgOjp60r7d3NyQmpqK5uZmZGRkKOUDAwPo6upSjnt6emC1WuHr64uwsDAMDg7i0KFDSE9PR1BQEN6/f4/Kykq8fPkSWVlZynlbt26FXq/H4cOHna5rNpuRkZEBPz8/l/O+f/8+6uvrlTktXLgQbm5uMJvNCAwMxKNHj5QtlwBw8+ZNrFy5UknrQkRERET0p7tw4QJsNhtyc3MnpDvZsGEDzGYzCgoKlLLy8nL4+fkhICAAu3fvxty5c5X3gKKiIqSlpcFgMMBms+H69etKwDU/Px8VFRXYsWMHCgoK0NnZCZPJhOLiYpd5vFNSUnDs2DEkJiZidHQUZWVlTguG5s2bB09PTzQ2NiIkJASzZ8/GnDlzsH//fuzcuRM6nQ5paWkYHh7G7du3YbPZUFxcPOm1KisrkZSUhKVLl6K8vByxsbGw2+24evUqqqqq8PDhQ6SmpiI2NhZbtmxBRUUF7HY78vPzYTQaZ5SGZcz8+fPR2tqKZ8+ewcvLC76+vjM+FwC+fv2K3Nxc7NmzB729vTCZTCgoKFDuY0pKCmpra7Fu3Tr4+Phg7969mDVr1oQxXLt2DcuXL4eHhwd8fHwmXKe0tBQmkwkRERGIj49HTU0NrFbrhB9WiOg3IEREJDk5ObJ+/fpp29XX10tycrJ4e3uLVquV2NhYKS8vF5vNprQBMOETHh4+Zb+NjY2i1+vF4XAoZU1NTZP2lZOTIyIiX758kczMTAkODhaNRiNBQUGSnp4ut27dcurbaDQq54zp7OwUAHLlyhWXYxodHZWkpCRpaGhwKm9oaJCwsDAJCAiQ6upqpzqDwSBnzpyZcq5ERERERH+StWvXypo1ayata2trEwDS1tamPN83NDRITEyMaDQaSUhIEKvVqrQvKCiQiIgI8fDwEH9/f8nOzpZ3794p9Tdu3JCEhATRaDQSGBgoZWVlMjIyotQbjUYpLCxUjvv6+mTVqlWi1WolKipKLl26JHPmzJGamhqlTXV1tYSGhoqbm5sYjUalvK6uTuLj40Wj0YiPj48kJyeLxWKZ8l68evVKtm/fLuHh4aLRaESv10t6ero0NTUpbXp7eyU9PV20Wq14e3tLVlaWvHnzRqk3mUwSFxfn1O/Ro0ed3qk6OzslMTFRPD09BYD09PQo9/f7d7PJ+ht799u3b5/4+fmJl5eX5OXlydDQkNKmv79fNm7cKDqdTkJDQ6W2tlbi4uLEZDIpbc6fPy+RkZGiVquVsY2/lsPhkAMHDoherxd3d3eJi4uTy5cvK/U9PT0CQO7cuaOU2Ww2AeB0z4jo36cSGZfkiYiI/nEigsTERBQVFWHTpk3/9nB+ysWLF1FaWoq7d+9CreZGJyIiIiIiIiL6MzAnOhHRL0ClUuHUqVNO+cZ/N4ODg6ipqWEAnYiIiIiIiIj+KFyJTkRERERERERERETkAleiExERERERERERERG5wCA6EREREREREREREZELDKITEREREREREREREbnAIDoRERERERERERERkQsMohMRERERERERERERucAgOhERERERERERERGRCwyiExERERERERERERG5wCA6EREREREREREREZELDKITEREREREREREREbnAIDoRERERERERERERkQt/AcO/gBKbp75bAAAAAElFTkSuQmCC",
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-### Personal Goals
- * Understand the processing and application of the full neuroimaging workflow, specifically from raw fMRI data to data visualization
- * Build and develop skills in open science applications, particularly for future neuroimaging projects
- * Gaining insight into fMRI data applications, specifically in creating connectivity matrices and brain parcellations
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-This project will rely on the following technologies:
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-An example of output from the SCanD project preprocessing pipeline fMRI prep anatomical step is shown below for subject 3, a child without ADHD. Specifically, the image below shows the template T1-weighted image with contours delineating the detected brain mask (red outline) and brain tissue (blue outline) segmentations. These outputs were reviewed for quality assurance purposes.
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-Our project GitHub repository can be accessed here: https://github.com/brainhack-school2023/csun_project/
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-Our preprocessing scripts adapted from Erin Dickie’s SCanD_project: https://github.com/sunclara/SCanD_project-brainhack2023
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-#### Deliverable 2: A jupyter notebook of the analysis codes and visualizations
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-Based on resources contained within Erin Dickie’s Krembil Centre for Neuroinformatics (KCNI) summer school slides and modules, we were able to produce graphs as well as brain images via the preprocessed data. For example, using nilearn, nibabel, and matplotlib, we were able to plot slices of the brain as well as a line graph which displayed fMRI signal vs time of any brain parcellation. In the context of our data, we chose to plot the somatomotor region vs the auditory region.
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-The code for parcellation graph comparing fMRI signal vs. time with the somatomotor and auditory regions:
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